wehub-resource-sync
54 lines
2.0 KiB
C++
54 lines
2.0 KiB
C++
// Copyright (c) 2024 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/phi/backends/gpu/gpu_primitives.h"
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#include "paddle/phi/core/dense_tensor.h"
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namespace phi {
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static constexpr int kNumCUDAThreads = 512;
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static constexpr int64_t kNumMaximumNumBlocks = 4096;
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static inline int NumBlocks(const int64_t N) {
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return std::min((N + kNumCUDAThreads - 1) / kNumCUDAThreads,
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kNumMaximumNumBlocks);
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}
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template <typename T>
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__global__ void ShuffleChannel(const int64_t nthreads,
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const int64_t feature_map_size,
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T* output,
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const T* input,
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int64_t group_row,
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int64_t group_column,
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int64_t len) {
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int64_t index =
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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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int64_t offset =
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static_cast<int64_t>(blockDim.x) * static_cast<int64_t>(gridDim.x);
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for (int64_t ii = index; ii < nthreads; ii += offset) {
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const int64_t n = ii / group_row / group_column / len;
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const int64_t i = (ii / group_column / len) % group_row;
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const int64_t j = ii / len % group_column;
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const int64_t k =
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ii - (n * feature_map_size + (i * group_column + j) * len);
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T* p_o = output + n * feature_map_size + (j * group_row + i) * len;
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p_o[k] = input[ii];
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}
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}
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} // namespace phi
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