508 lines
16 KiB
C++
508 lines
16 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, 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 Implements several possible threadblock-swizzling functions mapping blockIdx to
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GEMM problems.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/layout/matrix.h"
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#include "cutlass/platform/platform.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/conv/conv2d_problem_size.h"
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#include "cutlass/conv/conv3d_problem_size.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace gemm {
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namespace threadblock {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Helper to rematerialize block Idx. Reduces register liveness.
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CUTLASS_DEVICE
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int RematerializeThreadIdxX() {
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return threadIdx.x;
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}
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/// Helper to rematerialize block Idx. Reduces register liveness.
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CUTLASS_DEVICE
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int RematerializeThreadIdxY() {
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return threadIdx.y;
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}
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/// Helper to rematerialize block Idx. Reduces register liveness.
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CUTLASS_DEVICE
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int RematerializeThreadIdxZ() {
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return threadIdx.z;
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}
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/// Helper to rematerialize block Idx. Reduces register liveness.
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CUTLASS_DEVICE
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int RematerializeBlockIdxX() {
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return blockIdx.x;
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}
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/// Helper to rematerialize block Idx. Reduces register liveness.
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CUTLASS_DEVICE
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int RematerializeBlockIdxY() {
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return blockIdx.y;
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}
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/// Helper to rematerialize block Idx. Reduces register liveness.
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CUTLASS_DEVICE
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int RematerializeBlockIdxZ() {
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return blockIdx.z;
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}
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/// Helper to rematerialize block Dim. Reduces register liveness.
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CUTLASS_DEVICE
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int RematerializeBlockDimX() {
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return blockDim.x;
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}
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/// Helper to rematerialize block Dim. Reduces register liveness.
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CUTLASS_DEVICE
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int RematerializeBlockDimY() {
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return blockDim.y;
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}
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/// Helper to rematerialize block Dim. Reduces register liveness.
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CUTLASS_DEVICE
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int RematerializeBlockDimZ() {
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return blockDim.z;
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Threadblock swizzling function for GEMMs
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template <int N = 1>
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struct GemmIdentityThreadblockSwizzle {
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CUTLASS_HOST_DEVICE
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GemmIdentityThreadblockSwizzle() { }
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/// Returns the shape of the problem in units of logical tiles
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/// *Gemm* problem size: gemm(M, N, K)
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CUTLASS_HOST_DEVICE
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GemmCoord get_tiled_shape(
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GemmCoord problem_size,
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GemmCoord tile_size,
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int split_k_slices) const {
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return GemmCoord(
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(problem_size.m() + tile_size.m() - 1) / tile_size.m(),
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(problem_size.n() + tile_size.n() - 1) / tile_size.n(),
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split_k_slices);
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}
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/// Returns the shape of the problem in units of logical tiles
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/// *ImplicitGemm* Conv2d problem size: conv_operator(NPQK, NHWC, KRSC)
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CUTLASS_HOST_DEVICE
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GemmCoord get_tiled_shape(
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cutlass::conv::Operator conv_operator,
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cutlass::conv::Conv2dProblemSize const &problem_size,
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GemmCoord tile_size,
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int split_k_slices) const {
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gemm::GemmCoord implicit_gemm_problem_size =
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cutlass::conv::implicit_gemm_problem_size(conv_operator, problem_size);
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return get_tiled_shape(
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implicit_gemm_problem_size, tile_size, split_k_slices);
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}
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/// Returns the shape of the problem in units of logical tiles
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/// *ImplicitGemm* Conv3d problem size: conv_operator(NZPQK, NDHWC, KTRSC)
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CUTLASS_HOST_DEVICE
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GemmCoord get_tiled_shape(
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cutlass::conv::Operator conv_operator,
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cutlass::conv::Conv3dProblemSize const &problem_size,
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GemmCoord tile_size,
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int split_k_slices) const {
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gemm::GemmCoord implicit_gemm_problem_size =
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cutlass::conv::implicit_gemm_problem_size(conv_operator, problem_size);
