Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into dev_refine_bilinear_interp
fengjiayi
7 years ago
commit
8567d0424d
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <string>
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#include "paddle/fluid/operators/mean_op.h"
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namespace paddle {
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namespace operators {
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using framework::DataLayout;
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template <typename T>
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class GaussianMKLDNNKernel : public paddle::framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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float mean = context.Attr<float>("mean");
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float std = context.Attr<float>("std");
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auto* tensor = context.Output<framework::Tensor>("Out");
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T* data = tensor->mutable_data<T>(context.GetPlace());
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unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
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std::minstd_rand engine;
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if (seed == 0) {
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seed = std::random_device()();
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}
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engine.seed(seed);
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std::normal_distribution<T> dist(mean, std);
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int64_t size = tensor->numel();
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for (int64_t i = 0; i < size; ++i) {
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data[i] = dist(engine);
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}
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// The format of output is set as the mkldnn's format
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// TODO(@mozga-intel) The format of matrix sets inside the another layers.
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tensor->set_layout(DataLayout::kMKLDNN);
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tensor->set_format(mkldnn::memory::format::oihw);
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_KERNEL(gaussian_random, MKLDNN, ::paddle::platform::CPUPlace,
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ops::GaussianMKLDNNKernel<float>);
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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/*Licensed under the Apache License, Version 2.0(the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "mkldnn.hpp"
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#include "paddle/fluid/framework/tensor.h"
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#include "paddle/fluid/operators/math/selected_rows_functor.h"
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#include "paddle/fluid/operators/sum_op.h"
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#include "paddle/fluid/platform/device_context.h"
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#include "paddle/fluid/platform/mkldnn_helper.h"
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namespace paddle {
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namespace operators {
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using paddle::framework::Tensor;
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using paddle::platform::MKLDNNDeviceContext;
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using paddle::platform::CPUDeviceContext;
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using framework::DataLayout;
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using mkldnn::memory;
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using mkldnn::primitive;
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using mkldnn::stream;
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using mkldnn::sum;
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using mkldnn::reorder;
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using platform::to_void_cast;
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template <typename T>
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class SumMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
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public:
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void Compute(const paddle::framework::ExecutionContext& ctx) const override {
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PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
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"It must use CPUPlace.");
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auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
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const auto& mkldnn_engine = dev_ctx.GetEngine();
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auto in_vars = ctx.MultiInputVar("X");
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const int N = in_vars.size();
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auto out_var = ctx.OutputVar("Out");
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bool in_place = out_var == in_vars[0];
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if (out_var->IsType<framework::LoDTensor>()) {
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LoDTensor* output = ctx.Output<LoDTensor>("Out");
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T* output_data = output->mutable_data<T>(ctx.GetPlace());
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std::vector<int> dst_tz = framework::vectorize2int(output->dims());
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auto src_tz = dst_tz;
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memory::format output_format{memory::format::format_undef};
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std::vector<float> scales;
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std::vector<memory::primitive_desc> srcs_mpd;
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std::vector<mkldnn::memory> srcs_mem;
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PADDLE_ENFORCE(in_vars[0]->IsType<LoDTensor>(),
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"Input[0] must be LoDTensors");
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auto& input0 = in_vars[0]->Get<LoDTensor>();
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PADDLE_ENFORCE(input0.layout() == DataLayout::kMKLDNN &&
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input0.format() != memory::format::format_undef,
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"Wrong layout/format for inputs[0]");
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memory::format input_format = input0.format();
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if (src_tz.size() == 1 && (input_format == memory::format::nchw ||
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input_format == memory::format::nhwc)) {
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input_format = memory::format::x;
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}
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if (src_tz.size() == 2 && (input_format == memory::format::nchw ||
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input_format == memory::format::nhwc)) {
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input_format = memory::format::nc;
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}
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for (int i = in_place ? 1 : 0; i < N; i++) {
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PADDLE_ENFORCE(in_vars[i]->IsType<LoDTensor>(),
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"all inputs must be all LoDTensors");
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auto& input = in_vars[i]->Get<LoDTensor>();
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PADDLE_ENFORCE(input.layout() == DataLayout::kMKLDNN &&
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input.format() != memory::format::format_undef,
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"Wrong layout/format for inputs");
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if (input.numel() == 0) {
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continue;
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}
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const T* input_data = input.data<T>();
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auto src_md =
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memory::desc(src_tz, memory::data_type::f32, input_format);
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auto src_mpd = memory::primitive_desc(src_md, mkldnn_engine);
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auto src_mem = memory(src_mpd, to_void_cast(input_data));
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srcs_mpd.push_back(src_mpd);
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srcs_mem.push_back(src_mem);
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scales.push_back(1.0);
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}
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auto dst_md =
