Refactor MKL-DNN ElementwiseMul (#21061)

* Refactor MKL-DNN ElementwiseMul

remove manual fallback, remove format attrs
test=develop

* Refine PADDLE_ENFORCEs in eltwise_mul_op.h

test=develop

* Make ElementwiseMulOp inherit from ElementwiseOp

* Change type of simd_width to int

test=develop

* Remove Constructor extensions in ElementwiseOp and ElementwiseMulOp

test=develop

* Restore attributes

test=develop

* Fix test coverage for mkldnn eltwise mul

test=develop

* Conform to new is_run_common_broadcast API

test=develop

* Add UT for AreDimsAndFormatCorrect

test=develop

paddle/fluid/operators/elementwise/CMakeLists.txt

@@ -4,3 +4,4 @@ register_operators()
 cc_test(test_elementwise_add_op_inplace SRCS test_elementwise_add_op_inplace.cc DEPS op_registry elementwise_add_op scope device_context enforce executor)
 cc_test(test_elementwise_div_grad_grad SRCS test_elementwise_div_grad_grad.cc DEPS op_registry elementwise_div_op scope device_context enforce executor)
 cc_test(test_elementwise_add_grad_grad SRCS test_elementwise_add_grad_grad.cc DEPS op_registry elementwise_add_op scope device_context enforce executor)
+cc_test(test_elementwise_mul_op_correct_dims SRCS test_elementwise_mul_op_dim.cc DEPS op_registry elementwise_mul_op scope device_context enforce executor)

paddle/fluid/operators/elementwise/elementwise_mul_op.cc

@@ -117,7 +117,7 @@ class ElementwiseMulDoubleGradMaker : public framework::SingleGradOpMaker<T> {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(elementwise_mul, ops::ElementwiseOp,
+REGISTER_OPERATOR(elementwise_mul, ops::ElementwiseMulOp,
                   ops::ElementwiseMulOpMaker, ops::ElementwiseOpInferVarType,
                   ops::ElementwiseMulOpGradMaker<paddle::framework::OpDesc>,
                   ops::ElementwiseMulOpGradMaker<paddle::imperative::OpBase>);

paddle/fluid/operators/elementwise/elementwise_mul_op.h

@@ -13,14 +13,59 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <string>
 #include "paddle/fluid/operators/elementwise/elementwise_op.h"
 #include "paddle/fluid/operators/elementwise/elementwise_op_function.cu.h"
 #include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
 #include "paddle/fluid/operators/math/blas.h"
+#include "paddle/fluid/platform/cpu_info.h"
 
 namespace paddle {
 namespace operators {
 
+class ElementwiseMulOp : public ElementwiseOp {
+ public:
+  using Tensor = framework::Tensor;
+  using ElementwiseOp::ElementwiseOp;
+
+#ifdef PADDLE_WITH_MKLDNN
+  static bool AreDimsAndFormatCorrect(const framework::ExecutionContext& ctx,
+                                      int simd_width,
+                                      mkldnn::memory::format x_format) {
+    using Tensor = framework::Tensor;
+    using paddle::framework::vectorize;
+    using mkldnn::memory;
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto x_dims = vectorize(x->dims());
+    const bool are_dims_divisable = !(x_dims[1] % simd_width);
+    const bool is_x_format_correct = x->format() == x_format;
+    const bool is_y_format_correct = vectorize(y->dims()).size() == 2;
+    return are_dims_divisable && is_x_format_correct && is_y_format_correct;
+  }
+#endif
+
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    auto input_data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
+
+#ifdef PADDLE_WITH_MKLDNN
+    using mkldnn::memory;
+    if (platform::CanMKLDNNBeUsed(ctx)) {
+      bool can_use_avx512_kernel =
+          platform::MayIUse(platform::avx512f) &&
+          AreDimsAndFormatCorrect(ctx, 16, memory::format::nChw16c);
+      if (can_use_avx512_kernel) {
+        return framework::OpKernelType(input_data_type, ctx.GetPlace(),
+                                       framework::DataLayout::kMKLDNN,
+                                       framework::LibraryType::kMKLDNN);
+      }
+    }
+#endif
+    return framework::OpKernelType(input_data_type, ctx.GetPlace());
+  }
+};
+
 template <typename DeviceContext, typename T>
 void default_elementwise_mul(const framework::ExecutionContext& ctx,
                              const framework::Tensor* x,