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return get_tiled_shape(
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implicit_gemm_problem_size, tile_size, split_k_slices);
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}
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/// Computes CUDA grid dimensions given a size in units of logical tiles
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CUTLASS_HOST_DEVICE
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dim3 get_grid_shape(GemmCoord tiled_shape) const {
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int tile = 1 << get_log_tile(tiled_shape);
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return dim3(tiled_shape.m() * tile, (tiled_shape.n() + tile - 1) / tile, tiled_shape.k());
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}
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/// Calculates optimal swizzle width
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CUTLASS_HOST_DEVICE
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int get_log_tile(GemmCoord tiled_shape) const {
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auto n = tiled_shape.n();
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// Thresholds picked so that it doesn't cause too many no-op CTAs
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if (N >= 8 && n >= 6)
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return 3;
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else if (N >= 4 && n >= 3)
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return 2;
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else if (N >= 2 && n >= 2)
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return 1;
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else
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return 0;
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}
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/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
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CUTLASS_DEVICE
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GemmCoord get_tile_offset(int log_tile) const {
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int block_idx_x = RematerializeBlockIdxX();
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int block_idx_y = RematerializeBlockIdxY();
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int block_idx_z = RematerializeBlockIdxZ();
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return GemmCoord{(block_idx_x >> log_tile), //
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(block_idx_y << log_tile) + ((block_idx_x) & ((1 << (log_tile)) - 1)),
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block_idx_z};
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}
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/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
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CUTLASS_DEVICE
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GemmCoord get_tile_offset(GemmCoord tiled_shape) const {
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int const kTile = N;
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int block_idx_x = RematerializeBlockIdxX();
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int block_idx_y = RematerializeBlockIdxY();
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if ((tiled_shape.m() < kTile) || (tiled_shape.n() < kTile))
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return GemmCoord{block_idx_x, block_idx_y, RematerializeBlockIdxZ()};
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return GemmCoord{
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(block_idx_x / kTile),
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(block_idx_y * kTile) + (block_idx_x % kTile),
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RematerializeBlockIdxZ()
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};
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Threadblock swizzling function for GEMMs
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struct GemmHorizontalThreadblockSwizzle {
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CUTLASS_HOST_DEVICE
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GemmHorizontalThreadblockSwizzle() { }
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/// Returns the shape of the problem in units of logical tiles
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CUTLASS_HOST_DEVICE
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GemmCoord get_tiled_shape(
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GemmCoord problem_size,
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GemmCoord tile_size,
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int split_k_slices) const {
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return GemmCoord(
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(problem_size.m() + tile_size.m() - 1) / tile_size.m(),
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(problem_size.n() + tile_size.n() - 1) / tile_size.n(),
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split_k_slices);
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}
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/// Computes CUDA grid dimensions given a size in units of logical tiles
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CUTLASS_HOST_DEVICE
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dim3 get_grid_shape(GemmCoord tiled_shape) const {
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return dim3(tiled_shape.n(), tiled_shape.m(), tiled_shape.k());
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}
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/// Calculates optimal swizzle width
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CUTLASS_HOST_DEVICE
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int get_log_tile(GemmCoord tiled_shape) const {
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return 0;
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}
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/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
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CUTLASS_DEVICE
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GemmCoord get_tile_offset(GemmCoord tiled_shape) const {
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return GemmCoord{
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RematerializeBlockIdxY(),
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RematerializeBlockIdxX(),
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RematerializeBlockIdxZ()
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};
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Threadblock swizzling function for batched GEMMs
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struct GemmBatchedIdentityThreadblockSwizzle {
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/// Returns the shape of the problem in units of logical tiles
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CUTLASS_HOST_DEVICE
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GemmCoord get_tiled_shape(
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GemmCoord problem_size,
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GemmCoord tile_size,
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int batch_count) const {
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return GemmCoord(
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(problem_size.m() + tile_size.m() - 1) / tile_size.m(),
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(problem_size.n() + tile_size.n() - 1) / tile_size.n(),
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batch_count % (1 << 16));
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}
