From b78427e61997483a28af5270d631951e1af7b1fa Mon Sep 17 00:00:00 2001 From: WeibiaoYu Date: Tue, 31 Mar 2020 09:25:11 -0400 Subject: [PATCH 001/227] the size of tensor may be bigger than 2GB, should use memcpy instead of memcpy_s --- mindspore/ccsrc/transform/util.cc | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/mindspore/ccsrc/transform/util.cc b/mindspore/ccsrc/transform/util.cc index a106a20ad8..0a18763d12 100644 --- a/mindspore/ccsrc/transform/util.cc +++ b/mindspore/ccsrc/transform/util.cc @@ -361,12 +361,11 @@ MeTensorPtr TransformUtil::GenerateMeTensor(const GeTensorPtr& ge_tensor, const MS_LOG(ERROR) << "GE tensor data size is zero!"; return nullptr; } - errno_t ret = memcpy_s(me_data_ptr, me_data_size, ge_tensor->GetData(), ge_tensor->GetSize()); - if (ret != EOK) { - MS_LOG(INFO) << "GE tensor data size is " << ge_tensor->GetSize() << " bytes"; - MS_LOG(ERROR) << "Copy GE tensor data to me tensor failed"; - return nullptr; - } + + // Use memcpy here, not memcpy_s, just because the size of ge_tensor may be bigger than 2GB + // which is the size limit of memcpy_s + memcpy(me_data_ptr, ge_tensor->GetData(), ge_tensor->GetSize()); + return make_shared(me_tensor); } From b53c97451398fc0f8dd7d19f555c25fc68173de8 Mon Sep 17 00:00:00 2001 From: yanghaoran Date: Thu, 2 Apr 2020 17:15:41 +0800 Subject: [PATCH 002/227] modify reduceminD and reducemaxD IR --- .gitmodules | 2 +- graphengine | 2 +- mindspore/ccsrc/transform/convert.cc | 14 +-- mindspore/ccsrc/transform/op_declare.cc | 146 ++++++++++-------------- mindspore/ccsrc/transform/op_declare.h | 12 +- 5 files changed, 75 insertions(+), 101 deletions(-) diff --git a/.gitmodules b/.gitmodules index a241b6d69b..a024019b14 100644 --- a/.gitmodules +++ b/.gitmodules @@ -12,4 +12,4 @@ url = https://github.com/protocolbuffers/protobuf.git [submodule "graphengine"] path = graphengine - url = https://gitee.com/mindspore/graphengine.git + url = https://gitee.com/ms-incubator/graphengine.git diff --git a/graphengine b/graphengine index 5f763679fa..092c7a1f65 160000 --- a/graphengine +++ b/graphengine @@ -1 +1 @@ -Subproject commit 5f763679fa33de1608d07f7651c6f16012b953ea +Subproject commit 092c7a1f6548cac7d40e677af3498c3c49ea2bfd diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index 74b0695cff..d40553a56d 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -189,7 +189,7 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameApplyMomentum), ADPT_DESC(ApplyMomentum)}, {string(kNameMaxPool), ADPT_DESC(MaxPool)}, {string(kNameAvgPool), ADPT_DESC(AvgPool)}, - {string(kNameTopK), ADPT_DESC(TopKV2)}, + {string(kNameTopK), ADPT_DESC(TopK)}, {string(kNamePack), ADPT_DESC(Pack)}, {string(kNameSplitD), ADPT_DESC(SplitD)}, {string(kNameAllReduce), ADPT_DESC(HcomAllReduce)}, @@ -310,7 +310,7 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {prim::kPrimMinimum->name(), ADPT_DESC(Minimum)}, {prim::kPrimSelect->name(), ADPT_DESC(Select)}, {string(kNameLessEqual), ADPT_DESC(LessEqual)}, - {prim::kPrimLogSoftmax->name(), ADPT_DESC(LogSoftmax)}, + {prim::kPrimLogSoftmax->name(), ADPT_DESC(LogSoftmaxV2)}, {string(kNameTruncatedNormal), ADPT_DESC(TruncatedNormal)}, {string(kNameStridedSliceGrad), ADPT_DESC(StridedSliceGrad)}, {prim::kPrimGelu->name(), ADPT_DESC(Gelu)}, @@ -343,7 +343,7 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {prim::kPrimMatMul->name(), ADPT_DESC(MatMul)}, {string(kNameConst), ADPT_DESC(Constant, Const)}, - {string(kNameSoftmax), ADPT_DESC(Softmax)}, + {string(kNameSoftmax), ADPT_DESC(SoftmaxV2)}, {string(kNameSoftmaxGrad), ADPT_DESC(SoftmaxGrad)}, {string(kNameParam), ADPT_DESC(Data)}, {string(kNameROIAlign), ADPT_DESC(ROIAlign)}, @@ -1017,8 +1017,8 @@ DfGraphConvertor &DfGraphConvertor::BuildGraph() { } } - // set up dependices - MS_LOG(DEBUG) << "set up dependices"; + // set up dependencies + MS_LOG(DEBUG) << "set up dependencies"; std::vector nodes = ::mindspore::TopoSort(anf_graph_->get_return()); for (auto &it : nodes) { SetNodeInput(it); @@ -1115,8 +1115,8 @@ void DfGraphConvertor::UpdateDataOpDesc(const AnfNodePtr &it, const OperatorPtr if (desc == nullptr) { MS_LOG(ERROR) << "Update data op descriptor failed! TensorDesc is null."; } else { - (void)std::static_pointer_cast(op)->update_input_desc_data(*desc); - (void)std::static_pointer_cast(op)->update_output_desc_out(*desc); + (void)std::static_pointer_cast(op)->update_input_desc_x(*desc); + (void)std::static_pointer_cast(op)->update_output_desc_y(*desc); } } diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 78b949c525..5daf67e89a 100755 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -138,11 +138,10 @@ OUTPUT_MAP(ApplyMomentum) = {{0, OUTPUT_DESC(var)}}; INPUT_MAP(Summary) = {{2, INPUT_DESC(x)}}; ATTR_MAP(Summary) = EMPTY_ATTR_MAP; -// data +// Data INPUT_MAP(Data) = EMPTY_INPUT_MAP; ATTR_MAP(Data) = EMPTY_ATTR_MAP; -// resnet ops in ge // BatchNorm INPUT_MAP(BatchNorm) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(scale)}, @@ -194,9 +193,9 @@ OUTPUT_MAP(PRelu) = {{0, OUTPUT_DESC(y)}}; // PReluGrad INPUT_MAP(PReluGrad) = { - {1, INPUT_DESC(input_gradients)}, {2, INPUT_DESC(input_features)}, {3, INPUT_DESC(input_weights)}}; + {1, INPUT_DESC(grads)}, {2, INPUT_DESC(features)}, {3, INPUT_DESC(weights)}}; ATTR_MAP(PReluGrad) = EMPTY_ATTR_MAP; -OUTPUT_MAP(PReluGrad) = {{0, OUTPUT_DESC(output_backprops_dx)}, {1, OUTPUT_DESC(output_backprops_da)}}; +OUTPUT_MAP(PReluGrad) = {{0, OUTPUT_DESC(dx)}, {1, OUTPUT_DESC(da)}}; // Sigmoid INPUT_MAP(Sigmoid) = {{1, INPUT_DESC(x)}}; @@ -241,12 +240,12 @@ ATTR_MAP(CumsumD) = {{"exclusive", ATTR_DESC(exclusive, AnyTraits())}, {"reverse", ATTR_DESC(reverse, AnyTraits())}}; OUTPUT_MAP(CumsumD) = {{0, OUTPUT_DESC(y)}}; -// softmax -INPUT_MAP(Softmax) = {{1, INPUT_DESC(x)}}; -ATTR_MAP(Softmax) = { - {"axis", ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}, +// SoftmaxV2 +INPUT_MAP(SoftmaxV2) = {{1, INPUT_DESC(x)}}; +ATTR_MAP(SoftmaxV2) = { + {"axis", ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}, }; -OUTPUT_MAP(Softmax) = {{0, OUTPUT_DESC(y)}}; +OUTPUT_MAP(SoftmaxV2) = {{0, OUTPUT_DESC(y)}}; // SoftmaxGrad INPUT_MAP(SoftmaxGrad) = {{1, INPUT_DESC(softmax)}, {2, INPUT_DESC(grad_softmax)}}; @@ -269,21 +268,21 @@ ATTR_MAP(GatherV2) = EMPTY_ATTR_MAP; OUTPUT_MAP(GatherV2) = {{0, OUTPUT_DESC(y)}}; // ReduceSum -INPUT_MAP(ReduceSum) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axis)}}; +INPUT_MAP(ReduceSum) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axes)}}; ATTR_MAP(ReduceSum) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceSum) = {{0, OUTPUT_DESC(y)}}; // ReduceSumD INPUT_MAP(ReduceSumD) = {{1, INPUT_DESC(x)}}; INPUT_ATTR_MAP(ReduceSumD) = { - {2, ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}}; + {2, ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(ReduceSumD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceSumD) = {{0, OUTPUT_DESC(y)}}; // ReduceProdD INPUT_MAP(ReduceProdD) = {{1, INPUT_DESC(x)}}; INPUT_ATTR_MAP(ReduceProdD) = { - {2, ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}}; + {2, ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(ReduceProdD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceProdD) = {{0, OUTPUT_DESC(y)}}; @@ -294,7 +293,7 @@ ATTR_MAP(CumprodD) = {{"exclusive", ATTR_DESC(exclusive, AnyTraits())}, {"reverse", ATTR_DESC(reverse, AnyTraits())}}; OUTPUT_MAP(CumprodD) = {{0, OUTPUT_DESC(y)}}; -// SoftmaxCrossEntropyWithLogits/ +// SoftmaxCrossEntropyWithLogits INPUT_MAP(SoftmaxCrossEntropyWithLogits) = {{1, INPUT_DESC(features)}, {2, INPUT_DESC(labels)}}; ATTR_MAP(SoftmaxCrossEntropyWithLogits) = EMPTY_ATTR_MAP; OUTPUT_MAP(SoftmaxCrossEntropyWithLogits) = {{0, OUTPUT_DESC(loss)}, {1, OUTPUT_DESC(backprop)}}; @@ -306,7 +305,7 @@ INPUT_ATTR_MAP(MeanGrad) = {{2, ATTR_DESC(mean_grad_output_shape_value, kOpForma ATTR_MAP(MeanGrad) = {{"mode", ATTR_DESC(mode, AnyTraits())}}; INPUT_MAP(SliceD) = {{1, INPUT_DESC(x)}}; -INPUT_ATTR_MAP(SliceD) = {{2, ATTR_DESC(begin, AnyTraits(), AnyTraits>())}, +INPUT_ATTR_MAP(SliceD) = {{2, ATTR_DESC(offsets, AnyTraits(), AnyTraits>())}, {3, ATTR_DESC(size, AnyTraits(), AnyTraits>())}}; ATTR_MAP(SliceD) = EMPTY_ATTR_MAP; OUTPUT_MAP(SliceD) = {{0, OUTPUT_DESC(y)}}; @@ -401,42 +400,10 @@ ATTR_MAP(BoundingBoxDecode) = { }; OUTPUT_MAP(BoundingBoxDecode) = {{0, OUTPUT_DESC(bboxes)}}; -#ifdef VALID_CODE - -// Less -INPUT_MAP(Less) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(y)}}; -ATTR_MAP(Less) = EMPTY_ATTR_MAP; -OUTPUT_MAP(Less) = {{0, OUTPUT_DESC(z)}}; - -// Cast -INPUT_MAP(Cast) = {{1, INPUT_DESC(x)}}; -INPUT_ATTR_MAP(Cast) = {{2, ATTR_DESC(dst_type, AnyTraits())}}; -ATTR_MAP(Cast) = {{"Truncate", ATTR_DESC(truncate, AnyTraits())}}; -OUTPUT_MAP(Cast) = {{0, OUTPUT_DESC(y)}}; - -// Minimum -INPUT_MAP(Minimum) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(y)}}; -ATTR_MAP(Minimum) = {{"alpha", ATTR_DESC(alpha, AnyTraits())}, {"beta", ATTR_DESC(beta, AnyTraits())}}; -OUTPUT_MAP(Minimum) = {{0, OUTPUT_DESC(z)}}; - -// Sub -INPUT_MAP(Sub) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; -ATTR_MAP(Sub) = {{"alpha", ATTR_DESC(alpha, AnyTraits())}, {"beta", ATTR_DESC(beta, AnyTraits())}}; - -#endif - -// TopKV2 -INPUT_MAP(TopKV2) = { - {1, INPUT_DESC(input)}, - {2, INPUT_DESC(k)}, -}; - -ATTR_MAP(TopKV2) = {{"T", ATTR_DESC(T, AnyTraits())}, {"sorted", ATTR_DESC(sorted, AnyTraits())}}; - -OUTPUT_MAP(TopKV2) = { - {0, OUTPUT_DESC(values)}, - {1, OUTPUT_DESC(indices)}, -}; +// TopK +INPUT_MAP(TopK) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(k)}}; +ATTR_MAP(TopK) = {{"sorted", ATTR_DESC(sorted, AnyTraits())}}; +OUTPUT_MAP(TopK) = {{0, OUTPUT_DESC(values)}, {1, OUTPUT_DESC(indices)}}; // Multiply INPUT_MAP(Multiply) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(y)}}; @@ -476,7 +443,7 @@ ATTR_MAP(Iou) = {{"mode", ATTR_DESC(mode, AnyTraits())}}; OUTPUT_MAP(Iou) = {{0, OUTPUT_DESC(overlap)}}; // ResizeNearestNeighborD -INPUT_MAP(ResizeNearestNeighborD) = {{1, INPUT_DESC(images)}}; +INPUT_MAP(ResizeNearestNeighborD) = {{1, INPUT_DESC(x)}}; ATTR_MAP(ResizeNearestNeighborD) = { {"size", ATTR_DESC(size, AnyTraits>(), AnyTraits>())}, {"align_corners", ATTR_DESC(align_corners, AnyTraits())}}; @@ -506,17 +473,17 @@ ATTR_MAP(Relu6) = EMPTY_ATTR_MAP; OUTPUT_MAP(Relu6) = {{0, OUTPUT_DESC(activations)}}; // Relu6Grad -INPUT_MAP(Relu6Grad) = {{1, INPUT_DESC(dy)}, {2, INPUT_DESC(y)}}; +INPUT_MAP(Relu6Grad) = {{1, INPUT_DESC(features)}, {2, INPUT_DESC(gradients)}}; ATTR_MAP(Relu6Grad) = EMPTY_ATTR_MAP; -OUTPUT_MAP(Relu6Grad) = {{0, OUTPUT_DESC(z)}}; +OUTPUT_MAP(Relu6Grad) = {{0, OUTPUT_DESC(backprops)}}; // ResizeBilinearGrad INPUT_MAP(ResizeBilinearGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(original_image)}}; ATTR_MAP(ResizeBilinearGrad) = {{"align_corners", ATTR_DESC(align_corners, AnyTraits())}}; OUTPUT_MAP(ResizeBilinearGrad) = {{0, OUTPUT_DESC(y)}}; -// ResizeBilinear -INPUT_MAP(ResizeBilinearD) = {{1, INPUT_DESC(images)}}; +// ResizeBilinearD +INPUT_MAP(ResizeBilinearD) = {{1, INPUT_DESC(x)}}; ATTR_MAP(ResizeBilinearD) = { {"size", ATTR_DESC(size, AnyTraits>(), AnyTraits>())}, {"align_corners", ATTR_DESC(align_corners, AnyTraits())}}; @@ -539,9 +506,9 @@ OUTPUT_MAP(NMSWithMask) = { {0, OUTPUT_DESC(selected_boxes)}, {1, OUTPUT_DESC(selected_idx)}, {2, OUTPUT_DESC(selected_mask)}}; // Unpack -INPUT_MAP(Unpack) = {{1, INPUT_DESC(value)}}; +INPUT_MAP(Unpack) = {{1, INPUT_DESC(x)}}; ATTR_MAP(Unpack) = {{"axis", ATTR_DESC(axis, AnyTraits())}, {"num", ATTR_DESC(num, AnyTraits())}}; -DYN_OUTPUT_MAP(Unpack) = {{0, DYN_OUTPUT_DESC(output)}}; +DYN_OUTPUT_MAP(Unpack) = {{0, DYN_OUTPUT_DESC(y)}}; // ScatterNdUpdate INPUT_MAP(ScatterNdUpdate) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(indices)}, {3, INPUT_DESC(updates)}}; @@ -574,8 +541,8 @@ INPUT_MAP(SigmoidCrossEntropyWithLogitsGrad) = { ATTR_MAP(SigmoidCrossEntropyWithLogitsGrad) = EMPTY_ATTR_MAP; OUTPUT_MAP(SigmoidCrossEntropyWithLogitsGrad) = {{0, OUTPUT_DESC(gradient)}}; -// ScatterNd -INPUT_MAP(ScatterNdD) = {{1, INPUT_DESC(indices)}, {2, INPUT_DESC(updates)}}; +// ScatterNdD +INPUT_MAP(ScatterNdD) = {{1, INPUT_DESC(indices)}, {2, INPUT_DESC(x)}}; INPUT_ATTR_MAP(ScatterNdD) = { {3, ATTR_DESC(shape, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(ScatterNdD) = EMPTY_ATTR_MAP; @@ -587,7 +554,7 @@ ATTR_MAP(PadD) = {{"paddings", ATTR_DESC(paddings, AnyTraits())}, - {"output_type", ATTR_DESC(output_type, AnyTraits())}}; + {"output_type", ATTR_DESC(dtype, AnyTraits())}}; OUTPUT_MAP(ArgMaxD) = {{0, OUTPUT_DESC(y)}}; // ArgMinD INPUT_MAP(ArgMinD) = {{1, INPUT_DESC(x)}}; ATTR_MAP(ArgMinD) = {{"axis", ATTR_DESC(dimension, AnyTraits())}, - {"output_type", ATTR_DESC(output_type, AnyTraits())}}; + {"output_type", ATTR_DESC(dtype, AnyTraits())}}; OUTPUT_MAP(ArgMinD) = {{0, OUTPUT_DESC(y)}}; // ArgMaxWithValue @@ -634,14 +601,14 @@ ATTR_MAP(ArgMinWithValue) = {{"axis", ATTR_DESC(dimension, AnyTraits())}, OUTPUT_MAP(ArgMinWithValue) = {{0, OUTPUT_DESC(indice)}, {1, OUTPUT_DESC(values)}}; // ReduceAll -INPUT_MAP(ReduceAll) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axis)}}; +INPUT_MAP(ReduceAll) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axes)}}; ATTR_MAP(ReduceAll) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceAll) = {{0, OUTPUT_DESC(y)}}; // ReduceMeanD INPUT_MAP(ReduceMeanD) = {{1, INPUT_DESC(x)}}; INPUT_ATTR_MAP(ReduceMeanD) = { - {2, ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}}; + {2, ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(ReduceMeanD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceMeanD) = {{0, OUTPUT_DESC(y)}}; @@ -708,11 +675,12 @@ INPUT_MAP(BiasAddGrad) = {{1, INPUT_DESC(x)}}; ATTR_MAP(BiasAddGrad) = {{"data_format", ATTR_DESC(data_format, AnyTraits())}}; OUTPUT_MAP(BiasAddGrad) = {{0, OUTPUT_DESC(y)}}; -// maxpoolgrad +// MaxPoolGrad INPUT_MAP(MaxPoolGrad) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}, {3, INPUT_DESC(grad)}}; ATTR_MAP(MaxPoolGrad) = {{"ksize", ATTR_DESC(ksize, AnyTraits(), AnyTraits>())}, {"strides", ATTR_DESC(strides, AnyTraits(), AnyTraits>())}, - {"padding", ATTR_DESC(padding, AnyTraits())}}; + {"padding", ATTR_DESC(padding, AnyTraits())}, + {"data_format", ATTR_DESC(data_format, AnyTraits())}}; OUTPUT_MAP(MaxPoolGrad) = {{0, OUTPUT_DESC(y)}}; // avgpoolgrad @@ -739,28 +707,34 @@ ATTR_MAP(Conv2D) = { {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, + {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"group", ATTR_DESC(groups, AnyTraits())} }; OUTPUT_MAP(Conv2D) = {{0, OUTPUT_DESC(y)}}; // Conv2DBackpropInputD -INPUT_MAP(Conv2DBackpropInputD) = {{1, INPUT_DESC(out_backprop)}, {2, INPUT_DESC(filters)}}; +INPUT_MAP(Conv2DBackpropInputD) = {{1, INPUT_DESC(out_backprop)}, {2, INPUT_DESC(filter)}}; INPUT_ATTR_MAP(Conv2DBackpropInputD) = { - {3, ATTR_DESC(input_sizes, AnyTraits>(), AnyTraits>())}}; + {3, ATTR_DESC(input_size, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(Conv2DBackpropInputD) = { {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"stride", ATTR_DESC(strides, "strides", AnyTraits>())}, + {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, + {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"group", ATTR_DESC(groups, AnyTraits())} }; OUTPUT_MAP(Conv2DBackpropInputD) = {{0, OUTPUT_DESC(y)}}; // Conv2DBackpropFilterD INPUT_MAP(Conv2DBackpropFilterD) = {{1, INPUT_DESC(out_backprop)}, {2, INPUT_DESC(x)}}; INPUT_ATTR_MAP(Conv2DBackpropFilterD) = { - {3, ATTR_DESC(filter_sizes, AnyTraits>(), AnyTraits>())}}; + {3, ATTR_DESC(filter_size, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(Conv2DBackpropFilterD) = { {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"stride", ATTR_DESC(strides, "strides", AnyTraits>())}, + {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, + {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"group", ATTR_DESC(groups, AnyTraits())} }; OUTPUT_MAP(Conv2DBackpropFilterD) = {{0, OUTPUT_DESC(y)}}; @@ -798,8 +772,8 @@ OUTPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{0, OUTPUT_DESC(filter_grad)}}; // MatMul INPUT_MAP(MatMul) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; -ATTR_MAP(MatMul) = {{"transpose_a", ATTR_DESC(transpose_a, AnyTraits())}, - {"transpose_b", ATTR_DESC(transpose_b, AnyTraits())}}; +ATTR_MAP(MatMul) = {{"transpose_a", ATTR_DESC(transpose_x1, AnyTraits())}, + {"transpose_b", ATTR_DESC(transpose_x2, AnyTraits())}}; OUTPUT_MAP(MatMul) = {{0, OUTPUT_DESC(y)}}; // Merge @@ -846,10 +820,10 @@ ATTR_MAP(Sub) = EMPTY_ATTR_MAP; OUTPUT_MAP(Sub) = {{0, OUTPUT_DESC(y)}}; // SplitD -INPUT_MAP(SplitD) = {{1, INPUT_DESC(value)}}; +INPUT_MAP(SplitD) = {{1, INPUT_DESC(x)}}; ATTR_MAP(SplitD) = {{"axis", ATTR_DESC(split_dim, AnyTraits())}, {"output_num", ATTR_DESC(num_split, AnyTraits())}}; -DYN_OUTPUT_MAP(SplitD) = {{0, DYN_OUTPUT_DESC(output)}}; +DYN_OUTPUT_MAP(SplitD) = {{0, DYN_OUTPUT_DESC(y)}}; // Neg INPUT_MAP(Neg) = {{1, INPUT_DESC(x)}}; @@ -876,12 +850,12 @@ OUTPUT_MAP(Pack) = {{0, OUTPUT_DESC(y)}}; // ConcatD INPUT_MAP(ConcatD) = EMPTY_INPUT_MAP; -DYN_INPUT_MAP(ConcatD) = {{1, DYN_INPUT_DESC(input_values)}}; +DYN_INPUT_MAP(ConcatD) = {{1, DYN_INPUT_DESC(x)}}; ATTR_MAP(ConcatD) = { {"axis", ATTR_DESC(concat_dim, AnyTraits())}, {"inputNums", ATTR_DESC(N, AnyTraits())}, }; -OUTPUT_MAP(ConcatD) = {{0, OUTPUT_DESC(output_data)}}; +OUTPUT_MAP(ConcatD) = {{0, OUTPUT_DESC(y)}}; // Less INPUT_MAP(Less) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; @@ -916,14 +890,14 @@ OUTPUT_MAP(TanhGrad) = {{0, OUTPUT_DESC(z)}}; // ReduceMinD INPUT_MAP(ReduceMinD) = {{1, INPUT_DESC(x)}}; INPUT_ATTR_MAP(ReduceMinD) = { - {2, ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}}; + {2, ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(ReduceMinD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceMinD) = {{0, OUTPUT_DESC(y)}}; // ReduceMaxD INPUT_MAP(ReduceMaxD) = {{1, INPUT_DESC(x)}}; INPUT_ATTR_MAP(ReduceMaxD) = { - {2, ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}}; + {2, ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(ReduceMaxD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceMaxD) = {{0, OUTPUT_DESC(y)}}; @@ -1008,11 +982,11 @@ INPUT_MAP(LessEqual) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; ATTR_MAP(LessEqual) = EMPTY_ATTR_MAP; OUTPUT_MAP(LessEqual) = {{0, OUTPUT_DESC(y)}}; -// LogSoftmax -INPUT_MAP(LogSoftmax) = {{1, INPUT_DESC(logits)}}; -ATTR_MAP(LogSoftmax) = { - {"axis", ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}}; -OUTPUT_MAP(LogSoftmax) = {{0, OUTPUT_DESC(logsoftmax)}}; +// LogSoftmaxV2 +INPUT_MAP(LogSoftmaxV2) = {{1, INPUT_DESC(logits)}}; +ATTR_MAP(LogSoftmaxV2) = { + {"axis", ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}}; +OUTPUT_MAP(LogSoftmaxV2) = {{0, OUTPUT_DESC(logsoftmax)}}; // RandomChoiceWithMask INPUT_MAP(RandomChoiceWithMask) = {{1, INPUT_DESC(x)}}; @@ -1094,8 +1068,8 @@ OUTPUT_MAP(LayerNormGrad) = {{0, OUTPUT_DESC(pd_x)}, {1, OUTPUT_DESC(pd_gamma)}, // BatchMatMul INPUT_MAP(BatchMatMul) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; -ATTR_MAP(BatchMatMul) = {{"transpose_x1", ATTR_DESC(adj_x, AnyTraits())}, - {"transpose_x2", ATTR_DESC(adj_y, AnyTraits())}}; +ATTR_MAP(BatchMatMul) = {{"transpose_x1", ATTR_DESC(adj_x1, AnyTraits())}, + {"transpose_x2", ATTR_DESC(adj_x2, AnyTraits())}}; OUTPUT_MAP(BatchMatMul) = {{0, OUTPUT_DESC(y)}}; // DropoutDoMask diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index 03463b978f..59acfa4153 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -209,8 +209,8 @@ DECLARE_OP_USE_OUTPUT(Merge) DECLARE_OP_ADAPTER(Switch) DECLARE_OP_USE_OUTPUT(Switch) -DECLARE_OP_ADAPTER(TopKV2) -DECLARE_OP_USE_OUTPUT(TopKV2) +DECLARE_OP_ADAPTER(TopK) +DECLARE_OP_USE_OUTPUT(TopK) DECLARE_OP_ADAPTER(RealDiv) DECLARE_OP_USE_OUTPUT(RealDiv) @@ -260,8 +260,8 @@ DECLARE_OP_ADAPTER(Select) DECLARE_OP_USE_OUTPUT(Select) DECLARE_OP_ADAPTER(LessEqual) DECLARE_OP_USE_OUTPUT(LessEqual) -DECLARE_OP_ADAPTER(LogSoftmax) -DECLARE_OP_USE_OUTPUT(LogSoftmax) +DECLARE_OP_ADAPTER(LogSoftmaxV2) +DECLARE_OP_USE_OUTPUT(LogSoftmaxV2) DECLARE_OP_ADAPTER(TruncatedNormal) DECLARE_OP_USE_OUTPUT(TruncatedNormal) DECLARE_OP_ADAPTER(StridedSliceGrad) @@ -391,8 +391,8 @@ DECLARE_OP_ADAPTER(Sigmoid) DECLARE_OP_USE_OUTPUT(Sigmoid) DECLARE_OP_ADAPTER(SigmoidGrad) DECLARE_OP_USE_OUTPUT(SigmoidGrad) -DECLARE_OP_ADAPTER(Softmax) -DECLARE_OP_USE_OUTPUT(Softmax) +DECLARE_OP_ADAPTER(SoftmaxV2) +DECLARE_OP_USE_OUTPUT(SoftmaxV2) DECLARE_OP_ADAPTER(SoftmaxGrad) DECLARE_OP_USE_OUTPUT(SoftmaxGrad) DECLARE_OP_ADAPTER(Greater) From 96e1127edba7dec4ef917cfa556428b096389e22 Mon Sep 17 00:00:00 2001 From: zhangz0911gm Date: Thu, 2 Apr 2020 10:30:41 -0400 Subject: [PATCH 003/227] Adding new operator SparseApplyFtrlD in ME --- mindspore/ccsrc/transform/convert.cc | 2 + mindspore/ccsrc/transform/op_declare.cc | 13 +++++ mindspore/ccsrc/transform/op_declare.h | 2 + mindspore/ops/operations/__init__.py | 3 +- mindspore/ops/operations/nn_ops.py | 76 ++++++++++++++++++++++++- tests/ut/python/ops/test_ops.py | 5 ++ 6 files changed, 99 insertions(+), 2 deletions(-) diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index 74b0695cff..29f156426d 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -171,6 +171,7 @@ const char kNameAbsGrad[] = "AbsGrad"; const char kNameBinaryCrossEntropy[] = "BinaryCrossEntropy"; const char kNameBinaryCrossEntropyGrad[] = "BinaryCrossEntropyGrad"; const char kNameSparseApplyAdagrad[] = "SparseApplyAdagrad"; +const char kNameSparseApplyFtrlD[] = "SparseApplyFtrlD"; const char kNameSpaceToDepth[] = "SpaceToDepth"; const char kNameDepthToSpace[] = "DepthToSpace"; const char kNameSign[] = "Sign"; @@ -353,6 +354,7 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameBinaryCrossEntropy), ADPT_DESC(BinaryCrossEntropy)}, {string(kNameBinaryCrossEntropyGrad), ADPT_DESC(BinaryCrossEntropyGrad)}, {string(kNameSparseApplyAdagrad), ADPT_DESC(SparseApplyAdagradD)}, + {string(kNameSparseApplyFtrlD), ADPT_DESC(SparseApplyFtrlD)}, {string(kNameSpaceToDepth), ADPT_DESC(SpaceToDepth)}, {string(kNameDepthToSpace), ADPT_DESC(DepthToSpace)}, {string(kNameSign), ADPT_DESC(Sign)}, diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 78b949c525..474bd160ce 100755 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -1146,6 +1146,19 @@ ATTR_MAP(SparseApplyAdagradD) = {{"lr", ATTR_DESC(lr, AnyTraits())}, {"use_locking", ATTR_DESC(use_locking, AnyTraits())}}; OUTPUT_MAP(SparseApplyAdagradD) = {{0, OUTPUT_DESC(var)}}; +// SparseApplyFtrlD +INPUT_MAP(SparseApplyFtrlD) = {{1, INPUT_DESC(var)}, + {2, INPUT_DESC(accum)}, + {3, INPUT_DESC(linear)}, + {4, INPUT_DESC(grad)}, + {5, INPUT_DESC(indices)}}; +ATTR_MAP(SparseApplyFtrlD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits())}, + {"lr", ATTR_DESC(lr, AnyTraits())}, + {"l1", ATTR_DESC(l1, AnyTraits())}, + {"l2", ATTR_DESC(l2, AnyTraits())}, + {"lr_power", ATTR_DESC(lr_power, AnyTraits())}}; +OUTPUT_MAP(SparseApplyFtrlD) = {{0, OUTPUT_DESC(var)}}; + // SpaceToDepth INPUT_MAP(SpaceToDepth) = {{1, INPUT_DESC(x)}}; ATTR_MAP(SpaceToDepth) = {{"block_size", ATTR_DESC(block_size, AnyTraits())}}; diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index 03463b978f..1447052d56 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -435,6 +435,8 @@ DECLARE_OP_ADAPTER(Round) DECLARE_OP_USE_OUTPUT(Round) DECLARE_OP_ADAPTER(ApplyFtrl) DECLARE_OP_USE_OUTPUT(ApplyFtrl) +DECLARE_OP_ADAPTER(SparseApplyFtrlD) +DECLARE_OP_USE_OUTPUT(SparseApplyFtrlD) #ifdef ENABLE_GE DECLARE_OP_ADAPTER(Print) DECLARE_OP_USE_DYN_INPUT(Print) diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index a75b078df8..77bb6d0ff3 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -65,7 +65,7 @@ from .nn_ops import (LSTM, SGD, Adam, ApplyMomentum, BatchNorm, SmoothL1Loss, Softmax, SoftmaxCrossEntropyWithLogits, ROIAlign, SparseSoftmaxCrossEntropyWithLogits, Tanh, - TopK, BinaryCrossEntropy, SparseApplyAdagrad, LARSUpdate, ApplyFtrl) + TopK, BinaryCrossEntropy, SparseApplyAdagrad, LARSUpdate, ApplyFtrl, SparseApplyFtrlD) from .other_ops import Assign, IOU, BoundingBoxDecode, BoundingBoxEncode, CheckValid, MakeRefKey @@ -217,6 +217,7 @@ __all__ = [ "Abs", "BinaryCrossEntropy", "SparseApplyAdagrad", + "SparseApplyFtrlD", "SpaceToDepth", "DepthToSpace", "Conv2DBackpropInput", diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index afa4c7dfe3..f404d22ff9 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -2141,6 +2141,80 @@ class SparseApplyAdagrad(PrimitiveWithInfer): return var_type +class SparseApplyFtrlD(PrimitiveWithInfer): + r""" + Conduct experiment on updating on parameters related to FTRL optimization algorithm. + + .. math :: + \text{accum} = \text{grad} * \text{grad} + + .. math :: + \text{linear} += \text{grad} + (\text{accum} ^ {\text{-lr_power}} - + \frac{\text{accum} ^ \text{-lr_power}}{\text{lr}} * \text{var}) + + .. math :: + \text{quadratic} = {\text{1.0}/({\text{accum}^\text{lr_power} * \text{lr}}) + 2*\text{l2} + + .. math :: + \text{var} = {\text{sign}({linear}) * \text{l1} - \text{linear}})/{ quadratic } + if \vert linear \vert > l1 \ else \ 0.0 + + Args: + lr (float): Learning rate. + l1 (float): temp value NO.1. + l2 (float): temp value No.2. + lr_power (float): temp value used as power number. + use_locking (bool): If true, updating the var and accum tensors will be protected. Default: False. + + Inputs: + - **var** (Tensor) - Variable to be update. The type must be float32. + - **accum** (Tensor) - Accum to be update. The shape must be the same as `var`'s shape, + the type must be float32. + - **linear** (Tensor) - Linear to be update. The shape must be the same as `var`'s shape, + the type must be float32. + - **grad** (Tensor) - Gradient. The shape must be the same as `var`'s shape, + the type must be float32. + - **indices** (Tensor) - A vector of indices into the first dimension of 'var' and 'accum', + the shape of `indices` must be the same as `grad` in first dimension, the type must be int32. + + Output: + Tensors, has the same shape and type as `var`. + + """ + + @prim_attr_register + def __init__(self, lr, l1, l2, lr_power, use_locking=False): + """init SparseApplyFtrlD""" + self.lr = validator.check_type("lr", lr, [float]) + self.l1 = validator.check_type("l1", l1, [float]) + self.l2 = validator.check_type("l2", l2, [float]) + self.lr_power = validator.check_type("lr_power", lr_power, [float]) + self.use_locking = validator.check_type("use_locking", use_locaking, [bool]) + + def infer_shape(self, var_shape, accum_shape, linear_shape, grad_shape, indices_shape): + validator.check_param_equal('var shape', var_shape, 'accum shape', accum_shape) + validator.check_param_equal('len of var shape', len(var_shape), 'len of grad shape', len(grad_shape)) + validator.check_param_equal('len of var shape', len(var_shape), 'len of linear shape', len(linear_shape)) + if len(var_shape) > 1: + validator.check_param_equal('var_shape', var_shape[1:], 'grad_shape', grad_shape[1:]) + validator.check_param_equal('var_shape', var_shape[1:], 'linear_shape', linear_shape[1:]) + validator.check_integer("len of indices shape", len(indices_shape), 1, Rel.EQ) + validator.check('the first dimension of grad', grad_shape[0], + 'the shape of indices', indices_shape[0], Rel.EQ) + + return var_shape + + def infer_dtype(self, var_type, accum_type, linear_type, grad_type, indices_type): + validator.check_subclass("var_type", var_type, mstype.tensor) + validator.check_subclass("accum_type", accum_type, mstype.tensor) + validator.check_subclass("linear_type", linear_type, mstype.tensor) + validator.check_subclass("grad_type", grad_type, mstype.tensor) + validator.check_subclass("indices_type", indices_type, mstype.tensor) + validator.check_subclass('indices_type', indices_type, [mstype.int32]) + + return var_type + + class LARSUpdate(PrimitiveWithInfer): """ Conduct lars (layer-wise adaptive rate scaling) update on the square sum of gradient. @@ -2244,4 +2318,4 @@ class ApplyFtrl(PrimitiveWithInfer): validator.check_typename("l1", l1_type,[mstype.float16, mstype.float32]) validator.check_typename("l2", l2_type,[mstype.float16, mstype.float32]) validator.check_typename("lr_power", lr_power_type,[mstype.float16, mstype.float32]) - return var_type \ No newline at end of file + return var_type diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py index bfe8075972..8d7dd95072 100755 --- a/tests/ut/python/ops/test_ops.py +++ b/tests/ut/python/ops/test_ops.py @@ -749,6 +749,11 @@ test_case_nn_ops = [ 'desc_inputs': [[3, 3], [3, 3], [3, 3], Tensor(np.ones((3,), np.int32))], 'desc_bprop': [3, 3], 'skip': ['backward']}), + ('SparseApplyFtrlD', { + 'block': P.SparseApplyFtrlD(0.1, 0.1, 0.1, -0.1), + 'desc_inputs': [[3, 3], [3, 3], [3, 3], [3, 3], Tensor(2*np.ones((3,), np.int32))], + 'desc_bprop': [3, 3], + 'skip': ['backward']}), ('Flatten_1', { 'block': NetForFlatten(), 'desc_inputs': [Tensor(np.ones([2, 3, 4]).astype(np.int32)), Tensor(np.ones([2, 12]).astype(np.int32))], From b57b111947756eb6334c5dcca338e7fd0d18dfe6 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Fri, 3 Apr 2020 17:22:03 +0800 Subject: [PATCH 004/227] modify spell error --- mindspore/ops/operations/nn_ops.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index f404d22ff9..57e409b44f 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -2189,7 +2189,7 @@ class SparseApplyFtrlD(PrimitiveWithInfer): self.l1 = validator.check_type("l1", l1, [float]) self.l2 = validator.check_type("l2", l2, [float]) self.lr_power = validator.check_type("lr_power", lr_power, [float]) - self.use_locking = validator.check_type("use_locking", use_locaking, [bool]) + self.use_locking = validator.check_type("use_locking", use_locking, [bool]) def infer_shape(self, var_shape, accum_shape, linear_shape, grad_shape, indices_shape): validator.check_param_equal('var shape', var_shape, 'accum shape', accum_shape) @@ -2210,7 +2210,6 @@ class SparseApplyFtrlD(PrimitiveWithInfer): validator.check_subclass("linear_type", linear_type, mstype.tensor) validator.check_subclass("grad_type", grad_type, mstype.tensor) validator.check_subclass("indices_type", indices_type, mstype.tensor) - validator.check_subclass('indices_type', indices_type, [mstype.int32]) return var_type From b61ad0a5a77c2af155275f3beac87557691cbc0d Mon Sep 17 00:00:00 2001 From: chenhaozhe Date: Mon, 30 Mar 2020 20:10:56 +0800 Subject: [PATCH 005/227] use find instead of equal to distinguish training graph --- mindspore/ccsrc/pipeline/pipeline.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/ccsrc/pipeline/pipeline.cc b/mindspore/ccsrc/pipeline/pipeline.cc index 35336e975b..70ef9a5407 100644 --- a/mindspore/ccsrc/pipeline/pipeline.cc +++ b/mindspore/ccsrc/pipeline/pipeline.cc @@ -1071,7 +1071,7 @@ bool ExecutorPy::AddDFGraph(const py::dict& init_params, const std::string& phas } std::string init_graph = "init_subgraph." + net_id; std::string checkpoint_name = "save." + net_id; - if (phase == "train") { + if (phase.find("train") != std::string::npos) { (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph(), {{"ge.exec.variable_acc", "1"}}); } else { (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph()); From 2da8570a0111fcc37cf556b09d1ac753ec7a536f Mon Sep 17 00:00:00 2001 From: lvliang Date: Thu, 2 Apr 2020 15:39:22 +0800 Subject: [PATCH 006/227] pynative-add-lenet --- tests/st/pynative/test_ascend_lenet.py | 155 +++++++++++++++++++++++++ 1 file changed, 155 insertions(+) create mode 100644 tests/st/pynative/test_ascend_lenet.py diff --git a/tests/st/pynative/test_ascend_lenet.py b/tests/st/pynative/test_ascend_lenet.py new file mode 100644 index 0000000000..7f69b4585f --- /dev/null +++ b/tests/st/pynative/test_ascend_lenet.py @@ -0,0 +1,155 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +import pytest +import numpy as np +import time, math +import mindspore.nn as nn +from mindspore import context, Tensor, ParameterTuple +from mindspore.ops import operations as P +from mindspore.common.initializer import TruncatedNormal +from mindspore.ops import functional as F +from mindspore.ops import composite as C +from mindspore.common import dtype as mstype + +from mindspore.nn.wrap.cell_wrapper import WithLossCell +from mindspore.nn.optim import Momentum + +np.random.seed(1) + +def weight_variable(): + """weight initial""" + return TruncatedNormal(0.02) + + +def conv(in_channels, out_channels, kernel_size, stride=1, padding=0): + """weight initial for conv layer""" + weight = weight_variable() + return nn.Conv2d(in_channels, out_channels, + kernel_size=kernel_size, stride=stride, padding=padding, + weight_init=weight, has_bias=False, pad_mode="valid") + + +def fc_with_initialize(input_channels, out_channels): + """weight initial for fc layer""" + weight = weight_variable() + bias = weight_variable() + return nn.Dense(input_channels, out_channels, weight, bias) + + +class LeNet(nn.Cell): + """ + Lenet network + Args: + num_class (int): Num classes, Default: 10. + Returns: + Tensor, output tensor + Examples: + >>> LeNet(num_class=10) + """ + def __init__(self, num_class=10): + super(LeNet, self).__init__() + self.num_class = num_class + self.batch_size = 32 + self.conv1 = conv(1, 6, 5) + self.conv2 = conv(6, 16, 5) + self.fc1 = fc_with_initialize(16 * 5 * 5, 120) + self.fc2 = fc_with_initialize(120, 84) + self.fc3 = fc_with_initialize(84, self.num_class) + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.reshape = P.Reshape() + + def construct(self, x): + x = self.conv1(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.conv2(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.reshape(x, (self.batch_size, -1)) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.relu(x) + x = self.fc3(x) + return x + + +class CrossEntropyLoss(nn.Cell): + """ + Define loss for network + """ + def __init__(self): + super(CrossEntropyLoss, self).__init__() + self.cross_entropy = P.SoftmaxCrossEntropyWithLogits() + self.mean = P.ReduceMean() + self.one_hot = P.OneHot() + self.on_value = Tensor(1.0, mstype.float32) + self.off_value = Tensor(0.0, mstype.float32) + self.num = Tensor(32.0, mstype.float32) + + def construct(self, logits, label): + label = self.one_hot(label, F.shape(logits)[1], self.on_value, self.off_value) + loss = self.cross_entropy(logits, label)[0] + loss = P.RealDiv()(P.ReduceSum()(loss, -1), self.num) + return loss + + +class GradWrap(nn.Cell): + """ + GradWrap definition + """ + def __init__(self, network): + super(GradWrap, self).__init__() + self.network = network + self.weights = ParameterTuple(filter(lambda x: x.requires_grad, network.get_parameters())) + + def construct(self, x, label): + weights = self.weights + return C.grad_by_list(self.network, weights)(x, label) + + +@pytest.mark.level0 +@pytest.mark.platform_x86_ascend_training +@pytest.mark.env_single +def test_ascend_pynative_lenet(): + context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend") + + epoch_size = 20 + batch_size = 32 + inputs = Tensor(np.ones([batch_size, 1, 32, 32]).astype(np.float32)) + labels = Tensor(np.ones([batch_size]).astype(np.int32)) + + net = LeNet() + criterion = CrossEntropyLoss() + optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9) + + net_with_criterion = WithLossCell(net, criterion) + train_network = GradWrap(net_with_criterion) + train_network.set_train() + total_time = 0 + + for epoch in range(0, epoch_size): + start_time = time.time() + fw_output = net(inputs) + loss_output = criterion(fw_output, labels) + grads = train_network(inputs, labels) + success = optimizer(grads) + end_time = time.time() + cost_time = end_time - start_time + total_time = total_time + cost_time + assert(total_time < 20.0) + assert(loss_output.asnumpy() < 0.01) + \ No newline at end of file From 56ab3a1df32053d0ae1a6fa22eb8dc1753af77a3 Mon Sep 17 00:00:00 2001 From: seatea Date: Mon, 30 Mar 2020 12:10:35 +0800 Subject: [PATCH 007/227] Check if the shape of the input of NMSWithMask is (N, 5). --- mindspore/ops/operations/math_ops.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 213e04e351..ad928d792f 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1745,11 +1745,14 @@ class NMSWithMask(PrimitiveWithInfer): self.init_prim_io_names(inputs=['bboxes'], outputs=['selected_boxes', 'selected_idx', 'selected_mask']) def infer_shape(self, bboxes_shape): + validator.check_integer("bboxes rank", len(bboxes_shape), 2, Rel.EQ) + validator.check_integer("bboxes.shape()[0]", bboxes_shape[0], 0, Rel.GT) + validator.check_integer("bboxes.shape()[1]", bboxes_shape[1], 5, Rel.EQ) num = bboxes_shape[0] - validator.check_integer("bboxes_shape[0]", num, 0, Rel.GT) return (bboxes_shape, (num,), (num,)) def infer_dtype(self, bboxes_dtype): + validator.check_subclass("bboxes_dtype", bboxes_dtype, mstype.tensor) validator.check_typename("bboxes_dtype", bboxes_dtype, [mstype.float16, mstype.float32]) return (bboxes_dtype, mstype.int32, mstype.bool_) From 02ebf03c6e9a5ebb947780f7e858c878648828d2 Mon Sep 17 00:00:00 2001 From: yoonlee666 Date: Mon, 30 Mar 2020 16:21:57 +0800 Subject: [PATCH 008/227] add bert script to master --- example/Bert_NEZHA/config.py | 55 ---------------- example/Bert_NEZHA_cnwiki/config.py | 57 ++++++++++++++++ .../main.py => Bert_NEZHA_cnwiki/train.py} | 66 +++++++------------ 3 files changed, 82 insertions(+), 96 deletions(-) delete mode 100644 example/Bert_NEZHA/config.py create mode 100644 example/Bert_NEZHA_cnwiki/config.py rename example/{Bert_NEZHA/main.py => Bert_NEZHA_cnwiki/train.py} (57%) diff --git a/example/Bert_NEZHA/config.py b/example/Bert_NEZHA/config.py deleted file mode 100644 index 2f3b22fe50..0000000000 --- a/example/Bert_NEZHA/config.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright 2020 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ - -""" -network config setting, will be used in main.py -""" - -from easydict import EasyDict as edict -import mindspore.common.dtype as mstype -from mindspore.model_zoo.Bert_NEZHA import BertConfig -bert_cfg = edict({ - 'epoch_size': 10, - 'num_warmup_steps': 0, - 'start_learning_rate': 1e-4, - 'end_learning_rate': 1, - 'decay_steps': 1000, - 'power': 10.0, - 'save_checkpoint_steps': 2000, - 'keep_checkpoint_max': 10, - 'checkpoint_prefix': "checkpoint_bert", - 'DATA_DIR' = "/your/path/examples.tfrecord" - 'SCHEMA_DIR' = "/your/path/datasetSchema.json" - 'bert_config': BertConfig( - batch_size=16, - seq_length=128, - vocab_size=21136, - hidden_size=1024, - num_hidden_layers=24, - num_attention_heads=16, - intermediate_size=4096, - hidden_act="gelu", - hidden_dropout_prob=0.0, - attention_probs_dropout_prob=0.0, - max_position_embeddings=512, - type_vocab_size=2, - initializer_range=0.02, - use_relative_positions=True, - input_mask_from_dataset=True, - token_type_ids_from_dataset=True, - dtype=mstype.float32, - compute_type=mstype.float16, - ) -}) diff --git a/example/Bert_NEZHA_cnwiki/config.py b/example/Bert_NEZHA_cnwiki/config.py new file mode 100644 index 0000000000..a704d9a264 --- /dev/null +++ b/example/Bert_NEZHA_cnwiki/config.py @@ -0,0 +1,57 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +""" +network config setting, will be used in train.py +""" + +from easydict import EasyDict as edict +import mindspore.common.dtype as mstype +from mindspore.model_zoo.Bert_NEZHA import BertConfig +bert_train_cfg = edict({ + 'epoch_size': 10, + 'num_warmup_steps': 0, + 'start_learning_rate': 1e-4, + 'end_learning_rate': 0.0, + 'decay_steps': 1000, + 'power': 10.0, + 'save_checkpoint_steps': 2000, + 'keep_checkpoint_max': 10, + 'checkpoint_prefix': "checkpoint_bert", + # please add your own dataset path + 'DATA_DIR': "/your/path/examples.tfrecord", + # please add your own dataset schema path + 'SCHEMA_DIR': "/your/path/datasetSchema.json" +}) +bert_net_cfg = BertConfig( + batch_size=16, + seq_length=128, + vocab_size=21136, + hidden_size=1024, + num_hidden_layers=24, + num_attention_heads=16, + intermediate_size=4096, + hidden_act="gelu", + hidden_dropout_prob=0.0, + attention_probs_dropout_prob=0.0, + max_position_embeddings=512, + type_vocab_size=2, + initializer_range=0.02, + use_relative_positions=True, + input_mask_from_dataset=True, + token_type_ids_from_dataset=True, + dtype=mstype.float32, + compute_type=mstype.float16, +) diff --git a/example/Bert_NEZHA/main.py b/example/Bert_NEZHA_cnwiki/train.py similarity index 57% rename from example/Bert_NEZHA/main.py rename to example/Bert_NEZHA_cnwiki/train.py index a5500f25a9..87f425e21c 100644 --- a/example/Bert_NEZHA/main.py +++ b/example/Bert_NEZHA_cnwiki/train.py @@ -14,7 +14,8 @@ # ============================================================================ """ -NEZHA (NEural contextualiZed representation for CHinese lAnguage understanding) is the Chinese pretrained language model currently based on BERT developed by Huawei. +NEZHA (NEural contextualiZed representation for CHinese lAnguage understanding) is the Chinese pretrained language +model currently based on BERT developed by Huawei. 1. Prepare data Following the data preparation as in BERT, run command as below to get dataset for training: python ./create_pretraining_data.py \ @@ -28,35 +29,29 @@ Following the data preparation as in BERT, run command as below to get dataset f --random_seed=12345 \ --dupe_factor=5 2. Pretrain -First, prepare the distributed training environment, then adjust configurations in config.py, finally run main.py. +First, prepare the distributed training environment, then adjust configurations in config.py, finally run train.py. """ import os -import pytest import numpy as np -from numpy import allclose -from config import bert_cfg as cfg -import mindspore.common.dtype as mstype +from config import bert_train_cfg, bert_net_cfg import mindspore.dataset.engine.datasets as de import mindspore._c_dataengine as deMap from mindspore import context from mindspore.common.tensor import Tensor from mindspore.train.model import Model -from mindspore.train.callback import Callback -from mindspore.model_zoo.Bert_NEZHA import BertConfig, BertNetworkWithLoss, BertTrainOneStepCell +from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor +from mindspore.model_zoo.Bert_NEZHA import BertNetworkWithLoss, BertTrainOneStepCell from mindspore.nn.optim import Lamb -from mindspore import log as logger _current_dir = os.path.dirname(os.path.realpath(__file__)) -DATA_DIR = [cfg.DATA_DIR] -SCHEMA_DIR = cfg.SCHEMA_DIR -def me_de_train_dataset(batch_size): - """test me de train dataset""" +def create_train_dataset(batch_size): + """create train dataset""" # apply repeat operations - repeat_count = cfg.epoch_size - ds = de.StorageDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids", - "next_sentence_labels", "masked_lm_positions", - "masked_lm_ids", "masked_lm_weights"]) + repeat_count = bert_train_cfg.epoch_size + ds = de.StorageDataset([bert_train_cfg.DATA_DIR], bert_train_cfg.SCHEMA_DIR, + columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels", + "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"]) type_cast_op = deMap.TypeCastOp("int32") ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op) ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op) @@ -69,43 +64,32 @@ def me_de_train_dataset(batch_size): ds = ds.repeat(repeat_count) return ds - def weight_variable(shape): """weight variable""" np.random.seed(1) ones = np.random.uniform(-0.1, 0.1, size=shape).astype(np.float32) return Tensor(ones) - -class ModelCallback(Callback): - def __init__(self): - super(ModelCallback, self).__init__() - self.loss_list = [] - - def step_end(self, run_context): - cb_params = run_context.original_args() - self.loss_list.append(cb_params.net_outputs.asnumpy()[0]) - logger.info("epoch: {}, outputs are {}".format(cb_params.cur_epoch_num, str(cb_params.net_outputs))) - -def test_bert_tdt(): - """test bert tdt""" +def train_bert(): + """train bert""" context.set_context(mode=context.GRAPH_MODE) context.set_context(device_target="Ascend") context.set_context(enable_task_sink=True) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) - parallel_callback = ModelCallback() - ds = me_de_train_dataset(cfg.bert_config.batch_size) - config = cfg.bert_config - netwithloss = BertNetworkWithLoss(config, True) - optimizer = Lamb(netwithloss.trainable_params(), decay_steps=cfg.decay_steps, start_learning_rate=cfg.start_learning_rate, - end_learning_rate=cfg.end_learning_rate, power=cfg.power, warmup_steps=cfg.num_warmup_steps, decay_filter=lambda x: False) + ds = create_train_dataset(bert_net_cfg.batch_size) + netwithloss = BertNetworkWithLoss(bert_net_cfg, True) + optimizer = Lamb(netwithloss.trainable_params(), decay_steps=bert_train_cfg.decay_steps, + start_learning_rate=bert_train_cfg.start_learning_rate, + end_learning_rate=bert_train_cfg.end_learning_rate, power=bert_train_cfg.power, + warmup_steps=bert_train_cfg.num_warmup_steps, decay_filter=lambda x: False) netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer) netwithgrads.set_train(True) model = Model(netwithgrads) - config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, keep_checkpoint_max=cfg.keep_checkpoint_max) - ckpoint_cb = ModelCheckpoint(prefix=cfg.checkpoint_prefix, config=config_ck) - model.train(ds.get_repeat_count(), ds, callbacks=[parallel_callback, ckpoint_cb], dataset_sink_mode=False) + config_ck = CheckpointConfig(save_checkpoint_steps=bert_train_cfg.save_checkpoint_steps, + keep_checkpoint_max=bert_train_cfg.keep_checkpoint_max) + ckpoint_cb = ModelCheckpoint(prefix=bert_train_cfg.checkpoint_prefix, config=config_ck) + model.train(ds.get_repeat_count(), ds, callbacks=[LossMonitor(), ckpoint_cb], dataset_sink_mode=False) if __name__ == '__main__': - test_bert_tdt() + train_bert() From 04e4f6d92713e4aa4b073774375b483188fbc859 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Mon, 30 Mar 2020 09:34:34 +0800 Subject: [PATCH 009/227] adapt relu6grad and graphengine modified --- graphengine | 2 +- mindspore/ccsrc/transform/op_declare.cc | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/graphengine b/graphengine index 092c7a1f65..78a40081fc 160000 --- a/graphengine +++ b/graphengine @@ -1 +1 @@ -Subproject commit 092c7a1f6548cac7d40e677af3498c3c49ea2bfd +Subproject commit 78a40081fc1462ab9e2083d37e5fbe608a57dd06 diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 07c5e9f5fe..028b129756 100755 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -473,7 +473,7 @@ ATTR_MAP(Relu6) = EMPTY_ATTR_MAP; OUTPUT_MAP(Relu6) = {{0, OUTPUT_DESC(activations)}}; // Relu6Grad -INPUT_MAP(Relu6Grad) = {{1, INPUT_DESC(features)}, {2, INPUT_DESC(gradients)}}; +INPUT_MAP(Relu6Grad) = {{1, INPUT_DESC(gradients)}, {2, INPUT_DESC(features)}}; ATTR_MAP(Relu6Grad) = EMPTY_ATTR_MAP; OUTPUT_MAP(Relu6Grad) = {{0, OUTPUT_DESC(backprops)}}; From 1012790a3300db38753ddc14d9fe354596a829cb Mon Sep 17 00:00:00 2001 From: seatea Date: Mon, 30 Mar 2020 11:46:53 +0800 Subject: [PATCH 010/227] Fix dtype bug for loss_scale and weight_decay. 1.Change dtype of scale to dtype of grad in loss_scale.py; 2.Change dtype of weight_decay to dtype of weight in optimizer.py. --- mindspore/nn/optim/optimizer.py | 2 +- mindspore/nn/wrap/loss_scale.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/mindspore/nn/optim/optimizer.py b/mindspore/nn/optim/optimizer.py index e2b0cddb71..cd0ed93a10 100755 --- a/mindspore/nn/optim/optimizer.py +++ b/mindspore/nn/optim/optimizer.py @@ -84,7 +84,7 @@ apply_decay = C.MultitypeFuncGraph("apply_decay") def _tensor_apply_decay(weight_decay, if_apply, weight, gradient): """Get grad with weight_decay.""" if if_apply: - return op_add((gradient, weight * F.scalar_to_array(weight_decay))) + return op_add((gradient, weight * weight_decay)) return gradient diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py index f7c686f535..a11c753eda 100644 --- a/mindspore/nn/wrap/loss_scale.py +++ b/mindspore/nn/wrap/loss_scale.py @@ -32,7 +32,7 @@ reciprocal = P.Reciprocal() @_grad_scale.register("Tensor", "Tensor") def tensor_grad_scale(scale, grad): - return grad * reciprocal(scale) + return grad * F.cast(reciprocal(scale), F.dtype(grad)) class DynamicLossScaleUpdateCell(Cell): From ef1caad11f0b790ad60ab55ed156db6954a1d940 Mon Sep 17 00:00:00 2001 From: jonyguo Date: Mon, 30 Mar 2020 17:23:44 +0800 Subject: [PATCH 011/227] 1. add more log info for dataset & mindrecord, 2. add two new testcase for MindDataset --- mindspore/dataset/engine/iterators.py | 20 ++++----- mindspore/mindrecord/filewriter.py | 7 ++++ tests/ut/python/dataset/test_minddataset.py | 46 +++++++++++++++++++++ 3 files changed, 63 insertions(+), 10 deletions(-) diff --git a/mindspore/dataset/engine/iterators.py b/mindspore/dataset/engine/iterators.py index 806df64fdc..268a66c0cf 100644 --- a/mindspore/dataset/engine/iterators.py +++ b/mindspore/dataset/engine/iterators.py @@ -66,11 +66,10 @@ def _alter_node(node): class Iterator: """ - General Iterator over a dataset. - - Attributes: - dataset: Dataset to be iterated over + General Iterator over a dataset. + Attributes: + dataset: Dataset to be iterated over """ def __init__(self, dataset): @@ -86,6 +85,7 @@ class Iterator: root = self.__convert_node_postorder(self.dataset) self.depipeline.AssignRootNode(root) self.depipeline.LaunchTreeExec() + self._index = 0 def __is_tree_node(self, node): """Check if a node is tree node.""" @@ -185,10 +185,7 @@ class Iterator: Iterator.__print_local(input_op, level + 1) def print(self): - """ - Print the dataset tree - - """ + """Print the dataset tree""" self.__print_local(self.dataset, 0) def release(self): @@ -202,7 +199,10 @@ class Iterator: def __next__(self): data = self.get_next() if not data: + if self._index == 0: + logger.warning("No records available.") raise StopIteration + self._index += 1 return data def get_output_shapes(self): @@ -234,7 +234,7 @@ class DictIterator(Iterator): def get_next(self): """ - Returns the next record in the dataset as dictionary + Returns the next record in the dataset as dictionary Returns: Dict, the next record in the dataset. @@ -260,7 +260,7 @@ class TupleIterator(Iterator): def get_next(self): """ - Returns the next record in the dataset as a list + Returns the next record in the dataset as a list Returns: List, the next record in the dataset. diff --git a/mindspore/mindrecord/filewriter.py b/mindspore/mindrecord/filewriter.py index 0e44b61857..d1471f47cb 100644 --- a/mindspore/mindrecord/filewriter.py +++ b/mindspore/mindrecord/filewriter.py @@ -328,13 +328,20 @@ class FileWriter: self._generator.build() self._generator.write_to_db() + mindrecord_files = [] + index_files = [] # change the file mode to 600 for item in self._paths: if os.path.exists(item): os.chmod(item, stat.S_IRUSR | stat.S_IWUSR) + mindrecord_files.append(item) index_file = item + ".db" if os.path.exists(index_file): os.chmod(index_file, stat.S_IRUSR | stat.S_IWUSR) + index_files.append(index_file) + + logger.info("The list of mindrecord files created are: {}, and the list of index files are: {}".format( + mindrecord_files, index_files)) return ret diff --git a/tests/ut/python/dataset/test_minddataset.py b/tests/ut/python/dataset/test_minddataset.py index 99b31b64ec..8b8cbc807a 100644 --- a/tests/ut/python/dataset/test_minddataset.py +++ b/tests/ut/python/dataset/test_minddataset.py @@ -25,6 +25,7 @@ import mindspore.dataset.transforms.vision.c_transforms as vision import numpy as np import pytest from mindspore._c_dataengine import InterpolationMode +from mindspore.dataset.transforms.vision import Inter from mindspore import log as logger import mindspore.dataset as ds @@ -151,6 +152,51 @@ def test_cv_minddataset_dataset_size(add_and_remove_cv_file): assert data_set.get_dataset_size() == 3 +def test_cv_minddataset_repeat_reshuffle(add_and_remove_cv_file): + """tutorial for cv minddataset.""" + columns_list = ["data", "label"] + num_readers = 4 + data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers) + decode_op = vision.Decode() + data_set = data_set.map(input_columns=["data"], operations=decode_op, num_parallel_workers=2) + resize_op = vision.Resize((32, 32), interpolation=Inter.LINEAR) + data_set = data_set.map(input_columns="data", operations=resize_op, num_parallel_workers=2) + data_set = data_set.batch(2) + data_set = data_set.repeat(2) + num_iter = 0 + labels = [] + for item in data_set.create_dict_iterator(): + logger.info("-------------- get dataset size {} -----------------".format(num_iter)) + logger.info("-------------- item[label]: {} ---------------------".format(item["label"])) + logger.info("-------------- item[data]: {} ----------------------".format(item["data"])) + num_iter += 1 + labels.append(item["label"]) + assert num_iter == 10 + logger.info("repeat shuffle: {}".format(labels)) + assert len(labels) == 10 + assert labels[0:5] == labels[0:5] + assert labels[0:5] != labels[5:5] + + +def test_cv_minddataset_batch_size_larger_than_records(add_and_remove_cv_file): + """tutorial for cv minddataset.""" + columns_list = ["data", "label"] + num_readers = 4 + data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers) + decode_op = vision.Decode() + data_set = data_set.map(input_columns=["data"], operations=decode_op, num_parallel_workers=2) + resize_op = vision.Resize((32, 32), interpolation=Inter.LINEAR) + data_set = data_set.map(input_columns="data", operations=resize_op, num_parallel_workers=2) + data_set = data_set.batch(32, drop_remainder=True) + num_iter = 0 + for item in data_set.create_dict_iterator(): + logger.info("-------------- get dataset size {} -----------------".format(num_iter)) + logger.info("-------------- item[label]: {} ---------------------".format(item["label"])) + logger.info("-------------- item[data]: {} ----------------------".format(item["data"])) + num_iter += 1 + assert num_iter == 0 + + def test_cv_minddataset_issue_888(add_and_remove_cv_file): """issue 888 test.""" columns_list = ["data", "label"] From 295c00ac39aacd8cb61b182fd5985ad249349576 Mon Sep 17 00:00:00 2001 From: Jonathan Yan Date: Mon, 30 Mar 2020 11:02:08 -0400 Subject: [PATCH 012/227] Replace std::cout with MS_LOG in dataset unit test Signed-off-by: Jonathan Yan --- tests/ut/cpp/dataset/arena_test.cc | 2 +- tests/ut/cpp/dataset/btree_test.cc | 2 +- tests/ut/cpp/dataset/cifar_op_test.cc | 12 ++++-- tests/ut/cpp/dataset/circular_pool_test.cc | 6 +-- tests/ut/cpp/dataset/image_folder_op_test.cc | 33 ++++++++++----- tests/ut/cpp/dataset/manifest_op_test.cc | 12 ++++-- tests/ut/cpp/dataset/map_op_test.cc | 4 +- tests/ut/cpp/dataset/memory_pool_test.cc | 4 +- tests/ut/cpp/dataset/mind_record_op_test.cc | 12 +++--- tests/ut/cpp/dataset/mnist_op_test.cc | 6 ++- tests/ut/cpp/dataset/normalize_op_test.cc | 2 +- tests/ut/cpp/dataset/one_hot_op_test.cc | 4 +- tests/ut/cpp/dataset/path_test.cc | 4 +- tests/ut/cpp/dataset/queue_test.cc | 2 +- .../random_crop_decode_resizeOp_test.cc | 4 +- .../cpp/dataset/stand_alone_samplers_test.cc | 2 +- tests/ut/cpp/dataset/status_test.cc | 8 ++-- tests/ut/cpp/dataset/task_manager_test.cc | 2 +- tests/ut/cpp/dataset/tensor_test.cc | 2 +- tests/ut/cpp/dataset/type_cast_op_test.cc | 2 +- .../cpp/mindrecord/ut_shard_operator_test.cc | 42 ++++++++++++------- tests/ut/cpp/runtest.sh | 1 + 22 files changed, 102 insertions(+), 66 deletions(-) diff --git a/tests/ut/cpp/dataset/arena_test.cc b/tests/ut/cpp/dataset/arena_test.cc index 4b5f50c47f..0809c40ee3 100644 --- a/tests/ut/cpp/dataset/arena_test.cc +++ b/tests/ut/cpp/dataset/arena_test.cc @@ -45,5 +45,5 @@ TEST_F(MindDataTestArena, TestALLFunction) { for (int i = 0; i < 1000; i++) { mp->Deallocate(v.at(i)); } - std::cout << *mp; + MS_LOG(DEBUG) << *mp; } diff --git a/tests/ut/cpp/dataset/btree_test.cc b/tests/ut/cpp/dataset/btree_test.cc index 993f8f465f..3e0a867fba 100644 --- a/tests/ut/cpp/dataset/btree_test.cc +++ b/tests/ut/cpp/dataset/btree_test.cc @@ -196,6 +196,6 @@ TEST_F(MindDataTestBPlusTree, Test3) { EXPECT_EQ(it.value(), "b"); MS_LOG(INFO) << "Dump all the values using [] operator."; for (uint64_t i = min; i <= max; i++) { - std::cout << ai[i] << std::endl; + MS_LOG(DEBUG) << ai[i] << std::endl; } } diff --git a/tests/ut/cpp/dataset/cifar_op_test.cc b/tests/ut/cpp/dataset/cifar_op_test.cc index 48c332bc13..0cd1db65b5 100644 --- a/tests/ut/cpp/dataset/cifar_op_test.cc +++ b/tests/ut/cpp/dataset/cifar_op_test.cc @@ -81,7 +81,8 @@ TEST_F(MindDataTestCifarOp, TestSequentialSamplerCifar10) { uint32_t label = 0; while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 100); @@ -108,7 +109,8 @@ TEST_F(MindDataTestCifarOp, TestRandomSamplerCifar10) { uint32_t label = 0; while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 12); @@ -133,7 +135,8 @@ TEST_F(MindDataTestCifarOp, TestCifar10NumSample) { uint32_t label = 0; while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 100); @@ -159,8 +162,9 @@ TEST_F(MindDataTestCifarOp, TestSequentialSamplerCifar100) { while (tensor_map.size() != 0) { tensor_map["coarse_label"]->GetItemAt(&coarse, {}); tensor_map["fine_label"]->GetItemAt(&fine, {}); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << " coarse:" + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << " coarse:" << coarse << " fine:" << fine << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 100); diff --git a/tests/ut/cpp/dataset/circular_pool_test.cc b/tests/ut/cpp/dataset/circular_pool_test.cc index 47ceae5930..c42b08ddcd 100644 --- a/tests/ut/cpp/dataset/circular_pool_test.cc +++ b/tests/ut/cpp/dataset/circular_pool_test.cc @@ -52,7 +52,7 @@ Status TestMem(MindDataTestCircularPool *tp, int32_t num_iterations) { uint64_t new_sz = dist(gen); std::string str = "Allocate " + std::to_string(old_sz) + " bytes of memory and then resize to " + std::to_string(new_sz); - std::cout << str << std::endl; + MS_LOG(DEBUG) << str << std::endl; std::string id = Services::GetUniqueID(); void *p; RETURN_IF_NOT_OK(tp->mp_->Allocate(old_sz, &p)); @@ -76,9 +76,9 @@ TEST_F(MindDataTestCircularPool, TestALLFunction) { vg_.CreateAsyncTask("TestMem", f); } vg_.join_all(); - std::cout << vg_.GetTaskErrorIfAny() << std::endl; + MS_LOG(DEBUG) << vg_.GetTaskErrorIfAny() << std::endl; ASSERT_TRUE(vg_.GetTaskErrorIfAny().IsOk()); CircularPool *cp = dynamic_cast(mp_.get()); - std::cout << *cp << std::endl; + MS_LOG(DEBUG) << *cp << std::endl; } diff --git a/tests/ut/cpp/dataset/image_folder_op_test.cc b/tests/ut/cpp/dataset/image_folder_op_test.cc index 82513a29a5..5b118a629a 100644 --- a/tests/ut/cpp/dataset/image_folder_op_test.cc +++ b/tests/ut/cpp/dataset/image_folder_op_test.cc @@ -102,7 +102,8 @@ TEST_F(MindDataTestImageFolderSampler, TestSequentialImageFolderWithRepeat) { while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); EXPECT_TRUE(res[(i % 44) / 11] == label); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 88); @@ -126,7 +127,8 @@ TEST_F(MindDataTestImageFolderSampler, TestRandomImageFolder) { int32_t label = 0; while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 44); @@ -155,7 +157,8 @@ TEST_F(MindDataTestImageFolderSampler, TestRandomSamplerImageFolder) { while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); EXPECT_TRUE(res[i] == label); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 12); @@ -185,8 +188,9 @@ TEST_F(MindDataTestImageFolderSampler, TestSequentialImageFolderWithRepeatBatch) std::shared_ptr label; Create1DTensor(&label, 11, reinterpret_cast(res[i % 4]), DataType::DE_INT32); EXPECT_TRUE((*label) == (*tensor_map["label"])); - std::cout << "row: " << i++ << " " << tensor_map["image"]->shape() << " (*label):" << (*label) + MS_LOG(DEBUG) << "row: " << i << " " << tensor_map["image"]->shape() << " (*label):" << (*label) << " *tensor_map[label]: " << *tensor_map["label"] << std::endl; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 8); @@ -282,7 +286,8 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderClassIndex) { while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); EXPECT_TRUE(label == res[i / 11]); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 22); @@ -308,7 +313,8 @@ TEST_F(MindDataTestImageFolderSampler, TestDistributedSampler) { while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); EXPECT_EQ(i % 4, label); - std::cout << "row:" << i++ << "\tlabel:" << label << "\n"; + MS_LOG(DEBUG) << "row:" << i << "\tlabel:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 16); @@ -335,7 +341,8 @@ TEST_F(MindDataTestImageFolderSampler, TestPKSamplerImageFolder) { while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); EXPECT_TRUE(res[i] == label); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 12); @@ -360,7 +367,8 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderNumSamples) { while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); EXPECT_TRUE(0 == label); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 22); @@ -392,7 +400,8 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderDecode) { EXPECT_TRUE(label == res[i / 11]); EXPECT_TRUE( tensor_map["image"]->shape() == TensorShape({2268, 4032, 3})); // verify shapes are correct after decode - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 20); @@ -442,7 +451,8 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderSharding1) { while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); EXPECT_EQ(labels[i], label); - std::cout << "row:" << i++ << "\tlabel:" << label << "\n"; + MS_LOG(DEBUG) << "row:" << i << "\tlabel:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 5); @@ -470,7 +480,8 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderSharding2) { while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); EXPECT_EQ(labels[i], label); - std::cout << "row:" << i++ << "\tlabel:" << label << "\n"; + MS_LOG(DEBUG) << "row:" << i << "\tlabel:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 11); diff --git a/tests/ut/cpp/dataset/manifest_op_test.cc b/tests/ut/cpp/dataset/manifest_op_test.cc index e062335fa6..9e36f8c747 100644 --- a/tests/ut/cpp/dataset/manifest_op_test.cc +++ b/tests/ut/cpp/dataset/manifest_op_test.cc @@ -76,7 +76,8 @@ TEST_F(MindDataTestManifest, TestSequentialManifestWithRepeat) { while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); EXPECT_TRUE(res[i] == label); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 4); @@ -134,7 +135,8 @@ TEST_F(MindDataTestManifest, MindDataTestManifestClassIndex) { while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); EXPECT_TRUE(label == res[i]); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 2); @@ -159,7 +161,8 @@ TEST_F(MindDataTestManifest, MindDataTestManifestNumSamples) { while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); EXPECT_TRUE(0 == label); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 4); @@ -184,7 +187,8 @@ TEST_F(MindDataTestManifest, MindDataTestManifestEval) { while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); EXPECT_TRUE(0 == label); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 1); diff --git a/tests/ut/cpp/dataset/map_op_test.cc b/tests/ut/cpp/dataset/map_op_test.cc index 746c1e8d5f..271cbbe190 100644 --- a/tests/ut/cpp/dataset/map_op_test.cc +++ b/tests/ut/cpp/dataset/map_op_test.cc @@ -697,7 +697,7 @@ TEST_F(MindDataTestMapOp, ImageFolder_Decode_Repeat_Resize) { std::string result; while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); - std::cout << "row:" << i << "\tlabel:" << label << "\n"; + MS_LOG(DEBUG) << "row:" << i << "\tlabel:" << label << "\n"; EXPECT_TRUE(img_class[(i % 44) / 11] == label); // Dump all the image into string, to be used as a comparison later. result.append((char *) tensor_map["image"]->StartAddr(), (int64_t) tensor_map["image"]->Size()); @@ -743,7 +743,7 @@ TEST_F(MindDataTestMapOp, ImageFolder_Decode_Repeat_Resize) { std::string result2; while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); - std::cout << "row:" << i << "\tlabel:" << label << "\n"; + MS_LOG(DEBUG) << "row:" << i << "\tlabel:" << label << "\n"; EXPECT_TRUE(img_class[(i % 44) / 11] == label); result2.append((char *) tensor_map["image"]->StartAddr(), (int64_t) tensor_map["image"]->Size()); di2.GetNextAsMap(&tensor_map); diff --git a/tests/ut/cpp/dataset/memory_pool_test.cc b/tests/ut/cpp/dataset/memory_pool_test.cc index c988a09e89..136f3fe1b8 100644 --- a/tests/ut/cpp/dataset/memory_pool_test.cc +++ b/tests/ut/cpp/dataset/memory_pool_test.cc @@ -35,7 +35,7 @@ class MindDataTestMemoryPool : public UT::Common { }; TEST_F(MindDataTestMemoryPool, DumpPoolInfo) { - std::cout << *(std::dynamic_pointer_cast(mp_)) << std::endl; + MS_LOG(DEBUG) << *(std::dynamic_pointer_cast(mp_)) << std::endl; } TEST_F(MindDataTestMemoryPool, TestOperator1) { @@ -72,5 +72,5 @@ TEST_F(MindDataTestMemoryPool, TestAllocator) { std::shared_ptr obj_a = std::allocate_shared(alloc, 3); int v = obj_a->val_a(); ASSERT_EQ(v, 3); - std::cout << *(std::dynamic_pointer_cast(mp_)) << std::endl; + MS_LOG(DEBUG) << *(std::dynamic_pointer_cast(mp_)) << std::endl; } diff --git a/tests/ut/cpp/dataset/mind_record_op_test.cc b/tests/ut/cpp/dataset/mind_record_op_test.cc index 9664b66ecb..abe7faef14 100644 --- a/tests/ut/cpp/dataset/mind_record_op_test.cc +++ b/tests/ut/cpp/dataset/mind_record_op_test.cc @@ -69,7 +69,7 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordBasic) { rc = builder.Build(&my_mindrecord_op); ASSERT_TRUE(rc.IsOk()); - std::cout << (*my_mindrecord_op); + MS_LOG(DEBUG) << (*my_mindrecord_op); my_tree->AssociateNode(my_mindrecord_op); @@ -140,7 +140,7 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordSample) { rc = builder.Build(&my_mindrecord_op); ASSERT_TRUE(rc.IsOk()); - std::cout << (*my_mindrecord_op); + MS_LOG(DEBUG) << (*my_mindrecord_op); my_tree->AssociateNode(my_mindrecord_op); @@ -211,7 +211,7 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordShuffle) { rc = builder.Build(&my_mindrecord_op); ASSERT_TRUE(rc.IsOk()); - std::cout << (*my_mindrecord_op); + MS_LOG(DEBUG) << (*my_mindrecord_op); my_tree->AssociateNode(my_mindrecord_op); @@ -285,7 +285,7 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordCategory) { rc = builder.Build(&my_mindrecord_op); ASSERT_TRUE(rc.IsOk()); - std::cout << (*my_mindrecord_op); + MS_LOG(DEBUG) << (*my_mindrecord_op); my_tree->AssociateNode(my_mindrecord_op); @@ -352,7 +352,7 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordRepeat) { rc = builder.Build(&my_mindrecord_op); ASSERT_TRUE(rc.IsOk()); - std::cout << (*my_mindrecord_op); + MS_LOG(DEBUG) << (*my_mindrecord_op); rc = my_tree->AssociateNode(my_mindrecord_op); EXPECT_TRUE(rc.IsOk()); @@ -434,7 +434,7 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordBlockReaderRepeat) { rc = builder.Build(&my_mindrecord_op); ASSERT_TRUE(rc.IsOk()); - std::cout << (*my_mindrecord_op); + MS_LOG(DEBUG) << (*my_mindrecord_op); rc = my_tree->AssociateNode(my_mindrecord_op); EXPECT_TRUE(rc.IsOk()); diff --git a/tests/ut/cpp/dataset/mnist_op_test.cc b/tests/ut/cpp/dataset/mnist_op_test.cc index 3d5c4b05ca..2733597b35 100644 --- a/tests/ut/cpp/dataset/mnist_op_test.cc +++ b/tests/ut/cpp/dataset/mnist_op_test.cc @@ -91,7 +91,8 @@ TEST_F(MindDataTestMnistSampler, TestSequentialMnistWithRepeat) { while (tensor_map.size() != 0) { tensor_map["label"]->GetItemAt(&label, {}); EXPECT_TRUE(res[i % 10] == label); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << label << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 20); @@ -120,7 +121,8 @@ TEST_F(MindDataTestMnistSampler, TestSequentialImageFolderWithRepeatBatch) { std::shared_ptr label; Create1DTensor(&label, 5, reinterpret_cast(res[i % 4])); EXPECT_TRUE((*label) == (*tensor_map["label"])); - std::cout << "row: " << i++ << "\t" << tensor_map["image"]->shape() << "label:" << *tensor_map["label"] << "\n"; + MS_LOG(DEBUG) << "row: " << i << "\t" << tensor_map["image"]->shape() << "label:" << *tensor_map["label"] << "\n"; + i++; di.GetNextAsMap(&tensor_map); } EXPECT_TRUE(i == 4); diff --git a/tests/ut/cpp/dataset/normalize_op_test.cc b/tests/ut/cpp/dataset/normalize_op_test.cc index 373669a473..05ac3f6289 100644 --- a/tests/ut/cpp/dataset/normalize_op_test.cc +++ b/tests/ut/cpp/dataset/normalize_op_test.cc @@ -51,7 +51,7 @@ TEST_F(MindDataTestNormalizeOP, TestOp) { cv::Mat cv_output_image; cv_output_image = p->mat(); - std::cout << "Storing output file to : " << output_filename << std::endl; + MS_LOG(DEBUG) << "Storing output file to : " << output_filename << std::endl; cv::FileStorage file(output_filename, cv::FileStorage::WRITE); file << "imageData" << cv_output_image; } diff --git a/tests/ut/cpp/dataset/one_hot_op_test.cc b/tests/ut/cpp/dataset/one_hot_op_test.cc index 3928aeffd4..4b8bbc1bdd 100644 --- a/tests/ut/cpp/dataset/one_hot_op_test.cc +++ b/tests/ut/cpp/dataset/one_hot_op_test.cc @@ -47,8 +47,8 @@ TEST_F(MindDataTestOneHotOp, TestOp) { EXPECT_TRUE(s.IsOk()); ASSERT_TRUE(output->shape() == expected->shape()); ASSERT_TRUE(output->type() == expected->type()); - std::cout << *output << std::endl; - std::cout << *expected << std::endl; + MS_LOG(DEBUG) << *output << std::endl; + MS_LOG(DEBUG) << *expected << std::endl; ASSERT_TRUE(*output == *expected); MS_LOG(INFO) << "MindDataTestOneHotOp end."; diff --git a/tests/ut/cpp/dataset/path_test.cc b/tests/ut/cpp/dataset/path_test.cc index 7bd2abe7e5..3839d659aa 100644 --- a/tests/ut/cpp/dataset/path_test.cc +++ b/tests/ut/cpp/dataset/path_test.cc @@ -38,7 +38,7 @@ TEST_F(MindDataTestPath, Test1) { int i = 0; while (dir_it->hasNext()) { Path v = dir_it->next(); - std::cout << v.toString() << "\n"; + MS_LOG(DEBUG) << v.toString() << "\n"; i++; if (i == 10) { break; @@ -46,7 +46,7 @@ TEST_F(MindDataTestPath, Test1) { } // Test extension. Path g("file.jpeg"); - std::cout << g.Extension() << "\n"; + MS_LOG(DEBUG) << g.Extension() << "\n"; ASSERT_EQ(g.Extension(), ".jpeg"); } diff --git a/tests/ut/cpp/dataset/queue_test.cc b/tests/ut/cpp/dataset/queue_test.cc index 37560e5dfb..00366fcafd 100644 --- a/tests/ut/cpp/dataset/queue_test.cc +++ b/tests/ut/cpp/dataset/queue_test.cc @@ -44,7 +44,7 @@ class RefCount { explicit RefCount(int x) : v_(std::make_shared(x)) {} explicit RefCount(const RefCount &o) : v_(o.v_) {} ~RefCount() { - std::cout << "Destructor of RefCount called" << std::endl; + MS_LOG(DEBUG) << "Destructor of RefCount called" << std::endl; gRefCountDestructorCalled++; } RefCount& operator=(const RefCount &o) { diff --git a/tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc b/tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc index a4c4fbfdff..facd35c4f7 100644 --- a/tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc +++ b/tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc @@ -78,7 +78,7 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp2) { } else { mse = mse_sum; } - std::cout << "mse: " << mse << std::endl; + MS_LOG(DEBUG) << "mse: " << mse << std::endl; } MS_LOG(INFO) << "MindDataTestRandomCropDecodeResizeOp end!"; } @@ -150,7 +150,7 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) { } mse = (count == 0) ? mse_sum : static_cast(mse_sum) / count; - std::cout << "mse: " << mse << std::endl; + MS_LOG(DEBUG) << "mse: " << mse << std::endl; } MS_LOG(INFO) << "MindDataTestRandomCropDecodeResizeOp end!"; } diff --git a/tests/ut/cpp/dataset/stand_alone_samplers_test.cc b/tests/ut/cpp/dataset/stand_alone_samplers_test.cc index 2626add22e..c686a9486b 100644 --- a/tests/ut/cpp/dataset/stand_alone_samplers_test.cc +++ b/tests/ut/cpp/dataset/stand_alone_samplers_test.cc @@ -78,7 +78,7 @@ TEST_F(MindDataTestStandAloneSampler, TestDistributedSampler) { sampler->Init(&mock); sampler->GetNextBuffer(&db); db->GetTensor(&tensor, 0, 0); - std::cout << (*tensor); + MS_LOG(DEBUG) << (*tensor); if(i < 3) { // This is added due to std::shuffle() EXPECT_TRUE((*tensor) == (*row[i])); } diff --git a/tests/ut/cpp/dataset/status_test.cc b/tests/ut/cpp/dataset/status_test.cc index 835e6303b0..30bbd1e909 100644 --- a/tests/ut/cpp/dataset/status_test.cc +++ b/tests/ut/cpp/dataset/status_test.cc @@ -43,19 +43,19 @@ TEST_F(MindDataTestStatus, Test1) { Status rc; ASSERT_TRUE(rc.IsOk()); Status err1(StatusCode::kOutOfMemory, __LINE__, __FILE__); - std::cout << err1; + MS_LOG(DEBUG) << err1; ASSERT_TRUE(err1.IsOutofMemory()); ASSERT_TRUE(err1.IsError()); Status err2(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Oops"); - std::cout << err2; + MS_LOG(DEBUG) << err2; } TEST_F(MindDataTestStatus, Test2) { Status rc = f1(); - std::cout << rc; + MS_LOG(DEBUG) << rc; } TEST_F(MindDataTestStatus, Test3) { Status rc = f3(); - std::cout << rc; + MS_LOG(DEBUG) << rc; } diff --git a/tests/ut/cpp/dataset/task_manager_test.cc b/tests/ut/cpp/dataset/task_manager_test.cc index 182eee1882..c6fccd51cb 100644 --- a/tests/ut/cpp/dataset/task_manager_test.cc +++ b/tests/ut/cpp/dataset/task_manager_test.cc @@ -36,7 +36,7 @@ Status f(TaskGroup &vg){ RETURN_IF_NOT_OK(vg.CreateAsyncTask("Infinity", [&]() -> Status { TaskManager::FindMe()->Post(); int a = v.fetch_add(1); - std::cout << a << std::endl; + MS_LOG(DEBUG) << a << std::endl; return f(vg); })); } diff --git a/tests/ut/cpp/dataset/tensor_test.cc b/tests/ut/cpp/dataset/tensor_test.cc index 6686be15be..7437b3d942 100644 --- a/tests/ut/cpp/dataset/tensor_test.cc +++ b/tests/ut/cpp/dataset/tensor_test.cc @@ -311,7 +311,7 @@ TEST_F(MindDataTestTensorDE, CVTensorAs) { m = 2 * m; ASSERT_EQ(ctv->StartAddr(), addr); ASSERT_TRUE(*t2 == *ctv); - std::cout << *t2 << std::endl << *ctv; + MS_LOG(DEBUG) << *t2 << std::endl << *ctv; } TEST_F(MindDataTestTensorDE, CVTensorMatSlice) { diff --git a/tests/ut/cpp/dataset/type_cast_op_test.cc b/tests/ut/cpp/dataset/type_cast_op_test.cc index 0bd1e90eae..f5fa035579 100644 --- a/tests/ut/cpp/dataset/type_cast_op_test.cc +++ b/tests/ut/cpp/dataset/type_cast_op_test.cc @@ -54,7 +54,7 @@ void testCast(std::vector values, const DataType &from, const DataType &to EXPECT_TRUE(op->Compute(t, &output)); ASSERT_TRUE(t->shape() == output->shape()); ASSERT_TRUE(DataType(to)==output->type()); - std::cout << *output << std::endl; + MS_LOG(DEBUG) << *output << std::endl; auto out = output->begin(); auto v = values.begin(); for (; out != output->end(); out++, v++) { diff --git a/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc b/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc index acea1cefae..46ea1712b2 100644 --- a/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc +++ b/tests/ut/cpp/mindrecord/ut_shard_operator_test.cc @@ -58,7 +58,8 @@ TEST_F(TestShardOperator, TestShardSampleBasic) { while (true) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]); + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]); + i++; } dataset.Finish(); ASSERT_TRUE(i <= kSampleCount); @@ -83,7 +84,8 @@ TEST_F(TestShardOperator, TestShardSampleWrongNumber) { while (true) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]); + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]); + i++; } dataset.Finish(); ASSERT_TRUE(i <= 5); @@ -108,7 +110,8 @@ TEST_F(TestShardOperator, TestShardSampleRatio) { while (true) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]); + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]); + i++; } dataset.Finish(); ASSERT_TRUE(i <= 10); @@ -137,7 +140,8 @@ TEST_F(TestShardOperator, TestShardSamplePartition) { while (true) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]); + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]); + i++; } dataset.Finish(); ASSERT_TRUE(i <= 10); @@ -166,8 +170,9 @@ TEST_F(TestShardOperator, TestShardCategory) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump()); + i++; ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second); @@ -194,8 +199,9 @@ TEST_F(TestShardOperator, TestShardShuffle) { while (true) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump()); + i++; } dataset.Finish(); } @@ -218,8 +224,9 @@ TEST_F(TestShardOperator, TestShardSampleShuffle) { while (true) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump()); + i++; } dataset.Finish(); ASSERT_LE(i, 35); @@ -244,8 +251,9 @@ TEST_F(TestShardOperator, TestShardShuffleSample) { while (true) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump()); + i++; } dataset.Finish(); ASSERT_TRUE(i <= kSampleSize); @@ -270,8 +278,9 @@ TEST_F(TestShardOperator, TestShardSampleShuffleSample) { while (true) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump()); + i++; } dataset.Finish(); ASSERT_LE(i, 35); @@ -298,8 +307,9 @@ TEST_F(TestShardOperator, TestShardShuffleCompare) { while (true) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump()); + i++; auto y = compare_dataset.GetNext(); if ((std::get<1>(x[0]))["file_name"] != (std::get<1>(y[0]))["file_name"]) different = true; @@ -332,8 +342,9 @@ TEST_F(TestShardOperator, TestShardCategoryShuffle1) { while (true) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump()); + i++; ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second); category_no++; @@ -365,8 +376,9 @@ TEST_F(TestShardOperator, TestShardCategoryShuffle2) { while (true) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump()); + i++; ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second); category_no++; category_no %= static_cast(categories.size()); @@ -398,8 +410,9 @@ TEST_F(TestShardOperator, TestShardCategorySample) { while (true) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump()); + i++; ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second); category_no++; @@ -435,8 +448,9 @@ TEST_F(TestShardOperator, TestShardCategorySampleShuffle) { while (true) { auto x = dataset.GetNext(); if (x.empty()) break; - MS_LOG(INFO) << "index: " << i++ << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << + MS_LOG(INFO) << "index: " << i << ", filename: " << common::SafeCStr((std::get<1>(x[0]))["file_name"]) << ", label: " << common::SafeCStr((std::get<1>(x[0]))["label"].dump()); + i++; ASSERT_TRUE((std::get<1>(x[0]))["label"] == categories[category_no].second); category_no++; diff --git a/tests/ut/cpp/runtest.sh b/tests/ut/cpp/runtest.sh index a50e1d4933..283d6bc639 100755 --- a/tests/ut/cpp/runtest.sh +++ b/tests/ut/cpp/runtest.sh @@ -28,6 +28,7 @@ cd ${BUILD_PATH}/mindspore/tests/ut/cpp export LD_LIBRARY_PATH=${BUILD_PATH}/mindspore/googletest/googlemock/gtest:${PROJECT_PATH}/mindspore:${PROJECT_PATH}/mindspore/lib:$LD_LIBRARY_PATH export PYTHONPATH=${PROJECT_PATH}/tests/ut/cpp/python_input:$PYTHONPATH:${PROJECT_PATH} +export GLOG_v=2 ## prepare data for dataset & mindrecord cp -fr $PROJECT_PATH/tests/ut/data ${PROJECT_PATH}/build/mindspore/tests/ut/cpp/ From a6f6a376cbae9e310698eb5830a3d443f440d079 Mon Sep 17 00:00:00 2001 From: cathwong Date: Tue, 31 Mar 2020 04:37:59 +0800 Subject: [PATCH 013/227] update CONTRIBUTING.md. typo fix. --- CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 591beaef82..85fee704c2 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -78,7 +78,7 @@ Please follow this style to make MindSpore easy to review, maintain and develop. * Pull a request to MindSpore repository - In the last step, your need to pull a compare request between your new branch and MindSpore `master` branch. After finishing the pull request, the Jekins CI will be automatically set up for building test. + In the last step, your need to pull a compare request between your new branch and MindSpore `master` branch. After finishing the pull request, the Jenkins CI will be automatically set up for building test. ### Report issues From 24d1991cac01d3c5390cb98a79e7e640c85f0b65 Mon Sep 17 00:00:00 2001 From: buxue Date: Wed, 25 Mar 2020 20:04:12 +0800 Subject: [PATCH 014/227] Fix bug structure output when there is depend whose first input is constant in outputs --- mindspore/ccsrc/pipeline/pipeline.cc | 81 +++++++++++++++------ tests/ut/python/nn/test_structure_output.py | 32 ++++++-- 2 files changed, 84 insertions(+), 29 deletions(-) diff --git a/mindspore/ccsrc/pipeline/pipeline.cc b/mindspore/ccsrc/pipeline/pipeline.cc index 70ef9a5407..7b3cf06b5e 100644 --- a/mindspore/ccsrc/pipeline/pipeline.cc +++ b/mindspore/ccsrc/pipeline/pipeline.cc @@ -725,23 +725,15 @@ py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) { return BaseRefToPyData(value); } -py::object StructureOutput(const AbstractBasePtr& output, const py::tuple& data, size_t* count) { - MS_EXCEPTION_IF_NULL(output); +py::object ExtractGeneralCnodeRet(const AbstractBasePtr& cnode_data, const py::tuple& data, size_t* count) { + MS_EXCEPTION_IF_NULL(cnode_data); + if (*count >= data.size()) { + MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size() + << " less than the number of elements required. "; + } - if (!output->isa()) { - ValuePtr value = output->BuildValue(); - if (value != kAnyValue) { - return ValuePtrToPyData(value); - } - if (!output->isa()) { - MS_LOG(EXCEPTION) << "Output can only be tensor except for constants, but got " - << output->BuildValue()->ToString() << "."; - } - if (*count >= data.size()) { - MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size() - << " less than the number of elements required. "; - } - auto shape = output->BuildShape(); + if (cnode_data->isa()) { + BaseShapePtr shape = cnode_data->BuildShape(); auto shape_act = shape->cast()->shape(); Tensor tensor_exp = py::cast(data[*count]); if (shape_act != tensor_exp.shape()) { @@ -751,16 +743,58 @@ py::object StructureOutput(const AbstractBasePtr& output, const py::tuple& data, return data[(*count)++]; } - auto tuple_output = output->cast(); - AbstractBasePtrList elements = tuple_output->elements(); - size_t size = elements.size(); + if (!cnode_data->isa()) { + MS_LOG(EXCEPTION) << "The output of operator in the final anf graph could " + << "only be a tensor or a tuple of tensor, but got " << cnode_data->BuildValue()->ToString() + << "."; + } + auto data_tp = cnode_data->cast(); + auto elements = data_tp->elements(); + size_t size = data_tp->size(); py::tuple tp = py::tuple(size); for (size_t i = 0; i < size; i++) { - tp[i] = StructureOutput(elements[i], data, count); + tp[i] = ExtractGeneralCnodeRet(elements[i], data, count); } return std::move(tp); } +py::object StructureOutput(const AnfNodePtr& output_node, const py::tuple& data, size_t* count) { + MS_EXCEPTION_IF_NULL(output_node); + + if (output_node->isa()) { + return ValuePtrToPyData(GetValueNode(output_node)); + } + + if (*count >= data.size()) { + MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size() + << " less than the number of elements required. "; + } + if (output_node->isa()) { + return data[(*count)++]; + } + + auto output_c = output_node->cast(); + if (output_c == nullptr) { + MS_LOG(EXCEPTION) << "The final anf graph could only have constant, parameter, and operator, but got " + << output_node->ToString(); + } + + if (output_c->IsApply(prim::kPrimMakeTuple)) { + auto input_list = output_c->inputs(); + size_t size = input_list.size(); + py::tuple tp = py::tuple(size - 1); + for (size_t i = 1; i < size; i++) { + tp[i - 1] = StructureOutput(input_list[i], data, count); + } + return std::move(tp); + } + if (output_c->IsApply(prim::kPrimDepend)) { + return StructureOutput(output_c->input(1), data, count); + } + + return ExtractGeneralCnodeRet(output_c->abstract(), data, count); +} + std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::vector& inputs, const std::string& phase) { std::vector ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW); @@ -806,11 +840,10 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve std::shared_ptr ret = nullptr; #ifdef ENABLE_GE - AnfNodePtr root = graph->get_return(); - MS_EXCEPTION_IF_NULL(root); - AbstractBasePtr output = root->abstract(); + AnfNodePtr output_node = graph->get_return()->input(1); + MS_EXCEPTION_IF_NULL(output_node); size_t count = 0; - py::object oj = StructureOutput(output, outputs, &count); + py::object oj = StructureOutput(output_node, outputs, &count); ret = std::make_shared(oj); #else if (outputs.size() == 1) { diff --git a/tests/ut/python/nn/test_structure_output.py b/tests/ut/python/nn/test_structure_output.py index eb2722878a..f5f6d77a67 100644 --- a/tests/ut/python/nn/test_structure_output.py +++ b/tests/ut/python/nn/test_structure_output.py @@ -236,7 +236,7 @@ def test_soft(): def __init__(self): super(SoftmaxCrossEntropyWithLogitsNet, self).__init__() self.soft = P.SoftmaxCrossEntropyWithLogits() - self.value = (Tensor(np.zeros((2,)).astype(np.float32)), Tensor(np.ones((2,)).astype(np.float32))) + self.value = (Tensor(np.zeros((2, 2)).astype(np.float32)), Tensor(np.ones((2, 2)).astype(np.float32))) def construct(self, x, y, z): xx = x + y @@ -246,8 +246,30 @@ def test_soft(): ret = (ret, self.value) return ret - input1 = Tensor(np.zeros((2,)).astype(np.float32)) - input2 = Tensor(np.ones((2,)).astype(np.float32)) - input3 = Tensor((np.ones((2,)) + np.ones((2,))).astype(np.float32)) + input1 = Tensor(np.zeros((2, 2)).astype(np.float32)) + input2 = Tensor(np.ones((2, 2)).astype(np.float32)) + input3 = Tensor((np.ones((2, 2)) + np.ones((2, 2))).astype(np.float32)) net = SoftmaxCrossEntropyWithLogitsNet() - print(net(input1, input2, input3)) + net(input1, input2, input3) + + +def test_const_depend(): + class ConstDepend(Cell): + def __init__(self): + super(ConstDepend, self).__init__() + self.value = (Tensor(np.zeros((2, 3)).astype(np.float32)), Tensor(np.ones((2, 3)).astype(np.float32))) + self.soft = P.SoftmaxCrossEntropyWithLogits() + self.depend = depend + + def construct(self, x, y, z): + ret = x + y + ret = ret * z + ret = self.depend(self.value, ret) + ret = (ret, self.soft(x, y)) + return ret + + input1 = Tensor(np.zeros((2, 2)).astype(np.float32)) + input2 = Tensor(np.ones((2, 2)).astype(np.float32)) + input3 = Tensor((np.ones((2, 2)) + np.ones((2, 2))).astype(np.float32)) + net = ConstDepend() + net(input1, input2, input3) From df0668390a6ba4f49cbfaaccaf39c4de9419476a Mon Sep 17 00:00:00 2001 From: qianlong Date: Mon, 30 Mar 2020 20:16:45 +0800 Subject: [PATCH 015/227] Optimize the execution time of test case test_rgb_hsv.py --- tests/ut/python/dataset/test_rgb_hsv.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/ut/python/dataset/test_rgb_hsv.py b/tests/ut/python/dataset/test_rgb_hsv.py index e887acb01d..14ab9ddc2d 100644 --- a/tests/ut/python/dataset/test_rgb_hsv.py +++ b/tests/ut/python/dataset/test_rgb_hsv.py @@ -133,6 +133,7 @@ def test_rgb_hsv_pipeline(): # First dataset transforms1 = [ vision.Decode(), + vision.Resize([64, 64]), vision.ToTensor() ] transforms1 = vision.ComposeOp(transforms1) @@ -142,6 +143,7 @@ def test_rgb_hsv_pipeline(): # Second dataset transforms2 = [ vision.Decode(), + vision.Resize([64, 64]), vision.ToTensor(), vision.RgbToHsv(), vision.HsvToRgb() From 204a86c67a83926664e0d413010c0ccb577ca341 Mon Sep 17 00:00:00 2001 From: helloway Date: Mon, 30 Mar 2020 20:17:13 +0800 Subject: [PATCH 016/227] Add issue and PR templates for gitee and github --- .gitee/PULL_REQUEST_TEMPLATE.md | 26 +++++++++++++++ .github/ISSUE_TEMPLATE/RFC.md | 19 +++++++++++ .github/ISSUE_TEMPLATE/bug-report.md | 43 +++++++++++++++++++++++++ .github/ISSUE_TEMPLATE/task-tracking.md | 19 +++++++++++ .github/PULL_REQUEST_TEMPLATE.md | 24 ++++++++++++++ 5 files changed, 131 insertions(+) create mode 100644 .gitee/PULL_REQUEST_TEMPLATE.md create mode 100644 .github/ISSUE_TEMPLATE/RFC.md create mode 100644 .github/ISSUE_TEMPLATE/bug-report.md create mode 100644 .github/ISSUE_TEMPLATE/task-tracking.md create mode 100644 .github/PULL_REQUEST_TEMPLATE.md diff --git a/.gitee/PULL_REQUEST_TEMPLATE.md b/.gitee/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000000..b58f1abbf3 --- /dev/null +++ b/.gitee/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,26 @@ + + +**What type of PR is this?** +> Uncomment only one ` /kind <>` line, hit enter to put that in a new line, and remove leading whitespaces from that line: +> +> /kind bug +> /kind task +> /kind feature + + +**What does this PR do / why do we need it**: + + +**Which issue(s) this PR fixes**: + +Fixes # + +**Special notes for your reviewers**: + + diff --git a/.github/ISSUE_TEMPLATE/RFC.md b/.github/ISSUE_TEMPLATE/RFC.md new file mode 100644 index 0000000000..7dd17f56b6 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/RFC.md @@ -0,0 +1,19 @@ +--- +name: RFC +about: Use this template for the new feature or enhancement +labels: kind/feature or kind/enhancement + +--- + +## Background +- Describe the status of the problem you wish to solve +- Attach the relevant issue if have + +## Introduction +- Describe the general solution, design and/or pseudo-code + +## Trail +| No. | Task Description | Related Issue(URL) | +| --- | ---------------- | ------------------ | +| 1 | | | +| 2 | | | diff --git a/.github/ISSUE_TEMPLATE/bug-report.md b/.github/ISSUE_TEMPLATE/bug-report.md new file mode 100644 index 0000000000..2c0260ae2b --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug-report.md @@ -0,0 +1,43 @@ +--- +name: Bug Report +about: Use this template for reporting a bug +labels: kind/bug + +--- + + + +## Environment +### Hardware Environment(`Ascend`/`GPU`/`CPU`): +> Uncomment only one ` /device <>` line, hit enter to put that in a new line, and remove leading whitespaces from that line: +> +> `/device ascend`
+> `/device gpu`
+> `/device cpu`
+ +### Software Environment: +- **MindSpore version (source or binary)**: +- **Python version (e.g., Python 3.7.5)**: +- **OS platform and distribution (e.g., Linux Ubuntu 16.04)**: +- **GCC/Compiler version (if compiled from source)**: + +## Describe the current behavior + + +## Describe the expected behavior + + +## Steps to reproduce the issue +1. +2. +3. + +## Related log / screenshot + + +## Special notes for this issue + + diff --git a/.github/ISSUE_TEMPLATE/task-tracking.md b/.github/ISSUE_TEMPLATE/task-tracking.md new file mode 100644 index 0000000000..f2d3d23ae7 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/task-tracking.md @@ -0,0 +1,19 @@ +--- +name: Task +about: Use this template for task tracking +labels: kind/task + +--- + +## Task Description + + +## Task Goal + + +## Sub Task +| No. | Task Description | Issue ID | +| --- | ---------------- | -------- | +| 1 | | | +| 2 | | | + diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000000..96800b55f7 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,24 @@ + + +**What type of PR is this?** +> Uncomment only one ` /kind <>` line, hit enter to put that in a new line, and remove leading whitespaces from that line: +> +> `/kind bug`
+> `/kind task`
+> `/kind feature`
+ +**What does this PR do / why do we need it**: + + +**Which issue(s) this PR fixes**: + +Fixes # + +**Special notes for your reviewers**: + From c119f1c29e0ee160297a9104cf74e3c590aa8767 Mon Sep 17 00:00:00 2001 From: lichenever Date: Mon, 30 Mar 2020 11:31:45 +0800 Subject: [PATCH 017/227] fix_cast_bug --- .../ccsrc/parallel/step_auto_parallel.cc | 6 ++-- mindspore/ccsrc/parallel/step_parallel.cc | 21 ++++++++++---- .../parallel/test_element_wise_function.py | 29 +++++++++++++++++++ 3 files changed, 49 insertions(+), 7 deletions(-) diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc index c3e3f5893e..aee4247755 100644 --- a/mindspore/ccsrc/parallel/step_auto_parallel.cc +++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc @@ -346,6 +346,8 @@ bool IsAutoParallelCareNode(const CNodePtr &cnode) { } OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &cnode) { + MS_EXCEPTION_IF_NULL(prim); + MS_EXCEPTION_IF_NULL(cnode); auto attrs = prim->attrs(); std::vector shape_list = ExtractShape(cnode); if (shape_list.empty()) { @@ -381,8 +383,8 @@ OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr & operator_info->set_outputs_dtype(cnode->Type()); operator_info->set_cnode(cnode); // If no strategy has been configured for this operator, then candidate strategies are generated for - // auto-strategy searching - if (!StrategyFound(attrs)) { + // auto-strategy searching, if this primitive is Cast, we ignore the user-specified strategy + if (!StrategyFound(attrs) || prim->name() == CAST) { // Compute split_flag_list_, indicating which input has batch dimension. This is ONLY used for preparation for // BatchParallelInfo operator operator_info->ComputeBatchSplitFlagList(); diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc index 927acea705..2d948679d7 100644 --- a/mindspore/ccsrc/parallel/step_parallel.cc +++ b/mindspore/ccsrc/parallel/step_parallel.cc @@ -370,15 +370,12 @@ bool IsParallelCareNode(const CNodePtr& cnode) { if (prim == nullptr) { return false; } - auto attrs = prim->attrs(); if (IsInBlackList(prim)) { MS_LOG(INFO) << "Parallel don't care node: " << prim->name(); return false; } - if ((prim->name() == CAST)) { - if ((!attrs.count(STRATEGY)) && (cnode->operator_info() == nullptr)) { - return false; - } + if ((prim->name() == CAST) && (cnode->operator_info() == nullptr)) { + return false; } return cnode->in_forward_flag(); @@ -648,6 +645,13 @@ LossNodeInfo GetLossNodeInfo(const AnfNodePtr& loss_node) { MS_EXCEPTION_IF_NULL(pre_node); LossNodeInfo node_info; + // return -> cast + auto pre_cnode = pre_node->cast(); + MS_EXCEPTION_IF_NULL(pre_cnode); + auto pre_prim = GetValueNode(pre_cnode->input(0)); + if (pre_prim->name() == CAST && pre_cnode->operator_info() == nullptr) { + pre_node = pre_cnode->input(1); + } // return -> loss if (pre_node == loss_node) { @@ -1943,6 +1947,13 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) { MS_EXCEPTION_IF_NULL(current_value); PrimitivePtr current_prim = current_value->value()->cast(); MS_EXCEPTION_IF_NULL(current_prim); + // return -> cast + if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) { + pre_cnode = pre_cnode->input(1)->cast(); + MS_EXCEPTION_IF_NULL(pre_cnode); + current_prim = GetValueNode(pre_cnode->input(0)); + } + // notice: the GetNext op has not input if (INVALID_LOSS_OPS.find(current_prim->name()) != INVALID_LOSS_OPS.end()) { MS_LOG(INFO) << "The loss is: " << current_prim->name(); diff --git a/tests/ut/python/parallel/test_element_wise_function.py b/tests/ut/python/parallel/test_element_wise_function.py index dfcebdc5ab..a917dce9b6 100644 --- a/tests/ut/python/parallel/test_element_wise_function.py +++ b/tests/ut/python/parallel/test_element_wise_function.py @@ -272,3 +272,32 @@ def test_cast_before_mirror3(): y = Tensor(np.ones([32, 64]), dtype=ms.float16) b = Tensor(np.ones([64, 64]), dtype=ms.float32) _executor.compile(net, x, y, b) + + +def test_mul_two_cast(): + class Net(nn.Cell): + def __init__(self, strategy1, strategy2, strategy3): + super().__init__() + self.mul = P.Mul().set_strategy(strategy1) + self.mul2 = P.Mul().set_strategy(strategy2) + self.cast = P.Cast().set_strategy(strategy3) + self.cast2 = P.Cast().set_strategy(strategy3) + + def construct(self, x, y, b): + out = self.mul(x, y) + out = self.mul2(out, b) + out = self.cast(out, ms.int32) + out = self.cast2(out, ms.bool_) + return out + + context.set_auto_parallel_context(device_num=8, global_rank=0) + strategy1 = ((2, 2), (2, 2)) + strategy2 = ((8, 1), (8, 1)) + strategy3 = ((8, 1), ) + net = GradWrap(Net(strategy1, strategy2, strategy3)) + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") + + x = Tensor(np.ones([128, 32]), dtype=ms.float32) + y = Tensor(np.ones([128, 32]), dtype=ms.float32) + b = Tensor(np.ones([128, 32]), dtype=ms.float32) + _executor.compile(net, x, y, b) From 2e308e4bed9589b1ef9fc6c97d7a1cea5d312e14 Mon Sep 17 00:00:00 2001 From: shibeiji Date: Mon, 30 Mar 2020 17:55:28 +0800 Subject: [PATCH 018/227] add log for kernel runtime in order to trace performance --- mindspore/ccsrc/device/kernel_runtime.cc | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc index f320aa9cf6..99f5d491ac 100644 --- a/mindspore/ccsrc/device/kernel_runtime.cc +++ b/mindspore/ccsrc/device/kernel_runtime.cc @@ -50,12 +50,19 @@ bool KernelRuntime::Run(session::KernelGraph *graph) { bool ret = false; auto context_ptr = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(context_ptr); + struct timeval start_time, end_time; + (void)gettimeofday(&start_time, nullptr); bool is_task_sink = context_ptr->enable_task_sink(); if (is_task_sink) { ret = RunTask(graph); } else { ret = LaunchKernel(graph); } + (void)gettimeofday(&end_time, nullptr); + const uint64_t kUSecondInSecond = 1000000; + uint64_t cost = kUSecondInSecond * static_cast(end_time.tv_sec - start_time.tv_sec); + cost += static_cast(end_time.tv_usec - start_time.tv_usec); + MS_LOG(INFO) << "Call MS Run Success in " << cost << " us"; return ret; } From d3400cde0117f2c75d1b99ea9a7938f127645dfc Mon Sep 17 00:00:00 2001 From: lianliguang Date: Tue, 31 Mar 2020 09:35:44 +0800 Subject: [PATCH 019/227] fix bug of got a error transdata's dest format --- mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc index 6fff8ccd2a..7f11c8f2c7 100644 --- a/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc +++ b/mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc @@ -187,10 +187,10 @@ AnfNodePtr InsertTransOpForSingleOutput(const FuncGraphPtr &func_graph, const An return node; } -void GetTransDataInputFormat(const AnfNodePtr &node, std::string *input_format) { +void GetTransDataInputFormat(const AnfNodePtr &node, size_t idx, std::string *input_format) { MS_EXCEPTION_IF_NULL(input_format); if (AnfAlgo::IsRealKernel(node)) { - *input_format = AnfAlgo::GetOutputFormat(node, 0); + *input_format = AnfAlgo::GetOutputFormat(node, idx); } else { *input_format = AnfAlgo::GetPrevNodeOutputFormat(node, 0); } @@ -206,7 +206,7 @@ AnfNodePtr InsertTransOpForMultipleOutput(const FuncGraphPtr &func_graph, const bool padding_flag = false; std::string output_format; - GetTransDataInputFormat(node, &output_format); + GetTransDataInputFormat(node, output_idx, &output_format); if (output_format == kOpFormat_NC1KHKWHWC0) { MS_LOG(EXCEPTION) << "got the hw format" << output_format << " when insert the transdata node " << node->DebugString(); From 26a65bee0cd95922dcde12a227e650aee97f6a9f Mon Sep 17 00:00:00 2001 From: lichenever Date: Mon, 30 Mar 2020 21:41:59 +0800 Subject: [PATCH 020/227] fix auto parallel st --- .../st/auto_parallel/onehot_model_parallel.py | 9 +---- .../run_auto_parallel_loss_expand.sh | 3 +- .../run_auto_parallel_resnet50_expand_loss.sh | 2 +- .../run_onehot_model_parallel.sh | 2 +- .../soft_entropy_loss_expand_parallel.py | 40 +++++++------------ tests/st/auto_parallel/test_expand_loss.py | 4 +- .../test_model_parallel_onehot.py | 3 -- 7 files changed, 22 insertions(+), 41 deletions(-) diff --git a/tests/st/auto_parallel/onehot_model_parallel.py b/tests/st/auto_parallel/onehot_model_parallel.py index b0e6f1eb91..14b351c0ee 100644 --- a/tests/st/auto_parallel/onehot_model_parallel.py +++ b/tests/st/auto_parallel/onehot_model_parallel.py @@ -130,9 +130,7 @@ class OneHotFactory: context.reset_auto_parallel_context() assert np.allclose(out_mindspore_single, out_mindspore_parallel, 0.0001, 0.0001) -@pytest.mark.level0 -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_single + def test_reid_onehot_forward_int32_128_depth1024_model_parallel(): fact = OneHotFactory(batch_size=128, classes=1024, @@ -142,9 +140,7 @@ def test_reid_onehot_forward_int32_128_depth1024_model_parallel(): strategy=((1,device_num),(),())) fact.forward_cmp() -@pytest.mark.level0 -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_single + def test_reid_onehot_forward_int32_1024_depth128_model_parallel(): fact = OneHotFactory(batch_size=1024, classes=128, @@ -153,4 +149,3 @@ def test_reid_onehot_forward_int32_1024_depth128_model_parallel(): axis=-1, strategy=((1,device_num),(),())) fact.forward_cmp() - diff --git a/tests/st/auto_parallel/run_auto_parallel_loss_expand.sh b/tests/st/auto_parallel/run_auto_parallel_loss_expand.sh index 83acc6ad54..71dee820cb 100644 --- a/tests/st/auto_parallel/run_auto_parallel_loss_expand.sh +++ b/tests/st/auto_parallel/run_auto_parallel_loss_expand.sh @@ -18,7 +18,6 @@ BASE_PATH=$(cd "$(dirname $0)"; pwd) CONFIG_PATH=/home/workspace/mindspore_config export DEVICE_NUM=8 export RANK_SIZE=$DEVICE_NUM -ulimit -n 65535 source ${BASE_PATH}/env.sh unset SLOG_PRINT_TO_STDOUT export MINDSPORE_HCCL_CONFIG_PATH=$CONFIG_PATH/hccl/rank_table_${DEVICE_NUM}p.json @@ -27,7 +26,7 @@ process_pid=() for((i=0; i<$DEVICE_NUM; i++)); do rm -rf ${BASE_PATH}/loss_expand${i} mkdir ${BASE_PATH}/loss_expand${i} - cp -r soft_entropy_loss_expand_parallel.py ${BASE_PATH}/loss_expand${i}/ + cp -r ${BASE_PATH}/soft_entropy_loss_expand_parallel.py ${BASE_PATH}/loss_expand${i}/ cd ${BASE_PATH}/loss_expand${i} export RANK_ID=${i} export DEVICE_ID=${i} diff --git a/tests/st/auto_parallel/run_auto_parallel_resnet50_expand_loss.sh b/tests/st/auto_parallel/run_auto_parallel_resnet50_expand_loss.sh index efc1e0c0de..094668ba5c 100644 --- a/tests/st/auto_parallel/run_auto_parallel_resnet50_expand_loss.sh +++ b/tests/st/auto_parallel/run_auto_parallel_resnet50_expand_loss.sh @@ -27,7 +27,7 @@ process_pid=() for((i=0; i<$DEVICE_NUM; i++)); do rm -rf ${BASE_PATH}/resnet50_expand_loss${i} mkdir ${BASE_PATH}/resnet50_expand_loss${i} - cp -r resnet50_expand_loss.py ${BASE_PATH}/resnet50_expand_loss${i}/ + cp -r ${BASE_PATH}/resnet50_expand_loss.py ${BASE_PATH}/resnet50_expand_loss${i}/ cd ${BASE_PATH}/resnet50_expand_loss${i} export RANK_ID=${i} export DEVICE_ID=${i} diff --git a/tests/st/auto_parallel/run_onehot_model_parallel.sh b/tests/st/auto_parallel/run_onehot_model_parallel.sh index 93229aab4e..8e931c2cdd 100644 --- a/tests/st/auto_parallel/run_onehot_model_parallel.sh +++ b/tests/st/auto_parallel/run_onehot_model_parallel.sh @@ -27,7 +27,7 @@ process_pid=() for((i=0; i<$DEVICE_NUM; i++)); do rm -rf ${BASE_PATH}/onehot_model_parallel${i} mkdir ${BASE_PATH}/onehot_model_parallel${i} - cp -r onehot_model_parallel.py ${BASE_PATH}/onehot_model_parallel${i}/ + cp -r ${BASE_PATH}/onehot_model_parallel.py ${BASE_PATH}/onehot_model_parallel${i}/ cd ${BASE_PATH}/onehot_model_parallel${i} export RANK_ID=${i} export DEVICE_ID=${i} diff --git a/tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py b/tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py index 081c6bd647..17dbe8f304 100644 --- a/tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py +++ b/tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py @@ -118,6 +118,9 @@ class Dataset(): def get_dataset_size(self): return self.length + def get_repeat_count(self): + return self.length + class ModelCallback(Callback): def __init__(self): super(ModelCallback, self).__init__() @@ -177,7 +180,6 @@ class LossFactory(): dataGen = DataGenerator() self.input_full, self.input_part = dataGen.input_data((batch_size, embed)) self.label_full, self.label_part = dataGen.label_data((batch_size,),embed) - self.expect_out = np.array([0.9205861 , 0.9205861 , 0.9205861 , 0.9201946 , 0.91951686, 0.919343]) def single_matmul_trains(self): single_callback = ModelCallback() @@ -187,7 +189,8 @@ class LossFactory(): epoch_size = 6 dataset = Dataset(self.input_full, self.label_full) model.train(epoch_size, dataset, callbacks=single_callback, dataset_sink_mode=False) - print("---loss---",single_callback.loss_list) + loss_value = np.array(single_callback.loss_list) + return loss_value def data_parallel_matmul_trains(self): parallel_callback = ModelCallback() @@ -199,7 +202,7 @@ class LossFactory(): dataset = Dataset(self.input_part, self.label_part) model.train(epoch_size, dataset, callbacks=parallel_callback, dataset_sink_mode=False) loss_value = np.array(parallel_callback.loss_list) - assert allclose(loss_value, self.expect_out, 0.00001, 0.00001) + return loss_value def model_parallel_matmul_trains(self): parallel_callback = ModelCallback() @@ -224,7 +227,7 @@ class LossFactory(): dataset = Dataset(self.input_part, self.label_part) model.train(epoch_size, dataset, callbacks=parallel_callback, dataset_sink_mode=False) loss_value = np.array(parallel_callback.loss_list) - assert allclose(loss_value, self.expect_out, 0.00001, 0.00001) + return loss_value def mix_parallel_matmul_trains(self): parallel_callback = ModelCallback() @@ -249,28 +252,13 @@ class LossFactory(): dataset = Dataset(self.input_part, self.label_part) model.train(epoch_size, dataset, callbacks=parallel_callback, dataset_sink_mode=False) loss_value = np.array(parallel_callback.loss_list) - assert allclose(loss_value, self.expect_out, 0.00001, 0.00001) - -@pytest.mark.level0 -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_single -def test_matmul_loss_data_parallel_trains(): - loss_factory = LossFactory() - context.reset_auto_parallel_context() - loss_factory.data_parallel_matmul_trains() - -@pytest.mark.level0 -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_single -def test_matmul_loss_model_parallel_trains(): - loss_factory = LossFactory() - context.reset_auto_parallel_context() - loss_factory.model_parallel_matmul_trains() + return loss_value -@pytest.mark.level0 -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_single -def test_matmul_loss_mix_parallel_trains(): +def test_all_trains(): loss_factory = LossFactory() context.reset_auto_parallel_context() - loss_factory.mix_parallel_matmul_trains() + single_loss = loss_factory.single_matmul_trains() + model_parallel_loss = loss_factory.model_parallel_matmul_trains() + mix_parallel_loss = loss_factory.mix_parallel_matmul_trains() + assert allclose(single_loss, model_parallel_loss) + assert allclose(single_loss, mix_parallel_loss) diff --git a/tests/st/auto_parallel/test_expand_loss.py b/tests/st/auto_parallel/test_expand_loss.py index c3ab64395a..786cbff980 100644 --- a/tests/st/auto_parallel/test_expand_loss.py +++ b/tests/st/auto_parallel/test_expand_loss.py @@ -18,7 +18,9 @@ import pytest @pytest.mark.level0 @pytest.mark.platform_x86_ascend_training +@pytest.mark.platform_arm_ascend_training @pytest.mark.env_single def test_expand_loss(): - ret = os.system("sh run_auto_parallel_loss_expand.sh") + sh_path = os.path.split(os.path.realpath(__file__))[0] + ret = os.system(f"sh {sh_path}/run_auto_parallel_loss_expand.sh") assert(ret==0) diff --git a/tests/st/auto_parallel/test_model_parallel_onehot.py b/tests/st/auto_parallel/test_model_parallel_onehot.py index aeebf1b78b..1df7ad1e99 100644 --- a/tests/st/auto_parallel/test_model_parallel_onehot.py +++ b/tests/st/auto_parallel/test_model_parallel_onehot.py @@ -16,9 +16,6 @@ import os import pytest -@pytest.mark.level0 -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_single def test_expand_loss(): ret = os.system("sh run_onehot_model_parallel.sh") assert(ret==0) From c5da87254dcfe6dffa0e2c9b0ebe22603274677d Mon Sep 17 00:00:00 2001 From: lvliang Date: Mon, 30 Mar 2020 22:01:24 +0800 Subject: [PATCH 021/227] auto enbale dynamic mem pool --- .../device/ascend/ascend_kernel_runtime.cc | 44 ++++++------------- .../device/ascend/ascend_memory_allocator.cc | 2 +- .../device/ascend/ascend_memory_allocator.h | 2 +- mindspore/ccsrc/session/session_basic.cc | 2 +- 4 files changed, 17 insertions(+), 33 deletions(-) diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc index 7be480e5e3..a7dfc96b2f 100644 --- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc +++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc @@ -239,22 +239,11 @@ DeviceAddressPtr AscendKernelRuntime::CreateDeviceAddress(void *device_ptr, size return std::make_shared(device_ptr, device_size, format, type_id); } -void AscendKernelRuntime::MallocOpMemory(const DeviceAddressPtr address, size_t size, int flag) { - MS_EXCEPTION_IF_NULL(MsContext::GetInstance()); - if (MsContext::GetInstance()->enable_dynamic_mem_pool()) { - auto device_ptr = AscendMemoryAllocator::GetInstance().AllocTensorMem(size); - MS_EXCEPTION_IF_NULL(device_ptr); - address->ptr_ = device_ptr; - address->mem_dynamic_alloc_ = true; - return; - } - if (flag == kStaticMem) { - address->ptr_ = MallocStaticMem(size, false); - } else if (flag == kDynamicMem) { - address->ptr_ = MallocDynamicMem(size, false); - } else { - MS_LOG(EXCEPTION) << "Unknown memory type!"; - } +void AscendKernelRuntime::MallocOpMemory(const DeviceAddressPtr address, size_t size, int) { + auto device_ptr = AscendMemoryAllocator::GetInstance().AllocTensorMem(size); + MS_EXCEPTION_IF_NULL(device_ptr); + address->ptr_ = device_ptr; + address->mem_dynamic_alloc_ = true; } bool AscendKernelRuntime::GenTask(const session::KernelGraph *graph) { @@ -488,23 +477,18 @@ bool AscendKernelRuntime::DestroyHccl() { bool AscendKernelRuntime::MallocDeviceMemory() { device_mem_size_ = ASCEND_MEM_SIZE_BYTE; - MS_EXCEPTION_IF_NULL(MsContext::GetInstance()); - if (MsContext::GetInstance()->enable_dynamic_mem_pool()) { - static_mem_offset_ = FloatToSize(device_mem_size_ * GRAPH_INIT_DAVINCI_MEM_RATIO); - device_mem_pool_size_ = FloatToSize(device_mem_size_ * (1 - GRAPH_INIT_DAVINCI_MEM_RATIO)); - auto ret = rtMalloc(reinterpret_cast(&device_mem_pool_base_), device_mem_pool_size_, RT_MEMORY_HBM); - if (ret != RT_ERROR_NONE) { - MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]"; - } - AscendMemoryAllocator::GetInstance().set_device_mem_pool_base(device_mem_pool_base_); - AscendMemoryAllocator::GetInstance().set_device_mem_pool_size(device_mem_pool_size_); - } else { - static_mem_offset_ = device_mem_size_; + static_mem_offset_ = FloatToSize(device_mem_size_ * GRAPH_INIT_ASCEND_MEM_RATIO); + auto ret = rtMalloc(reinterpret_cast(&device_mem_base_), static_mem_offset_, RT_MEMORY_HBM); + if (ret != RT_ERROR_NONE) { + MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << static_mem_offset_ << "] fail, ret[" << ret << "]"; } - auto ret = rtMalloc(reinterpret_cast(&device_mem_base_), device_mem_size_, RT_MEMORY_HBM); + device_mem_pool_size_ = FloatToSize(device_mem_size_ * (1 - GRAPH_INIT_ASCEND_MEM_RATIO)); + ret = rtMalloc(reinterpret_cast(&device_mem_pool_base_), device_mem_pool_size_, RT_MEMORY_HBM); if (ret != RT_ERROR_NONE) { - MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_size_ << "] fail, ret[" << ret << "]"; + MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]"; } + AscendMemoryAllocator::GetInstance().set_device_mem_pool_base(device_mem_pool_base_); + AscendMemoryAllocator::GetInstance().set_device_mem_pool_size(device_mem_pool_size_); return true; } diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc b/mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc index 06b921f509..08a30a28b7 100644 --- a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc +++ b/mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc @@ -26,7 +26,7 @@ const uint64_t MEM_SIZE_BYTE = (MEM_SIZE << 30); AscendMemoryAllocator::AscendMemoryAllocator() { hasMalloc_ = false; - free_mem_size_ = FloatToSize(MEM_SIZE_BYTE * (1 - GRAPH_INIT_DAVINCI_MEM_RATIO)); + free_mem_size_ = FloatToSize(MEM_SIZE_BYTE * (1 - GRAPH_INIT_ASCEND_MEM_RATIO)); total_mem_size_ = free_mem_size_; } diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.h b/mindspore/ccsrc/device/ascend/ascend_memory_allocator.h index 74cc50834f..8b0f89a9b8 100644 --- a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.h +++ b/mindspore/ccsrc/device/ascend/ascend_memory_allocator.h @@ -24,7 +24,7 @@ namespace mindspore { namespace device { namespace ascend { // The fraction of total ascend memory used to compute the graph. -static const float GRAPH_INIT_DAVINCI_MEM_RATIO = 0.8; +static const float GRAPH_INIT_ASCEND_MEM_RATIO = 0.8; class AscendMemoryAllocator : public DynamicMemPoolBestFit { public: diff --git a/mindspore/ccsrc/session/session_basic.cc b/mindspore/ccsrc/session/session_basic.cc index 1d786708a2..be03f54f3c 100644 --- a/mindspore/ccsrc/session/session_basic.cc +++ b/mindspore/ccsrc/session/session_basic.cc @@ -497,7 +497,7 @@ void SessionBasic::LoadInputData(const std::shared_ptr &kernel_grap auto device_address = AnfAlgo::GetMutableOutputAddr(pk_node, 0); bool need_sync = false; if (ms_context->enable_pynative_infer()) { - if (tensor->device_address().get() == nullptr) { + if (tensor->device_address().get() == nullptr || tensor->device_address() != device_address) { need_sync = true; } } else { From d2348c7f87633c6cfe4d6f000d2251f2807aae59 Mon Sep 17 00:00:00 2001 From: leonwanghui Date: Tue, 31 Mar 2020 10:27:40 +0800 Subject: [PATCH 022/227] =?UTF-8?q?=E5=9B=9E=E9=80=80=20'Pull=20Request=20?= =?UTF-8?q?!17=20:=20[AutoParallel]Fix=20bug=20in=20the=20case=20of=20two?= =?UTF-8?q?=20cast'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../ccsrc/parallel/step_auto_parallel.cc | 6 ++-- mindspore/ccsrc/parallel/step_parallel.cc | 21 ++++---------- .../parallel/test_element_wise_function.py | 29 ------------------- 3 files changed, 7 insertions(+), 49 deletions(-) diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc index aee4247755..c3e3f5893e 100644 --- a/mindspore/ccsrc/parallel/step_auto_parallel.cc +++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc @@ -346,8 +346,6 @@ bool IsAutoParallelCareNode(const CNodePtr &cnode) { } OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &cnode) { - MS_EXCEPTION_IF_NULL(prim); - MS_EXCEPTION_IF_NULL(cnode); auto attrs = prim->attrs(); std::vector shape_list = ExtractShape(cnode); if (shape_list.empty()) { @@ -383,8 +381,8 @@ OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr & operator_info->set_outputs_dtype(cnode->Type()); operator_info->set_cnode(cnode); // If no strategy has been configured for this operator, then candidate strategies are generated for - // auto-strategy searching, if this primitive is Cast, we ignore the user-specified strategy - if (!StrategyFound(attrs) || prim->name() == CAST) { + // auto-strategy searching + if (!StrategyFound(attrs)) { // Compute split_flag_list_, indicating which input has batch dimension. This is ONLY used for preparation for // BatchParallelInfo operator operator_info->ComputeBatchSplitFlagList(); diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc index 2d948679d7..927acea705 100644 --- a/mindspore/ccsrc/parallel/step_parallel.cc +++ b/mindspore/ccsrc/parallel/step_parallel.cc @@ -370,12 +370,15 @@ bool IsParallelCareNode(const CNodePtr& cnode) { if (prim == nullptr) { return false; } + auto attrs = prim->attrs(); if (IsInBlackList(prim)) { MS_LOG(INFO) << "Parallel don't care node: " << prim->name(); return false; } - if ((prim->name() == CAST) && (cnode->operator_info() == nullptr)) { - return false; + if ((prim->name() == CAST)) { + if ((!attrs.count(STRATEGY)) && (cnode->operator_info() == nullptr)) { + return false; + } } return cnode->in_forward_flag(); @@ -645,13 +648,6 @@ LossNodeInfo GetLossNodeInfo(const AnfNodePtr& loss_node) { MS_EXCEPTION_IF_NULL(pre_node); LossNodeInfo node_info; - // return -> cast - auto pre_cnode = pre_node->cast(); - MS_EXCEPTION_IF_NULL(pre_cnode); - auto pre_prim = GetValueNode(pre_cnode->input(0)); - if (pre_prim->name() == CAST && pre_cnode->operator_info() == nullptr) { - pre_node = pre_cnode->input(1); - } // return -> loss if (pre_node == loss_node) { @@ -1947,13 +1943,6 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) { MS_EXCEPTION_IF_NULL(current_value); PrimitivePtr current_prim = current_value->value()->cast(); MS_EXCEPTION_IF_NULL(current_prim); - // return -> cast - if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) { - pre_cnode = pre_cnode->input(1)->cast(); - MS_EXCEPTION_IF_NULL(pre_cnode); - current_prim = GetValueNode(pre_cnode->input(0)); - } - // notice: the GetNext op has not input if (INVALID_LOSS_OPS.find(current_prim->name()) != INVALID_LOSS_OPS.end()) { MS_LOG(INFO) << "The loss is: " << current_prim->name(); diff --git a/tests/ut/python/parallel/test_element_wise_function.py b/tests/ut/python/parallel/test_element_wise_function.py index a917dce9b6..dfcebdc5ab 100644 --- a/tests/ut/python/parallel/test_element_wise_function.py +++ b/tests/ut/python/parallel/test_element_wise_function.py @@ -272,32 +272,3 @@ def test_cast_before_mirror3(): y = Tensor(np.ones([32, 64]), dtype=ms.float16) b = Tensor(np.ones([64, 64]), dtype=ms.float32) _executor.compile(net, x, y, b) - - -def test_mul_two_cast(): - class Net(nn.Cell): - def __init__(self, strategy1, strategy2, strategy3): - super().__init__() - self.mul = P.Mul().set_strategy(strategy1) - self.mul2 = P.Mul().set_strategy(strategy2) - self.cast = P.Cast().set_strategy(strategy3) - self.cast2 = P.Cast().set_strategy(strategy3) - - def construct(self, x, y, b): - out = self.mul(x, y) - out = self.mul2(out, b) - out = self.cast(out, ms.int32) - out = self.cast2(out, ms.bool_) - return out - - context.set_auto_parallel_context(device_num=8, global_rank=0) - strategy1 = ((2, 2), (2, 2)) - strategy2 = ((8, 1), (8, 1)) - strategy3 = ((8, 1), ) - net = GradWrap(Net(strategy1, strategy2, strategy3)) - context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") - - x = Tensor(np.ones([128, 32]), dtype=ms.float32) - y = Tensor(np.ones([128, 32]), dtype=ms.float32) - b = Tensor(np.ones([128, 32]), dtype=ms.float32) - _executor.compile(net, x, y, b) From 14223b6cc586c65a895b76d609969fba3b0e836a Mon Sep 17 00:00:00 2001 From: Ting Wang Date: Tue, 31 Mar 2020 12:01:58 +0800 Subject: [PATCH 023/227] fix typo Signed-off-by: Ting Wang --- docs/Automatic-parallel.png | Bin 124010 -> 36199 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/docs/Automatic-parallel.png b/docs/Automatic-parallel.png index abf572d568d35bbb61690b30d5c1d1c66be76f31..f52d8b757d11a07ce99aebf5458d10059ea6dd3b 100644 GIT binary patch literal 36199 zcmcG$2UJtf6AcQ1y;(zYU{nolOcV^A~SSw^Dr=Rn_&$FMs_mdc7Lv02+PPz*hE->io z+&8^&fgA_Cl4z)aZ~AtgzXsmOd`z|PT__*oMgt!xT{R6fFI=dCUOIQY2z;jX(s}B0 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12:18:02 +0800 Subject: [PATCH 024/227] Correct the comments for `RandomChoiceWithMask` op. --- mindspore/ops/operations/random_ops.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/mindspore/ops/operations/random_ops.py b/mindspore/ops/operations/random_ops.py index c8f59e898d..9ef5b301f9 100644 --- a/mindspore/ops/operations/random_ops.py +++ b/mindspore/ops/operations/random_ops.py @@ -25,20 +25,23 @@ class RandomChoiceWithMask(PrimitiveWithInfer): """ Generates a random samply as index tensor with a mask tensor from a given tensor. - The input must be a tensor of rank >= 2, the first dimension specify the number of sample. - The index tensor and the mask tensor have the same and fixed shape. The index tensor denotes the index - of the nonzero sample, while the mask tensor denotes which element in the index tensor are valid. + The input must be a tensor of rank >= 1. If its rank >= 2, the first dimension specify the number of sample. + The index tensor and the mask tensor have the fixed shapes. The index tensor denotes the index of the nonzero + sample, while the mask tensor denotes which elements in the index tensor are valid. Args: - count (int): Number of items expected to get. Default: 256. - seed (int): Random seed. - seed2 (int): Random seed2. + count (int): Number of items expected to get and the number should be greater than 0. Default: 256. + seed (int): Random seed. Default: 0. + seed2 (int): Random seed2. Default: 0. Inputs: - - **input_x** (Tensor) - The input tensor. + - **input_x** (Tensor[bool]) - The input tensor. Outputs: - Tuple, two tensors, the first one is the index tensor and the other one is the mask tensor. + Two tensors, the first one is the index tensor and the other one is the mask tensor. + + - **index** (Tensor) - The output has shape between 2-D and 5-D. + - **mask** (Tensor) - The output has shape 1-D. Examples: >>> rnd_choice_mask = RandomChoiceWithMask() From a6e626f02ebff4854adb3b7bdbe831545dd69dfd Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Tue, 31 Mar 2020 09:34:09 +0800 Subject: [PATCH 025/227] add operator diag and diag_part --- mindspore/ccsrc/transform/convert.cc | 6 +- mindspore/ccsrc/transform/op_declare.cc | 10 +++ mindspore/ccsrc/transform/op_declare.h | 4 ++ mindspore/ops/_grad/grad_array_ops.py | 22 +++++++ mindspore/ops/operations/__init__.py | 3 +- mindspore/ops/operations/array_ops.py | 85 +++++++++++++++++++++---- tests/ut/python/ops/test_ops.py | 10 +++ 7 files changed, 125 insertions(+), 15 deletions(-) diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index 87bfc8f6d8..b8d07594b7 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -179,6 +179,8 @@ const char kNameLARSUpdate[] = "LARSUpdate"; const char kNameRound[] = "Round"; const char kNamePrint[] = "Print"; const char kNameApplyFtrl[] = "ApplyFtrl"; +const char kNameDiag[] = "Diag"; +const char kNameDiagPart[] = "DiagPart"; // -----------------OpAdapter initialization-------------- std::unordered_map &DfGraphConvertor::get_adpt_map() { @@ -359,7 +361,9 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameDepthToSpace), ADPT_DESC(DepthToSpace)}, {string(kNameSign), ADPT_DESC(Sign)}, {string(kNameRound), ADPT_DESC(Round)}, - {string(kNameApplyFtrl), ADPT_DESC(ApplyFtrl)}}; + {string(kNameApplyFtrl), ADPT_DESC(ApplyFtrl)}, + {string(kNameDiag), ADPT_DESC(Diag)}, + {string(kNameDiagPart), ADPT_DESC(DiagPart)}}; #ifdef ENABLE_GE adpt_map[string(kNamePrint)] = ADPT_DESC(Print); #endif diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 028b129756..24043072fa 100755 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -1160,6 +1160,16 @@ INPUT_MAP(ApplyFtrl) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(accum)}, {3, INP ATTR_MAP(ApplyFtrl) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits())}}; OUTPUT_MAP(ApplyFtrl) = {{0, OUTPUT_DESC(var)}}; +// Diag +INPUT_MAP(Diag) = {{1, INPUT_DESC(x)}}; +ATTR_MAP(Diag) = EMPTY_ATTR_MAP; +OUTPUT_MAP(Diag) = {{0, OUTPUT_DESC(y)}}; + +// DiagPart +INPUT_MAP(DiagPart) = {{1, INPUT_DESC(x)}}; +ATTR_MAP(DiagPart) = EMPTY_ATTR_MAP; +OUTPUT_MAP(DiagPart) = {{0, OUTPUT_DESC(y)}}; + #ifdef ENABLE_GE // Print INPUT_MAP(Print) = EMPTY_INPUT_MAP; diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index 9e4f407ebb..65e4cdc998 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -437,6 +437,10 @@ DECLARE_OP_ADAPTER(ApplyFtrl) DECLARE_OP_USE_OUTPUT(ApplyFtrl) DECLARE_OP_ADAPTER(SparseApplyFtrlD) DECLARE_OP_USE_OUTPUT(SparseApplyFtrlD) +DECLARE_OP_ADAPTER(Diag) +DECLARE_OP_USE_OUTPUT(Diag) +DECLARE_OP_ADAPTER(DiagPart) +DECLARE_OP_USE_OUTPUT(DiagPart) #ifdef ENABLE_GE DECLARE_OP_ADAPTER(Print) DECLARE_OP_USE_DYN_INPUT(Print) diff --git a/mindspore/ops/_grad/grad_array_ops.py b/mindspore/ops/_grad/grad_array_ops.py index cf6247023e..79841cf27a 100644 --- a/mindspore/ops/_grad/grad_array_ops.py +++ b/mindspore/ops/_grad/grad_array_ops.py @@ -408,3 +408,25 @@ def get_bprop_depth_to_space(self): return (op(dout),) return bprop + + +@bprop_getters.register(P.Diag) +def get_bprop_diag(self): + """Generate bprop for Diag""" + op = P.DiagPart() + + def bprop(x, out, dout): + return (op(dout),) + + return bprop + + +@bprop_getters.register(P.DiagPart) +def get_bprop_diag_part(self): + """Generate bprop for DiagPart""" + op = P.Diag() + + def bprop(x, out, dout): + return (op(dout),) + + return bprop diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index 77bb6d0ff3..92ffdaf199 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -20,7 +20,7 @@ A collection of operators to build nerual networks or computing functions. """ from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat, - Diag, DType, ExpandDims, Eye, + Diag, DiagPart, DType, ExpandDims, Eye, Fill, GatherNd, GatherV2, InvertPermutation, IsInstance, IsSubClass, ArgMaxWithValue, OnesLike, ZerosLike, Rank, Reshape, ResizeNearestNeighbor, ArgMinWithValue, @@ -208,6 +208,7 @@ __all__ = [ "Cos", "ACos", "Diag", + "DiagPart", 'Eye', 'Assign', 'AssignAdd', diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index d0d3d5006c..6740f172b4 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -1615,37 +1615,96 @@ class StridedSlice(PrimitiveWithInfer): class Diag(PrimitiveWithInfer): r""" - Extract or construct a diagonal array. + Construct a diagonal tensor with a given diagonal values. - If input is a 2-D tensor, returns the diagonal of the input with the given offset. If - input is a 1-D tensor, returns the array of diagonals. If you use this function - to extract the diagonal and want to write to the result array, see the more - detailed documentation for "numpy.diagonal", whether you return a copy or a - view depends on the version of numpy you are using. + Assume `input_x` has dimensions :math:`[D_1,... D_k]`, the output is a tensor of + rank 2k with dimensions :math:`[D_1,..., D_k, D_1,..., D_k]` where: + :math:`output[i_1,..., i_k, i_1,..., i_k] = input_x[i_1,..., i_k]` and 0 everywhere else. Inputs: - - **input_x** (Tensor) - 1-D tensor or 2-D tensor. + - **input_x** (Tensor) - The input tensor. Outputs: Tensor. Examples: + >>> input_x = Tensor([1, 2, 3, 4]) >>> diag = P.Diag() >>> diag(x) + [[1, 0, 0, 0], + [0, 2, 0, 0], + [0, 0, 3, 0], + [0, 0, 0, 4]] """ @prim_attr_register def __init__(self): """init Diag""" - def infer_type(self, x): - args = {"x_dtype": x} - validator.check_subclass('input_x', x, mstype.tensor) - validator.check_type_same(args, mstype.number_type) - return x + def infer_dtype(self, x_type): + validator.check_subclass('input_x', x_type, mstype.tensor) + return x_type + + def infer_shape(self, x_shape): + validator.check("x rank", len(x_shape), "", 1, Rel.GE) + ret_shape = copy.deepcopy(x_shape) + ret_shape = ret_shape + ret_shape + return ret_shape + + def infer_value(self, x): + if x is None: + return None + validator.check("input x rank", len(x.shape()), "", 1) + ret = np.diag(x.asnumpy()) + return Tensor(ret) + + +class DiagPart(PrimitiveWithInfer): + r""" + + Extract the diagonal part from given tensor. + + Assume input has dimensions :math:`[D_1,..., D_k, D_1,..., D_k]`, the output is a tensor + of rank k with dimensions :math:`[D_1,..., D_k]` where: + :math:`output[i_1,..., i_k] = input[i_1,..., i_k, i_1,..., i_k]`. + + Inputs: + - **input_x** (Tensor) - The input Tensor. + + Outputs: + Tensor. + + Examples + >>> input_x = Tensor([[1, 0, 0, 0], + >>> [0, 2, 0, 0], + >>> [0, 0, 3, 0], + >>> [0, 0, 0, 4]]) + >>> diag_part = P.DiagPart() + >>> diag_part(x) + [1, 2, 3, 4] + """ + + @prim_attr_register + def __init__(self): + """init DiagPart""" + + def infer_dtype(self, x_type): + validator.check_subclass('input_x', x_type, mstype.tensor) + return x_type + + def infer_shape(self, x_shape): + if len(x_shape)%2 != 0 or \ + not x_shape: + raise ValueError(f"DiagPart input rank must be non-zero and even, but got rank {len(x_shape)}, " + f"with shapes {x_shape}") + length = len(x_shape) // 2 + ret_shape = x_shape[0:length] + return ret_shape def infer_value(self, x): - validator.check("shape_length", len(x.shape()), "length", [1, 2], Rel.IN) + if x is None: + return None + validator.check("x rank", len(x.shape()), "", 2) ret = np.diag(x.asnumpy()) return Tensor(ret) diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py index 8d7dd95072..7e32ac9454 100755 --- a/tests/ut/python/ops/test_ops.py +++ b/tests/ut/python/ops/test_ops.py @@ -947,6 +947,16 @@ test_case_array_ops = [ Tensor(np.array([1], np.float32)), Tensor(np.array([1], np.float32)))], 'desc_bprop': [[3,]]}), + ('Diag', { + 'block': P.Diag(), + 'desc_inputs': [[4]], + 'desc_bprop': [[4, 4]], + }), + ('DiagPart', { + 'block': P.DiagPart(), + 'desc_inputs': [[4, 4]], + 'desc_bprop': [[4]], + }), ] test_case_other_ops = [ From 2fd7f6710897e73b834d84e4ed68b9a216fb3351 Mon Sep 17 00:00:00 2001 From: GinFung Date: Mon, 30 Mar 2020 09:54:05 +0800 Subject: [PATCH 026/227] Add matmul biasadd fusion pass --- .../ascend/ascend_backend_optimization.cc | 2 + .../ascend/ir_fusion/matmul_biasadd_fusion.cc | 51 +++++++++++++++++ .../ascend/ir_fusion/matmul_biasadd_fusion.h | 34 +++++++++++ mindspore/ccsrc/pre_activate/common/helper.h | 1 + mindspore/ccsrc/utils/utils.h | 2 + .../ir_fusion/matmul_biasadd_fusion_test.cc | 56 +++++++++++++++++++ .../matmul_biasadd_fusion_test.py | 46 +++++++++++++++ 7 files changed, 192 insertions(+) create mode 100644 mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.cc create mode 100644 mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h create mode 100644 tests/ut/cpp/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion_test.cc create mode 100644 tests/ut/cpp/python_input/gtest_input/pre_activate/matmul_biasadd_fusion_test.py diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc index 9883443910..b9b324e5dd 100644 --- a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc +++ b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc @@ -43,6 +43,7 @@ #include "pre_activate/ascend/ir_fusion/momentum_lossscale_fusion.h" #include "pre_activate/ascend/ir_fusion/mul_add_fusion.h" #include "pre_activate/ascend/ir_fusion/mul_addn_fusion.h" +#include "pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h" #include "pre_activate/ascend/format_type/insert_trans_op.h" #include "pre_activate/pass/getitem_tuple.h" #include "pre_activate/pass/optimize_dependence.h" @@ -173,6 +174,7 @@ void AscendBackendIRFusionOptimization(const std::shared_ptrAddPass(std::make_shared()); ir_fusion_pm->AddPass(std::make_shared()); ir_fusion_pm->AddPass(std::make_shared()); + ir_fusion_pm->AddPass(std::make_shared()); ir_fusion_pm->AddPass(std::make_shared()); ir_fusion_pm->AddPass(std::make_shared()); } diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.cc b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.cc new file mode 100644 index 0000000000..e81c804b71 --- /dev/null +++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.cc @@ -0,0 +1,51 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h" +#include +#include "pre_activate/common/helper.h" +#include "session/anf_runtime_algorithm.h" +#include "utils/utils.h" + +namespace mindspore { +namespace opt { +namespace { +constexpr size_t kMatMulInputIndex = 1; +constexpr size_t kBiasInputIndex = 2; +} // namespace + +const BaseRef MatmulBiasaddFusion::DefinePattern() const { + VarPtr X0 = std::make_shared(); + VarPtr X1 = std::make_shared(); + VarPtr X2 = std::make_shared(); + const auto prim_bias_add = std::make_shared(kBiasAddOpName); + return VectorRef({prim_bias_add, VectorRef({prim::kPrimMatMul, X0, X1}), X2}); +} + +const AnfNodePtr MatmulBiasaddFusion::Process(const FuncGraphPtr &, const AnfNodePtr &node, const EquivPtr &) const { + MS_EXCEPTION_IF_NULL(node); + auto cnode = node->cast(); + MS_EXCEPTION_IF_NULL(cnode); + CheckCNodeInputSize(cnode, kBiasAddInputNum); + AnfNodePtr matmul = cnode->input(kMatMulInputIndex); + MS_EXCEPTION_IF_NULL(matmul); + auto matmul_cnode = matmul->cast(); + MS_EXCEPTION_IF_NULL(matmul_cnode); + matmul_cnode->add_input(cnode->input(kBiasInputIndex)); + AnfAlgo::SetNodeAttr(kAttrHasBias, MakeValue(true), matmul); + return matmul; +} +} // namespace opt +} // namespace mindspore diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h new file mode 100644 index 0000000000..56675243de --- /dev/null +++ b/mindspore/ccsrc/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h @@ -0,0 +1,34 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_MATMUL_BIASADD_FUSION_H_ +#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_MATMUL_BIASADD_FUSION_H_ + +#include "pre_activate/common/optimizer.h" + +namespace mindspore { +namespace opt { +class MatmulBiasaddFusion : public PatternProcessPass { + public: + explicit MatmulBiasaddFusion(bool multigraph = true) : PatternProcessPass("matmul_biasadd_fusion", multigraph) {} + + ~MatmulBiasaddFusion() override = default; + const BaseRef DefinePattern() const override; + const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override; +}; +} // namespace opt +} // namespace mindspore + +#endif // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_MATMUL_BIASADD_FUSION_H_ diff --git a/mindspore/ccsrc/pre_activate/common/helper.h b/mindspore/ccsrc/pre_activate/common/helper.h index ba8db8bd29..4f30a935af 100644 --- a/mindspore/ccsrc/pre_activate/common/helper.h +++ b/mindspore/ccsrc/pre_activate/common/helper.h @@ -84,6 +84,7 @@ constexpr size_t kLayerNormGradInputNum = 6; constexpr size_t kAdamApplyOneOutputNum = 3; constexpr size_t kBackendTransDataInputNum = 2; constexpr size_t kApplyMomentumInputNum = 6; +constexpr size_t kBiasAddInputNum = 3; enum FusedBatchNormInput { kX = 1, diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h index 3328480d00..229c0547cc 100644 --- a/mindspore/ccsrc/utils/utils.h +++ b/mindspore/ccsrc/utils/utils.h @@ -110,6 +110,7 @@ constexpr auto kResizeNearestNeighborGrad = "ResizeNearestNeighborGrad"; constexpr auto kFusedMulAddOpName = "FusedMulAdd"; constexpr auto kFusedMulAddNOpName = "FusedMulAddN"; constexpr auto kFusedMulApplyMomentumOpName = "FusedMulApplyMomentum"; +constexpr auto kBiasAddOpName = "BiasAdd"; // attr key name constexpr auto kAttrInputNames = "input_names"; @@ -140,6 +141,7 @@ constexpr auto kAttrDynInput = "dynamic"; constexpr auto kAttrDynInputSizes = "dyn_input_sizes"; constexpr auto kAttrSrcFormat = "src_format"; constexpr auto kAttrOutputUsedNum = "output_used_num"; +constexpr auto kAttrHasBias = "has_bias"; // attr value constexpr auto kValueTargetSwitch = "target_switch"; diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion_test.cc new file mode 100644 index 0000000000..c8f97be290 --- /dev/null +++ b/tests/ut/cpp/pre_activate/ascend/ir_fusion/matmul_biasadd_fusion_test.cc @@ -0,0 +1,56 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "pre_activate/ascend/ir_fusion/matmul_biasadd_fusion.h" +#include "common/backend_common_test.h" +#include "common/py_func_graph_fetcher.h" + +namespace mindspore { +namespace opt { +class TestHWMatmulBiasaddFusion : public BackendCommon { + public: + TestHWMatmulBiasaddFusion() : get_py_fun_("gtest_input.pre_activate.matmul_biasadd_fusion_test", true) {} + ~TestHWMatmulBiasaddFusion() override = default; + + UT::PyFuncGraphFetcher get_py_fun_; +}; + +TEST_F(TestHWMatmulBiasaddFusion, test_matmul_biasadd_fusion) { + FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_matmul_biasadd_fusion", "before"); + EXPECT_NE(g, nullptr); + std::vector shpx{1, 3}; + auto x_abstract = std::make_shared(kFloat32, shpx); + std::vector shpy{3, 4}; + auto y_abstract = std::make_shared(kFloat32, shpy); + std::vector shp_bias{4}; + auto bias_abstract = std::make_shared(kFloat32, shp_bias); + AbstractBasePtrList args_spec_list; + args_spec_list.push_back(x_abstract); + args_spec_list.push_back(y_abstract); + args_spec_list.push_back(bias_abstract); + auto kg = GetKernelGraph(g, args_spec_list); + + auto optimizer = std::make_shared(); + auto pm = std::make_shared(); + pm->AddPass(std::make_shared()); + optimizer->AddPassManager(pm); + FuncGraphPtr new_graph = optimizer->Optimize(kg); + + FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_matmul_biasadd_fusion", "after"); + EXPECT_TRUE(CheckEqualGraph(g_after, new_graph)); +} +} // namespace opt +} // namespace mindspore \ No newline at end of file diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/matmul_biasadd_fusion_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/matmul_biasadd_fusion_test.py new file mode 100644 index 0000000000..5d55e854f7 --- /dev/null +++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/matmul_biasadd_fusion_test.py @@ -0,0 +1,46 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +from mindspore.ops import operations as P +from mindspore.ops import Primitive + +MatMul = P.MatMul() +BiasAdd = P.BiasAdd() +make_tuple = Primitive('make_tuple') + +class FnDict: + def __init__(self): + self.fnDict = {} + + def __call__(self, fn): + self.fnDict[fn.__name__] = fn + + def __getitem__(self, name): + return self.fnDict[name] + + +def test_matmul_biasadd_fusion(tag): + fns = FnDict() + + @fns + def before(input0, input1, input2): + matmul = MatMul(input0, input1) + biasadd = BiasAdd(matmul, input2) + return biasadd + + @fns + def after(input0, input1, input2): + return make_tuple(MatMul(input0, input1, input2)) + + return fns[tag] From 10389d8f199e56eeff2ec29904025b69648942e3 Mon Sep 17 00:00:00 2001 From: Ting Wang Date: Tue, 31 Mar 2020 14:28:46 +0800 Subject: [PATCH 027/227] update the supported version of EulerOS Signed-off-by: Ting Wang --- RELEASE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/RELEASE.md b/RELEASE.md index 76d86c8bff..8920095bb5 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -3,7 +3,7 @@ ## Main Features ### Ascend 910 Training and Inference Framework -* Recommended OS: Ubuntu 16.04 (or later) or EulerOS 2.0 +* Recommended OS: Ubuntu 16.04 (or later) or EulerOS 2.5 or EulerOS 2.8 * Python version: 3.7.5 * Preset models * ResNet-50: residual structure-based convolutional neural network (CNN) for image classification, which is widely used. From 80b8d05c14b6c9f4fe59a765acc5975c105f4bbe Mon Sep 17 00:00:00 2001 From: lichenever Date: Tue, 31 Mar 2020 11:14:28 +0800 Subject: [PATCH 028/227] fix grpah mode loop sink bug in auto parallel --- .../ccsrc/parallel/auto_parallel/graph_costmodel.h | 4 ++-- mindspore/ccsrc/parallel/step_parallel.cc | 4 ++++ mindspore/train/dataset_helper.py | 2 +- .../parallel/test_auto_parallel_parameter_cast.py | 12 ++++++------ .../python/parallel/test_auto_parallel_two_matmul.py | 7 ++++--- 5 files changed, 17 insertions(+), 12 deletions(-) diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h index 4efb77055f..fde9514540 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h +++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h @@ -34,8 +34,8 @@ namespace parallel { #define OPERATOR_TO_OPERATOR_CONNECTOR "-" #define DEFAULT_DEVICE_MEMORY_CAPACITY (1024.0 * 1024.0 * 1024.0 * 16.0) #define DEFAULT_COST_MODEL_ALPHA 1.0 -#define DEFAULT_COST_MODEL_BETA 65.0 -#define DEFAULT_COST_MODEL_GAMMA 0.02 +#define DEFAULT_COST_MODEL_BETA 260.0 +#define DEFAULT_COST_MODEL_GAMMA 0.001 #define DEFAULT_COST_MODEL_SIMPLIFY_CALCULATION true #define DEFAULT_COST_MODEL_COMMUNI_THRESHOLD 2048.0 #define DEFAULT_COST_MODEL_COMMUNI_CONST 3072.0 diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc index 927acea705..21da11ec21 100644 --- a/mindspore/ccsrc/parallel/step_parallel.cc +++ b/mindspore/ccsrc/parallel/step_parallel.cc @@ -375,6 +375,10 @@ bool IsParallelCareNode(const CNodePtr& cnode) { MS_LOG(INFO) << "Parallel don't care node: " << prim->name(); return false; } + // get_next is not in the forward graph, we need mark the get_next as the forward node + if (prim->name() == GET_NEXT) { + return true; + } if ((prim->name() == CAST)) { if ((!attrs.count(STRATEGY)) && (cnode->operator_info() == nullptr)) { return false; diff --git a/mindspore/train/dataset_helper.py b/mindspore/train/dataset_helper.py index de26d72108..6252116efe 100644 --- a/mindspore/train/dataset_helper.py +++ b/mindspore/train/dataset_helper.py @@ -88,7 +88,7 @@ class _DatasetIter: # times the batch dimension of tensors for run if _get_parallel_mode() in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL): device_num = _get_device_num() - dataset_shapes = _to_full_shapes(dataset_shapes, device_num) + self.dataset_shapes = _to_full_shapes(dataset_shapes, device_num) def __iter__(self): self.ind = 0 diff --git a/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py b/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py index 67b8f98faf..b7a3255f7c 100644 --- a/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py +++ b/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py @@ -80,9 +80,9 @@ def test_common_parameter(): _executor.compile(net, x, y, z, w, phase='train') strategies = _executor._get_strategy(net) - expected_strategies = {'Default/network-Net/MatMul-op8': [[1, 1], [1, 8]], - 'Default/network-Net/MatMul-op9': [[1, 1], [1, 8]], - 'Default/network-Net/Cast-op10': [[1, 8]], - 'Default/network-Net/MatMul-op0': [[1, 1], [1, 8]], - 'Default/network-Net/Cast-op11': [[1, 8]]} - assert strategies == expected_strategies \ No newline at end of file + expected_strategies = {'Default/network-Net/MatMul-op6': [[8, 1], [1, 1]], + 'Default/network-Net/MatMul-op8': [[8, 1], [1, 1]], + 'Default/network-Net/Cast-op7': [[1, 1]], + 'Default/network-Net/MatMul-op0': [[8, 1], [1, 1]], + 'Default/network-Net/Cast-op9': [[1, 1]]} + assert strategies == expected_strategies diff --git a/tests/ut/python/parallel/test_auto_parallel_two_matmul.py b/tests/ut/python/parallel/test_auto_parallel_two_matmul.py index 5155db41f6..e7beed384e 100644 --- a/tests/ut/python/parallel/test_auto_parallel_two_matmul.py +++ b/tests/ut/python/parallel/test_auto_parallel_two_matmul.py @@ -86,9 +86,9 @@ def test_two_matmul(): costmodel_alpha = cost_model_context.get_cost_model_context("costmodel_alpha") assert costmodel_alpha == 1.0 costmodel_beta = cost_model_context.get_cost_model_context("costmodel_beta") - assert costmodel_beta == 65.0 + assert costmodel_beta == 260.0 costmodel_gamma = cost_model_context.get_cost_model_context("costmodel_gamma") - assert costmodel_gamma == 0.02 + assert costmodel_gamma == 0.001 costmodel_communi_threshold = cost_model_context.get_cost_model_context("costmodel_communi_threshold") assert costmodel_communi_threshold == 2048.0 costmodel_communi_const = cost_model_context.get_cost_model_context("costmodel_communi_const") @@ -137,4 +137,5 @@ def test_two_matmul(): strategies = _executor._get_strategy(net) expected_strategies = {'Default/network-Net/MatMul-op2': [[16, 1], [1, 1]], 'Default/network-Net/MatMul-op3': [[16, 1], [1, 1]]} - assert strategies == expected_strategies \ No newline at end of file + assert strategies == expected_strategies + From d735d47953d92922a4f52b74f3c4b274969d3f82 Mon Sep 17 00:00:00 2001 From: wangjun260 Date: Tue, 31 Mar 2020 10:43:24 +0800 Subject: [PATCH 029/227] add vgg scripts --- example/vgg16_cifar10/config.py | 31 +++++++++++++ example/vgg16_cifar10/dataset.py | 65 ++++++++++++++++++++++++++ example/vgg16_cifar10/eval.py | 53 ++++++++++++++++++++++ example/vgg16_cifar10/train.py | 78 ++++++++++++++++++++++++++++++++ mindspore/model_zoo/vgg.py | 30 +++++++----- 5 files changed, 246 insertions(+), 11 deletions(-) create mode 100644 example/vgg16_cifar10/config.py create mode 100644 example/vgg16_cifar10/dataset.py create mode 100644 example/vgg16_cifar10/eval.py create mode 100644 example/vgg16_cifar10/train.py diff --git a/example/vgg16_cifar10/config.py b/example/vgg16_cifar10/config.py new file mode 100644 index 0000000000..8c6ffee98b --- /dev/null +++ b/example/vgg16_cifar10/config.py @@ -0,0 +1,31 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +network config setting, will be used in main.py +""" +from easydict import EasyDict as edict + +cifar_cfg = edict({ + 'num_classes': 10, + 'lr_init': 0.05, + 'batch_size': 64, + 'epoch_size': 70, + 'momentum': 0.9, + 'weight_decay': 5e-4, + 'buffer_size': 10, + 'image_height': 224, + 'image_width': 224, + 'keep_checkpoint_max': 10 +}) diff --git a/example/vgg16_cifar10/dataset.py b/example/vgg16_cifar10/dataset.py new file mode 100644 index 0000000000..4e82beb2e3 --- /dev/null +++ b/example/vgg16_cifar10/dataset.py @@ -0,0 +1,65 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +Data operations, will be used in train.py and eval.py +""" +import os +import mindspore.dataset as ds +import mindspore.dataset.transforms.c_transforms as C +import mindspore.dataset.transforms.vision.c_transforms as vision +import mindspore.common.dtype as mstype +from config import cifar_cfg as cfg + +def create_dataset(data_home, repeat_num=1, training=True): + """Data operations.""" + ds.config.set_seed(1) + data_dir = os.path.join(data_home, "cifar-10-batches-bin") + if not training: + data_dir = os.path.join(data_home, "cifar-10-verify-bin") + data_set = ds.Cifar10Dataset(data_dir) + resize_height = cfg.image_height + resize_width = cfg.image_width + rescale = 1.0 / 255.0 + shift = 0.0 + + # define map operations + random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT + random_horizontal_op = vision.RandomHorizontalFlip() + resize_op = vision.Resize((resize_height, resize_width)) # interpolation default BILINEAR + rescale_op = vision.Rescale(rescale, shift) + normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023)) + changeswap_op = vision.HWC2CHW() + type_cast_op = C.TypeCast(mstype.int32) + + c_trans = [] + if training: + c_trans = [random_crop_op, random_horizontal_op] + c_trans += [resize_op, rescale_op, normalize_op, + changeswap_op] + + # apply map operations on images + data_set = data_set.map(input_columns="label", operations=type_cast_op) + data_set = data_set.map(input_columns="image", operations=c_trans) + + # apply repeat operations + data_set = data_set.repeat(repeat_num) + + # apply shuffle operations + data_set = data_set.shuffle(buffer_size=10) + + # apply batch operations + data_set = data_set.batch(batch_size=cfg.batch_size, drop_remainder=True) + + return data_set diff --git a/example/vgg16_cifar10/eval.py b/example/vgg16_cifar10/eval.py new file mode 100644 index 0000000000..b034183373 --- /dev/null +++ b/example/vgg16_cifar10/eval.py @@ -0,0 +1,53 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +##############test vgg16 example on cifar10################# +python eval.py --data_path=$DATA_HOME --device_id=$DEVICE_ID +""" +import argparse +import mindspore.nn as nn +from mindspore.nn.optim.momentum import Momentum +from mindspore.train.model import Model +from mindspore import context +from mindspore.train.serialization import load_checkpoint, load_param_into_net +from mindspore.model_zoo.vgg import vgg16 +from config import cifar_cfg as cfg +import dataset + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Cifar10 classification') + parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'], + help='device where the code will be implemented. (Default: Ascend)') + parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved') + parser.add_argument('--checkpoint_path', type=str, default=None, help='checkpoint file path.') + parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)') + args_opt = parser.parse_args() + + context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target) + context.set_context(device_id=args_opt.device_id) + context.set_context(enable_mem_reuse=True, enable_hccl=False) + + net = vgg16(batch_size=cfg.batch_size, num_classes=cfg.num_classes) + opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum, + weight_decay=cfg.weight_decay) + loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False) + model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'}) + + param_dict = load_checkpoint(args_opt.checkpoint_path) + load_param_into_net(net, param_dict) + net.set_train(False) + dataset = dataset.create_dataset(args_opt.data_path, 1, False) + res = model.eval(dataset) + print("result: ", res) diff --git a/example/vgg16_cifar10/train.py b/example/vgg16_cifar10/train.py new file mode 100644 index 0000000000..32cd344d50 --- /dev/null +++ b/example/vgg16_cifar10/train.py @@ -0,0 +1,78 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +#################train vgg16 example on cifar10######################## +python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID +""" +import argparse +import random +import numpy as np +import mindspore.nn as nn +from mindspore import Tensor +from mindspore.nn.optim.momentum import Momentum +from mindspore.train.model import Model +from mindspore import context +from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor +from mindspore.model_zoo.vgg import vgg16 +import dataset +from config import cifar_cfg as cfg +random.seed(1) +np.random.seed(1) + +def lr_steps(global_step, lr_max=None, total_epochs=None, steps_per_epoch=None): + """Set learning rate.""" + lr_each_step = [] + total_steps = steps_per_epoch * total_epochs + decay_epoch_index = [0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps] + for i in range(total_steps): + if i < decay_epoch_index[0]: + lr_each_step.append(lr_max) + elif i < decay_epoch_index[1]: + lr_each_step.append(lr_max * 0.1) + elif i < decay_epoch_index[2]: + lr_each_step.append(lr_max * 0.01) + else: + lr_each_step.append(lr_max * 0.001) + current_step = global_step + lr_each_step = np.array(lr_each_step).astype(np.float32) + learning_rate = lr_each_step[current_step:] + + return learning_rate + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Cifar10 classification') + parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'], + help='device where the code will be implemented. (Default: Ascend)') + parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved') + parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)') + args_opt = parser.parse_args() + + context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target) + context.set_context(device_id=args_opt.device_id) + context.set_context(enable_mem_reuse=True, enable_hccl=False) + + net = vgg16(batch_size=cfg.batch_size, num_classes=cfg.num_classes) + lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, steps_per_epoch=50000 // cfg.batch_size) + opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), Tensor(lr), cfg.momentum, weight_decay=cfg.weight_decay) + loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False) + model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'}) + + dataset = dataset.create_dataset(args_opt.data_path, cfg.epoch_size) + batch_num = dataset.get_dataset_size() + config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, keep_checkpoint_max=cfg.keep_checkpoint_max) + ckpoint_cb = ModelCheckpoint(prefix="train_vgg_cifar10", directory="./", config=config_ck) + loss_cb = LossMonitor() + model.train(cfg.epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb]) diff --git a/mindspore/model_zoo/vgg.py b/mindspore/model_zoo/vgg.py index 76f265c326..6fcd075cc8 100644 --- a/mindspore/model_zoo/vgg.py +++ b/mindspore/model_zoo/vgg.py @@ -15,7 +15,8 @@ """VGG.""" import mindspore.nn as nn from mindspore.ops import operations as P - +from mindspore.common.initializer import initializer +import mindspore.common.dtype as mstype def _make_layer(base, batch_norm): """Make stage network of VGG.""" @@ -25,11 +26,14 @@ def _make_layer(base, batch_norm): if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: + weight_shape = (v, in_channels, 3, 3) + weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32) conv2d = nn.Conv2d(in_channels=in_channels, out_channels=v, kernel_size=3, - padding=1, - pad_mode='pad') + padding=0, + pad_mode='same', + weight_init=weight) if batch_norm: layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU()] else: @@ -52,13 +56,13 @@ class Vgg(nn.Cell): Tensor, infer output tensor. Examples: - >>> VGG([64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], + >>> Vgg([64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], >>> num_classes=1000, batch_norm=False, batch_size=1) """ + def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1): super(Vgg, self).__init__() self.layers = _make_layer(base, batch_norm=batch_norm) - self.avgpool = nn.AvgPool2d(7) self.reshape = P.Reshape() self.shp = (batch_size, -1) self.classifier = nn.SequentialCell([ @@ -70,7 +74,6 @@ class Vgg(nn.Cell): def construct(self, x): x = self.layers(x) - x = self.avgpool(x) x = self.reshape(x, self.shp) x = self.classifier(x) return x @@ -84,15 +87,20 @@ cfg = { } -def vgg16(): +def vgg16(batch_size=1, num_classes=1000): """ - Get VGG16 neural network. + Get Vgg16 neural network with batch normalization. + + Args: + batch_size (int): Batch size. Default: 1. + num_classes (int): Class numbers. Default: 1000. Returns: - Cell, cell instance of VGG16 neural network. + Cell, cell instance of Vgg16 neural network with batch normalization. Examples: - >>> vgg16() + >>> vgg16(batch_size=1, num_classes=1000) """ - net = Vgg(cfg['16'], num_classes=1000, batch_norm=False, batch_size=1) + + net = Vgg(cfg['16'], num_classes=num_classes, batch_norm=True, batch_size=batch_size) return net From c4b03e854c892310617c0167be281b34b8a5ca43 Mon Sep 17 00:00:00 2001 From: c00425699 Date: Tue, 31 Mar 2020 12:29:14 +0800 Subject: [PATCH 030/227] use std::vector instead of std::list to promote performance for parallel module --- mindspore/ccsrc/parallel/context.cc | 1 - mindspore/ccsrc/parallel/context.h | 1 - mindspore/ccsrc/parallel/device.h | 1 - mindspore/ccsrc/parallel/device_manager.cc | 19 ++++++++-------- mindspore/ccsrc/parallel/device_manager.h | 20 ++++++++--------- mindspore/ccsrc/parallel/device_matrix.cc | 6 ++--- mindspore/ccsrc/parallel/device_matrix.h | 9 ++++---- .../parallel/graph_util/generate_graph.cc | 1 - .../parallel/graph_util/generate_graph.h | 1 - mindspore/ccsrc/parallel/group_manager.cc | 6 ++--- mindspore/ccsrc/parallel/group_manager.h | 10 ++++----- .../ccsrc/parallel/ops_info/activation_info.h | 1 - .../ccsrc/parallel/ops_info/arithmetic_info.h | 1 - .../parallel/ops_info/batch_parallel_info.h | 1 - .../ccsrc/parallel/ops_info/bias_add_info.h | 2 +- .../ops_info/comparison_function_info.h | 1 - .../parallel/ops_info/dropout_do_mask_info.h | 1 - .../ops_info/elementary_function_info.h | 1 - .../ccsrc/parallel/ops_info/generator_info.h | 1 - .../ccsrc/parallel/ops_info/get_next_info.h | 1 - .../parallel/ops_info/l2_normalize_info.h | 1 - mindspore/ccsrc/parallel/ops_info/loss_info.h | 2 +- .../ccsrc/parallel/ops_info/matmul_info.cc | 2 +- .../ccsrc/parallel/ops_info/matmul_info.h | 2 +- .../ccsrc/parallel/ops_info/onehot_info.h | 2 +- .../ccsrc/parallel/ops_info/operator_info.h | 1 - .../ccsrc/parallel/ops_info/prelu_info.h | 2 +- .../parallel/ops_info/reduce_method_info.cc | 2 +- .../parallel/ops_info/reduce_method_info.h | 1 - .../ccsrc/parallel/ops_info/reshape_info.h | 1 - .../parallel/ops_info/tmp_identity_info.h | 1 + .../ccsrc/parallel/ops_info/transpose_info.h | 2 +- .../parallel/ops_info/virtual_dataset_info.h | 2 +- mindspore/ccsrc/parallel/status.h | 1 - mindspore/ccsrc/parallel/step_parallel.cc | 2 +- mindspore/ccsrc/parallel/step_parallel.h | 2 +- mindspore/ccsrc/parallel/strategy.h | 1 - .../redistribution_operator_infer.h | 1 - .../parallel/auto_parallel/dp_algo_test.cc | 4 ++-- .../auto_parallel/edge_costmodel_test.cc | 4 ++-- .../auto_parallel/graph_costmodel_test.cc | 4 ++-- .../auto_parallel/operator_costmodel_test.cc | 12 +++++----- tests/ut/cpp/parallel/device_manager_test.cc | 16 +++++++------- tests/ut/cpp/parallel/device_matrix_test.cc | 16 +++++++------- tests/ut/cpp/parallel/group_manager_test.cc | 22 +++++++++---------- .../parallel/ops_info/activation_info_test.cc | 4 ++-- .../cpp/parallel/ops_info/activation_test.cc | 4 ++-- .../ops_info/dropout_do_mask_info_test.cc | 4 ++-- .../cpp/parallel/ops_info/gelu_info_test.cc | 4 ++-- .../ops_info/generate_strategy_test.cc | 4 ++-- .../parallel/ops_info/generator_info_test.cc | 4 ++-- .../parallel/ops_info/get_next_info_test.cc | 4 ++-- .../ops_info/l2_normalize_info_test.cc | 4 ++-- .../ops_info/log_softmax_info_test.cc | 4 ++-- .../cpp/parallel/ops_info/matmul_info_test.cc | 4 ++-- .../cpp/parallel/ops_info/onehot_info_test.cc | 4 ++-- .../ops_info/onehot_info_test_axis_0.cc | 4 ++-- .../ut/cpp/parallel/ops_info/pow_info_test.cc | 4 ++-- tests/ut/cpp/parallel/ops_info/prelu_test.cc | 4 ++-- .../parallel/ops_info/reduce_method_test.cc | 4 ++-- .../ut/cpp/parallel/ops_info/reshape_test.cc | 4 ++-- .../softmax_entropy_loss_info_test.cc | 4 ++-- .../parallel/ops_info/softmax_info_test.cc | 4 ++-- .../cpp/parallel/ops_info/tanh_info_test.cc | 4 ++-- .../parallel/ops_info/tensor_add_info_test.cc | 4 ++-- .../cpp/parallel/ops_info/tmpidentity_test.cc | 4 ++-- .../cpp/parallel/ops_info/transpose_test.cc | 4 ++-- .../cpp/parallel/step_auto_parallel_test.cc | 4 ++-- tests/ut/cpp/parallel/step_parallel_test.cc | 4 ++-- .../tensor_layout/construct_operator_test.cc | 4 ++-- .../redistribution_operator_infer_test.cc | 4 ++-- .../tensor_redistribution_test.cc | 2 +- tests/ut/cpp/parallel/virtual_dataset_test.cc | 4 ++-- 73 files changed, 141 insertions(+), 160 deletions(-) diff --git a/mindspore/ccsrc/parallel/context.cc b/mindspore/ccsrc/parallel/context.cc index 74ec948c49..64cecf1669 100644 --- a/mindspore/ccsrc/parallel/context.cc +++ b/mindspore/ccsrc/parallel/context.cc @@ -21,7 +21,6 @@ #include #include #include -#include #include #include "parallel/device_manager.h" diff --git a/mindspore/ccsrc/parallel/context.h b/mindspore/ccsrc/parallel/context.h index 22a5f89e38..866609fbfe 100644 --- a/mindspore/ccsrc/parallel/context.h +++ b/mindspore/ccsrc/parallel/context.h @@ -20,7 +20,6 @@ #include #include #include -#include #include #include "parallel/status.h" diff --git a/mindspore/ccsrc/parallel/device.h b/mindspore/ccsrc/parallel/device.h index 0373617ad5..8c3174ae55 100644 --- a/mindspore/ccsrc/parallel/device.h +++ b/mindspore/ccsrc/parallel/device.h @@ -18,7 +18,6 @@ #define MINDSPORE_CCSRC_PARALLEL_DEVICE_H_ #include -#include #include #include diff --git a/mindspore/ccsrc/parallel/device_manager.cc b/mindspore/ccsrc/parallel/device_manager.cc index 999d6b4432..3a553e08ec 100644 --- a/mindspore/ccsrc/parallel/device_manager.cc +++ b/mindspore/ccsrc/parallel/device_manager.cc @@ -30,7 +30,7 @@ namespace mindspore { namespace parallel { DeviceManagerPtr g_device_manager = nullptr; -Stage::Stage(const std::list& devices, int num, int rank) +Stage::Stage(const std::vector& devices, int num, int rank) : devices_(devices), number_(num), rank_(rank) { gm_ = GroupManager(); } @@ -104,7 +104,7 @@ int32_t GetListMemberByIndex(size_t index, const RankList& devices) { return result; } -std::shared_ptr GetListMemberByIndex(size_t index, const std::list>& device_list) { +std::shared_ptr GetListMemberByIndex(size_t index, const std::vector>& device_list) { size_t i = 0; std::shared_ptr result; if ((device_list.empty()) || (index >= device_list.size())) { @@ -178,7 +178,7 @@ Status DeviceManager::Init(const RankList& devices, int32_t global_device_rank, MS_LOG(ERROR) << "The number of 'devices' in a stage must be positive"; return Status::FAILED; } - std::list curr_dev_list; + std::vector curr_dev_list; for (int i = 0; i < num_device; ++i) { curr_dev_list.push_back(*GetListMemberByIndex(global_index, devices_)); global_index++; @@ -278,8 +278,8 @@ RankList DeviceManager::global_device_list(int32_t stage_id, int32_t rank, int32 Device DeviceManager::CreateNewDeviceByRank(int32_t rank) const { return Device(rank); } -std::list DeviceManager::CreateDeviceListByRankList(RankList ranks) { - std::list dev_list; +std::vector DeviceManager::CreateDeviceListByRankList(RankList ranks) { + std::vector dev_list; for (auto& rank : ranks) { Device one = CreateNewDeviceByRank(rank); dev_list.push_back(one); @@ -312,8 +312,8 @@ std::string HashName(const std::string& origin_name) { return std::to_string(std // is '0-1-3-5-7'. std::string DeviceManager::GenerateGroupNameByRanks(RankList ranks) { std::string rank_list_name; - std::list::iterator it; - ranks.sort(); // sorted in increasing order + std::vector::iterator it; + std::sort(ranks.begin(), ranks.end()); // sorted in increasing order for (it = ranks.begin(); it != ranks.end(); ++it) { if (it == ranks.begin()) { rank_list_name = std::to_string(*it); @@ -343,7 +343,8 @@ std::string DeviceManager::GenerateGroupNameByRanks(RankList ranks) { // Create the group with the given devices and the given name. The GroupManager // gm_ will create a new group only if there does not exit a group with the same // name. Otherwise, let the pointer g point to that group. -Group DeviceManager::CreateGroup(const std::string& group_name, const std::list& devices) { +Group DeviceManager::CreateGroup(const std::string& group_name, + const std::vector& devices) { if ((world_group() == NCCL_WORLD_GROUP) && (devices.size() != devices_.size())) { MS_LOG(EXCEPTION) << "Do not support sub group for nccl"; } @@ -360,7 +361,7 @@ Group DeviceManager::CreateGroup(const RankList& dev_ranks) { } std::string group_name = GenerateGroupNameByRanks(dev_ranks); - std::list dev_list = CreateDeviceListByRankList(dev_ranks); + auto dev_list = CreateDeviceListByRankList(dev_ranks); return CreateGroup(group_name, dev_list); } diff --git a/mindspore/ccsrc/parallel/device_manager.h b/mindspore/ccsrc/parallel/device_manager.h index 80beb15f86..798d99383d 100644 --- a/mindspore/ccsrc/parallel/device_manager.h +++ b/mindspore/ccsrc/parallel/device_manager.h @@ -19,7 +19,7 @@ #include #include -#include +#include #include #include #include @@ -50,19 +50,19 @@ class Stage { // This class is used in pipeline-parallelization. Available devices are partitioned into multiple stages. // Currently, the function of pipeline-parallelization and this class are NOT implemented. public: - explicit Stage(std::list devices) : devices_(std::move(devices)), number_(0), rank_(0) { + explicit Stage(std::vector devices) : devices_(std::move(devices)), number_(0), rank_(0) { gm_ = GroupManager(); } - Stage(const std::list& devices, int num, int rank); + Stage(const std::vector& devices, int num, int rank); ~Stage() = default; int GetStageNum() const { return number_; } size_t GetDevicesNum() const { return devices_.size(); } - std::list GetDevicesList() { return devices_; } + std::vector GetDevicesList() { return devices_; } int global_rank(Group* g) const; private: - std::list devices_; + std::vector devices_; int number_; int32_t rank_; GroupManager gm_; @@ -89,10 +89,10 @@ class DeviceManager { RankList global_device_list(int32_t stage_id, int32_t rank, int32_t split_num) const; Device CreateNewDeviceByRank(int32_t rank) const; - std::list CreateDeviceListByRankList(RankList ranks); + std::vector CreateDeviceListByRankList(RankList ranks); std::string GenerateGroupNameByRanks(RankList dev_ranks); - Group CreateGroup(const std::string& group_name, const std::list& devices); + Group CreateGroup(const std::string& group_name, const std::vector& devices); Group CreateGroup(const RankList& dev_ranks); std::shared_ptr GetStageById(int32_t stage_id); @@ -108,11 +108,11 @@ class DeviceManager { std::string FindRankListNameByHashName(const std::string& hash_name); private: - std::list> devices_; + std::vector> devices_; // each stage has a list of devices - std::list> stage_devices_; + std::vector> stage_devices_; std::shared_ptr device_; - std::list> stages_; + std::vector> stages_; GroupManager gm_; std::string backend_; diff --git a/mindspore/ccsrc/parallel/device_matrix.cc b/mindspore/ccsrc/parallel/device_matrix.cc index 5a7aa36c8e..f9f314d5a3 100644 --- a/mindspore/ccsrc/parallel/device_matrix.cc +++ b/mindspore/ccsrc/parallel/device_matrix.cc @@ -21,7 +21,7 @@ #include #include #include -#include +#include #include "parallel/status.h" #include "parallel/ops_info/operator_info.h" @@ -64,7 +64,7 @@ Status DeviceMatrix::GetDevicesAlongDim(const uint32_t& dim, RankList* devices) } RankList group; - std::list local_group_list; + std::vector local_group_list; // lower than dim int32_t step = 1; @@ -160,7 +160,7 @@ std::string ShapeToString(const Shape& shape) { return str + "]"; } -std::string ListToString(const std::list& list) { +std::string ListToString(const std::vector& list) { std::string str = "["; for (auto& element : list) { str += std::to_string(element) + ", "; diff --git a/mindspore/ccsrc/parallel/device_matrix.h b/mindspore/ccsrc/parallel/device_matrix.h index d3b0fdcb49..a912000604 100644 --- a/mindspore/ccsrc/parallel/device_matrix.h +++ b/mindspore/ccsrc/parallel/device_matrix.h @@ -20,7 +20,6 @@ #include #include #include -#include #include "parallel/status.h" #include "utils/convert_utils.h" @@ -28,7 +27,7 @@ namespace mindspore { namespace parallel { -using RankList = std::list; +using RankList = std::vector; using Shape = std::vector; class DeviceMatrix { @@ -36,7 +35,7 @@ class DeviceMatrix { DeviceMatrix(int32_t rank, RankList devices, Shape dev_shape); DeviceMatrix() = default; ~DeviceMatrix() = default; - std::list group_list() const { return group_list_; } + std::vector group_list() const { return group_list_; } Status CreateGroupList(); Status GetDevicesByTensorMap(const Shape& tensor_map, RankList* rank_list); Status GetDevicesAlongDim(const uint32_t& dim, RankList* devices); @@ -46,11 +45,11 @@ class DeviceMatrix { RankList dev_list_; // From low dim to high dim. eg: [D0 D1 D2 D3] Shape dev_shape_; - std::list group_list_; + std::vector group_list_; }; std::string ShapeToString(const Shape& shape); -std::string ListToString(const std::list& list); +std::string ListToString(const std::vector& list); } // namespace parallel } // namespace mindspore diff --git a/mindspore/ccsrc/parallel/graph_util/generate_graph.cc b/mindspore/ccsrc/parallel/graph_util/generate_graph.cc index d0548ec647..43df9fe802 100644 --- a/mindspore/ccsrc/parallel/graph_util/generate_graph.cc +++ b/mindspore/ccsrc/parallel/graph_util/generate_graph.cc @@ -17,7 +17,6 @@ #include "parallel/graph_util/generate_graph.h" #include -#include #include #include #include diff --git a/mindspore/ccsrc/parallel/graph_util/generate_graph.h b/mindspore/ccsrc/parallel/graph_util/generate_graph.h index 713c13dfb5..bb1f811f2f 100644 --- a/mindspore/ccsrc/parallel/graph_util/generate_graph.h +++ b/mindspore/ccsrc/parallel/graph_util/generate_graph.h @@ -18,7 +18,6 @@ #define MINDSPORE_CCSRC_PARALLEL_GRAPH_UTIL_GENERATE_GRAPH_H_ #include -#include #include #include #include diff --git a/mindspore/ccsrc/parallel/group_manager.cc b/mindspore/ccsrc/parallel/group_manager.cc index 35e81a7d83..cff5877f0a 100644 --- a/mindspore/ccsrc/parallel/group_manager.cc +++ b/mindspore/ccsrc/parallel/group_manager.cc @@ -30,13 +30,13 @@ Group::Group() { devices_.clear(); } -Status Group::Init(const std::string &name, const std::list &devices) { +Status Group::Init(const std::string &name, const std::vector &devices) { this->name_ = name; this->devices_ = devices; return Status::SUCCESS; } -std::list Group::GetDevicesList() const { return devices_; } +std::vector Group::GetDevicesList() const { return devices_; } bool Group::IsInThisGroup(int32_t device_rank) { for (auto &device : devices_) { @@ -66,7 +66,7 @@ Status Group::GetIndex(size_t *index) { GroupManager::GroupManager() { groups_.clear(); } -Status GroupManager::CreateGroup(const std::string &group_name, const std::list &devices, +Status GroupManager::CreateGroup(const std::string &group_name, const std::vector &devices, mindspore::parallel::Group *const group) { // it is simple to use size to determine whether it is a world group uint32_t world_size = 0; diff --git a/mindspore/ccsrc/parallel/group_manager.h b/mindspore/ccsrc/parallel/group_manager.h index c3b95b99e0..9e23569b15 100644 --- a/mindspore/ccsrc/parallel/group_manager.h +++ b/mindspore/ccsrc/parallel/group_manager.h @@ -18,7 +18,7 @@ #define MINDSPORE_CCSRC_PARALLEL_GROUP_MANAGER_H_ #include -#include +#include #include #include @@ -37,8 +37,8 @@ class Group { public: Group(); ~Group() = default; - Status Init(const std::string& name, const std::list& devices); - std::list GetDevicesList() const; + Status Init(const std::string& name, const std::vector& devices); + std::vector GetDevicesList() const; std::string name() const { return name_; } bool IsInThisGroup(int32_t device_rank); Status GetIndex(size_t* index); @@ -46,7 +46,7 @@ class Group { private: std::string name_; - std::list devices_; + std::vector devices_; }; class GroupManager { @@ -54,7 +54,7 @@ class GroupManager { GroupManager(); ~GroupManager() = default; - Status CreateGroup(const std::string& name, const std::list& devices, Group* group); + Status CreateGroup(const std::string& name, const std::vector& devices, Group* group); Status DestroyGroup(Group* group); Status DestroyAllGroups(); Status GetRankID(const std::string& name, unsigned int* rank_id); diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.h b/mindspore/ccsrc/parallel/ops_info/activation_info.h index e988ccf54c..d8de19b328 100644 --- a/mindspore/ccsrc/parallel/ops_info/activation_info.h +++ b/mindspore/ccsrc/parallel/ops_info/activation_info.h @@ -19,7 +19,6 @@ #include #include -#include #include #include #include diff --git a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h index 1b641152aa..734368a533 100644 --- a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h +++ b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h @@ -18,7 +18,6 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ARITHMETIC_INFO_H_ #include -#include #include #include #include diff --git a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h index fb08fbe9fc..0ffdea97f3 100644 --- a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h +++ b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h @@ -17,7 +17,6 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_BATCH_PARALLEL_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_BATCH_PARALLEL_INFO_H_ -#include #include #include #include diff --git a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h index ac263ec91f..e5001fc0d3 100644 --- a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h +++ b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h @@ -18,7 +18,7 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_BIAS_ADD_INFO_H_ #include -#include + #include #include #include diff --git a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h index 2762f94eb9..8760ce5666 100644 --- a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h +++ b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h @@ -18,7 +18,6 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_COMPARISON_FUNCTION_INFO_H_ #include -#include #include #include #include "ir/value.h" diff --git a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h index 2fc18b521d..45d4c28d8e 100644 --- a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h +++ b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h @@ -18,7 +18,6 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_DROPOUT_DO_MASK_INFO_H_ #include -#include #include #include #include diff --git a/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h b/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h index cf85d28fb0..33ad04023b 100644 --- a/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h +++ b/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h @@ -18,7 +18,6 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ELEMENTARY_FUNCTION_INFO_H_ #include -#include #include #include #include "ir/value.h" diff --git a/mindspore/ccsrc/parallel/ops_info/generator_info.h b/mindspore/ccsrc/parallel/ops_info/generator_info.h index 0f1d7209c8..1473fead67 100644 --- a/mindspore/ccsrc/parallel/ops_info/generator_info.h +++ b/mindspore/ccsrc/parallel/ops_info/generator_info.h @@ -18,7 +18,6 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GENERATOR_INFO_H_ #include -#include #include #include #include diff --git a/mindspore/ccsrc/parallel/ops_info/get_next_info.h b/mindspore/ccsrc/parallel/ops_info/get_next_info.h index 1280d3b191..3dd639ba57 100644 --- a/mindspore/ccsrc/parallel/ops_info/get_next_info.h +++ b/mindspore/ccsrc/parallel/ops_info/get_next_info.h @@ -18,7 +18,6 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GETNEXT_INFO_H_ #include -#include #include #include #include diff --git a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h index 10bf46d8ff..1a67073065 100644 --- a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h +++ b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h @@ -18,7 +18,6 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_L2_NORMALIZE_INFO_H_ #include -#include #include #include #include diff --git a/mindspore/ccsrc/parallel/ops_info/loss_info.h b/mindspore/ccsrc/parallel/ops_info/loss_info.h index 70de84fda3..585545302f 100644 --- a/mindspore/ccsrc/parallel/ops_info/loss_info.h +++ b/mindspore/ccsrc/parallel/ops_info/loss_info.h @@ -18,10 +18,10 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_LOSS_INFO_H_ #include -#include #include #include #include + #include "ir/value.h" #include "parallel/ops_info/operator_info.h" #include "parallel/ops_info/activation_info.h" diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc index 2cd8e0df8f..6103087a1d 100644 --- a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc @@ -397,7 +397,7 @@ Status MatMulBase::GenerateStrategies(int32_t stage_id) { return FAILED; } CheckGlobalDeviceManager(); - std::list dev_list = g_device_manager->GetDeviceListByStageId(stage_id); + std::vector dev_list = g_device_manager->GetDeviceListByStageId(stage_id); size_t dev_num = dev_list.size(); Shape input0_shape = inputs_shape_[0], input1_shape = inputs_shape_[1]; if (transpose_a_) { diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.h b/mindspore/ccsrc/parallel/ops_info/matmul_info.h index daf422e6d6..c9feae55b6 100644 --- a/mindspore/ccsrc/parallel/ops_info/matmul_info.h +++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.h @@ -18,10 +18,10 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_MATMUL_INFO_H_ #include -#include #include #include #include + #include "ir/value.h" #include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" diff --git a/mindspore/ccsrc/parallel/ops_info/onehot_info.h b/mindspore/ccsrc/parallel/ops_info/onehot_info.h index fd5d6be8f6..62c66695fa 100644 --- a/mindspore/ccsrc/parallel/ops_info/onehot_info.h +++ b/mindspore/ccsrc/parallel/ops_info/onehot_info.h @@ -18,10 +18,10 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ONEHOT_INFO_H_ #include -#include #include #include #include + #include "ir/value.h" #include "parallel/ops_info/operator_info.h" #include "parallel/auto_parallel/operator_costmodel.h" diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.h b/mindspore/ccsrc/parallel/ops_info/operator_info.h index 9902c69ce7..89fd73564f 100644 --- a/mindspore/ccsrc/parallel/ops_info/operator_info.h +++ b/mindspore/ccsrc/parallel/ops_info/operator_info.h @@ -18,7 +18,6 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_OPERATOR_INFO_H_ #include -#include #include #include #include diff --git a/mindspore/ccsrc/parallel/ops_info/prelu_info.h b/mindspore/ccsrc/parallel/ops_info/prelu_info.h index d7dac46291..2e74118f80 100644 --- a/mindspore/ccsrc/parallel/ops_info/prelu_info.h +++ b/mindspore/ccsrc/parallel/ops_info/prelu_info.h @@ -17,11 +17,11 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_PRELU_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_PRELU_INFO_H_ -#include #include #include #include #include + #include "ir/value.h" #include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc index 216357d5ab..5f9a67c22d 100644 --- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc @@ -198,7 +198,7 @@ ForwardOp CreatReduceMeanForwardOp(const std::vector &forward_group, cons // Creat RealDiv op OperatorName operator1_name = REAL_DIV; - std::list device_list = forward_group[0].GetDevicesList(); + std::vector device_list = forward_group[0].GetDevicesList(); auto divisor = static_cast(device_list.size()); py::tuple tuple = py::make_tuple(divisor); mindspore::tensor::TensorPtr tensor_ptr = std::make_shared(tuple, dtype); diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h index e9f03bcdee..6f26b99ffb 100644 --- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h +++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h @@ -18,7 +18,6 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_REDUCE_SUM_INFO_H_ #include -#include #include #include #include diff --git a/mindspore/ccsrc/parallel/ops_info/reshape_info.h b/mindspore/ccsrc/parallel/ops_info/reshape_info.h index a8a9323540..982894a8e0 100644 --- a/mindspore/ccsrc/parallel/ops_info/reshape_info.h +++ b/mindspore/ccsrc/parallel/ops_info/reshape_info.h @@ -19,7 +19,6 @@ #include -#include #include #include #include diff --git a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h index 1ef7341f3b..304e336adf 100644 --- a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h +++ b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h @@ -20,6 +20,7 @@ #include #include #include + #include "parallel/ops_info/operator_info.h" #include "parallel/auto_parallel/operator_costmodel.h" #include "parallel/strategy.h" diff --git a/mindspore/ccsrc/parallel/ops_info/transpose_info.h b/mindspore/ccsrc/parallel/ops_info/transpose_info.h index 6c5e98e3b9..c7c1c96675 100644 --- a/mindspore/ccsrc/parallel/ops_info/transpose_info.h +++ b/mindspore/ccsrc/parallel/ops_info/transpose_info.h @@ -17,11 +17,11 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_TRANSPOSE_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_TRANSPOSE_INFO_H_ -#include #include #include #include #include + #include "ir/value.h" #include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" diff --git a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h index d22565d305..c4fdfcef04 100644 --- a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h +++ b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h @@ -17,11 +17,11 @@ #ifndef PARALLEL_OPS_INFO_DATASET_INFO_H_ #define PARALLEL_OPS_INFO_DATASET_INFO_H_ -#include #include #include #include #include + #include "ir/value.h" #include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" diff --git a/mindspore/ccsrc/parallel/status.h b/mindspore/ccsrc/parallel/status.h index 4c018c7c17..9d773f0d9b 100644 --- a/mindspore/ccsrc/parallel/status.h +++ b/mindspore/ccsrc/parallel/status.h @@ -18,7 +18,6 @@ #define MINDSPORE_CCSRC_PARALLEL_STATUS_H_ #include -#include namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc index 21da11ec21..d8c2864dbf 100644 --- a/mindspore/ccsrc/parallel/step_parallel.cc +++ b/mindspore/ccsrc/parallel/step_parallel.cc @@ -19,7 +19,7 @@ #include #include #include -#include + #include #include #include diff --git a/mindspore/ccsrc/parallel/step_parallel.h b/mindspore/ccsrc/parallel/step_parallel.h index ac7376a09d..2d1982dc9c 100644 --- a/mindspore/ccsrc/parallel/step_parallel.h +++ b/mindspore/ccsrc/parallel/step_parallel.h @@ -18,7 +18,7 @@ #define MINDSPORE_CCSRC_PARALLEL_STEP_PARALLEL_H_ #include -#include + #include #include #include diff --git a/mindspore/ccsrc/parallel/strategy.h b/mindspore/ccsrc/parallel/strategy.h index 24879c12c1..68ba4962d7 100644 --- a/mindspore/ccsrc/parallel/strategy.h +++ b/mindspore/ccsrc/parallel/strategy.h @@ -18,7 +18,6 @@ #define MINDSPORE_CCSRC_PARALLEL_STRATEGY_H_ #include -#include #include #include #include diff --git a/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h b/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h index 77955b5d1a..3515c7383a 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h +++ b/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h @@ -22,7 +22,6 @@ #include #include #include -#include #include "parallel/tensor_layout/redistribution_layout_transfer.h" #include "parallel/tensor_layout/construct_operator.h" diff --git a/tests/ut/cpp/parallel/auto_parallel/dp_algo_test.cc b/tests/ut/cpp/parallel/auto_parallel/dp_algo_test.cc index 73eff99304..d0243d5327 100644 --- a/tests/ut/cpp/parallel/auto_parallel/dp_algo_test.cc +++ b/tests/ut/cpp/parallel/auto_parallel/dp_algo_test.cc @@ -154,13 +154,13 @@ class TestDPAlgo : public UT::Common { void TestDPAlgo::SetUp() { cost_graph = std::make_shared(); cost_graph->SetDeviceMemoryAndCostParameter(); - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 10; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(8); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/auto_parallel/edge_costmodel_test.cc b/tests/ut/cpp/parallel/auto_parallel/edge_costmodel_test.cc index adf06d8fcc..467f4976e8 100644 --- a/tests/ut/cpp/parallel/auto_parallel/edge_costmodel_test.cc +++ b/tests/ut/cpp/parallel/auto_parallel/edge_costmodel_test.cc @@ -42,13 +42,13 @@ class TestEdgeCostModel : public UT::Common { }; void TestEdgeCostModel::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 10; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(8); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc b/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc index 1428193481..83a9eceacc 100644 --- a/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc +++ b/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc @@ -53,13 +53,13 @@ class TestCostGraph : public UT::Common { void TestCostGraph::SetUp() { cost_graph.SetDeviceMemoryAndCostParameter(); - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 10; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(8); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc b/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc index 3b71c80c4b..3bd65c049c 100644 --- a/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc +++ b/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc @@ -33,13 +33,13 @@ class TestMatMulCost : public UT::Common { void TestMatMulCost::SetUp() { mmcost_ = MatMulCost(); - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 1050; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(1024); stage_map.push_back(26); @@ -90,13 +90,13 @@ class TestActivationCost : public UT::Common { void TestActivationCost::SetUp() { ac_cost_ = ActivationCost(); - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 1050; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(1024); stage_map.push_back(26); @@ -142,13 +142,13 @@ class TestPReLUCost : public UT::Common { void TestPReLUCost::SetUp() { prelu_cost_ = PReLUCost(); - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 1050; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(1024); stage_map.push_back(26); diff --git a/tests/ut/cpp/parallel/device_manager_test.cc b/tests/ut/cpp/parallel/device_manager_test.cc index 0814ca64d0..056896f514 100644 --- a/tests/ut/cpp/parallel/device_manager_test.cc +++ b/tests/ut/cpp/parallel/device_manager_test.cc @@ -69,8 +69,8 @@ void TestDeviceManager::TearDown() { } TEST_F(TestDeviceManager, test_dm_init_AND_get_device_list) { - std::list dev_list; - std::list stage_map; + std::vector dev_list; + std::vector stage_map; int32_t local_dev = 0; dev_list.push_back(5); @@ -85,12 +85,12 @@ TEST_F(TestDeviceManager, test_dm_init_AND_get_device_list) { ASSERT_EQ(dm_.DeviceNum(), 4); ASSERT_EQ(dm_.GetStageNum(), (int32_t)(2)); - std::list dev_list_0 = dm_.GetDeviceListByStageId(0); - std::list dev_list_1 = dm_.GetDeviceListByStageId(1); + std::vector dev_list_0 = dm_.GetDeviceListByStageId(0); + std::vector dev_list_1 = dm_.GetDeviceListByStageId(1); ASSERT_EQ(dev_list_0.size(), 2); ASSERT_EQ(dev_list_1.size(), 2); - std::list::iterator it = dev_list_0.begin(); + std::vector::iterator it = dev_list_0.begin(); ASSERT_EQ((*it), int32_t(5)); it++; ASSERT_EQ((*it), int32_t(3)); @@ -111,13 +111,13 @@ TEST_F(TestDeviceManager, test_CreateNewDeviceByRank) { } TEST_F(TestDeviceManager, test_CreateDeviceListByRankList) { - std::list dev_list; - std::list rlist; + std::vector dev_list; + std::vector rlist; rlist.push_back(int32_t(2)); rlist.push_back(int32_t(1)); dev_list = dm_.CreateDeviceListByRankList(rlist); - std::list::iterator it = dev_list.begin(); + std::vector::iterator it = dev_list.begin(); ASSERT_EQ(it->rank(), int32_t(2)); it++; ASSERT_EQ(it->rank(), int32_t(1)); diff --git a/tests/ut/cpp/parallel/device_matrix_test.cc b/tests/ut/cpp/parallel/device_matrix_test.cc index 9c3fa9a861..877a211df8 100644 --- a/tests/ut/cpp/parallel/device_matrix_test.cc +++ b/tests/ut/cpp/parallel/device_matrix_test.cc @@ -35,9 +35,9 @@ TEST_F(TestDeviceMatrix, Test2Dgroup_list) { Shape shape = {2, 3}; DeviceMatrix arr(0, dev_list, shape); - std::list group_list; + std::vector group_list; if (arr.CreateGroupList() == Status::SUCCESS) group_list = arr.group_list(); - std::list group_list_expect = {{0, 3}, {0, 1, 2}}; + std::vector group_list_expect = {{0, 3}, {0, 1, 2}}; ASSERT_EQ(group_list, group_list_expect); } @@ -46,9 +46,9 @@ TEST_F(TestDeviceMatrix, Test3Dgroup_list) { Shape shape = {2, 2, 3}; DeviceMatrix arr(5, dev_list, shape); - std::list group_list; + std::vector group_list; if (arr.CreateGroupList() == Status::SUCCESS) group_list = arr.group_list(); - std::list group_list_expect = {{5, 11}, {2, 5}, {3, 4, 5}}; + std::vector group_list_expect = {{5, 11}, {2, 5}, {3, 4, 5}}; ASSERT_EQ(group_list, group_list_expect); } @@ -57,9 +57,9 @@ TEST_F(TestDeviceMatrix, Test4DGetAlongDim) { Shape shape = {2, 1, 4, 2}; DeviceMatrix arr(5, dev_list, shape); - std::list group_list; + std::vector group_list; if (arr.CreateGroupList() == Status::SUCCESS) group_list = arr.group_list(); - std::list group_list_expect = {{5, 13}, {5}, {1, 3, 5, 7}, {4, 5}}; + std::vector group_list_expect = {{5, 13}, {5}, {1, 3, 5, 7}, {4, 5}}; ASSERT_EQ(group_list, group_list_expect); } @@ -69,9 +69,9 @@ TEST_F(TestDeviceMatrix, Test5DGetAlongDim) { Shape shape = {3, 4, 2, 3, 2}; DeviceMatrix arr(5, dev_list, shape); - std::list group_list; + std::vector group_list; if (arr.CreateGroupList() == Status::SUCCESS) group_list = arr.group_list(); - std::list group_list_expect = {{5, 53, 101}, {5, 17, 29, 41}, {5, 11}, {1, 3, 5}, {4, 5}}; + std::vector group_list_expect = {{5, 53, 101}, {5, 17, 29, 41}, {5, 11}, {1, 3, 5}, {4, 5}}; ASSERT_EQ(group_list, group_list_expect); } diff --git a/tests/ut/cpp/parallel/group_manager_test.cc b/tests/ut/cpp/parallel/group_manager_test.cc index 1e9ec9c060..e3d2b3a364 100644 --- a/tests/ut/cpp/parallel/group_manager_test.cc +++ b/tests/ut/cpp/parallel/group_manager_test.cc @@ -42,7 +42,7 @@ void TestGroup::TearDown() { Status TestGroup::Init() { std::string gname = "1-2"; - std::list dev_list; + std::vector dev_list; Device one = Device(int32_t(1)); dev_list.push_back(one); Device two = Device(int32_t(2)); @@ -55,8 +55,8 @@ TEST_F(TestGroup, test_Init) { ASSERT_EQ(Init(), Status::SUCCESS); } TEST_F(TestGroup, test_GetDevicesList) { Init(); - std::list res_dev_list = gp.GetDevicesList(); - std::list::iterator it = res_dev_list.begin(); + std::vector res_dev_list = gp.GetDevicesList(); + std::vector::iterator it = res_dev_list.begin(); ASSERT_EQ(it->rank(), int32_t(1)); it++; ASSERT_EQ(it->rank(), int32_t(2)); @@ -88,7 +88,7 @@ void TestGroupManager::TearDown() { Status TestGroupManager::Init(Group** gp_ptr) { std::string gname = "1-2"; - std::list dev_list; + std::vector dev_list; Device one = Device(int32_t(1)); dev_list.push_back(one); Device two = Device(int32_t(2)); @@ -102,15 +102,15 @@ TEST_F(TestGroupManager, test_CreateGroup) { Group* gp_ptr = new Group(); ASSERT_EQ(Init(&gp_ptr), Status::SUCCESS); - std::list res_dev_list = gp_ptr->GetDevicesList(); - std::list::iterator it = res_dev_list.begin(); + std::vector res_dev_list = gp_ptr->GetDevicesList(); + std::vector::iterator it = res_dev_list.begin(); ASSERT_EQ(it->rank(), int32_t(1)); it++; ASSERT_EQ(it->rank(), int32_t(2)); delete gp_ptr; // testing for creating a group with an existing group name - std::list dev_list2; + std::vector dev_list2; Device three = Device(int32_t(3)); dev_list2.push_back(three); Device four = Device(int32_t(4)); @@ -119,8 +119,8 @@ TEST_F(TestGroupManager, test_CreateGroup) { ASSERT_EQ(gm.CreateGroup("1-2", dev_list2, gp_ptr), Status::SUCCESS); ASSERT_STREQ(gp_ptr->name().data(), "1-2"); - std::list res_dev_list2 = gp_ptr->GetDevicesList(); - std::list::iterator it2 = res_dev_list2.begin(); + std::vector res_dev_list2 = gp_ptr->GetDevicesList(); + std::vector::iterator it2 = res_dev_list2.begin(); ASSERT_EQ(it2->rank(), int32_t(1)); it2++; ASSERT_EQ(it2->rank(), int32_t(2)); @@ -136,8 +136,8 @@ TEST_F(TestGroupManager, test_FindGroup) { ASSERT_EQ(gm.FindGroup(gname, &gp_ptr2), Status::SUCCESS); - std::list res_dev_list = gp_ptr2->GetDevicesList(); - std::list::iterator it = res_dev_list.begin(); + std::vector res_dev_list = gp_ptr2->GetDevicesList(); + std::vector::iterator it = res_dev_list.begin(); ASSERT_EQ(it->rank(), int32_t(1)); it++; ASSERT_EQ(it->rank(), int32_t(2)); diff --git a/tests/ut/cpp/parallel/ops_info/activation_info_test.cc b/tests/ut/cpp/parallel/ops_info/activation_info_test.cc index a725cb0bf7..a9fe9b4c48 100644 --- a/tests/ut/cpp/parallel/ops_info/activation_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/activation_info_test.cc @@ -38,13 +38,13 @@ class TestActivationInfo : public UT::Common { }; void TestActivationInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 1050; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(1024); stage_map.push_back(26); diff --git a/tests/ut/cpp/parallel/ops_info/activation_test.cc b/tests/ut/cpp/parallel/ops_info/activation_test.cc index 200b96eb2e..149aa9d5af 100644 --- a/tests/ut/cpp/parallel/ops_info/activation_test.cc +++ b/tests/ut/cpp/parallel/ops_info/activation_test.cc @@ -40,13 +40,13 @@ class TestActivation : public UT::Common { }; void TestActivation::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 1050; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(1024); stage_map.push_back(26); diff --git a/tests/ut/cpp/parallel/ops_info/dropout_do_mask_info_test.cc b/tests/ut/cpp/parallel/ops_info/dropout_do_mask_info_test.cc index 5ae79e2851..2f17fb4450 100644 --- a/tests/ut/cpp/parallel/ops_info/dropout_do_mask_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/dropout_do_mask_info_test.cc @@ -38,13 +38,13 @@ class TestDropoutDoMaskInfo : public UT::Common { }; void TestDropoutDoMaskInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 34; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(32); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/gelu_info_test.cc b/tests/ut/cpp/parallel/ops_info/gelu_info_test.cc index d07794f63d..e54d1f2423 100644 --- a/tests/ut/cpp/parallel/ops_info/gelu_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/gelu_info_test.cc @@ -38,13 +38,13 @@ class TestGeluInfo : public UT::Common { }; void TestGeluInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 130; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(128); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/generate_strategy_test.cc b/tests/ut/cpp/parallel/ops_info/generate_strategy_test.cc index 81b1d3a1ec..947ad60cca 100644 --- a/tests/ut/cpp/parallel/ops_info/generate_strategy_test.cc +++ b/tests/ut/cpp/parallel/ops_info/generate_strategy_test.cc @@ -34,13 +34,13 @@ class TestGenerateStrategy : public UT::Common { }; void TestGenerateStrategy::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 10; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(8); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/generator_info_test.cc b/tests/ut/cpp/parallel/ops_info/generator_info_test.cc index bfd14ced30..eb463066a6 100644 --- a/tests/ut/cpp/parallel/ops_info/generator_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/generator_info_test.cc @@ -38,13 +38,13 @@ class TestDropoutGenMaskInfo : public UT::Common { }; void TestDropoutGenMaskInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 10; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(8); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/get_next_info_test.cc b/tests/ut/cpp/parallel/ops_info/get_next_info_test.cc index 1276a30225..503edf2eda 100644 --- a/tests/ut/cpp/parallel/ops_info/get_next_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/get_next_info_test.cc @@ -38,13 +38,13 @@ class TestGetNextInfo : public UT::Common { }; void TestGetNextInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 8; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(8); int32_t local_dev = 0; // create a new g_device_manager diff --git a/tests/ut/cpp/parallel/ops_info/l2_normalize_info_test.cc b/tests/ut/cpp/parallel/ops_info/l2_normalize_info_test.cc index 07a160ec99..b59481e1f6 100644 --- a/tests/ut/cpp/parallel/ops_info/l2_normalize_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/l2_normalize_info_test.cc @@ -38,13 +38,13 @@ class TestL2NormalizeInfo : public UT::Common { }; void TestL2NormalizeInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 34; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(32); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/log_softmax_info_test.cc b/tests/ut/cpp/parallel/ops_info/log_softmax_info_test.cc index 1c31463c88..cf5a4239a2 100644 --- a/tests/ut/cpp/parallel/ops_info/log_softmax_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/log_softmax_info_test.cc @@ -38,13 +38,13 @@ class TestLogSoftmaxInfo : public UT::Common { }; void TestLogSoftmaxInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 130; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(128); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc index 02f4488744..978b792a0c 100644 --- a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc @@ -42,13 +42,13 @@ class TestMatmulInfo : public UT::Common { }; void TestMatmulInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 1050; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(1024); stage_map.push_back(26); diff --git a/tests/ut/cpp/parallel/ops_info/onehot_info_test.cc b/tests/ut/cpp/parallel/ops_info/onehot_info_test.cc index df67175ae6..07d150a294 100644 --- a/tests/ut/cpp/parallel/ops_info/onehot_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/onehot_info_test.cc @@ -38,13 +38,13 @@ class TestOneHotInfo : public UT::Common { }; void TestOneHotInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 10; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(8); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/onehot_info_test_axis_0.cc b/tests/ut/cpp/parallel/ops_info/onehot_info_test_axis_0.cc index 17fe365176..c89bf97fb3 100644 --- a/tests/ut/cpp/parallel/ops_info/onehot_info_test_axis_0.cc +++ b/tests/ut/cpp/parallel/ops_info/onehot_info_test_axis_0.cc @@ -38,13 +38,13 @@ class TestOneHotInfo2 : public UT::Common { }; void TestOneHotInfo2::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 10; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(8); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/pow_info_test.cc b/tests/ut/cpp/parallel/ops_info/pow_info_test.cc index 86d62190a7..f6ea2c3d3c 100644 --- a/tests/ut/cpp/parallel/ops_info/pow_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/pow_info_test.cc @@ -38,13 +38,13 @@ class TestPowInfo : public UT::Common { }; void TestPowInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 66; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(64); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/prelu_test.cc b/tests/ut/cpp/parallel/ops_info/prelu_test.cc index bb815921e0..5ff261234f 100644 --- a/tests/ut/cpp/parallel/ops_info/prelu_test.cc +++ b/tests/ut/cpp/parallel/ops_info/prelu_test.cc @@ -39,13 +39,13 @@ class TestPReLUInfo : public UT::Common { }; void TestPReLUInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 1050; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(1024); stage_map.push_back(26); int32_t local_dev = 0; diff --git a/tests/ut/cpp/parallel/ops_info/reduce_method_test.cc b/tests/ut/cpp/parallel/ops_info/reduce_method_test.cc index 0ce81b0cd0..a1fe46ca33 100644 --- a/tests/ut/cpp/parallel/ops_info/reduce_method_test.cc +++ b/tests/ut/cpp/parallel/ops_info/reduce_method_test.cc @@ -39,13 +39,13 @@ class TestReduceSumInfo : public UT::Common { void TestReduceSumInfo::SetUp() { UT::InitPythonPath(); - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 34; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(32); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/reshape_test.cc b/tests/ut/cpp/parallel/ops_info/reshape_test.cc index 73a0d68be5..7ff94e9af5 100644 --- a/tests/ut/cpp/parallel/ops_info/reshape_test.cc +++ b/tests/ut/cpp/parallel/ops_info/reshape_test.cc @@ -38,13 +38,13 @@ class TestReshapeInfo : public UT::Common { }; void TestReshapeInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 34; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(32); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/softmax_entropy_loss_info_test.cc b/tests/ut/cpp/parallel/ops_info/softmax_entropy_loss_info_test.cc index cba9b7ecd6..03634b9a6f 100644 --- a/tests/ut/cpp/parallel/ops_info/softmax_entropy_loss_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/softmax_entropy_loss_info_test.cc @@ -38,13 +38,13 @@ class TestSoftmaxLoss : public UT::Common { }; void TestSoftmaxLoss::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 65; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(64); stage_map.push_back(1); diff --git a/tests/ut/cpp/parallel/ops_info/softmax_info_test.cc b/tests/ut/cpp/parallel/ops_info/softmax_info_test.cc index 65fd46fb9c..bba6e89626 100644 --- a/tests/ut/cpp/parallel/ops_info/softmax_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/softmax_info_test.cc @@ -39,13 +39,13 @@ class TestSoftmaxInfo : public UT::Common { }; void TestSoftmaxInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 130; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(128); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/tanh_info_test.cc b/tests/ut/cpp/parallel/ops_info/tanh_info_test.cc index 3ee99d093f..a892c5c84a 100644 --- a/tests/ut/cpp/parallel/ops_info/tanh_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/tanh_info_test.cc @@ -38,13 +38,13 @@ class TestTanhInfo : public UT::Common { }; void TestTanhInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 130; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(128); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc index 612b344514..e7736a4b3e 100644 --- a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc @@ -38,13 +38,13 @@ class TestTensorAddInfo : public UT::Common { }; void TestTensorAddInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 34; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(32); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc index 7edeb5a553..ce1238baeb 100644 --- a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc +++ b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc @@ -38,13 +38,13 @@ class TestTmpIdentityInfo : public UT::Common { }; void TestTmpIdentityInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 1050; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(1024); stage_map.push_back(26); diff --git a/tests/ut/cpp/parallel/ops_info/transpose_test.cc b/tests/ut/cpp/parallel/ops_info/transpose_test.cc index d54258915d..991ec47820 100644 --- a/tests/ut/cpp/parallel/ops_info/transpose_test.cc +++ b/tests/ut/cpp/parallel/ops_info/transpose_test.cc @@ -38,13 +38,13 @@ class TestTransposeInfo : public UT::Common { }; void TestTransposeInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 34; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(32); stage_map.push_back(2); diff --git a/tests/ut/cpp/parallel/step_auto_parallel_test.cc b/tests/ut/cpp/parallel/step_auto_parallel_test.cc index 5503377ee2..a1474ca244 100644 --- a/tests/ut/cpp/parallel/step_auto_parallel_test.cc +++ b/tests/ut/cpp/parallel/step_auto_parallel_test.cc @@ -32,13 +32,13 @@ class TestStepAutoParallel : public UT::Common { }; void TestStepAutoParallel::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 20; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(16); stage_map.push_back(4); diff --git a/tests/ut/cpp/parallel/step_parallel_test.cc b/tests/ut/cpp/parallel/step_parallel_test.cc index 23debf11ac..afc898907b 100644 --- a/tests/ut/cpp/parallel/step_parallel_test.cc +++ b/tests/ut/cpp/parallel/step_parallel_test.cc @@ -34,13 +34,13 @@ class TestStepParallel : public UT::Common { void TestStepParallel::SetUp() { UT::InitPythonPath(); } void Init_Device_Manager() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 20; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(16); stage_map.push_back(4); diff --git a/tests/ut/cpp/parallel/tensor_layout/construct_operator_test.cc b/tests/ut/cpp/parallel/tensor_layout/construct_operator_test.cc index bebe55250b..2ba8cc9dfc 100644 --- a/tests/ut/cpp/parallel/tensor_layout/construct_operator_test.cc +++ b/tests/ut/cpp/parallel/tensor_layout/construct_operator_test.cc @@ -39,12 +39,12 @@ class TestConstructOperator : public UT::Common { }; void TestConstructOperator::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 1050; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(1024); stage_map.push_back(26); diff --git a/tests/ut/cpp/parallel/tensor_layout/redistribution_operator_infer_test.cc b/tests/ut/cpp/parallel/tensor_layout/redistribution_operator_infer_test.cc index 5ffd9b22ce..1b1dd4af04 100644 --- a/tests/ut/cpp/parallel/tensor_layout/redistribution_operator_infer_test.cc +++ b/tests/ut/cpp/parallel/tensor_layout/redistribution_operator_infer_test.cc @@ -28,13 +28,13 @@ class TestRedistributionOperatorInfer : public UT::Common { TestRedistributionOperatorInfer() {} void SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 1050; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(1024); stage_map.push_back(26); diff --git a/tests/ut/cpp/parallel/tensor_layout/tensor_redistribution_test.cc b/tests/ut/cpp/parallel/tensor_layout/tensor_redistribution_test.cc index ad20793417..572763faa3 100644 --- a/tests/ut/cpp/parallel/tensor_layout/tensor_redistribution_test.cc +++ b/tests/ut/cpp/parallel/tensor_layout/tensor_redistribution_test.cc @@ -33,7 +33,7 @@ class TestTensorRedistribution : public UT::Common { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(16); stage_map.push_back(4); diff --git a/tests/ut/cpp/parallel/virtual_dataset_test.cc b/tests/ut/cpp/parallel/virtual_dataset_test.cc index 602a7370f1..1d3ff081c7 100644 --- a/tests/ut/cpp/parallel/virtual_dataset_test.cc +++ b/tests/ut/cpp/parallel/virtual_dataset_test.cc @@ -37,13 +37,13 @@ class TestVirtualDatasetInfo : public UT::Common { }; void TestVirtualDatasetInfo::SetUp() { - std::list dev_list; + std::vector dev_list; for (int32_t i = 0; i < 130; i++) { dev_list.push_back(i); } - std::list stage_map; + std::vector stage_map; stage_map.push_back(16); stage_map.push_back(114); From ada46fc219e88eb105442f5b8c0885e41b5cfcc8 Mon Sep 17 00:00:00 2001 From: Ziyan Date: Mon, 30 Mar 2020 15:17:05 +0800 Subject: [PATCH 031/227] enable use float type learning rate in lars optimizer --- mindspore/nn/optim/lars.py | 9 ++++++--- tests/ut/python/nn/optim/test_lars.py | 19 ++++++++++++++++++- 2 files changed, 24 insertions(+), 4 deletions(-) diff --git a/mindspore/nn/optim/lars.py b/mindspore/nn/optim/lars.py index cdfe45de62..a69057215d 100755 --- a/mindspore/nn/optim/lars.py +++ b/mindspore/nn/optim/lars.py @@ -13,12 +13,14 @@ # limitations under the License. # ============================================================================ """lars optimizer""" +from typing import Iterable from mindspore.common import dtype as mstype +from mindspore.common import Tensor from mindspore.common.initializer import initializer +from mindspore.common.parameter import Parameter from mindspore.ops import operations as P from mindspore.ops import composite as C from mindspore.ops import functional as F -from mindspore.common.parameter import Parameter from mindspore.nn.cell import Cell from .optimizer import grad_scale @@ -111,7 +113,8 @@ class LARS(Cell): self.gather = None self.global_step = None self.axis = None - if not isinstance(self.learning_rate, float): + if isinstance(self.learning_rate.default_input, Iterable) or \ + (isinstance(self.learning_rate.default_input, Tensor) and self.learning_rate.default_input.dim() == 1): self.dynamic_lr = True self.assignadd = P.AssignAdd() self.gather = P.GatherV2() @@ -124,7 +127,7 @@ class LARS(Cell): lr = self.gather(self.learning_rate, self.global_step, self.axis) F.control_depend(lr, self.assignadd(self.global_step, 1)) else: - lr = F.scalar_to_array(self.learning_rate) + lr = self.learning_rate if self.reciprocal_scale != 1.0: gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients) diff --git a/tests/ut/python/nn/optim/test_lars.py b/tests/ut/python/nn/optim/test_lars.py index 92d218a32b..17bbe69fe6 100644 --- a/tests/ut/python/nn/optim/test_lars.py +++ b/tests/ut/python/nn/optim/test_lars.py @@ -46,7 +46,7 @@ class Net(nn.Cell): return x -def test_lars(): +def test_lars_multi_step_lr(): inputs = Tensor(np.ones([1, 64]).astype(np.float32)) label = Tensor(np.zeros([1, 10]).astype(np.float32)) net = Net() @@ -61,3 +61,20 @@ def test_lars(): net_with_loss = WithLossCell(net, loss) train_network = TrainOneStepCell(net_with_loss, optimizer) _executor.compile(train_network, inputs, label) + + +def test_lars_float_lr(): + inputs = Tensor(np.ones([1, 64]).astype(np.float32)) + label = Tensor(np.zeros([1, 10]).astype(np.float32)) + net = Net() + net.set_train() + loss = nn.SoftmaxCrossEntropyWithLogits() + + lr = 0.1 + SGD = Momentum(net.trainable_params(), lr, 0.9) + optimizer = LARS(SGD, epsilon=1e-08, hyperpara=0.02, decay_filter=lambda x: 'bn' not in x.name, + lars_filter=lambda x: 'bn' not in x.name) + + net_with_loss = WithLossCell(net, loss) + train_network = TrainOneStepCell(net_with_loss, optimizer) + _executor.compile(train_network, inputs, label) \ No newline at end of file From 33b92dddca629c1c72052f039bdb3b1cf29f35e3 Mon Sep 17 00:00:00 2001 From: panyifeng Date: Tue, 31 Mar 2020 14:55:29 +0800 Subject: [PATCH 032/227] fix parallel related valuenode merging error --- mindspore/ccsrc/parallel/step_parallel.cc | 1 + mindspore/ccsrc/pipeline/action.cc | 28 +++++++++++++++++++++++ 2 files changed, 29 insertions(+) diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc index d8c2864dbf..ad194a7961 100644 --- a/mindspore/ccsrc/parallel/step_parallel.cc +++ b/mindspore/ccsrc/parallel/step_parallel.cc @@ -112,6 +112,7 @@ void InsertNode(const Operator& op, const CNodePtr& node, size_t index, const An MS_EXCEPTION_IF_NULL(new_node_value); PrimitivePtr new_node_prim = new_node_value->value()->cast(); new_node_prim->set_instance_name(instance_name); + new_node_prim->set_attr("keep_value_node_input", MakeValue(true)); new_node->set_scope(scope); node_input[0]->set_scope(scope); manager->SetEdge(node, SizeToInt(index), new_node); diff --git a/mindspore/ccsrc/pipeline/action.cc b/mindspore/ccsrc/pipeline/action.cc index f3742ab654..392602f419 100644 --- a/mindspore/ccsrc/pipeline/action.cc +++ b/mindspore/ccsrc/pipeline/action.cc @@ -276,6 +276,31 @@ bool ExecuteAction(const ResourcePtr& res) { return true; } +// The parallel primitive related valuenode might be partitioned so that its value changes by device, +// that will result in a syncronization error due to different executing order. +// Here we temporarily avoid the problem by skipping valuenode merging used by parallel related primitive, +// the final solution will be proposed later as a parallel feature. +bool KeepValueNodeDuplication(const AnfNodePtr& value_node, const ResourcePtr& res) { + auto& node_users = res->manager()->node_users(); + auto& users = node_users[value_node]; + auto used_by_keep_value_prim = + std::any_of(users.begin(), users.end(), [](const std::pair& user) -> bool { + MS_EXCEPTION_IF_NULL(user.first); + auto cnode = user.first->cast(); + if (cnode == nullptr) { + return false; + } + auto prim_node = cnode->input(0); + if (IsValueNode(prim_node)) { + auto prim = GetValue(prim_node->cast()->value()); + // value_node is referenced by some parallel primitive + return prim->HasAttr("keep_value_node_input"); + } + return false; + }); + return used_by_keep_value_prim; +} + bool RemoveValueNodeDuplicationsAction(const ResourcePtr& res) { if (res->func_graph() == nullptr) { MS_LOG(EXCEPTION) << "Remove value node duplications error."; @@ -287,6 +312,9 @@ bool RemoveValueNodeDuplicationsAction(const ResourcePtr& res) { HashCache hash_cache; HashValue hashes; for (const auto& value_pair : value_nodes) { + if (KeepValueNodeDuplication(value_pair.first, res)) { + continue; + } TryToDoReplace(manager.get(), value_pair.first, &hash_cache, &hashes); } return true; From 1c5f17ba3c12e4ca81f0311b7b51f1cdf23525fb Mon Sep 17 00:00:00 2001 From: wenchunjiang Date: Mon, 30 Mar 2020 17:35:19 +0800 Subject: [PATCH 033/227] This fixes an issue about mindspore process cannot exit when calling python api op_select_format failed in select kernel steps. Previously function op_select_format and check_supported raise an exception directly on the tbe_process python side, but we don't deal with the exception, and raise an exeception on c++ side to frontend ME, that will cause some conflict when recycle resource on ME and tbe_process python interpreter. This changes adding try...catch in function op_select_format and check_supported on the python side, and return the Exception string to c++ side, so that we can raise an exception to frontend ME and ME will deal with resouce clearning and exit. --- .../parallel_compile/tbe_compiler/helper.py | 114 ++++++++++++++++++ .../tbe_compiler/tbe_process.py | 108 +++++------------ mindspore/ccsrc/kernel/tbe/tbe_adapter.cc | 2 +- .../ccsrc/kernel/tbe/tbe_kernel_select.cc | 6 +- .../ccsrc/kernel/tbe/tbe_python_funcs.cc | 18 ++- 5 files changed, 167 insertions(+), 81 deletions(-) create mode 100644 mindspore/_extends/parallel_compile/tbe_compiler/helper.py diff --git a/mindspore/_extends/parallel_compile/tbe_compiler/helper.py b/mindspore/_extends/parallel_compile/tbe_compiler/helper.py new file mode 100644 index 0000000000..b5f7750187 --- /dev/null +++ b/mindspore/_extends/parallel_compile/tbe_compiler/helper.py @@ -0,0 +1,114 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +"""tbe process""" +import sys +import os +from .common import get_args, get_build_in_impl_path, TBEException + +build_in_impl_path = get_build_in_impl_path() + + +def _op_select_format(kernel_info): + """ + call op's op_select_format to get op supported format + + Args: + kernel_info (dict): kernel info load by json string + + Returns: + op supported format + """ + try: + # import module + op_name = kernel_info['op_info']['name'] + impl_path = build_in_impl_path + custom_flag = False + if 'impl_path' in kernel_info and kernel_info['impl_path'] is not None: + op_impl_path = os.path.realpath(kernel_info['impl_path']) + if os.path.isfile(op_impl_path): + path, file_name = os.path.split(op_impl_path) + op_name, _ = os.path.splitext(file_name) + impl_path = path + custom_flag = True + if impl_path not in sys.path: + sys.path.insert(0, impl_path) + + if custom_flag: + op_module = __import__(op_name) + else: + op_module = __import__("impl." + op_name, globals(), locals(), [op_name], 0) + # get function + if not hasattr(op_module, "op_select_format"): + return "" + op_func = getattr(op_module, "op_select_format", None) + + # call function + inputs_args = get_args(kernel_info['op_info'], 'inputs') + outputs_args = get_args(kernel_info['op_info'], 'outputs') + attrs_args = get_args(kernel_info['op_info'], 'attrs') + kernel_name = kernel_info['op_info']['kernel_name'] + ret = op_func(*inputs_args, *outputs_args, *attrs_args, kernel_name=kernel_name) + + except Exception as e: + raise TBEException(str(e)) + + return ret + + +def _check_supported(kernel_info): + """ + call op's check_supported to check supported or not + + Args: + kernel_info (dict): kernel info load by json string + + Returns: + bool: check result, true or false + """ + try: + # import module + op_name = kernel_info['op_info']['name'] + impl_path = build_in_impl_path + custom_flag = False + if 'impl_path' in kernel_info and kernel_info['impl_path'] is not None: + op_impl_path = os.path.realpath(kernel_info['impl_path']) + if os.path.isfile(op_impl_path): + path, file_name = os.path.split(op_impl_path) + op_name, _ = os.path.splitext(file_name) + impl_path = path + custom_flag = True + if impl_path not in sys.path: + sys.path.insert(0, impl_path) + + if custom_flag: + op_module = __import__(op_name) + else: + op_module = __import__("impl." + op_name, globals(), locals(), [op_name], 0) + # get function + if not hasattr(op_module, "check_supported"): + return "" + op_func = getattr(op_module, "check_supported", None) + + # call function + inputs_args = get_args(kernel_info['op_info'], 'inputs') + outputs_args = get_args(kernel_info['op_info'], 'outputs') + attrs_args = get_args(kernel_info['op_info'], 'attrs') + kernel_name = kernel_info['op_info']['kernel_name'] + ret = op_func(*inputs_args, *outputs_args, *attrs_args, kernel_name=kernel_name) + + except Exception as e: + raise TBEException(str(e)) + + return ret diff --git a/mindspore/_extends/parallel_compile/tbe_compiler/tbe_process.py b/mindspore/_extends/parallel_compile/tbe_compiler/tbe_process.py index 9b970b7242..2f73ced061 100644 --- a/mindspore/_extends/parallel_compile/tbe_compiler/tbe_process.py +++ b/mindspore/_extends/parallel_compile/tbe_compiler/tbe_process.py @@ -19,10 +19,8 @@ import subprocess import sys import os import json -from .common import check_kernel_info, get_args, get_build_in_impl_path - -build_in_impl_path = get_build_in_impl_path() - +from .common import check_kernel_info, TBEException +from .helper import _op_select_format, _check_supported def create_tbe_parallel_compiler(): """ @@ -41,40 +39,17 @@ def op_select_format(op_json: str): op_json (str): json string of the op Returns: - op supported format + op supported format or exception message """ ret = "" - kernel_info = json.loads(op_json) - check_kernel_info(kernel_info) - - # import module - op_name = kernel_info['op_info']['name'] - impl_path = build_in_impl_path - custom_flag = False - if 'impl_path' in kernel_info and kernel_info['impl_path'] is not None: - op_impl_path = os.path.realpath(kernel_info['impl_path']) - if os.path.isfile(op_impl_path): - path, file_name = os.path.split(op_impl_path) - op_name, _ = os.path.splitext(file_name) - impl_path = path - custom_flag = True - sys.path.insert(0, impl_path) - - if custom_flag: - op_module = __import__(op_name) - else: - op_module = __import__("impl." + op_name, globals(), locals(), [op_name], 0) - # get function - if not hasattr(op_module, "op_select_format"): - return "" - op_func = getattr(op_module, "op_select_format", None) - - # call function - inputs_args = get_args(kernel_info['op_info'], 'inputs') - outputs_args = get_args(kernel_info['op_info'], 'outputs') - attrs_args = get_args(kernel_info['op_info'], 'attrs') - kernel_name = kernel_info['op_info']['kernel_name'] - ret = op_func(*inputs_args, *outputs_args, *attrs_args, kernel_name=kernel_name) + try: + kernel_info = json.loads(op_json) + check_kernel_info(kernel_info) + ret = _op_select_format(kernel_info) + + except TBEException as e: + return "TBEException: " + str(e) + return ret @@ -86,40 +61,18 @@ def check_supported(op_json: str): op_json (str): json string of the op Returns: - true or false + bool: check result, true or false + str: exception message when catch an Exception """ ret = "" - kernel_info = json.loads(op_json) - check_kernel_info(kernel_info) - - # import module - op_name = kernel_info['op_info']['name'] - impl_path = build_in_impl_path - custom_flag = False - if 'impl_path' in kernel_info and kernel_info['impl_path'] is not None: - op_impl_path = os.path.realpath(kernel_info['impl_path']) - if os.path.isfile(op_impl_path): - path, file_name = os.path.split(op_impl_path) - op_name, _ = os.path.splitext(file_name) - impl_path = path - custom_flag = True - sys.path.insert(0, impl_path) - - if custom_flag: - op_module = __import__(op_name) - else: - op_module = __import__("impl." + op_name, globals(), locals(), [op_name], 0) - # get function - if not hasattr(op_module, "check_supported"): - return "" - op_func = getattr(op_module, "check_supported", None) - - # call function - inputs_args = get_args(kernel_info['op_info'], 'inputs') - outputs_args = get_args(kernel_info['op_info'], 'outputs') - attrs_args = get_args(kernel_info['op_info'], 'attrs') - kernel_name = kernel_info['op_info']['kernel_name'] - ret = op_func(*inputs_args, *outputs_args, *attrs_args, kernel_name=kernel_name) + try: + kernel_info = json.loads(op_json) + check_kernel_info(kernel_info) + ret = _check_supported(kernel_info) + + except TBEException as e: + return "TBEException: " + str(e) + return ret @@ -149,12 +102,12 @@ class CompilerPool: """compiler pool""" def __init__(self): - processes = multiprocessing.cpu_count() + self.__processe_num = multiprocessing.cpu_count() # max_processes_num: Set the maximum number of concurrent processes for compiler max_processes_num = 16 - if processes > max_processes_num: - processes = max_processes_num - self.__pool = multiprocessing.Pool(processes=processes) + if self.__processe_num > max_processes_num: + self.__processe_num = max_processes_num + self.__pool = None self.__next_task_id = 1 self.__running_tasks = [] @@ -165,11 +118,10 @@ class CompilerPool: del self.__pool def exit(self): - return - # self.__pool.terminate() - # self.__pool.join() - # if self.__pool is not None: - # del self.__pool + if self.__pool is not None: + self.__pool.terminate() + self.__pool.join() + del self.__pool def start_compile_op(self, op_json): """ @@ -183,6 +135,8 @@ class CompilerPool: """ task_id = self.__next_task_id self.__next_task_id = self.__next_task_id + 1 + if self.__pool is None: + self.__pool = multiprocessing.Pool(processes=self.__processe_num) task_future = self.__pool.apply_async(func=run_compiler, args=(op_json,)) self.__running_tasks.append((task_id, task_future)) return task_id diff --git a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc index ddb78a08df..50fed77a9a 100644 --- a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc +++ b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc @@ -98,7 +98,7 @@ void TbeAdapter::NormalizeFuncName(std::string *func_name) { *func_name = name_tmp; auto iter = tbe_func_adapter_map.find(*func_name); if (iter != tbe_func_adapter_map.end()) { - MS_LOG(INFO) << "map actual op fron me " << func_name << "to tbe op" << iter->second; + MS_LOG(INFO) << "map actual op from me " << func_name << "to tbe op" << iter->second; *func_name = iter->second; } } diff --git a/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc b/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc index 25495d1e68..1953fd0c72 100644 --- a/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc +++ b/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc @@ -35,6 +35,8 @@ namespace kernel { constexpr auto kName = "name"; constexpr auto kDtype = "dtype"; constexpr auto kFormat = "format"; +constexpr auto kPrefixInput = "input"; +constexpr auto kPrefixOutput = "output"; const std::map DYNAMIC_FORMAT_MAP = {{"NCHW", "DefaultFormat"}, {"NHWC", "DefaultFormat"}, {"ND", "DefaultFormat"}, @@ -146,13 +148,13 @@ bool ParseDynamicFormatJson(const std::string &jsonStr, std::vector input = std::make_shared(); MS_EXCEPTION_IF_NULL(input); input->set_name(json_obj[key_name].at(kName)); ConvertFormatDtype(json_obj[key_name].at(kFormat), json_obj[key_name].at(kDtype), input); inputs->emplace_back(input); - } else if (key_name.find("output", 0) != std::string::npos) { + } else if (key_name.compare(0, strlen(kPrefixOutput), kPrefixOutput) == 0) { std::shared_ptr output = std::make_shared(); MS_EXCEPTION_IF_NULL(output); output->set_name(json_obj[key_name].at(kName)); diff --git a/mindspore/ccsrc/kernel/tbe/tbe_python_funcs.cc b/mindspore/ccsrc/kernel/tbe/tbe_python_funcs.cc index 1e60742fc4..7204fb7f96 100644 --- a/mindspore/ccsrc/kernel/tbe/tbe_python_funcs.cc +++ b/mindspore/ccsrc/kernel/tbe/tbe_python_funcs.cc @@ -26,6 +26,7 @@ constexpr auto kTbeProcessModule = "mindspore._extends.parallel_compile.tbe_comp constexpr auto kCreateTbeParallelCompilerFunc = "create_tbe_parallel_compiler"; constexpr auto kOpSelectFormatFunc = "op_select_format"; constexpr auto kCheckSupportedFunc = "check_supported"; +constexpr auto kTBEException = "TBEException"; PyObject *TbePythonFuncs::pCreateTbeParallelCompilerFunc_ = nullptr; PyObject *TbePythonFuncs::pTbeCompiler_ = nullptr; @@ -133,6 +134,10 @@ std::string TbePythonFuncs::OpSelectFormat(const nlohmann::json &kernel_json) { char *pstr = nullptr; (void)PyArg_Parse(pRet, "s", &pstr); res_json_str = pstr; + if (res_json_str.compare(0, strlen(kTBEException), kTBEException) == 0) { + MS_EXCEPTION(ArgumentError) << "Failed to call function [" << kOpSelectFormatFunc << "], " << res_json_str + << " ,function args:" << PyObjectToStr(pArg); + } return res_json_str; } @@ -167,7 +172,18 @@ bool TbePythonFuncs::CheckSupported(const nlohmann::json &kernel_json) { MS_EXCEPTION(ArgumentError) << "Failed to call function [" << kCheckSupportedFunc << "], function args: " << PyObjectToStr(pArg); } - ret = PyObject_IsTrue(pRes) != 0; + if (PyBool_Check(pRes)) { + ret = PyObject_IsTrue(pRes) != 0; + } else { + char *pstr = nullptr; + (void)PyArg_Parse(pRes, "s", &pstr); + std::string res_str = pstr; + if (res_str.compare(0, strlen(kTBEException), kTBEException) == 0) { + MS_EXCEPTION(ArgumentError) << "Failed to call function [" << kCheckSupportedFunc << "], " << res_str + << ", function args: " << PyObjectToStr(pArg); + } + } + return ret; } From bd4a206ae8670fc9a36abe289f7070776ad3a789 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Tue, 31 Mar 2020 16:18:04 +0800 Subject: [PATCH 034/227] modify longtime python ut --- mindspore/nn/layer/conv.py | 2 +- tests/ut/python/parallel/test_auto_parallel_reshape.py | 6 +++--- tests/ut/python/parallel/test_one_hot_net.py | 8 ++++---- tests/ut/python/parallel/test_reshape.py | 8 ++++---- tests/ut/python/train/summary/test_summary_performance.py | 6 +++--- 5 files changed, 15 insertions(+), 15 deletions(-) diff --git a/mindspore/nn/layer/conv.py b/mindspore/nn/layer/conv.py index 6c78e1a715..666be93451 100644 --- a/mindspore/nn/layer/conv.py +++ b/mindspore/nn/layer/conv.py @@ -159,7 +159,7 @@ class Conv2d(_Conv): >>> net = nn.Conv2d(120, 240, 4, has_bias=False, weight_init='normal') >>> input = mindspore.Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32) >>> net(input).shape() - (1, 240, 1024, 637) + (1, 240, 1024, 640) """ @cell_attr_register def __init__(self, diff --git a/tests/ut/python/parallel/test_auto_parallel_reshape.py b/tests/ut/python/parallel/test_auto_parallel_reshape.py index 26e7e95a9f..ed9f24cc0b 100644 --- a/tests/ut/python/parallel/test_auto_parallel_reshape.py +++ b/tests/ut/python/parallel/test_auto_parallel_reshape.py @@ -49,16 +49,16 @@ def test_reshape_matmul(): super().__init__() self.reshape = P.Reshape() self.matmul = P.MatMul() - self.matmul_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight") + self.matmul_weight = Parameter(Tensor(np.ones([28, 64]), dtype=ms.float32), name="weight") def construct(self, x): - out = self.reshape(x, (256, 25088)) + out = self.reshape(x, (64, 28)) out = self.matmul(out, self.matmul_weight) return out size = 8 context.set_auto_parallel_context(device_num=size, global_rank=0) - x = Tensor(np.ones([32*size, 512, 7, 7]), dtype=ms.float32) + x = Tensor(np.ones([8*size, 28, 1, 1]), dtype=ms.float32) net = GradWrap(NetWithLoss(Net())) context.set_auto_parallel_context(parallel_mode="auto_parallel") diff --git a/tests/ut/python/parallel/test_one_hot_net.py b/tests/ut/python/parallel/test_one_hot_net.py index bf2677056e..87b4acfe37 100644 --- a/tests/ut/python/parallel/test_one_hot_net.py +++ b/tests/ut/python/parallel/test_one_hot_net.py @@ -247,15 +247,15 @@ def fc_with_initialize(input_channels, out_channels): class BNReshapeDenseBNNet(nn.Cell): def __init__(self): super(BNReshapeDenseBNNet, self).__init__() - self.batch_norm = bn_with_initialize(512) + self.batch_norm = bn_with_initialize(2) self.reshape = P.Reshape() self.batch_norm2 = nn.BatchNorm1d(512, affine=False) - self.fc = fc_with_initialize(512 * 32 * 32, 512) + self.fc = fc_with_initialize(2 * 32 * 32, 512) self.loss = SemiAutoOneHotNet(args=Args(), strategy=StrategyBatch()) def construct(self, x, label): x = self.batch_norm(x) - x = self.reshape(x, (16, 512*32*32)) + x = self.reshape(x, (16, 2*32*32)) x = self.fc(x) x = self.batch_norm2(x) loss = self.loss(x, label) @@ -266,7 +266,7 @@ def test_bn_reshape_dense_bn_train_loss(): batch_size = 16 device_num = 16 context.set_auto_parallel_context(device_num=device_num, global_rank=0) - input = Tensor(np.ones([batch_size, 512, 32, 32]).astype(np.float32) * 0.01) + input = Tensor(np.ones([batch_size, 2, 32, 32]).astype(np.float32) * 0.01) label = Tensor(np.ones([batch_size]), dtype=ms.int32) net = GradWrap(NetWithLoss(BNReshapeDenseBNNet())) diff --git a/tests/ut/python/parallel/test_reshape.py b/tests/ut/python/parallel/test_reshape.py index 11ca435e5b..43906aec23 100644 --- a/tests/ut/python/parallel/test_reshape.py +++ b/tests/ut/python/parallel/test_reshape.py @@ -490,15 +490,15 @@ def fc_with_initialize(input_channels, out_channels): class BNReshapeDenseBNNet(nn.Cell): def __init__(self): super(BNReshapeDenseBNNet, self).__init__() - self.batch_norm = bn_with_initialize(512) + self.batch_norm = bn_with_initialize(2) self.reshape = P.Reshape() self.cast = P.Cast() self.batch_norm2 = nn.BatchNorm1d(512, affine=False) - self.fc = fc_with_initialize(512 * 32 * 32, 512) + self.fc = fc_with_initialize(2 * 32 * 32, 512) def construct(self, x): x = self.batch_norm(x) - x = self.reshape(x, (16, 512*32*32)) + x = self.reshape(x, (16, 2*32*32)) x = self.fc(x) x = self.batch_norm2(x) return x @@ -508,7 +508,7 @@ def test_bn_reshape_dense_bn_train(): batch_size = 16 device_num = 16 context.set_auto_parallel_context(device_num=device_num, global_rank=0) - input = Tensor(np.ones([batch_size, 512, 32, 32]).astype(np.float32) * 0.01) + input = Tensor(np.ones([batch_size, 2, 32, 32]).astype(np.float32) * 0.01) net = GradWrap(NetWithLoss(BNReshapeDenseBNNet())) context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") diff --git a/tests/ut/python/train/summary/test_summary_performance.py b/tests/ut/python/train/summary/test_summary_performance.py index f004d4e584..9ee9725d13 100644 --- a/tests/ut/python/train/summary/test_summary_performance.py +++ b/tests/ut/python/train/summary/test_summary_performance.py @@ -43,9 +43,9 @@ def get_test_data(step): tag1 = "xt1[:Tensor]" tag2 = "xt2[:Tensor]" tag3 = "xt3[:Tensor]" - np1 = np.random.random((50, 40, 30, 50)) - np2 = np.random.random((50, 50, 30, 50)) - np3 = np.random.random((40, 55, 30, 50)) + np1 = np.random.random((5, 4, 3, 5)) + np2 = np.random.random((5, 5, 3, 5)) + np3 = np.random.random((4, 5, 3, 5)) dict1 = {} dict1["name"] = tag1 From 51feea03a4151a373a6f14acad7289a672eec3af Mon Sep 17 00:00:00 2001 From: ms_yan Date: Tue, 31 Mar 2020 18:46:59 +0800 Subject: [PATCH 035/227] Repair parameter check problem in TFRecordDataset --- mindspore/dataset/engine/validators.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py index 4dc1867808..adfe54a02e 100644 --- a/mindspore/dataset/engine/validators.py +++ b/mindspore/dataset/engine/validators.py @@ -398,6 +398,7 @@ def check_tfrecorddataset(method): nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id'] nreq_param_list = ['columns_list'] + nreq_param_bool = ['shard_equal_rows'] # check dataset_files; required argument dataset_files = param_dict.get('dataset_files') @@ -410,6 +411,10 @@ def check_tfrecorddataset(method): check_param_type(nreq_param_list, param_dict, list) + check_param_type(nreq_param_bool, param_dict, bool) + + check_sampler_shuffle_shard_options(param_dict) + return method(*args, **kwargs) return new_method From 7a16dfe2bfcbbf98ebb5d30895757b312a8a4e65 Mon Sep 17 00:00:00 2001 From: ms_yan <6576637+ms_yan@user.noreply.gitee.com> Date: Tue, 31 Mar 2020 20:13:49 +0800 Subject: [PATCH 036/227] repair parameter check problem in random_resize_crop and random_crop --- mindspore/dataset/transforms/vision/validators.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/mindspore/dataset/transforms/vision/validators.py b/mindspore/dataset/transforms/vision/validators.py index 17f50e068d..443232d780 100644 --- a/mindspore/dataset/transforms/vision/validators.py +++ b/mindspore/dataset/transforms/vision/validators.py @@ -41,7 +41,7 @@ def check_crop_size(size): else: raise TypeError("Size should be a single integer or a list/tuple (h, w) of length 2.") for value in size: - check_value(value, (1, INT32_MAX)) + check_pos_int32(value) return size @@ -239,6 +239,7 @@ def check_random_resize_crop(method): kwargs["scale"] = scale if ratio is not None: check_range(ratio, [0, FLOAT_MAX_INTEGER]) + check_positive(ratio[0]) kwargs["ratio"] = ratio if interpolation is not None: check_inter_mode(interpolation) From cca1d7764a34c4c8774b0ada306c16fc42009515 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Tue, 31 Mar 2020 15:15:08 +0800 Subject: [PATCH 037/227] add operator SpaceToBatch and BatchToSpace for ge --- mindspore/ccsrc/transform/convert.cc | 6 +- mindspore/ccsrc/transform/op_adapter.h | 22 ++++ mindspore/ccsrc/transform/op_declare.cc | 13 +++ mindspore/ccsrc/transform/op_declare.h | 4 + mindspore/ops/_grad/grad_array_ops.py | 20 ++++ mindspore/ops/operations/__init__.py | 4 +- mindspore/ops/operations/array_ops.py | 141 ++++++++++++++++++++++++ tests/ut/python/ops/test_ops.py | 20 ++++ 8 files changed, 228 insertions(+), 2 deletions(-) diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index b8d07594b7..f2942b3de0 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -181,6 +181,8 @@ const char kNamePrint[] = "Print"; const char kNameApplyFtrl[] = "ApplyFtrl"; const char kNameDiag[] = "Diag"; const char kNameDiagPart[] = "DiagPart"; +const char kNameSpaceToBatch[] = "SpaceToBatch"; +const char kNameBatchToSpace[] = "BatchToSpace"; // -----------------OpAdapter initialization-------------- std::unordered_map &DfGraphConvertor::get_adpt_map() { @@ -363,7 +365,9 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameRound), ADPT_DESC(Round)}, {string(kNameApplyFtrl), ADPT_DESC(ApplyFtrl)}, {string(kNameDiag), ADPT_DESC(Diag)}, - {string(kNameDiagPart), ADPT_DESC(DiagPart)}}; + {string(kNameDiagPart), ADPT_DESC(DiagPart)}, + {string(kNameSpaceToBatch), ADPT_DESC(SpaceToBatchD)}, + {string(kNameBatchToSpace), ADPT_DESC(BatchToSpaceD)}}; #ifdef ENABLE_GE adpt_map[string(kNamePrint)] = ADPT_DESC(Print); #endif diff --git a/mindspore/ccsrc/transform/op_adapter.h b/mindspore/ccsrc/transform/op_adapter.h index aa466adbb8..3dd299f83d 100644 --- a/mindspore/ccsrc/transform/op_adapter.h +++ b/mindspore/ccsrc/transform/op_adapter.h @@ -744,6 +744,28 @@ class OpAdapter : public BaseOpAdapter { return list; } + static std::vector ConvertAny(const ValuePtr& value, const AnyTraits>>, + const AnyTraits>) { + MS_EXCEPTION_IF_NULL(value); + MS_LOG(DEBUG) << "Value: " << value->type_name(); + if (!value->isa()) { + MS_LOG(EXCEPTION) << "Value should be ValueList, but got " << value->type_name(); + } + auto vec = value->cast(); + std::vector list; + for (auto& it : vec->value()) { + MS_EXCEPTION_IF_NULL(it); + if (!it->isa()) { + MS_LOG(EXCEPTION) << "It should be ValueList, but got " << it->type_name(); + } + auto sub_vector = it->cast(); + for (auto& item : sub_vector->value()) { + list.push_back(static_cast(GetValue(item))); + } + } + return list; + } + static std::vector ConvertAny(const ValuePtr& value, const AnyTraits>, const AnyTraits>) { MS_EXCEPTION_IF_NULL(value); diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 24043072fa..efe12681f0 100755 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -1170,6 +1170,19 @@ INPUT_MAP(DiagPart) = {{1, INPUT_DESC(x)}}; ATTR_MAP(DiagPart) = EMPTY_ATTR_MAP; OUTPUT_MAP(DiagPart) = {{0, OUTPUT_DESC(y)}}; +// SpaceToBatchD +INPUT_MAP(SpaceToBatchD) = {{1, INPUT_DESC(x)}}; +ATTR_MAP(SpaceToBatchD) = { + {"block_size", ATTR_DESC(block_size, AnyTraits())}, + {"paddings", ATTR_DESC(paddings, AnyTraits>>(), AnyTraits>())}}; +OUTPUT_MAP(SpaceToBatchD) = {{0, OUTPUT_DESC(y)}}; + +// BatchToSpaceD +INPUT_MAP(BatchToSpaceD) = {{1, INPUT_DESC(x)}}; +ATTR_MAP(BatchToSpaceD) = { + {"block_size", ATTR_DESC(block_size, AnyTraits())}, + {"crops", ATTR_DESC(crops, AnyTraits>>(), AnyTraits>())}}; +OUTPUT_MAP(BatchToSpaceD) = {{0, OUTPUT_DESC(y)}}; #ifdef ENABLE_GE // Print INPUT_MAP(Print) = EMPTY_INPUT_MAP; diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index 65e4cdc998..7028e43975 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -441,6 +441,10 @@ DECLARE_OP_ADAPTER(Diag) DECLARE_OP_USE_OUTPUT(Diag) DECLARE_OP_ADAPTER(DiagPart) DECLARE_OP_USE_OUTPUT(DiagPart) +DECLARE_OP_ADAPTER(SpaceToBatchD) +DECLARE_OP_USE_OUTPUT(SpaceToBatchD) +DECLARE_OP_ADAPTER(BatchToSpaceD) +DECLARE_OP_USE_OUTPUT(BatchToSpaceD) #ifdef ENABLE_GE DECLARE_OP_ADAPTER(Print) DECLARE_OP_USE_DYN_INPUT(Print) diff --git a/mindspore/ops/_grad/grad_array_ops.py b/mindspore/ops/_grad/grad_array_ops.py index 79841cf27a..81d38a1e1e 100644 --- a/mindspore/ops/_grad/grad_array_ops.py +++ b/mindspore/ops/_grad/grad_array_ops.py @@ -430,3 +430,23 @@ def get_bprop_diag_part(self): return (op(dout),) return bprop + + +@bprop_getters.register(P.SpaceToBatch) +def get_bprop_space_to_batch(self): + """Generate bprop for SpaceToBatch""" + space_to_batch_grad = P.BatchToSpace(self.block_size, self.paddings) + def bprop(x, out, dout): + dx = space_to_batch_grad(dout) + return (dx,) + return bprop + + +@bprop_getters.register(P.BatchToSpace) +def get_bprop_batch_to_space(self): + """Generate bprop for BatchToSpace""" + batch_to_space_grad = P.SpaceToBatch(self.block_size, self.crops) + def bprop(x, out, dout): + dx = batch_to_space_grad(dout) + return (dx,) + return bprop diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index 92ffdaf199..07a5471d0a 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -29,7 +29,7 @@ from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat, Shape, Size, Slice, Split, Squeeze, StridedSlice, Tile, Transpose, TruncatedNormal, TupleToArray, - UnsortedSegmentSum, SpaceToDepth, DepthToSpace) + UnsortedSegmentSum, SpaceToDepth, DepthToSpace, SpaceToBatch, BatchToSpace) from .comm_ops import (AllGather, AllReduce, _AlltoAll, ReduceScatter, Broadcast, _MirrorOperator, ReduceOp, _VirtualDataset, _VirtualDiv, _GetTensorSlice) @@ -226,6 +226,8 @@ __all__ = [ "LARSUpdate", "Round", "ApplyFtrl", + "SpaceToBatch", + "BatchToSpace" ] __all__.sort() diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index 6740f172b4..8585f873e9 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -20,6 +20,7 @@ import copy import functools +import itertools import numbers import numpy as np @@ -2020,3 +2021,143 @@ class DepthToSpace(PrimitiveWithInfer): def infer_dtype(self, x_dtype): validator.check_subclass("x_dtype", x_dtype, mstype.tensor) return x_dtype + + +class SpaceToBatch(PrimitiveWithInfer): + r""" + Divide spatial dimensions into blocks and combine the block size with the original batch. + + This operation will divide spatial dimensions (H, W) into blocks with block_size, the output tensor's H and W + dimension is the corresponding number of blocks after division. The output tensor's batch dimension is the + product of the original batch and the square of block_size. Prior to division into blocks, the spatial dimensions + of the input are zero padded according to paddings if necessary. + + Args: + block_size (int): The block size of dividing block with value >= 1. + paddings (list): The padding value for H and W dimension, containing 2 sub list, each containing 2 int value. + All values must be >= 0. paddings[i] specifies the paddings for spatial dimension i, which corresponds to + input dimension i+2. It is required that input_shape[i+2]+paddings[i][0]+paddings[i][1] is divisible + by block_size. + + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, the output tensor with the same type as input. Assume input shape is :math:`(n, c, h, w)` with + :math:`block\_size` and :math:`padddings`. The output tensor shape will be :math:`(n', c', h', w')`, where + + :math:`n' = n*(block\_size*block\_size)` + + :math:`c' = c` + + :math:`h' = (h+paddings[0][0]+paddings[0][1])//block\_size` + + :math:`w' = (w+paddings[1][0]+paddings[1][1])//block\_size` + + Examples: + >>> block_size = 2 + >>> paddings = [[0, 0], [0, 0]] + >>> space_to_batch = P.SpaceToBatch(block_size, paddings) + >>> x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mstype.float32) + >>> space_to_batch(x) + [[[[1.]]], [[[2.]]], [[[3.]]], [[[4.]]]] + + """ + @prim_attr_register + def __init__(self, block_size, paddings): + """Init SpaceToBatch""" + validator.check_type('block_size', block_size, [int]) + validator.check('block_size', block_size, '', 1, Rel.GT) + self.block_size = block_size + validator.check('paddings shape', np.array(paddings).shape, '', (2, 2)) + for elem in itertools.chain(*paddings): + validator.check_type('paddings element', elem, [int]) + self.paddings = paddings + + def infer_dtype(self, x_dtype): + validator.check_subclass("input_x", x_dtype, mstype.tensor) + validator.check_typename('input_x', x_dtype, mstype.number_type) + return x_dtype + + def infer_shape(self, x_shape): + validator.check('rank of input_x', len(x_shape), '', 4) + out_shape = copy.deepcopy(x_shape) + for i in range(2): + padded = out_shape[i+2] + self.paddings[i][0] + \ + self.paddings[i][1] + if padded % self.block_size != 0: + raise ValueError(f'padded[{i}] {padded} should be divisible by ' + f'block_size {self.block_size}') + out_shape[i+2] = padded // self.block_size + out_shape[0] *= self.block_size * self.block_size + return out_shape + + +class BatchToSpace(PrimitiveWithInfer): + r""" + Divide batch dimension with blocks and interleaves these blocks back into spatial dimensions. + + This operation will divide batch dimension N into blocks with block_size, the output tensor's N dimension + is the corresponding number of blocks after division. The output tensor's H, W dimension is product of original H, W + dimension and block_size with given amount to crop from dimension, respectively. + + Args: + block_size (int): The block size of dividing block with value >= 1. + crops (list): The crop value for H and W dimension, containing 2 sub list, each containing 2 int value. + All values must be >= 0. crops[i] specifies the crop values for spatial dimension i, which corresponds to + input dimension i+2. It is required that input_shape[i+2]*block_size >= crops[i][0]+crops[i][1]. + + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, the output tensor with the same type as input. Assume input shape is (n, c, h, w) with block_size + and crops. The output shape will be (n', c', h', w'), where + + :math:`n' = n//(block\_size*block\_size)` + + :math:`c' = c` + + :math:`h' = h*block\_size-crops[0][0]-crops[0][1]` + + :math:`w' = w*block\_size-crops[1][0]-crops[1][1]` + + Examples: + >>> block_size = 2 + >>> crops = [[0, 0], [0, 0]] + >>> op = P.BatchToSpace(block_size, crops) + >>> x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mstype.float32) + >>> output = op(x) + [[[[1., 2.], [3., 4.]]]] + + """ + @prim_attr_register + def __init__(self, block_size, crops): + """Init BatchToSpace""" + validator.check_type('block_size', block_size, [int]) + validator.check('block_size', block_size, '', 1, Rel.GT) + self.block_size = block_size + validator.check('crops shape', np.array(crops).shape, '', (2, 2)) + for elem in itertools.chain(*crops): + validator.check_type('crops element', elem, [int]) + self.crops = crops + + def infer_dtype(self, x_dtype): + validator.check_subclass("input_x", x_dtype, mstype.tensor) + validator.check_typename('input_x', x_dtype, mstype.number_type) + return x_dtype + + def infer_shape(self, x_shape): + validator.check('rank of input_x', len(x_shape), '', 4) + out_shape = copy.deepcopy(x_shape) + for i in range(2): + x_block_prod = out_shape[i+2] * self.block_size + crops_sum = self.crops[i][0] + self.crops[i][1] + validator.check("x block shape prod", x_block_prod, 'crops sum', crops_sum, Rel.GT) + out_shape[i+2] = x_block_prod - crops_sum + block_size_prod = self.block_size * self.block_size + if out_shape[0] % block_size_prod != 0: + raise ValueError(f'input_x dimension 0 {out_shape[0]} should be divisible by ' + f'block_size_prod {block_size_prod}') + out_shape[0] = out_shape[0] // block_size_prod + return out_shape diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py index 7e32ac9454..f76cf39c72 100755 --- a/tests/ut/python/ops/test_ops.py +++ b/tests/ut/python/ops/test_ops.py @@ -957,6 +957,26 @@ test_case_array_ops = [ 'desc_inputs': [[4, 4]], 'desc_bprop': [[4]], }), + ('SpaceToBatch_1', { + 'block': P.SpaceToBatch(2, [[0, 0], [0, 0]]), + 'desc_inputs': [[1, 3, 2, 2]], + 'desc_bprop': [[4, 3, 1, 1]], + }), + ('SpaceToBatch_2', { + 'block': P.SpaceToBatch(2, [[1, 1], [0, 4]]), + 'desc_inputs': [[1, 3, 2, 2]], + 'desc_bprop': [[4, 3, 2, 4]], + }), + ('BatchToSpace_1', { + 'block': P.BatchToSpace(2, [[0, 0], [0, 0]]), + 'desc_inputs': [[4, 3, 1, 1]], + 'desc_bprop': [[1, 3, 2, 2]], + }), + ('BatchToSpace_2', { + 'block': P.BatchToSpace(2, [[0, 0], [0, 1]]), + 'desc_inputs': [[4, 3, 1, 1]], + 'desc_bprop': [[1, 3, 2, 1]], + }), ] test_case_other_ops = [ From 73e0bdcd4337523a1c6403a9d504bc92315d9119 Mon Sep 17 00:00:00 2001 From: fary86 Date: Sat, 28 Mar 2020 17:53:22 +0800 Subject: [PATCH 038/227] Dump graph with type info when static analysis failed --- mindspore/ccsrc/debug/anf_ir_utils.cc | 25 +++--- mindspore/ccsrc/debug/anf_ir_utils.h | 14 +-- mindspore/ccsrc/debug/trace.cc | 108 +++++++++++++++++++++--- mindspore/ccsrc/ir/primitive.cc | 21 +++++ mindspore/ccsrc/ir/primitive.h | 1 + tests/ut/cpp/operator/composite_test.cc | 2 + 6 files changed, 139 insertions(+), 32 deletions(-) diff --git a/mindspore/ccsrc/debug/anf_ir_utils.cc b/mindspore/ccsrc/debug/anf_ir_utils.cc index 0891e211a0..98cd2f4b2f 100644 --- a/mindspore/ccsrc/debug/anf_ir_utils.cc +++ b/mindspore/ccsrc/debug/anf_ir_utils.cc @@ -34,6 +34,7 @@ #include "utils/utils.h" #include "debug/trace.h" #include "utils/context/ms_context.h" +#include "operator/ops.h" namespace mindspore { // max number of elements in sequence @@ -69,7 +70,7 @@ py::object load_obj(const std::string& path) { // ============================================= MindSpore IR Exporter ============================================= -std::string GetNodeType(const AnfNodePtr& nd) { +std::string AnfExporter::GetNodeType(const AnfNodePtr& nd) { abstract::ShapePtr shape = nd->Shape() == nullptr ? nullptr : dyn_cast(nd->Shape()); TypePtr type = dyn_cast(nd->Type()); std::ostringstream oss; @@ -102,7 +103,7 @@ int AnfExporter::GetParamIndex(const FuncGraphPtr& func_graph, const AnfNodePtr& FuncGraphPtr fg = func_graph; while (fg != nullptr) { if (exported.find(fg) == exported.end()) { - if (!export_used_) { + if (!check_integrity_) { break; } MS_LOG(EXCEPTION) << "Can not find func graph '" << fg->DumpText() << "." << fg->debug_info()->get_id() << "'"; @@ -255,15 +256,15 @@ std::string AnfExporter::GetPrimitiveText(const PrimitivePtr& prim) { } // output primitive attributes - auto attrs = prim->attrs(); - if (attrs.size() > 0) { - oss << "["; - int i = 0; - for (auto& attr : attrs) { - oss << (i > 0 ? ", " : "") << attr.first << "=" << attr.second->DumpText(); - i++; + oss << prim->GetAttrsText(); + + if (prim->isa()) { + auto do_signature = dyn_cast(prim); + auto& func = do_signature->function(); + if (func->isa()) { + auto sig_prim = dyn_cast(func); + oss << sig_prim->GetAttrsText(); } - oss << "]"; } return oss.str(); @@ -351,7 +352,7 @@ std::string AnfExporter::GetDictText(const FuncGraphPtr& func_graph, const Value std::string AnfExporter::GetOtherValueText(const FuncGraphPtr&, const ValuePtr& value) { std::ostringstream oss; - if (export_used_) { + if (check_integrity_) { MS_LOG(EXCEPTION) << "Need to process type: " << value->type_name() << ", dump text: " << value->DumpText(); } oss << value->type_name() << "[" << value->DumpText() << "]"; @@ -420,7 +421,7 @@ std::string AnfExporter::GetAnfNodeText(const FuncGraphPtr& func_graph, const An } oss << "%" << iter->second; } else if (node->isa()) { - oss << "%para" << GetParamIndex(func_graph, node, export_used_); + oss << "%para" << GetParamIndex(func_graph, node, check_integrity_); } else if (IsValueNode(node)) { FuncGraphPtr fg = GetValueNode(node); oss << fg->type_name() << "::fg_" << fg->debug_info()->get_id(); diff --git a/mindspore/ccsrc/debug/anf_ir_utils.h b/mindspore/ccsrc/debug/anf_ir_utils.h index 5b9ac9d2f0..5342c1ab96 100644 --- a/mindspore/ccsrc/debug/anf_ir_utils.h +++ b/mindspore/ccsrc/debug/anf_ir_utils.h @@ -64,17 +64,18 @@ struct ParamPtrHasher { class AnfExporter { public: - explicit AnfExporter(const std::string& id, bool export_used = true) - : param_index(-1), id_(id), export_used_(export_used) { + explicit AnfExporter(const std::string& id, bool export_used = true, bool check_integrity = false) + : param_index(-1), id_(id), export_used_(export_used), check_integrity_(check_integrity) { func_graph_set.clear(); exported.clear(); } - ~AnfExporter() {} + virtual ~AnfExporter() {} void ExportFuncGraph(const std::string& filename, const FuncGraphPtr& func_graph); void ExportFuncGraph(const std::string& filename, const std::vector& graphs); - private: + protected: + virtual std::string GetNodeType(const AnfNodePtr& nd); int GetParamIndex(const FuncGraphPtr& func_graph, const AnfNodePtr& param, bool throw_excp = true); int GetParamIndexFromExported(const AnfNodePtr& param); std::string DumpObject(const py::object& obj, const std::string& category) const; @@ -101,8 +102,10 @@ class AnfExporter { OrderedSet func_graph_set{}; OrderedMap> exported; std::string id_; - bool export_used_ = true; // whether export function graphs used in current exporting function graph + bool export_used_ = true; // whether export function graphs used in current exporting function graph + bool check_integrity_ = false; // whether check integrity or not, when dumping ir for loading, must set it to true TaggedNodeMap tagged_cnodes_; + abstract::AnfNodeConfigPtr node_cfg_ = nullptr; }; void ExportIR(const std::string& filename, const std::string& id, const FuncGraphPtr& func_graph); @@ -115,7 +118,6 @@ std::string GetFuncGraphProtoString(const FuncGraphPtr& func_graph); void DumpIRProto(const FuncGraphPtr& func_graph, const std::string& suffix); std::string GetOnnxProtoString(const FuncGraphPtr& func_graph); -std::string GetNodeType(const AnfNodePtr& nd); } // namespace mindspore #endif // MINDSPORE_CCSRC_DEBUG_ANF_IR_UTILS_H_ diff --git a/mindspore/ccsrc/debug/trace.cc b/mindspore/ccsrc/debug/trace.cc index 4b0f4c4fb3..7ce13052c5 100644 --- a/mindspore/ccsrc/debug/trace.cc +++ b/mindspore/ccsrc/debug/trace.cc @@ -17,6 +17,7 @@ #include "debug/trace.h" #include +#include #include #include #include @@ -194,37 +195,116 @@ void TraceGraphInfer() { MS_LOG(INFO) << "\n*************************************************************************************"; } -void OutputAnalysisGraphInfo() { - MS_LOG(INFO) << "Output analysis graph begin"; - std::unordered_map index_map; - std::vector tagged_graphs; +class AnalyzedFuncGraphExporter : public AnfExporter { + public: + AnalyzedFuncGraphExporter() : AnfExporter("", true, false) {} + ~AnalyzedFuncGraphExporter() override = default; + void ExportFuncGraph(const std::string& filename, const std::vector& node_cfgs); + + private: + std::string GetNodeType(const AnfNodePtr& nd) override; +}; + +std::unordered_map CalcTaggedFuncGraphs() { + std::unordered_map tagged_func_graphs; auto& list = GetCNodeDebugStack(); for (size_t i = 0; i < list.size(); ++i) { - auto& node_cfg = list[i]; + auto node_cfg = list[i]; auto fg = node_cfg->context()->func_graph(); auto node = node_cfg->node(); - auto idx = tagged_graphs.size(); - std::pair item(fg, idx); - if (index_map.insert(item).second) { - tagged_graphs.emplace_back(TaggedGraph(fg, TaggedNodeMap())); + tagged_func_graphs[fg][node] = i; + } + return tagged_func_graphs; +} + +void OutputAnalyzedGraphWithType() { + AnalyzedFuncGraphExporter exporter; + exporter.ExportFuncGraph("analyze_fail.dat", GetCNodeDebugStack()); +} + +std::string AnalyzedFuncGraphExporter::GetNodeType(const AnfNodePtr& node) { + if (node_cfg_ == nullptr) { + return AnfExporter::GetNodeType(node); + } + auto ctx = node_cfg_->context(); + auto engine = node_cfg_->engine(); + auto cfg = engine->MakeConfig(node, ctx); + auto abs = engine->cache().GetValue(cfg); + + if (abs == nullptr) { + return "Undefined"; + } + auto dtype = abs->BuildType(); + auto shape = abs->BuildShape(); + std::ostringstream oss; + if (dtype != nullptr && abs->isa() && shape != nullptr) { + oss << dtype->DumpText() << shape->DumpText(); + } else if (dtype != nullptr) { + oss << dtype->DumpText(); + } else { + oss << "Undefined"; + } + return oss.str(); +} + +void AnalyzedFuncGraphExporter::ExportFuncGraph(const std::string& filename, + const std::vector& node_cfgs) { + if (node_cfgs.empty()) { + MS_LOG(DEBUG) << "Node configs is empty"; + return; + } + + std::ofstream ofs(filename); + if (!ofs.is_open()) { + MS_LOG(ERROR) << "Open file '" << filename << "' failed!"; + return; + } + + param_index = 1; + auto tagged_func_graphs = CalcTaggedFuncGraphs(); + + // first output grapn on the analysis stack + for (const auto& node_cfg : node_cfgs) { + auto fg = node_cfg->context()->func_graph(); + // the graph is already output, skip it + if (exported.find(fg) != exported.end()) { + continue; } - tagged_graphs[index_map[fg]].second[node] = i; + // set node_cfg info for getting type + node_cfg_ = node_cfg; + tagged_cnodes_ = tagged_func_graphs[fg]; + ExportOneFuncGraph(ofs, fg); + ofs << "\n\n"; + } + + node_cfg_ = nullptr; + tagged_cnodes_.clear(); + + // print seperator between function graphs on analyzed graph call stack and others + ofs << "#===============================================================================\n\n\n"; + + // second output other graphs + while (!func_graph_set.empty()) { + FuncGraphPtr fg = *func_graph_set.begin(); + ExportOneFuncGraph(ofs, fg); + ofs << "\n\n"; + (void)func_graph_set.erase(fg); } + ofs << "# num of total funcgraphs: " << exported.size(); - ExportIR("analyze_fail.dat", tagged_graphs); - MS_LOG(INFO) << "Output analysis graph *end*"; + ofs.close(); } void GetInferStackInfo(std::ostringstream& oss) { MS_LOG(INFO) << "Get graph analysis information begin"; - auto& stack = GetCNodeDebugStack(); + auto stack = GetCNodeDebugStack(); if (stack.empty()) { MS_LOG(INFO) << "Length of analysis information stack is empty."; return; } - OutputAnalysisGraphInfo(); + OutputAnalyzedGraphWithType(); oss << "\nThe function call stack:\n"; int index = 0; diff --git a/mindspore/ccsrc/ir/primitive.cc b/mindspore/ccsrc/ir/primitive.cc index d62553ef60..a576c1e76b 100644 --- a/mindspore/ccsrc/ir/primitive.cc +++ b/mindspore/ccsrc/ir/primitive.cc @@ -106,6 +106,27 @@ void Primitive::set_signatures( } } +std::string Primitive::GetAttrsText() const { + if (attrs_.empty()) { + return ""; + } + + std::ostringstream oss; + oss << "["; + bool is_first = true; + for (auto& attr : attrs_) { + if (is_first) { + is_first = false; + } else { + oss << ", "; + } + oss << attr.first << "=" << attr.second->DumpText(); + } + oss << "]"; + + return oss.str(); +} + py::function PrimitivePy::GetBpropFunction() { static const char* const get_bprop_func_name = "get_bprop"; if (py::hasattr(python_obj_, get_bprop_func_name)) { diff --git a/mindspore/ccsrc/ir/primitive.h b/mindspore/ccsrc/ir/primitive.h index 8a60412e44..7dd37eb15f 100644 --- a/mindspore/ccsrc/ir/primitive.h +++ b/mindspore/ccsrc/ir/primitive.h @@ -102,6 +102,7 @@ class Primitive : public Named { PrimType prim_type() const { return prim_type_; } std::string instance_name() const { return instance_name_; } + std::string GetAttrsText() const; bool operator==(const Value& other) const override; bool operator==(const Primitive& other) const; ~Primitive() override = default; diff --git a/tests/ut/cpp/operator/composite_test.cc b/tests/ut/cpp/operator/composite_test.cc index 984f24b326..d9dd9e5e99 100644 --- a/tests/ut/cpp/operator/composite_test.cc +++ b/tests/ut/cpp/operator/composite_test.cc @@ -22,6 +22,7 @@ #include "operator/ops.h" #include "pipeline/static_analysis/prim.h" #include "pipeline/static_analysis/abstract_function.h" +#include "debug/trace.h" namespace mindspore { using Shape = abstract::Shape; @@ -124,6 +125,7 @@ TEST_F(TestComposite, test_TupleSlice_arg_one_number) { AbstractBasePtrList args_spec_list = {tuple_tensor, start_index}; try { + trace::ClearTraceStack(); engine_->Run(tupleSliceGraphPtr, args_spec_list); FAIL() << "Excepted exception :Args type is wrong"; } catch (std::runtime_error const &err) { From 8854a6c9f74a19d1f682ba31609dfc097b0c1507 Mon Sep 17 00:00:00 2001 From: lichenever Date: Tue, 31 Mar 2020 18:43:42 +0800 Subject: [PATCH 039/227] fix two cast bug in auto parallel --- .../ccsrc/parallel/step_auto_parallel.cc | 6 ++-- mindspore/ccsrc/parallel/step_parallel.cc | 23 ++++++++++--- .../parallel/test_element_wise_function.py | 33 ++++++++++++++++--- 3 files changed, 51 insertions(+), 11 deletions(-) diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc index c3e3f5893e..400e985f18 100644 --- a/mindspore/ccsrc/parallel/step_auto_parallel.cc +++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc @@ -346,6 +346,8 @@ bool IsAutoParallelCareNode(const CNodePtr &cnode) { } OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &cnode) { + MS_EXCEPTION_IF_NULL(prim); + MS_EXCEPTION_IF_NULL(cnode); auto attrs = prim->attrs(); std::vector shape_list = ExtractShape(cnode); if (shape_list.empty()) { @@ -381,8 +383,8 @@ OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr & operator_info->set_outputs_dtype(cnode->Type()); operator_info->set_cnode(cnode); // If no strategy has been configured for this operator, then candidate strategies are generated for - // auto-strategy searching - if (!StrategyFound(attrs)) { + // auto-strategy searchingm if this primitive is Cast, we ignore the user-specified strategy + if (!StrategyFound(attrs) || prim->name() == CAST) { // Compute split_flag_list_, indicating which input has batch dimension. This is ONLY used for preparation for // BatchParallelInfo operator operator_info->ComputeBatchSplitFlagList(); diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc index ad194a7961..e2b4d55aad 100644 --- a/mindspore/ccsrc/parallel/step_parallel.cc +++ b/mindspore/ccsrc/parallel/step_parallel.cc @@ -371,7 +371,6 @@ bool IsParallelCareNode(const CNodePtr& cnode) { if (prim == nullptr) { return false; } - auto attrs = prim->attrs(); if (IsInBlackList(prim)) { MS_LOG(INFO) << "Parallel don't care node: " << prim->name(); return false; @@ -380,10 +379,8 @@ bool IsParallelCareNode(const CNodePtr& cnode) { if (prim->name() == GET_NEXT) { return true; } - if ((prim->name() == CAST)) { - if ((!attrs.count(STRATEGY)) && (cnode->operator_info() == nullptr)) { - return false; - } + if ((prim->name() == CAST) && (cnode->operator_info() == nullptr)) { + return false; } return cnode->in_forward_flag(); @@ -654,6 +651,14 @@ LossNodeInfo GetLossNodeInfo(const AnfNodePtr& loss_node) { LossNodeInfo node_info; + // return -> cast + auto pre_cnode = pre_node->cast(); + MS_EXCEPTION_IF_NULL(pre_cnode); + auto pre_prim = GetValueNode(pre_cnode->input(0)); + if (pre_prim->name() == CAST && pre_cnode->operator_info() == nullptr) { + pre_node = pre_cnode->input(1); + } + // return -> loss if (pre_node == loss_node) { node_info.has_tuple_getitem = false; @@ -1948,6 +1953,14 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) { MS_EXCEPTION_IF_NULL(current_value); PrimitivePtr current_prim = current_value->value()->cast(); MS_EXCEPTION_IF_NULL(current_prim); + + // return -> cast + if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) { + pre_cnode = pre_cnode->input(1)->cast(); + MS_EXCEPTION_IF_NULL(pre_cnode); + current_prim = GetValueNode(pre_cnode->input(0)); + } + // notice: the GetNext op has not input if (INVALID_LOSS_OPS.find(current_prim->name()) != INVALID_LOSS_OPS.end()) { MS_LOG(INFO) << "The loss is: " << current_prim->name(); diff --git a/tests/ut/python/parallel/test_element_wise_function.py b/tests/ut/python/parallel/test_element_wise_function.py index dfcebdc5ab..2eb3a22ed2 100644 --- a/tests/ut/python/parallel/test_element_wise_function.py +++ b/tests/ut/python/parallel/test_element_wise_function.py @@ -192,7 +192,6 @@ def test_cast_before_mirror(): net = GradWrap(NetWithLoss(Net(strategy1))) context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") - x = Tensor(np.ones([128, 32]), dtype=ms.float32) y = Tensor(np.ones([32, 64]), dtype=ms.float32) b = Tensor(np.ones([64, 64]), dtype=ms.float16) @@ -217,7 +216,6 @@ def test_cast_before_mirror1(): net = GradWrap(NetWithLoss(Net(strategy1))) context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") - x = Tensor(np.ones([128, 32]), dtype=ms.float16) y = Tensor(np.ones([32, 64]), dtype=ms.float16) b = Tensor(np.ones([64, 64]), dtype=ms.float32) @@ -242,7 +240,6 @@ def test_cast_before_mirror2(): net = GradWrap(NetWithLoss(Net(strategy1))) context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") - x = Tensor(np.ones([128, 32]), dtype=ms.float16) y = Tensor(np.ones([32, 64]), dtype=ms.float16) b = Tensor(np.ones([64, 64]), dtype=ms.float32) @@ -267,8 +264,36 @@ def test_cast_before_mirror3(): net = GradWrap(NetWithLoss(Net(strategy1))) context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") - x = Tensor(np.ones([128, 32]), dtype=ms.float16) y = Tensor(np.ones([32, 64]), dtype=ms.float16) b = Tensor(np.ones([64, 64]), dtype=ms.float32) _executor.compile(net, x, y, b) + + +def test_mul_two_cast(): + class Net(nn.Cell): + def __init__(self, strategy1, strategy2, strategy3): + super().__init__() + self.mul = P.Mul().set_strategy(strategy1) + self.mul2 = P.Mul().set_strategy(strategy2) + self.cast = P.Cast().set_strategy(strategy3) + self.cast2 = P.Cast().set_strategy(strategy3) + + def construct(self, x, y, b): + out = self.mul(x, y) + out = self.mul2(out, b) + out = self.cast(out, ms.int32) + out = self.cast2(out, ms.bool_) + return out + + context.set_auto_parallel_context(device_num=8, global_rank=0) + strategy1 = ((2, 2), (2, 2)) + strategy2 = ((8, 1), (8, 1)) + strategy3 = ((8, 1), ) + net = GradWrap(Net(strategy1, strategy2, strategy3)) + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") + + x = Tensor(np.ones([128, 32]), dtype=ms.float32) + y = Tensor(np.ones([128, 32]), dtype=ms.float32) + b = Tensor(np.ones([128, 32]), dtype=ms.float32) + _executor.compile(net, x, y, b) From 2c751b75b5fcba5de0967eb04f2ec35f1112428c Mon Sep 17 00:00:00 2001 From: jojobugfree Date: Tue, 31 Mar 2020 17:02:25 +0800 Subject: [PATCH 040/227] remove cce dependency --- mindspore/ccsrc/CMakeLists.txt | 3 --- mindspore/ccsrc/device/ascend/tasksink/task_generator.h | 3 --- mindspore/ccsrc/kernel/CMakeLists.txt | 3 +-- 3 files changed, 1 insertion(+), 8 deletions(-) diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt index 72ad4d7b09..1c684b6736 100644 --- a/mindspore/ccsrc/CMakeLists.txt +++ b/mindspore/ccsrc/CMakeLists.txt @@ -185,7 +185,6 @@ if(ENABLE_D) "device/ascend/*.cc" "device/ascend/profiling/*.cc" "device/ascend/tasksink/*.cc" - "kernel/akg/cce/*.cc" "device/kernel_adjust.cc" "kernel/kernel_fusion.cc" "kernel/tbe/*.cc" @@ -361,7 +360,6 @@ if(ENABLE_GPU) ${TVM_DIR}/src/node/*.cc ${TVM_DIR}/src/schedule/*.cc ${TVM_DIR}/src/runtime/*.cc - ${TVM_DIR}/src/runtime/cce/*.cc ${TVM_DIR}/src/runtime/vm/*.cc ${TVM_DIR}/src/runtime/vm/profiler/*.cc ${TVM_DIR}/src/codegen/stackvm/*.cc) @@ -379,7 +377,6 @@ if(ENABLE_GPU) file(GLOB RUNTIME_SRCS ${TVM_DIR}/src/runtime/*.cc - ${TVM_DIR}/src/runtime/cce/*.cc ${TVM_DIR}/src/runtime/vm/*.cc ${TVM_DIR}/src/runtime/stub/*.cc ${TVM_DIR}/src/runtime/stackvm/*.cc) diff --git a/mindspore/ccsrc/device/ascend/tasksink/task_generator.h b/mindspore/ccsrc/device/ascend/tasksink/task_generator.h index 4d1fba9db4..0c56fcc744 100644 --- a/mindspore/ccsrc/device/ascend/tasksink/task_generator.h +++ b/mindspore/ccsrc/device/ascend/tasksink/task_generator.h @@ -22,9 +22,6 @@ #include #include #include -#include "cce/aicpu_engine_struct.h" -#include "cce/taskdown_api.h" -#include "cce/fwk_adpt_struct.h" #include "device/kernel_runtime.h" #include "ir/anf.h" #include "kernel/kernel.h" diff --git a/mindspore/ccsrc/kernel/CMakeLists.txt b/mindspore/ccsrc/kernel/CMakeLists.txt index 4cd60b591c..9c5e2c1890 100644 --- a/mindspore/ccsrc/kernel/CMakeLists.txt +++ b/mindspore/ccsrc/kernel/CMakeLists.txt @@ -25,11 +25,10 @@ endif() if(ENABLE_D) file(GLOB_RECURSE _D_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} - "akg/cce/*.cc" "tbe/*.cc" "aicpu/*.cc" "mng/*.cc" "hccl/*.cc" ) target_sources(_mindspore_kernel_obj PRIVATE ${_D_SRC_LIST}) -endif() \ No newline at end of file +endif() From 7f6cb98779d43892f24d0bb0d06816cd830fc610 Mon Sep 17 00:00:00 2001 From: wukesong Date: Sat, 28 Mar 2020 18:26:56 +0800 Subject: [PATCH 041/227] add lenet & alexnet in master branch --- example/alexnet_cifar10/config.py | 32 ++++++ example/alexnet_cifar10/dataset.py | 54 ++++++++++ example/alexnet_cifar10/eval.py | 58 +++++++++++ example/alexnet_cifar10/train.py | 58 +++++++++++ example/lenet/main.py | 125 ----------------------- example/{lenet => lenet_mnist}/config.py | 4 +- example/lenet_mnist/dataset.py | 60 +++++++++++ example/lenet_mnist/eval.py | 64 ++++++++++++ example/lenet_mnist/train.py | 58 +++++++++++ mindspore/model_zoo/alexnet.py | 73 +++++++++++++ mindspore/model_zoo/lenet.py | 5 +- 11 files changed, 461 insertions(+), 130 deletions(-) create mode 100644 example/alexnet_cifar10/config.py create mode 100644 example/alexnet_cifar10/dataset.py create mode 100644 example/alexnet_cifar10/eval.py create mode 100644 example/alexnet_cifar10/train.py delete mode 100644 example/lenet/main.py rename example/{lenet => lenet_mnist}/config.py (93%) create mode 100644 example/lenet_mnist/dataset.py create mode 100644 example/lenet_mnist/eval.py create mode 100644 example/lenet_mnist/train.py create mode 100644 mindspore/model_zoo/alexnet.py diff --git a/example/alexnet_cifar10/config.py b/example/alexnet_cifar10/config.py new file mode 100644 index 0000000000..9edfec2b60 --- /dev/null +++ b/example/alexnet_cifar10/config.py @@ -0,0 +1,32 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +network config setting, will be used in train.py +""" + +from easydict import EasyDict as edict + +alexnet_cfg = edict({ + 'num_classes': 10, + 'learning_rate': 0.002, + 'momentum': 0.9, + 'epoch_size': 1, + 'batch_size': 32, + 'buffer_size': 1000, + 'image_height': 227, + 'image_width': 227, + 'save_checkpoint_steps': 1562, + 'keep_checkpoint_max': 10, +}) diff --git a/example/alexnet_cifar10/dataset.py b/example/alexnet_cifar10/dataset.py new file mode 100644 index 0000000000..d62ed2852d --- /dev/null +++ b/example/alexnet_cifar10/dataset.py @@ -0,0 +1,54 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +Produce the dataset +""" + +from config import alexnet_cfg as cfg +import mindspore.dataset as ds +import mindspore.dataset.transforms.c_transforms as C +import mindspore.dataset.transforms.vision.c_transforms as CV +from mindspore.common import dtype as mstype + + +def create_dataset(data_path, batch_size=32, repeat_size=1, status="train"): + """ + create dataset for train or test + """ + cifar_ds = ds.Cifar10Dataset(data_path) + rescale = 1.0 / 255.0 + shift = 0.0 + + resize_op = CV.Resize((cfg.image_height, cfg.image_width)) + rescale_op = CV.Rescale(rescale, shift) + normalize_op = CV.