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Unverified Commit 5063a39f authored by Illia Silin's avatar Illia Silin Committed by GitHub
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Merge pull request #285 from ROCm/merge_from_public 

Merge from public
parents bfdc2430 96a0d5f6
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example/01_gemm/run_gemm_example.inc
View file @ 5063a39f
...@@ -5,88 +5,6 @@ ...@@ -5,88 +5,6 @@
#include "ck/tensor_operation/gpu/device/device_gemm_streamk.hpp" #include "ck/tensor_operation/gpu/device/device_gemm_streamk.hpp"
template <typename DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 2e-1;
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 2e-1;
}
else
{
return 1e-3;
}
}
template <typename DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 2e-1;
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 2e-1;
}
else
{
return 1e-3;
}
}
template <typename ProblemType> template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config) bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{ {
......
example/01_gemm/run_gemm_example_streamk_v2.inc 100755 → 100644
View file @ 5063a39f
...@@ -3,88 +3,6 @@ ...@@ -3,88 +3,6 @@
#pragma once #pragma once
template <typename DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 1e-1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 1.5e-1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 16.1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 8192.1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename ProblemType> template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config) bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{ {
......
example/01_gemm/run_gemm_example_v2.inc
View file @ 5063a39f
...@@ -3,88 +3,6 @@ ...@@ -3,88 +3,6 @@
#pragma once #pragma once
template <typename DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 1e-1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 1.5e-1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 16.1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 8192.1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename ProblemType> template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config) bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{ {
......
example/CMakeLists.txt
View file @ 5063a39f
...@@ -5,6 +5,14 @@ include_directories(BEFORE ...@@ -5,6 +5,14 @@ include_directories(BEFORE
add_custom_target(examples) add_custom_target(examples)
# list of examples that are labelled as REGRESSION_EXAMPLE for make regression (runtime more than 30 seconds)
# all other tests are labelled as SMOKE_EXAMPLE
set(REGRESSION_EXAMPLES
example_sparse_embedding3_forward_layernorm
)
function(add_example_dependencies EXAMPLE_NAME FILE_NAME) function(add_example_dependencies EXAMPLE_NAME FILE_NAME)
if(FILE_NAME) if(FILE_NAME)
add_dependencies(EXAMPLE_NAME FILE_NAME) add_dependencies(EXAMPLE_NAME FILE_NAME)
...@@ -54,9 +62,9 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME) ...@@ -54,9 +62,9 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
list(REMOVE_ITEM FILE_NAME "${source}") list(REMOVE_ITEM FILE_NAME "${source}")
endif() endif()
endforeach() endforeach()
#Do not build any DPP examples if DL_KERNELS not set #Do not build any DPP examples if DPP_KERNELS not set
foreach(source IN LISTS FILE_NAME) foreach(source IN LISTS FILE_NAME)
if(NOT DEFINED DL_KERNELS AND source MATCHES "_dpp") if(NOT DEFINED DPP_KERNELS AND source MATCHES "_dpp")
message("removing dpp example ${source} ") message("removing dpp example ${source} ")
list(REMOVE_ITEM FILE_NAME "${source}") list(REMOVE_ITEM FILE_NAME "${source}")
endif() endif()
...@@ -107,6 +115,15 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME) ...@@ -107,6 +115,15 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
set(result 0) set(result 0)
endif() endif()
#message("add_example returns ${result}") #message("add_example returns ${result}")
if(result EQUAL 0 AND NOT "${EXAMPLE_NAME}" IN_LIST REGRESSION_EXAMPLES)
#message("adding to SMOKE EXAMPLE FILTER ${EXAMPLE_NAME}")
set_tests_properties(${EXAMPLE_NAME} PROPERTIES LABELS "SMOKE_TEST")
add_dependencies(smoke ${EXAMPLE_NAME})
elseif(result EQUAL 0 AND "${EXAMPLE_NAME}" IN_LIST REGRESSION_EXAMPLES)
#message("Adding to REGRESSION EXAMPLE FILTER ${EXAMPLE_NAME}")
set_tests_properties(${EXAMPLE_NAME} PROPERTIES LABELS "REGRESSION_TEST")
add_dependencies(regression ${EXAMPLE_NAME})
endif()
set(result ${result} PARENT_SCOPE) set(result ${result} PARENT_SCOPE)
endfunction(add_example_executable EXAMPLE_NAME) endfunction(add_example_executable EXAMPLE_NAME)
...@@ -188,8 +205,10 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME) ...@@ -188,8 +205,10 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
rocm_install(TARGETS ${EXAMPLE_NAME} COMPONENT examples) rocm_install(TARGETS ${EXAMPLE_NAME} COMPONENT examples)
set(result 0) set(result 0)
endif() endif()
#message("add_example returns ${result}") #message("add_example returns ${result}")
set(result ${result} PARENT_SCOPE) set(result ${result} PARENT_SCOPE)
endfunction(add_example_executable_no_testing EXAMPLE_NAME) endfunction(add_example_executable_no_testing EXAMPLE_NAME)
# add all example subdir # add all example subdir
......
example/ck_tile/01_fmha/README.md
View file @ 5063a39f
...@@ -15,8 +15,7 @@ This will result in an executable `build/bin/tile_example_fmha_fwd` ...@@ -15,8 +15,7 @@ This will result in an executable `build/bin/tile_example_fmha_fwd`
## kernel ## kernel
The kernel template is `fmha_fwd_kernel.hpp`, this is the grid-wise op in old ck_tile's terminology. We put it here purposely, to demonstrate one can construct a kernel by using various internal component from ck_tile. We may still have an implementation under ck_tile's include path (in the future) for the kernel template. The kernel template is `fmha_fwd_kernel.hpp`, this is the grid-wise op in old ck_tile's terminology. We put it here purposely, to demonstrate one can construct a kernel by using various internal component from ck_tile. We may still have an implementation under ck_tile's include path (in the future) for the kernel template.
There are 3 template parameters for this kernel template. There are 2 template parameters for this kernel template.
* `TilePartitioner` is used to map the workgroup to corresponding tile, `fmha_fwd_tile_partitioner.hpp` in this folder served as this purpose.
* `FmhaPipeline` is one of the block_tile_pipeline(under `include/ck_tile/tile_program/block_tile_pipeline`) which is a performance critical component. Indeed, we did a lot of optimization and trials to optimize the pipeline and may still workout more performance pipeline and update into that folder. People only need to replace this pipeline type and would be able to enjoy the benefit of different performant implementations (stay tuned for updated pipeline(s)). * `FmhaPipeline` is one of the block_tile_pipeline(under `include/ck_tile/tile_program/block_tile_pipeline`) which is a performance critical component. Indeed, we did a lot of optimization and trials to optimize the pipeline and may still workout more performance pipeline and update into that folder. People only need to replace this pipeline type and would be able to enjoy the benefit of different performant implementations (stay tuned for updated pipeline(s)).
* `EpiloguePipeline` will modify and store out the result in the last phase. People usually will do lot of post-fusion at this stage, so we also abstract this concept. Currently we didn't do much thing at the epilogue stage but leave the room for future possible support. * `EpiloguePipeline` will modify and store out the result in the last phase. People usually will do lot of post-fusion at this stage, so we also abstract this concept. Currently we didn't do much thing at the epilogue stage but leave the room for future possible support.
......
example/ck_tile/01_fmha/codegen/cpp_symbol_map.py
View file @ 5063a39f
...@@ -119,6 +119,7 @@ PIPELINE_MAP = { ...@@ -119,6 +119,7 @@ PIPELINE_MAP = {
PIPELINE_ENUM_MAP = { PIPELINE_ENUM_MAP = {
"qr" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS", "qr" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS",
"qr_async" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS_ASYNC", "qr_async" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS_ASYNC",
"qr_nwarp_sshuffle" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS",
} }
BOOL_MAP = { BOOL_MAP = {
......
