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Unverified Commit d71189ff authored by Rostyslav Geyyer's avatar Rostyslav Geyyer Committed by GitHub
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Merge branch 'develop' into lwpck-1815

parents f84e2020 73b67f29
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View file @ d71189ff
......@@ -262,10 +262,19 @@ def cmake_build(Map conf=[:]){
// reduce parallelism when compiling, clang uses too much memory
def nt = nthreads()
def cmd
def setup_cmd
def build_cmd
def execute_cmd = conf.get("execute_cmd", "")
if(!setup_args.contains("NO_CK_BUILD")){
def setup_cmd = conf.get("setup_cmd", "${cmake_envs} cmake ${setup_args} .. ")
def build_cmd = conf.get("build_cmd", "${build_envs} dumb-init make -j${nt} ${config_targets}")
if (setup_args.contains("gfx90a") && params.NINJA_BUILD_TRACE){
echo "running ninja build trace"
setup_cmd = conf.get("setup_cmd", "${cmake_envs} cmake -G Ninja ${setup_args} .. ")
build_cmd = conf.get("build_cmd", "${build_envs} ninja -j${nt} ${config_targets}")
}
else{
setup_cmd = conf.get("setup_cmd", "${cmake_envs} cmake ${setup_args} .. ")
build_cmd = conf.get("build_cmd", "${build_envs} dumb-init make -j${nt} ${config_targets}")
}
cmd = conf.get("cmd", """
${setup_cmd}
${build_cmd}
......@@ -281,7 +290,19 @@ def cmake_build(Map conf=[:]){
echo cmd
dir("build"){
//build CK
sh cmd
//run tests
if(!setup_args.contains("NO_CK_BUILD")){
if (setup_args.contains("gfx90a") && params.NINJA_BUILD_TRACE){
sh "/ninjatracing/ninjatracing .ninja_log > ck_build_trace.json"
archiveArtifacts "ck_build_trace.json"
sh "ninja test"
}
else{
sh "make check"
}
}
}
// Only archive from master or develop
......@@ -543,7 +564,7 @@ def Build_CK(Map conf=[:]){
cmake_build(conf)
dir("build"){
//run tests and examples
sh 'make -j check'
//sh 'make -j check'
if (params.RUN_PERFORMANCE_TESTS && do_perf_tests == 0 ){
//we only need the ckProfiler to run the performance tests, so we pack and stash it
//do not stash profiler on nodes where we don't need to run performance tests
......@@ -684,8 +705,8 @@ def process_results(Map conf=[:]){
//launch develop branch daily at 23:00 UT in FULL_QA mode and at 19:00 UT with latest staging compiler version
CRON_SETTINGS = BRANCH_NAME == "develop" ? '''0 23 * * * % RUN_FULL_QA=true;ROCMVERSION=6.2; RUN_CK_TILE_TESTS=true
0 21 * * * % ROCMVERSION=6.2;hipTensor_test=true
0 19 * * * % BUILD_DOCKER=true;DL_KERNELS=true;COMPILER_VERSION=amd-staging;BUILD_COMPILER=/llvm-project/build/bin/clang++;BUILD_GFX12=true;USE_SCCACHE=false
0 17 * * * % BUILD_DOCKER=true;DL_KERNELS=true;COMPILER_VERSION=amd-mainline-open;BUILD_COMPILER=/llvm-project/build/bin/clang++;BUILD_GFX12=true;USE_SCCACHE=false
0 19 * * * % BUILD_DOCKER=true;DL_KERNELS=true;COMPILER_VERSION=amd-staging;BUILD_COMPILER=/llvm-project/build/bin/clang++;BUILD_GFX12=true;USE_SCCACHE=false;NINJA_BUILD_TRACE=true
0 17 * * * % BUILD_DOCKER=true;DL_KERNELS=true;COMPILER_VERSION=amd-mainline-open;BUILD_COMPILER=/llvm-project/build/bin/clang++;BUILD_GFX12=true;USE_SCCACHE=false;NINJA_BUILD_TRACE=true
0 15 * * * % BUILD_INSTANCES_ONLY=true;RUN_CODEGEN_TESTS=false;RUN_PERFORMANCE_TESTS=false;USE_SCCACHE=false''' : ""
pipeline {
......@@ -765,7 +786,10 @@ pipeline {
name: "BUILD_GFX12",
defaultValue: false,
description: "Build CK and run tests on gfx12 (default: OFF)")
booleanParam(
name: "NINJA_BUILD_TRACE",
defaultValue: false,
description: "Generate a ninja build trace (default: OFF)")
}
environment{
dbuser = "${dbuser}"
......@@ -799,6 +823,7 @@ pipeline {
}
agent{ label rocmnode("nogpu") }
environment{
setup_args = "NO_CK_BUILD"
execute_cmd = "find .. -not -path \'*.git*\' -iname \'*.h\' \
-o -not -path \'*.git*\' -iname \'*.hpp\' \
-o -not -path \'*.git*\' -iname \'*.cpp\' \
......@@ -815,7 +840,7 @@ pipeline {
--file-filter=*.cpp --force --enable=all --output-file=ck_cppcheck.log"
}
steps{
buildHipClangJobAndReboot(setup_cmd: "", build_cmd: "", execute_cmd: execute_cmd, no_reboot:true)
buildHipClangJobAndReboot(setup_args:setup_args, setup_cmd: "", build_cmd: "", execute_cmd: execute_cmd, no_reboot:true)
archiveArtifacts "build/ck_cppcheck.log"
cleanWs()
}
......@@ -827,6 +852,7 @@ pipeline {
}
agent{ label rocmnode("nogpu") }
environment{
setup_args = "NO_CK_BUILD"
execute_cmd = "find .. -not -path \'*.git*\' -iname \'*.h\' \
-o -not -path \'*.git*\' -iname \'*.hpp\' \
-o -not -path \'*.git*\' -iname \'*.cpp\' \
......@@ -838,7 +864,7 @@ pipeline {
| xargs -n 1 -P 1 -I{} -t sh -c \'clang-format-12 -style=file {} | diff - {}\'"
}
steps{
buildHipClangJobAndReboot(setup_cmd: "", build_cmd: "", execute_cmd: execute_cmd, no_reboot:true)
buildHipClangJobAndReboot(setup_args:setup_args, setup_cmd: "", build_cmd: "", execute_cmd: execute_cmd, no_reboot:true)
cleanWs()
}
}
......@@ -967,10 +993,10 @@ pipeline {
}
agent{ label rocmnode("gfx90a") }
environment{
setup_args = """ -DCMAKE_INSTALL_PREFIX=../install -DGPU_TARGETS="gfx1100;gfx90a" -DCMAKE_CXX_FLAGS=" -O3 " """
setup_args = """ -DCMAKE_INSTALL_PREFIX=../install -DGPU_TARGETS="gfx90a" -DCMAKE_CXX_FLAGS=" -O3 " """
execute_args = """ cd ../client_example && rm -rf build && mkdir build && cd build && \
cmake -DCMAKE_PREFIX_PATH="${env.WORKSPACE}/install;/opt/rocm" \
-DGPU_TARGETS="gfx1100;gfx90a" \
-DGPU_TARGETS="gfx90a" \
-DCMAKE_CXX_COMPILER="${build_compiler()}" \
-DCMAKE_CXX_FLAGS=" -O3 " .. && make -j """
}
......@@ -1074,7 +1100,7 @@ pipeline {
options { retry(1) }
agent{ label rocmnode("gfx90a")}
environment{
setup_args = """ -DGPU_TARGETS="gfx90a" -DBUILD_DEV=On """
setup_args = "NO_CK_BUILD"
}
steps{
runPerfTest(setup_args:setup_args, config_targets: "ckProfiler", no_reboot:true, build_type: 'Release')
......
example/ck_tile/01_fmha/CMakeLists.txt
View file @ d71189ff
# generate a list of kernels, but not actually emit files at config stage
# validate user-specified fmha_fwd API list
set(FMHA_FWD_KNOWN_APIS "fwd;fwd_splitkv;fwd_appendkv")
set(FMHA_FWD_ENABLE_APIS "fwd" CACHE STRING
"semicolon-separated list of APIs to generate (${FMHA_FWD_KNOWN_APIS}) & link, or \"all\".")
if(FMHA_FWD_ENABLE_APIS STREQUAL "all")
set(FMHA_FWD_ENABLE_APIS ${FMHA_FWD_KNOWN_APIS})
endif()
foreach(api ${FMHA_FWD_ENABLE_APIS})
if(NOT "${api}" IN_LIST FMHA_FWD_KNOWN_APIS)
message(FATAL_ERROR "${api} isn't a known api: ${FMHA_FWD_KNOWN_APIS}.")
endif()
endforeach()
# "fwd" is a must-have api for the fmha_fwd example, add it if not specified
if(NOT "fwd" IN_LIST FMHA_FWD_ENABLE_APIS)
list(APPEND FMHA_FWD_ENABLE_APIS "fwd")
endif()
string(REPLACE ";" "," FMHA_FWD_APIS "${FMHA_FWD_ENABLE_APIS}")
# generate a list of kernels, but not actually emit files at config sta
execute_process(
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--api fwd,fwd_splitkv --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/fwd_blob_list.txt
--api ${FMHA_FWD_APIS} --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/fwd_blob_list.txt
)
execute_process(
......@@ -17,7 +37,7 @@ file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/bwd_blob_list.txt FMHA_BWD_GEN_BLOBS)
add_custom_command(
OUTPUT ${FMHA_FWD_GEN_BLOBS}
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--api fwd,fwd_splitkv --output_dir ${CMAKE_CURRENT_BINARY_DIR}
--api ${FMHA_FWD_APIS} --output_dir ${CMAKE_CURRENT_BINARY_DIR}
)
add_custom_command(
......@@ -60,6 +80,20 @@ else()
endif()
list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-undefined-func-template -fgpu-flush-denormals-to-zero)
# conditionally enable call to the fwd_splitkv API in fmha_fwd example
if("fwd_splitkv" IN_LIST FMHA_FWD_ENABLE_APIS)
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -DCK_TILE_FMHA_FWD_SPLITKV_API=1)
else()
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -DCK_TILE_FMHA_FWD_SPLITKV_API=0)
endif()
# conditionally enable call to the fwd_appendkv API in fmha_fwd example
if("fwd_appendkv" IN_LIST FMHA_FWD_ENABLE_APIS)
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -DCK_TILE_FMHA_FWD_APPENDKV_API=1)
else()
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -DCK_TILE_FMHA_FWD_APPENDKV_API=0)
endif()
# Allow comparing floating points directly in order to check sentinel values
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-float-equal)
list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-float-equal)
......
