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MKLDNN conv2d kernel added (#8451)

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* MKLDNN conv2 OP kernel added

* TODOs added

* mkldnn conv2d OP refactor

* CanCUDNNBeUsed and CanMKLDNNBeUsed moved
shanyi15-patch-2
pzelazko-intel 7 years ago committed by Tao Luo
parent 049383c615
commit 8c71adaa8c
10 changed files with 465 additions and 115 deletions
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26
paddle/fluid/operators/CMakeLists.txt
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@ -1,5 +1,7 @@
file(GLOB GENERAL_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*_op.cc")
string(REPLACE "_mkldnn" "" GENERAL_OPS "${GENERAL_OPS}")
string(REPLACE ".cc" "" GENERAL_OPS "${GENERAL_OPS}")
list(REMOVE_DUPLICATES GENERAL_OPS)
set(DEPS_OPS "")
set(pybind_file ${PADDLE_SOURCE_DIR}/paddle/fluid/pybind/pybind.h)
file(WRITE ${pybind_file} "// Generated by the paddle/operator/CMakeLists.txt. DO NOT EDIT!\n\n")
@ -13,6 +15,8 @@ function(op_library TARGET)
set(cu_cc_srcs)
set(cudnn_cu_cc_srcs)
set(CUDNN_FILE)
set(mkldnn_cc_srcs)
set(MKLDNN_FILE)
set(op_common_deps operator op_registry math_function)
set(options "")
set(oneValueArgs "")
@ -36,12 +40,20 @@ function(op_library TARGET)
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${CUDNN_FILE}.cu.cc)
list(APPEND cudnn_cu_cc_srcs ${CUDNN_FILE}.cu.cc)
endif()
if(WITH_MKLDNN)
string(REPLACE "_op" "_mkldnn_op" MKLDNN_FILE "${TARGET}")
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${MKLDNN_FILE}.cc)
list(APPEND mkldnn_cc_srcs ${MKLDNN_FILE}.cc)
endif()
endif()
else()
foreach(src ${op_library_SRCS})
if (${src} MATCHES ".*\\.cu$")
list(APPEND cu_srcs ${src})
elseif(${src} MATCHES ".*_cudnn_op.cu.cc$")
list(APPEND cudnn_cu_cc_srcs ${src})
elseif(WITH_MKLDNN AND ${src} MATCHES ".*_mkldnn_op.cc$")
list(APPEND mkldnn_cc_srcs ${src})
elseif(${src} MATCHES ".*\\.cu.cc$")
list(APPEND cu_cc_srcs ${src})
elseif(${src} MATCHES ".*\\.cc$")
@ -62,11 +74,11 @@ function(op_library TARGET)
set(DEPS_OPS ${TARGET} ${DEPS_OPS} PARENT_SCOPE)
endif()
if (WITH_GPU)
nv_library(${TARGET} SRCS ${cc_srcs} ${cu_cc_srcs} ${cudnn_cu_cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS}
nv_library(${TARGET} SRCS ${cc_srcs} ${cu_cc_srcs} ${cudnn_cu_cc_srcs} ${mkldnn_cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS}
${op_common_deps})
else()
cc_library(${TARGET} SRCS ${cc_srcs} DEPS ${op_library_DEPS}
${op_common_deps})
cc_library(${TARGET} SRCS ${cc_srcs} ${mkldnn_cc_srcs} DEPS ${op_library_DEPS}
${op_common_deps})
endif()
# Define operators that don't need pybind here.
@ -101,7 +113,8 @@ function(op_library TARGET)
# pybind USE_CPU_ONLY_OP
list(LENGTH cu_srcs cu_srcs_len)
list(LENGTH cu_cc_srcs cu_cc_srcs_len)
if (${pybind_flag} EQUAL 0 AND ${cu_srcs_len} EQUAL 0 AND ${cu_cc_srcs_len} EQUAL 0)
list(LENGTH mkldnn_cc_srcs mkldnn_cc_srcs_len)
if (${pybind_flag} EQUAL 0 AND ${mkldnn_cc_srcs_len} EQUAL 0 AND ${cu_srcs_len} EQUAL 0 AND ${cu_cc_srcs_len} EQUAL 0)
file(APPEND ${pybind_file} "USE_CPU_ONLY_OP(${TARGET});\n")
set(pybind_flag 1)
endif()
@ -112,6 +125,11 @@ function(op_library TARGET)
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, CUDNN);\n")
endif()
# pybind USE_OP_DEVICE_KERNEL for MKLDNN
if (WITH_MKLDNN AND ${mkldnn_cc_srcs_len} GREATER 0)
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, MKLDNN);\n")
endif()
# pybind USE_OP
if (${pybind_flag} EQUAL 0)
file(APPEND ${pybind_file} "USE_OP(${TARGET});\n")

