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Mkldnn layout (#11040)

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* Add MKLDNN layout support in Paddle

Add MKLDNN layout in Paddle so that MKLDNN friendly memory layout
can be used in MKLDNN enabled OP kernel. Before this commit, NCHW
is hardcode to be used in all MKLDNN op kernels. As a result,
non-optimized execution path is selected in MKLDNN primitive which
bring worse performance.
Besides framework change, three MKLDNN OP kernels were updated
for using new MKLDNN layout. They are conv/pool2d/batch_norm.
Other MKLDNN OP kernels need be also updated in similar way to
achieve best performance.

* Add MKLDNN layout support in activation OP

* Don't populate layout from input to output when kMKLDNN in

* Refine pool mkldnn op kernel

* MKLDNN layout

* Remove the inferitance from tensor file

* MKLDNN layout: refactoring

* Remove additional #define to register new operator

* Prepare mkldnn tests to work with layout
wangkuiyi-patch-1
mozga-intel 7 years ago committed by Tao Luo
parent a1e046bfc0
commit 3ff9ba0e6b
18 changed files with 287 additions and 43 deletions
Show Stats Download Patch File Download Diff File

7
paddle/fluid/framework/data_layout.h
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@ -27,6 +27,7 @@ enum class DataLayout {
kNHWC = 0,
kNCHW = 1,
kAnyLayout = 2,
kMKLDNN = 3, // all layouts supported by MKLDNN internally
};
inline DataLayout StringToDataLayout(const std::string& str) {
@ -41,6 +42,8 @@ inline DataLayout StringToDataLayout(const std::string& str) {
return DataLayout::kNCHW;
} else if (s == "ANYLAYOUT") {
return DataLayout::kAnyLayout;
} else if (s == "MKLDNNLAYOUT") {
return DataLayout::kMKLDNN;
} else {
PADDLE_THROW("Unknown storage order string: %s", s);
}
@ -54,8 +57,10 @@ inline std::string DataLayoutToString(const DataLayout& data_layout) {
return "NCHW";
case DataLayout::kAnyLayout:
return "ANY_LAYOUT";
case DataLayout::kMKLDNN:
return "MKLDNNLAYOUT";
default:
PADDLE_THROW("unknown DataLayou %d", data_layout);
PADDLE_THROW("unknown DataLayout %d", data_layout);
}
}