paddle/fluid/operators/elementwise/elementwise_op.h

@@ -130,21 +130,10 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
         .EqualGreaterThan(-1);
     AddAttr<bool>("use_mkldnn", "(bool, default false). Used by MKLDNN.")
         .SetDefault(false);
-    AddAttr<std::string>(
-        "x_data_format",
-        "(string, default NCHW) Only used in mkldnn"
-        "An optional string from: \"NHWC\", \"NCHW\", \"NCHW16C\", \"NCHW8C\". "
-        "Defaults to \"\". Specify the data format of the output data, "
-        "the input will be transformed automatically. ")
+    AddAttr<std::string>("x_data_format", "This parameter is no longer used.")
         .SetDefault("");
-    AddAttr<std::string>(
-        "y_data_format",
-        "(string, default \"\") Only used in mkldnn"
-        "An optional string from: \"NHWC\", \"NCHW\", \"NCHW16C\", \"NCHW8C\". "
-        "Defaults to \"\". Specify the data format of the output data, "
-        "the input will be transformed automatically. ")
+    AddAttr<std::string>("y_data_format", "This parameter is no longer used.")
         .SetDefault("");
-
     AddOpComment();
   }
 

paddle/fluid/operators/elementwise/mkldnn/elementwise_mul_mkldnn_op.cc

@@ -32,38 +32,28 @@ using framework::DataLayout;
 using mkldnn::memory;
 using platform::StringToMKLDNNFormat;
 
-static void UpdateDataFormat(const framework::ExecutionContext& ctx,
-                             framework::Tensor* tensor, const char* attribute) {
-  if (ctx.op().HasAttr(attribute)) {
-    auto format_as_string = ctx.Attr<std::string>(attribute);
-    auto format = StringToMKLDNNFormat(&format_as_string);
-    if (format != MKLDNNMemoryFormat::any) {
-      tensor->set_format(format);
+template <typename T>
+static void ComputeBroadcastedMultiply(const T* x_data, const T* y_data,
+                                       T* z_data, int64_t n, int64_t c,
+                                       int64_t h, int64_t w, int simd_width,
+                                       void (*multiply)(const T*, const T*, T*,
+                                                        int, int)) {
+  const int64_t C = c / simd_width;
+#pragma omp parallel for collapse(2)
+  for (int ni = 0; ni < n; ni++) {
+    for (int ci = 0; ci < C; ci++) {
+      auto ptr_x =
+          x_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
+
+      auto ptr_y = y_data + ni * C * simd_width + ci * simd_width;
+      auto ptr_z =
+          z_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
+
+      multiply(ptr_x, ptr_y, ptr_z, h, w);
     }
   }
 }
 
-template <typename T>
-static void ReorderInput(framework::Tensor* tensor,
-                         const platform::Place& place,
-                         const mkldnn::engine& engine, bool isFourDim) {
-  using platform::to_void_cast;
-  auto dims = paddle::framework::vectorize<int>(tensor->dims());
-  framework::Tensor out_tensor;
-  out_tensor.Resize(tensor->dims());
-  out_tensor.set_format(isFourDim ? MKLDNNMemoryFormat::nchw
-                                  : MKLDNNMemoryFormat::nc);
-  out_tensor.set_layout(tensor->layout());
-  mkldnn::memory input_memory = {
-      {{dims, platform::MKLDNNGetDataType<T>(), tensor->format()}, engine},
-      to_void_cast<T>(tensor->data<T>())};
-  mkldnn::memory output_memory = {
-      {{dims, platform::MKLDNNGetDataType<T>(), out_tensor.format()}, engine},
-      to_void_cast<T>(out_tensor.mutable_data<T>(place))};
-  platform::Reorder(input_memory, output_memory);
-  tensor->ShareDataWith(out_tensor);
-}
-
 template <typename T>
 class ElementwiseMulMKLDNNKernel : public framework::OpKernel<T> {
  public:
@@ -82,105 +72,26 @@ class ElementwiseMulMKLDNNKernel : public framework::OpKernel<T> {
     auto y_dims_untrimmed = y->dims();
     auto x_int_dims = paddle::framework::vectorize<int>(x_dims);
 