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memory::desc(dst_tz, memory::data_type::f32, memory::format::any);
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auto sum_pd = sum::primitive_desc(dst_md, scales, srcs_mpd);
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std::shared_ptr<memory> dst_mem;
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if (in_place) {
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dst_mem.reset(new memory(sum_pd.dst_primitive_desc()));
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} else {
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dst_mem.reset(new memory(sum_pd.dst_primitive_desc(), output_data));
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}
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std::vector<mkldnn::primitive::at> inputs;
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for (size_t i = 0; i < srcs_mem.size(); ++i) {
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inputs.push_back(srcs_mem[i]);
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}
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auto sum_prim = mkldnn::sum(sum_pd, inputs, *dst_mem);
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output_format = (memory::format)platform::GetMKLDNNFormat(sum_pd);
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primitive reorder_prim;
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std::shared_ptr<memory> target_mem;
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if (in_place) {
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output_format = input_format;
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target_mem.reset(new memory(
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{{{src_tz}, memory::data_type::f32, output_format}, mkldnn_engine},
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output_data));
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reorder_prim = reorder(*dst_mem, *target_mem);
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}
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std::vector<primitive> pipeline;
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pipeline.push_back(sum_prim);
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if (in_place) pipeline.push_back(reorder_prim);
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stream(stream::kind::eager).submit(pipeline).wait();
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output->set_layout(DataLayout::kMKLDNN);
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output->set_format(output_format);
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} else if (out_var->IsType<framework::SelectedRows>()) {
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// TODO(@mozga-intel) Add MKLDNN SelectedRows support
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std::unique_ptr<framework::SelectedRows> in0;
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if (in_place) {
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// If is in_place, we store the input[0] to in0
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auto& in_sel0 = in_vars[0]->Get<SelectedRows>();
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auto& rows = in_sel0.rows();
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in0.reset(new framework::SelectedRows(rows, in_sel0.height()));
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in0->mutable_value()->ShareDataWith(in_sel0.value());
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}
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auto get_selected_row = [&](size_t i) -> const SelectedRows& {
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if (i == 0 && in0) {
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return *in0.get();
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} else {
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return in_vars[i]->Get<SelectedRows>();
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}
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};
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auto* out = ctx.Output<SelectedRows>("Out");
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out->mutable_rows()->clear();
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auto* out_value = out->mutable_value();
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// Runtime InferShape
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size_t first_dim = 0;
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for (int i = 0; i < N; i++) {
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auto& sel_row = get_selected_row(i);
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first_dim += sel_row.rows().size();
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}
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auto in_dim =
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framework::vectorize(get_selected_row(N - 1).value().dims());
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in_dim[0] = static_cast<int64_t>(first_dim);
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out_value->Resize(framework::make_ddim(in_dim));
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// if all the input sparse vars are empty, no need to
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// merge these vars.
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if (first_dim == 0UL) {
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return;
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}
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out_value->mutable_data<T>(ctx.GetPlace());
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math::SelectedRowsAddTo<CPUDeviceContext, T> functor;
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int64_t offset = 0;
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for (int i = 0; i < N; i++) {
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auto& sel_row = get_selected_row(i);
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if (sel_row.rows().size() == 0) {
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continue;
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}
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PADDLE_ENFORCE_EQ(out->height(), sel_row.height());
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functor(ctx.template device_context<CPUDeviceContext>(), sel_row,
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offset, out);
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offset += sel_row.value().numel();
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}
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} else if (out_var->IsType<framework::LoDTensorArray>()) {
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// TODO(@mozga-intel) Add MKLDNN LoDTensorArray support
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auto& out_array = *out_var->GetMutable<framework::LoDTensorArray>();
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for (size_t i = in_place ? 1 : 0; i < in_vars.size(); ++i) {
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PADDLE_ENFORCE(in_vars[i]->IsType<framework::LoDTensorArray>(),
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"Only support all inputs are TensorArray");
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auto& in_array = in_vars[i]->Get<framework::LoDTensorArray>();
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for (size_t i = 0; i < in_array.size(); ++i) {
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if (in_array[i].numel() != 0) {
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if (i >= out_array.size()) {
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out_array.resize(i + 1);
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}
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if (out_array[i].numel() == 0) {
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framework::TensorCopy(in_array[i], in_array[i].place(),
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ctx.device_context(), &out_array[i]);
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out_array[i].set_lod(in_array[i].lod());
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} else {
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PADDLE_ENFORCE(out_array[i].lod() == in_array[i].lod());
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auto in = EigenVector<T>::Flatten(in_array[i]);
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auto result = EigenVector<T>::Flatten(out_array[i]);
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result.device(*ctx.template device_context<MKLDNNDeviceContext>()
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.eigen_device()) = result + in;
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}
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}
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}
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}
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} else {
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PADDLE_THROW("Unexpected branch, output variable type is %s",
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out_var->Type().name());
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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REGISTER_OP_KERNEL(sum, MKLDNN, ::paddle::platform::CPUPlace,
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paddle::operators::SumMKLDNNOpKernel<float>);
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