83
paddle/fluid/framework/data_layout_transform.cc
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@ -16,6 +16,9 @@
#include <vector>
#include "paddle/fluid/operators/math/math_function.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace framework {
@ -88,5 +91,85 @@ void TransDataLayout(const OpKernelType& kernel_type_for_var,
out->set_layout(expected_kernel_type.data_layout_);
}
#ifdef PADDLE_WITH_MKLDNN
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::reorder;
void* GetDataFromTensor(const Tensor& tensor, mkldnn::memory::data_type type) {
switch (type) {
case mkldnn::memory::data_type::f32:
return platform::to_void_cast(tensor.data<float>());
case mkldnn::memory::data_type::s8:
return platform::to_void_cast(tensor.data<char>());
case mkldnn::memory::data_type::u8:
return platform::to_void_cast(tensor.data<unsigned char>());
case mkldnn::memory::data_type::s16:
return platform::to_void_cast(tensor.data<int16_t>());
case mkldnn::memory::data_type::s32:
return platform::to_void_cast(tensor.data<int32_t>());
default:
PADDLE_THROW("wrong mkldnn type provided");
}
}
#endif
void TransDataLayoutFromMKLDNN(const OpKernelType& kernel_type_for_var,
const OpKernelType& expected_kernel_type,
const Tensor& in, Tensor* out) {
auto in_layout = kernel_type_for_var.data_layout_;
auto out_layout = expected_kernel_type.data_layout_;
PADDLE_ENFORCE(
in_layout == DataLayout::kMKLDNN && out_layout != DataLayout::kMKLDNN,
"TransDataLayoutFromMKLDNN only supports transform from MKLDNN to "
"non-MKLDNN");
#ifdef PADDLE_WITH_MKLDNN
PADDLE_ENFORCE(in.format() != memory::format::format_undef &&
in.format() != memory::format::any,
"Input tensor should have specified memory format");
// Set default as NCHW in case not specified
out_layout =
out_layout == DataLayout::kAnyLayout ? DataLayout::kNCHW : out_layout;
auto& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx = dynamic_cast<platform::MKLDNNDeviceContext*>(
pool.Get(expected_kernel_type.place_));
auto& cpu_engine = dev_ctx->GetEngine();
std::vector<int> in_tz = paddle::framework::vectorize2int(in.dims());
std::vector<int> out_tz = in_tz;
memory::data_type in_type = ToMKLDNNDataType(in.type());
PADDLE_ENFORCE(in_type != memory::data_type::data_undef,
"Input tensor type is not supported: ", in.type().name());
memory::data_type out_type = in_type;
memory::format in_format =
in_tz.size() == 2 ? memory::format::nc : in.format();
memory::format out_format =
out_tz.size() == 2 ? memory::format::nc : ToMKLDNNFormat(out_layout);
void* in_data = GetDataFromTensor(in, in_type);
// output tensor has the same dims as input. Reorder don't change dims
out->Resize(in.dims());
auto out_data = out->mutable_data(expected_kernel_type.place_, in.type());
auto in_memory = memory({{{in_tz}, in_type, in_format}, cpu_engine}, in_data);
auto out_memory =
memory({{{out_tz}, out_type, out_format}, cpu_engine}, out_data);
platform::Reorder(in_memory, out_memory);
out->set_layout(out_layout);
// reset format since the out tensor will be feed to non-MKLDNN OPkernel
out->set_format(memory::format::format_undef);
#endif
}
} // namespace framework
} // namespace paddle

45
paddle/fluid/framework/data_layout_transform.h
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@ -14,6 +14,7 @@
#pragma once
#include <map>
#include <vector>
#include "paddle/fluid/framework/op_kernel_type.h"
#include "paddle/fluid/framework/tensor.h"
@ -22,6 +23,50 @@
namespace paddle {
namespace framework {
#ifdef PADDLE_WITH_MKLDNN
using MKLDNNFormat = mkldnn::memory::format;
using MKLDNNDataType = mkldnn::memory::data_type;
inline MKLDNNFormat ToMKLDNNFormat(const DataLayout& layout) {
switch (layout) {
case DataLayout::kNHWC:
return MKLDNNFormat::nhwc;
case DataLayout::kNCHW:
return MKLDNNFormat::nchw;
default:
PADDLE_THROW("Fail to convert layout %s to MKLDNN format",
DataLayoutToString(layout));
}
}
inline DataLayout ToPaddleLayout(const MKLDNNFormat& format) {
switch (format) {
case MKLDNNFormat::nhwc:
return DataLayout::kNHWC;
case MKLDNNFormat::nchw:
return DataLayout::kNCHW;
default:
PADDLE_THROW("Fail to convert MKLDNN format to paddle layout");
}
}
inline MKLDNNDataType ToMKLDNNDataType(const std::type_index type) {
static const std::map<std::type_index, MKLDNNDataType> dict{
{std::type_index(typeid(float)), MKLDNNDataType::f32}, // NOLINT
{std::type_index(typeid(char)), MKLDNNDataType::s8}, // NOLINT
{std::type_index(typeid(unsigned char)), MKLDNNDataType::u8},
{std::type_index(typeid(int16_t)), MKLDNNDataType::s16},
{std::type_index(typeid(int32_t)), MKLDNNDataType::s32}};
auto iter = dict.find(type);
if (iter != dict.end()) return iter->second;
return MKLDNNDataType::data_undef;
}
#endif
void TransDataLayoutFromMKLDNN(const OpKernelType& kernel_type_for_var,
const OpKernelType& expected_kernel_type,
const Tensor& in, Tensor* out);
std::vector<int> GetAxis(const DataLayout& from, const DataLayout& to);
void TransDataLayout(const OpKernelType& kernel_type_for_var,