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) + if status == "train": + random_crop_op = CV.RandomCrop([32, 32], [4, 4, 4, 4]) + random_horizontal_op = CV.RandomHorizontalFlip() + channel_swap_op = CV.HWC2CHW() + typecast_op = C.TypeCast(mstype.int32) + cifar_ds = cifar_ds.map(input_columns="label", operations=typecast_op) + if status == "train": + cifar_ds = cifar_ds.map(input_columns="image", operations=random_crop_op) + cifar_ds = cifar_ds.map(input_columns="image", operations=random_horizontal_op) + cifar_ds = cifar_ds.map(input_columns="image", operations=resize_op) + cifar_ds = cifar_ds.map(input_columns="image", operations=rescale_op) + cifar_ds = cifar_ds.map(input_columns="image", operations=normalize_op) + cifar_ds = cifar_ds.map(input_columns="image", operations=channel_swap_op) + + cifar_ds = cifar_ds.shuffle(buffer_size=cfg.buffer_size) + cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True) + cifar_ds = cifar_ds.repeat(repeat_size) + return cifar_ds diff --git a/example/alexnet_cifar10/eval.py b/example/alexnet_cifar10/eval.py new file mode 100644 index 0000000000..be71e33995 --- /dev/null +++ b/example/alexnet_cifar10/eval.py @@ -0,0 +1,58 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +######################## eval alexnet example ######################## +eval alexnet according to model file: +python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt +""" + +import argparse +from config import alexnet_cfg as cfg +from dataset import create_dataset +import mindspore.nn as nn +from mindspore import context +from mindspore.model_zoo.alexnet import AlexNet +from mindspore.train.serialization import load_checkpoint, load_param_into_net +from mindspore.train import Model +from mindspore.nn.metrics import Accuracy + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='MindSpore AlexNet Example') + parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'], + help='device where the code will be implemented (default: Ascend)') + parser.add_argument('--data_path', type=str, default="./", help='path where the dataset is saved') + parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\ + path where the trained ckpt file') + parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True') + args = parser.parse_args() + + context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False) + + network = AlexNet(cfg.num_classes) + loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + repeat_size = cfg.epoch_size + opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum) + model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()}) # test + + print("============== Starting Testing ==============") + param_dict = load_checkpoint(args.ckpt_path) + load_param_into_net(network, param_dict) + ds_eval = create_dataset(args.data_path, + cfg.batch_size, + 1, + "test") + acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode) + print("============== Accuracy:{} ==============".format(acc)) diff --git a/example/alexnet_cifar10/train.py b/example/alexnet_cifar10/train.py new file mode 100644 index 0000000000..b97843902d --- /dev/null +++ b/example/alexnet_cifar10/train.py @@ -0,0 +1,58 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +######################## train alexnet example ######################## +train alexnet and get network model files(.ckpt) : +python train.py --data_path /YourDataPath +""" + +import argparse +from config import alexnet_cfg as cfg +from dataset import create_dataset +import mindspore.nn as nn +from mindspore import context +from mindspore.train import Model +from mindspore.nn.metrics import Accuracy +from mindspore.model_zoo.alexnet import AlexNet +from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='MindSpore AlexNet Example') + parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'], + help='device where the code will be implemented (default: Ascend)') + parser.add_argument('--data_path', type=str, default="./", help='path where the dataset is saved') + parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\ + path where the trained ckpt file') + parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True') + args = parser.parse_args() + + context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False) + + network = AlexNet(cfg.num_classes) + loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum) + model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()}) # test + + print("============== Starting Training ==============") + ds_train = create_dataset(args.data_path, + cfg.batch_size, + cfg.epoch_size, + "train") + config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, + keep_checkpoint_max=cfg.keep_checkpoint_max) + ckpoint_cb = ModelCheckpoint(prefix="checkpoint_alexnet", directory=args.ckpt_path, config=config_ck) + model.train(cfg.epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor()], + dataset_sink_mode=args.dataset_sink_mode) diff --git a/example/lenet/main.py b/example/lenet/main.py deleted file mode 100644 index fe20264d95..0000000000 --- a/example/lenet/main.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright 2020 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -""" -######################## train and test lenet example ######################## -1. train lenet and get network model files(.ckpt) : -python main.py --data_path /home/workspace/mindspore_dataset/Tutorial_Network/Lenet/MNIST_Data - -2. test lenet according to model file: -python main.py --data_path /home/workspace/mindspore_dataset/Tutorial_Network/Lenet/MNIST_Data - --mode test --ckpt_path checkpoint_lenet_1-1_1875.ckpt -""" -import os -import argparse -from config import mnist_cfg as cfg - -import mindspore.dataengine as de -import mindspore.nn as nn -from mindspore.model_zoo.lenet import LeNet5 -from mindspore import context, Tensor -from mindspore.train.serialization import load_checkpoint, load_param_into_net -from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor -from mindspore.train import Model -import mindspore.ops.operations as P -import mindspore.transforms.c_transforms as C -from mindspore.transforms import Inter -from mindspore.nn.metrics import Accuracy -from mindspore.ops import functional as F -from mindspore.common import dtype as mstype - - -class CrossEntropyLoss(nn.Cell): - """ - Define loss for network - """ - def __init__(self): - super(CrossEntropyLoss, self).__init__() - self.cross_entropy = P.SoftmaxCrossEntropyWithLogits() - self.mean = P.ReduceMean() - self.one_hot = P.OneHot() - self.on_value = Tensor(1.0, mstype.float32) - self.off_value = Tensor(0.0, mstype.float32) - - def construct(self, logits, label): - label = self.one_hot(label, F.shape(logits)[1], self.on_value, self.off_value) - loss = self.cross_entropy(logits, label)[0] - loss = self.mean(loss, (-1,)) - return loss - -def create_dataset(data_path, batch_size=32, repeat_size=1, - num_parallel_workers=1): - """ - create dataset for train or test - """ - # define dataset - ds1 = de.MnistDataset(data_path) - - # apply map operations on images - ds1 = ds1.map(input_columns="label", operations=C.TypeCast(mstype.int32)) - ds1 = ds1.map(input_columns="image", operations=C.Resize((cfg.image_height, cfg.image_width), - interpolation=Inter.LINEAR), - num_parallel_workers=num_parallel_workers) - ds1 = ds1.map(input_columns="image", operations=C.Rescale(1 / 0.3081, -1 * 0.1307 / 0.3081), - num_parallel_workers=num_parallel_workers) - ds1 = ds1.map(input_columns="image", operations=C.Rescale(1.0 / 255.0, 0.0), - num_parallel_workers=num_parallel_workers) - ds1 = ds1.map(input_columns="image", operations=C.HWC2CHW(), num_parallel_workers=num_parallel_workers) - - # apply DatasetOps - ds1 = ds1.shuffle(buffer_size=cfg.buffer_size) # 10000 as in LeNet train script - ds1 = ds1.batch(batch_size, drop_remainder=True) - ds1 = ds1.repeat(repeat_size) - - return ds1 - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description='MindSpore MNIST Example') - parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'], - help='device where the code will be implemented (default: Ascend)') - parser.add_argument('--mode', type=str, default="train", choices=['train', 'test'], - help='implement phase, set to train or test') - parser.add_argument('--data_path', type=str, default="./MNIST_Data", - help='path where the dataset is saved') - parser.add_argument('--ckpt_path', type=str, default="", help='if mode is test, must provide\ - path where the trained ckpt file') - - args = parser.parse_args() - - context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) - - network = LeNet5(cfg.num_classes) - network.set_train() - # net_loss = nn.SoftmaxCrossEntropyWithLogits() # support this loss soon - net_loss = CrossEntropyLoss() - net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) - config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, - keep_checkpoint_max=cfg.keep_checkpoint_max) - ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck) - model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) - - if args.mode == 'train': # train - ds = create_dataset(os.path.join(args.data_path, args.mode), batch_size=cfg.batch_size, - repeat_size=cfg.epoch_size) - print("============== Starting Training ==============") - model.train(cfg['epoch_size'], ds, callbacks=[ckpoint_cb, LossMonitor()], dataset_sink_mode=False) - elif args.mode == 'test': # test - print("============== Starting Testing ==============") - param_dict = load_checkpoint(args.ckpt_path) - load_param_into_net(network, param_dict) - ds_eval = create_dataset(os.path.join(args.data_path, "test"), 32, 1) - acc = model.eval(ds_eval, dataset_sink_mode=False) - print("============== Accuracy:{} ==============".format(acc)) - else: - raise RuntimeError('mode should be train or test, rather than {}'.format(args.mode)) diff --git a/example/lenet/config.py b/example/lenet_mnist/config.py similarity index 93% rename from example/lenet/config.py rename to example/lenet_mnist/config.py index 3ad78f3bde..9a13ae535f 100644 --- a/example/lenet/config.py +++ b/example/lenet_mnist/config.py @@ -13,8 +13,9 @@ # limitations under the License. # ============================================================================ """ -network config setting, will be used in main.py +network config setting, will be used in train.py """ + from easydict import EasyDict as edict mnist_cfg = edict({ @@ -23,7 +24,6 @@ mnist_cfg = edict({ 'momentum': 0.9, 'epoch_size': 1, 'batch_size': 32, - 'repeat_size': 1, 'buffer_size': 1000, 'image_height': 32, 'image_width': 32, diff --git a/example/lenet_mnist/dataset.py b/example/lenet_mnist/dataset.py new file mode 100644 index 0000000000..cef6973483 --- /dev/null +++ b/example/lenet_mnist/dataset.py @@ -0,0 +1,60 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +Produce the dataset +""" + +import mindspore.dataset as ds +import mindspore.dataset.transforms.vision.c_transforms as CV +import mindspore.dataset.transforms.c_transforms as C +from mindspore.dataset.transforms.vision import Inter +from mindspore.common import dtype as mstype + + +def create_dataset(data_path, batch_size=32, repeat_size=1, + num_parallel_workers=1): + """ + create dataset for train or test + """ + # define dataset + mnist_ds = ds.MnistDataset(data_path) + + resize_height, resize_width = 32, 32 + rescale = 1.0 / 255.0 + shift = 0.0 + rescale_nml = 1 / 0.3081 + shift_nml = -1 * 0.1307 / 0.3081 + + # define map operations + resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode + rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) + rescale_op = CV.Rescale(rescale, shift) + hwc2chw_op = CV.HWC2CHW() + type_cast_op = C.TypeCast(mstype.int32) + + # apply map operations on images + mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers) + + # apply DatasetOps + buffer_size = 10000 + mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script + mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) + mnist_ds = mnist_ds.repeat(repeat_size) + + return mnist_ds diff --git a/example/lenet_mnist/eval.py b/example/lenet_mnist/eval.py new file mode 100644 index 0000000000..3473a99532 --- /dev/null +++ b/example/lenet_mnist/eval.py @@ -0,0 +1,64 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +######################## eval lenet example ######################## +eval lenet according to model file: +python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt +""" + +import os +import argparse +from dataset import create_dataset +from config import mnist_cfg as cfg +import mindspore.nn as nn +from mindspore.model_zoo.lenet import LeNet5 +from mindspore import context +from mindspore.train.serialization import load_checkpoint, load_param_into_net +from mindspore.train.callback import ModelCheckpoint, CheckpointConfig +from mindspore.train import Model +from mindspore.nn.metrics import Accuracy + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='MindSpore MNIST Example') + parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'], + help='device where the code will be implemented (default: Ascend)') + parser.add_argument('--data_path', type=str, default="./MNIST_Data", + help='path where the dataset is saved') + parser.add_argument('--ckpt_path', type=str, default="", help='if mode is test, must provide\ + path where the trained ckpt file') + parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True') + + args = parser.parse_args() + + context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False) + + network = LeNet5(cfg.num_classes) + net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + repeat_size = cfg.epoch_size + net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) + config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, + keep_checkpoint_max=cfg.keep_checkpoint_max) + ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck) + model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) + + print("============== Starting Testing ==============") + param_dict = load_checkpoint(args.ckpt_path) + load_param_into_net(network, param_dict) + ds_eval = create_dataset(os.path.join(args.data_path, "test"), + cfg.batch_size, + 1) + acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode) + print("============== Accuracy:{} ==============".format(acc)) diff --git a/example/lenet_mnist/train.py b/example/lenet_mnist/train.py new file mode 100644 index 0000000000..2fa8d3c27f --- /dev/null +++ b/example/lenet_mnist/train.py @@ -0,0 +1,58 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +######################## train lenet example ######################## +train lenet and get network model files(.ckpt) : +python train.py --data_path /YourDataPath +""" + +import os +import argparse +from config import mnist_cfg as cfg +from dataset import create_dataset +import mindspore.nn as nn +from mindspore.model_zoo.lenet import LeNet5 +from mindspore import context +from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor +from mindspore.train import Model +from mindspore.nn.metrics import Accuracy + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='MindSpore MNIST Example') + parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'], + help='device where the code will be implemented (default: Ascend)') + parser.add_argument('--data_path', type=str, default="./MNIST_Data", + help='path where the dataset is saved') + parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True') + + args = parser.parse_args() + + context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False) + + network = LeNet5(cfg.num_classes) + net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) + config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, + keep_checkpoint_max=cfg.keep_checkpoint_max) + ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck) + model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) + + ds_train = create_dataset(os.path.join(args.data_path, "train"), + cfg.batch_size, + cfg.epoch_size) + print("============== Starting Training ==============") + model.train(cfg['epoch_size'], ds_train, callbacks=[ckpoint_cb, LossMonitor()], + dataset_sink_mode=args.dataset_sink_mode) diff --git a/mindspore/model_zoo/alexnet.py b/mindspore/model_zoo/alexnet.py new file mode 100644 index 0000000000..8cd316229c --- /dev/null +++ b/mindspore/model_zoo/alexnet.py @@ -0,0 +1,73 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +"""Alexnet.""" +import mindspore.nn as nn +from mindspore.common.initializer import TruncatedNormal + +def conv(in_channels, out_channels, kernel_size, stride=1, padding=0, pad_mode="valid"): + weight = weight_variable() + return nn.Conv2d(in_channels, out_channels, + kernel_size=kernel_size, stride=stride, padding=padding, + weight_init=weight, has_bias=False, pad_mode=pad_mode) + +def fc_with_initialize(input_channels, out_channels): + weight = weight_variable() + bias = weight_variable() + return nn.Dense(input_channels, out_channels, weight, bias) + +def weight_variable(): + return TruncatedNormal(0.02) # 0.02 + + +class AlexNet(nn.Cell): + """ + Alexnet + """ + def __init__(self, num_classes=10): + super(AlexNet, self).__init__() + self.batch_size = 32 + self.conv1 = conv(3, 96, 11, stride=4) + self.conv2 = conv(96, 256, 5, pad_mode="same") + self.conv3 = conv(256, 384, 3, pad_mode="same") + self.conv4 = conv(384, 384, 3, pad_mode="same") + self.conv5 = conv(384, 256, 3, pad_mode="same") + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2) + self.flatten = nn.Flatten() + self.fc1 = fc_with_initialize(6*6*256, 4096) + self.fc2 = fc_with_initialize(4096, 4096) + self.fc3 = fc_with_initialize(4096, num_classes) + + def construct(self, x): + x = self.conv1(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.conv2(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.conv3(x) + x = self.relu(x) + x = self.conv4(x) + x = self.relu(x) + x = self.conv5(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.flatten(x) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.relu(x) + x = self.fc3(x) + return x diff --git a/mindspore/model_zoo/lenet.py b/mindspore/model_zoo/lenet.py index a22eef1a96..6e39c439bf 100644 --- a/mindspore/model_zoo/lenet.py +++ b/mindspore/model_zoo/lenet.py @@ -13,7 +13,6 @@ # limitations under the License. # ============================================================================ """LeNet.""" -import mindspore.ops.operations as P import mindspore.nn as nn from mindspore.common.initializer import TruncatedNormal @@ -62,7 +61,7 @@ class LeNet5(nn.Cell): self.fc3 = fc_with_initialize(84, self.num_class) self.relu = nn.ReLU() self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) - self.reshape = P.Reshape() + self.flatten = nn.Flatten() def construct(self, x): x = self.conv1(x) @@ -71,7 +70,7 @@ class LeNet5(nn.Cell): x = self.conv2(x) x = self.relu(x) x = self.max_pool2d(x) - x = self.reshape(x, (self.batch_size, -1)) + x = self.flatten(x) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) From fb00dae17a9ac0a2ef61ed9b63f8855a89962e3a Mon Sep 17 00:00:00 2001 From: Xiaoda Zhang Date: Tue, 31 Mar 2020 14:55:31 +0800 Subject: [PATCH 042/227] change star elimination: remove some redundant and checking works --- .../ccsrc/parallel/auto_parallel/graph_costmodel.cc | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc index d30522c2fe..292cc4f5f0 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc @@ -1210,19 +1210,16 @@ void CostGraph::CreateStarEliminationCostList(std::vector> MS_EXCEPTION_IF_NULL(succ_node_cost); for (auto& succ_edge_cost : succ_edge_clist) { MS_EXCEPTION_IF_NULL(succ_edge_cost); - if ((succ_node_cost->memory_cost_ < DEVICE_MEMORY_CAPACITY) && - (succ_edge_cost->memory_cost_ < DEVICE_MEMORY_CAPACITY)) { - succ_nodes_stras[k] = succ_node_stra; - succ_edges_costs[k] = succ_edge_cost; - succ_nodes_costs[k] = succ_node_cost; - recursive(k + 1); - } + succ_nodes_stras[k] = succ_node_stra; + succ_edges_costs[k] = succ_edge_cost; + succ_nodes_costs[k] = succ_node_cost; + recursive(k + 1); } } } }; - recursive(0); + recursive(1); } std::vector> CostGraph::EliminationStar(const OperatorInfoPtr& merged_op) { From 3b4d8fce26e4e8258fe5ad089a67fd66403a29f9 Mon Sep 17 00:00:00 2001 From: VectorSL Date: Wed, 1 Apr 2020 11:49:37 +0800 Subject: [PATCH 043/227] update tests/st/ops/gpu/test_tensoradd.py. fix pytest.mark for testcase --- tests/st/ops/gpu/test_tensoradd.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/tests/st/ops/gpu/test_tensoradd.py b/tests/st/ops/gpu/test_tensoradd.py index a60f5dcf30..23c23f12d2 100644 --- a/tests/st/ops/gpu/test_tensoradd.py +++ b/tests/st/ops/gpu/test_tensoradd.py @@ -25,10 +25,6 @@ from mindspore.common.parameter import Parameter context.set_context(device_target='GPU') - -@pytest.mark.level0 -@pytest.mark.platform_x86_gpu_training -@pytest.mark.env_onecard class TensroAdd(nn.Cell): def __init__(self): super(TensroAdd, self).__init__() @@ -61,7 +57,9 @@ class TensroAdd(nn.Cell): self.add(self.x, self.y), self.add(self.x1, self.y1), self.add(self.x2, self.y2), self.add(self.x3, self.y3)) - +@pytest.mark.level0 +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard def test_TensroAdd(): add = TensroAdd() output = add() From fe1b73584d04253deab1e753fdf52093c03528a7 Mon Sep 17 00:00:00 2001 From: kswang Date: Tue, 31 Mar 2020 21:25:48 +0800 Subject: [PATCH 044/227] add cpu st lenet --- ...{test_cpu_network.py => test_cpu_lenet.py} | 67 ++++++++++--------- 1 file changed, 34 insertions(+), 33 deletions(-) rename tests/st/networks/{test_cpu_network.py => test_cpu_lenet.py} (59%) diff --git a/tests/st/networks/test_cpu_network.py b/tests/st/networks/test_cpu_lenet.py similarity index 59% rename from tests/st/networks/test_cpu_network.py rename to tests/st/networks/test_cpu_lenet.py index 6745a7626b..a3105721d3 100644 --- a/tests/st/networks/test_cpu_network.py +++ b/tests/st/networks/test_cpu_lenet.py @@ -12,25 +12,44 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ -""" -Function: - test network -Usage: - python test_network_main.py --net lenet --target Davinci -""" -import os -import time import pytest -import numpy as np -import argparse -import mindspore.nn as nn -from mindspore.common.tensor import Tensor from mindspore.nn import TrainOneStepCell, WithLossCell import mindspore.context as context from mindspore.nn.optim import Momentum -from models.lenet import LeNet -from models.resnetv1_5 import resnet50 -from models.alexnet import AlexNet +import numpy as np +import mindspore.nn as nn +from mindspore.ops import operations as P +from mindspore import Tensor + +class LeNet(nn.Cell): + def __init__(self): + super(LeNet, self).__init__() + self.relu = P.ReLU() + self.batch_size = 32 + + self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid') + self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid') + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + self.reshape = P.Reshape() + self.fc1 = nn.Dense(400, 120) + self.fc2 = nn.Dense(120, 84) + self.fc3 = nn.Dense(84, 10) + + def construct(self, input_x): + output = self.conv1(input_x) + output = self.relu(output) + output = self.pool(output) + output = self.conv2(output) + output = self.relu(output) + output = self.pool(output) + output = self.reshape(output, (self.batch_size, -1)) + output = self.fc1(output) + output = self.relu(output) + output = self.fc2(output) + output = self.relu(output) + output = self.fc3(output) + return output + context.set_context(mode=context.GRAPH_MODE, device_target="CPU") def train(net, data, label): @@ -48,15 +67,6 @@ def train(net, data, label): print("+++++++++++++++++++++++++++") assert res -@pytest.mark.level0 -@pytest.mark.platform_x86_cpu -@pytest.mark.env_onecard -def test_resnet50(): - data = Tensor(np.ones([32, 3 ,224, 224]).astype(np.float32) * 0.01) - label = Tensor(np.ones([32]).astype(np.int32)) - net = resnet50(32, 10) - train(net, data, label) - @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard @@ -65,12 +75,3 @@ def test_lenet(): label = Tensor(np.ones([32]).astype(np.int32)) net = LeNet() train(net, data, label) - -@pytest.mark.level0 -@pytest.mark.platform_x86_cpu -@pytest.mark.env_onecard -def test_alexnet(): - data = Tensor(np.ones([32, 3 ,227, 227]).astype(np.float32) * 0.01) - label = Tensor(np.ones([32]).astype(np.int32)) - net = AlexNet() - train(net, data, label) From c44e12713bfbd29efb2f1f215005a08e532154e2 Mon Sep 17 00:00:00 2001 From: yangzhenzhang <285824651@qq.com> Date: Tue, 31 Mar 2020 15:40:43 +0800 Subject: [PATCH 045/227] add parallel ops for neg and batchmatmul --- mindspore/ccsrc/parallel/dynamic_creator.h | 2 + .../ccsrc/parallel/ops_info/activation_info.h | 7 ++ .../ccsrc/parallel/ops_info/matmul_info.h | 8 ++ mindspore/ccsrc/parallel/ops_info/ops_utils.h | 2 + .../ccsrc/parallel/step_auto_parallel.cc | 2 + tests/ut/python/parallel/test_batch_matmul.py | 93 +++++++++++++++++++ tests/ut/python/parallel/test_neg.py | 84 +++++++++++++++++ 7 files changed, 198 insertions(+) create mode 100644 tests/ut/python/parallel/test_batch_matmul.py create mode 100644 tests/ut/python/parallel/test_neg.py diff --git a/mindspore/ccsrc/parallel/dynamic_creator.h b/mindspore/ccsrc/parallel/dynamic_creator.h index 59b8722435..e6e1b41d76 100644 --- a/mindspore/ccsrc/parallel/dynamic_creator.h +++ b/mindspore/ccsrc/parallel/dynamic_creator.h @@ -123,6 +123,8 @@ REGISTER(ReLUInfo); REGISTER(GatherV2Info); REGISTER(SqrtInfo); REGISTER(GetNextInfo); +REGISTER(NegInfo); +REGISTER(BatchMatMulInfo); } // namespace parallel } // namespace mindspore diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.h b/mindspore/ccsrc/parallel/ops_info/activation_info.h index d8de19b328..d05f8743b0 100644 --- a/mindspore/ccsrc/parallel/ops_info/activation_info.h +++ b/mindspore/ccsrc/parallel/ops_info/activation_info.h @@ -167,6 +167,13 @@ class SqrtInfo : public ActivationOther { : ActivationOther(name, inputs_shape, outputs_shape, attrs) {} ~SqrtInfo() override = default; }; + +class NegInfo : public ActivationOther { + public: + NegInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) + : ActivationOther(name, inputs_shape, outputs_shape, attrs) {} + ~NegInfo() override = default; +}; } // namespace parallel } // namespace mindspore #endif // MINDSPORE_CCSRC_OPTIMIZER_OPS_INFO_PARALLEL_ACTIVATION_INFO_H_ diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.h b/mindspore/ccsrc/parallel/ops_info/matmul_info.h index c9feae55b6..b434e4522d 100644 --- a/mindspore/ccsrc/parallel/ops_info/matmul_info.h +++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.h @@ -87,6 +87,14 @@ class MatMulInfo : public MatMul { : MatMul(name, inputs_shape, outputs_shape, attrs) {} ~MatMulInfo() override = default; }; + +class BatchMatMulInfo : public MatMul { + public: + BatchMatMulInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, + const PrimitiveAttrs& attrs) + : MatMul(name, inputs_shape, outputs_shape, attrs) {} + ~BatchMatMulInfo() override = default; +}; } // namespace parallel } // namespace mindspore #endif // MINDSPORE_CCSRC_PARALLEL_OPS_INFO_MATMUL_INFO_H_ diff --git a/mindspore/ccsrc/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/parallel/ops_info/ops_utils.h index a25200c3c1..2b8fc0ee3f 100644 --- a/mindspore/ccsrc/parallel/ops_info/ops_utils.h +++ b/mindspore/ccsrc/parallel/ops_info/ops_utils.h @@ -188,6 +188,8 @@ constexpr char SQRT[] = "Sqrt"; constexpr char ASSIGN[] = "Assign"; constexpr char GET_NEXT[] = "GetNext"; constexpr char SQUEEZE[] = "Squeeze"; +constexpr char Neg[] = "Neg"; +constexpr char BATCH_MATMUL[] = "BatchMatMul"; // Parallel don't care constexpr char TUPLE_GETITEM[] = "tuple_getitem"; diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc index 400e985f18..883e0d9f15 100644 --- a/mindspore/ccsrc/parallel/step_auto_parallel.cc +++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc @@ -101,6 +101,8 @@ std::vector splittable_op_ = {MATMUL, SQRT, GET_NEXT, CAST, + Neg, + BATCH_MATMUL, SQUEEZE}; std::vector elementwise_op_ = {ACTIVATION, GELU, TANH, SOFTMAX, LOG_SOFTMAX, RELU, SQRT, diff --git a/tests/ut/python/parallel/test_batch_matmul.py b/tests/ut/python/parallel/test_batch_matmul.py new file mode 100644 index 0000000000..88ba818c91 --- /dev/null +++ b/tests/ut/python/parallel/test_batch_matmul.py @@ -0,0 +1,93 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import mindspore as ms +from mindspore import context, Tensor, Parameter +from mindspore.nn import Cell, TrainOneStepCell, Momentum +from mindspore.ops import operations as P +from mindspore.common.api import _executor + + +class Net(Cell): + def __init__(self, mul_weight, batch_matmul_weight, transpose_b=False, strategy1=None, strategy2=None): + super().__init__() + self.mul = P.Mul().set_strategy(strategy1) + self.batch_matmul = P.BatchMatMul(transpose_b=transpose_b).set_strategy(strategy2) + self.mul_weight = Parameter(mul_weight, "w1") + self.batch_matmul_weight = Parameter(batch_matmul_weight, "w2") + + def construct(self, x, b): + out = self.mul(x, self.mul_weight) + out = self.batch_matmul(out, self.batch_matmul_weight) + return out + + +_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32) +_w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32) +_w2 = Tensor(np.ones([128, 32, 32]), dtype=ms.float32) +_b = Tensor(np.ones([128, 64, 16]), dtype=ms.float32) + + +def compile(net): + optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9) + train_net = TrainOneStepCell(net, optimizer) + _executor.compile(train_net, _x, _b) + context.reset_auto_parallel_context() + + +def test_batch_matmul_data_parallel(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((16, 1, 1), (16, 1, 1)) + strategy2 = ((16, 1, 1), (16, 1, 1)) + net = Net(_w1, _w2, False, strategy1, strategy2) + compile(net) + + +def test_batch_matmul_model_parallel(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((1, 1, 1), (1, 1, 1)) + strategy2 = ((1, 1, 1), (1, 1, 16)) + net = Net(_w1, _w2, False, strategy1, strategy2) + compile(net) + + +def test_batch_matmul_hybrid_parallel(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((2, 2, 2), (2, 2, 2)) + strategy2 = ((2, 2, 2), (2, 2, 2)) + net = Net(_w1, _w2, False, strategy1, strategy2) + compile(net) + + +def test_batch_matmul_auto_parallel(): + context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0) + net = Net(_w1, _w2, False) + compile(net) + + +def test_batch_matmul_repeat_calc(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((2, 2, 4), (2, 2, 4)) + strategy2 = ((1, 2, 2), (1, 2, 2)) + net = Net(_w1, _w2, False, strategy1, strategy2) + compile(net) + + +def test_batch_matmul_transpose_b(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((2, 2, 4), (2, 2, 4)) + strategy2 = ((1, 2, 2), (1, 2, 2)) + net = Net(_w1, _w2, True, strategy1, strategy2) + compile(net) diff --git a/tests/ut/python/parallel/test_neg.py b/tests/ut/python/parallel/test_neg.py new file mode 100644 index 0000000000..0e08e8c096 --- /dev/null +++ b/tests/ut/python/parallel/test_neg.py @@ -0,0 +1,84 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import mindspore as ms +from mindspore import context, Tensor, Parameter +from mindspore.nn import Cell, TrainOneStepCell, Momentum +from mindspore.ops import operations as P +from mindspore.common.api import _executor + + +class Net(Cell): + def __init__(self, mul_weight, strategy1=None, strategy2=None): + super().__init__() + self.mul = P.Mul().set_strategy(strategy1) + self.neg = P.Neg().set_strategy(strategy2) + self.mul_weight = Parameter(mul_weight, "w1") + + def construct(self, x, b): + out = self.mul(x, self.mul_weight) + out = self.neg(out) + return out + + +_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32) +_w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32) +_b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32) + + +def compile(net): + optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9) + train_net = TrainOneStepCell(net, optimizer) + _executor.compile(train_net, _x, _b) + context.reset_auto_parallel_context() + + +def test_neg_data_parallel(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((16, 1, 1), (16, 1, 1)) + strategy2 = ((16, 1, 1), ) + net = Net(_w1, strategy1, strategy2) + compile(net) + + +def test_neg_model_parallel(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((1, 1, 16), (1, 1, 16)) + strategy2 = ((1, 1, 16), ) + net = Net(_w1, strategy1, strategy2) + compile(net) + + +def test_neg_hybrid_parallel(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((2, 2, 4), (2, 2, 4)) + strategy2 = ((2, 2, 4), ) + net = Net(_w1, strategy1, strategy2) + compile(net) + + +def test_neg_auto_parallel(): + context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0) + net = Net(_w1) + compile(net) + + +def test_neg_repeat_calc(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((2, 2, 4), (2, 2, 4)) + strategy2 = ((1, 2, 2), ) + net = Net(_w1, strategy1, strategy2) + compile(net) + From 3c1d35295ce54fbbeba46e8c0152793e54f49d71 Mon Sep 17 00:00:00 2001 From: Peilin Wang Date: Mon, 30 Mar 2020 18:25:14 -0400 Subject: [PATCH 046/227] TFReaderOp fix, threads will exit after reading necessary amount of rows changes from yanpanhui 524009: added set_dataset_size and changed get_dataest_size according to ME requirements CI fixes --- .../engine/datasetops/source/tf_reader_op.cc | 41 ++++++++++++++++--- .../engine/datasetops/source/tf_reader_op.h | 1 + mindspore/dataset/engine/datasets.py | 20 ++++++--- 3 files changed, 51 insertions(+), 11 deletions(-) diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc index 16a7700012..c872c02015 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc @@ -105,6 +105,7 @@ TFReaderOp::TFReaderOp(int32_t num_workers, int32_t worker_connector_size, int64 data_schema_(std::move(data_schema)), filename_index_(make_unique()), load_io_block_queue_(true), + load_jagged_connector_(true), num_rows_(0), num_rows_per_shard_(0), equal_rows_per_shard_(equal_rows_per_shard) { @@ -203,6 +204,25 @@ Status TFReaderOp::operator()() { buffer_id++; RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(fetched_buffer))); } else { + // user specified number of rows they want, and we read enough rows + // + // IOBlockQueue thread needs to: + // -stop pushing stuff to IOBlockQueue + // -call PostEndOfEpoch (will send EOE) + // -wait for reset + // + // Worker threads need to: + // -stop reading the file they are currently reading and throw it away + // -keep pulling, but dont read other files (eventually skips all IOBlocks and will get EOE) + // + // Master thread needs to: + // -tell IOBlockQueue thread to stop pushing + // -tell worker threads to stop reading the file tey are currently reading + // -keep pulling until EOE + + // don't think we need a lock for now + load_jagged_connector_ = false; + std::unique_lock lock(load_io_block_queue_mutex_); load_io_block_queue_ = false; } @@ -245,12 +265,14 @@ Status TFReaderOp::WorkerEntry(int32_t worker_id) { while (!io_block->eof()) { if (!io_block->eoe()) { - std::string filename; - RETURN_IF_NOT_OK(io_block->GetFilename(&filename, *filename_index_)); - int64_t start_offset = io_block->GetStartOffset(); - int64_t end_offset = io_block->GetEndOffset(); - RETURN_IF_NOT_OK(LoadFile(filename, start_offset, end_offset, worker_id)); - MS_LOG(INFO) << "TFReader operator worker " << worker_id << " loaded file " << common::SafeCStr(filename) << "."; + if (load_jagged_connector_) { + std::string filename; + RETURN_IF_NOT_OK(io_block->GetFilename(&filename, *filename_index_)); + int64_t start_offset = io_block->GetStartOffset(); + int64_t end_offset = io_block->GetEndOffset(); + RETURN_IF_NOT_OK(LoadFile(filename, start_offset, end_offset, worker_id)); + MS_LOG(INFO) << "TFReader operator worker " << worker_id << " loaded file " << filename << "."; + } } else { std::unique_ptr eoe_buffer = mindspore::make_unique(1, DataBuffer::kDeBFlagEOE); RETURN_IF_NOT_OK(jagged_buffer_connector_->Add(worker_id, std::move(eoe_buffer))); @@ -478,6 +500,10 @@ Status TFReaderOp::LoadFile(const std::string &filename, const int64_t start_off std::unique_ptr new_tensor_table = make_unique(); while (reader.peek() != EOF) { + if (!load_jagged_connector_) { + break; + } + // read length int64_t record_length = 0; (void)reader.read(reinterpret_cast(&record_length), static_cast(sizeof(int64_t))); @@ -599,6 +625,9 @@ Status TFReaderOp::LoadFeature(const std::unique_ptr *tensor_table // Overrides base class reset method. Cleans up any state info from it's previous execution and // reinitializes itself so that it can be executed again, as if it was just created. Status TFReaderOp::Reset() { + // start workers first, otherwise IOBlokcs will fall through if workers see it before this is set to true + load_jagged_connector_ = true; + { std::unique_lock lock(load_io_block_queue_mutex_); load_io_block_queue_ = true; diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.h b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.h index 69de068f9b..560cff114f 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.h +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.h @@ -369,6 +369,7 @@ class TFReaderOp : public ParallelOp { std::unique_ptr data_schema_; std::unique_ptr filename_index_; bool load_io_block_queue_; + bool load_jagged_connector_; std::unique_ptr jagged_buffer_connector_; QueueList> io_block_queues_; diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py index 8aaa6212b6..ad3e7d8255 100644 --- a/mindspore/dataset/engine/datasets.py +++ b/mindspore/dataset/engine/datasets.py @@ -1906,11 +1906,21 @@ class TFRecordDataset(SourceDataset): Return: Number, number of batches. """ - num_rows = TFReaderOp.get_num_rows(self.dataset_files, 8, estimate) - num_rows = get_num_rows(num_rows, self.num_shards) - if self.num_samples is None: - return num_rows - return min(self.num_samples, num_rows) + if self._dataset_size is None: + num_rows = TFReaderOp.get_num_rows(self.dataset_files, 8, estimate) + num_rows = get_num_rows(num_rows, self.num_shards) + if self.num_samples is None: + return num_rows + return min(self.num_samples, num_rows) + return self._dataset_size + + # manually set dataset_size as a tempoary solution. + def set_dataset_size(self, value): + logger.warning("WARN_DEPRECATED: This method is deprecated. Please use get_dataset_size directly.") + if value >= 0: + self._dataset_size = value + else: + raise ValueError('set dataset_size with negative value {}'.format(value)) class ManifestDataset(SourceDataset): From 854308ff7567cb915b5b65ae5339d612d6ec7069 Mon Sep 17 00:00:00 2001 From: anzhengqi Date: Tue, 31 Mar 2020 20:18:40 +0800 Subject: [PATCH 047/227] check num_samples --- mindspore/dataset/engine/validators.py | 8 ++++++++ .../python/dataset/test_datasets_sharding.py | 6 +++--- tests/ut/python/dataset/test_exceptions.py | 19 +++++++++++++++++++ 3 files changed, 30 insertions(+), 3 deletions(-) diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py index adfe54a02e..b4d22a4a01 100644 --- a/mindspore/dataset/engine/validators.py +++ b/mindspore/dataset/engine/validators.py @@ -243,6 +243,8 @@ def check_param_type(param_list, param_dict, param_type): if param_dict.get(param_name) is not None: if param_name == 'num_parallel_workers': check_num_parallel_workers(param_dict.get(param_name)) + if param_name == 'num_samples': + check_num_samples(param_dict.get(param_name)) else: check_type(param_dict.get(param_name), param_name, param_type) @@ -262,6 +264,12 @@ def check_num_parallel_workers(value): raise ValueError("num_parallel_workers exceeds the boundary between 0 and {}!".format(cpu_count())) +def check_num_samples(value): + check_type(value, 'num_samples', int) + if value <= 0: + raise ValueError("num_samples must be greater than 0!") + + def check_dataset_dir(dataset_dir): if not os.path.isdir(dataset_dir) or not os.access(dataset_dir, os.R_OK): raise ValueError("The folder {} does not exist or permission denied!".format(dataset_dir)) diff --git a/tests/ut/python/dataset/test_datasets_sharding.py b/tests/ut/python/dataset/test_datasets_sharding.py index b178298e33..b398391fb7 100644 --- a/tests/ut/python/dataset/test_datasets_sharding.py +++ b/tests/ut/python/dataset/test_datasets_sharding.py @@ -33,14 +33,14 @@ def test_imagefolder_shardings(print_res=False): # total 44 rows in dataset assert (sharding_config(4, 0, 5, False, dict()) == [0, 0, 0, 1, 1]) # 5 rows assert (sharding_config(4, 0, 12, False, dict()) == [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3]) # 11 rows - assert (sharding_config(4, 3, 0, False, dict()) == [0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]) # 11 rows + assert (sharding_config(4, 3, None, False, dict()) == [0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]) # 11 rows # total 22 in dataset rows because of class indexing which takes only 2 folders - assert (len(sharding_config(4, 0, 0, True, {"class1": 111, "class2": 999})) == 6) + assert (len(sharding_config(4, 0, None, True, {"class1": 111, "class2": 999})) == 6) assert (len(sharding_config(4, 2, 3, True, {"class1": 111, "class2": 999})) == 3) # test with repeat assert (sharding_config(4, 0, 12, False, dict(), 3) == [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3] * 3) assert (sharding_config(4, 0, 5, False, dict(), 5) == [0, 0, 0, 1, 1] * 5) - assert (len(sharding_config(5, 1, 0, True, {"class1": 111, "class2": 999}, 4)) == 20) + assert (len(sharding_config(5, 1, None, True, {"class1": 111, "class2": 999}, 4)) == 20) def test_manifest_shardings(print_res=False): diff --git a/tests/ut/python/dataset/test_exceptions.py b/tests/ut/python/dataset/test_exceptions.py index 7668eeb2a8..631f2ddcbc 100644 --- a/tests/ut/python/dataset/test_exceptions.py +++ b/tests/ut/python/dataset/test_exceptions.py @@ -18,6 +18,7 @@ import pytest import mindspore.dataset as ds DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"] +SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json" def skip_test_exception(): @@ -29,5 +30,23 @@ def skip_test_exception(): assert "The shape size 1 of input tensor is invalid" in str(info.value) +def test_sample_exception(): + num_samples = 0 + with pytest.raises(ValueError) as info: + data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], num_samples=num_samples) + assert "num_samples must be greater than 0" in str(info.value) + num_samples = -1 + with pytest.raises(ValueError) as info: + data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], num_samples=num_samples) + assert "num_samples must be greater than 0" in str(info.value) + num_samples = 1 + data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], num_samples=num_samples) + data = data.map(input_columns=["image"], operations=vision.Decode()) + data = data.map(input_columns=["image"], operations=vision.Resize((100, 100))) + num_iters = 0 + for item in data.create_dict_iterator(): + num_iters += 1 + assert num_iters == 1 + if __name__ == '__main__': test_exception() From 913de7af69a79abe71134689007a16aca374ce03 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?= Date: Wed, 1 Apr 2020 11:14:21 +0800 Subject: [PATCH 048/227] fix typo in formula --- mindspore/nn/layer/basic.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py index e5ed147b85..0cf4cd5e99 100644 --- a/mindspore/nn/layer/basic.py +++ b/mindspore/nn/layer/basic.py @@ -156,10 +156,10 @@ class Dense(Cell): ValueError: If weight_init or bias_init shape is incorrect. Inputs: - - **input** (Tensor) - Tensor of shape :math:`(N, in_channels)`. + - **input** (Tensor) - Tensor of shape :math:`(N, in\_channels)`. Outputs: - Tensor of shape :math:`(N, out_channels)`. + Tensor of shape :math:`(N, out\_channels)`. Examples: >>> net = nn.Dense(3, 4) From 075a9f5f9a97b315e3e85d574a8964dfbf87e0be Mon Sep 17 00:00:00 2001 From: dengwentao Date: Tue, 31 Mar 2020 10:57:38 +0800 Subject: [PATCH 049/227] change MS LIB CACHE setting --- cmake/utils.cmake | 28 +++++++++++++++++++++++----- 1 file changed, 23 insertions(+), 5 deletions(-) diff --git a/cmake/utils.cmake b/cmake/utils.cmake index 4efaad61b9..060e400820 100644 --- a/cmake/utils.cmake +++ b/cmake/utils.cmake @@ -16,16 +16,34 @@ function(mindspore_add_submodule_obj des_submodule_objs sub_dir submodule_name_o endfunction() -get_filename_component(_MS_LIB_CACHE ~/.mslib REALPATH) +if (DEFINED ENV{MSLIBS_CACHE_PATH}) + set(_MS_LIB_CACHE $ENV{MSLIBS_CACHE_PATH}) +else() + set(_MS_LIB_CACHE ${CMAKE_BINARY_DIR}/.mslib) +endif () +message("MS LIBS CACHE PATH: ${_MS_LIB_CACHE}") + if (NOT EXISTS ${_MS_LIB_CACHE}) file(MAKE_DIRECTORY ${_MS_LIB_CACHE}) endif () -# set(FETCHCONTENT_BASE_DIR ${_MS_LIB_CACHE}) -# set(CMAKE_PREFIX_PATH ${_MS_LIB_CACHE}) + if (DEFINED ENV{MSLIBS_SERVER}) set(LOCAL_LIBS_SERVER $ENV{MSLIBS_SERVER}) message("LOCAL_LIBS_SERVER: ${LOCAL_LIBS_SERVER}") endif () + +include(ProcessorCount) +ProcessorCount(N) +if (JOBS) + set(THNUM ${JOBS}) +else() + set(JOBS 8) + if (${JOBS} GREATER ${N}) + set(THNUM ${N}) + endif() +endif () +message("set make thread num: ${THNUM}") + if(LOCAL_LIBS_SERVER) if (NOT ENV{no_proxy}) set(ENV{no_proxy} "${LOCAL_LIBS_SERVER}") @@ -287,7 +305,7 @@ function(mindspore_add_pkg pkg_name ) -DCMAKE_INSTALL_PREFIX=${${pkg_name}_BASE_DIR} .. WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR}/_build) - __exec_cmd(COMMAND ${CMAKE_COMMAND} --build . --target install -- -j8 + __exec_cmd(COMMAND ${CMAKE_COMMAND} --build . --target install -- -j${THNUM} WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR}/_build) else() @@ -318,7 +336,7 @@ function(mindspore_add_pkg pkg_name ) ${${pkg_name}_MAKE_CFLAGS} ${${pkg_name}_MAKE_CXXFLAGS} ${${pkg_name}_MAKE_LDFLAGS}) endif () # build - __exec_cmd(COMMAND ${CMAKE_MAKE_PROGRAM} ${${pkg_name}_BUILD_OPTION} -j8 + __exec_cmd(COMMAND ${CMAKE_MAKE_PROGRAM} ${${pkg_name}_BUILD_OPTION} -j${THNUM} WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR}) if (PKG_INSTALL_INCS OR PKG_INSTALL_LIBS) From 4520469e16df97bbeec7278c2ec3e5d5a645e013 Mon Sep 17 00:00:00 2001 From: Xiaoda Zhang Date: Wed, 1 Apr 2020 14:16:52 +0800 Subject: [PATCH 050/227] refining strategy-checking for resnet50 --- .../ut/python/parallel/test_auto_parallel_resnet.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tests/ut/python/parallel/test_auto_parallel_resnet.py b/tests/ut/python/parallel/test_auto_parallel_resnet.py index 667e3873a6..1e0e3570b9 100644 --- a/tests/ut/python/parallel/test_auto_parallel_resnet.py +++ b/tests/ut/python/parallel/test_auto_parallel_resnet.py @@ -295,11 +295,11 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576 model.train(5, dataset, dataset_sink_mode=False) strategies = _executor._get_strategy(model._train_network) for (k, v) in strategies.items(): - if re.match(k, 'Conv2D-op') is not None: + if re.search('Conv2D-op', k) is not None: assert v[0][0] == dev_num - elif re.match(k, 'MatMul-op') is not None: + elif re.search('MatMul-op', k) is not None: assert v == [[dev_num, 1], [1, 1]] - elif re.match(k, 'ReduceSum-op') is not None: + elif re.search('ReduceSum-op', k) is not None: assert v == [[dev_num, 1]] allreduce_fusion_dict = _executor._get_allreduce_fusion(model._train_network) @@ -490,9 +490,9 @@ def test_train_64k_8p(epoch_size=3, batch_size=32, num_classes=65536): #1048576 model.train(5, dataset, dataset_sink_mode=False) strategies = _executor._get_strategy(model._train_network) for (k, v) in strategies.items(): - if re.match(k, 'Conv2D-op') is not None: + if re.search('Conv2D-op', k ) is not None: assert v[0][0] == dev_num - elif re.match(k, 'MatMul-op') is not None: + elif re.search('MatMul-op', k) is not None: assert v == [[1, 1], [dev_num, 1]] - elif re.match(k, 'ReduceSum-op') is not None: + elif re.search('ReduceSum-op', k) is not None: assert v == [[1, dev_num]] From 99173fb0f5eb79487b55960fc520be2ce36a5197 Mon Sep 17 00:00:00 2001 From: chenhaozhe Date: Tue, 31 Mar 2020 19:37:57 +0800 Subject: [PATCH 051/227] modify log level in DfGraphManager --- mindspore/ccsrc/pipeline/pipeline.cc | 7 ++++--- mindspore/ccsrc/transform/convert.cc | 4 ++-- mindspore/ccsrc/transform/df_graph_manager.cc | 2 +- 3 files changed, 7 insertions(+), 6 deletions(-) diff --git a/mindspore/ccsrc/pipeline/pipeline.cc b/mindspore/ccsrc/pipeline/pipeline.cc index 7b3cf06b5e..3c3478e89d 100644 --- a/mindspore/ccsrc/pipeline/pipeline.cc +++ b/mindspore/ccsrc/pipeline/pipeline.cc @@ -1110,10 +1110,11 @@ bool ExecutorPy::AddDFGraph(const py::dict& init_params, const std::string& phas (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph()); } (void)DfGraphManager::GetInstance().AddGraph(init_graph, convertor.GetInitGraph()); - (void)DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph()); (void)DfGraphManager::GetInstance().AddGraph(BROADCAST_GRAPH_NAME, convertor.GetBroadcastGraph()); - - DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph); + Status ret = DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph()); + if (ret == Status::SUCCESS) { + DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph); + } return true; } diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index f2942b3de0..1c16684dfa 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -1106,12 +1106,12 @@ void DfGraphConvertor::UpdateDataOpDesc(const AnfNodePtr &it, const OperatorPtr auto normal_shape_ptr = dyn_cast(node->Shape()); vector shape; if (normal_shape_ptr == nullptr) { - MS_LOG(ERROR) << "Update data op descriptor failed! Invalid shape."; + MS_LOG(INFO) << "Invalid shape to update data op descriptor."; return; } shape = normal_shape_ptr->shape(); if (node->Type() == nullptr) { - MS_LOG(ERROR) << "Update data op descriptor failed! Invalid type."; + MS_LOG(INFO) << "Invalid type to update data op descriptor."; return; } TypeId me_type = node->Type()->type_id(); diff --git a/mindspore/ccsrc/transform/df_graph_manager.cc b/mindspore/ccsrc/transform/df_graph_manager.cc index 3339f43145..bfe4d9f5d2 100644 --- a/mindspore/ccsrc/transform/df_graph_manager.cc +++ b/mindspore/ccsrc/transform/df_graph_manager.cc @@ -112,7 +112,7 @@ DfGraphWrapperPtr DfGraphManager::GetGraphByName(const std::string& name) { auto it = graphs_.find(name); if (it == graphs_.end()) { - MS_LOG(ERROR) << "Can't found graph name: " << name; + MS_LOG(INFO) << "Can't found graph name: " << name; return nullptr; } MS_LOG(INFO) << "Return graph: " << name; From 2498d2c749c3cb31aa724425d950d7f5d96a01ed Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Sat, 28 Mar 2020 12:02:46 +0800 Subject: [PATCH 052/227] add atan2 operator --- mindspore/ccsrc/transform/convert.cc | 4 +++- mindspore/ccsrc/transform/op_declare.cc | 6 ++++++ mindspore/ccsrc/transform/op_declare.h | 2 ++ mindspore/ops/_grad/grad_math_ops.py | 13 +++++++++++++ mindspore/ops/operations/__init__.py | 5 +++-- mindspore/ops/operations/math_ops.py | 23 +++++++++++++++++++++++ tests/ut/python/ops/test_ops.py | 7 ++++++- 7 files changed, 56 insertions(+), 4 deletions(-) diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index 1c16684dfa..5ea3eef2a3 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -183,6 +183,7 @@ const char kNameDiag[] = "Diag"; const char kNameDiagPart[] = "DiagPart"; const char kNameSpaceToBatch[] = "SpaceToBatch"; const char kNameBatchToSpace[] = "BatchToSpace"; +const char kNameAtan2[] = "Atan2"; // -----------------OpAdapter initialization-------------- std::unordered_map &DfGraphConvertor::get_adpt_map() { @@ -367,7 +368,8 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameDiag), ADPT_DESC(Diag)}, {string(kNameDiagPart), ADPT_DESC(DiagPart)}, {string(kNameSpaceToBatch), ADPT_DESC(SpaceToBatchD)}, - {string(kNameBatchToSpace), ADPT_DESC(BatchToSpaceD)}}; + {string(kNameBatchToSpace), ADPT_DESC(BatchToSpaceD)}, + {string(kNameAtan2), ADPT_DESC(Atan2)}}; #ifdef ENABLE_GE adpt_map[string(kNamePrint)] = ADPT_DESC(Print); #endif diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index efe12681f0..752a66b8e2 100755 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -1183,6 +1183,12 @@ ATTR_MAP(BatchToSpaceD) = { {"block_size", ATTR_DESC(block_size, AnyTraits())}, {"crops", ATTR_DESC(crops, AnyTraits>>(), AnyTraits>())}}; OUTPUT_MAP(BatchToSpaceD) = {{0, OUTPUT_DESC(y)}}; + +// Atan2 +INPUT_MAP(Atan2) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; +ATTR_MAP(Atan2) = EMPTY_ATTR_MAP; +OUTPUT_MAP(Atan2) = {{0, OUTPUT_DESC(y)}}; + #ifdef ENABLE_GE // Print INPUT_MAP(Print) = EMPTY_INPUT_MAP; diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index 7028e43975..027264c06e 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -445,6 +445,8 @@ DECLARE_OP_ADAPTER(SpaceToBatchD) DECLARE_OP_USE_OUTPUT(SpaceToBatchD) DECLARE_OP_ADAPTER(BatchToSpaceD) DECLARE_OP_USE_OUTPUT(BatchToSpaceD) +DECLARE_OP_ADAPTER(Atan2) +DECLARE_OP_USE_OUTPUT(Atan2) #ifdef ENABLE_GE DECLARE_OP_ADAPTER(Print) DECLARE_OP_USE_DYN_INPUT(Print) diff --git a/mindspore/ops/_grad/grad_math_ops.py b/mindspore/ops/_grad/grad_math_ops.py index eb6ee9401d..9e90c5660c 100755 --- a/mindspore/ops/_grad/grad_math_ops.py +++ b/mindspore/ops/_grad/grad_math_ops.py @@ -738,3 +738,16 @@ def get_bprop_round(self): def bprop(x, out, dout): return (zeros_like(x),) return bprop + + +@bprop_getters.register(P.Atan2) +def get_bprop_atan2(self): + """Generate bprop for Atan2""" + + square = P.Square() + def bprop(x, y, out, dout): + tmp = dout / (square(x) + square(y)) + dx = tmp * y + dy = tmp * (-x) + return (dx, dy) + return bprop diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index 07a5471d0a..92b12f73e1 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -37,7 +37,7 @@ from .debug_ops import (ImageSummary, InsertGradientOf, ScalarSummary, TensorSummary, Print) from .control_ops import ControlDepend, GeSwitch, Merge from .inner_ops import ScalarCast -from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, BatchMatMul, +from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul, ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd, Cos, Div, Equal, EqualCount, Exp, Floor, FloorDiv, Greater, GreaterEqual, Less, LessEqual, Log, LogicalAnd, @@ -227,7 +227,8 @@ __all__ = [ "Round", "ApplyFtrl", "SpaceToBatch", - "BatchToSpace" + "BatchToSpace", + "Atan2", ] __all__.sort() diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index ad928d792f..398a7e6f1a 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1858,3 +1858,26 @@ class Round(PrimitiveWithInfer): validator.check_subclass("x_dtype", x_type, mstype.tensor) validator.check_typename('x_dtype', x_type, mstype.number_type) return x_type + + +class Atan2(_MathBinaryOp): + r""" + Returns arctangent of input_x/input_y element-wise. + + It returns :math:`\theta\ \in\ (-\frac{\pi}{2}, \frac{\pi}{2})` + such that :math:`x = r*\sin(\theta), y = r*\cos(\theta)`, where :math:`r = \sqrt{x^2 + y^2}`. + + Inputs: + - **input_x** (Tensor) - The input tensor. + - **input_y** (Tensor) - The input tensor. + + Outputs: + Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'. + + Examples: + >>> input_x = Tensor(np.array([[0, 1]]), mstype.float32) + >>> input_y = Tensor(np.array([[1, 1]]), mstype.float32) + >>> atan2 = Atan2() + >>> atan2(input_x, input_y) + [[0. 0.7853982]] + """ diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py index f76cf39c72..1a66b99450 100755 --- a/tests/ut/python/ops/test_ops.py +++ b/tests/ut/python/ops/test_ops.py @@ -481,7 +481,12 @@ test_case_math_ops = [ ('Round', { 'block': P.Round(), 'desc_inputs': [[3]], - 'desc_bprop': [[3]]}) + 'desc_bprop': [[3]]}), + ('Atan2', { + 'block': P.Atan2(), + 'desc_inputs': [Tensor(np.array([0, 1]).astype(np.float32)), + Tensor(np.array([1, 1]).astype(np.float32))], + 'desc_bprop': [[2]]}) ] test_case_nn_ops = [ From 04471939f53db761d2cf59a3b4f94ac0e1b94bff Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Wed, 1 Apr 2020 11:53:35 +0800 Subject: [PATCH 053/227] delete longtime python ut --- .../train/summary/test_summary_performance.py | 97 ------------------- tests/ut/python/train/test_amp.py | 9 +- 2 files changed, 1 insertion(+), 105 deletions(-) delete mode 100644 tests/ut/python/train/summary/test_summary_performance.py diff --git a/tests/ut/python/train/summary/test_summary_performance.py b/tests/ut/python/train/summary/test_summary_performance.py deleted file mode 100644 index 9ee9725d13..0000000000 --- a/tests/ut/python/train/summary/test_summary_performance.py +++ /dev/null @@ -1,97 +0,0 @@ -# Copyright 2020 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -""" -@File : test_summary.py -@Author: -@Date : 2019-07-4 -@Desc : test summary function -""" -import os -import logging -import time -import numpy as np -from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data -from mindspore.common.tensor import Tensor - -CUR_DIR = os.getcwd() -SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/" - -log = logging.getLogger("test") -log.setLevel(level=logging.ERROR) - -def get_now_time_ns(): - """get the time of second""" - time_second = int(time.time_ns()) - return time_second - -def get_test_data(step): - """ get_test_data """ - # pylint: disable=unused-argument - test_data_list = [] - tag1 = "xt1[:Tensor]" - tag2 = "xt2[:Tensor]" - tag3 = "xt3[:Tensor]" - np1 = np.random.random((5, 4, 3, 5)) - np2 = np.random.random((5, 5, 3, 5)) - np3 = np.random.random((4, 5, 3, 5)) - - dict1 = {} - dict1["name"] = tag1 - dict1["data"] = Tensor(np1) - - dict2 = {} - dict2["name"] = tag2 - dict2["data"] = Tensor(np2) - - dict3 = {} - dict3["name"] = tag3 - dict3["data"] = Tensor(np3) - - test_data_list.append(dict1) - test_data_list.append(dict2) - - return test_data_list - - -# Test 1: summary sample of scalar -def test_summary_performance(): - """ test_summary_performance """ - log.debug("begin test_scalar_summary_sample") - current_time = time.time() - print("time = ", current_time) - # step 0: create the thread - test_writer = SummaryRecord(SUMMARY_DIR, flush_time=120) - - # step 1: create the test data for summary - old_time = get_now_time_ns() - # step 2: create the Event - for i in range(1, 10): - test_data = get_test_data(i) - _cache_summary_tensor_data(test_data) - test_writer.record(i) - now_time = get_now_time_ns() - consume_time = (now_time - old_time)/1000/1000 - old_time = now_time - print("step test_summary_performance conusmer time is:", consume_time) - - - # step 3: send the event to mq - - # step 4: accept the event and write the file - test_writer.flush() - test_writer.close() - current_time = time.time() - current_time - print("consume time = ", current_time) - log.debug("finished test_scalar_summary_sample") diff --git a/tests/ut/python/train/test_amp.py b/tests/ut/python/train/test_amp.py index eebd188e60..1a26c21775 100644 --- a/tests/ut/python/train/test_amp.py +++ b/tests/ut/python/train/test_amp.py @@ -66,6 +66,7 @@ def test_amp_o2(): train_network = amp.build_train_network(net, optimizer, level="O2") output = train_network(inputs, label) + def test_amp_o2_loss(): inputs = Tensor(np.ones([16, 16]).astype(np.float32)) label = Tensor(np.zeros([16, 16]).astype(np.float32)) @@ -75,14 +76,6 @@ def test_amp_o2_loss(): train_network = amp.build_train_network(net, optimizer, loss, level="O2") output = train_network(inputs, label) -def test_amp_resnet50_loss(): - inputs = Tensor(np.ones([2, 3, 224, 224]).astype(np.float32)) - label = Tensor(np.zeros([2, 10]).astype(np.float32)) - net = resnet50() - loss = nn.SoftmaxCrossEntropyWithLogits(reduction='mean') - optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9) - train_network = amp.build_train_network(net, optimizer, loss, level="O2") - train_network(inputs, label) def test_amp_o0_loss(): inputs = Tensor(np.ones([16, 16]).astype(np.float32)) From 10ee3fbca54547cade57d262743bc23f4d06c6c3 Mon Sep 17 00:00:00 2001 From: huangdongrun Date: Wed, 1 Apr 2020 15:23:40 +0800 Subject: [PATCH 054/227] fix bug of auto control depend for bert pre training add comment --- mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py b/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py index 22a212a489..bc51ba5d48 100644 --- a/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py +++ b/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py @@ -403,9 +403,6 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell): sens=None): """Defines the computation performed.""" weights = self.weights - # alloc status - init = self.alloc_status() - self.clear_before_grad(init) loss = self.network(input_ids, input_mask, token_type_id, @@ -417,6 +414,9 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell): scaling_sens = self.loss_scale else: scaling_sens = sens + # alloc status and clear should be right before gradoperation + init = self.alloc_status() + self.clear_before_grad(init) grads = self.grad(self.network, weights)(input_ids, input_mask, token_type_id, From 47925a2006a8251a64a91e0f6f47769d6181938b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?= Date: Wed, 1 Apr 2020 11:42:24 +0800 Subject: [PATCH 055/227] fix typo in formula --- mindspore/nn/layer/normalization.py | 2 +- mindspore/ops/operations/array_ops.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py index fdfa25e183..d43c1c8ab4 100644 --- a/mindspore/nn/layer/normalization.py +++ b/mindspore/nn/layer/normalization.py @@ -249,7 +249,7 @@ class LayerNorm(Cell): 'he_uniform', etc. Default: 'zeros'. Inputs: - - **input_x** (Tensor) - The shape of 'input_x' is input_shape = `(x_1, x_2, ..., x_R)`, + - **input_x** (Tensor) - The shape of 'input_x' is input_shape = :math:`(x_1, x_2, ..., x_R)`, and `input_shape[begin_norm_axis:]` is equal to `normalized_shape`. Outputs: diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index 8585f873e9..f6d563321c 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -1790,7 +1790,7 @@ class ScatterNd(PrimitiveWithInfer): class ResizeNearestNeighbor(PrimitiveWithInfer): - """ + r""" Resize the input tensor by using nearest neighbor algorithm. Resize input tensor to given size by using nearest neighbor algorithm. The nearest @@ -1806,7 +1806,7 @@ class ResizeNearestNeighbor(PrimitiveWithInfer): - **input_x** (Tensor) - The input tensor. The shape of the tensor is :math:`(N, C, H, W)`. Outputs: - Tensor, the shape of the output tensor is :math:`(N, NEW_C, NEW_H, W)`. + Tensor, the shape of the output tensor is :math:`(N, NEW\_C, NEW\_H, W)`. Examples: >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) From 0c1aa0d566a8a5865e1089142ff7a244de9b3e2e Mon Sep 17 00:00:00 2001 From: kswang Date: Wed, 1 Apr 2020 21:37:02 +0800 Subject: [PATCH 056/227] add getptr for memreuse --- mindspore/ccsrc/device/kernel_runtime.cc | 33 ++++--------------- mindspore/ccsrc/device/kernel_runtime.h | 3 -- .../ccsrc/pre_activate/mem_reuse/mem_reuse.cc | 30 +++++++++++++++++ .../ccsrc/pre_activate/mem_reuse/mem_reuse.h | 4 +++ 4 files changed, 40 insertions(+), 30 deletions(-) diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc index 99f5d491ac..878fe4a7f8 100644 --- a/mindspore/ccsrc/device/kernel_runtime.cc +++ b/mindspore/ccsrc/device/kernel_runtime.cc @@ -42,7 +42,6 @@ KernelRuntime::~KernelRuntime() { #ifdef ENABLE_DUMP_E2E dump_conf_ptr_ = nullptr; #endif - reuse_mem_base_ = nullptr; mem_reuse_util_ptr_ = nullptr; } @@ -476,9 +475,9 @@ void KernelRuntime::ReuseAssignDynamicMemory(session::KernelGraph *graph) { bestfit_mem_reuse->Reuse(mem_reuse_util_ptr.get()); size_t total_allocated_size = bestfit_mem_reuse->GetAllocatedSize(); MS_LOG(INFO) << "TotalReuseDynamicSize [" << total_allocated_size << "]"; - auto base_ptr = MallocDynamicMem(total_allocated_size, false); - reuse_mem_base_ = base_ptr; mem_reuse_util_ptr_ = mem_reuse_util_ptr; + auto base_ptr = MallocDynamicMem(total_allocated_size, false); + mem_reuse_util_ptr_->set_mem_base(base_ptr); auto &kernels = graph->execution_order(); for (auto &kernel : kernels) { AssignNodeOutputMem(kReuseDynamicMem, kernel, kGetAllOuts); @@ -488,22 +487,13 @@ void KernelRuntime::ReuseAssignDynamicMemory(session::KernelGraph *graph) { void KernelRuntime::AssignReuseWorkSpaceMem(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); - auto key = node.get(); auto kernel_mod = AnfAlgo::GetKernelMod(node); MS_EXCEPTION_IF_NULL(kernel_mod); size_t index = 0; - auto iter = mem_reuse_util_ptr_->kernel_workspace_refs_.find(key); for (auto &size : kernel_mod->GetWorkspaceSizeList()) { - if (iter != mem_reuse_util_ptr_->kernel_workspace_refs_.end()) { - if (index >= iter->second.size()) { - MS_LOG(EXCEPTION) << "index:[" << index << "] is larger than it's workspace size:[" << iter->second.size() - << "]"; - } - auto wk_ref = iter->second[index]; - auto wk_ptr = reuse_mem_base_ + wk_ref->offset_; - AnfAlgo::SetWorkspaceAddr(CreateDeviceAddress(wk_ptr, size, "", kTypeUnknown), index, node.get()); - index++; - } + auto wk_ptr = mem_reuse_util_ptr_->GetNodeWorkSpacePtr(node, index); + AnfAlgo::SetWorkspaceAddr(CreateDeviceAddress(wk_ptr, size, "", kTypeUnknown), index, node.get()); + index++; } } @@ -554,18 +544,7 @@ uint8_t *KernelRuntime::CalDeviceMem(const AnfNodePtr &node, size_t size, int fl } else if (flag == kDynamicMem) { ptr = MallocDynamicMem(size, false); } else if (flag == kReuseDynamicMem) { - auto key = node.get(); - auto iter = mem_reuse_util_ptr_->kernel_output_refs_.find(key); - if (iter != mem_reuse_util_ptr_->kernel_output_refs_.end()) { - // private member form KernelRuntime - memreuse::KernelRefCountPtr kernel_ref_count_ptr = mem_reuse_util_ptr_->kernel_output_refs_[key][index]; - if (kernel_ref_count_ptr == nullptr) { - return ptr; - } - ptr = reuse_mem_base_ + kernel_ref_count_ptr->offset_; - } else { - MS_LOG(EXCEPTION) << "node [" << AnfAlgo::GetCNodeName(node) << "] don't exist in kernel_output_refs"; - } + ptr = mem_reuse_util_ptr_->GetNodeOutputPtr(node, index); } return ptr; } diff --git a/mindspore/ccsrc/device/kernel_runtime.h b/mindspore/ccsrc/device/kernel_runtime.h index afdb45a698..ac9a56ed4d 100644 --- a/mindspore/ccsrc/device/kernel_runtime.h +++ b/mindspore/ccsrc/device/kernel_runtime.h @@ -128,9 +128,6 @@ class KernelRuntime { size_t total_static_size_ = 0; size_t total_dynamic_size_ = 0; MemReuseUtilPtr mem_reuse_util_ptr_{nullptr}; - - private: - uint8_t *reuse_mem_base_{nullptr}; }; using KernelRuntimePtr = std::shared_ptr; } // namespace device diff --git a/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.cc b/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.cc index 0db3c35196..2113fec653 100644 --- a/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.cc +++ b/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.cc @@ -316,5 +316,35 @@ void MemReuseUtil::SetAllInfo(KernelGraph *graph) { MemReuseChecker::GetInstance().CheckMemReuseIR(total_refs_list_, kernel_def_ptr_list_, graph); #endif } + +uint8_t *MemReuseUtil::GetNodeOutputPtr(const AnfNodePtr &node, size_t index) const { + auto key = node.get(); + auto iter = kernel_output_refs_.find(key); + uint8_t *ptr = nullptr; + if (iter != kernel_output_refs_.end()) { + if (index >= iter->second.size()) { + MS_LOG(EXCEPTION) << "index:[" << index << "] is larger than it's workspace size:[" << iter->second.size() << "]"; + } + auto output_ref = iter->second[index]; + ptr = mem_base_ + output_ref->offset_; + } else { + MS_LOG(EXCEPTION) << "node [" << AnfAlgo::GetCNodeName(node) << "] don't exist in kernel_output_refs"; + } + return ptr; +} + +uint8_t *MemReuseUtil::GetNodeWorkSpacePtr(const AnfNodePtr &node, size_t index) const { + auto key = node.get(); + auto iter = kernel_workspace_refs_.find(key); + uint8_t *ptr = nullptr; + if (iter != kernel_workspace_refs_.end()) { + if (index >= iter->second.size()) { + MS_LOG(EXCEPTION) << "index:[" << index << "] is larger than it's workspace size:[" << iter->second.size() << "]"; + } + auto wk_ref = iter->second[index]; + ptr = mem_base_ + wk_ref->offset_; + } + return ptr; +} } // namespace memreuse } // namespace mindspore diff --git a/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.h b/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.h index 6ecd222688..cae0e4565f 100644 --- a/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.h +++ b/mindspore/ccsrc/pre_activate/mem_reuse/mem_reuse.h @@ -76,6 +76,9 @@ class MemReuseUtil { void set_kernel_def_ptr_list(const KernelDefPtrMaps &kernel_def_ptr_list) { kernel_def_ptr_list_ = kernel_def_ptr_list; } + void set_mem_base(uint8_t *mem_base) { mem_base_ = mem_base; } + uint8_t *GetNodeOutputPtr(const AnfNodePtr &node, size_t index) const; + uint8_t *GetNodeWorkSpacePtr(const AnfNodePtr &node, size_t index) const; private: int util_index_; @@ -88,6 +91,7 @@ class MemReuseUtil { size_t total_dy_size_ = 0; size_t total_workspace_size_ = 0; size_t total_reuseworkspace_size_ = 0; + uint8_t *mem_base_{nullptr}; }; using MemReuseUtilPtr = std::shared_ptr; } // namespace memreuse From 29ab2c109315b6083fe9ea1ef7016bc733d83d9a Mon Sep 17 00:00:00 2001 From: Cathy Wong Date: Tue, 31 Mar 2020 16:46:44 -0400 Subject: [PATCH 057/227] Enable skipped dataset zip python unit tests Signed-off-by: Cathy Wong --- .../ut/data/dataset/golden/zip_02_result.npz | Bin 428510 -> 1065228 bytes .../ut/data/dataset/golden/zip_03_result.npz | Bin 428553 -> 1065271 bytes 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z=AhH$jWx+*RoZ!<*_fnk$!_z{D6`0I&P*@?7iqn=ev0K^*BCQ%l-chzM@E??s(ERO zNpzdmc$KxmXLe3BelH^O4L}@rno^Hx9!=p~aF5ru$uWF9=P@V7gPgg4eExmmqR1q{ zTn^0ZAliM9=9>Ejgj7H5$_L?TI zD1l-~`+(~UN=$T_=?Ug+oGBSkK9qG*`FqmL46CWzy@J|!#LqJJ;hIn4Ouq}Ij|}87 zRS%|GT~ZpZj-(Yu(lOJ?`B5cBktz8uUZ>_eOTvYTB&iLrX)JCC*EWW0?+q8*3=NTt zqKKay%80Mmenf1Fgo+}!K%g1wO0Vf(beefJ z2-lpG&zD8cyc&ez=EMj96(hM0BWVbi*WjG(aO>+ukz28nEdLih|8S!+V#CBv@S&Pdn@moN$zm`3vNg@od5s; diff --git a/tests/ut/python/dataset/test_zip.py b/tests/ut/python/dataset/test_zip.py index 353b5a2471..16b61079db 100644 --- a/tests/ut/python/dataset/test_zip.py +++ b/tests/ut/python/dataset/test_zip.py @@ -48,7 +48,7 @@ def test_zip_01(): save_and_check_dict(dataz, parameters, filename, generate_golden=GENERATE_GOLDEN) -def skip_test_zip_02(): +def test_zip_02(): """ Test zip: zip 2 datasets, #rows-data1 < #rows-data2, #cols-data1 == #cols-data2 """ @@ -63,7 +63,7 @@ def skip_test_zip_02(): save_and_check_dict(dataz, parameters, filename, generate_golden=GENERATE_GOLDEN) -def skip_test_zip_03(): +def test_zip_03(): """ Test zip: zip 2 datasets, #rows-data1 > #rows-data2, #cols-data1 > #cols-data2 """ @@ -78,7 +78,7 @@ def skip_test_zip_03(): save_and_check_dict(dataz, parameters, filename, generate_golden=GENERATE_GOLDEN) -def skip_test_zip_04(): +def test_zip_04(): """ Test zip: zip >2 datasets """ From 9a717aa1f70c220cab5b198c670530bc825de83c Mon Sep 17 00:00:00 2001 From: Xiaoda Zhang Date: Wed, 1 Apr 2020 19:24:18 +0800 Subject: [PATCH 058/227] change_star_elimination: make the non-identity triangle_eliminatin exact --- .../parallel/auto_parallel/graph_costmodel.cc | 97 +++---------------- .../parallel/auto_parallel/graph_costmodel.h | 11 +-- 2 files changed, 16 insertions(+), 92 deletions(-) diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc index 292cc4f5f0..f99b271894 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc @@ -948,10 +948,12 @@ OperatorInfoPtr CostGraph::EliminationContract(const OperatorInfoPtr& op) { return target_op; } -void CostGraph::CreateTriangleEliminationSubCostListForIdentity( - StrategyPtr elimi_op_stra, StrategyPtr left_op_stra, StrategyPtr right_op_stra, const CostPtr& right_op_cost, - const CostPtrList& elimi_op_clist, const CostPtrList& left_edge_clist, const CostPtr& right_edge_cost, - const CostPtrList& left_node_clist_origin, CostPtrList* left_node_clist_new) { +void CostGraph::CreateTriangleEliminationSubCostList(StrategyPtr elimi_op_stra, StrategyPtr left_op_stra, + StrategyPtr right_op_stra, const CostPtr& right_op_cost, + const CostPtrList& elimi_op_clist, + const CostPtrList& left_edge_clist, const CostPtr& right_edge_cost, + const CostPtrList& left_node_clist_origin, + CostPtrList* left_node_clist_new) { MS_EXCEPTION_IF_NULL(right_edge_cost); MS_EXCEPTION_IF_NULL(right_op_cost); MS_EXCEPTION_IF_NULL(left_node_clist_new); @@ -985,93 +987,20 @@ void CostGraph::CreateTriangleEliminationSubCostListForIdentity( } } -void CostGraph::CreateTriangleEliminationSubCostListForOthers( - StrategyPtr elimi_op_stra, StrategyPtr left_node_stra, StrategyPtr right_node_stra, const CostPtr& right_op_cost, - const CostPtrList& elimi_op_clist, const CostPtrList& left_edge_clist, const CostPtr& right_edge_cost, - const CostPtrList& left_node_clist_origin, CostPtrList* left_node_clist_new) { - CostPtr elimi_op_determined = nullptr, left_edge_determined = nullptr, init_ele = nullptr; - std::function LocalCompare = [&](CostPtr init, const CostPtr& cost_x) { - MS_EXCEPTION_IF_NULL(cost_x); - if ((init == nullptr) || (cost_x->memory_cost_ < DEVICE_MEMORY_CAPACITY)) { - init = cost_x; - } - return init; - }; - - // Find a feasible elimi_op_clist - elimi_op_determined = std::accumulate(elimi_op_clist.begin(), elimi_op_clist.end(), init_ele, LocalCompare); - init_ele = nullptr; - // Find a feasible left_edge_cost - left_edge_determined = std::accumulate(left_edge_clist.begin(), left_edge_clist.end(), init_ele, LocalCompare); - if ((elimi_op_determined == nullptr) || (left_edge_determined == nullptr)) { - return; - } - if ((elimi_op_determined->memory_cost_ >= DEVICE_MEMORY_CAPACITY) || - (left_edge_determined->memory_cost_ >= DEVICE_MEMORY_CAPACITY)) { - return; - } - - for (auto& left_node_cost : left_node_clist_origin) { - MS_EXCEPTION_IF_NULL(left_node_cost); - MS_EXCEPTION_IF_NULL(right_op_cost); - double new_memory_cost = left_node_cost->memory_cost_ + elimi_op_determined->memory_cost_ + - left_edge_determined->memory_cost_ + right_edge_cost->memory_cost_ + - right_op_cost->memory_cost_; - double commu_cost = left_node_cost->communication_cost_ + elimi_op_determined->communication_cost_ + - left_edge_determined->communication_cost_ + right_edge_cost->communication_cost_ + - right_op_cost->communication_cost_; - double commu_without = - left_node_cost->communication_without_parameter_ + elimi_op_determined->communication_without_parameter_ + - left_edge_determined->communication_without_parameter_ + right_edge_cost->communication_without_parameter_ + - right_op_cost->communication_without_parameter_; - auto decision = std::make_shared(elimi_op_stra, elimi_op_determined, - left_edge_determined, right_edge_cost, left_node_stra, - left_node_cost, right_node_stra, right_op_cost); - - auto new_cost = std::make_shared(new_memory_cost, commu_cost, decision); - new_cost->communication_without_parameter_ = commu_without; - new_cost->communication_with_partial_para_ = commu_without + COST_MODEL_GAMMA * (commu_cost - commu_without); - left_node_clist_new->emplace_back(std::move(new_cost)); - } -} - void CostGraph::CreateTriangleEliminationCostList(const OperatorInfoPtr& elimi_op, const CostPtrList& right_node_clist, const CostPtrList& right_edge_clist, const StrategyPtr& elimi_op_stra, const StrategyPtr& left_node_stra, const StrategyPtr& right_node_stra, const CostPtrList& elimi_op_clist, const CostPtrList& left_edge_clist, const CostPtrList& left_node_clist_origin, CostPtrList* left_node_clist_new) { - // The reason for separately dealing with when the 'elimi_op' is 'TMPIDENTITY_INFO' or others is that - // when 'elimi_op' is TMPIDENTITY_INFO, the computation is limited, while 'elimi_op' is others, the computation - // may be huge MS_EXCEPTION_IF_NULL(elimi_op); - if (elimi_op->name().find(TMPIDENTITY_INFO_NAME) != std::string::npos) { - for (auto& right_node_cost : right_node_clist) { - MS_EXCEPTION_IF_NULL(right_node_cost); - for (auto& right_edge_cost : right_edge_clist) { - MS_EXCEPTION_IF_NULL(right_edge_cost); - if ((right_node_cost->memory_cost_ < DEVICE_MEMORY_CAPACITY) && - (right_edge_cost->memory_cost_ < DEVICE_MEMORY_CAPACITY)) { - // Exact computation for TMPIDENTITY_INFO_NAME case - CreateTriangleEliminationSubCostListForIdentity(elimi_op_stra, left_node_stra, right_node_stra, - right_node_cost, elimi_op_clist, left_edge_clist, - right_edge_cost, left_node_clist_origin, left_node_clist_new); - } - } - } - } else { - for (auto& right_node_cost : right_node_clist) { - MS_EXCEPTION_IF_NULL(right_node_cost); - for (auto& right_edge_cost : right_edge_clist) { - MS_EXCEPTION_IF_NULL(right_edge_cost); - if ((right_node_cost->memory_cost_ < DEVICE_MEMORY_CAPACITY) && - (right_edge_cost->memory_cost_ < DEVICE_MEMORY_CAPACITY)) { - // Approximate computation for other case - CreateTriangleEliminationSubCostListForOthers(elimi_op_stra, left_node_stra, right_node_stra, right_node_cost, - elimi_op_clist, left_edge_clist, right_edge_cost, - left_node_clist_origin, left_node_clist_new); - } - } + for (auto& right_node_cost : right_node_clist) { + MS_EXCEPTION_IF_NULL(right_node_cost); + for (auto& right_edge_cost : right_edge_clist) { + MS_EXCEPTION_IF_NULL(right_edge_cost); + CreateTriangleEliminationSubCostList(elimi_op_stra, left_node_stra, right_node_stra, right_node_cost, + elimi_op_clist, left_edge_clist, right_edge_cost, left_node_clist_origin, + left_node_clist_new); } } } diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h index fde9514540..3b04703a47 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h +++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h @@ -163,14 +163,9 @@ class CostGraph { void CreateTriangleEliminationCostList(const OperatorInfoPtr&, const CostPtrList&, const CostPtrList&, const StrategyPtr&, const StrategyPtr&, const StrategyPtr&, const CostPtrList&, const CostPtrList&, const CostPtrList&, CostPtrList*); - // Given the relevant costlist, create the TriangleElimination cost for eliminating TmpIdentityInfo - void CreateTriangleEliminationSubCostListForIdentity(StrategyPtr, StrategyPtr, StrategyPtr, const CostPtr&, - const CostPtrList&, const CostPtrList&, const CostPtr&, - const CostPtrList&, CostPtrList*); - // Given the relevant costlist, create the TriangleElimination cost for eliminating other operators - void CreateTriangleEliminationSubCostListForOthers(StrategyPtr, StrategyPtr, StrategyPtr, const CostPtr&, - const CostPtrList&, const CostPtrList&, const CostPtr&, - const CostPtrList&, CostPtrList*); + // Given the relevant costlist, create the TriangleElimination cost + void CreateTriangleEliminationSubCostList(StrategyPtr, StrategyPtr, StrategyPtr, const CostPtr&, const CostPtrList&, + const CostPtrList&, const CostPtr&, const CostPtrList&, CostPtrList*); // Applying the Star Elimination in DP algorithm. Return the successive edges of this merged_op // NOTE: this elimination MUST be performed only when the above 5 operation cannot be applied. From dcd1f0a504cb7de4bc258b6b1f4acae6ed408c85 Mon Sep 17 00:00:00 2001 From: zhaoting Date: Tue, 31 Mar 2020 09:14:08 +0800 Subject: [PATCH 059/227] add RMSProp optimizer --- mindspore/ccsrc/transform/convert.cc | 6 +- mindspore/ccsrc/transform/op_declare.cc | 16 ++ mindspore/ccsrc/transform/op_declare.h | 6 + mindspore/nn/optim/__init__.py | 3 +- mindspore/nn/optim/rmsprop.py | 187 ++++++++++++++++++ mindspore/ops/_grad/grad_math_ops.py | 4 +- mindspore/ops/operations/__init__.py | 5 +- mindspore/ops/operations/nn_ops.py | 152 ++++++++++++++ .../utils/block_util.py | 4 + tests/ut/python/ops/test_ops.py | 12 ++ 10 files changed, 390 insertions(+), 5 deletions(-) create mode 100644 mindspore/nn/optim/rmsprop.py diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index 5ea3eef2a3..b34a2aa28c 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -184,6 +184,8 @@ const char kNameDiagPart[] = "DiagPart"; const char kNameSpaceToBatch[] = "SpaceToBatch"; const char kNameBatchToSpace[] = "BatchToSpace"; const char kNameAtan2[] = "Atan2"; +const char kNameApplyRMSProp[] = "ApplyRMSProp"; +const char kNameApplyCenteredRMSProp[] = "ApplyCenteredRMSProp"; // -----------------OpAdapter initialization-------------- std::unordered_map &DfGraphConvertor::get_adpt_map() { @@ -369,7 +371,9 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameDiagPart), ADPT_DESC(DiagPart)}, {string(kNameSpaceToBatch), ADPT_DESC(SpaceToBatchD)}, {string(kNameBatchToSpace), ADPT_DESC(BatchToSpaceD)}, - {string(kNameAtan2), ADPT_DESC(Atan2)}}; + {string(kNameAtan2), ADPT_DESC(Atan2)}, + {string(kNameApplyRMSProp), ADPT_DESC(ApplyRMSPropD)}, + {string(kNameApplyCenteredRMSProp), ADPT_DESC(ApplyCenteredRMSProp)}}; #ifdef ENABLE_GE adpt_map[string(kNamePrint)] = ADPT_DESC(Print); #endif diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 752a66b8e2..8be036ec3c 100755 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -1189,6 +1189,22 @@ INPUT_MAP(Atan2) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; ATTR_MAP(Atan2) = EMPTY_ATTR_MAP; OUTPUT_MAP(Atan2) = {{0, OUTPUT_DESC(y)}}; +// ApplyRMSPropD +INPUT_MAP(ApplyRMSPropD) = { + {1, INPUT_DESC(var)}, {2, INPUT_DESC(ms)}, {3, INPUT_DESC(mom)}, {4, INPUT_DESC(grad)}, {5, INPUT_DESC(lr)}}; +INPUT_ATTR_MAP(ApplyRMSPropD) = {{6, ATTR_DESC(rho, AnyTraits())}, + {7, ATTR_DESC(momentum, AnyTraits())}, + {8, ATTR_DESC(epsilon, AnyTraits())}}; +ATTR_MAP(ApplyRMSPropD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits())}}; +OUTPUT_MAP(ApplyRMSPropD) = {{0, OUTPUT_DESC(var)}}; + +// ApplyCenteredRMSProp +INPUT_MAP(ApplyCenteredRMSProp) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(mg)}, {3, INPUT_DESC(ms)}, + {4, INPUT_DESC(mom)}, {5, INPUT_DESC(grad)}, {6, INPUT_DESC(lr)}, + {7, INPUT_DESC(rho)}, {8, INPUT_DESC(momentum)}, {9, INPUT_DESC(epsilon)}}; +ATTR_MAP(ApplyCenteredRMSProp) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits())}}; +OUTPUT_MAP(ApplyCenteredRMSProp) = {{0, OUTPUT_DESC(var)}}; + #ifdef ENABLE_GE // Print INPUT_MAP(Print) = EMPTY_INPUT_MAP; diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index 027264c06e..e31a4fa07e 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -447,6 +447,12 @@ DECLARE_OP_ADAPTER(BatchToSpaceD) DECLARE_OP_USE_OUTPUT(BatchToSpaceD) DECLARE_OP_ADAPTER(Atan2) DECLARE_OP_USE_OUTPUT(Atan2) +DECLARE_OP_ADAPTER(ApplyRMSPropD) +DECLARE_OP_USE_INPUT_ATTR(ApplyRMSPropD) +DECLARE_OP_USE_OUTPUT(ApplyRMSPropD) +DECLARE_OP_ADAPTER(ApplyCenteredRMSProp) +DECLARE_OP_USE_OUTPUT(ApplyCenteredRMSProp) + #ifdef ENABLE_GE DECLARE_OP_ADAPTER(Print) DECLARE_OP_USE_DYN_INPUT(Print) diff --git a/mindspore/nn/optim/__init__.py b/mindspore/nn/optim/__init__.py index 6f7f6fbd46..8f21179893 100644 --- a/mindspore/nn/optim/__init__.py +++ b/mindspore/nn/optim/__init__.py @@ -25,6 +25,7 @@ from .lamb import Lamb from .sgd import SGD from .lars import LARS from .ftrl import FTRL +from .rmsprop import RMSProp __all__ = ['Optimizer', 'Momentum', 'LARS', 'Adam', 'AdamWeightDecay', - 'AdamWeightDecayDynamicLR', 'Lamb', 'SGD', 'FTRL'] + 'AdamWeightDecayDynamicLR', 'Lamb', 'SGD', 'FTRL', 'RMSProp'] diff --git a/mindspore/nn/optim/rmsprop.py b/mindspore/nn/optim/rmsprop.py new file mode 100644 index 0000000000..3000fdeeee --- /dev/null +++ b/mindspore/nn/optim/rmsprop.py @@ -0,0 +1,187 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +"""rmsprop""" +from mindspore.ops import functional as F, composite as C, operations as P +from mindspore.common.initializer import initializer +from mindspore.common.parameter import Parameter +from mindspore._checkparam import ParamValidator as validator +import mindspore.common.dtype as mstype +from .optimizer import Optimizer, grad_scale + +rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt") +centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt") + + +@rmsprop_opt.register("Function", "Number", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor") +def _rmsprop_opt(opt, learning_rate, decay, epsilon, momentum, weight, ms, mom, grad): + """Apply rmsprop optimizer to the weight parameter.""" + success = True + success = F.depend(success, opt(weight, ms, mom, grad, learning_rate, decay, momentum, epsilon)) + return success + + +@rmsprop_opt.register("Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor") +def _rmsprop_opt_dynamic_lr(opt, learning_rate, decay, epsilon, momentum, weight, ms, mom, grad): + """Apply rmsprop optimizer to the weight parameter using dynamic learning rate.""" + success = True + success = F.depend(success, opt(weight, ms, mom, grad, learning_rate, decay, momentum, epsilon)) + return success + + +@centered_rmsprop_opt.register("Function", "Number", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", + "Tensor", "Tensor") +def _centered_rmsprop_opt(opt, learning_rate, decay, epsilon, momentum, weight, mg, ms, mom, grad): + """Apply centered rmsprop optimizer to the weight parameter.""" + success = True + success = F.depend(success, opt(weight, mg, ms, mom, grad, learning_rate, decay, momentum, epsilon)) + return success + + +@centered_rmsprop_opt.register("Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", + "Tensor", "Tensor") +def _centered_rmsprop_opt_dynamic_lr(opt, learning_rate, decay, epsilon, momentum, weight, mg, ms, mom, grad): + """Apply centered rmsprop optimizer to the weight parameter using dynamic learning rate.""" + success = True + success = F.depend(success, opt(weight, mg, ms, mom, grad, learning_rate, decay, momentum, epsilon)) + return success + + +class RMSProp(Optimizer): + """ + Implements Root Mean Squared Propagation (RMSProp) algorithm. + + Note: + Update `params` according to the RMSProp algorithm. + + The equation is as follows: + + .. math:: + s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2 + + .. math:: + m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} + \\epsilon}} \\nabla Q_{i}(w) + + .. math:: + w = w - m_{t} + + The first equation calculates moving average of the squared gradient for + each weight. Then dividing the gradient by :math:`\\sqrt{ms_{t} + \\epsilon}`. + + if centered is True: + + .. math:: + g_{t} = \\rho g_{t-1} + (1 - \\rho)\\nabla Q_{i}(w) + + .. math:: + s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2 + + .. math:: + m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} - g_{t}^2 + \\epsilon}} \\nabla Q_{i}(w) + + .. math:: + w = w - m_{t} + + where, :math:`w` represents `params`, which will be updated. + :math:`g_{t}` is mean gradients, :math:`g_{t-1}` is the last moment of :math:`g_{t}`. + :math:`s_{t}` is the mean square gradients, :math:`s_{t-1}` is the last moment of :math:`s_{t}`, + :math:`m_{t}` is moment, the delta of `w`, :math:`m_{t-1}` is the last moment of :math:`m_{t}`. + :math:`\\rho` represents `decay`. :math:`\\beta` is the momentum term, represents `momentum`. + :math:`\\epsilon` is a smoothing term to avoid division by zero, represents `epsilon`. + :math:`\\eta` is learning rate, represents `learning_rate`. :math:`\\nabla Q_{i}(w)` is gradientse, + represents `gradients`. + + Args: + params (list[Parameter]): A list of parameter, which will be updated. The element in `parameters` + should be class mindspore.Parameter. + learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is + Iterable or a Tensor and the dims of the Tensor is 1, + use dynamic learning rate, then the i-th step will + take the i-th value as the learning rate. + When the learning_rate is float or learning_rate is a Tensor + but the dims of the Tensor is 0, use fixed learning rate. + Other cases are not supported. + decay (float): Decay rate. + momentum (float): Hyperparameter of type float, means momentum for the moving average. + epsilon (float): Term added to the denominator to improve numerical stability. Should be greater than 0. + use_locking (bool): Enable a lock to protect the update of variable and accumlation tensors. Default: False. + centered (bool): If True, gradients are normalized by the estimated variance of the gradient. Default: False + loss_scale (float): A floating point value for the loss scale. Default: 1.0. + + Inputs: + - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`. + + Outputs: + Tensor[bool], the value is True. + + Examples: + >>> net = Net() + >>> loss = nn.SoftmaxCrossEntropyWithLogits() + >>> opt = RMSProp(params=net.trainable_params(), learning_rate=lr) + >>> model = Model(net, loss, opt) + """ + def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10, + use_locking=False, centered=False, loss_scale=1.0): + super(RMSProp, self).__init__(learning_rate, params) + + if isinstance(momentum, float) and momentum < 0.0: + raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum)) + + if decay < 0.0: + raise ValueError("decay should be at least 0.0, but got dampening {}".format(decay)) + self.decay = decay + self.epsilon = epsilon + + validator.check_type("use_locking", use_locking, [bool]) + validator.check_type("centered", centered, [bool]) + self.centered = centered + if centered: + self.opt = P.ApplyCenteredRMSProp(use_locking) + self.mg = self.parameters.clone(prefix="mean_grad", init='zeros') + else: + self.opt = P.ApplyRMSProp(use_locking) + + self.dynamic_lr = False + if not isinstance(learning_rate, float): + self.dynamic_lr = True + self.gather = P.GatherV2() + self.assignadd = P.AssignAdd() + self.global_step = Parameter(initializer(0, [1], mstype.int32), name="global_step") + self.axis = 0 + + self.momentum = momentum + + self.ms = self.parameters.clone(prefix="mean_square", init='zeros') + self.moment = self.parameters.clone(prefix="moment", init='zeros') + self.hyper_map = C.HyperMap() + + self.decay = decay + self.reciprocal_scale = 1.0 / loss_scale + + def construct(self, gradients): + params = self.parameters + if self.reciprocal_scale != 1.0: + gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients) + if self.dynamic_lr: + lr = self.gather(self.learning_rate, self.global_step, self.axis) + F.control_depend(lr, self.assignadd(self.global_step, self.one)) + else: + lr = self.learning_rate + if self.centered: + success = self.hyper_map(F.partial(centered_rmsprop_opt, self.opt, lr, self.decay, self.epsilon, + self.momentum), params, self.mg, self.ms, self.moment, gradients) + else: + success = self.hyper_map(F.partial(rmsprop_opt, self.opt, lr, self.decay, self.epsilon, + self.momentum), params, self.ms, self.moment, gradients) + return success diff --git a/mindspore/ops/_grad/grad_math_ops.py b/mindspore/ops/_grad/grad_math_ops.py index 9e90c5660c..1675855c88 100755 --- a/mindspore/ops/_grad/grad_math_ops.py +++ b/mindspore/ops/_grad/grad_math_ops.py @@ -394,8 +394,8 @@ def _split_shape_index(input_shape, axis): axis = tuple([axis]) reduction_indices = tuple([(i + rank) % rank for i in axis]) other_indices = tuple(set(range(rank)) - set(reduction_indices)) - reduced_num = reduce(lambda x, y: x * y, [input_shape[i] for i in reduction_indices]) - other_num = reduce(lambda x, y: x * y, [input_shape[i] for i in other_indices]) + reduced_num = reduce(lambda x, y: x * y, [1] + [input_shape[i] for i in reduction_indices]) + other_num = reduce(lambda x, y: x * y, [1] + [input_shape[i] for i in other_indices]) perm = reduction_indices + other_indices return tuple([reduced_num, other_num]), perm diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index 92b12f73e1..d23d5f02f3 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -65,7 +65,8 @@ from .nn_ops import (LSTM, SGD, Adam, ApplyMomentum, BatchNorm, SmoothL1Loss, Softmax, SoftmaxCrossEntropyWithLogits, ROIAlign, SparseSoftmaxCrossEntropyWithLogits, Tanh, - TopK, BinaryCrossEntropy, SparseApplyAdagrad, LARSUpdate, ApplyFtrl, SparseApplyFtrlD) + TopK, BinaryCrossEntropy, SparseApplyAdagrad, LARSUpdate, ApplyFtrl, SparseApplyFtrlD, + ApplyRMSProp, ApplyCenteredRMSProp) from .other_ops import Assign, IOU, BoundingBoxDecode, BoundingBoxEncode, CheckValid, MakeRefKey @@ -229,6 +230,8 @@ __all__ = [ "SpaceToBatch", "BatchToSpace", "Atan2", + "ApplyRMSProp", + "ApplyCenteredRMSProp" ] __all__.sort() diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 57e409b44f..a35766a417 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -1359,6 +1359,158 @@ class SGD(PrimitiveWithInfer): validator.check_typename("stat_dtype", stat_dtype, [mstype.float16, mstype.float32]) return parameters_dtype +class ApplyRMSProp(PrimitiveWithInfer): + """ + Optimizer that implements the Root Mean Square prop(RMSProp) algorithm. + + Note: + Update `var` according to the RMSProp algorithm. + + .. math:: + s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2 + + .. math:: + m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} + \\epsilon}} \\nabla Q_{i}(w) + + .. math:: + w = w - m_{t} + + where, :math:`w` represents `var`, which will be updated. + :math:`s_{t}` represents `mean_square`, :math:`s_{t-1}` is the last momentent of :math:`s_{t}`, + :math:`m_{t}` represents `moment`, :math:`m_{t-1}` is the last momentent of :math:`m_{t}`. + :math:`\\rho` represents `decay`. :math:`\\beta` is the momentum term, represents `momentum`. + :math:`\\epsilon` is a smoothing term to avoid division by zero, represents `epsilon`. + :math:`\\eta` represents `learning_rate`. :math:`\\nabla Q_{i}(w)` represents `grad`. + + Args: + use_locking (bool): Enable a lock to protect the update of variable tensors. Default: False. + + Inputs: + - **var** (Tensor) - Weights to be update. + - **mean_square** (Tensor) - Mean square gradients, must have the same type as `var`. + - **moment** (Tensor) - Delta of `var`, must have the same type as `var`. + - **grad** (Tensor) - Gradients, must have the same type as `var`. + - **learning_rate** (Union[Number, Tensor]) - Learning rate. + - **decay** (float) - Decay rate. + - **momentum** (float) - Momentum. + - **epsilon** (float) - Ridge term. + + Outputs: + Tensor, parameters to be update. + + Examples: + >>> net = Net() + >>> loss = nn.SoftmaxCrossEntropyWithLogits() + >>> opt = RMSProp(params=net.trainable_params(), learning_rate=learning_rate) + >>> model = Model(net, loss, opt) + """ + + @prim_attr_register + def __init__(self, use_locking=False): + self.use_locking = validator.check_type("use_locking", use_locking, [bool]) + + def infer_shape(self, var_shape, mean_square_shape, moment_shape, grad_shape, learning_rate_shape, decay_shape, + momentum_shape, epsilon_shape): + validator.check_param_equal("var_shape", var_shape, "mean_square_shape", mean_square_shape) + validator.check_param_equal("var_shape", var_shape, "moment_shape", moment_shape) + validator.check_param_equal("var_shape", var_shape, "grad_shape", grad_shape) + return var_shape + + def infer_dtype(self, var_dtype, mean_square_dtype, moment_dtype, grad_dtype, learning_rate_dtype, decay_dtype, + momentum_dtype, epsilon_dtype): + validator.check_subclass("var_dtype", var_dtype, mstype.tensor) + validator.check_subclass("mean_square_dtype", mean_square_dtype, mstype.tensor) + validator.check_subclass("moment_dtype", moment_dtype, mstype.tensor) + validator.check_subclass("grad_dtype", moment_dtype, mstype.tensor) + args = {"var_dtype": var_dtype, "mean_square_dtype": mean_square_dtype, "moment_dtype": moment_dtype, + "grad_dtype": grad_dtype} + validator.check_type_same(args, mstype.number_type) + + args = {"learning_rate_dtype": learning_rate_dtype, "decay_dtype": decay_dtype, + 'momentum_dtype': momentum_dtype, "epsilon_dtype": epsilon_dtype} + validator.check_type_same(args, [mstype.float16, mstype.float32]) + return var_dtype + + +class ApplyCenteredRMSProp(PrimitiveWithInfer): + """ + Optimizer that implements the centered RMSProp algorithm. + + Note: + Update `var` according to the centered RMSProp algorithm. + + .. math:: + g_{t} = \\rho g_{t-1} + (1 - \\rho)\\nabla Q_{i}(w) + + .. math:: + s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2 + + .. math:: + m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} - g_{t}^2 + \\epsilon}} \\nabla Q_{i}(w) + + .. math:: + w = w - m_{t} + + where, :math:`w` represents `var`, which will be updated. + :math:`g_{t}` represents `mean_gradient`, :math:`g_{t-1}` is the last momentent of :math:`g_{t}`. + :math:`s_{t}` represents `mean_square`, :math:`s_{t-1}` is the last momentent of :math:`s_{t}`, + :math:`m_{t}` represents `moment`, :math:`m_{t-1}` is the last momentent of :math:`m_{t}`. + :math:`\\rho` represents `decay`. :math:`\\beta` is the momentum term, represents `momentum`. + :math:`\\epsilon` is a smoothing term to avoid division by zero, represents `epsilon`. + :math:`\\eta` represents `learning_rate`. :math:`\\nabla Q_{i}(w)` represents `grad`. + + Args: + use_locking (bool): Enable a lock to protect the update of variable tensors. Default: False. + + Inputs: + - **var** (Tensor) - Weights to be update. + - **mean_gradient** (Tensor) - Mean gradients, must have the same type as `var`. + - **mean_square** (Tensor) - Mean square gradients, must have the same type as `var`. + - **moment** (Tensor) - Delta of `var`, must have the same type as `var`. + - **grad** (Tensor) - Gradients, must have the same type as `var`. + - **learning_rate** (Union[Number, Tensor]) - Learning rate. + - **decay** (float) - Decay rate. + - **momentum** (float) - Momentum. + - **epsilon** (float) - Ridge term. + + Outputs: + Tensor, parameters to be update. + + Examples: + >>> net = Net() + >>> loss = nn.SoftmaxCrossEntropyWithLogits() + >>> opt = RMSProp(params=net.trainable_params(), learning_rate=learning_rate, centered=True) + >>> model = Model(net, loss, opt) + """ + + @prim_attr_register + def __init__(self, use_locking=False): + self.use_locking = validator.check_type("use_locking", use_locking, [bool]) + + def infer_shape(self, var_shape, mean_gradient_shape, mean_square_shape, moment_shape, grad_shape, + learning_rate_shape, decay_shape, momentum_shape, epsilon_shape): + validator.check_param_equal("var_shape", var_shape, "mean_gradient_shape", mean_gradient_shape) + validator.check_param_equal("var_shape", var_shape, "mean_square_shape", mean_square_shape) + validator.check_param_equal("var_shape", var_shape, "moment_shape", moment_shape) + validator.check_param_equal("var_shape", var_shape, "grad_shape", grad_shape) + return var_shape + + def infer_dtype(self, var_dtype, mean_gradient_dtype, mean_square_dtype, moment_dtype, grad_dtype, + learning_rate_dtype, rho_dtype, momentum_dtype, epsilon_dtype): + validator.check_subclass("var_dtype", var_dtype, mstype.tensor) + validator.check_subclass("mean_gradient_dtype", mean_gradient_dtype, mstype.tensor) + validator.check_subclass("mean_square_dtype", mean_square_dtype, mstype.tensor) + validator.check_subclass("moment_dtype", moment_dtype, mstype.tensor) + validator.check_subclass("grad_dtype", moment_dtype, mstype.tensor) + args = {"var_dtype": var_dtype, "mean_gradient_dtype": mean_gradient_dtype, + "mean_square_dtype": mean_square_dtype, "moment_dtype": moment_dtype, "grad_dtype": grad_dtype} + validator.check_type_same(args, mstype.number_type) + + args = {"learning_rate_dtype": learning_rate_dtype, "rho_dtype": rho_dtype, 'momentum_dtype': momentum_dtype, + "epsilon_dtype": epsilon_dtype} + validator.check_type_same(args, [mstype.float16, mstype.float32]) + return var_dtype + class LayerNorm(Primitive): r""" diff --git a/tests/mindspore_test_framework/utils/block_util.py b/tests/mindspore_test_framework/utils/block_util.py index 9d75ae0888..b4a926c15d 100644 --- a/tests/mindspore_test_framework/utils/block_util.py +++ b/tests/mindspore_test_framework/utils/block_util.py @@ -223,6 +223,10 @@ class InputOpNet(nn.Cell): x = self.op(x1, x2, x3, x4, x5, self.c1) return x + def construct5_c4(self, x1, x2, x3, x4, x5): + x = self.op(x1, x2, x3, x4, x5, self.c1, self.c2, self.c3, self.c4) + return x + def gen_net(op, input_num, training=True, desc_const=(), const_first=False, add_fake_input=False): if isinstance(op, nn.Cell): return op diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py index 1a66b99450..c759c59c9f 100755 --- a/tests/ut/python/ops/test_ops.py +++ b/tests/ut/python/ops/test_ops.py @@ -810,6 +810,18 @@ test_case_nn_ops = [ 'desc_inputs': [[3, 3], [3, 3], [3, 3], [3, 3]], 'desc_bprop': [3, 3], 'skip': ['backward']}), + ('ApplyRMSProp', { + 'block': P.ApplyRMSProp(), + 'desc_const': [0.9, 0.0, 1e-10, 0.001], + 'desc_inputs': [[3, 3], [3, 3], [3, 3], [3, 3]], + 'desc_bprop': [3, 3], + 'skip': ['backward']}), + ('ApplyCenteredRMSProp', { + 'block': P.ApplyCenteredRMSProp(), + 'desc_const': [0.9, 0.0, 1e-10, 0.001], + 'desc_inputs': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], + 'desc_bprop': [3, 3], + 'skip': ['backward']}), ] test_case_array_ops = [ From 59d08edea7fe67963ca3c591c27b9c468acb3e1c Mon Sep 17 00:00:00 2001 From: ms_yan <6576637+ms_yan@user.noreply.gitee.com> Date: Wed, 1 Apr 2020 20:14:46 +0800 Subject: [PATCH 060/227] repair unsuitable warning in repeat count and epoch size check --- mindspore/train/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/train/model.py b/mindspore/train/model.py index fe655433fa..4174632cec 100755 --- a/mindspore/train/model.py +++ b/mindspore/train/model.py @@ -372,7 +372,7 @@ class Model: >>> model.train(2, dataset) """ repeat_count = train_dataset.get_repeat_count() - if epoch != repeat_count: + if epoch != repeat_count and dataset_sink_mode is True: logger.warning(f"The epoch_size {epoch} is not the same with dataset repeat_count {repeat_count}") check_bool(dataset_sink_mode) _device_number_check(self._parallel_mode, self._device_number) From 84e4942ca9120aec2c1aae2a47cca399d05f417c Mon Sep 17 00:00:00 2001 From: huangdongrun Date: Sat, 28 Mar 2020 10:33:10 +0000 Subject: [PATCH 061/227] support grad on graph with variable arguments use unpack graph primitive instead add testcases for all grad interface remove debug log format code remove dumpfuncgraph resolve clang-format resolve reviews resolve cpplint fix review --- .../ccsrc/operator/composite/composite.cc | 50 ------ .../ccsrc/operator/composite/composite.h | 14 +- .../ccsrc/operator/composite/unpack_call.cc | 94 +++++++++++ .../ccsrc/operator/composite/unpack_call.h | 54 +++++++ mindspore/ccsrc/operator/ops.h | 15 ++ mindspore/ccsrc/optimizer/irpass.cc | 6 + mindspore/ccsrc/optimizer/irpass.h | 8 + .../optimizer/irpass/grad_var_prepare.cc | 144 +++++++++++++++++ .../ccsrc/optimizer/irpass/grad_var_prepare.h | 55 +++++++ mindspore/ccsrc/pipeline/action.cc | 16 +- mindspore/ccsrc/pipeline/pass.cc | 17 ++ mindspore/ccsrc/pipeline/pass.h | 2 +- .../static_analysis/abstract_function.h | 1 + .../static_analysis/analysis_context.cc | 2 +- .../ccsrc/pipeline/static_analysis/prim.cc | 79 +++++++++ .../ccsrc/pipeline/static_analysis/prim.h | 15 ++ .../static_analysis/static_analysis.cc | 4 + .../python/parameter_feature/test_var_grad.py | 151 ++++++++++++++++++ 18 files changed, 660 insertions(+), 67 deletions(-) create mode 100644 mindspore/ccsrc/operator/composite/unpack_call.cc create mode 100644 mindspore/ccsrc/operator/composite/unpack_call.h create mode 100644 mindspore/ccsrc/optimizer/irpass/grad_var_prepare.cc create mode 100644 mindspore/ccsrc/optimizer/irpass/grad_var_prepare.h diff --git a/mindspore/ccsrc/operator/composite/composite.cc b/mindspore/ccsrc/operator/composite/composite.cc index 0c55b9480c..347641829d 100644 --- a/mindspore/ccsrc/operator/composite/composite.cc +++ b/mindspore/ccsrc/operator/composite/composite.cc @@ -1199,51 +1199,6 @@ FuncGraphPtr TensorSlice::GenerateFuncGraph(const AbstractBasePtrList& args_spec return ret_graph; } -FuncGraphPtr UnpackCall::GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) { - // slice a tensor - // args: tensor, slice or slice tuple - const std::string op_name = std::string("UnpackCall"); - size_t arg_length = args_spec_list.size(); - if (arg_length < 2) { - MS_LOG(EXCEPTION) << "" << op_name << " requires at least two args, but got " << arg_length << "."; - } - - (void)abstract::CheckArg(op_name, args_spec_list, 0); - FuncGraphPtr ret_graph = std::make_shared(); - ret_graph->set_flags(FUNC_GRAPH_FLAG_CORE, true); - - AnfNodePtr fnNode = ret_graph->add_parameter(); - std::vector elems; - elems.push_back(fnNode); - for (size_t index = 1; index < arg_length; index++) { - MS_EXCEPTION_IF_NULL(args_spec_list[index]); - if (args_spec_list[index]->isa()) { - AbstractTuplePtr arg_tuple = dyn_cast(args_spec_list[index]); - AnfNodePtr para_tuple = ret_graph->add_parameter(); - for (size_t i = 0; i < arg_tuple->size(); ++i) { - elems.push_back( - ret_graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), para_tuple, NewValueNode(SizeToInt(i))})); - } - } else if (args_spec_list[index]->isa()) { - AbstractDictionaryPtr arg_dict = dyn_cast(args_spec_list[index]); - AnfNodePtr para_dict = ret_graph->add_parameter(); - auto dict_elems = arg_dict->elements(); - (void)std::transform( - dict_elems.begin(), dict_elems.end(), std::back_inserter(elems), - [ret_graph, para_dict](const AbstractAttribute& item) { - return ret_graph->NewCNode( - {NewValueNode(prim::kPrimMakeKeywordArg), NewValueNode(item.first), - ret_graph->NewCNode({NewValueNode(prim::kPrimDictGetItem), para_dict, NewValueNode(item.first)})}); - }); - } else { - MS_LOG(EXCEPTION) << "" << op_name << " require args should be tuple or dict, but got " - << args_spec_list[index]->ToString(); - } - } - ret_graph->set_output(ret_graph->NewCNode(elems)); - return ret_graph; -} - REGISTER_PYBIND_DEFINE( TupleAdd_, ([](const py::module* m) { (void)py::class_>(*m, "TupleAdd_").def(py::init()); @@ -1258,10 +1213,5 @@ REGISTER_PYBIND_DEFINE(TensorSlice_, ([](const py::module* m) { (void)py::class_>(*m, "TensorSlice_") .def(py::init()); })); - -REGISTER_PYBIND_DEFINE(UnpackCall_, ([](const py::module* m) { - (void)py::class_>(*m, "UnpackCall_") - .def(py::init()); - })); } // namespace prim } // namespace mindspore diff --git a/mindspore/ccsrc/operator/composite/composite.h b/mindspore/ccsrc/operator/composite/composite.h index 176efb0425..dc8627ba61 100644 --- a/mindspore/ccsrc/operator/composite/composite.h +++ b/mindspore/ccsrc/operator/composite/composite.h @@ -29,6 +29,7 @@ #include "operator/composite/zip_operation.h" #include "operator/composite/list_append_operation.h" #include "operator/composite/do_signature.h" +#include "operator/composite/unpack_call.h" #include "pipeline/static_analysis/static_analysis.h" #include "utils/misc.h" #include "utils/any.h" @@ -154,7 +155,7 @@ class GradOperation : public MetaFuncGraph { FuncGraphPtr GetGrad(AnfNodePtr ptrNode, const AnfNodePtr& weights, const std::vector& ptrParams, bool applyJ = false); FuncGraphPtr GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) override; - + bool sens_param() const { return sens_param_; } bool get_all_; bool get_by_list_; bool sens_param_; @@ -208,17 +209,6 @@ class TensorSlice : public MetaFuncGraph { }; using TensorSlicePtr = std::shared_ptr; -// Expand the tuple and dict parameters generated when parsing the function call, -// and generate positional parameters and key-value pairs for function. -class UnpackCall : public MetaFuncGraph { - public: - explicit UnpackCall(const std::string& name) : MetaFuncGraph(name) {} - ~UnpackCall() override = default; - MS_DECLARE_PARENT(UnpackCall, MetaFuncGraph) - FuncGraphPtr GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) override; - friend bool operator==(const UnpackCall& lhs, const UnpackCall& rhs) { return lhs.name_ == rhs.name_; } -}; -using UnpackCallPtr = std::shared_ptr; } // namespace prim } // namespace mindspore diff --git a/mindspore/ccsrc/operator/composite/unpack_call.cc b/mindspore/ccsrc/operator/composite/unpack_call.cc new file mode 100644 index 0000000000..64d6b3433b --- /dev/null +++ b/mindspore/ccsrc/operator/composite/unpack_call.cc @@ -0,0 +1,94 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "operator/composite/unpack_call.h" +#include +#include + +#include "./common.h" +#include "pipeline/static_analysis/abstract_value.h" +#include "pipeline/static_analysis/dshape.h" +#include "pipeline/static_analysis/param_validator.h" +#include "operator/cc_implementations.h" +#include "ir/anf.h" +#include "optimizer/opt.h" +#include "utils/symbolic.h" +#include "pybind_api/api_register.h" + +namespace mindspore { +// namespace to support composite operators definition +namespace prim { +using mindspore::abstract::AbstractAttribute; +using mindspore::abstract::AbstractBase; +using mindspore::abstract::AbstractDictionary; +using mindspore::abstract::AbstractDictionaryPtr; +using mindspore::abstract::AbstractFunction; +using mindspore::abstract::AbstractKeywordArg; +using mindspore::abstract::AbstractTuple; +using mindspore::abstract::AbstractTuplePtr; + +FuncGraphPtr UnpackCall::GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) { + // slice a tensor + // args: tensor, slice or slice tuple + const std::string op_name = std::string("UnpackCall"); + size_t arg_length = args_spec_list.size(); + if (arg_length < 2) { + MS_LOG(EXCEPTION) << op_name << " requires at least two args, but got " << arg_length << "."; + } + + (void)abstract::CheckArg(op_name, args_spec_list, 0); + auto ret_graph = std::make_shared(); + ret_graph->set_flags(FUNC_GRAPH_FLAG_CORE, true); + + AnfNodePtr fnNode = ret_graph->add_parameter(); + std::vector elems; + elems.push_back(fnNode); + for (size_t index = 1; index < arg_length; index++) { + MS_EXCEPTION_IF_NULL(args_spec_list[index]); + if (args_spec_list[index]->isa()) { + auto arg_tuple = args_spec_list[index]->cast(); + AnfNodePtr para_tuple = ret_graph->add_parameter(); + for (size_t i = 0; i < arg_tuple->size(); ++i) { + elems.push_back( + ret_graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), para_tuple, NewValueNode(SizeToInt(i))})); + } + } else if (args_spec_list[index]->isa()) { + AbstractDictionaryPtr arg_dict = args_spec_list[index]->cast(); + AnfNodePtr para_dict = ret_graph->add_parameter(); + auto dict_elems = arg_dict->elements(); + (void)std::transform(dict_elems.begin(), dict_elems.end(), std::back_inserter(elems), + [ret_graph, para_dict](const AbstractAttribute& item) { + auto dict_get_item = ret_graph->NewCNode( + {NewValueNode(prim::kPrimDictGetItem), para_dict, NewValueNode(item.first)}); + return ret_graph->NewCNode( + {NewValueNode(prim::kPrimMakeKeywordArg), NewValueNode(item.first), dict_get_item}); + }); + } else { + MS_LOG(EXCEPTION) << op_name << " require args should be tuple or dict, but got " + << args_spec_list[index]->ToString(); + } + } + ret_graph->set_output(ret_graph->NewCNode(elems)); + return ret_graph; +} + +REGISTER_PYBIND_DEFINE(UnpackCall_, ([](const py::module* m) { + (void)py::class_>(*m, "UnpackCall_") + .def(py::init()); + })); + +} // namespace prim +} // namespace mindspore diff --git a/mindspore/ccsrc/operator/composite/unpack_call.h b/mindspore/ccsrc/operator/composite/unpack_call.h new file mode 100644 index 0000000000..7ec5f9ad33 --- /dev/null +++ b/mindspore/ccsrc/operator/composite/unpack_call.h @@ -0,0 +1,54 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_CCSRC_OPERATOR_COMPOSITE_UNPACK_CALL_H_ +#define MINDSPORE_CCSRC_OPERATOR_COMPOSITE_UNPACK_CALL_H_ + +#include +#include +#include +#include +#include +#include +#include + +#include "pipeline/static_analysis/static_analysis.h" +#include "utils/misc.h" +#include "utils/any.h" +#include "ir/dtype.h" +#include "ir/meta_func_graph.h" +#include "common/utils.h" + +namespace mindspore { +// namespace to support composite operators definition +namespace prim { + +// Expand the tuple and dict parameters generated when parsing the function call, +// and generate positional parameters and key-value pairs for function. +class UnpackCall : public MetaFuncGraph { + public: + explicit UnpackCall(const std::string& name) : MetaFuncGraph(name) {} + ~UnpackCall() override = default; + MS_DECLARE_PARENT(UnpackCall, MetaFuncGraph) + FuncGraphPtr GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) override; + friend bool operator==(const UnpackCall& lhs, const UnpackCall& rhs) { return lhs.name_ == rhs.name_; } +}; +using UnpackCallPtr = std::shared_ptr; + +} // namespace prim +} // namespace mindspore + +#endif // MINDSPORE_CCSRC_OPERATOR_COMPOSITE_UNPACK_CALL_H_ diff --git a/mindspore/ccsrc/operator/ops.h b/mindspore/ccsrc/operator/ops.h index f3f5dad5f1..727d66dfb3 100644 --- a/mindspore/ccsrc/operator/ops.h +++ b/mindspore/ccsrc/operator/ops.h @@ -246,6 +246,21 @@ class DoSignaturePrimitive : public Primitive { ValuePtr function_; }; using DoSignaturePrimitivePtr = std::shared_ptr; + +class UnpackGraphPrimitive : public Primitive { + public: + explicit UnpackGraphPrimitive(const std::string& name, const bool& with_sens, const bool& need_unpack_args) + : Primitive("UnpackGraph"), with_sens_in_args_(with_sens), need_unpack_args_(need_unpack_args) {} + ~UnpackGraphPrimitive() override = default; + MS_DECLARE_PARENT(UnpackGraphPrimitive, Primitive) + bool with_sens_in_args() const { return with_sens_in_args_; } + bool need_unpack_args() const { return need_unpack_args_; } + + private: + bool with_sens_in_args_; + bool need_unpack_args_; +}; +using UnpackGraphPrimitivePtr = std::shared_ptr; } // namespace prim } // namespace mindspore diff --git a/mindspore/ccsrc/optimizer/irpass.cc b/mindspore/ccsrc/optimizer/irpass.cc index ba78696b38..cdc960792f 100644 --- a/mindspore/ccsrc/optimizer/irpass.cc +++ b/mindspore/ccsrc/optimizer/irpass.cc @@ -39,6 +39,7 @@ #include "optimizer/irpass/specialize_transform.h" #include "optimizer/irpass/incorporate_getitem.h" #include "optimizer/irpass/incorporate_call.h" +#include "optimizer/irpass/grad_var_prepare.h" namespace mindspore { namespace opt { @@ -123,6 +124,11 @@ ResolveIRPassLib::ResolveIRPassLib() { resolver_resolve_ = MakeSubstitution(ResolverResolve(), "resolver_resolve", prim::kPrimResolve); resolver_getattr_ = MakeSubstitution(ResolverGetattr(), "resolver_getattr", prim::kPrimGetAttr); } + +InferenceOptPrepareLib::InferenceOptPrepareLib() { + grad_var_prepare_ = MakeSubstitution(GradVarPrepare(), "grad_var_prepare", IsCNode); +} + } // namespace irpass } // namespace opt } // namespace mindspore diff --git a/mindspore/ccsrc/optimizer/irpass.h b/mindspore/ccsrc/optimizer/irpass.h index c2af344e32..bdaf42b3ed 100644 --- a/mindspore/ccsrc/optimizer/irpass.h +++ b/mindspore/ccsrc/optimizer/irpass.h @@ -102,6 +102,13 @@ class ResolveIRPassLib { SubstitutionPtr resolver_getattr_; }; +class InferenceOptPrepareLib { + public: + InferenceOptPrepareLib(); + ~InferenceOptPrepareLib() = default; + SubstitutionPtr grad_var_prepare_; +}; + // predicate functions inline bool IsNode(const AnfNodePtr &) { return true; } @@ -151,6 +158,7 @@ inline bool IsCNodeDup(const AnfNodePtr &node) { } return false; } + } // namespace irpass } // namespace opt } // namespace mindspore diff --git a/mindspore/ccsrc/optimizer/irpass/grad_var_prepare.cc b/mindspore/ccsrc/optimizer/irpass/grad_var_prepare.cc new file mode 100644 index 0000000000..5daeced3a5 --- /dev/null +++ b/mindspore/ccsrc/optimizer/irpass/grad_var_prepare.cc @@ -0,0 +1,144 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "optimizer/irpass/grad_var_prepare.h" +#include +#include +#include +#include + +#include "operator/composite/composite.h" +#include "operator/ops.h" +#include "optimizer/irpass.h" +#include "optimizer/optimizer.h" +#include "ir/visitor.h" +#include "ir/func_graph.h" +#include "ir/func_graph_cloner.h" + +namespace mindspore { +namespace opt { +namespace irpass { + +static AnfNodePtr GenerateUnpackGraphNode(std::vector inputs_y, FuncGraphPtr func_graph, + AnfNodePtr func_node, bool is_unpack, bool sens_param) { + MS_EXCEPTION_IF_NULL(func_graph); + MS_EXCEPTION_IF_NULL(func_node); + std::vector nodes; + AnfNodePtr unpack_graph_node = nullptr; + if (is_unpack) { + auto unpack_graph = std::make_shared("unpack_graph", sens_param, true); + nodes.push_back(NewValueNode(unpack_graph)); + nodes.push_back(func_node); + // {unpackcall, {GradOperation, ...}, args...} + std::transform(inputs_y.begin() + 2, inputs_y.end(), std::back_inserter(nodes), + [](const AnfNodePtr& node) { return node; }); + unpack_graph_node = func_graph->NewCNode(nodes); + } else { + auto unpack_graph = std::make_shared("unpack_graph", sens_param, false); + nodes.push_back(NewValueNode(unpack_graph)); + nodes.push_back(func_node); + // {{GradOperation, ...}, args...} + std::transform(inputs_y.begin() + 1, inputs_y.end(), std::back_inserter(nodes), + [](const AnfNodePtr& node) { return node; }); + unpack_graph_node = func_graph->NewCNode(nodes); + } + return unpack_graph_node; +} + +// get metagraph of value node +MetaFuncGraphPtr GetMetaFuncGraphOfValueNode(const AnfNodePtr& node) { + ValuePtr value; + if (IsValueNode(node)) { + value = GetValueNode(node)->cast()->function(); + } else { + value = GetValueNode(node); + } + if (value == nullptr) { + return nullptr; + } + return value->cast(); +} + +// check if node is a specific metafuncgraph op +bool IsMetaFuncGraph(const AnfNodePtr& node, const MetaFuncGraphPtr meta_func_graph) { + if (node != nullptr) { + auto meta_func_graph_ptr = GetMetaFuncGraphOfValueNode(node); + if (meta_func_graph_ptr == nullptr) { + return false; + } + + if (meta_func_graph_ptr->type_name() == meta_func_graph->type_name()) { + return true; + } + } + return false; +} + +// {{GradOperation, g, w}, Ys} +// {UnPackCall, {GradOperation, g, w}, Ys} +AnfNodePtr GradVarPrepare::operator()(const OptimizerPtr&, const AnfNodePtr& node) { + if (!node->isa() || node->func_graph() == nullptr) { + return nullptr; + } + + // {{...}, Ys} + auto inputs_y = node->cast()->inputs(); + std::vector inputs_x; + if (IsCNode(inputs_y[0])) { + inputs_x = inputs_y[0]->cast()->inputs(); + } else if (IsMetaFuncGraph(inputs_y[0], unpack_op_) && IsCNode(inputs_y[1])) { + inputs_x = inputs_y[1]->cast()->inputs(); + } else { + return nullptr; + } + + // {{...}, Xs} + if (inputs_x.size() < 2) { + return nullptr; + } + + // {GradOperation, g, w} or {GradOperation, g} + if (!IsMetaFuncGraph(inputs_x[0], grad_op_)) { + return nullptr; + } + + auto meta_func = GetMetaFuncGraphOfValueNode(inputs_x[0]); + if (meta_func == nullptr) { + return nullptr; + } + auto grad_op_ptr = meta_func->cast(); + auto func_node = inputs_x[1]; + if (!IsValueNode(func_node)) { + return nullptr; + } + + AnfNodePtr unpack_graph_node = + GenerateUnpackGraphNode(inputs_y, node->cast()->func_graph(), func_node, + IsMetaFuncGraph(inputs_y[0], unpack_op_), grad_op_ptr->sens_param()); + // constuct new grad_opration + inputs_x[1] = unpack_graph_node; + auto grad_op_cnode = node->func_graph()->NewCNode(inputs_x); + if (IsMetaFuncGraph(inputs_y[0], unpack_op_)) { + inputs_y[1] = grad_op_cnode; + } else { + inputs_y[0] = grad_op_cnode; + } + auto cnode = node->func_graph()->NewCNode(inputs_y); + return cnode; +} +} // namespace irpass +} // namespace opt +} // namespace mindspore diff --git a/mindspore/ccsrc/optimizer/irpass/grad_var_prepare.h b/mindspore/ccsrc/optimizer/irpass/grad_var_prepare.h new file mode 100644 index 0000000000..599d1dca17 --- /dev/null +++ b/mindspore/ccsrc/optimizer/irpass/grad_var_prepare.h @@ -0,0 +1,55 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_CCSRC_OPTIMIZER_IRPASS_GRAD_VAR_PREPARE_H_ +#define MINDSPORE_CCSRC_OPTIMIZER_IRPASS_GRAD_VAR_PREPARE_H_ + +#include +#include +#include +#include + +#include "operator/composite/composite.h" +#include "operator/ops.h" +#include "optimizer/irpass.h" +#include "optimizer/optimizer.h" +#include "ir/visitor.h" +#include "ir/func_graph.h" +#include "ir/func_graph_cloner.h" + +namespace mindspore { +namespace opt { +namespace irpass { + +// {{GradOperation, g, w}, Ys} +// {UnPackCall, {GradOperation, g, w}, Ys} +class GradVarPrepare : public AnfVisitor { + public: + GradVarPrepare() + : grad_op_(std::make_shared("grad")), + unpack_op_(std::make_shared("unpack_call")) {} + ~GradVarPrepare() override = default; + + AnfNodePtr operator()(const OptimizerPtr &, const AnfNodePtr &node) override; + + private: + MetaFuncGraphPtr grad_op_; + MetaFuncGraphPtr unpack_op_; +}; +} // namespace irpass +} // namespace opt +} // namespace mindspore +#endif // MINDSPORE_CCSRC_OPTIMIZER_IRPASS_GRAD_VAR_PREPARE_H_ diff --git a/mindspore/ccsrc/pipeline/action.cc b/mindspore/ccsrc/pipeline/action.cc index 392602f419..83e524e5df 100644 --- a/mindspore/ccsrc/pipeline/action.cc +++ b/mindspore/ccsrc/pipeline/action.cc @@ -175,10 +175,10 @@ bool CombineLikeGraphs(const ResourcePtr&) { bool SymbolResolveAction(const ResourcePtr& res) { if (res->manager() == nullptr) { - MS_LOG(EXCEPTION) << "Resolve error."; + MS_LOG(EXCEPTION) << "SymbolResolve error, manager is null"; } if (res->func_graph() == nullptr) { - MS_LOG(EXCEPTION) << "Resolve error"; + MS_LOG(EXCEPTION) << "SymbolResolve error, graph is null"; } FuncGraphPtr func_graph = res->func_graph(); auto succ = parse::ResolveFuncGraph(func_graph, res); @@ -194,6 +194,16 @@ bool SymbolResolveAction(const ResourcePtr& res) { return succ; } +bool InferenceOptPrepareAction(const ResourcePtr& res) { + if (res->manager() == nullptr) { + MS_LOG(EXCEPTION) << "InferenceOptPrepare error, manager is null."; + } + if (res->func_graph() == nullptr) { + MS_LOG(EXCEPTION) << "InferenceOptPrepare error, graph is null."; + } + return InferenceOptPreparePass(res); +} + bool AbstractSpecializeAction(const ResourcePtr& res) { if (res->func_graph() == nullptr) { MS_LOG(EXCEPTION) << "AbstractSpecialize error"; @@ -331,7 +341,7 @@ static std::vector CommonPipeline() { // Resolve the python func actions.emplace_back(std::make_pair("symbol_resolve", SymbolResolveAction)); actions.emplace_back(std::make_pair("combine_like_graphs", CombineLikeGraphs)); - + actions.emplace_back(std::make_pair("inference_opt_prepare", InferenceOptPrepareAction)); // Evaluate type and shape, and specialize actions.emplace_back(std::make_pair("abstract_specialize", AbstractSpecializeAction)); diff --git a/mindspore/ccsrc/pipeline/pass.cc b/mindspore/ccsrc/pipeline/pass.cc index 02e8c5277b..e2626d5314 100644 --- a/mindspore/ccsrc/pipeline/pass.cc +++ b/mindspore/ccsrc/pipeline/pass.cc @@ -160,6 +160,13 @@ OptPassGroupMap GetControlPhases(const opt::irpass::OptimizeIRPassLib& irpass) { return map; } +OptPassGroupMap GetInferenceOptPreparePhases() { + opt::irpass::InferenceOptPrepareLib irpass; + auto grad_var_prepare = opt::OptPassConfig({irpass.grad_var_prepare_}); + opt::OptPassGroupMap prepare_map({{"inference_opt_prep", grad_var_prepare}}); + return prepare_map; +} + OptPassGroupMap GetPreparePhases(const opt::irpass::OptimizeIRPassLib& irpass) { opt::OptPassConfig prepare_group = opt::OptPassConfig({irpass.print_tuple_wrapper_}); OptPassGroupMap map({{"prepare_group", prepare_group}}); @@ -239,6 +246,16 @@ bool ValidatePass(const ResourcePtr& res) { return true; } +bool InferenceOptPreparePass(const ResourcePtr& res) { + FuncGraphPtr func_graph = res->func_graph(); + MS_EXCEPTION_IF_NULL(func_graph); + abstract::AbstractBasePtrList args_spec = res->args_spec(); + auto prepare_map = GetInferenceOptPreparePhases(); + auto infer_opt_prepare = opt::Optimizer::MakeOptimizer("inference_prepare", res, prepare_map); + (void)infer_opt_prepare->step(func_graph, args_spec, false); + return true; +} + std::vector kVmPasses = {{"simplify_data_structures", SimplifyDataStructuresPass}, {"opt_a", OptPassAGroup}, {"opt_b", OptPassBGroup}, diff --git a/mindspore/ccsrc/pipeline/pass.h b/mindspore/ccsrc/pipeline/pass.h index 03ed8eb370..3731d7e524 100644 --- a/mindspore/ccsrc/pipeline/pass.h +++ b/mindspore/ccsrc/pipeline/pass.h @@ -34,7 +34,7 @@ bool CconvPass(const ResourcePtr& res); bool ValidatePass(const ResourcePtr& res); bool ConvertPrepareAdapt(const ResourcePtr& res); bool AddControlDependPass(const ResourcePtr& res); - +bool InferenceOptPreparePass(const ResourcePtr& res); void ReclaimOptimizer(); } // namespace pipeline } // namespace mindspore diff --git a/mindspore/ccsrc/pipeline/static_analysis/abstract_function.h b/mindspore/ccsrc/pipeline/static_analysis/abstract_function.h index 3acb22d829..133d5e99a9 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/abstract_function.h +++ b/mindspore/ccsrc/pipeline/static_analysis/abstract_function.h @@ -133,6 +133,7 @@ class FuncGraphAbstractClosure : public AbstractFuncAtom { FuncGraphPtr func_graph_; AnalysisContextPtr context_; }; +using FuncGraphAbstractClosurePtr = std::shared_ptr; class MetaFuncGraphAbstractClosure : public AbstractFuncAtom { public: diff --git a/mindspore/ccsrc/pipeline/static_analysis/analysis_context.cc b/mindspore/ccsrc/pipeline/static_analysis/analysis_context.cc index 9326ded2d5..aeaa6b17f8 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/analysis_context.cc +++ b/mindspore/ccsrc/pipeline/static_analysis/analysis_context.cc @@ -41,7 +41,7 @@ AnalysisContextPtr AnalysisContext::NewFuncGraphContext(const FuncGraphPtr &func } else { oss << "nullptr"; } - MS_LOG(EXCEPTION) << "" << oss.str() << " NodeInfo: " << trace::GetDebugInfo(func_graph->debug_info()); + MS_LOG(EXCEPTION) << oss.str() << " NodeInfo: " << trace::GetDebugInfo(func_graph->debug_info()); } return NewContext(parent_context, func_graph, args_spec_list); } diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.cc b/mindspore/ccsrc/pipeline/static_analysis/prim.cc index 98d82de5d5..4110f25811 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/prim.cc +++ b/mindspore/ccsrc/pipeline/static_analysis/prim.cc @@ -180,6 +180,85 @@ AbstractBasePtr DoSignatureEvaluator::Run(AnalysisEnginePtr engine, const Config return engine->ForwardConfig(out_conf, fn_conf); } +static AbstractBasePtrList GetUnpackGraphSpecArgsList(AbstractBasePtrList args_spec_list, bool need_unpack) { + // arg[0] is the func graph to unpack, ignore it + AbstractBasePtrList sepcialize_args_before_unpack(args_spec_list.begin() + 1, args_spec_list.end()); + AbstractBasePtrList graph_sepcialize_args; + if (need_unpack) { + for (size_t index = 0; index < sepcialize_args_before_unpack.size(); index++) { + MS_EXCEPTION_IF_NULL(sepcialize_args_before_unpack[index]); + if (sepcialize_args_before_unpack[index]->isa()) { + AbstractTuplePtr arg_tuple = sepcialize_args_before_unpack[index]->cast(); + std::transform(arg_tuple->elements().begin(), arg_tuple->elements().end(), + std::back_inserter(graph_sepcialize_args), [](AbstractBasePtr abs) { return abs; }); + } else if (sepcialize_args_before_unpack[index]->isa()) { + AbstractDictionaryPtr arg_dict = sepcialize_args_before_unpack[index]->cast(); + auto dict_elems = arg_dict->elements(); + (void)std::transform( + dict_elems.begin(), dict_elems.end(), std::back_inserter(graph_sepcialize_args), + [](const AbstractAttribute &item) { return std::make_shared(item.first, item.second); }); + } else { + MS_LOG(EXCEPTION) << "UnpackGraph require args should be tuple or dict, but got " + << sepcialize_args_before_unpack[index]->ToString(); + } + } + } else { + graph_sepcialize_args = sepcialize_args_before_unpack; + } + return graph_sepcialize_args; +} + +AbstractBasePtr UnpackGraphEvaluator::Run(AnalysisEnginePtr engine, const ConfigPtrList &args_conf_list, + AnfNodeConfigPtr out_conf) { + if (out_conf->node() == nullptr || !out_conf->node()->isa()) { + MS_LOG(EXCEPTION) << "Node of out_conf should be CNode"; + } + if (!prim_->isa()) { + MS_LOG(EXCEPTION) << "Primitive should be UnpackGraphPrimitive, but got " << prim_->ToString(); + } + + auto unpack_graph = prim_->cast(); + auto out_node = out_conf->node()->cast(); + const auto &out_node_inputs = out_node->inputs(); + if (out_node->inputs().size() == 0 || (out_node_inputs.size() - 1) != args_conf_list.size()) { + MS_LOG(EXCEPTION) << "UnpackGraphPrimitive" + << " args size should equal to inputs size minus 1, but args size " << args_conf_list.size() + << ", inputs size " << out_node_inputs.size(); + } + AnfNodePtrList args_inputs{out_node_inputs.begin() + 1, out_node_inputs.end()}; + AbstractBasePtrList args_spec_list; + (void)std::transform(args_conf_list.begin(), args_conf_list.end(), std::back_inserter(args_spec_list), + [](const ConfigPtr &ref) -> AbstractBasePtr { return ref->GetEvaluatedValue(); }); + // get the forward graph + MS_EXCEPTION_IF_NULL(args_spec_list[0]); + AbstractFunctionPtr fn = args_spec_list[0]->cast(); + if (fn == nullptr) { + MS_LOG(EXCEPTION) << "UnpackGraphPrimitive arg0 must be AbstractFunction, but " << args_spec_list[0]->ToString(); + } + auto real_fn = fn->cast(); + MS_EXCEPTION_IF_NULL(real_fn); + FuncGraphPtr forward_graph = real_fn->func_graph(); + MS_EXCEPTION_IF_NULL(forward_graph); + AbstractBasePtrList graph_sepcialize_args = + GetUnpackGraphSpecArgsList(args_spec_list, unpack_graph->need_unpack_args()); + + AbstractBasePtrList graph_sepcialize_args_without_sens; + (void)std::transform(graph_sepcialize_args.begin(), + graph_sepcialize_args.end() - (unpack_graph->with_sens_in_args() ? 1 : 0), + std::back_inserter(graph_sepcialize_args_without_sens), [](AbstractBasePtr abs) { return abs; }); + auto new_graph = forward_graph->GenerateGraph(graph_sepcialize_args_without_sens); + engine->func_graph_manager()->AddFuncGraph(new_graph); + ScopePtr scope = kDefaultScope; + if (out_conf != nullptr) { + scope = out_conf->node()->scope(); + } + ScopeGuard scope_guard(scope); + AnfNodePtr new_vnode = NewValueNode(new_graph); + AnfNodeConfigPtr fn_conf = engine->MakeConfig(new_vnode, out_conf->context()); + + return engine->ForwardConfig(out_conf, fn_conf); +} + namespace { py::object BuildValue(const ValuePtr &value_ptr) { if (value_ptr == nullptr) { diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.h b/mindspore/ccsrc/pipeline/static_analysis/prim.h index 9dae576a4c..e154473dbb 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/prim.h +++ b/mindspore/ccsrc/pipeline/static_analysis/prim.h @@ -87,6 +87,21 @@ class DoSignatureEvaluator : public Evaluator { PrimitivePtr prim_; }; +class UnpackGraphEvaluator : public Evaluator { + public: + explicit UnpackGraphEvaluator(const PrimitivePtr primitive) : Evaluator("UnpackGraphEvaluator"), prim_(primitive) {} + ~UnpackGraphEvaluator() override = default; + AbstractBasePtr Run(AnalysisEnginePtr engine, const ConfigPtrList &argrefs, + AnfNodeConfigPtr out_config = nullptr) override; + + AbstractBasePtr Infer(AnalysisEnginePtr, const AbstractBasePtrList &) override { + MS_LOG(EXCEPTION) << "Infer() should not be called, Run() method should be called"; + } + + private: + PrimitivePtr prim_; +}; + bool IsInWhiteList(PrimitivePtr primitive); StandardPrimitiveEvalImpl GetPrimitiveInferImpl(const PrimitivePtr &primitive); diff --git a/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc b/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc index 0bfba265db..49182e8d09 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc +++ b/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc @@ -289,6 +289,10 @@ EvaluatorPtr GetPrimEvaluator(const PrimitivePtr &prim, const AnalysisEnginePtr evaluator = std::make_shared(prim); return evaluator; } + if (prim->isa()) { + evaluator = std::make_shared(prim); + return evaluator; + } if (prim->HasPyEvaluator()) { auto prim_py = dyn_cast(prim); if (prim_py != nullptr) { diff --git a/tests/ut/python/parameter_feature/test_var_grad.py b/tests/ut/python/parameter_feature/test_var_grad.py index d51b78ed9d..12c05d0594 100644 --- a/tests/ut/python/parameter_feature/test_var_grad.py +++ b/tests/ut/python/parameter_feature/test_var_grad.py @@ -19,6 +19,8 @@ from mindspore.nn import Cell from mindspore.ops import operations as P import mindspore.ops.composite as C from mindspore.common.api import _executor +from mindspore.common.parameter import ParameterTuple +from mindspore.common import dtype as mstype context.set_context(mode=context.GRAPH_MODE) @@ -34,3 +36,152 @@ def test_net_vargs_expand(): sens = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32)) net = AddNet() out = C.grad_all_with_sens(net, net.trainable_params())(x, y, sens) + +class VarNet(Cell): + def __init__(self, net): + super(VarNet, self).__init__() + self.b = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True) + self.w = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "w", requires_grad=True) + self.net = net + def construct(self, *args): + return self.net(*args)*self.w + self.b + +class SecondNet(Cell): + def __init__(self): + super(SecondNet, self).__init__() + self.b2 = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True) + def construct(self, *args): + res = args[0] + args[1] + return res + self.b2 +def test_all_var_args_grad_with_sens(): + """"test grad_by_list_with_sens with all var args input""" + class GradNet(Cell): + def __init__(self, net): + super(GradNet, self).__init__() + self.weights = ParameterTuple(net.trainable_params()) + self.net = net + def construct(self, *inputs): + return C.grad_by_list_with_sens(self.net, self.weights)(*inputs) + x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + sens = Tensor(1.0, dtype=mstype.float32) + net = VarNet(SecondNet()) + grad_net = GradNet(net) + out = grad_net(x, y, sens) + +def test_grad_list_var_args(): + class GradNet(Cell): + def __init__(self, net): + super(GradNet, self).__init__() + self.weights = ParameterTuple(net.trainable_params()) + self.net = net + def construct(self, *inputs): + return C.grad_by_list(self.net, self.weights)(*inputs) + x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + net = VarNet(SecondNet()) + grad_net = GradNet(net) + out = grad_net(x, y) + +def test_grad_all_var_args(): + class GradNet(Cell): + def __init__(self, net): + super(GradNet, self).__init__() + self.weights = ParameterTuple(net.trainable_params()) + self.net = net + def construct(self, *inputs): + return C.grad_all(self.net)(*inputs) + x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + net = VarNet(SecondNet()) + grad_net = GradNet(net) + out = grad_net(x, y) + +def test_grad_all_var_args_with_sens(): + class GradNet(Cell): + def __init__(self, net): + super(GradNet, self).__init__() + self.weights = ParameterTuple(net.trainable_params()) + self.net = net + def construct(self, *inputs): + return C.grad_all_with_sens(self.net)(*inputs) + x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + sens = Tensor(1.0, dtype=mstype.float32) + net = VarNet(SecondNet()) + grad_net = GradNet(net) + out = grad_net(x, y, sens) + +def test_grad_var_args_with_sens(): + class GradNet(Cell): + def __init__(self, net): + super(GradNet, self).__init__() + self.weights = ParameterTuple(net.trainable_params()) + self.net = net + def construct(self, *inputs): + return C.grad_with_sens(self.net)(*inputs) + x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + sens = Tensor(1.0, dtype=mstype.float32) + net = VarNet(SecondNet()) + grad_net = GradNet(net) + out = grad_net(x, y, sens) + +def test_var_args_grad(): + class VarNet(Cell): + def __init__(self, net): + super(VarNet, self).__init__() + self.b = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True) + self.net = net + def construct(self, *args): + return self.net(*args) + self.b + + class SecondNet(Cell): + def __init__(self): + super(SecondNet, self).__init__() + self.b2 = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True) + def construct(self, *args): + res = args[0] + args[1] + return res + self.b2 + class GradNet(Cell): + def __init__(self, net): + super(GradNet, self).__init__() + self.net = net + self.weights = ParameterTuple(net.trainable_params()) + def construct(self, x, y, sens): + return C.grad_by_list_with_sens(self.net, self.weights)(x, y, sens) + x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + sens = Tensor(1.0, dtype=mstype.float32) + net = VarNet(SecondNet()) + grad_net = GradNet(net) + out = grad_net(x, y, sens) + + +def test_var_args_positional(): + """"test grad_all with var args in inner graph""" + class VarNet(Cell): + def __init__(self, net): + super(VarNet, self).__init__() + self.net = net + def construct(self, x, y): + return self.net(x, y)*x + + class SecondNet(Cell): + def __init__(self): + super(SecondNet, self).__init__() + def construct(self, *args): + return args[0] + args[1] + + class GradNet(Cell): + def __init__(self, net): + super(GradNet, self).__init__() + self.net = net + self.weights = ParameterTuple(net.trainable_params()) + def construct(self, x, y): + return C.grad_all(self.net)(x, y) + x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32) + net = VarNet(SecondNet()) + grad_net = GradNet(net) + out = grad_net(x, y) From 3e68560023f499c6758ccc74e53787013ceaf252 Mon Sep 17 00:00:00 2001 From: laiyongqiang Date: Wed, 1 Apr 2020 14:51:43 +0800 Subject: [PATCH 062/227] fix bug when dump op with trans_flag true --- config/e2e_dump_config.json | 12 ++++++------ config/e2e_dump_config_0.json | 14 +++++++------- config/e2e_dump_config_1.json | 14 +++++++------- mindspore/ccsrc/common/trans.cc | 7 ++++++- .../ccsrc/device/ascend/ascend_device_address.cc | 6 +++--- .../ccsrc/device/ascend/ascend_kernel_runtime.cc | 12 ++++++------ 6 files changed, 35 insertions(+), 30 deletions(-) diff --git a/config/e2e_dump_config.json b/config/e2e_dump_config.json index ad75c2f27f..fdba941f97 100644 --- a/config/e2e_dump_config.json +++ b/config/e2e_dump_config.json @@ -6,17 +6,17 @@ "net_name": "ResNet50", "mode": 0, "iteration": 0, - "kernels": ["TensorAdd"] + "kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"] }, "DumpSettingsSpec": { - "enable": "true: dump enable false: dump disable", - "trans_flag": "true: trans to host format,false: not trans format", + "enable": "true: dump enable, false: dump disable", + "trans_flag": "true: trans to host format, false: not trans format", "path": "the dump file folder", "net_name": "net name eg:ResNet50", - "mode": "0: dump all kernels 1: dump kernels in kernels list", - "iteration": "0: all iteration others: specified iteration ", - "kernels": "kernel name list need to be dump" + "mode": "0: dump all kernels, 1: dump kernels in kernels list", + "iteration": "0: all iteration, others: specified iteration ", + "kernels": "op's full scope name which need to be dump" }, "other": {} } \ No newline at end of file diff --git a/config/e2e_dump_config_0.json b/config/e2e_dump_config_0.json index a67a4daba0..64b18b8b55 100644 --- a/config/e2e_dump_config_0.json +++ b/config/e2e_dump_config_0.json @@ -6,17 +6,17 @@ "net_name": "ResNet50", "mode": 0, "iteration": 0, - "kernels": ["AllReduce","BiasAddGrad","Conv2DBackpropFilter","SparseSoftmaxCrossEntropyWithLogits"] + "kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"] }, "DumpSettingsSpec": { - "enable": "true: dump enable false: dump disable", - "trans_flag": "true: trans to host format,false: not trans format", + "enable": "true: dump enable, false: dump disable", + "trans_flag": "true: trans to host format, false: not trans format", "path": "the dump file folder", "net_name": "net name eg:ResNet50", - "mode": "0: dump all kernels 1: dump kernels in kernels list", - "iteration": "0: all iteration others: specified iteration ", - "kernels": "kernel name list need to be dump" + "mode": "0: dump all kernels, 1: dump kernels in kernels list", + "iteration": "0: all iteration, others: specified iteration ", + "kernels": "op's full scope name which need to be dump" }, "other": {} -} +} \ No newline at end of file diff --git a/config/e2e_dump_config_1.json b/config/e2e_dump_config_1.json index 226b91ae09..1486487799 100644 --- a/config/e2e_dump_config_1.json +++ b/config/e2e_dump_config_1.json @@ -6,17 +6,17 @@ "net_name": "ResNet50", "mode": 0, "iteration": 0, - "kernels": ["AllReduce","BiasAddGrad","Conv2DBackpropFilter","SparseSoftmaxCrossEntropyWithLogits"] + "kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"] }, "DumpSettingsSpec": { - "enable": "true: dump enable false: dump disable", - "trans_flag": "true: trans to host format,false: not trans format", + "enable": "true: dump enable, false: dump disable", + "trans_flag": "true: trans to host format, false: not trans format", "path": "the dump file folder", "net_name": "net name eg:ResNet50", - "mode": "0: dump all kernels 1: dump kernels in kernels list", - "iteration": "0: all iteration others: specified iteration ", - "kernels": "kernel name list need to be dump" + "mode": "0: dump all kernels, 1: dump kernels in kernels list", + "iteration": "0: all iteration, others: specified iteration ", + "kernels": "op's full scope name which need to be dump" }, "other": {} -} +} \ No newline at end of file diff --git a/mindspore/ccsrc/common/trans.cc b/mindspore/ccsrc/common/trans.cc index ea84537c1a..4748d59286 100644 --- a/mindspore/ccsrc/common/trans.cc +++ b/mindspore/ccsrc/common/trans.cc @@ -53,6 +53,7 @@ enum DataTypeTransMode { FROM_INT8_TO_FLOAT, FROM_INT8_TO_INT32, FROM_INT64_TO_INT32, + FROM_UINT16_TO_INT32, }; const std::map, DataTypeTransMode> mode_map{ @@ -68,7 +69,8 @@ const std::map, DataTypeTransMode> mode_map{ {std::pair(kNumberTypeUInt8, kNumberTypeInt32), FROM_UINT8_TO_INT32}, {std::pair(kNumberTypeInt8, kNumberTypeFloat32), FROM_INT8_TO_FLOAT}, {std::pair(kNumberTypeInt8, kNumberTypeInt32), FROM_INT8_TO_INT32}, - {std::pair(kNumberTypeInt64, kNumberTypeInt32), FROM_INT64_TO_INT32}}; + {std::pair(kNumberTypeInt64, kNumberTypeInt32), FROM_INT64_TO_INT32}, + {std::pair(kNumberTypeUInt16, kNumberTypeInt32), FROM_UINT16_TO_INT32}}; template void TransDataSrc2Dst(const TypeIdArgs &args, void *dst, const size_t data_size) { @@ -116,6 +118,9 @@ bool CastKernel(const TypeIdArgs &args, void *dst, const size_t data_size, const case FROM_INT64_TO_INT32: TransDataSrc2Dst(args, dst, data_size); break; + case FROM_UINT16_TO_INT32: + TransDataSrc2Dst(args, dst, data_size); + break; default: MS_LOG(ERROR) << "unsupported datatype trans"; return false; diff --git a/mindspore/ccsrc/device/ascend/ascend_device_address.cc b/mindspore/ccsrc/device/ascend/ascend_device_address.cc index a521f1516f..b8b7f452e3 100644 --- a/mindspore/ccsrc/device/ascend/ascend_device_address.cc +++ b/mindspore/ccsrc/device/ascend/ascend_device_address.cc @@ -106,13 +106,13 @@ bool AscendDeviceAddress::SyncDeviceToHost(const std::vector &shape, size_t } else { auto shape_size = trans::ShapeSize(host_shape); auto host = std::vector(size_); - const trans::TypeIdArgs type_args{ptr_, shape_size, type_id_, type}; - sync_ok = trans::TransDataType(type_args, host.data()); + SyncMemory(host.data(), ptr_, size_, RT_MEMCPY_DEVICE_TO_HOST); + const trans::TypeIdArgs type_args{host.data(), shape_size, type_id_, type}; + sync_ok = trans::TransDataType(type_args, host_ptr); if (!sync_ok) { MS_LOG(ERROR) << "trans data type failed."; return false; } - SyncMemory(host_ptr, host.data(), size, RT_MEMCPY_DEVICE_TO_HOST); } } else if (format_ == kOpFormat_NC1HWC0 || format_ == kOpFormat_FRAC_Z || format_ == kOpFormat_FRAC_NZ) { sync_ok = SyncDeviceToHostAndConvertFormat(shape, size, type, host_ptr); diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc index a7dfc96b2f..dc7eb5449b 100644 --- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc +++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc @@ -150,9 +150,9 @@ void DumpOutput(mindspore::session::KernelGraph *graph, const string &dump_path, auto output_size = AnfAlgo::GetOutputTensorNum(node); for (size_t j = 0; j < output_size; ++j) { auto addr = AnfAlgo::GetOutputAddr(node, j); - auto shape = AnfAlgo::GetOutputDeviceShape(node, j); - auto type = AnfAlgo::GetOutputDeviceDataType(node, j); - auto format = AnfAlgo::GetOutputFormat(node, j); + auto shape = AnfAlgo::GetOutputInferShape(node, j); + auto type = AnfAlgo::GetOutputInferDataType(node, j); + auto format = kOpFormat_DEFAULT; string filepath = dump_path + '/' + kernel_name + '_' + "output_" + std::to_string(j); auto ascend_addr = dynamic_cast(addr); std::vector int_shapes; @@ -181,9 +181,9 @@ void DumpParameters(mindspore::session::KernelGraph *graph, const string &dump_p continue; } auto addr = AnfAlgo::GetOutputAddr(item, PRAMATER_OUTPUT_INDEX); - auto shape = AnfAlgo::GetOutputDeviceShape(item, PRAMATER_OUTPUT_INDEX); - auto type = AnfAlgo::GetOutputDeviceDataType(item, PRAMATER_OUTPUT_INDEX); - auto format = AnfAlgo::GetOutputFormat(item, PRAMATER_OUTPUT_INDEX); + auto shape = AnfAlgo::GetOutputInferShape(item, PRAMATER_OUTPUT_INDEX); + auto type = AnfAlgo::GetOutputInferDataType(item, PRAMATER_OUTPUT_INDEX); + auto format = kOpFormat_DEFAULT; string filepath = dump_path + '/' + parameter_name + '_' + "output_0"; auto ascend_addr = dynamic_cast(addr); std::vector int_shapes; From 244a82b2445cb86673fbec98e3879a303d7230fb Mon Sep 17 00:00:00 2001 From: lvliang Date: Wed, 1 Apr 2020 14:08:26 +0800 Subject: [PATCH 063/227] pynative-add-lenet --- tests/st/pynative/test_ascend_lenet.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/st/pynative/test_ascend_lenet.py b/tests/st/pynative/test_ascend_lenet.py index 7f69b4585f..c03b2eee85 100644 --- a/tests/st/pynative/test_ascend_lenet.py +++ b/tests/st/pynative/test_ascend_lenet.py @@ -150,6 +150,7 @@ def test_ascend_pynative_lenet(): end_time = time.time() cost_time = end_time - start_time total_time = total_time + cost_time + print("======epoch: ", epoch, " loss: ", loss_output.asnumpy(), " cost time: ", cost_time) + assert(total_time < 20.0) assert(loss_output.asnumpy() < 0.01) - \ No newline at end of file From b5ebfa3ff3579ec553c16c4ccd504b667df42a34 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?= Date: Wed, 1 Apr 2020 22:16:29 +0800 Subject: [PATCH 064/227] fix the examples of P.ReLU6 --- mindspore/ops/operations/nn_ops.py | 1 - 1 file changed, 1 deletion(-) diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index a35766a417..27e27e049a 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -190,7 +190,6 @@ class ReLU6(PrimitiveWithInfer): >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32)) >>> relu6 = ReLU6() >>> result = relu6(input_x) - >>> assert result.asnumpy() == Tensor(np.array([[0, 4.0, 0.0], [2.0, 0.0, 6.0]], np.float32)).asnumpy() """ @prim_attr_register From 408a1aa19f6a3221d5ba572a718e30368f45d097 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?= Date: Wed, 1 Apr 2020 22:03:01 +0800 Subject: [PATCH 065/227] fix examples of P.Fill --- mindspore/ops/operations/array_ops.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index f6d563321c..7b49cbf84c 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -713,7 +713,7 @@ class Fill(PrimitiveWithInfer): Examples: >>> fill = P.Fill() - >>> fill(P.DType()(x), (2, 2), 1) + >>> fill(mindspore.float32, (2, 2), 1) """ @prim_attr_register From 91283d0c74f215a4484f315ce800d41aee417e91 Mon Sep 17 00:00:00 2001 From: Su Teng Date: Thu, 2 Apr 2020 15:14:47 +0800 Subject: [PATCH 066/227] sort include file in parallel dir --- .../allreduce_fusion/allreduce_fusion.cc | 12 ++++----- .../allreduce_fusion/allreduce_fusion.h | 2 +- .../allreduce_fusion/allreduce_graph.cc | 2 +- .../allreduce_fusion/allreduce_graph.h | 6 ++--- .../allreduce_fusion/allreduce_node.h | 4 +-- .../allreduce_fusion/step_allreduce_fusion.cc | 6 ++--- .../ccsrc/parallel/auto_parallel/costmodel.cc | 4 +-- .../ccsrc/parallel/auto_parallel/costmodel.h | 4 +-- .../auto_parallel/dp_algo_costmodel.cc | 2 +- .../auto_parallel/dp_algo_costmodel.h | 2 +- .../parallel/auto_parallel/edge_costmodel.cc | 4 +-- .../parallel/auto_parallel/edge_costmodel.h | 12 ++++----- .../parallel/auto_parallel/graph_costmodel.cc | 4 +-- .../parallel/auto_parallel/graph_costmodel.h | 10 +++---- .../auto_parallel/operator_costmodel.h | 4 +-- .../auto_parallel/rec_core/rec_cost.cc | 8 +++--- .../auto_parallel/rec_core/rec_cost.h | 6 ++--- .../rec_core/rec_generate_strategy.cc | 6 ++--- .../rec_core/rec_generate_strategy.h | 4 +-- .../auto_parallel/rec_core/rec_graph.h | 4 +-- .../auto_parallel/rec_core/rec_parse_graph.cc | 8 +++--- .../auto_parallel/rec_core/rec_parse_graph.h | 6 ++--- .../auto_parallel/rec_core/rec_partition.cc | 8 +++--- .../auto_parallel/rec_core/rec_partition.h | 10 +++---- mindspore/ccsrc/parallel/context.cc | 8 +++--- mindspore/ccsrc/parallel/context.h | 4 +-- mindspore/ccsrc/parallel/costmodel_context.cc | 2 +- mindspore/ccsrc/parallel/costmodel_context.h | 2 +- mindspore/ccsrc/parallel/device_manager.h | 8 +++--- mindspore/ccsrc/parallel/device_matrix.cc | 8 +++--- .../parallel/graph_util/generate_graph.h | 6 ++--- .../parallel/graph_util/get_parallel_info.cc | 8 +++--- .../ccsrc/parallel/graph_util/graph_info.cc | 8 +++--- mindspore/ccsrc/parallel/group_manager.cc | 2 +- mindspore/ccsrc/parallel/group_manager.h | 2 +- mindspore/ccsrc/parallel/node_check.cc | 2 +- .../parallel/ops_info/activation_info.cc | 4 +-- .../ccsrc/parallel/ops_info/activation_info.h | 4 +-- .../parallel/ops_info/arithmetic_info.cc | 4 +-- .../ccsrc/parallel/ops_info/arithmetic_info.h | 4 +-- .../parallel/ops_info/batch_parallel_info.h | 2 +- .../ccsrc/parallel/ops_info/bias_add_info.cc | 4 +-- .../ccsrc/parallel/ops_info/bias_add_info.h | 4 +-- .../ops_info/comparison_function_info.h | 2 +- .../parallel/ops_info/dropout_do_mask_info.cc | 6 ++--- .../parallel/ops_info/dropout_do_mask_info.h | 4 +-- .../ops_info/elementary_function_info.h | 2 +- .../ccsrc/parallel/ops_info/generator_info.cc | 4 +-- .../ccsrc/parallel/ops_info/generator_info.h | 4 +-- .../ccsrc/parallel/ops_info/get_next_info.cc | 4 +-- .../ccsrc/parallel/ops_info/get_next_info.h | 4 +-- .../parallel/ops_info/l2_normalize_info.cc | 4 +-- .../parallel/ops_info/l2_normalize_info.h | 4 +-- .../ccsrc/parallel/ops_info/loss_info.cc | 4 +-- mindspore/ccsrc/parallel/ops_info/loss_info.h | 4 +-- .../ccsrc/parallel/ops_info/matmul_info.cc | 8 +++--- .../ccsrc/parallel/ops_info/matmul_info.h | 6 ++--- .../ccsrc/parallel/ops_info/onehot_info.cc | 8 +++--- .../ccsrc/parallel/ops_info/onehot_info.h | 4 +-- .../ccsrc/parallel/ops_info/operator_info.cc | 14 +++++----- .../ccsrc/parallel/ops_info/operator_info.h | 10 +++---- .../parallel/ops_info/ops_info_head_files.h | 22 ++++++++-------- .../ccsrc/parallel/ops_info/prelu_info.h | 2 +- .../parallel/ops_info/reduce_method_info.cc | 6 ++--- .../parallel/ops_info/reduce_method_info.h | 6 ++--- .../ccsrc/parallel/ops_info/reshape_info.h | 2 +- .../parallel/ops_info/tmp_identity_info.cc | 2 -- .../parallel/ops_info/tmp_identity_info.h | 4 +-- .../ccsrc/parallel/ops_info/transpose_info.h | 2 +- .../parallel/ops_info/virtual_dataset_info.h | 2 +- .../ccsrc/parallel/step_auto_parallel.cc | 26 +++++++++---------- mindspore/ccsrc/parallel/step_auto_parallel.h | 6 ++--- mindspore/ccsrc/parallel/step_parallel.cc | 18 ++++++------- mindspore/ccsrc/parallel/step_parallel.h | 4 +-- mindspore/ccsrc/parallel/strategy.h | 4 +-- .../parallel_strategy_checkpoint.cc | 4 +-- .../parallel_strategy_checkpoint.h | 2 +- .../parallel/tensor_layout/arrangement.cc | 8 +++--- .../parallel/tensor_layout/arrangement.h | 2 +- .../ccsrc/parallel/tensor_layout/array.cc | 2 +- .../tensor_layout/construct_operator.h | 2 +- .../parallel/tensor_layout/layout_transfer.cc | 2 +- mindspore/ccsrc/parallel/tensor_layout/map.cc | 8 +++--- mindspore/ccsrc/parallel/tensor_layout/map.h | 2 +- .../redistribution_layout_transfer.cc | 2 +- .../redistribution_operator_infer.h | 6 ++--- .../ccsrc/parallel/tensor_layout/shape_util.h | 4 +-- .../parallel/tensor_layout/tensor_info.h | 4 +-- .../parallel/tensor_layout/tensor_layout.cc | 12 ++++----- .../parallel/tensor_layout/tensor_layout.h | 2 +- .../tensor_layout/tensor_redistribution.cc | 2 +- .../tensor_layout/tensor_redistribution.h | 8 +++--- 92 files changed, 248 insertions(+), 250 deletions(-) diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc index 37b6eb42ed..e4f54056a8 100644 --- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc +++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc @@ -15,16 +15,16 @@ */ #include "parallel/allreduce_fusion/allreduce_fusion.h" +#include #include -#include #include -#include -#include "utils/log_adapter.h" -#include "parallel/status.h" +#include #include "ir/func_graph.h" -#include "parallel/step_parallel.h" -#include "parallel/graph_util/node_info.h" #include "parallel/costmodel_context.h" +#include "parallel/graph_util/node_info.h" +#include "parallel/status.h" +#include "parallel/step_parallel.h" +#include "utils/log_adapter.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h index de2844fa51..a2fea45187 100644 --- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h +++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h @@ -20,8 +20,8 @@ #include #include #include "ir/anf.h" -#include "parallel/status.h" #include "parallel/allreduce_fusion/allreduce_graph.h" +#include "parallel/status.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc index 5c97eda8d8..559ce02349 100644 --- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc +++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc @@ -17,9 +17,9 @@ #include "parallel/allreduce_fusion/allreduce_graph.h" #include #include -#include "utils/log_adapter.h" #include "ir/anf.h" #include "parallel/allreduce_fusion/allreduce_node.h" +#include "utils/log_adapter.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.h b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.h index da4cbf8800..f0db78a130 100644 --- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.h +++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.h @@ -18,14 +18,14 @@ #define MINDSPORE_CCSRC_PARALLEL_ALLREDUCE_FUSION_ALLREDUCE_GRAPH_H_ #include -#include +#include #include #include -#include #include +#include #include "ir/anf.h" -#include "parallel/status.h" #include "parallel/allreduce_fusion/allreduce_node.h" +#include "parallel/status.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h index f3eeb53ec7..dc6fbed4ba 100644 --- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h +++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h @@ -17,9 +17,9 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_ALLREDUCE_FUSION_ALLREDUCE_NODE_H_ #define MINDSPORE_CCSRC_PARALLEL_ALLREDUCE_FUSION_ALLREDUCE_NODE_H_ -#include -#include #include +#include +#include #include #include "ir/anf.h" #include "parallel/status.h" diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/step_allreduce_fusion.cc b/mindspore/ccsrc/parallel/allreduce_fusion/step_allreduce_fusion.cc index 4db7007448..9dbd3a0246 100644 --- a/mindspore/ccsrc/parallel/allreduce_fusion/step_allreduce_fusion.cc +++ b/mindspore/ccsrc/parallel/allreduce_fusion/step_allreduce_fusion.cc @@ -17,12 +17,12 @@ #include "parallel/allreduce_fusion/step_allreduce_fusion.h" #include #include -#include "utils/log_adapter.h" -#include "parallel/status.h" -#include "parallel/context.h" #include "optimizer/optimizer.h" #include "parallel/allreduce_fusion/allreduce_fusion.h" +#include "parallel/context.h" #include "parallel/graph_util/graph_info.h" +#include "parallel/status.h" +#include "utils/log_adapter.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc index 618e505eba..f5cf5069be 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc @@ -15,9 +15,9 @@ */ #include "parallel/auto_parallel/costmodel.h" -#include -#include #include +#include +#include #include "parallel/auto_parallel/graph_costmodel.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/auto_parallel/costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/costmodel.h index 97155fb9c3..361c19573f 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/costmodel.h +++ b/mindspore/ccsrc/parallel/auto_parallel/costmodel.h @@ -17,11 +17,11 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_AUTO_PARALLEL_COSTMODEL_H_ #define MINDSPORE_CCSRC_PARALLEL_AUTO_PARALLEL_COSTMODEL_H_ -#include #include -#include +#include #include #include +#include #include "parallel/strategy.h" #include "parallel/tensor_layout/tensor_info.h" diff --git a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.cc index 8e042eadab..060caa4cca 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.cc @@ -17,8 +17,8 @@ #include "parallel/auto_parallel/dp_algo_costmodel.h" #include -#include #include +#include namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h index de8ebc6fca..c9b6a07317 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h +++ b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h @@ -18,8 +18,8 @@ #define MINDSPORE_CCSRC_PARALLEL_AUTO_PARALLEL_DP_ALGO_COSTMODEL_H_ #include -#include #include +#include #include "ir/value.h" #include "parallel/auto_parallel/edge_costmodel.h" #include "parallel/auto_parallel/graph_costmodel.h" diff --git a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc index 30fa90457e..6381049f17 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc @@ -18,11 +18,11 @@ #include #include -#include #include +#include #include "parallel/auto_parallel/costmodel.h" -#include "parallel/tensor_layout/tensor_redistribution.h" #include "parallel/auto_parallel/graph_costmodel.h" +#include "parallel/tensor_layout/tensor_redistribution.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h index d2bad60215..1fa49029fa 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h +++ b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h @@ -17,16 +17,16 @@ #ifndef PARALLEL_AUTO_PARALLEL_EDGE_COSTMODEL_H_ #define PARALLEL_AUTO_PARALLEL_EDGE_COSTMODEL_H_ -#include -#include -#include #include +#include #include +#include +#include #include "common/utils.h" -#include "parallel/tensor_layout/tensor_layout.h" -#include "parallel/tensor_layout/tensor_info.h" -#include "parallel/ops_info/operator_info.h" #include "parallel/auto_parallel/costmodel.h" +#include "parallel/ops_info/operator_info.h" +#include "parallel/tensor_layout/tensor_info.h" +#include "parallel/tensor_layout/tensor_layout.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc index f99b271894..59b9d9e992 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc @@ -17,11 +17,11 @@ #include "parallel/auto_parallel/graph_costmodel.h" #include +#include +#include #include #include #include -#include -#include #include namespace mindspore { diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h index 3b04703a47..e4cbdffb61 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h +++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h @@ -17,17 +17,17 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_AUTO_PARALLEL_GRAPH_COSTMODEL_H_ #define MINDSPORE_CCSRC_PARALLEL_AUTO_PARALLEL_GRAPH_COSTMODEL_H_ -#include -#include #include -#include +#include #include +#include +#include #include "../../common.h" #include "common/utils.h" -#include "parallel/ops_info/operator_info.h" #include "parallel/auto_parallel/edge_costmodel.h" -#include "parallel/ops_info/tmp_identity_info.h" #include "parallel/costmodel_context.h" +#include "parallel/ops_info/operator_info.h" +#include "parallel/ops_info/tmp_identity_info.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h index ad856fc910..9fb86d467e 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h +++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h @@ -17,10 +17,10 @@ #ifndef PARALLEL_AUTO_PARALLEL_OPERATOR_COSTMODEL_H_ #define PARALLEL_AUTO_PARALLEL_OPERATOR_COSTMODEL_H_ -#include #include -#include "parallel/tensor_layout/tensor_info.h" +#include #include "parallel/device_manager.h" +#include "parallel/tensor_layout/tensor_info.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.cc b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.cc index 45407a89e6..4591753efe 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.cc @@ -16,13 +16,13 @@ #include "parallel/auto_parallel/rec_core/rec_cost.h" -#include -#include -#include -#include #include +#include #include #include +#include +#include +#include #include "ir/anf.h" diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.h b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.h index 7e07ff9be3..af37b9178e 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.h +++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.h @@ -18,13 +18,13 @@ #define PARALLEL_AUTO_PARALLEL_REC_COST_H_ #include -#include +#include #include #include -#include +#include -#include "parallel/auto_parallel/rec_core/rec_strategy.h" #include "parallel/auto_parallel/rec_core/rec_graph.h" +#include "parallel/auto_parallel/rec_core/rec_strategy.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.cc b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.cc index 06b7bf544a..60f3003a42 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.cc @@ -16,14 +16,14 @@ #include "parallel/auto_parallel/rec_core/rec_generate_strategy.h" -#include #include #include +#include -#include "parallel/ops_info/operator_info.h" +#include "ir/value.h" #include "parallel/auto_parallel/rec_core/rec_partition.h" +#include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" -#include "ir/value.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.h b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.h index 7445d3940e..4abef843a8 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.h +++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.h @@ -17,10 +17,10 @@ #ifndef PARALLEL_AUTO_PARALLEL_REC_GENERATE_STRATEGY_H_ #define PARALLEL_AUTO_PARALLEL_REC_GENERATE_STRATEGY_H_ -#include +#include #include #include -#include +#include #include "parallel/auto_parallel/rec_core/rec_graph.h" #include "parallel/ops_info/operator_info.h" diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_graph.h b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_graph.h index 209ce6b13e..6ab2782cb2 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_graph.h +++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_graph.h @@ -18,11 +18,11 @@ #define PARALLEL_AUTO_PARALLEL_REC_GRAPH_H_ #include -#include #include +#include -#include "parallel/auto_parallel/rec_core/rec_tensor.h" #include "parallel/auto_parallel/rec_core/rec_strategy.h" +#include "parallel/auto_parallel/rec_core/rec_tensor.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.cc b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.cc index 153ce2ea17..3ff3473298 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.cc @@ -17,14 +17,14 @@ #include "parallel/auto_parallel/rec_core/rec_parse_graph.h" #include +#include #include #include -#include -#include "parallel/ops_info/operator_info.h" -#include "parallel/auto_parallel/rec_core/rec_tensor.h" -#include "parallel/auto_parallel/rec_core/rec_graph.h" #include "ir/value.h" +#include "parallel/auto_parallel/rec_core/rec_graph.h" +#include "parallel/auto_parallel/rec_core/rec_tensor.h" +#include "parallel/ops_info/operator_info.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.h b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.h index 7904d260c0..7dfca86a21 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.h +++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.h @@ -17,11 +17,11 @@ #ifndef PARALLEL_AUTO_PARALLEL_REC_PARSE_GRAPH_H_ #define PARALLEL_AUTO_PARALLEL_REC_PARSE_GRAPH_H_ -#include +#include +#include #include #include -#include -#include +#include #include "parallel/auto_parallel/rec_core/rec_graph.h" #include "parallel/ops_info/operator_info.h" diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.cc b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.cc index a33b861f1a..24ad8ac203 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.cc @@ -16,13 +16,13 @@ #include "parallel/auto_parallel/rec_core/rec_partition.h" +#include +#include #include -#include +#include #include #include -#include -#include -#include +#include #include "ir/anf.h" #include "parallel/status.h" diff --git a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.h b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.h index 4e49305156..4f831f4f9a 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.h +++ b/mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.h @@ -17,17 +17,17 @@ #ifndef PARALLEL_AUTO_PARALLEL_REC_PARTITION_H_ #define PARALLEL_AUTO_PARALLEL_REC_PARTITION_H_ +#include #include -#include -#include #include -#include -#include #include +#include +#include +#include +#include "parallel/auto_parallel/rec_core/rec_cost.h" #include "parallel/auto_parallel/rec_core/rec_graph.h" #include "parallel/auto_parallel/rec_core/rec_strategy.h" -#include "parallel/auto_parallel/rec_core/rec_cost.h" #include "parallel/status.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/context.cc b/mindspore/ccsrc/parallel/context.cc index 64cecf1669..ab216cb22c 100644 --- a/mindspore/ccsrc/parallel/context.cc +++ b/mindspore/ccsrc/parallel/context.cc @@ -16,15 +16,15 @@ #include "parallel/context.h" -#include #include -#include -#include +#include #include #include +#include +#include -#include "parallel/device_manager.h" #include "common/utils.h" +#include "parallel/device_manager.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/context.h b/mindspore/ccsrc/parallel/context.h index 866609fbfe..265f5bac71 100644 --- a/mindspore/ccsrc/parallel/context.h +++ b/mindspore/ccsrc/parallel/context.h @@ -18,12 +18,12 @@ #define MINDSPORE_CCSRC_PARALLEL_CONTEXT_H_ #include +#include #include #include -#include -#include "parallel/status.h" #include "parallel/ops_info/ops_utils.h" +#include "parallel/status.h" #include "utils/convert_utils.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/costmodel_context.cc b/mindspore/ccsrc/parallel/costmodel_context.cc index 45b158bde5..0ebbd2c626 100644 --- a/mindspore/ccsrc/parallel/costmodel_context.cc +++ b/mindspore/ccsrc/parallel/costmodel_context.cc @@ -18,8 +18,8 @@ #include -#include "parallel/auto_parallel/graph_costmodel.h" #include "parallel/allreduce_fusion/allreduce_fusion.h" +#include "parallel/auto_parallel/graph_costmodel.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/costmodel_context.h b/mindspore/ccsrc/parallel/costmodel_context.h index a808fc556f..04782fa366 100644 --- a/mindspore/ccsrc/parallel/costmodel_context.h +++ b/mindspore/ccsrc/parallel/costmodel_context.h @@ -17,9 +17,9 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_COSTMODEL_CONTEXT_H_ #define MINDSPORE_CCSRC_PARALLEL_COSTMODEL_CONTEXT_H_ +#include #include #include -#include #include "utils/log_adapter.h" diff --git a/mindspore/ccsrc/parallel/device_manager.h b/mindspore/ccsrc/parallel/device_manager.h index 798d99383d..e87c1d740f 100644 --- a/mindspore/ccsrc/parallel/device_manager.h +++ b/mindspore/ccsrc/parallel/device_manager.h @@ -19,19 +19,19 @@ #include #include -#include #include #include #include #include +#include -#include "utils/convert_utils.h" #include "common/utils.h" #include "parallel/device.h" -#include "parallel/status.h" +#include "parallel/device_matrix.h" #include "parallel/group_manager.h" +#include "parallel/status.h" #include "parallel/strategy.h" -#include "parallel/device_matrix.h" +#include "utils/convert_utils.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/device_matrix.cc b/mindspore/ccsrc/parallel/device_matrix.cc index f9f314d5a3..a581dbf275 100644 --- a/mindspore/ccsrc/parallel/device_matrix.cc +++ b/mindspore/ccsrc/parallel/device_matrix.cc @@ -16,15 +16,15 @@ #include "parallel/device_matrix.h" -#include #include -#include -#include +#include #include +#include +#include #include -#include "parallel/status.h" #include "parallel/ops_info/operator_info.h" +#include "parallel/status.h" #include "utils/log_adapter.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/graph_util/generate_graph.h b/mindspore/ccsrc/parallel/graph_util/generate_graph.h index bb1f811f2f..c829e67b6a 100644 --- a/mindspore/ccsrc/parallel/graph_util/generate_graph.h +++ b/mindspore/ccsrc/parallel/graph_util/generate_graph.h @@ -17,12 +17,12 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_GRAPH_UTIL_GENERATE_GRAPH_H_ #define MINDSPORE_CCSRC_PARALLEL_GRAPH_UTIL_GENERATE_GRAPH_H_ -#include -#include -#include #include +#include #include +#include #include +#include #include "./common.h" #include "optimizer/opt.h" diff --git a/mindspore/ccsrc/parallel/graph_util/get_parallel_info.cc b/mindspore/ccsrc/parallel/graph_util/get_parallel_info.cc index 6619f2cc9c..3006cb7680 100644 --- a/mindspore/ccsrc/parallel/graph_util/get_parallel_info.cc +++ b/mindspore/ccsrc/parallel/graph_util/get_parallel_info.cc @@ -16,16 +16,16 @@ #include "parallel/graph_util/get_parallel_info.h" -#include #include +#include #include #include -#include "parallel/tensor_layout/tensor_layout.h" -#include "parallel/strategy.h" -#include "ir/func_graph.h" #include "common/utils.h" +#include "ir/func_graph.h" #include "parallel/graph_util/graph_info.h" +#include "parallel/strategy.h" +#include "parallel/tensor_layout/tensor_layout.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/graph_util/graph_info.cc b/mindspore/ccsrc/parallel/graph_util/graph_info.cc index be73683c57..46c9a37960 100644 --- a/mindspore/ccsrc/parallel/graph_util/graph_info.cc +++ b/mindspore/ccsrc/parallel/graph_util/graph_info.cc @@ -15,12 +15,12 @@ */ #include "parallel/graph_util/graph_info.h" -#include "ir/func_graph.h" -#include "utils/graph_utils.h" -#include "utils/context/ms_context.h" -#include "debug/draw.h" #include "debug/anf_ir_dump.h" #include "debug/anf_ir_utils.h" +#include "debug/draw.h" +#include "ir/func_graph.h" +#include "utils/context/ms_context.h" +#include "utils/graph_utils.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/group_manager.cc b/mindspore/ccsrc/parallel/group_manager.cc index cff5877f0a..1562cbc140 100644 --- a/mindspore/ccsrc/parallel/group_manager.cc +++ b/mindspore/ccsrc/parallel/group_manager.cc @@ -16,8 +16,8 @@ #include "parallel/group_manager.h" -#include #include +#include #include "parallel/device_manager.h" #include "parallel/ops_info/ops_utils.h" diff --git a/mindspore/ccsrc/parallel/group_manager.h b/mindspore/ccsrc/parallel/group_manager.h index 9e23569b15..430d2f64ed 100644 --- a/mindspore/ccsrc/parallel/group_manager.h +++ b/mindspore/ccsrc/parallel/group_manager.h @@ -18,9 +18,9 @@ #define MINDSPORE_CCSRC_PARALLEL_GROUP_MANAGER_H_ #include -#include #include #include +#include #include "parallel/device.h" #include "parallel/status.h" diff --git a/mindspore/ccsrc/parallel/node_check.cc b/mindspore/ccsrc/parallel/node_check.cc index db53bbc59f..fc6115c3c5 100644 --- a/mindspore/ccsrc/parallel/node_check.cc +++ b/mindspore/ccsrc/parallel/node_check.cc @@ -16,8 +16,8 @@ #include "parallel/node_check.h" -#include #include +#include #include "parallel/ops_info/ops_utils.h" diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.cc b/mindspore/ccsrc/parallel/ops_info/activation_info.cc index 6e581d6280..13155ee4f1 100644 --- a/mindspore/ccsrc/parallel/ops_info/activation_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/activation_info.cc @@ -17,13 +17,13 @@ #include "parallel/ops_info/activation_info.h" #include -#include #include +#include #include "ir/value.h" +#include "parallel/auto_parallel/costmodel.h" #include "parallel/device_matrix.h" #include "parallel/strategy.h" -#include "parallel/auto_parallel/costmodel.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.h b/mindspore/ccsrc/parallel/ops_info/activation_info.h index d05f8743b0..3cadad6b80 100644 --- a/mindspore/ccsrc/parallel/ops_info/activation_info.h +++ b/mindspore/ccsrc/parallel/ops_info/activation_info.h @@ -18,13 +18,13 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ACTIVATION_INFO_H_ #include +#include #include #include #include -#include -#include "parallel/ops_info/operator_info.h" #include "parallel/auto_parallel/operator_costmodel.h" +#include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.cc b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.cc index 48741690ba..02c26ea965 100644 --- a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.cc @@ -17,9 +17,9 @@ #include "parallel/ops_info/arithmetic_info.h" #include -#include -#include #include +#include +#include #include "parallel/device_matrix.h" #include "parallel/strategy.h" diff --git a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h index 734368a533..7cd0d66b1b 100644 --- a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h +++ b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h @@ -17,14 +17,14 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ARITHMETIC_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ARITHMETIC_INFO_H_ +#include #include #include #include -#include #include "ir/value.h" -#include "parallel/ops_info/operator_info.h" #include "parallel/auto_parallel/operator_costmodel.h" +#include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h index 0ffdea97f3..57711b5298 100644 --- a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h +++ b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h @@ -17,10 +17,10 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_BATCH_PARALLEL_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_BATCH_PARALLEL_INFO_H_ +#include #include #include #include -#include #include "ir/value.h" #include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" diff --git a/mindspore/ccsrc/parallel/ops_info/bias_add_info.cc b/mindspore/ccsrc/parallel/ops_info/bias_add_info.cc index f07606e4d3..005edaf7c7 100644 --- a/mindspore/ccsrc/parallel/ops_info/bias_add_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/bias_add_info.cc @@ -17,9 +17,9 @@ #include "parallel/ops_info/bias_add_info.h" #include -#include -#include #include +#include +#include #include "parallel/device_matrix.h" #include "parallel/strategy.h" diff --git a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h index e5001fc0d3..07f0bc00ff 100644 --- a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h +++ b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h @@ -19,13 +19,13 @@ #include +#include #include #include -#include #include "ir/value.h" -#include "parallel/ops_info/operator_info.h" #include "parallel/auto_parallel/operator_costmodel.h" +#include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h index 8760ce5666..110a9a6c38 100644 --- a/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h +++ b/mindspore/ccsrc/parallel/ops_info/comparison_function_info.h @@ -21,8 +21,8 @@ #include #include #include "ir/value.h" -#include "parallel/ops_info/arithmetic_info.h" #include "parallel/auto_parallel/operator_costmodel.h" +#include "parallel/ops_info/arithmetic_info.h" #include "parallel/strategy.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.cc b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.cc index 1b77b913da..c6cd94b7be 100644 --- a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.cc @@ -17,15 +17,15 @@ #include "parallel/ops_info/dropout_do_mask_info.h" #include -#include -#include #include +#include +#include #include "ir/value.h" +#include "parallel/auto_parallel/costmodel.h" #include "parallel/device_matrix.h" #include "parallel/strategy.h" #include "parallel/tensor_layout/tensor_redistribution.h" -#include "parallel/auto_parallel/costmodel.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h index 45d4c28d8e..e43601355a 100644 --- a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h +++ b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h @@ -17,14 +17,14 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_DROPOUT_DO_MASK_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_DROPOUT_DO_MASK_INFO_H_ +#include #include #include #include -#include #include "ir/value.h" -#include "parallel/ops_info/operator_info.h" #include "parallel/auto_parallel/operator_costmodel.h" +#include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h b/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h index 33ad04023b..57b4650f26 100644 --- a/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h +++ b/mindspore/ccsrc/parallel/ops_info/elementary_function_info.h @@ -22,8 +22,8 @@ #include #include "ir/value.h" #include "parallel/auto_parallel/operator_costmodel.h" -#include "parallel/strategy.h" #include "parallel/ops_info/activation_info.h" +#include "parallel/strategy.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/ops_info/generator_info.cc b/mindspore/ccsrc/parallel/ops_info/generator_info.cc index b0fd0eaa9a..a39f9faab9 100644 --- a/mindspore/ccsrc/parallel/ops_info/generator_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/generator_info.cc @@ -17,9 +17,9 @@ #include "parallel/ops_info/generator_info.h" #include -#include -#include #include +#include +#include #include "ir/value.h" #include "parallel/device_matrix.h" diff --git a/mindspore/ccsrc/parallel/ops_info/generator_info.h b/mindspore/ccsrc/parallel/ops_info/generator_info.h index 1473fead67..a280fac28e 100644 --- a/mindspore/ccsrc/parallel/ops_info/generator_info.h +++ b/mindspore/ccsrc/parallel/ops_info/generator_info.h @@ -17,13 +17,13 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GENERATOR_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GENERATOR_INFO_H_ +#include #include #include #include -#include -#include "parallel/ops_info/operator_info.h" #include "parallel/auto_parallel/operator_costmodel.h" +#include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/ops_info/get_next_info.cc b/mindspore/ccsrc/parallel/ops_info/get_next_info.cc index f38baa1e4e..ac9acff41b 100644 --- a/mindspore/ccsrc/parallel/ops_info/get_next_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/get_next_info.cc @@ -17,9 +17,9 @@ #include "parallel/ops_info/get_next_info.h" #include -#include -#include #include +#include +#include #include "ir/value.h" #include "parallel/device_matrix.h" diff --git a/mindspore/ccsrc/parallel/ops_info/get_next_info.h b/mindspore/ccsrc/parallel/ops_info/get_next_info.h index 3dd639ba57..32adce1165 100644 --- a/mindspore/ccsrc/parallel/ops_info/get_next_info.h +++ b/mindspore/ccsrc/parallel/ops_info/get_next_info.h @@ -17,13 +17,13 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GETNEXT_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_GETNEXT_INFO_H_ +#include #include #include #include -#include -#include "parallel/ops_info/operator_info.h" #include "parallel/auto_parallel/operator_costmodel.h" +#include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.cc b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.cc index 183c5dec45..2955f76506 100644 --- a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.cc @@ -17,9 +17,9 @@ #include "parallel/ops_info/l2_normalize_info.h" #include -#include -#include #include +#include +#include #include "parallel/device_matrix.h" #include "parallel/strategy.h" diff --git a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h index 1a67073065..c0af9dbcb9 100644 --- a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h +++ b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h @@ -17,15 +17,15 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_L2_NORMALIZE_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_L2_NORMALIZE_INFO_H_ +#include #include #include #include -#include #include "ir/value.h" +#include "parallel/auto_parallel/operator_costmodel.h" #include "parallel/ops_info/activation_info.h" #include "parallel/strategy.h" -#include "parallel/auto_parallel/operator_costmodel.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/ops_info/loss_info.cc b/mindspore/ccsrc/parallel/ops_info/loss_info.cc index 5ca383ebb5..31f80e338b 100644 --- a/mindspore/ccsrc/parallel/ops_info/loss_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/loss_info.cc @@ -17,9 +17,9 @@ #include "parallel/ops_info/loss_info.h" #include -#include -#include #include +#include +#include #include "ir/value.h" #include "parallel/device_matrix.h" diff --git a/mindspore/ccsrc/parallel/ops_info/loss_info.h b/mindspore/ccsrc/parallel/ops_info/loss_info.h index 585545302f..6a9697a447 100644 --- a/mindspore/ccsrc/parallel/ops_info/loss_info.h +++ b/mindspore/ccsrc/parallel/ops_info/loss_info.h @@ -17,14 +17,14 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_LOSS_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_LOSS_INFO_H_ +#include #include #include #include -#include #include "ir/value.h" -#include "parallel/ops_info/operator_info.h" #include "parallel/ops_info/activation_info.h" +#include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc index 6103087a1d..ad6409be0a 100644 --- a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc @@ -18,16 +18,16 @@ #include #include -#include -#include #include #include +#include +#include #include "ir/value.h" +#include "parallel/auto_parallel/graph_costmodel.h" +#include "parallel/device_manager.h" #include "parallel/device_matrix.h" #include "parallel/tensor_layout/tensor_redistribution.h" -#include "parallel/device_manager.h" -#include "parallel/auto_parallel/graph_costmodel.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.h b/mindspore/ccsrc/parallel/ops_info/matmul_info.h index b434e4522d..7ced12b14a 100644 --- a/mindspore/ccsrc/parallel/ops_info/matmul_info.h +++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.h @@ -17,16 +17,16 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_MATMUL_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_MATMUL_INFO_H_ +#include #include #include #include -#include +#include "common/utils.h" #include "ir/value.h" +#include "parallel/auto_parallel/operator_costmodel.h" #include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" -#include "parallel/auto_parallel/operator_costmodel.h" -#include "common/utils.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/ops_info/onehot_info.cc b/mindspore/ccsrc/parallel/ops_info/onehot_info.cc index 9b8e9071b0..e07609d3c4 100644 --- a/mindspore/ccsrc/parallel/ops_info/onehot_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/onehot_info.cc @@ -16,15 +16,15 @@ #include "parallel/ops_info/onehot_info.h" -#include -#include #include +#include +#include #include "ir/value.h" -#include "parallel/device_matrix.h" -#include "parallel/strategy.h" #include "parallel/auto_parallel/costmodel.h" +#include "parallel/device_matrix.h" #include "parallel/graph_util/generate_graph.h" +#include "parallel/strategy.h" #include "utils/log_adapter.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/ops_info/onehot_info.h b/mindspore/ccsrc/parallel/ops_info/onehot_info.h index 62c66695fa..4697e201a4 100644 --- a/mindspore/ccsrc/parallel/ops_info/onehot_info.h +++ b/mindspore/ccsrc/parallel/ops_info/onehot_info.h @@ -17,14 +17,14 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ONEHOT_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_ONEHOT_INFO_H_ +#include #include #include #include -#include #include "ir/value.h" -#include "parallel/ops_info/operator_info.h" #include "parallel/auto_parallel/operator_costmodel.h" +#include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/parallel/ops_info/operator_info.cc index f187d38673..8b96425bf7 100644 --- a/mindspore/ccsrc/parallel/ops_info/operator_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/operator_info.cc @@ -17,21 +17,21 @@ #include "parallel/ops_info/operator_info.h" #include +#include #include -#include -#include #include -#include #include +#include +#include -#include "ir/value.h" #include "ir/dtype.h" #include "ir/meta_tensor.h" -#include "utils/context/ms_context.h" -#include "utils/log_adapter.h" -#include "parallel/context.h" +#include "ir/value.h" #include "parallel/auto_parallel/edge_costmodel.h" #include "parallel/auto_parallel/graph_costmodel.h" +#include "parallel/context.h" +#include "utils/context/ms_context.h" +#include "utils/log_adapter.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.h b/mindspore/ccsrc/parallel/ops_info/operator_info.h index 89fd73564f..cc70f1b870 100644 --- a/mindspore/ccsrc/parallel/ops_info/operator_info.h +++ b/mindspore/ccsrc/parallel/ops_info/operator_info.h @@ -25,17 +25,17 @@ #include #include -#include "utils/log_adapter.h" -#include "ir/base.h" #include "common/utils.h" +#include "ir/base.h" +#include "parallel/auto_parallel/costmodel.h" +#include "parallel/auto_parallel/operator_costmodel.h" #include "parallel/device_manager.h" #include "parallel/device_matrix.h" #include "parallel/group_manager.h" +#include "parallel/ops_info/ops_utils.h" #include "parallel/strategy.h" #include "parallel/tensor_layout/tensor_info.h" -#include "parallel/auto_parallel/costmodel.h" -#include "parallel/auto_parallel/operator_costmodel.h" -#include "parallel/ops_info/ops_utils.h" +#include "utils/log_adapter.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h b/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h index 7eb24bd30a..cc13512b54 100644 --- a/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h +++ b/mindspore/ccsrc/parallel/ops_info/ops_info_head_files.h @@ -18,22 +18,22 @@ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_OPS_INFO_HEAD_FILES_H_ #include "parallel/ops_info/activation_info.h" +#include "parallel/ops_info/arithmetic_info.h" #include "parallel/ops_info/batch_parallel_info.h" +#include "parallel/ops_info/bias_add_info.h" +#include "parallel/ops_info/comparison_function_info.h" +#include "parallel/ops_info/dropout_do_mask_info.h" +#include "parallel/ops_info/elementary_function_info.h" +#include "parallel/ops_info/generator_info.h" +#include "parallel/ops_info/get_next_info.h" +#include "parallel/ops_info/l2_normalize_info.h" #include "parallel/ops_info/loss_info.h" #include "parallel/ops_info/matmul_info.h" #include "parallel/ops_info/onehot_info.h" -#include "parallel/ops_info/virtual_dataset_info.h" -#include "parallel/ops_info/arithmetic_info.h" -#include "parallel/ops_info/elementary_function_info.h" -#include "parallel/ops_info/comparison_function_info.h" -#include "parallel/ops_info/l2_normalize_info.h" -#include "parallel/ops_info/reduce_method_info.h" -#include "parallel/ops_info/transpose_info.h" #include "parallel/ops_info/prelu_info.h" +#include "parallel/ops_info/reduce_method_info.h" #include "parallel/ops_info/reshape_info.h" -#include "parallel/ops_info/generator_info.h" -#include "parallel/ops_info/dropout_do_mask_info.h" -#include "parallel/ops_info/get_next_info.h" -#include "parallel/ops_info/bias_add_info.h" +#include "parallel/ops_info/transpose_info.h" +#include "parallel/ops_info/virtual_dataset_info.h" #endif // MINDSPORE_CCSRC_PARALLEL_OPS_INFO_HEAD_FILES_H_ diff --git a/mindspore/ccsrc/parallel/ops_info/prelu_info.h b/mindspore/ccsrc/parallel/ops_info/prelu_info.h index 2e74118f80..d491ecb331 100644 --- a/mindspore/ccsrc/parallel/ops_info/prelu_info.h +++ b/mindspore/ccsrc/parallel/ops_info/prelu_info.h @@ -17,10 +17,10 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_PRELU_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_PRELU_INFO_H_ +#include #include #include #include -#include #include "ir/value.h" #include "parallel/ops_info/operator_info.h" diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc index 5f9a67c22d..5b07f8d0a9 100644 --- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc @@ -17,14 +17,14 @@ #include "parallel/ops_info/reduce_method_info.h" #include -#include -#include #include +#include +#include #include "ir/value.h" +#include "parallel/device_manager.h" #include "parallel/device_matrix.h" #include "parallel/tensor_layout/tensor_redistribution.h" -#include "parallel/device_manager.h" #include "utils/log_adapter.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h index 6f26b99ffb..8e2e17af99 100644 --- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h +++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h @@ -17,16 +17,16 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_REDUCE_SUM_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_REDUCE_SUM_INFO_H_ +#include #include #include #include -#include +#include "ir/meta_tensor.h" #include "ir/value.h" +#include "parallel/auto_parallel/operator_costmodel.h" #include "parallel/ops_info/activation_info.h" #include "parallel/strategy.h" -#include "parallel/auto_parallel/operator_costmodel.h" -#include "ir/meta_tensor.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/ops_info/reshape_info.h b/mindspore/ccsrc/parallel/ops_info/reshape_info.h index 982894a8e0..1d6a14b1f6 100644 --- a/mindspore/ccsrc/parallel/ops_info/reshape_info.h +++ b/mindspore/ccsrc/parallel/ops_info/reshape_info.h @@ -19,10 +19,10 @@ #include +#include #include #include #include -#include #include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" diff --git a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.cc b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.cc index 73b18ad473..772a4f83f6 100644 --- a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.cc @@ -1,6 +1,4 @@ /** -#include "utils/log_adapter.h" -#include "utils/log_adapter.h" #include "utils/log_adapter.h" * Copyright 2019 Huawei Technologies Co., Ltd * diff --git a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h index 304e336adf..6df5856e0c 100644 --- a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h +++ b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h @@ -17,12 +17,12 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_TMP_IDENTITY_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_TMP_IDENTITY_INFO_H_ -#include #include #include +#include -#include "parallel/ops_info/operator_info.h" #include "parallel/auto_parallel/operator_costmodel.h" +#include "parallel/ops_info/operator_info.h" #include "parallel/strategy.h" namespace mindspore { diff --git a/mindspore/ccsrc/parallel/ops_info/transpose_info.h b/mindspore/ccsrc/parallel/ops_info/transpose_info.h index c7c1c96675..4f6f6bb695 100644 --- a/mindspore/ccsrc/parallel/ops_info/transpose_info.h +++ b/mindspore/ccsrc/parallel/ops_info/transpose_info.h @@ -17,10 +17,10 @@ #ifndef MINDSPORE_CCSRC_PARALLEL_OPS_INFO_TRANSPOSE_INFO_H_ #define MINDSPORE_CCSRC_PARALLEL_OPS_INFO_TRANSPOSE_INFO_H_ +#include #include #include #include -#include #include "ir/value.h" #include "parallel/ops_info/operator_info.h" diff --git a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h index c4fdfcef04..d0278f27d9 100644 --- a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h +++ b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h @@ -17,10 +17,10 @@ #ifndef PARALLEL_OPS_INFO_DATASET_INFO_H_ #define PARALLEL_OPS_INFO_DATASET_INFO_H_ +#include #include #include #include -#include #include "ir/value.h" #include "parallel/ops_info/operator_info.h" diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc index 883e0d9f15..b06ec383fa 100644 --- a/mindspore/ccsrc/parallel/step_auto_parallel.cc +++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc @@ -18,30 +18,30 @@ #include #include -#include +#include +#include #include #include -#include -#include #include -#include #include +#include +#include #include "ir/anf.h" +#include "ir/meta_tensor.h" #include "optimizer/opt.h" #include "optimizer/optimizer.h" -#include "pipeline/pipeline.h" -#include "pipeline/parse/python_adapter.h" +#include "parallel/auto_parallel/dp_algo_costmodel.h" #include "parallel/auto_parallel/edge_costmodel.h" #include "parallel/auto_parallel/graph_costmodel.h" -#include "parallel/step_parallel.h" -#include "parallel/auto_parallel/dp_algo_costmodel.h" -#include "parallel/ops_info/tmp_identity_info.h" -#include "parallel/context.h" -#include "parallel/auto_parallel/rec_core/rec_partition.h" -#include "parallel/auto_parallel/rec_core/rec_parse_graph.h" #include "parallel/auto_parallel/rec_core/rec_generate_strategy.h" -#include "ir/meta_tensor.h" +#include "parallel/auto_parallel/rec_core/rec_parse_graph.h" +#include "parallel/auto_parallel/rec_core/rec_partition.h" +#include "parallel/context.h" +#include "parallel/ops_info/tmp_identity_info.h" +#include "parallel/step_parallel.h" +#include "pipeline/parse/python_adapter.h" +#include "pipeline/pipeline.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.h b/mindspore/ccsrc/parallel/step_auto_parallel.h index d0d603a4f4..5ee75ca162 100644 --- a/mindspore/ccsrc/parallel/step_auto_parallel.h +++ b/mindspore/ccsrc/parallel/step_auto_parallel.h @@ -17,14 +17,14 @@ #ifndef PARALLEL_STEP_AUTO_PARALLEL_H_ #define PARALLEL_STEP_AUTO_PARALLEL_H_ -#include +#include #include #include -#include +#include #include "ir/anf.h" #include "optimizer/opt.h" -#include "pipeline/pipeline.h" #include "parallel/status.h" +#include "pipeline/pipeline.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc index e2b4d55aad..886a0aebf4 100644 --- a/mindspore/ccsrc/parallel/step_parallel.cc +++ b/mindspore/ccsrc/parallel/step_parallel.cc @@ -22,26 +22,26 @@ #include #include +#include #include #include #include -#include -#include "parallel/graph_util/graph_info.h" #include "ir/meta_tensor.h" -#include "optimizer/optimizer.h" -#include "parallel/dynamic_creator.h" -#include "parallel/ops_info/matmul_info.h" -#include "utils/symbolic.h" #include "operator/ops.h" +#include "optimizer/optimizer.h" #include "parallel/auto_parallel/graph_costmodel.h" +#include "parallel/context.h" #include "parallel/device_manager.h" -#include "parallel/strategy_checkpoint/parallel_strategy_checkpoint.h" +#include "parallel/dynamic_creator.h" #include "parallel/graph_util/generate_graph.h" -#include "parallel/context.h" +#include "parallel/graph_util/graph_info.h" +#include "parallel/graph_util/node_info.h" #include "parallel/node_check.h" +#include "parallel/ops_info/matmul_info.h" +#include "parallel/strategy_checkpoint/parallel_strategy_checkpoint.h" #include "utils/comm_manager.h" -#include "parallel/graph_util/node_info.h" +#include "utils/symbolic.h" using mindspore::tensor::Tensor; diff --git a/mindspore/ccsrc/parallel/step_parallel.h b/mindspore/ccsrc/parallel/step_parallel.h index 2d1982dc9c..fd47a59bf5 100644 --- a/mindspore/ccsrc/parallel/step_parallel.h +++ b/mindspore/ccsrc/parallel/step_parallel.h @@ -19,10 +19,10 @@ #include -#include -#include #include +#include #include +#include #include #include "./common.h" diff --git a/mindspore/ccsrc/parallel/strategy.h b/mindspore/ccsrc/parallel/strategy.h index 68ba4962d7..acc6ca928f 100644 --- a/mindspore/ccsrc/parallel/strategy.h +++ b/mindspore/ccsrc/parallel/strategy.h @@ -18,10 +18,10 @@ #define MINDSPORE_CCSRC_PARALLEL_STRATEGY_H_ #include -#include -#include #include +#include #include +#include #include "parallel/status.h" diff --git a/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc b/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc index 4e008feee1..9e3573eee2 100644 --- a/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc +++ b/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc @@ -20,10 +20,10 @@ #include #include +#include "common/utils.h" +#include "utils/convert_utils.h" #include "utils/log_adapter.h" #include "utils/node_strategy.pb.h" -#include "utils/convert_utils.h" -#include "common/utils.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h b/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h index 3cbb116b42..b5d3626f53 100644 --- a/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h +++ b/mindspore/ccsrc/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h @@ -19,8 +19,8 @@ #include #include -#include "parallel/strategy.h" #include "parallel/ops_info/ops_utils.h" +#include "parallel/strategy.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/tensor_layout/arrangement.cc b/mindspore/ccsrc/parallel/tensor_layout/arrangement.cc index fea7e4ba65..68acae87f3 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/arrangement.cc +++ b/mindspore/ccsrc/parallel/tensor_layout/arrangement.cc @@ -15,14 +15,14 @@ */ #include "parallel/tensor_layout/arrangement.h" -#include -#include #include +#include +#include +#include "common/utils.h" #include "parallel/status.h" -#include "utils/log_adapter.h" #include "parallel/tensor_layout/shape_util.h" #include "utils/convert_utils.h" -#include "common/utils.h" +#include "utils/log_adapter.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/tensor_layout/arrangement.h b/mindspore/ccsrc/parallel/tensor_layout/arrangement.h index 582beeaff2..6d64e07f03 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/arrangement.h +++ b/mindspore/ccsrc/parallel/tensor_layout/arrangement.h @@ -21,8 +21,8 @@ #include #include #include -#include #include +#include #include "parallel/status.h" #include "parallel/tensor_layout/array.h" diff --git a/mindspore/ccsrc/parallel/tensor_layout/array.cc b/mindspore/ccsrc/parallel/tensor_layout/array.cc index e073c3905c..ce1b9b8ecf 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/array.cc +++ b/mindspore/ccsrc/parallel/tensor_layout/array.cc @@ -16,8 +16,8 @@ #include "parallel/tensor_layout/array.h" #include -#include "utils/log_adapter.h" #include "parallel/status.h" +#include "utils/log_adapter.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/tensor_layout/construct_operator.h b/mindspore/ccsrc/parallel/tensor_layout/construct_operator.h index 641b0975dd..91f5236037 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/construct_operator.h +++ b/mindspore/ccsrc/parallel/tensor_layout/construct_operator.h @@ -21,9 +21,9 @@ #include #include +#include "ir/value.h" #include "parallel/ops_info/operator_info.h" #include "parallel/status.h" -#include "ir/value.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.cc b/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.cc index 3321d751bb..b2ee51b40b 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.cc +++ b/mindspore/ccsrc/parallel/tensor_layout/layout_transfer.cc @@ -15,8 +15,8 @@ */ #include "parallel/tensor_layout/layout_transfer.h" -#include "parallel/status.h" #include "common/utils.h" +#include "parallel/status.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/tensor_layout/map.cc b/mindspore/ccsrc/parallel/tensor_layout/map.cc index 46c066b250..4f3f2369c7 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/map.cc +++ b/mindspore/ccsrc/parallel/tensor_layout/map.cc @@ -15,14 +15,14 @@ */ #include "parallel/tensor_layout/map.h" -#include -#include #include +#include +#include +#include "common/utils.h" #include "parallel/status.h" -#include "utils/log_adapter.h" #include "parallel/tensor_layout/shape_util.h" #include "utils/convert_utils.h" -#include "common/utils.h" +#include "utils/log_adapter.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/tensor_layout/map.h b/mindspore/ccsrc/parallel/tensor_layout/map.h index 55ed3e5577..f7bc061aa1 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/map.h +++ b/mindspore/ccsrc/parallel/tensor_layout/map.h @@ -23,8 +23,8 @@ #include #include #include "parallel/status.h" -#include "parallel/tensor_layout/array.h" #include "parallel/tensor_layout/arrangement.h" +#include "parallel/tensor_layout/array.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.cc b/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.cc index dea44ee60e..2ee682fad8 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.cc +++ b/mindspore/ccsrc/parallel/tensor_layout/redistribution_layout_transfer.cc @@ -16,8 +16,8 @@ #include "parallel/tensor_layout/redistribution_layout_transfer.h" #include "parallel/status.h" -#include "parallel/tensor_layout/shape_util.h" #include "parallel/tensor_layout/reshape_layout_transfer.h" +#include "parallel/tensor_layout/shape_util.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h b/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h index 3515c7383a..13f9e7af24 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h +++ b/mindspore/ccsrc/parallel/tensor_layout/redistribution_operator_infer.h @@ -18,13 +18,13 @@ #define MINDSPORE_CCSRC_PARALLEL_TENSOR_LAYOUT_REDISTRIBUTION_OPERATOR_INFER_H_ #include -#include #include -#include +#include #include +#include -#include "parallel/tensor_layout/redistribution_layout_transfer.h" #include "parallel/tensor_layout/construct_operator.h" +#include "parallel/tensor_layout/redistribution_layout_transfer.h" #include "utils/convert_utils.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/tensor_layout/shape_util.h b/mindspore/ccsrc/parallel/tensor_layout/shape_util.h index 5451af063e..85ca70969b 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/shape_util.h +++ b/mindspore/ccsrc/parallel/tensor_layout/shape_util.h @@ -18,10 +18,10 @@ #define MINDSPORE_CCSRC_PARALLEL_TENSOR_LAYOUT_SHAPE_UTIL_H_ #include -#include -#include #include #include +#include +#include #include "parallel/status.h" diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_info.h b/mindspore/ccsrc/parallel/tensor_layout/tensor_info.h index b24df6bbf2..9fc6a229e2 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/tensor_info.h +++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_info.h @@ -19,12 +19,12 @@ #include #include -#include #include +#include +#include "parallel/device_matrix.h" #include "parallel/status.h" #include "parallel/tensor_layout/tensor_layout.h" -#include "parallel/device_matrix.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.cc b/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.cc index 9463b99ce6..f49b967abc 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.cc +++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.cc @@ -15,15 +15,15 @@ */ #include "parallel/tensor_layout/tensor_layout.h" -#include #include -#include "parallel/status.h" -#include "utils/log_adapter.h" -#include "parallel/tensor_layout/shape_util.h" -#include "parallel/tensor_layout/array.h" +#include +#include "common/utils.h" #include "ir/value.h" #include "parallel/device_matrix.h" -#include "common/utils.h" +#include "parallel/status.h" +#include "parallel/tensor_layout/array.h" +#include "parallel/tensor_layout/shape_util.h" +#include "utils/log_adapter.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.h b/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.h index 0db356b8b1..238c9373d9 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.h +++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_layout.h @@ -22,11 +22,11 @@ #include #include #include +#include "parallel/device_manager.h" #include "parallel/status.h" #include "parallel/tensor_layout/arrangement.h" #include "parallel/tensor_layout/map.h" #include "utils/convert_utils.h" -#include "parallel/device_manager.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc index 365a2be1cb..93bda5da81 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc +++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc @@ -18,9 +18,9 @@ #include #include #include +#include "common/utils.h" #include "parallel/status.h" #include "parallel/tensor_layout/shape_util.h" -#include "common/utils.h" namespace mindspore { namespace parallel { diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h index daf474b3d7..38fb5959ad 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h +++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h @@ -18,18 +18,18 @@ #define MINDSPORE_CCSRC_PARALLEL_TENSOR_LAYOUT_TENSOR_REDISTRIBUTION_H_ #include -#include -#include #include #include +#include #include +#include #include "ir/value.h" -#include "parallel/status.h" -#include "parallel/tensor_layout/tensor_layout.h" #include "parallel/ops_info/operator_info.h" +#include "parallel/status.h" #include "parallel/tensor_layout/construct_operator.h" #include "parallel/tensor_layout/redistribution_operator_infer.h" +#include "parallel/tensor_layout/tensor_layout.h" namespace mindspore { namespace parallel { From 89945c15f53be268d74d318e3a64269460a0d1dc Mon Sep 17 00:00:00 2001 From: lichenever Date: Thu, 2 Apr 2020 11:14:45 +0800 Subject: [PATCH 067/227] fix refkey bug for auto parallel --- mindspore/ccsrc/parallel/step_parallel.cc | 21 ++++++++++-- tests/ut/python/parallel/test_arithmetic.py | 36 ++++++++++++++++++--- 2 files changed, 50 insertions(+), 7 deletions(-) diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc index 886a0aebf4..b79198ffca 100644 --- a/mindspore/ccsrc/parallel/step_parallel.cc +++ b/mindspore/ccsrc/parallel/step_parallel.cc @@ -49,6 +49,9 @@ namespace mindspore { namespace parallel { const std::set COMMUNICATION_OPS = {ALL_REDUCE, ALL_GATHER, ALL_TO_ALL, REDUCE_SCATTER}; const std::set INVALID_LOSS_OPS = {GET_NEXT, VIRTUALLOSS}; +// g_RefMap, for CNode B input i is a RefKey[Parameter C], +// it will be one item in map with key: C, and value: (B, i) +static std::map> g_RefMap; void SetCommunicationOpGroupLabel(std::vector new_node_input) { if (new_node_input.empty()) { @@ -1085,11 +1088,19 @@ std::vector ExtractShape(const CNodePtr& node) { std::vector all_inputs = node->inputs(); std::vector node_inputs{all_inputs.begin() + 1, all_inputs.end()}; - for (auto& input : node_inputs) { + size_t inputs_size = all_inputs.size(); + for (size_t i = 1; i < inputs_size; ++i) { Shapes input_shapes; + AnfNodePtr input = all_inputs[i]; if (IsValueNode(input)) { auto func_graph = node->func_graph(); MS_EXCEPTION_IF_NULL(func_graph); + std::vector parameters = FindParameterByRefKeyNode(input, func_graph); + if (parameters.size() != 1) { + MS_LOG(EXCEPTION) << "Find parameter by ref key node failed"; + } + std::pair node_pair = std::make_pair(node, SizeToInt(i)); + g_RefMap[parameters[0]] = node_pair; input_shapes = GetRefKeyNodeShape(input, func_graph); } else if (IsValueNode(input) || input->isa() || input->isa()) { input_shapes = GetNodeShape(input); @@ -1205,14 +1216,20 @@ void CoverSliceShape(const FuncGraphPtr& root) { auto parameters = root->parameters(); for (auto& parameter : parameters) { MS_EXCEPTION_IF_NULL(parameter->Shape()); + auto iter = g_RefMap.find(parameter); + if (iter != g_RefMap.end()) { + SetParallelShape(parameter, g_RefMap[parameter]); + continue; + } std::pair res = FindSubGraph(root, parameter); if (res.first == nullptr) { MS_LOG(INFO) << "Parameter " << parameter->ToString() << " don't need to set parallel shape"; } else { SetParallelShape(parameter, res); - MS_LOG(DEBUG) << "parameter " << parameter->ToString() << " shape " << parameter->Shape()->ToString(); + MS_LOG(DEBUG) << "Parameter " << parameter->ToString() << " shape " << parameter->Shape()->ToString(); } } + g_RefMap.clear(); } bool ParameterIsCloned(const FuncGraphPtr& root, const AnfNodePtr& parameter_node) { diff --git a/tests/ut/python/parallel/test_arithmetic.py b/tests/ut/python/parallel/test_arithmetic.py index 2c7eabc8f2..4c34c0371e 100644 --- a/tests/ut/python/parallel/test_arithmetic.py +++ b/tests/ut/python/parallel/test_arithmetic.py @@ -13,14 +13,13 @@ # limitations under the License. import numpy as np -from mindspore import context +import mindspore as ms +from mindspore import Parameter, Tensor, context import mindspore.nn as nn from mindspore.ops import operations as P -from mindspore import Tensor -from tests.ut.python.ops.test_math_ops import VirtualLoss -import mindspore as ms -from mindspore.common.api import _executor from mindspore.ops import composite as C +from mindspore.common.api import _executor +from tests.ut.python.ops.test_math_ops import VirtualLoss class NetWithLoss(nn.Cell): @@ -470,3 +469,30 @@ def test_matmul_floordiv_broadcast2(): y = Tensor(np.ones([32, 1]), dtype=ms.float32) b = Tensor(np.ones([1, 64]), dtype=ms.float32) _executor.compile(net, x, y, b) + + +def test_assign_sub(): + class Net(nn.Cell): + def __init__(self): + super().__init__() + self.assign_sub = P.AssignSub() + self.mul = P.Mul() + self.mul_weight = Parameter(Tensor(np.full([128, 32], + 0.5, dtype=np.float32)), + name="mul_weight") + self.assignsub_weight = Parameter(Tensor(np.full([128, 32], + 1.1, dtype=np.float32)), + name="assignsub_weight") + + def construct(self, x, y, z): + out = self.mul(x, self.mul_weight) + out = self.assign_sub(self.assignsub_weight, out) + return out + + context.set_auto_parallel_context(device_num=64, global_rank=15) + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") + net = GradWrap(NetWithLoss(Net())) + x = Tensor(np.ones([128, 32]), dtype=ms.float32) + y = Tensor(np.ones([128, 32]), dtype=ms.float32) + z = Tensor(np.ones([128, 32]), dtype=ms.float32) + _executor.compile(net, x, y, z) From b18f634912c626f46d5d846bbe6a09d86a96a62d Mon Sep 17 00:00:00 2001 From: Wei Luning Date: Mon, 23 Mar 2020 17:33:56 +0800 Subject: [PATCH 068/227] remove ge depend in cpu --- CMakeLists.txt | 12 +- cmake/mind_expression.cmake | 2 +- mindspore/ccsrc/CMakeLists.txt | 26 +- mindspore/ccsrc/debug/e2e_dump.cc | 2 +- .../device/ascend/kernel_build_ascend.cc | 2 +- .../ascend/profiling/profiling_manager.cc | 1 + .../device/ascend/tasksink/task_generator.cc | 4 +- .../device/ascend/tasksink/task_generator.h | 2 +- .../ccsrc/device/gpu/gpu_kernel_build.cc | 1 - .../ccsrc/device/gpu/kernel_info_setter.cc | 2 +- mindspore/ccsrc/device/kernel_adjust.cc | 1 + mindspore/ccsrc/ir/anf.cc | 17 + mindspore/ccsrc/ir/anf.h | 2 + mindspore/ccsrc/ir/meta_tensor.cc | 1 - .../ccsrc/kernel/aicpu/aicpu_kernel_mod.h | 4 +- mindspore/ccsrc/kernel/akg/akgkernelbuild.cc | 5 +- mindspore/ccsrc/kernel/ascend_kernel_mod.h | 36 + mindspore/ccsrc/kernel/common_utils.cc | 3 +- .../ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h | 4 +- .../kernel/gpu/nn/lstm_grad_data_gpu_kernel.h | 6 +- .../gpu/nn/lstm_grad_weight_gpu_kernel.h | 4 +- mindspore/ccsrc/kernel/hccl/hccl_kernel.h | 4 +- mindspore/ccsrc/kernel/kernel.h | 6 - mindspore/ccsrc/kernel/mng/rt_kernel.h | 4 +- mindspore/ccsrc/kernel/oplib/oplib.cc | 4 +- mindspore/ccsrc/kernel/tbe/tbe_kernel_build.h | 33 +- mindspore/ccsrc/kernel/tbe/tbe_kernel_mod.h | 4 +- .../kernel/tbe/tbe_kernel_parallel_build.h | 5 +- .../ccsrc/kernel/tbe/tbe_kernel_select.cc | 9 +- mindspore/ccsrc/kernel/tbe/tbe_utils.cc | 1 + mindspore/ccsrc/pipeline/base.h | 64 ++ mindspore/ccsrc/pipeline/init.cc | 10 +- .../ccsrc/pipeline/parse/python_adapter.cc | 1 + .../ccsrc/pipeline/parse/python_adapter.h | 1 + mindspore/ccsrc/pipeline/pipeline.cc | 692 +++--------------- mindspore/ccsrc/pipeline/pipeline.h | 35 +- mindspore/ccsrc/pipeline/pipeline_ge.cc | 545 ++++++++++++++ mindspore/ccsrc/pipeline/pipeline_ge.h | 57 ++ mindspore/ccsrc/pipeline/resource.cc | 29 - mindspore/ccsrc/pipeline/resource.h | 7 +- .../ascend/ascend_backend_optimization.cc | 2 +- .../ascend/buffer_fusion/buffer_fusion.cc | 4 +- .../ir_fusion/allreduce_fusion.cc | 2 +- .../ir_fusion/allreduce_fusion.h | 6 +- .../ccsrc/predict/converter/kernel2ms.cc | 6 +- mindspore/ccsrc/pynative/base.h | 67 ++ mindspore/ccsrc/pynative/pynative_execute.cc | 285 +------- mindspore/ccsrc/pynative/pynative_execute.h | 43 +- .../ccsrc/pynative/pynative_execute_ge.cc | 311 ++++++++ .../ccsrc/pynative/pynative_execute_ge.h | 46 ++ mindspore/ccsrc/session/ascend_session.cc | 1 + mindspore/ccsrc/session/gpu_session.cc | 2 +- mindspore/ccsrc/transform/convert.cc | 18 - mindspore/ccsrc/transform/convert.h | 1 - mindspore/ccsrc/utils/callbacks.cc | 13 +- mindspore/ccsrc/utils/callbacks.h | 9 + mindspore/ccsrc/utils/context/ms_context.cc | 7 +- mindspore/ccsrc/utils/context/ms_context.h | 1 - mindspore/ccsrc/utils/convert_utils.cc | 41 ++ mindspore/ccsrc/utils/convert_utils.h | 4 + mindspore/ccsrc/vm/segment_runner.cc | 4 +- mindspore/ccsrc/vm/transform.cc | 16 +- mindspore/ccsrc/vm/transform.h | 2 - mindspore/ccsrc/vm/vmimpl.cc | 35 - mindspore/ccsrc/vm/vmimpl.h | 6 - mindspore/common/api.py | 3 +- mindspore/common/parameter.py | 12 + mindspore/common/tensor.py | 10 + mindspore/ops/functional.py | 2 +- mindspore/train/model.py | 3 + .../cpp/device/ascend_kernel_select_test.cc | 2 +- tests/ut/cpp/device/ascend_profiling_test.cc | 2 +- .../ir_fusion/allreduce_fusion_test.cc | 2 +- ...onvert_const_input_to_tensor_input_test.cc | 2 +- tests/ut/python/ir/test_tensor.py | 18 + tests/ut/python/ops/test_array_ops.py | 14 - tests/ut/python/parallel/__init__.py | 5 +- tests/ut/python/parallel/test_alltoall.py | 11 +- .../parallel/test_auto_parallel_arithmetic.py | 18 +- ...t_auto_parallel_assign_sub_with_ref_key.py | 10 +- .../parallel/test_auto_parallel_cast.py | 12 +- .../test_auto_parallel_matmul_prelu.py | 8 +- .../test_auto_parallel_parameter_cast.py | 8 +- .../parallel/test_auto_parallel_transpose.py | 10 +- .../parallel/test_auto_parallel_two_matmul.py | 5 +- .../python/parallel/test_dataset_interface.py | 2 +- tests/ut/python/parallel/test_one_dev.py | 20 +- .../python/pipeline/parse/test_create_obj.py | 3 + tests/ut/python/pipeline/parse/test_dtype.py | 3 +- .../ut/python/pynative_mode/ops/test_grad.py | 16 + .../test_summary_ops_params_valid_check.py | 21 +- tests/ut/python/utils/test_serialize.py | 3 +- 92 files changed, 1571 insertions(+), 1234 deletions(-) create mode 100644 mindspore/ccsrc/kernel/ascend_kernel_mod.h create mode 100644 mindspore/ccsrc/pipeline/base.h create mode 100644 mindspore/ccsrc/pipeline/pipeline_ge.cc create mode 100644 mindspore/ccsrc/pipeline/pipeline_ge.h rename mindspore/ccsrc/pre_activate/{ascend => common}/ir_fusion/allreduce_fusion.cc (97%) rename mindspore/ccsrc/pre_activate/{ascend => common}/ir_fusion/allreduce_fusion.h (87%) create mode 100644 mindspore/ccsrc/pynative/base.h create mode 100644 mindspore/ccsrc/pynative/pynative_execute_ge.cc create mode 100644 mindspore/ccsrc/pynative/pynative_execute_ge.h rename tests/ut/cpp/pre_activate/{ascend => common}/ir_fusion/allreduce_fusion_test.cc (99%) diff --git a/CMakeLists.txt b/CMakeLists.txt index 9cb73935ee..bdac2da46e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -42,11 +42,13 @@ else() include(${CMAKE_SOURCE_DIR}/cmake/dependency_graphengine.cmake) endif() -include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc) -include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/external) -include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/framework) -include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc) -include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc/toolchain) +if (ENABLE_GE OR ENABLE_D OR ENABLE_TESTCASES) + include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc) + include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/external) + include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/framework) + include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc) + include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc/toolchain) +endif() set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=hidden") add_subdirectory(mindspore/ccsrc) diff --git a/cmake/mind_expression.cmake b/cmake/mind_expression.cmake index 103775a4c2..345fd4675e 100644 --- a/cmake/mind_expression.cmake +++ b/cmake/mind_expression.cmake @@ -40,7 +40,7 @@ if (ENABLE_GE) include_directories(${CMAKE_SOURCE_DIR}/third_party/ge/include) include_directories(${CMAKE_SOURCE_DIR}/third_party/ge/include/external) include_directories(${CMAKE_SOURCE_DIR}/third_party/ge/include/external/graph) -else() +elseif(ENABLE_D OR ENABLE_TESTCASES) include_directories(${CMAKE_SOURCE_DIR}/graphengine/inc) include_directories(${CMAKE_SOURCE_DIR}/graphengine/inc/ops) include_directories(${CMAKE_SOURCE_DIR}/graphengine/inc/external) diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt index 1c684b6736..0b4bb0d1df 100644 --- a/mindspore/ccsrc/CMakeLists.txt +++ b/mindspore/ccsrc/CMakeLists.txt @@ -34,6 +34,8 @@ if(ENABLE_GPU) "device/gpu/*.cu" "kernel/gpu/*.cu" "kernel/akg/gpu/*.cc" + "kernel/akg/akgkernelbuild.cc" + "kernel/akg/akg_kernel_attrs_process.cc" ) file(GLOB_RECURSE GPU_KERNEL_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "kernel/gpu/*.cc" @@ -100,14 +102,14 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "debug/*.cc" "onnx/onnx_exporter.cc" "operator/*.cc" - "transform/*.cc" "session/kernel_graph.cc" "utils/node_utils.cc" "session/session_basic.cc" "session/session_factory.cc" "session/anf_runtime_algorithm.cc" "vm/*.cc" - "pynative/*.cc" + "pynative/base.cc" + "pynative/pynative_execute.cc" "pybind_api/*.cc" "device/common/*.cc" "kernel/kernel_query.cc" @@ -117,7 +119,6 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "device/kernel_runtime.cc" "device/kernel_runtime_manager.cc" "device/convert_tensor_utils.cc" - "pre_activate/ascend/*.cc" "pre_activate/common/*.cc" "pre_activate/pass/*.cc" "pre_activate/gpu/*.cc" @@ -168,6 +169,15 @@ if(ENABLE_DUMP_PROTO) add_compile_definitions(ENABLE_DUMP_PROTO) endif() +if(ENABLE_GE) + file(GLOB_RECURSE GE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} + "transform/*.cc" + "pynative/pynative_execute_ge.cc" + "pipeline/pipeline_ge.cc" + ) + list(APPEND MINDSPORE_SRC_LIST ${GE_SRC_LIST}) +endif() + if(ENABLE_D) include_directories("${CMAKE_BINARY_DIR}/kernel/aicpu") file(GLOB_RECURSE PROTO_IN RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} @@ -188,6 +198,9 @@ if(ENABLE_D) "device/kernel_adjust.cc" "kernel/kernel_fusion.cc" "kernel/tbe/*.cc" + "pre_activate/ascend/*.cc" + "transform/*.cc" + "pipeline/pipeline_ge.cc" ) list(APPEND MINDSPORE_SRC_LIST ${D_SRC_LIST}) list(APPEND MINDSPORE_PROTO_AICPU_LIST ${PROTOSRCS}) @@ -246,9 +259,11 @@ if (ENABLE_GE) target_link_libraries(mindspore graph ge_client) endif() target_link_libraries(mindspore tsdclient) -else() +elseif(ENABLE_D) add_compile_definitions(NO_GE_CLIENT) target_link_libraries(mindspore graph) +else() + add_compile_definitions(NO_GE_CLIENT) endif() if(ENABLE_D) @@ -288,8 +303,6 @@ endif() set(PYTHON_MODULE_SOURCE pipeline/init.cc kernel/oplib/oplib.cc - kernel/akg/akgkernelbuild.cc - kernel/akg/akg_kernel_attrs_process.cc ${MS_STEPS_SRC_LIST} ${MS_CCE_SRC_LIST} ${MS_AICPU_SRC_LIST} ${MS_TASKINFO_LIST} ${MS_RT_SRC_LIST} ${GPU_NCCL_LIST} ${MS_HCCL_SRC_LIST} ${MS_PREDICT_SRC_LIST} ${CPU_SRC_LIST} ${MEM_REUSE_SRC_LIST} ${GPU_KERNEL_SRC_LIST}) @@ -350,6 +363,7 @@ if(ENABLE_GPU) assign_source_group("Include" ${GROUP_INCLUDE}) file(GLOB COMPILER_SRCS + "pre_activate/gpu/*.cc" ${TVM_DIR}/src/api/*.cc ${TVM_DIR}/src/arithmetic/*.cc ${TVM_DIR}/src/autotvm/*.cc diff --git a/mindspore/ccsrc/debug/e2e_dump.cc b/mindspore/ccsrc/debug/e2e_dump.cc index 3006bb66da..ba11eafa5f 100644 --- a/mindspore/ccsrc/debug/e2e_dump.cc +++ b/mindspore/ccsrc/debug/e2e_dump.cc @@ -49,7 +49,7 @@ bool Dump::IsKernelNeedDump(const std::string& kernel_name) { return false; } -bool Dump::ParseDumpConfig(const string& dump_config_file) { +bool Dump::ParseDumpConfig(const std::string& dump_config_file) { std::ifstream jsonFile(dump_config_file); if (!jsonFile.is_open()) { MS_LOG(ERROR) << dump_config_file << " open failed."; diff --git a/mindspore/ccsrc/device/ascend/kernel_build_ascend.cc b/mindspore/ccsrc/device/ascend/kernel_build_ascend.cc index 66ce697ffc..e7c1449360 100644 --- a/mindspore/ccsrc/device/ascend/kernel_build_ascend.cc +++ b/mindspore/ccsrc/device/ascend/kernel_build_ascend.cc @@ -94,7 +94,7 @@ static bool KernelBuildParallelCompile(const mindspore::session::KernelGraph *ke return ret; } -static vector CalCleanZerosSize(const CNodePtr &pre_node) { +static std::vector CalCleanZerosSize(const CNodePtr &pre_node) { MS_EXCEPTION_IF_NULL(pre_node); std::vector clean_size_list; // clean output diff --git a/mindspore/ccsrc/device/ascend/profiling/profiling_manager.cc b/mindspore/ccsrc/device/ascend/profiling/profiling_manager.cc index 4bc68e647a..29193e5cfa 100644 --- a/mindspore/ccsrc/device/ascend/profiling/profiling_manager.cc +++ b/mindspore/ccsrc/device/ascend/profiling/profiling_manager.cc @@ -27,6 +27,7 @@ #include "utils/log_adapter.h" #include "utils/context/ms_context.h" #include "common/utils.h" +#include "utils/convert_utils.h" using std::vector; using Json = nlohmann::json; diff --git a/mindspore/ccsrc/device/ascend/tasksink/task_generator.cc b/mindspore/ccsrc/device/ascend/tasksink/task_generator.cc index 88d00bc6c2..62cf809c21 100644 --- a/mindspore/ccsrc/device/ascend/tasksink/task_generator.cc +++ b/mindspore/ccsrc/device/ascend/tasksink/task_generator.cc @@ -121,8 +121,8 @@ bool TaskGenerator::LaunchKernel(const CNodePtr &anf_node_ptr, uint32_t stream_i LaunchAddrCleanKernel(anf_node_ptr, &kernel_inputs); } - std::vector task_info_ptrs = - kernel_mod->GenTask(kernel_inputs, kernel_workspaces, kernel_outputs, stream_id); + std::vector task_info_ptrs = dynamic_cast(kernel_mod) + ->GenTask(kernel_inputs, kernel_workspaces, kernel_outputs, stream_id); task_info_list->insert(task_info_list->end(), task_info_ptrs.begin(), task_info_ptrs.end()); return true; } diff --git a/mindspore/ccsrc/device/ascend/tasksink/task_generator.h b/mindspore/ccsrc/device/ascend/tasksink/task_generator.h index 0c56fcc744..ffedcd7930 100644 --- a/mindspore/ccsrc/device/ascend/tasksink/task_generator.h +++ b/mindspore/ccsrc/device/ascend/tasksink/task_generator.h @@ -24,7 +24,7 @@ #include #include "device/kernel_runtime.h" #include "ir/anf.h" -#include "kernel/kernel.h" +#include "kernel/ascend_kernel_mod.h" #include "framework/ge_runtime/task_info.h" namespace mindspore { diff --git a/mindspore/ccsrc/device/gpu/gpu_kernel_build.cc b/mindspore/ccsrc/device/gpu/gpu_kernel_build.cc index 0467b59e06..2a2a2be065 100644 --- a/mindspore/ccsrc/device/gpu/gpu_kernel_build.cc +++ b/mindspore/ccsrc/device/gpu/gpu_kernel_build.cc @@ -21,7 +21,6 @@ #include "kernel/gpu/gpu_kernel_factory.h" #include "operator/ops.h" #include "pybind11/stl.h" -#include "transform/convert.h" #include "session/anf_runtime_algorithm.h" namespace mindspore { namespace device { diff --git a/mindspore/ccsrc/device/gpu/kernel_info_setter.cc b/mindspore/ccsrc/device/gpu/kernel_info_setter.cc index 3faf7d01c8..05ecf380d1 100644 --- a/mindspore/ccsrc/device/gpu/kernel_info_setter.cc +++ b/mindspore/ccsrc/device/gpu/kernel_info_setter.cc @@ -91,7 +91,7 @@ std::string SupportedTypeList(const CNodePtr& kernel_node) { return supported_type_lists; } -bool SelectAkgKernel(const CNodePtr& kernel_node, const shared_ptr& selected_kernel_info) { +bool SelectAkgKernel(const CNodePtr& kernel_node, const std::shared_ptr& selected_kernel_info) { MS_EXCEPTION_IF_NULL(kernel_node); MS_EXCEPTION_IF_NULL(selected_kernel_info); std::vector> kernel_info_list; diff --git a/mindspore/ccsrc/device/kernel_adjust.cc b/mindspore/ccsrc/device/kernel_adjust.cc index a4d316d601..4d3ebf9490 100644 --- a/mindspore/ccsrc/device/kernel_adjust.cc +++ b/mindspore/ccsrc/device/kernel_adjust.cc @@ -32,6 +32,7 @@ #include "device/ascend/profiling/profiling_manager.h" #include "device/ascend/kernel_select_ascend.h" #include "device/kernel_info.h" +#include "runtime/base.h" constexpr auto kLoopCountParamName = "loop_count"; constexpr auto kIterLoopParamName = "iter_loop"; diff --git a/mindspore/ccsrc/ir/anf.cc b/mindspore/ccsrc/ir/anf.cc index c1348bf7d7..924453a7a6 100644 --- a/mindspore/ccsrc/ir/anf.cc +++ b/mindspore/ccsrc/ir/anf.cc @@ -197,6 +197,23 @@ PrimitivePtr GetCNodePrimitive(const AnfNodePtr& node) { return nullptr; } +std::string GetCNodeFuncName(const CNodePtr cnode) { + if (cnode->inputs().empty()) { + return ""; + } + + AnfNodePtr valuenode = cnode->input(0); + if (valuenode->isa()) { + auto value = GetValueNode(valuenode); + // check whether the valuenode is primitive + if (value->isa()) { + return value->cast()->name(); + } + return value->ToString(); + } + return ""; +} + bool IsPrimitive(const AnfNodePtr& node, const PrimitivePtr& value) { if (IsValueNode(node)) { PrimitivePtr fn_value = GetValueNode(node); diff --git a/mindspore/ccsrc/ir/anf.h b/mindspore/ccsrc/ir/anf.h index 9050a4ed16..e64b1329e9 100644 --- a/mindspore/ccsrc/ir/anf.h +++ b/mindspore/ccsrc/ir/anf.h @@ -384,6 +384,8 @@ static S GetValue(const ValuePtr &value) { return v; } +std::string GetCNodeFuncName(CNodePtr cnode); + // used to check whether an AnfNode is a cnode with a kind of Primitive as first input bool IsPrimitiveCNode(const AnfNodePtr &node, const PrimitivePtr &value); diff --git a/mindspore/ccsrc/ir/meta_tensor.cc b/mindspore/ccsrc/ir/meta_tensor.cc index d33bc10c27..e9221039a7 100644 --- a/mindspore/ccsrc/ir/meta_tensor.cc +++ b/mindspore/ccsrc/ir/meta_tensor.cc @@ -25,7 +25,6 @@ #include "device/device_address.h" #include "pybind_api/api_register.h" #include "pybind_api/export_flags.h" -#include "pynative/pynative_execute.h" #include "pipeline/static_analysis/abstract_value.h" namespace mindspore { diff --git a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_mod.h b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_mod.h index d1e6f69b23..dde2afe34a 100644 --- a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_mod.h +++ b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_mod.h @@ -18,11 +18,11 @@ #include #include #include -#include "kernel/kernel.h" +#include "kernel/ascend_kernel_mod.h" #include "kernel/aicpu/aicpu_util.h" namespace mindspore { namespace kernel { -class AicpuOpKernelMod : public KernelMod { +class AicpuOpKernelMod : public AscendKernelMod { public: AicpuOpKernelMod(); ~AicpuOpKernelMod() override; diff --git a/mindspore/ccsrc/kernel/akg/akgkernelbuild.cc b/mindspore/ccsrc/kernel/akg/akgkernelbuild.cc index 8413208c4d..c0759172a5 100644 --- a/mindspore/ccsrc/kernel/akg/akgkernelbuild.cc +++ b/mindspore/ccsrc/kernel/akg/akgkernelbuild.cc @@ -35,7 +35,6 @@ #include "utils/convert_utils.h" #include "utils/any.h" #include "utils/utils.h" -#include "transform/convert.h" #include "session/anf_runtime_algorithm.h" #include "kernel/akg/akg_kernel_attrs_process.h" @@ -240,8 +239,8 @@ bool AkgKernelBuild::CreateOutputDescJson(const AnfNodePtr &anf_node, nlohmann:: return true; } -void GetJson(const AnfNodePtr &anf_node, const vector &dyn_input_sizes, const shared_ptr &op_attr, - nlohmann::json *const attr_json, const ValuePtr &attr_value) { +void GetJson(const AnfNodePtr &anf_node, const std::vector &dyn_input_sizes, + const std::shared_ptr &op_attr, nlohmann::json *const attr_json, const ValuePtr &attr_value) { MS_EXCEPTION_IF_NULL(anf_node); MS_EXCEPTION_IF_NULL(op_attr); MS_EXCEPTION_IF_NULL(attr_json); diff --git a/mindspore/ccsrc/kernel/ascend_kernel_mod.h b/mindspore/ccsrc/kernel/ascend_kernel_mod.h new file mode 100644 index 0000000000..ff8595c1a2 --- /dev/null +++ b/mindspore/ccsrc/kernel/ascend_kernel_mod.h @@ -0,0 +1,36 @@ +/** + * Copyright 2019 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_CCSRC_KERNEL_ASCEND_KERNEL_MOD_H_ +#define MINDSPORE_CCSRC_KERNEL_ASCEND_KERNEL_MOD_H_ + +#include +#include +#include "framework/ge_runtime/task_info.h" +#include "kernel/kernel.h" + +using TaskInfoPtr = std::shared_ptr; +namespace mindspore { +namespace kernel { +class AscendKernelMod : public KernelMod { + public: + virtual std::vector GenTask(const std::vector &, const std::vector &, + const std::vector &, uint32_t) = 0; +}; +} // namespace kernel +} // namespace mindspore + +#endif // MINDSPORE_CCSRC_KERNEL_ASCEND_KERNEL_MOD_H_ diff --git a/mindspore/ccsrc/kernel/common_utils.cc b/mindspore/ccsrc/kernel/common_utils.cc index d610ea736d..c2f2638753 100644 --- a/mindspore/ccsrc/kernel/common_utils.cc +++ b/mindspore/ccsrc/kernel/common_utils.cc @@ -19,7 +19,6 @@ #include #include #include -#include "runtime/rt.h" #include "nlohmann/json.hpp" #include "session/anf_runtime_algorithm.h" #include "common/utils.h" @@ -490,7 +489,7 @@ void SaveJsonInfo(const std::string &json_name, const std::string &info) { if (!filewrite.is_open()) { return; } - filewrite << info << endl; + filewrite << info << std::endl; filewrite.close(); if (nullptr == realpath(path.c_str(), real_path)) { MS_LOG(DEBUG) << "dir " << path << " does not exit."; diff --git a/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h index 2e284f72e8..51a2da8574 100644 --- a/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h +++ b/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h @@ -226,12 +226,12 @@ class LstmGpuKernel : public GpuKernel { size_t reserved_size_; // input desc - unique_ptr x_desc_; + std::unique_ptr x_desc_; cudnnTensorDescriptor_t hx_desc_; cudnnTensorDescriptor_t cx_desc_; cudnnFilterDescriptor_t w_desc_; cudnnDropoutDescriptor_t dropout_desc_; - unique_ptr y_desc_; + std::unique_ptr y_desc_; cudnnTensorDescriptor_t hy_desc_; cudnnTensorDescriptor_t cy_desc_; cudnnRNNDescriptor_t rnn_desc_; diff --git a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h index 2fadccb8ea..a60ab78f7d 100644 --- a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h +++ b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h @@ -258,8 +258,8 @@ class LstmGradDataGpuKernel : public GpuKernel { cudnnRNNDescriptor_t rnn_desc_; // input desc - unique_ptr y_desc_; - unique_ptr dy_desc_; + std::unique_ptr y_desc_; + std::unique_ptr dy_desc_; cudnnTensorDescriptor_t dhy_desc_; cudnnTensorDescriptor_t dcy_desc_; cudnnFilterDescriptor_t w_desc_; @@ -269,7 +269,7 @@ class LstmGradDataGpuKernel : public GpuKernel { cudnnDropoutDescriptor_t dropout_desc_; // output desc - unique_ptr dx_desc_; + std::unique_ptr dx_desc_; cudnnTensorDescriptor_t dhx_desc_; cudnnTensorDescriptor_t dcx_desc_; diff --git a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h index 6cf512f14a..b28736cc96 100644 --- a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h +++ b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h @@ -214,9 +214,9 @@ class LstmGradWeightGpuKernel : public GpuKernel { cudnnDropoutDescriptor_t dropout_desc_; // input desc - unique_ptr x_desc_; + std::unique_ptr x_desc_; cudnnTensorDescriptor_t hx_desc_; - unique_ptr y_desc_; + std::unique_ptr y_desc_; // output desc cudnnFilterDescriptor_t dw_desc_; diff --git a/mindspore/ccsrc/kernel/hccl/hccl_kernel.h b/mindspore/ccsrc/kernel/hccl/hccl_kernel.h index 71d9e5ba6a..24e1feec0a 100644 --- a/mindspore/ccsrc/kernel/hccl/hccl_kernel.h +++ b/mindspore/ccsrc/kernel/hccl/hccl_kernel.h @@ -23,14 +23,14 @@ #include #include #include -#include "kernel/kernel.h" +#include "kernel/ascend_kernel_mod.h" #include "kernel/hccl/hcom_util.h" #include "hccl/hcom.h" #include "common/utils.h" namespace mindspore { namespace kernel { -class HcclKernel : public KernelMod { +class HcclKernel : public AscendKernelMod { public: HcclKernel(); ~HcclKernel() override; diff --git a/mindspore/ccsrc/kernel/kernel.h b/mindspore/ccsrc/kernel/kernel.h index aecc51794c..80d831269c 100644 --- a/mindspore/ccsrc/kernel/kernel.h +++ b/mindspore/ccsrc/kernel/kernel.h @@ -25,7 +25,6 @@ #include "ir/meta_tensor.h" #include "pipeline/static_analysis/dshape.h" #include "utils/log_adapter.h" -#include "framework/ge_runtime/task_info.h" namespace mindspore { enum KernelType : int { UNKNOWN_KERNEL_TYPE = 0, AUTO_DIFF_KERNEL, AICPU_KERNEL, RT_KERNEL, HCCL_KERNEL, TBE_KERNEL }; @@ -111,7 +110,6 @@ struct Address { size_t size; }; using AddressPtr = std::shared_ptr