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/// Computes CUDA grid dimensions given a size in units of logical tiles
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CUTLASS_HOST_DEVICE
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dim3 get_grid_shape(GemmCoord tiled_shape) const {
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return dim3(tiled_shape.m(), tiled_shape.n(), tiled_shape.k());
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}
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/// Calculates optimal swizzle width
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CUTLASS_HOST_DEVICE
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int get_log_tile(GemmCoord tiled_shape) const {
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return 0;
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}
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/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
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CUTLASS_DEVICE
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GemmCoord get_tile_offset(GemmCoord tiled_shape) const {
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return GemmCoord{
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RematerializeBlockIdxX(),
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RematerializeBlockIdxY(),
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RematerializeBlockIdxZ()
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};
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}
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/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
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CUTLASS_DEVICE
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GemmCoord get_tile_offset(int log_tile) const {
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int block_idx_x = RematerializeBlockIdxX();
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int block_idx_y = RematerializeBlockIdxY();
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int block_idx_z = RematerializeBlockIdxZ();
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return GemmCoord{(block_idx_x >> log_tile), //
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(block_idx_y << log_tile) + ((block_idx_x) & ((1 << (log_tile)) - 1)),
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block_idx_z};
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}
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/// Gets the batch index
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CUTLASS_DEVICE
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int get_batch_idx() const {
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return RematerializeBlockIdxZ();
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Threadblock swizzling function for split-K GEMMs
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template <int N = 1>
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struct GemmSplitKIdentityThreadblockSwizzle {
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int const kTile = N;
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/// Returns the shape of the problem in units of logical tiles
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CUTLASS_HOST_DEVICE
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GemmCoord get_tiled_shape(
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GemmCoord problem_size,
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GemmCoord tile_size,
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int partitions) const {
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return GemmCoord(
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(problem_size.m() + tile_size.m() - 1) / tile_size.m(),
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(problem_size.n() + tile_size.n() - 1) / tile_size.n(),
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partitions);
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}
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/// Calculates optimal swizzle width
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CUTLASS_HOST_DEVICE
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int get_log_tile(GemmCoord tiled_shape) const {
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auto n = tiled_shape.n();
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// Thresholds picked so that it doesn't cause too many no-op CTAs
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if (N >= 8 && n >= 6)
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return 3;
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else if (N >= 4 && n >= 3)
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return 2;
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else if (N >= 2 && n >= 2)
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return 1;
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else
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return 0;
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}
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/// Computes CUDA grid dimensions given a size in units of logical tiles
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CUTLASS_HOST_DEVICE
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dim3 get_grid_shape(GemmCoord tiled_shape) const {
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int tile = 1 << get_log_tile(tiled_shape);
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return dim3(tiled_shape.m() * tile, (tiled_shape.n() + tile - 1) / tile, tiled_shape.k());
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}
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/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
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CUTLASS_DEVICE
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GemmCoord get_tile_offset(int log_tile) const {
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int block_idx_x = RematerializeBlockIdxX();
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int block_idx_y = RematerializeBlockIdxY();
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int block_idx_z = RematerializeBlockIdxZ();
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return GemmCoord{(block_idx_x >> log_tile), //
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(block_idx_y << log_tile) + ((block_idx_x) & ((1 << (log_tile)) - 1)),
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block_idx_z};
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}
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/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
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CUTLASS_DEVICE
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GemmCoord get_tile_offset(GemmCoord tiled_shape) const {
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int const kTile = N;
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int block_idx_x = RematerializeBlockIdxX();
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int block_idx_y = RematerializeBlockIdxY();
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if ((tiled_shape.m() < kTile) || (tiled_shape.n() < kTile))
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return GemmCoord{block_idx_x, block_idx_y, RematerializeBlockIdxZ()};
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return GemmCoord{
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(block_idx_x / kTile),
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(block_idx_y * kTile) + (block_idx_x % kTile),
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RematerializeBlockIdxZ()
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};
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Threadblock swizzling function for split-K GEMMs
