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120 lines
4.2 KiB
120 lines
4.2 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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#pragma once
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#include <utility>
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/concat.h"
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#include "paddle/fluid/operators/strided_memcpy.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class ConcatKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto ins = ctx.MultiInput<framework::Tensor>("X");
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framework::Tensor* out = ctx.Output<framework::Tensor>("Out");
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int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
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auto place = ctx.GetPlace();
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out->mutable_data<T>(place);
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// Sometimes direct copies will be faster, this maybe need deeply analysis.
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if (axis == 0 && ins.size() < 10) {
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size_t output_offset = 0;
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for (auto* in : ins) {
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auto in_stride = framework::stride_numel(in->dims());
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auto out_stride = framework::stride_numel(out->dims());
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StridedNumelCopyWithAxis<T>(ctx.device_context(), axis,
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out->data<T>() + output_offset, out_stride,
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in->data<T>(), in_stride, in_stride[axis]);
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output_offset += in_stride[axis];
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}
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} else {
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std::vector<framework::Tensor> inputs(ins.size());
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for (size_t j = 0; j < ins.size(); ++j) {
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inputs[j] = *ins[j];
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}
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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paddle::operators::math::ConcatFunctor<DeviceContext, T> concat_functor;
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concat_functor(dev_ctx, inputs, static_cast<int>(axis), out);
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}
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}
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};
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template <typename DeviceContext, typename T>
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class ConcatGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* out_grad =
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ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto ins = ctx.MultiInput<framework::LoDTensor>("X");
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auto out_var_names = ctx.Outputs(framework::GradVarName("X"));
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auto outs =
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ctx.MultiOutput<framework::LoDTensor>(framework::GradVarName("X"));
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{
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auto dx = outs;
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auto x = ins;
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for (size_t i = 0; i < dx.size(); ++i) {
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if (dx[i] != nullptr) {
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dx[i]->set_lod(x[i]->lod());
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}
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}
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}
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int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
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// get output tensor that the name is not kEmptyVarName
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std::vector<framework::Tensor*> outputs;
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for (size_t j = 0; j < outs.size(); ++j) {
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if (out_var_names[j] != framework::kEmptyVarName) {
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outs[j]->mutable_data<T>(ctx.GetPlace());
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outputs.push_back(outs[j]);
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} else {
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outputs.push_back(nullptr);
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}
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}
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// Sometimes direct copies will be faster, this maybe need deeply analysis.
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if (axis == 0 && outs.size() < 10) {
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size_t input_offset = 0;
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const auto in_stride = framework::stride_numel(out_grad->dims());
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for (size_t i = 0; i < outs.size(); ++i) {
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auto out_stride = framework::stride_numel(ins[i]->dims());
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auto* out = outputs[i];
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if (out != nullptr) {
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StridedNumelCopyWithAxis<T>(
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ctx.device_context(), axis, out->data<T>(), out_stride,
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out_grad->data<T>() + input_offset, in_stride, out_stride[axis]);
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}
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input_offset += out_stride[axis];
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}
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} else {
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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paddle::operators::math::ConcatGradFunctor<DeviceContext, T>
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concat_grad_functor;
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concat_grad_functor(dev_ctx, *out_grad, ins, static_cast<int>(axis),
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&outputs);
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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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