    ; -using TaskInfoPtr = std::shared_ptr; class KernelMod { public: @@ -120,10 +118,6 @@ class KernelMod { virtual const std::vector &GetWorkspaceSizeList() const = 0; virtual bool Launch(const std::vector &inputs, const std::vector &workspace, const std::vector &outputs, uintptr_t stream_ptr) = 0; - virtual std::vector GenTask(const std::vector &, const std::vector &, - const std::vector &, uint32_t) { - return {}; - } virtual std::vector GenParameters() { return {}; } virtual ~KernelMod() = default; diff --git a/mindspore/ccsrc/kernel/mng/rt_kernel.h b/mindspore/ccsrc/kernel/mng/rt_kernel.h index f86a86ef5d..54823f73cc 100644 --- a/mindspore/ccsrc/kernel/mng/rt_kernel.h +++ b/mindspore/ccsrc/kernel/mng/rt_kernel.h @@ -22,12 +22,12 @@ #include #include #include -#include "kernel/kernel.h" +#include "kernel/ascend_kernel_mod.h" #include "kernel/task_stream.h" namespace mindspore { namespace kernel { -class RtKernel : public KernelMod { +class RtKernel : public AscendKernelMod { public: RtKernel(); ~RtKernel() override; diff --git a/mindspore/ccsrc/kernel/oplib/oplib.cc b/mindspore/ccsrc/kernel/oplib/oplib.cc index 23e7014104..b20bd741f1 100644 --- a/mindspore/ccsrc/kernel/oplib/oplib.cc +++ b/mindspore/ccsrc/kernel/oplib/oplib.cc @@ -19,7 +19,7 @@ #include #include #include "utils/log_adapter.h" -#include "kernel/oplib/opinfo.h" +#include "utils/overload.h" #include "utils/context/ms_context.h" namespace mindspore { @@ -50,7 +50,7 @@ constexpr auto kNeedCompile = "need_compile"; constexpr auto kShape = "shape"; std::vector> OpLib::op_info_; -string ImplTypeToStr(OpImplyType impl_type) { +std::string ImplTypeToStr(OpImplyType impl_type) { switch (impl_type) { case kTBE: return kTbe; diff --git a/mindspore/ccsrc/kernel/tbe/tbe_kernel_build.h b/mindspore/ccsrc/kernel/tbe/tbe_kernel_build.h index bc4895ac6f..de5ed84e41 100644 --- a/mindspore/ccsrc/kernel/tbe/tbe_kernel_build.h +++ b/mindspore/ccsrc/kernel/tbe/tbe_kernel_build.h @@ -48,7 +48,7 @@ class TbeKernelBuild { private: TbeKernelBuild() = default; ~TbeKernelBuild() = default; - static bool GenFusionDataInputJson(const shared_ptr &data_input, nlohmann::json *data_str, + static bool GenFusionDataInputJson(const std::shared_ptr &data_input, nlohmann::json *data_str, size_t *index); static bool GenFusionComputeJson(const mindspore::AnfNodePtr &compute_node, std::vector>::iterator *layer_iter, @@ -56,12 +56,13 @@ class TbeKernelBuild { static bool GenFusionComputeInputeJson(const mindspore::CNodePtr &cnode, std::vector>::iterator *layer_iter, std::vector *input_desc_list, size_t *index); - static void GenDescJson(const shared_ptr &anf_node, size_t out_idx, nlohmann::json *output_desc); - static void GenReusedOutputDesc(const shared_ptr &anf_node, size_t index, size_t output_index, - nlohmann::json *output_desc); + static void GenDescJson(const std::shared_ptr &anf_node, size_t out_idx, + nlohmann::json *output_desc); + static void GenReusedOutputDesc(const std::shared_ptr &anf_node, size_t index, + size_t output_index, nlohmann::json *output_desc); static size_t GetIOSizeImpl(const nlohmann::json &desc); - static bool GetInputLayers(const vector &input_nodes, - const vector &compute_nodes, + static bool GetInputLayers(const std::vector &input_nodes, + const std::vector &compute_nodes, std::vector> *input_layers); static bool IsDynamicInput(const CNodePtr &cnode); static size_t GetOptionalInput(const CNodePtr &cnode, bool is_dynamic_input); @@ -82,15 +83,17 @@ class TbeKernelJsonCreator { bool GenTbeAttrJson(const std::shared_ptr &anf_node, const std::shared_ptr &op_info, nlohmann::json *attrs_json); void ParseAttrValue(const std::string &type, const ValuePtr &value, nlohmann::json *attr_obj); - bool GenInputDescJson(const shared_ptr &anf_node, size_t real_input_index, bool value, - const shared_ptr &input_ptr, const string &op_input_name, size_t input_i, - vector *input_list); - bool GenOutputDescJson(const shared_ptr &anf_node, const vector> &outputs_ptr, - nlohmann::json *outputs_json); - bool GenInputList(const shared_ptr &anf_node, size_t input_tensor_num, const shared_ptr &input_ptr, - size_t *real_input_index, string *op_input_name, vector *input_list); - void GenOutputList(const shared_ptr &anf_node, const size_t &output_obj_num, - const shared_ptr &output_ptr, size_t *output_idx, vector *output_list); + bool GenInputDescJson(const std::shared_ptr &anf_node, size_t real_input_index, bool value, + const std::shared_ptr &input_ptr, const string &op_input_name, size_t input_i, + std::vector *input_list); + bool GenOutputDescJson(const std::shared_ptr &anf_node, + const std::vector> &outputs_ptr, nlohmann::json *outputs_json); + bool GenInputList(const std::shared_ptr &anf_node, size_t input_tensor_num, + const std::shared_ptr &input_ptr, size_t *real_input_index, string *op_input_name, + std::vector *input_list); + void GenOutputList(const std::shared_ptr &anf_node, const size_t &output_obj_num, + const std::shared_ptr &output_ptr, size_t *output_idx, + std::vector *output_list); kCreaterType creater_type_; std::string json_name_; std::string json_info_; diff --git a/mindspore/ccsrc/kernel/tbe/tbe_kernel_mod.h b/mindspore/ccsrc/kernel/tbe/tbe_kernel_mod.h index 35fc7f517d..f4fb75038e 100644 --- a/mindspore/ccsrc/kernel/tbe/tbe_kernel_mod.h +++ b/mindspore/ccsrc/kernel/tbe/tbe_kernel_mod.h @@ -21,12 +21,12 @@ #include #include #include -#include "kernel/kernel.h" +#include "kernel/ascend_kernel_mod.h" #include "kernel/tbe/tbe_utils.h" namespace mindspore { namespace kernel { -class TbeKernelMod : public KernelMod { +class TbeKernelMod : public AscendKernelMod { public: explicit TbeKernelMod(KernelPackPtr kernel_pack) : kernel_pack_(std::move(kernel_pack)) {} ~TbeKernelMod() override = default; diff --git a/mindspore/ccsrc/kernel/tbe/tbe_kernel_parallel_build.h b/mindspore/ccsrc/kernel/tbe/tbe_kernel_parallel_build.h index 45f56fdd0b..5066e9457f 100644 --- a/mindspore/ccsrc/kernel/tbe/tbe_kernel_parallel_build.h +++ b/mindspore/ccsrc/kernel/tbe/tbe_kernel_parallel_build.h @@ -55,8 +55,9 @@ class ParallelBuildManager { bool WaitOne(int *task_id, char **task_result) const; bool IsAllTaskFinish() const; std::pair TaskFinishProcess(int32_t task_id, bool set_kernel_mod = true); - KernelModPtr GenKernelMod(const string &json_name, const string &processor, const vector &input_size_list, - const vector &output_size_list, const KernelPackPtr &kernel_pack) const; + KernelModPtr GenKernelMod(const string &json_name, const string &processor, + const std::vector &input_size_list, const std::vector &output_size_list, + const KernelPackPtr &kernel_pack) const; private: PyObject *tbe_parallel_compiler_; diff --git a/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc b/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc index 1953fd0c72..92798aa6bc 100644 --- a/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc +++ b/mindspore/ccsrc/kernel/tbe/tbe_kernel_select.cc @@ -168,7 +168,7 @@ bool ParseDynamicFormatJson(const std::string &jsonStr, std::vector &anf_node) { +std::string OpSelectFormat(const std::shared_ptr &anf_node) { nlohmann::json kernel_json; std::string res_json_str; TbeKernelJsonCreator creator(OP_SELECT_FORMAT); @@ -182,7 +182,7 @@ std::string OpSelectFormat(const shared_ptr &anf_node) { return res_json_str; } -void SetTidyInputsInfo(const shared_ptr &anf_node, +void SetTidyInputsInfo(const std::shared_ptr &anf_node, const std::shared_ptr &builder, const std::vector> &inputs) { std::vector inputs_type; @@ -231,7 +231,7 @@ void SetTidyInputsInfo(const shared_ptr &anf_node, builder->SetInputsFormat(inputs_format); } -void SetTidyOutputsInfo(const shared_ptr &anf_node, +void SetTidyOutputsInfo(const std::shared_ptr &anf_node, const std::shared_ptr &builder, const std::vector> &outputs) { std::vector outputs_type; @@ -268,7 +268,8 @@ void SetTidyOutputsInfo(const shared_ptr &anf_node, builder->SetOutputsFormat(outputs_format); } -void GenTidyKernelBuildInfo(const shared_ptr &anf_node, const std::vector> &inputs, +void GenTidyKernelBuildInfo(const std::shared_ptr &anf_node, + const std::vector> &inputs, const std::vector> &outputs) { auto builder_tmp = std::make_shared(); builder_tmp->SetKernelType(TBE_KERNEL); diff --git a/mindspore/ccsrc/kernel/tbe/tbe_utils.cc b/mindspore/ccsrc/kernel/tbe/tbe_utils.cc index ab29ca69bb..5980a0fd88 100644 --- a/mindspore/ccsrc/kernel/tbe/tbe_utils.cc +++ b/mindspore/ccsrc/kernel/tbe/tbe_utils.cc @@ -26,6 +26,7 @@ #include #include +#include "runtime/kernel.h" #include "kernel/oplib/oplib.h" #include "utils/utils.h" #include "session/anf_runtime_algorithm.h" diff --git a/mindspore/ccsrc/pipeline/base.h b/mindspore/ccsrc/pipeline/base.h new file mode 100644 index 0000000000..d007eac294 --- /dev/null +++ b/mindspore/ccsrc/pipeline/base.h @@ -0,0 +1,64 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_CCSRC_PIPELINE_BASE_H_ +#define MINDSPORE_CCSRC_PIPELINE_BASE_H_ + +#include +#include +#include +#include + +#include "ir/anf.h" +#include "pipeline/resource.h" +#include "utils/context/ms_context.h" + +namespace mindspore { +namespace pipeline { + +struct ExecutorInfo { + FuncGraphPtr func_graph; + ResourcePtr resource; + std::size_t arg_list_size; +}; + +using ExecutorInfoPtr = std::shared_ptr; + +inline std::string GetPhasePrefix(const std::string& phase) { + auto pos = phase.find('.'); + if (pos == std::string::npos) { + MS_LOG(EXCEPTION) << "phase has no . for prefix" << phase; + } + return phase.substr(0, pos); +} + +inline std::string GetFilePathName(const std::string& file_name) { + std::ostringstream oss; + auto ms_context = MsContext::GetInstance(); + if (ms_context == nullptr) { + MS_LOG(EXCEPTION) << "ms_context is nullptr"; + } + auto save_graphs_path = ms_context->save_graphs_path(); + if (save_graphs_path.empty()) { + save_graphs_path = "."; + } + oss << save_graphs_path << "/" << file_name; + return oss.str(); +} +} // namespace pipeline +} // namespace mindspore + +#endif // MINDSPORE_CCSRC_PIPELINE_BASE_H_ diff --git a/mindspore/ccsrc/pipeline/init.cc b/mindspore/ccsrc/pipeline/init.cc index f42ae0cf7b..24ead047d3 100644 --- a/mindspore/ccsrc/pipeline/init.cc +++ b/mindspore/ccsrc/pipeline/init.cc @@ -73,7 +73,7 @@ PYBIND11_MODULE(_c_expression, m) { "Get CNode Strategy Dictionary.") .def("get_allreduce_fusion", &ExecutorPy::GetAllreduceFusion, py::arg("phase") = py::str("train"), "Get Allreduce Fusion Dictionary.") - .def("build_data_graph", &ExecutorPy::BuildDFGraph, py::arg("build_params"), py::arg("phase") = py::str("train"), + .def("build_data_graph", &ExecutorPy::BuildGraph, py::arg("build_params"), py::arg("phase") = py::str("train"), py::arg("broadcast_params") = py::dict(), "Build data graph.") .def("has_compiled", &ExecutorPy::HasCompiled, py::arg("phase") = py::str(""), "get if cell compiled.") .def("run_init_graph", &ExecutorPy::RunInitGraph, "Run init Graph."); @@ -86,19 +86,17 @@ PYBIND11_MODULE(_c_expression, m) { (void)m.def("generate_key", &mindspore::pipeline::GenerateKey, "Generate the function graph key."); (void)m.def("real_run_op", &mindspore::pynative::RunOp, "Run op pynatively."); - (void)m.def("initialize_distribute", &mindspore::pipeline::InitDistribute, "Initialize for Distribute.") - .def("init_ge", &mindspore::pipeline::InitGe, "Init GE"); (void)m.def("reset_op_id", &mindspore::pipeline::ResetOpId, "Reset Operator Id"); (void)m.def("init_hccl", &mindspore::pipeline::InitHccl, "Init Hccl"); - (void)m.def("finalize_ge", &mindspore::pipeline::FinalizeGe, "Finalize Ge"); (void)m.def("finalize_hccl", &mindspore::pipeline::FinalizeHccl, "Finalize Hccl"); - (void)m.def("set_ge_option", &mindspore::pipeline::SetGeOption, "API for set ge option."); (void)m.def("verify_inputs_signature", &mindspore::pipeline::VerifyInputSignature, "Verify input signature."); (void)m.def("init_exec_dataset", &mindspore::pipeline::InitExecDataset, py::arg("queue_name"), py::arg("size"), py::arg("batch_size"), py::arg("types"), py::arg("shapes"), py::arg("input_indexs"), py::arg("phase") = py::str("dataset"), "Init and exec dataset."); (void)m.def("_set_dataset_mode_config", &mindspore::ConfigManager::SetDatasetModeConfig, "API for set dataset mode."); - (void)m.def("export_graph", &mindspore::pipeline::ExportDFGraph, "Export Graph."); + (void)m.def("init_ge", &mindspore::pipeline::InitGe, "Init GE"); + + (void)m.def("export_graph", &mindspore::pipeline::ExportGraph, "Export Graph."); (void)py::class_>(m, "MSContext") .def_static("get_instance", &mindspore::MsContext::GetInstance, "Get ms context instance.") diff --git a/mindspore/ccsrc/pipeline/parse/python_adapter.cc b/mindspore/ccsrc/pipeline/parse/python_adapter.cc index 776e33235e..db40238729 100644 --- a/mindspore/ccsrc/pipeline/parse/python_adapter.cc +++ b/mindspore/ccsrc/pipeline/parse/python_adapter.cc @@ -27,6 +27,7 @@ static std::shared_ptr scoped_ = nullptr; // true: start process from python, false: start process from c++ static bool python_env_ = false; static bool use_signature_in_resolve_ = true; +void ResetPythonScope() { scoped_ = nullptr; } void set_use_signature_in_resolve(bool use_signature) noexcept { use_signature_in_resolve_ = use_signature; } bool UseSignatureInResolve() { return use_signature_in_resolve_; } void set_python_env_flag(bool python_env) noexcept { python_env_ = python_env; } diff --git a/mindspore/ccsrc/pipeline/parse/python_adapter.h b/mindspore/ccsrc/pipeline/parse/python_adapter.h index 4b9cbff251..12cfc27186 100644 --- a/mindspore/ccsrc/pipeline/parse/python_adapter.h +++ b/mindspore/ccsrc/pipeline/parse/python_adapter.h @@ -55,6 +55,7 @@ void set_use_signature_in_resolve(bool use_signature) noexcept; bool UseSignatureInResolve(); std::shared_ptr set_python_scoped(); +void ResetPythonScope(); bool IsPythonEnv(); void SetPythonPath(const std::string& path); void set_python_env_flag(bool python_env) noexcept; diff --git a/mindspore/ccsrc/pipeline/pipeline.cc b/mindspore/ccsrc/pipeline/pipeline.cc index 3c3478e89d..861862b849 100644 --- a/mindspore/ccsrc/pipeline/pipeline.cc +++ b/mindspore/ccsrc/pipeline/pipeline.cc @@ -27,11 +27,6 @@ #include "pipeline/pass.h" #include "pipeline/parse/data_converter.h" #include "optimizer/ad/dfunctor.h" -#include "ir/meta_tensor.h" -#include "transform/convert.h" -#include "transform/df_graph_manager.h" -#include "transform/graph_builder.h" -#include "transform/graph_runner.h" #include "debug/anf_ir_dump.h" #include "debug/anf_ir_utils.h" #include "utils/config_manager.h" @@ -44,6 +39,12 @@ #include "device/kernel_runtime_manager.h" #include "debug/trace.h" +#if (ENABLE_GE || ENABLE_D) +#include "pipeline/pipeline_ge.h" +#include "transform/convert.h" +#include "transform/df_graph_manager.h" +#endif + namespace mindspore { // namespace to support intermediate representation definition namespace pipeline { @@ -54,12 +55,6 @@ using mindspore::abstract::AbstractTensor; using mindspore::abstract::AbstractTensorPtr; using mindspore::abstract::AbstractTuple; using mindspore::abstract::AbstractTuplePtr; -using mindspore::transform::DfGraphConvertor; -using mindspore::transform::DfGraphManager; -using mindspore::transform::GeTensorPtr; -using mindspore::transform::MeTensorPtr; -using mindspore::transform::Status; -using mindspore::transform::TransformUtil; const char IR_TYPE_ANF[] = "anf_ir"; const char IR_TYPE_ONNX[] = "onnx_ir"; @@ -85,65 +80,8 @@ std::string GetBaseNameForIR(int stage_idx, const std::string& action_name) { oss << save_graphs_path << "/" << stage_idx << "_" << action_name; return oss.str(); } - -std::string GetFilePathName(const std::string& file_name) { - std::ostringstream oss; - auto ms_context = MsContext::GetInstance(); - if (ms_context == nullptr) { - MS_LOG(EXCEPTION) << "ms_context is nullptr"; - } - auto save_graphs_path = ms_context->save_graphs_path(); - if (save_graphs_path.empty()) { - save_graphs_path = "."; - } - oss << save_graphs_path << "/" << file_name; - return oss.str(); -} } // namespace -// We will not execute graph when output is constant or just input itself. -static bool IsGraphOutputValueNodeOrParameter(const AnfNodePtr& output, const py::tuple& args, - const std::shared_ptr& ret_val) { - if (output->isa()) { - MS_LOG(INFO) << "Graph's output is a constant. No need to execute."; - ValuePtr value = GetValueNode(output); - *ret_val = ValuePtrToPyData(value); - return true; - } - - // Adapter will transform values in __init__() and construct() to parameters, this could cause - // inputs (a.k.a args in current function) size less than parameters'. - if (output->isa()) { - MS_LOG(INFO) << "Graph's output is a parameter. If all params are inputs, no need to execute."; - if (args.empty()) { - MS_LOG(EXCEPTION) << "Inputs size is 0, let graph to be executed."; - } - // Find the right parameter as ret_val. - auto func_graph = output->func_graph(); - MS_EXCEPTION_IF_NULL(func_graph); - auto params = func_graph->parameters(); - if (params.empty()) { - MS_EXCEPTION(UnknownError) << "Graph's parameters size is 0"; - } - if (args.size() != params.size()) { - MS_LOG(EXCEPTION) << "Input size " << args.size() << " not equal to params size " << params.size() - << ", let graph to be executed."; - } - - auto it = std::find(params.begin(), params.end(), output); - if (it == params.end()) { - MS_EXCEPTION(UnknownError) << "When graph output is Parameter, it should be found in graph parameters"; - } - size_t index = it - params.cbegin(); - if (index >= args.size()) { - MS_EXCEPTION(UnknownError) << "Index " << index << " equal or larger than args size " << args.size() << "."; - } - *ret_val = args[index]; - return true; - } - return false; -} - py::tuple GenerateKey(const std::string& name, const std::unordered_map& defaults) { MS_LOG(DEBUG) << "GenerateKey args size:" << defaults.size(); abstract::AbstractBasePtrList args_spec; @@ -207,11 +145,7 @@ py::bool_ VerifyInputSignature(const py::list input_signature, const py::tuple i return true; } -ExecutorPy::ExecutorPy() { - // because Ge only support one Session exist at the same time ,so we delete the old one - DfGraphManager::GetInstance().DeleteGraphRunner(); - DfGraphManager::GetInstance().DeleteGeSession(); -} +ExecutorPy::ExecutorPy() {} ResourcePtr ExecutorPy::GetResource(const std::string& phase) { MS_LOG(DEBUG) << "phase size:" << info_.size(); @@ -221,14 +155,6 @@ ResourcePtr ExecutorPy::GetResource(const std::string& phase) { return info_[phase]->resource; } -std::string GetPhasePrefix(const std::string& phase) { - auto pos = phase.find('.'); - if (pos == std::string::npos) { - MS_LOG(EXCEPTION) << "phase has no . for prefix" << phase; - } - return phase.substr(0, pos); -} - FuncGraphPtr ExecutorPy::GetFuncGraph(const std::string& phase) { if (info_.count(phase) == 0) { MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase); @@ -323,11 +249,15 @@ void ExecutorPy::DelNetRes(const std::string& id) { } } + MS_LOG(INFO) << "Delete flag:" << flag; +#ifdef ENABLE_GE if (flag && info_.size() == 0) { - DfGraphManager::GetInstance().DeleteGraphRunner(); - DfGraphManager::GetInstance().EraseAnfGraph(); - DfGraphManager::GetInstance().DeleteGeSession(); + // because Ge only support one Session exist at the same time ,so we delete the old one + transform::DfGraphManager::GetInstance().DeleteGraphRunner(); + transform::DfGraphManager::GetInstance().EraseAnfGraph(); + transform::DfGraphManager::GetInstance().DeleteGeSession(); } +#endif } } @@ -405,7 +335,8 @@ bool ExecutorPy::CompileInner(const py::object& obj, const py::tuple& args, cons use_vm = ChangeExportGeirUseVmFlag(use_vm, phase_s); - if (use_vm) { + std::string backend = MsContext::GetInstance()->backend_policy(); + if (use_vm && backend != "ge") { // Create backend and session resource->results()[kBackend] = compile::CreateBackend(); p_actions = VmPipeline(); @@ -497,30 +428,6 @@ bool ExecutorPy::Compile(const py::object& obj, const py::tuple& args, const py: return ret_value; } -void SetGeOption(const std::map& options) { - ConfigManager::GetInstance().set_ge_initialize_options(options); -} - -bool InitDistribute(const std::map& options) { - ConfigManager::GetInstance().set_parallel_strategy(ParallelStrategy::DISTRIBUTION); - MS_LOG(INFO) << "ME run in DISTRIBUTION strategy mode"; - - SetGeOption(options); -#ifdef ENABLE_GE - auto ge_options = ConfigManager::GetInstance().ge_initialize_options(); - { - // Release GIL before calling into (potentially long-running) C++ code - py::gil_scoped_release release; - if (ge::GEInitialize(ge_options) != ge::GRAPH_SUCCESS) { - MS_LOG(ERROR) << "Initialize GE failed!"; - return false; - } - } -#endif - MS_LOG(DEBUG) << "Initialize Ge success"; - return true; -} - #ifdef ENABLE_LOAD_ANF_IR // get MindSpore Intermediate Representation File std::string GetMsIrFile(void) { @@ -704,9 +611,25 @@ py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) { } auto phase_s = py::cast(phase); std::string backend = MsContext::GetInstance()->backend_policy(); +#ifdef ENABLE_GE + if (backend == "ge") { + return ExecDFGraph(info_, args, phase_s); + } +#else + MS_LOG(WARNING) << "In ut test " << size << phase_s; if (backend == "ge") { - return ExecDFGraph(args, phase_s); + std::shared_ptr ret_val = std::make_shared(); + if (info_.count(phase_s) != 0 && info_[phase_s]->func_graph != nullptr) { + if (IsGraphOutputValueNodeOrParameter(info_[phase_s]->func_graph->output(), args, ret_val)) { + return *ret_val; + } + } + if (args.size() > 0) { + return args[0]; + } + return args; } +#endif std::size_t full_arg_size = ArgListSize(phase_s); if (size > full_arg_size) { MS_LOG(WARNING) << "The arg num : size = " << size << ". full_arg_size = " << full_arg_size; @@ -719,435 +642,25 @@ py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) { MS_LOG(EXCEPTION) << "Can't find run graph func for " << phase_s; } - MS_LOG(DEBUG) << "eval run"; + MS_LOG(DEBUG) << "eval run" << backend; BaseRef value = (*run)(arg_list); MS_LOG(DEBUG) << "run end"; return BaseRefToPyData(value); } -py::object ExtractGeneralCnodeRet(const AbstractBasePtr& cnode_data, const py::tuple& data, size_t* count) { - MS_EXCEPTION_IF_NULL(cnode_data); - if (*count >= data.size()) { - MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size() - << " less than the number of elements required. "; - } - - if (cnode_data->isa()) { - BaseShapePtr shape = cnode_data->BuildShape(); - auto shape_act = shape->cast()->shape(); - Tensor tensor_exp = py::cast(data[*count]); - if (shape_act != tensor_exp.shape()) { - MS_LOG(EXCEPTION) << "The shape of the tensor returned from GE is not the same as " - "the shape of the tensor derived from ME."; - } - return data[(*count)++]; - } - - if (!cnode_data->isa()) { - MS_LOG(EXCEPTION) << "The output of operator in the final anf graph could " - << "only be a tensor or a tuple of tensor, but got " << cnode_data->BuildValue()->ToString() - << "."; - } - auto data_tp = cnode_data->cast(); - auto elements = data_tp->elements(); - size_t size = data_tp->size(); - py::tuple tp = py::tuple(size); - for (size_t i = 0; i < size; i++) { - tp[i] = ExtractGeneralCnodeRet(elements[i], data, count); - } - return std::move(tp); -} - -py::object StructureOutput(const AnfNodePtr& output_node, const py::tuple& data, size_t* count) { - MS_EXCEPTION_IF_NULL(output_node); - - if (output_node->isa()) { - return ValuePtrToPyData(GetValueNode(output_node)); - } - - if (*count >= data.size()) { - MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size() - << " less than the number of elements required. "; - } - if (output_node->isa()) { - return data[(*count)++]; - } - - auto output_c = output_node->cast(); - if (output_c == nullptr) { - MS_LOG(EXCEPTION) << "The final anf graph could only have constant, parameter, and operator, but got " - << output_node->ToString(); - } - - if (output_c->IsApply(prim::kPrimMakeTuple)) { - auto input_list = output_c->inputs(); - size_t size = input_list.size(); - py::tuple tp = py::tuple(size - 1); - for (size_t i = 1; i < size; i++) { - tp[i - 1] = StructureOutput(input_list[i], data, count); - } - return std::move(tp); - } - if (output_c->IsApply(prim::kPrimDepend)) { - return StructureOutput(output_c->input(1), data, count); - } - - return ExtractGeneralCnodeRet(output_c->abstract(), data, count); -} - -std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::vector& inputs, - const std::string& phase) { - std::vector ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW); - if (ge_tensors.size() != inputs.size()) { - MS_LOG(ERROR) << "args convert to ge tensor error"; - return nullptr; - } - - std::vector ge_outputs; - transform::RunOptions run_options; - - run_options.name = phase; - - auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner(); - - if (graph_runner == nullptr) { - MS_LOG(ERROR) << "Can not found GraphRunner"; - return nullptr; - } - - { - // Release GIL before calling into (potentially long-running) C++ code - py::gil_scoped_release release; - MS_LOG(DEBUG) << "Run graph begin, inputs size is: " << inputs.size(); - Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs); - MS_LOG(DEBUG) << "Run graph finish, outputs size is: " << ge_outputs.size(); - if (ret != Status::SUCCESS) { - MS_LOG(ERROR) << "Exec graph failed"; - return nullptr; - } - } - - std::vector me_outputs = TransformUtil::ConvertGeTensors(ge_outputs); - if (me_outputs.size() != ge_outputs.size()) { - MS_LOG(ERROR) << "Convert output Ge tensor to Me tensor failed"; - } - - py::tuple outputs(me_outputs.size()); - for (std::size_t i = 0; i < outputs.size(); i++) { - outputs[i] = *me_outputs[i]; - } - - std::shared_ptr ret = nullptr; - -#ifdef ENABLE_GE - AnfNodePtr output_node = graph->get_return()->input(1); - MS_EXCEPTION_IF_NULL(output_node); - size_t count = 0; - py::object oj = StructureOutput(output_node, outputs, &count); - ret = std::make_shared(oj); +FuncGraphPtr ExecutorPy::BuildGraph(const py::dict& init_params, const std::string& phase, + const py::object& broadcast_params) { +#if (ENABLE_GE || ENABLE_D) + return BuildDFGraph(info_, init_params, phase, broadcast_params); #else - if (outputs.size() == 1) { - ret = std::make_shared(outputs[0]); - } else { - ret = std::make_shared(outputs); - } -#endif - - return ret; -} - -void DoExecNonInputGraph(const std::string& phase) { - std::vector ge_tensors; - std::vector ge_outputs; - transform::RunOptions run_options; - run_options.name = phase; - auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner(); - - if (graph_runner == nullptr) { - MS_LOG(ERROR) << "Can not found GraphRunner"; - return; - } - { - // Release GIL before calling into (potentially long-running) C++ code - py::gil_scoped_release release; - Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs); - if (ret != Status::SUCCESS) { - MS_LOG(ERROR) << "Exec graph:" << run_options.name << " failed"; - return; - } - } -} - -void ExecutorPy::ProcessGeArg(const py::tuple& args, const std::string& phase, std::vector* inputs) { - // check the arg and use the ExecutorPy args - std::size_t size = args.size(); - if (size != ArgListSize(phase)) { - MS_LOG(EXCEPTION) << "The real arg num : size = " << size << ". graph_arg_size = " << ArgListSize(phase); - } - - // process the first args of tensor - // only in Dataset Feed Mode, fp_bp graph need input tensors - if (ConfigManager::GetInstance().dataset_mode() == DS_FEED_MODE) { - for (std::size_t i = 0; i < size; i++) { - ValuePtr converted = nullptr; - bool succ = parse::ConvertData(args[i], &converted); - if (!succ) { - MS_LOG(EXCEPTION) << "args convert error"; - } - if (converted->isa()) { - (*inputs).push_back(converted->cast()); - } else { - MS_LOG(EXCEPTION) << "args, " << converted->ToString() << " is not tensor"; - } - } - } -} - -py::object ExecutorPy::ExecDFGraph(const py::tuple& args, const std::string& phase) { - std::string phase_prefix = GetPhasePrefix(phase); - - if (phase_prefix == "save") { - DoExecNonInputGraph(phase); - ConfigManager::GetInstance().ResetConfig(); - return py::none(); - } - - if (info_.count(phase) == 0) { - MS_LOG(EXCEPTION) << "has no phase:" << phase; - } - -#if (!defined ENABLE_GE) || (defined ENABLE_INFER) - // Now don't use the graph because the exec ge function don't take effect - MS_EXCEPTION_IF_NULL(info_[phase]->func_graph); - if (ENABLE_TRAIN != info_[phase]->func_graph->flags()["training"]) { - MS_LOG(ERROR) << "Graph training mode mismatch mode of libraries"; - ConfigManager::GetInstance().ResetConfig(); - return py::none(); - } + return nullptr; #endif - - std::shared_ptr ret_val = std::make_shared(); - if (IsGraphOutputValueNodeOrParameter(info_[phase]->func_graph->output(), args, ret_val)) { - ConfigManager::GetInstance().ResetConfig(); - return *ret_val; - } - - std::vector inputs; - ProcessGeArg(args, phase, &inputs); - - std::shared_ptr ret = DoExecGraph(GetFuncGraph(phase), inputs, phase); - ConfigManager::GetInstance().ResetConfig(); - if (ret != nullptr) { - return *ret; - } else { - MS_LOG(EXCEPTION) << "exec graph failed"; - } } void ExecutorPy::RunInitGraph(const py::dict& init_params, const std::string& phase) { - MS_LOG(DEBUG) << "ExecInitGraph start."; - TensorOrderMap inputs_with_name{}; - ConvertObjectToTensors(init_params, &inputs_with_name); - std::vector inputs; - (void)std::transform(inputs_with_name.begin(), inputs_with_name.end(), std::back_inserter(inputs), - [](const std::pair& item) { return item.second; }); - - std::vector ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW); - if (ge_tensors.size() != inputs.size()) { - MS_LOG(ERROR) << "Args convert to ge tensor error."; - return; - } - MS_LOG(DEBUG) << "Run graph begin, inputs size is: " << inputs.size() << "."; - - std::vector ge_outputs; - transform::RunOptions run_options; - - run_options.name = phase; - if (DfGraphManager::GetInstance().GetGraphByName(phase) == nullptr) { - MS_LOG(WARNING) << "Can not find " << phase << " sub graph, don't need data init subgraph in INFER mode."; - return; - } - auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner(); - if (graph_runner == nullptr) { - MS_LOG(EXCEPTION) << "Can not found GraphRunner."; - } - { - // Release GIL before calling into (potentially long-running) C++ code - py::gil_scoped_release release; - Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs); - if (ret != Status::SUCCESS) { - MS_LOG(EXCEPTION) << "Exec " << phase << " graph failed."; - } - - MS_LOG(INFO) << "Exec " << phase << " graph success."; - - if ((ConfigManager::GetInstance().parallel_strategy() == ParallelStrategy::DISTRIBUTION) && - (DfGraphManager::GetInstance().GetGraphByName(BROADCAST_GRAPH_NAME) != nullptr)) { - run_options.name = BROADCAST_GRAPH_NAME; - ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs); - if (ret != Status::SUCCESS) { - MS_LOG(EXCEPTION) << "Exec BROADCAST_GRAPH_NAME failed."; - } - MS_LOG(INFO) << "Exec broadcast graph success."; - } - } -} - -Status CreateSessionAndGraphRunner(bool is_training = true) { - std::shared_ptr sess = DfGraphManager::GetInstance().GetGeSession(); - if (sess == nullptr) { - transform::SessionOptions options; - if (is_training) { - options["ge.trainFlag"] = "1"; - options["ge.streamNum"] = "100"; - options["ge.enabledLocalFmkop"] = "1"; - options["ge.hcomParallel"] = "1"; - } else { - options["ge.trainFlag"] = "0"; - } - - options["ge.enablePrintOpPass"] = "0"; - sess = transform::GraphRunner::NewSession(options); - if (sess == nullptr) { - MS_LOG(ERROR) << "Init data graph failed, because of create Ge session failed"; - return Status::FAILED; - } else { - DfGraphManager::GetInstance().SetGeSession(sess); - } - } - - transform::GraphRunnerOptions options; - options.sess_ptr = sess; - auto graph_runner = std::make_shared(options); - if (graph_runner == nullptr) { - MS_LOG(ERROR) << "Create new graph runner failed"; - return Status::FAILED; - } else { - DfGraphManager::GetInstance().SetGraphRunner(graph_runner); - } - - return Status::SUCCESS; -} - -void ExecutorPy::ConvertObjectToTensors(const py::dict& dict, TensorOrderMap* const tensors) { - for (auto item : dict) { - if ((!py::isinstance(item.first))) { - MS_LOG(WARNING) << "Type of key of py_dict is not string, ignore it."; - continue; - } - std::shared_ptr tensor; - std::string name = py::cast(item.first); - if (py::isinstance(item.second.attr("default_input"))) { - // convert float to tensor with shape([1]) - tensor = std::make_shared(kNumberTypeFloat32, std::vector({1})); - *(static_cast(tensor->data_c(true))) = py::cast(item.second.attr("default_input")); - } else if (py::isinstance(item.second.attr("default_input"))) { - // convert int to tensor with shape([1]) - tensor = std::make_shared(kNumberTypeInt32, std::vector({1})); - *(static_cast(tensor->data_c(true))) = py::cast(item.second.attr("default_input")); - } else if (py::hasattr(item.second.attr("default_input"), PYTHON_TENSOR_FLAG)) { - // cast tensor - tensor = py::cast>(item.second.attr("default_input")); - } - - if (tensor == nullptr) { - MS_LOG(EXCEPTION) << "Get default value for " << name << " failed"; - } - (void)tensors->emplace(name, tensor); - } -} - -bool ExecutorPy::AddDFGraph(const py::dict& init_params, const std::string& phase, const py::object& broadcast_params) { - FuncGraphPtr anf_graph = info_[phase]->func_graph; - DfGraphConvertor convertor(anf_graph); - - size_t pos = phase.find('.'); - std::string net_id = ((pos == std::string::npos || pos == phase.size() - 1) ? phase : phase.substr(pos + 1)); - std::string phase_prefix = phase.substr(0, pos); - - if (phase_prefix == "export") { - MS_LOG(INFO) << "Set DfGraphConvertor training : false"; - convertor.set_training(false); - } - - TensorOrderMap init_tensors{}; - ConvertObjectToTensors(init_params, &init_tensors); - (void)convertor.ConvertAllNode().InitParam(init_tensors).BuildGraph(); - - if (broadcast_params != py::none()) { - if (!py::isinstance(broadcast_params)) { - MS_LOG(ERROR) << "Invalid broadcast params, it must be py::dict type"; - return false; - } - py::dict broadcast = broadcast_params.cast(); - if (broadcast.empty()) { - (void)convertor.GenerateBroadcastGraph(init_tensors); - } else { - TensorOrderMap broadcast_tensors{}; - ConvertObjectToTensors(broadcast, &broadcast_tensors); - (void)convertor.GenerateBroadcastGraph(broadcast_tensors); - } - MS_LOG(INFO) << "Generate broadcast graph with params and broadcast_empty is " << broadcast.empty(); - } - - (void)convertor.GenerateCheckpointGraph(); - if (convertor.ErrCode() != 0) { - DfGraphManager::GetInstance().ClearGraph(); - MS_LOG(ERROR) << "convert df graph failed, err:" << convertor.ErrCode(); - return false; - } - - if (MsContext::GetInstance()->save_graphs_flag()) { - convertor.DrawComputeGraph(GetFilePathName("ge_graph.dot")); // for debug - convertor.DrawInitGraph(GetFilePathName("init_graph.dot")); // for debug - convertor.DrawSaveCheckpointGraph(GetFilePathName("save_checkpoint_graph.dot")); // for debug - } - std::string init_graph = "init_subgraph." + net_id; - std::string checkpoint_name = "save." + net_id; - if (phase.find("train") != std::string::npos) { - (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph(), {{"ge.exec.variable_acc", "1"}}); - } else { - (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph()); - } - (void)DfGraphManager::GetInstance().AddGraph(init_graph, convertor.GetInitGraph()); - (void)DfGraphManager::GetInstance().AddGraph(BROADCAST_GRAPH_NAME, convertor.GetBroadcastGraph()); - Status ret = DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph()); - if (ret == Status::SUCCESS) { - DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph); - } - - return true; -} - -FuncGraphPtr ExecutorPy::BuildDFGraph(const py::dict& init_params, const std::string& phase, - const py::object& broadcast_params) { - if (info_.count(phase) == 0) { - MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase); - } - FuncGraphPtr anf_graph = info_[phase]->func_graph; - - if (MsContext::GetInstance()->save_graphs_flag()) { - draw::Draw(GetFilePathName("anf_graph.dot"), anf_graph); // for debug - DumpIR(GetFilePathName("anf_graph.ir"), anf_graph, true); - } - - if (!AddDFGraph(init_params, phase, broadcast_params)) { - MS_LOG(ERROR) << "GenConvertor failed"; - return nullptr; - } - -#if ENABLE_TRAIN - (void)setenv("GE_TRAIN", "1", 1); -#else - (void)setenv("GE_TRAIN", "0", 1); +#if ENABLE_GE + RunGEInitGraph(init_params, phase); #endif - - if (CreateSessionAndGraphRunner(static_cast(ENABLE_TRAIN)) != Status::SUCCESS) { - MS_LOG(ERROR) << "Create GE Session or GraphRunner failed."; - return nullptr; - } - - return anf_graph; } bool InitExecDataset(const std::string& queue_name, int64_t iter_num, int64_t batch_size, @@ -1156,47 +669,16 @@ bool InitExecDataset(const std::string& queue_name, int64_t iter_num, int64_t ba std::string name = MsContext::GetInstance()->backend_policy(); if (name == kMsConvert || name == kMsVm) { return InitExecDatasetVm(queue_name, iter_num, batch_size, types, shapes, input_indexes); - } else { - return InitExecDatasetGe(queue_name, iter_num, batch_size, types, shapes, input_indexes, phase); - } -} - -bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batch_size, - const std::vector& types, const std::vector>& shapes, - const std::vector& input_indexes, const std::string& phase) { - // Convert types to GE types and TF types - std::vector ge_types; - (void)std::transform(types.begin(), types.end(), std::back_inserter(ge_types), [](const TypePtr& i) -> int64_t { - return transform::TransformUtil::ConvertDataType(i->type_id()); - }); - - ConfigManager::GetInstance().set_dataset_mode(DatasetMode::DS_GRAPH_MODE); - ConfigManager::GetInstance().set_iter_num(size); - ConfigManager::GetInstance().set_dataset_phase(phase); - - DatasetGraphParam param(queue_name, size, batch_size, ge_types, shapes, input_indexes); - ConfigManager::GetInstance().set_dataset_param(param); - - if (transform::BuildDatasetGraph(param, phase) != transform::SUCCESS) { - MS_LOG(ERROR) << "Build dateset graph failed."; - return false; } - -#if ENABLE_TRAIN - (void)setenv("GE_TRAIN", "1", 1); +#if ENABLE_GE + return InitExecDatasetGe(queue_name, iter_num, batch_size, types, shapes, input_indexes, phase); #else - (void)setenv("GE_TRAIN", "0", 1); -#endif - - if (CreateSessionAndGraphRunner(static_cast(ENABLE_TRAIN)) != Status::SUCCESS) { - MS_LOG(ERROR) << "Create GE Session or GraphRunner failed."; - return false; + std::string backend = MsContext::GetInstance()->backend_policy(); + if (backend == "ge") { + return true; } - - MS_LOG(INFO) << "DoExecNonInputGraph:" << phase; - DoExecNonInputGraph(phase); - - return true; +#endif + return false; } bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batch_size, @@ -1259,25 +741,6 @@ bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batc return true; } -void InitGe() { - // set python env flag - mindspore::parse::python_adapter::set_python_env_flag(true); - // open tsd before ge initialize - auto ms_context = MsContext::GetInstance(); - MS_EXCEPTION_IF_NULL(ms_context); - if (!ms_context->OpenTsd()) { - MS_LOG(EXCEPTION) << "open tsd failed"; - } - (void)ms_context->InitGe(); -} - -void FinalizeGe() { - auto context_ptr = MsContext::GetInstance(); - MS_EXCEPTION_IF_NULL(context_ptr); - (void)context_ptr->FinalizeGe(); - (void)context_ptr->CloseTsd(); -} - void ResetOpId() { mindspore::id_generator::reset_id(); } void InitHccl() { @@ -1309,24 +772,57 @@ void FinalizeHccl() { device::KernelRuntimeManager::Instance().ClearRuntimeResource(); #endif } -void ExportDFGraph(const std::string& file_name, const std::string&, const std::string& phase) { - MS_LOG(DEBUG) << "ExportGraph Begin"; - transform::DfGraphWrapperPtr wrap_ptr = DfGraphManager::GetInstance().GetGraphByName(phase); - if (wrap_ptr == nullptr) { - MS_LOG(ERROR) << "Get graph form DfGraphManager failed!"; - return; - } - transform::DfGraphPtr ge_graph = wrap_ptr->graph_ptr_; - if (nullptr == ge_graph) { - MS_LOG(ERROR) << "The export graph is null"; - return; +void ExportGraph(const std::string& file_name, const std::string&, const std::string& phase) { +#if (ENABLE_GE || ENABLE_D) + ExportDFGraph(file_name, phase); +#endif + MS_LOG(WARNING) << "In ut test no export_graph"; +} + +void ReleaseGeTsd() { + auto context_ptr = MsContext::GetInstance(); + if (context_ptr != nullptr) { + (void)context_ptr->FinalizeGe(true); + (void)context_ptr->CloseTsd(true); } +} - (void)ge_graph->SaveToFile(file_name); +void InitGe() { + // set python env flag + mindspore::parse::python_adapter::set_python_env_flag(true); + // open tsd before ge initialize + auto ms_context = MsContext::GetInstance(); + MS_EXCEPTION_IF_NULL(ms_context); + if (!ms_context->OpenTsd()) { + MS_LOG(EXCEPTION) << "open tsd failed"; + } + (void)ms_context->InitGe(); +} - MS_LOG(DEBUG) << "ExportGraph End"; +void FinalizeGe() { + auto context_ptr = MsContext::GetInstance(); + MS_EXCEPTION_IF_NULL(context_ptr); + (void)context_ptr->FinalizeGe(); + (void)context_ptr->CloseTsd(); } +void ClearResAtexit() { + MS_LOG(DEBUG) << "Pipeline clear all resource"; + device::KernelRuntimeManager::Instance().ClearRuntimeResource(); + + ad::g_k_prims.clear(); + + abstract::ClearPrimEvaluatorMap(); + compile::ClearConvertCache(); + pipeline::GetMethodMap().clear(); + pipeline::ExecutorPy::ClearRes(); +#ifdef ENABLE_GE + transform::DfGraphManager::GetInstance().ClearGraph(); + transform::DfGraphConvertor::get_adpt_map().clear(); +#endif + ReleaseGeTsd(); + parse::python_adapter::ResetPythonScope(); +} } // namespace pipeline } // namespace mindspore diff --git a/mindspore/ccsrc/pipeline/pipeline.h b/mindspore/ccsrc/pipeline/pipeline.h index b075306682..a0d7a19198 100644 --- a/mindspore/ccsrc/pipeline/pipeline.h +++ b/mindspore/ccsrc/pipeline/pipeline.h @@ -30,6 +30,7 @@ #include "pipeline/action.h" #include "vm/segment_runner.h" #include "vm/transform.h" +#include "pipeline/base.h" namespace mindspore { extern const char kMsConvert[]; @@ -55,14 +56,6 @@ class Pipeline { std::vector actions_; }; -struct ExecutorInfo { - FuncGraphPtr func_graph; - ResourcePtr resource; - std::size_t arg_list_size; -}; - -using ExecutorInfoPtr = std::shared_ptr; - // A function pipeline. class ExecutorPy : public std::enable_shared_from_this { public: @@ -80,11 +73,7 @@ class ExecutorPy : public std::enable_shared_from_this { bool CompileInner(const py::object& obj, const py::tuple& args, const py::object& phase, bool use_vm); bool Compile(const py::object& obj, const py::tuple& args, const py::object& phase, bool use_vm); - // for graph mode - py::object ExecDFGraph(const py::tuple& args, const std::string& phase = "train"); - void ProcessVmArg(const py::tuple& args, const std::string& phase, VectorRef* arg_list); - void ProcessGeArg(const py::tuple& args, const std::string& phase, std::vector* inputs); // for pynative mode when use_vm is on py::object Run(const py::tuple& args, const py::object& phase); @@ -95,9 +84,8 @@ class ExecutorPy : public std::enable_shared_from_this { compile::VmEvalFuncPtr GetVmEvalFunc(const std::string& phase); bool HasCompiled(const std::string& phase) const; - bool AddDFGraph(const py::dict& init_params, const std::string& phase, const py::object& broadcast_params); - FuncGraphPtr BuildDFGraph(const py::dict& init_params, const std::string& phase, - const py::object& broadcast_params = {}); + FuncGraphPtr BuildGraph(const py::dict& init_params, const std::string& phase, + const py::object& broadcast_params = {}); void RunInitGraph(const py::dict& init_params, const std::string& phase); py::dict GetParameterLayout(const std::string& phase); py::dict GetCNodeStrategy(const std::string& phase); @@ -122,32 +110,29 @@ using ExecutorPyPtr = std::shared_ptr; py::tuple GenerateKey(const std::string& name, const std::unordered_map& defaults); py::bool_ VerifyInputSignature(const py::list input_signature, const py::tuple inputs); -void SetGeOption(const std::map& options); bool InitDistribute(const std::map& options); void ResetOpId(); -void InitGe(); -void FinalizeGe(); void InitHccl(); void FinalizeHccl(); +void InitGe(); +void FinalizeGe(); + +void ClearResAtexit(); +void ReleaseGeTsd(); + +void ExportGraph(const std::string& file_name, const std::string&, const std::string& phase); // init and exec dataset sub graph bool InitExecDataset(const std::string& queue_name, int64_t iter_num, int64_t batch_size, const std::vector& types, const std::vector>& shapes, const std::vector& input_indexes, const std::string& phase); -// init and exec dataset sub graph for GE backend -bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batch_size, - const std::vector& types, const std::vector>& shapes, - const std::vector& input_indexes, const std::string& phase); - // Build and run dataset subgraph for ms backend bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batch_size, const std::vector& types, const std::vector>& shapes, const std::vector& input_indexes); -void ExportDFGraph(const std::string& file_name, const std::string&, const std::string& phase); - } // namespace pipeline } // namespace mindspore diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.cc b/mindspore/ccsrc/pipeline/pipeline_ge.cc new file mode 100644 index 0000000000..4617884ca0 --- /dev/null +++ b/mindspore/ccsrc/pipeline/pipeline_ge.cc @@ -0,0 +1,545 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "pipeline/pipeline_ge.h" + +#include +#include +#include +#include +#include + +#include "debug/anf_ir_dump.h" +#include "ir/meta_tensor.h" +#include "transform/convert.h" +#include "transform/df_graph_manager.h" +#include "transform/graph_builder.h" +#include "transform/graph_runner.h" +#include "debug/draw.h" +#include "pipeline/static_analysis/abstract_value.h" + +namespace mindspore { +namespace pipeline { +using Tensor = mindspore::tensor::Tensor; +using MetaTensor = mindspore::tensor::MetaTensor; +using TensorOrderMap = std::map>; +using mindspore::abstract::AbstractTensor; +using mindspore::abstract::AbstractTuple; +using mindspore::abstract::AbstractTuplePtr; +using mindspore::transform::DfGraphConvertor; +using mindspore::transform::DfGraphManager; +using mindspore::transform::GeTensorPtr; +using mindspore::transform::MeTensorPtr; +using mindspore::transform::Status; +using mindspore::transform::TransformUtil; + +void DoExecNonInputGraph(const std::string& phase) { + std::vector ge_tensors; + std::vector ge_outputs; + transform::RunOptions run_options; + run_options.name = phase; + auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner(); + + if (graph_runner == nullptr) { + MS_LOG(ERROR) << "Can not found GraphRunner"; + return; + } + { + // Release GIL before calling into (potentially long-running) C++ code + py::gil_scoped_release release; + Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs); + if (ret != Status::SUCCESS) { + MS_LOG(ERROR) << "Exec graph:" << run_options.name << " failed"; + return; + } + } +} + +void SetGeOption(const std::map& options) { + ConfigManager::GetInstance().set_ge_initialize_options(options); +} + +Status CreateSessionAndGraphRunner(bool is_training = true) { + std::shared_ptr sess = DfGraphManager::GetInstance().GetGeSession(); + if (sess == nullptr) { + transform::SessionOptions options; + if (is_training) { + options["ge.trainFlag"] = "1"; + options["ge.streamNum"] = "100"; + options["ge.enabledLocalFmkop"] = "1"; + options["ge.hcomParallel"] = "1"; + } else { + options["ge.trainFlag"] = "0"; + } + + options["ge.enablePrintOpPass"] = "0"; + sess = transform::GraphRunner::NewSession(options); + if (sess == nullptr) { + MS_LOG(ERROR) << "Init data graph failed, because of create Ge session failed"; + return Status::FAILED; + } else { + DfGraphManager::GetInstance().SetGeSession(sess); + } + } + + transform::GraphRunnerOptions options; + options.sess_ptr = sess; + auto graph_runner = std::make_shared(options); + if (graph_runner == nullptr) { + MS_LOG(ERROR) << "Create new graph runner failed"; + return Status::FAILED; + } else { + DfGraphManager::GetInstance().SetGraphRunner(graph_runner); + } + + return Status::SUCCESS; +} + +bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batch_size, + const std::vector& types, const std::vector>& shapes, + const std::vector& input_indexes, const std::string& phase) { + std::vector ge_types; + (void)std::transform(types.begin(), types.end(), std::back_inserter(ge_types), [](const TypePtr& i) -> int64_t { + return transform::TransformUtil::ConvertDataType(i->type_id()); + }); + + ConfigManager::GetInstance().set_dataset_mode(DatasetMode::DS_GRAPH_MODE); + ConfigManager::GetInstance().set_iter_num(size); + ConfigManager::GetInstance().set_dataset_phase(phase); + + DatasetGraphParam param(queue_name, size, batch_size, ge_types, shapes, input_indexes); + ConfigManager::GetInstance().set_dataset_param(param); + + if (transform::BuildDatasetGraph(param, phase) != transform::SUCCESS) { + MS_LOG(ERROR) << "Build dateset graph failed."; + return false; + } + +#if ENABLE_TRAIN + (void)setenv("GE_TRAIN", "1", 1); +#else + (void)setenv("GE_TRAIN", "0", 1); +#endif + + if (CreateSessionAndGraphRunner(static_cast(ENABLE_TRAIN)) != Status::SUCCESS) { + MS_LOG(ERROR) << "Create GE Session or GraphRunner failed."; + return false; + } + + MS_LOG(INFO) << "DoExecNonInputGraph:" << phase; + DoExecNonInputGraph(phase); + + return true; +} + +void ConvertObjectToTensors(const py::dict& dict, TensorOrderMap* const tensors) { + for (auto item : dict) { + if ((!py::isinstance(item.first))) { + MS_LOG(WARNING) << "Type of key of py_dict is not string, ignore it."; + continue; + } + std::shared_ptr tensor; + std::string name = py::cast(item.first); + if (py::isinstance(item.second.attr("default_input"))) { + // convert float to tensor with shape([1]) + tensor = std::make_shared(kNumberTypeFloat32, std::vector({1})); + *(static_cast(tensor->data_c(true))) = py::cast(item.second.attr("default_input")); + } else if (py::isinstance(item.second.attr("default_input"))) { + // convert int to tensor with shape([1]) + tensor = std::make_shared(kNumberTypeInt32, std::vector({1})); + *(static_cast(tensor->data_c(true))) = py::cast(item.second.attr("default_input")); + } else if (py::hasattr(item.second.attr("default_input"), PYTHON_TENSOR_FLAG)) { + // cast tensor + tensor = py::cast>(item.second.attr("default_input")); + } + + if (tensor == nullptr) { + MS_LOG(EXCEPTION) << "Get default value for " << name << " failed"; + } + (void)tensors->emplace(name, tensor); + } +} + +bool AddDFGraph(const std::map& info, const py::dict& init_params, + const std::string& phase, const py::object& broadcast_params) { + FuncGraphPtr anf_graph = info.at(phase)->func_graph; + DfGraphConvertor convertor(anf_graph); + + size_t pos = phase.find('.'); + std::string net_id = ((pos == std::string::npos || pos == phase.size() - 1) ? phase : phase.substr(pos + 1)); + std::string phase_prefix = phase.substr(0, pos); + + if (phase_prefix == "export") { + MS_LOG(INFO) << "Set DfGraphConvertor training : false"; + convertor.set_training(false); + } + + TensorOrderMap init_tensors{}; + ConvertObjectToTensors(init_params, &init_tensors); + (void)convertor.ConvertAllNode().InitParam(init_tensors).BuildGraph(); + + if (broadcast_params != py::none()) { + if (!py::isinstance(broadcast_params)) { + MS_LOG(ERROR) << "Invalid broadcast params, it must be py::dict type"; + return false; + } + py::dict broadcast = broadcast_params.cast(); + if (broadcast.empty()) { + (void)convertor.GenerateBroadcastGraph(init_tensors); + } else { + TensorOrderMap broadcast_tensors{}; + ConvertObjectToTensors(broadcast, &broadcast_tensors); + (void)convertor.GenerateBroadcastGraph(broadcast_tensors); + } + MS_LOG(INFO) << "Generate broadcast graph with params and broadcast_empty is " << broadcast.empty(); + } + + (void)convertor.GenerateCheckpointGraph(); + if (convertor.ErrCode() != 0) { + DfGraphManager::GetInstance().ClearGraph(); + MS_LOG(ERROR) << "convert df graph failed, err:" << convertor.ErrCode(); + return false; + } + + if (MsContext::GetInstance()->save_graphs_flag()) { + convertor.DrawComputeGraph(GetFilePathName("ge_graph.dot")); // for debug + convertor.DrawInitGraph(GetFilePathName("init_graph.dot")); // for debug + convertor.DrawSaveCheckpointGraph(GetFilePathName("save_checkpoint_graph.dot")); // for debug + } + std::string init_graph = "init_subgraph." + net_id; + std::string checkpoint_name = "save." + net_id; + if (phase.find("train") != std::string::npos) { + (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph(), {{"ge.exec.variable_acc", "1"}}); + } else { + (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph()); + } + (void)DfGraphManager::GetInstance().AddGraph(init_graph, convertor.GetInitGraph()); + (void)DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph()); + (void)DfGraphManager::GetInstance().AddGraph(BROADCAST_GRAPH_NAME, convertor.GetBroadcastGraph()); + + DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph); + + return true; +} + +FuncGraphPtr BuildDFGraph(const std::map& info, const py::dict& init_params, + const std::string& phase, const py::object& broadcast_params) { + if (info.count(phase) == 0) { + MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase); + } + FuncGraphPtr anf_graph = info.at(phase)->func_graph; + + if (MsContext::GetInstance()->save_graphs_flag()) { + draw::Draw(GetFilePathName("anf_graph.dot"), anf_graph); // for debug + DumpIR(GetFilePathName("anf_graph.ir"), anf_graph, true); + } + + if (!AddDFGraph(info, init_params, phase, broadcast_params)) { + MS_LOG(ERROR) << "GenConvertor failed"; + return nullptr; + } + +#if ENABLE_TRAIN + (void)setenv("GE_TRAIN", "1", 1); +#else + (void)setenv("GE_TRAIN", "0", 1); +#endif + + if (CreateSessionAndGraphRunner(static_cast(ENABLE_TRAIN)) != Status::SUCCESS) { + MS_LOG(ERROR) << "Create GE Session or GraphRunner failed."; + return nullptr; + } + + return anf_graph; +} + +void RunGEInitGraph(const py::dict& init_params, const std::string& phase) { + MS_LOG(DEBUG) << "ExecInitGraph start."; + TensorOrderMap inputs_with_name{}; + ConvertObjectToTensors(init_params, &inputs_with_name); + std::vector inputs; + (void)std::transform(inputs_with_name.begin(), inputs_with_name.end(), std::back_inserter(inputs), + [](const std::pair& item) { return item.second; }); + + std::vector ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW); + if (ge_tensors.size() != inputs.size()) { + MS_LOG(ERROR) << "Args convert to ge tensor error."; + return; + } + MS_LOG(DEBUG) << "Run graph begin, inputs size is: " << inputs.size() << "."; + + std::vector ge_outputs; + transform::RunOptions run_options; + + run_options.name = phase; + if (DfGraphManager::GetInstance().GetGraphByName(phase) == nullptr) { + MS_LOG(WARNING) << "Can not find " << phase << " sub graph, don't need data init subgraph in INFER mode."; + return; + } + auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner(); + if (graph_runner == nullptr) { + MS_LOG(EXCEPTION) << "Can not found GraphRunner."; + } + { + // Release GIL before calling into (potentially long-running) C++ code + py::gil_scoped_release release; + Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs); + if (ret != Status::SUCCESS) { + MS_LOG(EXCEPTION) << "Exec " << phase << " graph failed."; + } + + MS_LOG(INFO) << "Exec " << phase << " graph success."; + + if ((ConfigManager::GetInstance().parallel_strategy() == ParallelStrategy::DISTRIBUTION) && + (DfGraphManager::GetInstance().GetGraphByName(BROADCAST_GRAPH_NAME) != nullptr)) { + run_options.name = BROADCAST_GRAPH_NAME; + ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs); + if (ret != Status::SUCCESS) { + MS_LOG(EXCEPTION) << "Exec BROADCAST_GRAPH_NAME failed."; + } + MS_LOG(INFO) << "Exec broadcast graph success."; + } + } +} + +py::object ExtractGeneralCnodeRet(const AbstractBasePtr& cnode_data, const py::tuple& data, size_t* count) { + MS_EXCEPTION_IF_NULL(cnode_data); + if (*count >= data.size()) { + MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size() + << " less than the number of elements required. "; + } + + if (cnode_data->isa()) { + BaseShapePtr shape = cnode_data->BuildShape(); + auto shape_act = shape->cast()->shape(); + Tensor tensor_exp = py::cast(data[*count]); + if (shape_act != tensor_exp.shape()) { + MS_LOG(EXCEPTION) << "The shape of the tensor returned from GE is not the same as " + "the shape of the tensor derived from ME."; + } + return data[(*count)++]; + } + + if (!cnode_data->isa()) { + MS_LOG(EXCEPTION) << "The output of operator in the final anf graph could " + << "only be a tensor or a tuple of tensor, but got " << cnode_data->BuildValue()->ToString() + << "."; + } + auto data_tp = cnode_data->cast(); + auto elements = data_tp->elements(); + size_t size = data_tp->size(); + py::tuple tp = py::tuple(size); + for (size_t i = 0; i < size; i++) { + tp[i] = ExtractGeneralCnodeRet(elements[i], data, count); + } + return std::move(tp); +} + +py::object StructureOutput(const AnfNodePtr& output_node, const py::tuple& data, size_t* count) { + MS_EXCEPTION_IF_NULL(output_node); + + if (output_node->isa()) { + return ValuePtrToPyData(GetValueNode(output_node)); + } + + if (*count >= data.size()) { + MS_LOG(EXCEPTION) << "The number of elements in the outputs : " << data.size() + << " less than the number of elements required. "; + } + if (output_node->isa()) { + return data[(*count)++]; + } + + auto output_c = output_node->cast(); + if (output_c == nullptr) { + MS_LOG(EXCEPTION) << "The final anf graph could only have constant, parameter, and operator, but got " + << output_node->ToString(); + } + + if (output_c->IsApply(prim::kPrimMakeTuple)) { + auto input_list = output_c->inputs(); + size_t size = input_list.size(); + py::tuple tp = py::tuple(size - 1); + for (size_t i = 1; i < size; i++) { + tp[i - 1] = StructureOutput(input_list[i], data, count); + } + return std::move(tp); + } + if (output_c->IsApply(prim::kPrimDepend)) { + return StructureOutput(output_c->input(1), data, count); + } + + return ExtractGeneralCnodeRet(output_c->abstract(), data, count); +} + +std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::vector& inputs, + const std::string& phase) { + std::vector ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW); + if (ge_tensors.size() != inputs.size()) { + MS_LOG(ERROR) << "args convert to ge tensor error"; + return nullptr; + } + + std::vector ge_outputs; + transform::RunOptions run_options; + + run_options.name = phase; + + auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner(); + + if (graph_runner == nullptr) { + MS_LOG(ERROR) << "Can not found GraphRunner"; + return nullptr; + } + + { + // Release GIL before calling into (potentially long-running) C++ code + py::gil_scoped_release release; + MS_LOG(DEBUG) << "Run graph begin, inputs size is: " << inputs.size(); + Status ret = graph_runner->RunGraph(run_options, ge_tensors, &ge_outputs); + MS_LOG(DEBUG) << "Run graph finish, outputs size is: " << ge_outputs.size(); + if (ret != Status::SUCCESS) { + MS_LOG(ERROR) << "Exec graph failed"; + return nullptr; + } + } + + std::vector me_outputs = TransformUtil::ConvertGeTensors(ge_outputs); + if (me_outputs.size() != ge_outputs.size()) { + MS_LOG(ERROR) << "Convert output Ge tensor to Me tensor failed"; + } + + py::tuple outputs(me_outputs.size()); + for (std::size_t i = 0; i < outputs.size(); i++) { + outputs[i] = *me_outputs[i]; + } + + std::shared_ptr ret = nullptr; + +#ifdef ENABLE_GE + AnfNodePtr root = graph->get_return(); + MS_EXCEPTION_IF_NULL(root); + AbstractBasePtr output = root->abstract(); + size_t count = 0; + py::object oj = StructureOutput(output, outputs, &count); + ret = std::make_shared(oj); +#else + if (outputs.size() == 1) { + ret = std::make_shared(outputs[0]); + } else { + ret = std::make_shared(outputs); + } +#endif + + return ret; +} + +void ProcessGeArg(const std::map& info, const py::tuple& args, const std::string& phase, + std::vector* inputs) { + // check the arg and use the ExecutorPy args + std::size_t size = args.size(); + + if (info.count(phase) == 0) { + MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase); + } + + auto arg_size = info.at(phase)->arg_list_size; + if (size != arg_size) { + MS_LOG(EXCEPTION) << "The real arg num : size = " << size << ". graph_arg_size = " << arg_size; + } + + // process the first args of tensor + // only in Dataset Feed Mode, fp_bp graph need input tensors + if (ConfigManager::GetInstance().dataset_mode() == DS_FEED_MODE) { + for (std::size_t i = 0; i < size; i++) { + ValuePtr converted = nullptr; + bool succ = parse::ConvertData(args[i], &converted); + if (!succ) { + MS_LOG(EXCEPTION) << "args convert error"; + } + if (converted->isa()) { + (*inputs).push_back(converted->cast()); + } else { + MS_LOG(EXCEPTION) << "args, " << converted->ToString() << " is not tensor"; + } + } + } +} + +py::object ExecDFGraph(const std::map& info, const py::tuple& args, + const std::string& phase) { + std::string phase_prefix = GetPhasePrefix(phase); + + if (phase_prefix == "save") { + DoExecNonInputGraph(phase); + ConfigManager::GetInstance().ResetConfig(); + return py::none(); + } + + if (info.count(phase) == 0) { + MS_LOG(EXCEPTION) << "has no phase:" << phase; + } + + FuncGraphPtr anf_graph = info.at(phase)->func_graph; + +#if (!defined ENABLE_GE) || (defined ENABLE_INFER) + // Now don't use the graph because the exec ge function don't take effect + MS_EXCEPTION_IF_NULL(info.at(phase)->func_graph); + if (ENABLE_TRAIN != info.at(phase)->func_graph->flags()["training"]) { + MS_LOG(ERROR) << "Graph training mode mismatch mode of libraries"; + ConfigManager::GetInstance().ResetConfig(); + return py::none(); + } +#endif + + std::shared_ptr ret_val = std::make_shared(); + // We will not execute graph when output is constant or just input itself. + if (IsGraphOutputValueNodeOrParameter(info.at(phase)->func_graph->output(), args, ret_val)) { + ConfigManager::GetInstance().ResetConfig(); + return *ret_val; + } + + std::vector inputs; + ProcessGeArg(info, args, phase, &inputs); + + std::shared_ptr ret = DoExecGraph(anf_graph, inputs, phase); + ConfigManager::GetInstance().ResetConfig(); + if (ret != nullptr) { + return *ret; + } else { + MS_LOG(EXCEPTION) << "exec graph failed"; + } +} +void ExportDFGraph(const std::string& file_name, const std::string& phase) { + MS_LOG(DEBUG) << "ExportGraph Begin"; + transform::DfGraphWrapperPtr wrap_ptr = DfGraphManager::GetInstance().GetGraphByName(phase); + if (wrap_ptr == nullptr) { + MS_LOG(ERROR) << "Get graph form DfGraphManager failed!"; + return; + } + + transform::DfGraphPtr ge_graph = wrap_ptr->graph_ptr_; + if (nullptr == ge_graph) { + MS_LOG(ERROR) << "The export graph is null"; + return; + } + + (void)ge_graph->SaveToFile(file_name); + + MS_LOG(DEBUG) << "ExportGraph End"; +} +} // namespace pipeline +} // namespace mindspore diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.h b/mindspore/ccsrc/pipeline/pipeline_ge.h new file mode 100644 index 0000000000..c3779fd982 --- /dev/null +++ b/mindspore/ccsrc/pipeline/pipeline_ge.h @@ -0,0 +1,57 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_CCSRC_PIPELINE_PIPELINE_GE_H_ +#define MINDSPORE_CCSRC_PIPELINE_PIPELINE_GE_H_ + +#include +#include +#include +#include +#include +#include +#include + +#include "pybind11/pybind11.h" +#include "pipeline/base.h" +#include "operator/ops.h" + +namespace mindspore { +namespace pipeline { + +namespace py = pybind11; + +void SetGeOption(const std::map& options); + +void RunGEInitGraph(const py::dict& init_params, const std::string& phase); + +py::object ExecDFGraph(const std::map& info, const py::tuple& args, + const std::string& phase = "train"); + +FuncGraphPtr BuildDFGraph(const std::map& info, const py::dict& init_params, + const std::string& phase, const py::object& broadcast_params = {}); + +// init and exec dataset sub graph for GE backend +bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batch_size, + const std::vector& types, const std::vector>& shapes, + const std::vector& input_indexes, const std::string& phase); + +void ExportDFGraph(const std::string& file_name, const std::string& phase); + +} // namespace pipeline +} // namespace mindspore + +#endif // MINDSPORE_CCSRC_PIPELINE_PIPELINE_GE_H_ diff --git a/mindspore/ccsrc/pipeline/resource.cc b/mindspore/ccsrc/pipeline/resource.cc index 2998ff1dbb..59ee04ad15 100644 --- a/mindspore/ccsrc/pipeline/resource.cc +++ b/mindspore/ccsrc/pipeline/resource.cc @@ -25,19 +25,13 @@ #include "pipeline/parse/data_converter.h" #include "operator/ops.h" #include "utils/graph_utils.h" -#include "transform/convert.h" #include "optimizer/ad/dfunctor.h" #include "vm/segment_runner.h" -#include "utils/context/ms_context.h" -#include "transform/df_graph_manager.h" -#include "device/kernel_runtime_manager.h" namespace mindspore { // namespace to support opmap definition namespace pipeline { -using MethodMap = std::unordered_map>; - MethodMap& GetMethodMap() { static MethodMap method_map = {{kObjectTypeString, { @@ -255,28 +249,5 @@ void Resource::Clean() { trace::ClearTraceStack(); is_cleaned_ = true; } - -void ReleaseGeTsd() { - auto context_ptr = MsContext::GetInstance(); - if (context_ptr != nullptr) { - (void)context_ptr->FinalizeGe(true); - (void)context_ptr->CloseTsd(true); - } -} - -void ClearResAtexit() { - MS_LOG(DEBUG) << "pipeline clear all resource"; - device::KernelRuntimeManager::Instance().ClearRuntimeResource(); - transform::DfGraphManager::GetInstance().ClearGraph(); - ad::g_k_prims.clear(); - - abstract::ClearPrimEvaluatorMap(); - compile::ClearConvertCache(); - transform::DfGraphConvertor::get_adpt_map().clear(); - pipeline::GetMethodMap().clear(); - pipeline::ExecutorPy::ClearRes(); - - ReleaseGeTsd(); -} } // namespace pipeline } // namespace mindspore diff --git a/mindspore/ccsrc/pipeline/resource.h b/mindspore/ccsrc/pipeline/resource.h index 43159ddbdd..15ab70db14 100644 --- a/mindspore/ccsrc/pipeline/resource.h +++ b/mindspore/ccsrc/pipeline/resource.h @@ -44,6 +44,10 @@ const char kOutput[] = "output"; class InferenceResource; +using MethodMap = std::unordered_map>; + +MethodMap& GetMethodMap(); + class ResourceBase { public: ResourceBase() { manager_ = MakeManager(); } @@ -110,9 +114,6 @@ class Resource : public ResourceBase { using ResourcePtr = std::shared_ptr; -void ClearResAtexit(); -void ReleaseGeTsd(); - } // namespace pipeline } // namespace mindspore diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc index b9b324e5dd..8212d64c27 100644 --- a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc +++ b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc @@ -21,7 +21,7 @@ #include "pre_activate/ascend/ir_fission/bn_grad_split.h" #include "pre_activate/ascend/ir_fusion/fused_batch_norm_fusion.h" #include "pre_activate/ascend/ir_fission/layer_norm_grad_split.h" -#include "pre_activate/ascend/ir_fusion/allreduce_fusion.h" +#include "pre_activate/common/ir_fusion/allreduce_fusion.h" #include "pre_activate/ascend/ir_fusion/square_sum_fusion.h" #include "pre_activate/ascend/ir_fusion/clip_by_norm_no_div_square_sum_fusion.h" #include "pre_activate/ascend/ir_fusion/lamb_update_with_lr_rule_fusion.h" diff --git a/mindspore/ccsrc/pre_activate/ascend/buffer_fusion/buffer_fusion.cc b/mindspore/ccsrc/pre_activate/ascend/buffer_fusion/buffer_fusion.cc index 49c5e89641..58b8a93516 100644 --- a/mindspore/ccsrc/pre_activate/ascend/buffer_fusion/buffer_fusion.cc +++ b/mindspore/ccsrc/pre_activate/ascend/buffer_fusion/buffer_fusion.cc @@ -237,11 +237,11 @@ CNodePtr CreateFusionOp(const std::vector &inputs_list, const std::v std::vector input_names; for (uint8_t i = 0; i < inputs_list.size(); i++) { - input_names.emplace_back("input" + to_string(i)); + input_names.emplace_back("input" + std::to_string(i)); } std::vector output_names; for (uint8_t i = 0; i < outputs_list.size(); i++) { - output_names.emplace_back("output" + to_string(i)); + output_names.emplace_back("output" + std::to_string(i)); } ValuePtr input_names_v = MakeValue(input_names); diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.cc b/mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.cc similarity index 97% rename from mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.cc rename to mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.cc index 7b862b21b9..55efcf9058 100644 --- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.cc +++ b/mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.cc @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#include "pre_activate/ascend/ir_fusion/allreduce_fusion.h" +#include "pre_activate/common/ir_fusion/allreduce_fusion.h" #include #include diff --git a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.h b/mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.h similarity index 87% rename from mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.h rename to mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.h index c26dbc20d9..b49b8373c6 100644 --- a/mindspore/ccsrc/pre_activate/ascend/ir_fusion/allreduce_fusion.h +++ b/mindspore/ccsrc/pre_activate/common/ir_fusion/allreduce_fusion.h @@ -13,8 +13,8 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_ALLREDUCE_FUSION_H_ -#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_ALLREDUCE_FUSION_H_ +#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_COMMON_IR_FUSION_ALLREDUCE_FUSION_H_ +#define MINDSPORE_CCSRC_PRE_ACTIVATE_COMMON_IR_FUSION_ALLREDUCE_FUSION_H_ #include #include "pre_activate/common/pass.h" @@ -46,4 +46,4 @@ class AllReduceFusion : public Pass { }; } // namespace opt } // namespace mindspore -#endif // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_IR_FUSION_ALLREDUCE_FUSION_H_ +#endif // MINDSPORE_CCSRC_PRE_ACTIVATE_COMMON_IR_FUSION_ALLREDUCE_FUSION_H_ diff --git a/mindspore/ccsrc/predict/converter/kernel2ms.cc b/mindspore/ccsrc/predict/converter/kernel2ms.cc index 30b1960e41..32cdee1350 100644 --- a/mindspore/ccsrc/predict/converter/kernel2ms.cc +++ b/mindspore/ccsrc/predict/converter/kernel2ms.cc @@ -16,7 +16,7 @@ #include "predict/converter/kernel2ms.h" #include -#include "transform/convert.h" +#include "ir/anf.h" #include "predict/converter/lite_model/op_attr_packer.h" #include "mindspore/ccsrc/operator/ops.h" @@ -135,7 +135,7 @@ void Kernel2Ms::GetRealInpoutsPtr(const AnfNodePtr &node, std::vectorisa()) { auto c_node = node->cast(); MS_EXCEPTION_IF_NULL(c_node); - std::string c_node_name = transform::GetCNodeFuncName(c_node); + std::string c_node_name = GetCNodeFuncName(c_node); if (c_node_name == prim::kPrimTupleGetItem->name()) { auto v_node = c_node->inputs()[kTupleGetItemIndex]->cast(); MS_EXCEPTION_IF_NULL(v_node); @@ -321,7 +321,7 @@ bool Kernel2Ms::SetGraphInputTensors(const KernelGraphPtr &kernel_graph_ptr, con } for (const auto &input_node : kernel_graph_ptr->inputs()) { if (input_node->isa()) { - ParameterPtr pk_node = dynamic_pointer_cast(input_node); + ParameterPtr pk_node = std::dynamic_pointer_cast(input_node); TensorPtr device_tensor; if (convert_mode_ == kConvertCpuMode) { device_tensor = predict::utils::GetParaCpuTensor(input_node); diff --git a/mindspore/ccsrc/pynative/base.h b/mindspore/ccsrc/pynative/base.h new file mode 100644 index 0000000000..7405f621cb --- /dev/null +++ b/mindspore/ccsrc/pynative/base.h @@ -0,0 +1,67 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_CCSRC_PYNATIVE_BASE_H_ +#define MINDSPORE_CCSRC_PYNATIVE_BASE_H_ + +#include +#include +#include +#include +#include +#include + +#include "pybind11/pybind11.h" +#include "ir/primitive.h" +#include "pipeline/static_analysis/abstract_value.h" + +namespace mindspore { +namespace pynative { + +namespace py = pybind11; + +enum PynativeStatusCode { + PYNATIVE_SUCCESS = 0, + PYNATIVE_OP_NOT_IMPLEMENTED_ERR = 1, + PYNATIVE_OP_INPUTS_ERR = 2, + PYNATIVE_OP_PARAMS_ERR = 3, + PYNATIVE_OP_ATTRS_ERR = 4, + PYNATIVE_GRAPH_MANAGER_ERR = 5, + PYNATIVE_GRAPH_GE_BUILD_ERR = 6, + PYNATIVE_GRAPH_GE_RUN_ERR = 7, + PYNATIVE_UNKNOWN_STATE = 0XFF +}; + +enum RunOpArgsEnum { PY_PRIM = 0, PY_NAME, PY_INPUTS, PY_INPUT_MASK, PY_ARGS_NUM }; + +struct OpExecInfo { + PrimitivePyPtr py_primitive; + std::string op_name; + AbstractBasePtr abstract; + + py::tuple op_inputs; + py::tuple inputs_mask; + py::dict op_attrs; +}; +using OpExecInfoPtr = std::shared_ptr; +OpExecInfoPtr GenerateOpExecInfo(const py::args& args); + +const std::unordered_set ignore_infer_prim = {"partial"}; + +} // namespace pynative +} // namespace mindspore + +#endif // MINDSPORE_CCSRC_PYNATIVE_BASE_H_ diff --git a/mindspore/ccsrc/pynative/pynative_execute.cc b/mindspore/ccsrc/pynative/pynative_execute.cc index 27cfd89106..927e768bbe 100644 --- a/mindspore/ccsrc/pynative/pynative_execute.cc +++ b/mindspore/ccsrc/pynative/pynative_execute.cc @@ -29,16 +29,18 @@ #include "pipeline/static_analysis/prim.h" #include "session/session_factory.h" +#include "pynative/base.h" + +#ifdef ENABLE_GE +#include "pynative/pynative_execute_ge.h" +#endif + const char SINGLE_OP_GRAPH[] = "single_op_graph"; // primitive unable to infer value for constant input in pynative mode -const std::unordered_set ignore_infer_prim = {"partial"}; const std::unordered_set vm_operators = {"partial", "depend"}; namespace mindspore { namespace pynative { -using transform::GraphRunner; -using transform::GraphRunnerOptions; -using transform::OperatorPtr; inline ValuePtr PyAttrValue(const py::object& obj) { ValuePtr converted_ret = nullptr; bool converted = parse::ConvertData(obj, &converted_ret); @@ -48,32 +50,12 @@ inline ValuePtr PyAttrValue(const py::object& obj) { return converted_ret; } -MeTensorPtr ConvertPyObjToTensor(const py::object& obj) { - MeTensorPtr me_tensor_ptr = nullptr; - if (py::isinstance(obj)) { - me_tensor_ptr = py::cast(obj); - } else if (py::isinstance(obj)) { - me_tensor_ptr = std::make_shared(py::cast(obj), nullptr); - } else if (py::isinstance(obj)) { - me_tensor_ptr = std::make_shared(py::cast(obj), nullptr); - } else if (py::isinstance(obj)) { - me_tensor_ptr = std::make_shared(py::cast(obj), nullptr); - } else if (py::isinstance(obj)) { - me_tensor_ptr = std::make_shared(py::cast(obj), nullptr); - } else if (py::isinstance(obj)) { - me_tensor_ptr = std::make_shared(py::cast(obj), nullptr); - } else { - MS_LOG(EXCEPTION) << "run op inputs type is invalid!"; - } - return me_tensor_ptr; -} - void PynativeInfer(const PrimitivePyPtr& prim, const py::tuple& py_args, OpExecInfo* const op_exec_info) { size_t size = py_args.size(); AbstractBasePtrList args_spec_list; for (size_t i = 0; i < size; i++) { ValuePtr input_value = PyAttrValue(py_args[i]); - if (py::isinstance(py_args[i])) { + if (py::isinstance(py_args[i])) { args_spec_list.emplace_back(abstract::FromValueInside(input_value, true)); } else { args_spec_list.emplace_back(abstract::FromValueInside(input_value, false)); @@ -140,241 +122,6 @@ std::string GetSingleOpGraphInfo(const OpExecInfoPtr& op_exec_info) { return graph_info; } -bool SetInputsForSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector& inputs, - const OperatorPtr& op, std::vector* graph_input_nodes) { - MS_EXCEPTION_IF_NULL(op_exec_info); - MS_EXCEPTION_IF_NULL(graph_input_nodes); - auto op_inputs = op_exec_info->op_inputs; - std::string op_name = op_exec_info->op_name; - transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true); - if (adapter == nullptr) { - return false; - } - - int op_input_idx = 1; - size_t size = inputs.size(); - for (size_t i = 0; i < size; i++) { - if (inputs[i] == nullptr) { - continue; - } - auto const_op = std::make_shared(); - MS_EXCEPTION_IF_NULL(const_op); - (void)const_op->set_attr_value(*inputs[i]); - MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]); - MS_EXCEPTION_IF_NULL(me_tensor_ptr); - auto const_op_desc = - transform::TransformUtil::GetGeTensorDesc(me_tensor_ptr->shape_c(), me_tensor_ptr->data_type(), kOpFormat_NCHW); - if (const_op_desc == nullptr) { - MS_LOG(ERROR) << "Create variable " << op_name << " ouptut descriptor failed!"; - return false; - } - auto pointer_cast_const_op = std::static_pointer_cast(const_op); - MS_EXCEPTION_IF_NULL(pointer_cast_const_op); - (void)pointer_cast_const_op->update_output_desc_y(*const_op_desc); - auto& input_map = adapter->getInputMap(); - if (input_map.find(op_input_idx) == input_map.end()) { - continue; - } - if (adapter->setInput(op, op_input_idx++, const_op)) { - MS_LOG(ERROR) << "fail to set params, index is " << op_input_idx; - return false; - } - graph_input_nodes->push_back(*const_op); - } - return true; -} - -bool BuildSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector& inputs, - const std::unordered_map& attrs, const GeGraphPtr& graph) { - MS_EXCEPTION_IF_NULL(op_exec_info); - std::string op_name = op_exec_info->op_name; - auto op_inputs = op_exec_info->op_inputs; - transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true); - if (adapter == nullptr) { - MS_LOG(ERROR) << "Unable to find Adapter for " << ((std::string)py::str(op_name)); - return false; - } - OperatorPtr op = adapter->generate(op_name); - MS_EXCEPTION_IF_NULL(op); - - std::vector graph_input_nodes; - // hold param nodes after setting input and output for the graph - // set input - if (!SetInputsForSingleOpGraph(op_exec_info, inputs, op, &graph_input_nodes)) { - return false; - } - // set attributes - for (auto attr : attrs) { - (void)adapter->setAttr(op, attr.first, attr.second); - } - // set default attributes - auto extra_attrs = adapter->GetExtraAttr(); - for (auto attr : extra_attrs) { - (void)adapter->setAttr(op, attr.first, attr.second); - } - // set input attributes - auto& input_attr_map = adapter->getInputAttrMap(); - for (auto& it : input_attr_map) { - if (op_inputs.size() < it.first) { - continue; - } - auto const_value = PyAttrValue(op_inputs[it.first - 1]); - if (const_value->isa()) { - continue; - } - it.second.set_attr(op, const_value); - } - // construct output data nodes - std::vector graph_outputs{*op}; - // set input and output nodes for the graph - MS_EXCEPTION_IF_NULL(graph); - (void)graph->SetInputs(graph_input_nodes).SetOutputs(graph_outputs); - MS_LOG(INFO) << "BuildSingleOpGraph done"; - return true; -} - -void ToTensorPtr(const OpExecInfoPtr op_exec_info, std::vector* const inputs) { - MS_EXCEPTION_IF_NULL(inputs); - MS_EXCEPTION_IF_NULL(op_exec_info); - auto op_inputs = op_exec_info->op_inputs; - size_t size = op_inputs.size(); - for (size_t i = 0; i < size; i++) { - if (py::isinstance(op_inputs[i])) { - inputs->emplace_back(nullptr); - continue; - } - MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]); - auto ge_tensor_ptr = transform::TransformUtil::ConvertTensor(me_tensor_ptr, kOpFormat_NCHW); - if (ge_tensor_ptr == nullptr) { - MS_LOG(EXCEPTION) << "convert inputs to GE tensor failed in op " << op_exec_info->op_name << "."; - } - // set inputs for operator to build single node graph - inputs->push_back(ge_tensor_ptr); - } -} - -PynativeStatusCode ConvertAttributes(const OpExecInfoPtr& op_exec_info, const std::vector& inputs) { - MS_EXCEPTION_IF_NULL(op_exec_info); - auto op_attrs = op_exec_info->op_attrs; - std::unordered_map attrs{}; - - for (auto& item : op_attrs) { - if (!py::isinstance(item.first)) { - MS_LOG(ERROR) << "type error in py dict convert"; - return PYNATIVE_OP_ATTRS_ERR; - } - std::string name = py::cast(item.first); - auto attr_value = PyAttrValue(py::cast(item.second)); - (void)attrs.emplace(name, attr_value); - } - - // build graph - GeGraphPtr graph = std::make_shared(op_exec_info->op_name); - if (BuildSingleOpGraph(op_exec_info, inputs, attrs, graph) == false) { - MS_LOG(ERROR) << "Fail to BuildSingleOpGraph"; - return PYNATIVE_GRAPH_GE_BUILD_ERR; - } - - // add the single op graph into the graph manager, which will be iterated by session. - transform::Status ret = - transform::DfGraphManager::GetInstance().AddGraph(SINGLE_OP_GRAPH, std::shared_ptr(graph)); - if (ret != transform::SUCCESS) { - MS_LOG(ERROR) << "Fail to AddGraph into graph manager"; - return PYNATIVE_GRAPH_MANAGER_ERR; - } - - return PYNATIVE_SUCCESS; -} - -std::vector ConvertOutputTensors(const OpExecInfoPtr& op_exec_info, - const std::vector& ge_tensors) { - std::vector outputs; - AbstractBasePtr abs_base = op_exec_info->abstract; - std::vector> shapes; - if (abs_base != nullptr && abs_base->isa()) { - auto arg_tensor = dyn_cast(abs_base); - shapes.emplace_back(arg_tensor->shape()->shape()); - outputs = transform::TransformUtil::ConvertGeTensors(ge_tensors, shapes); - return outputs; - } - if (abs_base != nullptr && abs_base->isa()) { - auto arg_tuple = dyn_cast(abs_base); - size_t len = arg_tuple->size(); - - for (size_t i = 0; i < len; i++) { - if (arg_tuple->elements()[i]->isa()) { - auto arg_tensor = dyn_cast(arg_tuple->elements()[i]); - shapes.emplace_back(arg_tensor->shape()->shape()); - } - } - outputs = transform::TransformUtil::ConvertGeTensors(ge_tensors, shapes); - return outputs; - } - for (auto& it : ge_tensors) { - auto tensor = transform::TransformUtil::ConvertGeTensor(it); - if (tensor != nullptr) { - outputs.emplace_back(tensor); - } - } - return outputs; -} - -py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status) { - MS_LOG(INFO) << "RunOpInGe start"; - MS_EXCEPTION_IF_NULL(op_exec_info); - MS_EXCEPTION_IF_NULL(status); - - // returns a null py::tuple on error - py::tuple err_ret(0); - auto op_name = op_exec_info->op_name; - transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true); - if (adapter == nullptr) { - MS_LOG(ERROR) << "Unable to find GE Adapter for " << ((std::string)py::str(op_name)); - *status = PYNATIVE_OP_NOT_IMPLEMENTED_ERR; - return std::move(err_ret); - } - - std::vector inputs{}; - ToTensorPtr(op_exec_info, &inputs); - // convert me attr to ge AttrValue - PynativeStatusCode ret = ConvertAttributes(op_exec_info, inputs); - if (ret != PYNATIVE_SUCCESS) { - *status = ret; - return std::move(err_ret); - } - // run graph - transform::RunOptions run_options; - run_options.name = SINGLE_OP_GRAPH; - std::vector ge_inputs; - std::vector ge_outputs; - transform::GraphRunnerOptions graph_runner_options; - graph_runner_options.options["ge.trainFlag"] = "1"; - auto graph_runner = std::make_shared(graph_runner_options); - transform::Status run_ret; - { - // Release GIL before calling into (potentially long-running) C++ code - py::gil_scoped_release release; - run_ret = graph_runner->RunGraph(run_options, ge_inputs, &ge_outputs); - } - if (run_ret != transform::Status::SUCCESS) { - MS_LOG(ERROR) << "GraphRunner Fails to Run Graph"; - *status = PYNATIVE_GRAPH_GE_RUN_ERR; - return std::move(err_ret); - } - - std::vector graph_outputs = ConvertOutputTensors(op_exec_info, ge_outputs); - size_t output_size = graph_outputs.size(); - py::tuple result(output_size); - for (size_t i = 0; i < output_size; i++) { - MS_EXCEPTION_IF_NULL(graph_outputs[i]); - result[i] = *graph_outputs[i]; - } - - *status = PYNATIVE_SUCCESS; - MS_LOG(INFO) << "RunOpInGe end"; - return std::move(result); -} - py::object RunOpInVM(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status) { MS_LOG(INFO) << "RunOpInVM start"; @@ -423,12 +170,6 @@ py::object RunOpWithBackendPolicy(MsBackendPolicy backend_policy, const OpExecIn MS_EXCEPTION_IF_NULL(status); py::object result; switch (backend_policy) { - case kMsBackendGeOnly: { - // use GE only - MS_LOG(INFO) << "RunOp use GE only backend"; - result = RunOpInGE(op_exec_info, status); - break; - } case kMsBackendVmOnly: { // use vm only MS_LOG(INFO) << "RunOp use VM only backend"; @@ -436,22 +177,14 @@ py::object RunOpWithBackendPolicy(MsBackendPolicy backend_policy, const OpExecIn break; } case kMsBackendGePrior: { +#ifdef ENABLE_GE // use GE first, use vm when GE fails MS_LOG(INFO) << "RunOp use GE first backend"; result = RunOpInGE(op_exec_info, status); if (*status != PYNATIVE_SUCCESS) { result = RunOpInVM(op_exec_info, status); } - break; - } - case kMsBackendVmPrior: { - // GE_VM_SILENT - // (should not use this policy) use vm first, use GE when vm fails - MS_LOG(INFO) << "RunOp use VM first backend"; - result = RunOpInVM(op_exec_info, status); - if (*status != PYNATIVE_SUCCESS) { - result = RunOpInGE(op_exec_info, status); - } +#endif break; } case kMsBackendMsPrior: { diff --git a/mindspore/ccsrc/pynative/pynative_execute.h b/mindspore/ccsrc/pynative/pynative_execute.h index e3d7649106..17b5610bfd 100644 --- a/mindspore/ccsrc/pynative/pynative_execute.h +++ b/mindspore/ccsrc/pynative/pynative_execute.h @@ -25,55 +25,14 @@ #include "pybind11/pybind11.h" -#include "transform/convert.h" -#include "transform/graph_runner.h" -#include "transform/types.h" +#include "pynative/base.h" #include "utils/context/ms_context.h" namespace mindspore { namespace pynative { -using MeTensor = mindspore::tensor::Tensor; -using MeTensorPtr = mindspore::tensor::TensorPtr; -using GeTensor = ge::Tensor; -using GeTensorPtr = std::shared_ptr; -using GeGraph = ge::Graph; -using GeGraphPtr = std::shared_ptr; -using GeOperator = ge::Operator; -using GeOperatorPtr = std::shared_ptr; - namespace py = pybind11; -enum PynativeStatusCode { - PYNATIVE_SUCCESS = 0, - PYNATIVE_OP_NOT_IMPLEMENTED_ERR = 1, - PYNATIVE_OP_INPUTS_ERR = 2, - PYNATIVE_OP_PARAMS_ERR = 3, - PYNATIVE_OP_ATTRS_ERR = 4, - PYNATIVE_GRAPH_MANAGER_ERR = 5, - PYNATIVE_GRAPH_GE_BUILD_ERR = 6, - PYNATIVE_GRAPH_GE_RUN_ERR = 7, - PYNATIVE_UNKNOWN_STATE = 0XFF -}; - -enum RunOpArgsEnum { PY_PRIM = 0, PY_NAME, PY_INPUTS, PY_INPUT_MASK, PY_ARGS_NUM }; - -struct OpExecInfo { - PrimitivePyPtr py_primitive; - std::string op_name; - AbstractBasePtr abstract; - - py::tuple op_inputs; - py::tuple inputs_mask; - py::dict op_attrs; -}; -using OpExecInfoPtr = std::shared_ptr; -OpExecInfoPtr GenerateOpExecInfo(const py::args& args); -bool BuildSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector& inputs, - const std::unordered_map& attrs, const GeGraphPtr& graph); - -py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status); - py::object RunOpInVM(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status); py::tuple RunOp(const py::args& args); diff --git a/mindspore/ccsrc/pynative/pynative_execute_ge.cc b/mindspore/ccsrc/pynative/pynative_execute_ge.cc new file mode 100644 index 0000000000..4ed6088494 --- /dev/null +++ b/mindspore/ccsrc/pynative/pynative_execute_ge.cc @@ -0,0 +1,311 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "pynative/pynative_execute_ge.h" + +#include +#include +#include +#include + +#include "utils/any.h" +#include "utils/utils.h" +#include "utils/context/ms_context.h" +#include "operator/ops.h" +#include "pipeline/parse/data_converter.h" +#include "pipeline/static_analysis/prim.h" +#include "session/session_factory.h" + +const char SINGLE_OP_GRAPH[] = "single_op_graph"; + +namespace mindspore { +namespace pynative { + +using MeTensor = mindspore::tensor::Tensor; +using MeTensorPtr = mindspore::tensor::TensorPtr; +using GeOperator = ge::Operator; +using GeOperatorPtr = std::shared_ptr; + +using transform::GraphRunner; +using transform::GraphRunnerOptions; +using transform::OperatorPtr; +static std::shared_ptr session = nullptr; +inline ValuePtr PyAttrValue(const py::object& obj) { + ValuePtr converted_ret = nullptr; + bool converted = parse::ConvertData(obj, &converted_ret); + if (!converted) { + MS_LOG(EXCEPTION) << "attribute convert error with type:" << std::string(py::str(obj)); + } + return converted_ret; +} + +MeTensorPtr ConvertPyObjToTensor(const py::object& obj) { + MeTensorPtr me_tensor_ptr = nullptr; + if (py::isinstance(obj)) { + me_tensor_ptr = py::cast(obj); + } else if (py::isinstance(obj)) { + me_tensor_ptr = std::make_shared(py::cast(obj), nullptr); + } else if (py::isinstance(obj)) { + me_tensor_ptr = std::make_shared(py::cast(obj), nullptr); + } else if (py::isinstance(obj)) { + me_tensor_ptr = std::make_shared(py::cast(obj), nullptr); + } else if (py::isinstance(obj)) { + me_tensor_ptr = std::make_shared(py::cast(obj), nullptr); + } else if (py::isinstance(obj)) { + me_tensor_ptr = std::make_shared(py::cast(obj), nullptr); + } else { + MS_LOG(EXCEPTION) << "run op inputs type is invalid!"; + } + return me_tensor_ptr; +} + +bool SetInputsForSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector& inputs, + const OperatorPtr& op, std::vector* graph_input_nodes) { + MS_EXCEPTION_IF_NULL(op_exec_info); + MS_EXCEPTION_IF_NULL(graph_input_nodes); + auto op_inputs = op_exec_info->op_inputs; + std::string op_name = op_exec_info->op_name; + transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true); + if (adapter == nullptr) { + return false; + } + + int op_input_idx = 1; + size_t size = inputs.size(); + for (size_t i = 0; i < size; i++) { + if (inputs[i] == nullptr) { + continue; + } + auto const_op = std::make_shared(); + MS_EXCEPTION_IF_NULL(const_op); + (void)const_op->set_attr_value(*inputs[i]); + MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]); + MS_EXCEPTION_IF_NULL(me_tensor_ptr); + auto const_op_desc = + transform::TransformUtil::GetGeTensorDesc(me_tensor_ptr->shape_c(), me_tensor_ptr->data_type(), kOpFormat_NCHW); + if (const_op_desc == nullptr) { + MS_LOG(ERROR) << "Create variable " << op_name << " ouptut descriptor failed!"; + return false; + } + auto pointer_cast_const_op = std::static_pointer_cast(const_op); + MS_EXCEPTION_IF_NULL(pointer_cast_const_op); + (void)pointer_cast_const_op->update_output_desc_y(*const_op_desc); + auto& input_map = adapter->getInputMap(); + if (input_map.find(op_input_idx) == input_map.end()) { + continue; + } + if (adapter->setInput(op, op_input_idx++, const_op)) { + MS_LOG(ERROR) << "fail to set params, index is " << op_input_idx; + return false; + } + graph_input_nodes->push_back(*const_op); + } + return true; +} + +bool BuildSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector& inputs, + const std::unordered_map& attrs, const GeGraphPtr& graph) { + MS_EXCEPTION_IF_NULL(op_exec_info); + std::string op_name = op_exec_info->op_name; + auto op_inputs = op_exec_info->op_inputs; + transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true); + if (adapter == nullptr) { + MS_LOG(ERROR) << "Unable to find Adapter for " << ((std::string)py::str(op_name)); + return false; + } + OperatorPtr op = adapter->generate(op_name); + MS_EXCEPTION_IF_NULL(op); + + std::vector graph_input_nodes; + // hold param nodes after setting input and output for the graph + // set input + if (!SetInputsForSingleOpGraph(op_exec_info, inputs, op, &graph_input_nodes)) { + return false; + } + // set attributes + for (auto attr : attrs) { + (void)adapter->setAttr(op, attr.first, attr.second); + } + // set default attributes + auto extra_attrs = adapter->GetExtraAttr(); + for (auto attr : extra_attrs) { + (void)adapter->setAttr(op, attr.first, attr.second); + } + // set input attributes + auto& input_attr_map = adapter->getInputAttrMap(); + for (auto& it : input_attr_map) { + if (op_inputs.size() < it.first) { + continue; + } + auto const_value = PyAttrValue(op_inputs[it.first - 1]); + if (const_value->isa()) { + continue; + } + it.second.set_attr(op, const_value); + } + // construct output data nodes + std::vector graph_outputs{*op}; + // set input and output nodes for the graph + MS_EXCEPTION_IF_NULL(graph); + (void)graph->SetInputs(graph_input_nodes).SetOutputs(graph_outputs); + MS_LOG(INFO) << "BuildSingleOpGraph done"; + return true; +} + +void ToTensorPtr(const OpExecInfoPtr op_exec_info, std::vector* const inputs) { + MS_EXCEPTION_IF_NULL(inputs); + MS_EXCEPTION_IF_NULL(op_exec_info); + auto op_inputs = op_exec_info->op_inputs; + size_t size = op_inputs.size(); + for (size_t i = 0; i < size; i++) { + if (py::isinstance(op_inputs[i])) { + inputs->emplace_back(nullptr); + continue; + } + MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]); + auto ge_tensor_ptr = transform::TransformUtil::ConvertTensor(me_tensor_ptr, kOpFormat_NCHW); + if (ge_tensor_ptr == nullptr) { + MS_LOG(EXCEPTION) << "convert inputs to GE tensor failed in op " << op_exec_info->op_name << "."; + } + // set inputs for operator to build single node graph + inputs->push_back(ge_tensor_ptr); + } +} + +PynativeStatusCode ConvertAttributes(const OpExecInfoPtr& op_exec_info, const std::vector& inputs) { + MS_EXCEPTION_IF_NULL(op_exec_info); + auto op_attrs = op_exec_info->op_attrs; + std::unordered_map attrs{}; + + for (auto& item : op_attrs) { + if (!py::isinstance(item.first)) { + MS_LOG(ERROR) << "type error in py dict convert"; + return PYNATIVE_OP_ATTRS_ERR; + } + std::string name = py::cast(item.first); + auto attr_value = PyAttrValue(py::cast(item.second)); + (void)attrs.emplace(name, attr_value); + } + + // build graph + GeGraphPtr graph = std::make_shared(op_exec_info->op_name); + if (BuildSingleOpGraph(op_exec_info, inputs, attrs, graph) == false) { + MS_LOG(ERROR) << "Fail to BuildSingleOpGraph"; + return PYNATIVE_GRAPH_GE_BUILD_ERR; + } + + // add the single op graph into the graph manager, which will be iterated by session. + transform::Status ret = + transform::DfGraphManager::GetInstance().AddGraph(SINGLE_OP_GRAPH, std::shared_ptr(graph)); + if (ret != transform::SUCCESS) { + MS_LOG(ERROR) << "Fail to AddGraph into graph manager"; + return PYNATIVE_GRAPH_MANAGER_ERR; + } + + return PYNATIVE_SUCCESS; +} + +std::vector ConvertOutputTensors(const OpExecInfoPtr& op_exec_info, + const std::vector& ge_tensors) { + std::vector outputs; + AbstractBasePtr abs_base = op_exec_info->abstract; + std::vector> shapes; + if (abs_base != nullptr && abs_base->isa()) { + auto arg_tensor = dyn_cast(abs_base); + shapes.emplace_back(arg_tensor->shape()->shape()); + outputs = transform::TransformUtil::ConvertGeTensors(ge_tensors, shapes); + return outputs; + } + if (abs_base != nullptr && abs_base->isa()) { + auto arg_tuple = dyn_cast(abs_base); + size_t len = arg_tuple->size(); + + for (size_t i = 0; i < len; i++) { + if (arg_tuple->elements()[i]->isa()) { + auto arg_tensor = dyn_cast(arg_tuple->elements()[i]); + shapes.emplace_back(arg_tensor->shape()->shape()); + } + } + outputs = transform::TransformUtil::ConvertGeTensors(ge_tensors, shapes); + return outputs; + } + for (auto& it : ge_tensors) { + auto tensor = transform::TransformUtil::ConvertGeTensor(it); + if (tensor != nullptr) { + outputs.emplace_back(tensor); + } + } + return outputs; +} + +py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status) { + MS_LOG(INFO) << "RunOpInGe start"; + MS_EXCEPTION_IF_NULL(op_exec_info); + MS_EXCEPTION_IF_NULL(status); + + // returns a null py::tuple on error + py::tuple err_ret(0); + auto op_name = op_exec_info->op_name; + transform::OpAdapterPtr adapter = transform::DfGraphConvertor::FindAdapter(op_name, true); + if (adapter == nullptr) { + MS_LOG(ERROR) << "Unable to find GE Adapter for " << ((std::string)py::str(op_name)); + *status = PYNATIVE_OP_NOT_IMPLEMENTED_ERR; + return std::move(err_ret); + } + + std::vector inputs{}; + ToTensorPtr(op_exec_info, &inputs); + // convert me attr to ge AttrValue + PynativeStatusCode ret = ConvertAttributes(op_exec_info, inputs); + if (ret != PYNATIVE_SUCCESS) { + *status = ret; + return std::move(err_ret); + } + // run graph + transform::RunOptions run_options; + run_options.name = SINGLE_OP_GRAPH; + std::vector ge_inputs; + std::vector ge_outputs; + transform::GraphRunnerOptions graph_runner_options; + graph_runner_options.options["ge.trainFlag"] = "1"; + auto graph_runner = std::make_shared(graph_runner_options); + transform::Status run_ret; + { + // Release GIL before calling into (potentially long-running) C++ code + py::gil_scoped_release release; + run_ret = graph_runner->RunGraph(run_options, ge_inputs, &ge_outputs); + } + if (run_ret != transform::Status::SUCCESS) { + MS_LOG(ERROR) << "GraphRunner Fails to Run Graph"; + *status = PYNATIVE_GRAPH_GE_RUN_ERR; + return std::move(err_ret); + } + + std::vector graph_outputs = ConvertOutputTensors(op_exec_info, ge_outputs); + size_t output_size = graph_outputs.size(); + py::tuple result(output_size); + for (size_t i = 0; i < output_size; i++) { + MS_EXCEPTION_IF_NULL(graph_outputs[i]); + result[i] = *graph_outputs[i]; + } + + *status = PYNATIVE_SUCCESS; + MS_LOG(INFO) << "RunOpInGe end"; + return std::move(result); +} +} // namespace pynative + +} // namespace mindspore diff --git a/mindspore/ccsrc/pynative/pynative_execute_ge.h b/mindspore/ccsrc/pynative/pynative_execute_ge.h new file mode 100644 index 0000000000..af0efec3e3 --- /dev/null +++ b/mindspore/ccsrc/pynative/pynative_execute_ge.h @@ -0,0 +1,46 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_CCSRC_PYNATIVE_PYNATIVE_EXECUTE_GE_H_ +#define MINDSPORE_CCSRC_PYNATIVE_PYNATIVE_EXECUTE_GE_H_ + +#include +#include +#include +#include +#include + +#include "pynative/base.h" +#include "transform/convert.h" +#include "transform/graph_runner.h" +#include "transform/types.h" +#include "utils/context/ms_context.h" + +using GeTensor = ge::Tensor; +using GeTensorPtr = std::shared_ptr; +using GeGraph = ge::Graph; +using GeGraphPtr = std::shared_ptr; + +namespace mindspore { +namespace pynative { +bool BuildSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vector& inputs, + const std::unordered_map& attrs, const GeGraphPtr& graph); + +py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status); +} // namespace pynative +} // namespace mindspore + +#endif // MINDSPORE_CCSRC_PYNATIVE_PYNATIVE_EXECUTE_GE_H_ diff --git a/mindspore/ccsrc/session/ascend_session.cc b/mindspore/ccsrc/session/ascend_session.cc index 1a29450313..64647cd036 100644 --- a/mindspore/ccsrc/session/ascend_session.cc +++ b/mindspore/ccsrc/session/ascend_session.cc @@ -35,6 +35,7 @@ #include "pre_activate/common/helper.h" #include "device/kernel_runtime_manager.h" #include "kernel/tbe/tbe_python_funcs.h" +#include "utils/config_manager.h" namespace mindspore { namespace session { diff --git a/mindspore/ccsrc/session/gpu_session.cc b/mindspore/ccsrc/session/gpu_session.cc index 196a2f300f..293ca4f2ba 100644 --- a/mindspore/ccsrc/session/gpu_session.cc +++ b/mindspore/ccsrc/session/gpu_session.cc @@ -19,7 +19,7 @@ #include "device/gpu/gpu_kernel_runtime.h" #include "pre_activate/common/optimizer.h" #include "pre_activate/common/pass_manager.h" -#include "pre_activate/ascend/ir_fusion/allreduce_fusion.h" +#include "pre_activate/common/ir_fusion/allreduce_fusion.h" #include "device/kernel_runtime_manager.h" #include "predict/predict.h" #include "common/utils.h" diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index b34a2aa28c..dc3eb3f0b0 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -381,24 +381,6 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma } // ---------------implement of DfGraphConvertor------------- -std::string GetCNodeFuncName(const CNodePtr cnode) { - if (cnode->inputs().empty()) { - return ""; - } - - AnfNodePtr valuenode = cnode->input(0); - if (valuenode->isa()) { - auto value = GetValueNode(valuenode); - // check whether the valuenode is primitive - if (value->isa()) { - return value->cast()->name(); - } else { - return value->ToString(); - } - } - return ""; -} - PrimType GetCNodeFuncType(const CNodePtr cnode) { if (cnode->inputs().empty()) { return kPrimTypeUnknown; diff --git a/mindspore/ccsrc/transform/convert.h b/mindspore/ccsrc/transform/convert.h index e38b0b2b3a..556db5acee 100644 --- a/mindspore/ccsrc/transform/convert.h +++ b/mindspore/ccsrc/transform/convert.h @@ -253,7 +253,6 @@ class DfGraphConvertor { bool distribute_ = false; }; -extern std::string GetCNodeFuncName(CNodePtr cnode); } // namespace transform } // namespace mindspore diff --git a/mindspore/ccsrc/utils/callbacks.cc b/mindspore/ccsrc/utils/callbacks.cc index ab3f7d883b..cdee0be82d 100644 --- a/mindspore/ccsrc/utils/callbacks.cc +++ b/mindspore/ccsrc/utils/callbacks.cc @@ -20,16 +20,16 @@ #include #include #include "pybind11/pybind11.h" +#ifdef ENABLE_GE #include "transform/df_graph_manager.h" #include "transform/util.h" +#endif #include "pipeline/parse/data_converter.h" #include "pipeline/parse/python_adapter.h" #include "utils/visible.h" namespace mindspore { namespace callbacks { -using mindspore::transform::Status; -using mindspore::transform::TransformUtil; const char PYTHON_MOD_CALLBACK_MODULE[] = "mindspore.train.callback"; const char PYTHON_FUN_PROCESS_CHECKPOINT[] = "_checkpoint_cb_for_save_op"; @@ -38,6 +38,10 @@ const char kSummary[] = "Summary"; const char kCheckPoint[] = "Save"; const int ONE_SHAPE = 1; +#ifdef ENABLE_GE +using mindspore::transform::Status; +using mindspore::transform::TransformUtil; + bool GetParameterShape(const FuncGraphPtr& graph, const std::string& param_name, const std::shared_ptr>& shape) { if (graph == nullptr) { @@ -181,6 +185,7 @@ uint32_t MS_EXPORT SummarySaveCallback(uint32_t graph_id, const std::map(ret); if (!bool_ret) { MS_LOG(ERROR) << "Python checkpoint return false during callback"; - return Status::FAILED; + return kCallbackFalied; } MS_LOG(DEBUG) << "End the summary save callback function."; - return Status::SUCCESS; + return kCallbackOk; } } // namespace callbacks } // namespace mindspore diff --git a/mindspore/ccsrc/utils/callbacks.h b/mindspore/ccsrc/utils/callbacks.h index 2a18b21b16..778b0a9ba2 100644 --- a/mindspore/ccsrc/utils/callbacks.h +++ b/mindspore/ccsrc/utils/callbacks.h @@ -20,8 +20,11 @@ #include #include #include +#include "ir/meta_tensor.h" +#ifdef ENABLE_GE #include "transform/types.h" #include "transform/util.h" +#endif namespace mindspore { namespace callbacks { @@ -36,10 +39,16 @@ extern const char kSummary[]; extern const char kCheckPoint[]; extern const std::string kPythonCheckpointModuleName; extern const std::string kPythonCheckpointFuncName; + +const int kCallbackOk = 0; +const int kCallbackFalied = 1; + bool GetParameterShape(const FuncGraphPtr& anf_graph, const std::string& param_name, const std::shared_ptr>& shape); +#ifdef ENABLE_GE uint32_t CheckpointSaveCallback(uint32_t, const std::map&); uint32_t SummarySaveCallback(uint32_t, const std::map&); +#endif uint32_t SummarySaveCallback(uint32_t, const std::map&); } // namespace callbacks diff --git a/mindspore/ccsrc/utils/context/ms_context.cc b/mindspore/ccsrc/utils/context/ms_context.cc index bf05af9858..7b531536ac 100644 --- a/mindspore/ccsrc/utils/context/ms_context.cc +++ b/mindspore/ccsrc/utils/context/ms_context.cc @@ -26,13 +26,15 @@ #include "tdt/tdt_host_interface.h" #include "tdt/data_common.h" #endif +#ifdef ENABLE_GE #include "transform/df_graph_manager.h" +#endif #include "ir/meta_tensor.h" namespace mindspore { +#ifdef ENABLE_GE using mindspore::transform::DfGraphManager; -using transform::GraphRunner; -using transform::GraphRunnerOptions; +#endif std::atomic thread_1_must_end(false); @@ -81,6 +83,7 @@ MsContext::MsContext(const std::string& policy, const std::string& target) { std::shared_ptr MsContext::GetInstance() { if (inst_context_ == nullptr) { + MS_LOG(DEBUG) << "Create new mindspore context"; #ifdef ENABLE_GE inst_context_.reset(new (std::nothrow) MsContext("ge", kAscendDevice)); #elif defined(ENABLE_D) diff --git a/mindspore/ccsrc/utils/context/ms_context.h b/mindspore/ccsrc/utils/context/ms_context.h index e7d8dc769f..06704ff9c6 100644 --- a/mindspore/ccsrc/utils/context/ms_context.h +++ b/mindspore/ccsrc/utils/context/ms_context.h @@ -23,7 +23,6 @@ #include #include #include -#include "transform/graph_runner.h" #include "utils/log_adapter.h" namespace mindspore { diff --git a/mindspore/ccsrc/utils/convert_utils.cc b/mindspore/ccsrc/utils/convert_utils.cc index ccd21f6801..e840ff8734 100644 --- a/mindspore/ccsrc/utils/convert_utils.cc +++ b/mindspore/ccsrc/utils/convert_utils.cc @@ -373,4 +373,45 @@ AbstractBasePtr PyListDtype2AbstractTensor(const py::object &shape_obj, const py MS_LOG(EXCEPTION) << "Python evaluator return invalid shape or type. " << (std::string)py::str(type_obj); } } +bool IsGraphOutputValueNodeOrParameter(const AnfNodePtr &output, const py::tuple &args, + const std::shared_ptr &ret_val) { + if (output->isa()) { + MS_LOG(INFO) << "Graph's output is a constant. No need to execute."; + ValuePtr value = GetValueNode(output); + *ret_val = ValuePtrToPyData(value); + return true; + } + + // Adapter will transform values in __init__() and construct() to parameters, this could cause + // inputs (a.k.a args in current function) size less than parameters'. + if (output->isa()) { + MS_LOG(INFO) << "Graph's output is a parameter. If all params are inputs, no need to execute."; + if (args.empty()) { + MS_LOG(EXCEPTION) << "Inputs size is 0, let graph to be executed."; + } + // Find the right parameter as ret_val. + auto func_graph = output->func_graph(); + MS_EXCEPTION_IF_NULL(func_graph); + auto params = func_graph->parameters(); + if (params.empty()) { + MS_EXCEPTION(UnknownError) << "Graph's parameters size is 0"; + } + if (args.size() != params.size()) { + MS_LOG(EXCEPTION) << "Input size " << args.size() << " not equal to params size " << params.size() + << ", let graph to be executed."; + } + + auto it = std::find(params.begin(), params.end(), output); + if (it == params.end()) { + MS_EXCEPTION(UnknownError) << "When graph output is Parameter, it should be found in graph parameters"; + } + size_t index = it - params.cbegin(); + if (index >= args.size()) { + MS_EXCEPTION(UnknownError) << "Index " << index << " equal or larger than args size " << args.size() << "."; + } + *ret_val = args[index]; + return true; + } + return false; +} } // namespace mindspore diff --git a/mindspore/ccsrc/utils/convert_utils.h b/mindspore/ccsrc/utils/convert_utils.h index f190f98e6a..fbd4485a3f 100644 --- a/mindspore/ccsrc/utils/convert_utils.h +++ b/mindspore/ccsrc/utils/convert_utils.h @@ -18,6 +18,7 @@ #define MINDSPORE_CCSRC_UTILS_CONVERT_UTILS_H_ #include +#include #include "pybind11/pybind11.h" #include "utils/any.h" @@ -120,6 +121,9 @@ inline uint8_t *AddressOffset(void *address, size_t offset) { AbstractBasePtr PyListDtype2AbstractTensor(const py::object &shape_obj, const py::object &type_obj); +bool IsGraphOutputValueNodeOrParameter(const AnfNodePtr &output, const py::tuple &args, + const std::shared_ptr &ret_val); + } // namespace mindspore #endif // MINDSPORE_CCSRC_UTILS_CONVERT_UTILS_H_ diff --git a/mindspore/ccsrc/vm/segment_runner.cc b/mindspore/ccsrc/vm/segment_runner.cc index 82a61c010d..d7d5a4c096 100644 --- a/mindspore/ccsrc/vm/segment_runner.cc +++ b/mindspore/ccsrc/vm/segment_runner.cc @@ -178,14 +178,12 @@ LinConvertResult Convert(const AnfNodePtrList& lst) { } LinkFuncType MsVmConvert = Convert; -LinkFuncType GeVmConvert = Convert; -std::unordered_map backends = {{kMsVm, MsVmConvert}, {kGeVm, GeVmConvert}}; +std::unordered_map backends = {{kMsVm, MsVmConvert}}; std::set backend_list = { kMsConvert, kMsVm, - kGeVm, }; } // namespace compile diff --git a/mindspore/ccsrc/vm/transform.cc b/mindspore/ccsrc/vm/transform.cc index d5933db1ab..be7aaf5baa 100644 --- a/mindspore/ccsrc/vm/transform.cc +++ b/mindspore/ccsrc/vm/transform.cc @@ -24,7 +24,9 @@ #include #include "pipeline/static_analysis/abstract_value.h" +#ifdef ENABLE_GE #include "transform/convert.h" +#endif #include "utils/graph_utils.h" #include "utils/context/ms_context.h" #include "debug/trace.h" @@ -55,7 +57,6 @@ CompileGraph::CompileGraph(const BackendPtr& backend, const std::vectorIsGraphCut()) { - return nullptr; - } -#endif - FinalVMPtr rt = Link(graph); Reset(); MS_LOG(DEBUG) << "End"; diff --git a/mindspore/ccsrc/vm/transform.h b/mindspore/ccsrc/vm/transform.h index 206fd00431..f862444a82 100644 --- a/mindspore/ccsrc/vm/transform.h +++ b/mindspore/ccsrc/vm/transform.h @@ -55,7 +55,6 @@ class CompileGraph { InstSet Run(const FuncGraphPtr& func_graph); InstSet GenMultiGraphsSinkInst(const FuncGraphPtr& graph); - bool IsGraphCut() const { return is_graph_cut; } bool IsCut(const AnfNodePtr& node); void Push(const AnfNodePtr& node); void Tie(const AnfNodePtr& n1, const AnfNodePtr& n2) { slots_[n2] = slots_[n1]; } @@ -101,7 +100,6 @@ class CompileGraph { BackendPtr backend_; LinkFuncType lin_convert_; bool is_gevm_convert_; - bool is_graph_cut; int height_{0}; int max_height_{0}; std::vector cut_list_; diff --git a/mindspore/ccsrc/vm/vmimpl.cc b/mindspore/ccsrc/vm/vmimpl.cc index e64cd16fcf..ee9a817dd8 100644 --- a/mindspore/ccsrc/vm/vmimpl.cc +++ b/mindspore/ccsrc/vm/vmimpl.cc @@ -26,8 +26,6 @@ #include #include -#include "transform/graph_runner.h" -#include "transform/convert.h" #include "ir/meta_tensor.h" #include "operator/ops.h" #include "ir/manager.h" @@ -40,39 +38,6 @@ namespace compile { using PrimitivePyPtr = std::shared_ptr; -static const char SEGMENT_GRAPH_NAME[] = "runnable_segment"; - -VectorRef GeVM::RunGraph(const FuncGraphPtr& anf_graph, const VectorRef& args) { - // Convert graph - transform::DfGraphConvertor convertor(anf_graph); - - (void)convertor.ConvertAllNode().BuildGraph(); - if (convertor.ErrCode() == 0) { - (void)transform::DfGraphManager::GetInstance().AddGraph(SEGMENT_GRAPH_NAME, convertor.GetComputeGraph()); - } else { - MS_LOG(EXCEPTION) << "convert df graph failed"; - } - - // Run graph - transform::GraphRunnerOptions options; - transform::GraphRunner graph_runner(options); - transform::RunOptions run_options; - run_options.name = SEGMENT_GRAPH_NAME; - - std::vector inputs; - (void)std::transform(std::begin(args), std::end(args), std::back_inserter(inputs), - [](const BaseRef& arg) -> tensor::TensorPtr { - auto value_ref = utils::cast(arg); - auto value = value_ref.object_; - return py::cast(value); - }); - std::vector outputs; - (void)graph_runner.RunGraph(run_options, inputs, &outputs); - std::vector ret; - (void)std::copy(outputs.begin(), outputs.end(), std::back_inserter(ret)); - return VectorRef(ret); -} - // Indicate a call to a new frame. struct CallWrap : public Base { explicit CallWrap(const VMFramePtr& vm_frame) : frame(vm_frame) {} diff --git a/mindspore/ccsrc/vm/vmimpl.h b/mindspore/ccsrc/vm/vmimpl.h index 8ff02ae946..4ef507af82 100644 --- a/mindspore/ccsrc/vm/vmimpl.h +++ b/mindspore/ccsrc/vm/vmimpl.h @@ -64,12 +64,6 @@ class VMImpl { virtual ~VMImpl() = default; }; -class GeVM : public VMImpl { - public: - VectorRef RunGraph(const FuncGraphPtr& fg, const VectorRef& args) override; - ~GeVM() override = default; -}; - // An execution frame. // This holds the state for an application of a graph. The nodes list // must contain free variables of graphs encountered before the diff --git a/mindspore/common/api.py b/mindspore/common/api.py index 8e23e9184d..9ee95ef772 100644 --- a/mindspore/common/api.py +++ b/mindspore/common/api.py @@ -22,7 +22,7 @@ from mindspore import context from mindspore import log as logger from mindspore.parallel._utils import _get_parallel_mode from .._c_expression import generate_key, Executor_, Tensor, MetaTensor -from .._c_expression import verify_inputs_signature, init_exec_dataset, export_graph, _set_dataset_mode_config, init_ge +from .._c_expression import verify_inputs_signature, init_exec_dataset, _set_dataset_mode_config, init_ge from .tensor import Tensor as MsTensor # store ms_function class compiled pipeline cache @@ -501,6 +501,7 @@ class _Executor: file_name (str): File name of model to export file_format (str): MindSpore currently support 'GEIR' and 'ONNX' format for exported model """ + from .._c_expression import export_graph phase = 'export' + '.' + str(net.create_time) export_graph(file_name, file_format, phase) diff --git a/mindspore/common/parameter.py b/mindspore/common/parameter.py index b1992c8b82..c8ddf0eac6 100644 --- a/mindspore/common/parameter.py +++ b/mindspore/common/parameter.py @@ -155,6 +155,18 @@ class Parameter: def data(self): return self.default_input + def __add__(self, other): + return self.default_input + other + + def __sub__(self, other): + return self.default_input - other + + def __mul__(self, other): + return self.default_input * other + + def __truediv__(self, other): + return self.default_input / other + def set_parameter_data(self, data): if isinstance(data, (Tensor, list, int, float, np.float16, np.float32, np.int32, np.int16, np.ndarray)) and not isinstance(data, bool): diff --git a/mindspore/common/tensor.py b/mindspore/common/tensor.py index 4c7f31921b..d17661595f 100644 --- a/mindspore/common/tensor.py +++ b/mindspore/common/tensor.py @@ -89,6 +89,16 @@ class Tensor(Tensor_): out = self.__mul__(other) return out + def __truediv__(self, other): + if isinstance(other, (int, float)): + other_tensor = Tensor(other, self.dtype()) + elif isinstance(other, Tensor): + other_tensor = other + else: + raise TypeError("unsupported type for div operation") + out = tensor_operator_registry.get('__div__')(self, other_tensor) + return out + def __sub__(self, other): if not isinstance(other, Tensor): raise TypeError("input_data must be a tensor") diff --git a/mindspore/ops/functional.py b/mindspore/ops/functional.py index 5adb6fac57..b8411d42c1 100644 --- a/mindspore/ops/functional.py +++ b/mindspore/ops/functional.py @@ -125,5 +125,5 @@ shape_mul = Primitive("shape_mul") stop_gradient = Primitive("stop_gradient") tensor_operator_registry.register('__add__', tensor_add) - tensor_operator_registry.register('__mul__', tensor_mul) +tensor_operator_registry.register('__div__', tensor_div) diff --git a/mindspore/train/model.py b/mindspore/train/model.py index 4174632cec..bcfd897f58 100755 --- a/mindspore/train/model.py +++ b/mindspore/train/model.py @@ -161,6 +161,9 @@ class Model: def _update_metrics(self, outputs): """Update metrics local values.""" + if not isinstance(outputs, tuple): + raise ValueError("The `outputs` is not tuple.") + if self._eval_indexes is not None and len(outputs) < 3: raise ValueError("The length of `outputs` must be greater than or equal to 3, \ but got {}".format(len(outputs))) diff --git a/tests/ut/cpp/device/ascend_kernel_select_test.cc b/tests/ut/cpp/device/ascend_kernel_select_test.cc index d522a5adc0..79986d375d 100644 --- a/tests/ut/cpp/device/ascend_kernel_select_test.cc +++ b/tests/ut/cpp/device/ascend_kernel_select_test.cc @@ -231,7 +231,7 @@ void test_select(const CNodePtr &kernel_node, std::vector parent_list, std::vector> shapes, +void SetParentAbstract(std::vector parent_list, std::vector> shapes, std::vector types) { for (const auto &node : parent_list) { AnfAlgo::SetOutputInferTypeAndShape(types, shapes, node.get()); diff --git a/tests/ut/cpp/device/ascend_profiling_test.cc b/tests/ut/cpp/device/ascend_profiling_test.cc index 2bfdc9fcae..2829a5fd4a 100644 --- a/tests/ut/cpp/device/ascend_profiling_test.cc +++ b/tests/ut/cpp/device/ascend_profiling_test.cc @@ -16,10 +16,10 @@ #include #include +#include "./prof_reporter.h" #include "common/common_test.h" #include "device/ascend/profiling/profiling_manager.h" #include "./common.h" -#include "./prof_reporter.h" #define private public #include "device/ascend/profiling/plugin_impl.h" #undef private diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fusion/allreduce_fusion_test.cc b/tests/ut/cpp/pre_activate/common/ir_fusion/allreduce_fusion_test.cc similarity index 99% rename from tests/ut/cpp/pre_activate/ascend/ir_fusion/allreduce_fusion_test.cc rename to tests/ut/cpp/pre_activate/common/ir_fusion/allreduce_fusion_test.cc index 1a7a103a99..79a1cf1a8a 100644 --- a/tests/ut/cpp/pre_activate/ascend/ir_fusion/allreduce_fusion_test.cc +++ b/tests/ut/cpp/pre_activate/common/ir_fusion/allreduce_fusion_test.cc @@ -20,7 +20,7 @@ #include "ir/manager.h" #include "debug/anf_ir_dump.h" #include "session/anf_runtime_algorithm.h" -#include "pre_activate/ascend/ir_fusion/allreduce_fusion.h" +#include "pre_activate/common/ir_fusion/allreduce_fusion.h" #include "pre_activate/common/optimizer.h" #include "device/kernel_info.h" #include "pre_activate/common/pass_manager.h" diff --git a/tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc b/tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc index 083e4168ce..014b0aed55 100644 --- a/tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc +++ b/tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc @@ -105,7 +105,7 @@ TEST_F(TestHWConstInputToTensorInput, test_value_tuple_tensor_input) { auto tensor = input1->cast()->value()->cast(); ASSERT_TRUE(tensor != nullptr); auto data = tensor->data_c(false); - EXPECT_EQ(vector((int *)data, (int *)data + 4), vector({2, 4, 2, 2})); + EXPECT_EQ(std::vector((int *)data, (int *)data + 4), std::vector({2, 4, 2, 2})); } } // namespace opt } // namespace mindspore diff --git a/tests/ut/python/ir/test_tensor.py b/tests/ut/python/ir/test_tensor.py index d4f96b54eb..1757567db5 100644 --- a/tests/ut/python/ir/test_tensor.py +++ b/tests/ut/python/ir/test_tensor.py @@ -24,6 +24,8 @@ import pytest import mindspore as ms import mindspore.common.api as me import mindspore.nn as nn +from mindspore.common.parameter import Parameter +from mindspore.common.initializer import initializer from ..ut_filter import non_graph_engine @@ -199,6 +201,21 @@ def test_sub(): z = x - y assert isinstance(z, ms.Tensor) +@non_graph_engine +def test_div(): + x = ms.Tensor(np.array([[2,6,10],[12, 4, 8]]).astype(np.float32)) + y = ms.Tensor(np.array([[2,2,5],[6, 1, 2]]).astype(np.float32)) + z = x / y + z2 = x / 2 + assert isinstance(z, ms.Tensor) + assert isinstance(z2, ms.Tensor) + +@non_graph_engine +def test_parameter(): + x = Parameter(initializer(1, [1], ms.float32), name="beta1_power") + z = x / 2 + print(z) + class Net(nn.Cell): """Net definition""" @@ -378,3 +395,4 @@ def test_tensor_dtype_fp32_to_bool(): input = np.random.randn(2, 3, 4, 5).astype(np.float32) input = ms.Tensor(input) input_me = ms.Tensor(input, dtype=ms.bool_) + diff --git a/tests/ut/python/ops/test_array_ops.py b/tests/ut/python/ops/test_array_ops.py index 4164ce6e8f..1c4895465f 100644 --- a/tests/ut/python/ops/test_array_ops.py +++ b/tests/ut/python/ops/test_array_ops.py @@ -97,20 +97,6 @@ def test_select(): assert np.all(output.asnumpy() == expect) -def test_scalar_cast_grad(): - """ test_scalar_cast_grad """ - input_x = 255.5 - input_t = get_py_obj_dtype(ms.int8) - - def fx_cast(x): - output = F.scalar_cast(x, input_t) - return output - - gfn = C.grad(fx_cast)(input_x) - expect_dx = 1 - assert gfn == expect_dx - - class CustomOP(PrimitiveWithInfer): __mindspore_signature__ = (sig_dtype.T, sig_dtype.T, sig_dtype.T1, sig_dtype.T1, sig_dtype.T2, sig_dtype.T2, diff --git a/tests/ut/python/parallel/__init__.py b/tests/ut/python/parallel/__init__.py index ffe03896ab..c08f8e247b 100644 --- a/tests/ut/python/parallel/__init__.py +++ b/tests/ut/python/parallel/__init__.py @@ -13,11 +13,14 @@ # limitations under the License. import mindspore.context as context +from mindspore.parallel._utils import _reset_op_id def setup_module(module): - context.set_context(mode=context.GRAPH_MODE) + context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False) + _reset_op_id() def teardown_module(): context.reset_auto_parallel_context() + _reset_op_id() diff --git a/tests/ut/python/parallel/test_alltoall.py b/tests/ut/python/parallel/test_alltoall.py index ddceece8ec..7365dd941c 100644 --- a/tests/ut/python/parallel/test_alltoall.py +++ b/tests/ut/python/parallel/test_alltoall.py @@ -97,13 +97,10 @@ def test_all_to_all(): strategys = all_to_all_common(strategy1) print(strategys) expect_dict = {'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits' - '/SoftmaxCrossEntropyWithLogits-op43': [[8, 1], [8, 1]], - 'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits' - '/OneHot-op44': [[8, 1], [], []], - 'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/Transpose-op1': - [[8, 1]], - 'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/MatMul-op0': - [[1, 1], [1, 8]]} + '/SoftmaxCrossEntropyWithLogits-op3': [[8, 1], [8, 1]], + 'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits/OneHot-op4': [[8, 1], [], []], + 'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/Transpose-op1': [[8, 1]], + 'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/MatMul-op0': [[1, 1], [1, 8]]} assert (strategys == expect_dict) context.set_context(save_graphs=False) diff --git a/tests/ut/python/parallel/test_auto_parallel_arithmetic.py b/tests/ut/python/parallel/test_auto_parallel_arithmetic.py index e6f72d8019..7c928be376 100644 --- a/tests/ut/python/parallel/test_auto_parallel_arithmetic.py +++ b/tests/ut/python/parallel/test_auto_parallel_arithmetic.py @@ -65,8 +65,8 @@ def test_auto_parallel_arithmetic(): b = Tensor(np.ones([64, 128]), dtype=ms.float32) _executor.compile(net, x, y, b, phase='train') strategies = _executor._get_strategy(net) - expected_strategies = {'Default/network-Net/FloorDiv-op2': [[2, 4], [2, 4]], - 'Default/network-Net/MatMul-op3': [[2, 1], [1, 4]]} + expected_strategies = {'Default/network-Net/FloorDiv-op0': [[2, 4], [2, 4]], + 'Default/network-Net/MatMul-op1': [[2, 1], [1, 4]]} assert strategies == expected_strategies def test_auto_parallel_arithmetic_broadcast_both(): @@ -91,8 +91,8 @@ def test_auto_parallel_arithmetic_broadcast_both(): b = Tensor(np.ones([1, 64]), dtype=ms.float32) _executor.compile(net, x, y, b, phase='train') strategies = _executor._get_strategy(net) - expected_strategies = {'Default/network-Net/FloorDiv-op2': [[8, 1], [1, 1]], - 'Default/network-Net/MatMul-op3': [[8, 1], [1, 1]]} + expected_strategies = {'Default/network-Net/FloorDiv-op0': [[8, 1], [1, 1]], + 'Default/network-Net/MatMul-op1': [[8, 1], [1, 1]]} assert strategies == expected_strategies @@ -118,8 +118,8 @@ def test_auto_parallel_arithmetic_broadcast_right(): b = Tensor(np.ones([32]), dtype=ms.float32) _executor.compile(net, x, y, b, phase='train') strategies = _executor._get_strategy(net) - expected_strategies = {'Default/network-Net/FloorDiv-op2': [[4, 2], [2]], - 'Default/network-Net/MatMul-op3': [[4, 1], [1, 2]]} + expected_strategies = {'Default/network-Net/FloorDiv-op0': [[4, 2], [2]], + 'Default/network-Net/MatMul-op1': [[4, 1], [1, 2]]} assert strategies == expected_strategies @@ -145,6 +145,6 @@ def test_auto_parallel_arithmetic_broadcast_left(): b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32) _executor.compile(net, x, y, b, phase="train") strategies = _executor._get_strategy(net) - expected_strategies = {'Default/network-Net/FloorDiv-op2': [[4, 2], [1, 4, 2]], - 'Default/network-Net/MatMul-op3': [[4, 1], [1, 2]]} - assert strategies == expected_strategies \ No newline at end of file + expected_strategies = {'Default/network-Net/FloorDiv-op0': [[4, 2], [1, 4, 2]], + 'Default/network-Net/MatMul-op1': [[4, 1], [1, 2]]} + assert strategies == expected_strategies diff --git a/tests/ut/python/parallel/test_auto_parallel_assign_sub_with_ref_key.py b/tests/ut/python/parallel/test_auto_parallel_assign_sub_with_ref_key.py index e436f9faf7..5364263695 100755 --- a/tests/ut/python/parallel/test_auto_parallel_assign_sub_with_ref_key.py +++ b/tests/ut/python/parallel/test_auto_parallel_assign_sub_with_ref_key.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import re import numpy as np from mindspore import context import mindspore.nn as nn @@ -55,6 +56,9 @@ def test_auto_parallel_assign_sub_with_ref_key(): _executor.compile(net, x, phase="train") strategies = _executor._get_strategy(net) - expected_strategies = {'Default/network-PReLU/PReLU-op2': [[1, 1, 1, 8], [1]], - 'Default/network-PReLU/ReLU-op3': [[1]]} - assert strategies == expected_strategies + for (k, v) in strategies.items(): + if re.search('PReLU-op', k) is not None: + assert v == [[1, 1, 1, 8], [1]] + elif re.search('ReLU-op', k) is not None: + assert v == [[1]] + diff --git a/tests/ut/python/parallel/test_auto_parallel_cast.py b/tests/ut/python/parallel/test_auto_parallel_cast.py index be7b5082d6..4cfeb59fc1 100644 --- a/tests/ut/python/parallel/test_auto_parallel_cast.py +++ b/tests/ut/python/parallel/test_auto_parallel_cast.py @@ -75,9 +75,9 @@ def test_double_star_graph(): _executor.compile(net, x, y, z, w, phase='train') strategies = _executor._get_strategy(net) - expected_strategies = {'Default/network-Net/MatMul-op0': [[1, 8], [8, 1]], - 'Default/network-Net/Cast-op7': [[8, 1]], - 'Default/network-Net/MatMul-op8': [[8, 1], [1, 1]], - 'Default/network-Net/Cast-op9': [[1, 8]], - 'Default/network-Net/MatMul-op10': [[1, 1], [1, 8]]} - assert strategies == expected_strategies \ No newline at end of file + expected_strategies = {'Default/network-Net/Cast-op1': [[8, 1]], + 'Default/network-Net/Cast-op3': [[1, 8]], + 'Default/network-Net/MatMul-op2': [[8, 1], [1, 1]], + 'Default/network-Net/MatMul-op4': [[1, 1], [1, 8]], + 'Default/network-Net/MatMul-op0': [[1, 8], [8, 1]]} + assert strategies == expected_strategies diff --git a/tests/ut/python/parallel/test_auto_parallel_matmul_prelu.py b/tests/ut/python/parallel/test_auto_parallel_matmul_prelu.py index ca9b561145..4d68a7f883 100644 --- a/tests/ut/python/parallel/test_auto_parallel_matmul_prelu.py +++ b/tests/ut/python/parallel/test_auto_parallel_matmul_prelu.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import re import numpy as np from mindspore import context import mindspore.nn as nn @@ -66,7 +67,10 @@ def test_matmul_prelu(): _executor.compile(net, x, y, b, phase='train') strategies = _executor._get_strategy(net) - assert strategies['Default/network-Net/PReLU-op2'] == [[16, 1, 1, 1], [1]] - assert strategies['Default/network-Net/Mul-op3'] == [[16, 1, 1, 1], [16, 1, 1, 1]] + for (k, v) in strategies.items(): + if re.search('PReLU-op', k) is not None: + assert v == [[16, 1, 1, 1], [1]] + elif re.search('Mul-op', k) is not None: + assert v == [[16, 1, 1, 1], [16, 1, 1, 1]] diff --git a/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py b/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py index b7a3255f7c..29e81f7f90 100644 --- a/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py +++ b/tests/ut/python/parallel/test_auto_parallel_parameter_cast.py @@ -80,9 +80,9 @@ def test_common_parameter(): _executor.compile(net, x, y, z, w, phase='train') strategies = _executor._get_strategy(net) - expected_strategies = {'Default/network-Net/MatMul-op6': [[8, 1], [1, 1]], - 'Default/network-Net/MatMul-op8': [[8, 1], [1, 1]], - 'Default/network-Net/Cast-op7': [[1, 1]], + expected_strategies = {'Default/network-Net/MatMul-op1': [[8, 1], [1, 1]], + 'Default/network-Net/MatMul-op3': [[8, 1], [1, 1]], + 'Default/network-Net/Cast-op2': [[1, 1]], 'Default/network-Net/MatMul-op0': [[8, 1], [1, 1]], - 'Default/network-Net/Cast-op9': [[1, 1]]} + 'Default/network-Net/Cast-op4': [[1, 1]]} assert strategies == expected_strategies diff --git a/tests/ut/python/parallel/test_auto_parallel_transpose.py b/tests/ut/python/parallel/test_auto_parallel_transpose.py index 62fdc11120..2d4dcbae81 100644 --- a/tests/ut/python/parallel/test_auto_parallel_transpose.py +++ b/tests/ut/python/parallel/test_auto_parallel_transpose.py @@ -71,8 +71,8 @@ def test_two_matmul_transpose(): _executor.compile(net, x, y, b, phase='train') strategies = _executor._get_strategy(net) - expected_strategies = {'Default/network-Net/Transpose-op4': [[1, 16]], - 'Default/network-Net/Transpose-op5': [[16, 1]], - 'Default/network-Net/MatMul-op6': [[16, 1], [1, 1]], - 'Default/network-Net/MatMul-op7': [[16, 1], [1, 1]]} - assert strategies == expected_strategies \ No newline at end of file + expected_strategies = {'Default/network-Net/Transpose-op0': [[1, 16]], + 'Default/network-Net/Transpose-op1': [[16, 1]], + 'Default/network-Net/MatMul-op2': [[16, 1], [1, 1]], + 'Default/network-Net/MatMul-op3': [[16, 1], [1, 1]]} + assert strategies == expected_strategies diff --git a/tests/ut/python/parallel/test_auto_parallel_two_matmul.py b/tests/ut/python/parallel/test_auto_parallel_two_matmul.py index e7beed384e..bd6639a501 100644 --- a/tests/ut/python/parallel/test_auto_parallel_two_matmul.py +++ b/tests/ut/python/parallel/test_auto_parallel_two_matmul.py @@ -135,7 +135,6 @@ def test_two_matmul(): _executor.compile(net, x, y, b, phase='train') strategies = _executor._get_strategy(net) - expected_strategies = {'Default/network-Net/MatMul-op2': [[16, 1], [1, 1]], - 'Default/network-Net/MatMul-op3': [[16, 1], [1, 1]]} + expected_strategies = {'Default/network-Net/MatMul-op0': [[16, 1], [1, 1]], + 'Default/network-Net/MatMul-op1': [[16, 1], [1, 1]]} assert strategies == expected_strategies - diff --git a/tests/ut/python/parallel/test_dataset_interface.py b/tests/ut/python/parallel/test_dataset_interface.py index da8821199e..17b8d3cc6d 100644 --- a/tests/ut/python/parallel/test_dataset_interface.py +++ b/tests/ut/python/parallel/test_dataset_interface.py @@ -84,7 +84,7 @@ def loss_scale_manager_common(strategy1): opt = Momentum(net.trainable_params(), learning_rate, momentum) scale_manager = DynamicLossScaleManager(32, 2, 2000) model = Model(net, loss, opt, loss_scale_manager=scale_manager) - # if no GE exists, outputs = self._train_network(*next_element) outputs is None, TypeError is caught. + # if no GE exists, outputs = self._train_network(*next_element) outputs inputs tensor. try: model.train(epoch_size, dataset, dataset_sink_mode=False) except TypeError: diff --git a/tests/ut/python/parallel/test_one_dev.py b/tests/ut/python/parallel/test_one_dev.py index a5867eb5f4..efd4889ce6 100644 --- a/tests/ut/python/parallel/test_one_dev.py +++ b/tests/ut/python/parallel/test_one_dev.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import re from mindspore.train import Model, ParallelMode from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits from mindspore.nn.optim.momentum import Momentum @@ -89,16 +90,13 @@ def all_to_all_common(): def test_one_dev(): - _reset_op_id() - strategys = all_to_all_common() - expect_dict = {'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits' - '/SoftmaxCrossEntropyWithLogits-op9': [[1, 1], [1, 1]], - 'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits' - '/OneHot-op10': [[1, 1], [], []], - 'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/Transpose-op11': - [[1, 1]], - 'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/MatMul-op12': - [[1, 1], [1, 1]]} - assert (strategys == expect_dict) + strategies = all_to_all_common() + for (k, v) in strategies.items(): + if re.search('SoftmaxCrossEntropyWithLogits-op', k) is not None: + assert v == [[1, 1], [1, 1]] + elif re.search('Transpose-op', k) is not None: + assert v == [[1, 1]] + elif re.search('MatMul-op', k) is not None: + assert v == [[1, 1], [1, 1]] diff --git a/tests/ut/python/pipeline/parse/test_create_obj.py b/tests/ut/python/pipeline/parse/test_create_obj.py index a702f37e0b..370445cf99 100644 --- a/tests/ut/python/pipeline/parse/test_create_obj.py +++ b/tests/ut/python/pipeline/parse/test_create_obj.py @@ -24,6 +24,7 @@ import logging import numpy as np import mindspore.nn as nn +from mindspore import context from mindspore.ops import operations as P from mindspore.common.api import ms_function from mindspore.common.tensor import Tensor @@ -50,6 +51,7 @@ class Net(nn.Cell): def test_create_cell_object_on_construct(): """ test_create_cell_object_on_construct """ log.debug("begin test_create_object_on_construct") + context.set_context(mode=context.GRAPH_MODE) np1 = np.random.randn(2, 3, 4, 5).astype(np.float32) input_me = Tensor(np1) @@ -118,6 +120,7 @@ class NetC(nn.Cell): def test_create_cell_object_on_construct_use_many_parameter(): """ test_create_cell_object_on_construct_use_many_parameter """ log.debug("begin test_create_object_on_construct") + context.set_context(mode=context.GRAPH_MODE) np1 = np.random.randn(2, 3, 4, 5).astype(np.float32) input_me = Tensor(np1) diff --git a/tests/ut/python/pipeline/parse/test_dtype.py b/tests/ut/python/pipeline/parse/test_dtype.py index a282c82f83..645eba8004 100644 --- a/tests/ut/python/pipeline/parse/test_dtype.py +++ b/tests/ut/python/pipeline/parse/test_dtype.py @@ -28,5 +28,4 @@ def try_type(): def test_dtype_convert(): - with pytest.raises(RuntimeError): - try_type() + try_type() diff --git a/tests/ut/python/pynative_mode/ops/test_grad.py b/tests/ut/python/pynative_mode/ops/test_grad.py index 25db6b15d2..b927da5b04 100644 --- a/tests/ut/python/pynative_mode/ops/test_grad.py +++ b/tests/ut/python/pynative_mode/ops/test_grad.py @@ -19,8 +19,10 @@ from mindspore.common.api import ms_function from mindspore import Tensor from mindspore.ops import composite as C from mindspore.ops.composite import grad_all_with_sens +from mindspore.common.dtype import get_py_obj_dtype import mindspore.nn as nn import mindspore.ops.operations as P +from mindspore.ops import functional as F from ...ut_filter import non_graph_engine @@ -78,6 +80,20 @@ def test_cast_grad(): assert np.all(gout[0].asnumpy() == expect) +def test_scalar_cast_grad(): + """ test_scalar_cast_grad """ + input_x = 255.5 + input_t = get_py_obj_dtype(ms.int8) + + def fx_cast(x): + output = F.scalar_cast(x, input_t) + return output + + gfn = C.grad(fx_cast)(input_x) + expect_dx = 1 + assert gfn == expect_dx + + @non_graph_engine def test_reshape_grad(): """ test_reshape_grad """ diff --git a/tests/ut/python/train/summary/test_summary_ops_params_valid_check.py b/tests/ut/python/train/summary/test_summary_ops_params_valid_check.py index b6e1ab992c..98dfd6aaef 100644 --- a/tests/ut/python/train/summary/test_summary_ops_params_valid_check.py +++ b/tests/ut/python/train/summary/test_summary_ops_params_valid_check.py @@ -163,12 +163,7 @@ def test_scalar_summary_use_invalid_tag_None(): def test_scalar_summary_use_invalid_tag_Bool(): log.debug("begin test_scalar_summary_use_invalid_tag_Bool") net = SummaryDemoTag(True, True, True) - try: - run_case(net) - except: - assert True - else: - assert False + run_case(net) log.debug("finished test_scalar_summary_use_invalid_tag_Bool") @@ -176,12 +171,7 @@ def test_scalar_summary_use_invalid_tag_Bool(): def test_scalar_summary_use_invalid_tag_null(): log.debug("begin test_scalar_summary_use_invalid_tag_null") net = SummaryDemoTag("", "", "") - try: - run_case(net) - except: - assert True - else: - assert False + run_case(net) log.debug("finished test_scalar_summary_use_invalid_tag_null") @@ -189,12 +179,7 @@ def test_scalar_summary_use_invalid_tag_null(): def test_scalar_summary_use_invalid_tag_Int(): log.debug("begin test_scalar_summary_use_invalid_tag_Int") net = SummaryDemoTag(1, 2, 3) - try: - run_case(net) - except: - assert True - else: - assert False + run_case(net) log.debug("finished test_scalar_summary_use_invalid_tag_Int") diff --git a/tests/ut/python/utils/test_serialize.py b/tests/ut/python/utils/test_serialize.py index 12937e5a83..41da45ab25 100644 --- a/tests/ut/python/utils/test_serialize.py +++ b/tests/ut/python/utils/test_serialize.py @@ -30,7 +30,7 @@ from mindspore.nn import WithLossCell, TrainOneStepCell from mindspore.train.callback import _CheckpointManager from mindspore.train.serialization import save_checkpoint, load_checkpoint,load_param_into_net, \ _exec_save_checkpoint, export, _save_graph -from ..ut_filter import run_on_onnxruntime +from ..ut_filter import run_on_onnxruntime, non_graph_engine from mindspore import context @@ -306,6 +306,7 @@ class MYNET(nn.Cell): return out +@non_graph_engine def test_export(): net = MYNET() input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]).astype(np.float32)) From fb92b5b16e710b547232bf419c094628876e2618 Mon Sep 17 00:00:00 2001 From: geekun Date: Mon, 30 Mar 2020 19:20:26 +0800 Subject: [PATCH 069/227] fix custom op bug and add custom op check --- mindspore/ccsrc/transform/op_adapter.h | 25 ++++++++++++++++++++ mindspore/ccsrc/transform/op_adapter_util.cc | 8 ++++++- 2 files changed, 32 insertions(+), 1 deletion(-) mode change 100755 => 100644 mindspore/ccsrc/transform/op_adapter_util.cc diff --git a/mindspore/ccsrc/transform/op_adapter.h b/mindspore/ccsrc/transform/op_adapter.h index 3dd299f83d..68d89ae3f9 100644 --- a/mindspore/ccsrc/transform/op_adapter.h +++ b/mindspore/ccsrc/transform/op_adapter.h @@ -279,6 +279,31 @@ class OpAdapter : public BaseOpAdapter { } OutHandler getOutput(const OperatorPtr& op, int index) override { + MS_EXCEPTION_IF_NULL(op); + if (IsCustomOp(op)) { + return getCustomOutput(op, index); + } + return getNormalOutput(op, index); + } + + OutHandler getCustomOutput(const OperatorPtr& op, int index) { + MS_EXCEPTION_IF_NULL(op); + auto it = cus_output_map_.find(op->GetOpType()); + if (it == cus_output_map_.end()) { + MS_LOG(ERROR) << "OpAdpator(" << op->GetName() << ") has both OUTPUT is not supported!"; + return OutHandler(); + } + + std::unordered_map& output_map = it->second; + + if ((output_map.find(index) != output_map.end())) { + return OutHandler(op, output_map[index]); + } + MS_LOG(ERROR) << "OpAdpator(" << op->GetName() << ") has no OUTPUT index(" << index << ")!"; + return OutHandler(); + } + + OutHandler getNormalOutput(const OperatorPtr& op, int index) { MS_EXCEPTION_IF_NULL(op); if (!dyn_output_map_.empty() && !output_map_.empty()) { MS_LOG(ERROR) << "OpAdpator(" << op->GetName() << ") has both OUTPUT and DYN_OUTPUT is not supported!"; diff --git a/mindspore/ccsrc/transform/op_adapter_util.cc b/mindspore/ccsrc/transform/op_adapter_util.cc old mode 100755 new mode 100644 index 49b8714837..d52699fa8f --- a/mindspore/ccsrc/transform/op_adapter_util.cc +++ b/mindspore/ccsrc/transform/op_adapter_util.cc @@ -223,7 +223,13 @@ bool IsCustomPrim(const PrimitivePtr& prim) { return false; } - return GetValue(flag); + bool is_custom_op = GetValue(flag); + if (!is_custom_op && prim->GetAttr("_custom_op_impl_config_path") != nullptr) { + MS_LOG(EXCEPTION) << "The custom op flag is false, but the op information config path is not null, non-custom op " + "can not assign the op information config path."; + } + + return is_custom_op; } bool IsCustomCNode(const AnfNodePtr& anf) { From 65cd9d48fd9f0d2885d70cecc33f934203678663 Mon Sep 17 00:00:00 2001 From: zhangz0911gm Date: Tue, 31 Mar 2020 23:14:21 -0400 Subject: [PATCH 070/227] Add FloorMod, Acosh in ME --- mindspore/ccsrc/transform/convert.cc | 7 ++++ mindspore/ccsrc/transform/op_declare.cc | 10 +++++ mindspore/ccsrc/transform/op_declare.h | 5 ++- mindspore/ops/_grad/grad_math_ops.py | 25 +++++++++++ mindspore/ops/operations/__init__.py | 4 +- mindspore/ops/operations/math_ops.py | 56 +++++++++++++++++++++++++ tests/ut/python/ops/test_ops.py | 9 ++++ 7 files changed, 114 insertions(+), 2 deletions(-) diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index dc3eb3f0b0..f6697dbbd2 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -172,6 +172,8 @@ const char kNameBinaryCrossEntropy[] = "BinaryCrossEntropy"; const char kNameBinaryCrossEntropyGrad[] = "BinaryCrossEntropyGrad"; const char kNameSparseApplyAdagrad[] = "SparseApplyAdagrad"; const char kNameSparseApplyFtrlD[] = "SparseApplyFtrlD"; +const char kNameAcosh[] = "Acosh"; +const char kNameFloorMod[] = "FloorMod"; const char kNameSpaceToDepth[] = "SpaceToDepth"; const char kNameDepthToSpace[] = "DepthToSpace"; const char kNameSign[] = "Sign"; @@ -361,7 +363,12 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameBinaryCrossEntropy), ADPT_DESC(BinaryCrossEntropy)}, {string(kNameBinaryCrossEntropyGrad), ADPT_DESC(BinaryCrossEntropyGrad)}, {string(kNameSparseApplyAdagrad), ADPT_DESC(SparseApplyAdagradD)}, +<<<<<<< HEAD {string(kNameSparseApplyFtrlD), ADPT_DESC(SparseApplyFtrlD)}, +======= + {string(kNameAcosh), ADPT_DESC(Acosh)}, + {string(kNameFloorMod), ADPT_DESC(FloorMod)}, +>>>>>>> Add FloorMod, Acosh in ME {string(kNameSpaceToDepth), ADPT_DESC(SpaceToDepth)}, {string(kNameDepthToSpace), ADPT_DESC(DepthToSpace)}, {string(kNameSign), ADPT_DESC(Sign)}, diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 8be036ec3c..16e089ed21 100755 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -356,6 +356,11 @@ INPUT_MAP(AcosGrad) = {{1, INPUT_DESC(y)}, {2, INPUT_DESC(dy)}}; ATTR_MAP(AcosGrad) = EMPTY_ATTR_MAP; OUTPUT_MAP(AcosGrad) = {{0, OUTPUT_DESC(z)}}; +// Acosh +INPUT_MAP(Acosh) = {{1, INPUT_DESC(x)}}; +ATTR_MAP(Acosh) = EMPTY_ATTR_MAP; +OUTPUT_MAP(Acosh) = {{0, OUTPUT_DESC(y)}}; + // Floor INPUT_MAP(Floor) = {{1, INPUT_DESC(x)}}; ATTR_MAP(Floor) = EMPTY_ATTR_MAP; @@ -366,6 +371,11 @@ INPUT_MAP(FloorDiv) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; ATTR_MAP(FloorDiv) = EMPTY_ATTR_MAP; OUTPUT_MAP(FloorDiv) = {{0, OUTPUT_DESC(y)}}; +// FloorMod +INPUT_MAP(FloorMod) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; +ATTR_MAP(FloorMod) = EMPTY_ATTR_MAP; +OUTPUT_MAP(FloorMod) = {{0, OUTPUT_DESC(y)}}; + // Sin INPUT_MAP(Sin) = {{1, INPUT_DESC(x)}}; ATTR_MAP(Sin) = EMPTY_ATTR_MAP; diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index e31a4fa07e..d358647d26 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -324,11 +324,15 @@ DECLARE_OP_USE_OUTPUT(Acos) DECLARE_OP_ADAPTER(AcosGrad) DECLARE_OP_USE_OUTPUT(AcosGrad) +DECLARE_OP_ADAPTER(Acosh) +DECLARE_OP_USE_OUTPUT(Acosh) DECLARE_OP_ADAPTER(Floor) DECLARE_OP_USE_OUTPUT(Floor) DECLARE_OP_ADAPTER(FloorDiv) DECLARE_OP_USE_OUTPUT(FloorDiv) +DECLARE_OP_ADAPTER(FloorMod) +DECLARE_OP_USE_OUTPUT(FloorMod) DECLARE_OP_ADAPTER(Sin) DECLARE_OP_USE_OUTPUT(Sin) DECLARE_OP_ADAPTER(Exp) @@ -452,7 +456,6 @@ DECLARE_OP_USE_INPUT_ATTR(ApplyRMSPropD) DECLARE_OP_USE_OUTPUT(ApplyRMSPropD) DECLARE_OP_ADAPTER(ApplyCenteredRMSProp) DECLARE_OP_USE_OUTPUT(ApplyCenteredRMSProp) - #ifdef ENABLE_GE DECLARE_OP_ADAPTER(Print) DECLARE_OP_USE_DYN_INPUT(Print) diff --git a/mindspore/ops/_grad/grad_math_ops.py b/mindspore/ops/_grad/grad_math_ops.py index 1675855c88..1863ac8fdd 100755 --- a/mindspore/ops/_grad/grad_math_ops.py +++ b/mindspore/ops/_grad/grad_math_ops.py @@ -251,6 +251,20 @@ def get_bprop_floordiv(self): return bprop +@bprop_getters.register(P.FloorMod) +def get_bprop_floormod(self): + """Grad definition for `FloorMod` operation.""" + div_op = P.FloorMod() + neg = P.Neg() + mul_op = P.Mul() + + def bprop(x, y, out, dout): + bc_x = div_op(dout, y) + bc_y = neg(mul_op(bc_x, out)) + return binop_grad_common(x, y, bc_x, bc_y) + return bprop + + @bprop_getters.register(P.Square) def get_bprop_square(self): """Grad definition for `Square` operation.""" @@ -690,6 +704,17 @@ def get_bprop_acos(self): return bprop +@bprop_getters.register(P.Acosh) +def get_bprop_acosh(self): + """Grad definition for `Acosh` operation.""" + input_grad = G.AcoshGrad() + + def bprop(x, out, dout): + dx = input_grad(x, dout) + return (dx,) + return bprop + + @bprop_getters.register(P.Abs) def get_bprop_abs(self): """Grad definition for `Abs` operation.""" diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index d23d5f02f3..37a3b38bb6 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -39,7 +39,7 @@ from .control_ops import ControlDepend, GeSwitch, Merge from .inner_ops import ScalarCast from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul, ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd, - Cos, Div, Equal, EqualCount, Exp, Floor, FloorDiv, + Cos, Div, Equal, EqualCount, Exp, Floor, FloorDiv, FloorMod, Acosh, Greater, GreaterEqual, Less, LessEqual, Log, LogicalAnd, LogicalNot, LogicalOr, MatMul, Maximum, Minimum, Mul, Neg, NMSWithMask, NotEqual, @@ -205,6 +205,8 @@ __all__ = [ 'Log', 'SigmoidCrossEntropyWithLogits', 'FloorDiv', + 'FloorMod', + 'Acosh', "PReLU", "Cos", "ACos", diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 398a7e6f1a..9600c736b2 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1183,6 +1183,62 @@ class Floor(PrimitiveWithInfer): return x_dtype +class FloorMod(_MathBinaryOp): + """ + Compute element-wise remainder of division. + + The inputs must be two tensors or one tensor and one scalar. + When the inputs are two tensors, the shapes of them could be broadcast, + and the data types of them should be same. + When the inputs are one tensor and one scalar, the scalar cannot be a parameter, only can be a constant, + and the type of the scalar is the same as the data type of the tensor. + + Inputs: + - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number. + - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or + a number. + + Outputs: + Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'. + + Examples: + >>> input_x = Tensor(np.array([2, 4, -1]), mindspore.int32) + >>> input_y = Tensor(np.array([3, 3, 3]), mindspore.int32) + >>> floor_mod = FloorMod() + >>> floor_mod(input_x, input_y) + [2, 1, 2] + """ + + +class Acosh(PrimitiveWithInfer): + """ + Compute inverse hyperbolic cosine of x element-wise. + + Inputs: + - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`. + + Outputs: + Tensor, has the same shape as `input_x`. + + Examples: + >>> acosh = Acosh() + >>> X = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), ms.float32) + >>> output = acosh(X) + """ + + @prim_attr_register + def __init__(self): + """init Acosh""" + + def infer_shape(self, x): + return x + + def infer_dtype(self, x): + validator.check_subclass("x_dtype", x, mstype.tensor) + validator.check_typename('x_dtype', x, mstype.number_type) + return x + + class _LogicBinaryOp(_BinaryOp): """ Define logic binary operators. diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py index c759c59c9f..01c1db1f29 100755 --- a/tests/ut/python/ops/test_ops.py +++ b/tests/ut/python/ops/test_ops.py @@ -219,6 +219,10 @@ test_case_math_ops = [ 'block': P.ACos(), 'desc_inputs': [[2, 3]], 'desc_bprop': [[2, 3]]}), + ('Acosh', { + 'block': P.Acosh(), + 'desc_inputs': [Tensor(np.random.rand(4).astype(np.float16))], + 'skip': ['backward']}), ('Sin', { 'block': P.Sin(), 'desc_inputs': [[2, 3]], @@ -301,6 +305,11 @@ test_case_math_ops = [ 'desc_inputs': [Tensor(np.random.rand(4).astype(np.float16)), Tensor(np.random.rand(4).astype(np.float16))], 'skip': ['backward']}), + ('FloorMod', { + 'block': P.FloorMod(), + 'desc_inputs': [Tensor(np.random.rand(4).astype(np.float16)), + Tensor(np.random.rand(4).astype(np.float16))], + 'skip': ['backward']}), ('identity', { 'block': ops.functional.identity, 'desc_inputs': [[2, 2]], From 10312620d2d946f95dfdbaad841a3299f376eaaf Mon Sep 17 00:00:00 2001 From: lichenever Date: Thu, 2 Apr 2020 15:42:00 +0800 Subject: [PATCH 071/227] register not equal distributed op --- mindspore/ccsrc/parallel/ops_info/ops_utils.h | 1 + mindspore/ccsrc/parallel/step_auto_parallel.cc | 1 + mindspore/ccsrc/parallel/step_parallel.cc | 2 +- 3 files changed, 3 insertions(+), 1 deletion(-) diff --git a/mindspore/ccsrc/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/parallel/ops_info/ops_utils.h index 2b8fc0ee3f..4062847d73 100644 --- a/mindspore/ccsrc/parallel/ops_info/ops_utils.h +++ b/mindspore/ccsrc/parallel/ops_info/ops_utils.h @@ -180,6 +180,7 @@ constexpr char SIGMOID[] = "Sigmoid"; constexpr char POW[] = "Pow"; constexpr char MAXIMUM[] = "Maximum"; constexpr char EQUAL[] = "Equal"; +constexpr char NOT_EQUAL[] = "NotEqual"; constexpr char LOGICALNOT[] = "LogicalNot"; constexpr char GATHERV2[] = "GatherV2"; constexpr char STRIDEDSLICE[] = "StridedSlice"; diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc index b06ec383fa..a359905494 100644 --- a/mindspore/ccsrc/parallel/step_auto_parallel.cc +++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc @@ -95,6 +95,7 @@ std::vector splittable_op_ = {MATMUL, POW, MAXIMUM, EQUAL, + NOT_EQUAL, LOGICALNOT, GATHERV2, STRIDEDSLICE, diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc index b79198ffca..78bec00bcf 100644 --- a/mindspore/ccsrc/parallel/step_parallel.cc +++ b/mindspore/ccsrc/parallel/step_parallel.cc @@ -1226,7 +1226,7 @@ void CoverSliceShape(const FuncGraphPtr& root) { MS_LOG(INFO) << "Parameter " << parameter->ToString() << " don't need to set parallel shape"; } else { SetParallelShape(parameter, res); - MS_LOG(DEBUG) << "Parameter " << parameter->ToString() << " shape " << parameter->Shape()->ToString(); + MS_LOG(DEBUG) << "Parameter " << parameter->ToString() << " shape " << parameter->Shape()->ToString(); } } g_RefMap.clear(); From b3fd38c23a15896040c7e6862dbca02114173164 Mon Sep 17 00:00:00 2001 From: c00425699 Date: Wed, 1 Apr 2020 20:47:46 +0800 Subject: [PATCH 072/227] fix bug for allreduce fusion and add resnet unit test --- .../allreduce_fusion/allreduce_fusion.cc | 2 +- .../allreduce_fusion/allreduce_fusion.h | 1 - .../allreduce_fusion/allreduce_graph.cc | 22 +- .../allreduce_fusion/allreduce_node.cc | 24 +- .../allreduce_fusion/allreduce_node.h | 7 +- .../python/parallel/test_allreduce_fusion.py | 4 +- .../parallel/test_auto_parallel_resnet.py | 223 ++++++++++++++++-- 7 files changed, 246 insertions(+), 37 deletions(-) diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc index e4f54056a8..03f7d054e0 100644 --- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc +++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc @@ -359,7 +359,7 @@ Status AllreduceFusion::SetFusionByBackwardCompAndAllreduceTime() { return FAILED; } double para_size = (tail_time_ - allreduce_inherent_time_) / allreduce_bandwidth_; - double to_cost = allreduce_graph_.max() + FUSION_COST_EPS; + double to_cost = allreduce_graph_.max(); int32_t fusion = 1; while (to_cost != 0) { MS_LOG(INFO) << "to_cost: " << to_cost << " para_size: " << para_size; diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h index a2fea45187..67dc55836a 100644 --- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h +++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.h @@ -38,7 +38,6 @@ constexpr double DEFAULT_COST_MODEL_ALLREDUCE_FUSION_COMPUTATION_TIME_PARAMETER constexpr char FUSION[] = "fusion"; constexpr char PARAMETER[] = "parameter"; const uint32_t MAX_RECURSIVE_CALL_TIMES = 100; -const double FUSION_COST_EPS = 1e-7; class AllreduceFusion { public: AllreduceFusion() diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc index 559ce02349..9e04593c83 100644 --- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc +++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_graph.cc @@ -24,7 +24,19 @@ namespace mindspore { namespace parallel { Status AllreduceGraph::AddNode(const CNodePtr& node, const AnfNodePtr& para) { - auto arnode = std::make_shared(AllreduceNode()); + AllreduceNodePtr arnode; + auto cnode_emplace_return = cnode_set_.emplace(node); + if (!cnode_emplace_return.second) { + MS_LOG(INFO) << "node: " << node->DebugString() << " has already been added!"; + auto cnode_arnode_pair = cnode_arnode_map_.find(node); + if (cnode_arnode_pair == cnode_arnode_map_.end()) { + MS_LOG(EXCEPTION) << "node is not in cnode_arnode_map_!"; + } + arnode = cnode_arnode_pair->second; + } else { + arnode = std::make_shared(AllreduceNode()); + } + if (arnode->Init(node) != SUCCESS) { MS_LOG(ERROR) << "AllreduceNode Init failed"; return FAILED; @@ -39,10 +51,6 @@ Status AllreduceGraph::AddNode(const CNodePtr& node, const AnfNodePtr& para) { if (!arnode_emplace_return.second) { MS_LOG(INFO) << "node: " << node->DebugString() << "'s arnode has already been added!"; } - auto cnode_emplace_return = cnode_set_.emplace(node); - if (!cnode_emplace_return.second) { - MS_LOG(INFO) << "node: " << node->DebugString() << " has already been added!"; - } cnode_emplace_return = para_cnodeset_map_[para].emplace(node); if (!cnode_emplace_return.second) { MS_LOG(INFO) << "node: " << node->DebugString() << " already in para: " << para->fullname_with_scope() @@ -75,7 +83,7 @@ Status AllreduceGraph::AddEdge(const CNodePtr& from, const CNodePtr& to, double MS_LOG(ERROR) << "from_arnode AddNext failed"; return FAILED; } - if (to_arnode->AddPrev(from_arnode, dist) != SUCCESS) { + if (to_arnode->AddPrev(from_arnode, dist, &max_) != SUCCESS) { MS_LOG(ERROR) << "to_arnode AddPrev failed"; return FAILED; } @@ -110,7 +118,7 @@ std::pair, double> AllreduceGraph::GetParaByParaSize(dou double cur_para_size = 0; double from = to; for (auto& arnode : arnode_vec_) { - if (arnode.depend_feat_size() >= to) { + if (arnode.depend_feat_size() != max_ && arnode.depend_feat_size() >= to) { continue; } if (para_size > 0 && cur_para_size >= para_size && arnode.depend_feat_size() < from) { diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.cc b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.cc index 8bed29f2f2..6be588928a 100644 --- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.cc +++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.cc @@ -15,6 +15,7 @@ */ #include "parallel/allreduce_fusion/allreduce_node.h" +#include #include "parallel/tensor_layout/tensor_layout.h" #include "utils/log_adapter.h" @@ -29,7 +30,7 @@ Status AllreduceNode::AddNext(const AllreduceNodePtr& next_node) { return SUCCESS; } -Status AllreduceNode::AddPrev(const AllreduceNodePtr& prev_node, double dist) { +Status AllreduceNode::AddPrev(const AllreduceNodePtr& prev_node, double dist, double* max) { if (prev_node == nullptr) { MS_LOG(ERROR) << "next_node is nullptr!"; return FAILED; @@ -39,7 +40,26 @@ Status AllreduceNode::AddPrev(const AllreduceNodePtr& prev_node, double dist) { return FAILED; } prev_.emplace_back(prev_node); - depend_feat_size_ += prev_node->depend_feat_size() + dist; + double add_dist = prev_node->depend_feat_size() + dist; + depend_feat_size_ += add_dist; + if (depend_feat_size_ > *max) { + *max = depend_feat_size_; + } + std::queue next_queue; + for (auto& next : next_) { + next_queue.push(next); + } + while (!next_queue.empty()) { + auto ele = next_queue.front(); + ele->AddDependFeatSize(add_dist); + if (ele->depend_feat_size() > *max) { + *max = ele->depend_feat_size(); + } + for (auto& next : ele->next()) { + next_queue.push(next); + } + next_queue.pop(); + } return SUCCESS; } diff --git a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h index dc6fbed4ba..d9ba98c3a2 100644 --- a/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h +++ b/mindspore/ccsrc/parallel/allreduce_fusion/allreduce_node.h @@ -39,9 +39,14 @@ class AllreduceNode { const std::unordered_set& paras() const { return paras_; } double curr_para_size() const { return curr_para_size_; } virtual ~AllreduceNode() = default; - Status AddPrev(const AllreduceNodePtr& prev_node, double dist); + // Add previous node + // prev_node is the previous to be added + // max is the current max depend_feat_size of the AllreduceGraph + Status AddPrev(const AllreduceNodePtr& prev_node, double dist, double* max); Status AddNext(const AllreduceNodePtr& next_node); double depend_feat_size() const { return depend_feat_size_; } + void AddDependFeatSize(double add_dist) { depend_feat_size_ += add_dist; } + const std::vector& next() const { return next_; } void ToString() const; bool operator<(const AllreduceNode& node) const { return depend_feat_size_ < node.depend_feat_size(); } bool operator>(const AllreduceNode& node) const { return depend_feat_size_ > node.depend_feat_size(); } diff --git a/tests/ut/python/parallel/test_allreduce_fusion.py b/tests/ut/python/parallel/test_allreduce_fusion.py index e59ce38051..fcbee10587 100644 --- a/tests/ut/python/parallel/test_allreduce_fusion.py +++ b/tests/ut/python/parallel/test_allreduce_fusion.py @@ -275,7 +275,7 @@ def test_allreduce_fusion5(): expect_dict = {'backbone2.fc8.weight': 3, 'backbone2.fc7.weight': 3, 'backbone2.fc6.weight': 3, - 'backbone2.fc5.weight': 2, + 'backbone2.fc5.weight': 3, 'backbone2.fc4.weight': 2, 'backbone2.fc3.weight': 2, 'backbone2.fc2.weight': 1, @@ -283,7 +283,7 @@ def test_allreduce_fusion5(): 'backbone1.fc8.weight': 3, 'backbone1.fc7.weight': 3, 'backbone1.fc6.weight': 3, - 'backbone1.fc5.weight': 2, + 'backbone1.fc5.weight': 3, 'backbone1.fc4.weight': 2, 'backbone1.fc3.weight': 2, 'backbone1.fc2.weight': 1, diff --git a/tests/ut/python/parallel/test_auto_parallel_resnet.py b/tests/ut/python/parallel/test_auto_parallel_resnet.py index 1e0e3570b9..9b4e1fda23 100644 --- a/tests/ut/python/parallel/test_auto_parallel_resnet.py +++ b/tests/ut/python/parallel/test_auto_parallel_resnet.py @@ -273,13 +273,9 @@ class DatasetLenet(): return 1 -def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576 #131072 #32768 #8192 +def train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): dev_num = 8 context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, device_num=dev_num) - cost_model_context.set_cost_model_context(costmodel_gamma=0.001, costmodel_beta=260.0) - cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_algorithm=1) - cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_times=2) - cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_tail_percent=0.5) set_algo_parameters(elementwise_op_strategy_follow=True) resset_op_id() np.random.seed(6) @@ -303,8 +299,16 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576 assert v == [[dev_num, 1]] allreduce_fusion_dict = _executor._get_allreduce_fusion(model._train_network) - print(allreduce_fusion_dict) + return allreduce_fusion_dict + + +def test_train_32k_8p_fusion1(epoch_size=3, batch_size=32, num_classes=32768): #1048576 #131072 #32768 #8192 + cost_model_context.set_cost_model_context(costmodel_gamma=0.001, costmodel_beta=260.0) + cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_algorithm=1) + cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_times=2) + cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_tail_percent=0.5) + allreduce_fusion_dict = train_32k_8p(epoch_size, batch_size, num_classes) expect_dict = {'end_point.bias': 2, 'end_point.weight': 2, 'layer4.2.bn3.beta': 2, @@ -382,11 +386,11 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576 'layer3.1.bn1.beta': 2, 'layer3.1.bn1.gamma': 2, 'layer3.1.conv1.weight': 2, - 'layer3.0.bn_down_sample.beta': 1, - 'layer3.0.bn_down_sample.gamma': 1, + 'layer3.0.bn_down_sample.beta': 2, + 'layer3.0.bn_down_sample.gamma': 2, 'layer3.0.conv_down_sample.weight': 2, - 'layer3.0.bn3.beta': 1, - 'layer3.0.bn3.gamma': 1, + 'layer3.0.bn3.beta': 2, + 'layer3.0.bn3.gamma': 2, 'layer3.0.conv3.weight': 2, 'layer3.0.bn2.beta': 2, 'layer3.0.bn2.gamma': 2, @@ -412,8 +416,8 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576 'layer2.2.bn1.beta': 2, 'layer2.2.bn1.gamma': 2, 'layer2.2.conv1.weight': 2, - 'layer2.1.bn3.beta': 1, - 'layer2.1.bn3.gamma': 1, + 'layer2.1.bn3.beta': 2, + 'layer2.1.bn3.gamma': 2, 'layer2.1.conv3.weight': 2, 'layer2.1.bn2.beta': 2, 'layer2.1.bn2.gamma': 2, @@ -421,11 +425,11 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576 'layer2.1.bn1.beta': 2, 'layer2.1.bn1.gamma': 2, 'layer2.1.conv1.weight': 2, - 'layer2.0.bn_down_sample.beta': 1, - 'layer2.0.bn_down_sample.gamma': 1, + 'layer2.0.bn_down_sample.beta': 2, + 'layer2.0.bn_down_sample.gamma': 2, 'layer2.0.conv_down_sample.weight': 2, - 'layer2.0.bn3.beta': 1, - 'layer2.0.bn3.gamma': 1, + 'layer2.0.bn3.beta': 2, + 'layer2.0.bn3.gamma': 2, 'layer2.0.conv3.weight': 2, 'layer2.0.bn2.beta': 2, 'layer2.0.bn2.gamma': 2, @@ -442,8 +446,8 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576 'layer1.2.bn1.beta': 2, 'layer1.2.bn1.gamma': 2, 'layer1.2.conv1.weight': 2, - 'layer1.1.bn3.beta': 1, - 'layer1.1.bn3.gamma': 1, + 'layer1.1.bn3.beta': 2, + 'layer1.1.bn3.gamma': 2, 'layer1.1.conv3.weight': 2, 'layer1.1.bn2.beta': 2, 'layer1.1.bn2.gamma': 2, @@ -451,11 +455,11 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576 'layer1.1.bn1.beta': 2, 'layer1.1.bn1.gamma': 2, 'layer1.1.conv1.weight': 2, - 'layer1.0.bn_down_sample.beta': 1, - 'layer1.0.bn_down_sample.gamma': 1, + 'layer1.0.bn_down_sample.beta': 2, + 'layer1.0.bn_down_sample.gamma': 2, 'layer1.0.conv_down_sample.weight': 2, - 'layer1.0.bn3.beta': 1, - 'layer1.0.bn3.gamma': 1, + 'layer1.0.bn3.beta': 2, + 'layer1.0.bn3.gamma': 2, 'layer1.0.conv3.weight': 2, 'layer1.0.bn2.beta': 2, 'layer1.0.bn2.gamma': 2, @@ -465,7 +469,180 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576 'layer1.0.conv1.weight': 2, 'bn1.beta': 1, 'bn1.gamma': 1, - 'conv1.weight': 2} + 'conv1.weight': 1} + + assert (allreduce_fusion_dict == expect_dict) + cost_model_context.reset_cost_model_context() + + +def test_train_32k_8p_fusion2(epoch_size=3, batch_size=32, num_classes=32768): #1048576 #131072 #32768 #8192 + cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_algorithm=2) + cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_tail_time=0.1) + cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_allreduce_inherent_time=0.05) + cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_allreduce_bandwidth=0.000001) + cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_computation_time_parameter=0.0000015) + allreduce_fusion_dict = train_32k_8p(epoch_size, batch_size, num_classes) + expect_dict = {'end_point.bias': 2, + 'end_point.weight': 2, + 'layer4.2.bn3.beta': 2, + 'layer4.2.bn3.gamma': 2, + 'layer4.2.conv3.weight': 2, + 'layer4.2.bn2.beta': 2, + 'layer4.2.bn2.gamma': 2, + 'layer4.2.conv2.weight': 2, + 'layer4.2.bn1.beta': 2, + 'layer4.2.bn1.gamma': 2, + 'layer4.2.conv1.weight': 2, + 'layer4.1.bn3.beta': 2, + 'layer4.1.bn3.gamma': 2, + 'layer4.1.conv3.weight': 2, + 'layer4.1.bn2.beta': 2, + 'layer4.1.bn2.gamma': 2, + 'layer4.1.conv2.weight': 2, + 'layer4.1.bn1.beta': 2, + 'layer4.1.bn1.gamma': 2, + 'layer4.1.conv1.weight': 2, + 'layer4.0.bn_down_sample.beta': 2, + 'layer4.0.bn_down_sample.gamma': 2, + 'layer4.0.conv_down_sample.weight': 2, + 'layer4.0.bn3.beta': 2, + 'layer4.0.bn3.gamma': 2, + 'layer4.0.conv3.weight': 2, + 'layer4.0.bn2.beta': 2, + 'layer4.0.bn2.gamma': 2, + 'layer4.0.conv2.weight': 2, + 'layer4.0.bn1.beta': 2, + 'layer4.0.bn1.gamma': 2, + 'layer4.0.conv1.weight': 2, + 'layer3.5.bn3.beta': 2, + 'layer3.5.bn3.gamma': 2, + 'layer3.5.conv3.weight': 2, + 'layer3.5.bn2.beta': 2, + 'layer3.5.bn2.gamma': 2, + 'layer3.5.conv2.weight': 2, + 'layer3.5.bn1.beta': 2, + 'layer3.5.bn1.gamma': 2, + 'layer3.5.conv1.weight': 2, + 'layer3.4.bn3.beta': 2, + 'layer3.4.bn3.gamma': 2, + 'layer3.4.conv3.weight': 2, + 'layer3.4.bn2.beta': 2, + 'layer3.4.bn2.gamma': 2, + 'layer3.4.conv2.weight': 2, + 'layer3.4.bn1.beta': 2, + 'layer3.4.bn1.gamma': 2, + 'layer3.4.conv1.weight': 2, + 'layer3.3.bn3.beta': 2, + 'layer3.3.bn3.gamma': 2, + 'layer3.3.conv3.weight': 2, + 'layer3.3.bn2.beta': 2, + 'layer3.3.bn2.gamma': 2, + 'layer3.3.conv2.weight': 2, + 'layer3.3.bn1.beta': 2, + 'layer3.3.bn1.gamma': 2, + 'layer3.3.conv1.weight': 2, + 'layer3.2.bn3.beta': 2, + 'layer3.2.bn3.gamma': 2, + 'layer3.2.conv3.weight': 2, + 'layer3.2.bn2.beta': 2, + 'layer3.2.bn2.gamma': 2, + 'layer3.2.conv2.weight': 2, + 'layer3.2.bn1.beta': 2, + 'layer3.2.bn1.gamma': 2, + 'layer3.2.conv1.weight': 2, + 'layer3.1.bn3.beta': 2, + 'layer3.1.bn3.gamma': 2, + 'layer3.1.conv3.weight': 2, + 'layer3.1.bn2.beta': 2, + 'layer3.1.bn2.gamma': 2, + 'layer3.1.conv2.weight': 2, + 'layer3.1.bn1.beta': 2, + 'layer3.1.bn1.gamma': 2, + 'layer3.1.conv1.weight': 2, + 'layer3.0.bn_down_sample.beta': 2, + 'layer3.0.bn_down_sample.gamma': 2, + 'layer3.0.conv_down_sample.weight': 2, + 'layer3.0.bn3.beta': 2, + 'layer3.0.bn3.gamma': 2, + 'layer3.0.conv3.weight': 2, + 'layer3.0.bn2.beta': 2, + 'layer3.0.bn2.gamma': 2, + 'layer3.0.conv2.weight': 2, + 'layer3.0.bn1.beta': 2, + 'layer3.0.bn1.gamma': 2, + 'layer3.0.conv1.weight': 2, + 'layer2.3.bn3.beta': 2, + 'layer2.3.bn3.gamma': 2, + 'layer2.3.conv3.weight': 2, + 'layer2.3.bn2.beta': 2, + 'layer2.3.bn2.gamma': 2, + 'layer2.3.conv2.weight': 2, + 'layer2.3.bn1.beta': 2, + 'layer2.3.bn1.gamma': 2, + 'layer2.3.conv1.weight': 2, + 'layer2.2.bn3.beta': 2, + 'layer2.2.bn3.gamma': 2, + 'layer2.2.conv3.weight': 2, + 'layer2.2.bn2.beta': 2, + 'layer2.2.bn2.gamma': 2, + 'layer2.2.conv2.weight': 2, + 'layer2.2.bn1.beta': 2, + 'layer2.2.bn1.gamma': 2, + 'layer2.2.conv1.weight': 2, + 'layer2.1.bn3.beta': 2, + 'layer2.1.bn3.gamma': 2, + 'layer2.1.conv3.weight': 2, + 'layer2.1.bn2.beta': 2, + 'layer2.1.bn2.gamma': 2, + 'layer2.1.conv2.weight': 2, + 'layer2.1.bn1.beta': 2, + 'layer2.1.bn1.gamma': 2, + 'layer2.1.conv1.weight': 2, + 'layer2.0.bn_down_sample.beta': 2, + 'layer2.0.bn_down_sample.gamma': 2, + 'layer2.0.conv_down_sample.weight': 2, + 'layer2.0.bn3.beta': 2, + 'layer2.0.bn3.gamma': 2, + 'layer2.0.conv3.weight': 2, + 'layer2.0.bn2.beta': 2, + 'layer2.0.bn2.gamma': 2, + 'layer2.0.conv2.weight': 2, + 'layer2.0.bn1.beta': 2, + 'layer2.0.bn1.gamma': 2, + 'layer2.0.conv1.weight': 2, + 'layer1.2.bn3.beta': 2, + 'layer1.2.bn3.gamma': 2, + 'layer1.2.conv3.weight': 2, + 'layer1.2.bn2.beta': 2, + 'layer1.2.bn2.gamma': 2, + 'layer1.2.conv2.weight': 2, + 'layer1.2.bn1.beta': 2, + 'layer1.2.bn1.gamma': 2, + 'layer1.2.conv1.weight': 2, + 'layer1.1.bn3.beta': 2, + 'layer1.1.bn3.gamma': 2, + 'layer1.1.conv3.weight': 2, + 'layer1.1.bn2.beta': 2, + 'layer1.1.bn2.gamma': 2, + 'layer1.1.conv2.weight': 2, + 'layer1.1.bn1.beta': 2, + 'layer1.1.bn1.gamma': 2, + 'layer1.1.conv1.weight': 2, + 'layer1.0.bn_down_sample.beta': 2, + 'layer1.0.bn_down_sample.gamma': 2, + 'layer1.0.conv_down_sample.weight': 2, + 'layer1.0.bn3.beta': 2, + 'layer1.0.bn3.gamma': 2, + 'layer1.0.conv3.weight': 2, + 'layer1.0.bn2.beta': 2, + 'layer1.0.bn2.gamma': 2, + 'layer1.0.conv2.weight': 1, + 'layer1.0.bn1.beta': 1, + 'layer1.0.bn1.gamma': 1, + 'layer1.0.conv1.weight': 1, + 'bn1.beta': 1, + 'bn1.gamma': 1, + 'conv1.weight': 1} assert (allreduce_fusion_dict == expect_dict) cost_model_context.reset_cost_model_context() From f2263572fb63dc4d5314f0543c5700afd689a4be Mon Sep 17 00:00:00 2001 From: ms_yan <6576637+ms_yan@user.noreply.gitee.com> Date: Thu, 2 Apr 2020 16:07:17 +0800 Subject: [PATCH 073/227] remove (feed mode) in comment --- mindspore/dataset/engine/datasets.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py index ad3e7d8255..db2b5169d2 100644 --- a/mindspore/dataset/engine/datasets.py +++ b/mindspore/dataset/engine/datasets.py @@ -395,7 +395,7 @@ class Dataset: Note: The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. - If dataset_sink_mode is False (feed mode), here repeat operation is invalid. + If dataset_sink_mode is False, here repeat operation is invalid. Args: count (int): Number of times the dataset should be repeated (default=None). From d75745bce073253ea338c22b3825f2e991255d4f Mon Sep 17 00:00:00 2001 From: Yanjun Peng Date: Thu, 2 Apr 2020 16:37:39 +0800 Subject: [PATCH 074/227] fix random crop parameter check --- mindspore/dataset/transforms/vision/validators.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/dataset/transforms/vision/validators.py b/mindspore/dataset/transforms/vision/validators.py index 443232d780..caab120af4 100644 --- a/mindspore/dataset/transforms/vision/validators.py +++ b/mindspore/dataset/transforms/vision/validators.py @@ -325,7 +325,7 @@ def check_random_crop(method): @wraps(method) def new_method(self, *args, **kwargs): - args = (list(args) + 4 * [None])[:5] + args = (list(args) + 5 * [None])[:5] size, padding, pad_if_needed, fill_value, padding_mode = args if "size" in kwargs: From 948ff63adda422f2ff68be2d32074819493b0fa3 Mon Sep 17 00:00:00 2001 From: yanzhenxiang2020 Date: Wed, 1 Apr 2020 17:48:30 +0800 Subject: [PATCH 075/227] fix mindrecord ut long time --- mindspore/mindrecord/tools/mnist_to_mr.py | 18 +++++++++--------- .../testMnistData/t10k-images-idx3-ubyte.gz | Bin 61261 -> 407 bytes .../testMnistData/t10k-labels-idx1-ubyte.gz | Bin 92 -> 74 bytes .../testMnistData/train-images-idx3-ubyte.gz | Bin 340460 -> 474 bytes .../testMnistData/train-labels-idx1-ubyte.gz | Bin 140 -> 82 bytes .../python/mindrecord/test_mindrecord_base.py | 10 +++++----- .../ut/python/mindrecord/test_mnist_to_mr.py | 12 ++++++------ 7 files changed, 20 insertions(+), 20 deletions(-) diff --git a/mindspore/mindrecord/tools/mnist_to_mr.py b/mindspore/mindrecord/tools/mnist_to_mr.py index 462ab7fb53..046788535d 100644 --- a/mindspore/mindrecord/tools/mnist_to_mr.py +++ b/mindspore/mindrecord/tools/mnist_to_mr.py @@ -77,20 +77,20 @@ class MnistToMR: self.mnist_schema_json = {"label": {"type": "int64"}, "data": {"type": "bytes"}} - def _extract_images(self, filename, num_images): + def _extract_images(self, filename): """Extract the images into a 4D tensor [image index, y, x, channels].""" with gzip.open(filename) as bytestream: bytestream.read(16) - buf = bytestream.read(self.image_size * self.image_size * num_images * self.num_channels) + buf = bytestream.read() data = np.frombuffer(buf, dtype=np.uint8) - data = data.reshape(num_images, self.image_size, self.image_size, self.num_channels) + data = data.reshape(-1, self.image_size, self.image_size, self.num_channels) return data - def _extract_labels(self, filename, num_images): + def _extract_labels(self, filename): """Extract the labels into a vector of int64 label IDs.""" with gzip.open(filename) as bytestream: bytestream.read(8) - buf = bytestream.read(1 * num_images) + buf = bytestream.read() labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64) return labels @@ -101,8 +101,8 @@ class MnistToMR: Yields: data (dict of list): mnist data list which contains dict. """ - train_data = self._extract_images(self.train_data_filename_, 60000) - train_labels = self._extract_labels(self.train_labels_filename_, 60000) + train_data = self._extract_images(self.train_data_filename_) + train_labels = self._extract_labels(self.train_labels_filename_) for data, label in zip(train_data, train_labels): _, img = cv2.imencode(".jpeg", data) yield {"label": int(label), "data": img.tobytes()} @@ -114,8 +114,8 @@ class MnistToMR: Yields: data (dict of list): mnist data list which contains dict. 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zN=8aXN=8aXN=8aXN=8aXO7@?mWbTgwYQODIfkFL+mb?1?_Mr~jh}45lw(LLKvcoQN^fYR57X;Rg5Y|6{Ct##i(LbF{&6gDWNVnX>KiSVPa)$b1i9Pcrh(?VtI6B09}S#4geqw!iulW ofA4=*jWLTFpaopOkVq@!UCJ3XK)Z-N!avjFOw2g?lwtq?0KJ$Wy8r+H literal 140 zcmb2|=HO7xFOFkiE-6aP%+t+DOiIlu*3C?*Fw`wgsw_!mcze*1u|a`>WrK6bhNVA! z6FYwAd4KcRXU+fu4!K~8VJkD3Uf~O-7L+l8>8;!l(Ys*EA(s&%$_u8h$bqQ^-vto= diff --git a/tests/ut/python/mindrecord/test_mindrecord_base.py b/tests/ut/python/mindrecord/test_mindrecord_base.py index 576063295a..7fdf1f0f94 100644 --- a/tests/ut/python/mindrecord/test_mindrecord_base.py +++ b/tests/ut/python/mindrecord/test_mindrecord_base.py @@ -203,9 +203,9 @@ def test_nlp_page_reader_tutorial(): os.remove("{}".format(x)) os.remove("{}.db".format(x)) -def test_cv_file_writer_shard_num_1000(): - """test file writer when shard num equals 1000.""" - writer = FileWriter(CV_FILE_NAME, 1000) +def test_cv_file_writer_shard_num_10(): + """test file writer when shard num equals 10.""" + writer = FileWriter(CV_FILE_NAME, 10) data = get_data("../data/mindrecord/testImageNetData/") cv_schema_json = {"file_name": {"type": "string"}, "label": {"type": "int64"}, "data": {"type": "bytes"}} @@ -214,8 +214,8 @@ def test_cv_file_writer_shard_num_1000(): writer.write_raw_data(data) writer.commit() - paths = ["{}{}".format(CV_FILE_NAME, str(x).rjust(3, '0')) - for x in range(1000)] + paths = ["{}{}".format(CV_FILE_NAME, str(x).rjust(1, '0')) + for x in range(10)] for x in paths: os.remove("{}".format(x)) os.remove("{}.db".format(x)) diff --git a/tests/ut/python/mindrecord/test_mnist_to_mr.py b/tests/ut/python/mindrecord/test_mnist_to_mr.py index c299a1f719..505b0d6b43 100644 --- a/tests/ut/python/mindrecord/test_mnist_to_mr.py +++ b/tests/ut/python/mindrecord/test_mnist_to_mr.py @@ -37,7 +37,7 @@ def read(train_name, test_name): count = count + 1 if count == 1: logger.info("data: {}".format(x)) - assert count == 60000 + assert count == 20 reader.close() count = 0 @@ -47,7 +47,7 @@ def read(train_name, test_name): count = count + 1 if count == 1: logger.info("data: {}".format(x)) - assert count == 10000 + assert count == 10 reader.close() @@ -102,10 +102,10 @@ def test_mnist_to_mindrecord_compare_data(): 't10k-images-idx3-ubyte.gz') test_labels_filename_ = os.path.join(MNIST_DIR, 't10k-labels-idx1-ubyte.gz') - train_data = _extract_images(train_data_filename_, 60000) - train_labels = _extract_labels(train_labels_filename_, 60000) - test_data = _extract_images(test_data_filename_, 10000) - test_labels = _extract_labels(test_labels_filename_, 10000) + train_data = _extract_images(train_data_filename_, 20) + train_labels = _extract_labels(train_labels_filename_, 20) + test_data = _extract_images(test_data_filename_, 10) + test_labels = _extract_labels(test_labels_filename_, 10) reader = FileReader(train_name) for x, data, label in zip(reader.get_next(), train_data, train_labels): From 024706f951555665da1d7489fdef27f7b0ed91d9 Mon Sep 17 00:00:00 2001 From: YuJianfeng Date: Wed, 1 Apr 2020 19:42:13 +0800 Subject: [PATCH 076/227] Optimize depend edge with make tuple input --- .../pre_activate/pass/optimize_dependence.cc | 93 +++++++++++++++---- .../pass/optimize_dependence_test.cc | 20 ++++ .../pre_activate/optimize_dependence_test.py | 22 +++++ 3 files changed, 116 insertions(+), 19 deletions(-) diff --git a/mindspore/ccsrc/pre_activate/pass/optimize_dependence.cc b/mindspore/ccsrc/pre_activate/pass/optimize_dependence.cc index 6ae3f3be36..db32354abf 100644 --- a/mindspore/ccsrc/pre_activate/pass/optimize_dependence.cc +++ b/mindspore/ccsrc/pre_activate/pass/optimize_dependence.cc @@ -27,6 +27,69 @@ namespace mindspore { namespace opt { constexpr auto kSingleInputIndex = 1; +namespace { +AnfNodePtr GetReplaceNode(const FuncGraphPtr &func_graph, const AnfNodePtr &node) { + MS_EXCEPTION_IF_NULL(func_graph); + MS_EXCEPTION_IF_NULL(node); + if (!node->isa()) { + return nullptr; + } + auto cnode = node->cast(); + MS_EXCEPTION_IF_NULL(cnode); + string op_name = AnfAlgo::GetCNodeName(cnode); + // Currently we only eliminate transdata or cast nodes. + if (op_name != kTransDataOpName && op_name != prim::kPrimCast->name()) { + return nullptr; + } + auto manager = func_graph->manager(); + MS_EXCEPTION_IF_NULL(manager); + // Check whether the node has only one output node. + if (manager->node_users().find(cnode) == manager->node_users().end()) { + MS_LOG(EXCEPTION) << "The node should be used by at least another node's input"; + } + if (manager->node_users()[cnode].size() > 1) { + return nullptr; + } + CheckCNodeInputSize(cnode, kSingleInputIndex + 1); + return cnode->input(kSingleInputIndex); +} + +bool ReplaceMakeTuple(const FuncGraphPtr &func_graph, const CNodePtr &cnode) { + MS_EXCEPTION_IF_NULL(func_graph); + MS_EXCEPTION_IF_NULL(cnode); + if (AnfAlgo::GetCNodeName(cnode) != prim::kPrimMakeTuple->name()) { + return false; + } + std::vector new_make_tuple_inputs; + bool need_update = false; + for (const auto &input : cnode->inputs()) { + AnfNodePtr replace_input = GetReplaceNode(func_graph, input); + // If replace input is not null, it will be the input of the TransData or Cast. + if (replace_input == nullptr) { + new_make_tuple_inputs.push_back(input); + continue; + } + new_make_tuple_inputs.push_back(replace_input); + need_update = true; + } + if (need_update) { + auto kernel_graph = func_graph->cast>(); + CNodePtr new_make_tuple = nullptr; + if (kernel_graph == nullptr) { + new_make_tuple = func_graph->NewCNode(new_make_tuple_inputs); + } else { + new_make_tuple = kernel_graph->NewCNode(cnode); + } + MS_EXCEPTION_IF_NULL(new_make_tuple); + new_make_tuple->set_inputs(new_make_tuple_inputs); + auto manager = func_graph->manager(); + MS_EXCEPTION_IF_NULL(manager); + manager->Replace(cnode, new_make_tuple); + } + return true; +} +} // namespace + const BaseRef OptimizeDependence::DefinePattern() const { VarPtr X = std::make_shared("X"); MS_EXCEPTION_IF_NULL(X); @@ -43,9 +106,8 @@ const AnfNodePtr OptimizeDependence::Process(const FuncGraphPtr &func_graph, con return nullptr; } auto depend_cnode = node->cast(); - if (depend_cnode->inputs().size() < kDependInputNum) { - return nullptr; - } + MS_EXCEPTION_IF_NULL(depend_cnode); + CheckCNodeInputSize(depend_cnode, kDependInputNum); auto replacing_node = depend_cnode->input(kDependInputNum - 1); MS_EXCEPTION_IF_NULL(replacing_node); if (!replacing_node->isa()) { @@ -53,36 +115,29 @@ const AnfNodePtr OptimizeDependence::Process(const FuncGraphPtr &func_graph, con } auto replacing_cnode = replacing_node->cast(); MS_EXCEPTION_IF_NULL(replacing_cnode); - // Currently we only optimize transdata or cast nodes. - string replacing_cnode_op_name = AnfAlgo::GetCNodeName(replacing_cnode); - if (replacing_cnode_op_name != kTransDataOpName && replacing_cnode_op_name != prim::kPrimCast->name()) { + // Deal with the make_tuple with TransData or Cast inputs. + if (ReplaceMakeTuple(func_graph, replacing_cnode)) { return nullptr; } - auto manager = func_graph->manager(); - MS_EXCEPTION_IF_NULL(manager); - // Check whether the replacing node has only one input and one output. - if (replacing_cnode->inputs().size() != kSingleInputIndex + 1) { - return nullptr; - } - if (manager->node_users().find(replacing_node) == manager->node_users().end()) { - MS_LOG(EXCEPTION) << "The node should be used by at least another node input"; - } - if (manager->node_users()[replacing_node].size() > 1) { + AnfNodePtr replace_node = GetReplaceNode(func_graph, replacing_cnode); + if (replace_node == nullptr) { + MS_LOG(DEBUG) << "Can not find the TransData or Cast with single output node. Depend node: " << node->DebugString(); return nullptr; } std::vector new_depend_inputs = {depend_cnode->input(kAnfPrimitiveIndex), - depend_cnode->input(kRealInputIndexInDepend), - replacing_cnode->input(kSingleInputIndex)}; + depend_cnode->input(kRealInputIndexInDepend), replace_node}; auto kernel_graph = func_graph->cast>(); CNodePtr new_depend; if (kernel_graph == nullptr) { new_depend = func_graph->NewCNode(new_depend_inputs); + MS_EXCEPTION_IF_NULL(new_depend); + new_depend->set_abstract(node->abstract()); + new_depend->set_scope(node->scope()); } else { new_depend = kernel_graph->NewCNode(depend_cnode); MS_EXCEPTION_IF_NULL(new_depend); new_depend->set_inputs(new_depend_inputs); } - new_depend->set_abstract(node->abstract()); return new_depend; } } // namespace opt diff --git a/tests/ut/cpp/pre_activate/pass/optimize_dependence_test.cc b/tests/ut/cpp/pre_activate/pass/optimize_dependence_test.cc index 3f59b6159a..e95d63e93e 100644 --- a/tests/ut/cpp/pre_activate/pass/optimize_dependence_test.cc +++ b/tests/ut/cpp/pre_activate/pass/optimize_dependence_test.cc @@ -48,5 +48,25 @@ TEST_F(TestHWOptimizeDependence, test_optimize_dependence) { FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_optimize_dependence", "after"); EXPECT_TRUE(CheckEqualGraph(g_after, new_graph)); } + +TEST_F(TestHWOptimizeDependence, test_optimize_dependence_with_make_tuple) { + /* + * def before(x, y, a, b): + * z = make_tuple(TransData(a), TransData(b)) + * depend_intput = depend(y, z) + * sum = add(x, depend_intput) + * return sum + */ + FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_optimize_dependence_with_make_tuple", "before"); + + auto optimizer = std::make_shared(); + auto pm = std::make_shared(); + pm->AddPass(std::make_shared()); + optimizer->AddPassManager(pm); + FuncGraphPtr new_graph = optimizer->Optimize(g); + + FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_optimize_dependence_with_make_tuple", "after"); + EXPECT_TRUE(CheckEqualGraph(g_after, new_graph)); +} } // namespace opt } // namespace mindspore diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/optimize_dependence_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/optimize_dependence_test.py index 45c419d25d..05eb057327 100644 --- a/tests/ut/cpp/python_input/gtest_input/pre_activate/optimize_dependence_test.py +++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/optimize_dependence_test.py @@ -18,6 +18,8 @@ from mindspore.ops import Primitive depend = Primitive('depend') TransData = Primitive('TransData') add = P.TensorAdd() +make_tuple = Primitive('make_tuple') + class FnDict: def __init__(self): @@ -47,3 +49,23 @@ def test_optimize_dependence(tag): return sum return fns[tag] + + +def test_optimize_dependence_with_make_tuple(tag): + fns = FnDict() + + @fns + def before(x, y, a, b): + z = make_tuple(TransData(a), TransData(b)) + depend_intput = depend(y, z) + sum = add(x, depend_intput) + return sum + + @fns + def after(x, y, a, b): + z = make_tuple(a, b) + depend_intput = depend(y, z) + sum = add(x, depend_intput) + return sum + + return fns[tag] From e4041879708d902a9982997f985b7742791e8b63 Mon Sep 17 00:00:00 2001 From: leonwanghui Date: Thu, 2 Apr 2020 16:29:14 +0800 Subject: [PATCH 077/227] Fix some typo errors in session and device module Signed-off-by: leonwanghui --- .../ccsrc/device/cpu/cpu_kernel_runtime.cc | 10 +- .../ccsrc/device/cpu/cpu_resource_manager.cc | 4 +- mindspore/ccsrc/device/cpu/cpu_session.cc | 14 +- mindspore/ccsrc/device/kernel_adjust.cc | 12 +- mindspore/ccsrc/device/kernel_runtime.cc | 12 +- .../ccsrc/device/kernel_runtime_manager.cc | 2 +- .../ccsrc/session/anf_runtime_algorithm.cc | 36 ++-- .../ccsrc/session/anf_runtime_algorithm.h | 4 +- mindspore/ccsrc/session/ascend_session.cc | 177 +++++++++--------- mindspore/ccsrc/session/ascend_session.h | 13 +- mindspore/ccsrc/session/gpu_session.cc | 4 +- mindspore/ccsrc/session/kernel_graph.cc | 42 ++--- mindspore/ccsrc/session/kernel_graph.h | 4 +- mindspore/ccsrc/session/session_basic.cc | 26 +-- mindspore/ccsrc/session/session_basic.h | 4 +- mindspore/ccsrc/utils/utils.h | 2 +- 16 files changed, 184 insertions(+), 182 deletions(-) diff --git a/mindspore/ccsrc/device/cpu/cpu_kernel_runtime.cc b/mindspore/ccsrc/device/cpu/cpu_kernel_runtime.cc index 5bf9689f0b..1dcddfa994 100644 --- a/mindspore/ccsrc/device/cpu/cpu_kernel_runtime.cc +++ b/mindspore/ccsrc/device/cpu/cpu_kernel_runtime.cc @@ -66,7 +66,7 @@ void CPUKernelRuntime::AssignValueNodeAddress(session::KernelGraph *kernel_graph address->ptr_ = resource_manager_.MemMalloc(tensor_size); if (!address->SyncHostToDevice(data_shape, LongToSize(tensor->data().nbytes()), tensor->data_type(), tensor->data_c(false))) { - MS_LOG(EXCEPTION) << "value node sync host to device failed!"; + MS_LOG(EXCEPTION) << "Value node sync host to device failed!"; } } address->ref_count_ = INIT_NODE_REF; @@ -141,7 +141,7 @@ BaseRef CPUKernelRuntime::CreatTensorForOutput(const AnfNodePtr &input_node, siz MS_EXCEPTION_IF_NULL(node); size_t output_size = AnfAlgo::GetOutputTensorNum(node); if (index >= output_size) { - MS_LOG(EXCEPTION) << "invalid input index " << index; + MS_LOG(EXCEPTION) << "Invalid input index " << index; } auto address = AnfAlgo::GetMutableOutputAddr(node, index); MS_EXCEPTION_IF_NULL(address); @@ -157,7 +157,7 @@ BaseRef CPUKernelRuntime::CreatTensorForOutput(const AnfNodePtr &input_node, siz type_id = kNumberTypeFloat32; } if (type_id != kNumberTypeInt32 && type_id != kNumberTypeFloat32) { - MS_LOG(EXCEPTION) << "check output type failed."; + MS_LOG(EXCEPTION) << "Check output type failed."; } tensor::TensorPtr tensor = std::make_shared(type_id, temp_shape); MS_EXCEPTION_IF_NULL(tensor); @@ -181,7 +181,7 @@ void CPUKernelRuntime::BindInputOutput(const session::KernelGraph *kernel_graph, // bind input ptr auto &input_nodes = kernel_graph->inputs(); if (input_nodes.size() != inputs.size()) { - MS_LOG(EXCEPTION) << "input size not equal to input node size!"; + MS_LOG(EXCEPTION) << "Input size not equal to input node size!"; } std::unordered_map input_map; @@ -203,7 +203,7 @@ void CPUKernelRuntime::BindInputOutput(const session::KernelGraph *kernel_graph, address->ptr_ = resource_manager_.MemMalloc(tensor_size); if (!address->SyncHostToDevice(data_shape, LongToSize(tensor->data().nbytes()), tensor->data_type(), tensor->data_c(false))) { - MS_LOG(EXCEPTION) << "parameter node sync host to device failed!"; + MS_LOG(EXCEPTION) << "Parameter node sync host to device failed!"; } tensor->set_dirty(true); } diff --git a/mindspore/ccsrc/device/cpu/cpu_resource_manager.cc b/mindspore/ccsrc/device/cpu/cpu_resource_manager.cc index 5b2b7a0750..97df7d4487 100644 --- a/mindspore/ccsrc/device/cpu/cpu_resource_manager.cc +++ b/mindspore/ccsrc/device/cpu/cpu_resource_manager.cc @@ -44,7 +44,7 @@ void CPUResourceManager::MemPlan(const session::KernelGraph *graph) { mem_size_ = graph_mem_size; dynamic_malloc_ = false; } else { - MS_LOG(INFO) << "switch to dynamic malloc"; + MS_LOG(INFO) << "Switch to dynamic malloc"; dynamic_malloc_ = true; } } @@ -63,7 +63,7 @@ void *CPUResourceManager::MemMalloc(size_t mem_size) { dynamic_mem_[ptr] = mem_size; return ptr; } else { - MS_LOG(EXCEPTION) << "malloc memory failed: size " << mem_size; + MS_LOG(EXCEPTION) << "Malloc memory failed: size " << mem_size; } } diff --git a/mindspore/ccsrc/device/cpu/cpu_session.cc b/mindspore/ccsrc/device/cpu/cpu_session.cc index 59d0318874..1613f9f91e 100644 --- a/mindspore/ccsrc/device/cpu/cpu_session.cc +++ b/mindspore/ccsrc/device/cpu/cpu_session.cc @@ -31,12 +31,12 @@ GraphId CPUSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList auto graph_id = graph_sum_; auto graph = ConstructKernelGraph(lst, outputs); MS_EXCEPTION_IF_NULL(graph); - MS_LOG(INFO) << "set kernel info"; + MS_LOG(INFO) << "Set kernel info"; SetKernelInfo(graph.get()); predictmodel::StepConvertGraph(graph); - MS_LOG(INFO) << "build kernel"; + MS_LOG(INFO) << "Build kernel"; BuildKernel(graph.get()); - MS_LOG(INFO) << "assign kernel address"; + MS_LOG(INFO) << "Assign kernel address"; runtime_.AssignKernelAddress(graph.get()); return graph_id; } @@ -44,18 +44,18 @@ GraphId CPUSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList void CPUSession::RunGraph(const GraphId &graph_id, const std::vector &inputs, VectorRef *outputs) { auto &kernel_graph = graphs_[graph_id]; MS_EXCEPTION_IF_NULL(kernel_graph); - MS_LOG(INFO) << "bind input output address"; + MS_LOG(INFO) << "Bind input output address"; runtime_.BindInputOutput(kernel_graph.get(), inputs, outputs); - MS_LOG(INFO) << "run graph start"; + MS_LOG(INFO) << "Run graph start"; predictmodel::StepConvertWeight(inputs); auto execution_order = kernel_graph->execution_order(); Reorder(&execution_order); kernel_graph->set_execution_order(execution_order); bool ret = runtime_.Run(kernel_graph.get()); if (!ret) { - MS_LOG(EXCEPTION) << "run graph failed"; + MS_LOG(EXCEPTION) << "Run graph failed"; } - MS_LOG(INFO) << "run graph end"; + MS_LOG(INFO) << "Run graph end"; } void CPUSession::SetKernelInfo(const KernelGraph *kernel_graph) { diff --git a/mindspore/ccsrc/device/kernel_adjust.cc b/mindspore/ccsrc/device/kernel_adjust.cc index 4d3ebf9490..c422d50b51 100644 --- a/mindspore/ccsrc/device/kernel_adjust.cc +++ b/mindspore/ccsrc/device/kernel_adjust.cc @@ -50,7 +50,7 @@ void KernelAdjust::Reorder(const std::shared_ptr &kernel_g std::vector momentum_list; std::vector other_list; for (const auto &cnode : origin_cnode_list) { - if (kOptOpeatorSet.find(AnfAlgo::GetCNodeName(cnode)) != kOptOpeatorSet.end()) { + if (kOptOperatorSet.find(AnfAlgo::GetCNodeName(cnode)) != kOptOperatorSet.end()) { momentum_list.emplace_back(cnode); } else { other_list.emplace_back(cnode); @@ -119,7 +119,7 @@ void KernelAdjust::CreateSwitchOpParameters(const std::shared_ptrToAbstract(); if (paremeter_abstract_ptr == nullptr) { - MS_LOG(EXCEPTION) << "create abstract brfore insert switch op failed!"; + MS_LOG(EXCEPTION) << "create abstract before insert switch op failed!"; } ParameterPtr loop_count = std::make_shared(kernel_graph_ptr); @@ -372,7 +372,7 @@ bool KernelAdjust::StepLoadCtrlInputs(const std::shared_ptr &c auto tensor = inputs[i]; size_t deal_index = input_nodes.size() - input_ctrl_size + i; if (deal_index >= input_nodes.size()) { - MS_LOG(EXCEPTION) << "deak_index[" << deal_index << "] outof range"; + MS_LOG(EXCEPTION) << "deal_index[" << deal_index << "] out of range"; } auto input_node = input_nodes[deal_index]; bool need_sync = false; @@ -440,7 +440,7 @@ void KernelAdjust::LoadSwitchInputs(std::vector *inputs) { void KernelAdjust::Profiling(const std::shared_ptr &kernel_graph_ptr) { if (!ascend::ProfilingManager::GetInstance().IsProfiling()) { - MS_LOG(INFO) << "no need to profiling"; + MS_LOG(INFO) << "No need to profiling"; return; } ProfilingTraceInfo profiling_trace_info; @@ -453,10 +453,10 @@ void KernelAdjust::Profiling(const std::shared_ptr &kernel void KernelAdjust::InsertProfilingKernel(const std::shared_ptr &kernel_graph_ptr, const ProfilingTraceInfo &profiling_trace_info) { - MS_LOG(INFO) << "[profiling] insert profiling kernel start"; + MS_LOG(INFO) << "[profiling] Insert profiling kernel start"; MS_EXCEPTION_IF_NULL(kernel_graph_ptr); if (!profiling_trace_info.IsValid()) { - MS_LOG(WARNING) << "profiling trace point not found"; + MS_LOG(WARNING) << "Profiling trace point not found"; return; } std::vector new_cnode_list; diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc index 878fe4a7f8..0a9be35fb5 100644 --- a/mindspore/ccsrc/device/kernel_runtime.cc +++ b/mindspore/ccsrc/device/kernel_runtime.cc @@ -241,7 +241,7 @@ void KernelRuntime::AssignStaticMemoryInput(const session::KernelGraph *graph) { auto output_size = AnfAlgo::GetOutputTensorNum(item); for (size_t index = 0; index < output_size; index++) { TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(item, index); - // if graph output is a weight and doesn't link to any cnode,it's data type will be unkonwn + // if graph output is a weight and doesn't link to any cnode, it's data type will be unknown if (output_type_id == kTypeUnknown) { MS_LOG(WARNING) << "It is not suggested to use a lonely weight parameter as the output of graph"; output_type_id = AnfAlgo::GetOutputInferDataType(item, index); @@ -372,7 +372,7 @@ void KernelRuntime::AssignNodeOutputMem(int flag, const AnfNodePtr &node, int in continue; } if (AnfAlgo::OutputAddrExist(node, i)) { - MS_LOG(INFO) << "already malloc index:" << i; + MS_LOG(INFO) << "Already malloc index:" << i; continue; } auto ptr = CalDeviceMem(node, output_sizes[i], flag, i); @@ -392,7 +392,7 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const MS_EXCEPTION_IF_NULL(node_value); auto tensor = node_value->cast(); if (tensor == nullptr) { - MS_LOG(WARNING) << "tensor is null"; + MS_LOG(WARNING) << "Tensor is null"; return; } size_t tensor_size = tensor->data().nbytes(); @@ -595,7 +595,7 @@ void KernelRuntime::GenLaunchArgs(const mindspore::kernel::KernelMod &kernel_mod void KernelRuntime::GenAddrCleanLaunchArgs(const CNodePtr &cnode, AddressPtrList *kernel_inputs) { if (cnode->inputs().size() != 2) { - MS_LOG(EXCEPTION) << "atomic Addr clean Node Input nodes not equal 2."; + MS_LOG(EXCEPTION) << "Atomic Addr clean Node Input nodes not equal 2."; } auto pre_node = cnode->inputs()[1]; // set clean output address @@ -721,11 +721,11 @@ uint8_t *KernelRuntime::MallocDynamicMem(size_t size, bool communication_mem) { bool KernelRuntime::LaunchKernel(const session::KernelGraph *graph) { MS_EXCEPTION_IF_NULL(graph); if (!LaunchKernelMod(*graph)) { - MS_LOG(ERROR) << "LaunchKernelMod failed."; + MS_LOG(ERROR) << "LaunchKernelMod failed!"; return false; } if (!SyncStream()) { - MS_LOG(ERROR) << "SyncStream failed."; + MS_LOG(ERROR) << "SyncStream failed!"; return false; } return true; diff --git a/mindspore/ccsrc/device/kernel_runtime_manager.cc b/mindspore/ccsrc/device/kernel_runtime_manager.cc index 6303bc88a8..5a70d75cf9 100644 --- a/mindspore/ccsrc/device/kernel_runtime_manager.cc +++ b/mindspore/ccsrc/device/kernel_runtime_manager.cc @@ -67,7 +67,7 @@ KernelRuntime *KernelRuntimeManager::GetKernelRuntime(const std::string &device_ MS_EXCEPTION_IF_NULL(kernel_runtime); runtime_map_[runtime_key] = kernel_runtime; } else { - MS_LOG(EXCEPTION) << "no kernel runtime creator for " << device_name << " with device id " << device_id; + MS_LOG(EXCEPTION) << "No kernel runtime creator for " << device_name << " with device id " << device_id; } return kernel_runtime.get(); diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.cc b/mindspore/ccsrc/session/anf_runtime_algorithm.cc index c0dca4522a..cc23dbbdd2 100644 --- a/mindspore/ccsrc/session/anf_runtime_algorithm.cc +++ b/mindspore/ccsrc/session/anf_runtime_algorithm.cc @@ -65,7 +65,7 @@ KernelWithIndex AnfRuntimeAlgorithm::VisitKernel(const AnfNodePtr &anf_node, siz return VisitKernel(node, 0); } else if (IsPrimitive(input0, prim::kPrimTupleGetItem)) { if (cnode->inputs().size() != kTupleGetItemInputSize) { - MS_LOG(EXCEPTION) << "the node tuple_get_item must have 2 inputs!"; + MS_LOG(EXCEPTION) << "The node tuple_get_item must have 2 inputs!"; } auto input2 = cnode->input(kInputNodeOutputIndexInTupleGetItem); MS_EXCEPTION_IF_NULL(input2); @@ -102,7 +102,7 @@ KernelWithIndex AnfRuntimeAlgorithm::VisitKernelWithReturnType(const AnfNodePtr MS_EXCEPTION_IF_NULL(input0); if (IsPrimitive(input0, prim::kPrimTupleGetItem)) { if (cnode->inputs().size() != kTupleGetItemInputSize) { - MS_LOG(EXCEPTION) << "the node tuple_get_item must have 2 inputs!"; + MS_LOG(EXCEPTION) << "The node tuple_get_item must have 2 inputs!"; } auto input2 = cnode->input(kInputNodeOutputIndexInTupleGetItem); MS_EXCEPTION_IF_NULL(input2); @@ -188,7 +188,7 @@ std::string AnfRuntimeAlgorithm::GetNodeDebugString(const AnfNodePtr &node) { void AnfRuntimeAlgorithm::SetNodeAttr(const std::string &key, const ValuePtr &value, const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); if (!node->isa()) { - MS_LOG(EXCEPTION) << "only cnode has attr,but this anf is " << node->DebugString(); + MS_LOG(EXCEPTION) << "Only cnode has attr, but this anf is " << node->DebugString(); } auto primitive = AnfAlgo::GetCNodePrimitive(node); MS_EXCEPTION_IF_NULL(primitive); @@ -204,7 +204,7 @@ void AnfRuntimeAlgorithm::CopyNodeAttr(const std::string &old_key, const std::st MS_EXCEPTION_IF_NULL(from); MS_EXCEPTION_IF_NULL(to); if (!from->isa() || !to->isa()) { - MS_LOG(EXCEPTION) << "only cnode has attr,but this from_anf is " << from->DebugString() << " ,to_node is " + MS_LOG(EXCEPTION) << "Only cnode has attr, but this from_anf is " << from->DebugString() << " ,to_node is " << to->DebugString(); } auto from_primitive = AnfAlgo::GetCNodePrimitive(from); @@ -218,7 +218,7 @@ void AnfRuntimeAlgorithm::CopyNodeAttrs(const AnfNodePtr &from, const AnfNodePtr MS_EXCEPTION_IF_NULL(from); MS_EXCEPTION_IF_NULL(to); if (!from->isa() || !to->isa()) { - MS_LOG(EXCEPTION) << "only cnode has attr,but this from_anf is " << from->DebugString() << ",to_node is " + MS_LOG(EXCEPTION) << "Only cnode has attr, but this from_anf is " << from->DebugString() << ",to_node is " << from->DebugString(); } auto from_primitive = AnfAlgo::GetCNodePrimitive(from); @@ -231,7 +231,7 @@ void AnfRuntimeAlgorithm::CopyNodeAttrs(const AnfNodePtr &from, const AnfNodePtr void AnfRuntimeAlgorithm::EraseNodeAttr(const std::string &key, const AnfNodePtr node) { MS_EXCEPTION_IF_NULL(node); if (!node->isa()) { - MS_LOG(EXCEPTION) << "only cnode has attr,but this anf is " << node->DebugString(); + MS_LOG(EXCEPTION) << "Only cnode has attr, but this anf is " << node->DebugString(); } auto primitive = AnfAlgo::GetCNodePrimitive(node); MS_EXCEPTION_IF_NULL(primitive); @@ -241,7 +241,7 @@ void AnfRuntimeAlgorithm::EraseNodeAttr(const std::string &key, const AnfNodePtr bool AnfRuntimeAlgorithm::HasNodeAttr(const std::string &key, const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); if (!node->isa()) { - MS_LOG(WARNING) << "only cnode has attr,but this anf is " << node->DebugString(); + MS_LOG(WARNING) << "Only cnode has attr, but this anf is " << node->DebugString(); return false; } auto primitive = AnfAlgo::GetCNodePrimitive(node); @@ -252,7 +252,7 @@ bool AnfRuntimeAlgorithm::HasNodeAttr(const std::string &key, const AnfNodePtr & size_t AnfRuntimeAlgorithm::GetInputTensorNum(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); if (!node->isa()) { - MS_LOG(EXCEPTION) << "only cnode has real input,but this anf is " << node->DebugString(); + MS_LOG(EXCEPTION) << "Only cnode has real input, but this anf is " << node->DebugString(); } auto cnode = node->cast(); MS_EXCEPTION_IF_NULL(cnode); @@ -404,7 +404,7 @@ std::vector AnfRuntimeAlgorithm::GetInputReshapeType(const AnfNode MS_EXCEPTION_IF_NULL(build_info); std::vector result; if (!build_info->GetInputReshapeType(input_idx, &result)) { - MS_LOG(EXCEPTION) << "filed to ge the node's[ " << node->DebugString() << "] reshape type !"; + MS_LOG(EXCEPTION) << "Failed to get the node's[ " << node->DebugString() << "] reshape type !"; } return result; } @@ -417,7 +417,7 @@ std::vector AnfRuntimeAlgorithm::GetOutputReshapeType(const AnfNod MS_EXCEPTION_IF_NULL(build_info); std::vector result; if (!build_info->GetOutputReshapeType(output_idx, &result)) { - MS_LOG(EXCEPTION) << "filed to ge the node's[ " << node->DebugString() << "] reshape type !"; + MS_LOG(EXCEPTION) << "Failed to get the node's[ " << node->DebugString() << "] reshape type !"; } return result; } @@ -593,7 +593,7 @@ void AnfRuntimeAlgorithm::SetOutputAddr(const DeviceAddressPtr &addr, size_t out auto kernel_info = node->kernel_info(); MS_EXCEPTION_IF_NULL(kernel_info); if (!kernel_info->SetOutputAddr(addr, output_idx)) { - MS_LOG(EXCEPTION) << "node " << node->DebugString() << "set adr" << output_idx << " fail"; + MS_LOG(EXCEPTION) << "Node " << node->DebugString() << "set adr" << output_idx << " fail"; } } @@ -603,7 +603,7 @@ void AnfRuntimeAlgorithm::SetWorkspaceAddr(const DeviceAddressPtr &addr, size_t auto kernel_info = node->kernel_info(); MS_EXCEPTION_IF_NULL(kernel_info); if (!kernel_info->SetWorkspaceAddr(addr, output_idx)) { - MS_LOG(EXCEPTION) << "node " << node->DebugString() << "set adr" << output_idx << " fail"; + MS_LOG(EXCEPTION) << "Node " << node->DebugString() << "set adr" << output_idx << " fail"; } } @@ -614,7 +614,7 @@ DeviceAddress *AnfRuntimeAlgorithm::GetWorkspaceAddr(const AnfNodePtr &node, siz MS_EXCEPTION_IF_NULL(kernel_info); auto addr = kernel_info->GetWorkspaceAddr(output_idx); if (addr == nullptr) { - MS_LOG(EXCEPTION) << "output_idx " << output_idx << " of node " << node->DebugString() + MS_LOG(EXCEPTION) << "Output_idx " << output_idx << " of node " << node->DebugString() << "] workspace addr is not exist"; } return addr; @@ -625,7 +625,7 @@ void AnfRuntimeAlgorithm::SetOutputInferTypeAndShape(const std::vector & const std::vector> &shapes, AnfNode *node) { MS_EXCEPTION_IF_NULL(node); if (types.size() != shapes.size()) { - MS_LOG(EXCEPTION) << "types size " << types.size() << "should be same with shapes size " << shapes.size(); + MS_LOG(EXCEPTION) << "Types size " << types.size() << "should be same with shapes size " << shapes.size(); } if (shapes.empty()) { MS_LOG(EXCEPTION) << "Illegal empty output_types_shapes"; @@ -636,7 +636,7 @@ void AnfRuntimeAlgorithm::SetOutputInferTypeAndShape(const std::vector & auto abstract = std::make_shared(TypeIdToType(types[0]), shape_int); node->set_abstract(abstract); } else { - // mutiple output handle + // multiple output handle std::vector abstract_list; for (size_t i = 0; i < types.size(); ++i) { std::vector shape_int; @@ -647,12 +647,12 @@ void AnfRuntimeAlgorithm::SetOutputInferTypeAndShape(const std::vector & node->set_abstract(abstract_tuple); } } -// copy a abstract of a node to another node +// copy an abstract of a node to another node void AnfRuntimeAlgorithm::CopyAbstract(const AnfNodePtr &from_node, AnfNode *to_node) { to_node->set_abstract(from_node->abstract()); } -// get KernelBuildType of node ,such as ATT,RT,FWK and so on +// get KernelBuildType of node, such as ATT,RT,FWK and so on KernelType AnfRuntimeAlgorithm::GetKernelType(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); auto kernel_info = node->kernel_info(); @@ -846,7 +846,7 @@ size_t AnfRuntimeAlgorithm::GetRealInputIndex(const mindspore::AnfNodePtr &anf_n auto find = spec_node_list.find(node_name); if (find != spec_node_list.end()) { ret = find->second[cur_index]; - MS_LOG(INFO) << "real input index change to" << ret << ", node name:" << node_name; + MS_LOG(INFO) << "Real input index change to" << ret << ", node name:" << node_name; } } return ret; diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.h b/mindspore/ccsrc/session/anf_runtime_algorithm.h index 60d373d5ad..2de68f0098 100644 --- a/mindspore/ccsrc/session/anf_runtime_algorithm.h +++ b/mindspore/ccsrc/session/anf_runtime_algorithm.h @@ -61,7 +61,7 @@ class AnfRuntimeAlgorithm { MS_EXCEPTION_IF_NULL(node); if (!node->isa()) { std::string node_debug_log = node->DebugString(); - MS_LOG(EXCEPTION) << "only cnode has attr,but this anf is " << node_debug_log.c_str(); + MS_LOG(EXCEPTION) << "Only cnode has attr, but this anf is " << node_debug_log.c_str(); } auto primitive = GetCNodePrimitive(node); MS_EXCEPTION_IF_NULL(primitive); @@ -105,7 +105,7 @@ class AnfRuntimeAlgorithm { static TypeId GetOutputInferDataType(const AnfNodePtr &node, size_t output_idx); // get output original data type from prev node,input_index is the input index of current node related to prev node static TypeId GetPrevNodeOutputInferDataType(const AnfNodePtr &node, size_t input_idx); - // get output select data typpe of anf node + // get output select data type of anf node static TypeId GetOutputDeviceDataType(const AnfNodePtr &node, size_t output_idx); // get input select data type of anf node static TypeId GetInputDeviceDataType(const AnfNodePtr &node, size_t input_idx); diff --git a/mindspore/ccsrc/session/ascend_session.cc b/mindspore/ccsrc/session/ascend_session.cc index 64647cd036..9d351f3199 100644 --- a/mindspore/ccsrc/session/ascend_session.cc +++ b/mindspore/ccsrc/session/ascend_session.cc @@ -56,7 +56,7 @@ void DumpGraphExeOrder(const std::vector &execution_order) { } void DumpGraphInputArgs(const VectorRef &args) { - MS_LOG(INFO) << "args size[%lu]" << args.size(); + MS_LOG(INFO) << "Args size[%lu]" << args.size(); for (size_t i = 0; i < args.size(); i++) { if (utils::isa(args[i])) { auto anf = utils::cast(args[i]); @@ -67,7 +67,7 @@ void DumpGraphInputArgs(const VectorRef &args) { MS_EXCEPTION_IF_NULL(value); MS_LOG(INFO) << "Tensor arg" << i << " = " << value->ToString(); } else { - MS_LOG(INFO) << "Unknonwn arg" << i << " = " << args[i].ToString(); + MS_LOG(INFO) << "Unknown arg" << i << " = " << args[i].ToString(); } } } @@ -96,7 +96,7 @@ GraphId GetDistinctionLabel(const KernelGraphPtr &graph) { GraphId AscendSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) { MS_LOG(INFO) << "start"; auto graph_id = graph_sum_; - // construct graph,if construct successs,graph_sum_ + 1 + // construct graph, if successfully, graph_sum_ + 1 auto graph = ConstructKernelGraph(lst, outputs); MS_EXCEPTION_IF_NULL(graph); opt::AscendBackendIRFusionOptimization(graph); @@ -128,7 +128,7 @@ void AscendSession::BuildGraph(GraphId graph_id) { // merge child graph MergeGraphExecOrder(); } else { - // set the distinciton label of single graph + // set the distinction label of single graph SetStreamDistinctionLabel(GetGraph(graph_id), graph_id, false); } // adjust execution order because merge child graph and other special operations @@ -144,7 +144,7 @@ void AscendSession::BuildGraph(GraphId graph_id) { if (ms_context->precompile_only()) { MS_LOG(INFO) << "Precompile only, stop in build kernel step"; } else { - // alloc memeory,include static memory and dynamic memory + // alloc memory, including static memory and dynamic memory MemoryAlloc(graph.get()); // generate task info for task sink mode GenerateTaskInfo(graph); @@ -159,9 +159,9 @@ void AscendSession::RunGraph(const GraphId &graph_id, const std::vectorexecutable()) { - MS_LOG(INFO) << "no child graph but has anf output"; + MS_LOG(INFO) << "No child graph has anf output"; UpdateOutputs(kernel_graph, outputs, inputs); return; } @@ -184,16 +184,16 @@ void AscendSession::RunGraph(const GraphId &graph_id, const std::vector &kernel_graph) const { - MS_LOG(INFO) << "start !"; + MS_LOG(INFO) << "Start"; // data layout optimization opt::AscendDataLayout(kernel_graph); // mixed precision optimization opt::AscendMixPrecision(kernel_graph); - MS_LOG(INFO) << "Finish!"; + MS_LOG(INFO) << "Finish"; } void AscendSession::RunOpExecTask(const std::shared_ptr &kernel_graph) const { - MS_LOG(INFO) << "start !"; + MS_LOG(INFO) << "Start!"; auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_); MS_EXCEPTION_IF_NULL(runtime_instance); bool ret_ok = runtime_instance->LaunchKernel(kernel_graph.get()); @@ -252,26 +252,26 @@ py::tuple AscendSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &gr // compile graph steps void AscendSession::SelectKernel(const KernelGraph &kernel_graph) const { - MS_LOG(INFO) << "start !"; + MS_LOG(INFO) << "Start!"; for (const auto &cnode : kernel_graph.execution_order()) { device::ascend::SelectKernelInfo(cnode); - MS_LOG(INFO) << "select ApplyKernel: " << cnode->DebugString(); + MS_LOG(INFO) << "Select ApplyKernel: " << cnode->DebugString(); } MS_LOG(INFO) << "Finish!"; } void AscendSession::InitRuntimeResource() { - MS_LOG(INFO) << "start !"; + MS_LOG(INFO) << "Start!"; auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_); MS_EXCEPTION_IF_NULL(runtime_instance); if (!runtime_instance->Init()) { - MS_LOG(EXCEPTION) << "kernel runtime init error."; + MS_LOG(EXCEPTION) << "Kernel runtime init error."; } MS_LOG(INFO) << "Finish!"; } void AscendSession::HardwareOptimize(const std::shared_ptr &kernel_graph) const { - MS_LOG(INFO) << "HardwareOptimize start !"; + MS_LOG(INFO) << "HardwareOptimize start!"; opt::AscendBackendOptimization(kernel_graph); MS_EXCEPTION_IF_NULL(kernel_graph); kernel_graph->SetExecOrderByDefault(); @@ -279,7 +279,7 @@ void AscendSession::HardwareOptimize(const std::shared_ptr &kernel_ } void AscendSession::AdjustKernel(const std::shared_ptr &kernel_graph) const { - MS_LOG(INFO) << "start !"; + MS_LOG(INFO) << "Start!"; device::KernelAdjust::GetInstance().Reorder(kernel_graph); opt::HideNopNode(kernel_graph.get()); // Insert CLearZero op @@ -302,7 +302,7 @@ void AscendSession::AdjustKernel(const std::shared_ptr &kernel_grap } void AscendSession::RunOpAdjustKernel(const std::shared_ptr &kernel_graph) const { - MS_LOG(INFO) << "start !"; + MS_LOG(INFO) << "Start!"; opt::HideNopNode(kernel_graph.get()); // Insert CLearZero op // prepare for next step from json get atomic info @@ -312,18 +312,18 @@ void AscendSession::RunOpAdjustKernel(const std::shared_ptr &kernel } void AscendSession::AssignStream(const std::shared_ptr &kernel_graph) const { - MS_LOG(INFO) << "start !"; + MS_LOG(INFO) << "Start!"; device::ascend::AscendStreamAssign::GetInstance().AssignStreamNew(kernel_graph); MS_LOG(INFO) << "Finish!"; } void AscendSession::BuildKernel(const std::shared_ptr &kernel_graph) const { - MS_LOG(INFO) << "start !"; + MS_LOG(INFO) << "Start!"; struct timeval start_time, end_time; (void)gettimeofday(&start_time, nullptr); auto ret = device::ascend::KernelBuild(kernel_graph.get()); if (!ret) { - MS_LOG(EXCEPTION) << "kernel build error."; + MS_LOG(EXCEPTION) << "Kernel build error."; } (void)gettimeofday(&end_time, nullptr); const uint64_t kUSecondInSecond = 1000000; @@ -334,7 +334,7 @@ void AscendSession::BuildKernel(const std::shared_ptr &kernel_graph } void AscendSession::MemoryAlloc(KernelGraph *kernel_graph) const { - MS_LOG(INFO) << "start !"; + MS_LOG(INFO) << "Start!"; MS_EXCEPTION_IF_NULL(kernel_graph); opt::RemoveNopNode(kernel_graph); auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_); @@ -345,7 +345,7 @@ void AscendSession::MemoryAlloc(KernelGraph *kernel_graph) const { void AscendSession::RunOpMemoryAlloc(const std::vector &input_tensors, KernelGraph *kernel_graph) const { - MS_LOG(INFO) << "start memory alloc!"; + MS_LOG(INFO) << "Start memory alloc!"; MS_EXCEPTION_IF_NULL(kernel_graph); opt::RemoveNopNode(kernel_graph); auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_); @@ -355,30 +355,30 @@ void AscendSession::RunOpMemoryAlloc(const std::vector &input } void AscendSession::GenerateTaskInfo(const std::shared_ptr &kernel_graph) const { - MS_LOG(INFO) << "start !"; + MS_LOG(INFO) << "Start!"; (void)device::KernelAdjust::GetInstance().StepLoadCtrlInputs(context_, kernel_graph); auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_); MS_EXCEPTION_IF_NULL(runtime_instance); bool ret_ok = runtime_instance->GenTask(kernel_graph.get()); if (!ret_ok) { - MS_LOG(EXCEPTION) << "generate task error!"; + MS_LOG(EXCEPTION) << "Generate task error!"; } MS_LOG(INFO) << "Finish!"; } void AscendSession::LoadTask(const std::shared_ptr &kernel_graph) const { - MS_LOG(INFO) << "start !"; + MS_LOG(INFO) << "Start!"; auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_); MS_EXCEPTION_IF_NULL(runtime_instance); bool ret_ok = runtime_instance->LoadTask(kernel_graph.get()); if (!ret_ok) { - MS_LOG(EXCEPTION) << "load task error!"; + MS_LOG(EXCEPTION) << "Load task error!"; } MS_LOG(INFO) << "Finish!"; } void AscendSession::ExecTask(const std::shared_ptr &kernel_graph) const { - MS_LOG(INFO) << "start !"; + MS_LOG(INFO) << "Start!"; auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_); MS_EXCEPTION_IF_NULL(runtime_instance); bool ret_ok = runtime_instance->Run(kernel_graph.get()); @@ -389,7 +389,7 @@ void AscendSession::ExecTask(const std::shared_ptr &kernel_graph) c } void AscendSession::Dump(const std::shared_ptr &kernel_graph) const { - MS_LOG(INFO) << "start !"; + MS_LOG(INFO) << "Start!"; MS_EXCEPTION_IF_NULL(kernel_graph); auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_); MS_EXCEPTION_IF_NULL(runtime_instance); @@ -398,7 +398,7 @@ void AscendSession::Dump(const std::shared_ptr &kernel_graph) const } GraphId AscendSession::SetFinalGraphInput(const std::vector &args) { - MS_LOG(INFO) << "start!args size " << args.size(); + MS_LOG(INFO) << "Start! Args size " << args.size(); auto final_graph = std::make_shared(); final_graph_id_ = graph_sum_++; graphs_[final_graph_id_] = final_graph; @@ -418,12 +418,12 @@ GraphId AscendSession::SetFinalGraphInput(const std::vector &args) { if (parameter_belong_graph_id == kInvalidGraphId) { parameter_backend = final_graph->NewParameter(parameter->cast()); final_graph->FrontBackendlMapAdd(parameter, parameter_backend); - MS_LOG(INFO) << "new parameter" << parameter->DebugString() << "in final_graph"; + MS_LOG(INFO) << "New parameter" << parameter->DebugString() << "in final_graph"; } else { // parametr is a parameter of child graph auto graph = GetGraph(parameter_belong_graph_id); MS_EXCEPTION_IF_NULL(graph); - MS_LOG(INFO) << "reuse parameter [" << parameter->DebugString() << "] of child graph [" + MS_LOG(INFO) << "Reuse parameter [" << parameter->DebugString() << "] of child graph [" << parameter_belong_graph_id << "]"; parameter_backend = graph->GetBackendAnfByFrontAnf(parameter); } @@ -435,7 +435,7 @@ GraphId AscendSession::SetFinalGraphInput(const std::vector &args) { MS_EXCEPTION_IF_NULL(final_graph_inputs); final_graph_inputs->push_back(parameter_backend); } - MS_LOG(INFO) << "end final_graph_id " << final_graph_id_; + MS_LOG(INFO) << "End final_graph_id " << final_graph_id_; return final_graph_id_; } @@ -454,7 +454,7 @@ void AscendSession::SetFinalGraphOutput(const BaseRef &output) { value_node->set_abstract(abstract::FromValue(value_ptr)); final_graph->set_output(final_graph->NewCNode({NewValueNode(prim::kPrimMakeTuple), value_node})); final_graph->set_executable(false); - MS_LOG(INFO) << "not anf output[" << output.ToString() << "]"; + MS_LOG(INFO) << "Not anf output[" << output.ToString() << "]"; return; } // get the backend anf node related to the output node of front @@ -462,12 +462,12 @@ void AscendSession::SetFinalGraphOutput(const BaseRef &output) { auto output_from_graph_id = GetGraphIdByNode(output_anf_node); auto output_from_graph = GetGraph(output_from_graph_id); MS_EXCEPTION_IF_NULL(output_anf_node); - MS_LOG(INFO) << "set the output[" << output_anf_node->DebugString() << "] of graph[" << output_from_graph_id + MS_LOG(INFO) << "Set the output[" << output_anf_node->DebugString() << "] of graph[" << output_from_graph_id << "] to final graph"; MS_EXCEPTION_IF_NULL(output_from_graph); // if output is from final graph,it remarks no child graph exist if (final_graph_id_ == output_from_graph_id) { - MS_LOG(INFO) << "no child graph,output is " << output_anf_node->DebugString(); + MS_LOG(INFO) << "No child graph,output is " << output_anf_node->DebugString(); final_graph->set_output(ConstructOutput({output_anf_node}, final_graph)); final_graph->set_executable(false); return; @@ -478,15 +478,15 @@ void AscendSession::SetFinalGraphOutput(const BaseRef &output) { KernelGraphPtr AscendSession::GetGraph(mindspore::GraphId graph_id) { auto it = graphs_.find(graph_id); if (it == graphs_.end()) { - MS_LOG(WARNING) << "can't find graph " << graph_id; + MS_LOG(WARNING) << "Can't find graph " << graph_id; return nullptr; } return it->second; } void AscendSession::InsertSwitchToGraph(GraphId condition_graph_id, GraphId true_graph_id) { - MS_LOG(INFO) << "start"; - MS_LOG(INFO) << "condition graph id[" << condition_graph_id << "],true graph id[" << true_graph_id << "]"; + MS_LOG(INFO) << "Start!"; + MS_LOG(INFO) << "Condition graph id[" << condition_graph_id << "],true graph id[" << true_graph_id << "]"; auto condition_graph = GetGraph(condition_graph_id); MS_EXCEPTION_IF_NULL(condition_graph); tensor::TensorPtr tensor = std::make_shared(kNumberTypeInt32, std::vector{1}); @@ -508,7 +508,7 @@ void AscendSession::InsertSwitchToGraph(GraphId condition_graph_id, GraphId true kernel_build_info_builder->SetKernelType(KernelType::RT_KERNEL); // condition graph's output must be single output if (condition_graph->outputs().size() != 1) { - MS_LOG(EXCEPTION) << "condition_graph output num " << condition_graph_id << " should be 1"; + MS_LOG(EXCEPTION) << "Condition_graph output num " << condition_graph_id << " should be 1"; } AnfNodePtr cond_output_kernel = condition_graph->outputs()[0]; std::vector inputs = {NewValueNode(switch_primitive), cond_output_kernel, counter_const}; @@ -528,7 +528,7 @@ void AscendSession::InsertSwitchToGraph(GraphId condition_graph_id, GraphId true std::vector exec_order = condition_graph->execution_order(); exec_order.push_back(switch_node); condition_graph->set_execution_order(exec_order); - MS_LOG(INFO) << "end"; + MS_LOG(INFO) << "Finish!"; } void AscendSession::CopyOutputOfIf(GraphId false_graph_id) { @@ -541,11 +541,11 @@ void AscendSession::CopyOutputOfIf(GraphId false_graph_id) { for (int i = SizeToInt(false_index) - 1; i >= 0; i--) { size_t graph_index = IntToSize(i); if (graph_index >= graph_execute_order.size()) { - MS_LOG(EXCEPTION) << "graph index[" << graph_index << "] out of range[" << graph_execute_order.size() << "]"; + MS_LOG(EXCEPTION) << "Graph index[" << graph_index << "] out of range[" << graph_execute_order.size() << "]"; } if (graph_order_type[graph_index] == COMMON_GRAPH) { auto true_last_id = graph_execute_order[graph_index]; - MS_LOG(INFO) << "the last graph of if true branch is " << true_last_id; + MS_LOG(INFO) << "The last graph of if true branch is " << true_last_id; auto true_last = GetGraph(true_last_id); auto final_graph = GetGraph(final_graph_id_); MS_EXCEPTION_IF_NULL(final_graph); @@ -553,10 +553,10 @@ void AscendSession::CopyOutputOfIf(GraphId false_graph_id) { auto false_last = GetGraph(false_last_id); MS_EXCEPTION_IF_NULL(true_last); MS_EXCEPTION_IF_NULL(false_last); - MS_LOG(INFO) << "the last graph of false branch is " << false_last_id; + MS_LOG(INFO) << "The last graph of false branch is " << false_last_id; // now only consider the single output InsertMultipleAssignToGraph(true_last_id, true_last->output(), false_last->output()); - // insert stream acitve for loop sink + // insert stream active for loop sink auto context_ptr = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(context_ptr); if (context_ptr->enable_task_sink() && context_ptr->loop_sink_flag() && @@ -570,32 +570,32 @@ void AscendSession::CopyOutputOfIf(GraphId false_graph_id) { } void AscendSession::SwitchCompile(GraphId cond_graph_id, GraphId true_graph_id, GraphId false_graph_id) { - if (switchs_.find(cond_graph_id) != switchs_.end()) { - MS_LOG(WARNING) << "condition graph" << cond_graph_id << " has been set before "; + if (switches_.find(cond_graph_id) != switches_.end()) { + MS_LOG(WARNING) << "Condition graph" << cond_graph_id << " has been set before "; return; } - switchs_[cond_graph_id] = std::pair(true_graph_id, false_graph_id); - MS_LOG(INFO) << "new switch compile " << cond_graph_id << " " << true_graph_id << " " << false_graph_id; - // set the type of condtion graph + switches_[cond_graph_id] = std::pair(true_graph_id, false_graph_id); + MS_LOG(INFO) << "New switch compile " << cond_graph_id << " " << true_graph_id << " " << false_graph_id; + // set the type of condition graph auto cond_graph_index = ExecOrderOfChildGraph(final_graph_id_, cond_graph_id); auto &graph_order_type = GetGraphOrderType(final_graph_id_); if (cond_graph_index >= graph_order_type.size()) { MS_LOG(EXCEPTION) << "cond_graph_index " << cond_graph_index << " out of range " << graph_order_types_.size(); } graph_order_type[cond_graph_index] = CONDITION_GRAPH; - // update disinction label of false graph,update before merge to sure the distinction + // update distinction label of false graph,update before merge to sure the distinction if (false_graph_id != kInvalidGraphId) { // false graph and condition in graph same stream - auto conditon_graph = GetGraph(cond_graph_id); - SetStreamDistinctionLabel(GetGraph(false_graph_id), GetDistinctionLabel(conditon_graph), true); + auto condition_graph = GetGraph(cond_graph_id); + SetStreamDistinctionLabel(GetGraph(false_graph_id), GetDistinctionLabel(condition_graph), true); // if false graph is a condition graph and has been switch compiled before,it's false should be updated again - auto cond_it = switchs_.find(false_graph_id); - while (cond_it != switchs_.end() && cond_it->second.second != kInvalidGraphId) { + auto cond_it = switches_.find(false_graph_id); + while (cond_it != switches_.end() && cond_it->second.second != kInvalidGraphId) { cond_graph_id = cond_it->first; false_graph_id = cond_it->second.second; - conditon_graph = GetGraph(cond_graph_id); - SetStreamDistinctionLabel(GetGraph(false_graph_id), GetDistinctionLabel(conditon_graph), true); - cond_it = switchs_.find(false_graph_id); + condition_graph = GetGraph(cond_graph_id); + SetStreamDistinctionLabel(GetGraph(false_graph_id), GetDistinctionLabel(condition_graph), true); + cond_it = switches_.find(false_graph_id); } } } // namespace session @@ -603,11 +603,11 @@ void AscendSession::SwitchCompile(GraphId cond_graph_id, GraphId true_graph_id, void AscendSession::MergeSwitchCompile() { auto graph_execute_order = GetGraphOrder(final_graph_id_); auto &graph_order_type = GetGraphOrderType(final_graph_id_); - for (auto switch_compile : switchs_) { + for (auto switch_compile : switches_) { auto cond_graph_id = switch_compile.first; auto true_graph_id = switch_compile.second.first; auto false_graph_id = switch_compile.second.second; - MS_LOG(INFO) << "switch compile: " << cond_graph_id << " " << true_graph_id << " " << false_graph_id; + MS_LOG(INFO) << "Switch compile: " << cond_graph_id << " " << true_graph_id << " " << false_graph_id; auto condition_graph = GetGraph(cond_graph_id); auto final_graph = GetGraph(final_graph_id_); MS_EXCEPTION_IF_NULL(condition_graph); @@ -631,8 +631,8 @@ void AscendSession::MergeSwitchCompile() { InsertStreamActiveToGraph(prev_graph_id, GetDistinctionLabel(condition_graph)); } // if this is a 'if' condition - auto it = while_condtion_graphs_.find(cond_graph_id); - if (it == while_condtion_graphs_.end()) { + auto it = while_condition_graphs_.find(cond_graph_id); + if (it == while_condition_graphs_.end()) { CopyOutputOfIf(false_graph_id); } else { // if it is a while,insert a stream active to true graph @@ -640,17 +640,17 @@ void AscendSession::MergeSwitchCompile() { InsertStreamActiveToGraph(from_graph, GetDistinctionLabel(condition_graph)); } } - MS_LOG(INFO) << "end"; + MS_LOG(INFO) << "Finish!"; } // insert active to graph void AscendSession::SetActive(GraphId from, GraphId to) { - if (while_condtion_graphs_.find(to) != while_condtion_graphs_.end()) { + if (while_condition_graphs_.find(to) != while_condition_graphs_.end()) { MS_LOG(WARNING) << " to " << to << " has been exits in map,from " << from << ",exist from " - << while_condtion_graphs_[to]; + << while_condition_graphs_[to]; return; } - MS_LOG(INFO) << "from " << from << " to " << to; + MS_LOG(INFO) << "From " << from << " to " << to; auto &graph_order = GetGraphOrder(final_graph_id_); auto &graph_type = GetGraphOrderType(final_graph_id_); std::vector graph_order_new; @@ -669,21 +669,21 @@ void AscendSession::SetActive(GraphId from, GraphId to) { // set the graph type of condition graph graph_type[ExecOrderOfChildGraph(final_graph_id_, to)] = CONDITION_GRAPH; // record the condition graph into while condition set - while_condtion_graphs_[to] = from; + while_condition_graphs_[to] = from; } void AscendSession::SetChildGraphParameter(const AnfNodePtr &front_anf, const AnfNodePtr &backend_parameter) { - MS_LOG(INFO) << "start"; + MS_LOG(INFO) << "Start!"; MS_EXCEPTION_IF_NULL(backend_parameter); MS_EXCEPTION_IF_NULL(front_anf); if (!backend_parameter->isa()) { - MS_LOG(EXCEPTION) << "backend parameter's type is not a parameter,but is " << backend_parameter->ToString(); + MS_LOG(EXCEPTION) << "Backend parameter's type is not a parameter,but is " << backend_parameter->ToString(); } auto from_graph_id = GetGraphIdByNode(front_anf); auto from_graph = GetGraph(from_graph_id); MS_EXCEPTION_IF_NULL(from_graph); - MS_LOG(INFO) << "set node[" << front_anf->DebugString() << "] of graph[" << from_graph_id << "]to node[" + MS_LOG(INFO) << "Set node[" << front_anf->DebugString() << "] of graph[" << from_graph_id << "]to node[" << backend_parameter->DebugString() << "] of graph[" << AnfAlgo::GetGraphId(backend_parameter.get()) << "]"; // a node should not assign to itself @@ -697,26 +697,27 @@ void AscendSession::SetChildGraphParameter(const AnfNodePtr &front_anf, const An if (!AnfAlgo::OutputAddrExist(backend_arg, 0)) { // set parameter's addr in child graph to parameter in final graph AnfAlgo::SetOutputAddr(AnfAlgo::GetMutableOutputAddr(backend_parameter, 0), 0, backend_arg.get()); - MS_LOG(INFO) << "assign mem of node" << backend_parameter->DebugString() << " of graph " + MS_LOG(INFO) << "Assign mem of node" << backend_parameter->DebugString() << " of graph " << AnfAlgo::GetGraphId(backend_parameter.get()) << " to node" << backend_arg->DebugString() << "of graph " << AnfAlgo::GetGraphId(backend_arg.get()); return; } } InsertMultipleAssignToGraph(from_graph_id, backend_arg, backend_parameter); - // if front anf is a parameter,we can assign the value back,because backend_parameter won't be change in it's graph - // unless it's a weigth.If backend_parameter is a weight,we do should assign the value back + // if front anf is a parameter, we can assign the value back, because backend_parameter + // won't be changed in it's graph unless it's a weight. If backend_parameter is a weight, + // we do should assign the value back. auto to_graph_id = AnfAlgo::GetGraphId(backend_parameter.get()); auto to_graph = GetGraph(to_graph_id); MS_EXCEPTION_IF_NULL(to_graph); if (backend_arg->isa() && !to_graph->execution_order().empty()) { InsertMultipleAssignToGraph(to_graph_id, backend_parameter, backend_arg); } - MS_LOG(INFO) << "end"; + MS_LOG(INFO) << "Finish!"; } void AscendSession::SetChildGraphParameter(const tensor::TensorPtr &front_tensor, const AnfNodePtr &backend_parameter) { - MS_LOG(INFO) << "start"; + MS_LOG(INFO) << "Start!"; // sync data from host to device MS_EXCEPTION_IF_NULL(front_tensor); size_t tensor_size = front_tensor->data().nbytes(); @@ -724,9 +725,9 @@ void AscendSession::SetChildGraphParameter(const tensor::TensorPtr &front_tensor MS_EXCEPTION_IF_NULL(addr); if (!addr->SyncHostToDevice(front_tensor->shape(), tensor_size, front_tensor->data_type(), front_tensor->data_c(false))) { - MS_LOG(EXCEPTION) << "tensor SyncHostToDevice fail!"; + MS_LOG(EXCEPTION) << "Tensor SyncHostToDevice fail!"; } - MS_LOG(INFO) << "end"; + MS_LOG(INFO) << "Finish!"; } void AscendSession::UpdateGraphOrder(GraphId to_graph_id) { @@ -743,7 +744,7 @@ void AscendSession::UpdateGraphOrder(GraphId to_graph_id) { graph_order.push_back(to_graph_id); graph_type.push_back(COMMON_GRAPH); for (size_t i = 0; i < graph_order.size(); i++) { - MS_LOG(INFO) << "index " << i << ",graph_id " << graph_order[i] << ",graph_type" << graph_type[i]; + MS_LOG(INFO) << "Index " << i << ",graph_id " << graph_order[i] << ",graph_type" << graph_type[i]; } } @@ -775,10 +776,10 @@ void AscendSession::SetChildGraphInput(GraphId g, const VectorRef &args) { SetChildGraphParameter(value->cast(), graph_inputs[input_index]); input_index++; } else { - MS_LOG(EXCEPTION) << "Unxpected arg type " << args[i].ToString(); + MS_LOG(EXCEPTION) << "Unexpected arg type " << args[i].ToString(); } } - MS_LOG(INFO) << "end"; + MS_LOG(INFO) << "Finish!"; } GraphId AscendSession::GetGraphIdByNode(const AnfNodePtr &front_anf) const { @@ -796,7 +797,7 @@ GraphId AscendSession::GetGraphIdByNode(const AnfNodePtr &front_anf) const { } void AscendSession::MergeGraphExecOrder() { - MS_LOG(INFO) << "start"; + MS_LOG(INFO) << "Start!"; // insert switch to graph MergeSwitchCompile(); // merge graph order @@ -805,7 +806,7 @@ void AscendSession::MergeGraphExecOrder() { auto final_graph = GetGraph(final_graph_id_); MS_EXCEPTION_IF_NULL(final_graph); if (graph_order.empty()) { - MS_LOG(WARNING) << "graph output is a lonely variable not linked to any op!"; + MS_LOG(WARNING) << "Graph output is a lonely variable not linked to any op!"; return; } // if first graph is common,the final graph has no label,then set the stream of final graph same with the first graph @@ -821,7 +822,7 @@ void AscendSession::MergeGraphExecOrder() { last_graph = child_graph; MS_EXCEPTION_IF_NULL(child_graph); auto exec_order = child_graph->execution_order(); - MS_LOG(INFO) << "merge graph,graph_id " << graph_id; + MS_LOG(INFO) << "Merge graph,graph_id " << graph_id; (void)std::copy(exec_order.begin(), exec_order.end(), std::back_inserter(final_exec_order)); // add all value nodes of child graphs to final graph for (auto &value_node : child_graph->graph_value_nodes()) { @@ -875,11 +876,11 @@ void AscendSession::InsertAssignToGraph(GraphId graph_id, const AnfNodePtr &from void AscendSession::InsertMultipleAssignToGraph(GraphId graph_id, const AnfNodePtr &from, const AnfNodePtr &to) { std::vector from_outputs = AnfAlgo::GetAllOutput(from, {prim::kPrimTupleGetItem}); std::vector to_outputs = AnfAlgo::GetAllOutput(to, {prim::kPrimTupleGetItem}); - MS_LOG(INFO) << "insert assigns from [" << AnfAlgo::GetGraphId(from.get()) << "] to [" + MS_LOG(INFO) << "Insert assigns from [" << AnfAlgo::GetGraphId(from.get()) << "] to [" << AnfAlgo::GetGraphId(to.get()) << "]"; if (from_outputs.size() != to_outputs.size()) { - MS_LOG(INFO) << "from[" << from->DebugString(5) << "] to[" << to->DebugString(5) << "]"; - MS_LOG(EXCEPTION) << "from outputs size[" << from_outputs.size() << "] is not equal to to outputs size[" + MS_LOG(INFO) << "From[" << from->DebugString(5) << "] to[" << to->DebugString(5) << "]"; + MS_LOG(EXCEPTION) << "From outputs size[" << from_outputs.size() << "] is not equal to to outputs size[" << to_outputs.size() << "]"; } for (size_t i = 0; i < from_outputs.size(); i++) { @@ -898,7 +899,7 @@ void AscendSession::InsertStreamActiveToGraph(GraphId graph_id, uint32_t actived auto kernel_build_info_builder = std::make_shared(); kernel_build_info_builder->SetKernelType(KernelType::RT_KERNEL); AnfAlgo::SetSelectKernelBuildInfo(kernel_build_info_builder->Build(), active_node.get()); - // set the actived stream id into the attr of active node + // set the active stream id into the attr of active node std::vector active_index_value = {}; active_index_value.push_back(actived_stream); AnfAlgo::SetNodeAttr(kAttrActiveStreamList, MakeValue>(active_index_value), active_node); @@ -922,7 +923,7 @@ size_t AscendSession::ExecOrderOfChildGraph(GraphId final_graph, GraphId child_g std::vector &AscendSession::GetGraphOrder(GraphId final_graph_id) { auto graph_order_iter = graph_execute_orders_.find(final_graph_id); if (graph_order_iter == graph_execute_orders_.end()) { - MS_LOG(EXCEPTION) << "final graph" << final_graph_id << "has no child graph"; + MS_LOG(EXCEPTION) << "Final graph" << final_graph_id << "has no child graph"; } return graph_order_iter->second; } @@ -931,7 +932,7 @@ std::vector &AscendSession::GetGraphOrder(GraphId final_graph_id) { std::vector &AscendSession::GetGraphOrderType(GraphId final_graph_id) { auto graph_type_iter = graph_order_types_.find(final_graph_id); if (graph_type_iter == graph_order_types_.end()) { - MS_LOG(EXCEPTION) << "final graph" << final_graph_id << "has no graph_order_types_"; + MS_LOG(EXCEPTION) << "Final graph" << final_graph_id << "has no graph_order_types_"; } return graph_type_iter->second; } diff --git a/mindspore/ccsrc/session/ascend_session.h b/mindspore/ccsrc/session/ascend_session.h index e5cfb52f2a..caec4b35f7 100644 --- a/mindspore/ccsrc/session/ascend_session.h +++ b/mindspore/ccsrc/session/ascend_session.h @@ -48,13 +48,14 @@ class AscendSession : public SessionBasic { GraphId SetFinalGraphInput(const std::vector &args) override; // set output of final graph void SetFinalGraphOutput(const BaseRef &output) override; - // insert switch and set the relative acitve ops + // insert switch and set the relative active ops void SwitchCompile(GraphId cond_g, GraphId true_g, GraphId false_g) override; - // set args of child graph.the arg maybe come from a output of other child graphs,or from final graph's parameter + // set args of child graph. the arg maybe come from a output of other child graphs, + // or from final graph's parameter void SetChildGraphInput(GraphId g, const VectorRef &args) override; // get graph id in child graphs by ME front anf node pointer GraphId GetGraphIdByNode(const AnfNodePtr &front_anf) const override; - // get grpah id of final graph + // get graph id of final graph GraphId GetFinalRunGraph() const override { return final_graph_id_; } // insert active to graph void SetActive(GraphId, GraphId) override; @@ -112,9 +113,9 @@ class AscendSession : public SessionBasic { // key is final_graph_id,value is the graph types of child graphs std::unordered_map> graph_order_types_; // record condition graph of while - std::unordered_map while_condtion_graphs_; - // record all conditons - std::unordered_map> switchs_; + std::unordered_map while_condition_graphs_; + // record all conditions + std::unordered_map> switches_; // final_graph_id is used in every root graph has it's own session situation GraphId final_graph_id_; }; diff --git a/mindspore/ccsrc/session/gpu_session.cc b/mindspore/ccsrc/session/gpu_session.cc index 293ca4f2ba..29330fb193 100644 --- a/mindspore/ccsrc/session/gpu_session.cc +++ b/mindspore/ccsrc/session/gpu_session.cc @@ -83,7 +83,7 @@ void GPUSession::Execute(const std::shared_ptr &kernel_graph) const } GraphId GPUSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) { - // Construct graph, if construct successs, graph_sum_ + 1 + // Construct graph, if successfully, graph_sum_ + 1 auto graph_id = graph_sum_; auto graph = ConstructKernelGraph(lst, outputs); // Select kernel build info @@ -100,7 +100,7 @@ GraphId GPUSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList auto execution_order = graph->execution_order(); Reorder(&execution_order); graph->set_execution_order(execution_order); - // Alloc memeory, include static memory and dynamic memory + // Alloc memory, including static memory and dynamic memory AllocateMemory(graph.get()); // Reset memory resource auto runtime_instance = device::KernelRuntimeManager::Instance().GetSingleKernelRuntime(kGPUDevice, device_id_); diff --git a/mindspore/ccsrc/session/kernel_graph.cc b/mindspore/ccsrc/session/kernel_graph.cc index b07840aa98..84ff6b81a2 100644 --- a/mindspore/ccsrc/session/kernel_graph.cc +++ b/mindspore/ccsrc/session/kernel_graph.cc @@ -34,7 +34,7 @@ void PushNoVisitedNode(const AnfNodePtr &node, std::queue *que, if (visited_nodes->find(node) == visited_nodes->end()) { que->push(node); (void)visited_nodes->insert(node); - MS_LOG(DEBUG) << "push que:" << node->DebugString(); + MS_LOG(DEBUG) << "Push que:" << node->DebugString(); } } } // namespace @@ -58,7 +58,7 @@ void KernelGraph::SetExecOrderByDefault() { auto clear_output = [&zero_output_nodes, &allreduce_nodes, &visited_nodes, this](const AnfNodePtr &input) -> void { if (node_output_num_[input] == 0 && visited_nodes.find(input) == visited_nodes.end()) { MS_EXCEPTION_IF_NULL(input); - MS_LOG(DEBUG) << "clear output num:" << input->DebugString(); + MS_LOG(DEBUG) << "Clear output num:" << input->DebugString(); (void)visited_nodes.insert(input); if (input->isa() && AnfAlgo::GetCNodeName(input) == kAllReduceOpName) { allreduce_nodes.push(input); @@ -85,21 +85,21 @@ void KernelGraph::SetExecOrderByDefault() { if (it == node_input_edges_.end()) { // value node and parameter has no input,no need to print log if (node->isa()) { - MS_LOG(DEBUG) << "can not find node [" << node->DebugString() << "]"; + MS_LOG(DEBUG) << "Can not find node [" << node->DebugString() << "]"; } continue; } for (const auto &input_edge : it->second) { if (node_output_num_.find(input_edge.first) == node_output_num_.end()) { MS_EXCEPTION_IF_NULL(input_edge.first); - MS_LOG(EXCEPTION) << "can't find node[" << input_edge.first->DebugString() << "]"; + MS_LOG(EXCEPTION) << "Can't find node[" << input_edge.first->DebugString() << "]"; } MS_EXCEPTION_IF_NULL(input_edge.first); - MS_LOG(DEBUG) << "decrese input:" << input_edge.first->DebugString() << ",node:" << node->DebugString() + MS_LOG(DEBUG) << "Decrease input:" << input_edge.first->DebugString() << ",node:" << node->DebugString() << ",num: " << node_output_num_[input_edge.first] << ",decrease num:" << input_edge.second; if (node_output_num_[input_edge.first] < input_edge.second) { - MS_LOG(EXCEPTION) << "input node:" << input_edge.first->DebugString() << ",node_output_num" - << node_output_num_[input_edge.first] << "depend edege:" << input_edge.second; + MS_LOG(EXCEPTION) << "Input node:" << input_edge.first->DebugString() << ",node_output_num" + << node_output_num_[input_edge.first] << "depend edge:" << input_edge.second; } node_output_num_[input_edge.first] = node_output_num_[input_edge.first] - input_edge.second; clear_output(input_edge.first); @@ -120,20 +120,20 @@ void KernelGraph::CheckLoop() { string str; auto node_output_it = node_output_edges_.find(it.first); if (node_output_it == node_output_edges_.end()) { - MS_LOG(EXCEPTION) << "can't find node [" << it.first->DebugString() << "]"; + MS_LOG(EXCEPTION) << "Can't find node [" << it.first->DebugString() << "]"; } for (const auto &output_edge : node_output_edges_[it.first]) { MS_EXCEPTION_IF_NULL(output_edge.first); str = str.append(output_edge.first->DebugString()).append("|"); } if (it.second != 0) { - MS_LOG(WARNING) << "node:" << it.first->DebugString() << ",outputs:" << str << ",output num:" << it.second; + MS_LOG(WARNING) << "Node:" << it.first->DebugString() << ",outputs:" << str << ",output num:" << it.second; none_zero_output[it.first] = it.second; } } // if don't consider control depend and loop exit,a exception will be throw if (!none_zero_output.empty()) { - MS_LOG(EXCEPTION) << "nodes have loop,left node num:" << none_zero_output.size(); + MS_LOG(EXCEPTION) << "Nodes have loop, left node num:" << none_zero_output.size(); } } @@ -152,7 +152,7 @@ CNodePtr KernelGraph::NewCNode(const CNodePtr &cnode) { MS_EXCEPTION_IF_NULL(cnode); auto new_cnode = std::make_shared(*cnode); // if a cnode is created not from front,this cnode won't be in map,so when replace it,we shouldn't update map - if (BakcendNodeExistInFrontBackendMap(cnode)) { + if (BackendNodeExistInFrontBackendMap(cnode)) { FrontBackendlMapUpdate(cnode, new_cnode); } AnfAlgo::SetGraphId(graph_id_, cnode.get()); @@ -299,7 +299,7 @@ AnfNodePtr KernelGraph::GetBackendAnfByFrontAnf(const AnfNodePtr &front_anf) { return front_backend_anf_map_[front_anf]; } -bool KernelGraph::BakcendNodeExistInFrontBackendMap(const AnfNodePtr &backend_anf) { +bool KernelGraph::BackendNodeExistInFrontBackendMap(const AnfNodePtr &backend_anf) { return backend_front_anf_map_.find(backend_anf) != backend_front_anf_map_.end(); } @@ -317,9 +317,9 @@ void KernelGraph::TensorValueNodeMapAdd(const tensor::TensorPtr &tensor, const V } void KernelGraph::AddDependEdge(const AnfNodePtr &node, const AnfNodePtr &input, size_t depend_edge_num) { - MS_LOG(DEBUG) << "input:" << input->DebugString() << ", node:" << node->DebugString() << ",num:" << depend_edge_num; + MS_LOG(DEBUG) << "Input:" << input->DebugString() << ", node:" << node->DebugString() << ",num:" << depend_edge_num; auto output_depend_edge = std::pair(node, depend_edge_num); - // add output depend eddge of input + // add output depend edge of input auto output_it = node_output_edges_.find(input); if (output_it == node_output_edges_.end()) { node_output_edges_[input] = std::vector>{output_depend_edge}; @@ -346,7 +346,7 @@ std::vector KernelGraph::GetOutputNodes(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); auto it = node_output_edges_.find(node); if (it == node_output_edges_.end()) { - MS_LOG(EXCEPTION) << "can'f find node[" << node->DebugString() << "]"; + MS_LOG(EXCEPTION) << "Can't find node[" << node->DebugString() << "]"; } std::vector output_nodes; auto trans = [](const std::pair &pair) -> AnfNodePtr { return pair.first; }; @@ -372,7 +372,7 @@ void KernelGraph::UpdateControlDependRelations(const std::vector &de MS_EXCEPTION_IF_NULL(depend_node); std::vector prior_nodes = {prior_node}; std::vector depend_nodes = {depend_node}; - MS_LOG(INFO) << "prior node[" << prior_node->DebugString() << "],depend node[" << depend_node->DebugString() + MS_LOG(INFO) << "Prior node[" << prior_node->DebugString() << "],depend node[" << depend_node->DebugString() << "],depend_mode=[" << AnfAlgo::GetNodeAttr(cnode, "depend_mode") << "]"; if (prior_node->isa()) { prior_nodes = GetOutputNodes(prior_node); @@ -384,7 +384,7 @@ void KernelGraph::UpdateControlDependRelations(const std::vector &de for (auto &second_node : depend_nodes) { MS_EXCEPTION_IF_NULL(first_node); MS_EXCEPTION_IF_NULL(second_node); - MS_LOG(INFO) << "add first node:" << first_node->DebugString() << ",second node:" << second_node->DebugString(); + MS_LOG(INFO) << "Add first node:" << first_node->DebugString() << ",second node:" << second_node->DebugString(); AddDependEdge(second_node, first_node, 1); } } @@ -437,18 +437,18 @@ void KernelGraph::BfsToUpdateNodeOutput() { MS_EXCEPTION_IF_NULL(cnode); // handle data links for (const auto &input : cnode->inputs()) { - size_t dpend_edge_num = 1; + size_t depend_edge_num = 1; // handle control depend,all inputs of control depend has no depend edge if (HandleControlDependNode(input, &que, &visited_nodes)) { control_depends.push_back(input); - dpend_edge_num = 0; + depend_edge_num = 0; } // the 2rd input of depend is no depend edge if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend) && input == cnode->input(kDependAttachNodeIndex)) { - dpend_edge_num = 0; + depend_edge_num = 0; } PushNoVisitedNode(input, &que, &visited_nodes); - AddDependEdge(node, input, dpend_edge_num); + AddDependEdge(node, input, depend_edge_num); } } UpdateControlDependRelations(control_depends); diff --git a/mindspore/ccsrc/session/kernel_graph.h b/mindspore/ccsrc/session/kernel_graph.h index d94638aa99..e11f6807f5 100644 --- a/mindspore/ccsrc/session/kernel_graph.h +++ b/mindspore/ccsrc/session/kernel_graph.h @@ -62,8 +62,8 @@ class KernelGraph : public FuncGraph { void FrontBackendlMapUpdate(const AnfNodePtr &old_backend_anf, const AnfNodePtr &new_backend_anf); // get backend anf by front anf AnfNodePtr GetBackendAnfByFrontAnf(const AnfNodePtr &front_anf); - // check backend node wheteher exist in map - bool BakcendNodeExistInFrontBackendMap(const AnfNodePtr &backend_anf); + // check backend node whether exist in map + bool BackendNodeExistInFrontBackendMap(const AnfNodePtr &backend_anf); // get value node by tensor ValueNodePtr GetValueNodeByTensor(const tensor::TensorPtr &tensor); // add value node tensor relation map diff --git a/mindspore/ccsrc/session/session_basic.cc b/mindspore/ccsrc/session/session_basic.cc index be03f54f3c..ede3ae7419 100644 --- a/mindspore/ccsrc/session/session_basic.cc +++ b/mindspore/ccsrc/session/session_basic.cc @@ -281,7 +281,7 @@ std::vector CreateParameterFromTuple(const AnfNodePtr &node, KernelG } continue; } - // creata single parameter if is a abstract real kernel + // create single parameter if is a abstract real kernel create_parameter(out_node->abstract()); } return parameters; @@ -413,7 +413,7 @@ CNodePtr SessionBasic::CreateNewCNode(const CNodePtr &cnode, KernelGraph *graph) cnode_inputs.emplace_back(graph->GetBackendAnfByFrontAnf(anf)); continue; } else if (anf->isa() && !IsValueNode(anf)) { - // if input is a value ndoe, + // if input is a value node, auto new_value_node = CreateNewValueNode(anf, graph); if (new_value_node != nullptr) { cnode_inputs.emplace_back(new_value_node); @@ -549,7 +549,7 @@ void SessionBasic::Reorder(std::vector *node_list) { for (const auto &node : *node_list) { MS_EXCEPTION_IF_NULL(node); - if (kOptOpeatorSet.find(AnfAlgo::GetCNodeName(node)) != kOptOpeatorSet.end()) { + if (kOptOperatorSet.find(AnfAlgo::GetCNodeName(node)) != kOptOperatorSet.end()) { all_opt_list.emplace_back(node); } else { non_opt_list.emplace_back(node); @@ -599,7 +599,7 @@ void SessionBasic::ToTensorPtr(const OpRunInfo &op_run_info, std::vector &graph) { - MS_LOG(INFO) << "start"; + MS_LOG(INFO) << "Start!"; std::vector make_tuple_inputs; make_tuple_inputs.push_back(NewValueNode(prim::kPrimMakeTuple)); if (AnfRuntimeAlgorithm::GetOutputTensorNum(cnode) > 1) { @@ -667,14 +667,14 @@ void SessionBasic::CreateOutputNode(const CNodePtr &cnode, const std::shared_ptr // create output auto g_output = graph->NewCNode(make_tuple_inputs); graph->set_output(g_output); - // set graph manager,which now is only used to get valuendoes and hardware optimizing + // set graph manager,which now is only used to get valuenodes and hardware optimizing MS_EXCEPTION_IF_NULL(context_); FuncGraphManagerPtr manager = context_->manager(); if (manager != nullptr) { manager->AddFuncGraph(graph); graph->set_manager(manager); } - MS_LOG(INFO) << "end"; + MS_LOG(INFO) << "Finish!"; } std::shared_ptr SessionBasic::ConstructSingleOpGraph(const OpRunInfo &op_run_info) { @@ -694,9 +694,9 @@ std::shared_ptr SessionBasic::ConstructSingleOpGraph(const OpRunInf std::vector input_tensors; std::vector tensors_mask; ToTensorPtr(op_run_info, &input_tensors, &tensors_mask); - MS_LOG(INFO) << "input tensor size" << input_tensors.size(); + MS_LOG(INFO) << "Input tensor size" << input_tensors.size(); if (input_tensors.size() != tensors_mask.size()) { - MS_LOG(EXCEPTION) << "input tensors size " << input_tensors.size() << " should be equal to tensors mask size " + MS_LOG(EXCEPTION) << "Input tensors size " << input_tensors.size() << " should be equal to tensors mask size " << tensors_mask.size(); } for (size_t i = 0; i < input_tensors.size(); ++i) { @@ -711,7 +711,7 @@ std::shared_ptr SessionBasic::ConstructSingleOpGraph(const OpRunInf cnode->set_abstract(op_run_info.abstract); // set const input to attr if value is not a tensor,such as scalar or tuple RunOpConvertConstInputToAttr(op_run_info, cnode); - // set exectuion order + // set execution order std::vector exe_order = {cnode}; graph->set_execution_order(exe_order); // set output @@ -734,14 +734,14 @@ BaseRef SessionBasic::TransformBaseRefListToTuple(const BaseRef &base_ref) { py::tuple tensor_tuple = py::cast(obj); output_tensors[i] = tensor_tuple; } else { - MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor !"; + MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor!"; } } return output_tensors; // turn tuple to py::object and store in PyObjectRef } else if (utils::isa(base_ref)) { return base_ref; } else { - MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor !"; + MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor!"; } } } // namespace session diff --git a/mindspore/ccsrc/session/session_basic.h b/mindspore/ccsrc/session/session_basic.h index f80e69bd9f..9aadb78cb2 100644 --- a/mindspore/ccsrc/session/session_basic.h +++ b/mindspore/ccsrc/session/session_basic.h @@ -56,7 +56,7 @@ class SessionBasic { virtual ~SessionBasic() { summary_callback_ = nullptr; } virtual GraphId CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) = 0; - // build graph ,used to handle mupltiple child graphs + // build graph, used to handle multiple child graphs virtual void BuildGraph(GraphId) {} virtual void RunGraph(const GraphId &graph_id, const std::vector &inputs, VectorRef *outputs) = 0; @@ -75,7 +75,7 @@ class SessionBasic { virtual GraphId SetFinalGraphInput(const std::vector &) { return kInvalidGraphId; } // set output of final graph virtual void SetFinalGraphOutput(const BaseRef &) {} - // insert switch and set the relative acitve ops + // insert switch and set the relative active ops virtual void SwitchCompile(GraphId, GraphId, GraphId) {} // set args of child graph.the arg maybe come from a output of other child graphs,or from final graph's parameter virtual void SetChildGraphInput(GraphId, const VectorRef &) {} diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h index 229c0547cc..ee1eeaddfc 100644 --- a/mindspore/ccsrc/utils/utils.h +++ b/mindspore/ccsrc/utils/utils.h @@ -186,7 +186,7 @@ const std::vector> kShapeSupportFormatMap = {k1DSupportFor k4DSupportFormat}; const std::set kDefaultCompatibleFormat = {kOpFormat_ND, kOpFormat_NCHW, kOpFormat_NHWC, kOpFormat_HWCN}; -const std::set kOptOpeatorSet = { +const std::set kOptOperatorSet = { kMomentumOpName, kApplyMomentumOpName, kApplyAdadeltaOpName, kApplyAdagradOpName, kApplyAdagradDAName, kApplyAdamOpName, kApplyAdaMaxOpName, kApplyAddSignOpName, kApplyCenteredRMSPOpName, From b9ccd6d5d08822b65d24d8dabd49dd382fbe6c68 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?= Date: Mon, 23 Mar 2020 15:33:01 +0800 Subject: [PATCH 078/227] 1. add Note refer to nn.SGD for detail 2. delete default value of stat 3. delete examples 4. some comments error from wangting review 5. modify comments from jinyaohui 6. modify examples from wanghao 7. modify Select operation examples --- mindspore/nn/layer/activation.py | 4 ++-- mindspore/nn/layer/basic.py | 4 ++-- mindspore/nn/layer/conv.py | 2 +- mindspore/nn/metrics/fbeta.py | 6 +++--- mindspore/ops/operations/array_ops.py | 7 +++++-- mindspore/ops/operations/comm_ops.py | 12 ++++++++---- mindspore/ops/operations/control_ops.py | 1 - mindspore/ops/operations/math_ops.py | 1 - mindspore/ops/operations/nn_ops.py | 13 +++++-------- mindspore/train/amp.py | 3 +-- 10 files changed, 27 insertions(+), 26 deletions(-) diff --git a/mindspore/nn/layer/activation.py b/mindspore/nn/layer/activation.py index 00f2afe703..ad63dde8bc 100644 --- a/mindspore/nn/layer/activation.py +++ b/mindspore/nn/layer/activation.py @@ -40,7 +40,7 @@ class Softmax(Cell): where :math:`x_{i}` is the :math:`i`-th slice along the given dim of the input Tensor. Args: - axis (Union[int, tuple[int]]): The axis to apply Softmax operation. Default: -1, means the last dimension. + axis (Union[int, tuple[int]]): The axis to apply Softmax operation, -1 means the last dimension. Default: -1. Inputs: - **x** (Tensor) - The input of Softmax. @@ -70,7 +70,7 @@ class LogSoftmax(Cell): where :math:`x_{i}` is the :math:`i`-th slice along the given dim of the input Tensor. Args: - axis (int): The axis to apply LogSoftmax operation. Default: -1, means the last dimension. + axis (int): The axis to apply LogSoftmax operation, -1 means the last dimension. Default: -1. Inputs: - **x** (Tensor) - The input of LogSoftmax. diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py index 0cf4cd5e99..9dc0d5e623 100644 --- a/mindspore/nn/layer/basic.py +++ b/mindspore/nn/layer/basic.py @@ -32,13 +32,13 @@ class Dropout(Cell): r""" Dropout layer for the input. - Randomly set some elements of the input tensor to zero with probability :math:`1 - keep_prob` during training + Randomly set some elements of the input tensor to zero with probability :math:`1 - keep\_prob` during training using samples from a Bernoulli distribution. Note: Each channel will be zeroed out independently on every construct call. - The outputs are scaled by a factor of :math:`\frac{1}{keep_prob}` during training so + The outputs are scaled by a factor of :math:`\frac{1}{keep\_prob}` during training so that the output layer remains at a similar scale. During inference, this layer returns the same tensor as the input. diff --git a/mindspore/nn/layer/conv.py b/mindspore/nn/layer/conv.py index 666be93451..dfbf96e150 100644 --- a/mindspore/nn/layer/conv.py +++ b/mindspore/nn/layer/conv.py @@ -241,7 +241,7 @@ class Conv2dTranspose(_Conv): in_channels (int): The number of channels in the input space. out_channels (int): The number of channels in the output space. kernel_size (Union[int, tuple]): int or tuple with 2 integers, which specifies the height - and width of the 2D convolution window.Single int means the value if for both height and width of + and width of the 2D convolution window. Single int means the value is for both height and width of the kernel. A tuple of 2 ints means the first value is for the height and the other is for the width of the kernel. stride (int): Specifies the same value for all spatial dimensions. Default: 1. diff --git a/mindspore/nn/metrics/fbeta.py b/mindspore/nn/metrics/fbeta.py index f38febf3b1..6771b6ba36 100755 --- a/mindspore/nn/metrics/fbeta.py +++ b/mindspore/nn/metrics/fbeta.py @@ -26,8 +26,8 @@ class Fbeta(Metric): Fbeta score is a weighted mean of precison and recall. .. math:: - F_\beta=\frac{(1+\beta^2) \cdot true positive} - {(1+\beta^2) \cdot true positive +\beta^2 \cdot false negative + false positive} + F_\beta=\frac{(1+\beta^2) \cdot true\_positive} + {(1+\beta^2) \cdot true\_positive +\beta^2 \cdot false\_negative + false\_positive} Args: beta (float): The weight of precision. @@ -123,7 +123,7 @@ class F1(Fbeta): Refer to class `Fbeta` for more details. .. math:: - F_\beta=\frac{2\cdot true positive}{2\cdot true positive + false negative + false positive} + F_\beta=\frac{2\cdot true\_positive}{2\cdot true\_positive + false\_negative + false\_positive} Examples: >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index 7b49cbf84c..3dabf2ab0a 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -881,7 +881,7 @@ class ScalarToTensor(PrimitiveWithInfer): Inputs: - **input_x** (Union[int, float]) - The input is a scalar. Only constant value is allowed. - **dtype** (mindspore.dtype) - The target data type. Default: mindspore.float32. Only - constant value is allowed. + constant value is allowed. Outputs: Tensor. 0-D Tensor and the content is the input. @@ -1458,7 +1458,10 @@ class Select(PrimitiveWithInfer): Examples: >>> select = Select() - >>> select([True, False],[2,3],[1,2]) + >>> input_x = Tensor([True, False]) + >>> input_y = Tensor([2,3], mindspore.float32) + >>> input_z = Tensor([1,2], mindspore.float32) + >>> select(input_x, input_y, input_z) """ @prim_attr_register diff --git a/mindspore/ops/operations/comm_ops.py b/mindspore/ops/operations/comm_ops.py index 53a3686367..1644c5800a 100644 --- a/mindspore/ops/operations/comm_ops.py +++ b/mindspore/ops/operations/comm_ops.py @@ -66,11 +66,12 @@ class AllReduce(PrimitiveWithInfer): Examples: >>> from mindspore.communication.management import init + >>> import mindspore.ops.operations as P >>> init('nccl') >>> class Net(nn.Cell): >>> def __init__(self): >>> super(Net, self).__init__() - >>> self.allreduce_sum = AllReduce(ReduceOp.SUM, group="nccl_world_group") + >>> self.allreduce_sum = P.AllReduce(ReduceOp.SUM, group="nccl_world_group") >>> >>> def construct(self, x): >>> return self.allreduce_sum(x) @@ -130,11 +131,12 @@ class AllGather(PrimitiveWithInfer): Examples: >>> from mindspore.communication.management import init + >>> import mindspore.ops.operations as P >>> init('nccl') >>> class Net(nn.Cell): >>> def __init__(self): >>> super(Net, self).__init__() - >>> self.allgather = AllGather(group="nccl_world_group") + >>> self.allgather = P.AllGather(group="nccl_world_group") >>> >>> def construct(self, x): >>> return self.allgather(x) @@ -184,11 +186,12 @@ class ReduceScatter(PrimitiveWithInfer): Examples: >>> from mindspore.communication.management import init + >>> import mindspore.ops.operations as P >>> init('nccl') >>> class Net(nn.Cell): >>> def __init__(self): >>> super(Net, self).__init__() - >>> self.reducescatter = ReduceScatter(ReduceOp.SUM, group="nccl_world_group") + >>> self.reducescatter = P.ReduceScatter(ReduceOp.SUM, group="nccl_world_group") >>> >>> def construct(self, x): >>> return self.reducescatter(x) @@ -246,11 +249,12 @@ class Broadcast(PrimitiveWithInfer): Examples: >>> from mindspore.communication.management import init + >>> import mindspore.ops.operations as P >>> init('nccl') >>> class Net(nn.Cell): >>> def __init__(self): >>> super(Net, self).__init__() - >>> self.broadcast = Broadcast(1) + >>> self.broadcast = P.Broadcast(1) >>> >>> def construct(self, x): >>> return self.broadcast((x,)) diff --git a/mindspore/ops/operations/control_ops.py b/mindspore/ops/operations/control_ops.py index 242a3b155d..1bffc09c04 100644 --- a/mindspore/ops/operations/control_ops.py +++ b/mindspore/ops/operations/control_ops.py @@ -150,7 +150,6 @@ class Merge(PrimitiveWithInfer): raise NotImplementedError def infer_shape(self, inputs): - """merge select one input as its output""" return (inputs[0], [1]) def infer_dtype(self, inputs): diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 9600c736b2..b7373416f9 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1319,7 +1319,6 @@ class EqualCount(PrimitiveWithInfer): self.init_prim_io_names(inputs=['x', 'y'], outputs=['output']) def infer_shape(self, x_shape, w_shape): - """Infer shape.""" output_shape = (1,) return output_shape diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 27e27e049a..fd2c3f5614 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -1309,6 +1309,9 @@ class SGD(PrimitiveWithInfer): Nesterov momentum is based on the formula from On the importance of initialization and momentum in deep learning. + Note: + For details, please refer to `nn.SGD` source code. + Args: dampening (float): The dampening for momentum. Default: 0.0. weight_decay (float): Weight decay (L2 penalty). Default: 0.0. @@ -1320,16 +1323,10 @@ class SGD(PrimitiveWithInfer): - **learning_rate** (Tensor) - Learning rate. e.g. Tensor(0.1, mindspore.float32). - **accum** (Tensor) - Accum(velocity) to be update. - **momentum** (Tensor) - Momentum. e.g. Tensor(0.1, mindspore.float32). - - **stat** (Tensor) - States to be updated with the same shape as gradient. Default: 1.0. + - **stat** (Tensor) - States to be updated with the same shape as gradient. Outputs: Tensor, parameters to be update. - - Examples: - >>> net = ResNet50() - >>> loss = SoftmaxCrossEntropyWithLogits() - >>> opt = SGD(params=net.trainable_params(), learning_rate=lr, momentum=0.9) - >>> model = Model(net, loss, opt) """ @prim_attr_register @@ -1919,7 +1916,7 @@ class LSTM(PrimitiveWithInfer): """ Performs the long short term memory(LSTM) on the input. - Detailed information, please refer to `nn.layer.LSTM`. + Detailed information, please refer to `nn.LSTM`. """ @prim_attr_register diff --git a/mindspore/train/amp.py b/mindspore/train/amp.py index 5a70a86fdd..e909b44e40 100644 --- a/mindspore/train/amp.py +++ b/mindspore/train/amp.py @@ -91,13 +91,12 @@ def build_train_network(network, optimizer, loss_fn=None, level='O0', **kwargs): loss_fn (Union[None, Cell]): Definition of the loss_fn. If None, the `network` should have the loss inside. Default: None. optimizer (Optimizer): Optimizer to update the Parameter. - level (str): Supports [O0, O2]. + level (str): Supports [O0, O2]. Default: "O0". - O0: Do not change. - O2: Cast network to float16, keep batchnorm and `loss_fn` (if set) run in float32, using dynamic loss scale. - Default: "O0" cast_model_type (:class:`mindspore.dtype`): Supports `mstype.float16` or `mstype.float32`. If set to `mstype.float16`, use `float16` mode to train. If set, overwrite the level setting. keep_batchnorm_fp32 (bool): Keep Batchnorm run in `float32`. If set, overwrite the level setting. From 5583532ec6d48ffae4bb34056b6b54862f0ced3b Mon Sep 17 00:00:00 2001 From: jjfeing Date: Thu, 2 Apr 2020 22:03:36 +0800 Subject: [PATCH 079/227] tr5 run package update --- cmake/dependency_graphengine.cmake | 25 ++++++++++--------- .../parallel_compile/tbe_compiler/common.py | 2 +- mindspore/ccsrc/CMakeLists.txt | 22 +++++++++------- 3 files changed, 27 insertions(+), 22 deletions(-) diff --git a/cmake/dependency_graphengine.cmake b/cmake/dependency_graphengine.cmake index dfe90de836..d2f80c4d04 100644 --- a/cmake/dependency_graphengine.cmake +++ b/cmake/dependency_graphengine.cmake @@ -38,19 +38,20 @@ elseif (DEFINED ENV{D_LINK_PATH}) find_library(cce libcce.so ${GE_LIB_PATH}) find_library(resource libresource.so ${GE_LIB_PATH}) else() - set(HIAI_INSTALLED_DIR /usr/local/HiAI) - set(HIAI_DRIVER_DIR ${HIAI_INSTALLED_DIR}/driver/lib64) - set(HIAI_RUNTIME_DIR ${HIAI_INSTALLED_DIR}/runtime/lib64) - find_library(c_sec libc_sec.so ${HIAI_DRIVER_DIR}) - find_library(slog libslog.so ${HIAI_DRIVER_DIR}) - find_library(mmpa libmmpa.so ${HIAI_DRIVER_DIR}) + # Ascend mode + set(ASCEND_PATH /usr/local/Ascend) + set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common) + set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64) - find_library(cce libcce.so ${HIAI_RUNTIME_DIR}) - find_library(hccl libhccl.so ${HIAI_RUNTIME_DIR}) - find_library(runtime libruntime.so ${HIAI_RUNTIME_DIR}) - find_library(msprof libmsprof.so ${HIAI_RUNTIME_DIR}) - find_library(register libregister.so ${HIAI_RUNTIME_DIR}) - find_library(resource libresource.so ${HIAI_RUNTIME_DIR}) + find_library(c_sec libc_sec.so ${ASCEND_DRIVER_PATH}) + find_library(slog libslog.so ${ASCEND_DRIVER_PATH}) + find_library(mmpa libmmpa.so ${ASCEND_DRIVER_PATH}) + find_library(cce libcce.so ${ASCEND_RUNTIME_PATH}) + find_library(hccl libhccl.so ${ASCEND_RUNTIME_PATH}) + find_library(runtime libruntime.so ${ASCEND_RUNTIME_PATH}) + find_library(msprof libmsprof.so ${ASCEND_RUNTIME_PATH}) + find_library(register libregister.so ${ASCEND_RUNTIME_PATH}) + find_library(resource libresource.so ${ASCEND_RUNTIME_PATH}) endif() # compile libraries from following directories diff --git a/mindspore/_extends/parallel_compile/tbe_compiler/common.py b/mindspore/_extends/parallel_compile/tbe_compiler/common.py index 6cfa2afd0d..6258cf8d45 100644 --- a/mindspore/_extends/parallel_compile/tbe_compiler/common.py +++ b/mindspore/_extends/parallel_compile/tbe_compiler/common.py @@ -50,7 +50,7 @@ def get_build_in_impl_path(): tbe_impl_path = os.environ.get("TBE_IMPL_PATH") if tbe_impl_path is None: default_install_path = '/usr/local/HiAI/runtime/ops/op_impl/built-in/ai_core/tbe/' - backup_install_path = '/usr/local/Ascend/Ascend/opp/op_impl/built-in/ai_core/tbe/' + backup_install_path = '/usr/local/Ascend/opp/op_impl/built-in/ai_core/tbe/' if os.path.exists(default_install_path): tbe_impl_path = default_install_path elif os.path.exists(backup_install_path): diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt index 0b4bb0d1df..c4deb53e68 100644 --- a/mindspore/ccsrc/CMakeLists.txt +++ b/mindspore/ccsrc/CMakeLists.txt @@ -279,15 +279,17 @@ if(ENABLE_D) endif() else() MESSAGE("use system default lib") - set(D_LIB_PATH "/usr/local/HiAI/runtime/lib64/") + set(ASCEND_PATH /usr/local/Ascend) + set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common) + set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64) endif() - MESSAGE("USE DAV LIB PATH: ${D_LIB_PATH}") - find_library(HCCL hccl ${D_LIB_PATH}) - find_library(CCE_LIB cce ${D_LIB_PATH}) - find_library(RUNTIME_LIB runtime ${D_LIB_PATH}) - find_library(TSDCLIENT tsdclient ${D_LIB_PATH}) - find_library(PROFILING msprof ${D_LIB_PATH}) + MESSAGE("USE DAV LIB PATH: ${ASCEND_PATH}") + find_library(HCCL hccl ${ASCEND_RUNTIME_PATH}) + find_library(CCE_LIB cce ${ASCEND_RUNTIME_PATH}) + find_library(RUNTIME_LIB runtime ${ASCEND_RUNTIME_PATH}) + find_library(TSDCLIENT tsdclient ${ASCEND_RUNTIME_PATH}) + find_library(PROFILING msprof ${ASCEND_DRIVER_PATH}) target_link_libraries(mindspore ge_runtime ${CCE_LIB} ${RUNTIME_LIB} ${TSDCLIENT} ${PROFILING} ${HCCL} ${TSDCLIENT}) endif() @@ -481,12 +483,14 @@ add_dependencies(add_ms_lib _c_expression) if (NOT ENABLE_GE) if (ENABLE_D) + set(ASCEND_PATH /usr/local/Ascend) + set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common) add_custom_target(add_ge_lib ALL COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/common/graph/libgraph.so ${MS_LIB_PATH} COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/ge/common/libge_common.so ${MS_LIB_PATH} COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/ge/ge_runtime/libge_runtime.so ${MS_LIB_PATH} - COMMAND cp /usr/local/HiAI/driver/lib64/libslog.so ${MS_LIB_PATH} - COMMAND cp /usr/local/HiAI/driver/lib64/libc_sec.so ${MS_LIB_PATH} + COMMAND cp ${ASCEND_DRIVER_PATH}/libslog.so ${MS_LIB_PATH} + COMMAND cp ${ASCEND_DRIVER_PATH}/libc_sec.so ${MS_LIB_PATH} ) add_dependencies(add_ge_lib add_ms_lib) add_dependencies(add_ge_lib graph) From db405872e7bc7e3b2c3905d46457de12de7b33de Mon Sep 17 00:00:00 2001 From: guohongzilong <2713219276@qq.com> Date: Thu, 2 Apr 2020 12:05:28 +0800 Subject: [PATCH 080/227] pass str to ge.exec.EnableDump --- mindspore/ccsrc/utils/context/ms_context.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/ccsrc/utils/context/ms_context.cc b/mindspore/ccsrc/utils/context/ms_context.cc index 7b531536ac..cdc7af62e6 100644 --- a/mindspore/ccsrc/utils/context/ms_context.cc +++ b/mindspore/ccsrc/utils/context/ms_context.cc @@ -277,7 +277,7 @@ void MsContext::SetHcclOptions(std::map* ge_options) c void MsContext::GetGeOptions(std::map* ge_options) const { #ifdef ENABLE_GE (*ge_options)["device_id"] = "0"; - (*ge_options)["ge.exec.enableDump"] = enable_dump_; + (*ge_options)["ge.exec.enableDump"] = std::to_string(enable_dump_); (*ge_options)["ge.exec.dumpPath"] = save_dump_path_; // only not supported in ge auto tbe_plugin_path = common::GetEnv("ME_TBE_PLUGIN_PATH"); From a62a9f4395b242d588cc3aec9f16a25c81473b3f Mon Sep 17 00:00:00 2001 From: zhaoting Date: Wed, 1 Apr 2020 10:22:48 +0800 Subject: [PATCH 081/227] add YOLOv3 infer scipt and change dataset to MindRecord --- example/yolov3_coco2017/config.py | 3 + example/yolov3_coco2017/dataset.py | 265 +++++++----------- example/yolov3_coco2017/eval.py | 107 +++++++ .../yolov3_coco2017/run_distribute_train.sh | 24 +- example/yolov3_coco2017/run_eval.sh | 23 ++ .../yolov3_coco2017/run_standalone_train.sh | 8 +- example/yolov3_coco2017/train.py | 114 +++++--- example/yolov3_coco2017/util.py | 146 ++++++++++ mindspore/ccsrc/kernel/tbe/tbe_adapter.cc | 1 + mindspore/model_zoo/yolov3.py | 97 ++++++- mindspore/ops/_op_impl/tbe/__init__.py | 2 + mindspore/ops/_op_impl/tbe/reduce_min.py | 76 +++++ mindspore/ops/_op_impl/tbe/round.py | 65 +++++ 13 files changed, 704 insertions(+), 227 deletions(-) create mode 100644 example/yolov3_coco2017/eval.py create mode 100644 example/yolov3_coco2017/run_eval.sh create mode 100644 example/yolov3_coco2017/util.py create mode 100644 mindspore/ops/_op_impl/tbe/reduce_min.py create mode 100644 mindspore/ops/_op_impl/tbe/round.py diff --git a/example/yolov3_coco2017/config.py b/example/yolov3_coco2017/config.py index f00eb89dc7..6d858bcbcb 100644 --- a/example/yolov3_coco2017/config.py +++ b/example/yolov3_coco2017/config.py @@ -26,6 +26,7 @@ class ConfigYOLOV3ResNet18: img_shape = [352, 640] feature_shape = [32, 3, 352, 640] num_classes = 80 + nms_max_num = 50 backbone_input_shape = [64, 64, 128, 256] backbone_shape = [64, 128, 256, 512] @@ -33,6 +34,8 @@ class ConfigYOLOV3ResNet18: backbone_stride = [1, 2, 2, 2] ignore_threshold = 0.5 + obj_threshold = 0.3 + nms_threshold = 0.4 anchor_scales = [(10, 13), (16, 30), diff --git a/example/yolov3_coco2017/dataset.py b/example/yolov3_coco2017/dataset.py index 56520704b4..826fe16c53 100644 --- a/example/yolov3_coco2017/dataset.py +++ b/example/yolov3_coco2017/dataset.py @@ -16,16 +16,14 @@ """YOLOv3 dataset""" from __future__ import division -import abc -import io import os -import math -import json import numpy as np from PIL import Image from matplotlib.colors import rgb_to_hsv, hsv_to_rgb import mindspore.dataset as de +from mindspore.mindrecord import FileWriter import mindspore.dataset.transforms.vision.py_transforms as P +import mindspore.dataset.transforms.vision.c_transforms as C from config import ConfigYOLOV3ResNet18 iter_cnt = 0 @@ -114,6 +112,29 @@ def preprocess_fn(image, box, is_training): return y_true[0], y_true[1], y_true[2], pad_gt_box0, pad_gt_box1, pad_gt_box2 + def _infer_data(img_data, input_shape, box): + w, h = img_data.size + input_h, input_w = input_shape + scale = min(float(input_w) / float(w), float(input_h) / float(h)) + nw = int(w * scale) + nh = int(h * scale) + img_data = img_data.resize((nw, nh), Image.BICUBIC) + + new_image = np.zeros((input_h, input_w, 3), np.float32) + new_image.fill(128) + img_data = np.array(img_data) + if len(img_data.shape) == 2: + img_data = np.expand_dims(img_data, axis=-1) + img_data = np.concatenate([img_data, img_data, img_data], axis=-1) + + dh = int((input_h - nh) / 2) + dw = int((input_w - nw) / 2) + new_image[dh:(nh + dh), dw:(nw + dw), :] = img_data + new_image /= 255. + new_image = np.transpose(new_image, (2, 0, 1)) + new_image = np.expand_dims(new_image, 0) + return new_image, np.array([h, w], np.float32), box + def _data_aug(image, box, is_training, jitter=0.3, hue=0.1, sat=1.5, val=1.5, image_size=(352, 640)): """Data augmentation function.""" if not isinstance(image, Image.Image): @@ -124,32 +145,7 @@ def preprocess_fn(image, box, is_training): h, w = image_size if not is_training: - image = image.resize((w, h), Image.BICUBIC) - image_data = np.array(image) / 255. - if len(image_data.shape) == 2: - image_data = np.expand_dims(image_data, axis=-1) - image_data = np.concatenate([image_data, image_data, image_data], axis=-1) - image_data = image_data.astype(np.float32) - - # correct boxes - box_data = np.zeros((max_boxes, 5)) - if len(box) >= 1: - np.random.shuffle(box) - if len(box) > max_boxes: - box = box[:max_boxes] - # xmin ymin xmax ymax - box[:, [0, 2]] = box[:, [0, 2]] * float(w) / float(iw) - box[:, [1, 3]] = box[:, [1, 3]] * float(h) / float(ih) - box_data[:len(box)] = box - else: - image_data, box_data = None, None - - # preprocess bounding boxes - bbox_true_1, bbox_true_2, bbox_true_3, gt_box1, gt_box2, gt_box3 = \ - _preprocess_true_boxes(box_data, anchors, image_size) - - return image_data, bbox_true_1, bbox_true_2, bbox_true_3, \ - ori_image_shape, gt_box1, gt_box2, gt_box3 + return _infer_data(image, image_size, box) flip = _rand() < .5 # correct boxes @@ -235,12 +231,16 @@ def preprocess_fn(image, box, is_training): return image_data, bbox_true_1, bbox_true_2, bbox_true_3, \ ori_image_shape, gt_box1, gt_box2, gt_box3 - images, bbox_1, bbox_2, bbox_3, _, gt_box1, gt_box2, gt_box3 = _data_aug(image, box, is_training) - return images, bbox_1, bbox_2, bbox_3, gt_box1, gt_box2, gt_box3 + if is_training: + images, bbox_1, bbox_2, bbox_3, _, gt_box1, gt_box2, gt_box3 = _data_aug(image, box, is_training) + return images, bbox_1, bbox_2, bbox_3, gt_box1, gt_box2, gt_box3 + + images, shape, anno = _data_aug(image, box, is_training) + return images, shape, anno def anno_parser(annos_str): - """Annotation parser.""" + """Parse annotation from string to list.""" annos = [] for anno_str in annos_str: anno = list(map(int, anno_str.strip().split(','))) @@ -248,142 +248,71 @@ def anno_parser(annos_str): return annos -def expand_path(path): - """Get file list from path.""" - files = [] - if os.path.isdir(path): - for file in os.listdir(path): - if os.path.isfile(os.path.join(path, file)): - files.append(file) - else: +def filter_valid_data(image_dir, anno_path): + """Filter valid image file, which both in image_dir and anno_path.""" + image_files = [] + image_anno_dict = {} + if not os.path.isdir(image_dir): raise RuntimeError("Path given is not valid.") - return files - - -def read_image(img_path): - """Read image with PIL.""" - with open(img_path, "rb") as f: - img = f.read() - data = io.BytesIO(img) - img = Image.open(data) - return np.array(img) - - -class BaseDataset(): - """BaseDataset for GeneratorDataset iterator.""" - def __init__(self, image_dir, anno_path): - self.image_dir = image_dir - self.anno_path = anno_path - self.cur_index = 0 - self.samples = [] - self.image_anno_dict = {} - self._load_samples() - - def __getitem__(self, item): - sample = self.samples[item] - return self._next_data(sample, self.image_dir, self.image_anno_dict) - - def __len__(self): - return len(self.samples) - - @staticmethod - def _next_data(sample, image_dir, image_anno_dict): - """Get next data.""" - image = read_image(os.path.join(image_dir, sample)) - annos = image_anno_dict[sample] - return [np.array(image), np.array(annos)] - - @abc.abstractmethod - def _load_samples(self): - """Base load samples.""" - - -class YoloDataset(BaseDataset): - """YoloDataset for GeneratorDataset iterator.""" - def _load_samples(self): - """Load samples.""" - image_files_raw = expand_path(self.image_dir) - self.samples = self._filter_valid_data(self.anno_path, image_files_raw) - self.dataset_size = len(self.samples) - if self.dataset_size == 0: - raise RuntimeError("Valid dataset is none!") - - def _filter_valid_data(self, anno_path, image_files_raw): - """Filter valid data.""" - image_files = [] - anno_dict = {} - print("Start filter valid data.") - with open(anno_path, "rb") as f: - lines = f.readlines() - for line in lines: - line_str = line.decode("utf-8") - line_split = str(line_str).split(' ') - anno_dict[line_split[0].split("/")[-1]] = line_split[1:] - anno_set = set(anno_dict.keys()) - image_set = set(image_files_raw) - for image_file in (anno_set & image_set): - image_files.append(image_file) - self.image_anno_dict[image_file] = anno_parser(anno_dict[image_file]) - image_files.sort() - print("Filter valid data done!") - return image_files - - -class DistributedSampler(): - """DistributedSampler for YOLOv3""" - def __init__(self, dataset_size, batch_size, num_replicas=None, rank=None, shuffle=True): - if num_replicas is None: - num_replicas = 1 - if rank is None: - rank = 0 - self.dataset_size = dataset_size - self.num_replicas = num_replicas - self.rank = rank % num_replicas - self.epoch = 0 - self.num_samples = max(batch_size, int(math.ceil(dataset_size * 1.0 / self.num_replicas))) - self.total_size = self.num_samples * self.num_replicas - self.shuffle = shuffle - - def __iter__(self): - # deterministically shuffle based on epoch - if self.shuffle: - indices = np.random.RandomState(seed=self.epoch).permutation(self.dataset_size) - indices = indices.tolist() - else: - indices = list(range(self.dataset_size)) - - # add extra samples to make it evenly divisible - indices += indices[:(self.total_size - len(indices))] - assert len(indices) == self.total_size - - # subsample - indices = indices[self.rank:self.total_size:self.num_replicas] - assert len(indices) == self.num_samples - - return iter(indices) - - def __len__(self): - return self.num_samples - - def set_epoch(self, epoch): - self.epoch = epoch - - -def create_yolo_dataset(image_dir, anno_path, batch_size=32, repeat_num=10, device_num=1, rank=0, + if not os.path.isfile(anno_path): + raise RuntimeError("Annotation file is not valid.") + + with open(anno_path, "rb") as f: + lines = f.readlines() + for line in lines: + line_str = line.decode("utf-8").strip() + line_split = str(line_str).split(' ') + file_name = line_split[0] + if os.path.isfile(os.path.join(image_dir, file_name)): + image_anno_dict[file_name] = anno_parser(line_split[1:]) + image_files.append(file_name) + return image_files, image_anno_dict + + +def data_to_mindrecord_byte_image(image_dir, anno_path, mindrecord_dir, prefix="yolo.mindrecord", file_num=8): + """Create MindRecord file by image_dir and anno_path.""" + mindrecord_path = os.path.join(mindrecord_dir, prefix) + writer = FileWriter(mindrecord_path, file_num) + image_files, image_anno_dict = filter_valid_data(image_dir, anno_path) + + yolo_json = { + "image": {"type": "bytes"}, + "annotation": {"type": "int64", "shape": [-1, 5]}, + } + writer.add_schema(yolo_json, "yolo_json") + + for image_name in image_files: + image_path = os.path.join(image_dir, image_name) + with open(image_path, 'rb') as f: + img = f.read() + annos = np.array(image_anno_dict[image_name]) + row = {"image": img, "annotation": annos} + writer.write_raw_data([row]) + writer.commit() + + +def create_yolo_dataset(mindrecord_dir, batch_size=32, repeat_num=10, device_num=1, rank=0, is_training=True, num_parallel_workers=8): - """Creatr YOLOv3 dataset with GeneratorDataset.""" - yolo_dataset = YoloDataset(image_dir=image_dir, anno_path=anno_path) - distributed_sampler = DistributedSampler(yolo_dataset.dataset_size, batch_size, device_num, rank) - ds = de.GeneratorDataset(yolo_dataset, column_names=["image", "annotation"], sampler=distributed_sampler) - ds.set_dataset_size(len(distributed_sampler)) + """Creatr YOLOv3 dataset with MindDataset.""" + ds = de.MindDataset(mindrecord_dir, columns_list=["image", "annotation"], num_shards=device_num, shard_id=rank, + num_parallel_workers=num_parallel_workers, shuffle=is_training) + decode = C.Decode() + ds = ds.map(input_columns=["image"], operations=decode) compose_map_func = (lambda image, annotation: preprocess_fn(image, annotation, is_training)) - hwc_to_chw = P.HWC2CHW() - ds = ds.map(input_columns=["image", "annotation"], - output_columns=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"], - columns_order=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"], - operations=compose_map_func, num_parallel_workers=num_parallel_workers) - ds = ds.map(input_columns=["image"], operations=hwc_to_chw, num_parallel_workers=num_parallel_workers) - ds = ds.shuffle(buffer_size=256) - ds = ds.batch(batch_size, drop_remainder=True) - ds = ds.repeat(repeat_num) + + if is_training: + hwc_to_chw = P.HWC2CHW() + ds = ds.map(input_columns=["image", "annotation"], + output_columns=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"], + columns_order=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"], + operations=compose_map_func, num_parallel_workers=num_parallel_workers) + ds = ds.map(input_columns=["image"], operations=hwc_to_chw, num_parallel_workers=num_parallel_workers) + ds = ds.shuffle(buffer_size=256) + ds = ds.batch(batch_size, drop_remainder=True) + ds = ds.repeat(repeat_num) + else: + ds = ds.map(input_columns=["image", "annotation"], + output_columns=["image", "image_shape", "annotation"], + columns_order=["image", "image_shape", "annotation"], + operations=compose_map_func, num_parallel_workers=num_parallel_workers) return ds diff --git a/example/yolov3_coco2017/eval.py b/example/yolov3_coco2017/eval.py new file mode 100644 index 0000000000..8347f5be37 --- /dev/null +++ b/example/yolov3_coco2017/eval.py @@ -0,0 +1,107 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# less required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""Evaluation for yolo_v3""" +import os +import argparse +import time +from mindspore import context, Tensor +from mindspore.train.serialization import load_checkpoint, load_param_into_net +from mindspore.model_zoo.yolov3 import yolov3_resnet18, YoloWithEval +from dataset import create_yolo_dataset, data_to_mindrecord_byte_image +from config import ConfigYOLOV3ResNet18 +from util import metrics + +def yolo_eval(dataset_path, ckpt_path): + """Yolov3 evaluation.""" + + ds = create_yolo_dataset(dataset_path, is_training=False) + config = ConfigYOLOV3ResNet18() + net = yolov3_resnet18(config) + eval_net = YoloWithEval(net, config) + print("Load Checkpoint!") + param_dict = load_checkpoint(ckpt_path) + load_param_into_net(net, param_dict) + + + eval_net.set_train(False) + i = 1. + total = ds.get_dataset_size() + start = time.time() + pred_data = [] + print("\n========================================\n") + print("total images num: ", total) + print("Processing, please wait a moment.") + for data in ds.create_dict_iterator(): + img_np = data['image'] + image_shape = data['image_shape'] + annotation = data['annotation'] + + eval_net.set_train(False) + output = eval_net(Tensor(img_np), Tensor(image_shape)) + for batch_idx in range(img_np.shape[0]): + pred_data.append({"boxes": output[0].asnumpy()[batch_idx], + "box_scores": output[1].asnumpy()[batch_idx], + "annotation": annotation}) + percent = round(i / total * 100, 2) + + print(' %s [%d/%d]' % (str(percent) + '%', i, total), end='\r') + i += 1 + print(' %s [%d/%d] cost %d ms' % (str(100.0) + '%', total, total, int((time.time() - start) * 1000)), end='\n') + + precisions, recalls = metrics(pred_data) + print("\n========================================\n") + for i in range(config.num_classes): + print("class {} precision is {:.2f}%, recall is {:.2f}%".format(i, precisions[i] * 100, recalls[i] * 100)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Yolov3 evaluation') + parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.") + parser.add_argument("--mindrecord_dir", type=str, default="./Mindrecord_eval", + help="Mindrecord directory. If the mindrecord_dir is empty, it wil generate mindrecord file by" + "image_dir and anno_path. Note if mindrecord_dir isn't empty, it will use mindrecord_dir " + "rather than image_dir and anno_path. Default is ./Mindrecord_eval") + parser.add_argument("--image_dir", type=str, default="", help="Dataset directory, " + "the absolute image path is joined by the image_dir " + "and the relative path in anno_path.") + parser.add_argument("--anno_path", type=str, default="", help="Annotation path.") + parser.add_argument("--ckpt_path", type=str, required=True, help="Checkpoint path.") + args_opt = parser.parse_args() + + context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id) + context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True, + enable_auto_mixed_precision=False) + + # It will generate mindrecord file in args_opt.mindrecord_dir, + # and the file name is yolo.mindrecord0, 1, ... file_num. + if not os.path.isdir(args_opt.mindrecord_dir): + os.makedirs(args_opt.mindrecord_dir) + + prefix = "yolo.mindrecord" + mindrecord_file = os.path.join(args_opt.mindrecord_dir, prefix + "0") + if not os.path.exists(mindrecord_file): + if os.path.isdir(args_opt.image_dir) and os.path.exists(args_opt.anno_path): + print("Create Mindrecord") + data_to_mindrecord_byte_image(args_opt.image_dir, + args_opt.anno_path, + args_opt.mindrecord_dir, + prefix=prefix, + file_num=8) + print("Create Mindrecord Done, at {}".format(args_opt.mindrecord_dir)) + else: + print("image_dir or anno_path not exits") + print("Start Eval!") + yolo_eval(mindrecord_file, args_opt.ckpt_path) diff --git a/example/yolov3_coco2017/run_distribute_train.sh b/example/yolov3_coco2017/run_distribute_train.sh index 98e6690b8c..0c43e776b9 100644 --- a/example/yolov3_coco2017/run_distribute_train.sh +++ b/example/yolov3_coco2017/run_distribute_train.sh @@ -14,17 +14,26 @@ # limitations under the License. # ============================================================================ +echo "==============================================================================================================" echo "Please run the scipt as: " -echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE IMAGE_DIR ANNO_PATH MINDSPORE_HCCL_CONFIG_PATH" -echo "for example: sh run_distribute_train.sh 8 100 ./dataset/coco/train2017 ./dataset/train.txt　./hccl.json" -echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt" +echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE MINDRECORD_DIR IMAGE_DIR ANNO_PATH MINDSPORE_HCCL_CONFIG_PATH" +echo "for example: sh run_distribute_train.sh 8 100 /data/Mindrecord_train /data /data/train.txt /data/hccl.json" +echo "It is better to use absolute path." +echo "==============================================================================================================" -export RANK_SIZE=$1 EPOCH_SIZE=$2 -IMAGE_DIR=$3 -ANNO_PATH=$4 -export MINDSPORE_HCCL_CONFIG_PATH=$5 +MINDRECORD_DIR=$3 +IMAGE_DIR=$4 +ANNO_PATH=$5 + +# Before start distribute train, first create mindrecord files. +python train.py --only_create_dataset=1 --mindrecord_dir=$MINDRECORD_DIR --image_dir=$IMAGE_DIR \ +--anno_path=$ANNO_PATH +echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt" + +export MINDSPORE_HCCL_CONFIG_PATH=$6 +export RANK_SIZE=$1 for((i=0;i log.txt 2>&1 & diff --git a/example/yolov3_coco2017/run_eval.sh b/example/yolov3_coco2017/run_eval.sh new file mode 100644 index 0000000000..4608e92589 --- /dev/null +++ b/example/yolov3_coco2017/run_eval.sh @@ -0,0 +1,23 @@ +#!/bin/bash +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +echo "==============================================================================================================" +echo "Please run the scipt as: " +echo "sh run_eval.sh DEVICE_ID CKPT_PATH MINDRECORD_DIR IMAGE_DIR ANNO_PATH" +echo "for example: sh run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt" +echo "==============================================================================================================" + +python eval.py --device_id=$1 --ckpt_path=$2 --mindrecord_dir=$3 --image_dir=$4 --anno_path=$5 diff --git a/example/yolov3_coco2017/run_standalone_train.sh b/example/yolov3_coco2017/run_standalone_train.sh index b3b8b0cd9a..d91d537008 100644 --- a/example/yolov3_coco2017/run_standalone_train.sh +++ b/example/yolov3_coco2017/run_standalone_train.sh @@ -14,8 +14,10 @@ # limitations under the License. # ============================================================================ +echo "==============================================================================================================" echo "Please run the scipt as: " -echo "sh run_standalone_train.sh DEVICE_ID EPOCH_SIZE IMAGE_DIR ANNO_PATH" -echo "for example: sh run_standalone_train.sh 0 50 ./dataset/coco/train2017 ./dataset/train.txt" +echo "sh run_standalone_train.sh DEVICE_ID EPOCH_SIZE MINDRECORD_DIR IMAGE_DIR ANNO_PATH" +echo "for example: sh run_standalone_train.sh 0 50 ./Mindrecord_train ./dataset ./dataset/train.txt" +echo "==============================================================================================================" -python train.py --device_id=$1 --epoch_size=$2 --image_dir=$3 --anno_path=$4 +python train.py --device_id=$1 --epoch_size=$2 --mindrecord_dir=$3 --image_dir=$4 --anno_path=$5 diff --git a/example/yolov3_coco2017/train.py b/example/yolov3_coco2017/train.py index a3dd8f34fe..3ac3816f4a 100644 --- a/example/yolov3_coco2017/train.py +++ b/example/yolov3_coco2017/train.py @@ -16,26 +16,30 @@ """ ######################## train YOLOv3 example ######################## train YOLOv3 and get network model files(.ckpt) : -python train.py --image_dir dataset/coco/coco/train2017 --anno_path dataset/coco/train_coco.txt +python train.py --image_dir /data --anno_path /data/coco/train_coco.txt --mindrecord_dir=/data/Mindrecord_train + +If the mindrecord_dir is empty, it wil generate mindrecord file by image_dir and anno_path. +Note if mindrecord_dir isn't empty, it will use mindrecord_dir rather than image_dir and anno_path. """ +import os import argparse import numpy as np import mindspore.nn as nn from mindspore import context, Tensor -from mindspore.common.initializer import initializer from mindspore.communication.management import init from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMonitor, TimeMonitor from mindspore.train import Model, ParallelMode from mindspore.train.serialization import load_checkpoint, load_param_into_net +from mindspore.common.initializer import initializer from mindspore.model_zoo.yolov3 import yolov3_resnet18, YoloWithLossCell, TrainingWrapper -from dataset import create_yolo_dataset +from dataset import create_yolo_dataset, data_to_mindrecord_byte_image from config import ConfigYOLOV3ResNet18 def get_lr(learning_rate, start_step, global_step, decay_step, decay_rate, steps=False): - """Set learning rate""" + """Set learning rate.""" lr_each_step = [] lr = learning_rate for i in range(global_step): @@ -57,7 +61,9 @@ def init_net_param(net, init='ones'): if __name__ == '__main__': - parser = argparse.ArgumentParser(description="YOLOv3") + parser = argparse.ArgumentParser(description="YOLOv3 train") + parser.add_argument("--only_create_dataset", type=bool, default=False, help="If set it true, only create " + "Mindrecord, default is false.") parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.") parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.") parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.") @@ -67,12 +73,19 @@ if __name__ == '__main__': parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path") parser.add_argument("--save_checkpoint_epochs", type=int, default=5, help="Save checkpoint epochs, default is 5.") parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.") - parser.add_argument("--image_dir", type=str, required=True, help="Dataset image dir.") - parser.add_argument("--anno_path", type=str, required=True, help="Dataset anno path.") + parser.add_argument("--mindrecord_dir", type=str, default="./Mindrecord_train", + help="Mindrecord directory. If the mindrecord_dir is empty, it wil generate mindrecord file by" + "image_dir and anno_path. Note if mindrecord_dir isn't empty, it will use mindrecord_dir " + "rather than image_dir and anno_path. Default is ./Mindrecord_train") + parser.add_argument("--image_dir", type=str, default="", help="Dataset directory, " + "the absolute image path is joined by the image_dir " + "and the relative path in anno_path") + parser.add_argument("--anno_path", type=str, default="", help="Annotation path.") args_opt = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id) - context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True) + context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True, + enable_auto_mixed_precision=False) if args_opt.distribute: device_num = args_opt.device_num context.reset_auto_parallel_context() @@ -80,36 +93,65 @@ if __name__ == '__main__': context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True, device_num=device_num) init() - rank = args_opt.device_id + rank = args_opt.device_id % device_num else: context.set_context(enable_hccl=False) rank = 0 device_num = 1 - loss_scale = float(args_opt.loss_scale) - dataset = create_yolo_dataset(args_opt.image_dir, args_opt.anno_path, repeat_num=args_opt.epoch_size, - batch_size=args_opt.batch_size, device_num=device_num, rank=rank) - dataset_size = dataset.get_dataset_size() - net = yolov3_resnet18(ConfigYOLOV3ResNet18()) - net = YoloWithLossCell(net, ConfigYOLOV3ResNet18()) - init_net_param(net, "XavierUniform") - - # checkpoint - ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs) - ckpoint_cb = ModelCheckpoint(prefix="yolov3", directory=None, config=ckpt_config) - if args_opt.checkpoint_path != "": - param_dict = load_checkpoint(args_opt.checkpoint_path) - load_param_into_net(net, param_dict) - - lr = Tensor(get_lr(learning_rate=0.001, start_step=0, global_step=args_opt.epoch_size * dataset_size, - decay_step=1000, decay_rate=0.95)) - opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale) - net = TrainingWrapper(net, opt, loss_scale) - callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb] - - model = Model(net) - dataset_sink_mode = False - if args_opt.mode == "graph": - dataset_sink_mode = True - print("Start train YOLOv3.") - model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode) + print("Start create dataset!") + + # It will generate mindrecord file in args_opt.mindrecord_dir, + # and the file name is yolo.mindrecord0, 1, ... file_num. + if not os.path.isdir(args_opt.mindrecord_dir): + os.makedirs(args_opt.mindrecord_dir) + + prefix = "yolo.mindrecord" + mindrecord_file = os.path.join(args_opt.mindrecord_dir, prefix + "0") + if not os.path.exists(mindrecord_file): + if os.path.isdir(args_opt.image_dir) and os.path.exists(args_opt.anno_path): + print("Create Mindrecord.") + data_to_mindrecord_byte_image(args_opt.image_dir, + args_opt.anno_path, + args_opt.mindrecord_dir, + prefix=prefix, + file_num=8) + print("Create Mindrecord Done, at {}".format(args_opt.mindrecord_dir)) + else: + print("image_dir or anno_path not exits.") + + if not args_opt.only_create_dataset: + loss_scale = float(args_opt.loss_scale) + + # When create MindDataset, using the fitst mindrecord file, such as yolo.mindrecord0. + dataset = create_yolo_dataset(mindrecord_file, repeat_num=args_opt.epoch_size, + batch_size=args_opt.batch_size, device_num=device_num, rank=rank) + dataset_size = dataset.get_dataset_size() + print("Create dataset done!") + + net = yolov3_resnet18(ConfigYOLOV3ResNet18()) + net = YoloWithLossCell(net, ConfigYOLOV3ResNet18()) + init_net_param(net, "XavierUniform") + + # checkpoint + ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs) + ckpoint_cb = ModelCheckpoint(prefix="yolov3", directory=None, config=ckpt_config) + + lr = Tensor(get_lr(learning_rate=0.001, start_step=0, global_step=args_opt.epoch_size * dataset_size, + decay_step=1000, decay_rate=0.95)) + opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale) + net = TrainingWrapper(net, opt, loss_scale) + + if args_opt.checkpoint_path != "": + param_dict = load_checkpoint(args_opt.checkpoint_path) + load_param_into_net(net, param_dict) + + callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb] + + model = Model(net) + dataset_sink_mode = False + if args_opt.mode == "graph": + print("In graph mode, one epoch return a loss.") + dataset_sink_mode = True + print("Start train YOLOv3, the first epoch will be slower because of the graph compilation.") + model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode) diff --git a/example/yolov3_coco2017/util.py b/example/yolov3_coco2017/util.py new file mode 100644 index 0000000000..62e15afe38 --- /dev/null +++ b/example/yolov3_coco2017/util.py @@ -0,0 +1,146 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +"""metrics utils""" + +import numpy as np +from config import ConfigYOLOV3ResNet18 + + +def calc_iou(bbox_pred, bbox_ground): + """Calculate iou of predicted bbox and ground truth.""" + x1 = bbox_pred[0] + y1 = bbox_pred[1] + width1 = bbox_pred[2] - bbox_pred[0] + height1 = bbox_pred[3] - bbox_pred[1] + + x2 = bbox_ground[0] + y2 = bbox_ground[1] + width2 = bbox_ground[2] - bbox_ground[0] + height2 = bbox_ground[3] - bbox_ground[1] + + endx = max(x1 + width1, x2 + width2) + startx = min(x1, x2) + width = width1 + width2 - (endx - startx) + + endy = max(y1 + height1, y2 + height2) + starty = min(y1, y2) + height = height1 + height2 - (endy - starty) + + if width <= 0 or height <= 0: + iou = 0 + else: + area = width * height + area1 = width1 * height1 + area2 = width2 * height2 + iou = area * 1. / (area1 + area2 - area) + + return iou + + +def apply_nms(all_boxes, all_scores, thres, max_boxes): + """Apply NMS to bboxes.""" + x1 = all_boxes[:, 0] + y1 = all_boxes[:, 1] + x2 = all_boxes[:, 2] + y2 = all_boxes[:, 3] + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + + order = all_scores.argsort()[::-1] + keep = [] + + while order.size > 0: + i = order[0] + keep.append(i) + + if len(keep) >= max_boxes: + break + + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= thres)[0] + + order = order[inds + 1] + return keep + + +def metrics(pred_data): + """Calculate precision and recall of predicted bboxes.""" + config = ConfigYOLOV3ResNet18() + num_classes = config.num_classes + count_corrects = [1e-6 for _ in range(num_classes)] + count_grounds = [1e-6 for _ in range(num_classes)] + count_preds = [1e-6 for _ in range(num_classes)] + + for i, sample in enumerate(pred_data): + gt_anno = sample["annotation"] + box_scores = sample['box_scores'] + boxes = sample['boxes'] + mask = box_scores >= config.obj_threshold + boxes_ = [] + scores_ = [] + classes_ = [] + max_boxes = config.nms_max_num + for c in range(num_classes): + class_boxes = np.reshape(boxes, [-1, 4])[np.reshape(mask[:, c], [-1])] + class_box_scores = np.reshape(box_scores[:, c], [-1])[np.reshape(mask[:, c], [-1])] + nms_index = apply_nms(class_boxes, class_box_scores, config.nms_threshold, max_boxes) + class_boxes = class_boxes[nms_index] + class_box_scores = class_box_scores[nms_index] + classes = np.ones_like(class_box_scores, 'int32') * c + boxes_.append(class_boxes) + scores_.append(class_box_scores) + classes_.append(classes) + + boxes = np.concatenate(boxes_, axis=0) + classes = np.concatenate(classes_, axis=0) + + + # metric + count_correct = [1e-6 for _ in range(num_classes)] + count_ground = [1e-6 for _ in range(num_classes)] + count_pred = [1e-6 for _ in range(num_classes)] + + for anno in gt_anno: + count_ground[anno[4]] += 1 + + for box_index, box in enumerate(boxes): + bbox_pred = [box[1], box[0], box[3], box[2]] + count_pred[classes[box_index]] += 1 + + for anno in gt_anno: + class_ground = anno[4] + + if classes[box_index] == class_ground: + iou = calc_iou(bbox_pred, anno) + if iou >= 0.5: + count_correct[class_ground] += 1 + break + + count_corrects = [count_corrects[i] + count_correct[i] for i in range(num_classes)] + count_preds = [count_preds[i] + count_pred[i] for i in range(num_classes)] + count_grounds = [count_grounds[i] + count_ground[i] for i in range(num_classes)] + + precision = np.array([count_corrects[ix] / count_preds[ix] for ix in range(num_classes)]) + recall = np.array([count_corrects[ix] / count_grounds[ix] for ix in range(num_classes)]) + return precision, recall diff --git a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc index 50fed77a9a..c0416f648b 100644 --- a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc +++ b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc @@ -34,6 +34,7 @@ static std::map tbe_func_adapter_map = { {"tensor_add", "add"}, {"reduce_mean", "reduce_mean_d"}, {"reduce_max", "reduce_max_d"}, + {"reduce_min", "reduce_min_d"}, {"conv2d_backprop_filter", "conv2d_backprop_filter_d"}, {"conv2d_backprop_input", "conv2d_backprop_input_d"}, {"top_kv2", "top_k"}, diff --git a/mindspore/model_zoo/yolov3.py b/mindspore/model_zoo/yolov3.py index 5ac3b67086..0ac6b21070 100644 --- a/mindspore/model_zoo/yolov3.py +++ b/mindspore/model_zoo/yolov3.py @@ -15,6 +15,7 @@ """YOLOv3 based on ResNet18.""" +import numpy as np import mindspore as ms import mindspore.nn as nn from mindspore import context, Tensor @@ -31,19 +32,14 @@ def weight_variable(): return TruncatedNormal(0.02) -class _conv_with_pad(nn.Cell): +class _conv2d(nn.Cell): """Create Conv2D with padding.""" def __init__(self, in_channels, out_channels, kernel_size, stride=1): - super(_conv_with_pad, self).__init__() - total_pad = kernel_size - 1 - pad_begin = total_pad // 2 - pad_end = total_pad - pad_begin - self.pad = P.Pad(((0, 0), (0, 0), (pad_begin, pad_end), (pad_begin, pad_end))) + super(_conv2d, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, - kernel_size=kernel_size, stride=stride, padding=0, pad_mode='valid', + kernel_size=kernel_size, stride=stride, padding=0, pad_mode='same', weight_init=weight_variable()) def construct(self, x): - x = self.pad(x) x = self.conv(x) return x @@ -101,15 +97,15 @@ class BasicBlock(nn.Cell): momentum=0.99): super(BasicBlock, self).__init__() - self.conv1 = _conv_with_pad(in_channels, out_channels, 3, stride=stride) + self.conv1 = _conv2d(in_channels, out_channels, 3, stride=stride) self.bn1 = _fused_bn(out_channels, momentum=momentum) - self.conv2 = _conv_with_pad(out_channels, out_channels, 3) + self.conv2 = _conv2d(out_channels, out_channels, 3) self.bn2 = _fused_bn(out_channels, momentum=momentum) self.relu = P.ReLU() self.down_sample_layer = None self.downsample = (in_channels != out_channels) if self.downsample: - self.down_sample_layer = _conv_with_pad(in_channels, out_channels, 1, stride=stride) + self.down_sample_layer = _conv2d(in_channels, out_channels, 1, stride=stride) self.add = P.TensorAdd() def construct(self, x): @@ -166,7 +162,7 @@ class ResNet(nn.Cell): raise ValueError("the length of " "layer_num, inchannel, outchannel list must be 4!") - self.conv1 = _conv_with_pad(3, 64, 7, stride=2) + self.conv1 = _conv2d(3, 64, 7, stride=2) self.bn1 = _fused_bn(64) self.relu = P.ReLU() self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same') @@ -452,7 +448,7 @@ class DetectionBlock(nn.Cell): if self.training: return grid, prediction, box_xy, box_wh - return self.concat((box_xy, box_wh, box_confidence, box_probs)) + return box_xy, box_wh, box_confidence, box_probs class Iou(nn.Cell): @@ -675,3 +671,78 @@ class TrainingWrapper(nn.Cell): # apply grad reducer on grads grads = self.grad_reducer(grads) return F.depend(loss, self.optimizer(grads)) + + +class YoloBoxScores(nn.Cell): + """ + Calculate the boxes of the original picture size and the score of each box. + + Args: + config (Class): YOLOv3 config. + + Returns: + Tensor, the boxes of the original picture size. + Tensor, the score of each box. + """ + def __init__(self, config): + super(YoloBoxScores, self).__init__() + self.input_shape = Tensor(np.array(config.img_shape), ms.float32) + self.num_classes = config.num_classes + + def construct(self, box_xy, box_wh, box_confidence, box_probs, image_shape): + batch_size = F.shape(box_xy)[0] + x = box_xy[:, :, :, :, 0:1] + y = box_xy[:, :, :, :, 1:2] + box_yx = P.Concat(-1)((y, x)) + w = box_wh[:, :, :, :, 0:1] + h = box_wh[:, :, :, :, 1:2] + box_hw = P.Concat(-1)((h, w)) + + new_shape = P.Round()(image_shape * P.ReduceMin()(self.input_shape / image_shape)) + offset = (self.input_shape - new_shape) / 2.0 / self.input_shape + scale = self.input_shape / new_shape + box_yx = (box_yx - offset) * scale + box_hw = box_hw * scale + + box_min = box_yx - box_hw / 2.0 + box_max = box_yx + box_hw / 2.0 + boxes = P.Concat(-1)((box_min[:, :, :, :, 0:1], + box_min[:, :, :, :, 1:2], + box_max[:, :, :, :, 0:1], + box_max[:, :, :, :, 1:2])) + image_scale = P.Tile()(image_shape, (1, 2)) + boxes = boxes * image_scale + boxes = F.reshape(boxes, (batch_size, -1, 4)) + boxes_scores = box_confidence * box_probs + boxes_scores = F.reshape(boxes_scores, (batch_size, -1, self.num_classes)) + return boxes, boxes_scores + + +class YoloWithEval(nn.Cell): + """ + Encapsulation class of YOLOv3 evaluation. + + Args: + network (Cell): The training network. Note that loss function and optimizer must not be added. + config (Class): YOLOv3 config. + + Returns: + Tensor, the boxes of the original picture size. + Tensor, the score of each box. + Tensor, the original picture size. + """ + def __init__(self, network, config): + super(YoloWithEval, self).