460 lines
15 KiB
C++
460 lines
15 KiB
C++
/***************************************************************************************************
|
|
* Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
* SPDX-License-Identifier: BSD-3-Clause
|
|
*
|
|
* Redistribution and use in source and binary forms, with or without
|
|
* modification, are permitted provided that the following conditions are met:
|
|
*
|
|
* 1. Redistributions of source code must retain the above copyright notice, this
|
|
* list of conditions and the following disclaimer.
|
|
*
|
|
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
|
* this list of conditions and the following disclaimer in the documentation
|
|
* and/or other materials provided with the distribution.
|
|
*
|
|
* 3. Neither the name of the copyright holder nor the names of its
|
|
* contributors may be used to endorse or promote products derived from
|
|
* this software without specific prior written permission.
|
|
*
|
|
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
|
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
|
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
|
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
|
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
|
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
|
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
|
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
|
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
*
|
|
**************************************************************************************************/
|
|
/*! \file
|
|
\brief Implements several possible threadblock-swizzling functions mapping blockIdx to
|
|
GEMM problems.
|
|
*/
|
|
|
|
#pragma once
|
|
|
|
#include "cutlass/cutlass.h"
|
|
#include "cutlass/layout/matrix.h"
|
|
#include "cutlass/platform/platform.h"
|
|
#include "cutlass/gemm/gemm.h"
|
|
#include "cutlass/conv/conv2d_problem_size.h"
|
|
#include "cutlass/conv/conv3d_problem_size.h"
|
|
#include "cutlass/gemm/threadblock/index_remat.h"
|
|
#include "cutlass/gemm/threadblock/threadblock_swizzle_streamk.h"
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace cutlass {
|
|
namespace gemm {
|
|
namespace threadblock {
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Threadblock swizzling function for GEMMs
|
|
template <int N = 1>
|
|
struct GemmIdentityThreadblockSwizzle {
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
GemmIdentityThreadblockSwizzle() { }
|
|
|
|
/// Returns the shape of the problem in units of logical tiles
|
|
/// *Gemm* problem size: gemm(M, N, K)
|
|
CUTLASS_HOST_DEVICE
|
|
static GemmCoord get_tiled_shape(
|
|
GemmCoord problem_size,
|
|
GemmCoord tile_size,
|
|
int split_k_slices) {
|
|
|
|
return GemmCoord(
|
|
(problem_size.m() + tile_size.m() - 1) / tile_size.m(),
|
|
(problem_size.n() + tile_size.n() - 1) / tile_size.n(),
|
|
split_k_slices);
|
|
}
|
|
|
|
/// Returns the shape of the problem in units of logical tiles
|
|
/// *ImplicitGemm* Conv2d problem size: conv_operator(NPQK, NHWC, KRSC)
|
|
CUTLASS_HOST_DEVICE
|
|
static GemmCoord get_tiled_shape(
|
|
cutlass::conv::Operator conv_operator,
|
|
cutlass::conv::Conv2dProblemSize const &problem_size,
|
|
GemmCoord tile_size,
|
|
int split_k_slices) {
|
|
|
|
gemm::GemmCoord implicit_gemm_problem_size =
|
|
cutlass::conv::implicit_gemm_problem_size(conv_operator, problem_size);
|
|
|
|
return get_tiled_shape(
|
|
implicit_gemm_problem_size, tile_size, split_k_slices);
|
|
}
|
|
|
|
/// Returns the shape of the problem in units of logical tiles
|
|
/// *ImplicitGemm* Conv3d problem size: conv_operator(NZPQK, NDHWC, KTRSC)
