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Commit c881136b authored by Po Yen Chen's avatar Po Yen Chen
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Merge branch 'develop' into ck_tile/support-vllm-kcache-layout

parents c5e8e14f 4e076909
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example/ck_tile/01_fmha/README.md
View file @ c881136b
......@@ -15,8 +15,7 @@ This will result in an executable `build/bin/tile_example_fmha_fwd`
## 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.
There are 3 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.
There are 2 template parameters for this kernel template.
* `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.
......
example/ck_tile/01_fmha/codegen/cpp_symbol_map.py
View file @ c881136b
......@@ -119,6 +119,7 @@ PIPELINE_MAP = {
PIPELINE_ENUM_MAP = {
"qr" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS",
"qr_async" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS_ASYNC",
"qr_nwarp_sshuffle" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS",
}
BOOL_MAP = {
......
example/ck_tile/01_fmha/codegen/ops/fmha_fwd.py
View file @ c881136b
......@@ -29,11 +29,6 @@ K0_MAX_SUBMAX_MAP = {
256: 256
}
TILE_PARTITIONER_MAP = {
"shb" : "ck_tile::FmhaFwdTilePartitioner_SHB",
"hbs" : "ck_tile::FmhaFwdTilePartitioner_HBS",
}
FMHA_FWD_KERNEL_HEADER = """// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.\n
// auto generated by generate.py
......@@ -44,13 +39,12 @@ FMHA_FWD_KERNEL_BODY="""
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_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},
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}>,
fmha_warp_tile_{F_idx},
ck_tile::sequence<{F_wm1}, {F_wn1}, {F_wk1}>,
{F_vlayout}>;
using fmha_trait_{F_idx} = ck_tile::TileFmhaTraits<{F_spad},
......@@ -91,9 +85,7 @@ using fmha_epilogue_{F_idx} =
{F_spad}, {F_dvpad}>>;
using fmha_kernel_{F_idx} =
ck_tile::FmhaFwdKernel<{F_tile_partitioner}<fmha_shape_{F_idx}>,
fmha_pipeline_{F_idx},
fmha_epilogue_{F_idx}>;
ck_tile::FmhaFwdKernel<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},
{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:
F_rm1 : int # number of warps for gemm1 along q seqlen
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_wm : int # warp size along m (warp size)
F_wn : int # warp size along n
F_wk : int # warp size along k
F_wm0 : int # gemm0 warp size along m
F_wn0 : int # gemm0 warp size along n
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
@property
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}" +\
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
class FmhaFwdKernel:
......@@ -326,12 +322,6 @@ class FmhaFwdKernel:
F_pipeline : FmhaFwdPipeline
mask_impl : str
def get_tp(self) -> str:
if self.F_mode == 'group':
return 'hbs'
else:
return 'shb'
@property
def template(self) -> str:
kernel_body = str()
......@@ -352,9 +342,12 @@ class FmhaFwdKernel:
F_rm1 = self.F_tile.F_rm1,
F_rn1 = self.F_tile.F_rn1,
F_rk1 = self.F_tile.F_rk1,
F_wm = self.F_tile.F_wm,
F_wn = self.F_tile.F_wn,
F_wk = self.F_tile.F_wk,
F_wm0 = self.F_tile.F_wm0,
F_wn0 = self.F_tile.F_wn0,
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_spad = BOOL_MAP[self.F_pipeline.F_spad],
F_skpad = BOOL_MAP[self.F_pipeline.F_skpad],
......@@ -368,13 +361,12 @@ class FmhaFwdKernel:
F_pipeline_enum = PIPELINE_ENUM_MAP[self.F_pipeline.tag],
F_mask = get_mask_map(self.mask_impl)[self.F_pipeline.F_mask],
F_mode = MODE_MAP[self.F_mode],
F_pipeline = PIPELINE_MAP[self.F_pipeline.tag],
F_tile_partitioner = TILE_PARTITIONER_MAP[self.get_tp()])
F_pipeline = PIPELINE_MAP[self.F_pipeline.tag])
@property
def name(self) -> str:
# 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
@property
......@@ -409,17 +401,17 @@ class FmhaFwdKernel:
def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
if dtype == 'fp16' or dtype == 'bf16':
return {
'32' : FmhaFwdTileSize(128, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 32, 32, 16, -1),
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 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, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 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, -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, 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, 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':
return {
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 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, -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, 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:
return None
......
