Withdraw mkldnn mul

Author: mozga-intel

paddle/fluid/operators/mul_mkldnn_op.cc

@@ -16,10 +16,6 @@ limitations under the License. */
 #include <string>
 #include <vector>
 
-#ifdef PADDLE_WITH_MKLDNN
-#include "paddle/fluid/platform/mkldnn_helper.h"
-#endif
-
 namespace paddle {
 namespace operators {
 
@@ -76,22 +72,6 @@ class MulOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim("Out", framework::make_ddim(output_dims));
     ctx->ShareLoD("X", /*->*/ "Out");
   }
-
- private:
-  framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
-    framework::LibraryType library{framework::LibraryType::kPlain};
-#ifdef PADDLE_WITH_MKLDNN
-    if (library == framework::LibraryType::kPlain &&
-        platform::CanMKLDNNBeUsed(ctx)) {
-      library = framework::LibraryType::kMKLDNN;
-    }
-#endif
-    framework::DataLayout layout{framework::DataLayout::kAnyLayout};
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
-        layout, library);
-  }
 };
 
 class MulOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -120,9 +100,6 @@ class MulOpMaker : public framework::OpProtoAndCheckerMaker {
         )DOC")
         .SetDefault(1)
         .EqualGreaterThan(1);
-    AddAttr<bool>("use_mkldnn",
-                  "(bool, default false) Only used in mkldnn kernel")
-        .SetDefault(false);
     AddAttr<int>(
         "y_num_col_dims",
         R"DOC((int, default 1), The mul_op can take tensors with more than two,
@@ -177,22 +154,6 @@ class MulGradOp : public framework::OperatorWithKernel {
       ctx->SetOutputDim(y_grad_name, y_dims);
     }
   }
-
- private:
-  framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
-    framework::LibraryType library{framework::LibraryType::kPlain};
-#ifdef PADDLE_WITH_MKLDNN
-    if (library == framework::LibraryType::kPlain &&
-        platform::CanMKLDNNBeUsed(ctx)) {
-      library = framework::LibraryType::kMKLDNN;
-    }
-#endif
-    framework::DataLayout layout{framework::DataLayout::kAnyLayout};
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
-        layout, library);
-  }
 };
 
 }  // namespace operators

paddle/fluid/operators/mul_op.cc

@@ -177,11 +177,8 @@ def fc(input,
             inputs={"X": input_var,
                     "Y": w},
             outputs={"Out": tmp},
-            attrs={
-                "x_num_col_dims": num_flatten_dims,
-                "y_num_col_dims": 1,
-                "use_mkldnn": use_mkldnn
-            })
+            attrs={"x_num_col_dims": num_flatten_dims,
+                   "y_num_col_dims": 1})
         mul_results.append(tmp)
 
     if len(mul_results) == 1:
@@ -3929,10 +3926,10 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
     Bilinear interpolation is an extension of linear interpolation for
     interpolating functions of two variables (e.g. H-direction and
     W-direction in this layer) on a rectilinear 2D grid.
-    
+
     For details, please refer to Wikipedia:
     https://en.wikipedia.org/wiki/Bilinear_interpolation
-    
+
     Args:
         input (Variable): The input tensor of bilinear interpolation,
                           This is a 4-D tensor of the shape
@@ -3950,7 +3947,7 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
     Returns:
         out (Variable): The output is a 4-D tensor of the shape
                         (num_batches, channls, out_h, out_w).
-   
+
     Examples:
         .. code-block:: python