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提交 4730a4be 编写于 作者: P pzelazko-intel 提交者： Tao Luo
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MKLDNN pool2d OP kernel added (#8879) 

* MKLDNN pool2d OP kernel added

* conv2d and pool2d MKLDNN kernels renamed

* MKLDNN conv2d kernel refactoring
上级 ccc54188
隐藏空白更改
内联 并排
Showing with 417 addition and 186 deletion
paddle/fluid/operators/conv_mkldnn_op.cc
浏览文件 @ 4730a4be
......@@ -12,58 +12,21 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "mkldnn.hpp"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/conv_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace paddle {
namespace operators {
using paddle::framework::Tensor;
using paddle::platform::MKLDNNDeviceContext;
using paddle::platform::MKLDNNMemDesc;
using mkldnn::memory; // Note: paddle has also "memory" namespace
using mkldnn::primitive;
using mkldnn::convolution_forward;
using mkldnn::convolution_backward_weights;
using mkldnn::convolution_backward_data;
using mkldnn::convolution_direct;
using mkldnn::prop_kind;
using mkldnn::padding_kind;
using mkldnn::stream;
namespace {
std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
const memory::desc& dst, const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::engine& engine);
convolution_backward_weights::primitive_desc ConvBwdWeightsPrimitiveDesc(
const memory::desc& src, const memory::desc& diff_weights,
const memory::desc& diff_dst, const std::vector<int>& strides,
const std::vector<int>& paddings,
const convolution_forward::primitive_desc& conv_pd,
const mkldnn::engine& engine);
convolution_backward_data::primitive_desc ConvBwdDataPrimitiveDesc(
const memory::desc& diff_src, const memory::desc& weights,
const memory::desc& diff_dst, const std::vector<int>& strides,
const std::vector<int>& paddings,
const convolution_forward::primitive_desc& conv_pd,
const mkldnn::engine& engine);
} // anonymous namespace
template <typename T>
class ConvOpMkldnnKernel : public paddle::framework::OpKernel<T> {
class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
auto& dev_ctx =
ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
auto* input = ctx.Input<Tensor>("Input");
......@@ -88,7 +51,6 @@ class ConvOpMkldnnKernel : public paddle::framework::OpKernel<T> {
const T* input_data = input->data<T>();
const T* filter_data = filter->data<T>();
// allocate memory for output
T* output_data = output->mutable_data<T>(ctx.GetPlace());
PADDLE_ENFORCE(input->dims().size() == 4,
......@@ -102,48 +64,69 @@ class ConvOpMkldnnKernel : public paddle::framework::OpKernel<T> {
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
// TODO(pzelazko-intel): support more formats
// memory descriptors for convolution src/weight/dst
auto conv_src_md =
MKLDNNMemDesc(src_tz, memory::data_type::f32, memory::format::nchw);
auto conv_weights_md =
MKLDNNMemDesc(weights_tz, memory::data_type::f32, memory::format::oihw);
auto conv_dst_md =
MKLDNNMemDesc(dst_tz, memory::data_type::f32, memory::format::nchw);
// create memory primitives
auto conv_src_memory =
memory({conv_src_md, mkldnn_engine}, (void*)input_data);
auto conv_weights_memory =
memory({conv_weights_md, mkldnn_engine}, (void*)filter_data);
auto conv_dst_memory = memory({conv_dst_md, mkldnn_engine}, output_data);
std::unique_ptr<convolution_forward::primitive_desc> conv_pd =
ConvFwdPrimitiveDesc(conv_src_md, conv_weights_md, conv_dst_md, strides,
paddings, mkldnn_engine);
// save p_conv_pd into dev_ctx to be referred in backward path
auto p_conv_pd = conv_pd.get();
std::shared_ptr<void> conv_pd_value = std::move(conv_pd);
dev_ctx.SetBlob(key_conv_pd, conv_pd_value);
auto src_md = platform::MKLDNNMemDesc(
src_tz, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw);
auto weights_md =
platform::MKLDNNMemDesc(weights_tz, mkldnn::memory::data_type::f32,
mkldnn::memory::format::oihw);
auto dst_md = platform::MKLDNNMemDesc(
dst_tz, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw);
auto src_memory =
mkldnn::memory({src_md, mkldnn_engine}, (void*)input_data);
auto weights_memory =
