Fix mkldnn invalid infershape impl

paddle/fluid/eager/legacy/infer_shape_context.h

@@ -31,15 +31,18 @@ class EagerInferShapeContext : public paddle::framework::InferShapeContext {
   using DDim = paddle::framework::DDim;
 
  public:
-  EagerInferShapeContext(const NameTensorMap* in, const NameTensorMap* out,
-                         const paddle::framework::AttributeMap* attr,
-                         const paddle::framework::AttributeMap* default_attr,
-                         const std::string op_type)
+  EagerInferShapeContext(
+      const NameTensorMap* in, const NameTensorMap* out,
+      const paddle::framework::AttributeMap* attr,
+      const paddle::framework::AttributeMap* default_attr,
+      const std::string op_type,
+      const paddle::framework::OpKernelType* op_kernel_type = nullptr)
       : tensor_in_(in),
         tensor_out_(out),
         attrs_(attr),
         default_attrs_(default_attr),
-        op_type_(op_type) {}
+        op_type_(op_type),
+        op_kernel_type_(op_kernel_type) {}
 
   bool HasInput(const std::string& name) const override {
     // has only one input
@@ -214,6 +217,11 @@ class EagerInferShapeContext : public paddle::framework::InferShapeContext {
 
   bool IsRuntime() const override { return true; }
 
+  bool IsRunMKLDNNKernel() const override {
+    return (op_kernel_type_ && (op_kernel_type_->data_layout_ ==
+                                paddle::framework::DataLayout::kMKLDNN));
+  }
+
   // TODO(paddle-dev): Can this be template?
   std::vector<paddle::framework::InferShapeVarPtr> GetInputVarPtrs(
       const std::string& name) const override {
@@ -400,6 +408,7 @@ class EagerInferShapeContext : public paddle::framework::InferShapeContext {
   const paddle::framework::AttributeMap* attrs_;
   const paddle::framework::AttributeMap* default_attrs_;
   const std::string op_type_;
+  const paddle::framework::OpKernelType* op_kernel_type_;
 };
 
 }  // namespace legacy

paddle/fluid/eager/legacy/prepared_operator.cc

@@ -173,7 +173,7 @@ static void PreparedOpRunImpl(
   paddle::framework::Scope scope;
 
   EagerInferShapeContext infer_shape_ctx(&ins, &outs, &attrs, &default_attrs,
-                                         op.Type());
+                                         op.Type(), &kernel_type);
   op.Info().infer_shape_(&infer_shape_ctx);
 
   func(EagerExecutionContext(op, scope, *dev_ctx, ctx, ins, outs, attrs,

paddle/fluid/framework/new_executor/new_executor_defs.cc

@@ -307,6 +307,17 @@ void InterpretercoreInferShapeContext::SetLoDLevel(const std::string& out,
 
 bool InterpretercoreInferShapeContext::IsRuntime() const { return true; }
 
+bool InterpretercoreInferShapeContext::IsRunMKLDNNKernel() const {
+  try {
+    auto& op_with_kernel = dynamic_cast<const OperatorWithKernel&>(op_);
+    return ((op_with_kernel.kernel_type()) &&
+            (op_with_kernel.kernel_type()->data_layout_ ==
+             framework::DataLayout::kMKLDNN));
+  } catch (std::bad_cast exp) {
+    return false;
+  }
+}
+
 // TODO(paddle-dev): Can this be template?
 std::vector<InferShapeVarPtr> InterpretercoreInferShapeContext::GetInputVarPtrs(
     const std::string& name) const {

paddle/fluid/framework/new_executor/new_executor_defs.h

@@ -84,6 +84,8 @@ class InterpretercoreInferShapeContext : public InferShapeContext {
 
   bool IsRuntime() const override;
 
+  bool IsRunMKLDNNKernel() const override;
+
   // TODO(paddle-dev): Can this be template?
   std::vector<InferShapeVarPtr> GetInputVarPtrs(
       const std::string& name) const override;

paddle/fluid/framework/op_desc.cc

@@ -240,6 +240,8 @@ class CompileTimeInferShapeContext : public InferShapeContext {
 
   bool IsRuntime() const override;
 
