wehub-resource-sync
67 lines
2.2 KiB
C++
67 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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#include "paddle/phi/kernels/multiplex_grad_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/common/memory_utils.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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namespace phi {
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template <typename T, typename Context>
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void MultiplexGradKernel(const Context& dev_ctx,
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const DenseTensor& ids,
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const DenseTensor& out_grad,
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std::vector<DenseTensor*> ins_grad) {
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size_t idx = -1UL;
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for (size_t i = 0; i < ins_grad.size(); i++) {
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if (ins_grad[i]) {
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dev_ctx.template Alloc<T>(ins_grad[i]);
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auto t = EigenVector<T>::Flatten(*ins_grad[i]);
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t.device(*dev_ctx.eigen_device()) = t.constant(static_cast<T>(0));
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idx = i;
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}
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}
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if (idx == -1UL) return;
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auto rows = ins_grad[idx]->dims()[0];
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auto cols = ins_grad[idx]->numel() / rows;
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auto* index = ids.data<int32_t>();
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for (auto i = 0; i < rows; i++) {
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size_t k = static_cast<size_t>(index[i]);
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if (ins_grad[k]) {
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memory_utils::Copy(dev_ctx.GetPlace(),
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ins_grad[k]->data<T>() + i * cols,
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dev_ctx.GetPlace(),
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out_grad.data<T>() + i * cols,
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cols * sizeof(T));
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}
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(multiplex_grad,
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CPU,
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ALL_LAYOUT,
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phi::MultiplexGradKernel,
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float,
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double,
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int,
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int64_t,
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phi::complex64,
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phi::complex128) {}
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