313
paddle/fluid/operators/conv_mkldnn_op.cc
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File diff suppressed because it is too large Load Diff

51
paddle/fluid/operators/conv_op.cc
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@ -13,6 +13,12 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/conv_op.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cudnn_helper.h"
#endif
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
@ -64,22 +70,21 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
framework::OpKernelType ConvOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
bool use_cudnn = ctx.Attr<bool>("use_cudnn");
use_cudnn &= platform::is_gpu_place(ctx.GetPlace());
framework::LibraryType library_{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_CUDA
if (platform::is_gpu_place(ctx.GetPlace())) {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
use_cudnn &= dev_ctx.cudnn_handle() != nullptr;
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;
}
#endif
framework::LibraryType library_;
if (use_cudnn) {
library_ = framework::LibraryType::kCUDNN;
} else {
library_ = framework::LibraryType::kPlain;
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
}
#endif
std::string data_format = ctx.Attr<std::string>("data_format");
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
@ -131,6 +136,9 @@ Conv2DOpMaker::Conv2DOpMaker(OpProto* proto, OpAttrChecker* op_checker)
"use_cudnn",
"(bool, default false) Only used in cudnn kernel, need install cudnn")
.SetDefault(false);
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddAttr<std::string>(
"data_format",
"(string, default NCHW) Only used in "
@ -224,6 +232,9 @@ Conv3DOpMaker::Conv3DOpMaker(OpProto* proto, OpAttrChecker* op_checker)
"use_cudnn",
"(bool, default false) Only used in cudnn kernel, need install cudnn")
.SetDefault(false);
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddAttr<std::string>(
"data_format",
"(string, default NCHW) Only used in "
@ -284,23 +295,21 @@ void ConvOpGrad::InferShape(framework::InferShapeContext* ctx) const {
framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
bool use_cudnn = ctx.Attr<bool>("use_cudnn");
use_cudnn &= platform::is_gpu_place(ctx.GetPlace());
framework::LibraryType library_{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_CUDA
if (platform::is_gpu_place(ctx.GetPlace())) {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
use_cudnn &= dev_ctx.cudnn_handle() != nullptr;
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;
}
#endif
framework::LibraryType library_;
if (use_cudnn) {
library_ = framework::LibraryType::kCUDNN;
} else {
library_ = framework::LibraryType::kPlain;
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
}
#endif
std::string data_format = ctx.Attr<std::string>("data_format");
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),

14
paddle/fluid/platform/cudnn_helper.h
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@ -15,6 +15,8 @@ limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/dynload/cudnn.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/macros.h"
@ -282,5 +284,17 @@ class ScopedPoolingDescriptor {
DISABLE_COPY_AND_ASSIGN(ScopedPoolingDescriptor);
};
inline bool CanCUDNNBeUsed(const framework::ExecutionContext& ctx) {
bool use_cudnn = ctx.Attr<bool>("use_cudnn");
use_cudnn &= paddle::platform::is_gpu_place(ctx.GetPlace());
#ifdef PADDLE_WITH_CUDA
if (use_cudnn) {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
use_cudnn &= dev_ctx.cudnn_handle() != nullptr;
}
#endif
return use_cudnn;
}
} // namespace platform
} // namespace paddle

