wehub-resource-sync
49 lines
1.7 KiB
C++
49 lines
1.7 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/bce_loss_grad_kernel.h"
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#include <algorithm> // for max
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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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namespace phi {
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template <typename T, typename Context>
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void BCELossGradKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const DenseTensor& label,
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const DenseTensor& out_grad,
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DenseTensor* input_grad) {
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auto dx_data = dev_ctx.template Alloc<T>(input_grad);
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auto dout_data = out_grad.data<T>();
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auto x_data = input.data<T>();
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auto label_data = label.data<T>();
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int x_numel = static_cast<int>(input.numel());
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// dx = dout * ((x - label)/(x - x^2))
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for (int i = 0; i < x_numel; ++i) {
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dx_data[i] =
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dout_data[i] * ((x_data[i] - label_data[i]) /
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std::max((static_cast<T>(1) - x_data[i]) * x_data[i],
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static_cast<T>(1e-12)));
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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bce_loss_grad, CPU, ALL_LAYOUT, phi::BCELossGradKernel, float, double) {}
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