wehub-resource-sync
71 lines
2.5 KiB
Plaintext
71 lines
2.5 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/common/amp_type_traits.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/mean_all_kernel.h"
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namespace phi {
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template <typename T>
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__global__ void MeanRunKernel(const T* in_data, T* out_data, int64_t N) {
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using MT = typename MPTypeTrait<T>::Type;
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int64_t idx = static_cast<int64_t>(blockDim.x) * blockIdx.x + threadIdx.x;
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auto data = static_cast<MT>(in_data[0]);
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for (; idx < N; idx += static_cast<int64_t>(blockDim.x) * gridDim.x) {
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out_data[idx] = static_cast<T>(data / (static_cast<MT>(N)));
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}
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}
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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,
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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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1,
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common::errors::InvalidArgument(
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"Mean Gradient Input Tensor len should be 1. But "
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"received 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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auto in_data = out_grad.data<T>();
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auto size_prob = x_grad->numel();
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auto out_data = x_grad->data<T>();
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int threads = 512;
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int64_t grid_64 = (size_prob + threads - 1) / threads;
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PADDLE_ENFORCE_LE_UINT32_MAX(grid_64, "grid");
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uint32_t grid = static_cast<uint32_t>(grid_64);
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auto stream = dev_ctx.stream();
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MeanRunKernel<T><<<grid, threads, 0, stream>>>(in_data, out_data, size_prob);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(mean_all_grad,
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GPU,
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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::float16,
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phi::complex64,
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phi::complex128) {}
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