mkldnn::memory({weights_md, mkldnn_engine}, (void*)filter_data);
auto dst_memory = mkldnn::memory({dst_md, mkldnn_engine}, output_data);
std::shared_ptr<mkldnn::convolution_forward::primitive_desc> conv_pd =
ConvFwdPrimitiveDesc(src_md, weights_md, dst_md, strides, paddings,
mkldnn_engine);
// save conv_pd into global device context to be referred in backward path
dev_ctx.SetBlob(key_conv_pd, conv_pd);
// create convolution op primitive
auto conv_prim = convolution_forward(*p_conv_pd, conv_src_memory,
conv_weights_memory, conv_dst_memory);
auto conv_prim = mkldnn::convolution_forward(*conv_pd, src_memory,
weights_memory, dst_memory);
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline{conv_prim};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
// push op to stream and wait MKLDNN until it's executed
std::vector<primitive> pipeline{conv_prim};
stream(stream::kind::eager).submit(pipeline).wait();
private:
std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
ConvFwdPrimitiveDesc(const mkldnn::memory::desc& src,
const mkldnn::memory::desc& weights,
const mkldnn::memory::desc& dst,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::engine& engine) const {
mkldnn::memory::dims stride_dims = {strides[0], strides[1]};
mkldnn::memory::dims padding_dims = {paddings[0], paddings[1]};
auto conv_desc = mkldnn::convolution_forward::desc(
mkldnn::prop_kind::forward, mkldnn::convolution_direct, src, weights,
dst, stride_dims, padding_dims, padding_dims,
mkldnn::padding_kind::zero);
auto p_conv_pd =
new mkldnn::convolution_forward::primitive_desc(conv_desc, engine);
return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
p_conv_pd);
}
};
template <typename T>
class ConvGradOpMkldnnKernel : public paddle::framework::OpKernel<T> {
class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
const Tensor* input = ctx.Input<Tensor>("Input");
......@@ -170,7 +153,6 @@ class ConvGradOpMkldnnKernel : public paddle::framework::OpKernel<T> {
T* input_grad_data = nullptr;
T* filter_grad_data = nullptr;
// allocate memory for gradient of input/filter
if (input_grad) {
input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
}
......@@ -184,130 +166,111 @@ class ConvGradOpMkldnnKernel : public paddle::framework::OpKernel<T> {
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
// TODO(pzelazko-intel): support more formats
auto conv_src_md =
MKLDNNMemDesc(src_tz, memory::data_type::f32, memory::format::nchw);
auto conv_diff_src_md =
MKLDNNMemDesc(src_tz, memory::data_type::f32, memory::format::nchw);
auto conv_weights_md =
MKLDNNMemDesc(weights_tz, memory::data_type::f32, memory::format::oihw);
auto conv_diff_weights_md =
MKLDNNMemDesc(weights_tz, memory::data_type::f32, memory::format::oihw);
auto conv_diff_dst_md =
MKLDNNMemDesc(dst_tz, memory::data_type::f32, memory::format::nchw);
auto src_md = platform::MKLDNNMemDesc(
src_tz, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw);
auto diff_src_md = platform::MKLDNNMemDesc(
src_tz, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw);
auto weights_md =
platform::MKLDNNMemDesc(weights_tz, mkldnn::memory::data_type::f32,
mkldnn::memory::format::oihw);
auto diff_weights_md =
platform::MKLDNNMemDesc(weights_tz, mkldnn::memory::data_type::f32,
mkldnn::memory::format::oihw);
auto diff_dst_md = platform::MKLDNNMemDesc(
dst_tz, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw);
// create memory
auto conv_diff_dst_memory =
memory({conv_diff_weights_md, mkldnn_engine}, (void*)output_grad_data);
auto diff_dst_memory = mkldnn::memory({diff_weights_md, mkldnn_engine},
(void*)output_grad_data);
// Retrieve conv_pd from device context
std::shared_ptr<void> conv_pd;
convolution_forward::primitive_desc* p_conv_pd;
conv_pd = dev_ctx.GetBlob(key_conv_pd);
auto conv_pd =
std::static_pointer_cast<mkldnn::convolution_forward::primitive_desc>(
dev_ctx.GetBlob(key_conv_pd));
PADDLE_ENFORCE(conv_pd != nullptr,
"Fail to find conv_pd in device context");
p_conv_pd =
static_cast<convolution_forward::primitive_desc*>(conv_pd.get());
// create backward conv primitive for weights