example/ck_tile/01_fmha/codegen/ops/fmha_fwd.py
View file @ 5063a39f
...@@ -29,11 +29,6 @@ K0_MAX_SUBMAX_MAP = { ...@@ -29,11 +29,6 @@ K0_MAX_SUBMAX_MAP = {
256: 256 256: 256
} }
TILE_PARTITIONER_MAP = {
"shb" : "ck_tile::FmhaFwdTilePartitioner_SHB",
"hbs" : "ck_tile::FmhaFwdTilePartitioner_HBS",
}
FMHA_FWD_KERNEL_HEADER = """// SPDX-License-Identifier: MIT FMHA_FWD_KERNEL_HEADER = """// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.\n // Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.\n
// auto generated by generate.py // auto generated by generate.py
...@@ -44,13 +39,12 @@ FMHA_FWD_KERNEL_BODY=""" ...@@ -44,13 +39,12 @@ FMHA_FWD_KERNEL_BODY="""
using fmha_dtype_{F_idx} = {F_dtype}; using fmha_dtype_{F_idx} = {F_dtype};
using fmha_block_tile_{F_idx} = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}>; using fmha_block_tile_{F_idx} = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}>;
using fmha_warp_tile_{F_idx} = ck_tile::sequence<{F_wm}, {F_wn}, {F_wk}>;
using fmha_shape_{F_idx} = ck_tile::TileFmhaShape<fmha_block_tile_{F_idx}, using fmha_shape_{F_idx} = ck_tile::TileFmhaShape<fmha_block_tile_{F_idx},
ck_tile::sequence<{F_rm0}, {F_rn0}, {F_rk0}>, ck_tile::sequence<{F_rm0}, {F_rn0}, {F_rk0}>,
fmha_warp_tile_{F_idx}, ck_tile::sequence<{F_wm0}, {F_wn0}, {F_wk0}>,
ck_tile::sequence<{F_rm1}, {F_rn1}, {F_rk1}>, ck_tile::sequence<{F_rm1}, {F_rn1}, {F_rk1}>,
fmha_warp_tile_{F_idx}, ck_tile::sequence<{F_wm1}, {F_wn1}, {F_wk1}>,
{F_vlayout}>; {F_vlayout}>;
using fmha_trait_{F_idx} = ck_tile::TileFmhaTraits<{F_spad}, using fmha_trait_{F_idx} = ck_tile::TileFmhaTraits<{F_spad},
...@@ -91,9 +85,7 @@ using fmha_epilogue_{F_idx} = ...@@ -91,9 +85,7 @@ using fmha_epilogue_{F_idx} =
{F_spad}, {F_dvpad}>>; {F_spad}, {F_dvpad}>>;
using fmha_kernel_{F_idx} = using fmha_kernel_{F_idx} =
ck_tile::FmhaFwdKernel<{F_tile_partitioner}<fmha_shape_{F_idx}>, ck_tile::FmhaFwdKernel<fmha_pipeline_{F_idx}, fmha_epilogue_{F_idx}>;
fmha_pipeline_{F_idx},
fmha_epilogue_{F_idx}>;
using trait_{F_idx} = fmha_fwd_traits_<{F_hdim}, {F_dtype}, {F_mode},{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}, {F_vlayout}, using trait_{F_idx} = fmha_fwd_traits_<{F_hdim}, {F_dtype}, {F_mode},{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}, {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}>; {F_pipeline_enum}, fmha_mask_{F_idx}, {F_bias}, {F_lse}, {F_dropout}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
...@@ -306,15 +298,19 @@ class FmhaFwdTileSize: ...@@ -306,15 +298,19 @@ class FmhaFwdTileSize:
F_rm1 : int # number of warps for gemm1 along q seqlen F_rm1 : int # number of warps for gemm1 along q seqlen
F_rn1 : int # number of warps for gemm1 along head dim v F_rn1 : int # number of warps for gemm1 along head dim v
F_rk1 : int # number of warps for gemm1 along k seqlen (not used) F_rk1 : int # number of warps for gemm1 along k seqlen (not used)
F_wm : int # warp size along m (warp size) F_wm0 : int # gemm0 warp size along m
F_wn : int # warp size along n F_wn0 : int # gemm0 warp size along n
F_wk : int # warp size along k F_wk0 : int # gemm0 warp size along k
F_wm1 : int # gemm1 warp size along m
F_wn1 : int # gemm1 warp size along n
F_wk1 : int # gemm1 warp size along k
F_occupancy : int # occupancy, -1 will let pipeline decide the occupancy, other value will overwrite occupancy F_occupancy : int # occupancy, -1 will let pipeline decide the occupancy, other value will overwrite occupancy
@property @property
def name(self) -> str: def name(self) -> str:
return f"b{self.F_bm0}x{self.F_bn0}x{self.F_bk0}x{self.F_bn1}x{self.F_bk1}x{self.F_bk0max}" +\ return f"b{self.F_bm0}x{self.F_bn0}x{self.F_bk0}x{self.F_bn1}x{self.F_bk1}x{self.F_bk0max}" +\
f"_r{self.F_rm0}x{self.F_rn0}x{self.F_rk0}_r{self.F_rm1}x{self.F_rn1}x{self.F_rk1}" +\ f"_r{self.F_rm0}x{self.F_rn0}x{self.F_rk0}_r{self.F_rm1}x{self.F_rn1}x{self.F_rk1}" +\
f"_w{self.F_wm}x{self.F_wn}x{self.F_wk}" + ("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}") f"_w{self.F_wm0}x{self.F_wn0}x{self.F_wk0}_w{self.F_wm1}x{self.F_wn1}x{self.F_wk1}" +\
("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}")
@dataclass @dataclass
class FmhaFwdKernel: class FmhaFwdKernel:
...@@ -326,12 +322,6 @@ class FmhaFwdKernel: ...@@ -326,12 +322,6 @@ class FmhaFwdKernel:
F_pipeline : FmhaFwdPipeline F_pipeline : FmhaFwdPipeline
mask_impl : str mask_impl : str
def get_tp(self) -> str:
if self.F_mode == 'group':
return 'hbs'
else:
return 'shb'
@property @property
def template(self) -> str: def template(self) -> str:
kernel_body = str() kernel_body = str()
...@@ -352,9 +342,12 @@ class FmhaFwdKernel: ...@@ -352,9 +342,12 @@ class FmhaFwdKernel:
F_rm1 = self.F_tile.F_rm1, F_rm1 = self.F_tile.F_rm1,
F_rn1 = self.F_tile.F_rn1, F_rn1 = self.F_tile.F_rn1,
F_rk1 = self.F_tile.F_rk1, F_rk1 = self.F_tile.F_rk1,
F_wm = self.F_tile.F_wm, F_wm0 = self.F_tile.F_wm0,
F_wn = self.F_tile.F_wn, F_wn0 = self.F_tile.F_wn0,
F_wk = self.F_tile.F_wk, F_wk0 = self.F_tile.F_wk0,
F_wm1 = self.F_tile.F_wm1,
F_wn1 = self.F_tile.F_wn1,
F_wk1 = self.F_tile.F_wk1,
F_vlayout = LAYOUT_MAP[self.F_pipeline.F_vlayout], F_vlayout = LAYOUT_MAP[self.F_pipeline.F_vlayout],
F_spad = BOOL_MAP[self.F_pipeline.F_spad], F_spad = BOOL_MAP[self.F_pipeline.F_spad],
F_skpad = BOOL_MAP[self.F_pipeline.F_skpad], F_skpad = BOOL_MAP[self.F_pipeline.F_skpad],
...@@ -368,13 +361,12 @@ class FmhaFwdKernel: ...@@ -368,13 +361,12 @@ class FmhaFwdKernel:
F_pipeline_enum = PIPELINE_ENUM_MAP[self.F_pipeline.tag], F_pipeline_enum = PIPELINE_ENUM_MAP[self.F_pipeline.tag],
F_mask = get_mask_map(self.mask_impl)[self.F_pipeline.F_mask], F_mask = get_mask_map(self.mask_impl)[self.F_pipeline.F_mask],
F_mode = MODE_MAP[self.F_mode], F_mode = MODE_MAP[self.F_mode],
F_pipeline = PIPELINE_MAP[self.F_pipeline.tag], F_pipeline = PIPELINE_MAP[self.F_pipeline.tag])
F_tile_partitioner = TILE_PARTITIONER_MAP[self.get_tp()])
@property @property
def name(self) -> str: def name(self) -> str:
# TODO: we don't encode idx here # TODO: we don't encode idx here
return f"fmha_fwd_d{self.F_hdim}_{self.F_dtype}_{self.F_mode}_{self.get_tp()}_" + \ return f"fmha_fwd_d{self.F_hdim}_{self.F_dtype}_{self.F_mode}_" + \
self.F_tile.name + '_' + self.F_pipeline.name self.F_tile.name + '_' + self.F_pipeline.name
@property @property
...@@ -409,17 +401,17 @@ class FmhaFwdKernel: ...@@ -409,17 +401,17 @@ class FmhaFwdKernel:
def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]: def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
if dtype == 'fp16' or dtype == 'bf16': if dtype == 'fp16' or dtype == 'bf16':
return { return {
'32' : FmhaFwdTileSize(128, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 32, 32, 16, -1), '32' : FmhaFwdTileSize(128, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 32, 32, 16, 32, 32, 16, -1),
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 4, 1, 1, 4, 1, 1, 32, 32, 16, -1), '64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
## '96' : FmhaFwdTileSize(128, 128, 32, 128, 32, 96, 4, 1, 1, 4, 1, 1, 32, 32, 16, -1), ### '96' : FmhaFwdTileSize(128, 128, 32, 128, 32, 96, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 16, -1), '128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 16, -1), '256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
} }
elif dtype == 'fp8' or dtype == 'bf8': elif dtype == 'fp8' or dtype == 'bf8':
return { return {
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, -1), '64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, 32, 32, 32, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, -1), '128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, -1) '256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
} }
else: else:
return None return None
......
