1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
|
/* 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
#ifndef TENSORFLOW_CORE_KERNELS_MKL_TFCONV_OP_H_
#define TENSORFLOW_CORE_KERNELS_MKL_TFCONV_OP_H_
#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 "mkl_dnn.h"
#include "mkl_dnn_types.h"
#include "tensorflow/core/util/mkl_util.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 {
ConvertMklToTf(this, context, data_format_str, op_data_type, has_avx512f_,
0);
VLOG(1) << "MKLToTFConversion complete successfully.";
}
static void ConvertMklToTf(OpKernel* op_kernel, OpKernelContext* context,
string data_format_str, DataType op_data_type,
bool has_avx512f, uint input_number) {
// Check that input tensor is in MKL format.
const Tensor& input_tensor = MklGetInput(context, input_number);
MklShape input_shape;
GetMklShape(context, input_number, &input_shape);
// if input is already in Tf format, then just copy input tensor to output.
if (!input_shape.IsMklTensor()) {
context->set_output(input_number, 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 = op_kernel->input_type(input_number);
DataType output_data_type = op_kernel->output_type(input_number);
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(input_number, 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_NUMBER_TYPES(REGISTER_CPU);
#undef REGISTER_CPU
} // namespace tensorflow
#endif // TENSORFLOW_CORE_KERNELS_MKL_TFCONV_OP_H_
#endif // INTEL_MKL
|