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_appendkv.py
View file @ c881136b
......@@ -46,9 +46,7 @@ using fmha_pipeline_problem_{F_idx} = ck_tile::BlockFmhaFwdAppendKVPipelineProbl
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 fmha_kernel_{F_idx} = ck_tile::FmhaFwdAppendKVKernel<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}>;
......@@ -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)
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
f.write(str(file_path.parent / GEN_DIR / FMHA_FWD_APPENDKV_API_FILENAME) + "\n")
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_splitkv.py
View file @ c881136b
......@@ -39,6 +39,7 @@ K0_MAX_SUBMAX_MAP = {
FMHA_FWD_SPLITKV_PIPELINE_MAP = {
"qr" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVS",
"qr_nwarp_sshuffle" : "ck_tile::BlockFmhaFwdSplitKVPipelineNWarpSShuffleQRKSVS",
"qr_async" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVSAsync",
}
......@@ -50,13 +51,12 @@ namespace {{
template <bool kIsMultipleSplits, bool kHasUnevenSplits = kIsMultipleSplits>
struct kernel_runner {{
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,
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}>,
fmha_warp_tile,
ck_tile::sequence<{F_wm1}, {F_wn1}, {F_wk1}>,
{F_vlayout}>;
using fmha_trait = ck_tile::TileFmhaFwdSplitKVTraits<{F_spad},
......@@ -175,9 +175,8 @@ using fmha_pipeline_problem = ck_tile::BlockFmhaSplitKVCombinePipelineProblem<
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::OaccDataType,
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::ODataType,
{F_hdim},
{F_bm0},
{F_bn1},
{F_mode},
{F_bn1},
fmha_trait>;
using fmha_pipeline = ck_tile::BlockFmhaFwdSplitKVCombinePipeline<
......@@ -191,9 +190,7 @@ using fmha_epilogue =
false, false>>;
using fmha_kernel =
ck_tile::FmhaFwdSplitKVCombineKernel<ck_tile::FmhaFwdSplitKVCombineTilePartitioner<{F_bm0}, {F_bn1}>,
fmha_pipeline,
fmha_epilogue>;
ck_tile::FmhaFwdSplitKVCombineKernel<fmha_pipeline, fmha_epilogue>;
static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
{{
......@@ -206,7 +203,7 @@ 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}>;
#include <iostream>
......@@ -276,19 +273,28 @@ 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}) &&
((a.block_table_ptr != nullptr) == {F_pagedkv}) && ({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{
if (1 < a.num_splits) {{
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 constexpr (std::is_same_v<{F_dtype}, FmhaFwdFp8>) {{
return -1;
}} 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);
}}
}} 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);
}}
......@@ -346,7 +352,7 @@ class FmhaFwdSplitKVApiTrait:
if self.pipeline_tag == 'qr_async':
if self.spad == 't' : return 'true' # always support
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)
else : return f'a.seqlen_q % {self.bm0} == 0'
else: assert False
......@@ -357,7 +363,7 @@ class FmhaFwdSplitKVApiTrait:
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']:
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)
else : return f'a.seqlen_k % {self.bn0} == 0'
else: assert False
......@@ -368,7 +374,7 @@ class FmhaFwdSplitKVApiTrait:
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']:
elif self.pipeline_tag in ['qr', 'qr_nwarp_sshuffle']:
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)
else : return f'a.hdim_q % {bk0submax} == 0'
......@@ -380,7 +386,7 @@ class FmhaFwdSplitKVApiTrait:
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']:
elif self.pipeline_tag in ['qr', 'qr_nwarp_sshuffle']:
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)
else : return f'a.hdim_v % {bk0submax} == 0'
......@@ -491,12 +497,11 @@ class FmhaFwdSplitKVApiPool:
@dataclass
class FmhaFwdSplitKVCombineTileSize:
F_bm0 : int # tile size along q seqlen
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
@property
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}")
@dataclass
......@@ -529,9 +534,12 @@ class FmhaFwdSplitKVKernel:
F_rm1 = self.F_tile.F_rm1,
F_rn1 = self.F_tile.F_rn1,
F_rk1 = self.F_tile.F_rk1,
F_wm = self.F_tile.F_wm,
F_wn = self.F_tile.F_wn,
F_wk = self.F_tile.F_wk,
F_wm0 = self.F_tile.F_wm0,
F_wn0 = self.F_tile.F_wn0,
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_spad = BOOL_MAP[self.F_pipeline.F_spad],
F_skpad = BOOL_MAP[self.F_pipeline.F_skpad],
......@@ -597,7 +605,6 @@ class FmhaFwdSplitKVCombineKernel:
F_idx = self.F_idx,
F_hdim = self.F_hdim,
F_dtype = FWD_DTYPE_MAP[self.F_dtype],
F_bm0 = self.F_tile.F_bm0,
F_bn1 = self.F_tile.F_bn1,
F_spad = BOOL_MAP[self.F_pipeline.F_spad],
F_dvpad = BOOL_MAP[self.F_pipeline.F_dvpad],
......@@ -621,17 +628,17 @@ class FmhaFwdSplitKVCombineKernel:
def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
if dtype == 'fp16' or dtype == 'bf16':
return {
'32' : FmhaFwdTileSize(32, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 16, 16, 16, -1),
'64' : FmhaFwdTileSize(64, 64, 32, 64, 32, 64, 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, -1),
'128' : FmhaFwdTileSize(64, 128, 32, 128, 32, 128, 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, 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, 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, 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':
return {
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 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, -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, 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:
return None
......@@ -639,18 +646,17 @@ 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]:
if dtype == 'fp16' or dtype == 'bf16':
return {
# tile size for decode tile size for prefill
'32' : [FmhaFwdSplitKVCombineTileSize(16, 16, -1), FmhaFwdSplitKVCombineTileSize(64, 16, -1)],
'64' : [FmhaFwdSplitKVCombineTileSize(32, 32, -1), FmhaFwdSplitKVCombineTileSize(64, 32, -1)],
### '96' : [FmhaFwdSplitKVCombineTileSize(32, 64, -1), FmhaFwdSplitKVCombineTileSize(64, 64, -1)],
'128' : [FmhaFwdSplitKVCombineTileSize(32, 64, -1), FmhaFwdSplitKVCombineTileSize(64, 64, -1)],
'256' : [FmhaFwdSplitKVCombineTileSize(32, 128, -1), FmhaFwdSplitKVCombineTileSize(64, 128, -1)],
}
'32' : FmhaFwdSplitKVCombineTileSize(32, -1),
'64' : FmhaFwdSplitKVCombineTileSize(32, -1),
### '96' : FmhaFwdSplitKVCombineTileSize(32, -1),
'128' : FmhaFwdSplitKVCombineTileSize(32, -1),
'256' : FmhaFwdSplitKVCombineTileSize(32, -1),
}
elif dtype == 'fp8' or dtype == 'bf8':
return {
'64' : [FmhaFwdSplitKVCombineTileSize(64, 32, -1)],
'128' : [FmhaFwdSplitKVCombineTileSize(64, 64, -1)],
'256' : [FmhaFwdSplitKVCombineTileSize(64, 128, -1)],
'64' : FmhaFwdSplitKVCombineTileSize(32, -1),
'128' : FmhaFwdSplitKVCombineTileSize(32, -1),
'256' : FmhaFwdSplitKVCombineTileSize(32, -1),
}
else:
return None
......@@ -710,7 +716,6 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