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_appendkv.py
View file @ 5063a39f
...@@ -46,9 +46,7 @@ using fmha_pipeline_problem_{F_idx} = ck_tile::BlockFmhaFwdAppendKVPipelineProbl ...@@ -46,9 +46,7 @@ using fmha_pipeline_problem_{F_idx} = ck_tile::BlockFmhaFwdAppendKVPipelineProbl
using fmha_pipeline_{F_idx} = ck_tile::BlockFmhaFwdAppendKVPipeline< using fmha_pipeline_{F_idx} = ck_tile::BlockFmhaFwdAppendKVPipeline<
fmha_pipeline_problem_{F_idx}>; fmha_pipeline_problem_{F_idx}>;
using fmha_kernel_{F_idx} = using fmha_kernel_{F_idx} = ck_tile::FmhaFwdAppendKVKernel<fmha_pipeline_{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}, 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}>; {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}, {F_rope}, {F_pagedkv}>;
...@@ -355,4 +353,4 @@ def list_blobs(file_path : Path, kernel_filter : Optional[str], receipt, mask_im ...@@ -355,4 +353,4 @@ def list_blobs(file_path : Path, kernel_filter : Optional[str], receipt, mask_im
_, kernels = get_fwd_appendkv_blobs(kernel_filter, receipt, mask_impl) _, kernels = get_fwd_appendkv_blobs(kernel_filter, receipt, mask_impl)
for kernel in kernels: for kernel in kernels:
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n") 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") 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 @ 5063a39f
...@@ -39,6 +39,7 @@ K0_MAX_SUBMAX_MAP = { ...@@ -39,6 +39,7 @@ K0_MAX_SUBMAX_MAP = {
FMHA_FWD_SPLITKV_PIPELINE_MAP = { FMHA_FWD_SPLITKV_PIPELINE_MAP = {
"qr" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVS", "qr" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVS",
"qr_nwarp_sshuffle" : "ck_tile::BlockFmhaFwdSplitKVPipelineNWarpSShuffleQRKSVS",
"qr_async" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVSAsync", "qr_async" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVSAsync",
} }
...@@ -47,16 +48,15 @@ using fmha_dtype_{F_idx} = {F_dtype}; ...@@ -47,16 +48,15 @@ using fmha_dtype_{F_idx} = {F_dtype};
using fmha_mask_{F_idx} = {F_mask}; using fmha_mask_{F_idx} = {F_mask};
namespace {{ namespace {{
template <bool kHasUnevenSplits> template <bool kHasUnevenSplits, bool kMergeNumHeadGroupsSeqLenQ = false>
struct kernel_runner {{ struct instance {{
using fmha_block_tile = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}>; using fmha_block_tile = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}>;
using fmha_warp_tile = ck_tile::sequence<{F_wm}, {F_wn}, {F_wk}>;
using fmha_shape = ck_tile::TileFmhaShape<fmha_block_tile, using fmha_shape = ck_tile::TileFmhaShape<fmha_block_tile,
ck_tile::sequence<{F_rm0}, {F_rn0}, {F_rk0}>, ck_tile::sequence<{F_rm0}, {F_rn0}, {F_rk0}>,
fmha_warp_tile, ck_tile::sequence<{F_wm0}, {F_wn0}, {F_wk0}>,
ck_tile::sequence<{F_rm1}, {F_rn1}, {F_rk1}>, ck_tile::sequence<{F_rm1}, {F_rn1}, {F_rk1}>,
fmha_warp_tile, ck_tile::sequence<{F_wm1}, {F_wn1}, {F_wk1}>,
{F_vlayout}>; {F_vlayout}>;
using fmha_trait = ck_tile::TileFmhaFwdSplitKVTraits<{F_spad}, using fmha_trait = ck_tile::TileFmhaFwdSplitKVTraits<{F_spad},
...@@ -64,11 +64,12 @@ using fmha_trait = ck_tile::TileFmhaFwdSplitKVTraits<{F_spad}, ...@@ -64,11 +64,12 @@ using fmha_trait = ck_tile::TileFmhaFwdSplitKVTraits<{F_spad},
{F_dpad}, {F_dpad},
{F_dvpad}, {F_dvpad},
{F_bias}, {F_bias},
false, /*kHasBiasGrad=*/false,
{F_lse}, {F_lse},
{F_squant}, {F_squant},
{F_pagedkv}, {F_pagedkv},
kHasUnevenSplits, kHasUnevenSplits,
kMergeNumHeadGroupsSeqLenQ,
{F_occupancy}>; {F_occupancy}>;
using fmha_pipeline_problem = ck_tile::BlockFmhaFwdSplitKVPipelineProblem< using fmha_pipeline_problem = ck_tile::BlockFmhaFwdSplitKVPipelineProblem<
...@@ -96,9 +97,7 @@ using fmha_epilogue = ...@@ -96,9 +97,7 @@ using fmha_epilogue =
{F_spad}, {F_dvpad}>>; {F_spad}, {F_dvpad}>>;
using fmha_kernel = using fmha_kernel =
ck_tile::FmhaFwdSplitKVKernel<ck_tile::FmhaFwdSplitKVTilePartitioner<fmha_shape>, ck_tile::FmhaFwdSplitKVKernel<fmha_pipeline, fmha_epilogue>;
fmha_pipeline,
fmha_epilogue>;
static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a) static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
{{ {{
...@@ -117,28 +116,50 @@ using trait_{F_idx} = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F ...@@ -117,28 +116,50 @@ using trait_{F_idx} = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F
#include <iostream> #include <iostream>
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wtautological-compare"
namespace {{
template <bool kHasUnevenSplits>
void run_instance(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a) {{
if constexpr ({F_hdim} == 128 && {F_bias} == ck_tile::BlockAttentionBiasEnum::NO_BIAS
&& (std::is_same_v<{F_mask}, ck_tile::SimplifiedGenericAttentionMask<false>>
|| std::is_same_v<{F_mask}, FmhaMasks::NoMask>)) {{
if (a.max_seqlen_q == 1 && a.nhead_k < a.nhead_q) {{
instance<kHasUnevenSplits, /*kMergeNumHeadGroupsSeqLenQ=*/true>::run(s, a);
}} else {{
instance<kHasUnevenSplits>::run(s, a);
}}
}} else {{
instance<kHasUnevenSplits>::run(s, a);
}}
}}
}} // anonymous namespace
#pragma clang diagnostic pop
template<> template<>
void fmha_fwd_splitkv_oneshot_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_splitkv_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 constexpr({F_mode} == false) {{ // batch mode
// we don't check every seqlen_k values for kvcache // we don't check every seqlen_k values for kvcache
if (a.seqlen_k_ptr != nullptr) {{ if (a.seqlen_k_ptr != nullptr) {{
kernel_runner<true>::run(s, a); run_instance</*kHasUnevenSplits=*/true>(s, a);
// make sure F_bn0 is divisible by F_bk1 // make sure F_bn0 is divisible by F_bk1
}} else if (a.seqlen_k % (a.num_splits * {F_bn0}) == 0) {{ }} else if (a.seqlen_k % (a.num_splits * {F_bn0}) == 0) {{
kernel_runner<false>::run(s, a); run_instance</*kHasUnevenSplits=*/false>(s, a);
}} else {{ }} else {{
kernel_runner<true>::run(s, a); run_instance</*kHasUnevenSplits=*/true>(s, a);
}} }}
}} else {{ }} else {{
kernel_runner<true>::run(s, a); run_instance</*kHasUnevenSplits=*/true>(s, a);
}} }}
}} }}
template<> template<>
std::string fmha_fwd_splitkv_get_name_<trait_{F_idx}>() std::string fmha_fwd_splitkv_get_name_<trait_{F_idx}>()
{{ {{
using k_ = kernel_runner<true>::fmha_kernel; /// FIXME: choose real kernel type using k_ = instance<true>::fmha_kernel; /// FIXME: choose real kernel type
return k_::GetName(); return k_::GetName();
}} }}
""" """
...@@ -148,7 +169,7 @@ using fmha_dtype_{F_idx} = {F_dtype}; ...@@ -148,7 +169,7 @@ using fmha_dtype_{F_idx} = {F_dtype};
namespace {{ namespace {{
template <ck_tile::index_t kLogMaxSplits> template <ck_tile::index_t kLogMaxSplits>
struct kernel_runner {{ struct instance {{
using fmha_trait = ck_tile::TileFmhaFwdSplitKVCombineTraits<{F_spad}, using fmha_trait = ck_tile::TileFmhaFwdSplitKVCombineTraits<{F_spad},
{F_dvpad}, {F_dvpad},
{F_lse}, {F_lse},
...@@ -161,9 +182,8 @@ using fmha_pipeline_problem = ck_tile::BlockFmhaSplitKVCombinePipelineProblem< ...@@ -161,9 +182,8 @@ using fmha_pipeline_problem = ck_tile::BlockFmhaSplitKVCombinePipelineProblem<
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::OaccDataType, typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::OaccDataType,
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::ODataType, typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::ODataType,
{F_hdim}, {F_hdim},
{F_bm0},
{F_bn1},
{F_mode}, {F_mode},
{F_bn1},
fmha_trait>; fmha_trait>;
using fmha_pipeline = ck_tile::BlockFmhaFwdSplitKVCombinePipeline< using fmha_pipeline = ck_tile::BlockFmhaFwdSplitKVCombinePipeline<
...@@ -177,9 +197,7 @@ using fmha_epilogue = ...@@ -177,9 +197,7 @@ using fmha_epilogue =
false, false>>; false, false>>;
using fmha_kernel = using fmha_kernel =
ck_tile::FmhaFwdSplitKVCombineKernel<ck_tile::FmhaFwdSplitKVCombineTilePartitioner<{F_bm0}, {F_bn1}>, ck_tile::FmhaFwdSplitKVCombineKernel<fmha_pipeline, fmha_epilogue>;
fmha_pipeline,
fmha_epilogue>;
static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a) static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
{{ {{
...@@ -192,7 +210,7 @@ static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a) ...@@ -192,7 +210,7 @@ static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
}}; }};
}} }}
using trait_{F_idx} = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn1}, using trait_{F_idx} = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bn1},
{F_lse}, {F_squant}, {F_spad}, {F_dvpad}>; {F_lse}, {F_squant}, {F_spad}, {F_dvpad}>;
#include <iostream> #include <iostream>
...@@ -201,22 +219,22 @@ template<> ...@@ -201,22 +219,22 @@ template<>
void fmha_fwd_splitkv_combine_oneshot_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_splitkv_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 <= 8) {{ if (a.num_splits <= 8) {{