example/ck_tile/01_fmha/codegen/cpp_symbol_map.py
View file @ d71189ff
......@@ -82,6 +82,18 @@ DROPOUT_CHECK_MAP = {
"dropout_wg16_storerandval" : "t.has_dropout == true && t.is_store_randval == true",
}
ROPE_MAP = {
"no" : "ck_tile::RotaryEmbeddingEnum::NONE",
"inter" : "ck_tile::RotaryEmbeddingEnum::INTERLEAVED",
"half" : "ck_tile::RotaryEmbeddingEnum::HALF_ROTATED"
}
ROPE_CHECK_MAP = {
"no" : "rope_enum::none",
"inter" : "rope_enum::interleaved",
"half" : "rope_enum::half_rotated"
}
MODE_MAP = {
"batch" : "false",
"group" : "true"
......@@ -105,4 +117,4 @@ PIPELINE_ENUM_MAP = {
BOOL_MAP = {
"t" : "true",
"f" : "false"
}
\ No newline at end of file
}
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_appendkv.py 0 → 100644
View file @ d71189ff
# SPDX-License-Identifier: MIT
# Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
# generate kernel instances to speed up compilation
import copy
from dataclasses import dataclass
import fnmatch
import itertools
from pathlib import Path
from typing import List, Optional, Tuple
from codegen.cmake_config import *
from codegen.cpp_symbol_map import *
from codegen.ops.fmha_fwd import (
FmhaFwdApiTrait,
DTYPE_BITS,
FMHA_FWD_KERNEL_HEADER,
FMHA_FWD_API_PER_DTYPE,
FMHA_FWD_API_PER_HDIM_CASE,
)
FMHA_FWD_APPENDKV_KERNEL_BODY="""
using fmha_dtype_{F_idx} = {F_dtype};
using fmha_trait_{F_idx} = ck_tile::TileFmhaFwdAppendKVTraits<{F_spad},
{F_skpad},
{F_dpad},
{F_dvpad},
{F_occupancy}>;
using fmha_pipeline_problem_{F_idx} = ck_tile::BlockFmhaFwdAppendKVPipelineProblem<
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::QDataType,
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::KDataType,
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::VDataType,
{F_bs},
{F_bsk},
{F_bd},
{F_bdv},
{F_vlayout},
{F_rope},
{F_pagedkv},
fmha_trait_{F_idx}>;
using fmha_pipeline_{F_idx} = ck_tile::BlockFmhaFwdAppendKVPipeline<
fmha_pipeline_problem_{F_idx}>;
using fmha_kernel_{F_idx} =
ck_tile::FmhaFwdAppendKVKernel<ck_tile::FmhaFwdAppendKVTilePartitioner<{F_bs}, {F_bsk}, {F_bd}, {F_bdv}>,
fmha_pipeline_{F_idx}>;
using trait_{F_idx} = fmha_fwd_appendkv_traits_<{F_hdim}, {F_dtype}, {F_bs}, {F_bsk}, {F_bd}, {F_bdv}, {F_vlayout},
{F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}, {F_rope}, {F_pagedkv}>;
#include <iostream>
template<>
float fmha_fwd_appendkv_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_appendkv_args a)
{{
using k_ = fmha_kernel_{F_idx};
if(s.log_level_ > 0)
std::cout << ", " << k_::GetName() << std::flush;
auto [kargs, grids] = fmha_fwd_appendkv_create_kargs_and_grids<k_>(a);
constexpr dim3 blocks = k_::BlockSize();
constexpr ck_tile::index_t kBlockPerCu = k_::kBlockPerCu;
return ck_tile::launch_kernel(s, ck_tile::make_kernel<blocks.x, kBlockPerCu>(k_{{}}, grids, blocks, 0, kargs));
}}
"""
FMHA_FWD_APPENDKV_API_FILENAME="fmha_fwd_appendkv_api.cpp"
FMHA_FWD_APPENDKV_API="""
float fmha_fwd_appendkv(fmha_fwd_appendkv_traits t, fmha_fwd_appendkv_args a, const ck_tile::stream_config& s){{
float r = -1;
{F_dispatch}
return r;
}}
"""
FMHA_FWD_APPENDKV_API_INNER_DISPATCH=""" {F_if}((t.is_v_rowmajor == {F_vlayout}) &&
({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck}) && (t.rope_type == {F_rope_check}) &&
((a.block_table_ptr != nullptr) == {F_pagedkv})) {{
using trait_ = fmha_fwd_appendkv_traits_<{F_hdim}, {F_dtype}, {F_bs}, {F_bsk}, {F_bd}, {F_bdv}, {F_vlayout}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}, {F_rope}, {F_pagedkv}>;
return fmha_fwd_appendkv_<trait_>(s, a);
}}
"""
@dataclass
class FmhaFwdAppendKVApiTrait:
# sync with fmha_fwd_traits<>, to generate fallback calls
hdim : str
dtype : str # data type
bs : int # tile size along q seqlen
bsk : int # tile size along k seqlen
bd : int # tile size along qk gemm unroll
bdv : int # tile size along kv gemm unroll
vlayout : str
spad : str
skpad : str
dpad : str
dvpad : str
rope : str # key from ROPE_MAP
pagedkv : str
@property
def name(self) -> str:
return f'{self.hdim}-{self.dtype}-{self.bs}-{self.bsk}-{self.bd}-{self.bdv}-{self.vlayout}-'+\
f'{self.spad}-{self.skpad}-{self.dpad}-{self.dvpad}-{self.rope}-{self.pagedkv}'
@property
def scheck(self) -> str:
if self.spad == 't' : return f'true /*a.seqlen_q % {self.bs} != 0*/'
else : return f'a.seqlen_q % {self.bs} == 0'
@property
def skcheck(self) -> str:
# we do not check all the values in a.seqlen_k_ptr
return 'true'
@property
def dcheck(self) -> str:
if self.dpad == 't': return f'true /*a.hdim_q % {self.bd} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_q % {self.bd} == 0'
@property
def dvcheck(self) -> str:
if self.dvpad == 't': return f'true /*a.hdim_v % {self.bdv} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_v % {self.bdv} == 0'
@dataclass
class FmhaFwdAppendKVPipeline:
F_vlayout : str # row/col
F_spad : str # true/false
F_skpad : str #
F_dpad : str #
F_dvpad : str #
F_rope : str # key from ROPE_MAP
F_pagedkv : str # t/f
@property
def name(self) -> str:
def pad_name() -> str:
n = ''
if self.F_spad == 't': n += 's'
if self.F_skpad == 't' : n += 'sk'
if self.F_dpad == 't' : n += 'd'
if self.F_dvpad == 't' : n += 'dv'
if n != '' : n = 'p' + n
return n
pn = pad_name()
n = f'v{self.F_vlayout[0]}'
if pn != '' : n += f'_{pn}'
if self.F_rope != 'no': n += f'_{self.F_rope}'
if self.F_pagedkv == 't': n += '_pagedkv'
return n
class FmhaFwdAppendKVApiPool:
def __init__(self, mask_impl):
self.pool = dict()
self.mask_impl = mask_impl
def register_traits(self, trait : FmhaFwdApiTrait) -> None:
# TODO: do we need to check duplication?
if trait.dtype not in self.pool.keys():
self.pool[trait.dtype] = dict()
if trait.hdim not in self.pool[trait.dtype].keys():
self.pool[trait.dtype][trait.hdim] = list()
self.pool[trait.dtype][trait.hdim].append(copy.copy(trait))
@property
def api(self) -> str:
per_dtypes=str()
for i, dtype in enumerate(self.pool.keys()):
per_hdim_case=str()
for j, hdim in enumerate(self.pool[dtype].keys()):
traits=self.pool[dtype][hdim]
inners=str()
for k, trait in enumerate(traits):
if_k = 'if' if k == 0 else 'else if'
inners = inners + FMHA_FWD_APPENDKV_API_INNER_DISPATCH.format(F_if=if_k, F_vlayout=LAYOUT_MAP[trait.vlayout],
F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_rope_check=ROPE_CHECK_MAP[trait.rope],
F_pagedkv=BOOL_MAP[trait.pagedkv], F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
F_rope=ROPE_MAP[trait.rope], F_bs=trait.bs, F_bsk=trait.bsk, F_bd=trait.bd, F_bdv=trait.bdv, F_hdim=hdim, F_dtype=DTYPE_MAP[dtype])
if_j = 'if' if j == 0 else 'else if'
per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners)
if_i = 'if' if i == 0 else 'else if'
per_dtypes = per_dtypes + FMHA_FWD_API_PER_DTYPE.format(F_if=if_i, F_dtype=dtype, F_hdim_case=per_hdim_case)
return FMHA_FWD_KERNEL_HEADER + FMHA_FWD_APPENDKV_API.format(F_dispatch = per_dtypes)
@dataclass
class FmhaFwdAppendKVTileSize:
F_bs : int # tile size along q seqlen
F_bsk : int # tile size along k seqlen
F_bd : int # tile size along qk gemm unroll
F_bdv : int # tile size along kv gemm unroll
F_occupancy : int # occupancy, -1 will let pipeline decide the occupancy, other value will overwrite occupancy
@property
def name(self) -> str:
return f"b{self.F_bs}x{self.F_bsk}x{self.F_bd}x{self.F_bdv}" +\
("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}")
@dataclass
class FmhaFwdAppendKVKernel:
F_idx : int # this is not a tunable, but a counter to differentiate symbol
F_hdim : int # hdim
F_dtype : str # data type
F_tile : FmhaFwdAppendKVTileSize
F_pipeline : FmhaFwdAppendKVPipeline
mask_impl : str
@property
def template(self) -> str:
kernel_body = str()
return FMHA_FWD_KERNEL_HEADER + \
FMHA_FWD_APPENDKV_KERNEL_BODY.format(
F_idx = self.F_idx,
F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype],
F_bs = self.F_tile.F_bs,
F_bsk = self.F_tile.F_bsk,
F_bd = self.F_tile.F_bd,
F_bdv = self.F_tile.F_bdv,
F_vlayout = LAYOUT_MAP[self.F_pipeline.F_vlayout],
F_spad = BOOL_MAP[self.F_pipeline.F_spad],
F_skpad = BOOL_MAP[self.F_pipeline.F_skpad],
F_dpad = BOOL_MAP[self.F_pipeline.F_dpad],
F_dvpad = BOOL_MAP[self.F_pipeline.F_dvpad],
F_rope = ROPE_MAP[self.F_pipeline.F_rope],
F_pagedkv = BOOL_MAP[self.F_pipeline.F_pagedkv],
F_occupancy = self.F_tile.F_occupancy)
@property
def name(self) -> str:
# TODO: we don't encode idx here
return f"fmha_fwd_appendkv_d{self.F_hdim}_{self.F_dtype}_" + \
self.F_tile.name + '_' + self.F_pipeline.name
@property
def filename(self) -> str:
return self.name + ".cpp"
def api_trait(self) -> FmhaFwdAppendKVApiTrait:
return FmhaFwdAppendKVApiTrait(
hdim=str(self.F_hdim),
dtype=self.F_dtype,
bs=self.F_tile.F_bs,
bsk=self.F_tile.F_bsk,
bd=self.F_tile.F_bd,
bdv=self.F_tile.F_bdv,
vlayout=self.F_pipeline.F_vlayout,
spad=self.F_pipeline.F_spad,
skpad=self.F_pipeline.F_skpad,
dpad=self.F_pipeline.F_dpad,
dvpad=self.F_pipeline.F_dvpad,
rope=self.F_pipeline.F_rope,
pagedkv=self.F_pipeline.F_pagedkv)
# TODO: design a more practical way to do it
# this is current supported tile size per hdim
def get_fmha_fwd_appendkv_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
if dtype == 'fp16' or dtype == 'bf16':
return {
'32' : FmhaFwdAppendKVTileSize(64, 64, 32, 32, -1),
'64' : FmhaFwdAppendKVTileSize(64, 64, 64, 64, -1),
'128' : FmhaFwdAppendKVTileSize(64, 64, 128, 128, -1),
'256' : FmhaFwdAppendKVTileSize(64, 64, 256, 256, -1),
}
elif dtype == 'fp8' or dtype == 'bf8':
return {
'64' : FmhaFwdAppendKVTileSize(64, 64, 64, 64, -1),
'128' : FmhaFwdAppendKVTileSize(64, 64, 128, 128, -1),
'256' : FmhaFwdAppendKVTileSize(64, 64, 256, 256, -1)
}
else:
return None
def get_fwd_appendkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaFwdAppendKVApiPool, List[FmhaFwdAppendKVKernel]]:
# TODO: we don't support tuning yet, so pick up one value for vlayout/pipeline/pad
# support this in future
def get_pipelines(dtype, hdim) -> List[FmhaFwdAppendKVPipeline]:
# this function will populate a list possible pipelines
# TODO: the order of List matters! the later in this list will be also be checked later
# TODO: currently for qr pipeline, let 't' padding to appear later!!
# TODO: how to design this more generic?
squant = 't' if dtype == 'fp8' else 'f'
pipelines = []
if dtype in ['fp16', 'bf16']:
# NOTICE: it will be very complicated if we consider all the hdim_q padding cases while
# applying rotary embedding, so I just use 't' in inter/half pipelines
for vlayout in ['row', 'col']:
for pagedkv in ["t", "f"]:
pipelines.append(FmhaFwdAppendKVPipeline(vlayout, 'f', 't', 'f', 'f', 'no', pagedkv))
pipelines.append(FmhaFwdAppendKVPipeline(vlayout, 't', 't', 't', 't', 'no', pagedkv))
pipelines.append(FmhaFwdAppendKVPipeline(vlayout, 'f', 't', 't', 'f', 'inter', pagedkv))
pipelines.append(FmhaFwdAppendKVPipeline(vlayout, 't', 't', 't', 't', 'inter', pagedkv))
pipelines.append(FmhaFwdAppendKVPipeline(vlayout, 'f', 't', 't', 'f', 'half', pagedkv))
pipelines.append(FmhaFwdAppendKVPipeline(vlayout, 't', 't', 't', 't', 'half', pagedkv))
elif dtype in ['fp8', 'bf8']:
# rope/paged-kv is not supported
pipelines.append(FmhaFwdAppendKVPipeline('col', 't', 't', 't', 't', 'no', 'f'))
else:
assert False
return pipelines
gen = list()
api_pool = FmhaFwdAppendKVApiPool(mask_impl)
for dtype in DTYPE_MAP.keys():
d = get_fmha_fwd_appendkv_tile_dict_from_dtype(dtype)
if d == None:
continue
for hdim_str in d.keys():
tile = d[hdim_str]
hdim = int(hdim_str)
for pipeline in get_pipelines(dtype, hdim):
k = FmhaFwdAppendKVKernel(F_idx=0,
F_hdim=hdim,
F_dtype=dtype,
F_tile=tile,
F_pipeline=pipeline,
mask_impl=mask_impl)
if kernel_filter != None:
if not fnmatch.fnmatch(k.name, kernel_filter):
continue
if receipt == 2:
cond = dtype in ['fp16', 'bf16']
cond &= pipeline.F_vlayout == 'row'
if not cond:
continue
api_pool.register_traits(k.api_trait())
gen.append(k)
return (api_pool, gen)
def write_single_kernel(kernel: FmhaFwdAppendKVKernel, autogen_dir: Path) -> None:
(autogen_dir / kernel.filename).write_text(kernel.template)
def write_fwd_appendkv_api(api_pool : FmhaFwdAppendKVApiPool, autogen_dir: Path) -> None:
(autogen_dir / FMHA_FWD_APPENDKV_API_FILENAME).write_text(api_pool.api)
def write_blobs(output_dir : Path, kernel_filter : Optional[str], receipt, mask_impl) -> None:
api_pool, kernels = get_fwd_appendkv_blobs(kernel_filter, receipt, mask_impl)
for kernel in kernels:
write_single_kernel(kernel, output_dir)
write_fwd_appendkv_api(api_pool, output_dir)
def list_blobs(file_path : Path, kernel_filter : Optional[str], receipt, mask_impl) -> None:
with file_path.open('a') as f:
_, kernels = get_fwd_appendkv_blobs(kernel_filter, receipt, mask_impl)
for kernel in kernels:
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
f.write(str(file_path.parent / GEN_DIR / FMHA_FWD_APPENDKV_API_FILENAME) + "\n")
\ No newline at end of file
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_splitkv.py
View file @ d71189ff
......@@ -21,6 +21,14 @@ from codegen.ops.fmha_fwd import (
)
DTYPE_BITS = {
"fp32": 32,
"fp16": 16,
"bf16": 16,
"fp8" : 8,
"bf8" : 8
}
FMHA_FWD_SPLITKV_PIPELINE_MAP = {
"qr" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVS",
"qr_async" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVSAsync",
......@@ -51,8 +59,8 @@ using fmha_trait = ck_tile::TileFmhaFwdSplitKVTraits<{F_spad},
{F_bias},
false,
{F_lse},
{F_dropout},
{F_squant},
{F_pagedkv},
kHasUnevenSplits,
{F_occupancy}>;
......@@ -63,7 +71,6 @@ using fmha_pipeline_problem = ck_tile::BlockFmhaFwdSplitKVPipelineProblem<
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::SaccDataType,
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::SMPLComputeDataType,
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::BiasDataType,
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::RandValOutputDataType,
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::LSEDataType,
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::PDataType,
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::OaccDataType,
......@@ -86,7 +93,7 @@ using fmha_kernel =
fmha_pipeline,
fmha_epilogue>;
static void run(const ck_tile::stream_config& s, fmha_fwd_args a)
static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
{{
using k_ = fmha_kernel;
auto [kargs, grids] = fmha_fwd_splitkv_create_kargs_and_grids<k_>(a);
......@@ -97,16 +104,21 @@ static void run(const ck_tile::stream_config& s, fmha_fwd_args a)
}};
}}
using trait_{F_idx} = fmha_fwd_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout},
{F_pipeline_enum}, fmha_mask_{F_idx}, {F_bias}, {F_lse}, {F_dropout}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
using trait_{F_idx} = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout},
{F_pipeline_enum}, fmha_mask_{F_idx}, {F_bias}, {F_lse}, {F_squant}, {F_pagedkv}, {F_spad}, {F_skpad}, {F_dpad},
{F_dvpad}>;
#include <iostream>
template<>
void fmha_fwd_splitkv_oneshot_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_args a)
void fmha_fwd_splitkv_oneshot_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
{{
if constexpr({F_mode} == false) {{ // batch mode
if (a.seqlen_k % (a.num_splits * {F_bn0}) == 0) {{
// we don't check every seqlen_k values for kvcache
if (a.seqlen_k_ptr != nullptr) {{
kernel_runner<true>::run(s, a);
// make sure F_bn0 is divisible by F_bk1
}} else if (a.seqlen_k % (a.num_splits * {F_bn0}) == 0) {{
kernel_runner<false>::run(s, a);
}} else {{
kernel_runner<true>::run(s, a);
......@@ -160,7 +172,7 @@ using fmha_kernel =
fmha_pipeline,
fmha_epilogue>;
static void run(const ck_tile::stream_config& s, fmha_fwd_args a)
static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
{{
using k_ = fmha_kernel;
auto [kargs, grids] = fmha_fwd_splitkv_combine_create_kargs_and_grids<k_>(a);
......@@ -177,7 +189,7 @@ using trait_{F_idx} = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_m
#include <iostream>
template<>
void fmha_fwd_splitkv_combine_oneshot_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_args a)
void fmha_fwd_splitkv_combine_oneshot_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
{{
if (a.num_splits <= 16) {{
kernel_runner<4>::run(s, a);
......@@ -203,7 +215,7 @@ FMHA_FWD_SPLITKV_API="""
#include <iostream>
template<typename fmha_fwd_splitkv_traits_, typename fmha_fwd_splitkv_combine_traits_>
float fmha_fwd_splitkv_(const ck_tile::stream_config& s, fmha_fwd_args a)
float fmha_fwd_splitkv_(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
{{
if(s.log_level_ > 0)
std::cout
......@@ -217,22 +229,96 @@ float fmha_fwd_splitkv_(const ck_tile::stream_config& s, fmha_fwd_args a)
);
}}
float fmha_fwd_splitkv(fmha_fwd_traits t, fmha_fwd_args a, const ck_tile::stream_config& s){{
float fmha_fwd_splitkv(fmha_fwd_splitkv_traits t, fmha_fwd_splitkv_args a, const ck_tile::stream_config& s){{
float r = -1;
{F_dispatch}
return r;
}}
"""
FMHA_FWD_SPLITKV_API_INNER_DISPATCH=""" {F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_lse == {F_lse}) && (t.has_dropout == {F_dropout}) && (t.do_fp8_static_quant == {F_squant}) &&
({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{
using traits_ = fmha_fwd_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, {F_lse}, {F_dropout}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
FMHA_FWD_SPLITKV_API_INNER_DISPATCH=""" {F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_lse == {F_lse}) && (t.do_fp8_static_quant == {F_squant}) &&
((a.block_table_ptr != nullptr) == {F_pagedkv}) && ({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{
using traits_ = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, {F_lse}, {F_squant}, {F_pagedkv}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
using traits2_ = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}/2, {F_bn1}, {F_lse}, {F_squant}, {F_spad}, {F_dvpad}>;
return fmha_fwd_splitkv_<traits_, traits2_>(s, a);
}}
"""
@dataclass
class FmhaFwdSplitKVApiTrait:
pipeline_tag : str
# sync with fmha_fwd_traits<>, to generate fallback calls
hdim : str
dtype : str # data type
mode : str # value from MODE_MAP
bm0 : int # tile size along q seqlen (block size)
bn0 : int # tile size along qk seqlen
bk0 : int # tile size along qk gemm unroll
bn1 : int # tile size along v head_dim
bk1 : int # tile size along kv gemm unroll
bk0blen : int
vlayout : str
mask : str
bias : str #
lse : str #
squant : str #
spad : str
skpad : str
dpad : str
dvpad : str
pagedkv : str
@property
def name(self) -> str:
return f'{self.hdim}-{self.dtype}-{self.mode}-{self.bm0}-{self.bn0}-{self.bk0}-{self.bn0}-{self.bk1}-{self.bk0blen}-'+\
f'{self.vlayout}-{self.mask}-{self.bias}-{self.lse}-{self.squant}-{self.spad}-{self.skpad}-{self.dpad}-'+\
f'{self.dvpad}-{self.pagedkv}'
@property
def scheck(self) -> str:
if self.mode == 'group': return 'true/*group mode spad always true*/' # group mode only generate spad/skpad == true
if self.pipeline_tag == 'qr_async':
if self.spad == 't' : return 'true' # always support
else : return 'true'
elif self.pipeline_tag in ['qr']:
if self.spad == 't' : return f'true /*a.seqlen_q % {self.bm0} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.seqlen_q % {self.bm0} == 0'
else: assert False
@property
def skcheck(self) -> str:
if self.mode == 'group': return 'true/*group mode skpad always true*/' # group mode only generate spad/skpad == true
if self.pipeline_tag == 'qr_async':
if self.skpad == 't' : return f'a.seqlen_k == 0 || a.seqlen_k % {self.bn0} != 0'
else : return f'a.seqlen_k != 0 && a.seqlen_k % {self.bn0} == 0'
elif self.pipeline_tag in ['qr', 'qr_fp8']:
if self.skpad == 't' : return f'true /*a.seqlen_k % {self.bn0} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.seqlen_k % {self.bn0} == 0'
else: assert False
@property
def dcheck(self) -> str:
if self.pipeline_tag == 'qr_async':
vec = int((32 * 4) / DTYPE_BITS[self.dtype])
if self.dpad == 't': return f'a.hdim_q % {vec} == 0'
else : assert False
elif self.pipeline_tag in ['qr']:
if self.dpad == 't': return f'true /*a.hdim_q % {self.bk0blen} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_q % {self.bk0blen} == 0'
else: assert False
@property
def dvcheck(self) -> str:
if self.pipeline_tag == 'qr_async':
vec = int((32 * 4) / DTYPE_BITS[self.dtype])
if self.dvpad == 't': return f'a.hdim_v % {vec} == 0'
else : assert False
elif self.pipeline_tag in ['qr']:
if self.dvpad == 't': return f'true /*a.hdim_v % {self.bk0blen} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_v % {self.bk0blen} == 0'
else: assert False
@dataclass
class FmhaFwdSplitKVPipeline:
tag : str
......@@ -244,8 +330,8 @@ class FmhaFwdSplitKVPipeline:
F_dvpad : str #
F_bias : str # true/false
F_lse : str #
F_dropout : str #
F_squant : str #
F_pagedkv : str # t/f
F_mask : str # value from MASK_MAP
@property
......@@ -267,8 +353,8 @@ class FmhaFwdSplitKVPipeline:
else:
if self.F_mask != 'no' : n += f'_m{self.F_mask[0]}'
if self.F_lse == 't' : n += '_lse'
if self.F_dropout == 't' : n += '_dropout'
if self.F_squant == 't' : n += '_squant'
if self.F_pagedkv == 't' : n += '_pagedkv'
return n
@dataclass
......@@ -300,7 +386,7 @@ class FmhaFwdSplitKVApiPool:
self.pool = dict()
self.mask_impl = mask_impl
def register_traits(self, trait : FmhaFwdApiTrait) -> None:
def register_traits(self, trait : FmhaFwdSplitKVApiTrait) -> None:
# TODO: do we need to check duplication?
if trait.dtype not in self.pool.keys():
self.pool[trait.dtype] = dict()
......@@ -322,8 +408,8 @@ class FmhaFwdSplitKVApiPool:
inners = inners + FMHA_FWD_SPLITKV_API_INNER_DISPATCH.format(F_if=if_k, F_mode=MODE_MAP[trait.mode], F_vlayout=LAYOUT_MAP[trait.vlayout],
F_pipeline_enum=PIPELINE_ENUM_MAP[trait.pipeline_tag], F_mask=get_mask_map(self.mask_impl)[trait.mask],
F_mask_check=get_mask_check_map(self.mask_impl)[trait.mask], F_bias_check=BIAS_CHECK_MAP[trait.bias], F_bias=BIAS_MAP[trait.bias],
F_lse=BOOL_MAP[trait.lse], F_dropout=BOOL_MAP[trait.dropout] ,
F_squant=BOOL_MAP[trait.squant], F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck,
F_lse=BOOL_MAP[trait.lse], F_squant=BOOL_MAP[trait.squant], F_pagedkv=BOOL_MAP[trait.pagedkv],
F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck,
F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
F_bm0=trait.bm0, F_bn0=trait.bn0, F_bk0=trait.bk0, F_bn1=trait.bn1, F_bk1=trait.bk1, F_bk0blen=trait.bk0blen,
F_hdim=hdim, F_dtype=DTYPE_MAP[dtype])
......@@ -383,8 +469,8 @@ class FmhaFwdSplitKVKernel:
F_dvpad = BOOL_MAP[self.F_pipeline.F_dvpad],
F_bias = BIAS_MAP[self.F_pipeline.F_bias],
F_lse = BOOL_MAP[self.F_pipeline.F_lse],
F_dropout = BOOL_MAP[self.F_pipeline.F_dropout],
F_squant = BOOL_MAP[self.F_pipeline.F_squant],
F_pagedkv = BOOL_MAP[self.F_pipeline.F_pagedkv],
F_occupancy = self.F_tile.F_occupancy,
F_pipeline_enum = PIPELINE_ENUM_MAP[self.F_pipeline.tag],
F_mask = get_mask_map(self.mask_impl)[self.F_pipeline.F_mask],
......@@ -401,8 +487,8 @@ class FmhaFwdSplitKVKernel:
def filename(self) -> str:
return self.name + ".cpp"
def api_trait(self) -> FmhaFwdApiTrait:
return FmhaFwdApiTrait(
def api_trait(self) -> FmhaFwdSplitKVApiTrait:
return FmhaFwdSplitKVApiTrait(
pipeline_tag=self.F_pipeline.tag,
hdim=str(self.F_hdim),
dtype=self.F_dtype,
......@@ -417,8 +503,8 @@ class FmhaFwdSplitKVKernel:
mask=self.F_pipeline.F_mask,
bias=self.F_pipeline.F_bias,
lse=self.F_pipeline.F_lse,
dropout=self.F_pipeline.F_dropout,
squant=self.F_pipeline.F_squant,
pagedkv=self.F_pipeline.F_pagedkv,
spad=self.F_pipeline.F_spad,
skpad=self.F_pipeline.F_skpad,
dpad=self.F_pipeline.F_dpad,
......@@ -460,29 +546,6 @@ class FmhaFwdSplitKVCombineKernel:
def filename(self) -> str:
return self.name + ".cpp"
def api_trait(self) -> FmhaFwdApiTrait:
return FmhaFwdApiTrait(
pipeline_tag=self.F_pipeline.tag,
hdim=str(self.F_hdim),
dtype=self.F_dtype,
mode=self.F_mode,
bm0=self.F_tile.F_bm0,
bn0=self.F_tile.F_bn0,
bk0=self.F_tile.F_bk0,
bn1=self.F_tile.F_bn1,
bk1=self.F_tile.F_bk1,
bk0blen=self.F_tile.F_bk0blen,
vlayout=self.F_pipeline.F_vlayout,
mask=self.F_pipeline.F_mask,
bias=self.F_pipeline.F_bias,
lse=self.F_pipeline.F_lse,
dropout=self.F_pipeline.F_dropout,
squant=self.F_pipeline.F_squant,
spad=self.F_pipeline.F_spad,
skpad=self.F_pipeline.F_skpad,
dpad=self.F_pipeline.F_dpad,
dvpad=self.F_pipeline.F_dvpad)
# TODO: design a more practical way to do it
# this is current supported tile size per hdim
def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
......@@ -533,27 +596,27 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
squant = 't' if dtype == 'fp8' else 'f'
pipelines = []
if dtype in ['fp16', 'bf16']:
# splitkv kernel donot support dropout
for mask, bias, lse, dropout in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"], ["f"]):
if hdim == 256:
for mask, bias, lse, pagedkv in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"], ["t", "f"]):
# TODO: use async pipeline when compiler is more stable
if hdim == 256 or hdim in [32, 64, 128]:
# if True:
pipelines.append(Pipeline('qr', 'row', 'f', 'f', 'f', 'f', bias, lse, dropout, squant, mask))
pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, lse, dropout, squant, mask))
pipelines.append(Pipeline('qr', 'row', 'f', 'f', 'f', 'f', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
pipelines.append(Pipeline('qr', 'col', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'col', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
else:
pipelines.append(Pipeline('qr_async', 'row', 't', 'f', 't', 't', bias, lse, dropout, squant, mask))
pipelines.append(Pipeline('qr_async', 'row', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
pipelines.append(Pipeline('qr_async', 'col', 't', 'f', 't', 't', bias, lse, dropout, squant, mask))
pipelines.append(Pipeline('qr_async', 'col', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
pipelines.append(Pipeline('qr_async', 'row', 't', 'f', 't', 't', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr_async', 'row', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr_async', 'col', 't', 'f', 't', 't', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr_async', 'col', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
if receipt == 1:
pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, lse, dropout, squant, mask)) # TODO: cover arbitraty hdim
pipelines.append(Pipeline('qr', 'col', 't', 'f', 't', 't', bias, lse, dropout, squant, mask)) # TODO: cover arbitraty hdim
pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask)) # TODO: cover arbitraty hdim
pipelines.append(Pipeline('qr', 'col', 't', 'f', 't', 't', bias, lse, squant, pagedkv, mask)) # TODO: cover arbitraty hdim
elif dtype in ['fp8', 'bf8']:
# no need lse/dropout kernels
# no need lse/paged-kv kernels
for mask, bias in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys()):
pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 'f', 'f', squant, mask))
pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 'f', squant, 'f', mask))
else:
assert False
return pipelines
......@@ -574,6 +637,9 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
if pipeline.F_spad != 't' or pipeline.F_skpad != 't':
# in group mode, spad/skpad must be true, since we can't predict if seqlen of current batch need pad or not
continue
if pipeline.F_pagedkv == 't':
# we only use batch mode kernels to handle (paged-) kvcache problems
continue
k = Kernel(F_idx=0,
F_hdim=hdim,
F_dtype=dtype,
......
example/ck_tile/01_fmha/fmha_fwd.cpp
View file @ d71189ff
This diff is collapsed.
example/ck_tile/01_fmha/fmha_fwd.hpp
View file @ d71189ff
......@@ -5,10 +5,13 @@
#include "ck_tile/core.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/ops/fmha.hpp"
#include "ck_tile/ops/epilogue.hpp"
#include "mask.hpp"
#include "ck_tile/ops/fmha.hpp"
#include "bias.hpp"
#include "mask.hpp"
#include "rotary.hpp"
#include <type_traits>
template <typename DataType>
......@@ -93,13 +96,86 @@ struct fmha_fwd_args
const void* v_ptr;
const void* bias_ptr; // bias or alibi_slope pointer
void* rand_val_ptr;
void* lse_ptr;
void* o_ptr;
const void* seqstart_q_ptr;
const void* seqstart_k_ptr;
const void*
seqlen_k_ptr; // only used if both 'seqstart_q_ptr' & 'seqstart_k_ptr' are not nullptr
ck_tile::index_t seqlen_q;
ck_tile::index_t seqlen_k;
ck_tile::index_t batch;
ck_tile::index_t max_seqlen_q;
ck_tile::index_t hdim_q;
ck_tile::index_t hdim_v;
ck_tile::index_t nhead_q;
ck_tile::index_t nhead_k;
float scale_s;
float scale_p;
float scale_o;
ck_tile::index_t stride_q;
ck_tile::index_t stride_k;
ck_tile::index_t stride_v;
ck_tile::index_t stride_bias; // if alibi, b*h need set this to h, 1*h need set this to 0
ck_tile::index_t stride_randval;
ck_tile::index_t stride_o;
ck_tile::index_t nhead_stride_q;
ck_tile::index_t nhead_stride_k;
ck_tile::index_t nhead_stride_v;
ck_tile::index_t nhead_stride_bias;
ck_tile::index_t nhead_stride_randval;
ck_tile::index_t nhead_stride_lse;
ck_tile::index_t nhead_stride_o;
ck_tile::index_t batch_stride_q;
ck_tile::index_t batch_stride_k;
ck_tile::index_t batch_stride_v;
ck_tile::index_t batch_stride_bias;
ck_tile::index_t batch_stride_randval;
ck_tile::index_t batch_stride_lse;
ck_tile::index_t batch_stride_o;
ck_tile::index_t window_size_left;
ck_tile::index_t window_size_right;
ck_tile::index_t mask_type;
float p_drop;
bool s_randval;
std::tuple<uint64_t, uint64_t> drop_seed_offset;
};
struct fmha_fwd_splitkv_args
{
const void* q_ptr;
const void* k_ptr;
const void* v_ptr;
const void* bias_ptr; // bias or alibi_slope pointer
void* lse_acc_ptr;
void* o_acc_ptr;
void* lse_ptr;
void* o_ptr;
void* block_table_ptr;
ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr
ck_tile::index_t page_block_size; // only used if 'block_table_ptr' is not nullptr
const void* cache_batch_idx;
// the real seqlen_q & seqlen_k are decided by following:
// batch mode: seqlen_q = kargs.seqlen_q
// seqlen_k = kargs.seqlen_k
// group mode: seqlen_q = kargs.seqstart_q_ptr[b + 1] - kargs.seqstart_q_ptr[b]
// seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b]
// kvcache mode (use same kernel as batch mode):
// seqlen_q = kargs.seqlen_q
// seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b]
const void* seqstart_q_ptr;
const void* seqstart_k_ptr;
const void* seqlen_k_ptr;
ck_tile::index_t seqlen_q;
ck_tile::index_t seqlen_k;
ck_tile::index_t batch;
......@@ -109,21 +185,21 @@ struct fmha_fwd_args
ck_tile::index_t nhead_q;
ck_tile::index_t nhead_k;
ck_tile::index_t num_splits;
float scale_s;
float scale_p;
float scale_o;
ck_tile::index_t stride_q;
ck_tile::index_t stride_k;
ck_tile::index_t stride_v;
ck_tile::index_t stride_bias; // if alibi, b*h need set this to h, 1*h need set this to 0
ck_tile::index_t stride_randval;
ck_tile::index_t stride_o_acc;
ck_tile::index_t stride_o;
ck_tile::index_t nhead_stride_q;
ck_tile::index_t nhead_stride_k;
ck_tile::index_t nhead_stride_v;
ck_tile::index_t nhead_stride_bias;
ck_tile::index_t nhead_stride_randval;
ck_tile::index_t nhead_stride_lse;
ck_tile::index_t nhead_stride_lse_acc;
ck_tile::index_t nhead_stride_o_acc;
......@@ -132,19 +208,62 @@ struct fmha_fwd_args
ck_tile::index_t batch_stride_k;
ck_tile::index_t batch_stride_v;