__init__() + self.yolo_network = network + self.box_score_0 = YoloBoxScores(config) + self.box_score_1 = YoloBoxScores(config) + self.box_score_2 = YoloBoxScores(config) + + def construct(self, x, image_shape): + yolo_output = self.yolo_network(x) + boxes_0, boxes_scores_0 = self.box_score_0(*yolo_output[0], image_shape) + boxes_1, boxes_scores_1 = self.box_score_1(*yolo_output[1], image_shape) + boxes_2, boxes_scores_2 = self.box_score_2(*yolo_output[2], image_shape) + boxes = P.Concat(1)((boxes_0, boxes_1, boxes_2)) + boxes_scores = P.Concat(1)((boxes_scores_0, boxes_scores_1, boxes_scores_2)) + return boxes, boxes_scores, image_shape diff --git a/mindspore/ops/_op_impl/tbe/__init__.py b/mindspore/ops/_op_impl/tbe/__init__.py index b9b9af4396..0b79ae845b 100644 --- a/mindspore/ops/_op_impl/tbe/__init__.py +++ b/mindspore/ops/_op_impl/tbe/__init__.py @@ -85,7 +85,9 @@ from .logical_and import _logical_and_tbe from .logical_not import _logical_not_tbe from .logical_or import _logical_or_tbe from .reduce_max import _reduce_max_tbe +from .reduce_min import _reduce_min_tbe from .reduce_sum import _reduce_sum_tbe +from .round import _round_tbe from .tanh import _tanh_tbe from .tanh_grad import _tanh_grad_tbe from .softmax import _softmax_tbe diff --git a/mindspore/ops/_op_impl/tbe/reduce_min.py b/mindspore/ops/_op_impl/tbe/reduce_min.py new file mode 100644 index 0000000000..f440b1fff6 --- /dev/null +++ b/mindspore/ops/_op_impl/tbe/reduce_min.py @@ -0,0 +1,76 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""ReduceMin op""" +from mindspore.ops.op_info_register import op_info_register + + +@op_info_register("""{ + "op_name": "ReduceMin", + "imply_type": "TBE", + "fusion_type": "OPAQUE", + "async_flag": false, + "binfile_name": "reduce_min_d.so", + "compute_cost": 10, + "kernel_name": "reduce_min_d", + "partial_flag": true, + "attr": [ + { + "name": "axis", + "param_type": "required", + "type": "listInt", + "value": "all" + }, + { + "name": "keep_dims", + "param_type": "required", + "type": "bool", + "value": "all" + } + ], + "inputs": [ + { + "index": 0, + "dtype": [ + "float16", "float16", "float", "float", "int8", "int8", "uint8", "uint8" + ], + "format": [ + "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ" + ], + "name": "x", + "need_compile": false, + "param_type": "required", + "shape": "all" + } + ], + "outputs": [ + { + "index": 0, + "dtype": [ + "float16", "float16", "float", "float", "int8", "int8", "uint8", "uint8" + ], + "format": [ + "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ" + ], + "name": "y", + "need_compile": false, + "param_type": "required", + "shape": "all" + } + ] +}""") +def _reduce_min_tbe(): + """ReduceMin TBE register""" + return diff --git a/mindspore/ops/_op_impl/tbe/round.py b/mindspore/ops/_op_impl/tbe/round.py new file mode 100644 index 0000000000..1368100a8e --- /dev/null +++ b/mindspore/ops/_op_impl/tbe/round.py @@ -0,0 +1,65 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""Round op""" +from mindspore.ops.op_info_register import op_info_register + + +@op_info_register("""{ + "op_name": "Round", + "imply_type": "TBE", + "fusion_type": "ELEMWISE", + "async_flag": false, + "binfile_name": "round.so", + "compute_cost": 10, + "kernel_name": "round", + "partial_flag": true, + "attr": [ + + ], + "inputs": [ + { + "index": 0, + "dtype": [ + "float16", "float16", "float16", "float", "float", "float" + ], + "format": [ + "DefaultFormat", "NC1HWC0", "FracZ", "DefaultFormat", "NC1HWC0", "FracZ" + ], + "name": "x", + "need_compile": false, + "param_type": "required", + "shape": "all" + } + ], + "outputs": [ + { + "index": 0, + "dtype": [ + "float16", "float16", "float16", "float", "float", "float" + ], + "format": [ + "DefaultFormat", "NC1HWC0", "FracZ", "DefaultFormat", "NC1HWC0", "FracZ" + ], + "name": "y", + "need_compile": false, + "param_type": "required", + "shape": "all" + } + ] +}""") +def _round_tbe(): + """Round TBE register""" + return From d4dead939bcbd413af2cbd3cf8722c5879fddd8e Mon Sep 17 00:00:00 2001 From: zhaoting Date: Fri, 3 Apr 2020 11:45:49 +0800 Subject: [PATCH 082/227] add weight decay in RMSProp optimizer --- mindspore/nn/optim/rmsprop.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/mindspore/nn/optim/rmsprop.py b/mindspore/nn/optim/rmsprop.py index 3000fdeeee..faaeacfaa8 100644 --- a/mindspore/nn/optim/rmsprop.py +++ b/mindspore/nn/optim/rmsprop.py @@ -18,7 +18,8 @@ from mindspore.common.initializer import initializer from mindspore.common.parameter import Parameter from mindspore._checkparam import ParamValidator as validator import mindspore.common.dtype as mstype -from .optimizer import Optimizer, grad_scale +from mindspore.common import Tensor +from .optimizer import Optimizer, grad_scale, apply_decay rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt") centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt") @@ -118,6 +119,9 @@ class RMSProp(Optimizer): use_locking (bool): Enable a lock to protect the update of variable and accumlation tensors. Default: False. centered (bool): If True, gradients are normalized by the estimated variance of the gradient. Default: False loss_scale (float): A floating point value for the loss scale. Default: 1.0. + weight_decay (float): Weight decay (L2 penalty). Default: 0.0. + decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default: + lambda x: 'beta' not in x.name and 'gamma' not in x.name. Inputs: - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`. @@ -132,7 +136,8 @@ class RMSProp(Optimizer): >>> model = Model(net, loss, opt) """ def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10, - use_locking=False, centered=False, loss_scale=1.0): + use_locking=False, centered=False, loss_scale=1.0, weight_decay=0.0, + decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name): super(RMSProp, self).__init__(learning_rate, params) if isinstance(momentum, float) and momentum < 0.0: @@ -159,6 +164,7 @@ class RMSProp(Optimizer): self.assignadd = P.AssignAdd() self.global_step = Parameter(initializer(0, [1], mstype.int32), name="global_step") self.axis = 0 + self.one = Tensor(1, mstype.int32) self.momentum = momentum @@ -167,10 +173,14 @@ class RMSProp(Optimizer): self.hyper_map = C.HyperMap() self.decay = decay + self.decay_tf = tuple(decay_filter(x) for x in self.parameters) self.reciprocal_scale = 1.0 / loss_scale + self.weight_decay = weight_decay * loss_scale def construct(self, gradients): params = self.parameters + if self.weight_decay > 0: + gradients = self.hyper_map(F.partial(apply_decay, self.weight_decay), self.decay_tf, params, gradients) if self.reciprocal_scale != 1.0: gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients) if self.dynamic_lr: From 8d554d432282629801a4c4780475eaac9e95ae99 Mon Sep 17 00:00:00 2001 From: xiefangqi Date: Mon, 30 Mar 2020 15:34:48 +0800 Subject: [PATCH 083/227] fix and remove useless import of example, st, ut --- example/Bert_NEZHA_cnwiki/train.py | 4 +- tests/st/mem_reuse/resnet_cifar_memreuse.py | 3 +- tests/st/mem_reuse/resnet_cifar_normal.py | 3 +- .../models/bert/bert_tdt_no_lossscale.py | 4 +- tests/st/ops/davinci/test_tdt_data_ms.py | 26 ++++++------- tests/ut/python/dataset/test_minddataset.py | 1 - tests/ut/python/dataset/test_project.py | 38 +++++++++---------- 7 files changed, 37 insertions(+), 42 deletions(-) diff --git a/example/Bert_NEZHA_cnwiki/train.py b/example/Bert_NEZHA_cnwiki/train.py index 87f425e21c..86e033fc9f 100644 --- a/example/Bert_NEZHA_cnwiki/train.py +++ b/example/Bert_NEZHA_cnwiki/train.py @@ -36,7 +36,7 @@ import os import numpy as np from config import bert_train_cfg, bert_net_cfg import mindspore.dataset.engine.datasets as de -import mindspore._c_dataengine as deMap +import mindspore.dataset.transforms.c_transforms as C from mindspore import context from mindspore.common.tensor import Tensor from mindspore.train.model import Model @@ -52,7 +52,7 @@ def create_train_dataset(batch_size): ds = de.StorageDataset([bert_train_cfg.DATA_DIR], bert_train_cfg.SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels", "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"]) - type_cast_op = deMap.TypeCastOp("int32") + type_cast_op = C.TypeCast(mstype.int32) ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op) ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op) ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op) diff --git a/tests/st/mem_reuse/resnet_cifar_memreuse.py b/tests/st/mem_reuse/resnet_cifar_memreuse.py index 4699c00e73..4edcdd8fb8 100644 --- a/tests/st/mem_reuse/resnet_cifar_memreuse.py +++ b/tests/st/mem_reuse/resnet_cifar_memreuse.py @@ -24,8 +24,7 @@ import numpy as np import mindspore.ops.functional as F from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor from mindspore.train.serialization import load_checkpoint, load_param_into_net -import mindspore.dataengine as de -import mindspore._c_dataengine as deMap +import mindspore.dataset as de import mindspore.dataset.transforms.c_transforms as C import mindspore.dataset.transforms.vision.c_transforms as vision from mindspore.communication.management import init diff --git a/tests/st/mem_reuse/resnet_cifar_normal.py b/tests/st/mem_reuse/resnet_cifar_normal.py index bff0c2d6e6..39f6e7fe59 100644 --- a/tests/st/mem_reuse/resnet_cifar_normal.py +++ b/tests/st/mem_reuse/resnet_cifar_normal.py @@ -24,8 +24,7 @@ import numpy as np import mindspore.ops.functional as F from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor from mindspore.train.serialization import load_checkpoint, load_param_into_net -import mindspore.dataengine as de -import mindspore._c_dataengine as deMap +import mindspore.dataset as de import mindspore.dataset.transforms.c_transforms as C import mindspore.dataset.transforms.vision.c_transforms as vision from mindspore.communication.management import init diff --git a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py index c1ca6f6499..7c50707fbd 100644 --- a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py +++ b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py @@ -21,7 +21,7 @@ import numpy as np from numpy import allclose import mindspore.common.dtype as mstype import mindspore.dataset.engine.datasets as de -import mindspore._c_dataengine as deMap +import mindspore.dataset.transforms.c_transforms as C from mindspore import context from mindspore.common.tensor import Tensor from mindspore.train.model import Model @@ -106,7 +106,7 @@ def me_de_train_dataset(): ds = de.StorageDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels", "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"]) - type_cast_op = deMap.TypeCastOp("int32") + type_cast_op = C.TypeCast(mstype.int32) ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op) ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op) ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op) diff --git a/tests/st/ops/davinci/test_tdt_data_ms.py b/tests/st/ops/davinci/test_tdt_data_ms.py index 6463401d82..89f6f212d0 100644 --- a/tests/st/ops/davinci/test_tdt_data_ms.py +++ b/tests/st/ops/davinci/test_tdt_data_ms.py @@ -12,11 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ -import mindspore._c_dataengine as deMap import mindspore.dataset as ds +import mindspore.dataset.transforms.vision.c_transforms as vision +from mindspore.dataset.transforms.vision import Inter import numpy as np import sys -from mindspore._c_dataengine import InterpolationMode import mindspore.context as context import mindspore.nn as nn @@ -32,7 +32,7 @@ SCHEMA_DIR = "{0}/resnet_all_datasetSchema.json".format(data_path) def test_me_de_train_dataset(): data_list = ["{0}/train-00001-of-01024.data".format(data_path)] data_set = ds.StorageDataset(data_list, schema=SCHEMA_DIR, - columns_list=["image/encoded", "image/class/label"]) + columns_list=["image/encoded", "image/class/label"]) resize_height = 224 resize_width = 224 @@ -41,19 +41,17 @@ def test_me_de_train_dataset(): # define map operations - decode_op = deMap.DecodeOp() - resize_op = deMap.ResizeOp(resize_height, resize_width, - InterpolationMode.DE_INTER_LINEAR) # Bilinear as default - rescale_op = deMap.RescaleOp(rescale, shift) - changemode_op = deMap.ChangeModeOp() + decode_op = vision.Decode() + resize_op = vision.Resize(resize_height, resize_width, + Inter.LINEAR) # Bilinear as default + rescale_op = vision.Rescale(rescale, shift) # apply map operations on images - data_set = data_set.map(input_column_names="image/encoded", operation=decode_op) - data_set = data_set.map(input_column_names="image/encoded", operation=resize_op) - data_set = data_set.map(input_column_names="image/encoded", operation=rescale_op) - data_set = data_set.map(input_column_names="image/encoded", operation=changemode_op) - changeswap_op = deMap.ChannelSwapOp() - data_set = data_set.map(input_column_names="image/encoded", operation=changeswap_op) + data_set = data_set.map(input_columns="image/encoded", operations=decode_op) + data_set = data_set.map(input_columns="image/encoded", operations=resize_op) + data_set = data_set.map(input_columns="image/encoded", operations=rescale_op) + hwc2chw_op = vision.HWC2CHW() + data_set = data_set.map(input_columns="image/encoded", operations=hwc2chw_op) data_set = data_set.repeat(1) # apply batch operations batch_size = 32 diff --git a/tests/ut/python/dataset/test_minddataset.py b/tests/ut/python/dataset/test_minddataset.py index 8b8cbc807a..da22f5c3b7 100644 --- a/tests/ut/python/dataset/test_minddataset.py +++ b/tests/ut/python/dataset/test_minddataset.py @@ -24,7 +24,6 @@ import string import mindspore.dataset.transforms.vision.c_transforms as vision import numpy as np import pytest -from mindspore._c_dataengine import InterpolationMode from mindspore.dataset.transforms.vision import Inter from mindspore import log as logger diff --git a/tests/ut/python/dataset/test_project.py b/tests/ut/python/dataset/test_project.py index de600e07db..522788ac37 100644 --- a/tests/ut/python/dataset/test_project.py +++ b/tests/ut/python/dataset/test_project.py @@ -13,7 +13,8 @@ # limitations under the License. # ============================================================================== import mindspore.dataset.transforms.vision.c_transforms as vision -import mindspore._c_dataengine as de_map +import mindspore.dataset.transforms.c_transforms as C +from mindspore.common import dtype as mstype from util import ordered_save_and_check import mindspore.dataset as ds @@ -63,9 +64,8 @@ def test_case_project_map(): data1 = ds.TFRecordDataset(DATA_DIR_TF, SCHEMA_DIR_TF, shuffle=False) data1 = data1.project(columns=columns) - no_op = de_map.NoOp() - - data1 = data1.map(input_columns=["col_3d"], operations=no_op) + type_cast_op = C.TypeCast(mstype.int64) + data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op) filename = "project_map_after_result.npz" ordered_save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) @@ -77,8 +77,8 @@ def test_case_map_project(): data1 = ds.TFRecordDataset(DATA_DIR_TF, SCHEMA_DIR_TF, shuffle=False) - no_op = de_map.NoOp() - data1 = data1.map(input_columns=["col_sint64"], operations=no_op) + type_cast_op = C.TypeCast(mstype.int64) + data1 = data1.map(input_columns=["col_sint64"], operations=type_cast_op) data1 = data1.project(columns=columns) @@ -92,19 +92,19 @@ def test_case_project_between_maps(): data1 = ds.TFRecordDataset(DATA_DIR_TF, SCHEMA_DIR_TF, shuffle=False) - no_op = de_map.NoOp() - data1 = data1.map(input_columns=["col_3d"], operations=no_op) - data1 = data1.map(input_columns=["col_3d"], operations=no_op) - data1 = data1.map(input_columns=["col_3d"], operations=no_op) - data1 = data1.map(input_columns=["col_3d"], operations=no_op) + type_cast_op = C.TypeCast(mstype.int64) + data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op) + data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op) + data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op) + data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op) data1 = data1.project(columns=columns) - data1 = data1.map(input_columns=["col_3d"], operations=no_op) - data1 = data1.map(input_columns=["col_3d"], operations=no_op) - data1 = data1.map(input_columns=["col_3d"], operations=no_op) - data1 = data1.map(input_columns=["col_3d"], operations=no_op) - data1 = data1.map(input_columns=["col_3d"], operations=no_op) + data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op) + data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op) + data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op) + data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op) + data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op) filename = "project_between_maps_result.npz" ordered_save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) @@ -145,12 +145,12 @@ def test_case_map_project_map_project(): data1 = ds.TFRecordDataset(DATA_DIR_TF, SCHEMA_DIR_TF, shuffle=False) - no_op = de_map.NoOp() - data1 = data1.map(input_columns=["col_sint64"], operations=no_op) + type_cast_op = C.TypeCast(mstype.int64) + data1 = data1.map(input_columns=["col_sint64"], operations=type_cast_op) data1 = data1.project(columns=columns) - data1 = data1.map(input_columns=["col_2d"], operations=no_op) + data1 = data1.map(input_columns=["col_2d"], operations=type_cast_op) data1 = data1.project(columns=columns) From 9b9a85ba34f99d46030ca40ec2e15fe2cb0f123e Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Fri, 3 Apr 2020 09:38:04 +0800 Subject: [PATCH 084/227] modify runtest py ut for parallel exec --- tests/ut/python/runtest.sh | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/tests/ut/python/runtest.sh b/tests/ut/python/runtest.sh index d1c3299821..035e454f24 100755 --- a/tests/ut/python/runtest.sh +++ b/tests/ut/python/runtest.sh @@ -39,9 +39,13 @@ if [ "x${ENABLE_GE}" == "xON" -o "x${ENABLE_GE}" == "xOn" -o "x${ENABLE_GE}" == fi if [ $# -gt 0 ]; then - pytest -s --ignore=$1/pynative_mode $IGNORE_EXEC $1 + pytest -s --ignore=$1/pynative_mode --ignore=$1/parallel --ignore=$1/train $IGNORE_EXEC $1 + pytest -n 4 --dist=loadfile -v $1/parallel + pytest -n 4 --dist=loadfile -v $1/train else - pytest --ignore=$CURRPATH/pynative_mode $IGNORE_EXEC $CURRPATH + pytest --ignore=$CURRPATH/pynative_mode --ignore=$CURRPATH/parallel --ignore=$CURRPATH/train $IGNORE_EXEC $CURRPATH + pytest -n 4 --dist=loadfile -v $CURRPATH/parallel + pytest -n 4 --dist=loadfile -v $CURRPATH/train fi RET=$? From 7329a1ef8737bf4142086e959137aa6c2683aea3 Mon Sep 17 00:00:00 2001 From: kingfo Date: Fri, 3 Apr 2020 10:53:46 +0800 Subject: [PATCH 085/227] fix ME+GE compile error --- CMakeLists.txt | 2 +- mindspore/ccsrc/CMakeLists.txt | 2 +- mindspore/ccsrc/pipeline/pipeline_ge.cc | 17 ++++------------- 3 files changed, 6 insertions(+), 15 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index bdac2da46e..d11314408e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -38,7 +38,7 @@ set(MS_CCSRC_BUILD_PATH ${BUILD_PATH}/mindspore/mindspore/ccsrc) if (ENABLE_GE) link_directories(${CMAKE_SOURCE_DIR}/third_party/ge/lib) -else() +elseif(ENABLE_D OR ENABLE_TESTCASES) include(${CMAKE_SOURCE_DIR}/cmake/dependency_graphengine.cmake) endif() diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt index c4deb53e68..fdefdce6a2 100644 --- a/mindspore/ccsrc/CMakeLists.txt +++ b/mindspore/ccsrc/CMakeLists.txt @@ -495,7 +495,7 @@ if (NOT ENABLE_GE) add_dependencies(add_ge_lib add_ms_lib) add_dependencies(add_ge_lib graph) add_dependencies(add_ge_lib ge_runtime) - else() + elseif(ENABLE_TESTCASES) add_custom_target(add_ge_lib ALL COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/common/graph/libgraph.so ${MS_LIB_PATH} COMMAND cp ${CMAKE_SOURCE_DIR}/graphengine/third_party/prebuild/${CMAKE_HOST_SYSTEM_PROCESSOR}/libslog.so ${MS_LIB_PATH} diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.cc b/mindspore/ccsrc/pipeline/pipeline_ge.cc index 4617884ca0..5a2a8039dd 100644 --- a/mindspore/ccsrc/pipeline/pipeline_ge.cc +++ b/mindspore/ccsrc/pipeline/pipeline_ge.cc @@ -429,20 +429,11 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve std::shared_ptr ret = nullptr; -#ifdef ENABLE_GE - AnfNodePtr root = graph->get_return(); - MS_EXCEPTION_IF_NULL(root); - AbstractBasePtr output = root->abstract(); + AnfNodePtr output_node = graph->get_return()->input(1); + MS_EXCEPTION_IF_NULL(output_node); size_t count = 0; - py::object oj = StructureOutput(output, outputs, &count); + py::object oj = StructureOutput(output_node, outputs, &count); ret = std::make_shared(oj); -#else - if (outputs.size() == 1) { - ret = std::make_shared(outputs[0]); - } else { - ret = std::make_shared(outputs); - } -#endif return ret; } @@ -495,7 +486,7 @@ py::object ExecDFGraph(const std::map& info, const FuncGraphPtr anf_graph = info.at(phase)->func_graph; -#if (!defined ENABLE_GE) || (defined ENABLE_INFER) +#ifdef ENABLE_INFER // Now don't use the graph because the exec ge function don't take effect MS_EXCEPTION_IF_NULL(info.at(phase)->func_graph); if (ENABLE_TRAIN != info.at(phase)->func_graph->flags()["training"]) { From d12a720fc55d03fde2b03de234875d166f5494ca Mon Sep 17 00:00:00 2001 From: huangdongrun Date: Thu, 2 Apr 2020 15:33:46 +0800 Subject: [PATCH 086/227] add comparison ops fix pylint use scalar_lt primitive directly fix review --- mindspore/_extends/parse/resources.py | 10 +- .../ops/composite/multitype_ops/__init__.py | 12 +- .../ops/composite/multitype_ops/equal_impl.py | 3 +- .../multitype_ops/greater_equal_impl.py | 53 ++++ .../composite/multitype_ops/greater_impl.py | 53 ++++ .../multitype_ops/less_equal_impl.py | 3 +- .../ops/composite/multitype_ops/less_impl.py | 5 +- .../composite/multitype_ops/logic_not_impl.py | 48 ++++ .../composite/multitype_ops/not_equal_impl.py | 237 ++++++++++++++++++ .../ops/composite/multitype_ops/uadd_impl.py | 26 ++ mindspore/ops/functional.py | 4 + .../python_input/gtest_input/vm/vm_test.py | 3 +- tests/ut/python/ops/test_python_operators.py | 20 +- 13 files changed, 462 insertions(+), 15 deletions(-) create mode 100644 mindspore/ops/composite/multitype_ops/greater_equal_impl.py create mode 100644 mindspore/ops/composite/multitype_ops/greater_impl.py create mode 100644 mindspore/ops/composite/multitype_ops/logic_not_impl.py create mode 100644 mindspore/ops/composite/multitype_ops/not_equal_impl.py create mode 100644 mindspore/ops/composite/multitype_ops/uadd_impl.py diff --git a/mindspore/_extends/parse/resources.py b/mindspore/_extends/parse/resources.py index 1f6f4b91b5..5dd24ccf80 100644 --- a/mindspore/_extends/parse/resources.py +++ b/mindspore/_extends/parse/resources.py @@ -92,16 +92,16 @@ convert_object_map = { T.and_: multitype_ops.logical_and, T.or_: multitype_ops.logical_or, T.xor: NO_IMPLEMENT, - T.pos: F.scalar_uadd, + T.pos: multitype_ops.uadd, T.neg: multitype_ops.negative, T.invert: NO_IMPLEMENT, - T.not_: F.bool_not, + T.not_: multitype_ops.logical_not, T.eq: multitype_ops.equal, - T.ne: F.scalar_ne, + T.ne: multitype_ops.not_equal, T.lt: multitype_ops.less, - T.gt: F.scalar_gt, + T.gt: multitype_ops.greater, T.le: multitype_ops.less_equal, - T.ge: F.scalar_ge, + T.ge: multitype_ops.greater_equal, T.is_: F.is_, T.is_not: F.is_not, T.contains: NO_IMPLEMENT, diff --git a/mindspore/ops/composite/multitype_ops/__init__.py b/mindspore/ops/composite/multitype_ops/__init__.py index 0ab8527ab4..db28b1b5f6 100644 --- a/mindspore/ops/composite/multitype_ops/__init__.py +++ b/mindspore/ops/composite/multitype_ops/__init__.py @@ -23,23 +23,33 @@ from .getitem_impl import getitem from .zeros_like_impl import zeros_like from .ones_like_impl import ones_like from .equal_impl import equal +from .not_equal_impl import not_equal from .less_impl import less from .less_equal_impl import less_equal +from .greater_impl import greater +from .greater_equal_impl import greater_equal from .negative_impl import negative from .logical_and_impl import logical_and from .logical_or_impl import logical_or +from .logic_not_impl import logical_not +from .uadd_impl import uadd __all__ = [ 'add', 'sub', 'mul', 'div', + 'uadd', 'zeros_like', 'ones_like', 'equal', + 'not_equal', 'less', 'less_equal', + 'greater', + 'greater_equal', 'negative', 'getitem', 'logical_and', - 'logical_or' + 'logical_or', + 'logical_not' ] diff --git a/mindspore/ops/composite/multitype_ops/equal_impl.py b/mindspore/ops/composite/multitype_ops/equal_impl.py index 9ff7e6671e..428cdf4705 100644 --- a/mindspore/ops/composite/multitype_ops/equal_impl.py +++ b/mindspore/ops/composite/multitype_ops/equal_impl.py @@ -190,7 +190,8 @@ def _none_equal_tuple(x, y): """ return False - +@equal.register("Tensor", "Number") +@equal.register("Number", "Tensor") @equal.register("Tensor", "Tensor") def _tensor_equal_tensor(x, y): """ diff --git a/mindspore/ops/composite/multitype_ops/greater_equal_impl.py b/mindspore/ops/composite/multitype_ops/greater_equal_impl.py new file mode 100644 index 0000000000..2073abb762 --- /dev/null +++ b/mindspore/ops/composite/multitype_ops/greater_equal_impl.py @@ -0,0 +1,53 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""greater_equal_impl""" +from mindspore.ops.composite import base +from mindspore.ops import functional as F + +# greater_equal is a metagraph object which will determine if two objects are greater_equal according to input type +# using ".register" decorator +greater_equal = base.MultitypeFuncGraph("greater_equal") + + +@greater_equal.register("Number", "Number") +def _greater_equal_scala(x, y): + """ + Determine whether x is greater equal than y + + Args: + x(Number): Number. + y(Number): Number. + + Returns: + bool, if x >= y return true, x < y return false. + """ + return F.scalar_ge(x, y) + +@greater_equal.register("Tensor", "Number") +@greater_equal.register("Number", "Tensor") +@greater_equal.register("Tensor", "Tensor") +def _greater_equal_tensor(x, y): + """ + Determine whether tensor x is greater equal than tensor y elementwise + + Args: + x(Tensor): Tensor. + y(Tensor): Tensor. + + Returns: + Tensor, return value by operator P.GreaterEqual. + """ + return F.tensor_ge(x, y) diff --git a/mindspore/ops/composite/multitype_ops/greater_impl.py b/mindspore/ops/composite/multitype_ops/greater_impl.py new file mode 100644 index 0000000000..7bbf53da49 --- /dev/null +++ b/mindspore/ops/composite/multitype_ops/greater_impl.py @@ -0,0 +1,53 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Ungreater required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""equal_impl""" +from mindspore.ops.composite import base +from mindspore.ops import functional as F + +# greater is a metafuncgraph object which will determine if two objects are greater according to input type +# using ".register" decorator +greater = base.MultitypeFuncGraph("greater") + + +@greater.register("Number", "Number") +def _greater_scala(x, y): + """ + Determine whether two numbers are greater. + + Args: + x(Number): Number. + y(Number): Number. + + Returns: + bool, if x > y return true, x <= y return false. + """ + return F.scalar_gt(x, y) + +@greater.register("Tensor", "Number") +@greater.register("Number", "Tensor") +@greater.register("Tensor", "Tensor") +def _greater_tensor(x, y): + """ + Determine whether two tensor are greater by element. + + Args: + x(Tensor): Tensor. + y(Tensor): Tensor. + + Returns: + tensor, return operation of x and y by P.Greater + """ + return F.tensor_gt(x, y) diff --git a/mindspore/ops/composite/multitype_ops/less_equal_impl.py b/mindspore/ops/composite/multitype_ops/less_equal_impl.py index f02ab61da1..dc1438da2c 100644 --- a/mindspore/ops/composite/multitype_ops/less_equal_impl.py +++ b/mindspore/ops/composite/multitype_ops/less_equal_impl.py @@ -36,7 +36,8 @@ def _less_equal_scala(x, y): """ return F.scalar_le(x, y) - +@less_equal.register("Tensor", "Number") +@less_equal.register("Number", "Tensor") @less_equal.register("Tensor", "Tensor") def _less_equal_tensor(x, y): """ diff --git a/mindspore/ops/composite/multitype_ops/less_impl.py b/mindspore/ops/composite/multitype_ops/less_impl.py index c9c20657e5..6e50e54c82 100644 --- a/mindspore/ops/composite/multitype_ops/less_impl.py +++ b/mindspore/ops/composite/multitype_ops/less_impl.py @@ -36,7 +36,8 @@ def _less_scala(x, y): """ return F.scalar_lt(x, y) - +@less.register("Tensor", "Number") +@less.register("Number", "Tensor") @less.register("Tensor", "Tensor") def _less_tensor(x, y): """ @@ -47,6 +48,6 @@ def _less_tensor(x, y): y(Tensor): Tensor. Returns: - bool, if x and y are less elements by element return true, else return false. + Tensor, return value of x and y by operation P.Less() """ return F.tensor_lt(x, y) diff --git a/mindspore/ops/composite/multitype_ops/logic_not_impl.py b/mindspore/ops/composite/multitype_ops/logic_not_impl.py new file mode 100644 index 0000000000..35ae766433 --- /dev/null +++ b/mindspore/ops/composite/multitype_ops/logic_not_impl.py @@ -0,0 +1,48 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""logical_not_impl""" +from mindspore.ops.composite import base +from mindspore.ops import functional as F + +# logical_not is a metagraph object which will generate function according to input type +# using ".register" decorator +logical_not = base.MultitypeFuncGraph("logical_not") + + +@logical_not.register("Number") +def _logical_not_scala(x): + """ + Return logical not operation result of x + + Args: + x(Number): Number. + + Returns: + bool, Return logical not operation result of x + """ + return F.bool_not(x.__bool__()) + + +@logical_not.register("Tensor") +def _logical_not_tensor(x): + """ + Return logical not operation result of x + Args: + x(Tensor): Tensor. + Returns: + Tensor, Return logical not operation result of x + """ + return F.logical_not(x) diff --git a/mindspore/ops/composite/multitype_ops/not_equal_impl.py b/mindspore/ops/composite/multitype_ops/not_equal_impl.py new file mode 100644 index 0000000000..de099a2b8f --- /dev/null +++ b/mindspore/ops/composite/multitype_ops/not_equal_impl.py @@ -0,0 +1,237 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""not_equal_impl""" + +from ...composite import base +from ... import functional as F + + +not_equal = base.MultitypeFuncGraph("not_equal") +""" +not_equal is a metafuncgraph object which will determine if two objects are not_equal according to input type +using ".register" decorator +""" + + +@not_equal.register("Number", "Number") +def _not_equal_scalar(x, y): + """ + Determine if two numbers is not equal. + + Args: + x (Number): x + y (NUmber): y + + Returns: + bool, if x != y return true, x == y return false. + """ + return not F.scalar_eq(x, y) + + +@not_equal.register("String", "String") +def _not_equal_string(x, y): + """ + Determine if two strings are not equal. + + Args: + x: str + y: str + + Returns: + bool, if x != y return true, x == y return false. + """ + return not F.string_eq(x, y) + + +@not_equal.register("String", "None") +def _string_not_equal_none(x, y): + """ + Determine if string not equals none. + + Args: + x: str. + y: None. + + Returns: + bool, return True. + """ + return True + + +@not_equal.register("None", "String") +def _none_not_equal_string(x, y): + """ + Determine if string not equals none. + + Args: + x: None. + y: str. + + Returns: + bool, return True. + """ + return True + + +@not_equal.register("None", "None") +def _none_not_equal_none(x, y): + """ + Determine if none not equals none. + + Args: + x: None. + y: None. + + Returns: + bool, return False. + """ + return False + + +@not_equal.register("Number", "None") +def _scalar_not_equal_none(x, y): + """ + Determine if number not equals none. + + Args: + x: Number. + y: None. + + Returns: + bool, return True. + """ + return True + + +@not_equal.register("None", "Number") +def _none_not_equal_scalar(x, y): + """ + Determine if number not_equals none. + + Args: + x: None. + y: NUmber. + + Returns: + bool, return True. + """ + return True + + +@not_equal.register("Tuple", "Tuple") +def _euqal_tuple(x, y): + """ + Determine if two tuples are not equal by element. + + Args: + x (tuple): x + y (tuple): y + + Returns: + bool, if x and y are not equal by element return true, else return false. + """ + return not F.tuple_equal(x, y) + + +@not_equal.register("List", "List") +def _euqal_list(x, y): + """ + Determine if two lists are not equal by element. + + Args: + x (list): x + y (list): y + + Returns: + bool, if x and y are not equal by element return true, else return false. + """ + return not F.list_equal(x, y) + + +@not_equal.register("Tuple", "None") +def _tuple_euqal_none(x, y): + """ + Determine if tuple element not equals none element. + + Args: + x: Tuple. + y: None. + + Returns: + bool, return True. + """ + return True + + +@not_equal.register("None", "Tuple") +def _none_not_equal_tuple(x, y): + """ + Determine if tuple element not equals none element. + + Args: + x: None. + y: Tuple. + + Returns: + bool, return True. + """ + return True + +@not_equal.register("Tensor", "Number") +@not_equal.register("Number", "Tensor") +@not_equal.register("Tensor", "Tensor") +def _tensor_not_equal_tensor(x, y): + """ + Determine if two tensors are not_equal. + + Args: + x : Tensor. + y : Tensor. + + Returns: + bool, if x == y return true, x != y return false. + """ + return F.not_equal(x, y) + + +@not_equal.register("Tensor", "None") +def _tensor_not_equal_none(x, y): + """ + Determine if tensor not_equal none. + + Args: + x : Tensor. + y : None. + + Returns: + bool, return True. + """ + return True + + +@not_equal.register("None", "Tensor") +def _none_not_equal_tensor(x, y): + """ + Determine if tensor not equal none. + + Args: + x : None. + y : Tensor. + + Returns: + bool, return True. + """ + return True diff --git a/mindspore/ops/composite/multitype_ops/uadd_impl.py b/mindspore/ops/composite/multitype_ops/uadd_impl.py new file mode 100644 index 0000000000..163120b541 --- /dev/null +++ b/mindspore/ops/composite/multitype_ops/uadd_impl.py @@ -0,0 +1,26 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""uadd_impl""" +from mindspore.ops.composite import base + +# uadd is a metagraph object which will return operation result regarding input +# using ".register" decorator +uadd = base.MultitypeFuncGraph("uadd") + +@uadd.register("Tensor") +@uadd.register("Number") +def _uadd_scala(x): + return x diff --git a/mindspore/ops/functional.py b/mindspore/ops/functional.py index b8411d42c1..4da725145f 100644 --- a/mindspore/ops/functional.py +++ b/mindspore/ops/functional.py @@ -43,12 +43,15 @@ tensor_add = P.TensorAdd() neg_tensor = P.Neg() tensor_lt = P.Less() tensor_le = P.LessEqual() +tensor_gt = P.Greater() +tensor_ge = P.GreaterEqual() tensor_sub = P.Sub() tensor_mul = P.Mul() tensor_div = P.RealDiv() strided_slice = P.StridedSlice() same_type_shape = P.SameTypeShape() equal = P.Equal() +not_equal = P.NotEqual() assign_sub = P.AssignSub() assign = P.Assign() square = P.Square() @@ -97,6 +100,7 @@ bool_or = Primitive("bool_or") bool_and = Primitive("bool_and") logical_and = P.LogicalAnd() logical_or = P.LogicalOr() +logical_not = P.LogicalNot() array_to_scalar = Primitive('array_to_scalar') is_ = Primitive("is_") is_not = Primitive("is_not") diff --git a/tests/ut/cpp/python_input/gtest_input/vm/vm_test.py b/tests/ut/cpp/python_input/gtest_input/vm/vm_test.py index bdd3c900d6..947e9fa2c3 100644 --- a/tests/ut/cpp/python_input/gtest_input/vm/vm_test.py +++ b/tests/ut/cpp/python_input/gtest_input/vm/vm_test.py @@ -17,6 +17,7 @@ from mindspore.ops import Primitive scala_add = Primitive('scalar_add') scala_mul = Primitive('scalar_mul') +scalar_gt = Primitive('scalar_gt') def scalar_add(x, y): """Implement `scalar_add`.""" return scala_add(x, y) @@ -26,6 +27,6 @@ def scalar_mul(x, y): return scala_mul(x, y) def test_if(x, y): - if x > y: + if scalar_gt(x, y): return x return y diff --git a/tests/ut/python/ops/test_python_operators.py b/tests/ut/python/ops/test_python_operators.py index d6c6c03760..eb65a7f373 100644 --- a/tests/ut/python/ops/test_python_operators.py +++ b/tests/ut/python/ops/test_python_operators.py @@ -31,8 +31,20 @@ class ComparisonOpsNet(nn.Cell): def __init__(self): super(ComparisonOpsNet, self).__init__() def construct(self, x, y): - ret = x <= y - return ret + a = x <= y + b = x <= 1.0 + c = y >= 1.0 + d = y >= x + e = x < y + f = x < 1.0 + g = 1.0 > y + h = y > x + i = y == 3.0 + j = x != 4 + k = + x + l = + 1.0 + m = k != l + return a or b or c or d or e or f or g or h or i or j or m class LogicalNumberOpsNet(nn.Cell): def __init__(self): @@ -41,7 +53,7 @@ class LogicalNumberOpsNet(nn.Cell): self.one = 0 self.zero = 0.0 def construct(self, x, y): - if self.cond and self.one or self.zero: + if self.cond and self.one or self.zero and not self.one: return x + y return x - y @@ -51,7 +63,7 @@ class LogicalTensorOpsNet(nn.Cell): super(LogicalTensorOpsNet, self).__init__() self.const_true = Tensor(True, dtype=mstype.bool_) def construct(self, x, y): - ret = x and y and (y or self.const_true) + ret = x and y and (y or self.const_true) and (not self.const_true) return ret From 8f74aef65d6d40594194cdf9e94fd2e5430f3339 Mon Sep 17 00:00:00 2001 From: yangzhenzhang <285824651@qq.com> Date: Thu, 2 Apr 2020 15:18:34 +0800 Subject: [PATCH 087/227] add parallel ops for expand dims --- mindspore/ccsrc/parallel/dynamic_creator.h | 1 + .../parallel/ops_info/activation_info.cc | 163 ++++++++++++++++++ .../ccsrc/parallel/ops_info/activation_info.h | 20 +++ mindspore/ccsrc/parallel/ops_info/ops_utils.h | 4 + .../ccsrc/parallel/step_auto_parallel.cc | 1 + tests/ut/python/parallel/test_expand_dims.py | 110 ++++++++++++ 6 files changed, 299 insertions(+) create mode 100644 tests/ut/python/parallel/test_expand_dims.py diff --git a/mindspore/ccsrc/parallel/dynamic_creator.h b/mindspore/ccsrc/parallel/dynamic_creator.h index e6e1b41d76..1270116f50 100644 --- a/mindspore/ccsrc/parallel/dynamic_creator.h +++ b/mindspore/ccsrc/parallel/dynamic_creator.h @@ -125,6 +125,7 @@ REGISTER(SqrtInfo); REGISTER(GetNextInfo); REGISTER(NegInfo); REGISTER(BatchMatMulInfo); +REGISTER(ExpandDimsInfo); } // namespace parallel } // namespace mindspore diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.cc b/mindspore/ccsrc/parallel/ops_info/activation_info.cc index 13155ee4f1..9ba3624b01 100644 --- a/mindspore/ccsrc/parallel/ops_info/activation_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/activation_info.cc @@ -381,5 +381,168 @@ Status CastInfo::InferMirrorOps() { return SUCCESS; } + +Status ExpandDimsInfo::GetAttrs() { + if (input_value_.size() != EXPANDDIMS_INPUT_SIZE) { + MS_LOG(ERROR) << name_ << ": Invalid inputs size " << input_value_.size(); + return FAILED; + } + + if (!input_value_.back()->isa()) { + MS_LOG(ERROR) << name_ << ": The type of axis is not int"; + return FAILED; + } + + int32_t axis = GetValue(input_value_.back()); + + if (inputs_shape_.empty()) { + MS_LOG(ERROR) << name_ << ": The inputs shape is empty"; + return FAILED; + } + + int32_t dim = SizeToInt(inputs_shape_[0].size()); + if ((axis > dim) || (axis < -dim - 1)) { + MS_LOG(ERROR) << name_ << ": The axis(" << axis << ") is out of range[" << -dim - 1 << ", " << dim << "]"; + return FAILED; + } + + if (axis < 0) { + positive_axis_ = dim + axis + 1; + } else { + positive_axis_ = axis; + } + MS_LOG(INFO) << name_ << ": The axis is " << axis << ", and the positive axis is " << positive_axis_; + return SUCCESS; +} + +Status ExpandDimsInfo::InferTensorMap() { + if (inputs_shape_.empty()) { + MS_LOG(ERROR) << name_ << ": The inputs shape is empty"; + return FAILED; + } + + // for example: if the dimension of input is 3, and the axis is 2, + // then the input_tensor_map is [2, 1, 0], the output_tensor_map is [2, 1, -1, 0] + std::vector input_tensor_map, output_tensor_map; + size_t size = inputs_shape_[0].size(); + for (size_t i = 0; i < size; ++i) { + input_tensor_map.push_back(SizeToInt(size - i - 1)); + } + + inputs_tensor_map_.push_back(input_tensor_map); + + output_tensor_map = input_tensor_map; + if ((positive_axis_ < 0) || (positive_axis_ > SizeToInt(size))) { + MS_LOG(ERROR) << name_ << ": Invalid positive axis " << positive_axis_; + return FAILED; + } + (void)output_tensor_map.insert(output_tensor_map.begin() + positive_axis_, NO_SPLIT_MAP); + outputs_tensor_map_.push_back(output_tensor_map); + + MS_LOG(INFO) << name_ << ": The tensor map of input is " << ShapeToString(input_tensor_map) + << ", and the tensor map of output is " << ShapeToString(output_tensor_map); + return SUCCESS; +} + +Status ExpandDimsInfo::InferTensorStrategy() { + if (strategy_ == nullptr) { + MS_LOG(ERROR) << name_ << ": The strategy is null"; + return FAILED; + } + + inputs_strategy_ = strategy_->GetInputDim(); + if (inputs_strategy_.empty()) { + MS_LOG(ERROR) << name_ << ": The strategy is empty"; + return FAILED; + } + + Shape output_strategy = inputs_strategy_[0]; + if ((positive_axis_ < 0) || (positive_axis_ > SizeToInt(output_strategy.size()))) { + MS_LOG(ERROR) << name_ << ": Invalid positive axis " << positive_axis_; + return FAILED; + } + (void)output_strategy.insert(output_strategy.begin() + positive_axis_, NO_SPLIT_STRATEGY); + outputs_strategy_ = {output_strategy}; + return SUCCESS; +} + +Status ExpandDimsInfo::InferTensorInfo() { + if (inputs_shape_.empty() || outputs_shape_.empty()) { + MS_LOG(ERROR) << name_ << ": The shape of inputs or outputs is empty"; + return FAILED; + } + + if (inputs_tensor_map_.empty() || outputs_tensor_map_.empty()) { + MS_LOG(ERROR) << name_ << ": The tensor map of inputs or outputs is empty"; + return FAILED; + } + + Shape input_shape = inputs_shape_[0]; + Shape output_shape = outputs_shape_[0]; + + // infer slice shape + if (InferTensorStrategy() != SUCCESS) { + MS_LOG(ERROR) << name_ << ": Infer tensor strategy failed"; + return FAILED; + } + Shapes inputs_slice_shape, outputs_slice_shape; + if (InferSliceShape(inputs_strategy_, outputs_strategy_, &inputs_slice_shape, &outputs_slice_shape) != SUCCESS) { + MS_LOG(ERROR) << name_ << ": Infer slice shape failed"; + return FAILED; + } + + if (inputs_slice_shape.empty() || outputs_slice_shape.empty()) { + MS_LOG(ERROR) << name_ << ": The slice shape of inputs or outputs is empty"; + return FAILED; + } + + Shape input_slice_shape = inputs_slice_shape[0]; + Shape output_slice_shape = outputs_slice_shape[0]; + + TensorLayout input_tensor_layout, output_tensor_layout; + if (input_tensor_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[0], input_shape) != SUCCESS) { + MS_LOG(ERROR) << name_ << ": Init tensor layout for input failed"; + return FAILED; + } + + if (output_tensor_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[0], output_shape) != SUCCESS) { + MS_LOG(ERROR) << name_ << ": Init tensor layout for output failed"; + return FAILED; + } + + TensorInfo input_tensor_info(input_tensor_layout, input_shape, input_slice_shape); + TensorInfo output_tensor_info(output_tensor_layout, output_shape, output_slice_shape); + + inputs_tensor_info_.push_back(input_tensor_info); + outputs_tensor_info_.push_back(output_tensor_info); + return SUCCESS; +} + +Status ExpandDimsInfo::InferMirrorOps() { + mirror_ops_.clear(); + + if (inputs_tensor_map_.empty()) { + MS_LOG(ERROR) << name_ << ": The tensor map of inputs is empty"; + return FAILED; + } + + std::vector group; + if (CreateGroupByTensorMap(inputs_tensor_map_[0], &group) != SUCCESS) { + MS_LOG(ERROR) << name_ << ": Create group failed"; + return FAILED; + } + + if (group.empty()) { + MS_LOG(INFO) << name_ << ": No need to create mirror ops"; + return SUCCESS; + } + + OperatorVector mirror_op, placeholder_op; + mirror_op = CreateMirrorOps(group[0].name(), group[0].GetDevNum()); + mirror_ops_.push_back(mirror_op); + mirror_ops_.push_back(placeholder_op); + MS_LOG(INFO) << name_ << ": Create mirror ops success, the group name is " << group[0].name(); + return SUCCESS; +} } // namespace parallel } // namespace mindspore diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.h b/mindspore/ccsrc/parallel/ops_info/activation_info.h index 3cadad6b80..183b593e23 100644 --- a/mindspore/ccsrc/parallel/ops_info/activation_info.h +++ b/mindspore/ccsrc/parallel/ops_info/activation_info.h @@ -174,6 +174,26 @@ class NegInfo : public ActivationOther { : ActivationOther(name, inputs_shape, outputs_shape, attrs) {} ~NegInfo() override = default; }; + +class ExpandDimsInfo : public ActivationOther { + public: + ExpandDimsInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, + const PrimitiveAttrs& attrs) + : ActivationOther(name, inputs_shape, outputs_shape, attrs) {} + ~ExpandDimsInfo() override = default; + + protected: + Status GetAttrs() override; + Status InferTensorMap() override; + Status InferTensorInfo() override; + Status InferMirrorOps() override; + Status InferTensorStrategy(); + + private: + int32_t positive_axis_ = -1; + Strategys inputs_strategy_; + Strategys outputs_strategy_; +}; } // namespace parallel } // namespace mindspore #endif // MINDSPORE_CCSRC_OPTIMIZER_OPS_INFO_PARALLEL_ACTIVATION_INFO_H_ diff --git a/mindspore/ccsrc/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/parallel/ops_info/ops_utils.h index 4062847d73..fe2a5d2c86 100644 --- a/mindspore/ccsrc/parallel/ops_info/ops_utils.h +++ b/mindspore/ccsrc/parallel/ops_info/ops_utils.h @@ -24,6 +24,8 @@ constexpr size_t PRELU_OUTPUTS_SIZE = 1; constexpr size_t PRELU_SECOND_INPUT_SIZE = 1; constexpr int32_t PRELU_CHANNEL_INDEX = 1; constexpr int32_t PRELU_CHANNEL_STRATEGY = 1; +constexpr int32_t NO_SPLIT_MAP = -1; +constexpr int32_t NO_SPLIT_STRATEGY = 1; constexpr size_t MATMUL_ATTRS_SIZE = 2; constexpr size_t MATMUL_INPUTS_SIZE = 2; constexpr size_t MATMUL_OUTPUTS_SIZE = 1; @@ -31,6 +33,7 @@ constexpr size_t ACTIVATION_ATTR_SIZE = 1; constexpr size_t SOFTMAX_ATTR_SIZE = 1; constexpr size_t ACTIVATION_INPUTS_SIZE = 1; constexpr size_t ACTIVATION_OUTPUTS_SIZE = 1; +constexpr size_t EXPANDDIMS_INPUT_SIZE = 2; constexpr size_t SoftmaxCrossEntropyWithLogitsAttrSize = 1; constexpr size_t SoftmaxCrossEntropyWithLogitsInputsSize = 2; constexpr size_t SoftmaxCrossEntropyWithLogitsOutputsSize = 2; @@ -191,6 +194,7 @@ constexpr char GET_NEXT[] = "GetNext"; constexpr char SQUEEZE[] = "Squeeze"; constexpr char Neg[] = "Neg"; constexpr char BATCH_MATMUL[] = "BatchMatMul"; +constexpr char EXPAND_DIMS[] = "ExpandDims"; // Parallel don't care constexpr char TUPLE_GETITEM[] = "tuple_getitem"; diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc index a359905494..50e6a1e84e 100644 --- a/mindspore/ccsrc/parallel/step_auto_parallel.cc +++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc @@ -104,6 +104,7 @@ std::vector splittable_op_ = {MATMUL, CAST, Neg, BATCH_MATMUL, + EXPAND_DIMS, SQUEEZE}; std::vector elementwise_op_ = {ACTIVATION, GELU, TANH, SOFTMAX, LOG_SOFTMAX, RELU, SQRT, diff --git a/tests/ut/python/parallel/test_expand_dims.py b/tests/ut/python/parallel/test_expand_dims.py new file mode 100644 index 0000000000..676e9ed523 --- /dev/null +++ b/tests/ut/python/parallel/test_expand_dims.py @@ -0,0 +1,110 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import mindspore as ms +from mindspore import context, Tensor, Parameter +from mindspore.nn import Cell, TrainOneStepCell, Momentum +from mindspore.ops import operations as P +from mindspore.common.api import _executor + + +class Net(Cell): + def __init__(self, mul_weight, strategy1=None, strategy2=None, strategy3=None): + super().__init__() + self.mul = P.Mul().set_strategy(strategy1) + self.expand_dims = P.ExpandDims().set_strategy(strategy2) + self.mul2 = P.Mul().set_strategy(strategy3) + self.mul_weight = Parameter(mul_weight, "w1") + + def construct(self, x, b): + out = self.mul(x, self.mul_weight) + out = self.expand_dims(out, -1) + out = self.mul2(out, b) + return out + + +class Net2(Cell): + def __init__(self, mul_weight, strategy1=None, strategy2=None): + super().__init__() + self.expand_dims = P.ExpandDims().set_strategy(strategy1) + self.mul = P.Mul().set_strategy(strategy2) + self.mul_weight = Parameter(mul_weight, "w1") + + def construct(self, x, b): + out = self.expand_dims(self.mul_weight, -1) + out = self.mul(out, b) + return out + + +_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32) +_w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32) +_b = Tensor(np.ones([128, 64, 32, 1]), dtype=ms.float32) + + +def compile(net): + optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9) + train_net = TrainOneStepCell(net, optimizer) + _executor.compile(train_net, _x, _b) + context.reset_auto_parallel_context() + + +def test_expand_dims_data_parallel(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((16, 1, 1), (16, 1, 1)) + strategy2 = ((16, 1, 1), ) + strategy3 = ((16, 1, 1, 1), (16, 1, 1, 1)) + net = Net(_w1, strategy1, strategy2, strategy3) + compile(net) + + +def test_expand_dims_model_parallel(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((1, 1, 16), (1, 1, 16)) + strategy2 = ((1, 1, 16), ) + strategy3 = ((1, 1, 16, 1), (1, 1, 16, 1)) + net = Net(_w1, strategy1, strategy2, strategy3) + compile(net) + + +def test_expand_dims_hybrid_parallel(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((2, 2, 4), (2, 2, 4)) + strategy2 = ((2, 2, 4), ) + strategy3 = ((2, 2, 4, 1), (2, 2, 4, 1)) + net = Net(_w1, strategy1, strategy2, strategy3) + compile(net) + + +def test_expand_dims_auto_parallel(): + context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0) + net = Net(_w1) + compile(net) + + +def test_expand_dims_repeat_calc(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((2, 2, 4), (2, 2, 4)) + strategy2 = ((1, 2, 2), ) + strategy3 = ((2, 2, 4, 1), (2, 2, 4, 1)) + net = Net(_w1, strategy1, strategy2, strategy3) + compile(net) + + +def test_expand_dims_parameter(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((1, 2, 2), ) + strategy2 = ((2, 2, 4, 1), (2, 2, 4, 1)) + net = Net2(_w1, strategy1, strategy2) + compile(net) From 51ca769d098888634986ae4965cfa4fb85998221 Mon Sep 17 00:00:00 2001 From: zjun Date: Wed, 1 Apr 2020 19:41:21 +0800 Subject: [PATCH 088/227] add new mode for operator info register --- mindspore/ccsrc/kernel/oplib/opinfo.h | 9 + mindspore/ccsrc/kernel/oplib/oplib.cc | 61 ++- mindspore/ccsrc/kernel/oplib/oplib.h | 4 +- mindspore/ops/__init__.py | 4 +- .../_op_impl/tbe/adam_apply_one_with_decay.py | 231 ++------- mindspore/ops/op_info_register.py | 441 +++++++++++++++++- 6 files changed, 535 insertions(+), 215 deletions(-) diff --git a/mindspore/ccsrc/kernel/oplib/opinfo.h b/mindspore/ccsrc/kernel/oplib/opinfo.h index 7861da34d9..56abea9269 100644 --- a/mindspore/ccsrc/kernel/oplib/opinfo.h +++ b/mindspore/ccsrc/kernel/oplib/opinfo.h @@ -61,6 +61,7 @@ class OpIOInfo { std::string name() const { return name_; } bool need_compile() const { return need_compile_; } std::string param_type() const { return param_type_; } + std::string reshape_type() const { return reshape_type_; } std::string shape() const { return shape_; } std::vector dtypes() const { return dtypes_; } std::vector formats() const { return formats_; } @@ -69,6 +70,7 @@ class OpIOInfo { void set_name(const std::string& name) { name_ = name; } void set_need_compile(const bool need_compile) { need_compile_ = need_compile; } void set_param_type(const std::string& param_type) { param_type_ = param_type; } + void set_reshape_type(const std::string& reshape_type) { reshape_type_ = reshape_type; } void set_shape(const std::string& shape) { shape_ = shape; } void set_dtypes(const std::vector& dtype) { dtypes_ = dtype; } void set_formats(const std::vector& formats) { formats_ = formats; } @@ -78,6 +80,7 @@ class OpIOInfo { std::string name_; bool need_compile_ = false; std::string param_type_; + std::string reshape_type_; std::string shape_; std::vector dtypes_; std::vector formats_; @@ -96,6 +99,8 @@ class OpInfo { int compute_cost() const { return compute_cost_; } std::string kernel_name() const { return kernel_name_; } bool partial_flag() const { return partial_flag_; } + bool dynamic_format() const { return dynamic_format_; } + std::string op_pattern() const { return op_pattern_; } std::vector> attrs_ptr() const { return attrs_ptr_; } std::vector> inputs_ptr() const { return inputs_ptr_; } std::vector> outputs_ptr() const { return outputs_ptr_; } @@ -110,6 +115,8 @@ class OpInfo { void set_compute_cost(const int compute_cost) { compute_cost_ = compute_cost; } void set_kernel_name(const std::string& kernel_name) { kernel_name_ = kernel_name; } void set_partial_flag(const bool partial_flag) { partial_flag_ = partial_flag; } + void set_dynamic_format(const bool dynamic_format) { dynamic_format_ = dynamic_format; } + void set_op_pattern(const std::string op_pattern) { op_pattern_ = op_pattern; } void add_attrs_ptr(const std::shared_ptr& attr) { attrs_ptr_.push_back(attr); } void add_inputs_ptr(const std::shared_ptr& input) { inputs_ptr_.push_back(input); } void add_outputs_ptr(const std::shared_ptr& output) { outputs_ptr_.push_back(output); } @@ -129,6 +136,8 @@ class OpInfo { int compute_cost_ = 0; std::string kernel_name_; bool partial_flag_ = false; + bool dynamic_format_ = false; + std::string op_pattern_; std::vector> attrs_ptr_; std::vector> inputs_ptr_; std::vector> outputs_ptr_; diff --git a/mindspore/ccsrc/kernel/oplib/oplib.cc b/mindspore/ccsrc/kernel/oplib/oplib.cc index b20bd741f1..4059b8e246 100644 --- a/mindspore/ccsrc/kernel/oplib/oplib.cc +++ b/mindspore/ccsrc/kernel/oplib/oplib.cc @@ -26,18 +26,22 @@ namespace mindspore { namespace kernel { constexpr auto kImplyType = "imply_type"; constexpr auto kOpName = "op_name"; -constexpr auto kTbe = "TBE"; -constexpr auto kAkg = "akg"; -constexpr auto kAutodiff = "AutoDiff"; constexpr auto kFusionType = "fusion_type"; constexpr auto kAsyncFlag = "async_flag"; constexpr auto kBinfileName = "binfile_name"; constexpr auto kComputeCost = "compute_cost"; constexpr auto kKernelName = "kernel_name"; constexpr auto kPartialFlag = "partial_flag"; +constexpr auto kReshapeType = "reshape_type"; +constexpr auto kOpPattern = "op_pattern"; +constexpr auto kDynamicFormat = "dynamic_format"; +constexpr auto kDtypeFormat = "dtype_format"; constexpr auto kAttr = "attr"; constexpr auto kIputs = "inputs"; constexpr auto kOutputs = "outputs"; +constexpr auto kTbe = "TBE"; +constexpr auto kAkg = "akg"; +constexpr auto kAutodiff = "AutoDiff"; constexpr auto kName = "name"; constexpr auto kParamType = "param_type"; constexpr auto kDtype = "dtype"; @@ -89,8 +93,8 @@ bool OpLib::DecodeOpInfo(const nlohmann::json& obj, const mindspore::kernel::OpI std::shared_ptr op_info = std::make_shared(); MS_EXCEPTION_IF_NULL(op_info); op_info->set_op_name(obj.at(kOpName)); - op_info->set_imply_type(imply_type); op_info->set_impl_path(impl_path); + op_info->set_imply_type(imply_type); op_info->set_fusion_type(obj.at(kFusionType)); if (imply_type == kTBE) { op_info->set_async_flag(obj.at(kAsyncFlag)); @@ -98,6 +102,12 @@ bool OpLib::DecodeOpInfo(const nlohmann::json& obj, const mindspore::kernel::OpI op_info->set_compute_cost(obj.at(kComputeCost)); op_info->set_kernel_name(obj.at(kKernelName)); op_info->set_partial_flag(obj.at(kPartialFlag)); + if (obj.find(kOpPattern) != obj.end()) { + op_info->set_op_pattern(obj.at(kOpPattern)); + } + if (obj.find(kDynamicFormat) != obj.end()) { + op_info->set_dynamic_format(obj.at(kDynamicFormat)); + } } auto attrs = obj.at(kAttr); for (const auto& attr : attrs) { @@ -106,16 +116,20 @@ bool OpLib::DecodeOpInfo(const nlohmann::json& obj, const mindspore::kernel::OpI return false; } } + nlohmann::json dtype_format; + if (obj.find(kDtypeFormat) != obj.end()) { + dtype_format = obj.at(kDtypeFormat); + } auto inputs = obj.at(kIputs); for (const auto& input : inputs) { - if (!DecodeInputOutput(input, imply_type, kInput, op_info)) { + if (!DecodeInputOutput(input, imply_type, kInput, op_info, dtype_format)) { MS_LOG(DEBUG) << "DecodeInputOutput Failed"; return false; } } auto outputs = obj.at(kOutputs); for (const auto& output : outputs) { - if (!DecodeInputOutput(output, imply_type, kOutput, op_info)) { + if (!DecodeInputOutput(output, imply_type, kOutput, op_info, dtype_format)) { MS_LOG(DEBUG) << "DecodeInputOutput Failed"; return false; } @@ -156,16 +170,42 @@ bool OpLib::DecodeAttr(const nlohmann::json& obj, const OpImplyType imply_type, return ret; } +bool OpLib::DecodeDtypeFormat(const nlohmann::json& dtype_format, const std::shared_ptr& op_io, + size_t index) { + bool ret = true; + try { + std::vector dtype; + std::vector format; + for (const auto& it : dtype_format) { + dtype.emplace_back(it[index][0]); + format.emplace_back(it[index][1]); + } + op_io->set_dtypes(dtype); + op_io->set_formats(format); + } catch (const std::exception& e) { + MS_LOG(ERROR) << "DecodeDtypeFormat falied" << e.what(); + ret = false; + } + return ret; +} + bool OpLib::DecodeInputOutput(const nlohmann::json& obj, const OpImplyType imply_type, const OpIOType io_type, - const std::shared_ptr& op_info) { + const std::shared_ptr& op_info, const nlohmann::json& dtype_format) { bool ret = true; try { std::shared_ptr op_io = std::make_shared(); MS_EXCEPTION_IF_NULL(op_io); op_io->set_index(obj.at(kIndex)); op_io->set_name(obj.at(kName)); - op_io->set_dtypes(obj.at(kDtype)); - op_io->set_formats(obj.at(kFormat)); + if (!dtype_format.empty()) { + if (!DecodeDtypeFormat(dtype_format, op_io, op_info->inputs_ptr().size() + op_info->outputs_ptr().size())) { + MS_LOG(ERROR) << "Decode dtype format failed"; + return false; + } + } else { + op_io->set_dtypes(obj.at(kDtype)); + op_io->set_formats(obj.at(kFormat)); + } if (op_io->dtypes().size() != op_io->formats().size()) { MS_LOG(DEBUG) << "op" << op_io->name() << "dtype size:" << op_io->dtypes() << "is not equal to format size:" << op_io->formats(); @@ -181,6 +221,9 @@ bool OpLib::DecodeInputOutput(const nlohmann::json& obj, const OpImplyType imply if (obj.find(kShape) != obj.end()) { op_io->set_shape(obj.at(kShape)); } + if (obj.find(kReshapeType) != obj.end()) { + op_io->set_reshape_type(obj.at(kReshapeType)); + } } if (io_type == kInput) { diff --git a/mindspore/ccsrc/kernel/oplib/oplib.h b/mindspore/ccsrc/kernel/oplib/oplib.h index 37c3fdcfc7..a4c5e04bb1 100644 --- a/mindspore/ccsrc/kernel/oplib/oplib.h +++ b/mindspore/ccsrc/kernel/oplib/oplib.h @@ -38,8 +38,10 @@ class OpLib { static bool DecodeOpInfo(const nlohmann::json& obj, const OpImplyType imply_type, const std::string& impl_path); static bool DecodeAttr(const nlohmann::json& obj, const OpImplyType imply_type, const std::shared_ptr& op_info); + static bool DecodeDtypeFormat(const nlohmann::json& dtype_format, const std::shared_ptr& op_io, + size_t index); static bool DecodeInputOutput(const nlohmann::json& obj, const OpImplyType imply_type, const OpIOType io_type, - const std::shared_ptr& op_info); + const std::shared_ptr& op_info, const nlohmann::json& dtype_format); static bool GetRefInfo(const std::shared_ptr& op_info); static bool CheckRepetition(const std::shared_ptr& op_info); }; diff --git a/mindspore/ops/__init__.py b/mindspore/ops/__init__.py index 23109b386a..6f4f680672 100644 --- a/mindspore/ops/__init__.py +++ b/mindspore/ops/__init__.py @@ -30,7 +30,7 @@ Note: from .primitive import Primitive, PrimitiveWithInfer, prim_attr_register from .vm_impl_registry import get_vm_impl_fn, vm_impl_registry -from .op_info_register import op_info_register +from .op_info_register import op_info_register, TBERegOp, DataType from .primitive import constexpr from .._c_expression import signature_rw, signature_kind @@ -40,6 +40,6 @@ __primitive__ = [ ] __all__ = ["get_vm_impl_fn", "vm_impl_registry", - "op_info_register", + "op_info_register", "TBERegOp", "DataType", "constexpr"] __all__.extend(__primitive__) diff --git a/mindspore/ops/_op_impl/tbe/adam_apply_one_with_decay.py b/mindspore/ops/_op_impl/tbe/adam_apply_one_with_decay.py index d1c43ca957..a8911e81bd 100644 --- a/mindspore/ops/_op_impl/tbe/adam_apply_one_with_decay.py +++ b/mindspore/ops/_op_impl/tbe/adam_apply_one_with_decay.py @@ -14,208 +14,41 @@ # ============================================================================ """AdamApplyOneWithDecay op""" -from mindspore.ops.op_info_register import op_info_register +from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType +adam_apply_one_with_decay_op_info = TBERegOp("AdamApplyOneWithDecay") \ + .fusion_type("OPAQUE") \ + .async_flag(False) \ + .binfile_name("adam_apply_one_with_decay.so") \ + .compute_cost(10) \ + .kernel_name("adam_apply_one_with_decay") \ + .partial_flag(True) \ + .input(0, "input0", False, "required", "all") \ + .input(1, "input1", False, "required", "all") \ + .input(2, "input2", False, "required", "all") \ + .input(3, "input3", False, "required", "all") \ + .input(4, "input4", False, "required", "all") \ + .input(5, "mul0_x", False, "required", "all") \ + .input(6, "mul1_x", False, "required", "all") \ + .input(7, "mul2_x", False, "required", "all") \ + .input(8, "mul3_x", False, "required", "all") \ + .input(9, "mul4_x", False, "required", "all") \ + .input(10, "add2_y", False, "required", "all") \ + .output(0, "output0", False, "required", "all") \ + .output(1, "output1", False, "required", "all") \ + .output(2, "output2", False, "required", "all") \ + .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, + DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, + DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, + DataType.F16_Default, DataType.F16_Default) \ + .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, + DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, + DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, + DataType.F32_Default, DataType.F32_Default) \ + .get_op_info() -@op_info_register("""{ - "op_name": "AdamApplyOneWithDecay", - "imply_type": "TBE", - "fusion_type": "OPAQUE", - "async_flag": false, - "binfile_name": "adam_apply_one_with_decay.so", - "compute_cost": 10, - "kernel_name": "adam_apply_one_with_decay", - "partial_flag": true, - "attr": [ - ], - "inputs": [ - { - "index": 0, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "input0", - "need_compile": false, - "param_type": "required", - "shape": "all" - }, - { - "index": 1, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "input1", - "need_compile": false, - "param_type": "required", - "shape": "all" - }, - { - "index": 2, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "input2", - "need_compile": false, - "param_type": "required", - "shape": "all" - }, - { - "index": 3, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "input3", - "need_compile": false, - "param_type": "required", - "shape": "all" - }, - { - "index": 4, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "input4", - "need_compile": false, - "param_type": "required", - "shape": "all" - }, - { - "index": 5, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "mul0_x", - "need_compile": false, - "param_type": "required", - "shape": "all" - }, - { - "index": 6, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "mul1_x", - "need_compile": false, - "param_type": "required", - "shape": "all" - }, - { - "index": 7, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "mul2_x", - "need_compile": false, - "param_type": "required", - "shape": "all" - }, - { - "index": 8, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "mul3_x", - "need_compile": false, - "param_type": "required", - "shape": "all" - }, - { - "index": 9, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "mul4_x", - "need_compile": false, - "param_type": "required", - "shape": "all" - }, - { - "index": 10, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "add2_y", - "need_compile": false, - "param_type": "required", - "shape": "all" - } - ], - "outputs": [ - { - "index": 0, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "output0", - "need_compile": true, - "param_type": "required", - "shape": "all" - }, - { - "index": 1, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "output1", - "need_compile": true, - "param_type": "required", - "shape": "all" - }, - { - "index": 2, - "dtype": [ - "float16", "float" - ], - "format": [ - "DefaultFormat", "DefaultFormat" - ], - "name": "output2", - "need_compile": true, - "param_type": "required", - "shape": "all" - } - ] -}""") +@op_info_register(adam_apply_one_with_decay_op_info) def _adam_apply_one_with_decay_tbe(): """AdamApplyOneWithDecay TBE register""" return diff --git a/mindspore/ops/op_info_register.py b/mindspore/ops/op_info_register.py index 80f40ff1d6..6a42099c89 100644 --- a/mindspore/ops/op_info_register.py +++ b/mindspore/ops/op_info_register.py @@ -16,6 +16,7 @@ """Operators info register.""" import os +import json import inspect from mindspore._c_expression import Oplib from mindspore._checkparam import ParamValidator as validator @@ -32,21 +33,453 @@ def op_info_register(op_info): 'op_info' must be a str of json format represent the op info, the op info will be added into oplib. Args: - op_info (str): op info of json format. + op_info (str or dict): op info of json format. Returns: Function, returns a decorator for op info register. """ def register_decorator(func): - validator.check_type("op_info", op_info, [str]) + if isinstance(op_info, dict): + op_info_real = json.dumps(op_info) + else: + op_info_real = op_info + validator.check_type("op_info", op_info_real, [str]) op_lib = Oplib() file_path = os.path.realpath(inspect.getfile(func)) # keep the path custom ops implementation. imply_path = "" if BUILT_IN_OPS_REGISTER_PATH in file_path else file_path - if not op_lib.reg_op(op_info, imply_path): - raise ValueError('Invalid op info {}:\n{}\n'.format(file_path, op_info)) + if not op_lib.reg_op(op_info_real, imply_path): + raise ValueError('Invalid op info {}:\n{}\n'.format(file_path, op_info_real)) def wrapped_function(*args, **kwargs): return func(*args, **kwargs) return wrapped_function return register_decorator + + +class RegOp(): + """ + Base class for op info register. + + Args: + op_name (str): Name of op. + inputs (list): Inputs inoformation of the op. + outputs (list): Outputs information of the op. + attr_ (list): Attribute information of the op. + dtype_format_ (list): Dtype and format information of the op. + """ + + def __init__(self, op_name=""): + if not isinstance(op_name, str): + raise ValueError("op name value must be string") + if not op_name.strip(): + raise ValueError("op name is empty") + self.op_name = op_name + self.inputs = [] + self.outputs = [] + self.attr_ = [] + self.dtype_format_ = [] + + def is_string(self, value): + """ + Check if the value is a str type. + + Args: + value: Parameter to to check. + + Raises: + TypeError: If the type of value is not a str. + """ + if not isinstance(value, str): + raise TypeError("%s value must be str" % str(value)) + + def is_int(self, value): + """ + Check if the value is a int. + + Args: + value: Parameter to to check. + + Raises: + TypeError: If the type of value is not a int. + """ + if not isinstance(value, int): + raise TypeError("%s value must be int" % str(value)) + + def is_bool(self, value): + """ + Check if the value is a bool. + + Args: + value: Parameter to to check. + + Raises: + TypeError: If the type of value is not a bool. + """ + if not isinstance(value, bool): + raise TypeError("%s value must be bool" % str(value)) + + def dtype_format(self, *args): + """ + Register dtype and format. + + Args: + args (tuple): Value of dtype and format. + + Raises: + ValueError: If the size of args not equal to input size add output size. + TypeError: If the type of args is not tuple. + """ + if len(self.inputs) + len(self.outputs) != len(args): + raise ValueError("input size add output size must be equal to detype format size") + dtype_format = [] + for arg in args: + if not isinstance(arg, tuple) or len(arg) != 2: + raise ValueError("dtype and format value must be tuple of two elements") + self.is_string(arg[0]) + self.is_string(arg[1]) + dtype_format.append(arg) + self.dtype_format_.append(tuple(dtype_format)) + return self + + def get_op_info(self): + """ + Return all registration information for this instance. + + The '_' character ending the key is removed here for compatibility with previous version. + + Key will be unified into an underlined form later. + """ + op_info = {} + for key, value in self.__dict__.items(): + if isinstance(key, str) and key.endswith('_'): + op_info[key.rstrip('_')] = value + else: + op_info[key] = value + return op_info + + +class TBERegOp(RegOp): + """Class for TBE op info register.""" + + def __init__(self, op_name=""): + super(TBERegOp, self).__init__(op_name) + self.imply_type = "TBE" + self.fusion_type_ = '' + self.async_flag_ = False + self.binfile_name_ = '' + self.compute_cost_ = 10 + self.kernel_name_ = '' + self.partial_flag_ = False + self.reshape_type_ = '' + self.dynamic_format_ = False + self.op_pattern_ = "" + + def fusion_type(self, fusion_type): + """ + Register fusion type. + + Args: + fusion_type (str): Value of fusion type. + """ + self.is_string(fusion_type) + self.fusion_type_ = fusion_type + return self + + def async_flag(self, async_flag): + """ + Register async flag. + + Args: + async_flag (bool): Value of async flag. + """ + self.is_bool(async_flag) + self.async_flag_ = async_flag + return self + + def binfile_name(self, binfile_name): + """ + Register binfile name. + + Args: + binfile_name (str): Name of op binfile. + """ + self.is_string(binfile_name) + self.binfile_name_ = binfile_name + return self + + def compute_cost(self, compute_cost): + """ + Register compute cost. + + Args: + compute_cost (int): Value of compute cost. + """ + self.is_int(compute_cost) + self.compute_cost_ = compute_cost + return self + + def kernel_name(self, kernel_name): + """ + Register kernel name. + + Args: + kernel_name (str): Name of op kernel. + """ + self.is_string(kernel_name) + self.kernel_name_ = kernel_name + return self + + def partial_flag(self, partial_flag): + """ + Register partial flag. + + Args: + partial_flag (bool): Value of partial flag. + """ + self.is_bool(partial_flag) + self.partial_flag_ = partial_flag + return self + + def reshape_type(self, reshape_type): + """ + Register reshape type. + + Args: + reshape_type (str): Value of reshape type. + """ + self.is_string(reshape_type) + self.reshape_type_ = reshape_type + return self + + def dynamic_format(self, dynamic_format): + """ + Register dynamic format. + + Args: + reshape_type (bool): Value of dynamic format. + """ + self.is_bool(dynamic_format) + self.dynamic_format_ = dynamic_format + return self + + def op_pattern(self, pattern=None): + """ + Register op pattern information. + + Args: + pattern (str): Value of op pattern. + """ + if pattern is not None and self.istring(pattern): + self.op_pattern_ = pattern + return self + + def attr(self, name=None, param_type=None, value_type=None, value=None, default_value=None, **kwargs): + """ + Register op attribute information. + + Args: + name (str): Name of the attribute. Default: None. + param_type (str): Param type of the attribute. Default: None. + type (str): Type of the attribute. Default: None. + value (str): Value of the attribute. Default: None. + default_value (str): Default value of attribute. Default: None. + kwargs (dict): Other information for the attribute. + """ + param_list = [name, param_type, value_type, value, default_value] + attr_dict = {} + for index, element in enumerate(param_list): + if element is not None: + self.is_string(element) + if index == 0: + attr_dict["name"] = element + elif index == 1: + attr_dict["param_type"] = element + elif index == 2: + attr_dict["type"] = element + elif index == 3: + attr_dict["value"] = element + elif index == 4: + attr_dict["default_value"] = element + if kwargs: + attr_dict = dict(attr_dict, **kwargs) + self.attr_.append(attr_dict) + return self + + def input(self, index=None, name=None, need_compile=None, param_type=None, shape=None, **kwargs): + """ + Register op input information. + + Args: + index (int): Order of the input. Default: None. + name (str): Name of the input. Default: None. + need_compile (bool): The input need compile whether or not. Default: None. + param_type (str): Type of the input. Default: None. + shape (str): Shape of the input. Default: None. + kwargs (dict): Other information for the input. + """ + param_list = [index, name, need_compile, param_type, shape] + input_dict = {} + for idx, element in enumerate(param_list): + if element is not None: + if idx == 0: + self.is_int(element) + input_dict["index"] = element + elif idx == 1: + self.is_string(element) + input_dict["name"] = element + elif idx == 2: + self.is_bool(element) + input_dict["need_compile"] = element + elif idx == 3: + self.is_string(element) + input_dict["param_type"] = element + elif idx == 4: + self.is_string(element) + input_dict["shape"] = element + if kwargs: + input_dict = dict(input_dict, **kwargs) + self.inputs.append(input_dict) + return self + + def output(self, index=None, name=None, need_compile=None, param_type=None, shape=None, **kwargs): + """ + Register op output information. + + Args: + index (int): Order of the output. Default: None. + name (str): Name of the output. Default: None. + need_compile (bool): The output need compile whether or not. Default: None. + param_type (str): Type of the output. Default: None. + shape (str): Shape of the output. Default: None. + kwargs (dict): Other information for the output. + """ + param_list = [index, name, need_compile, param_type, shape] + output_dict = {} + for idx, element in enumerate(param_list): + if element is not None: + if idx == 0: + self.is_int(element) + output_dict["index"] = element + elif idx == 1: + self.is_string(element) + output_dict["name"] = element + elif idx == 2: + self.is_bool(element) + output_dict["need_compile"] = element + elif idx == 3: + self.is_string(element) + output_dict["param_type"] = element + elif idx == 4: + self.is_string(element) + output_dict["shape"] = element + if kwargs: + output_dict = dict(output_dict, **kwargs) + self.outputs.append(output_dict) + return self + +class DataType(): + """ + Various combinations of dtype and formatself. + + The current list below maybe not completed. If necessary, please add it. + """ + + BOOL_None = ("bool", "") + BOOL_Default = ("bool", "DefaultFormat") + BOOL_5HD = ("bool", "NC1HWC0") + BOOL_NCHW = ("bool", "NCHW") + BOOL_NHWC = ("bool", "NHWC") + BOOL_HWCN = ("bool", "HWCN") + + I8_None = ("int8", "") + I8_Default = ("int8", "DefaultFormat") + I8_5HD = ("int8", "NC1HWC0") + I8_FracZ = ("int8", "Fracz") + I8_FracNZ = ("int8", "FRACTAL_NZ") + I8_NCHW = ("int8", "NCHW") + I8_NHWC = ("int8", "NHWC") + I8_HWCN = ("int8", "HWCN") + + U8_None = ("uint8", "") + U8_Default = ("uint8", "DefaultFormat") + U8_5HD = ("uint8", "NC1HWC0") + U8_FracZ = ("uint8", "Fracz") + U8_FracNZ = ("uint8", "FRACTAL_NZ") + U8_NCHW = ("uint8", "NCHW") + U8_NHWC = ("uint8", "NHWC") + U8_HWCN = ("uint8", "HWCN") + + I16_None = ("int16", "") + I16_Default = ("int16", "DefaultFormat") + I16_5HD = ("int16", "NC1HWC0") + I16_FracZ = ("int16", "Fracz") + I16_FracNZ = ("int16", "FRACTAL_NZ") + I16_NCHW = ("int16", "NCHW") + I16_NHWC = ("int16", "NHWC") + I16_HWCN = ("int16", "HWCN") + + U16_None = ("uint16", "") + U16_Default = ("uint16", "DefaultFormat") + U16_5HD = ("uint16", "NC1HWC0") + U16_FracZ = ("uint16", "Fracz") + U16_FracNZ = ("uint16", "FRACTAL_NZ") + U16_NCHW = ("uint16", "NCHW") + U16_NHWC = ("uint16", "NHWC") + U16_HWCN = ("uint16", "HWCN") + + I32_None = ("int32", "") + I32_Default = ("int32", "DefaultFormat") + I32_5HD = ("int32", "NC1HWC0") + I32_FracZ = ("int32", "Fracz") + I32_FracNZ = ("int32", "FRACTAL_NZ") + I32_NCHW = ("int32", "NCHW") + I32_NHWC = ("int32", "NHWC") + I32_HWCN = ("int32", "HWCN") + + U32_None = ("uint32", "") + U32_Default = ("uint32", "DefaultFormat") + U32_5HD = ("uint32", "NC1HWC0") + U32_FracZ = ("uint32", "Fracz") + U32_FracNZ = ("uint32", "FRACTAL_NZ") + U32_NCHW = ("uint32", "NCHW") + U32_NHWC = ("uint32", "NHWC") + U32_HWCN = ("uint32", "HWCN") + + I64_None = ("int64", "") + I64_Default = ("int64", "DefaultFormat") + I64_5HD = ("int64", "NC1HWC0") + I64_FracZ = ("int64", "Fracz") + I64_FracNZ = ("int64", "FRACTAL_NZ") + I64_NCHW = ("int64", "NCHW") + I64_NHWC = ("int64", "NHWC") + I64_HWCN = ("int64", "HWCN") + + U64_None = ("uint64", "") + U64_Default = ("uint64", "DefaultFormat") + U64_5HD = ("uint64", "NC1HWC0") + U64_FracZ = ("uint64", "Fracz") + U64_FracNZ = ("uint64", "FRACTAL_NZ") + U64_NCHW = ("uint64", "NCHW") + U64_NHWC = ("uint64", "NHWC") + U64_HWCN = ("uint64", "HWCN") + + F16_None = ("float16", "") + F16_Default = ("float16", "DefaultFormat") + F16_5HD = ("float16", "NC1HWC0") + F16_FracZ = ("float16", "Fracz") + F16_FracNZ = ("float16", "FRACTAL_NZ") + F16_C1HWNCoC0 = ("float16", "C1HWNCoC0") + F16_NCHW = ("float16", "NCHW") + F16_NHWC = ("float16", "NHWC") + F16_HWCN = ("float16", "HWCN") + + F32_None = ("float32", "") + F32_Default = ("float32", "DefaultFormat") + F32_5HD = ("float32", "NC1HWC0") + F32_FracZ = ("float32", "Fracz") + F32_FracNZ = ("float32", "FRACTAL_NZ") + F32_C1HWNCoC0 = ("float32", "C1HWNCoC0") + F32_NCHW = ("float32", "NCHW") + F32_NHWC = ("float32", "NHWC") + F32_HWCN = ("float32", "HWCN") From 9d25700d88b2834d83519855a709ef3c6d5010cd Mon Sep 17 00:00:00 2001 From: leonwanghui Date: Fri, 3 Apr 2020 15:20:52 +0800 Subject: [PATCH 089/227] Update some required system packages in dockerfile Signed-off-by: leonwanghui --- docker/mindspore-cpu/Dockerfile | 23 +++++++++++++---------- docker/mindspore-cuda10.1/Dockerfile | 23 +++++++++++++---------- docker/mindspore-cuda9.2/Dockerfile | 23 +++++++++++++---------- 3 files changed, 39 insertions(+), 30 deletions(-) diff --git a/docker/mindspore-cpu/Dockerfile b/docker/mindspore-cpu/Dockerfile index dea2fd2149..d24d23cf6b 100644 --- a/docker/mindspore-cpu/Dockerfile +++ b/docker/mindspore-cpu/Dockerfile @@ -12,20 +12,22 @@ RUN apt update \ && DEBIAN_FRONTEND=noninteractive apt install -y \ vim \ wget \ + curl \ xz-utils \ net-tools \ openssh-client \ git \ - subversion \ ntpdate \ tzdata \ tcl \ - sudo + sudo \ + bash-completion # Install compile tools RUN DEBIAN_FRONTEND=noninteractive apt install -y \ gcc \ g++ \ + zlibc \ make \ libgmp-dev \ patch \ @@ -39,7 +41,8 @@ RUN echo "dash dash/sh boolean false" | debconf-set-selections RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash # Install python (v3.7.5) -RUN apt install -y --no-install-recommends libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev libgdbm-dev liblzma-dev libreadline-dev \ +RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \ + libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \ && cd /tmp \ && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \ && tar -xvf v3.7.5.tar.gz \ @@ -62,12 +65,12 @@ RUN mkdir -pv /root/.pip \ && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf # Install pip package -RUN pip install numpy \ - && pip install wheel \ - && pip install nose \ - && pip install pytest \ - && pip install pytest-xdist \ - && pip list +RUN pip install --no-cache-dir \ + numpy \ + wheel \ + nose \ + pytest \ + pytest-xdist # Install cmake (v3.14.1) RUN cd /tmp \ @@ -77,4 +80,4 @@ RUN cd /tmp \ && rm -f /tmp/cmake-3.14.1-Linux-x86_64.sh # Install MindSpore cpu whl package -RUN pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/cpu/ubuntu-x86/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl +RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/cpu/ubuntu-x86/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl diff --git a/docker/mindspore-cuda10.1/Dockerfile b/docker/mindspore-cuda10.1/Dockerfile index 83b23ff878..e2a1ee955a 100644 --- a/docker/mindspore-cuda10.1/Dockerfile +++ b/docker/mindspore-cuda10.1/Dockerfile @@ -12,20 +12,22 @@ RUN apt update \ && DEBIAN_FRONTEND=noninteractive apt install -y \ vim \ wget \ + curl \ xz-utils \ net-tools \ openssh-client \ git \ - subversion \ ntpdate \ tzdata \ tcl \ - sudo + sudo \ + bash-completion # Install compile tools RUN DEBIAN_FRONTEND=noninteractive apt install -y \ gcc \ g++ \ + zlibc \ make \ libgmp-dev \ patch \ @@ -39,7 +41,8 @@ RUN echo "dash dash/sh boolean false" | debconf-set-selections RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash # Install python (v3.7.5) -RUN apt install -y --no-install-recommends libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev libgdbm-dev liblzma-dev libreadline-dev \ +RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \ + libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \ && cd /tmp \ && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \ && tar -xvf v3.7.5.tar.gz \ @@ -62,12 +65,12 @@ RUN mkdir -pv /root/.pip \ && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf # Install pip package -RUN pip install numpy \ - && pip install wheel \ - && pip install nose \ - && pip install pytest \ - && pip install pytest-xdist \ - && pip list +RUN pip install --no-cache-dir \ + numpy \ + wheel \ + nose \ + pytest \ + pytest-xdist # Install cmake (v3.14.1) RUN cd /tmp \ @@ -77,4 +80,4 @@ RUN cd /tmp \ && rm -f /tmp/cmake-3.14.1-Linux-x86_64.sh # Install MindSpore cuda-10.1 whl package -RUN pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/gpu/cuda-10.1/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl +RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/gpu/cuda-10.1/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl diff --git a/docker/mindspore-cuda9.2/Dockerfile b/docker/mindspore-cuda9.2/Dockerfile index cbfcfd42ac..6e40106396 100644 --- a/docker/mindspore-cuda9.2/Dockerfile +++ b/docker/mindspore-cuda9.2/Dockerfile @@ -12,20 +12,22 @@ RUN apt update \ && DEBIAN_FRONTEND=noninteractive apt install -y \ vim \ wget \ + curl \ xz-utils \ net-tools \ openssh-client \ git \ - subversion \ ntpdate \ tzdata \ tcl \ - sudo + sudo \ + bash-completion # Install compile tools RUN DEBIAN_FRONTEND=noninteractive apt install -y \ gcc \ g++ \ + zlibc \ make \ libgmp-dev \ patch \ @@ -39,7 +41,8 @@ RUN echo "dash dash/sh boolean false" | debconf-set-selections RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash # Install python (v3.7.5) -RUN apt install -y --no-install-recommends libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev libgdbm-dev liblzma-dev libreadline-dev \ +RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \ + libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \ && cd /tmp \ && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \ && tar -xvf v3.7.5.tar.gz \ @@ -62,12 +65,12 @@ RUN mkdir -pv /root/.pip \ && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf # Install pip package -RUN pip install numpy \ - && pip install wheel \ - && pip install nose \ - && pip install pytest \ - && pip install pytest-xdist \ - && pip list +RUN pip install --no-cache-dir \ + numpy \ + wheel \ + nose \ + pytest \ + pytest-xdist # Install cmake (v3.14.1) RUN cd /tmp \ @@ -77,4 +80,4 @@ RUN cd /tmp \ && rm -f /tmp/cmake-3.14.1-Linux-x86_64.sh # Install MindSpore cuda-9.2 whl package -RUN pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/gpu/cuda-9.2/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl +RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/gpu/cuda-9.2/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl From 26e6138247d464a647840a5c1bbf41eeb7d5c286 Mon Sep 17 00:00:00 2001 From: y00445136 Date: Thu, 2 Apr 2020 11:39:24 +0800 Subject: [PATCH 090/227] add support for bool --- mindspore/ccsrc/utils/tensorprint_utils.cc | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mindspore/ccsrc/utils/tensorprint_utils.cc b/mindspore/ccsrc/utils/tensorprint_utils.cc index ee58ed418c..1036b424ba 100644 --- a/mindspore/ccsrc/utils/tensorprint_utils.cc +++ b/mindspore/ccsrc/utils/tensorprint_utils.cc @@ -37,13 +37,13 @@ static std::map print_type_map = { {"int32_t", TypeId::kNumberTypeInt32}, {"uint32_t", TypeId::kNumberTypeUInt32}, {"int64_t", TypeId::kNumberTypeInt64}, {"uint64_t", TypeId::kNumberTypeUInt64}, {"float16", TypeId::kNumberTypeFloat16}, {"float", TypeId::kNumberTypeFloat32}, - {"double", TypeId::kNumberTypeFloat64}}; + {"double", TypeId::kNumberTypeFloat64}, {"bool", TypeId::kNumberTypeBool}}; static std::map type_size_map = { {"int8_t", sizeof(int8_t)}, {"uint8_t", sizeof(uint8_t)}, {"int16_t", sizeof(int16_t)}, {"uint16_t", sizeof(uint16_t)}, {"int32_t", sizeof(int32_t)}, {"uint32_t", sizeof(uint32_t)}, {"int64_t", sizeof(int64_t)}, {"uint64_t", sizeof(uint64_t)}, {"float16", sizeof(float) / 2}, - {"float", sizeof(float)}, {"double", sizeof(double)}}; + {"float", sizeof(float)}, {"double", sizeof(double)}, {"bool", sizeof(bool)}}; bool ParseTensorShape(const std::string &input_shape_str, std::vector *const tensor_shape, size_t *dims) { if (tensor_shape == nullptr) { @@ -107,7 +107,7 @@ bool ConvertDataItem2Tensor(const std::vector &items) { } else { auto type_iter = print_type_map.find(item.tensorType_); if (type_iter == print_type_map.end()) { - MS_LOG(ERROR) << "type of tensor need to print is not soupport" << item.tensorType_; + MS_LOG(ERROR) << "type of tensor need to print is not support " << item.tensorType_; continue; } auto type_id = type_iter->second; From 1a82ff0a7261cd0ce56c00df8b4b2bd9a3cb0183 Mon Sep 17 00:00:00 2001 From: wanghua Date: Fri, 3 Apr 2020 14:55:25 +0800 Subject: [PATCH 091/227] fix bert precison bug --- .../device/ascend/kernel_select_ascend.cc | 20 ++++++++++++------- mindspore/ccsrc/utils/utils.h | 3 +++ mindspore/ops/_op_impl/tbe/gelu.py | 8 ++++---- .../models/bert/bert_tdt_no_lossscale.py | 9 ++++----- 4 files changed, 24 insertions(+), 16 deletions(-) diff --git a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc index 347d63be39..a7c8d131fb 100644 --- a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc +++ b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc @@ -35,6 +35,7 @@ enum MatchCountPriority : int { MATCH_COUNT_PRIORITY_BEGIN = 0, MATCH_DTYPE_COUNT = MATCH_COUNT_PRIORITY_BEGIN, MATCH_FORMAT_COUNT, + MATCH_SPECIAL_FORMAT_COUNT, MATCH_5D_FORMAT_COUNT, MATCH_OUTPUT_DTYPE_COUNT, MATCH_COUNT_PRIORITY_END @@ -81,6 +82,12 @@ bool IsValidKernelInfo(const std::shared_ptr &kernel_node, const kernel:: } return true; }; + if (AnfAlgo::GetCNodeName(kernel_node) == "LayerNormBetaGammaBackprop" || + AnfAlgo::GetCNodeName(kernel_node) == "LayerNormXBackprop") { + if (AnfAlgo::GetPrevNodeOutputFormat(kernel_node, 0) != kernel_build_info.GetInputFormat(0)) { + return true; + } + } if (AnfAlgo::GetCNodeName(kernel_node) == prim::kPrimCast->name()) { return AnfAlgo::GetOutputInferDataType(kernel_node, 0) == kernel_build_info.GetOutputDeviceType(0) && AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0) == kernel_build_info.GetInputDeviceType(0); @@ -154,7 +161,7 @@ bool PriorityChooseItem(const std::vector &cur_item, std::vector *best return false; } } - return false; + return true; } void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, const std::shared_ptr &kernel_node, @@ -174,12 +181,11 @@ void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, cons continue; } } - if (input_anf_node->isa()) { - if (AnfAlgo::GetOutputDeviceDataType(input_anf_node, 0) == kTypeUnknown) { - continue; - } - } if (kernel_build_info.GetInputFormat(input_index) == AnfAlgo::GetPrevNodeOutputFormat(kernel_node, input_index)) { + if (AnfAlgo::IsFeatureMapInput(kernel_node, input_index) && + kSpecialFormatSet.find(kernel_build_info.GetInputFormat(input_index)) != kSpecialFormatSet.end()) { + (*cur_kernelinfo_match_counts)[MATCH_SPECIAL_FORMAT_COUNT]++; + } (*cur_kernelinfo_match_counts)[MATCH_FORMAT_COUNT]++; } if (kernel_build_info.GetInputDeviceType(input_index) == @@ -203,7 +209,7 @@ void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, cons (*cur_kernelinfo_match_counts)[MATCH_OUTPUT_DTYPE_COUNT]++; } } -} +} // namespace void SetTensorDeviceInfo(const kernel::KernelBuildInfo &selected_kernel_info, const CNodePtr &kernel_node) { MS_EXCEPTION_IF_NULL(kernel_node); diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h index ee1eeaddfc..ea5e969e52 100644 --- a/mindspore/ccsrc/utils/utils.h +++ b/mindspore/ccsrc/utils/utils.h @@ -195,6 +195,9 @@ const std::set kOptOperatorSet = { kApplyRMSPropOpName, }; +const std::set kSpecialFormatSet = {kOpFormat_FRAC_Z, kOpFormat_NC1KHKWHWC0, kOpFormat_NC1HWC0, + kOpFormat_FRAC_NZ, kOpFormat_C1HWNCoC0}; + static inline void ChangeFileMode(const std::string& file_name, mode_t mode) { if (access(file_name.c_str(), F_OK) != 0) { MS_LOG(DEBUG) << "File `" << file_name << "` does not exist."; diff --git a/mindspore/ops/_op_impl/tbe/gelu.py b/mindspore/ops/_op_impl/tbe/gelu.py index 8093312547..171d97c043 100644 --- a/mindspore/ops/_op_impl/tbe/gelu.py +++ b/mindspore/ops/_op_impl/tbe/gelu.py @@ -32,10 +32,10 @@ from mindspore.ops.op_info_register import op_info_register { "index": 0, "dtype": [ - "float16","float","float16","float16","float16","float16","float","float","float","float" + "float16","float","float16","float","float16","float16","float16","float16","float","float","float","float" ], "format": [ - "FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat" + "FRACTAL_NZ","FRACTAL_NZ","FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat" ], "name": "x", "need_compile": false, @@ -47,10 +47,10 @@ from mindspore.ops.op_info_register import op_info_register { "index": 0, "dtype": [ - "float16","float","float16","float16","float16","float16","float","float","float","float" + "float16","float","float16","float","float16","float16","float16","float16","float","float","float","float" ], "format": [ - "FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat" + "FRACTAL_NZ","FRACTAL_NZ","FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat" ], "name": "y", "need_compile": true, diff --git a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py index 7c50707fbd..6f3ffc7dad 100644 --- a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py +++ b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py @@ -153,8 +153,7 @@ def test_bert_tdt(): batch_size = int(os.getenv('BATCH_SIZE', '16')) config = get_config(version=version, batch_size=batch_size) netwithloss = BertNetworkWithLoss(config, True) - optimizer = Lamb(netwithloss.trainable_params(), decay_steps=10000, start_learning_rate=1e-4, - end_learning_rate=0.0, power=10.0, warmup_steps=0, decay_filter=lambda x: False) + optimizer = Momentum(netwithloss.trainable_params(), learning_rate=2e-5, momentum=0.9) netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer) netwithgrads.set_train(True) model = Model(netwithgrads) @@ -178,10 +177,10 @@ def test_bert_tdt(): param.default_input = weight_variable(value.asnumpy().shape) model.train(ds.get_repeat_count(), ds, callbacks=parallel_callback, dataset_sink_mode=False) loss_value = np.array(parallel_callback.loss_list) - expect_out = [12.191790, 11.739655, 11.523477, 11.320723, 11.113152, 11.203759, 10.841681, 10.826849, - 10.616718, 10.486609] + expect_out = [12.19179, 11.965041, 11.969687, 11.97815, 11.969171, 12.603289, 12.165594, + 12.824818, 12.38842, 12.604046] logger.info("expected loss value output: {}".format(expect_out)) - assert allclose(loss_value, expect_out, 0.001, 0.001) + assert allclose(loss_value, expect_out, 0.00001, 0.00001) if __name__ == '__main__': test_bert_tdt() From 434abe3f8c0a0bdc428604dcca01a7ff349dc903 Mon Sep 17 00:00:00 2001 From: guohongzilong <2713219276@qq.com> Date: Wed, 1 Apr 2020 21:08:40 +0800 Subject: [PATCH 092/227] import comment and function of op print --- mindspore/ccsrc/transform/op_adapter.h | 2 +- mindspore/ops/operations/debug_ops.py | 14 +++++++++----- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/mindspore/ccsrc/transform/op_adapter.h b/mindspore/ccsrc/transform/op_adapter.h index 68d89ae3f9..7f20a88035 100644 --- a/mindspore/ccsrc/transform/op_adapter.h +++ b/mindspore/ccsrc/transform/op_adapter.h @@ -538,7 +538,7 @@ class OpAdapter : public BaseOpAdapter { return; } } else { - MS_LOG(ERROR) << "Update output desc failed, unknow output shape type"; + MS_LOG(WARNING) << "Update output desc failed, unknow output shape type"; return; } MS_EXCEPTION_IF_NULL(node); diff --git a/mindspore/ops/operations/debug_ops.py b/mindspore/ops/operations/debug_ops.py index 6640ef87ca..a69dcc2df1 100644 --- a/mindspore/ops/operations/debug_ops.py +++ b/mindspore/ops/operations/debug_ops.py @@ -14,6 +14,7 @@ # ============================================================================ """debug_ops""" +from ..._checkparam import ParamValidator as validator from ...common import dtype as mstype from ..primitive import Primitive, prim_attr_register, PrimitiveWithInfer @@ -157,19 +158,20 @@ class InsertGradientOf(PrimitiveWithInfer): class Print(PrimitiveWithInfer): """ - Output tensor to stdout. + Output tensor or string to stdout. Inputs: - - **input_x** (Tensor) - The graph node to attach to. + - **input_x** (Union[Tensor, str]) - The graph node to attach to. The input supports + multiple strings and tensors which are separated by ','. Examples: >>> class PrintDemo(nn.Cell): - >>> def __init__(self,): + >>> def __init__(self): >>> super(PrintDemo, self).__init__() >>> self.print = P.Print() >>> - >>> def construct(self, x): - >>> self.print(x) + >>> def construct(self, x, y): + >>> self.print('Print Tensor x and Tensor y:', x, y) >>> return x """ @@ -181,4 +183,6 @@ class Print(PrimitiveWithInfer): return [1] def infer_dtype(self, *inputs): + for dtype in inputs: + validator.check_subclass("input", dtype, (mstype.tensor, mstype.string)) return mstype.int32 From 2de97f256fbdefed35340f1cc021a3f89227a7d2 Mon Sep 17 00:00:00 2001 From: guohongzilong <2713219276@qq.com> Date: Tue, 31 Mar 2020 16:12:43 +0800 Subject: [PATCH 093/227] iterfaces change: _Constant to Constant --- mindspore/common/initializer.py | 21 +++++++++++++++------ tests/ut/python/utils/test_initializer.py | 9 +++++++-- 2 files changed, 22 insertions(+), 8 deletions(-) diff --git a/mindspore/common/initializer.py b/mindspore/common/initializer.py index 99b4501307..bdc3418129 100644 --- a/mindspore/common/initializer.py +++ b/mindspore/common/initializer.py @@ -180,18 +180,18 @@ class HeUniform(Initializer): _assignment(arr, data) -class _Constant(Initializer): +class Constant(Initializer): """ Initialize a constant. Args: - value (int or numpy.ndarray): The value to initialize. + value (Union[int, numpy.ndarray]): The value to initialize. Returns: Array, initialize array. """ def __init__(self, value): - super(_Constant, self).__init__(value=value) + super(Constant, self).__init__(value=value) self.value = value def _initialize(self, arr): @@ -266,8 +266,16 @@ def initializer(init, shape=None, dtype=mstype.float32): Args: init (Union[Tensor, str, Initializer, numbers.Number]): Initialize value. + + - `str`: The `init` should be the alias of the class inheriting from `Initializer` and the corresponding + class will be called. + + - `Initializer`: The `init` should be the class inheriting from `Initializer` to initialize tensor. + + - `numbers.Number`: The `Constant` will be called to initialize tensor. + shape (Union[tuple, list, int]): A list of integers, a tuple of integers or an integer as the shape of - output. Default: None. + output. Default: None. dtype (:class:`mindspore.dtype`): The type of data in initialized tensor. Default: mstype.float32. Returns: @@ -295,7 +303,7 @@ def initializer(init, shape=None, dtype=mstype.float32): raise ValueError(msg) if isinstance(init, numbers.Number): - init_obj = _Constant(init) + init_obj = Constant(init) elif isinstance(init, str): init_obj = _INITIALIZER_ALIAS[init.lower()]() else: @@ -314,4 +322,5 @@ __all__ = [ 'HeUniform', 'XavierUniform', 'One', - 'Zero'] + 'Zero', + 'Constant'] diff --git a/tests/ut/python/utils/test_initializer.py b/tests/ut/python/utils/test_initializer.py index ff7ab8d119..31d2434341 100644 --- a/tests/ut/python/utils/test_initializer.py +++ b/tests/ut/python/utils/test_initializer.py @@ -37,8 +37,8 @@ def _check_value(tensor, value_min, value_max): for ele in nd.flatten(): if value_min <= ele <= value_max: continue - raise TypeError('value_min = %d, ele = %d, value_max = %d' - % (value_min, ele, value_max)) + raise ValueError('value_min = %d, ele = %d, value_max = %d' + % (value_min, ele, value_max)) def _check_uniform(tensor, boundary_a, boundary_b): @@ -92,6 +92,11 @@ def test_init_one_alias(): _check_value(tensor, 1, 1) +def test_init_constant(): + tensor = init.initializer(init.Constant(1), [2, 2], ms.float32) + _check_value(tensor, 1, 1) + + def test_init_uniform(): scale = 10 tensor = init.initializer(init.Uniform(scale=scale), [5, 4], ms.float32) From d42954652cf2e8cd8f553d395c5ebef20a0c776f Mon Sep 17 00:00:00 2001 From: wanghua Date: Fri, 3 Apr 2020 17:51:56 +0800 Subject: [PATCH 094/227] modify bert test file --- mindspore/ccsrc/device/ascend/kernel_select_ascend.cc | 8 +------- tests/st/networks/models/bert/bert_tdt_no_lossscale.py | 2 +- 2 files changed, 2 insertions(+), 8 deletions(-) diff --git a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc index a7c8d131fb..d05b9fafa1 100644 --- a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc +++ b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc @@ -82,12 +82,6 @@ bool IsValidKernelInfo(const std::shared_ptr &kernel_node, const kernel:: } return true; }; - if (AnfAlgo::GetCNodeName(kernel_node) == "LayerNormBetaGammaBackprop" || - AnfAlgo::GetCNodeName(kernel_node) == "LayerNormXBackprop") { - if (AnfAlgo::GetPrevNodeOutputFormat(kernel_node, 0) != kernel_build_info.GetInputFormat(0)) { - return true; - } - } if (AnfAlgo::GetCNodeName(kernel_node) == prim::kPrimCast->name()) { return AnfAlgo::GetOutputInferDataType(kernel_node, 0) == kernel_build_info.GetOutputDeviceType(0) && AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0) == kernel_build_info.GetInputDeviceType(0); @@ -161,7 +155,7 @@ bool PriorityChooseItem(const std::vector &cur_item, std::vector *best return false; } } - return true; + return false; } void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, const std::shared_ptr &kernel_node, diff --git a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py index 6f3ffc7dad..9cc11997e6 100644 --- a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py +++ b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py @@ -27,7 +27,7 @@ from mindspore.common.tensor import Tensor from mindspore.train.model import Model from mindspore.train.callback import Callback from mindspore.model_zoo.Bert_NEZHA import BertConfig, BertNetworkWithLoss, BertTrainOneStepCell -from mindspore.nn.optim import Lamb +from mindspore.nn.optim import Momentum from mindspore import log as logger _current_dir = os.path.dirname(os.path.realpath(__file__)) DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"] From 95f02396bd25ec5824a3d66d65af7e9cf2bbf28b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?= Date: Mon, 23 Mar 2020 15:33:01 +0800 Subject: [PATCH 095/227] modify comments --- mindspore/ops/operations/nn_ops.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index fd2c3f5614..93a07880a2 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -1219,14 +1219,14 @@ class ApplyMomentum(PrimitiveWithInfer): gradient_scale (float): The scale of the gradient. Default: 1.0. Inputs: - - **variable** (Tensor) - Weights to be update. + - **variable** (Tensor) - Weights to be updated. - **accumulation** (Tensor) - Accumulated gradient value by moment weight. - **learning_rate** (float) - Learning rate. - **gradient** (Tensor) - Gradients. - **momentum** (float) - Momentum. Outputs: - Tensor, parameters to be update. + Tensor, parameters to be updated. Examples: >>> net = ResNet50() @@ -1318,15 +1318,15 @@ class SGD(PrimitiveWithInfer): nesterov (bool): Enable Nesterov momentum. Default: False. Inputs: - - **parameters** (Tensor) - Parameters to be update. + - **parameters** (Tensor) - Parameters to be updated. - **gradient** (Tensor) - Gradients. - **learning_rate** (Tensor) - Learning rate. e.g. Tensor(0.1, mindspore.float32). - - **accum** (Tensor) - Accum(velocity) to be update. + - **accum** (Tensor) - Accum(velocity) to be updated. - **momentum** (Tensor) - Momentum. e.g. Tensor(0.1, mindspore.float32). - **stat** (Tensor) - States to be updated with the same shape as gradient. Outputs: - Tensor, parameters to be update. + Tensor, parameters to be updated. """ @prim_attr_register @@ -2141,7 +2141,7 @@ class Adam(PrimitiveWithInfer): If False, updates the gradients without using NAG. Default: False. Inputs: - - **var** (Tensor) - Weights to be update. + - **var** (Tensor) - Weights to be updated. - **m** (Tensor) - The 1st moment vector in the updating formula. - **v** (Tensor) - the 2nd moment vector in the updating formula. - **beta1_power** (float) - :math:`beta_1^t` in the updating formula. @@ -2251,8 +2251,8 @@ class SparseApplyAdagrad(PrimitiveWithInfer): use_locking (bool): If True, updating of the var and accum tensors will be protected. Default: False. Inputs: - - **var** (Tensor) - Variable to be update. The type must be float32. - - **accum** (Tensor) - Accum to be update. The shape must be the same as `var`'s shape, + - **var** (Tensor) - Variable to be updated. The type must be float32. + - **accum** (Tensor) - Accum to be updated. The shape must be the same as `var`'s shape, the type must be float32. - **grad** (Tensor) - Gradient. The shape must be the same as `var`'s shape except first dimension, the type must be float32. @@ -2372,7 +2372,7 @@ class LARSUpdate(PrimitiveWithInfer): use_clip (bool): Whether to use clip operation for calculating the local learning rate. Default: False. Inputs: - - **weight** (Tensor) - The weight to be update. + - **weight** (Tensor) - The weight to be updated. - **gradient** (Tensor) - The gradient of weight, which has the same shape and dtype with weight. - **norm_weight** (Tensor) - A scalar tensor, representing the square sum of weight. - **norm_gradient** (Tensor) - A scalar tensor, representing the square sum of gradient. From cd1395cf30aa0dae459328b6c041f65a4757676c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?= Date: Thu, 2 Apr 2020 11:13:16 +0800 Subject: [PATCH 096/227] fix issue I1DBRX delete duplicated words in comments fix issue I1CJAP fix issue I1D3WS fix issue I1DBA8 fix issue I1CJ77 fix issue I1CJG3 fix review suggestion --- mindspore/nn/layer/normalization.py | 2 +- mindspore/ops/operations/array_ops.py | 3 +- mindspore/ops/operations/control_ops.py | 4 +- mindspore/ops/operations/math_ops.py | 122 ++++++++++++------------ mindspore/ops/operations/nn_ops.py | 7 +- 5 files changed, 69 insertions(+), 69 deletions(-) diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py index d43c1c8ab4..4aded20ab3 100644 --- a/mindspore/nn/layer/normalization.py +++ b/mindspore/nn/layer/normalization.py @@ -249,7 +249,7 @@ class LayerNorm(Cell): 'he_uniform', etc. Default: 'zeros'. Inputs: - - **input_x** (Tensor) - The shape of 'input_x' is input_shape = :math:`(x_1, x_2, ..., x_R)`, + - **input_x** (Tensor) - The shape of 'input_x' is :math:`(x_1, x_2, ..., x_R)`, and `input_shape[begin_norm_axis:]` is equal to `normalized_shape`. Outputs: diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index 3dabf2ab0a..ff6289a4ad 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -443,7 +443,6 @@ class Transpose(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]), mindspore.float32) >>> perm = (0, 2, 1) - >>> expect = np.array([[[1, 4], [2, 5], [3, 6]], [[7, 10], [8, 11], [9, 12]]]) >>> transpose = Transpose() >>> output = transpose(input_tensor, perm) """ @@ -1634,7 +1633,7 @@ class Diag(PrimitiveWithInfer): Examples: >>> input_x = Tensor([1, 2, 3, 4]) >>> diag = P.Diag() - >>> diag(x) + >>> diag(input_x) [[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], diff --git a/mindspore/ops/operations/control_ops.py b/mindspore/ops/operations/control_ops.py index 1bffc09c04..167739b89a 100644 --- a/mindspore/ops/operations/control_ops.py +++ b/mindspore/ops/operations/control_ops.py @@ -107,8 +107,8 @@ class GeSwitch(PrimitiveWithInfer): >>> ret = self.merge((add_ret, sq_ret)) >>> return ret[0] >>> - >>> x = Tensor(x_init, dtype=mindspore.float32) - >>> y = Tensor(y_init, dtype=mindspore.float32) + >>> x = Tensor(10.0, dtype=mindspore.float32) + >>> y = Tensor(5.0, dtype=mindspore.float32) >>> net = Net() >>> output = net(x, y) """ diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index b7373416f9..97a992ca05 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -140,6 +140,7 @@ class AssignAdd(PrimitiveWithInfer): Examples: >>> class Net(Cell): >>> def __init__(self): + >>> super(Net, self).__init__() >>> self.AssignAdd = P.AssignAdd() >>> self.inputdata = Parameter(initializer(1, [1], mindspore.int64), name="global_step") >>> @@ -272,7 +273,7 @@ class ReduceMean(_Reduce): Examples: >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) - >>> op = ReduceMean(keep_dims=True) + >>> op = P.ReduceMean(keep_dims=True) >>> output = op(data, 1) """ @@ -304,7 +305,7 @@ class ReduceSum(_Reduce): Examples: >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) - >>> op = ReduceSum(keep_dims=True) + >>> op = P.ReduceSum(keep_dims=True) >>> output = op(data, 1) """ @@ -337,7 +338,7 @@ class ReduceAll(_Reduce): Examples: >>> data = Tensor(np.array([[True, False], [True, True]])) - >>> op = ReduceAll(keep_dims=True) + >>> op = P.ReduceAll(keep_dims=True) >>> output = op(data, 1) """ @@ -373,7 +374,7 @@ class ReduceMax(_Reduce): Examples: >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) - >>> op = ReduceMax(keep_dims=True) + >>> op = P.ReduceMax(keep_dims=True) >>> output = op(data, 1) """ @@ -406,7 +407,7 @@ class ReduceMin(_Reduce): Examples: >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) - >>> op = ReduceMin(keep_dims=True) + >>> op = P.ReduceMin(keep_dims=True) >>> output = op(data, 1) """ @@ -438,7 +439,7 @@ class ReduceProd(_Reduce): Examples: >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) - >>> op = ReduceProd(keep_dims=True) + >>> op = P.ReduceProd(keep_dims=True) >>> output = op(data, 1) """ @@ -460,13 +461,13 @@ class CumProd(PrimitiveWithInfer): Examples: >>> data = Tensor(np.array([a, b, c]).astype(np.float32)) - >>> op0 = CumProd() + >>> op0 = P.CumProd() >>> output = op0(data, 0) # output=[a, a * b, a * b * c] - >>> op1 = CumProd(exclusive=True) + >>> op1 = P.CumProd(exclusive=True) >>> output = op1(data, 0) # output=[1, a, a * b] - >>> op2 = CumProd(reverse=True) + >>> op2 = P.CumProd(reverse=True) >>> output = op2(data, 0) # output=[a * b * c, b * c, c] - >>> op3 = CumProd(exclusive=True, reverse=True) + >>> op3 = P.CumProd(exclusive=True, reverse=True) >>> output = op3(data, 0) # output=[b * c, c, 1] """ @prim_attr_register @@ -506,7 +507,7 @@ class MatMul(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.ones(shape=[1, 3]), mindspore.float32) >>> input_y = Tensor(np.ones(shape=[3, 4]), mindspore.float32) - >>> matmul = MatMul() + >>> matmul = P.MatMul() >>> output = matmul(input_x, input_y) """ @@ -582,12 +583,12 @@ class BatchMatMul(MatMul): Examples: >>> input_x = Tensor(np.ones(shape=[2, 4, 1, 3]), mindspore.float32) >>> input_y = Tensor(np.ones(shape=[2, 4, 3, 4]), mindspore.float32) - >>> batmatmul = BatchMatMul() + >>> batmatmul = P.BatchMatMul() >>> output = batmatmul(input_x, input_y) >>> >>> input_x = Tensor(np.ones(shape=[2, 4, 3, 1]), mindspore.float32) >>> input_y = Tensor(np.ones(shape=[2, 4, 3, 4]), mindspore.float32) - >>> batmatmul = BatchMatMul(transpose_a=True) + >>> batmatmul = P.BatchMatMul(transpose_a=True) >>> output = batmatmul(input_x, input_y) """ @@ -621,7 +622,7 @@ class CumSum(PrimitiveWithInfer): Examples: >>> input = Tensor(np.array([[3, 4, 6, 10],[1, 6, 7, 9],[4, 3, 8, 7],[1, 3, 7, 9]]).astype(np.float32)) - >>> cumsum = CumSum() + >>> cumsum = P.CumSum() >>> output = cumsum(input, 1) [[ 3. 7. 13. 23.] [ 1. 7. 14. 23.] @@ -666,7 +667,7 @@ class AddN(PrimitiveWithInfer): >>> class NetAddN(nn.Cell): >>> def __init__(self): >>> super(NetAddN, self).__init__() - >>> self.addN = AddN() + >>> self.addN = P.AddN() >>> >>> def construct(self, *z): >>> return self.addN(z) @@ -748,7 +749,7 @@ class Sub(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([4, 5, 6]), mindspore.int32) - >>> sub = Sub() + >>> sub = P.Sub() >>> sub(input_x, input_y) [-3, -3, -3] """ @@ -775,7 +776,7 @@ class Mul(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([4, 5, 6]), mindspore.int32) - >>> mul = Mul() + >>> mul = P.Mul() >>> mul(input_x, input_y) [4, 10, 18] """ @@ -793,7 +794,7 @@ class Square(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) - >>> square = Square() + >>> square = P.Square() >>> square(input_x) [1.0, 4.0, 9.0] """ @@ -823,7 +824,7 @@ class Rsqrt(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor([[4, 4], [9, 9]], mindspore.float32) - >>> rsqrt = Rsqrt() + >>> rsqrt = P.Rsqrt() >>> rsqrt(input_tensor) [[0.5, 0.5], [0.333333, 0.333333]] """ @@ -853,7 +854,7 @@ class Sqrt(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.0, 4.0, 9.0]), mindspore.float32) - >>> sqrt = Sqrt() + >>> sqrt = P.Sqrt() >>> sqrt(input_x) [1.0, 2.0, 3.0] """ @@ -883,7 +884,7 @@ class Reciprocal(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) - >>> reciprocal = Reciprocal() + >>> reciprocal = P.Reciprocal() >>> reciprocal(input_x) [1.0, 0.5, 0.25] """ @@ -916,13 +917,13 @@ class Pow(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) >>> input_y = 3.0 - >>> pow = Pow() + >>> pow = P.Pow() >>> pow(input_x, input_y) [1.0, 8.0, 64.0] >>> >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) >>> input_y = Tensor(np.array([2.0, 4.0, 3.0]), mindspore.float32) - >>> pow = Pow() + >>> pow = P.Pow() >>> pow(input_x, input_y) [1.0, 16.0, 64.0] """ @@ -952,7 +953,7 @@ class Exp(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) - >>> exp = Exp() + >>> exp = P.Exp() >>> exp(input_x) [ 2.71828183, 7.3890561 , 54.59815003] """ @@ -982,7 +983,7 @@ class Log(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) - >>> log = Log() + >>> log = P.Log() >>> log(input_x) [0.0, 0.69314718, 1.38629436] """ @@ -1020,7 +1021,7 @@ class Minimum(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.float32) >>> input_y = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32) - >>> minimum = Minimum() + >>> minimum = P.Minimum() >>> minimum(input_x, input_y) [1.0, 2.0, 3.0] """ @@ -1047,7 +1048,7 @@ class Maximum(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.float32) >>> input_y = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32) - >>> maximum = Maximum() + >>> maximum = P.Maximum() >>> maximum(input_x, input_y) [4.0, 5.0, 6.0] """ @@ -1074,7 +1075,7 @@ class RealDiv(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) >>> input_y = Tensor(np.array([4.0, 5.0, 6.0]), mindspore.float32) - >>> realdiv = RealDiv() + >>> realdiv = P.RealDiv() >>> realdiv(input_x, input_y) [0.25, 0.4, 0.5] """ @@ -1113,9 +1114,8 @@ class Div(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([-4.0, 5.0, 6.0]), mindspore.float32) >>> input_y = Tensor(np.array([3.0, 2.0, 3.0]), mindspore.float32) - >>> div = Div() + >>> div = P.Div() >>> div(input_x, input_y) - [-2.0, 2.0, 2.0] """ def infer_value(self, x, y): @@ -1147,7 +1147,7 @@ class FloorDiv(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([2, 4, -1]), mindspore.int32) >>> input_y = Tensor(np.array([3, 3, 3]), mindspore.int32) - >>> floor_div = FloorDiv() + >>> floor_div = P.FloorDiv() >>> floor_div(input_x, input_y) [0, 1, -1] """ @@ -1165,7 +1165,7 @@ class Floor(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1.1, 2.5, -1.5]), mindspore.float32) - >>> floor = Floor() + >>> floor = P.Floor() >>> floor(input_x) [1.0, 2.0, -2.0] """ @@ -1277,13 +1277,13 @@ class Equal(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32) - >>> equal = Equal() + >>> equal = P.Equal() >>> equal(input_x, 2.0) [False, True, False] >>> >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 2, 4]), mindspore.int32) - >>> equal = Equal() + >>> equal = P.Equal() >>> equal(input_x, input_y) [True, True, False] """ @@ -1308,7 +1308,7 @@ class EqualCount(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 2, 4]), mindspore.int32) - >>> equal_count = EqualCount() + >>> equal_count = P.EqualCount() >>> equal_count(input_x, input_y) [2] """ @@ -1347,13 +1347,13 @@ class NotEqual(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32) - >>> not_equal = NotEqual() + >>> not_equal = P.NotEqual() >>> not_equal(input_x, 2.0) [True, False, True] >>> >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 2, 4]), mindspore.int32) - >>> not_equal = NotEqual() + >>> not_equal = P.NotEqual() >>> not_equal(input_x, input_y) [False, False, True] """ @@ -1383,7 +1383,7 @@ class Greater(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 1, 4]), mindspore.int32) - >>> greater = Greater() + >>> greater = P.Greater() >>> greater(input_x, input_y) [False, True, False] """ @@ -1410,7 +1410,7 @@ class GreaterEqual(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 1, 4]), mindspore.int32) - >>> greater_equal = GreaterEqual() + >>> greater_equal = P.GreaterEqual() >>> greater_equal(input_x, input_y) [True, True, False] """ @@ -1437,7 +1437,7 @@ class Less(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 1, 4]), mindspore.int32) - >>> less = Less() + >>> less = P.Less() >>> less(input_x, input_y) [False, False, True] """ @@ -1464,7 +1464,7 @@ class LessEqual(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_y = Tensor(np.array([1, 1, 4]), mindspore.int32) - >>> less_equal = LessEqual() + >>> less_equal = P.LessEqual() >>> less_equal(input_x, input_y) [True, False, True] """ @@ -1482,7 +1482,7 @@ class LogicalNot(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([True, False, True]), mindspore.bool_) - >>> logical_not = LogicalNot() + >>> logical_not = P.LogicalNot() >>> logical_not(input_x) [False, True, False] """ @@ -1520,7 +1520,7 @@ class LogicalAnd(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([True, False, True]), mindspore.bool_) >>> input_y = Tensor(np.array([True, True, False]), mindspore.bool_) - >>> logical_and = LogicalAnd() + >>> logical_and = P.LogicalAnd() >>> logical_and(input_x, input_y) [True, False, False] """ @@ -1549,7 +1549,7 @@ class LogicalOr(_LogicBinaryOp): Examples: >>> input_x = Tensor(np.array([True, False, True]), mindspore.bool_) >>> input_y = Tensor(np.array([True, True, False]), mindspore.bool_) - >>> logical_or = LogicalOr() + >>> logical_or = P.LogicalOr() >>> logical_or(input_x, input_y) [True, True, True] """ @@ -1571,7 +1571,7 @@ class NPUAllocFloatStatus(PrimitiveWithInfer): Tensor, has the shape of `(8,)`. Examples: - >>> alloc_status = NPUAllocFloatStatus() + >>> alloc_status = P.NPUAllocFloatStatus() >>> init = alloc_status() Tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=(8,), dtype=mindspore.float32) """ @@ -1603,8 +1603,8 @@ class NPUGetFloatStatus(PrimitiveWithInfer): Tensor, has the same shape as `input_x`. All the elements in the tensor will be zero. Examples: - >>> alloc_status = NPUAllocFloatStatus() - >>> get_status = NPUGetFloatStatus() + >>> alloc_status = P.NPUAllocFloatStatus() + >>> get_status = P.NPUGetFloatStatus() >>> init = alloc_status() >>> flag = get_status(init) Tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=(8,), dtype=mindspore.float32) @@ -1643,9 +1643,9 @@ class NPUClearFloatStatus(PrimitiveWithInfer): Tensor, has the same shape as `input_x`. All the elements in the tensor will be zero. Examples: - >>> alloc_status = NPUAllocFloatStatus() - >>> get_status = NPUGetFloatStatus() - >>> clear_status = NPUClearFloatStatus() + >>> alloc_status = P.NPUAllocFloatStatus() + >>> get_status = P.NPUGetFloatStatus() + >>> clear_status = P.NPUClearFloatStatus() >>> init = alloc_status() >>> flag = get_status(init) >>> clear = clear_status(init) @@ -1679,7 +1679,7 @@ class Cos(PrimitiveWithInfer): Tensor, has the same shape as `input_x`. Examples: - >>> cos = Cos() + >>> cos = P.Cos() >>> X = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), ms.float32) >>> output = cos(X) """ @@ -1708,8 +1708,8 @@ class ACos(PrimitiveWithInfer): Tensor, has the same shape as `input_x`. Examples: - >>> acos = ACos() - >>> X = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), ms.float32) + >>> acos = P.ACos() + >>> X = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32) >>> output = acos(X) """ @@ -1737,9 +1737,9 @@ class Sin(PrimitiveWithInfer): Tensor, has the same shape as `input_x`. Examples: - >>> sin = Sin() - >>> X = Tensor(np.array([0.62, 0.28, 0.43, 0.62]), ms.float32) - >>> output = sin(X) + >>> sin = P.Sin() + >>> input_x = Tensor(np.array([0.62, 0.28, 0.43, 0.62]), ms.float32) + >>> output = sin(input_x) """ @prim_attr_register @@ -1789,7 +1789,7 @@ class NMSWithMask(PrimitiveWithInfer): >>> bbox[:, 2] += bbox[:, 0] >>> bbox[:, 3] += bbox[:, 1] >>> inputs = Tensor(bbox) - >>> nms = NMSWithMask(0.5) + >>> nms = P.NMSWithMask(0.5) >>> output_boxes, indices, mask = nms(inputs) """ @@ -1824,7 +1824,7 @@ class Abs(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([-1.0, 1.0, 0.0]), mindspore.float32) - >>> abs = Abs() + >>> abs = P.Abs() >>> abs(input_x) [1.0, 1.0, 0.0] """ @@ -1867,7 +1867,7 @@ class Sign(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[2.0, 0.0, -1.0]]), mindspore.float32) - >>> sign = Sign() + >>> sign = P.Sign() >>> output = sign(input_x) [[1.0, 0.0, -1.0]] """ @@ -1897,7 +1897,7 @@ class Round(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([0.8, 1.5, 2.3, 2.5, -4.5]), mindspore.float32) - >>> round = Round() + >>> round = P.Round() >>> round(input_x) [1.0, 2.0, 2.0, 2.0, -4.0] """ @@ -1932,7 +1932,7 @@ class Atan2(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([[0, 1]]), mstype.float32) >>> input_y = Tensor(np.array([[1, 1]]), mstype.float32) - >>> atan2 = Atan2() + >>> atan2 = P.Atan2() >>> atan2(input_x, input_y) [[0. 0.7853982]] """ diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 93a07880a2..952f7fc0d6 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -1089,9 +1089,10 @@ class TopK(PrimitiveWithInfer): - **indices** (Tensor) - The indices of values within the last dimension of input. Examples: - >>> topk = TopK(sorted=True) - >>> x = Tensor(np.array([1, 2, 3, 4, 5]).astype(np.float16)) - >>> values, indices = topk(x) + >>> topk = P.TopK(sorted=True) + >>> input_x = Tensor([1, 2, 3, 4, 5], mindspore.float16)) + >>> k = 3 + >>> values, indices = topk(input_x, k) >>> assert values == Tensor(np.array([5, 4, 3])) >>> assert indices == Tensor(np.array([4, 3, 2])) """ From 48d80d591aa80c38dd057abdcc7dfa8ce7c77a0f Mon Sep 17 00:00:00 2001 From: leonwanghui Date: Sat, 4 Apr 2020 13:00:18 +0800 Subject: [PATCH 097/227] Fix some typo errors in pipeline module Signed-off-by: leonwanghui --- mindspore/ccsrc/ir/func_graph.cc | 12 ++-- mindspore/ccsrc/ir/func_graph.h | 4 +- mindspore/ccsrc/pipeline/action.cc | 2 +- mindspore/ccsrc/pipeline/base.h | 2 +- .../ccsrc/pipeline/parse/data_converter.cc | 21 +++--- .../ccsrc/pipeline/parse/function_block.cc | 4 +- mindspore/ccsrc/pipeline/parse/parse.cc | 50 ++++++------- mindspore/ccsrc/pipeline/parse/parse.h | 6 +- mindspore/ccsrc/pipeline/parse/parse_base.h | 4 +- .../ccsrc/pipeline/parse/python_adapter.cc | 2 + mindspore/ccsrc/pipeline/parse/resolve.cc | 6 +- mindspore/ccsrc/pipeline/pass.cc | 4 +- mindspore/ccsrc/pipeline/pipeline.cc | 70 +++++++++---------- mindspore/ccsrc/pipeline/pipeline_ge.cc | 16 ++--- mindspore/ccsrc/pipeline/resource.cc | 2 +- .../static_analysis/abstract_value.cc | 2 +- .../pipeline/static_analysis/abstract_value.h | 4 +- .../pipeline/static_analysis/evaluator.cc | 2 +- .../static_analysis/param_validator.cc | 10 +-- .../ccsrc/pipeline/static_analysis/prim.cc | 46 ++++++------ .../static_analysis/program_specialize.cc | 10 +-- .../static_analysis/static_analysis.cc | 12 ++-- 22 files changed, 149 insertions(+), 142 deletions(-) diff --git a/mindspore/ccsrc/ir/func_graph.cc b/mindspore/ccsrc/ir/func_graph.cc index f515367635..7404db4af0 100644 --- a/mindspore/ccsrc/ir/func_graph.cc +++ b/mindspore/ccsrc/ir/func_graph.cc @@ -64,14 +64,14 @@ AbstractFunctionPtr FuncGraph::abstract() { for (auto& p : parameters_) { MS_EXCEPTION_IF_NULL(p); if (p->abstract() == nullptr) { - MS_LOG(ERROR) << "error!!"; + MS_LOG(ERROR) << "Error!!"; return nullptr; } args_spec_list.push_back(p->abstract()); } if (nullptr == output()) { - MS_LOG(ERROR) << "error func graph no output"; + MS_LOG(ERROR) << "Error func graph no output"; return nullptr; } @@ -543,6 +543,7 @@ void FuncGraph::GenerateKwargReplNode(const FuncGraphPtr& specialized_graph, TraceManager::EndTrace(); } } + bool FuncGraph::NeedGenerate(const std::vector& kwarg_list) { // if the function does not have any vararg/kwarg/kwonly/default value/kw args input // return the original graph @@ -556,6 +557,7 @@ bool FuncGraph::NeedGenerate(const std::vector& } return true; } + void FuncGraph::GenerateDefaultValue(const FuncGraphPtr& specialized_graph, const std::vector& specialized_parameter_list, std::unordered_map* repl_nodes) { @@ -664,7 +666,7 @@ void FuncGraph::EraseUnusedNodeInOrder() { auto mng = manager_.lock(); if (mng) { auto nodes = mng->nodes()[shared_from_base()]; - // Erase unusued cnode. + // Erase unused cnode. for (auto it = order_.begin(); it != order_.end();) { if (nodes.count(*it)) { (void)it++; @@ -695,7 +697,7 @@ void FuncGraph::CheckOrder() { if (found == it) { DumpCNodeList(); MS_LOG(EXCEPTION) << "The cnode " << (*it)->DebugString() << " order in " << ToString() - << " doesn't obey the input denpency, " + << " doesn't obey the input dependency, " << "as input " << input_node->DebugString() << " is not ahead of itself."; } } @@ -842,5 +844,5 @@ void FuncGraph::SetEffectDepends(const std::vector& depend_inputs) { } const PrimitivePtr FuncGraphTransform::func_graph_prim_ = std::make_shared("FuncGraph"); -const char kFuncGraphFlagUndetermin[] = "Undeterminate"; +const char kFuncGraphFlagUndetermined[] = "Undeterminate"; } // namespace mindspore diff --git a/mindspore/ccsrc/ir/func_graph.h b/mindspore/ccsrc/ir/func_graph.h index 95b26de473..13e8c9dc8f 100644 --- a/mindspore/ccsrc/ir/func_graph.h +++ b/mindspore/ccsrc/ir/func_graph.h @@ -96,7 +96,7 @@ class FuncGraphBase : public Value { MS_DECLARE_PARENT(FuncGraphBase, Value); }; -extern const char kFuncGraphFlagUndetermin[]; +extern const char kFuncGraphFlagUndetermined[]; class FuncGraph : public FuncGraphBase { public: @@ -200,7 +200,7 @@ class FuncGraph : public FuncGraphBase { // get all func graphs directly used by this func graph const FuncGraphCounterMap &func_graphs_used(); - // get all func graphs nestedly used by this func graph + // get all func graphs nested used by this func graph const FuncGraphSet &func_graphs_used_total(); // get all users of this func graph diff --git a/mindspore/ccsrc/pipeline/action.cc b/mindspore/ccsrc/pipeline/action.cc index 83e524e5df..baf4bea7ec 100644 --- a/mindspore/ccsrc/pipeline/action.cc +++ b/mindspore/ccsrc/pipeline/action.cc @@ -183,7 +183,7 @@ bool SymbolResolveAction(const ResourcePtr& res) { FuncGraphPtr func_graph = res->func_graph(); auto succ = parse::ResolveFuncGraph(func_graph, res); - // Remove usued nodes in cnode order list. + // Remove unused nodes in cnode order list. func_graph->EraseUnusedNodeInOrder(); func_graph->ReleaseFullOrderToEffectOrder(); for (auto fg : func_graph->func_graphs_used_total()) { diff --git a/mindspore/ccsrc/pipeline/base.h b/mindspore/ccsrc/pipeline/base.h index d007eac294..30524e84f6 100644 --- a/mindspore/ccsrc/pipeline/base.h +++ b/mindspore/ccsrc/pipeline/base.h @@ -40,7 +40,7 @@ using ExecutorInfoPtr = std::shared_ptr; inline std::string GetPhasePrefix(const std::string& phase) { auto pos = phase.find('.'); if (pos == std::string::npos) { - MS_LOG(EXCEPTION) << "phase has no . for prefix" << phase; + MS_LOG(EXCEPTION) << "Phase has no . for prefix" << phase; } return phase.substr(0, pos); } diff --git a/mindspore/ccsrc/pipeline/parse/data_converter.cc b/mindspore/ccsrc/pipeline/parse/data_converter.cc index aee7c35ba6..d25a202afc 100644 --- a/mindspore/ccsrc/pipeline/parse/data_converter.cc +++ b/mindspore/ccsrc/pipeline/parse/data_converter.cc @@ -39,7 +39,7 @@ using TensorPtr = mindspore::tensor::TensorPtr; namespace { bool ConvertTuple(const py::object& obj, ValuePtr* const data, bool use_signature) { - MS_LOG(DEBUG) << "converting python tuple"; + MS_LOG(DEBUG) << "Converting python tuple"; py::tuple tuple = obj.cast(); std::vector value_list; for (size_t it = 0; it < tuple.size(); ++it) { @@ -56,7 +56,7 @@ bool ConvertTuple(const py::object& obj, ValuePtr* const data, bool use_signatur } bool ConvertList(const py::object& obj, ValuePtr* const data, bool use_signature) { - MS_LOG(DEBUG) << "converting python list"; + MS_LOG(DEBUG) << "Converting python list"; py::list list = obj.cast(); std::vector value_list; @@ -73,7 +73,7 @@ bool ConvertList(const py::object& obj, ValuePtr* const data, bool use_signature } bool ConvertCellList(const py::object& obj, ValuePtr* const data, bool use_signature) { - MS_LOG(DEBUG) << "converting cell list"; + MS_LOG(DEBUG) << "Converting cell list"; py::sequence list = obj; std::vector value_list; for (size_t it = 0; it < list.size(); ++it) { @@ -89,7 +89,7 @@ bool ConvertCellList(const py::object& obj, ValuePtr* const data, bool use_signa } bool ConvertDict(const py::object& obj, ValuePtr* data, bool use_signature) { - MS_LOG(DEBUG) << "converting python dict"; + MS_LOG(DEBUG) << "Converting python dict"; py::dict dict_values = obj.cast(); std::vector> key_values; @@ -110,14 +110,14 @@ bool ConvertDict(const py::object& obj, ValuePtr* data, bool use_signature) { } void ConvertNameSpace(const py::object& obj, ValuePtr* const data) { - MS_LOG(DEBUG) << "converting python module"; + MS_LOG(DEBUG) << "Converting python module"; py::module mod = python_adapter::GetPyModule(PYTHON_MOD_PARSE_MODULE); py::object module_namespace = python_adapter::CallPyModFn(mod, PYTHON_MOD_GET_MODULE_NAMESPACE, obj); *data = std::make_shared(RESOLVE_NAMESPACE_NAME_MODULE, py::cast(module_namespace)); } void ConvertDataClass(py::object obj, ValuePtr* const data) { - MS_LOG(DEBUG) << "converting dataclass"; + MS_LOG(DEBUG) << "Converting dataclass"; // Maybe the obj is dataclass define auto desc = py::cast(python_adapter::CallPyObjMethod(obj, PYTHON_GET_OBJ_DESC, obj)); // desc has format "", strip the '<' and '>' by offset 1; @@ -247,7 +247,7 @@ bool ConvertOtherObj(py::object obj, ValuePtr* const data) { bool ConvertData(const py::object& obj, ValuePtr* const data, bool use_signature) { // check parameter valid if (data == nullptr) { - MS_LOG(ERROR) << " data is null pointer"; + MS_LOG(ERROR) << "Data is null pointer"; return false; } @@ -386,9 +386,9 @@ py::object CreatePythonObject(const py::object& type, const py::tuple& params) { py::module mod = python_adapter::GetPyModule(PYTHON_MOD_PARSE_MODULE); py::object obj; if (params.size() == 0) { - obj = python_adapter::CallPyModFn(mod, PYTHON_MOD_CRETAE_OBJ_INSTANCE, type); + obj = python_adapter::CallPyModFn(mod, PYTHON_MOD_CREATE_OBJ_INSTANCE, type); } else { - obj = python_adapter::CallPyModFn(mod, PYTHON_MOD_CRETAE_OBJ_INSTANCE, type, params); + obj = python_adapter::CallPyModFn(mod, PYTHON_MOD_CREATE_OBJ_INSTANCE, type, params); } return obj; } @@ -421,6 +421,7 @@ ValuePtr PyDataToValue(const py::object& obj) { (void)ConvertData(to_convert, &value); return value; } + void ClearObjectCache() { object_map_.clear(); object_graphs_map_.clear(); @@ -445,7 +446,7 @@ ClassPtr ParseDataClass(const py::object& cls_obj) { for (auto& item : names) { TypePtr type_value = item.second.cast(); MS_EXCEPTION_IF_NULL(type_value); - MS_LOG(DEBUG) << "(name: " << py::cast(item.first) << ", type: " << type_value->ToString() << ")"; + MS_LOG(DEBUG) << "(Name: " << py::cast(item.first) << ", type: " << type_value->ToString() << ")"; attributes.push_back(std::make_pair(py::cast(item.first), type_value)); } diff --git a/mindspore/ccsrc/pipeline/parse/function_block.cc b/mindspore/ccsrc/pipeline/parse/function_block.cc index 25cc3ab4d8..423e76c1d8 100644 --- a/mindspore/ccsrc/pipeline/parse/function_block.cc +++ b/mindspore/ccsrc/pipeline/parse/function_block.cc @@ -254,7 +254,7 @@ void FunctionBlock::Mature() { matured_ = true; } -// Force the conditon node to bool using bool operation +// Force the conditIon node to bool using bool operation CNodePtr FunctionBlock::ForceToBoolNode(const AnfNodePtr& cond) { TraceManager::DebugTrace(std::make_shared(cond->debug_info())); CNodePtr op_apply_node = func_graph()->NewCNode({MakeResolveOperation(NAMED_PRIMITIVE_BOOL), cond}); @@ -347,7 +347,7 @@ void FunctionBlock::InsertDependItemsBeforeReturn() { auto return_node = func_graph()->get_return(); if (return_node) { if (return_node->inputs().size() < 1) { - MS_LOG(EXCEPTION) << "length of inputs of output node is less than 2"; + MS_LOG(EXCEPTION) << "Length of inputs of output node is less than 2"; } old_ret = return_node->input(1); } else { diff --git a/mindspore/ccsrc/pipeline/parse/parse.cc b/mindspore/ccsrc/pipeline/parse/parse.cc index 60cc00a307..231b98ab00 100644 --- a/mindspore/ccsrc/pipeline/parse/parse.cc +++ b/mindspore/ccsrc/pipeline/parse/parse.cc @@ -294,14 +294,14 @@ FunctionBlockPtr Parser::ParseStatement(const FunctionBlockPtr &block, const py: TraceManager::EndTrace(); return stmt_block; } else { - errcode_ = PARSE_NODE_METHOD_UNSUPPORT; + errcode_ = PARSE_NODE_METHOD_UNSUPPORTED; py::list location = ast_->CallParserObjMethod(PYTHON_PARSE_GET_LOCATION, node); if (location.size() < 2) { MS_LOG(EXCEPTION) << "List size should not be less than 2."; } auto filename = location[0].cast(); auto line_no = location[1].cast(); - MS_LOG(EXCEPTION) << "unsupported syntax '" << node_name << "' at " << filename << ":" << line_no; + MS_LOG(EXCEPTION) << "Unsupported syntax '" << node_name << "' at " << filename << ":" << line_no; } } @@ -324,11 +324,11 @@ AnfNodePtr Parser::ParseExprNode(const FunctionBlockPtr &block, const py::object TraceManager::EndTrace(); return expr_node; } else { - errcode_ = PARSE_NODE_METHOD_UNSUPPORT; + errcode_ = PARSE_NODE_METHOD_UNSUPPORTED; py::list ret = ast_->CallParserObjMethod(PYTHON_PARSE_GET_LOCATION, node); auto filename = ret[0].cast(); auto line_no = ret[1].cast(); - MS_LOG(EXCEPTION) << "unsupported syntax '" << node_name << "' at " << filename << ":" << line_no; + MS_LOG(EXCEPTION) << "Unsupported syntax '" << node_name << "' at " << filename << ":" << line_no; } } @@ -339,7 +339,7 @@ FunctionBlockPtr Parser::ParseExpr(const FunctionBlockPtr &block, const py::obje // Expr only have value , no target py::tuple expand_info = ast_->CallParserObjMethod(PYTHON_PARSE_EXPAND_EXPR_STATEMENT, node); - // refer pypthon function expand_expr_statement, expand_info is one of the following: + // refer python function expand_expr_statement, expand_info is one of the following: // True, expr.value, x // True, expr.value // False, None, None @@ -453,8 +453,8 @@ AnfNodePtr Parser::ParseNum(const FunctionBlockPtr &, const py::object &node) { return NewValueNode(data); } else { // no else actually - MS_LOG(ERROR) << "unsupported Num type : " << (std::string)py::str(obj) << GetLocation(node)->ToString(); - errcode_ = PARSE_NODE_TYPE_UNKONW; + MS_LOG(ERROR) << "Unsupported Num type : " << (std::string)py::str(obj) << GetLocation(node)->ToString(); + errcode_ = PARSE_NODE_TYPE_UNKOWN; return nullptr; } } @@ -478,8 +478,8 @@ AnfNodePtr Parser::ParseNameConstant(const FunctionBlockPtr &, const py::object return NewValueNode(kNone); } else { // no else actually - MS_LOG(ERROR) << "unsupported NameConstant type: " << (std::string)py::str(obj) << GetLocation(node)->ToString(); - errcode_ = PARSE_NODE_TYPE_UNKONW; + MS_LOG(ERROR) << "Unsupported NameConstant type: " << (std::string)py::str(obj) << GetLocation(node)->ToString(); + errcode_ = PARSE_NODE_TYPE_UNKOWN; return nullptr; } } @@ -497,7 +497,7 @@ AnfNodePtr Parser::ParseCall(const FunctionBlockPtr &block, const py::object &no // process function call py::object function_ast_node = python_adapter::GetPyObjAttr(node, "func"); AnfNodePtr call_function_anf_node = ParseExprNode(block, function_ast_node); - // function call arguments should be passed in as groups and upacked later using unpack call + // function call arguments should be passed in as groups and unpacked later using unpack call py::list args = python_adapter::GetPyObjAttr(node, "args"); std::vector packed_arguments; std::vector group_arguments; @@ -614,7 +614,7 @@ AnfNodePtr Parser::ParseAttribute(const FunctionBlockPtr &block, const py::objec py::object value_body = python_adapter::GetPyObjAttr(node, "value"); AnfNodePtr value_node = ParseExprNode(block, value_body); if (value_node == nullptr) { - MS_LOG(WARNING) << "Parse Attribut failed"; + MS_LOG(WARNING) << "Parse attribute failed"; return nullptr; } @@ -637,7 +637,7 @@ AnfNodePtr Parser::ParseCompare(const FunctionBlockPtr &block, const py::object // which there is two ops , but we only support one now py::list ops = python_adapter::GetPyObjAttr(node, "ops"); if (ops.size() > MAX_COMPARISON_OPS_SUPPORTED) { - MS_LOG(ERROR) << "mindspore does not support comparison with operators more than one now, ops size =" << ops.size(); + MS_LOG(ERROR) << "MindSpore does not support comparison with operators more than one now, ops size =" << ops.size(); return nullptr; } @@ -817,7 +817,7 @@ AnfNodePtr Parser::ParseIndex(const FunctionBlockPtr &block, const py::object &n // process a UnaryOp, +a, -b AnfNodePtr Parser::ParseUnaryOp(const FunctionBlockPtr &block, const py::object &node) { - MS_LOG(DEBUG) << "process ast UnaryOp"; + MS_LOG(DEBUG) << "Process ast UnaryOp"; py::object op = python_adapter::GetPyObjAttr(node, "op"); MS_EXCEPTION_IF_NULL(block); @@ -831,7 +831,7 @@ AnfNodePtr Parser::ParseUnaryOp(const FunctionBlockPtr &block, const py::object // process a dict ast node expression AnfNodePtr Parser::ParseDict(const FunctionBlockPtr &block, const py::object &node) { - MS_LOG(DEBUG) << "process ast Dict"; + MS_LOG(DEBUG) << "Process ast Dict"; py::list keys = node.attr("keys"); py::list values = node.attr("values"); std::vector key_nodes; @@ -849,7 +849,7 @@ AnfNodePtr Parser::ParseDict(const FunctionBlockPtr &block, const py::object &no // process a augment assign such as a += b; FunctionBlockPtr Parser::ParseAugAssign(const FunctionBlockPtr &block, const py::object &node) { - MS_LOG(DEBUG) << "process ast AugAssign"; + MS_LOG(DEBUG) << "Process ast AugAssign"; py::object op = python_adapter::GetPyObjAttr(node, "op"); MS_EXCEPTION_IF_NULL(block); @@ -864,10 +864,10 @@ FunctionBlockPtr Parser::ParseAugAssign(const FunctionBlockPtr &block, const py: } else if (ast_->IsClassMember(target_node)) { read_node = ParseAttribute(block, target_node); } else { - MS_LOG(EXCEPTION) << "not supported augassign"; + MS_LOG(EXCEPTION) << "Not supported augassign"; } if (read_node == nullptr) { - MS_LOG(EXCEPTION) << "can not get target node "; + MS_LOG(EXCEPTION) << "Can not get target node "; } py::object value = python_adapter::GetPyObjAttr(node, "value"); @@ -879,7 +879,7 @@ FunctionBlockPtr Parser::ParseAugAssign(const FunctionBlockPtr &block, const py: // process global declaration such as 'global x'; FunctionBlockPtr Parser::ParseGlobal(const FunctionBlockPtr &block, const py::object &node) { - MS_LOG(DEBUG) << "process ast Global"; + MS_LOG(DEBUG) << "Process ast Global"; MS_EXCEPTION_IF_NULL(block); py::list vars = python_adapter::GetPyObjAttr(node, "names"); for (auto &item : vars) { @@ -890,7 +890,7 @@ FunctionBlockPtr Parser::ParseGlobal(const FunctionBlockPtr &block, const py::ob // process a if statement FunctionBlockPtr Parser::ParseIf(const FunctionBlockPtr &block, const py::object &node) { - MS_LOG(DEBUG) << "process ast If"; + MS_LOG(DEBUG) << "Process ast If"; py::object test_node = python_adapter::GetPyObjAttr(node, "test"); AnfNodePtr condition_node = ParseExprNode(block, test_node); MS_EXCEPTION_IF_NULL(block); @@ -934,7 +934,7 @@ FunctionBlockPtr Parser::ParseIf(const FunctionBlockPtr &block, const py::object } FunctionBlockPtr Parser::ParseWhile(const FunctionBlockPtr &block, const py::object &node) { - MS_LOG(DEBUG) << "process ast While"; + MS_LOG(DEBUG) << "Process ast While"; MS_EXCEPTION_IF_NULL(block); MS_LOG(INFO) << "Parse while statement"; TraceManager::DebugTrace(std::make_shared(block->func_graph()->debug_info())); @@ -999,7 +999,7 @@ FunctionBlockPtr Parser::GenerateBlockInFor(const TraceInfoPtr &trace_info) { // x, it = next(it) // body FunctionBlockPtr Parser::ParseFor(const FunctionBlockPtr &block, const py::object &node) { - MS_LOG(DEBUG) << "process ast For"; + MS_LOG(DEBUG) << "Process ast For"; MS_EXCEPTION_IF_NULL(block); AnfNodePtr op_iter = block->MakeResolveOperation(NAMED_PRIMITIVE_ITER); AnfNodePtr op_next = block->MakeResolveOperation(NAMED_PRIMITIVE_NEXT); @@ -1054,7 +1054,7 @@ FunctionBlockPtr Parser::ParseFor(const FunctionBlockPtr &block, const py::objec return after_block; } AnfNodePtr Parser::ParseIfExp(const FunctionBlockPtr &block, const py::object &node) { - MS_LOG(DEBUG) << "process ast IfExp"; + MS_LOG(DEBUG) << "Process ast IfExp"; MS_EXCEPTION_IF_NULL(block); py::object test_node = python_adapter::GetPyObjAttr(node, "test"); AnfNodePtr condition_node = ParseExprNode(block, test_node); @@ -1163,7 +1163,7 @@ void Parser::HandleAssignSubscript(const FunctionBlockPtr &block, const py::obje void Parser::WriteAssignVars(const FunctionBlockPtr &block, const py::object &targ, const AnfNodePtr &value_node) { MS_EXCEPTION_IF_NULL(value_node); - MS_LOG(DEBUG) << "process WriteAssignVars"; + MS_LOG(DEBUG) << "Process WriteAssignVars"; auto ast_type = AstSubType(py::cast(ast_->CallParserObjMethod(PYTHON_PARSE_GET_AST_TYPE, targ))); if (ast_type == AST_SUB_TYPE_NAME) { HandleAssignName(block, targ, value_node); @@ -1174,7 +1174,7 @@ void Parser::WriteAssignVars(const FunctionBlockPtr &block, const py::object &ta } else if (ast_->IsClassMember(targ)) { HandleAssignClassMember(block, targ, value_node); } else { - MS_LOG(EXCEPTION) << "not supported assign type: " << ast_type + MS_LOG(EXCEPTION) << "Not supported assign type: " << ast_type << " NodeInfo: " << trace::GetDebugInfo(value_node->debug_info()); } } @@ -1340,7 +1340,7 @@ bool ParseAst::UpdateFuncGraphFlags(const FuncGraphPtr &func_graph) { py::dict flags = python_adapter::GetPyObjAttr(obj_, PYTHON_EXTERN_MINDSPORE_FLAG); for (auto &item : flags) { if (!py::isinstance(item.first) || !py::isinstance(item.second)) { - MS_LOG(ERROR) << "type error in flags dict convert"; + MS_LOG(ERROR) << "Type error in flags dict convert"; return false; } auto name = py::cast(item.first); diff --git a/mindspore/ccsrc/pipeline/parse/parse.h b/mindspore/ccsrc/pipeline/parse/parse.h index 3e891e47dd..4dd1bc62aa 100644 --- a/mindspore/ccsrc/pipeline/parse/parse.h +++ b/mindspore/ccsrc/pipeline/parse/parse.h @@ -40,8 +40,8 @@ enum ParseStatusCode : int { PARSE_PARAMETER_INVALID, // parameter is invalid PARSE_NO_RETURN, // function no return node PARSE_NODE_TYPE_NO_MATCH, // ast node type is error - PARSE_NODE_TYPE_UNKONW, // node type is unkonw - PARSE_NODE_METHOD_UNSUPPORT, // no method to parse the node + PARSE_NODE_TYPE_UNKOWN, // node type is unkown + PARSE_NODE_METHOD_UNSUPPORTED, // no method to parse the node PARSE_DONT_RESOLVE_SYMBOL, // can't resolve the string PARSE_NOT_SUPPORTED_COMPARE_EXPR, // the comparison is not supported PARSE_FAILURE = 0xFF @@ -102,7 +102,7 @@ class Parser { AnfNodePtr ParseCall(const FunctionBlockPtr &block, const py::object &node); // process the if expression AnfNodePtr ParseIfExp(const FunctionBlockPtr &block, const py::object &node); - // process calss type define + // process class type define AnfNodePtr ParseAttribute(const FunctionBlockPtr &block, const py::object &node); // process a compare expression AnfNodePtr ParseCompare(const FunctionBlockPtr &block, const py::object &node); diff --git a/mindspore/ccsrc/pipeline/parse/parse_base.h b/mindspore/ccsrc/pipeline/parse/parse_base.h index 9f92687b6f..df2d1968a5 100644 --- a/mindspore/ccsrc/pipeline/parse/parse_base.h +++ b/mindspore/ccsrc/pipeline/parse/parse_base.h @@ -45,7 +45,7 @@ enum AstSubType : int { // define the parse target type enum ParseTargetTypeDef { - PARSE_TARGET_FUNCTION = 0, // funciton + PARSE_TARGET_FUNCTION = 0, // function PARSE_TARGET_METHOD = 1, // method PARSE_TARGET_OBJECT_INSTANCE = 2, // object instance PARSE_TARGET_UNKNOW = 0xFF // ERROR TYPE @@ -59,7 +59,7 @@ const char PYTHON_MOD_RESOLVE_GET_OBJ_KEY[] = "get_object_key"; const char PYTHON_MOD_PARSE_CHECK_IS_CLASS_MEMBER[] = "is_class_member"; const char PYTHON_MOD_RESOLVE_GET_OBJ_TYPE[] = "get_obj_type"; const char PYTHON_MOD_GET_CLASS_INSTANCE_TYPE[] = "get_class_instance_type"; -const char PYTHON_MOD_CRETAE_OBJ_INSTANCE[] = "create_obj_instance"; +const char PYTHON_MOD_CREATE_OBJ_INSTANCE[] = "create_obj_instance"; const char PYTHON_MOD_GET_DATACLASS_ATTRS[] = "get_dataclass_attributes"; const char PYTHON_MOD_GET_DATACLASS_METHODS[] = "get_dataclass_methods"; const char PYTHON_MOD_GET_MODULE_NAMESPACE[] = "get_module_namespace"; diff --git a/mindspore/ccsrc/pipeline/parse/python_adapter.cc b/mindspore/ccsrc/pipeline/parse/python_adapter.cc index db40238729..e2c86164d4 100644 --- a/mindspore/ccsrc/pipeline/parse/python_adapter.cc +++ b/mindspore/ccsrc/pipeline/parse/python_adapter.cc @@ -50,6 +50,7 @@ void SetPythonPath(const std::string& path) { (void)sys_path.attr("append")(path.c_str()); } } + std::shared_ptr set_python_scoped() { // if start process from python, no need set the python scope. if (!python_env_) { @@ -79,6 +80,7 @@ py::object GetPyObjAttr(const py::object& obj, const std::string& attr) { } return py::none(); } + py::object GetPyFn(const std::string& module, const std::string& name) { (void)python_adapter::set_python_scoped(); if (!module.empty() && !name.empty()) { diff --git a/mindspore/ccsrc/pipeline/parse/resolve.cc b/mindspore/ccsrc/pipeline/parse/resolve.cc index 976c474aa4..ebc1f65486 100644 --- a/mindspore/ccsrc/pipeline/parse/resolve.cc +++ b/mindspore/ccsrc/pipeline/parse/resolve.cc @@ -53,6 +53,7 @@ abstract::AbstractBasePtr ClassType::ToAbstract() { ret_val->set_value_desc(ToString()); return ret_val; } + // call python PYTHON_MOD_RESOLVE_FUNCTION interface to resolve the symbol in corresponding namespace bool SymbolResolver::Resolve() { py::module mod = python_adapter::GetPyModule(PYTHON_MOD_PARSE_MODULE); @@ -127,7 +128,7 @@ bool ResolveObjectToNode(const FuncGraphPtr& func_graph, const py::object& obj, MS_LOG(ERROR) << "Resolve parameter object failed, got nullptr"; return false; } - MS_LOG(DEBUG) << "add param graph:" << func_graph->ToString() << ", " << param->DebugString(); + MS_LOG(DEBUG) << "Add param graph:" << func_graph->ToString() << ", " << param->DebugString(); output = param; } else if (py::hasattr(obj, "__parameter_tuple__")) { @@ -160,6 +161,7 @@ bool ResolveObjectToNode(const FuncGraphPtr& func_graph, const py::object& obj, *node = output; return true; } + // transform the ValueTuple or ValueList of graph node to make tuple of const graph node bool TransformVectorGraphValueNode(const FuncGraphManagerPtr& manager, const AnfNodePtr& node, const ValueNodePtr& value_node, AnfNodePtr* const transformed) { @@ -175,7 +177,7 @@ bool TransformVectorGraphValueNode(const FuncGraphManagerPtr& manager, const Anf continue; } if (has_graph_in_list) { - MS_LOG(EXCEPTION) << "list has graph in it , but not all is graph"; + MS_LOG(EXCEPTION) << "List has graph in it, but not all is graph"; } } // The celllist or ordered_cell will be parsed as valuetuple of const graph in it, diff --git a/mindspore/ccsrc/pipeline/pass.cc b/mindspore/ccsrc/pipeline/pass.cc index e2626d5314..d89a0090a7 100644 --- a/mindspore/ccsrc/pipeline/pass.cc +++ b/mindspore/ccsrc/pipeline/pass.cc @@ -195,13 +195,13 @@ void ReclaimOptimizer() { bool OptPassGroup(const ResourcePtr& res, const std::string& name) { if (res->func_graph() == nullptr) { - MS_LOG(ERROR) << "opt passes int error"; + MS_LOG(ERROR) << "Opt passes int error"; return false; } abstract::AbstractBasePtrList args = res->args_spec(); FuncGraphPtr func_graph = res->func_graph(); - MS_LOG(DEBUG) << "start " << name << " func graph:" << func_graph->ToString() << ", " + MS_LOG(DEBUG) << "Start " << name << " func graph:" << func_graph->ToString() << ", " << func_graph->get_return()->DebugString(true); InitOpt(res); if (g_pass_opts.find(name) != g_pass_opts.end()) { diff --git a/mindspore/ccsrc/pipeline/pipeline.cc b/mindspore/ccsrc/pipeline/pipeline.cc index 861862b849..0d7790fb36 100644 --- a/mindspore/ccsrc/pipeline/pipeline.cc +++ b/mindspore/ccsrc/pipeline/pipeline.cc @@ -98,7 +98,7 @@ py::tuple GenerateKey(const std::string& name, const std::unordered_mapfunc_graph; } std::size_t ExecutorPy::ArgListSize(const std::string& phase) { if (info_.count(phase) == 0) { - MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase); + MS_LOG(EXCEPTION) << "No phase in executor:" << GetPhasePrefix(phase); } return info_[phase]->arg_list_size; } @@ -243,7 +243,7 @@ void ExecutorPy::DelNetRes(const std::string& id) { auto tmp_info = info_; for (auto& item : tmp_info) { if (item.first.find(id) != string::npos) { - MS_LOG(INFO) << "delete network res:" << item.first; + MS_LOG(INFO) << "Delete network res:" << item.first; (void)info_.erase(item.first); flag = true; } @@ -262,7 +262,7 @@ void ExecutorPy::DelNetRes(const std::string& id) { } void ExecutorPy::ClearRes() { - MS_LOG(INFO) << "clean executor Resrouce!"; + MS_LOG(INFO) << "Clean executor resource!"; executor_ = nullptr; } @@ -278,27 +278,27 @@ void ExecutorPy::SaveCompiledGraph(const std::string& phase_s) { MS_EXCEPTION_IF_NULL(parallel::ParallelContext::GetInstance()); std::string parallel_mode = parallel::ParallelContext::GetInstance()->parallel_mode(); - MS_LOG(INFO) << "save compiled func graph(" << func_graph->ToString() << ") phase(" << phase_s << ")!"; + MS_LOG(INFO) << "Save compiled func graph(" << func_graph->ToString() << ") phase(" << phase_s << ")!"; info_[phase_s]->func_graph = func_graph; if ((func_graph != nullptr) && ((parallel_mode == parallel::AUTO_PARALLEL) || (parallel_mode == parallel::SEMI_AUTO_PARALLEL))) { - MS_LOG(DEBUG) << "save model parallel parameter layout graph!"; + MS_LOG(DEBUG) << "Save model parallel parameter layout graph!"; func_graph = info_[phase_s]->resource->results()[kStepParallelGraph].cast(); - ExecutorInfoPtr excutor_info = std::make_shared(); + ExecutorInfoPtr executor_info = std::make_shared(); std::string layout_graph = phase_s + kStepParallelGraph; - excutor_info->func_graph = func_graph; - info_[layout_graph] = excutor_info; + executor_info->func_graph = func_graph; + info_[layout_graph] = executor_info; } else { - MS_LOG(DEBUG) << "save model parallel parameter layout graph null!"; + MS_LOG(DEBUG) << "Save model parallel parameter layout graph null!"; } - MS_LOG(INFO) << "end save compiled func graph!"; + MS_LOG(INFO) << "End save compiled func graph!"; } bool ExecutorPy::ChangeExportGeirUseVmFlag(bool use_vm, const std::string& phase_s) const { std::string phase_prefix = GetPhasePrefix(phase_s); if (use_vm && phase_prefix == "export") { - MS_LOG(INFO) << "use ge backend to export geir"; + MS_LOG(INFO) << "Use ge backend to export geir"; use_vm = false; } return use_vm; @@ -316,7 +316,7 @@ void ExecutorPy::GetGeBackendPolicy() const { bool ExecutorPy::CompileInner(const py::object& obj, const py::tuple& args, const py::object& phase, bool use_vm) { MS_LOG(DEBUG) << "Start ExecutorPy compile!"; if ((!py::isinstance(phase))) { - MS_LOG(ERROR) << "arg phase must be string."; + MS_LOG(ERROR) << "Arg phase must be string."; return false; } // check the arg valid? @@ -327,7 +327,7 @@ bool ExecutorPy::CompileInner(const py::object& obj, const py::tuple& args, cons #ifdef ENABLE_GE GetGeBackendPolicy(); #endif - ExecutorInfoPtr excutor_info = std::make_shared(); + ExecutorInfoPtr executor_info = std::make_shared(); std::string phase_s = py::cast(phase); MS_LOG(INFO) << "ExecutorPy compile phase:" << phase_s << "!"; ResourcePtr resource = std::make_shared(obj); @@ -353,16 +353,16 @@ bool ExecutorPy::CompileInner(const py::object& obj, const py::tuple& args, cons ValuePtr converted = nullptr; bool succ = parse::ConvertData(args[i], &converted); if (!succ) { - MS_LOG(EXCEPTION) << "args convert error"; + MS_LOG(EXCEPTION) << "Args convert error"; } bool broaden = true; args_spec.push_back(abstract::FromValue(converted, broaden)); } resource->set_args_spec(args_spec); - excutor_info->arg_list_size = size; - excutor_info->resource = resource; - info_[phase_s] = excutor_info; + executor_info->arg_list_size = size; + executor_info->resource = resource; + info_[phase_s] = executor_info; pip->Run(); // save the run graph func to MsPipeLine @@ -439,7 +439,7 @@ std::string GetMsIrFile(void) { char real_path[PATH_MAX] = {0}; if (realpath(path, real_path) == nullptr) { - MS_LOG(ERROR) << "MS IR Path error, " << path; + MS_LOG(ERROR) << "MS IR path error, " << path; return file; } file = real_path; @@ -485,7 +485,7 @@ void RunPipelineAction(const ActionItem& action, pipeline::ResourcePtr resource, #endif void Pipeline::Run() { - MS_LOG(INFO) << "pipeline run"; + MS_LOG(INFO) << "Pipeline run"; MS_EXCEPTION_IF_NULL(resource_); FuncGraphPtr user_graph = nullptr; @@ -507,7 +507,7 @@ void Pipeline::Run() { MS_LOG(DEBUG) << "Action " << action.first << " end."; }; if (!result) { - MS_LOG(EXCEPTION) << "pipeline running to end, failed in step:" << action.first; + MS_LOG(EXCEPTION) << "Pipeline running to end, failed in step:" << action.first; } if (MsContext::GetInstance()->save_graphs_flag() && resource_->func_graph() != nullptr) { auto graph = resource_->func_graph(); @@ -555,7 +555,7 @@ void Pipeline::Run() { if (MsContext::GetInstance()->save_graphs_flag() && (user_graph != nullptr)) { std::string user_graph_file = GetFilePathName("ModelDigraph.dot"); - MS_LOG(DEBUG) << "save user graph to: " << user_graph_file; + MS_LOG(DEBUG) << "Save user graph to: " << user_graph_file; draw::DrawUserFuncGraph(user_graph_file, user_graph); #ifdef ENABLE_DUMP_IR @@ -572,7 +572,7 @@ void Pipeline::Run() { ChangeFileMode(filename, S_IRUSR); #endif } - MS_LOG(INFO) << "end"; + MS_LOG(INFO) << "End"; } void ExecutorPy::ProcessVmArg(const py::tuple& args, const std::string& phase, VectorRef* arg_list) { @@ -582,7 +582,7 @@ void ExecutorPy::ProcessVmArg(const py::tuple& args, const std::string& phase, V py::object arg = args[i]; auto ms_context = MsContext::GetInstance(); if (ms_context->backend_policy() == kMsConvert && py::isinstance(arg)) { - MS_LOG(EXCEPTION) << "args[" << i << "] is numpy array, not tensor"; + MS_LOG(EXCEPTION) << "Args[" << i << "] is numpy array, not tensor"; } (*arg_list).push_back(arg); } @@ -642,9 +642,9 @@ py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) { MS_LOG(EXCEPTION) << "Can't find run graph func for " << phase_s; } - MS_LOG(DEBUG) << "eval run" << backend; + MS_LOG(DEBUG) << "Eval run" << backend; BaseRef value = (*run)(arg_list); - MS_LOG(DEBUG) << "run end"; + MS_LOG(DEBUG) << "Run end"; return BaseRefToPyData(value); } @@ -704,9 +704,9 @@ bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batc p_init->set_attr("shapes", MakeValue(int_shapes)); p_init->set_attr("input_indexes", MakeValue(int_input_indexes)); - const std::vector emply_str_list; - p_init->set_attr("input_names", MakeValue(emply_str_list)); - p_init->set_attr("output_names", MakeValue(emply_str_list)); + const std::vector empty_str_list; + p_init->set_attr("input_names", MakeValue(empty_str_list)); + p_init->set_attr("output_names", MakeValue(empty_str_list)); FuncGraphPtr func_graph = std::make_shared(); auto app_init = std::make_shared(AnfNodePtrList{NewValueNode(p_init)}, func_graph); @@ -730,7 +730,7 @@ bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batc if (!(*runner.run)) { // empty function - MS_LOG(EXCEPTION) << "Backend " << backend->name() << " unsupports tdt dataset."; + MS_LOG(EXCEPTION) << "Backend " << backend->name() << " unsupported tdt dataset."; } // launch init dataset runner without inputs and outputs @@ -758,7 +758,7 @@ void InitHccl() { auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(device_name, device_id); MS_EXCEPTION_IF_NULL(runtime_instance); if (!runtime_instance->Init()) { - MS_LOG(ERROR) << "kernel runtime init error."; + MS_LOG(ERROR) << "Kernel runtime init error."; return; } } @@ -795,7 +795,7 @@ void InitGe() { auto ms_context = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(ms_context); if (!ms_context->OpenTsd()) { - MS_LOG(EXCEPTION) << "open tsd failed"; + MS_LOG(EXCEPTION) << "Open tsd failed"; } (void)ms_context->InitGe(); } diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.cc b/mindspore/ccsrc/pipeline/pipeline_ge.cc index 5a2a8039dd..abf55bb2d8 100644 --- a/mindspore/ccsrc/pipeline/pipeline_ge.cc +++ b/mindspore/ccsrc/pipeline/pipeline_ge.cc @@ -210,7 +210,7 @@ bool AddDFGraph(const std::map& info, const py::di (void)convertor.GenerateCheckpointGraph(); if (convertor.ErrCode() != 0) { DfGraphManager::GetInstance().ClearGraph(); - MS_LOG(ERROR) << "convert df graph failed, err:" << convertor.ErrCode(); + MS_LOG(ERROR) << "Convert df graph failed, err:" << convertor.ErrCode(); return false; } @@ -238,7 +238,7 @@ bool AddDFGraph(const std::map& info, const py::di FuncGraphPtr BuildDFGraph(const std::map& info, const py::dict& init_params, const std::string& phase, const py::object& broadcast_params) { if (info.count(phase) == 0) { - MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase); + MS_LOG(EXCEPTION) << "No phase in executor:" << GetPhasePrefix(phase); } FuncGraphPtr anf_graph = info.at(phase)->func_graph; @@ -389,7 +389,7 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve const std::string& phase) { std::vector ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW); if (ge_tensors.size() != inputs.size()) { - MS_LOG(ERROR) << "args convert to ge tensor error"; + MS_LOG(ERROR) << "Args convert to ge tensor error"; return nullptr; } @@ -444,7 +444,7 @@ void ProcessGeArg(const std::map& info, const py:: std::size_t size = args.size(); if (info.count(phase) == 0) { - MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase); + MS_LOG(EXCEPTION) << "No phase in executor:" << GetPhasePrefix(phase); } auto arg_size = info.at(phase)->arg_list_size; @@ -459,12 +459,12 @@ void ProcessGeArg(const std::map& info, const py:: ValuePtr converted = nullptr; bool succ = parse::ConvertData(args[i], &converted); if (!succ) { - MS_LOG(EXCEPTION) << "args convert error"; + MS_LOG(EXCEPTION) << "Args convert error"; } if (converted->isa()) { (*inputs).push_back(converted->cast()); } else { - MS_LOG(EXCEPTION) << "args, " << converted->ToString() << " is not tensor"; + MS_LOG(EXCEPTION) << "Args " << converted->ToString() << " is not tensor"; } } } @@ -481,7 +481,7 @@ py::object ExecDFGraph(const std::map& info, const } if (info.count(phase) == 0) { - MS_LOG(EXCEPTION) << "has no phase:" << phase; + MS_LOG(EXCEPTION) << "There is no phase:" << phase; } FuncGraphPtr anf_graph = info.at(phase)->func_graph; @@ -511,7 +511,7 @@ py::object ExecDFGraph(const std::map& info, const if (ret != nullptr) { return *ret; } else { - MS_LOG(EXCEPTION) << "exec graph failed"; + MS_LOG(EXCEPTION) << "Exec graph failed"; } } void ExportDFGraph(const std::string& file_name, const std::string& phase) { diff --git a/mindspore/ccsrc/pipeline/resource.cc b/mindspore/ccsrc/pipeline/resource.cc index 59ee04ad15..18695518be 100644 --- a/mindspore/ccsrc/pipeline/resource.cc +++ b/mindspore/ccsrc/pipeline/resource.cc @@ -189,7 +189,7 @@ Resource::~Resource() { // If exit normally, these global variables will be cleaned // in Resource::Clean call by MsPipeline::Compile, but if exit with MS_LOGEXCEPTION, // these global variables may not being cleaned, it may - // cause segmentfault when free python object inside these global varaibles + // cause segmentfault when free python object inside these global variables // after python interpreter got freed, so these global variables // are cleaned here. // So if exit normally, these global variable will be cleaned twice, diff --git a/mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc b/mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc index eef4e8b4ad..555a6d87c0 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc +++ b/mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc @@ -443,7 +443,7 @@ bool AbstractTensor::operator==(const AbstractTensor &other) const { auto v1 = GetValueTrack(); auto v2 = other.GetValueTrack(); if (v1 == nullptr || v2 == nullptr) { - MS_LOG(EXCEPTION) << "the value of AbstractTensor is nullptr"; + MS_LOG(EXCEPTION) << "The value of AbstractTensor is nullptr"; } bool is_value_equal = (v1 == v2); diff --git a/mindspore/ccsrc/pipeline/static_analysis/abstract_value.h b/mindspore/ccsrc/pipeline/static_analysis/abstract_value.h index 9d9585bba3..9e0dd82003 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/abstract_value.h +++ b/mindspore/ccsrc/pipeline/static_analysis/abstract_value.h @@ -77,7 +77,7 @@ class AbstractBase : public Base { } protected: - // default implementation, it can be overrided by subclass; + // default implementation, it can be overwritten by subclass; virtual ValuePtr RealBuildValue() const { return kAnyValue; } private: @@ -495,7 +495,7 @@ class AbstractNone : public AbstractBase { }; using AbstractNonePtr = std::shared_ptr; -// the un assgined state value for variable, which means the variable is not assigned +// the un assigned state value for variable, which means the variable is not assigned class AbstractNull : public AbstractBase { public: AbstractNull() : AbstractBase(kNullObj) { set_type(std::make_shared()); } diff --git a/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc b/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc index 251f218145..9b120f731c 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc +++ b/mindspore/ccsrc/pipeline/static_analysis/evaluator.cc @@ -116,7 +116,7 @@ AbstractBasePtrList FuncGraphEvaluator::NormalizeArgs(const AbstractBasePtrList return broaded_list; } - if (func_graph_->has_flag(kFuncGraphFlagUndetermin)) { + if (func_graph_->has_flag(kFuncGraphFlagUndetermined)) { if (parent_context_) { MS_LOG(DEBUG) << "Undeterminate FuncGraphEvaluator " << ToString() << ", context: " << parent_context_->ToString(); diff --git a/mindspore/ccsrc/pipeline/static_analysis/param_validator.cc b/mindspore/ccsrc/pipeline/static_analysis/param_validator.cc index 1b70e2fe22..69f6af0dc0 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/param_validator.cc +++ b/mindspore/ccsrc/pipeline/static_analysis/param_validator.cc @@ -56,7 +56,7 @@ TypePtr CheckTensorDType(const AbstractTensorPtr &tensor, const TypePtrList &acc } TypePtr ele_type = tensor->element()->BuildType(); if (ele_type == nullptr) { - MS_LOG(EXCEPTION) << "abstract tensor element type nullptr"; + MS_LOG(EXCEPTION) << "Abstract tensor element type nullptr"; } return CheckType(ele_type, accepts, error_message_prefix); } @@ -64,7 +64,7 @@ TypePtr CheckTensorDType(const AbstractTensorPtr &tensor, const TypePtrList &acc TypePtr CheckTensorsDTypeSame(const AbstractTensorPtrList &tensor_list, const TypePtrList &accepts, const std::string &error_message_prefix) { if (tensor_list.empty()) { - MS_LOG(EXCEPTION) << "array list is empty"; + MS_LOG(EXCEPTION) << "Array list is empty"; } auto sample_tensor = tensor_list[0]; @@ -78,7 +78,7 @@ TypePtr CheckTensorsDTypeSame(const AbstractTensorPtrList &tensor_list, const Ty auto aType = tensor_list[index]->element()->BuildType(); loginfoBuffer << " " << aType->ToString(); if (sample_type->type_id() != aType->type_id()) { - MS_LOG(EXCEPTION) << "expected type " << sample_type->ToString() << ", but got " << aType->ToString() + MS_LOG(EXCEPTION) << "Expected type " << sample_type->ToString() << ", but got " << aType->ToString() << ", index " << index; } } @@ -89,11 +89,11 @@ TypePtr CheckTensorsDTypeSame(const AbstractTensorPtrList &tensor_list, const Ty TypePtr CheckScalarType(const AbstractScalarPtr &scalar, const TypePtrList &accepts, const std::string &error_message_prefix) { if (scalar == nullptr) { - MS_LOG(EXCEPTION) << "scalar nullptr"; + MS_LOG(EXCEPTION) << "Scalar nullptr"; } auto type = scalar->BuildType(); if (type == nullptr) { - MS_LOG(EXCEPTION) << "scalar value nullptr"; + MS_LOG(EXCEPTION) << "Scalar value nullptr"; } return CheckType(type, accepts, error_message_prefix); diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.cc b/mindspore/ccsrc/pipeline/static_analysis/prim.cc index 4110f25811..56bcd77f67 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/prim.cc +++ b/mindspore/ccsrc/pipeline/static_analysis/prim.cc @@ -182,30 +182,30 @@ AbstractBasePtr DoSignatureEvaluator::Run(AnalysisEnginePtr engine, const Config static AbstractBasePtrList GetUnpackGraphSpecArgsList(AbstractBasePtrList args_spec_list, bool need_unpack) { // arg[0] is the func graph to unpack, ignore it - AbstractBasePtrList sepcialize_args_before_unpack(args_spec_list.begin() + 1, args_spec_list.end()); - AbstractBasePtrList graph_sepcialize_args; + AbstractBasePtrList specialize_args_before_unpack(args_spec_list.begin() + 1, args_spec_list.end()); + AbstractBasePtrList graph_specialize_args; if (need_unpack) { - for (size_t index = 0; index < sepcialize_args_before_unpack.size(); index++) { - MS_EXCEPTION_IF_NULL(sepcialize_args_before_unpack[index]); - if (sepcialize_args_before_unpack[index]->isa()) { - AbstractTuplePtr arg_tuple = sepcialize_args_before_unpack[index]->cast(); + for (size_t index = 0; index < specialize_args_before_unpack.size(); index++) { + MS_EXCEPTION_IF_NULL(specialize_args_before_unpack[index]); + if (specialize_args_before_unpack[index]->isa()) { + AbstractTuplePtr arg_tuple = specialize_args_before_unpack[index]->cast(); std::transform(arg_tuple->elements().begin(), arg_tuple->elements().end(), - std::back_inserter(graph_sepcialize_args), [](AbstractBasePtr abs) { return abs; }); - } else if (sepcialize_args_before_unpack[index]->isa()) { - AbstractDictionaryPtr arg_dict = sepcialize_args_before_unpack[index]->cast(); + std::back_inserter(graph_specialize_args), [](AbstractBasePtr abs) { return abs; }); + } else if (specialize_args_before_unpack[index]->isa()) { + AbstractDictionaryPtr arg_dict = specialize_args_before_unpack[index]->cast(); auto dict_elems = arg_dict->elements(); (void)std::transform( - dict_elems.begin(), dict_elems.end(), std::back_inserter(graph_sepcialize_args), + dict_elems.begin(), dict_elems.end(), std::back_inserter(graph_specialize_args), [](const AbstractAttribute &item) { return std::make_shared(item.first, item.second); }); } else { MS_LOG(EXCEPTION) << "UnpackGraph require args should be tuple or dict, but got " - << sepcialize_args_before_unpack[index]->ToString(); + << specialize_args_before_unpack[index]->ToString(); } } } else { - graph_sepcialize_args = sepcialize_args_before_unpack; + graph_specialize_args = specialize_args_before_unpack; } - return graph_sepcialize_args; + return graph_specialize_args; } AbstractBasePtr UnpackGraphEvaluator::Run(AnalysisEnginePtr engine, const ConfigPtrList &args_conf_list, @@ -239,14 +239,14 @@ AbstractBasePtr UnpackGraphEvaluator::Run(AnalysisEnginePtr engine, const Config MS_EXCEPTION_IF_NULL(real_fn); FuncGraphPtr forward_graph = real_fn->func_graph(); MS_EXCEPTION_IF_NULL(forward_graph); - AbstractBasePtrList graph_sepcialize_args = + AbstractBasePtrList graph_specialize_args = GetUnpackGraphSpecArgsList(args_spec_list, unpack_graph->need_unpack_args()); - AbstractBasePtrList graph_sepcialize_args_without_sens; - (void)std::transform(graph_sepcialize_args.begin(), - graph_sepcialize_args.end() - (unpack_graph->with_sens_in_args() ? 1 : 0), - std::back_inserter(graph_sepcialize_args_without_sens), [](AbstractBasePtr abs) { return abs; }); - auto new_graph = forward_graph->GenerateGraph(graph_sepcialize_args_without_sens); + AbstractBasePtrList graph_specialize_args_without_sens; + (void)std::transform(graph_specialize_args.begin(), + graph_specialize_args.end() - (unpack_graph->with_sens_in_args() ? 1 : 0), + std::back_inserter(graph_specialize_args_without_sens), [](AbstractBasePtr abs) { return abs; }); + auto new_graph = forward_graph->GenerateGraph(graph_specialize_args_without_sens); engine->func_graph_manager()->AddFuncGraph(new_graph); ScopePtr scope = kDefaultScope; if (out_conf != nullptr) { @@ -635,8 +635,8 @@ AbstractBasePtr GetEvaluatedValueForClassAttrOrMethod(const AnalysisEnginePtr &e MS_LOG(EXCEPTION) << "Attribute type error"; } std::string item_name = item_v->cast()->value(); - MS_LOG(DEBUG) << "Resovle name: " << cls->tag().name(); - MS_LOG(DEBUG) << "Resovle item: " << item_name; + MS_LOG(DEBUG) << "Resolve name: " << cls->tag().name(); + MS_LOG(DEBUG) << "Resolve item: " << item_name; AbstractBasePtr attr = cls->GetAttribute(item_name); if (attr != nullptr) { @@ -720,7 +720,7 @@ class EmbedEvaluator : public SymbolicPrimEvaluator { ~EmbedEvaluator() override = default; MS_DECLARE_PARENT(EmbedEvaluator, SymbolicPrimEvaluator); AbstractBasePtr EvalPrim(const ConfigPtrList &args_conf_list) override { - // arg: free variable to be embeded + // arg: free variable to be embedded if (args_conf_list.size() != 1) { MS_LOG(EXCEPTION) << "EmbedEvaluator requires 1 parameter, but got " << args_conf_list.size(); } @@ -939,7 +939,7 @@ class PartialEvaluator : public Evaluator { AbstractBasePtr Run(AnalysisEnginePtr engine, const ConfigPtrList &args_conf_list, AnfNodeConfigPtr out_conf = nullptr) override { if (args_conf_list.size() == 0) { - MS_LOG(EXCEPTION) << "args size should be greater than 0"; + MS_LOG(EXCEPTION) << "Args size should be greater than 0"; } auto arg0_value = args_conf_list[0]->GetEvaluatedValue(); AbstractBasePtrList args_spec_list{arg0_value}; diff --git a/mindspore/ccsrc/pipeline/static_analysis/program_specialize.cc b/mindspore/ccsrc/pipeline/static_analysis/program_specialize.cc index bfa1e43ceb..987c5d1db0 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/program_specialize.cc +++ b/mindspore/ccsrc/pipeline/static_analysis/program_specialize.cc @@ -190,7 +190,7 @@ void FuncGraphSpecializer::FirstPass() { } if (node->func_graph() != func_graph_) { if (parent_ == nullptr) { - MS_LOG(EXCEPTION) << "parent must not null NodeInfo: " << trace::GetDebugInfo(node->debug_info()); + MS_LOG(EXCEPTION) << "Parent must not null NodeInfo: " << trace::GetDebugInfo(node->debug_info()); } parent_->AddTodoItem(node); parent_->FirstPass(); @@ -365,16 +365,16 @@ AnfNodePtr FuncGraphSpecializer::BuildSpecializedNodeInner(const AbstractBasePtr } if (!eval->isa()) { - MS_LOG(EXCEPTION) << "eval is not BaseGraphEvaluator, but " << eval->ToString(); + MS_LOG(EXCEPTION) << "Eval is not BaseGraphEvaluator, but " << eval->ToString(); } auto real_eval = dyn_cast(eval); if (func->context() != nullptr) { if (!IsVisible(func_graph_, func->context()->func_graph())) { - MS_LOG(EXCEPTION) << "func is not visible NodeInfo: " << trace::GetDebugInfo(func_graph_->debug_info()); + MS_LOG(EXCEPTION) << "Func is not visible NodeInfo: " << trace::GetDebugInfo(func_graph_->debug_info()); } } else { - MS_LOG(EXCEPTION) << "func context is nullptr NodeInfo: " << trace::GetDebugInfo(func_graph_->debug_info()); + MS_LOG(EXCEPTION) << "Func context is nullptr NodeInfo: " << trace::GetDebugInfo(func_graph_->debug_info()); } AnalysisContextPtr context = real_eval->MakeContext(engine_, argvals); MS_LOG(DEBUG) << "Specialize function graph: " << context->func_graph()->ToString() << ", args: " << argvals.size() @@ -556,7 +556,7 @@ SpecializeStatusCode FuncGraphSpecializer::FindUniqueArgvals(const AbstractFunct if (!result->first.empty()) { return kSpecializeSuccess; } - MS_LOG(DEBUG) << "Find POLY code, it may be unused code or unresoved polymorphism."; + MS_LOG(DEBUG) << "Find POLY code, it may be unused code or unresolved polymorphism."; return kSpecializeFindUniqueArgvalPoly; } } diff --git a/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc b/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc index 49182e8d09..4ab7b9d20b 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc +++ b/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc @@ -89,7 +89,7 @@ std::size_t AnfNodeConfigHasher::operator()(const AnfNodeConfigPtr conf) const { MS_EXCEPTION_IF_NULL(conf->node()); std::size_t hash_value = hash_combine(conf->node()->hash(), conf->context()->hash()); if (conf->context() != nullptr && conf->context()->func_graph() != nullptr) { - MS_LOG(DEBUG) << "NodeConfgHasher Node: " << conf->node()->DebugString() + MS_LOG(DEBUG) << "NodeConfigHasher Node: " << conf->node()->DebugString() << ", Graph: " << conf->context()->func_graph()->ToString() << " ### , hash value: " << hash_value; } else { MS_LOG(DEBUG) << "NodeConfigHasher Node: " << conf->node()->DebugString() << " ### , hash value: " << hash_value; @@ -456,13 +456,13 @@ AbstractBasePtr AnalysisEngine::ExecuteMultipleEvaluators(const std::vectorcast(); if (fg_eval) { - auto undetermin_fgs = fg_eval->func_graph()->recursive_graphs(); - if (undetermin_fgs) { - for (auto undetermin_fg : *undetermin_fgs) { - MS_LOG(DEBUG) << "Set graph undetermin: " << undetermin_fg->ToString(); + auto undetermined_fgs = fg_eval->func_graph()->recursive_graphs(); + if (undetermined_fgs) { + for (auto undetermined_fg : *undetermined_fgs) { + MS_LOG(DEBUG) << "Set graph undetermined: " << undetermined_fg->ToString(); // As the current evaluator has multiple possibles, all the func_graphs which // are recursive with the current func_graph are undetermined in control flow. - undetermin_fg->set_flags(kFuncGraphFlagUndetermin, true); + undetermined_fg->set_flags(kFuncGraphFlagUndetermined, true); } } } From a86b31eeee8515243ce37013706821bfe507a517 Mon Sep 17 00:00:00 2001 From: zhongligeng Date: Fri, 3 Apr 2020 10:45:03 +0800 Subject: [PATCH 098/227] resolve some issue in nn comments --- mindspore/nn/layer/basic.py | 23 +++++++++------------ mindspore/nn/layer/container.py | 5 ++--- mindspore/nn/layer/conv.py | 7 ++----- mindspore/nn/layer/embedding.py | 2 +- mindspore/nn/layer/lstm.py | 6 +++--- mindspore/nn/layer/normalization.py | 4 ++-- mindspore/nn/layer/pooling.py | 16 +++++++-------- mindspore/nn/loss/loss.py | 31 ++++++++++++++--------------- mindspore/nn/metrics/accuracy.py | 17 ++++++++-------- mindspore/nn/metrics/error.py | 8 ++++---- mindspore/nn/metrics/fbeta.py | 11 +++++----- mindspore/nn/metrics/loss.py | 3 +-- mindspore/nn/metrics/precision.py | 18 ++++++++--------- mindspore/nn/metrics/recall.py | 18 ++++++++--------- mindspore/nn/metrics/topk.py | 15 +++++++------- mindspore/nn/optim/adam.py | 6 +++--- mindspore/nn/optim/ftrl.py | 2 +- mindspore/nn/optim/lamb.py | 2 +- mindspore/nn/optim/lars.py | 4 ++-- mindspore/nn/optim/momentum.py | 2 +- mindspore/nn/optim/rmsprop.py | 2 +- mindspore/nn/optim/sgd.py | 2 +- mindspore/nn/wrap/cell_wrapper.py | 29 +++------------------------ mindspore/nn/wrap/grad_reducer.py | 23 +++++++++++++++------ mindspore/nn/wrap/loss_scale.py | 22 ++++++++++---------- 25 files changed, 128 insertions(+), 150 deletions(-) diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py index 9dc0d5e623..30b94c738d 100644 --- a/mindspore/nn/layer/basic.py +++ b/mindspore/nn/layer/basic.py @@ -65,7 +65,7 @@ class Dropout(Cell): Tensor, output tensor with the same shape as the input. Examples: - >>> x = mindspore.Tensor(np.ones([20, 16, 50]), mindspore.float32) + >>> x = Tensor(np.ones([20, 16, 50]), mindspore.float32) >>> net = nn.Dropout(keep_prob=0.8) >>> net(x) """ @@ -111,7 +111,7 @@ class Flatten(Cell): Examples: >>> net = nn.Flatten() - >>> input = mindspore.Tensor(np.array([[[1.2, 1.2], [2.1, 2.1]], [[2.2, 2.2], [3.2, 3.2]]]), mindspore.float32) + >>> input = Tensor(np.array([[[1.2, 1.2], [2.1, 2.1]], [[2.2, 2.2], [3.2, 3.2]]]), mindspore.float32) >>> input.shape() (2, 2, 2) >>> net(input) @@ -149,9 +149,6 @@ class Dense(Cell): has_bias (bool): Specifies whether the layer uses a bias vector. Default: True. activation (str): Regularizer function applied to the output of the layer, eg. 'relu'. Default: None. - Returns: - Tensor, output tensor. - Raises: ValueError: If weight_init or bias_init shape is incorrect. @@ -163,7 +160,7 @@ class Dense(Cell): Examples: >>> net = nn.Dense(3, 4) - >>> input = mindspore.Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32) + >>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32) >>> net(input) [[ 2.5246444 2.2738023 0.5711005 -3.9399147 ] [ 1.0739875 4.0155234 0.94188046 -5.459526 ]] @@ -243,8 +240,8 @@ class ClipByNorm(Cell): Examples: >>> net = nn.ClipByNorm() - >>> input = mindspore.Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32) - >>> clip_norm = mindspore.Tensor(np.array([100]).astype(np.float32)) + >>> input = Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32) + >>> clip_norm = Tensor(np.array([100]).astype(np.float32)) >>> net(input, clip_norm) """ @@ -290,9 +287,6 @@ class Norm(Cell): keep_dims (bool): If True, the axis indicated in `axis` are kept with size 1. Otherwise, the dimensions in `axis` are removed from the output shape. Default: False. - Returns: - Tensor, a Tensor of the same type as input, containing the vector or matrix norms. - Inputs: - **input** (Tensor) - Tensor which is not empty. @@ -302,7 +296,7 @@ class Norm(Cell): Examples: >>> net = nn.Norm(axis=0) - >>> input = mindspore.Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32) + >>> input = Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32) >>> net(input) """ def __init__(self, axis=(), keep_dims=False): @@ -344,7 +338,8 @@ class OneHot(Cell): when indices[j] = i. Default: 1.0. off_value (float): A scalar defining the value to fill in output[i][j] when indices[j] != i. Default: 0.0. - dtype (:class:`mindspore.dtype`): Default: mindspore.float32. + dtype (:class:`mindspore.dtype`): Data type of 'on_value' and 'off_value', not the + data type of indices. Default: mindspore.float32. Inputs: - **indices** (Tensor) - A tensor of indices of data type mindspore.int32 and arbitrary shape. @@ -355,7 +350,7 @@ class OneHot(Cell): Examples: >>> net = nn.OneHot(depth=4, axis=1) - >>> indices = mindspore.Tensor([[1, 3], [0, 2]], dtype=mindspore.int32) + >>> indices = Tensor([[1, 3], [0, 2]], dtype=mindspore.int32) >>> net(indices) [[[0. 0.] [1. 0.] diff --git a/mindspore/nn/layer/container.py b/mindspore/nn/layer/container.py index 76c72ce421..709b3ef8ef 100644 --- a/mindspore/nn/layer/container.py +++ b/mindspore/nn/layer/container.py @@ -86,7 +86,7 @@ class SequentialCell(Cell): >>> relu = nn.ReLU() >>> seq = nn.SequentialCell([conv, bn, relu]) >>> - >>> x = mindspore.Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32) + >>> x = Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32) >>> seq(x) [[[[0.02531557 0. ] [0.04933941 0.04880078]] @@ -138,7 +138,6 @@ class SequentialCell(Cell): return len(self._cells) def construct(self, input_data): - """Processes the input with the defined sequence of Cells.""" for cell in self.cell_list: input_data = cell(input_data) return input_data @@ -161,7 +160,7 @@ class CellList(_CellListBase, Cell): >>> cell_ls = nn.CellList([bn]) >>> cell_ls.insert(0, conv) >>> cell_ls.append(relu) - >>> x = mindspore.Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32) + >>> x = Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32) >>> # not same as nn.SequentialCell, `cell_ls(x)` is not correct >>> cell_ls CellList< (0): Conv2d diff --git a/mindspore/nn/layer/conv.py b/mindspore/nn/layer/conv.py index dfbf96e150..eb73a9ce5a 100644 --- a/mindspore/nn/layer/conv.py +++ b/mindspore/nn/layer/conv.py @@ -146,9 +146,6 @@ class Conv2d(_Conv): Initializer and string are the same as 'weight_init'. Refer to the values of Initializer for more details. Default: 'zeros'. - Returns: - Tensor, output tensor. - Inputs: - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. @@ -157,7 +154,7 @@ class Conv2d(_Conv): Examples: >>> net = nn.Conv2d(120, 240, 4, has_bias=False, weight_init='normal') - >>> input = mindspore.Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32) + >>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32) >>> net(input).shape() (1, 240, 1024, 640) """ @@ -277,7 +274,7 @@ class Conv2dTranspose(_Conv): Examples: >>> net = nn.Conv2dTranspose(3, 64, 4, has_bias=False, weight_init='normal') - >>> input = Tensor(np.ones([1, 3, 16, 50]), mstype.float32) + >>> input = Tensor(np.ones([1, 3, 16, 50]), mindspore.float32) >>> net(input) """ def __init__(self, diff --git a/mindspore/nn/layer/embedding.py b/mindspore/nn/layer/embedding.py index 9579f35226..dfa8e66469 100755 --- a/mindspore/nn/layer/embedding.py +++ b/mindspore/nn/layer/embedding.py @@ -50,7 +50,7 @@ class Embedding(Cell): Examples: >>> net = nn.Embedding(20000, 768, True) - >>> input_data = mindspore.Tensor(np.ones([8, 128]), mindspore.int32) + >>> input_data = Tensor(np.ones([8, 128]), mindspore.int32) >>> >>> # Maps the input word IDs to word embedding. >>> output = net(input_data) diff --git a/mindspore/nn/layer/lstm.py b/mindspore/nn/layer/lstm.py index 317f754f67..cef926d365 100755 --- a/mindspore/nn/layer/lstm.py +++ b/mindspore/nn/layer/lstm.py @@ -96,9 +96,9 @@ class LSTM(Cell): >>> return self.lstm(inp, (h0, c0)) >>> >>> net = LstmNet(10, 12, 2, has_bias=True, batch_first=True, bidirectional=False) - >>> input = mindspore.Tensor(np.ones([3, 5, 10]).astype(np.float32)) - >>> h0 = mindspore.Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32)) - >>> c0 = mindspore.Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32)) + >>> input = Tensor(np.ones([3, 5, 10]).astype(np.float32)) + >>> h0 = Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32)) + >>> c0 = Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32)) >>> output, (hn, cn) = net(input, h0, c0) """ def __init__(self, diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py index 4aded20ab3..1ca2221122 100644 --- a/mindspore/nn/layer/normalization.py +++ b/mindspore/nn/layer/normalization.py @@ -159,7 +159,7 @@ class BatchNorm1d(_BatchNorm): Examples: >>> net = nn.BatchNorm1d(num_features=16) - >>> input = mindspore.Tensor(np.random.randint(0, 255, [3, 16]), mindspore.float32) + >>> input = Tensor(np.random.randint(0, 255, [3, 16]), mindspore.float32) >>> net(input) """ def _check_data_dim(self, x): @@ -258,7 +258,7 @@ class LayerNorm(Cell): Examples: >>> x = Tensor(np.ones([20, 5, 10, 10], np.float32)) >>> shape1 = x.shape()[1:] - >>> m = LayerNorm(shape1, begin_norm_axis=1, begin_params_axis=1) + >>> m = nn.LayerNorm(shape1, begin_norm_axis=1, begin_params_axis=1) >>> m(x) """ def __init__(self, diff --git a/mindspore/nn/layer/pooling.py b/mindspore/nn/layer/pooling.py index 0a4bd1662b..6ff28dd362 100644 --- a/mindspore/nn/layer/pooling.py +++ b/mindspore/nn/layer/pooling.py @@ -63,8 +63,8 @@ class MaxPool2d(_PoolNd): pad_mode for training only supports "same" and "valid". Args: - kernel_size (int): Size of the window to take a max over. - stride (int): Stride size of the window. Default: None. + kernel_size (int): Size of the window to take a max over. Default 1. + stride (int): Stride size of the window. Default: 1. pad_mode (str): Select the mode of the pad. The optional values are "same" and "valid". Default: "valid". @@ -75,7 +75,7 @@ class MaxPool2d(_PoolNd): - valid: Adopts the way of discarding. The possibly largest height and width of output will be return without padding. Extra pixels will be discarded. - padding (int): Now is not supported, mplicit zero padding to be added on both sides. Default: 0. + padding (int): Implicit zero padding to be added on both sides. Default: 0. Inputs: - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. @@ -85,7 +85,7 @@ class MaxPool2d(_PoolNd): Examples: >>> pool = MaxPool2d(kernel_size=3, stride=1) - >>> x = mindspore.Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32) + >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32) [[[[1. 5. 5. 1.] [0. 3. 4. 8.] [4. 2. 7. 6.] @@ -149,8 +149,8 @@ class AvgPool2d(_PoolNd): pad_mode for training only supports "same" and "valid". Args: - kernel_size (int): Size of the window to take a max over. - stride (int): Stride size of the window. Default: None. + kernel_size (int): Size of the window to take a max over. Default: 1. + stride (int): Stride size of the window. Default: 1. pad_mode (str): Select the mode of the pad. The optional values are "same", "valid". Default: "valid". @@ -161,7 +161,7 @@ class AvgPool2d(_PoolNd): - valid: Adopts the way of discarding. The possibly largest height and width of output will be return without padding. Extra pixels will be discarded. - padding (int): Now is not supported, implicit zero padding to be added on both sides. Default: 0. + padding (int): Implicit zero padding to be added on both sides. Default: 0. Inputs: - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. @@ -171,7 +171,7 @@ class AvgPool2d(_PoolNd): Examples: >>> pool = AvgPool2d(kernel_size=3, stride=1) - >>> x = mindspore.Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32) + >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32) [[[[5. 5. 9. 9.] [8. 4. 3. 0.] [2. 7. 1. 2.] diff --git a/mindspore/nn/loss/loss.py b/mindspore/nn/loss/loss.py index 340cbe73d8..806456e561 100644 --- a/mindspore/nn/loss/loss.py +++ b/mindspore/nn/loss/loss.py @@ -86,9 +86,9 @@ class L1Loss(_Loss): Tensor, loss float tensor. Examples: - >>> loss = L1Loss() - >>> input_data = Tensor(np.array([1, 2, 3]), mstype.float32) - >>> target_data = Tensor(np.array([1, 2, 2]), mstype.float32) + >>> loss = nn.L1Loss() + >>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32) + >>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32) >>> loss(input_data, target_data) """ def __init__(self, reduction='mean'): @@ -126,9 +126,9 @@ class MSELoss(_Loss): Tensor, weighted loss float tensor. Examples: - >>> loss = MSELoss() - >>> input_data = Tensor(np.array([1, 2, 3]), mstype.float32) - >>> target_data = Tensor(np.array([1, 2, 2]), mstype.float32) + >>> loss = nn.MSELoss() + >>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32) + >>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32) >>> loss(input_data, target_data) """ def construct(self, base, target): @@ -171,9 +171,9 @@ class SmoothL1Loss(_Loss): Tensor, loss float tensor. Examples: - >>> loss = SmoothL1Loss() - >>> input_data = Tensor(np.array([1, 2, 3]), mstype.float32) - >>> target_data = Tensor(np.array([1, 2, 2]), mstype.float32) + >>> loss = nn.SmoothL1Loss() + >>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32) + >>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32) >>> loss(input_data, target_data) """ def __init__(self, sigma=1.0): @@ -219,17 +219,16 @@ class SoftmaxCrossEntropyWithLogits(_Loss): Inputs: - **logits** (Tensor) - Tensor of shape :math:`(x_1, x_2, ..., x_R)`. - **labels** (Tensor) - Tensor of shape :math:`(y_1, y_2, ..., y_S)`. If `sparse` is True, The type of - `labels` is mstype.int32. If `sparse` is False, the type of `labels` is same as the type of `logits`. + `labels` is mindspore.int32. If `sparse` is False, the type of `labels` is same as the type of `logits`. Outputs: Tensor, a tensor of the same shape as logits with the component-wise logistic losses. Examples: - >>> loss = SoftmaxCrossEntropyWithLogits(sparse=True) - >>> logits = Tensor(np.random.randint(0, 9, [1, 10]), mstype.float32) - >>> labels_np = np.zeros([1, 10]).astype(np.int32) - >>> labels_np[0][0] = 1 + >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True) + >>> logits = Tensor(np.random.randint(0, 9, [1, 10]), mindspore.float32) + >>> labels_np = np.ones([1,]).astype(np.int32) >>> labels = Tensor(labels_np) >>> loss(logits, labels) """ @@ -286,8 +285,8 @@ class SoftmaxCrossEntropyExpand(Cell): Examples: >>> loss = SoftmaxCrossEntropyExpand(sparse=True) - >>> input_data = Tensor(np.ones([64, 512]), dtype=mstype.float32) - >>> label = Tensor(np.ones([64]), dtype=mstype.int32) + >>> input_data = Tensor(np.ones([64, 512]), dtype=mindspore.float32) + >>> label = Tensor(np.ones([64]), dtype=mindspore.int32) >>> loss(input_data, label) """ def __init__(self, sparse=False): diff --git a/mindspore/nn/metrics/accuracy.py b/mindspore/nn/metrics/accuracy.py index 5a11fa9d08..f131432ddd 100644 --- a/mindspore/nn/metrics/accuracy.py +++ b/mindspore/nn/metrics/accuracy.py @@ -35,8 +35,8 @@ class Accuracy(EvaluationBase): Default: 'classification'. Examples: - >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32) - >>> y = mindspore.Tensor(np.array([1, 0, 1]), mindspore.float32) + >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32) + >>> y = Tensor(np.array([1, 0, 1]), mindspore.float32) >>> metric = nn.Accuracy('classification') >>> metric.clear() >>> metric.update(x, y) @@ -58,13 +58,14 @@ class Accuracy(EvaluationBase): Args: inputs: Input `y_pred` and `y`. `y_pred` and `y` are a `Tensor`, a list or an array. - `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]` + For 'classification' evaluation type, `y_pred` is in most cases (not strictly) a list + of floating numbers in range :math:`[0, 1]` and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C` - is the number of categories. For 'multilabel' evaluation type, `y_pred` can only be one-hot - encoding with values 0 or 1. Indices with 1 indicate positive category. `y` contains values - of integers. The shape is :math:`(N, C)` if one-hot encoding is used. One-hot encoding - should be used when 'eval_type' is 'multilabel'. Shape can also be :math:`(N, 1)` if category - index is used in 'classification' evaluation type. + is the number of categories. Shape of `y` can be :math:`(N, C)` with values 0 and 1 if one-hot + encoding is used or the shape is :math:`(N,)` with integer values if index of category is used. + For 'multilabel' evaluation type, `y_pred` and `y` can only be one-hot encoding with + values 0 or 1. Indices with 1 indicate positive category. The shape of `y_pred` and `y` + are both :math:`(N, C)`. Raises: ValueError: If the number of the input is not 2. diff --git a/mindspore/nn/metrics/error.py b/mindspore/nn/metrics/error.py index 5dbd83645b..c803000192 100644 --- a/mindspore/nn/metrics/error.py +++ b/mindspore/nn/metrics/error.py @@ -33,8 +33,8 @@ class MAE(Metric): The method `update` must be called with the form `update(y_pred, y)`. Examples: - >>> x = mindspore.Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32) - >>> y = mindspore.Tensor(np.array([0.1, 0.25, 0.7, 0.9]), mindspore.float32) + >>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32) + >>> y = Tensor(np.array([0.1, 0.25, 0.7, 0.9]), mindspore.float32) >>> error = nn.MAE() >>> error.clear() >>> error.update(x, y) @@ -95,8 +95,8 @@ class MSE(Metric): where :math:`n` is batch size. Examples: - >>> x = mindspore.Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32) - >>> y = mindspore.Tensor(np.array([0.1, 0.25, 0.5, 0.9]), mindspore.float32) + >>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32) + >>> y = Tensor(np.array([0.1, 0.25, 0.5, 0.9]), mindspore.float32) >>> error = MSE() >>> error.clear() >>> error.update(x, y) diff --git a/mindspore/nn/metrics/fbeta.py b/mindspore/nn/metrics/fbeta.py index 6771b6ba36..68df4318b0 100755 --- a/mindspore/nn/metrics/fbeta.py +++ b/mindspore/nn/metrics/fbeta.py @@ -33,12 +33,11 @@ class Fbeta(Metric): beta (float): The weight of precision. Examples: - >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) - >>> y = mindspore.Tensor(np.array([1, 0, 1])) + >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) + >>> y = Tensor(np.array([1, 0, 1])) >>> metric = nn.Fbeta(1) >>> metric.update(x, y) >>> fbeta = metric.eval() - [0.66666667 0.66666667] """ def __init__(self, beta): super(Fbeta, self).__init__() @@ -64,7 +63,7 @@ class Fbeta(Metric): `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]` and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C` is the number of categories. y contains values of integers. The shape is :math:`(N, C)` - if one-hot encoding is used. Shape can also be :math:`(N, 1)` if category index is used. + if one-hot encoding is used. Shape can also be :math:`(N,)` if category index is used. """ if len(inputs) != 2: raise ValueError('Fbeta need 2 inputs (y_pred, y), but got {}'.format(len(inputs))) @@ -126,8 +125,8 @@ class F1(Fbeta): F_\beta=\frac{2\cdot true\_positive}{2\cdot true\_positive + false\_negative + false\_positive} Examples: - >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) - >>> y = mindspore.Tensor(np.array([1, 0, 1])) + >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) + >>> y = Tensor(np.array([1, 0, 1])) >>> metric = nn.F1() >>> metric.update(x, y) >>> fbeta = metric.eval() diff --git a/mindspore/nn/metrics/loss.py b/mindspore/nn/metrics/loss.py index bc4c58ef2f..3828fcdef5 100644 --- a/mindspore/nn/metrics/loss.py +++ b/mindspore/nn/metrics/loss.py @@ -25,12 +25,11 @@ class Loss(Metric): loss = \frac{\sum_{k=1}^{n}loss_k}{n} Examples: - >>> x = mindspore.Tensor(np.array(0.2), mindspore.float32) + >>> x = Tensor(np.array(0.2), mindspore.float32) >>> loss = nn.Loss() >>> loss.clear() >>> loss.update(x) >>> result = loss.eval() - 0.20000000298023224 """ def __init__(self): super(Loss, self).__init__() diff --git a/mindspore/nn/metrics/precision.py b/mindspore/nn/metrics/precision.py index a2c8502002..ad7b6c576f 100644 --- a/mindspore/nn/metrics/precision.py +++ b/mindspore/nn/metrics/precision.py @@ -41,13 +41,12 @@ class Precision(EvaluationBase): multilabel. Default: 'classification'. Examples: - >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) - >>> y = mindspore.Tensor(np.array([1, 0, 1])) + >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) + >>> y = Tensor(np.array([1, 0, 1])) >>> metric = nn.Precision('classification') >>> metric.clear() >>> metric.update(x, y) >>> precision = metric.eval() - [0.5 1. ] """ def __init__(self, eval_type='classification'): super(Precision, self).__init__(eval_type) @@ -72,13 +71,14 @@ class Precision(EvaluationBase): Args: inputs: Input `y_pred` and `y`. `y_pred` and `y` are Tensor, list or numpy.ndarray. - `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]` + For 'classification' evaluation type, `y_pred` is in most cases (not strictly) a list + of floating numbers in range :math:`[0, 1]` and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C` - is the number of categories. For 'multilabel' evaluation type, `y_pred` can only be one-hot - encoding with values 0 or 1. Indices with 1 indicate positive category. `y` contains values - of integers. The shape is :math:`(N, C)` if one-hot encoding is used. One-hot encoding - should be used when 'eval_type' is 'multilabel'. Shape can also be :math:`(N, 1)` if category - index is used in 'classification' evaluation type. + is the number of categories. Shape of `y` can be :math:`(N, C)` with values 0 and 1 if one-hot + encoding is used or the shape is :math:`(N,)` with integer values if index of category is used. + For 'multilabel' evaluation type, `y_pred` and `y` can only be one-hot encoding with + values 0 or 1. Indices with 1 indicate positive category. The shape of `y_pred` and `y` + are both :math:`(N, C)`. Raises: ValueError: If the number of input is not 2. diff --git a/mindspore/nn/metrics/recall.py b/mindspore/nn/metrics/recall.py index 2ea284ec41..45ebf0d7db 100644 --- a/mindspore/nn/metrics/recall.py +++ b/mindspore/nn/metrics/recall.py @@ -41,13 +41,12 @@ class Recall(EvaluationBase): multilabel. Default: 'classification'. Examples: - >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) - >>> y = mindspore.Tensor(np.array([1, 0, 1])) + >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) + >>> y = Tensor(np.array([1, 0, 1])) >>> metric = nn.Recall('classification') >>> metric.clear() >>> metric.update(x, y) >>> recall = metric.eval() - [1. 0.5] """ def __init__(self, eval_type='classification'): super(Recall, self).__init__(eval_type) @@ -72,13 +71,14 @@ class Recall(EvaluationBase): Args: inputs: Input `y_pred` and `y`. `y_pred` and `y` are a `Tensor`, a list or an array. - `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]` + For 'classification' evaluation type, `y_pred` is in most cases (not strictly) a list + of floating numbers in range :math:`[0, 1]` and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C` - is the number of categories. For 'multilabel' evaluation type, `y_pred` can only be one-hot - encoding with values 0 or 1. Indices with 1 indicate positive category. `y` contains values - of integers. The shape is :math:`(N, C)` if one-hot encoding is used. One-hot encoding - should be used when 'eval_type' is 'multilabel'. Shape can also be :math:`(N, 1)` if category - index is used in 'classification' evaluation type. + is the number of categories. Shape of `y` can be :math:`(N, C)` with values 0 and 1 if one-hot + encoding is used or the shape is :math:`(N,)` with integer values if index of category is used. + For 'multilabel' evaluation type, `y_pred` and `y` can only be one-hot encoding with + values 0 or 1. Indices with 1 indicate positive category. The shape of `y_pred` and `y` + are both :math:`(N, C)`. Raises: diff --git a/mindspore/nn/metrics/topk.py b/mindspore/nn/metrics/topk.py index 6afa631940..eab08a498d 100644 --- a/mindspore/nn/metrics/topk.py +++ b/mindspore/nn/metrics/topk.py @@ -33,14 +33,13 @@ class TopKCategoricalAccuracy(Metric): ValueError: If `k` is less than 1. Examples: - >>> x = mindspore.Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.], + >>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.], >>> [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32) - >>> y = mindspore.Tensor(np.array([2, 0, 1]), mindspore.float32) + >>> y = Tensor(np.array([2, 0, 1]), mindspore.float32) >>> topk = nn.TopKCategoricalAccuracy(3) >>> topk.clear() >>> topk.update(x, y) >>> result = topk.eval() - 0.6666666666666666 """ def __init__(self, k): super(TopKCategoricalAccuracy, self).__init__() @@ -65,7 +64,7 @@ class TopKCategoricalAccuracy(Metric): y_pred is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]` and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C` is the number of categories. y contains values of integers. The shape is :math:`(N, C)` - if one-hot encoding is used. Shape can also be :math:`(N, 1)` if category index is used. + if one-hot encoding is used. Shape can also be :math:`(N,)` if category index is used. """ if len(inputs) != 2: raise ValueError('Topk need 2 inputs (y_pred, y), but got {}'.format(len(inputs))) @@ -98,9 +97,9 @@ class Top1CategoricalAccuracy(TopKCategoricalAccuracy): Refer to class 'TopKCategoricalAccuracy' for more details. Examples: - >>> x = mindspore.Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.], + >>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.], >>> [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32) - >>> y = mindspore.Tensor(np.array([2, 0, 1]), mindspore.float32) + >>> y = Tensor(np.array([2, 0, 1]), mindspore.float32) >>> topk = nn.Top1CategoricalAccuracy() >>> topk.clear() >>> topk.update(x, y) @@ -116,9 +115,9 @@ class Top5CategoricalAccuracy(TopKCategoricalAccuracy): Refer to class 'TopKCategoricalAccuracy' for more details. Examples: - >>> x = mindspore.Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.], + >>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.], >>> [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32) - >>> y = mindspore.Tensor(np.array([2, 0, 1]), mindspore.float32) + >>> y = Tensor(np.array([2, 0, 1]), mindspore.float32) >>> topk = nn.Top5CategoricalAccuracy() >>> topk.clear() >>> topk.update(x, y) diff --git a/mindspore/nn/optim/adam.py b/mindspore/nn/optim/adam.py index 2c901ae081..86ce2b2147 100755 --- a/mindspore/nn/optim/adam.py +++ b/mindspore/nn/optim/adam.py @@ -161,7 +161,7 @@ class Adam(Optimizer): Examples: >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() - >>> optim = Adam(params=net.trainable_params()) + >>> optim = nn.Adam(params=net.trainable_params()) >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None) """ @@ -252,7 +252,7 @@ class AdamWeightDecay(Optimizer): Examples: >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() - >>> optim = AdamWeightDecay(params=net.trainable_params()) + >>> optim = nn.AdamWeightDecay(params=net.trainable_params()) >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None) """ def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0): @@ -306,7 +306,7 @@ class AdamWeightDecayDynamicLR(Optimizer): Examples: >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() - >>> optim = AdamWeightDecayDynamicLR(params=net.trainable_params(), decay_steps=10) + >>> optim = nn.AdamWeightDecayDynamicLR(params=net.trainable_params(), decay_steps=10) >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None) """ def __init__(self, diff --git a/mindspore/nn/optim/ftrl.py b/mindspore/nn/optim/ftrl.py index 3f4da483ea..ee8fc9355f 100644 --- a/mindspore/nn/optim/ftrl.py +++ b/mindspore/nn/optim/ftrl.py @@ -87,7 +87,7 @@ class FTRL(Optimizer): Examples: >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() - >>> opt = FTRL(net.trainable_params()) + >>> opt = nn.FTRL(net.trainable_params()) >>> model = Model(net, loss_fn=loss, optimizer=opt, metrics=None) """ def __init__(self, params, initial_accum=0.1, learning_rate=0.001, lr_power=-0.5, l1=0.0, l2=0.0, diff --git a/mindspore/nn/optim/lamb.py b/mindspore/nn/optim/lamb.py index e4fd3bf1d7..e74d6fc6a8 100755 --- a/mindspore/nn/optim/lamb.py +++ b/mindspore/nn/optim/lamb.py @@ -163,7 +163,7 @@ class Lamb(Optimizer): Examples: >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() - >>> optim = Lamb(params=net.trainable_params(), decay_steps=10) + >>> optim = nn.Lamb(params=net.trainable_params(), decay_steps=10) >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None) """ diff --git a/mindspore/nn/optim/lars.py b/mindspore/nn/optim/lars.py index a69057215d..c0cb71cfa6 100755 --- a/mindspore/nn/optim/lars.py +++ b/mindspore/nn/optim/lars.py @@ -90,8 +90,8 @@ class LARS(Cell): Examples: >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() - >>> opt = Momentum(net.trainable_params(), 0.1, 0.9) - >>> opt_lars = LARS(opt, epsilon=1e-08, hyperpara=0.02) + >>> opt = nn.Momentum(net.trainable_params(), 0.1, 0.9) + >>> opt_lars = nn.LARS(opt, epsilon=1e-08, hyperpara=0.02) >>> model = Model(net, loss_fn=loss, optimizer=opt_lars, metrics=None) """ diff --git a/mindspore/nn/optim/momentum.py b/mindspore/nn/optim/momentum.py index 2cc6d76a86..21d3cc864e 100755 --- a/mindspore/nn/optim/momentum.py +++ b/mindspore/nn/optim/momentum.py @@ -83,7 +83,7 @@ class Momentum(Optimizer): Examples: >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() - >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) + >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None) """ def __init__(self, params, learning_rate, momentum, weight_decay=0.0, loss_scale=1.0, diff --git a/mindspore/nn/optim/rmsprop.py b/mindspore/nn/optim/rmsprop.py index faaeacfaa8..b17a101708 100644 --- a/mindspore/nn/optim/rmsprop.py +++ b/mindspore/nn/optim/rmsprop.py @@ -132,7 +132,7 @@ class RMSProp(Optimizer): Examples: >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() - >>> opt = RMSProp(params=net.trainable_params(), learning_rate=lr) + >>> opt = nn.RMSProp(params=net.trainable_params(), learning_rate=lr) >>> model = Model(net, loss, opt) """ def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10, diff --git a/mindspore/nn/optim/sgd.py b/mindspore/nn/optim/sgd.py index 92e9a11070..dbc81ecdd6 100755 --- a/mindspore/nn/optim/sgd.py +++ b/mindspore/nn/optim/sgd.py @@ -77,7 +77,7 @@ class SGD(Optimizer): Examples: >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() - >>> optim = SGD(params=net.trainable_params()) + >>> optim = nn.SGD(params=net.trainable_params()) >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None) """ def __init__(self, params, learning_rate=0.1, momentum=0.0, dampening=0.0, weight_decay=0.0, nesterov=False, diff --git a/mindspore/nn/wrap/cell_wrapper.py b/mindspore/nn/wrap/cell_wrapper.py index efdfc9367e..53a535781d 100644 --- a/mindspore/nn/wrap/cell_wrapper.py +++ b/mindspore/nn/wrap/cell_wrapper.py @@ -50,8 +50,8 @@ class WithLossCell(Cell): >>> net_with_criterion = nn.WithLossCell(net, loss_fn) >>> >>> batch_size = 2 - >>> data = mindspore.Tensor(np.ones([batch_size, 3, 64, 64]).astype(np.float32) * 0.01) - >>> label = mindspore.Tensor(np.ones([batch_size, 1, 1, 1]).astype(np.int32)) + >>> data = Tensor(np.ones([batch_size, 3, 64, 64]).astype(np.float32) * 0.01) + >>> label = Tensor(np.ones([batch_size, 1, 1, 1]).astype(np.int32)) >>> >>> net_with_criterion(data, label) """ @@ -62,16 +62,6 @@ class WithLossCell(Cell): self._loss_fn = loss_fn def construct(self, data, label): - """ - Computes loss based on the wrapped loss cell. - - Args: - data (Tensor): Tensor data to train. - label (Tensor): Tensor label data. - - Returns: - Tensor, compute result. - """ out = self._backbone(data) return self._loss_fn(out, label) @@ -137,19 +127,6 @@ class WithGradCell(Cell): self.network_with_loss.set_train() def construct(self, data, label): - """ - Computes gradients based on the wrapped gradients cell. - - Note: - Run in PyNative mode. - - Args: - data (Tensor): Tensor data to train. - label (Tensor): Tensor label data. - - Returns: - Tensor, return compute gradients. - """ weights = self.weights if self.sens is None: grads = self.grad(self.network_with_loss, weights)(data, label) @@ -355,7 +332,7 @@ class ParameterUpdate(Cell): >>> param = network.parameters_dict()['learning_rate'] >>> update = nn.ParameterUpdate(param) >>> update.phase = "update_param" - >>> lr = mindspore.Tensor(0.001, mindspore.float32) + >>> lr = Tensor(0.001, mindspore.float32) >>> update(lr) """ diff --git a/mindspore/nn/wrap/grad_reducer.py b/mindspore/nn/wrap/grad_reducer.py index 8b34abc47b..01346698ee 100644 --- a/mindspore/nn/wrap/grad_reducer.py +++ b/mindspore/nn/wrap/grad_reducer.py @@ -120,25 +120,36 @@ class DistributedGradReducer(Cell): ValueError: If degree is not a int or less than 0. Examples: - >>> from mindspore.communication import get_group_size + >>> from mindspore.communication import init, get_group_size >>> from mindspore.ops import composite as C >>> from mindspore.ops import operations as P >>> from mindspore.ops import functional as F >>> from mindspore import context + >>> from mindspore import nn + >>> from mindspore import ParallelMode, ParameterTuple + >>> + >>> device_id = int(os.environ["DEVICE_ID"]) + >>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True, + >>> device_id=int(device_id), enable_hccl=True) + >>> init() + >>> context.reset_auto_parallel_context() + >>> context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL) + >>> >>> >>> class TrainingWrapper(nn.Cell): >>> def __init__(self, network, optimizer, sens=1.0): >>> super(TrainingWrapper, self).__init__(auto_prefix=False) >>> self.network = network - >>> self.weights = mindspore.ParameterTuple(network.trainable_params()) + >>> self.network.add_flags(defer_inline=True) + >>> self.weights = ParameterTuple(network.trainable_params()) >>> self.optimizer = optimizer >>> self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True) >>> self.sens = sens >>> self.reducer_flag = False >>> self.grad_reducer = None >>> self.parallel_mode = context.get_auto_parallel_context("parallel_mode") - >>> if self.parallel_mode in [mindspore.ParallelMode.DATA_PARALLEL, - >>> mindspore.ParallelMode.HYBRID_PARALLEL]: + >>> if self.parallel_mode in [ParallelMode.DATA_PARALLEL, + >>> ParallelMode.HYBRID_PARALLEL]: >>> self.reducer_flag = True >>> if self.reducer_flag: >>> mean = context.get_auto_parallel_context("mirror_mean") @@ -161,8 +172,8 @@ class DistributedGradReducer(Cell): >>> network = Net() >>> optimizer = nn.Momentum(network.trainable_params(), learning_rate=0.1, momentum=0.9) >>> train_cell = TrainingWrapper(network, optimizer) - >>> inputs = mindspore.Tensor(np.ones([16, 16]).astype(np.float32)) - >>> label = mindspore.Tensor(np.zeros([16, 16]).astype(np.float32)) + >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32)) + >>> label = Tensor(np.zeros([16, 16]).astype(np.float32)) >>> grads = train_cell(inputs, label) """ diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py index a11c753eda..1ce3179273 100644 --- a/mindspore/nn/wrap/loss_scale.py +++ b/mindspore/nn/wrap/loss_scale.py @@ -65,9 +65,10 @@ class DynamicLossScaleUpdateCell(Cell): >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager) >>> train_network.set_train() >>> - >>> inputs = mindspore.Tensor(np.ones([16, 16]).astype(np.float32)) - >>> label = mindspore.Tensor(np.zeros([16, 16]).astype(np.float32)) - >>> output = train_network(inputs, label) + >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32)) + >>> label = Tensor(np.zeros([16, 16]).astype(np.float32)) + >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32) + >>> output = train_network(inputs, label, scaling_sens) """ def __init__(self, @@ -126,13 +127,14 @@ class FixedLossScaleUpdateCell(Cell): Examples: >>> net_with_loss = Net() >>> optimizer = nn.Momentum(net_with_loss.trainable_params(), learning_rate=0.1, momentum=0.9) - >>> manager = nn.FixedLossScaleUpdateCell(loss_scale_value=2**12, scale_factor=2, scale_window=1000) + >>> manager = nn.FixedLossScaleUpdateCell(loss_scale_value=2**12) >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager) >>> train_network.set_train() >>> - >>> inputs = mindspore.Tensor(np.ones([16, 16]).astype(np.float32)) - >>> label = mindspore.Tensor(np.zeros([16, 16]).astype(np.float32)) - >>> output = train_network(inputs, label) + >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32)) + >>> label = Tensor(np.zeros([16, 16]).astype(np.float32)) + >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32) + >>> output = train_network(inputs, label, scaling_sens) """ def __init__(self, loss_scale_value): @@ -181,9 +183,9 @@ class TrainOneStepWithLossScaleCell(Cell): >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager) >>> train_network.set_train() >>> - >>> inputs = mindspore.Tensor(np.ones([16, 16]).astype(np.float32)) - >>> label = mindspore.Tensor(np.zeros([16, 16]).astype(np.float32)) - >>> scaling_sens = mindspore.Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32) + >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32)) + >>> label = Tensor(np.zeros([16, 16]).astype(np.float32)) + >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32) >>> output = train_network(inputs, label, scaling_sens) """ From e8ae576d1003786e0398d2360c33144f035436dd Mon Sep 17 00:00:00 2001 From: panyifeng Date: Fri, 3 Apr 2020 17:09:04 +0800 Subject: [PATCH 099/227] fix grad missing due to indirect dependent free morphism --- mindspore/ccsrc/optimizer/ad/dfunctor.cc | 43 +++++++++++++------ mindspore/ccsrc/optimizer/ad/dfunctor.h | 1 + mindspore/ccsrc/pipeline/pass.cc | 2 +- .../python/pynative_mode/test_cell_bprop.py | 3 +- .../python/pynative_mode/test_framstruct.py | 22 ++++++++++ 5 files changed, 54 insertions(+), 17 deletions(-) diff --git a/mindspore/ccsrc/optimizer/ad/dfunctor.cc b/mindspore/ccsrc/optimizer/ad/dfunctor.cc index 128e4463e6..3e1aa6e555 100644 --- a/mindspore/ccsrc/optimizer/ad/dfunctor.cc +++ b/mindspore/ccsrc/optimizer/ad/dfunctor.cc @@ -185,19 +185,32 @@ AdjointPtr DFunctor::MapMorphism(const AnfNodePtr &morph) { return node_adjoint; } +bool DFunctor::IsFreeMorphism(const AnfNodePtr &node) { + // Do not care about non-CNode + if (!node->isa()) { + return false; + } + // Do not care about kPrimReturn + if (IsPrimitiveCNode(node, prim::kPrimReturn)) { + return false; + } + auto &users = primal_graph_->manager()->node_users()[node]; + // Do not care about isolated morphisms + if (users.empty()) { + return false; + } + // Not free if it's used by some node in primal_graph + bool nonfree = std::any_of(std::begin(users), std::end(users), [&](const auto &kv) { + auto &user = kv.first; + return user->func_graph() == primal_graph_; + }); + return !nonfree; +} + void DFunctor::MapFreeMorphism() { // Handle cnode not attached to output, that might be refered in other functions. for (auto &node : primal_graph_->nodes()) { - auto adjoint = FindAdjoint(node); - if (adjoint != nullptr) { - continue; - } - if (!node->isa()) { - MS_LOG(DEBUG) << "MapFreeMorphism noncnode not mapped after MapMorphism " << node->ToString() << " " - << node->type_name() << "."; - continue; - } - if (IsPrimitiveCNode(node, prim::kPrimReturn)) { + if (!IsFreeMorphism(node)) { continue; } MS_LOG(DEBUG) << "MapFreeMorphism map nonoutput cnode after MapMorphism " << node->ToString() << "."; @@ -256,9 +269,10 @@ void DFunctor::MapMorphism() { // Set stop_gradient before MapMorphism. BroadCastStopFlag(); + // Handle free morphism before output, because in some case, free morphism might depend on output's fv tangent + MapFreeMorphism(); // Handle morphism from output. (void)MapMorphism(primal_graph_->output()); - MapFreeMorphism(); // Construct K for primal_graph_ auto output_adjoint = anfnode_to_adjoin_.find(primal_graph_->output()); @@ -298,9 +312,10 @@ FuncGraphPtr DFunctor::KUserDefined(const FuncGraphPtr &primal) { const size_t param_diff = 1; if (bprop_graph->output()->isa() && bprop_graph->output()->cast()->size() + param_diff != bprop_graph->parameters().size()) { - MS_LOG(EXCEPTION) << "User defined Cell bprop " << primal->ToString() << " in scope " - << primal->output()->scope()->name() - << " output must be a tuple and output number should be the same with inputs."; + // It does not matter with the final tangents, just a tip for debugging + MS_LOG(DEBUG) << "User defined Cell bprop " << primal->ToString() << " in scope " + << primal->output()->scope()->name() + << " output must be a tuple and output number should be the same with inputs."; } resources_->manager()->AddFuncGraph(bprop_graph); diff --git a/mindspore/ccsrc/optimizer/ad/dfunctor.h b/mindspore/ccsrc/optimizer/ad/dfunctor.h index f50f866efa..3059736171 100644 --- a/mindspore/ccsrc/optimizer/ad/dfunctor.h +++ b/mindspore/ccsrc/optimizer/ad/dfunctor.h @@ -61,6 +61,7 @@ class DFunctor { private: // Map one morphism. AdjointPtr MapMorphism(const AnfNodePtr &morph); + bool IsFreeMorphism(const AnfNodePtr &node); // Map morphism that's not attached to output. void MapFreeMorphism(); void BackPropagateFv(const AnfNodePtr &fv, const AnfNodePtr &din); diff --git a/mindspore/ccsrc/pipeline/pass.cc b/mindspore/ccsrc/pipeline/pass.cc index d89a0090a7..e0336443e6 100644 --- a/mindspore/ccsrc/pipeline/pass.cc +++ b/mindspore/ccsrc/pipeline/pass.cc @@ -111,7 +111,7 @@ OptPassGroupMap GetOptPassesA(const opt::irpass::OptimizeIRPassLib& irpass) { irpass.replace_applicator_, }); opt::OptPassConfig virtual_dataset = opt::OptPassConfig({irpass.virtual_dataset_eliminate_}); - opt::OptPassConfig grad = opt::OptPassConfig({irpass.inline_, irpass.expand_jprim_}, true); + opt::OptPassConfig grad = opt::OptPassConfig({irpass.expand_jprim_}, true); OptPassGroupMap map_a({{"a_1", a_1}, {"a_2", a_2}, diff --git a/tests/ut/python/pynative_mode/test_cell_bprop.py b/tests/ut/python/pynative_mode/test_cell_bprop.py index 03ae1affa5..054afe36c9 100644 --- a/tests/ut/python/pynative_mode/test_cell_bprop.py +++ b/tests/ut/python/pynative_mode/test_cell_bprop.py @@ -304,5 +304,4 @@ class MulAddWithWrongOutputNum(nn.Cell): def test_grad_mul_add_with_wrong_output_num(): mul_add = MulAddWithWrongOutputNum() - with pytest.raises(RuntimeError): - C.grad_all(mul_add)(1, 2) + C.grad_all(mul_add)(1, 2) diff --git a/tests/ut/python/pynative_mode/test_framstruct.py b/tests/ut/python/pynative_mode/test_framstruct.py index 2939337211..ff7cf67f52 100644 --- a/tests/ut/python/pynative_mode/test_framstruct.py +++ b/tests/ut/python/pynative_mode/test_framstruct.py @@ -15,6 +15,7 @@ """ test_framstruct """ import pytest import numpy as np +import mindspore as ms import mindspore.nn as nn from mindspore import context from mindspore.ops import composite as C @@ -706,3 +707,24 @@ def grad_refactor_14(a, b): return inner1(b) + inner2(a) + inner3(a) def test_grad_refactor_14(): assert C.grad_all(grad_refactor_14)(2, 3) == (3, 9) + + +class IfDeferInline(nn.Cell): + def __init__(self, mul_size): + super().__init__() + self.mul_weight = Tensor(np.full(mul_size, 0.6, dtype=np.float32)) + self.mul = P.Mul() + + def construct(self, inputs): + x = self.mul(inputs, self.mul_weight) + if True: + x = x + return x + +def test_grad_if_defer_inline(): + """ test_grad_if_defer_inline """ + network = IfDeferInline([128, 96]) + network.add_flags(defer_inline=False) + inp = Tensor(np.ones([128, 96]).astype(np.float32)) + grads = C.grad_all(network)(inp) + assert grads == (Tensor(np.full([128, 96], 0.6, dtype=np.float32)),) From 2ebd60496a0d14f7e62d239c8801317e45b1b8e4 Mon Sep 17 00:00:00 2001 From: jjfeing Date: Tue, 7 Apr 2020 09:11:45 +0800 Subject: [PATCH 100/227] adapt graphengine upgrade --- cmake/dependency_graphengine.cmake | 1 - 1 file changed, 1 deletion(-) diff --git a/cmake/dependency_graphengine.cmake b/cmake/dependency_graphengine.cmake index d2f80c4d04..2420f47736 100644 --- a/cmake/dependency_graphengine.cmake +++ b/cmake/dependency_graphengine.cmake @@ -42,7 +42,6 @@ else() set(ASCEND_PATH /usr/local/Ascend) set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common) set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64) - find_library(c_sec libc_sec.so ${ASCEND_DRIVER_PATH}) find_library(slog libslog.so ${ASCEND_DRIVER_PATH}) find_library(mmpa libmmpa.so ${ASCEND_DRIVER_PATH}) From 826a7393db781656e52dd53fe0e4d02be9fcb22c Mon Sep 17 00:00:00 2001 From: Wei Luning Date: Sun, 5 Apr 2020 09:27:29 +0000 Subject: [PATCH 101/227] fix InsertGradientOf with class method --- mindspore/ccsrc/pipeline/parse/resolve.cc | 8 ++++ mindspore/ccsrc/pipeline/pass.cc | 8 ++++ .../ccsrc/pipeline/static_analysis/prim.cc | 37 +++---------------- .../pynative_mode/test_insert_grad_of.py | 36 +++++++++++++++++- 4 files changed, 57 insertions(+), 32 deletions(-) diff --git a/mindspore/ccsrc/pipeline/parse/resolve.cc b/mindspore/ccsrc/pipeline/parse/resolve.cc index ebc1f65486..f90fc5039c 100644 --- a/mindspore/ccsrc/pipeline/parse/resolve.cc +++ b/mindspore/ccsrc/pipeline/parse/resolve.cc @@ -103,6 +103,14 @@ AnfNodePtr ResolveParameterObj(const FuncGraphPtr& func_graph, const py::object& if (para_node == nullptr) { ParameterPtr node = top_graph->AddWeightParameter(param_name); node->set_default_param(obj); + + // set_abstract for parameter + auto to_convert = py::cast(python_adapter::GetPyObjAttr(obj, "default_input")); + ValuePtr converted = nullptr; + (void)ConvertData(to_convert, &converted); + bool broaden = true; + node->set_abstract(abstract::FromValue(converted, broaden)); + para_node = node; } auto iter = func_graph->make_ref_params().find(para_node); diff --git a/mindspore/ccsrc/pipeline/pass.cc b/mindspore/ccsrc/pipeline/pass.cc index e0336443e6..a58ecf41b6 100644 --- a/mindspore/ccsrc/pipeline/pass.cc +++ b/mindspore/ccsrc/pipeline/pass.cc @@ -112,6 +112,13 @@ OptPassGroupMap GetOptPassesA(const opt::irpass::OptimizeIRPassLib& irpass) { }); opt::OptPassConfig virtual_dataset = opt::OptPassConfig({irpass.virtual_dataset_eliminate_}); opt::OptPassConfig grad = opt::OptPassConfig({irpass.expand_jprim_}, true); + opt::irpass::ResolveIRPassLib resolve_irpass; + + opt::OptPassConfig resolve_pass = opt::OptPassConfig({ + resolve_irpass.resolver_resolve_, + resolve_irpass.resolver_getattr_, + irpass.get_make_ref_eliminate_, + }); OptPassGroupMap map_a({{"a_1", a_1}, {"a_2", a_2}, @@ -120,6 +127,7 @@ OptPassGroupMap GetOptPassesA(const opt::irpass::OptimizeIRPassLib& irpass) { {"allreduce_fusion", opt::OptPassConfig(parallel::StepAllreduceFusion)}, {"virtual_dataset", virtual_dataset}, {"grad", grad}, + {"resolve", resolve_pass}, {"renormalize", opt::OptPassConfig::Renormalize()}, {"cse", opt::OptPassConfig(opt::CSE(false))}, {"a_3", a_3}}); diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.cc b/mindspore/ccsrc/pipeline/static_analysis/prim.cc index 56bcd77f67..d71e098009 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/prim.cc +++ b/mindspore/ccsrc/pipeline/static_analysis/prim.cc @@ -554,24 +554,6 @@ AbstractBasePtr StaticGetterInferred(const ValuePtr &value, const ConfigPtr &dat return eng->ForwardConfig(old_conf, fn_conf); } -AbstractBasePtr GenerateResolveAbstract(const AnfNodeConfigPtr &out_conf, const py::object &obj, - const ValuePtr &converted_ret) { - if (py::hasattr(obj, PYTHON_DATACLASS_FIELDS)) { - TypePtr cls_ptr = parse::ParseDataClass(converted_ret->cast>()->obj()); - - std::vector input = {NewValueNode(prim::kPrimPartial), NewValueNode(prim::kPrimMakeRecord), - NewValueNode(cls_ptr)}; - MS_EXCEPTION_IF_NULL(out_conf); - FuncGraphPtr func_graph = out_conf->node()->func_graph(); - CNodePtr new_cnode = func_graph->NewCNode(input); - AnalysisEnginePtr eng = out_conf->engine(); - AnfNodeConfigPtr fn_conf = eng->MakeConfig(new_cnode, out_conf->context()); - return eng->ForwardConfig(out_conf, fn_conf); - } else { - return ToAbstract(converted_ret, AnalysisContext::DummyContext(), out_conf); - } -} - AbstractBasePtr GetEvaluatedValueForNameSpaceString(const AnalysisEnginePtr &engine, const AbstractBasePtrList &args_spec_list, const AnfNodeConfigPtr &out_conf) { @@ -602,23 +584,16 @@ AbstractBasePtr GetEvaluatedValueForNameSpaceString(const AnalysisEnginePtr &eng // item_name to func addr from obj_map parse::SymbolPtr symbol = item_v->cast(); parse::NameSpacePtr name_space = data_v->cast(); + FuncGraphPtr func_graph = out_conf->node()->func_graph(); - parse::SymbolResolverPtr symbol_resolver = - std::make_shared(name_space, symbol, out_conf->node()); - if (!symbol_resolver->Resolve()) { + auto new_node = parse::ResolveSymbol(func_graph->manager(), name_space, symbol, out_conf->node()); + if (new_node == nullptr) { MS_LOG(EXCEPTION) << "Resolve node failed"; } - py::object obj = symbol_resolver->result(); - ValuePtr converted_ret = nullptr; - bool converted = parse::ConvertData(obj, &converted_ret, true); - if (!converted) { - MS_LOG(EXCEPTION) << "Convert data failed"; - } - if (converted_ret->isa()) { - AddToManager(engine, converted_ret->cast()); - } - return GenerateResolveAbstract(out_conf, obj, converted_ret); + AnalysisEnginePtr eng = out_conf->engine(); + AnfNodeConfigPtr fn_conf = eng->MakeConfig(new_node, out_conf->context()); + return eng->ForwardConfig(out_conf, fn_conf); } AbstractBasePtr GetEvaluatedValueForClassAttrOrMethod(const AnalysisEnginePtr &engine, diff --git a/tests/ut/python/pynative_mode/test_insert_grad_of.py b/tests/ut/python/pynative_mode/test_insert_grad_of.py index 38432d79f3..104ac4d1c7 100644 --- a/tests/ut/python/pynative_mode/test_insert_grad_of.py +++ b/tests/ut/python/pynative_mode/test_insert_grad_of.py @@ -17,13 +17,14 @@ import numpy as np import mindspore.nn as nn from mindspore.ops import composite as C from mindspore.ops import operations as P +from mindspore.ops import functional as F from mindspore.common.api import ms_function from ....mindspore_test_framework.utils.bprop_util import bprop from ....mindspore_test_framework.utils.debug_util import PrintShapeTypeCell, PrintGradShapeTypeCell from mindspore import Tensor from mindspore import context - +import mindspore def setup_module(module): context.set_context(mode=context.PYNATIVE_MODE) @@ -107,3 +108,36 @@ def test_print_shape_type(): return z bprop(Mul(), Tensor(np.ones([2, 2]).astype(np.float32)), Tensor(np.ones([2, 2]).astype(np.float32))) + +def test_cell_assign(): + context.set_context(mode=context.GRAPH_MODE, save_graphs=True) + class GradNetWrap(nn.Cell): + """ GradNetWrap definition """ + def __init__(self, net): + super(GradNetWrap, self).__init__() + self.net = net + self.weights = mindspore.ParameterTuple(net.get_parameters()) + + def construct(self, x, y): + return C.grad_by_list(self.net, self.weights)(x, y) + + class Mul(nn.Cell): + def __init__(self): + super(Mul, self).__init__() + self.get_g = P.InsertGradientOf(self.save_gradient) + self.matrix_w = mindspore.Parameter(Tensor(np.ones([2, 2], np.float32)), name="matrix_w") + self.matrix_g = mindspore.Parameter(Tensor(np.ones([2, 2], np.float32)), name="matrix_g") + + def save_gradient(self, dout): + self.matrix_g = dout + return dout + + def construct(self, x, y): + z = x * self.matrix_w + z = self.get_g(z) + z = z * y + return z + + input_x = Tensor(np.ones([2, 2], np.float32)) + input_y = Tensor(np.ones([2, 2], np.float32)) + GradNetWrap(Mul())(input_x, input_y) From b1de026c98d8e75b5c9bec2bfdfe66f9fdcaad1d Mon Sep 17 00:00:00 2001 From: fary86 Date: Thu, 2 Apr 2020 07:50:13 +0800 Subject: [PATCH 102/227] Add primitive name to param error message for math_ops.py --- mindspore/_checkparam.py | 126 ++- mindspore/ops/_utils/broadcast.py | 6 +- mindspore/ops/operations/array_ops.py | 13 +- mindspore/ops/operations/math_ops.py | 206 +++-- mindspore/ops/primitive.py | 3 + .../components/executor/check_exceptions.py | 11 +- .../utils/config_util.py | 5 +- .../utils/facade_util.py | 8 +- .../mindspore_test_framework/utils/keyword.py | 1 + tests/ut/python/ops/test_array_ops.py | 2 +- tests/ut/python/ops/test_math_ops_check.py | 751 ++++++++++++++++++ 11 files changed, 1003 insertions(+), 129 deletions(-) create mode 100755 tests/ut/python/ops/test_math_ops_check.py diff --git a/mindspore/_checkparam.py b/mindspore/_checkparam.py index 61b7cc9818..d553bcd364 100644 --- a/mindspore/_checkparam.py +++ b/mindspore/_checkparam.py @@ -15,6 +15,7 @@ """Check parameters.""" import re from enum import Enum +from functools import reduce from itertools import repeat from collections import Iterable @@ -93,8 +94,131 @@ rel_strs = { } +class Validator: + """validator for checking input parameters""" + + @staticmethod + def check(arg_name, arg_value, value_name, value, rel=Rel.EQ, prim_name=None): + """ + Method for judging relation between two int values or list/tuple made up of ints. + + This method is not suitable for judging relation between floats, since it does not consider float error. + """ + + rel_fn = Rel.get_fns(rel) + if not rel_fn(arg_value, value): + rel_str = Rel.get_strs(rel).format(f'{value_name}: {value}') + msg_prefix = f'For {prim_name} the' if prim_name else "The" + raise ValueError(f'{msg_prefix} `{arg_name}` should be {rel_str}, but got {arg_value}.') + + @staticmethod + def check_integer(arg_name, arg_value, value, rel, prim_name): + """Integer value judgment.""" + rel_fn = Rel.get_fns(rel) + type_mismatch = not isinstance(arg_value, int) or isinstance(arg_value, bool) + if type_mismatch or not rel_fn(arg_value, value): + rel_str = Rel.get_strs(rel).format(value) + raise ValueError(f'For {prim_name} the `{arg_name}` should be an int and must {rel_str},' + f' but got {arg_value}.') + return arg_value + + @staticmethod + def check_int_range(arg_name, arg_value, lower_limit, upper_limit, rel, prim_name): + """Method for checking whether an int value is in some range.""" + rel_fn = Rel.get_fns(rel) + type_mismatch = not isinstance(arg_value, int) + if type_mismatch or not rel_fn(arg_value, lower_limit, upper_limit): + rel_str = Rel.get_strs(rel).format(lower_limit, upper_limit) + raise ValueError(f'For \'{prim_name}\' the `{arg_name}` should be an int in range {rel_str},' + f' but got {arg_value}.') + return arg_value + + @staticmethod + def check_subclass(arg_name, type_, template_type, prim_name): + """Check whether some type is sublcass of another type""" + if not isinstance(template_type, Iterable): + template_type = (template_type,) + if not any([mstype.issubclass_(type_, x) for x in template_type]): + type_str = (type(type_).__name__ if isinstance(type_, (tuple, list)) else "") + str(type_) + raise TypeError(f'For \'{prim_name}\' the type of `{arg_name}` should be subclass' + f' of {",".join((str(x) for x in template_type))}, but got {type_str}.') + + @staticmethod + def check_tensor_type_same(args, valid_values, prim_name): + """check whether the element types of input tensors are the same.""" + def _check_tensor_type(arg): + arg_key, arg_val = arg + Validator.check_subclass(arg_key, arg_val, mstype.tensor, prim_name) + elem_type = arg_val.element_type() + if not elem_type in valid_values: + raise TypeError(f'For \'{prim_name}\' element type of `{arg_key}` should be in {valid_values},' + f' but `{arg_key}` is {elem_type}.') + return (arg_key, elem_type) + + def _check_types_same(arg1, arg2): + arg1_name, arg1_type = arg1 + arg2_name, arg2_type = arg2 + if arg1_type != arg2_type: + raise TypeError(f'For \'{prim_name}\' element type of `{arg2_name}` should be same as `{arg1_name}`,' + f' but `{arg1_name}` is {arg1_type} and `{arg2_name}` is {arg2_type}.') + return arg1 + + elem_types = map(_check_tensor_type, args.items()) + reduce(_check_types_same, elem_types) + + + @staticmethod + def check_scalar_or_tensor_type_same(args, valid_values, prim_name): + """check whether the types of inputs are the same. if the input args are tensors, check their element types""" + def _check_argument_type(arg): + arg_key, arg_val = arg + if isinstance(arg_val, type(mstype.tensor)): + arg_val = arg_val.element_type() + if not arg_val in valid_values: + raise TypeError(f'For \'{prim_name}\' the `{arg_key}` should be in {valid_values},' + f' but `{arg_key}` is {arg_val}.') + return arg + + def _check_types_same(arg1, arg2): + arg1_name, arg1_type = arg1 + arg2_name, arg2_type = arg2 + excp_flag = False + if isinstance(arg1_type, type(mstype.tensor)) and isinstance(arg2_type, type(mstype.tensor)): + arg1_type = arg1_type.element_type() + arg2_type = arg2_type.element_type() + elif not (isinstance(arg1_type, type(mstype.tensor)) or isinstance(arg2_type, type(mstype.tensor))): + pass + else: + excp_flag = True + + if excp_flag or arg1_type != arg2_type: + raise TypeError(f'For \'{prim_name}\' type of `{arg2_name}` should be same as `{arg1_name}`,' + f' but `{arg1_name}` is {arg1_type} and `{arg2_name}` is {arg2_type}.') + return arg1 + reduce(_check_types_same, map(_check_argument_type, args.items())) + + @staticmethod + def check_value_type(arg_name, arg_value, valid_types, prim_name): + """Check whether a values is instance of some types.""" + def raise_error_msg(): + """func for raising error message when check failed""" + type_names = [t.__name__ for t in valid_types] + num_types = len(valid_types) + raise TypeError(f'For \'{prim_name}\' the type of `{arg_name}` should be ' + f'{"one of " if num_types > 1 else ""}' + f'{type_names if num_types > 1 else type_names[0]}, but got {type(arg_value).__name__}.') + + # Notice: bool is subclass of int, so `check_value_type('x', True, [int])` will check fail, and + # `check_value_type('x', True, [bool, int])` will check pass + if isinstance(arg_value, bool) and bool not in tuple(valid_types): + raise_error_msg() + if isinstance(arg_value, tuple(valid_types)): + return arg_value + raise_error_msg() + + class ParamValidator: - """Parameter validator.""" + """Parameter validator. NOTICE: this class will be replaced by `class Validator`""" @staticmethod def equal(arg_name, arg_value, cond_str, cond): diff --git a/mindspore/ops/_utils/broadcast.py b/mindspore/ops/_utils/broadcast.py index 2c9eb8a54b..c71158de57 100644 --- a/mindspore/ops/_utils/broadcast.py +++ b/mindspore/ops/_utils/broadcast.py @@ -16,13 +16,14 @@ """broadcast""" -def _get_broadcast_shape(x_shape, y_shape): +def _get_broadcast_shape(x_shape, y_shape, prim_name): """ Doing broadcast between tensor x and tensor y. Args: x_shape (list): The shape of tensor x. y_shape (list): The shape of tensor y. + prim_name (str): Primitive name. Returns: List, the shape that broadcast between tensor x and tensor y. @@ -50,7 +51,8 @@ def _get_broadcast_shape(x_shape, y_shape): elif x_shape[i] == y_shape[i]: broadcast_shape_back.append(x_shape[i]) else: - raise ValueError("The x_shape {} and y_shape {} can not broadcast.".format(x_shape, y_shape)) + raise ValueError("For '{}' the x_shape {} and y_shape {} can not broadcast.".format( + prim_name, x_shape, y_shape)) broadcast_shape_front = y_shape[0: y_len - length] if length == x_len else x_shape[0: x_len - length] broadcast_shape = broadcast_shape_front + broadcast_shape_back diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index ff6289a4ad..fdad46a31f 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -28,9 +28,16 @@ from ..._checkparam import ParamValidator as validator from ..._checkparam import Rel from ...common import dtype as mstype from ...common.tensor import Tensor -from ..operations.math_ops import _check_infer_attr_reduce, _infer_shape_reduce +from ..operations.math_ops import _infer_shape_reduce from ..primitive import Primitive, PrimitiveWithInfer, prim_attr_register +def _check_infer_attr_reduce(axis, keep_dims): + validator.check_type('keep_dims', keep_dims, [bool]) + validator.check_type('axis', axis, [int, tuple]) + if isinstance(axis, tuple): + for index, value in enumerate(axis): + validator.check_type('axis[%d]' % index, value, [int]) + class ExpandDims(PrimitiveWithInfer): """ @@ -1090,7 +1097,7 @@ class ArgMaxWithValue(PrimitiveWithInfer): axis = self.axis x_rank = len(x_shape) validator.check_int_range("axis", axis, -x_rank, x_rank, Rel.INC_LEFT) - ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims) + ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims, self.prim_name()) return ouput_shape, ouput_shape def infer_dtype(self, x_dtype): @@ -1136,7 +1143,7 @@ class ArgMinWithValue(PrimitiveWithInfer): axis = self.axis x_rank = len(x_shape) validator.check_int_range("axis", axis, -x_rank, x_rank, Rel.INC_LEFT) - ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims) + ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims, self.prim_name()) return ouput_shape, ouput_shape def infer_dtype(self, x_dtype): diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 97a992ca05..175b72560f 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -19,7 +19,7 @@ import numpy as np from ..._c_expression import signature_rw as sig_rw from ..._c_expression import signature_kind as sig_kind from ..._c_expression import signature_dtype as sig_dtype -from ..._checkparam import ParamValidator as validator +from ..._checkparam import Validator as validator from ..._checkparam import Rel from ...common import dtype as mstype from ...common.tensor import Tensor @@ -27,16 +27,16 @@ from .._utils import _get_broadcast_shape from ..primitive import PrimitiveWithInfer, prim_attr_register -def _infer_shape_reduce(x, axis, keep_dims): +def _infer_shape_reduce(x, axis, keep_dims, prim_name): """Common infer for reduce operator""" def reduce_one_axis(one_axis): - validator.check_int_range('axis', one_axis, -dim, dim, Rel.INC_LEFT) + validator.check_int_range('axis', one_axis, -dim, dim, Rel.INC_LEFT, prim_name) if one_axis < 0: one_axis += dim axis_reduce.add(one_axis) - validator.check_type('axis', axis, [int, tuple, list]) + validator.check_value_type('axis', axis, [int, tuple, list], prim_name) dim = len(x) axis_reduce = set() @@ -48,7 +48,7 @@ def _infer_shape_reduce(x, axis, keep_dims): return [1] * dim return [] for index, one_axis in enumerate(axis): - validator.check_type('axis[%d]' % index, one_axis, [int]) + validator.check_value_type('axis[%d]' % index, one_axis, [int], prim_name) reduce_one_axis(one_axis) out_shape = [] @@ -61,14 +61,6 @@ def _infer_shape_reduce(x, axis, keep_dims): return out_shape -def _check_infer_attr_reduce(axis, keep_dims): - validator.check_type('keep_dims', keep_dims, [bool]) - validator.check_type('axis', axis, [int, tuple]) - if isinstance(axis, tuple): - for index, value in enumerate(axis): - validator.check_type('axis[%d]' % index, value, [int]) - - class _BinaryOp(PrimitiveWithInfer): """ Define binary operators. @@ -82,7 +74,7 @@ class _BinaryOp(PrimitiveWithInfer): self.init_prim_io_names(inputs=['x', 'y'], outputs=['output']) def infer_shape(self, x_shape, y_shape): - return _get_broadcast_shape(x_shape, y_shape) + return _get_broadcast_shape(x_shape, y_shape, self.prim_name()) class _MathBinaryOp(_BinaryOp): @@ -91,15 +83,13 @@ class _MathBinaryOp(_BinaryOp): """ @staticmethod - def do_infer_dtype(x_dtype, y_dtype, valid_dtype=mstype.number_type): + def do_infer_dtype(x_dtype, y_dtype, valid_dtype=mstype.number_type, prim_name=None): args_type = {"x": x_dtype, "y": y_dtype} - validator.check_args_tensor(args_type) - args_dtype = {"x_dtype": x_dtype, "y_dtype": y_dtype} - validator.check_type_same(args_dtype, valid_dtype) + validator.check_tensor_type_same(args_type, valid_dtype, prim_name) return x_dtype def infer_dtype(self, x_dtype, y_dtype): - return _MathBinaryOp.do_infer_dtype(x_dtype, y_dtype) + return _MathBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type, self.prim_name()) class TensorAdd(_MathBinaryOp): @@ -167,7 +157,7 @@ class AssignAdd(PrimitiveWithInfer): def infer_dtype(self, variable, value): args = {"value": value} - validator.check_type_same(args, mstype.number_type) + validator.check_scalar_or_tensor_type_same(args, mstype.number_type, self.prim_name()) return value @@ -208,7 +198,7 @@ class AssignSub(PrimitiveWithInfer): def infer_dtype(self, variable, value): args = {"value": value} - validator.check_type_same(args, mstype.number_type) + validator.check_scalar_or_tensor_type_same(args, mstype.number_type, self.prim_name()) return value @@ -229,15 +219,16 @@ class _Reduce(PrimitiveWithInfer): @prim_attr_register def __init__(self, keep_dims=False): """init Reduce""" - validator.check_type('keep_dims', keep_dims, [bool]) + validator.check_value_type('keep_dims', keep_dims, [bool], self.prim_name()) self.init_prim_io_names(inputs=['input_x', 'axis'], outputs=['y']) def do_infer(self, input_x, axis, valid_dtype=mstype.number_type): axis_v = axis['value'] input_shp = input_x['shape'] - validator.check_subclass('input_x', input_x['dtype'], mstype.tensor) - validator.check_typename('input_x', input_x['dtype'], valid_dtype) - input_shp = _infer_shape_reduce(input_shp, axis_v, self.keep_dims) + args = {'input_x': input_x['dtype']} + validator.check_tensor_type_same(args, valid_dtype, self.prim_name()) + + input_shp = _infer_shape_reduce(input_shp, axis_v, self.keep_dims, self.prim_name()) return {'shape': input_shp, 'dtype': input_x['dtype'], 'value': None} @@ -472,16 +463,17 @@ class CumProd(PrimitiveWithInfer): """ @prim_attr_register def __init__(self, exclusive=False, reverse=False): - self.exclusive = validator.check_type("exclusive", exclusive, [bool]) - self.reverse = validator.check_type("reverse", reverse, [bool]) + cls_name = self.prim_name() + self.exclusive = validator.check_value_type("exclusive", exclusive, [bool], cls_name) + self.reverse = validator.check_value_type("reverse", reverse, [bool], cls_name) def infer_shape(self, x_shape, axis_shape): return x_shape def infer_dtype(self, x_type, axis_type): - validator.check_subclass('x_type', x_type, mstype.tensor) - validator.check_typename('x_type', x_type, mstype.number_type) - validator.check_subclass("axis_type", axis_type, mstype.int_) + cls_name = self.prim_name() + validator.check_tensor_type_same({'x': x_type}, mstype.number_type, cls_name) + validator.check_subclass("axis", axis_type, mstype.int_, cls_name) return x_type @@ -515,8 +507,9 @@ class MatMul(PrimitiveWithInfer): def __init__(self, transpose_a=False, transpose_b=False): self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['output']) self.__setattr_flag__ = True - validator.check_type("transpose_a", transpose_a, [bool]) - validator.check_type("transpose_b", transpose_b, [bool]) + cls_name = self.prim_name() + validator.check_value_type("transpose_a", transpose_a, [bool], cls_name) + validator.check_value_type("transpose_b", transpose_b, [bool], cls_name) def check_shape_size(self, x, y): if len(x) != 2 or len(y) != 2: @@ -525,11 +518,11 @@ class MatMul(PrimitiveWithInfer): def infer_shape(self, x, y): self.check_shape_size(x, y) - cls_name = self.__class__.__name__ + cls_name = self.prim_name() # expected dimension of x, y, x:[...,a,b] y:[..., c,d], the dim size should be the same except the last two for i in range(len(x) - 2): if x[i] != y[i]: - raise ValueError(f'{cls_name} shape in dim[{i}] not the same, while x is {x[i]}, y is {y[i]}') + raise ValueError(f'For \'{cls_name}\' shape in dim[{i}] not the same, while x is {x[i]}, y is {y[i]}') # validate whether last two dims satifing matrix multiply x_last = x[-2:] @@ -538,8 +531,8 @@ class MatMul(PrimitiveWithInfer): x_col = x_last[not self.transpose_a] # x_col = x_last[1] if (not transpose_a) else x_last[0] y_row = y_last[self.transpose_b] # y_row = y_last[0] if (not transpose_b) else y_last[1] if x_col != y_row: - raise ValueError(f'{cls_name} evaluator shapes of inputs can not do this operator, got {x_col} and {y_row}' - + f' for {cls_name}, with x shape {x}(transpose_a={self.transpose_a})' + raise ValueError(f'For \'{cls_name}\' evaluator shapes of inputs can not do this operator,' + + f' got {x_col} and {y_row}, with x shape {x}(transpose_a={self.transpose_a})' + f', y shape {y}(transpose_b={self.transpose_b}).') # set attribute self.add_prim_attr('transpose_x1', self.transpose_a) @@ -549,10 +542,8 @@ class MatMul(PrimitiveWithInfer): return ret_dims def infer_dtype(self, x, y): - validator.check_subclass("x", x, mstype.tensor) - validator.check_subclass("y", y, mstype.tensor) - args = {"x dtype": x, "y dtype": y} - validator.check_type_same(args, mstype.float_type + mstype.int_type) + args = {"x": x, "y": y} + validator.check_tensor_type_same(args, mstype.float_type + mstype.int_type, self.prim_name()) return x @@ -596,12 +587,13 @@ class BatchMatMul(MatMul): def __init__(self, transpose_a=False, transpose_b=False): self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['output']) self.__setattr_flag__ = True - validator.check_type("transpose_a", transpose_a, [bool]) - validator.check_type("transpose_b", transpose_b, [bool]) + cls_name = self.prim_name() + validator.check_value_type("transpose_a", transpose_a, [bool], cls_name) + validator.check_value_type("transpose_b", transpose_b, [bool], cls_name) def check_shape_size(self, x, y): if len(x) != len(y) or len(x) < 3: - raise ValueError('BatchMatMul input x, y should be the same dimension size and should be ' + raise ValueError('For \'BatchMatMul\' input x, y should be the same dimension size and should be ' 'greater or equal to 3,' + f' while x size = {len(x)}, y size= {len(y)}') @@ -633,18 +625,17 @@ class CumSum(PrimitiveWithInfer): @prim_attr_register def __init__(self, exclusive=False, reverse=False): """init cumsum""" - self.exclusive = validator.check_type('exclusive', exclusive, [bool]) - self.add_prim_attr("exclusive", self.exclusive) - self.reverse = validator.check_type('reverse', reverse, [bool]) - self.add_prim_attr("reverse", self.reverse) + cls_name = self.prim_name() + validator.check_value_type('exclusive', exclusive, [bool], cls_name) + validator.check_value_type('reverse', reverse, [bool], cls_name) self.init_prim_io_names(inputs=['x', 'axis'], outputs=['y']) def __infer__(self, x, axis): + cls_name = self.prim_name() x_shp = x['shape'] - validator.check_type('axis', axis['value'], [int]) - validator.check_subclass('x', x['dtype'], mstype.tensor) - validator.check_typename('x', x['dtype'], [mstype.uint8, mstype.int8, - mstype.int32, mstype.float16, mstype.float32]) + validator.check_value_type('axis', axis['value'], [int], cls_name) + valid_types = [mstype.uint8, mstype.int8, mstype.int32, mstype.float16, mstype.float32] + validator.check_tensor_type_same({'x': x['dtype']}, valid_types, cls_name) return {'shape': x_shp, 'dtype': x['dtype'], 'value': None} @@ -685,21 +676,22 @@ class AddN(PrimitiveWithInfer): self.init_prim_io_names(inputs=["inputs"], outputs=["sum"]) def infer_shape(self, inputs): - validator.check_integer("inputs", len(inputs), 1, Rel.GE) + cls_name = self.prim_name() + validator.check_integer("inputs", len(inputs), 1, Rel.GE, cls_name) self.add_prim_attr('n', len(inputs)) shp0 = inputs[0] for i, shp in enumerate(inputs): - validator.check(f"shape of inputs[{i}]", shp, 'shape of inputs[0]', shp0) + validator.check(f"shape of inputs[{i}]", shp, 'shape of inputs[0]', shp0, Rel.EQ, cls_name) return shp0 def infer_dtype(self, inputs): - validator.check_type("inputs", inputs, [tuple, list]) - validator.check_integer("inputs", len(inputs), 1, Rel.GE) + cls_name = self.prim_name() + validator.check_value_type("inputs", inputs, [tuple, list], cls_name) + validator.check_integer("inputs", len(inputs), 1, Rel.GE, cls_name) args = {} for i, dtype in enumerate(inputs): - validator.check_subclass(f"inputs[{i}]", dtype, mstype.tensor) args[f"inputs[{i}]"] = dtype - validator.check_type_same(args, mstype.number_type + (mstype.bool_,)) + validator.check_tensor_type_same(args, mstype.number_type + (mstype.bool_,), cls_name) return inputs[0] @@ -723,8 +715,7 @@ class Neg(PrimitiveWithInfer): return input_x def infer_dtype(self, input_x): - validator.check_subclass("input_x", input_x, mstype.tensor) - validator.check_typename("input_x", input_x, mstype.number_type) + validator.check_tensor_type_same({"input_x": input_x}, mstype.number_type, self.prim_name()) return input_x @@ -807,8 +798,7 @@ class Square(PrimitiveWithInfer): return x_shape def infer_dtype(self, x_type): - validator.check_subclass("x", x_type, mstype.tensor) - validator.check_typename("x_dtype", x_type, mstype.number_type) + validator.check_tensor_type_same({"x": x_type}, mstype.number_type, self.prim_name()) return x_type @@ -837,8 +827,7 @@ class Rsqrt(PrimitiveWithInfer): return x_shape def infer_dtype(self, x_type): - validator.check_subclass("x", x_type, mstype.tensor) - validator.check_typename("x_dtype", x_type, mstype.number_type) + validator.check_tensor_type_same({"x": x_type}, mstype.number_type, self.prim_name()) return x_type @@ -867,8 +856,7 @@ class Sqrt(PrimitiveWithInfer): return x_shape def infer_dtype(self, x_type): - validator.check_subclass("x", x_type, mstype.tensor) - validator.check_typename("x_dtype", x_type, mstype.number_type) + validator.check_tensor_type_same({"x": x_type}, mstype.number_type, self.prim_name()) return x_type @@ -898,7 +886,7 @@ class Reciprocal(PrimitiveWithInfer): return x def infer_dtype(self, x): - validator.check_subclass("x", x, mstype.tensor) + validator.check_subclass("x", x, mstype.tensor, self.prim_name()) return x @@ -936,8 +924,7 @@ class Pow(PrimitiveWithInfer): return x def infer_dtype(self, x, power): - validator.check_subclass("x", x, mstype.tensor) - validator.check_typename("power", power, mstype.number_type) + validator.check_tensor_type_same({"x": x}, mstype.number_type, self.prim_name()) return x @@ -967,7 +954,7 @@ class Exp(PrimitiveWithInfer): return x_shape def infer_dtype(self, x_type): - validator.check_subclass("x", x_type, mstype.tensor) + validator.check_subclass("x", x_type, mstype.tensor, self.prim_name()) return x_type @@ -996,7 +983,7 @@ class Log(PrimitiveWithInfer): return x def infer_dtype(self, x): - validator.check_subclass("x", x, mstype.tensor) + validator.check_subclass("x", x, mstype.tensor, self.prim_name()) return x @@ -1178,8 +1165,7 @@ class Floor(PrimitiveWithInfer): return x_shape def infer_dtype(self, x_dtype): - validator.check_subclass("x", x_dtype, mstype.tensor) - validator.check_typename("x_dtype", x_dtype, mstype.float_type) + validator.check_tensor_type_same({"x": x_dtype}, mstype.float_type, self.prim_name()) return x_dtype @@ -1234,8 +1220,7 @@ class Acosh(PrimitiveWithInfer): return x def infer_dtype(self, x): - validator.check_subclass("x_dtype", x, mstype.tensor) - validator.check_typename('x_dtype', x, mstype.number_type) + validator.check_tensor_type_same({'x': x}, mstype.number_type, self.prim_name()) return x @@ -1245,15 +1230,13 @@ class _LogicBinaryOp(_BinaryOp): """ @staticmethod - def do_infer_dtype(x_dtype, y_dtype, valid_type=mstype.number_type): - args_type = {"x": x_dtype, "y": y_dtype} - validator.check_args_tensor(args_type) - args_dtype = {"x_dtype": x_dtype, "y_dtype": y_dtype} - validator.check_type_same(args_dtype, valid_type) + def do_infer_dtype(x_dtype, y_dtype, valid_type=mstype.number_type, prim_name=None): + args_dtype = {"x": x_dtype, "y": y_dtype} + validator.check_tensor_type_same(args_dtype, valid_type, prim_name) return mstype.tensor_type(mstype.bool_) def infer_dtype(self, x_dtype, y_dtype): - return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype) + return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, prim_name=self.prim_name()) class Equal(_LogicBinaryOp): @@ -1289,7 +1272,7 @@ class Equal(_LogicBinaryOp): """ def infer_dtype(self, x_dtype, y_dtype): - return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type + (mstype.bool_,)) + return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type + (mstype.bool_,), self.prim_name()) class EqualCount(PrimitiveWithInfer): @@ -1318,11 +1301,13 @@ class EqualCount(PrimitiveWithInfer): """init EqualCount""" self.init_prim_io_names(inputs=['x', 'y'], outputs=['output']) - def infer_shape(self, x_shape, w_shape): + def infer_shape(self, x_shape, y_shape): output_shape = (1,) return output_shape - def infer_dtype(self, x_dtype, w_dtype): + def infer_dtype(self, x_dtype, y_dtype): + args = {'x': x_dtype, 'y': y_dtype} + validator.check_tensor_type_same(args, mstype.number_type + (mstype.bool_,), self.prim_name()) return x_dtype @@ -1359,7 +1344,7 @@ class NotEqual(_LogicBinaryOp): """ def infer_dtype(self, x_dtype, y_dtype): - return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type + (mstype.bool_,)) + return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type + (mstype.bool_,), self.prim_name()) class Greater(_LogicBinaryOp): @@ -1495,8 +1480,7 @@ class LogicalNot(PrimitiveWithInfer): return x_shape def infer_dtype(self, x_dtype): - validator.check_subclass("x", x_dtype, mstype.tensor) - validator.check_typename("x_dtype", x_dtype, [mstype.bool_]) + validator.check_tensor_type_same({"x": x_dtype}, [mstype.bool_], self.prim_name()) return mstype.tensor_type(mstype.bool_) @@ -1526,7 +1510,7 @@ class LogicalAnd(_LogicBinaryOp): """ def infer_dtype(self, x_dtype, y_dtype): - return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,)) + return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.prim_name()) class LogicalOr(_LogicBinaryOp): @@ -1555,7 +1539,7 @@ class LogicalOr(_LogicBinaryOp): """ def infer_dtype(self, x_dtype, y_dtype): - return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,)) + return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.prim_name()) class NPUAllocFloatStatus(PrimitiveWithInfer): @@ -1616,13 +1600,13 @@ class NPUGetFloatStatus(PrimitiveWithInfer): self.add_prim_attr("_side_effect_flag", True) def infer_shape(self, x_shape): - validator.check_integer("len(x_shape)", len(x_shape), 1, Rel.EQ) - validator.check_integer("x_shape[0]", x_shape[0], 8, Rel.EQ) + cls_name = self.prim_name() + validator.check_integer("len(x_shape)", len(x_shape), 1, Rel.EQ, cls_name) + validator.check_integer("x_shape[0]", x_shape[0], 8, Rel.EQ, cls_name) return [8] def infer_dtype(self, x_dtype): - args = {"x_dtype": x_dtype} - validator.check_type_same(args, [mstype.float32]) + validator.check_tensor_type_same({'x': x_dtype}, [mstype.float32], self.prim_name()) return mstype.float32 @@ -1658,13 +1642,13 @@ class NPUClearFloatStatus(PrimitiveWithInfer): self.add_prim_attr("_side_effect_flag", True) def infer_shape(self, x_shape): - validator.check_integer("len(x_shape)", len(x_shape), 1, Rel.EQ) - validator.check_integer("x_shape[0]", x_shape[0], 8, Rel.EQ) + cls_name = self.prim_name() + validator.check_integer("len(x_shape)", len(x_shape), 1, Rel.EQ, cls_name) + validator.check_integer("x_shape[0]", x_shape[0], 8, Rel.EQ, cls_name) return [8] def infer_dtype(self, x_dtype): - args = {"x_dtype": x_dtype} - validator.check_type_same(args, [mstype.float32]) + validator.check_tensor_type_same({'x': x_dtype}, [mstype.float32], self.prim_name()) return mstype.float32 @@ -1692,8 +1676,7 @@ class Cos(PrimitiveWithInfer): return x def infer_dtype(self, x): - validator.check_subclass("x_dtype", x, mstype.tensor) - validator.check_typename('x_dtype', x, mstype.number_type) + validator.check_tensor_type_same({'x': x}, mstype.number_type, self.prim_name()) return x @@ -1721,8 +1704,7 @@ class ACos(PrimitiveWithInfer): return x def infer_dtype(self, x): - validator.check_subclass("x_dtype", x, mstype.tensor) - validator.check_typename('x_dtype', x, mstype.number_type) + validator.check_tensor_type_same({'x': x}, mstype.number_type, self.prim_name()) return x @@ -1750,8 +1732,7 @@ class Sin(PrimitiveWithInfer): return x def infer_dtype(self, x): - validator.check_subclass("x_dtype", x, mstype.tensor) - validator.check_typename('x_dtype', x, mstype.number_type) + validator.check_tensor_type_same({'x': x}, mstype.number_type, self.prim_name()) return x @@ -1796,19 +1777,19 @@ class NMSWithMask(PrimitiveWithInfer): @prim_attr_register def __init__(self, iou_threshold=0.5): """Init NMSWithMask""" - validator.check_type("iou_threshold", iou_threshold, [float]) + validator.check_value_type("iou_threshold", iou_threshold, [float], self.prim_name()) self.init_prim_io_names(inputs=['bboxes'], outputs=['selected_boxes', 'selected_idx', 'selected_mask']) def infer_shape(self, bboxes_shape): - validator.check_integer("bboxes rank", len(bboxes_shape), 2, Rel.EQ) - validator.check_integer("bboxes.shape()[0]", bboxes_shape[0], 0, Rel.GT) - validator.check_integer("bboxes.shape()[1]", bboxes_shape[1], 5, Rel.EQ) + cls_name = self.prim_name() + validator.check_integer("bboxes rank", len(bboxes_shape), 2, Rel.EQ, cls_name) + validator.check_integer("bboxes.shape()[0]", bboxes_shape[0], 0, Rel.GT, cls_name) + validator.check_integer("bboxes.shape()[1]", bboxes_shape[1], 5, Rel.EQ, cls_name) num = bboxes_shape[0] return (bboxes_shape, (num,), (num,)) def infer_dtype(self, bboxes_dtype): - validator.check_subclass("bboxes_dtype", bboxes_dtype, mstype.tensor) - validator.check_typename("bboxes_dtype", bboxes_dtype, [mstype.float16, mstype.float32]) + validator.check_tensor_type_same({"bboxes": bboxes_dtype}, [mstype.float16, mstype.float32], self.prim_name()) return (bboxes_dtype, mstype.int32, mstype.bool_) @@ -1837,8 +1818,7 @@ class Abs(PrimitiveWithInfer): return x_shape def infer_dtype(self, x_type): - validator.check_subclass("x_dtype", x_type, mstype.tensor) - validator.check_typename('x_dtype', x_type, mstype.number_type) + validator.check_tensor_type_same({'x': x_type}, mstype.number_type, self.prim_name()) return x_type def infer_value(self, x): @@ -1880,8 +1860,7 @@ class Sign(PrimitiveWithInfer): return x_shape def infer_dtype(self, x_dtype): - validator.check_subclass('x', x_dtype, mstype.tensor) - validator.check_typename('x_dtype', x_dtype, mstype.number_type) + validator.check_tensor_type_same({'x': x_dtype}, mstype.number_type, self.prim_name()) return x_dtype @@ -1910,8 +1889,7 @@ class Round(PrimitiveWithInfer): return x_shape def infer_dtype(self, x_type): - validator.check_subclass("x_dtype", x_type, mstype.tensor) - validator.check_typename('x_dtype', x_type, mstype.number_type) + validator.check_tensor_type_same({'x': x_type}, mstype.number_type, self.prim_name()) return x_type diff --git a/mindspore/ops/primitive.py b/mindspore/ops/primitive.py index d281b4f76c..96e754f5f7 100644 --- a/mindspore/ops/primitive.py +++ b/mindspore/ops/primitive.py @@ -194,6 +194,9 @@ class PrimitiveWithInfer(Primitive): Primitive.__init__(self, name) self.set_prim_type(prim_type.py_infer_shape) + def prim_name(self): + return self.__class__.__name__ + def _clone(self): """ Deeply clones the primitive object. diff --git a/tests/mindspore_test_framework/components/executor/check_exceptions.py b/tests/mindspore_test_framework/components/executor/check_exceptions.py index eca0578b7f..fe57a3d287 100644 --- a/tests/mindspore_test_framework/components/executor/check_exceptions.py +++ b/tests/mindspore_test_framework/components/executor/check_exceptions.py @@ -23,20 +23,25 @@ from ...utils import keyword class CheckExceptionsEC(IExectorComponent): """ - Check if the function raises the expected Exception. + Check if the function raises the expected Exception and the error message contains specified keywords if not None. Examples: { 'block': f, - 'exception': Exception + 'exception': Exception, + 'error_keywords': ['TensorAdd', 'shape'] } """ def run_function(self, function, inputs, verification_set): f = function[keyword.block] args = inputs[keyword.desc_inputs] e = function.get(keyword.exception, Exception) + error_kws = function.get(keyword.error_keywords, None) try: - with pytest.raises(e): + with pytest.raises(e) as exec_info: f(*args) except: raise Exception(f"Expect {e}, but got {sys.exc_info()[0]}") + if error_kws and any(keyword not in str(exec_info.value) for keyword in error_kws): + raise ValueError('Error message `{}` does not contain all keywords `{}`'.format( + str(exec_info.value), error_kws)) diff --git a/tests/mindspore_test_framework/utils/config_util.py b/tests/mindspore_test_framework/utils/config_util.py index d2eb91d042..b7be15ac23 100644 --- a/tests/mindspore_test_framework/utils/config_util.py +++ b/tests/mindspore_test_framework/utils/config_util.py @@ -87,8 +87,9 @@ def get_function_config(function): init_param_with = function.get(keyword.init_param_with, None) split_outputs = function.get(keyword.split_outputs, True) exception = function.get(keyword.exception, Exception) + error_keywords = function.get(keyword.error_keywords, None) return delta, max_error, input_selector, output_selector, sampling_times, \ - reduce_output, init_param_with, split_outputs, exception + reduce_output, init_param_with, split_outputs, exception, error_keywords def get_grad_checking_options(function, inputs): """ @@ -104,6 +105,6 @@ def get_grad_checking_options(function, inputs): """ f = function[keyword.block] args = inputs[keyword.desc_inputs] - delta, max_error, input_selector, output_selector, sampling_times, reduce_output, _, _, _ = \ + delta, max_error, input_selector, output_selector, sampling_times, reduce_output, _, _, _, _ = \ get_function_config(function) return f, args, delta, max_error, input_selector, output_selector, sampling_times, reduce_output diff --git a/tests/mindspore_test_framework/utils/facade_util.py b/tests/mindspore_test_framework/utils/facade_util.py index bbbf1dd375..4c5896b3df 100644 --- a/tests/mindspore_test_framework/utils/facade_util.py +++ b/tests/mindspore_test_framework/utils/facade_util.py @@ -54,11 +54,12 @@ def fill_block_config(ret, block_config, tid, group, desc_inputs, desc_bprop, ex block = block_config delta, max_error, input_selector, output_selector, \ - sampling_times, reduce_output, init_param_with, split_outputs, exception = get_function_config({}) + sampling_times, reduce_output, init_param_with, split_outputs, exception, error_keywords = get_function_config({}) if isinstance(block_config, tuple) and isinstance(block_config[-1], dict): block = block_config[0] delta, max_error, input_selector, output_selector, \ - sampling_times, reduce_output, init_param_with, split_outputs, exception = get_function_config(block_config[-1]) + sampling_times, reduce_output, init_param_with, \ + split_outputs, exception, error_keywords = get_function_config(block_config[-1]) if block: func_list.append({ @@ -78,7 +79,8 @@ def fill_block_config(ret, block_config, tid, group, desc_inputs, desc_bprop, ex keyword.const_first: const_first, keyword.add_fake_input: add_fake_input, keyword.split_outputs: split_outputs, - keyword.exception: exception + keyword.exception: exception, + keyword.error_keywords: error_keywords }) if desc_inputs or desc_const: diff --git a/tests/mindspore_test_framework/utils/keyword.py b/tests/mindspore_test_framework/utils/keyword.py index 16618be70a..79f1f91d8f 100644 --- a/tests/mindspore_test_framework/utils/keyword.py +++ b/tests/mindspore_test_framework/utils/keyword.py @@ -73,5 +73,6 @@ keyword.const_first = "const_first" keyword.add_fake_input = "add_fake_input" keyword.fake_input_type = "fake_input_type" keyword.exception = "exception" +keyword.error_keywords = "error_keywords" sys.modules[__name__] = keyword diff --git a/tests/ut/python/ops/test_array_ops.py b/tests/ut/python/ops/test_array_ops.py index 1c4895465f..faaa9d5402 100644 --- a/tests/ut/python/ops/test_array_ops.py +++ b/tests/ut/python/ops/test_array_ops.py @@ -234,7 +234,7 @@ raise_set = [ 'block': (lambda x: P.Squeeze(axis=((1.2, 1.3))), {'exception': ValueError}), 'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5]))]}), ('ReduceSum_Error', { - 'block': (lambda x: P.ReduceSum(keep_dims=1), {'exception': ValueError}), + 'block': (lambda x: P.ReduceSum(keep_dims=1), {'exception': TypeError}), 'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5]))]}), ] diff --git a/tests/ut/python/ops/test_math_ops_check.py b/tests/ut/python/ops/test_math_ops_check.py new file mode 100755 index 0000000000..be6e5691ea --- /dev/null +++ b/tests/ut/python/ops/test_math_ops_check.py @@ -0,0 +1,751 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" test ops """ +import functools +import numpy as np +from mindspore import ops +from mindspore.ops import functional as F +from mindspore.ops import operations as P +from mindspore.ops.operations import _grad_ops as G +import mindspore.ops.composite as C +import mindspore.nn as nn +from mindspore import Tensor +from mindspore.common import dtype as mstype +from mindspore.common.parameter import Parameter +from ..ut_filter import non_graph_engine +from mindspore.common.api import _executor + +from ....mindspore_test_framework.mindspore_test import mindspore_test +from ....mindspore_test_framework.pipeline.forward.compile_forward\ + import (pipeline_for_compile_forward_ge_graph_for_case_by_case_config, + pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception) +from ....mindspore_test_framework.pipeline.gradient.compile_gradient\ + import pipeline_for_compile_grad_ge_graph_for_case_by_case_config + + +class AssignAddNet(nn.Cell): + def __init__(self,): + super(AssignAddNet, self).__init__() + self.op = P.AssignAdd() + self.inputdata = Parameter(Tensor(np.zeros([1]).astype(np.bool_), mstype.bool_), name="assign_add1") + + def construct(self, x): + self.op(self.inputdata, x) + return self.inputdata + + +class AssignSubNet(nn.Cell): + def __init__(self,): + super(AssignSubNet, self).__init__() + self.op = P.AssignSub() + self.inputdata = Parameter(Tensor(np.zeros([1]).astype(np.bool_), mstype.bool_), name="assign_sub1") + + def construct(self, x): + self.op(self.inputdata, x) + return self.inputdata + + +class ReduceNet(nn.Cell): + def __init__(self, op_class, keep_dims, axis): + super(ReduceNet, self).__init__() + self.axis = axis + self.op = op_class(keep_dims=keep_dims) + + def construct(self, x): + return self.op(x, self.axis) + + +class CumProdNet(nn.Cell): + def __init__(self): + super(CumProdNet, self).__init__() + self.op = P.CumProd() + + def construct(self, x, axis): + return self.op(x, axis) + + +class CumSumNet(nn.Cell): + def __init__(self, axis): + super(CumSumNet, self).