345 lines
12 KiB
C++
345 lines
12 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Contains additional metadata about layout permute functions used in the example.
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*/
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#include "cutlass/tensor_coord.h"
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#include "cutlass/layout/permute.h"
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/// Additional permutation metadata to facilitate testing/printing
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template<typename PermuteLayout>
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struct PermuteInfo;
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/// Specialization for default case (no permute). Other specializations must follow this template.
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template<>
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struct PermuteInfo<cutlass::layout::NoPermute> {
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/// Whether this is a BMM or GEMM permutation (NoPermute can actually be either)
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static bool constexpr kBatched = false;
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/// Minimal divisor for row extent
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static int constexpr kRowFactor = 1;
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/// Minimum divisor for column extent
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static int constexpr kColumnFactor = 1;
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/// Minimum divisor for batch size dimension
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static int constexpr kBatchFactor = 1;
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/// Tensor layout used in permutation operation
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using Layout = cutlass::layout::PackedVectorLayout;
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static std::string name() {
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return "NoPermute";
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}
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/// User-friendly description of the permute operation
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static std::string desc() {
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return "no permutation";
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}
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/// Infer original higher-rank tensor shape from GEMM/BMM matrix extents.
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/// For direct (output) permutations, must be a simple reshape of extent.
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/// For inverse (input) permutations, must return shape *before* permute operation.
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/// In case of NoPermute, simply use a linear (rank 1) view of the memory
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static Layout::TensorCoord original_shape(cutlass::MatrixCoord extent, int batch_count) {
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return Layout::TensorCoord(extent.row() * extent.column() * batch_count);
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}
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/// Compute the permuted higher-rank tensor shape from the original shape.
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static Layout::TensorCoord permute(Layout::TensorCoord const &s) {
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return s;
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}
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};
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template<int D1>
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struct PermuteInfo<cutlass::layout::Tensor4DPermuteBMM0213RowMajor<D1>> {
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static bool constexpr kBatched = true;
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static int constexpr kRowFactor = 1;
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static int constexpr kColumnFactor = 1;
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static int constexpr kBatchFactor = D1;
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using Layout = cutlass::layout::TensorNHWC;
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static std::string name() {
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return "Tensor4DPermuteBMM0213<" + std::to_string(D1) + ">";
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}
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static std::string desc() {
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return "batched GEMM permutation [0, 2, 1, 3]";
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}
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static Layout::TensorCoord original_shape(cutlass::MatrixCoord extent, int batch_count) {
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int D0 = batch_count / D1;
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int D2 = extent.row();
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int D3 = extent.column();
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return {D0, D1, D2, D3};
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}
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static Layout::TensorCoord permute(Layout::TensorCoord const &s) {
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return {s[0], s[2], s[1], s[3]};
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}
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};
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template<int D1>
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struct PermuteInfo<cutlass::layout::Tensor4DPermuteBMM0213RowMajorInverse<D1>>
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: public PermuteInfo<cutlass::layout::Tensor4DPermuteBMM0213RowMajor<D1>> {
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static bool constexpr kBatched = true;
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static int constexpr kRowFactor = 1;
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static int constexpr kColumnFactor = D1;
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static int constexpr kBatchFactor = 1;
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using Base = PermuteInfo<cutlass::layout::Tensor4DPermuteBMM0213RowMajor<D1>>;
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using Layout = typename Base::Layout;
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static typename Layout::TensorCoord original_shape(cutlass::MatrixCoord extent, int batch_count) {
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int D0 = batch_count;
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int D2 = extent.row();
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int D3 = extent.column() / D1;
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return {D0, D1, D2, D3};
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}
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};
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template<int D1>
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struct PermuteInfo<cutlass::layout::Tensor4DPermuteBMM0321ColumnMajor<D1>> {
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static bool constexpr kBatched = true;