-    UpdateDataFormat(ctx, const_cast<Tensor*>(x), "x_data_format");
-    UpdateDataFormat(ctx, const_cast<Tensor*>(y), "y_data_format");
-
-    const bool is_avx512_enabled = platform::MayIUse(platform::avx512f);
-    const bool are_dims_divisable = !(x_int_dims[1] % 16);
-    const bool is_x_format_correct = x->format() == MKLDNNMemoryFormat::nChw16c;
-    const bool is_y_format_correct = y->format() == MKLDNNMemoryFormat::nc;
-    if (is_x_format_correct && is_y_format_correct && are_dims_divisable &&
-        is_avx512_enabled) {
-      int pre, n, post, is_run_common_broadcast;
-      get_mid_dims(x_dims, y_dims_untrimmed, axis, &pre, &n, &post,
-                   &is_run_common_broadcast);
-
-      if (post == 1) {
-        PADDLE_THROW("Not implemented when post is 1");
-      } else {
-        // Just check whether it works for RE-Resnext.
-        PADDLE_ENFORCE_EQ(x_dims.size(), 4, "X should have 4 dimensions");
-
-        int n = x_dims[0];
-        int c = x_dims[1];
-        int h = x_dims[2];
-        int w = x_dims[3];
-
-        PADDLE_ENFORCE(y_dims_untrimmed[0] == n && y_dims_untrimmed[1] == c,
-                       "Y should be in nc format");
-
-        constexpr int simd_width = 16;
-        int C = c / simd_width;
-
-        auto multiply = jit::KernelFuncs<jit::NCHW16CMulNCTuple<T>,
-                                         platform::CPUPlace>::Cache()
-                            .At(0);
-#pragma omp parallel for collapse(2)
-        for (int ni = 0; ni < n; ni++) {
-          for (int ci = 0; ci < C; ci++) {
-            auto ptr_x =
-                x_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
-
-            auto ptr_y = y_data + ni * C * simd_width + ci * simd_width;
-            auto ptr_z =
-                z_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
-
-            multiply(ptr_x, ptr_y, ptr_z, h, w);
-          }
-        }
-      }
-
-      z->set_layout(DataLayout::kMKLDNN);
-      z->set_format(x->format());
-    } else {
-      // Fallback to naive version:
-      const bool are_inputs_in_same_format = x->format() == y->format();
-      const bool is_x_nchw = x->format() == MKLDNNMemoryFormat::nchw;
-      const bool is_x_nc = x->format() == MKLDNNMemoryFormat::nc;
-      const bool is_x_x = x->format() == MKLDNNMemoryFormat::x;
-      const bool is_y_nchw = y->format() == MKLDNNMemoryFormat::nchw;
-      const bool is_y_nc = y->format() == MKLDNNMemoryFormat::nc;
-      const bool is_y_x = y->format() == MKLDNNMemoryFormat::x;
-      if (!are_inputs_in_same_format) {
-        using platform::MKLDNNDeviceContext;
-        auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
-        const auto& mkldnn_engine = dev_ctx.GetEngine();
-        if (!(is_x_nchw || is_x_nc || is_x_x))
-          ReorderInput<T>(const_cast<Tensor*>(x), ctx.GetPlace(), mkldnn_engine,
-                          x->dims().size() == 4);
-        if (!(is_y_nchw || is_y_nc || is_y_x))
-          ReorderInput<T>(const_cast<Tensor*>(y), ctx.GetPlace(), mkldnn_engine,
-                          y->dims().size() == 4);
-      }
-
-      auto mul_func = [](T a, T b) -> T { return a * b; };
-
-      TransformFunctor<decltype(mul_func), T,
-                       paddle::platform::CPUDeviceContext, T>
-          functor(
-              x, y, z,
-              ctx.template device_context<paddle::platform::CPUDeviceContext>(),
-              mul_func);
-
-      axis = (axis == -1 ? x_dims.size() - y_dims_untrimmed.size() : axis);
-      PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
-                     "Axis should be in range [0, x_dims)");
-
-      auto y_dims = trim_trailing_singular_dims(y_dims_untrimmed);
-      axis = (y_dims.size() == 0) ? x_dims.size() : axis;
-
-      int pre, n, post, is_run_common_broadcast;
-      get_mid_dims(x_dims, y_dims, axis, &pre, &n, &post,
-                   &is_run_common_broadcast);
-
-      if (post == 1) {
-        functor.RunRowWise(n, pre);
-      } else {
-        functor.RunMidWise(n, pre, post);
-      }
-      z->set_layout(DataLayout::kMKLDNN);
-      z->set_format(x->format());
-    }
+    int pre, num, post, is_run_common_broadcast;
+    get_mid_dims(x_dims, y_dims_untrimmed, axis, &pre, &num, &post,
+                 &is_run_common_broadcast);
+
+    if (post == 1) PADDLE_THROW("Not implemented when post is 1");
+
+    const int64_t n = x_dims[0];
+    const int64_t c = x_dims[1];
+    const int64_t h = x_dims[2];
+    const int64_t w = x_dims[3];
+
+    const int simd_width = 16;
+    auto multiply =
+        jit::KernelFuncs<jit::NCHW16CMulNCTuple<T>, platform::CPUPlace>::Cache()
+            .At(0);
+    ComputeBroadcastedMultiply(x_data, y_data, z_data, n, c, h, w, simd_width,
+                               multiply);
+
+    z->set_layout(DataLayout::kMKLDNN);
+    z->set_format(x->format());
   }
 };
 }  // namespace operators