__init__() + self.axis = axis + self.op = P.CumSum() + + def construct(self, x): + return self.op(x, self.axis) + + +raise_set = [ + # one input is scalar, and another is Tensor(float32) + ('TensorAdd0', { + 'block': (P.TensorAdd(), {'exception': TypeError, 'error_keywords': ['TensorAdd']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, but element types are not same + ('TensorAdd1', { + 'block': (P.TensorAdd(), {'exception': TypeError, 'error_keywords': ['TensorAdd']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, their shapes do not match + ('TensorAdd2', { + 'block': (P.TensorAdd(), {'exception': ValueError, 'error_keywords': ['TensorAdd']}), + 'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + + # check input Tensor(bool_) + ('AssignAdd', { + 'block': (AssignAddNet(), {'exception': TypeError, 'error_keywords': ['AssignAdd']}), + 'desc_inputs': [Tensor(np.ones([1]).astype(np.bool_), mstype.bool_)], + 'skip': ['backward']}), + + # check input Tensor(bool_) + ('AssignSub', { + 'block': (AssignSubNet(), {'exception': TypeError, 'error_keywords': ['AssignSub']}), + 'desc_inputs': [Tensor(np.ones([1]).astype(np.bool_), mstype.bool_)], + 'skip': ['backward']}), + + # type of axis is float, not int + ('ReduceMean1', { + 'block': (ReduceNet(P.ReduceMean, keep_dims=True, axis=5.0), + {'exception': TypeError, 'error_keywords': ['ReduceMean']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))], + 'skip': ['backward']}), + # axis is out of range + ('ReduceMean2', { + 'block': (ReduceNet(P.ReduceMean, keep_dims=True, axis=5), + {'exception': ValueError, 'error_keywords': ['ReduceMean']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))], + 'skip': ['backward']}), + + # type of axis is float, not int + ('ReduceSum1', { + 'block': (ReduceNet(P.ReduceSum, keep_dims=True, axis=5.0), + {'exception': TypeError, 'error_keywords': ['ReduceSum']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))], + 'skip': ['backward']}), + # axis is out of range + ('ReduceSum2', { + 'block': (ReduceNet(P.ReduceSum, keep_dims=True, axis=5), + {'exception': ValueError, 'error_keywords': ['ReduceSum']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))], + 'skip': ['backward']}), + + # type of axis is float, not int + ('ReduceAll1', { + 'block': (ReduceNet(P.ReduceAll, keep_dims=True, axis=5.0), + {'exception': TypeError, 'error_keywords': ['ReduceAll']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool_))], + 'skip': ['backward']}), + # axis is out of range + ('ReduceAll2', { + 'block': (ReduceNet(P.ReduceAll, keep_dims=True, axis=5), + {'exception': ValueError, 'error_keywords': ['ReduceAll']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool_))], + 'skip': ['backward']}), + + # type of axis is float, not int + ('ReduceMax1', { + 'block': (ReduceNet(P.ReduceMax, keep_dims=True, axis=5.0), + {'exception': TypeError, 'error_keywords': ['ReduceMax']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))], + 'skip': ['backward']}), + # axis is out of range + ('ReduceMax2', { + 'block': (ReduceNet(P.ReduceMax, keep_dims=True, axis=5), + {'exception': ValueError, 'error_keywords': ['ReduceMax']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))], + 'skip': ['backward']}), + + # type of axis is float, not int + ('ReduceMin1', { + 'block': (ReduceNet(P.ReduceMin, keep_dims=True, axis=5.0), + {'exception': TypeError, 'error_keywords': ['ReduceMin']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))], + 'skip': ['backward']}), + # axis is out of range + ('ReduceMin2', { + 'block': (ReduceNet(P.ReduceMin, keep_dims=True, axis=5), + {'exception': ValueError, 'error_keywords': ['ReduceMin']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))], + 'skip': ['backward']}), + + # type of axis is float, not int + ('ReduceProd1', { + 'block': (ReduceNet(P.ReduceProd, keep_dims=True, axis=5.0), + {'exception': TypeError, 'error_keywords': ['ReduceProd']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))], + 'skip': ['backward']}), + # axis is out of range + ('ReduceProd2', { + 'block': (ReduceNet(P.ReduceProd, keep_dims=True, axis=5), + {'exception': ValueError, 'error_keywords': ['ReduceProd']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))], + 'skip': ['backward']}), + + # type of x is Tensor(bool) + ('CumProd1', { + 'block': (CumProdNet(), + {'exception': TypeError, 'error_keywords': ['CumProd']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool)), 1], + 'skip': ['backward']}), + # type of axis in float, not int + ('CumProd2', { + 'block': (CumProdNet(), + {'exception': TypeError, 'error_keywords': ['CumProd']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32)), 5.0], + 'skip': ['backward']}), + + # type of x and y are Tensor(uint32) + ('MatMul1', { + 'block': (P.MatMul(), + {'exception': TypeError, 'error_keywords': ['MatMul']}), + 'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.uint32)), Tensor(np.ones([3, 2]).astype(np.uint32))], + 'skip': ['backward']}), + # type of x and y not match + ('MatMul2', { + 'block': (P.MatMul(), + {'exception': TypeError, 'error_keywords': ['MatMul']}), + 'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.int32))], + 'skip': ['backward']}), + # shape of x and y not match + ('MatMul3', { + 'block': (P.MatMul(), + {'exception': ValueError, 'error_keywords': ['MatMul']}), + 'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([2, 3]).astype(np.float32))], + 'skip': ['backward']}), + + # dims of x and y are less than 3 + ('BatchMatMul1', { + 'block': (P.BatchMatMul(), + {'exception': ValueError, 'error_keywords': ['BatchMatMul']}), + 'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.int32)), Tensor(np.ones([3, 2]).astype(np.int32))], + 'skip': ['backward']}), + + # type of x is Tensor(bool) + ('CumSum1', { + 'block': (CumSumNet(axis=1), + {'exception': TypeError, 'error_keywords': ['CumSum']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool))], + 'skip': ['backward']}), + # type of axis in float, not int + ('CumSum2', { + 'block': (CumSumNet(axis=1.0), + {'exception': TypeError, 'error_keywords': ['CumSum']}), + 'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool))], + 'skip': ['backward']}), + + # intput is not tuple or list + ('AddN1', { + 'block': (P.AddN(), + {'exception': TypeError, 'error_keywords': ['AddN']}), + 'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.uint32))], + 'skip': ['backward']}), + # type not match + ('AddN2', { + 'block': (P.AddN(), + {'exception': TypeError, 'error_keywords': ['AddN']}), + 'desc_inputs': [(Tensor(np.ones([2, 3]).astype(np.uint32)), Tensor(np.ones([3, 2]).astype(np.int32)))], + 'skip': ['backward']}), + # shape not match + ('AddN3', { + 'block': (P.AddN(), + {'exception': ValueError, 'error_keywords': ['AddN']}), + 'desc_inputs': [(Tensor(np.ones([2, 3]).astype(np.int32)), Tensor(np.ones([3, 2]).astype(np.int32)))], + 'skip': ['backward']}), + + # input is Tensor(bool) + ('Neg1', { + 'block': (P.Neg(), + {'exception': TypeError, 'error_keywords': ['Neg']}), + 'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))], + 'skip': ['backward']}), + + # one input is scalar, and another is Tensor(float32) + ('Sub0', { + 'block': (P.Sub(), {'exception': TypeError, 'error_keywords': ['Sub']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, but element types are not same + ('Sub1', { + 'block': (P.Sub(), {'exception': TypeError, 'error_keywords': ['Sub']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, their shapes do not match + ('Sub2', { + 'block': (P.Sub(), {'exception': ValueError, 'error_keywords': ['Sub']}), + 'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + + # one input is scalar, and another is Tensor(float32) + ('Mul0', { + 'block': (P.Mul(), {'exception': TypeError, 'error_keywords': ['Mul']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, but element types are not same + ('Mul1', { + 'block': (P.Mul(), {'exception': TypeError, 'error_keywords': ['Mul']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, their shapes do not match + ('Mul2', { + 'block': (P.Mul(), {'exception': ValueError, 'error_keywords': ['Mul']}), + 'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + + # input is Tensor(bool) + ('Square1', { + 'block': (P.Square(), + {'exception': TypeError, 'error_keywords': ['Square']}), + 'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))], + 'skip': ['backward']}), + + # input is Tensor(bool) + ('Rsqrt1', { + 'block': (P.Rsqrt(), + {'exception': TypeError, 'error_keywords': ['Rsqrt']}), + 'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))], + 'skip': ['backward']}), + + # input is Tensor(bool) + ('Sqrt1', { + 'block': (P.Sqrt(), + {'exception': TypeError, 'error_keywords': ['Sqrt']}), + 'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))], + 'skip': ['backward']}), + + # input is not Tensor + ('Reciprocal1', { + 'block': (P.Reciprocal(), + {'exception': TypeError, 'error_keywords': ['Reciprocal']}), + 'desc_inputs': [5.0], + 'skip': ['backward']}), + + # input x is Tensor(bool) + ('Pow1', { + 'block': (P.Pow(), + {'exception': TypeError, 'error_keywords': ['Pow']}), + 'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_)), 2.0], + 'skip': ['backward']}), + + # input is not Tensor + ('Exp1', { + 'block': (P.Exp(), + {'exception': TypeError, 'error_keywords': ['Exp']}), + 'desc_inputs': [5.0], + 'skip': ['backward']}), + + # input is not Tensor + ('Log1', { + 'block': (P.Log(), + {'exception': TypeError, 'error_keywords': ['Log']}), + 'desc_inputs': [5.0], + 'skip': ['backward']}), + + # one input is scalar, and another is Tensor(float32) + ('Minimum0', { + 'block': (P.Minimum(), {'exception': TypeError, 'error_keywords': ['Minimum']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, but element types are not same + ('Minimum1', { + 'block': (P.Minimum(), {'exception': TypeError, 'error_keywords': ['Minimum']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, their shapes do not match + ('Minimum2', { + 'block': (P.Minimum(), {'exception': ValueError, 'error_keywords': ['Minimum']}), + 'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + + # one input is scalar, and another is Tensor(float32) + ('Maximum0', { + 'block': (P.Maximum(), {'exception': TypeError, 'error_keywords': ['Maximum']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, but element types are not same + ('Maximum1', { + 'block': (P.Maximum(), {'exception': TypeError, 'error_keywords': ['Maximum']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, their shapes do not match + ('Maximum2', { + 'block': (P.Maximum(), {'exception': ValueError, 'error_keywords': ['Maximum']}), + 'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + + # one input is scalar, and another is Tensor(float32) + ('RealDiv0', { + 'block': (P.RealDiv(), {'exception': TypeError, 'error_keywords': ['RealDiv']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, but element types are not same + ('RealDiv1', { + 'block': (P.RealDiv(), {'exception': TypeError, 'error_keywords': ['RealDiv']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, their shapes do not match + ('RealDiv2', { + 'block': (P.RealDiv(), {'exception': ValueError, 'error_keywords': ['RealDiv']}), + 'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + + # one input is scalar, and another is Tensor(float32) + ('Div0', { + 'block': (P.Div(), {'exception': TypeError, 'error_keywords': ['Div']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, but element types are not same + ('Div1', { + 'block': (P.Div(), {'exception': TypeError, 'error_keywords': ['Div']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, their shapes do not match + ('Div2', { + 'block': (P.Div(), {'exception': ValueError, 'error_keywords': ['Div']}), + 'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + + # one input is scalar, and another is Tensor(float32) + ('FloorDiv0', { + 'block': (P.FloorDiv(), {'exception': TypeError, 'error_keywords': ['FloorDiv']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, but element types are not same + ('FloorDiv1', { + 'block': (P.FloorDiv(), {'exception': TypeError, 'error_keywords': ['FloorDiv']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, their shapes do not match + ('FloorDiv2', { + 'block': (P.FloorDiv(), {'exception': ValueError, 'error_keywords': ['FloorDiv']}), + 'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + + # input x is Tensor(int32), not Tensor(float) + ('Floor1', { + 'block': (P.Floor(), + {'exception': TypeError, 'error_keywords': ['Floor']}), + 'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.int32))], + 'skip': ['backward']}), + + # one input is scalar, and another is Tensor(float32) + ('FloorMod0', { + 'block': (P.FloorMod(), {'exception': TypeError, 'error_keywords': ['FloorMod']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, but element types are not same + ('FloorMod1', { + 'block': (P.FloorMod(), {'exception': TypeError, 'error_keywords': ['FloorMod']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, their shapes do not match + ('FFloorMod2', { + 'block': (P.FloorMod(), {'exception': ValueError, 'error_keywords': ['FloorMod']}), + 'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + + # input x is Tensor(int32), not Tensor(float) + ('Acosh1', { + 'block': (P.Acosh(), + {'exception': TypeError, 'error_keywords': ['Acosh']}), + 'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))], + 'skip': ['backward']}), + + # input is not tensor + ('Equal0', { + 'block': (P.Equal(), {'exception': TypeError, 'error_keywords': ['Equal']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # type of x and y not match + ('Equal1', { + 'block': (P.Equal(), {'exception': TypeError, 'error_keywords': ['Equal']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # shape of x and y not match + ('Equal2', { + 'block': (P.Equal(), {'exception': ValueError, 'error_keywords': ['Equal']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))], + 'skip': ['backward']}), + + # input is not tensor + ('EqualCount0', { + 'block': (P.EqualCount(), {'exception': TypeError, 'error_keywords': ['EqualCount']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # type of x and y not match + ('EqualCount1', { + 'block': (P.EqualCount(), {'exception': TypeError, 'error_keywords': ['EqualCount']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # shape of x and y not match + + # input is not tensor + ('NotEqual0', { + 'block': (P.NotEqual(), {'exception': TypeError, 'error_keywords': ['NotEqual']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # type of x and y not match + ('NotEqual1', { + 'block': (P.NotEqual(), {'exception': TypeError, 'error_keywords': ['NotEqual']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # shape of x and y not match + ('NotEqual2', { + 'block': (P.NotEqual(), {'exception': ValueError, 'error_keywords': ['NotEqual']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))], + 'skip': ['backward']}), + + # input is not tensor + ('Greater0', { + 'block': (P.Greater(), {'exception': TypeError, 'error_keywords': ['Greater']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # type of x and y not match + ('Greater1', { + 'block': (P.Greater(), {'exception': TypeError, 'error_keywords': ['Greater']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # shape of x and y not match + ('Greater2', { + 'block': (P.Greater(), {'exception': ValueError, 'error_keywords': ['Greater']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))], + 'skip': ['backward']}), + + # input is not tensor + ('GreaterEqual0', { + 'block': (P.GreaterEqual(), {'exception': TypeError, 'error_keywords': ['GreaterEqual']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # type of x and y not match + ('GreaterEqual1', { + 'block': (P.GreaterEqual(), {'exception': TypeError, 'error_keywords': ['GreaterEqual']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # shape of x and y not match + ('GreaterEqual2', { + 'block': (P.GreaterEqual(), {'exception': ValueError, 'error_keywords': ['GreaterEqual']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))], + 'skip': ['backward']}), + + # input is not tensor + ('Less0', { + 'block': (P.Less(), {'exception': TypeError, 'error_keywords': ['Less']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # type of x and y not match + ('Less1', { + 'block': (P.Less(), {'exception': TypeError, 'error_keywords': ['Less']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # shape of x and y not match + ('Less2', { + 'block': (P.Less(), {'exception': ValueError, 'error_keywords': ['Less']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))], + 'skip': ['backward']}), + + # input is not tensor + ('LessEqual0', { + 'block': (P.LessEqual(), {'exception': TypeError, 'error_keywords': ['LessEqual']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # type of x and y not match + ('LessEqual1', { + 'block': (P.LessEqual(), {'exception': TypeError, 'error_keywords': ['LessEqual']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # shape of x and y not match + ('LessEqual2', { + 'block': (P.LessEqual(), {'exception': ValueError, 'error_keywords': ['LessEqual']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))], + 'skip': ['backward']}), + + # input x is not Tensor(bool) + ('LogicalNot1', { + 'block': (P.LogicalNot(), + {'exception': TypeError, 'error_keywords': ['LogicalNot']}), + 'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.int32))], + 'skip': ['backward']}), + + # type of x and y not match + ('LogicalAnd1', { + 'block': (P.LogicalAnd(), {'exception': TypeError, 'error_keywords': ['LogicalAnd']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.bool_))], + 'skip': ['backward']}), + # shape of x and y not match + ('LogicalAnd2', { + 'block': (P.LogicalAnd(), {'exception': ValueError, 'error_keywords': ['LogicalAnd']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_)), Tensor(np.ones([3, 2]).astype(np.bool_))], + 'skip': ['backward']}), + + # type of x and y not match + ('LogicalOr1', { + 'block': (P.LogicalOr(), {'exception': TypeError, 'error_keywords': ['LogicalOr']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.bool_))], + 'skip': ['backward']}), + # shape of x and y not match + ('LogicalOr2', { + 'block': (P.LogicalOr(), {'exception': ValueError, 'error_keywords': ['LogicalOr']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_)), Tensor(np.ones([3, 2]).astype(np.bool_))], + 'skip': ['backward']}), + + # input is not tensor + ('NPUGetFloatStatus0', { + 'block': (P.NPUGetFloatStatus(), {'exception': TypeError, 'error_keywords': ['NPUGetFloatStatus']}), + 'desc_inputs': [5.0], + 'skip': ['backward']}), + # input is Tensor(int32), not Tensor(float32) + ('NPUGetFloatStatus1', { + 'block': (P.NPUGetFloatStatus(), {'exception': TypeError, 'error_keywords': ['NPUGetFloatStatus']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32))], + 'skip': ['backward']}), + # dims is not 1 + ('NPUGetFloatStatus2', { + 'block': (P.NPUGetFloatStatus(), {'exception': ValueError, 'error_keywords': ['NPUGetFloatStatus']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # shape[0] is not 8 + ('NPUGetFloatStatus3', { + 'block': (P.NPUGetFloatStatus(), {'exception': ValueError, 'error_keywords': ['NPUGetFloatStatus']}), + 'desc_inputs': [Tensor(np.ones([3]).astype(np.float32))], + 'skip': ['backward']}), + + # input is not tensor + ('NPUClearFloatStatus0', { + 'block': (P.NPUClearFloatStatus(), {'exception': TypeError, 'error_keywords': ['NPUClearFloatStatus']}), + 'desc_inputs': [5.0], + 'skip': ['backward']}), + # input is Tensor(int32), not Tensor(float32) + ('NPUClearFloatStatus1', { + 'block': (P.NPUClearFloatStatus(), {'exception': TypeError, 'error_keywords': ['NPUClearFloatStatus']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32))], + 'skip': ['backward']}), + # dims is not 1 + ('NPUClearFloatStatus2', { + 'block': (P.NPUClearFloatStatus(), {'exception': ValueError, 'error_keywords': ['NPUClearFloatStatus']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # shape[0] is not 8 + ('NPUClearFloatStatus3', { + 'block': (P.NPUClearFloatStatus(), {'exception': ValueError, 'error_keywords': ['NPUClearFloatStatus']}), + 'desc_inputs': [Tensor(np.ones([3]).astype(np.float32))], + 'skip': ['backward']}), + + # input is not tensor + ('Cos0', { + 'block': (P.Cos(), {'exception': TypeError, 'error_keywords': ['Cos']}), + 'desc_inputs': [5.0], + 'skip': ['backward']}), + # input is Tensor(bool) + ('Cos1', { + 'block': (P.Cos(), {'exception': TypeError, 'error_keywords': ['Cos']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))], + 'skip': ['backward']}), + + # input is not tensor + ('ACos0', { + 'block': (P.ACos(), {'exception': TypeError, 'error_keywords': ['ACos']}), + 'desc_inputs': [5.0], + 'skip': ['backward']}), + # input is Tensor(bool) + ('ACos1', { + 'block': (P.ACos(), {'exception': TypeError, 'error_keywords': ['ACos']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))], + 'skip': ['backward']}), + + # input is not tensor + ('Sin0', { + 'block': (P.Sin(), {'exception': TypeError, 'error_keywords': ['Sin']}), + 'desc_inputs': [5.0], + 'skip': ['backward']}), + # input is Tensor(bool) + ('Sin1', { + 'block': (P.Sin(), {'exception': TypeError, 'error_keywords': ['Sin']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))], + 'skip': ['backward']}), + + # input is not tensor + ('NMSWithMask0', { + 'block': (P.NMSWithMask(), {'exception': TypeError, 'error_keywords': ['NMSWithMask']}), + 'desc_inputs': [5.0], + 'skip': ['backward']}), + # input is not Tensor(float16) or Tensor(float32) + ('NMSWithMask1', { + 'block': (P.NMSWithMask(), {'exception': TypeError, 'error_keywords': ['NMSWithMask']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32))], + 'skip': ['backward']}), + # dims is not 2 + ('NMSWithMask2', { + 'block': (P.NMSWithMask(), {'exception': ValueError, 'error_keywords': ['NMSWithMask']}), + 'desc_inputs': [Tensor(np.ones([3, 4, 2]).astype(np.float32))], + 'skip': ['backward']}), + # shape[1] is not 5 + ('NMSWithMask3', { + 'block': (P.NMSWithMask(), {'exception': ValueError, 'error_keywords': ['NMSWithMask']}), + 'desc_inputs': [Tensor(np.ones([3, 2]).astype(np.float32))], + 'skip': ['backward']}), + + # input is not tensor + ('Abs0', { + 'block': (P.Abs(), {'exception': TypeError, 'error_keywords': ['Abs']}), + 'desc_inputs': [5.0], + 'skip': ['backward']}), + # input is Tensor(bool) + ('Abs1', { + 'block': (P.Abs(), {'exception': TypeError, 'error_keywords': ['Abs']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))], + 'skip': ['backward']}), + + # input is not tensor + ('Sign0', { + 'block': (P.Sign(), {'exception': TypeError, 'error_keywords': ['Sign']}), + 'desc_inputs': [5.0], + 'skip': ['backward']}), + # input is Tensor(bool) + ('Sign1', { + 'block': (P.Sign(), {'exception': TypeError, 'error_keywords': ['Sign']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))], + 'skip': ['backward']}), + + # input is not tensor + ('Round0', { + 'block': (P.Round(), {'exception': TypeError, 'error_keywords': ['Round']}), + 'desc_inputs': [5.0], + 'skip': ['backward']}), + # input is Tensor(bool) + ('Round1', { + 'block': (P.Round(), {'exception': TypeError, 'error_keywords': ['Round']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))], + 'skip': ['backward']}), + + # one input is scalar, and another is Tensor(float32) + ('Atan20', { + 'block': (P.Atan2(), {'exception': TypeError, 'error_keywords': ['Atan2']}), + 'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, but element types are not same + ('Atan21', { + 'block': (P.Atan2(), {'exception': TypeError, 'error_keywords': ['Atan2']}), + 'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), + # input two tensors, their shapes do not match + ('Atan22', { + 'block': (P.Atan2(), {'exception': ValueError, 'error_keywords': ['Atan2']}), + 'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))], + 'skip': ['backward']}), +] + + +@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception) +def test_check_exception(): + return raise_set From 36899e0b1d0abd224adccc9fbc9e2f26e64fe167 Mon Sep 17 00:00:00 2001 From: chenhaozhe Date: Sat, 4 Apr 2020 15:52:58 +0800 Subject: [PATCH 103/227] change log level in pipeline_ge --- mindspore/ccsrc/pipeline/pipeline_ge.cc | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.cc b/mindspore/ccsrc/pipeline/pipeline_ge.cc index abf55bb2d8..2f68935591 100644 --- a/mindspore/ccsrc/pipeline/pipeline_ge.cc +++ b/mindspore/ccsrc/pipeline/pipeline_ge.cc @@ -227,10 +227,12 @@ bool AddDFGraph(const std::map& info, const py::di (void)DfGraphManager::GetInstance().AddGraph(phase, convertor.GetComputeGraph()); } (void)DfGraphManager::GetInstance().AddGraph(init_graph, convertor.GetInitGraph()); - (void)DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph()); (void)DfGraphManager::GetInstance().AddGraph(BROADCAST_GRAPH_NAME, convertor.GetBroadcastGraph()); - DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph); + Status ret = DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph()); + if (ret == Status::SUCCESS) { + DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph); + } return true; } @@ -389,8 +391,7 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve const std::string& phase) { std::vector ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW); if (ge_tensors.size() != inputs.size()) { - MS_LOG(ERROR) << "Args convert to ge tensor error"; - return nullptr; + MS_LOG(EXCEPTION) << "Convert me args to ge tensor error."; } std::vector ge_outputs; @@ -401,8 +402,7 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner(); if (graph_runner == nullptr) { - MS_LOG(ERROR) << "Can not found GraphRunner"; - return nullptr; + MS_LOG(EXCEPTION) << "Can not found GraphRunner."; } { @@ -419,7 +419,7 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve std::vector me_outputs = TransformUtil::ConvertGeTensors(ge_outputs); if (me_outputs.size() != ge_outputs.size()) { - MS_LOG(ERROR) << "Convert output Ge tensor to Me tensor failed"; + MS_LOG(WARNING) << "Convert output Ge tensor to Me tensor failed"; } py::tuple outputs(me_outputs.size()); From 04a46e36c6569d733d625447ab495cd5b367dc6c Mon Sep 17 00:00:00 2001 From: ms_yan <6576637+ms_yan@user.noreply.gitee.com> Date: Thu, 2 Apr 2020 21:56:48 +0800 Subject: [PATCH 104/227] add parameter check for Class Schema --- mindspore/dataset/engine/datasets.py | 20 +++++++---- mindspore/dataset/engine/validators.py | 49 ++++++++++++++++++++++++++ 2 files changed, 63 insertions(+), 6 deletions(-) diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py index db2b5169d2..2d5c219b71 100644 --- a/mindspore/dataset/engine/datasets.py +++ b/mindspore/dataset/engine/datasets.py @@ -38,7 +38,7 @@ from .iterators import DictIterator, TupleIterator from .validators import check, check_batch, check_shuffle, check_map, check_repeat, check_zip, check_rename, \ check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \ check_tfrecorddataset, check_vocdataset, check_celebadataset, check_minddataset, check_generatordataset, \ - check_zip_dataset + check_zip_dataset, check_add_column, check_columns from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist try: @@ -2334,13 +2334,20 @@ class Schema: self.dataset_type = '' self.num_rows = 0 else: + if not os.path.isfile(schema_file) or not os.access(schema_file, os.R_OK): + raise ValueError("The file %s does not exist or permission denied!" % schema_file) try: with open(schema_file, 'r') as load_f: json_obj = json.load(load_f) - self.from_json(json_obj) except json.decoder.JSONDecodeError: - raise RuntimeError("Schema file failed to load") + raise RuntimeError("Schema file failed to load.") + except UnicodeDecodeError: + raise RuntimeError("Schema file failed to decode.") + except Exception: + raise RuntimeError("Schema file failed to open.") + self.from_json(json_obj) + @check_add_column def add_column(self, name, de_type, shape=None): """ Add new column to the schema. @@ -2359,10 +2366,8 @@ class Schema: if isinstance(de_type, typing.Type): de_type = mstype_to_detype(de_type) new_column["type"] = str(de_type) - elif isinstance(de_type, str): - new_column["type"] = str(DataType(de_type)) else: - raise ValueError("Unknown column type") + new_column["type"] = str(DataType(de_type)) if shape is not None: new_column["shape"] = shape @@ -2399,6 +2404,7 @@ class Schema: RuntimeError: If column's name field is missing. RuntimeError: If column's type field is missing. """ + check_columns(columns, columns) self.columns = [] for col in columns: name = None @@ -2443,6 +2449,8 @@ class Schema: RuntimeError: if dataset type is missing in the object. RuntimeError: if columns are missing in the object. """ + if not isinstance(json_obj, dict) or json_obj is None: + raise ValueError("Expected non-empty dict.") for k, v in json_obj.items(): if k == "datasetType": self.dataset_type = v diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py index b4d22a4a01..1c374ae879 100644 --- a/mindspore/dataset/engine/validators.py +++ b/mindspore/dataset/engine/validators.py @@ -19,10 +19,15 @@ import inspect as ins import os from functools import wraps from multiprocessing import cpu_count +from mindspore._c_expression import typing from . import samplers from . import datasets INT32_MAX = 2147483647 +valid_detype = [ + "bool", "int8", "int16", "int32", "int64", "uint8", "uint16", + "uint32", "uint64", "float16", "float32", "float64" +] def check(method): @@ -188,6 +193,12 @@ def check(method): return wrapper +def check_valid_detype(type_): + if type_ not in valid_detype: + raise ValueError("Unknown column type") + return True + + def check_filename(path): """ check the filename in the path @@ -743,3 +754,41 @@ def check_project(method): return method(*args, **kwargs) return new_method + + +def check_shape(shape, name): + if isinstance(shape, list): + for element in shape: + if not isinstance(element, int): + raise TypeError( + "Each element in {0} should be of type int. Got {1}.".format(name, type(element))) + else: + raise TypeError("Expected int list.") + + +def check_add_column(method): + """check the input arguments of add_column.""" + @wraps(method) + def new_method(*args, **kwargs): + param_dict = make_param_dict(method, args, kwargs) + + # check name; required argument + name = param_dict.get("name") + if not isinstance(name, str) or not name: + raise TypeError("Expected non-empty string.") + + # check type; required argument + de_type = param_dict.get("de_type") + if not isinstance(de_type, str) or not de_type: + raise TypeError("Expected non-empty string.") + if not isinstance(de_type, typing.Type) and not check_valid_detype(de_type): + raise ValueError("Unknown column type.") + + # check shape + shape = param_dict.get("shape") + if shape is not None: + check_shape(shape, "shape") + + return method(*args, **kwargs) + + return new_method From 286db9b513e66d35667b1ff4611b2e24af94a8d5 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Tue, 7 Apr 2020 11:39:10 +0800 Subject: [PATCH 105/227] fix runtest.sh for python ut --- mindspore/dataset/engine/datasets.py | 20 ++++------- mindspore/dataset/engine/validators.py | 49 -------------------------- tests/ut/python/ops/test_ops_check.py | 4 +-- tests/ut/python/runtest.sh | 16 ++++++--- 4 files changed, 20 insertions(+), 69 deletions(-) diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py index 2d5c219b71..db2b5169d2 100644 --- a/mindspore/dataset/engine/datasets.py +++ b/mindspore/dataset/engine/datasets.py @@ -38,7 +38,7 @@ from .iterators import DictIterator, TupleIterator from .validators import check, check_batch, check_shuffle, check_map, check_repeat, check_zip, check_rename, \ check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \ check_tfrecorddataset, check_vocdataset, check_celebadataset, check_minddataset, check_generatordataset, \ - check_zip_dataset, check_add_column, check_columns + check_zip_dataset from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist try: @@ -2334,20 +2334,13 @@ class Schema: self.dataset_type = '' self.num_rows = 0 else: - if not os.path.isfile(schema_file) or not os.access(schema_file, os.R_OK): - raise ValueError("The file %s does not exist or permission denied!" % schema_file) try: with open(schema_file, 'r') as load_f: json_obj = json.load(load_f) + self.from_json(json_obj) except json.decoder.JSONDecodeError: - raise RuntimeError("Schema file failed to load.") - except UnicodeDecodeError: - raise RuntimeError("Schema file failed to decode.") - except Exception: - raise RuntimeError("Schema file failed to open.") - self.from_json(json_obj) + raise RuntimeError("Schema file failed to load") - @check_add_column def add_column(self, name, de_type, shape=None): """ Add new column to the schema. @@ -2366,8 +2359,10 @@ class Schema: if isinstance(de_type, typing.Type): de_type = mstype_to_detype(de_type) new_column["type"] = str(de_type) - else: + elif isinstance(de_type, str): new_column["type"] = str(DataType(de_type)) + else: + raise ValueError("Unknown column type") if shape is not None: new_column["shape"] = shape @@ -2404,7 +2399,6 @@ class Schema: RuntimeError: If column's name field is missing. RuntimeError: If column's type field is missing. """ - check_columns(columns, columns) self.columns = [] for col in columns: name = None @@ -2449,8 +2443,6 @@ class Schema: RuntimeError: if dataset type is missing in the object. RuntimeError: if columns are missing in the object. """ - if not isinstance(json_obj, dict) or json_obj is None: - raise ValueError("Expected non-empty dict.") for k, v in json_obj.items(): if k == "datasetType": self.dataset_type = v diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py index 1c374ae879..b4d22a4a01 100644 --- a/mindspore/dataset/engine/validators.py +++ b/mindspore/dataset/engine/validators.py @@ -19,15 +19,10 @@ import inspect as ins import os from functools import wraps from multiprocessing import cpu_count -from mindspore._c_expression import typing from . import samplers from . import datasets INT32_MAX = 2147483647 -valid_detype = [ - "bool", "int8", "int16", "int32", "int64", "uint8", "uint16", - "uint32", "uint64", "float16", "float32", "float64" -] def check(method): @@ -193,12 +188,6 @@ def check(method): return wrapper -def check_valid_detype(type_): - if type_ not in valid_detype: - raise ValueError("Unknown column type") - return True - - def check_filename(path): """ check the filename in the path @@ -754,41 +743,3 @@ def check_project(method): return method(*args, **kwargs) return new_method - - -def check_shape(shape, name): - if isinstance(shape, list): - for element in shape: - if not isinstance(element, int): - raise TypeError( - "Each element in {0} should be of type int. Got {1}.".format(name, type(element))) - else: - raise TypeError("Expected int list.") - - -def check_add_column(method): - """check the input arguments of add_column.""" - @wraps(method) - def new_method(*args, **kwargs): - param_dict = make_param_dict(method, args, kwargs) - - # check name; required argument - name = param_dict.get("name") - if not isinstance(name, str) or not name: - raise TypeError("Expected non-empty string.") - - # check type; required argument - de_type = param_dict.get("de_type") - if not isinstance(de_type, str) or not de_type: - raise TypeError("Expected non-empty string.") - if not isinstance(de_type, typing.Type) and not check_valid_detype(de_type): - raise ValueError("Unknown column type.") - - # check shape - shape = param_dict.get("shape") - if shape is not None: - check_shape(shape, "shape") - - return method(*args, **kwargs) - - return new_method diff --git a/tests/ut/python/ops/test_ops_check.py b/tests/ut/python/ops/test_ops_check.py index 5083878dae..a7e1b41c4a 100644 --- a/tests/ut/python/ops/test_ops_check.py +++ b/tests/ut/python/ops/test_ops_check.py @@ -62,7 +62,7 @@ def test_net_without_construct(): try: _executor.compile(net, inp) except RuntimeError as err: - if str(err).find("unsupported syntax 'Raise' at ") >= 0: + if str(err).find("Unsupported syntax 'Raise' at ") >= 0: print(str(err)) else: raise err @@ -86,7 +86,7 @@ def test_net_with_raise(): try: _executor.compile(net, inp) except RuntimeError as err: - if str(err).find("unsupported syntax 'Raise' at ") >= 0: + if str(err).find("Unsupported syntax 'Raise' at ") >= 0: print(str(err)) else: raise err diff --git a/tests/ut/python/runtest.sh b/tests/ut/python/runtest.sh index 035e454f24..6502d3a1bf 100755 --- a/tests/ut/python/runtest.sh +++ b/tests/ut/python/runtest.sh @@ -40,12 +40,8 @@ fi if [ $# -gt 0 ]; then pytest -s --ignore=$1/pynative_mode --ignore=$1/parallel --ignore=$1/train $IGNORE_EXEC $1 - pytest -n 4 --dist=loadfile -v $1/parallel - pytest -n 4 --dist=loadfile -v $1/train else pytest --ignore=$CURRPATH/pynative_mode --ignore=$CURRPATH/parallel --ignore=$CURRPATH/train $IGNORE_EXEC $CURRPATH - pytest -n 4 --dist=loadfile -v $CURRPATH/parallel - pytest -n 4 --dist=loadfile -v $CURRPATH/train fi RET=$? @@ -57,6 +53,18 @@ if [ ${RET} -ne 0 ]; then exit ${RET} fi +if [ $# -gt 0 ]; then + pytest -n 4 --dist=loadfile -v $1/parallel $1/train +else + pytest -n 4 --dist=loadfile -v $CURRPATH/parallel $CURRPATH/train +fi + +RET=$? + +if [ ${RET} -ne 0 ]; then + exit ${RET} +fi + if [ $# -gt 0 ]; then pytest -s $1/pynative_mode else From e8d2a6351e058cd4da67a4ecf4641a27103858a9 Mon Sep 17 00:00:00 2001 From: leonwanghui Date: Mon, 6 Apr 2020 16:14:59 +0800 Subject: [PATCH 106/227] Fix some typo errors in mindspore ir module Signed-off-by: leonwanghui --- mindspore/ccsrc/common/trans.cc | 90 +++++++++---------- mindspore/ccsrc/debug/anf_ir_dump.cc | 16 ++-- mindspore/ccsrc/debug/anf_ir_utils.cc | 36 ++++---- mindspore/ccsrc/debug/draw.cc | 2 +- mindspore/ccsrc/debug/dump_proto.cc | 2 +- mindspore/ccsrc/debug/e2e_dump.cc | 24 ++--- mindspore/ccsrc/debug/info.cc | 4 +- mindspore/ccsrc/debug/trace.cc | 10 +-- mindspore/ccsrc/debug/trace_info.cc | 2 +- mindspore/ccsrc/ir/anf.h | 6 +- mindspore/ccsrc/ir/dtype/number.cc | 6 +- mindspore/ccsrc/ir/dtype/type.cc | 6 +- mindspore/ccsrc/ir/func_graph.h | 2 +- mindspore/ccsrc/ir/manager.cc | 4 +- mindspore/ccsrc/ir/manager.h | 2 +- mindspore/ccsrc/ir/meta_tensor.h | 8 +- mindspore/ccsrc/ir/visitor.cc | 7 +- mindspore/ccsrc/pynative/pynative_execute.cc | 14 +-- .../ccsrc/pynative/pynative_execute_ge.cc | 18 ++-- 19 files changed, 129 insertions(+), 130 deletions(-) diff --git a/mindspore/ccsrc/common/trans.cc b/mindspore/ccsrc/common/trans.cc index 4748d59286..380c51bcf9 100644 --- a/mindspore/ccsrc/common/trans.cc +++ b/mindspore/ccsrc/common/trans.cc @@ -122,7 +122,7 @@ bool CastKernel(const TypeIdArgs &args, void *dst, const size_t data_size, const TransDataSrc2Dst(args, dst, data_size); break; default: - MS_LOG(ERROR) << "unsupported datatype trans"; + MS_LOG(ERROR) << "Unsupported datatype trans"; return false; } return true; @@ -132,7 +132,7 @@ size_t CubeSizeByType(const TypeId data_type) { const size_t default_error = 0; auto dt_size = TypeIdSize(data_type); if (dt_size < 1) { - MS_LOG(ERROR) << "illegal dtype."; + MS_LOG(ERROR) << "Illegal dtype."; return default_error; } else if (dt_size == 1) { return kCubeSize * 2; @@ -146,12 +146,12 @@ size_t ShapeSize(const std::vector &shape) { } size_t TypeIdSize(const TypeId data_type) { - const size_t unsupport_type_error = 0; + const size_t unsupported_type_error = 0; auto iter = type_map.find(data_type); if (iter != type_map.end()) { return iter->second; } - return unsupport_type_error; + return unsupported_type_error; } std::vector TransShapeTo4d(const std::vector &shape) { @@ -174,7 +174,7 @@ std::vector TransShapeTo4d(const std::vector &shape) { } break; default: - MS_LOG(EXCEPTION) << "Unexpeted shape size = " << shape.size(); + MS_LOG(EXCEPTION) << "Unexpected shape size = " << shape.size(); } return shape_4d; } @@ -183,7 +183,7 @@ std::vector TransShapeToDevice(const std::vector &shape, const s std::vector device_shape; if (format == kOpFormat_FRAC_NZ) { if (shape.size() < 2) { - MS_EXCEPTION(NotSupportError) << "format " << format << " is not support shape " << shape.size(); + MS_EXCEPTION(NotSupportError) << "Format " << format << " is not support shape " << shape.size(); } if (shape.size() > 2) { (void)std::copy(shape.begin(), shape.end() - 2, std::back_inserter(device_shape)); @@ -231,37 +231,37 @@ std::vector TransShapeToDevice(const std::vector &shape, const s } bool TransDataType(const TypeIdArgs &args, void *result) { - MS_LOG(DEBUG) << "begin trans datatype from " << TypeIdLabel(args.host_data_type) << " to " + MS_LOG(DEBUG) << "Begin trans datatype from " << TypeIdLabel(args.host_data_type) << " to " << TypeIdLabel(args.device_data_type); MS_EXCEPTION_IF_NULL(result); std::pair type_info(args.host_data_type, args.device_data_type); auto iter = mode_map.find(type_info); if (iter == mode_map.end()) { - MS_LOG(ERROR) << "unsupported datatype trans. src_type :" << TypeIdLabel(args.host_data_type) + MS_LOG(ERROR) << "Unsupported datatype trans. src_type :" << TypeIdLabel(args.host_data_type) << ", dst_type:" << TypeIdLabel(args.device_data_type); return false; } auto trans_mode = iter->second; auto type_size = TypeIdSize(args.device_data_type); if (type_size < 1) { - MS_LOG(ERROR) << "invalid host data type."; + MS_LOG(ERROR) << "Invalid host data type."; return false; } if (args.host_shape_size < 1) { - MS_LOG(ERROR) << "invalid host data size."; + MS_LOG(ERROR) << "Invalid host data size."; return false; } if (!CastKernel(args, result, args.host_shape_size, trans_mode)) { - MS_LOG(ERROR) << "failed to trans datatype.."; + MS_LOG(ERROR) << "Failed to trans datatype.."; return false; } return true; } bool TransFormat(const FormatArgs &args, void *result) { - MS_LOG(DEBUG) << "start trans format."; + MS_LOG(DEBUG) << "Start trans format."; if (TypeIdSize(args.src_data_type) < 1) { - MS_LOG(ERROR) << "invalid datatype.."; + MS_LOG(ERROR) << "Invalid datatype.."; return false; } if ((args.host_format == kOpFormat_NCHW || args.host_format == kOpFormat_ND) && @@ -276,9 +276,9 @@ bool TransFormat(const FormatArgs &args, void *result) { } bool TransFormatFromDeviceToHost(const FormatArgs &args, void *result) { - MS_LOG(DEBUG) << "start trans format."; + MS_LOG(DEBUG) << "Start trans format."; if (TypeIdSize(args.src_data_type) < 1) { - MS_LOG(ERROR) << "invalid datatype.."; + MS_LOG(ERROR) << "Invalid datatype.."; return false; } if ((args.host_format == kOpFormat_NCHW || args.host_format == kOpFormat_ND) && @@ -293,15 +293,15 @@ bool TransFormatFromDeviceToHost(const FormatArgs &args, void *result) { } bool NchwToFracZ(const FormatArgs &args, void *result) { - MS_LOG(DEBUG) << "trans format from nchw to frac_z"; + MS_LOG(DEBUG) << "Trans format from nchw to frac_z"; MS_EXCEPTION_IF_NULL(result); if (args.host_shape.size() != kNchwDims) { - MS_LOG(ERROR) << "invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims; + MS_LOG(ERROR) << "Invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims; return false; } size_t size = TypeIdSize(args.src_data_type); if (size < 1) { - MS_LOG(ERROR) << "illegal dtype."; + MS_LOG(ERROR) << "Illegal dtype."; return false; } auto n = args.host_shape[0]; @@ -311,7 +311,7 @@ bool NchwToFracZ(const FormatArgs &args, void *result) { size_t c0 = CubeSizeByType(args.src_data_type); if (c0 < 1) { - MS_LOG(ERROR) << "illegal dtype."; + MS_LOG(ERROR) << "Illegal dtype."; return false; } size_t c1 = Ceil(c, c0); @@ -327,7 +327,7 @@ bool NchwToFracZ(const FormatArgs &args, void *result) { size_t dst_size = total_ele_cnt * size; if (dst_size != args.device_size) { - MS_LOG(ERROR) << "illegal total data size." + MS_LOG(ERROR) << "Illegal total data size." << "dst size is :" << dst_size << "device size is :" << args.device_size; return false; } @@ -369,20 +369,20 @@ bool NchwToFracZ(const FormatArgs &args, void *result) { } bool FracZToNchw(const FormatArgs &args, void *result) { - MS_LOG(DEBUG) << "trans format from frac_z to nchw"; + MS_LOG(DEBUG) << "Trans format from frac_z to nchw"; MS_EXCEPTION_IF_NULL(result); if (args.host_shape.size() != kNchwDims) { - MS_LOG(ERROR) << "invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims; + MS_LOG(ERROR) << "Invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims; return false; } size_t size = TypeIdSize(args.src_data_type); if (size < 1) { - MS_LOG(ERROR) << "illegal dtype."; + MS_LOG(ERROR) << "Illegal dtype."; return false; } size_t total_size = ShapeSize(args.device_shape) * size; if (total_size != args.device_size) { - MS_LOG(ERROR) << "illegal total data size, total_size:" << total_size << ", device_size:" << args.device_size; + MS_LOG(ERROR) << "Illegal total data size, total_size:" << total_size << ", device_size:" << args.device_size; return false; } @@ -435,7 +435,7 @@ bool FracZToNchw(const FormatArgs &args, void *result) { bool TransShapeToNz(const std::vector &host_shape, std::vector *hw_shape) { MS_EXCEPTION_IF_NULL(hw_shape); if (host_shape.empty()) { - MS_LOG(ERROR) << "size of vector is 0."; + MS_LOG(ERROR) << "Size of vector is 0."; return false; } switch (host_shape.size()) { @@ -447,7 +447,7 @@ bool TransShapeToNz(const std::vector &host_shape, std::vector * default: auto size = host_shape.size(); if (size < 2) { - MS_LOG(ERROR) << "illegal size."; + MS_LOG(ERROR) << "Illegal size."; return false; } size_t times = 1; @@ -462,26 +462,26 @@ bool TransShapeToNz(const std::vector &host_shape, std::vector * } bool NchwToFracNz(const FormatArgs &args, void *result) { - MS_LOG(DEBUG) << "trans format from nchw to frac_nz."; + MS_LOG(DEBUG) << "Trans format from nchw to frac_nz."; MS_EXCEPTION_IF_NULL(result); std::vector hw_shape; if (!TransShapeToNz(args.host_shape, &hw_shape)) { - MS_LOG(ERROR) << "trans shape failed.."; + MS_LOG(ERROR) << "Trans shape failed.."; return false; } if (hw_shape.size() < 3 || args.device_shape.size() < 4) { - MS_LOG(ERROR) << "invalid shape size."; + MS_LOG(ERROR) << "Invalid shape size."; return false; } auto size = TypeIdSize(args.src_data_type); if (size < 1) { - MS_LOG(ERROR) << "illegal dtype"; + MS_LOG(ERROR) << "Illegal dtype"; return false; } auto dst_size = ShapeSize(args.device_shape) * size; if (dst_size != args.device_size) { - MS_LOG(ERROR) << "illegal total data size, total_size:" << dst_size << ", device_size:" << args.device_size; + MS_LOG(ERROR) << "Illegal total data size, total_size:" << dst_size << ", device_size:" << args.device_size; return false; } auto times = hw_shape.at(0); @@ -538,26 +538,26 @@ bool NchwToFracNz(const FormatArgs &args, void *result) { } bool FracNzToNchw(const FormatArgs &args, void *result) { - MS_LOG(DEBUG) << "trans format from frac_nz to nchw"; + MS_LOG(DEBUG) << "Trans format from frac_nz to nchw"; MS_EXCEPTION_IF_NULL(result); std::vector hw_shape; if (!TransShapeToNz(args.host_shape, &hw_shape)) { - MS_LOG(ERROR) << "trans shape failed.."; + MS_LOG(ERROR) << "Trans shape failed.."; return false; } if (hw_shape.size() < 3 || args.device_shape.size() < 4) { - MS_LOG(ERROR) << "invalid shape size."; + MS_LOG(ERROR) << "Invalid shape size."; return false; } auto size = TypeIdSize(args.src_data_type); if (size < 1) { - MS_LOG(ERROR) << "illegal dtype"; + MS_LOG(ERROR) << "Illegal dtype"; return false; } auto dst_size = ShapeSize(args.device_shape) * size; if (dst_size != args.device_size) { - MS_LOG(ERROR) << "illegal total data size, total_size:" << dst_size << ", device_size:" << args.device_size; + MS_LOG(ERROR) << "Illegal total data size, total_size:" << dst_size << ", device_size:" << args.device_size; return false; } auto times = hw_shape.at(0); @@ -614,20 +614,20 @@ bool FracNzToNchw(const FormatArgs &args, void *result) { } bool NchwToNc1hwc0(const FormatArgs &args, void *result) { - MS_LOG(DEBUG) << "trans format from nchw to Nc1h1wc0"; + MS_LOG(DEBUG) << "Trans format from nchw to Nc1h1wc0"; MS_EXCEPTION_IF_NULL(result); if (args.host_shape.size() != kNchwDims) { - MS_LOG(ERROR) << "invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims; + MS_LOG(ERROR) << "Invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims; return false; } size_t size = TypeIdSize(args.src_data_type); if (size < 1) { - MS_LOG(ERROR) << "illegal dtype."; + MS_LOG(ERROR) << "Illegal dtype."; return false; } auto total_size = ShapeSize(args.device_shape) * size; if (total_size != args.device_size) { - MS_LOG(ERROR) << "illegal total data size, total_size:" << total_size << ", device_size:" << args.device_size; + MS_LOG(ERROR) << "Illegal total data size, total_size:" << total_size << ", device_size:" << args.device_size; return false; } @@ -637,7 +637,7 @@ bool NchwToNc1hwc0(const FormatArgs &args, void *result) { auto w = args.host_shape[3]; size_t c0 = CubeSizeByType(args.src_data_type); if (c0 < 1) { - MS_LOG(ERROR) << "illegal dtype."; + MS_LOG(ERROR) << "Illegal dtype."; return false; } size_t c1 = Ceil(c, c0); @@ -687,20 +687,20 @@ bool NchwToNc1hwc0(const FormatArgs &args, void *result) { } bool Nc1hwc0ToNchw(const FormatArgs &args, void *result) { - MS_LOG(DEBUG) << "trans format from nc1h1wc0 to nchw"; + MS_LOG(DEBUG) << "Trans format from nc1h1wc0 to nchw"; MS_EXCEPTION_IF_NULL(result); if (args.host_shape.size() != kNchwDims) { - MS_LOG(ERROR) << "invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims; + MS_LOG(ERROR) << "Invalid host shape, host shape dims:" << args.host_shape.size() << ", expect dims:" << kNchwDims; return false; } size_t size = TypeIdSize(args.src_data_type); if (size < 1) { - MS_LOG(ERROR) << "illegal dtype."; + MS_LOG(ERROR) << "Illegal dtype."; return false; } size_t total_size = ShapeSize(args.device_shape) * size; if (total_size != args.device_size) { - MS_LOG(ERROR) << "illegal total data size, total_size:" << total_size << ", device_size:" << args.device_size; + MS_LOG(ERROR) << "Illegal total data size, total_size:" << total_size << ", device_size:" << args.device_size; return false; } diff --git a/mindspore/ccsrc/debug/anf_ir_dump.cc b/mindspore/ccsrc/debug/anf_ir_dump.cc index 9e24a17ede..9eb0a376cc 100644 --- a/mindspore/ccsrc/debug/anf_ir_dump.cc +++ b/mindspore/ccsrc/debug/anf_ir_dump.cc @@ -141,7 +141,7 @@ void DumpKernelInfo(const CNodePtr &node, const std::shared_ptr void DumpParams(const FuncGraphPtr &graph, std::ostringstream &buffer, OrderedMap *para_map) { if (graph == nullptr) { - MS_LOG(INFO) << "param graph is nullptr."; + MS_LOG(INFO) << "Param graph is nullptr."; return; } std::vector parameters = graph->parameters(); @@ -175,17 +175,17 @@ void DumpParams(const FuncGraphPtr &graph, std::ostringstream &buffer, OrderedMa if (para_map != nullptr) { (*para_map)[p] = para++; } - MS_LOG(DEBUG) << "record param: " << p->ToString() << " graph belong : " << p->func_graph()->ToString(); + MS_LOG(DEBUG) << "Record param: " << p->ToString() << " graph belong : " << p->func_graph()->ToString(); } } void DumpOperator(const AnfNodePtr &op, const std::shared_ptr &gsub) { if (op == nullptr) { - MS_LOG(INFO) << "param op is nullptr"; + MS_LOG(INFO) << "Param op is nullptr"; return; } if (gsub == nullptr) { - MS_LOG(INFO) << "param gsub is nullptr"; + MS_LOG(INFO) << "Param gsub is nullptr"; return; } @@ -338,7 +338,7 @@ void DumpCNode(const CNodePtr &nd, const FuncGraphPtr &sub_graph, OrderedMapinputs().empty()) { - MS_LOG(EXCEPTION) << "input of apply node is empty"; + MS_LOG(EXCEPTION) << "Input of apply node is empty"; } // print operator @@ -376,7 +376,7 @@ void DumpIRInSubgraph(const std::vector &nodes, OrderedMapfunc_graph(); if (sub_graph == nullptr) { - MS_LOG(DEBUG) << "node[" << nd->ToString() << "] belongs to no graph!"; + MS_LOG(DEBUG) << "Node[" << nd->ToString() << "] belongs to no graph!"; continue; } std::shared_ptr gsub = (*sub_graphs)[sub_graph]; @@ -430,12 +430,12 @@ void DumpIR(const std::string &filename, const FuncGraphPtr &graph, bool dump_fu return; } if (filename.size() > PATH_MAX) { - MS_LOG(ERROR) << "file path " << filename << " is too long."; + MS_LOG(ERROR) << "File path " << filename << " is too long."; return; } char real_path[PATH_MAX] = {0}; if (nullptr == realpath(filename.c_str(), real_path)) { - MS_LOG(DEBUG) << "dir " << filename << " does not exit."; + MS_LOG(DEBUG) << "Dir " << filename << " does not exit."; } OrderedMap para_map; diff --git a/mindspore/ccsrc/debug/anf_ir_utils.cc b/mindspore/ccsrc/debug/anf_ir_utils.cc index 98cd2f4b2f..c25ad862df 100644 --- a/mindspore/ccsrc/debug/anf_ir_utils.cc +++ b/mindspore/ccsrc/debug/anf_ir_utils.cc @@ -49,7 +49,7 @@ std::string GetMsIrPath(void) { path = path_ptr; char real_path[PATH_MAX] = {0}; if (path.size() > PATH_MAX || nullptr == realpath(path.c_str(), real_path)) { - MS_LOG(EXCEPTION) << "MS IR Path error, " << path_ptr; + MS_LOG(EXCEPTION) << "MS IR path error, " << path_ptr; } path = real_path; } @@ -144,8 +144,8 @@ std::string AnfExporter::GetValueNodeText(const FuncGraphPtr& fg, const ValueNod } std::string AnfExporter::GetMultitypeFuncGraphText(const prim::MultitypeFuncGraphPtr& mt_func_graph) { - auto py_funs = mt_func_graph->GetPyFunctions(); - if (py_funs.empty()) { + auto py_funcs = mt_func_graph->GetPyFunctions(); + if (py_funcs.empty()) { return ""; } @@ -153,7 +153,7 @@ std::string AnfExporter::GetMultitypeFuncGraphText(const prim::MultitypeFuncGrap oss << "{"; bool is_first = true; - for (const auto& py_func : py_funs) { + for (const auto& py_func : py_funcs) { if (is_first) { is_first = false; } else { @@ -626,7 +626,7 @@ void AnfExporter::ExportFuncGraph(const std::string& filename, const FuncGraphPt ofs << "\n\n"; (void)func_graph_set.erase(fg); } - ofs << "# num of total funcgraphs: " << exported.size(); + ofs << "# num of total function graphs: " << exported.size(); ofs.close(); } @@ -651,7 +651,7 @@ void AnfExporter::ExportFuncGraph(const std::string& filename, const std::vector ofs << "\n\n"; } - ofs << "# num of total funcgraphs: " << graphs.size(); + ofs << "# num of total function graphs: " << graphs.size(); ofs.close(); } @@ -763,7 +763,7 @@ class Lexer { fin.close(); } } catch (const std::exception& e) { - MS_LOG(ERROR) << "exception when closing file"; + MS_LOG(ERROR) << "Exception when closing file"; } catch (...) { std::string exName(abi::__cxa_current_exception_type()->name()); MS_LOG(ERROR) << "Error occurred when closing file. Exception name: " << exName; @@ -802,7 +802,7 @@ class Lexer { Token token = GetNextTokenInner(); const char* str = token_text[token]; std::string text = (str == nullptr ? GetTokenText() : str); - MS_LOG(DEBUG) << "------parse token] " << text; + MS_LOG(DEBUG) << "------Parse token] " << text; return token; } @@ -1642,7 +1642,7 @@ class IrParser { MS_LOG(EXCEPTION) << "Expect @file at line " << lexer_.GetLineNo(); } - // load prameter default value from serialized file + // load parameter default value from serialized file py::object default_obj = LoadObject(lexer_.GetTokenText()); param->set_default_param(default_obj); @@ -1950,7 +1950,7 @@ class IrParser { return TOK_ERROR; } - // restore python funciton of PrimitivePy from serialized file + // restore python function of PrimitivePy from serialized file py::object py_obj = LoadObject(lexer_.GetTokenText()); PrimitivePyPtr ptr = nullptr; if (py::hasattr(py_obj, "__setattr_flag__") && py::hasattr(py_obj, "_clone")) { @@ -1958,7 +1958,7 @@ class IrParser { py::object new_obj = clone_fn(); ptr = new_obj.cast(); if (ptr == nullptr) { - MS_LOG(EXCEPTION) << "cast to type 'PrimitivePyPtr' error"; + MS_LOG(EXCEPTION) << "Cast to type 'PrimitivePyPtr' error"; } } else { ptr = std::make_shared(id.substr(strlen("PrimitivePy::")), py_obj); @@ -2221,15 +2221,15 @@ class IrParser { }; std::vector ImportIR(const std::string& filename) { - IrParser paser(filename.c_str()); - paser.ParseFile(); - return paser.GetFuncGraphs(); + IrParser parser(filename.c_str()); + parser.ParseFile(); + return parser.GetFuncGraphs(); } #ifdef ENABLE_DUMP_IR void DumpIRProto(const FuncGraphPtr& func_graph, const std::string& suffix) { if (func_graph == nullptr) { - MS_LOG(ERROR) << "func graph is nullptr"; + MS_LOG(ERROR) << "Func graph is nullptr"; return; } auto ms_context = MsContext::GetInstance(); @@ -2243,16 +2243,16 @@ void DumpIRProto(const FuncGraphPtr& func_graph, const std::string& suffix) { } std::string file_path = save_graphs_path + "/" + "ms_output_" + suffix + ".pb"; if (file_path.size() > PATH_MAX) { - MS_LOG(ERROR) << "file path " << file_path << " is too long."; + MS_LOG(ERROR) << "File path " << file_path << " is too long."; return; } char real_path[PATH_MAX] = {0}; if (nullptr == realpath(file_path.c_str(), real_path)) { - MS_LOG(DEBUG) << "dir " << file_path << " does not exit."; + MS_LOG(DEBUG) << "Dir " << file_path << " does not exit."; } else { std::string path_string = real_path; if (chmod(common::SafeCStr(path_string), S_IRUSR | S_IWUSR) == -1) { - MS_LOG(ERROR) << "modify file:" << real_path << " to rw fail."; + MS_LOG(ERROR) << "Modify file:" << real_path << " to rw fail."; return; } } diff --git a/mindspore/ccsrc/debug/draw.cc b/mindspore/ccsrc/debug/draw.cc index e0949c0419..3e8cbfba19 100644 --- a/mindspore/ccsrc/debug/draw.cc +++ b/mindspore/ccsrc/debug/draw.cc @@ -362,7 +362,7 @@ Digraph::~Digraph() { fout_.close(); } } catch (const std::exception& e) { - MS_LOG(ERROR) << "exception when closing file " << filename_; + MS_LOG(ERROR) << "Exception when closing file " << filename_; } } diff --git a/mindspore/ccsrc/debug/dump_proto.cc b/mindspore/ccsrc/debug/dump_proto.cc index a12cf8b47f..a7a1e208a4 100644 --- a/mindspore/ccsrc/debug/dump_proto.cc +++ b/mindspore/ccsrc/debug/dump_proto.cc @@ -208,7 +208,7 @@ void ProtoExporter::SetValueToProto(const ValuePtr& val, irpb::ValueProto* value TypePtr elem_type = dyn_cast(val)->element(); type_proto->mutable_tensor_type()->set_elem_type(GetNumberDataType(elem_type)); } else { - MS_LOG(WARNING) << "Not supported type " << val->type_name(); + MS_LOG(WARNING) << "Unsupported type " << val->type_name(); } } diff --git a/mindspore/ccsrc/debug/e2e_dump.cc b/mindspore/ccsrc/debug/e2e_dump.cc index ba11eafa5f..fbe76cdc47 100644 --- a/mindspore/ccsrc/debug/e2e_dump.cc +++ b/mindspore/ccsrc/debug/e2e_dump.cc @@ -101,7 +101,7 @@ bool Dump::IsConfigValid(const nlohmann::json& dumpSettings) { auto kernels = dumpSettings.at("kernels"); if (!(enable.is_boolean() && trans_flag.is_boolean() && mode.is_number() && path.is_string() && net_name.is_string() && iteration.is_number() && kernels.is_array())) { - MS_LOG(ERROR) << "element's type in Dump config json is invalid."; + MS_LOG(ERROR) << "Element's type in Dump config json is invalid."; dump_enable_ = false; return false; } @@ -121,7 +121,7 @@ bool Dump::IsConfigValid(const nlohmann::json& dumpSettings) { bool Dump::SetDumpConfFromJsonFile() { const char* config_path_str = std::getenv("MINDSPORE_CONFIG_PATH"); if (config_path_str != nullptr) { - MS_LOG(INFO) << "getenv MINDSPORE_CONFIG_PATH :" << config_path_str; + MS_LOG(INFO) << "Getenv MINDSPORE_CONFIG_PATH :" << config_path_str; } else { MS_LOG(INFO) << "No need E2E Dump. please export MINDSPORE_CONFIG_PATH eg: MINDSPORE_CONFIG_PATH=/etc"; dump_enable_ = false; @@ -132,7 +132,7 @@ bool Dump::SetDumpConfFromJsonFile() { auto id = context_ptr->device_id(); char real_path[PATH_MAX] = {0}; if (nullptr == realpath(config_path_str, real_path)) { - MS_LOG(ERROR) << "env e2e dump path error, " << config_path_str; + MS_LOG(ERROR) << "Env e2e dump path error, " << config_path_str; dump_enable_ = false; return false; } @@ -150,20 +150,20 @@ bool Dump::SetDumpConfFromJsonFile() { bool Dump::DumpToFile(const std::string& filename, const void* data, size_t len) { if (filename.empty() || data == nullptr || len == 0) { - MS_LOG(ERROR) << "incorrect parameter."; + MS_LOG(ERROR) << "Incorrect parameter."; return false; } std::string realpath; bool ret = GetRealPath(filename, &realpath); if (!ret) { - MS_LOG(ERROR) << "get real path failed."; + MS_LOG(ERROR) << "Get real path failed."; return false; } std::ofstream fd; fd.open(realpath, std::ios::binary | std::ios::out); if (!fd.is_open()) { - MS_LOG(ERROR) << "open file " << realpath << " fail."; + MS_LOG(ERROR) << "Open file " << realpath << " fail."; return false; } (void)fd.write(reinterpret_cast(data), SizeToLong(len)); @@ -182,7 +182,7 @@ bool Dump::GetRealPath(const std::string& inpath, std::string* outpath) { if (path_split_pos != std::string::npos) { std::string prefix_path = inpath.substr(0, path_split_pos); if (prefix_path.length() >= PATH_MAX) { - MS_LOG(ERROR) << "prefix path is too longer!"; + MS_LOG(ERROR) << "Prefix path is too longer!"; return false; } std::string last_path = inpath.substr(path_split_pos, inpath.length() - path_split_pos); @@ -201,11 +201,11 @@ bool Dump::GetRealPath(const std::string& inpath, std::string* outpath) { if (path_split_pos == std::string::npos) { if (inpath.length() >= PATH_MAX) { - MS_LOG(ERROR) << "prefix path is too longer!"; + MS_LOG(ERROR) << "Prefix path is too longer!"; return false; } if (nullptr == realpath(inpath.c_str(), real_path)) { - MS_LOG(ERROR) << "file " << inpath << " does not exit, it will be created."; + MS_LOG(ERROR) << "File " << inpath << " does not exit, it will be created."; } *outpath = std::string(real_path); } @@ -218,7 +218,7 @@ bool Dump::CreateNotExistDirs(const std::string& path) { MS_EXCEPTION_IF_NULL(fs); char temp_path[PATH_MAX] = {0}; if (path.length() > PATH_MAX) { - MS_LOG(ERROR) << "path lens is max than " << PATH_MAX; + MS_LOG(ERROR) << "Path lens is max than " << PATH_MAX; return false; } for (uint32_t i = 0; i < path.length(); i++) { @@ -229,7 +229,7 @@ bool Dump::CreateNotExistDirs(const std::string& path) { temp_path[i] = '\0'; std::string path_handle(temp_path); if (!fs->FileExist(temp_path)) { - MS_LOG(INFO) << "dir " << path_handle << " does not exit, creating..."; + MS_LOG(INFO) << "Dir " << path_handle << " does not exit, creating..."; if (!fs->CreateDir(temp_path)) { MS_LOG(ERROR) << "Create " << path_handle << " dir error"; return false; @@ -241,7 +241,7 @@ bool Dump::CreateNotExistDirs(const std::string& path) { } if (!fs->FileExist(path)) { - MS_LOG(INFO) << "dir " << path << " does not exit, creating..."; + MS_LOG(INFO) << "Dir " << path << " does not exit, creating..."; if (!fs->CreateDir(path)) { MS_LOG(ERROR) << "Create " << path << " dir error"; return false; diff --git a/mindspore/ccsrc/debug/info.cc b/mindspore/ccsrc/debug/info.cc index 6f966e335c..5c1fc372c5 100644 --- a/mindspore/ccsrc/debug/info.cc +++ b/mindspore/ccsrc/debug/info.cc @@ -193,7 +193,7 @@ void TraceManager::DebugTrace(const TraceInfoPtr& trace_info) { } TraceContextPtr context = std::make_shared(trace_info); if (trace_info->debug_info() == nullptr) { - MS_LOG(EXCEPTION) << "trace debug info is null"; + MS_LOG(EXCEPTION) << "Trace debug info is null"; } TraceManager::trace_context_stack_.push(context); } @@ -205,7 +205,7 @@ void TraceManager::DebugTrace(const DebugInfoPtr& debug_info, const TraceInfoPtr auto cloned_info = trace_info->clone(); cloned_info->set_debug_info(debug_info); if (cloned_info->debug_info() == nullptr) { - MS_LOG(EXCEPTION) << "trace debug info is null with cloned trace"; + MS_LOG(EXCEPTION) << "Trace debug info is null with cloned trace"; } TraceContextPtr context = std::make_shared(cloned_info); TraceManager::trace_context_stack_.push(context); diff --git a/mindspore/ccsrc/debug/trace.cc b/mindspore/ccsrc/debug/trace.cc index 7ce13052c5..51cdd34946 100644 --- a/mindspore/ccsrc/debug/trace.cc +++ b/mindspore/ccsrc/debug/trace.cc @@ -89,7 +89,7 @@ std::string GetDebugInfo(const DebugInfoPtr& info, SourceLineTip tip) { return ""; } -// a trace info identifys a node transform, so we can trace the node transform through +// a trace info identifies a node transform, so we can trace the node transform through // a link of trace info and debug info std::string GetInfoWithAction(const std::vector& info_vec, SourceLineTip tip) { if (info_vec.size() < 1) { @@ -173,7 +173,7 @@ void DumpInferStack(std::ostringstream& oss) { } auto graph_context = graph_infer->graph_context(); if (graph_context == nullptr) { - MS_LOG(INFO) << "null context continue"; + MS_LOG(INFO) << "Null context continue"; continue; } auto graph = graph_context->func_graph(); @@ -264,7 +264,7 @@ void AnalyzedFuncGraphExporter::ExportFuncGraph(const std::string& filename, param_index = 1; auto tagged_func_graphs = CalcTaggedFuncGraphs(); - // first output grapn on the analysis stack + // first output graph on the analysis stack for (const auto& node_cfg : node_cfgs) { auto fg = node_cfg->context()->func_graph(); // the graph is already output, skip it @@ -291,7 +291,7 @@ void AnalyzedFuncGraphExporter::ExportFuncGraph(const std::string& filename, ofs << "\n\n"; (void)func_graph_set.erase(fg); } - ofs << "# num of total funcgraphs: " << exported.size(); + ofs << "# num of total function graphs: " << exported.size(); ofs.close(); } @@ -332,7 +332,7 @@ void GetInferStackInfo(std::ostringstream& oss) { MS_LOG(INFO) << "Get graph analysis information *end*"; } -// trace the graph evaluator statck +// trace the graph evaluator stack static std::stack> graph_infer_stack; // trace the cnode infer debug info static std::vector cnode_debug_stack{}; diff --git a/mindspore/ccsrc/debug/trace_info.cc b/mindspore/ccsrc/debug/trace_info.cc index e2da4ffcce..b01cd15010 100644 --- a/mindspore/ccsrc/debug/trace_info.cc +++ b/mindspore/ccsrc/debug/trace_info.cc @@ -36,6 +36,6 @@ std::string TraceInfo::GetActionBetweenNode(const DebugInfoPtr& info) { } else if (debug_info()->trace_info() != nullptr) { return act_name + debug_info()->trace_info()->GetActionBetweenNode(info); } - return "not in the traced info"; + return "Not in the traced info"; } } // namespace mindspore diff --git a/mindspore/ccsrc/ir/anf.h b/mindspore/ccsrc/ir/anf.h index e64b1329e9..16ccb15c43 100644 --- a/mindspore/ccsrc/ir/anf.h +++ b/mindspore/ccsrc/ir/anf.h @@ -83,7 +83,7 @@ class AnfVisitor; // Methods: // func_graph: return FuncGraph that this AnfNode belongs to. // scope: return the scope namespace of this AnfNode. Set it using set_scope. -// abstract: return the cached inferred abstract value. It cantains type, shape +// abstract: return the cached inferred abstract value. It contains type, shape // value. Set New cache using set_abstract. // intermediate_abstract: return the cached inferring abstract value. // Type/Shape: return the related info of this AnfNode. When this AnfNode is an @@ -284,7 +284,7 @@ class Parameter : public ANode { }; using ParameterPtr = std::shared_ptr; -// Value is used to represent the atomic expression metioned in BNF. +// Value is used to represent the atomic expression mentioned in BNF. // It mainly be stored in ValueNode. Value and ValueNode is related definition. class Value : public Base { public: @@ -313,7 +313,7 @@ using ValuePtr = std::shared_ptr; using ValuePtrList = std::vector; // ValueNode is used to hold value. Unlike CNode and Parameter, ValueNode -// do not belong to any particular function graph. +// does not belong to any particular function graph. class ValueNode : public ANode { public: explicit ValueNode(const ValuePtr &value) : value_(value) {} diff --git a/mindspore/ccsrc/ir/dtype/number.cc b/mindspore/ccsrc/ir/dtype/number.cc index 70d0aaeeaa..d9ef6bb3bd 100644 --- a/mindspore/ccsrc/ir/dtype/number.cc +++ b/mindspore/ccsrc/ir/dtype/number.cc @@ -34,19 +34,19 @@ bool Number::operator==(const Type& other) const { Int::Int(const int nbits) : Number(IntBitsToTypeId(nbits), nbits, false) { if (nbits != 8 && nbits != 16 && nbits != 32 && nbits != 64) { - MS_LOG(EXCEPTION) << "wrong number of bits."; + MS_LOG(EXCEPTION) << "Wrong number of bits."; } } UInt::UInt(const int nbits) : Number(UIntBitsToTypeId(nbits), nbits, false) { if (nbits != 8 && nbits != 16 && nbits != 32 && nbits != 64) { - MS_LOG(EXCEPTION) << "wrong number of bits."; + MS_LOG(EXCEPTION) << "Wrong number of bits."; } } Float::Float(const int nbits) : Number(FloatBitsToTypeId(nbits), nbits, false) { if (nbits != 16 && nbits != 32 && nbits != 64) { - MS_LOG(EXCEPTION) << "wrong number of bits."; + MS_LOG(EXCEPTION) << "Wrong number of bits."; } } diff --git a/mindspore/ccsrc/ir/dtype/type.cc b/mindspore/ccsrc/ir/dtype/type.cc index 6169acabbc..6fbd7f8111 100644 --- a/mindspore/ccsrc/ir/dtype/type.cc +++ b/mindspore/ccsrc/ir/dtype/type.cc @@ -37,7 +37,7 @@ TypeId IntBitsToTypeId(const int nbits) { case 64: return kNumberTypeInt64; default: - MS_LOG(EXCEPTION) << "wrong number of bits."; + MS_LOG(EXCEPTION) << "Wrong number of bits."; } } @@ -52,7 +52,7 @@ TypeId UIntBitsToTypeId(const int nbits) { case 64: return kNumberTypeUInt64; default: - MS_LOG(EXCEPTION) << "wrong number of bits."; + MS_LOG(EXCEPTION) << "Wrong number of bits."; } } @@ -65,7 +65,7 @@ TypeId FloatBitsToTypeId(const int nbits) { case 64: return kNumberTypeFloat64; default: - MS_LOG(EXCEPTION) << "wrong number of bits."; + MS_LOG(EXCEPTION) << "Wrong number of bits."; } } diff --git a/mindspore/ccsrc/ir/func_graph.h b/mindspore/ccsrc/ir/func_graph.h index 13e8c9dc8f..1d58c90755 100644 --- a/mindspore/ccsrc/ir/func_graph.h +++ b/mindspore/ccsrc/ir/func_graph.h @@ -174,7 +174,7 @@ class FuncGraph : public FuncGraphBase { GraphDebugInfoPtr debug_info(); void set_debug_info(const GraphDebugInfoPtr &info) { if (info == nullptr) { - MS_LOG(EXCEPTION) << "graph set null debug info"; + MS_LOG(EXCEPTION) << "Graph set null debug info"; } this->debug_info_ = info; } diff --git a/mindspore/ccsrc/ir/manager.cc b/mindspore/ccsrc/ir/manager.cc index 09bc7127c1..c1459014bb 100644 --- a/mindspore/ccsrc/ir/manager.cc +++ b/mindspore/ccsrc/ir/manager.cc @@ -817,7 +817,7 @@ void FuncGraphChildDirect::OnMoveAllCNode(FuncGraphPtr src, FuncGraphPtr dst) { void FuncGraphParentsDirectCollector::OnModEdge(AnfNodePtr node, int, AnfNodePtr inp, EdgeProcessDirection direction) { MS_EXCEPTION_IF_NULL(node); FuncGraphPtr fg1 = node->func_graph(); - // possible chirld parent + // possible child parent if (IsValueNode(inp)) { FuncGraphPtr fg2 = GetValueNode(inp); if (Mod(fg1, ParentProxy(fg2), direction)) { @@ -1181,7 +1181,7 @@ bool FuncGraphJTotalComputer::SeekJ(const FuncGraphPtr& fg, const FuncGraphSetPt } path->add(fg); - // checkg if func graphs used contains J(func_graph); + // check if func graphs used contains J(func_graph); auto& used = this->manager_->func_graphs_used(); for (auto& item : used[fg]) { auto used_g = item.first; diff --git a/mindspore/ccsrc/ir/manager.h b/mindspore/ccsrc/ir/manager.h index 8036bd68c0..dc8f656ae7 100644 --- a/mindspore/ccsrc/ir/manager.h +++ b/mindspore/ccsrc/ir/manager.h @@ -650,7 +650,7 @@ class FuncGraphTransaction { explicit FuncGraphTransaction(FuncGraphManager* manager) : manager_(manager), changes_() { MS_EXCEPTION_IF_NULL(manager_); if (!manager_->IsManaged()) { - MS_LOG(DEBUG) << "the manager is not managed yet"; + MS_LOG(DEBUG) << "The manager is not managed yet"; } } diff --git a/mindspore/ccsrc/ir/meta_tensor.h b/mindspore/ccsrc/ir/meta_tensor.h index 57c37851b6..3e28f29f37 100644 --- a/mindspore/ccsrc/ir/meta_tensor.h +++ b/mindspore/ccsrc/ir/meta_tensor.h @@ -148,7 +148,7 @@ class MetaTensor : public Value { // // The constructed MetaTensor object has the same type and shape with meta_tensor. // - // param meta_tensor An exisiting MetaTensor object. + // param meta_tensor An existing MetaTensor object. virtual MetaTensor& operator=(const MetaTensor& meta_tensor); // brief Compares two MetaTensor objects. @@ -166,7 +166,7 @@ class MetaTensor : public Value { TypeId data_type() const { return data_type_; } std::string ToString() const override; std::string DumpText() const override; - // bried Sets the data type of a tensor in its MetaTensor. + // brief Sets the data type of a tensor in its MetaTensor. // // param data_type The data type of the tensor to be set. virtual TypeId set_data_type(const TypeId data_type) { @@ -314,7 +314,7 @@ class Tensor : public MetaTensor { // // The constructed Tensor object has the same type and shape with tensor. // - // param tensor An exisiting Tensor object. + // param tensor An existing Tensor object. Tensor& operator=(const Tensor& tensor); // brief Compares two Tensor objects. @@ -383,7 +383,7 @@ class Tensor : public MetaTensor { // return The [TypeId] of the tensor data. TypeId GetDataType(const py::buffer_info& buf) const; - // bried Sets the data type of a tensor. + // brief Sets the data type of a tensor. // // param data_type The data type of the tensor to be set. // diff --git a/mindspore/ccsrc/ir/visitor.cc b/mindspore/ccsrc/ir/visitor.cc index 70e22b2808..efebe3124a 100644 --- a/mindspore/ccsrc/ir/visitor.cc +++ b/mindspore/ccsrc/ir/visitor.cc @@ -43,14 +43,13 @@ VisitFuncType AnfVisitor::Match(const PrimitivePtr &prim, const std::vectorcast()->inputs(); - // infact, funcs_size == inps_size - 1 auto funcs_size = funcs.size(); - auto inps_size = inputs.size(); + auto inputs_size = inputs.size(); // check the inputs are matched with the predicate functions if (funcs_size > 0) { // use the predicate function list to check the number of inputs - if (funcs_size != (inps_size - 1)) { + if (funcs_size != (inputs_size - 1)) { return; } @@ -63,7 +62,7 @@ VisitFuncType AnfVisitor::Match(const PrimitivePtr &prim, const std::vectorVisit(inputs[i]); } }; diff --git a/mindspore/ccsrc/pynative/pynative_execute.cc b/mindspore/ccsrc/pynative/pynative_execute.cc index 927e768bbe..e5fb0c6949 100644 --- a/mindspore/ccsrc/pynative/pynative_execute.cc +++ b/mindspore/ccsrc/pynative/pynative_execute.cc @@ -36,7 +36,7 @@ #endif const char SINGLE_OP_GRAPH[] = "single_op_graph"; -// primitive unable to infer value for constant input in pynative mode +// primitive unable to infer value for constant input in PyNative mode const std::unordered_set vm_operators = {"partial", "depend"}; namespace mindspore { @@ -45,7 +45,7 @@ inline ValuePtr PyAttrValue(const py::object& obj) { ValuePtr converted_ret = nullptr; bool converted = parse::ConvertData(obj, &converted_ret); if (!converted) { - MS_LOG(EXCEPTION) << "attribute convert error with type:" << std::string(py::str(obj)); + MS_LOG(EXCEPTION) << "Attribute convert error with type:" << std::string(py::str(obj)); } return converted_ret; } @@ -67,7 +67,7 @@ void PynativeInfer(const PrimitivePyPtr& prim, const py::tuple& py_args, OpExecI OpExecInfoPtr GenerateOpExecInfo(const py::args& args) { if (args.size() != PY_ARGS_NUM) { - MS_LOG(ERROR) << "four args are needed by RunOp"; + MS_LOG(ERROR) << "Four args are needed by RunOp"; return nullptr; } auto op_exec_info = std::make_shared(); @@ -145,13 +145,13 @@ py::object RunOpInVM(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* stat py::object RunOpInMs(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* status) { MS_EXCEPTION_IF_NULL(op_exec_info); - MS_LOG(INFO) << "start run op[" << op_exec_info->op_name << "] with backend policy ms"; + MS_LOG(INFO) << "Start run op[" << op_exec_info->op_name << "] with backend policy ms"; auto ms_context = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(ms_context); ms_context->set_enable_pynative_infer(true); std::string device_target = ms_context->device_target(); if (device_target != kAscendDevice && device_target != kGPUDevice) { - MS_EXCEPTION(ArgumentError) << "device target [" << device_target << "] is not supported in Pynative mode"; + MS_EXCEPTION(ArgumentError) << "Device target [" << device_target << "] is not supported in Pynative mode"; } std::shared_ptr session = session::SessionFactory::Get().Create(device_target); MS_EXCEPTION_IF_NULL(session); @@ -197,7 +197,7 @@ py::object RunOpWithBackendPolicy(MsBackendPolicy backend_policy, const OpExecIn break; } default: - MS_LOG(ERROR) << "No backend configed for run op"; + MS_LOG(ERROR) << "No backend configured for run op"; } return result; } @@ -240,7 +240,7 @@ py::tuple RunOp(const py::args& args) { } result = RunOpWithBackendPolicy(backend_policy, op_exec_info, &status); if (status != PYNATIVE_SUCCESS) { - MS_LOG(ERROR) << "Fail to run " << op_exec_info->op_name; + MS_LOG(ERROR) << "Failed to run " << op_exec_info->op_name; return err_ret; } diff --git a/mindspore/ccsrc/pynative/pynative_execute_ge.cc b/mindspore/ccsrc/pynative/pynative_execute_ge.cc index 4ed6088494..180b0006ff 100644 --- a/mindspore/ccsrc/pynative/pynative_execute_ge.cc +++ b/mindspore/ccsrc/pynative/pynative_execute_ge.cc @@ -47,7 +47,7 @@ inline ValuePtr PyAttrValue(const py::object& obj) { ValuePtr converted_ret = nullptr; bool converted = parse::ConvertData(obj, &converted_ret); if (!converted) { - MS_LOG(EXCEPTION) << "attribute convert error with type:" << std::string(py::str(obj)); + MS_LOG(EXCEPTION) << "Attribute convert error with type:" << std::string(py::str(obj)); } return converted_ret; } @@ -67,7 +67,7 @@ MeTensorPtr ConvertPyObjToTensor(const py::object& obj) { } else if (py::isinstance(obj)) { me_tensor_ptr = std::make_shared(py::cast(obj), nullptr); } else { - MS_LOG(EXCEPTION) << "run op inputs type is invalid!"; + MS_LOG(EXCEPTION) << "Run op inputs type is invalid!"; } return me_tensor_ptr; } @@ -97,7 +97,7 @@ bool SetInputsForSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vec auto const_op_desc = transform::TransformUtil::GetGeTensorDesc(me_tensor_ptr->shape_c(), me_tensor_ptr->data_type(), kOpFormat_NCHW); if (const_op_desc == nullptr) { - MS_LOG(ERROR) << "Create variable " << op_name << " ouptut descriptor failed!"; + MS_LOG(ERROR) << "Create variable " << op_name << " output descriptor failed!"; return false; } auto pointer_cast_const_op = std::static_pointer_cast(const_op); @@ -108,7 +108,7 @@ bool SetInputsForSingleOpGraph(const OpExecInfoPtr& op_exec_info, const std::vec continue; } if (adapter->setInput(op, op_input_idx++, const_op)) { - MS_LOG(ERROR) << "fail to set params, index is " << op_input_idx; + MS_LOG(ERROR) << "Failed to set params, index is " << op_input_idx; return false; } graph_input_nodes->push_back(*const_op); @@ -178,7 +178,7 @@ void ToTensorPtr(const OpExecInfoPtr op_exec_info, std::vector* con MeTensorPtr me_tensor_ptr = ConvertPyObjToTensor(op_inputs[i]); auto ge_tensor_ptr = transform::TransformUtil::ConvertTensor(me_tensor_ptr, kOpFormat_NCHW); if (ge_tensor_ptr == nullptr) { - MS_LOG(EXCEPTION) << "convert inputs to GE tensor failed in op " << op_exec_info->op_name << "."; + MS_LOG(EXCEPTION) << "Convert inputs to GE tensor failed in op " << op_exec_info->op_name << "."; } // set inputs for operator to build single node graph inputs->push_back(ge_tensor_ptr); @@ -192,7 +192,7 @@ PynativeStatusCode ConvertAttributes(const OpExecInfoPtr& op_exec_info, const st for (auto& item : op_attrs) { if (!py::isinstance(item.first)) { - MS_LOG(ERROR) << "type error in py dict convert"; + MS_LOG(ERROR) << "Type error in py dict convert"; return PYNATIVE_OP_ATTRS_ERR; } std::string name = py::cast(item.first); @@ -203,7 +203,7 @@ PynativeStatusCode ConvertAttributes(const OpExecInfoPtr& op_exec_info, const st // build graph GeGraphPtr graph = std::make_shared(op_exec_info->op_name); if (BuildSingleOpGraph(op_exec_info, inputs, attrs, graph) == false) { - MS_LOG(ERROR) << "Fail to BuildSingleOpGraph"; + MS_LOG(ERROR) << "Failed to BuildSingleOpGraph"; return PYNATIVE_GRAPH_GE_BUILD_ERR; } @@ -211,7 +211,7 @@ PynativeStatusCode ConvertAttributes(const OpExecInfoPtr& op_exec_info, const st transform::Status ret = transform::DfGraphManager::GetInstance().AddGraph(SINGLE_OP_GRAPH, std::shared_ptr(graph)); if (ret != transform::SUCCESS) { - MS_LOG(ERROR) << "Fail to AddGraph into graph manager"; + MS_LOG(ERROR) << "Failed to AddGraph into graph manager"; return PYNATIVE_GRAPH_MANAGER_ERR; } @@ -289,7 +289,7 @@ py::object RunOpInGE(const OpExecInfoPtr& op_exec_info, PynativeStatusCode* stat run_ret = graph_runner->RunGraph(run_options, ge_inputs, &ge_outputs); } if (run_ret != transform::Status::SUCCESS) { - MS_LOG(ERROR) << "GraphRunner Fails to Run Graph"; + MS_LOG(ERROR) << "GraphRunner fails to run graph"; *status = PYNATIVE_GRAPH_GE_RUN_ERR; return std::move(err_ret); } From 99ce88ba6285fdb8012f734aaa9397cefa3f2c51 Mon Sep 17 00:00:00 2001 From: VectorSL Date: Fri, 3 Apr 2020 16:55:37 +0800 Subject: [PATCH 107/227] edit loss_scale for gpu --- mindspore/nn/wrap/loss_scale.py | 40 ++++++++++---- mindspore/ops/operations/__init__.py | 6 +- mindspore/ops/operations/math_ops.py | 83 ++++++++++++++++++++++++++++ 3 files changed, 117 insertions(+), 12 deletions(-) diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py index 1ce3179273..4d929352b3 100644 --- a/mindspore/nn/wrap/loss_scale.py +++ b/mindspore/nn/wrap/loss_scale.py @@ -25,6 +25,7 @@ from ...ops import operations as P from ...ops.operations import NPUGetFloatStatus, NPUAllocFloatStatus, NPUClearFloatStatus, ReduceSum, LessEqual, \ ControlDepend from ...common import dtype as mstype +import mindspore.context as context _grad_scale = C.MultitypeFuncGraph("grad_scale") reciprocal = P.Reciprocal() @@ -34,6 +35,12 @@ reciprocal = P.Reciprocal() def tensor_grad_scale(scale, grad): return grad * F.cast(reciprocal(scale), F.dtype(grad)) +_grad_overflow = C.MultitypeFuncGraph("_grad_overflow") +grad_overflow = P.FloatStatus() + +@_grad_overflow.register("Tensor") +def _tensor_grad_overflow(grad): + return grad_overflow(grad) class DynamicLossScaleUpdateCell(Cell): r""" @@ -197,9 +204,15 @@ class TrainOneStepWithLossScaleCell(Cell): self.optimizer = optimizer self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True) self.hyper_map = C.HyperMap() - self.alloc_status = NPUAllocFloatStatus() - self.get_status = NPUGetFloatStatus() - self.clear_status = NPUClearFloatStatus() + if context.get_context("device_target") == "GPU": + self.gpu_target = True + self.float_status = P.FloatStatus() + self.addn = P.AddN() + else: + self.gpu_target = False + self.alloc_status = NPUAllocFloatStatus() + self.get_status = NPUGetFloatStatus() + self.clear_status = NPUClearFloatStatus() self.reduce_sum = ReduceSum(keep_dims=False) self.base = Tensor(1, mstype.float32) self.less_equal = LessEqual() @@ -224,10 +237,11 @@ class TrainOneStepWithLossScaleCell(Cell): def construct(self, data, label, sens=None): weights = self.weights loss = self.network(data, label) - # init overflow buffer - init = self.alloc_status() - # clear overflow buffer - self.clear_status(init) + if not self.gpu_target: + # init overflow buffer + init = self.alloc_status() + # clear overflow buffer + self.clear_status(init) if sens is None: scaling_sens = self.loss_scale else: @@ -237,10 +251,14 @@ class TrainOneStepWithLossScaleCell(Cell): if self.reducer_flag: # apply grad reducer on grads grads = self.grad_reducer(grads) - # get the overflow buffer - self.get_status(init) - # sum overflow buffer elements, 0:not overflow , >0:overflow - flag_sum = self.reduce_sum(init, (0,)) + if not self.gpu_target: + # get the overflow buffer + self.get_status(init) + # sum overflow buffer elements, 0:not overflow , >0:overflow + flag_sum = self.reduce_sum(init, (0,)) + else: + flag_sum = self.hyper_map(F.partial(_grad_overflow), grads) + flag_sum = self.addn(flag_sum) if self.is_distributed: # sum overflow flag over devices flag_reduce = self.allreduce(flag_sum) diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index 37a3b38bb6..48a985b33d 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -44,7 +44,7 @@ from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul LogicalNot, LogicalOr, MatMul, Maximum, Minimum, Mul, Neg, NMSWithMask, NotEqual, NPUAllocFloatStatus, NPUClearFloatStatus, - NPUGetFloatStatus, Pow, RealDiv, + NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus, Reciprocal, CumSum, Sin, Sqrt, Rsqrt, Square, Sub, TensorAdd, Sign, Round) @@ -151,6 +151,10 @@ __all__ = [ 'Neg', 'Slice', 'DType', + 'IsNan', + 'IsInf', + 'IsFinite', + 'FloatStatus', 'NPUAllocFloatStatus', 'NPUGetFloatStatus', 'NPUClearFloatStatus', diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 175b72560f..5f8c24d78b 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1541,6 +1541,89 @@ class LogicalOr(_LogicBinaryOp): def infer_dtype(self, x_dtype, y_dtype): return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.prim_name()) +class IsNan(PrimitiveWithInfer): + """ + Judging which elements are nan for each position + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the same shape of input. + """ + + @prim_attr_register + def __init__(self): + """init IsNan""" + self.init_prim_io_names(inputs=['x'], outputs=['output']) + + def infer_shape(self, x_shape): + return x_shape + + def infer_dtype(self, x_dtype): + return mstype.bool_ + +class IsInf(PrimitiveWithInfer): + """ + Judging which elements are inf or -inf for each position + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the same shape of input. + """ + + @prim_attr_register + def __init__(self): + """init IsInf""" + self.init_prim_io_names(inputs=['x'], outputs=['output']) + + def infer_shape(self, x_shape): + return x_shape + + def infer_dtype(self, x_dtype): + return mstype.bool_ + +class IsFinite(PrimitiveWithInfer): + """ + Judging which elements are finite for each position + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the same shape of input. + """ + + @prim_attr_register + def __init__(self): + """init IsFinite""" + self.init_prim_io_names(inputs=['x'], outputs=['output']) + + def infer_shape(self, x_shape): + return x_shape + + def infer_dtype(self, x_dtype): + return mstype.bool_ + +class FloatStatus(PrimitiveWithInfer): + """ + Determine if the elements contains nan, inf or -inf + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the shape of `(1,)`. + """ + + @prim_attr_register + def __init__(self): + """init FloatStatus""" + self.init_prim_io_names(inputs=['x'], outputs=['output']) + + def infer_shape(self, x_shape): + return [1] + + def infer_dtype(self, x_dtype): + return x_dtype class NPUAllocFloatStatus(PrimitiveWithInfer): """ From cdbef85e5ea3f7d92012816fedb85b8a23dbcfa1 Mon Sep 17 00:00:00 2001 From: kingfo Date: Fri, 3 Apr 2020 12:02:41 +0800 Subject: [PATCH 108/227] refactor callback for ge backend --- mindspore/ccsrc/CMakeLists.txt | 18 ++- mindspore/ccsrc/pipeline/pipeline.cc | 1 - mindspore/ccsrc/transform/graph_runner.cc | 3 + mindspore/ccsrc/utils/callbacks.cc | 153 ------------------ mindspore/ccsrc/utils/callbacks.h | 8 - mindspore/ccsrc/utils/callbacks_ge.cc | 182 ++++++++++++++++++++++ mindspore/ccsrc/utils/callbacks_ge.h | 38 +++++ mindspore/ccsrc/vm/backend.cc | 3 + tests/ut/cpp/utils/callback_test.cc | 3 + 9 files changed, 246 insertions(+), 163 deletions(-) create mode 100644 mindspore/ccsrc/utils/callbacks_ge.cc create mode 100644 mindspore/ccsrc/utils/callbacks_ge.h diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt index fdefdce6a2..befe86f3c0 100644 --- a/mindspore/ccsrc/CMakeLists.txt +++ b/mindspore/ccsrc/CMakeLists.txt @@ -87,7 +87,22 @@ ms_build_flatbuffers("${FLATBUFFER_IN}" "${FLATBUFFER_IN}" GENERATED_OUTPUT_DIR file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "ir/*.cc" "ir/dtype/*.cc" - "utils/*.cc" + "utils/context/ms_context.cc" + "utils/symbolic.cc" + "utils/tensorprint_utils.cc" + "utils/convert_utils.cc" + "utils/graph_utils.cc" + "utils/misc.cc" + "utils/callbacks.cc" + "utils/profile.cc" + "utils/base_ref.cc" + "utils/summary/event_writer.cc" + "utils/log_adapter.cc" + "utils/comm_manager.cc" + "utils/any.cc" + "utils/config_manager.cc" + "utils/system/file_system.cc" + "utils/system/crc32c.cc" "common/*.cc" "parallel/*.cc" "pipeline/pipeline.cc" @@ -173,6 +188,7 @@ if(ENABLE_GE) file(GLOB_RECURSE GE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "transform/*.cc" "pynative/pynative_execute_ge.cc" + "utils/callbacks_ge.cc" "pipeline/pipeline_ge.cc" ) list(APPEND MINDSPORE_SRC_LIST ${GE_SRC_LIST}) diff --git a/mindspore/ccsrc/pipeline/pipeline.cc b/mindspore/ccsrc/pipeline/pipeline.cc index 0d7790fb36..003d4c15e9 100644 --- a/mindspore/ccsrc/pipeline/pipeline.cc +++ b/mindspore/ccsrc/pipeline/pipeline.cc @@ -616,7 +616,6 @@ py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) { return ExecDFGraph(info_, args, phase_s); } #else - MS_LOG(WARNING) << "In ut test " << size << phase_s; if (backend == "ge") { std::shared_ptr ret_val = std::make_shared(); if (info_.count(phase_s) != 0 && info_[phase_s]->func_graph != nullptr) { diff --git a/mindspore/ccsrc/transform/graph_runner.cc b/mindspore/ccsrc/transform/graph_runner.cc index e77b1bcd73..f1f270cdb6 100644 --- a/mindspore/ccsrc/transform/graph_runner.cc +++ b/mindspore/ccsrc/transform/graph_runner.cc @@ -24,6 +24,9 @@ #include "utils/callbacks.h" #include "utils/utils.h" #include "./common.h" +#ifdef ENABLE_GE +#include "utils/callbacks_ge.h" +#endif #ifdef NO_GE_CLIENT namespace ge { diff --git a/mindspore/ccsrc/utils/callbacks.cc b/mindspore/ccsrc/utils/callbacks.cc index cdee0be82d..03c6322afe 100644 --- a/mindspore/ccsrc/utils/callbacks.cc +++ b/mindspore/ccsrc/utils/callbacks.cc @@ -20,10 +20,6 @@ #include #include #include "pybind11/pybind11.h" -#ifdef ENABLE_GE -#include "transform/df_graph_manager.h" -#include "transform/util.h" -#endif #include "pipeline/parse/data_converter.h" #include "pipeline/parse/python_adapter.h" #include "utils/visible.h" @@ -38,155 +34,6 @@ const char kSummary[] = "Summary"; const char kCheckPoint[] = "Save"; const int ONE_SHAPE = 1; -#ifdef ENABLE_GE -using mindspore::transform::Status; -using mindspore::transform::TransformUtil; - -bool GetParameterShape(const FuncGraphPtr& graph, const std::string& param_name, - const std::shared_ptr>& shape) { - if (graph == nullptr) { - MS_LOG(ERROR) << "Graph is null, can not get graph parameter"; - return false; - } - - auto parameter_nodes = graph->parameters(); - for (auto& node : parameter_nodes) { - ParameterPtr param_node = std::static_pointer_cast(node); - if (param_node == nullptr) { - MS_LOG(ERROR) << "Parameter node is null, can not get graph parameter"; - return false; - } - if (param_node->name() == param_name) { - py::object parameter = param_node->default_param(); - ValuePtr value = parse::data_converter::PyDataToValue(parameter); - TensorPtr tensor = std::dynamic_pointer_cast(value); - if (tensor == nullptr) { - shape->push_back(ONE_SHAPE); - } else { - *shape = tensor->shape(); - } - return true; - } - } - MS_LOG(ERROR) << "Can not find parameter of name:" << param_name; - return false; -} - -static TensorPtr GetMeTensorTransformed(uint32_t graph_id, const std::string& parameter_name, - const std::shared_ptr& ge_tensor_ptr) { - FuncGraphPtr anf_graph = transform::DfGraphManager::GetInstance().GetAnfGraph(graph_id); - if (anf_graph == nullptr) { - MS_LOG(ERROR) << "Get anf graph failed during callback"; - return nullptr; - } - - std::shared_ptr> parameter_shape_ptr = std::make_shared>(); - if (!GetParameterShape(anf_graph, parameter_name, parameter_shape_ptr)) { - MS_LOG(ERROR) << "Can not get parameter shape during callback"; - return nullptr; - } - - return TransformUtil::ConvertGeTensor(ge_tensor_ptr, *parameter_shape_ptr); -} - -uint32_t CheckpointSaveCallback(uint32_t graph_id, const std::map& params_list) { - // Acquire GIL before calling Python code - py::gil_scoped_acquire acquire; - - MS_LOG(DEBUG) << "Start the checkpoint save callback function in checkpoint save process."; - py::list parameter_list = py::list(); - for (auto& item : params_list) { - std::string name = item.first; - std::shared_ptr ge_tensor_ptr = std::make_shared(item.second); - TensorPtr tensor_ptr = GetMeTensorTransformed(graph_id, name, ge_tensor_ptr); - if (tensor_ptr == nullptr) { - MS_LOG(EXCEPTION) << "Transform ge tensor to me tensor failed"; - } - py::dict param_dict; - param_dict["name"] = name; - param_dict["data"] = tensor_ptr; - parameter_list.append(param_dict); - } - py::bool_ ret = - parse::python_adapter::CallPyFn(PYTHON_MOD_CALLBACK_MODULE, PYTHON_FUN_PROCESS_CHECKPOINT, parameter_list); - auto bool_ret = py::cast(ret); - - uint32_t status = Status::SUCCESS; - if (!bool_ret) { - status = Status::FAILED; - MS_LOG(ERROR) << "python checkpoint return false during callback"; - } - return status; -} - -static TensorPtr GetMeTensorForSummary(const std::string& name, const std::shared_ptr& ge_tensor_ptr) { - // confirm the type by name - // Format: xxx[:Scalar] xxx[:Image] xxx[:Tensor] - if (name.empty()) { - MS_LOG(EXCEPTION) << "The summary name is empty."; - } - auto bpos = name.rfind("[:"); - if (bpos >= name.size()) { - MS_LOG(EXCEPTION) << "The summary name(" << name << ") is invalid."; - } - auto tname = name.substr(bpos); - if (tname == "[:Scalar]") { - MS_LOG(DEBUG) << "The summary(" << name << ") is Scalar"; - // process the scalar type summary - // Because the ge tensor is dim = 4, so set the (1,1,1,1)-->(1,) - // We do the (1,) shape is scalar - auto shape = std::vector({ONE_SHAPE}); - return TransformUtil::ConvertGeTensor(ge_tensor_ptr, shape); - } - if (tname == "[:Tensor]") { - MS_LOG(DEBUG) << "The summary(" << name << ") is Tensor"; - // process the tensor summary - // Now we can't get the real shape, so we keep same shape with GE - return TransformUtil::ConvertGeTensor(ge_tensor_ptr); - } - if (tname == "[:Image]") { - MS_LOG(DEBUG) << "The summary(" << name << ") is Image"; - // process the Image summary - // Image dim = 4, is same with ge, so we keep same shape with GE - return TransformUtil::ConvertGeTensor(ge_tensor_ptr); - } - - MS_LOG(EXCEPTION) << "The summary name(" << name << ") is invalid."; -} - -// Cache the summary callback data -// Output Format: [{"name": tag_name, "data": tensor}, {"name": tag_name, "data": tensor},...] -uint32_t MS_EXPORT SummarySaveCallback(uint32_t graph_id, const std::map& params_list) { - // Acquire GIL before calling Python code - py::gil_scoped_acquire acquire; - - MS_LOG(DEBUG) << "Start the summary save callback function for graph " << graph_id << "."; - py::list summary_list = py::list(); - MS_LOG(DEBUG) << "Param list size = " << params_list.size(); - for (auto& item : params_list) { - std::string tag_name = item.first; - std::shared_ptr ge_tensor_ptr = std::make_shared(item.second); - TensorPtr tensor_ptr = GetMeTensorForSummary(tag_name, ge_tensor_ptr); - if (tensor_ptr == nullptr) { - MS_LOG(EXCEPTION) << "ConvertGeTensor return tensor is null"; - } - py::dict summary_value_dict; - summary_value_dict["name"] = tag_name; - summary_value_dict["data"] = tensor_ptr; - summary_list.append(summary_value_dict); - } - - py::bool_ ret = parse::python_adapter::CallPyFn(PYTHON_MOD_CALLBACK_MODULE, PYTHON_FUN_PROCESS_SUMMARY, summary_list); - auto bool_ret = py::cast(ret); - if (!bool_ret) { - MS_LOG(ERROR) << "Python checkpoint return false during callback"; - return Status::FAILED; - } - MS_LOG(DEBUG) << "End the summary save callback function."; - return Status::SUCCESS; -} -#endif - // Cache the summary callback data from ME session // Remove the GE module on new architecture // Output Format: [{"name": tag_name, "data": tensor}, {"name": tag_name, "data": tensor},...] diff --git a/mindspore/ccsrc/utils/callbacks.h b/mindspore/ccsrc/utils/callbacks.h index 778b0a9ba2..a1e4e75d5b 100644 --- a/mindspore/ccsrc/utils/callbacks.h +++ b/mindspore/ccsrc/utils/callbacks.h @@ -21,10 +21,6 @@ #include #include #include "ir/meta_tensor.h" -#ifdef ENABLE_GE -#include "transform/types.h" -#include "transform/util.h" -#endif namespace mindspore { namespace callbacks { @@ -45,10 +41,6 @@ const int kCallbackFalied = 1; bool GetParameterShape(const FuncGraphPtr& anf_graph, const std::string& param_name, const std::shared_ptr>& shape); -#ifdef ENABLE_GE -uint32_t CheckpointSaveCallback(uint32_t, const std::map&); -uint32_t SummarySaveCallback(uint32_t, const std::map&); -#endif uint32_t SummarySaveCallback(uint32_t, const std::map&); } // namespace callbacks diff --git a/mindspore/ccsrc/utils/callbacks_ge.cc b/mindspore/ccsrc/utils/callbacks_ge.cc new file mode 100644 index 0000000000..50fd2f0b11 --- /dev/null +++ b/mindspore/ccsrc/utils/callbacks_ge.cc @@ -0,0 +1,182 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "utils/callbacks_ge.h" +#include "pybind11/pybind11.h" +#include "transform/df_graph_manager.h" +#include "transform/util.h" +#include "pipeline/parse/data_converter.h" +#include "pipeline/parse/python_adapter.h" +#include "utils/visible.h" + +namespace mindspore { +namespace callbacks { + +const char PYTHON_MOD_CALLBACK_MODULE[] = "mindspore.train.callback"; +const char PYTHON_FUN_PROCESS_CHECKPOINT[] = "_checkpoint_cb_for_save_op"; +const char PYTHON_FUN_PROCESS_SUMMARY[] = "_summary_cb_for_save_op"; +const char kSummary[] = "Summary"; +const char kCheckPoint[] = "Save"; +const int ONE_SHAPE = 1; + +using mindspore::transform::Status; +using mindspore::transform::TransformUtil; + +bool GetParameterShape(const FuncGraphPtr& graph, const std::string& param_name, + const std::shared_ptr>& shape) { + if (graph == nullptr) { + MS_LOG(ERROR) << "Graph is null, can not get graph parameter"; + return false; + } + + auto parameter_nodes = graph->parameters(); + for (auto& node : parameter_nodes) { + ParameterPtr param_node = std::static_pointer_cast(node); + if (param_node == nullptr) { + MS_LOG(ERROR) << "Parameter node is null, can not get graph parameter"; + return false; + } + if (param_node->name() == param_name) { + py::object parameter = param_node->default_param(); + ValuePtr value = parse::data_converter::PyDataToValue(parameter); + TensorPtr tensor = std::dynamic_pointer_cast(value); + if (tensor == nullptr) { + shape->push_back(ONE_SHAPE); + } else { + *shape = tensor->shape(); + } + return true; + } + } + MS_LOG(ERROR) << "Can not find parameter of name:" << param_name; + return false; +} + +static TensorPtr GetMeTensorTransformed(uint32_t graph_id, const std::string& parameter_name, + const std::shared_ptr& ge_tensor_ptr) { + FuncGraphPtr anf_graph = transform::DfGraphManager::GetInstance().GetAnfGraph(graph_id); + if (anf_graph == nullptr) { + MS_LOG(ERROR) << "Get anf graph failed during callback"; + return nullptr; + } + + std::shared_ptr> parameter_shape_ptr = std::make_shared>(); + if (!GetParameterShape(anf_graph, parameter_name, parameter_shape_ptr)) { + MS_LOG(ERROR) << "Can not get parameter shape during callback"; + return nullptr; + } + + return TransformUtil::ConvertGeTensor(ge_tensor_ptr, *parameter_shape_ptr); +} + +uint32_t CheckpointSaveCallback(uint32_t graph_id, const std::map& params_list) { + // Acquire GIL before calling Python code + py::gil_scoped_acquire acquire; + + MS_LOG(DEBUG) << "Start the checkpoint save callback function in checkpoint save process."; + py::list parameter_list = py::list(); + for (auto& item : params_list) { + std::string name = item.first; + std::shared_ptr ge_tensor_ptr = std::make_shared(item.second); + TensorPtr tensor_ptr = GetMeTensorTransformed(graph_id, name, ge_tensor_ptr); + if (tensor_ptr == nullptr) { + MS_LOG(EXCEPTION) << "Transform ge tensor to me tensor failed"; + } + py::dict param_dict; + param_dict["name"] = name; + param_dict["data"] = tensor_ptr; + parameter_list.append(param_dict); + } + py::bool_ ret = + parse::python_adapter::CallPyFn(PYTHON_MOD_CALLBACK_MODULE, PYTHON_FUN_PROCESS_CHECKPOINT, parameter_list); + auto bool_ret = py::cast(ret); + + uint32_t status = Status::SUCCESS; + if (!bool_ret) { + status = Status::FAILED; + MS_LOG(ERROR) << "Python checkpoint return false during callback"; + } + return status; +} + +static TensorPtr GetMeTensorForSummary(const std::string& name, const std::shared_ptr& ge_tensor_ptr) { + // confirm the type by name + // Format: xxx[:Scalar] xxx[:Image] xxx[:Tensor] + if (name.empty()) { + MS_LOG(EXCEPTION) << "The summary name is empty."; + } + auto bpos = name.rfind("[:"); + if (bpos >= name.size()) { + MS_LOG(EXCEPTION) << "The summary name(" << name << ") is invalid."; + } + auto tname = name.substr(bpos); + if (tname == "[:Scalar]") { + MS_LOG(DEBUG) << "The summary(" << name << ") is Scalar"; + // process the scalar type summary + // Because the ge tensor is dim = 4, so set the (1,1,1,1)-->(1,) + // We do the (1,) shape is scalar + auto shape = std::vector({ONE_SHAPE}); + return TransformUtil::ConvertGeTensor(ge_tensor_ptr, shape); + } + if (tname == "[:Tensor]") { + MS_LOG(DEBUG) << "The summary(" << name << ") is Tensor"; + // process the tensor summary + // Now we can't get the real shape, so we keep same shape with GE + return TransformUtil::ConvertGeTensor(ge_tensor_ptr); + } + if (tname == "[:Image]") { + MS_LOG(DEBUG) << "The summary(" << name << ") is Image"; + // process the Image summary + // Image dim = 4, is same with ge, so we keep same shape with GE + return TransformUtil::ConvertGeTensor(ge_tensor_ptr); + } + + MS_LOG(EXCEPTION) << "The summary name(" << name << ") is invalid."; +} + +// Cache the summary callback data +// Output Format: [{"name": tag_name, "data": tensor}, {"name": tag_name, "data": tensor},...] +uint32_t MS_EXPORT SummarySaveCallback(uint32_t graph_id, const std::map& params_list) { + // Acquire GIL before calling Python code + py::gil_scoped_acquire acquire; + + MS_LOG(DEBUG) << "Start the summary save callback function for graph " << graph_id << "."; + py::list summary_list = py::list(); + MS_LOG(DEBUG) << "Param list size = " << params_list.size(); + for (auto& item : params_list) { + std::string tag_name = item.first; + std::shared_ptr ge_tensor_ptr = std::make_shared(item.second); + TensorPtr tensor_ptr = GetMeTensorForSummary(tag_name, ge_tensor_ptr); + if (tensor_ptr == nullptr) { + MS_LOG(EXCEPTION) << "ConvertGeTensor return tensor is null"; + } + py::dict summary_value_dict; + summary_value_dict["name"] = tag_name; + summary_value_dict["data"] = tensor_ptr; + summary_list.append(summary_value_dict); + } + + py::bool_ ret = parse::python_adapter::CallPyFn(PYTHON_MOD_CALLBACK_MODULE, PYTHON_FUN_PROCESS_SUMMARY, summary_list); + auto bool_ret = py::cast(ret); + if (!bool_ret) { + MS_LOG(ERROR) << "Python checkpoint return false during callback"; + return Status::FAILED; + } + MS_LOG(DEBUG) << "End the summary save callback function."; + return Status::SUCCESS; +} +} // namespace callbacks +} // namespace mindspore diff --git a/mindspore/ccsrc/utils/callbacks_ge.h b/mindspore/ccsrc/utils/callbacks_ge.h new file mode 100644 index 0000000000..750ec74666 --- /dev/null +++ b/mindspore/ccsrc/utils/callbacks_ge.h @@ -0,0 +1,38 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef MINDSPORE_CCSRC_UTILS_CALLBACKS_GE_H_ +#define MINDSPORE_CCSRC_UTILS_CALLBACKS_GE_H_ + +#include +#include +#include +#include +#include "transform/types.h" +#include "transform/util.h" +#include "ir/meta_tensor.h" + +namespace mindspore { +namespace callbacks { + +using mindspore::tensor::TensorPtr; + +uint32_t CheckpointSaveCallback(uint32_t, const std::map&); +uint32_t SummarySaveCallback(uint32_t, const std::map&); + +} // namespace callbacks +} // namespace mindspore + +#endif // MINDSPORE_CCSRC_UTILS_CALLBACKS_GE_H_ diff --git a/mindspore/ccsrc/vm/backend.cc b/mindspore/ccsrc/vm/backend.cc index 28609abfa9..9355cca99c 100644 --- a/mindspore/ccsrc/vm/backend.cc +++ b/mindspore/ccsrc/vm/backend.cc @@ -24,6 +24,9 @@ #include "utils/graph_utils.h" #include "session/session_factory.h" #include "common/utils.h" +#ifdef ENABLE_GE +#include "utils/callbacks_ge.h" +#endif namespace mindspore { namespace compile { diff --git a/tests/ut/cpp/utils/callback_test.cc b/tests/ut/cpp/utils/callback_test.cc index 758e99ff59..c63f68f000 100644 --- a/tests/ut/cpp/utils/callback_test.cc +++ b/tests/ut/cpp/utils/callback_test.cc @@ -22,6 +22,9 @@ #include "pipeline/parse/python_adapter.h" #include "transform/df_graph_manager.h" #include "debug/draw.h" +#ifdef ENABLE_GE +#include "utils/callbacks_ge.h" +#endif namespace mindspore { namespace python_adapter = mindspore::parse::python_adapter; From ef2f03c1660cd74bae8c63145da26fe930295ebc Mon Sep 17 00:00:00 2001 From: chengang Date: Tue, 7 Apr 2020 16:56:33 +0800 Subject: [PATCH 109/227] =?UTF-8?q?=E5=9B=9E=E9=80=80=20'Pull=20Request=20?= =?UTF-8?q?!133=20:=20Edit=20loss=5Fscale=20to=20fit=20GPU'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- mindspore/nn/wrap/loss_scale.py | 40 ++++---------- mindspore/ops/operations/__init__.py | 6 +- mindspore/ops/operations/math_ops.py | 83 ---------------------------- 3 files changed, 12 insertions(+), 117 deletions(-) diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py index 4d929352b3..1ce3179273 100644 --- a/mindspore/nn/wrap/loss_scale.py +++ b/mindspore/nn/wrap/loss_scale.py @@ -25,7 +25,6 @@ from ...ops import operations as P from ...ops.operations import NPUGetFloatStatus, NPUAllocFloatStatus, NPUClearFloatStatus, ReduceSum, LessEqual, \ ControlDepend from ...common import dtype as mstype -import mindspore.context as context _grad_scale = C.MultitypeFuncGraph("grad_scale") reciprocal = P.Reciprocal() @@ -35,12 +34,6 @@ reciprocal = P.Reciprocal() def tensor_grad_scale(scale, grad): return grad * F.cast(reciprocal(scale), F.dtype(grad)) -_grad_overflow = C.MultitypeFuncGraph("_grad_overflow") -grad_overflow = P.FloatStatus() - -@_grad_overflow.register("Tensor") -def _tensor_grad_overflow(grad): - return grad_overflow(grad) class DynamicLossScaleUpdateCell(Cell): r""" @@ -204,15 +197,9 @@ class TrainOneStepWithLossScaleCell(Cell): self.optimizer = optimizer self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True) self.hyper_map = C.HyperMap() - if context.get_context("device_target") == "GPU": - self.gpu_target = True - self.float_status = P.FloatStatus() - self.addn = P.AddN() - else: - self.gpu_target = False - self.alloc_status = NPUAllocFloatStatus() - self.get_status = NPUGetFloatStatus() - self.clear_status = NPUClearFloatStatus() + self.alloc_status = NPUAllocFloatStatus() + self.get_status = NPUGetFloatStatus() + self.clear_status = NPUClearFloatStatus() self.reduce_sum = ReduceSum(keep_dims=False) self.base = Tensor(1, mstype.float32) self.less_equal = LessEqual() @@ -237,11 +224,10 @@ class TrainOneStepWithLossScaleCell(Cell): def construct(self, data, label, sens=None): weights = self.weights loss = self.network(data, label) - if not self.gpu_target: - # init overflow buffer - init = self.alloc_status() - # clear overflow buffer - self.clear_status(init) + # init overflow buffer + init = self.alloc_status() + # clear overflow buffer + self.clear_status(init) if sens is None: scaling_sens = self.loss_scale else: @@ -251,14 +237,10 @@ class TrainOneStepWithLossScaleCell(Cell): if self.reducer_flag: # apply grad reducer on grads grads = self.grad_reducer(grads) - if not self.gpu_target: - # get the overflow buffer - self.get_status(init) - # sum overflow buffer elements, 0:not overflow , >0:overflow - flag_sum = self.reduce_sum(init, (0,)) - else: - flag_sum = self.hyper_map(F.partial(_grad_overflow), grads) - flag_sum = self.addn(flag_sum) + # get the overflow buffer + self.get_status(init) + # sum overflow buffer elements, 0:not overflow , >0:overflow + flag_sum = self.reduce_sum(init, (0,)) if self.is_distributed: # sum overflow flag over devices flag_reduce = self.allreduce(flag_sum) diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index 48a985b33d..37a3b38bb6 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -44,7 +44,7 @@ from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul LogicalNot, LogicalOr, MatMul, Maximum, Minimum, Mul, Neg, NMSWithMask, NotEqual, NPUAllocFloatStatus, NPUClearFloatStatus, - NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus, + NPUGetFloatStatus, Pow, RealDiv, Reciprocal, CumSum, Sin, Sqrt, Rsqrt, Square, Sub, TensorAdd, Sign, Round) @@ -151,10 +151,6 @@ __all__ = [ 'Neg', 'Slice', 'DType', - 'IsNan', - 'IsInf', - 'IsFinite', - 'FloatStatus', 'NPUAllocFloatStatus', 'NPUGetFloatStatus', 'NPUClearFloatStatus', diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 5f8c24d78b..175b72560f 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1541,89 +1541,6 @@ class LogicalOr(_LogicBinaryOp): def infer_dtype(self, x_dtype, y_dtype): return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.prim_name()) -class IsNan(PrimitiveWithInfer): - """ - Judging which elements are nan for each position - Inputs: - - **input_x** (Tensor) - The input tensor. - - Outputs: - Tensor, has the same shape of input. - """ - - @prim_attr_register - def __init__(self): - """init IsNan""" - self.init_prim_io_names(inputs=['x'], outputs=['output']) - - def infer_shape(self, x_shape): - return x_shape - - def infer_dtype(self, x_dtype): - return mstype.bool_ - -class IsInf(PrimitiveWithInfer): - """ - Judging which elements are inf or -inf for each position - Inputs: - - **input_x** (Tensor) - The input tensor. - - Outputs: - Tensor, has the same shape of input. - """ - - @prim_attr_register - def __init__(self): - """init IsInf""" - self.init_prim_io_names(inputs=['x'], outputs=['output']) - - def infer_shape(self, x_shape): - return x_shape - - def infer_dtype(self, x_dtype): - return mstype.bool_ - -class IsFinite(PrimitiveWithInfer): - """ - Judging which elements are finite for each position - Inputs: - - **input_x** (Tensor) - The input tensor. - - Outputs: - Tensor, has the same shape of input. - """ - - @prim_attr_register - def __init__(self): - """init IsFinite""" - self.init_prim_io_names(inputs=['x'], outputs=['output']) - - def infer_shape(self, x_shape): - return x_shape - - def infer_dtype(self, x_dtype): - return mstype.bool_ - -class FloatStatus(PrimitiveWithInfer): - """ - Determine if the elements contains nan, inf or -inf - Inputs: - - **input_x** (Tensor) - The input tensor. - - Outputs: - Tensor, has the shape of `(1,)`. - """ - - @prim_attr_register - def __init__(self): - """init FloatStatus""" - self.init_prim_io_names(inputs=['x'], outputs=['output']) - - def infer_shape(self, x_shape): - return [1] - - def infer_dtype(self, x_dtype): - return x_dtype class NPUAllocFloatStatus(PrimitiveWithInfer): """ From 9f427befd16eb56471758966161fe40d6a38a399 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Tue, 7 Apr 2020 22:59:52 +0800 Subject: [PATCH 110/227] utf --- graphengine | 2 +- mindspore/ccsrc/transform/convert.cc | 5 +---- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/graphengine b/graphengine index 78a40081fc..32f0696599 160000 --- a/graphengine +++ b/graphengine @@ -1 +1 @@ -Subproject commit 78a40081fc1462ab9e2083d37e5fbe608a57dd06 +Subproject commit 32f06965993ca25ea936239d284e992c5512d2ff diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index f6697dbbd2..b194a0b3ca 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -171,9 +171,9 @@ const char kNameAbsGrad[] = "AbsGrad"; const char kNameBinaryCrossEntropy[] = "BinaryCrossEntropy"; const char kNameBinaryCrossEntropyGrad[] = "BinaryCrossEntropyGrad"; const char kNameSparseApplyAdagrad[] = "SparseApplyAdagrad"; -const char kNameSparseApplyFtrlD[] = "SparseApplyFtrlD"; const char kNameAcosh[] = "Acosh"; const char kNameFloorMod[] = "FloorMod"; +const char kNameSparseApplyFtrlD[] = "SparseApplyFtrlD"; const char kNameSpaceToDepth[] = "SpaceToDepth"; const char kNameDepthToSpace[] = "DepthToSpace"; const char kNameSign[] = "Sign"; @@ -363,12 +363,9 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameBinaryCrossEntropy), ADPT_DESC(BinaryCrossEntropy)}, {string(kNameBinaryCrossEntropyGrad), ADPT_DESC(BinaryCrossEntropyGrad)}, {string(kNameSparseApplyAdagrad), ADPT_DESC(SparseApplyAdagradD)}, -<<<<<<< HEAD {string(kNameSparseApplyFtrlD), ADPT_DESC(SparseApplyFtrlD)}, -======= {string(kNameAcosh), ADPT_DESC(Acosh)}, {string(kNameFloorMod), ADPT_DESC(FloorMod)}, ->>>>>>> Add FloorMod, Acosh in ME {string(kNameSpaceToDepth), ADPT_DESC(SpaceToDepth)}, {string(kNameDepthToSpace), ADPT_DESC(DepthToSpace)}, {string(kNameSign), ADPT_DESC(Sign)}, From 88d173cc48724078289e3c18a93368c12f3fc0d5 Mon Sep 17 00:00:00 2001 From: simson <526422051@qq.com> Date: Thu, 9 Apr 2020 09:20:08 +0800 Subject: [PATCH 111/227] rebuild graph if needed --- mindspore/ccsrc/transform/graph_runner.cc | 7 +++++++ mindspore/ccsrc/utils/context/ms_context.cc | 4 +++- 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/mindspore/ccsrc/transform/graph_runner.cc b/mindspore/ccsrc/transform/graph_runner.cc index e77b1bcd73..2bff1a740c 100644 --- a/mindspore/ccsrc/transform/graph_runner.cc +++ b/mindspore/ccsrc/transform/graph_runner.cc @@ -135,6 +135,13 @@ Status GraphRunner::RunGraph(const RunOptions& options, const std::vectorIsGraphNeedRebuild(wrap_ptr->id_)) { + sess_->RemoveGraph(wrap_ptr->id_); + sess_->AddGraph(wrap_ptr->id_, *(wrap_ptr->graph_ptr_), wrap_ptr->options_); + } + ge::Status ret = sess_->RunGraph(wrap_ptr->id_, ge_inputs, ge_outputs); if (ret != ge::GRAPH_SUCCESS) { MS_LOG(ERROR) << "Call GE RunGraph Failed, ret is: " << ret; diff --git a/mindspore/ccsrc/utils/context/ms_context.cc b/mindspore/ccsrc/utils/context/ms_context.cc index bf05af9858..e9b4586b21 100644 --- a/mindspore/ccsrc/utils/context/ms_context.cc +++ b/mindspore/ccsrc/utils/context/ms_context.cc @@ -355,7 +355,9 @@ void MsContext::GetGeOptions(std::map* ge_options) con MS_LOG(ERROR) << "Set proto lib path failed!"; } - // Disbale the global variable acc, only enable it whlie adding training graph in pipeline + // Enable auto mixed precision according to the context options + (*ge_options)["ge.exec.auto_mix_precision"] = std::to_string(auto_mixed_precision_flag_); + // Disable the global variable acc, only enable it whlie adding training graph in pipeline (*ge_options)["ge.exec.variable_acc"] = "0"; #endif } From 1d3bb0b7312ea74202748280d9c9fde12e1d1936 Mon Sep 17 00:00:00 2001 From: buxue Date: Thu, 2 Apr 2020 11:58:45 +0800 Subject: [PATCH 112/227] Develop op MaxPoolWithArgMax --- mindspore/ccsrc/kernel/tbe/tbe_adapter.cc | 44 --- mindspore/ccsrc/transform/convert.cc | 2 + mindspore/ccsrc/transform/op_declare.cc | 39 +- mindspore/ccsrc/transform/op_declare.h | 4 +- mindspore/model_zoo/resnet.py | 2 +- mindspore/nn/layer/pooling.py | 141 ++++---- mindspore/ops/_grad/grad_nn_ops.py | 11 +- .../_op_impl/tbe/max_pool_grad_with_argmax.py | 6 +- .../ops/_op_impl/tbe/max_pool_with_argmax.py | 6 +- mindspore/ops/operations/_grad_ops.py | 121 +++---- mindspore/ops/operations/nn_ops.py | 338 +++++++++--------- tests/perf_test/resnet_example.py | 2 +- tests/st/networks/test_cpu_lenet.py | 6 +- tests/st/networks/test_gpu_alexnet.py | 2 +- .../ops/davinci/test_maxpool_with_argmax.py | 15 +- .../davinci/test_maxpool_with_argmax_grad.py | 6 +- tests/st/tbe_networks/resnet.py | 2 +- .../gtest_input/pre_activate/hw_opt_test.py | 2 +- .../pre_activate/insert_trans_op_test.py | 2 +- .../pre_activate/mixed_precision_test.py | 2 +- .../pre_activate/transdata_split_test.py | 2 +- .../transpose_transdata_fusion_test.py | 2 +- .../gtest_input/session/session_test.py | 2 +- .../test_data_parallel_resnet.py | 4 +- tests/ut/python/exec/resnet_example.py | 2 +- tests/ut/python/exec/test_pooling.py | 6 +- tests/ut/python/model/res18_example.py | 4 +- tests/ut/python/nn/test_cell.py | 2 +- tests/ut/python/nn/test_pooling.py | 3 +- tests/ut/python/ops/test_nn_ops.py | 105 +++--- tests/ut/python/ops/test_ops.py | 2 +- tests/ut/python/ops/test_ops_check.py | 8 +- .../pynative_mode/ge/ops/test_pooling.py | 4 +- tests/ut/python/pynative_mode/nn/test_cell.py | 2 +- .../python/pynative_mode/nn/test_pooling.py | 11 +- tests/ut/python/pynative_mode/vm/test_vm.py | 8 +- tests/ut/python/utils/test_serialize.py | 2 +- tests/vm_impl/nn_ops_vm_impl.py | 90 ++++- tests/vm_impl/vm_me.py | 46 ++- 39 files changed, 542 insertions(+), 516 deletions(-) mode change 100755 => 100644 mindspore/ccsrc/transform/op_declare.cc diff --git a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc index c0416f648b..229a3eb34a 100644 --- a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc +++ b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc @@ -148,8 +148,6 @@ void TbeAdapter::InputOrderPass(const std::string &op_name, std::vector TbeAdapter::build_json_attr_pass_map_ = { - {"MaxPoolWithArgmax", TbeAdapter::MaxPoolWithArgmaxAttrJsonPass}, - {"MaxPoolGradWithArgmax", TbeAdapter::MaxPoolGradWithArgmaxAttrJsonPass}, {"Conv2D", TbeAdapter::Conv2DAttrJsonPass}, {"Conv2DBackpropFilter", TbeAdapter::Conv2DBackpropFilterAttrJsonPass}, {"Conv2DBackpropInput", TbeAdapter::Conv2DBackpropInputAttrJsonPass}, @@ -170,48 +168,6 @@ bool TbeAdapter::RunAttrPass(const mindspore::AnfNodePtr &anf_node, return false; } -void TbeAdapter::MaxPoolWithArgmaxAttrJsonPass( - const mindspore::AnfNodePtr &anf_node, const std::vector> &op_info_attrs, - nlohmann::json *attrs_json) { - MS_EXCEPTION_IF_NULL(anf_node); - MS_EXCEPTION_IF_NULL(attrs_json); - auto attr_num = op_info_attrs.size(); - auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); - MS_EXCEPTION_IF_NULL(primitive); - for (size_t i = 0; i < attr_num; i++) { - nlohmann::json attr_obj; - MS_EXCEPTION_IF_NULL(op_info_attrs[i]); - std::string attr_name = op_info_attrs[i]->name(); - if (primitive->GetAttr(attr_name) != nullptr) { - auto value = primitive->GetAttr(attr_name); - if (attr_name == "pad_mode") { - std::string attr_value = GetValue(value); - (void)transform(attr_value.begin(), attr_value.end(), attr_value.begin(), ::toupper); - attr_obj["value"] = attr_value; - } else { - std::vector attr_value; - int data = GetValue(value); - attr_value.push_back(1); - attr_value.push_back(data); - attr_value.push_back(data); - attr_value.push_back(1); - attr_obj["value"] = attr_value; - } - attr_obj["valid"] = true; - } else { - attr_obj["valid"] = false; - } - attr_obj["name"] = attr_name; - attrs_json->push_back(attr_obj); - } -} - -void TbeAdapter::MaxPoolGradWithArgmaxAttrJsonPass( - const mindspore::AnfNodePtr &anf_node, const std::vector> &op_info_attrs, - nlohmann::json *attrs_json) { - MaxPoolWithArgmaxAttrJsonPass(anf_node, op_info_attrs, attrs_json); -} - void TbeAdapter::Conv2DAttrJsonPass(const mindspore::AnfNodePtr &anf_node, const std::vector> &op_info_attrs, nlohmann::json *attrs_json) { diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index b194a0b3ca..d2a11948ef 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -161,6 +161,7 @@ const char kNameTopK[] = "TopK"; const char kNameSoftmaxGrad[] = "SoftmaxGrad"; const char kNameMaxPool[] = "MaxPool"; const char kNameAvgPool[] = "AvgPool"; +const char kNameMaxPoolWithArgmax[] = "MaxPoolWithArgmax"; const char kNameBatchNorm[] = "BatchNorm"; const char kNameBatchNormGrad[] = "BatchNormGrad"; const char kNameROIAlign[] = "ROIAlign"; @@ -199,6 +200,7 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameApplyMomentum), ADPT_DESC(ApplyMomentum)}, {string(kNameMaxPool), ADPT_DESC(MaxPool)}, {string(kNameAvgPool), ADPT_DESC(AvgPool)}, + {string(kNameMaxPoolWithArgmax), ADPT_DESC(MaxPoolWithArgmax)}, {string(kNameTopK), ADPT_DESC(TopK)}, {string(kNamePack), ADPT_DESC(Pack)}, {string(kNameSplitD), ADPT_DESC(SplitD)}, diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc old mode 100755 new mode 100644 index 16e089ed21..4149280404 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -192,8 +192,7 @@ ATTR_MAP(PRelu) = EMPTY_ATTR_MAP; OUTPUT_MAP(PRelu) = {{0, OUTPUT_DESC(y)}}; // PReluGrad -INPUT_MAP(PReluGrad) = { - {1, INPUT_DESC(grads)}, {2, INPUT_DESC(features)}, {3, INPUT_DESC(weights)}}; +INPUT_MAP(PReluGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(features)}, {3, INPUT_DESC(weights)}}; ATTR_MAP(PReluGrad) = EMPTY_ATTR_MAP; OUTPUT_MAP(PReluGrad) = {{0, OUTPUT_DESC(dx)}, {1, OUTPUT_DESC(da)}}; @@ -702,24 +701,30 @@ ATTR_MAP(AvgPoolGrad) = {{"ksize", ATTR_DESC(ksize, AnyTraits(), AnyTraits< OUTPUT_MAP(AvgPoolGrad) = {{0, OUTPUT_DESC(out_grad)}}; // MaxPoolWithArgmax +INPUT_MAP(MaxPoolWithArgmax) = {{1, INPUT_DESC(x)}}; +ATTR_MAP(MaxPoolWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits(), AnyTraits>())}, + {"strides", ATTR_DESC(strides, AnyTraits(), AnyTraits>())}, + {"padding", ATTR_DESC(padding, AnyTraits())}}; +OUTPUT_MAP(MaxPoolWithArgmax) = {{0, OUTPUT_DESC(y)}, {1, OUTPUT_DESC(argmax)}}; + +// MaxPoolGradWithArgmax INPUT_MAP(MaxPoolGradWithArgmax) = { {1, INPUT_DESC(x)}, - {2, INPUT_DESC(argmax)}, - {3, INPUT_DESC(grad)}, + {2, INPUT_DESC(grad)}, + {3, INPUT_DESC(argmax)}, }; -ATTR_MAP(MaxPoolGradWithArgmax) = {{"pad_mode", ATTR_DESC(padding, AnyTraits())}, - {"window", ATTR_DESC(ksize, "window", AnyTraits>())}, - {"stride", ATTR_DESC(strides, "stride", AnyTraits>())}}; +ATTR_MAP(MaxPoolGradWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits(), AnyTraits>())}, + {"strides", ATTR_DESC(strides, AnyTraits(), AnyTraits>())}, + {"padding", ATTR_DESC(padding, AnyTraits())}}; +OUTPUT_MAP(MaxPoolGradWithArgmax) = {{0, OUTPUT_DESC(y)}}; // Conv2D INPUT_MAP(Conv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}}; -ATTR_MAP(Conv2D) = { - {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, - {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, - {"data_format", ATTR_DESC(data_format, AnyTraits())}, - {"group", ATTR_DESC(groups, AnyTraits())} -}; +ATTR_MAP(Conv2D) = {{"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, + {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, + {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, + {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"group", ATTR_DESC(groups, AnyTraits())}}; OUTPUT_MAP(Conv2D) = {{0, OUTPUT_DESC(y)}}; // Conv2DBackpropInputD @@ -731,8 +736,7 @@ ATTR_MAP(Conv2DBackpropInputD) = { {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, {"data_format", ATTR_DESC(data_format, AnyTraits())}, - {"group", ATTR_DESC(groups, AnyTraits())} -}; + {"group", ATTR_DESC(groups, AnyTraits())}}; OUTPUT_MAP(Conv2DBackpropInputD) = {{0, OUTPUT_DESC(y)}}; // Conv2DBackpropFilterD @@ -744,8 +748,7 @@ ATTR_MAP(Conv2DBackpropFilterD) = { {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, {"data_format", ATTR_DESC(data_format, AnyTraits())}, - {"group", ATTR_DESC(groups, AnyTraits())} -}; + {"group", ATTR_DESC(groups, AnyTraits())}}; OUTPUT_MAP(Conv2DBackpropFilterD) = {{0, OUTPUT_DESC(y)}}; // DepthwiseConv2D diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index d358647d26..9fbc97f3c9 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -88,8 +88,10 @@ DECLARE_OP_ADAPTER(FusedBatchNormGrad) DECLARE_OP_USE_OUTPUT(FusedBatchNormGrad) DECLARE_OP_ADAPTER(BiasAddGrad) DECLARE_OP_USE_OUTPUT(BiasAddGrad) +DECLARE_OP_ADAPTER(MaxPoolWithArgmax) +DECLARE_OP_USE_OUTPUT(MaxPoolWithArgmax) DECLARE_OP_ADAPTER(MaxPoolGradWithArgmax) -DECLARE_OP_USE_ENUM(MaxPoolGradWithArgmax) +DECLARE_OP_USE_OUTPUT(MaxPoolGradWithArgmax) DECLARE_OP_ADAPTER(Conv2D) DECLARE_OP_USE_ENUM(Conv2D) DECLARE_OP_USE_OUTPUT(Conv2D) diff --git a/mindspore/model_zoo/resnet.py b/mindspore/model_zoo/resnet.py index 403f66e415..9d010eede1 100755 --- a/mindspore/model_zoo/resnet.py +++ b/mindspore/model_zoo/resnet.py @@ -168,7 +168,7 @@ class ResNet(nn.Cell): self.conv1 = _conv7x7(3, 64, stride=2) self.bn1 = _bn(64) self.relu = P.ReLU() - self.maxpool = P.MaxPoolWithArgmax(pad_mode='same', window=3, stride=2) + self.maxpool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2) self.layer1 = self._make_layer(block, layer_nums[0], diff --git a/mindspore/nn/layer/pooling.py b/mindspore/nn/layer/pooling.py index 6ff28dd362..5d9b0ffa6c 100644 --- a/mindspore/nn/layer/pooling.py +++ b/mindspore/nn/layer/pooling.py @@ -13,33 +13,49 @@ # limitations under the License. # ============================================================================ """pooling""" - from mindspore.ops import operations as P from mindspore._checkparam import ParamValidator as validator from mindspore._checkparam import Rel +from ... import context from ..cell import Cell class _PoolNd(Cell): """N-D AvgPool""" - def __init__(self, - kernel_size, - stride, - pad_mode, - padding=0, - pool=None): + def __init__(self, kernel_size, stride, pad_mode): + name = self.__class__.__name__ super(_PoolNd, self).__init__() + validator.check_type('kernel_size', kernel_size, [int, tuple]) + validator.check_type('stride', stride, [int, tuple]) + self.pad_mode = validator.check_string('pad_mode', pad_mode.upper(), ['VALID', 'SAME']) + + if isinstance(kernel_size, int): + validator.check_integer("kernel_size", kernel_size, 1, Rel.GE) + else: + if (len(kernel_size) != 2 or + (not isinstance(kernel_size[0], int)) or + (not isinstance(kernel_size[1], int)) or + kernel_size[0] <= 0 or + kernel_size[1] <= 0): + raise ValueError(f'The kernel_size passed to cell {name} should be an positive int number or' + f'a tuple of two positive int numbers, but got {kernel_size}') self.kernel_size = kernel_size + + if isinstance(stride, int): + validator.check_integer("stride", stride, 1, Rel.GE) + else: + if (len(stride) != 2 or + (not isinstance(stride[0], int)) or + (not isinstance(stride[1], int)) or + stride[0] <= 0 or + stride[1] <= 0): + raise ValueError(f'The stride passed to cell {name} should be an positive int number or' + f'a tuple of two positive int numbers, but got {stride}') self.stride = stride - self.pad_mode = pad_mode - self.padding = validator.check_integer('padding', padding, 0, Rel.GE) - self.pool = pool - if self.pool is None: - raise NotImplementedError - def construct(self, x): - return self.pool(x) + def construct(self, *inputs): + pass def extend_repr(self): return 'kernel_size={kernel_size}, stride={stride}, pad_mode={pad_mode}'.format(**self.__dict__) @@ -63,19 +79,23 @@ class MaxPool2d(_PoolNd): pad_mode for training only supports "same" and "valid". Args: - kernel_size (int): Size of the window to take a max over. Default 1. - stride (int): Stride size of the window. Default: 1. - pad_mode (str): Select the mode of the pad. The optional values are - "same" and "valid". Default: "valid". + kernel_size (Union[int, tuple[int]]): The size of kernel used to take the max value, + is an int number that represents height and width are both kernel_size, + or a tuple of two int numbers that represent height and width respectively. + Default: 1. + stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents + the height and width of movement are both strides, or a tuple of two int numbers that + represent height and width of movement respectively. Default: 1. + pad_mode (str): The optional values for pad mode, is "same" or "valid", not case sensitive. + Default: "valid". - same: Adopts the way of completion. Output height and width will be the same as the input. Total number of padding will be calculated for horizontal and vertical - direction and evenly distributed to top and bottom, left and right if possible. Otherwise, the - last extra padding will be done from the bottom and the right side. + direction and evenly distributed to top and bottom, left and right if possible. + Otherwise, the last extra padding will be done from the bottom and the right side. - - valid: Adopts the way of discarding. The possibly largest height and width of output will be return - without padding. Extra pixels will be discarded. - padding (int): Implicit zero padding to be added on both sides. Default: 0. + - valid: Adopts the way of discarding. The possibly largest height and width of output + will be return without padding. Extra pixels will be discarded. Inputs: - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. @@ -103,31 +123,22 @@ class MaxPool2d(_PoolNd): [[7. 8.] [8. 8.]]]] """ - def __init__(self, - kernel_size=1, - stride=1, - pad_mode="VALID", - padding=0): - max_pool = P.MaxPool(ksize=kernel_size, - strides=stride, - padding=pad_mode) - self.is_autodiff_backend = False - if self.is_autodiff_backend: - - # At present, pad mode of max pool is not unified, so it is a temporarily avoided - pad_mode = validator.check_string('pad_mode', pad_mode.lower(), ['valid', 'same']) - - max_pool = P.MaxPoolWithArgmax(window=kernel_size, - stride=stride, - pad_mode=pad_mode, - pad=padding) - super(MaxPool2d, self).__init__(kernel_size, stride, pad_mode, padding, max_pool) + + def __init__(self, kernel_size=1, stride=1, pad_mode="valid"): + super(MaxPool2d, self).__init__(kernel_size, stride, pad_mode) + self.max_pool = P.MaxPool(ksize=self.kernel_size, + strides=self.stride, + padding=self.pad_mode) + self.max_pool_with_arg_max = P.MaxPoolWithArgmax(ksize=self.kernel_size, + strides=self.stride, + padding=self.pad_mode) + self.is_tbe = context.get_context("device_target") == "Ascend" def construct(self, x): - if self.is_autodiff_backend: - out = self.pool(x)[0] + if self.is_tbe and self.training: + out = self.max_pool_with_arg_max(x)[0] else: - out = self.pool(x) + out = self.max_pool(x) return out @@ -149,19 +160,24 @@ class AvgPool2d(_PoolNd): pad_mode for training only supports "same" and "valid". Args: - kernel_size (int): Size of the window to take a max over. Default: 1. - stride (int): Stride size of the window. Default: 1. - pad_mode (str): Select the mode of the pad. The optional values are - "same", "valid". Default: "valid". + kernel_size (Union[int, tuple[int]]): The size of kernel used to take the average value, + is an int number that represents height and width are both kernel_size, + or a tuple of two int numbers that represent height and width respectively. + Default: 1. + stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents + the height and width of movement are both strides, or a tuple of two int numbers that + represent height and width of movement respectively. Default: 1. + pad_mode (str): The optional values for pad mode, is "same" or "valid", not case sensitive. + Default: "valid". - same: Adopts the way of completion. Output height and width will be the same as the input. Total number of padding will be calculated for horizontal and vertical - direction and evenly distributed to top and bottom, left and right if possible. Otherwise, the - last extra padding will be done from the bottom and the right side. + direction and evenly distributed to top and bottom, left and right if possible. + Otherwise, the last extra padding will be done from the bottom and the right side. + + - valid: Adopts the way of discarding. The possibly largest height and width of output + will be return without padding. Extra pixels will be discarded. - - valid: Adopts the way of discarding. The possibly largest height and width of output will be return - without padding. Extra pixels will be discarded. - padding (int): Implicit zero padding to be added on both sides. Default: 0. Inputs: - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. @@ -170,7 +186,7 @@ class AvgPool2d(_PoolNd): Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`. Examples: - >>> pool = AvgPool2d(kernel_size=3, stride=1) + >>> pool = AvgPool2d(kernel_size=3, strides=1) >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32) [[[[5. 5. 9. 9.] [8. 4. 3. 0.] @@ -189,12 +205,15 @@ class AvgPool2d(_PoolNd): [[4.2222223 4.5555553] [3.2222223 4.5555553]]]] """ + def __init__(self, kernel_size=1, stride=1, - pad_mode="VALID", - padding=0): - avg_pool = P.AvgPool(ksize=kernel_size, - strides=stride, - padding=pad_mode) - super(AvgPool2d, self).__init__(kernel_size, stride, pad_mode, padding, avg_pool) + pad_mode="valid"): + super(AvgPool2d, self).__init__(kernel_size, stride, pad_mode) + self.avg_pool = P.AvgPool(ksize=self.kernel_size, + strides=self.stride, + padding=self.pad_mode) + + def construct(self, x): + return self.avg_pool(x) diff --git a/mindspore/ops/_grad/grad_nn_ops.py b/mindspore/ops/_grad/grad_nn_ops.py index bad99351a5..fbe48aff97 100755 --- a/mindspore/ops/_grad/grad_nn_ops.py +++ b/mindspore/ops/_grad/grad_nn_ops.py @@ -76,14 +76,9 @@ def get_bprop_depthwise_conv2d_native(self): def get_bprop_max_pool_with_argmax(self): """Grad definition for `MaxPoolWithArgmax` operation.""" maxpool_grad = G.MaxPoolGradWithArgmax( - pad_mode=self.pad_mode, - window=self.window, - pad=self.pad, - stride=self.stride, - data_mode=self.data_mode, - ceil_mode=self.ceil_mode, - alpha=self.alpha, - beta=self.beta) + ksize=self.ksize, + strides=self.strides, + padding=self.padding,) def bprop(x, out, dout): dx = maxpool_grad(x, dout[0], out[1]) diff --git a/mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py b/mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py index a167ef85f8..3730ee1b93 100644 --- a/mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py +++ b/mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py @@ -28,19 +28,19 @@ from mindspore.ops.op_info_register import op_info_register "partial_flag": true, "attr": [ { - "name": "window", + "name": "ksize", "param_type": "required", "type": "listInt", "value": "all" }, { - "name": "stride", + "name": "strides", "param_type": "required", "type": "listInt", "value": "all" }, { - "name": "pad_mode", + "name": "padding", "param_type": "required", "type": "str", "value": "all" diff --git a/mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py b/mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py index 04d0eeb92c..2e081c1082 100644 --- a/mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py +++ b/mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py @@ -28,19 +28,19 @@ from mindspore.ops.op_info_register import op_info_register "partial_flag": true, "attr": [ { - "name": "window", + "name": "ksize", "param_type": "required", "type": "listInt", "value": "all" }, { - "name": "stride", + "name": "strides", "param_type": "required", "type": "listInt", "value": "all" }, { - "name": "pad_mode", + "name": "padding", "param_type": "required", "type": "str", "value": "all" diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py index a699c23adc..f38044ab6a 100644 --- a/mindspore/ops/operations/_grad_ops.py +++ b/mindspore/ops/operations/_grad_ops.py @@ -15,7 +15,6 @@ """Operators for gradients.""" -import math from ..._c_expression import signature_rw as sig_rw from ..._c_expression import signature_kind as sig_kind from ..primitive import Primitive, PrimitiveWithInfer, prim_attr_register @@ -340,59 +339,60 @@ class _PoolGrad(PrimitiveWithInfer): """Gradients of the max/avg pool operation.""" @prim_attr_register - def __init__(self, ksize=1, strides=1, padding="VALID"): + def __init__(self, ksize, strides, padding="VALID"): self.init_prim_io_names(inputs=['x_origin', 'out_origin', 'grad'], outputs=['output']) - self.ksize = ksize - self.strides = strides - self.padding = padding - - self.ksize = validator.check_type('ksize', self.ksize, [int, tuple]) - self.strides = validator.check_type('strides', self.strides, [int, tuple]) - validator.check_type('padding', self.padding, [str]) - self.padding = validator.check_string('padding', self.padding, ['VALID', 'SAME']) + validator.check_type('ksize', ksize, [int, tuple]) + validator.check_type('strides', strides, [int, tuple]) + self.padding = validator.check_string('padding', padding.upper(), ['VALID', 'SAME']) self.add_prim_attr("padding", self.padding) - self.add_prim_attr('data_format', "NCHW") - - if isinstance(self.ksize, int): - self.pool_h = validator.check_integer("ksize", self.ksize, 1, Rel.GE) - self.pool_w = self.pool_h - self.add_prim_attr("ksize", (1, 1, self.ksize, self.ksize)) - elif isinstance(self.ksize, tuple): - if (len(self.ksize) != 2 and len(self.ksize) != 4): - raise ValueError('Attr \'ksize\' of \'Pool\' Op passed ' + - str(self.ksize)+', should be a int or a tuple of length 2 or 4.') - for ksize_val in self.ksize: - if (not isinstance(ksize_val, int)) or (ksize_val <= 0): - raise ValueError('Each value of attr \'ksize\' of \'MaxPool\' Op passed ' + - str(self.ksize)+', should be int and greater than 0.') - self.pool_h = self.ksize[-2] - self.pool_w = self.ksize[-1] - self.add_prim_attr("ksize", (1, 1, self.ksize[-2], self.ksize[-1])) - - if isinstance(self.strides, int): - self.stride_h = validator.check_integer("strides", self.strides, 1, Rel.GE) - self.stride_w = self.stride_h - self.add_prim_attr("strides", (1, 1, self.strides, self.strides)) - elif isinstance(self.strides, tuple): - if (len(self.strides) != 2 and len(self.strides) != 4): - raise ValueError('Attr \'strides\' of \'MaxPool\' Op passed ' + - str(self.strides)+', should be a int or a tuple of length 2 or 4.') - for stride_val in self.strides: - if (not isinstance(stride_val, int)) or (stride_val <= 0): - raise ValueError('Each value of attr \'strides\' of \'MaxPool\' Op passed ' + - str(self.strides)+', should be int and greater than 0.') - self.stride_h = self.strides[-2] - self.stride_w = self.strides[-1] - self.add_prim_attr("strides", (1, 1, self.strides[-2], self.strides[-1])) - - if self.padding == "VALID": - self.pad = 0 - elif self.padding == "SAME": - self.pad = math.floor((self.pool_h - 1) / 2) + self.is_maxpoolgradwithargmax = (self.name == "MaxPoolGradWithArgmax") + if not self.is_maxpoolgradwithargmax: + self.add_prim_attr('data_format', "NCHW") + + if isinstance(ksize, int): + validator.check_integer("ksize", ksize, 1, Rel.GE) + if self.is_maxpoolgradwithargmax: + self.ksize = (1, ksize, ksize, 1) + else: + self.ksize = (1, 1, ksize, ksize) else: - raise ValueError('The padding should be str and must be SAME or VALID,' - ' but got {}.'.format(self.padding)) + ksize_error = ValueError(f"The 'ksize' passed to operator {self.name} should be an positive int number" + f"or a tuple of two or four positive int numbers, but got {ksize}") + if len(ksize) != 2 and len(ksize) != 4: + raise ksize_error + for ksize_val in ksize: + if not isinstance(ksize_val, int) or (ksize_val <= 0): + raise ksize_error + if len(ksize) == 2 and self.is_maxpoolgradwithargmax: + self.ksize = (1, ksize[0], ksize[1], 1) + elif len(ksize) == 2 and not self.is_maxpoolgradwithargmax: + self.ksize = (1, 1, ksize[0], ksize[1]) + else: + self.ksize = ksize + self.add_prim_attr("ksize", self.ksize) + + if isinstance(strides, int): + validator.check_integer("strides", strides, 1, Rel.GE) + if self.is_maxpoolgradwithargmax: + self.strides = (1, strides, strides, 1) + else: + self.strides = (1, 1, strides, strides) + else: + strides_error = ValueError(f"The 'strides' passed to operator {self.name} should be an positive int number" + f"or a tuple of two or four positive int numbers, but got {strides}") + if len(strides) != 2 and len(strides) != 4: + raise strides_error + for strides_val in strides: + if not isinstance(strides_val, int) or (strides_val <= 0): + raise strides_error + if len(strides) == 2 and self.is_maxpoolgradwithargmax: + self.strides = (1, strides[0], strides[1], 1) + elif len(strides) == 2 and not self.is_maxpoolgradwithargmax: + self.strides = (1, 1, strides[0], strides[1]) + else: + self.strides = strides + self.add_prim_attr("strides", self.strides) class AvgPoolGrad(_PoolGrad): @@ -451,28 +451,13 @@ class MaximumGrad(Primitive): raise NotImplementedError -class MaxPoolGradWithArgmax(PrimitiveWithInfer): +class MaxPoolGradWithArgmax(_PoolGrad): """Computes the gradients of MaxPoolWithArgmax.""" @prim_attr_register - def __init__(self, - pad_mode="valid", - window=0, - pad=0, - stride=1, - data_mode=1, - ceil_mode=0, - alpha=1.0, - beta=0.0): + def __init__(self, ksize=1, strides=1, padding="VALID",): self.init_prim_io_names(inputs=['x', 'grad', 'argmax'], outputs=['output']) - - self.window = window - self.pool_h = self.pool_w = window - self.pad = pad - self.pad_mode = pad_mode - self.stride = stride - self.data_mode = data_mode - self.ceil_mode = ceil_mode + super(MaxPoolGradWithArgmax, self).__init__(ksize, strides, padding) def infer_shape(self, x_shape, grad_shape, argmax_shape): if not grad_shape: diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 952f7fc0d6..c7c823aeb6 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -682,186 +682,83 @@ class DepthwiseConv2dNative(PrimitiveWithInfer): return x_dtype -class MaxPoolWithArgmax(PrimitiveWithInfer): - r""" - Performs max pooling on the input Tensor and return both max values and indices. - - Typically the input is of shape :math:`(N_{in}, C_{in}, H_{in}, W_{in})`, MaxPool outputs - regional maximum in the :math:`(H_{in}, W_{in})`-dimension. Given kernel size - :math:`ks = (h_{ker}, w_{ker})` and stride :math:`s = (s_0, s_1)`, the operation is as follows. - - .. math:: - \text{output}(N_i, C_j, h, w) = \max_{m=0, \ldots, h_{ker}-1} \max_{n=0, \ldots, w_{ker}-1} - \text{input}(N_i, C_j, s_0 \times h + m, s_1 \times w + n) - - Args: - pad_mode (str): "valid", "same", "pad" the mode to fill padding. Default: "valid". - window (Union[int, tuple[int]]): The size of window, which is the kernel size, two `int` for width - and height. Default: 1. - pad (Union[int, tuple[int]]): If `pad_mode` is `pad`, the pad value to fill, two `int` for width - and height. Default: 0. - stride (Union[int, tuple[int]]): The stride of the window, that should be a tuple of two `int` for - width and height. Default: 1. - - Inputs: - - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. - - Outputs: - Tuple of 2 Tensor, the maxpool result and where max values from. - - - **output** (Tensor) - Maxpooling result, with shape :math:`(N, C_{out}, H_{out}, W_{out})`. - - **mask** (Tensor) - Max values' index represented by the mask. - """ - - @prim_attr_register - def __init__(self, - pad_mode="valid", - window=1, - pad=0, - stride=1, - data_mode=1, - ceil_mode=0, - alpha=1.0, - beta=0.0): - self.init_prim_io_names(inputs=['x'], outputs=['output', 'argmax']) - self.window = validator.check_type('window', window, [int, tuple]) - if isinstance(window, int) and window <= 0: - raise ValueError('Attr \'window\' of \'MaxPoolWithArgmax\' Op passed ' - + str(self.window)+', should be a int or tuple and greater than 0.') - if isinstance(window, tuple) and (len(window) != 2 or - (not isinstance(window[0], int)) or - (not isinstance(window[1], int)) or - window[0] <= 0 or window[1] <= 0): - raise ValueError('Attr \'window\' of \'MaxPoolWithArgmax\' Op passed ' - + str(self.window)+', should be a int or tuple and greater than 0.') - self.pool_h = self.pool_w = window - self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad']) - if self.pad_mode == "valid": - self.pad = 0 - elif self.pad_mode == "same": - self.pad = math.floor((self.window - 1) / 2) - elif self.pad_mode == "pad": - self.pad = validator.check_integer('pad', pad, 0, Rel.GE) - - self.data_mode = validator.check_integer('data_mode', data_mode, 1, Rel.EQ) - self.ceil_mode = validator.check_integer('ceil_mode', ceil_mode, 0, Rel.EQ) - self.stride = validator.check_integer('stride', stride, 1, Rel.GE) - self.alpha = validator.check_type('alpha', alpha, [int, float]) - self.beta = validator.check_type('beta', beta, [int, float]) - self.is_tbe = not context.get_context("enable_ge") and context.get_context("device_target") == "Ascend" - - def infer_shape(self, x_shape): - validator.check_integer("x_shape", len(x_shape), 4, Rel.EQ) - pad = self.pad - h_input = x_shape[2] - w_input = x_shape[3] - h_out = (h_input + 2 * pad - (self.window - 1) - 1) / self.stride + 1 - h_out = math.floor(h_out) - w_out = (w_input + 2 * pad - (self.window - 1) - 1) / self.stride + 1 - w_out = math.floor(w_out) - out_shape = [x_shape[0], x_shape[1], h_out, w_out] - for shape_value in out_shape: - if shape_value <= 0: - raise ValueError("The kernel size is not valid please check it if is larger than data's shape size.") - k_size_vec = [1, self.window, self.window, 1] - argmax_shape = [] - if self.is_tbe: - for i in range(4): - if i == 2: - dim = k_size_vec[i - 1] * k_size_vec[i] - argmax_shape.append(dim) - elif i == 3: - dim = math.ceil(out_shape[i - 1] * out_shape[i] / 16) + 1 - argmax_shape.append(dim) - else: - argmax_shape.append(x_shape[i]) - else: - argmax_shape = out_shape - return out_shape, argmax_shape - - def infer_dtype(self, x_dtype): - out_dtype = x_dtype - validator.check_typename("x_type", x_dtype, (mstype.float16, mstype.float32)) - argmax_dtype = mstype.int32 - return out_dtype, argmax_dtype - - class _Pool(PrimitiveWithInfer): r""" Performs max/avg pooling operation. Args: - ksize (Union[int, tuple[int]]): The size of the window to take a max over, that should be a tuple - of two `int` for width and height. Default: 1. - stride (Union[int, tuple[int]]): The stride of the window, that should be a tuple of two `int` for - width and height. Default: 1. - padding (str): The optional values for pad mode "SAME", "VALID". Default: "VALID". + ksize (Union[int, tuple[int]]): The size of the kernel, that should be a tuple + of two `int` for height and width. Default: 1. + strides (Union[int, tuple[int]]): The stride of the window, that should be + a tuple of two `int` for height and width. Default: 1. + padding (str): The optional values for pad mode, is "same" or "valid", not case sensitive. + Default: "valid". """ @prim_attr_register - def __init__(self, ksize=1, strides=1, padding="VALID"): - self.init_prim_io_names(inputs=['x'], outputs=['output']) - validator.check_type('padding', padding, [str]) - self.ksize = ksize - self.strides = strides - self.padding = padding.upper() - self.ksize = validator.check_type('ksize', self.ksize, [int, tuple]) - self.strides = validator.check_type('strides', self.strides, [int, tuple]) - self.padding = validator.check_string('padding', self.padding, ['VALID', 'SAME']) + def __init__(self, ksize=1, strides=1, padding="valid"): self.init_prim_io_names(inputs=['x'], outputs=['output']) + validator.check_type('ksize', ksize, [int, tuple]) + validator.check_type('strides', strides, [int, tuple]) + self.padding = validator.check_string('padding', padding.upper(), ['VALID', 'SAME']) self.add_prim_attr("padding", self.padding) - self.add_prim_attr('data_format', "NCHW") - - if isinstance(self.ksize, int): - self.pool_h = validator.check_integer("ksize", self.ksize, 1, Rel.GE) - self.pool_w = self.pool_h - self.add_prim_attr("ksize", (1, 1, self.ksize, self.ksize)) - elif isinstance(self.ksize, tuple): - if (len(self.ksize) != 2 or (not isinstance(self.ksize[0], int)) or (not isinstance(self.ksize[1], int)) - or self.ksize[0] <= 0 or self.ksize[1] <= 0): - raise ValueError('Each value of attr \'ksize\' of \'MaxPool\' Op passed ' + - str(self.ksize) + ', should be a int or a tuple of length 2 and greater than 0.') - self.pool_h = self.ksize[0] - self.pool_w = self.ksize[1] - self.add_prim_attr("ksize", (1, 1, self.ksize[0], self.ksize[1])) - - if isinstance(self.strides, int): - self.stride_h = validator.check_integer("strides", self.strides, 1, Rel.GE) - self.stride_w = self.stride_h - self.add_prim_attr("strides", (1, 1, self.strides, self.strides)) - elif isinstance(self.strides, tuple): - if (len(self.strides) != 2 or (not isinstance(self.strides[0], int)) or - (not isinstance(self.strides[1], int)) or self.strides[0] <= 0 or self.strides[1] <= 0): - raise ValueError('Each value of attr \'strides\' of \'MaxPool\' Op passed ' + - str(self.strides) + ', should be a int or a tuple of length 2 and greater than 0.') - self.stride_h = self.strides[0] - self.stride_w = self.strides[1] - self.add_prim_attr("strides", (1, 1, self.strides[0], self.strides[1])) + self.is_maxpoolwithargmax = (self.name == "MaxPoolWithArgmax") + if not self.is_maxpoolwithargmax: + self.add_prim_attr('data_format', "NCHW") - if self.padding == "VALID": - self.pad = 0 - elif self.padding == "SAME": - self.pad = math.floor((self.pool_h - 1) / 2) + if isinstance(ksize, int): + validator.check_integer("ksize", ksize, 1, Rel.GE) + self.ksize = (1, 1, ksize, ksize) else: - raise ValueError('The padding should be str and must be SAME or VALID,' - ' but got {}.'.format(self.padding)) - self.add_prim_attr('pad', self.pad) + if (len(ksize) != 2 or + (not isinstance(ksize[0], int)) or + (not isinstance(ksize[1], int)) or + ksize[0] <= 0 or + ksize[1] <= 0): + raise ValueError(f"The 'ksize' passed to operator {self.name} should be an positive int number or" + f"a tuple of two positive int numbers, but got {ksize}") + self.ksize = (1, 1, ksize[0], ksize[1]) + if self.is_maxpoolwithargmax: + self.ksize = (1, self.ksize[-2], self.ksize[-1], 1) + self.add_prim_attr("ksize", self.ksize) + + if isinstance(strides, int): + validator.check_integer("strides", strides, 1, Rel.GE) + self.strides = (1, 1, strides, strides) + else: + if (len(strides) != 2 or + (not isinstance(strides[0], int)) or + (not isinstance(strides[1], int)) or + strides[0] <= 0 or + strides[1] <= 0): + raise ValueError(f"The 'strides' passed to operator {self.name} should be an positive int number or" + f"a tuple of two positive int numbers, but got {strides}") + self.strides = (1, 1, strides[0], strides[1]) + if self.is_maxpoolwithargmax: + self.strides = (1, self.strides[-2], self.strides[-1], 1) + self.add_prim_attr("strides", self.strides) def infer_shape(self, x_shape): validator.check_integer("x_shape", len(x_shape), 4, Rel.EQ) - h_input = x_shape[2] - w_input = x_shape[3] + batch, channel, input_h, input_w = x_shape + if self.is_maxpoolwithargmax: + _, kernel_h, kernel_w, _ = self.ksize + _, stride_h, stride_w, _ = self.strides + else: + _, _, kernel_h, kernel_w = self.ksize + _, _, stride_h, stride_w = self.strides + if self.padding == "VALID": - h_out = math.ceil((h_input - (self.pool_h - 1)) / self.stride_h) - w_out = math.ceil((w_input - (self.pool_w - 1)) / self.stride_w) + out_h = math.ceil((input_h - (kernel_h - 1)) / stride_h) + out_w = math.ceil((input_w - (kernel_w - 1)) / stride_w) elif self.padding == "SAME": - h_out = math.ceil(h_input / self.stride_h) - w_out = math.ceil(w_input / self.stride_w) + out_h = math.ceil(input_h / stride_h) + out_w = math.ceil(input_w / stride_w) else: - raise ValueError('The padding should be str and must be SAME or VALID,' - ' but got {}.'.format(self.padding)) + raise ValueError(f"The padding of operator {self.name} should be a str and must be 'SAME' or 'VALID', " + f"but got {self.padding}.") + out_shape = [batch, channel, out_h, out_w] - out_shape = [x_shape[0], x_shape[1], h_out, w_out] for shape_value in out_shape: if shape_value <= 0: raise ValueError("The kernel size is not valid please check it if is larger than data's shape size.") @@ -887,11 +784,22 @@ class MaxPool(_Pool): \text{input}(N_i, C_j, s_0 \times h + m, s_1 \times w + n) Args: - ksize (Union[int, tuple[int]]): The size of the window to take a max over, that should be a tuple - of two `int` for width and height. Default: 1. - stride (Union[int, tuple[int]]): The stride of the window, that should be a tuple of two `int` for - width and height. Default: 1. - padding (str): The optional values for pad mode "SAME", "VALID". Default: "VALID". + ksize (Union[int, tuple[int]]): The size of kernel used to take the maximum value, + is an int number that represents height and width are both ksize, or a tuple + of two int numbers that represent height and width respectively. Default: 1. + strides (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents + the height and width of movement are both strides, or a tuple of two int numbers that + represent height and width of movement respectively. Default: 1. + padding (str): The optional values for pad mode, is "same" or "valid", not case sensitive. + Default: "valid". + + - same: Adopts the way of completion. Output height and width will be the same as + the input. Total number of padding will be calculated for horizontal and vertical + direction and evenly distributed to top and bottom, left and right if possible. + Otherwise, the last extra padding will be done from the bottom and the right side. + + - valid: Adopts the way of discarding. The possibly largest height and width of output + will be return without padding. Extra pixels will be discarded. Inputs: - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. @@ -901,10 +809,83 @@ class MaxPool(_Pool): """ @prim_attr_register - def __init__(self, ksize=1, strides=1, padding="VALID"): + def __init__(self, ksize=1, strides=1, padding="valid"): super(MaxPool, self).__init__(ksize, strides, padding) +class MaxPoolWithArgmax(_Pool): + r""" + Performs max pooling on the input Tensor and return both max values and indices. + + Typically the input is of shape :math:`(N_{in}, C_{in}, H_{in}, W_{in})`, MaxPool outputs + regional maximum in the :math:`(H_{in}, W_{in})`-dimension. Given kernel size + :math:`ks = (h_{ker}, w_{ker})` and stride :math:`s = (s_0, s_1)`, the operation is as follows. + + .. math:: + \text{output}(N_i, C_j, h, w) = \max_{m=0, \ldots, h_{ker}-1} \max_{n=0, \ldots, w_{ker}-1} + \text{input}(N_i, C_j, s_0 \times h + m, s_1 \times w + n) + + Args: + ksize (Union[int, tuple[int]]): The size of kernel used to take the maximum value and arg value, + is an int number that represents height and width are both ksize, or a tuple of + two int numbers that represent height and width respectively. Default: 1. + strides (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents + the height and width of movement are both strides, or a tuple of two int numbers that + represent height and width of movement respectively. Default: 1. + padding (str): The optional values for pad mode, is "same" or "valid", not case sensitive. + Default: "valid". + + - same: Adopts the way of completion. Output height and width will be the same as + the input. Total number of padding will be calculated for horizontal and vertical + direction and evenly distributed to top and bottom, left and right if possible. + Otherwise, the last extra padding will be done from the bottom and the right side. + + - valid: Adopts the way of discarding. The possibly largest height and width of output + will be return without padding. Extra pixels will be discarded. + + + Inputs: + - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. + + Outputs: + Tuple of 2 Tensor, the maxpool result and where max values from. + + - **output** (Tensor) - Maxpooling result, with shape :math:`(N, C_{out}, H_{out}, W_{out})`. + - **mask** (Tensor) - Max values' index represented by the mask. + """ + + def __init__(self, ksize=1, strides=1, padding="valid"): + super(MaxPoolWithArgmax, self).__init__(ksize, strides, padding) + self.is_tbe = context.get_context("device_target") == "Ascend" + + def infer_shape(self, x_shape): + out_shape = _Pool.infer_shape(self, x_shape) + _, _, out_h, out_w = out_shape + _, kernel_h, kernel_w, _ = self.ksize + + argmax_shape = [] + if self.is_tbe: + for i in range(4): + if i == 2: + dim = kernel_h * kernel_w + argmax_shape.append(dim) + elif i == 3: + dim = math.ceil(out_h * out_w / 16) + 1 + argmax_shape.append(dim) + else: + argmax_shape.append(x_shape[i]) + else: + argmax_shape = out_shape + + return out_shape, argmax_shape + + def infer_dtype(self, x_dtype): + out_dtype = x_dtype + validator.check_typename("x_type", x_dtype, (mstype.float16, mstype.float32)) + argmax_dtype = mstype.uint16 + return out_dtype, argmax_dtype + + class AvgPool(_Pool): r""" Average pooling operation. @@ -919,11 +900,22 @@ class AvgPool(_Pool): \text{input}(N_i, C_j, s_0 \times h + m, s_1 \times w + n) Args: - ksize (Union[int, tuple[int]]): The size of the window to take a average over, that should be a tuple - of two `int` for width and height. Default: 1. - stride (Union[int, tuple[int]]): The stride of the window, that should be a tuple of two `int` for - width and height. Default: 1. - padding (str): The optional values for pad mode "SAME", "VALID". Default: "VALID". + ksize (Union[int, tuple[int]]): The size of kernel used to take the average value, + is an int number that represents height and width are both ksize, or a tuple + of two int numbers that represent height and width respectively. Default: 1. + strides (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents + the height and width of movement are both strides, or a tuple of two int numbers that + represent height and width of movement respectively. Default: 1. + padding (str): The optional values for pad mode, is "same" or "valid", not case sensitive. + Default: "valid". + + - same: Adopts the way of completion. Output height and width will be the same as + the input. Total number of padding will be calculated for horizontal and vertical + direction and evenly distributed to top and bottom, left and right if possible. + Otherwise, the last extra padding will be done from the bottom and the right side. + + - valid: Adopts the way of discarding. The possibly largest height and width of output + will be return without padding. Extra pixels will be discarded. Inputs: - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. @@ -933,7 +925,7 @@ class AvgPool(_Pool): """ @prim_attr_register - def __init__(self, ksize=1, strides=1, padding="VALID"): + def __init__(self, ksize=1, strides=1, padding="valid"): if context.get_context("device_target") == "GPU": self.target = "GPU" else: diff --git a/tests/perf_test/resnet_example.py b/tests/perf_test/resnet_example.py index 19d235c2b1..34413109de 100644 --- a/tests/perf_test/resnet_example.py +++ b/tests/perf_test/resnet_example.py @@ -103,7 +103,7 @@ class ResNet50(nn.Cell): self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad') self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU() - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, pad_mode='valid') + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='valid') self.layer1 = self.MakeLayer( block, 3, in_channels=64, out_channels=256, stride=1) diff --git a/tests/st/networks/test_cpu_lenet.py b/tests/st/networks/test_cpu_lenet.py index a3105721d3..9fd50f5d9b 100644 --- a/tests/st/networks/test_cpu_lenet.py +++ b/tests/st/networks/test_cpu_lenet.py @@ -21,6 +21,7 @@ import mindspore.nn as nn from mindspore.ops import operations as P from mindspore import Tensor + class LeNet(nn.Cell): def __init__(self): super(LeNet, self).__init__() @@ -50,8 +51,10 @@ class LeNet(nn.Cell): output = self.fc3(output) return output + context.set_context(mode=context.GRAPH_MODE, device_target="CPU") + def train(net, data, label): learning_rate = 0.01 momentum = 0.9 @@ -67,11 +70,12 @@ def train(net, data, label): print("+++++++++++++++++++++++++++") assert res + @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_lenet(): - data = Tensor(np.ones([32, 1 ,32, 32]).astype(np.float32) * 0.01) + data = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01) label = Tensor(np.ones([32]).astype(np.int32)) net = LeNet() train(net, data, label) diff --git a/tests/st/networks/test_gpu_alexnet.py b/tests/st/networks/test_gpu_alexnet.py index 3b193e17d6..9f92fc630e 100644 --- a/tests/st/networks/test_gpu_alexnet.py +++ b/tests/st/networks/test_gpu_alexnet.py @@ -38,7 +38,7 @@ class AlexNet(nn.Cell): self.conv4 = nn.Conv2d(384, 384, 3, stride=1, pad_mode="same") self.conv5 = nn.Conv2d(384, 256, 3, stride=1, pad_mode="same") self.relu = nn.ReLU() - self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2,pad_mode="valid",padding=0) + self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="valid") self.flatten = nn.Flatten() self.fc1 = nn.Dense(6*6*256, 4096) self.fc2 = nn.Dense(4096, 4096) diff --git a/tests/st/ops/davinci/test_maxpool_with_argmax.py b/tests/st/ops/davinci/test_maxpool_with_argmax.py index c9312d666c..a6c875a9e8 100644 --- a/tests/st/ops/davinci/test_maxpool_with_argmax.py +++ b/tests/st/ops/davinci/test_maxpool_with_argmax.py @@ -20,26 +20,29 @@ import numpy as np import mindspore.context as context from mindspore.common.initializer import initializer from mindspore.common.parameter import Parameter + context.set_context(device_target="Ascend") + + class Net(nn.Cell): def __init__(self): super(Net, self).__init__() - self.maxpool = P.MaxPoolWithArgmax(pad_mode="same", - window=3, - stride=2) + self.maxpool = P.MaxPoolWithArgmax(padding="same", + ksize=3, + strides=2) self.x = Parameter(initializer( - 'normal', [1, 64, 112, 112]), name='w') + 'normal', [1, 64, 112, 112]), name='w') self.add = P.TensorAdd() - @ms_function def construct(self): output = self.maxpool(self.x) return output[0] + def test_net(): - x = np.random.randn(1,64,112,112).astype(np.float32) + x = np.random.randn(1, 64, 112, 112).astype(np.float32) maxpool = Net() output = maxpool() print("***********output output*********") diff --git a/tests/st/ops/davinci/test_maxpool_with_argmax_grad.py b/tests/st/ops/davinci/test_maxpool_with_argmax_grad.py index d97e2a06f8..3bbc835c1b 100644 --- a/tests/st/ops/davinci/test_maxpool_with_argmax_grad.py +++ b/tests/st/ops/davinci/test_maxpool_with_argmax_grad.py @@ -37,9 +37,9 @@ class Net(nn.Cell): def __init__(self): super(Net, self).__init__() - self.maxpool = P.MaxPoolWithArgmax(pad_mode="same", - window=3, - stride=2) + self.maxpool = P.MaxPoolWithArgmax(padding="same", + ksize=3, + strides=2) @ms_function def construct(self, x): diff --git a/tests/st/tbe_networks/resnet.py b/tests/st/tbe_networks/resnet.py index a1ece6556e..2024286b8f 100644 --- a/tests/st/tbe_networks/resnet.py +++ b/tests/st/tbe_networks/resnet.py @@ -267,7 +267,7 @@ class ResNet(nn.Cell): self.bn1 = bn_with_initialize(64) self.relu = P.ReLU() - self.maxpool = P.MaxPoolWithArgmax(window=3, stride=2, pad_mode="same") + self.maxpool = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="SAME") self.layer1 = MakeLayer0(block, layer_num[0], in_channels=64, out_channels=256, stride=1) self.layer2 = MakeLayer1(block, layer_num[1], in_channels=256, out_channels=512, stride=2) diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/hw_opt_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/hw_opt_test.py index 0afffc99df..2877bc7c7a 100644 --- a/tests/ut/cpp/python_input/gtest_input/pre_activate/hw_opt_test.py +++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/hw_opt_test.py @@ -21,7 +21,7 @@ addn = P.AddN() add = P.TensorAdd() sub = P.Sub() mul = P.Mul() -max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2) +max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2) make_tuple = Primitive('make_tuple') four2five = Primitive('Four2Five') five2four = Primitive('Five2Four') diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_trans_op_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_trans_op_test.py index 57bd2000c4..a24501e8b1 100644 --- a/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_trans_op_test.py +++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/insert_trans_op_test.py @@ -17,7 +17,7 @@ from mindspore.ops import Primitive tuple_getitem = Primitive('tuple_getitem') add = P.TensorAdd() -max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2) +max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2) make_tuple = Primitive('make_tuple') transdata = Primitive("TransData") diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/mixed_precision_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/mixed_precision_test.py index 8cbad52db1..7d3985376b 100644 --- a/tests/ut/cpp/python_input/gtest_input/pre_activate/mixed_precision_test.py +++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/mixed_precision_test.py @@ -21,7 +21,7 @@ addn = P.AddN() add = P.TensorAdd() sub = P.Sub() mul = P.Mul() -max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2) +max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2) make_tuple = Primitive('make_tuple') cast = Primitive('Cast') diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/transdata_split_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/transdata_split_test.py index 8cd18d1ac3..e353cf8fbe 100644 --- a/tests/ut/cpp/python_input/gtest_input/pre_activate/transdata_split_test.py +++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/transdata_split_test.py @@ -17,7 +17,7 @@ from mindspore.ops import Primitive tuple_getitem = Primitive('tuple_getitem') add = P.TensorAdd() -max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2) +max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2) make_tuple = Primitive('make_tuple') four2five = Primitive('Four2Five') five2four = Primitive('Five2Four') diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/transpose_transdata_fusion_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/transpose_transdata_fusion_test.py index ea3def743d..c4fc50e0da 100644 --- a/tests/ut/cpp/python_input/gtest_input/pre_activate/transpose_transdata_fusion_test.py +++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/transpose_transdata_fusion_test.py @@ -17,7 +17,7 @@ from mindspore.ops import Primitive tuple_getitem = Primitive('tuple_getitem') add = P.TensorAdd() -max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2) +max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2) make_tuple = Primitive('make_tuple') transdata = Primitive("TransData") Transpose = P.Transpose() diff --git a/tests/ut/cpp/python_input/gtest_input/session/session_test.py b/tests/ut/cpp/python_input/gtest_input/session/session_test.py index ed074fc8d6..ee034a1ae0 100644 --- a/tests/ut/cpp/python_input/gtest_input/session/session_test.py +++ b/tests/ut/cpp/python_input/gtest_input/session/session_test.py @@ -22,7 +22,7 @@ add = P.TensorAdd() reshape = P.Reshape() cast = P.Cast() tuple_getitem = Primitive('tuple_getitem') -max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2) +max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2) def test_addn_cast(x, y, z): sum = addn((x, y)) diff --git a/tests/ut/python/communication/test_data_parallel_resnet.py b/tests/ut/python/communication/test_data_parallel_resnet.py index 037152a0b7..220e553b4f 100644 --- a/tests/ut/python/communication/test_data_parallel_resnet.py +++ b/tests/ut/python/communication/test_data_parallel_resnet.py @@ -107,7 +107,7 @@ class ResNet18(nn.Cell): self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad') self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU() - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, pad_mode='pad') + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same') self.layer1 = self.MakeLayer( block, 2, in_channels=64, out_channels=256, stride=1) @@ -176,7 +176,7 @@ class ResNet9(nn.Cell): self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad') self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU() - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, pad_mode='same') + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same') self.layer1 = self.MakeLayer( block, 1, in_channels=64, out_channels=256, stride=1) diff --git a/tests/ut/python/exec/resnet_example.py b/tests/ut/python/exec/resnet_example.py index bfbb64f732..913e90a0bb 100644 --- a/tests/ut/python/exec/resnet_example.py +++ b/tests/ut/python/exec/resnet_example.py @@ -189,7 +189,7 @@ class ResNet50(nn.Cell): self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, weight_init=weight_conv) self.bn1 = bn_with_initialize(64) self.relu = nn.ReLU() - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2) self.layer1 = MakeLayer3( block, in_channels=64, out_channels=256, stride=1) diff --git a/tests/ut/python/exec/test_pooling.py b/tests/ut/python/exec/test_pooling.py index 9c378c15c2..0e526ff8d6 100644 --- a/tests/ut/python/exec/test_pooling.py +++ b/tests/ut/python/exec/test_pooling.py @@ -23,12 +23,10 @@ class MaxNet(nn.Cell): """MaxNet definition""" def __init__(self, kernel_size, - stride=None, - padding=0): + stride=None): super(MaxNet, self).__init__() self.maxpool = nn.MaxPool2d(kernel_size, - stride, - padding=padding) + stride) def construct(self, input_x): return self.maxpool(input_x) diff --git a/tests/ut/python/model/res18_example.py b/tests/ut/python/model/res18_example.py index eaf8bbc387..8875333465 100644 --- a/tests/ut/python/model/res18_example.py +++ b/tests/ut/python/model/res18_example.py @@ -106,7 +106,7 @@ class ResNet18(nn.Cell): self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad') self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU() - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, pad_mode='pad') + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same') self.layer1 = self.MakeLayer( block, 2, in_channels=64, out_channels=256, stride=1) @@ -175,7 +175,7 @@ class ResNet9(nn.Cell): self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU() - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2) self.layer1 = self.MakeLayer( block, 1, in_channels=64, out_channels=256, stride=1) diff --git a/tests/ut/python/nn/test_cell.py b/tests/ut/python/nn/test_cell.py index 882756f3d2..c583b27c1d 100644 --- a/tests/ut/python/nn/test_cell.py +++ b/tests/ut/python/nn/test_cell.py @@ -87,7 +87,7 @@ class ConvNet(nn.Cell): self.conv1 = nn.Conv2d(3, ConvNet.output_ch, kernel_size=7, stride=2, pad_mode="pad", padding=3) self.bn1 = nn.BatchNorm2d(ConvNet.output_ch) self.relu = nn.ReLU() - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="pad", padding=1) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same") self.flatten = nn.Flatten() self.fc = nn.Dense( int(ConvNet.image_h*ConvNet.image_w*ConvNet.output_ch/(4*4)), diff --git a/tests/ut/python/nn/test_pooling.py b/tests/ut/python/nn/test_pooling.py index 694d202d13..10bb7632b2 100644 --- a/tests/ut/python/nn/test_pooling.py +++ b/tests/ut/python/nn/test_pooling.py @@ -46,8 +46,7 @@ class MaxNet(nn.Cell): padding=0): super(MaxNet, self).__init__() self.maxpool = nn.MaxPool2d(kernel_size, - stride, - padding=padding) + stride) def construct(self, x): return self.maxpool(x) diff --git a/tests/ut/python/ops/test_nn_ops.py b/tests/ut/python/ops/test_nn_ops.py index 5b9f37864c..cadac6dfb4 100644 --- a/tests/ut/python/ops/test_nn_ops.py +++ b/tests/ut/python/ops/test_nn_ops.py @@ -108,6 +108,7 @@ class ResidualBlock(nn.Cell): class VirtualLossGrad(PrimitiveWithInfer): """ VirtualLossGrad definition """ + @prim_attr_register def __init__(self): """init VirtualLossGrad""" @@ -124,6 +125,7 @@ class VirtualLossGrad(PrimitiveWithInfer): class VirtualLoss(PrimitiveWithInfer): """ VirtualLoss definition """ + @prim_attr_register def __init__(self): """init VirtualLoss""" @@ -138,6 +140,7 @@ class VirtualLoss(PrimitiveWithInfer): # pylint: disable=unused-argument dx = loss_grad(x, out, dout) return (dx,) + return bprop def infer_shape(self, x_shape): @@ -149,6 +152,7 @@ class VirtualLoss(PrimitiveWithInfer): class VirtualNetWithLoss(nn.Cell): """ VirtualNetWithLoss definition """ + def __init__(self, network): super(VirtualNetWithLoss, self).__init__() self.loss = VirtualLoss() @@ -161,6 +165,7 @@ class VirtualNetWithLoss(nn.Cell): class SoftMaxGrad(nn.Cell): """ SoftMaxGrad definition """ + def __init__(self, network): super(SoftMaxGrad, self).__init__() self.network = network @@ -171,6 +176,7 @@ class SoftMaxGrad(nn.Cell): class DropoutGrad(nn.Cell): """ DropoutGrad definition """ + def __init__(self, network): super(DropoutGrad, self).__init__() self.network = network @@ -181,6 +187,7 @@ class DropoutGrad(nn.Cell): class ScalarSummaryNet(nn.Cell): """ ScalarSummaryNet definition """ + def __init__(self): super(ScalarSummaryNet, self).__init__() self.summary = P.ScalarSummary() @@ -193,6 +200,7 @@ class ScalarSummaryNet(nn.Cell): class FusedBatchNormGrad(nn.Cell): """ FusedBatchNormGrad definition """ + def __init__(self, network): super(FusedBatchNormGrad, self).__init__() self.grad = C.GradOperation(name="get_all", get_all=True, sens_param=True) @@ -204,6 +212,7 @@ class FusedBatchNormGrad(nn.Cell): class NetWithLoss(nn.Cell): """ NetWithLoss definition """ + def __init__(self, network): super(NetWithLoss, self).__init__() self.loss = P.SmoothL1Loss() @@ -216,6 +225,7 @@ class NetWithLoss(nn.Cell): class Grad(nn.Cell): """ GradWrap definition """ + def __init__(self, network): super(Grad, self).__init__() self.network = network @@ -227,6 +237,7 @@ class Grad(nn.Cell): class BatchnormNet(nn.Cell): """ BatchnormNet definition """ + def __init__(self): super(BatchnormNet, self).__init__() self.conv1 = nn.Conv2d(3, 4, kernel_size=8, stride=2, pad_mode="pad", padding=3) @@ -247,6 +258,7 @@ class BatchnormNet(nn.Cell): class NetWithLossClass(nn.Cell): """ NetWithLossClass definition """ + def __init__(self, network): super(NetWithLossClass, self).__init__(auto_prefix=False) self.loss = nn.SoftmaxCrossEntropyWithLogits() @@ -259,12 +271,13 @@ class NetWithLossClass(nn.Cell): class BlockNet(nn.Cell): """ BlockNet definition """ + def __init__(self): super(BlockNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, pad_mode="pad", padding=3) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU() - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2) self.block_down_sample = ResidualBlock( 64, 256, stride=1, down_sample=True ) @@ -281,6 +294,7 @@ class BlockNet(nn.Cell): class Conv2dWithBiasNet(nn.Cell): """ Conv2dWithBiasNet definition """ + def __init__(self): super(Conv2dWithBiasNet, self).__init__() self.conv = nn.Conv2d(3, 10, 1, bias_init='zeros') @@ -292,6 +306,7 @@ class Conv2dWithBiasNet(nn.Cell): class Conv2dNativeNet(nn.Cell): """ Conv2dNativeNet definition """ + def __init__(self): super(Conv2dNativeNet, self).__init__() self.conv = P.DepthwiseConv2dNative(channel_multiplier=3, kernel_size=(3, 3)) @@ -309,9 +324,10 @@ class Conv2dNativeNet(nn.Cell): class MakeRefKeyNet(nn.Cell): """ MakeRefKeyNet definition """ + def __init__(self): super(MakeRefKeyNet, self).__init__() - self.y= Parameter(Tensor([1.0], mindspore.float32), name="y") + self.y = Parameter(Tensor([1.0], mindspore.float32), name="y") def construct(self, x): key = P.MakeRefKey("y")() @@ -321,6 +337,7 @@ class MakeRefKeyNet(nn.Cell): class StateNet(nn.Cell): """ StateTestTensor definition """ + def __init__(self): super(StateNet, self).__init__() weight = Tensor(np.ones([2, 1, 2, 2], np.float32)) @@ -347,6 +364,24 @@ class ComparisonNet(nn.Cell): return ret +def test_max_pool_with_arg_max(): + class NetMaxPoolWithArgMax(nn.Cell): + def __init__(self): + """ ComparisonNet definition """ + super(NetMaxPoolWithArgMax, self).__init__() + self.max_pool_with_arg_max = P.MaxPoolWithArgmax(padding="valid", ksize=2, strides=1) + + def construct(self, x): + ret = self.max_pool_with_arg_max(x) + return ret + + x = Tensor(np.ones([1, 1, 3, 3], np.float32)) + net = NetMaxPoolWithArgMax() + context.set_context(mode=context.GRAPH_MODE, save_graphs=True) + ret = net(x) + print(ret) + + test_cases = [ ('SoftMaxGrad', { 'block': SoftMaxGrad(VirtualNetWithLoss(P.Softmax())), @@ -382,7 +417,7 @@ test_cases = [ 'desc_inputs': [Tensor(np.ones([1, 3, 8, 8], np.float32)), Tensor(np.zeros([1, 64, 4, 4], np.float32))], }), ('Conv2dWithBiasGrad', { - 'block': Grad(NetWithLossClass(Conv2dWithBiasNet())), + 'block': Grad(NetWithLossClass(Conv2dWithBiasNet())), 'desc_inputs': [Tensor(np.ones([1, 3, 16, 16], np.float32)), Tensor(np.zeros([1, 2560], np.float32))], }), ('Conv2dNativeGrad', { @@ -407,114 +442,93 @@ test_cases = [ }), ] - test_cases_for_verify_exception = [ ('Conv2d_ValueError_1', { - 'block': (lambda _ : P.Conv2D(3, 4, mode=-2.0), {'exception': ValueError}), + 'block': (lambda _: P.Conv2D(3, 4, mode=-2.0), {'exception': ValueError}), 'desc_inputs': [0], }), ('Conv2d_ValueError_2', { - 'block': (lambda _ : P.Conv2D(3, 4, mode=-2), {'exception': ValueError}), + 'block': (lambda _: P.Conv2D(3, 4, mode=-2), {'exception': ValueError}), 'desc_inputs': [0], }), ('MaxPoolWithArgmax_ValueError_1', { - 'block': (lambda _ : P.MaxPoolWithArgmax(pad_mode='sane'), {'exception': ValueError}), + 'block': (lambda _: P.MaxPoolWithArgmax(padding='sane'), {'exception': ValueError}), 'desc_inputs': [0], }), ('MaxPoolWithArgmax_ValueError_2', { - 'block': (lambda _ : P.MaxPoolWithArgmax(data_mode=2), {'exception': ValueError}), + 'block': (lambda _: P.MaxPoolWithArgmax(ksize='1'), {'exception': ValueError}), 'desc_inputs': [0], }), ('MaxPoolWithArgmax_ValueError_3', { - 'block': (lambda _ : P.MaxPoolWithArgmax(ceil_mode=2), {'exception': ValueError}), + 'block': (lambda _: P.MaxPoolWithArgmax(ksize=-2), {'exception': ValueError}), 'desc_inputs': [0], }), ('MaxPoolWithArgmax_ValueError_4', { - 'block': (lambda _ : P.MaxPoolWithArgmax(pad_mode="pad", pad=-1), {'exception': ValueError}), - 'desc_inputs': [0], - }), - ('MaxPoolWithArgmax_ValueError_5', { - 'block': (lambda _ : P.MaxPoolWithArgmax(pad_mode="pad", pad='1'), {'exception': ValueError}), - 'desc_inputs': [0], - }), - ('MaxPoolWithArgmax_ValueError_6', { - 'block': (lambda _ : P.MaxPoolWithArgmax(window='1'), {'exception': ValueError}), - 'desc_inputs': [0], - }), - ('MaxPoolWithArgmax_ValueError_7', { - 'block': (lambda _ : P.MaxPoolWithArgmax(window=-2), {'exception': ValueError}), - 'desc_inputs': [0], - }), - ('MaxPoolWithArgmax_ValueError_8', { - 'block': (lambda _ : P.MaxPoolWithArgmax(stride=-1), {'exception': ValueError}), - 'desc_inputs': [0], - }), - ('MaxPoolWithArgmax_ValueError_9', { - 'block': (lambda _ : P.MaxPoolWithArgmax(alpha='1'), {'exception': ValueError}), + 'block': (lambda _: P.MaxPoolWithArgmax(strides=-1), {'exception': ValueError}), 'desc_inputs': [0], }), ('FusedBatchNorm_ValueError_1', { - 'block': (lambda _ : P.FusedBatchNorm(mode="1", epsilon=1e-5, momentum=0.1), {'exception': ValueError}), + 'block': (lambda _: P.FusedBatchNorm(mode="1", epsilon=1e-5, momentum=0.1), {'exception': ValueError}), 'desc_inputs': [0], }), ('FusedBatchNorm_ValueError_2', { - 'block': (lambda _ : P.FusedBatchNorm(mode=2, epsilon=1e-5, momentum=0.1), {'exception': ValueError}), + 'block': (lambda _: P.FusedBatchNorm(mode=2, epsilon=1e-5, momentum=0.1), {'exception': ValueError}), 'desc_inputs': [0], }), ('FusedBatchNorm_ValueError_3', { - 'block': (lambda _ : P.FusedBatchNorm(mode=0, epsilon=-1e-5, momentum=0.1), {'exception': ValueError}), + 'block': (lambda _: P.FusedBatchNorm(mode=0, epsilon=-1e-5, momentum=0.1), {'exception': ValueError}), 'desc_inputs': [0], }), ('FusedBatchNorm_ValueError_4', { - 'block': (lambda _ : P.FusedBatchNorm(mode=0, epsilon=1e-5, momentum=-0.1), {'exception': ValueError}), + 'block': (lambda _: P.FusedBatchNorm(mode=0, epsilon=1e-5, momentum=-0.1), {'exception': ValueError}), 'desc_inputs': [0], }), ('FusedBatchNorm_ValueError_5', { - 'block': (lambda _ : P.FusedBatchNorm(mode=1, epsilon=-0.001, momentum=0.0), {'exception': ValueError}), + 'block': (lambda _: P.FusedBatchNorm(mode=1, epsilon=-0.001, momentum=0.0), {'exception': ValueError}), 'desc_inputs': [0], }), ('Softmax_ValueError_1', { - 'block': (lambda _ : P.Softmax("1"), {'exception': ValueError}), + 'block': (lambda _: P.Softmax("1"), {'exception': ValueError}), 'desc_inputs': [0], }), ('Softmax_ValueError_2', { - 'block': (lambda _ : P.Softmax(1.1), {'exception': ValueError}), + 'block': (lambda _: P.Softmax(1.1), {'exception': ValueError}), 'desc_inputs': [0], }), ('Softmax_ValueError_3', { - 'block': (lambda _ : P.Softmax(axis="1"), {'exception': ValueError}), + 'block': (lambda _: P.Softmax(axis="1"), {'exception': ValueError}), 'desc_inputs': [0], }), ('DropoutGenMask_ValueError_1', { - 'block': (lambda _ : P.DropoutGenMask(Seed0="seed0"), {'exception': ValueError}), + 'block': (lambda _: P.DropoutGenMask(Seed0="seed0"), {'exception': ValueError}), 'desc_inputs': [0], }), ('DropoutGenMask_ValueError_2', { - 'block': (lambda _ : P.DropoutGenMask(Seed0=1.0), {'exception': ValueError}), + 'block': (lambda _: P.DropoutGenMask(Seed0=1.0), {'exception': ValueError}), 'desc_inputs': [0], }), ('DropoutGenMask_ValueError_3', { - 'block': (lambda _ : P.DropoutGenMask(Seed1="seed1"), {'exception': ValueError}), + 'block': (lambda _: P.DropoutGenMask(Seed1="seed1"), {'exception': ValueError}), 'desc_inputs': [0], }), ('DropoutGenMask_ValueError_4', { - 'block': (lambda _ : P.DropoutGenMask(Seed1=2.0), {'exception': ValueError}), + 'block': (lambda _: P.DropoutGenMask(Seed1=2.0), {'exception': ValueError}), 'desc_inputs': [0], }), ('MaxPool2d_ValueError_1', { - 'block': (nn.MaxPool2d(kernel_size=120, stride=1, pad_mode="valid", padding=0), {'exception': ValueError}), + 'block': (nn.MaxPool2d(kernel_size=120, stride=1, pad_mode="valid"), {'exception': ValueError}), 'desc_inputs': [Tensor(np.random.randn(32, 3, 112, 112).astype(np.float32).transpose(0, 3, 1, 2))], }), ('MaxPool2d_ValueError_2', { 'block': ( - lambda _ : nn.MaxPool2d(kernel_size=120, stride=True, pad_mode="valid", padding=0), + lambda _: nn.MaxPool2d(kernel_size=120, stride=True, pad_mode="valid"), {'exception': ValueError}, ), 'desc_inputs': [Tensor(np.random.randn(32, 3, 112, 112).astype(np.float32).transpose(0, 3, 1, 2))], }), ('MaxPool2d_ValueError_3', { 'block': ( - lambda _ : nn.MaxPool2d(kernel_size=3, stride=True, pad_mode="valid", padding=0), + lambda _: nn.MaxPool2d(kernel_size=3, stride=True, pad_mode="valid"), {'exception': ValueError}, ), 'desc_inputs': [Tensor(np.random.randn(32, 3, 112, 112).astype(np.float32).transpose(0, 3, 1, 2))], @@ -532,4 +546,3 @@ def test_compile(): @mindspore_test(pipeline_for_verify_exception_for_case_by_case_config) def test_check_exception(): return test_cases_for_verify_exception - diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py index 01c1db1f29..3e4acb12f3 100755 --- a/tests/ut/python/ops/test_ops.py +++ b/tests/ut/python/ops/test_ops.py @@ -571,7 +571,7 @@ test_case_nn_ops = [ 'desc_bprop': [[3, 4, 6, 6]], 'skip': ['backward']}), ('MaxPoolWithArgmax', { - 'block': P.MaxPoolWithArgmax(window=2, stride=2), + 'block': P.MaxPoolWithArgmax(ksize=2, strides=2), 'desc_inputs': [[128, 32, 32, 64]], 'desc_bprop': [[128, 32, 8, 16], [128, 32, 8, 16]]}), ('SoftmaxCrossEntropyWithLogits', { diff --git a/tests/ut/python/ops/test_ops_check.py b/tests/ut/python/ops/test_ops_check.py index a7e1b41c4a..aa379cc64e 100644 --- a/tests/ut/python/ops/test_ops_check.py +++ b/tests/ut/python/ops/test_ops_check.py @@ -160,16 +160,16 @@ test_case_check_ops = [ 'block': nn.Dense(1, 6, has_bias=False, bias_init=Tensor(np.ones([6]).astype(np.float32))), 'desc_inputs': [Tensor(np.ones(shape=[6, 1]).astype(np.float32))]}), ('MaxPool2d_1', { - 'block': nn.MaxPool2d(5, pad_mode='same', padding=0), + 'block': nn.MaxPool2d(5, pad_mode='same'), 'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}), ('MaxPool2d_2', { - 'block': nn.MaxPool2d(5, pad_mode='valid', padding=0), + 'block': nn.MaxPool2d(5, pad_mode='valid'), 'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}), ('AvgPool2d_1', { - 'block': nn.AvgPool2d(5, pad_mode='same', padding=0), + 'block': nn.AvgPool2d(5, pad_mode='same'), 'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}), ('AvgPool2d_2', { - 'block': nn.AvgPool2d(5, pad_mode='valid', padding=0), + 'block': nn.AvgPool2d(5, pad_mode='valid'), 'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}), ('Conv2D_1', { 'block': P.Conv2D(1, 6, pad_mode='same', pad=0), diff --git a/tests/ut/python/pynative_mode/ge/ops/test_pooling.py b/tests/ut/python/pynative_mode/ge/ops/test_pooling.py index e6cf88a9ca..d5b90b6edd 100644 --- a/tests/ut/python/pynative_mode/ge/ops/test_pooling.py +++ b/tests/ut/python/pynative_mode/ge/ops/test_pooling.py @@ -42,12 +42,10 @@ def test_maxpool2d(): """ test_maxpool2d """ kernel_size = 3 stride = 3 - padding = 0 - max_pool = nn.MaxPool2d(kernel_size, stride, padding=padding) + max_pool = nn.MaxPool2d(kernel_size, stride) assert max_pool.kernel_size == 3 assert max_pool.stride == 3 - assert max_pool.padding == 0 input_data = Tensor(np.random.randint(0, 255, [1, 3, 6, 6]).astype(np.float32)) output = max_pool(input_data) output_np = output.asnumpy() diff --git a/tests/ut/python/pynative_mode/nn/test_cell.py b/tests/ut/python/pynative_mode/nn/test_cell.py index 16adcd6119..2d5196b80d 100644 --- a/tests/ut/python/pynative_mode/nn/test_cell.py +++ b/tests/ut/python/pynative_mode/nn/test_cell.py @@ -89,7 +89,7 @@ class ConvNet(nn.Cell): self.conv1 = nn.Conv2d(3, ConvNet.output_ch, kernel_size=7, stride=2, pad_mode='pad', padding=3) self.bn1 = nn.BatchNorm2d(ConvNet.output_ch) self.relu = nn.ReLU() - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='pad', padding=1) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same") self.flatten = nn.Flatten() self.fc = nn.Dense( int(ConvNet.image_h*ConvNet.image_w*ConvNet.output_ch/(4*4)), diff --git a/tests/ut/python/pynative_mode/nn/test_pooling.py b/tests/ut/python/pynative_mode/nn/test_pooling.py index ab95fec091..bb1822f8a8 100644 --- a/tests/ut/python/pynative_mode/nn/test_pooling.py +++ b/tests/ut/python/pynative_mode/nn/test_pooling.py @@ -49,23 +49,14 @@ def test_maxpool2d(): """ test_maxpool2d """ kernel_size = 3 stride = 3 - padding = 2 - max_pool = nn.MaxPool2d(kernel_size, stride, pad_mode='SAME', padding=padding) + max_pool = nn.MaxPool2d(kernel_size, stride, pad_mode='SAME') assert max_pool.kernel_size == 3 assert max_pool.stride == 3 - assert max_pool.padding == 2 input_data = Tensor(np.random.randint(0, 255, [1, 3, 6, 6])*0.1) output = max_pool(input_data) output_np = output.asnumpy() assert isinstance(output_np[0][0][0][0], (np.float32, np.float64)) -def test_maxpool2d_error_padding(): - """ test_maxpool2d_error_padding """ - kernel_size = 3.5 - stride = 3 - padding = 1 - with pytest.raises(ValueError): - nn.MaxPool2d(kernel_size, stride, padding=padding) diff --git a/tests/ut/python/pynative_mode/vm/test_vm.py b/tests/ut/python/pynative_mode/vm/test_vm.py index 4ea0abd753..77510337b0 100644 --- a/tests/ut/python/pynative_mode/vm/test_vm.py +++ b/tests/ut/python/pynative_mode/vm/test_vm.py @@ -23,7 +23,7 @@ def test_avg_pooling(): [-9., -1., 3., 4.], [1., -1., -3., -6.], [-2., -1., -2., -15.]]]]).astype(np.float32) - out = vm.avg_pooling(input_data, pool_h=2, pool_w=2, stride=1, pad=0) + out = vm.avg_pooling(input_data, pool_h=2, pool_w=2, stride=1) expect_out = [[[[-4.25, 0.0, 4.25], [-2.5, -0.5, -0.5], [-0.75, -1.75, -6.5]]]] @@ -37,9 +37,9 @@ def test_avg_pool_grad(): [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]]]).astype(np.float32) - dout = vm.avg_pooling(input_data, pool_h=2, pool_w=2, stride=1, pad=0) + dout = vm.avg_pooling(input_data, pool_h=2, pool_w=2, stride=1) print("vm.avg_pooling dout: ", dout) - out = vm.avg_pool_grad(dout, input_data.shape, 2, 2, 1, 0) + out = vm.avg_pool_grad(dout, input_data.shape, 2, 2, 1) print("vm.avg_pool_grad: ", out) assert True @@ -202,7 +202,7 @@ def test_max_pooling(): [-9., -1., 3., 4.], [1., -1., -3., -6.], [-2., -1., -2., -15.]]]]).astype(np.float32) - out = vm.max_pooling(input_data, pool_h=2, pool_w=2, stride=1, pad=0) + out = vm.max_pooling(input_data, pool_h=2, pool_w=2, stride=1) expect_out = [[[[-1., 3., 9.], [1., 3., 4.], [1., -1., -2.]]]] diff --git a/tests/ut/python/utils/test_serialize.py b/tests/ut/python/utils/test_serialize.py index 41da45ab25..cc6f346b77 100644 --- a/tests/ut/python/utils/test_serialize.py +++ b/tests/ut/python/utils/test_serialize.py @@ -44,7 +44,7 @@ class Net(nn.Cell): self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=0, weight_init="zeros") self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU() - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2) self.flatten = nn.Flatten() self.fc = nn.Dense(int(224*224*64/16), num_classes) diff --git a/tests/vm_impl/nn_ops_vm_impl.py b/tests/vm_impl/nn_ops_vm_impl.py index f6bbdca55a..fc1fa95024 100644 --- a/tests/vm_impl/nn_ops_vm_impl.py +++ b/tests/vm_impl/nn_ops_vm_impl.py @@ -19,66 +19,82 @@ from mindspore.ops.operations import _grad_ops as G from mindspore.common.tensor import Tensor from mindspore.ops.vm_impl_registry import vm_impl_registry as vm_impl_getters from .vm_interface import vm + + # pylint: disable=unused-argument @vm_impl_getters.register(P.ScalarSummary) def vm_impl_scalar_summary(self): """Generate vm_impl function for ScalarSummary""" + def vm_impl(string_in, scalar): """Implement by vm mode.""" return scalar + return vm_impl @vm_impl_getters.register(P.ReLU) def vm_impl_relu(self): """Generate vm_impl function for ReLU""" + def vm_impl(x): x = x.asnumpy() output = Tensor(vm.relu(x)) return output + return vm_impl + @vm_impl_getters.register(P.Flatten) def vm_impl_flatten(self): """Generate vm_impl function for Flatten""" + def vm_impl(x): x = x.asnumpy() return Tensor(vm.flatten_batch(x)) + return vm_impl @vm_impl_getters.register(P.Softmax) def vm_impl_softmax(self): """Generate vm_impl function for Softmax""" + def vm_impl(x): x = x.asnumpy() return Tensor(vm.softmax(x)) + return vm_impl @vm_impl_getters.register(P.LogSoftmax) def vm_impl_log_softmax(self): """Generate vm_impl function for LogSoftmax""" + def vm_impl(x): x = x.asnumpy() return Tensor(vm.logsoftmax(x)) + return vm_impl @vm_impl_getters.register(P.Tanh) def vm_impl_tanh(self): """Generate vm_impl function for Tanh""" + def vm_impl(x): x = x.asnumpy() return Tensor(vm.tanh(x)) + return vm_impl @vm_impl_getters.register(P.FusedBatchNorm) def vm_impl_fused_batch_norm(self): """Generate vm_impl function for FusedBatchNorm""" + def vm_impl(x, scale, b, mean, variance): # pylint: disable=unused-argument x = x.asnumpy() @@ -92,12 +108,14 @@ def vm_impl_fused_batch_norm(self): momentum=self.momentum) return Tensor(out), Tensor(x_mean), Tensor(x_var), \ Tensor(running_mean), Tensor(running_var) + return vm_impl @vm_impl_getters.register(P.BatchNorm) def vm_impl_batch_norm(self): """Generate vm_impl function for BatchNorm""" + def vm_impl(x, scale, b, mean, variance): # pylint: disable=unused-argument x = x.asnumpy() @@ -110,83 +128,106 @@ def vm_impl_batch_norm(self): eps=self.epsilon) return Tensor(out), Tensor(x_mean), Tensor(x_var), \ Tensor(running_mean), Tensor(running_var) + return vm_impl @vm_impl_getters.register(P.Conv2D) def vm_impl_conv2d(self): """Generate vm_impl function for Conv2D""" + def vm_impl(x, w): x = x.asnumpy() weight = w.asnumpy() bias = None out = vm.conv2d(x, weight, bias, self.stride, self.pad, self.dilation) return Tensor(out) + return vm_impl @vm_impl_getters.register(G.MaxPoolGradWithArgmax) def vm_impl_max_pool_grad_with_argmax(self): """Generate vm_impl function for MaxPoolGradWithArgmax""" - def vm_impl(x, argmax, dout): + + def vm_impl(x, dout, argmax): + print("buxue") + print(argmax) x = x.asnumpy() dout = dout.asnumpy() arg_max = argmax.asnumpy() - dx = vm.max_pool_grad_with_argmax(x, arg_max, dout, self.pool_h, self.pool_w, self.stride, self.pad) + dx = vm.max_pool_grad_with_argmax(x, dout, arg_max, + self.ksize[1], self.ksize[2], self.strides[1]) return Tensor(dx) + return vm_impl @vm_impl_getters.register(P.MaxPoolWithArgmax) def vm_impl_max_pool_with_argmax(self): """Generate vm_impl function for MaxPoolWithArgmax""" + def vm_impl(x): x = x.asnumpy() - out, out_argmax = vm.max_pool_with_argmax(x, self.pool_h, self.pool_w, self.stride, self.pad) + out, out_argmax = vm.max_pool_with_argmax(x, self.ksize[1], self.ksize[2], self.strides[1]) return Tensor(out), Tensor(out_argmax) + return vm_impl + @vm_impl_getters.register(P.MaxPool) def vm_impl_max_pool(self): """Generate vm_impl function for MaxPool""" + def vm_impl(x): x = x.asnumpy() - out = vm.max_pooling(x, self.pool_h, self.pool_w, self.stride_h, self.pad) + out = vm.max_pooling(x, self.ksize[-2], self.ksize[-1], self.strides[-2]) return Tensor(out) + return vm_impl + @vm_impl_getters.register(G.MaxPoolGrad) def vm_impl_max_pool_grad(self): """Generate vm_impl function for MaxPoolGrad""" + def vm_impl(x, out, dout): x = x.asnumpy() dout = dout.asnumpy() - out = vm.max_pool_grad(x, dout, self.pool_h, self.pool_w, self.stride_h, self.pad) + out = vm.max_pool_grad(x, dout, self.ksize[-2], self.ksize[-1], self.strides[-2]) return Tensor(out) + return vm_impl + @vm_impl_getters.register(P.AvgPool) -def vm_impl_max_pool(self): +def vm_impl_avg_pool(self): """Generate vm_impl function for AvgPool""" + def vm_impl(x): x = x.asnumpy() - out = vm.avg_pooling(x, self.pool_h, self.pool_w, self.stride_h, self.pad) + out = vm.avg_pooling(x, self.ksize[-2], self.ksize[-1], self.strides[-2]) return Tensor(out) + return vm_impl + @vm_impl_getters.register(G.AvgPoolGrad) def vm_impl_avg_pool_grad(self): """Generate vm_impl function for AvgPoolGrad""" + def vm_impl(dout, origin_shape): dout = dout.asnumpy() - out = vm.avg_pool_grad(dout, origin_shape, self.pool_h, self.pool_w, self.stride_h, self.pad) + out = vm.avg_pool_grad(dout, origin_shape, self.ksize[-2], self.ksize[-1], self.strides[-2]) return Tensor(out) + return vm_impl @vm_impl_getters.register(G.FusedBatchNormGrad) def vm_impl_fused_batch_norm_grad(self): """Generate vm_impl function for FusedBatchNormGrad""" + def vm_impl(dy, x, scale, save_mean, save_inv_variance): dy = dy.asnumpy() x = x.asnumpy() @@ -195,11 +236,14 @@ def vm_impl_fused_batch_norm_grad(self): save_inv_variance = save_inv_variance.asnumpy() dx, dscale, dshift = vm.batch_norm_grad(dy, x, scale, save_mean, save_inv_variance) return (Tensor(dx), Tensor(dscale), Tensor(dshift)) + return vm_impl + @vm_impl_getters.register(G.BatchNormGrad) def vm_impl_fused_batch_norm_grad(self): """Generate vm_impl function for BatchNormGrad""" + def vm_impl(dy, x, scale, save_mean, save_inv_variance): dy = dy.asnumpy() x = x.asnumpy() @@ -208,104 +252,123 @@ def vm_impl_fused_batch_norm_grad(self): save_inv_variance = save_inv_variance.asnumpy() dx, dscale, dshift = vm.batch_norm_grad(dy, x, scale, save_mean, save_inv_variance) return (Tensor(dx), Tensor(dscale), Tensor(dshift)) + return vm_impl @vm_impl_getters.register(G.ReluGrad) def vm_impl_relu_grad(self): """Generate vm_impl function for ReluGrad""" + def vm_impl(y_backprop, x): x = x.asnumpy() y_backprop = y_backprop.asnumpy() - y_backprop = vm.relu_grad(x.copy())*y_backprop + y_backprop = vm.relu_grad(x.copy()) * y_backprop return Tensor(y_backprop) + return vm_impl @vm_impl_getters.register(P.Conv2DBackpropInput) def vm_impl_conv2d_backprop_input(self): """Generate vm_impl function for Conv2DBackpropInput""" + def vm_impl(dout, w, x_size): dout = dout.asnumpy() w = w.asnumpy() dx = vm.conv2d_backprop_input(dout, x_size, w, self.stride, self.pad) return Tensor(dx) + return vm_impl @vm_impl_getters.register(G.Conv2DBackpropFilter) def vm_impl_conv2d_backprop_filter(self): """Generate vm_impl function for Conv2DBackpropFilter""" + def vm_impl(dout, x, w_size): x = x.asnumpy() dout = dout.asnumpy() dw = vm.conv2d_backprop_filter(dout, x, w_size, self.stride, self.pad) return Tensor(dw) + return vm_impl @vm_impl_getters.register(G.FlattenGrad) def vm_impl_flatten_grad(self): """Generate vm_impl function for FlattenGrad""" + def vm_impl(dout, x): dout = dout.asnumpy() dout = vm.flatten_grad(dout, x) return Tensor(dout) + return vm_impl @vm_impl_getters.register(P.BiasAdd) def vm_impl_bias_add(self): """Generate vm_impl function for BiasAdd""" + def vm_impl(wx, bias): wx = wx.asnumpy() bias = bias.asnumpy() out = wx + bias return Tensor(out) + return vm_impl @vm_impl_getters.register(G.BiasAddGrad) def vm_impl_bias_add_grad(self): """Generate vm_impl function for BiasAddGrad""" + def vm_impl(dout): dout = dout.asnumpy() shape = np.shape(dout) return Tensor(np.add.reduce(dout, axis=tuple(range(len(shape) - 1)))) + return vm_impl @vm_impl_getters.register(P.SoftmaxCrossEntropyWithLogits) def vm_impl_softmax_cross_entropy_with_logits(self): """Generate vm_impl function for SoftmaxCrossEntropyWithLogits""" + def vm_impl(logits, labels): logits = logits.asnumpy() labels = labels.asnumpy() loss, dx = vm.softmax_cross_entropy_with_logits(logits, labels) return (Tensor(np.array(loss)), Tensor(dx)) + return vm_impl @vm_impl_getters.register(P.SparseSoftmaxCrossEntropyWithLogits) def vm_impl_sparse_softmax_cross_entropy_with_logits(self): """Generate vm_impl function for SparseSoftmaxCrossEntropyWithLogits""" + def vm_impl(logits, labels): logits = logits.asnumpy() labels = labels.asnumpy() n_class = labels.max() + 1 n_sample = labels.shape[0] - one_hot_label = np.zeros((n_sample, n_class))#3个样本，4个类别 - one_hot_label[:, labels] = 1#非零列赋值为1 + one_hot_label = np.zeros((n_sample, n_class)) # 3个样本，4个类别 + one_hot_label[:, labels] = 1 # 非零列赋值为1 loss, dx = vm.softmax_cross_entropy_with_logits(logits, one_hot_label) if self.is_grad: return (Tensor(dx),) return (Tensor(np.array(loss)),) + return vm_impl + @vm_impl_getters.register(P.ApplyMomentum) def vm_impl_momentum(self): """Generate vm_impl function for Momentum""" + def vm_impl(variable, accumulation, learning_rate, @@ -327,19 +390,24 @@ def vm_impl_momentum(self): return vm_impl + @vm_impl_getters.register(P.ResizeBilinear) def vm_impl_resize_bilinear(self): """Generate vm_impl function for ResizeBilinear""" + def vm_impl(x): out = vm.ResizeBilinear(x) return Tensor(out) + return vm_impl @vm_impl_getters.register(G.ResizeBilinearGrad) def vm_impl_resize_bilinear_grad(self): """Generate vm_impl function for ResizeBilinearGrad""" + def vm_impl(dout, original_image): out = vm.ResizeBilinearGrad(dout, original_image) return Tensor(out) + return vm_impl diff --git a/tests/vm_impl/vm_me.py b/tests/vm_impl/vm_me.py index 03a0e1a885..ba51a3b13b 100644 --- a/tests/vm_impl/vm_me.py +++ b/tests/vm_impl/vm_me.py @@ -19,7 +19,7 @@ from mindspore._checkparam import Rel from mindspore._checkparam import ParamValidator as validator -def avg_pooling(x, pool_h, pool_w, stride, pad): +def avg_pooling(x, pool_h, pool_w, stride): """ Applies average pooling over an input array. @@ -28,26 +28,25 @@ def avg_pooling(x, pool_h, pool_w, stride, pad): pool_h (int): Height of the pooling window. pool_w (int): Width of the pooling window. stride (int): The stride of the sliding window. - pad (int): Padding to be added on height and width. Returns: numpy.ndarray, an output array after applying average pooling on input array. """ validator.check_integer("stride", stride, 0, Rel.GT) num, channel, height, width = x.shape - out_h = (height + 2*pad - pool_h)//stride + 1 - out_w = (width + 2*pad - pool_w)//stride + 1 + out_h = (height - pool_h)//stride + 1 + out_w = (width - pool_w)//stride + 1 - col = im2col(x, pool_h, pool_w, stride, pad) + col = im2col(x, pool_h, pool_w, stride) col = col.reshape(-1, pool_h*pool_w) out = np.mean(col, axis=1) - out = out.reshape(num, out_h, out_w, channel).transpose(0, 3, 1, 2) + out = out.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2) return out -def avg_pool_grad(dout, origin_shape, pool_h, pool_w, stride, pad): +def avg_pool_grad(dout, origin_shape, pool_h, pool_w, stride): """ Gets grad of average pooling. @@ -57,7 +56,6 @@ def avg_pool_grad(dout, origin_shape, pool_h, pool_w, stride, pad): pool_h (int): Height of the pooling window. pool_w (int): Width of the pooling window. stride (int): The stride of the sliding window. - pad (int): Padding to be added on height and width. Returns: numpy.ndarray, grad of avgerage pooling. @@ -324,38 +322,38 @@ def matmul(x, w, b=None): return y -def max_pooling(x, pool_h, pool_w, stride, pad): +def max_pooling(x, pool_h, pool_w, stride): """Max pooling.""" validator.check_integer("stride", stride, 0, Rel.GT) num, channel, height, width = x.shape - out_h = (height + 2*pad - pool_h)//stride + 1 - out_w = (width + 2*pad - pool_w)//stride + 1 + out_h = (height - pool_h)//stride + 1 + out_w = (width - pool_w)//stride + 1 - col = im2col(x, pool_h, pool_w, stride, pad) + col = im2col(x, pool_h, pool_w, stride) col = col.reshape(-1, pool_h*pool_w) out = np.max(col, axis=1) - out = out.reshape(num, out_h, out_w, channel).transpose(0, 3, 1, 2) + out = out.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2) return out -def max_pool_grad(x, dout, pool_h, pool_w, stride, pad): +def max_pool_grad(x, dout, pool_h, pool_w, stride): """Grad of max pooling.""" dout = dout.transpose(0, 2, 3, 1) pool_size = pool_h * pool_w dmax = np.zeros((dout.size, pool_size)) - col = im2col(x, pool_h, pool_w, stride, pad) + col = im2col(x, pool_h, pool_w, stride) col = col.reshape(-1, pool_h*pool_w) arg_max = np.argmax(col, axis=1) dmax[np.arange(arg_max.size), arg_max.flatten()] = dout.flatten() dmax = dmax.reshape(dout.shape + (pool_size,)) dcol = dmax.reshape(dmax.shape[0]*dmax.shape[1]*dmax.shape[2], -1) - dx = col2im(dcol, x.shape, pool_h, pool_w, stride, pad) + dx = col2im(dcol, x.shape, pool_h, pool_w, stride) return dx -def max_pool_grad_with_argmax(x, arg_max, dout, pool_h, pool_w, stride, pad): +def max_pool_grad_with_argmax(x, dout, arg_max, pool_h, pool_w, stride): """Grad of max pooling with argmax.""" dout = dout.transpose(0, 2, 3, 1) pool_size = pool_h * pool_w @@ -363,22 +361,22 @@ def max_pool_grad_with_argmax(x, arg_max, dout, pool_h, pool_w, stride, pad): dmax[np.arange(arg_max.size), arg_max.flatten()] = dout.flatten() dmax = dmax.reshape(dout.shape + (pool_size,)) dcol = dmax.reshape(dmax.shape[0]*dmax.shape[1]*dmax.shape[2], -1) - dx = col2im(dcol, x.shape, pool_h, pool_w, stride, pad) + dx = col2im(dcol, x.shape, pool_h, pool_w, stride) return dx -def max_pool_with_argmax(x, pool_h, pool_w, stride, pad): +def max_pool_with_argmax(x, pool_h, pool_w, stride): """Max pooling with argmax.""" validator.check_integer("stride", stride, 0, Rel.GT) num, channel, height, width = x.shape - out_h = (height + 2*pad - pool_h)//stride + 1 - out_w = (width + 2*pad - pool_w)//stride + 1 - col = im2col(x, pool_h, pool_w, stride, pad) + out_h = (height - pool_h)//stride + 1 + out_w = (width - pool_w)//stride + 1 + col = im2col(x, pool_h, pool_w, stride) col = col.reshape(-1, pool_h*pool_w) out = np.max(col, axis=1) out_argmax = np.argmax(col, axis=1) - out = out.reshape(num, out_h, out_w, channel).transpose(0, 3, 1, 2) - out_argmax = out_argmax.reshape(num, out_h, out_w, channel).transpose(0, 3, 1, 2) + out = out.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2) + out_argmax = out_argmax.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2) return out, out_argmax From 87c8f9a9a147f0b52b5c13ecefb0db2fc0d94f0c Mon Sep 17 00:00:00 2001 From: biffex Date: Thu, 9 Apr 2020 15:04:24 +0800 Subject: [PATCH 113/227] constant duplicate mul for momentum --- mindspore/ccsrc/optimizer/irpass.cc | 6 +-- .../optimizer/irpass/arithmetic_simplify.h | 54 ++++++++++++++++++- mindspore/ccsrc/utils/graph_utils.cc | 2 + mindspore/ops/operations/math_ops.py | 8 +++ tests/ut/cpp/optimizer/lib_test.cc | 13 +++++ .../gtest_input/optimizer/opt_test.py | 33 ++++++++++++ 6 files changed, 112 insertions(+), 4 deletions(-) diff --git a/mindspore/ccsrc/optimizer/irpass.cc b/mindspore/ccsrc/optimizer/irpass.cc index cdc960792f..0991c31b00 100644 --- a/mindspore/ccsrc/optimizer/irpass.cc +++ b/mindspore/ccsrc/optimizer/irpass.cc @@ -45,9 +45,9 @@ namespace mindspore { namespace opt { namespace irpass { OptimizeIRPassLib::OptimizeIRPassLib() { - arithmetic_simplify_ = MakeSubstitution( - ArithmeticSimplify(), "arithmetic_simplify", - {prim::kPrimScalarAdd, prim::kPrimScalarMul, prim::kPrimTensorAdd, prim::kPrimIdentity, prim::kPrimMomentum}); + arithmetic_simplify_ = MakeSubstitution(ArithmeticSimplify(), "arithmetic_simplify", + {prim::kPrimScalarAdd, prim::kPrimScalarMul, prim::kPrimTensorAdd, + prim::kPrimIdentity, prim::kPrimMomentum, prim::kPrimMul}); special_op_eliminate_ = MakeSubstitution(SpecialOpEliminater(), "special_op_eliminate", {prim::kPrimInsertGradientOf, prim::kPrimPrintShapeType, prim::kPrimGetRefKey, prim::kPrimMirror, prim::kPrimVirtualDiv}); diff --git a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h index 8c5610ed1b..ab191aab20 100644 --- a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h +++ b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h @@ -179,6 +179,55 @@ class OptUpdateZeroTensor : public AnfVisitor { } }; +// {prim::kPrimMul, Tensor1, {orim::kPrimMul, Tensor2, {...}}} -> +// {prim::kPrimMul, {...}, {prim::kPrimMul, Tensor1, Tensor2}} +class ConstantDuplicateMul : public AnfVisitor { + public: + AnfNodePtr operator()(const OptimizerPtr &, const AnfNodePtr &node) override { + Reset(); + // {prim::kPrimMul, Tensor1, {...}} + AnfVisitor::Match(prim::kPrimMul, {IsNode, IsNode})(node); + if (vnode_ == nullptr || cnode_ == nullptr) { + return nullptr; + } + auto tensor1 = vnode_; + auto mul = cnode_; + + Reset(); + // {prim::kPrimMul, Tensor2, {...}} + AnfVisitor::Match(prim::kPrimMul, {IsNode, IsNode})(mul); + if (vnode_ == nullptr || cnode_ == nullptr) { + return nullptr; + } + auto tensor2 = vnode_; + auto cnode = cnode_; + + auto PrimMul = GetValueNode(mul->input(0)); + auto fg = node->func_graph(); + auto ttmul = NewCNode({NewValueNode(PrimMul), tensor1, tensor2}, fg); + return NewCNode({NewValueNode(PrimMul), cnode, ttmul}, fg); + } + + void Visit(const AnfNodePtr &node) override { + if (IsValueNode(node)) { + vnode_ = node; + } + + if (IsCNode(node)) { + cnode_ = node->cast(); + } + } + + void Reset() { + vnode_ = nullptr; + cnode_ = nullptr; + } + + private: + AnfNodePtr vnode_; + CNodePtr cnode_; +}; + class ArithmeticSimplify { public: ArithmeticSimplify() @@ -186,12 +235,14 @@ class ArithmeticSimplify { add_by_zero_(), tensor_add_by_zero_(), identity_(prim::kPrimIdentity), - opt_update_zero_tensor_() { + opt_update_zero_tensor_(), + constant_duplicate_mul_() { eliminaters_.emplace_back(multiply_by_zero_or_one_); eliminaters_.emplace_back(add_by_zero_); eliminaters_.emplace_back(tensor_add_by_zero_); eliminaters_.emplace_back(identity_); eliminaters_.emplace_back(opt_update_zero_tensor_); + eliminaters_.emplace_back(constant_duplicate_mul_); } ~ArithmeticSimplify() = default; @@ -212,6 +263,7 @@ class ArithmeticSimplify { TensorAddByZero tensor_add_by_zero_; PrimEliminater identity_; OptUpdateZeroTensor opt_update_zero_tensor_; + ConstantDuplicateMul constant_duplicate_mul_; std::vector eliminaters_{}; }; } // namespace irpass diff --git a/mindspore/ccsrc/utils/graph_utils.cc b/mindspore/ccsrc/utils/graph_utils.cc index 938df2c291..55ef8dc3d5 100644 --- a/mindspore/ccsrc/utils/graph_utils.cc +++ b/mindspore/ccsrc/utils/graph_utils.cc @@ -400,6 +400,8 @@ static bool SameNodeShallow(const AnfNodePtr& node1, const AnfNodePtr& node2, Fu auto a2 = GetValueNode(node2); if (a1->isa() && a2->isa()) { return a1->cast()->name() == a2->cast()->name(); + } else if (a1->isa() && a2->isa()) { + return a1->cast()->ValueEqual(*(a2->cast())); } else { return *a1 == *a2; } diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 175b72560f..a2e7368ea1 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -771,6 +771,14 @@ class Mul(_MathBinaryOp): >>> mul(input_x, input_y) [4, 10, 18] """ + def infer_value(self, x, y): + if x is not None and y is not None: + x = x.asnumpy() + y = y.asnumpy() + out = x * y + out = np.array(out, x.dtype) + return Tensor(out) + return None class Square(PrimitiveWithInfer): diff --git a/tests/ut/cpp/optimizer/lib_test.cc b/tests/ut/cpp/optimizer/lib_test.cc index ff3c00d37a..2d4cf0e78e 100644 --- a/tests/ut/cpp/optimizer/lib_test.cc +++ b/tests/ut/cpp/optimizer/lib_test.cc @@ -543,5 +543,18 @@ TEST_F(TestOptLib, test_print_tuple_wrapper) { ASSERT_TRUE(CheckOpt(before2, after2, patterns)); ASSERT_TRUE(CheckOpt(before3, before3, patterns)); } + +TEST_F(TestOptLib, test_constant_duplicate_mul) { + FuncGraphPtr beforell = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "beforell"); + FuncGraphPtr beforelr = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "beforelr"); + FuncGraphPtr beforerl = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "beforerl"); + FuncGraphPtr beforerr = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "beforerr"); + FuncGraphPtr after = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "after"); + auto patterns = std::vector({irpass.arithmetic_simplify_}); + ASSERT_TRUE(CheckOpt(beforell, after, patterns)); + ASSERT_TRUE(CheckOpt(beforelr, after, patterns)); + ASSERT_TRUE(CheckOpt(beforerl, after, patterns)); + ASSERT_TRUE(CheckOpt(beforerr, after, patterns)); +} } // namespace opt } // namespace mindspore diff --git a/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py b/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py index 53eb2130f0..d494ad27d3 100644 --- a/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py +++ b/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py @@ -16,6 +16,8 @@ from mindspore.ops import Primitive, PrimitiveWithInfer from mindspore.ops import operations as P from mindspore.ops.operations import _grad_ops as G +from mindspore import Tensor +import numpy as np # pylint: disable=unused-variable @@ -903,3 +905,34 @@ def test_print_tuple_wrapper(tag): return print_(make_tuple(x, y, z)) return fns[tag] + +def test_constant_duplicate_mul(tag): + fns = FnDict() + Mul = Primitive('Mul'); + Sqrt = Primitive('Sqrt'); + + x = Tensor(np.array([[2, 2], [2, 3]]).astype('float32')) + tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32')) + tensor2 = Tensor(np.array([[2.2, 3.1], [3.2, 4.2]]).astype('float32')) + + @fns + def beforell(): + return Mul(tensor1, Mul(tensor2, Sqrt(x))) + + @fns + def beforelr(): + return Mul(tensor1, Mul(Sqrt(x), tensor2)) + + @fns + def beforerl(): + return Mul(Mul(Sqrt(x), tensor2), tensor1) + + @fns + def beforerr(): + return Mul(Mul(Sqrt(x), tensor2), tensor1) + + @fns + def after(): + return Mul(Sqrt(x), Mul(tensor1, tensor2)) + + return fns[tag] From f0f55ad7e826199d96498b3efbae7bd305751870 Mon Sep 17 00:00:00 2001 From: lichenever Date: Mon, 30 Mar 2020 11:31:45 +0800 Subject: [PATCH 114/227] fix_cast_bug --- mindspore/ccsrc/parallel/step_parallel.cc | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc index 78bec00bcf..31dc77b595 100644 --- a/mindspore/ccsrc/parallel/step_parallel.cc +++ b/mindspore/ccsrc/parallel/step_parallel.cc @@ -653,6 +653,13 @@ LossNodeInfo GetLossNodeInfo(const AnfNodePtr& loss_node) { MS_EXCEPTION_IF_NULL(pre_node); LossNodeInfo node_info; + // return -> cast + auto pre_cnode = pre_node->cast(); + MS_EXCEPTION_IF_NULL(pre_cnode); + auto pre_prim = GetValueNode(pre_cnode->input(0)); + if (pre_prim->name() == CAST && pre_cnode->operator_info() == nullptr) { + pre_node = pre_cnode->input(1); + } // return -> cast auto pre_cnode = pre_node->cast(); @@ -1970,7 +1977,10 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) { MS_EXCEPTION_IF_NULL(current_value); PrimitivePtr current_prim = current_value->value()->cast(); MS_EXCEPTION_IF_NULL(current_prim); +<<<<<<< HEAD +======= +>>>>>>> fix_cast_bug // return -> cast if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) { pre_cnode = pre_cnode->input(1)->cast(); From 4c2aa41f1d859ba3a57d12593736bb7cc6b52ed6 Mon Sep 17 00:00:00 2001 From: leonwanghui Date: Tue, 31 Mar 2020 10:27:40 +0800 Subject: [PATCH 115/227] =?UTF-8?q?=E5=9B=9E=E9=80=80=20'Pull=20Request=20?= =?UTF-8?q?!17=20:=20[AutoParallel]Fix=20bug=20in=20the=20case=20of=20two?= =?UTF-8?q?=20cast'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../ccsrc/parallel/step_auto_parallel.cc | 2 -- mindspore/ccsrc/parallel/step_parallel.cc | 11 +++---- .../parallel/test_element_wise_function.py | 29 ------------------- 3 files changed, 4 insertions(+), 38 deletions(-) diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc index 50e6a1e84e..7a895a9458 100644 --- a/mindspore/ccsrc/parallel/step_auto_parallel.cc +++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc @@ -350,8 +350,6 @@ bool IsAutoParallelCareNode(const CNodePtr &cnode) { } OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &cnode) { - MS_EXCEPTION_IF_NULL(prim); - MS_EXCEPTION_IF_NULL(cnode); auto attrs = prim->attrs(); std::vector shape_list = ExtractShape(cnode); if (shape_list.empty()) { diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc index 31dc77b595..af5eb0159f 100644 --- a/mindspore/ccsrc/parallel/step_parallel.cc +++ b/mindspore/ccsrc/parallel/step_parallel.cc @@ -374,6 +374,7 @@ bool IsParallelCareNode(const CNodePtr& cnode) { if (prim == nullptr) { return false; } + auto attrs = prim->attrs(); if (IsInBlackList(prim)) { MS_LOG(INFO) << "Parallel don't care node: " << prim->name(); return false; @@ -653,13 +654,6 @@ LossNodeInfo GetLossNodeInfo(const AnfNodePtr& loss_node) { MS_EXCEPTION_IF_NULL(pre_node); LossNodeInfo node_info; - // return -> cast - auto pre_cnode = pre_node->cast(); - MS_EXCEPTION_IF_NULL(pre_cnode); - auto pre_prim = GetValueNode(pre_cnode->input(0)); - if (pre_prim->name() == CAST && pre_cnode->operator_info() == nullptr) { - pre_node = pre_cnode->input(1); - } // return -> cast auto pre_cnode = pre_node->cast(); @@ -1978,6 +1972,7 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) { PrimitivePtr current_prim = current_value->value()->cast(); MS_EXCEPTION_IF_NULL(current_prim); <<<<<<< HEAD +<<<<<<< HEAD ======= >>>>>>> fix_cast_bug @@ -1988,6 +1983,8 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) { current_prim = GetValueNode(pre_cnode->input(0)); } +======= +>>>>>>> 回退 'Pull Request !17 : [AutoParallel]Fix bug in the case of two cast' // notice: the GetNext op has not input if (INVALID_LOSS_OPS.find(current_prim->name()) != INVALID_LOSS_OPS.end()) { MS_LOG(INFO) << "The loss is: " << current_prim->name(); diff --git a/tests/ut/python/parallel/test_element_wise_function.py b/tests/ut/python/parallel/test_element_wise_function.py index 2eb3a22ed2..0c65593d6a 100644 --- a/tests/ut/python/parallel/test_element_wise_function.py +++ b/tests/ut/python/parallel/test_element_wise_function.py @@ -268,32 +268,3 @@ def test_cast_before_mirror3(): y = Tensor(np.ones([32, 64]), dtype=ms.float16) b = Tensor(np.ones([64, 64]), dtype=ms.float32) _executor.compile(net, x, y, b) - - -def test_mul_two_cast(): - class Net(nn.Cell): - def __init__(self, strategy1, strategy2, strategy3): - super().__init__() - self.mul = P.Mul().set_strategy(strategy1) - self.mul2 = P.Mul().set_strategy(strategy2) - self.cast = P.Cast().set_strategy(strategy3) - self.cast2 = P.Cast().set_strategy(strategy3) - - def construct(self, x, y, b): - out = self.mul(x, y) - out = self.mul2(out, b) - out = self.cast(out, ms.int32) - out = self.cast2(out, ms.bool_) - return out - - context.set_auto_parallel_context(device_num=8, global_rank=0) - strategy1 = ((2, 2), (2, 2)) - strategy2 = ((8, 1), (8, 1)) - strategy3 = ((8, 1), ) - net = GradWrap(Net(strategy1, strategy2, strategy3)) - context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") - - x = Tensor(np.ones([128, 32]), dtype=ms.float32) - y = Tensor(np.ones([128, 32]), dtype=ms.float32) - b = Tensor(np.ones([128, 32]), dtype=ms.float32) - _executor.compile(net, x, y, b) From 1984e4a1ffd938a9d24e6f3db215c7a70cafb114 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Tue, 31 Mar 2020 09:34:09 +0800 Subject: [PATCH 116/227] add operator diag and diag_part --- mindspore/ccsrc/transform/convert.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index d2a11948ef..8eed207f59 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -190,6 +190,7 @@ const char kNameAtan2[] = "Atan2"; const char kNameApplyRMSProp[] = "ApplyRMSProp"; const char kNameApplyCenteredRMSProp[] = "ApplyCenteredRMSProp"; + // -----------------OpAdapter initialization-------------- std::unordered_map &DfGraphConvertor::get_adpt_map() { static std::unordered_map adpt_map = { From b27129c9da5c4f54f2ec8d5170fd0628cbbe87bb Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Tue, 31 Mar 2020 16:18:04 +0800 Subject: [PATCH 117/227] modify longtime python ut --- .../train/summary/test_summary_performance.py | 97 +++++++++++++++++++ 1 file changed, 97 insertions(+) create mode 100644 tests/ut/python/train/summary/test_summary_performance.py diff --git a/tests/ut/python/train/summary/test_summary_performance.py b/tests/ut/python/train/summary/test_summary_performance.py new file mode 100644 index 0000000000..9ee9725d13 --- /dev/null +++ b/tests/ut/python/train/summary/test_summary_performance.py @@ -0,0 +1,97 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +@File : test_summary.py +@Author: +@Date : 2019-07-4 +@Desc : test summary function +""" +import os +import logging +import time +import numpy as np +from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data +from mindspore.common.tensor import Tensor + +CUR_DIR = os.getcwd() +SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/" + +log = logging.getLogger("test") +log.setLevel(level=logging.ERROR) + +def get_now_time_ns(): + """get the time of second""" + time_second = int(time.time_ns()) + return time_second + +def get_test_data(step): + """ get_test_data """ + # pylint: disable=unused-argument + test_data_list = [] + tag1 = "xt1[:Tensor]" + tag2 = "xt2[:Tensor]" + tag3 = "xt3[:Tensor]" + np1 = np.random.random((5, 4, 3, 5)) + np2 = np.random.random((5, 5, 3, 5)) + np3 = np.random.random((4, 5, 3, 5)) + + dict1 = {} + dict1["name"] = tag1 + dict1["data"] = Tensor(np1) + + dict2 = {} + dict2["name"] = tag2 + dict2["data"] = Tensor(np2) + + dict3 = {} + dict3["name"] = tag3 + dict3["data"] = Tensor(np3) + + test_data_list.append(dict1) + test_data_list.append(dict2) + + return test_data_list + + +# Test 1: summary sample of scalar +def test_summary_performance(): + """ test_summary_performance """ + log.debug("begin test_scalar_summary_sample") + current_time = time.time() + print("time = ", current_time) + # step 0: create the thread + test_writer = SummaryRecord(SUMMARY_DIR, flush_time=120) + + # step 1: create the test data for summary + old_time = get_now_time_ns() + # step 2: create the Event + for i in range(1, 10): + test_data = get_test_data(i) + _cache_summary_tensor_data(test_data) + test_writer.record(i) + now_time = get_now_time_ns() + consume_time = (now_time - old_time)/1000/1000 + old_time = now_time + print("step test_summary_performance conusmer time is:", consume_time) + + + # step 3: send the event to mq + + # step 4: accept the event and write the file + test_writer.flush() + test_writer.close() + current_time = time.time() - current_time + print("consume time = ", current_time) + log.debug("finished test_scalar_summary_sample") From 07449cd1cc6e44ba4d51f2891ada4410df02a725 Mon Sep 17 00:00:00 2001 From: lichenever Date: Tue, 31 Mar 2020 18:43:42 +0800 Subject: [PATCH 118/227] fix two cast bug in auto parallel --- .../ccsrc/parallel/step_auto_parallel.cc | 2 ++ mindspore/ccsrc/parallel/step_parallel.cc | 8 +---- .../parallel/test_element_wise_function.py | 29 +++++++++++++++++++ 3 files changed, 32 insertions(+), 7 deletions(-) diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc index 7a895a9458..50e6a1e84e 100644 --- a/mindspore/ccsrc/parallel/step_auto_parallel.cc +++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc @@ -350,6 +350,8 @@ bool IsAutoParallelCareNode(const CNodePtr &cnode) { } OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &cnode) { + MS_EXCEPTION_IF_NULL(prim); + MS_EXCEPTION_IF_NULL(cnode); auto attrs = prim->attrs(); std::vector shape_list = ExtractShape(cnode); if (shape_list.empty()) { diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc index af5eb0159f..9a08ead584 100644 --- a/mindspore/ccsrc/parallel/step_parallel.cc +++ b/mindspore/ccsrc/parallel/step_parallel.cc @@ -374,7 +374,6 @@ bool IsParallelCareNode(const CNodePtr& cnode) { if (prim == nullptr) { return false; } - auto attrs = prim->attrs(); if (IsInBlackList(prim)) { MS_LOG(INFO) << "Parallel don't care node: " << prim->name(); return false; @@ -1971,11 +1970,7 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) { MS_EXCEPTION_IF_NULL(current_value); PrimitivePtr current_prim = current_value->value()->cast(); MS_EXCEPTION_IF_NULL(current_prim); -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> fix_cast_bug // return -> cast if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) { pre_cnode = pre_cnode->input(1)->cast(); @@ -1983,8 +1978,7 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) { current_prim = GetValueNode(pre_cnode->input(0)); } -======= ->>>>>>> 回退 'Pull Request !17 : [AutoParallel]Fix bug in the case of two cast' + // notice: the GetNext op has not input if (INVALID_LOSS_OPS.find(current_prim->name()) != INVALID_LOSS_OPS.end()) { MS_LOG(INFO) << "The loss is: " << current_prim->name(); diff --git a/tests/ut/python/parallel/test_element_wise_function.py b/tests/ut/python/parallel/test_element_wise_function.py index 0c65593d6a..2eb3a22ed2 100644 --- a/tests/ut/python/parallel/test_element_wise_function.py +++ b/tests/ut/python/parallel/test_element_wise_function.py @@ -268,3 +268,32 @@ def test_cast_before_mirror3(): y = Tensor(np.ones([32, 64]), dtype=ms.float16) b = Tensor(np.ones([64, 64]), dtype=ms.float32) _executor.compile(net, x, y, b) + + +def test_mul_two_cast(): + class Net(nn.Cell): + def __init__(self, strategy1, strategy2, strategy3): + super().__init__() + self.mul = P.Mul().set_strategy(strategy1) + self.mul2 = P.Mul().set_strategy(strategy2) + self.cast = P.Cast().set_strategy(strategy3) + self.cast2 = P.Cast().set_strategy(strategy3) + + def construct(self, x, y, b): + out = self.mul(x, y) + out = self.mul2(out, b) + out = self.cast(out, ms.int32) + out = self.cast2(out, ms.bool_) + return out + + context.set_auto_parallel_context(device_num=8, global_rank=0) + strategy1 = ((2, 2), (2, 2)) + strategy2 = ((8, 1), (8, 1)) + strategy3 = ((8, 1), ) + net = GradWrap(Net(strategy1, strategy2, strategy3)) + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") + + x = Tensor(np.ones([128, 32]), dtype=ms.float32) + y = Tensor(np.ones([128, 32]), dtype=ms.float32) + b = Tensor(np.ones([128, 32]), dtype=ms.float32) + _executor.compile(net, x, y, b) From 079df4c909dcfb3a5c665e3cceca64fe3038e097 Mon Sep 17 00:00:00 2001 From: kswang Date: Tue, 31 Mar 2020 21:25:48 +0800 Subject: [PATCH 119/227] add cpu st lenet --- tests/st/networks/test_cpu_lenet.py | 2 +- tests/st/networks/test_network_main.py | 51 ++++++++++++++++++++++++++ 2 files changed, 52 insertions(+), 1 deletion(-) diff --git a/tests/st/networks/test_cpu_lenet.py b/tests/st/networks/test_cpu_lenet.py index 9fd50f5d9b..bdcbc32382 100644 --- a/tests/st/networks/test_cpu_lenet.py +++ b/tests/st/networks/test_cpu_lenet.py @@ -78,4 +78,4 @@ def test_lenet(): data = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01) label = Tensor(np.ones([32]).astype(np.int32)) net = LeNet() - train(net, data, label) + train(net, data, label) \ No newline at end of file diff --git a/tests/st/networks/test_network_main.py b/tests/st/networks/test_network_main.py index 7601739f8c..730602c0ae 100644 --- a/tests/st/networks/test_network_main.py +++ b/tests/st/networks/test_network_main.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ +<<<<<<< HEAD:tests/st/networks/test_network_main.py """ Function: test network @@ -31,6 +32,47 @@ from models.lenet import LeNet from models.resnetv1_5 import resnet50 from models.alexnet import AlexNet context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") +======= +import pytest +from mindspore.nn import TrainOneStepCell, WithLossCell +import mindspore.context as context +from mindspore.nn.optim import Momentum +import numpy as np +import mindspore.nn as nn +from mindspore.ops import operations as P +from mindspore import Tensor + +class LeNet(nn.Cell): + def __init__(self): + super(LeNet, self).__init__() + self.relu = P.ReLU() + self.batch_size = 32 + + self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid') + self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid') + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + self.reshape = P.Reshape() + self.fc1 = nn.Dense(400, 120) + self.fc2 = nn.Dense(120, 84) + self.fc3 = nn.Dense(84, 10) + + def construct(self, input_x): + output = self.conv1(input_x) + output = self.relu(output) + output = self.pool(output) + output = self.conv2(output) + output = self.relu(output) + output = self.pool(output) + output = self.reshape(output, (self.batch_size, -1)) + output = self.fc1(output) + output = self.relu(output) + output = self.fc2(output) + output = self.relu(output) + output = self.fc3(output) + return output + +context.set_context(mode=context.GRAPH_MODE, device_target="CPU") +>>>>>>> add cpu st lenet:tests/st/networks/test_cpu_lenet.py def train(net, data, label): learning_rate = 0.01 @@ -47,17 +89,24 @@ def train(net, data, label): print("+++++++++++++++++++++++++++") assert res +<<<<<<< HEAD:tests/st/networks/test_network_main.py def test_resnet50(): data = Tensor(np.ones([32, 3 ,224, 224]).astype(np.float32) * 0.01) label = Tensor(np.ones([32]).astype(np.int32)) net = resnet50(32, 10) train(net, data, label) +======= +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.env_onecard +>>>>>>> add cpu st lenet:tests/st/networks/test_cpu_lenet.py def test_lenet(): data = Tensor(np.ones([32, 1 ,32, 32]).astype(np.float32) * 0.01) label = Tensor(np.ones([32]).astype(np.int32)) net = LeNet() train(net, data, label) +<<<<<<< HEAD:tests/st/networks/test_network_main.py def test_alexnet(): data = Tensor(np.ones([32, 3 ,227, 227]).astype(np.float32) * 0.01) @@ -79,3 +128,5 @@ if __name__ == "__main__": test_alexnet() else: print("Please add net name like --net lenet") +======= +>>>>>>> add cpu st lenet:tests/st/networks/test_cpu_lenet.py From 8adbcdbc4cd572d0bc57d9167841baee87b01501 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Wed, 1 Apr 2020 11:53:35 +0800 Subject: [PATCH 120/227] delete longtime python ut --- .../train/summary/test_summary_performance.py | 97 ------------------- 1 file changed, 97 deletions(-) delete mode 100644 tests/ut/python/train/summary/test_summary_performance.py diff --git a/tests/ut/python/train/summary/test_summary_performance.py b/tests/ut/python/train/summary/test_summary_performance.py deleted file mode 100644 index 9ee9725d13..0000000000 --- a/tests/ut/python/train/summary/test_summary_performance.py +++ /dev/null @@ -1,97 +0,0 @@ -# Copyright 2020 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -""" -@File : test_summary.py -@Author: -@Date : 2019-07-4 -@Desc : test summary function -""" -import os -import logging -import time -import numpy as np -from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data -from mindspore.common.tensor import Tensor - -CUR_DIR = os.getcwd() -SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/" - -log = logging.getLogger("test") -log.setLevel(level=logging.ERROR) - -def get_now_time_ns(): - """get the time of second""" - time_second = int(time.time_ns()) - return time_second - -def get_test_data(step): - """ get_test_data """ - # pylint: disable=unused-argument - test_data_list = [] - tag1 = "xt1[:Tensor]" - tag2 = "xt2[:Tensor]" - tag3 = "xt3[:Tensor]" - np1 = np.random.random((5, 4, 3, 5)) - np2 = np.random.random((5, 5, 3, 5)) - np3 = np.random.random((4, 5, 3, 5)) - - dict1 = {} - dict1["name"] = tag1 - dict1["data"] = Tensor(np1) - - dict2 = {} - dict2["name"] = tag2 - dict2["data"] = Tensor(np2) - - dict3 = {} - dict3["name"] = tag3 - dict3["data"] = Tensor(np3) - - test_data_list.append(dict1) - test_data_list.append(dict2) - - return test_data_list - - -# Test 1: summary sample of scalar -def test_summary_performance(): - """ test_summary_performance """ - log.debug("begin test_scalar_summary_sample") - current_time = time.time() - print("time = ", current_time) - # step 0: create the thread - test_writer = SummaryRecord(SUMMARY_DIR, flush_time=120) - - # step 1: create the test data for summary - old_time = get_now_time_ns() - # step 2: create the Event - for i in range(1, 10): - test_data = get_test_data(i) - _cache_summary_tensor_data(test_data) - test_writer.record(i) - now_time = get_now_time_ns() - consume_time = (now_time - old_time)/1000/1000 - old_time = now_time - print("step test_summary_performance conusmer time is:", consume_time) - - - # step 3: send the event to mq - - # step 4: accept the event and write the file - test_writer.flush() - test_writer.close() - current_time = time.time() - current_time - print("consume time = ", current_time) - log.debug("finished test_scalar_summary_sample") From 9b5d4eff89cd47856e1ee181207d4b62d05d542b Mon Sep 17 00:00:00 2001 From: zhaoting Date: Tue, 31 Mar 2020 09:14:08 +0800 Subject: [PATCH 121/227] add RMSProp optimizer --- mindspore/ccsrc/transform/op_declare.h | 1 + mindspore/nn/optim/rmsprop.py | 15 +++++++++++++++ 2 files changed, 16 insertions(+) diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index 9fbc97f3c9..339a5027c5 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -458,6 +458,7 @@ DECLARE_OP_USE_INPUT_ATTR(ApplyRMSPropD) DECLARE_OP_USE_OUTPUT(ApplyRMSPropD) DECLARE_OP_ADAPTER(ApplyCenteredRMSProp) DECLARE_OP_USE_OUTPUT(ApplyCenteredRMSProp) + #ifdef ENABLE_GE DECLARE_OP_ADAPTER(Print) DECLARE_OP_USE_DYN_INPUT(Print) diff --git a/mindspore/nn/optim/rmsprop.py b/mindspore/nn/optim/rmsprop.py index b17a101708..a34de449d1 100644 --- a/mindspore/nn/optim/rmsprop.py +++ b/mindspore/nn/optim/rmsprop.py @@ -18,8 +18,12 @@ from mindspore.common.initializer import initializer from mindspore.common.parameter import Parameter from mindspore._checkparam import ParamValidator as validator import mindspore.common.dtype as mstype +<<<<<<< HEAD from mindspore.common import Tensor from .optimizer import Optimizer, grad_scale, apply_decay +======= +from .optimizer import Optimizer, grad_scale +>>>>>>> add RMSProp optimizer rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt") centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt") @@ -119,9 +123,12 @@ class RMSProp(Optimizer): use_locking (bool): Enable a lock to protect the update of variable and accumlation tensors. Default: False. centered (bool): If True, gradients are normalized by the estimated variance of the gradient. Default: False loss_scale (float): A floating point value for the loss scale. Default: 1.0. +<<<<<<< HEAD weight_decay (float): Weight decay (L2 penalty). Default: 0.0. decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default: lambda x: 'beta' not in x.name and 'gamma' not in x.name. +======= +>>>>>>> add RMSProp optimizer Inputs: - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`. @@ -132,12 +139,20 @@ class RMSProp(Optimizer): Examples: >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() +<<<<<<< HEAD >>> opt = nn.RMSProp(params=net.trainable_params(), learning_rate=lr) >>> model = Model(net, loss, opt) """ def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10, use_locking=False, centered=False, loss_scale=1.0, weight_decay=0.0, decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name): +======= + >>> opt = RMSProp(params=net.trainable_params(), learning_rate=lr) + >>> model = Model(net, loss, opt) + """ + def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10, + use_locking=False, centered=False, loss_scale=1.0): +>>>>>>> add RMSProp optimizer super(RMSProp, self).__init__(learning_rate, params) if isinstance(momentum, float) and momentum < 0.0: From 2ba026dbf2e8791281cbd4e98cf74a6464b6a991 Mon Sep 17 00:00:00 2001 From: Wei Luning Date: Mon, 23 Mar 2020 17:33:56 +0800 Subject: [PATCH 122/227] remove ge depend in cpu --- mindspore/ccsrc/pipeline/pipeline_ge.cc | 55 +++++++++++++++++++++++++ mindspore/ccsrc/utils/callbacks.h | 2 +- 2 files changed, 56 insertions(+), 1 deletion(-) diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.cc b/mindspore/ccsrc/pipeline/pipeline_ge.cc index 2f68935591..0b37e8f930 100644 --- a/mindspore/ccsrc/pipeline/pipeline_ge.cc +++ b/mindspore/ccsrc/pipeline/pipeline_ge.cc @@ -391,7 +391,12 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve const std::string& phase) { std::vector ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW); if (ge_tensors.size() != inputs.size()) { +<<<<<<< HEAD MS_LOG(EXCEPTION) << "Convert me args to ge tensor error."; +======= + MS_LOG(ERROR) << "args convert to ge tensor error"; + return nullptr; +>>>>>>> remove ge depend in cpu } std::vector ge_outputs; @@ -402,7 +407,12 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner(); if (graph_runner == nullptr) { +<<<<<<< HEAD MS_LOG(EXCEPTION) << "Can not found GraphRunner."; +======= + MS_LOG(ERROR) << "Can not found GraphRunner"; + return nullptr; +>>>>>>> remove ge depend in cpu } { @@ -419,7 +429,11 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve std::vector me_outputs = TransformUtil::ConvertGeTensors(ge_outputs); if (me_outputs.size() != ge_outputs.size()) { +<<<<<<< HEAD MS_LOG(WARNING) << "Convert output Ge tensor to Me tensor failed"; +======= + MS_LOG(ERROR) << "Convert output Ge tensor to Me tensor failed"; +>>>>>>> remove ge depend in cpu } py::tuple outputs(me_outputs.size()); @@ -429,11 +443,28 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve std::shared_ptr ret = nullptr; +<<<<<<< HEAD AnfNodePtr output_node = graph->get_return()->input(1); MS_EXCEPTION_IF_NULL(output_node); size_t count = 0; py::object oj = StructureOutput(output_node, outputs, &count); ret = std::make_shared(oj); +======= +#ifdef ENABLE_GE + AnfNodePtr root = graph->get_return(); + MS_EXCEPTION_IF_NULL(root); + AbstractBasePtr output = root->abstract(); + size_t count = 0; + py::object oj = StructureOutput(output, outputs, &count); + ret = std::make_shared(oj); +#else + if (outputs.size() == 1) { + ret = std::make_shared(outputs[0]); + } else { + ret = std::make_shared(outputs); + } +#endif +>>>>>>> remove ge depend in cpu return ret; } @@ -444,7 +475,11 @@ void ProcessGeArg(const std::map& info, const py:: std::size_t size = args.size(); if (info.count(phase) == 0) { +<<<<<<< HEAD MS_LOG(EXCEPTION) << "No phase in executor:" << GetPhasePrefix(phase); +======= + MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase); +>>>>>>> remove ge depend in cpu } auto arg_size = info.at(phase)->arg_list_size; @@ -459,12 +494,20 @@ void ProcessGeArg(const std::map& info, const py:: ValuePtr converted = nullptr; bool succ = parse::ConvertData(args[i], &converted); if (!succ) { +<<<<<<< HEAD MS_LOG(EXCEPTION) << "Args convert error"; +======= + MS_LOG(EXCEPTION) << "args convert error"; +>>>>>>> remove ge depend in cpu } if (converted->isa()) { (*inputs).push_back(converted->cast()); } else { +<<<<<<< HEAD MS_LOG(EXCEPTION) << "Args " << converted->ToString() << " is not tensor"; +======= + MS_LOG(EXCEPTION) << "args, " << converted->ToString() << " is not tensor"; +>>>>>>> remove ge depend in cpu } } } @@ -481,12 +524,20 @@ py::object ExecDFGraph(const std::map& info, const } if (info.count(phase) == 0) { +<<<<<<< HEAD MS_LOG(EXCEPTION) << "There is no phase:" << phase; +======= + MS_LOG(EXCEPTION) << "has no phase:" << phase; +>>>>>>> remove ge depend in cpu } FuncGraphPtr anf_graph = info.at(phase)->func_graph; +<<<<<<< HEAD #ifdef ENABLE_INFER +======= +#if (!defined ENABLE_GE) || (defined ENABLE_INFER) +>>>>>>> remove ge depend in cpu // Now don't use the graph because the exec ge function don't take effect MS_EXCEPTION_IF_NULL(info.at(phase)->func_graph); if (ENABLE_TRAIN != info.at(phase)->func_graph->flags()["training"]) { @@ -511,7 +562,11 @@ py::object ExecDFGraph(const std::map& info, const if (ret != nullptr) { return *ret; } else { +<<<<<<< HEAD MS_LOG(EXCEPTION) << "Exec graph failed"; +======= + MS_LOG(EXCEPTION) << "exec graph failed"; +>>>>>>> remove ge depend in cpu } } void ExportDFGraph(const std::string& file_name, const std::string& phase) { diff --git a/mindspore/ccsrc/utils/callbacks.h b/mindspore/ccsrc/utils/callbacks.h index a1e4e75d5b..6f099ef4ca 100644 --- a/mindspore/ccsrc/utils/callbacks.h +++ b/mindspore/ccsrc/utils/callbacks.h @@ -40,7 +40,7 @@ const int kCallbackOk = 0; const int kCallbackFalied = 1; bool GetParameterShape(const FuncGraphPtr& anf_graph, const std::string& param_name, - const std::shared_ptr>& shape); + const std::shared_ptr>& shape) uint32_t SummarySaveCallback(uint32_t, const std::map&); } // namespace callbacks From 23c21e191ff2ebdcdbc7340e3748ce2d4c9c25f0 Mon Sep 17 00:00:00 2001 From: zhangz0911gm Date: Tue, 31 Mar 2020 23:14:21 -0400 Subject: [PATCH 123/227] Add FloorMod, Acosh in ME --- mindspore/ccsrc/transform/op_declare.h | 1 - 1 file changed, 1 deletion(-) diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index 339a5027c5..9fbc97f3c9 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -458,7 +458,6 @@ DECLARE_OP_USE_INPUT_ATTR(ApplyRMSPropD) DECLARE_OP_USE_OUTPUT(ApplyRMSPropD) DECLARE_OP_ADAPTER(ApplyCenteredRMSProp) DECLARE_OP_USE_OUTPUT(ApplyCenteredRMSProp) - #ifdef ENABLE_GE DECLARE_OP_ADAPTER(Print) DECLARE_OP_USE_DYN_INPUT(Print) From 2a82eb450efa5b26b8722807c1ff33db192594a9 Mon Sep 17 00:00:00 2001 From: zhaoting Date: Fri, 3 Apr 2020 11:45:49 +0800 Subject: [PATCH 124/227] add weight decay in RMSProp optimizer --- mindspore/nn/optim/rmsprop.py | 17 +---------------- 1 file changed, 1 insertion(+), 16 deletions(-) diff --git a/mindspore/nn/optim/rmsprop.py b/mindspore/nn/optim/rmsprop.py index a34de449d1..e252f89f2f 100644 --- a/mindspore/nn/optim/rmsprop.py +++ b/mindspore/nn/optim/rmsprop.py @@ -18,12 +18,8 @@ from mindspore.common.initializer import initializer from mindspore.common.parameter import Parameter from mindspore._checkparam import ParamValidator as validator import mindspore.common.dtype as mstype -<<<<<<< HEAD from mindspore.common import Tensor from .optimizer import Optimizer, grad_scale, apply_decay -======= -from .optimizer import Optimizer, grad_scale ->>>>>>> add RMSProp optimizer rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt") centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt") @@ -123,12 +119,9 @@ class RMSProp(Optimizer): use_locking (bool): Enable a lock to protect the update of variable and accumlation tensors. Default: False. centered (bool): If True, gradients are normalized by the estimated variance of the gradient. Default: False loss_scale (float): A floating point value for the loss scale. Default: 1.0. -<<<<<<< HEAD weight_decay (float): Weight decay (L2 penalty). Default: 0.0. decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default: lambda x: 'beta' not in x.name and 'gamma' not in x.name. -======= ->>>>>>> add RMSProp optimizer Inputs: - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`. @@ -139,20 +132,12 @@ class RMSProp(Optimizer): Examples: >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() -<<<<<<< HEAD >>> opt = nn.RMSProp(params=net.trainable_params(), learning_rate=lr) >>> model = Model(net, loss, opt) """ def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10, use_locking=False, centered=False, loss_scale=1.0, weight_decay=0.0, decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name): -======= - >>> opt = RMSProp(params=net.trainable_params(), learning_rate=lr) - >>> model = Model(net, loss, opt) - """ - def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10, - use_locking=False, centered=False, loss_scale=1.0): ->>>>>>> add RMSProp optimizer super(RMSProp, self).__init__(learning_rate, params) if isinstance(momentum, float) and momentum < 0.0: @@ -209,4 +194,4 @@ class RMSProp(Optimizer): else: success = self.hyper_map(F.partial(rmsprop_opt, self.opt, lr, self.decay, self.epsilon, self.momentum), params, self.ms, self.moment, gradients) - return success + return success \ No newline at end of file From 93fe493cf46fdca43f2b0ecc486b210864629cc5 Mon Sep 17 00:00:00 2001 From: kingfo Date: Fri, 3 Apr 2020 10:53:46 +0800 Subject: [PATCH 125/227] fix ME+GE compile error --- mindspore/ccsrc/pipeline/pipeline_ge.cc | 63 ++----------------------- 1 file changed, 4 insertions(+), 59 deletions(-) diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.cc b/mindspore/ccsrc/pipeline/pipeline_ge.cc index 0b37e8f930..ee67d46cf7 100644 --- a/mindspore/ccsrc/pipeline/pipeline_ge.cc +++ b/mindspore/ccsrc/pipeline/pipeline_ge.cc @@ -116,7 +116,7 @@ bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batc return transform::TransformUtil::ConvertDataType(i->type_id()); }); - ConfigManager::GetInstance().set_dataset_mode(DatasetMode::DS_GRAPH_MODE); + ConfigManager::GetInstance().set_dataset_mode(DatasetMode::DS_SINK_MODE); ConfigManager::GetInstance().set_iter_num(size); ConfigManager::GetInstance().set_dataset_phase(phase); @@ -391,12 +391,7 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve const std::string& phase) { std::vector ge_tensors = TransformUtil::ConvertInputTensors(inputs, kOpFormat_NCHW); if (ge_tensors.size() != inputs.size()) { -<<<<<<< HEAD MS_LOG(EXCEPTION) << "Convert me args to ge tensor error."; -======= - MS_LOG(ERROR) << "args convert to ge tensor error"; - return nullptr; ->>>>>>> remove ge depend in cpu } std::vector ge_outputs; @@ -407,12 +402,7 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve auto graph_runner = DfGraphManager::GetInstance().GetGraphRunner(); if (graph_runner == nullptr) { -<<<<<<< HEAD MS_LOG(EXCEPTION) << "Can not found GraphRunner."; -======= - MS_LOG(ERROR) << "Can not found GraphRunner"; - return nullptr; ->>>>>>> remove ge depend in cpu } { @@ -429,11 +419,7 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve std::vector me_outputs = TransformUtil::ConvertGeTensors(ge_outputs); if (me_outputs.size() != ge_outputs.size()) { -<<<<<<< HEAD MS_LOG(WARNING) << "Convert output Ge tensor to Me tensor failed"; -======= - MS_LOG(ERROR) << "Convert output Ge tensor to Me tensor failed"; ->>>>>>> remove ge depend in cpu } py::tuple outputs(me_outputs.size()); @@ -443,28 +429,11 @@ std::shared_ptr DoExecGraph(const FuncGraphPtr& graph, const std::ve std::shared_ptr ret = nullptr; -<<<<<<< HEAD AnfNodePtr output_node = graph->get_return()->input(1); MS_EXCEPTION_IF_NULL(output_node); size_t count = 0; py::object oj = StructureOutput(output_node, outputs, &count); ret = std::make_shared(oj); -======= -#ifdef ENABLE_GE - AnfNodePtr root = graph->get_return(); - MS_EXCEPTION_IF_NULL(root); - AbstractBasePtr output = root->abstract(); - size_t count = 0; - py::object oj = StructureOutput(output, outputs, &count); - ret = std::make_shared(oj); -#else - if (outputs.size() == 1) { - ret = std::make_shared(outputs[0]); - } else { - ret = std::make_shared(outputs); - } -#endif ->>>>>>> remove ge depend in cpu return ret; } @@ -475,11 +444,7 @@ void ProcessGeArg(const std::map& info, const py:: std::size_t size = args.size(); if (info.count(phase) == 0) { -<<<<<<< HEAD MS_LOG(EXCEPTION) << "No phase in executor:" << GetPhasePrefix(phase); -======= - MS_LOG(EXCEPTION) << "no phase in executor:" << GetPhasePrefix(phase); ->>>>>>> remove ge depend in cpu } auto arg_size = info.at(phase)->arg_list_size; @@ -488,26 +453,18 @@ void ProcessGeArg(const std::map& info, const py:: } // process the first args of tensor - // only in Dataset Feed Mode, fp_bp graph need input tensors - if (ConfigManager::GetInstance().dataset_mode() == DS_FEED_MODE) { + // only in dataset normal(non-sink) mode, fp_bp graph need input tensors + if (ConfigManager::GetInstance().dataset_mode() == DS_NORMAL_MODE) { for (std::size_t i = 0; i < size; i++) { ValuePtr converted = nullptr; bool succ = parse::ConvertData(args[i], &converted); if (!succ) { -<<<<<<< HEAD MS_LOG(EXCEPTION) << "Args convert error"; -======= - MS_LOG(EXCEPTION) << "args convert error"; ->>>>>>> remove ge depend in cpu } if (converted->isa()) { (*inputs).push_back(converted->cast()); } else { -<<<<<<< HEAD MS_LOG(EXCEPTION) << "Args " << converted->ToString() << " is not tensor"; -======= - MS_LOG(EXCEPTION) << "args, " << converted->ToString() << " is not tensor"; ->>>>>>> remove ge depend in cpu } } } @@ -524,20 +481,12 @@ py::object ExecDFGraph(const std::map& info, const } if (info.count(phase) == 0) { -<<<<<<< HEAD MS_LOG(EXCEPTION) << "There is no phase:" << phase; -======= - MS_LOG(EXCEPTION) << "has no phase:" << phase; ->>>>>>> remove ge depend in cpu } FuncGraphPtr anf_graph = info.at(phase)->func_graph; -<<<<<<< HEAD #ifdef ENABLE_INFER -======= -#if (!defined ENABLE_GE) || (defined ENABLE_INFER) ->>>>>>> remove ge depend in cpu // Now don't use the graph because the exec ge function don't take effect MS_EXCEPTION_IF_NULL(info.at(phase)->func_graph); if (ENABLE_TRAIN != info.at(phase)->func_graph->flags()["training"]) { @@ -562,11 +511,7 @@ py::object ExecDFGraph(const std::map& info, const if (ret != nullptr) { return *ret; } else { -<<<<<<< HEAD MS_LOG(EXCEPTION) << "Exec graph failed"; -======= - MS_LOG(EXCEPTION) << "exec graph failed"; ->>>>>>> remove ge depend in cpu } } void ExportDFGraph(const std::string& file_name, const std::string& phase) { @@ -588,4 +533,4 @@ void ExportDFGraph(const std::string& file_name, const std::string& phase) { MS_LOG(DEBUG) << "ExportGraph End"; } } // namespace pipeline -} // namespace mindspore +} // namespace mindspore \ No newline at end of file From 3aa51f35c1dd33344f40c8d475d1bd8b60bd3028 Mon Sep 17 00:00:00 2001 From: wanghua Date: Fri, 3 Apr 2020 14:55:25 +0800 Subject: [PATCH 126/227] fix bert precison bug --- mindspore/ccsrc/device/ascend/kernel_select_ascend.cc | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc index d05b9fafa1..a7c8d131fb 100644 --- a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc +++ b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc @@ -82,6 +82,12 @@ bool IsValidKernelInfo(const std::shared_ptr &kernel_node, const kernel:: } return true; }; + if (AnfAlgo::GetCNodeName(kernel_node) == "LayerNormBetaGammaBackprop" || + AnfAlgo::GetCNodeName(kernel_node) == "LayerNormXBackprop") { + if (AnfAlgo::GetPrevNodeOutputFormat(kernel_node, 0) != kernel_build_info.GetInputFormat(0)) { + return true; + } + } if (AnfAlgo::GetCNodeName(kernel_node) == prim::kPrimCast->name()) { return AnfAlgo::GetOutputInferDataType(kernel_node, 0) == kernel_build_info.GetOutputDeviceType(0) && AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0) == kernel_build_info.GetInputDeviceType(0); @@ -155,7 +161,7 @@ bool PriorityChooseItem(const std::vector &cur_item, std::vector *best return false; } } - return false; + return true; } void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, const std::shared_ptr &kernel_node, From a8f95e6d2c94565dadff47f39c30dcb676773f5d Mon Sep 17 00:00:00 2001 From: wanghua Date: Fri, 3 Apr 2020 17:51:56 +0800 Subject: [PATCH 127/227] modify bert test file --- mindspore/ccsrc/device/ascend/kernel_select_ascend.cc | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc index a7c8d131fb..d05b9fafa1 100644 --- a/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc +++ b/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc @@ -82,12 +82,6 @@ bool IsValidKernelInfo(const std::shared_ptr &kernel_node, const kernel:: } return true; }; - if (AnfAlgo::GetCNodeName(kernel_node) == "LayerNormBetaGammaBackprop" || - AnfAlgo::GetCNodeName(kernel_node) == "LayerNormXBackprop") { - if (AnfAlgo::GetPrevNodeOutputFormat(kernel_node, 0) != kernel_build_info.GetInputFormat(0)) { - return true; - } - } if (AnfAlgo::GetCNodeName(kernel_node) == prim::kPrimCast->name()) { return AnfAlgo::GetOutputInferDataType(kernel_node, 0) == kernel_build_info.GetOutputDeviceType(0) && AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0) == kernel_build_info.GetInputDeviceType(0); @@ -161,7 +155,7 @@ bool PriorityChooseItem(const std::vector &cur_item, std::vector *best return false; } } - return true; + return false; } void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, const std::shared_ptr &kernel_node, From 1efa4ffc39b9599769ea1ef8cb1deb6b9d4bd280 Mon Sep 17 00:00:00 2001 From: ms_yan <6576637+ms_yan@user.noreply.gitee.com> Date: Thu, 2 Apr 2020 21:56:48 +0800 Subject: [PATCH 128/227] add parameter check for Class Schema --- mindspore/dataset/engine/datasets.py | 20 +++++++---- mindspore/dataset/engine/validators.py | 49 ++++++++++++++++++++++++++ 2 files changed, 63 insertions(+), 6 deletions(-) diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py index db2b5169d2..2d5c219b71 100644 --- a/mindspore/dataset/engine/datasets.py +++ b/mindspore/dataset/engine/datasets.py @@ -38,7 +38,7 @@ from .iterators import DictIterator, TupleIterator from .validators import check, check_batch, check_shuffle, check_map, check_repeat, check_zip, check_rename, \ check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \ check_tfrecorddataset, check_vocdataset, check_celebadataset, check_minddataset, check_generatordataset, \ - check_zip_dataset + check_zip_dataset, check_add_column, check_columns from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist try: @@ -2334,13 +2334,20 @@ class Schema: self.dataset_type = '' self.num_rows = 0 else: + if not os.path.isfile(schema_file) or not os.access(schema_file, os.R_OK): + raise ValueError("The file %s does not exist or permission denied!" % schema_file) try: with open(schema_file, 'r') as load_f: json_obj = json.load(load_f) - self.from_json(json_obj) except json.decoder.JSONDecodeError: - raise RuntimeError("Schema file failed to load") + raise RuntimeError("Schema file failed to load.") + except UnicodeDecodeError: + raise RuntimeError("Schema file failed to decode.") + except Exception: + raise RuntimeError("Schema file failed to open.") + self.from_json(json_obj) + @check_add_column def add_column(self, name, de_type, shape=None): """ Add new column to the schema. @@ -2359,10 +2366,8 @@ class Schema: if isinstance(de_type, typing.Type): de_type = mstype_to_detype(de_type) new_column["type"] = str(de_type) - elif isinstance(de_type, str): - new_column["type"] = str(DataType(de_type)) else: - raise ValueError("Unknown column type") + new_column["type"] = str(DataType(de_type)) if shape is not None: new_column["shape"] = shape @@ -2399,6 +2404,7 @@ class Schema: RuntimeError: If column's name field is missing. RuntimeError: If column's type field is missing. """ + check_columns(columns, columns) self.columns = [] for col in columns: name = None @@ -2443,6 +2449,8 @@ class Schema: RuntimeError: if dataset type is missing in the object. RuntimeError: if columns are missing in the object. """ + if not isinstance(json_obj, dict) or json_obj is None: + raise ValueError("Expected non-empty dict.") for k, v in json_obj.items(): if k == "datasetType": self.dataset_type = v diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py index b4d22a4a01..1c374ae879 100644 --- a/mindspore/dataset/engine/validators.py +++ b/mindspore/dataset/engine/validators.py @@ -19,10 +19,15 @@ import inspect as ins import os from functools import wraps from multiprocessing import cpu_count +from mindspore._c_expression import typing from . import samplers from . import datasets INT32_MAX = 2147483647 +valid_detype = [ + "bool", "int8", "int16", "int32", "int64", "uint8", "uint16", + "uint32", "uint64", "float16", "float32", "float64" +] def check(method): @@ -188,6 +193,12 @@ def check(method): return wrapper +def check_valid_detype(type_): + if type_ not in valid_detype: + raise ValueError("Unknown column type") + return True + + def check_filename(path): """ check the filename in the path @@ -743,3 +754,41 @@ def check_project(method): return method(*args, **kwargs) return new_method + + +def check_shape(shape, name): + if isinstance(shape, list): + for element in shape: + if not isinstance(element, int): + raise TypeError( + "Each element in {0} should be of type int. Got {1}.".format(name, type(element))) + else: + raise TypeError("Expected int list.") + + +def check_add_column(method): + """check the input arguments of add_column.""" + @wraps(method) + def new_method(*args, **kwargs): + param_dict = make_param_dict(method, args, kwargs) + + # check name; required argument + name = param_dict.get("name") + if not isinstance(name, str) or not name: + raise TypeError("Expected non-empty string.") + + # check type; required argument + de_type = param_dict.get("de_type") + if not isinstance(de_type, str) or not de_type: + raise TypeError("Expected non-empty string.") + if not isinstance(de_type, typing.Type) and not check_valid_detype(de_type): + raise ValueError("Unknown column type.") + + # check shape + shape = param_dict.get("shape") + if shape is not None: + check_shape(shape, "shape") + + return method(*args, **kwargs) + + return new_method From 0a595b4749525b94b10ddf33770255ad5effe674 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Tue, 7 Apr 2020 11:39:10 +0800 Subject: [PATCH 129/227] fix runtest.sh for python ut --- mindspore/dataset/engine/datasets.py | 20 ++++------- mindspore/dataset/engine/validators.py | 49 -------------------------- 2 files changed, 6 insertions(+), 63 deletions(-) diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py index 2d5c219b71..db2b5169d2 100644 --- a/mindspore/dataset/engine/datasets.py +++ b/mindspore/dataset/engine/datasets.py @@ -38,7 +38,7 @@ from .iterators import DictIterator, TupleIterator from .validators import check, check_batch, check_shuffle, check_map, check_repeat, check_zip, check_rename, \ check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \ check_tfrecorddataset, check_vocdataset, check_celebadataset, check_minddataset, check_generatordataset, \ - check_zip_dataset, check_add_column, check_columns + check_zip_dataset from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist try: @@ -2334,20 +2334,13 @@ class Schema: self.dataset_type = '' self.num_rows = 0 else: - if not os.path.isfile(schema_file) or not os.access(schema_file, os.R_OK): - raise ValueError("The file %s does not exist or permission denied!" % schema_file) try: with open(schema_file, 'r') as load_f: json_obj = json.load(load_f) + self.from_json(json_obj) except json.decoder.JSONDecodeError: - raise RuntimeError("Schema file failed to load.") - except UnicodeDecodeError: - raise RuntimeError("Schema file failed to decode.") - except Exception: - raise RuntimeError("Schema file failed to open.") - self.from_json(json_obj) + raise RuntimeError("Schema file failed to load") - @check_add_column def add_column(self, name, de_type, shape=None): """ Add new column to the schema. @@ -2366,8 +2359,10 @@ class Schema: if isinstance(de_type, typing.Type): de_type = mstype_to_detype(de_type) new_column["type"] = str(de_type) - else: + elif isinstance(de_type, str): new_column["type"] = str(DataType(de_type)) + else: + raise ValueError("Unknown column type") if shape is not None: new_column["shape"] = shape @@ -2404,7 +2399,6 @@ class Schema: RuntimeError: If column's name field is missing. RuntimeError: If column's type field is missing. """ - check_columns(columns, columns) self.columns = [] for col in columns: name = None @@ -2449,8 +2443,6 @@ class Schema: RuntimeError: if dataset type is missing in the object. RuntimeError: if columns are missing in the object. """ - if not isinstance(json_obj, dict) or json_obj is None: - raise ValueError("Expected non-empty dict.") for k, v in json_obj.items(): if k == "datasetType": self.dataset_type = v diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py index 1c374ae879..b4d22a4a01 100644 --- a/mindspore/dataset/engine/validators.py +++ b/mindspore/dataset/engine/validators.py @@ -19,15 +19,10 @@ import inspect as ins import os from functools import wraps from multiprocessing import cpu_count -from mindspore._c_expression import typing from . import samplers from . import datasets INT32_MAX = 2147483647 -valid_detype = [ - "bool", "int8", "int16", "int32", "int64", "uint8", "uint16", - "uint32", "uint64", "float16", "float32", "float64" -] def check(method): @@ -193,12 +188,6 @@ def check(method): return wrapper -def check_valid_detype(type_): - if type_ not in valid_detype: - raise ValueError("Unknown column type") - return True - - def check_filename(path): """ check the filename in the path @@ -754,41 +743,3 @@ def check_project(method): return method(*args, **kwargs) return new_method - - -def check_shape(shape, name): - if isinstance(shape, list): - for element in shape: - if not isinstance(element, int): - raise TypeError( - "Each element in {0} should be of type int. Got {1}.".format(name, type(element))) - else: - raise TypeError("Expected int list.") - - -def check_add_column(method): - """check the input arguments of add_column.""" - @wraps(method) - def new_method(*args, **kwargs): - param_dict = make_param_dict(method, args, kwargs) - - # check name; required argument - name = param_dict.get("name") - if not isinstance(name, str) or not name: - raise TypeError("Expected non-empty string.") - - # check type; required argument - de_type = param_dict.get("de_type") - if not isinstance(de_type, str) or not de_type: - raise TypeError("Expected non-empty string.") - if not isinstance(de_type, typing.Type) and not check_valid_detype(de_type): - raise ValueError("Unknown column type.") - - # check shape - shape = param_dict.get("shape") - if shape is not None: - check_shape(shape, "shape") - - return method(*args, **kwargs) - - return new_method From bf4c09931eec2c2516cfe0a2cc790ea9f92d8eb1 Mon Sep 17 00:00:00 2001 From: VectorSL Date: Fri, 3 Apr 2020 16:55:37 +0800 Subject: [PATCH 130/227] edit loss_scale for gpu --- mindspore/nn/wrap/loss_scale.py | 40 ++++++++++---- mindspore/ops/operations/__init__.py | 6 +- mindspore/ops/operations/math_ops.py | 83 ++++++++++++++++++++++++++++ 3 files changed, 117 insertions(+), 12 deletions(-) diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py index 1ce3179273..4d929352b3 100644 --- a/mindspore/nn/wrap/loss_scale.py +++ b/mindspore/nn/wrap/loss_scale.py @@ -25,6 +25,7 @@ from ...ops import operations as P from ...ops.operations import NPUGetFloatStatus, NPUAllocFloatStatus, NPUClearFloatStatus, ReduceSum, LessEqual, \ ControlDepend from ...common import dtype as mstype +import mindspore.context as context _grad_scale = C.MultitypeFuncGraph("grad_scale") reciprocal = P.Reciprocal() @@ -34,6 +35,12 @@ reciprocal = P.Reciprocal() def tensor_grad_scale(scale, grad): return grad * F.cast(reciprocal(scale), F.dtype(grad)) +_grad_overflow = C.MultitypeFuncGraph("_grad_overflow") +grad_overflow = P.FloatStatus() + +@_grad_overflow.register("Tensor") +def _tensor_grad_overflow(grad): + return grad_overflow(grad) class DynamicLossScaleUpdateCell(Cell): r""" @@ -197,9 +204,15 @@ class TrainOneStepWithLossScaleCell(Cell): self.optimizer = optimizer self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True) self.hyper_map = C.HyperMap() - self.alloc_status = NPUAllocFloatStatus() - self.get_status = NPUGetFloatStatus() - self.clear_status = NPUClearFloatStatus() + if context.get_context("device_target") == "GPU": + self.gpu_target = True + self.float_status = P.FloatStatus() + self.addn = P.AddN() + else: + self.gpu_target = False + self.alloc_status = NPUAllocFloatStatus() + self.get_status = NPUGetFloatStatus() + self.clear_status = NPUClearFloatStatus() self.reduce_sum = ReduceSum(keep_dims=False) self.base = Tensor(1, mstype.float32) self.less_equal = LessEqual() @@ -224,10 +237,11 @@ class TrainOneStepWithLossScaleCell(Cell): def construct(self, data, label, sens=None): weights = self.weights loss = self.network(data, label) - # init overflow buffer - init = self.alloc_status() - # clear overflow buffer - self.clear_status(init) + if not self.gpu_target: + # init overflow buffer + init = self.alloc_status() + # clear overflow buffer + self.clear_status(init) if sens is None: scaling_sens = self.loss_scale else: @@ -237,10 +251,14 @@ class TrainOneStepWithLossScaleCell(Cell): if self.reducer_flag: # apply grad reducer on grads grads = self.grad_reducer(grads) - # get the overflow buffer - self.get_status(init) - # sum overflow buffer elements, 0:not overflow , >0:overflow - flag_sum = self.reduce_sum(init, (0,)) + if not self.gpu_target: + # get the overflow buffer + self.get_status(init) + # sum overflow buffer elements, 0:not overflow , >0:overflow + flag_sum = self.reduce_sum(init, (0,)) + else: + flag_sum = self.hyper_map(F.partial(_grad_overflow), grads) + flag_sum = self.addn(flag_sum) if self.is_distributed: # sum overflow flag over devices flag_reduce = self.allreduce(flag_sum) diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index 37a3b38bb6..48a985b33d 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -44,7 +44,7 @@ from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul LogicalNot, LogicalOr, MatMul, Maximum, Minimum, Mul, Neg, NMSWithMask, NotEqual, NPUAllocFloatStatus, NPUClearFloatStatus, - NPUGetFloatStatus, Pow, RealDiv, + NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus, Reciprocal, CumSum, Sin, Sqrt, Rsqrt, Square, Sub, TensorAdd, Sign, Round) @@ -151,6 +151,10 @@ __all__ = [ 'Neg', 'Slice', 'DType', + 'IsNan', + 'IsInf', + 'IsFinite', + 'FloatStatus', 'NPUAllocFloatStatus', 'NPUGetFloatStatus', 'NPUClearFloatStatus', diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 175b72560f..5f8c24d78b 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1541,6 +1541,89 @@ class LogicalOr(_LogicBinaryOp): def infer_dtype(self, x_dtype, y_dtype): return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.prim_name()) +class IsNan(PrimitiveWithInfer): + """ + Judging which elements are nan for each position + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the same shape of input. + """ + + @prim_attr_register + def __init__(self): + """init IsNan""" + self.init_prim_io_names(inputs=['x'], outputs=['output']) + + def infer_shape(self, x_shape): + return x_shape + + def infer_dtype(self, x_dtype): + return mstype.bool_ + +class IsInf(PrimitiveWithInfer): + """ + Judging which elements are inf or -inf for each position + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the same shape of input. + """ + + @prim_attr_register + def __init__(self): + """init IsInf""" + self.init_prim_io_names(inputs=['x'], outputs=['output']) + + def infer_shape(self, x_shape): + return x_shape + + def infer_dtype(self, x_dtype): + return mstype.bool_ + +class IsFinite(PrimitiveWithInfer): + """ + Judging which elements are finite for each position + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the same shape of input. + """ + + @prim_attr_register + def __init__(self): + """init IsFinite""" + self.init_prim_io_names(inputs=['x'], outputs=['output']) + + def infer_shape(self, x_shape): + return x_shape + + def infer_dtype(self, x_dtype): + return mstype.bool_ + +class FloatStatus(PrimitiveWithInfer): + """ + Determine if the elements contains nan, inf or -inf + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the shape of `(1,)`. + """ + + @prim_attr_register + def __init__(self): + """init FloatStatus""" + self.init_prim_io_names(inputs=['x'], outputs=['output']) + + def infer_shape(self, x_shape): + return [1] + + def infer_dtype(self, x_dtype): + return x_dtype class NPUAllocFloatStatus(PrimitiveWithInfer): """ From 22578e983949129e5f1c60f53cd2426072d55fd5 Mon Sep 17 00:00:00 2001 From: chengang Date: Tue, 7 Apr 2020 16:56:33 +0800 Subject: [PATCH 131/227] =?UTF-8?q?=E5=9B=9E=E9=80=80=20'Pull=20Request=20?