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125 lines
4.7 KiB
125 lines
4.7 KiB
/* Copyright (c) 2018 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 "paddle/fluid/framework/data_layout_transform.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/memory/malloc.h"
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#include "paddle/fluid/platform/mkldnn_reuse.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using framework::DataLayout;
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template <typename T>
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class TransposeMKLDNNOpKernel : 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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const bool is_test = ctx.Attr<bool>("is_test");
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PADDLE_ENFORCE(
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is_test == true,
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"ConvTransposeMKLDNN works only for inference!. Set is_test = True");
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auto& dev_ctx =
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ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
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const auto& mkldnn_engine = dev_ctx.GetEngine();
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std::vector<int> axis = ctx.Attr<std::vector<int>>("axis");
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int ndims = axis.size();
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auto* input = ctx.Input<Tensor>("X");
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auto* output = ctx.Output<Tensor>("Out");
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const T* input_data = input->data<T>();
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if (ndims == 1) {
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output->ShareDataWith(*input);
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return;
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}
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std::vector<int> nchw_axis(ndims, 0);
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for (size_t i = 0; i < nchw_axis.size(); ++i) {
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nchw_axis[i] = i;
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}
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std::vector<int> nchw_tz = paddle::framework::vectorize2int(input->dims());
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std::string data_format = ctx.Attr<std::string>("data_format");
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auto src_md =
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input->format() != mkldnn::memory::format::nchw
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? platform::MKLDNNMemDesc(nchw_tz, platform::MKLDNNGetDataType<T>(),
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input->format())
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: Axis2MemoryDesc(nchw_tz, nchw_axis);
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this->TransposeKernel(ctx.GetPlace(), Axis2MemoryDesc(nchw_tz, axis),
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src_md, output, input_data, nchw_tz, mkldnn_engine);
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}
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protected:
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mkldnn::memory::desc Axis2MemoryDesc(std::vector<int>& nchw_tz,
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std::vector<int>& axis) const {
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mkldnn_memory_desc_t mem_fmt;
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mem_fmt.primitive_kind = mkldnn_memory;
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mem_fmt.ndims = axis.size();
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for (unsigned int i = 0; i < nchw_tz.size(); ++i) {
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mem_fmt.dims[i] = nchw_tz[i]; // logical dimensions (nchw format,
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// regardless physical layout)
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}
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mem_fmt.data_type = mkldnn_f32;
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mem_fmt.format = mkldnn_blocked;
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unsigned int total_stride = 1;
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for (int i = nchw_tz.size() - 1; i >= 0; --i) {
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mem_fmt.layout_desc.blocking.padding_dims[i] =
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nchw_tz[i]; // logical dimensions (nchw format, regardless physical
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// layout)
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mem_fmt.layout_desc.blocking.block_dims[i] = 1;
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mem_fmt.layout_desc.blocking.offset_padding_to_data[i] = 0; // no offset
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mem_fmt.layout_desc.blocking.strides[0][axis[i]] = total_stride;
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mem_fmt.layout_desc.blocking.strides[1][axis[i]] = 1;
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total_stride *= nchw_tz[axis[i]];
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}
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mem_fmt.layout_desc.blocking.offset_padding = 0; // no initial offset
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return mem_fmt;
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}
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void TransposeKernel(platform::Place place, mkldnn::memory::desc md_o,
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mkldnn::memory::desc md_i, Tensor* output,
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const T* data_i, std::vector<int>& nchw_dims,
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const mkldnn::engine& eng) const {
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// Make Memory primitive descriptors
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auto mpd_o = mkldnn::memory::primitive_desc(md_o, eng);
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auto mpd_i = mkldnn::memory::primitive_desc(md_i, eng);
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auto data_o = output->mutable_data<T>(
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place, paddle::memory::Allocator::kDefault, mpd_o.get_size());
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auto src = mkldnn::memory(mpd_i, (T*)(data_i));
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auto dst = mkldnn::memory(mpd_o, data_o);
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auto r = mkldnn::reorder(src, dst);
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mkldnn::stream(mkldnn::stream::kind::eager).submit({r}).wait();
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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(transpose2, MKLDNN, ::paddle::platform::CPUPlace,
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ops::TransposeMKLDNNOpKernel<float>);
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REGISTER_OP_KERNEL(transpose, MKLDNN, ::paddle::platform::CPUPlace,
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ops::TransposeMKLDNNOpKernel<float>);
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