modify reduceminD and reducemaxD IR

graphengine

@@ -1 +1 @@
-Subproject commit 21d3700f661576edc37607a3bc961874ee5189a7
+Subproject commit 092c7a1f6548cac7d40e677af3498c3c49ea2bfd

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());

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";
@@ -189,7 +190,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &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 +311,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &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 +344,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &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)},
@@ -353,6 +354,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &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)},
@@ -1017,8 +1019,8 @@ DfGraphConvertor &DfGraphConvertor::BuildGraph() {
     }
   }
 
-  // set up dependices
-  MS_LOG(DEBUG) << "set up dependices";
+  // set up dependencies
+  MS_LOG(DEBUG) << "set up dependencies";
   std::vector<AnfNodePtr> nodes = ::mindspore::TopoSort(anf_graph_->get_return());
   for (auto &it : nodes) {
     SetNodeInput(it);
@@ -1115,8 +1117,8 @@ 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<Data>(op)->update_input_desc_data(*desc);
-    (void)std::static_pointer_cast<Data>(op)->update_output_desc_out(*desc);
+    (void)std::static_pointer_cast<Data>(op)->update_input_desc_x(*desc);
+    (void)std::static_pointer_cast<Data>(op)->update_output_desc_y(*desc);
   }
 }
 

mindspore/ccsrc/transform/graph_runner.cc

@@ -135,6 +135,13 @@ Status GraphRunner::RunGraph(const RunOptions& options, const std::vector<GeTens
     return Status::FAILED;
   }
 
+  // The information of some nodes could be changed after fusion in some cases
+  // Therefore a graph needs to be rebuilt in above situation
+  if (sess_->IsGraphNeedRebuild(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;

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<bool>())},
                      {"reverse", ATTR_DESC(reverse, AnyTraits<bool>())}};
 OUTPUT_MAP(CumsumD) = {{0, OUTPUT_DESC(y)}};
 
-// softmax
-INPUT_MAP(Softmax) = {{1, INPUT_DESC(x)}};
-ATTR_MAP(Softmax) = {
-  {"axis", ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+// SoftmaxV2
+INPUT_MAP(SoftmaxV2) = {{1, INPUT_DESC(x)}};
+ATTR_MAP(SoftmaxV2) = {
+  {"axis", ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
 };
-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<bool>())}};
 OUTPUT_MAP(ReduceSum) = {{0, OUTPUT_DESC(y)}};
 
 // ReduceSumD
 INPUT_MAP(ReduceSumD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceSumD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceSumD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceSumD) = {{0, OUTPUT_DESC(y)}};
 
 // ReduceProdD
 INPUT_MAP(ReduceProdD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceProdD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceProdD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceProdD) = {{0, OUTPUT_DESC(y)}};
 
@@ -294,7 +293,7 @@ ATTR_MAP(CumprodD) = {{"exclusive", ATTR_DESC(exclusive, AnyTraits<bool>())},
                       {"reverse", ATTR_DESC(reverse, AnyTraits<bool>())}};
 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<int64_t>())}};
 
 INPUT_MAP(SliceD) = {{1, INPUT_DESC(x)}};
-INPUT_ATTR_MAP(SliceD) = {{2, ATTR_DESC(begin, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
+INPUT_ATTR_MAP(SliceD) = {{2, ATTR_DESC(offsets, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
                           {3, ATTR_DESC(size, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())}};
 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<GEType>())}};
-ATTR_MAP(Cast) = {{"Truncate", ATTR_DESC(truncate, AnyTraits<bool>())}};
-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<float>())}, {"beta", ATTR_DESC(beta, AnyTraits<float>())}};
-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<float>())}, {"beta", ATTR_DESC(beta, AnyTraits<float>())}};
-
-#endif
-
-// TopKV2
-INPUT_MAP(TopKV2) = {
-  {1, INPUT_DESC(input)},
-  {2, INPUT_DESC(k)},
-};
-
-ATTR_MAP(TopKV2) = {{"T", ATTR_DESC(T, AnyTraits<GEType>())}, {"sorted", ATTR_DESC(sorted, AnyTraits<bool>())}};
-
-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<bool>())}};
+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<std::string>())}};
 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<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
@@ -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<bool>())}};
 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<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
@@ -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<int>())}, {"num", ATTR_DESC(num, AnyTraits<int>())}};
-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<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ScatterNdD) = EMPTY_ATTR_MAP;
@@ -587,7 +554,7 @@ ATTR_MAP(PadD) = {{"paddings", ATTR_DESC(paddings, AnyTraits<std::vector<std::ve
 OUTPUT_MAP(PadD) = {{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)}};
 
@@ -612,13 +579,13 @@ ATTR_MAP(ROIAlignGrad) = {
 // ArgMaxD
 INPUT_MAP(ArgMaxD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(ArgMaxD) = {{"axis", ATTR_DESC(dimension, AnyTraits<int>())},
-                     {"output_type", ATTR_DESC(output_type, AnyTraits<GEType>())}};
+                     {"output_type", ATTR_DESC(dtype, AnyTraits<GEType>())}};
 OUTPUT_MAP(ArgMaxD) = {{0, OUTPUT_DESC(y)}};
 