kernel_runner<3>::run(s, a); instance<3>::run(s, a);
}} else if (a.num_splits <= 16) {{ }} else if (a.num_splits <= 16) {{
kernel_runner<4>::run(s, a); instance<4>::run(s, a);
}} else if (a.num_splits <= 32) {{ }} else if (a.num_splits <= 32) {{
kernel_runner<5>::run(s, a); instance<5>::run(s, a);
}} else if (a.num_splits <= 64) {{ }} else if (a.num_splits <= 64) {{
kernel_runner<6>::run(s, a); instance<6>::run(s, a);
}} else if (a.num_splits <= 128) {{ }} else if (a.num_splits <= 128) {{
kernel_runner<7>::run(s, a); instance<7>::run(s, a);
}} }}
}} }}
template<> template<>
std::string fmha_fwd_splitkv_combine_get_name_<trait_{F_idx}>() std::string fmha_fwd_splitkv_combine_get_name_<trait_{F_idx}>()
{{ {{
using k_ = kernel_runner<6>::fmha_kernel; /// FIXME: choose real kernel type using k_ = instance<6>::fmha_kernel; /// FIXME: choose real kernel type
return k_::GetName(); return k_::GetName();
}} }}
""" """
...@@ -250,16 +268,25 @@ float fmha_fwd_splitkv(fmha_fwd_splitkv_traits t, fmha_fwd_splitkv_args a, const ...@@ -250,16 +268,25 @@ float fmha_fwd_splitkv(fmha_fwd_splitkv_traits t, fmha_fwd_splitkv_args a, const
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.do_fp8_static_quant == {F_squant}) && 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.do_fp8_static_quant == {F_squant}) &&
((a.block_table_ptr != nullptr) == {F_pagedkv}) && ({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{ ((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_bk0max}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, true, {F_squant}, {F_pagedkv}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>; using traits_ = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, true, {F_squant}, {F_pagedkv}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
// get combine kernel tile sizes
using OaccDataType = typename FmhaFwdTypeConfig<{F_dtype}>::OaccDataType;
constexpr ck_tile::index_t kM0 = ck_tile::BlockFmhaSplitKVCombinePipelineTileSizes<OaccDataType, /*F_bn1=*/32>::kM0;
// make sure we can reuse the padding flags in combine kernels
static_assert({F_bm0} % kM0 == 0);
static_assert({F_bn1} % 32 == 0);
if (t.has_lse) {{ if (t.has_lse) {{
if constexpr (std::is_same_v<{F_dtype}, ck_tile::fp8_t>) {{ if constexpr (std::is_same_v<{F_dtype}, FmhaFwdFp8>) {{
return -1; return -1;
}} else {{ }} else {{
using traits2_ = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}/2, {F_bn1}/2, true, {F_squant}, {F_spad}, {F_dvpad}>; using traits2_ = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, /*F_bn1=*/32, true, {F_squant}, {F_spad}, {F_dvpad}>;
return fmha_fwd_splitkv_<traits_, traits2_>(s, a); return fmha_fwd_splitkv_<traits_, traits2_>(s, a);
}} }}
}} else {{ }} else {{
using traits2_ = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}/2, {F_bn1}/2, false, {F_squant}, {F_spad}, {F_dvpad}>; using traits2_ = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, /*F_bn1=*/32, false, {F_squant}, {F_spad}, {F_dvpad}>;
return fmha_fwd_splitkv_<traits_, traits2_>(s, a); return fmha_fwd_splitkv_<traits_, traits2_>(s, a);
}} }}
...@@ -302,7 +329,7 @@ class FmhaFwdSplitKVApiTrait: ...@@ -302,7 +329,7 @@ class FmhaFwdSplitKVApiTrait:
if self.pipeline_tag == 'qr_async': if self.pipeline_tag == 'qr_async':
if self.spad == 't' : return 'true' # always support if self.spad == 't' : return 'true' # always support
else : return 'true' else : return 'true'
elif self.pipeline_tag in ['qr']: elif self.pipeline_tag in ['qr', 'qr_nwarp_sshuffle']:
if self.spad == 't' : return f'true /*a.seqlen_q % {self.bm0} != 0*/' # TODO: order of get_pipelines() matters! (ugly) 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 : return f'a.seqlen_q % {self.bm0} == 0'
else: assert False else: assert False
...@@ -313,7 +340,7 @@ class FmhaFwdSplitKVApiTrait: ...@@ -313,7 +340,7 @@ class FmhaFwdSplitKVApiTrait:
if self.pipeline_tag == 'qr_async': if self.pipeline_tag == 'qr_async':
if self.skpad == 't' : return f'a.seqlen_k == 0 || a.seqlen_k % {self.bn0} != 0' 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' else : return f'a.seqlen_k != 0 && a.seqlen_k % {self.bn0} == 0'
elif self.pipeline_tag in ['qr', 'qr_fp8']: elif self.pipeline_tag in ['qr', 'qr_nwarp_sshuffle']:
if self.skpad == 't' : return f'true /*a.seqlen_k % {self.bn0} != 0*/' # TODO: order of get_pipelines() matters! (ugly) 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 : return f'a.seqlen_k % {self.bn0} == 0'
else: assert False else: assert False
...@@ -324,7 +351,7 @@ class FmhaFwdSplitKVApiTrait: ...@@ -324,7 +351,7 @@ class FmhaFwdSplitKVApiTrait:
vec = int((32 * 4) / DTYPE_BITS[self.dtype]) vec = int((32 * 4) / DTYPE_BITS[self.dtype])
if self.dpad == 't': return f'a.hdim_q % {vec} == 0' if self.dpad == 't': return f'a.hdim_q % {vec} == 0'
else : assert False else : assert False
elif self.pipeline_tag in ['qr']: elif self.pipeline_tag in ['qr', 'qr_nwarp_sshuffle']:
bk0submax = K0_MAX_SUBMAX_MAP[self.bk0max] bk0submax = K0_MAX_SUBMAX_MAP[self.bk0max]
if self.dpad == 't': return f'true /*a.hdim_q % {bk0submax} != 0*/' # TODO: order of get_pipelines() matters! (ugly) if self.dpad == 't': return f'true /*a.hdim_q % {bk0submax} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_q % {bk0submax} == 0' else : return f'a.hdim_q % {bk0submax} == 0'
...@@ -336,7 +363,7 @@ class FmhaFwdSplitKVApiTrait: ...@@ -336,7 +363,7 @@ class FmhaFwdSplitKVApiTrait:
vec = int((32 * 4) / DTYPE_BITS[self.dtype]) vec = int((32 * 4) / DTYPE_BITS[self.dtype])
if self.dvpad == 't': return f'a.hdim_v % {vec} == 0' if self.dvpad == 't': return f'a.hdim_v % {vec} == 0'
else : assert False else : assert False
elif self.pipeline_tag in ['qr']: elif self.pipeline_tag in ['qr', 'qr_nwarp_sshuffle']:
bk0submax = K0_MAX_SUBMAX_MAP[self.bk0max] bk0submax = K0_MAX_SUBMAX_MAP[self.bk0max]
if self.dvpad == 't': return f'true /*a.hdim_v % {bk0submax} != 0*/' # TODO: order of get_pipelines() matters! (ugly) if self.dvpad == 't': return f'true /*a.hdim_v % {bk0submax} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_v % {bk0submax} == 0' else : return f'a.hdim_v % {bk0submax} == 0'
...@@ -447,12 +474,11 @@ class FmhaFwdSplitKVApiPool: ...@@ -447,12 +474,11 @@ class FmhaFwdSplitKVApiPool:
@dataclass @dataclass
class FmhaFwdSplitKVCombineTileSize: class FmhaFwdSplitKVCombineTileSize:
F_bm0 : int # tile size along q seqlen
F_bn1 : int # tile size along v head_dim F_bn1 : int # tile size along v head_dim
F_occupancy : int # occupancy, -1 will let pipeline decide the occupancy, other value will overwrite occupancy F_occupancy : int # occupancy, -1 will let pipeline decide the occupancy, other value will overwrite occupancy
@property @property
def name(self) -> str: def name(self) -> str:
return f"b{self.F_bm0}x{self.F_bn1}" +\ return f"b{self.F_bn1}" +\
("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}") ("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}")
@dataclass @dataclass
...@@ -485,9 +511,12 @@ class FmhaFwdSplitKVKernel: ...@@ -485,9 +511,12 @@ class FmhaFwdSplitKVKernel:
F_rm1 = self.F_tile.F_rm1, F_rm1 = self.F_tile.F_rm1,
F_rn1 = self.F_tile.F_rn1, F_rn1 = self.F_tile.F_rn1,
F_rk1 = self.F_tile.F_rk1, F_rk1 = self.F_tile.F_rk1,
F_wm = self.F_tile.F_wm, F_wm0 = self.F_tile.F_wm0,
F_wn = self.F_tile.F_wn, F_wn0 = self.F_tile.F_wn0,
F_wk = self.F_tile.F_wk, F_wk0 = self.F_tile.F_wk0,
F_wm1 = self.F_tile.F_wm1,
F_wn1 = self.F_tile.F_wn1,
F_wk1 = self.F_tile.F_wk1,
F_vlayout = LAYOUT_MAP[self.F_pipeline.F_vlayout], F_vlayout = LAYOUT_MAP[self.F_pipeline.F_vlayout],
F_spad = BOOL_MAP[self.F_pipeline.F_spad], F_spad = BOOL_MAP[self.F_pipeline.F_spad],
F_skpad = BOOL_MAP[self.F_pipeline.F_skpad], F_skpad = BOOL_MAP[self.F_pipeline.F_skpad],
...@@ -553,7 +582,6 @@ class FmhaFwdSplitKVCombineKernel: ...@@ -553,7 +582,6 @@ class FmhaFwdSplitKVCombineKernel:
F_idx = self.F_idx, F_idx = self.F_idx,
F_hdim = self.F_hdim, F_hdim = self.F_hdim,
F_dtype = FWD_DTYPE_MAP[self.F_dtype], F_dtype = FWD_DTYPE_MAP[self.F_dtype],
F_bm0 = self.F_tile.F_bm0,
F_bn1 = self.F_tile.F_bn1, F_bn1 = self.F_tile.F_bn1,
F_spad = BOOL_MAP[self.F_pipeline.F_spad], F_spad = BOOL_MAP[self.F_pipeline.F_spad],
F_dvpad = BOOL_MAP[self.F_pipeline.F_dvpad], F_dvpad = BOOL_MAP[self.F_pipeline.F_dvpad],
...@@ -577,17 +605,17 @@ class FmhaFwdSplitKVCombineKernel: ...@@ -577,17 +605,17 @@ class FmhaFwdSplitKVCombineKernel:
def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]: def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
if dtype == 'fp16' or dtype == 'bf16': if dtype == 'fp16' or dtype == 'bf16':
return { return {