+  bool IsRunMKLDNNKernel() const override;
+
   std::vector<proto::VarType::Type> GetInputsVarType(
       const std::string &name) const override {
     return GetVarTypes(Inputs(name));
@@ -930,6 +932,8 @@ void CompileTimeInferShapeContext::SetRepeatedDims(
 
 bool CompileTimeInferShapeContext::IsRuntime() const { return false; }
 
+bool CompileTimeInferShapeContext::IsRunMKLDNNKernel() const { return false; }
+
 proto::VarType::Type CompileTimeInferShapeContext::GetVarType(
     const std::string &name) const {
   return block_.FindVarRecursive(name)->GetType();

paddle/fluid/framework/operator.cc

@@ -884,6 +884,17 @@ class RuntimeInferShapeContext : public InferShapeContext {
 
   bool IsRuntime() const override { return true; }
 
+  bool IsRunMKLDNNKernel() const override {
+    try {
+      auto& op_with_kernel = dynamic_cast<const OperatorWithKernel&>(op_);
+      return ((op_with_kernel.kernel_type()) &&
+              (op_with_kernel.kernel_type()->data_layout_ ==
+               framework::DataLayout::kMKLDNN));
+    } catch (std::bad_cast exp) {
+      return false;
+    }
+  }
+
   // TODO(paddle-dev): Can this be template?
   std::vector<InferShapeVarPtr> GetInputVarPtrs(
       const std::string& name) const override {
@@ -1178,9 +1189,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
     platform::RecordEvent record_event("infer_shape",
                                        platform::EventRole::kInnerOp);
     RuntimeInferShapeContext infer_shape_ctx(*this, *runtime_ctx);
-    // TODO(chenweihang): replace this after removing `this->IsMKLDNNType()`
-    // in some mkldnn infershape functions, such conv2d infershape
-    this->InferShape(&infer_shape_ctx);
+    this->Info().infer_shape_(&infer_shape_ctx);
   }
 
   if (FLAGS_enable_unused_var_check) {

paddle/fluid/framework/operator.h

@@ -528,11 +528,6 @@ class OperatorWithKernel : public OperatorBase {
     return g_all_op_kernels;
   }
 
-  bool IsMKLDNNType() const {
-    return ((this->kernel_type_) && (this->kernel_type_->data_layout_ ==
-                                     framework::DataLayout::kMKLDNN));
-  }
-
   bool SupportGPU() const override {
     auto& op_kernels = OperatorWithKernel::AllOpKernels().at(type_);
     return std::any_of(op_kernels.begin(), op_kernels.end(),
@@ -609,6 +604,8 @@ class OperatorWithKernel : public OperatorBase {
     return pt_kernel_context_.get();
   }
 
+  const OpKernelType* kernel_type() const { return kernel_type_.get(); }
+
  private:
   void RunImpl(const Scope& scope, const platform::Place& place) const final;
   void RunImpl(const Scope& scope, const platform::Place& place,

paddle/fluid/framework/shape_inference.h

@@ -102,6 +102,8 @@ class InferShapeContext {
 
   virtual bool IsRuntime() const = 0;
 
+  virtual bool IsRunMKLDNNKernel() const = 0;
+
   virtual std::vector<InferShapeVarPtr> GetInputVarPtrs(
       const std::string &name) const = 0;
   virtual std::vector<InferShapeVarPtr> GetOutputVarPtrs(

paddle/fluid/imperative/infer_shape_context.h

@@ -32,16 +32,17 @@ class DygraphInferShapeContext : public framework::InferShapeContext {
   using DDim = framework::DDim;
 