= =?UTF-8?q?!133=20:=20Edit=20loss=5Fscale=20to=20fit=20GPU'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- mindspore/nn/wrap/loss_scale.py | 40 ++++---------- mindspore/ops/operations/__init__.py | 6 +- mindspore/ops/operations/math_ops.py | 83 ---------------------------- 3 files changed, 12 insertions(+), 117 deletions(-) diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py index 4d929352b3..1ce3179273 100644 --- a/mindspore/nn/wrap/loss_scale.py +++ b/mindspore/nn/wrap/loss_scale.py @@ -25,7 +25,6 @@ from ...ops import operations as P from ...ops.operations import NPUGetFloatStatus, NPUAllocFloatStatus, NPUClearFloatStatus, ReduceSum, LessEqual, \ ControlDepend from ...common import dtype as mstype -import mindspore.context as context _grad_scale = C.MultitypeFuncGraph("grad_scale") reciprocal = P.Reciprocal() @@ -35,12 +34,6 @@ reciprocal = P.Reciprocal() def tensor_grad_scale(scale, grad): return grad * F.cast(reciprocal(scale), F.dtype(grad)) -_grad_overflow = C.MultitypeFuncGraph("_grad_overflow") -grad_overflow = P.FloatStatus() - -@_grad_overflow.register("Tensor") -def _tensor_grad_overflow(grad): - return grad_overflow(grad) class DynamicLossScaleUpdateCell(Cell): r""" @@ -204,15 +197,9 @@ class TrainOneStepWithLossScaleCell(Cell): self.optimizer = optimizer self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True) self.hyper_map = C.HyperMap() - if context.get_context("device_target") == "GPU": - self.gpu_target = True - self.float_status = P.FloatStatus() - self.addn = P.AddN() - else: - self.gpu_target = False - self.alloc_status = NPUAllocFloatStatus() - self.get_status = NPUGetFloatStatus() - self.clear_status = NPUClearFloatStatus() + self.alloc_status = NPUAllocFloatStatus() + self.get_status = NPUGetFloatStatus() + self.clear_status = NPUClearFloatStatus() self.reduce_sum = ReduceSum(keep_dims=False) self.base = Tensor(1, mstype.float32) self.less_equal = LessEqual() @@ -237,11 +224,10 @@ class TrainOneStepWithLossScaleCell(Cell): def construct(self, data, label, sens=None): weights = self.weights loss = self.network(data, label) - if not self.gpu_target: - # init overflow buffer - init = self.alloc_status() - # clear overflow buffer - self.clear_status(init) + # init overflow buffer + init = self.alloc_status() + # clear overflow buffer + self.clear_status(init) if sens is None: scaling_sens = self.loss_scale else: @@ -251,14 +237,10 @@ class TrainOneStepWithLossScaleCell(Cell): if self.reducer_flag: # apply grad reducer on grads grads = self.grad_reducer(grads) - if not self.gpu_target: - # get the overflow buffer - self.get_status(init) - # sum overflow buffer elements, 0:not overflow , >0:overflow - flag_sum = self.reduce_sum(init, (0,)) - else: - flag_sum = self.hyper_map(F.partial(_grad_overflow), grads) - flag_sum = self.addn(flag_sum) + # get the overflow buffer + self.get_status(init) + # sum overflow buffer elements, 0:not overflow , >0:overflow + flag_sum = self.reduce_sum(init, (0,)) if self.is_distributed: # sum overflow flag over devices flag_reduce = self.allreduce(flag_sum) diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index 48a985b33d..37a3b38bb6 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -44,7 +44,7 @@ from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul LogicalNot, LogicalOr, MatMul, Maximum, Minimum, Mul, Neg, NMSWithMask, NotEqual, NPUAllocFloatStatus, NPUClearFloatStatus, - NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus, + NPUGetFloatStatus, Pow, RealDiv, Reciprocal, CumSum, Sin, Sqrt, Rsqrt, Square, Sub, TensorAdd, Sign, Round) @@ -151,10 +151,6 @@ __all__ = [ 'Neg', 'Slice', 'DType', - 'IsNan', - 'IsInf', - 'IsFinite', - 'FloatStatus', 'NPUAllocFloatStatus', 'NPUGetFloatStatus', 'NPUClearFloatStatus', diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 5f8c24d78b..175b72560f 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1541,89 +1541,6 @@ class LogicalOr(_LogicBinaryOp): def infer_dtype(self, x_dtype, y_dtype): return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.prim_name()) -class IsNan(PrimitiveWithInfer): - """ - Judging which elements are nan for each position - Inputs: - - **input_x** (Tensor) - The input tensor. - - Outputs: - Tensor, has the same shape of input. - """ - - @prim_attr_register - def __init__(self): - """init IsNan""" - self.init_prim_io_names(inputs=['x'], outputs=['output']) - - def infer_shape(self, x_shape): - return x_shape - - def infer_dtype(self, x_dtype): - return mstype.bool_ - -class IsInf(PrimitiveWithInfer): - """ - Judging which elements are inf or -inf for each position - Inputs: - - **input_x** (Tensor) - The input tensor. - - Outputs: - Tensor, has the same shape of input. - """ - - @prim_attr_register - def __init__(self): - """init IsInf""" - self.init_prim_io_names(inputs=['x'], outputs=['output']) - - def infer_shape(self, x_shape): - return x_shape - - def infer_dtype(self, x_dtype): - return mstype.bool_ - -class IsFinite(PrimitiveWithInfer): - """ - Judging which elements are finite for each position - Inputs: - - **input_x** (Tensor) - The input tensor. - - Outputs: - Tensor, has the same shape of input. - """ - - @prim_attr_register - def __init__(self): - """init IsFinite""" - self.init_prim_io_names(inputs=['x'], outputs=['output']) - - def infer_shape(self, x_shape): - return x_shape - - def infer_dtype(self, x_dtype): - return mstype.bool_ - -class FloatStatus(PrimitiveWithInfer): - """ - Determine if the elements contains nan, inf or -inf - Inputs: - - **input_x** (Tensor) - The input tensor. - - Outputs: - Tensor, has the shape of `(1,)`. - """ - - @prim_attr_register - def __init__(self): - """init FloatStatus""" - self.init_prim_io_names(inputs=['x'], outputs=['output']) - - def infer_shape(self, x_shape): - return [1] - - def infer_dtype(self, x_dtype): - return x_dtype class NPUAllocFloatStatus(PrimitiveWithInfer): """ From b9701db887348513da47b7d301f25e7f7420a8f3 Mon Sep 17 00:00:00 2001 From: Alexey Shevlyakov Date: Thu, 2 Apr 2020 14:17:46 -0400 Subject: [PATCH 132/227] fix RandomCropDecodeResize test --- tests/ut/cpp/dataset/CMakeLists.txt | 2 +- .../dataset/random_crop_and_resize_op_test.cc | 36 ++---- ...c => random_crop_decode_resize_op_test.cc} | 105 +++++++++--------- 3 files changed, 62 insertions(+), 81 deletions(-) rename tests/ut/cpp/dataset/{random_crop_decode_resizeOp_test.cc => random_crop_decode_resize_op_test.cc} (56%) diff --git a/tests/ut/cpp/dataset/CMakeLists.txt b/tests/ut/cpp/dataset/CMakeLists.txt index 0da470ac89..086a67c7d7 100644 --- a/tests/ut/cpp/dataset/CMakeLists.txt +++ b/tests/ut/cpp/dataset/CMakeLists.txt @@ -32,7 +32,7 @@ SET(DE_UT_SRCS project_op_test.cc queue_test.cc random_crop_op_test.cc - random_crop_decode_resizeOp_test.cc + random_crop_decode_resize_op_test.cc random_crop_and_resize_op_test.cc random_color_adjust_op_test.cc random_horizontal_flip_op_test.cc diff --git a/tests/ut/cpp/dataset/random_crop_and_resize_op_test.cc b/tests/ut/cpp/dataset/random_crop_and_resize_op_test.cc index 864d713ed3..7be18fb02c 100644 --- a/tests/ut/cpp/dataset/random_crop_and_resize_op_test.cc +++ b/tests/ut/cpp/dataset/random_crop_and_resize_op_test.cc @@ -20,35 +20,17 @@ #include "utils/log_adapter.h" using namespace mindspore::dataset; -using mindspore::MsLogLevel::INFO; -using mindspore::ExceptionType::NoExceptionType; using mindspore::LogStream; +using mindspore::ExceptionType::NoExceptionType; +using mindspore::MsLogLevel::INFO; class MindDataTestRandomCropAndResizeOp : public UT::CVOP::CVOpCommon { public: MindDataTestRandomCropAndResizeOp() : CVOpCommon() {} }; -TEST_F(MindDataTestRandomCropAndResizeOp, TestOpDefault) { - MS_LOG(INFO) << "Doing testRandomCropAndResize."; - TensorShape s_in = input_tensor_->shape(); - std::shared_ptr output_tensor; - int h_out = 512; - int w_out = 512; - - TensorShape s_out({(uint32_t) h_out, (uint32_t) w_out, (uint32_t) s_in[2]}); - - std::unique_ptr op(new RandomCropAndResizeOp(h_out, w_out)); - Status s; - for (auto i = 0; i < 100; i++) { - s = op->Compute(input_tensor_, &output_tensor); - } - EXPECT_TRUE(s.IsOk()); - MS_LOG(INFO) << "testRandomCropAndResize end."; -} - -TEST_F(MindDataTestRandomCropAndResizeOp, TestOpExtended) { - MS_LOG(INFO) << "Doing testRandomCropAndResize."; +TEST_F(MindDataTestRandomCropAndResizeOp, TestOpSimpleTest) { + MS_LOG(INFO) << " starting RandomCropAndResizeOp simple test"; TensorShape s_in = input_tensor_->shape(); std::shared_ptr output_tensor; int h_out = 1024; @@ -58,14 +40,14 @@ TEST_F(MindDataTestRandomCropAndResizeOp, TestOpExtended) { float scale_lb = 0.0001; float scale_ub = 1.0; - TensorShape s_out({(uint32_t) h_out, (uint32_t) w_out, (uint32_t) s_in[2]}); + TensorShape s_out({h_out, w_out, s_in[2]}); - std::unique_ptr op( - new RandomCropAndResizeOp(h_out, w_out, scale_lb, scale_ub, aspect_lb, aspect_ub)); + auto op = std::make_unique(h_out, w_out, scale_lb, scale_ub, aspect_lb, aspect_ub); Status s; for (auto i = 0; i < 100; i++) { s = op->Compute(input_tensor_, &output_tensor); + EXPECT_TRUE(s.IsOk()); } - EXPECT_TRUE(s.IsOk()); - MS_LOG(INFO) << "testRandomCropAndResize end."; + + MS_LOG(INFO) << "RandomCropAndResizeOp simple test finished"; } diff --git a/tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc b/tests/ut/cpp/dataset/random_crop_decode_resize_op_test.cc similarity index 56% rename from tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc rename to tests/ut/cpp/dataset/random_crop_decode_resize_op_test.cc index facd35c4f7..d7e0b16aff 100644 --- a/tests/ut/cpp/dataset/random_crop_decode_resizeOp_test.cc +++ b/tests/ut/cpp/dataset/random_crop_decode_resize_op_test.cc @@ -23,9 +23,10 @@ #include "utils/log_adapter.h" using namespace mindspore::dataset; -using mindspore::MsLogLevel::INFO; -using mindspore::ExceptionType::NoExceptionType; using mindspore::LogStream; +using mindspore::ExceptionType::NoExceptionType; +using mindspore::MsLogLevel::INFO; +constexpr double kMseThreshold = 2.0; class MindDataTestRandomCropDecodeResizeOp : public UT::CVOP::CVOpCommon { public: @@ -33,39 +34,38 @@ class MindDataTestRandomCropDecodeResizeOp : public UT::CVOP::CVOpCommon { }; TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp2) { - MS_LOG(INFO) << "Doing testRandomCropDecodeResizeOp Test"; + MS_LOG(INFO) << "starting RandomCropDecodeResizeOp test 1"; - std::shared_ptr output_tensor1; - std::shared_ptr output_tensor2; + std::shared_ptr decode_and_crop_output; + std::shared_ptr crop_and_decode_output; - int target_height = 884; - int target_width = 718; - float scale_lb = 0.08; - float scale_ub = 1.0; - float aspect_lb = 0.75; - float aspect_ub = 1.333333; - InterpolationMode interpolation = InterpolationMode::kLinear; - uint32_t max_iter = 10; - std::unique_ptr op1(new RandomCropAndResizeOp( - target_height, target_width, scale_lb, scale_ub, aspect_lb, aspect_ub, interpolation, max_iter)); - EXPECT_TRUE(op1->OneToOne()); - std::unique_ptr op2(new RandomCropDecodeResizeOp( - target_height, target_width, scale_lb, scale_ub, aspect_lb, aspect_ub, interpolation, max_iter)); - EXPECT_TRUE(op2->OneToOne()); - Status s1, s2; + constexpr int target_height = 884; + constexpr int target_width = 718; + constexpr float scale_lb = 0.08; + constexpr float scale_ub = 1.0; + constexpr float aspect_lb = 0.75; + constexpr float aspect_ub = 1.333333; + const InterpolationMode interpolation = InterpolationMode::kLinear; + constexpr uint32_t max_iter = 10; + auto crop_and_decode = RandomCropDecodeResizeOp(target_height, target_width, scale_lb, scale_ub, aspect_lb, aspect_ub, + interpolation, max_iter); + auto crop_and_decode_copy = crop_and_decode; + auto decode_and_crop = static_cast(crop_and_decode_copy); + EXPECT_TRUE(crop_and_decode.OneToOne()); + GlobalContext::config_manager()->set_seed(42); for (int i = 0; i < 100; i++) { - s1 = op1->Compute(input_tensor_, &output_tensor1); - s2 = op2->Compute(raw_input_tensor_, &output_tensor2); - cv::Mat output1(target_height, target_width, CV_8UC3, output_tensor1->StartAddr()); - cv::Mat output2(target_height, target_width, CV_8UC3, output_tensor2->StartAddr()); + (void)crop_and_decode.Compute(raw_input_tensor_, &crop_and_decode_output); + (void)decode_and_crop.Compute(input_tensor_, &decode_and_crop_output); + cv::Mat output1(target_height, target_width, CV_8UC3, crop_and_decode_output->StartAddr()); + cv::Mat output2(target_height, target_width, CV_8UC3, decode_and_crop_output->StartAddr()); long int mse_sum = 0; long int count = 0; int a, b; - for (int i = 0; i < target_height; i++) { - for (int j = 0; j < target_width; j++) { - a = (int)output1.at(i, j)[1]; - b = (int)output2.at(i, j)[1]; + for (int j = 0; j < target_height; j++) { + for (int k = 0; k < target_width; k++) { + a = static_cast(output1.at(i, j)[1]); + b = static_cast(output2.at(i, j)[1]); mse_sum += sqrt((a - b) * (a - b)); if (a != b) { count++; @@ -73,24 +73,22 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp2) { } } double mse; - if (count > 0) { - mse = (double) mse_sum / count; - } else { - mse = mse_sum; - } - MS_LOG(DEBUG) << "mse: " << mse << std::endl; + mse = count > 0 ? static_cast(mse_sum) / count : mse_sum; + MS_LOG(INFO) << "mse: " << mse << std::endl; + EXPECT_LT(mse, kMseThreshold); } - MS_LOG(INFO) << "MindDataTestRandomCropDecodeResizeOp end!"; + + MS_LOG(INFO) << "RandomCropDecodeResizeOp test 1 finished"; } TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) { - MS_LOG(INFO) << "Doing MindDataTestRandomCropDecodeResizeOp"; - const unsigned int h = 884; - const unsigned int w = 718; - const float scale_lb = 0.1; - const float scale_ub = 1; - const float aspect_lb = 0.1; - const float aspect_ub = 10; + MS_LOG(INFO) << "starting RandomCropDecodeResizeOp test 2"; + constexpr int h = 884; + constexpr int w = 718; + constexpr float scale_lb = 0.1; + constexpr float scale_ub = 1; + constexpr float aspect_lb = 0.1; + constexpr float aspect_ub = 10; std::shared_ptr decoded, decoded_and_cropped, cropped_and_decoded; std::mt19937 rd; @@ -98,14 +96,14 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) { std::uniform_real_distribution rd_aspect(aspect_lb, aspect_ub); DecodeOp op(true); op.Compute(raw_input_tensor_, &decoded); - Status s1, s2; + Status crop_and_decode_status, decode_and_crop_status; float scale, aspect; int crop_width, crop_height; bool crop_success = false; - unsigned int mse_sum, m1, m2, count; - float mse; + int mse_sum, m1, m2, count; + double mse; - for (unsigned int k = 0; k < 100; ++k) { + for (int k = 0; k < 100; ++k) { mse_sum = 0; count = 0; for (auto i = 0; i < 100; i++) { @@ -132,13 +130,13 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) { int y = rd_y(rd); op.Compute(raw_input_tensor_, &decoded); - s1 = Crop(decoded, &decoded_and_cropped, x, y, crop_width, crop_height); - s2 = JpegCropAndDecode(raw_input_tensor_, &cropped_and_decoded, x, y, crop_width, crop_height); + crop_and_decode_status = Crop(decoded, &decoded_and_cropped, x, y, crop_width, crop_height); + decode_and_crop_status = JpegCropAndDecode(raw_input_tensor_, &cropped_and_decoded, x, y, crop_width, crop_height); { cv::Mat M1(crop_height, crop_width, CV_8UC3, decoded_and_cropped->StartAddr()); cv::Mat M2(crop_height, crop_width, CV_8UC3, cropped_and_decoded->StartAddr()); - for (unsigned int i = 0; i < crop_height; ++i) { - for (unsigned int j = 0; j < crop_width; ++j) { + for (int i = 0; i < crop_height; ++i) { + for (int j = 0; j < crop_width; ++j) { m1 = M1.at(i, j)[1]; m2 = M2.at(i, j)[1]; mse_sum += sqrt((m1 - m2) * (m1 - m2)); @@ -149,8 +147,9 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) { } } - mse = (count == 0) ? mse_sum : static_cast(mse_sum) / count; - MS_LOG(DEBUG) << "mse: " << mse << std::endl; + mse = count > 0 ? static_cast(mse_sum) / count : mse_sum; + MS_LOG(INFO) << "mse: " << mse << std::endl; + EXPECT_LT(mse, kMseThreshold); } - MS_LOG(INFO) << "MindDataTestRandomCropDecodeResizeOp end!"; + MS_LOG(INFO) << "RandomCropDecodeResizeOp test 2 finished"; } From c6c8c94d3f40b04b736da3d5d411c1a41629f6e6 Mon Sep 17 00:00:00 2001 From: dengwentao Date: Tue, 7 Apr 2020 11:22:53 +0800 Subject: [PATCH 133/227] updata mkl-dnn link and md5 --- Third_Party_Open_Source_Software_Notice | 2 +- cmake/external_libs/mkl_dnn.cmake | 18 +++++++++--------- cmake/utils.cmake | 2 ++ mindspore/ccsrc/CMakeLists.txt | 2 +- 4 files changed, 13 insertions(+), 11 deletions(-) diff --git a/Third_Party_Open_Source_Software_Notice b/Third_Party_Open_Source_Software_Notice index 498b5b8d1b..60ad7cf47c 100644 --- a/Third_Party_Open_Source_Software_Notice +++ b/Third_Party_Open_Source_Software_Notice @@ -368,7 +368,7 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -Software: MKL-DNN 1.1.2 +Software: oneDNN 1.1.2 Copyright (c) 2009-2018 The MathJax Consortium Copyright 2018 Intel Corporation Copyright 2019 Intel Corporation diff --git a/cmake/external_libs/mkl_dnn.cmake b/cmake/external_libs/mkl_dnn.cmake index 17d8020d3a..6f033fa565 100644 --- a/cmake/external_libs/mkl_dnn.cmake +++ b/cmake/external_libs/mkl_dnn.cmake @@ -1,11 +1,11 @@ -set(mkl_dnn_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2") -set(mkl_dnn_CFLAGS "-D_FORTIFY_SOURCE=2 -O2") -mindspore_add_pkg(mkl_dnn - VER 1.1.1 +set(onednn_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2") +set(onednn_CFLAGS "-D_FORTIFY_SOURCE=2 -O2") +mindspore_add_pkg(onednn + VER 1.1.2 LIBS dnnl mkldnn - URL https://github.com/intel/mkl-dnn/archive/v1.1.1.tar.gz - MD5 d6a422b00459600bdc22242590953f38 + URL https://github.com/oneapi-src/oneDNN/archive/v1.1.2.tar.gz + MD5 ab40d52230f3ad1d7a6f06ce0f6bc17a CMAKE_OPTION -DDNNL_ARCH_OPT_FLAGS='' -DDNNL_CPU_RUNTIME='SEQ' -DDNNL_BUILD_EXAMPLES=OFF -DDNNL_BUILD_TESTS=OFF) -include_directories(${mkl_dnn_INC}) -add_library(mindspore::dnnl ALIAS mkl_dnn::dnnl) -add_library(mindspore::mkldnn ALIAS mkl_dnn::mkldnn) +include_directories(${onednn_INC}) +add_library(mindspore::dnnl ALIAS onednn::dnnl) +add_library(mindspore::mkldnn ALIAS onednn::mkldnn) diff --git a/cmake/utils.cmake b/cmake/utils.cmake index 060e400820..99c064fdd4 100644 --- a/cmake/utils.cmake +++ b/cmake/utils.cmake @@ -40,6 +40,8 @@ else() set(JOBS 8) if (${JOBS} GREATER ${N}) set(THNUM ${N}) + else() + set(THNUM ${JOBS}) endif() endif () message("set make thread num: ${THNUM}") diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt index befe86f3c0..9f559a51eb 100644 --- a/mindspore/ccsrc/CMakeLists.txt +++ b/mindspore/ccsrc/CMakeLists.txt @@ -542,7 +542,7 @@ endif() if (ENABLE_CPU) add_custom_target(add_cpu_lib ALL - COMMAND cp ${mkl_dnn_LIBPATH}/libdnnl.so.1.1 ${MS_LIB_PATH}/libdnnl.so.1 + COMMAND cp ${onednn_LIBPATH}/libdnnl.so.1.1 ${MS_LIB_PATH}/libdnnl.so.1 ) add_dependencies(add_cpu_lib add_ms_lib) endif() From 6dc6d6bc83113431542b6d21e4005331f1d20a4d Mon Sep 17 00:00:00 2001 From: jonyguo Date: Fri, 3 Apr 2020 16:53:45 +0800 Subject: [PATCH 134/227] fix: when use MindDataset block_reade=True hung --- mindspore/ccsrc/mindrecord/io/shard_reader.cc | 2 ++ mindspore/mindrecord/filewriter.py | 1 + mindspore/mindrecord/tools/cifar100_to_mr.py | 9 ++++--- tests/ut/python/dataset/test_minddataset.py | 27 ++++++++++++++++--- .../mindrecord/test_cifar100_to_mindrecord.py | 4 ++- .../mindrecord/test_mindrecord_exception.py | 8 +++++- 6 files changed, 43 insertions(+), 8 deletions(-) diff --git a/mindspore/ccsrc/mindrecord/io/shard_reader.cc b/mindspore/ccsrc/mindrecord/io/shard_reader.cc index 791de6c60b..32825fd9df 100644 --- a/mindspore/ccsrc/mindrecord/io/shard_reader.cc +++ b/mindspore/ccsrc/mindrecord/io/shard_reader.cc @@ -785,6 +785,8 @@ vector ShardReader::GetAllColumns() { MSRStatus ShardReader::CreateTasksByBlock(const std::vector> &row_group_summary, const std::vector> &operators) { + vector columns = GetAllColumns(); + CheckIfColumnInIndex(columns); for (const auto &rg : row_group_summary) { auto shard_id = std::get<0>(rg); auto group_id = std::get<1>(rg); diff --git a/mindspore/mindrecord/filewriter.py b/mindspore/mindrecord/filewriter.py index d1471f47cb..4056825ff3 100644 --- a/mindspore/mindrecord/filewriter.py +++ b/mindspore/mindrecord/filewriter.py @@ -143,6 +143,7 @@ class FileWriter: ParamTypeError: If index field is invalid. MRMDefineIndexError: If index field is not primitive type. MRMAddIndexError: If failed to add index field. + MRMGetMetaError: If the schema is not set or get meta failed. """ if not index_fields or not isinstance(index_fields, list): raise ParamTypeError('index_fields', 'list') diff --git a/mindspore/mindrecord/tools/cifar100_to_mr.py b/mindspore/mindrecord/tools/cifar100_to_mr.py index a359de853d..c011c8f4b0 100644 --- a/mindspore/mindrecord/tools/cifar100_to_mr.py +++ b/mindspore/mindrecord/tools/cifar100_to_mr.py @@ -24,7 +24,7 @@ from mindspore import log as logger from .cifar100 import Cifar100 from ..common.exceptions import PathNotExistsError from ..filewriter import FileWriter -from ..shardutils import check_filename +from ..shardutils import check_filename, SUCCESS try: cv2 = import_module("cv2") except ModuleNotFoundError: @@ -98,8 +98,11 @@ class Cifar100ToMR: data_list = _construct_raw_data(images, fine_labels, coarse_labels) test_data_list = _construct_raw_data(test_images, test_fine_labels, test_coarse_labels) - _generate_mindrecord(self.destination, data_list, fields, "img_train") - _generate_mindrecord(self.destination + "_test", test_data_list, fields, "img_test") + if _generate_mindrecord(self.destination, data_list, fields, "img_train") != SUCCESS: + return FAILED + if _generate_mindrecord(self.destination + "_test", test_data_list, fields, "img_test") != SUCCESS: + return FAILED + return SUCCESS def _construct_raw_data(images, fine_labels, coarse_labels): """ diff --git a/tests/ut/python/dataset/test_minddataset.py b/tests/ut/python/dataset/test_minddataset.py index da22f5c3b7..460a728b5c 100644 --- a/tests/ut/python/dataset/test_minddataset.py +++ b/tests/ut/python/dataset/test_minddataset.py @@ -47,7 +47,9 @@ def add_and_remove_cv_file(): os.remove("{}.db".format(x)) if os.path.exists("{}.db".format(x)) else None writer = FileWriter(CV_FILE_NAME, FILES_NUM) data = get_data(CV_DIR_NAME) - cv_schema_json = {"file_name": {"type": "string"}, "label": {"type": "int32"}, + cv_schema_json = {"id": {"type": "int32"}, + "file_name": {"type": "string"}, + "label": {"type": "int32"}, "data": {"type": "bytes"}} writer.add_schema(cv_schema_json, "img_schema") writer.add_index(["file_name", "label"]) @@ -226,6 +228,24 @@ def test_cv_minddataset_blockreader_tutorial(add_and_remove_cv_file): num_iter += 1 assert num_iter == 20 +def test_cv_minddataset_blockreader_some_field_not_in_index_tutorial(add_and_remove_cv_file): + """tutorial for cv minddataset.""" + columns_list = ["id", "data", "label"] + num_readers = 4 + data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers, shuffle=False, + block_reader=True) + assert data_set.get_dataset_size() == 10 + repeat_num = 2 + data_set = data_set.repeat(repeat_num) + num_iter = 0 + for item in data_set.create_dict_iterator(): + logger.info("-------------- block reader repeat tow {} -----------------".format(num_iter)) + logger.info("-------------- item[id]: {} ----------------------------".format(item["id"])) + logger.info("-------------- item[label]: {} ----------------------------".format(item["label"])) + logger.info("-------------- item[data]: {} -----------------------------".format(item["data"])) + num_iter += 1 + assert num_iter == 20 + def test_cv_minddataset_reader_basic_tutorial(add_and_remove_cv_file): """tutorial for cv minderdataset.""" @@ -359,13 +379,14 @@ def get_data(dir_name): lines = file_reader.readlines() data_list = [] - for line in lines: + for i, line in enumerate(lines): try: filename, label = line.split(",") label = label.strip("\n") with open(os.path.join(img_dir, filename), "rb") as file_reader: img = file_reader.read() - data_json = {"file_name": filename, + data_json = {"id": i, + "file_name": filename, "data": img, "label": int(label)} data_list.append(data_json) diff --git a/tests/ut/python/mindrecord/test_cifar100_to_mindrecord.py b/tests/ut/python/mindrecord/test_cifar100_to_mindrecord.py index b3a8d94589..e95f25aae4 100644 --- a/tests/ut/python/mindrecord/test_cifar100_to_mindrecord.py +++ b/tests/ut/python/mindrecord/test_cifar100_to_mindrecord.py @@ -18,6 +18,7 @@ import pytest from mindspore.mindrecord import Cifar100ToMR from mindspore.mindrecord import FileReader from mindspore.mindrecord import MRMOpenError +from mindspore.mindrecord import SUCCESS from mindspore import log as logger CIFAR100_DIR = "../data/mindrecord/testCifar100Data" @@ -26,7 +27,8 @@ MINDRECORD_FILE = "./cifar100.mindrecord" def test_cifar100_to_mindrecord_without_index_fields(): """test transform cifar100 dataset to mindrecord without index fields.""" cifar100_transformer = Cifar100ToMR(CIFAR100_DIR, MINDRECORD_FILE) - cifar100_transformer.transform() + ret = cifar100_transformer.transform() + assert ret == SUCCESS, "Failed to tranform from cifar100 to mindrecord" assert os.path.exists(MINDRECORD_FILE) assert os.path.exists(MINDRECORD_FILE + "_test") read() diff --git a/tests/ut/python/mindrecord/test_mindrecord_exception.py b/tests/ut/python/mindrecord/test_mindrecord_exception.py index 0a51fbf4e7..1f7a3f859d 100644 --- a/tests/ut/python/mindrecord/test_mindrecord_exception.py +++ b/tests/ut/python/mindrecord/test_mindrecord_exception.py @@ -16,7 +16,7 @@ import os import pytest from mindspore.mindrecord import FileWriter, FileReader, MindPage -from mindspore.mindrecord import MRMOpenError, MRMGenerateIndexError, ParamValueError +from mindspore.mindrecord import MRMOpenError, MRMGenerateIndexError, ParamValueError, MRMGetMetaError from mindspore import log as logger from utils import get_data @@ -280,3 +280,9 @@ def test_cv_file_writer_shard_num_greater_than_1000(): with pytest.raises(ParamValueError) as err: FileWriter(CV_FILE_NAME, 1001) assert 'Shard number should between' in str(err.value) + +def test_add_index_without_add_schema(): + with pytest.raises(MRMGetMetaError) as err: + fw = FileWriter(CV_FILE_NAME) + fw.add_index(["label"]) + assert 'Failed to get meta info' in str(err.value) From 180c1750de1a0b4a3b62fe383cb90f63e5df75cf Mon Sep 17 00:00:00 2001 From: ms_yan <6576637+ms_yan@user.noreply.gitee.com> Date: Thu, 2 Apr 2020 21:56:48 +0800 Subject: [PATCH 135/227] add parameter check for Class Schema --- mindspore/dataset/engine/datasets.py | 23 ++++++++---- mindspore/dataset/engine/validators.py | 50 ++++++++++++++++++++++++++ 2 files changed, 66 insertions(+), 7 deletions(-) diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py index db2b5169d2..de604a67e9 100644 --- a/mindspore/dataset/engine/datasets.py +++ b/mindspore/dataset/engine/datasets.py @@ -38,7 +38,7 @@ from .iterators import DictIterator, TupleIterator from .validators import check, check_batch, check_shuffle, check_map, check_repeat, check_zip, check_rename, \ check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \ check_tfrecorddataset, check_vocdataset, check_celebadataset, check_minddataset, check_generatordataset, \ - check_zip_dataset + check_zip_dataset, check_add_column from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist try: @@ -2334,13 +2334,20 @@ class Schema: self.dataset_type = '' self.num_rows = 0 else: + if not os.path.isfile(schema_file) or not os.access(schema_file, os.R_OK): + raise ValueError("The file %s does not exist or permission denied!" % schema_file) try: with open(schema_file, 'r') as load_f: json_obj = json.load(load_f) - self.from_json(json_obj) except json.decoder.JSONDecodeError: - raise RuntimeError("Schema file failed to load") + raise RuntimeError("Schema file failed to load.") + except UnicodeDecodeError: + raise RuntimeError("Schema file failed to decode.") + except Exception: + raise RuntimeError("Schema file failed to open.") + self.from_json(json_obj) + @check_add_column def add_column(self, name, de_type, shape=None): """ Add new column to the schema. @@ -2359,10 +2366,8 @@ class Schema: if isinstance(de_type, typing.Type): de_type = mstype_to_detype(de_type) new_column["type"] = str(de_type) - elif isinstance(de_type, str): - new_column["type"] = str(DataType(de_type)) else: - raise ValueError("Unknown column type") + new_column["type"] = str(DataType(de_type)) if shape is not None: new_column["shape"] = shape @@ -2391,7 +2396,7 @@ class Schema: Parse the columns and add it to self. Args: - columns (list[str]): names of columns. + columns (dict or list[str]): names of columns. Raises: RuntimeError: If failed to parse schema file. @@ -2399,6 +2404,8 @@ class Schema: RuntimeError: If column's name field is missing. RuntimeError: If column's type field is missing. """ + if columns is None: + raise TypeError("Expected non-empty dict or string list.") self.columns = [] for col in columns: name = None @@ -2443,6 +2450,8 @@ class Schema: RuntimeError: if dataset type is missing in the object. RuntimeError: if columns are missing in the object. """ + if not isinstance(json_obj, dict) or json_obj is None: + raise ValueError("Expected non-empty dict.") for k, v in json_obj.items(): if k == "datasetType": self.dataset_type = v diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py index b4d22a4a01..26d6241945 100644 --- a/mindspore/dataset/engine/validators.py +++ b/mindspore/dataset/engine/validators.py @@ -19,10 +19,15 @@ import inspect as ins import os from functools import wraps from multiprocessing import cpu_count +from mindspore._c_expression import typing from . import samplers from . import datasets INT32_MAX = 2147483647 +valid_detype = [ + "bool", "int8", "int16", "int32", "int64", "uint8", "uint16", + "uint32", "uint64", "float16", "float32", "float64" +] def check(method): @@ -188,6 +193,12 @@ def check(method): return wrapper +def check_valid_detype(type_): + if type_ not in valid_detype: + raise ValueError("Unknown column type") + return True + + def check_filename(path): """ check the filename in the path @@ -743,3 +754,42 @@ def check_project(method): return method(*args, **kwargs) return new_method + + +def check_shape(shape, name): + if isinstance(shape, list): + for element in shape: + if not isinstance(element, int): + raise TypeError( + "Each element in {0} should be of type int. Got {1}.".format(name, type(element))) + else: + raise TypeError("Expected int list.") + + +def check_add_column(method): + """check the input arguments of add_column.""" + @wraps(method) + def new_method(*args, **kwargs): + param_dict = make_param_dict(method, args, kwargs) + + # check name; required argument + name = param_dict.get("name") + if not isinstance(name, str) or not name: + raise TypeError("Expected non-empty string.") + + # check type; required argument + de_type = param_dict.get("de_type") + if de_type is not None: + if not isinstance(de_type, typing.Type) and not check_valid_detype(de_type): + raise ValueError("Unknown column type.") + else: + raise TypeError("Expected non-empty string.") + + # check shape + shape = param_dict.get("shape") + if shape is not None: + check_shape(shape, "shape") + + return method(*args, **kwargs) + + return new_method From f01098bc12be06f440ae166bf28a17587d9e50cb Mon Sep 17 00:00:00 2001 From: Jonathan Yan Date: Sat, 4 Apr 2020 06:48:58 -0400 Subject: [PATCH 136/227] remove ENABLE_MINDRECORD flag --- mindspore/ccsrc/dataset/CMakeLists.txt | 2 -- mindspore/ccsrc/dataset/api/de_pipeline.cc | 12 +++--------- mindspore/ccsrc/dataset/api/de_pipeline.h | 4 ---- mindspore/ccsrc/dataset/api/python_bindings.cc | 6 ------ .../engine/datasetops/source/mindrecord_op.cc | 3 --- .../dataset/engine/datasetops/source/mindrecord_op.h | 2 -- tests/ut/cpp/CMakeLists.txt | 1 - tests/ut/cpp/dataset/mind_record_op_test.cc | 2 -- 8 files changed, 3 insertions(+), 29 deletions(-) diff --git a/mindspore/ccsrc/dataset/CMakeLists.txt b/mindspore/ccsrc/dataset/CMakeLists.txt index d6791f2b9b..477d37051e 100644 --- a/mindspore/ccsrc/dataset/CMakeLists.txt +++ b/mindspore/ccsrc/dataset/CMakeLists.txt @@ -17,8 +17,6 @@ if (ENABLE_TDTQUE) message(STATUS "TDT queue is enabled") endif () -add_definitions(-D ENABLE_MINDRECORD) - # conde coverage # option(ENABLE_COVERAGE "Enable code coverage report" OFF) # if (ENABLE_COVERAGE) diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.cc b/mindspore/ccsrc/dataset/api/de_pipeline.cc index d51204f659..65ec8d30f2 100644 --- a/mindspore/ccsrc/dataset/api/de_pipeline.cc +++ b/mindspore/ccsrc/dataset/api/de_pipeline.cc @@ -29,11 +29,9 @@ #include "dataset/engine/datasetops/source/manifest_op.h" #include "dataset/engine/datasetops/source/cifar_op.h" #include "dataset/engine/datasetops/source/celeba_op.h" -#ifdef ENABLE_MINDRECORD -#include "./shard_category.h" -#include "./shard_sample.h" -#include "./shard_shuffle.h" -#endif +#include "mindrecord/include/shard_category.h" +#include "mindrecord/include/shard_sample.h" +#include "mindrecord/include/shard_shuffle.h" #include "dataset/util/random.h" #include "dataset/util/status.h" @@ -46,9 +44,7 @@ using pFunction = Status (DEPipeline::*)(const py::dict &, std::shared_ptr g_parse_op_func_ = {{kStorage, &DEPipeline::ParseStorageOp}, {kShuffle, &DEPipeline::ParseShuffleOp}, -#ifdef ENABLE_MINDRECORD {kMindrecord, &DEPipeline::ParseMindRecordOp}, -#endif {kMap, &DEPipeline::ParseMapOp}, {kBatch, &DEPipeline::ParseBatchOp}, {kRepeat, &DEPipeline::ParseRepeatOp}, @@ -364,7 +360,6 @@ Status DEPipeline::ParseShuffleOp(const py::dict &args, std::shared_ptr *in_partitions) { if (args["partitions"].is_none()) { std::string err_msg = "Error: partitions is not set (None)"; @@ -450,7 +445,6 @@ Status DEPipeline::ParseMindRecordOp(const py::dict &args, std::shared_ptr *ptr) { std::shared_ptr builder = std::make_shared(); diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.h b/mindspore/ccsrc/dataset/api/de_pipeline.h index e8dde85a77..acffc390cc 100644 --- a/mindspore/ccsrc/dataset/api/de_pipeline.h +++ b/mindspore/ccsrc/dataset/api/de_pipeline.h @@ -38,9 +38,7 @@ using DsOpPtr = std::shared_ptr; enum OpName { kStorage = 0, kShuffle, -#ifdef ENABLE_MINDRECORD kMindrecord, -#endif kBatch, kCache, kRepeat, @@ -101,11 +99,9 @@ class DEPipeline { Status ParseShuffleOp(const py::dict &args, std::shared_ptr *ptr); -#ifdef ENABLE_MINDRECORD Status CheckMindRecordPartitionInfo(const py::dict &args, std::vector *ptr); Status ParseMindRecordOp(const py::dict &args, std::shared_ptr *ptr); -#endif Status ParseMapOp(const py::dict &args, std::shared_ptr *ptr); diff --git a/mindspore/ccsrc/dataset/api/python_bindings.cc b/mindspore/ccsrc/dataset/api/python_bindings.cc index 86b0a5d66a..e6c2691281 100644 --- a/mindspore/ccsrc/dataset/api/python_bindings.cc +++ b/mindspore/ccsrc/dataset/api/python_bindings.cc @@ -44,9 +44,7 @@ #include "dataset/engine/datasetops/source/io_block.h" #include "dataset/engine/datasetops/source/mnist_op.h" #include "dataset/engine/datasetops/source/manifest_op.h" -#ifdef ENABLE_MINDRECORD #include "dataset/engine/datasetops/source/mindrecord_op.h" -#endif #include "dataset/engine/datasetops/source/sampler/distributed_sampler.h" #include "dataset/engine/datasetops/source/sampler/pk_sampler.h" #include "dataset/engine/datasetops/source/sampler/random_sampler.h" @@ -146,14 +144,12 @@ void bindDatasetOps(py::module *m) { return py::make_tuple(count, num_classes); }); -#ifdef ENABLE_MINDRECORD (void)py::class_>(*m, "MindRecordOp") .def_static("get_num_rows", [](const std::string &path) { int64_t count = 0; THROW_IF_ERROR(MindRecordOp::CountTotalRows(path, &count)); return count; }); -#endif (void)py::class_>(*m, "ManifestOp") .def_static("get_num_rows_and_classes", @@ -424,9 +420,7 @@ PYBIND11_MODULE(_c_dataengine, m) { .value("STORAGE", OpName::kStorage) .value("SHUFFLE", OpName::kShuffle) .value("BATCH", OpName::kBatch) -#ifdef ENABLE_MINDRECORD .value("MINDRECORD", OpName::kMindrecord) -#endif .value("CACHE", OpName::kCache) .value("REPEAT", OpName::kRepeat) .value("TAKE", OpName::kTake) diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc index b062371d7f..b5bea5416c 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc @@ -13,8 +13,6 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#ifdef ENABLE_MINDRECORD - #include "dataset/engine/datasetops/source/mindrecord_op.h" #include @@ -665,4 +663,3 @@ Status MindRecordOp::CountTotalRows(const std::string dataset_path, int64_t *cou } } // namespace dataset } // namespace mindspore -#endif diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h index 2535acbc50..0b16391b20 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h @@ -15,7 +15,6 @@ */ #ifndef DATASET_ENGINE_DATASETOPS_SOURCE_MINDRECORD_OP_H_ #define DATASET_ENGINE_DATASETOPS_SOURCE_MINDRECORD_OP_H_ -#ifdef ENABLE_MINDRECORD #pragma once #include @@ -276,5 +275,4 @@ class MindRecordOp : public ParallelOp { }; } // namespace dataset } // namespace mindspore -#endif #endif // DATASET_ENGINE_DATASETOPS_SOURCE_MINDRECORD_OP_H_ diff --git a/tests/ut/cpp/CMakeLists.txt b/tests/ut/cpp/CMakeLists.txt index 5f4bd41b3b..8d3f8a8138 100644 --- a/tests/ut/cpp/CMakeLists.txt +++ b/tests/ut/cpp/CMakeLists.txt @@ -26,7 +26,6 @@ MESSAGE("check ut_test ${CMAKE_BINARY_DIR}") link_directories(${MS_CCSRC_BUILD_PATH}) if(ENABLE_MINDDATA) - add_definitions(-D ENABLE_MINDRECORD) add_definitions(-D ENABLE_MINDDATA) link_directories(${MS_CCSRC_BUILD_PATH}/dataset) link_directories(${MS_CCSRC_BUILD_PATH}/mindrecord) diff --git a/tests/ut/cpp/dataset/mind_record_op_test.cc b/tests/ut/cpp/dataset/mind_record_op_test.cc index abe7faef14..3d5c80b3f4 100644 --- a/tests/ut/cpp/dataset/mind_record_op_test.cc +++ b/tests/ut/cpp/dataset/mind_record_op_test.cc @@ -13,7 +13,6 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#ifdef ENABLE_MINDRECORD #include #include #include @@ -480,4 +479,3 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordBlockReaderRepeat) { row_count++; } } -#endif From 5f1fedaae7bed936b22e1f7e4a0adde4f1fb11e8 Mon Sep 17 00:00:00 2001 From: WeibiaoYu Date: Thu, 2 Apr 2020 08:43:13 -0400 Subject: [PATCH 137/227] Support to config whether to save integeated checkpoint, in auto model parallel scene --- mindspore/common/api.py | 35 -------------------------- mindspore/train/callback.py | 16 +++++++++--- mindspore/train/serialization.py | 7 +++--- tests/ut/python/utils/test_callback.py | 4 +-- 4 files changed, 19 insertions(+), 43 deletions(-) diff --git a/mindspore/common/api.py b/mindspore/common/api.py index 9ee95ef772..5a74febe53 100644 --- a/mindspore/common/api.py +++ b/mindspore/common/api.py @@ -374,9 +374,6 @@ class _Executor: obj.parameter_layout_dict = self._executor.get_parameter_layout(phase) obj.load_parameter_slice(params) - if _get_parallel_mode() in ["hybrid_parallel"]: - obj.parameter_layout_dict = self._build_parameter_layout(obj) - # the following GE init process is not needed when use vm or ms backend if enable_ge: # decide whether to sink based on whether the inputs is virtual or not @@ -449,38 +446,6 @@ class _Executor: return self._exec_pip(obj, *args, phase=phase_real) raise KeyError('{} graph is not exist.'.format(phase_real)) - def _build_parameter_layout(self, obj): - """ - Build parameter layout, for layerwise_parallel parameter. - - Args: - obj (Function or Cell): The function or cell instance need to be compiled. - - Returns: - Dictionary, parameter layout info. - """ - parameter_layout_dict = {} - layerwise_parallel_parameters = [] - for key in obj.parameters_dict(): - if obj.parameters_dict()[key].layerwise_parallel is True: - layerwise_parallel_parameters.append(key) - - if not layerwise_parallel_parameters: - return parameter_layout_dict - - from ..communication.management import get_group_size - group_size = [get_group_size()] - for key in layerwise_parallel_parameters: - tensor_map = [0] - shape = obj.parameters_dict()[key].data.shape() - for x in range(len(shape)): # dim 0 set 0, others set -1 - if x: - tensor_map.append(-1) - layout = [group_size, tensor_map] - parameter_layout_dict[key] = layout - - return parameter_layout_dict - def del_net_res(self, net_id): self._executor.del_net_res(net_id) diff --git a/mindspore/train/callback.py b/mindspore/train/callback.py index 62f847089d..dcf630342c 100644 --- a/mindspore/train/callback.py +++ b/mindspore/train/callback.py @@ -24,7 +24,7 @@ import mindspore.context as context from mindspore.train.serialization import _exec_save_checkpoint, _fill_param_into_net, _save_graph from mindspore.train._utils import _make_directory from mindspore import log as logger -from mindspore._checkparam import check_int_non_negative +from mindspore._checkparam import check_int_non_negative, check_bool from mindspore.common.tensor import Tensor from .summary.summary_record import _cache_summary_tensor_data @@ -150,6 +150,8 @@ class CheckpointConfig: keep_checkpoint_max (int): Maximum step to save checkpoint. Default: 5. keep_checkpoint_per_n_minutes (int): Keep one checkpoint every n minutes. Default: 0. Can't be used with keep_checkpoint_max at the same time. + integrated_save (bool): Whether to intergrated save in automatic model parall scene. Default: True. + Integrated save function is only supported in automatic parall scene, not supported in manual parallel. Raises: ValueError: If the input_param is None or 0. @@ -163,7 +165,8 @@ class CheckpointConfig: save_checkpoint_steps=1, save_checkpoint_seconds=0, keep_checkpoint_max=5, - keep_checkpoint_per_n_minutes=0): + keep_checkpoint_per_n_minutes=0, + integrated_save=True): if not save_checkpoint_steps and not save_checkpoint_seconds and \ not keep_checkpoint_max and not keep_checkpoint_per_n_minutes: @@ -191,6 +194,8 @@ class CheckpointConfig: if not self._keep_checkpoint_per_n_minutes or self._keep_checkpoint_per_n_minutes == 0: self._keep_checkpoint_max = 1 + self._integrated_save = check_bool(integrated_save) + @property def save_checkpoint_steps(self): """Get the value of _save_checkpoint_steps.""" @@ -211,6 +216,11 @@ class CheckpointConfig: """Get the value of _keep_checkpoint_per_n_minutes.""" return self._keep_checkpoint_per_n_minutes + @property + def integrated_save(self): + """Get the value of _integrated_save.""" + return self._integrated_save + def get_checkpoint_policy(self): """Get the policy of checkpoint.""" checkpoint_policy = {'save_checkpoint_steps': self._save_checkpoint_steps, @@ -619,7 +629,7 @@ class ModelCheckpoint(Callback): _set_cur_net(cb_params.train_network) cb_params.train_network.exec_checkpoint_graph() - _exec_save_checkpoint(cb_params.train_network, gen_file) + _exec_save_checkpoint(cb_params.train_network, gen_file, self._config.integrated_save) if os.path.exists(gen_file): shutil.move(gen_file, cur_file) diff --git a/mindspore/train/serialization.py b/mindspore/train/serialization.py index 0478bbc071..b334c3e9d8 100644 --- a/mindspore/train/serialization.py +++ b/mindspore/train/serialization.py @@ -279,13 +279,14 @@ def _save_graph(network, file_name): os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR) -def _exec_save_checkpoint(train_network, ckpoint_file_name): +def _exec_save_checkpoint(train_network, ckpoint_file_name, integrated_save=True): """ Saves checkpoint for 'ms' backend. Args: train_network (Network): The train network for training. ckpoint_file_name (str): The name of checkpoint file. + integrated_save (bool): Whether to intergrated save in automatic model parallel scene. """ param_dict = {} @@ -300,9 +301,9 @@ def _exec_save_checkpoint(train_network, ckpoint_file_name): else: param_data = Tensor(value.data) - # in model parallel scenario, some parameters were spliteds to all the devices, + # in automatic model parallel scenario, some parameters were spliteds to all the devices, # which should be combined before saving - if key in train_network.parameter_layout_dict: + if integrated_save and key in train_network.parameter_layout_dict: param_data = _get_merged_param_data(train_network, key, param_data) each_param["data"] = param_data diff --git a/tests/ut/python/utils/test_callback.py b/tests/ut/python/utils/test_callback.py index 60e4c6527a..43cf827330 100644 --- a/tests/ut/python/utils/test_callback.py +++ b/tests/ut/python/utils/test_callback.py @@ -308,10 +308,10 @@ def test_RunContext(): def test_Checkpoint_Config(): """Test CheckpointConfig all None or 0.""" with pytest.raises(ValueError): - CheckpointConfig(0, 0, 0, 0) + CheckpointConfig(0, 0, 0, 0, True) with pytest.raises(ValueError): - CheckpointConfig(0, None, 0, 0) + CheckpointConfig(0, None, 0, 0, True) def test_step_end_save_graph(): From 5b915155427293e60ecb229810b7e8d0f67912fd Mon Sep 17 00:00:00 2001 From: VectorSL Date: Tue, 7 Apr 2020 19:48:34 +0800 Subject: [PATCH 138/227] update lossscale for gpu --- mindspore/nn/wrap/loss_scale.py | 40 +++++++++---- mindspore/ops/operations/__init__.py | 6 +- mindspore/ops/operations/math_ops.py | 88 ++++++++++++++++++++++++++++ 3 files changed, 123 insertions(+), 11 deletions(-) diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py index 1ce3179273..6a1f15a402 100644 --- a/mindspore/nn/wrap/loss_scale.py +++ b/mindspore/nn/wrap/loss_scale.py @@ -13,6 +13,7 @@ # limitations under the License. # ============================================================================ """Loss scale cell for loss scale training.""" +import mindspore.context as context from mindspore.nn.wrap.grad_reducer import DistributedGradReducer from mindspore.train.parallel_utils import ParallelMode from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_mirror_mean @@ -34,6 +35,13 @@ reciprocal = P.Reciprocal() def tensor_grad_scale(scale, grad): return grad * F.cast(reciprocal(scale), F.dtype(grad)) +_grad_overflow = C.MultitypeFuncGraph("_grad_overflow") +grad_overflow = P.FloatStatus() + + +@_grad_overflow.register("Tensor") +def _tensor_grad_overflow(grad): + return grad_overflow(grad) class DynamicLossScaleUpdateCell(Cell): r""" @@ -197,9 +205,15 @@ class TrainOneStepWithLossScaleCell(Cell): self.optimizer = optimizer self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True) self.hyper_map = C.HyperMap() - self.alloc_status = NPUAllocFloatStatus() - self.get_status = NPUGetFloatStatus() - self.clear_status = NPUClearFloatStatus() + if context.get_context("device_target") == "GPU": + self.gpu_target = True + self.float_status = P.FloatStatus() + self.addn = P.AddN() + else: + self.gpu_target = False + self.alloc_status = NPUAllocFloatStatus() + self.get_status = NPUGetFloatStatus() + self.clear_status = NPUClearFloatStatus() self.reduce_sum = ReduceSum(keep_dims=False) self.base = Tensor(1, mstype.float32) self.less_equal = LessEqual() @@ -224,10 +238,12 @@ class TrainOneStepWithLossScaleCell(Cell): def construct(self, data, label, sens=None): weights = self.weights loss = self.network(data, label) - # init overflow buffer - init = self.alloc_status() - # clear overflow buffer - self.clear_status(init) + init = False + if not self.gpu_target: + # init overflow buffer + init = self.alloc_status() + # clear overflow buffer + self.clear_status(init) if sens is None: scaling_sens = self.loss_scale else: @@ -238,9 +254,13 @@ class TrainOneStepWithLossScaleCell(Cell): # apply grad reducer on grads grads = self.grad_reducer(grads) # get the overflow buffer - self.get_status(init) - # sum overflow buffer elements, 0:not overflow , >0:overflow - flag_sum = self.reduce_sum(init, (0,)) + if not self.gpu_target: + self.get_status(init) + # sum overflow buffer elements, 0:not overflow , >0:overflow + flag_sum = self.reduce_sum(init, (0,)) + else: + flag_sum = self.hyper_map(F.partial(_grad_overflow), grads) + flag_sum = self.addn(flag_sum) if self.is_distributed: # sum overflow flag over devices flag_reduce = self.allreduce(flag_sum) diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index 37a3b38bb6..d255796bae 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -44,7 +44,7 @@ from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul LogicalNot, LogicalOr, MatMul, Maximum, Minimum, Mul, Neg, NMSWithMask, NotEqual, NPUAllocFloatStatus, NPUClearFloatStatus, - NPUGetFloatStatus, Pow, RealDiv, + NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus, Reciprocal, CumSum, Sin, Sqrt, Rsqrt, Square, Sub, TensorAdd, Sign, Round) @@ -154,6 +154,10 @@ __all__ = [ 'NPUAllocFloatStatus', 'NPUGetFloatStatus', 'NPUClearFloatStatus', + 'IsNan', + 'IsFinite', + 'IsInf', + 'FloatStatus', 'Reciprocal', 'SmoothL1Loss', 'ReduceAll', diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 175b72560f..127d3c513c 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1541,6 +1541,94 @@ class LogicalOr(_LogicBinaryOp): def infer_dtype(self, x_dtype, y_dtype): return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.prim_name()) +class IsNan(PrimitiveWithInfer): + """ + Judging which elements are nan for each position + + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the same shape of input, and the dtype is bool. + """ + + @prim_attr_register + def __init__(self): + """init IsNan""" + self.init_prim_io_names(inputs=['x'], outputs=['output']) + + def infer_shape(self, x_shape): + return x_shape + + def infer_dtype(self, x_dtype): + return mstype.bool_ + +class IsInf(PrimitiveWithInfer): + """ + Judging which elements are inf or -inf for each position + + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the same shape of input, and the dtype is bool. + """ + + @prim_attr_register + def __init__(self): + """init IsInf""" + self.init_prim_io_names(inputs=['x'], outputs=['output']) + + def infer_shape(self, x_shape): + return x_shape + + def infer_dtype(self, x_dtype): + return mstype.bool_ + +class IsFinite(PrimitiveWithInfer): + """ + Judging which elements are finite for each position + + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the same shape of input, and the dtype is bool. + """ + + @prim_attr_register + def __init__(self): + """init IsFinite""" + self.init_prim_io_names(inputs=['x'], outputs=['output']) + + def infer_shape(self, x_shape): + return x_shape + + def infer_dtype(self, x_dtype): + return mstype.bool_ + +class FloatStatus(PrimitiveWithInfer): + """ + Determine if the elements contains nan, inf or -inf. `0` for normal, `1` for overflow. + + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the shape of `(1,)`, and has the same dtype of input `mindspore.dtype.float32` or + `mindspore.dtype.float16`. + """ + + @prim_attr_register + def __init__(self): + """init FloatStatus""" + self.init_prim_io_names(inputs=['x'], outputs=['output']) + + def infer_shape(self, x_shape): + return [1] + + def infer_dtype(self, x_dtype): + return x_dtype class NPUAllocFloatStatus(PrimitiveWithInfer): """ From 14c5c1b57d89e6a80f9d5272c566d199515b8b59 Mon Sep 17 00:00:00 2001 From: guohongzilong <2713219276@qq.com> Date: Tue, 7 Apr 2020 20:39:51 +0800 Subject: [PATCH 139/227] usr mindspore. instead of mstype. --- mindspore/common/api.py | 4 ++-- mindspore/common/initializer.py | 2 +- mindspore/dataset/engine/datasets.py | 4 ++-- mindspore/ops/operations/array_ops.py | 7 ++++--- mindspore/ops/operations/math_ops.py | 4 ++-- mindspore/train/model.py | 2 +- 6 files changed, 12 insertions(+), 11 deletions(-) diff --git a/mindspore/common/api.py b/mindspore/common/api.py index 5a74febe53..7f0b2bfeaa 100644 --- a/mindspore/common/api.py +++ b/mindspore/common/api.py @@ -230,8 +230,8 @@ def ms_function(fn=None, obj=None, input_signature=None): >>> z = F.tensor_add(x, y) >>> return z >>> - >>> @ms_function(input_signature=(MetaTensor(mstype.float32, (1, 1, 3, 3)), - >>> MetaTensor(mstype.float32, (1, 1, 3, 3)))) + >>> @ms_function(input_signature=(MetaTensor(mindspore.float32, (1, 1, 3, 3)), + >>> MetaTensor(mindspore.float32, (1, 1, 3, 3)))) >>> def tensor_add_with_sig(x, y): >>> z = F.tensor_add(x, y) >>> return z diff --git a/mindspore/common/initializer.py b/mindspore/common/initializer.py index bdc3418129..4261621272 100644 --- a/mindspore/common/initializer.py +++ b/mindspore/common/initializer.py @@ -282,7 +282,7 @@ def initializer(init, shape=None, dtype=mstype.float32): Tensor, initialized tensor. Examples: - >>> tensor = initializer('ones', [1, 2, 3], mstype.float32) + >>> tensor = initializer('ones', [1, 2, 3], mindspore.float32) """ if not isinstance(init, (Tensor, numbers.Number, str, Initializer)): raise TypeError('Unsupported init type.') diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py index de604a67e9..ab2290c13c 100644 --- a/mindspore/dataset/engine/datasets.py +++ b/mindspore/dataset/engine/datasets.py @@ -1814,7 +1814,7 @@ class TFRecordDataset(SourceDataset): >>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files) >>> # 2) get all rows from dataset_files with user-defined schema: >>> schema = ds.Schema() - >>> schema.add_column('col_1d', de_type=mstype.int64, shape=[2]) + >>> schema.add_column('col_1d', de_type=mindspore.int64, shape=[2]) >>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files, schema=schema) >>> # 3) get all rows from dataset_files with schema file "./schema.json": >>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files, schema="./schema.json") @@ -2325,7 +2325,7 @@ class Schema: >>> import mindspore.common.dtype as mstype >>> # create schema, specify column name, mindspore.dtype and shape of the column >>> schema = ds.Schema() - >>> schema.add_column('col1', de_type=mstype.int64, shape=[2]) + >>> schema.add_column('col1', de_type=mindspore.int64, shape=[2]) """ def __init__(self, schema_file=None): diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index fdad46a31f..b91c2cbc7d 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -1535,7 +1535,8 @@ class StridedSlice(PrimitiveWithInfer): - Finally, the output is [3, 3, 3]. Examples - >>> input_x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]]) + >>> input_x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], + >>> [[5, 5, 5], [6, 6, 6]]], mindspore.float32) >>> slice = StridedSlice() >>> output = slice(input_x, (1, 0, 0), (2, 1, 3), (1, 1, 1)) >>> output.shape() @@ -2067,7 +2068,7 @@ class SpaceToBatch(PrimitiveWithInfer): >>> block_size = 2 >>> paddings = [[0, 0], [0, 0]] >>> space_to_batch = P.SpaceToBatch(block_size, paddings) - >>> x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mstype.float32) + >>> x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32) >>> space_to_batch(x) [[[[1.]]], [[[2.]]], [[[3.]]], [[[4.]]]] @@ -2135,7 +2136,7 @@ class BatchToSpace(PrimitiveWithInfer): >>> block_size = 2 >>> crops = [[0, 0], [0, 0]] >>> op = P.BatchToSpace(block_size, crops) - >>> x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mstype.float32) + >>> x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32) >>> output = op(x) [[[[1., 2.], [3., 4.]]]] diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 127d3c513c..47b9e490f1 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1996,8 +1996,8 @@ class Atan2(_MathBinaryOp): Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'. Examples: - >>> input_x = Tensor(np.array([[0, 1]]), mstype.float32) - >>> input_y = Tensor(np.array([[1, 1]]), mstype.float32) + >>> input_x = Tensor(np.array([[0, 1]]), mindspore.float32) + >>> input_y = Tensor(np.array([[1, 1]]), mindspore.float32) >>> atan2 = P.Atan2() >>> atan2(input_x, input_y) [[0. 0.7853982]] diff --git a/mindspore/train/model.py b/mindspore/train/model.py index bcfd897f58..41b372f85a 100755 --- a/mindspore/train/model.py +++ b/mindspore/train/model.py @@ -528,7 +528,7 @@ class Model: Tensor, array(s) of predictions. Examples: - >>> input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mstype.float32) + >>> input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mindspore.float32) >>> model = Model(Net()) >>> model.predict(input_data) """ From 951e094dd4868c871f61006487f1e6d8d7672fb6 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 3 Apr 2020 15:37:42 +0800 Subject: [PATCH 140/227] add api image gradients --- mindspore/nn/layer/__init__.py | 4 +- mindspore/nn/layer/basic.py | 45 ++++++++++++++ tests/st/ops/davinci/test_image_gradients.py | 62 ++++++++++++++++++++ tests/ut/python/nn/test_image_gradients.py | 49 ++++++++++++++++ 4 files changed, 158 insertions(+), 2 deletions(-) create mode 100644 tests/st/ops/davinci/test_image_gradients.py create mode 100644 tests/ut/python/nn/test_image_gradients.py diff --git a/mindspore/nn/layer/__init__.py b/mindspore/nn/layer/__init__.py index bb29935602..dae18fe663 100644 --- a/mindspore/nn/layer/__init__.py +++ b/mindspore/nn/layer/__init__.py @@ -22,7 +22,7 @@ from .normalization import BatchNorm1d, BatchNorm2d, LayerNorm from .container import SequentialCell, CellList from .conv import Conv2d, Conv2dTranspose from .lstm import LSTM -from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot +from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot, ImageGradients from .embedding import Embedding from .pooling import AvgPool2d, MaxPool2d @@ -31,7 +31,7 @@ __all__ = ['Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid', 'SequentialCell', 'CellList', 'Conv2d', 'Conv2dTranspose', 'LSTM', - 'Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', + 'Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', 'ImageGradients', 'Embedding', 'AvgPool2d', 'MaxPool2d', ] diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py index 30b94c738d..de49685dac 100644 --- a/mindspore/nn/layer/basic.py +++ b/mindspore/nn/layer/basic.py @@ -370,3 +370,48 @@ class OneHot(Cell): def construct(self, indices): return self.onehot(indices, self.depth, self.on_value, self.off_value) + + +class ImageGradients(Cell): + r""" + Returns two tensors, the first is along the height dimension and the second is along the width dimension. + + Assume an image shape is :math:`h*w`. The gradients along the height and the width are :math:`dy` and :math:`dx`, + respectively. + + .. math:: + dy[i] = \begin{cases} image[i+1, :]-image[i, :], &if\ 0<=i>> net = nn.ImageGradients() + >>> image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32) + >>> net(image) + [[[[2,2] + [0,0]]]] + [[[[1,0] + [1,0]]]] + """ + def __init__(self): + super(ImageGradients, self).__init__() + + def construct(self, images): + batch_size, depth, height, width = P.Shape()(images) + dy = images[:, :, 1:, :] - images[:, :, :height - 1, :] + dy_last = P.Fill()(P.DType()(images), (batch_size, depth, 1, width), 0) + dy = P.Concat(2)((dy, dy_last)) + + dx = images[:, :, :, 1:] - images[:, :, :, :width - 1] + dx_last = P.Fill()(P.DType()(images), (batch_size, depth, height, 1), 0) + dx = P.Concat(3)((dx, dx_last)) + return dy, dx diff --git a/tests/st/ops/davinci/test_image_gradients.py b/tests/st/ops/davinci/test_image_gradients.py new file mode 100644 index 0000000000..ea385158c9 --- /dev/null +++ b/tests/st/ops/davinci/test_image_gradients.py @@ -0,0 +1,62 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +import numpy as np +import mindspore.nn as nn +import mindspore.context as context +import mindspore.common.dtype as mstype +from mindspore import Tensor +from mindspore.common.api import ms_function + +context.set_context(device_target="Ascend") +class Net(nn.Cell): + def __init__(self): + super(Net, self).__init__() + self.image_gradients = nn.ImageGradients() + + @ms_function + def construct(self, x): + return self.image_gradients(x) + + +def test_image_gradients(): + image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32) + expected_dy = np.array([[[[2,2],[0,0]]]]).astype(np.int32) + expected_dx = np.array([[[[1,0],[1,0]]]]).astype(np.int32) + net = Net() + dy, dx = net(image) + assert np.any(dx.asnumpy()-expected_dx) == False + assert np.any(dy.asnumpy()-expected_dy) == False + + +def test_image_gradients_multi_channel_depth(): + # 4 x 2 x 2 x 2 + dtype = mstype.int32 + image = Tensor(np.array([[[[1,2],[3,4]], [[5,6],[7,8]]], + [[[3,5],[7,9]], [[11,13],[15,17]]], + [[[5,10],[15,20]], [[25,30],[35,40]]], + [[[10,20],[30,40]], [[50,60],[70,80]]]]), dtype=dtype) + expected_dy = Tensor(np.array([[[[2,2],[0,0]], [[2,2],[0,0]]], + [[[4,4],[0,0]], [[4,4],[0,0]]], + [[[10,10],[0,0]], [[10,10],[0,0]]], + [[[20,20],[0,0]], [[20,20],[0,0]]]]), dtype=dtype) + expected_dx = Tensor(np.array([[[[1,0],[1,0]], [[1,0],[1,0]]], + [[[2,0],[2,0]], [[2,0],[2,0]]], + [[[5,0],[5,0]], [[5,0],[5,0]]], + [[[10,0],[10,0]], [[10,0],[10,0]]]]), dtype=dtype) + net = Net() + dy, dx = net(image) + + assert np.any(dx.asnumpy()-expected_dx.asnumpy()) == False + assert np.any(dy.asnumpy()-expected_dy.asnumpy()) == False diff --git a/tests/ut/python/nn/test_image_gradients.py b/tests/ut/python/nn/test_image_gradients.py new file mode 100644 index 0000000000..f65f38ec0a --- /dev/null +++ b/tests/ut/python/nn/test_image_gradients.py @@ -0,0 +1,49 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" test loss """ +import numpy as np +import mindspore.nn as nn +import mindspore.context as context +import mindspore.common.dtype as mstype +from mindspore import Tensor +from mindspore.common.api import _executor +from mindspore.common.api import ms_function + +context.set_context(device_target="Ascend") +class Net(nn.Cell): + def __init__(self): + super(Net, self).__init__() + self.image_gradients = nn.ImageGradients() + + @ms_function + def construct(self, x): + return self.image_gradients(x) + +def test_compile(): + # input shape 1 x 1 x 2 x 2 + image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32) + net = Net() + _executor.compile(net, image) + + +def test_compile_multi_channel(): + # input shape 4 x 2 x 2 x 2 + dtype = mstype.int32 + image = Tensor(np.array([[[[1,2],[3,4]], [[5,6],[7,8]]], + [[[3,5],[7,9]], [[11,13],[15,17]]], + [[[5,10],[15,20]], [[25,30],[35,40]]], + [[[10,20],[30,40]], [[50,60],[70,80]]]]), dtype=dtype) + net = Net() + _executor.compile(net, image) From 7798c85e70ebe474a2832102d15707b9b2b40eb4 Mon Sep 17 00:00:00 2001 From: Xiaoda Zhang Date: Fri, 3 Apr 2020 14:33:04 +0800 Subject: [PATCH 141/227] This commit is to separate the computation cost and memory cost in auto_parallel. Some related memory correction is removed. --- .../ccsrc/parallel/auto_parallel/costmodel.cc | 12 +- .../ccsrc/parallel/auto_parallel/costmodel.h | 10 +- .../auto_parallel/dp_algo_costmodel.h | 2 +- .../parallel/auto_parallel/edge_costmodel.cc | 51 ++-- .../parallel/auto_parallel/edge_costmodel.h | 2 +- .../parallel/auto_parallel/graph_costmodel.cc | 105 +++----- .../parallel/auto_parallel/graph_costmodel.h | 8 +- .../auto_parallel/operator_costmodel.cc | 114 ++++---- .../auto_parallel/operator_costmodel.h | 244 +++++++++--------- .../ccsrc/parallel/ops_info/matmul_info.cc | 8 +- .../ccsrc/parallel/ops_info/operator_info.cc | 25 +- .../ccsrc/parallel/ops_info/operator_info.h | 6 +- .../ccsrc/parallel/step_auto_parallel.cc | 30 +-- .../tensor_layout/tensor_redistribution.cc | 24 +- .../tensor_layout/tensor_redistribution.h | 9 +- .../auto_parallel/graph_costmodel_test.cc | 4 +- .../auto_parallel/operator_costmodel_test.cc | 12 +- .../cpp/parallel/ops_info/activation_test.cc | 8 +- .../cpp/parallel/ops_info/matmul_info_test.cc | 8 +- .../parallel/ops_info/tensor_add_info_test.cc | 8 +- .../cpp/parallel/ops_info/tmpidentity_test.cc | 4 +- 21 files changed, 322 insertions(+), 372 deletions(-) diff --git a/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc index f5cf5069be..190f589bb5 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/costmodel.cc @@ -23,8 +23,8 @@ namespace mindspore { namespace parallel { void Simplify(CostPtrList* clist_ptrs) { - // Sort the cost_list with the memory_cost increasing, and communication_cost decreasing order. This method - // excludes the cost with greater memory_cost and greater communication_cost. + // Sort the cost_list with the computation_cost_ increasing, and communication_cost decreasing order. This method + // excludes the cost with greater computation_cost_ and greater communication_cost. // E.g. clist_ptrs = {<100, 20>, <200, 10>, <300, 50>}. After this method, clist_ptrs = {<200, 10>, <100, 20>} if (!COST_MODEL_SIMPLIFY_CALCULATION) { return; @@ -33,7 +33,7 @@ void Simplify(CostPtrList* clist_ptrs) { std::vector id(clist_ptrs->size()); std::iota(id.begin(), id.end(), size_t(0)); std::sort(id.begin(), id.end(), [&clist_ptrs](size_t x, size_t y) { - return clist_ptrs->at(x)->memory_cost_ < clist_ptrs->at(y)->memory_cost_; + return clist_ptrs->at(x)->computation_cost_ < clist_ptrs->at(y)->computation_cost_; }); CostPtrList ret; for (size_t i = 0; i < clist_ptrs->size(); ++i) { @@ -45,8 +45,8 @@ void Simplify(CostPtrList* clist_ptrs) { } void SimplifyForDreasingCommunicationWithPartialPara(CostPtrList* clist_ptrs) { - // Sort the cost_list with the memory_cost increasing, and communication_with_partial_para_cost decreasing order. - // This method excludes the cost with greater memory_cost and greater communication_without_para_cost. + // Sort the cost_list with the computation_cost_ increasing, and communication_with_partial_para_cost decreasing + // order. This method excludes the cost with greater computation_cost_ and greater communication_without_para_cost. if (!COST_MODEL_SIMPLIFY_CALCULATION) { return; } @@ -54,7 +54,7 @@ void SimplifyForDreasingCommunicationWithPartialPara(CostPtrList* clist_ptrs) { std::vector id(clist_ptrs->size()); std::iota(id.begin(), id.end(), size_t(0)); std::sort(id.begin(), id.end(), [&clist_ptrs](size_t x, size_t y) { - return clist_ptrs->at(x)->memory_cost_ < clist_ptrs->at(y)->memory_cost_; + return clist_ptrs->at(x)->computation_cost_ < clist_ptrs->at(y)->computation_cost_; }); CostPtrList ret; for (size_t i = 0; i < clist_ptrs->size(); ++i) { diff --git a/mindspore/ccsrc/parallel/auto_parallel/costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/costmodel.h index 361c19573f..229f0fbf5e 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/costmodel.h +++ b/mindspore/ccsrc/parallel/auto_parallel/costmodel.h @@ -44,14 +44,18 @@ using RedistributionOpListPtr = std::shared_ptr& decision_ = nullptr) - : memory_cost_(memory), communication_cost_(commuication), decision_ptr_(std::move(decision_)) { + Cost(double computation, double commuication, const std::shared_ptr& decision_ = nullptr) + : computation_cost_(computation), communication_cost_(commuication), decision_ptr_(std::move(decision_)) { + memory_with_reuse_ = 0.0; communication_without_parameter_ = 0.0; communication_with_partial_para_ = 0.0; communication_redis_forward_ = 0.0; communication_redis_backward_ = 0.0; } - double memory_cost_; + // 'memory_with_reuse_' calculates the peak memory usage in a training phase + double memory_with_reuse_; + // 'computation_cost_' models the training time of an iteration in a training phase + double computation_cost_; // 'communication_cost_' includes communications from operators (forward and backward) and edges double communication_cost_; // communication_without_parameter_ = communication_cost_ - (backward communication from operators) diff --git a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h index c9b6a07317..0cb58c49da 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h +++ b/mindspore/ccsrc/parallel/auto_parallel/dp_algo_costmodel.h @@ -35,7 +35,7 @@ namespace parallel { // interpretation of 6 operations in costmodel.h. // Phase 2: Search the cost_list in the final graph, and determine the optimal one // Create the cost_list for the final graph, and choose the optimal one: one the minimum quantity -// COST_MODEL_ALPHA * memory_cost + COST_MODEL_BETA * communication_cost +// COST_MODEL_ALPHA * computation_cost + COST_MODEL_BETA * communication_cost // Phase 3: Recover the original CostGraph, the determine strategy for each operator // After determining the optimal cost for the final graph, the algorithm recovers the original graph by applying // the 4 operations in the reverse order in the Phase 1. Because each operation decision contains the strategy, diff --git a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc index 6381049f17..653f6c903d 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc @@ -69,7 +69,7 @@ Status Edge::InitEdgeCost() { MS_LOG(EXCEPTION) << "Failure: redistribution cost calculation failed"; } MS_EXCEPTION_IF_NULL(cost); - MS_LOG(DEBUG) << "The redistribution cost: memory_cost: " << cost->memory_cost_ + MS_LOG(DEBUG) << "The redistribution cost: computation_cost: " << cost->computation_cost_ << ", communication_cost: " << cost->communication_cost_ << ", communication_without_parameter_: " << cost->communication_without_parameter_ << ", communication_with_partial_para_: " << cost->communication_with_partial_para_ << "."; @@ -117,9 +117,9 @@ Status Edge::GetRedistributionCost(const TensorLayout& prev_op_output_layout, co double comm_cost = tensor_redistribution.comm_cost(); double forward_comm_cost = tensor_redistribution.forward_comm_cost(); double backward_comm_cost = tensor_redistribution.backward_comm_cost(); - double mem_cost = tensor_redistribution.mem_cost(); + double computation_cost = tensor_redistribution.computation_cost(); - *cost = std::make_shared(type_length * mem_cost, type_length * comm_cost); + *cost = std::make_shared(type_length * computation_cost, type_length * comm_cost); (*cost)->communication_without_parameter_ = type_length * comm_cost; (*cost)->communication_with_partial_para_ = (*cost)->communication_without_parameter_ + @@ -150,26 +150,26 @@ CostPtrList Edge::CreateEdgeEliminationCostList(const StrategyPtr& output_st_ptr (void)std::transform(edges.begin(), edges.end(), all_cost_list.begin(), LocalGetCostList); CostPtrList selected_cost_list(all_cost_list.size(), nullptr); - std::function recursive = [&](size_t k, double memory, double communication, - double communication_without_para) { - if (k == edges.size()) { - auto decision = std::make_shared(selected_cost_list); - CostPtr new_cost = std::make_shared(memory, communication); - MS_EXCEPTION_IF_NULL(new_cost); - new_cost->communication_without_parameter_ = communication_without_para; - new_cost->communication_with_partial_para_ = - communication_without_para + COST_MODEL_GAMMA * (communication - communication_without_para); - new_cost->decision_ptr_ = decision; - result.push_back(new_cost); - return; - } - for (auto& c : all_cost_list[k]) { - MS_EXCEPTION_IF_NULL(c); - selected_cost_list[k] = c; - recursive(k + 1, memory + c->memory_cost_, communication + c->communication_cost_, - communication_without_para + c->communication_without_parameter_); - } - }; + std::function recursive = + [&](size_t k, double computation, double communication, double communication_without_para) { + if (k == edges.size()) { + auto decision = std::make_shared(selected_cost_list); + CostPtr new_cost = std::make_shared(computation, communication); + MS_EXCEPTION_IF_NULL(new_cost); + new_cost->communication_without_parameter_ = communication_without_para; + new_cost->communication_with_partial_para_ = + communication_without_para + COST_MODEL_GAMMA * (communication - communication_without_para); + new_cost->decision_ptr_ = decision; + result.push_back(new_cost); + return; + } + for (auto& c : all_cost_list[k]) { + MS_EXCEPTION_IF_NULL(c); + selected_cost_list[k] = c; + recursive(k + 1, computation + c->computation_cost_, communication + c->communication_cost_, + communication_without_para + c->communication_without_parameter_); + } + }; recursive(0, 0, 0, 0); SimplifyForDreasingCommunicationWithPartialPara(&result); return result; @@ -203,7 +203,8 @@ void Edge::CreateOpEliminationSubCostList(StrategyPtr op_strategy, const CostPtr MS_EXCEPTION_IF_NULL(middle_cost); for (auto& right_cost : right_cost_list) { MS_EXCEPTION_IF_NULL(right_cost); - double memory = left_cost->memory_cost_ + middle_cost->memory_cost_ + right_cost->memory_cost_; + double computation = + left_cost->computation_cost_ + middle_cost->computation_cost_ + right_cost->computation_cost_; double communication = left_cost->communication_cost_ + middle_cost->communication_cost_ + right_cost->communication_cost_; double communication_without_para = left_cost->communication_without_parameter_ + @@ -211,7 +212,7 @@ void Edge::CreateOpEliminationSubCostList(StrategyPtr op_strategy, const CostPtr right_cost->communication_without_parameter_; auto decision = std::make_shared(op_strategy, left_cost, middle_cost, right_cost); - auto cost = std::make_shared(memory, communication, decision); + auto cost = std::make_shared(computation, communication, decision); MS_EXCEPTION_IF_NULL(cost); cost->communication_without_parameter_ = communication_without_para; cost->communication_with_partial_para_ = diff --git a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h index 1fa49029fa..eb89466d7c 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h +++ b/mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h @@ -133,7 +133,7 @@ class Edge { void set_parameter_involve(int para_invol) { is_output_parameter_involve_ = para_invol; } // When the input of a operator contains WEIGHT or a output from other operators involving WEIGHT, then these input // should stay in memory until it is used in the backward phase, which is kept in memory at the end of forward phase. - Status CorrectStrategyCostForMemoryReuse() const { return SUCCESS; } + Status CalculateMemoryCost() const { return SUCCESS; } private: std::string edge_name_; diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc index 59b9d9e992..88a54662d3 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc @@ -247,7 +247,7 @@ CostPtrList CostGraph::CreateFinalCostList(const OperatorInfoPtr& u, const std:: MS_EXCEPTION_IF_NULL(cost1); MS_EXCEPTION_IF_NULL(cost2); MS_EXCEPTION_IF_NULL(cost3); - double memory = cost1->memory_cost_ + cost2->memory_cost_ + cost3->memory_cost_; + double computation = cost1->computation_cost_ + cost2->computation_cost_ + cost3->computation_cost_; double commmunication = cost1->communication_cost_ + cost2->communication_cost_ + cost3->communication_cost_; double communication_without_para = cost1->communication_without_parameter_ + @@ -255,7 +255,7 @@ CostPtrList CostGraph::CreateFinalCostList(const OperatorInfoPtr& u, const std:: cost3->communication_without_parameter_; auto decision = std::make_shared(u_strategy->strategy_ptr, v_strategy->strategy_ptr, cost1, cost2, cost3); - auto cost = std::make_shared(memory, commmunication, decision); + auto cost = std::make_shared(computation, commmunication, decision); MS_EXCEPTION_IF_NULL(cost); cost->communication_without_parameter_ = communication_without_para; cost->communication_with_partial_para_ = @@ -282,7 +282,7 @@ CostPtrList CostGraph::CreateFinalSingleCostList(const OperatorInfoPtr& u) { for (const auto& cost1 : clist1) { MS_EXCEPTION_IF_NULL(cost1); auto decision = std::make_shared(u_strategy_ptr, cost1); - auto new_cost = std::make_shared(cost1->memory_cost_, cost1->communication_cost_, decision); + auto new_cost = std::make_shared(cost1->computation_cost_, cost1->communication_cost_, decision); MS_EXCEPTION_IF_NULL(new_cost); new_cost->communication_without_parameter_ = cost1->communication_without_parameter_; new_cost->communication_with_partial_para_ = @@ -297,12 +297,12 @@ CostPtrList CostGraph::CreateFinalSingleCostList(const OperatorInfoPtr& u) { } CostPtr CostGraph::SelectCostWithMemoryConstraint(const CostPtrList& cost_list, double memory) { - if (cost_list.empty() || cost_list[0]->memory_cost_ >= memory) { + if (cost_list.empty() || cost_list[0]->computation_cost_ >= memory) { return nullptr; } std::function LocalCompare = [&](CostPtr init, const CostPtr& cost_x) { MS_EXCEPTION_IF_NULL(cost_x); - if (init == nullptr || cost_x->memory_cost_ < memory) { + if (init == nullptr || cost_x->computation_cost_ < memory) { init = cost_x; } return init; @@ -313,36 +313,36 @@ CostPtr CostGraph::SelectCostWithMemoryConstraint(const CostPtrList& cost_list, CostPtr CostGraph::SelectCostWithMinTrainingTime(const CostPtrList& cost_list, double memory) { // Select the cost with minimum training time. Currently, the training time is modeled as = - // costmodel_alpha_ * memory_cost + costmodel_beta_ * communication_with_partial_para_ + // costmodel_alpha_ * computation_cost + costmodel_beta_ * communication_with_partial_para_ if (cost_list.empty()) { MS_LOG(ERROR) << "Final cost list is null."; return nullptr; } CostPtr ret = cost_list[0]; MS_EXCEPTION_IF_NULL(ret); - if (ret->memory_cost_ >= memory) { - MS_LOG(ERROR) << "No available cost; the minimum cost is " << ret->memory_cost_ + if (ret->computation_cost_ >= memory) { + MS_LOG(ERROR) << "No available cost; the minimum cost is " << ret->computation_cost_ << ", the memory capacity is: " << memory << "."; return nullptr; } - double minimum = costmodel_alpha_ * ret->memory_cost_ + costmodel_beta_ * ret->communication_with_partial_para_; - MS_LOG(INFO) << "minimum: " << minimum << ", memory_cost_: " << ret->memory_cost_ + double minimum = costmodel_alpha_ * ret->computation_cost_ + costmodel_beta_ * ret->communication_with_partial_para_; + MS_LOG(INFO) << "minimum: " << minimum << ", computation_cost_: " << ret->computation_cost_ << ", communication_with_partial_para_: " << ret->communication_with_partial_para_ << ", communication_cost_: " << ret->communication_cost_ << ", communication_without_parameter_: " << ret->communication_without_parameter_ << "."; for (size_t i = 1; i < cost_list.size(); ++i) { MS_EXCEPTION_IF_NULL(cost_list[i]); - if (cost_list[i]->memory_cost_ >= memory) { - MS_LOG(INFO) << "cost_list " << i << " memory_cost_: " << cost_list[i]->memory_cost_ + if (cost_list[i]->computation_cost_ >= memory) { + MS_LOG(INFO) << "cost_list " << i << " computation_cost_: " << cost_list[i]->computation_cost_ << ", is larger than the memory capacity: " << memory << "."; break; } - MS_LOG(INFO) << "cost_list " << i << " memory_cost_: " << cost_list[i]->memory_cost_ + MS_LOG(INFO) << "cost_list " << i << " computation_cost_: " << cost_list[i]->computation_cost_ << ", communication_with_partial_para_: " << cost_list[i]->communication_with_partial_para_ << ", communication_cost_: " << cost_list[i]->communication_cost_ << ", communication_without_parameter_: " << cost_list[i]->communication_without_parameter_ << "."; - auto tmp = - costmodel_alpha_ * cost_list[i]->memory_cost_ + costmodel_beta_ * cost_list[i]->communication_with_partial_para_; + auto tmp = costmodel_alpha_ * cost_list[i]->computation_cost_ + + costmodel_beta_ * cost_list[i]->communication_with_partial_para_; MS_LOG(INFO) << "tmp: " << tmp; if (minimum > tmp) { minimum = tmp; @@ -363,8 +363,8 @@ CostPtrList CostGraph::SelectCostListWithMinTrainingTimeMultiple(const std::vect MS_LOG(ERROR) << "The cost list " << i << " is empty."; return ret; } else { - total_memory += all_cost_list[i][0]->memory_cost_; - minimum += costmodel_alpha_ * all_cost_list[i][0]->memory_cost_ + + total_memory += all_cost_list[i][0]->computation_cost_; + minimum += costmodel_alpha_ * all_cost_list[i][0]->computation_cost_ + costmodel_beta_ * all_cost_list[i][0]->communication_with_partial_para_; ret[i] = all_cost_list[i][0]; } @@ -381,8 +381,8 @@ CostPtrList CostGraph::SelectCostListWithMinTrainingTimeMultiple(const std::vect double tmp_memory = 0.0, tmp_minimum = 0.0; for (size_t i = 0; i < selected_cost_list.size(); ++i) { MS_EXCEPTION_IF_NULL(selected_cost_list[i]); - tmp_memory += selected_cost_list[i]->memory_cost_; - tmp_minimum += costmodel_alpha_ * selected_cost_list[i]->memory_cost_ + + tmp_memory += selected_cost_list[i]->computation_cost_; + tmp_minimum += costmodel_alpha_ * selected_cost_list[i]->computation_cost_ + costmodel_beta_ * selected_cost_list[i]->communication_with_partial_para_; } MS_LOG(INFO) << "tmp_memory: " << tmp_memory << ", tmp_minimum: " << tmp_minimum << ", minimum: " << minimum @@ -394,6 +394,7 @@ CostPtrList CostGraph::SelectCostListWithMinTrainingTimeMultiple(const std::vect } return; } + MS_LOG(DEBUG) << "The value minimum: " << minimum << ", available_memory: " << available_memory << "."; for (auto& c : all_cost_list[k]) { selected_cost_list[k] = c; @@ -814,7 +815,7 @@ void CostGraph::CreateMergeEliminationSubCostList(StrategyPtr op_strategy, const for (size_t k = 0; k < tar_cost_list.size(); ++k) { auto& tar_cost = tar_cost_list[k]; MS_EXCEPTION_IF_NULL(tar_cost); - double memory = op_cost->memory_cost_ + edge_cost->memory_cost_ + tar_cost->memory_cost_; + double computation = op_cost->computation_cost_ + edge_cost->computation_cost_ + tar_cost->computation_cost_; double communication = op_cost->communication_cost_ + edge_cost->communication_cost_ + tar_cost->communication_cost_; double communication_without_para = op_cost->communication_without_parameter_ + @@ -823,7 +824,7 @@ void CostGraph::CreateMergeEliminationSubCostList(StrategyPtr op_strategy, const auto decision = std::make_shared(op_strategy, op_cost, edge_cost, tar_op_strategy, tar_cost); - auto new_cost = std::make_shared(memory, communication, decision); + auto new_cost = std::make_shared(computation, communication, decision); MS_EXCEPTION_IF_NULL(new_cost); new_cost->communication_without_parameter_ = communication_without_para; new_cost->communication_with_partial_para_ = @@ -891,7 +892,8 @@ void CostGraph::CreateContractEliminationSubCostList(StrategyPtr contract_op_str for (size_t k = 0; k < tar_cost_list.size(); ++k) { auto& tar_cost = tar_cost_list[k]; MS_EXCEPTION_IF_NULL(tar_cost); - double memory = contract_op_cost->memory_cost_ + edge_cost->memory_cost_ + tar_cost->memory_cost_; + double computation = + contract_op_cost->computation_cost_ + edge_cost->computation_cost_ + tar_cost->computation_cost_; double communication = contract_op_cost->communication_cost_ + edge_cost->communication_cost_ + tar_cost->communication_cost_; double communication_without_para = contract_op_cost->communication_without_parameter_ + @@ -900,7 +902,7 @@ void CostGraph::CreateContractEliminationSubCostList(StrategyPtr contract_op_str auto decision = std::make_shared(contract_op_stra, contract_op_cost, edge_cost, target_op_stra, tar_cost); - auto new_cost = std::make_shared(memory, communication, decision); + auto new_cost = std::make_shared(computation, communication, decision); new_cost->communication_without_parameter_ = communication_without_para; new_cost->communication_with_partial_para_ = communication_without_para + COST_MODEL_GAMMA * (communication - communication_without_para); @@ -963,9 +965,9 @@ void CostGraph::CreateTriangleEliminationSubCostList(StrategyPtr elimi_op_stra, MS_EXCEPTION_IF_NULL(left_edge_cost); for (auto& left_node_cost : left_node_clist_origin) { MS_EXCEPTION_IF_NULL(left_node_cost); - double new_memory_cost = elimi_op_cost->memory_cost_ + left_edge_cost->memory_cost_ + - left_node_cost->memory_cost_ + right_edge_cost->memory_cost_ + - right_op_cost->memory_cost_; + double new_computation = elimi_op_cost->computation_cost_ + left_edge_cost->computation_cost_ + + left_node_cost->computation_cost_ + right_edge_cost->computation_cost_ + + right_op_cost->computation_cost_; double new_commu_cost = elimi_op_cost->communication_cost_ + left_edge_cost->communication_cost_ + left_node_cost->communication_cost_ + right_edge_cost->communication_cost_ + right_op_cost->communication_cost_; @@ -977,7 +979,7 @@ void CostGraph::CreateTriangleEliminationSubCostList(StrategyPtr elimi_op_stra, auto decision = std::make_shared(elimi_op_stra, elimi_op_cost, left_edge_cost, right_edge_cost, left_op_stra, left_node_cost, right_op_stra, right_op_cost); - auto new_cost = std::make_shared(new_memory_cost, new_commu_cost, decision); + auto new_cost = std::make_shared(new_computation, new_commu_cost, decision); new_cost->communication_without_parameter_ = new_commu_without; new_cost->communication_with_partial_para_ = new_commu_without + COST_MODEL_GAMMA * (new_commu_cost - new_commu_without); @@ -1082,11 +1084,12 @@ void CostGraph::CreateStarEliminationSubCostList(const StrategyPtr& first_succ_n succ_edges_costs[0] = first_succ_edge_cost; succ_nodes_costs[0] = first_succ_node_cost; - double memory_cost = merged_node_cost->memory_cost_, commu_cost = merged_node_cost->communication_cost_, + double computation_cost = merged_node_cost->computation_cost_, + commu_cost = merged_node_cost->communication_cost_, commu_without = merged_node_cost->communication_without_parameter_; for (size_t i = 0; i < succ_nodes_stras.size(); ++i) { MS_EXCEPTION_IF_NULL(succ_edges_costs[i]); - memory_cost += succ_edges_costs[i]->memory_cost_ + succ_nodes_costs[i]->memory_cost_; + computation_cost += succ_edges_costs[i]->computation_cost_ + succ_nodes_costs[i]->computation_cost_; commu_cost += succ_edges_costs[i]->communication_cost_ + succ_nodes_costs[i]->communication_cost_; commu_without += succ_edges_costs[i]->communication_without_parameter_ + succ_nodes_costs[i]->communication_without_parameter_; @@ -1094,7 +1097,7 @@ void CostGraph::CreateStarEliminationSubCostList(const StrategyPtr& first_succ_n auto decision = std::make_shared(merged_op_stra, merged_node_cost, succ_edges_costs, succ_nodes_stras, succ_nodes_costs); - auto new_cost = std::make_shared(memory_cost, commu_cost, decision); + auto new_cost = std::make_shared(computation_cost, commu_cost, decision); new_cost->communication_without_parameter_ = commu_without; new_cost->communication_with_partial_para_ = commu_without + COST_MODEL_GAMMA * (commu_cost - commu_without); first_succ_node_clist_new->emplace_back(std::move(new_cost)); @@ -1210,36 +1213,6 @@ Status CostGraph::InitSelectedStrategy() { return SUCCESS; } -Status CostGraph::CorrectOpsStrategyCostForMultiOutputUse() { - for (auto& op : ops_) { - MS_EXCEPTION_IF_NULL(op); - if (op->GetAliveSuccEdges().size() > 1) { - // Filter out the case of a output being used by multiple operators - std::map output_count; - for (size_t i = 0; i < op->GetAliveSuccEdges().size(); ++i) { - auto output_index = op->GetAliveSuccEdges()[i]->prev_op_output_index(); - output_count[output_index]++; - } - for (size_t i = 0; i < op->GetAliveSuccEdges().size(); ++i) { - auto output_index = op->GetAliveSuccEdges()[i]->prev_op_output_index(); - if (output_count[output_index] <= 1) { - continue; - } - auto next_op = op->GetAliveSuccEdges()[i]->next_operator(); - MS_EXCEPTION_IF_NULL(next_op); - auto input_index = op->GetAliveSuccEdges()[i]->next_op_input_index(); - if (next_op->CorrectStrategyCostForMultiOutputUse(input_index) != SUCCESS) { - MS_LOG(ERROR) << "The operator name: " << op->name() << ", the next operator name: " << next_op->name() - << ", the output_index: " << output_index << ", the input_index: " << input_index << "."; - return FAILED; - } - output_count[output_index]--; - } - } - } - return SUCCESS; -} - Status CostGraph::ComputeOpsAndEdgesParameterInvolved() { for (auto& op : ops_) { MS_EXCEPTION_IF_NULL(op); @@ -1252,23 +1225,23 @@ Status CostGraph::ComputeOpsAndEdgesParameterInvolved() { return SUCCESS; } -Status CostGraph::CorrectOpsStrategyCostForMemoryReuse() { +Status CostGraph::CalculateOpsMemoryCost() { for (auto& op : ops_) { MS_EXCEPTION_IF_NULL(op); - if (op->CorrectStrategyCostForMemoryReuse() != SUCCESS) { - MS_LOG(ERROR) << "Correcting Operator: " << op->name() << " cost for memory reuse failed."; + if (op->CalculateMemoryCost() != SUCCESS) { + MS_LOG(ERROR) << "Calculate Operator: " << op->name() << " cost for memory usage failed."; return FAILED; } } return SUCCESS; } -Status CostGraph::CorrectEdgesStrategyCostForMemoryReuse() { +Status CostGraph::CalculateEdgesMemoryCost() { for (auto& edge_pair : edges_) { const auto& edges = edge_pair.second; for (auto& one_edge : edges) { - if (one_edge->CorrectStrategyCostForMemoryReuse() != SUCCESS) { - MS_LOG(ERROR) << "Correcting Edge: " << one_edge->edge_name() << " cost for memory reuse failed."; + if (one_edge->CalculateMemoryCost() != SUCCESS) { + MS_LOG(ERROR) << "Calculate Edge: " << one_edge->edge_name() << " cost for memory usage failed."; return FAILED; } } diff --git a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h index e4cbdffb61..c149534826 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h +++ b/mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h @@ -175,16 +175,12 @@ class CostGraph { void CreateStarEliminationSubCostList(const StrategyPtr&, const CostPtrList&, const CostPtrList&, const StrategyPtr&, const CostPtrList&, std::vector, CostPtrList&, CostPtrList&, CostPtrList*); - - // When a output of a operator is being used by multiple operators, the memory cost of this part should be calculated - // only once. This method is for correcting the 'strategy_cost_' for operators - Status CorrectOpsStrategyCostForMultiOutputUse(); // When the input of a operator is neither a WEIGHT, nor a output of a subsequent operator involving WEIGHT, then // the memory cost can be resused. - Status CorrectOpsStrategyCostForMemoryReuse(); + Status CalculateOpsMemoryCost(); // When the input of the edge is neither a WEIGHT, nor a output of a subsequent operator involving WEIGHT, then // the memory cost can be resused. - Status CorrectEdgesStrategyCostForMemoryReuse(); + Status CalculateEdgesMemoryCost(); Status ComputeOpsAndEdgesParameterInvolved(); std::vector GetOperators() const { return ops_; } diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc index 6958932fd6..7c17b499b1 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc @@ -74,8 +74,8 @@ double MatMulCost::GetBackwardCommCost(const std::vector& inputs, co // Return the per device memory cost in the forward phase. The cost is calculated according to the bytes // this operator uses -double MatMulCost::GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t&) const { +double MatMulCost::GetForwardComputationCost(const std::vector& inputs, + const std::vector& outputs, const int32_t&) const { // In forward phase, the memory cost = slice(A) + slice(B) + (0 or 1) allreduce(slice(C)) double result = 0.0; TensorInfo output0 = outputs[0]; @@ -93,8 +93,8 @@ double MatMulCost::GetForwardMemoryCost(const std::vector& inputs, c // Return the per device memory cost in the forward phase. The cost is calculated according to the bytes // this operator uses -double MatMulCost::GetBackwardMemoryCost(const std::vector& inputs, const std::vector&, - const int32_t& stage_id) const { +double MatMulCost::GetBackwardComputationCost(const std::vector& inputs, const std::vector&, + const int32_t& stage_id) const { // In backward phase, the memory cost = (0 or 1) allreduce(slice(B)) double result = 0.0; if (is_parameter_[1]) { @@ -147,8 +147,8 @@ double ActivationCost::GetBackwardCommCost(const std::vector& inputs // Return the per memory cost in the forward phase. The cost is calculated according to the bytes // this operator uses -double ActivationCost::GetForwardMemoryCost(const std::vector& inputs, const std::vector&, - const int32_t&) const { +double ActivationCost::GetForwardComputationCost(const std::vector& inputs, const std::vector&, + const int32_t&) const { TensorInfo input0_info = inputs[0]; Shape input0_slice_shape = input0_info.slice_shape(); return ListProduct(input0_slice_shape) * static_cast(inputs_type_lengths_[0]); @@ -156,8 +156,8 @@ double ActivationCost::GetForwardMemoryCost(const std::vector& input // Return the per memory cost in the forward phase. The cost is calculated according to the bytes // this operator uses -double ActivationCost::GetBackwardMemoryCost(const std::vector&, const std::vector&, - const int32_t&) const { +double ActivationCost::GetBackwardComputationCost(const std::vector&, const std::vector&, + const int32_t&) const { return 0.0; } @@ -191,8 +191,8 @@ double SoftmaxCost::GetBackwardCommCost(const std::vector& inputs, c // Return the per memory cost in the forward phase. The cost is calculated according to the bytes // this operator uses -double SoftmaxCost::GetForwardMemoryCost(const std::vector& inputs, const std::vector&, - const int32_t&) const { +double SoftmaxCost::GetForwardComputationCost(const std::vector& inputs, const std::vector&, + const int32_t&) const { // In the forward phase, the memory cost = slice(A) TensorInfo input0 = inputs[0]; Shape input0_slice_shape = input0.slice_shape(); @@ -201,8 +201,9 @@ double SoftmaxCost::GetForwardMemoryCost(const std::vector& inputs, // Return the per memory cost in the forward phase. The cost is calculated according to the bytes // this operator uses -double SoftmaxCost::GetBackwardMemoryCost(const std::vector&, - const std::vector&, const int32_t&) const { +double SoftmaxCost::GetBackwardComputationCost(const std::vector&, + const std::vector&, + const int32_t&) const { return 0.0; } @@ -222,9 +223,9 @@ double TmpIdentityCost::GetBackwardCommCost(const std::vector& inputs, - const std::vector&, - const int32_t&) const { +double TmpIdentityCost::GetForwardComputationCost(const std::vector& inputs, + const std::vector&, + const int32_t&) const { TensorInfo input0_info = inputs[0]; Shape input0_slice_shape = input0_info.slice_shape(); return ListProduct(input0_slice_shape) * static_cast(inputs_type_lengths_[0]); @@ -232,15 +233,15 @@ double TmpIdentityCost::GetForwardMemoryCost(const std::vector&, - const std::vector&, - const int32_t&) const { +double TmpIdentityCost::GetBackwardComputationCost(const std::vector&, + const std::vector&, + const int32_t&) const { return 0.0; } -double BatchParallelCost::GetForwardMemoryCost(const std::vector& inputs, - const std::vector&, - const int32_t&) const { +double BatchParallelCost::GetForwardComputationCost(const std::vector& inputs, + const std::vector&, + const int32_t&) const { double cost = 0.0; for (size_t i = 0; i < inputs.size(); ++i) { cost += ListProduct(inputs[i].slice_shape()) * static_cast(inputs_type_lengths_[i]); @@ -248,9 +249,9 @@ double BatchParallelCost::GetForwardMemoryCost(const std::vector&, - const std::vector&, - const int32_t&) const { +double BatchParallelCost::GetBackwardComputationCost(const std::vector&, + const std::vector&, + const int32_t&) const { return 0.0; } @@ -285,8 +286,8 @@ double PReLUCost::GetBackwardCommCost(const std::vector& inputs, con // Return the per memory cost in the forward phase. The cost is calculated according to the bytes // this operator uses -double PReLUCost::GetForwardMemoryCost(const std::vector& inputs, const std::vector&, - const int32_t&) const { +double PReLUCost::GetForwardComputationCost(const std::vector& inputs, const std::vector&, + const int32_t&) const { // In forward phase, the memory cost = slice(A) + slice(B) Shape input0_slice_shape = inputs[0].slice_shape(); Shape input1_slice_shape = inputs[1].slice_shape(); @@ -297,9 +298,9 @@ double PReLUCost::GetForwardMemoryCost(const std::vector& inputs, co // Return the per memory cost in the backward phase. The cost is calculated according to the bytes // this operator uses -double PReLUCost::GetBackwardMemoryCost(const std::vector& inputs, - const std::vector&, - const int32_t& stage_id) const { +double PReLUCost::GetBackwardComputationCost(const std::vector& inputs, + const std::vector&, + const int32_t& stage_id) const { // In backward phase, the memory cost = (0 or 1) allreduce(slice(B)) double result = 0.0; if (is_parameter_[1]) { @@ -338,8 +339,8 @@ double OneHotCost::GetBackwardCommCost(const std::vector&, const std // Return the per memory cost in the forward phase. The cost is calculated according to the bytes // this operator uses -double OneHotCost::GetForwardMemoryCost(const std::vector& inputs, const std::vector&, - const int32_t&) const { +double OneHotCost::GetForwardComputationCost(const std::vector& inputs, const std::vector&, + const int32_t&) const { // In onehot's forward phase, the memory cost = slice(A) Shape input0_slice_shape = inputs[0].slice_shape(); return ListProduct(input0_slice_shape) * static_cast(inputs_type_lengths_[0]); @@ -347,8 +348,8 @@ double OneHotCost::GetForwardMemoryCost(const std::vector& inputs, c // Return the per memory cost in the backward phase. The cost is calculated according to the bytes // this operator uses -double OneHotCost::GetBackwardMemoryCost(const std::vector&, const std::vector&, - const int32_t&) const { +double OneHotCost::GetBackwardComputationCost(const std::vector&, const std::vector&, + const int32_t&) const { return 0.0; } @@ -368,8 +369,9 @@ double SoftmaxCrossEntropyWithLogitsCost::GetBackwardCommCost(const std::vector< // Return the per memory cost in the forward phase. The cost is calculated according to the bytes // this operator uses -double SoftmaxCrossEntropyWithLogitsCost::GetForwardMemoryCost(const std::vector& inputs, - const std::vector&, const int32_t&) const { +double SoftmaxCrossEntropyWithLogitsCost::GetForwardComputationCost(const std::vector& inputs, + const std::vector&, + const int32_t&) const { // In forward phase, the memory cost = slice(A) + slice(B) Shape input0_slice_shape = inputs[0].slice_shape(); Shape input1_slice_shape = inputs[1].slice_shape(); @@ -380,8 +382,9 @@ double SoftmaxCrossEntropyWithLogitsCost::GetForwardMemoryCost(const std::vector // Return the per memory cost in the backward phase. The cost is calculated according to the bytes // this operator uses -double SoftmaxCrossEntropyWithLogitsCost::GetBackwardMemoryCost(const std::vector&, - const std::vector&, const int32_t&) const { +double SoftmaxCrossEntropyWithLogitsCost::GetBackwardComputationCost(const std::vector&, + const std::vector&, + const int32_t&) const { return 0.0; } @@ -409,8 +412,8 @@ double ReshapeCost::GetBackwardCommCost(const std::vector&, const st // Return the per memory cost in the forward phase. The cost is calculated according to the bytes // this operator uses -double ReshapeCost::GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const { +double ReshapeCost::GetForwardComputationCost(const std::vector& inputs, + const std::vector& outputs, const int32_t& stage_id) const { CheckGlobalDeviceManager(); MS_EXCEPTION_IF_NULL(g_device_manager); RankList dev_list = g_device_manager->GetDeviceListByStageId(stage_id); @@ -421,26 +424,27 @@ double ReshapeCost::GetForwardMemoryCost(const std::vector& inputs, if (tensor_redistribution.ComputeCost() == FAILED) { MS_LOG(EXCEPTION) << "Failure: tensor_redistribution ComputeCost failed."; } - return (inputs_type_lengths_[0] * tensor_redistribution.mem_cost()); + return (inputs_type_lengths_[0] * tensor_redistribution.computation_cost()); } // Return the per memory cost in the backward phase. The cost is calculated according to the bytes // this operator uses -double ReshapeCost::GetBackwardMemoryCost(const std::vector&, - const std::vector&, const int32_t&) const { +double ReshapeCost::GetBackwardComputationCost(const std::vector&, + const std::vector&, + const int32_t&) const { return 0.0; } -double ArithmeticCost::GetForwardMemoryCost(const std::vector& inputs, const std::vector&, - const int32_t&) const { +double ArithmeticCost::GetForwardComputationCost(const std::vector& inputs, const std::vector&, + const int32_t&) const { double result; result = ListProduct(inputs[0].slice_shape()) * static_cast(inputs_type_lengths_[0]) + ListProduct(inputs[1].slice_shape()) * static_cast(inputs_type_lengths_[1]); return result; } -double ArithmeticCost::GetBackwardMemoryCost(const std::vector& inputs, const std::vector&, - const int32_t& stage_id) const { +double ArithmeticCost::GetBackwardComputationCost(const std::vector& inputs, const std::vector&, + const int32_t& stage_id) const { double result = 0.0; CheckGlobalDeviceManager(); MS_EXCEPTION_IF_NULL(g_device_manager); @@ -533,15 +537,15 @@ double L2NormalizeCost::GetBackwardCommCost(const std::vector& input return result; } -double L2NormalizeCost::GetForwardMemoryCost(const std::vector& inputs, const std::vector&, - const int32_t&) const { +double L2NormalizeCost::GetForwardComputationCost(const std::vector& inputs, const std::vector&, + const int32_t&) const { TensorInfo input0_info = inputs[0]; Shape input0_slice_shape = input0_info.slice_shape(); return ListProduct(input0_slice_shape) * static_cast(inputs_type_lengths_[0]); } -double L2NormalizeCost::GetBackwardMemoryCost(const std::vector& inputs, const std::vector&, - const int32_t& stage_id) const { +double L2NormalizeCost::GetBackwardComputationCost(const std::vector& inputs, + const std::vector&, const int32_t& stage_id) const { double result = 0.0; if (is_parameter_[0]) { @@ -618,8 +622,9 @@ double ReduceMethodCost::GetBackwardCommCost(const std::vector& inpu return result; } -double ReduceMethodCost::GetForwardMemoryCost(const std::vector& inputs, - const std::vector& outputs, const int32_t& stage_id) const { +double ReduceMethodCost::GetForwardComputationCost(const std::vector& inputs, + const std::vector& outputs, + const int32_t& stage_id) const { double result = 0.0; TensorInfo input0 = inputs[0]; TensorInfo output0 = outputs[0]; @@ -640,8 +645,9 @@ double ReduceMethodCost::GetForwardMemoryCost(const std::vector& inp return result; } -double ReduceMeanCost::GetForwardMemoryCost(const std::vector& inputs, - const std::vector& outputs, const int32_t& stage_id) const { +double ReduceMeanCost::GetForwardComputationCost(const std::vector& inputs, + const std::vector& outputs, + const int32_t& stage_id) const { double result = 0.0; TensorInfo input0 = inputs[0]; TensorInfo output0 = outputs[0]; diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h index 9fb86d467e..8f0099bba3 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h +++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h @@ -65,12 +65,12 @@ class OperatorCost { virtual double GetBackwardCommCost(const std::vector& inputs, const std::vector& outputs, const int32_t& stage_id) const = 0; // per device computation cost - virtual double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const = 0; - virtual double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const = 0; - virtual double GetBackwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const = 0; + virtual double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const = 0; + virtual double GetForwardComputationCost(const std::vector& inputs, + const std::vector& outputs, const int32_t& stage_id) const = 0; + virtual double GetBackwardComputationCost(const std::vector& inputs, + const std::vector& outputs, const int32_t& stage_id) const = 0; protected: // for each input in 'inputs_', there is a bool variable indicating whether that the corresponding input is parameter @@ -96,14 +96,14 @@ class MatMulCost : public OperatorCost { const int32_t& stage_id) const override; // per device computation cost - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); - } - double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; - double GetBackwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); + } + double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; + double GetBackwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; }; using MatMulCostPtr = std::shared_ptr; @@ -121,14 +121,14 @@ class ActivationCost : public OperatorCost { const int32_t& stage_id) const override; double GetBackwardCommCost(const std::vector& inputs, const std::vector& outputs, const int32_t& stage_id) const override; - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); - } - double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; - double GetBackwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); + } + double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; + double GetBackwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; }; using ActivationCostPtr = std::shared_ptr; @@ -146,14 +146,14 @@ class SoftmaxCost : public OperatorCost { const int32_t& stage_id) const override; double GetBackwardCommCost(const std::vector& inputs, const std::vector& outputs, const int32_t& stage_id) const override; - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); - } - double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; - double GetBackwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t&) const override; + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); + } + double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; + double GetBackwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t&) const override; }; using SoftmaxCostPtr = std::shared_ptr; @@ -171,14 +171,14 @@ class TmpIdentityCost : public OperatorCost { const int32_t& stage_id) const override; double GetBackwardCommCost(const std::vector& inputs, const std::vector& outputs, const int32_t& stage_id) const override; - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); - } - double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; - double GetBackwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); + } + double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; + double GetBackwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; }; using TmpIdentityCostPtr = std::shared_ptr; @@ -199,14 +199,14 @@ class BatchParallelCost : public OperatorCost { const int32_t&) const override { return 0.0; } - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); } - double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; - double GetBackwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; + double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; + double GetBackwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; }; using BatchParallelCostPtr = std::shared_ptr; @@ -227,16 +227,16 @@ class VirtualDatasetCost : public OperatorCost { const int32_t&) const override { return 0.0; } - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); } - double GetForwardMemoryCost(const std::vector&, const std::vector&, - const int32_t&) const override { + double GetForwardComputationCost(const std::vector&, const std::vector&, + const int32_t&) const override { return 0.0; } - double GetBackwardMemoryCost(const std::vector&, const std::vector&, - const int32_t&) const override { + double GetBackwardComputationCost(const std::vector&, const std::vector&, + const int32_t&) const override { return 0.0; } }; @@ -259,18 +259,18 @@ class GeneratorBaseCost : public OperatorCost { const int32_t&) const override { return 0.0; } - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); } // Inputs vector is empty for generator ops. - double GetForwardMemoryCost(const std::vector&, const std::vector&, - const int32_t&) const override { + double GetForwardComputationCost(const std::vector&, const std::vector&, + const int32_t&) const override { return 0.0; } // Generator ops don't have backward steps. - double GetBackwardMemoryCost(const std::vector&, const std::vector&, - const int32_t&) const override { + double GetBackwardComputationCost(const std::vector&, const std::vector&, + const int32_t&) const override { return 0.0; } }; @@ -292,14 +292,14 @@ class PReLUCost : public OperatorCost { const int32_t& stage_id) const override; // per device computation cost - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); - } - double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; - double GetBackwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); + } + double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; + double GetBackwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; }; using PReLUCostPtr = std::shared_ptr; @@ -319,14 +319,14 @@ class OneHotCost : public OperatorCost { const int32_t& stage_id) const override; // per device computation cost - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); - } - double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; - double GetBackwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); + } + double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; + double GetBackwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; }; using OneHotCostPtr = std::shared_ptr; @@ -346,14 +346,14 @@ class SoftmaxCrossEntropyWithLogitsCost : public OperatorCost { const int32_t& stage_id) const override; // per device computation cost - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); - } - double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; - double GetBackwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); + } + double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; + double GetBackwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; }; using SoftmaxCrossEntropyWithLogitsCostPtr = std::shared_ptr; @@ -376,16 +376,16 @@ class ReshapeCost : public OperatorCost { const int32_t& stage_id) const override; // per device computation cost - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); } - double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; + double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; - double GetBackwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; + double GetBackwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; }; using ReshapeCostPtr = std::shared_ptr; @@ -405,14 +405,14 @@ class ArithmeticCost : public OperatorCost { double GetBackwardCommCost(const std::vector&, const std::vector&, const int32_t&) const override; - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); } - double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; - double GetBackwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; + double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; + double GetBackwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; }; using ArithmeticCostPtr = std::shared_ptr; @@ -431,14 +431,14 @@ class L2NormalizeCost : public OperatorCost { } double GetBackwardCommCost(const std::vector& inputs, const std::vector& outputs, const int32_t& stage_id) const override; - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); - } - double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; - double GetBackwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); + } + double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; + double GetBackwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; }; using L2NormalizeCostPtr = std::shared_ptr; @@ -455,14 +455,14 @@ class ReduceMethodCost : public OperatorCost { const int32_t& stage_id) const override; double GetBackwardCommCost(const std::vector& inputs, const std::vector& outputs, const int32_t& stage_id) const override; - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id); - } - double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; - double GetBackwardMemoryCost(const std::vector&, const std::vector&, - const int32_t&) const override { + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id); + } + double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; + double GetBackwardComputationCost(const std::vector&, const std::vector&, + const int32_t&) const override { return 0.0; } void set_cross_batch(bool cb) { cross_batch_ = cb; } @@ -477,8 +477,8 @@ class ReduceMeanCost : public ReduceMethodCost { ReduceMeanCost() = default; ~ReduceMeanCost() override = default; - double GetForwardMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; + double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override; }; using ReduceMeanCostPtr = std::shared_ptr; @@ -499,18 +499,18 @@ class GetNextCost : public OperatorCost { const int32_t&) const override { return 0.0; } - double GetMemoryCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardMemoryCost(inputs, outputs, stage_id) + GetBackwardMemoryCost(inputs, outputs, stage_id); + double GetComputationCost(const std::vector& inputs, const std::vector& outputs, + const int32_t& stage_id) const override { + return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); } // Inputs vector is empty for generator ops. - double GetForwardMemoryCost(const std::vector&, const std::vector&, - const int32_t&) const override { + double GetForwardComputationCost(const std::vector&, const std::vector&, + const int32_t&) const override { return 0.0; } // Generator ops don't have backward steps. - double GetBackwardMemoryCost(const std::vector&, const std::vector&, - const int32_t&) const override { + double GetBackwardComputationCost(const std::vector&, const std::vector&, + const int32_t&) const override { return 0.0; } }; diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc index ad6409be0a..2b02dc100d 100644 --- a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc @@ -592,10 +592,10 @@ Status MatMulBase::SetCostUnderStrategy(const mindspore::parallel::StrategyPtr& int32_t stage_id = strategy->GetInputStage(); // Here, we use the origin outputs_, because we only use the slice size of the output tensor. // It does not matter whether the output tensor is transposed or not. - double memory_cost = - matmulcost_ptr->GetForwardMemoryCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id); + double computation_cost = + matmulcost_ptr->GetForwardComputationCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id); double communication_cost = matmulcost_ptr->GetCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id); - std::shared_ptr result = std::make_shared(memory_cost, communication_cost); + std::shared_ptr result = std::make_shared(computation_cost, communication_cost); result->communication_without_parameter_ = matmulcost_ptr->GetForwardCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id); result->communication_with_partial_para_ = @@ -604,7 +604,7 @@ Status MatMulBase::SetCostUnderStrategy(const mindspore::parallel::StrategyPtr& // Breaking ties for preferring data parallelization BreakingTiesForPerferringDataParallel(strategy, result); - MS_LOG(DEBUG) << name_ << " : memory_cost: " << result->memory_cost_ + MS_LOG(DEBUG) << name_ << " : computation_cost: " << result->computation_cost_ << ", communication_cost: " << result->communication_cost_ << ", communication_without_parameter_: " << result->communication_without_parameter_ << ", communication_with_partial_para_: " << result->communication_with_partial_para_; diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/parallel/ops_info/operator_info.cc index 8b96425bf7..11c518d844 100644 --- a/mindspore/ccsrc/parallel/ops_info/operator_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/operator_info.cc @@ -1034,9 +1034,10 @@ Status OperatorInfo::SetCostUnderStrategyBase(const StrategyPtr& strategy) { return FAILED; } int32_t stage_id = strategy->GetInputStage(); - double memory_cost = GetOperatorCost()->GetForwardMemoryCost(inputs_tensor_info_, outputs_tensor_info_, stage_id); + double computation_cost = + GetOperatorCost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, stage_id); double communication_cost = GetOperatorCost()->GetCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id); - std::shared_ptr result = std::make_shared(memory_cost, communication_cost); + std::shared_ptr result = std::make_shared(computation_cost, communication_cost); result->communication_without_parameter_ = GetOperatorCost()->GetForwardCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id); result->communication_with_partial_para_ = @@ -1056,22 +1057,6 @@ Status OperatorInfo::SetCostUnderStrategyBase(const StrategyPtr& strategy) { return SUCCESS; } -Status OperatorInfo::CorrectStrategyCostForMultiOutputUse(size_t input_index) { - for (auto& swc : strategy_cost_) { - double parameter_memory_cost = ListProduct(swc->inputs_ptr[input_index].slice_shape()) * - static_cast(GetOperatorCost()->inputs_type_lengths()[input_index]); - // remove the parameter memory cost - swc->cost_list[0]->memory_cost_ -= parameter_memory_cost; - if (swc->cost_list[0]->memory_cost_ < -1) { - MS_LOG(ERROR) << "The memory cost after correction is " << swc->cost_list[0]->memory_cost_ - << ", the parameter_memory_cost is " << parameter_memory_cost; - return FAILED; - } - } - corrected_input_indices_.push_back(input_index); - return SUCCESS; -} - int OperatorInfo::ComputeOpAndPrevEdgeParameterInvolved() { if (is_output_parameter_involve_ != -1) { return is_output_parameter_involve_; @@ -1217,7 +1202,7 @@ void OperatorInfo::BreakingTiesForPerferringDataParallel(const StrategyPtr& stra CheckGlobalDeviceManager(); auto total_device_num = g_device_manager->GetDeviceListByStageId(stra->GetInputStage()).size(); if (IntToSize(stra->GetInputDim()[0][0]) == total_device_num) { - cost->memory_cost_ -= 1.0; + cost->computation_cost_ -= 1.0; cost->communication_cost_ -= 1.0; cost->communication_with_partial_para_ -= 1.0; cost->communication_without_parameter_ -= 1.0; @@ -1226,7 +1211,7 @@ void OperatorInfo::BreakingTiesForPerferringDataParallel(const StrategyPtr& stra } double OperatorInfo::GetForwardMemoryCostFromCNode() { - return GetOperatorCost()->GetForwardMemoryCost(inputs_tensor_info_, outputs_tensor_info_, 0); + return GetOperatorCost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, 0); } } // namespace parallel diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.h b/mindspore/ccsrc/parallel/ops_info/operator_info.h index cc70f1b870..e7b8af0a7e 100644 --- a/mindspore/ccsrc/parallel/ops_info/operator_info.h +++ b/mindspore/ccsrc/parallel/ops_info/operator_info.h @@ -87,13 +87,9 @@ class OperatorInfo { // is checked Status SetCostUnderStrategyBase(const StrategyPtr& strategy); std::vector> GetStrategyCost() { return strategy_cost_; } - // In the case of a Parameter (or a output) being used by multiple operators, the memory cost induced by - // the parameter (or a output) should be calculated only once. This method is used to - // remove this part from the 'strategy_cost_'. - Status CorrectStrategyCostForMultiOutputUse(size_t input_index); // When the input of a operator contains WEIGHT or a output from other operators involving WEIGHT, then these input // should stay in memory until it is used in the backward phase, which is kept in memory at the end of forward phase. - Status CorrectStrategyCostForMemoryReuse() const { return SUCCESS; } + Status CalculateMemoryCost() const { return SUCCESS; } int ComputeOpAndPrevEdgeParameterInvolved(); ForwardOp forward_op() const { return forward_op_; } diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc index 50e6a1e84e..d7d48c35bb 100644 --- a/mindspore/ccsrc/parallel/step_auto_parallel.cc +++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc @@ -387,7 +387,7 @@ OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr & operator_info->set_outputs_dtype(cnode->Type()); operator_info->set_cnode(cnode); // If no strategy has been configured for this operator, then candidate strategies are generated for - // auto-strategy searchingm if this primitive is Cast, we ignore the user-specified strategy + // auto-strategy searching; if this primitive is CAST, we ignore the user-specified strategy if (!StrategyFound(attrs) || prim->name() == CAST) { // Compute split_flag_list_, indicating which input has batch dimension. This is ONLY used for preparation for // BatchParallelInfo operator @@ -600,13 +600,7 @@ void ConstructCostGraphEdges(const std::vector &all_nodes) { } MS_LOG(INFO) << "Successfully created " << edge_count << " edges for: " << cnode->operator_info()->name(); } - // For the case of a output being used by multiple subsequent operators, the output induced memory cost should be - // calculated only once. This method is for correct the operators' memory cost calculation. - if (entire_costgraph->CorrectOpsStrategyCostForMultiOutputUse() != SUCCESS) { - MS_LOG(EXCEPTION) << "Correcting strategy_cost_ for operators failed."; - } else { - MS_LOG(INFO) << "Correcting strategy_cost_ for operators succeeded."; - } + MS_LOG(INFO) << "Constructing edges for cost graph ends."; } @@ -803,14 +797,6 @@ void AugmentCostGraph(const std::vector &all_nodes) { std::shared_ptr edge_ptr = std::make_shared( edge_name, tmp_identity_ptr, target_cnode->operator_info(), 0, input_index - 1, false, true); - // Correct the memory calculation for a parameter being used by multiple operators. The parameter is calculated - // only once - if (target_cnode->operator_info()->CorrectStrategyCostForMultiOutputUse(IntToSize(input_index - 1)) != SUCCESS) { - MS_LOG(EXCEPTION) << "Correcting strategy_cost_ failed : " << prim->name(); - } else { - MS_LOG(INFO) << "Correcting strategy_cost_ succeeded. " << prim->name(); - } - if (edge_ptr->InitEdgeCost() != SUCCESS) { MS_LOG(EXCEPTION) << "Edge cost initialization failed"; } @@ -840,7 +826,7 @@ Status ParallelStrategySearch(const std::vector &all_nodes, const Fu // taking care for the case of a single Parameter being used by multiple operators. Create a TmpIdentity // operator for this Parameter, and add an edge for the use of this Parameter by each // subsequent operator; - // Step 3.1: Correct the memory calculation for memory reuse + // Step 3.1: Calculate memory usage // Step 4: Run the Dynamic Programming algorithm: // in this process, cost is calculated based on not only the operators, but also the edges. Here, the edge // cost is caused by the redistribution of a operator's output tensor layout to the next operator's input @@ -867,14 +853,14 @@ Status ParallelStrategySearch(const std::vector &all_nodes, const Fu MS_LOG(INFO) << "After the augmenting procedure, there are " << entire_costgraph->GetOperators().size() << " operators, and " << entire_costgraph->GetNumPairs() << " edges."; - // Step 3.1: Correcting calculation for memory reuse + // Step 3.1: Calculate the memory usage if (entire_costgraph->ComputeOpsAndEdgesParameterInvolved() == SUCCESS) { - // Correcting operators' memory usage - if (entire_costgraph->CorrectOpsStrategyCostForMemoryReuse() != SUCCESS) { + // Calculate operators' memory usage + if (entire_costgraph->CalculateOpsMemoryCost() != SUCCESS) { MS_LOG(EXCEPTION) << "Correcting operators' cost for memory reuse failed."; } - // Correcting edges' memory usage - if (entire_costgraph->CorrectEdgesStrategyCostForMemoryReuse() != SUCCESS) { + // Calculate edges' memory usage + if (entire_costgraph->CalculateEdgesMemoryCost() != SUCCESS) { MS_LOG(EXCEPTION) << "Correcting edges' cost for memory reuse failed."; } } else { diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc index 93bda5da81..55e6a300e0 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc +++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.cc @@ -144,7 +144,7 @@ Status TensorRedistribution::ComputeCost() { MS_LOG(ERROR) << "Failure: InferTensorRedistribution failed"; return Status::FAILED; } - // Compute redistribution communication cost and memory cost + // Compute redistribution communication cost and computation cost for (auto& op_cost : operator_list_) { OperatorR op = op_cost.first; Shape slice_shape = op_cost.second; @@ -154,14 +154,14 @@ Status TensorRedistribution::ComputeCost() { if (str == PERMUTE_BY_AXIS) { // The shape does not change after PermuteByAxis operation. // communication cost = all_to_all + all_to_all = 2 * slice_shape - // memory cost = slice_shape + // computation cost = slice_shape forward_comm_cost_ += prod; backward_comm_cost_ += prod; comm_cost_ += 2.0 * prod; - mem_cost_ += prod; + computation_cost_ += prod; } else if (str == CONCAT_BY_AXIS) { // communication cost = all_gather + reduce_scatter = before_slice_shape + after_slice_shape - // memory cost = before_slice_shape + // computation cost = before_slice_shape if (op.second.size() < 3) { MS_LOG(ERROR) << "op.second size should not be less than 3!"; return Status::FAILED; @@ -173,22 +173,22 @@ Status TensorRedistribution::ComputeCost() { comm_cost_ += prod * (dev_num + 1.0); int32_t concat_dim = op.second[0]; if (concat_dim == 0) { - // memory cost = all_gather - mem_cost_ += prod; + // computation cost = all_gather + computation_cost_ += prod; } else { - // memory cost = all_gather + split + concat - mem_cost_ += (prod + prod * dev_num + prod * dev_num); + // computation cost = all_gather + split + concat + computation_cost_ += (prod + prod * dev_num + prod * dev_num); } } else { - // There is only memory cost in SplitByAxis. - // memory cost = before_slice_shape - mem_cost_ += prod; + // There is only computation cost in SplitByAxis. + // computation cost = before_slice_shape + computation_cost_ += prod; } } if (reshape_flag()) { Shape prev_slice_shape = from_.slice_shape().array(); double prev_prod = std::accumulate(prev_slice_shape.begin(), prev_slice_shape.end(), 1, std::multiplies()); - mem_cost_ += 2.0 * prev_prod; + computation_cost_ += 2.0 * prev_prod; } return Status::SUCCESS; } diff --git a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h index 38fb5959ad..e933b9b8eb 100644 --- a/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h +++ b/mindspore/ccsrc/parallel/tensor_layout/tensor_redistribution.h @@ -41,7 +41,7 @@ class TensorRedistribution { comm_cost_(0.0), forward_comm_cost_(0.0), backward_comm_cost_(0.0), - mem_cost_(0.0), + computation_cost_(0.0), construct_op_flag_(construct_op_flag), keep_reshape_(keep_reshape) {} Status Init(const TensorLayout& from, const TensorLayout& to, const RankList& dev_list); @@ -51,7 +51,7 @@ class TensorRedistribution { bool reshape_flag() const { return reshape_flag_; } Status ComputeCost(); double comm_cost() const { return comm_cost_; } - double mem_cost() const { return mem_cost_; } + double computation_cost() const { return computation_cost_; } double forward_comm_cost() const { return forward_comm_cost_; } double backward_comm_cost() const { return backward_comm_cost_; } @@ -66,10 +66,13 @@ class TensorRedistribution { RankList dev_list_; OperatorList operator_list_; bool reshape_flag_; + // communication cost double comm_cost_; + // forward communication cost double forward_comm_cost_; + // backward communication cost double backward_comm_cost_; - double mem_cost_; + double computation_cost_; bool construct_op_flag_; bool keep_reshape_; }; diff --git a/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc b/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc index 83a9eceacc..415a1fdd55 100644 --- a/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc +++ b/tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc @@ -322,8 +322,8 @@ TEST_F(TestCostGraph, test_SelectCostListWithMinTrainingTimeMultiple) { auto ret_list = entire_cost_graph.SelectCostListWithMinTrainingTimeMultiple(all_list, memory); ASSERT_EQ(ret_list.size(), 2); - ASSERT_DOUBLE_EQ(ret_list[0]->memory_cost_, 10); - ASSERT_DOUBLE_EQ(ret_list[1]->memory_cost_, 1010); + ASSERT_DOUBLE_EQ(ret_list[0]->computation_cost_, 10); + ASSERT_DOUBLE_EQ(ret_list[1]->computation_cost_, 1010); } TEST_F(TestCostGraph, test_CheckOpElimination) { diff --git a/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc b/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc index 3bd65c049c..919c5b43ec 100644 --- a/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc +++ b/tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc @@ -76,8 +76,8 @@ TEST_F(TestMatMulCost, test_CostGeneration) { mmcost_.SetInputAndOutputTypeLength(inputs_length, outputs_length); mmcost_.GetForwardCommCost(inputs, outputs, 0); mmcost_.GetBackwardCommCost(inputs, outputs, 0); - mmcost_.GetForwardMemoryCost(inputs, outputs, 0); - mmcost_.GetBackwardMemoryCost(inputs, outputs, 0); + mmcost_.GetForwardComputationCost(inputs, outputs, 0); + mmcost_.GetForwardComputationCost(inputs, outputs, 0); } class TestActivationCost : public UT::Common { @@ -128,8 +128,8 @@ TEST_F(TestActivationCost, test_CostGeneration) { std::vector inputs_length = {4, 4}; std::vector outputs_length = {4}; ac_cost_.SetInputAndOutputTypeLength(inputs_length, outputs_length); - ac_cost_.GetForwardMemoryCost(inputs, outputs, 0); - ac_cost_.GetBackwardMemoryCost(inputs, outputs, 0); + ac_cost_.GetForwardComputationCost(inputs, outputs, 0); + ac_cost_.GetBackwardComputationCost(inputs, outputs, 0); } class TestPReLUCost : public UT::Common { @@ -184,8 +184,8 @@ TEST_F(TestPReLUCost, test_CostGeneration) { prelu_cost_.SetInputAndOutputTypeLength(inputs_length, outputs_length); double BCC, FMC, GMC; BCC = prelu_cost_.GetBackwardCommCost(inputs, outputs, 0); - FMC = prelu_cost_.GetForwardMemoryCost(inputs, outputs, 0); - GMC = prelu_cost_.GetBackwardMemoryCost(inputs, outputs, 0); + FMC = prelu_cost_.GetForwardComputationCost(inputs, outputs, 0); + GMC = prelu_cost_.GetBackwardComputationCost(inputs, outputs, 0); ASSERT_EQ(BCC, 32 * 4); ASSERT_EQ(FMC, 8 * 32 * 8 * 8 * 4 + 32 * 4); ASSERT_EQ(GMC, 128); diff --git a/tests/ut/cpp/parallel/ops_info/activation_test.cc b/tests/ut/cpp/parallel/ops_info/activation_test.cc index 149aa9d5af..5d18c5372f 100644 --- a/tests/ut/cpp/parallel/ops_info/activation_test.cc +++ b/tests/ut/cpp/parallel/ops_info/activation_test.cc @@ -84,8 +84,8 @@ TEST_F(TestActivation, test_activation_strategies) { act_ptr_->InitForCostModel(sp); std::vector inputs_info = act_ptr_->inputs_tensor_info(); std::vector outputs_info = act_ptr_->outputs_tensor_info(); - ASSERT_DOUBLE_EQ(act_ptr_->GetOperatorCost()->GetMemoryCost(inputs_info, outputs_info, sp->GetInputStage()), - cost.memory_cost_); + ASSERT_DOUBLE_EQ(act_ptr_->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()), + cost.computation_cost_); ASSERT_DOUBLE_EQ(act_ptr_->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()), cost.communication_cost_); } @@ -109,8 +109,8 @@ TEST_F(TestActivation, test_softmax_strategies) { soft_ptr_->InitForCostModel(sp); std::vector inputs_info = soft_ptr_->inputs_tensor_info(); std::vector outputs_info = soft_ptr_->outputs_tensor_info(); - ASSERT_DOUBLE_EQ(soft_ptr_->GetOperatorCost()->GetMemoryCost(inputs_info, outputs_info, sp->GetInputStage()), - cost.memory_cost_); + ASSERT_DOUBLE_EQ(soft_ptr_->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()), + cost.computation_cost_); ASSERT_DOUBLE_EQ(soft_ptr_->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()), cost.communication_cost_); } diff --git a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc index 978b792a0c..99ca9f8e0e 100644 --- a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc @@ -569,8 +569,8 @@ TEST_F(TestMatmulInfo, test_GenerateStrategies1) { matmul1->InitForCostModel(sp); std::vector inputs_info = matmul1->inputs_tensor_info(); std::vector outputs_info = matmul1->outputs_tensor_info(); - ASSERT_DOUBLE_EQ(matmul1->GetOperatorCost()->GetMemoryCost(inputs_info, outputs_info, sp->GetInputStage()), - cost.memory_cost_); + ASSERT_DOUBLE_EQ(matmul1->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()), + cost.computation_cost_); break; } } @@ -599,8 +599,8 @@ TEST_F(TestMatmulInfo, test_GenerateStrategies2) { TensorInfo replica_input1_info(tly, input1_shape, input1_slice_shape); replica_inputs_info.push_back(replica_input1_info); - ASSERT_DOUBLE_EQ(matmul3->GetOperatorCost()->GetMemoryCost(replica_inputs_info, outputs_info, sp->GetInputStage()), - cost.memory_cost_); + ASSERT_DOUBLE_EQ(matmul3->GetOperatorCost()->GetComputationCost(replica_inputs_info, outputs_info, sp->GetInputStage()), + cost.computation_cost_); break; } } diff --git a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc index e7736a4b3e..6cb9739b1c 100644 --- a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc @@ -188,8 +188,8 @@ TEST_F(TestTensorAddInfo, GenerateStrategies) { tensor_add->InitForCostModel(sp); std::vector inputs_info = tensor_add->inputs_tensor_info(); std::vector outputs_info = tensor_add->outputs_tensor_info(); - double memory_cost0 = tensor_add->GetOperatorCost()->GetMemoryCost(inputs_info, outputs_info, sp->GetInputStage()); - double memory_cost1 = cost.memory_cost_; + double memory_cost0 = tensor_add->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()); + double memory_cost1 = cost.computation_cost_; bool memory = memory_cost0 - memory_cost1 <= 1.0; double comm_cost0 = tensor_add->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()); @@ -210,8 +210,8 @@ TEST_F(TestTensorAddInfo, GenerateStrategies1) { tensor_add1->InitForCostModel(sp); std::vector inputs_info = tensor_add1->inputs_tensor_info(); std::vector outputs_info = tensor_add1->outputs_tensor_info(); - double memory_cost0 = tensor_add1->GetOperatorCost()->GetMemoryCost(inputs_info, outputs_info, sp->GetInputStage()); - double memory_cost1 = cost.memory_cost_; + double memory_cost0 = tensor_add1->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()); + double memory_cost1 = cost.computation_cost_; bool memory = memory_cost0 - memory_cost1 <= 1.0; double comm_cost0 = tensor_add1->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()); diff --git a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc index ce1238baeb..043746498f 100644 --- a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc +++ b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc @@ -145,8 +145,8 @@ TEST_F(TestTmpIdentityInfo, test_generate_strategies) { identity_ptr->Init(sp); std::vector inputs_info = identity_ptr->inputs_tensor_info(); std::vector outputs_info = identity_ptr->outputs_tensor_info(); - ASSERT_DOUBLE_EQ(identity_ptr->GetOperatorCost()->GetMemoryCost(inputs_info, outputs_info, sp->GetInputStage()), - cost.memory_cost_); + ASSERT_DOUBLE_EQ(identity_ptr->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()), + cost.computation_cost_); ASSERT_DOUBLE_EQ(identity_ptr->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()), cost.communication_cost_); } From 89f0b3b1bb7af5e638dd9e76cd444edec4260856 Mon Sep 17 00:00:00 2001 From: jojobugfree Date: Tue, 7 Apr 2020 19:59:36 +0800 Subject: [PATCH 142/227] profiling feature enhancement --- .../device/ascend/ascend_stream_assign.cc | 2 +- .../ascend/profiling/profiling_manager.h | 4 - .../ascend/profiling/profiling_utils.cc | 229 ++++++++---------- .../device/ascend/profiling/profiling_utils.h | 93 ++++--- mindspore/ccsrc/device/kernel_adjust.cc | 31 +-- mindspore/ccsrc/device/kernel_adjust.h | 6 +- .../ascend/ascend_backend_optimization.cc | 2 +- mindspore/ccsrc/session/ascend_session.cc | 2 +- .../tasksink/ascend_stream_assign_stub.cc | 2 +- 9 files changed, 190 insertions(+), 181 deletions(-) diff --git a/mindspore/ccsrc/device/ascend/ascend_stream_assign.cc b/mindspore/ccsrc/device/ascend/ascend_stream_assign.cc index 4f16c596c7..8c4d1f4a8f 100644 --- a/mindspore/ccsrc/device/ascend/ascend_stream_assign.cc +++ b/mindspore/ccsrc/device/ascend/ascend_stream_assign.cc @@ -702,7 +702,7 @@ void AscendStreamAssign::PrintGraphExeOrders(const shared_ptr(primitive->GetAttr(kAttrEventId)) << "]"; } else { - MS_LOG(INFO) << "node name[" << AnfAlgo::GetCNodeName(cur_cnode_ptr) << "], logic id[" + MS_LOG(INFO) << "node name[" << cur_cnode_ptr->fullname_with_scope() << "], logic id[" << AnfAlgo::GetStreamDistinctionLabel(cur_cnode_ptr.get()) << "], stream id[" << AnfAlgo::GetStreamId(cur_cnode_ptr) << "]"; } diff --git a/mindspore/ccsrc/device/ascend/profiling/profiling_manager.h b/mindspore/ccsrc/device/ascend/profiling/profiling_manager.h index de8f6a7d0a..b826c4cf36 100644 --- a/mindspore/ccsrc/device/ascend/profiling/profiling_manager.h +++ b/mindspore/ccsrc/device/ascend/profiling/profiling_manager.h @@ -29,10 +29,6 @@ namespace ascend { // PROFILING_CUSTOM_LOGID_START 3 const uint64_t kProfilingFpStartLogId = 1; const uint64_t kProfilingBpEndLogId = 2; -const uint64_t kProfilingAllReduce1Start = 3; -const uint64_t kProfilingAllReduce1End = 4; -const uint64_t kProfilingAllReduce2Start = 5; -const uint64_t kProfilingAllReduce2End = 6; const uint64_t kProfilingIterEndLogId = 255; class ProfilingEngineImpl; diff --git a/mindspore/ccsrc/device/ascend/profiling/profiling_utils.cc b/mindspore/ccsrc/device/ascend/profiling/profiling_utils.cc index 0d7088300e..aa71aa0566 100644 --- a/mindspore/ccsrc/device/ascend/profiling/profiling_utils.cc +++ b/mindspore/ccsrc/device/ascend/profiling/profiling_utils.cc @@ -14,10 +14,8 @@ * limitations under the License. */ -#include "device/ascend/profiling/profiling_utils.h" - #include - +#include "device/ascend/profiling/profiling_utils.h" #include "kernel/kernel.h" #include "device/ascend/profiling/profiling_manager.h" #include "session/anf_runtime_algorithm.h" @@ -27,82 +25,61 @@ namespace mindspore { namespace device { namespace ascend { -const char ProfilingUtils::kProfiling[] = "Profiling"; -const char ProfilingUtils::kNotify[] = "notify"; -const char ProfilingUtils::kProfilerTraceId[] = "profiler_trace_id"; -const char ProfilingUtils::kFlags[] = "flags"; +constexpr uint32_t kMaxProfilingNodeNum = 100; +constexpr char kCustomNode[] = "PROFILING_CUSTOM_"; +constexpr char kFpStartNode[] = "PROFILING_FP_START"; +constexpr char kBpEndNode[] = "PROFILING_BP_END"; +constexpr char kIterEndNode[] = "PROFILING_ITER_END"; std::unordered_map> ProfilingUtils::graph_kernel_name_; -bool ProfilingUtils::GetProfilingTraceInfo(const std::shared_ptr &graph_ptr, - ProfilingTraceInfo *profiling_trace_info) { - MS_EXCEPTION_IF_NULL(profiling_trace_info); - MS_EXCEPTION_IF_NULL(graph_ptr); - bool find_begin = false; - bool first_allreduce = true; - for (const auto &anf_node : graph_ptr->execution_order()) { - if (anf_node->isa()) { - const std::string kernel_name = AnfAlgo::GetCNodeName(anf_node); - if ((kernel_name == "Cast" || kernel_name == "Four2Five") && !find_begin) { - profiling_trace_info->profiling_trace_begin = anf_node->fullname_with_scope(); - find_begin = true; - } - if (kernel_name == "Conv2DBackpropFilter") { - profiling_trace_info->profiling_trace_bp_end = anf_node->fullname_with_scope(); - } - if (kernel_name == kFusedMulApplyMomentumOpName || kernel_name == kApplyMomentumOpName) { - profiling_trace_info->profiling_trace_netoutput = anf_node->fullname_with_scope(); - } - if (kernel_name == kAllReduceOpName) { - if (first_allreduce) { - profiling_trace_info->profiling_allreduce1_start = anf_node->fullname_with_scope(); - profiling_trace_info->profiling_allreduce1_end = anf_node->fullname_with_scope(); - first_allreduce = false; - } else { - profiling_trace_info->profiling_allreduce2_start = anf_node->fullname_with_scope(); - profiling_trace_info->profiling_allreduce2_end = anf_node->fullname_with_scope(); - } - } +uint32_t ProfilingUtils::custom_node_index_ = 1; + +ProfilingTraceInfo ProfilingUtils::GetProfilingTraceFromEnv(NotNull graph_ptr) { + MS_LOG(INFO) << "get env start"; + custom_node_index_ = 1; + auto &cnode_exec_order = graph_ptr->execution_order(); + ProfilingTraceInfo profiling_trace; + profiling_trace.trace_begin = GetTraceBegin(cnode_exec_order); + profiling_trace.trace_bp_end = GetTraceBpEnd(); + profiling_trace.trace_netoutput = GetTraceNetoutput(cnode_exec_order); + + MS_LOG(INFO) << "[profiling] trace_begin:" << profiling_trace.trace_begin + << " trace_bp_end:" << profiling_trace.trace_bp_end + << " trace_netoutput:" << profiling_trace.trace_netoutput; + + for (uint32_t i = 1; i <= kMaxProfilingNodeNum; ++i) { + std::string env_str = std::string(kCustomNode) + std::to_string(i); + const char *node_full_name = std::getenv(env_str.c_str()); + if (node_full_name == nullptr) { + break; } + MS_LOG(INFO) << "Get profiling node:" << node_full_name; + profiling_trace.trace_custom_node.insert(node_full_name); } - MS_LOG(INFO) << "[profiling]begin:" << profiling_trace_info->profiling_trace_begin - << ", net_output:" << profiling_trace_info->profiling_trace_netoutput - << ", end:" << profiling_trace_info->profiling_trace_bp_end - << ", allreduce1:" << profiling_trace_info->profiling_allreduce1_start - << ", allreduce2:" << profiling_trace_info->profiling_allreduce2_start; - return profiling_trace_info->IsValid(); + MS_LOG(INFO) << "get env end"; + return profiling_trace; } -bool ProfilingUtils::GetNetOutput(AnfNodePtr anf_node, std::string *profiling_trace_net_output) { - MS_EXCEPTION_IF_NULL(anf_node); - MS_EXCEPTION_IF_NULL(profiling_trace_net_output); - MS_LOG(INFO) << "[profiling]Anf node's full name with scope:" << anf_node->fullname_with_scope(); - if (!profiling_trace_net_output->empty()) { - MS_LOG(INFO) << "[profiling]Has got the net_output:" << profiling_trace_net_output->c_str(); - return true; - } - - if (AnfAlgo::IsRealKernel(anf_node)) { - *profiling_trace_net_output = anf_node->fullname_with_scope(); - return true; - } +std::string ProfilingUtils::GetTraceBegin(const std::vector &cnode_exec_order) { + const char *trace_begin = std::getenv(kFpStartNode); + auto &first_cnode = cnode_exec_order.front(); + MS_EXCEPTION_IF_NULL(first_cnode); + return trace_begin == nullptr ? first_cnode->fullname_with_scope() : std::string(trace_begin); +} - auto cnode = anf_node->cast(); - if (cnode == nullptr) { - MS_LOG(ERROR) << "[profiling]Anf node should be a CNode"; - return false; - } +std::string ProfilingUtils::GetTraceBpEnd() { + const char *trace_bp_end = std::getenv(kBpEndNode); + return trace_bp_end == nullptr ? "" : std::string(trace_bp_end); +} - auto inputs = cnode->inputs(); - auto input_size = inputs.size(); - if (input_size < 2) { - MS_LOG(ERROR) << "[profiling]Anf node' input size(" << input_size << ") < 2, don't support get apply kernel node."; - return false; - } - return GetNetOutput(inputs[1], profiling_trace_net_output); +std::string ProfilingUtils::GetTraceNetoutput(const std::vector &cnode_exec_order) { + const char *trace_netoutput = std::getenv(kIterEndNode); + auto &last_cnode = cnode_exec_order.back(); + MS_EXCEPTION_IF_NULL(last_cnode); + return trace_netoutput == nullptr ? last_cnode->fullname_with_scope() : std::string(trace_netoutput); } -CNodePtr ProfilingUtils::CreateProfilingCNode(const std::shared_ptr &graph_ptr, bool notify, - uint64_t profiler_trace_id, uint32_t flags) { - MS_EXCEPTION_IF_NULL(graph_ptr); +NotNull ProfilingUtils::CreateProfilingCNode(const ProfilingContent &profiling_content, + NotNull graph_ptr) { kernel::KernelBuildInfo::KernelBuildInfoBuilder selected_kernel_builder; selected_kernel_builder.SetInputsFormat({kOpFormat_DEFAULT, kOpFormat_DEFAULT}); selected_kernel_builder.SetInputsDeviceType({TypeId::kNumberTypeInt32, TypeId::kNumberTypeInt32}); @@ -118,75 +95,79 @@ CNodePtr ProfilingUtils::CreateProfilingCNode(const std::shared_ptrset_abstract(type_none_abstract); // set attr - ValuePtr notify_value = MakeValue(notify); - ValuePtr trace_id_value = MakeValue(profiler_trace_id); - ValuePtr flags_value = MakeValue(flags); + ValuePtr notify_value = MakeValue(profiling_content.notify); + ValuePtr trace_id_value = MakeValue(profiling_content.profiler_trace_id); + ValuePtr flags_value = MakeValue(profiling_content.flags); AnfAlgo::SetNodeAttr(ProfilingUtils::kNotify, notify_value, cnode_ptr); AnfAlgo::SetNodeAttr(ProfilingUtils::kProfilerTraceId, trace_id_value, cnode_ptr); AnfAlgo::SetNodeAttr(ProfilingUtils::kFlags, flags_value, cnode_ptr); - return cnode_ptr; + return NOT_NULL(cnode_ptr); } -void ProfilingUtils::ProfilingTraceFpStart(const std::shared_ptr &graph_ptr, - const mindspore::AnfNodePtr &anf_node, - const mindspore::device::ascend::ProfilingTraceInfo &profiling_trace_info, - std::vector *kernel_list) { - if (profiling_trace_info.IsValid() && profiling_trace_info.profiling_trace_begin == anf_node->fullname_with_scope()) { - if (graph_ptr == nullptr || kernel_list == nullptr || anf_node == nullptr) { - MS_LOG(ERROR) << "[profiling]input param invalid"; - return; - } +void ProfilingUtils::ProfilingTraceFpStart(const mindspore::AnfNodePtr &anf_node, + const ProfilingTraceInfo &profiling_trace_info, + NotNull graph_ptr, + NotNull *> kernel_list) { + if (profiling_trace_info.trace_begin == anf_node->fullname_with_scope()) { auto job_id = ProfilingManager::GetInstance().GetJobId(); - // job task info - CNodePtr job_kernel_ptr = CreateProfilingCNode(graph_ptr, false, job_id, 0); - AnfAlgo::SetStreamDistinctionLabel(AnfAlgo::GetStreamDistinctionLabel(anf_node.get()), job_kernel_ptr.get()); - AnfAlgo::SetStreamId(AnfAlgo::GetStreamId(anf_node), job_kernel_ptr.get()); - // fp task info - CNodePtr start_kernel_ptr = CreateProfilingCNode(graph_ptr, false, kProfilingFpStartLogId, 0); - AnfAlgo::SetStreamDistinctionLabel(AnfAlgo::GetStreamDistinctionLabel(anf_node.get()), start_kernel_ptr.get()); - AnfAlgo::SetStreamId(AnfAlgo::GetStreamId(anf_node), start_kernel_ptr.get()); - kernel_list->emplace_back(job_kernel_ptr); - kernel_list->emplace_back(start_kernel_ptr); + ProfilingContent job_profiling_context = {false, job_id, 0}; + auto job_profiling_node = CreateProfilingCNodeWithStream(anf_node, job_profiling_context, graph_ptr); + kernel_list->emplace_back(job_profiling_node); + + ProfilingContent fp_profiling_content = {false, kProfilingFpStartLogId, 0}; + auto fp_profiling_node = CreateProfilingCNodeWithStream(anf_node, fp_profiling_content, graph_ptr); + kernel_list->emplace_back(fp_profiling_node); } } -void ProfilingUtils::ProfilingAllReduce(const std::shared_ptr &graph_ptr, - const AnfNodePtr &anf_node, int job_id, const std::string &profiling_node_name, - std::vector *kernel_list) { - MS_EXCEPTION_IF_NULL(graph_ptr); +CNodePtr ProfilingUtils::CreateProfilingCNodeWithStream(const mindspore::AnfNodePtr &anf_node, + const ProfilingContent &profiling_content, + NotNull graph_ptr) { + CNodePtr profiling_node = CreateProfilingCNode(profiling_content, graph_ptr); + AnfAlgo::SetStreamDistinctionLabel(AnfAlgo::GetStreamDistinctionLabel(anf_node.get()), profiling_node.get()); + AnfAlgo::SetStreamId(AnfAlgo::GetStreamId(anf_node), profiling_node.get()); + return profiling_node; +} + +void ProfilingUtils::ProfilingCustomOp(const AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info, + NotNull graph_ptr, + NotNull *> kernel_list) { MS_EXCEPTION_IF_NULL(anf_node); - MS_EXCEPTION_IF_NULL(kernel_list); - auto full_scope_name = anf_node->fullname_with_scope(); - if (profiling_node_name == full_scope_name) { - CNodePtr allreduce_kernel_ptr = CreateProfilingCNode(graph_ptr, false, job_id, 0); - AnfAlgo::SetStreamDistinctionLabel(AnfAlgo::GetStreamDistinctionLabel(anf_node.get()), allreduce_kernel_ptr.get()); - AnfAlgo::SetStreamId(AnfAlgo::GetStreamId(anf_node), allreduce_kernel_ptr.get()); - kernel_list->emplace_back(allreduce_kernel_ptr); + auto iter = profiling_trace_info.trace_custom_node.find(anf_node->fullname_with_scope()); + if (iter == profiling_trace_info.trace_custom_node.end()) { + return; } + // custom op profiling job start from 3. + ProfilingContent front_profiling_content = {false, 2 * custom_node_index_ + 1, 0}; + CNodePtr front_node = CreateProfilingCNodeWithStream(anf_node, front_profiling_content, graph_ptr); + kernel_list->insert(kernel_list->end() - 1, front_node); + + ProfilingContent back_profiling_content = {false, 2 * custom_node_index_ + 2, 0}; + CNodePtr back_node = CreateProfilingCNodeWithStream(anf_node, back_profiling_content, graph_ptr); + kernel_list->insert(kernel_list->end(), back_node); + ++custom_node_index_; } -void ProfilingUtils::ProfilingTraceEnd(const std::shared_ptr &graph_ptr, - const mindspore::AnfNodePtr &anf_node, - const mindspore::device::ascend::ProfilingTraceInfo &profiling_trace_info, - std::vector *kernel_list) { - MS_EXCEPTION_IF_NULL(graph_ptr); +void ProfilingUtils::ProfilingTraceBpEnd(const AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info, + NotNull graph_ptr, + NotNull *> kernel_list) { MS_EXCEPTION_IF_NULL(anf_node); - MS_EXCEPTION_IF_NULL(kernel_list); - if (profiling_trace_info.IsValid()) { - auto full_scope_name = anf_node->fullname_with_scope(); - if (profiling_trace_info.profiling_trace_netoutput == full_scope_name) { - CNodePtr bp_kernel_ptr = CreateProfilingCNode(graph_ptr, true, kProfilingIterEndLogId, 0); - AnfAlgo::SetStreamDistinctionLabel(AnfAlgo::GetStreamDistinctionLabel(anf_node.get()), bp_kernel_ptr.get()); - AnfAlgo::SetStreamId(AnfAlgo::GetStreamId(anf_node), bp_kernel_ptr.get()); - kernel_list->emplace_back(bp_kernel_ptr); - } + if (profiling_trace_info.trace_bp_end == anf_node->fullname_with_scope()) { + ProfilingContent bp_end_profiling_content = {false, kProfilingBpEndLogId, 0}; + CNodePtr bp_end_node = CreateProfilingCNodeWithStream(anf_node, bp_end_profiling_content, graph_ptr); + kernel_list->emplace_back(bp_end_node); + } +} - if (profiling_trace_info.profiling_trace_bp_end == full_scope_name) { - CNodePtr end_task_info = CreateProfilingCNode(graph_ptr, false, kProfilingBpEndLogId, 0); - AnfAlgo::SetStreamDistinctionLabel(AnfAlgo::GetStreamDistinctionLabel(anf_node.get()), end_task_info.get()); - AnfAlgo::SetStreamId(AnfAlgo::GetStreamId(anf_node), end_task_info.get()); - kernel_list->emplace_back(end_task_info); - } +void ProfilingUtils::ProfilingTraceEnd(const AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info, + NotNull graph_ptr, + NotNull *> kernel_list) { + MS_EXCEPTION_IF_NULL(anf_node); + auto full_scope_name = anf_node->fullname_with_scope(); + if (profiling_trace_info.trace_netoutput == full_scope_name) { + ProfilingContent bp_end_profiling_content = {true, kProfilingIterEndLogId, 0}; + CNodePtr bp_kernel_ptr = CreateProfilingCNodeWithStream(anf_node, bp_end_profiling_content, graph_ptr); + kernel_list->emplace_back(bp_kernel_ptr); } } diff --git a/mindspore/ccsrc/device/ascend/profiling/profiling_utils.h b/mindspore/ccsrc/device/ascend/profiling/profiling_utils.h index ca0ef6f1f0..c59e856249 100644 --- a/mindspore/ccsrc/device/ascend/profiling/profiling_utils.h +++ b/mindspore/ccsrc/device/ascend/profiling/profiling_utils.h @@ -19,63 +19,102 @@ #include #include #include +#include #include #include "session/kernel_graph.h" +#include "utils/contract.h" namespace mindspore { namespace device { namespace ascend { struct ProfilingTraceInfo { // execute order's first execute op(like: Cast or Four2Five ...), except tdt op(GetNext ...) - std::string profiling_trace_begin; + std::string trace_begin; // get first net_output(apply kernel) from graph outputs: fp ->net_output<- bp - std::string profiling_trace_bp_end; + std::string trace_bp_end; // execute order's end execute (like: Conv2DBackpropFilter) - std::string profiling_trace_netoutput; + std::string trace_netoutput; - std::string profiling_allreduce1_start; - - std::string profiling_allreduce1_end; - - std::string profiling_allreduce2_start; - - std::string profiling_allreduce2_end; + // profiling specific op, such as AllReduce; + std::set trace_custom_node; // 1. insert profiling_trace_begin if profiling_trace_bp_end is not empty. // 2. op lanuch get task info with callback func. // 3. insert profiling_trace_bp_end. // 4. insert profiling_trace_net_output if profiling_trace_bp_end is not empty. - bool IsValid() const { return !(profiling_trace_begin.empty() || profiling_trace_bp_end.empty()); } + bool IsValid() const { return !(trace_begin.empty() || trace_bp_end.empty() || trace_netoutput.empty()); } +}; + +struct ProfilingContent { + // true -send data from device to host and finish profiling + bool notify; + uint64_t profiler_trace_id; + uint32_t flags; }; class ProfilingUtils { public: ProfilingUtils() = default; ~ProfilingUtils() = default; - static bool GetProfilingTraceInfo(const std::shared_ptr &graph_ptr, - ProfilingTraceInfo *profiling_trace_info); - static void ProfilingTraceFpStart(const std::shared_ptr &graph_ptr, const AnfNodePtr &anf_node, - const ProfilingTraceInfo &profiling_trace_info, std::vector *kernel_list); - static void ProfilingAllReduce(const std::shared_ptr &graph_ptr, const AnfNodePtr &anf_node, - int job_id, const std::string &profiling_node_name, - std::vector *kernel_list); - static void ProfilingTraceEnd(const std::shared_ptr &graph_ptr, const AnfNodePtr &anf_node, - const ProfilingTraceInfo &profiling_trace_info, std::vector *kernel_list); + + // Insert job_id profiling node and fp_start profiling node. + // Job_id is got from envs, which shound be a number greater than 255 + // Fp_start node should been inserted in the start of a network, and the log_id is hard code to 1. + static void ProfilingTraceFpStart(const AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info, + NotNull graph_ptr, + NotNull *> kernel_list); + + // Insert net output profiling node, which tells the device to stop profiling. + // The notify in struct ProfilingContent should be 'true', which tells the device to send data to host. + static void ProfilingTraceEnd(const AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info, + NotNull graph_ptr, + NotNull *> kernel_list); + + // Insert bp_end profiling node, which should been inserted after the last backpropagation CNode in the network. + static void ProfilingTraceBpEnd(const mindspore::AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info, + NotNull graph_ptr, + NotNull *> kernel_list); + + // Mapping graph id and the kernels' name in the graph static void SetGraphKernelName(uint32_t graph_id, const std::vector &kernel_names); + + // Mapping task_id and kernel name for device to generate the time cost of specific kernel. + // Device calculate the time cost of the task which is marked by task id. + // But we need data of (kernel name , time cost) static void ReportProfilingData(uint32_t graph_id, const std::vector &task_ids); - static const char kProfiling[]; - static const char kNotify[]; - static const char kProfilerTraceId[]; - static const char kFlags[]; + // Get profiling trace point from envs. + // export PROFILING_FP_START='full name of the first cnode to execute' + // export PROFILING_BP_END='full name of the last backpropagation cnode to execute' + // export PROFILING_ITER_END='full name of last cnode in graph to execute' + // And other cnode, like AllReduce, export PROFILING_CUSTOM_1='full name of AllReduce cnode' + // GetNext, export PROFIFLING_CUSTOM_2='full name fo GetNext cnode' + // The variable i in PROFILING_CUSTOM_i should start from 1 without interruption. + static ProfilingTraceInfo GetProfilingTraceFromEnv(NotNull graph_ptr); + + // Insert two profiling trace points, one in front and one behind + static void ProfilingCustomOp(const mindspore::AnfNodePtr &anf_node, const ProfilingTraceInfo &profiling_trace_info, + NotNull graph_ptr, + NotNull *> kernel_list); + + inline static constexpr char kProfiling[] = "Profiling"; + inline static constexpr char kNotify[] = "notify"; + inline static constexpr char kProfilerTraceId[] = "profiler_trace_id"; + inline static constexpr char kFlags[] = "flags"; private: - static bool GetNetOutput(AnfNodePtr anf_node, std::string *profiling_trace_net_output); - static CNodePtr CreateProfilingCNode(const std::shared_ptr &graph_ptr, bool notify, - uint64_t profiler_trace_id, uint32_t flags); + static NotNull CreateProfilingCNode(const ProfilingContent &profiling_content, + NotNull graph_ptr); + static CNodePtr CreateProfilingCNodeWithStream(const AnfNodePtr &anf_node, const ProfilingContent &profiling_content, + NotNull graph_ptr); + static std::string GetTraceBegin(const std::vector &cnode_exec_order); + static std::string GetTraceBpEnd(); + static std::string GetTraceNetoutput(const std::vector &cnode_exec_order); + // graph id --> (kernel name list) static std::unordered_map> graph_kernel_name_; + static uint32_t custom_node_index_; }; } // namespace ascend } // namespace device diff --git a/mindspore/ccsrc/device/kernel_adjust.cc b/mindspore/ccsrc/device/kernel_adjust.cc index c422d50b51..9a6f48025f 100644 --- a/mindspore/ccsrc/device/kernel_adjust.cc +++ b/mindspore/ccsrc/device/kernel_adjust.cc @@ -438,23 +438,22 @@ void KernelAdjust::LoadSwitchInputs(std::vector *inputs) { MS_LOG(INFO) << "---------------- LoadSwitchInputs End--"; } -void KernelAdjust::Profiling(const std::shared_ptr &kernel_graph_ptr) { +void KernelAdjust::Profiling(NotNull kernel_graph_ptr) { if (!ascend::ProfilingManager::GetInstance().IsProfiling()) { MS_LOG(INFO) << "No need to profiling"; return; } - ProfilingTraceInfo profiling_trace_info; - if (ProfilingUtils::GetProfilingTraceInfo(kernel_graph_ptr, &profiling_trace_info)) { - InsertProfilingKernel(kernel_graph_ptr, profiling_trace_info); - } else { - MS_LOG(WARNING) << "[profiling] GetProfilingTraceInfo failed"; + ProfilingTraceInfo profiling_trace_info = ProfilingUtils::GetProfilingTraceFromEnv(kernel_graph_ptr); + if (!profiling_trace_info.IsValid()) { + MS_LOG(WARNING) << "[profiling] no profiling node found!"; + return; } + InsertProfilingKernel(profiling_trace_info, kernel_graph_ptr); } -void KernelAdjust::InsertProfilingKernel(const std::shared_ptr &kernel_graph_ptr, - const ProfilingTraceInfo &profiling_trace_info) { +void KernelAdjust::InsertProfilingKernel(const ProfilingTraceInfo &profiling_trace_info, + NotNull kernel_graph_ptr) { MS_LOG(INFO) << "[profiling] Insert profiling kernel start"; - MS_EXCEPTION_IF_NULL(kernel_graph_ptr); if (!profiling_trace_info.IsValid()) { MS_LOG(WARNING) << "Profiling trace point not found"; return; @@ -462,18 +461,12 @@ void KernelAdjust::InsertProfilingKernel(const std::shared_ptr new_cnode_list; std::vector cnode_ptr_list = kernel_graph_ptr->execution_order(); for (const auto &cnode_ptr : cnode_ptr_list) { - ProfilingUtils::ProfilingTraceFpStart(kernel_graph_ptr, cnode_ptr, profiling_trace_info, &new_cnode_list); - ProfilingUtils::ProfilingAllReduce(kernel_graph_ptr, cnode_ptr, ascend::kProfilingAllReduce1Start, - profiling_trace_info.profiling_allreduce1_start, &new_cnode_list); - ProfilingUtils::ProfilingAllReduce(kernel_graph_ptr, cnode_ptr, ascend::kProfilingAllReduce2Start, - profiling_trace_info.profiling_allreduce2_start, &new_cnode_list); + ProfilingUtils::ProfilingTraceFpStart(cnode_ptr, profiling_trace_info, kernel_graph_ptr, NOT_NULL(&new_cnode_list)); new_cnode_list.emplace_back(cnode_ptr); - ProfilingUtils::ProfilingAllReduce(kernel_graph_ptr, cnode_ptr, ascend::kProfilingAllReduce1End, - profiling_trace_info.profiling_allreduce1_end, &new_cnode_list); - ProfilingUtils::ProfilingAllReduce(kernel_graph_ptr, cnode_ptr, ascend::kProfilingAllReduce2End, - profiling_trace_info.profiling_allreduce2_end, &new_cnode_list); - ProfilingUtils::ProfilingTraceEnd(kernel_graph_ptr, cnode_ptr, profiling_trace_info, &new_cnode_list); + ProfilingUtils::ProfilingCustomOp(cnode_ptr, profiling_trace_info, kernel_graph_ptr, NOT_NULL(&new_cnode_list)); + ProfilingUtils::ProfilingTraceBpEnd(cnode_ptr, profiling_trace_info, kernel_graph_ptr, NOT_NULL(&new_cnode_list)); + ProfilingUtils::ProfilingTraceEnd(cnode_ptr, profiling_trace_info, kernel_graph_ptr, NOT_NULL(&new_cnode_list)); } kernel_graph_ptr->set_execution_order(new_cnode_list); } diff --git a/mindspore/ccsrc/device/kernel_adjust.h b/mindspore/ccsrc/device/kernel_adjust.h index 62c64d98b9..ca01d51e54 100644 --- a/mindspore/ccsrc/device/kernel_adjust.h +++ b/mindspore/ccsrc/device/kernel_adjust.h @@ -48,7 +48,7 @@ class KernelAdjust { void SetStreamSwitchOps(const std::shared_ptr &kernel_graph_ptr); bool StepLoadCtrlInputs(const std::shared_ptr &context, const std::shared_ptr &kernel_graph_ptr); - void Profiling(const std::shared_ptr &kernel_graph_ptr); + void Profiling(NotNull kernel_graph_ptr); static bool NeedInsertSwitch(); CNodePtr CreateSteamActiveOp(const std::shared_ptr &kernel_graph_ptr); @@ -66,8 +66,8 @@ class KernelAdjust { kernel::KernelBuildInfo::KernelBuildInfoBuilder CreateMngKernelBuilder(const std::vector &formats, const std::vector &type_ids); void LoadSwitchInputs(std::vector *inputs); - void InsertProfilingKernel(const std::shared_ptr &kernel_graph_ptr, - const ProfilingTraceInfo &profiling_trace_info); + void InsertProfilingKernel(const ProfilingTraceInfo &profiling_trace_info, + NotNull kernel_graph_ptr); }; } // namespace device } // namespace mindspore diff --git a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc index 8212d64c27..432d88e7a4 100644 --- a/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc +++ b/mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc @@ -246,7 +246,7 @@ void AscendBackendOptimization(const std::shared_ptr &kern kernel_graph->SetExecOrderByDefault(); if (save_graphs) { std::string file_path = save_graphs_path + "/" + "hwopt_d_end.ir"; - DumpIR(file_path, kernel_graph); + DumpIR(file_path, kernel_graph, true); DumpIRProto(kernel_graph, "after_hwopt"); } } diff --git a/mindspore/ccsrc/session/ascend_session.cc b/mindspore/ccsrc/session/ascend_session.cc index 9d351f3199..34c05aed08 100644 --- a/mindspore/ccsrc/session/ascend_session.cc +++ b/mindspore/ccsrc/session/ascend_session.cc @@ -136,7 +136,7 @@ void AscendSession::BuildGraph(GraphId graph_id) { // Assign streams for control sink and hccl and so on AssignStream(graph); - device::KernelAdjust::GetInstance().Profiling(graph); + device::KernelAdjust::GetInstance().Profiling(NOT_NULL(graph.get())); // build kernel if node is cnode BuildKernel(graph); auto ms_context = MsContext::GetInstance(); diff --git a/tests/ut/cpp/stub/tasksink/ascend_stream_assign_stub.cc b/tests/ut/cpp/stub/tasksink/ascend_stream_assign_stub.cc index ebd2ac8b46..e0b5ab0d61 100755 --- a/tests/ut/cpp/stub/tasksink/ascend_stream_assign_stub.cc +++ b/tests/ut/cpp/stub/tasksink/ascend_stream_assign_stub.cc @@ -42,6 +42,6 @@ bool KernelAdjust::StepLoadCtrlInputs(const std::shared_ptr &c return true; } bool KernelAdjust::NeedInsertSwitch() { return true; } -void KernelAdjust::Profiling(const std::shared_ptr &kernel_graph_ptr) { return; } +void KernelAdjust::Profiling(NotNull kernel_graph_ptr) { return; } } // namespace device } // namespace mindspore From 0ba72a6885df59659e5a6162805941f42a428476 Mon Sep 17 00:00:00 2001 From: guohongzilong <2713219276@qq.com> Date: Wed, 8 Apr 2020 18:00:33 +0800 Subject: [PATCH 143/227] unified tensor and mindspore.type --- mindspore/common/tensor.py | 8 ++++---- mindspore/ops/operations/math_ops.py | 6 +++--- mindspore/ops/operations/random_ops.py | 2 +- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/mindspore/common/tensor.py b/mindspore/common/tensor.py index d17661595f..709b2ae280 100644 --- a/mindspore/common/tensor.py +++ b/mindspore/common/tensor.py @@ -42,14 +42,14 @@ class Tensor(Tensor_): Examples: >>> # init a tensor with input data - >>> t1 = mindspore.Tensor(np.zeros([1, 2, 3]), mindspore.float32) - >>> assert isinstance(t1, mindspore.Tensor) + >>> t1 = Tensor(np.zeros([1, 2, 3]), mindspore.float32) + >>> assert isinstance(t1, Tensor) >>> assert t1.shape() == (1, 2, 3) >>> assert t1.dtype() == mindspore.float32 >>> >>> # init a tensor with a float scalar - >>> t2 = mindspore.Tensor(0.1) - >>> assert isinstance(t2, mindspore.Tensor) + >>> t2 = Tensor(0.1) + >>> assert isinstance(t2, Tensor) >>> assert t2.dtype() == mindspore.float64 """ diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 47b9e490f1..d003f6ee8b 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1208,7 +1208,7 @@ class Acosh(PrimitiveWithInfer): Examples: >>> acosh = Acosh() - >>> X = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), ms.float32) + >>> X = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), mindspore.float32) >>> output = acosh(X) """ @@ -1752,7 +1752,7 @@ class Cos(PrimitiveWithInfer): Examples: >>> cos = P.Cos() - >>> X = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), ms.float32) + >>> X = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32) >>> output = cos(X) """ @@ -1808,7 +1808,7 @@ class Sin(PrimitiveWithInfer): Examples: >>> sin = P.Sin() - >>> input_x = Tensor(np.array([0.62, 0.28, 0.43, 0.62]), ms.float32) + >>> input_x = Tensor(np.array([0.62, 0.28, 0.43, 0.62]), mindspore.float32) >>> output = sin(input_x) """ diff --git a/mindspore/ops/operations/random_ops.py b/mindspore/ops/operations/random_ops.py index 9ef5b301f9..95692a622e 100644 --- a/mindspore/ops/operations/random_ops.py +++ b/mindspore/ops/operations/random_ops.py @@ -45,7 +45,7 @@ class RandomChoiceWithMask(PrimitiveWithInfer): Examples: >>> rnd_choice_mask = RandomChoiceWithMask() - >>> input_x = Tensor(np.ones(shape=[240000, 4]), ms.bool_) + >>> input_x = Tensor(np.ones(shape=[240000, 4]), mindspore.bool_) >>> output_y, output_mask = rnd_choice_mask(input_x) """ From ee519aa0d188695caf61a277ddb2d3c915e67046 Mon Sep 17 00:00:00 2001 From: yoonlee666 Date: Wed, 8 Apr 2020 14:31:18 +0800 Subject: [PATCH 144/227] use TFRecordDataset in bert ci script and add absolute position embedding code in bert model --- mindspore/model_zoo/Bert_NEZHA/bert_model.py | 14 ++++++++++++++ .../networks/models/bert/bert_tdt_no_lossscale.py | 4 ++-- 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/mindspore/model_zoo/Bert_NEZHA/bert_model.py b/mindspore/model_zoo/Bert_NEZHA/bert_model.py index f20c57dd75..d7f9355b3c 100644 --- a/mindspore/model_zoo/Bert_NEZHA/bert_model.py +++ b/mindspore/model_zoo/Bert_NEZHA/bert_model.py @@ -165,6 +165,7 @@ class EmbeddingPostprocessor(nn.Cell): def __init__(self, embedding_size, embedding_shape, + use_relative_positions=False, use_token_type=False, token_type_vocab_size=16, use_one_hot_embeddings=False, @@ -192,6 +193,13 @@ class EmbeddingPostprocessor(nn.Cell): self.layernorm = nn.LayerNorm(embedding_size) self.dropout = nn.Dropout(1 - dropout_prob) self.gather = P.GatherV2() + self.use_relative_positions = use_relative_positions + self.slice = P.Slice() + self.full_position_embeddings = Parameter(initializer + (TruncatedNormal(initializer_range), + [max_position_embeddings, + embedding_size]), + name='full_position_embeddings') def construct(self, token_type_ids, word_embeddings): output = word_embeddings @@ -206,6 +214,11 @@ class EmbeddingPostprocessor(nn.Cell): token_type_embeddings = self.gather(self.embedding_table, flat_ids, 0) token_type_embeddings = self.reshape(token_type_embeddings, self.shape) output += token_type_embeddings + if not self.use_relative_positions: + _, seq, width = self.shape + position_embeddings = self.slice(self.full_position_embeddings, [0, 0], [seq, width]) + position_embeddings = self.reshape(position_embeddings, (1, seq, width)) + output += position_embeddings output = self.layernorm(output) output = self.dropout(output) return output @@ -853,6 +866,7 @@ class BertModel(nn.Cell): self.bert_embedding_postprocessor = EmbeddingPostprocessor( embedding_size=self.embedding_size, embedding_shape=output_embedding_shape, + use_relative_positions=config.use_relative_positions, use_token_type=True, token_type_vocab_size=config.type_vocab_size, use_one_hot_embeddings=use_one_hot_embeddings, diff --git a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py index 9cc11997e6..5b6268505b 100644 --- a/tests/st/networks/models/bert/bert_tdt_no_lossscale.py +++ b/tests/st/networks/models/bert/bert_tdt_no_lossscale.py @@ -103,9 +103,9 @@ def me_de_train_dataset(): """test me de train dataset""" # apply repeat operations repeat_count = 1 - ds = de.StorageDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids", + ds = de.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels", "masked_lm_positions", - "masked_lm_ids", "masked_lm_weights"]) + "masked_lm_ids", "masked_lm_weights"], shuffle=False) type_cast_op = C.TypeCast(mstype.int32) ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op) ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op) From 599543932c566fd7f8b7a9245e455d53c81b396d Mon Sep 17 00:00:00 2001 From: liuxiao Date: Mon, 6 Apr 2020 10:22:47 +0800 Subject: [PATCH 145/227] Add pack and unpack --- mindspore/ccsrc/operator/ops.h | 1 + mindspore/ccsrc/transform/convert.cc | 3 +- mindspore/ops/_grad/grad_array_ops.py | 24 +++++ mindspore/ops/operations/__init__.py | 4 +- mindspore/ops/operations/array_ops.py | 144 ++++++++++++++++++++++++++ tests/ut/python/ops/test_ops.py | 53 ++++++++++ 6 files changed, 227 insertions(+), 2 deletions(-) diff --git a/mindspore/ccsrc/operator/ops.h b/mindspore/ccsrc/operator/ops.h index 727d66dfb3..5fbf2b7067 100644 --- a/mindspore/ccsrc/operator/ops.h +++ b/mindspore/ccsrc/operator/ops.h @@ -135,6 +135,7 @@ extern const PrimitivePtr kPrimGatherV2; extern const PrimitivePtr kPrimSize; extern const PrimitivePtr kPrimArgMax; extern const PrimitivePtr kPrimPack; +extern const PrimitivePtr kPrimUnpack; extern const PrimitivePtr kPrimUnsortedSegmentSum; extern const PrimitivePtr kPrimConcatOffset; extern const PrimitivePtr kPrimReshape; diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index 8eed207f59..8b14a8f338 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -148,7 +148,8 @@ const char kNameSlice[] = "Slice"; const char kNameAddN[] = "AddN"; const char kNameLess[] = "Less"; const char kNameGreater[] = "Greater"; -const char kNamePack[] = "Stack"; +const char kNameStack[] = "Stack"; +const char kNameUnstack[] = "Unstack"; const char kNameMerge[] = "Merge"; const char kNameGeSwitch[] = "GeSwitch"; diff --git a/mindspore/ops/_grad/grad_array_ops.py b/mindspore/ops/_grad/grad_array_ops.py index 81d38a1e1e..0a0caf471e 100644 --- a/mindspore/ops/_grad/grad_array_ops.py +++ b/mindspore/ops/_grad/grad_array_ops.py @@ -266,6 +266,30 @@ def get_bprop_gather_v2(self): return bprop +@bprop_getters.register(P.Stack) +def get_bprop_stack(self): + """Generate bprop for Stack""" + axis = self.axis + + def bprop(x, out, dout): + stack_grad = P.Unstack(axis) + out = stack_grad(dout) + return (out,) + return bprop + + +@bprop_getters.register(P.Unstack) +def get_bprop_unstack(self): + """Generate bprop for Unstack""" + axis = self.axis + + def bprop(x, out, dout): + unstack_grad = P.Stack(axis) + out = unstack_grad(dout) + return (out,) + return bprop + + @bprop_getters.register(P.StridedSlice) def get_bprop_strided_slice(self): """Generate bprop for StridedSlice""" diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index d255796bae..5fd3f07876 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -19,7 +19,7 @@ Primitive operator classes. A collection of operators to build nerual networks or computing functions. """ -from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat, +from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat, Stack, Unstack, Diag, DiagPart, DType, ExpandDims, Eye, Fill, GatherNd, GatherV2, InvertPermutation, IsInstance, IsSubClass, ArgMaxWithValue, OnesLike, ZerosLike, @@ -112,6 +112,8 @@ __all__ = [ 'OneHot', 'GatherV2', 'Concat', + 'Stack', + 'Unstack', 'Tile', 'BiasAdd', 'Gelu', diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index b91c2cbc7d..59d3083c5d 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -1350,6 +1350,150 @@ class Concat(PrimitiveWithInfer): return out +def _get_stack_shape(x_shape, x_type, axis): + """for satck output shape""" + validator.check_type("shape", x_shape, [tuple]) + validator.check_integer("len of input_x shape", len(x_shape), 0, Rel.GT) + validator.check_subclass("shape0", x_type[0], mstype.tensor) + validator.check_integer("len of input_x0 shape", len(x_shape[0]), 0, Rel.GT) + rank_base = len(x_shape[0]) + N = len(x_shape) + out_shape = x_shape[0] + validator.check_int_range('axis', axis, -rank_base - 1, rank_base, Rel.INC_BOTH) + if axis < 0: + axis = axis + rank_base + 1 + for i in range(1, N): + v = x_shape[i] + validator.check('len of x_shape[%d]' % i, len(v), 'len of rank_base', rank_base) + validator.check('x_type[%d]' % i, x_type[i], 'base', x_type[0]) + for j in range(rank_base): + if v[j] != x_shape[0][j]: + raise ValueError("Stack evaluator element %d shape in input can not stack with first element" % i) + out_shape.insert(axis, N) + return out_shape + +class Stack(PrimitiveWithInfer): + r""" + Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor. + + Packs the list of tensors in `input_x` into a tensor with rank one higher than + each tensor in `input_x`, by packing them along the `axis` dimension. + Given a list of length `N` of tensors of shape `(A, B, C)`; + + If `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`. + + If `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`. Etc. + + Args: + axis (int): The axis to stack along. Negative values wrap around, + so the valid range is [-(R+1), R+1). Default: 0. + + Inputs: + - **input_x** (Union[tuple, list]) - A Tuple or list of Tensor objects with the same shape and type. + + Outputs: + Tensor. A stacked Tensor with the same type as values. + + Examples: + >>> data1 = Tensor(np.array([0, 1]).astype(np.float32)) + >>> data2 = Tensor(np.array([2, 3]).astype(np.float32)) + >>> op = P.Stack() + >>> output = op([data1, data2]) + [[0, 1], [2, 3]] + """ + + @prim_attr_register + def __init__(self, axis=0): + """init Stack""" + self.__setattr_flag__ = True + validator.check_type("axis", axis, [int]) + self.axis = axis + + def __infer__(self, value): + x_shape = value['shape'] + x_type = value['dtype'] + self.add_prim_attr('num', len(x_shape)) + all_shape = _get_stack_shape(x_shape, x_type, self.axis) + out = {'shape': all_shape, + 'dtype': x_type[0], + 'value': None} + return out + + +class Unstack(PrimitiveWithInfer): + r""" + Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors. + + Unpacks num tensors from value by chipping it along the axis dimension. + If num is not specified (the default), it is inferred from value's shape. + If value.shape[axis] is not known, ValueError is raised. + + For example, given a tensor of shape (A, B, C, D); + + If axis == 0 then the i'th tensor in output is the slice value[i, :, :, :] and + each tensor in output will have shape (B, C, D). (Note that the dimension unpacked along is gone, unlike split). + + If axis == 1 then the i'th tensor in output is the slice value[:, i, :, :] and + each tensor in output will have shape (A, C, D). Etc. + + This is the opposite of stack. + + Args: + axis (int): The axis to unstack along. Defaults to the first dimension. + Negative values wrap around, so the valid range is [-R, R). + + Inputs: + - **input_x** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_R)`. + A rank R > 0 Tensor to be unstacked. + + Outputs: + A tuple of Tensors, the shape of each objects is same. + + Raises: + ValueError: If axis is out of the range [-len(input_x.shape()), len(input_x.shape())), + or if len(input_x.shape[axis]) not equal to num. + + Examples: + >>> unstack = P.Unstack() + >>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]])) + >>> output = unstack(x) + ([1, 1, 1, 1], [2, 2, 2, 2]) + """ + + @prim_attr_register + def __init__(self, axis=0): + """init Unstack""" + self.__setattr_flag__ = True + validator.check_type("axis", axis, [int]) + self.axis = axis + + def __infer__(self, x): + validator.check_subclass("x", x['dtype'], mstype.tensor) + x_shape = list(x['shape']) + dim = len(x_shape) + validator.check_int_range('axis value', self.axis, -dim, dim, Rel.INC_LEFT) + if self.axis < 0: + self.axis = self.axis + dim + output_num = x_shape[self.axis] + validator.check_type("num", output_num, [int]) + validator.check_integer("output_num", output_num, 0, Rel.GT) + self.add_prim_attr('num', output_num) + output_valid_check = x_shape[self.axis] - output_num + validator.check_integer("the dimension which to unstack divides output_num", output_valid_check, 0, Rel.EQ) + out_shapes = [] + out_dtypes = [] + out_shape = x_shape[:self.axis] + x_shape[self.axis + 1:] + for _ in range(output_num): + out_shapes.append(tuple(out_shape)) + out_dtypes.append(x['dtype']) + out_shapes = tuple(out_shapes) + out_dtypes = tuple(out_dtypes) + out = {'shape': out_shapes, + 'dtype': out_dtypes, + 'value': None} + return out + + class Slice(PrimitiveWithInfer): """ Slice a tensor in specified shape. diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py index 3e4acb12f3..97481e69a2 100755 --- a/tests/ut/python/ops/test_ops.py +++ b/tests/ut/python/ops/test_ops.py @@ -80,6 +80,29 @@ class NetForConcat1(nn.Cell): return self.concat((x1, x2)) +class NetForStackInput(nn.Cell): + def __init__(self, op): + super(NetForStackInput, self).__init__() + self.op = op + self.mul = P.Mul() + + def construct(self, *args): + t = () + for i in range(len(args)): + t = t + (self.mul(args[i], args[i]),) + return self.op(t) + + +class NetForUnstackInput(nn.Cell): + def __init__(self, op): + super(NetForUnstackInput, self).__init__() + self.op = op + self.mul = P.Mul() + + def construct(self, x1): + return self.op((self.mul(x1, x1))) + + class NetForFlatten(nn.Cell): def __init__(self): super(NetForFlatten, self).__init__() @@ -973,6 +996,36 @@ test_case_array_ops = [ Tensor(np.array([1], np.float32)), Tensor(np.array([1], np.float32)))], 'desc_bprop': [[3,]]}), + ('StackV2_0', { + 'block': NetForStackInput(P.Stack()), + 'desc_inputs':[[2, 2], [2, 2], [2, 2]], + 'desc_bprop':[[3, 2, 2]], + }), + ('StackV2_1', { + 'block': NetForStackInput(P.Stack(axis=-2)), + 'desc_inputs':[[3, 2, 3], [3, 2, 3], [3, 2, 3]], + 'desc_bprop':[[3, 2, 3, 3]], + }), + ('StackV2_2', { + 'block': NetForStackInput(P.Stack()), + 'desc_inputs':[[2, 2]], + 'desc_bprop':[[2, 2, 2]], + }), + ('StackV2_3', { + 'block': NetForStackInput(P.Stack()), + 'desc_inputs':[[128, 128], [128, 128]], + 'desc_bprop':[[2, 128, 128]], + }), + ('UnstackV2_0', { + 'block': NetForUnstackInput(P.Unstack(axis=0)), + 'desc_inputs':[[2, 4]], + 'desc_bprop':[[4], [4]], + }), + ('UnstackV2_1', { + 'block': NetForUnstackInput(P.Unstack(axis=-1)), + 'desc_inputs':[Tensor(np.array([[1, 1, 1]], np.float32))], + 'desc_bprop':[[1], [1], [1]], + }), ('Diag', { 'block': P.Diag(), 'desc_inputs': [[4]], From e01df479151f16782f6b7865d220ec0e647c3e0a Mon Sep 17 00:00:00 2001 From: jinyaohui Date: Tue, 7 Apr 2020 17:23:17 +0800 Subject: [PATCH 146/227] modify set_dataset_mode_config api param --- example/yolov3_coco2017/train.py | 6 +++--- mindspore/ccsrc/transform/convert.cc | 12 ++++++------ mindspore/ccsrc/utils/config_manager.cc | 4 ++-- mindspore/ccsrc/utils/config_manager.h | 4 ++-- mindspore/common/api.py | 4 ++-- mindspore/nn/wrap/loss_scale.py | 2 +- tests/ut/python/utils/test_callback.py | 16 +++++++++------- 7 files changed, 25 insertions(+), 23 deletions(-) diff --git a/example/yolov3_coco2017/train.py b/example/yolov3_coco2017/train.py index 3ac3816f4a..0a32a6d30d 100644 --- a/example/yolov3_coco2017/train.py +++ b/example/yolov3_coco2017/train.py @@ -67,7 +67,7 @@ if __name__ == '__main__': parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.") parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.") parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.") - parser.add_argument("--mode", type=str, default="graph", help="Run graph mode or feed mode, default is graph") + parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or non-sink mode, default is sink") parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10") parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.") parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path") @@ -150,8 +150,8 @@ if __name__ == '__main__': model = Model(net) dataset_sink_mode = False - if args_opt.mode == "graph": - print("In graph mode, one epoch return a loss.") + if args_opt.mode == "sink": + print("In sink mode, one epoch return a loss.") dataset_sink_mode = True print("Start train YOLOv3, the first epoch will be slower because of the graph compilation.") model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode) diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index 8b14a8f338..59985c8ae3 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -446,10 +446,10 @@ void DfGraphConvertor::InitLoopVar(std::vector *init_input) { int64_t value = 0; auto const_iter_num = std::make_shared("const/npu_runconfig/iterations_per_loop"); - if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) { + if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) { value = ConfigManager::GetInstance().iter_num(); } else { - MS_LOG(INFO) << "Run with feed mode, the iterator number will always be 1"; + MS_LOG(INFO) << "Run with non-sink mode, the iterator number will always be 1"; value = 1; ConfigManager::GetInstance().set_iter_num(value); } @@ -580,7 +580,7 @@ void DfGraphConvertor::SetupParamInitSubGraph(const TensorOrderMap &tensors, std void DfGraphConvertor::MakeDatasetHandler(const std::string &name, const size_t &input_idx, const AnfNodePtr &it) { MS_LOG(INFO) << "The " << name << " is the " << input_idx << "(st/nd/th) input"; - if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) { + if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) { auto getnext_idx = static_cast(input_idx); DatasetGraphParam param = ConfigManager::GetInstance().dataset_param(); if (!param.input_indexes().empty() && input_idx <= param.input_indexes().size()) { @@ -872,7 +872,7 @@ DfGraphConvertor &DfGraphConvertor::ConvertAllNode() { } // Create dataset iterator and iterator_getnext node - if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) { + if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) { DatasetGraphParam param = ConfigManager::GetInstance().dataset_param(); MS_LOG(INFO) << "Dataset param is " << param.ToString() << "."; // GetNext @@ -981,7 +981,7 @@ void DfGraphConvertor::TraceOutputFromParameter(const AnfNodePtr &anf_out) { } void SetupDatasetIterGetNextNode(const OperatorPtr &op) { - if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) { + if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) { DatasetGraphParam param = ConfigManager::GetInstance().dataset_param(); size_t output_num = param.ge_types().size(); MS_LOG(INFO) << "Set iterator_getnext op's output num = " << output_num << "."; @@ -1040,7 +1040,7 @@ DfGraphConvertor &DfGraphConvertor::BuildGraph() { // set graph input according to the order from anf graph std::vector inputs; - if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) { + if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) { inputs.push_back(*dataset_iter_getnext_); } else { auto params = anf_graph_->parameters(); diff --git a/mindspore/ccsrc/utils/config_manager.cc b/mindspore/ccsrc/utils/config_manager.cc index ac8a965878..6d66b37436 100644 --- a/mindspore/ccsrc/utils/config_manager.cc +++ b/mindspore/ccsrc/utils/config_manager.cc @@ -28,7 +28,7 @@ ConfigManager& ConfigManager::GetInstance() noexcept { } void ConfigManager::SetDatasetModeConfig(const std::string& mode) { - static const std::map mode_map = {{"feed", DS_FEED_MODE}, {"graph", DS_GRAPH_MODE}}; + static const std::map mode_map = {{"normal", DS_NORMAL_MODE}, {"sink", DS_SINK_MODE}}; if (mode_map.find(mode) == mode_map.end()) { MS_LOG(ERROR) << "Invalid dataset mode:" << mode; return; @@ -38,7 +38,7 @@ void ConfigManager::SetDatasetModeConfig(const std::string& mode) { void ConfigManager::ResetConfig() noexcept { parallel_strategy_ = ONE_DEVICE; - dataset_mode_ = DS_FEED_MODE; + dataset_mode_ = DS_NORMAL_MODE; dataset_param_ = DatasetGraphParam("", 0, 0, {}, {}, {}); iter_num_ = 1; } diff --git a/mindspore/ccsrc/utils/config_manager.h b/mindspore/ccsrc/utils/config_manager.h index 31137f6243..db7d7d0c14 100644 --- a/mindspore/ccsrc/utils/config_manager.h +++ b/mindspore/ccsrc/utils/config_manager.h @@ -33,7 +33,7 @@ enum ParallelStrategy { DISTRIBUTION, }; -enum DatasetMode { DS_FEED_MODE = 0, DS_GRAPH_MODE }; +enum DatasetMode { DS_NORMAL_MODE = 0, DS_SINK_MODE }; class DatasetGraphParam { public: @@ -106,7 +106,7 @@ class ConfigManager { ~ConfigManager() = default; ParallelStrategy parallel_strategy_{ONE_DEVICE}; - DatasetMode dataset_mode_{DS_FEED_MODE}; + DatasetMode dataset_mode_{DS_NORMAL_MODE}; DatasetGraphParam dataset_param_{"", 0, 0, {}, {}, {}}; int64_t iter_num_{1}; std::string dataset_phase_{""}; diff --git a/mindspore/common/api.py b/mindspore/common/api.py index 7f0b2bfeaa..b5450bc5a3 100644 --- a/mindspore/common/api.py +++ b/mindspore/common/api.py @@ -378,9 +378,9 @@ class _Executor: if enable_ge: # decide whether to sink based on whether the inputs is virtual or not if args_list and isinstance(args_list[0], Tensor) and args_list[0].virtual_flag: - _set_dataset_mode_config('graph') + _set_dataset_mode_config('sink') else: - _set_dataset_mode_config('feed') + _set_dataset_mode_config('normal') self._build_data_graph(obj, params, phase) diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py index 6a1f15a402..fd1c22be1f 100644 --- a/mindspore/nn/wrap/loss_scale.py +++ b/mindspore/nn/wrap/loss_scale.py @@ -51,7 +51,7 @@ class DynamicLossScaleUpdateCell(Cell): In every training step, the loss scaling value will be updated by loss scaling value/`scale_factor` when there is overflow. And it will be increased by loss scaling value * `scale_factor` if there is no overflow for a continuous `scale_window` steps. This cell is used for Graph mode training in which all - logic will be executed on device side(Another training mode is feed mode in which some logic will be + logic will be executed on device side(Another training mode is non-sink mode in which some logic will be executed on host). Args: diff --git a/tests/ut/python/utils/test_callback.py b/tests/ut/python/utils/test_callback.py index 43cf827330..7e7b893e0c 100644 --- a/tests/ut/python/utils/test_callback.py +++ b/tests/ut/python/utils/test_callback.py @@ -24,11 +24,12 @@ from mindspore import context from mindspore.common.tensor import Tensor from mindspore.nn.optim import Momentum from mindspore.nn import TrainOneStepCell, WithLossCell -from mindspore.train.callback import ModelCheckpoint, _check_file_name_prefix, RunContext,_checkpoint_cb_for_save_op,\ - LossMonitor, _InternalCallbackParam, _chg_ckpt_file_name_if_same_exist,\ - _build_callbacks, CheckpointConfig, _set_cur_net +from mindspore.train.callback import ModelCheckpoint, _check_file_name_prefix, RunContext, _checkpoint_cb_for_save_op, \ + LossMonitor, _InternalCallbackParam, _chg_ckpt_file_name_if_same_exist, \ + _build_callbacks, CheckpointConfig, _set_cur_net from mindspore.common.api import ms_function + class Net(nn.Cell): """Net definition.""" @@ -52,6 +53,7 @@ class Net(nn.Cell): class LossNet(nn.Cell): """ LossNet definition """ + def __init__(self): super(LossNet, self).__init__() self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal', pad_mode='valid') @@ -110,8 +112,8 @@ def test_save_checkpoint(): os.remove('./test_files/test_ckpt-model.pkl') -def test_loss_monitor_graph_model(): - """Test lossmonitor Graph model.""" +def test_loss_monitor_sink_model(): + """Test loss monitor sink model.""" cb_params = _InternalCallbackParam() cb_params.cur_epoch_num = 4 cb_params.cur_step_num = 2 @@ -129,8 +131,8 @@ def test_loss_monitor_graph_model(): callbacklist.end(run_context) -def test_Loss_Monitor_feed_feed_model(): - """Test Loss Monitor feed feed mode.""" +def test_loss_monitor_feed_model(): + """Test loss monitor non-sink mode.""" cb_params = _InternalCallbackParam() run_context = RunContext(cb_params) loss_cb = LossMonitor(1) From 82896411e06f7c5372ebef500553cd62b1701aa9 Mon Sep 17 00:00:00 2001 From: anzhengqi Date: Wed, 8 Apr 2020 14:47:16 +0800 Subject: [PATCH 147/227] modify part of comments --- .../ccsrc/dataset/kernels/image/image_utils.h | 14 ++------------ 1 file changed, 2 insertions(+), 12 deletions(-) diff --git a/mindspore/ccsrc/dataset/kernels/image/image_utils.h b/mindspore/ccsrc/dataset/kernels/image/image_utils.h index d289f6f56e..a4ddef40d6 100644 --- a/mindspore/ccsrc/dataset/kernels/image/image_utils.h +++ b/mindspore/ccsrc/dataset/kernels/image/image_utils.h @@ -84,18 +84,8 @@ Status Resize(const std::shared_ptr &input, std::shared_ptr *out // Returns Decoded image // Supported images: -// - Windows bitmaps - \*.bmp, \*.dib (always supported) -// - JPEG files - \*.jpeg, \*.jpg, \*.jpe (see the *Note* section) -// - JPEG 2000 files - \*.jp2 (see the *Note* section) -// - Portable Network Graphics - \*.png (see the *Note* section) -// - WebP - \*.webp (see the *Note* section) -// - Portable image format - \*.pbm, \*.pgm, \*.ppm \*.pxm, \*.pnm (always supported) -// - PFM files - \*.pfm (see the *Note* section) -// - Sun rasters - \*.sr, \*.ras (always supported) -// - TIFF files - \*.tiff, \*.tif (see the *Note* section) -// - OpenEXR Image files - \*.exr (see the *Note* section) -// - Radiance HDR - \*.hdr, \*.pic (always supported) -// - Raster and Vector geospatial data supported by GDAL (see the *Note* section) +// BMP JPEG JPG PNG TIFF +// supported by opencv, if user need more image analysis capabilities, please compile opencv particularlly. // @param input: CVTensor containing the not decoded image 1D bytes // @param output: Decoded image Tensor of shape and type DE_UINT8. Pixel order is RGB Status Decode(const std::shared_ptr &input, std::shared_ptr *output); From 6690a7fd7a3108ce408220342bd750455067ccab Mon Sep 17 00:00:00 2001 From: jonyguo Date: Wed, 8 Apr 2020 15:52:53 +0800 Subject: [PATCH 148/227] fix: error info is not exactly when column list invalid --- .../engine/datasetops/source/mindrecord_op.cc | 3 +- .../engine/datasetops/source/mindrecord_op.h | 1 + .../ccsrc/mindrecord/common/shard_error.cc | 178 ++++++++++++++++++ .../ccsrc/mindrecord/include/shard_error.h | 102 +++++----- mindspore/ccsrc/mindrecord/io/shard_reader.cc | 2 +- tests/ut/cpp/dataset/mind_record_op_test.cc | 36 ++++ .../ut/cpp/mindrecord/ut_shard_reader_test.cc | 2 +- 7 files changed, 271 insertions(+), 53 deletions(-) create mode 100644 mindspore/ccsrc/mindrecord/common/shard_error.cc diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc index b5bea5416c..cb0f135a0d 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc @@ -103,7 +103,8 @@ Status MindRecordOp::Init() { shard_reader_ = mindspore::make_unique(); auto rc = shard_reader_->Open(dataset_file_, num_mind_record_workers_, columns_to_load_, operators_, block_reader_); - CHECK_FAIL_RETURN_UNEXPECTED(rc != MSRStatus::FAILED, "MindRecordOp init failed."); + CHECK_FAIL_RETURN_UNEXPECTED(rc != MSRStatus::FAILED, + "MindRecordOp init failed. Error message: " + ErrnoToMessage(rc)); data_schema_ = mindspore::make_unique(); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h index 0b16391b20..aca5c86c2c 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h @@ -32,6 +32,7 @@ #include "dataset/engine/datasetops/source/io_block.h" #include "dataset/util/queue.h" #include "dataset/util/status.h" +#include "mindrecord/include/shard_error.h" #include "mindrecord/include/shard_reader.h" #include "mindrecord/include/common/shard_utils.h" #include "dataset/util/wait_post.h" diff --git a/mindspore/ccsrc/mindrecord/common/shard_error.cc b/mindspore/ccsrc/mindrecord/common/shard_error.cc new file mode 100644 index 0000000000..cf43dcb315 --- /dev/null +++ b/mindspore/ccsrc/mindrecord/common/shard_error.cc @@ -0,0 +1,178 @@ +/** + * Copyright 2019 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "mindrecord/include/shard_error.h" + +namespace mindspore { +namespace mindrecord { +std::string ErrnoToMessage(MSRStatus status) { + switch (status) { + case FAILED: + return "operator failed"; + break; + case SUCCESS: + return "operator success"; + break; + case OPEN_FILE_FAILED: + return "open file failed"; + break; + case CLOSE_FILE_FAILED: + return "close file failed"; + break; + case WRITE_METADATA_FAILED: + return "write metadata failed"; + break; + case WRITE_RAWDATA_FAILED: + return "write rawdata failed"; + break; + case GET_SCHEMA_FAILED: + return "get schema failed"; + break; + case ILLEGAL_RAWDATA: + return "illegal raw data"; + break; + case PYTHON_TO_JSON_FAILED: + return "pybind: python object to json failed"; + break; + case DIR_CREATE_FAILED: + return "directory create failed"; + break; + case OPEN_DIR_FAILED: + return "open directory failed"; + break; + case INVALID_STATISTICS: + return "invalid statistics object"; + break; + case OPEN_DATABASE_FAILED: + return "open database failed"; + break; + case CLOSE_DATABASE_FAILED: + return "close database failed"; + break; + case DATABASE_OPERATE_FAILED: + return "database operate failed"; + break; + case BUILD_SCHEMA_FAILED: + return "build schema failed"; + break; + case DIVISOR_IS_ILLEGAL: + return "divisor is illegal"; + break; + case INVALID_FILE_PATH: + return "file path is invalid"; + break; + case SECURE_FUNC_FAILED: + return "secure function failed"; + break; + case ALLOCATE_MEM_FAILED: + return "allocate memory failed"; + break; + case ILLEGAL_FIELD_NAME: + return "illegal field name"; + break; + case ILLEGAL_FIELD_TYPE: + return "illegal field type"; + break; + case SET_METADATA_FAILED: + return "set metadata failed"; + break; + case ILLEGAL_SCHEMA_DEFINITION: + return "illegal schema definition"; + break; + case ILLEGAL_COLUMN_LIST: + return "illegal column list"; + break; + case SQL_ERROR: + return "sql error"; + break; + case ILLEGAL_SHARD_COUNT: + return "illegal shard count"; + break; + case ILLEGAL_SCHEMA_COUNT: + return "illegal schema count"; + break; + case VERSION_ERROR: + return "data version is not matched"; + break; + case ADD_SCHEMA_FAILED: + return "add schema failed"; + break; + case ILLEGAL_Header_SIZE: + return "illegal header size"; + break; + case ILLEGAL_Page_SIZE: + return "illegal page size"; + break; + case ILLEGAL_SIZE_VALUE: + return "illegal size value"; + break; + case INDEX_FIELD_ERROR: + return "add index fields failed"; + break; + case GET_CANDIDATE_CATEGORYFIELDS_FAILED: + return "get candidate category fields failed"; + break; + case GET_CATEGORY_INFO_FAILED: + return "get category information failed"; + break; + case ILLEGAL_CATEGORY_ID: + return "illegal category id"; + break; + case ILLEGAL_ROWNUMBER_OF_PAGE: + return "illegal row number of page"; + break; + case ILLEGAL_SCHEMA_ID: + return "illegal schema id"; + break; + case DESERIALIZE_SCHEMA_FAILED: + return "deserialize schema failed"; + break; + case DESERIALIZE_STATISTICS_FAILED: + return "deserialize statistics failed"; + break; + case ILLEGAL_DB_FILE: + return "illegal db file"; + break; + case OVERWRITE_DB_FILE: + return "overwrite db file"; + break; + case OVERWRITE_MINDRECORD_FILE: + return "overwrite mindrecord file"; + break; + case ILLEGAL_MINDRECORD_FILE: + return "illegal mindrecord file"; + break; + case PARSE_JSON_FAILED: + return "parse json failed"; + break; + case ILLEGAL_PARAMETERS: + return "illegal parameters"; + break; + case GET_PAGE_BY_GROUP_ID_FAILED: + return "get page by group id failed"; + break; + case GET_SYSTEM_STATE_FAILED: + return "get system state failed"; + break; + case IO_FAILED: + return "io operate failed"; + break; + default: + return "invalid error no"; + } +} +} // namespace mindrecord +} // namespace mindspore diff --git a/mindspore/ccsrc/mindrecord/include/shard_error.h b/mindspore/ccsrc/mindrecord/include/shard_error.h index 026ee836e3..b85eeb71c0 100644 --- a/mindspore/ccsrc/mindrecord/include/shard_error.h +++ b/mindspore/ccsrc/mindrecord/include/shard_error.h @@ -18,63 +18,65 @@ #define MINDRECORD_INCLUDE_SHARD_ERROR_H_ #include -#include "utils/error_code.h" +#include namespace mindspore { namespace mindrecord { -DE_ERRORNO_MINDRECORD(OPEN_FILE_FAILED, 0, "open file failed"); -DE_ERRORNO_MINDRECORD(CLOSE_FILE_FAILED, 1, "close file failed"); -DE_ERRORNO_MINDRECORD(WRITE_METADATA_FAILED, 2, "write metadata failed"); -DE_ERRORNO_MINDRECORD(WRITE_RAWDATA_FAILED, 3, "write rawdata failed"); -DE_ERRORNO_MINDRECORD(GET_SCHEMA_FAILED, 4, "get schema failed"); -DE_ERRORNO_MINDRECORD(ILLEGAL_RAWDATA, 5, "illegal raw data"); -DE_ERRORNO_MINDRECORD(PYTHON_TO_JSON_FAILED, 6, "pybind: python object to json failed"); -DE_ERRORNO_MINDRECORD(DIR_CREATE_FAILED, 7, "directory create failed"); -DE_ERRORNO_MINDRECORD(OPEN_DIR_FAILED, 8, "open directory failed"); -DE_ERRORNO_MINDRECORD(INVALID_STATISTICS, 9, "invalid statistics object"); -DE_ERRORNO_MINDRECORD(OPEN_DATABASE_FAILED, 10, "open database failed"); -DE_ERRORNO_MINDRECORD(CLOSE_DATABASE_FAILED, 11, "close database failed"); -DE_ERRORNO_MINDRECORD(DATABASE_OPERATE_FAILED, 12, "database operate failed"); -DE_ERRORNO_MINDRECORD(BUILD_SCHEMA_FAILED, 13, "build schema failed"); -DE_ERRORNO_MINDRECORD(DIVISOR_IS_ILLEGAL, 14, "divisor is illegal"); -DE_ERRORNO_MINDRECORD(INVALID_FILE_PATH, 15, "file path is invalid"); -DE_ERRORNO_MINDRECORD(SECURE_FUNC_FAILED, 16, "secure function failed"); -DE_ERRORNO_MINDRECORD(ALLOCATE_MEM_FAILED, 17, "allocate memory failed"); -DE_ERRORNO_MINDRECORD(ILLEGAL_FIELD_NAME, 18, "illegal field name"); -DE_ERRORNO_MINDRECORD(ILLEGAL_FIELD_TYPE, 19, "illegal field type"); -DE_ERRORNO_MINDRECORD(SET_METADATA_FAILED, 20, "set metadata failed"); -DE_ERRORNO_MINDRECORD(ILLEGAL_SCHEMA_DEFINITION, 21, "illegal schema definition"); -DE_ERRORNO_MINDRECORD(ILLEGAL_COLUMN_LIST, 22, "illegal column list"); -DE_ERRORNO_MINDRECORD(SQL_ERROR, 23, "sql error"); -DE_ERRORNO_MINDRECORD(ILLEGAL_SHARD_COUNT, 24, "illegal shard count"); -DE_ERRORNO_MINDRECORD(ILLEGAL_SCHEMA_COUNT, 25, "illegal schema count"); -DE_ERRORNO_MINDRECORD(VERSION_ERROR, 26, "data version is not matched"); -DE_ERRORNO_MINDRECORD(ADD_SCHEMA_FAILED, 27, "add schema failed"); -DE_ERRORNO_MINDRECORD(ILLEGAL_Header_SIZE, 28, "illegal header size"); -DE_ERRORNO_MINDRECORD(ILLEGAL_Page_SIZE, 29, "illegal page size"); -DE_ERRORNO_MINDRECORD(ILLEGAL_SIZE_VALUE, 30, "illegal size value"); -DE_ERRORNO_MINDRECORD(INDEX_FIELD_FAILED, 31, "add index fields failed"); -DE_ERRORNO_MINDRECORD(GET_CANDIDATE_CATEGORYFIELDS_FAILED, 32, "get candidate categoryFields failed"); -DE_ERRORNO_MINDRECORD(GET_CATEGORY_INFO, 33, "get category information failed"); -DE_ERRORNO_MINDRECORD(ILLEGAL_CATEGORY_ID, 34, "illegal category id"); -DE_ERRORNO_MINDRECORD(ILLEGAL_ROWNUMBER_OF_PAGE, 35, "illegal row number of page"); -DE_ERRORNO_MINDRECORD(ILLEGAL_SCHEMA_ID, 36, "illegal schema id"); -DE_ERRORNO_MINDRECORD(DESERIALIZE_SCHEMA_FAILED, 37, "deserialize schema failed"); -DE_ERRORNO_MINDRECORD(DESERIALIZE_STATISTICS_FAILED, 38, "deserialize statistics failed"); -DE_ERRORNO_MINDRECORD(ILLEGAL_DB_FILE, 39, "illegal db file."); -DE_ERRORNO_MINDRECORD(OVERWRITE_DB_FILE, 40, "overwrite db file."); -DE_ERRORNO_MINDRECORD(OVERWRITE_MINDRECORD_FILE, 41, "overwrite mindrecord file."); -DE_ERRORNO_MINDRECORD(ILLEGAL_MINDRECORD_FILE, 42, "illegal mindrecord file."); -DE_ERRORNO_MINDRECORD(PARSE_JSON_FAILED, 43, "parse json failed."); -DE_ERRORNO_MINDRECORD(ILLEGAL_PARAMETERS, 44, "illegal parameters."); -DE_ERRORNO_MINDRECORD(GET_PAGE_BY_GROUP_ID_FAILED, 46, "get page by group id failed."); -DE_ERRORNO_MINDRECORD(GET_SYSTEM_STATE_FAILED, 47, "get system state failed."); -DE_ERRORNO_MINDRECORD(IO_FAILED, 48, "io operate failed."); - enum MSRStatus { SUCCESS = 0, FAILED = 1, + OPEN_FILE_FAILED, + CLOSE_FILE_FAILED, + WRITE_METADATA_FAILED, + WRITE_RAWDATA_FAILED, + GET_SCHEMA_FAILED, + ILLEGAL_RAWDATA, + PYTHON_TO_JSON_FAILED, + DIR_CREATE_FAILED, + OPEN_DIR_FAILED, + INVALID_STATISTICS, + OPEN_DATABASE_FAILED, + CLOSE_DATABASE_FAILED, + DATABASE_OPERATE_FAILED, + BUILD_SCHEMA_FAILED, + DIVISOR_IS_ILLEGAL, + INVALID_FILE_PATH, + SECURE_FUNC_FAILED, + ALLOCATE_MEM_FAILED, + ILLEGAL_FIELD_NAME, + ILLEGAL_FIELD_TYPE, + SET_METADATA_FAILED, + ILLEGAL_SCHEMA_DEFINITION, + ILLEGAL_COLUMN_LIST, + SQL_ERROR, + ILLEGAL_SHARD_COUNT, + ILLEGAL_SCHEMA_COUNT, + VERSION_ERROR, + ADD_SCHEMA_FAILED, + ILLEGAL_Header_SIZE, + ILLEGAL_Page_SIZE, + ILLEGAL_SIZE_VALUE, + INDEX_FIELD_ERROR, + GET_CANDIDATE_CATEGORYFIELDS_FAILED, + GET_CATEGORY_INFO_FAILED, + ILLEGAL_CATEGORY_ID, + ILLEGAL_ROWNUMBER_OF_PAGE, + ILLEGAL_SCHEMA_ID, + DESERIALIZE_SCHEMA_FAILED, + DESERIALIZE_STATISTICS_FAILED, + ILLEGAL_DB_FILE, + OVERWRITE_DB_FILE, + OVERWRITE_MINDRECORD_FILE, + ILLEGAL_MINDRECORD_FILE, + PARSE_JSON_FAILED, + ILLEGAL_PARAMETERS, + GET_PAGE_BY_GROUP_ID_FAILED, + GET_SYSTEM_STATE_FAILED, + IO_FAILED }; + +// convert error no to string message +std::string ErrnoToMessage(MSRStatus status); } // namespace mindrecord } // namespace mindspore diff --git a/mindspore/ccsrc/mindrecord/io/shard_reader.cc b/mindspore/ccsrc/mindrecord/io/shard_reader.cc index 32825fd9df..f91d28544e 100644 --- a/mindspore/ccsrc/mindrecord/io/shard_reader.cc +++ b/mindspore/ccsrc/mindrecord/io/shard_reader.cc @@ -676,7 +676,7 @@ MSRStatus ShardReader::Open(const std::string &file_path, int n_consumer, if (CheckColumnList(selected_columns_) == FAILED) { MS_LOG(ERROR) << "Illegal column list"; - return FAILED; + return ILLEGAL_COLUMN_LIST; } // Initialize argument diff --git a/tests/ut/cpp/dataset/mind_record_op_test.cc b/tests/ut/cpp/dataset/mind_record_op_test.cc index 3d5c80b3f4..90f41fdeb9 100644 --- a/tests/ut/cpp/dataset/mind_record_op_test.cc +++ b/tests/ut/cpp/dataset/mind_record_op_test.cc @@ -21,6 +21,7 @@ #include "common/utils.h" #include "gtest/gtest.h" #include "mindrecord/include/shard_category.h" +#include "mindrecord/include/shard_error.h" #include "mindrecord/include/shard_sample.h" #include "mindrecord/include/shard_shuffle.h" #include "utils/log_adapter.h" @@ -479,3 +480,38 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordBlockReaderRepeat) { row_count++; } } + +TEST_F(MindDataTestMindRecordOp, TestMindRecordInvalidColumnList) { + // single MindRecord op and nothing else + // + // MindRecordOp + + MS_LOG(INFO) << "UT test TestMindRecordInvalidColumnList"; + + Status rc; + + // Start with an empty execution tree + auto my_tree = std::make_shared(); + + // Test info: + // Dataset from testDataset1 has 10 rows, 2 columns. + // RowsPerBuffer buffer setting of 3 yields 4 buffers with the last buffer having single row + // only. 2 workers. + // Test a column selection instead of all columns as well. + + std::vector column_list; + std::string label_col_name("file_name_2"); + column_list.push_back(label_col_name); + label_col_name = "label"; + column_list.push_back(label_col_name); + + std::shared_ptr my_mindrecord_op; + MindRecordOp::Builder builder; + builder.SetDatasetFile(mindrecord_root_path_ + "/testMindDataSet/testImageNetData/imagenet.mindrecord0") + .SetRowsPerBuffer(3) + .SetNumMindRecordWorkers(4) + .SetColumnsToLoad(column_list); + rc = builder.Build(&my_mindrecord_op); + ASSERT_TRUE(rc.IsError()); + ASSERT_TRUE(rc.ToString().find_first_of("illegal column list") != std::string::npos); +} diff --git a/tests/ut/cpp/mindrecord/ut_shard_reader_test.cc b/tests/ut/cpp/mindrecord/ut_shard_reader_test.cc index fd63373e20..f7ed39a006 100644 --- a/tests/ut/cpp/mindrecord/ut_shard_reader_test.cc +++ b/tests/ut/cpp/mindrecord/ut_shard_reader_test.cc @@ -155,7 +155,7 @@ TEST_F(TestShardReader, TestShardReaderColumnNotInSchema) { auto column_list = std::vector{"file_namex"}; ShardReader dataset; MSRStatus ret = dataset.Open(file_name, 4, column_list); - ASSERT_EQ(ret, FAILED); + ASSERT_EQ(ret, ILLEGAL_COLUMN_LIST); } TEST_F(TestShardReader, TestShardVersion) { From 824d9e49565dfc3401e3c3fb54484bbf647daeb9 Mon Sep 17 00:00:00 2001 From: buxue Date: Thu, 2 Apr 2020 11:58:45 +0800 Subject: [PATCH 149/227] Develop op MaxPoolWithArgMax --- mindspore/nn/layer/pooling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/nn/layer/pooling.py b/mindspore/nn/layer/pooling.py index 5d9b0ffa6c..bf90fcc9de 100644 --- a/mindspore/nn/layer/pooling.py +++ b/mindspore/nn/layer/pooling.py @@ -58,7 +58,7 @@ class _PoolNd(Cell): pass def extend_repr(self): - return 'kernel_size={kernel_size}, stride={stride}, pad_mode={pad_mode}'.format(**self.__dict__) + return 'kernel_size={kernel_size}, strides={strides}, pad_mode={pad_mode}'.format(**self.__dict__) class MaxPool2d(_PoolNd): From 71b81c8f1b25ac9fd2431d3b9f292df793197a13 Mon Sep 17 00:00:00 2001 From: Zirui Wu Date: Wed, 1 Apr 2020 11:24:25 -0400 Subject: [PATCH 150/227] implemented multi-thread index writer for mindrecord num threads cannot be more than num shards minor fix clang style fix address review comments --- .../include/shard_index_generator.h | 10 +- .../mindrecord/io/shard_index_generator.cc | 98 ++++++++++++------- 2 files changed, 72 insertions(+), 36 deletions(-) diff --git a/mindspore/ccsrc/mindrecord/include/shard_index_generator.h b/mindspore/ccsrc/mindrecord/include/shard_index_generator.h index f59dbe9bf0..1febd28fc2 100644 --- a/mindspore/ccsrc/mindrecord/include/shard_index_generator.h +++ b/mindspore/ccsrc/mindrecord/include/shard_index_generator.h @@ -85,14 +85,14 @@ class ShardIndexGenerator { /// \param sql /// \param data /// \return - MSRStatus BindParamaterExecuteSQL( + MSRStatus BindParameterExecuteSQL( sqlite3 *db, const std::string &sql, const std::vector>> &data); INDEX_FIELDS GenerateIndexFields(const std::vector &schema_detail); - MSRStatus ExcuteTransaction(const int &shard_no, const std::pair &db, - const std::vector &raw_page_ids, const std::map &blob_id_to_page_id); + MSRStatus ExecuteTransaction(const int &shard_no, const std::pair &db, + const std::vector &raw_page_ids, const std::map &blob_id_to_page_id); MSRStatus CreateShardNameTable(sqlite3 *db, const std::string &shard_name); @@ -103,12 +103,16 @@ class ShardIndexGenerator { void AddIndexFieldByRawData(const std::vector &schema_detail, std::vector> &row_data); + void DatabaseWriter(); // worker thread + std::string file_path_; bool append_; ShardHeader shard_header_; uint64_t page_size_; uint64_t header_size_; int schema_count_; + std::atomic_int task_; + std::atomic_bool write_success_; std::vector> fields_; }; } // namespace mindrecord diff --git a/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc b/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc index 1c14d30f30..c0108241a1 100644 --- a/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc +++ b/mindspore/ccsrc/mindrecord/io/shard_index_generator.cc @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ +#include #include "mindrecord/include/shard_index_generator.h" #include "common/utils.h" @@ -26,7 +27,13 @@ using mindspore::MsLogLevel::INFO; namespace mindspore { namespace mindrecord { ShardIndexGenerator::ShardIndexGenerator(const std::string &file_path, bool append) - : file_path_(file_path), append_(append), page_size_(0), header_size_(0), schema_count_(0) {} + : file_path_(file_path), + append_(append), + page_size_(0), + header_size_(0), + schema_count_(0), + task_(0), + write_success_(true) {} MSRStatus ShardIndexGenerator::Build() { ShardHeader header = ShardHeader(); @@ -284,7 +291,7 @@ std::pair ShardIndexGenerator::GenerateRawSQL( return {SUCCESS, sql}; } -MSRStatus ShardIndexGenerator::BindParamaterExecuteSQL( +MSRStatus ShardIndexGenerator::BindParameterExecuteSQL( sqlite3 *db, const std::string &sql, const std::vector>> &data) { sqlite3_stmt *stmt = nullptr; @@ -471,9 +478,9 @@ INDEX_FIELDS ShardIndexGenerator::GenerateIndexFields(const std::vector &s return {SUCCESS, std::move(fields)}; } -MSRStatus ShardIndexGenerator::ExcuteTransaction(const int &shard_no, const std::pair &db, - const std::vector &raw_page_ids, - const std::map &blob_id_to_page_id) { +MSRStatus ShardIndexGenerator::ExecuteTransaction(const int &shard_no, const std::pair &db, + const std::vector &raw_page_ids, + const std::map &blob_id_to_page_id) { // Add index data to database std::string shard_address = shard_header_.get_shard_address_by_id(shard_no); if (shard_address.empty()) { @@ -493,7 +500,7 @@ MSRStatus ShardIndexGenerator::ExcuteTransaction(const int &shard_no, const std: if (data.first != SUCCESS) { return FAILED; } - if (BindParamaterExecuteSQL(db.second, sql.second, data.second) == FAILED) { + if (BindParameterExecuteSQL(db.second, sql.second, data.second) == FAILED) { return FAILED; } MS_LOG(INFO) << "Insert " << data.second.size() << " rows to index db."; @@ -514,37 +521,62 @@ MSRStatus ShardIndexGenerator::WriteToDatabase() { page_size_ = shard_header_.get_page_size(); header_size_ = shard_header_.get_header_size(); schema_count_ = shard_header_.get_schema_count(); - if (shard_header_.get_shard_count() <= kMaxShardCount) { - // Create one database per shard - for (int shard_no = 0; shard_no < shard_header_.get_shard_count(); ++shard_no) { - // Create database - auto db = CreateDatabase(shard_no); - if (db.first != SUCCESS || db.second == nullptr) { - return FAILED; - } - MS_LOG(INFO) << "Init index db for shard: " << shard_no << " successfully."; - - // Pre-processing page information - auto total_pages = shard_header_.GetLastPageId(shard_no) + 1; - - std::map blob_id_to_page_id; - std::vector raw_page_ids; - for (uint64_t i = 0; i < total_pages; ++i) { - std::shared_ptr cur_page = shard_header_.GetPage(shard_no, i).first; - if (cur_page->get_page_type() == "RAW_DATA") { - raw_page_ids.push_back(i); - } else if (cur_page->get_page_type() == "BLOB_DATA") { - blob_id_to_page_id[cur_page->get_page_type_id()] = i; - } - } + if (shard_header_.get_shard_count() > kMaxShardCount) { + MS_LOG(ERROR) << "num shards: " << shard_header_.get_shard_count() << " exceeds max count:" << kMaxSchemaCount; + return FAILED; + } + task_ = 0; // set two atomic vars to initial value + write_success_ = true; - if (ExcuteTransaction(shard_no, db, raw_page_ids, blob_id_to_page_id) != SUCCESS) { - return FAILED; + // spawn half the physical threads or total number of shards whichever is smaller + const unsigned int num_workers = + std::min(std::thread::hardware_concurrency() / 2 + 1, static_cast(shard_header_.get_shard_count())); + + std::vector threads; + threads.reserve(num_workers); + + for (size_t t = 0; t < threads.capacity(); t++) { + threads.emplace_back(std::thread(&ShardIndexGenerator::DatabaseWriter, this)); + } + + for (size_t t = 0; t < threads.capacity(); t++) { + threads[t].join(); + } + return write_success_ ? SUCCESS : FAILED; +} + +void ShardIndexGenerator::DatabaseWriter() { + int shard_no = task_++; + while (shard_no < shard_header_.get_shard_count()) { + auto db = CreateDatabase(shard_no); + if (db.first != SUCCESS || db.second == nullptr || write_success_ == false) { + write_success_ = false; + return; + } + + MS_LOG(INFO) << "Init index db for shard: " << shard_no << " successfully."; + + // Pre-processing page information + auto total_pages = shard_header_.GetLastPageId(shard_no) + 1; + + std::map blob_id_to_page_id; + std::vector raw_page_ids; + for (uint64_t i = 0; i < total_pages; ++i) { + std::shared_ptr cur_page = shard_header_.GetPage(shard_no, i).first; + if (cur_page->get_page_type() == "RAW_DATA") { + raw_page_ids.push_back(i); + } else if (cur_page->get_page_type() == "BLOB_DATA") { + blob_id_to_page_id[cur_page->get_page_type_id()] = i; } - MS_LOG(INFO) << "Generate index db for shard: " << shard_no << " successfully."; } + + if (ExecuteTransaction(shard_no, db, raw_page_ids, blob_id_to_page_id) != SUCCESS) { + write_success_ = false; + return; + } + MS_LOG(INFO) << "Generate index db for shard: " << shard_no << " successfully."; + shard_no = task_++; } - return SUCCESS; } } // namespace mindrecord } // namespace mindspore From 6d1ea7af8e13c10795ddfe8bdd59db4767bf48bf Mon Sep 17 00:00:00 2001 From: Alexey Shevlyakov Date: Tue, 7 Apr 2020 13:32:52 -0400 Subject: [PATCH 151/227] remove make_unique.h --- mindspore/ccsrc/dataset/api/de_pipeline.cc | 7 ++- .../ccsrc/dataset/core/global_context.cc | 6 +-- mindspore/ccsrc/dataset/core/tensor.cc | 5 +-- mindspore/ccsrc/dataset/engine/data_buffer.cc | 2 +- mindspore/ccsrc/dataset/engine/data_schema.cc | 8 ++-- .../dataset/engine/datasetops/batch_op.cc | 16 +++---- .../dataset/engine/datasetops/dataset_op.cc | 10 ++--- .../engine/datasetops/device_queue_op.cc | 2 +- .../ccsrc/dataset/engine/datasetops/map_op.cc | 2 +- .../dataset/engine/datasetops/parallel_op.cc | 2 +- .../dataset/engine/datasetops/project_op.cc | 2 +- .../dataset/engine/datasetops/rename_op.cc | 4 +- .../dataset/engine/datasetops/shuffle_op.cc | 12 +++--- .../engine/datasetops/source/celeba_op.cc | 24 +++++------ .../engine/datasetops/source/cifar_op.cc | 24 +++++------ .../engine/datasetops/source/generator_op.cc | 8 ++-- .../datasetops/source/image_folder_op.cc | 26 +++++------ .../engine/datasetops/source/manifest_op.cc | 22 +++++----- .../engine/datasetops/source/mindrecord_op.cc | 43 ++++++++++--------- .../engine/datasetops/source/mnist_op.cc | 26 +++++------ .../source/sampler/distributed_sampler.cc | 6 +-- .../datasetops/source/sampler/pk_sampler.cc | 6 +-- .../source/sampler/random_sampler.cc | 8 ++-- .../datasetops/source/sampler/sampler.cc | 2 +- .../datasetops/source/sampler/sampler.h | 1 - .../source/sampler/sequential_sampler.cc | 6 +-- .../source/sampler/subset_random_sampler.cc | 6 +-- .../source/sampler/weighted_random_sampler.cc | 10 ++--- .../datasetops/source/storage_client.cc | 9 ++-- .../engine/datasetops/source/storage_op.cc | 8 ++-- .../engine/datasetops/source/tf_buffer.cc | 7 ++- .../engine/datasetops/source/tf_reader_op.cc | 37 ++++++++-------- .../engine/datasetops/source/voc_op.cc | 22 +++++----- .../ccsrc/dataset/engine/datasetops/zip_op.cc | 14 +++--- mindspore/ccsrc/dataset/engine/db_connector.h | 2 +- .../ccsrc/dataset/engine/execution_tree.cc | 2 +- .../dataset/kernels/image/image_utils.cc | 3 +- mindspore/ccsrc/dataset/kernels/py_func_op.cc | 1 - mindspore/ccsrc/dataset/util/arena.cc | 1 - mindspore/ccsrc/dataset/util/circular_pool.cc | 4 +- mindspore/ccsrc/dataset/util/de_error.h | 7 +++ mindspore/ccsrc/dataset/util/list.h | 3 +- mindspore/ccsrc/dataset/util/lock.cc | 1 + mindspore/ccsrc/dataset/util/lock.h | 1 - mindspore/ccsrc/dataset/util/make_unique.h | 37 ---------------- mindspore/ccsrc/dataset/util/queue.h | 2 +- mindspore/ccsrc/dataset/util/task.h | 1 - mindspore/ccsrc/device/gpu/blocking_queue.cc | 3 +- .../kernel/gpu/math/bias_add_gpu_kernel.h | 5 +-- .../kernel/gpu/nn/bias_add_grad_gpu_kenel.h | 5 +-- .../ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h | 5 +-- .../kernel/gpu/nn/lstm_grad_data_gpu_kernel.h | 7 ++- .../gpu/nn/lstm_grad_weight_gpu_kernel.h | 5 +-- tests/ut/cpp/dataset/celeba_op_test.cc | 2 +- tests/ut/cpp/dataset/cifar_op_test.cc | 2 +- tests/ut/cpp/dataset/image_folder_op_test.cc | 14 +++--- tests/ut/cpp/dataset/manifest_op_test.cc | 2 +- tests/ut/cpp/dataset/project_op_test.cc | 2 +- .../cpp/dataset/stand_alone_samplers_test.cc | 2 +- tests/ut/cpp/dataset/tfReader_op_test.cc | 20 ++++----- 60 files changed, 241 insertions(+), 291 deletions(-) delete mode 100644 mindspore/ccsrc/dataset/util/make_unique.h diff --git a/mindspore/ccsrc/dataset/api/de_pipeline.cc b/mindspore/ccsrc/dataset/api/de_pipeline.cc index 65ec8d30f2..1812c0421a 100644 --- a/mindspore/ccsrc/dataset/api/de_pipeline.cc +++ b/mindspore/ccsrc/dataset/api/de_pipeline.cc @@ -23,7 +23,6 @@ #include "dataset/engine/datasetops/source/image_folder_op.h" #include "dataset/engine/datasetops/source/mnist_op.h" #include "dataset/engine/datasetops/source/voc_op.h" -#include "dataset/util/make_unique.h" #include "dataset/core/tensor.h" #include "dataset/engine/dataset_iterator.h" #include "dataset/engine/datasetops/source/manifest_op.h" @@ -119,7 +118,7 @@ Status DEPipeline::AssignRootNode(const DsOpPtr &dataset_op) { return (tree_->As Status DEPipeline::LaunchTreeExec() { RETURN_IF_NOT_OK(tree_->Prepare()); RETURN_IF_NOT_OK(tree_->Launch()); - iterator_ = make_unique(tree_); + iterator_ = std::make_unique(tree_); if (iterator_ == nullptr) RETURN_STATUS_UNEXPECTED("Cannot create an Iterator."); return Status::OK(); } @@ -307,7 +306,7 @@ Status DEPipeline::ParseStorageOp(const py::dict &args, std::shared_ptrSetSchemaFile(ToString(args["schema"])); } else if (!args["schema_json_string"].is_none()) { - std::unique_ptr schema = make_unique(); + std::unique_ptr schema = std::make_unique(); std::string s = ToString(args["schema_json_string"]); RETURN_IF_NOT_OK(schema->LoadSchemaString(s, std::vector())); (void)builder->SetNumRows(schema->num_rows()); @@ -683,7 +682,7 @@ Status DEPipeline::ParseTFReaderOp(const py::dict &args, std::shared_ptr schema = make_unique(); + std::unique_ptr schema = std::make_unique(); if (args.contains("schema_file_path")) { RETURN_IF_NOT_OK(schema->LoadSchemaFile(ToString(args["schema_file_path"]), columns_to_load)); } else { diff --git a/mindspore/ccsrc/dataset/core/global_context.cc b/mindspore/ccsrc/dataset/core/global_context.cc index 7e361a1f2c..3de8e0fcd8 100644 --- a/mindspore/ccsrc/dataset/core/global_context.cc +++ b/mindspore/ccsrc/dataset/core/global_context.cc @@ -55,9 +55,9 @@ Status GlobalContext::Init() { // For testing we can use Dummy pool instead // Create some tensor allocators for the different types and hook them into the pool. - tensor_allocator_ = mindspore::make_unique>(mem_pool_); - cv_tensor_allocator_ = mindspore::make_unique>(mem_pool_); - int_allocator_ = mindspore::make_unique(mem_pool_); + tensor_allocator_ = std::make_unique>(mem_pool_); + cv_tensor_allocator_ = std::make_unique>(mem_pool_); + int_allocator_ = std::make_unique(mem_pool_); return Status::OK(); } diff --git a/mindspore/ccsrc/dataset/core/tensor.cc b/mindspore/ccsrc/dataset/core/tensor.cc index 6aa34fa342..8f0eae459a 100644 --- a/mindspore/ccsrc/dataset/core/tensor.cc +++ b/mindspore/ccsrc/dataset/core/tensor.cc @@ -28,7 +28,6 @@ #include "dataset/core/global_context.h" #include "dataset/core/pybind_support.h" #include "dataset/core/tensor_shape.h" -#include "dataset/util/make_unique.h" namespace py = pybind11; namespace mindspore { @@ -53,7 +52,7 @@ namespace dataset { Tensor::Tensor(const TensorShape &shape, const DataType &type) : shape_(shape), type_(type), data_(nullptr) { // grab the mem pool from global context and create the allocator for char data area std::shared_ptr global_pool = GlobalContext::Instance()->mem_pool(); - data_allocator_ = mindspore::make_unique>(global_pool); + data_allocator_ = std::make_unique>(global_pool); } Tensor::Tensor(const TensorShape &shape, const DataType &type, const unsigned char *data) : Tensor(shape, type) { @@ -137,7 +136,7 @@ Status Tensor::CreateTensor(std::shared_ptr *ptr, py::array arr) { if ((*ptr)->type_ == DataType::DE_UNKNOWN) RETURN_STATUS_UNEXPECTED("Invalid data type."); std::shared_ptr global_pool = GlobalContext::Instance()->mem_pool(); - (*ptr)->data_allocator_ = mindspore::make_unique>(global_pool); + (*ptr)->data_allocator_ = std::make_unique>(global_pool); static_cast((*ptr)->StartAddr()); int64_t byte_size = (*ptr)->SizeInBytes(); unsigned char *data = static_cast(arr.request().ptr); diff --git a/mindspore/ccsrc/dataset/engine/data_buffer.cc b/mindspore/ccsrc/dataset/engine/data_buffer.cc index a0f47512ec..4aed994d3c 100644 --- a/mindspore/ccsrc/dataset/engine/data_buffer.cc +++ b/mindspore/ccsrc/dataset/engine/data_buffer.cc @@ -40,7 +40,7 @@ Status DataBuffer::CreateDataBuffer( case DatasetType::kTf: { // This type of buffer is for TF record data. // Allocate derived class version for a TF buffers - new_data_buffer = mindspore::make_unique(id, kDeBFlagNone, storage_client); + new_data_buffer = std::make_unique(id, kDeBFlagNone, storage_client); break; } default: { diff --git a/mindspore/ccsrc/dataset/engine/data_schema.cc b/mindspore/ccsrc/dataset/engine/data_schema.cc index 68666796be..4fe5d665c6 100644 --- a/mindspore/ccsrc/dataset/engine/data_schema.cc +++ b/mindspore/ccsrc/dataset/engine/data_schema.cc @@ -26,8 +26,8 @@ #include "common/utils.h" #include "dataset/util/status.h" #include "dataset/core/tensor_shape.h" -#include "dataset/util/make_unique.h" #include "utils/log_adapter.h" +#include "dataset/util/de_error.h" namespace mindspore { namespace dataset { @@ -58,7 +58,7 @@ ColDescriptor::ColDescriptor(const std::string &col_name, DataType col_type, Ten // our shape. Otherwise, set our shape to be empty. if (in_shape != nullptr) { // Create a shape and copy construct it into our column's shape. - tensor_shape_ = mindspore::make_unique(*in_shape); + tensor_shape_ = std::make_unique(*in_shape); } else { tensor_shape_ = nullptr; } @@ -75,7 +75,7 @@ ColDescriptor::ColDescriptor(const std::string &col_name, DataType col_type, Ten ColDescriptor::ColDescriptor(const ColDescriptor &in_cd) : type_(in_cd.type_), rank_(in_cd.rank_), tensor_impl_(in_cd.tensor_impl_), col_name_(in_cd.col_name_) { // If it has a tensor shape, make a copy of it with our own unique_ptr. - tensor_shape_ = in_cd.hasShape() ? mindspore::make_unique(in_cd.shape()) : nullptr; + tensor_shape_ = in_cd.hasShape() ? std::make_unique(in_cd.shape()) : nullptr; } // Assignment overload @@ -86,7 +86,7 @@ ColDescriptor &ColDescriptor::operator=(const ColDescriptor &in_cd) { tensor_impl_ = in_cd.tensor_impl_; col_name_ = in_cd.col_name_; // If it has a tensor shape, make a copy of it with our own unique_ptr. - tensor_shape_ = in_cd.hasShape() ? mindspore::make_unique(in_cd.shape()) : nullptr; + tensor_shape_ = in_cd.hasShape() ? std::make_unique(in_cd.shape()) : nullptr; } return *this; } diff --git a/mindspore/ccsrc/dataset/engine/datasetops/batch_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/batch_op.cc index 7c5d4bd4c8..8778fe1b45 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/batch_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/batch_op.cc @@ -59,8 +59,8 @@ Status BatchOp::operator()() { TaskManager::FindMe()->Post(); int32_t epoch_num = 0, batch_num = 0, cnt = 0; TensorRow new_row; - std::unique_ptr table = make_unique(); - child_iterator_ = mindspore::make_unique(this, 0, 0); + std::unique_ptr table = std::make_unique(); + child_iterator_ = std::make_unique(this, 0, 0); RETURN_IF_NOT_OK(child_iterator_->FetchNextTensorRow(&new_row)); column_name_map_ = child_iterator_->col_name_id_map(); int32_t cur_batch_size = 0; @@ -72,7 +72,7 @@ Status BatchOp::operator()() { if (table->size() == static_cast(cur_batch_size)) { RETURN_IF_NOT_OK(worker_queues_[cnt++ % num_workers_]->EmplaceBack( std::make_pair(std::move(table), CBatchInfo(epoch_num, batch_num++, cnt - epoch_num)))); - table = make_unique(); + table = std::make_unique(); RETURN_IF_NOT_OK(GetBatchSize(&cur_batch_size, CBatchInfo(epoch_num, batch_num, cnt - epoch_num))); } RETURN_IF_NOT_OK(child_iterator_->FetchNextTensorRow(&new_row)); @@ -82,7 +82,7 @@ Status BatchOp::operator()() { RETURN_IF_NOT_OK(worker_queues_[cnt++ % num_workers_]->EmplaceBack( std::make_pair(std::move(table), CBatchInfo(epoch_num, batch_num++, cnt - epoch_num)))); } - table = make_unique(); // this drops when drop == true + table = std::make_unique(); // this drops when drop == true // end of the current epoch, batch_num should start from 0 again batch_num = 0; epoch_num++; @@ -153,9 +153,9 @@ Status BatchOp::WorkerEntry(int32_t workerId) { RETURN_IF_NOT_OK(worker_queues_[workerId]->PopFront(&table_pair)); while (table_pair.second.ctrl_ != batchCtrl::kQuit) { if (table_pair.second.ctrl_ == batchCtrl::kEOE) { - RETURN_IF_NOT_OK(out_connector_->Add(workerId, make_unique(0, DataBuffer::kDeBFlagEOE))); + RETURN_IF_NOT_OK(out_connector_->Add(workerId, std::make_unique(0, DataBuffer::kDeBFlagEOE))); } else if (table_pair.second.ctrl_ == batchCtrl::kEOF) { - RETURN_IF_NOT_OK(out_connector_->Add(workerId, make_unique(0, DataBuffer::kDeBFlagEOF))); + RETURN_IF_NOT_OK(out_connector_->Add(workerId, std::make_unique(0, DataBuffer::kDeBFlagEOF))); } else if (table_pair.second.ctrl_ == batchCtrl::kNoCtrl) { std::unique_ptr db = nullptr; RETURN_IF_NOT_OK(MakeBatchedBuffer(std::move(table_pair), &db)); @@ -170,8 +170,8 @@ Status BatchOp::MakeBatchedBuffer(std::pair, CBatc std::unique_ptr *db) { RETURN_UNEXPECTED_IF_NULL(table_pair.first); if (!input_column_names_.empty()) RETURN_IF_NOT_OK(MapColumns(&table_pair)); // pass it through pyfunc - (*db) = make_unique(table_pair.second.batch_num_, DataBuffer::kDeBFlagNone); - std::unique_ptr dest_table = make_unique(); + (*db) = std::make_unique(table_pair.second.batch_num_, DataBuffer::kDeBFlagNone); + std::unique_ptr dest_table = std::make_unique(); RETURN_IF_NOT_OK(BatchRows(&table_pair.first, &dest_table, table_pair.first->size())); (*db)->set_tensor_table(std::move(dest_table)); (*db)->set_column_name_map(column_name_map_); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc index d3b85b84fb..f51c2a1539 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/dataset_op.cc @@ -80,9 +80,9 @@ void DatasetOp::CreateConnector(int32_t num_producers, int32_t num_consumers) { MS_LOG(INFO) << "Creating connector in tree operator: " << operator_id_ << ". Producer: " << num_producers << ". Consumer: " << num_consumers << "."; if (oc_queue_size_ > 0) { - out_connector_ = mindspore::make_unique(num_producers, // The number of producers - num_consumers, // Only one consumer (the training App) - oc_queue_size_); + out_connector_ = std::make_unique(num_producers, // The number of producers + num_consumers, // Only one consumer (the training App) + oc_queue_size_); } else { // Some op's may choose not to have an output connector MS_LOG(INFO) << "Bypassed connector creation for tree operator: " << operator_id_ << "."; @@ -149,7 +149,7 @@ Status DatasetOp::GetNextInput(std::unique_ptr *p_buffer, int32_t wo // The base class implementation simply flows the eoe message to output. Derived classes // may override if they need to perform special eoe handling. Status DatasetOp::EoeReceived(int32_t worker_id) { - std::unique_ptr eoe_buffer = mindspore::make_unique(0, DataBuffer::kDeBFlagEOE); + std::unique_ptr eoe_buffer = std::make_unique(0, DataBuffer::kDeBFlagEOE); return (out_connector_->Add(static_cast(worker_id), std::move(eoe_buffer))); } @@ -157,7 +157,7 @@ Status DatasetOp::EoeReceived(int32_t worker_id) { // The base class implementation simply flows the eof message to output. Derived classes // may override if they need to perform special eof handling. Status DatasetOp::EofReceived(int32_t worker_id) { - std::unique_ptr eof_buffer = mindspore::make_unique(0, DataBuffer::kDeBFlagEOF); + std::unique_ptr eof_buffer = std::make_unique(0, DataBuffer::kDeBFlagEOF); return (out_connector_->Add(static_cast(worker_id), std::move(eof_buffer))); } diff --git a/mindspore/ccsrc/dataset/engine/datasetops/device_queue_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/device_queue_op.cc index 3c2eea16ee..71e4ce64a4 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/device_queue_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/device_queue_op.cc @@ -225,7 +225,7 @@ Status DeviceQueueOp::SendDataToCPU() { MS_LOG(INFO) << "Device queue, sending data to CPU."; int64_t total_batch = 0; - std::unique_ptr child_iterator = mindspore::make_unique(this, 0, 0); + std::unique_ptr child_iterator = std::make_unique(this, 0, 0); while (!(child_iterator->eof_handled())) { TensorRow curr_row; RETURN_IF_NOT_OK(child_iterator->FetchNextTensorRow(&curr_row)); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/map_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/map_op.cc index c9f1f98ae0..b6d603bac9 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/map_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/map_op.cc @@ -179,7 +179,7 @@ Status MapOp::WorkerEntry(int32_t worker_id) { RETURN_IF_NOT_OK(WorkerEntryInit(in_buffer.get(), &keep_input_columns, &to_process_indices, &final_col_name_id_map, &input_columns, &output_columns)); - std::unique_ptr new_tensor_table(mindspore::make_unique()); + std::unique_ptr new_tensor_table(std::make_unique()); // Perform the compute function of TensorOp(s) and store the result in new_tensor_table. RETURN_IF_NOT_OK(WorkerCompute(in_buffer.get(), to_process_indices, new_tensor_table.get(), keep_input_columns, &input_columns, &output_columns)); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/parallel_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/parallel_op.cc index d9792312a3..4b2af2250a 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/parallel_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/parallel_op.cc @@ -48,7 +48,7 @@ Status ParallelOp::CreateWorkerConnector(int32_t worker_connector_size) { // Instantiate the worker connector. This is the internal connector, not the operators // output connector. It has single master consuming from it (num producers is 1), and the number // of workers is the defined count from the op. - worker_connector_ = mindspore::make_unique(num_workers_, num_producers_, worker_connector_size); + worker_connector_ = std::make_unique(num_workers_, num_producers_, worker_connector_size); return Status::OK(); } diff --git a/mindspore/ccsrc/dataset/engine/datasetops/project_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/project_op.cc index 11296f84f4..b87967dde8 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/project_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/project_op.cc @@ -79,7 +79,7 @@ Status ProjectOp::Project(std::unique_ptr *data_buffer) { new_column_name_mapping[current_column] = i; projected_column_indices.push_back(column_name_mapping[current_column]); } - std::unique_ptr new_tensor_table = mindspore::make_unique(); + std::unique_ptr new_tensor_table = std::make_unique(); while ((*data_buffer)->NumRows() > 0) { TensorRow current_row; RETURN_IF_NOT_OK((*data_buffer)->PopRow(¤t_row)); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/rename_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/rename_op.cc index c09f56141e..725476bf91 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/rename_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/rename_op.cc @@ -84,13 +84,13 @@ Status RenameOp::operator()() { // we got eoe, now try again until we get eof MS_LOG(INFO) << "Rename operator EOE Received."; - RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(mindspore::make_unique(0, DataBuffer::kDeBFlagEOE)))); + RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(std::make_unique(0, DataBuffer::kDeBFlagEOE)))); MS_LOG(DEBUG) << "Rename operator fetching buffer after EOE."; RETURN_IF_NOT_OK(GetNextInput(&curr_buffer)); } // end of while eof loop MS_LOG(INFO) << "Rename opeerator EOF Received."; - RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(mindspore::make_unique(0, DataBuffer::kDeBFlagEOF)))); + RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(std::make_unique(0, DataBuffer::kDeBFlagEOF)))); return Status::OK(); } diff --git a/mindspore/ccsrc/dataset/engine/datasetops/shuffle_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/shuffle_op.cc index 5dae48ad73..2afafe2128 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/shuffle_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/shuffle_op.cc @@ -70,7 +70,7 @@ ShuffleOp::ShuffleOp(int32_t shuffle_size, uint32_t shuffle_seed, int32_t op_con rng_(shuffle_seed), buffer_counter_(0), rows_per_buffer_(rows_per_buffer), - shuffle_buffer_(mindspore::make_unique()), + shuffle_buffer_(std::make_unique()), shuffle_last_row_idx_(0), shuffle_buffer_state_(kShuffleStateInit) {} @@ -90,7 +90,7 @@ Status ShuffleOp::SelfReset() { shuffle_seed_ = distribution(random_device); rng_ = std::mt19937_64(shuffle_seed_); } - shuffle_buffer_ = mindspore::make_unique(); + shuffle_buffer_ = std::make_unique(); buffer_counter_ = 0; shuffle_last_row_idx_ = 0; shuffle_buffer_state_ = kShuffleStateInit; @@ -142,7 +142,7 @@ Status ShuffleOp::operator()() { // Create the child iterator to fetch our data from. int32_t worker_id = 0; int32_t child_idx = 0; - child_iterator_ = mindspore::make_unique(this, worker_id, child_idx); + child_iterator_ = std::make_unique(this, worker_id, child_idx); // Main operator loop while (true) { @@ -161,7 +161,7 @@ Status ShuffleOp::operator()() { // Step 1) // Create an output tensor table if one is not created yet. if (!new_buffer_table) { - new_buffer_table = mindspore::make_unique(); + new_buffer_table = std::make_unique(); } // Step 2) @@ -176,7 +176,7 @@ Status ShuffleOp::operator()() { // and send this buffer on it's way up the pipeline. Special case is if this is the // last row then we also send it. if (new_buffer_table->size() == rows_per_buffer_ || shuffle_last_row_idx_ == 0) { - auto new_buffer = mindspore::make_unique(buffer_counter_, DataBuffer::kDeBFlagNone); + auto new_buffer = std::make_unique(buffer_counter_, DataBuffer::kDeBFlagNone); new_buffer->set_tensor_table(std::move(new_buffer_table)); new_buffer->set_column_name_map(column_name_map_); buffer_counter_++; @@ -218,7 +218,7 @@ Status ShuffleOp::operator()() { // Since we overloaded eoeReceived function, we are responsible to flow the EOE up the // pipepline manually now that we are done draining the shuffle buffer MS_LOG(INFO) << "Shuffle operator sending EOE."; - auto eoe_buffer = mindspore::make_unique(0, DataBuffer::kDeBFlagEOE); + auto eoe_buffer = std::make_unique(0, DataBuffer::kDeBFlagEOE); RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eoe_buffer))); // Do not wait for any reset to be flown down from operators above us. diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/celeba_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/celeba_op.cc index 570fc9f454..0c2e20729e 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/celeba_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/celeba_op.cc @@ -40,7 +40,7 @@ Status CelebAOp::Builder::Build(std::shared_ptr *op) { builder_sampler_ = std::make_shared(); } - builder_schema_ = make_unique(); + builder_schema_ = std::make_unique(); RETURN_IF_NOT_OK( builder_schema_->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1))); // label is like this:0 1 0 0 1...... @@ -83,7 +83,7 @@ CelebAOp::CelebAOp(int32_t num_workers, int32_t rows_per_buffer, const std::stri col_name_map_[data_schema_->column(index).name()] = index; } - attr_info_queue_ = make_unique>>(queue_size); + attr_info_queue_ = std::make_unique>>(queue_size); io_block_queues_.Init(num_workers_, queue_size); } @@ -311,7 +311,7 @@ Status CelebAOp::AddIOBlock(std::unique_ptr *data_buffer) { row_count++; if (row_count % rows_per_buffer_ == 0) { RETURN_IF_NOT_OK(io_block_queues_[buff_count++ % num_workers_]->Add( - make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); + std::make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); keys.clear(); } } @@ -320,21 +320,21 @@ Status CelebAOp::AddIOBlock(std::unique_ptr *data_buffer) { if (!keys.empty()) { RETURN_IF_NOT_OK(io_block_queues_[(buff_count++) % num_workers_]->Add( - make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); + std::make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); } if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) { RETURN_IF_NOT_OK( - io_block_queues_[(buff_count++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEoe))); + io_block_queues_[(buff_count++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEoe))); RETURN_IF_NOT_OK( - io_block_queues_[(buff_count++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEof))); + io_block_queues_[(buff_count++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEof))); for (int32_t i = 0; i < num_workers_; i++) { RETURN_IF_NOT_OK( - io_block_queues_[i]->Add(std::move(make_unique(std::vector(), IOBlock::kDeIoBlockNone)))); + io_block_queues_[i]->Add(std::make_unique(std::vector(), IOBlock::kDeIoBlockNone))); } return Status::OK(); } else { // not the last repeat. Acquire lock, sleeps master thread, wait for the wake-up from reset RETURN_IF_NOT_OK( - io_block_queues_[(buff_count++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEoe))); + io_block_queues_[(buff_count++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEoe))); RETURN_IF_NOT_OK(wp_.Wait()); // Master thread goes to sleep after it has made all the IOBlocks wp_.Clear(); RETURN_IF_NOT_OK(sampler_->GetNextBuffer(data_buffer)); @@ -349,17 +349,17 @@ Status CelebAOp::WorkerEntry(int32_t worker_id) { RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block)); while (io_block != nullptr) { if (io_block->eoe() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(make_unique(0, DataBuffer::kDeBFlagEOE)))); + RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique(0, DataBuffer::kDeBFlagEOE))); buffer_id = worker_id; } else if (io_block->eof() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(make_unique(0, DataBuffer::kDeBFlagEOF)))); + RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique(0, DataBuffer::kDeBFlagEOF))); } else { std::vector keys; RETURN_IF_NOT_OK(io_block->GetKeys(&keys)); if (keys.empty()) { return Status::OK(); // empty key is a quit signal for workers } - std::unique_ptr db = make_unique(buffer_id, DataBuffer::kDeBFlagNone); + std::unique_ptr db = std::make_unique(buffer_id, DataBuffer::kDeBFlagNone); RETURN_IF_NOT_OK(LoadBuffer(keys, &db)); RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db))); buffer_id += num_workers_; @@ -370,7 +370,7 @@ Status CelebAOp::WorkerEntry(int32_t worker_id) { } Status CelebAOp::LoadBuffer(const std::vector &keys, std::unique_ptr *db) { - std::unique_ptr deq = make_unique(); + std::unique_ptr deq = std::make_unique(); for (const auto &key : keys) { TensorRow row; RETURN_IF_NOT_OK(LoadTensorRow(image_labels_vec_[key], &row)); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/cifar_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/cifar_op.cc index 260a4a4dc5..3e64c8a3e6 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/cifar_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/cifar_op.cc @@ -47,7 +47,7 @@ Status CifarOp::Builder::Build(std::shared_ptr *ptr) { if (sampler_ == nullptr) { sampler_ = std::make_shared(); } - schema_ = make_unique(); + schema_ = std::make_unique(); TensorShape scalar = TensorShape::CreateScalar(); RETURN_IF_NOT_OK(schema_->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1))); if (cifar_type_ == kCifar10) { @@ -91,7 +91,7 @@ CifarOp::CifarOp(CifarType type, int32_t num_works, int32_t rows_per_buf, const col_name_map_[data_schema_->column(i).name()] = i; } constexpr uint64_t kUtilQueueSize = 512; - cifar_raw_data_block_ = make_unique>>(kUtilQueueSize); + cifar_raw_data_block_ = std::make_unique>>(kUtilQueueSize); io_block_queues_.Init(num_workers_, queue_size); } @@ -114,7 +114,7 @@ Status CifarOp::operator()() { if (row_cnt_ >= num_samples_) break; // enough row read, break for loop if (row_cnt_ % rows_per_buffer_ == 0) { RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add( - make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); + std::make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); keys.clear(); } } @@ -122,21 +122,21 @@ Status CifarOp::operator()() { } if (keys.empty() == false) { RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add( - make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); + std::make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); } if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) { RETURN_IF_NOT_OK( - io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEoe))); + io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEoe))); RETURN_IF_NOT_OK( - io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEof))); + io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEof))); for (int32_t i = 0; i < num_workers_; i++) { RETURN_IF_NOT_OK( - io_block_queues_[i]->Add(make_unique(std::vector(), IOBlock::kDeIoBlockNone))); + io_block_queues_[i]->Add(std::make_unique(std::vector(), IOBlock::kDeIoBlockNone))); } return Status::OK(); } else { // not the last repeat. Acquire lock, sleeps master thread, wait for the wake-up from reset RETURN_IF_NOT_OK( - io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEoe))); + io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEoe))); RETURN_IF_NOT_OK(wp_.Wait()); // Master thread goes to sleep after it has made all the IOBlocks wp_.Clear(); RETURN_IF_NOT_OK(sampler_->GetNextBuffer(&sampler_buffer)); @@ -169,17 +169,17 @@ Status CifarOp::WorkerEntry(int32_t worker_id) { RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block)); while (io_block != nullptr) { if (io_block->eoe() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique(0, DataBuffer::kDeBFlagEOE))); + RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique(0, DataBuffer::kDeBFlagEOE))); buffer_id = worker_id; } else if (io_block->eof() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique(0, DataBuffer::kDeBFlagEOF))); + RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique(0, DataBuffer::kDeBFlagEOF))); } else { std::vector keys; RETURN_IF_NOT_OK(io_block->GetKeys(&keys)); if (keys.empty() == true) { return Status::OK(); // empty key is a quit signal for workers } - std::unique_ptr db = make_unique(buffer_id, DataBuffer::kDeBFlagNone); + std::unique_ptr db = std::make_unique(buffer_id, DataBuffer::kDeBFlagNone); RETURN_IF_NOT_OK(LoadBuffer(keys, &db)); RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db))); buffer_id += num_workers_; @@ -213,7 +213,7 @@ Status CifarOp::LoadTensorRow(uint64_t index, TensorRow *trow) { // Looping over LoadTensorRow to make 1 DataBuffer. 1 function call produces 1 buffer Status CifarOp::LoadBuffer(const std::vector &keys, std::unique_ptr *db) { - std::unique_ptr deq = make_unique(); + std::unique_ptr deq = std::make_unique(); for (const int64_t &key : keys) { TensorRow trow; RETURN_IF_NOT_OK(LoadTensorRow(key, &trow)); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/generator_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/generator_op.cc index ceb88ceb0e..37a74f019a 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/generator_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/generator_op.cc @@ -173,9 +173,9 @@ Status GeneratorOp::operator()() { bool eof = false; while (!eof) { // Create new buffer each iteration - fetched_buffer = mindspore::make_unique(buffer_id_++, DataBuffer::kDeBFlagNone); + fetched_buffer = std::make_unique(buffer_id_++, DataBuffer::kDeBFlagNone); fetched_buffer->set_column_name_map(column_names_map_); - std::unique_ptr fetched_table = mindspore::make_unique(); + std::unique_ptr fetched_table = std::make_unique(); bool eoe = false; { py::gil_scoped_acquire gil_acquire; @@ -201,12 +201,12 @@ Status GeneratorOp::operator()() { if (eoe) { // Push out EOE upon StopIteration exception from generator MS_LOG(INFO) << "Generator operator sends out EOE."; - std::unique_ptr eoe_buffer = mindspore::make_unique(0, DataBuffer::kDeBFlagEOE); + std::unique_ptr eoe_buffer = std::make_unique(0, DataBuffer::kDeBFlagEOE); RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eoe_buffer))); if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) { // If last repeat or not repeated, push out EOF and exit master loop MS_LOG(INFO) << "Generator operator sends out EOF."; - std::unique_ptr eof_buffer = mindspore::make_unique(0, DataBuffer::kDeBFlagEOF); + std::unique_ptr eof_buffer = std::make_unique(0, DataBuffer::kDeBFlagEOF); RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eof_buffer))); MS_LOG(INFO) << "Generator operator main execution loop complete."; eof = true; diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/image_folder_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/image_folder_op.cc index b8044fb38a..f6cf377666 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/image_folder_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/image_folder_op.cc @@ -39,7 +39,7 @@ Status ImageFolderOp::Builder::Build(std::shared_ptr *ptr) { if (builder_sampler_ == nullptr) { builder_sampler_ = std::make_shared(); } - builder_schema_ = make_unique(); + builder_schema_ = std::make_unique(); TensorShape scalar = TensorShape::CreateScalar(); RETURN_IF_NOT_OK( builder_schema_->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1))); @@ -82,8 +82,8 @@ ImageFolderOp::ImageFolderOp(int32_t num_wkrs, int32_t rows_per_buffer, std::str for (int32_t i = 0; i < data_schema_->NumColumns(); ++i) { col_name_map_[data_schema_->column(i).name()] = i; } - folder_name_queue_ = make_unique>(num_wkrs * queue_size); - image_name_queue_ = make_unique>(num_wkrs * queue_size); + folder_name_queue_ = std::make_unique>(num_wkrs * queue_size); + image_name_queue_ = std::make_unique>(num_wkrs * queue_size); io_block_queues_.Init(num_workers_, queue_size); } @@ -143,7 +143,7 @@ Status ImageFolderOp::operator()() { row_cnt_++; if (row_cnt_ % rows_per_buffer_ == 0) { RETURN_IF_NOT_OK( - io_block_queues_[buf_cnt_++ % num_workers_]->Add(make_unique(keys, IOBlock::kDeIoBlockNone))); + io_block_queues_[buf_cnt_++ % num_workers_]->Add(std::make_unique(keys, IOBlock::kDeIoBlockNone))); keys.clear(); } } @@ -151,21 +151,21 @@ Status ImageFolderOp::operator()() { } if (keys.empty() == false) { RETURN_IF_NOT_OK( - io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(keys, IOBlock::kDeIoBlockNone))); + io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(keys, IOBlock::kDeIoBlockNone))); } if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) { - std::unique_ptr eoe_block = make_unique(IOBlock::kDeIoBlockFlagEoe); - std::unique_ptr eof_block = make_unique(IOBlock::kDeIoBlockFlagEof); + std::unique_ptr eoe_block = std::make_unique(IOBlock::kDeIoBlockFlagEoe); + std::unique_ptr eof_block = std::make_unique(IOBlock::kDeIoBlockFlagEof); RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eoe_block))); RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eof_block))); for (int32_t i = 0; i < num_workers_; ++i) { RETURN_IF_NOT_OK( - io_block_queues_[i]->Add(make_unique(std::vector(), IOBlock::kDeIoBlockNone))); + io_block_queues_[i]->Add(std::make_unique(std::vector(), IOBlock::kDeIoBlockNone))); } return Status::OK(); } else { // not the last repeat. Sleep master thread, wait for the wake-up from reset RETURN_IF_NOT_OK( - io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEoe))); + io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEoe))); RETURN_IF_NOT_OK(wp_.Wait()); // Master thread goes to sleep after it has made all the IOBlocks wp_.Clear(); RETURN_IF_NOT_OK(sampler_->GetNextBuffer(&sampler_buffer)); @@ -182,15 +182,15 @@ Status ImageFolderOp::WorkerEntry(int32_t worker_id) { RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block)); while (io_block != nullptr) { if (io_block->eoe() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique(0, DataBuffer::kDeBFlagEOE))); + RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique(0, DataBuffer::kDeBFlagEOE))); buffer_id = worker_id; } else if (io_block->eof() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique(0, DataBuffer::kDeBFlagEOF))); + RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique(0, DataBuffer::kDeBFlagEOF))); } else { std::vector keys; RETURN_IF_NOT_OK(io_block->GetKeys(&keys)); if (keys.empty() == true) return Status::OK(); // empty key is a quit signal for workers - std::unique_ptr db = make_unique(buffer_id, DataBuffer::kDeBFlagNone); + std::unique_ptr db = std::make_unique(buffer_id, DataBuffer::kDeBFlagNone); RETURN_IF_NOT_OK(LoadBuffer(keys, &db)); RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db))); buffer_id += num_workers_; @@ -231,7 +231,7 @@ Status ImageFolderOp::LoadTensorRow(ImageLabelPair pairPtr, TensorRow *trow) { // Looping over LoadTensorRow to make 1 DataBuffer. 1 function call produces 1 buffer Status ImageFolderOp::LoadBuffer(const std::vector &keys, std::unique_ptr *db) { - std::unique_ptr deq = make_unique(); + std::unique_ptr deq = std::make_unique(); TensorRow trow; for (const int64_t &key : keys) { RETURN_IF_NOT_OK(this->LoadTensorRow(image_label_pairs_[key], &trow)); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/manifest_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/manifest_op.cc index 52db199e5b..6907647952 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/manifest_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/manifest_op.cc @@ -40,7 +40,7 @@ Status ManifestOp::Builder::Build(std::shared_ptr *ptr) { if (builder_sampler_ == nullptr) { builder_sampler_ = std::make_shared(); } - builder_schema_ = make_unique(); + builder_schema_ = std::make_unique(); RETURN_IF_NOT_OK( builder_schema_->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1))); RETURN_IF_NOT_OK( @@ -105,7 +105,7 @@ Status ManifestOp::AddIoBlock(std::unique_ptr *sampler_buffer) { row_cnt_++; if (row_cnt_ % rows_per_buffer_ == 0) { RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add( - make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); + std::make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); keys.clear(); } } @@ -113,21 +113,21 @@ Status ManifestOp::AddIoBlock(std::unique_ptr *sampler_buffer) { } if (keys.empty() == false) { RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add( - make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); + std::make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); } if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) { RETURN_IF_NOT_OK( - io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEoe))); + io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEoe))); RETURN_IF_NOT_OK( - io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEof))); + io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEof))); for (int32_t i = 0; i < num_workers_; i++) { RETURN_IF_NOT_OK( - io_block_queues_[i]->Add(make_unique(std::vector(), IOBlock::kDeIoBlockNone))); + io_block_queues_[i]->Add(std::make_unique(std::vector(), IOBlock::kDeIoBlockNone))); } return Status::OK(); } else { RETURN_IF_NOT_OK( - io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEoe))); + io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEoe))); RETURN_IF_NOT_OK(wp_.Wait()); // Master thread goes to sleep after it has made all the IOBlocks wp_.Clear(); RETURN_IF_NOT_OK(sampler_->GetNextBuffer(sampler_buffer)); @@ -160,17 +160,17 @@ Status ManifestOp::WorkerEntry(int32_t worker_id) { RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block)); while (io_block != nullptr) { if (io_block->eoe() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique(0, DataBuffer::kDeBFlagEOE))); + RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique(0, DataBuffer::kDeBFlagEOE))); buffer_id = worker_id; } else if (io_block->eof() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique(0, DataBuffer::kDeBFlagEOF))); + RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique(0, DataBuffer::kDeBFlagEOF))); } else { std::vector keys; RETURN_IF_NOT_OK(io_block->GetKeys(&keys)); if (keys.empty()) { return Status::OK(); // empty key is a quit signal for workers } - std::unique_ptr db = make_unique(buffer_id, DataBuffer::kDeBFlagNone); + std::unique_ptr db = std::make_unique(buffer_id, DataBuffer::kDeBFlagNone); RETURN_IF_NOT_OK(LoadBuffer(keys, &db)); RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db))); buffer_id += num_workers_; @@ -227,7 +227,7 @@ Status ManifestOp::LoadTensorRow(const std::pair &keys, std::unique_ptr *db) { - std::unique_ptr deq = make_unique(); + std::unique_ptr deq = std::make_unique(); for (const auto &key : keys) { TensorRow trow; RETURN_IF_NOT_OK(LoadTensorRow(image_labelname_[static_cast(key)], &trow)); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc index cb0f135a0d..fbb772af59 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc @@ -28,7 +28,6 @@ #include "dataset/engine/datasetops/dataset_op.h" #include "dataset/engine/db_connector.h" #include "dataset/engine/execution_tree.h" -#include "dataset/util/make_unique.h" #include "utils/log_adapter.h" namespace mindspore { @@ -94,19 +93,19 @@ MindRecordOp::MindRecordOp(int32_t num_mind_record_workers, int32_t rows_per_buf io_blk_queues_.Init(num_workers_, op_connector_queue_size); if (!block_reader_) return; for (int32_t i = 0; i < num_workers_; ++i) { - block_buffer_.emplace_back(make_unique>(std::vector{})); + block_buffer_.emplace_back(std::make_unique>(std::vector{})); } } // Private helper method to encapsulate some common construction/reset tasks Status MindRecordOp::Init() { - shard_reader_ = mindspore::make_unique(); + shard_reader_ = std::make_unique(); auto rc = shard_reader_->Open(dataset_file_, num_mind_record_workers_, columns_to_load_, operators_, block_reader_); CHECK_FAIL_RETURN_UNEXPECTED(rc != MSRStatus::FAILED, "MindRecordOp init failed. Error message: " + ErrnoToMessage(rc)); - data_schema_ = mindspore::make_unique(); + data_schema_ = std::make_unique(); std::vector> schema_vec = shard_reader_->get_shard_header()->get_schemas(); // check whether schema exists, if so use the first one @@ -143,7 +142,7 @@ Status MindRecordOp::Init() { } if (!load_all_cols) { - std::unique_ptr tmp_schema = make_unique(); + std::unique_ptr tmp_schema = std::make_unique(); for (std::string colname : columns_to_load_) { CHECK_FAIL_RETURN_UNEXPECTED(colname_to_ind.find(colname) != colname_to_ind.end(), colname + ": doesn't exist"); RETURN_IF_NOT_OK(tmp_schema->AddColumn(data_schema_->column(colname_to_ind[colname]))); @@ -297,7 +296,7 @@ Status MindRecordOp::LoadFloat(TensorShape *new_shape, std::unique_ptr *arr RETURN_IF_NOT_OK(GetFloat(&value, columns_json[column_name], use_double)); *new_shape = TensorShape::CreateScalar(); - *array_data = mindspore::make_unique(1); + *array_data = std::make_unique(1); (*array_data)[0] = value; } else { if (column.hasShape()) { @@ -308,7 +307,7 @@ Status MindRecordOp::LoadFloat(TensorShape *new_shape, std::unique_ptr *arr } int idx = 0; - *array_data = mindspore::make_unique(new_shape->NumOfElements()); + *array_data = std::make_unique(new_shape->NumOfElements()); for (auto &element : columns_json[column_name]) { T value = 0; RETURN_IF_NOT_OK(GetFloat(&value, element, use_double)); @@ -349,7 +348,7 @@ Status MindRecordOp::LoadInt(TensorShape *new_shape, std::unique_ptr *array RETURN_IF_NOT_OK(GetInt(&value, columns_json[column_name])); *new_shape = TensorShape::CreateScalar(); - *array_data = mindspore::make_unique(1); + *array_data = std::make_unique(1); (*array_data)[0] = value; } else { if (column.hasShape()) { @@ -360,7 +359,7 @@ Status MindRecordOp::LoadInt(TensorShape *new_shape, std::unique_ptr *array } int idx = 0; - *array_data = mindspore::make_unique(new_shape->NumOfElements()); + *array_data = std::make_unique(new_shape->NumOfElements()); for (auto &element : columns_json[column_name]) { T value = 0; RETURN_IF_NOT_OK(GetInt(&value, element)); @@ -430,12 +429,14 @@ Status MindRecordOp::WorkerEntry(int32_t worker_id) { RETURN_IF_NOT_OK(io_blk_queues_[worker_id]->PopFront(&io_block)); while (io_block != nullptr) { if (io_block->eoe() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(make_unique(0, DataBuffer::kDeBFlagEOE)))); + RETURN_IF_NOT_OK( + out_connector_->Add(worker_id, std::move(std::make_unique(0, DataBuffer::kDeBFlagEOE)))); RETURN_IF_NOT_OK(io_blk_queues_[worker_id]->PopFront(&io_block)); continue; } if (io_block->eof() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(make_unique(0, DataBuffer::kDeBFlagEOF)))); + RETURN_IF_NOT_OK( + out_connector_->Add(worker_id, std::move(std::make_unique(0, DataBuffer::kDeBFlagEOF)))); RETURN_IF_NOT_OK(io_blk_queues_[worker_id]->PopFront(&io_block)); continue; } @@ -485,9 +486,9 @@ Status MindRecordOp::WorkerEntry(int32_t worker_id) { Status MindRecordOp::GetBufferFromReader(std::unique_ptr *fetched_buffer, int64_t buffer_id, int32_t worker_id) { - *fetched_buffer = mindspore::make_unique(buffer_id, DataBuffer::kDeBFlagNone); + *fetched_buffer = std::make_unique(buffer_id, DataBuffer::kDeBFlagNone); (*fetched_buffer)->set_column_name_map(column_name_mapping_); - std::unique_ptr tensor_table = mindspore::make_unique(); + std::unique_ptr tensor_table = std::make_unique(); for (int32_t i = 0; i < rows_per_buffer_; ++i) { ShardTuple tupled_buffer; if (block_reader_) { @@ -596,22 +597,22 @@ Status MindRecordOp::operator()() { for (int32_t i = 0; i < buffers_needed_; ++i) { if (block_reader_) RETURN_IF_NOT_OK(FetchBlockBuffer(i)); std::vector keys(1, i); - RETURN_IF_NOT_OK( - io_blk_queues_[buf_cnt_++ % num_workers_]->Add(make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); + RETURN_IF_NOT_OK(io_blk_queues_[buf_cnt_++ % num_workers_]->Add( + std::make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); } if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) { RETURN_IF_NOT_OK( - io_blk_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEoe))); + io_blk_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEoe))); RETURN_IF_NOT_OK( - io_blk_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEof))); + io_blk_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEof))); for (int32_t i = 0; i < num_workers_; i++) { - RETURN_IF_NOT_OK( - io_blk_queues_[i]->Add(std::move(make_unique(std::vector(), IOBlock::kDeIoBlockNone)))); + RETURN_IF_NOT_OK(io_blk_queues_[i]->Add( + std::move(std::make_unique(std::vector(), IOBlock::kDeIoBlockNone)))); } return Status::OK(); } else { // not the last repeat. Acquire lock, sleeps master thread, wait for the wake-up from reset RETURN_IF_NOT_OK( - io_blk_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEoe))); + io_blk_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEoe))); // reset our buffer count and go to loop again. RETURN_IF_NOT_OK(shard_reader_wait_post_.Wait()); @@ -655,7 +656,7 @@ Status MindRecordOp::LaunchThreadAndInitOp() { } Status MindRecordOp::CountTotalRows(const std::string dataset_path, int64_t *count) { - std::unique_ptr shard_reader = mindspore::make_unique(); + std::unique_ptr shard_reader = std::make_unique(); MSRStatus rc = shard_reader->CountTotalRows(dataset_path, count); if (rc == MSRStatus::FAILED) { RETURN_STATUS_UNEXPECTED("MindRecordOp count total rows failed."); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/mnist_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/mnist_op.cc index f76fb9314d..3431e58aea 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/mnist_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/mnist_op.cc @@ -43,7 +43,7 @@ Status MnistOp::Builder::Build(std::shared_ptr *ptr) { if (builder_sampler_ == nullptr) { builder_sampler_ = std::make_shared(); } - builder_schema_ = make_unique(); + builder_schema_ = std::make_unique(); RETURN_IF_NOT_OK( builder_schema_->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kCv, 1))); TensorShape scalar = TensorShape::CreateScalar(); @@ -89,7 +89,7 @@ Status MnistOp::TraversalSampleIds(const std::shared_ptr &sample_ids, st row_cnt_++; if (row_cnt_ % rows_per_buffer_ == 0) { RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add( - make_unique(IOBlock(*keys, IOBlock::kDeIoBlockNone)))); + std::make_unique(IOBlock(*keys, IOBlock::kDeIoBlockNone)))); keys->clear(); } } @@ -115,21 +115,21 @@ Status MnistOp::operator()() { } if (keys.empty() == false) { RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add( - make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); + std::make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); } if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) { RETURN_IF_NOT_OK( - io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEoe))); + io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEoe))); RETURN_IF_NOT_OK( - io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEof))); + io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEof))); for (int32_t i = 0; i < num_workers_; ++i) { RETURN_IF_NOT_OK( - io_block_queues_[i]->Add(make_unique(std::vector(), IOBlock::kDeIoBlockNone))); + io_block_queues_[i]->Add(std::make_unique(std::vector(), IOBlock::kDeIoBlockNone))); } return Status::OK(); } else { RETURN_IF_NOT_OK( - io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEoe))); + io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEoe))); RETURN_IF_NOT_OK(wp_.Wait()); // Master thread goes to sleep after it has made all the IOBlocks wp_.Clear(); RETURN_IF_NOT_OK(sampler_->GetNextBuffer(&sampler_buffer)); @@ -145,15 +145,15 @@ Status MnistOp::WorkerEntry(int32_t worker_id) { RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&iOBlock)); while (iOBlock != nullptr) { if (iOBlock->eoe() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique(0, DataBuffer::kDeBFlagEOE))); + RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique(0, DataBuffer::kDeBFlagEOE))); buffer_id = worker_id; } else if (iOBlock->eof() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique(0, DataBuffer::kDeBFlagEOF))); + RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique(0, DataBuffer::kDeBFlagEOF))); } else { std::vector keys; RETURN_IF_NOT_OK(iOBlock->GetKeys(&keys)); if (keys.empty() == true) return Status::OK(); // empty key is a quit signal for workers - std::unique_ptr db = make_unique(buffer_id, DataBuffer::kDeBFlagNone); + std::unique_ptr db = std::make_unique(buffer_id, DataBuffer::kDeBFlagNone); RETURN_IF_NOT_OK(LoadBuffer(keys, &db)); RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db))); buffer_id += num_workers_; @@ -178,7 +178,7 @@ Status MnistOp::LoadTensorRow(const MnistLabelPair &mnist_pair, TensorRow *trow) // Looping over LoadTensorRow to make 1 DataBuffer. 1 function call produces 1 buffer Status MnistOp::LoadBuffer(const std::vector &keys, std::unique_ptr *db) { - std::unique_ptr deq = make_unique(); + std::unique_ptr deq = std::make_unique(); TensorRow trow; for (const int64_t &key : keys) { RETURN_IF_NOT_OK(this->LoadTensorRow(image_label_pairs_[key], &trow)); @@ -309,8 +309,8 @@ Status MnistOp::ReadImageAndLabel(std::ifstream *image_reader, std::ifstream *la CHECK_FAIL_RETURN_UNEXPECTED((num_images == num_labels), "num_images != num_labels"); // The image size of the Mnist dataset is fixed at [28,28] int64_t size = kMnistImageRows * kMnistImageCols; - auto images_buf = mindspore::make_unique(size * num_images); - auto labels_buf = mindspore::make_unique(num_images); + auto images_buf = std::make_unique(size * num_images); + auto labels_buf = std::make_unique(num_images); if (images_buf == nullptr || labels_buf == nullptr) { std::string err_msg = "Fail to allocate memory for MNIST Buffer."; MS_LOG(ERROR) << err_msg.c_str(); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.cc index 51ad71e8cf..28a5705648 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/distributed_sampler.cc @@ -52,9 +52,9 @@ Status DistributedSampler::GetNextBuffer(std::unique_ptr *out_buffer if (cnt_ > samples_per_buffer_) { RETURN_STATUS_UNEXPECTED("Distributed Sampler Error"); } else if (cnt_ == samples_per_buffer_) { - (*out_buffer) = mindspore::make_unique(0, DataBuffer::kDeBFlagEOE); + (*out_buffer) = std::make_unique(0, DataBuffer::kDeBFlagEOE); } else { - (*out_buffer) = mindspore::make_unique(cnt_, DataBuffer::kDeBFlagNone); + (*out_buffer) = std::make_unique(cnt_, DataBuffer::kDeBFlagNone); std::shared_ptr sample_ids; RETURN_IF_NOT_OK(CreateSamplerTensor(&sample_ids, samples_per_buffer_)); int64_t *id_ptr = reinterpret_cast(sample_ids->StartAddr()); @@ -63,7 +63,7 @@ Status DistributedSampler::GetNextBuffer(std::unique_ptr *out_buffer *(id_ptr++) = shuffle_ ? shuffle_vec_[static_cast(next_id)] : next_id; } TensorRow row(1, sample_ids); - (*out_buffer)->set_tensor_table(make_unique(1, row)); + (*out_buffer)->set_tensor_table(std::make_unique(1, row)); } return Status::OK(); } diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.cc index 04a6ad17a2..8c8c12fce2 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/pk_sampler.cc @@ -53,9 +53,9 @@ Status PKSampler::GetNextBuffer(std::unique_ptr *out_buffer) { if (next_id_ > num_pk_samples_ || num_pk_samples_ == 0) { RETURN_STATUS_UNEXPECTED("Index out of bound in PKSampler"); } else if (next_id_ == num_pk_samples_) { - (*out_buffer) = mindspore::make_unique(0, DataBuffer::kDeBFlagEOE); + (*out_buffer) = std::make_unique(0, DataBuffer::kDeBFlagEOE); } else { - (*out_buffer) = mindspore::make_unique(next_id_, DataBuffer::kDeBFlagNone); + (*out_buffer) = std::make_unique(next_id_, DataBuffer::kDeBFlagNone); std::shared_ptr sample_ids; int64_t last_id = (samples_per_buffer_ + next_id_ > num_pk_samples_) ? num_pk_samples_ : samples_per_buffer_ + next_id_; @@ -68,7 +68,7 @@ Status PKSampler::GetNextBuffer(std::unique_ptr *out_buffer) { *(id_ptr++) = samples[rnd_ind]; } TensorRow row(1, sample_ids); - (*out_buffer)->set_tensor_table(make_unique(1, row)); + (*out_buffer)->set_tensor_table(std::make_unique(1, row)); } return Status::OK(); } diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.cc index de4d89d950..216f322052 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/random_sampler.cc @@ -32,9 +32,9 @@ Status RandomSampler::GetNextBuffer(std::unique_ptr *out_buffer) { if (next_id_ > num_samples_) { RETURN_STATUS_UNEXPECTED("RandomSampler Internal Error"); } else if (next_id_ == num_samples_) { - (*out_buffer) = make_unique(0, DataBuffer::kDeBFlagEOE); + (*out_buffer) = std::make_unique(0, DataBuffer::kDeBFlagEOE); } else { - (*out_buffer) = make_unique(next_id_, DataBuffer::kDeBFlagNone); + (*out_buffer) = std::make_unique(next_id_, DataBuffer::kDeBFlagNone); std::shared_ptr sampleIds; int64_t last_id = samples_per_buffer_ + next_id_ > num_samples_ ? num_samples_ : samples_per_buffer_ + next_id_; RETURN_IF_NOT_OK(CreateSamplerTensor(&sampleIds, last_id - next_id_)); @@ -44,7 +44,7 @@ Status RandomSampler::GetNextBuffer(std::unique_ptr *out_buffer) { } next_id_ = last_id; TensorRow row(1, sampleIds); - (*out_buffer)->set_tensor_table(make_unique(1, row)); + (*out_buffer)->set_tensor_table(std::make_unique(1, row)); } return Status::OK(); } @@ -61,7 +61,7 @@ Status RandomSampler::Init(const RandomAccessOp *op) { } std::shuffle(shuffled_ids_.begin(), shuffled_ids_.end(), rnd_); } else { - dist = make_unique>(0, num_rows_ - 1); + dist = std::make_unique>(0, num_rows_ - 1); } rnd_.seed(seed_++); return Status::OK(); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc index 9818cd8a17..aa3838f8b5 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc @@ -35,7 +35,7 @@ Status Sampler::CreateSamplerTensor(std::shared_ptr *sample_ids, int64_t } if (col_desc_ == nullptr) { // a ColDescriptor for Tensor that holds SampleIds - col_desc_ = make_unique("sampleIds", DataType(DataType::DE_INT64), TensorImpl::kFlexible, 1); + col_desc_ = std::make_unique("sampleIds", DataType(DataType::DE_INT64), TensorImpl::kFlexible, 1); } TensorShape shape(std::vector(1, num_elements)); RETURN_IF_NOT_OK(Tensor::CreateTensor(sample_ids, col_desc_->tensorImpl(), shape, col_desc_->type())); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.h b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.h index d9a20f9170..801565508b 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.h +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.h @@ -27,7 +27,6 @@ #include "dataset/engine/data_buffer.h" #include "dataset/engine/data_schema.h" #include "dataset/engine/datasetops/dataset_op.h" -#include "dataset/util/make_unique.h" namespace mindspore { namespace dataset { diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.cc index 71c3dd07c4..72131a6de1 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sequential_sampler.cc @@ -25,9 +25,9 @@ Status SequentialSampler::GetNextBuffer(std::unique_ptr *out_buffer) if (next_id_ > num_samples_) { RETURN_STATUS_UNEXPECTED("Sequential Sampler Internal Error"); } else if (next_id_ == num_samples_) { - (*out_buffer) = make_unique(0, DataBuffer::kDeBFlagEOE); + (*out_buffer) = std::make_unique(0, DataBuffer::kDeBFlagEOE); } else { - (*out_buffer) = make_unique(next_id_, DataBuffer::kDeBFlagNone); + (*out_buffer) = std::make_unique(next_id_, DataBuffer::kDeBFlagNone); std::shared_ptr sampleIds; int64_t lastId = (samples_per_buffer_ + next_id_ > num_samples_) ? num_samples_ : samples_per_buffer_ + next_id_; RETURN_IF_NOT_OK(CreateSamplerTensor(&sampleIds, lastId - next_id_)); @@ -36,7 +36,7 @@ Status SequentialSampler::GetNextBuffer(std::unique_ptr *out_buffer) *(idPtr++) = next_id_++; } TensorRow row(1, sampleIds); - (*out_buffer)->set_tensor_table(make_unique(1, row)); + (*out_buffer)->set_tensor_table(std::make_unique(1, row)); } return Status::OK(); } diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.cc index 4f727fcd04..16603939b3 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/subset_random_sampler.cc @@ -64,9 +64,9 @@ Status SubsetRandomSampler::Reset() { Status SubsetRandomSampler::GetNextBuffer(std::unique_ptr *out_buffer) { // All samples have been drawn if (sample_id_ == indices_.size()) { - (*out_buffer) = make_unique(buffer_id_++, DataBuffer::kDeBFlagEOE); + (*out_buffer) = std::make_unique(buffer_id_++, DataBuffer::kDeBFlagEOE); } else { - (*out_buffer) = make_unique(buffer_id_++, DataBuffer::kDeBFlagNone); + (*out_buffer) = std::make_unique(buffer_id_++, DataBuffer::kDeBFlagNone); std::shared_ptr outputIds; int64_t last_id = sample_id_ + samples_per_buffer_; @@ -92,7 +92,7 @@ Status SubsetRandomSampler::GetNextBuffer(std::unique_ptr *out_buffe } // Create a TensorTable from that single tensor and push into DataBuffer - (*out_buffer)->set_tensor_table(make_unique(1, TensorRow(1, outputIds))); + (*out_buffer)->set_tensor_table(std::make_unique(1, TensorRow(1, outputIds))); } return Status::OK(); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.cc index f305474182..f2957e74be 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/sampler/weighted_random_sampler.cc @@ -46,10 +46,10 @@ Status WeightedRandomSampler::Init(const RandomAccessOp *op) { CHECK_FAIL_RETURN_UNEXPECTED(num_samples_ > 0 && samples_per_buffer_ > 0, "Fail to init WeightedRandomSampler"); if (!replacement_) { - exp_dist_ = mindspore::make_unique>(1); + exp_dist_ = std::make_unique>(1); InitOnePassSampling(); } else { - discrete_dist_ = mindspore::make_unique>(weights_.begin(), weights_.end()); + discrete_dist_ = std::make_unique>(weights_.begin(), weights_.end()); } return Status::OK(); @@ -96,9 +96,9 @@ Status WeightedRandomSampler::GetNextBuffer(std::unique_ptr *out_buf } if (sample_id_ == num_samples_) { - (*out_buffer) = make_unique(buffer_id_++, DataBuffer::kDeBFlagEOE); + (*out_buffer) = std::make_unique(buffer_id_++, DataBuffer::kDeBFlagEOE); } else { - (*out_buffer) = make_unique(buffer_id_++, DataBuffer::kDeBFlagNone); + (*out_buffer) = std::make_unique(buffer_id_++, DataBuffer::kDeBFlagNone); std::shared_ptr outputIds; int64_t last_id = sample_id_ + samples_per_buffer_; @@ -132,7 +132,7 @@ Status WeightedRandomSampler::GetNextBuffer(std::unique_ptr *out_buf } // Create a TensorTable from that single tensor and push into DataBuffer - (*out_buffer)->set_tensor_table(make_unique(1, TensorRow(1, outputIds))); + (*out_buffer)->set_tensor_table(std::make_unique(1, TensorRow(1, outputIds))); } return Status::OK(); diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/storage_client.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/storage_client.cc index 95720a97be..862edcf63a 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/storage_client.