Files
FastDeploy/custom_ops/gpu_ops/cutlass_extensions/gemm_configs.h
2025-06-09 19:20:15 +08:00

255 lines
8.5 KiB
C++

/*
* Copyright (c) 2020-2023, NVIDIA CORPORATION. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#pragma once
#include <cassert>
#include <iostream>
#include <sstream>
#include <string>
namespace cutlass_extensions
{
// Note: The shapes are in the format MxNxK. The K shape of the runtime config MUST match the K shape
// in the kernel layout details when doing weight only quantization.
enum class CutlassTileConfig
{
// Signals that we should run heuristics do choose a config
Undefined,
// Signals that we should run heuristics do choose a config
ChooseWithHeuristic,
// SiMT config
CtaShape128x128x8_WarpShape64x64x8,
// TensorCore configs CTA_N = 128, CTA_K = 64
// Warp configs for M=16
CtaShape16x128x64_WarpShape16x32x64,
// Warp configs for M=32
CtaShape32x128x64_WarpShape32x32x64,
// Warp configs for M=64
CtaShape64x128x64_WarpShape32x64x64,
CtaShape64x64x128_WarpShape32x64x64,
CtaShape64x128x64_WarpShape64x32x64,
// Warp configs for M=128
CtaShape128x64x64_WarpShape64x32x64,
CtaShape128x128x64_WarpShape64x32x64,
CtaShape128x128x64_WarpShape64x64x64,
CtaShape128x128x64_WarpShape128x32x64,
CtaShape128x256x64_WarpShape64x64x64,
// Warp configs for M=256
CtaShape256x128x64_WarpShape64x64x64,
// TensorCore config CTA_N = 64, CTA_K = 128
CtaShape128x64x128_WarpShape64x32x128,
// TensorCore config CTA_N = 256, CTA_K = 64
CtaShape16x256x64_WarpShape16x64x64,
// TensorCore config CTA_N = 256, CTA_K = 128
CtaShape16x256x128_WarpShape16x64x128
};
enum class SplitKStyle
{
NO_SPLIT_K,
SPLIT_K_SERIAL,
STREAM_K, // Sm80+
// SPLIT_K_PARALLEL // Not supported yet
};
enum class CutlassTileConfigSM90
{
// Signals that we should run heuristics do choose a config
Undefined,
// Signals that we should run heuristics do choose a config
ChooseWithHeuristic,
// CTA configs for M=64
CtaShape64x16x128B,
CtaShape64x32x128B,
CtaShape64x64x128B,
CtaShape64x128x128B,
CtaShape64x256x128B,
// CTA configs for M=128
CtaShape128x16x128B,
CtaShape128x32x128B,
CtaShape128x64x128B,
CtaShape128x128x128B,
CtaShape128x256x128B,
// CTA configs for M=128
CtaShape256x128x128B,
};
enum class MainloopScheduleType
{
AUTO // Automatically selects between pingpong and cooperative schedules on Hopper. On older architectures, this
// defaults to the "legacy" main loop schedule.
};
enum class EpilogueScheduleType
{
AUTO // Automatically chooses an epilogue schedule compatible with the selected main loop schedule for Hopper. For
// architectures older than hopper, the epilogue is always performed by the same thread block as the main loop.
