wehub-resource-sync
84 lines
2.7 KiB
C++
84 lines
2.7 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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#pragma once
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#include "paddle/common/array.h"
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#include "paddle/phi/backends/gpu/gpu_primitives.h"
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#include "paddle/phi/kernels/primitive/kernel_primitives.h"
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namespace phi {
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template <typename T>
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__global__ void RollCudaKernel(const T* input,
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T* output,
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const int rank,
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const int64_t numel,
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Array<int64_t, DDim::kMaxRank> shifts,
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Array<int64_t, DDim::kMaxRank> strides,
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Array<int64_t, DDim::kMaxRank> sizes) {
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int64_t idx =
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static_cast<int64_t>(blockIdx.x) * static_cast<int64_t>(blockDim.x) +
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static_cast<int64_t>(threadIdx.x);
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if (idx >= numel) {
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return;
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}
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int64_t output_idx = idx;
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int64_t new_dim_idx = 0;
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#pragma unroll
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for (size_t i = 0; i < DDim::kMaxRank; i++) {
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if (i >= rank) {
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break;
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}
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new_dim_idx = (output_idx / strides[i]) % sizes[i] + shifts[i];
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if (new_dim_idx >= sizes[i]) {
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output_idx += (shifts[i] - sizes[i]) * strides[i];
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} else {
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output_idx += shifts[i] * strides[i];
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}
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}
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output[output_idx] = input[idx];
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}
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template <typename T, typename Context>
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void LaunchRollKernel(const Context& dev_ctx,
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const T* input,
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T* output,
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const int rank,
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const int64_t numel,
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const std::vector<int64_t> shifts,
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const std::vector<int64_t> strides,
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const std::vector<int64_t> sizes) {
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Array<int64_t, DDim::kMaxRank> strides_array;
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Array<int64_t, DDim::kMaxRank> shifts_array;
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Array<int64_t, DDim::kMaxRank> sizes_array;
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for (int i = 0; i < rank; ++i) {
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strides_array[i] = strides[i];
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shifts_array[i] = shifts[i];
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sizes_array[i] = sizes[i];
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}
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auto stream = dev_ctx.stream();
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RollCudaKernel<T>
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<<<(numel + PADDLE_CUDA_NUM_THREADS - 1) / PADDLE_CUDA_NUM_THREADS,
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PADDLE_CUDA_NUM_THREADS,
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0,
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stream>>>(
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input, output, rank, numel, shifts_array, strides_array, sizes_array);
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}
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} // namespace phi
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