1 files changed, 0 insertions, 124 deletions
diff --git a/tensorflow/core/kernels/mkl_tfconv_op.cc b/tensorflow/core/kernels/mkl_tfconv_op.cc
deleted file mode 100644
index b48c735d12..0000000000
--- a/tensorflow/core/kernels/mkl_tfconv_op.cc
+++ /dev/null
@@ -1,124 +0,0 @@
-/* Copyright 2017 The TensorFlow 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.
-==============================================================================*/
-
-#ifdef INTEL_MKL
-
-#include <algorithm>
-#include <vector>
-#include "tensorflow/core/framework/numeric_op.h"
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/register_types.h"
-#include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/framework/tensor_shape.h"
-#include "tensorflow/core/kernels/ops_util.h"
-#include "tensorflow/core/platform/cpu_info.h"
-#include "tensorflow/core/platform/macros.h"
-#include "tensorflow/core/util/tensor_format.h"
-
-#include "tensorflow/core/util/mkl_util.h"
-#include "mkl_dnn.h"
-#include "mkl_dnn_types.h"
-
-namespace tensorflow {
-typedef Eigen::ThreadPoolDevice CPUDevice;
-
-///////////////////////////////////////////////////////////
-//               Op kernel
-///////////////////////////////////////////////////////////
-
-template <typename Device, typename T>
-class MklToTfOp : public OpKernel {
- public:
-  explicit MklToTfOp(OpKernelConstruction* context) : OpKernel(context) {
-    OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format_str));
-    OP_REQUIRES_OK(context, context->GetAttr("T", &op_data_type));
-    has_avx512f_ = port::TestCPUFeature(port::CPUFeature::AVX512F);
-  }
-
-  void Compute(OpKernelContext* context) override {
-    // Check that input tensor is in MKL format.
-    const Tensor& input_tensor = MklGetInput(context, 0);
-    MklShape input_shape;
-    GetMklShape(context, 0, &input_shape);
-
-    // if input is already in Tf format, then just copy input tensor to output.
-    if (!input_shape.IsMklTensor()) {
-      context->set_output(0, input_tensor);
-      VLOG(1) << "MKLToTFConversion: No conversion needed, "
-              << "copying input to output";
-      return;
-    }
-
-    // Check that input data type is same as operator data type and that it is
-    // same as output data type.
-    DataType input_data_type = input_type(0);
-    DataType output_data_type = output_type(0);
-    CHECK_EQ(op_data_type, input_data_type);
-    CHECK_EQ(op_data_type, output_data_type);
-
-    TensorShape output_shape;
-    size_t ndims = input_shape.GetDimension();
-    size_t* in_sizes = new size_t[ndims];
-    for (size_t i = 0; i < ndims; i++) {
-      // Outermost to innermost dimension
-      output_shape.AddDim(input_shape.GetSizes()[input_shape.tf_dim_idx(i)]);
-      in_sizes[i] = input_shape.GetSizes()[i];
-    }
-
-    // Allocate output tensor.
-    Tensor* output_tensor = NULL;
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(0, output_shape, &output_tensor));
-
-    dnnLayout_t output_layout =
-        static_cast<dnnLayout_t>(input_shape.GetTfLayout());
-    // Execute DNNConversion.
-    void* input_buffer =
-        static_cast<void*>(const_cast<T*>(input_tensor.flat<T>().data()));
-    delete[] in_sizes;
-    void* output_buffer =
-        static_cast<void*>(const_cast<T*>(output_tensor->flat<T>().data()));
-    input_shape.GetConvertedFlatData(output_layout, input_buffer,
-                                     output_buffer);
-    VLOG(1) << "MKLToTFConversion complete successfully.";
-  }
-
- private:
-  /// Data format of the operation
-  string data_format_str;
-
-  /// Data type of the operation
-  DataType op_data_type;
-
-  /// CPUIDInfo
-  bool has_avx512f_ = false;
-};
-
-///////////////////////////////////////////////////////////
-//               Register kernel
-///////////////////////////////////////////////////////////
-
-#define REGISTER_CPU(T)                                             \
-  REGISTER_KERNEL_BUILDER(Name("_MklToTf")                          \
-                              .Device(DEVICE_CPU)                   \
-                              .TypeConstraint<T>("T")               \
-                              .Label(mkl_op_registry::kMklOpLabel), \
-                          MklToTfOp<CPUDevice, T>);
-
-TF_CALL_float(REGISTER_CPU);
-#undef REGISTER_CPU
-}  // namespace tensorflow
-#endif /* INTEL_MKL */