wehub-resource-sync
94 lines
2.8 KiB
Plaintext
94 lines
2.8 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/mv_grad_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_launch_config.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/blas/blas.h"
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namespace phi {
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template <typename T>
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__global__ void MVGradDxCUDAKernel(
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const int64_t m, const int64_t n, const T *dout, const T *vec, T *dx) {
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int64_t idx = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
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for (; idx < m * n; idx += static_cast<int64_t>(blockDim.x) * gridDim.x) {
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int64_t i = idx / n;
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int64_t j = idx % n;
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dx[idx] = dout[i] * vec[j];
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}
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}
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template <typename T, typename Context>
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void MvGradKernel(const Context &dev_ctx,
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const DenseTensor &x,
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const DenseTensor &vec,
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const DenseTensor &out_grad,
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DenseTensor *x_grad,
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DenseTensor *vec_grad) {
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auto dout = out_grad;
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auto dx = x_grad;
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auto dvec = vec_grad;
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if (x.numel() == 0 || vec.numel() == 0) {
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if (dx) {
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Full<T, Context>(dev_ctx, dx->dims(), static_cast<T>(0), dx);
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}
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if (dvec) {
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Full<T, Context>(dev_ctx, dvec->dims(), static_cast<T>(0), dvec);
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}
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return;
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}
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auto dim_x = x.dims();
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int64_t m = dim_x[0];
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int64_t n = dim_x[1];
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// get data ptr
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const T *x_data = x.data<T>();
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const T *vec_data = vec.data<T>();
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const T *dout_data = dout.data<T>();
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auto blas = funcs::GetBlas<Context, T>(dev_ctx);
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auto stream = dev_ctx.stream();
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auto config = backends::gpu::GetGpuLaunchConfig1D(dev_ctx, m * n);
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if (dx) {
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T *dx_data = dev_ctx.template Alloc<T>(dx);
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MVGradDxCUDAKernel<T>
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<<<config.block_per_grid.x, config.thread_per_block.x, 0, stream>>>(
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m, n, dout_data, vec_data, dx_data);
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}
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if (dvec) {
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T *dvec_data = dev_ctx.template Alloc<T>(dvec);
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blas.GEMV(true,
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dim_x[0],
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dim_x[1],
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static_cast<T>(1),
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x_data,
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dout_data,
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static_cast<T>(0),
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dvec_data);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(mv_grad, GPU, ALL_LAYOUT, phi::MvGradKernel, float, double) {
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}
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