ck_tile::index_t batch_stride_bias;
ck_tile::index_t batch_stride_randval;
ck_tile::index_t batch_stride_lse;
ck_tile::index_t batch_stride_lse_acc;
ck_tile::index_t batch_stride_o_acc;
ck_tile::index_t batch_stride_o;
ck_tile::index_t split_stride_lse_acc;
ck_tile::index_t split_stride_o_acc;
ck_tile::index_t window_size_left;
ck_tile::index_t window_size_right;
ck_tile::index_t mask_type;
float p_drop;
bool s_randval;
std::tuple<uint64_t, uint64_t> drop_seed_offset;
};
struct fmha_fwd_appendkv_args
{
void* q_ptr;
void* k_ptr;
const void* knew_ptr;
void* v_ptr;
const void* vnew_ptr;
const void* seqlen_k_ptr;
ck_tile::index_t seqlen_q;
ck_tile::index_t seqlen_knew;
ck_tile::index_t batch;
ck_tile::index_t hdim_q;
ck_tile::index_t hdim_v;
ck_tile::index_t nhead_q;
ck_tile::index_t nhead_k;
const void* rotary_cos_ptr; // only used if 'rotary_dim' > 0
const void* rotary_sin_ptr; // only used if 'rotary_dim' > 0
ck_tile::index_t rotary_dim;
bool has_mask;
void* block_table_ptr;
ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr
ck_tile::index_t page_block_size; // only used if 'block_table_ptr' is not nullptr
const void* cache_batch_idx;
ck_tile::index_t stride_q;
ck_tile::index_t stride_k;
ck_tile::index_t stride_knew;
ck_tile::index_t stride_v;
ck_tile::index_t stride_vnew;
ck_tile::index_t nhead_stride_q;
ck_tile::index_t nhead_stride_k;
ck_tile::index_t nhead_stride_knew;
ck_tile::index_t nhead_stride_v;
ck_tile::index_t nhead_stride_vnew;
ck_tile::index_t batch_stride_q;
ck_tile::index_t batch_stride_k;
ck_tile::index_t batch_stride_knew;
ck_tile::index_t batch_stride_v;
ck_tile::index_t batch_stride_vnew;
};
template <typename FmhaKernel>
......@@ -244,7 +363,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
}
template <typename Kernel>
auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_args args)
auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args)
{
assert(args.nhead_q % args.nhead_k == 0);
auto kargs = [&] {
......@@ -255,11 +374,9 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_args args)
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.rand_val_ptr,
args.lse_acc_ptr,
args.o_acc_ptr,
args.batch,
args.max_seqlen_q,
args.seqstart_q_ptr,
args.seqstart_k_ptr,
args.seqlen_k_ptr,
......@@ -274,24 +391,22 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_args args)
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_o_acc,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse_acc,
args.nhead_stride_o_acc,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_lse_acc,
args.batch_stride_o_acc,
args.split_stride_lse_acc,
args.split_stride_o_acc,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
args.mask_type);
}
else
{ // create batch mode kernel arguments
......@@ -299,48 +414,45 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_args args)
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.rand_val_ptr,
args.lse_acc_ptr,
args.o_acc_ptr,
args.batch,
args.max_seqlen_q,
args.seqlen_q,
args.seqlen_k,
args.seqlen_k_ptr,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.num_splits,
args.block_table_ptr,
args.batch_stride_block_table,
args.page_block_size,
args.cache_batch_idx,
args.scale_s,
args.scale_p,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_o_acc,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse_acc,
args.nhead_stride_o_acc,
args.batch_stride_q,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_bias,
args.batch_stride_randval,
args.batch_stride_lse_acc,
args.batch_stride_o_acc,
args.split_stride_lse_acc,
args.split_stride_o_acc,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
args.mask_type);
}
}();
......@@ -351,7 +463,7 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_args args)
}
template <typename Kernel>
auto fmha_fwd_splitkv_combine_create_kargs_and_grids(fmha_fwd_args args)
auto fmha_fwd_splitkv_combine_create_kargs_and_grids(fmha_fwd_splitkv_args args)
{
assert(args.nhead_q % args.nhead_k == 0);
auto kargs = [&] {
......@@ -410,6 +522,51 @@ auto fmha_fwd_splitkv_combine_create_kargs_and_grids(fmha_fwd_args args)
return ck_tile::make_tuple(kargs, grids);
}
template <typename Kernel>
auto fmha_fwd_appendkv_create_kargs_and_grids(fmha_fwd_appendkv_args args)
{
assert(args.nhead_q % args.nhead_k == 0);
auto kargs = Kernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.knew_ptr,
args.v_ptr,
args.vnew_ptr,
args.seqlen_q,
args.seqlen_k_ptr,
args.seqlen_knew,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.rotary_cos_ptr,
args.rotary_sin_ptr,
args.rotary_dim,
args.has_mask,
args.block_table_ptr,
args.batch_stride_block_table,
args.page_block_size,
args.cache_batch_idx,
args.stride_q,
args.stride_k,
args.stride_knew,
args.stride_v,
args.stride_vnew,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_knew,
args.nhead_stride_v,
args.nhead_stride_vnew,
args.batch_stride_q,
args.batch_stride_k,
args.batch_stride_knew,
args.batch_stride_v,
args.batch_stride_vnew);
dim3 grids = Kernel::GridSize(args.batch, args.nhead_q, args.seqlen_q, args.seqlen_knew);
return ck_tile::make_tuple(kargs, grids);
}
// this is used to pattern-match internl kernel implementation, not to instantiate kernel
template <ck_tile::index_t HDim_,
typename DataType_,
......@@ -458,8 +615,52 @@ struct fmha_fwd_traits_
template <typename Traits_>
float fmha_fwd_(const ck_tile::stream_config&, fmha_fwd_args);
template <ck_tile::index_t HDim_,
typename DataType_,
bool kIsGroupMode_,
ck_tile::index_t kM0_,
ck_tile::index_t kN0_,
ck_tile::index_t kK0_,
ck_tile::index_t kN1_,
ck_tile::index_t kK1_,
ck_tile::index_t kK0BlockLength_,
bool kIsVLayoutRowMajor_,
ck_tile::BlockFmhaPipelineEnum FmhaPipelineEnum_,
typename FmhaMask_,
ck_tile::BlockAttentionBiasEnum BiasEnum_,
bool kStoreLse_,
bool kDoFp8StaticQuant_,
bool kIsPagedKV_,
bool kPadS_,
bool kPadSK_,
bool kPadD_,
bool kPadDv_>
struct fmha_fwd_splitkv_traits_
{
static constexpr ck_tile::index_t HDim = HDim_;
using DataType = ck_tile::remove_cvref_t<DataType_>;
static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr ck_tile::index_t kM0 = kM0_;
static constexpr ck_tile::index_t kN0 = kN0_;
static constexpr ck_tile::index_t kK0 = kK0_;
static constexpr ck_tile::index_t kN1 = kN1_;
static constexpr ck_tile::index_t kK1 = kK1_;
static constexpr ck_tile::index_t kK0BlockLength = kK0BlockLength_;
static constexpr bool kIsVLayoutRowMajor = kIsVLayoutRowMajor_;
static constexpr auto FmhaPipelineEnum = FmhaPipelineEnum_;
using FmhaMask = ck_tile::remove_cvref_t<FmhaMask_>;
static constexpr auto BiasEnum = BiasEnum_;
static constexpr bool kStoreLse = kStoreLse_;
static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
static constexpr bool kPadS = kPadS_;
static constexpr bool kPadSK = kPadSK_;
static constexpr bool kPadD = kPadD_;
static constexpr bool kPadDv = kPadDv_;
static constexpr bool kIsPagedKV = kIsPagedKV_;
};
template <typename Traits_>
void fmha_fwd_splitkv_oneshot_(const ck_tile::stream_config&, fmha_fwd_args);
void fmha_fwd_splitkv_oneshot_(const ck_tile::stream_config&, fmha_fwd_splitkv_args);
template <typename Traits_>
std::string fmha_fwd_splitkv_get_name_();
......@@ -487,11 +688,45 @@ struct fmha_fwd_splitkv_combine_traits_
};
template <typename Traits_>
void fmha_fwd_splitkv_combine_oneshot_(const ck_tile::stream_config&, fmha_fwd_args);
void fmha_fwd_splitkv_combine_oneshot_(const ck_tile::stream_config&, fmha_fwd_splitkv_args);
template <typename Traits_>
std::string fmha_fwd_splitkv_combine_get_name_();
// this is used to pattern-match internl kernel implementation, not to instantiate kernel
template <ck_tile::index_t HDim_,
typename DataType_,
ck_tile::index_t kTileSizeS_,
ck_tile::index_t kTileSizeSk_,
ck_tile::index_t kTileSizeD_,
ck_tile::index_t kTileSizeDv_,
bool kIsVLayoutRowMajor_,
bool kPadS_,
bool kPadSk_,
bool kPadD_,
bool kPadDv_,
ck_tile::RotaryEmbeddingEnum RotaryEnum_,
bool kIsPagedKV_>
struct fmha_fwd_appendkv_traits_
{
static constexpr ck_tile::index_t HDim = HDim_;
using DataType = ck_tile::remove_cvref_t<DataType_>;
static constexpr ck_tile::index_t kTileSizeS = kTileSizeS_;
static constexpr ck_tile::index_t kTileSizeSk = kTileSizeSk_;
static constexpr ck_tile::index_t kTileSizeD = kTileSizeD_;
static constexpr ck_tile::index_t kTileSizeDv = kTileSizeDv_;
static constexpr bool kIsVLayoutRowMajor = kIsVLayoutRowMajor_;
static constexpr bool kPadS = kPadS_;
static constexpr bool kPadSk = kPadSk_;
static constexpr bool kPadD = kPadD_;
static constexpr bool kPadDv = kPadDv_;
static constexpr auto RotaryEnum = RotaryEnum_;
static constexpr bool kIsPagedKV = kIsPagedKV_;
};
template <typename Traits_>
float fmha_fwd_appendkv_(const ck_tile::stream_config&, fmha_fwd_appendkv_args);
// This is the public API, will be generated by script
struct fmha_fwd_traits
{
......@@ -508,4 +743,32 @@ struct fmha_fwd_traits
// TODO: padding check is inside this api
};
float fmha_fwd(fmha_fwd_traits, fmha_fwd_args, const ck_tile::stream_config&);
float fmha_fwd_splitkv(fmha_fwd_traits, fmha_fwd_args, const ck_tile::stream_config&);
struct fmha_fwd_splitkv_traits
{
int hdim_q;
int hdim_v;
std::string data_type;
bool is_group_mode;
bool is_v_rowmajor;
mask_enum mask_type;
bias_enum bias_type; // 0:no bias, 1:elementwise bias, 2:alibi. sync with BlockAttentionBiasEnum
bool has_lse;
bool do_fp8_static_quant;
// TODO: padding check is inside this api
};
float fmha_fwd_splitkv(fmha_fwd_splitkv_traits,
fmha_fwd_splitkv_args,
const ck_tile::stream_config&);
struct fmha_fwd_appendkv_traits
{
int hdim_q;
int hdim_v;
std::string data_type;
bool is_v_rowmajor;
rope_enum rope_type;
};
float fmha_fwd_appendkv(fmha_fwd_appendkv_traits,
fmha_fwd_appendkv_args,
const ck_tile::stream_config&);
example/ck_tile/01_fmha/generate.py
View file @ d71189ff