if (filter_grad) {
// create primitive descriptor
convolution_backward_weights::primitive_desc conv_bwd_weights_pd =
ConvBwdWeightsPrimitiveDesc(conv_src_md, conv_diff_weights_md,
conv_diff_dst_md, strides, paddings,
*p_conv_pd, mkldnn_engine);
mkldnn::convolution_backward_weights::primitive_desc conv_bwd_weights_pd =
ConvBwdWeightsPrimitiveDesc(src_md, diff_weights_md, diff_dst_md,
strides, paddings, *conv_pd,
mkldnn_engine);
// create memory
auto conv_diff_weights_memory = memory(
{conv_diff_weights_md, mkldnn_engine}, (void*)filter_grad_data);
auto conv_src_memory =
memory({conv_src_md, mkldnn_engine}, (void*)input_data);
auto diff_weights_memory = mkldnn::memory(
{diff_weights_md, mkldnn_engine}, (void*)filter_grad_data);
auto src_memory =
mkldnn::memory({src_md, mkldnn_engine}, (void*)input_data);
// create backward conv primitive for weights
auto conv_bwd_weights_prim = convolution_backward_weights(
conv_bwd_weights_pd, conv_src_memory, conv_diff_dst_memory,
conv_diff_weights_memory);
auto conv_bwd_weights_prim = mkldnn::convolution_backward_weights(
conv_bwd_weights_pd, src_memory, diff_dst_memory,
diff_weights_memory);
// push primitive and execute it
std::vector<primitive> pipeline{conv_bwd_weights_prim};
stream(stream::kind::eager).submit(pipeline).wait();
std::vector<mkldnn::primitive> pipeline{conv_bwd_weights_prim};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
if (input_grad) {
// create primitive descriptor
convolution_backward_data::primitive_desc conv_bwd_data_pd =
ConvBwdDataPrimitiveDesc(conv_diff_src_md, conv_weights_md,
conv_diff_dst_md, strides, paddings,
*p_conv_pd, mkldnn_engine);
mkldnn::convolution_backward_data::primitive_desc conv_bwd_data_pd =
ConvBwdDataPrimitiveDesc(diff_src_md, weights_md, diff_dst_md,
strides, paddings, *conv_pd, mkldnn_engine);
// create memory
auto conv_diff_src_memory =
memory({conv_diff_src_md, mkldnn_engine}, (void*)input_grad_data);
auto conv_weights_memory =
memory({conv_weights_md, mkldnn_engine}, (void*)filter_data);
auto diff_src_memory =
mkldnn::memory({diff_src_md, mkldnn_engine}, (void*)input_grad_data);
auto weights_memory =
mkldnn::memory({weights_md, mkldnn_engine}, (void*)filter_data);
// create backward conv primitive for data
auto conv_bwd_data_prim =
convolution_backward_data(conv_bwd_data_pd, conv_diff_dst_memory,
conv_weights_memory, conv_diff_src_memory);
auto conv_bwd_data_prim = mkldnn::convolution_backward_data(
conv_bwd_data_pd, diff_dst_memory, weights_memory, diff_src_memory);
// push primitive and execute it
std::vector<primitive> pipeline{conv_bwd_data_prim};
stream(stream::kind::eager).submit(pipeline).wait();
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline{conv_bwd_data_prim};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
} // Compute()
private:
mkldnn::convolution_backward_weights::primitive_desc
ConvBwdWeightsPrimitiveDesc(
const mkldnn::memory::desc& src, const mkldnn::memory::desc& diff_weights,
const mkldnn::memory::desc& diff_dst, const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::convolution_forward::primitive_desc& conv_pd,
const mkldnn::engine& engine) const {
auto conv_bwd_weights_desc = mkldnn::convolution_backward_weights::desc(
mkldnn::convolution_direct, src, diff_weights, diff_dst, strides,
paddings, paddings, mkldnn::padding_kind::zero);
return mkldnn::convolution_backward_weights::primitive_desc(
conv_bwd_weights_desc, engine, conv_pd);
}
mkldnn::convolution_backward_data::primitive_desc ConvBwdDataPrimitiveDesc(
const mkldnn::memory::desc& diff_src, const mkldnn::memory::desc& weights,
const mkldnn::memory::desc& diff_dst, const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::convolution_forward::primitive_desc& conv_pd,
const mkldnn::engine& engine) const {
auto conv_bwd_data_desc = mkldnn::convolution_backward_data::desc(
mkldnn::convolution_direct, diff_src, weights, diff_dst, strides,
paddings, paddings, mkldnn::padding_kind::zero);
return mkldnn::convolution_backward_data::primitive_desc(conv_bwd_data_desc,
engine, conv_pd);
}
};
namespace {
