205 lines
6.5 KiB
C++
205 lines
6.5 KiB
C++
/***************************************************************************************************
|
|
* Copyright (c) 2024 - 2025 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.
|
|
*
|
|
**************************************************************************************************/
|
|
|
|
#pragma once
|
|
|
|
#include "cutlass/cutlass.h"
|
|
#include "cutlass/fast_math.h"
|
|
#include "cutlass/kernel_hardware_info.h"
|
|
|
|
namespace cutlass::fmha::kernel {
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
struct IndividualTileScheduler {
|
|
|
|
struct Params {
|
|
dim3 grid;
|
|
};
|
|
|
|
bool valid_ = true;
|
|
|
|
CUTLASS_DEVICE
|
|
IndividualTileScheduler(Params const&) {}
|
|
|
|
template<class ProblemSize, class ClusterShape, class TileShape>
|
|
static Params to_underlying_arguments(
|
|
ProblemSize const& problem_size, KernelHardwareInfo hw_info,
|
|
ClusterShape const& cluster_shape, TileShape const& tile_shape)
|
|
{
|
|
using namespace cute;
|
|
dim3 grid(round_up(ceil_div(size<2>(problem_size), size<0>(tile_shape)), size<0>(cluster_shape)), size<0>(problem_size), size<1>(problem_size));
|
|
return Params{ grid };
|
|
}
|
|
|
|
static dim3 get_grid_shape(Params const& params) {
|
|
return params.grid;
|
|
}
|
|
|
|
CUTLASS_DEVICE
|
|
bool is_valid() {
|
|
return valid_;
|
|
}
|
|
|
|
CUTLASS_DEVICE
|
|
auto get_block_coord() {
|
|
using namespace cute;
|
|
return make_coord(blockIdx.x, _0{}, make_coord(blockIdx.y, blockIdx.z));
|
|
}
|
|
|
|
CUTLASS_DEVICE
|
|
IndividualTileScheduler& operator++() {
|
|
valid_ = false;
|
|
return *this;
|
|
}
|
|
};
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
struct PersistentTileScheduler {
|
|
|
|
struct Params {
|
|
int num_blocks;
|
|
FastDivmod divmod_m_block;
|
|
FastDivmod divmod_b;
|
|
FastDivmod divmod_h;
|
|
|
|
KernelHardwareInfo hw_info;
|
|
};
|
|
|
|
int block_idx = 0;
|
|
Params params;
|
|
|
|
CUTLASS_DEVICE
|
|
PersistentTileScheduler(Params const& params) : block_idx(blockIdx.x), params(params) {}
|
|
|
|
template<class ProblemSize, class ClusterShape, class TileShape>
|
|
static Params to_underlying_arguments(
|
|
ProblemSize const& problem_size, KernelHardwareInfo hw_info,
|
|
ClusterShape const& cluster_shape, TileShape const& tile_shape)
|
|
{
|
|
using namespace cute;
|
|
// Get SM count if needed, otherwise use user supplied SM count
|
|
int sm_count = hw_info.sm_count;
|
|
if (sm_count <= 0) {
|
|
CUTLASS_TRACE_HOST(" WARNING: Arguments do not include a valid SM count.\n"
|
|
" For optimal performance, populate the arguments KernelHardwareInfo struct with the SM count.");
|
|
sm_count = KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
|
|
}
|
|
|
|
CUTLASS_TRACE_HOST("to_underlying_arguments(): Setting persistent grid SM count to " << sm_count);
|
|
hw_info.sm_count = sm_count;
|
|
|
|
int num_m_blocks = cutlass::round_up(ceil_div(size<2>(problem_size), size<0>(tile_shape)), size<0>(cluster_shape));
|
|
int num_blocks = num_m_blocks * size<0>(problem_size) * size<1>(problem_size);
|
|
|
|
return Params {
|
|
num_blocks,
|
|
{ num_m_blocks}, { size<0>(problem_size) }, { size<1>(problem_size) },
|
|
hw_info
|
|
};
|
|
}
|
|
|
|
static dim3 get_grid_shape(Params const& params) {
|
|
dim3 grid(std::min(params.num_blocks, params.hw_info.sm_count), 1, 1);
|
|
return grid;
|
|
}
|
|
|
|
CUTLASS_DEVICE
|
|
bool is_valid() {
|
|
return block_idx < params.num_blocks;
|
|
}
|
|
|
|
CUTLASS_DEVICE
|
|
auto get_block_coord() {
|
|
using namespace cute;
|
|
int block_decode = block_idx;
|
|
int m_block, bidb, bidh;
|
|
params.divmod_m_block(block_decode, m_block, block_decode);
|
|
params.divmod_b(block_decode, bidb, block_decode);
|
|
params.divmod_h(block_decode, bidh, block_decode);
|
|
return make_coord(m_block, _0{}, make_coord(bidb, bidh));
|
|
}
|
|
|
|
CUTLASS_DEVICE
|
|
PersistentTileScheduler& operator++() {
|
|
block_idx += gridDim.x;
|
|
return *this;
|
|
}
|
|
};
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template<typename Base>
|
|
struct TileSchedulerBwdAdapter {
|
|
|
|
using Params = typename Base::Params;
|
|
|
|
Base base_;
|
|
|
|
CUTLASS_DEVICE
|
|
TileSchedulerBwdAdapter(Params const& params) : base_(params) {}
|
|
|
|
template<class ProblemSize, class ClusterShape, class TileShape>
|
|
static Params to_underlying_arguments(
|
|
ProblemSize const& problem_size, KernelHardwareInfo hw_info,
|
|
ClusterShape const& cluster_shape, TileShape const& tile_shape)
|
|
{
|
|
using namespace cute;
|
|
return Base::to_underlying_arguments(select<0,1,3,2,4>(problem_size), hw_info, select<1,0,2>(cluster_shape), select<1,0,2>(tile_shape));
|
|
}
|
|
|
|
static dim3 get_grid_shape(Params const& params) {
|
|
return Base::get_grid_shape(params);
|
|
}
|
|
|
|
CUTLASS_DEVICE
|
|
bool is_valid() {
|
|
return base_.is_valid();
|
|
}
|
|
|
|
CUTLASS_DEVICE
|
|
auto get_block_coord() {
|
|
using namespace cute;
|
|
return select<1,0,2>(base_.get_block_coord());
|
|
}
|
|
|
|
CUTLASS_DEVICE
|
|
TileSchedulerBwdAdapter& operator++() {
|
|
++base_;
|
|
return *this;
|
|
}
|
|
};
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace cutlass::fmha::kernel
|