wehub-resource-sync
69 lines
2.2 KiB
C++
69 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/where_grad_kernel.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void WhereGradKernel(const Context& dev_ctx,
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const DenseTensor& condition,
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const DenseTensor& x UNUSED,
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const DenseTensor& y UNUSED,
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const DenseTensor& out_grad,
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DenseTensor* x_grad,
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DenseTensor* y_grad) {
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const auto* cond_data = condition.data<bool>();
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auto numel = condition.numel();
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auto* dout = out_grad.data<T>();
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if (out_grad.numel() == 0) {
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if (x_grad) {
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Full<T, Context>(dev_ctx, x_grad->dims(), static_cast<T>(0), x_grad);
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}
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if (y_grad) {
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Full<T, Context>(dev_ctx, y_grad->dims(), static_cast<T>(0), y_grad);
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}
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return;
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}
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if (x_grad != nullptr) {
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auto* dx = dev_ctx.template Alloc<T>(x_grad);
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for (int i = 0; i < numel; i++) {
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dx[i] = cond_data[i] ? dout[i] : T{};
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}
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}
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if (y_grad != nullptr) {
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auto* dy = dev_ctx.template Alloc<T>(y_grad);
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for (int i = 0; i < numel; i++) {
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dy[i] = cond_data[i] ? T{} : dout[i];
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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(where_grad,
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CPU,
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ALL_LAYOUT,
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phi::WhereGradKernel,
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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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bool,
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phi::complex64,
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phi::complex128) {}
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