std::unique_ptr<convolution_forward::primitive_desc> ConvFwdPrimitiveDesc(
const memory::desc& src, const memory::desc& weights,
const memory::desc& dst, const std::vector<int>& strides,
const std::vector<int>& paddings, const mkldnn::engine& engine) {
mkldnn::memory::dims stride_dims = {strides[0], strides[1]};
mkldnn::memory::dims padding_dims = {paddings[0], paddings[1]};
auto conv_desc = mkldnn::convolution_forward::desc(
mkldnn::prop_kind::forward, mkldnn::convolution_direct, src, weights, dst,
stride_dims, padding_dims, padding_dims, mkldnn::padding_kind::zero);
auto p_conv_pd = new convolution_forward::primitive_desc(conv_desc, engine);
return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
p_conv_pd);
}
convolution_backward_weights::primitive_desc ConvBwdWeightsPrimitiveDesc(
const memory::desc& src, const memory::desc& diff_weights,
const memory::desc& diff_dst, const std::vector<int>& strides,
const std::vector<int>& paddings,
const convolution_forward::primitive_desc& conv_pd,
const mkldnn::engine& engine) {
auto conv_bwd_weights_desc = convolution_backward_weights::desc(
convolution_direct, src, diff_weights, diff_dst, strides, paddings,
paddings, padding_kind::zero);
return convolution_backward_weights::primitive_desc(conv_bwd_weights_desc,
engine, conv_pd);
}
convolution_backward_data::primitive_desc ConvBwdDataPrimitiveDesc(
const memory::desc& diff_src, const memory::desc& weights,
const memory::desc& diff_dst, const std::vector<int>& strides,
const std::vector<int>& paddings,
const convolution_forward::primitive_desc& conv_pd,
const mkldnn::engine& engine) {
auto conv_bwd_data_desc = convolution_backward_data::desc(
convolution_direct, diff_src, weights, diff_dst, strides, paddings,
paddings, padding_kind::zero);
return convolution_backward_data::primitive_desc(conv_bwd_data_desc, engine,
conv_pd);
}
} // anonymous namespace
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(conv2d, MKLDNN, ::paddle::platform::CPUPlace,
ops::ConvOpMkldnnKernel<float>);
ops::ConvMKLDNNOpKernel<float>);
REGISTER_OP_KERNEL(conv2d_grad, MKLDNN, ::paddle::platform::CPUPlace,
ops::ConvGradOpMkldnnKernel<float>);
ops::ConvMKLDNNGradOpKernel<float>);
paddle/fluid/operators/pool_mkldnn_op.cc 0 → 100644
浏览文件 @ 4730a4be
/* Copyright (c) 2018 PaddlePaddle 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. */
#include "paddle/fluid/operators/pool_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace paddle {
namespace operators {
template <typename T>
class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
const Tensor* input = ctx.Input<Tensor>("X");
Tensor* output = ctx.Output<Tensor>("Out");
// Get an unique name from "argument" name of "Out" variable
// This name will be used as key when saving info into device context
const std::string key = ctx.op().Output("Out");
const std::string key_pool_pd = key + "@pool_pd";
const std::string key_pool_workspace_memory =
key + "@pool_workspace_memory";
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
if (ctx.Attr<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(input->dims()[i + 2]);
}
}
// Only 2D pooling is supported now
PADDLE_ENFORCE(ksize.size() == 2, "ksize must be 2D, i.e. 2D pooling");
PADDLE_ENFORCE(pooling_type == "max" || pooling_type == "avg",
"pooling_type must be 'max' or 'avg'");
PADDLE_ENFORCE(input->dims().size() == 4,
"Input dim must be with 4, i.e. NCHW");
const T* input_data = input->data<T>();
T* output_data = output->mutable_data<T>(ctx.GetPlace());
std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
// TODO(pzelazko-intel): support more formats
auto src_md = platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
auto dst_md = platform::MKLDNNMemDesc(dst_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
std::shared_ptr<mkldnn::pooling_forward::primitive_desc> pool_pd =
CreatePrimitiveDesc(src_md, dst_md, strides, paddings, ksize,
pooling_type, mkldnn_engine);
// save pool_pd into global device context to be referred in backward path
dev_ctx.SetBlob(key_pool_pd, pool_pd);
std::shared_ptr<mkldnn::memory> workspace_memory =
