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80 lines
3.0 KiB
80 lines
3.0 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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"TransposeMKLDNN 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_tz = paddle::framework::vectorize2int(input->dims());
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const std::string key = platform::TransposeMKLDNNHandler::GetHash(
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nchw_tz, axis, ctx.op().Output("Out"));
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platform::TransposeMKLDNNHandler handler(nchw_tz, axis, dev_ctx,
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mkldnn_engine, key);
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auto transpose_src_memory_p = handler.AcquireSrcMemory(
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input->format(), platform::to_void_cast<T>(input_data));
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auto transpose_dst_memory_p =
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handler.AcquireDstMemory(output, ctx.GetPlace());
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auto transpose_p = handler.AcquireTranspose(transpose_dst_memory_p,
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transpose_src_memory_p);
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std::vector<mkldnn::primitive> pipeline;
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pipeline.push_back(*transpose_p);
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mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).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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