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Added identity op and a fix for LRN (#9641)

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* relu grad and maxpooling grad fixes for perf

* Graph layout pass and conversion pass changes

This commit makes following changes:

- Enables support for ReluGrad and BiasAddGrad
- Adds support for detecting depthwise/batchwise pooling
- Adds more unit tests for Graph rewrite pass
- Improvements to handling control-flow edges
- Bug fixes

* Defaulting to Eigen when LRN depth_radius!=2

* Fixed mkl_conv_grad_filter.cc for conv_ops_tests.py

* Style fix to mkl_matmul and remove unnecessary 'MKL' label on matmul kernel

* Style fixes based on clang-format to mkl_conv_* and mkl_matmul

* Bug fixes

* Adding OP_REQUIRES_OK check in Concat

* Making some style changes

* Enabled the configuration of MKL settings

* relu grad and maxpooling grad fixes for perf

* Graph layout pass and conversion pass changes

This commit makes following changes:

- Enables support for ReluGrad and BiasAddGrad
- Adds support for detecting depthwise/batchwise pooling
- Adds more unit tests for Graph rewrite pass
- Improvements to handling control-flow edges
- Bug fixes

* Defaulting to Eigen when LRN depth_radius!=2

* Fixed mkl_conv_grad_filter.cc for conv_ops_tests.py

* Style fix to mkl_matmul and remove unnecessary 'MKL' label on matmul kernel

* Style fixes based on clang-format to mkl_conv_* and mkl_matmul

* Bug fixes

* Adding OP_REQUIRES_OK check in Concat

* Making some style changes

* Enabled the configuration of MKL settings

* Fixing graph unit tests with Mkl op name change to _Mkl; Fixed missing _ in MklToTf op

* Fixed missing libdl.so.2 in BUILD file

* Fixes for unit test build failures.

* Changes in mkl_conv_grad_filter_ops.cc for Google code style

* Fixes to remove dead code

* removed the dead code and added a TODO for mkl implementation to handle this case in the future

* Enabling MklIdentityOp

* Calling MKL for all values of depth radius in LRN

* Fixed buildifier sanity check error

* Adding support for google's CI automation

* Updated link to new MKL version

* Enabling MklIdentityOp

* Calling MKL for all values of depth radius in LRN

* Fix for missing locate binary

* Fix for missing locate command in CI

* Adding updatedb to populate the database after installing mlocate

* Fixed buildifier issue

* setting tf_need_mkl=0 in libtf files

* Added third_party/mkl/* to .gitignore

* Added third_party/eigen3/mkl_include to .gitignore

* In configured, set MKL-enabling options only for Linux.

* Enabling MklIdentityOp

* Calling MKL for all values of depth radius in LRN

* Making style fix in LRN

* Fixed Indentation
This commit is contained in:
Vivek Rane 2017-05-04 16:32:16 -07:00 committed by Vijay Vasudevan
parent 27dd167c5f
commit 243ac01320
8 changed files with 160 additions and 22 deletions
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2
tensorflow/core/BUILD
View File

@ -736,6 +736,7 @@ cc_library(
"//tensorflow/core/kernels:mkl_concat_op",
"//tensorflow/core/kernels:mkl_conv_op",
"//tensorflow/core/kernels:mkl_fused_batch_norm_op",
"//tensorflow/core/kernels:mkl_identity_op",
"//tensorflow/core/kernels:mkl_lrn_op",
"//tensorflow/core/kernels:mkl_pooling_ops",
"//tensorflow/core/kernels:mkl_relu_op",
@ -2123,6 +2124,7 @@ tf_cc_test_mkl(
"//tensorflow/core/kernels:mkl_concat_op",
"//tensorflow/core/kernels:mkl_conv_op",
"//tensorflow/core/kernels:mkl_fused_batch_norm_op",
"//tensorflow/core/kernels:mkl_identity_op",
"//tensorflow/core/kernels:mkl_lrn_op",
"//tensorflow/core/kernels:mkl_pooling_ops",
"//tensorflow/core/kernels:mkl_relu_op",

