wehub-resource-sync
59 lines
2.0 KiB
C++
59 lines
2.0 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/mean_all_grad_kernel.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 MeanAllGradKernel(const Context& dev_ctx,
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const DenseTensor& x UNUSED,
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const DenseTensor& out_grad,
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DenseTensor* x_grad) {
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if (x_grad && x_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(x_grad);
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return;
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}
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PADDLE_ENFORCE_EQ(out_grad.numel(),
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1UL,
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common::errors::InvalidArgument(
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"Mean Gradient should be scalar. But received "
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"Out@GRAD's elements num is %d.",
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out_grad.numel()));
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dev_ctx.template Alloc<T>(x_grad);
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T x_numel = static_cast<T>(x_grad->numel());
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Eigen::DSizes<int, 1> bcast(static_cast<int>(x_numel));
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auto eigen_x = EigenVector<T>::Flatten(*x_grad);
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auto eigen_dout = EigenVector<T>::Flatten(out_grad);
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eigen_x.device(*dev_ctx.eigen_device()) =
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(eigen_dout / x_numel).broadcast(bcast);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(mean_all_grad,
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CPU,
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ALL_LAYOUT,
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phi::MeanAllGradKernel,
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float,
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double,
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phi::bfloat16,
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phi::complex64,
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phi::complex128) {}
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