......@@ -5,25 +5,30 @@
import argparse
from enum import IntEnum
from pathlib import Path
import pkgutil
import sys
from typing import List, Optional
import codegen.ops
from codegen.cmake_config import *
from codegen.ops import (
fmha_fwd,
fmha_fwd_splitkv,
fmha_bwd
)
class HandlerId(IntEnum):
LIST_BLOBS = 0
WRITE_BLOBS = 1
handlers = {
'fwd' : (fmha_fwd.list_blobs, fmha_fwd.write_blobs),
'fwd_splitkv' : (fmha_fwd_splitkv.list_blobs, fmha_fwd_splitkv.write_blobs),
'bwd' : (fmha_bwd.list_blobs, fmha_bwd.write_blobs),
}
# inspect all modules under 'codegen.ops' and register API handlers
ops = []
for importer, module_name, _ in pkgutil.iter_modules(codegen.ops.__path__):
full_module_name = '%s.%s' % (codegen.ops.__name__, module_name)
if full_module_name not in sys.modules:
ops.append(importer.find_spec(module_name).loader.load_module(module_name))
unwanted_prefix = 'fmha_'
handlers = dict(
[(op.__name__[len(unwanted_prefix):] if op.__name__.startswith(unwanted_prefix) else op.__name__,
(op.list_blobs, op.write_blobs)) for op in ops]
)
assert 0 < len(handlers)
def write_blobs(output_dir: Optional[str], api_list : List[str], kernel_filter : Optional[str], receipt, mask_impl) -> None:
if output_dir is None:
......@@ -103,4 +108,4 @@ if __name__ == "__main__":
if args.list_blobs is not None:
list_blobs(args.list_blobs, api_list, args.filter, int(args.receipt), mask_impl=args.mask)
else:
write_blobs(args.output_dir, api_list, args.filter, int(args.receipt), mask_impl=args.mask)
\ No newline at end of file
write_blobs(args.output_dir, api_list, args.filter, int(args.receipt), mask_impl=args.mask)
example/ck_tile/01_fmha/rotary.hpp 0 → 100644
View file @ d71189ff
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/host/host_tensor.hpp"
#include <cassert>
#include <cmath>
#include <functional>
#include <iterator>
#include <optional>
#include <random>
#include <tuple>
// keep sync with RotaryEmbeddingEnum
enum class rope_enum
{
none = 0,
interleaved = 1,
half_rotated = 2,
};
template <typename DataType>
std::tuple<ck_tile::HostTensor<DataType>, ck_tile::HostTensor<DataType>>
generate_rotary_cos_sin(ck_tile::index_t seqlen,
ck_tile::index_t rotary_dim,
std::optional<unsigned> seed = std::nullopt)
{
// return dummy tensors if we won't apply RoPE at all
if(rotary_dim <= 0)
{
ck_tile::HostTensor<DataType> dummy({1, 1});
return std::make_tuple(dummy, dummy);
}
std::mt19937 random_engine(seed.has_value() ? *seed : std::random_device{}());
std::uniform_real_distribution<float> generator(0.0f, 1.0f);
const ck_tile::index_t num_rows = seqlen * 2;
const ck_tile::index_t num_cols = rotary_dim / 2;
using std::begin, std::end;
ck_tile::HostTensor<float> angle({num_rows, num_cols});
std::generate(begin(angle), end(angle), [&] { return generator(random_engine) * 2 * M_PI; });
ck_tile::HostTensor<DataType> cos({num_rows, num_cols});
std::transform(begin(angle), end(angle), begin(cos), [](float origin_value) {
return ck_tile::type_convert<DataType>(std::cos(origin_value));
});
ck_tile::HostTensor<DataType> sin({num_rows, num_cols});
std::transform(begin(angle), end(angle), begin(sin), [](float origin_value) {
return ck_tile::type_convert<DataType>(std::sin(origin_value));
});
return std::make_tuple(cos, sin);
}
template <typename DataType>
std::tuple<ck_tile::HostTensor<DataType>, ck_tile::HostTensor<DataType>>
slice_rotary_cos_sin(const ck_tile::HostTensor<DataType>& cos,
const ck_tile::HostTensor<DataType>& sin,
ck_tile::index_t seqlen_offset,
ck_tile::index_t seqlen)
{
assert(cos.get_num_of_dimension() == 2 && sin.get_num_of_dimension() == 2);
assert(cos.get_length(0) == sin.get_length(0) && cos.get_length(1) == sin.get_length(1));
assert(static_cast<std::size_t>(seqlen_offset + seqlen) <= cos.get_length(0));
const ck_tile::index_t num_rows = seqlen;
const ck_tile::index_t num_cols = cos.get_length(1);
ck_tile::HostTensor<DataType> cos_pt({num_rows, num_cols});
cos_pt.ForEach([&](auto& self, auto i) { self(i) = cos(i[0] + seqlen_offset, i[1]); });
ck_tile::HostTensor<DataType> sin_pt({num_rows, num_cols});
sin_pt.ForEach([&](auto& self, auto i) { self(i) = sin(i[0] + seqlen_offset, i[1]); });
return std::make_tuple(cos_pt, sin_pt);
}
example/ck_tile/01_fmha/script/benchmark_bwd.sh
View file @ d71189ff
#!/bin/sh
# TODO: run this script from CK root
BUILD=build
EXE=$BUILD/bin/tile_example_fmha_bwd
# TODO: run this script from CK root or build directory
EXE="$(find . -name tile_example_fmha_bwd -type f | head -n 1)"
VALID=0
for prec in "fp16" "bf16" ; do
......
example/ck_tile/01_fmha/script/benchmark_fwd.sh
View file @ d71189ff
#!/bin/sh
# TODO: run this script from CK root
BUILD=build
EXE=$BUILD/bin/tile_example_fmha_fwd
# TODO: run this script from CK root or build directory
EXE="$(find . -name tile_example_fmha_fwd -type f | head -n 1)"
VALID=0
for prec in "fp16" "bf16" ; do
......
example/ck_tile/01_fmha/script/smoke_test_bwd.sh
View file @ d71189ff
#!/bin/sh
# TODO: run this script from CK root
BUILD=build
EXE=$BUILD/bin/tile_example_fmha_bwd
# TODO: run this script from CK root or build directory
EXE="$(find . -name tile_example_fmha_bwd -type f | head -n 1)"
KNAME=1
export CK_WARMUP=0
......
example/ck_tile/01_fmha/script/smoke_test_fwd.sh
View file @ d71189ff
#!/bin/sh
# TODO: run this script from CK root
BUILD=build
EXE=$BUILD/bin/tile_example_fmha_fwd
#!/bin/bash
# TODO: run this script from CK root or build directory
EXE="$(find . -name tile_example_fmha_fwd -type f | head -n 1)"
KNAME=1
export CK_WARMUP=0
......@@ -10,44 +9,98 @@ export CK_REPEAT=1
COMMON_ARGS='-v=1 -warmup=0 -repeat=1'
# mode=0
# export HIP_VISIBLE_DEVICES=4
set -x
for prec in "fp16" "bf16" ; do
for mode in 1 0 ; do
for perm in 0 1 ; do
for vlayout in "r" "c" ; do
for hdim in 32 64 128 256 ; do
for lse in 0 1 ; do
for bias in "n" "e" "a" ; do
for p_drop in 0.0 0.2; do
# $EXE -prec=$prec -mode=$mode -b=1 -h=1 -d=$hdim -s=1024 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=2 -h=2 -h_k=1 -d=16, -d_v=$hdim -s=55 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=1 -h=3 -d=$hdim -s=100 -s_k=51 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=16 -d_v=$hdim -s=99 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=1 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1024 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -d_v=24 -s=3 -s_k=99 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=3 -h=2 -h_k=1 -d=$hdim -s=200 -s_k=520 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=t:128,30 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -s=99 -s_k=32 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=b:4,35 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=33 -s_k=0 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1 -s_k=10 -s_kpad=32 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
done
done
done
done
done
done
done
TEST_SPLITKV=0
TEST_APPENDKV=0
# options:
# -s: run splitkv tests
# -a: run appendkv tests
while getopts ":sa" opt; do
case "${opt}" in
s)
TEST_SPLITKV=1
;;
a)
TEST_APPENDKV=1
;;
*)
;;
esac
done
run_fp16_bf16_tests() {
local NUM_SPLITS=(1)
local PAGE_BLOCK_SIZE=(0)
local CACHE_BATCH_IDX=(0)
for perm in 0 1 ; do
for bias in "n" "e" "a" ; do
for b in 1 2 ; do
for hdim in 64 128 256 ; do
$EXE -prec=fp8 -init=3 -b=$b -h=1 -d=128 -s=128 -bias=$bias -iperm=$perm -operm=$perm -vlayout=c -squant=1 -kname=$KNAME $COMMON_ARGS
done
done
done
done
set +x
if [ $TEST_SPLITKV -eq 1 ] ; then
NUM_SPLITS+=(2 3)
PAGE_BLOCK_SIZE+=(128)
CACHE_BATCH_IDX+=(1)
fi
for prec in "fp16" "bf16" ; do
for mode in 1 0 ; do
for perm in 0 1 ; do
for vlayout in "r" "c" ; do
for hdim in 32 64 128 256 ; do
for lse in 0 1 ; do
for bias in "n" "e" "a" ; do
for p_drop in 0.0 0.2 ; do
for num_splits in "${NUM_SPLITS[@]}" ; do
for page_block_size in "${PAGE_BLOCK_SIZE[@]}" ; do
for cache_batch_idx in "${CACHE_BATCH_IDX[@]}" ; do
# $EXE -prec=$prec -mode=$mode -b=1 -h=1 -d=$hdim -s=1024 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=2 -h=2 -h_k=1 -d=16, -d_v=$hdim -s=55 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=1 -h=3 -d=$hdim -s=100 -s_k=51 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=16 -d_v=$hdim -s=99 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=1 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1024 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -d_v=24 -s=3 -s_k=99 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=3 -h=2 -h_k=1 -d=$hdim -s=200 -s_k=520 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=t:128,30 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -s=99 -s_k=32 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=b:4,35 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=33 -s_k=0 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1 -s_k=10 -s_kpad=32 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS
done ; done ; done ; done ; done
done ; done ; done ; done ; done
done ;
}
run_fp8_tests() {
for perm in 0 1 ; do
for bias in "n" "e" "a" ; do
for b in 1 2 ; do
for hdim in 64 128 256 ; do
$EXE -prec=fp8 -init=3 -b=$b -h=1 -d=128 -s=128 -bias=$bias -iperm=$perm -operm=$perm -vlayout=c -squant=1 -kname=$KNAME $COMMON_ARGS
done ; done ; done ; done
}
run_fp16_appendkv_tests() {
for s in $(seq 63 1 65) ; do
for s_k in 65 129 ; do
for s_knew in 0 64 $s_k ; do
for hdim in 32 64 128 256 ; do
for ri in 0 1 ; do
for rdim in 0 16 32 $hdim ; do
for page_block_size in 0 128 ; do
for cache_batch_idx in 0 1 ; do
$EXE -prec=fp16 -b=3 -h=3 -d=$hdim -s=$s -s_k=$s_k -s_knew=$s_knew -rotary_dim=$rdim -rotary_interleaved=$ri -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -iperm=1 -operm=1 -kname=1 $COMMON_ARGS
done ; done ; done ; done ; done
done ; done ; done
}
set -x
run_fp16_bf16_tests
run_fp8_tests
if [ $TEST_APPENDKV -eq 1 ] ; then