'32' : FmhaFwdTileSize(32, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 16, 16, 16, -1), '32' : FmhaFwdTileSize(32, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 16, 16, 16, 16, 16, 16, -1),
'64' : FmhaFwdTileSize(64, 64, 32, 64, 32, 64, 4, 1, 1, 4, 1, 1, 16, 16, 16, -1), '64' : FmhaFwdTileSize(64, 64, 32, 64, 32, 64, 4, 1, 1, 4, 1, 1, 16, 16, 16, 16, 16, 16, -1),
## '96' : FmhaFwdTileSize(64, 128, 32, 128, 32, 96, 4, 1, 1, 4, 1, 1, 16, 16, 16, -1), ### '96' : FmhaFwdTileSize(64, 128, 32, 128, 32, 96, 4, 1, 1, 4, 1, 1, 16, 16, 16, 16, 16, 16, -1),
'128' : FmhaFwdTileSize(64, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 16, 16, 16, -1), '128' : FmhaFwdTileSize(64, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 16, 16, 16, 16, 16, 16, -1),
'256' : FmhaFwdTileSize(64, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 16, 16, 16, -1), '256' : FmhaFwdTileSize(64, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 16, 16, 16, 16, 16, 16, -1),
} }
elif dtype == 'fp8' or dtype == 'bf8': elif dtype == 'fp8' or dtype == 'bf8':
return { return {
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, -1), '64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, 32, 32, 32, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, -1), '128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, -1) '256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
} }
else: else:
return None return None
...@@ -595,17 +623,17 @@ def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]: ...@@ -595,17 +623,17 @@ def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
def get_fmha_fwd_splitkv_combine_tile_dict_from_dtype(dtype : str) -> Optional[dict]: def get_fmha_fwd_splitkv_combine_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
if dtype == 'fp16' or dtype == 'bf16': if dtype == 'fp16' or dtype == 'bf16':
return { return {
'32' : FmhaFwdSplitKVCombineTileSize(16, 16, -1), '32' : FmhaFwdSplitKVCombineTileSize(32, -1),
'64' : FmhaFwdSplitKVCombineTileSize(32, 32, -1), '64' : FmhaFwdSplitKVCombineTileSize(32, -1),
## '96' : FmhaFwdSplitKVCombineTileSize(32, 64, -1), ### '96' : FmhaFwdSplitKVCombineTileSize(32, -1),
'128' : FmhaFwdSplitKVCombineTileSize(32, 64, -1), '128' : FmhaFwdSplitKVCombineTileSize(32, -1),
'256' : FmhaFwdSplitKVCombineTileSize(32, 128, -1), '256' : FmhaFwdSplitKVCombineTileSize(32, -1),
} }
elif dtype == 'fp8' or dtype == 'bf8': elif dtype == 'fp8' or dtype == 'bf8':
return { return {
'64' : FmhaFwdSplitKVCombineTileSize(64, 32, -1), '64' : FmhaFwdSplitKVCombineTileSize(32, -1),
'128' : FmhaFwdSplitKVCombineTileSize(64, 64, -1), '128' : FmhaFwdSplitKVCombineTileSize(32, -1),
'256' : FmhaFwdSplitKVCombineTileSize(64, 128, -1), '256' : FmhaFwdSplitKVCombineTileSize(32, -1),
} }
else: else:
return None return None
......
example/ck_tile/01_fmha/fmha_fwd.cpp
View file @ 5063a39f
...@@ -1131,15 +1131,16 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -1131,15 +1131,16 @@ bool run(const ck_tile::ArgParser& arg_parser)
{ {
// NOTE: use gpu to do validation // NOTE: use gpu to do validation
ck_tile::naive_attention_fwd_traits naive_t; ck_tile::naive_attention_fwd_traits naive_t;
naive_t.q_type = data_type; naive_t.q_type = data_type;
naive_t.k_type = data_type; naive_t.k_type = data_type;
naive_t.v_type = data_type; naive_t.v_type = data_type;
naive_t.o_type = data_type; naive_t.o_type = data_type;
naive_t.q_layout = i_perm == 1 ? "bhsd" : "bshd"; naive_t.q_layout = i_perm == 1 ? "bhsd" : "bshd";
naive_t.k_layout = i_perm == 1 ? "bhsd" : "bshd"; naive_t.k_layout = i_perm == 1 ? "bhsd" : "bshd";
naive_t.v_layout = i_perm == 1 ? "bhsd" : "bshd"; naive_t.v_layout = i_perm == 1 ? "bhsd" : "bshd";
naive_t.o_layout = o_perm == 1 ? "bhsd" : "bshd"; naive_t.o_layout = o_perm == 1 ? "bhsd" : "bshd";
naive_t.variation = 0; // TODO? naive_t.variation = 0; // TODO?
naive_t.quant_algo = 0;
ck_tile::DeviceMem o_naive_buf(o_host.get_element_space_size_in_bytes()); ck_tile::DeviceMem o_naive_buf(o_host.get_element_space_size_in_bytes());
......
example/ck_tile/01_fmha/fmha_fwd.hpp
View file @ 5063a39f
...@@ -400,8 +400,18 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args) ...@@ -400,8 +400,18 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
} }
}(); }();
dim3 grids = FmhaKernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v); if constexpr(FmhaKernel::kIsGroupMode)
return ck_tile::make_tuple(kargs, grids); {
dim3 grids = FmhaKernel::GridSize(
args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v, args.seqlen_k_ptr != nullptr);
return ck_tile::make_tuple(kargs, grids);
}
else
{
dim3 grids =
FmhaKernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v, false);
return ck_tile::make_tuple(kargs, grids);
}
} }
template <typename Kernel> template <typename Kernel>
...@@ -500,8 +510,8 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args) ...@@ -500,8 +510,8 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args)
} }
}(); }();
dim3 grids = dim3 grids = Kernel::GridSize(
Kernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v, args.num_splits); args.batch, args.nhead_q, args.nhead_k, args.max_seqlen_q, args.hdim_v, args.num_splits);
return ck_tile::make_tuple(kargs, grids); return ck_tile::make_tuple(kargs, grids);
} }
...@@ -709,7 +719,6 @@ std::string fmha_fwd_splitkv_get_name_(); ...@@ -709,7 +719,6 @@ std::string fmha_fwd_splitkv_get_name_();
template <ck_tile::index_t HDim_, template <ck_tile::index_t HDim_,
typename DataType_, typename DataType_,
bool kIsGroupMode_, bool kIsGroupMode_,
ck_tile::index_t kM0_,
ck_tile::index_t kN1_, ck_tile::index_t kN1_,
bool kStoreLse_, bool kStoreLse_,
bool kDoFp8StaticQuant_, bool kDoFp8StaticQuant_,
...@@ -720,7 +729,6 @@ struct fmha_fwd_splitkv_combine_traits_ ...@@ -720,7 +729,6 @@ struct fmha_fwd_splitkv_combine_traits_
static constexpr ck_tile::index_t HDim = HDim_; static constexpr ck_tile::index_t HDim = HDim_;
using DataType = ck_tile::remove_cvref_t<DataType_>; using DataType = ck_tile::remove_cvref_t<DataType_>;
static constexpr bool kIsGroupMode = kIsGroupMode_; static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr ck_tile::index_t kM0 = kM0_;
static constexpr ck_tile::index_t kN1 = kN1_; static constexpr ck_tile::index_t kN1 = kN1_;
static constexpr bool kStoreLse = kStoreLse_; static constexpr bool kStoreLse = kStoreLse_;
static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_; static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
......
example/ck_tile/02_layernorm2d/README.md
View file @ 5063a39f
...@@ -59,7 +59,7 @@ args: ...@@ -59,7 +59,7 @@ args:
-kname print kernel name or not (default:1) -kname print kernel name or not (default:1)
-prec_i input precision (default:fp16) -prec_i input precision (default:fp16)
-prec_o output precision, set auto will be the same as input (default:auto) -prec_o output precision, set auto will be the same as input (default:auto)
-prec_sx output quant scale type, set auto will be the same as input. used when fquant=1 (default:auto) -prec_sm output quant scale type, set auto will be the same as input. used when fquant=1 (default:auto)
-prec_sy output quant scale type, set auto will be the same as input. used when fquant=1 or 2 (default:auto) -prec_sy output quant scale type, set auto will be the same as input. used when fquant=1 or 2 (default:auto)
-fadd fused-add, 0:no fused add, 1:preadd+store, 2:preadd only (default:0) -fadd fused-add, 0:no fused add, 1:preadd+store, 2:preadd only (default:0)
-fquant fused-quant, 0:no, 1:smooth-dynamic-quant, 2:dynamic-quant (default:0) -fquant fused-quant, 0:no, 1:smooth-dynamic-quant, 2:dynamic-quant (default:0)
...@@ -69,7 +69,7 @@ args: ...@@ -69,7 +69,7 @@ args:
``` ```
## limitations ## limitations
Note that `fquant=2`, `fadd=2`, `prec_sx/prec_sy` other than `fp32` are not by default generated. Though our kernel template suppor this. (TBD: add some flag in generate.py) to generate those instance on demand. Beside, `N>8192` case will by default using two-pass pipeline, and `-fquant=1/2` are not supported yet. If need suport `N>8192` and `fused+residual+store`, you can use this example together with `12_smoothquant`, to construct layernorm+residual, and smoothquant, 2 kernels for this purpose. Note that `fquant=2`, `fadd=2`, `prec_sm/prec_sy` other than `fp32` are not by default generated. Though our kernel template suppor this. (TBD: add some flag in generate.py) to generate those instance on demand. Beside, `N>8192` case will by default using two-pass pipeline, and `-fquant=1/2` are not supported yet. If need suport `N>8192` and `fused+residual+store`, you can use this example together with `12_smoothquant`, to construct layernorm+residual, and smoothquant, 2 kernels for this purpose.