paddle/fluid/operators/elementwise/test_elementwise_mul_op_dim.cc

+/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+
+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 "gtest/gtest.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/operators/elementwise/elementwise_mul_op.h"
+
+USE_OP(elementwise_mul);
+
+namespace paddle {
+namespace operators {
+#ifdef PADDLE_WITH_MKLDNN
+
+using framework::Scope;
+using framework::LoDTensor;
+using framework::OpRegistry;
+using framework::OperatorBase;
+using framework::RuntimeContext;
+using framework::ExecutionContext;
+
+struct TestData {
+  int64_t channel_num;
+  MKLDNNMemoryFormat format;
+  framework::DDim y_dims;
+  bool supposed_to_fail;
+};
+
+void MainTest(const TestData& test_data) {
+  auto place = platform::CPUPlace();
+  Scope scope;
+
+  auto* x = scope.Var("x")->GetMutable<LoDTensor>();
+  auto* y = scope.Var("y")->GetMutable<LoDTensor>();
+  scope.Var("out")->GetMutable<LoDTensor>();
+
+  x->Resize({1, test_data.channel_num, 3, 3});
+  y->Resize(test_data.y_dims);
+
+  x->set_format(test_data.format);
+  y->set_format(MKLDNNMemoryFormat::nc);
+
+  std::unique_ptr<OperatorBase> op = OpRegistry::CreateOp(
+      "elementwise_mul", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {});
+
+  auto& pool = platform::DeviceContextPool::Instance();
+  auto* dev_ctx = dynamic_cast<platform::MKLDNNDeviceContext*>(pool.Get(place));
+
+  RuntimeContext runtime_ctx =
+      RuntimeContext(op->Inputs(), op->Outputs(), scope);
+  ExecutionContext ctx =
+      ExecutionContext(*op, scope, *dev_ctx, runtime_ctx, nullptr);
+  bool result = ElementwiseMulOp::AreDimsAndFormatCorrect(
+      ctx, 16, MKLDNNMemoryFormat::nChw16c);
+  if (test_data.supposed_to_fail)
+    ASSERT_FALSE(result);
+  else
+    ASSERT_TRUE(result);
+}
+
+// Checks if AreDimsAndFormatCorrect returns true when supplied with expected
+// data
+TEST(ElementwiseMulOpTester, correct_dims) {
+  TestData test_data;
+  test_data.channel_num = 16;
+  test_data.format = MKLDNNMemoryFormat::nChw16c;
+  test_data.y_dims = {1, test_data.channel_num};
+  test_data.supposed_to_fail = false;
+  MainTest(test_data);
+}
+
+// Checks if AreDimsAndFormatCorrect fails when channel_num is not divisable by
+// 16
+TEST(ElementwiseMulOpTester, incorrect_channel_num) {
+  TestData test_data;
+  test_data.channel_num = 17;
+  test_data.format = MKLDNNMemoryFormat::nChw16c;
+  test_data.y_dims = {1, test_data.channel_num};
+  test_data.supposed_to_fail = true;
+  MainTest(test_data);
+}
+
+// Checks if AreDimsAndFormatCorrect fails when x format is different from
+// nchw16c
+TEST(ElementwiseMulOpTester, incorrect_format) {
+  TestData test_data;
+  test_data.channel_num = 16;
+  test_data.format = MKLDNNMemoryFormat::nchw;
+  test_data.y_dims = {1, test_data.channel_num};
+  test_data.supposed_to_fail = true;
+  MainTest(test_data);
+}
+
+// Checks if AreDimsAndFormatCorrect fails when y input is not 2-dimensional
+TEST(ElementwiseMulOpTester, incorrect_y_dims) {
+  TestData test_data;
+  test_data.channel_num = 16;
+  test_data.format = MKLDNNMemoryFormat::nChw16c;
+  test_data.y_dims = {1, test_data.channel_num, 1};
+  test_data.supposed_to_fail = true;
+  MainTest(test_data);
+}
+#endif
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/tests/unittests/mkldnn/test_elementwise_mul_mkldnn_op.py