run_fp16_appendkv_tests
fi
set +x
\ No newline at end of file
example/ck_tile/01_fmha/utils.hpp
View file @ d71189ff
......@@ -3,15 +3,17 @@
#pragma once
#include <algorithm>
#include <cstdint>
#include <cstdlib>
#include <functional>
#include <optional>
#include <ostream>
#include <sstream>
#include <string>
#include <tuple>
#include <utility>
#include <vector>
#include <functional>
#include <string>
#include "ck_tile/core/container/span.hpp"
......@@ -40,13 +42,17 @@ std::vector<int32_t> to_seqstarts(ck_tile::span<const int32_t> seqlens)
std::vector<int32_t> generate_seqlens(mode_enum mode,
unsigned count,
int32_t seqlen_avg,
int32_t seqlen_min = -1, // if not negative, clamp min
int32_t seqlen_max = -1, // if not negative, clamp max
std::optional<unsigned> seed = std::nullopt)
{
assert(0 < count);
std::vector<int32_t> seqlens(
count, seqlen_max > 0 ? (seqlen_avg < seqlen_max ? seqlen_avg : seqlen_max) : seqlen_avg);
seqlen_min = (0 < seqlen_min ? seqlen_min : 1);
seqlen_max = (0 < seqlen_max ? seqlen_max : std::numeric_limits<int32_t>::max());
assert(seqlen_min <= seqlen_max);
std::vector<int32_t> seqlens(count, std::clamp(seqlen_avg, seqlen_min, seqlen_max));
if(mode == mode_enum::group && 1 < count)
{
......@@ -62,15 +68,15 @@ std::vector<int32_t> generate_seqlens(mode_enum mode,
for(unsigned repeat = seqlen_avg * (count / 2); 0 < repeat; --repeat)
{
const size_type to_decrease = next_idx();
// make sure each elements of seqlens is always greater than 0
if(seqlens[to_decrease] == 1)
// make sure each elements of seqlens is in range [seqlen_min, seqlen_max]
if(seqlens[to_decrease] == seqlen_min)
{
continue;
}
const size_type to_increase = (to_decrease + next_step()) % count;
if(seqlen_max > 0 && seqlens[to_increase] >= seqlen_max)
if(seqlens[to_increase] >= seqlen_max)
{
continue;
}
......@@ -86,10 +92,36 @@ std::vector<int32_t> generate_seqlens(mode_enum mode,
std::vector<int32_t> generate_seqstarts(mode_enum mode,
unsigned count,
int32_t seqlen_avg,
int32_t seqlen_min = -1,
int32_t seqlen_max = -1,
std::optional<unsigned> seed = std::nullopt)
{
return to_seqstarts(generate_seqlens(mode, count, seqlen_avg, seqlen_max, seed));
return to_seqstarts(generate_seqlens(mode, count, seqlen_avg, seqlen_min, seqlen_max, seed));
}
// return random integer generated uniformly in range [low, high]
template <typename Int = int>
auto randint(Int low, Int high, std::optional<unsigned> seed = std::nullopt)
-> std::enable_if_t<std::is_integral_v<Int>, Int>
{
std::mt19937 engine(seed.has_value() ? *seed : std::random_device{}());
std::uniform_int_distribution<Int> dist(low, high);
return dist(engine);
}
// return random integers generated uniformly in range [low, high]
template <typename Int, typename ForwardIterator>
auto randints(ForwardIterator first,
ForwardIterator last,
Int low,
Int high,
std::optional<unsigned> seed = std::nullopt)
-> std::enable_if_t<std::is_integral_v<Int>>
{
std::mt19937 engine(seed.has_value() ? *seed : std::random_device{}());
std::uniform_int_distribution<Int> dist(low, high);
std::generate(first, last, [&] { return dist(engine); });
}
/*
......@@ -112,16 +144,45 @@ decode_seqlen(mode_enum mode,
std::string q_val,
std::string k_val,
std::string k_pad_val,
std::optional<unsigned> seed = std::nullopt)
ck_tile::index_t seqlen_k_min = 0,
bool use_kvcache = false,
std::optional<unsigned> seed = std::nullopt)
{
#define _S2I_(str_) static_cast<ck_tile::index_t>(std::atoi((str_).c_str()))
if(mode == mode_enum::batch)
{
ck_tile::index_t q = _S2I_(q_val);
ck_tile::index_t k = _S2I_(k_val);
auto s_q = std::vector<ck_tile::index_t>(batch, q);
auto s_k = std::vector<ck_tile::index_t>(batch, k < 0 ? q : k);
auto s_q = std::vector<ck_tile::index_t>(batch, q);
auto s_k = [&] {
const ck_tile::index_t seqlen_k_max = (k < 0 ? q : k);
std::vector<ck_tile::index_t> seqlen_ks(batch, seqlen_k_max);
if(1 < batch && use_kvcache)
{
// to keep the original s_k value, we always use seqlen_k_max in first batch
randints(std::next(seqlen_ks.begin()),
seqlen_ks.end(),
seqlen_k_min,
seqlen_k_max,
seed);
return seqlen_ks;
}
return seqlen_ks;
}();
auto s_kpad = std::vector<ck_tile::index_t>(batch, -1); // TODO: batch not support k_padding
// s_k should be greater than or equal to seqlen_k_min if provided
if(s_k.back() < seqlen_k_min)
{
std::ostringstream msg;
msg << __FILE__ << ":" << __LINE__ << ": seqlen_k (=" << s_k.back()
<< ") is less than minimum seqlen_k (=" << seqlen_k_min << ")";
throw std::runtime_error(msg.str());
}
return std::make_tuple(s_q, s_k, s_kpad);
}
else
......@@ -149,6 +210,16 @@ decode_seqlen(mode_enum mode,
s_q.push_back(q);
s_k.push_back(k < 0 ? q : k);
s_kpad.push_back(kp);
// s_k should be greater than or equal to seqlen_k_min
if(s_k.back() < seqlen_k_min)
{
std::ostringstream msg;
msg << __FILE__ << ":" << __LINE__ << ": seqlen_k (=" << s_k.back()
<< ") is less than minimum seqlen_k (=" << seqlen_k_min << ")";
throw std::runtime_error(msg.str());
}
idx++;
if(found_q == std::string::npos || idx >= batch)
{
......@@ -160,8 +231,9 @@ decode_seqlen(mode_enum mode,
}
if(idx < batch)
{
auto rem_q = generate_seqlens(mode, batch - idx, s_q.back(), s_kpad.back(), seed);
auto rem_k = generate_seqlens(mode, batch - idx, s_k.back(), s_kpad.back(), seed);
auto rem_q = generate_seqlens(mode, batch - idx, s_q.back(), 1, s_kpad.back(), seed);
auto rem_k =
generate_seqlens(mode, batch - idx, s_k.back(), seqlen_k_min, s_kpad.back(), seed);
s_q.insert(s_q.end(), rem_q.begin(), rem_q.end());
s_k.insert(s_k.end(), rem_k.begin(), rem_k.end());
......@@ -180,3 +252,15 @@ int env_get_int(const char* var_name, int default_int)
r = std::atoi(v);
return r;
}
template <typename RandomAccessIterator, typename Int>
std::enable_if_t<std::is_integral_v<Int>> iota_shuffle(RandomAccessIterator first,
RandomAccessIterator last,
Int value,
std::optional<unsigned> seed = std::nullopt)
{
std::iota(first, last, value);
std::mt19937 engine(seed.has_value() ? *seed : std::random_device{}());
std::shuffle(first, last, engine);
}
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_dl.hpp
View file @ d71189ff
......@@ -1039,14 +1039,14 @@ struct DeviceGroupedConvBwdWeight_Dl : public DeviceGroupedConvBwdWeight<NDimSpa
return false;
if constexpr(!((NDimSpatial == 1 &&
(is_NWGK_GKXC_NWGC<InLayout, WeiLayout, OutLayout>() ||
is_GNWK_GKXC_GNWC<InLayout, WeiLayout, OutLayout>())) ||
(is_NWGC_GKXC_NWGK<InLayout, WeiLayout, OutLayout>() ||
is_GNWC_GKXC_GNWK<InLayout, WeiLayout, OutLayout>())) ||
(NDimSpatial == 2 &&
(is_NHWGK_GKYXC_NHWGC<InLayout, WeiLayout, OutLayout>() ||
is_GNHWK_GKYXC_GNHWC<InLayout, WeiLayout, OutLayout>())) ||
(is_NHWGC_GKYXC_NHWGK<InLayout, WeiLayout, OutLayout>() ||
is_GNHWC_GKYXC_GNHWK<InLayout, WeiLayout, OutLayout>())) ||
(NDimSpatial == 3 &&
(is_NDHWGK_GKZYXC_NDHWGC<InLayout, WeiLayout, OutLayout>() ||
is_GNDHWK_GKZYXC_GNDHWC<InLayout, WeiLayout, OutLayout>()))))
(is_NDHWGC_GKZYXC_NDHWGK<InLayout, WeiLayout, OutLayout>() ||
is_GNDHWC_GKZYXC_GNDHWK<InLayout, WeiLayout, OutLayout>()))))
{
return false;
}
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_multiple_d_xdl_cshuffle.hpp
View file @ d71189ff
......@@ -864,23 +864,23 @@ struct DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle
}
if constexpr(NDimSpatial == 1)
{
if constexpr(!is_GNWK_GKXC_GNWC<InLayout, WeiLayout, OutLayout>())
if constexpr(!is_GNWC_GKXC_GNWK<InLayout, WeiLayout, OutLayout>())
{
return false;
}
}
else if constexpr(NDimSpatial == 2)
{
if constexpr(!(is_NHWGK_GKYXC_NHWGC<InLayout, WeiLayout, OutLayout>() ||
is_GNHWK_GKYXC_GNHWC<InLayout, WeiLayout, OutLayout>()))
if constexpr(!(is_NHWGC_GKYXC_NHWGK<InLayout, WeiLayout, OutLayout>() ||
is_GNHWC_GKYXC_GNHWK<InLayout, WeiLayout, OutLayout>()))
{
return false;
}
}
else if constexpr(NDimSpatial == 3)
{
if constexpr(!(is_NDHWGK_GKZYXC_NDHWGC<InLayout, WeiLayout, OutLayout>() ||
is_GNDHWK_GKZYXC_GNDHWC<InLayout, WeiLayout, OutLayout>()))
if constexpr(!(is_NDHWGC_GKZYXC_NDHWGK<InLayout, WeiLayout, OutLayout>() ||
is_GNDHWC_GKZYXC_GNDHWK<InLayout, WeiLayout, OutLayout>()))
{
return false;
}
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_wmma_cshuffle.hpp
View file @ d71189ff
......@@ -710,8 +710,8 @@ struct DeviceGroupedConvBwdWeight_Wmma_CShuffle
return false;
}
if constexpr(!(is_NDHWGK_GKZYXC_NDHWGC<InLayout, WeiLayout, OutLayout>() ||
is_GNDHWK_GKZYXC_GNDHWC<InLayout, WeiLayout, OutLayout>()))
if constexpr(!(is_NDHWGC_GKZYXC_NDHWGK<InLayout, WeiLayout, OutLayout>() ||
is_GNDHWC_GKZYXC_GNDHWK<InLayout, WeiLayout, OutLayout>()))
{
return false;
}
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_xdl_cshuffle.hpp
View file @ d71189ff
......@@ -586,23 +586,23 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
}
if constexpr(NDimSpatial == 1)
{
if constexpr(!is_GNWK_GKXC_GNWC<InLayout, WeiLayout, OutLayout>())
if constexpr(!is_GNWC_GKXC_GNWK<InLayout, WeiLayout, OutLayout>())
{
return false;
}
}
else if constexpr(NDimSpatial == 2)
{
if constexpr(!(is_NHWGK_GKYXC_NHWGC<InLayout, WeiLayout, OutLayout>() ||
is_GNHWK_GKYXC_GNHWC<InLayout, WeiLayout, OutLayout>()))
if constexpr(!(is_NHWGC_GKYXC_NHWGK<InLayout, WeiLayout, OutLayout>() ||
is_GNHWC_GKYXC_GNHWK<InLayout, WeiLayout, OutLayout>()))
{
return false;
}
}
else if constexpr(NDimSpatial == 3)
{
if constexpr(!(is_NDHWGK_GKZYXC_NDHWGC<InLayout, WeiLayout, OutLayout>() ||
is_GNDHWK_GKZYXC_GNDHWC<InLayout, WeiLayout, OutLayout>()))
if constexpr(!(is_NDHWGC_GKZYXC_NDHWGK<InLayout, WeiLayout, OutLayout>() ||
is_GNDHWC_GKZYXC_GNDHWK<InLayout, WeiLayout, OutLayout>()))
{
return false;
}
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp
View file @ d71189ff
......@@ -102,10 +102,9 @@ __global__ void
// offset base pointer for each work-group
const index_t g_idx = __builtin_amdgcn_readfirstlane(blockIdx.y);