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static int constexpr kRowFactor = 1;
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static int constexpr kColumnFactor = 1;
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static int constexpr kBatchFactor = D1;
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using Layout = cutlass::layout::TensorNHCW;
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static std::string name() {
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return "Tensor4DPermuteBMM0321<" + std::to_string(D1) + ">";
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}
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static std::string desc() {
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return "batched GEMM permutation [0, 3, 2, 1]";
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}
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static Layout::TensorCoord original_shape(cutlass::MatrixCoord extent, int batch_count) {
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int D0 = batch_count / D1;
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int D2 = extent.row();
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int D3 = extent.column();
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return {D0, D1, D2, D3};
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}
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static Layout::TensorCoord permute(Layout::TensorCoord const &s) {
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return {s[0], s[3], s[2], s[1]};
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}
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};
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template<int D1>
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struct PermuteInfo<cutlass::layout::Tensor4DPermuteBMM0321ColumnMajorInverse<D1>>
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: public PermuteInfo<cutlass::layout::Tensor4DPermuteBMM0321ColumnMajor<D1>> {
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static bool constexpr kBatched = true;
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static int constexpr kRowFactor = D1;
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static int constexpr kColumnFactor = 1;
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static int constexpr kBatchFactor = 1;
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using Base = PermuteInfo<cutlass::layout::Tensor4DPermuteBMM0321ColumnMajor<D1>>;
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using Layout = typename Base::Layout;
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static typename Layout::TensorCoord original_shape(cutlass::MatrixCoord extent, int batch_count) {
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int D0 = batch_count;
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int D2 = extent.row() / D1;
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int D3 = extent.column();
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return {D0, D1, D2, D3};
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}
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};
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template<int D1, int D2>
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struct PermuteInfo<cutlass::layout::Tensor4DPermute0213RowMajor<D1, D2>> {
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static bool constexpr kBatched = false;
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static int constexpr kRowFactor = D1;
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static int constexpr kColumnFactor = D2;
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static int constexpr kBatchFactor = 1;
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using Layout = cutlass::layout::TensorNHWC;
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static std::string name() {
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return "Tensor4DPermute0213<" + std::to_string(D1) + "," + std::to_string(D2) + ">";
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}
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static std::string desc() {
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return "normal GEMM permutation [0, 2, 1, 3]";
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}
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static Layout::TensorCoord original_shape(cutlass::MatrixCoord extent, int batch_count) {
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int D0 = extent.row() / D1;
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int D3 = extent.column() / D2;
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return {D0, D1, D2, D3};
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}
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static Layout::TensorCoord permute(Layout::TensorCoord const &s) {
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return {s[0], s[2], s[1], s[3]};
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}
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};
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template<int D1, int D2>
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struct PermuteInfo<cutlass::layout::Tensor4DPermute0213RowMajorInverse<D1, D2>>
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: public PermuteInfo<cutlass::layout::Tensor4DPermute0213RowMajor<D1, D2>> {
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static bool constexpr kBatched = false;
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static int constexpr kRowFactor = D2;
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static int constexpr kColumnFactor = D1;
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static int constexpr kBatchFactor = 1;
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using Base = PermuteInfo<cutlass::layout::Tensor4DPermute0213RowMajor<D1, D2>>;
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using Layout = typename Base::Layout;
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static typename Layout::TensorCoord original_shape(cutlass::MatrixCoord extent, int batch_count) {
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int D0 = extent.row() / D2;
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int D3 = extent.column() / D1;
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return {D0, D1, D2, D3};
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}
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};
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template<int D1, int D2>
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struct PermuteInfo<cutlass::layout::Tensor4DPermute0213ColumnMajor<D1, D2>>
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: public PermuteInfo<cutlass::layout::Tensor4DPermute0213RowMajor<D1, D2>> {
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using Layout = cutlass::layout::TensorCWHN;
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};
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template<int D1, int D2>
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struct PermuteInfo<cutlass::layout::Tensor4DPermute0213ColumnMajorInverse<D1, D2>>
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: public PermuteInfo<cutlass::layout::Tensor4DPermute0213RowMajorInverse<D1, D2>> {
