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95 lines
3.8 KiB
95 lines
3.8 KiB
/* 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 <iostream>
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#include "mkldnn.hpp"
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#include "paddle/fluid/operators/softmax_op.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::MKLDNNMemDesc;
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using mkldnn::memory; // Note: paddle has also "memory" namespace
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using mkldnn::primitive;
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using mkldnn::softmax_forward;
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using mkldnn::prop_kind;
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using mkldnn::stream;
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template <typename T>
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class SoftmaxMKLDNNKernel : 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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auto mkldnn_engine = dev_ctx.GetEngine();
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const Tensor* input = ctx.Input<Tensor>("X");
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Tensor* output = ctx.Output<Tensor>("Out");
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PADDLE_ENFORCE(input->dims().size() == 2UL,
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"The input of softmax op must be a 2D matrix.");
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const T* input_data = input->data<T>();
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// allocate memory for output
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T* output_data = output->mutable_data<T>(ctx.GetPlace());
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std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
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std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
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// MKL-DNN does support softmax over selected axis. Having 2D Tensor,
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// we will make normalization after final eg. axis: 1
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PADDLE_ENFORCE(((src_tz[0] == dst_tz[0]) && (src_tz[1] == dst_tz[1])),
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"Softmax input and output dimensions should match");
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// Same memory descriptor to be used for input and output
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memory::dims softmax_tz = {src_tz[0], src_tz[1]};
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// Currently only supports NC data format
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// TODO(jczaja-intel): support more formats
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auto softmax_md =
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MKLDNNMemDesc({softmax_tz}, memory::f32, memory::format::nc);
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// Normalization is made after innermost dimension eg. C out of NC
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auto softmax_desc = softmax_forward::desc(prop_kind::forward_scoring,
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softmax_md, 1 /*dim: C*/);
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// create memory primitives
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auto softmax_src_memory =
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memory({softmax_md, mkldnn_engine},
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static_cast<void*>(const_cast<T*>(input_data)));
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auto softmax_dst_memory =
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memory({softmax_md, mkldnn_engine},
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static_cast<void*>(const_cast<T*>(output_data)));
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auto softmax_prim_desc =
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softmax_forward::primitive_desc(softmax_desc, mkldnn_engine);
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auto softmax = softmax_forward(softmax_prim_desc, softmax_src_memory,
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softmax_dst_memory);
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std::vector<primitive> pipeline{softmax};
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stream(stream::kind::eager).submit(pipeline).wait();
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const bool is_test = ctx.Attr<bool>("is_test");
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if (!is_test) {
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T threshold = exp(-64);
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for (size_t i = 0; i < dst_tz[0] * dst_tz[1]; ++i) {
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output_data[i] =
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output_data[i] < threshold ? threshold : output_data[i];
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}
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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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namespace ops = paddle::operators;
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REGISTER_OP_KERNEL(softmax, MKLDNN, ::paddle::platform::CPUPlace,
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ops::SoftmaxMKLDNNKernel<float>);
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