16
tensorflow/core/graph/mkl_layout_pass.cc
View File

@ -273,6 +273,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
csinfo_.conv2d_grad_filter = "Conv2DBackpropFilter";
csinfo_.fused_batch_norm = "FusedBatchNorm";
csinfo_.fused_batch_norm_grad = "FusedBatchNormGrad";
csinfo_.identity = "Identity";
csinfo_.lrn = "LRN";
csinfo_.lrn_grad = "LRNGrad";
csinfo_.matmul = "MatMul";
@ -326,6 +327,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
rinfo_.push_back({csinfo_.fused_batch_norm_grad,
GetMklOpName(csinfo_.fused_batch_norm_grad),
CopyAttrsFusedBatchNorm, AlwaysRewrite, nullptr});
rinfo_.push_back({csinfo_.identity,
GetMklOpName(csinfo_.identity),
CopyAttrsIdentity, AlwaysRewrite, nullptr});
rinfo_.push_back({csinfo_.lrn,
GetMklOpName(csinfo_.lrn),
CopyAttrsLRN, AlwaysRewrite, nullptr});
@ -446,6 +450,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
string conv2d_grad_filter;
string fused_batch_norm;
string fused_batch_norm_grad;
string identity;
string lrn;
string lrn_grad;
string matmul;
@ -767,6 +772,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
static void CopyAttrsConcatV2(const Node* orig_node, NodeBuilder* nb);
static void CopyAttrsConv2D(const Node* orig_node, NodeBuilder* nb);
static void CopyAttrsFusedBatchNorm(const Node* orig_node, NodeBuilder* nb);
static void CopyAttrsIdentity(const Node* orig_node, NodeBuilder* nb);
static void CopyAttrsLRN(const Node* orig_node, NodeBuilder* nb);
static void CopyAttrsPooling(const Node* orig_node, NodeBuilder* nb);
static void CopyAttrsRelu(const Node* orig_node, NodeBuilder* nb);
@ -1275,6 +1281,16 @@ void MklLayoutRewritePass::CopyAttrsBiasAddGrad(const Node* orig_node,
nb->Attr("data_format", data_format);
}
void MklLayoutRewritePass::CopyAttrsIdentity(const Node* orig_node,
NodeBuilder* nb) {
DataType T;
// Get all attributes from old node.
TF_CHECK_OK(GetNodeAttr(orig_node->def(), "T", &T));
// Add attributes to new node.
nb->Attr("T", T);
}
void MklLayoutRewritePass::CopyAttrsLRN(const Node* orig_node,
NodeBuilder* nb) {
DataType T;

8
tensorflow/core/kernels/BUILD
View File

@ -4918,6 +4918,14 @@ tf_mkl_kernel_library(
],
)
tf_mkl_kernel_library(
name = "mkl_identity_op",
prefix = "mkl_identity_op",
deps = ARRAY_DEPS + [
"//third_party/mkl:intel_binary_blob",
],
)
tf_mkl_kernel_library(
name = "mkl_lrn_op",
prefix = "mkl_lrn_op",

63
tensorflow/core/kernels/mkl_identity_op.cc Normal file
View File

@ -0,0 +1,63 @@
/* Copyright 2015 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.
==============================================================================*/
// See docs in ../ops/array_ops.cc.
#ifdef INTEL_MKL
#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/framework/types.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/util/mkl_util.h"
#include "third_party/mkl/include/mkl_dnn.h"
#include "third_party/mkl/include/mkl_dnn_types.h"
namespace tensorflow {
typedef Eigen::ThreadPoolDevice CPUDevice;
template <typename Device, typename T>
class MklIdentityOp : public OpKernel {
public:
explicit MklIdentityOp(OpKernelConstruction* context) : OpKernel(context) {}
void Compute(OpKernelContext* context) override {
MklShape mkl_shape_input;
GetMklShape(context, 0, &mkl_shape_input);
bool input_in_mkl_format = mkl_shape_input.IsMklTensor();
if (input_in_mkl_format) {
ForwarMklTensorInToOut(context, 0, 0);
} else {
FowardTfTensorInToOut(context, 0, 0);
}
}
bool IsExpensive() override { return false; }
};
#define REGISTER_MKL_CPU(T) \
REGISTER_KERNEL_BUILDER(Name("_MklIdentity") \
.Device(DEVICE_CPU) \
.TypeConstraint<T>("T") \
.Label(mkl_op_registry::kMklOpLabel), \
MklIdentityOp<CPUDevice, T>); \
TF_CALL_float(REGISTER_MKL_CPU);
#undef REGISTER_MKL_CPU
} // namespace tensorflow
#endif // INTEL_MKL