 // ArgMinD
 INPUT_MAP(ArgMinD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(ArgMinD) = {{"axis", ATTR_DESC(dimension, AnyTraits<int>())},
-                     {"output_type", ATTR_DESC(output_type, AnyTraits<GEType>())}};
+                     {"output_type", ATTR_DESC(dtype, AnyTraits<GEType>())}};
 OUTPUT_MAP(ArgMinD) = {{0, OUTPUT_DESC(y)}};
 
 // ArgMaxWithValue
@@ -634,14 +601,14 @@ ATTR_MAP(ArgMinWithValue) = {{"axis", ATTR_DESC(dimension, AnyTraits<int>())},
 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<bool>())}};
 OUTPUT_MAP(ReduceAll) = {{0, OUTPUT_DESC(y)}};
 
 // ReduceMeanD
 INPUT_MAP(ReduceMeanD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceMeanD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceMeanD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 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<std::string>())}};
 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<int>(), AnyTraits<std::vector<int64_t>>())},
                          {"strides", ATTR_DESC(strides, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
-                         {"padding", ATTR_DESC(padding, AnyTraits<std::string>())}};
+                         {"padding", ATTR_DESC(padding, AnyTraits<std::string>())},
+                         {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
 OUTPUT_MAP(MaxPoolGrad) = {{0, OUTPUT_DESC(y)}};
 
 // avgpoolgrad
@@ -739,28 +707,34 @@ ATTR_MAP(Conv2D) = {
   {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
   {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
+  {"group", ATTR_DESC(groups, AnyTraits<int>())}
 };
 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<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {3, ATTR_DESC(input_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(Conv2DBackpropInputD) = {
   {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"stride", ATTR_DESC(strides, "strides", AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
   {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
+  {"group", ATTR_DESC(groups, AnyTraits<int>())}
 };
 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<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {3, ATTR_DESC(filter_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(Conv2DBackpropFilterD) = {
   {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"stride", ATTR_DESC(strides, "strides", AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
   {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
+  {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
+  {"group", ATTR_DESC(groups, AnyTraits<int>())}
 };
 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<bool>())},
-                    {"transpose_b", ATTR_DESC(transpose_b, AnyTraits<bool>())}};
+ATTR_MAP(MatMul) = {{"transpose_a", ATTR_DESC(transpose_x1, AnyTraits<bool>())},
+                    {"transpose_b", ATTR_DESC(transpose_x2, AnyTraits<bool>())}};
 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<int>())},
                     {"output_num", ATTR_DESC(num_split, AnyTraits<int>())}};
-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<int>())},
   {"inputNums", ATTR_DESC(N, AnyTraits<int>())},
 };
-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<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceMinD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceMinD) = {{0, OUTPUT_DESC(y)}};
 
 // ReduceMaxD
 INPUT_MAP(ReduceMaxD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceMaxD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceMaxD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 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<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
-OUTPUT_MAP(LogSoftmax) = {{0, OUTPUT_DESC(logsoftmax)}};
+// LogSoftmaxV2
+INPUT_MAP(LogSoftmaxV2) = {{1, INPUT_DESC(logits)}};
+ATTR_MAP(LogSoftmaxV2) = {
+  {"axis", ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+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<bool>())},
-                         {"transpose_x2", ATTR_DESC(adj_y, AnyTraits<bool>())}};
+ATTR_MAP(BatchMatMul) = {{"transpose_x1", ATTR_DESC(adj_x1, AnyTraits<bool>())},
+                         {"transpose_x2", ATTR_DESC(adj_x2, AnyTraits<bool>())}};
 OUTPUT_MAP(BatchMatMul) = {{0, OUTPUT_DESC(y)}};
 
 // DropoutDoMask
@@ -1146,6 +1120,19 @@ ATTR_MAP(SparseApplyAdagradD) = {{"lr", ATTR_DESC(lr, AnyTraits<float>())},
                                  {"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
 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<bool>())},
+                              {"lr", ATTR_DESC(lr, AnyTraits<float>())},
+                              {"l1", ATTR_DESC(l1, AnyTraits<float>())},
+                              {"l2", ATTR_DESC(l2, AnyTraits<float>())},
+                              {"lr_power", ATTR_DESC(lr_power, AnyTraits<float>())}};
+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<int64_t>())}};

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)
@@ -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)

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<MeTensor>(me_tensor);
 }
 

mindspore/ccsrc/utils/context/ms_context.cc

@@ -355,7 +355,9 @@ void MsContext::GetGeOptions(std::map<std::string, std::string>* 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
 }

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",

mindspore/ops/operations/nn_ops.py

@@ -2141,6 +2141,79 @@ 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_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)
+        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)
+
+        return var_type
+
+
 class LARSUpdate(PrimitiveWithInfer):
     """
     Conduct lars (layer-wise adaptive rate scaling) update on the square sum of gradient.
@@ -2244,4 +2317,4 @@ 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
+        return var_type

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))],