};
enum class ClusterShape
{
ClusterShape_1x1x1,
ClusterShape_2x1x1,
ClusterShape_1x2x1,
ClusterShape_2x2x1,
ClusterShape_1x8x1,
ClusterShape_8x1x1
};
struct CutlassGemmConfig
{
enum CandidateConfigTypeParam : int
{
NONE = 0,
WEIGHT_ONLY = 1u << 0,
SIMT_ONLY = 1u << 1,
INT8_ONLY = 1u << 2,
HOPPER = 1u << 3,
GROUPED_GEMM = 1u << 4,
FP8_ONLY = 1u << 5,
};
CutlassTileConfig tile_config = CutlassTileConfig::ChooseWithHeuristic;
SplitKStyle split_k_style = SplitKStyle::NO_SPLIT_K;
int split_k_factor = -1;
int stages = -1;
// config options for sm90
CutlassTileConfigSM90 tile_config_sm90 = CutlassTileConfigSM90::ChooseWithHeuristic;
MainloopScheduleType mainloop_schedule = MainloopScheduleType::AUTO;
EpilogueScheduleType epilogue_schedule = EpilogueScheduleType::AUTO;
ClusterShape cluster_shape = ClusterShape::ClusterShape_1x1x1;
bool is_sm90 = false;
CutlassGemmConfig() {}
CutlassGemmConfig(CutlassTileConfig tile_config, SplitKStyle split_k_style, int split_k_factor, int stages)
: tile_config(tile_config)
, split_k_style(split_k_style)
, split_k_factor(split_k_factor)
, stages(stages)
, is_sm90(false)
{
}
CutlassGemmConfig(CutlassTileConfigSM90 tile_config_sm90, MainloopScheduleType mainloop_schedule,
EpilogueScheduleType epilogue_schedule, ClusterShape cluster_shape)
: tile_config_sm90(tile_config_sm90)
, mainloop_schedule(mainloop_schedule)
, epilogue_schedule(epilogue_schedule)
, cluster_shape(cluster_shape)
, is_sm90(true)
{
}
std::string toString() const
{
std::stringstream tactic;
tactic << "Cutlass GEMM Tactic";
if (tile_config_sm90 != cutlass_extensions::CutlassTileConfigSM90::ChooseWithHeuristic)
{
assert(is_sm90 && "Invalid cutlass GEMM config");
tactic << "\n\tstyle=TMA"
<< "\n\ttile shape ID: " << (int) tile_config_sm90
<< "\n\tcluster shape ID: " << (int) cluster_shape
<< "\n\tmainloop sched: " << (int) mainloop_schedule
<< "\n\tepi sched: " << (int) epilogue_schedule;
}
else if (tile_config != cutlass_extensions::CutlassTileConfig::ChooseWithHeuristic)
{
assert(!is_sm90 && "Invalid cutlass GEMM config");
tactic << "\n\tstyle=compatible"
<< "\n\ttile shape ID: " << (int) tile_config
<< "\n\tstages: " << (int) stages
<< "\n\tsplit_k_style: " << (int) split_k_style
<< "\n\tsplit k: " << (int) split_k_factor;
}
else
{
tactic << "\n\tundefined";
}
tactic << "\n";
return tactic.str();
}
void fromString(const std::string& str) {
std::istringstream stream(str);
std::string line;
while (std::getline(stream, line)) {
if (line.find("style=TMA") != std::string::npos) {
is_sm90 = true;
std::getline(stream, line);
tile_config_sm90 = static_cast<cutlass_extensions::CutlassTileConfigSM90>(std::stoi(line.substr(line.find(':') + 1)));
std::getline(stream, line);
cluster_shape = static_cast<cutlass_extensions::ClusterShape>(std::stoi(line.substr(line.find(':') + 1)));
std::getline(stream, line);
mainloop_schedule = static_cast<cutlass_extensions::MainloopScheduleType>(std::stoi(line.substr(line.find(':') + 1)));
std::getline(stream, line);
epilogue_schedule = static_cast<cutlass_extensions::EpilogueScheduleType>(std::stoi(line.substr(line.find(':') + 1)));
} else if (line.find("style=compatible") != std::string::npos) {
is_sm90 = false;
std::getline(stream, line);
tile_config = static_cast<cutlass_extensions::CutlassTileConfig>(std::stoi(line.substr(line.find(':') + 1)));
std::getline(stream, line);
stages = std::stoi(line.substr(line.find(':') + 1));
std::getline(stream, line);
split_k_style = static_cast<cutlass_extensions::SplitKStyle>(std::stoi(line.substr(line.find(':') + 1)));
std::getline(stream, line);
split_k_factor = std::stoi(line.substr(line.find(':') + 1));
}
}
}
};
inline std::ostream& operator<<(std::ostream& out, CutlassGemmConfig const& config)
{
// clang-format off
if (config.is_sm90)
{
out << "tile_config_sm90_enum: " << int(config.tile_config_sm90)
<< ", mainloop_schedule_enum: " << int(config.mainloop_schedule)
<< ", epilogue_schedule_enum: " << int(config.epilogue_schedule)
<< ", cluster_shape_enum: " << int(config.cluster_shape);
}
else
{
out << "tile_config_enum: " << int(config.tile_config)
<< ", split_k_style_enum: " << int(config.split_k_style)
<< ", split_k_factor: " << config.split_k_factor
<< ", stages: " << config.stages;
}
// clang-format on
return out;
}
} // namespace cutlass_extensions