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/storage_client.cc @@ -24,7 +24,6 @@ #include "dataset/engine/datasetops/source/storage_client.h" #include "dataset/engine/datasetops/source/storage_op.h" #include "dataset/engine/datasetops/source/tf_client.h" -#include "dataset/util/make_unique.h" #include "dataset/util/status.h" namespace mindspore { @@ -57,7 +56,7 @@ static Status CreateStorageClientSwitch( case DatasetType::kTf: { // Construct the derived class TFClient, stored as base class StorageClient store_op->set_rows_per_buffer(32); - *out_client = mindspore::make_unique(std::move(schema), store_op); + *out_client = std::make_unique(std::move(schema), store_op); break; } case DatasetType::kUnknown: @@ -83,7 +82,7 @@ Status StorageClient::CreateStorageClient( std::shared_ptr *out_client) { // Out: the created storage client // Make a new schema first. This only assigns the dataset type. It does not // create the columns yet. - auto new_schema = mindspore::make_unique(); + auto new_schema = std::make_unique(); RETURN_IF_NOT_OK(new_schema->LoadDatasetType(dataset_schema_path)); RETURN_IF_NOT_OK(CreateStorageClientSwitch(std::move(new_schema), store_op, out_client)); return Status::OK(); @@ -99,7 +98,7 @@ Status StorageClient::CreateStorageClient( std::shared_ptr *out_client) { // Out: the created storage client // The dataset type is passed in by the user. Create an empty schema with only // only the dataset type filled in and then create the client with it. - auto new_schema = mindspore::make_unique(); + auto new_schema = std::make_unique(); new_schema->set_dataset_type(in_type); RETURN_IF_NOT_OK(CreateStorageClientSwitch(std::move(new_schema), store_op, out_client)); return Status::OK(); @@ -147,7 +146,7 @@ Status StorageClient::AssignDatasetLayout(uint32_t num_rows, // In: Th // The current schema was just an empty one with only the dataset field populated. // Let's copy construct a new one that will be a copy of the input schema (releasing the old // one) and then set the number of rows that the user requested. - data_schema_ = mindspore::make_unique(schema); + data_schema_ = std::make_unique(schema); CHECK_FAIL_RETURN_UNEXPECTED(num_rows <= MAX_INTEGER_INT32, "numRows exceeds the boundary numRows>2147483647"); num_rows_in_dataset_ = num_rows; diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/storage_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/storage_op.cc index 9da27eac04..2ca957ae6d 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/storage_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/storage_op.cc @@ -303,7 +303,7 @@ Status StorageOp::init() { // For simplicity, we'll make both of them 3 so they are the same size. int32_t action_queue_size = (buffers_needed / num_workers_) + 1; for (int32_t i = 0; i < num_workers_; ++i) { - auto new_queue = mindspore::make_unique>(action_queue_size); + auto new_queue = std::make_unique>(action_queue_size); action_queue_.push_back(std::move(new_queue)); } } @@ -483,10 +483,10 @@ Status StorageOp::operator()() { // Post the control message to tell the workers to stop waiting on action queue // because we are done! RETURN_IF_NOT_OK(this->PostEndOfData()); - std::unique_ptr eoeBuffer = mindspore::make_unique(0, DataBuffer::kDeBFlagEOE); + std::unique_ptr eoeBuffer = std::make_unique(0, DataBuffer::kDeBFlagEOE); RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eoeBuffer))); MS_LOG(INFO) << "StorageOp master: Flow end-of-data eof message."; - std::unique_ptr eofBuffer = mindspore::make_unique(0, DataBuffer::kDeBFlagEOF); + std::unique_ptr eofBuffer = std::make_unique(0, DataBuffer::kDeBFlagEOF); RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eofBuffer))); MS_LOG(INFO) << "StorageOp master: Main execution loop complete."; done = true; // while loop exit @@ -496,7 +496,7 @@ Status StorageOp::operator()() { // RepeatOp above us somewhere in the tree will re-init us with the data to fetch again // once it gets the end-of-epoch message. MS_LOG(INFO) << "StorageOp master: Flow end-of-epoch eoe message."; - std::unique_ptr eoe_buffer = mindspore::make_unique(0, DataBuffer::kDeBFlagEOE); + std::unique_ptr eoe_buffer = std::make_unique(0, DataBuffer::kDeBFlagEOE); RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eoe_buffer))); // reset our buffer count and go to loop again. diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_buffer.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_buffer.cc index 766c2149c4..372dcd2c1c 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_buffer.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_buffer.cc @@ -27,7 +27,6 @@ #include "dataset/core/data_type.h" #include "dataset/engine/datasetops/source/storage_client.h" #include "dataset/engine/data_schema.h" -#include "dataset/util/make_unique.h" namespace mindspore { namespace dataset { @@ -72,7 +71,7 @@ Status TFBuffer::Load() { } // Construct the Tensor table for this buffer. - tensor_table_ = mindspore::make_unique(); + tensor_table_ = std::make_unique(); // At each position in the tensor table, instantiate the shared pointer to it's Tensor. uint32_t row = 0; @@ -272,7 +271,7 @@ Status TFBuffer::LoadFloatList(const ColDescriptor ¤t_col, const dataengin // Identify how many values we have and then create a local array of these // to deserialize into *num_elements = float_list.value_size(); - *float_array = mindspore::make_unique(*num_elements); + *float_array = std::make_unique(*num_elements); for (int i = 0; i < float_list.value_size(); i++) { (*float_array)[i] = float_list.value(i); } @@ -294,7 +293,7 @@ Status TFBuffer::LoadIntList(const ColDescriptor ¤t_col, const dataengine: // Identify how many values we have and then create a local array of these // to deserialize into *num_elements = int64_list.value_size(); - *int_array = mindspore::make_unique(*num_elements); + *int_array = std::make_unique(*num_elements); for (int i = 0; i < int64_list.value_size(); i++) { (*int_array)[i] = int64_list.value(i); } diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc index c872c02015..0764d7e0ad 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc @@ -36,7 +36,6 @@ #include "dataset/engine/db_connector.h" #include "dataset/engine/execution_tree.h" #include "dataset/engine/jagged_connector.h" -#include "dataset/util/make_unique.h" #include "dataset/util/path.h" #include "dataset/util/queue.h" #include "dataset/util/random.h" @@ -54,7 +53,7 @@ TFReaderOp::Builder::Builder() builder_op_connector_size_ = config_manager->op_connector_size(); builder_rows_per_buffer_ = config_manager->rows_per_buffer(); builder_shuffle_files_ = false; - builder_data_schema_ = make_unique(); + builder_data_schema_ = std::make_unique(); } Status TFReaderOp::Builder::ValidateInputs() const { @@ -103,7 +102,7 @@ TFReaderOp::TFReaderOp(int32_t num_workers, int32_t worker_connector_size, int64 finished_reading_dataset_(false), shuffle_files_(shuffle_files), data_schema_(std::move(data_schema)), - filename_index_(make_unique()), + filename_index_(std::make_unique()), load_io_block_queue_(true), load_jagged_connector_(true), num_rows_(0), @@ -129,7 +128,7 @@ Status TFReaderOp::Init() { // parallel op base. RETURN_IF_NOT_OK(ParallelOp::CreateWorkerConnector(worker_connector_size_)); - jagged_buffer_connector_ = mindspore::make_unique(num_workers_, 1, worker_connector_size_); + jagged_buffer_connector_ = std::make_unique(num_workers_, 1, worker_connector_size_); // temporary: make size large enough to hold all files + EOE to avoid hangs int32_t safe_queue_size = static_cast(std::ceil(dataset_files_list_.size() / num_workers_)) + 1; @@ -229,7 +228,7 @@ Status TFReaderOp::operator()() { } // all workers finished reading for this epoch, and we have read all the data from all workers - std::unique_ptr eoe_buffer = mindspore::make_unique(0, DataBuffer::kDeBFlagEOE); + std::unique_ptr eoe_buffer = std::make_unique(0, DataBuffer::kDeBFlagEOE); RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eoe_buffer))); if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) { @@ -241,7 +240,7 @@ Status TFReaderOp::operator()() { } } - std::unique_ptr eof_buffer = mindspore::make_unique(0, DataBuffer::kDeBFlagEOF); + std::unique_ptr eof_buffer = std::make_unique(0, DataBuffer::kDeBFlagEOF); RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eof_buffer))); RETURN_IF_NOT_OK(PostEndOfData()); @@ -274,7 +273,7 @@ Status TFReaderOp::WorkerEntry(int32_t worker_id) { MS_LOG(INFO) << "TFReader operator worker " << worker_id << " loaded file " << filename << "."; } } else { - std::unique_ptr eoe_buffer = mindspore::make_unique(1, DataBuffer::kDeBFlagEOE); + std::unique_ptr eoe_buffer = std::make_unique(1, DataBuffer::kDeBFlagEOE); RETURN_IF_NOT_OK(jagged_buffer_connector_->Add(worker_id, std::move(eoe_buffer))); } @@ -288,7 +287,7 @@ Status TFReaderOp::WorkerEntry(int32_t worker_id) { // When the worker pops this control indicator, it will shut itself down gracefully. Status TFReaderOp::PostEndOfData() { for (int i = 0; i < num_workers_; ++i) { - std::unique_ptr eof = mindspore::make_unique(IOBlock::kDeIoBlockFlagEof); + std::unique_ptr eof = std::make_unique(IOBlock::kDeIoBlockFlagEof); RETURN_IF_NOT_OK(PushIoBlockQueue(i, std::move(eof))); } @@ -299,7 +298,7 @@ Status TFReaderOp::PostEndOfData() { // pops this control indicator, it will wait until the next epoch starts and then resume execution. Status TFReaderOp::PostEndOfEpoch(int32_t queue_index) { for (int i = 0; i < num_workers_; ++i) { - std::unique_ptr eoe = mindspore::make_unique(IOBlock::kDeIoBlockFlagEoe); + std::unique_ptr eoe = std::make_unique(IOBlock::kDeIoBlockFlagEoe); RETURN_IF_NOT_OK(PushIoBlockQueue((queue_index + i) % num_workers_, std::move(eoe))); } @@ -358,7 +357,7 @@ Status TFReaderOp::FillIOBlockShuffle(const std::vector &i_keys) { } if (!equal_rows_per_shard_) { if (key_index++ % num_devices_ == device_id_) { - auto ioBlock = make_unique(*it, kInvalidOffset, kInvalidOffset, IOBlock::kDeIoBlockNone); + auto ioBlock = std::make_unique(*it, kInvalidOffset, kInvalidOffset, IOBlock::kDeIoBlockNone); RETURN_IF_NOT_OK(PushIoBlockQueue(queue_index, std::move(ioBlock))); queue_index = (queue_index + 1) % num_workers_; } @@ -367,7 +366,7 @@ Status TFReaderOp::FillIOBlockShuffle(const std::vector &i_keys) { auto file_it = filename_index_->Search(*it); std::string file_name = file_it.value(); if (NeedPushFileToblockQueue(file_name, &start_offset, &end_offset, pre_count)) { - auto ioBlock = make_unique(*it, start_offset, end_offset, IOBlock::kDeIoBlockNone); + auto ioBlock = std::make_unique(*it, start_offset, end_offset, IOBlock::kDeIoBlockNone); RETURN_IF_NOT_OK(PushIoBlockQueue(queue_index, std::move(ioBlock))); MS_LOG(DEBUG) << "File name " << *it << " start offset " << start_offset << " end_offset " << end_offset; queue_index = (queue_index + 1) % num_workers_; @@ -404,14 +403,15 @@ Status TFReaderOp::FillIOBlockNoShuffle() { } if (!equal_rows_per_shard_) { if (key_index++ % num_devices_ == device_id_) { - auto ioBlock = make_unique(it.key(), kInvalidOffset, kInvalidOffset, IOBlock::kDeIoBlockNone); + auto ioBlock = + std::make_unique(it.key(), kInvalidOffset, kInvalidOffset, IOBlock::kDeIoBlockNone); RETURN_IF_NOT_OK(PushIoBlockQueue(queue_index, std::move(ioBlock))); queue_index = (queue_index + 1) % num_workers_; } } else { std::string file_name = it.value(); if (NeedPushFileToblockQueue(file_name, &start_offset, &end_offset, pre_count)) { - auto ioBlock = make_unique(it.key(), start_offset, end_offset, IOBlock::kDeIoBlockNone); + auto ioBlock = std::make_unique(it.key(), start_offset, end_offset, IOBlock::kDeIoBlockNone); RETURN_IF_NOT_OK(PushIoBlockQueue(queue_index, std::move(ioBlock))); queue_index = (queue_index + 1) % num_workers_; } @@ -490,14 +490,13 @@ Status TFReaderOp::LoadFile(const std::string &filename, const int64_t start_off int64_t rows_read = 0; int64_t rows_total = 0; - std::unique_ptr current_buffer = - mindspore::make_unique(0, DataBuffer::BufferFlags::kDeBFlagNone); + std::unique_ptr current_buffer = std::make_unique(0, DataBuffer::BufferFlags::kDeBFlagNone); std::unordered_map column_name_map; for (int32_t i = 0; i < data_schema_->NumColumns(); ++i) { column_name_map[data_schema_->column(i).name()] = i; } current_buffer->set_column_name_map(column_name_map); - std::unique_ptr new_tensor_table = make_unique(); + std::unique_ptr new_tensor_table = std::make_unique(); while (reader.peek() != EOF) { if (!load_jagged_connector_) { @@ -532,9 +531,9 @@ Status TFReaderOp::LoadFile(const std::string &filename, const int64_t start_off current_buffer->set_tensor_table(std::move(new_tensor_table)); RETURN_IF_NOT_OK(jagged_buffer_connector_->Add(worker_id, std::move(current_buffer))); - current_buffer = make_unique(0, DataBuffer::BufferFlags::kDeBFlagNone); + current_buffer = std::make_unique(0, DataBuffer::BufferFlags::kDeBFlagNone); current_buffer->set_column_name_map(column_name_map); - new_tensor_table = make_unique(); + new_tensor_table = std::make_unique(); rows_read = 0; } } @@ -742,7 +741,7 @@ Status TFReaderOp::LoadFloatList(const ColDescriptor ¤t_col, const dataeng // Identify how many values we have and then create a local array of these // to deserialize into *num_elements = float_list.value_size(); - *float_array = mindspore::make_unique(*num_elements); + *float_array = std::make_unique(*num_elements); for (int i = 0; i < float_list.value_size(); ++i) { (*float_array)[i] = float_list.value(i); } diff --git a/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.cc index d3785f4660..71b4c47cf5 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.cc @@ -38,7 +38,7 @@ Status VOCOp::Builder::Build(std::shared_ptr *ptr) { if (builder_sampler_ == nullptr) { builder_sampler_ = std::make_shared(); } - builder_schema_ = make_unique(); + builder_schema_ = std::make_unique(); RETURN_IF_NOT_OK( builder_schema_->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1))); RETURN_IF_NOT_OK( @@ -85,7 +85,7 @@ Status VOCOp::TraverseSampleIds(const std::shared_ptr &sample_ids, std:: row_cnt_++; if (row_cnt_ % rows_per_buffer_ == 0) { RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add( - make_unique(IOBlock(*keys, IOBlock::kDeIoBlockNone)))); + std::make_unique(IOBlock(*keys, IOBlock::kDeIoBlockNone)))); keys->clear(); } } @@ -110,21 +110,21 @@ Status VOCOp::operator()() { } if (keys.empty() == false) { RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add( - make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); + std::make_unique(IOBlock(keys, IOBlock::kDeIoBlockNone)))); } if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) { - std::unique_ptr eoe_block = make_unique(IOBlock::kDeIoBlockFlagEoe); - std::unique_ptr eof_block = make_unique(IOBlock::kDeIoBlockFlagEof); + std::unique_ptr eoe_block = std::make_unique(IOBlock::kDeIoBlockFlagEoe); + std::unique_ptr eof_block = std::make_unique(IOBlock::kDeIoBlockFlagEof); RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eoe_block))); RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eof_block))); for (int32_t i = 0; i < num_workers_; i++) { RETURN_IF_NOT_OK( - io_block_queues_[i]->Add(make_unique(std::vector(), IOBlock::kDeIoBlockNone))); + io_block_queues_[i]->Add(std::make_unique(std::vector(), IOBlock::kDeIoBlockNone))); } return Status::OK(); } else { RETURN_IF_NOT_OK( - io_block_queues_[(buf_cnt_++) % num_workers_]->Add(make_unique(IOBlock::kDeIoBlockFlagEoe))); + io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique(IOBlock::kDeIoBlockFlagEoe))); RETURN_IF_NOT_OK(wp_.Wait()); wp_.Clear(); RETURN_IF_NOT_OK(sampler_->GetNextBuffer(&sampler_buffer)); @@ -164,7 +164,7 @@ Status VOCOp::LoadTensorRow(const std::string &image_id, TensorRow *trow) { } Status VOCOp::LoadBuffer(const std::vector &keys, std::unique_ptr *db) { - std::unique_ptr deq = make_unique(); + std::unique_ptr deq = std::make_unique(); TensorRow trow; for (const uint64_t &key : keys) { RETURN_IF_NOT_OK(this->LoadTensorRow(image_ids_[key], &trow)); @@ -182,15 +182,15 @@ Status VOCOp::WorkerEntry(int32_t worker_id) { RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block)); while (io_block != nullptr) { if (io_block->eoe() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, make_unique(0, DataBuffer::kDeBFlagEOE))); + RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique(0, DataBuffer::kDeBFlagEOE))); buffer_id = worker_id; } else if (io_block->eof() == true) { - RETURN_IF_NOT_OK(out_connector_->Add(worker_id, (make_unique(0, DataBuffer::kDeBFlagEOF)))); + RETURN_IF_NOT_OK(out_connector_->Add(worker_id, (std::make_unique(0, DataBuffer::kDeBFlagEOF)))); } else { std::vector keys; RETURN_IF_NOT_OK(io_block->GetKeys(&keys)); if (keys.empty() == true) return Status::OK(); - std::unique_ptr db = make_unique(buffer_id, DataBuffer::kDeBFlagNone); + std::unique_ptr db = std::make_unique(buffer_id, DataBuffer::kDeBFlagNone); RETURN_IF_NOT_OK(LoadBuffer(keys, &db)); RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db))); buffer_id += num_workers_; diff --git a/mindspore/ccsrc/dataset/engine/datasetops/zip_op.cc b/mindspore/ccsrc/dataset/engine/datasetops/zip_op.cc index 716c853488..ec771740c1 100644 --- a/mindspore/ccsrc/dataset/engine/datasetops/zip_op.cc +++ b/mindspore/ccsrc/dataset/engine/datasetops/zip_op.cc @@ -65,13 +65,13 @@ Status ZipOp::operator()() { // initialize the iterators for (int32_t i = 0; i < children_num_; ++i) { // magic number 0 since Zip is not a parallel Op - child_iterators_.push_back(mindspore::make_unique(this, 0, i)); + child_iterators_.push_back(std::make_unique(this, 0, i)); } // Loop until eof is true while (!eof_) { // Create tensor table and prepare it by fetching and packing the first zipped row into it. - std::unique_ptr curr_table = mindspore::make_unique(); + std::unique_ptr curr_table = std::make_unique(); RETURN_IF_NOT_OK(prepare(curr_table.get())); // If an eof got picked up during the above prepare, then we're done @@ -81,7 +81,7 @@ Status ZipOp::operator()() { while (!draining_) { // 1. If a previous loop iteration sent the current table out, then create a new one. if (curr_table == nullptr) { - curr_table = mindspore::make_unique(); + curr_table = std::make_unique(); } // 2 fill the table. Note: draining mode might get turned on if any of the child inputs were done @@ -89,8 +89,7 @@ Status ZipOp::operator()() { // 3 create and update buffer and send it to the out connector if (!curr_table->empty()) { - std::unique_ptr curr_buffer = - mindspore::make_unique(buffer_id_, DataBuffer::kDeBFlagNone); + std::unique_ptr curr_buffer = std::make_unique(buffer_id_, DataBuffer::kDeBFlagNone); curr_buffer->set_tensor_table(std::move(curr_table)); curr_buffer->set_column_name_map(col_name_id_map_); MS_LOG(DEBUG) << "Zip operator finished one buffer, pushing, rows " << curr_buffer->NumRows() << ", cols " @@ -105,15 +104,14 @@ Status ZipOp::operator()() { MS_LOG(DEBUG) << "Zip operator is now draining child inputs."; RETURN_IF_NOT_OK(drainPipeline()); // Now that we have drained child inputs, send the eoe up. - RETURN_IF_NOT_OK( - out_connector_->Add(0, std::move(mindspore::make_unique(0, DataBuffer::kDeBFlagEOE)))); + RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(std::make_unique(0, DataBuffer::kDeBFlagEOE)))); } } // 5 handle eof // propagate eof here. MS_LOG(INFO) << "Zip operator got EOF, propagating."; - RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(mindspore::make_unique(0, DataBuffer::kDeBFlagEOF)))); + RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(std::make_unique(0, DataBuffer::kDeBFlagEOF)))); return Status::OK(); } diff --git a/mindspore/ccsrc/dataset/engine/db_connector.h b/mindspore/ccsrc/dataset/engine/db_connector.h index 243e77e6ac..7ea9837c44 100644 --- a/mindspore/ccsrc/dataset/engine/db_connector.h +++ b/mindspore/ccsrc/dataset/engine/db_connector.h @@ -65,7 +65,7 @@ class DbConnector : public Connector> { RETURN_IF_NOT_OK(cv_.Wait(&lk, [this, worker_id]() { return expect_consumer_ == worker_id; })); // Once an EOF message is encountered this flag will be set and we can return early. if (end_of_file_) { - *result = mindspore::make_unique(0, DataBuffer::kDeBFlagEOF); + *result = std::make_unique(0, DataBuffer::kDeBFlagEOF); } else { RETURN_IF_NOT_OK(queues_[pop_from_]->PopFront(result)); if (*result == nullptr) { diff --git a/mindspore/ccsrc/dataset/engine/execution_tree.cc b/mindspore/ccsrc/dataset/engine/execution_tree.cc index 3dbeaa5ed1..20fcb836c5 100644 --- a/mindspore/ccsrc/dataset/engine/execution_tree.cc +++ b/mindspore/ccsrc/dataset/engine/execution_tree.cc @@ -24,7 +24,7 @@ namespace mindspore { namespace dataset { // Constructor ExecutionTree::ExecutionTree() : id_count_(0) { - tg_ = mindspore::make_unique(); + tg_ = std::make_unique(); tree_state_ = kDeTStateInit; prepare_flags_ = kDePrepNone; } diff --git a/mindspore/ccsrc/dataset/kernels/image/image_utils.cc b/mindspore/ccsrc/dataset/kernels/image/image_utils.cc index 0412f07636..8735cf7a05 100644 --- a/mindspore/ccsrc/dataset/kernels/image/image_utils.cc +++ b/mindspore/ccsrc/dataset/kernels/image/image_utils.cc @@ -24,7 +24,6 @@ #include "dataset/core/cv_tensor.h" #include "dataset/core/tensor.h" #include "dataset/core/tensor_shape.h" -#include "dataset/util/make_unique.h" #include "dataset/util/random.h" #define MAX_INT_PRECISION 16777216 // float int precision is 16777216 @@ -376,7 +375,7 @@ Status HwcToChw(std::shared_ptr input, std::shared_ptr *output) int width = input_cv->shape()[1]; int num_channels = input_cv->shape()[2]; - auto output_cv = mindspore::make_unique(TensorShape{num_channels, height, width}, input_cv->type()); + auto output_cv = std::make_unique(TensorShape{num_channels, height, width}, input_cv->type()); for (int i = 0; i < num_channels; ++i) { cv::Mat mat; RETURN_IF_NOT_OK(output_cv->Mat({i}, &mat)); diff --git a/mindspore/ccsrc/dataset/kernels/py_func_op.cc b/mindspore/ccsrc/dataset/kernels/py_func_op.cc index 69bd3443c4..c9e5d5b169 100644 --- a/mindspore/ccsrc/dataset/kernels/py_func_op.cc +++ b/mindspore/ccsrc/dataset/kernels/py_func_op.cc @@ -20,7 +20,6 @@ #include "dataset/core/tensor.h" #include "dataset/kernels/tensor_op.h" -#include "dataset/util/make_unique.h" #include "dataset/util/status.h" namespace mindspore { diff --git a/mindspore/ccsrc/dataset/util/arena.cc b/mindspore/ccsrc/dataset/util/arena.cc index 856f7fef24..68673529ff 100644 --- a/mindspore/ccsrc/dataset/util/arena.cc +++ b/mindspore/ccsrc/dataset/util/arena.cc @@ -16,7 +16,6 @@ #include "dataset/util/arena.h" #include #include -#include "dataset/util/make_unique.h" #include "dataset/util/system_pool.h" #include "dataset/util/de_error.h" #include "./securec.h" diff --git a/mindspore/ccsrc/dataset/util/circular_pool.cc b/mindspore/ccsrc/dataset/util/circular_pool.cc index f6e43f35bf..92b169c94a 100644 --- a/mindspore/ccsrc/dataset/util/circular_pool.cc +++ b/mindspore/ccsrc/dataset/util/circular_pool.cc @@ -18,10 +18,8 @@ #include #include #include - #include "./securec.h" - -#include "dataset/util/make_unique.h" +#include "dataset/util/de_error.h" #include "dataset/util/system_pool.h" namespace mindspore { diff --git a/mindspore/ccsrc/dataset/util/de_error.h b/mindspore/ccsrc/dataset/util/de_error.h index 07d7c125f7..d4988c58db 100644 --- a/mindspore/ccsrc/dataset/util/de_error.h +++ b/mindspore/ccsrc/dataset/util/de_error.h @@ -16,6 +16,13 @@ #ifndef DATASET_UTIL_DE_ERROR_H_ #define DATASET_UTIL_DE_ERROR_H_ +#ifdef DEBUG +#include +#define DS_ASSERT(f) assert(f) +#else +#define DS_ASSERT(f) ((void)0) +#endif + #include #include "utils/error_code.h" diff --git a/mindspore/ccsrc/dataset/util/list.h b/mindspore/ccsrc/dataset/util/list.h index f01201e34d..5a08f4514e 100644 --- a/mindspore/ccsrc/dataset/util/list.h +++ b/mindspore/ccsrc/dataset/util/list.h @@ -18,8 +18,7 @@ #include #include - -#include "dataset/util/make_unique.h" +#include "dataset/util/de_error.h" namespace mindspore { namespace dataset { diff --git a/mindspore/ccsrc/dataset/util/lock.cc b/mindspore/ccsrc/dataset/util/lock.cc index 7e92a1e53f..13a43e3e84 100644 --- a/mindspore/ccsrc/dataset/util/lock.cc +++ b/mindspore/ccsrc/dataset/util/lock.cc @@ -14,6 +14,7 @@ * limitations under the License. */ #include "dataset/util/lock.h" +#include "dataset/util/de_error.h" namespace mindspore { namespace dataset { diff --git a/mindspore/ccsrc/dataset/util/lock.h b/mindspore/ccsrc/dataset/util/lock.h index 8fef6a143a..9492d34bdf 100644 --- a/mindspore/ccsrc/dataset/util/lock.h +++ b/mindspore/ccsrc/dataset/util/lock.h @@ -19,7 +19,6 @@ #include #include #include -#include "dataset/util/make_unique.h" namespace mindspore { namespace dataset { diff --git a/mindspore/ccsrc/dataset/util/make_unique.h b/mindspore/ccsrc/dataset/util/make_unique.h deleted file mode 100644 index 2fe0bf4550..0000000000 --- a/mindspore/ccsrc/dataset/util/make_unique.h +++ /dev/null @@ -1,37 +0,0 @@ -/** - * Copyright 2019 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef DATASET_UTIL_MAKE_UNIQUE_H_ -#define DATASET_UTIL_MAKE_UNIQUE_H_ - -#ifdef DEBUG -#include -#define DS_ASSERT(f) assert(f) -#else -#define DS_ASSERT(f) ((void)0) -#endif - -#include -#include -#include -#include "dataset/util/de_error.h" -#include "utils/log_adapter.h" - -namespace mindspore { -using std::make_unique; -} // namespace mindspore - -#endif // DATASET_UTIL_MAKE_UNIQUE_H_ diff --git a/mindspore/ccsrc/dataset/util/queue.h b/mindspore/ccsrc/dataset/util/queue.h index 4048deb86b..f0b087cf6d 100644 --- a/mindspore/ccsrc/dataset/util/queue.h +++ b/mindspore/ccsrc/dataset/util/queue.h @@ -212,7 +212,7 @@ class QueueList { void Init(int num_queues, int capacity) { queue_list_.reserve(num_queues); for (int i = 0; i < num_queues; i++) { - queue_list_.emplace_back(mindspore::make_unique>(capacity)); + queue_list_.emplace_back(std::make_unique>(capacity)); } } diff --git a/mindspore/ccsrc/dataset/util/task.h b/mindspore/ccsrc/dataset/util/task.h index aaf2f80a3d..d6149caec8 100644 --- a/mindspore/ccsrc/dataset/util/task.h +++ b/mindspore/ccsrc/dataset/util/task.h @@ -27,7 +27,6 @@ #include #include #include "dataset/util/de_error.h" -#include "dataset/util/make_unique.h" #include "dataset/util/intrp_resource.h" #include "dataset/util/list.h" #include "dataset/util/memory_pool.h" diff --git a/mindspore/ccsrc/device/gpu/blocking_queue.cc b/mindspore/ccsrc/device/gpu/blocking_queue.cc index 7417115ae9..c36b1cdbf5 100644 --- a/mindspore/ccsrc/device/gpu/blocking_queue.cc +++ b/mindspore/ccsrc/device/gpu/blocking_queue.cc @@ -17,7 +17,6 @@ #include "device/gpu/blocking_queue.h" #include #include "device/gpu/gpu_common.h" -#include "dataset/util/make_unique.h" #include "common/utils.h" namespace mindspore { @@ -32,7 +31,7 @@ GpuQueue::GpuQueue(void *addr, size_t feature_size, size_t label_size, size_t ca stream_(0), node_info_(nullptr) { CHECK_CUDA_RET_WITH_ERROR(cudaStreamCreate(&stream_), "Cuda Create Stream Failed"); - node_info_ = mindspore::make_unique(capacity); + node_info_ = std::make_unique(capacity); } GpuQueue::~GpuQueue() { buffer_ = nullptr; } diff --git a/mindspore/ccsrc/kernel/gpu/math/bias_add_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/math/bias_add_gpu_kernel.h index 265180afe6..90609c3be5 100644 --- a/mindspore/ccsrc/kernel/gpu/math/bias_add_gpu_kernel.h +++ b/mindspore/ccsrc/kernel/gpu/math/bias_add_gpu_kernel.h @@ -23,7 +23,6 @@ #include #include "kernel/gpu/gpu_kernel.h" #include "kernel/gpu/gpu_kernel_factory.h" -#include "dataset/util/make_unique.h" #include "kernel/gpu/kernel_constants.h" namespace mindspore { @@ -74,8 +73,8 @@ class BiasAddGpuKernel : public GpuKernel { // Expand to 4 dims for cudnnSetTensorNdDescriptorEx. auto cudnn_dims = std::max(num_dims, 4UL); - std::unique_ptr x_dims = mindspore::make_unique(cudnn_dims); - std::unique_ptr b_dims = mindspore::make_unique(cudnn_dims); + std::unique_ptr x_dims = std::make_unique(cudnn_dims); + std::unique_ptr b_dims = std::make_unique(cudnn_dims); for (size_t i = 0; i < cudnn_dims; i++) { x_dims[i] = (i < num_dims) ? SizeToInt(x_shape[i]) : 1; b_dims[i] = (i == pos) ? SizeToInt(x_shape[i]) : 1; diff --git a/mindspore/ccsrc/kernel/gpu/nn/bias_add_grad_gpu_kenel.h b/mindspore/ccsrc/kernel/gpu/nn/bias_add_grad_gpu_kenel.h index b0e8102ee3..fd73f378d8 100644 --- a/mindspore/ccsrc/kernel/gpu/nn/bias_add_grad_gpu_kenel.h +++ b/mindspore/ccsrc/kernel/gpu/nn/bias_add_grad_gpu_kenel.h @@ -26,7 +26,6 @@ #include "kernel/gpu/gpu_kernel.h" #include "kernel/gpu/gpu_kernel_factory.h" #include "kernel/gpu/kernel_constants.h" -#include "dataset/util/make_unique.h" namespace mindspore { namespace kernel { @@ -84,8 +83,8 @@ class BiasAddGradGpuKernel : public GpuKernel { // Expand to 4 dims for cudnnSetTensorNdDescriptorEx. auto cudnn_dims = std::max(num_dims, 4UL); - std::unique_ptr dy_dims = mindspore::make_unique(cudnn_dims); - std::unique_ptr db_dims = mindspore::make_unique(cudnn_dims); + std::unique_ptr dy_dims = std::make_unique(cudnn_dims); + std::unique_ptr db_dims = std::make_unique(cudnn_dims); for (size_t i = 0; i < cudnn_dims; i++) { dy_dims[i] = (i < num_dims) ? SizeToInt(dy_shape[i]) : 1; db_dims[i] = (i == pos) ? SizeToInt(dy_shape[i]) : 1; diff --git a/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h index 51a2da8574..c3e839b9c5 100644 --- a/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h +++ b/mindspore/ccsrc/kernel/gpu/nn/lstm_gpu_kernel.h @@ -22,7 +22,6 @@ #include #include "kernel/gpu/gpu_kernel.h" #include "kernel/gpu/gpu_kernel_factory.h" -#include "dataset/util/make_unique.h" #include "kernel/gpu/kernel_constants.h" namespace mindspore { @@ -144,8 +143,8 @@ class LstmGpuKernel : public GpuKernel { int x_dims[3]{batch_size_, input_size_, 1}; int y_dims[3]{batch_size_, hidden_size_ * (bidirectional_ ? 2 : 1), 1}; - x_desc_ = mindspore::make_unique(seq_len_); - y_desc_ = mindspore::make_unique(seq_len_); + x_desc_ = std::make_unique(seq_len_); + y_desc_ = std::make_unique(seq_len_); for (size_t i = 0; i < IntToSize(seq_len_); ++i) { CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&x_desc_[i]), "create x_desc failed"); diff --git a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h index a60ab78f7d..b12fa3bea5 100644 --- a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h +++ b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_data_gpu_kernel.h @@ -23,7 +23,6 @@ #include "kernel/gpu/gpu_kernel.h" #include "kernel/gpu/gpu_kernel_factory.h" #include "kernel/gpu/kernel_constants.h" -#include "dataset/util/make_unique.h" namespace mindspore { namespace kernel { @@ -212,9 +211,9 @@ class LstmGradDataGpuKernel : public GpuKernel { int x_dims[3]{batch_size_, input_size_, 1}; int y_dims[3]{batch_size_, hidden_size_ * (bidirectional_ ? 2 : 1), 1}; - dx_desc_ = mindspore::make_unique(seq_len_); - y_desc_ = mindspore::make_unique(seq_len_); - dy_desc_ = mindspore::make_unique(seq_len_); + dx_desc_ = std::make_unique(seq_len_); + y_desc_ = std::make_unique(seq_len_); + dy_desc_ = std::make_unique(seq_len_); for (size_t i = 0; i < IntToSize(seq_len_); ++i) { CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&dx_desc_[i]), "create x_desc failed"); diff --git a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h index b28736cc96..e081b9d070 100644 --- a/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h +++ b/mindspore/ccsrc/kernel/gpu/nn/lstm_grad_weight_gpu_kernel.h @@ -22,7 +22,6 @@ #include #include "kernel/gpu/gpu_kernel.h" #include "kernel/gpu/gpu_kernel_factory.h" -#include "dataset/util/make_unique.h" #include "kernel/gpu/kernel_constants.h" namespace mindspore { namespace kernel { @@ -169,8 +168,8 @@ class LstmGradWeightGpuKernel : public GpuKernel { int x_dims[3]{batch_size_, input_size_, 1}; int y_dims[3]{batch_size_, hidden_size_ * (bidirectional_ ? 2 : 1), 1}; - x_desc_ = mindspore::make_unique(seq_len_); - y_desc_ = mindspore::make_unique(seq_len_); + x_desc_ = std::make_unique(seq_len_); + y_desc_ = std::make_unique(seq_len_); for (size_t i = 0; i < IntToSize(seq_len_); ++i) { CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&x_desc_[i]), "create x_desc failed"); diff --git a/tests/ut/cpp/dataset/celeba_op_test.cc b/tests/ut/cpp/dataset/celeba_op_test.cc index 69314771a3..35be4d7378 100644 --- a/tests/ut/cpp/dataset/celeba_op_test.cc +++ b/tests/ut/cpp/dataset/celeba_op_test.cc @@ -116,7 +116,7 @@ TEST_F(MindDataTestCelebaDataset, TestCelebaRepeat) { TEST_F(MindDataTestCelebaDataset, TestSubsetRandomSamplerCeleba) { std::vector indices({1}); - std::unique_ptr sampler = mindspore::make_unique(indices); + std::unique_ptr sampler = std::make_unique(indices); uint32_t expect_labels[1][40] = {{0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1}}; std::string dir = datasets_root_path_ + "/testCelebAData/"; uint32_t count = 0; diff --git a/tests/ut/cpp/dataset/cifar_op_test.cc b/tests/ut/cpp/dataset/cifar_op_test.cc index 0cd1db65b5..dcbea83df4 100644 --- a/tests/ut/cpp/dataset/cifar_op_test.cc +++ b/tests/ut/cpp/dataset/cifar_op_test.cc @@ -92,7 +92,7 @@ TEST_F(MindDataTestCifarOp, TestSequentialSamplerCifar10) { TEST_F(MindDataTestCifarOp, TestRandomSamplerCifar10) { uint32_t original_seed = GlobalContext::config_manager()->seed(); GlobalContext::config_manager()->set_seed(0); - std::unique_ptr sampler = mindspore::make_unique(true, 12); + std::unique_ptr sampler = std::make_unique(true, 12); std::string folder_path = datasets_root_path_ + "/testCifar10Data/"; auto tree = Build({Cifarop(16, 2, 32, folder_path, std::move(sampler), 100)}); tree->Prepare(); diff --git a/tests/ut/cpp/dataset/image_folder_op_test.cc b/tests/ut/cpp/dataset/image_folder_op_test.cc index 5b118a629a..e149e687c6 100644 --- a/tests/ut/cpp/dataset/image_folder_op_test.cc +++ b/tests/ut/cpp/dataset/image_folder_op_test.cc @@ -138,7 +138,7 @@ TEST_F(MindDataTestImageFolderSampler, TestRandomImageFolder) { TEST_F(MindDataTestImageFolderSampler, TestRandomSamplerImageFolder) { int32_t original_seed = GlobalContext::config_manager()->seed(); GlobalContext::config_manager()->set_seed(0); - std::unique_ptr sampler = mindspore::make_unique(true, 12); + std::unique_ptr sampler = std::make_unique(true, 12); int32_t res[] = {2, 2, 2, 3, 2, 3, 2, 3, 1, 2, 2, 1}; // ground truth label std::string folder_path = datasets_root_path_ + "/testPK/data"; auto tree = Build({ImageFolder(16, 2, 32, folder_path, false, std::move(sampler))}); @@ -200,7 +200,7 @@ TEST_F(MindDataTestImageFolderSampler, TestSequentialImageFolderWithRepeatBatch) TEST_F(MindDataTestImageFolderSampler, TestSubsetRandomSamplerImageFolder) { // id range 0 - 10 is label 0, and id range 11 - 21 is label 1 std::vector indices({0, 1, 2, 3, 4, 5, 12, 13, 14, 15, 16, 11}); - std::unique_ptr sampler = mindspore::make_unique(indices); + std::unique_ptr sampler = std::make_unique(indices); std::string folder_path = datasets_root_path_ + "/testPK/data"; // Expect 6 samples for label 0 and 1 int res[2] = {6, 6}; @@ -238,7 +238,7 @@ TEST_F(MindDataTestImageFolderSampler, TestWeightedRandomSamplerImageFolder) { // create sampler with replacement = replacement std::unique_ptr sampler = - mindspore::make_unique(weights, num_samples, true, samples_per_buffer); + std::make_unique(weights, num_samples, true, samples_per_buffer); std::string folder_path = datasets_root_path_ + "/testPK/data"; auto tree = Build({ImageFolder(16, 2, 32, folder_path, false, std::move(sampler))}); @@ -295,7 +295,7 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderClassIndex) { } TEST_F(MindDataTestImageFolderSampler, TestDistributedSampler) { - std::unique_ptr sampler = mindspore::make_unique(11, 10, false); + std::unique_ptr sampler = std::make_unique(11, 10, false); std::string folder_path = datasets_root_path_ + "/testPK/data"; auto tree = Build({ImageFolder(16, 2, 32, folder_path, false, std::move(sampler)), Repeat(4)}); tree->Prepare(); @@ -322,7 +322,7 @@ TEST_F(MindDataTestImageFolderSampler, TestDistributedSampler) { } TEST_F(MindDataTestImageFolderSampler, TestPKSamplerImageFolder) { - std::unique_ptr sampler = mindspore::make_unique(3, false, 4); + std::unique_ptr sampler = std::make_unique(3, false, 4); int32_t res[] = {0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3}; // ground truth label std::string folder_path = datasets_root_path_ + "/testPK/data"; auto tree = Build({ImageFolder(16, 2, 32, folder_path, false, std::move(sampler))}); @@ -431,7 +431,7 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderDatasetSize) { } TEST_F(MindDataTestImageFolderSampler, TestImageFolderSharding1) { - std::unique_ptr sampler = mindspore::make_unique(4, 0, false); + std::unique_ptr sampler = std::make_unique(4, 0, false); std::string folder_path = datasets_root_path_ + "/testPK/data"; // numWrks, rows, conns, path, shuffle, sampler, map, numSamples, decode auto tree = Build({ImageFolder(16, 2, 32, folder_path, false, std::move(sampler), {}, 5)}); @@ -460,7 +460,7 @@ TEST_F(MindDataTestImageFolderSampler, TestImageFolderSharding1) { } TEST_F(MindDataTestImageFolderSampler, TestImageFolderSharding2) { - std::unique_ptr sampler = mindspore::make_unique(4, 3, false); + std::unique_ptr sampler = std::make_unique(4, 3, false); std::string folder_path = datasets_root_path_ + "/testPK/data"; // numWrks, rows, conns, path, shuffle, sampler, map, numSamples, decode auto tree = Build({ImageFolder(16, 16, 32, folder_path, false, std::move(sampler), {}, 12)}); diff --git a/tests/ut/cpp/dataset/manifest_op_test.cc b/tests/ut/cpp/dataset/manifest_op_test.cc index 9e36f8c747..f662f98fc8 100644 --- a/tests/ut/cpp/dataset/manifest_op_test.cc +++ b/tests/ut/cpp/dataset/manifest_op_test.cc @@ -86,7 +86,7 @@ TEST_F(MindDataTestManifest, TestSequentialManifestWithRepeat) { TEST_F(MindDataTestManifest, TestSubsetRandomSamplerManifest) { std::vector indices({1}); - std::unique_ptr sampler = mindspore::make_unique(indices); + std::unique_ptr sampler = std::make_unique(indices); std::string file = datasets_root_path_ + "/testManifestData/cpp.json"; // Expect 6 samples for label 0 and 1 auto tree = Build({Manifest(16, 2, 32, file, "train", std::move(sampler))}); diff --git a/tests/ut/cpp/dataset/project_op_test.cc b/tests/ut/cpp/dataset/project_op_test.cc index 1df2ce05bb..484396321c 100644 --- a/tests/ut/cpp/dataset/project_op_test.cc +++ b/tests/ut/cpp/dataset/project_op_test.cc @@ -45,7 +45,7 @@ TEST_F(MindDataTestProjectOp, TestProjectProject) { .SetRowsPerBuffer(16) .SetWorkerConnectorSize(16) .SetNumWorkers(16); - std::unique_ptr schema = mindspore::make_unique(); + std::unique_ptr schema = std::make_unique(); schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {}); builder.SetDataSchema(std::move(schema)); Status rc = builder.Build(&my_tfreader_op); ASSERT_TRUE(rc.IsOk()); diff --git a/tests/ut/cpp/dataset/stand_alone_samplers_test.cc b/tests/ut/cpp/dataset/stand_alone_samplers_test.cc index c686a9486b..48cc811615 100644 --- a/tests/ut/cpp/dataset/stand_alone_samplers_test.cc +++ b/tests/ut/cpp/dataset/stand_alone_samplers_test.cc @@ -74,7 +74,7 @@ TEST_F(MindDataTestStandAloneSampler, TestDistributedSampler) { std::unique_ptr db; std::shared_ptr tensor; for (int i = 0; i < 6; i++) { - std::unique_ptr sampler = mindspore::make_unique(3, i % 3, (i < 3 ? false : true)); + std::unique_ptr sampler = std::make_unique(3, i % 3, (i < 3 ? false : true)); sampler->Init(&mock); sampler->GetNextBuffer(&db); db->GetTensor(&tensor, 0, 0); diff --git a/tests/ut/cpp/dataset/tfReader_op_test.cc b/tests/ut/cpp/dataset/tfReader_op_test.cc index c70d5fb6ee..5fb1f4e909 100644 --- a/tests/ut/cpp/dataset/tfReader_op_test.cc +++ b/tests/ut/cpp/dataset/tfReader_op_test.cc @@ -48,7 +48,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderBasic1) { builder.SetDatasetFilesList({dataset_path}) .SetRowsPerBuffer(16) .SetNumWorkers(16); - std::unique_ptr schema = mindspore::make_unique(); + std::unique_ptr schema = std::make_unique(); schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {}); builder.SetDataSchema(std::move(schema)); Status rc = builder.Build(&my_tfreader_op); @@ -102,7 +102,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderLargeRowsPerBuffer) { builder.SetDatasetFilesList({dataset_path}) .SetRowsPerBuffer(500) .SetNumWorkers(16); - std::unique_ptr schema = mindspore::make_unique(); + std::unique_ptr schema = std::make_unique(); schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {}); builder.SetDataSchema(std::move(schema)); Status rc = builder.Build(&my_tfreader_op); @@ -156,7 +156,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderSmallRowsPerBuffer) { builder.SetDatasetFilesList({dataset_path}) .SetRowsPerBuffer(1) .SetNumWorkers(16); - std::unique_ptr schema = mindspore::make_unique(); + std::unique_ptr schema = std::make_unique(); schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {}); builder.SetDataSchema(std::move(schema)); Status rc = builder.Build(&my_tfreader_op); @@ -211,7 +211,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderLargeQueueSize) { .SetWorkerConnectorSize(1) .SetRowsPerBuffer(16) .SetNumWorkers(16); - std::unique_ptr schema = mindspore::make_unique(); + std::unique_ptr schema = std::make_unique(); schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {}); builder.SetDataSchema(std::move(schema)); Status rc = builder.Build(&my_tfreader_op); @@ -265,7 +265,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderOneThread) { builder.SetDatasetFilesList({dataset_path}) .SetRowsPerBuffer(16) .SetNumWorkers(1); - std::unique_ptr schema = mindspore::make_unique(); + std::unique_ptr schema = std::make_unique(); schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {}); builder.SetDataSchema(std::move(schema)); Status rc = builder.Build(&my_tfreader_op); @@ -321,7 +321,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderRepeat) { .SetRowsPerBuffer(16) .SetWorkerConnectorSize(16) .SetNumWorkers(16); - std::unique_ptr schema = mindspore::make_unique(); + std::unique_ptr schema = std::make_unique(); schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {}); builder.SetDataSchema(std::move(schema)); Status rc= builder.Build(&my_tfreader_op); @@ -379,7 +379,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderSchemaConstructor) { std::string dataset_path; dataset_path = datasets_root_path_ + "/testTFTestAllTypes"; - std::unique_ptr data_schema = mindspore::make_unique(); + std::unique_ptr data_schema = std::make_unique(); std::vector columns_to_load; columns_to_load.push_back("col_sint32"); columns_to_load.push_back("col_binary"); @@ -445,7 +445,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderTake1Row) { std::shared_ptr my_tfreader_op; TFReaderOp::Builder builder; builder.SetDatasetFilesList({dataset_path + "/test.data"}).SetRowsPerBuffer(5).SetNumWorkers(16); - std::unique_ptr schema = mindspore::make_unique(); + std::unique_ptr schema = std::make_unique(); schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema1Row.json", {}); builder.SetDataSchema(std::move(schema)); @@ -503,7 +503,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderTake1Buffer) { std::shared_ptr my_tfreader_op; TFReaderOp::Builder builder; builder.SetDatasetFilesList({dataset_path + "/test.data"}).SetRowsPerBuffer(5).SetNumWorkers(16); - std::unique_ptr schema = mindspore::make_unique(); + std::unique_ptr schema = std::make_unique(); schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema5Rows.json", {}); builder.SetDataSchema(std::move(schema)); @@ -561,7 +561,7 @@ TEST_F(MindDataTestTFReaderOp, TestTFReaderTake7Rows) { std::shared_ptr my_tfreader_op; TFReaderOp::Builder builder; builder.SetDatasetFilesList({dataset_path + "/test.data"}).SetRowsPerBuffer(5).SetNumWorkers(16); - std::unique_ptr schema = mindspore::make_unique(); + std::unique_ptr schema = std::make_unique(); schema->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema7Rows.json", {}); builder.SetDataSchema(std::move(schema)); From d3757240551c610545b63efbc1f9c63ecafddd9e Mon Sep 17 00:00:00 2001 From: mxm <83028974@qq.com> Date: Tue, 31 Mar 2020 12:08:02 +0800 Subject: [PATCH 152/227] fixed: PrimitiveToInferImplMap map is global, and key of the map PrimitivePtr also a global variable. If key is initialized later than the map initialized during compilation, will cause the primitive map initialize failed. Variable initialization order is not guaranteed during compilation. --- .../ccsrc/pipeline/static_analysis/prim.cc | 248 +++++++++--------- .../static_analysis/static_analysis.cc | 49 ++-- 2 files changed, 148 insertions(+), 149 deletions(-) diff --git a/mindspore/ccsrc/pipeline/static_analysis/prim.cc b/mindspore/ccsrc/pipeline/static_analysis/prim.cc index d71e098009..403bbdf433 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/prim.cc +++ b/mindspore/ccsrc/pipeline/static_analysis/prim.cc @@ -42,92 +42,95 @@ namespace mindspore { namespace abstract { -PrimitiveEvalImplMap PrimitiveToInferImplMap = { - // Statements - {prim::kPrimReturn, {InferImplReturn, true}}, - {prim::kPrimTypeOf, {InferImplTypeof, false}}, - {prim::kPrimHasType, {InferImplHasType, false}}, - {prim::kPrimDot, {InferImplDot, true}}, - {prim::kPrimSwitch, {InferImplSwitch, true}}, - {prim::kPrimIs_, {InferImplIs_, true}}, - {prim::kPrimIsNot, {InferImplIsNot, true}}, - // Maths - {prim::kPrimMaximumGrad, {InferImplMinOrMaxGrad, true}}, - {prim::kPrimMinimumGrad, {InferImplMinOrMaxGrad, true}}, - // Array - {prim::kPrimScalarToArray, {InferImplScalarToArray, true}}, - {prim::kPrimArrayToScalar, {InferImplArrayToScalar, true}}, - {prim::kPrimBroadcastShape, {InferImplBroadCastShape, true}}, - {prim::kPrimShape, {InferImplShape, true}}, - {prim::kPrimPack, {InferImplPack, true}}, - // Structure - {prim::kPrimMakeTuple, {InferImplMakeTuple, true}}, - {prim::kPrimMakeList, {InferImplMakeList, true}}, - {prim::kPrimMakeDict, {InferImplMakeDict, true}}, - {prim::kPrimMakeSlice, {InferImplMakeSlice, true}}, - {prim::kPrimMakeKeywordArg, {InferImplMakeKwarg, true}}, - {prim::kPrimExtractKeywordArg, {InferImplExtractKwarg, true}}, - {prim::kPrimMakeRecord, {InferImplMakeRecord, false}}, - {prim::kPrimTupleGetItem, {InferImplTupleGetItem, true}}, - {prim::kPrimListGetItem, {InferImplListGetItem, true}}, - {prim::kPrimTupleSetItem, {InferImplTupleSetItem, true}}, - {prim::kPrimListSetItem, {InferImplListSetItem, true}}, - {prim::kPrimDictGetItem, {InferImplDictGetItem, true}}, - {prim::kPrimDictSetItem, {InferImplDictSetItem, true}}, - {prim::kPrimListAppend, {InferImplListAppend, true}}, - {prim::kPrimTupleLen, {InferImplTupleLen, true}}, - {prim::kPrimListLen, {InferImplListLen, true}}, - {prim::kPrimArrayLen, {InferImplArrayLen, true}}, - {prim::kPrimListMap, {InferImplListMap, false}}, - {prim::kPrimListReduce, {InferImplListReduce, false}}, - {prim::kPrimTupleReversed, {InferImplTupleReversed, false}}, - {prim::kPrimReducedShape, {InferImplReduceShape, false}}, - {prim::kPrimTupleDiv, {InferImplTupleDiv, false}}, - {prim::kPrimTupleToArray, {InferImplTuple2Array, false}}, - {prim::kPrimShapeMul, {InferImplShapeMul, false}}, - {prim::kPrimTupleEqual, {InferImplTupleEqual, false}}, - {prim::kPrimListEqual, {InferImplListEqual, false}}, - {prim::kPrimMakeRange, {InferImplMakeRange, false}}, - {prim::kPrimStopGradient, {InferImplStopGradient, false}}, - {prim::kPrimStringEqual, {InferImplStringEqual, false}}, - {prim::kPrimDictLen, {InferImplDictLen, false}}, - // NN - {prim::kPrimPooling, {InferImplPooling, true}}, - {prim::kPrimPoolingGrad, {InferImplPoolingGrad, true}}, - {prim::kPrimFusedBatchNorm, {InferImplFusedBatchNorm, true}}, - {prim::kPrimFusedBatchNormGrad, {InferImplFusedBatchNormGrad, true}}, - {prim::kPrimReluGrad, {InferImplReluGrad, true}}, - {prim::kPrimConv2DBackpropInput, {InferImplConv2DBackpropInput, true}}, - {prim::kPrimConv2DBackpropFilter, {InferImplConv2DBackpropFilter, true}}, - {prim::kPrimBiasAddGrad, {InferImplBiasAddGrad, true}}, - {prim::kPrimRelu, {InferImplRelu, true}}, - {prim::kPrimZerosLikeTensor, {InferImplZerosLikeTensor, true}}, - {prim::kPrimFakeBprop, {InferImplFakeBprop, false}}, - {prim::kPrimLayerNorm, {InferImplLayerNorm, true}}, - {prim::kPrimLayerNormGrad, {InferImplLayerNormGrad, true}}, - {prim::kPrimDropoutGenMask, {InferImplDropoutGenMask, true}}, - // Others - {prim::kPrimIdentity, {InferImplIdentity, true}}, - // Set impl to null as it will use PartialEvaluator; - {prim::kPrimPartial, {nullptr, true}}, - {prim::kPrimJ, {InferImplJ, false}}, - {prim::kPrimEnvGetItem, {InferImplEnvGetItem, true}}, - {prim::kPrimEnvSetItem, {InferImplEnvSetItem, true}}, - {prim::kPrimEnvAdd, {InferImplEnvAdd, true}}, - {prim::kPrimMakeRefKey, {InferImplMakeRefKey, true}}, - {prim::kPrimMakeRef, {InferImplMakeRef, true}}, - {prim::kPrimGetRefKey, {InferImplGetRefKey, true}}, - {prim::kPrimGetRefValue, {InferImplGetRefValue, true}}, - {prim::kPrimGetRefOrigin, {InferImplGetRefOrigin, true}}, - {prim::kPrimStateSetItem, {InferImplStateSetItem, true}}, - {prim::kPrimDepend, {InferImplDepend, true}}, - {prim::kPrimBroadcastGradientArgs, {InferImplBroadcastGradientArgs, false}}, - {prim::kPrimControlDepend, {InferImplControlDepend, true}}, - // Debug - {prim::kPrimScalarSummary, {InferImplScalarSummary, true}}, - {prim::kPrimImageSummary, {InferImplTensorSummary, true}}, - {prim::kPrimTensorSummary, {InferImplTensorSummary, true}}, -}; +PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() { + static PrimitiveEvalImplMap prim_eval_implement_map = { + // Statements + {prim::kPrimReturn, {InferImplReturn, true}}, + {prim::kPrimTypeOf, {InferImplTypeof, false}}, + {prim::kPrimHasType, {InferImplHasType, false}}, + {prim::kPrimDot, {InferImplDot, true}}, + {prim::kPrimSwitch, {InferImplSwitch, true}}, + {prim::kPrimIs_, {InferImplIs_, true}}, + {prim::kPrimIsNot, {InferImplIsNot, true}}, + // Maths + {prim::kPrimMaximumGrad, {InferImplMinOrMaxGrad, true}}, + {prim::kPrimMinimumGrad, {InferImplMinOrMaxGrad, true}}, + // Array + {prim::kPrimScalarToArray, {InferImplScalarToArray, true}}, + {prim::kPrimArrayToScalar, {InferImplArrayToScalar, true}}, + {prim::kPrimBroadcastShape, {InferImplBroadCastShape, true}}, + {prim::kPrimShape, {InferImplShape, true}}, + {prim::kPrimPack, {InferImplPack, true}}, + // Structure + {prim::kPrimMakeTuple, {InferImplMakeTuple, true}}, + {prim::kPrimMakeList, {InferImplMakeList, true}}, + {prim::kPrimMakeDict, {InferImplMakeDict, true}}, + {prim::kPrimMakeSlice, {InferImplMakeSlice, true}}, + {prim::kPrimMakeKeywordArg, {InferImplMakeKwarg, true}}, + {prim::kPrimExtractKeywordArg, {InferImplExtractKwarg, true}}, + {prim::kPrimMakeRecord, {InferImplMakeRecord, false}}, + {prim::kPrimTupleGetItem, {InferImplTupleGetItem, true}}, + {prim::kPrimListGetItem, {InferImplListGetItem, true}}, + {prim::kPrimTupleSetItem, {InferImplTupleSetItem, true}}, + {prim::kPrimListSetItem, {InferImplListSetItem, true}}, + {prim::kPrimDictGetItem, {InferImplDictGetItem, true}}, + {prim::kPrimDictSetItem, {InferImplDictSetItem, true}}, + {prim::kPrimListAppend, {InferImplListAppend, true}}, + {prim::kPrimTupleLen, {InferImplTupleLen, true}}, + {prim::kPrimListLen, {InferImplListLen, true}}, + {prim::kPrimArrayLen, {InferImplArrayLen, true}}, + {prim::kPrimListMap, {InferImplListMap, false}}, + {prim::kPrimListReduce, {InferImplListReduce, false}}, + {prim::kPrimTupleReversed, {InferImplTupleReversed, false}}, + {prim::kPrimReducedShape, {InferImplReduceShape, false}}, + {prim::kPrimTupleDiv, {InferImplTupleDiv, false}}, + {prim::kPrimTupleToArray, {InferImplTuple2Array, false}}, + {prim::kPrimShapeMul, {InferImplShapeMul, false}}, + {prim::kPrimTupleEqual, {InferImplTupleEqual, false}}, + {prim::kPrimListEqual, {InferImplListEqual, false}}, + {prim::kPrimMakeRange, {InferImplMakeRange, false}}, + {prim::kPrimStopGradient, {InferImplStopGradient, false}}, + {prim::kPrimStringEqual, {InferImplStringEqual, false}}, + {prim::kPrimDictLen, {InferImplDictLen, false}}, + // NN + {prim::kPrimPooling, {InferImplPooling, true}}, + {prim::kPrimPoolingGrad, {InferImplPoolingGrad, true}}, + {prim::kPrimFusedBatchNorm, {InferImplFusedBatchNorm, true}}, + {prim::kPrimFusedBatchNormGrad, {InferImplFusedBatchNormGrad, true}}, + {prim::kPrimReluGrad, {InferImplReluGrad, true}}, + {prim::kPrimConv2DBackpropInput, {InferImplConv2DBackpropInput, true}}, + {prim::kPrimConv2DBackpropFilter, {InferImplConv2DBackpropFilter, true}}, + {prim::kPrimBiasAddGrad, {InferImplBiasAddGrad, true}}, + {prim::kPrimRelu, {InferImplRelu, true}}, + {prim::kPrimZerosLikeTensor, {InferImplZerosLikeTensor, true}}, + {prim::kPrimFakeBprop, {InferImplFakeBprop, false}}, + {prim::kPrimLayerNorm, {InferImplLayerNorm, true}}, + {prim::kPrimLayerNormGrad, {InferImplLayerNormGrad, true}}, + {prim::kPrimDropoutGenMask, {InferImplDropoutGenMask, true}}, + // Others + {prim::kPrimIdentity, {InferImplIdentity, true}}, + // Set impl to null as it will use PartialEvaluator; + {prim::kPrimPartial, {nullptr, true}}, + {prim::kPrimJ, {InferImplJ, false}}, + {prim::kPrimEnvGetItem, {InferImplEnvGetItem, true}}, + {prim::kPrimEnvSetItem, {InferImplEnvSetItem, true}}, + {prim::kPrimEnvAdd, {InferImplEnvAdd, true}}, + {prim::kPrimMakeRefKey, {InferImplMakeRefKey, true}}, + {prim::kPrimMakeRef, {InferImplMakeRef, true}}, + {prim::kPrimGetRefKey, {InferImplGetRefKey, true}}, + {prim::kPrimGetRefValue, {InferImplGetRefValue, true}}, + {prim::kPrimGetRefOrigin, {InferImplGetRefOrigin, true}}, + {prim::kPrimStateSetItem, {InferImplStateSetItem, true}}, + {prim::kPrimDepend, {InferImplDepend, true}}, + {prim::kPrimBroadcastGradientArgs, {InferImplBroadcastGradientArgs, false}}, + {prim::kPrimControlDepend, {InferImplControlDepend, true}}, + // Debug + {prim::kPrimScalarSummary, {InferImplScalarSummary, true}}, + {prim::kPrimImageSummary, {InferImplTensorSummary, true}}, + {prim::kPrimTensorSummary, {InferImplTensorSummary, true}}, + }; + return prim_eval_implement_map; +} using mindspore::parse::PyObjectWrapper; @@ -961,10 +964,7 @@ class PartialEvaluator : public Evaluator { new_nodes_inputs[1] = NewValueNode(new_signature_value); FuncGraphPtr func_graph = cnode->func_graph(); - ScopePtr scope = kDefaultScope; - if (out_conf != nullptr) { - scope = out_conf->node()->scope(); - } + ScopePtr scope = out_conf->node()->scope(); ScopeGuard scope_guard(scope); CNodePtr new_cnode = func_graph->NewCNode(new_nodes_inputs); @@ -981,39 +981,41 @@ struct PrimitiveImplInferValue { }; using PrimitiveToImplMap = std::unordered_map; - -PrimitiveToImplMap UniformPrimitiveToImplMapValue = { - {prim::kPrimScalarAdd, {prim::ScalarAdd, true, nullptr, true}}, - {prim::kPrimScalarSub, {prim::ScalarSub, true, nullptr, true}}, - {prim::kPrimScalarMul, {prim::ScalarMul, true, nullptr, true}}, - {prim::kPrimScalarDiv, {prim::ScalarDiv, true, nullptr, true}}, - {prim::kPrimScalarMod, {prim::ScalarMod, true, nullptr, true}}, - {prim::kPrimScalarUadd, {prim::ScalarUAdd, true, nullptr, true}}, - {prim::kPrimScalarUsub, {prim::ScalarUSub, true, nullptr, true}}, - {prim::kPrimScalarLog, {prim::ScalarLog, true, nullptr, true}}, - {prim::kPrimScalarEq, {prim::ScalarEq, true, std::make_shared(), true}}, - {prim::kPrimScalarLt, {prim::ScalarLt, true, std::make_shared(), true}}, - {prim::kPrimScalarGt, {prim::ScalarGt, true, std::make_shared(), true}}, - {prim::kPrimScalarNe, {prim::ScalarNe, true, std::make_shared(), true}}, - {prim::kPrimScalarLe, {prim::ScalarLe, true, std::make_shared(), true}}, - {prim::kPrimScalarGe, {prim::ScalarGe, true, std::make_shared(), true}}, - {prim::kPrimBoolNot, {prim::BoolNot, true, std::make_shared(), true}}, - {prim::kPrimBoolAnd, {prim::BoolAnd, true, std::make_shared(), true}}, - {prim::kPrimBoolEq, {prim::BoolEq, true, std::make_shared(), true}}, - {prim::kPrimBoolOr, {prim::BoolOr, true, std::make_shared(), true}}, -}; +PrimitiveToImplMap &GetUniformPrimitiveToImplMap() { + static PrimitiveToImplMap uniform_prim_implement_map = { + {prim::kPrimScalarAdd, {prim::ScalarAdd, true, nullptr, true}}, + {prim::kPrimScalarSub, {prim::ScalarSub, true, nullptr, true}}, + {prim::kPrimScalarMul, {prim::ScalarMul, true, nullptr, true}}, + {prim::kPrimScalarDiv, {prim::ScalarDiv, true, nullptr, true}}, + {prim::kPrimScalarMod, {prim::ScalarMod, true, nullptr, true}}, + {prim::kPrimScalarUadd, {prim::ScalarUAdd, true, nullptr, true}}, + {prim::kPrimScalarUsub, {prim::ScalarUSub, true, nullptr, true}}, + {prim::kPrimScalarLog, {prim::ScalarLog, true, nullptr, true}}, + {prim::kPrimScalarEq, {prim::ScalarEq, true, std::make_shared(), true}}, + {prim::kPrimScalarLt, {prim::ScalarLt, true, std::make_shared(), true}}, + {prim::kPrimScalarGt, {prim::ScalarGt, true, std::make_shared(), true}}, + {prim::kPrimScalarNe, {prim::ScalarNe, true, std::make_shared(), true}}, + {prim::kPrimScalarLe, {prim::ScalarLe, true, std::make_shared(), true}}, + {prim::kPrimScalarGe, {prim::ScalarGe, true, std::make_shared(), true}}, + {prim::kPrimBoolNot, {prim::BoolNot, true, std::make_shared(), true}}, + {prim::kPrimBoolAnd, {prim::BoolAnd, true, std::make_shared(), true}}, + {prim::kPrimBoolEq, {prim::BoolEq, true, std::make_shared(), true}}, + {prim::kPrimBoolOr, {prim::BoolOr, true, std::make_shared(), true}}, + }; + return uniform_prim_implement_map; +} PrimEvaluatorMap PrimEvaluatorConstructors = PrimEvaluatorMap(); std::mutex PrimEvaluatorConstructorMutex; -void InitPrimEvaluatorConstructors(const PrimitiveEvalImplMap &prim_eval_impl_map) { +void InitPrimEvaluatorConstructors() { PrimEvaluatorMap &constructor = PrimEvaluatorConstructors; - for (const auto &iter : prim_eval_impl_map) { + for (const auto &iter : GetPrimitiveToEvalImplMap()) { constructor[iter.first] = InitStandardPrimEvaluator(iter.first, iter.second.impl_); } - for (const auto &iter : UniformPrimitiveToImplMapValue) { + for (const auto &iter : GetUniformPrimitiveToImplMap()) { constructor[iter.first] = InitUniformPrimEvaluator(iter.first, iter.second.impl_, iter.second.eval_value_, iter.second.specify_out_type_); } @@ -1028,20 +1030,20 @@ void InitPrimEvaluatorConstructors(const PrimitiveEvalImplMap &prim_eval_impl_ma void ClearPrimEvaluatorMap() { PrimEvaluatorConstructors.clear(); - PrimitiveToInferImplMap.clear(); - UniformPrimitiveToImplMapValue.clear(); + GetPrimitiveToEvalImplMap().clear(); + GetUniformPrimitiveToImplMap().clear(); } bool IsInWhiteList(const PrimitivePtr primitive) { MS_EXCEPTION_IF_NULL(primitive); - auto iter = PrimitiveToInferImplMap.find(primitive); - if (iter != PrimitiveToInferImplMap.end()) { + auto iter = GetPrimitiveToEvalImplMap().find(primitive); + if (iter != GetPrimitiveToEvalImplMap().end()) { return iter->second.in_white_list_; } - auto uni_iter = UniformPrimitiveToImplMapValue.find(primitive); - if (uni_iter != UniformPrimitiveToImplMapValue.end()) { + auto uni_iter = GetUniformPrimitiveToImplMap().find(primitive); + if (uni_iter != GetUniformPrimitiveToImplMap().end()) { return uni_iter->second.in_white_list_; } @@ -1050,8 +1052,8 @@ bool IsInWhiteList(const PrimitivePtr primitive) { StandardPrimitiveEvalImpl GetPrimitiveInferImpl(const PrimitivePtr &primitive) { MS_EXCEPTION_IF_NULL(primitive); - auto iter = PrimitiveToInferImplMap.find(primitive); - if (iter == PrimitiveToInferImplMap.end()) { + auto iter = GetPrimitiveToEvalImplMap().find(primitive); + if (iter == GetPrimitiveToEvalImplMap().end()) { return nullptr; } return iter->second.impl_; @@ -1064,7 +1066,7 @@ PrimEvaluatorMap &GetPrimEvaluatorConstructors() { } std::lock_guard initLock(PrimEvaluatorConstructorMutex); if (constructor.empty()) { - InitPrimEvaluatorConstructors(PrimitiveToInferImplMap); + InitPrimEvaluatorConstructors(); } return constructor; diff --git a/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc b/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc index 4ab7b9d20b..6230df44a5 100644 --- a/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc +++ b/mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc @@ -296,38 +296,35 @@ EvaluatorPtr GetPrimEvaluator(const PrimitivePtr &prim, const AnalysisEnginePtr if (prim->HasPyEvaluator()) { auto prim_py = dyn_cast(prim); if (prim_py != nullptr) { - evaluator = std::make_shared(prim_py); - } else { - MS_LOG(EXCEPTION) << "The primitive with python evaluator should be a python primitive."; + return std::make_shared(prim_py); + } + MS_LOG(EXCEPTION) << "The primitive with python evaluator should be a python primitive."; + } + + if (prim->isa() || prim->HasAttr()) { + if (engine == nullptr) { + (void)GetPrimEvaluatorConstructors(); } - } else if (prim->isa() || prim->HasAttr()) { // If a primitive may have attr, try to create a new evaluator. StandardPrimitiveEvalImpl eval_impl = GetPrimitiveInferImpl(prim); if (eval_impl != nullptr) { - std::shared_ptr standard_evaluator = - std::make_shared(prim, eval_impl); - evaluator = standard_evaluator; + return std::make_shared(prim, eval_impl); } } - if (evaluator == nullptr) { - if (engine == nullptr) { - // If engine is nullptr, get constructor from default. - const PrimEvaluatorMap &prim_evaluator_map = GetPrimEvaluatorConstructors(); - auto iter = prim_evaluator_map.find(prim); - if (iter == prim_evaluator_map.end()) { - evaluator = nullptr; - } else { - evaluator = iter->second; - } - } else { - // If engine is given, get constructor from engine resource. - const PrimEvaluatorMap &prim_evaluator_map = engine->PrimConstructors(); - auto iter = prim_evaluator_map.find(prim); - if (iter == prim_evaluator_map.end()) { - evaluator = nullptr; - } else { - evaluator = iter->second; - } + + if (engine == nullptr) { + // If engine is nullptr, get constructor from default. + const PrimEvaluatorMap &prim_evaluator_map = GetPrimEvaluatorConstructors(); + auto iter = prim_evaluator_map.find(prim); + if (iter != prim_evaluator_map.end()) { + evaluator = iter->second; + } + } else { + // If engine is given, get constructor from engine resource. + const PrimEvaluatorMap &prim_evaluator_map = engine->PrimConstructors(); + auto iter = prim_evaluator_map.find(prim); + if (iter != prim_evaluator_map.end()) { + evaluator = iter->second; } } if (evaluator == nullptr) { From d84cfb01080b650c16fed895f7717ba667b60ca3 Mon Sep 17 00:00:00 2001 From: kswang Date: Wed, 8 Apr 2020 17:05:17 +0800 Subject: [PATCH 153/227] add mem manager --- mindspore/ccsrc/CMakeLists.txt | 1 + .../device/ascend/ascend_kernel_runtime.cc | 57 +---- .../device/ascend/ascend_kernel_runtime.h | 5 +- .../device/ascend/ascend_memory_manager.cc | 65 +++++ .../device/ascend/ascend_memory_manager.h | 35 +++ mindspore/ccsrc/device/device_address.h | 5 + .../ccsrc/device/gpu/gpu_kernel_runtime.cc | 108 ++------ .../ccsrc/device/gpu/gpu_kernel_runtime.h | 9 +- .../ccsrc/device/gpu/gpu_memory_manager.cc | 88 +++++++ .../ccsrc/device/gpu/gpu_memory_manager.h | 40 +++ mindspore/ccsrc/device/kernel_runtime.cc | 231 +++--------------- mindspore/ccsrc/device/kernel_runtime.h | 38 +-- mindspore/ccsrc/device/memory_manager.cc | 170 +++++++++++++ mindspore/ccsrc/device/memory_manager.h | 71 ++++++ .../ccsrc/session/anf_runtime_algorithm.cc | 10 + .../ccsrc/session/anf_runtime_algorithm.h | 1 + mindspore/ccsrc/session/gpu_session.cc | 4 - tests/ut/cpp/CMakeLists.txt | 2 + 18 files changed, 562 insertions(+), 378 deletions(-) create mode 100644 mindspore/ccsrc/device/ascend/ascend_memory_manager.cc create mode 100644 mindspore/ccsrc/device/ascend/ascend_memory_manager.h create mode 100644 mindspore/ccsrc/device/gpu/gpu_memory_manager.cc create mode 100644 mindspore/ccsrc/device/gpu/gpu_memory_manager.h create mode 100644 mindspore/ccsrc/device/memory_manager.cc create mode 100644 mindspore/ccsrc/device/memory_manager.h diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt index 9f559a51eb..1d104148c3 100644 --- a/mindspore/ccsrc/CMakeLists.txt +++ b/mindspore/ccsrc/CMakeLists.txt @@ -132,6 +132,7 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "kernel/kash/*.cc" "device/kernel_info.cc" "device/kernel_runtime.cc" + "device/memory_manager.cc" "device/kernel_runtime_manager.cc" "device/convert_tensor_utils.cc" "pre_activate/common/*.cc" diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc index dc7eb5449b..0c2a97a5a6 100644 --- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc +++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc @@ -37,6 +37,7 @@ #include "kernel/tbe/tbe_utils.h" #include "kernel/tbe/tbe_python_funcs.h" #include "pre_activate/mem_reuse/mem_reuse_checker.h" +#include "device/ascend/ascend_memory_manager.h" using mindspore::device::ascend::ProfilingManager; using mindspore::device::ascend::ProfilingUtils; @@ -47,8 +48,6 @@ using std::vector; namespace mindspore { namespace device { namespace ascend { -static const uint64_t ASCEND_MEM_SIZE = 20; -static const uint64_t ASCEND_MEM_SIZE_BYTE = (ASCEND_MEM_SIZE << 30); static const size_t PRAMATER_OUTPUT_INDEX = 0; AscendKernelRuntime::~AscendKernelRuntime() { graph_model_map_.clear(); } @@ -86,7 +85,8 @@ void AscendKernelRuntime::ReleaseDeviceRes() { MS_EXCEPTION(DeviceProcessError) << "rtSetDevice, ret[" << static_cast(ret) << "]"; } - FreeDeviceMemory(); + MS_EXCEPTION_IF_NULL(mem_manager_); + mem_manager_->FreeDeviceMemory(); (void)DestroyHccl(); (void)ResetDevice(); (void)ProfilingManager::GetInstance().StopProfiling(); @@ -109,11 +109,9 @@ bool AscendKernelRuntime::Init() { if (!ret) { return ret; } - - ret = MallocDeviceMemory(); - if (!ret) { - return ret; - } + mem_manager_ = std::make_shared(); + MS_EXCEPTION_IF_NULL(mem_manager_); + mem_manager_->MallocDeviceMemory(); ret = ProfilingManager::GetInstance().StartupProfiling(device_id_); if (!ret) { @@ -239,13 +237,6 @@ DeviceAddressPtr AscendKernelRuntime::CreateDeviceAddress(void *device_ptr, size return std::make_shared(device_ptr, device_size, format, type_id); } -void AscendKernelRuntime::MallocOpMemory(const DeviceAddressPtr address, size_t size, int) { - auto device_ptr = AscendMemoryAllocator::GetInstance().AllocTensorMem(size); - MS_EXCEPTION_IF_NULL(device_ptr); - address->ptr_ = device_ptr; - address->mem_dynamic_alloc_ = true; -} - bool AscendKernelRuntime::GenTask(const session::KernelGraph *graph) { auto context_ptr = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(context_ptr); @@ -474,42 +465,6 @@ bool AscendKernelRuntime::DestroyHccl() { context_ptr->set_enable_hccl(false); return true; } - -bool AscendKernelRuntime::MallocDeviceMemory() { - device_mem_size_ = ASCEND_MEM_SIZE_BYTE; - static_mem_offset_ = FloatToSize(device_mem_size_ * GRAPH_INIT_ASCEND_MEM_RATIO); - auto ret = rtMalloc(reinterpret_cast(&device_mem_base_), static_mem_offset_, RT_MEMORY_HBM); - if (ret != RT_ERROR_NONE) { - MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << static_mem_offset_ << "] fail, ret[" << ret << "]"; - } - device_mem_pool_size_ = FloatToSize(device_mem_size_ * (1 - GRAPH_INIT_ASCEND_MEM_RATIO)); - ret = rtMalloc(reinterpret_cast(&device_mem_pool_base_), device_mem_pool_size_, RT_MEMORY_HBM); - if (ret != RT_ERROR_NONE) { - MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]"; - } - AscendMemoryAllocator::GetInstance().set_device_mem_pool_base(device_mem_pool_base_); - AscendMemoryAllocator::GetInstance().set_device_mem_pool_size(device_mem_pool_size_); - return true; -} - -void AscendKernelRuntime::FreeDeviceMemory() { - if (device_mem_base_ != nullptr) { - auto ret = rtFree(device_mem_base_); - if (ret != RT_ERROR_NONE) { - MS_LOG(ERROR) << "rtFree mem size[" << device_mem_size_ << "] fail, ret[" << ret << "]"; - } - device_mem_base_ = nullptr; - } - if (device_mem_pool_base_ != nullptr) { - auto ret = rtFree(device_mem_pool_base_); - if (ret != RT_ERROR_NONE) { - MS_LOG(ERROR) << "rtFree mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]"; - } - device_mem_pool_base_ = nullptr; - } -} - -void AscendKernelRuntime::FreeHostMemory() { dynamic_mem_offset_ = 0; } } // namespace ascend } // namespace device } // namespace mindspore diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h index dbd1460d24..0eedad3d2b 100644 --- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h +++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.h @@ -39,13 +39,11 @@ class AscendKernelRuntime : public KernelRuntime { bool GenTask(const session::KernelGraph *graph) override; bool RunTask(const session::KernelGraph *graph) override; bool LoadTask(const session::KernelGraph *graph) override; - void FreeHostMemory() override; protected: DeviceAddressPtr CreateDeviceAddress(void *device_ptr, size_t device_size, const string &format, TypeId type_id) override; bool SyncStream() override; - void MallocOpMemory(const DeviceAddressPtr address, size_t size, int flag) override; private: bool InitDevice(); @@ -53,8 +51,7 @@ class AscendKernelRuntime : public KernelRuntime { bool HcclInit(); bool NeedDestroyHccl(); bool DestroyHccl(); - bool MallocDeviceMemory(); - void FreeDeviceMemory(); + void ClearGraphModelMap(); void ReleaseDeviceRes() override; uint32_t GetGraphModelId(const session::KernelGraph *kernel_graph); diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc b/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc new file mode 100644 index 0000000000..f033d81d82 --- /dev/null +++ b/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc @@ -0,0 +1,65 @@ +/** + * Copyright 2019 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "device/ascend/ascend_memory_manager.h" +#include "device/ascend/ascend_memory_allocator.h" +#include "utils/context/ms_context.h" +#include "runtime/mem.h" +namespace mindspore { +namespace device { +namespace ascend { +static const uint64_t ASCEND_MEM_SIZE = 20; +static const uint64_t ASCEND_MEM_SIZE_BYTE = (ASCEND_MEM_SIZE << 30); + +void AscendMemoryManager::MallocDeviceMemory() { + device_mem_size_ = ASCEND_MEM_SIZE_BYTE; + static_mem_offset_ = FloatToSize(device_mem_size_ * GRAPH_INIT_ASCEND_MEM_RATIO); + auto ret = rtMalloc(reinterpret_cast(&device_mem_base_), static_mem_offset_, RT_MEMORY_HBM); + if (ret != RT_ERROR_NONE) { + MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << static_mem_offset_ << "] fail, ret[" << ret << "]"; + } + device_mem_pool_size_ = FloatToSize(device_mem_size_ * (1 - GRAPH_INIT_ASCEND_MEM_RATIO)); + ret = rtMalloc(reinterpret_cast(&device_mem_pool_base_), device_mem_pool_size_, RT_MEMORY_HBM); + if (ret != RT_ERROR_NONE) { + MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]"; + } + AscendMemoryAllocator::GetInstance().set_device_mem_pool_base(device_mem_pool_base_); + AscendMemoryAllocator::GetInstance().set_device_mem_pool_size(device_mem_pool_size_); +} + +void AscendMemoryManager::FreeDeviceMemory() { + if (device_mem_base_ != nullptr) { + auto ret = rtFree(device_mem_base_); + if (ret != RT_ERROR_NONE) { + MS_LOG(ERROR) << "rtFree mem size[" << device_mem_size_ << "] fail, ret[" << ret << "]"; + } + device_mem_base_ = nullptr; + } + if (device_mem_pool_base_ != nullptr) { + auto ret = rtFree(device_mem_pool_base_); + if (ret != RT_ERROR_NONE) { + MS_LOG(ERROR) << "rtFree mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]"; + } + device_mem_pool_base_ = nullptr; + } +} + +void *AscendMemoryManager::AllocTensorMemDynamic(size_t size) { + return AscendMemoryAllocator::GetInstance().AllocTensorMem(size); +} +} // namespace ascend +} // namespace device +} // namespace mindspore diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_manager.h b/mindspore/ccsrc/device/ascend/ascend_memory_manager.h new file mode 100644 index 0000000000..8639fb5c72 --- /dev/null +++ b/mindspore/ccsrc/device/ascend/ascend_memory_manager.h @@ -0,0 +1,35 @@ +/** + * Copyright 2019 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_MANAGER_H_ +#define MINDSPORE_MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_MANAGER_H_ +#include "device/memory_manager.h" +namespace mindspore { +namespace device { +namespace ascend { +class AscendMemoryManager : public MemoryManager { + public: + AscendMemoryManager() = default; + virtual ~AscendMemoryManager() = default; + + void MallocDeviceMemory() override; + void FreeDeviceMemory() override; + void *AllocTensorMemDynamic(size_t size) override; +}; +} // namespace ascend +} // namespace device +} // namespace mindspore +#endif // MINDSPORE_MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_MANAGER_H_ diff --git a/mindspore/ccsrc/device/device_address.h b/mindspore/ccsrc/device/device_address.h index 1610d43372..cb022427e3 100644 --- a/mindspore/ccsrc/device/device_address.h +++ b/mindspore/ccsrc/device/device_address.h @@ -33,12 +33,14 @@ class CPUKernelRuntime; } // namespace cpu namespace ascend { class AscendKernelRuntime; +class AscendMemoryManager; namespace tasksink { class TaskGenerator; } // namespace tasksink } // namespace ascend namespace gpu { class GPUKernelRuntime; +class GPUMemoryManager; } // namespace gpu } // namespace device } // namespace mindspore @@ -70,12 +72,15 @@ class DeviceAddress { TypeId type_id_{kNumberTypeFloat16}; bool mem_dynamic_alloc_{false}; friend class KernelRuntime; + friend class MemoryManager; friend class mindspore::device::ascend::tasksink::TaskGenerator; friend class mindspore::device::cpu::CPUSimpleMemPlan; friend class mindspore::device::cpu::CPUResourceManager; friend class mindspore::device::cpu::CPUKernelRuntime; friend class mindspore::device::gpu::GPUKernelRuntime; + friend class mindspore::device::gpu::GPUMemoryManager; friend class mindspore::device::ascend::AscendKernelRuntime; + friend class mindspore::device::ascend::AscendMemoryManager; }; using DeviceAddressPtr = std::shared_ptr; diff --git a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc index 9eeb1062f7..597e188e9d 100644 --- a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc +++ b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc @@ -26,6 +26,7 @@ #include "device/kernel_runtime_manager.h" #include "device/gpu/gpu_common.h" #include "common/utils.h" +#include "device/gpu/gpu_memory_manager.h" namespace mindspore { namespace device { @@ -36,26 +37,14 @@ bool GPUKernelRuntime::Init() { if (device_init_ == true) { return true; } - auto ret = InitDevice(); if (!ret) { MS_LOG(ERROR) << "InitDevice error."; return ret; } - - auto context_ptr = MsContext::GetInstance(); - MS_EXCEPTION_IF_NULL(context_ptr); - // If use the dynamic memory pool, then alloc the first memory block to init. - if (context_ptr->enable_dynamic_mem_pool()) { - auto device_addr = AllocTensorMemDynamic(1); - if (!device_addr) { - MS_LOG(ERROR) << "Dynamic memory pool init error."; - return false; - } - } else { - MallocDeviceMemory(); - } - + mem_manager_ = std::make_shared(); + MS_EXCEPTION_IF_NULL(mem_manager_); + mem_manager_->MallocDeviceMemory(); const void *collective_handle_ = CollectiveInitializer::instance().collective_handle(); bool collective_inited = CollectiveInitializer::instance().collective_inited(); if (collective_inited && collective_handle_ != nullptr) { @@ -101,16 +90,6 @@ bool GPUKernelRuntime::InitDevice() { return true; } -void GPUKernelRuntime::MallocDeviceMemory() { - // Need to reserve 20% space for dynamic memory - const float init_gpu_mem_ratio = 0.8; - size_t mem_size = FloatToSize(GPUMemoryAllocator::GetInstance().free_mem_size() * init_gpu_mem_ratio); - auto alloc_size = - GPUMemoryAllocator::GetInstance().AllocDeviceMem(mem_size, reinterpret_cast(&device_mem_base_)); - device_mem_size_ = alloc_size; - static_mem_offset_ = device_mem_size_; -} - void GPUKernelRuntime::ReleaseDeviceRes() { // For dataset mode. if (GpuBufferMgr::GetInstance().IsInit()) { @@ -122,39 +101,22 @@ void GPUKernelRuntime::ReleaseDeviceRes() { CHECK_OP_RET_WITH_EXCEPT(GpuBufferMgr::GetInstance().Destroy(), "Could not destroy gpu data queue."); } GPUDeviceManager::GetInstance().ReleaseDevice(); - if (device_mem_base_ != nullptr) { - if (!GPUMemoryAllocator::GetInstance().FreeDeviceMem(device_mem_base_)) { - MS_LOG(EXCEPTION) << "Could not free gpu device memory."; - } - } - GPUMemoryAllocator::GetInstance().ReleaseDeviceRes(); -} - -void GPUKernelRuntime::FreeHostMemory() { dynamic_mem_offset_ = 0; } - -void *GPUKernelRuntime::AllocTensorMemDynamic(size_t size) { - return GPUMemoryAllocator::GetInstance().AllocTensorMem(size); -} - -void GPUKernelRuntime::FreeTensorMemDynamic(void *device_ptr) { - GPUMemoryAllocator::GetInstance().FreeTensorMem(device_ptr); + MS_EXCEPTION_IF_NULL(mem_manager_); + mem_manager_->FreeDeviceMemory(); } void GPUKernelRuntime::AssignMemory(session::KernelGraph *graph) { auto context_ptr = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(context_ptr); + MS_EXCEPTION_IF_NULL(mem_manager_); + mem_manager_->ResetDynamicMemory(); AssignStaticMemory(graph); - bool is_enable_mem_reuse = context_ptr->enable_mem_reuse(); bool is_enable_dynamic_mem = context_ptr->enable_dynamic_mem_pool(); if (is_enable_dynamic_mem) { // Use the dynamic memory pool. InitKernelRefCount(graph); InitKernelOutputAddress(graph); - } else if (is_enable_mem_reuse) { - // Use the memory reuse. - ReuseAssignDynamicMemory(graph); } else { - // Normal way. AssignDynamicMemory(graph); } } @@ -179,32 +141,6 @@ bool GPUKernelRuntime::Run(session::KernelGraph *graph) { return ret; } -uint8_t *GPUKernelRuntime::MallocStaticMem(size_t size, bool) { - auto context_ptr = MsContext::GetInstance(); - MS_EXCEPTION_IF_NULL(context_ptr); - if (context_ptr->enable_dynamic_mem_pool()) { - auto device_ptr = AllocTensorMemDynamic(size); - MS_EXCEPTION_IF_NULL(device_ptr); - return AddressOffset(device_ptr, 0); - } - - auto align_size = GetCommonAlignSize(size); - if (static_mem_offset_ < align_size) { - MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_ - << "] static[" << total_static_size_ << "])" - << " malloc [" << align_size << "] failed!"; - } - auto offset = static_mem_offset_ - align_size; - if (dynamic_mem_offset_ > offset) { - MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_ - << "] static[" << total_static_size_ << "])" - << " malloc [" << align_size << "] failed!"; - } - total_static_size_ += align_size; - static_mem_offset_ = offset; - return device_mem_base_ + offset; -} - void GPUKernelRuntime::InitKernelRefCount(const session::KernelGraph *graph) { MS_EXCEPTION_IF_NULL(graph); MemReuseUtilPtr mem_reuse_util_ptr = std::make_shared(); @@ -273,6 +209,7 @@ void GPUKernelRuntime::AllocKernelDynamicRes(const mindspore::kernel::KernelMod MS_EXCEPTION_IF_NULL(kernel_inputs); MS_EXCEPTION_IF_NULL(kernel_workspaces); MS_EXCEPTION_IF_NULL(kernel_outputs); + MS_EXCEPTION_IF_NULL(mem_manager_); for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(kernel); ++i) { auto device_address = AnfAlgo::GetPrevNodeOutputAddr(kernel, i); MS_EXCEPTION_IF_NULL(device_address); @@ -290,7 +227,7 @@ void GPUKernelRuntime::AllocKernelDynamicRes(const mindspore::kernel::KernelMod MS_EXCEPTION_IF_NULL(device_address); auto device_ptr = device_address->ptr_; if (device_ptr == nullptr) { - device_ptr = AllocTensorMemDynamic(output_sizes[i]); + device_ptr = mem_manager_->AllocTensorMemDynamic(output_sizes[i]); MS_EXCEPTION_IF_NULL(device_ptr); device_address->ptr_ = device_ptr; } @@ -307,7 +244,7 @@ void GPUKernelRuntime::AllocKernelDynamicRes(const mindspore::kernel::KernelMod kernel_workspaces->emplace_back(nullptr); continue; } - auto device_ptr = AllocTensorMemDynamic(workspace_sizes[i]); + auto device_ptr = mem_manager_->AllocTensorMemDynamic(workspace_sizes[i]); MS_EXCEPTION_IF_NULL(device_ptr); kernel::AddressPtr workspace = std::make_shared(); MS_EXCEPTION_IF_NULL(workspace); @@ -333,6 +270,7 @@ void GPUKernelRuntime::AllocCommunicationOpDynamicRes(const session::KernelGraph void GPUKernelRuntime::AllocCommunicationOpInputDynamicRes(const mindspore::AnfNodePtr &kernel) { MS_EXCEPTION_IF_NULL(kernel); + MS_EXCEPTION_IF_NULL(mem_manager_); // The reference count of communication kernel input is not 0. if (communication_op_input_ref_count_ != 0) { MS_LOG(ERROR) << "The reference count of communication kernel input is not 0."; @@ -354,7 +292,7 @@ void GPUKernelRuntime::AllocCommunicationOpInputDynamicRes(const mindspore::AnfN addr_size.emplace_back(device_address.get(), output_size); } - auto device_mem_ptr = AllocTensorMemDynamic(total); + auto device_mem_ptr = mem_manager_->AllocTensorMemDynamic(total); MS_EXCEPTION_IF_NULL(device_mem_ptr); for (const auto &iter : addr_size) { MS_EXCEPTION_IF_NULL(iter.first); @@ -366,6 +304,7 @@ void GPUKernelRuntime::AllocCommunicationOpInputDynamicRes(const mindspore::AnfN void GPUKernelRuntime::AllocCommunicationOpOutputDynamicRes(const mindspore::AnfNodePtr &kernel) { MS_EXCEPTION_IF_NULL(kernel); + MS_EXCEPTION_IF_NULL(mem_manager_); // The reference count of communication kernel output is not 0. if (communication_op_output_ref_count_ != 0) { MS_LOG(ERROR) << "The reference count of communication kernel output is not 0."; @@ -389,7 +328,7 @@ void GPUKernelRuntime::AllocCommunicationOpOutputDynamicRes(const mindspore::Anf addr_size.emplace_back(device_address.get(), output_sizes[i]); } - auto device_mem_ptr = AllocTensorMemDynamic(total); + auto device_mem_ptr = mem_manager_->AllocTensorMemDynamic(total); MS_EXCEPTION_IF_NULL(device_mem_ptr); for (const auto &iter : addr_size) { MS_EXCEPTION_IF_NULL(iter.first); @@ -402,6 +341,7 @@ void GPUKernelRuntime::AllocCommunicationOpOutputDynamicRes(const mindspore::Anf void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel, const AddressPtrList &kernel_workspaces) { MS_EXCEPTION_IF_NULL(kernel); + MS_EXCEPTION_IF_NULL(mem_manager_); auto cnode = kernel->cast(); MS_EXCEPTION_IF_NULL(cnode); // Free the input of kernel by reference count. @@ -421,7 +361,7 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel, auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i); MS_EXCEPTION_IF_NULL(device_address); MS_EXCEPTION_IF_NULL(device_address->ptr_); - FreeTensorMemDynamic(device_address->ptr_); + mem_manager_->FreeTensorMemDynamic(device_address->ptr_); device_address->ptr_ = nullptr; } } @@ -432,7 +372,7 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel, auto workspace = kernel_workspaces[i]; if (workspace != nullptr) { MS_EXCEPTION_IF_NULL(workspace->addr); - FreeTensorMemDynamic(workspace->addr); + mem_manager_->FreeTensorMemDynamic(workspace->addr); workspace->addr = nullptr; } } @@ -441,6 +381,7 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel, void GPUKernelRuntime::FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr &kernel, size_t input_idx, bool *is_communication_op) { MS_EXCEPTION_IF_NULL(kernel); + MS_EXCEPTION_IF_NULL(mem_manager_); // The inputs memory of communication kernel is one piece memory, need release together. if (AnfAlgo::GetCNodeName(kernel) == kAllReduceOpName) { communication_op_input_ref_count_--; @@ -448,7 +389,7 @@ void GPUKernelRuntime::FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, 0); MS_EXCEPTION_IF_NULL(device_address); MS_EXCEPTION_IF_NULL(device_address->ptr_); - FreeTensorMemDynamic(device_address->ptr_); + mem_manager_->FreeTensorMemDynamic(device_address->ptr_); device_address->ptr_ = nullptr; } *is_communication_op = true; @@ -470,19 +411,12 @@ void GPUKernelRuntime::FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr auto device_address = AnfAlgo::GetMutableOutputAddr(kernel_input.first, 0); MS_EXCEPTION_IF_NULL(device_address); MS_EXCEPTION_IF_NULL(device_address->ptr_); - FreeTensorMemDynamic(device_address->ptr_); + mem_manager_->FreeTensorMemDynamic(device_address->ptr_); device_address->ptr_ = nullptr; } *is_communication_op = true; } } - -void GPUKernelRuntime::MallocOpMemory(const DeviceAddressPtr address, size_t size, int) { - auto device_ptr = AllocTensorMemDynamic(size); - MS_EXCEPTION_IF_NULL(device_ptr); - address->ptr_ = device_ptr; - address->mem_dynamic_alloc_ = true; -} } // namespace gpu } // namespace device } // namespace mindspore diff --git a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.h b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.h index f3fdb5fa98..6f761342d3 100644 --- a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.h +++ b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.h @@ -33,7 +33,6 @@ class GPUKernelRuntime : public KernelRuntime { ~GPUKernelRuntime() override = default; bool Init() override; void ReleaseDeviceRes() override; - void FreeHostMemory() override; void AssignMemory(session::KernelGraph *graph) override; bool Run(session::KernelGraph *graph) override; @@ -41,18 +40,11 @@ class GPUKernelRuntime : public KernelRuntime { DeviceAddressPtr CreateDeviceAddress(void *device_ptr, size_t device_size, const string &format, TypeId type_id) override; bool SyncStream() override; - // Alloc memory use the dynamic memory pool. - void *AllocTensorMemDynamic(size_t size) override; - // Free memory use the dynamic memory pool. - void FreeTensorMemDynamic(void *device_ptr) override; - void MallocOpMemory(const DeviceAddressPtr address, size_t size, int flag) override; - uint8_t *MallocStaticMem(size_t size, bool communication_mem) override; private: GPUKernelRuntime(const GPUKernelRuntime &); GPUKernelRuntime &operator=(const GPUKernelRuntime &); bool InitDevice(); - void MallocDeviceMemory(); bool device_init_{false}; // The related functions and members for using dynamic memory pool. @@ -69,6 +61,7 @@ class GPUKernelRuntime : public KernelRuntime { void FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr &kernel, size_t input_idx, bool *is_communication_op); size_t communication_op_input_ref_count_{0}; size_t communication_op_output_ref_count_{0}; + MemReuseUtilPtr mem_reuse_util_ptr_{nullptr}; }; MS_REG_KERNEL_RUNTIME(kGPUDevice, GPUKernelRuntime); } // namespace gpu diff --git a/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc b/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc new file mode 100644 index 0000000000..3944b504e4 --- /dev/null +++ b/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc @@ -0,0 +1,88 @@ +/** + * Copyright 2019 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "device/gpu/gpu_memory_manager.h" +#include "device/gpu/gpu_memory_allocator.h" +#include "utils/context/ms_context.h" +#include "utils/convert_utils.h" +namespace mindspore { +namespace device { +namespace gpu { +void *GPUMemoryManager::AllocTensorMemDynamic(size_t size) { + return GPUMemoryAllocator::GetInstance().AllocTensorMem(size); +} + +void GPUMemoryManager::FreeTensorMemDynamic(void *device_ptr) { + GPUMemoryAllocator::GetInstance().FreeTensorMem(device_ptr); +} + +void GPUMemoryManager::MallocDeviceMemory() { + auto context_ptr = MsContext::GetInstance(); + MS_EXCEPTION_IF_NULL(context_ptr); + // If use the dynamic memory pool, then alloc the first memory block to init. + if (context_ptr->enable_dynamic_mem_pool()) { + auto device_addr = AllocTensorMemDynamic(1); + if (!device_addr) { + MS_LOG(ERROR) << "Dynamic memory pool init error."; + } + } else { + // Need to reserve 20% space for dynamic memory + const float init_gpu_mem_ratio = 0.8; + size_t mem_size = FloatToSize(GPUMemoryAllocator::GetInstance().free_mem_size() * init_gpu_mem_ratio); + auto alloc_size = + GPUMemoryAllocator::GetInstance().AllocDeviceMem(mem_size, reinterpret_cast(&device_mem_base_)); + device_mem_size_ = alloc_size; + static_mem_offset_ = device_mem_size_; + } +} + +void GPUMemoryManager::FreeDeviceMemory() { + if (device_mem_base_ != nullptr) { + if (!GPUMemoryAllocator::GetInstance().FreeDeviceMem(device_mem_base_)) { + MS_LOG(EXCEPTION) << "Could not free gpu device memory."; + } + } + GPUMemoryAllocator::GetInstance().ReleaseDeviceRes(); +} + +uint8_t *GPUMemoryManager::MallocStaticMem(size_t size, bool) { + auto context_ptr = MsContext::GetInstance(); + MS_EXCEPTION_IF_NULL(context_ptr); + if (context_ptr->enable_dynamic_mem_pool()) { + auto device_ptr = AllocTensorMemDynamic(size); + MS_EXCEPTION_IF_NULL(device_ptr); + return AddressOffset(device_ptr, 0); + } + + auto align_size = GetCommonAlignSize(size); + if (static_mem_offset_ < align_size) { + MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_ + << "] static[" << total_static_size_ << "])" + << " malloc [" << align_size << "] failed!"; + } + auto offset = static_mem_offset_ - align_size; + if (dynamic_mem_offset_ > offset) { + MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_ + << "] static[" << total_static_size_ << "])" + << " malloc [" << align_size << "] failed!"; + } + total_static_size_ += align_size; + static_mem_offset_ = offset; + return device_mem_base_ + offset; +} +} // namespace gpu +} // namespace device +} // namespace mindspore diff --git a/mindspore/ccsrc/device/gpu/gpu_memory_manager.h b/mindspore/ccsrc/device/gpu/gpu_memory_manager.h new file mode 100644 index 0000000000..a18226bdf3 --- /dev/null +++ b/mindspore/ccsrc/device/gpu/gpu_memory_manager.h @@ -0,0 +1,40 @@ +/** + * Copyright 2019 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_MINDSPORE_CCSRC_DEVICE_GPU_GPU_MEMORY_MANAGER_H_ +#define MINDSPORE_MINDSPORE_CCSRC_DEVICE_GPU_GPU_MEMORY_MANAGER_H_ +#include "device/memory_manager.h" +namespace mindspore { +namespace device { +namespace gpu { +class GPUMemoryManager : public MemoryManager { + public: + GPUMemoryManager() = default; + virtual ~GPUMemoryManager() = default; + + void MallocDeviceMemory() override; + void FreeDeviceMemory() override; + + void *AllocTensorMemDynamic(size_t size) override; + void FreeTensorMemDynamic(void *device_ptr) override; + + protected: + uint8_t *MallocStaticMem(size_t size, bool communication_mem); +}; +} // namespace gpu +} // namespace device +} // namespace mindspore +#endif // MINDSPORE_MINDSPORE_CCSRC_DEVICE_GPU_GPU_MEMORY_MANAGER_H_ diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc index 0a9be35fb5..16025ed8a4 100644 --- a/mindspore/ccsrc/device/kernel_runtime.cc +++ b/mindspore/ccsrc/device/kernel_runtime.cc @@ -31,18 +31,13 @@ #include "ir/value.h" using mindspore::kernel::Address; using mindspore::kernel::AddressPtr; -using mindspore::memreuse::BestFitMemReuse; -using mindspore::memreuse::MemReuseUtilPtr; namespace mindspore { namespace device { KernelRuntime::~KernelRuntime() { - device_mem_base_ = nullptr; - device_mem_pool_base_ = nullptr; #ifdef ENABLE_DUMP_E2E dump_conf_ptr_ = nullptr; #endif - mem_reuse_util_ptr_ = nullptr; } bool KernelRuntime::Run(session::KernelGraph *graph) { @@ -88,11 +83,6 @@ bool KernelRuntime::LoadTask(const session::KernelGraph *graph) { return false; } -void KernelRuntime::FreeHostMemory() { - dynamic_mem_offset_ = 0; - static_mem_offset_ = 0; -} - // for D to impl bool KernelRuntime::RunTask(const session::KernelGraph *graph) { if (graph != nullptr) { @@ -126,13 +116,11 @@ size_t KernelRuntime::CountNodeDeviceMemorySize(const mindspore::AnfNodePtr &nod void KernelRuntime::AssignMemory(session::KernelGraph *graph) { auto context_ptr = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(context_ptr); + MS_EXCEPTION_IF_NULL(mem_manager_); + mem_manager_->ResetDynamicMemory(); AssignStaticMemory(graph); - bool is_enable_mem_reuse = context_ptr->enable_mem_reuse(); - if (is_enable_mem_reuse) { - ReuseAssignDynamicMemory(graph); - } else { - AssignDynamicMemory(graph); - } + AssignDynamicMemory(graph); + UpdateRefNodeOutputMem(graph); } @@ -159,6 +147,7 @@ void KernelRuntime::AssignStaticMemory(session::KernelGraph *graph) { void KernelRuntime::RunOpAssignInputMemory(const std::vector &input_tensors, const session::KernelGraph *graph) { MS_EXCEPTION_IF_NULL(graph); + MS_EXCEPTION_IF_NULL(mem_manager_); for (size_t input_index = 0; input_index < graph->inputs().size(); ++input_index) { auto item = graph->inputs()[input_index]; MS_EXCEPTION_IF_NULL(item); @@ -180,7 +169,7 @@ void KernelRuntime::RunOpAssignInputMemory(const std::vector auto device_address = CreateDeviceAddress(nullptr, tensor_size, AnfAlgo::GetOutputFormat(item, index), output_type_id); MS_EXCEPTION_IF_NULL(device_address); - MallocOpMemory(device_address, tensor_size, kStaticMem); + mem_manager_->MallocOpMemory(device_address, tensor_size); AnfAlgo::SetOutputAddr(device_address, index, item.get()); } } @@ -188,6 +177,7 @@ void KernelRuntime::RunOpAssignInputMemory(const std::vector void KernelRuntime::RunOpAssignOutputMemory(const AnfNodePtr &kernel) { MS_EXCEPTION_IF_NULL(kernel); + MS_EXCEPTION_IF_NULL(mem_manager_); auto kernel_mod = AnfAlgo::GetKernelMod(kernel); MS_EXCEPTION_IF_NULL(kernel_mod); auto output_sizes = kernel_mod->GetOutputSizeList(); @@ -208,13 +198,14 @@ void KernelRuntime::RunOpAssignOutputMemory(const AnfNodePtr &kernel) { auto output_type = AnfAlgo::GetOutputDeviceDataType(kernel, i); auto device_address = CreateDeviceAddress(nullptr, output_sizes[i], output_format, output_type); MS_EXCEPTION_IF_NULL(device_address); - MallocOpMemory(device_address, output_sizes[i], kDynamicMem); + mem_manager_->MallocOpMemory(device_address, output_sizes[i]); AnfAlgo::SetOutputAddr(device_address, i, kernel.get()); } } void KernelRuntime::RunOpAssignWorkSpaceMemory(const AnfNodePtr &kernel) { MS_EXCEPTION_IF_NULL(kernel); + MS_EXCEPTION_IF_NULL(mem_manager_); if (kernel->isa()) { auto kernel_mod = AnfAlgo::GetKernelMod(kernel); MS_EXCEPTION_IF_NULL(kernel_mod); @@ -222,7 +213,7 @@ void KernelRuntime::RunOpAssignWorkSpaceMemory(const AnfNodePtr &kernel) { for (size_t i = 0; i < workspace_lists.size(); ++i) { auto device_address = CreateDeviceAddress(nullptr, workspace_lists[i], "", kTypeUnknown); MS_EXCEPTION_IF_NULL(device_address); - MallocOpMemory(device_address, workspace_lists[i], kDynamicMem); + mem_manager_->MallocOpMemory(device_address, workspace_lists[i]); AnfAlgo::SetWorkspaceAddr(device_address, i, kernel.get()); } } @@ -230,6 +221,7 @@ void KernelRuntime::RunOpAssignWorkSpaceMemory(const AnfNodePtr &kernel) { void KernelRuntime::AssignStaticMemoryInput(const session::KernelGraph *graph) { MS_EXCEPTION_IF_NULL(graph); + MS_EXCEPTION_IF_NULL(mem_manager_); for (auto &item : graph->inputs()) { MS_EXCEPTION_IF_NULL(item); if (!item->isa()) { @@ -247,7 +239,7 @@ void KernelRuntime::AssignStaticMemoryInput(const session::KernelGraph *graph) { output_type_id = AnfAlgo::GetOutputInferDataType(item, index); } auto tensor_size = CountNodeDeviceMemorySize(item, index); - auto ptr = MallocStaticMem(tensor_size, false); + auto ptr = mem_manager_->MallocMem(kStaticMem, tensor_size); auto address = CreateDeviceAddress(ptr, tensor_size, AnfAlgo::GetOutputFormat(item, index), output_type_id); AnfAlgo::SetOutputAddr(address, index, item.get()); } @@ -301,6 +293,7 @@ void KernelRuntime::UpdateRefNodeOutputMem(const session::KernelGraph *graph) { void KernelRuntime::AssignCommunicationNodeOutputMem(int flag, const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); + MS_EXCEPTION_IF_NULL(mem_manager_); auto kernel_mod = AnfAlgo::GetKernelMod(node); MS_EXCEPTION_IF_NULL(kernel_mod); auto output_sizes = kernel_mod->GetOutputSizeList(); @@ -314,12 +307,12 @@ void KernelRuntime::AssignCommunicationNodeOutputMem(int flag, const AnfNodePtr std::vector align_size_list; for (uint64_t mem_size : output_sizes) { if (context_ptr->enable_hccl()) { - mem_size = GetCommonAlignSize(mem_size); + mem_size = mem_manager_->GetCommonAlignSize(mem_size); } total_size += mem_size; align_size_list.emplace_back(mem_size); } - uint8_t *output_ptr = CalDeviceMem(node, total_size, flag, 0); + uint8_t *output_ptr = mem_manager_->MallocOutputMem(node, 0, flag, total_size); for (size_t j = 0; j < align_size_list.size(); ++j) { std::string output_format = AnfAlgo::GetOutputFormat(node, j); auto output_type = AnfAlgo::GetOutputDeviceDataType(node, j); @@ -333,6 +326,7 @@ void KernelRuntime::UpdateCommunicationOpInputMem(const AnfNodePtr &node) { auto context_ptr = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(context_ptr); MS_EXCEPTION_IF_NULL(node); + MS_EXCEPTION_IF_NULL(mem_manager_); size_t total_size = 0; std::vector> addr_size; for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(node); ++i) { @@ -340,12 +334,12 @@ void KernelRuntime::UpdateCommunicationOpInputMem(const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(address); auto mem_size = address->size(); if (context_ptr->enable_hccl()) { - mem_size = GetCommonAlignSize(mem_size); + mem_size = mem_manager_->GetCommonAlignSize(mem_size); } total_size += mem_size; addr_size.emplace_back(address.get(), mem_size); } - uint8_t *input_ptr = CalDeviceMem(node, total_size, kDynamicMem, 0); + uint8_t *input_ptr = mem_manager_->MallocOutputMem(node, 0, kDynamicMem, total_size); for (const auto &iter : addr_size) { MS_EXCEPTION_IF_NULL(iter.first); iter.first->set_ptr(input_ptr); @@ -355,7 +349,8 @@ void KernelRuntime::UpdateCommunicationOpInputMem(const AnfNodePtr &node) { void KernelRuntime::AssignNodeOutputMem(int flag, const AnfNodePtr &node, int index) { MS_EXCEPTION_IF_NULL(node); - if (IsCommunicationOp(node)) { + MS_EXCEPTION_IF_NULL(mem_manager_); + if (AnfAlgo::IsCommunicationOp(node)) { UpdateCommunicationOpInputMem(node); AssignCommunicationNodeOutputMem(flag, node); return; @@ -375,7 +370,7 @@ void KernelRuntime::AssignNodeOutputMem(int flag, const AnfNodePtr &node, int in MS_LOG(INFO) << "Already malloc index:" << i; continue; } - auto ptr = CalDeviceMem(node, output_sizes[i], flag, i); + auto ptr = mem_manager_->MallocOutputMem(node, i, flag, output_sizes[i]); if (ptr == nullptr) { // reused ptr, no need alloc, continue; continue; @@ -390,6 +385,7 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const size_t output_idx) { MS_EXCEPTION_IF_NULL(value_node); MS_EXCEPTION_IF_NULL(node_value); + MS_EXCEPTION_IF_NULL(mem_manager_); auto tensor = node_value->cast(); if (tensor == nullptr) { MS_LOG(WARNING) << "Tensor is null"; @@ -397,7 +393,7 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const } size_t tensor_size = tensor->data().nbytes(); auto node_size = CountNodeDeviceMemorySize(value_node, output_idx); - auto ptr = MallocStaticMem(node_size, false); + auto ptr = mem_manager_->MallocMem(kStaticMem, node_size); TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(value_node, output_idx); if (output_type_id == kTypeUnknown) { output_type_id = AnfAlgo::GetOutputInferDataType(value_node, output_idx); @@ -414,6 +410,7 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const void KernelRuntime::AssignStaticMemoryValueNode(session::KernelGraph *graph) { MS_EXCEPTION_IF_NULL(graph); + MS_EXCEPTION_IF_NULL(mem_manager_); for (auto &value_node : graph->graph_value_nodes()) { MS_EXCEPTION_IF_NULL(value_node); if (AnfAlgo::OutputAddrExist(value_node, 0)) { @@ -440,7 +437,7 @@ void KernelRuntime::AssignStaticMemoryValueNode(session::KernelGraph *graph) { } else if (node_value->isa()) { auto value = GetValue(node_value); size_t tensor_size = value.size(); - auto ptr = MallocStaticMem(tensor_size, false); + auto ptr = mem_manager_->MallocMem(kStaticMem, tensor_size); auto address = CreateDeviceAddress(ptr, tensor_size, kOpFormat_DEFAULT, kNumberTypeUInt8); MS_EXCEPTION_IF_NULL(address); AnfAlgo::SetOutputAddr(address, 0, value_node.get()); @@ -452,103 +449,37 @@ void KernelRuntime::AssignStaticMemoryValueNode(session::KernelGraph *graph) { } } -void KernelRuntime::AssignDynamicMemory(const session::KernelGraph *graph) { +void KernelRuntime::AssignDynamicMemory(session::KernelGraph *graph) { MS_EXCEPTION_IF_NULL(graph); - // reset dynamic mem offset - dynamic_mem_offset_ = 0; - auto &kernels = graph->execution_order(); - for (auto &kernel : kernels) { - AssignNodeOutputMem(kDynamicMem, kernel, kGetAllOuts); - AssignWorkSpaceMem(kernel); + MS_EXCEPTION_IF_NULL(mem_manager_); + auto context_ptr = MsContext::GetInstance(); + MS_EXCEPTION_IF_NULL(context_ptr); + bool is_enable_mem_reuse = context_ptr->enable_mem_reuse(); + auto mem_flag = kDynamicMem; + if (is_enable_mem_reuse) { + mem_manager_->InitReuseDynamicMemory(graph); + mem_flag = kReuseDynamicMem; } -} - -void KernelRuntime::ReuseAssignDynamicMemory(session::KernelGraph *graph) { - MS_EXCEPTION_IF_NULL(graph); - dynamic_mem_offset_ = 0; - MemReuseUtilPtr mem_reuse_util_ptr = std::make_shared(); - MS_EXCEPTION_IF_NULL(mem_reuse_util_ptr); - // set all infos - mem_reuse_util_ptr->SetAllInfo(graph); - auto bestfit_mem_reuse = std::make_shared(); - MS_EXCEPTION_IF_NULL(bestfit_mem_reuse); - bestfit_mem_reuse->Reuse(mem_reuse_util_ptr.get()); - size_t total_allocated_size = bestfit_mem_reuse->GetAllocatedSize(); - MS_LOG(INFO) << "TotalReuseDynamicSize [" << total_allocated_size << "]"; - mem_reuse_util_ptr_ = mem_reuse_util_ptr; - auto base_ptr = MallocDynamicMem(total_allocated_size, false); - mem_reuse_util_ptr_->set_mem_base(base_ptr); auto &kernels = graph->execution_order(); for (auto &kernel : kernels) { - AssignNodeOutputMem(kReuseDynamicMem, kernel, kGetAllOuts); - AssignReuseWorkSpaceMem(kernel); + AssignNodeOutputMem(mem_flag, kernel, kGetAllOuts); + AssignWorkSpaceMem(mem_flag, kernel); } } -void KernelRuntime::AssignReuseWorkSpaceMem(const AnfNodePtr &node) { +void KernelRuntime::AssignWorkSpaceMem(int flag, const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(node); + MS_EXCEPTION_IF_NULL(mem_manager_); auto kernel_mod = AnfAlgo::GetKernelMod(node); MS_EXCEPTION_IF_NULL(kernel_mod); size_t index = 0; for (auto &size : kernel_mod->GetWorkspaceSizeList()) { - auto wk_ptr = mem_reuse_util_ptr_->GetNodeWorkSpacePtr(node, index); - AnfAlgo::SetWorkspaceAddr(CreateDeviceAddress(wk_ptr, size, "", kTypeUnknown), index, node.get()); + auto ptr = mem_manager_->MallocWorkSpaceMem(node, flag, index, size); + AnfAlgo::SetWorkspaceAddr(CreateDeviceAddress(ptr, size, "", kTypeUnknown), index, node.get()); index++; } } -void KernelRuntime::AssignWorkSpaceMem(const AnfNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - if (node->isa()) { - auto kernel_mod = AnfAlgo::GetKernelMod(node); - MS_EXCEPTION_IF_NULL(kernel_mod); - size_t index = 0; - for (auto &size : kernel_mod->GetWorkspaceSizeList()) { - auto ptr = MallocDynamicMem(size, false); - AnfAlgo::SetWorkspaceAddr(CreateDeviceAddress(ptr, size, "", kTypeUnknown), index, node.get()); - index++; - } - } -} - -bool KernelRuntime::IsCommunicationOp(const AnfNodePtr &node) { - MS_EXCEPTION_IF_NULL(node); - auto kernel_name = AnfAlgo::GetCNodeName(node); - auto kernel_type = AnfAlgo::GetKernelType(node); - if (kernel_name == kAllReduceOpName || kernel_type == HCCL_KERNEL) { - return true; - } - return false; -} - -uint8_t *KernelRuntime::CalDeviceMem(const AnfNodePtr &node, size_t size, int flag, size_t index) { - MS_EXCEPTION_IF_NULL(node); - auto context_ptr = MsContext::GetInstance(); - MS_EXCEPTION_IF_NULL(context_ptr); - uint8_t *ptr = nullptr; - if (IsCommunicationOp(node)) { - bool communication_mem = false; - if (context_ptr->enable_hccl()) { - communication_mem = true; - } - if (flag == kStaticMem) { - ptr = MallocStaticMem(size, communication_mem); - } else { - ptr = MallocDynamicMem(size, communication_mem); - } - return ptr; - } - - if (flag == kStaticMem) { - ptr = MallocStaticMem(size, false); - } else if (flag == kDynamicMem) { - ptr = MallocDynamicMem(size, false); - } else if (flag == kReuseDynamicMem) { - ptr = mem_reuse_util_ptr_->GetNodeOutputPtr(node, index); - } - return ptr; -} - void KernelRuntime::GenLaunchArgs(const mindspore::kernel::KernelMod &kernel_mod, const mindspore::AnfNodePtr &kernel, AddressPtrList *kernel_inputs, AddressPtrList *const kernel_workspaces, AddressPtrList *kernel_outputs) { @@ -659,65 +590,6 @@ bool KernelRuntime::LaunchKernelMod(const session::KernelGraph &graph) { return true; } -size_t KernelRuntime::GetCommonAlignSize(size_t input_size) const { - return (input_size + mem_align_size_ + 31) / mem_align_size_ * mem_align_size_; -} - -size_t KernelRuntime::GetCommunicationAlignSize(size_t input_size) const { - return (input_size + mem_align_size_ - 1) / mem_align_size_ * mem_align_size_ + 2 * mem_align_size_; -} - -uint8_t *KernelRuntime::MallocStaticMem(size_t size, bool communication_mem) { - size_t align_size = 0; - if (communication_mem) { - align_size = GetCommunicationAlignSize(size); - } else { - align_size = GetCommonAlignSize(size); - } - if (static_mem_offset_ < align_size) { - MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_ - << "] static[" << total_static_size_ << "])" - << " malloc [" << align_size << "] failed!"; - } - total_static_size_ += align_size; - auto offset = static_mem_offset_ - align_size; - if (dynamic_mem_offset_ > offset) { - MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_ - << "] static[" << total_static_size_ << "])" - << " malloc [" << align_size << "] failed!"; - } - static_mem_offset_ = offset; - if (communication_mem) { - return device_mem_base_ + offset + mem_align_size_; - } else { - return device_mem_base_ + offset; - } -} - -uint8_t *KernelRuntime::MallocDynamicMem(size_t size, bool communication_mem) { - size_t align_size = 0; - if (communication_mem) { - align_size = GetCommunicationAlignSize(size); - } else { - align_size = GetCommonAlignSize(size); - } - uint64_t offset = dynamic_mem_offset_; - auto new_offset = dynamic_mem_offset_ + align_size; - if (new_offset > static_mem_offset_) { - MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_ - << "] static[" << total_static_size_ << "])" - << " malloc [" << align_size << "] failed!"; - } - total_dynamic_size_ += align_size; - dynamic_mem_offset_ = new_offset; - - if (communication_mem) { - return device_mem_base_ + offset + mem_align_size_; - } else { - return device_mem_base_ + offset; - } -} - bool KernelRuntime::LaunchKernel(const session::KernelGraph *graph) { MS_EXCEPTION_IF_NULL(graph); if (!LaunchKernelMod(*graph)) { @@ -731,29 +603,6 @@ bool KernelRuntime::LaunchKernel(const session::KernelGraph *graph) { return true; } -void KernelRuntime::MallocOpMemory(const DeviceAddressPtr address, size_t size, int flag) { - if (flag == kStaticMem) { - address->ptr_ = MallocStaticMem(size, false); - } else if (flag == kDynamicMem) { - address->ptr_ = MallocDynamicMem(size, false); - } else { - MS_LOG(EXCEPTION) << "Unknown memory type!"; - } -} - -void *KernelRuntime::AllocTensorMemDynamic(size_t size) { - if (size == 0) { - MS_LOG(ERROR) << "AllocTensorMemDynamic size is 0."; - } - return nullptr; -} - -void KernelRuntime::FreeTensorMemDynamic(void *device_ptr) { - if (device_ptr == nullptr) { - MS_LOG(ERROR) << "FreeTensorMemDynamic device_ptr is null."; - } -} - #ifdef ENABLE_DUMP_E2E bool KernelRuntime::SetDumpConf() { dump_conf_ptr_ = std::make_shared(); diff --git a/mindspore/ccsrc/device/kernel_runtime.h b/mindspore/ccsrc/device/kernel_runtime.h index ac9a56ed4d..1224bf14eb 100644 --- a/mindspore/ccsrc/device/kernel_runtime.h +++ b/mindspore/ccsrc/device/kernel_runtime.h @@ -20,8 +20,7 @@ #include #include #include -#include "pre_activate/mem_reuse/mem_reuse.h" -#include "pre_activate/mem_reuse/mem_reuse_allocator.h" + #include "device/device_address.h" #include "ir/meta_tensor.h" #include "predict/generator/utils/ir_model_util.h" @@ -32,21 +31,16 @@ #include "session/anf_runtime_algorithm.h" #include "kernel/kernel.h" #include "utils/context/ms_context.h" +#include "device/memory_manager.h" // using mindspore::session::KernelGraph; using mindspore::tensor::Tensor; using TensorPtr = std::shared_ptr; -using MemReuseUtilPtr = mindspore::memreuse::MemReuseUtilPtr; using mindspore::kernel::AddressPtr; using AddressPtrList = std::vector; namespace mindspore { namespace device { -const int kStaticMem = 0; -const int kDynamicMem = 1; -const int kReuseDynamicMem = 2; -const int kGetAllOuts = -1; - class KernelRuntime { public: KernelRuntime() = default; @@ -65,7 +59,6 @@ class KernelRuntime { DumpConfPtr GetDumpConf(); #endif virtual bool LoadTask(const session::KernelGraph *graph); - virtual void FreeHostMemory(); // for GPU and D to impl virtual void ReleaseDeviceRes() {} void set_device_id(uint32_t device_id) { device_id_ = device_id; } @@ -75,29 +68,17 @@ class KernelRuntime { TypeId type_id) = 0; virtual bool SyncStream() = 0; void AssignStaticMemory(session::KernelGraph *graph); - void AssignDynamicMemory(const session::KernelGraph *graph); + void AssignDynamicMemory(session::KernelGraph *graph); void ReuseAssignDynamicMemory(session::KernelGraph *graph); void AssignNodeOutputMem(int flag, const AnfNodePtr &node, int index); - void AssignWorkSpaceMem(const AnfNodePtr &node); + void AssignWorkSpaceMem(int flag, const AnfNodePtr &node); void AssignReuseWorkSpaceMem(const AnfNodePtr &node); void AssignCommunicationNodeOutputMem(int flag, const AnfNodePtr &node); void UpdateRefNodeOutputMem(const session::KernelGraph *graph); void UpdateCommunicationOpInputMem(const AnfNodePtr &node); - bool IsCommunicationOp(const AnfNodePtr &node); - size_t GetCommonAlignSize(size_t input_size) const; - size_t GetCommunicationAlignSize(size_t input_size) const; - - uint8_t *CalDeviceMem(const AnfNodePtr &node, size_t size, int flag, size_t index); - virtual uint8_t *MallocStaticMem(size_t size, bool communication_mem); - uint8_t *MallocDynamicMem(size_t size, bool communication_mem); #ifdef ENABLE_DUMP_E2E bool SetDumpConf(); #endif - // Alloc memory use the dynamic memory pool. - virtual void *AllocTensorMemDynamic(size_t size); - // Free memory use the dynamic memory pool. - virtual void FreeTensorMemDynamic(void *device_ptr); - virtual void MallocOpMemory(const DeviceAddressPtr address, size_t size, int flag); private: void AssignStaticMemoryOutput(const session::KernelGraph *graph); @@ -114,20 +95,11 @@ class KernelRuntime { protected: uint32_t device_id_{0}; - uint8_t *device_mem_base_{nullptr}; - uint8_t *device_mem_pool_base_{nullptr}; - uint64_t device_mem_size_{0}; - uint64_t device_mem_pool_size_{0}; - uint64_t dynamic_mem_offset_{0}; - uint64_t static_mem_offset_{0}; - const uint64_t mem_align_size_ = 512; #ifdef ENABLE_DUMP_E2E DumpConfPtr dump_conf_ptr_; #endif void *stream_ = nullptr; - size_t total_static_size_ = 0; - size_t total_dynamic_size_ = 0; - MemReuseUtilPtr mem_reuse_util_ptr_{nullptr}; + std::shared_ptr mem_manager_{nullptr}; }; using KernelRuntimePtr = std::shared_ptr; } // namespace device diff --git a/mindspore/ccsrc/device/memory_manager.cc b/mindspore/ccsrc/device/memory_manager.cc new file mode 100644 index 0000000000..3c1ddee6bc --- /dev/null +++ b/mindspore/ccsrc/device/memory_manager.cc @@ -0,0 +1,170 @@ +/** + * Copyright 2019 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "device/memory_manager.h" +#include "session/anf_runtime_algorithm.h" +#include "utils/context/ms_context.h" +using mindspore::memreuse::BestFitMemReuse; +using mindspore::memreuse::MemReuseUtilPtr; +namespace mindspore { +namespace device { +MemoryManager::~MemoryManager() { + device_mem_base_ = nullptr; + device_mem_pool_base_ = nullptr; + mem_reuse_util_ptr_ = nullptr; +} + +size_t MemoryManager::GetCommonAlignSize(size_t input_size) const { + return (input_size + kMemAlignSize + 31) / kMemAlignSize * kMemAlignSize; +} + +size_t MemoryManager::GetCommunicationAlignSize(size_t input_size) const { + return (input_size + kMemAlignSize - 1) / kMemAlignSize * kMemAlignSize + 2 * kMemAlignSize; +} + +void MemoryManager::InitReuseDynamicMemory(session::KernelGraph *graph) { + MS_EXCEPTION_IF_NULL(graph); + MemReuseUtilPtr mem_reuse_util_ptr = std::make_shared(); + MS_EXCEPTION_IF_NULL(mem_reuse_util_ptr); + // set all infos + mem_reuse_util_ptr->SetAllInfo(graph); + auto bestfit_mem_reuse = std::make_shared(); + MS_EXCEPTION_IF_NULL(bestfit_mem_reuse); + bestfit_mem_reuse->Reuse(mem_reuse_util_ptr.get()); + size_t total_allocated_size = bestfit_mem_reuse->GetAllocatedSize(); + MS_LOG(INFO) << "TotalReuseDynamicSize [" << total_allocated_size << "]"; + mem_reuse_util_ptr_ = mem_reuse_util_ptr; + auto base_ptr = MallocDynamicMem(total_allocated_size, false); + mem_reuse_util_ptr_->set_mem_base(base_ptr); +} + +uint8_t *MemoryManager::MallocOutputMem(const AnfNodePtr &node, size_t index, int flag, size_t size) { + MS_EXCEPTION_IF_NULL(node); + auto context_ptr = MsContext::GetInstance(); + MS_EXCEPTION_IF_NULL(context_ptr); + uint8_t *ptr = nullptr; + if (AnfAlgo::IsCommunicationOp(node)) { + bool communication_mem = false; + if (context_ptr->enable_hccl()) { + communication_mem = true; + } + if (flag == kStaticMem) { + ptr = MallocStaticMem(size, communication_mem); + } else { + ptr = MallocDynamicMem(size, communication_mem); + } + return ptr; + } + + if (flag == kStaticMem) { + ptr = MallocStaticMem(size, false); + } else if (flag == kDynamicMem) { + ptr = MallocDynamicMem(size, false); + } else if (flag == kReuseDynamicMem) { + ptr = mem_reuse_util_ptr_->GetNodeOutputPtr(node, index); + } + return ptr; +} + +uint8_t *MemoryManager::MallocWorkSpaceMem(const AnfNodePtr &node, size_t index, int flag, size_t size) { + if (flag == kReuseDynamicMem) { + return mem_reuse_util_ptr_->GetNodeWorkSpacePtr(node, index); + } + return MallocDynamicMem(size, false); +} + +uint8_t *MemoryManager::MallocMem(int flag, size_t size) { + uint8_t *ptr = nullptr; + if (flag == kStaticMem) { + ptr = MallocStaticMem(size, false); + } else if (flag == kDynamicMem) { + ptr = MallocDynamicMem(size, false); + } + return ptr; +} + +uint8_t *MemoryManager::MallocStaticMem(size_t size, bool communication_mem) { + size_t align_size = 0; + if (communication_mem) { + align_size = GetCommunicationAlignSize(size); + } else { + align_size = GetCommonAlignSize(size); + } + if (static_mem_offset_ < align_size) { + MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_ + << "] static[" << total_static_size_ << "])" + << " malloc [" << align_size << "] failed!"; + } + total_static_size_ += align_size; + auto offset = static_mem_offset_ - align_size; + if (dynamic_mem_offset_ > offset) { + MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_ + << "] static[" << total_static_size_ << "])" + << " malloc [" << align_size << "] failed!"; + } + static_mem_offset_ = offset; + if (communication_mem) { + return device_mem_base_ + offset + kMemAlignSize; + } else { + return device_mem_base_ + offset; + } +} + +uint8_t *MemoryManager::MallocDynamicMem(size_t size, bool communication_mem) { + size_t align_size = 0; + if (communication_mem) { + align_size = GetCommunicationAlignSize(size); + } else { + align_size = GetCommonAlignSize(size); + } + uint64_t offset = dynamic_mem_offset_; + auto new_offset = dynamic_mem_offset_ + align_size; + if (new_offset > static_mem_offset_) { + MS_LOG(EXCEPTION) << "Out of memory!!! total[" << device_mem_size_ << "](dynamic[" << total_dynamic_size_ + << "] static[" << total_static_size_ << "])" + << " malloc [" << align_size << "] failed!"; + } + total_dynamic_size_ += align_size; + dynamic_mem_offset_ = new_offset; + + if (communication_mem) { + return device_mem_base_ + offset + kMemAlignSize; + } else { + return device_mem_base_ + offset; + } +} + +void MemoryManager::MallocOpMemory(const DeviceAddressPtr address, size_t size) { + auto device_ptr = AllocTensorMemDynamic(size); + MS_EXCEPTION_IF_NULL(device_ptr); + address->ptr_ = device_ptr; + address->mem_dynamic_alloc_ = true; +} + +void *MemoryManager::AllocTensorMemDynamic(size_t size) { + if (size == 0) { + MS_LOG(ERROR) << "AllocTensorMemDynamic size is 0."; + } + return nullptr; +} + +void MemoryManager::FreeTensorMemDynamic(void *device_ptr) { + if (device_ptr == nullptr) { + MS_LOG(ERROR) << "FreeTensorMemDynamic device_ptr is null."; + } +} +} // namespace device +} // namespace mindspore diff --git a/mindspore/ccsrc/device/memory_manager.h b/mindspore/ccsrc/device/memory_manager.h new file mode 100644 index 0000000000..2e47237def --- /dev/null +++ b/mindspore/ccsrc/device/memory_manager.h @@ -0,0 +1,71 @@ +/** + * Copyright 2019 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_MINDSPORE_CCSRC_DEVICE_MEMORY_MANAGER_H_ +#define MINDSPORE_MINDSPORE_CCSRC_DEVICE_MEMORY_MANAGER_H_ +#include +#include "pre_activate/mem_reuse/mem_reuse.h" +#include "pre_activate/mem_reuse/mem_reuse_allocator.h" +namespace mindspore { +namespace device { +const int kStaticMem = 0; +const int kDynamicMem = 1; +const int kReuseDynamicMem = 2; +const int kGetAllOuts = -1; +const uint64_t kMemAlignSize = 512; +using MemReuseUtilPtr = mindspore::memreuse::MemReuseUtilPtr; + +class MemoryManager { + public: + MemoryManager() = default; + virtual ~MemoryManager(); + + virtual void MallocDeviceMemory() = 0; + virtual void FreeDeviceMemory() = 0; + void ResetDynamicMemory() { + total_dynamic_size_ = 0; + dynamic_mem_offset_ = 0; + } + + void InitReuseDynamicMemory(session::KernelGraph *graph); + uint8_t *MallocOutputMem(const AnfNodePtr &node, size_t index, int flag, size_t size); + uint8_t *MallocWorkSpaceMem(const AnfNodePtr &node, size_t index, int flag, size_t size); + virtual uint8_t *MallocMem(int flag, size_t size); + + // Alloc memory use the dynamic memory pool. + virtual void *AllocTensorMemDynamic(size_t size); + // Free memory use the dynamic memory pool. + virtual void FreeTensorMemDynamic(void *device_ptr); + virtual void MallocOpMemory(const DeviceAddressPtr address, size_t size); + size_t GetCommonAlignSize(size_t input_size) const; + size_t GetCommunicationAlignSize(size_t input_size) const; + + protected: + virtual uint8_t *MallocStaticMem(size_t size, bool communication_mem); + virtual uint8_t *MallocDynamicMem(size_t size, bool communication_mem); + uint8_t *device_mem_base_{nullptr}; + uint8_t *device_mem_pool_base_{nullptr}; + uint64_t device_mem_size_{0}; + uint64_t device_mem_pool_size_{0}; + uint64_t dynamic_mem_offset_{0}; + uint64_t static_mem_offset_{0}; + size_t total_static_size_ = 0; + size_t total_dynamic_size_ = 0; + MemReuseUtilPtr mem_reuse_util_ptr_{nullptr}; +}; +} // namespace device +} // namespace mindspore +#endif // MINDSPORE_MINDSPORE_CCSRC_DEVICE_MEMORY_MANAGER_H_ diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.cc b/mindspore/ccsrc/session/anf_runtime_algorithm.cc index cc23dbbdd2..78922448af 100644 --- a/mindspore/ccsrc/session/anf_runtime_algorithm.cc +++ b/mindspore/ccsrc/session/anf_runtime_algorithm.cc @@ -857,5 +857,15 @@ void AnfRuntimeAlgorithm::SetNodeInput(const CNodePtr &node, const AnfNodePtr &i MS_EXCEPTION_IF_NULL(input_node); node->set_input(index + 1, input_node); } + +bool AnfRuntimeAlgorithm::IsCommunicationOp(const AnfNodePtr &node) { + MS_EXCEPTION_IF_NULL(node); + auto kernel_name = AnfAlgo::GetCNodeName(node); + auto kernel_type = AnfAlgo::GetKernelType(node); + if (kernel_name == kAllReduceOpName || kernel_type == HCCL_KERNEL) { + return true; + } + return false; +} } // namespace session } // namespace mindspore diff --git a/mindspore/ccsrc/session/anf_runtime_algorithm.h b/mindspore/ccsrc/session/anf_runtime_algorithm.h index 2de68f0098..55650ac31e 100644 --- a/mindspore/ccsrc/session/anf_runtime_algorithm.h +++ b/mindspore/ccsrc/session/anf_runtime_algorithm.h @@ -166,6 +166,7 @@ class AnfRuntimeAlgorithm { static bool IsFeatureMapInput(const AnfNodePtr &node, size_t input_index); // get real input index for some tbe ops which input order is different between me and tbe impl static size_t GetRealInputIndex(const AnfNodePtr &anf_node, const size_t cur_index); + static bool IsCommunicationOp(const AnfNodePtr &node); }; } // namespace session using AnfAlgo = session::AnfRuntimeAlgorithm; diff --git a/mindspore/ccsrc/session/gpu_session.cc b/mindspore/ccsrc/session/gpu_session.cc index 29330fb193..bbcf2228cc 100644 --- a/mindspore/ccsrc/session/gpu_session.cc +++ b/mindspore/ccsrc/session/gpu_session.cc @@ -102,10 +102,6 @@ GraphId GPUSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList graph->set_execution_order(execution_order); // Alloc memory, including static memory and dynamic memory AllocateMemory(graph.get()); - // Reset memory resource - auto runtime_instance = device::KernelRuntimeManager::Instance().GetSingleKernelRuntime(kGPUDevice, device_id_); - MS_EXCEPTION_IF_NULL(runtime_instance); - runtime_instance->FreeHostMemory(); return graph_id; } diff --git a/tests/ut/cpp/CMakeLists.txt b/tests/ut/cpp/CMakeLists.txt index 8d3f8a8138..3c1351a857 100644 --- a/tests/ut/cpp/CMakeLists.txt +++ b/tests/ut/cpp/CMakeLists.txt @@ -85,6 +85,7 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "../../../mindspore/ccsrc/kernel/oplib/*.cc" "../../../mindspore/ccsrc/kernel/tbe/*.cc" "../../../mindspore/ccsrc/device/kernel_runtime.cc" + "../../../mindspore/ccsrc/device/memory_manager.cc" "../../../mindspore/ccsrc/device/kernel_runtime_manager.cc" "../../../mindspore/ccsrc/device/kernel_info.cc" "../../../mindspore/ccsrc/device/ascend/profiling/*.cc" @@ -92,6 +93,7 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "../../../mindspore/ccsrc/device/convert_tensor_utils.cc" "../../../mindspore/ccsrc/device/ascend/kernel_build_ascend.cc" "../../../mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc" + "../../../mindspore/ccsrc/device/ascend/ascend_memory_manager.cc" "../../../mindspore/ccsrc/device/ascend/ascend_device_address.cc" "../../../mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc" "../../../mindspore/ccsrc/predict/generator/utils/ir_model_util.cc" From ed04b8e165e65645b74e7cb532151356ad14ad45 Mon Sep 17 00:00:00 2001 From: zhoufeng Date: Mon, 6 Apr 2020 11:22:47 +0800 Subject: [PATCH 154/227] default build command "-z" (minddata) and "-M on" (gpu) by default in build.sh --- build.sh | 24 +++++++++++++++++------- cmake/mind_expression.cmake | 8 ++++---- 2 files changed, 21 insertions(+), 11 deletions(-) diff --git a/build.sh b/build.sh index 9d812d6dcc..8bb5f3d95a 100755 --- a/build.sh +++ b/build.sh @@ -26,7 +26,7 @@ usage() echo "Usage:" echo "bash build.sh [-d] [-r] [-v] [-c on|off] [-t on|off] [-g on|off] [-h] [-s] [-b ge|cpu] [-m infer|train] \\" echo " [-a on|off] [-g on|off] [-p on|off] [-i] [-L] [-R] [-D on|off] [-j[n]] [-e gpu|d|cpu] \\" - echo " [-P on|off] [-z] [-M on|off] [-V 9.2|10.1] [-I] [-K]" + echo " [-P on|off] [-z [on|off]] [-M on|off] [-V 9.2|10.1] [-I] [-K]" echo "" echo "Options:" echo " -d Debug mode" @@ -50,8 +50,8 @@ usage() echo " -P Enable dump anf graph to file in ProtoBuffer format, default on" echo " -Q Enable dump end to end, default off" echo " -D Enable dumping of function graph ir, default on" - echo " -z Compile dataset & mindrecord, default off" - echo " -M Enable MPI and NCCL for GPU training, default off" + echo " -z Compile dataset & mindrecord, default on" + echo " -M Enable MPI and NCCL for GPU training, default on" echo " -V Specify the minimum required cuda version, default CUDA 9.2" echo " -I Compile predict, default off" echo " -K Compile with AKG, default off" @@ -88,8 +88,8 @@ checkopts() ENABLE_DUMP2PROTO="on" ENABLE_DUMPE2E="off" ENABLE_DUMP_IR="on" - COMPILE_MINDDATA="off" - ENABLE_MPI="off" + COMPILE_MINDDATA="on" + ENABLE_MPI="on" CUDA_VERSION="9.2" COMPILE_PREDICT="off" USE_GLOG="on" @@ -177,7 +177,7 @@ checkopts() if [[ "X$OPTARG" == "Xgpu" ]]; then ENABLE_GPU="on" ENABLE_CPU="on" - elif [[ "X$OPTARG" == "Xd" ]]; then + elif [[ "X$OPTARG" == "Xd" || "X$OPTARG" == "Xascend" ]]; then ENABLE_D="on" ENABLE_CPU="on" elif [[ "X$OPTARG" == "Xcpu" ]]; then @@ -216,7 +216,17 @@ checkopts() echo "enable dump function graph ir" ;; z) - COMPILE_MINDDATA="on" + eval ARG=\$\{$OPTIND\} + if [[ -n $ARG && $ARG != -* ]]; then + OPTARG=$ARG + check_on_off $OPTARG z + OPTIND=$((OPTIND + 1)) + else + OPTARG="" + fi + if [[ "X$OPTARG" == "Xoff" ]]; then + COMPILE_MINDDATA="off" + fi ;; I) COMPILE_PREDICT="on" diff --git a/cmake/mind_expression.cmake b/cmake/mind_expression.cmake index 345fd4675e..af122d4117 100644 --- a/cmake/mind_expression.cmake +++ b/cmake/mind_expression.cmake @@ -29,11 +29,11 @@ if (ENABLE_GPU) include(${CMAKE_SOURCE_DIR}/cmake/external_libs/dmlc_core.cmake) include(${CMAKE_SOURCE_DIR}/cmake/external_libs/rang.cmake) include(${CMAKE_SOURCE_DIR}/cmake/external_libs/tvm_gpu.cmake) -endif() -if (ENABLE_MPI) - include(${CMAKE_SOURCE_DIR}/cmake/external_libs/nccl.cmake) - include(${CMAKE_SOURCE_DIR}/cmake/external_libs/ompi.cmake) + if (ENABLE_MPI) + include(${CMAKE_SOURCE_DIR}/cmake/external_libs/nccl.cmake) + include(${CMAKE_SOURCE_DIR}/cmake/external_libs/ompi.cmake) + endif() endif() if (ENABLE_GE) From 849543ac96c76c876195192bb94ff5a6008fbc6a Mon Sep 17 00:00:00 2001 From: zhoufeng Date: Mon, 6 Apr 2020 12:17:43 +0800 Subject: [PATCH 155/227] Distinguish package name according to hardware platform --- build.sh | 6 ++++-- package.sh | 17 ++++++++++++++++- setup_package.py | 2 +- 3 files changed, 21 insertions(+), 4 deletions(-) diff --git a/build.sh b/build.sh index 8bb5f3d95a..6dc699000a 100755 --- a/build.sh +++ b/build.sh @@ -462,8 +462,10 @@ if [[ "X$INC_BUILD" = "Xoff" ]]; then bash "${PROJECT_PATH}/package.sh" ge elif [[ "X$ENABLE_GPU" = "Xon" ]]; then bash "${PROJECT_PATH}/package.sh" ms gpu - elif [[ "X$ENABLE_D" = "Xon" ]] || [[ "X$ENABLE_CPU" = "Xon" ]]; then - bash "${PROJECT_PATH}/package.sh" ms + elif [[ "X$ENABLE_D" = "Xon" ]]; then + bash "${PROJECT_PATH}/package.sh" ms ascend + elif [[ "X$ENABLE_CPU" = "Xon" ]]; then + bash "${PROJECT_PATH}/package.sh" ms cpu else bash "${PROJECT_PATH}/package.sh" debug fi diff --git a/package.sh b/package.sh index 0d4147c9f6..67f4761f37 100755 --- a/package.sh +++ b/package.sh @@ -96,14 +96,29 @@ if [ -n "$1" ];then else export BACKEND_POLICY="ms" fi + +# package name +if [[ "X$1" = "Xge" ]]; then + export MS_PACKAGE_NAME="mindspore" +elif [[ "X$1" = "Xms" && "X$2" = "Xgpu" ]]; then + export MS_PACKAGE_NAME="mindspore-gpu" +elif [[ "X$1" = "Xms" && "X$2" = "Xascend" ]]; then + export MS_PACKAGE_NAME="mindspore-ascend" +elif [[ "X$1" = "Xms" && "X$2" = "Xcpu" ]]; then + export MS_PACKAGE_NAME="mindspore" +else + export MS_PACKAGE_NAME="mindspore" +fi + ${PYTHON} "${BASEPATH}/setup_package.py" bdist_wheel chmod -R 700 ${PACKAGE_PATH}/mindspore/ -chmod -R 700 ${PACKAGE_PATH}/mindspore.egg-info/ +chmod -R 700 ${PACKAGE_PATH}/${MS_PACKAGE_NAME//-/_}.egg-info/ # rename package PACKAGE_FULL_NAME=$(find "${PACKAGE_PATH}" -iname "*.whl") PACKAGE_BASE_NAME=$(echo ${PACKAGE_FULL_NAME} | awk -F / '{print $NF}' | awk -F - '{print $1"-"$2}') +PACKAGE_BASE_NAME=${PACKAGE_BASE_NAME//_*-/-} PACKAGE_NEW_NAME="${PACKAGE_BASE_NAME}-${PY_TAGS}-${PLATFORM_TAG}.whl" cp -rf "${PACKAGE_PATH}/dist"/*.whl "${PACKAGE_PATH}/${PACKAGE_NEW_NAME}" diff --git a/setup_package.py b/setup_package.py index 8b6889cd34..87b5718de2 100644 --- a/setup_package.py +++ b/setup_package.py @@ -21,7 +21,6 @@ from setuptools import setup, find_packages from setuptools.command.egg_info import egg_info from setuptools.command.build_py import build_py -package_name = 'mindspore' version = '0.1.0' author = 'The MindSpore Authors' author_email = 'contact@mindspore.cn' @@ -29,6 +28,7 @@ home_page = 'https://www.mindspore.cn' backend_policy = os.getenv('BACKEND_POLICY') commit_id = os.getenv('COMMIT_ID').replace("\n", "") +package_name = os.getenv('MS_PACKAGE_NAME').replace("\n", "") pwd = os.path.dirname(os.path.realpath(__file__)) pkg_dir = os.path.join(pwd, 'build/package') From 513f384c43f5d850fabdfc9ca878ed7cd7f403a3 Mon Sep 17 00:00:00 2001 From: yao_yf Date: Wed, 8 Apr 2020 17:24:22 +0800 Subject: [PATCH 156/227] fix auto parallel prelu --- mindspore/ccsrc/parallel/ops_info/prelu_info.cc | 2 +- tests/ut/cpp/parallel/ops_info/prelu_test.cc | 6 ++---- tests/ut/python/parallel/test_prelu.py | 17 +++++++++++++++++ 3 files changed, 20 insertions(+), 5 deletions(-) diff --git a/mindspore/ccsrc/parallel/ops_info/prelu_info.cc b/mindspore/ccsrc/parallel/ops_info/prelu_info.cc index 9aa8513331..1a44501f42 100644 --- a/mindspore/ccsrc/parallel/ops_info/prelu_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/prelu_info.cc @@ -52,7 +52,7 @@ Status PReLUInfo::CheckStrategy(const StrategyPtr& strategy) { } return FAILED; } - if ((stra[0][PRELU_CHANNEL_INDEX] != PRELU_CHANNEL_STRATEGY) || (stra[1][0] != PRELU_CHANNEL_STRATEGY)) { + if (stra[0][PRELU_CHANNEL_INDEX] != stra[1][0]) { if (is_auto_parallel_) { MS_LOG(DEBUG) << name_ << ": Invalid channel strategy."; } else { diff --git a/tests/ut/cpp/parallel/ops_info/prelu_test.cc b/tests/ut/cpp/parallel/ops_info/prelu_test.cc index 5ff261234f..d6db1b8460 100644 --- a/tests/ut/cpp/parallel/ops_info/prelu_test.cc +++ b/tests/ut/cpp/parallel/ops_info/prelu_test.cc @@ -146,11 +146,10 @@ TEST_F(TestPReLUInfo, CheckStrategy1) { } TEST_F(TestPReLUInfo, CheckStrategy2) { - // Success: {{2,1,8,16},{1}} std::vector inputs = {{2, 4, 8, 16}, {4}}; StrategyPtr strategy = NewStrategy(0, inputs); Status ret = prelu->Init(strategy); - ASSERT_EQ(ret, FAILED); + ASSERT_EQ(ret, SUCCESS); } TEST_F(TestPReLUInfo, AutoStrategy1) { @@ -252,11 +251,10 @@ TEST_F(TestPReLUInfo, CheckStrategy_2d1) { } TEST_F(TestPReLUInfo, CheckStrategy_2d2) { - // Success: {{2,1,8,16},{1}} std::vector inputs = {{128, 4}, {4}}; StrategyPtr strategy = NewStrategy(0, inputs); Status ret = prelu_2d->Init(strategy); - ASSERT_EQ(ret, FAILED); + ASSERT_EQ(ret, SUCCESS); } TEST_F(TestPReLUInfo, AutoStrategy_2d1) { diff --git a/tests/ut/python/parallel/test_prelu.py b/tests/ut/python/parallel/test_prelu.py index c601045491..d3ad1cc710 100755 --- a/tests/ut/python/parallel/test_prelu.py +++ b/tests/ut/python/parallel/test_prelu.py @@ -149,3 +149,20 @@ def test_prelu_parallel_success3(): w = Tensor(np.random.rand(16),dtype=ms.float32) net = GradWrap(NetWithLoss(Net(strategy1, strategy2))) _executor.compile(net, x, y, w) + +def test_prelu_parallel_success4(): + class Net(nn.Cell): + def __init__(self, strategy): + super().__init__() + self.prelu = P.PReLU().set_strategy(strategy) + def construct(self, x, y): + out = self.prelu(x, y) + return out + context.reset_auto_parallel_context() + context.set_auto_parallel_context(device_num=64, global_rank=0) + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") + strategy = ((2, 4, 4, 2), (4, )) + x = Tensor(np.random.rand(4, 16, 32, 64),dtype=ms.float32) + w = Tensor(np.random.rand(16),dtype=ms.float32) + net = GradWrap(NetWithLoss(Net(strategy))) + _executor.compile(net, x, w) From c0e2a63fdb989ff598869b38e184c5049cea1948 Mon Sep 17 00:00:00 2001 From: Cathy Wong Date: Wed, 8 Apr 2020 16:36:06 -0400 Subject: [PATCH 157/227] Correct dataset error checking --- mindspore/dataset/engine/datasets.py | 2 -- mindspore/dataset/engine/validators.py | 8 ++++---- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py index ab2290c13c..2058bbf826 100644 --- a/mindspore/dataset/engine/datasets.py +++ b/mindspore/dataset/engine/datasets.py @@ -82,8 +82,6 @@ def zip(datasets): if len(datasets) <= 1: raise ValueError( "Can't zip empty or just one dataset!") - if not isinstance(datasets, tuple): - raise TypeError("The zip function %s type error!" % (datasets)) return ZipDataset(datasets) diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py index 26d6241945..4c84cfe354 100644 --- a/mindspore/dataset/engine/validators.py +++ b/mindspore/dataset/engine/validators.py @@ -105,13 +105,13 @@ def check(method): "The %s function %s exceeds the boundary!" % ( func_name, param_name)) if isinstance(arg, int) and param_name == "num_parallel_workers" and ( - arg <= 0 or arg > cpu_count()): + arg < 1 or arg > cpu_count()): raise ValueError( "The %s function %s exceeds the boundary(%s)!" % ( func_name, param_name, cpu_count())) if isinstance(arg, int) and param_name != "seed" \ and param_name != "count" and param_name != "prefetch_size" \ - and param_name != "num_parallel_workers" and (arg <= 0 or arg > 2147483647): + and param_name != "num_parallel_workers" and (arg < 1 or arg > 2147483647): raise ValueError( "The %s function %s exceeds the boundary!" % ( func_name, param_name)) @@ -271,8 +271,8 @@ def check_interval_closed(param, param_name, valid_range): def check_num_parallel_workers(value): check_type(value, 'num_parallel_workers', int) - if value <= 0 or value > cpu_count(): - raise ValueError("num_parallel_workers exceeds the boundary between 0 and {}!".format(cpu_count())) + if value < 1 or value > cpu_count(): + raise ValueError("num_parallel_workers exceeds the boundary between 1 and {}!".format(cpu_count())) def check_num_samples(value): From 0b1ae67418a588a0f58e3f7bdadfb42cf53e97ef Mon Sep 17 00:00:00 2001 From: jinyaohui Date: Thu, 9 Apr 2020 09:23:39 +0800 Subject: [PATCH 158/227] modify comment --- example/yolov3_coco2017/train.py | 2 +- mindspore/ccsrc/transform/convert.cc | 2 +- mindspore/nn/wrap/loss_scale.py | 2 +- tests/ut/python/utils/test_callback.py | 8 ++++---- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/example/yolov3_coco2017/train.py b/example/yolov3_coco2017/train.py index 0a32a6d30d..121e2aa810 100644 --- a/example/yolov3_coco2017/train.py +++ b/example/yolov3_coco2017/train.py @@ -67,7 +67,7 @@ if __name__ == '__main__': parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.") parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.") parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.") - parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or non-sink mode, default is sink") + parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink") parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10") parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.") parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path") diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index 59985c8ae3..bebd000958 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -449,7 +449,7 @@ void DfGraphConvertor::InitLoopVar(std::vector *init_input) { if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) { value = ConfigManager::GetInstance().iter_num(); } else { - MS_LOG(INFO) << "Run with non-sink mode, the iterator number will always be 1"; + MS_LOG(INFO) << "Run with normal(non-sink) mode, the iterator number will always be 1"; value = 1; ConfigManager::GetInstance().set_iter_num(value); } diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py index fd1c22be1f..c6d61e6983 100644 --- a/mindspore/nn/wrap/loss_scale.py +++ b/mindspore/nn/wrap/loss_scale.py @@ -51,7 +51,7 @@ class DynamicLossScaleUpdateCell(Cell): In every training step, the loss scaling value will be updated by loss scaling value/`scale_factor` when there is overflow. And it will be increased by loss scaling value * `scale_factor` if there is no overflow for a continuous `scale_window` steps. This cell is used for Graph mode training in which all - logic will be executed on device side(Another training mode is non-sink mode in which some logic will be + logic will be executed on device side(Another training mode is normal(non-sink) mode in which some logic will be executed on host). Args: diff --git a/tests/ut/python/utils/test_callback.py b/tests/ut/python/utils/test_callback.py index 7e7b893e0c..8c10c8886d 100644 --- a/tests/ut/python/utils/test_callback.py +++ b/tests/ut/python/utils/test_callback.py @@ -112,8 +112,8 @@ def test_save_checkpoint(): os.remove('./test_files/test_ckpt-model.pkl') -def test_loss_monitor_sink_model(): - """Test loss monitor sink model.""" +def test_loss_monitor_sink_mode(): + """Test loss monitor sink mode.""" cb_params = _InternalCallbackParam() cb_params.cur_epoch_num = 4 cb_params.cur_step_num = 2 @@ -131,8 +131,8 @@ def test_loss_monitor_sink_model(): callbacklist.end(run_context) -def test_loss_monitor_feed_model(): - """Test loss monitor non-sink mode.""" +def test_loss_monitor_normal_mode(): + """Test loss monitor normal(non-sink) mode.""" cb_params = _InternalCallbackParam() run_context = RunContext(cb_params) loss_cb = LossMonitor(1) From 25cdf4833679e14d1cda2ae4d523f6856af9c2d9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=87=E4=B8=87=E6=B2=A1=E6=83=B3=E5=88=B0?= Date: Tue, 7 Apr 2020 14:56:05 +0800 Subject: [PATCH 159/227] fix review opinions in doc/comments --- mindspore/common/initializer.py | 2 +- mindspore/ops/operations/array_ops.py | 86 ++++++++++----------- mindspore/ops/operations/math_ops.py | 105 +++++++++++++------------- mindspore/ops/operations/nn_ops.py | 44 +++++------ mindspore/train/serialization.py | 2 +- 5 files changed, 116 insertions(+), 123 deletions(-) diff --git a/mindspore/common/initializer.py b/mindspore/common/initializer.py index 4261621272..d55e03314d 100644 --- a/mindspore/common/initializer.py +++ b/mindspore/common/initializer.py @@ -276,7 +276,7 @@ def initializer(init, shape=None, dtype=mstype.float32): shape (Union[tuple, list, int]): A list of integers, a tuple of integers or an integer as the shape of output. Default: None. - dtype (:class:`mindspore.dtype`): The type of data in initialized tensor. Default: mstype.float32. + dtype (:class:`mindspore.dtype`): The type of data in initialized tensor. Default: mindspore.float32. Returns: Tensor, initialized tensor. diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index 59d3083c5d..dda490566f 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -62,7 +62,7 @@ class ExpandDims(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) - >>> expand_dims = ExpandDims() + >>> expand_dims = P.ExpandDims() >>> output = expand_dims(input_tensor, 0) """ @@ -101,7 +101,7 @@ class DType(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) - >>> type = DType()(input_tensor) + >>> type = P.DType()(input_tensor) """ @prim_attr_register @@ -134,7 +134,7 @@ class SameTypeShape(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> input_y = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) - >>> out = SameTypeShape()(input_x, input_y) + >>> out = P.SameTypeShape()(input_x, input_y) """ @prim_attr_register @@ -175,7 +175,7 @@ class Cast(PrimitiveWithInfer): >>> input_np = np.random.randn(2, 3, 4, 5).astype(np.float32) >>> input_x = Tensor(input_np) >>> type_dst = mindspore.int32 - >>> cast = Cast() + >>> cast = P.Cast() >>> result = cast(input_x, type_dst) >>> expect = input_np.astype(type_dst) """ @@ -227,7 +227,7 @@ class IsSubClass(PrimitiveWithInfer): bool, the check result. Examples: - >>> result = IsSubClass()(mindspore.int32, mindspore.intc) + >>> result = P.IsSubClass()(mindspore.int32, mindspore.intc) """ @prim_attr_register @@ -262,7 +262,7 @@ class IsInstance(PrimitiveWithInfer): Examples: >>> a = 1 - >>> result = IsInstance()(a, mindspore.int32) + >>> result = P.IsInstance()(a, mindspore.int32) """ @prim_attr_register @@ -303,7 +303,7 @@ class Reshape(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) - >>> reshape = Reshape() + >>> reshape = P.Reshape() >>> output = reshape(input_tensor, (3, 2)) """ @@ -366,7 +366,7 @@ class Shape(Primitive): Examples: >>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) - >>> shape = Shape() + >>> shape = P.Shape() >>> output = shape(input_tensor) """ @@ -398,7 +398,7 @@ class Squeeze(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) - >>> squeeze = Squeeze(2) + >>> squeeze = P.Squeeze(2) >>> output = squeeze(input_tensor) """ @@ -450,7 +450,7 @@ class Transpose(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]), mindspore.float32) >>> perm = (0, 2, 1) - >>> transpose = Transpose() + >>> transpose = P.Transpose() >>> output = transpose(input_tensor, perm) """ @@ -504,10 +504,10 @@ class GatherV2(PrimitiveWithInfer): Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`. Examples: - >>> params = Tensor(np.array([[1, 2, 7, 42], [3, 4, 54, 22], [2, 2, 55, 3]]), mindspore.float32) - >>> indices = Tensor(np.array([1, 2]), mindspore.int32) + >>> input_params = Tensor(np.array([[1, 2, 7, 42], [3, 4, 54, 22], [2, 2, 55, 3]]), mindspore.float32) + >>> input_indices = Tensor(np.array([1, 2]), mindspore.int32) >>> axis = 1 - >>> out = GatherV2()(params, indices, axis) + >>> out = P.GatherV2()(input_params, input_indices, axis) """ @prim_attr_register @@ -556,7 +556,7 @@ class Split(PrimitiveWithInfer): :math:`(y_1, y_2, ..., y_S)`. Examples: - >>> split = Split(1, 2) + >>> split = P.Split(1, 2) >>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]])) >>> output = split(x) """ @@ -606,7 +606,7 @@ class Rank(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) - >>> rank = Rank() + >>> rank = P.Rank() >>> rank(input_tensor) """ @@ -640,7 +640,7 @@ class TruncatedNormal(PrimitiveWithInfer): Examples: >>> input_shape = Tensor(np.array([1, 2, 3])) - >>> truncated_normal = TruncatedNormal() + >>> truncated_normal = P.TruncatedNormal() >>> output = truncated_normal(input_shape) """ @@ -681,7 +681,7 @@ class Size(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) - >>> size = Size() + >>> size = P.Size() >>> output = size(input_tensor) """ @@ -826,7 +826,7 @@ class TupleToArray(PrimitiveWithInfer): Tensor, if the input tuple contain `N` numbers, then the output tensor shape is (N,). Examples: - >>> type = TupleToArray()((1,2,3)) + >>> type = P.TupleToArray()((1,2,3)) """ @prim_attr_register @@ -861,7 +861,7 @@ class ScalarToArray(PrimitiveWithInfer): Tensor. 0-D Tensor and the content is the input. Examples: - >>> op = ScalarToArray() + >>> op = P.ScalarToArray() >>> data = 1.0 >>> output = op(data) """ @@ -893,7 +893,7 @@ class ScalarToTensor(PrimitiveWithInfer): Tensor. 0-D Tensor and the content is the input. Examples: - >>> op = ScalarToTensor() + >>> op = P.ScalarToTensor() >>> data = 1 >>> output = op(data, mindspore.float32) """ @@ -934,7 +934,7 @@ class InvertPermutation(PrimitiveWithInfer): tuple[int]. the lenth is same as input. Examples: - >>> invert = InvertPermutation() + >>> invert = P.InvertPermutation() >>> input_data = (3, 4, 0, 2, 1) >>> output = invert(input_data) >>> output == (2, 4, 3, 0, 1) @@ -982,8 +982,8 @@ class Argmax(PrimitiveWithInfer): Tensor, indices of the max value of input tensor across the axis. Examples: - >>> input = Tensor(np.array([2.0, 3.1, 1.2])) - >>> index = Argmax()(input) + >>> input_x = Tensor(np.array([2.0, 3.1, 1.2])) + >>> index = P.Argmax()(input_x) >>> assert index == Tensor(1, mindspore.int64) """ @@ -1030,8 +1030,8 @@ class Argmin(PrimitiveWithInfer): Tensor, indices of the min value of input tensor across the axis. Examples: - >>> input = Tensor(np.array([2.0, 3.1, 1.2])) - >>> index = Argmin()(input) + >>> input_x = Tensor(np.array([2.0, 3.1, 1.2])) + >>> index = P.Argmin()(input_x) >>> assert index == Tensor(2, mindspore.int64) """ @@ -1082,8 +1082,8 @@ class ArgMaxWithValue(PrimitiveWithInfer): :math:`(x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)`. Examples: - >>> input = Tensor(np.random.rand(5)) - >>> index, output = ArgMaxWithValue()(input) + >>> input_x = Tensor(np.random.rand(5)) + >>> index, output = P.ArgMaxWithValue()(input_x) """ @prim_attr_register @@ -1129,8 +1129,8 @@ class ArgMinWithValue(PrimitiveWithInfer): :math:`(x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)`. Examples: - >>> input = Tensor(np.random.rand(5)) - >>> index, output = ArgMinWithValue()(input) + >>> input_x = Tensor(np.random.rand(5)) + >>> index, output = P.ArgMinWithValue()(input_x) """ @prim_attr_register def __init__(self, axis=0, keep_dims=False): @@ -1325,7 +1325,7 @@ class Concat(PrimitiveWithInfer): Examples: >>> data1 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32)) >>> data2 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32)) - >>> op = Concat() + >>> op = P.Concat() >>> output = op((data1, data2)) """ @@ -1607,7 +1607,7 @@ class Select(PrimitiveWithInfer): Tensor, has the same shape as input_y. The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`. Examples: - >>> select = Select() + >>> select = P.Select() >>> input_x = Tensor([True, False]) >>> input_y = Tensor([2,3], mindspore.float32) >>> input_z = Tensor([1,2], mindspore.float32) @@ -1681,7 +1681,7 @@ class StridedSlice(PrimitiveWithInfer): Examples >>> input_x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], >>> [[5, 5, 5], [6, 6, 6]]], mindspore.float32) - >>> slice = StridedSlice() + >>> slice = P.StridedSlice() >>> output = slice(input_x, (1, 0, 0), (2, 1, 3), (1, 1, 1)) >>> output.shape() (1, 1, 3) @@ -1913,9 +1913,9 @@ class ScatterNd(PrimitiveWithInfer): Tensor, the new tensor, has the same type as `update` and the same shape as `shape`. Examples: - >>> op = ScatterNd() - >>> update = Tensor(np.array([3.2, 1.1]), mindspore.float32) + >>> op = P.ScatterNd() >>> indices = Tensor(np.array([[0, 1], [1, 1]]), mindspore.int32) + >>> update = Tensor(np.array([3.2, 1.1]), mindspore.float32) >>> shape = (3, 3) >>> output = op(indices, update, shape) """ @@ -1964,7 +1964,7 @@ class ResizeNearestNeighbor(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) - >>> resize = ResizeNearestNeighbor((2, 2)) + >>> resize = P.ResizeNearestNeighbor((2, 2)) >>> output = resize(input_tensor) """ @@ -1997,7 +1997,7 @@ class GatherNd(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) >>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32) - >>> op = GatherNd() + >>> op = P.GatherNd() >>> output = op(input_x, indices) """ @@ -2039,7 +2039,7 @@ class ScatterNdUpdate(PrimitiveWithInfer): >>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) >>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32) >>> update = Tensor(np.array([1.0, 2.2]), mindspore.float32) - >>> op = ScatterNdUpdate() + >>> op = P.ScatterNdUpdate() >>> output = op(input_x, indices, update) """ @@ -2090,7 +2090,7 @@ class SpaceToDepth(PrimitiveWithInfer): Examples: >>> x = Tensor(np.random.rand(1,3,2,2), mindspore.float32) >>> block_size = 2 - >>> op = SpaceToDepth(block_size) + >>> op = P.SpaceToDepth(block_size) >>> output = op(x) >>> output.asnumpy().shape == (1,12,1,1) """ @@ -2148,7 +2148,7 @@ class DepthToSpace(PrimitiveWithInfer): Examples: >>> x = Tensor(np.random.rand(1,12,1,1), mindspore.float32) >>> block_size = 2 - >>> op = DepthToSpace(block_size) + >>> op = P.DepthToSpace(block_size) >>> output = op(x) >>> output.asnumpy().shape == (1,3,2,2) """ @@ -2212,8 +2212,8 @@ class SpaceToBatch(PrimitiveWithInfer): >>> block_size = 2 >>> paddings = [[0, 0], [0, 0]] >>> space_to_batch = P.SpaceToBatch(block_size, paddings) - >>> x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32) - >>> space_to_batch(x) + >>> input_x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32) + >>> space_to_batch(input_x) [[[[1.]]], [[[2.]]], [[[3.]]], [[[4.]]]] """ @@ -2280,8 +2280,8 @@ class BatchToSpace(PrimitiveWithInfer): >>> block_size = 2 >>> crops = [[0, 0], [0, 0]] >>> op = P.BatchToSpace(block_size, crops) - >>> x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32) - >>> output = op(x) + >>> input_x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32) + >>> output = op(input_x) [[[[1., 2.], [3., 4.]]]] """ diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index d003f6ee8b..1294a65d02 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -112,9 +112,9 @@ class TensorAdd(_MathBinaryOp): Examples: >>> add = P.TensorAdd() - >>> x = Tensor(np.array([1,2,3]).astype(np.float32)) - >>> y = Tensor(np.array([4,5,6]).astype(np.float32)) - >>> add(x, y) + >>> input_x = Tensor(np.array([1,2,3]).astype(np.float32)) + >>> input_y = Tensor(np.array([4,5,6]).astype(np.float32)) + >>> add(input_x, input_y) [5,7,9] """ @@ -124,23 +124,24 @@ class AssignAdd(PrimitiveWithInfer): Updates a `Parameter` by adding a value to it. Inputs: - - **input_x** (Parameter) - The `Parameter`. - - **input_y** (Union[scalar, Tensor]) - Has the same shape as `input_x`. + - **variable** (Parameter) - The `Parameter`. + - **value** (Union[numbers.Number, Tensor]) - The value to be added to the `variable`. + It should have the same shape as `variable` if it is a Tensor. Examples: >>> class Net(Cell): >>> def __init__(self): >>> super(Net, self).__init__() >>> self.AssignAdd = P.AssignAdd() - >>> self.inputdata = Parameter(initializer(1, [1], mindspore.int64), name="global_step") + >>> self.variable = Parameter(initializer(1, [1], mindspore.int64), name="global_step") >>> >>> def construct(self, x): - >>> self.AssignAdd(self.inputdata, x) - >>> return self.inputdata + >>> self.AssignAdd(self.variable, x) + >>> return self.variable >>> >>> net = Net() - >>> x = Tensor(np.ones([1]).astype(np.int64)*100) - >>> net(x) + >>> value = Tensor(np.ones([1]).astype(np.int64)*100) + >>> net(value) """ __mindspore_signature__ = ( ('variable', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD), @@ -166,22 +167,24 @@ class AssignSub(PrimitiveWithInfer): Updates a `Parameter` by subtracting a value from it. Inputs: - - **input_x** (Parameter) - The `Parameter`. - - **input_y** (Union[scalar, Tensor]) - Has the same shape as `input_x`. + - **variable** (Parameter) - The `Parameter`. + - **value** (Union[numbers.Number, Tensor]) - The value to be subtracted from the `variable`. + It should have the same shape as `variable` if it is a Tensor. Examples: >>> class Net(Cell): >>> def __init__(self): + >>> super(Net, self).__init__() >>> self.AssignSub = P.AssignSub() - >>> self.inputdata = Parameter(initializer(1, [1], mindspore.int64), name="global_step") + >>> self.variable = Parameter(initializer(1, [1], mindspore.int64), name="global_step") >>> >>> def construct(self, x): - >>> self.AssignSub(self.inputdata, x) - >>> return self.inputdata + >>> self.AssignSub(self.variable, x) + >>> return self.variable >>> >>> net = Net() - >>> x = Tensor(np.ones([1]).astype(np.int64)*100) - >>> net(x) + >>> value = Tensor(np.ones([1]).astype(np.int64)*100) + >>> net(value) """ __mindspore_signature__ = ( @@ -263,9 +266,9 @@ class ReduceMean(_Reduce): the shape of output is :math:`(x_1, x_4, ..., x_R)`. Examples: - >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) + >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> op = P.ReduceMean(keep_dims=True) - >>> output = op(data, 1) + >>> output = op(input_x, 1) """ @@ -295,9 +298,9 @@ class ReduceSum(_Reduce): the shape of output is :math:`(x_1, x_4, ..., x_R)`. Examples: - >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) + >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> op = P.ReduceSum(keep_dims=True) - >>> output = op(data, 1) + >>> output = op(input_x, 1) """ @@ -328,9 +331,9 @@ class ReduceAll(_Reduce): the shape of output is :math:`(x_1, x_4, ..., x_R)`. Examples: - >>> data = Tensor(np.array([[True, False], [True, True]])) + >>> input_x = Tensor(np.array([[True, False], [True, True]])) >>> op = P.ReduceAll(keep_dims=True) - >>> output = op(data, 1) + >>> output = op(input_x, 1) """ def __infer__(self, input_x, axis): @@ -364,9 +367,9 @@ class ReduceMax(_Reduce): the shape of output is :math:`(x_1, x_4, ..., x_R)`. Examples: - >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) + >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> op = P.ReduceMax(keep_dims=True) - >>> output = op(data, 1) + >>> output = op(input_x, 1) """ @@ -397,9 +400,9 @@ class ReduceMin(_Reduce): the shape of output is :math:`(x_1, x_4, ..., x_R)`. Examples: - >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) + >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> op = P.ReduceMin(keep_dims=True) - >>> output = op(data, 1) + >>> output = op(input_x, 1) """ @@ -429,9 +432,9 @@ class ReduceProd(_Reduce): the shape of output is :math:`(x_1, x_4, ..., x_R)`. Examples: - >>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) + >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> op = P.ReduceProd(keep_dims=True) - >>> output = op(data, 1) + >>> output = op(input_x, 1) """ @@ -451,15 +454,15 @@ class CumProd(PrimitiveWithInfer): Tensor, has the same shape and dtype as the 'input_x'. Examples: - >>> data = Tensor(np.array([a, b, c]).astype(np.float32)) + >>> input_x = Tensor(np.array([a, b, c]).astype(np.float32)) >>> op0 = P.CumProd() - >>> output = op0(data, 0) # output=[a, a * b, a * b * c] + >>> output = op0(input_x, 0) # output=[a, a * b, a * b * c] >>> op1 = P.CumProd(exclusive=True) - >>> output = op1(data, 0) # output=[1, a, a * b] + >>> output = op1(input_x, 0) # output=[1, a, a * b] >>> op2 = P.CumProd(reverse=True) - >>> output = op2(data, 0) # output=[a * b * c, b * c, c] + >>> output = op2(input_x, 0) # output=[a * b * c, b * c, c] >>> op3 = P.CumProd(exclusive=True, reverse=True) - >>> output = op3(data, 0) # output=[b * c, c, 1] + >>> output = op3(input_x, 0) # output=[b * c, c, 1] """ @prim_attr_register def __init__(self, exclusive=False, reverse=False): @@ -1190,7 +1193,7 @@ class FloorMod(_MathBinaryOp): Examples: >>> input_x = Tensor(np.array([2, 4, -1]), mindspore.int32) >>> input_y = Tensor(np.array([3, 3, 3]), mindspore.int32) - >>> floor_mod = FloorMod() + >>> floor_mod = P.FloorMod() >>> floor_mod(input_x, input_y) [2, 1, 2] """ @@ -1207,9 +1210,9 @@ class Acosh(PrimitiveWithInfer): Tensor, has the same shape as `input_x`. Examples: - >>> acosh = Acosh() - >>> X = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), mindspore.float32) - >>> output = acosh(X) + >>> acosh = P.Acosh() + >>> input_x = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), mindspore.float32) + >>> output = acosh(input_x) """ @prim_attr_register @@ -1286,7 +1289,7 @@ class EqualCount(PrimitiveWithInfer): - **input_y** (Tensor) - The second input tensor. Outputs: - Tensor, has the same shape as the `input_x`. + Tensor, with the type as `mindspore.int32` and size as (1,). Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) @@ -1324,7 +1327,7 @@ class NotEqual(_LogicBinaryOp): Inputs: - **input_x** (Union[Tensor, Number, bool]) - The first input is a tensor whose data type is number or bool, or a number or a bool object. - - **input_y** (Union[Tensor, Number, bool]) - The second input tensor whose data type is same as 'input_x' or + - **input_y** (Union[Tensor, Number, bool]) - The second input tensor whose data type is same as `input_x` or a number or a bool object. Outputs: @@ -1359,11 +1362,11 @@ class Greater(_LogicBinaryOp): Inputs: - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number. - - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or + - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as `input_x` or a number. Outputs: - Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'. + Tensor, the shape is same as the shape after broadcasting, and the data type is bool. Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) @@ -1386,11 +1389,11 @@ class GreaterEqual(_LogicBinaryOp): Inputs: - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number. - - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or + - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as `input_x` or a number. Outputs: - Tensor, the shape is same as the shape after broadcasting, and the data type is bool'. + Tensor, the shape is same as the shape after broadcasting, and the data type is bool. Examples: >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) @@ -1413,7 +1416,7 @@ class Less(_LogicBinaryOp): Inputs: - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number. - - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or + - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as `input_x` or a number. Outputs: @@ -1440,7 +1443,7 @@ class LessEqual(_LogicBinaryOp): Inputs: - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number. - - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or + - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as `input_x` or a number. Outputs: @@ -1752,8 +1755,8 @@ class Cos(PrimitiveWithInfer): Examples: >>> cos = P.Cos() - >>> X = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32) - >>> output = cos(X) + >>> input_x = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32) + >>> output = cos(input_x) """ @prim_attr_register @@ -1780,8 +1783,8 @@ class ACos(PrimitiveWithInfer): Examples: >>> acos = P.ACos() - >>> X = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32) - >>> output = acos(X) + >>> input_x = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32) + >>> output = acos(input_x) """ @prim_attr_register @@ -1993,7 +1996,7 @@ class Atan2(_MathBinaryOp): - **input_y** (Tensor) - The input tensor. Outputs: - Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'. + Tensor, the shape is same as the shape after broadcasting, and the data type is same as `input_x`. Examples: >>> input_x = Tensor(np.array([[0, 1]]), mindspore.float32) diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index c7c823aeb6..3cc6718484 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -41,7 +41,7 @@ class Flatten(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.ones(shape=[1, 2, 3, 4]), mindspore.float32) - >>> flatten = Flatten() + >>> flatten = P.Flatten() >>> output = flatten(input_tensor) >>> assert output.shape() == (1, 24) """ @@ -155,7 +155,7 @@ class ReLU(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32)) - >>> relu = ReLU() + >>> relu = P.ReLU() >>> result = relu(input_x) [[0, 4.0, 0.0], [2.0, 0.0, 9.0]] """ @@ -188,7 +188,7 @@ class ReLU6(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32)) - >>> relu6 = ReLU6() + >>> relu6 = P.ReLU6() >>> result = relu6(input_x) """ @@ -222,10 +222,10 @@ class Elu(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32)) - >>> elu = Elu() + >>> elu = P.Elu() >>> result = elu(input_x) Tensor([[-0.632 4.0 -0.999] - [2.0 -0.993 9.0 ]], shape=(2, 3), dtype=ms.float32) + [2.0 -0.993 9.0 ]], shape=(2, 3), dtype=mindspore.float32) """ @prim_attr_register @@ -1082,7 +1082,7 @@ class TopK(PrimitiveWithInfer): Examples: >>> topk = P.TopK(sorted=True) - >>> input_x = Tensor([1, 2, 3, 4, 5], mindspore.float16)) + >>> input_x = Tensor([1, 2, 3, 4, 5], mindspore.float16) >>> k = 3 >>> values, indices = topk(input_x, k) >>> assert values == Tensor(np.array([5, 4, 3])) @@ -1223,8 +1223,8 @@ class ApplyMomentum(PrimitiveWithInfer): Examples: >>> net = ResNet50() - >>> loss = SoftmaxCrossEntropyWithLogits() - >>> opt = ApplyMomentum(Tensor(np.array([0.001])), Tensor(np.array([0.9])), + >>> loss = nn.SoftmaxCrossEntropyWithLogits() + >>> opt = P.ApplyMomentum(Tensor(np.array([0.001])), Tensor(np.array([0.9])), filter(lambda x: x.requires_grad, net.get_parameters())) >>> model = Model(net, loss, opt) """ @@ -1351,6 +1351,7 @@ class SGD(PrimitiveWithInfer): class ApplyRMSProp(PrimitiveWithInfer): """ Optimizer that implements the Root Mean Square prop(RMSProp) algorithm. + Please refer to the usage in source code of `nn.RMSProp`. Note: Update `var` according to the RMSProp algorithm. @@ -1386,12 +1387,6 @@ class ApplyRMSProp(PrimitiveWithInfer): Outputs: Tensor, parameters to be update. - - Examples: - >>> net = Net() - >>> loss = nn.SoftmaxCrossEntropyWithLogits() - >>> opt = RMSProp(params=net.trainable_params(), learning_rate=learning_rate) - >>> model = Model(net, loss, opt) """ @prim_attr_register @@ -1424,6 +1419,7 @@ class ApplyRMSProp(PrimitiveWithInfer): class ApplyCenteredRMSProp(PrimitiveWithInfer): """ Optimizer that implements the centered RMSProp algorithm. + Please refer to the usage in source code of `nn.RMSProp`. Note: Update `var` according to the centered RMSProp algorithm. @@ -1464,12 +1460,6 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer): Outputs: Tensor, parameters to be update. - - Examples: - >>> net = Net() - >>> loss = nn.SoftmaxCrossEntropyWithLogits() - >>> opt = RMSProp(params=net.trainable_params(), learning_rate=learning_rate, centered=True) - >>> model = Model(net, loss, opt) """ @prim_attr_register @@ -1596,7 +1586,7 @@ class DropoutGenMask(Primitive): Tensor, the value of generated mask for input shape. Examples: - >>> dropout_gen_mask = DropoutGenMask() + >>> dropout_gen_mask = P.DropoutGenMask() >>> shape = (20, 16, 50) >>> keep_prob = Tensor(0.5, mindspore.float32) >>> mask = dropout_gen_mask(shape, keep_prob) @@ -1631,8 +1621,8 @@ class DropoutDoMask(PrimitiveWithInfer): >>> x = Tensor(np.ones([20, 16, 50]), mindspore.float32) >>> shape = (20, 16, 50) >>> keep_prob = Tensor(0.5, mindspore.float32) - >>> dropout_gen_mask = DropoutGenMask() - >>> dropout_do_mask = DropoutDoMask() + >>> dropout_gen_mask = P.DropoutGenMask() + >>> dropout_do_mask = P.DropoutDoMask() >>> mask = dropout_gen_mask(shape, keep_prob) >>> output = dropout_do_mask(x, mask, keep_prob) >>> assert output.shape() == (20, 16, 50) @@ -1737,7 +1727,7 @@ class OneHot(PrimitiveWithInfer): Examples: >>> indices = Tensor(np.array([0, 1, 2]), mindspore.int32) >>> depth, on_value, off_value = 3, Tensor(1.0, mindspore.float32), Tensor(0.0, mindspore.float32) - >>> onehot = OneHot() + >>> onehot = P.OneHot() >>> result = onehot(indices, depth, on_value, off_value) [[1, 0, 0], [0, 1, 0], [0, 0, 1]] """ @@ -1793,7 +1783,7 @@ class Gelu(PrimitiveWithInfer): Examples: >>> tensor = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) - >>> gelu = Gelu() + >>> gelu = P.Gelu() >>> result = gelu(tensor) """ @@ -1834,7 +1824,7 @@ class GetNext(PrimitiveWithInfer): and the type is described is `types`. Examples: - >>> get_next = GetNext([mindspore.float32, mindspore.int32], [[32, 1, 28, 28], [10]], 'shared_name') + >>> get_next = P.GetNext([mindspore.float32, mindspore.int32], [[32, 1, 28, 28], [10]], 'shared_name') >>> feature, label = get_next() """ @@ -2015,7 +2005,7 @@ class Pad(PrimitiveWithInfer): Examples: >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) - >>> pad_op = Pad(((1, 2), (2, 1))) + >>> pad_op = P.Pad(((1, 2), (2, 1))) >>> output_tensor = pad_op(input_tensor) >>> assert output_tensor == Tensor(np.array([[ 0. , 0. , 0. , 0. , 0. , 0. ], >>> [ 0. , 0. , -0.1, 0.3, 3.6, 0. ], diff --git a/mindspore/train/serialization.py b/mindspore/train/serialization.py index b334c3e9d8..90d8816094 100644 --- a/mindspore/train/serialization.py +++ b/mindspore/train/serialization.py @@ -406,7 +406,7 @@ def export(net, *inputs, file_name, file_format='GEIR'): file_format (str): MindSpore currently supports 'GEIR', 'ONNX' and 'LITE' format for exported model. - GEIR: Graph Engine Intermidiate Representation. An intermidiate representation format of - Ascend model. + Ascend model. - ONNX: Open Neural Network eXchange. An open format built to represent machine learning models. - LITE: Huawei model format for mobile. """ From a6747c522fdc03da9ea9b24b1f9086308120efdf Mon Sep 17 00:00:00 2001 From: kswang Date: Thu, 9 Apr 2020 15:08:47 +0800 Subject: [PATCH 160/227] add ascend mem pool --- .../device/ascend/ascend_device_address.cc | 4 +-- .../device/ascend/ascend_device_address.h | 2 +- .../device/ascend/ascend_kernel_runtime.cc | 2 +- .../device/ascend/ascend_memory_manager.cc | 22 +++++++------ .../device/ascend/ascend_memory_manager.h | 6 +++- ...ory_allocator.cc => ascend_memory_pool.cc} | 31 +++++++------------ ...emory_allocator.h => ascend_memory_pool.h} | 31 ++++++++++--------- mindspore/ccsrc/device/device_address.h | 2 +- .../ccsrc/device/gpu/gpu_device_address.cc | 2 +- .../ccsrc/device/gpu/gpu_kernel_runtime.cc | 16 +++++----- .../ccsrc/device/gpu/gpu_memory_manager.cc | 8 ++--- .../ccsrc/device/gpu/gpu_memory_manager.h | 6 ++-- mindspore/ccsrc/device/kernel_runtime.cc | 8 ++--- mindspore/ccsrc/device/kernel_runtime.h | 1 - mindspore/ccsrc/device/memory_manager.cc | 22 +++++-------- mindspore/ccsrc/device/memory_manager.h | 15 ++++----- tests/ut/cpp/CMakeLists.txt | 2 +- 17 files changed, 84 insertions(+), 96 deletions(-) rename mindspore/ccsrc/device/ascend/{ascend_memory_allocator.cc => ascend_memory_pool.cc} (62%) rename mindspore/ccsrc/device/ascend/{ascend_memory_allocator.h => ascend_memory_pool.h} (67%) diff --git a/mindspore/ccsrc/device/ascend/ascend_device_address.cc b/mindspore/ccsrc/device/ascend/ascend_device_address.cc index b8b7f452e3..93f039af0e 100644 --- a/mindspore/ccsrc/device/ascend/ascend_device_address.cc +++ b/mindspore/ccsrc/device/ascend/ascend_device_address.cc @@ -262,8 +262,8 @@ AscendDeviceAddress::~AscendDeviceAddress() { if (ptr_ == nullptr) { return; } - if (mem_dynamic_alloc_) { - AscendMemoryAllocator::GetInstance().FreeTensorMem(ptr_); + if (from_mem_pool_) { + AscendMemoryPool::GetInstance().FreeTensorMem(ptr_); ptr_ = nullptr; } } diff --git a/mindspore/ccsrc/device/ascend/ascend_device_address.h b/mindspore/ccsrc/device/ascend/ascend_device_address.h index 60cc64cca7..93746082c1 100644 --- a/mindspore/ccsrc/device/ascend/ascend_device_address.h +++ b/mindspore/ccsrc/device/ascend/ascend_device_address.h @@ -21,7 +21,7 @@ #include #include #include "device/device_address.h" -#include "device/ascend/ascend_memory_allocator.h" +#include "device/ascend/ascend_memory_pool.h" #include "ir/dtype.h" namespace mindspore { diff --git a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc index 0c2a97a5a6..0c6861e21f 100644 --- a/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc +++ b/mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc @@ -29,7 +29,7 @@ #include "hccl/hcom.h" #include "runtime/context.h" #include "device/ascend/ascend_stream_assign.h" -#include "device/ascend/ascend_memory_allocator.h" +#include "device/ascend/ascend_memory_pool.h" #include "framework/ge_runtime/model_runner.h" #include "device/ascend/tasksink/task_generator.h" #include "session/anf_runtime_algorithm.h" diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc b/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc index f033d81d82..42830f54fa 100644 --- a/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc +++ b/mindspore/ccsrc/device/ascend/ascend_memory_manager.cc @@ -15,29 +15,31 @@ */ #include "device/ascend/ascend_memory_manager.h" -#include "device/ascend/ascend_memory_allocator.h" +#include "device/ascend/ascend_memory_pool.h" #include "utils/context/ms_context.h" #include "runtime/mem.h" namespace mindspore { namespace device { namespace ascend { -static const uint64_t ASCEND_MEM_SIZE = 20; -static const uint64_t ASCEND_MEM_SIZE_BYTE = (ASCEND_MEM_SIZE << 30); +const uint64_t kAscendDeviceMemGB = 20; +const uint64_t kAscendMemPoolGB = 5; +const uint64_t kAscendDeviceMemSize = (kAscendDeviceMemGB << 30); +const uint64_t kAscendMemPoolSize = (kAscendMemPoolGB << 30); void AscendMemoryManager::MallocDeviceMemory() { - device_mem_size_ = ASCEND_MEM_SIZE_BYTE; - static_mem_offset_ = FloatToSize(device_mem_size_ * GRAPH_INIT_ASCEND_MEM_RATIO); + device_mem_size_ = kAscendDeviceMemSize; + static_mem_offset_ = device_mem_size_; auto ret = rtMalloc(reinterpret_cast(&device_mem_base_), static_mem_offset_, RT_MEMORY_HBM); if (ret != RT_ERROR_NONE) { MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << static_mem_offset_ << "] fail, ret[" << ret << "]"; } - device_mem_pool_size_ = FloatToSize(device_mem_size_ * (1 - GRAPH_INIT_ASCEND_MEM_RATIO)); + device_mem_pool_size_ = kAscendMemPoolSize; ret = rtMalloc(reinterpret_cast(&device_mem_pool_base_), device_mem_pool_size_, RT_MEMORY_HBM); if (ret != RT_ERROR_NONE) { MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_pool_size_ << "] fail, ret[" << ret << "]"; } - AscendMemoryAllocator::GetInstance().set_device_mem_pool_base(device_mem_pool_base_); - AscendMemoryAllocator::GetInstance().set_device_mem_pool_size(device_mem_pool_size_); + AscendMemoryPool::GetInstance().set_device_mem_pool_base(device_mem_pool_base_); + AscendMemoryPool::GetInstance().set_device_mem_pool_size(device_mem_pool_size_); } void AscendMemoryManager::FreeDeviceMemory() { @@ -57,8 +59,8 @@ void AscendMemoryManager::FreeDeviceMemory() { } } -void *AscendMemoryManager::AllocTensorMemDynamic(size_t size) { - return AscendMemoryAllocator::GetInstance().AllocTensorMem(size); +void *AscendMemoryManager::MallocMemFromMemPool(size_t size) { + return AscendMemoryPool::GetInstance().AllocTensorMem(size); } } // namespace ascend } // namespace device diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_manager.h b/mindspore/ccsrc/device/ascend/ascend_memory_manager.h index 8639fb5c72..dea88ac10a 100644 --- a/mindspore/ccsrc/device/ascend/ascend_memory_manager.h +++ b/mindspore/ccsrc/device/ascend/ascend_memory_manager.h @@ -27,7 +27,11 @@ class AscendMemoryManager : public MemoryManager { void MallocDeviceMemory() override; void FreeDeviceMemory() override; - void *AllocTensorMemDynamic(size_t size) override; + void *MallocMemFromMemPool(size_t size) override; + + private: + uint8_t *device_mem_pool_base_{nullptr}; + uint64_t device_mem_pool_size_{0}; }; } // namespace ascend } // namespace device diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc b/mindspore/ccsrc/device/ascend/ascend_memory_pool.cc similarity index 62% rename from mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc rename to mindspore/ccsrc/device/ascend/ascend_memory_pool.cc index 08a30a28b7..2c38e4290d 100644 --- a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc +++ b/mindspore/ccsrc/device/ascend/ascend_memory_pool.cc @@ -14,24 +14,15 @@ * limitations under the License. */ -#include "device/ascend/ascend_memory_allocator.h" +#include "device/ascend/ascend_memory_pool.h" #include "device/ascend/ascend_kernel_runtime.h" #include "utils/log_adapter.h" namespace mindspore { namespace device { namespace ascend { -const uint64_t MEM_SIZE = 20; -const uint64_t MEM_SIZE_BYTE = (MEM_SIZE << 30); - -AscendMemoryAllocator::AscendMemoryAllocator() { - hasMalloc_ = false; - free_mem_size_ = FloatToSize(MEM_SIZE_BYTE * (1 - GRAPH_INIT_ASCEND_MEM_RATIO)); - total_mem_size_ = free_mem_size_; -} - -size_t AscendMemoryAllocator::AllocDeviceMem(size_t size, DeviceMemPtr* addr) { - if (hasMalloc_) { +size_t AscendMemoryPool::AllocDeviceMem(size_t size, DeviceMemPtr* addr) { + if (has_malloc_) { MS_LOG(EXCEPTION) << "Has alloc memory pool memory !"; } if (size == 0 || size > free_mem_size_) { @@ -41,35 +32,35 @@ size_t AscendMemoryAllocator::AllocDeviceMem(size_t size, DeviceMemPtr* addr) { if (*addr == nullptr) { MS_LOG(EXCEPTION) << "Device memory pool base is nullptr, failed to alloc memory pool memory!"; } - hasMalloc_ = true; + has_malloc_ = true; free_mem_size_ -= size; return size; } -bool AscendMemoryAllocator::FreeDeviceMem(const DeviceMemPtr& addr) { +bool AscendMemoryPool::FreeDeviceMem(const DeviceMemPtr& addr) { MS_EXCEPTION_IF_NULL(addr); - hasMalloc_ = false; + has_malloc_ = false; free_mem_size_ = total_mem_size_; return true; } -size_t AscendMemoryAllocator::AlignMemorySize(size_t size) const { +size_t AscendMemoryPool::AlignMemorySize(size_t size) const { if (size == 0) { return DYNAMIC_MEM_ALIGN_SIZE; } return ((size + DYNAMIC_MEM_ALIGN_SIZE + 31) / DYNAMIC_MEM_ALIGN_SIZE) * DYNAMIC_MEM_ALIGN_SIZE; } -size_t AscendMemoryAllocator::mem_alloc_unit_size() const { return free_mem_size_ - 512; } +size_t AscendMemoryPool::mem_alloc_unit_size() const { return free_mem_size_ - 512; } -void AscendMemoryAllocator::set_device_mem_pool_base(uint8_t* device_mem_pool_base) { +void AscendMemoryPool::set_device_mem_pool_base(uint8_t* device_mem_pool_base) { MS_EXCEPTION_IF_NULL(device_mem_pool_base); device_mem_pool_base_ = device_mem_pool_base; } -size_t AscendMemoryAllocator::free_mem_size() { return free_mem_size_; } +size_t AscendMemoryPool::free_mem_size() { return free_mem_size_; } -size_t AscendMemoryAllocator::total_mem_size() { return total_mem_size_; } +size_t AscendMemoryPool::total_mem_size() { return total_mem_size_; } } // namespace ascend } // namespace device } // namespace mindspore diff --git a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.h b/mindspore/ccsrc/device/ascend/ascend_memory_pool.h similarity index 67% rename from mindspore/ccsrc/device/ascend/ascend_memory_allocator.h rename to mindspore/ccsrc/device/ascend/ascend_memory_pool.h index 8b0f89a9b8..c2a29725f4 100644 --- a/mindspore/ccsrc/device/ascend/ascend_memory_allocator.h +++ b/mindspore/ccsrc/device/ascend/ascend_memory_pool.h @@ -14,8 +14,8 @@ * limitations under the License. */ -#ifndef MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_ALLOCATOR_H_ -#define MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_ALLOCATOR_H_ +#ifndef MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_POOL_H_ +#define MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_POOL_H_ #include #include "pre_activate/mem_reuse/mem_dynamic_allocator.h" @@ -23,22 +23,23 @@ namespace mindspore { namespace device { namespace ascend { -// The fraction of total ascend memory used to compute the graph. -static const float GRAPH_INIT_ASCEND_MEM_RATIO = 0.8; - -class AscendMemoryAllocator : public DynamicMemPoolBestFit { +class AscendMemoryPool : public DynamicMemPoolBestFit { public: - ~AscendMemoryAllocator() override = default; + ~AscendMemoryPool() override = default; size_t AllocDeviceMem(size_t size, DeviceMemPtr* addr) override; bool FreeDeviceMem(const DeviceMemPtr& addr) override; void set_device_mem_pool_base(uint8_t* device_mem_pool_base); - void set_device_mem_pool_size(uint64_t device_mem_pool_size) { device_mem_pool_size_ = device_mem_pool_size; } + void set_device_mem_pool_size(uint64_t device_mem_pool_size) { + device_mem_pool_size_ = device_mem_pool_size; + free_mem_size_ = device_mem_pool_size_; + total_mem_size_ = free_mem_size_; + } size_t free_mem_size() override; size_t total_mem_size() override; - static AscendMemoryAllocator& GetInstance() { - static AscendMemoryAllocator instance; + static AscendMemoryPool& GetInstance() { + static AscendMemoryPool instance; return instance; } @@ -49,10 +50,10 @@ class AscendMemoryAllocator : public DynamicMemPoolBestFit { size_t mem_alloc_unit_size() const override; private: - AscendMemoryAllocator(); - AscendMemoryAllocator(const AscendMemoryAllocator&) = delete; - AscendMemoryAllocator& operator=(const AscendMemoryAllocator&) = delete; - bool hasMalloc_; + AscendMemoryPool() = default; + AscendMemoryPool(const AscendMemoryPool&) = delete; + AscendMemoryPool& operator=(const AscendMemoryPool&) = delete; + bool has_malloc_{false}; uint8_t* device_mem_pool_base_{nullptr}; uint64_t device_mem_pool_size_{0}; size_t free_mem_size_; @@ -62,4 +63,4 @@ class AscendMemoryAllocator : public DynamicMemPoolBestFit { } // namespace device } // namespace mindspore -#endif // MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_ALLOCATOR_H_ +#endif // MINDSPORE_CCSRC_DEVICE_ASCEND_ASCEND_MEMORY_POOL_H_ diff --git a/mindspore/ccsrc/device/device_address.h b/mindspore/ccsrc/device/device_address.h index cb022427e3..2d43963934 100644 --- a/mindspore/ccsrc/device/device_address.h +++ b/mindspore/ccsrc/device/device_address.h @@ -70,7 +70,7 @@ class DeviceAddress { size_t ref_count_{0}; string format_{"DefaultFormat"}; TypeId type_id_{kNumberTypeFloat16}; - bool mem_dynamic_alloc_{false}; + bool from_mem_pool_{false}; friend class KernelRuntime; friend class MemoryManager; friend class mindspore::device::ascend::tasksink::TaskGenerator; diff --git a/mindspore/ccsrc/device/gpu/gpu_device_address.cc b/mindspore/ccsrc/device/gpu/gpu_device_address.cc index 36391d27db..c27a1aa65b 100644 --- a/mindspore/ccsrc/device/gpu/gpu_device_address.cc +++ b/mindspore/ccsrc/device/gpu/gpu_device_address.cc @@ -46,7 +46,7 @@ GPUDeviceAddress::~GPUDeviceAddress() { } auto ms_context = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(ms_context); - if (mem_dynamic_alloc_) { + if (from_mem_pool_) { GPUMemoryAllocator::GetInstance().FreeTensorMem(ptr_); ptr_ = nullptr; } diff --git a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc index 597e188e9d..2ec1a5df29 100644 --- a/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc +++ b/mindspore/ccsrc/device/gpu/gpu_kernel_runtime.cc @@ -227,7 +227,7 @@ void GPUKernelRuntime::AllocKernelDynamicRes(const mindspore::kernel::KernelMod MS_EXCEPTION_IF_NULL(device_address); auto device_ptr = device_address->ptr_; if (device_ptr == nullptr) { - device_ptr = mem_manager_->AllocTensorMemDynamic(output_sizes[i]); + device_ptr = mem_manager_->MallocMemFromMemPool(output_sizes[i]); MS_EXCEPTION_IF_NULL(device_ptr); device_address->ptr_ = device_ptr; } @@ -244,7 +244,7 @@ void GPUKernelRuntime::AllocKernelDynamicRes(const mindspore::kernel::KernelMod kernel_workspaces->emplace_back(nullptr); continue; } - auto device_ptr = mem_manager_->AllocTensorMemDynamic(workspace_sizes[i]); + auto device_ptr = mem_manager_->MallocMemFromMemPool(workspace_sizes[i]); MS_EXCEPTION_IF_NULL(device_ptr); kernel::AddressPtr workspace = std::make_shared(); MS_EXCEPTION_IF_NULL(workspace); @@ -292,7 +292,7 @@ void GPUKernelRuntime::AllocCommunicationOpInputDynamicRes(const mindspore::AnfN addr_size.emplace_back(device_address.get(), output_size); } - auto device_mem_ptr = mem_manager_->AllocTensorMemDynamic(total); + auto device_mem_ptr = mem_manager_->MallocMemFromMemPool(total); MS_EXCEPTION_IF_NULL(device_mem_ptr); for (const auto &iter : addr_size) { MS_EXCEPTION_IF_NULL(iter.first); @@ -328,7 +328,7 @@ void GPUKernelRuntime::AllocCommunicationOpOutputDynamicRes(const mindspore::Anf addr_size.emplace_back(device_address.get(), output_sizes[i]); } - auto device_mem_ptr = mem_manager_->AllocTensorMemDynamic(total); + auto device_mem_ptr = mem_manager_->MallocMemFromMemPool(total); MS_EXCEPTION_IF_NULL(device_mem_ptr); for (const auto &iter : addr_size) { MS_EXCEPTION_IF_NULL(iter.first); @@ -361,7 +361,7 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel, auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i); MS_EXCEPTION_IF_NULL(device_address); MS_EXCEPTION_IF_NULL(device_address->ptr_); - mem_manager_->FreeTensorMemDynamic(device_address->ptr_); + mem_manager_->FreeMemFromMemPool(device_address->ptr_); device_address->ptr_ = nullptr; } } @@ -372,7 +372,7 @@ void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel, auto workspace = kernel_workspaces[i]; if (workspace != nullptr) { MS_EXCEPTION_IF_NULL(workspace->addr); - mem_manager_->FreeTensorMemDynamic(workspace->addr); + mem_manager_->FreeMemFromMemPool(workspace->addr); workspace->addr = nullptr; } } @@ -389,7 +389,7 @@ void GPUKernelRuntime::FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, 0); MS_EXCEPTION_IF_NULL(device_address); MS_EXCEPTION_IF_NULL(device_address->ptr_); - mem_manager_->FreeTensorMemDynamic(device_address->ptr_); + mem_manager_->FreeMemFromMemPool(device_address->ptr_); device_address->ptr_ = nullptr; } *is_communication_op = true; @@ -411,7 +411,7 @@ void GPUKernelRuntime::FreeCommunicationOpDynamicRes(const mindspore::AnfNodePtr auto device_address = AnfAlgo::GetMutableOutputAddr(kernel_input.first, 0); MS_EXCEPTION_IF_NULL(device_address); MS_EXCEPTION_IF_NULL(device_address->ptr_); - mem_manager_->FreeTensorMemDynamic(device_address->ptr_); + mem_manager_->FreeMemFromMemPool(device_address->ptr_); device_address->ptr_ = nullptr; } *is_communication_op = true; diff --git a/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc b/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc index 3944b504e4..7d042264b6 100644 --- a/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc +++ b/mindspore/ccsrc/device/gpu/gpu_memory_manager.cc @@ -21,11 +21,11 @@ namespace mindspore { namespace device { namespace gpu { -void *GPUMemoryManager::AllocTensorMemDynamic(size_t size) { +void *GPUMemoryManager::MallocMemFromMemPool(size_t size) { return GPUMemoryAllocator::GetInstance().AllocTensorMem(size); } -void GPUMemoryManager::FreeTensorMemDynamic(void *device_ptr) { +void GPUMemoryManager::FreeMemFromMemPool(void *device_ptr) { GPUMemoryAllocator::GetInstance().FreeTensorMem(device_ptr); } @@ -34,7 +34,7 @@ void GPUMemoryManager::MallocDeviceMemory() { MS_EXCEPTION_IF_NULL(context_ptr); // If use the dynamic memory pool, then alloc the first memory block to init. if (context_ptr->enable_dynamic_mem_pool()) { - auto device_addr = AllocTensorMemDynamic(1); + auto device_addr = MallocMemFromMemPool(1); if (!device_addr) { MS_LOG(ERROR) << "Dynamic memory pool init error."; } @@ -62,7 +62,7 @@ uint8_t *GPUMemoryManager::MallocStaticMem(size_t size, bool) { auto context_ptr = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(context_ptr); if (context_ptr->enable_dynamic_mem_pool()) { - auto device_ptr = AllocTensorMemDynamic(size); + auto device_ptr = MallocMemFromMemPool(size); MS_EXCEPTION_IF_NULL(device_ptr); return AddressOffset(device_ptr, 0); } diff --git a/mindspore/ccsrc/device/gpu/gpu_memory_manager.h b/mindspore/ccsrc/device/gpu/gpu_memory_manager.h index a18226bdf3..cc5dac2a5e 100644 --- a/mindspore/ccsrc/device/gpu/gpu_memory_manager.h +++ b/mindspore/ccsrc/device/gpu/gpu_memory_manager.h @@ -28,11 +28,11 @@ class GPUMemoryManager : public MemoryManager { void MallocDeviceMemory() override; void FreeDeviceMemory() override; - void *AllocTensorMemDynamic(size_t size) override; - void FreeTensorMemDynamic(void *device_ptr) override; + void *MallocMemFromMemPool(size_t size) override; + void FreeMemFromMemPool(void *device_ptr) override; protected: - uint8_t *MallocStaticMem(size_t size, bool communication_mem); + uint8_t *MallocStaticMem(size_t size, bool communication_mem) override; }; } // namespace gpu } // namespace device diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc index 16025ed8a4..eebc650347 100644 --- a/mindspore/ccsrc/device/kernel_runtime.cc +++ b/mindspore/ccsrc/device/kernel_runtime.cc @@ -169,7 +169,7 @@ void KernelRuntime::RunOpAssignInputMemory(const std::vector auto device_address = CreateDeviceAddress(nullptr, tensor_size, AnfAlgo::GetOutputFormat(item, index), output_type_id); MS_EXCEPTION_IF_NULL(device_address); - mem_manager_->MallocOpMemory(device_address, tensor_size); + mem_manager_->MallocMemFromMemPool(device_address, tensor_size); AnfAlgo::SetOutputAddr(device_address, index, item.get()); } } @@ -198,7 +198,7 @@ void KernelRuntime::RunOpAssignOutputMemory(const AnfNodePtr &kernel) { auto output_type = AnfAlgo::GetOutputDeviceDataType(kernel, i); auto device_address = CreateDeviceAddress(nullptr, output_sizes[i], output_format, output_type); MS_EXCEPTION_IF_NULL(device_address); - mem_manager_->MallocOpMemory(device_address, output_sizes[i]); + mem_manager_->MallocMemFromMemPool(device_address, output_sizes[i]); AnfAlgo::SetOutputAddr(device_address, i, kernel.get()); } } @@ -213,7 +213,7 @@ void KernelRuntime::RunOpAssignWorkSpaceMemory(const AnfNodePtr &kernel) { for (size_t i = 0; i < workspace_lists.size(); ++i) { auto device_address = CreateDeviceAddress(nullptr, workspace_lists[i], "", kTypeUnknown); MS_EXCEPTION_IF_NULL(device_address); - mem_manager_->MallocOpMemory(device_address, workspace_lists[i]); + mem_manager_->MallocMemFromMemPool(device_address, workspace_lists[i]); AnfAlgo::SetWorkspaceAddr(device_address, i, kernel.get()); } } @@ -457,7 +457,7 @@ void KernelRuntime::AssignDynamicMemory(session::KernelGraph *graph) { bool is_enable_mem_reuse = context_ptr->enable_mem_reuse(); auto mem_flag = kDynamicMem; if (is_enable_mem_reuse) { - mem_manager_->InitReuseDynamicMemory(graph); + mem_manager_->MallocReusedDynamicMem(graph); mem_flag = kReuseDynamicMem; } auto &kernels = graph->execution_order(); diff --git a/mindspore/ccsrc/device/kernel_runtime.h b/mindspore/ccsrc/device/kernel_runtime.h index 1224bf14eb..61b43fd5c0 100644 --- a/mindspore/ccsrc/device/kernel_runtime.h +++ b/mindspore/ccsrc/device/kernel_runtime.h @@ -33,7 +33,6 @@ #include "utils/context/ms_context.h" #include "device/memory_manager.h" -// using mindspore::session::KernelGraph; using mindspore::tensor::Tensor; using TensorPtr = std::shared_ptr; using mindspore::kernel::AddressPtr; diff --git a/mindspore/ccsrc/device/memory_manager.cc b/mindspore/ccsrc/device/memory_manager.cc index 3c1ddee6bc..6977628eb1 100644 --- a/mindspore/ccsrc/device/memory_manager.cc +++ b/mindspore/ccsrc/device/memory_manager.cc @@ -21,12 +21,6 @@ using mindspore::memreuse::BestFitMemReuse; using mindspore::memreuse::MemReuseUtilPtr; namespace mindspore { namespace device { -MemoryManager::~MemoryManager() { - device_mem_base_ = nullptr; - device_mem_pool_base_ = nullptr; - mem_reuse_util_ptr_ = nullptr; -} - size_t MemoryManager::GetCommonAlignSize(size_t input_size) const { return (input_size + kMemAlignSize + 31) / kMemAlignSize * kMemAlignSize; } @@ -35,7 +29,7 @@ size_t MemoryManager::GetCommunicationAlignSize(size_t input_size) const { return (input_size + kMemAlignSize - 1) / kMemAlignSize * kMemAlignSize + 2 * kMemAlignSize; } -void MemoryManager::InitReuseDynamicMemory(session::KernelGraph *graph) { +void MemoryManager::MallocReusedDynamicMem(session::KernelGraph *graph) { MS_EXCEPTION_IF_NULL(graph); MemReuseUtilPtr mem_reuse_util_ptr = std::make_shared(); MS_EXCEPTION_IF_NULL(mem_reuse_util_ptr); @@ -147,23 +141,23 @@ uint8_t *MemoryManager::MallocDynamicMem(size_t size, bool communication_mem) { } } -void MemoryManager::MallocOpMemory(const DeviceAddressPtr address, size_t size) { - auto device_ptr = AllocTensorMemDynamic(size); +void MemoryManager::MallocMemFromMemPool(const DeviceAddressPtr address, size_t size) { + auto device_ptr = MallocMemFromMemPool(size); MS_EXCEPTION_IF_NULL(device_ptr); address->ptr_ = device_ptr; - address->mem_dynamic_alloc_ = true; + address->from_mem_pool_ = true; } -void *MemoryManager::AllocTensorMemDynamic(size_t size) { +void *MemoryManager::MallocMemFromMemPool(size_t size) { if (size == 0) { - MS_LOG(ERROR) << "AllocTensorMemDynamic size is 0."; + MS_LOG(ERROR) << "MallocMemFromMemPool size is 0."; } return nullptr; } -void MemoryManager::FreeTensorMemDynamic(void *device_ptr) { +void MemoryManager::FreeMemFromMemPool(void *device_ptr) { if (device_ptr == nullptr) { - MS_LOG(ERROR) << "FreeTensorMemDynamic device_ptr is null."; + MS_LOG(ERROR) << "FreeMemFromMemPool device_ptr is null."; } } } // namespace device diff --git a/mindspore/ccsrc/device/memory_manager.h b/mindspore/ccsrc/device/memory_manager.h index 2e47237def..82c22f4548 100644 --- a/mindspore/ccsrc/device/memory_manager.h +++ b/mindspore/ccsrc/device/memory_manager.h @@ -31,7 +31,7 @@ using MemReuseUtilPtr = mindspore::memreuse::MemReuseUtilPtr; class MemoryManager { public: MemoryManager() = default; - virtual ~MemoryManager(); + virtual ~MemoryManager() = default; virtual void MallocDeviceMemory() = 0; virtual void FreeDeviceMemory() = 0; @@ -40,16 +40,15 @@ class MemoryManager { dynamic_mem_offset_ = 0; } - void InitReuseDynamicMemory(session::KernelGraph *graph); + void MallocReusedDynamicMem(session::KernelGraph *graph); uint8_t *MallocOutputMem(const AnfNodePtr &node, size_t index, int flag, size_t size); uint8_t *MallocWorkSpaceMem(const AnfNodePtr &node, size_t index, int flag, size_t size); virtual uint8_t *MallocMem(int flag, size_t size); - // Alloc memory use the dynamic memory pool. - virtual void *AllocTensorMemDynamic(size_t size); - // Free memory use the dynamic memory pool. - virtual void FreeTensorMemDynamic(void *device_ptr); - virtual void MallocOpMemory(const DeviceAddressPtr address, size_t size); + virtual void MallocMemFromMemPool(const DeviceAddressPtr address, size_t size); + virtual void *MallocMemFromMemPool(size_t size); + virtual void FreeMemFromMemPool(void *device_ptr); + size_t GetCommonAlignSize(size_t input_size) const; size_t GetCommunicationAlignSize(size_t input_size) const; @@ -57,9 +56,7 @@ class MemoryManager { virtual uint8_t *MallocStaticMem(size_t size, bool communication_mem); virtual uint8_t *MallocDynamicMem(size_t size, bool communication_mem); uint8_t *device_mem_base_{nullptr}; - uint8_t *device_mem_pool_base_{nullptr}; uint64_t device_mem_size_{0}; - uint64_t device_mem_pool_size_{0}; uint64_t dynamic_mem_offset_{0}; uint64_t static_mem_offset_{0}; size_t total_static_size_ = 0; diff --git a/tests/ut/cpp/CMakeLists.txt b/tests/ut/cpp/CMakeLists.txt index 3c1351a857..f5bc07ff69 100644 --- a/tests/ut/cpp/CMakeLists.txt +++ b/tests/ut/cpp/CMakeLists.txt @@ -95,7 +95,7 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "../../../mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc" "../../../mindspore/ccsrc/device/ascend/ascend_memory_manager.cc" "../../../mindspore/ccsrc/device/ascend/ascend_device_address.cc" - "../../../mindspore/ccsrc/device/ascend/ascend_memory_allocator.cc" + "../../../mindspore/ccsrc/device/ascend/ascend_memory_pool.cc" "../../../mindspore/ccsrc/predict/generator/utils/ir_model_util.cc" "../../../mindspore/ccsrc/predict/predict.cc" "../../../mindspore/ccsrc/predict/converter/*.cc" From 735923c0ce8765d885ece6530fa35ae03cf8cb48 Mon Sep 17 00:00:00 2001 From: zjun Date: Tue, 7 Apr 2020 21:52:28 +0800 Subject: [PATCH 161/227] add aicpu opinfo register --- .../ccsrc/kernel/aicpu/aicpu_kernel_build.cc | 116 +++++---- .../kernel/aicpu/aicpu_kernel_metadata.cc | 78 ++---- mindspore/ccsrc/kernel/aicpu/aicpu_util.h | 3 +- mindspore/ccsrc/kernel/common_utils.cc | 9 + mindspore/ccsrc/kernel/oplib/opinfo.h | 2 +- mindspore/ccsrc/kernel/oplib/oplib.cc | 21 +- mindspore/ops/__init__.py | 4 +- mindspore/ops/_op_impl/__init__.py | 1 + mindspore/ops/_op_impl/aicpu/__init__.py | 19 ++ .../ops/_op_impl/aicpu/dropout_genmask.py | 32 +++ mindspore/ops/_op_impl/aicpu/get_next.py | 39 +++ .../ops/_op_impl/aicpu/init_data_set_queue.py | 27 +++ mindspore/ops/_op_impl/aicpu/print_tensor.py | 39 +++ mindspore/ops/op_info_register.py | 225 +++++++++++------- 14 files changed, 409 insertions(+), 206 deletions(-) create mode 100644 mindspore/ops/_op_impl/aicpu/__init__.py create mode 100644 mindspore/ops/_op_impl/aicpu/dropout_genmask.py create mode 100644 mindspore/ops/_op_impl/aicpu/get_next.py create mode 100644 mindspore/ops/_op_impl/aicpu/init_data_set_queue.py create mode 100644 mindspore/ops/_op_impl/aicpu/print_tensor.py diff --git a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_build.cc b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_build.cc index c89e27c8ce..cf23779415 100644 --- a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_build.cc +++ b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_build.cc @@ -39,45 +39,7 @@ namespace mindspore { namespace kernel { using FNodeAttrHandle = std::function &anf_node, mindspore::NodeDef *proto)>; -const std::vector local_framework_op_vec = {kInitDataSetQueue, kGetNext, kDropoutGenMask, kPrint}; - -void InitDataSetQueueAttr(const std::shared_ptr &anf_node, mindspore::NodeDef *proto) { - MS_EXCEPTION_IF_NULL(anf_node); - MS_EXCEPTION_IF_NULL(proto); - - ::google::protobuf::Map<::std::string, ::mindspore::AttrValue> *node_attr = proto->mutable_attrs(); - MS_EXCEPTION_IF_NULL(node_attr); - std::string channel_name = AnfAlgo::GetNodeAttr(anf_node, kQueueName); - (*node_attr)[kChannelName].set_s(channel_name); -} - -void GetNextAttr(const std::shared_ptr &anf_node, mindspore::NodeDef *proto) { - MS_EXCEPTION_IF_NULL(anf_node); - MS_EXCEPTION_IF_NULL(proto); - - ::google::protobuf::Map<::std::string, ::mindspore::AttrValue> *node_attr = proto->mutable_attrs(); - MS_EXCEPTION_IF_NULL(node_attr); - std::string shared_name = AnfAlgo::GetNodeAttr(anf_node, kSharedName); - (*node_attr)[kChannelName].set_s(shared_name); -} - -void DropoutGenMaskAttr(const std::shared_ptr &anf_node, mindspore::NodeDef *proto) { - MS_EXCEPTION_IF_NULL(anf_node); - MS_EXCEPTION_IF_NULL(proto); - - ::google::protobuf::Map<::std::string, ::mindspore::AttrValue> *node_attr = proto->mutable_attrs(); - MS_EXCEPTION_IF_NULL(node_attr); - int seed = AnfAlgo::GetNodeAttr(anf_node, kSeed); - int seed2 = AnfAlgo::GetNodeAttr(anf_node, kSeed2); - (*node_attr)["seed"].set_i(seed); - (*node_attr)["seed2"].set_i(seed2); -} - -void CreateAttrFuncMap(std::map *mOpAttrFuncMap) { - (void)mOpAttrFuncMap->emplace(std::pair(kInitDataSetQueue, InitDataSetQueueAttr)); - (void)mOpAttrFuncMap->emplace(std::pair(kGetNext, GetNextAttr)); - (void)mOpAttrFuncMap->emplace(std::pair(kDropoutGenMask, DropoutGenMaskAttr)); -} +const std::vector local_framework_op_vec = {kInitData, kGetNext, kDropoutGenMask, kPrint}; bool SetIOIputSize(const std::shared_ptr &anf_node, const size_t &input_num, std::vector *input_size_list) { @@ -147,24 +109,74 @@ bool SetIOSize(const std::shared_ptr &anf_node, const std::shared_ptr *node_attr) { + MS_EXCEPTION_IF_NULL(node_attr); + if (type == "int") { + auto attr_value = GetValue(value); + (*node_attr)[attr_name].set_i(attr_value); + } else if (type == "str") { + auto attr_value = GetValue(value); + (*node_attr)[attr_name].set_s(attr_value); + } else if (type == "bool") { + auto attr_value = GetValue(value); + (*node_attr)[attr_name].set_b(attr_value); + } else if (type == "float") { + auto attr_value = GetValue(value); + (*node_attr)[attr_name].set_f(attr_value); + } else if (type == "listInt") { + std::vector attr_value; + auto value_type = value->type(); + MS_EXCEPTION_IF_NULL(value_type); + auto value_type_str = value_type->ToString(); + if (value_type_str == "Int32") { + int data = GetValue(value); + attr_value.push_back(data); + } else { + attr_value = GetValue>(value); + } + mindspore::AttrValue input_shape_attr; + mindspore::AttrValue_ArrayValue *input_shape_attr_list = input_shape_attr.mutable_array(); + MS_EXCEPTION_IF_NULL(input_shape_attr_list); + for (const auto shape : attr_value) { + input_shape_attr_list->add_i(shape); + } + (*node_attr)[attr_name] = input_shape_attr; + } else { + MS_LOG(EXCEPTION) << "type: " << type << "not support"; + } +} + void SetNodeAttr(const std::shared_ptr &anf_node, mindspore::NodeDef *proto) { std::string op_name = AnfAlgo::GetCNodeName(anf_node); - if (op_name == "InitDataSetQueue") { - op_name = "InitData"; + if (op_name == kInitDataSetQueue) { + op_name = kInitData; } - if (op_name == "Print") { + if (op_name == kPrint) { return; } - std::map mOpAttrFuncMap; - CreateAttrFuncMap(&mOpAttrFuncMap); - FNodeAttrHandle func_ptr = nullptr; - auto iter = mOpAttrFuncMap.find(op_name); - if (iter != mOpAttrFuncMap.end()) { - func_ptr = iter->second; - MS_EXCEPTION_IF_NULL(func_ptr); - func_ptr(anf_node, proto); - } else { - MS_LOG(ERROR) << "Don't support node [" << op_name << "] to set nodedef of attr"; + + auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, OpImplyType::kAICPU); + MS_EXCEPTION_IF_NULL(op_info_ptr); + auto attrs_ptr = op_info_ptr->attrs_ptr(); + auto primitive = AnfAlgo::GetCNodePrimitive(anf_node); + MS_EXCEPTION_IF_NULL(primitive); + ::google::protobuf::Map<::std::string, ::mindspore::AttrValue> *node_attr = proto->mutable_attrs(); + for (const auto &attr_ptr : attrs_ptr) { + std::string attr_name = attr_ptr->name(); + std::string real_name; + auto value = primitive->GetAttr(attr_name); + if (value != nullptr) { + if (attr_name == kQueueName || attr_name == kSharedName) { + real_name = kChannelName; + } else if (attr_name == kSeed) { + real_name = "seed"; + } else if (attr_name == kSeed2) { + real_name = "seed2"; + } + std::string type = attr_ptr->type(); + ParseAttrValue(type, real_name, value, node_attr); + } } MS_LOG(INFO) << "Set node attr end!"; } diff --git a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc index ac0b0d9f7a..6675051069 100644 --- a/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc +++ b/mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc @@ -17,68 +17,27 @@ #include "kernel/aicpu/aicpu_kernel_metadata.h" #include #include +#include "kernel/oplib/oplib.h" +#include "kernel/common_utils.h" +#include "kernel/aicpu/aicpu_util.h" #include "session/anf_runtime_algorithm.h" namespace mindspore { namespace kernel { -constexpr auto kInitDataSetQueueOpName = "InitDataSetQueue"; -constexpr auto kGetNext = "GetNext"; -constexpr auto kDropoutGenMask = "DropoutGenMask"; -constexpr auto kPrint = "Print"; -const std::vector AICPU_OPS = {kInitDataSetQueueOpName, kGetNext, kDropoutGenMask, kPrint}; - -std::shared_ptr CreateKernelInfo(const std::vector &inputs_format, - const std::vector &inputs_device_type, - const std::vector &outputs_format, - const std::vector &outputs_device_type) { - auto builder = KernelBuildInfo::KernelBuildInfoBuilder(); - builder.SetInputsFormat(inputs_format); - builder.SetInputsDeviceType(inputs_device_type); - builder.SetOutputsFormat(outputs_format); - builder.SetOutputsDeviceType(outputs_device_type); - builder.SetProcessor(AICPU); - builder.SetKernelType(AICPU_KERNEL); - builder.SetFusionType(OPAQUE); - return builder.Build(); -} - -bool CheckIfExistAicpuMeta(const std::string &op_name) { - if (std::find(AICPU_OPS.begin(), AICPU_OPS.end(), op_name) != AICPU_OPS.end()) { - return false; - } - return true; -} - void AicpuMetadataInfo(const CNodePtr &kernel_node, std::vector> *kernel_info_list) { MS_LOG(INFO) << "AicpuMetadataInfo."; MS_EXCEPTION_IF_NULL(kernel_node); MS_EXCEPTION_IF_NULL(kernel_info_list); std::string op_name = AnfAlgo::GetCNodeName(kernel_node); - if (CheckIfExistAicpuMeta(op_name)) { - MS_LOG(DEBUG) << "Aicpu doesn't have metadata of op [" << op_name << "]."; - return; - } - - if (op_name == kInitDataSetQueueOpName) { - kernel_info_list->push_back(CreateKernelInfo({}, {}, {}, {})); + if (op_name == kInitDataSetQueue) { + op_name = kInitData; } - - if (op_name == kGetNext) { - std::vector outputs_format; - std::vector outputs_type; - for (size_t output_index = 0; output_index < AnfAlgo::GetOutputTensorNum(kernel_node); ++output_index) { - outputs_format.emplace_back(kOpFormat_DEFAULT); - outputs_type.push_back(AnfAlgo::GetOutputInferDataType(kernel_node, output_index)); - } - kernel_info_list->push_back(CreateKernelInfo({}, {}, outputs_format, outputs_type)); - } - - if (op_name == kDropoutGenMask) { - kernel_info_list->push_back(CreateKernelInfo({kOpFormat_NCHW, kOpFormat_NCHW}, - {kInt32->type_id(), kFloat16->type_id()}, {kOpFormat_NCHW}, - {kUInt8->type_id()})); + auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, OpImplyType::kAICPU); + if (op_info_ptr == nullptr) { + MS_LOG(WARNING) << "Aicpu doestn't have metadata of op [" << op_name << "]"; + return; } - + // For compatibility with the current framework if (op_name == kPrint) { std::vector inputs_format; std::vector inputs_type; @@ -92,11 +51,20 @@ void AicpuMetadataInfo(const CNodePtr &kernel_node, std::vectorpush_back(CreateKernelInfo(inputs_format, inputs_type, outputs_format, outputs_type)); + auto builder = KernelBuildInfo::KernelBuildInfoBuilder(); + builder.SetInputsFormat(inputs_format); + builder.SetInputsDeviceType(inputs_type); + builder.SetOutputsFormat(outputs_format); + builder.SetOutputsDeviceType(outputs_type); + builder.SetProcessor(AICPU); + builder.SetKernelType(AICPU_KERNEL); + builder.SetFusionType(OPAQUE); + kernel_info_list->push_back(builder.Build()); + return; } - - if (kernel_info_list->empty()) { - MS_LOG(INFO) << "Aicpu dose not has metadata of op[ " << op_name << "]."; + if (!ParseMetadata(kernel_node, op_info_ptr, AICPU, kernel_info_list)) { + MS_LOG(WARNING) << "Aicpu parsed metadata op [" << op_name << "] failed"; + return; } } } // namespace kernel diff --git a/mindspore/ccsrc/kernel/aicpu/aicpu_util.h b/mindspore/ccsrc/kernel/aicpu/aicpu_util.h index f521418f6b..08fca16a3b 100644 --- a/mindspore/ccsrc/kernel/aicpu/aicpu_util.h +++ b/mindspore/ccsrc/kernel/aicpu/aicpu_util.h @@ -24,7 +24,8 @@ namespace mindspore { namespace kernel { -constexpr auto kInitDataSetQueue = "InitData"; +constexpr auto kInitDataSetQueue = "InitDataSetQueue"; +constexpr auto kInitData = "InitData"; constexpr auto kGetNext = "GetNext"; constexpr auto kDropoutGenMask = "DropoutGenMask"; constexpr auto kPrint = "Print"; diff --git a/mindspore/ccsrc/kernel/common_utils.cc b/mindspore/ccsrc/kernel/common_utils.cc index c2f2638753..137ae65414 100644 --- a/mindspore/ccsrc/kernel/common_utils.cc +++ b/mindspore/ccsrc/kernel/common_utils.cc @@ -417,6 +417,8 @@ void SetKernelBuildInfo(const std::shared_ptrSetKernelType(AUTO_DIFF_KERNEL); + } else if (imply_type == kAICPU) { + builder->SetKernelType(AICPU_KERNEL); } else { builder->SetKernelType(TBE_KERNEL); } @@ -471,6 +473,13 @@ bool ParseMetadata(const CNodePtr &kernel_node, const std::shared_ptrpush_back(builder->Build()); + } + } else { + if (processor == AICPU) { + auto builder = std::make_shared(); + MS_EXCEPTION_IF_NULL(builder); + SetKernelBuildInfo(builder, processor, op_info_ptr); kernel_info_list->push_back(builder->Build()); } } diff --git a/mindspore/ccsrc/kernel/oplib/opinfo.h b/mindspore/ccsrc/kernel/oplib/opinfo.h index 56abea9269..215df21776 100644 --- a/mindspore/ccsrc/kernel/oplib/opinfo.h +++ b/mindspore/ccsrc/kernel/oplib/opinfo.h @@ -24,7 +24,7 @@ namespace mindspore { namespace kernel { -enum OpImplyType { kAKG = 0, kTBE }; +enum OpImplyType { kAKG = 0, kTBE = 1, kAICPU }; enum OpIOType { kInput = 0, kOutput }; class OpAttr { diff --git a/mindspore/ccsrc/kernel/oplib/oplib.cc b/mindspore/ccsrc/kernel/oplib/oplib.cc index 4059b8e246..d2464bce47 100644 --- a/mindspore/ccsrc/kernel/oplib/oplib.cc +++ b/mindspore/ccsrc/kernel/oplib/oplib.cc @@ -39,6 +39,7 @@ constexpr auto kDtypeFormat = "dtype_format"; constexpr auto kAttr = "attr"; constexpr auto kIputs = "inputs"; constexpr auto kOutputs = "outputs"; +constexpr auto kAiCPU = "AiCPU"; constexpr auto kTbe = "TBE"; constexpr auto kAkg = "akg"; constexpr auto kAutodiff = "AutoDiff"; @@ -60,6 +61,8 @@ std::string ImplTypeToStr(OpImplyType impl_type) { return kTbe; case kAKG: return kAkg; + case kAICPU: + return kAiCPU; default: return "unknow"; } @@ -76,6 +79,9 @@ bool OpLib::RegOp(const std::string& json_string, const std::string& impl_path) } else if (imply_type_string == kAutodiff) { OpImplyType imply_type = kAKG; ret = DecodeOpInfo(op_json, imply_type, impl_path); + } else if (imply_type_string == kAiCPU) { + OpImplyType imply_type = kAICPU; + ret = DecodeOpInfo(op_json, imply_type, impl_path); } else { MS_LOG(DEBUG) << "Not support imply_type"; } @@ -154,7 +160,9 @@ bool OpLib::DecodeAttr(const nlohmann::json& obj, const OpImplyType imply_type, std::shared_ptr op_attr = std::make_shared(); MS_EXCEPTION_IF_NULL(op_attr); op_attr->set_name(obj.at(kName)); - op_attr->set_param_type(obj.at(kParamType)); + if (imply_type != kAICPU) { + op_attr->set_param_type(obj.at(kParamType)); + } op_attr->set_type(obj.at(kType)); if (imply_type == kTBE) { op_attr->set_value(obj.at(kValue)); @@ -242,9 +250,10 @@ std::shared_ptr OpLib::FindOp(const std::string& op_name, OpImplyType im auto context = MsContext::GetInstance(); MS_EXCEPTION_IF_NULL(context); bool is_gpu = (context->device_target() == kGPUDevice); - if ((is_gpu && imply_type == kTBE) || (!is_gpu && imply_type != kTBE)) { - MS_LOG(DEBUG) << "FindOp failed: opname:" << op_name << "imply_type:" << ImplTypeToStr(imply_type) - << "current op num:" << op_info_.size(); + if ((is_gpu && (imply_type == kTBE || imply_type == kAICPU)) || + (!is_gpu && (imply_type != kTBE && imply_type != kAICPU))) { + MS_LOG(ERROR) << "FindOp failed: opname:" << op_name << ", imply_type:" << ImplTypeToStr(imply_type) + << ", current op num:" << op_info_.size(); return nullptr; } for (const auto& op_info : op_info_) { @@ -253,8 +262,8 @@ std::shared_ptr OpLib::FindOp(const std::string& op_name, OpImplyType im return op_info; } } - MS_LOG(DEBUG) << "FindOp failed: opname:" << op_name << "imply_type:" << ImplTypeToStr(imply_type) - << "current op num:" << op_info_.size(); + MS_LOG(DEBUG) << "FindOp failed: opname:" << op_name << ", imply_type:" << ImplTypeToStr(imply_type) + << ", current op num:" << op_info_.size(); return nullptr; } diff --git a/mindspore/ops/__init__.py b/mindspore/ops/__init__.py index 6f4f680672..0e6c114566 100644 --- a/mindspore/ops/__init__.py +++ b/mindspore/ops/__init__.py @@ -30,7 +30,7 @@ Note: from .primitive import Primitive, PrimitiveWithInfer, prim_attr_register from .vm_impl_registry import get_vm_impl_fn, vm_impl_registry -from .op_info_register import op_info_register, TBERegOp, DataType +from .op_info_register import op_info_register, AiCPURegOp, TBERegOp, DataType from .primitive import constexpr from .._c_expression import signature_rw, signature_kind @@ -40,6 +40,6 @@ __primitive__ = [ ] __all__ = ["get_vm_impl_fn", "vm_impl_registry", - "op_info_register", "TBERegOp", "DataType", + "op_info_register", "AiCPURegOp", "TBERegOp", "DataType", "constexpr"] __all__.extend(__primitive__) diff --git a/mindspore/ops/_op_impl/__init__.py b/mindspore/ops/_op_impl/__init__.py index b8370cc64e..76444881cc 100644 --- a/mindspore/ops/_op_impl/__init__.py +++ b/mindspore/ops/_op_impl/__init__.py @@ -16,5 +16,6 @@ from .akg.gpu import * from .tbe import * +from .aicpu import * __all__ = [] diff --git a/mindspore/ops/_op_impl/aicpu/__init__.py b/mindspore/ops/_op_impl/aicpu/__init__.py new file mode 100644 index 0000000000..b0f90a629b --- /dev/null +++ b/mindspore/ops/_op_impl/aicpu/__init__.py @@ -0,0 +1,19 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""aicpu ops""" +from .init_data_set_queue import _init_data_set_queue_aicpu +from .dropout_genmask import _dropout_genmask_aicpu +from .get_next import _get_next_aicpu +from .print_tensor import _print_aicpu diff --git a/mindspore/ops/_op_impl/aicpu/dropout_genmask.py b/mindspore/ops/_op_impl/aicpu/dropout_genmask.py new file mode 100644 index 0000000000..96707a5010 --- /dev/null +++ b/mindspore/ops/_op_impl/aicpu/dropout_genmask.py @@ -0,0 +1,32 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""InitDataSetQueue op""" +from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType + +dropout_genmask_op_info = AiCPURegOp("DropoutGenMask") \ + .fusion_type("OPAQUE") \ + .input(0, "x1", "required") \ + .input(1, "x2", "required") \ + .output(0, "y", "required") \ + .attr("Seed0", "int") \ + .attr("Seed1", "int") \ + .dtype_format(DataType.I32_NCHW, DataType.F16_NCHW, DataType.U8_NCHW) \ + .get_op_info() + +@op_info_register(dropout_genmask_op_info) +def _dropout_genmask_aicpu(): + """Dropout AiCPU register""" + return diff --git a/mindspore/ops/_op_impl/aicpu/get_next.py b/mindspore/ops/_op_impl/aicpu/get_next.py new file mode 100644 index 0000000000..ce32014211 --- /dev/null +++ b/mindspore/ops/_op_impl/aicpu/get_next.py @@ -0,0 +1,39 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""InitDataSetQueue op""" +from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType + +get_next_op_info = AiCPURegOp("GetNext") \ + .fusion_type("OPAQUE") \ + .output(0, "y", "dynamic") \ + .attr("shared_name", "str") \ + .dtype_format(DataType.BOOL_Default) \ + .dtype_format(DataType.I8_Default) \ + .dtype_format(DataType.I16_Default) \ + .dtype_format(DataType.I32_Default) \ + .dtype_format(DataType.I64_Default) \ + .dtype_format(DataType.F16_Default) \ + .dtype_format(DataType.U8_Default) \ + .dtype_format(DataType.U16_Default) \ + .dtype_format(DataType.U32_Default) \ + .dtype_format(DataType.U64_Default) \ + .dtype_format(DataType.F32_Default) \ + .get_op_info() + +@op_info_register(get_next_op_info) +def _get_next_aicpu(): + """GetNext AiCPU register""" + return diff --git a/mindspore/ops/_op_impl/aicpu/init_data_set_queue.py b/mindspore/ops/_op_impl/aicpu/init_data_set_queue.py new file mode 100644 index 0000000000..a48e01eced --- /dev/null +++ b/mindspore/ops/_op_impl/aicpu/init_data_set_queue.py @@ -0,0 +1,27 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""InitDataSetQueue op""" +from mindspore.ops.op_info_register import op_info_register, AiCPURegOp + +init_data_set_queue_op_info = AiCPURegOp("InitData") \ + .fusion_type("OPAQUE") \ + .attr("queue_name", "str") \ + .get_op_info() + +@op_info_register(init_data_set_queue_op_info) +def _init_data_set_queue_aicpu(): + """InitDataSetQueue AiCPU register""" + return diff --git a/mindspore/ops/_op_impl/aicpu/print_tensor.py b/mindspore/ops/_op_impl/aicpu/print_tensor.py new file mode 100644 index 0000000000..011f4a3d9d --- /dev/null +++ b/mindspore/ops/_op_impl/aicpu/print_tensor.py @@ -0,0 +1,39 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""InitDataSetQueue op""" +from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType + +print_op_info = AiCPURegOp("Print") \ + .fusion_type("OPAQUE") \ + .input(0, "x", "dynamic") \ + .output(0, "y", "required") \ + .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.I8_Default, DataType.I8_Default) \ + .dtype_format(DataType.I16_Default, DataType.I16_Default) \ + .dtype_format(DataType.I32_Default, DataType.I32_Default) \ + .dtype_format(DataType.I64_Default, DataType.I64_Default) \ + .dtype_format(DataType.F16_Default, DataType.F16_Default) \ + .dtype_format(DataType.U8_Default, DataType.U8_Default) \ + .dtype_format(DataType.U16_Default, DataType.U16_Default) \ + .dtype_format(DataType.U32_Default, DataType.U32_Default) \ + .dtype_format(DataType.U64_Default, DataType.U64_Default) \ + .dtype_format(DataType.F32_Default, DataType.F32_Default) \ + .get_op_info() + +@op_info_register(print_op_info) +def _print_aicpu(): + """Print AiCPU register""" + return diff --git a/mindspore/ops/op_info_register.py b/mindspore/ops/op_info_register.py index 6a42099c89..0750094e18 100644 --- a/mindspore/ops/op_info_register.py +++ b/mindspore/ops/op_info_register.py @@ -78,14 +78,15 @@ class RegOp(): self.inputs = [] self.outputs = [] self.attr_ = [] + self.fusion_type_ = '' self.dtype_format_ = [] - def is_string(self, value): + def _is_string(self, value): """ Check if the value is a str type. Args: - value: Parameter to to check. + value: Parameter to be checked. Raises: TypeError: If the type of value is not a str. @@ -93,12 +94,12 @@ class RegOp(): if not isinstance(value, str): raise TypeError("%s value must be str" % str(value)) - def is_int(self, value): + def _is_int(self, value): """ Check if the value is a int. Args: - value: Parameter to to check. + value: Parameter to be checked. Raises: TypeError: If the type of value is not a int. @@ -106,12 +107,12 @@ class RegOp(): if not isinstance(value, int): raise TypeError("%s value must be int" % str(value)) - def is_bool(self, value): + def _is_bool(self, value): """ Check if the value is a bool. Args: - value: Parameter to to check. + value: Parameter to be checked. Raises: TypeError: If the type of value is not a bool. @@ -119,6 +120,51 @@ class RegOp(): if not isinstance(value, bool): raise TypeError("%s value must be bool" % str(value)) + def _check_param(self, param_list, key_list, fn_list, kwargs): + """ + Check if the parameter type is correct. + + Args: + param_list (list): Parameter list to be checked. + key_list (list): The keys of output dict. + fn_list (list): Function used for parameter checking. If the function list has only one element, + all parameters will use the same function. + kwargs (dict): Other parameter information. + + Raises: + TypeError: If the type of value is not list. + ValueError: If the size of param list is not equal to the size of key list, or + the size of param list is not equal to the size of funtion list. + """ + for i in [param_list, key_list, fn_list]: + if not isinstance(i, list): + raise TypeError("%s value must be list type" % str(i)) + if len(param_list) != len(key_list) or (len(fn_list) != 1 and len(param_list) != len(fn_list)): + raise ValueError("param_list size {}, key_list size {}, must be equal.And fn_list size {}.". + format(len(param_list), len(key_list), len(fn_list))) + out_dict = {} + for idx, element in enumerate(param_list): + if element is not None: + if len(fn_list) == 1: + fn_list[0](element) + else: + fn_list[idx](element) + out_dict[key_list[idx]] = element + if kwargs: + out_dict = dict(out_dict, kwargs) + return out_dict + + def fusion_type(self, fusion_type): + """ + Register fusion type. + + Args: + fusion_type (str): Value of fusion type. + """ + self._is_string(fusion_type) + self.fusion_type_ = fusion_type + return self + def dtype_format(self, *args): """ Register dtype and format. @@ -136,8 +182,8 @@ class RegOp(): for arg in args: if not isinstance(arg, tuple) or len(arg) != 2: raise ValueError("dtype and format value must be tuple of two elements") - self.is_string(arg[0]) - self.is_string(arg[1]) + self._is_string(arg[0]) + self._is_string(arg[1]) dtype_format.append(arg) self.dtype_format_.append(tuple(dtype_format)) return self @@ -159,13 +205,71 @@ class RegOp(): return op_info +class AiCPURegOp(RegOp): + """Class for AiCPU op info register""" + + def __init__(self, op_name): + super(AiCPURegOp, self).__init__(op_name) + self.imply_type = "AiCPU" + + def input(self, index=None, name=None, param_type=None, **kwargs): + """ + Register AiCPU op input information. + + Args: + index (int): Order of the input. Default: None. + name (str): Name of the input. Default: None. + param_type (str): Param type of the input. Default: None. + kwargs (dict): Other information for the input. + """ + param_list = [index, name, param_type] + key_list = ["index", "name", "param_type"] + fn_list = [self._is_int, self._is_string, self._is_string] + input_dict = self._check_param(param_list, key_list, fn_list, kwargs) + self.inputs.append(input_dict) + return self + + def output(self, index=None, name=None, param_type=None, **kwargs): + """ + Register AiCPU op output information. + + Args: + index (int): Order of the output. Default: None. + name (str): Name of the output. Default: None. + param_type (str): Param type of the output. Default: None. + kwargs (dict): Other information for the output. + """ + param_list = [index, name, param_type] + key_list = ["index", "name", "param_type"] + fn_list = [self._is_int, self._is_string, self._is_string] + output_dict = self._check_param(param_list, key_list, fn_list, kwargs) + self.outputs.append(output_dict) + return self + + def attr(self, name=None, value_type=None, value=None, **kwargs): + """ + Register AiCPU op attribute information. + + Args: + name (str): Name of the attribute. Default: None. + value_type (str): Value type of the attribute. Default: None. + value (str): Value type of the attribute. Default: None. + kwargs (dict): Other information for the attribute. + """ + param_list = [name, value_type, value] + key_list = ["name", "type", "value"] + fn_list = [self._is_string] + attr_dict = self._check_param(param_list, key_list, fn_list, kwargs) + self.attr_.append(attr_dict) + return self + + class TBERegOp(RegOp): """Class for TBE op info register.""" def __init__(self, op_name=""): super(TBERegOp, self).__init__(op_name) self.imply_type = "TBE" - self.fusion_type_ = '' self.async_flag_ = False self.binfile_name_ = '' self.compute_cost_ = 10 @@ -175,17 +279,6 @@ class TBERegOp(RegOp): self.dynamic_format_ = False self.op_pattern_ = "" - def fusion_type(self, fusion_type): - """ - Register fusion type. - - Args: - fusion_type (str): Value of fusion type. - """ - self.is_string(fusion_type) - self.fusion_type_ = fusion_type - return self - def async_flag(self, async_flag): """ Register async flag. @@ -193,7 +286,7 @@ class TBERegOp(RegOp): Args: async_flag (bool): Value of async flag. """ - self.is_bool(async_flag) + self._is_bool(async_flag) self.async_flag_ = async_flag return self @@ -204,7 +297,7 @@ class TBERegOp(RegOp): Args: binfile_name (str): Name of op binfile. """ - self.is_string(binfile_name) + self._is_string(binfile_name) self.binfile_name_ = binfile_name return self @@ -215,7 +308,7 @@ class TBERegOp(RegOp): Args: compute_cost (int): Value of compute cost. """ - self.is_int(compute_cost) + self._is_int(compute_cost) self.compute_cost_ = compute_cost return self @@ -226,7 +319,7 @@ class TBERegOp(RegOp): Args: kernel_name (str): Name of op kernel. """ - self.is_string(kernel_name) + self._is_string(kernel_name) self.kernel_name_ = kernel_name return self @@ -237,7 +330,7 @@ class TBERegOp(RegOp): Args: partial_flag (bool): Value of partial flag. """ - self.is_bool(partial_flag) + self._is_bool(partial_flag) self.partial_flag_ = partial_flag return self @@ -248,7 +341,7 @@ class TBERegOp(RegOp): Args: reshape_type (str): Value of reshape type. """ - self.is_string(reshape_type) + self._is_string(reshape_type) self.reshape_type_ = reshape_type return self @@ -259,56 +352,43 @@ class TBERegOp(RegOp): Args: reshape_type (bool): Value of dynamic format. """ - self.is_bool(dynamic_format) + self._is_bool(dynamic_format) self.dynamic_format_ = dynamic_format return self def op_pattern(self, pattern=None): """ - Register op pattern information. + Register TBE op pattern information. Args: pattern (str): Value of op pattern. """ - if pattern is not None and self.istring(pattern): + if pattern is not None and self._is_string(pattern): self.op_pattern_ = pattern return self def attr(self, name=None, param_type=None, value_type=None, value=None, default_value=None, **kwargs): """ - Register op attribute information. + Register TBE op attribute information. Args: name (str): Name of the attribute. Default: None. param_type (str): Param type of the attribute. Default: None. - type (str): Type of the attribute. Default: None. + value_type (str): Type of the attribute. Default: None. value (str): Value of the attribute. Default: None. default_value (str): Default value of attribute. Default: None. kwargs (dict): Other information for the attribute. """ param_list = [name, param_type, value_type, value, default_value] - attr_dict = {} - for index, element in enumerate(param_list): - if element is not None: - self.is_string(element) - if index == 0: - attr_dict["name"] = element - elif index == 1: - attr_dict["param_type"] = element - elif index == 2: - attr_dict["type"] = element - elif index == 3: - attr_dict["value"] = element - elif index == 4: - attr_dict["default_value"] = element - if kwargs: - attr_dict = dict(attr_dict, **kwargs) + key_list = ["name", "param_type", "type", "value", "default_value"] + fn_list = [self._is_string] + attr_dict = self._check_param(param_list, key_list, fn_list, kwargs) self.attr_.append(attr_dict) return self def input(self, index=None, name=None, need_compile=None, param_type=None, shape=None, **kwargs): """ - Register op input information. + Register TBE op input information. Args: index (int): Order of the input. Default: None. @@ -319,32 +399,15 @@ class TBERegOp(RegOp): kwargs (dict): Other information for the input. """ param_list = [index, name, need_compile, param_type, shape] - input_dict = {} - for idx, element in enumerate(param_list): - if element is not None: - if idx == 0: - self.is_int(element) - input_dict["index"] = element - elif idx == 1: - self.is_string(element) - input_dict["name"] = element - elif idx == 2: - self.is_bool(element) - input_dict["need_compile"] = element - elif idx == 3: - self.is_string(element) - input_dict["param_type"] = element - elif idx == 4: - self.is_string(element) - input_dict["shape"] = element - if kwargs: - input_dict = dict(input_dict, **kwargs) + key_list = ["index", "name", "need_compile", "param_type", "shape"] + fn_list = [self._is_int, self._is_string, self._is_bool, self._is_string, self._is_string] + input_dict = self._check_param(param_list, key_list, fn_list, kwargs) self.inputs.append(input_dict) return self def output(self, index=None, name=None, need_compile=None, param_type=None, shape=None, **kwargs): """ - Register op output information. + Register TBE op output information. Args: index (int): Order of the output. Default: None. @@ -355,29 +418,13 @@ class TBERegOp(RegOp): kwargs (dict): Other information for the output. """ param_list = [index, name, need_compile, param_type, shape] - output_dict = {} - for idx, element in enumerate(param_list): - if element is not None: - if idx == 0: - self.is_int(element) - output_dict["index"] = element - elif idx == 1: - self.is_string(element) - output_dict["name"] = element - elif idx == 2: - self.is_bool(element) - output_dict["need_compile"] = element - elif idx == 3: - self.is_string(element) - output_dict["param_type"] = element - elif idx == 4: - self.is_string(element) - output_dict["shape"] = element - if kwargs: - output_dict = dict(output_dict, **kwargs) + key_list = ["index", "name", "need_compile", "param_type", "shape"] + fn_list = [self._is_int, self._is_string, self._is_bool, self._is_string, self._is_string] + output_dict = self._check_param(param_list, key_list, fn_list, kwargs) self.outputs.append(output_dict) return self + class DataType(): """ Various combinations of dtype and formatself. From 99c353e6966cf14a23d421e3ac490fffa7cce324 Mon Sep 17 00:00:00 2001 From: yanghaoran Date: Thu, 9 Apr 2020 19:07:01 +0800 Subject: [PATCH 162/227] add custom environment variable ASCEND_CUSTOM_PATH for customized Ascend software installation --- cmake/dependency_graphengine.cmake | 6 +++++- mindspore/ccsrc/CMakeLists.txt | 12 ++++++++++-- 2 files changed, 15 insertions(+), 3 deletions(-) diff --git a/cmake/dependency_graphengine.cmake b/cmake/dependency_graphengine.cmake index 2420f47736..2a90cc1458 100644 --- a/cmake/dependency_graphengine.cmake +++ b/cmake/dependency_graphengine.cmake @@ -39,7 +39,11 @@ elseif (DEFINED ENV{D_LINK_PATH}) find_library(resource libresource.so ${GE_LIB_PATH}) else() # Ascend mode - set(ASCEND_PATH /usr/local/Ascend) + if(DEFINED ENV{ASCEND_CUSTOM_PATH}) + set(ASCEND_PATH $ENV{ASCEND_CUSTOM_PATH}) + else() + set(ASCEND_PATH /usr/local/Ascend) + endif() set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common) set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64) find_library(c_sec libc_sec.so ${ASCEND_DRIVER_PATH}) diff --git a/mindspore/ccsrc/CMakeLists.txt b/mindspore/ccsrc/CMakeLists.txt index 1d104148c3..c49c962bdd 100644 --- a/mindspore/ccsrc/CMakeLists.txt +++ b/mindspore/ccsrc/CMakeLists.txt @@ -296,7 +296,11 @@ if(ENABLE_D) endif() else() MESSAGE("use system default lib") - set(ASCEND_PATH /usr/local/Ascend) + if(DEFINED ENV{ASCEND_CUSTOM_PATH}) + set(ASCEND_PATH $ENV{ASCEND_CUSTOM_PATH}) + else() + set(ASCEND_PATH /usr/local/Ascend) + endif() set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common) set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64) endif() @@ -500,7 +504,11 @@ add_dependencies(add_ms_lib _c_expression) if (NOT ENABLE_GE) if (ENABLE_D) - set(ASCEND_PATH /usr/local/Ascend) + if(DEFINED ENV{ASCEND_CUSTOM_PATH}) + set(ASCEND_PATH $ENV{ASCEND_CUSTOM_PATH}) + else() + set(ASCEND_PATH /usr/local/Ascend) + endif() set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common) add_custom_target(add_ge_lib ALL COMMAND cp ${MS_CCSRC_BUILD_PATH}/../../graphengine/src/common/graph/libgraph.so ${MS_LIB_PATH} From fc4ad192dc11006f25fee75095072a6d5d5f2019 Mon Sep 17 00:00:00 2001 From: liuxiao Date: Thu, 9 Apr 2020 14:37:00 +0800 Subject: [PATCH 163/227] modified api name Stack -> Pack, Unstack -> Unpack --- mindspore/ccsrc/transform/convert.cc | 4 +- mindspore/ops/_grad/grad_array_ops.py | 20 ++++---- mindspore/ops/operations/__init__.py | 6 +-- mindspore/ops/operations/array_ops.py | 70 ++++++++++++--------------- tests/ut/python/ops/test_ops.py | 32 ++++++------ 5 files changed, 62 insertions(+), 70 deletions(-) diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index bebd000958..20adec5b97 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -148,8 +148,8 @@ const char kNameSlice[] = "Slice"; const char kNameAddN[] = "AddN"; const char kNameLess[] = "Less"; const char kNameGreater[] = "Greater"; -const char kNameStack[] = "Stack"; -const char kNameUnstack[] = "Unstack"; +const char kNamePack[] = "Pack"; +const char kNameUnpack[] = "Unpack"; const char kNameMerge[] = "Merge"; const char kNameGeSwitch[] = "GeSwitch"; diff --git a/mindspore/ops/_grad/grad_array_ops.py b/mindspore/ops/_grad/grad_array_ops.py index 0a0caf471e..abad030ae9 100644 --- a/mindspore/ops/_grad/grad_array_ops.py +++ b/mindspore/ops/_grad/grad_array_ops.py @@ -266,26 +266,26 @@ def get_bprop_gather_v2(self): return bprop -@bprop_getters.register(P.Stack) -def get_bprop_stack(self): - """Generate bprop for Stack""" +@bprop_getters.register(P.Pack) +def get_bprop_pack(self): + """Generate bprop for Pack""" axis = self.axis def bprop(x, out, dout): - stack_grad = P.Unstack(axis) - out = stack_grad(dout) + pack_grad = P.Unpack(axis) + out = pack_grad(dout) return (out,) return bprop -@bprop_getters.register(P.Unstack) -def get_bprop_unstack(self): - """Generate bprop for Unstack""" +@bprop_getters.register(P.Unpack) +def get_bprop_unpack(self): + """Generate bprop for Unpack""" axis = self.axis def bprop(x, out, dout): - unstack_grad = P.Stack(axis) - out = unstack_grad(dout) + unpack_grad = P.Pack(axis) + out = unpack_grad(dout) return (out,) return bprop diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index 5fd3f07876..c10aef1ac0 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -19,7 +19,7 @@ Primitive operator classes. A collection of operators to build nerual networks or computing functions. """ -from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat, Stack, Unstack, +from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat, Pack, Unpack, Diag, DiagPart, DType, ExpandDims, Eye, Fill, GatherNd, GatherV2, InvertPermutation, IsInstance, IsSubClass, ArgMaxWithValue, OnesLike, ZerosLike, @@ -112,8 +112,8 @@ __all__ = [ 'OneHot', 'GatherV2', 'Concat', - 'Stack', - 'Unstack', + 'Pack', + 'Unpack', 'Tile', 'BiasAdd', 'Gelu', diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index dda490566f..ac7f8ed699 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -1350,8 +1350,8 @@ class Concat(PrimitiveWithInfer): return out -def _get_stack_shape(x_shape, x_type, axis): - """for satck output shape""" +def _get_pack_shape(x_shape, x_type, axis): + """for pack output shape""" validator.check_type("shape", x_shape, [tuple]) validator.check_integer("len of input_x shape", len(x_shape), 0, Rel.GT) validator.check_subclass("shape0", x_type[0], mstype.tensor) @@ -1368,43 +1368,40 @@ def _get_stack_shape(x_shape, x_type, axis): validator.check('x_type[%d]' % i, x_type[i], 'base', x_type[0]) for j in range(rank_base): if v[j] != x_shape[0][j]: - raise ValueError("Stack evaluator element %d shape in input can not stack with first element" % i) + raise ValueError("Pack evaluator element %d shape in input can not pack with first element" % i) out_shape.insert(axis, N) return out_shape -class Stack(PrimitiveWithInfer): +class Pack(PrimitiveWithInfer): r""" - Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor. + Packs a list of tensors in specified axis. - Packs the list of tensors in `input_x` into a tensor with rank one higher than - each tensor in `input_x`, by packing them along the `axis` dimension. - Given a list of length `N` of tensors of shape `(A, B, C)`; + Packs the list of input tensors with the same rank `R`, output is a tensor of rank `(R+1)`. - If `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`. - - If `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`. Etc. + Given input tensors of shape :math:`(x_1, x_2, ..., x_R)`. Set the number of input tensors as `N`. + If :math:`0 \le axis`, the output tensor shape is :math:`(x_1, x_2, ..., x_{axis}, N, x_{axis+1}, ..., x_R)`. Args: - axis (int): The axis to stack along. Negative values wrap around, - so the valid range is [-(R+1), R+1). Default: 0. + axis (int): Dimension along which to pack. Default: 0. + Negative values wrap around. The range is [-(R+1), R+1). Inputs: - **input_x** (Union[tuple, list]) - A Tuple or list of Tensor objects with the same shape and type. Outputs: - Tensor. A stacked Tensor with the same type as values. + Tensor. A packed Tensor with the same type as `input_x`. Examples: >>> data1 = Tensor(np.array([0, 1]).astype(np.float32)) >>> data2 = Tensor(np.array([2, 3]).astype(np.float32)) - >>> op = P.Stack() - >>> output = op([data1, data2]) + >>> pack = P.Pack() + >>> output = pack([data1, data2]) [[0, 1], [2, 3]] """ @prim_attr_register def __init__(self, axis=0): - """init Stack""" + """init Pack""" self.__setattr_flag__ = True validator.check_type("axis", axis, [int]) self.axis = axis @@ -1413,38 +1410,33 @@ class Stack(PrimitiveWithInfer): x_shape = value['shape'] x_type = value['dtype'] self.add_prim_attr('num', len(x_shape)) - all_shape = _get_stack_shape(x_shape, x_type, self.axis) + all_shape = _get_pack_shape(x_shape, x_type, self.axis) out = {'shape': all_shape, 'dtype': x_type[0], 'value': None} return out -class Unstack(PrimitiveWithInfer): +class Unpack(PrimitiveWithInfer): r""" - Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors. - - Unpacks num tensors from value by chipping it along the axis dimension. - If num is not specified (the default), it is inferred from value's shape. - If value.shape[axis] is not known, ValueError is raised. + Unpacks tensor in specified axis. - For example, given a tensor of shape (A, B, C, D); + Unpacks a tensor of rank `R` along axis dimension, output tensors will have rank `(R-1)`. - If axis == 0 then the i'th tensor in output is the slice value[i, :, :, :] and - each tensor in output will have shape (B, C, D). (Note that the dimension unpacked along is gone, unlike split). + Given a tensor of shape :math:`(x_1, x_2, ..., x_R)`. If :math:`0 \le axis`, + the shape of tensor in output is :math:`(x_1, x_2, ..., x_{axis}, x_{axis+2}, ..., x_R)`. - If axis == 1 then the i'th tensor in output is the slice value[:, i, :, :] and - each tensor in output will have shape (A, C, D). Etc. - - This is the opposite of stack. + This is the opposite of pack. Args: - axis (int): The axis to unstack along. Defaults to the first dimension. - Negative values wrap around, so the valid range is [-R, R). + axis (int): Dimension along which to pack. Default: 0. + Negative values wrap around. The range is [-R, R). + num (int): The number of tensors to be unpacked to. Default : "None". + If `num` is not specified, it is inferred from the shape of `input_x`. Inputs: - **input_x** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_R)`. - A rank R > 0 Tensor to be unstacked. + A rank R > 0 Tensor to be unpacked. Outputs: A tuple of Tensors, the shape of each objects is same. @@ -1454,15 +1446,15 @@ class Unstack(PrimitiveWithInfer): or if len(input_x.shape[axis]) not equal to num. Examples: - >>> unstack = P.Unstack() - >>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]])) - >>> output = unstack(x) + >>> unpack = P.Unpack() + >>> input_x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]])) + >>> output = unpack(input_x) ([1, 1, 1, 1], [2, 2, 2, 2]) """ @prim_attr_register def __init__(self, axis=0): - """init Unstack""" + """init Unpack""" self.__setattr_flag__ = True validator.check_type("axis", axis, [int]) self.axis = axis @@ -1479,7 +1471,7 @@ class Unstack(PrimitiveWithInfer): validator.check_integer("output_num", output_num, 0, Rel.GT) self.add_prim_attr('num', output_num) output_valid_check = x_shape[self.axis] - output_num - validator.check_integer("the dimension which to unstack divides output_num", output_valid_check, 0, Rel.EQ) + validator.check_integer("The dimension which to unpack divides output_num", output_valid_check, 0, Rel.EQ) out_shapes = [] out_dtypes = [] out_shape = x_shape[:self.axis] + x_shape[self.axis + 1:] diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py index 97481e69a2..a3d771d7ec 100755 --- a/tests/ut/python/ops/test_ops.py +++ b/tests/ut/python/ops/test_ops.py @@ -80,9 +80,9 @@ class NetForConcat1(nn.Cell): return self.concat((x1, x2)) -class NetForStackInput(nn.Cell): +class NetForPackInput(nn.Cell): def __init__(self, op): - super(NetForStackInput, self).__init__() + super(NetForPackInput, self).__init__() self.op = op self.mul = P.Mul() @@ -93,9 +93,9 @@ class NetForStackInput(nn.Cell): return self.op(t) -class NetForUnstackInput(nn.Cell): +class NetForUnpackInput(nn.Cell): def __init__(self, op): - super(NetForUnstackInput, self).__init__() + super(NetForUnpackInput, self).__init__() self.op = op self.mul = P.Mul() @@ -996,33 +996,33 @@ test_case_array_ops = [ Tensor(np.array([1], np.float32)), Tensor(np.array([1], np.float32)))], 'desc_bprop': [[3,]]}), - ('StackV2_0', { - 'block': NetForStackInput(P.Stack()), + ('Pack_0', { + 'block': NetForPackInput(P.Pack()), 'desc_inputs':[[2, 2], [2, 2], [2, 2]], 'desc_bprop':[[3, 2, 2]], }), - ('StackV2_1', { - 'block': NetForStackInput(P.Stack(axis=-2)), + ('Pack_1', { + 'block': NetForPackInput(P.Pack(axis=-2)), 'desc_inputs':[[3, 2, 3], [3, 2, 3], [3, 2, 3]], 'desc_bprop':[[3, 2, 3, 3]], }), - ('StackV2_2', { - 'block': NetForStackInput(P.Stack()), + ('Pack_2', { + 'block': NetForPackInput(P.Pack()), 'desc_inputs':[[2, 2]], 'desc_bprop':[[2, 2, 2]], }), - ('StackV2_3', { - 'block': NetForStackInput(P.Stack()), + ('Pack_3', { + 'block': NetForPackInput(P.Pack()), 'desc_inputs':[[128, 128], [128, 128]], 'desc_bprop':[[2, 128, 128]], }), - ('UnstackV2_0', { - 'block': NetForUnstackInput(P.Unstack(axis=0)), + ('Unpack_0', { + 'block': NetForUnpackInput(P.Unpack(axis=0)), 'desc_inputs':[[2, 4]], 'desc_bprop':[[4], [4]], }), - ('UnstackV2_1', { - 'block': NetForUnstackInput(P.Unstack(axis=-1)), + ('Unpack_1', { + 'block': NetForUnpackInput(P.Unpack(axis=-1)), 'desc_inputs':[Tensor(np.array([[1, 1, 1]], np.float32))], 'desc_bprop':[[1], [1], [1]], }), From f57bd919e08432e7fe4a6177371936cd4680c34c Mon Sep 17 00:00:00 2001 From: yao_yf Date: Thu, 9 Apr 2020 15:02:33 +0800 Subject: [PATCH 164/227] Integrate two allreduce fusion set interfaces into one --- mindspore/parallel/__init__.py | 4 +--- mindspore/parallel/_auto_parallel_context.py | 14 ++++++++++---- ...allreduce_fusion.py => _dp_allreduce_fusion.py} | 4 ++-- 3 files changed, 13 insertions(+), 9 deletions(-) rename mindspore/parallel/{dp_allreduce_fusion.py => _dp_allreduce_fusion.py} (94%) diff --git a/mindspore/parallel/__init__.py b/mindspore/parallel/__init__.py index c79704f110..79d8e67a8d 100644 --- a/mindspore/parallel/__init__.py +++ b/mindspore/parallel/__init__.py @@ -15,9 +15,7 @@ """ This interface is ONLY used in Auto-parallel procedure. """ -from .dp_allreduce_fusion import set_fusion_strategy_by_idx, set_fusion_strategy_by_size from .algo_parameter_config import get_algo_parameters, reset_algo_parameters, \ set_algo_parameters -__all__ = ["set_fusion_strategy_by_idx", "set_fusion_strategy_by_size", "get_algo_parameters", - "reset_algo_parameters", "set_algo_parameters"] +__all__ = ["get_algo_parameters", "reset_algo_parameters", "set_algo_parameters"] diff --git a/mindspore/parallel/_auto_parallel_context.py b/mindspore/parallel/_auto_parallel_context.py index 3564ad4395..c99ac4a3c7 100644 --- a/mindspore/parallel/_auto_parallel_context.py +++ b/mindspore/parallel/_auto_parallel_context.py @@ -14,6 +14,8 @@ # ============================================================================ """Context of auto parallel""" import threading +import mindspore.context as context +from mindspore.parallel._dp_allreduce_fusion import _set_fusion_strategy_by_idx, _set_fusion_strategy_by_size from mindspore._c_expression import AutoParallelContext from mindspore._extends.pynative_helper import args_type_check @@ -219,13 +221,15 @@ class _AutoParallelContext: indices (list): Indices list. Raises: - ValueError: If type of indices item is not int. + TypeError: If type of indices item is not int. """ self.check_context_handle() for index in indices: if not isinstance(index, int): raise TypeError('indices has invalid value') - return self._context_handle.set_all_reduce_fusion_split_indices(indices) + self._context_handle.set_all_reduce_fusion_split_indices(indices) + if context.get_context("device_target") == "Ascend": + _set_fusion_strategy_by_idx(indices) def get_all_reduce_fusion_split_indices(self): """Get allreduce fusion split indices.""" @@ -240,13 +244,15 @@ class _AutoParallelContext: sizes (list): Sizes list. Raises: - ValueError: If type of sizes item is not int. + TypeError: If type of sizes item is not int. """ self.check_context_handle() for size in sizes: if not isinstance(size, int): raise TypeError('sizes has invalid value') - return self._context_handle.set_all_reduce_fusion_split_sizes(sizes) + self._context_handle.set_all_reduce_fusion_split_sizes(sizes) + if context.get_context("device_target") == "Ascend": + _set_fusion_strategy_by_size(sizes) def get_all_reduce_fusion_split_sizes(self): """Get allreduce fusion split sizes.""" diff --git a/mindspore/parallel/dp_allreduce_fusion.py b/mindspore/parallel/_dp_allreduce_fusion.py similarity index 94% rename from mindspore/parallel/dp_allreduce_fusion.py rename to mindspore/parallel/_dp_allreduce_fusion.py index 979823bd80..3c7039dbd6 100644 --- a/mindspore/parallel/dp_allreduce_fusion.py +++ b/mindspore/parallel/_dp_allreduce_fusion.py @@ -43,7 +43,7 @@ def _c_array(ctype, values): return (ctype * len(values))(*values) -def set_fusion_strategy_by_idx(idxList, group="hccl_world_group"): +def _set_fusion_strategy_by_idx(idxList, group="hccl_world_group"): """ A function set gradient segment strategy according to the index list. @@ -100,7 +100,7 @@ def set_fusion_strategy_by_idx(idxList, group="hccl_world_group"): raise RuntimeError('Allreduce split error') -def set_fusion_strategy_by_size(dataSizeList, group="hccl_world_group"): +def _set_fusion_strategy_by_size(dataSizeList, group="hccl_world_group"): """ A function set gradient segment strategy according to the data size percentage list. From dd112c98fc4efe734617dab0c7729c9e51390837 Mon Sep 17 00:00:00 2001 From: jojobugfree Date: Thu, 9 Apr 2020 14:47:05 +0800 Subject: [PATCH 165/227] change logging to mindspore.log --- mindspore/context.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/mindspore/context.py b/mindspore/context.py index 89365f3d1c..2938b87119 100644 --- a/mindspore/context.py +++ b/mindspore/context.py @@ -17,16 +17,14 @@ The context of mindspore, used to configure the current execution environment, including execution mode, execution backend and other feature switchs. """ import threading -import logging from collections import namedtuple from types import FunctionType +from mindspore import log as logger from mindspore._c_expression import MSContext from mindspore._extends.pynative_helper import args_type_check from mindspore.parallel._auto_parallel_context import _set_auto_parallel_context, _get_auto_parallel_context, \ _reset_auto_parallel_context -logger = logging.getLogger('Context') - __all__ = ['GRAPH_MODE', 'PYNATIVE_MODE', 'set_context', 'get_context', 'set_auto_parallel_context', 'get_auto_parallel_context', 'reset_auto_parallel_context'] From a73347db6a70429d45dbcf6c2889760dc95f8033 Mon Sep 17 00:00:00 2001 From: Yanjun Peng Date: Thu, 9 Apr 2020 11:04:13 +0800 Subject: [PATCH 166/227] fix dataset para validator check --- mindspore/dataset/engine/samplers.py | 1 - mindspore/dataset/engine/validators.py | 5 +++++ mindspore/dataset/transforms/vision/validators.py | 4 ++++ 3 files changed, 9 insertions(+), 1 deletion(-) diff --git a/mindspore/dataset/engine/samplers.py b/mindspore/dataset/engine/samplers.py index ed36e72b65..62a3dbed18 100644 --- a/mindspore/dataset/engine/samplers.py +++ b/mindspore/dataset/engine/samplers.py @@ -127,7 +127,6 @@ class RandomSampler(): Raises: ValueError: If replacement is not boolean. - ValueError: If num_samples is not None and replacement is false. ValueError: If num_samples is not positive. """ diff --git a/mindspore/dataset/engine/validators.py b/mindspore/dataset/engine/validators.py index 4c84cfe354..63d7c58270 100644 --- a/mindspore/dataset/engine/validators.py +++ b/mindspore/dataset/engine/validators.py @@ -556,6 +556,11 @@ def check_generatordataset(method): if column_names is None: raise ValueError("column_names is not provided.") + # check prefetch_size range + prefetch_size = param_dict.get('prefetch_size') + if prefetch_size is not None and (prefetch_size <= 0 or prefetch_size > 1024): + raise ValueError("prefetch_size exceeds the boundary.") + check_param_type(nreq_param_int, param_dict, int) check_param_type(nreq_param_list, param_dict, list) diff --git a/mindspore/dataset/transforms/vision/validators.py b/mindspore/dataset/transforms/vision/validators.py index caab120af4..ef4b879f8c 100644 --- a/mindspore/dataset/transforms/vision/validators.py +++ b/mindspore/dataset/transforms/vision/validators.py @@ -104,6 +104,10 @@ def check_padding(padding): raise ValueError("The size of the padding list or tuple should be 2 or 4.") else: raise TypeError("Padding can be any of: a number, a tuple or list of size 2 or 4.") + if not (isinstance(left, int) and isinstance(top, int) and isinstance(right, int) and isinstance(bottom, int)): + raise TypeError("Padding value should be integer.") + if left < 0 or top < 0 or right < 0 or bottom < 0: + raise ValueError("Padding value could not be negative.") return left, top, right, bottom From d87fc50e3542ba1ad039bc209ea0f760e68af37a Mon Sep 17 00:00:00 2001 From: Cathy Wong Date: Thu, 9 Apr 2020 15:22:33 -0400 Subject: [PATCH 167/227] Correct shuffle UT buffer_size > #dataset-row as valid --- .../data/dataset/golden/shuffle_05_result.npz | Bin 0 -> 1507 bytes tests/ut/python/dataset/test_shuffle.py | 39 +++++++++--------- 2 files changed, 20 insertions(+), 19 deletions(-) create mode 100644 tests/ut/data/dataset/golden/shuffle_05_result.npz diff --git a/tests/ut/data/dataset/golden/shuffle_05_result.npz b/tests/ut/data/dataset/golden/shuffle_05_result.npz new file mode 100644 index 0000000000000000000000000000000000000000..27eb0a470d370fab9d4938e5fb9e3eb50d603bd1 GIT binary patch literal 1507 zcmbW1OH&hB6vw+00!c(c4LUmWtAJtD)+OVZ!lkD_Ooy{uH(5UT@6!HZoH}d``mL*e)pVH>6?PTC89M~ul9TG z?dO$%rm1g8i)dNL$qdDd<(jT_vQs6hpjvN}i*u=EU3;$m94*+Dyc3-;qA%v7F(bNK za;i?Ym?=30Te)Ym8x@=Um9=cyro9-C8N(w3G2<8GyH~jG&lk5g%e8pEZTT|wXz+S zZA(6CbAC()p+SEwLklu2;9t_o`$FzYCXf0b zj85?;sAfj14xJo(1@>V-8T$kd;2;_M1@@qWjEF!B0%UXvv|~3J2Lyr$k#SHUf-W)+ z3ACb(jKcz9>>{IEz(6nhaDp~R7~!TI{mSkI>AIpj#sBTIS#Pf0IggLD*Yq+`gFj^hdG1Z>hvct&~|1=1^6CB15y z{nj4_{h??xX-$6dHm3F0=SJi2X5^Y>M*scfZmwI_c4W#j`#${nxS2L>nE?+lW0^hw zyfm7TS(izJLF*8M$GZ9bVc^%k1_d2?VJqKqtMG(CUo_8FL7cCg&Qz(qkTN zq$&7FZ=;oT0WG9=5GK6~Kj|U@r1zM0pIJ)@I=N2HWoA84Z=Ry9Fzq4J(oB2Av<%Z8 zGcC)s9MkelD{Q}##IPB;`X;DeXyDERm#Z%CI5&yUDWg3FJ>gp=mzNJ$Lv?_;ApHX_g H`G6I{m literal 0 HcmV?d00001 diff --git a/tests/ut/python/dataset/test_shuffle.py b/tests/ut/python/dataset/test_shuffle.py index 2b7a251d2c..4a823c5fb7 100644 --- a/tests/ut/python/dataset/test_shuffle.py +++ b/tests/ut/python/dataset/test_shuffle.py @@ -98,6 +98,25 @@ def test_shuffle_04(): save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) +def test_shuffle_05(): + """ + Test shuffle: buffer_size > number-of-rows-in-dataset + """ + logger.info("test_shuffle_05") + # define parameters + buffer_size = 13 + seed = 1 + parameters = {"params": {'buffer_size': buffer_size, "seed": seed}} + + # apply dataset operations + data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) + ds.config.set_seed(seed) + data1 = data1.shuffle(buffer_size=buffer_size) + + filename = "shuffle_05_result.npz" + save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) + + def test_shuffle_exception_01(): """ Test shuffle exception: buffer_size<0 @@ -152,24 +171,6 @@ def test_shuffle_exception_03(): assert "buffer_size" in str(e) -def test_shuffle_exception_04(): - """ - Test shuffle exception: buffer_size > number-of-rows-in-dataset - """ - logger.info("test_shuffle_exception_04") - - # apply dataset operations - data1 = ds.TFRecordDataset(DATA_DIR) - ds.config.set_seed(1) - try: - data1 = data1.shuffle(buffer_size=13) - sum([1 for _ in data1]) - - except BaseException as e: - logger.info("Got an exception in DE: {}".format(str(e))) - assert "buffer_size" in str(e) - - def test_shuffle_exception_05(): """ Test shuffle exception: Missing mandatory buffer_size input parameter @@ -229,10 +230,10 @@ if __name__ == '__main__': test_shuffle_02() test_shuffle_03() test_shuffle_04() + test_shuffle_05() test_shuffle_exception_01() test_shuffle_exception_02() test_shuffle_exception_03() - test_shuffle_exception_04() test_shuffle_exception_05() test_shuffle_exception_06() test_shuffle_exception_07() From cc1416bfc2e5d1a06e66da466c7bee14aa0d1e42 Mon Sep 17 00:00:00 2001 From: biffex Date: Thu, 9 Apr 2020 15:04:24 +0800 Subject: [PATCH 168/227] constant duplicate mul for momentum --- mindspore/ccsrc/optimizer/irpass.cc | 6 +-- .../optimizer/irpass/arithmetic_simplify.h | 54 ++++++++++++++++++- mindspore/ccsrc/utils/graph_utils.cc | 2 + mindspore/ops/operations/math_ops.py | 8 +++ tests/ut/cpp/optimizer/lib_test.cc | 13 +++++ .../gtest_input/optimizer/opt_test.py | 33 ++++++++++++ 6 files changed, 112 insertions(+), 4 deletions(-) diff --git a/mindspore/ccsrc/optimizer/irpass.cc b/mindspore/ccsrc/optimizer/irpass.cc index cdc960792f..0991c31b00 100644 --- a/mindspore/ccsrc/optimizer/irpass.cc +++ b/mindspore/ccsrc/optimizer/irpass.cc @@ -45,9 +45,9 @@ namespace mindspore { namespace opt { namespace irpass { OptimizeIRPassLib::OptimizeIRPassLib() { - arithmetic_simplify_ = MakeSubstitution( - ArithmeticSimplify(), "arithmetic_simplify", - {prim::kPrimScalarAdd, prim::kPrimScalarMul, prim::kPrimTensorAdd, prim::kPrimIdentity, prim::kPrimMomentum}); + arithmetic_simplify_ = MakeSubstitution(ArithmeticSimplify(), "arithmetic_simplify", + {prim::kPrimScalarAdd, prim::kPrimScalarMul, prim::kPrimTensorAdd, + prim::kPrimIdentity, prim::kPrimMomentum, prim::kPrimMul}); special_op_eliminate_ = MakeSubstitution(SpecialOpEliminater(), "special_op_eliminate", {prim::kPrimInsertGradientOf, prim::kPrimPrintShapeType, prim::kPrimGetRefKey, prim::kPrimMirror, prim::kPrimVirtualDiv}); diff --git a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h index 8c5610ed1b..ab191aab20 100644 --- a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h +++ b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h @@ -179,6 +179,55 @@ class OptUpdateZeroTensor : public AnfVisitor { } }; +// {prim::kPrimMul, Tensor1, {orim::kPrimMul, Tensor2, {...}}} -> +// {prim::kPrimMul, {...}, {prim::kPrimMul, Tensor1, Tensor2}} +class ConstantDuplicateMul : public AnfVisitor { + public: + AnfNodePtr operator()(const OptimizerPtr &, const AnfNodePtr &node) override { + Reset(); + // {prim::kPrimMul, Tensor1, {...}} + AnfVisitor::Match(prim::kPrimMul, {IsNode, IsNode})(node); + if (vnode_ == nullptr || cnode_ == nullptr) { + return nullptr; + } + auto tensor1 = vnode_; + auto mul = cnode_; + + Reset(); + // {prim::kPrimMul, Tensor2, {...