  public:
-  DygraphInferShapeContext(const NameVarMap<VarType>* in,
-                           const NameVarMap<VarType>* out,
-                           const framework::AttributeMap* attr,
-                           const framework::AttributeMap* default_attr,
-                           const std::string op_type)
+  DygraphInferShapeContext(
+      const NameVarMap<VarType>* in, const NameVarMap<VarType>* out,
+      const framework::AttributeMap* attr,
+      const framework::AttributeMap* default_attr, const std::string op_type,
+      const framework::OpKernelType* op_kernel_type = nullptr)
       : var_base_map_in_(in),
         var_base_map_out_(out),
         attrs_(attr),
         default_attrs_(default_attr),
-        op_type_(op_type) {}
+        op_type_(op_type),
+        op_kernel_type_(op_kernel_type) {}
 
   bool HasInput(const std::string& name) const override {
     // has only one input
@@ -214,6 +215,11 @@ class DygraphInferShapeContext : public framework::InferShapeContext {
 
   bool IsRuntime() const override { return true; }
 
+  bool IsRunMKLDNNKernel() const override {
+    return (op_kernel_type_ &&
+            (op_kernel_type_->data_layout_ == framework::DataLayout::kMKLDNN));
+  }
+
   // TODO(paddle-dev): Can this be template?
   std::vector<framework::InferShapeVarPtr> GetInputVarPtrs(
       const std::string& name) const override {
@@ -399,6 +405,7 @@ class DygraphInferShapeContext : public framework::InferShapeContext {
   const framework::AttributeMap* attrs_;
   const framework::AttributeMap* default_attrs_;
   const std::string op_type_;
+  const framework::OpKernelType* op_kernel_type_;
 };
 
 }  // namespace imperative

paddle/fluid/imperative/prepared_operator.cc

@@ -514,8 +514,8 @@ static void PreparedOpRunImpl(
   // TODO(zjl): remove scope in dygraph
   framework::Scope scope;
 
-  DygraphInferShapeContext<VarType> infer_shape_ctx(&ins, &outs, &attrs,
-                                                    &default_attrs, op.Type());
+  DygraphInferShapeContext<VarType> infer_shape_ctx(
+      &ins, &outs, &attrs, &default_attrs, op.Type(), &kernel_type);
   op.Info().infer_shape_(&infer_shape_ctx);
 
   func(DygraphExecutionContext<VarType>(op, scope, *dev_ctx, ctx, ins, outs,
@@ -560,8 +560,8 @@ static void PreparedOpRunPtImpl(
     platform::DeviceContext* dev_ctx, const NameVarMap<VarType>& ins,
     const NameVarMap<VarType>& outs, const framework::AttributeMap& attrs,
     const framework::AttributeMap& default_attrs) {
-  DygraphInferShapeContext<VarType> infer_shape_ctx(&ins, &outs, &attrs,
-                                                    &default_attrs, op.Type());
+  DygraphInferShapeContext<VarType> infer_shape_ctx(
+      &ins, &outs, &attrs, &default_attrs, op.Type(), &kernel_type);
   op.Info().infer_shape_(&infer_shape_ctx);
 
   BuildDygraphPtenKernelContext<VarType>(pt_kernel_signature, pt_kernel, ins,

paddle/fluid/operators/batch_norm_op.cc

@@ -93,7 +93,7 @@ void BatchNormOp::InferShape(framework::InferShapeContext *ctx) const {
           x_dims, x_dims.size()));
 
   const int64_t C =
-      ((this->IsMKLDNNType() == true) || (data_layout == DataLayout::kNCHW)
+      ((ctx->IsRunMKLDNNKernel() == true) || (data_layout == DataLayout::kNCHW)
            ? x_dims[1]
            : x_dims[x_dims.size() - 1]);
 
@@ -508,7 +508,7 @@ void BatchNormGradOp::InferShape(framework::InferShapeContext *ctx) const {
       ctx->Attrs().Get<std::string>("data_layout"));
 
   const int C =
-      ((this->IsMKLDNNType() == true) || (data_layout == DataLayout::kNCHW)
+      ((ctx->IsRunMKLDNNKernel() == true) || (data_layout == DataLayout::kNCHW)
            ? x_dims[1]
            : x_dims[x_dims.size() - 1]);
 
