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Merge branch 'develop' into lwpck-4181

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This commit is contained in:
khushbu
2025-12-15 15:28:16 -05:00
parent 9ad0687b28 2544e394cf
commit 5a3e7de060
166 changed files with 8195 additions and 2289 deletions
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15
example/26_contraction/run_contraction_bilinear_example.inc
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@@ -233,7 +233,20 @@ int run_contraction_bilinear_example(int argc, char* argv[])
}
}
return ck::utils::check_err(e_ms_ns_device_result, e_ms_ns_host_result) ? 0 : 1;
if(ck::is_gfx11_supported())
{
return ck::utils::check_err(e_ms_ns_device_result,
e_ms_ns_host_result,
"Error: Incorrect results!",
1e-4,
1e-4)
? 0
: 1;
}
else
{
return ck::utils::check_err(e_ms_ns_device_result, e_ms_ns_host_result) ? 0 : 1;
}
}
return 0;

15
example/26_contraction/run_contraction_scale_example.inc
View File

@@ -216,7 +216,20 @@ int run_contraction_scale_example(int argc, char* argv[])
}
}
return ck::utils::check_err(e_ms_ns_device_result, e_ms_ns_host_result) ? 0 : 1;
if(ck::is_gfx11_supported())
{
return ck::utils::check_err(e_ms_ns_device_result,
e_ms_ns_host_result,
"Error: Incorrect results!",
1e-4,
1e-4)
? 0
: 1;
}
else
{
return ck::utils::check_err(e_ms_ns_device_result, e_ms_ns_host_result) ? 0 : 1;
}
}
return 0;

2
example/ck_tile/01_fmha/CMakeLists.txt
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@@ -65,7 +65,7 @@ set(FMHA_BWD_CODE_GEN_COMMON_ARGS
# there is no corresponding instance for parameters).
if(BUILD_TESTING)
# Filters are in the order of FMHA_FWD_KNOWN_APIS: fwd,fwd_splitkv_combine@fwd_splitkv,fwd_appendkv,pagedkv_prefill
list(APPEND FMHA_FWD_CODE_GEN_COMMON_ARGS --filter *_nlogits*_nskip*,*@*_nlogits*_nbias*,*,*_nlogits*_nskip*_pagedkv)
list(APPEND FMHA_FWD_CODE_GEN_COMMON_ARGS --filter *_nlogits*_nskip*_nsink*,*@*_nlogits*_nbias*_nsink*,*,*_nlogits*_nskip*_pagedkv*)
endif()
# generate a list of kernels, but not actually emit files at config sta