const index_t n_idx = __builtin_amdgcn_readfirstlane(blockIdx.z);
const long_index_t e_batch_offset =
const long_index_t e_group_offset =
amd_wave_read_first_lane(compute_ptr_offset_of_groups.GetEPtrOffset(g_idx));
const auto& ds_batch_offset = compute_ptr_offset_of_groups.GetDsPtrOffset(g_idx);
const auto& ds_group_offset = compute_ptr_offset_of_groups.GetDsPtrOffset(g_idx);
const long_index_t e_n_offset =
amd_wave_read_first_lane(compute_ptr_offset_of_n.GetEPtrOffset(n_idx));
......@@ -118,14 +117,14 @@ __global__ void
DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock::Size();
static_for<0, NumDTensor, 1>{}(
[&](auto i) { p_ds_grid_grp(i) = p_ds_grid[i] + ds_batch_offset[i]; });
[&](auto i) { p_ds_grid_grp(i) = p_ds_grid[i] + ds_group_offset[i]; });
if constexpr(isMultiA || isMultiB)
{
AsPointer p_as_grid_grp;
BsPointer p_bs_grid_grp;
const auto& as_batch_offset = compute_ptr_offset_of_groups.GetAsPtrOffset(g_idx);
const auto& as_group_offset = compute_ptr_offset_of_groups.GetAsPtrOffset(g_idx);
// compute_ptr_offset_of_n_ not need BatchStrideB so
// in case of MultiA is false but isMultiB is true
......@@ -136,27 +135,27 @@ __global__ void
static constexpr index_t NumATensor = AGridDesc_AK0_M_AK1::Size();
static_for<0, NumATensor, 1>{}([&](auto i) {
p_as_grid_grp(i) = p_as_grid[i] + as_batch_offset[i] + as_n_offset[i];
p_as_grid_grp(i) = p_as_grid[i] + as_group_offset[i] + as_n_offset[i];
});
}
else
{
const long_index_t a_n_offset = compute_ptr_offset_of_n.GetAPtrOffset(n_idx);
static_for<0, 1, 1>{}(
[&](auto i) { p_as_grid_grp(i) = p_as_grid[i] + as_batch_offset[i] + a_n_offset; });
[&](auto i) { p_as_grid_grp(i) = p_as_grid[i] + as_group_offset[i] + a_n_offset; });
}
const auto& bs_batch_offset = compute_ptr_offset_of_groups.GetBsPtrOffset(g_idx);
const auto& bs_group_offset = compute_ptr_offset_of_groups.GetBsPtrOffset(g_idx);
static constexpr index_t NumBTensor = BGridDesc_BK0_N_BK1::Size();
static_for<0, NumBTensor, 1>{}(
[&](auto i) { p_bs_grid_grp(i) = p_bs_grid[i] + bs_batch_offset[i]; });
[&](auto i) { p_bs_grid_grp(i) = p_bs_grid[i] + bs_group_offset[i]; });
GridwiseGemm::template Run<HasMainKBlockLoop>(
p_as_grid_grp,
p_bs_grid_grp,
p_ds_grid_grp,
p_e_grid + e_batch_offset + e_n_offset,
p_e_grid + e_group_offset + e_n_offset,
p_shared,
a_element_op,
b_element_op,
......@@ -169,19 +168,19 @@ __global__ void
}
else
{
const long_index_t a_batch_offset =
const long_index_t a_group_offset =
amd_wave_read_first_lane(compute_ptr_offset_of_groups.GetAPtrOffset(g_idx));
const long_index_t b_batch_offset =
const long_index_t b_group_offset =
amd_wave_read_first_lane(compute_ptr_offset_of_groups.GetBPtrOffset(g_idx));
const long_index_t a_n_offset =
amd_wave_read_first_lane(compute_ptr_offset_of_n.GetAPtrOffset(n_idx));
GridwiseGemm::template Run<HasMainKBlockLoop>(
p_as_grid + a_batch_offset + a_n_offset,
p_bs_grid + b_batch_offset,
p_as_grid + a_group_offset + a_n_offset,
p_bs_grid + b_group_offset,
p_ds_grid_grp,
p_e_grid + e_batch_offset + e_n_offset,
p_e_grid + e_group_offset + e_n_offset,
p_shared,
a_element_op,
b_element_op,
......@@ -283,7 +282,8 @@ template <index_t NDimSpatial,
// in tuple for MultiAB), unpack if tuple was
// passed
typename BComputeDataType = AComputeDataType,
LoopScheduler LoopSched = make_default_loop_scheduler()>
LoopScheduler LoopSched = make_default_loop_scheduler(),
index_t NumGroupsToMerge = 1>
struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
: public DeviceGroupedConvFwdMultipleABD<NDimSpatial,
ALayout,
......@@ -302,6 +302,8 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
{
using DeviceOp = DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle;
static_assert(NumGroupsToMerge >= 1);
static constexpr bool isMultiA = is_detected<is_tuple, ADataType>::value;
static constexpr bool isMultiB = is_detected<is_tuple, BDataType>::value;
......@@ -318,7 +320,8 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
ConvForwardSpecialization,
true /*SplitN*/,
ADataType,
EDataType>;
EDataType,
NumGroupsToMerge>;
static constexpr auto matrix_padder =
MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, KPerBlock};
......@@ -517,7 +520,8 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
{
static_for<0, NumATensor, 1>{}([&](auto i) {
// Init compute_ptr_offset_of_groups_ for multiple AB
compute_ptr_offset_of_groups_.BatchStrideA_(i) = a_g_n_c_wis_strides[0];
compute_ptr_offset_of_groups_.BatchStrideA_(i) =
a_g_n_c_wis_strides[0] * NumGroupsToMerge;
// Use GemmADataType/GemmBDataType to iterate over tuple (even if passed data
// type is not tuple)
......@@ -545,7 +549,8 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
});
static_for<0, NumBTensor, 1>{}([&](auto i) {
// Init compute_ptr_offset_of_groups_ for multiple AB
compute_ptr_offset_of_groups_.BatchStrideB_(i) = b_g_k_c_xs_strides[0];
compute_ptr_offset_of_groups_.BatchStrideB_(i) =
b_g_k_c_xs_strides[0] * NumGroupsToMerge;
using DataType = remove_cvref_t<tuple_element_t<i.value, GemmBDataType>>;
// It is possible that one of the AB is a pointer and one is a tuple.
......@@ -565,8 +570,10 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
}
else
{
compute_ptr_offset_of_groups_.BatchStrideA_ = a_g_n_c_wis_strides[0];
compute_ptr_offset_of_groups_.BatchStrideB_ = b_g_k_c_xs_strides[0];
compute_ptr_offset_of_groups_.BatchStrideA_ =
a_g_n_c_wis_strides[0] * NumGroupsToMerge;
compute_ptr_offset_of_groups_.BatchStrideB_ =
b_g_k_c_xs_strides[0] * NumGroupsToMerge;
compute_ptr_offset_of_n_.BatchStrideA_ = a_g_n_c_wis_strides[1] * conv_N_per_block_;
// p_as and p_bs are pointers
......@@ -583,7 +590,8 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
p_ds_grid_(i) = static_cast<const DDataType*>(p_ds[i]);
// D batch stride
compute_ptr_offset_of_groups_.BatchStrideDs_(i) = ds_g_n_k_wos_strides[i][0];
compute_ptr_offset_of_groups_.BatchStrideDs_(i) =
ds_g_n_k_wos_strides[i][0] * NumGroupsToMerge;
compute_ptr_offset_of_n_.BatchStrideDs_(i) =
ds_g_n_k_wos_strides[i][1] * conv_N_per_block_;
......@@ -602,7 +610,7 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
ds_grid_desc_m_n_(i) =
DeviceOp::MakeEGridDescriptor_M_N<DLayout>(conv_to_gemm_transformer_d);
});
compute_ptr_offset_of_groups_.BatchStrideE_ = e_g_n_k_wos_strides[0];
compute_ptr_offset_of_groups_.BatchStrideE_ = e_g_n_k_wos_strides[0] * NumGroupsToMerge;
compute_ptr_offset_of_n_.BatchStrideE_ = e_g_n_k_wos_strides[1] * conv_N_per_block_;
// populate desc for Ds/E
......@@ -726,7 +734,7 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
arg.a_g_n_c_wis_lengths_[I1] / arg.conv_N_per_block_;
const index_t gdx = arg.block_2_etile_map_.CalculateGridSize(arg.e_grid_desc_m_n_);
const index_t gdy = arg.num_group_;
const index_t gdy = arg.num_group_ / NumGroupsToMerge;
const index_t gdz = num_workgroups_per_Conv_N;
const auto K =
......@@ -850,6 +858,10 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
{
namespace ctc = tensor_layout::convolution;
const index_t G = arg.b_g_k_c_xs_lengths_[I0];
const index_t K = arg.b_g_k_c_xs_lengths_[I1];
const index_t C = arg.b_g_k_c_xs_lengths_[I2];
// check device
if(get_device_name() == "gfx908")
{
......@@ -898,6 +910,42 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
}
}
}
else if constexpr(ConvForwardSpecialization == ConvolutionForwardSpecialization::Filter3x3)
{
if(C != 1)
{
return false;
}
for(index_t i = 0; i < NDimSpatial; ++i)
{
const index_t filter_spatial_dim = arg.b_g_k_c_xs_lengths_[i + I3];
if(filter_spatial_dim != I3)
{
return false;
}
}
if constexpr(!is_NSpatialGC_GKSpatial_NSpatialGK<ALayout, BLayout, ELayout>())
{
return false;
}
}
if constexpr(NumGroupsToMerge > 1)
{
if(!(C == 1))
{
return false;
}
if(G % NumGroupsToMerge != 0)
{
return false;
}
if constexpr(!is_NSpatialGC_GKSpatial_NSpatialGK<ALayout, BLayout, ELayout>())
{
return false;
}
}
// check vector access of A
// FIXME: layout
......@@ -907,11 +955,16 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
is_same_v<ALayout, ctc::NWGC> || is_same_v<ALayout, ctc::NHWGC> ||
is_same_v<ALayout, ctc::NDHWGC>)
{
const index_t C = arg.a_g_n_c_wis_lengths_[2];
// Check access per C
if(!(ABlockTransferSrcVectorDim == 2 && C % ABlockTransferSrcScalarPerVector == 0))
{
return false;
// If not possible, check access per G
if(!(ABlockTransferSrcVectorDim == 1 && C == 1 &&
is_NSpatialGC_GKSpatial_NSpatialGK<ALayout, BLayout, ELayout>() &&
G % ABlockTransferSrcScalarPerVector == 0))
{
return false;
}
}
}
else
......@@ -928,8 +981,6 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
is_same_v<BLayout, ctc::KZYXGC>)
{
const index_t C = arg.b_g_k_c_xs_lengths_[2];
if(!(BBlockTransferSrcVectorDim == 2 && C % BBlockTransferSrcScalarPerVector == 0))
{
return false;
......@@ -953,8 +1004,6 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
is_same_v<DLayout, ctc::NWGK> || is_same_v<DLayout, ctc::NHWGK> ||
is_same_v<DLayout, ctc::NDHWGK> || is_same_v<DLayout, ctc::G_K>)
{
const index_t K = arg.ds_g_n_k_wos_lengths_[i][2];
if(!(K % CDEBlockTransferScalarPerVector_NPerBlock == 0))
{
valid = false;
......@@ -999,8 +1048,6 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
is_same_v<ELayout, ctc::NWGK> || is_same_v<ELayout, ctc::NHWGK> ||
is_same_v<ELayout, ctc::NDHWGK>)
{
const index_t K = arg.e_g_n_k_wos_lengths_[2];
if(!(K % CDEBlockTransferScalarPerVector_NPerBlock == 0))
{
return false;
......@@ -1298,7 +1345,8 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
<< BBlockTransferSrcScalarPerVector << ", "
<< CDEBlockTransferScalarPerVector_NPerBlock << ", "
<< CShuffleMXdlPerWavePerShuffle << ", "
<< CShuffleNXdlPerWavePerShuffle
<< CShuffleNXdlPerWavePerShuffle << ", "
<< NumGroupsToMerge
<< ">";
// clang-format on
......
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