# make sure if all the hdim str keys in decode_tiles are also available in prefill_tiles
assert all(tile in prefill_tiles.keys() for tile in decode_tiles.keys())
#for hdim_str, mode, mask, bias, lse in itertools.product(d.keys(), MODE_MAP.keys(), MASK_MAP.keys(), ["t", "f"], ["t", "f"]):
for hdim_str, mode in itertools.product(decode_tiles.keys(), MODE_MAP.keys()):
prefill_tile = prefill_tiles[hdim_str]
decode_tile = decode_tiles[hdim_str]
......@@ -778,12 +783,10 @@ def get_fwd_splitkv_combine_blobs(kernel_filter : Optional[str], receipt) -> Lis
d = get_fmha_fwd_splitkv_combine_tile_dict_from_dtype(dtype)
if d == None:
continue
#for hdim_str, mode, mask, bias, lse in itertools.product(d.keys(), MODE_MAP.keys(), MASK_MAP.keys(), ["t", "f"], ["t", "f"]):
for hdim_str, mode in itertools.product(d.keys(), MODE_MAP.keys()):
# include prefill tile size if in group mode
tiles = d[hdim_str][0 : 2 if mode == 'group' else 1]
tile = d[hdim_str]
hdim = int(hdim_str)
for tile, pipeline in itertools.product(tiles, get_pipelines(dtype, hdim)):
for pipeline in get_pipelines(dtype, hdim):
if mode == 'group':
if pipeline.F_spad != 't':
# in group mode, spad/skpad must be true, since we can't predict if seqlen of current batch need pad or not
......
example/ck_tile/01_fmha/fmha_fwd.hpp
View file @ c881136b
......@@ -401,8 +401,18 @@ 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);
return ck_tile::make_tuple(kargs, grids);
if constexpr(FmhaKernel::kIsGroupMode)
{
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>
......@@ -712,7 +722,6 @@ std::string fmha_fwd_splitkv_get_name_();
template <ck_tile::index_t HDim_,
typename DataType_,
bool kIsGroupMode_,
ck_tile::index_t kM0_,
ck_tile::index_t kN1_,
bool kStoreLse_,
bool kDoFp8StaticQuant_,
......@@ -723,7 +732,6 @@ struct fmha_fwd_splitkv_combine_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 kN1 = kN1_;
static constexpr bool kStoreLse = kStoreLse_;
static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
......
example/ck_tile/03_gemm/gemm_basic.hpp
View file @ c881136b
......@@ -54,8 +54,7 @@ using CDataType = Types::CDataType;
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("b", "1", "batch size")
.insert("m", "3840", "m dimension")
arg_parser.insert("m", "3840", "m dimension")
.insert("n", "4096", "n dimension")
.insert("k", "2048", "k dimension")
.insert("a_layout", "R", "A tensor data layout - Row by default")
......@@ -68,7 +67,8 @@ auto create_args(int argc, char* argv[])
.insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8")
.insert("warmup", "50", "number of iterations before 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);
return std::make_tuple(result, arg_parser);
......
example/ck_tile/03_gemm/run_gemm_example.inc
View file @ c881136b
......@@ -64,9 +64,9 @@ int run_gemm_example_with_layouts(int argc,
ck_tile::index_t stride_B = arg_parser.get_int("stride_b");
ck_tile::index_t stride_C = arg_parser.get_int("stride_c");
ck_tile::index_t batch_size = arg_parser.get_int("b");
int n_warmup = arg_parser.get_int("warmup");
int n_repeat = arg_parser.get_int("repeat");
ck_tile::index_t kbatch = arg_parser.get_int("split_k");
int n_warmup = arg_parser.get_int("warmup");
int n_repeat = arg_parser.get_int("repeat");
using namespace ck_tile::literals;
......@@ -133,7 +133,7 @@ int run_gemm_example_with_layouts(int argc,
stride_A,
stride_B,
stride_C,
batch_size,
kbatch,
n_warmup,
n_repeat);
......