|
|
CUTLASS_HOST_DEVICE
|
|
static GemmCoord get_tiled_shape(
|
|
cutlass::conv::Operator conv_operator,
|
|
cutlass::conv::Conv3dProblemSize const &problem_size,
|
|
GemmCoord tile_size,
|
|
int split_k_slices) {
|
|
|
|
gemm::GemmCoord implicit_gemm_problem_size =
|
|
cutlass::conv::implicit_gemm_problem_size(conv_operator, problem_size);
|
|
|
|
return get_tiled_shape(
|
|
implicit_gemm_problem_size, tile_size, split_k_slices);
|
|
}
|
|
|
|
/// Computes CUDA grid dimensions given a size in units of logical tiles
|
|
CUTLASS_HOST_DEVICE
|
|
static dim3 get_grid_shape(GemmCoord tiled_shape) {
|
|
int tile = 1 << get_log_tile(tiled_shape);
|
|
return dim3(tiled_shape.m() * tile, (tiled_shape.n() + tile - 1) / tile, tiled_shape.k());
|
|
}
|
|
|
|
/// Calculates optimal swizzle width
|
|
CUTLASS_HOST_DEVICE
|
|
static int get_log_tile(GemmCoord tiled_shape) {
|
|
auto n = tiled_shape.n();
|
|
// Thresholds picked so that it doesn't cause too many no-op CTAs
|
|
if (N >= 8 && n >= 6)
|
|
return 3;
|
|
else if (N >= 4 && n >= 3)
|
|
return 2;
|
|
else if (N >= 2 && n >= 2)
|
|
return 1;
|
|
else
|
|
return 0;
|
|
}
|
|
|
|
/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
|
|
CUTLASS_DEVICE
|
|
static GemmCoord get_tile_offset(int log_tile) {
|
|
int block_idx_x = RematerializeBlockIdxX();
|
|
int block_idx_y = RematerializeBlockIdxY();
|
|
int block_idx_z = RematerializeBlockIdxZ();
|
|
|
|
return GemmCoord{(block_idx_x >> log_tile), //
|
|
(block_idx_y << log_tile) + ((block_idx_x) & ((1 << (log_tile)) - 1)),
|
|
block_idx_z};
|
|
}
|
|
|
|
/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
|
|
CUTLASS_DEVICE
|
|
static GemmCoord get_tile_offset(GemmCoord tiled_shape) {
|
|
|
|
int const kTile = N;
|
|
int block_idx_x = RematerializeBlockIdxX();
|
|
int block_idx_y = RematerializeBlockIdxY();
|
|
|
|
if ((tiled_shape.m() < kTile) || (tiled_shape.n() < kTile))
|
|
return GemmCoord{block_idx_x, block_idx_y, RematerializeBlockIdxZ()};
|
|
|
|
return GemmCoord{
|
|
(block_idx_x / kTile),
|
|
(block_idx_y * kTile) + (block_idx_x % kTile),
|
|
RematerializeBlockIdxZ()
|
|
};
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Threadblock swizzling function for GEMMs
|
|
struct GemmHorizontalThreadblockSwizzle {
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
GemmHorizontalThreadblockSwizzle() { }
|
|
|
|
/// Returns the shape of the problem in units of logical tiles
|
|
CUTLASS_HOST_DEVICE
|
|
static GemmCoord get_tiled_shape(
|
|
GemmCoord problem_size,
|
|
GemmCoord tile_size,
|
|
int split_k_slices) {
|
|
|
|
return GemmCoord(
|
|
(problem_size.m() + tile_size.m() - 1) / tile_size.m(),
|
|
(problem_size.n() + tile_size.n() - 1) / tile_size.n(),
|
|
split_k_slices);
|
|
}
|
|
|
|
/// Computes CUDA grid dimensions given a size in units of logical tiles
|
|
CUTLASS_HOST_DEVICE
|
|
static dim3 get_grid_shape(GemmCoord tiled_shape) {
|
|
return dim3(tiled_shape.n(), tiled_shape.m(), tiled_shape.k());
|
|
}
|
|
|
|
/// Calculates optimal swizzle width
|
|
CUTLASS_HOST_DEVICE
|
|
static int get_log_tile(GemmCoord tiled_shape) {
|
|
return 0;
|
|
}
|
|
|
|
/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
|
|
CUTLASS_DEVICE
|
|
static GemmCoord get_tile_offset(GemmCoord tiled_shape) {
|
|
return GemmCoord{
|
|
RematerializeBlockIdxY(),
|
|
RematerializeBlockIdxX(),
|
|
RematerializeBlockIdxZ()
|
|
};
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Threadblock swizzling function for batched GEMMs
|
|
struct GemmBatchedIdentityThreadblockSwizzle {
|
|
|
|
/// Returns the shape of the problem in units of logical tiles
|
|