29
paddle/fluid/framework/data_transform.cc
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@ -33,11 +33,34 @@ void DataTransform(const OpKernelType& expected_kernel_type,
Tensor in;
in.ShareDataWith(input_tensor);
Tensor out;
DataLayout lin = kernel_type_for_var.data_layout_;
DataLayout lout = expected_kernel_type.data_layout_;
// do layout transform
if (NeedTransformLayout(expected_kernel_type.data_layout_,
kernel_type_for_var.data_layout_)) {
TransDataLayout(kernel_type_for_var, expected_kernel_type, in, &out);
if (NeedTransformLayout(lout, lin)) {
if (lin == DataLayout::kMKLDNN || lout == DataLayout::kMKLDNN) {
PADDLE_ENFORCE(
!(lin == DataLayout::kMKLDNN && lout == DataLayout::kMKLDNN),
"No layout transform needed between two MKLDNN OPKernels");
if (lin != DataLayout::kMKLDNN && lout == DataLayout::kMKLDNN) {
#ifdef PADDLE_WITH_MKLDNN
// Case1 - transform from Non-MKLDNN OPKernel to MKLDNN OPKernel
// Just set layout/format. No real transform occur
out.ShareDataWith(input_tensor);
out.set_layout(DataLayout::kMKLDNN);
out.set_format(ToMKLDNNFormat(lin));
#endif
} else {
// Case2 - transfrom from MKLDNN OPKernel to Non-MKLDNN OPKernel
// Do transform via MKLDNN lib
TransDataLayoutFromMKLDNN(kernel_type_for_var, expected_kernel_type, in,
&out);
}
} else {
// Case3 - transfrom between Non-MKLDNN OPKernels
TransDataLayout(kernel_type_for_var, expected_kernel_type, in, &out);
}
transformed = true;
PassTensorData(&out, &in);
}

9
paddle/fluid/framework/op_kernel_type.h
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@ -87,7 +87,14 @@ inline std::string KernelTypeToString(const OpKernelType& kernel_key) {
}
inline bool NeedTransformLayout(const DataLayout& l, const DataLayout& r) {
return l != DataLayout::kAnyLayout && r != DataLayout::kAnyLayout && l != r;
bool ret =
(l != DataLayout::kAnyLayout && r != DataLayout::kAnyLayout && l != r);
#ifdef PADDLE_WITH_MKLDNN
// Layout transform needed for either non-MKLDNN to MKLDNN or vice versa
ret |= (l != DataLayout::kMKLDNN && r == DataLayout::kMKLDNN);
ret |= (l == DataLayout::kMKLDNN && r != DataLayout::kMKLDNN);
#endif
return ret;
}
inline bool TransFromNeeded(const OpKernelType& l, const OpKernelType& r) {

11
paddle/fluid/framework/op_registry.h
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@ -83,8 +83,14 @@ struct OpKernelRegistrarFunctor<PlaceType, false, I, KernelTypes...> {
void operator()(const char* op_type, const char* library_type) const {
using T = typename KERNEL_TYPE::ELEMENT_TYPE;
std::string library(library_type);
std::string data_layout = "ANYLAYOUT";
if (library == "MKLDNN") {
data_layout = "MKLDNNLAYOUT";
}
OpKernelType key(ToDataType(std::type_index(typeid(T))), PlaceType(),
DataLayout::kAnyLayout, StringToLibraryType(library_type));
StringToDataLayout(data_layout),
StringToLibraryType(library_type));
OperatorWithKernel::AllOpKernels()[op_type][key].reset(new KERNEL_TYPE);
constexpr auto size = std::tuple_size<std::tuple<KernelTypes...>>::value;
@ -99,7 +105,8 @@ struct OpKernelRegistrarFunctor<PlaceType, true, I, KernelType...> {
void operator()(const char* op_type, const char* library_type) const {}
};
// User can register many kernel in one place. The data type could be different.
// User can register many kernel in one place. The data type could be
// different.
template <typename PlaceType, typename... KernelType>
class OpKernelRegistrar : public Registrar {
public:

