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Merge pull request #9123 from tpatejko/tpatejko/mkldnn-lrn
Implementation of MKLDNN LRN
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. | ||
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. */ | ||
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#include "paddle/fluid/framework/tensor.h" | ||
#include "paddle/fluid/operators/lrn_op.h" | ||
#include "paddle/fluid/platform/mkldnn_helper.h" | ||
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namespace paddle { | ||
namespace operators { | ||
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using paddle::framework::Tensor; | ||
using paddle::platform::MKLDNNDeviceContext; | ||
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namespace { | ||
template <typename T, typename... Args> | ||
std::shared_ptr<T> insert_to_context(const std::string& key, | ||
const MKLDNNDeviceContext& dev_ctx, | ||
Args&&... args) { | ||
auto p = std::static_pointer_cast<T, void>(dev_ctx.GetBlob(key)); | ||
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if (!p) { | ||
p = std::make_shared<T>(args...); | ||
dev_ctx.SetBlob(key, std::static_pointer_cast<void, T>(p)); | ||
} | ||
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return p; | ||
} | ||
} // namespace | ||
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template <typename T> | ||
class LRNMKLDNNOpKernel : public paddle::framework::OpKernel<T> { | ||
public: | ||
void Compute(const paddle::framework::ExecutionContext& ctx) const override { | ||
PADDLE_ENFORCE(std::is_same<T, float>::value, | ||
"MKLDNN LRN must use float data."); | ||
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()), | ||
"MKLDNN LRN must use CPUPlace."); | ||
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auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>(); | ||
const auto& mkldnn_engine = dev_ctx.GetEngine(); | ||
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auto x = ctx.Input<Tensor>("X"); | ||
auto out = ctx.Output<Tensor>("Out"); | ||
auto mid = ctx.Output<Tensor>("MidOut"); | ||
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auto input_data = x->data<T>(); | ||
auto output_data = out->mutable_data<T>(ctx.GetPlace()); | ||
mid->mutable_data<T>(ctx.GetPlace()); | ||
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const int n = ctx.Attr<int>("n"); | ||
const float alpha = ctx.Attr<float>("alpha"); | ||
const float beta = ctx.Attr<float>("beta"); | ||
const float k = ctx.Attr<float>("k"); | ||
const bool is_test = ctx.Attr<bool>("is_test"); | ||
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auto e_mid = framework::EigenTensor<T, 4>::From(*mid); | ||
e_mid = e_mid.constant(k); | ||
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auto dims = paddle::framework::vectorize2int(x->dims()); | ||
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auto src_md = paddle::platform::MKLDNNMemDesc( | ||
dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); | ||
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auto dst_md = paddle::platform::MKLDNNMemDesc( | ||
dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); | ||
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auto forward_desc = mkldnn::lrn_forward::desc{mkldnn::prop_kind::forward, | ||
mkldnn::lrn_across_channels, | ||
src_md, | ||
n, | ||
alpha, | ||
beta, | ||
k}; | ||
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auto src_memory_pd = mkldnn::memory::primitive_desc{src_md, mkldnn_engine}; | ||
auto dst_memory = mkldnn::memory{{dst_md, mkldnn_engine}, | ||
static_cast<void*>(output_data)}; | ||
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std::unique_ptr<mkldnn::lrn_forward> forward_op = nullptr; | ||
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if (!is_test) { | ||
const std::string key = ctx.op().Output("Out"); | ||
const std::string key_src_memory = key + "@lrn_src_memory"; | ||
const std::string key_pd = key + "@lrn_pd"; | ||
const std::string key_workspace_memory = key + "@lrn_workspace_memory"; | ||
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auto forward_pd = insert_to_context<mkldnn::lrn_forward::primitive_desc>( | ||
key_pd, dev_ctx, forward_desc, mkldnn_engine); | ||
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auto src_memory = insert_to_context<mkldnn::memory>( | ||
key_src_memory, dev_ctx, src_memory_pd); | ||
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src_memory->set_data_handle( | ||
static_cast<void*>(const_cast<T*>(input_data))); | ||
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auto workspace_memory = insert_to_context<mkldnn::memory>( | ||
key_workspace_memory, dev_ctx, | ||
forward_pd->workspace_primitive_desc()); | ||
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forward_op.reset(new mkldnn::lrn_forward{*forward_pd, *src_memory, | ||
*workspace_memory, dst_memory}); | ||