example/ck_tile/03_gemm/universal_gemm.cpp
View file @ c881136b
......@@ -22,7 +22,7 @@
#endif
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)
{
#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_MEMORY)
// Memory friendly for Interwave scheduler
......@@ -78,7 +78,9 @@ float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s)
#endif
ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>>;
const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(args.K);
const ck_tile::index_t k_grain = args.k_batch * K_Tile;
const ck_tile::index_t K_split = (args.K + k_grain - 1) / k_grain * K_Tile;
const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(K_split);
const bool has_hot_loop = BaseGemmPipeline::BlockHasHotloop(num_loop);
const ck_tile::TailNumber tail_num = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);
......@@ -106,17 +108,9 @@ float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s)
has_hot_loop_v,
tail_number_v>>;
using Kernel = ck_tile::GemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
auto kargs = Kernel::MakeKargs(args.p_a,
args.p_b,
args.p_c,
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);
auto kargs = Kernel::MakeKernelArgs(args);
const dim3 grids = Kernel::GridSize(args.M, args.N, args.k_batch);
constexpr dim3 blocks = Kernel::BlockSize();
if(!Kernel::IsSupportedArgument(kargs))
......
example/ck_tile/13_moe_sorting/moe_sorting_api.cpp
View file @ c881136b
......@@ -3,18 +3,42 @@
#include "moe_sorting_api.hpp"
#define MOE_SORTING_DISPATCH(unroll_num_) \
constexpr ck_tile::index_t unroll_num = unroll_num_; \
using ms_problem = ck_tile::MoeSortingProblem<index_t, ms_weight_type, unroll_num>; \
using kernel = ck_tile::MoeSortingKernel<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
const dim3 blocks = kernel::BlockSize(a); \
const auto lds_bytes = kernel::GetSmemSize(a); \
float ave_time = ck_tile::launch_kernel( \
s, ck_tile::make_kernel(kernel{}, grids, blocks, lds_bytes, kargs)); \
#define MOE_SORTING_DISPATCH_ETILE(unroll_num_, expert_tile_) \
constexpr ck_tile::index_t unroll_num = unroll_num_; \
constexpr ck_tile::index_t expert_tile = expert_tile_; \
using ms_problem = \
ck_tile::MoeSortingProblem<index_t, ms_weight_type, unroll_num, expert_tile>; \
using kernel = ck_tile::MoeSortingKernel<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
const dim3 blocks = kernel::BlockSize(a); \
const auto lds_bytes = kernel::GetSmemSize(a); \
float ave_time = ck_tile::launch_kernel( \
s, ck_tile::make_kernel(kernel{}, grids, blocks, lds_bytes, kargs)); \
return ave_time;
#define MOE_SORTING_DISPATCH(unroll_num_) \
if(a.num_experts <= 8) \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 8) \
} \
else if(a.num_experts <= 16) \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 16) \
} \
else if(a.num_experts <= 32) \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 32) \
} \
else if(a.num_experts <= 64) \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 64) \
} \
else \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 0) \
}
float moe_sorting(moe_sorting_trait t, moe_sorting_args a, ck_tile::stream_config s)
{
if(t.weight_type == "fp32" && t.index_type == "int32")
......@@ -49,21 +73,12 @@ float moe_sorting(moe_sorting_trait t, moe_sorting_args a, ck_tile::stream_confi
case(6): {
MOE_SORTING_DISPATCH(6);
}
case(7): {
MOE_SORTING_DISPATCH(7);
}
case(8): {
MOE_SORTING_DISPATCH(8);
}
case(9): {
MOE_SORTING_DISPATCH(9);
}
case(10): {
MOE_SORTING_DISPATCH(10);
}
case(11): {
MOE_SORTING_DISPATCH(11);
}
default: {
MOE_SORTING_DISPATCH(4);
}
......
