wehub-resource-sync
108 lines
3.1 KiB
C++
108 lines
3.1 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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// CUDA, XPU and HIP use same api
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#if defined(__NVCC__) || defined(__HIPCC__) || defined(__xpu__)
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#include <algorithm>
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#include <cmath>
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#include <numeric>
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#include <set>
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#include <vector>
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#include "paddle/phi/kernels/primitive/kernel_primitives.h"
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namespace phi {
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namespace funcs {
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constexpr int kMaxRank = DDim::kMaxRank;
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namespace details {
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// Convert dims from vector to array
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template <typename T, size_t ElementCount, typename VectorLikeType>
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static inline Array<T, ElementCount> VectorToArray(const VectorLikeType& vec) {
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PADDLE_ENFORCE_LE(vec.size(),
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ElementCount,
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common::errors::InvalidArgument(
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"Vector to Array: size not match. Received "
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"vec.size() %d > ElementCount %d.",
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vec.size(),
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ElementCount));
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size_t n = static_cast<size_t>(vec.size());
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Array<T, ElementCount> ret;
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for (size_t i = 0; i < n; ++i) {
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ret[i] = vec[i];
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}
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return ret;
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}
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} // namespace details
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template <typename IndexType>
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struct IndexCalculator {
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IndexCalculator(int dim,
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const std::vector<int64_t>& cal_dims,
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const std::vector<int64_t>& cal_strides,
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const std::vector<int64_t>& full_strides)
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: dim(dim) {
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std::vector<int64_t> dim_strides;
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for (auto i : cal_dims) {
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dim_strides.push_back(full_strides[i]);
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}
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strides = details::VectorToArray<int64_t, kMaxRank>(dim_strides);
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#ifdef PADDLE_WITH_XPU_KP
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reduce_strides = details::VectorToArray<int64_t, kMaxRank>(cal_strides);
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#else
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std::vector<FastDivMod<IndexType>> cal_divmoders;
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for (auto i : cal_strides) {
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cal_divmoders.emplace_back(i);
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}
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divmoders =
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details::VectorToArray<FastDivMod<IndexType>, kMaxRank>(cal_divmoders);
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#endif
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}
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__device__ inline IndexType operator()(IndexType offset) const {
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IndexType index = 0;
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#pragma unroll
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for (int i = 0; i < kMaxRank; ++i) {
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if (i == dim) {
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break;
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}
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#ifdef PADDLE_WITH_XPU_KP
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index += (offset / reduce_strides[i]) * strides[i];
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offset = offset % reduce_strides[i];
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#else
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auto divmod = divmoders[i].Divmod(offset);
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index += (divmod.val[0] * strides[i]);
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offset = divmod.val[1];
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#endif
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}
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return index;
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}
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int dim;
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Array<int64_t, kMaxRank> strides;
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#ifdef PADDLE_WITH_XPU_KP
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Array<int64_t, kMaxRank> reduce_strides;
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#else
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Array<FastDivMod<IndexType>, kMaxRank> divmoders;
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#endif
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};
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#endif
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} // namespace funcs
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} // namespace phi
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