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} else { | ||
auto forward_pd = | ||
mkldnn::lrn_forward::primitive_desc{forward_desc, mkldnn_engine}; | ||
auto src_memory = mkldnn::memory{ | ||
src_memory_pd, static_cast<void*>(const_cast<T*>(input_data))}; | ||
auto workspace_memory = | ||
mkldnn::memory{forward_pd.workspace_primitive_desc()}; | ||
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forward_op.reset(new mkldnn::lrn_forward{forward_pd, src_memory, | ||
workspace_memory, dst_memory}); | ||
} | ||
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std::vector<mkldnn::primitive> pipeline = {*forward_op}; | ||
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); | ||
} | ||
}; | ||
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template <typename T> | ||
class LRNMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> { | ||
public: | ||
void Compute(const paddle::framework::ExecutionContext& ctx) const override { | ||
PADDLE_ENFORCE(std::is_same<T, float>::value, | ||
"MKLDNN LRN must use float data."); | ||
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()), | ||
"MKLDNN LRN must use CPUPlace."); | ||
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auto x = ctx.Input<Tensor>("X"); | ||
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auto out_grad = ctx.Input<Tensor>(framework::GradVarName("Out")); | ||
auto x_grad = ctx.Output<Tensor>(framework::GradVarName("X")); | ||
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const std::string key = ctx.op().Input("Out"); | ||
const std::string key_src_memory = key + "@lrn_src_memory"; | ||
const std::string key_pd = key + "@lrn_pd"; | ||
const std::string key_workspace_memory = key + "@lrn_workspace_memory"; | ||
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const int n = ctx.Attr<int>("n"); | ||
const float alpha = ctx.Attr<float>("alpha"); | ||
const float beta = ctx.Attr<float>("beta"); | ||
const float k = ctx.Attr<float>("k"); | ||
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auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>(); | ||
const auto& mkldnn_engine = dev_ctx.GetEngine(); | ||
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auto x_grad_data = x_grad->mutable_data<T>(ctx.GetPlace()); | ||
auto out_grad_data = out_grad->data<T>(); | ||
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auto dims = paddle::framework::vectorize2int(x->dims()); | ||
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auto src_md = paddle::platform::MKLDNNMemDesc( | ||
dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); | ||
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auto diff_src_md = paddle::platform::MKLDNNMemDesc( | ||
dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); | ||
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auto diff_dst_md = paddle::platform::MKLDNNMemDesc( | ||
dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); | ||
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auto diff_dst_memory = | ||
mkldnn::memory{{diff_dst_md, mkldnn_engine}, | ||
static_cast<void*>(const_cast<float*>(out_grad_data))}; | ||
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auto diff_src_memory = mkldnn::memory{{diff_src_md, mkldnn_engine}, | ||
static_cast<void*>(x_grad_data)}; | ||
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auto backward_desc = mkldnn::lrn_backward::desc{ | ||
mkldnn::lrn_across_channels, src_md, diff_src_md, n, alpha, beta, k}; | ||
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auto forward_pd = dev_ctx.GetBlob(key_pd); | ||
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auto backward_pd = mkldnn::lrn_backward::primitive_desc{ | ||
backward_desc, mkldnn_engine, | ||
*static_cast<mkldnn::lrn_forward::primitive_desc*>(forward_pd.get())}; | ||
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std::shared_ptr<void> workspace_memory = | ||
dev_ctx.GetBlob(key_workspace_memory); | ||
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auto src_memory = dev_ctx.GetBlob(key_src_memory); | ||
auto backward_op = mkldnn::lrn_backward{ | ||
backward_pd, *static_cast<mkldnn::memory*>(src_memory.get()), | ||
diff_dst_memory, *static_cast<mkldnn::memory*>(workspace_memory.get()), | ||
diff_src_memory}; | ||
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std::vector<mkldnn::primitive> pipeline = {backward_op}; | ||
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); | ||
} | ||
}; | ||
} // namespace operators | ||
} // namespace paddle | ||
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namespace ops = paddle::operators; | ||
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REGISTER_OP_KERNEL(lrn, MKLDNN, paddle::platform::CPUPlace, | ||
ops::LRNMKLDNNOpKernel<float>); | ||
REGISTER_OP_KERNEL(lrn_grad, MKLDNN, paddle::platform::CPUPlace, | ||
ops::LRNMKLDNNGradOpKernel<float>); |
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cool