75
example/ck_tile/01_fmha/codegen/ops/fmha_fwd.py
View File

@@ -76,7 +76,8 @@ using fmha_traits = ck_tile::TileFmhaTraits<{F_spad},
{F_dropout},
{F_qscale},
{F_occupancy},
{F_skip}>;
{F_skip},
{F_sink}>;
using fmha_variant = ck_tile::ComposedAttention<{F_logits} * ck_tile::LOGITS_SOFT_CAP, CK_TILE_FMHA_FWD_FAST_EXP2>;
@@ -113,7 +114,7 @@ using fmha_kernel = {F_kernel}<fmha_pipeline, fmha_epilogue>;
using trait = 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}, {F_logits}, fmha_mask, {F_bias}, {F_lse}, {F_dropout}, {F_qscale}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}, {F_trload}, {F_skip}>;
{F_pipeline_enum}, {F_logits}, fmha_mask, {F_bias}, {F_lse}, {F_dropout}, {F_qscale}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}, {F_trload}, {F_skip}, {F_sink}>;
template<>
float fmha_fwd_<trait, {F_arch.tag}>(const ck_tile::stream_config& s, fmha_fwd_args a)
@@ -229,9 +230,9 @@ FMHA_FWD_API_PER_HDIM_CASE = """{F_if}(t.hdim_q <= {F_hdim} && t.hdim_v <= {F_hd
}}
"""
FMHA_FWD_API_INNER_DISPATCH = """{F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && (t.has_logits_soft_cap == {F_logits}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_lse == {F_lse}) && (t.has_dropout == {F_dropout}) && (t.qscale_type == {F_qscale_check}) && (t.skip_min_seqlen_q == {F_skip}) &&
FMHA_FWD_API_INNER_DISPATCH = """{F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && (t.has_logits_soft_cap == {F_logits}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_lse == {F_lse}) && (t.has_dropout == {F_dropout}) && (t.qscale_type == {F_qscale_check}) && (t.skip_min_seqlen_q == {F_skip}) &&(t.has_sink == {F_sink}) &&
({F_scheck}) && ({F_seqtune}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck}) && ({F_constraint})) {{
using trait_ = 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}, {F_logits}, {F_mask}, {F_bias}, {F_lse}, {F_dropout}, {F_qscale}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}, {F_trload}, {F_skip}>;
using trait_ = 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}, {F_logits}, {F_mask}, {F_bias}, {F_lse}, {F_dropout}, {F_qscale}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}, {F_trload}, {F_skip}, {F_sink}>;
return fmha_fwd_<trait_, {F_arch.tag}>(s, a);
}}
"""
@@ -278,13 +279,14 @@ class FmhaFwdApiTrait:
dvpad: str
skip: str
tr_load: str
sink: str
constraint: CppConstraint
@property
def name(self) -> str:
return (
f"{self.hdim}-{self.dtype}-{self.mode}-{self.bm0}-{self.bn0}-{self.bk0}-{self.bn0}-{self.bk1}-{self.bk0max}-"
+ f"{self.vlayout}-{self.logits}-{self.mask}-{self.bias}-{self.lse}-{self.dropout}-{self.qscale}-{self.spad}-{self.skpad}-{self.dpad}-{self.dvpad}-{self.skip}"
+ f"{self.vlayout}-{self.logits}-{self.mask}-{self.bias}-{self.lse}-{self.dropout}-{self.qscale}-{self.spad}-{self.skpad}-{self.dpad}-{self.dvpad}-{self.skip}-{self.sink}"
)
@property
@@ -384,6 +386,7 @@ class FmhaFwdPipeline:
F_mask: str # value from MASK_MAP
F_skip: str # true/false
F_trload: str # true/false
F_sink: str # true/false
F_constraint: CppConstraint = field(default_factory=lambda: CppConstraint())
@property
@@ -454,6 +457,10 @@ class FmhaFwdPipeline:
n += "_trload"
else:
n += "_ntrload"
if self.F_sink == "t":
n += "_sink"
else:
n += "_nsink"
return n
@@ -543,6 +550,7 @@ class FmhaFwdApiPool:
F_trload=BOOL_MAP[trait.tr_load],
F_qscale_check=QSCALE_CHECK_MAP[trait.qscale],
F_qscale=QSCALE_MAP[trait.qscale],
F_sink=BOOL_MAP[trait.sink],
F_scheck=trait.scheck,
F_seqtune=trait.seqtune(max_bm0),
F_skcheck=trait.skcheck,
@@ -683,6 +691,7 @@ class FmhaFwdKernel:
F_pipeline=PIPELINE_MAP[self.F_pipeline.tag],
F_kernel=self._get_cpp_kernel_class_name(self.F_pipeline.tag),
F_kargs_creator=self._get_cpp_kargs_creator_func_name(self.F_pipeline.tag),
F_sink=BOOL_MAP[self.F_pipeline.F_sink],
)
@property
@@ -725,6 +734,7 @@ class FmhaFwdKernel:
dvpad=self.F_pipeline.F_dvpad,
skip=self.F_pipeline.F_skip,
tr_load=self.F_pipeline.F_trload,
sink=self.F_pipeline.F_sink,
constraint=self.F_tile.F_constraint & self.F_pipeline.F_constraint,
)
@@ -957,52 +967,55 @@ class KernelComponentFactoryGfx9(CompatibilityRuleFactoryGfx9):
pipelines = []
if dtype in cls._DT_FP32:
qscale = "no"
for logits, mask, bias, lse, dropout, skip in itertools.product(
for logits, mask, bias, lse, dropout, skip, sink in itertools.product(
["t", "f"],
get_mask_map(mask_impl).keys(),
BIAS_MAP.keys(),
["t", "f"],
["t", "f"],
["t", "f"],
["t", "f"],
):
pipelines.append(FmhaFwdPipeline("qr", "row", "f", "f", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "f", "t", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "f", "f", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "f", sink)) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "f", "t", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "f", sink)) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f", sink)) # fmt: skip
elif dtype in cls._DT_FP16_BF16:
qscale = "no"
for logits, mask, bias, lse, dropout, skip in itertools.product(
for logits, mask, bias, lse, dropout, skip, sink in itertools.product(
["t", "f"],
get_mask_map(mask_impl).keys(),
BIAS_MAP.keys(),
["t", "f"],
["t", "f"],
["t", "f"],
["t", "f"],
):
if hdim == 256 and hdim_v == 256:
