fix(mgb/gopt): fix testcase for enable nchw64 pass

GitOrigin-RevId: 2ae8d1608d

dnn/src/common/conv_bias.cpp

@@ -65,8 +65,8 @@ void do_check_exec_common(
                           bias.to_string().c_str(), dst.to_string().c_str());
             megdnn_assert(bias.shape[2] == 1);
             megdnn_assert(bias.shape[3] == 1);
-        } else if (param().format == param::ConvBias::Format::NHWC ||
-                   param().format == param::ConvBias::Format::NCHW4_NHWC) {
+        } else if (opr->param().format == param::ConvBias::Format::NHWC ||
+                   opr->param().format == param::ConvBias::Format::NCHW4_NHWC) {
             megdnn_assert(bias.shape[0] == 1);
             megdnn_assert(bias.shape[1] == 1);
             megdnn_assert(bias.shape[2] == 1);

src/gopt/impl/tensor_reformat.cpp

@@ -420,7 +420,8 @@ void TensorReformatPass::RelayoutPlaceholder::init_output_static_infer_desc() {
             dst[4] = 32;
         } else if (layout_type() ==
                    RelayoutPlaceholder::LayoutType::NCHW_TO_NCHW64) {
-            mgb_assert(inp_shape.ndim == 4 && inp_shape[1] % 64 == 0, "%s", inp_shape.to_string().c_str());
+            mgb_assert(inp_shape.ndim == 4 && inp_shape[1] % 64 == 0, "%s",
+                       inp_shape.to_string().c_str());
             dst.ndim = 5;
             dst[0] = inp_shape[0];
             dst[1] = inp_shape[1] / 64;
@@ -438,8 +439,6 @@ void TensorReformatPass::RelayoutPlaceholder::init_output_static_infer_desc() {
             dst[4] = 32;
         } else if (layout_type() ==
                    RelayoutPlaceholder::LayoutType::NCHW4_TO_NCHW64) {
-            mgb_assert(layout_type() ==
-                       RelayoutPlaceholder::LayoutType::NCHW4_TO_NCHW64);
             mgb_assert(inp_shape.ndim == 5 && inp_shape[1] % 16 == 0);
             dst.ndim = 5;
             dst[0] = inp_shape[0];
@@ -499,18 +498,17 @@ void TensorReformatPass::RelayoutPlaceholder::init_output_static_infer_desc() {
         } else if (layout_type() ==
                    RelayoutPlaceholder::LayoutType::NHWC_TO_NCHW32) {
             mgb_assert(inp_shape.ndim == 4 && inp_shape[3] % 32 == 0);
-            dst.ndim = 4;
+            dst.ndim = 5;
             dst[0] = inp_shape[0];
             dst[1] = inp_shape[3] / 32;
             dst[2] = inp_shape[1];
             dst[3] = inp_shape[2];
             dst[4] = 32;
-        } else if (layout_type() ==
-                   RelayoutPlaceholder::LayoutType::NHWC_TO_NCHW64) {
+        } else {
             mgb_assert(layout_type() ==
                        RelayoutPlaceholder::LayoutType::NHWC_TO_NCHW64);
             mgb_assert(inp_shape.ndim == 4 && inp_shape[3] % 64 == 0);
-            dst.ndim = 4;
+            dst.ndim = 5;
             dst[0] = inp_shape[0];
             dst[1] = inp_shape[3] / 64;
             dst[2] = inp_shape[1];
@@ -3729,21 +3727,6 @@ void FoldingConvBiasDimshufflePass::apply(OptState& opt) const {
         return y1.node();
     };
 
-    auto nhwc2nchw64 = [](VarNode* inp) -> VarNode* {
-        mgb_assert(inp->shape().ndim == 4);
-        auto x = SymbolVar(inp);
-        auto xshp = opr::GetVarShape::make(x);
-        auto cv = [&x](int v) { return x.make_scalar(v); };
-        auto sub = [&xshp, &cv](int idx) {
-            return opr::IndexAt::make(xshp, {{0, cv(idx)}});
-        };
-        auto tshp = opr::Concat::make(
-                     {sub(0), sub(1), sub(2), sub(3) / 64, cv(64)}, 0);
-        auto y0 = opr::Reshape::make(x, tshp);
-        auto y1 = opr::Dimshuffle::make(y0, {0, 3, 1, 2, 4});
-        return y1.node();
-    };
-
     auto try_conv_dimshuffle_reshape_typecvt = [&rewriter, &readers,
                                                 &nchw42nchw](
                                                        OperatorNodeBase* opr) {
@@ -3915,31 +3898,29 @@ void FoldingConvBiasDimshufflePass::apply(OptState& opt) const {
         return true;
     };
 