example/ck_tile/13_moe_sorting/script/smoke_test.sh
View file @ c881136b
......@@ -16,4 +16,5 @@ $EXE -t=127 -e=99 -k=19
$EXE -t=71 -e=11 -k=11
$EXE -t=1 -e=1 -k=1
$EXE -t=99 -e=2 -k=1
$EXE -t=333 -e=99 -k=13
\ No newline at end of file
$EXE -t=333 -e=99 -k=13
$EXE -t=128 -e=32 -k=5 -moe_buf_size=262144
example/ck_tile/15_fused_moe/instances/fused_moesorting_api.cpp
View file @ c881136b
......@@ -3,18 +3,42 @@
#include "fused_moesorting.hpp"
#define MOE_SORTING_DISPATCH(unroll_num_) \
constexpr ck_tile::index_t unroll_num = unroll_num_; \
using ms_problem = ck_tile::MoeSortingProblem<index_t, ms_weight_type, unroll_num>; \
using kernel = ck_tile::MoeSortingKernel<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
const dim3 blocks = kernel::BlockSize(a); \
const auto lds_bytes = kernel::GetSmemSize(a); \
float ave_time = ck_tile::launch_kernel( \
s, ck_tile::make_kernel(kernel{}, grids, blocks, lds_bytes, kargs)); \
#define MOE_SORTING_DISPATCH_ETILE(unroll_num_, expert_tile_) \
constexpr ck_tile::index_t unroll_num = unroll_num_; \
constexpr ck_tile::index_t expert_tile = expert_tile_; \
using ms_problem = \
ck_tile::MoeSortingProblem<index_t, ms_weight_type, unroll_num, expert_tile>; \
using kernel = ck_tile::MoeSortingKernel<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
const dim3 blocks = kernel::BlockSize(a); \
const auto lds_bytes = kernel::GetSmemSize(a); \
float ave_time = ck_tile::launch_kernel( \
s, ck_tile::make_kernel(kernel{}, grids, blocks, lds_bytes, kargs)); \
return ave_time;
#define MOE_SORTING_DISPATCH(unroll_num_) \
if(a.num_experts <= 8) \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 8) \
} \
else if(a.num_experts <= 16) \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 16) \
} \
else if(a.num_experts <= 32) \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 32) \
} \
else if(a.num_experts <= 64) \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 64) \
} \
else \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 0) \
}
float fused_moesorting(fused_moesorting_trait t, fused_moesorting_args a, ck_tile::stream_config s)
{
if(t.weight_type == "fp32" && t.index_type == "int32")
......@@ -49,21 +73,12 @@ float fused_moesorting(fused_moesorting_trait t, fused_moesorting_args a, ck_til
case(6): {
MOE_SORTING_DISPATCH(6);
}
case(7): {
MOE_SORTING_DISPATCH(7);
}
case(8): {
MOE_SORTING_DISPATCH(8);
}
case(9): {
MOE_SORTING_DISPATCH(9);
}
case(10): {
MOE_SORTING_DISPATCH(10);
}
case(11): {
MOE_SORTING_DISPATCH(11);
}
default: {
MOE_SORTING_DISPATCH(4);
}
......
example/ck_tile/16_batched_gemm/batched_gemm.cpp
View file @ c881136b
......@@ -70,20 +70,25 @@ float batched_gemm(const ck_tile::BatchedGemmHostArgs& args, const ck_tile::stre
using CodegenGemmTraits =
ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
using CodegenPipelineProblem = ck_tile::
GemmPipelineProblem<ADataType, BDataType, AccDataType, CodegenGemmShape, CodegenGemmTraits>;
using CodegenGemmPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
using CodegenGemmPolicy = ck_tile::UniversalGemmPipelineAgBgCrPolicy;
using CodegenGemmPipeline =
ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem, CodegenGemmPolicy>;
// ToDo: Will add the codegen part to test different pipeline policies in GEMM.