``` ```
# some case # some case
......
example/ck_tile/02_layernorm2d/generate.py
View file @ 5063a39f
This diff is collapsed.
example/ck_tile/02_layernorm2d/layernorm2d_fwd.cpp
View file @ 5063a39f
...@@ -35,12 +35,13 @@ auto create_args(int argc, char* argv[]) ...@@ -35,12 +35,13 @@ auto create_args(int argc, char* argv[])
.insert("kname", "1", "print kernel name or not") .insert("kname", "1", "print kernel name or not")
.insert("prec_i", "fp16", "input precision") .insert("prec_i", "fp16", "input precision")
.insert("prec_o", "auto", "output precision, set auto will be the same as input") .insert("prec_o", "auto", "output precision, set auto will be the same as input")
.insert("prec_sx", .insert("prec_sm",
"auto", "auto",
"output quant scale type, set auto will use fp32. used when fquant=1") "output quant scale type, set auto will use fp32. used when fquant=1")
.insert("prec_sy", .insert("prec_sy",
"auto", "auto",
"output quant scale type, set auto will use fp32. used when fquant=1 or 2") "output quant scale type, set auto will use fp32. used when fquant=1 or 2")
.insert("xbias", "0", "add bias, 0:no add, 1:add bias before fadd")
.insert("fadd", "0", "fused-add, 0:no fused add, 1:preadd+store, 2:preadd only") .insert("fadd", "0", "fused-add, 0:no fused add, 1:preadd+store, 2:preadd only")
.insert("fquant", "0", "fused-quant, 0:no, 1:smooth-dynamic-quant, 2:dynamic-quant") .insert("fquant", "0", "fused-quant, 0:no, 1:smooth-dynamic-quant, 2:dynamic-quant")
.insert("warmup", "5", "cold iter") .insert("warmup", "5", "cold iter")
...@@ -52,7 +53,7 @@ auto create_args(int argc, char* argv[]) ...@@ -52,7 +53,7 @@ auto create_args(int argc, char* argv[])
template <typename InDataType, template <typename InDataType,
typename OutDataType, typename OutDataType,
typename XScaleDataType, typename SmoothScaleDataType,
typename YScaleDataType, typename YScaleDataType,
bool SaveMeanVar> bool SaveMeanVar>
bool run(const ck_tile::ArgParser& arg_parser) bool run(const ck_tile::ArgParser& arg_parser)
...@@ -74,15 +75,15 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -74,15 +75,15 @@ bool run(const ck_tile::ArgParser& arg_parser)
float epsilon = arg_parser.get_float("e"); float epsilon = arg_parser.get_float("e");
std::string prec_i = arg_parser.get_str("prec_i"); std::string prec_i = arg_parser.get_str("prec_i");
std::string prec_o = arg_parser.get_str("prec_o"); std::string prec_o = arg_parser.get_str("prec_o");
std::string prec_sx = arg_parser.get_str("prec_sx"); std::string prec_sm = arg_parser.get_str("prec_sm");
std::string prec_sy = arg_parser.get_str("prec_sy"); std::string prec_sy = arg_parser.get_str("prec_sy");
if(prec_o == "auto") if(prec_o == "auto")
{ {
prec_o = prec_i; prec_o = prec_i;
} }
if(prec_sx == "auto") if(prec_sm == "auto")
{ {
prec_sx = "fp32"; prec_sm = "fp32";
} }
if(prec_sy == "auto") if(prec_sy == "auto")
{ {
...@@ -93,6 +94,7 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -93,6 +94,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
int do_validation = arg_parser.get_int("v"); int do_validation = arg_parser.get_int("v");
int warmup = arg_parser.get_int("warmup"); int warmup = arg_parser.get_int("warmup");
int repeat = arg_parser.get_int("repeat"); int repeat = arg_parser.get_int("repeat");
int xbias = arg_parser.get_int("xbias");
int fused_add = arg_parser.get_int("fadd"); int fused_add = arg_parser.get_int("fadd");
int fused_quant = arg_parser.get_int("fquant"); int fused_quant = arg_parser.get_int("fquant");
if(fused_quant == 1 && prec_o != "int8") if(fused_quant == 1 && prec_o != "int8")
...@@ -103,10 +105,12 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -103,10 +105,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
assert(x_stride >= n); assert(x_stride >= n);
using TypeConfig = LayerNormTypeConfig<InDataType, OutDataType, XScaleDataType, YScaleDataType>; using TypeConfig =
LayerNormTypeConfig<InDataType, OutDataType, SmoothScaleDataType, YScaleDataType>;
using XDataType = typename TypeConfig::XDataType; using XDataType = typename TypeConfig::XDataType;
using YDataType = typename TypeConfig::YDataType; using YDataType = typename TypeConfig::YDataType;
using XBiasDataType = typename TypeConfig::XBiasDataType;
using GammaDataType = typename TypeConfig::GammaDataType; using GammaDataType = typename TypeConfig::GammaDataType;
using BetaDataType = typename TypeConfig::BetaDataType; using BetaDataType = typename TypeConfig::BetaDataType;
using XResidualDataType = XDataType; using XResidualDataType = XDataType;
...@@ -121,6 +125,7 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -121,6 +125,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
// host verify // host verify
ck_tile::HostTensor<XDataType> x_host({m, n}, {x_stride, 1}); ck_tile::HostTensor<XDataType> x_host({m, n}, {x_stride, 1});
ck_tile::HostTensor<XBiasDataType> x_bias_host({n});
ck_tile::HostTensor<GammaDataType> gamma_host({n}); ck_tile::HostTensor<GammaDataType> gamma_host({n});
ck_tile::HostTensor<BetaDataType> beta_host({n}); ck_tile::HostTensor<BetaDataType> beta_host({n});
...@@ -135,30 +140,33 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -135,30 +140,33 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::HostTensor<YScaleDataType> y_scale_host_ref({m}); ck_tile::HostTensor<YScaleDataType> y_scale_host_ref({m});
ck_tile::HostTensor<YScaleDataType> y_scale_host_dev({m}); ck_tile::HostTensor<YScaleDataType> y_scale_host_dev({m});
ck_tile::HostTensor<XScaleDataType> x_scale_host({n}); ck_tile::HostTensor<SmoothScaleDataType> sm_scale_host({n});
ck_tile::HostTensor<XScaleDataType> x_scale_host_dev({n}); ck_tile::HostTensor<SmoothScaleDataType> sm_scale_host_dev({n});
ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host); ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
ck_tile::FillUniformDistribution<XResidualDataType>{-.5f, .5f}(x_residual_host); ck_tile::FillUniformDistribution<XResidualDataType>{-.5f, .5f}(x_residual_host);
ck_tile::FillUniformDistribution<XScaleDataType>{-1.f, 1.f}(x_scale_host); ck_tile::FillUniformDistribution<SmoothScaleDataType>{-1.f, 1.f}(sm_scale_host);
ck_tile::FillUniformDistribution<XBiasDataType>{-.5f, .5f}(x_bias_host);
ck_tile::FillUniformDistribution<GammaDataType>{-.5f, .5f}(gamma_host); ck_tile::FillUniformDistribution<GammaDataType>{-.5f, .5f}(gamma_host);
ck_tile::FillUniformDistribution<BetaDataType>{-.5f, .5f}(beta_host); ck_tile::FillUniformDistribution<BetaDataType>{-.5f, .5f}(beta_host);
ck_tile::DeviceMem x_buf(x_host.get_element_space_size_in_bytes()); ck_tile::DeviceMem x_buf(x_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem x_bias_buf(x_bias_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem gamma_buf(gamma_host.get_element_space_size_in_bytes()); ck_tile::DeviceMem gamma_buf(gamma_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem beta_buf(beta_host.get_element_space_size_in_bytes()); ck_tile::DeviceMem beta_buf(beta_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem y_buf(y_host_dev.get_element_space_size_in_bytes()); ck_tile::DeviceMem y_buf(y_host_dev.get_element_space_size_in_bytes());
ck_tile::DeviceMem y_scale_buf(y_scale_host_dev.get_element_space_size_in_bytes()); ck_tile::DeviceMem y_scale_buf(y_scale_host_dev.get_element_space_size_in_bytes());
ck_tile::DeviceMem x_scale_buf(x_scale_host_dev.get_element_space_size_in_bytes()); ck_tile::DeviceMem sm_scale_buf(sm_scale_host_dev.get_element_space_size_in_bytes());
ck_tile::DeviceMem x_residual_buf(x_residual_host.get_element_space_size_in_bytes()); ck_tile::DeviceMem x_residual_buf(x_residual_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem y_residual_buf(y_residual_host.get_element_space_size_in_bytes()); ck_tile::DeviceMem y_residual_buf(y_residual_host.get_element_space_size_in_bytes());
x_buf.ToDevice(x_host.data()); x_buf.ToDevice(x_host.data());
x_bias_buf.ToDevice(x_bias_host.data());
gamma_buf.ToDevice(gamma_host.data()); gamma_buf.ToDevice(gamma_host.data());
beta_buf.ToDevice(beta_host.data()); beta_buf.ToDevice(beta_host.data());
x_residual_buf.ToDevice(x_residual_host.data()); x_residual_buf.ToDevice(x_residual_host.data());
x_scale_buf.ToDevice(x_scale_host.data()); sm_scale_buf.ToDevice(sm_scale_host.data());
auto prec_str = [&]() { auto prec_str = [&]() {
auto base_str = prec_i; auto base_str = prec_i;
...@@ -179,11 +187,12 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -179,11 +187,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
<< ", yr_stride:" << yr_stride << std::flush; << ", yr_stride:" << yr_stride << std::flush;
layernorm2d_fwd_traits traits{ layernorm2d_fwd_traits traits{
prec_i, prec_o, prec_sx, prec_sy, SaveMeanVar, fused_add, fused_quant}; prec_i, prec_o, prec_sm, prec_sy, SaveMeanVar, xbias, fused_add, fused_quant};
layernorm2d_fwd_args args{x_buf.GetDeviceBuffer(), layernorm2d_fwd_args args{x_buf.GetDeviceBuffer(),
fused_add != 0 ? x_residual_buf.GetDeviceBuffer() : nullptr, fused_add != 0 ? x_residual_buf.GetDeviceBuffer() : nullptr,
fused_quant == 1 ? x_scale_buf.GetDeviceBuffer() : nullptr, fused_quant == 1 ? sm_scale_buf.GetDeviceBuffer() : nullptr,
x_bias_buf.GetDeviceBuffer(),
gamma_buf.GetDeviceBuffer(), gamma_buf.GetDeviceBuffer(),
beta_buf.GetDeviceBuffer(), beta_buf.GetDeviceBuffer(),