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using Layout = cutlass::layout::TensorCWHN;
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};
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template<int T1, int T2, int T3>
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struct PermuteInfo<cutlass::layout::Tensor5DPermute20314RowMajor<T1, T2, T3>> {
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static bool constexpr kBatched = false;
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static int constexpr kRowFactor = T1;
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static int constexpr kColumnFactor = T2 * T3;
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static int constexpr kBatchFactor = 1;
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using Layout = cutlass::layout::TensorNDHWC;
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static std::string name() {
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return "Tensor5DPermute20314<" + std::to_string(T1) + "," + std::to_string(T2) + "," + std::to_string(T3) + ">";
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}
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static std::string desc() {
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return "normal GEMM permutation [2, 0, 3, 1, 4]";
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}
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static Layout::TensorCoord original_shape(cutlass::MatrixCoord extent, int batch_count)
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{
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int const T0 = extent.row() / T1;
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int const T4 = extent.column() / (T2 * T3);
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return {T0, T1, T2, T3, T4};
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}
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static Layout::TensorCoord permute(Layout::TensorCoord const &s)
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{
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return {s[2], s[0], s[3], s[1], s[4]};
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}
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};
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template<int T1, int T2, int T3>
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struct PermuteInfo<cutlass::layout::Tensor5DPermute20314RowMajorInverse<T1, T2, T3>>
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: public PermuteInfo<cutlass::layout::Tensor5DPermute20314RowMajor<T1, T2, T3>> {
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static bool constexpr kBatched = false;
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static int constexpr kRowFactor = T2;
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static int constexpr kColumnFactor = T1 * T3;
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static int constexpr kBatchFactor = 1;
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using Base = PermuteInfo<cutlass::layout::Tensor5DPermute20314RowMajor<T1, T2, T3>>;
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using Layout = typename Base::Layout;
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static typename Layout::TensorCoord original_shape(cutlass::MatrixCoord extent, int batch_count) {
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int const T0 = extent.row() / T2;
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int const T4 = extent.column() / (T1 * T3);
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return {T0, T1, T2, T3, T4};
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}
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};
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template<int T1, int T2, int T3>
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struct PermuteInfo<cutlass::layout::Tensor5DPermute02413ColumnMajor<T1, T2, T3>> {
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static bool constexpr kBatched = false;
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static int constexpr kRowFactor = T1;
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static int constexpr kColumnFactor = T2 * T3;
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static int constexpr kBatchFactor = 1;
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using Layout = cutlass::layout::TensorCWHDN;
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static std::string name() {
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return "Tensor5DPermute02413<" + std::to_string(T1) + "," + std::to_string(T2) + "," + std::to_string(T3) + ">";
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}
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static std::string desc() {
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return "normal GEMM permutation [0, 2, 4, 1, 3]";
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}
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using Coord = cutlass::Tensor5DCoord;
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static Layout::TensorCoord original_shape(cutlass::MatrixCoord extent, int batch_count)
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{
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int const T0 = extent.row() / T1;
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int const T4 = extent.column() / (T2 * T3);
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return {T0, T1, T2, T3, T4};
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}
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static Layout::TensorCoord permute(Layout::TensorCoord const &s)
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{
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return {s[0], s[2], s[4], s[1], s[3]};
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}
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};
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template<int T1, int T2, int T3>
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struct PermuteInfo<cutlass::layout::Tensor5DPermute02413ColumnMajorInverse<T1, T2, T3>>
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: public PermuteInfo<cutlass::layout::Tensor5DPermute02413ColumnMajor<T1, T2, T3>> {
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static bool constexpr kBatched = false;
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static int constexpr kRowFactor = T2;
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static int constexpr kColumnFactor = T1 * T3;
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static int constexpr kBatchFactor = 1;
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using Base = PermuteInfo<cutlass::layout::Tensor5DPermute02413ColumnMajor<T1, T2, T3>>;
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using Layout = typename Base::Layout;
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static typename Layout::TensorCoord original_shape(cutlass::MatrixCoord extent, int batch_count) {
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int const T0 = extent.row() / T2;
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int const T4 = extent.column() / (T1 * T3);
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return {T0, T1, T2, T3, T4};
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}
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};
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