wehub-resource-sync
89 lines
2.9 KiB
Plaintext
89 lines
2.9 KiB
Plaintext
// 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/rrelu_grad_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_meta.h"
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#include "paddle/phi/kernels/empty_kernel.h"
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#include "paddle/phi/kernels/funcs/reduce_function.h"
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#include "paddle/phi/kernels/gpu/prelu_funcs.h"
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#include "paddle/phi/kernels/primitive/functor_primitives.h"
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namespace phi {
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template <typename T>
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__global__ void RReluOpGradKernel(const T* x_ptr,
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const T* noise_ptr,
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const T* out_grad_ptr,
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T* x_grad_ptr,
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int64_t numel) {
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CUDA_KERNEL_LOOP_TYPE(index, numel, int64_t) {
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T scale = noise_ptr[index];
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T x = x_ptr[index];
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T out_grad = out_grad_ptr[index];
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T zero = static_cast<T>(0);
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x_grad_ptr[index] = (x < zero) ? scale * out_grad : out_grad;
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}
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}
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template <typename T>
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class RReluOpGradFunctor {
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public:
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void operator()(gpuStream_t stream,
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const T* x,
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const T* noise,
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const T* out_grad,
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T* x_grad,
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int64_t numel) {
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RReluOpGradKernel<T>
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<<<PADDLE_GET_BLOCKS(numel), CUDA_NUM_THREADS, 0, stream>>>(
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x, noise, out_grad, x_grad, numel);
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}
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};
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template <typename T, typename Context>
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void RReluGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& noise,
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const DenseTensor& out_grad,
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DenseTensor* x_grad) {
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if (!x_grad) return;
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dev_ctx.template Alloc<T>(x_grad);
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if (x_grad->numel() == 0) return;
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const T* x_ptr = x.data<T>();
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const T* n_ptr = noise.data<T>();
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const T* out_grad_ptr = out_grad.data<T>();
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T* x_grad_ptr = dev_ctx.template Alloc<T>(x_grad);
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int64_t numel = x.numel();
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auto stream = dev_ctx.stream();
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RReluOpGradFunctor<T> rrelu_grad;
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rrelu_grad(stream, x_ptr, n_ptr, out_grad_ptr, x_grad_ptr, numel);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(rrelu_grad,
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GPU,
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ALL_LAYOUT,
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phi::RReluGradKernel,
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float,
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phi::float16,
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phi::bfloat16,
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double) {}
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