-    auto try_conv_reformat_nchw42nchw64 = [&rewriter, &nchw42nhwc, &nhwc2nchw64,
-                                           &readers](OperatorNodeBase* opr) {
+    auto try_conv_reformat_nchw42nhwc = [&rewriter, &nchw42nhwc,
+                                         &readers](OperatorNodeBase* opr) {
         ThinHashSet<OperatorNodeBase*> opr_set;
         ThinHashSet<OperatorNodeBase*> reader_set;
         // check reshape
-        auto reshape1 =
-                try_cast_as_op<opr::Reshape>(opr);
-        if (reshape1 == nullptr)
+        auto reshape = try_cast_as_op<opr::Reshape>(opr);
+        if (reshape == nullptr)
             return false;
         opr_set.insert(opr);
 
         // check dimshuffle
         auto shuffle = try_cast_as_op<opr::Dimshuffle>(
-                reshape1->input(0)->owner_opr());
+                reshape->input(0)->owner_opr());
         if (shuffle == nullptr)
             return false;
         auto&& param = shuffle->param();
-        if (param.pattern_len != 6)
+        if (param.pattern_len != 5)
             return false;
-        bool is_nchw42nchw64 = param.pattern[0] == 0 && param.pattern[1] == 1 &&
-                               param.pattern[2] == 3 && param.pattern[3] == 4 &&
-                               param.pattern[4] == 2 && param.pattern[5] == 5 &&
-                               shuffle->output(0)->shape()[5] == 4 &&
-                               shuffle->output(0)->shape()[4] == 16;
-        if (!is_nchw42nchw64)
+        bool is_nchw42nhwc = param.pattern[0] == 0 && param.pattern[1] == 2 &&
+                             param.pattern[2] == 3 && param.pattern[3] == 1 &&
+                             param.pattern[4] == 4 &&
+                             shuffle->output(0)->shape()[4] == 4;
+        if (!is_nchw42nhwc)
             return false;
         opr_set.insert(shuffle);
         for (auto&& i : readers[shuffle]) {
@@ -3948,20 +3929,8 @@ void FoldingConvBiasDimshufflePass::apply(OptState& opt) const {
             }
         }
 
-        // check reshape
-        auto reshape2 =
-                try_cast_as_op<opr::Reshape>(shuffle->input(0)->owner_opr());
-        if (reshape2 == nullptr)
-            return false;
-        opr_set.insert(reshape2);
-        for (auto&& i : readers[reshape2]) {
-            if (i.second & DepType::DEV_VALUE) {
-                reader_set.insert(i.first);
-            }
-        }
-
         auto typecvt =
-                try_cast_as_op<opr::TypeCvt>(reshape2->input(0)->owner_opr());
+                try_cast_as_op<opr::TypeCvt>(shuffle->input(0)->owner_opr());
         if (typecvt == nullptr)
             return false;
         auto in_dtype = typecvt->input(0)->dtype(),
@@ -3972,6 +3941,11 @@ void FoldingConvBiasDimshufflePass::apply(OptState& opt) const {
         if (!is_s82s4)
             return false;
         opr_set.insert(typecvt);
+        for (auto&& i : readers[typecvt]) {
+            if (i.second & DepType::DEV_VALUE) {
+                reader_set.insert(i.first);
+            }
+        }
 
         // check conv bias
         auto conv_bias =
@@ -4006,11 +3980,10 @@ void FoldingConvBiasDimshufflePass::apply(OptState& opt) const {
         auto conv_bias_shuffle = opr::ConvBias::make(
                 src, filter, new_bias, new_param, conv_bias->execution_policy(),
                 OperatorNodeConfig{out_dtype});
-        auto new_var = nhwc2nchw64(conv_bias_shuffle.node());
         rewriter.replace_var(
-                opr->output(0), new_var,
+                opr->output(0), conv_bias_shuffle.node(),
                 mgb_cstr_log("replace conv_bias + "
-                             "reformat to conv_bias(NCHW4_NCHW64)"));
+                             "reformat to conv_bias(NCHW4_NHWC)"));
         return true;
     };
 