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struct GemmSplitKHorizontalThreadblockSwizzle {
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/// Returns the shape of the problem in units of logical tiles
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CUTLASS_HOST_DEVICE
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GemmCoord get_tiled_shape(
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GemmCoord problem_size,
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GemmCoord tile_size,
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int partitions) const {
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return GemmCoord(
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(problem_size.m() + tile_size.m() - 1) / tile_size.m(),
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(problem_size.n() + tile_size.n() - 1) / tile_size.n(),
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partitions);
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}
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/// Computes CUDA grid dimensions given a size in units of logical tiles
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CUTLASS_HOST_DEVICE
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dim3 get_grid_shape(GemmCoord tiled_shape) const {
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return dim3(tiled_shape.n(), tiled_shape.m(), tiled_shape.k());
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}
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/// Calculates optimal swizzle width
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CUTLASS_HOST_DEVICE
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int get_log_tile(GemmCoord tiled_shape) const {
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return 0;
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}
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/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
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CUTLASS_DEVICE
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GemmCoord get_tile_offset(int log_tile) const {
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return GemmCoord{
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RematerializeBlockIdxY(),
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RematerializeBlockIdxX(),
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RematerializeBlockIdxZ()
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};
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}
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/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
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CUTLASS_DEVICE
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GemmCoord get_tile_offset(GemmCoord tiled_shape) const {
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return GemmCoord{
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RematerializeBlockIdxY(),
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RematerializeBlockIdxX(),
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RematerializeBlockIdxZ()
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};
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Threadblock swizzling function for batched GEMVs
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struct GemvBatchedStridedThreadblockDefaultSwizzle {
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/// Returns the shape of the problem in units of logical tiles
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CUTLASS_HOST_DEVICE
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BatchedGemmCoord get_tiled_shape(
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BatchedGemmCoord problem_size,
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BatchedGemmCoord tile_size) const {
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return BatchedGemmCoord(
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1, // M is always 1
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(problem_size.n() + tile_size.n() - 1) / tile_size.n(),
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(problem_size.k() + tile_size.k() - 1) / tile_size.k(),
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(problem_size.batch() + tile_size.batch() - 1) / tile_size.batch());
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}
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/// Computes CUDA grid dimensions given a size in units of logical tiles
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CUTLASS_HOST_DEVICE
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dim3 get_grid_shape(BatchedGemmCoord tiled_shape) const {
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return dim3(tiled_shape.n(), tiled_shape.batch(), tiled_shape.k());
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}
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/// Calculates optimal swizzle width
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CUTLASS_HOST_DEVICE
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int get_log_tile(GemmCoord tiled_shape) const {
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return 0;
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}
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/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
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CUTLASS_DEVICE
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BatchedGemmCoord get_tile_offset(int log_tile) const {
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return BatchedGemmCoord{
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0, // M is always 1
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RematerializeBlockIdxX(),
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RematerializeBlockIdxZ(),
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RematerializeBlockIdxY(),
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};
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}
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/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
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CUTLASS_DEVICE
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BatchedGemmCoord get_tile_offset() const {
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return BatchedGemmCoord{
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0, // M is always 1
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RematerializeBlockIdxX(),
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RematerializeBlockIdxZ(),
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RematerializeBlockIdxY(),
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};
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}
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/// Gets the batch tile index
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CUTLASS_DEVICE
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int get_batch_tile_idx() const {
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return RematerializeBlockIdxY();
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}
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/// Gets the absolute batch index
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CUTLASS_DEVICE
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int get_batch_idx() const {
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return RematerializeBlockDimY()*RematerializeBlockIdxY() + RematerializeThreadIdxY();
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace threadblock
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} // namespace gemm
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} // namespace cutlass
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