76
paddle/fluid/platform/device_context.cc
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@ -33,9 +33,15 @@ DeviceContextPool::DeviceContextPool(
PADDLE_ENFORCE_GT(places.size(), 0);
for (size_t i = 0; i < places.size(); i++) {
if (platform::is_cpu_place(places[i])) {
#ifdef PADDLE_WITH_MKLDNN
device_contexts_.emplace(places[i],
new platform::MKLDNNDeviceContext(
boost::get<platform::CPUPlace>(places[i])));
#else
device_contexts_.emplace(places[i],
new platform::CPUDeviceContext(
boost::get<platform::CPUPlace>(places[i])));
#endif
} else if (platform::is_gpu_place(places[i])) {
#ifdef PADDLE_WITH_CUDA
device_contexts_.emplace(places[i],
@ -170,64 +176,38 @@ cudaStream_t CUDADeviceContext::stream() const { return stream_; }
#ifdef PADDLE_WITH_MKLDNN
MKLDNNDeviceContext::MKLDNNDeviceContext(CPUPlace place)
: CPUDeviceContext(place), ready_(false) {
stream_.reset(new mkldnn::stream(mkldnn::stream::kind::eager));
engine_.reset(new mkldnn::engine(mkldnn::engine::cpu, 0));
: CPUDeviceContext(place), engine_(mkldnn::engine::cpu, 0), p_blobs_() {
p_blobs_.reset(new std::unordered_map<std::string, std::shared_ptr<void>>());
}
template <typename T>
void MKLDNNDeviceContext::AddElement(const std::string& op_key,
const T& value) {
if (GetElement<T>(op_key)) {
return;
}
GetElementPool<T>().emplace(op_key, std::move(value));
}
void MKLDNNDeviceContext::SetBlob(const std::string& name,
std::shared_ptr<void> data) const {
std::unordered_map<std::string, std::shared_ptr<void>>* p;
p = p_blobs_.get();
template <typename T>
const T& MKLDNNDeviceContext::GetElement(const std::string& op_key) const {
auto it = GetElementPool<T>().find(op_key);
return it == GetElementPool<T>().end() ? nullptr : it->second;
}
auto it = p->find(name);
template <>
const std::unordered_map<const std::string, const MKLDNNMemoryPtr,
std::hash<std::string>>&
MKLDNNDeviceContext::GetElementPool<MKLDNNMemoryPtr>() const {
return memory_pool_;
}
if (it == p->end()) {
(*p)[name] = data; // create new blob
} else {
it->second = data; // set data to existing blob
}
template <>
const std::unordered_map<const std::string, const MKLDNNPrimitivePtr,
std::hash<std::string>>&
MKLDNNDeviceContext::GetElementPool<MKLDNNPrimitivePtr>() const {
return primitive_pool_;
return;
}
template <>
const std::unordered_map<const std::string, const MKLDNNPrimitiveDescPtr,
std::hash<std::string>>&
MKLDNNDeviceContext::GetElementPool<MKLDNNPrimitiveDescPtr>() const {
return primitive_desc_pool_;
}
std::shared_ptr<void> MKLDNNDeviceContext::GetBlob(
const std::string& name) const {
std::unordered_map<std::string, std::shared_ptr<void>>* p;
p = p_blobs_.get();
void MKLDNNDeviceContext::Execute(bool block) {
if (pipeline_.empty()) {
return;
}
ResetStream();
stream_->submit(pipeline_).wait(block);
ready_ = false;
pipeline_.clear();
}
auto it = p->find(name);
void MKLDNNDeviceContext::ResetStream() {
if (ready_) {
return;
if (it != p->end()) {
return it->second;
}
// TODO(TJ): change me when mkldnn have specific method to reset this state
stream_.reset(new mkldnn::stream(mkldnn::stream::kind::eager));
ready_ = true;
return nullptr;
}
#endif

45
paddle/fluid/platform/device_context.h
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@ -22,7 +22,7 @@ limitations under the License. */
#endif
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#include <mkldnn.hpp>
#endif
#include "paddle/fluid/platform/enforce.h"
@ -114,46 +114,19 @@ class MKLDNNDeviceContext : public CPUDeviceContext {
public:
explicit MKLDNNDeviceContext(CPUPlace place);
/* \brief Add new element: memory, primitive or primitive desc */
template <typename T>
void AddElement(const std::string& op_key, const T& value);
/* \brief Get existed element: memory, primitive or primitive desc */
template <typename T>
const T& GetElement(const std::string& op_key) const;
/* \brief Get element pool: memory, primitive or primitive desc pool */
template <typename T>
const std::unordered_map<const std::string, const T, std::hash<std::string>>&
GetElementPool() const;
/* \brief Get the active engine */
const MKLDNNEngine& engine() const { return *engine_; }
/* \brief Submit primitive to pipeline */
void Submit(const MKLDNNPrimitivePtr& p) { pipeline_.push_back(*p); }
const mkldnn::engine& GetEngine() const { return engine_; }
/*! \brief Execute all submitted primitives in pipeline */
void Execute(bool block = true);
// Set data to blob (i.e. name/data pair). Create blob if not existing
void SetBlob(const std::string& name, std::shared_ptr<void> data) const;
protected:
/*! \brief Reset the stream to prepare next exectue */
void ResetStream();
// Find a saved blob. Return nullptr if not found
std::shared_ptr<void> GetBlob(const std::string& name) const;
private:
std::unordered_map<const std::string, const MKLDNNMemoryPtr,
std::hash<std::string>>
memory_pool_;
std::unordered_map<const std::string, const MKLDNNPrimitivePtr,
std::hash<std::string>>
primitive_pool_;
std::unordered_map<const std::string, const MKLDNNPrimitiveDescPtr,
std::hash<std::string>>
primitive_desc_pool_;
std::vector<MKLDNNPrimitive> pipeline_;
MKLDNNStreamPtr stream_;
MKLDNNEnginePtr engine_;
bool ready_;
mkldnn::engine engine_;
std::shared_ptr<std::unordered_map<std::string, std::shared_ptr<void>>>
p_blobs_;
};
#endif