@@ -4098,14 +4071,14 @@ void FoldingConvBiasDimshufflePass::apply(OptState& opt) const {
 
     auto on_opr = [&try_conv_dimshuffle_reshape_typecvt,
                    &try_conv_reformat_nchw42nchw32,
-                   &try_conv_reformat_nchw42nchw64,
+                   &try_conv_reformat_nchw42nhwc,
 #if CUDA_VERSION >= 10020
                    &try_conv_reformat_nchw322nchw4,
 #endif
                    &rewriter](OperatorNodeBase* opr) {
         if (!try_conv_dimshuffle_reshape_typecvt(opr) &&
-            !try_conv_reformat_nchw42nchw32(opr) && 
-            !try_conv_reformat_nchw42nchw64(opr) 
+            !try_conv_reformat_nchw42nchw32(opr) &&
+            !try_conv_reformat_nchw42nhwc(opr)
 #if CUDA_VERSION >= 10020
             && !try_conv_reformat_nchw322nchw4(opr)
 #endif
@@ -4546,6 +4519,9 @@ VarNode* EnableNCHW64Pass::on_graph_endpoint_var(VarNode* new_var,
             case Format::NCHW64:
                 type = LayoutType::NCHW64_TO_NCHW;
                 break;
+            case Format::NHWC:
+                type = LayoutType::NHWC_TO_NCHW;
+                break;
             default:
                 mgb_throw(AssertionError,
                           "format(%d) is not supported, related var "
@@ -4980,7 +4956,7 @@ EnableNCHW64Pass::make_nchw64_converter() {
                 case Format::NHWC:
                     inps[1] = RelayoutPlaceholder::make(
                                       inps[1], RelayoutPlaceholder::LayoutType::
-                                                       NCHW_TO_NHWC)
+                                                       NHWC_TO_NCHW4)
                                       .node();
                     break;
                 case Format::NCHW32:

src/gopt/test/inference.cpp

@@ -4404,10 +4404,10 @@ TEST(TestGoptInference, FoldingConvDimshuffleNCHW4NHWC) {
     };
 
     auto x = mkvar("x", {32, 4, 23, 40}, dtype::QuantizedS8(2.5f)),
-         w = mkcvar("w", {64, 4, 3, 3}, dtype::QuantizedS8(2.5f)),
-         b = mkcvar("b", {1, 64, 1, 1}, dtype::QuantizedS32(6.25f)),
-         w1 = mkcvar("w1", {64, 64, 3, 3}, dtype::QuantizedS4(1.234f)),
-         b1 = mkcvar("b1", {1, 64, 1, 1}, dtype::QuantizedS32(12.34567f*1.234f));
+         w = mkcvar("w", {32, 4, 3, 3}, dtype::QuantizedS8(2.5f)),
+         b = mkcvar("b", {1, 32, 1, 1}, dtype::QuantizedS32(6.25f)),
+         w1 = mkcvar("w1", {32, 32, 3, 3}, dtype::QuantizedS4(1.234f)),
+         b1 = mkcvar("b1", {1, 32, 1, 1}, dtype::QuantizedS32(12.34567f*1.234f));
     opr::ConvBias::Param param;
     param.format = opr::ConvBias::Param::Format::NCHW;
     param.nonlineMode = opr::ConvBias::Param::NonlineMode::RELU;
@@ -4438,7 +4438,7 @@ TEST(TestGoptInference, FoldingConvDimshuffleNCHW4NHWC) {
             ->writeto_fpath(output_file(
                     "TestGoptInference.FoldingConvDimshuffleNCHW4NHWC.json"));
     size_t nr_dimshuffle = find_opr_num<opr::TypeCvt>(y_fuse);
-    ASSERT_EQ(3u, find_opr_num<opr::Dimshuffle>(y_fuse));
+    ASSERT_EQ(2u, nr_dimshuffle);
     bool found = false;
     cg::DepOprIter{[&found](cg::OperatorNodeBase* opr) {
         if (!found && opr->same_type<opr::ConvBias>()) {
@@ -4735,101 +4735,6 @@ TEST(TestGoptInference, PaddingChannelsWithWarpPerspective) {
     MGB_ASSERT_TENSOR_EQ(t1, t2);
 }
 
