wehub-resource-sync
53 lines
2.0 KiB
C++
53 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/huber_loss_grad_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <typename T, typename Context>
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void HuberLossGradKernel(const Context& dev_ctx,
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const DenseTensor& residual,
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const DenseTensor& out_grad,
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float delta,
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DenseTensor* input_grad,
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DenseTensor* label_grad) {
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T* input_grad_data = nullptr;
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T* label_grad_data = nullptr;
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if (input_grad) {
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input_grad_data = dev_ctx.template Alloc<T>(input_grad);
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}
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if (label_grad) {
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label_grad_data = dev_ctx.template Alloc<T>(label_grad);
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}
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auto out_grad_data = out_grad.data<T>();
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auto residual_data = residual.data<T>();
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int r = xpu::huber_loss_grad<T>(dev_ctx.x_context(),
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residual_data,
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out_grad_data,
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input_grad_data,
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label_grad_data,
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out_grad.numel(),
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1,
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delta);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "huber_loss_grad");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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huber_loss_grad, XPU, ALL_LAYOUT, phi::HuberLossGradKernel, float) {}
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