15
paddle/fluid/platform/mkldnn_helper.h
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@ -16,12 +16,15 @@ limitations under the License. */
#include <mkldnn.hpp>
#include "paddle/fluid/framework/operator.h"
namespace paddle {
namespace platform {
using MKLDNNStream = mkldnn::stream;
using MKLDNNEngine = mkldnn::engine;
using MKLDNNMemory = mkldnn::memory;
using MKLDNNMemoryDescriptor = mkldnn::memory::desc;
using MKLDNNPrimitive = mkldnn::primitive;
using MKLDNNPrimitiveDesc = mkldnn::handle<mkldnn_primitive_desc_t>;
@ -31,5 +34,17 @@ typedef std::unique_ptr<MKLDNNMemory> MKLDNNMemoryPtr;
typedef std::unique_ptr<MKLDNNPrimitive> MKLDNNPrimitivePtr;
typedef std::unique_ptr<MKLDNNPrimitiveDesc> MKLDNNPrimitiveDescPtr;
inline mkldnn::memory::desc MKLDNNMemDesc(const std::vector<int>& dims,
mkldnn::memory::data_type data_type,
mkldnn::memory::format format) {
mkldnn::memory::dims tz = dims;
return mkldnn::memory::desc({tz}, data_type, format);
}
inline bool CanMKLDNNBeUsed(const framework::ExecutionContext& ctx) {
bool use_mkldnn = ctx.Attr<bool>("use_mkldnn");
return use_mkldnn && platform::is_cpu_place(ctx.GetPlace());
}
} // namespace platform
} // namespace paddle

4
python/paddle/fluid/layers/nn.py
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@ -1111,6 +1111,7 @@ def conv2d(input,
param_attr=None,
bias_attr=None,
use_cudnn=True,
use_mkldnn=False,
act=None):
"""
**Convlution2D Layer**
@ -1252,7 +1253,8 @@ def conv2d(input,
'strides': stride,
'paddings': padding,
'groups': groups,
'use_cudnn': use_cudnn
'use_cudnn': use_cudnn,
'use_mkldnn': use_mkldnn
})
pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)

12
python/paddle/fluid/nets.py
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@ -29,14 +29,16 @@ def simple_img_conv_pool(input,
act,
param_attr=None,
pool_type='max',
use_cudnn=True):
use_cudnn=True,
use_mkldnn=False):
conv_out = layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
param_attr=param_attr,
act=act,
use_cudnn=use_cudnn)
use_cudnn=use_cudnn,
use_mkldnn=use_mkldnn)
pool_out = layers.pool2d(
input=conv_out,
@ -58,7 +60,8 @@ def img_conv_group(input,
conv_batchnorm_drop_rate=0.0,
pool_stride=1,
pool_type=None,
use_cudnn=True):
use_cudnn=True,
use_mkldnn=False):
"""
Image Convolution Group, Used for vgg net.
"""
@ -90,7 +93,8 @@ def img_conv_group(input,
padding=conv_padding[i],
param_attr=param_attr[i],
act=local_conv_act,
use_cudnn=use_cudnn)
use_cudnn=use_cudnn,
use_mkldnn=use_mkldnn)
if conv_with_batchnorm[i]:
tmp = layers.batch_norm(input=tmp, act=conv_act)

24
python/paddle/fluid/tests/unittests/test_conv2d_op.py
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@ -64,6 +64,7 @@ def conv2d_forward_naive(input, filter, group, conv_param):
class TestConv2dOp(OpTest):
def setUp(self):
self.use_cudnn = False
self.use_mkldnn = False
self.init_op_type()
self.init_group()
self.init_dilation()
@ -85,7 +86,8 @@ class TestConv2dOp(OpTest):
'paddings': self.pad,
'groups': self.groups,
'dilations': self.dilations,
'use_cudnn': self.use_cudnn
'use_cudnn': self.use_cudnn,
'use_mkldnn': self.use_mkldnn
}
self.outputs = {'Output': output}
@ -290,5 +292,25 @@ class TestDepthwiseConv2(TestConv2dOp):
# def init_op_type(self):
# self.op_type = "conv_cudnn"
#----------------Conv2dMKLDNN----------------
class TestMKLDNN(TestConv2dOp):
def init_op_type(self):
self.use_mkldnn = True
self.op_type = "conv2d"
class TestMKLDNNWithPad(TestWithPad):
def init_op_type(self):
self.use_mkldnn = True
self.op_type = "conv2d"
class TestMKLDNNWithStride(TestWithStride):
def init_op_type(self):
self.use_mkldnn = True
self.op_type = "conv2d"
if __name__ == '__main__':
unittest.main()

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