pipelines.append(FmhaFwdPipeline("qr", "row", "f", "f", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "f", "f", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "f", sink)) # fmt: skip
# the below two is used for hdim vectorize load
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "f", sink)) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f", sink)) # fmt: skip
else:
if bias == "bias":
# TODO: rocm 6.2 compiler problem if using qr_async for bias case
pipelines.append(FmhaFwdPipeline("qr", "row", "f", "f", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "f", "f", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "f", sink)) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f", sink)) # fmt: skip
else:
pipelines.append(FmhaFwdPipeline("qr_async", "row", "t", "f", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_async", "row", "t", "t", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_async", "row", "t", "f", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f", sink)) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_async", "row", "t", "t", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f", sink)) # fmt: skip
if receipt == 1 and bias != "bias":
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f")) # fmt: skip # TODO: cover arbitraty hdim# fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f", sink)) # fmt: skip # TODO: cover arbitraty hdim# fmt: skip
elif dtype in cls._DT_FP8BF16 or dtype in cls._DT_FP8FP32:
# no need lse/dropout kernels
for logits, qscale, mask, bias in itertools.product(
for logits, qscale, mask, bias, sink in itertools.product(
["f"],
["no", "pertensor"],
get_mask_map(mask_impl).keys(),
["no"],
["f", "t"],
):
pipelines.append(FmhaFwdPipeline("qr_async", "row", "t", "f", "t", "t", logits, bias, "f", "f", qscale, mask, "f", "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_async", "row", "t", "t", "t", "t", logits, bias, "f", "f", qscale, mask, "f", "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_async", "row", "t", "f", "t", "t", logits, bias, "f", "f", qscale, mask, "f", "f", sink)) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_async", "row", "t", "t", "t", "t", logits, bias, "f", "f", qscale, mask, "f", "f", sink)) # fmt: skip
elif dtype in ["fp8", "fp8fp16", "bf8"]:
# TODO
pass
@@ -1033,13 +1046,14 @@ class KernelComponentFactoryGfx950(
)
if dtype in cls._DT_FP16_BF16:
qscale = "no"
for logits, mask, bias, lse, dropout, skip in itertools.product(
for logits, mask, bias, lse, dropout, skip, sink in itertools.product(
["t", "f"],
get_mask_map(mask_impl).keys(),
BIAS_MAP.keys(),
["t", "f"],
["t", "f"],
["t", "f"],
["t", "f"],
):
if (
(hdim, hdim_v) in [(64, 64), (128, 128)]
@@ -1048,15 +1062,15 @@ class KernelComponentFactoryGfx950(
and dropout == "f"
and skip == "f"
):
pipelines.append(FmhaFwdPipeline("qr_async_trload", "row", "f", "f", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "t")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_async_trload", "row", "f", "f", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "t")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_async_trload", "row", "f", "f", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "t", sink)) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_async_trload", "row", "f", "f", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "t", sink)) # fmt: skip
# qr_async_trload_v3 only supports hdim=hdim_v=128 for now
if (hdim, hdim_v) == (128, 128):
# qr_async_trload_v3 only supports (generic) causal mask
for mask in ["no", "causal"]:
pipelines.append(FmhaFwdPipeline("qr_async_trload_v3", "row", "t", "t", "f", "f",
F_logits="f", F_bias="no", F_lse="f", F_dropout="f", F_qscale=qscale, F_mask=mask, F_skip="f", F_trload="t")) # fmt: skip
F_logits="f", F_bias="no", F_lse="f", F_dropout="f", F_qscale=qscale, F_mask=mask, F_skip="f", F_trload="t", F_sink="f")) # fmt: skip
return pipelines
@@ -1105,23 +1119,24 @@ class KernelComponentFactoryGfx12(CompatibilityRuleFactory):
pipelines = []
if dtype in cls._DT_FP16_BF16:
qscale = "no"
for logits, mask, bias, lse, dropout, skip in itertools.product(
for logits, mask, bias, lse, dropout, skip, sink in itertools.product(
["t", "f"],
get_mask_map(mask_impl).keys(),
BIAS_MAP.keys(),
["t", "f"],
["t", "f"],
["t", "f"],
["t", "f"],
):
pipelines.append(FmhaFwdPipeline("qr", "row", "f", "f", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "f", "f", "f", "f", logits, bias, lse, dropout, qscale, mask, skip, "f", sink)) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "t", "t", logits, bias, lse, dropout, qscale, mask, skip, "f", sink)) # fmt: skip
elif dtype in cls._DT_FP8_FP8BF16 or dtype in cls._DT_FP8FP32:
# no need lse/dropout kernels
for logits, qscale, mask, bias in itertools.product(
["f"], ["no", "pertensor"], get_mask_map(mask_impl).keys(), ["no"]
):
pipelines.append(FmhaFwdPipeline("qr", "row", "f", "f", "f", "f", logits, bias, "f", "f", qscale, mask, "f", "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "t", "t", logits, bias, "f", "f", qscale, mask, "f", "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "f", "f", "f", "f", logits, bias, "f", "f", qscale, mask, "f", "f", "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr", "row", "t", "t", "t", "t", logits, bias, "f", "f", qscale, mask, "f", "f", "f")) # fmt: skip
return pipelines

