wehub-resource-sync
71 lines
2.2 KiB
C++
71 lines
2.2 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/funcs/stack_functor.h"
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#if defined(__NVCC__) || defined(__HIPCC__)
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#include <thrust/device_vector.h>
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#endif
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namespace phi {
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template <typename T, typename Context>
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void UnStackGradKernel(const Context &dev_ctx,
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const std::vector<const DenseTensor *> &x,
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int axis,
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DenseTensor *x_grad) {
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if (axis < 0) axis += (x[0]->dims().size() + 1);
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int64_t n = static_cast<int64_t>(x.size());
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auto *x_grad_data = dev_ctx.template Alloc<T>(x_grad);
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std::vector<const T *> x_datas(n);
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for (int64_t i = 0; i < n; i++) x_datas[i] = x[i]->data<T>();
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int64_t pre = 1;
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int64_t post = 1;
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auto &dim = x[0]->dims();
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for (auto i = 0; i < axis; ++i) pre *= dim[i];
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for (auto i = axis; i < dim.size(); ++i) post *= dim[i];
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#if defined(__NVCC__) || defined(__HIPCC__)
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int total_num = pre * n * post;
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thrust::device_vector<const T *> device_x_vec(x_datas);
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auto x_data_arr = device_x_vec.data().get();
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funcs::StackFunctorForRange(
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dev_ctx, x_data_arr, x_grad_data, total_num, n, post);
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// Wait() must be called because device_x_vec may be destructed before
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// kernel ends
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dev_ctx.Wait();
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#else
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auto x_data_arr = x_datas.data();
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size_t x_offset = 0;
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size_t y_offset = 0;
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for (int64_t i = 0; i < pre; i++) {
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for (int64_t j = 0; j < n; j++) {
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std::memcpy(
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x_grad_data + y_offset, x_data_arr[j] + x_offset, post * sizeof(T));
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y_offset += post;
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}
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x_offset += post;
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}
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#endif
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}
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} // namespace phi
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