26
paddle/fluid/framework/operator.cc
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@ -444,10 +444,25 @@ class RuntimeInferShapeContext : public InferShapeContext {
auto* out_tensor = out_var->GetMutable<LoDTensor>();
out_tensor->set_lod(in_tensor.lod());
// TODO(dzhwinter) : reuse ShareLoD in most operators.
// Need to call ShareLayout explicitly in sequence related ops.
// Shall we have a better method to shared info between in/out Tensor?
out_tensor->set_layout(in_tensor.layout());
// TODO(dzhwinter) : reuse ShareLoD in most operators.
// Need to call ShareLayout explicitly in sequence related ops.
// Shall we have a better method to shared info between in/out Tensor?
#ifdef PADDLE_WITH_MKLDNN
// Fix me: ugly workaround below
// Correct solution:
// set_layout() should NOT be called here (i.e. ShareLoD). Instead,
// layout of output tensor should be set "manually" in Compute()
// of each OPKernel. The reason layout should NOT be shared between
// input and output "automatically" (now by InferShape()->ShareLoD())
// is that layout transform may occur after InferShape().
// Workaround:
// Skip set_layout() when input layout is kMKLDNN
// This is to avoid kMKLDNN is populated wrongly into a non-MKLDNN
// OPKernel. In all MKLDNN OPkernel, set_layout(kMKLDNN) should be called
// in Compute()
if (in_tensor.layout() != DataLayout::kMKLDNN)
#endif
out_tensor->set_layout(in_tensor.layout());
}
void ShareLayout(const std::string& in, const std::string& out, size_t i = 0,
@ -665,7 +680,8 @@ OpKernelType OperatorWithKernel::GetExpectedKernelType(
OpKernelType OperatorWithKernel::GetKernelTypeForVar(
const std::string& var_name, const Tensor& tensor,
const OpKernelType& expected_kernel_type) const {
return OpKernelType(expected_kernel_type.data_type_, tensor.place());
return OpKernelType(expected_kernel_type.data_type_, tensor.place(),
tensor.layout());
}
} // namespace framework

28
paddle/fluid/framework/tensor.h
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@ -34,6 +34,28 @@ namespace framework {
class LoDTensor;
class Tensor {
#ifdef PADDLE_WITH_MKLDNN
public:
inline mkldnn::memory::format format() const { return format_; }
inline void set_format(const mkldnn::memory::format format) {
format_ = format;
}
protected:
/**
* @brief the detail format of memory block which have layout as kMKLDNN
*
* @note MKLDNN lib support various memory format like nchw, nhwc, nChw8C,
* nChw16c, etc. For a MKLDNN memory block, layout will be set as
* DataLayout::kMKLDNN meanwhile detail memory format will be kept in
* this field.
*/
mkldnn::memory::format format_ = mkldnn::memory::format::format_undef;
#endif
public:
template <typename T, size_t D, int MajorType, typename IndexType>
friend struct EigenTensor;
@ -195,8 +217,10 @@ class Tensor {
* N,C,H,W for respectively the batch size, the number of
* feature maps, the height.
*/
DataLayout layout_ = DataLayout::kNHWC;
// Fix me: here just change the default layout to kNCHW
// it doesn't fix the real issue, i.e. feeder should set up tensor layout
// according to actual input data
DataLayout layout_ = DataLayout::kNCHW;
/**
* @brief A PlaceHolder may be shared by more than one tensor.