CreateWorkspaceMemory(pool_pd, pooling_type, mkldnn_engine);
// save pool_workspace_memory to be referred in backward path
dev_ctx.SetBlob(key_pool_workspace_memory, workspace_memory);
auto src_memory =
mkldnn::memory({src_md, mkldnn_engine}, (void*)input_data);
auto dst_memory =
mkldnn::memory({dst_md, mkldnn_engine}, (void*)output_data);
auto pool_prim = mkldnn::pooling_forward(*pool_pd, src_memory, dst_memory,
*workspace_memory);
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline{pool_prim};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
private:
std::unique_ptr<mkldnn::pooling_forward::primitive_desc> CreatePrimitiveDesc(
const mkldnn::memory::desc& src, const mkldnn::memory::desc& dst,
const std::vector<int>& stride, const std::vector<int>& padding,
const std::vector<int>& kernel, const std::string& pooling_type,
const mkldnn::engine& engine) const {
auto pool_desc = mkldnn::pooling_forward::desc(
mkldnn::prop_kind::forward,
pooling_type == "max" ? mkldnn::algorithm::pooling_max
: mkldnn::algorithm::pooling_avg,
src, dst, stride, kernel, padding, padding, mkldnn::padding_kind::zero);
auto p_pool_pd =
new mkldnn::pooling_forward::primitive_desc(pool_desc, engine);
return std::unique_ptr<mkldnn::pooling_forward::primitive_desc>(p_pool_pd);
}
std::unique_ptr<mkldnn::memory> CreateWorkspaceMemory(
std::shared_ptr<mkldnn::pooling_forward::primitive_desc> pool_pd,
const std::string& pooling_type, const mkldnn::engine& engine) const {
mkldnn::memory::primitive_desc workspace_md =
pooling_type == "max"
? pool_pd->workspace_primitive_desc()
: mkldnn::memory::primitive_desc(
{{}, mkldnn::memory::f32, mkldnn::memory::format::nchw},
engine);
auto p_workspace_memory = new mkldnn::memory(workspace_md);
return std::unique_ptr<mkldnn::memory>(p_workspace_memory);
}
};
template <typename T>
class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
const Tensor* in_x = ctx.Input<Tensor>("X");
const Tensor* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
Tensor* in_x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
// Get an unique name from "argument" name of "Out" variable
// This name will be used as key when referring info from device context
const std::string key = ctx.op().Input("Out");
const std::string key_pool_pd = key + "@pool_pd";
const std::string key_pool_workspace_memory =
key + "@pool_workspace_memory";
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
if (ctx.Attr<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
}
}
auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const mkldnn::engine& mkldnn_engine = dev_ctx.GetEngine();
const T* out_grad_data = out_grad->data<T>();
T* in_x_grad_data = in_x_grad->mutable_data<T>(ctx.GetPlace());
std::vector<int> diff_src_tz =
paddle::framework::vectorize2int(in_x_grad->dims());
std::vector<int> diff_dst_tz =
paddle::framework::vectorize2int(out_grad->dims());
auto diff_src_md = platform::MKLDNNMemDesc(diff_src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
auto diff_dst_md = platform::MKLDNNMemDesc(diff_dst_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
// Retrieve pool_pd/pool_workspace_memory from device context
auto pool_pd =
std::static_pointer_cast<mkldnn::pooling_forward::primitive_desc>(
dev_ctx.GetBlob(key_pool_pd));
PADDLE_ENFORCE(pool_pd != nullptr,
"Fail to find pool_pd in device context");
auto workspace_memory = std::static_pointer_cast<mkldnn::memory>(
dev_ctx.GetBlob(key_pool_workspace_memory));
PADDLE_ENFORCE(workspace_memory != nullptr,
"Fail to find workspace_memory in device context");
auto pool_bwd_desc = mkldnn::pooling_backward::desc(
pooling_type == "max" ? mkldnn::algorithm::pooling_max
: mkldnn::algorithm::pooling_avg,
diff_src_md, diff_dst_md, strides, ksize, paddings, paddings,
mkldnn::padding_kind::zero);
auto pool_bwd_pd = mkldnn::pooling_backward::primitive_desc(
pool_bwd_desc, mkldnn_engine, *pool_pd);
auto diff_src_memory =
mkldnn::memory({diff_src_md, mkldnn_engine}, (void*)in_x_grad_data);
auto diff_dst_memory =
mkldnn::memory({diff_dst_md, mkldnn_engine}, (void*)out_grad_data);