-TEST(TestGoptInference, PaddingChannelsB4) {
-    REQUIRE_GPU(1);
-    auto cn = CompNode::load("gpu0");
-    cn.activate();
-    REQUIRE_CUDA_COMPUTE_CAPABILITY(7, 5);
-
-    HostTensorGenerator<dtype::Int8> gen;
-    auto graph = ComputingGraph::make();
-    graph->options().graph_opt_level = 0;
-    auto mkvar = [&](const char* name, const TensorShape& shp,
-                     const DType& dtype) {
-        return opr::TypeCvt::make(
-                opr::Host2DeviceCopy::make(*graph, gen(shp, cn)).rename(name),
-                dtype);
-    };
-    auto mkcvar = [&](const char* name, const TensorShape& shp,
-                      const DType& dtype) {
-        return opr::TypeCvt::make(
-                opr::SharedDeviceTensor::make(*graph, *gen(shp, cn))
-                        .rename(name),
-                dtype);
-    };
-
-    auto x = mkvar("x", {16, 3, 14, 14}, dtype::QuantizedS8(2.5f)),
-         w = mkcvar("w", {16, 3, 3, 3}, dtype::QuantizedS8(2.5f)),
-         b = mkcvar("b", {1, 16, 1, 1}, dtype::QuantizedS32(6.25f));
-    opr::ConvBias::Param param;
-    param.format = opr::ConvBias::Param::Format::NCHW;
-    param.nonlineMode = opr::ConvBias::Param::NonlineMode::RELU;
-    param.stride_h = param.stride_w = 1;
-    param.pad_h = param.pad_w = 1;
-
-    auto y = opr::ConvBias::make(x, w, b, param, {},
-                                 OperatorNodeConfig{dtype::QuantizedS8(2.5f)});
-    y = opr::TypeCvt::make(y, dtype::Quantized4Asymm{20.f, 8});
-    opr::Pooling::Param pool;
-    pool.format = opr::Pooling::Param::Format::NCHW;
-    y = opr::Pooling::make(y, pool);
-    auto w1 = mkcvar("w1", {48, 16, 3, 3}, dtype::QuantizedS4(1.234f)),
-         b1 = mkcvar("b1", {1, 48, 1, 1}, dtype::QuantizedS32(20.f*1.234f));
-    auto y1 = opr::ConvBias::make(y, w1, b1, param, {},
-                            OperatorNodeConfig{dtype::Quantized4Asymm(20.f, 8)});
-    auto w2 = mkcvar("w2", {48, 48, 3, 3}, dtype::QuantizedS4(1.234f)),
-         b2 = mkcvar("b2", {1, 48, 1, 1}, dtype::QuantizedS32(20.f*1.234f));
-    auto y2 = opr::ConvBias::make(
-            y1, w2, b2, param, {},
-            OperatorNodeConfig{dtype::Quantized4Asymm(20.f, 8)});
-    auto w3 = mkcvar("w2", {16, 48, 3, 3}, dtype::QuantizedS4(1.234f)),
-         b3 = mkcvar("b2", {1, 16, 1, 1}, dtype::QuantizedS32(20.f*1.234f));
-    auto y3 = opr::ConvBias::make(
-            y2, w3, b3, param, {},
-            OperatorNodeConfig{dtype::Quantized4Asymm(20.f, 8)});
-    using ElemMultiMode = opr::ElemwiseMultiType::Param::Mode;
-    auto y4 = opr::ElemwiseMultiType::make(
-            {y, y3}, {ElemMultiMode::QFUSE_ADD_RELU},
-            OperatorNodeConfig{dtype::Quantized4Asymm{20.f, 7}});
-    y4 = opr::TypeCvt::make(y4, dtype::Float32());
-    SymbolVar y4_pad;
-    unpack_vector(gopt::GraphOptimizer{}
-                          .add_pass<gopt::PaddingChannelPass>()
-                          .add_pass<gopt::ParamFusePass>()
-                          .apply({{y4}})
-                          .endpoint_vars(),
-                  y4_pad);
-    ASSERT_EQ(y4_pad.node()->shape()[1], y4.node()->shape()[1]);
-    SmallVector<cg::OperatorNodeBase*> oprs;
-    auto cb1 = [&oprs](cg::OperatorNodeBase* opr) {
-        if (opr->same_type<opr::ConvBias>()) {
-            oprs.push_back(opr);
-        }
-    };
-    cg::DepOprIter{cb1}.add(y4_pad.node()->owner_opr());
-    ASSERT_EQ(oprs.size(), 4);
-    ASSERT_EQ(oprs[0]->output(0)->shape()[1], 16);
-    ASSERT_EQ(oprs[1]->output(0)->shape()[1], 64);
-    ASSERT_EQ(oprs[2]->output(0)->shape()[1], 64);
-    ASSERT_EQ(oprs[3]->output(0)->shape()[1], 16);
-    size_t nr_concat = find_opr_num<opr::Concat>(y4_pad);
-    ASSERT_EQ(nr_concat, 1);
-    cg::OperatorNodeBase* concat = nullptr;
-    auto cb2 = [&concat](cg::OperatorNodeBase* opr) {
-        if (opr->same_type<opr::Concat>()) {
-            concat = opr;
-        }
-    };
-    cg::DepOprIter{cb2}.add(y4_pad.node()->owner_opr());
-    ASSERT_EQ(oprs[0]->input(0)->owner_opr(), concat);
-    HostTensorND t1, t2;
-    auto func1 = graph->compile({make_callback_copy(y4, t1)});
-    func1->execute();
-    auto func2 = graph->compile({make_callback_copy(y4_pad, t2)});
-    func2->execute();
-    MGB_ASSERT_TENSOR_EQ(t1, t2);
-}
-
 
 #endif