// Now we only use the BlockGemmASmemBSmemCRegV1DefaultPolicy.
using Kernel = ck_tile::BatchedGemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
auto kargs = Kernel::MakeKernelArgs(args);
const dim3 grids = Kernel::GridSize(args.M, args.N, args.batch_count);
const dim3 grids = Kernel::GridSize(args.M, args.N, args.k_batch, args.batch_count);
constexpr dim3 blocks = Kernel::BlockSize();
if(!Kernel::IsSupportedArgument(kargs))
{
throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
}
if(s.log_level_ > 0)
{
std::cout << "Launching kernel with args:"
......
example/ck_tile/16_batched_gemm/batched_gemm.hpp
View file @ c881136b
......@@ -49,7 +49,8 @@ auto create_args(int argc, char* argv[])
.insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8")
.insert("warmup", "50", "number of iterations before 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);
return std::make_tuple(result, arg_parser);
......
example/ck_tile/16_batched_gemm/run_batched_gemm_example.inc
View file @ c881136b
......@@ -17,6 +17,7 @@ float invoke_batched_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
ck_tile::index_t batch_stride_B,
ck_tile::index_t batch_stride_C,
ck_tile::index_t batch_count,
ck_tile::index_t kbatch,
int n_warmup,
int n_repeat)
{
......@@ -24,6 +25,7 @@ float invoke_batched_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
args.a_ptr = a_m_k_dev_buf.GetDeviceBuffer();
args.b_ptr = b_k_n_dev_buf.GetDeviceBuffer();
args.c_ptr = c_m_n_dev_buf.GetDeviceBuffer();
args.k_batch = kbatch;
args.M = M;
args.N = N;
args.K = K;
......@@ -79,6 +81,7 @@ int run_batched_gemm_example_with_layouts(int argc,
ck_tile::index_t batch_stride_B = arg_parser.get_int("batch_stride_b");
ck_tile::index_t batch_stride_C = arg_parser.get_int("batch_stride_c");
ck_tile::index_t batch_count = arg_parser.get_int("batch_count");
ck_tile::index_t kbatch = arg_parser.get_int("split_k");
int n_warmup = arg_parser.get_int("warmup");
int n_repeat = arg_parser.get_int("repeat");
......@@ -159,6 +162,7 @@ int run_batched_gemm_example_with_layouts(int argc,
batch_stride_B,
batch_stride_C,
batch_count,
kbatch,
n_warmup,
n_repeat);
......
example/ck_tile/17_grouped_gemm/grouped_gemm.hpp
View file @ c881136b
......@@ -34,13 +34,19 @@ using grouped_gemm_kargs = ck_tile::GroupedGemmHostArgs;
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("a_layout", "R", "A tensor data layout - Row by default")
.insert("b_layout", "R", "B tensor data layout - Row by default")
.insert("c_layout", "R", "C tensor data layout - Row by default")
.insert("validate", "1", "0. No validation, 1. Validation on CPU")
.insert("warmup", "10", "number of iterations before benchmark the kernel")
.insert("repeat", "100", "number of iterations to benchmark the kernel")
.insert("group_count", "16", "group count");
arg_parser.insert("Ms", "", "M dimensions - empty by default.")
.insert("Ns", "", "N dimensions - empty by default.")
.insert("Ks", "", "K dimensions - empty by default.")
.insert("stride_As", "", "Tensor A strides - it is empty by default.")
.insert("stride_Bs", "", "Tensor B strides - it is empty by default.")
.insert("stride_Cs", "", "Tensor C strides - it is empty by default.")
.insert("a_layout", "R", "A tensor data layout - Row by default.")
.insert("b_layout", "R", "B tensor data layout - Row by default.")