@@ -911,7 +911,7 @@ void BatchNormDoubleGradOp::InferShape(
   const DataLayout data_layout = framework::StringToDataLayout(
       ctx->Attrs().Get<std::string>("data_layout"));
   const int C =
-      ((this->IsMKLDNNType() == true) || (data_layout == DataLayout::kNCHW)
+      ((ctx->IsRunMKLDNNKernel() == true) || (data_layout == DataLayout::kNCHW)
            ? x_dims[1]
            : x_dims[x_dims.size() - 1]);
 

paddle/fluid/operators/conv_op.cc

@@ -57,7 +57,7 @@ std::vector<int64_t> ConvOp::ComputeOutputShape(
 
   // MKL-DNN Kernels are using NCHW order of dims description
   // so we ignore data_format consideration for MKL-DNN kernel
-  const bool channel_last = (this->IsMKLDNNType() == false) &&
+  const bool channel_last = (ctx->IsRunMKLDNNKernel() == false) &&
                             (data_format == "NHWC" || data_format == "NDHWC");
 
   PADDLE_ENFORCE_EQ(

paddle/fluid/operators/conv_transpose_op.cc

@@ -49,8 +49,8 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
   const std::string data_layout_str =
       ctx->Attrs().Get<std::string>("data_format");
   const DataLayout data_layout =
-      this->IsMKLDNNType() ? DataLayout::kNCHW
-                           : framework::StringToDataLayout(data_layout_str);
+      ctx->IsRunMKLDNNKernel() ? DataLayout::kNCHW
+                               : framework::StringToDataLayout(data_layout_str);
 
   PADDLE_ENFORCE_EQ(in_dims.size() == 4 || in_dims.size() == 5, true,
                     platform::errors::InvalidArgument(

paddle/fluid/operators/inplace_abn_op.cc

@@ -100,10 +100,10 @@ class InplaceABNGradOp : public paddle::operators::BatchNormGradOp {
     const DataLayout data_layout = framework::StringToDataLayout(
         ctx->Attrs().Get<std::string>("data_layout"));
 
-    const int C =
-        ((this->IsMKLDNNType() == true) || (data_layout == DataLayout::kNCHW)
-             ? y_dims[1]
-             : y_dims[y_dims.size() - 1]);
+    const int C = ((ctx->IsRunMKLDNNKernel() == true) ||
+                           (data_layout == DataLayout::kNCHW)
+                       ? y_dims[1]
+                       : y_dims[y_dims.size() - 1]);
 
     ctx->SetOutputDim(framework::GradVarName("X"), y_dims);
     // has_scale_grad == has_bias_grad, judge has_scale_grad is enough

paddle/fluid/operators/pool_op.cc

@@ -97,7 +97,7 @@ void PoolOp::InferShape(framework::InferShapeContext* ctx) const {
 
   // MKL-DNN Kernels are using NCHW order of dims description
   // so we ignore data_format consideration for MKL-DNN kernel
-  const bool channel_last = (this->IsMKLDNNType() == false) &&
+  const bool channel_last = (ctx->IsRunMKLDNNKernel() == false) &&
                             (data_format == "NHWC" || data_format == "NDHWC");
 
   // update paddings if "SAME" or global_pooling

python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py

@@ -14,6 +14,7 @@
 
 from __future__ import print_function
 
+import os
 import unittest
 import numpy as np
 
@@ -232,6 +233,15 @@ def init_group(self):
         self.groups = 3
 
 
+# TODO(chenweihang): To solve the coverage problem, add this unittest,
+# remove this unittest after new executor set to default executor
+class TestConv2dMKLDNNByNewExecutor(TestConv2DMKLDNNOp):
+    def test_check_output_by_new_executor(self):
+        os.environ['FLAGS_USE_STANDALONE_EXECUTOR'] = '1'
+        self.test_check_output()
+        del os.environ['FLAGS_USE_STANDALONE_EXECUTOR']
+
+
 if __name__ == '__main__':
     from paddle import enable_static
     enable_static()