30
tensorflow/core/kernels/mkl_lrn_op.cc
View File

@ -171,9 +171,7 @@ class MklLRNOp : public OpKernel {
MklShape input_shape;
dnnPrimitive_t lrn_fwd = nullptr;
dnnPrimitive_t convert_input = nullptr;
/* dnnPrimitive_t convert_output; */
dnnLayout_t lt_input = nullptr;
/* dnnLayout_t lt_output; */
dnnLayout_t lt_internal_input = nullptr;
dnnLayout_t lt_internal_workspace = nullptr;
dnnLayout_t lt_internal_output = nullptr;
@ -390,11 +388,6 @@ class MklLRNGradOp : public OpKernel {
return;
}
if (depth_radius_ != 2) {
mkl_context.MklDefaultToEigen(context);
return;
}
if (ingrad_in_mkl_format || inimage_in_mkl_format) {
const MklShape* tmp_mkl_shape = (ingrad_in_mkl_format)
? &mkl_context.ingrad_shape
@ -476,11 +469,11 @@ class MklLRNGradOp : public OpKernel {
const_cast<void*>(static_cast<const void*>(output->flat<T>().data()));
Tensor mkl_tmp_input_buf_tensor, mkl_tmp_image_buf_tensor,
mkl_tmp_outimage_buf_tensor, mkl_tmp_workspace_buf_tensor;
mkl_tmp_outimage_buf_tensor;
// Convert Inputs if needed
mkl_context.MklPrepareLRNGradInput(
context, &mkl_tmp_input_buf_tensor, &mkl_tmp_image_buf_tensor,
&mkl_tmp_outimage_buf_tensor, &mkl_tmp_workspace_buf_tensor);
mkl_context.MklPrepareLRNGradInput(context, &mkl_tmp_input_buf_tensor,
&mkl_tmp_image_buf_tensor,
&mkl_tmp_outimage_buf_tensor);
// We do not do any conversion for output. But we simply emit it
// in MKL format.
@ -537,11 +530,13 @@ class MklLRNGradOp : public OpKernel {
void MklPrepareLRNGradInput(OpKernelContext* context,
Tensor* mkl_tmp_input_buf_tensor,
Tensor* mkl_tmp_image_buf_tensor,
Tensor* mkl_tmp_outimage_buf_tensor,
Tensor* mkl_tmp_workspace_buf_tensor) {
Tensor* mkl_tmp_outimage_buf_tensor) {
const Tensor& in_grads = MklGetInput(context, 0);
const Tensor& in_image = MklGetInput(context, 1);
const Tensor& out_image = MklGetInput(context, 2);
const Tensor& workspace = MklGetInput(
context,
3); /*Worskpsace is enabled, get the buffer to the workspace */
void* user_input = const_cast<void*>(
static_cast<const void*>(in_grads.flat<T>().data()));
@ -549,6 +544,9 @@ class MklLRNGradOp : public OpKernel {
static_cast<const void*>(in_image.flat<T>().data()));
void* user_fwd_output = const_cast<void*>(
static_cast<const void*>(out_image.flat<T>().data()));
void* workspace_buffer = const_cast<void*>(
static_cast<const void*>(workspace.flat<T>().data()));
CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&lt_workspace, lrn_bwd,
dnnResourceWorkspace),
E_SUCCESS);
@ -623,9 +621,7 @@ class MklLRNGradOp : public OpKernel {
res_lrn_bwd[dnnResourceDst] = user_fwd_output;
}
// Allocate buffer for workspace.
AllocTmpBuffer(context, mkl_tmp_workspace_buf_tensor, lt_workspace,
&res_lrn_bwd[dnnResourceWorkspace]);
res_lrn_bwd[dnnResourceWorkspace] = workspace_buffer;
}
// Fallback implementation - Taken from lrn_op.cc
@ -713,7 +709,7 @@ class MklLRNGradOp : public OpKernel {
Shard(worker_threads.num_threads, worker_threads.workers, nodes * batch,
depth * depth, shard);
}
// release mkl resources
void Mklcleanup() {
bool ingrad_in_mkl_format = ingrad_shape.IsMklTensor();