CUTLASS_HOST_DEVICE
|
|
static GemmCoord get_tiled_shape(
|
|
GemmCoord problem_size,
|
|
GemmCoord tile_size,
|
|
int batch_count) {
|
|
|
|
return GemmCoord(
|
|
(problem_size.m() + tile_size.m() - 1) / tile_size.m(),
|
|
(problem_size.n() + tile_size.n() - 1) / tile_size.n(),
|
|
batch_count % (1 << 16));
|
|
}
|
|
|
|
/// Computes CUDA grid dimensions given a size in units of logical tiles
|
|
CUTLASS_HOST_DEVICE
|
|
static dim3 get_grid_shape(GemmCoord tiled_shape) {
|
|
return dim3(tiled_shape.m(), tiled_shape.n(), tiled_shape.k());
|
|
}
|
|
|
|
/// Calculates optimal swizzle width
|
|
CUTLASS_HOST_DEVICE
|
|
static int get_log_tile(GemmCoord tiled_shape) {
|
|
return 0;
|
|
}
|
|
|
|
/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
|
|
CUTLASS_DEVICE
|
|
static GemmCoord get_tile_offset(GemmCoord tiled_shape) {
|
|
return GemmCoord{
|
|
RematerializeBlockIdxX(),
|
|
RematerializeBlockIdxY(),
|
|
RematerializeBlockIdxZ()
|
|
};
|
|
}
|
|
|
|
/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
|
|
CUTLASS_DEVICE
|
|
static GemmCoord get_tile_offset(int log_tile) {
|
|
int block_idx_x = RematerializeBlockIdxX();
|
|
int block_idx_y = RematerializeBlockIdxY();
|
|
int block_idx_z = RematerializeBlockIdxZ();
|
|
|
|
return GemmCoord{(block_idx_x >> log_tile), //
|
|
(block_idx_y << log_tile) + ((block_idx_x) & ((1 << (log_tile)) - 1)),
|
|
block_idx_z};
|
|
}
|
|
|
|
/// Gets the batch index
|
|
CUTLASS_DEVICE
|
|
static int get_batch_idx() {
|
|
return RematerializeBlockIdxZ();
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Threadblock swizzling function for split-K GEMMs
|
|
template <int N = 1>
|
|
struct GemmSplitKIdentityThreadblockSwizzle {
|
|
|
|
int const kTile = N;
|
|
|
|
/// Returns the shape of the problem in units of logical tiles
|
|
CUTLASS_HOST_DEVICE
|
|
static GemmCoord get_tiled_shape(
|
|
GemmCoord problem_size,
|
|
GemmCoord tile_size,
|
|
int partitions) {
|
|
|
|
return GemmCoord(
|
|
(problem_size.m() + tile_size.m() - 1) / tile_size.m(),
|
|
(problem_size.n() + tile_size.n() - 1) / tile_size.n(),
|
|
partitions);
|
|
}
|
|
|
|
/// Calculates optimal swizzle width
|
|
CUTLASS_HOST_DEVICE
|
|
static int get_log_tile(GemmCoord tiled_shape) {
|
|
auto n = tiled_shape.n();
|
|
// Thresholds picked so that it doesn't cause too many no-op CTAs
|
|
if (N >= 8 && n >= 6)
|
|
return 3;
|
|
else if (N >= 4 && n >= 3)
|
|
return 2;
|
|
else if (N >= 2 && n >= 2)
|
|
return 1;
|
|
else
|
|
return 0;
|
|
}
|
|
|
|
/// Computes CUDA grid dimensions given a size in units of logical tiles
|
|
CUTLASS_HOST_DEVICE
|
|
static dim3 get_grid_shape(GemmCoord tiled_shape) {
|
|
int tile = 1 << get_log_tile(tiled_shape);
|
|
return dim3(tiled_shape.m() * tile, (tiled_shape.n() + tile - 1) / tile, tiled_shape.k());
|
|
}
|
|
|
|
/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
|
|
CUTLASS_DEVICE
|
|
static GemmCoord get_tile_offset(int log_tile) {
|
|
int block_idx_x = RematerializeBlockIdxX();
|
|
int block_idx_y = RematerializeBlockIdxY();
|
|
int block_idx_z = RematerializeBlockIdxZ();
|
|
|
|
return GemmCoord{(block_idx_x >> log_tile), //
|
|
(block_idx_y << log_tile) + ((block_idx_x) & ((1 << (log_tile)) - 1)),
|
|
block_idx_z};
|
|
}
|
|
|
|
/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
|
|
CUTLASS_DEVICE
|
|
static GemmCoord get_tile_offset(GemmCoord tiled_shape) {
|
|
|
|
int const kTile = N;
|
|
int block_idx_x = RematerializeBlockIdxX();
|
|
int block_idx_y = RematerializeBlockIdxY();
|
|
|
|
if ((tiled_shape.m() < kTile) || (tiled_shape.n() < kTile))
|
|
return GemmCoord{block_idx_x, block_idx_y, RematerializeBlockIdxZ()};
|