...@@ -210,8 +219,9 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -210,8 +219,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
return false; return false;
} }
std::size_t num_byte = sizeof(XDataType) * m * n + sizeof(GammaDataType) * n + std::size_t num_byte = sizeof(XDataType) * m * n + sizeof(XBiasDataType) * n +
sizeof(BetaDataType) * n + sizeof(YDataType) * m * n; sizeof(GammaDataType) * n + sizeof(BetaDataType) * n +
sizeof(YDataType) * m * n;
float gb_per_sec = num_byte / 1.E6 / ave_time; float gb_per_sec = num_byte / 1.E6 / ave_time;
std::cout << ", " << ave_time * 1.E3 << " us, " << gb_per_sec << " GB/s" << std::flush; std::cout << ", " << ave_time * 1.E3 << " us, " << gb_per_sec << " GB/s" << std::flush;
...@@ -221,6 +231,22 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -221,6 +231,22 @@ bool run(const ck_tile::ArgParser& arg_parser)
if(do_validation) if(do_validation)
{ {
// reference // reference
if(xbias != 0)
{
// add bias before fadd
int M = x_host.mDesc.get_lengths()[0];
int N = x_host.mDesc.get_lengths()[1];
for(int idx_m = 0; idx_m < M; ++idx_m)
{
for(int idx_n = 0; idx_n < N; ++idx_n)
{
x_host(idx_m, idx_n) = ck_tile::type_convert<XDataType>(
ck_tile::type_convert<ComputeDataType>(x_host(idx_m, idx_n)) +
ck_tile::type_convert<ComputeDataType>(x_bias_host(idx_n)));
}
}
}
if(fused_add != 0) if(fused_add != 0)
{ {
// fused pre_add/pre_add_store // fused pre_add/pre_add_store
...@@ -254,8 +280,8 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -254,8 +280,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
for(int n_ = 0; n_ < N_; n_++) for(int n_ = 0; n_ < N_; n_++)
{ {
// input smooth outlier // input smooth outlier
acc_(m_, n_) = acc_(m_, n_) = acc_(m_, n_) *
acc_(m_, n_) * ck_tile::type_convert<ComputeDataType>(x_scale_host(n_)); ck_tile::type_convert<ComputeDataType>(sm_scale_host(n_));
} }
} }
ComputeDataType absmax = static_cast<ComputeDataType>(0); ComputeDataType absmax = static_cast<ComputeDataType>(0);
...@@ -377,16 +403,16 @@ int main(int argc, char* argv[]) ...@@ -377,16 +403,16 @@ int main(int argc, char* argv[])
std::string prec_i = arg_parser.get_str("prec_i"); std::string prec_i = arg_parser.get_str("prec_i");
std::string prec_o = arg_parser.get_str("prec_o"); std::string prec_o = arg_parser.get_str("prec_o");
std::string prec_sx = arg_parser.get_str("prec_sx"); std::string prec_sm = arg_parser.get_str("prec_sm");
std::string prec_sy = arg_parser.get_str("prec_sy"); std::string prec_sy = arg_parser.get_str("prec_sy");
if(prec_o == "auto") if(prec_o == "auto")
{ {
prec_o = prec_i; prec_o = prec_i;
} }
if(prec_sx == "auto") if(prec_sm == "auto")
{ {
prec_sx = "fp32"; prec_sm = "fp32";
} }
if(prec_sy == "auto") if(prec_sy == "auto")
{ {
...@@ -395,33 +421,33 @@ int main(int argc, char* argv[]) ...@@ -395,33 +421,33 @@ int main(int argc, char* argv[])
int save_mv = arg_parser.get_int("save_mv"); int save_mv = arg_parser.get_int("save_mv");
// no dynamic quant case // no dynamic quant case
if(prec_i == "fp16" && prec_o == "fp16" && prec_sx == "fp32" && prec_sy == "fp32" && save_mv) if(prec_i == "fp16" && prec_o == "fp16" && prec_sm == "fp32" && prec_sy == "fp32" && save_mv)
{ {
return run<ck_tile::half_t, ck_tile::half_t, float, float, true>(arg_parser) ? 0 : -2; return run<ck_tile::half_t, ck_tile::half_t, float, float, true>(arg_parser) ? 0 : -2;
} }
else if(prec_i == "fp16" && prec_o == "fp16" && prec_sx == "fp32" && prec_sy == "fp32" && else if(prec_i == "fp16" && prec_o == "fp16" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_mv) !save_mv)
{ {
return run<ck_tile::half_t, ck_tile::half_t, float, float, false>(arg_parser) ? 0 : -2; return run<ck_tile::half_t, ck_tile::half_t, float, float, false>(arg_parser) ? 0 : -2;
} }
else if(prec_i == "bf16" && prec_o == "bf16" && prec_sx == "fp32" && prec_sy == "fp32" && else if(prec_i == "bf16" && prec_o == "bf16" && prec_sm == "fp32" && prec_sy == "fp32" &&
save_mv) save_mv)
{ {
return run<ck_tile::bf16_t, ck_tile::bf16_t, float, float, true>(arg_parser) ? 0 : -2; return run<ck_tile::bf16_t, ck_tile::bf16_t, float, float, true>(arg_parser) ? 0 : -2;
} }
else if(prec_i == "bf16" && prec_o == "bf16" && prec_sx == "fp32" && prec_sy == "fp32" && else if(prec_i == "bf16" && prec_o == "bf16" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_mv) !save_mv)
{ {
return run<ck_tile::bf16_t, ck_tile::bf16_t, float, float, true>(arg_parser) ? 0 : -2; return run<ck_tile::bf16_t, ck_tile::bf16_t, float, float, true>(arg_parser) ? 0 : -2;
} }
// dynamic quant case, only in inference // dynamic quant case, only in inference
else if(prec_i == "fp16" && prec_o == "int8" && prec_sx == "fp32" && prec_sy == "fp32" && else if(prec_i == "fp16" && prec_o == "int8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_mv) !save_mv)
{ {
return run<ck_tile::half_t, ck_tile::int8_t, float, float, false>(arg_parser) ? 0 : -2; return run<ck_tile::half_t, ck_tile::int8_t, float, float, false>(arg_parser) ? 0 : -2;
} }
else if(prec_i == "bf16" && prec_o == "int8" && prec_sx == "fp32" && prec_sy == "fp32" && else if(prec_i == "bf16" && prec_o == "int8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_mv) !save_mv)
{ {
return run<ck_tile::bf16_t, ck_tile::int8_t, float, float, false>(arg_parser) ? 0 : -2; return run<ck_tile::bf16_t, ck_tile::int8_t, float, float, false>(arg_parser) ? 0 : -2;
......
example/ck_tile/02_layernorm2d/layernorm2d_fwd.hpp
View file @ 5063a39f
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
...@@ -8,35 +8,40 @@ ...@@ -8,35 +8,40 @@
#include "ck_tile/ops/layernorm2d.hpp" #include "ck_tile/ops/layernorm2d.hpp"
#include <string> #include <string>
template <typename InType, typename OutType, typename XScaleDataType_, typename YScaleDataType_> template <typename InType,
typename OutType,
typename SmoothSScaleDataType_,
typename YScaleDataType_>
struct LayerNormTypeConfig; struct LayerNormTypeConfig;
template <typename OutType, typename XScaleDataType_, typename YScaleDataType_> template <typename OutType, typename SmoothScaleDataType_, typename YScaleDataType_>
struct LayerNormTypeConfig<ck_tile::half_t, OutType, XScaleDataType_, YScaleDataType_> struct LayerNormTypeConfig<ck_tile::half_t, OutType, SmoothScaleDataType_, YScaleDataType_>
{ {
using XDataType = ck_tile::half_t; using XDataType = ck_tile::half_t;
using YDataType = OutType; using YDataType = OutType;
using GammaDataType = ck_tile::half_t; using XBiasDataType = ck_tile::half_t;
using BetaDataType = ck_tile::half_t; using GammaDataType = ck_tile::half_t;
using MeanDataType = ck_tile::half_t; using BetaDataType = ck_tile::half_t;
using InvStdDataType = ck_tile::half_t; using MeanDataType = ck_tile::half_t;
using ComputeDataType = float; using InvStdDataType = ck_tile::half_t;
using XScaleDataType = XScaleDataType_; using ComputeDataType = float;
using YScaleDataType = YScaleDataType_; using SmoothScaleDataType = SmoothScaleDataType_;
using YScaleDataType = YScaleDataType_;
}; };
template <typename OutType, typename XScaleDataType_, typename YScaleDataType_> template <typename OutType, typename SmoothScaleDataType_, typename YScaleDataType_>
struct LayerNormTypeConfig<ck_tile::bf16_t, OutType, XScaleDataType_, YScaleDataType_> struct LayerNormTypeConfig<ck_tile::bf16_t, OutType, SmoothScaleDataType_, YScaleDataType_>
{ {
using XDataType = ck_tile::bf16_t; using XDataType = ck_tile::bf16_t;
using YDataType = OutType; using YDataType = OutType;
using GammaDataType = ck_tile::bf16_t; using XBiasDataType = ck_tile::bf16_t;
using BetaDataType = ck_tile::bf16_t; using GammaDataType = ck_tile::bf16_t;
using MeanDataType = ck_tile::bf16_t; using BetaDataType = ck_tile::bf16_t;
using InvStdDataType = ck_tile::bf16_t; using MeanDataType = ck_tile::bf16_t;
using ComputeDataType = float; using InvStdDataType = ck_tile::bf16_t;
using XScaleDataType = XScaleDataType_; using ComputeDataType = float;
using YScaleDataType = YScaleDataType_; using SmoothScaleDataType = SmoothScaleDataType_;
using YScaleDataType = YScaleDataType_;
}; };
// runtime args // runtime args
...@@ -50,13 +55,14 @@ struct layernorm2d_fwd_traits ...@@ -50,13 +55,14 @@ struct layernorm2d_fwd_traits
std::string prec_i; // input precision std::string prec_i; // input precision
std::string prec_o; // output precision std::string prec_o; // output precision
// if fused_quant == 1, need set prec_sx/prec_sy to proper string, otherwise can set // if fused_quant == 1, need set prec_sm/prec_sy to proper string, otherwise can set
// arbitrary(will skip check) if fused_quant == 2, need set prec_sy to proper string, otherwise // arbitrary(will skip check) if fused_quant == 2, need set prec_sy to proper string, otherwise
// can set arbitrary(will skip check) // can set arbitrary(will skip check)
std::string prec_sx; // x-scale, used for [1*N] input smooth quant std::string prec_sm; // x-scale, used for [1*N] input smooth quant
std::string prec_sy; // y-scale, used for [M*1] output for next layer std::string prec_sy; // y-scale, used for [M*1] output for next layer
bool save_mean_var; // bool save_mean_var; //
int xbias; // 0:no-bias, 1:add bias
int fused_add; // 0:no-add, 1:pre-add-store, 2:pre-add int fused_add; // 0:no-add, 1:pre-add-store, 2:pre-add
int fused_quant; // 0:no-sweep, 1:smooth-dynamic-quant, 2:dynamic-quant int fused_quant; // 0:no-sweep, 1:smooth-dynamic-quant, 2:dynamic-quant
}; };
......