@@ -40,7 +40,7 @@ class TestElementwiseMulMKLDNNOp_Integrated_With_Convs(ElementwiseMulOp):
         self.filter_size2 = [1, 16, 2, 2]
         self.dilations = [1, 1]
         self.use_cudnn = False
-        self.data_format = "NCHW"
+        self.data_format = "ANYLAYOUT"
         self.input = np.random.random(self.input_size).astype(self.dtype)
         self.filter = np.random.random(self.filter_size).astype(self.dtype)
         self.filter2 = np.random.random(self.filter_size2).astype(self.dtype)
@@ -97,7 +97,8 @@ class TestElementwiseMulMKLDNNOp_Integrated_With_Convs(ElementwiseMulOp):
                     'groups': self.groups,
                     'dilations': self.dilations,
                     'use_cudnn': self.use_cudnn,
-                    'use_mkldnn': self.use_mkldnn
+                    'use_mkldnn': self.use_mkldnn,
+                    'data_format': self.data_format
                 })
             elementwise_mul_op = block.append_op(
                 type="elementwise_mul",
@@ -152,179 +153,5 @@ class TestElementwiseMulMKLDNNOp_Integrated_With_Convs(ElementwiseMulOp):
         pass
 
 
-class TestElementwiseMulMKLDNNOp_FallbackNCHW(ElementwiseMulOp):
-    def init_input_output(self):
-        self.x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
-        self.y = np.random.rand(1, 16).astype(self.dtype)
-
-        self.out = self.x * self.y.reshape(1, 16, 1, 1)
-
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-
-    def init_axis(self):
-        self.axis = 0
-
-    def test_check_grad_normal(self):
-        pass
-
-    def test_check_grad_ingore_x(self):
-        pass
-
-    def test_check_grad_ingore_y(self):
-        pass
-
-
-class TestElementwiseMulMKLDNNOp_FallbackNCHW16C(ElementwiseMulOp):
-    def init_input_output(self):
-        x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
-        self.x = x.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
-        y = np.random.rand(1, 16, 2, 2).astype(self.dtype)
-        self.y = y.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
-
-        self.out = x * y
-
-    def setUp(self):
-        super(TestElementwiseMulMKLDNNOp_FallbackNCHW16C, self).setUp()
-        self.attrs["x_data_format"] = "nchw16c"
-        self.attrs["y_data_format"] = "nchw16c"
-        self._cpu_only = True
-
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-
-    def init_axis(self):
-        self.axis = 0
-
-    def test_check_grad_normal(self):
-        pass
-
-    def test_check_grad_ingore_x(self):
-        pass
-
-    def test_check_grad_ingore_y(self):
-        pass
-
-
-class TestElementwiseMulMKLDNNOp_FallbackNoReorders(ElementwiseMulOp):
-    def init_input_output(self):
-        x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
-        self.x = x.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
-        y = np.random.rand(1, 16, 2, 2).astype(self.dtype)
-        self.y = y.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
-
-        self.out = x * y
-