auto bwd_prim = mkldnn::pooling_backward(
pool_bwd_pd, diff_dst_memory, *workspace_memory, diff_src_memory);
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline{bwd_prim};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
} // Compute()
};
} // namespace operators
} // namespace paddle
REGISTER_OP_KERNEL(pool2d, MKLDNN, ::paddle::platform::CPUPlace,
paddle::operators::PoolMKLDNNOpKernel<float>);
REGISTER_OP_KERNEL(pool2d_grad, MKLDNN, ::paddle::platform::CPUPlace,
paddle::operators::PoolMKLDNNGradOpKernel<float>);
paddle/fluid/operators/pool_op.cc
浏览文件 @ 4730a4be
......@@ -13,6 +13,12 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/pool_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 {
......@@ -76,20 +82,18 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
framework::OpKernelType PoolOp::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");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
......@@ -107,20 +111,18 @@ void PoolOpGrad::InferShape(framework::InferShapeContext *ctx) const {
framework::OpKernelType PoolOpGrad::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");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
......@@ -181,6 +183,9 @@ Pool2dOpMaker::Pool2dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
"output height and width. False is the default. If it is set to False, "
"the floor function will be used.")
.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 "
......@@ -276,6 +281,9 @@ Pool3dOpMaker::Pool3dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
"output height and width. False is the default. If it is set to False, "
"the floor function will be used.")
.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 "
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 4730a4be
......@@ -1406,6 +1406,7 @@ def pool2d(input,
global_pooling=False,
use_cudnn=True,
ceil_mode=False,
use_mkldnn=False,
name=None):
"""
This function adds the operator for pooling in 2 dimensions, using the
......@@ -1443,7 +1444,8 @@ def pool2d(input,
"strides": pool_stride,
"paddings": pool_padding,
"use_cudnn": use_cudnn,
"ceil_mode": ceil_mode
"ceil_mode": ceil_mode,
"use_mkldnn": use_mkldnn
})
return pool_out
......
python/paddle/fluid/nets.py
浏览文件 @ 4730a4be
......@@ -45,7 +45,8 @@ def simple_img_conv_pool(input,
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
use_cudnn=use_cudnn)
use_cudnn=use_cudnn,
use_mkldnn=use_mkldnn)
return pool_out
......@@ -107,7 +108,8 @@ def img_conv_group(input,
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
use_cudnn=use_cudnn)
use_cudnn=use_cudnn,
use_mkldnn=use_mkldnn)
return pool_out
......
python/paddle/fluid/tests/unittests/test_pool2d_op.py
浏览文件 @ 4730a4be
......@@ -79,6 +79,7 @@ def avg_pool2D_forward_naive(x,
class TestPool2d_Op(OpTest):
def setUp(self):
self.use_cudnn = False
self.use_mkldnn = False
self.init_test_case()
self.init_global_pool()
self.init_op_type()
......@@ -99,6 +100,7 @@ class TestPool2d_Op(OpTest):
'pooling_type': self.pool_type,
'global_pooling': self.global_pool,
'use_cudnn': self.use_cudnn,
'use_mkldnn': self.use_mkldnn,
'ceil_mode': self.ceil_mode,
'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter
}
......@@ -260,5 +262,42 @@ class TestCeilModeCase4(TestCase2):
self.ceil_mode = True
#--------------------test pool2d MKLDNN--------------------
class TestMKLDNNCase1(TestPool2d_Op):
def init_op_type(self):
self.use_mkldnn = True
self.op_type = "pool2d"
class TestMKLDNNCase2(TestCase1):
def init_op_type(self):
self.use_mkldnn = True
self.op_type = "pool2d"
class TestMKLDNNCase3(TestCase2):
def init_op_type(self):
self.use_mkldnn = True
self.op_type = "pool2d"
class TestMKLDNNCase4(TestCase3):
def init_op_type(self):
self.use_mkldnn = True
self.op_type = "pool2d"
class TestMKLDNNCase5(TestCase4):
def init_op_type(self):
self.use_mkldnn = True
self.op_type = "pool2d"
class TestMKLDNNCase6(TestCase5):
def init_op_type(self):
self.use_mkldnn = True
self.op_type = "pool2d"
if __name__ == '__main__':
unittest.main()
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