|
|
|
return GemmCoord{
|
|
(block_idx_x / kTile),
|
|
(block_idx_y * kTile) + (block_idx_x % kTile),
|
|
RematerializeBlockIdxZ()
|
|
};
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Threadblock swizzling function for split-K GEMMs
|
|
struct GemmSplitKHorizontalThreadblockSwizzle {
|
|
|
|
/// Returns the shape of the problem in units of logical tiles
|
|
CUTLASS_HOST_DEVICE
|
|
static GemmCoord get_tiled_shape(
|
|
GemmCoord problem_size,
|
|
GemmCoord tile_size,
|
|
int partitions) {
|
|
|
|
return GemmCoord(
|
|
(problem_size.m() + tile_size.m() - 1) / tile_size.m(),
|
|
(problem_size.n() + tile_size.n() - 1) / tile_size.n(),
|
|
partitions);
|
|
}
|
|
|
|
/// Computes CUDA grid dimensions given a size in units of logical tiles
|
|
CUTLASS_HOST_DEVICE
|
|
static dim3 get_grid_shape(GemmCoord tiled_shape) {
|
|
return dim3(tiled_shape.n(), tiled_shape.m(), tiled_shape.k());
|
|
}
|
|
|
|
/// Calculates optimal swizzle width
|
|
CUTLASS_HOST_DEVICE
|
|
static int get_log_tile(GemmCoord tiled_shape) {
|
|
return 0;
|
|
}
|
|
|
|
/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
|
|
CUTLASS_DEVICE
|
|
static GemmCoord get_tile_offset(int log_tile) {
|
|
return GemmCoord{
|
|
RematerializeBlockIdxY(),
|
|
RematerializeBlockIdxX(),
|
|
RematerializeBlockIdxZ()
|
|
};
|
|
}
|
|
|
|
/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
|
|
CUTLASS_DEVICE
|
|
static GemmCoord get_tile_offset(GemmCoord tiled_shape) {
|
|
return GemmCoord{
|
|
RematerializeBlockIdxY(),
|
|
RematerializeBlockIdxX(),
|
|
RematerializeBlockIdxZ()
|
|
};
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Threadblock swizzling function for batched GEMVs
|
|
struct GemvBatchedStridedThreadblockDefaultSwizzle {
|
|
|
|
/// Returns the shape of the problem in units of logical tiles
|
|
CUTLASS_HOST_DEVICE
|
|
static BatchedGemmCoord get_tiled_shape(
|
|
BatchedGemmCoord problem_size,
|
|
BatchedGemmCoord tile_size) {
|
|
|
|
return BatchedGemmCoord(
|
|
1, // M is always 1
|
|
(problem_size.n() + tile_size.n() - 1) / tile_size.n(),
|
|
(problem_size.k() + tile_size.k() - 1) / tile_size.k(),
|
|
(problem_size.batch() + tile_size.batch() - 1) / tile_size.batch());
|
|
}
|
|
|
|
/// Computes CUDA grid dimensions given a size in units of logical tiles
|
|
CUTLASS_HOST_DEVICE
|
|
static dim3 get_grid_shape(BatchedGemmCoord tiled_shape) {
|
|
return dim3(tiled_shape.n(), tiled_shape.batch(), tiled_shape.k());
|
|
}
|
|
|
|
/// Calculates optimal swizzle width
|
|
CUTLASS_HOST_DEVICE
|
|
static int get_log_tile(GemmCoord tiled_shape) {
|
|
return 0;
|
|
}
|
|
|
|
/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
|
|
CUTLASS_DEVICE
|
|
static BatchedGemmCoord get_tile_offset(int log_tile) {
|
|
return BatchedGemmCoord{
|
|
0, // M is always 1
|
|
RematerializeBlockIdxX(),
|
|
RematerializeBlockIdxZ(),
|
|
RematerializeBlockIdxY(),
|
|
};
|
|
}
|
|
|
|
/// Obtains the threadblock offset (in units of threadblock-scoped tiles)
|
|
CUTLASS_DEVICE
|
|
static BatchedGemmCoord get_tile_offset() {
|
|
return BatchedGemmCoord{
|
|
0, // M is always 1
|
|
RematerializeBlockIdxX(),
|
|
RematerializeBlockIdxZ(),
|
|
RematerializeBlockIdxY(),
|
|
};
|
|
}
|
|
|
|
/// Gets the batch tile index
|
|
CUTLASS_DEVICE
|
|
static int get_batch_tile_idx() {
|
|
return RematerializeBlockIdxY();
|
|
}
|
|
|
|
/// Gets the absolute batch index
|
|
CUTLASS_DEVICE
|
|
static int get_batch_idx() {
|
|
return RematerializeBlockDimY()*RematerializeBlockIdxY() + RematerializeThreadIdxY();
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace threadblock
|
|
} // namespace gemm
|
|
} // namespace cutlass
|
|
|