2
paddle/fluid/framework/tensor_test.cc
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@ -209,7 +209,7 @@ TEST(Tensor, ReshapeToMatrix) {
TEST(Tensor, Layout) {
framework::Tensor src;
ASSERT_EQ(src.layout(), framework::DataLayout::kNHWC);
ASSERT_EQ(src.layout(), framework::DataLayout::kNCHW);
src.set_layout(framework::DataLayout::kAnyLayout);
ASSERT_EQ(src.layout(), framework::DataLayout::kAnyLayout);
}

4
paddle/fluid/operators/activation_op.cc
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@ -58,14 +58,16 @@ framework::OpKernelType GetKernelType(const framework::ExecutionContext& ctx,
const framework::OperatorWithKernel& oper,
const std::string& name) {
framework::LibraryType library{framework::LibraryType::kPlain};
framework::DataLayout layout = framework::DataLayout::kAnyLayout;
#ifdef PADDLE_WITH_MKLDNN
auto it = oper.Attrs().find("use_mkldnn");
if (library == framework::LibraryType::kPlain && it != oper.Attrs().end() &&
platform::CanMKLDNNBeUsed(ctx)) {
library = framework::LibraryType::kMKLDNN;
layout = framework::DataLayout::kMKLDNN;
}
#endif
framework::DataLayout layout = framework::DataLayout::kAnyLayout;
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>(name)->type()),
ctx.GetPlace(), layout, library);

13
paddle/fluid/operators/batch_norm_op.cc
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@ -111,14 +111,16 @@ class BatchNormOp : public framework::OperatorWithKernel {
"Variance input should be of float type");
framework::LibraryType library_{framework::LibraryType::kPlain};
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
framework::DataLayout layout = framework::DataLayout::kAnyLayout;
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
layout = framework::DataLayout::kMKLDNN;
}
#endif
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
framework::DataLayout layout = framework::DataLayout::kAnyLayout;
return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout,
library_);
}
@ -367,17 +369,18 @@ class BatchNormGradOp : public framework::OperatorWithKernel {
}
framework::LibraryType library_{framework::LibraryType::kPlain};
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
framework::DataLayout layout_ = framework::DataLayout::kAnyLayout;
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
}
#endif
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
framework::DataLayout layout = framework::DataLayout::kAnyLayout;
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
layout, library_);
layout_, library_);
}
};

17
paddle/fluid/operators/conv_op.cc
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@ -75,6 +75,11 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
framework::OpKernelType ConvOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
framework::LibraryType library{framework::LibraryType::kPlain};
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);
#ifdef PADDLE_WITH_CUDA
if (platform::CanCUDNNBeUsed(ctx)) {
library = framework::LibraryType::kCUDNN;
@ -84,6 +89,7 @@ framework::OpKernelType ConvOp::GetExpectedKernelType(
if (library == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library = framework::LibraryType::kMKLDNN;
layout = framework::DataLayout::kMKLDNN;
}
#endif
@ -99,9 +105,6 @@ framework::OpKernelType ConvOp::GetExpectedKernelType(
"float16 can only be used when CUDNN is used");
}
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(input_data_type, ctx.GetPlace(), layout,
library);
}
@ -309,6 +312,10 @@ void ConvOpGrad::InferShape(framework::InferShapeContext* ctx) const {
framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
framework::LibraryType library_{framework::LibraryType::kPlain};
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
#ifdef PADDLE_WITH_CUDA
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;
@ -318,12 +325,10 @@ framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::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(),
layout_, library_);

4
paddle/fluid/operators/fc_op.cc
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@ -43,7 +43,7 @@ void FCOp::InferShape(framework::InferShapeContext* ctx) const {
framework::OpKernelType FCOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
framework::LibraryType library{framework::LibraryType::kMKLDNN};
framework::DataLayout layout{framework::DataLayout::kAnyLayout};
framework::DataLayout layout{framework::DataLayout::kMKLDNN};
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
@ -65,7 +65,7 @@ void FCOpGrad::InferShape(framework::InferShapeContext* ctx) const {
framework::OpKernelType FCOpGrad::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
framework::LibraryType library{framework::LibraryType::kMKLDNN};
framework::DataLayout layout{framework::DataLayout::kAnyLayout};
framework::DataLayout layout{framework::DataLayout::kMKLDNN};
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),

7
paddle/fluid/operators/lrn_op.cc
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@ -124,16 +124,17 @@ namespace {
framework::OpKernelType GetExpectedLRNKernel(
const framework::ExecutionContext& ctx) {
framework::LibraryType library_{framework::LibraryType::kPlain};
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);
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::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>("X")->type()), ctx.GetPlace(),
layout_, library_);