42
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_splitkv.py
View File

@@ -73,7 +73,8 @@ using fmha_trait = ck_tile::TileFmhaFwdSplitKVTraits<{F_spad},
{F_pagedkv},
kHasUnevenSplits,
kMergeNumHeadGroupsSeqLenQ,
{F_occupancy}>;
{F_occupancy},
{F_sink}>;
using fmha_pipeline_problem = ck_tile::BlockFmhaFwdSplitKVPipelineProblem<
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::QDataType,
@@ -117,7 +118,7 @@ static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
}} // anonymous namespace
using trait_{F_idx} = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}, {F_vlayout},
{F_pipeline_enum}, {F_logits}, fmha_mask_{F_idx}, {F_bias}, {F_lse}, {F_squant}, {F_pagedkv}, {F_spad}, {F_skpad}, {F_dpad},
{F_pipeline_enum}, {F_logits}, fmha_mask_{F_idx}, {F_bias}, {F_lse}, {F_squant}, {F_pagedkv}, {F_sink}, {F_spad}, {F_skpad}, {F_dpad},
{F_dvpad}>;
#pragma clang diagnostic push
@@ -279,8 +280,8 @@ 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}) && (t.has_logits_soft_cap == {F_logits}) && ({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})) {{
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_logits}, {F_mask}, {F_bias}, true, {F_squant}, {F_pagedkv}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
((a.block_table_ptr != nullptr) == {F_pagedkv}) && (t.has_sink == {F_sink}) && ({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_logits}, {F_mask}, {F_bias}, true, {F_squant}, {F_pagedkv},{F_sink}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
// get combine kernel tile sizes
using OaccDataType = typename FmhaFwdTypeConfig<{F_dtype}>::OaccDataType;
@@ -332,6 +333,7 @@ class FmhaFwdSplitKVApiTrait:
dpad: str
dvpad: str
pagedkv: str
sink: str # sink or not
bn1comb: int # tile size along v head_dim of combine kernel
@property
@@ -339,7 +341,7 @@ class FmhaFwdSplitKVApiTrait:
return (
f"{self.hdim}-{self.dtype}-{self.mode}-{self.bm0}-{self.bn0}-{self.bk0}-{self.bn0}-{self.bk1}-{self.bk0max}-"
+ f"{self.vlayout}-{self.logits}-{self.mask}-{self.bias}-{self.lse}-{self.squant}-{self.spad}-{self.skpad}-{self.dpad}-"
+ f"{self.dvpad}-{self.pagedkv}"
+ f"{self.dvpad}-{self.pagedkv}-{self.sink}"
)
@property
@@ -425,6 +427,7 @@ class FmhaFwdSplitKVPipeline:
F_lse: str #
F_squant: str #
F_pagedkv: str # t/f
F_sink: str # t/f
F_mask: str # value from MASK_MAP
@property
@@ -485,6 +488,10 @@ class FmhaFwdSplitKVPipeline:
n += "_pagedkv"
else:
n += "_npagedkv"
if self.F_sink == "t":
n += "_sink"
else:
n += "_nsink"
return n
@@ -567,6 +574,7 @@ class FmhaFwdSplitKVApiPool:
F_lse=BOOL_MAP[trait.lse],
F_squant=BOOL_MAP[trait.squant],
F_pagedkv=BOOL_MAP[trait.pagedkv],
F_sink=BOOL_MAP[trait.sink],
F_scheck=trait.scheck,
F_skcheck=trait.skcheck,
F_dcheck=trait.dcheck,
@@ -667,6 +675,7 @@ class FmhaFwdSplitKVKernel:
F_squant=BOOL_MAP[self.F_pipeline.F_squant],
F_pagedkv=BOOL_MAP[self.F_pipeline.F_pagedkv],
F_occupancy=self.F_tile.F_occupancy,
F_sink=BOOL_MAP[self.F_pipeline.F_sink],
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],
@@ -740,19 +749,23 @@ class KernelComponentFactoryBase:
squant = "t" if dtype == "fp8" else "f"
pipelines = []
if dtype in ["fp16", "bf16"]:
for logits, mask, bias, pagedkv in itertools.product(
["t", "f"], get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"]