-    def setUp(self):
-        super(TestElementwiseMulMKLDNNOp_FallbackNoReorders, self).setUp()
-        self.attrs["x_data_format"] = "nchw16c"
-        self.attrs["y_data_format"] = "nchw16c"
-        self._cpu_only = True
-
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-
-    def init_axis(self):
-        self.axis = 0
-
-    def test_check_grad_normal(self):
-        pass
-
-    def test_check_grad_ingore_x(self):
-        pass
-
-    def test_check_grad_ingore_y(self):
-        pass
-
-
-class TestElementwiseMulMKLDNNOp_FallbackWithReorder1(ElementwiseMulOp):
-    def init_input_output(self):
-        self.x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
-        y = np.random.rand(1, 16, 2, 2).astype(self.dtype)
-        self.y = y.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
-
-        self.out = self.x * y
-
-    def setUp(self):
-        super(TestElementwiseMulMKLDNNOp_FallbackWithReorder1, self).setUp()
-        self.attrs["x_data_format"] = "nchw"
-        self.attrs["y_data_format"] = "nchw16c"
-        self._cpu_only = True
-
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-
-    def init_axis(self):
-        self.axis = 0
-
-    def test_check_grad_normal(self):
-        pass
-
-    def test_check_grad_ingore_x(self):
-        pass
-
-    def test_check_grad_ingore_y(self):
-        pass
-
-
-class TestElementwiseMulMKLDNNOp_FallbackWithReorder2(ElementwiseMulOp):
-    def init_input_output(self):
-        self.y = np.random.rand(1, 16, 2, 2).astype(self.dtype)
-        x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
-        self.x = x.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
-
-        self.out = x * self.y
-
-    def setUp(self):
-        super(TestElementwiseMulMKLDNNOp_FallbackWithReorder2, self).setUp()
-        self.attrs["x_data_format"] = "nchw16c"
-        self.attrs["y_data_format"] = "nchw"
-        self._cpu_only = True
-
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-
-    def init_axis(self):
-        self.axis = 0
-
-    def test_check_grad_normal(self):
-        pass
-
-    def test_check_grad_ingore_x(self):
-        pass
-
-    def test_check_grad_ingore_y(self):
-        pass
-
-
-class TestElementwiseMulMKLDNNOp_FallbackNoReorders2(ElementwiseMulOp):
-    def init_input_output(self):
-        self.x = np.random.rand(1, 16).astype(self.dtype)
-        self.y = np.random.rand(1, 16).astype(self.dtype)
-
-        self.out = self.x * self.y
-
-    def setUp(self):
-        super(TestElementwiseMulMKLDNNOp_FallbackNoReorders2, self).setUp()
-        self.attrs["x_data_format"] = "nc"
-        self.attrs["y_data_format"] = "nc"
-        self._cpu_only = True
-
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-
-    def init_axis(self):
-        self.axis = 0
-
-    def test_check_grad_normal(self):
-        pass
-
-    def test_check_grad_ingore_x(self):
-        pass
-
-    def test_check_grad_ingore_y(self):
-        pass
-
-
 if __name__ == '__main__':
     unittest.main()