11
paddle/fluid/operators/pool_mkldnn_op.cc
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@ -24,10 +24,13 @@ using mkldnn::pooling_backward;
// Generate keys for storing/retriving primitives for this operator
// TODO(jczaja): Make hashing function more optimial
static std::string gethash(memory::dims& input_dims, std::string& pooling_type,
std::vector<int>& ksize, std::vector<int>& strides,
std::vector<int>& paddings, std::string suffix) {
auto dims2str = [](memory::dims& operand_dims) {
static std::string gethash(const memory::dims& input_dims,
const std::string& pooling_type,
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::string& suffix) {
auto dims2str = [](const memory::dims& operand_dims) {
std::string dstr = "";
for (size_t i = 0; i < operand_dims.size(); ++i) {
dstr += std::to_string(operand_dims[i]) + "-";

12
paddle/fluid/operators/pool_op.cc
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@ -83,6 +83,9 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
framework::OpKernelType PoolOp::GetExpectedKernelType(
const framework::ExecutionContext &ctx) const {
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
#ifdef PADDLE_WITH_CUDA
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;
@ -92,11 +95,10 @@ framework::OpKernelType PoolOp::GetExpectedKernelType(
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
}
#endif
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
layout_, library_);
@ -112,6 +114,9 @@ void PoolOpGrad::InferShape(framework::InferShapeContext *ctx) const {
framework::OpKernelType PoolOpGrad::GetExpectedKernelType(
const framework::ExecutionContext &ctx) const {
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
#ifdef PADDLE_WITH_CUDA
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;
@ -121,6 +126,7 @@ framework::OpKernelType PoolOpGrad::GetExpectedKernelType(
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
}
#endif
@ -129,8 +135,6 @@ framework::OpKernelType PoolOpGrad::GetExpectedKernelType(
PADDLE_ENFORCE_EQ(library_, framework::LibraryType::kCUDNN,
"float16 can only be used when CUDNN is used");
}
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout_,
library_);
}

9
paddle/fluid/operators/softmax_op.cc
Unescape View File

@ -49,6 +49,9 @@ class SoftmaxOp : public framework::OperatorWithKernel {
const framework::ExecutionContext& ctx) const override {
// choose cudnn kernel if the runtime supported.
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
#ifdef PADDLE_WITH_CUDA
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;
@ -58,6 +61,7 @@ class SoftmaxOp : public framework::OperatorWithKernel {
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
}
#endif
@ -68,9 +72,7 @@ class SoftmaxOp : public framework::OperatorWithKernel {
"float16 can only be used on GPU place");
}
std::string data_format = ctx.Attr<std::string>("data_format");
return framework::OpKernelType(input_data_type, ctx.GetPlace(),
framework::StringToDataLayout(data_format),
return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout_,
library_);
}
};
@ -142,6 +144,7 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
const framework::ExecutionContext& ctx) const override {
// choose cudnn kernel if the runtime supported.
framework::LibraryType library_{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_CUDA
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;

13
paddle/fluid/platform/mkldnn_helper.h
Unescape View File

@ -16,6 +16,7 @@ limitations under the License. */
#include <mkldnn.h>
#include <vector>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/place.h"
namespace paddle {
namespace platform {
@ -86,5 +87,17 @@ inline mkldnn::memory::data_type MKLDNNGetDataType<float>() {
return mkldnn::memory::f32;
}
inline void Reorder(const mkldnn::memory& src, const mkldnn::memory& dst) {
auto reorder_prim = mkldnn::reorder(src, dst);
std::vector<mkldnn::primitive> pipeline;
pipeline.push_back(reorder_prim);
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
inline mkldnn::memory::format GetMKLDNNFormat(const mkldnn::memory memory) {
return static_cast<mkldnn::memory::format>(
memory.get_primitive_desc().desc().data.format);
}
} // namespace platform
} // namespace paddle

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