example/ck_tile/02_layernorm2d/script/smoke_test.sh
View file @ 5063a39f
...@@ -27,7 +27,8 @@ $EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=7 -n=2734 ...@@ -27,7 +27,8 @@ $EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=7 -n=2734
$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=1 -n=3182 $EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=1 -n=3182
$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=9 -n=4096 $EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=9 -n=4096
$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=3 -n=8192 $EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=3 -n=8192
#$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=1 -n=10547 $EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=3 -n=9120
$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=1 -n=10547
#$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=3 -n=17134 #$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=3 -n=17134
done done
done done
......
example/ck_tile/03_gemm/CMakeLists.txt
View file @ 5063a39f
add_executable(tile_example_gemm_basic EXCLUDE_FROM_ALL gemm_basic.cpp) add_executable(tile_example_gemm_basic EXCLUDE_FROM_ALL gemm_basic.cpp)
add_executable(tile_example_universal_gemm EXCLUDE_FROM_ALL universal_gemm.cpp) add_executable(tile_example_gemm_universal EXCLUDE_FROM_ALL universal_gemm.cpp)
example/ck_tile/03_gemm/README.md
View file @ 5063a39f
...@@ -11,9 +11,9 @@ sh ../script/cmake-ck-dev.sh ../ <arch> ...@@ -11,9 +11,9 @@ sh ../script/cmake-ck-dev.sh ../ <arch>
# The basic pipeline method on the gemm calculation # The basic pipeline method on the gemm calculation
make tile_example_gemm_basic -j make tile_example_gemm_basic -j
# The memory bound pipeline on the gemm calculation # The memory bound pipeline on the gemm calculation
make tile_example_gemm_mem_pipeline -j make tile_example_gemm_universal -j
``` ```
This will result in an executable `build/bin/tile_example_gemm_basic` This will result in an executable `build/bin/tile_example_gemm_basic` & `build/bin/tile_example_gemm_universal`
## example ## example
``` ```
...@@ -22,6 +22,9 @@ args: ...@@ -22,6 +22,9 @@ args:
-m m dimension (default:1024) -m m dimension (default:1024)
-n n dimension (default:2048) -n n dimension (default:2048)
-k k dimension (default:64) -k k dimension (default:64)
-a_layout Tensor A data layout (default: R)
-b_layout Tensor B data layout (default: R)
-c_layout Tensor C data layout (default: R)
-stride_a Tensor A stride (default:0) -stride_a Tensor A stride (default:0)
-stride_b Tensor B stride (default:0) -stride_b Tensor B stride (default:0)
-stride_c Tensor C stride (default:0) -stride_c Tensor C stride (default:0)
......
example/ck_tile/03_gemm/gemm_basic.cpp
View file @ 5063a39f
...@@ -9,13 +9,11 @@ ...@@ -9,13 +9,11 @@
#include <string> #include <string>
#include <tuple> #include <tuple>
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/host.hpp" #include "ck_tile/host.hpp"
#include "gemm_basic.hpp" #include "gemm_basic.hpp"
template <typename ALayout, typename BLayout, typename CLayout> template <typename ALayout, typename BLayout, typename CLayout>
float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s) float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config& s)
{ {
// The kPadM, kPadN, kPadK & kBlockPerCu should also come from the Codegen part. // The kPadM, kPadN, kPadK & kBlockPerCu should also come from the Codegen part.
constexpr bool kPadM = false; constexpr bool kPadM = false;
...@@ -79,17 +77,9 @@ float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s) ...@@ -79,17 +77,9 @@ float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s)
// Now we only use the BlockGemmASmemBSmemCRegV1DefaultPolicy. // Now we only use the BlockGemmASmemBSmemCRegV1DefaultPolicy.
using Kernel = ck_tile::GemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>; using Kernel = ck_tile::GemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
auto kargs = Kernel::MakeKargs(args.p_a, auto kargs = Kernel::MakeKernelArgs(args);
args.p_b,
args.p_c, const dim3 grids = Kernel::GridSize(args.M, args.N, args.k_batch);
args.M,
args.N,
args.K,
args.stride_A,
args.stride_B,
args.stride_C);
const dim3 grids = Kernel::GridSize(args.M, args.N, args.kbatch);
constexpr dim3 blocks = Kernel::BlockSize(); constexpr dim3 blocks = Kernel::BlockSize();
if(!Kernel::IsSupportedArgument(kargs)) if(!Kernel::IsSupportedArgument(kargs))
......
example/ck_tile/03_gemm/gemm_basic.hpp
View file @ 5063a39f
...@@ -8,6 +8,27 @@ ...@@ -8,6 +8,27 @@
#include "ck_tile/core.hpp" #include "ck_tile/core.hpp"
#include "ck_tile/host/kernel_launch.hpp" #include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"
#define CK_TILE_PIPELINE_COMPUTE 1
#define CK_TILE_PIPELINE_MEMORY 2
#ifndef CK_TILE_PIPELINE_DEFAULT
#define CK_TILE_PIPELINE_DEFAULT CK_TILE_PIPELINE_COMPUTE
#endif
#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_MEMORY)
#define GEMM_PIPELINE ck_tile::GemmPipelineAgBgCrMem
#define UNIVERSAL_GEMM_PIPELINE ck_tile::BaseGemmPipelineAgBgCrMem
#define GEMM_PIPELINE_SCHEDULER ck_tile::GemmPipelineScheduler::Interwave
#elif(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE)
#define GEMM_PIPELINE ck_tile::GemmPipelineAgBgCrCompV3
#define UNIVERSAL_GEMM_PIPELINE ck_tile::BaseGemmPipelineAgBgCrCompV3
#define GEMM_PIPELINE_SCHEDULER ck_tile::GemmPipelineScheduler::Intrawave
#else
#error "unsupported CK_TILE_PIPELINE_DEFAULT value"
#endif
template <typename DataType> template <typename DataType>
struct GemmBasicTypeConfig; struct GemmBasicTypeConfig;
...@@ -51,25 +72,10 @@ using BDataType = Types::BDataType; ...@@ -51,25 +72,10 @@ using BDataType = Types::BDataType;
using AccDataType = Types::AccDataType; using AccDataType = Types::AccDataType;
using CDataType = Types::CDataType; using CDataType = Types::CDataType;
struct gemm_basic_args
{
const void* p_a;
const void* p_b;
void* p_c;
ck_tile::index_t kbatch;
ck_tile::index_t M;
ck_tile::index_t N;
ck_tile::index_t K;
ck_tile::index_t stride_A;
ck_tile::index_t stride_B;
ck_tile::index_t stride_C;
};
auto create_args(int argc, char* argv[]) auto create_args(int argc, char* argv[])
{ {
ck_tile::ArgParser arg_parser; ck_tile::ArgParser arg_parser;
arg_parser.insert("b", "1", "batch size") arg_parser.insert("m", "3840", "m dimension")
.insert("m", "3840", "m dimension")
.insert("n", "4096", "n dimension") .insert("n", "4096", "n dimension")
.insert("k", "2048", "k dimension") .insert("k", "2048", "k dimension")
.insert("a_layout", "R", "A tensor data layout - Row by default") .insert("a_layout", "R", "A tensor data layout - Row by default")
...@@ -82,11 +88,12 @@ auto create_args(int argc, char* argv[]) ...@@ -82,11 +88,12 @@ auto create_args(int argc, char* argv[])
.insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8") .insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8")
.insert("warmup", "50", "number of iterations before benchmark the kernel") .insert("warmup", "50", "number of iterations before benchmark the kernel")
.insert("repeat", "100", "number of iterations to benchmark the kernel") .insert("repeat", "100", "number of iterations to benchmark the kernel")
.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer"); .insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
.insert("split_k", "1", "splitK value");
bool result = arg_parser.parse(argc, argv); bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser); return std::make_tuple(result, arg_parser);
} }
// host API // host API
float gemm_calc(gemm_basic_args args, const ck_tile::stream_config& s); float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config& s);
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