wehub-resource-sync
77 lines
2.3 KiB
C++
77 lines
2.3 KiB
C++
/* Copyright (c) 2022 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/phi/kernels/stack_grad_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/stack_functor.h"
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namespace phi {
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template <typename T, typename Context>
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void StackGradKernel(const Context& dev_ctx,
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const DenseTensor& out,
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int axis,
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std::vector<DenseTensor*> x_grad) {
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if (axis < 0) axis += out.dims().size();
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int n = static_cast<int>(out.dims()[axis]);
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std::vector<T*> dx_datas(n); // NOLINT
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for (int i = 0; i < n; i++) {
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if (x_grad[i] == nullptr) {
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dx_datas[i] = nullptr;
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} else {
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dx_datas[i] = dev_ctx.template Alloc<T>(x_grad[i]);
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}
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}
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auto dy_data = out.data<T>();
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// zero sized tensor case
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if (out.numel() == 0) {
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for (int i = 0; i < n; i++) {
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auto x_grad_dim = x_grad[i]->dims();
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x_grad[i]->Resize(x_grad_dim);
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}
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return;
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}
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int pre = 1;
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for (int i = 0; i < axis; ++i) pre *= static_cast<int>(out.dims()[i]);
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int total_num = static_cast<int>(out.numel());
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int post = total_num / (n * pre);
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auto dx_data_arr = dx_datas.data();
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funcs::StackGradFunctorForRange(
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dev_ctx, dx_data_arr, dy_data, total_num, n, post);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(stack_grad,
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CPU,
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ALL_LAYOUT,
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phi::StackGradKernel,
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bool,
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float,
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double,
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int,
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int8_t,
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int16_t,
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int64_t,
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uint8_t,
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phi::float16,
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phi::bfloat16,
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phi::complex64,
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phi::complex128) {}
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