.insert("c_layout", "R", "C tensor data layout - Row by default.")
.insert("validate", "1", "0. No validation, 1. Validation on CPU.")
.insert("warmup", "10", "number of iterations before benchmark the kernel.")
.insert("repeat", "100", "number of iterations to benchmark the kernel.")
.insert("group_count", "16", "group count.");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);
......
example/ck_tile/17_grouped_gemm/run_grouped_gemm_example.inc
View file @ c881136b
......@@ -53,26 +53,34 @@ int run_grouped_gemm_example_with_layouts(int argc,
return -1;
};
auto valid_input_data = [&](int group_count, const auto&... args) {
return !(args.empty() || ...) && group_count == (args.size() == ...);
};
const int group_count = arg_parser.get_int("group_count");
const int repeat = arg_parser.get_int("repeat");
const int warmup = arg_parser.get_int("warmup");
std::vector<ck_tile::index_t> Ms;
std::vector<ck_tile::index_t> Ns;
std::vector<ck_tile::index_t> Ks;
std::vector<ck_tile::index_t> stride_As;
std::vector<ck_tile::index_t> stride_Bs;
std::vector<ck_tile::index_t> stride_Cs;
std::vector<ck_tile::index_t> Ms = arg_parser.get_int_vec("Ms");
std::vector<ck_tile::index_t> Ns = arg_parser.get_int_vec("Ns");
std::vector<ck_tile::index_t> Ks = arg_parser.get_int_vec("Ks");
std::vector<ck_tile::index_t> stride_As = arg_parser.get_int_vec("stride_As");
std::vector<ck_tile::index_t> stride_Bs = arg_parser.get_int_vec("stride_Bs");
std::vector<ck_tile::index_t> stride_Cs = arg_parser.get_int_vec("stride_Cs");
for(int i = 0; i < group_count; i++)
if(!valid_input_data(group_count, Ms, Ns, Ks, stride_As, stride_Bs, stride_Cs))
{
Ms.push_back(256 + 256 * i);
Ns.push_back(128 + 128 * i);
Ks.push_back(128 + 64 * i);
std::cout << "Please check the input data. Default values will be used." << std::endl;
for(int i = 0; i < group_count; i++)
{
Ms.push_back(256 + 256 * i);
Ns.push_back(128 + 128 * i);
Ks.push_back(128 + 64 * i);
stride_As.push_back(Ks[i]);
stride_Bs.push_back(Ks[i]);
stride_Cs.push_back(Ns[i]);
stride_As.push_back(Ks[i]);
stride_Bs.push_back(Ks[i]);
stride_Cs.push_back(Ns[i]);
}
}
std::vector<ck_tile::HostTensor<ADataType>> a_m_k_tensors;
......
include/ck/config.h.in
View file @ c881136b
......@@ -115,8 +115,8 @@
#cmakedefine CK_USE_GFX94 @CK_USE_GFX94@
#endif
#ifndef DCK_USE_OCP_FP8
#cmakedefine DCK_USE_OCP_FP8 @DCK_USE_OCP_FP8@
#ifndef CK_USE_OCP_FP8
#cmakedefine CK_USE_OCP_FP8 @CK_USE_OCP_FP8@
#endif
#ifndef CK_USE_FNUZ_FP8
......
include/ck_tile/core.hpp
View file @ c881136b
// 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
......
include/ck_tile/core/arch/amd_buffer_addressing.hpp
View file @ c881136b
......@@ -1303,8 +1303,8 @@ CK_TILE_DEVICE thread_buffer<T, N> amd_buffer_load_impl(int32x4_t src_wave_buffe
static_assert(
(std::is_same<T, double>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
(std::is_same<T, float>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(std::is_same<T, fp16_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(std::is_same<T, bf16_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(std::is_same<T, fp16_t>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
(std::is_same<T, bf16_t>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
(std::is_same<T, int32_t>::value &&
(N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(std::is_same<T, fp8_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
......
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