2
tensorflow/core/kernels/mkl_reshape_op.cc
View File

@ -129,7 +129,7 @@ class MklReshapeOp : public OpKernel {
return;
}
} else {
CopyTFTensorInToOut(context, 0, 0, shape);
CopyTfTensorInToOutWithShape(context, 0, 0, shape);
}
}
};

17
tensorflow/core/ops/array_ops.cc
View File

@ -1506,6 +1506,23 @@ REGISTER_OP("Identity")
Return a tensor with the same shape and contents as the input tensor or value.
)Doc");
#ifdef INTEL_MKL
REGISTER_OP("_MklIdentity")
.Input("input: T")
.Input("mkl_input: uint8")
.Output("output: T")
.Output("mkl_output: uint8")
.Attr("T: type")
.SetShapeFn([](shape_inference::InferenceContext* c) {
c->set_output(0, c->input(0));
c->set_output_handle_dtype(0, c->input_handle_dtype(0));
c->set_output_handle_shape(0, c->input_handle_shape(0));
return Status::OK();
})
.Doc(R"Doc( Mkl implementation of IdentityOp
)Doc");
#endif
// --------------------------------------------------------------------------
REGISTER_OP("RefIdentity")
.Input("input: Ref(T)")

44
tensorflow/core/util/mkl_util.h
View File

@ -542,8 +542,8 @@ inline int64 GetMklTensorDim(const MklShape& mkl_shape, char dimension) {
return mkl_shape.dim_size(index);
}
inline void CopyMklTensorInToOut(OpKernelContext* context, int idx_in,
int idx_out) {
inline void CopyMklTensorInToOut(OpKernelContext* context,
int idx_in, int idx_out) {
int num_inputs = context->num_inputs();
int num_outputs = context->num_outputs();
int idx_data_in = GetTensorDataIndex(idx_in, num_inputs);
@ -563,8 +563,9 @@ inline void CopyMklTensorInToOut(OpKernelContext* context, int idx_in,
context->set_output(idx_meta_out, meta_output);
}
inline void CopyTFTensorInToOut(OpKernelContext* context, int idx_in,
int idx_out, const TensorShape& shape) {
inline void CopyTfTensorInToOutWithShape(OpKernelContext* context,
int idx_in, int idx_out,
const TensorShape& shape) {
int num_inputs = context->num_inputs();
int num_outputs = context->num_outputs();
int idx_data_in = GetTensorDataIndex(idx_in, num_inputs);
@ -580,6 +581,41 @@ inline void CopyTFTensorInToOut(OpKernelContext* context, int idx_in,
context->set_output(idx_data_out, output);
}
inline void FowardTfTensorInToOut(OpKernelContext* context,
int idx_in, int idx_out) {
int num_inputs = context->num_inputs();
int num_outputs = context->num_outputs();
int idx_data_in = GetTensorDataIndex(idx_in, num_inputs);
int idx_data_out = GetTensorDataIndex(idx_out, num_outputs);
MklShape mkl_shape_output;
mkl_shape_output.SetMklTensor(false);
AllocateOutputSetMklShape(context, idx_out, mkl_shape_output);
if (IsRefType(context->input_dtype(idx_data_in))) {
context->forward_ref_input_to_ref_output(idx_data_in, idx_data_out);
} else {
context->set_output(idx_data_out, context->input(idx_data_in));
}
}
inline void ForwarMklTensorInToOut(OpKernelContext* context,
int idx_in, int idx_out) {
int num_inputs = context->num_inputs();
int num_outputs = context->num_outputs();
int idx_data_in = GetTensorDataIndex(idx_in, num_inputs);
int idx_meta_in = GetTensorMetaDataIndex(idx_in, num_inputs);
int idx_data_out = GetTensorDataIndex(idx_out, num_outputs);
int idx_meta_out = GetTensorMetaDataIndex(idx_out, num_outputs);
if (IsRefType(context->input_dtype(idx_data_in))) {
context->forward_ref_input_to_ref_output(idx_data_in, idx_data_out);
context->forward_ref_input_to_ref_output(idx_meta_in, idx_meta_out);
} else {
context->set_output(idx_data_out, context->input(idx_data_in));
context->set_output(idx_meta_out, context->input(idx_meta_in));
}
}
namespace mkl_op_registry {
static const char* kMklOpLabel = "MklOp";
static const char* kMklOpLabelPattern = "label='MklOp'";