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140 lines
4.3 KiB
140 lines
4.3 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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 "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename Place, typename T>
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void NaiveCpuTranspose(const framework::ExecutionContext& context,
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const framework::Tensor& in, framework::Tensor& out,
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std::vector<int> axis) {
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auto in_data = in.data<T>();
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auto out_data = out.mutable_data<T>(context.GetPlace());
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auto in_dim = in.dims();
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auto out_dim = out.dims();
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size_t ndims = in_dim.size();
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std::vector<int> in_offset(ndims, 1);
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std::vector<int> out_offset(ndims, 1);
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for (int i = ndims - 2; i >= 0; i--) {
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in_offset[i] = in_offset[i + 1] * in_dim[i + 1];
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out_offset[i] = out_offset[i + 1] * out_dim[i + 1];
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}
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size_t data_size = product(in_dim);
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for (size_t to_index = 0; to_index < data_size; to_index++) {
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int from_index = 0;
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int temp = to_index;
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for (size_t i = 0; i < ndims; i++) {
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from_index += (temp / out_offset[i]) * in_offset[axis[i]];
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temp = temp % out_offset[i];
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}
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out_data[to_index] = in_data[from_index];
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}
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}
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template <typename Place, typename T, int Dims>
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void DoTranspose(const framework::ExecutionContext& context,
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const framework::Tensor& in, framework::Tensor& out,
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std::vector<int> axis) {
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Eigen::array<int, Dims> permute;
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for (int i = 0; i < Dims; i++) {
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permute[i] = axis[i];
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}
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auto in_dim = in.dims();
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auto out_dim = out.dims();
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auto eigen_in = framework::EigenTensor<T, Dims>::From(in);
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auto eigen_out = framework::EigenTensor<T, Dims>::From(out);
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auto& dev = context.GetEigenDevice<Place>();
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eigen_out.device(dev) = eigen_in.shuffle(permute);
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}
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template <typename Place, typename T>
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class TransposeKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* in = context.Input<framework::Tensor>("X");
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auto* out = context.Output<framework::Tensor>("Out");
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out->mutable_data<T>(context.GetPlace());
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auto axis = context.Attr<std::vector<int>>("axis");
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int ndims = axis.size();
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switch (ndims) {
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case 2:
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DoTranspose<Place, T, 2>(context, *in, *out, axis);
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break;
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case 3:
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DoTranspose<Place, T, 3>(context, *in, *out, axis);
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break;
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case 4:
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DoTranspose<Place, T, 4>(context, *in, *out, axis);
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break;
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case 5:
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DoTranspose<Place, T, 5>(context, *in, *out, axis);
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break;
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default:
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NaiveCpuTranspose<Place, T>(context, *in, *out, axis);
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break;
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}
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}
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};
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template <typename Place, typename T>
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class TransposeGradKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* in = context.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* out = context.Output<framework::Tensor>(framework::GradVarName("X"));
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out->mutable_data<T>(context.GetPlace());
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auto axis_temp = context.Attr<std::vector<int>>("axis");
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std::vector<int> axis(axis_temp);
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for (size_t i = 0; i < axis.size(); i++) {
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axis[axis_temp[i]] = i;
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}
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int ndims = axis.size();
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switch (ndims) {
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case 2:
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DoTranspose<Place, T, 2>(context, *in, *out, axis);
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break;
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case 3:
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DoTranspose<Place, T, 3>(context, *in, *out, axis);
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break;
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case 4:
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DoTranspose<Place, T, 4>(context, *in, *out, axis);
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break;
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case 5:
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DoTranspose<Place, T, 5>(context, *in, *out, axis);
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break;
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default:
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NaiveCpuTranspose<Place, T>(context, *in, *out, axis);
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break;
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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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