}} + AnfVisitor::Match(prim::kPrimMul, {IsNode, IsNode})(mul); + if (vnode_ == nullptr || cnode_ == nullptr) { + return nullptr; + } + auto tensor2 = vnode_; + auto cnode = cnode_; + + auto PrimMul = GetValueNode(mul->input(0)); + auto fg = node->func_graph(); + auto ttmul = NewCNode({NewValueNode(PrimMul), tensor1, tensor2}, fg); + return NewCNode({NewValueNode(PrimMul), cnode, ttmul}, fg); + } + + void Visit(const AnfNodePtr &node) override { + if (IsValueNode(node)) { + vnode_ = node; + } + + if (IsCNode(node)) { + cnode_ = node->cast(); + } + } + + void Reset() { + vnode_ = nullptr; + cnode_ = nullptr; + } + + private: + AnfNodePtr vnode_; + CNodePtr cnode_; +}; + class ArithmeticSimplify { public: ArithmeticSimplify() @@ -186,12 +235,14 @@ class ArithmeticSimplify { add_by_zero_(), tensor_add_by_zero_(), identity_(prim::kPrimIdentity), - opt_update_zero_tensor_() { + opt_update_zero_tensor_(), + constant_duplicate_mul_() { eliminaters_.emplace_back(multiply_by_zero_or_one_); eliminaters_.emplace_back(add_by_zero_); eliminaters_.emplace_back(tensor_add_by_zero_); eliminaters_.emplace_back(identity_); eliminaters_.emplace_back(opt_update_zero_tensor_); + eliminaters_.emplace_back(constant_duplicate_mul_); } ~ArithmeticSimplify() = default; @@ -212,6 +263,7 @@ class ArithmeticSimplify { TensorAddByZero tensor_add_by_zero_; PrimEliminater identity_; OptUpdateZeroTensor opt_update_zero_tensor_; + ConstantDuplicateMul constant_duplicate_mul_; std::vector eliminaters_{}; }; } // namespace irpass diff --git a/mindspore/ccsrc/utils/graph_utils.cc b/mindspore/ccsrc/utils/graph_utils.cc index 938df2c291..55ef8dc3d5 100644 --- a/mindspore/ccsrc/utils/graph_utils.cc +++ b/mindspore/ccsrc/utils/graph_utils.cc @@ -400,6 +400,8 @@ static bool SameNodeShallow(const AnfNodePtr& node1, const AnfNodePtr& node2, Fu auto a2 = GetValueNode(node2); if (a1->isa() && a2->isa()) { return a1->cast()->name() == a2->cast()->name(); + } else if (a1->isa() && a2->isa()) { + return a1->cast()->ValueEqual(*(a2->cast())); } else { return *a1 == *a2; } diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 1294a65d02..106886c45c 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -774,6 +774,14 @@ class Mul(_MathBinaryOp): >>> mul(input_x, input_y) [4, 10, 18] """ + def infer_value(self, x, y): + if x is not None and y is not None: + x = x.asnumpy() + y = y.asnumpy() + out = x * y + out = np.array(out, x.dtype) + return Tensor(out) + return None class Square(PrimitiveWithInfer): diff --git a/tests/ut/cpp/optimizer/lib_test.cc b/tests/ut/cpp/optimizer/lib_test.cc index ff3c00d37a..2d4cf0e78e 100644 --- a/tests/ut/cpp/optimizer/lib_test.cc +++ b/tests/ut/cpp/optimizer/lib_test.cc @@ -543,5 +543,18 @@ TEST_F(TestOptLib, test_print_tuple_wrapper) { ASSERT_TRUE(CheckOpt(before2, after2, patterns)); ASSERT_TRUE(CheckOpt(before3, before3, patterns)); } + +TEST_F(TestOptLib, test_constant_duplicate_mul) { + FuncGraphPtr beforell = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "beforell"); + FuncGraphPtr beforelr = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "beforelr"); + FuncGraphPtr beforerl = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "beforerl"); + FuncGraphPtr beforerr = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "beforerr"); + FuncGraphPtr after = getPyFun.CallAndParseRet("test_constant_duplicate_mul", "after"); + auto patterns = std::vector({irpass.arithmetic_simplify_}); + ASSERT_TRUE(CheckOpt(beforell, after, patterns)); + ASSERT_TRUE(CheckOpt(beforelr, after, patterns)); + ASSERT_TRUE(CheckOpt(beforerl, after, patterns)); + ASSERT_TRUE(CheckOpt(beforerr, after, patterns)); +} } // namespace opt } // namespace mindspore diff --git a/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py b/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py index 53eb2130f0..d494ad27d3 100644 --- a/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py +++ b/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py @@ -16,6 +16,8 @@ from mindspore.ops import Primitive, PrimitiveWithInfer from mindspore.ops import operations as P from mindspore.ops.operations import _grad_ops as G +from mindspore import Tensor +import numpy as np # pylint: disable=unused-variable @@ -903,3 +905,34 @@ def test_print_tuple_wrapper(tag): return print_(make_tuple(x, y, z)) return fns[tag] + +def test_constant_duplicate_mul(tag): + fns = FnDict() + Mul = Primitive('Mul'); + Sqrt = Primitive('Sqrt'); + + x = Tensor(np.array([[2, 2], [2, 3]]).astype('float32')) + tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32')) + tensor2 = Tensor(np.array([[2.2, 3.1], [3.2, 4.2]]).astype('float32')) + + @fns + def beforell(): + return Mul(tensor1, Mul(tensor2, Sqrt(x))) + + @fns + def beforelr(): + return Mul(tensor1, Mul(Sqrt(x), tensor2)) + + @fns + def beforerl(): + return Mul(Mul(Sqrt(x), tensor2), tensor1) + + @fns + def beforerr(): + return Mul(Mul(Sqrt(x), tensor2), tensor1) + + @fns + def after(): + return Mul(Sqrt(x), Mul(tensor1, tensor2)) + + return fns[tag] From 1c7d0c0b39c34d851d93132facddaa34e4b37fab Mon Sep 17 00:00:00 2001 From: leonwanghui Date: Thu, 9 Apr 2020 15:58:36 +0800 Subject: [PATCH 169/227] Update setuptool info Signed-off-by: leonwanghui --- CONTRIBUTING.md | 4 +- README.md | 2 +- RELEASE.md | 2 +- package.sh | 2 +- setup_package.py => setup.py | 129 +++++++++++++++++++++++------------ 5 files changed, 90 insertions(+), 49 deletions(-) rename setup_package.py => setup.py (53%) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 85fee704c2..105c620942 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -105,11 +105,11 @@ When reporting issues, refer to this format: * If it is a new feature that needs lots of design details, a design proposal should also be submitted. * After reaching consensus in the issue discussions and design proposal reviews, complete the development on the forked repo and submit a PR. * None of PRs is not permitted until it receives **2+ LGTM** from approvers. Please NOTICE that approver is NOT allowed to add *LGTM* on his own PR. -* After PR is sufficiently discussed, it will get merged, abondoned or rejected depending on the outcome of the discussion. +* After PR is sufficiently discussed, it will get merged, abandoned or rejected depending on the outcome of the discussion. **PRs advisory:** - Any irrelevant changes should be avoided. - Make sure your commit history being ordered. - Always keep your branch up with the master branch. -- For bug-fix PRs, make sure all related issues being linked. +- For bug-fix PRs, make sure all related issues being linked. diff --git a/README.md b/README.md index 925c22591d..be8ca5189a 100644 --- a/README.md +++ b/README.md @@ -129,7 +129,7 @@ Check out how MindSpore Open Governance [works](https://gitee.com/mindspore/comm - [MindSpore Slack](https://join.slack.com/t/mindspore/shared_invite/enQtOTcwMTIxMDI3NjM0LTNkMWM2MzI5NjIyZWU5ZWQ5M2EwMTQ5MWNiYzMxOGM4OWFhZjI4M2E5OGI2YTg3ODU1ODE2Njg1MThiNWI3YmQ) - Communication platform for developers. - IRC channel at `#mindspore` (only for meeting minutes logging purpose) - Video Conferencing: meet.jit.si -- Mailing-list: https://mailweb.mindspore.cn/postorius/lists +- Mailing-list: https://mailweb.mindspore.cn/postorius/lists ## Contributing diff --git a/RELEASE.md b/RELEASE.md index 8920095bb5..ce9064e4b1 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -70,4 +70,4 @@ * [MindSpore Official Website] (https://www.mindspore.cn/) * [MindInsight Visualization Debugging and Optimization] (https://gitee.com/mindspore/mindinsight) * [MindArmour Model Security Hardening Package] (https://gitee.com/mindspore/mindarmour) -* [GraphEngine Computational Graph Engine] (https://gitee.com/mindspore/graphengine) \ No newline at end of file +* [GraphEngine Computational Graph Engine] (https://gitee.com/mindspore/graphengine) diff --git a/package.sh b/package.sh index 67f4761f37..0c75a1bbfd 100755 --- a/package.sh +++ b/package.sh @@ -110,7 +110,7 @@ else export MS_PACKAGE_NAME="mindspore" fi -${PYTHON} "${BASEPATH}/setup_package.py" bdist_wheel +${PYTHON} "${BASEPATH}/setup.py" bdist_wheel chmod -R 700 ${PACKAGE_PATH}/mindspore/ chmod -R 700 ${PACKAGE_PATH}/${MS_PACKAGE_NAME//-/_}.egg-info/ diff --git a/setup_package.py b/setup.py similarity index 53% rename from setup_package.py rename to setup.py index 87b5718de2..e009a9b312 100644 --- a/setup_package.py +++ b/setup.py @@ -14,17 +14,15 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ -"""setup_package.""" +"""setup package.""" import os import stat + from setuptools import setup, find_packages from setuptools.command.egg_info import egg_info from setuptools.command.build_py import build_py version = '0.1.0' -author = 'The MindSpore Authors' -author_email = 'contact@mindspore.cn' -home_page = 'https://www.mindspore.cn' backend_policy = os.getenv('BACKEND_POLICY') commit_id = os.getenv('COMMIT_ID').replace("\n", "") @@ -33,56 +31,70 @@ package_name = os.getenv('MS_PACKAGE_NAME').replace("\n", "") pwd = os.path.dirname(os.path.realpath(__file__)) pkg_dir = os.path.join(pwd, 'build/package') -def write_version(file): + +def _read_file(filename): + with open(os.path.join(pwd, filename)) as f: + return f.read() + + +readme = _read_file('README.md') +release = _read_file('RELEASE.md') + + +def _write_version(file): file.write("__version__ = '{}'\n".format(version)) -def write_config(file): + +def _write_config(file): file.write("__backend__ = '{}'\n".format(backend_policy)) -def write_commit_file(file): + +def _write_commit_file(file): file.write("__commit_id__ = '{}'\n".format(commit_id)) -def build_depends(): + +def build_dependencies(): """generate python file""" - version_file = os.path.join(pwd, 'build/package/mindspore', 'version.py') + version_file = os.path.join(pkg_dir, 'mindspore', 'version.py') with open(version_file, 'w') as f: - write_version(f) + _write_version(f) - version_file = os.path.join(pwd, 'mindspore/', 'version.py') + version_file = os.path.join(pwd, 'mindspore', 'version.py') with open(version_file, 'w') as f: - write_version(f) + _write_version(f) - config_file = os.path.join(pwd, 'build/package/mindspore', 'default_config.py') + config_file = os.path.join(pkg_dir, 'mindspore', 'default_config.py') with open(config_file, 'w') as f: - write_config(f) + _write_config(f) - config_file = os.path.join(pwd, 'mindspore/', 'default_config.py') + config_file = os.path.join(pwd, 'mindspore', 'default_config.py') with open(config_file, 'w') as f: - write_config(f) + _write_config(f) - commit_file = os.path.join(pwd, 'build/package/mindspore', '.commit_id') + commit_file = os.path.join(pkg_dir, 'mindspore', '.commit_id') with open(commit_file, 'w') as f: - write_commit_file(f) + _write_commit_file(f) - commit_file = os.path.join(pwd, 'mindspore/', '.commit_id') + commit_file = os.path.join(pwd, 'mindspore', '.commit_id') with open(commit_file, 'w') as f: - write_commit_file(f) - -descriptions = 'An AI computing framework that supports development for AI applications in all scenarios.' - -requires = [ - 'numpy >= 1.17.0', - 'protobuf >= 3.8.0', - 'asttokens >= 1.1.13', - 'pillow >= 6.2.0', - 'scipy == 1.3.3', - 'easydict >= 1.9', - 'sympy >= 1.4', - 'cffi >= 1.13.2', - 'decorator >= 4.4.0' - ], + _write_commit_file(f) + -package_datas = { +build_dependencies() + +required_package = [ + 'numpy >= 1.17.0', + 'protobuf >= 3.8.0', + 'asttokens >= 1.1.13', + 'pillow >= 6.2.0', + 'scipy == 1.3.3', + 'easydict >= 1.9', + 'sympy >= 1.4', + 'cffi >= 1.13.2', + 'decorator >= 4.4.0' +] + +package_data = { '': [ '*.so*', 'lib/*.so*', @@ -91,7 +103,6 @@ package_datas = { ] } -build_depends() def update_permissions(path): """ @@ -103,20 +114,25 @@ def update_permissions(path): for dirpath, dirnames, filenames in os.walk(path): for dirname in dirnames: dir_fullpath = os.path.join(dirpath, dirname) - os.chmod(dir_fullpath, stat.S_IREAD | stat.S_IWRITE | stat.S_IEXEC | stat.S_IRGRP | stat.S_IXGRP) + os.chmod(dir_fullpath, stat.S_IREAD | stat.S_IWRITE | + stat.S_IEXEC | stat.S_IRGRP | stat.S_IXGRP) for filename in filenames: file_fullpath = os.path.join(dirpath, filename) os.chmod(file_fullpath, stat.S_IREAD) + class EggInfo(egg_info): """Egg info.""" + def run(self): super().run() egg_info_dir = os.path.join(pkg_dir, 'mindspore.egg-info') update_permissions(egg_info_dir) + class BuildPy(build_py): """BuildPy.""" + def run(self): super().run() mindspore_dir = os.path.join(pkg_dir, 'build', 'lib', 'mindspore') @@ -124,21 +140,46 @@ class BuildPy(build_py): mindspore_dir = os.path.join(pkg_dir, 'build', 'lib', 'akg') update_permissions(mindspore_dir) + setup( - python_requires='>=3.7', name=package_name, version=version, - author=author, - author_email=author_email, - url=home_page, + author='The MindSpore Authors', + author_email='contact@mindspore.cn', + url='https://www.mindspore.cn', + download_url='https://gitee.com/mindspore/mindspore/tags', + project_urls={ + 'Sources': 'https://gitee.com/mindspore/mindspore', + 'Issue Tracker': 'https://gitee.com/mindspore/mindspore/issues', + }, + description='MindSpore is a new open source deep learning training/inference ' + 'framework that could be used for mobile, edge and cloud scenarios.', + long_description="\n\n".join([readme, release]), packages=find_packages(), - package_data=package_datas, + package_data=package_data, include_package_data=True, cmdclass={ 'egg_info': EggInfo, 'build_py': BuildPy, }, - install_requires=requires, - description=descriptions, + python_requires='>=3.7', + install_requires=required_package, + classifiers=[ + 'Development Status :: 4 - Beta', + 'Environment :: Console', + 'Intended Audience :: Science/Research', + 'Intended Audience :: Developers', + 'License :: OSI Approved :: Apache Software License', + 'Programming Language :: Python :: 3 :: Only', + 'Programming Language :: Python :: 3.7', + 'Programming Language :: Python :: 3.8', + 'Programming Language :: C++', + 'Topic :: Scientific/Engineering', + 'Topic :: Scientific/Engineering :: Artificial Intelligence', + 'Topic :: Software Development', + 'Topic :: Software Development :: Libraries', + 'Topic :: Software Development :: Libraries :: Python Modules', + ], license='Apache 2.0', + keywords='mindspore machine learning', ) From 406475160f0d604e1021e42da2c22a5330128d5b Mon Sep 17 00:00:00 2001 From: c00425699 Date: Thu, 9 Apr 2020 14:40:43 +0800 Subject: [PATCH 170/227] refactor OperatorCostPtr in OperatorInfo --- .../auto_parallel/operator_costmodel.cc | 54 ------------------- .../auto_parallel/operator_costmodel.h | 30 ++--------- .../ccsrc/parallel/ops_info/activation_info.h | 18 ++----- .../ccsrc/parallel/ops_info/arithmetic_info.h | 6 +-- .../parallel/ops_info/batch_parallel_info.h | 6 +-- .../ccsrc/parallel/ops_info/bias_add_info.h | 6 +-- .../parallel/ops_info/dropout_do_mask_info.h | 8 +-- .../ccsrc/parallel/ops_info/generator_info.h | 6 +-- .../ccsrc/parallel/ops_info/get_next_info.h | 6 +-- .../parallel/ops_info/l2_normalize_info.h | 6 +-- mindspore/ccsrc/parallel/ops_info/loss_info.h | 6 +-- .../ccsrc/parallel/ops_info/matmul_info.cc | 6 +-- .../ccsrc/parallel/ops_info/matmul_info.h | 7 +-- .../ccsrc/parallel/ops_info/onehot_info.h | 6 +-- .../ccsrc/parallel/ops_info/operator_info.cc | 13 +++-- .../ccsrc/parallel/ops_info/operator_info.h | 13 +++-- .../ccsrc/parallel/ops_info/prelu_info.h | 6 +-- .../parallel/ops_info/reduce_method_info.cc | 8 ++- .../parallel/ops_info/reduce_method_info.h | 8 +-- .../ccsrc/parallel/ops_info/reshape_info.h | 8 +-- .../parallel/ops_info/tmp_identity_info.h | 8 +-- .../ccsrc/parallel/ops_info/transpose_info.h | 6 +-- .../parallel/ops_info/virtual_dataset_info.h | 8 +-- .../cpp/parallel/ops_info/activation_test.cc | 8 +-- .../cpp/parallel/ops_info/matmul_info_test.cc | 4 +- .../parallel/ops_info/tensor_add_info_test.cc | 8 +-- .../cpp/parallel/ops_info/tmpidentity_test.cc | 4 +- 27 files changed, 62 insertions(+), 211 deletions(-) diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc index 7c17b499b1..93d7dc56c5 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc +++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc @@ -514,60 +514,6 @@ double ArithmeticCost::GetBackwardCommCost(const std::vector& inputs return result; } -double L2NormalizeCost::GetBackwardCommCost(const std::vector& inputs, const std::vector&, - const int32_t& stage_id) const { - double result = 0.0; - if (is_parameter_[0]) { - TensorInfo input_tensor_info = inputs[0]; - CheckGlobalDeviceManager(); - MS_EXCEPTION_IF_NULL(g_device_manager); - auto total_device_num = g_device_manager->GetDeviceListByStageId(stage_id).size(); - - Shape input_shape = input_tensor_info.shape(); - Shape input_slice_shape = input_tensor_info.slice_shape(); - int32_t used_device_num = 1; - for (size_t i = 0; i < input_shape.size(); ++i) { - used_device_num *= input_shape[i] / input_slice_shape[i]; - } - - if (total_device_num != IntToSize(used_device_num)) - result += ListProduct(input_slice_shape) * static_cast(inputs_type_lengths_[0]); - } - - return result; -} - -double L2NormalizeCost::GetForwardComputationCost(const std::vector& inputs, const std::vector&, - const int32_t&) const { - TensorInfo input0_info = inputs[0]; - Shape input0_slice_shape = input0_info.slice_shape(); - return ListProduct(input0_slice_shape) * static_cast(inputs_type_lengths_[0]); -} - -double L2NormalizeCost::GetBackwardComputationCost(const std::vector& inputs, - const std::vector&, const int32_t& stage_id) const { - double result = 0.0; - - if (is_parameter_[0]) { - TensorInfo input_tensor_info = inputs[0]; - CheckGlobalDeviceManager(); - MS_EXCEPTION_IF_NULL(g_device_manager); - auto total_device_num = g_device_manager->GetDeviceListByStageId(stage_id).size(); - - Shape input_shape = input_tensor_info.shape(); - Shape input_slice_shape = input_tensor_info.slice_shape(); - int32_t used_device_num = 1; - for (size_t i = 0; i < input_shape.size(); ++i) { - used_device_num *= input_shape[i] / input_slice_shape[i]; - } - - if (total_device_num != IntToSize(used_device_num)) - result += ListProduct(input_slice_shape) * static_cast(inputs_type_lengths_[0]); - } - - return result; -} - bool IsDataParallel(const Shape& shape, const Shape& slice_shape, const int32_t& stage_id) { CheckGlobalDeviceManager(); MS_EXCEPTION_IF_NULL(g_device_manager); diff --git a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h index 8f0099bba3..73f3ff139f 100644 --- a/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h +++ b/mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h @@ -132,6 +132,8 @@ class ActivationCost : public OperatorCost { }; using ActivationCostPtr = std::shared_ptr; +using TransposeCost = ActivationCost; +using TransposeCostPtr = std::shared_ptr; class SoftmaxCost : public OperatorCost { public: @@ -415,32 +417,8 @@ class ArithmeticCost : public OperatorCost { const int32_t& stage_id) const override; }; using ArithmeticCostPtr = std::shared_ptr; - -class L2NormalizeCost : public OperatorCost { - public: - L2NormalizeCost() = default; - ~L2NormalizeCost() override = default; - - double GetCommCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id); - } - double GetForwardCommCost(const std::vector&, const std::vector&, - const int32_t&) const override { - return 0.0; - } - double GetBackwardCommCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; - double GetComputationCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override { - return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id); - } - double GetForwardComputationCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; - double GetBackwardComputationCost(const std::vector& inputs, const std::vector& outputs, - const int32_t& stage_id) const override; -}; -using L2NormalizeCostPtr = std::shared_ptr; +using BiasAddCost = ArithmeticCost; +using BiasAddCostPtr = std::shared_ptr; class ReduceMethodCost : public OperatorCost { public: diff --git a/mindspore/ccsrc/parallel/ops_info/activation_info.h b/mindspore/ccsrc/parallel/ops_info/activation_info.h index 183b593e23..21774c43ee 100644 --- a/mindspore/ccsrc/parallel/ops_info/activation_info.h +++ b/mindspore/ccsrc/parallel/ops_info/activation_info.h @@ -32,8 +32,8 @@ namespace parallel { class ActivationBase : public OperatorInfo { public: ActivationBase(const std::string& operator_name, const Shapes& inputs_shape, const Shapes& outputs_shape, - const PrimitiveAttrs& attrs) - : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs) {} + const PrimitiveAttrs& attrs, OperatorCostPtr cost) + : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, cost) {} ~ActivationBase() override = default; Status Init(const StrategyPtr& strategy) override; @@ -51,19 +51,13 @@ class Activation : public ActivationBase { public: Activation(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : ActivationBase(name, inputs_shape, outputs_shape, attrs) { - ac_cost_ptr_ = std::make_shared(); - } + : ActivationBase(name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~Activation() override = default; Status GenerateStrategies(int32_t stage_id) override; Status SetCostUnderStrategy(const StrategyPtr& strategy) override; - OperatorCostPtr GetOperatorCost() const override { return ac_cost_ptr_; } protected: Status CheckStrategy(const StrategyPtr& strategy) override; - - private: - ActivationCostPtr ac_cost_ptr_; }; class ActivationInfo : public Activation { @@ -108,13 +102,10 @@ class Softmax : public ActivationBase { public: explicit Softmax(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : ActivationBase(name, inputs_shape, outputs_shape, attrs) { - sm_cost_ptr_ = std::make_shared(); - } + : ActivationBase(name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~Softmax() override = default; Status GenerateStrategies(int32_t stage_id) override; Status SetCostUnderStrategy(const StrategyPtr& strategy) override; - OperatorCostPtr GetOperatorCost() const override { return sm_cost_ptr_; } protected: Status CheckStrategy(const StrategyPtr& strategy) override; @@ -122,7 +113,6 @@ class Softmax : public ActivationBase { private: std::vector axis_; - SoftmaxCostPtr sm_cost_ptr_; }; class SoftmaxInfo : public Softmax { diff --git a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h index 7cd0d66b1b..daa2ad595c 100644 --- a/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h +++ b/mindspore/ccsrc/parallel/ops_info/arithmetic_info.h @@ -33,15 +33,12 @@ class ArithmeticBase : public OperatorInfo { public: ArithmeticBase(const std::string& operator_name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs) { - arithmeticcost_ptr_ = std::make_shared(); - } + : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~ArithmeticBase() override = default; Status Init(const StrategyPtr& strategy) override; Status InitForCostModel(const StrategyPtr& strategy) override; Status GenerateStrategies(int32_t) override; Status SetCostUnderStrategy(const StrategyPtr&) override; - OperatorCostPtr GetOperatorCost() const override { return arithmeticcost_ptr_; } void ReComputeBatchSplitFlagList() override; protected: @@ -54,7 +51,6 @@ class ArithmeticBase : public OperatorInfo { Status InferTensorMap() override; Status InferTensorLayout(TensorLayouts* inputs_layout, TensorLayouts* outputs_layout, const Shape& dev_matrix_array); Shapes InferExpendShape(); - ArithmeticCostPtr arithmeticcost_ptr_; }; class SubInfo : public ArithmeticBase { diff --git a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h index 57711b5298..fae96dcab5 100644 --- a/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h +++ b/mindspore/ccsrc/parallel/ops_info/batch_parallel_info.h @@ -31,16 +31,13 @@ class BatchParallelInfo : public OperatorInfo { public: BatchParallelInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : OperatorInfo(name, inputs_shape, outputs_shape, attrs), dev_num_(1) { - bp_cost_ptr_ = std::make_shared(); - } + : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared()), dev_num_(1) {} ~BatchParallelInfo() override = default; Status Init(const StrategyPtr& strategy) override; Status InitForCostModel(const StrategyPtr& strategy) override; Status GenerateStrategies(int32_t stage_id) override; Status SetCostUnderStrategy(const StrategyPtr& strategy) override; - OperatorCostPtr GetOperatorCost() const override { return bp_cost_ptr_; } protected: Status CheckStrategy(const StrategyPtr& strategy) override; @@ -55,7 +52,6 @@ class BatchParallelInfo : public OperatorInfo { private: int32_t dev_num_; - BatchParallelCostPtr bp_cost_ptr_; }; class SparseSoftmaxCrossEntropyWithLogitsInfo : public BatchParallelInfo { diff --git a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h index 07f0bc00ff..dea5c90c88 100644 --- a/mindspore/ccsrc/parallel/ops_info/bias_add_info.h +++ b/mindspore/ccsrc/parallel/ops_info/bias_add_info.h @@ -34,16 +34,13 @@ class BiasAddInfo : public OperatorInfo { public: BiasAddInfo(const std::string& operator_name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs) { - biasaddcost_ptr_ = std::make_shared(); - } + : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~BiasAddInfo() override = default; Status Init(const StrategyPtr& strategy) override; Status InitForCostModel(const StrategyPtr& strategy) override; Status GenerateStrategies(int32_t) override; Status SetCostUnderStrategy(const StrategyPtr&) override; - OperatorCostPtr GetOperatorCost() const override { return biasaddcost_ptr_; } void ReComputeBatchSplitFlagList() override; protected: @@ -55,7 +52,6 @@ class BiasAddInfo : public OperatorInfo { Status InferDevMatrixShape() override; Status InferTensorMap() override; Status InferTensorLayout(TensorLayouts* inputs_layout, TensorLayouts* outputs_layout, const Shape& dev_matrix_array); - ArithmeticCostPtr biasaddcost_ptr_; }; } // namespace parallel } // namespace mindspore diff --git a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h index e43601355a..859b3e06a4 100644 --- a/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h +++ b/mindspore/ccsrc/parallel/ops_info/dropout_do_mask_info.h @@ -33,15 +33,12 @@ class DropoutDoMaskInfo : public OperatorInfo { public: DropoutDoMaskInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : OperatorInfo(name, inputs_shape, outputs_shape, attrs) { - bpcost_ptr_ = std::make_shared(); - } + : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~DropoutDoMaskInfo() override = default; Status Init(const StrategyPtr& strategy) override; Status GenerateStrategies(int32_t stage_id) override; Status SetCostUnderStrategy(const StrategyPtr& strategy) override; - OperatorCostPtr GetOperatorCost() const override { return bpcost_ptr_; } Status InitForCostModel(const StrategyPtr& strategy) override; std::shared_ptr>> GenerateBatchStrategies() override; @@ -53,9 +50,6 @@ class DropoutDoMaskInfo : public OperatorInfo { Status GetAttrs() override { return SUCCESS; } Status InferTensorInfo() override; Status InferDevMatrixShape() override; - - private: - BatchParallelCostPtr bpcost_ptr_; }; } // namespace parallel } // namespace mindspore diff --git a/mindspore/ccsrc/parallel/ops_info/generator_info.h b/mindspore/ccsrc/parallel/ops_info/generator_info.h index a280fac28e..68024593f3 100644 --- a/mindspore/ccsrc/parallel/ops_info/generator_info.h +++ b/mindspore/ccsrc/parallel/ops_info/generator_info.h @@ -32,15 +32,12 @@ class GeneratorBase : public OperatorInfo { public: GeneratorBase(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs) - : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs) { - generatorbasecost_ptr_ = std::make_shared(); - } + : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~GeneratorBase() override = default; Status Init(const StrategyPtr &strategy) override; Status SetCostUnderStrategy(const StrategyPtr &strategy) override; - OperatorCostPtr GetOperatorCost() const override { return generatorbasecost_ptr_; } Status InitForCostModel(const StrategyPtr &strategy) override; protected: @@ -52,7 +49,6 @@ class GeneratorBase : public OperatorInfo { Status InferMirrorOps() override { return SUCCESS; } Status InferForwardCommunication() override { return SUCCESS; } virtual Status InferReplaceOps(const StrategyPtr &strategy) = 0; - GeneratorBaseCostPtr generatorbasecost_ptr_; }; class DropoutGenMaskInfo : public GeneratorBase { diff --git a/mindspore/ccsrc/parallel/ops_info/get_next_info.h b/mindspore/ccsrc/parallel/ops_info/get_next_info.h index 32adce1165..9a65eff035 100644 --- a/mindspore/ccsrc/parallel/ops_info/get_next_info.h +++ b/mindspore/ccsrc/parallel/ops_info/get_next_info.h @@ -32,14 +32,11 @@ class GetNextInfo : public OperatorInfo { public: GetNextInfo(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs) - : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs) { - getnextcost_ptr_ = std::make_shared(); - } + : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~GetNextInfo() override = default; Status Init(const StrategyPtr &strategy) override; Status SetCostUnderStrategy(const StrategyPtr &strategy) override; - OperatorCostPtr GetOperatorCost() const override { return getnextcost_ptr_; } Status InitForCostModel(const StrategyPtr &strategy) override; Status GenerateStrategies(int32_t stage_id) override; @@ -65,7 +62,6 @@ class GetNextInfo : public OperatorInfo { Shapes shapes_; int32_t output_num_ = 0; std::string shared_name_; - GetNextCostPtr getnextcost_ptr_; }; } // namespace parallel } // namespace mindspore diff --git a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h index c0af9dbcb9..22ed5a965b 100644 --- a/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h +++ b/mindspore/ccsrc/parallel/ops_info/l2_normalize_info.h @@ -33,12 +33,9 @@ class L2NormalizeInfo : public Activation { public: L2NormalizeInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : Activation(name, inputs_shape, outputs_shape, attrs) { - l2normalizecost_ptr_ = std::make_shared(); - } + : Activation(name, inputs_shape, outputs_shape, attrs) {} ~L2NormalizeInfo() override = default; Status GenerateStrategies(int32_t stage_id) override; - OperatorCostPtr GetOperatorCost() const override { return l2normalizecost_ptr_; } protected: Status GetAttrs() override; @@ -47,7 +44,6 @@ class L2NormalizeInfo : public Activation { private: int32_t axis_ = 0; // Default value = 0 - L2NormalizeCostPtr l2normalizecost_ptr_; }; } // namespace parallel } // namespace mindspore diff --git a/mindspore/ccsrc/parallel/ops_info/loss_info.h b/mindspore/ccsrc/parallel/ops_info/loss_info.h index 6a9697a447..f1c2537a39 100644 --- a/mindspore/ccsrc/parallel/ops_info/loss_info.h +++ b/mindspore/ccsrc/parallel/ops_info/loss_info.h @@ -36,16 +36,13 @@ class SoftmaxCrossEntropyWithLogitsInfo : public OperatorInfo { public: SoftmaxCrossEntropyWithLogitsInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : OperatorInfo(name, inputs_shape, outputs_shape, attrs) { - softmax_loss_cost_ptr_ = std::make_shared(); - } + : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~SoftmaxCrossEntropyWithLogitsInfo() override = default; Status Init(const StrategyPtr& strategy) override; Status InitForCostModel(const StrategyPtr& strategy) override; Status GenerateStrategies(int32_t stage_id) override; Status SetCostUnderStrategy(const StrategyPtr& strategy) override; - OperatorCostPtr GetOperatorCost() const override { return softmax_loss_cost_ptr_; } void ReComputeBatchSplitFlagList() override; protected: @@ -59,7 +56,6 @@ class SoftmaxCrossEntropyWithLogitsInfo : public OperatorInfo { // There are two outputs for SoftmaxCrossEntropyWithLogits, and outputs[1] is used for grad and overload // the InferAsLossDivisor. Status InferAsLossDivisor() override; - SoftmaxCrossEntropyWithLogitsCostPtr softmax_loss_cost_ptr_; private: int32_t axis_ = -1; // default -1 diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc index 2b02dc100d..848116d68a 100644 --- a/mindspore/ccsrc/parallel/ops_info/matmul_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.cc @@ -593,11 +593,11 @@ Status MatMulBase::SetCostUnderStrategy(const mindspore::parallel::StrategyPtr& // Here, we use the origin outputs_, because we only use the slice size of the output tensor. // It does not matter whether the output tensor is transposed or not. double computation_cost = - matmulcost_ptr->GetForwardComputationCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id); - double communication_cost = matmulcost_ptr->GetCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id); + cost()->GetForwardComputationCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id); + double communication_cost = cost()->GetCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id); std::shared_ptr result = std::make_shared(computation_cost, communication_cost); result->communication_without_parameter_ = - matmulcost_ptr->GetForwardCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id); + cost()->GetForwardCommCost(relica_inputs_tensor_vector, outputs_tensor_info_, stage_id); result->communication_with_partial_para_ = result->communication_without_parameter_ + COST_MODEL_GAMMA * (communication_cost - result->communication_without_parameter_); diff --git a/mindspore/ccsrc/parallel/ops_info/matmul_info.h b/mindspore/ccsrc/parallel/ops_info/matmul_info.h index 7ced12b14a..2d3312774d 100644 --- a/mindspore/ccsrc/parallel/ops_info/matmul_info.h +++ b/mindspore/ccsrc/parallel/ops_info/matmul_info.h @@ -34,9 +34,7 @@ class MatMulBase : public OperatorInfo { public: MatMulBase(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : OperatorInfo(name, inputs_shape, outputs_shape, attrs) { - matmulcost_ptr = std::make_shared(); - } + : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~MatMulBase() override = default; Status Init(const StrategyPtr& strategy) override; @@ -48,7 +46,6 @@ class MatMulBase : public OperatorInfo { Status PrepareStrategy(int32_t stage_id, size_t dev_num, Dimensions combined_partitions, size_t input0_shape_size, size_t input1_shape_size, StrategyPtr* sp); - OperatorCostPtr GetOperatorCost() const override { return matmulcost_ptr; } Status SwapLastTwoElements(Shape* shape); protected: @@ -66,8 +63,6 @@ class MatMulBase : public OperatorInfo { bool transpose_b_ = false; size_t mat_a_dimension_ = 0; size_t mat_b_dimension_ = 0; - - MatMulCostPtr matmulcost_ptr; }; class MatMul : public MatMulBase { diff --git a/mindspore/ccsrc/parallel/ops_info/onehot_info.h b/mindspore/ccsrc/parallel/ops_info/onehot_info.h index 4697e201a4..a54d8479b3 100644 --- a/mindspore/ccsrc/parallel/ops_info/onehot_info.h +++ b/mindspore/ccsrc/parallel/ops_info/onehot_info.h @@ -33,16 +33,13 @@ class OneHotInfo : public OperatorInfo { public: OneHotInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : OperatorInfo(name, inputs_shape, outputs_shape, attrs) { - onehot_cost_ptr_ = std::make_shared(); - } + : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~OneHotInfo() override = default; Status Init(const StrategyPtr& strategy) override; Status InitForCostModel(const StrategyPtr& strategy) override; Status GenerateStrategies(int32_t stage_id) override; Status SetCostUnderStrategy(const StrategyPtr& strategy) override; - OperatorCostPtr GetOperatorCost() const override { return onehot_cost_ptr_; } ReplaceGraphPtr replace_graph(const CNodePtr& cnode) override; std::shared_ptr>> GenerateBatchStrategies() override; @@ -60,7 +57,6 @@ class OneHotInfo : public OperatorInfo { Status ComputeReplaceGraph(const CNodePtr& cnode); int axis_ = -1; - OneHotCostPtr onehot_cost_ptr_; int32_t rank_ = 0; int32_t total_class_number_ = 1; int32_t classes_each_device_ = 1; diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/parallel/ops_info/operator_info.cc index 11c518d844..a24f3e616b 100644 --- a/mindspore/ccsrc/parallel/ops_info/operator_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/operator_info.cc @@ -1034,12 +1034,11 @@ Status OperatorInfo::SetCostUnderStrategyBase(const StrategyPtr& strategy) { return FAILED; } int32_t stage_id = strategy->GetInputStage(); - double computation_cost = - GetOperatorCost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, stage_id); - double communication_cost = GetOperatorCost()->GetCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id); + double computation_cost = cost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, stage_id); + double communication_cost = cost()->GetCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id); std::shared_ptr result = std::make_shared(computation_cost, communication_cost); result->communication_without_parameter_ = - GetOperatorCost()->GetForwardCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id); + cost()->GetForwardCommCost(inputs_tensor_info_, outputs_tensor_info_, stage_id); result->communication_with_partial_para_ = result->communication_without_parameter_ + COST_MODEL_GAMMA * (communication_cost - result->communication_without_parameter_); @@ -1096,7 +1095,7 @@ Status OperatorInfo::set_is_parameter(const std::vector& is_parameter) { return FAILED; } is_parameter_ = is_parameter; - GetOperatorCost()->set_is_parameter(is_parameter); + cost()->set_is_parameter(is_parameter); return SUCCESS; } @@ -1193,7 +1192,7 @@ Status OperatorInfo::SetInputAndOutputTypeLength(const std::vector& inpu } inputs_type_lengths_ = input_lengths; outputs_type_lengths_ = output_lengths; - GetOperatorCost()->SetInputAndOutputTypeLength(input_lengths, output_lengths); + cost()->SetInputAndOutputTypeLength(input_lengths, output_lengths); return SUCCESS; } @@ -1211,7 +1210,7 @@ void OperatorInfo::BreakingTiesForPerferringDataParallel(const StrategyPtr& stra } double OperatorInfo::GetForwardMemoryCostFromCNode() { - return GetOperatorCost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, 0); + return cost()->GetForwardComputationCost(inputs_tensor_info_, outputs_tensor_info_, 0); } } // namespace parallel diff --git a/mindspore/ccsrc/parallel/ops_info/operator_info.h b/mindspore/ccsrc/parallel/ops_info/operator_info.h index e7b8af0a7e..8fcae8ad33 100644 --- a/mindspore/ccsrc/parallel/ops_info/operator_info.h +++ b/mindspore/ccsrc/parallel/ops_info/operator_info.h @@ -53,12 +53,13 @@ class Edge; class OperatorInfo { public: - OperatorInfo(std::string name, Shapes inputs_shape, Shapes outputs_shape, PrimitiveAttrs attrs) + OperatorInfo(std::string name, Shapes inputs_shape, Shapes outputs_shape, PrimitiveAttrs attrs, OperatorCostPtr cost) : name_(std::move(name)), inputs_shape_(std::move(inputs_shape)), outputs_shape_(std::move(outputs_shape)), attrs_(std::move(attrs)), - is_alive_(true) { + is_alive_(true), + cost_(cost) { std::vector not_parameteter(inputs_shape_.size(), false); is_parameter_ = not_parameteter; refkey_parameter_name_ = ""; @@ -75,7 +76,8 @@ class OperatorInfo { // Given the stage_id (which indicates the number of devices), // generate all strategies for this operator virtual Status GenerateStrategies(int32_t stage_id) = 0; - virtual OperatorCostPtr GetOperatorCost() const = 0; + const OperatorCostPtr& cost() const { return cost_; } + void set_cost(const OperatorCostPtr& cost) { cost_ = cost; } virtual Status SetCostUnderStrategy(const StrategyPtr& strategy) = 0; virtual std::shared_ptr>> GenerateBatchStrategies(); @@ -115,7 +117,7 @@ class OperatorInfo { void ReplaceSuccEdge(const std::shared_ptr& op, const std::shared_ptr& new_edge); void ReplacePreEdges(const std::shared_ptr& op, const std::shared_ptr& new_edge); void ReplaceSuccEdges(const std::shared_ptr& op, const std::shared_ptr& new_edge); - std::vector GetOutputTypeLengths() const { return GetOperatorCost()->outputs_type_lengths(); } + std::vector GetOutputTypeLengths() const { return cost()->outputs_type_lengths(); } void SetSelectedStrategyAndCost(const StrategyPtr& s_strategy, const CostPtr& cost) { selected_strategy_ = s_strategy; selected_cost_ = cost; @@ -221,6 +223,9 @@ class OperatorInfo { std::string refkey_parameter_name_; CNodePtr cnode_; int32_t used_devices_ = -1; + + private: + OperatorCostPtr cost_; }; Shape GetSliceShape(const Shape& tensor_shape, const Dimensions& strategy); diff --git a/mindspore/ccsrc/parallel/ops_info/prelu_info.h b/mindspore/ccsrc/parallel/ops_info/prelu_info.h index d491ecb331..bdfb11550b 100644 --- a/mindspore/ccsrc/parallel/ops_info/prelu_info.h +++ b/mindspore/ccsrc/parallel/ops_info/prelu_info.h @@ -35,15 +35,12 @@ class PReLUInfo : public OperatorInfo { public: PReLUInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : OperatorInfo(name, inputs_shape, outputs_shape, attrs) { - prelucost_ptr = std::make_shared(); - } + : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~PReLUInfo() override = default; Status Init(const StrategyPtr& strategy) override; Status InitForCostModel(const StrategyPtr& strategy) override; Status GenerateStrategies(int32_t stage_id) override; - OperatorCostPtr GetOperatorCost() const override { return prelucost_ptr; } Status SetCostUnderStrategy(const StrategyPtr& strategy) override; protected: @@ -59,7 +56,6 @@ class PReLUInfo : public OperatorInfo { private: Dimensions input_strategy_; - PReLUCostPtr prelucost_ptr; }; } // namespace parallel } // namespace mindspore diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc index 5b07f8d0a9..aa64e72d05 100644 --- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc +++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.cc @@ -109,8 +109,12 @@ Status ReduceMethod::GetAttrs() { } cross_batch_ = cross_batch_iter->second->cast()->value(); } - reducemethodcost_ptr_->set_cross_batch(cross_batch_); - + auto reducemethodcost = std::dynamic_pointer_cast(cost()); + if (reducemethodcost == nullptr) { + MS_LOG(ERROR) << "Cost cast to ReduceMethodCostPtr failed!"; + return FAILED; + } + reducemethodcost->set_cross_batch(cross_batch_); return SUCCESS; } diff --git a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h index 8e2e17af99..c2ddbc87ce 100644 --- a/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h +++ b/mindspore/ccsrc/parallel/ops_info/reduce_method_info.h @@ -34,9 +34,7 @@ class ReduceMethod : public OperatorInfo { public: ReduceMethod(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs) - : OperatorInfo(name, inputs_shape, outputs_shape, attrs) { - reducemethodcost_ptr_ = std::make_shared(); - } + : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~ReduceMethod() override = default; Status Init(const StrategyPtr &strategy) override; @@ -44,13 +42,11 @@ class ReduceMethod : public OperatorInfo { Status GenerateStrategies(int32_t stage_id) override; Status SetCostUnderStrategy(const StrategyPtr &strategy) override; - OperatorCostPtr GetOperatorCost() const override { return reducemethodcost_ptr_; } protected: std::string reduce_method_; bool keepdims_ = false; bool cross_batch_ = false; - ReduceMethodCostPtr reducemethodcost_ptr_; Status CheckStrategy(const StrategyPtr &strategy) override; Status GetAttrs() override; Dimensions InferOutputStrategy(); @@ -110,7 +106,7 @@ class ReduceMeanInfo : public ReduceMethod { ReduceMeanInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs) : ReduceMethod(name, inputs_shape, outputs_shape, attrs) { - reducemethodcost_ptr_ = std::make_shared(); + set_cost(std::make_shared()); } ~ReduceMeanInfo() override = default; diff --git a/mindspore/ccsrc/parallel/ops_info/reshape_info.h b/mindspore/ccsrc/parallel/ops_info/reshape_info.h index 1d6a14b1f6..38192a5d01 100644 --- a/mindspore/ccsrc/parallel/ops_info/reshape_info.h +++ b/mindspore/ccsrc/parallel/ops_info/reshape_info.h @@ -36,12 +36,10 @@ class ReshapeInfo : public OperatorInfo { public: ReshapeInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : OperatorInfo(name, inputs_shape, outputs_shape, attrs), + : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared()), dev_num_(0), input_layout_set_flag_(false), - output_layout_set_flag_(false) { - reshape_cost_ptr_ = std::make_shared(); - } + output_layout_set_flag_(false) {} ~ReshapeInfo() override = default; Status Init(const StrategyPtr& strategy) override; void SetInputLayout(const TensorLayout& input_layout) { @@ -55,7 +53,6 @@ class ReshapeInfo : public OperatorInfo { Status InitForCostModel(const StrategyPtr& strategy) override; Status GenerateStrategies(int32_t stage_id) override; Status SetCostUnderStrategy(const StrategyPtr& strategy) override; - OperatorCostPtr GetOperatorCost() const override { return reshape_cost_ptr_; } protected: Status CheckStrategy(const StrategyPtr& strategy) override; @@ -67,7 +64,6 @@ class ReshapeInfo : public OperatorInfo { Status InferTensorLayout(TensorLayouts* inputs_layout, TensorLayouts* outputs_layout); Status GetAttrs() override; Strategys GetOutputsStrategy(); - ReshapeCostPtr reshape_cost_ptr_; private: Status GetParameterInput(); diff --git a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h index 6df5856e0c..cf850683a6 100644 --- a/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h +++ b/mindspore/ccsrc/parallel/ops_info/tmp_identity_info.h @@ -34,9 +34,7 @@ class TmpIdentityInfo : public OperatorInfo { public: TmpIdentityInfo(const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs, const std::string& name = IDENTITY_INFO) - : OperatorInfo(name, inputs_shape, outputs_shape, attrs) { - id_cost_ptr_ = std::make_shared(); - } + : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~TmpIdentityInfo() override = default; Status Init(const StrategyPtr& strategy) override; @@ -44,7 +42,6 @@ class TmpIdentityInfo : public OperatorInfo { Status GenerateStrategies(int32_t stage_id) override; Status SetCostUnderStrategy(const StrategyPtr& strategy) override; - OperatorCostPtr GetOperatorCost() const override { return id_cost_ptr_; } protected: Status CheckStrategy(const StrategyPtr& strategy) override; @@ -54,9 +51,6 @@ class TmpIdentityInfo : public OperatorInfo { Status InferTensorInfo() override; Status InferDevMatrixShape() override; Status InferTensorMap() override; - - private: - TmpIdentityCostPtr id_cost_ptr_; }; } // namespace parallel } // namespace mindspore diff --git a/mindspore/ccsrc/parallel/ops_info/transpose_info.h b/mindspore/ccsrc/parallel/ops_info/transpose_info.h index 4f6f6bb695..2714b352b6 100644 --- a/mindspore/ccsrc/parallel/ops_info/transpose_info.h +++ b/mindspore/ccsrc/parallel/ops_info/transpose_info.h @@ -35,15 +35,12 @@ class TransposeInfo : public OperatorInfo { public: TransposeInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : OperatorInfo(name, inputs_shape, outputs_shape, attrs) { - transpose_cost_ptr_ = std::make_shared(); - } + : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~TransposeInfo() override = default; Status Init(const StrategyPtr& strategy) override; Status InitForCostModel(const StrategyPtr& strategy) override; Status GenerateStrategies(int32_t stage_id) override; Status SetCostUnderStrategy(const StrategyPtr& strategy) override; - OperatorCostPtr GetOperatorCost() const override { return transpose_cost_ptr_; } protected: Status CheckStrategy(const StrategyPtr& strategy) override; @@ -60,7 +57,6 @@ class TransposeInfo : public OperatorInfo { Status ComputeAxis(); std::vector axis_v_; Dimensions input_strategy_; - ActivationCostPtr transpose_cost_ptr_; }; } // namespace parallel } // namespace mindspore diff --git a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h index d0278f27d9..b958adeabe 100644 --- a/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h +++ b/mindspore/ccsrc/parallel/ops_info/virtual_dataset_info.h @@ -32,16 +32,13 @@ class VirtualDatasetInfo : public OperatorInfo { public: VirtualDatasetInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs) - : OperatorInfo(name, inputs_shape, outputs_shape, attrs) { - vd_cost_ptr_ = std::make_shared(); - } + : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared()) {} ~VirtualDatasetInfo() override = default; Status Init(const StrategyPtr& strategy) override; Status InitForCostModel(const StrategyPtr& strategy) override; Status GenerateStrategies(int32_t stage_id) override; Status SetCostUnderStrategy(const StrategyPtr& strategy) override; - OperatorCostPtr GetOperatorCost() const override { return vd_cost_ptr_; } void ReComputeBatchSplitFlagList() override; protected: @@ -53,9 +50,6 @@ class VirtualDatasetInfo : public OperatorInfo { Status InferTensorMap() override; Status GetAttrs() override; Status InferAsLossDivisor() override; - - private: - VirtualDatasetCostPtr vd_cost_ptr_; }; } // namespace parallel diff --git a/tests/ut/cpp/parallel/ops_info/activation_test.cc b/tests/ut/cpp/parallel/ops_info/activation_test.cc index 5d18c5372f..a8f8425ae9 100644 --- a/tests/ut/cpp/parallel/ops_info/activation_test.cc +++ b/tests/ut/cpp/parallel/ops_info/activation_test.cc @@ -84,9 +84,9 @@ TEST_F(TestActivation, test_activation_strategies) { act_ptr_->InitForCostModel(sp); std::vector inputs_info = act_ptr_->inputs_tensor_info(); std::vector outputs_info = act_ptr_->outputs_tensor_info(); - ASSERT_DOUBLE_EQ(act_ptr_->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()), + ASSERT_DOUBLE_EQ(act_ptr_->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()), cost.computation_cost_); - ASSERT_DOUBLE_EQ(act_ptr_->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()), + ASSERT_DOUBLE_EQ(act_ptr_->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()), cost.communication_cost_); } } @@ -109,9 +109,9 @@ TEST_F(TestActivation, test_softmax_strategies) { soft_ptr_->InitForCostModel(sp); std::vector inputs_info = soft_ptr_->inputs_tensor_info(); std::vector outputs_info = soft_ptr_->outputs_tensor_info(); - ASSERT_DOUBLE_EQ(soft_ptr_->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()), + ASSERT_DOUBLE_EQ(soft_ptr_->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()), cost.computation_cost_); - ASSERT_DOUBLE_EQ(soft_ptr_->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()), + ASSERT_DOUBLE_EQ(soft_ptr_->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()), cost.communication_cost_); } } diff --git a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc index 99ca9f8e0e..2fece098e8 100644 --- a/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/matmul_info_test.cc @@ -569,7 +569,7 @@ TEST_F(TestMatmulInfo, test_GenerateStrategies1) { matmul1->InitForCostModel(sp); std::vector inputs_info = matmul1->inputs_tensor_info(); std::vector outputs_info = matmul1->outputs_tensor_info(); - ASSERT_DOUBLE_EQ(matmul1->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()), + ASSERT_DOUBLE_EQ(matmul1->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()), cost.computation_cost_); break; } @@ -599,7 +599,7 @@ TEST_F(TestMatmulInfo, test_GenerateStrategies2) { TensorInfo replica_input1_info(tly, input1_shape, input1_slice_shape); replica_inputs_info.push_back(replica_input1_info); - ASSERT_DOUBLE_EQ(matmul3->GetOperatorCost()->GetComputationCost(replica_inputs_info, outputs_info, sp->GetInputStage()), + ASSERT_DOUBLE_EQ(matmul3->cost()->GetComputationCost(replica_inputs_info, outputs_info, sp->GetInputStage()), cost.computation_cost_); break; } diff --git a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc index 6cb9739b1c..8c956328a7 100644 --- a/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc +++ b/tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc @@ -188,11 +188,11 @@ TEST_F(TestTensorAddInfo, GenerateStrategies) { tensor_add->InitForCostModel(sp); std::vector inputs_info = tensor_add->inputs_tensor_info(); std::vector outputs_info = tensor_add->outputs_tensor_info(); - double memory_cost0 = tensor_add->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()); + double memory_cost0 = tensor_add->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()); double memory_cost1 = cost.computation_cost_; bool memory = memory_cost0 - memory_cost1 <= 1.0; - double comm_cost0 = tensor_add->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()); + double comm_cost0 = tensor_add->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()); double comm_cost1 = cost.communication_cost_; bool comm = comm_cost0 - comm_cost1 <= 1.0; @@ -210,11 +210,11 @@ TEST_F(TestTensorAddInfo, GenerateStrategies1) { tensor_add1->InitForCostModel(sp); std::vector inputs_info = tensor_add1->inputs_tensor_info(); std::vector outputs_info = tensor_add1->outputs_tensor_info(); - double memory_cost0 = tensor_add1->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()); + double memory_cost0 = tensor_add1->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()); double memory_cost1 = cost.computation_cost_; bool memory = memory_cost0 - memory_cost1 <= 1.0; - double comm_cost0 = tensor_add1->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()); + double comm_cost0 = tensor_add1->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()); double comm_cost1 = cost.communication_cost_; bool comm = comm_cost0 - comm_cost1 <= 1.0; diff --git a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc index 043746498f..3971a2b471 100644 --- a/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc +++ b/tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc @@ -145,9 +145,9 @@ TEST_F(TestTmpIdentityInfo, test_generate_strategies) { identity_ptr->Init(sp); std::vector inputs_info = identity_ptr->inputs_tensor_info(); std::vector outputs_info = identity_ptr->outputs_tensor_info(); - ASSERT_DOUBLE_EQ(identity_ptr->GetOperatorCost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()), + ASSERT_DOUBLE_EQ(identity_ptr->cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()), cost.computation_cost_); - ASSERT_DOUBLE_EQ(identity_ptr->GetOperatorCost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()), + ASSERT_DOUBLE_EQ(identity_ptr->cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()), cost.communication_cost_); } } From 5e9cfaf6effad2cf6121e63ca434a31bd00f7d4d Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Fri, 10 Apr 2020 19:20:37 +0800 Subject: [PATCH 171/227] syn-code1 --- mindspore/ccsrc/pipeline/pipeline_ge.cc | 2 +- mindspore/ccsrc/utils/callbacks.h | 2 +- mindspore/nn/optim/rmsprop.py | 2 +- tests/st/networks/test_network_main.py | 55 +------------------------ 4 files changed, 5 insertions(+), 56 deletions(-) diff --git a/mindspore/ccsrc/pipeline/pipeline_ge.cc b/mindspore/ccsrc/pipeline/pipeline_ge.cc index ee67d46cf7..6ce0ea5316 100644 --- a/mindspore/ccsrc/pipeline/pipeline_ge.cc +++ b/mindspore/ccsrc/pipeline/pipeline_ge.cc @@ -533,4 +533,4 @@ void ExportDFGraph(const std::string& file_name, const std::string& phase) { MS_LOG(DEBUG) << "ExportGraph End"; } } // namespace pipeline -} // namespace mindspore \ No newline at end of file +} // namespace mindspore diff --git a/mindspore/ccsrc/utils/callbacks.h b/mindspore/ccsrc/utils/callbacks.h index 6f099ef4ca..a1e4e75d5b 100644 --- a/mindspore/ccsrc/utils/callbacks.h +++ b/mindspore/ccsrc/utils/callbacks.h @@ -40,7 +40,7 @@ const int kCallbackOk = 0; const int kCallbackFalied = 1; bool GetParameterShape(const FuncGraphPtr& anf_graph, const std::string& param_name, - const std::shared_ptr>& shape) + const std::shared_ptr>& shape); uint32_t SummarySaveCallback(uint32_t, const std::map&); } // namespace callbacks diff --git a/mindspore/nn/optim/rmsprop.py b/mindspore/nn/optim/rmsprop.py index e252f89f2f..b17a101708 100644 --- a/mindspore/nn/optim/rmsprop.py +++ b/mindspore/nn/optim/rmsprop.py @@ -194,4 +194,4 @@ class RMSProp(Optimizer): else: success = self.hyper_map(F.partial(rmsprop_opt, self.opt, lr, self.decay, self.epsilon, self.momentum), params, self.ms, self.moment, gradients) - return success \ No newline at end of file + return success diff --git a/tests/st/networks/test_network_main.py b/tests/st/networks/test_network_main.py index 730602c0ae..4689adee54 100644 --- a/tests/st/networks/test_network_main.py +++ b/tests/st/networks/test_network_main.py @@ -12,11 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ -<<<<<<< HEAD:tests/st/networks/test_network_main.py """ -Function: +Function: test network -Usage: +Usage: python test_network_main.py --net lenet --target Ascend """ import os @@ -32,47 +31,6 @@ from models.lenet import LeNet from models.resnetv1_5 import resnet50 from models.alexnet import AlexNet context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") -======= -import pytest -from mindspore.nn import TrainOneStepCell, WithLossCell -import mindspore.context as context -from mindspore.nn.optim import Momentum -import numpy as np -import mindspore.nn as nn -from mindspore.ops import operations as P -from mindspore import Tensor - -class LeNet(nn.Cell): - def __init__(self): - super(LeNet, self).__init__() - self.relu = P.ReLU() - self.batch_size = 32 - - self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid') - self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid') - self.pool = nn.MaxPool2d(kernel_size=2, stride=2) - self.reshape = P.Reshape() - self.fc1 = nn.Dense(400, 120) - self.fc2 = nn.Dense(120, 84) - self.fc3 = nn.Dense(84, 10) - - def construct(self, input_x): - output = self.conv1(input_x) - output = self.relu(output) - output = self.pool(output) - output = self.conv2(output) - output = self.relu(output) - output = self.pool(output) - output = self.reshape(output, (self.batch_size, -1)) - output = self.fc1(output) - output = self.relu(output) - output = self.fc2(output) - output = self.relu(output) - output = self.fc3(output) - return output - -context.set_context(mode=context.GRAPH_MODE, device_target="CPU") ->>>>>>> add cpu st lenet:tests/st/networks/test_cpu_lenet.py def train(net, data, label): learning_rate = 0.01 @@ -89,24 +47,17 @@ def train(net, data, label): print("+++++++++++++++++++++++++++") assert res -<<<<<<< HEAD:tests/st/networks/test_network_main.py def test_resnet50(): data = Tensor(np.ones([32, 3 ,224, 224]).astype(np.float32) * 0.01) label = Tensor(np.ones([32]).astype(np.int32)) net = resnet50(32, 10) train(net, data, label) -======= -@pytest.mark.level0 -@pytest.mark.platform_x86_cpu -@pytest.mark.env_onecard ->>>>>>> add cpu st lenet:tests/st/networks/test_cpu_lenet.py def test_lenet(): data = Tensor(np.ones([32, 1 ,32, 32]).astype(np.float32) * 0.01) label = Tensor(np.ones([32]).astype(np.int32)) net = LeNet() train(net, data, label) -<<<<<<< HEAD:tests/st/networks/test_network_main.py def test_alexnet(): data = Tensor(np.ones([32, 3 ,227, 227]).astype(np.float32) * 0.01) @@ -128,5 +79,3 @@ if __name__ == "__main__": test_alexnet() else: print("Please add net name like --net lenet") -======= ->>>>>>> add cpu st lenet:tests/st/networks/test_cpu_lenet.py From f707be65fd2edb97abdc2e307bff8aceddd11d09 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Mon, 13 Apr 2020 10:37:27 +0800 Subject: [PATCH 172/227] adapt Relu6 and ROIAlign --- graphengine | 2 +- mindspore/ccsrc/transform/op_declare.cc | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/graphengine b/graphengine index 32f0696599..71e3e4ac0f 160000 --- a/graphengine +++ b/graphengine @@ -1 +1 @@ -Subproject commit 32f06965993ca25ea936239d284e992c5512d2ff +Subproject commit 71e3e4ac0fd9a1a229f0f07ba273162d27bdbb65 diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 4149280404..858b9b6b39 100644 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -477,9 +477,9 @@ OUTPUT_MAP(ApplyAdam) = {{0, OUTPUT_DESC(var)}}; #endif // Relu6 -INPUT_MAP(Relu6) = {{1, INPUT_DESC(features)}}; +INPUT_MAP(Relu6) = {{1, INPUT_DESC(x)}}; ATTR_MAP(Relu6) = EMPTY_ATTR_MAP; -OUTPUT_MAP(Relu6) = {{0, OUTPUT_DESC(activations)}}; +OUTPUT_MAP(Relu6) = {{0, OUTPUT_DESC(y)}}; // Relu6Grad INPUT_MAP(Relu6Grad) = {{1, INPUT_DESC(gradients)}, {2, INPUT_DESC(features)}}; @@ -569,7 +569,7 @@ OUTPUT_MAP(GatherNd) = {{0, OUTPUT_DESC(y)}}; // ROIAlign INPUT_MAP(ROIAlign) = {{1, INPUT_DESC(features)}, {2, INPUT_DESC(rois)}}; -OUTPUT_MAP(ROIAlign) = {{0, OUTPUT_DESC(output)}}; +OUTPUT_MAP(ROIAlign) = {{0, OUTPUT_DESC(y)}}; ATTR_MAP(ROIAlign) = {{"pooled_height", ATTR_DESC(pooled_height, AnyTraits())}, {"pooled_width", ATTR_DESC(pooled_width, AnyTraits())}, {"spatial_scale", ATTR_DESC(spatial_scale, AnyTraits())}, From d4473644171d990ffc54cf0dca13113966d5f508 Mon Sep 17 00:00:00 2001 From: Wei Luning Date: Sun, 12 Apr 2020 23:18:04 +0800 Subject: [PATCH 173/227] add pattern AdjustAllReduceMulAdd --- mindspore/ccsrc/operator/ops.cc | 1 + mindspore/ccsrc/operator/ops.h | 1 + mindspore/ccsrc/optimizer/irpass.cc | 2 +- .../optimizer/irpass/arithmetic_simplify.h | 78 +++++++++++++++++++ mindspore/ops/operations/array_ops.py | 4 +- mindspore/ops/operations/nn_ops.py | 2 +- tests/ut/cpp/optimizer/lib_test.cc | 19 +++++ .../gtest_input/optimizer/opt_test.py | 45 ++++++++++- 8 files changed, 146 insertions(+), 6 deletions(-) diff --git a/mindspore/ccsrc/operator/ops.cc b/mindspore/ccsrc/operator/ops.cc index 12e6b70a6f..ed287b03b7 100644 --- a/mindspore/ccsrc/operator/ops.cc +++ b/mindspore/ccsrc/operator/ops.cc @@ -223,6 +223,7 @@ const PrimitivePtr kPrimIsNot = std::make_shared("is_not"); const PrimitivePtr kPrimMirror = std::make_shared("_MirrorOperator"); const PrimitivePtr kPrimVirtualDiv = std::make_shared("_VirtualDiv"); const PrimitivePtr kPrimVirtualDataset = std::make_shared("_VirtualDataset"); +const PrimitivePtr kPrimAllReduce = std::make_shared("AllReduce"); // Debug ops const PrimitivePtr kPrimScalarSummary = std::make_shared("ScalarSummary"); diff --git a/mindspore/ccsrc/operator/ops.h b/mindspore/ccsrc/operator/ops.h index 5fbf2b7067..6c88e30e70 100644 --- a/mindspore/ccsrc/operator/ops.h +++ b/mindspore/ccsrc/operator/ops.h @@ -228,6 +228,7 @@ extern const PrimitivePtr kPrimMinimumGrad; extern const PrimitivePtr kPrimMaximumGrad; // Comm ops +extern const PrimitivePtr kPrimAllReduce; extern const PrimitivePtr kPrimMirror; extern const PrimitivePtr kPrimVirtualDiv; extern const PrimitivePtr kPrimVirtualDataset; diff --git a/mindspore/ccsrc/optimizer/irpass.cc b/mindspore/ccsrc/optimizer/irpass.cc index 0991c31b00..25784c8a0b 100644 --- a/mindspore/ccsrc/optimizer/irpass.cc +++ b/mindspore/ccsrc/optimizer/irpass.cc @@ -47,7 +47,7 @@ namespace irpass { OptimizeIRPassLib::OptimizeIRPassLib() { arithmetic_simplify_ = MakeSubstitution(ArithmeticSimplify(), "arithmetic_simplify", {prim::kPrimScalarAdd, prim::kPrimScalarMul, prim::kPrimTensorAdd, - prim::kPrimIdentity, prim::kPrimMomentum, prim::kPrimMul}); + prim::kPrimAddN, prim::kPrimIdentity, prim::kPrimMomentum, prim::kPrimMul}); special_op_eliminate_ = MakeSubstitution(SpecialOpEliminater(), "special_op_eliminate", {prim::kPrimInsertGradientOf, prim::kPrimPrintShapeType, prim::kPrimGetRefKey, prim::kPrimMirror, prim::kPrimVirtualDiv}); diff --git a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h index ab191aab20..0d48fc1463 100644 --- a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h +++ b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h @@ -228,6 +228,82 @@ class ConstantDuplicateMul : public AnfVisitor { CNodePtr cnode_; }; +// grad = AllReduce(grad) / worker_number +// grad = grad + weight * decy +// -> +// grad = grad + weight * decy +// grad = AllReduce(grad) / worker_number + +// {prim::kPrimAddN, {prim::kPrimMakeTuple, {prim::kPrimMul, {prim::kPrimAllReduce, X}, Y}, Z}} -> +// {prim::kPrimMul, {prim::kPrimAllReduce, {prim::kPrimAddN,{prim::kPrimMakeTuple, Z, X}}}, Y} +class AdjustAllReduceMulAdd : public AnfVisitor { + public: + AnfNodePtr operator()(const OptimizerPtr &, const AnfNodePtr &node) override { + Reset(); + // {prim::kPrimAddN, Zs} + if (!IsPrimitiveCNode(node, prim::kPrimAddN)) { + return nullptr; + } + auto addn = node->cast(); + if (addn->size() != 2) { + return nullptr; + } + + AnfVisitor::Match(prim::kPrimMakeTuple, {IsNode, IsNode})(addn->input(1)); + if (x_ == nullptr || y_ == nullptr || z_ == nullptr) { + return nullptr; + } + + auto fg = node->func_graph(); + AnfNodePtr tuple = NewCNode({NewValueNode(prim::kPrimMakeTuple), z_, x_}, fg); + AnfNodePtr add = NewCNode({NewValueNode(prim::kPrimAddN), tuple}, fg); + AnfNodePtr all_reduce = NewCNode({NewValueNode(prim::kPrimAllReduce), add}, fg); + return NewCNode({NewValueNode(prim::kPrimMul), all_reduce, y_}, fg); + } + + void Visit(const AnfNodePtr &node) override { + if (level_ == 0) { + level_ = 1; + is_reduce_match_ = false; + // {prim::kPrimMul, {prim::kPrimAllReduce, X}, Y} + AnfVisitor::Match(prim::kPrimMul)(node); + level_ = 0; + if (is_reduce_match_) { + y_ = tmp_; + } else { + z_ = node; + } + } + + if (level_ == 1) { + // {prim::kPrimAllReduce, X} + if (IsPrimitiveCNode(node, prim::kPrimAllReduce)) { + auto cnode = node->cast(); + if (cnode->size() > 1) { + x_ = cnode->input(1); + is_reduce_match_ = true; + } + } else { + tmp_ = node; + } + } + } + + void Reset() { + level_ = 0; + is_reduce_match_ = false; + x_ = nullptr; + y_ = nullptr; + z_ = nullptr; + tmp_ = nullptr; + } + + private: + int level_{0}; + bool is_reduce_match_{false}; + AnfNodePtr x_{nullptr}, y_{nullptr}, z_{nullptr}, tmp_{nullptr}; +}; + class ArithmeticSimplify { public: ArithmeticSimplify() @@ -243,6 +319,7 @@ class ArithmeticSimplify { eliminaters_.emplace_back(identity_); eliminaters_.emplace_back(opt_update_zero_tensor_); eliminaters_.emplace_back(constant_duplicate_mul_); + eliminaters_.emplace_back(adjust_allreduce_mul_add_); } ~ArithmeticSimplify() = default; @@ -264,6 +341,7 @@ class ArithmeticSimplify { PrimEliminater identity_; OptUpdateZeroTensor opt_update_zero_tensor_; ConstantDuplicateMul constant_duplicate_mul_; + AdjustAllReduceMulAdd adjust_allreduce_mul_add_; std::vector eliminaters_{}; }; } // namespace irpass diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index ac7f8ed699..2264e72773 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -1235,8 +1235,8 @@ class UnsortedSegmentSum(PrimitiveWithInfer): Tensor, the shape is :math:`(z, x_{N+1}, ..., x_R)`. Examples: - >>> input_x = [1, 2, 3, 4] - >>> segment_ids = [0, 0, 1, 2] + >>> input_x = Tensor([1, 2, 3, 4], mindspore.float32) + >>> segment_ids = Tensor([0, 0, 1, 2], mindspore.int32) >>> num_segments = 4 >>> type = P.UnsortedSegmentSum()(input_x, segment_ids, num_segments) """ diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 3cc6718484..e780500af7 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -1499,7 +1499,7 @@ class LayerNorm(Primitive): `Layer Normalization `_. .. math:: - y = \frac{x - mean]}{\sqrt{variance + \epsilon}} * \gamma + \beta + y = \frac{x - mean}{\sqrt{variance + \epsilon}} * \gamma + \beta where :math:`\gamma` is scale, :math:`\beta` is bias, :math:`\epsilon` is epsilon. diff --git a/tests/ut/cpp/optimizer/lib_test.cc b/tests/ut/cpp/optimizer/lib_test.cc index 2d4cf0e78e..8e348c698a 100644 --- a/tests/ut/cpp/optimizer/lib_test.cc +++ b/tests/ut/cpp/optimizer/lib_test.cc @@ -556,5 +556,24 @@ TEST_F(TestOptLib, test_constant_duplicate_mul) { ASSERT_TRUE(CheckOpt(beforerl, after, patterns)); ASSERT_TRUE(CheckOpt(beforerr, after, patterns)); } + +TEST_F(TestOptLib, test_adjust_allreduce_mul_add) { + FuncGraphPtr beforell = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforell"); + FuncGraphPtr beforelr = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforelr"); + FuncGraphPtr beforerl = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforerl"); + FuncGraphPtr beforerr = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforerr"); + FuncGraphPtr after1 = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "after1"); + FuncGraphPtr before2r = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "before2r"); + FuncGraphPtr before2l = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "before2l"); + FuncGraphPtr after2 = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "after2"); + auto patterns = std::vector({irpass.arithmetic_simplify_}); + ASSERT_TRUE(CheckOpt(beforell, after1, patterns)); + ASSERT_TRUE(CheckOpt(beforelr, after1, patterns)); + ASSERT_TRUE(CheckOpt(beforerl, after1, patterns)); + ASSERT_TRUE(CheckOpt(beforerr, after1, patterns)); + ASSERT_TRUE(CheckOpt(before2l, after2, patterns)); + ASSERT_TRUE(CheckOpt(before2r, after2, patterns)); +} + } // namespace opt } // namespace mindspore diff --git a/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py b/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py index d494ad27d3..d74aa15952 100644 --- a/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py +++ b/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py @@ -908,8 +908,8 @@ def test_print_tuple_wrapper(tag): def test_constant_duplicate_mul(tag): fns = FnDict() - Mul = Primitive('Mul'); - Sqrt = Primitive('Sqrt'); + Mul = Primitive('Mul') + Sqrt = Primitive('Sqrt') x = Tensor(np.array([[2, 2], [2, 3]]).astype('float32')) tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32')) @@ -936,3 +936,44 @@ def test_constant_duplicate_mul(tag): return Mul(Sqrt(x), Mul(tensor1, tensor2)) return fns[tag] + + +def test_adjust_allreduce_mul_add(tag): + fns = FnDict() + Mul = Primitive('Mul') + AddN = Primitive('AddN') + AllReduce = Primitive('AllReduce') + + @fns + def beforell(x, y, z): + return AddN((z, Mul(y, AllReduce(x)))) + + @fns + def beforelr(x, y, z): + return AddN((z, Mul(AllReduce(x), y))) + + @fns + def beforerl(x, y, z): + return AddN((Mul(y, AllReduce(x)), z)) + + @fns + def beforerr(x, y, z): + return AddN((Mul(AllReduce(x), y), z)) + + @fns + def after1(x, y, z): + return Mul(AllReduce(AddN((z, x))), y) + + @fns + def before2r(x, y, z): + return AddN((Mul(AllReduce(x), y), Mul(z, z))) + + @fns + def before2l(x, y, z): + return AddN((Mul(z, z), Mul(AllReduce(x), y))) + + @fns + def after2(x, y, z): + return Mul(AllReduce(AddN((Mul(z, z), x))), y) + + return fns[tag] From 698c29eedfaf7922036130878946b75252410099 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Mon, 20 Apr 2020 17:47:34 +0800 Subject: [PATCH 174/227] del graph --- graphengine | 1 - 1 file changed, 1 deletion(-) delete mode 160000 graphengine diff --git a/graphengine b/graphengine deleted file mode 160000 index 5869ad8541..0000000000 --- a/graphengine +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 5869ad8541403235113812f872f462bf19ecd11e From aadf06d70bdbde6e409e22520c755ece6c4bcfbf Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Mon, 20 Apr 2020 17:48:43 +0800 Subject: [PATCH 175/227] add graph --- graphengine | 1 + 1 file changed, 1 insertion(+) create mode 160000 graphengine diff --git a/graphengine b/graphengine new file mode 160000 index 0000000000..5869ad8541 --- /dev/null +++ b/graphengine @@ -0,0 +1 @@ +Subproject commit 5869ad8541403235113812f872f462bf19ecd11e From a67ff264a86087070ee11bedabce659a116c7037 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Mon, 20 Apr 2020 20:15:34 +0800 Subject: [PATCH 176/227] fetch graph --- graphengine | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/graphengine b/graphengine index 5869ad8541..70bb745b45 160000 --- a/graphengine +++ b/graphengine @@ -1 +1 @@ -Subproject commit 5869ad8541403235113812f872f462bf19ecd11e +Subproject commit 70bb745b459ff9a0e7fc1008d15fe4b510f03da7 From 9dc561b7c68db208c65a99ca116b9eb12c2e67c6 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Tue, 21 Apr 2020 10:00:45 +0800 Subject: [PATCH 177/227] modify topk --- mindspore/ccsrc/transform/op_declare.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index 8b32e16b35..2d15bfa181 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -217,8 +217,8 @@ DECLARE_OP_USE_OUTPUT(Merge) DECLARE_OP_ADAPTER(Switch) DECLARE_OP_USE_OUTPUT(Switch) -DECLARE_OP_ADAPTER(TopKV2) -DECLARE_OP_USE_OUTPUT(TopKV2) +DECLARE_OP_ADAPTER(TopK) +DECLARE_OP_USE_OUTPUT(TopK) DECLARE_OP_ADAPTER(RealDiv) DECLARE_OP_USE_OUTPUT(RealDiv) From ea3fa240b5fdb90e29bf99bb87ed3825f1038fcb Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Tue, 21 Apr 2020 11:26:02 +0800 Subject: [PATCH 178/227] modify LogSoftmaxV2 --- mindspore/ccsrc/transform/op_declare.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index 2d15bfa181..6fdb4b5434 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -268,8 +268,8 @@ DECLARE_OP_ADAPTER(Select) DECLARE_OP_USE_OUTPUT(Select) DECLARE_OP_ADAPTER(LessEqual) DECLARE_OP_USE_OUTPUT(LessEqual) -DECLARE_OP_ADAPTER(LogSoftmax) -DECLARE_OP_USE_OUTPUT(LogSoftmax) +DECLARE_OP_ADAPTER(LogSoftmaxV2) +DECLARE_OP_USE_OUTPUT(LogSoftmaxV2) DECLARE_OP_ADAPTER(TruncatedNormal) DECLARE_OP_USE_OUTPUT(TruncatedNormal) DECLARE_OP_ADAPTER(StridedSliceGrad) From fd344cb8f7a843b077fc950b940d981b876d5c3a Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Tue, 21 Apr 2020 11:38:18 +0800 Subject: [PATCH 179/227] add softmaxv2 --- mindspore/ccsrc/transform/op_declare.h | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index 6fdb4b5434..933896a275 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -403,8 +403,8 @@ DECLARE_OP_ADAPTER(Sigmoid) DECLARE_OP_USE_OUTPUT(Sigmoid) DECLARE_OP_ADAPTER(SigmoidGrad) DECLARE_OP_USE_OUTPUT(SigmoidGrad) -DECLARE_OP_ADAPTER(Softmax) -DECLARE_OP_USE_OUTPUT(Softmax) +DECLARE_OP_ADAPTER(SoftmaxV2) +DECLARE_OP_USE_OUTPUT(SoftmaxV2) DECLARE_OP_ADAPTER(SoftmaxGrad) DECLARE_OP_USE_OUTPUT(SoftmaxGrad) DECLARE_OP_ADAPTER(Greater) @@ -447,6 +447,8 @@ DECLARE_OP_ADAPTER(Round) DECLARE_OP_USE_OUTPUT(Round) DECLARE_OP_ADAPTER(ApplyFtrl) DECLARE_OP_USE_OUTPUT(ApplyFtrl) +DECLARE_OP_ADAPTER(SparseApplyFtrlD) +DECLARE_OP_USE_OUTPUT(SparseApplyFtrlD) DECLARE_OP_ADAPTER(Diag) DECLARE_OP_USE_OUTPUT(Diag) DECLARE_OP_ADAPTER(DiagPart) From 9221ad8c8d8ca1a9372b0e38a606603c1a67be87 Mon Sep 17 00:00:00 2001 From: Ziyan Date: Tue, 21 Apr 2020 12:41:02 +0800 Subject: [PATCH 180/227] fix lars base class --- mindspore/nn/optim/lars.py | 7 +++---- mindspore/nn/optim/optimizer.py | 2 +- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/mindspore/nn/optim/lars.py b/mindspore/nn/optim/lars.py index c0cb71cfa6..02538aa61a 100755 --- a/mindspore/nn/optim/lars.py +++ b/mindspore/nn/optim/lars.py @@ -21,8 +21,7 @@ from mindspore.common.parameter import Parameter from mindspore.ops import operations as P from mindspore.ops import composite as C from mindspore.ops import functional as F -from mindspore.nn.cell import Cell -from .optimizer import grad_scale +from .optimizer import grad_scale, Optimizer lars_opt = C.MultitypeFuncGraph("lars_opt") @@ -61,7 +60,7 @@ def _tensor_run_opt_v2(lars, weight_decay, learning_rate, gradient, weight, deca return gradient -class LARS(Cell): +class LARS(Optimizer): """ Implements the LARS algorithm with LARSUpdate Operator. @@ -98,7 +97,7 @@ class LARS(Cell): def __init__(self, optimizer, epsilon=1e-05, hyperpara=0.001, weight_decay=0.0, use_clip=False, decay_filter=lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name, lars_filter=lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name, loss_scale=1.0): - super(LARS, self).__init__(auto_prefix=False) + super(LARS, self).__init__(0.0, [Parameter(Tensor(0.0), name="trivial")]) self.opt = optimizer self.parameters = optimizer.parameters self.learning_rate = optimizer.learning_rate diff --git a/mindspore/nn/optim/optimizer.py b/mindspore/nn/optim/optimizer.py index cd0ed93a10..4b1fc5e12c 100755 --- a/mindspore/nn/optim/optimizer.py +++ b/mindspore/nn/optim/optimizer.py @@ -51,7 +51,7 @@ class Optimizer(Cell): """ def __init__(self, learning_rate, parameters): - super(Optimizer, self).__init__() + super(Optimizer, self).__init__(auto_prefix=False) if isinstance(learning_rate, float): validator.check_number_range("learning rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT) elif isinstance(learning_rate, Iterable): From e5dc50533c4ba3b9925948a8842ff08960936cc2 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Tue, 21 Apr 2020 11:50:35 +0800 Subject: [PATCH 181/227] modify op_declare.h --- mindspore/ccsrc/transform/convert.cc | 8 +- mindspore/ccsrc/transform/op_declare.cc | 212 ++++++++++-------------- mindspore/ccsrc/transform/op_declare.h | 6 - 3 files changed, 95 insertions(+), 131 deletions(-) diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index 417989247e..46e5586122 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -176,6 +176,7 @@ const char kNameAbsGrad[] = "AbsGrad"; const char kNameBinaryCrossEntropy[] = "BinaryCrossEntropy"; const char kNameBinaryCrossEntropyGrad[] = "BinaryCrossEntropyGrad"; const char kNameSparseApplyAdagrad[] = "SparseApplyAdagrad"; +const char kNameSparseApplyFtrlD[] = "SparseApplyFtrlD"; const char kNameAcosh[] = "Acosh"; const char kNameFloorMod[] = "FloorMod"; const char kNameSpaceToDepth[] = "SpaceToDepth"; @@ -204,7 +205,7 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameMaxPool), ADPT_DESC(MaxPool)}, {string(kNameAvgPool), ADPT_DESC(AvgPool)}, {string(kNameMaxPoolWithArgmax), ADPT_DESC(MaxPoolWithArgmax)}, - {string(kNameTopK), ADPT_DESC(TopKV2)}, + {string(kNameTopK), ADPT_DESC(TopK)}, {string(kNamePack), ADPT_DESC(Pack)}, {string(kNameUnpack), ADPT_DESC(Unpack)}, {string(kNameSplitD), ADPT_DESC(SplitD)}, @@ -329,7 +330,7 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {prim::kPrimMinimum->name(), ADPT_DESC(Minimum)}, {prim::kPrimSelect->name(), ADPT_DESC(Select)}, {string(kNameLessEqual), ADPT_DESC(LessEqual)}, - {prim::kPrimLogSoftmax->name(), ADPT_DESC(LogSoftmax)}, + {prim::kPrimLogSoftmax->name(), ADPT_DESC(LogSoftmaxV2)}, {string(kNameTruncatedNormal), ADPT_DESC(TruncatedNormal)}, {string(kNameStridedSliceGrad), ADPT_DESC(StridedSliceGrad)}, {prim::kPrimGelu->name(), ADPT_DESC(Gelu)}, @@ -363,7 +364,7 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {prim::kPrimMatMul->name(), ADPT_DESC(MatMul)}, {string(kNameConst), ADPT_DESC(Constant, Const)}, - {string(kNameSoftmax), ADPT_DESC(Softmax)}, + {string(kNameSoftmax), ADPT_DESC(SoftmaxV2)}, {string(kNameSoftmaxGrad), ADPT_DESC(SoftmaxGrad)}, {string(kNameParam), ADPT_DESC(Data)}, {string(kNameROIAlign), ADPT_DESC(ROIAlign)}, @@ -373,6 +374,7 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameBinaryCrossEntropy), ADPT_DESC(BinaryCrossEntropy)}, {string(kNameBinaryCrossEntropyGrad), ADPT_DESC(BinaryCrossEntropyGrad)}, {string(kNameSparseApplyAdagrad), ADPT_DESC(SparseApplyAdagradD)}, + {string(kNameSparseApplyFtrlD), ADPT_DESC(SparseApplyFtrlD)}, {string(kNameAcosh), ADPT_DESC(Acosh)}, {string(kNameFloorMod), ADPT_DESC(FloorMod)}, {string(kNameSpaceToDepth), ADPT_DESC(SpaceToDepth)}, diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 420edc685a..858b9b6b39 100644 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -138,11 +138,10 @@ OUTPUT_MAP(ApplyMomentum) = {{0, OUTPUT_DESC(var)}}; INPUT_MAP(Summary) = {{2, INPUT_DESC(x)}}; ATTR_MAP(Summary) = EMPTY_ATTR_MAP; -// data +// Data INPUT_MAP(Data) = EMPTY_INPUT_MAP; ATTR_MAP(Data) = EMPTY_ATTR_MAP; -// resnet ops in ge // BatchNorm INPUT_MAP(BatchNorm) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(scale)}, @@ -193,10 +192,9 @@ ATTR_MAP(PRelu) = EMPTY_ATTR_MAP; OUTPUT_MAP(PRelu) = {{0, OUTPUT_DESC(y)}}; // PReluGrad -INPUT_MAP(PReluGrad) = { - {1, INPUT_DESC(input_gradients)}, {2, INPUT_DESC(input_features)}, {3, INPUT_DESC(input_weights)}}; +INPUT_MAP(PReluGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(features)}, {3, INPUT_DESC(weights)}}; ATTR_MAP(PReluGrad) = EMPTY_ATTR_MAP; -OUTPUT_MAP(PReluGrad) = {{0, OUTPUT_DESC(output_backprops_dx)}, {1, OUTPUT_DESC(output_backprops_da)}}; +OUTPUT_MAP(PReluGrad) = {{0, OUTPUT_DESC(dx)}, {1, OUTPUT_DESC(da)}}; // Sigmoid INPUT_MAP(Sigmoid) = {{1, INPUT_DESC(x)}}; @@ -241,12 +239,12 @@ ATTR_MAP(CumsumD) = {{"exclusive", ATTR_DESC(exclusive, AnyTraits())}, {"reverse", ATTR_DESC(reverse, AnyTraits())}}; OUTPUT_MAP(CumsumD) = {{0, OUTPUT_DESC(y)}}; -// softmax -INPUT_MAP(Softmax) = {{1, INPUT_DESC(x)}}; -ATTR_MAP(Softmax) = { - {"axis", ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}, +// SoftmaxV2 +INPUT_MAP(SoftmaxV2) = {{1, INPUT_DESC(x)}}; +ATTR_MAP(SoftmaxV2) = { + {"axis", ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}, }; -OUTPUT_MAP(Softmax) = {{0, OUTPUT_DESC(y)}}; +OUTPUT_MAP(SoftmaxV2) = {{0, OUTPUT_DESC(y)}}; // SoftmaxGrad INPUT_MAP(SoftmaxGrad) = {{1, INPUT_DESC(softmax)}, {2, INPUT_DESC(grad_softmax)}}; @@ -269,21 +267,21 @@ ATTR_MAP(GatherV2) = EMPTY_ATTR_MAP; OUTPUT_MAP(GatherV2) = {{0, OUTPUT_DESC(y)}}; // ReduceSum -INPUT_MAP(ReduceSum) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axis)}}; +INPUT_MAP(ReduceSum) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axes)}}; ATTR_MAP(ReduceSum) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceSum) = {{0, OUTPUT_DESC(y)}}; // ReduceSumD INPUT_MAP(ReduceSumD) = {{1, INPUT_DESC(x)}}; INPUT_ATTR_MAP(ReduceSumD) = { - {2, ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}}; + {2, ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(ReduceSumD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceSumD) = {{0, OUTPUT_DESC(y)}}; // ReduceProdD INPUT_MAP(ReduceProdD) = {{1, INPUT_DESC(x)}}; INPUT_ATTR_MAP(ReduceProdD) = { - {2, ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}}; + {2, ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(ReduceProdD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceProdD) = {{0, OUTPUT_DESC(y)}}; @@ -294,7 +292,7 @@ ATTR_MAP(CumprodD) = {{"exclusive", ATTR_DESC(exclusive, AnyTraits())}, {"reverse", ATTR_DESC(reverse, AnyTraits())}}; OUTPUT_MAP(CumprodD) = {{0, OUTPUT_DESC(y)}}; -// SoftmaxCrossEntropyWithLogits/ +// SoftmaxCrossEntropyWithLogits INPUT_MAP(SoftmaxCrossEntropyWithLogits) = {{1, INPUT_DESC(features)}, {2, INPUT_DESC(labels)}}; ATTR_MAP(SoftmaxCrossEntropyWithLogits) = EMPTY_ATTR_MAP; OUTPUT_MAP(SoftmaxCrossEntropyWithLogits) = {{0, OUTPUT_DESC(loss)}, {1, OUTPUT_DESC(backprop)}}; @@ -306,7 +304,7 @@ INPUT_ATTR_MAP(MeanGrad) = {{2, ATTR_DESC(mean_grad_output_shape_value, kOpForma ATTR_MAP(MeanGrad) = {{"mode", ATTR_DESC(mode, AnyTraits())}}; INPUT_MAP(SliceD) = {{1, INPUT_DESC(x)}}; -INPUT_ATTR_MAP(SliceD) = {{2, ATTR_DESC(begin, AnyTraits(), AnyTraits>())}, +INPUT_ATTR_MAP(SliceD) = {{2, ATTR_DESC(offsets, AnyTraits(), AnyTraits>())}, {3, ATTR_DESC(size, AnyTraits(), AnyTraits>())}}; ATTR_MAP(SliceD) = EMPTY_ATTR_MAP; OUTPUT_MAP(SliceD) = {{0, OUTPUT_DESC(y)}}; @@ -411,42 +409,10 @@ ATTR_MAP(BoundingBoxDecode) = { }; OUTPUT_MAP(BoundingBoxDecode) = {{0, OUTPUT_DESC(bboxes)}}; -#ifdef VALID_CODE - -// Less -INPUT_MAP(Less) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(y)}}; -ATTR_MAP(Less) = EMPTY_ATTR_MAP; -OUTPUT_MAP(Less) = {{0, OUTPUT_DESC(z)}}; - -// Cast -INPUT_MAP(Cast) = {{1, INPUT_DESC(x)}}; -INPUT_ATTR_MAP(Cast) = {{2, ATTR_DESC(dst_type, AnyTraits())}}; -ATTR_MAP(Cast) = {{"Truncate", ATTR_DESC(truncate, AnyTraits())}}; -OUTPUT_MAP(Cast) = {{0, OUTPUT_DESC(y)}}; - -// Minimum -INPUT_MAP(Minimum) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(y)}}; -ATTR_MAP(Minimum) = {{"alpha", ATTR_DESC(alpha, AnyTraits())}, {"beta", ATTR_DESC(beta, AnyTraits())}}; -OUTPUT_MAP(Minimum) = {{0, OUTPUT_DESC(z)}}; - -// Sub -INPUT_MAP(Sub) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; -ATTR_MAP(Sub) = {{"alpha", ATTR_DESC(alpha, AnyTraits())}, {"beta", ATTR_DESC(beta, AnyTraits())}}; - -#endif - -// TopKV2 -INPUT_MAP(TopKV2) = { - {1, INPUT_DESC(input)}, - {2, INPUT_DESC(k)}, -}; - -ATTR_MAP(TopKV2) = {{"T", ATTR_DESC(T, AnyTraits())}, {"sorted", ATTR_DESC(sorted, AnyTraits())}}; - -OUTPUT_MAP(TopKV2) = { - {0, OUTPUT_DESC(values)}, - {1, OUTPUT_DESC(indices)}, -}; +// TopK +INPUT_MAP(TopK) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(k)}}; +ATTR_MAP(TopK) = {{"sorted", ATTR_DESC(sorted, AnyTraits())}}; +OUTPUT_MAP(TopK) = {{0, OUTPUT_DESC(values)}, {1, OUTPUT_DESC(indices)}}; // Multiply INPUT_MAP(Multiply) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(y)}}; @@ -486,7 +452,7 @@ ATTR_MAP(Iou) = {{"mode", ATTR_DESC(mode, AnyTraits())}}; OUTPUT_MAP(Iou) = {{0, OUTPUT_DESC(overlap)}}; // ResizeNearestNeighborD -INPUT_MAP(ResizeNearestNeighborD) = {{1, INPUT_DESC(images)}}; +INPUT_MAP(ResizeNearestNeighborD) = {{1, INPUT_DESC(x)}}; ATTR_MAP(ResizeNearestNeighborD) = { {"size", ATTR_DESC(size, AnyTraits>(), AnyTraits>())}, {"align_corners", ATTR_DESC(align_corners, AnyTraits())}}; @@ -511,9 +477,9 @@ OUTPUT_MAP(ApplyAdam) = {{0, OUTPUT_DESC(var)}}; #endif // Relu6 -INPUT_MAP(Relu6) = {{1, INPUT_DESC(features)}}; +INPUT_MAP(Relu6) = {{1, INPUT_DESC(x)}}; ATTR_MAP(Relu6) = EMPTY_ATTR_MAP; -OUTPUT_MAP(Relu6) = {{0, OUTPUT_DESC(activations)}}; +OUTPUT_MAP(Relu6) = {{0, OUTPUT_DESC(y)}}; // Relu6Grad INPUT_MAP(Relu6Grad) = {{1, INPUT_DESC(gradients)}, {2, INPUT_DESC(features)}}; @@ -525,8 +491,8 @@ INPUT_MAP(ResizeBilinearGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(original ATTR_MAP(ResizeBilinearGrad) = {{"align_corners", ATTR_DESC(align_corners, AnyTraits())}}; OUTPUT_MAP(ResizeBilinearGrad) = {{0, OUTPUT_DESC(y)}}; -// ResizeBilinear -INPUT_MAP(ResizeBilinearD) = {{1, INPUT_DESC(images)}}; +// ResizeBilinearD +INPUT_MAP(ResizeBilinearD) = {{1, INPUT_DESC(x)}}; ATTR_MAP(ResizeBilinearD) = { {"size", ATTR_DESC(size, AnyTraits>(), AnyTraits>())}, {"align_corners", ATTR_DESC(align_corners, AnyTraits())}}; @@ -549,9 +515,9 @@ OUTPUT_MAP(NMSWithMask) = { {0, OUTPUT_DESC(selected_boxes)}, {1, OUTPUT_DESC(selected_idx)}, {2, OUTPUT_DESC(selected_mask)}}; // Unpack -INPUT_MAP(Unpack) = {{1, INPUT_DESC(value)}}; +INPUT_MAP(Unpack) = {{1, INPUT_DESC(x)}}; ATTR_MAP(Unpack) = {{"axis", ATTR_DESC(axis, AnyTraits())}, {"num", ATTR_DESC(num, AnyTraits())}}; -DYN_OUTPUT_MAP(Unpack) = {{0, DYN_OUTPUT_DESC(output)}}; +DYN_OUTPUT_MAP(Unpack) = {{0, DYN_OUTPUT_DESC(y)}}; // ScatterNdUpdate INPUT_MAP(ScatterNdUpdate) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(indices)}, {3, INPUT_DESC(updates)}}; @@ -584,8 +550,8 @@ INPUT_MAP(SigmoidCrossEntropyWithLogitsGrad) = { ATTR_MAP(SigmoidCrossEntropyWithLogitsGrad) = EMPTY_ATTR_MAP; OUTPUT_MAP(SigmoidCrossEntropyWithLogitsGrad) = {{0, OUTPUT_DESC(gradient)}}; -// ScatterNd -INPUT_MAP(ScatterNdD) = {{1, INPUT_DESC(indices)}, {2, INPUT_DESC(updates)}}; +// ScatterNdD +INPUT_MAP(ScatterNdD) = {{1, INPUT_DESC(indices)}, {2, INPUT_DESC(x)}}; INPUT_ATTR_MAP(ScatterNdD) = { {3, ATTR_DESC(shape, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(ScatterNdD) = EMPTY_ATTR_MAP; @@ -596,24 +562,14 @@ INPUT_MAP(PadD) = {{1, INPUT_DESC(x)}}; ATTR_MAP(PadD) = {{"paddings", ATTR_DESC(paddings, AnyTraits>>())}}; OUTPUT_MAP(PadD) = {{0, OUTPUT_DESC(y)}}; -// MirrorPad -INPUT_MAP(MirrorPad) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(paddings)}}; -ATTR_MAP(MirrorPad) = {{"mode", ATTR_DESC(mode, AnyTraits())}}; -OUTPUT_MAP(MirrorPad) = {{0, OUTPUT_DESC(y)}}; - -// MirrorPadGrad -INPUT_MAP(MirrorPadGrad) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(paddings)}}; -ATTR_MAP(MirrorPadGrad) = {{"mode", ATTR_DESC(mode, AnyTraits())}}; -OUTPUT_MAP(MirrorPadGrad) = {{0, OUTPUT_DESC(y)}}; - // GatherNd -INPUT_MAP(GatherNd) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; +INPUT_MAP(GatherNd) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(indices)}}; ATTR_MAP(GatherNd) = EMPTY_ATTR_MAP; OUTPUT_MAP(GatherNd) = {{0, OUTPUT_DESC(y)}}; // ROIAlign INPUT_MAP(ROIAlign) = {{1, INPUT_DESC(features)}, {2, INPUT_DESC(rois)}}; -OUTPUT_MAP(ROIAlign) = {{0, OUTPUT_DESC(output)}}; +OUTPUT_MAP(ROIAlign) = {{0, OUTPUT_DESC(y)}}; ATTR_MAP(ROIAlign) = {{"pooled_height", ATTR_DESC(pooled_height, AnyTraits())}, {"pooled_width", ATTR_DESC(pooled_width, AnyTraits())}, {"spatial_scale", ATTR_DESC(spatial_scale, AnyTraits())}, @@ -632,13 +588,13 @@ ATTR_MAP(ROIAlignGrad) = { // ArgMaxD INPUT_MAP(ArgMaxD) = {{1, INPUT_DESC(x)}}; ATTR_MAP(ArgMaxD) = {{"axis", ATTR_DESC(dimension, AnyTraits())}, - {"output_type", ATTR_DESC(output_type, AnyTraits())}}; + {"output_type", ATTR_DESC(dtype, AnyTraits())}}; OUTPUT_MAP(ArgMaxD) = {{0, OUTPUT_DESC(y)}}; // ArgMinD INPUT_MAP(ArgMinD) = {{1, INPUT_DESC(x)}}; ATTR_MAP(ArgMinD) = {{"axis", ATTR_DESC(dimension, AnyTraits())}, - {"output_type", ATTR_DESC(output_type, AnyTraits())}}; + {"output_type", ATTR_DESC(dtype, AnyTraits())}}; OUTPUT_MAP(ArgMinD) = {{0, OUTPUT_DESC(y)}}; // ArgMaxWithValue @@ -654,14 +610,14 @@ ATTR_MAP(ArgMinWithValue) = {{"axis", ATTR_DESC(dimension, AnyTraits())}, OUTPUT_MAP(ArgMinWithValue) = {{0, OUTPUT_DESC(indice)}, {1, OUTPUT_DESC(values)}}; // ReduceAll -INPUT_MAP(ReduceAll) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axis)}}; +INPUT_MAP(ReduceAll) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axes)}}; ATTR_MAP(ReduceAll) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceAll) = {{0, OUTPUT_DESC(y)}}; // ReduceMeanD INPUT_MAP(ReduceMeanD) = {{1, INPUT_DESC(x)}}; INPUT_ATTR_MAP(ReduceMeanD) = { - {2, ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}}; + {2, ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(ReduceMeanD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceMeanD) = {{0, OUTPUT_DESC(y)}}; @@ -728,11 +684,12 @@ INPUT_MAP(BiasAddGrad) = {{1, INPUT_DESC(x)}}; ATTR_MAP(BiasAddGrad) = {{"data_format", ATTR_DESC(data_format, AnyTraits())}}; OUTPUT_MAP(BiasAddGrad) = {{0, OUTPUT_DESC(y)}}; -// maxpoolgrad +// MaxPoolGrad INPUT_MAP(MaxPoolGrad) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}, {3, INPUT_DESC(grad)}}; ATTR_MAP(MaxPoolGrad) = {{"ksize", ATTR_DESC(ksize, AnyTraits(), AnyTraits>())}, {"strides", ATTR_DESC(strides, AnyTraits(), AnyTraits>())}, - {"padding", ATTR_DESC(padding, AnyTraits())}}; + {"padding", ATTR_DESC(padding, AnyTraits())}, + {"data_format", ATTR_DESC(data_format, AnyTraits())}}; OUTPUT_MAP(MaxPoolGrad) = {{0, OUTPUT_DESC(y)}}; // avgpoolgrad @@ -751,57 +708,55 @@ ATTR_MAP(MaxPoolWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits(), AnyT OUTPUT_MAP(MaxPoolWithArgmax) = {{0, OUTPUT_DESC(y)}, {1, OUTPUT_DESC(argmax)}}; // MaxPoolGradWithArgmax -INPUT_MAP(MaxPoolGradWithArgmax) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(grad)}, {3, INPUT_DESC(argmax)}}; +INPUT_MAP(MaxPoolGradWithArgmax) = { + {1, INPUT_DESC(x)}, + {2, INPUT_DESC(grad)}, + {3, INPUT_DESC(argmax)}, +}; ATTR_MAP(MaxPoolGradWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits(), AnyTraits>())}, {"strides", ATTR_DESC(strides, AnyTraits(), AnyTraits>())}, {"padding", ATTR_DESC(padding, AnyTraits())}}; OUTPUT_MAP(MaxPoolGradWithArgmax) = {{0, OUTPUT_DESC(y)}}; -// ExtractImagePatches -INPUT_MAP(ExtractImagePatches) = {{1, INPUT_DESC(images)}}; -ATTR_MAP(ExtractImagePatches) = {{"ksizes", ATTR_DESC(ksizes, AnyTraits(), AnyTraits>())}, - {"strides", ATTR_DESC(strides, AnyTraits(), AnyTraits>())}, - {"rates", ATTR_DESC(rates, AnyTraits(), AnyTraits>())}, - {"padding", ATTR_DESC(padding, AnyTraits())}}; -OUTPUT_MAP(ExtractImagePatches) = {{0, OUTPUT_DESC(y)}}; - // Conv2D INPUT_MAP(Conv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}}; -ATTR_MAP(Conv2D) = { - {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, - {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, -}; +ATTR_MAP(Conv2D) = {{"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, + {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, + {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, + {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"group", ATTR_DESC(groups, AnyTraits())}}; OUTPUT_MAP(Conv2D) = {{0, OUTPUT_DESC(y)}}; // Conv2DBackpropInputD -INPUT_MAP(Conv2DBackpropInputD) = {{1, INPUT_DESC(out_backprop)}, {2, INPUT_DESC(filters)}}; +INPUT_MAP(Conv2DBackpropInputD) = {{1, INPUT_DESC(out_backprop)}, {2, INPUT_DESC(filter)}}; INPUT_ATTR_MAP(Conv2DBackpropInputD) = { - {3, ATTR_DESC(input_sizes, AnyTraits>(), AnyTraits>())}}; + {3, ATTR_DESC(input_size, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(Conv2DBackpropInputD) = { {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, - {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, -}; + {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, + {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, + {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"group", ATTR_DESC(groups, AnyTraits())}}; OUTPUT_MAP(Conv2DBackpropInputD) = {{0, OUTPUT_DESC(y)}}; // Conv2DBackpropFilterD INPUT_MAP(Conv2DBackpropFilterD) = {{1, INPUT_DESC(out_backprop)}, {2, INPUT_DESC(x)}}; INPUT_ATTR_MAP(Conv2DBackpropFilterD) = { - {3, ATTR_DESC(filter_sizes, AnyTraits>(), AnyTraits>())}}; + {3, ATTR_DESC(filter_size, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(Conv2DBackpropFilterD) = { {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, - {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, -}; + {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, + {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, + {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"group", ATTR_DESC(groups, AnyTraits())}}; OUTPUT_MAP(Conv2DBackpropFilterD) = {{0, OUTPUT_DESC(y)}}; // DepthwiseConv2D INPUT_MAP(DepthwiseConv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}}; ATTR_MAP(DepthwiseConv2D) = { - {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, + {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, {"pads", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, + {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, {"data_format", ATTR_DESC(data_format, AnyTraits())}, }; OUTPUT_MAP(DepthwiseConv2D) = {{0, OUTPUT_DESC(y)}}; @@ -811,9 +766,9 @@ INPUT_MAP(DepthwiseConv2DBackpropInputD) = {{2, INPUT_DESC(filter)}, {3, INPUT_D INPUT_ATTR_MAP(DepthwiseConv2DBackpropInputD) = { {1, ATTR_DESC(input_size, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(DepthwiseConv2DBackpropInputD) = { - {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, + {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, {"pads", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, + {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, }; OUTPUT_MAP(DepthwiseConv2DBackpropInputD) = {{0, OUTPUT_DESC(input_grad)}}; @@ -822,16 +777,16 @@ INPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{1, INPUT_DESC(input)}, {3, INPUT_D INPUT_ATTR_MAP(DepthwiseConv2DBackpropFilterD) = { {2, ATTR_DESC(filter_size, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(DepthwiseConv2DBackpropFilterD) = { - {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, + {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, {"pads", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, + {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, }; OUTPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{0, OUTPUT_DESC(filter_grad)}}; // MatMul INPUT_MAP(MatMul) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; -ATTR_MAP(MatMul) = {{"transpose_a", ATTR_DESC(transpose_a, AnyTraits())}, - {"transpose_b", ATTR_DESC(transpose_b, AnyTraits())}}; +ATTR_MAP(MatMul) = {{"transpose_a", ATTR_DESC(transpose_x1, AnyTraits())}, + {"transpose_b", ATTR_DESC(transpose_x2, AnyTraits())}}; OUTPUT_MAP(MatMul) = {{0, OUTPUT_DESC(y)}}; // Merge @@ -878,10 +833,10 @@ ATTR_MAP(Sub) = EMPTY_ATTR_MAP; OUTPUT_MAP(Sub) = {{0, OUTPUT_DESC(y)}}; // SplitD -INPUT_MAP(SplitD) = {{1, INPUT_DESC(value)}}; +INPUT_MAP(SplitD) = {{1, INPUT_DESC(x)}}; ATTR_MAP(SplitD) = {{"axis", ATTR_DESC(split_dim, AnyTraits())}, {"output_num", ATTR_DESC(num_split, AnyTraits())}}; -DYN_OUTPUT_MAP(SplitD) = {{0, DYN_OUTPUT_DESC(output)}}; +DYN_OUTPUT_MAP(SplitD) = {{0, DYN_OUTPUT_DESC(y)}}; // Neg INPUT_MAP(Neg) = {{1, INPUT_DESC(x)}}; @@ -908,12 +863,12 @@ OUTPUT_MAP(Pack) = {{0, OUTPUT_DESC(y)}}; // ConcatD INPUT_MAP(ConcatD) = EMPTY_INPUT_MAP; -DYN_INPUT_MAP(ConcatD) = {{1, DYN_INPUT_DESC(input_values)}}; +DYN_INPUT_MAP(ConcatD) = {{1, DYN_INPUT_DESC(x)}}; ATTR_MAP(ConcatD) = { {"axis", ATTR_DESC(concat_dim, AnyTraits())}, {"inputNums", ATTR_DESC(N, AnyTraits())}, }; -OUTPUT_MAP(ConcatD) = {{0, OUTPUT_DESC(output_data)}}; +OUTPUT_MAP(ConcatD) = {{0, OUTPUT_DESC(y)}}; // Less INPUT_MAP(Less) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; @@ -948,14 +903,14 @@ OUTPUT_MAP(TanhGrad) = {{0, OUTPUT_DESC(z)}}; // ReduceMinD INPUT_MAP(ReduceMinD) = {{1, INPUT_DESC(x)}}; INPUT_ATTR_MAP(ReduceMinD) = { - {2, ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}}; + {2, ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(ReduceMinD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceMinD) = {{0, OUTPUT_DESC(y)}}; // ReduceMaxD INPUT_MAP(ReduceMaxD) = {{1, INPUT_DESC(x)}}; INPUT_ATTR_MAP(ReduceMaxD) = { - {2, ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}}; + {2, ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(ReduceMaxD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceMaxD) = {{0, OUTPUT_DESC(y)}}; @@ -1040,11 +995,11 @@ INPUT_MAP(LessEqual) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; ATTR_MAP(LessEqual) = EMPTY_ATTR_MAP; OUTPUT_MAP(LessEqual) = {{0, OUTPUT_DESC(y)}}; -// LogSoftmax -INPUT_MAP(LogSoftmax) = {{1, INPUT_DESC(logits)}}; -ATTR_MAP(LogSoftmax) = { - {"axis", ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}}; -OUTPUT_MAP(LogSoftmax) = {{0, OUTPUT_DESC(logsoftmax)}}; +// LogSoftmaxV2 +INPUT_MAP(LogSoftmaxV2) = {{1, INPUT_DESC(logits)}}; +ATTR_MAP(LogSoftmaxV2) = { + {"axis", ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}}; +OUTPUT_MAP(LogSoftmaxV2) = {{0, OUTPUT_DESC(logsoftmax)}}; // RandomChoiceWithMask INPUT_MAP(RandomChoiceWithMask) = {{1, INPUT_DESC(x)}}; @@ -1126,8 +1081,8 @@ OUTPUT_MAP(LayerNormGrad) = {{0, OUTPUT_DESC(pd_x)}, {1, OUTPUT_DESC(pd_gamma)}, // BatchMatMul INPUT_MAP(BatchMatMul) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}}; -ATTR_MAP(BatchMatMul) = {{"transpose_x1", ATTR_DESC(adj_x, AnyTraits())}, - {"transpose_x2", ATTR_DESC(adj_y, AnyTraits())}}; +ATTR_MAP(BatchMatMul) = {{"transpose_x1", ATTR_DESC(adj_x1, AnyTraits())}, + {"transpose_x2", ATTR_DESC(adj_x2, AnyTraits())}}; OUTPUT_MAP(BatchMatMul) = {{0, OUTPUT_DESC(y)}}; // DropoutDoMask @@ -1178,6 +1133,19 @@ ATTR_MAP(SparseApplyAdagradD) = {{"lr", ATTR_DESC(lr, AnyTraits())}, {"use_locking", ATTR_DESC(use_locking, AnyTraits())}}; OUTPUT_MAP(SparseApplyAdagradD) = {{0, OUTPUT_DESC(var)}}; +// SparseApplyFtrlD +INPUT_MAP(SparseApplyFtrlD) = {{1, INPUT_DESC(var)}, + {2, INPUT_DESC(accum)}, + {3, INPUT_DESC(linear)}, + {4, INPUT_DESC(grad)}, + {5, INPUT_DESC(indices)}}; +ATTR_MAP(SparseApplyFtrlD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits())}, + {"lr", ATTR_DESC(lr, AnyTraits())}, + {"l1", ATTR_DESC(l1, AnyTraits())}, + {"l2", ATTR_DESC(l2, AnyTraits())}, + {"lr_power", ATTR_DESC(lr_power, AnyTraits())}}; +OUTPUT_MAP(SparseApplyFtrlD) = {{0, OUTPUT_DESC(var)}}; + // SpaceToDepth INPUT_MAP(SpaceToDepth) = {{1, INPUT_DESC(x)}}; ATTR_MAP(SpaceToDepth) = {{"block_size", ATTR_DESC(block_size, AnyTraits())}}; diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index 933896a275..9fbc97f3c9 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -95,8 +95,6 @@ DECLARE_OP_USE_OUTPUT(MaxPoolGradWithArgmax) DECLARE_OP_ADAPTER(Conv2D) DECLARE_OP_USE_ENUM(Conv2D) DECLARE_OP_USE_OUTPUT(Conv2D) -DECLARE_OP_ADAPTER(ExtractImagePatches) -DECLARE_OP_USE_OUTPUT(ExtractImagePatches) DECLARE_OP_ADAPTER(Conv2DBackpropInputD) DECLARE_OP_USE_ENUM(Conv2DBackpropInputD) DECLARE_OP_USE_INPUT_ATTR(Conv2DBackpropInputD) @@ -157,10 +155,6 @@ DECLARE_OP_USE_INPUT_ATTR(ScatterNdD) DECLARE_OP_USE_OUTPUT(ScatterNdD) DECLARE_OP_ADAPTER(PadD) DECLARE_OP_USE_OUTPUT(PadD) -DECLARE_OP_ADAPTER(MirrorPad) -DECLARE_OP_USE_OUTPUT(MirrorPad) -DECLARE_OP_ADAPTER(MirrorPadGrad) -DECLARE_OP_USE_OUTPUT(MirrorPadGrad) DECLARE_OP_ADAPTER(BoundingBoxEncode) DECLARE_OP_USE_OUTPUT(BoundingBoxEncode) DECLARE_OP_ADAPTER(BoundingBoxDecode) From 4166e266c64b98145ee6195f048510c538113ee6 Mon Sep 17 00:00:00 2001 From: Wei Luning Date: Tue, 21 Apr 2020 11:16:44 +0800 Subject: [PATCH 182/227] use the old op --- .../ccsrc/optimizer/irpass/arithmetic_simplify.h | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h index 0d48fc1463..ff6e4f6170 100644 --- a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h +++ b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h @@ -248,17 +248,18 @@ class AdjustAllReduceMulAdd : public AnfVisitor { if (addn->size() != 2) { return nullptr; } - AnfVisitor::Match(prim::kPrimMakeTuple, {IsNode, IsNode})(addn->input(1)); if (x_ == nullptr || y_ == nullptr || z_ == nullptr) { return nullptr; } + auto addn_op_node = addn->input(0); + auto make_tuple_op_node = addn->input(1)->cast()->input(0); auto fg = node->func_graph(); - AnfNodePtr tuple = NewCNode({NewValueNode(prim::kPrimMakeTuple), z_, x_}, fg); - AnfNodePtr add = NewCNode({NewValueNode(prim::kPrimAddN), tuple}, fg); - AnfNodePtr all_reduce = NewCNode({NewValueNode(prim::kPrimAllReduce), add}, fg); - return NewCNode({NewValueNode(prim::kPrimMul), all_reduce, y_}, fg); + AnfNodePtr tuple = NewCNode({make_tuple_op_node, z_, x_}, fg); + AnfNodePtr add = NewCNode({addn_op_node, tuple}, fg); + AnfNodePtr all_reduce = NewCNode({all_reduce_, add}, fg); + return NewCNode({mul_, all_reduce, y_}, fg); } void Visit(const AnfNodePtr &node) override { @@ -269,6 +270,7 @@ class AdjustAllReduceMulAdd : public AnfVisitor { AnfVisitor::Match(prim::kPrimMul)(node); level_ = 0; if (is_reduce_match_) { + mul_ = node->cast()->input(0); y_ = tmp_; } else { z_ = node; @@ -280,6 +282,7 @@ class AdjustAllReduceMulAdd : public AnfVisitor { if (IsPrimitiveCNode(node, prim::kPrimAllReduce)) { auto cnode = node->cast(); if (cnode->size() > 1) { + all_reduce_ = cnode->input(0); x_ = cnode->input(1); is_reduce_match_ = true; } @@ -302,6 +305,7 @@ class AdjustAllReduceMulAdd : public AnfVisitor { int level_{0}; bool is_reduce_match_{false}; AnfNodePtr x_{nullptr}, y_{nullptr}, z_{nullptr}, tmp_{nullptr}; + AnfNodePtr all_reduce_{nullptr}, mul_{nullptr}; }; class ArithmeticSimplify { From d2b80031407e3b0d6dbf3a5a15b3581f6ca4efdf Mon Sep 17 00:00:00 2001 From: Wei Luning Date: Thu, 23 Apr 2020 10:05:22 +0800 Subject: [PATCH 183/227] Revert "use the old op" This reverts commit 4166e266c64b98145ee6195f048510c538113ee6. --- .../ccsrc/optimizer/irpass/arithmetic_simplify.h | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h index ff6e4f6170..0d48fc1463 100644 --- a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h +++ b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h @@ -248,18 +248,17 @@ class AdjustAllReduceMulAdd : public AnfVisitor { if (addn->size() != 2) { return nullptr; } + AnfVisitor::Match(prim::kPrimMakeTuple, {IsNode, IsNode})(addn->input(1)); if (x_ == nullptr || y_ == nullptr || z_ == nullptr) { return nullptr; } - auto addn_op_node = addn->input(0); - auto make_tuple_op_node = addn->input(1)->cast()->input(0); auto fg = node->func_graph(); - AnfNodePtr tuple = NewCNode({make_tuple_op_node, z_, x_}, fg); - AnfNodePtr add = NewCNode({addn_op_node, tuple}, fg); - AnfNodePtr all_reduce = NewCNode({all_reduce_, add}, fg); - return NewCNode({mul_, all_reduce, y_}, fg); + AnfNodePtr tuple = NewCNode({NewValueNode(prim::kPrimMakeTuple), z_, x_}, fg); + AnfNodePtr add = NewCNode({NewValueNode(prim::kPrimAddN), tuple}, fg); + AnfNodePtr all_reduce = NewCNode({NewValueNode(prim::kPrimAllReduce), add}, fg); + return NewCNode({NewValueNode(prim::kPrimMul), all_reduce, y_}, fg); } void Visit(const AnfNodePtr &node) override { @@ -270,7 +269,6 @@ class AdjustAllReduceMulAdd : public AnfVisitor { AnfVisitor::Match(prim::kPrimMul)(node); level_ = 0; if (is_reduce_match_) { - mul_ = node->cast()->input(0); y_ = tmp_; } else { z_ = node; @@ -282,7 +280,6 @@ class AdjustAllReduceMulAdd : public AnfVisitor { if (IsPrimitiveCNode(node, prim::kPrimAllReduce)) { auto cnode = node->cast(); if (cnode->size() > 1) { - all_reduce_ = cnode->input(0); x_ = cnode->input(1); is_reduce_match_ = true; } @@ -305,7 +302,6 @@ class AdjustAllReduceMulAdd : public AnfVisitor { int level_{0}; bool is_reduce_match_{false}; AnfNodePtr x_{nullptr}, y_{nullptr}, z_{nullptr}, tmp_{nullptr}; - AnfNodePtr all_reduce_{nullptr}, mul_{nullptr}; }; class ArithmeticSimplify { From ac25cbae0e3332a7c777a3bb8cae5347f17d3666 Mon Sep 17 00:00:00 2001 From: Wei Luning Date: Thu, 23 Apr 2020 10:05:33 +0800 Subject: [PATCH 184/227] Revert "add pattern AdjustAllReduceMulAdd" This reverts commit d4473644171d990ffc54cf0dca13113966d5f508. --- mindspore/ccsrc/operator/ops.cc | 1 - mindspore/ccsrc/operator/ops.h | 1 - mindspore/ccsrc/optimizer/irpass.cc | 2 +- .../optimizer/irpass/arithmetic_simplify.h | 78 ------------------- mindspore/ops/operations/array_ops.py | 4 +- mindspore/ops/operations/nn_ops.py | 2 +- tests/ut/cpp/optimizer/lib_test.cc | 19 ----- .../gtest_input/optimizer/opt_test.py | 45 +---------- 8 files changed, 6 insertions(+), 146 deletions(-) diff --git a/mindspore/ccsrc/operator/ops.cc b/mindspore/ccsrc/operator/ops.cc index ed287b03b7..12e6b70a6f 100644 --- a/mindspore/ccsrc/operator/ops.cc +++ b/mindspore/ccsrc/operator/ops.cc @@ -223,7 +223,6 @@ const PrimitivePtr kPrimIsNot = std::make_shared("is_not"); const PrimitivePtr kPrimMirror = std::make_shared("_MirrorOperator"); const PrimitivePtr kPrimVirtualDiv = std::make_shared("_VirtualDiv"); const PrimitivePtr kPrimVirtualDataset = std::make_shared("_VirtualDataset"); -const PrimitivePtr kPrimAllReduce = std::make_shared("AllReduce"); // Debug ops const PrimitivePtr kPrimScalarSummary = std::make_shared("ScalarSummary"); diff --git a/mindspore/ccsrc/operator/ops.h b/mindspore/ccsrc/operator/ops.h index 6c88e30e70..5fbf2b7067 100644 --- a/mindspore/ccsrc/operator/ops.h +++ b/mindspore/ccsrc/operator/ops.h @@ -228,7 +228,6 @@ extern const PrimitivePtr kPrimMinimumGrad; extern const PrimitivePtr kPrimMaximumGrad; // Comm ops -extern const PrimitivePtr kPrimAllReduce; extern const PrimitivePtr kPrimMirror; extern const PrimitivePtr kPrimVirtualDiv; extern const PrimitivePtr kPrimVirtualDataset; diff --git a/mindspore/ccsrc/optimizer/irpass.cc b/mindspore/ccsrc/optimizer/irpass.cc index 25784c8a0b..0991c31b00 100644 --- a/mindspore/ccsrc/optimizer/irpass.cc +++ b/mindspore/ccsrc/optimizer/irpass.cc @@ -47,7 +47,7 @@ namespace irpass { OptimizeIRPassLib::OptimizeIRPassLib() { arithmetic_simplify_ = MakeSubstitution(ArithmeticSimplify(), "arithmetic_simplify", {prim::kPrimScalarAdd, prim::kPrimScalarMul, prim::kPrimTensorAdd, - prim::kPrimAddN, prim::kPrimIdentity, prim::kPrimMomentum, prim::kPrimMul}); + prim::kPrimIdentity, prim::kPrimMomentum, prim::kPrimMul}); special_op_eliminate_ = MakeSubstitution(SpecialOpEliminater(), "special_op_eliminate", {prim::kPrimInsertGradientOf, prim::kPrimPrintShapeType, prim::kPrimGetRefKey, prim::kPrimMirror, prim::kPrimVirtualDiv}); diff --git a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h index 0d48fc1463..ab191aab20 100644 --- a/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h +++ b/mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h @@ -228,82 +228,6 @@ class ConstantDuplicateMul : public AnfVisitor { CNodePtr cnode_; }; -// grad = AllReduce(grad) / worker_number -// grad = grad + weight * decy -// -> -// grad = grad + weight * decy -// grad = AllReduce(grad) / worker_number - -// {prim::kPrimAddN, {prim::kPrimMakeTuple, {prim::kPrimMul, {prim::kPrimAllReduce, X}, Y}, Z}} -> -// {prim::kPrimMul, {prim::kPrimAllReduce, {prim::kPrimAddN,{prim::kPrimMakeTuple, Z, X}}}, Y} -class AdjustAllReduceMulAdd : public AnfVisitor { - public: - AnfNodePtr operator()(const OptimizerPtr &, const AnfNodePtr &node) override { - Reset(); - // {prim::kPrimAddN, Zs} - if (!IsPrimitiveCNode(node, prim::kPrimAddN)) { - return nullptr; - } - auto addn = node->cast(); - if (addn->size() != 2) { - return nullptr; - } - - AnfVisitor::Match(prim::kPrimMakeTuple, {IsNode, IsNode})(addn->input(1)); - if (x_ == nullptr || y_ == nullptr || z_ == nullptr) { - return nullptr; - } - - auto fg = node->func_graph(); - AnfNodePtr tuple = NewCNode({NewValueNode(prim::kPrimMakeTuple), z_, x_}, fg); - AnfNodePtr add = NewCNode({NewValueNode(prim::kPrimAddN), tuple}, fg); - AnfNodePtr all_reduce = NewCNode({NewValueNode(prim::kPrimAllReduce), add}, fg); - return NewCNode({NewValueNode(prim::kPrimMul), all_reduce, y_}, fg); - } - - void Visit(const AnfNodePtr &node) override { - if (level_ == 0) { - level_ = 1; - is_reduce_match_ = false; - // {prim::kPrimMul, {prim::kPrimAllReduce, X}, Y} - AnfVisitor::Match(prim::kPrimMul)(node); - level_ = 0; - if (is_reduce_match_) { - y_ = tmp_; - } else { - z_ = node; - } - } - - if (level_ == 1) { - // {prim::kPrimAllReduce, X} - if (IsPrimitiveCNode(node, prim::kPrimAllReduce)) { - auto cnode = node->cast(); - if (cnode->size() > 1) { - x_ = cnode->input(1); - is_reduce_match_ = true; - } - } else { - tmp_ = node; - } - } - } - - void Reset() { - level_ = 0; - is_reduce_match_ = false; - x_ = nullptr; - y_ = nullptr; - z_ = nullptr; - tmp_ = nullptr; - } - - private: - int level_{0}; - bool is_reduce_match_{false}; - AnfNodePtr x_{nullptr}, y_{nullptr}, z_{nullptr}, tmp_{nullptr}; -}; - class ArithmeticSimplify { public: ArithmeticSimplify() @@ -319,7 +243,6 @@ class ArithmeticSimplify { eliminaters_.emplace_back(identity_); eliminaters_.emplace_back(opt_update_zero_tensor_); eliminaters_.emplace_back(constant_duplicate_mul_); - eliminaters_.emplace_back(adjust_allreduce_mul_add_); } ~ArithmeticSimplify() = default; @@ -341,7 +264,6 @@ class ArithmeticSimplify { PrimEliminater identity_; OptUpdateZeroTensor opt_update_zero_tensor_; ConstantDuplicateMul constant_duplicate_mul_; - AdjustAllReduceMulAdd adjust_allreduce_mul_add_; std::vector eliminaters_{}; }; } // namespace irpass diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index 2264e72773..ac7f8ed699 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -1235,8 +1235,8 @@ class UnsortedSegmentSum(PrimitiveWithInfer): Tensor, the shape is :math:`(z, x_{N+1}, ..., x_R)`. Examples: - >>> input_x = Tensor([1, 2, 3, 4], mindspore.float32) - >>> segment_ids = Tensor([0, 0, 1, 2], mindspore.int32) + >>> input_x = [1, 2, 3, 4] + >>> segment_ids = [0, 0, 1, 2] >>> num_segments = 4 >>> type = P.UnsortedSegmentSum()(input_x, segment_ids, num_segments) """ diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index e780500af7..3cc6718484 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -1499,7 +1499,7 @@ class LayerNorm(Primitive): `Layer Normalization `_. .. math:: - y = \frac{x - mean}{\sqrt{variance + \epsilon}} * \gamma + \beta + y = \frac{x - mean]}{\sqrt{variance + \epsilon}} * \gamma + \beta where :math:`\gamma` is scale, :math:`\beta` is bias, :math:`\epsilon` is epsilon. diff --git a/tests/ut/cpp/optimizer/lib_test.cc b/tests/ut/cpp/optimizer/lib_test.cc index 8e348c698a..2d4cf0e78e 100644 --- a/tests/ut/cpp/optimizer/lib_test.cc +++ b/tests/ut/cpp/optimizer/lib_test.cc @@ -556,24 +556,5 @@ TEST_F(TestOptLib, test_constant_duplicate_mul) { ASSERT_TRUE(CheckOpt(beforerl, after, patterns)); ASSERT_TRUE(CheckOpt(beforerr, after, patterns)); } - -TEST_F(TestOptLib, test_adjust_allreduce_mul_add) { - FuncGraphPtr beforell = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforell"); - FuncGraphPtr beforelr = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforelr"); - FuncGraphPtr beforerl = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforerl"); - FuncGraphPtr beforerr = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforerr"); - FuncGraphPtr after1 = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "after1"); - FuncGraphPtr before2r = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "before2r"); - FuncGraphPtr before2l = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "before2l"); - FuncGraphPtr after2 = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "after2"); - auto patterns = std::vector({irpass.arithmetic_simplify_}); - ASSERT_TRUE(CheckOpt(beforell, after1, patterns)); - ASSERT_TRUE(CheckOpt(beforelr, after1, patterns)); - ASSERT_TRUE(CheckOpt(beforerl, after1, patterns)); - ASSERT_TRUE(CheckOpt(beforerr, after1, patterns)); - ASSERT_TRUE(CheckOpt(before2l, after2, patterns)); - ASSERT_TRUE(CheckOpt(before2r, after2, patterns)); -} - } // namespace opt } // namespace mindspore diff --git a/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py b/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py index d74aa15952..d494ad27d3 100644 --- a/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py +++ b/tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py @@ -908,8 +908,8 @@ def test_print_tuple_wrapper(tag): def test_constant_duplicate_mul(tag): fns = FnDict() - Mul = Primitive('Mul') - Sqrt = Primitive('Sqrt') + Mul = Primitive('Mul'); + Sqrt = Primitive('Sqrt'); x = Tensor(np.array([[2, 2], [2, 3]]).astype('float32')) tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32')) @@ -936,44 +936,3 @@ def test_constant_duplicate_mul(tag): return Mul(Sqrt(x), Mul(tensor1, tensor2)) return fns[tag] - - -def test_adjust_allreduce_mul_add(tag): - fns = FnDict() - Mul = Primitive('Mul') - AddN = Primitive('AddN') - AllReduce = Primitive('AllReduce') - - @fns - def beforell(x, y, z): - return AddN((z, Mul(y, AllReduce(x)))) - - @fns - def beforelr(x, y, z): - return AddN((z, Mul(AllReduce(x), y))) - - @fns - def beforerl(x, y, z): - return AddN((Mul(y, AllReduce(x)), z)) - - @fns - def beforerr(x, y, z): - return AddN((Mul(AllReduce(x), y), z)) - - @fns - def after1(x, y, z): - return Mul(AllReduce(AddN((z, x))), y) - - @fns - def before2r(x, y, z): - return AddN((Mul(AllReduce(x), y), Mul(z, z))) - - @fns - def before2l(x, y, z): - return AddN((Mul(z, z), Mul(AllReduce(x), y))) - - @fns - def after2(x, y, z): - return Mul(AllReduce(AddN((Mul(z, z), x))), y) - - return fns[tag] From bd13f9ba339e2cdffdaf9a2d192a6aff5ca92e85 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Thu, 23 Apr 2020 15:17:06 +0800 Subject: [PATCH 185/227] modify ResizeNearestNeighborV2D --- .../gpu/nn/fused_batch_norm_gpu_kernel.cc | 2 - .../gpu/nn/fused_batch_norm_gpu_kernel.h | 3 - mindspore/ccsrc/transform/op_declare.cc | 59 +++++++++---------- mindspore/ops/_grad/grad_nn_ops.py | 4 +- mindspore/ops/operations/_grad_ops.py | 4 +- mindspore/ops/operations/nn_ops.py | 8 +-- tests/ut/python/ops/test_ops.py | 2 +- 7 files changed, 35 insertions(+), 47 deletions(-) diff --git a/mindspore/ccsrc/kernel/gpu/nn/fused_batch_norm_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/nn/fused_batch_norm_gpu_kernel.cc index 4ddc710a4c..91747d24d8 100644 --- a/mindspore/ccsrc/kernel/gpu/nn/fused_batch_norm_gpu_kernel.cc +++ b/mindspore/ccsrc/kernel/gpu/nn/fused_batch_norm_gpu_kernel.cc @@ -55,7 +55,6 @@ MS_REG_GPU_KERNEL_ONE(BatchNorm, .AddOutputAttr(kNumberTypeFloat32) .AddOutputAttr(kNumberTypeFloat32) .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) .AddOutputAttr(kNumberTypeFloat32), FusedBatchNormGpuKernel, float) MS_REG_GPU_KERNEL_ONE(BatchNorm, @@ -69,7 +68,6 @@ MS_REG_GPU_KERNEL_ONE(BatchNorm, .AddOutputAttr(kNumberTypeFloat16) .AddOutputAttr(kNumberTypeFloat16) .AddOutputAttr(kNumberTypeFloat16) - .AddOutputAttr(kNumberTypeFloat16) .AddOutputAttr(kNumberTypeFloat16), FusedBatchNormGpuKernel, half) } // namespace kernel diff --git a/mindspore/ccsrc/kernel/gpu/nn/fused_batch_norm_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/nn/fused_batch_norm_gpu_kernel.h index 6f0c59e29a..5ca85f8e63 100644 --- a/mindspore/ccsrc/kernel/gpu/nn/fused_batch_norm_gpu_kernel.h +++ b/mindspore/ccsrc/kernel/gpu/nn/fused_batch_norm_gpu_kernel.h @@ -156,9 +156,6 @@ class FusedBatchNormGpuKernel : public GpuKernel { output_size_list_.push_back(para_size); // running variance output_size_list_.push_back(para_size); // save mean output_size_list_.push_back(para_size); // save variance - if (!is_train_) { - output_size_list_.push_back(para_size); // reserve - } return; } diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index d6ca3f4cbe..299ac4f44d 100644 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -154,14 +154,14 @@ ATTR_MAP(BatchNorm) = {{"data_format", ATTR_DESC(data_format, AnyTraits())}, {"epsilon", ATTR_DESC(epsilon, AnyTraits())}, {"is_training", ATTR_DESC(is_training, AnyTraits())}}; @@ -266,11 +266,6 @@ INPUT_MAP(GatherV2) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(indices)}, {3, INPUT_D ATTR_MAP(GatherV2) = EMPTY_ATTR_MAP; OUTPUT_MAP(GatherV2) = {{0, OUTPUT_DESC(y)}}; -// ReduceSum -INPUT_MAP(ReduceSum) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axes)}}; -ATTR_MAP(ReduceSum) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; -OUTPUT_MAP(ReduceSum) = {{0, OUTPUT_DESC(y)}}; - // ReduceSumD INPUT_MAP(ReduceSumD) = {{1, INPUT_DESC(x)}}; INPUT_ATTR_MAP(ReduceSumD) = { @@ -451,17 +446,17 @@ INPUT_MAP(Iou) = {{1, INPUT_DESC(bboxes)}, {2, INPUT_DESC(gtboxes)}}; ATTR_MAP(Iou) = {{"mode", ATTR_DESC(mode, AnyTraits())}}; OUTPUT_MAP(Iou) = {{0, OUTPUT_DESC(overlap)}}; -// ResizeNearestNeighborD -INPUT_MAP(ResizeNearestNeighborD) = {{1, INPUT_DESC(x)}}; -ATTR_MAP(ResizeNearestNeighborD) = { +// ResizeNearestNeighborV2D +INPUT_MAP(ResizeNearestNeighborV2D) = {{1, INPUT_DESC(x)}}; +ATTR_MAP(ResizeNearestNeighborV2D) = { {"size", ATTR_DESC(size, AnyTraits>(), AnyTraits>())}, {"align_corners", ATTR_DESC(align_corners, AnyTraits())}}; -OUTPUT_MAP(ResizeNearestNeighborD) = {{0, OUTPUT_DESC(y)}}; +OUTPUT_MAP(ResizeNearestNeighborV2D) = {{0, OUTPUT_DESC(y)}}; -// ResizeNearestNeighborGrad -INPUT_MAP(ResizeNearestNeighborGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(size)}}; -ATTR_MAP(ResizeNearestNeighborGrad) = {{"align_corners", ATTR_DESC(align_corners, AnyTraits())}}; -OUTPUT_MAP(ResizeNearestNeighborGrad) = {{0, OUTPUT_DESC(y)}}; +// ResizeNearestNeighborV2Grad +INPUT_MAP(ResizeNearestNeighborV2Grad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(size)}}; +ATTR_MAP(ResizeNearestNeighborV2Grad) = {{"align_corners", ATTR_DESC(align_corners, AnyTraits())}}; +OUTPUT_MAP(ResizeNearestNeighborV2Grad) = {{0, OUTPUT_DESC(y)}}; // ApplyAdam INPUT_MAP(ApplyAdam) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(m)}, {3, INPUT_DESC(v)}, @@ -486,17 +481,17 @@ INPUT_MAP(Relu6Grad) = {{1, INPUT_DESC(gradients)}, {2, INPUT_DESC(features)}}; ATTR_MAP(Relu6Grad) = EMPTY_ATTR_MAP; OUTPUT_MAP(Relu6Grad) = {{0, OUTPUT_DESC(backprops)}}; -// ResizeBilinearGrad -INPUT_MAP(ResizeBilinearGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(original_image)}}; -ATTR_MAP(ResizeBilinearGrad) = {{"align_corners", ATTR_DESC(align_corners, AnyTraits())}}; -OUTPUT_MAP(ResizeBilinearGrad) = {{0, OUTPUT_DESC(y)}}; +// ResizeBilinearV2Grad +INPUT_MAP(ResizeBilinearV2Grad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(original_image)}}; +ATTR_MAP(ResizeBilinearV2Grad) = {{"align_corners", ATTR_DESC(align_corners, AnyTraits())}}; +OUTPUT_MAP(ResizeBilinearV2Grad) = {{0, OUTPUT_DESC(y)}}; -// ResizeBilinearD -INPUT_MAP(ResizeBilinearD) = {{1, INPUT_DESC(x)}}; -ATTR_MAP(ResizeBilinearD) = { +// ResizeBilinearV2D +INPUT_MAP(ResizeBilinearV2D) = {{1, INPUT_DESC(x)}}; +ATTR_MAP(ResizeBilinearV2D) = { {"size", ATTR_DESC(size, AnyTraits>(), AnyTraits>())}, {"align_corners", ATTR_DESC(align_corners, AnyTraits())}}; -OUTPUT_MAP(ResizeBilinearD) = {{0, OUTPUT_DESC(y)}}; +OUTPUT_MAP(ResizeBilinearV2D) = {{0, OUTPUT_DESC(y)}}; // ZerosLike INPUT_MAP(ZerosLike) = {{1, INPUT_DESC(x)}}; @@ -609,10 +604,12 @@ ATTR_MAP(ArgMinWithValue) = {{"axis", ATTR_DESC(dimension, AnyTraits())}, {"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ArgMinWithValue) = {{0, OUTPUT_DESC(indice)}, {1, OUTPUT_DESC(values)}}; -// ReduceAll -INPUT_MAP(ReduceAll) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axes)}}; -ATTR_MAP(ReduceAll) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; -OUTPUT_MAP(ReduceAll) = {{0, OUTPUT_DESC(y)}} +// ReduceAllD +INPUT_MAP(ReduceAllD) = {{1, INPUT_DESC(x)}}; +INPUT_ATTR_MAP(ReduceAllD) = { + {2, ATTR_DESC(axis, AnyTraits>(), AnyTraits>())}}; +ATTR_MAP(ReduceAllD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; +OUTPUT_MAP(ReduceAllD) = {{0, OUTPUT_DESC(y)}}; // ReduceMeanD INPUT_MAP(ReduceMeanD) = {{1, INPUT_DESC(x)}}; diff --git a/mindspore/ops/_grad/grad_nn_ops.py b/mindspore/ops/_grad/grad_nn_ops.py index e43d3d5d3a..6db059a7bb 100755 --- a/mindspore/ops/_grad/grad_nn_ops.py +++ b/mindspore/ops/_grad/grad_nn_ops.py @@ -356,12 +356,10 @@ def get_bprop_batch_norm(self): if is_training: saved_reserve_1 = out[3] saved_reserve_2 = out[4] - saved_reserve_3 = out[5] else: saved_reserve_1 = mean saved_reserve_2 = variance - saved_reserve_3 = variance - out = input_grad(dout[0], x, scale, saved_reserve_1, saved_reserve_2, saved_reserve_3) + out = input_grad(dout[0], x, scale, saved_reserve_1, saved_reserve_2) dx = out[0] dscale = out[1] dbias = out[2] diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py index c29832dcb7..9f277908ed 100644 --- a/mindspore/ops/operations/_grad_ops.py +++ b/mindspore/ops/operations/_grad_ops.py @@ -69,11 +69,11 @@ class BatchNormGrad(PrimitiveWithInfer): self.epsilon = validator.check_number_range('epsilon', epsilon, 0, 1, Rel.INC_RIGHT) self.add_prim_attr('data_format', "NCHW") - def infer_shape(self, y_backprop_shape, x_shape, scale_shape, reserve_1_shape, reserve_2_shape, reserve_3_shape): + def infer_shape(self, y_backprop_shape, x_shape, scale_shape, reserve_1_shape, reserve_2_shape): validator.check("BatchNorm y_backprop_shape", y_backprop_shape, "BatchNorm x_shape", x_shape) return (x_shape, scale_shape, scale_shape, reserve_1_shape, reserve_2_shape) - def infer_dtype(self, y_backprop_type, x_type, scale_type, reserve_1_type, reserve_2_type, reserve_3_type): + def infer_dtype(self, y_backprop_type, x_type, scale_type, reserve_1_type, reserve_2_type): return (x_type, scale_type, scale_type, reserve_1_type, reserve_2_type) diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 49145fb072..93359c7dd9 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -537,7 +537,6 @@ class BatchNorm(PrimitiveWithInfer): - **updated_bias** (Tensor) - Tensor of shape :math:`(C,)`. - **reserve_space_1** (Tensor) - Tensor of shape :math:`(C,)`. - **reserve_space_2** (Tensor) - Tensor of shape :math:`(C,)`. - - **reserve_space_3** (Tensor) - Tensor of shape :math:`(C,)`. """ @prim_attr_register @@ -546,8 +545,7 @@ class BatchNorm(PrimitiveWithInfer): validator.check_number_range('epsilon', epsilon, 0, 1, Rel.INC_RIGHT, self.name) self.add_prim_attr('data_format', "NCHW") self.init_prim_io_names(inputs=['x', 'scale', 'offset', 'mean', 'variance'], - outputs=['y', 'batch_mean', 'batch_variance', 'reserve_space_1', 'reserve_space_2', - 'reserve_space_3']) + outputs=['y', 'batch_mean', 'batch_variance', 'reserve_space_1', 'reserve_space_2']) def infer_shape(self, input_x, scale, bias, mean, variance): validator.check_integer("scale rank", len(scale), 1, Rel.EQ, self.name) @@ -557,7 +555,7 @@ class BatchNorm(PrimitiveWithInfer): validator.check_integer("mean rank", len(mean), 1, Rel.EQ, self.name) validator.check("mean shape", mean, "variance shape", variance, Rel.EQ, self.name) validator.check("mean shape", mean, "scale shape", scale, Rel.EQ, self.name) - return (input_x, scale, scale, scale, scale, scale) + return (input_x, scale, scale, scale, scale) def infer_dtype(self, input_x, scale, bias, mean, variance): validator.check_tensor_type_same({"input_x": input_x}, [mstype.float16, mstype.float32], self.name) @@ -570,7 +568,7 @@ class BatchNorm(PrimitiveWithInfer): else: args_moving = {"mean": mean, "variance": variance} validator.check_tensor_type_same(args_moving, [mstype.float16, mstype.float32], self.name) - return (input_x, scale, bias, input_x, input_x, input_x) + return (input_x, scale, bias, input_x, input_x) class Conv2D(PrimitiveWithInfer): diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py index 8b14ea2366..1dea7b6502 100755 --- a/tests/ut/python/ops/test_ops.py +++ b/tests/ut/python/ops/test_ops.py @@ -671,7 +671,7 @@ test_case_nn_ops = [ 'skip': []}), ('BatchNormGrad', { 'block': G.BatchNormGrad(), - 'desc_inputs': [[128, 64, 32, 32], [128, 64, 32, 32], [64], [64], [64], [64]], + 'desc_inputs': [[128, 64, 32, 32], [128, 64, 32, 32], [64], [64], [64]], 'desc_bprop': [[128, 64, 32, 32], [64], [64], [64], [64]], 'skip': ['backward']}), ('ApplyMomentum', { From 57bc30505ca3de0f5314b65da9fdd6a1053441c5 Mon Sep 17 00:00:00 2001 From: zhaojichen Date: Thu, 23 Apr 2020 05:29:26 -0400 Subject: [PATCH 186/227] change error type --- tests/ut/python/ops/test_array_ops.py | 4 ++-- tests/ut/python/ops/test_math_ops.py | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tests/ut/python/ops/test_array_ops.py b/tests/ut/python/ops/test_array_ops.py index faaa9d5402..01e7e32d50 100644 --- a/tests/ut/python/ops/test_array_ops.py +++ b/tests/ut/python/ops/test_array_ops.py @@ -228,10 +228,10 @@ def test_exec(): raise_set = [ ('Squeeze_1_Error', { - 'block': (lambda x: P.Squeeze(axis=1.2), {'exception': ValueError}), + 'block': (lambda x: P.Squeeze(axis=1.2), {'exception': TypeError}), 'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5]))]}), ('Squeeze_2_Error', { - 'block': (lambda x: P.Squeeze(axis=((1.2, 1.3))), {'exception': ValueError}), + 'block': (lambda x: P.Squeeze(axis=((1.2, 1.3))), {'exception': TypeError}), 'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5]))]}), ('ReduceSum_Error', { 'block': (lambda x: P.ReduceSum(keep_dims=1), {'exception': TypeError}), diff --git a/tests/ut/python/ops/test_math_ops.py b/tests/ut/python/ops/test_math_ops.py index 8b7f627e81..595bf35e2c 100755 --- a/tests/ut/python/ops/test_math_ops.py +++ b/tests/ut/python/ops/test_math_ops.py @@ -385,16 +385,16 @@ def test_exec(): raise_set = [ ('StridedSlice_1_Error', { - 'block': (lambda x: P.StridedSlice(begin_mask="1"), {'exception': ValueError}), + 'block': (lambda x: P.StridedSlice(begin_mask="1"), {'exception': TypeError}), 'desc_inputs': [0]}), ('StridedSlice_2_Error', { - 'block': (lambda x: P.StridedSlice(end_mask="1"), {'exception': ValueError}), + 'block': (lambda x: P.StridedSlice(end_mask="1"), {'exception': TypeError}), 'desc_inputs': [0]}), ('StridedSlice_3_Error', { - 'block': (lambda x: P.StridedSlice(ellipsis_mask=1.1), {'exception': ValueError}), + 'block': (lambda x: P.StridedSlice(ellipsis_mask=1.1), {'exception': TypeError}), 'desc_inputs': [0]}), ('StridedSlice_4_Error', { - 'block': (lambda x: P.StridedSlice(new_axis_mask="1.1"), {'exception': ValueError}), + 'block': (lambda x: P.StridedSlice(new_axis_mask="1.1"), {'exception': TypeError}), 'desc_inputs': [0]}), ] From d3981615e57406d2108b9931f0b62848bb8d7d30 Mon Sep 17 00:00:00 2001 From: zhaojichen Date: Thu, 23 Apr 2020 08:08:41 -0400 Subject: [PATCH 187/227] change error type --- mindspore/_checkparam.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/_checkparam.py b/mindspore/_checkparam.py index cb3dbc0d50..d01de7396a 100644 --- a/mindspore/_checkparam.py +++ b/mindspore/_checkparam.py @@ -314,7 +314,7 @@ class ParamValidator: """func for raising error message when check failed""" type_names = [t.__name__ for t in valid_types] num_types = len(valid_types) - raise ValueError(f'The type of `{arg_name}` should be {"one of " if num_types > 1 else ""}' + raise TypeError(f'The type of `{arg_name}` should be {"one of " if num_types > 1 else ""}' f'{type_names if num_types > 1 else type_names[0]}, but got {type(arg_value).__name__}.') if isinstance(arg_value, type(mstype.tensor)): From f66f234517153b7e4b191bce36a189188203a21c Mon Sep 17 00:00:00 2001 From: zhaojichen Date: Thu, 23 Apr 2020 08:26:19 -0400 Subject: [PATCH 188/227] change error type --- mindspore/_checkparam.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/_checkparam.py b/mindspore/_checkparam.py index d01de7396a..dc2f71cc18 100644 --- a/mindspore/_checkparam.py +++ b/mindspore/_checkparam.py @@ -315,7 +315,7 @@ class ParamValidator: type_names = [t.__name__ for t in valid_types] num_types = len(valid_types) raise TypeError(f'The type of `{arg_name}` should be {"one of " if num_types > 1 else ""}' - f'{type_names if num_types > 1 else type_names[0]}, but got {type(arg_value).__name__}.') + f'{type_names if num_types > 1 else type_names[0]}, but got {type(arg_value).__name__}.') if isinstance(arg_value, type(mstype.tensor)): arg_value = arg_value.element_type() From 0949ea19afb02ecfa986b0cc29d950141bf7dd28 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Thu, 23 Apr 2020 19:58:32 +0800 Subject: [PATCH 189/227] modify ReduceAllD --- mindspore/ccsrc/transform/op_declare.cc | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 299ac4f44d..718751e9f5 100644 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -154,6 +154,7 @@ ATTR_MAP(BatchNorm) = {{"data_format", ATTR_DESC(data_format, AnyTraits>(), AnyTraits>())}}; + {2, ATTR_DESC(axes, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(ReduceAllD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits())}}; OUTPUT_MAP(ReduceAllD) = {{0, OUTPUT_DESC(y)}}; From c6f5efaab279e78d36c79e34738968f4d44442eb Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Thu, 23 Apr 2020 21:20:14 +0800 Subject: [PATCH 190/227] modify ge --- graphengine | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/graphengine b/graphengine index cfc99f95f7..43a715bc46 160000 --- a/graphengine +++ b/graphengine @@ -1 +1 @@ -Subproject commit cfc99f95f722918025b0eaeb93440d92265f09fe +Subproject commit 43a715bc461fd70b7837051a2f47f0a1b19c5859 From 1af894e25fb497f388a042cb4798556f7747506e Mon Sep 17 00:00:00 2001 From: zhaojichen Date: Thu, 23 Apr 2020 09:21:05 -0400 Subject: [PATCH 191/227] change error type --- tests/ut/python/nn/test_dynamic_lr.py | 12 ++++++------ tests/ut/python/nn/test_psnr.py | 2 +- tests/ut/python/nn/test_ssim.py | 2 +- tests/ut/python/ops/test_nn_ops.py | 20 ++++++++++---------- 4 files changed, 18 insertions(+), 18 deletions(-) diff --git a/tests/ut/python/nn/test_dynamic_lr.py b/tests/ut/python/nn/test_dynamic_lr.py index cb959956d6..96f9d5afde 100644 --- a/tests/ut/python/nn/test_dynamic_lr.py +++ b/tests/ut/python/nn/test_dynamic_lr.py @@ -32,7 +32,7 @@ power = 0.5 class TestInputs: def test_milestone1(self): milestone1 = 1 - with pytest.raises(ValueError): + with pytest.raises(TypeError): dr.piecewise_constant_lr(milestone1, learning_rates) def test_milestone2(self): @@ -46,12 +46,12 @@ class TestInputs: def test_learning_rates1(self): lr = True - with pytest.raises(ValueError): + with pytest.raises(TypeError): dr.piecewise_constant_lr(milestone, lr) def test_learning_rates2(self): lr = [1, 2, 1] - with pytest.raises(ValueError): + with pytest.raises(TypeError): dr.piecewise_constant_lr(milestone, lr) def test_learning_rate_type(self): @@ -158,7 +158,7 @@ class TestInputs: def test_is_stair(self): is_stair = 1 - with pytest.raises(ValueError): + with pytest.raises(TypeError): dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair) def test_min_lr_type(self): @@ -183,12 +183,12 @@ class TestInputs: def test_power(self): power1 = True - with pytest.raises(ValueError): + with pytest.raises(TypeError): dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power1) def test_update_decay_epoch(self): update_decay_epoch = 1 - with pytest.raises(ValueError): + with pytest.raises(TypeError): dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power, update_decay_epoch) diff --git a/tests/ut/python/nn/test_psnr.py b/tests/ut/python/nn/test_psnr.py index 5a908b308d..32e7b570aa 100644 --- a/tests/ut/python/nn/test_psnr.py +++ b/tests/ut/python/nn/test_psnr.py @@ -52,7 +52,7 @@ def test_psnr_max_val_negative(): def test_psnr_max_val_bool(): max_val = True - with pytest.raises(ValueError): + with pytest.raises(TypeError): net = PSNRNet(max_val) def test_psnr_max_val_zero(): diff --git a/tests/ut/python/nn/test_ssim.py b/tests/ut/python/nn/test_ssim.py index a698b59f69..c1a652de57 100644 --- a/tests/ut/python/nn/test_ssim.py +++ b/tests/ut/python/nn/test_ssim.py @@ -51,7 +51,7 @@ def test_ssim_max_val_negative(): def test_ssim_max_val_bool(): max_val = True - with pytest.raises(ValueError): + with pytest.raises(TypeError): net = SSIMNet(max_val) def test_ssim_max_val_zero(): diff --git a/tests/ut/python/ops/test_nn_ops.py b/tests/ut/python/ops/test_nn_ops.py index bb2bb3ea9f..ec5d25cccb 100644 --- a/tests/ut/python/ops/test_nn_ops.py +++ b/tests/ut/python/ops/test_nn_ops.py @@ -528,7 +528,7 @@ test_cases_for_verify_exception = [ 'desc_inputs': [0], }), ('MaxPoolWithArgmax_ValueError_2', { - 'block': (lambda _: P.MaxPoolWithArgmax(ksize='1'), {'exception': ValueError}), + 'block': (lambda _: P.MaxPoolWithArgmax(ksize='1'), {'exception': TypeError}), 'desc_inputs': [0], }), ('MaxPoolWithArgmax_ValueError_3', { @@ -560,31 +560,31 @@ test_cases_for_verify_exception = [ 'desc_inputs': [0], }), ('Softmax_ValueError_1', { - 'block': (lambda _: P.Softmax("1"), {'exception': ValueError}), + 'block': (lambda _: P.Softmax("1"), {'exception': TypeError}), 'desc_inputs': [0], }), ('Softmax_ValueError_2', { - 'block': (lambda _: P.Softmax(1.1), {'exception': ValueError}), + 'block': (lambda _: P.Softmax(1.1), {'exception': TypeError}), 'desc_inputs': [0], }), ('Softmax_ValueError_3', { - 'block': (lambda _: P.Softmax(axis="1"), {'exception': ValueError}), + 'block': (lambda _: P.Softmax(axis="1"), {'exception': TypeError}), 'desc_inputs': [0], }), ('DropoutGenMask_ValueError_1', { - 'block': (lambda _: P.DropoutGenMask(Seed0="seed0"), {'exception': ValueError}), + 'block': (lambda _: P.DropoutGenMask(Seed0="seed0"), {'exception': TypeError}), 'desc_inputs': [0], }), ('DropoutGenMask_ValueError_2', { - 'block': (lambda _: P.DropoutGenMask(Seed0=1.0), {'exception': ValueError}), + 'block': (lambda _: P.DropoutGenMask(Seed0=1.0), {'exception': TypeError}), 'desc_inputs': [0], }), ('DropoutGenMask_ValueError_3', { - 'block': (lambda _: P.DropoutGenMask(Seed1="seed1"), {'exception': ValueError}), + 'block': (lambda _: P.DropoutGenMask(Seed1="seed1"), {'exception': TypeError}), 'desc_inputs': [0], }), ('DropoutGenMask_ValueError_4', { - 'block': (lambda _: P.DropoutGenMask(Seed1=2.0), {'exception': ValueError}), + 'block': (lambda _: P.DropoutGenMask(Seed1=2.0), {'exception': TypeError}), 'desc_inputs': [0], }), ('MaxPool2d_ValueError_1', { @@ -594,14 +594,14 @@ test_cases_for_verify_exception = [ ('MaxPool2d_ValueError_2', { 'block': ( lambda _: nn.MaxPool2d(kernel_size=120, stride=True, pad_mode="valid"), - {'exception': ValueError}, + {'exception': TypeError}, ), 'desc_inputs': [Tensor(np.random.randn(32, 3, 112, 112).astype(np.float32).transpose(0, 3, 1, 2))], }), ('MaxPool2d_ValueError_3', { 'block': ( lambda _: nn.MaxPool2d(kernel_size=3, stride=True, pad_mode="valid"), - {'exception': ValueError}, + {'exception': TypeError}, ), 'desc_inputs': [Tensor(np.random.randn(32, 3, 112, 112).astype(np.float32).transpose(0, 3, 1, 2))], }), From 1587eef5299ade3496aaaf79b64a2735af1da7b2 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Thu, 23 Apr 2020 21:29:43 +0800 Subject: [PATCH 192/227] modify Conv2DBackpropFilterD pad --- mindspore/ccsrc/transform/op_declare.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 718751e9f5..3e6b029f6b 100644 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -742,7 +742,7 @@ INPUT_ATTR_MAP(Conv2DBackpropFilterD) = { {3, ATTR_DESC(filter_size, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(Conv2DBackpropFilterD) = { {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, + {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, }; OUTPUT_MAP(Conv2DBackpropFilterD) = {{0, OUTPUT_DESC(y)}}; From 1c39c52f72f35b5ed16623b1ec75c6fcbadb2733 Mon Sep 17 00:00:00 2001 From: chang zherui <760161589@qq.com> Date: Thu, 23 Apr 2020 21:39:03 +0800 Subject: [PATCH 193/227] asd --- mindspore/ccsrc/transform/op_declare.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 3e6b029f6b..718751e9f5 100644 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -742,7 +742,7 @@ INPUT_ATTR_MAP(Conv2DBackpropFilterD) = { {3, ATTR_DESC(filter_size, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(Conv2DBackpropFilterD) = { {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, + {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, }; OUTPUT_MAP(Conv2DBackpropFilterD) = {{0, OUTPUT_DESC(y)}}; From d1add07b5afdf1248ab07c93494556d153597062 Mon Sep 17 00:00:00 2001 From: zhaojichen Date: Thu, 23 Apr 2020 09:49:56 -0400 Subject: [PATCH 194/227] change error type --- tests/ut/python/pynative_mode/nn/test_pooling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/ut/python/pynative_mode/nn/test_pooling.py b/tests/ut/python/pynative_mode/nn/test_pooling.py index bb1822f8a8..f8df3ada3f 100644 --- a/tests/ut/python/pynative_mode/nn/test_pooling.py +++ b/tests/ut/python/pynative_mode/nn/test_pooling.py @@ -38,7 +38,7 @@ def test_avgpool2d_error_input(): """ test_avgpool2d_error_input """ kernel_size = 5 stride = 2.3 - with pytest.raises(ValueError): + with pytest.raises(TypeError): nn.AvgPool2d(kernel_size, stride) From ee9eb86901c3f0a163582ed0e63b30ac2baec1f5 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 11:07:18 +0800 Subject: [PATCH 195/227] Conv2d and backprops --- mindspore/ccsrc/transform/op_declare.cc | 6 ++ mindspore/ops/operations/_grad_ops.py | 109 ++++++++++++++++------ mindspore/ops/operations/nn_ops.py | 119 +++++++++++++++++------- 3 files changed, 171 insertions(+), 63 deletions(-) diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 718751e9f5..b8aec05c3b 100644 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -722,6 +722,8 @@ ATTR_MAP(Conv2D) = { {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, + {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"group", ATTR_DESC(groups, AnyTraits())}, }; OUTPUT_MAP(Conv2D) = {{0, OUTPUT_DESC(y)}}; @@ -733,6 +735,8 @@ ATTR_MAP(Conv2DBackpropInputD) = { {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, + {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"group", ATTR_DESC(groups, AnyTraits())}, }; OUTPUT_MAP(Conv2DBackpropInputD) = {{0, OUTPUT_DESC(y)}}; @@ -744,6 +748,8 @@ ATTR_MAP(Conv2DBackpropFilterD) = { {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, + {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"group", ATTR_DESC(groups, AnyTraits())}, }; OUTPUT_MAP(Conv2DBackpropFilterD) = {{0, OUTPUT_DESC(y)}}; diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py index 9f277908ed..7c118994c6 100644 --- a/mindspore/ops/operations/_grad_ops.py +++ b/mindspore/ops/operations/_grad_ops.py @@ -137,9 +137,9 @@ class ConcatOffset(PrimitiveWithInfer): return out -class Conv2DBackpropFilter(PrimitiveWithInfer): +class Conv2DBackpropInput(PrimitiveWithInfer): """ - Computes the gradients of convolution with respect to the filter. + Computes the gradients of convolution with respect to the input. Args: out_channel (int): The dimensionality of the output space. @@ -147,9 +147,9 @@ class Conv2DBackpropFilter(PrimitiveWithInfer): pad_mode (str): "valid", "same", "pad" the mode to fill padding. Default: "valid". pad (int): The pad value to fill. Default: 0. mode (int): 0 Math convolutiuon, 1 cross-correlation convolution , - 2 deconvolution, 3 depthwise convolution. Default: 1. - stride (tuple): The stride to apply conv filter. Default: (1, 1). - dilation (tuple): Specifies the dilation rate to use for dilated convolution. Default: (1, 1, 1, 1). + 2 deconvolution, 3 depthwise convolution. Default: 1. + stride (Union[int. tuple[int]]): The stride to apply conv filter. Default: 1. + dilation (Union[int. tuple[int]]): Specifies the dilation rate to use for dilated convolution. Default: 1. group (int): Splits input into groups. Default: 1. Returns: @@ -162,38 +162,91 @@ class Conv2DBackpropFilter(PrimitiveWithInfer): kernel_size, pad_mode="valid", pad=0, - pad_list=(0, 0, 0, 0), + pad_list=None, mode=1, - stride=(1, 1), - dilation=(1, 1, 1, 1), + stride=1, + dilation=1, group=1): - """init Convolution""" - self.init_prim_io_names(inputs=['out_backprop', 'input', 'filter_sizes'], outputs=['output']) - self.out_channel = out_channel - self.kernel_size = kernel_size - self.mode = mode + """init Conv2DBackpropInput""" + self.init_prim_io_names(inputs=['out_backprop', 'filter', 'input_sizes'], outputs=['output']) + self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT) + self.kernel_size = validator.check_type('kernel_size', kernel_size, (int, tuple)) + if isinstance(kernel_size, int): + self.kernel_size = (kernel_size, kernel_size) + if len(self.kernel_size) != 2 or (not isinstance(self.kernel_size[0], int)) or \ + (not isinstance(self.kernel_size[1], int)) or \ + self.kernel_size[0] < 1 or self.kernel_size[1] < 1: + raise ValueError(f"The \'kernel_size\' of \'Conv2DBackpropInput\' should be an positive int number or " + f"a tuple of two positive int numbers, but got {kernel_size}") + self.stride = validator.check_type('stride', stride, (int, tuple)) + if isinstance(stride, int): + self.stride = (stride, stride) + elif isinstance(stride, tuple) and len(stride) == 4: + self.stride = (stride[2], stride[3]) + if len(self.stride) != 2 or (not isinstance(self.stride[0], int)) or (not isinstance(self.stride[1], int)) or \ + self.stride[0] < 1 or self.stride[1] < 1: + raise ValueError(f"The \'stride\' of \'Conv2DBackpropInput\' should be an positive int number or " + f"a tuple of two or four positive int numbers, but got {stride}") + self.add_prim_attr('stride', self.stride) + self.dilation = validator.check_type('dilation', dilation, (tuple, int)) + if isinstance(dilation, int): + self.dilation = (1, 1, dilation, dilation) + elif len(dilation) == 2: + self.dilation = (1, 1, dilation[0], dilation[1]) + if len(self.dilation) != 4 or (not isinstance(self.dilation[0], int) or self.dilation[0] < 1) or \ + (not isinstance(self.dilation[1], int) or self.dilation[1] < 1) or \ + (not isinstance(self.dilation[2], int) or self.dilation[2] < 1) or \ + (not isinstance(self.dilation[3], int) or self.dilation[3] < 1): + raise ValueError(f"The \'dilation\' of \'Conv2DBackpropInput\' should be an positive int number or " + f"a tuple of two or four positive int numbers, but got {dilation}") + self.add_prim_attr('dilation', self.dilation) + validator.equal('type of pad', type(pad), 'not bool', not isinstance(pad, bool)) + validator.equal('type of pad', type(pad), 'int', isinstance(pad, int)) + self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad']) + self.pad = validator.check_pad_value_by_mode(self.__class__.__name__, pad_mode, pad) + self.mode = validator.check_integer('mode', mode, 1, Rel.EQ) + self.group = validator.check_integer('group', group, 0, Rel.GT) pad_mode = pad_mode.upper() self.add_prim_attr('pad_mode', pad_mode) - self.pad = pad - if isinstance(stride, tuple) and len(stride) == 4: - self.stride = (stride[2], stride[3]) - self.add_prim_attr('stride', self.stride) - self.dilation = dilation - self.group = group self.add_prim_attr('data_format', "NCHW") + if pad_list: + self.pad_lsit = (validator.check_integer('pad_list', x, 0, Rel.GE) for x in pad_list) - def __infer__(self, doutput, x, w_size): - w_size_v = w_size['value'] - validator.check_type('w_size', w_size_v, [tuple]) - for i, dim_len in enumerate(w_size_v): - validator.check_type("w_size[%d]" % i, dim_len, [int]) - validator.check_typename('x_dtype', x['dtype'], [mstype.int8, mstype.int32, mstype.float16, mstype.float32]) - validator.check_two_types_same('doutput_dtype', doutput['dtype'], 'x_dtype', x['dtype']) + def __infer__(self, doutput, w, x_size): + x_size_v = x_size['value'] + validator.check_type('x_size', x_size_v, [tuple]) + for i, dim_len in enumerate(x_size_v): + validator.check_type("x_size[%d]" % i, dim_len, [int]) + validator.check_typename('w_dtype', w['dtype'], [mstype.int8, mstype.int32, mstype.float16, mstype.float32]) + validator.check_two_types_same('doutput_dtype', doutput['dtype'], 'w_dtype', w['dtype']) + + # infer shape + dout_shape = doutput['shape'] + kernel_h = self.kernel_size[0] + kernel_w = self.kernel_size[1] + stride_h = self.stride[0] + stride_w = self.stride[1] + # default pad mode is valid + pad_list = (0, 0, 0, 0) + if self.pad_list: + pad_list = tuple(self.pad_list) + elif self.pad_mode == "SAME": + pad_needed_h = max(0, (dout_shape[2] - 1) * stride_h + kernel_h - x_size_v[2]) + pad_top = math.floor(pad_needed_h / 2) + pad_bottom = pad_needed_h - pad_top + + pad_needed_w = max(0, (dout_shape[3] - 1) * stride_w + kernel_w - x_size_v[3]) + pad_left = math.floor(pad_needed_w / 2) + pad_right = pad_needed_w - pad_left + pad_list = (pad_top, pad_bottom, pad_left, pad_right) + elif self.pad_mode == 'PAD': + pad_list = (self.pad,) * 4 + self.add_prim_attr('pad_list', pad_list) out = { 'value': None, - 'shape': w_size_v, + 'shape': x_size_v, 'dtype': doutput['dtype'], - } + } return out diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 93359c7dd9..00af0155ae 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -637,28 +637,53 @@ class Conv2D(PrimitiveWithInfer): group=1): """init Conv2D""" self.init_prim_io_names(inputs=['x', 'w'], outputs=['output']) - self.kernel_size = _check_positive_int_or_tuple('kernel_size', kernel_size, self.name) - self.stride = _check_positive_int_or_tuple('stride', stride, self.name) + self.kernel_size = validator.check_type('kernel_size', kernel_size, (int, tuple)) + if isinstance(kernel_size, int): + self.kernel_size = (kernel_size, kernel_size) + if len(self.kernel_size) != 2 or (not isinstance(self.kernel_size[0], int)) or \ + (not isinstance(self.kernel_size[1], int)) or \ + self.kernel_size[0] < 1 or self.kernel_size[1] < 1: + raise ValueError(f"The \'kernel_size\' of \'Conv2D\' should be an positive int number or " + f"a tuple of two positive int numbers, but got {kernel_size}") + self.stride = validator.check_type('stride', stride, (int, tuple)) + if isinstance(stride, int): + self.stride = (stride, stride) + if len(self.stride) != 2 or (not isinstance(self.stride[0], int)) or \ + (not isinstance(self.stride[1], int)) or \ + self.stride[0] < 1 or self.stride[1] < 1: + raise ValueError(f"The \'stride\' of \'Conv2D\' should be an positive int number or " + f"a tuple of two positive int numbers, but got {stride}") self.add_prim_attr('stride', (1, 1, self.stride[0], self.stride[1])) - self.dilation = _check_positive_int_or_tuple('dilation', dilation, self.name, allow_four=True, ret_four=True) + self.dilation = validator.check_type('dilation', dilation, (tuple, int)) + if isinstance(dilation, int): + self.dilation = (1, 1, dilation, dilation) + elif len(dilation) == 2: + self.dilation = (1, 1, dilation[0], dilation[1]) + if len(self.dilation) != 4 or (not isinstance(self.dilation[0], int) or self.dilation[0] < 1) or \ + (not isinstance(self.dilation[1], int) or self.dilation[1] < 1) or \ + (not isinstance(self.dilation[2], int) or self.dilation[2] < 1) or \ + (not isinstance(self.dilation[3], int) or self.dilation[3] < 1): + raise ValueError(f"The \'dilation\' of \'Conv2D\' should be an positive int number or " + f"a tuple of two or four positive int numbers, but got {dilation}") self.add_prim_attr('dilation', self.dilation) - validator.check_value_type('pad', pad, (int,), self.name) - self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad'], self.name) - self.pad = validator.check_pad_value_by_mode(pad_mode, pad, self.name) + validator.equal('type of pad', type(pad), 'not bool', not isinstance(pad, bool)) + validator.equal('type of pad', type(pad), 'int', isinstance(pad, int)) + self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad']) + self.pad = validator.check_pad_value_by_mode(self.__class__.__name__, pad_mode, pad) if self.pad_mode == 'pad': - validator.check_integer('pad', self.pad, 0, Rel.GE, self.name) + validator.check_integer('pad', self.pad, 0, Rel.GE) - self.mode = validator.check_integer('mode', mode, 1, Rel.EQ, self.name) + self.mode = validator.check_integer('mode', mode, 1, Rel.EQ) self.add_prim_attr('data_format', "NCHW") - self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT, self.name) - self.group = validator.check_integer('group', group, 0, Rel.GT, self.name) + self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT) + self.group = validator.check_integer('group', group, 0, Rel.GT) def infer_shape(self, x_shape, w_shape): - validator.check_integer("weight rank", len(w_shape), 4, Rel.EQ, self.name) - validator.check_integer("x rank", len(x_shape), 4, Rel.EQ, self.name) - validator.check("x_shape[1] / group", x_shape[1] // self.group, "w_shape[1]", w_shape[1], Rel.EQ, self.name) - validator.check('out_channel', self.out_channel, 'w_shape[0]', w_shape[0], Rel.EQ, self.name) - validator.check('kernel_size', self.kernel_size, 'w_shape[2:4]', tuple(w_shape[2:4]), Rel.EQ, self.name) + validator.check_integer("weight_shape", len(w_shape), 4, Rel.EQ) + validator.check_integer("x_shape", len(x_shape), 4, Rel.EQ) + validator.check_param_equal("x_shape[1]", x_shape[1] // self.group, "w_shape[1]", w_shape[1]) + validator.check_param_equal('out_channel', self.out_channel, 'w_shape[0]', w_shape[0]) + validator.check_param_equal('kernel_size', self.kernel_size, 'w_shape[2:4]', tuple(w_shape[2:4])) kernel_size_h = w_shape[2] kernel_size_w = w_shape[3] @@ -700,9 +725,10 @@ class Conv2D(PrimitiveWithInfer): return out_shape def infer_dtype(self, x_dtype, w_dtype): - args = {'x': x_dtype, 'w': w_dtype} - valid_types = [mstype.int8, mstype.int32, mstype.float16, mstype.float32] - validator.check_tensor_type_same(args, valid_types, self.name) + args = {'x_dtype': x_dtype, 'w_dtype': w_dtype} + validator.check_subclass('input', x_dtype, mstype.tensor) + validator.check_subclass('weight', w_dtype, mstype.tensor) + validator.check_type_same(args, [mstype.int8, mstype.int32, mstype.float16, mstype.float32]) return x_dtype @@ -1082,33 +1108,56 @@ class Conv2DBackpropInput(PrimitiveWithInfer): group=1): """init Conv2DBackpropInput""" self.init_prim_io_names(inputs=['out_backprop', 'filter', 'input_sizes'], outputs=['output']) - self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT, self.name) - self.kernel_size = _check_positive_int_or_tuple('kernel_size', kernel_size, self.name) - self.stride = _check_positive_int_or_tuple('stride', stride, self.name, allow_four=True, ret_four=False) + self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT) + self.kernel_size = validator.check_type('kernel_size', kernel_size, (int, tuple)) + if isinstance(kernel_size, int): + self.kernel_size = (kernel_size, kernel_size) + if len(self.kernel_size) != 2 or (not isinstance(self.kernel_size[0], int)) or \ + (not isinstance(self.kernel_size[1], int)) or \ + self.kernel_size[0] < 1 or self.kernel_size[1] < 1: + raise ValueError(f"The \'kernel_size\' of \'Conv2DBackpropInput\' should be an positive int number or " + f"a tuple of two positive int numbers, but got {kernel_size}") + self.stride = validator.check_type('stride', stride, (int, tuple)) + if isinstance(stride, int): + self.stride = (stride, stride) + elif isinstance(stride, tuple) and len(stride) == 4: + self.stride = (stride[2], stride[3]) + if len(self.stride) != 2 or (not isinstance(self.stride[0], int)) or (not isinstance(self.stride[1], int)) or \ + self.stride[0] < 1 or self.stride[1] < 1: + raise ValueError(f"The \'stride\' of \'Conv2DBackpropInput\' should be an positive int number or " + f"a tuple of two or four positive int numbers, but got {stride}") self.add_prim_attr('stride', self.stride) - self.dilation = _check_positive_int_or_tuple('dilation', dilation, self.name, allow_four=True, ret_four=True) + self.dilation = validator.check_type('dilation', dilation, (tuple, int)) + if isinstance(dilation, int): + self.dilation = (1, 1, dilation, dilation) + elif len(dilation) == 2: + self.dilation = (1, 1, dilation[0], dilation[1]) + if len(self.dilation) != 4 or (not isinstance(self.dilation[0], int) or self.dilation[0] < 1) or \ + (not isinstance(self.dilation[1], int) or self.dilation[1] < 1) or \ + (not isinstance(self.dilation[2], int) or self.dilation[2] < 1) or \ + (not isinstance(self.dilation[3], int) or self.dilation[3] < 1): + raise ValueError(f"The \'dilation\' of \'Conv2DBackpropInput\' should be an positive int number or " + f"a tuple of two or four positive int numbers, but got {dilation}") self.add_prim_attr('dilation', self.dilation) - validator.check_value_type('pad', pad, (int,), self.name) - self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad'], self.name) - self.pad = validator.check_pad_value_by_mode(pad_mode, pad, self.name) + validator.equal('type of pad', type(pad), 'not bool', not isinstance(pad, bool)) + validator.equal('type of pad', type(pad), 'int', isinstance(pad, int)) + self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad']) + self.pad = validator.check_pad_value_by_mode(self.__class__.__name__, pad_mode, pad) + self.mode = validator.check_integer('mode', mode, 1, Rel.EQ) + self.group = validator.check_integer('group', group, 0, Rel.GT) pad_mode = pad_mode.upper() self.add_prim_attr('pad_mode', pad_mode) - self.mode = validator.check_integer('mode', mode, 1, Rel.EQ, self.name) - self.group = validator.check_integer('group', group, 0, Rel.GT, self.name) self.add_prim_attr('data_format', "NCHW") if pad_list: - for x in pad_list: - validator.check_integer('element of pad_list', x, 0, Rel.GE, self.name) - self.pad_list = pad_list + self.pad_lsit = (validator.check_integer('pad_list', x, 0, Rel.GE) for x in pad_list) def __infer__(self, doutput, w, x_size): x_size_v = x_size['value'] - validator.check_value_type('x_size', x_size_v, [tuple], self.name) + validator.check_type('x_size', x_size_v, [tuple]) for i, dim_len in enumerate(x_size_v): - validator.check_value_type("x_size[%d]" % i, dim_len, [int], self.name) - args = {'doutput': doutput['dtype'], 'w': w['dtype']} - valid_types = [mstype.int8, mstype.int32, mstype.float16, mstype.float32] - validator.check_tensor_type_same(args, valid_types, self.name) + validator.check_type("x_size[%d]" % i, dim_len, [int]) + validator.check_typename('w_dtype', w['dtype'], [mstype.int8, mstype.int32, mstype.float16, mstype.float32]) + validator.check_two_types_same('doutput_dtype', doutput['dtype'], 'w_dtype', w['dtype']) # infer shape dout_shape = doutput['shape'] From f3ed97a7f028b0ba5e3f9a106af3d04cfe3119a7 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 11:46:06 +0800 Subject: [PATCH 196/227] fix conv2d --- mindspore/ops/operations/nn_ops.py | 121 +++++++++-------------------- 1 file changed, 36 insertions(+), 85 deletions(-) diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 00af0155ae..9750549dc5 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -637,53 +637,28 @@ class Conv2D(PrimitiveWithInfer): group=1): """init Conv2D""" self.init_prim_io_names(inputs=['x', 'w'], outputs=['output']) - self.kernel_size = validator.check_type('kernel_size', kernel_size, (int, tuple)) - if isinstance(kernel_size, int): - self.kernel_size = (kernel_size, kernel_size) - if len(self.kernel_size) != 2 or (not isinstance(self.kernel_size[0], int)) or \ - (not isinstance(self.kernel_size[1], int)) or \ - self.kernel_size[0] < 1 or self.kernel_size[1] < 1: - raise ValueError(f"The \'kernel_size\' of \'Conv2D\' should be an positive int number or " - f"a tuple of two positive int numbers, but got {kernel_size}") - self.stride = validator.check_type('stride', stride, (int, tuple)) - if isinstance(stride, int): - self.stride = (stride, stride) - if len(self.stride) != 2 or (not isinstance(self.stride[0], int)) or \ - (not isinstance(self.stride[1], int)) or \ - self.stride[0] < 1 or self.stride[1] < 1: - raise ValueError(f"The \'stride\' of \'Conv2D\' should be an positive int number or " - f"a tuple of two positive int numbers, but got {stride}") + self.kernel_size = _check_positive_int_or_tuple('kernel_size', kernel_size, self.name) + self.stride = _check_positive_int_or_tuple('stride', stride, self.name) self.add_prim_attr('stride', (1, 1, self.stride[0], self.stride[1])) - self.dilation = validator.check_type('dilation', dilation, (tuple, int)) - if isinstance(dilation, int): - self.dilation = (1, 1, dilation, dilation) - elif len(dilation) == 2: - self.dilation = (1, 1, dilation[0], dilation[1]) - if len(self.dilation) != 4 or (not isinstance(self.dilation[0], int) or self.dilation[0] < 1) or \ - (not isinstance(self.dilation[1], int) or self.dilation[1] < 1) or \ - (not isinstance(self.dilation[2], int) or self.dilation[2] < 1) or \ - (not isinstance(self.dilation[3], int) or self.dilation[3] < 1): - raise ValueError(f"The \'dilation\' of \'Conv2D\' should be an positive int number or " - f"a tuple of two or four positive int numbers, but got {dilation}") + self.dilation = _check_positive_int_or_tuple('dilation', dilation, self.name, allow_four=True, ret_four=True) self.add_prim_attr('dilation', self.dilation) - validator.equal('type of pad', type(pad), 'not bool', not isinstance(pad, bool)) - validator.equal('type of pad', type(pad), 'int', isinstance(pad, int)) - self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad']) - self.pad = validator.check_pad_value_by_mode(self.__class__.__name__, pad_mode, pad) + validator.check_value_type('pad', pad, (int,), self.name) + self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad'], self.name) + self.pad = validator.check_pad_value_by_mode(pad_mode, pad, self.name) if self.pad_mode == 'pad': - validator.check_integer('pad', self.pad, 0, Rel.GE) + validator.check_integer('pad', self.pad, 0, Rel.GE, self.name) - self.mode = validator.check_integer('mode', mode, 1, Rel.EQ) + self.mode = validator.check_integer('mode', mode, 1, Rel.EQ, self.name) self.add_prim_attr('data_format', "NCHW") - self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT) - self.group = validator.check_integer('group', group, 0, Rel.GT) + self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT, self.name) + self.group = validator.check_integer('group', group, 0, Rel.GT, self.name) def infer_shape(self, x_shape, w_shape): - validator.check_integer("weight_shape", len(w_shape), 4, Rel.EQ) - validator.check_integer("x_shape", len(x_shape), 4, Rel.EQ) - validator.check_param_equal("x_shape[1]", x_shape[1] // self.group, "w_shape[1]", w_shape[1]) - validator.check_param_equal('out_channel', self.out_channel, 'w_shape[0]', w_shape[0]) - validator.check_param_equal('kernel_size', self.kernel_size, 'w_shape[2:4]', tuple(w_shape[2:4])) + validator.check_integer("weight rank", len(w_shape), 4, Rel.EQ, self.name) + validator.check_integer("x rank", len(x_shape), 4, Rel.EQ, self.name) + validator.check("x_shape[1] / group", x_shape[1] // self.group, "w_shape[1]", w_shape[1], Rel.EQ, self.name) + validator.check('out_channel', self.out_channel, 'w_shape[0]', w_shape[0], Rel.EQ, self.name) + validator.check('kernel_size', self.kernel_size, 'w_shape[2:4]', tuple(w_shape[2:4]), Rel.EQ, self.name) kernel_size_h = w_shape[2] kernel_size_w = w_shape[3] @@ -725,10 +700,9 @@ class Conv2D(PrimitiveWithInfer): return out_shape def infer_dtype(self, x_dtype, w_dtype): - args = {'x_dtype': x_dtype, 'w_dtype': w_dtype} - validator.check_subclass('input', x_dtype, mstype.tensor) - validator.check_subclass('weight', w_dtype, mstype.tensor) - validator.check_type_same(args, [mstype.int8, mstype.int32, mstype.float16, mstype.float32]) + args = {'x': x_dtype, 'w': w_dtype} + valid_types = [mstype.int8, mstype.int32, mstype.float16, mstype.float32] + validator.check_tensor_type_same(args, valid_types, self.name) return x_dtype @@ -1108,56 +1082,33 @@ class Conv2DBackpropInput(PrimitiveWithInfer): group=1): """init Conv2DBackpropInput""" self.init_prim_io_names(inputs=['out_backprop', 'filter', 'input_sizes'], outputs=['output']) - self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT) - self.kernel_size = validator.check_type('kernel_size', kernel_size, (int, tuple)) - if isinstance(kernel_size, int): - self.kernel_size = (kernel_size, kernel_size) - if len(self.kernel_size) != 2 or (not isinstance(self.kernel_size[0], int)) or \ - (not isinstance(self.kernel_size[1], int)) or \ - self.kernel_size[0] < 1 or self.kernel_size[1] < 1: - raise ValueError(f"The \'kernel_size\' of \'Conv2DBackpropInput\' should be an positive int number or " - f"a tuple of two positive int numbers, but got {kernel_size}") - self.stride = validator.check_type('stride', stride, (int, tuple)) - if isinstance(stride, int): - self.stride = (stride, stride) - elif isinstance(stride, tuple) and len(stride) == 4: - self.stride = (stride[2], stride[3]) - if len(self.stride) != 2 or (not isinstance(self.stride[0], int)) or (not isinstance(self.stride[1], int)) or \ - self.stride[0] < 1 or self.stride[1] < 1: - raise ValueError(f"The \'stride\' of \'Conv2DBackpropInput\' should be an positive int number or " - f"a tuple of two or four positive int numbers, but got {stride}") + self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT, self.name) + self.kernel_size = _check_positive_int_or_tuple('kernel_size', kernel_size, self.name) + self.stride = _check_positive_int_or_tuple('stride', stride, self.name, allow_four=True, ret_four=False) self.add_prim_attr('stride', self.stride) - self.dilation = validator.check_type('dilation', dilation, (tuple, int)) - if isinstance(dilation, int): - self.dilation = (1, 1, dilation, dilation) - elif len(dilation) == 2: - self.dilation = (1, 1, dilation[0], dilation[1]) - if len(self.dilation) != 4 or (not isinstance(self.dilation[0], int) or self.dilation[0] < 1) or \ - (not isinstance(self.dilation[1], int) or self.dilation[1] < 1) or \ - (not isinstance(self.dilation[2], int) or self.dilation[2] < 1) or \ - (not isinstance(self.dilation[3], int) or self.dilation[3] < 1): - raise ValueError(f"The \'dilation\' of \'Conv2DBackpropInput\' should be an positive int number or " - f"a tuple of two or four positive int numbers, but got {dilation}") + self.dilation = _check_positive_int_or_tuple('dilation', dilation, self.name, allow_four=True, ret_four=True) self.add_prim_attr('dilation', self.dilation) - validator.equal('type of pad', type(pad), 'not bool', not isinstance(pad, bool)) - validator.equal('type of pad', type(pad), 'int', isinstance(pad, int)) - self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad']) - self.pad = validator.check_pad_value_by_mode(self.__class__.__name__, pad_mode, pad) - self.mode = validator.check_integer('mode', mode, 1, Rel.EQ) - self.group = validator.check_integer('group', group, 0, Rel.GT) + validator.check_value_type('pad', pad, (int,), self.name) + self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad'], self.name) + self.pad = validator.check_pad_value_by_mode(pad_mode, pad, self.name) pad_mode = pad_mode.upper() self.add_prim_attr('pad_mode', pad_mode) + self.mode = validator.check_integer('mode', mode, 1, Rel.EQ, self.name) + self.group = validator.check_integer('group', group, 0, Rel.GT, self.name) self.add_prim_attr('data_format', "NCHW") if pad_list: - self.pad_lsit = (validator.check_integer('pad_list', x, 0, Rel.GE) for x in pad_list) + for x in pad_list: + validator.check_integer('element of pad_list', x, 0, Rel.GE, self.name) + self.pad_list = pad_list def __infer__(self, doutput, w, x_size): x_size_v = x_size['value'] - validator.check_type('x_size', x_size_v, [tuple]) + validator.check_value_type('x_size', x_size_v, [tuple], self.name) for i, dim_len in enumerate(x_size_v): - validator.check_type("x_size[%d]" % i, dim_len, [int]) - validator.check_typename('w_dtype', w['dtype'], [mstype.int8, mstype.int32, mstype.float16, mstype.float32]) - validator.check_two_types_same('doutput_dtype', doutput['dtype'], 'w_dtype', w['dtype']) + validator.check_value_type("x_size[%d]" % i, dim_len, [int], self.name) + args = {'doutput': doutput['dtype'], 'w': w['dtype']} + valid_types = [mstype.int8, mstype.int32, mstype.float16, mstype.float32] + validator.check_tensor_type_same(args, valid_types, self.name) # infer shape dout_shape = doutput['shape'] @@ -1677,7 +1628,7 @@ class LayerNorm(Primitive): `Layer Normalization `_. .. math:: - y = \frac{x - mean]}{\sqrt{variance + \epsilon}} * \gamma + \beta + y = \frac{x - mean}{\sqrt{variance + \epsilon}} * \gamma + \beta where :math:`\gamma` is scale, :math:`\beta` is bias, :math:`\epsilon` is epsilon. From 366b6d6803dac354d80da5562676a4581bf29a58 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 11:57:33 +0800 Subject: [PATCH 197/227] fix conv2d 2 --- mindspore/ops/operations/_grad_ops.py | 107 +++++++------------------- 1 file changed, 27 insertions(+), 80 deletions(-) diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py index 7c118994c6..b54d515b7a 100644 --- a/mindspore/ops/operations/_grad_ops.py +++ b/mindspore/ops/operations/_grad_ops.py @@ -137,9 +137,9 @@ class ConcatOffset(PrimitiveWithInfer): return out -class Conv2DBackpropInput(PrimitiveWithInfer): +class Conv2DBackpropFilter(PrimitiveWithInfer): """ - Computes the gradients of convolution with respect to the input. + Computes the gradients of convolution with respect to the filter. Args: out_channel (int): The dimensionality of the output space. @@ -147,9 +147,9 @@ class Conv2DBackpropInput(PrimitiveWithInfer): pad_mode (str): "valid", "same", "pad" the mode to fill padding. Default: "valid". pad (int): The pad value to fill. Default: 0. mode (int): 0 Math convolutiuon, 1 cross-correlation convolution , - 2 deconvolution, 3 depthwise convolution. Default: 1. - stride (Union[int. tuple[int]]): The stride to apply conv filter. Default: 1. - dilation (Union[int. tuple[int]]): Specifies the dilation rate to use for dilated convolution. Default: 1. + 2 deconvolution, 3 depthwise convolution. Default: 1. + stride (tuple): The stride to apply conv filter. Default: (1, 1). + dilation (tuple): Specifies the dilation rate to use for dilated convolution. Default: (1, 1, 1, 1). group (int): Splits input into groups. Default: 1. Returns: @@ -162,89 +162,36 @@ class Conv2DBackpropInput(PrimitiveWithInfer): kernel_size, pad_mode="valid", pad=0, - pad_list=None, + pad_list=(0, 0, 0, 0), mode=1, - stride=1, - dilation=1, + stride=(1, 1), + dilation=(1, 1, 1, 1), group=1): - """init Conv2DBackpropInput""" - self.init_prim_io_names(inputs=['out_backprop', 'filter', 'input_sizes'], outputs=['output']) - self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT) - self.kernel_size = validator.check_type('kernel_size', kernel_size, (int, tuple)) - if isinstance(kernel_size, int): - self.kernel_size = (kernel_size, kernel_size) - if len(self.kernel_size) != 2 or (not isinstance(self.kernel_size[0], int)) or \ - (not isinstance(self.kernel_size[1], int)) or \ - self.kernel_size[0] < 1 or self.kernel_size[1] < 1: - raise ValueError(f"The \'kernel_size\' of \'Conv2DBackpropInput\' should be an positive int number or " - f"a tuple of two positive int numbers, but got {kernel_size}") - self.stride = validator.check_type('stride', stride, (int, tuple)) - if isinstance(stride, int): - self.stride = (stride, stride) - elif isinstance(stride, tuple) and len(stride) == 4: - self.stride = (stride[2], stride[3]) - if len(self.stride) != 2 or (not isinstance(self.stride[0], int)) or (not isinstance(self.stride[1], int)) or \ - self.stride[0] < 1 or self.stride[1] < 1: - raise ValueError(f"The \'stride\' of \'Conv2DBackpropInput\' should be an positive int number or " - f"a tuple of two or four positive int numbers, but got {stride}") - self.add_prim_attr('stride', self.stride) - self.dilation = validator.check_type('dilation', dilation, (tuple, int)) - if isinstance(dilation, int): - self.dilation = (1, 1, dilation, dilation) - elif len(dilation) == 2: - self.dilation = (1, 1, dilation[0], dilation[1]) - if len(self.dilation) != 4 or (not isinstance(self.dilation[0], int) or self.dilation[0] < 1) or \ - (not isinstance(self.dilation[1], int) or self.dilation[1] < 1) or \ - (not isinstance(self.dilation[2], int) or self.dilation[2] < 1) or \ - (not isinstance(self.dilation[3], int) or self.dilation[3] < 1): - raise ValueError(f"The \'dilation\' of \'Conv2DBackpropInput\' should be an positive int number or " - f"a tuple of two or four positive int numbers, but got {dilation}") - self.add_prim_attr('dilation', self.dilation) - validator.equal('type of pad', type(pad), 'not bool', not isinstance(pad, bool)) - validator.equal('type of pad', type(pad), 'int', isinstance(pad, int)) - self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad']) - self.pad = validator.check_pad_value_by_mode(self.__class__.__name__, pad_mode, pad) - self.mode = validator.check_integer('mode', mode, 1, Rel.EQ) - self.group = validator.check_integer('group', group, 0, Rel.GT) + """init Convolution""" + self.init_prim_io_names(inputs=['out_backprop', 'input', 'filter_sizes'], outputs=['output']) + self.out_channel = out_channel + self.kernel_size = kernel_size + self.mode = mode pad_mode = pad_mode.upper() self.add_prim_attr('pad_mode', pad_mode) + self.pad = pad + if isinstance(stride, tuple) and len(stride) == 4: + self.stride = (stride[2], stride[3]) + self.add_prim_attr('stride', self.stride) + self.dilation = dilation + self.group = group self.add_prim_attr('data_format', "NCHW") - if pad_list: - self.pad_lsit = (validator.check_integer('pad_list', x, 0, Rel.GE) for x in pad_list) - def __infer__(self, doutput, w, x_size): - x_size_v = x_size['value'] - validator.check_type('x_size', x_size_v, [tuple]) - for i, dim_len in enumerate(x_size_v): - validator.check_type("x_size[%d]" % i, dim_len, [int]) - validator.check_typename('w_dtype', w['dtype'], [mstype.int8, mstype.int32, mstype.float16, mstype.float32]) - validator.check_two_types_same('doutput_dtype', doutput['dtype'], 'w_dtype', w['dtype']) - - # infer shape - dout_shape = doutput['shape'] - kernel_h = self.kernel_size[0] - kernel_w = self.kernel_size[1] - stride_h = self.stride[0] - stride_w = self.stride[1] - # default pad mode is valid - pad_list = (0, 0, 0, 0) - if self.pad_list: - pad_list = tuple(self.pad_list) - elif self.pad_mode == "SAME": - pad_needed_h = max(0, (dout_shape[2] - 1) * stride_h + kernel_h - x_size_v[2]) - pad_top = math.floor(pad_needed_h / 2) - pad_bottom = pad_needed_h - pad_top - - pad_needed_w = max(0, (dout_shape[3] - 1) * stride_w + kernel_w - x_size_v[3]) - pad_left = math.floor(pad_needed_w / 2) - pad_right = pad_needed_w - pad_left - pad_list = (pad_top, pad_bottom, pad_left, pad_right) - elif self.pad_mode == 'PAD': - pad_list = (self.pad,) * 4 - self.add_prim_attr('pad_list', pad_list) + def __infer__(self, doutput, x, w_size): + w_size_v = w_size['value'] + validator.check_value_type('w_size', w_size_v, [tuple], self.name) + for i, dim_len in enumerate(w_size_v): + validator.check_value_type("w_size[%d]" % i, dim_len, [int], self.name) + args = {"x": x['dtype'], "doutput": doutput['dtype']} + validator.check_tensor_type_same(args, [mstype.int8, mstype.int32, mstype.float16, mstype.float32], self.name) out = { 'value': None, - 'shape': x_size_v, + 'shape': w_size_v, 'dtype': doutput['dtype'], } return out From 61c41614539bbb7d8ccea232e4ab45c993148c3a Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 14:31:07 +0800 Subject: [PATCH 198/227] fix depthwise conv2d --- mindspore/ccsrc/transform/op_declare.cc | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index b8aec05c3b..377403cc89 100644 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -756,9 +756,9 @@ OUTPUT_MAP(Conv2DBackpropFilterD) = {{0, OUTPUT_DESC(y)}}; // DepthwiseConv2D INPUT_MAP(DepthwiseConv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}}; ATTR_MAP(DepthwiseConv2D) = { - {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, + {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, {"pads", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, + {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, {"data_format", ATTR_DESC(data_format, AnyTraits())}, }; OUTPUT_MAP(DepthwiseConv2D) = {{0, OUTPUT_DESC(y)}}; @@ -768,9 +768,9 @@ INPUT_MAP(DepthwiseConv2DBackpropInputD) = {{2, INPUT_DESC(filter)}, {3, INPUT_D INPUT_ATTR_MAP(DepthwiseConv2DBackpropInputD) = { {1, ATTR_DESC(input_size, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(DepthwiseConv2DBackpropInputD) = { - {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, + {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, {"pads", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, + {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, }; OUTPUT_MAP(DepthwiseConv2DBackpropInputD) = {{0, OUTPUT_DESC(input_grad)}}; @@ -779,9 +779,9 @@ INPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{1, INPUT_DESC(input)}, {3, INPUT_D INPUT_ATTR_MAP(DepthwiseConv2DBackpropFilterD) = { {2, ATTR_DESC(filter_size, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(DepthwiseConv2DBackpropFilterD) = { - {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, + {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, {"pads", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"dilation", ATTR_DESC(dilations, "pad", AnyTraits>())}, + {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, }; OUTPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{0, OUTPUT_DESC(filter_grad)}}; From 75b49640433ee3a6ba8bbfdb6bca270c3cd94270 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 14:51:02 +0800 Subject: [PATCH 199/227] update fanrui validator related --- mindspore/_checkparam.py | 14 +- mindspore/ops/operations/_grad_ops.py | 269 ++++++++++++-------------- 2 files changed, 126 insertions(+), 157 deletions(-) diff --git a/mindspore/_checkparam.py b/mindspore/_checkparam.py index 3543f58cf5..707ca748b4 100644 --- a/mindspore/_checkparam.py +++ b/mindspore/_checkparam.py @@ -206,8 +206,8 @@ class Validator: def _check_tensor_type(arg): arg_key, arg_val = arg elem_type = arg_val - type_names = [] if not elem_type in valid_values: + type_names = [] for t in valid_values: type_names.append(str(t)) types_info = '[' + ", ".join(type_names) + ']' @@ -304,10 +304,10 @@ class Validator: type_names = [get_typename(t) for t in valid_types] msg_prefix = f'For \'{prim_name}\' the' if prim_name else 'The' if len(valid_types) == 1: - raise ValueError(f'{msg_prefix} type of `{arg_name}` should be {type_names[0]},' - f' but got {get_typename(arg_type)}.') - raise ValueError(f'{msg_prefix} type of `{arg_name}` should be one of {type_names},' - f' but got {get_typename(arg_type)}.') + raise TypeError(f'{msg_prefix} type of `{arg_name}` should be {type_names[0]},' + f' but got {get_typename(arg_type)}.') + raise TypeError(f'{msg_prefix} type of `{arg_name}` should be one of {type_names},' + f' but got {get_typename(arg_type)}.') @staticmethod def check_float_legal_value(arg_name, arg_value, prim_name): @@ -417,8 +417,8 @@ class ParamValidator: """func for raising error message when check failed""" type_names = [t.__name__ for t in valid_types] num_types = len(valid_types) - raise ValueError(f'The type of `{arg_name}` should be {"one of " if num_types > 1 else ""}' - f'{type_names if num_types > 1 else type_names[0]}, but got {type(arg_value).__name__}.') + raise TypeError(f'The type of `{arg_name}` should be {"one of " if num_types > 1 else ""}' + f'{type_names if num_types > 1 else type_names[0]}, but got {type(arg_value).__name__}.') if isinstance(arg_value, type(mstype.tensor)): arg_value = arg_value.element_type() diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py index b54d515b7a..07857ca27b 100644 --- a/mindspore/ops/operations/_grad_ops.py +++ b/mindspore/ops/operations/_grad_ops.py @@ -18,8 +18,7 @@ from ..._c_expression import signature_rw as sig_rw from ..._c_expression import signature_kind as sig_kind from ..primitive import Primitive, PrimitiveWithInfer, prim_attr_register -from ..._checkparam import ParamValidator as validator -from ..._checkparam import Rel, check_int_positive, check_bool +from ..._checkparam import Validator as validator, Rel from .._utils import _get_concat_offset from ...common import dtype as mstype @@ -51,12 +50,12 @@ class ACosGrad(PrimitiveWithInfer): """init ACosGrad""" def infer_shape(self, x, dout): - validator.check_param_equal("x", x, "dout", dout) + validator.check("x shape", x, "dout shape", dout, Rel.EQ, self.name) return x def infer_dtype(self, x, dout): args = {"x": x, "dout": dout} - validator.check_type_same(args, mstype.number_type) + validator.check_tensor_type_same(args, mstype.number_type, self.name) return x @@ -65,15 +64,15 @@ class BatchNormGrad(PrimitiveWithInfer): @prim_attr_register def __init__(self, is_training=False, epsilon=1e-5): - self.is_training = validator.check_type('is_training', is_training, (bool,)) - self.epsilon = validator.check_number_range('epsilon', epsilon, 0, 1, Rel.INC_RIGHT) + self.is_training = validator.check_value_type('is_training', is_training, (bool,), self.name) + self.epsilon = validator.check_number_range('epsilon', epsilon, 0, 1, Rel.INC_RIGHT, self.name) self.add_prim_attr('data_format', "NCHW") - def infer_shape(self, y_backprop_shape, x_shape, scale_shape, reserve_1_shape, reserve_2_shape): + def infer_shape(self, y_backprop_shape, x_shape, scale_shape, reserve_1_shape, reserve_2_shape, reserve_3_shape): validator.check("BatchNorm y_backprop_shape", y_backprop_shape, "BatchNorm x_shape", x_shape) return (x_shape, scale_shape, scale_shape, reserve_1_shape, reserve_2_shape) - def infer_dtype(self, y_backprop_type, x_type, scale_type, reserve_1_type, reserve_2_type): + def infer_dtype(self, y_backprop_type, x_type, scale_type, reserve_1_type, reserve_2_type, reserve_3_type): return (x_type, scale_type, scale_type, reserve_1_type, reserve_2_type) @@ -93,19 +92,19 @@ class BinaryCrossEntropyGrad(PrimitiveWithInfer): """Computes gradients for `BinaryCrossEntropy` operation.""" @prim_attr_register def __init__(self, reduction='mean'): - self.reduction = validator.check_string('reduction', reduction, ['none', 'mean', 'sum']) + self.reduction = validator.check_string('reduction', reduction, ['none', 'mean', 'sum'], self.name) def infer_shape(self, x_shape, y_shape, doutput_shape, weight_shape): - validator.check_param_equal('x_shape', x_shape, 'y_shape', y_shape) + validator.check('x_shape', x_shape, 'y_shape', y_shape, Rel.EQ, self.name) if weight_shape: - validator.check_param_equal('y_shape', y_shape, 'weight_shape', weight_shape) + validator.check('y_shape', y_shape, 'weight_shape', weight_shape, Rel.EQ, self.name) return x_shape def infer_dtype(self, x_type, y_type, doutput_type, weight_type): args = {'x_type': x_type, 'y_type': y_type, 'doutput_type': doutput_type} - validator.check_type_same(args, (mstype.float16, mstype.float32)) + validator.check_tensor_type_same(args, (mstype.float16, mstype.float32), self.name) if weight_type: - validator.check_two_types_same('x_type', x_type, 'weight_type', weight_type) + validator.check('x_type', x_type, 'weight_type', weight_type, Rel.EQ, TypeError) return x_type @@ -120,7 +119,7 @@ class ConcatOffset(PrimitiveWithInfer): axis = self.axis x_shp = input_x['shape'] x_type = input_x['dtype'] - offset, _, axis = _get_concat_offset(x_shp, x_type, axis) + offset, _, axis = _get_concat_offset(x_shp, x_type, axis, self.name) self.add_prim_attr('T', x_type[0].element_type()) offset_values = [] for i in range(len(x_shp)): @@ -250,8 +249,8 @@ class DepthwiseConv2dNativeBackpropFilter(PrimitiveWithInfer): def __infer__(self, x, w_size, dout): w_size_v = w_size['value'] - args = {'x_dtype': x['dtype'], 'dout_type': dout['dtype']} - validator.check_type_same(args, mstype.number_type) + args = {'x': x['dtype'], 'dout': dout['dtype']} + validator.check_tensor_type_same(args, mstype.number_type, self.name) out = { 'value': None, 'shape': w_size_v, @@ -310,8 +309,8 @@ class DepthwiseConv2dNativeBackpropInput(PrimitiveWithInfer): raise NotImplementedError def __infer__(self, x_size, w, dout): - args = {'w_dtype': w['dtype'], 'dout_type': dout['dtype']} - validator.check_type_same(args, mstype.number_type) + args = {'w': w['dtype'], 'dout': dout['dtype']} + validator.check_tensor_type_same(args, mstype.number_type, self.name) x_size_v = x_size['value'] out = { 'value': None, @@ -333,7 +332,7 @@ class FlattenGrad(PrimitiveWithInfer): 'value': None, 'shape': args[1]['value'], 'dtype': args[0]['dtype'], - } + } return out @@ -360,9 +359,9 @@ class GeluGrad(PrimitiveWithInfer): return x_shape def infer_dtype(self, y_backprop_dtype, x_dtype, y_dtype): - validator.check_typename("y_backprop_dtype", y_backprop_dtype, (mstype.float16, mstype.float32)) - validator.check_typename("x_dtype", x_dtype, (mstype.float16, mstype.float32)) - validator.check_typename("y_dtype", y_dtype, (mstype.float16, mstype.float32)) + validator.check_tensor_type_same({"y_backprop": y_backprop_dtype}, (mstype.float16, mstype.float32), self.name) + validator.check_tensor_type_same({"x": x_dtype}, (mstype.float16, mstype.float32), self.name) + validator.check_tensor_type_same({"y": y_dtype}, (mstype.float16, mstype.float32), self.name) return x_dtype @@ -373,56 +372,36 @@ class _PoolGrad(PrimitiveWithInfer): def __init__(self, ksize, strides, padding="VALID"): self.init_prim_io_names(inputs=['x_origin', 'out_origin', 'grad'], outputs=['output']) - validator.check_type('ksize', ksize, [int, tuple]) - validator.check_type('strides', strides, [int, tuple]) - self.padding = validator.check_string('padding', padding.upper(), ['VALID', 'SAME']) + validator.check_value_type('ksize', ksize, [int, tuple], self.name) + validator.check_value_type('strides', strides, [int, tuple], self.name) + self.padding = validator.check_string('padding', padding.upper(), ['VALID', 'SAME'], self.name) self.add_prim_attr("padding", self.padding) self.is_maxpoolgradwithargmax = (self.name == "MaxPoolGradWithArgmax") if not self.is_maxpoolgradwithargmax: self.add_prim_attr('data_format', "NCHW") - if isinstance(ksize, int): - validator.check_integer("ksize", ksize, 1, Rel.GE) - if self.is_maxpoolgradwithargmax: - self.ksize = (1, ksize, ksize, 1) - else: - self.ksize = (1, 1, ksize, ksize) - else: - ksize_error = ValueError(f"The 'ksize' passed to operator {self.name} should be an positive int number" - f"or a tuple of two or four positive int numbers, but got {ksize}") - if len(ksize) != 2 and len(ksize) != 4: - raise ksize_error - for ksize_val in ksize: - if not isinstance(ksize_val, int) or (ksize_val <= 0): - raise ksize_error - if len(ksize) == 2 and self.is_maxpoolgradwithargmax: - self.ksize = (1, ksize[0], ksize[1], 1) - elif len(ksize) == 2 and not self.is_maxpoolgradwithargmax: - self.ksize = (1, 1, ksize[0], ksize[1]) + def _grad_check_int_or_tuple(arg_name, arg_val, is_argmax): + validator.check_value_type(arg_name, arg_val, (int, tuple), self.name) + error_msg = ValueError(f"For '{self.name}' the '{arg_name}' should be an positive int number " + f"or a tuple of two or four positive int numbers, but got {arg_val}") + if isinstance(arg_val, int): + ret = (1, arg_val, arg_val, 1) if is_argmax else (1, 1, arg_val, arg_val) + elif len(arg_val) == 2: + ret = (1, arg_val[0], arg_val[1], 1) if is_argmax else (1, 1, arg_val[0], arg_val[1]) + elif len(arg_val) == 4: + ret = arg_val else: - self.ksize = ksize + raise error_msg + # whether all elements of tuple are positive integers + for item in ret: + if not isinstance(item, int) or item <= 0: + raise error_msg + return ret + + self.ksize = _grad_check_int_or_tuple("ksize", ksize, self.is_maxpoolgradwithargmax) self.add_prim_attr("ksize", self.ksize) - if isinstance(strides, int): - validator.check_integer("strides", strides, 1, Rel.GE) - if self.is_maxpoolgradwithargmax: - self.strides = (1, strides, strides, 1) - else: - self.strides = (1, 1, strides, strides) - else: - strides_error = ValueError(f"The 'strides' passed to operator {self.name} should be an positive int number" - f"or a tuple of two or four positive int numbers, but got {strides}") - if len(strides) != 2 and len(strides) != 4: - raise strides_error - for strides_val in strides: - if not isinstance(strides_val, int) or (strides_val <= 0): - raise strides_error - if len(strides) == 2 and self.is_maxpoolgradwithargmax: - self.strides = (1, strides[0], strides[1], 1) - elif len(strides) == 2 and not self.is_maxpoolgradwithargmax: - self.strides = (1, 1, strides[0], strides[1]) - else: - self.strides = strides + self.strides = _grad_check_int_or_tuple("strides", strides, self.is_maxpoolgradwithargmax) self.add_prim_attr("strides", self.strides) @@ -529,17 +508,17 @@ class L2NormalizeGrad(PrimitiveWithInfer): @prim_attr_register def __init__(self, axis=0, epsilon=1e-4): - validator.check_type('axis', axis, [int]) - validator.check_type('epsilon', epsilon, [int, float]) + validator.check_value_type('axis', axis, [int], self.name) + validator.check_value_type('epsilon', epsilon, [int, float], self.name) def infer_shape(self, input_x, out, dout): - validator.check_param_equal('input_x', input_x, 'out', out) - validator.check_param_equal('input_x', input_x, 'dout', dout) + validator.check('input_x shape', input_x, 'out shape', out, Rel.EQ, self.name) + validator.check('input_x shape', input_x, 'dout shape', dout, Rel.EQ, self.name) return input_x def infer_dtype(self, input_x, out, dout): args = {'input_x': input_x, 'out': out, 'dout': dout} - validator.check_type_same(args, mstype.number_type) + validator.check_tensor_type_same(args, mstype.number_type, self.name) return input_x @@ -560,8 +539,8 @@ class LayerNormGrad(Primitive): @prim_attr_register def __init__(self, begin_norm_axis=1, begin_params_axis=1): """init""" - self.begin_norm_axis = validator.check_type('begin_norm_axis', begin_norm_axis, [int]) - self.begin_params_axis = validator.check_type('begin_params_axis', begin_params_axis, [int]) + self.begin_norm_axis = validator.check_value_type('begin_norm_axis', begin_norm_axis, [int], self.name) + self.begin_params_axis = validator.check_value_type('begin_params_axis', begin_params_axis, [int], self.name) def __call__(self, x, dy, variance, mean, gamma): raise NotImplementedError @@ -573,15 +552,15 @@ class LogSoftmaxGrad(PrimitiveWithInfer): @prim_attr_register def __init__(self, axis=-1): """init LogSoftmaxGrad""" - validator.check_type("axis", axis, [int]) + validator.check_value_type("axis", axis, [int], self.name) def infer_shape(self, dout, logits): rank = len(logits) - validator.check_int_range('axis', self.axis, -rank - 1, rank, Rel.INC_BOTH) + validator.check_int_range('axis', self.axis, -rank - 1, rank, Rel.INC_BOTH, self.name) return logits def infer_dtype(self, dout, logits): - validator.check_subclass("logits", logits, mstype.tensor) + validator.check_subclass("logits", logits, mstype.tensor, self.name) return logits @@ -590,13 +569,13 @@ class LSTMGradData(PrimitiveWithInfer): @prim_attr_register def __init__(self, input_size, hidden_size, num_layers, has_bias, bidirectional, dropout): - self.input_size = check_int_positive(input_size) - self.hidden_size = check_int_positive(hidden_size) - self.num_layers = check_int_positive(num_layers) - self.has_bias = check_bool(has_bias) - self.bidirectional = check_bool(bidirectional) - self.dropout = validator.check_type("dropout", dropout, [float]) - self.dropout = validator.check_number_range('dropout', dropout, 0, 1, Rel.INC_BOTH) + self.input_size = validator.check_integer('input_size', input_size, 0, Rel.GT, self.name) + self.hidden_size = validator.check_integer('hidden_size', hidden_size, 0, Rel.GT, self.name) + self.num_layers = validator.check_integer('num_layers', num_layers, 0, Rel.GT, self.name) + self.has_bias = validator.check_value_type('has_bias', has_bias, (bool,), self.name) + self.bidirectional = validator.check_value_type('bidirectional', bidirectional, (bool,), self.name) + self.dropout = validator.check_value_type("dropout", dropout, [float], self.name) + self.dropout = validator.check_number_range('dropout', dropout, 0, 1, Rel.INC_BOTH, self.name) if bidirectional: self.num_directions = 2 @@ -606,19 +585,19 @@ class LSTMGradData(PrimitiveWithInfer): def infer_shape(self, y_shape, dy_shape, dhy_shape, dcy_shape, w_shape, hx_shape, cx_shape, reserve_shape, state_shape): # dhy and dcy should be same shape - validator.check_integer("h_shape", len(dhy_shape), 3, Rel.EQ) - validator.check_integer("h_shape", len(dhy_shape), len(dcy_shape), Rel.EQ) - validator.check_integer("h_shape[0]", dhy_shape[0], dcy_shape[0], Rel.EQ) - validator.check_integer("h_shape[1]", dhy_shape[1], dcy_shape[1], Rel.EQ) - validator.check_integer("h_shape[2]", dhy_shape[2], dcy_shape[2], Rel.EQ) + validator.check_integer("h_shape", len(dhy_shape), 3, Rel.EQ, self.name) + validator.check_integer("h_shape", len(dhy_shape), len(dcy_shape), Rel.EQ, self.name) + validator.check_integer("h_shape[0]", dhy_shape[0], dcy_shape[0], Rel.EQ, self.name) + validator.check_integer("h_shape[1]", dhy_shape[1], dcy_shape[1], Rel.EQ, self.name) + validator.check_integer("h_shape[2]", dhy_shape[2], dcy_shape[2], Rel.EQ, self.name) - validator.check_integer("h_shape[0]", dhy_shape[0], self.num_layers * self.num_directions, Rel.EQ) - validator.check_integer("h_shape[2]", dhy_shape[2], self.hidden_size, Rel.EQ) + validator.check_integer("h_shape[0]", dhy_shape[0], self.num_layers * self.num_directions, Rel.EQ, self.name) + validator.check_integer("h_shape[2]", dhy_shape[2], self.hidden_size, Rel.EQ, self.name) # dy: (seq_len, batch_size, hidden_size * num_directions) - validator.check_integer("dy_shape", len(dy_shape), 3, Rel.EQ) - validator.check_integer("dy[1]", dy_shape[1], dhy_shape[1], Rel.EQ) - validator.check_integer("dy[2]", dy_shape[2], self.hidden_size * self.num_directions, Rel.EQ) + validator.check_integer("dy_shape", len(dy_shape), 3, Rel.EQ, self.name) + validator.check_integer("dy[1]", dy_shape[1], dhy_shape[1], Rel.EQ, self.name) + validator.check_integer("dy[2]", dy_shape[2], self.hidden_size * self.num_directions, Rel.EQ, self.name) # (seq_len, batch_size, input_size) dx_shape = (y_shape[0], y_shape[1], self.input_size) @@ -629,11 +608,8 @@ class LSTMGradData(PrimitiveWithInfer): def infer_dtype(self, y_dtype, dy_dtype, dhy_dtype, dcy_dtype, w_dtype, hx_dtype, cx_dtype, reserve_dtype, state_dtype): - validator.check_typename("dy_dtype", dy_dtype, (mstype.float32, mstype.float16)) - validator.check_typename("dhy_dtype", dhy_dtype, (mstype.float32, mstype.float16)) - validator.check_typename("dcy_dtype", dcy_dtype, (mstype.float32, mstype.float16)) - validator.check_typename("datatype", dy_dtype, (dhy_dtype.element_type(),)) - validator.check_typename("datatype", dy_dtype, (dcy_dtype.element_type(),)) + args = {"dy": dy_dtype, "dhy": dhy_dtype, "dcy": dcy_dtype} + validator.check_tensor_type_same(args, (mstype.float32, mstype.float16), self.name) return (dy_dtype, dy_dtype, dy_dtype) @@ -642,13 +618,13 @@ class LSTMGradWeight(PrimitiveWithInfer): @prim_attr_register def __init__(self, input_size, hidden_size, num_layers, has_bias, bidirectional, dropout): - self.input_size = check_int_positive(input_size) - self.hidden_size = check_int_positive(hidden_size) - self.num_layers = check_int_positive(num_layers) - self.has_bias = check_bool(has_bias) - self.bidirectional = check_bool(bidirectional) - self.dropout = validator.check_type("dropout", dropout, [float]) - self.dropout = validator.check_number_range('dropout', dropout, 0, 1, Rel.INC_BOTH) + self.input_size = validator.check_integer('input_size', input_size, 0, Rel.GT, self.name) + self.hidden_size = validator.check_integer('hidden_size', hidden_size, 0, Rel.GT, self.name) + self.num_layers = validator.check_integer('num_layers', num_layers, 0, Rel.GT, self.name) + self.has_bias = validator.check_value_type('has_bias', has_bias, (bool,), self.name) + self.bidirectional = validator.check_value_type('bidirectional', bidirectional, (bool,), self.name) + self.dropout = validator.check_value_type("dropout", dropout, [float], self.name) + self.dropout = validator.check_number_range('dropout', dropout, 0, 1, Rel.INC_BOTH, self.name) if bidirectional: self.num_directions = 2 @@ -693,9 +669,10 @@ class PReLUGrad(PrimitiveWithInfer): return y_backprop_shape, w_shape def infer_dtype(self, y_backprop_dtype, A_dtype, w_dtype): - validator.check_typename("y_backprop_dtype", y_backprop_dtype, (mstype.float16, mstype.float32)) - validator.check_typename("A_dtype", A_dtype, (mstype.float16, mstype.float32)) - validator.check_typename("w_dtype", w_dtype, (mstype.float16, mstype.float32)) + valid_types = (mstype.float16, mstype.float32) + validator.check_tensor_type_same({"y_backprop": y_backprop_dtype}, valid_types, self.name) + validator.check_tensor_type_same({"A_dtype": A_dtype}, valid_types, self.name) + validator.check_tensor_type_same({"w_dtype": w_dtype}, valid_types, self.name) return y_backprop_dtype, w_dtype @@ -725,8 +702,8 @@ class ReLU6Grad(PrimitiveWithInfer): return x_shape def infer_dtype(self, y_grad_dtype, x_dtype): - validator.check_typename("y_grad_dtype", y_grad_dtype, (mstype.float16, mstype.float32)) - validator.check_typename("x_dtype", x_dtype, (mstype.float16, mstype.float32)) + validator.check_tensor_type_same({"y_grad": y_grad_dtype}, (mstype.float16, mstype.float32), self.name) + validator.check_tensor_type_same({"x": x_dtype}, (mstype.float16, mstype.float32), self.name) return x_dtype @@ -744,10 +721,8 @@ class ReluGradV2(PrimitiveWithInfer): return gradients_shape def infer_dtype(self, gradients_dtype, mask_dtype): - args_type = {'gradients': gradients_dtype, 'mask': mask_dtype} - validator.check_args_tensor(args_type) - validator.check_typename("gradients_dtype", gradients_dtype, mstype.number_type) - validator.check_typename("mask_dtype", mask_dtype, (mstype.uint8,)) + validator.check_tensor_type_same({'gradients': gradients_dtype}, mstype.number_type, self.name) + validator.check_tensor_type_same({'mask': mask_dtype}, (mstype.uint8,), self.name) return gradients_dtype @@ -762,10 +737,8 @@ class EluGrad(PrimitiveWithInfer): return x_shape def infer_dtype(self, y_grad_dtype, x_dtype): - args_type = {'y_grad': y_grad_dtype, 'x': x_dtype} - validator.check_args_tensor(args_type) - args_dtype = {'y_grad_dtype': y_grad_dtype, 'x_dtype': x_dtype} - validator.check_type_same(args_dtype, mstype.float_type) + args = {'y_grad': y_grad_dtype, 'x': x_dtype} + validator.check_tensor_type_same(args, mstype.float_type, self.name) return x_dtype @@ -821,11 +794,11 @@ class ROIAlignGrad(PrimitiveWithInfer): @prim_attr_register def __init__(self, xdiff_shape, pooled_height, pooled_width, spatial_scale, sample_num=2): """init ROIAlignGrad""" - validator.check_type("pooled_height", pooled_height, [int]) - validator.check_type("pooled_width", pooled_width, [int]) - validator.check_type("spatial_scale", spatial_scale, [float]) - validator.check_type("sample_num", sample_num, [int]) - validator.check_type("xdiff_shape", xdiff_shape, [tuple]) + validator.check_value_type("pooled_height", pooled_height, [int], self.name) + validator.check_value_type("pooled_width", pooled_width, [int], self.name) + validator.check_value_type("spatial_scale", spatial_scale, [float], self.name) + validator.check_value_type("sample_num", sample_num, [int], self.name) + validator.check_value_type("xdiff_shape", xdiff_shape, [tuple], self.name) self.xdiff_shape = xdiff_shape self.pooled_height = pooled_height self.pooled_width = pooled_width @@ -850,10 +823,8 @@ class SigmoidGrad(PrimitiveWithInfer): return out def infer_dtype(self, out, dout): - validator.check_typename("dout dtype", dout, (mstype.float16, mstype.float32)) - validator.check_typename("out dtype", out, (mstype.float16, mstype.float32)) - args = {"out type": out, "dout type": dout} - validator.check_type_same(args, mstype.number_type) + args = {'out': out, 'dout': dout} + validator.check_tensor_type_same(args, mstype.number_type, self.name) return out @@ -868,8 +839,8 @@ class HSigmoidGrad(PrimitiveWithInfer): return x_shape def infer_dtype(self, y_grad_dtype, x_dtype): - validator.check_typename("y_grad dtype", y_grad_dtype, (mstype.float16, mstype.float32)) - validator.check_typename("x dtype", x_dtype, (mstype.float16, mstype.float32)) + validator.check_tensor_type_same({"y_grad": y_grad_dtype}, (mstype.float16, mstype.float32), self.name) + validator.check_tensor_type_same({"x": x_dtype}, (mstype.float16, mstype.float32), self.name) return x_dtype @@ -884,8 +855,8 @@ class HSwishGrad(PrimitiveWithInfer): return x_shape def infer_dtype(self, y_grad_dtype, x_dtype): - validator.check_typename("y_grad dtype", y_grad_dtype, (mstype.float16, mstype.float32)) - validator.check_typename("x_ dtype", x_dtype, (mstype.float16, mstype.float32)) + validator.check_tensor_type_same({"y_grad": y_grad_dtype}, (mstype.float16, mstype.float32), self.name) + validator.check_tensor_type_same({"x": x_dtype}, (mstype.float16, mstype.float32), self.name) return x_dtype @@ -898,13 +869,13 @@ class SigmoidCrossEntropyWithLogitsGrad(PrimitiveWithInfer): self.init_prim_io_names(inputs=['x', 'y', 'dout'], outputs=['x_grad']) def infer_shape(self, x_shape, y_shape, dout_shape): - validator.check_param_equal("x_shape", x_shape, "y_shape", y_shape) - validator.check_param_equal("x_shape", x_shape, "dout_shape", dout_shape) + validator.check("x_shape", x_shape, "y_shape", y_shape, Rel.EQ, self.name) + validator.check("x_shape", x_shape, "dout_shape", dout_shape, Rel.EQ, self.name) return x_shape def infer_dtype(self, x_dtype, y_dtype, dout_dtype): args = {"x_dtype": x_dtype, "y_dtype": y_dtype, 'dout_dtype': dout_dtype} - validator.check_type_same(args, mstype.number_type) + validator.check_tensor_type_same(args, mstype.number_type, self.name) return dout_dtype @@ -920,8 +891,8 @@ class SliceGrad(PrimitiveWithInfer): dy_shape, x_shape, size_value = dy['shape'], x['shape'], size['value'] dy_shape_len = len(dy_shape) for i in range(dy_shape_len): - validator.check(f'dy_shape[{i}]', dy_shape[i], f'x_shape[{i}]', x_shape[i], Rel.LE) - validator.check(f'dy_shape[{i}]', dy_shape[i], f'size_shape[{i}]', size_value[i], Rel.EQ) + validator.check(f'dy_shape[{i}]', dy_shape[i], f'x_shape[{i}]', x_shape[i], Rel.LE, self.name) + validator.check(f'dy_shape[{i}]', dy_shape[i], f'size_shape[{i}]', size_value[i], Rel.EQ, self.name) return {'shape': x_shape, 'dtype': x['dtype'], 'value': None} @@ -935,13 +906,13 @@ class SmoothL1LossGrad(PrimitiveWithInfer): pass def infer_shape(self, prediction, target, dloss): - validator.check_param_equal('prediction', prediction, 'target', target) - validator.check_param_equal('prediction', prediction, 'dloss', dloss) + validator.check('prediction shape', prediction, 'target shape', target, Rel.EQ, self.name) + validator.check('prediction shape', prediction, 'dloss shape', dloss, Rel.EQ, self.name) return prediction def infer_dtype(self, prediction, target, dloss): args = {"prediction": prediction, "target": target, 'dloss': dloss} - validator.check_type_same(args, mstype.number_type) + validator.check_tensor_type_same(args, mstype.number_type, self.name) return dloss @@ -968,11 +939,11 @@ class StridedSliceGrad(PrimitiveWithInfer): new_axis_mask=0, shrink_axis_mask=0): """init StrideSliceGrad""" - validator.check_type('begin_mask', begin_mask, [int]) - validator.check_type('end_mask', end_mask, [int]) - validator.check_type('ellipsis_mask', ellipsis_mask, [int]) - validator.check_type('new_axis_mask', new_axis_mask, [int]) - validator.check_type('shrink_axis_mask', shrink_axis_mask, [int]) + validator.check_value_type('begin_mask', begin_mask, [int], self.name) + validator.check_value_type('end_mask', end_mask, [int], self.name) + validator.check_value_type('ellipsis_mask', ellipsis_mask, [int], self.name) + validator.check_value_type('new_axis_mask', new_axis_mask, [int], self.name) + validator.check_value_type('shrink_axis_mask', shrink_axis_mask, [int], self.name) self.init_prim_io_names(inputs=['dy', 'shapex', 'begin', 'end', 'strides'], outputs=['output']) def __infer__(self, dy, shapex, begin, end, strides): @@ -992,10 +963,8 @@ class TanhGrad(PrimitiveWithInfer): return out def infer_dtype(self, out, dout): - validator.check_subclass("out", out, mstype.tensor) - validator.check_subclass("dout", dout, mstype.tensor) - args = {"out type": out, "dout type": dout} - validator.check_type_same(args, mstype.number_type) + args = {"out": out, "dout": dout} + validator.check_tensor_type_same(args, mstype.number_type, self.name) return out @@ -1005,13 +974,13 @@ class MirrorPadGrad(PrimitiveWithInfer): @prim_attr_register def __init__(self, mode="REFLECT"): """init MirrorPad""" - validator.check_string('mode', mode, ['REFLECT', 'SYMMETRIC']) + validator.check_string('mode', mode, ['REFLECT', 'SYMMETRIC'], self.name) self.mode = mode def __infer__(self, dout, paddings, x): - validator.check_subclass("dout", dout['dtype'], mstype.tensor) - validator.check_subclass("paddings", paddings['dtype'], mstype.tensor) - validator.check_subclass("input_x", x['dtype'], mstype.tensor) + validator.check_subclass("dout", dout['dtype'], mstype.tensor, self.name) + validator.check_subclass("paddings", paddings['dtype'], mstype.tensor, self.name) + validator.check_subclass("input_x", x['dtype'], mstype.tensor, self.name) return {'shape': x['shape'], 'dtype': dout['dtype'], 'value': None} From 2f89b75b2d5bc0485910ffdd19ef7bc613172b88 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 15:02:24 +0800 Subject: [PATCH 200/227] remove batchnorm grad 6 output to 5 output --- mindspore/ops/operations/_grad_ops.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py index 07857ca27b..782784ca00 100644 --- a/mindspore/ops/operations/_grad_ops.py +++ b/mindspore/ops/operations/_grad_ops.py @@ -68,11 +68,11 @@ class BatchNormGrad(PrimitiveWithInfer): self.epsilon = validator.check_number_range('epsilon', epsilon, 0, 1, Rel.INC_RIGHT, self.name) self.add_prim_attr('data_format', "NCHW") - def infer_shape(self, y_backprop_shape, x_shape, scale_shape, reserve_1_shape, reserve_2_shape, reserve_3_shape): + def infer_shape(self, y_backprop_shape, x_shape, scale_shape, reserve_1_shape, reserve_2_shape): validator.check("BatchNorm y_backprop_shape", y_backprop_shape, "BatchNorm x_shape", x_shape) return (x_shape, scale_shape, scale_shape, reserve_1_shape, reserve_2_shape) - def infer_dtype(self, y_backprop_type, x_type, scale_type, reserve_1_type, reserve_2_type, reserve_3_type): + def infer_dtype(self, y_backprop_type, x_type, scale_type, reserve_1_type, reserve_2_type): return (x_type, scale_type, scale_type, reserve_1_type, reserve_2_type) From 4c37420890e3a1efca34838a8c8fb15fca738ffa Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 15:13:21 +0800 Subject: [PATCH 201/227] make conv2d bp filter stride attr len 4 --- mindspore/ops/operations/_grad_ops.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py index 782784ca00..c821063da8 100644 --- a/mindspore/ops/operations/_grad_ops.py +++ b/mindspore/ops/operations/_grad_ops.py @@ -174,9 +174,9 @@ class Conv2DBackpropFilter(PrimitiveWithInfer): pad_mode = pad_mode.upper() self.add_prim_attr('pad_mode', pad_mode) self.pad = pad - if isinstance(stride, tuple) and len(stride) == 4: - self.stride = (stride[2], stride[3]) - self.add_prim_attr('stride', self.stride) + if isinstance(stride, tuple) and len(stride) == 2: + self.stride = stride + self.add_prim_attr('stride', (1, 1, self.stride[0], self.stride[1])) self.dilation = dilation self.group = group self.add_prim_attr('data_format', "NCHW") From c547f434a1be28c8487b951b0da6b5c65fc9576b Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 15:22:58 +0800 Subject: [PATCH 202/227] conv2d bp input stride len 2 to len 4 --- mindspore/ops/operations/nn_ops.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 9750549dc5..78a512fab7 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -1084,7 +1084,7 @@ class Conv2DBackpropInput(PrimitiveWithInfer): self.init_prim_io_names(inputs=['out_backprop', 'filter', 'input_sizes'], outputs=['output']) self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT, self.name) self.kernel_size = _check_positive_int_or_tuple('kernel_size', kernel_size, self.name) - self.stride = _check_positive_int_or_tuple('stride', stride, self.name, allow_four=True, ret_four=False) + self.stride = _check_positive_int_or_tuple('stride', stride, self.name, allow_four=True, ret_four=True) self.add_prim_attr('stride', self.stride) self.dilation = _check_positive_int_or_tuple('dilation', dilation, self.name, allow_four=True, ret_four=True) self.add_prim_attr('dilation', self.dilation) From 0e98430f4d5b4b06cd19b73fa9a1abfc0253ede2 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 15:55:58 +0800 Subject: [PATCH 203/227] get concat offset refactor --- mindspore/ops/_utils/utils.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/mindspore/ops/_utils/utils.py b/mindspore/ops/_utils/utils.py index fbd81c4f0d..90496afc9b 100644 --- a/mindspore/ops/_utils/utils.py +++ b/mindspore/ops/_utils/utils.py @@ -15,7 +15,7 @@ """utils for operator""" -from ..._checkparam import ParamValidator as validator +from ..._checkparam import Validator as validator from ..._checkparam import Rel from ...common import dtype as mstype @@ -62,25 +62,25 @@ def _get_broadcast_shape(x_shape, y_shape, prim_name): return broadcast_shape -def _get_concat_offset(x_shp, x_type, axis): +def _get_concat_offset(x_shp, x_type, axis, prim_name): """for concat and concatoffset check args and compute offset""" - validator.check_type("shape", x_shp, [tuple]) - validator.check_integer("len of input_x shape", len(x_shp), 0, Rel.GT) - validator.check_subclass("shape0", x_type[0], mstype.tensor) - validator.check_integer("len of input_x0 shape", len(x_shp[0]), 0, Rel.GT) + validator.check_value_type("shape", x_shp, [tuple], prim_name) + validator.check_integer("input_x rank", len(x_shp), 0, Rel.GT, prim_name) + validator.check_subclass("shape0", x_type[0], mstype.tensor, prim_name) + validator.check_integer("len of x_shp[0]", len(x_shp[0]), 0, Rel.GT, prim_name) rank_base = len(x_shp[0]) - validator.check_int_range('axis', axis, -rank_base - 1, rank_base, Rel.INC_BOTH) + validator.check_int_range('axis', axis, -rank_base - 1, rank_base, Rel.INC_BOTH, prim_name) if axis < 0: axis = axis + rank_base all_shp = x_shp[0][axis] offset = [0,] for i in range(1, len(x_shp)): v = x_shp[i] - validator.check('len of x_shp[%d]' % i, len(v), 'len of base', len(x_shp[0])) - validator.check('x_type[%d]' % i, x_type[i], 'base', x_type[0]) + validator.check('len of x_shp[%d]' % i, len(v), 'len of x_shp[0]', len(x_shp[0]), Rel.EQ, prim_name) + validator.check('x_type[%d]' % i, x_type[i], 'x_type[0]', x_type[0], Rel.EQ, prim_name) for j in range(rank_base): if j != axis and v[j] != x_shp[0][j]: - raise ValueError("Concat evaluator element %d shape in input can not concat with first element" % i) + raise ValueError(f"For \'{prim_name}\' element {i} shape in input can not concat with first element") offset.append(all_shp) all_shp += v[axis] return offset, all_shp, axis From cbb4136b626ef048c6c17e38890d7018896e4ed2 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 16:02:38 +0800 Subject: [PATCH 204/227] concat offset update --- mindspore/ops/operations/array_ops.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index 46239855f2..21dbf81730 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -1316,7 +1316,7 @@ class Concat(PrimitiveWithInfer): axis = self.axis x_shp = input_x['shape'] x_type = input_x['dtype'] - _, all_shp, _ = _get_concat_offset(x_shp, x_type, axis) + _, all_shp, _ = _get_concat_offset(x_shp, x_type, axis, self.name) self.add_prim_attr('T', x_type[0].element_type()) self.add_prim_attr('inputNums', len(x_shp)) ret_shp = x_shp[0].copy() From 3209687b9a1c569177027ce4db9384d5249dc5f7 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 17:07:29 +0800 Subject: [PATCH 205/227] conv2d bp filter input primitive stride 2 and adapt to 4 --- mindspore/ccsrc/transform/op_adapter.h | 2 +- mindspore/ccsrc/transform/op_adapter_util.cc | 14 ++++++++++---- mindspore/ccsrc/transform/op_declare.cc | 4 ++-- mindspore/ops/operations/_grad_ops.py | 6 +++--- mindspore/ops/operations/nn_ops.py | 2 +- 5 files changed, 17 insertions(+), 11 deletions(-) diff --git a/mindspore/ccsrc/transform/op_adapter.h b/mindspore/ccsrc/transform/op_adapter.h index 2039dfa7d6..ae678606a4 100644 --- a/mindspore/ccsrc/transform/op_adapter.h +++ b/mindspore/ccsrc/transform/op_adapter.h @@ -736,7 +736,7 @@ class OpAdapter : public BaseOpAdapter { return static_cast(GetValue(value)); } - // specialization for int to Vector + // specialization for int or tuple broadcast to Vector static std::vector ConvertAny(const ValuePtr &value, const std::string &name, const AnyTraits> anyTraitsInt) { return ConvertAnyUtil(value, name, anyTraitsInt); diff --git a/mindspore/ccsrc/transform/op_adapter_util.cc b/mindspore/ccsrc/transform/op_adapter_util.cc index 0163b80f08..0d9e56e510 100644 --- a/mindspore/ccsrc/transform/op_adapter_util.cc +++ b/mindspore/ccsrc/transform/op_adapter_util.cc @@ -35,14 +35,20 @@ GeTensor ConvertAnyUtil(const ValuePtr &value, const AnyTraits ConvertAnyUtil(const ValuePtr &value, const std::string &name, const AnyTraits>) { + MS_EXCEPTION_IF_NULL(value); int64_t data = GetValue(value); std::vector list; int size = 2; // 2 int in list if (name == "pad") { - size = 4; // 4 int in list - list = TransformUtil::ConvertIntToList(data, size); - list[0] = 1; - list[1] = 1; + if (!value->isa()) { + MS_LOG(EXCEPTION) << "Value should be ValueTuple, but got" << value->type_name(); + } + auto vec = value->cast(); + list.push_back(1); + list.push_back(1); + for (auto &it : vec->value()) { + list.push_back(static_cast(GetValue(it))); + } } else { list = TransformUtil::ConvertIntToList(data, size); } diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 377403cc89..5ec54b2037 100644 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -733,7 +733,7 @@ INPUT_ATTR_MAP(Conv2DBackpropInputD) = { {3, ATTR_DESC(input_size, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(Conv2DBackpropInputD) = { {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, + {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, {"data_format", ATTR_DESC(data_format, AnyTraits())}, {"group", ATTR_DESC(groups, AnyTraits())}, @@ -746,7 +746,7 @@ INPUT_ATTR_MAP(Conv2DBackpropFilterD) = { {3, ATTR_DESC(filter_size, AnyTraits>(), AnyTraits>())}}; ATTR_MAP(Conv2DBackpropFilterD) = { {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, - {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, + {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, {"data_format", ATTR_DESC(data_format, AnyTraits())}, {"group", ATTR_DESC(groups, AnyTraits())}, diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py index c821063da8..782784ca00 100644 --- a/mindspore/ops/operations/_grad_ops.py +++ b/mindspore/ops/operations/_grad_ops.py @@ -174,9 +174,9 @@ class Conv2DBackpropFilter(PrimitiveWithInfer): pad_mode = pad_mode.upper() self.add_prim_attr('pad_mode', pad_mode) self.pad = pad - if isinstance(stride, tuple) and len(stride) == 2: - self.stride = stride - self.add_prim_attr('stride', (1, 1, self.stride[0], self.stride[1])) + if isinstance(stride, tuple) and len(stride) == 4: + self.stride = (stride[2], stride[3]) + self.add_prim_attr('stride', self.stride) self.dilation = dilation self.group = group self.add_prim_attr('data_format', "NCHW") diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 78a512fab7..9750549dc5 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -1084,7 +1084,7 @@ class Conv2DBackpropInput(PrimitiveWithInfer): self.init_prim_io_names(inputs=['out_backprop', 'filter', 'input_sizes'], outputs=['output']) self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT, self.name) self.kernel_size = _check_positive_int_or_tuple('kernel_size', kernel_size, self.name) - self.stride = _check_positive_int_or_tuple('stride', stride, self.name, allow_four=True, ret_four=True) + self.stride = _check_positive_int_or_tuple('stride', stride, self.name, allow_four=True, ret_four=False) self.add_prim_attr('stride', self.stride) self.dilation = _check_positive_int_or_tuple('dilation', dilation, self.name, allow_four=True, ret_four=True) self.add_prim_attr('dilation', self.dilation) From ba4a34853103b81bef096a0956f4f74ce3e3429b Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 17:35:17 +0800 Subject: [PATCH 206/227] fix Cppcheck --- mindspore/ccsrc/transform/op_adapter_util.cc | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/mindspore/ccsrc/transform/op_adapter_util.cc b/mindspore/ccsrc/transform/op_adapter_util.cc index 0d9e56e510..07266c9eb2 100644 --- a/mindspore/ccsrc/transform/op_adapter_util.cc +++ b/mindspore/ccsrc/transform/op_adapter_util.cc @@ -38,18 +38,18 @@ std::vector ConvertAnyUtil(const ValuePtr &value, const std::string &na MS_EXCEPTION_IF_NULL(value); int64_t data = GetValue(value); std::vector list; - int size = 2; // 2 int in list if (name == "pad") { if (!value->isa()) { MS_LOG(EXCEPTION) << "Value should be ValueTuple, but got" << value->type_name(); } auto vec = value->cast(); - list.push_back(1); - list.push_back(1); - for (auto &it : vec->value()) { - list.push_back(static_cast(GetValue(it))); - } + list.resize(vec->value().size()+2); + list[0]=1; + list[1]=1; + (void)std::transform(vec->value().begin(), vec->value().end(), list.begin()+2, + [](const ValuePtr &val) { return static_cast(GetValue(val)); }); } else { + int size = 2; // 2 int in list list = TransformUtil::ConvertIntToList(data, size); } From 17de1b789012c83576f33b409aea33a49781d558 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 17:40:24 +0800 Subject: [PATCH 207/227] fix Cppcheck --- mindspore/ccsrc/transform/op_adapter_util.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mindspore/ccsrc/transform/op_adapter_util.cc b/mindspore/ccsrc/transform/op_adapter_util.cc index 07266c9eb2..2f5f3ddc7e 100644 --- a/mindspore/ccsrc/transform/op_adapter_util.cc +++ b/mindspore/ccsrc/transform/op_adapter_util.cc @@ -44,8 +44,8 @@ std::vector ConvertAnyUtil(const ValuePtr &value, const std::string &na } auto vec = value->cast(); list.resize(vec->value().size()+2); - list[0]=1; - list[1]=1; + list[0] = 1; + list[1] = 1; (void)std::transform(vec->value().begin(), vec->value().end(), list.begin()+2, [](const ValuePtr &val) { return static_cast(GetValue(val)); }); } else { From de6ca6bd2f4c28e5f875f0e72f88afa9c2b2e4ed Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 17:45:40 +0800 Subject: [PATCH 208/227] fix convert value tuple --- mindspore/ccsrc/transform/op_adapter_util.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/ccsrc/transform/op_adapter_util.cc b/mindspore/ccsrc/transform/op_adapter_util.cc index 2f5f3ddc7e..203acac10f 100644 --- a/mindspore/ccsrc/transform/op_adapter_util.cc +++ b/mindspore/ccsrc/transform/op_adapter_util.cc @@ -36,7 +36,6 @@ GeTensor ConvertAnyUtil(const ValuePtr &value, const AnyTraits ConvertAnyUtil(const ValuePtr &value, const std::string &name, const AnyTraits>) { MS_EXCEPTION_IF_NULL(value); - int64_t data = GetValue(value); std::vector list; if (name == "pad") { if (!value->isa()) { @@ -49,6 +48,7 @@ std::vector ConvertAnyUtil(const ValuePtr &value, const std::string &na (void)std::transform(vec->value().begin(), vec->value().end(), list.begin()+2, [](const ValuePtr &val) { return static_cast(GetValue(val)); }); } else { + int64_t data = GetValue(value); int size = 2; // 2 int in list list = TransformUtil::ConvertIntToList(data, size); } From 89f82e1a3799b9474f1901f99ccd0affeb1841aa Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Fri, 24 Apr 2020 19:04:40 +0800 Subject: [PATCH 209/227] fix cpp ut test TestConvertConvBackpropFilter --- tests/ut/cpp/transform/convert_test.cc | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tests/ut/cpp/transform/convert_test.cc b/tests/ut/cpp/transform/convert_test.cc index 4388312592..277aaa15c3 100644 --- a/tests/ut/cpp/transform/convert_test.cc +++ b/tests/ut/cpp/transform/convert_test.cc @@ -189,7 +189,8 @@ TEST_F(TestConvert, TestConvertBatchNorm) { TEST_F(TestConvert, TestConvertConvBackpropInput) { auto prim = prim::kPrimConv2DBackpropInput; - prim->AddAttr("stride", MakeValue(1)); + const std::vector list{1,1}; + prim->AddAttr("stride", MakeValue(list)); prim->AddAttr("pad", MakeValue(0)); prim->AddAttr("pad_mode", MakeValue(std::string("pad"))); prim->AddAttr("dilation", MakeValue(1)); @@ -218,7 +219,8 @@ TEST_F(TestConvert, TestConvertConvBackpropInput) { TEST_F(TestConvert, TestConvertConvBackpropFilter) { auto prim = prim::kPrimConv2DBackpropFilter; - prim->AddAttr("stride", MakeValue(1)); + const std::vector list{1,1}; + prim->AddAttr("stride", MakeValue(list)); prim->AddAttr("pad", MakeValue(0)); prim->AddAttr("pad_mode", MakeValue(std::string("pad"))); prim->AddAttr("dilation", MakeValue(1)); From 691337a6e14da3b04f4ba163bc45340fb1db32ca Mon Sep 17 00:00:00 2001 From: yanzhenxiang2020 Date: Tue, 21 Apr 2020 21:17:14 +0800 Subject: [PATCH 210/227] add aicpu ops of Reshape/Flatten/Squeeze/ExpandDims/IsFinite --- mindspore/ccsrc/kernel/aicpu/aicpu_util.h | 1 - mindspore/ccsrc/pre_activate/common/helper.cc | 18 +++ mindspore/ops/_op_impl/aicpu/__init__.py | 5 + mindspore/ops/_op_impl/aicpu/expand_dims.py | 52 ++++++++ mindspore/ops/_op_impl/aicpu/flatten.py | 48 ++++++++ mindspore/ops/_op_impl/aicpu/is_finite.py | 52 ++++++++ mindspore/ops/_op_impl/aicpu/reshape.py | 52 ++++++++ mindspore/ops/_op_impl/aicpu/squeeze.py | 52 ++++++++ mindspore/ops/_op_impl/tbe/__init__.py | 3 - mindspore/ops/op_info_register.py | 11 +- .../test_aicpu_ops/test_expand_dims.py | 114 ++++++++++++++++++ .../davinci/test_aicpu_ops/test_flatten.py | 99 +++++++++++++++ .../davinci/test_aicpu_ops/test_is_finite.py | 114 ++++++++++++++++++ .../davinci/test_aicpu_ops/test_reshape.py | 114 ++++++++++++++++++ .../davinci/test_aicpu_ops/test_squeeze.py | 113 +++++++++++++++++ 15 files changed, 843 insertions(+), 5 deletions(-) create mode 100644 mindspore/ops/_op_impl/aicpu/expand_dims.py create mode 100644 mindspore/ops/_op_impl/aicpu/flatten.py create mode 100644 mindspore/ops/_op_impl/aicpu/is_finite.py create mode 100644 mindspore/ops/_op_impl/aicpu/reshape.py create mode 100644 mindspore/ops/_op_impl/aicpu/squeeze.py create mode 100644 tests/st/ops/davinci/test_aicpu_ops/test_expand_dims.py create mode 100644 tests/st/ops/davinci/test_aicpu_ops/test_flatten.py create mode 100644 tests/st/ops/davinci/test_aicpu_ops/test_is_finite.py create mode 100644 tests/st/ops/davinci/test_aicpu_ops/test_reshape.py create mode 100644 tests/st/ops/davinci/test_aicpu_ops/test_squeeze.py diff --git a/mindspore/ccsrc/kernel/aicpu/aicpu_util.h b/mindspore/ccsrc/kernel/aicpu/aicpu_util.h index 08fca16a3b..b6f43414e3 100644 --- a/mindspore/ccsrc/kernel/aicpu/aicpu_util.h +++ b/mindspore/ccsrc/kernel/aicpu/aicpu_util.h @@ -27,7 +27,6 @@ namespace kernel { constexpr auto kInitDataSetQueue = "InitDataSetQueue"; constexpr auto kInitData = "InitData"; constexpr auto kGetNext = "GetNext"; -constexpr auto kDropoutGenMask = "DropoutGenMask"; constexpr auto kPrint = "Print"; constexpr auto kOutputTypes = "output_types"; diff --git a/mindspore/ccsrc/pre_activate/common/helper.cc b/mindspore/ccsrc/pre_activate/common/helper.cc index de45239268..15ca1ca838 100644 --- a/mindspore/ccsrc/pre_activate/common/helper.cc +++ b/mindspore/ccsrc/pre_activate/common/helper.cc @@ -340,8 +340,23 @@ bool IsNopNode(const AnfNodePtr &node) { return true; } +bool IsAllNopNode(session::KernelGraph *const graph) { + MS_EXCEPTION_IF_NULL(graph); + auto execution_order = graph->execution_order(); + for (auto &cnode : execution_order) { + MS_EXCEPTION_IF_NULL(cnode); + if (!IsNopNode(cnode)) { + return false; + } + } + return true; +} + void HideNopNode(session::KernelGraph *const graph) { MS_EXCEPTION_IF_NULL(graph); + if (IsAllNopNode(graph) == true) { + return; + } auto execution_order = graph->execution_order(); MS_LOG(INFO) << "nop node info (Before Remove) size: " << execution_order.size(); std::vector new_nodes; @@ -357,6 +372,9 @@ void HideNopNode(session::KernelGraph *const graph) { void RemoveNopNode(session::KernelGraph *const graph) { MS_EXCEPTION_IF_NULL(graph); + if (IsAllNopNode(graph) == true) { + return; + } bool changed = true; while (changed) { changed = false; diff --git a/mindspore/ops/_op_impl/aicpu/__init__.py b/mindspore/ops/_op_impl/aicpu/__init__.py index b0f90a629b..5aede8c820 100644 --- a/mindspore/ops/_op_impl/aicpu/__init__.py +++ b/mindspore/ops/_op_impl/aicpu/__init__.py @@ -17,3 +17,8 @@ from .init_data_set_queue import _init_data_set_queue_aicpu from .dropout_genmask import _dropout_genmask_aicpu from .get_next import _get_next_aicpu from .print_tensor import _print_aicpu +from .is_finite import _is_finite_aicpu +from .reshape import _reshape_aicpu +from .flatten import _flatten_aicpu +from .squeeze import _squeeze_aicpu +from .expand_dims import _expand_dims_aicpu diff --git a/mindspore/ops/_op_impl/aicpu/expand_dims.py b/mindspore/ops/_op_impl/aicpu/expand_dims.py new file mode 100644 index 0000000000..e1a79f24da --- /dev/null +++ b/mindspore/ops/_op_impl/aicpu/expand_dims.py @@ -0,0 +1,52 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""ExpandDims op""" +from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType + +expand_dims_op_info = AiCPURegOp("ExpandDims") \ + .fusion_type("OPAQUE") \ + .input(0, "x", "required") \ + .output(0, "y", "required") \ + .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.I8_Default, DataType.I8_Default) \ + .dtype_format(DataType.I16_Default, DataType.I16_Default) \ + .dtype_format(DataType.I32_Default, DataType.I32_Default) \ + .dtype_format(DataType.I64_Default, DataType.I64_Default) \ + .dtype_format(DataType.U8_Default, DataType.U8_Default) \ + .dtype_format(DataType.U16_Default, DataType.U16_Default) \ + .dtype_format(DataType.U32_Default, DataType.U32_Default) \ + .dtype_format(DataType.U64_Default, DataType.U64_Default) \ + .dtype_format(DataType.F16_Default, DataType.F16_Default) \ + .dtype_format(DataType.F32_Default, DataType.F32_Default) \ + .dtype_format(DataType.F64_Default, DataType.F64_Default) \ + .dtype_format(DataType.BOOL_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.I8_NCHW, DataType.I8_NCHW) \ + .dtype_format(DataType.I16_NCHW, DataType.I16_NCHW) \ + .dtype_format(DataType.I32_NCHW, DataType.I32_NCHW) \ + .dtype_format(DataType.I64_NCHW, DataType.I64_NCHW) \ + .dtype_format(DataType.U8_NCHW, DataType.U8_NCHW) \ + .dtype_format(DataType.U16_NCHW, DataType.U16_NCHW) \ + .dtype_format(DataType.U32_NCHW, DataType.U32_NCHW) \ + .dtype_format(DataType.U64_NCHW, DataType.U64_NCHW) \ + .dtype_format(DataType.F16_NCHW, DataType.F16_NCHW) \ + .dtype_format(DataType.F32_NCHW, DataType.F32_NCHW) \ + .dtype_format(DataType.F64_NCHW, DataType.F64_NCHW) \ + .get_op_info() + +@op_info_register(expand_dims_op_info) +def _expand_dims_aicpu(): + """ExpandDims AiCPU register""" + return diff --git a/mindspore/ops/_op_impl/aicpu/flatten.py b/mindspore/ops/_op_impl/aicpu/flatten.py new file mode 100644 index 0000000000..dd6c2e9124 --- /dev/null +++ b/mindspore/ops/_op_impl/aicpu/flatten.py @@ -0,0 +1,48 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""Flatten op""" +from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType + +flatten_op_info = AiCPURegOp("Flatten") \ + .fusion_type("OPAQUE") \ + .input(0, "x", "required") \ + .output(0, "y", "required") \ + .dtype_format(DataType.I8_Default, DataType.I8_Default) \ + .dtype_format(DataType.I16_Default, DataType.I16_Default) \ + .dtype_format(DataType.I32_Default, DataType.I32_Default) \ + .dtype_format(DataType.I64_Default, DataType.I64_Default) \ + .dtype_format(DataType.U8_Default, DataType.U8_Default) \ + .dtype_format(DataType.U16_Default, DataType.U16_Default) \ + .dtype_format(DataType.U32_Default, DataType.U32_Default) \ + .dtype_format(DataType.U64_Default, DataType.U64_Default) \ + .dtype_format(DataType.F16_Default, DataType.F16_Default) \ + .dtype_format(DataType.F32_Default, DataType.F32_Default) \ + .dtype_format(DataType.I8_NCHW, DataType.I8_NCHW) \ + .dtype_format(DataType.I16_NCHW, DataType.I16_NCHW) \ + .dtype_format(DataType.I32_NCHW, DataType.I32_NCHW) \ + .dtype_format(DataType.I64_NCHW, DataType.I64_NCHW) \ + .dtype_format(DataType.U8_NCHW, DataType.U8_NCHW) \ + .dtype_format(DataType.U16_NCHW, DataType.U16_NCHW) \ + .dtype_format(DataType.U32_NCHW, DataType.U32_NCHW) \ + .dtype_format(DataType.U64_NCHW, DataType.U64_NCHW) \ + .dtype_format(DataType.F16_NCHW, DataType.F16_NCHW) \ + .dtype_format(DataType.F32_NCHW, DataType.F32_NCHW) \ + .get_op_info() + +@op_info_register(flatten_op_info) +def _flatten_aicpu(): + """Flatten AiCPU register""" + return diff --git a/mindspore/ops/_op_impl/aicpu/is_finite.py b/mindspore/ops/_op_impl/aicpu/is_finite.py new file mode 100644 index 0000000000..3a3f8e07ae --- /dev/null +++ b/mindspore/ops/_op_impl/aicpu/is_finite.py @@ -0,0 +1,52 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""IsFinite op""" +from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType + +is_finite_op_info = AiCPURegOp("IsFinite") \ + .fusion_type("OPAQUE") \ + .input(0, "x", "required") \ + .output(0, "y", "required") \ + .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.I8_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.I16_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.I32_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.I64_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.U8_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.U16_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.U32_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.U64_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.F16_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.F32_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.F64_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.BOOL_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.I8_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.I16_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.I32_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.I64_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.U8_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.U16_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.U32_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.U64_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.F16_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.F32_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.F64_NCHW, DataType.BOOL_NCHW) \ + .get_op_info() + +@op_info_register(is_finite_op_info) +def _is_finite_aicpu(): + """IsFinite AiCPU register""" + return diff --git a/mindspore/ops/_op_impl/aicpu/reshape.py b/mindspore/ops/_op_impl/aicpu/reshape.py new file mode 100644 index 0000000000..47d1ea2f75 --- /dev/null +++ b/mindspore/ops/_op_impl/aicpu/reshape.py @@ -0,0 +1,52 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""Reshape op""" +from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType + +reshape_op_info = AiCPURegOp("Reshape") \ + .fusion_type("OPAQUE") \ + .input(0, "x", "required") \ + .output(0, "y", "required") \ + .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.I8_Default, DataType.I8_Default) \ + .dtype_format(DataType.I16_Default, DataType.I16_Default) \ + .dtype_format(DataType.I32_Default, DataType.I32_Default) \ + .dtype_format(DataType.I64_Default, DataType.I64_Default) \ + .dtype_format(DataType.U8_Default, DataType.U8_Default) \ + .dtype_format(DataType.U16_Default, DataType.U16_Default) \ + .dtype_format(DataType.U32_Default, DataType.U32_Default) \ + .dtype_format(DataType.U64_Default, DataType.U64_Default) \ + .dtype_format(DataType.F16_Default, DataType.F16_Default) \ + .dtype_format(DataType.F32_Default, DataType.F32_Default) \ + .dtype_format(DataType.F64_Default, DataType.F64_Default) \ + .dtype_format(DataType.BOOL_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.I8_NCHW, DataType.I8_NCHW) \ + .dtype_format(DataType.I16_NCHW, DataType.I16_NCHW) \ + .dtype_format(DataType.I32_NCHW, DataType.I32_NCHW) \ + .dtype_format(DataType.I64_NCHW, DataType.I64_NCHW) \ + .dtype_format(DataType.U8_NCHW, DataType.U8_NCHW) \ + .dtype_format(DataType.U16_NCHW, DataType.U16_NCHW) \ + .dtype_format(DataType.U32_NCHW, DataType.U32_NCHW) \ + .dtype_format(DataType.U64_NCHW, DataType.U64_NCHW) \ + .dtype_format(DataType.F16_NCHW, DataType.F16_NCHW) \ + .dtype_format(DataType.F32_NCHW, DataType.F32_NCHW) \ + .dtype_format(DataType.F64_NCHW, DataType.F64_NCHW) \ + .get_op_info() + +@op_info_register(reshape_op_info) +def _reshape_aicpu(): + """Rpeshape AiCPU register""" + return diff --git a/mindspore/ops/_op_impl/aicpu/squeeze.py b/mindspore/ops/_op_impl/aicpu/squeeze.py new file mode 100644 index 0000000000..0172ec5c39 --- /dev/null +++ b/mindspore/ops/_op_impl/aicpu/squeeze.py @@ -0,0 +1,52 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""Squeeze op""" +from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType + +squeeze_op_info = AiCPURegOp("Squeeze") \ + .fusion_type("OPAQUE") \ + .input(0, "x", "required") \ + .output(0, "y", "required") \ + .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \ + .dtype_format(DataType.I8_Default, DataType.I8_Default) \ + .dtype_format(DataType.I16_Default, DataType.I16_Default) \ + .dtype_format(DataType.I32_Default, DataType.I32_Default) \ + .dtype_format(DataType.I64_Default, DataType.I64_Default) \ + .dtype_format(DataType.U8_Default, DataType.U8_Default) \ + .dtype_format(DataType.U16_Default, DataType.U16_Default) \ + .dtype_format(DataType.U32_Default, DataType.U32_Default) \ + .dtype_format(DataType.U64_Default, DataType.U64_Default) \ + .dtype_format(DataType.F16_Default, DataType.F16_Default) \ + .dtype_format(DataType.F32_Default, DataType.F32_Default) \ + .dtype_format(DataType.F64_Default, DataType.F64_Default) \ + .dtype_format(DataType.BOOL_NCHW, DataType.BOOL_NCHW) \ + .dtype_format(DataType.I8_NCHW, DataType.I8_NCHW) \ + .dtype_format(DataType.I16_NCHW, DataType.I16_NCHW) \ + .dtype_format(DataType.I32_NCHW, DataType.I32_NCHW) \ + .dtype_format(DataType.I64_NCHW, DataType.I64_NCHW) \ + .dtype_format(DataType.U8_NCHW, DataType.U8_NCHW) \ + .dtype_format(DataType.U16_NCHW, DataType.U16_NCHW) \ + .dtype_format(DataType.U32_NCHW, DataType.U32_NCHW) \ + .dtype_format(DataType.U64_NCHW, DataType.U64_NCHW) \ + .dtype_format(DataType.F16_NCHW, DataType.F16_NCHW) \ + .dtype_format(DataType.F32_NCHW, DataType.F32_NCHW) \ + .dtype_format(DataType.F64_NCHW, DataType.F64_NCHW) \ + .get_op_info() + +@op_info_register(squeeze_op_info) +def _squeeze_aicpu(): + """Squeeze AiCPU register""" + return diff --git a/mindspore/ops/_op_impl/tbe/__init__.py b/mindspore/ops/_op_impl/tbe/__init__.py index 8030aac5c6..10b916a2c8 100644 --- a/mindspore/ops/_op_impl/tbe/__init__.py +++ b/mindspore/ops/_op_impl/tbe/__init__.py @@ -61,9 +61,6 @@ from .reduce_mean_d import _reduce_mean_d_tbe from .scatter_nd import _scatter_nd_tbe from .scatter_nd_d import _scatter_nd_d_tbe from .reduce_mean import _reduce_mean_tbe -from .reshape import _reshape_tbe -from .expand_dims import _expand_dims_tbe -from .squeeze import _squeeze_tbe from .tile import _tile_tbe from .atomic_addr_clean import _atomic_addr_clean_tbe from .gather_v2 import _gather_v2_tbe diff --git a/mindspore/ops/op_info_register.py b/mindspore/ops/op_info_register.py index 752b367023..2921759bb4 100644 --- a/mindspore/ops/op_info_register.py +++ b/mindspore/ops/op_info_register.py @@ -599,4 +599,13 @@ class DataType: F32_NCHW = ("float32", "NCHW") F32_NHWC = ("float32", "NHWC") F32_HWCN = ("float32", "HWCN") - \ No newline at end of file + + F64_None = ("float64", "") + F64_Default = ("float64", "DefaultFormat") + F64_5HD = ("float64", "NC1HWC0") + F64_FracZ = ("float64", "FracZ") + F64_FracNZ = ("float64", "FRACTAL_NZ") + F64_C1HWNCoC0 = ("float64", "C1HWNCoC0") + F64_NCHW = ("float64", "NCHW") + F64_NHWC = ("float64", "NHWC") + F64_HWCN = ("float64", "HWCN") diff --git a/tests/st/ops/davinci/test_aicpu_ops/test_expand_dims.py b/tests/st/ops/davinci/test_aicpu_ops/test_expand_dims.py new file mode 100644 index 0000000000..f47159af3b --- /dev/null +++ b/tests/st/ops/davinci/test_aicpu_ops/test_expand_dims.py @@ -0,0 +1,114 @@ +# Copyright 2019 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +from mindspore import Tensor +from mindspore.ops import operations as P +import mindspore.nn as nn +from mindspore.common.api import ms_function +import numpy as np +import mindspore.context as context +context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend") +class Net(nn.Cell): + def __init__(self): + super(Net, self).__init__() + self.expand_dims = P.ExpandDims() + + def construct(self, tensor, dim): + return self.expand_dims(tensor, dim) + + +def test_net_bool(): + x = np.random.randn(1, 16, 1, 1).astype(np.bool) + net = Net() + output = net(Tensor(x), -1) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.expand_dims(x, -1))) + +def test_net_int8(): + x = np.random.randn(1, 16, 1, 1).astype(np.int8) + net = Net() + output = net(Tensor(x), -1) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.expand_dims(x, -1))) + +def test_net_uint8(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint8) + net = Net() + output = net(Tensor(x), -1) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.expand_dims(x, -1))) + +def test_net_int16(): + x = np.random.randn(1, 16, 1, 1).astype(np.int16) + net = Net() + output = net(Tensor(x), -1) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.expand_dims(x, -1))) + +def test_net_uint16(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint16) + net = Net() + output = net(Tensor(x), -1) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.expand_dims(x, -1))) + +def test_net_int32(): + x = np.random.randn(1, 16, 1, 1).astype(np.int32) + net = Net() + output = net(Tensor(x), -1) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.expand_dims(x, -1))) + +def test_net_uint32(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint32) + net = Net() + output = net(Tensor(x), -1) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.expand_dims(x, -1))) + +def test_net_int64(): + x = np.random.randn(1, 16, 1, 1).astype(np.int64) + net = Net() + output = net(Tensor(x), -1) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.expand_dims(x, -1))) + +def test_net_uint64(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint64) + net = Net() + output = net(Tensor(x), -1) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.expand_dims(x, -1))) + +def test_net_float16(): + x = np.random.randn(1, 16, 1, 1).astype(np.float16) + net = Net() + output = net(Tensor(x), -1) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.expand_dims(x, -1))) + +def test_net_float32(): + x = np.random.randn(1, 16, 1, 1).astype(np.float32) + net = Net() + output = net(Tensor(x), -1) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.expand_dims(x, -1))) + +def test_net_float64(): + x = np.random.randn(1, 16, 1, 1).astype(np.float64) + net = Net() + output = net(Tensor(x), -1) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.expand_dims(x, -1))) + diff --git a/tests/st/ops/davinci/test_aicpu_ops/test_flatten.py b/tests/st/ops/davinci/test_aicpu_ops/test_flatten.py new file mode 100644 index 0000000000..45a336a0e1 --- /dev/null +++ b/tests/st/ops/davinci/test_aicpu_ops/test_flatten.py @@ -0,0 +1,99 @@ +# Copyright 2019 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +from mindspore import Tensor +from mindspore.ops import operations as P +import mindspore.nn as nn +import numpy as np +import mindspore.context as context +context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend") +class Net(nn.Cell): + def __init__(self): + super(Net, self).__init__() + self.flatten = P.Flatten() + + def construct(self, tensor): + return self.flatten(tensor) + + +def test_net_int8(): + x = np.random.randn(1, 16, 1, 1).astype(np.int8) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.flatten())) + +def test_net_uint8(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint8) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.flatten())) + +def test_net_int16(): + x = np.random.randn(1, 16, 1, 1).astype(np.int16) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.flatten())) + +def test_net_uint16(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint16) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.flatten())) + +def test_net_int32(): + x = np.random.randn(1, 16, 1, 1).astype(np.int32) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.flatten())) + +def test_net_uint32(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint32) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.flatten())) + +def test_net_int64(): + x = np.random.randn(1, 16, 1, 1).astype(np.int64) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.flatten())) + +def test_net_uint64(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint64) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.flatten())) + +def test_net_float16(): + x = np.random.randn(1, 16, 1, 1).astype(np.float16) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.flatten())) + +def test_net_float32(): + x = np.random.randn(1, 16, 1, 1).astype(np.float32) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.flatten())) + diff --git a/tests/st/ops/davinci/test_aicpu_ops/test_is_finite.py b/tests/st/ops/davinci/test_aicpu_ops/test_is_finite.py new file mode 100644 index 0000000000..149d48ce04 --- /dev/null +++ b/tests/st/ops/davinci/test_aicpu_ops/test_is_finite.py @@ -0,0 +1,114 @@ +# Copyright 2019 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +from mindspore import Tensor +from mindspore.ops import operations as P +import mindspore.nn as nn +from mindspore.common.api import ms_function +import numpy as np +import mindspore.context as context +context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend") +class Net(nn.Cell): + def __init__(self): + super(Net, self).__init__() + self.isfinite = P.IsFinite() + + def construct(self, tensor): + return self.isfinite(tensor) + + +def test_net_bool(): + x = np.random.randn(1, 16, 1, 1).astype(np.bool) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.isfinite(x))) + +def test_net_int8(): + x = np.random.randn(1, 16, 1, 1).astype(np.int8) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.isfinite(x))) + +def test_net_uint8(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint8) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.isfinite(x))) + +def test_net_int16(): + x = np.random.randn(1, 16, 1, 1).astype(np.int16) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.isfinite(x))) + +def test_net_uint16(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint16) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.isfinite(x))) + +def test_net_int32(): + x = np.random.randn(1, 16, 1, 1).astype(np.int32) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.isfinite(x))) + +def test_net_uint32(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint32) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.isfinite(x))) + +def test_net_int64(): + x = np.random.randn(1, 16, 1, 1).astype(np.int64) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.isfinite(x))) + +def test_net_uint64(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint64) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.isfinite(x))) + +def test_net_float16(): + x = np.random.randn(1, 16, 1, 1).astype(np.float16) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.isfinite(x))) + +def test_net_float32(): + x = np.random.randn(1, 16, 1, 1).astype(np.float32) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.isfinite(x))) + +def test_net_float64(): + x = np.random.randn(1, 16, 1, 1).astype(np.float64) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.isfinite(x))) + diff --git a/tests/st/ops/davinci/test_aicpu_ops/test_reshape.py b/tests/st/ops/davinci/test_aicpu_ops/test_reshape.py new file mode 100644 index 0000000000..7d380fabf8 --- /dev/null +++ b/tests/st/ops/davinci/test_aicpu_ops/test_reshape.py @@ -0,0 +1,114 @@ +# Copyright 2019 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +from mindspore import Tensor +from mindspore.ops import operations as P +import mindspore.nn as nn +from mindspore.common.api import ms_function +import numpy as np +import mindspore.context as context +context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend") +class Net(nn.Cell): + def __init__(self): + super(Net, self).__init__() + self.reshape = P.Reshape() + + def construct(self, tensor): + return self.reshape(tensor, (4,4)) + + +def test_net_bool(): + x = np.random.randn(1, 16, 1, 1).astype(np.bool) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.reshape(x, (4,4)))) + +def test_net_int8(): + x = np.random.randn(1, 16, 1, 1).astype(np.int8) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.reshape(x, (4,4)))) + +def test_net_uint8(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint8) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.reshape(x, (4,4)))) + +def test_net_int16(): + x = np.random.randn(1, 16, 1, 1).astype(np.int16) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.reshape(x, (4,4)))) + +def test_net_uint16(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint16) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.reshape(x, (4,4)))) + +def test_net_int32(): + x = np.random.randn(1, 16, 1, 1).astype(np.int32) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.reshape(x, (4,4)))) + +def test_net_uint32(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint32) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.reshape(x, (4,4)))) + +def test_net_int64(): + x = np.random.randn(1, 16, 1, 1).astype(np.int64) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.reshape(x, (4,4)))) + +def test_net_uint64(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint64) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.reshape(x, (4,4)))) + +def test_net_float16(): + x = np.random.randn(1, 16, 1, 1).astype(np.float16) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.reshape(x, (4,4)))) + +def test_net_float32(): + x = np.random.randn(1, 16, 1, 1).astype(np.float32) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.reshape(x, (4,4)))) + +def test_net_float64(): + x = np.random.randn(1, 16, 1, 1).astype(np.float64) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == np.reshape(x, (4,4)))) + diff --git a/tests/st/ops/davinci/test_aicpu_ops/test_squeeze.py b/tests/st/ops/davinci/test_aicpu_ops/test_squeeze.py new file mode 100644 index 0000000000..4a17feeff0 --- /dev/null +++ b/tests/st/ops/davinci/test_aicpu_ops/test_squeeze.py @@ -0,0 +1,113 @@ +# Copyright 2019 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +from mindspore import Tensor +from mindspore.ops import operations as P +import mindspore.nn as nn +import numpy as np +import mindspore.context as context +context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend") +class Net(nn.Cell): + def __init__(self): + super(Net, self).__init__() + self.squeeze = P.Squeeze() + + def construct(self, tensor): + return self.squeeze(tensor) + + +def test_net_bool(): + x = np.random.randn(1, 16, 1, 1).astype(np.bool) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.squeeze())) + +def test_net_int8(): + x = np.random.randn(1, 16, 1, 1).astype(np.int8) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.squeeze())) + +def test_net_uint8(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint8) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.squeeze())) + +def test_net_int16(): + x = np.random.randn(1, 16, 1, 1).astype(np.int16) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.squeeze())) + +def test_net_uint16(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint16) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.squeeze())) + +def test_net_int32(): + x = np.random.randn(1, 16, 1, 1).astype(np.int32) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.squeeze())) + +def test_net_uint32(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint32) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.squeeze())) + +def test_net_int64(): + x = np.random.randn(1, 16, 1, 1).astype(np.int64) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.squeeze())) + +def test_net_uint64(): + x = np.random.randn(1, 16, 1, 1).astype(np.uint64) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.squeeze())) + +def test_net_float16(): + x = np.random.randn(1, 16, 1, 1).astype(np.float16) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.squeeze())) + +def test_net_float32(): + x = np.random.randn(1, 16, 1, 1).astype(np.float32) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.squeeze())) + +def test_net_float64(): + x = np.random.randn(1, 16, 1, 1).astype(np.float64) + net = Net() + output = net(Tensor(x)) + print(output.asnumpy()) + assert(np.all(output.asnumpy() == x.squeeze())) + From a837e3cb7d20c27f45e0b55e426a86e9eda86e82 Mon Sep 17 00:00:00 2001 From: yankai Date: Sun, 26 Apr 2020 14:50:45 +0800 Subject: [PATCH 211/227] support print scalar --- mindspore/ccsrc/utils/tensorprint_utils.cc | 80 +++++++++++++++++++++- 1 file changed, 78 insertions(+), 2 deletions(-) diff --git a/mindspore/ccsrc/utils/tensorprint_utils.cc b/mindspore/ccsrc/utils/tensorprint_utils.cc index 1036b424ba..ec1953c56d 100644 --- a/mindspore/ccsrc/utils/tensorprint_utils.cc +++ b/mindspore/ccsrc/utils/tensorprint_utils.cc @@ -31,6 +31,7 @@ namespace mindspore { const char kShapeSeperator[] = ","; +const char kShapeScalar[] = "[0]"; static std::map print_type_map = { {"int8_t", TypeId::kNumberTypeInt8}, {"uint8_t", TypeId::kNumberTypeUInt8}, {"int16_t", TypeId::kNumberTypeInt16}, {"uint16_t", TypeId::kNumberTypeUInt16}, @@ -81,6 +82,73 @@ bool PrintTensorToString(const char *str_data_ptr, mindspore::tensor::Tensor *co return true; } +template +void PrintScalarToString(const char *str_data_ptr, const string &tensor_type) { + const T *data_ptr = reinterpret_cast(str_data_ptr); + std::ostringstream buf_scalar; + buf_scalar << "Tensor shape :1 " << tensor_type; + buf_scalar << "\nval:"; + buf_scalar << *data_ptr; + std::cout << buf_scalar.str() << std::endl; +} + +void PrintScalarToBoolString(const char *str_data_ptr, const string &tensor_type) { + const bool *data_ptr = reinterpret_cast(str_data_ptr); + std::ostringstream buf_scalar; + buf_scalar << "Tensor shape :1 " << tensor_type; + buf_scalar << "\nval:"; + if (*data_ptr == true) { + buf_scalar << "True"; + } else { + buf_scalar << "False"; + } + std::cout << buf_scalar.str() << std::endl; +} + +void convertDataItem2Scalar(const char *str_data_ptr, const string &tensor_type) { + auto type_iter = print_type_map.find(tensor_type); + auto type_id = type_iter->second; + if (type_id == TypeId::kNumberTypeBool) { + PrintScalarToBoolString(str_data_ptr, tensor_type); + } else if (type_id == TypeId::kNumberTypeInt8) { + PrintScalarToString(str_data_ptr, tensor_type); + } else if (type_id == TypeId::kNumberTypeUInt8) { + PrintScalarToString(str_data_ptr, tensor_type); + } else if (type_id == TypeId::kNumberTypeInt16) { + PrintScalarToString(str_data_ptr, tensor_type); + } else if (type_id == TypeId::kNumberTypeUInt16) { + PrintScalarToString(str_data_ptr, tensor_type); + } else if (type_id == TypeId::kNumberTypeInt32) { + PrintScalarToString(str_data_ptr, tensor_type); + } else if (type_id == TypeId::kNumberTypeUInt32) { + PrintScalarToString(str_data_ptr, tensor_type); + } else if (type_id == TypeId::kNumberTypeInt64) { + PrintScalarToString(str_data_ptr, tensor_type); + } else if (type_id == TypeId::kNumberTypeUInt64) { + PrintScalarToString(str_data_ptr, tensor_type); + } else if (type_id == TypeId::kNumberTypeFloat16) { + PrintScalarToString(str_data_ptr, tensor_type); + } else if (type_id == TypeId::kNumberTypeFloat32) { + PrintScalarToString(str_data_ptr, tensor_type); + } else if (type_id == TypeId::kNumberTypeFloat64) { + PrintScalarToString(str_data_ptr, tensor_type); + } else { + MS_LOG(EXCEPTION) << "Cannot print scalar because of unsupport data type: " << tensor_type << "."; + } +} // namespace mindspore + +bool judgeLengthValid(const size_t str_len, const string &tensor_type) { + auto type_iter = type_size_map.find(tensor_type); + if (type_iter == type_size_map.end()) { + MS_LOG(EXCEPTION) << "type of scalar to print is not support."; + } + + if (str_len != type_iter->second) { + return false; + } + return true; +} + #ifndef NO_DLIB bool ConvertDataItem2Tensor(const std::vector &items) { // Acquire Python GIL @@ -92,14 +160,22 @@ bool ConvertDataItem2Tensor(const std::vector &items) { ret_end_sequence = true; break; } + std::shared_ptr str_data_ptr = std::static_pointer_cast(item.dataPtr_); + MS_EXCEPTION_IF_NULL(str_data_ptr); + if (item.tensorShape_ == kShapeScalar) { + if (!judgeLengthValid(str_data_ptr->size(), item.tensorType_)) { + MS_LOG(EXCEPTION) << "Print op receive data length is invalid."; + } + convertDataItem2Scalar(str_data_ptr->data(), item.tensorType_); + continue; + } + std::vector tensor_shape; size_t totaldims = 1; if (!ParseTensorShape(item.tensorShape_, &tensor_shape, &totaldims)) { MS_LOG(ERROR) << "Tensor print can not parse tensor shape, receive info" << item.tensorShape_; continue; } - std::shared_ptr str_data_ptr = std::static_pointer_cast(item.dataPtr_); - MS_EXCEPTION_IF_NULL(str_data_ptr); if (item.tensorType_ == "string") { std::string data(reinterpret_cast(str_data_ptr->c_str()), item.dataLen_); From d1002883592360410c0efcbd05252ad5983b2c36 Mon Sep 17 00:00:00 2001 From: zhaojichen Date: Sun, 26 Apr 2020 02:24:00 -0400 Subject: [PATCH 212/227] fix groupnorm bug and change globalbn parameter name --- mindspore/nn/layer/normalization.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py index 3ef2381ba1..09a0b4bb27 100644 --- a/mindspore/nn/layer/normalization.py +++ b/mindspore/nn/layer/normalization.py @@ -41,7 +41,7 @@ class _BatchNorm(Cell): moving_mean_init='zeros', moving_var_init='ones', use_batch_statistics=True, - group=1): + device_num_each_group=1): super(_BatchNorm, self).__init__() if num_features < 1: raise ValueError("num_features must be at least 1") @@ -60,7 +60,7 @@ class _BatchNorm(Cell): gamma_init, num_features), name="gamma", requires_grad=affine) self.beta = Parameter(initializer( beta_init, num_features), name="beta", requires_grad=affine) - self.group = check_int_positive(group) + self.group = check_int_positive(device_num_each_group) if self.group != 1: self.rank_id = get_rank() self.rank_size = get_group_size() @@ -324,7 +324,7 @@ class GlobalBatchNorm(_BatchNorm): Args: num_features (int): `C` from an expected input of size (N, C, H, W). - group (int): The number of device in each group. + device_num_each_group (int): The number of device in each group. eps (float): A value added to the denominator for numerical stability. Default: 1e-5. momentum (float): A floating hyperparameter of the momentum for the running_mean and running_var computation. Default: 0.9. @@ -364,7 +364,7 @@ class GlobalBatchNorm(_BatchNorm): moving_mean_init='zeros', moving_var_init='ones', use_batch_statistics=True, - group=1): + device_num_each_group=1): super(GlobalBatchNorm, self).__init__(num_features, eps, momentum, @@ -374,8 +374,8 @@ class GlobalBatchNorm(_BatchNorm): moving_mean_init, moving_var_init, use_batch_statistics, - group) - self.group = check_int_positive(group) + device_num_each_group) + self.group = check_int_positive(device_num_each_group) if self.group <= 1: raise ValueError("the number of group must be greater than 1.") def _check_data_dim(self, x): @@ -482,17 +482,17 @@ class GroupNorm(Cell): >>> x = Tensor(np.ones([1, 64, 256, 256], np.float32)) >>> goup_norm_op(x) """ - def __init__(self, num_groups, num_channels, eps=1e-05, affine=True): + def __init__(self, num_groups, num_channels, eps=1e-05, affine=True, gamma_init='ones', beta_init='zeros'): super(GroupNorm, self).__init__() self.num_groups = check_int_positive(num_groups) self.num_channels = check_int_positive(num_channels) if num_channels % num_groups != 0: raise ValueError("num_channels should be divided by num_groups") - self.eps = Tensor(check_typename('eps', eps, (float,)), mstype.float32) + self.eps = check_typename('eps', eps, (float,)) self.affine = check_bool(affine) - gamma = initializer('ones', [num_channels, 1, 1], mstype.float32) - beta = initializer('zeros', [num_channels, 1, 1], mstype.float32) + gamma = initializer(gamma_init, [num_channels, 1, 1]) + beta = initializer(beta_init, [num_channels, 1, 1]) if self.affine: self.gamma = Parameter(gamma, name='gamma') self.beta = Parameter(beta, name='beta') From f53bd08c80810659caa094baf3e137102054b1f7 Mon Sep 17 00:00:00 2001 From: zhaojichen Date: Sun, 26 Apr 2020 08:08:56 -0400 Subject: [PATCH 213/227] fix groupnorm bug and change globalbn parameter name --- mindspore/train/model.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mindspore/train/model.py b/mindspore/train/model.py index 698105889a..3fccb7aa2b 100755 --- a/mindspore/train/model.py +++ b/mindspore/train/model.py @@ -83,7 +83,7 @@ class Model: >>> return out >>> >>> net = Net() - >>> loss = nn.SoftmaxCrossEntropyWithLogits() + >>> loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None) >>> dataset = get_dataset() @@ -395,7 +395,7 @@ class Model: Examples: >>> dataset = get_dataset() >>> net = Net() - >>> loss = nn.SoftmaxCrossEntropyWithLogits() + >>> loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) >>> loss_scale_manager = FixedLossScaleManager() >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None, loss_scale_manager=loss_scale_manager) @@ -518,7 +518,7 @@ class Model: Examples: >>> dataset = get_dataset() >>> net = Net() - >>> loss = nn.SoftmaxCrossEntropyWithLogits() + >>> loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) >>> model = Model(net, loss_fn=loss, optimizer=None, metrics={'acc'}) >>> model.eval(dataset) """ From f4de3741243c0cb4b519ff4faed6efd81f100b23 Mon Sep 17 00:00:00 2001 From: zhaojichen Date: Sun, 26 Apr 2020 22:11:32 -0400 Subject: [PATCH 214/227] fix groupnorm bug and change globalbn parameter name --- mindspore/nn/layer/normalization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py index 09a0b4bb27..1066d7f94c 100644 --- a/mindspore/nn/layer/normalization.py +++ b/mindspore/nn/layer/normalization.py @@ -350,7 +350,7 @@ class GlobalBatchNorm(_BatchNorm): Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out}, H_{out}, W_{out})`. Examples: - >>> global_bn_op = nn.GlobalBatchNorm(num_features=3, group=4) + >>> global_bn_op = nn.GlobalBatchNorm(num_features=3, device_num_each_group=4) >>> input = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mindspore.float32) >>> global_bn_op(input) """ From 56323f54a3b6857f17f7439842ed73147c7a9b09 Mon Sep 17 00:00:00 2001 From: zhaojichen Date: Mon, 27 Apr 2020 02:55:11 -0400 Subject: [PATCH 215/227] fix groupnorm bug and change globalbn parameter name --- mindspore/nn/layer/normalization.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py index 1066d7f94c..ddd1bab1bf 100644 --- a/mindspore/nn/layer/normalization.py +++ b/mindspore/nn/layer/normalization.py @@ -17,6 +17,7 @@ from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore.common.parameter import Parameter from mindspore.common.initializer import initializer +from mindspore.ops.primitive import constexpr from mindspore.common.tensor import Tensor import mindspore.common.dtype as mstype import mindspore.context as context @@ -166,6 +167,10 @@ class _BatchNorm(Cell): return 'num_features={}, eps={}, momentum={}, gamma={}, beta={}, moving_mean={}, moving_variance={}'.format( self.num_features, self.eps, self.momentum, self.gamma, self.beta, self.moving_mean, self.moving_variance) +@constexpr +def _channel_check(channel, num_channel): + if channel != num_channel: + raise ValueError("the input channel is not equal with num_channel") class BatchNorm1d(_BatchNorm): r""" @@ -508,6 +513,7 @@ class GroupNorm(Cell): def construct(self, x): batch, channel, height, width = self.shape(x) + _channel_check(channel, self.num_channels) x = self.reshape(x, (batch, self.num_groups, channel*height*width/self.num_groups)) mean = self.reduce_mean(x, 2) var = self.reduce_sum(self.square(x - mean), 2) / (channel * height * width / self.num_groups - 1) From 22349df2409843f5453440428a90853cab09ed9d Mon Sep 17 00:00:00 2001 From: fary86 Date: Mon, 27 Apr 2020 22:41:27 +0800 Subject: [PATCH 216/227] Fix bug of ApplyRMSProp's check --- mindspore/ops/operations/nn_ops.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 9750549dc5..4b89d520c5 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -1544,9 +1544,11 @@ class ApplyRMSProp(PrimitiveWithInfer): args = {"var": var_dtype, "mean_square": mean_square_dtype, "moment": moment_dtype, "grad": grad_dtype} validator.check_tensor_type_same(args, mstype.number_type, self.name) - args = {"learning_rate": learning_rate_dtype, "decay": decay_dtype, - 'momentum': momentum_dtype, "epsilon": epsilon_dtype} - validator.check_scalar_or_tensor_type_same(args, [mstype.float16, mstype.float32], self.name) + valid_types = [mstype.float16, mstype.float32] + args_decay = {"decay": decay_dtype, 'momentum': momentum_dtype, "epsilon": epsilon_dtype} + validator.check_type_same(args_decay, valid_types, self.name) + args_lr = {"learning_rate": learning_rate_dtype, "decay": decay_dtype} + validator.check_scalar_or_tensor_type_same(args_lr, valid_types, self.name, allow_mix=True) return var_dtype From 1b298015bc207bb61493aad0c518a963967c1843 Mon Sep 17 00:00:00 2001 From: yanzhenxiang2020 Date: Tue, 28 Apr 2020 19:53:01 +0800 Subject: [PATCH 217/227] add IsFinite input check --- mindspore/ops/operations/math_ops.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 8de4108435..6eebde3a84 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1656,6 +1656,8 @@ class IsFinite(PrimitiveWithInfer): return x_shape def infer_dtype(self, x_dtype): + validator.check_subclass("x", x_dtype, mstype.tensor, self.name) + validator.check_tensor_type_same({'x': x_dtype}, mstype.number_type + (mstype.bool_,), self.name) return mstype.bool_ class FloatStatus(PrimitiveWithInfer): From 243cd33203097f0f77bffe8916c6f90f2a86e251 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Sun, 26 Apr 2020 17:21:13 +0800 Subject: [PATCH 218/227] adapt SparseApplyAdagradD adding output accum --- graphengine | 2 +- mindspore/ccsrc/transform/op_declare.cc | 2 +- mindspore/ops/operations/nn_ops.py | 9 ++++++--- tests/ut/python/ops/test_ops.py | 2 +- 4 files changed, 9 insertions(+), 6 deletions(-) diff --git a/graphengine b/graphengine index 43a715bc46..d345a800a4 160000 --- a/graphengine +++ b/graphengine @@ -1 +1 @@ -Subproject commit 43a715bc461fd70b7837051a2f47f0a1b19c5859 +Subproject commit d345a800a4f7c32eb768ea48667d1ce00b841748 diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 5ec54b2037..76fec9e21c 100644 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -1133,7 +1133,7 @@ INPUT_MAP(SparseApplyAdagradD) = { {1, INPUT_DESC(var)}, {2, INPUT_DESC(accum)}, {3, INPUT_DESC(grad)}, {4, INPUT_DESC(indices)}}; ATTR_MAP(SparseApplyAdagradD) = {{"lr", ATTR_DESC(lr, AnyTraits())}, {"use_locking", ATTR_DESC(use_locking, AnyTraits())}}; -OUTPUT_MAP(SparseApplyAdagradD) = {{0, OUTPUT_DESC(var)}}; +OUTPUT_MAP(SparseApplyAdagradD) = {{0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(accum)}}; // SparseApplyFtrlD INPUT_MAP(SparseApplyFtrlD) = {{1, INPUT_DESC(var)}, diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 9750549dc5..8f877adc18 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -2433,7 +2433,10 @@ class SparseApplyAdagrad(PrimitiveWithInfer): The shape of `indices` must be the same as `grad` in first dimension, the type must be int32. Outputs: - Tensor, has the same shape and type as `var`. + Tuple of 2 Tensor, the updated parameters. + + - **var** (Tensor) - The same shape and data type as `var`. + - **accum** (Tensor) - The same shape and data type as `accum`. """ @prim_attr_register @@ -2448,13 +2451,13 @@ class SparseApplyAdagrad(PrimitiveWithInfer): validator.check('var_shape[1:]', var_shape[1:], 'grad_shape[1:]', grad_shape[1:], Rel.EQ, self.name) validator.check_integer("indices rank", len(indices_shape), 1, Rel.EQ, self.name) validator.check('grad_shape[0]', grad_shape[0], 'indices_shape[0]', indices_shape[0], Rel.EQ, self.name) - return var_shape + return var_shape, accum_shape def infer_dtype(self, var_type, accum_type, grad_type, indices_type): args = {'var': var_type, 'accum': accum_type, 'grad': grad_type} validator.check_tensor_type_same(args, (mstype.float32,), self.name) validator.check_tensor_type_same({'indices': indices_type}, [mstype.int32], self.name) - return var_type + return var_type, accum_type class LARSUpdate(PrimitiveWithInfer): diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py index 1dea7b6502..1c0f5eb5fe 100755 --- a/tests/ut/python/ops/test_ops.py +++ b/tests/ut/python/ops/test_ops.py @@ -814,7 +814,7 @@ test_case_nn_ops = [ ('SparseApplyAdagrad', { 'block': P.SparseApplyAdagrad(0.5), 'desc_inputs': [[3, 3], [3, 3], [3, 3], Tensor(np.ones((3,), np.int32))], - 'desc_bprop': [3, 3], + 'desc_bprop': [[3, 3], [3, 3]], 'skip': ['backward']}), ('Flatten_1', { 'block': NetForFlatten(), From 81077588fe7cae6b0c0b78099394e05ecf3d4a32 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Wed, 29 Apr 2020 10:00:51 +0800 Subject: [PATCH 219/227] adapt ApplyAdam remove outputs m and v --- graphengine | 2 +- mindspore/ccsrc/transform/op_declare.cc | 4 ---- mindspore/ops/operations/nn_ops.py | 8 ++------ 3 files changed, 3 insertions(+), 11 deletions(-) diff --git a/graphengine b/graphengine index d345a800a4..976d1e31b7 160000 --- a/graphengine +++ b/graphengine @@ -1 +1 @@ -Subproject commit d345a800a4f7c32eb768ea48667d1ce00b841748 +Subproject commit 976d1e31b777d65f87333c3a125093946e682a6e diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 76fec9e21c..79b2808b2e 100644 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -466,11 +466,7 @@ INPUT_MAP(ApplyAdam) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(m)}, {10, INPUT_DESC(grad)}}; ATTR_MAP(ApplyAdam) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits())}, {"use_nesterov", ATTR_DESC(use_nesterov, AnyTraits())}}; -#ifdef ENABLE_GE -OUTPUT_MAP(ApplyAdam) = {{0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(m)}, {2, OUTPUT_DESC(v)}}; -#else OUTPUT_MAP(ApplyAdam) = {{0, OUTPUT_DESC(var)}}; -#endif // Relu6 INPUT_MAP(Relu6) = {{1, INPUT_DESC(x)}}; diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 0687806bb2..6371fc4654 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -2323,11 +2323,7 @@ class Adam(PrimitiveWithInfer): - **gradient** (Tensor) - Gradients. Outputs: - Tuple of 3 Tensor, the updated parameters. - - **var** (Tensor) - The same shape and data type as `var`. - - **m** (Tensor) - The same shape and data type as `m`. - - **v** (Tensor) - The same shape and data type as `v`. """ @prim_attr_register @@ -2340,7 +2336,7 @@ class Adam(PrimitiveWithInfer): validator.check("var_shape", var_shape, "m_shape", m_shape, Rel.EQ, self.name) validator.check("var_shape", var_shape, "v_shape", v_shape, Rel.EQ, self.name) validator.check("var_shape", var_shape, "grad_shape", grad_shape, Rel.EQ, self.name) - return var_shape, m_shape, v_shape + return var_shape def infer_dtype(self, var_dtype, m_dtype, v_dtype, beta1_power_dtype, beta2_power_dtype, lr_dtype, beta1_dtype, beta2_dtype, epsilon_dtype, grad_dtype): @@ -2350,7 +2346,7 @@ class Adam(PrimitiveWithInfer): args = {"beta1_power": beta1_power_dtype, "beta2_power": beta2_power_dtype, 'lr': lr_dtype, "beta1": beta1_dtype, "beta2": beta2_dtype, "epsilon": epsilon_dtype} validator.check_scalar_or_tensor_type_same(args, [mstype.float16, mstype.float32], self.name, True) - return var_dtype, m_dtype, v_dtype + return var_dtype class BinaryCrossEntropy(PrimitiveWithInfer): From 3f225e2ee58cc24b7087b3f394a5e3d60afdce77 Mon Sep 17 00:00:00 2001 From: changzherui Date: Tue, 28 Apr 2020 15:52:03 +0800 Subject: [PATCH 220/227] add ExtractImagePatches MirrorPad MirrorPadGrad --- mindspore/ccsrc/transform/convert.cc | 6 ++++++ mindspore/ccsrc/transform/op_declare.cc | 24 +++++++++++++++++++----- mindspore/ccsrc/transform/op_declare.h | 6 ++++++ 3 files changed, 31 insertions(+), 5 deletions(-) diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index 162ef8f0c1..da70f402e6 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -96,6 +96,7 @@ const char kNameConfusionMatrix[] = "ConfusionMatrix"; const char kNameResizeNearestNeighborD[] = "ResizeNearestNeighbor"; const char kNameResizeNearestNeighborGrad[] = "ResizeNearestNeighborGrad"; const char kNameApplyAdam[] = "Adam"; +const char kNameExtractImagePatches[] = "ExtractImagePatches"; const char kNameReLU6[] = "ReLU6"; const char kNameReLU6Grad[] = "ReLU6Grad"; const char kNameElu[] = "Elu"; @@ -110,6 +111,8 @@ const char kNameSigmoidCrossEntropyWithLogits[] = "SigmoidCrossEntropyWithLogits const char kNameSigmoidCrossEntropyWithLogitsGrad[] = "SigmoidCrossEntropyWithLogitsGrad"; const char kNameScatterNdD[] = "ScatterNd"; const char kNamePadD[] = "Pad"; +const char kNameMirrorPad[] = "MirrorPad"; +const char kNameMirrorPadGrad[] = "MirrorPadGrad"; const char kNameGatherNd[] = "GatherNd"; const char kNameArgmax[] = "Argmax"; const char kNameArgmin[] = "Argmin"; @@ -213,6 +216,7 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameMaxPoolGrad), ADPT_DESC(MaxPoolGrad)}, {string(kNameAvgPoolGrad), ADPT_DESC(AvgPoolGrad)}, {string(kNameMaxPoolGradWithArgmax), ADPT_DESC(MaxPoolGradWithArgmax)}, + {string(kNameExtractImagePatches), ADPT_DESC(ExtractImagePatches)}, {prim::kPrimAssign->name(), ADPT_DESC(Assign)}, {prim::kPrimStateSetItem->name(), ADPT_DESC(Assign)}, {prim::kPrimReluGrad->name(), ADPT_DESC(ReluGrad)}, @@ -257,6 +261,8 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameSigmoidCrossEntropyWithLogitsGrad), ADPT_DESC(SigmoidCrossEntropyWithLogitsGrad)}, {string(kNameScatterNdD), ADPT_DESC(ScatterNdD)}, {string(kNamePadD), ADPT_DESC(PadD)}, + {string(kNameMirrorPad), ADPT_DESC(MirrorPad)}, + {string(kNameMirrorPadGrad), ADPT_DESC(MirrorPadGrad)}, {string(kNameGatherNd), ADPT_DESC(GatherNd)}, {string(kNameArgmax), ADPT_DESC(ArgMaxD)}, {string(kNameArgmin), ADPT_DESC(ArgMinD)}, diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 5ec54b2037..42c80d9734 100644 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -558,6 +558,16 @@ INPUT_MAP(PadD) = {{1, INPUT_DESC(x)}}; ATTR_MAP(PadD) = {{"paddings", ATTR_DESC(paddings, AnyTraits>>())}}; OUTPUT_MAP(PadD) = {{0, OUTPUT_DESC(y)}}; +// MirrorPad +INPUT_MAP(MirrorPad) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(paddings)}}; +ATTR_MAP(MirrorPad) = {{"mode", ATTR_DESC(mode, AnyTraits())}}; +OUTPUT_MAP(MirrorPad) = {{0, OUTPUT_DESC(y)}}; + +// MirrorPadGrad +INPUT_MAP(MirrorPadGrad) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(paddings)}}; +ATTR_MAP(MirrorPadGrad) = {{"mode", ATTR_DESC(mode, AnyTraits())}}; +OUTPUT_MAP(MirrorPadGrad) = {{0, OUTPUT_DESC(y)}}; + // GatherNd INPUT_MAP(GatherNd) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(indices)}}; ATTR_MAP(GatherNd) = EMPTY_ATTR_MAP; @@ -706,16 +716,20 @@ ATTR_MAP(MaxPoolWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits(), AnyT OUTPUT_MAP(MaxPoolWithArgmax) = {{0, OUTPUT_DESC(y)}, {1, OUTPUT_DESC(argmax)}}; // MaxPoolGradWithArgmax -INPUT_MAP(MaxPoolGradWithArgmax) = { - {1, INPUT_DESC(x)}, - {2, INPUT_DESC(grad)}, - {3, INPUT_DESC(argmax)}, -}; +INPUT_MAP(MaxPoolGradWithArgmax) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(grad)}, {3, INPUT_DESC(argmax)}}; ATTR_MAP(MaxPoolGradWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits(), AnyTraits>())}, {"strides", ATTR_DESC(strides, AnyTraits(), AnyTraits>())}, {"padding", ATTR_DESC(padding, AnyTraits())}}; OUTPUT_MAP(MaxPoolGradWithArgmax) = {{0, OUTPUT_DESC(y)}}; +// ExtractImagePatches +INPUT_MAP(ExtractImagePatches) = {{1, INPUT_DESC(x)}}; +ATTR_MAP(ExtractImagePatches) = {{"ksizes", ATTR_DESC(ksizes, AnyTraits(), AnyTraits>())}, + {"strides", ATTR_DESC(strides, AnyTraits(), AnyTraits>())}, + {"rates", ATTR_DESC(rates, AnyTraits(), AnyTraits>())}, + {"padding", ATTR_DESC(padding, AnyTraits())}}; +OUTPUT_MAP(ExtractImagePatches) = {{0, OUTPUT_DESC(y)}}; + // Conv2D INPUT_MAP(Conv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}}; ATTR_MAP(Conv2D) = { diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index 59c95df8e1..cfec43a43c 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -95,6 +95,8 @@ DECLARE_OP_USE_OUTPUT(MaxPoolGradWithArgmax) DECLARE_OP_ADAPTER(Conv2D) DECLARE_OP_USE_ENUM(Conv2D) DECLARE_OP_USE_OUTPUT(Conv2D) +DECLARE_OP_ADAPTER(ExtractImagePatches) +DECLARE_OP_USE_OUTPUT(ExtractImagePatches) DECLARE_OP_ADAPTER(Conv2DBackpropInputD) DECLARE_OP_USE_ENUM(Conv2DBackpropInputD) DECLARE_OP_USE_INPUT_ATTR(Conv2DBackpropInputD) @@ -155,6 +157,10 @@ DECLARE_OP_USE_INPUT_ATTR(ScatterNdD) DECLARE_OP_USE_OUTPUT(ScatterNdD) DECLARE_OP_ADAPTER(PadD) DECLARE_OP_USE_OUTPUT(PadD) +DECLARE_OP_ADAPTER(MirrorPad) +DECLARE_OP_USE_OUTPUT(MirrorPad) +DECLARE_OP_ADAPTER(MirrorPadGrad) +DECLARE_OP_USE_OUTPUT(MirrorPadGrad) DECLARE_OP_ADAPTER(BoundingBoxEncode) DECLARE_OP_USE_OUTPUT(BoundingBoxEncode) DECLARE_OP_ADAPTER(BoundingBoxDecode) From b79382ab324a56d47a81c01bb3b9767bd1617715 Mon Sep 17 00:00:00 2001 From: zhaozhenlong Date: Wed, 29 Apr 2020 12:42:06 +0800 Subject: [PATCH 221/227] me ge use ApplyAdamD --- graphengine | 2 +- mindspore/ccsrc/transform/convert.cc | 1 + mindspore/ccsrc/transform/op_declare.cc | 9 +++++++++ mindspore/ccsrc/transform/op_declare.h | 2 ++ mindspore/ops/operations/nn_ops.py | 8 ++++++-- 5 files changed, 19 insertions(+), 3 deletions(-) diff --git a/graphengine b/graphengine index 976d1e31b7..1ab4fa8eb5 160000 --- a/graphengine +++ b/graphengine @@ -1 +1 @@ -Subproject commit 976d1e31b777d65f87333c3a125093946e682a6e +Subproject commit 1ab4fa8eb55b4f98e9e5e871a54909a1eaedffd3 diff --git a/mindspore/ccsrc/transform/convert.cc b/mindspore/ccsrc/transform/convert.cc index da70f402e6..d15f4ae43a 100755 --- a/mindspore/ccsrc/transform/convert.cc +++ b/mindspore/ccsrc/transform/convert.cc @@ -391,6 +391,7 @@ std::unordered_map &DfGraphConvertor::get_adpt_ma {string(kNameApplyCenteredRMSProp), ADPT_DESC(ApplyCenteredRMSProp)}}; #ifdef ENABLE_GE adpt_map[string(kNamePrint)] = ADPT_DESC(Print); + adpt_map[string(kNameApplyAdam)] = ADPT_DESC(ApplyAdamD); #endif return adpt_map; } diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc index 8407e46519..d6da49f85d 100644 --- a/mindspore/ccsrc/transform/op_declare.cc +++ b/mindspore/ccsrc/transform/op_declare.cc @@ -468,6 +468,15 @@ ATTR_MAP(ApplyAdam) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits()) {"use_nesterov", ATTR_DESC(use_nesterov, AnyTraits())}}; OUTPUT_MAP(ApplyAdam) = {{0, OUTPUT_DESC(var)}}; +// ApplyAdamD +INPUT_MAP(ApplyAdamD) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(m)}, {3, INPUT_DESC(v)}, + {4, INPUT_DESC(beta1_power)}, {5, INPUT_DESC(beta2_power)}, {6, INPUT_DESC(lr)}, + {7, INPUT_DESC(beta1)}, {8, INPUT_DESC(beta2)}, {9, INPUT_DESC(epsilon)}, + {10, INPUT_DESC(grad)}}; +ATTR_MAP(ApplyAdamD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits())}, + {"use_nesterov", ATTR_DESC(use_nesterov, AnyTraits())}}; +OUTPUT_MAP(ApplyAdamD) = {{0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(m)}, {2, OUTPUT_DESC(v)}}; + // Relu6 INPUT_MAP(Relu6) = {{1, INPUT_DESC(x)}}; ATTR_MAP(Relu6) = EMPTY_ATTR_MAP; diff --git a/mindspore/ccsrc/transform/op_declare.h b/mindspore/ccsrc/transform/op_declare.h index cfec43a43c..ccc6578a61 100755 --- a/mindspore/ccsrc/transform/op_declare.h +++ b/mindspore/ccsrc/transform/op_declare.h @@ -124,6 +124,8 @@ DECLARE_OP_ADAPTER(ResizeNearestNeighborV2Grad) DECLARE_OP_USE_OUTPUT(ResizeNearestNeighborV2Grad) DECLARE_OP_ADAPTER(ApplyAdam) DECLARE_OP_USE_OUTPUT(ApplyAdam) +DECLARE_OP_ADAPTER(ApplyAdamD) +DECLARE_OP_USE_OUTPUT(ApplyAdamD) DECLARE_OP_ADAPTER(Relu6) DECLARE_OP_USE_OUTPUT(Relu6) DECLARE_OP_ADAPTER(Relu6Grad) diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 6371fc4654..0687806bb2 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -2323,7 +2323,11 @@ class Adam(PrimitiveWithInfer): - **gradient** (Tensor) - Gradients. Outputs: + Tuple of 3 Tensor, the updated parameters. + - **var** (Tensor) - The same shape and data type as `var`. + - **m** (Tensor) - The same shape and data type as `m`. + - **v** (Tensor) - The same shape and data type as `v`. """ @prim_attr_register @@ -2336,7 +2340,7 @@ class Adam(PrimitiveWithInfer): validator.check("var_shape", var_shape, "m_shape", m_shape, Rel.EQ, self.name) validator.check("var_shape", var_shape, "v_shape", v_shape, Rel.EQ, self.name) validator.check("var_shape", var_shape, "grad_shape", grad_shape, Rel.EQ, self.name) - return var_shape + return var_shape, m_shape, v_shape def infer_dtype(self, var_dtype, m_dtype, v_dtype, beta1_power_dtype, beta2_power_dtype, lr_dtype, beta1_dtype, beta2_dtype, epsilon_dtype, grad_dtype): @@ -2346,7 +2350,7 @@ class Adam(PrimitiveWithInfer): args = {"beta1_power": beta1_power_dtype, "beta2_power": beta2_power_dtype, 'lr': lr_dtype, "beta1": beta1_dtype, "beta2": beta2_dtype, "epsilon": epsilon_dtype} validator.check_scalar_or_tensor_type_same(args, [mstype.float16, mstype.float32], self.name, True) - return var_dtype + return var_dtype, m_dtype, v_dtype class BinaryCrossEntropy(PrimitiveWithInfer): From 99c9b48a4d249fb0191d7e2a10f274686253f4d5 Mon Sep 17 00:00:00 2001 From: changzherui Date: Mon, 4 May 2020 19:39:17 +0800 Subject: [PATCH 222/227] delete TestHWBatchNormGradSplit --- .../ir_fission/batch_norm_grad_split_test.cc | 59 ------------------- 1 file changed, 59 deletions(-) delete mode 100644 tests/ut/cpp/pre_activate/ascend/ir_fission/batch_norm_grad_split_test.cc diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fission/batch_norm_grad_split_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fission/batch_norm_grad_split_test.cc deleted file mode 100644 index 68c327ade1..0000000000 --- a/tests/ut/cpp/pre_activate/ascend/ir_fission/batch_norm_grad_split_test.cc +++ /dev/null @@ -1,59 +0,0 @@ -/** - * Copyright 2020 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "common/backend_common_test.h" -#include "common/py_func_graph_fetcher.h" -#include "operator/ops.h" -#include "ir/meta_tensor.h" -#include "debug/anf_ir_dump.h" -#include "utils/utils.h" -#include "pre_activate/common/optimizer.h" -#include "pre_activate/ascend/ir_fission/batch_norm_grad_split.h" -#include "session/anf_runtime_algorithm.h" - -namespace mindspore { -namespace opt { -class TestHWBatchNormGradSplit : public BackendCommon { - public: - TestHWBatchNormGradSplit() : get_py_fun_("gtest_input.pre_activate.batch_norm_grad_split", true) {} - - public: - UT::PyFuncGraphFetcher get_py_fun_; -}; - -TEST_F(TestHWBatchNormGradSplit, test_split) { - get_py_fun_.SetDoResolve(true); - FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_batch_norm_grad_split", "before"); - EXPECT_NE(g, nullptr); - std::vector shp_x{1, 64, 112, 112}; - std::vector shp_b{64}; - auto x_abstract = std::make_shared(kFloat32, shp_x); - auto b_abstract = std::make_shared(kFloat32, shp_b); - AbstractBasePtrList args_spec_list{x_abstract, x_abstract, b_abstract, b_abstract, b_abstract, b_abstract}; - auto kernel_graph = GetKernelGraph(g, args_spec_list); - EXPECT_NE(kernel_graph, nullptr); - - auto optimizer = std::make_shared(); - auto pm = std::make_shared(); - auto pass = std::make_shared(); - pm->AddPass(pass); - optimizer->AddPassManager(pm); - auto new_graph = optimizer->Optimize(kernel_graph); - - FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_batch_norm_grad_split", "after"); - EXPECT_TRUE(CheckEqualGraph(g_after, new_graph)); -} -} // namespace opt -} // namespace mindspore From 354d8b0354ac0ea922dfbbbcf17c0c64b4bb6c0e Mon Sep 17 00:00:00 2001 From: changzherui Date: Tue, 5 May 2020 16:02:02 +0800 Subject: [PATCH 223/227] mod softmax --- mindspore/ccsrc/kernel/tbe/tbe_adapter.cc | 3 +++ 1 file changed, 3 insertions(+) diff --git a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc index fec4729b23..959d21a1b8 100644 --- a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc +++ b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc @@ -30,6 +30,9 @@ namespace mindspore { namespace kernel { namespace tbe { static std::map tbe_func_adapter_map = { + {"softmax", "softmax_v2"}, + {"log_softmax", "log_softmax_v2"}, + {"applymomentum", "applymomentum_d"}, {"re_lu6", "relu6"}, {"re_lu6_grad", "relu6_grad"}, {"re_lu", "relu"}, From 84c029d1caf21ec2c7a0473b3a7ae49b98254633 Mon Sep 17 00:00:00 2001 From: changzherui Date: Tue, 5 May 2020 16:36:01 +0800 Subject: [PATCH 224/227] add apply_momentum --- mindspore/ccsrc/kernel/tbe/tbe_adapter.cc | 2 +- mindspore/ops/_op_impl/tbe/batchnorm.py | 9 ++++----- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc index 959d21a1b8..8ce5504b8e 100644 --- a/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc +++ b/mindspore/ccsrc/kernel/tbe/tbe_adapter.cc @@ -32,7 +32,7 @@ namespace tbe { static std::map tbe_func_adapter_map = { {"softmax", "softmax_v2"}, {"log_softmax", "log_softmax_v2"}, - {"applymomentum", "applymomentum_d"}, + {"apply_momentum", "apply_momentum_d"}, {"re_lu6", "relu6"}, {"re_lu6_grad", "relu6_grad"}, {"re_lu", "relu"}, diff --git a/mindspore/ops/_op_impl/tbe/batchnorm.py b/mindspore/ops/_op_impl/tbe/batchnorm.py index 6dd79245a3..ddb24ac3e7 100644 --- a/mindspore/ops/_op_impl/tbe/batchnorm.py +++ b/mindspore/ops/_op_impl/tbe/batchnorm.py @@ -36,19 +36,18 @@ batch_norm_op_info = TBERegOp("BatchNorm") \ .output(2, "batch_variance", False, "required", "all") \ .output(3, "reserve_space_1", False, "optional", "all") \ .output(4, "reserve_space_2", False, "optional", "all") \ - .output(5, "reserve_space_3", False, "optional", "all") \ .dtype_format(DataType.F16_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F16_Default, DataType.F32_Default, DataType.F32_Default, - DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \ + DataType.F32_Default, DataType.F32_Default) \ .dtype_format(DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD, - DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \ + DataType.F32_5HD, DataType.F32_5HD) \ .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, - DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \ + DataType.F32_Default, DataType.F32_Default) \ .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, - DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \ + DataType.F32_5HD, DataType.F32_5HD) \ .get_op_info() From e01692addaa2753dbf6b8565995b429c04463e8f Mon Sep 17 00:00:00 2001 From: changzherui Date: Tue, 5 May 2020 17:08:26 +0800 Subject: [PATCH 225/227] modify apply_momentum output var --- mindspore/ops/_op_impl/tbe/apply_momentum.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mindspore/ops/_op_impl/tbe/apply_momentum.py b/mindspore/ops/_op_impl/tbe/apply_momentum.py index 42ce9d0e41..920a48e48d 100644 --- a/mindspore/ops/_op_impl/tbe/apply_momentum.py +++ b/mindspore/ops/_op_impl/tbe/apply_momentum.py @@ -29,7 +29,7 @@ apply_momentum_op_info = TBERegOp("ApplyMomentum") \ .input(2, "lr", False, "required", "all") \ .input(3, "grad", False, "required", "all") \ .input(4, "momentum", False, "required", "all") \ - .output(0, "var", False, "required", "all") \ + .output(0, "out", False, "required", "all") \ .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \ .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_Default, DataType.F16_5HD, From 48735c25caa8bd0f29267ebb7dcfac3d32ce9266 Mon Sep 17 00:00:00 2001 From: changzherui Date: Tue, 5 May 2020 17:39:10 +0800 Subject: [PATCH 226/227] modify applr momentum output 2 --- mindspore/ccsrc/device/kernel_runtime.cc | 1 + mindspore/ops/_op_impl/tbe/apply_momentum.py | 19 ++++++++++--------- mindspore/ops/operations/nn_ops.py | 5 +++++ 3 files changed, 16 insertions(+), 9 deletions(-) diff --git a/mindspore/ccsrc/device/kernel_runtime.cc b/mindspore/ccsrc/device/kernel_runtime.cc index d3fccc11fd..e77d348630 100644 --- a/mindspore/ccsrc/device/kernel_runtime.cc +++ b/mindspore/ccsrc/device/kernel_runtime.cc @@ -201,6 +201,7 @@ void KernelRuntime::RunOpAssignOutputMemory(const AnfNodePtr &kernel) { if (AnfAlgo::GetCNodeName(kernel) == "ApplyMomentum") { auto device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, 0); AnfAlgo::SetOutputAddr(device_address, 0, kernel.get()); + AnfAlgo::SetOutputAddr(device_address, 1, kernel.get()); return; } diff --git a/mindspore/ops/_op_impl/tbe/apply_momentum.py b/mindspore/ops/_op_impl/tbe/apply_momentum.py index 920a48e48d..deb8f0d387 100644 --- a/mindspore/ops/_op_impl/tbe/apply_momentum.py +++ b/mindspore/ops/_op_impl/tbe/apply_momentum.py @@ -29,23 +29,24 @@ apply_momentum_op_info = TBERegOp("ApplyMomentum") \ .input(2, "lr", False, "required", "all") \ .input(3, "grad", False, "required", "all") \ .input(4, "momentum", False, "required", "all") \ - .output(0, "out", False, "required", "all") \ + .output(0, "var", False, "required", "all") \ + .output(1, "accum", False, "required", "all") \ .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, - DataType.F16_Default, DataType.F16_Default) \ + DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \ .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_Default, DataType.F16_5HD, - DataType.F16_Default, DataType.F16_5HD) \ + DataType.F16_Default, DataType.F16_5HD, DataType.F16_5HD) \ .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_Default, DataType.F16_C1HWNCoC0, - DataType.F16_Default, DataType.F16_C1HWNCoC0) \ + DataType.F16_Default, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \ .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_Default, DataType.F16_FracZ, - DataType.F16_Default, DataType.F16_FracZ) \ + DataType.F16_Default, DataType.F16_FracZ, DataType.F16_FracZ) \ .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, - DataType.F32_Default, DataType.F32_Default) \ + DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \ .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_Default, DataType.F32_5HD, - DataType.F32_Default, DataType.F32_5HD) \ + DataType.F32_Default, DataType.F32_5HD, DataType.F32_5HD) \ .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_Default, DataType.F32_C1HWNCoC0, - DataType.F32_Default, DataType.F32_C1HWNCoC0) \ + DataType.F32_Default, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \ .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_Default, DataType.F32_FracZ, - DataType.F32_Default, DataType.F32_FracZ) \ + DataType.F32_Default, DataType.F32_FracZ, DataType.F32_FracZ) \ .get_op_info() diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index fb8e908924..2a2dbe08a8 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -1427,8 +1427,11 @@ class ApplyMomentum(PrimitiveWithInfer): def __init__(self, use_nesterov=False, use_locking=False, gradient_scale=1.0): self.init_prim_io_names(inputs=['variable', 'accumulation', 'learning_rate', 'gradient', 'momentum'], outputs=['output']) + self.is_tbe = context.get_context("device_target") == "Ascend" def infer_shape(self, v_shape, a_shape, l_shape, g_shape, m_shape): + if self.is_tbe: + return v_shape, v_shape return v_shape def infer_dtype(self, v_dtype, a_dtype, l_dtype, g_dtype, m_dtype): @@ -1439,6 +1442,8 @@ class ApplyMomentum(PrimitiveWithInfer): validator.check_scalar_or_tensor_type_same({"l_dtype": l_dtype}, valid_types, self.name) validator.check_scalar_or_tensor_type_same({"g_dtype": g_dtype}, valid_types, self.name) validator.check_scalar_or_tensor_type_same({"m_dtype": m_dtype}, valid_types, self.name) + if self.is_tbe: + return g_dtype, g_dtype return g_dtype From 446828f221709a6f7995e09087a31b675da73c16 Mon Sep 17 00:00:00 2001 From: changzherui Date: Tue, 5 May 2020 21:56:13 +0800 Subject: [PATCH 227/227] modify momentum test --- tests/ut/python/ops/test_momentum.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/ut/python/ops/test_momentum.py b/tests/ut/python/ops/test_momentum.py index 28b9637015..f25e4faf2d 100644 --- a/tests/ut/python/ops/test_momentum.py +++ b/tests/ut/python/ops/test_momentum.py @@ -38,7 +38,7 @@ def tensor_run_opt(opt, iters, learning_rate, momentum, gradient, variable, moment): """ tensor_run_opt """ success = True - new_weight = opt(variable, moment, learning_rate, gradient, momentum) + new_weight = opt(variable, moment, learning_rate, gradient, momentum)[0] success = F.depend(success, F.assign(variable, new_weight)) return success

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