for logits, mask, bias, pagedkv, sink in itertools.product(
["t", "f"],
get_mask_map(mask_impl).keys(),
BIAS_MAP.keys(),
["t", "f"],
["t", "f"],
):
pipelines.append(Pipeline("qr", "row", "f", "t", "f", "f", logits, bias, "t", squant, pagedkv, mask)) # fmt: skip
pipelines.append(Pipeline("qr", "row", "t", "f", "f", "f", logits, bias, "t", squant, pagedkv, mask)) # fmt: skip
pipelines.append(Pipeline("qr", "row", "t", "t", "f", "f", logits, bias, "t", squant, pagedkv, mask)) # fmt: skip
pipelines.append(Pipeline("qr", "row", "t", "t", "t", "t", logits, bias, "t", squant, pagedkv, mask)) # fmt: skip
pipelines.append(Pipeline("qr", "row", "f", "t", "f", "f", logits, bias, "t", squant, pagedkv, sink, mask)) # fmt: skip
pipelines.append(Pipeline("qr", "row", "t", "f", "f", "f", logits, bias, "t", squant, pagedkv, sink, mask)) # fmt: skip
pipelines.append(Pipeline("qr", "row", "t", "t", "f", "f", logits, bias, "t", squant, pagedkv, sink, mask)) # fmt: skip
pipelines.append(Pipeline("qr", "row", "t", "t", "t", "t", logits, bias, "t", squant, pagedkv, sink, mask)) # fmt: skip
elif dtype in ["fp8", "bf8"]:
for logits, mask, bias in itertools.product(
["t", "f"], get_mask_map(mask_impl).keys(), BIAS_MAP.keys()
):
pipelines.append(Pipeline("qr", "row", "f", "f", "f", "f", logits, bias, "t", squant, "f", mask)) # fmt: skip
pipelines.append(Pipeline("qr", "row", "t", "t", "f", "f", logits, bias, "t", squant, "f", mask)) # fmt: skip
pipelines.append(Pipeline("qr", "row", "f", "f", "f", "f", logits, bias, "t", squant, "f", "f", mask)) # fmt: skip
pipelines.append(Pipeline("qr", "row", "t", "t", "f", "f", logits, bias, "t", squant, "f", "f", mask)) # fmt: skip
elif dtype in ["fp8fp16", "fp8bf16"]:
# TODO
None
@@ -908,6 +921,7 @@ def get_fwd_splitkv_blobs(
cond &= pipeline.F_vlayout == "row"
cond &= pipeline.F_bias in ["no", "alibi"]
cond &= pipeline.F_squant == "f"
cond &= pipeline.F_sink == "f"
if not cond:
continue
# PyTorch integration
@@ -917,6 +931,7 @@ def get_fwd_splitkv_blobs(
cond &= pipeline.F_bias in ["no", "bias"]
cond &= pipeline.F_squant == "f"
cond &= mode == "batch"
cond &= pipeline.F_sink == "f"
if not cond:
continue
# Aiter(mha_varlen_fwd) integration
@@ -1075,6 +1090,7 @@ def write_blobs(
lse=kernel.F_pipeline.F_lse,
squant=kernel.F_pipeline.F_squant,
pagedkv=kernel.F_pipeline.F_pagedkv,
sink=kernel.F_pipeline.F_sink,
spad=kernel.F_pipeline.F_spad,
skpad=kernel.F_pipeline.F_skpad,
dpad=kernel.F_pipeline.F_dpad,

33
example/ck_tile/01_fmha/codegen/ops/fmha_pagedkv_prefill.py
View File

@@ -65,7 +65,8 @@ using fmha_trait_{F_idx} = ck_tile::TileFmhaFwdPagedKVTraits<{F_spad},
{F_pagedkv}, //pagedkv
{F_squant},
{F_occupancy},
{F_skip}>;
{F_skip},
{F_sink}>;
using fmha_variant_{F_idx} = ck_tile::ComposedAttention<{F_logits} * ck_tile::LOGITS_SOFT_CAP, CK_TILE_FMHA_FWD_FAST_EXP2>;
@@ -100,7 +101,7 @@ using fmha_kernel_{F_idx} =
ck_tile::FmhaFwdPagedKVKernel<fmha_pipeline_{F_idx}, fmha_epilogue_{F_idx}>;
using trait_{F_idx} = fmha_fwd_pagedkv_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_logits}, fmha_mask_{F_idx}, {F_bias}, {F_lse}, {F_pagedkv}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}, {F_skip}>;
{F_pipeline_enum}, {F_logits}, fmha_mask_{F_idx}, {F_bias}, {F_lse}, {F_pagedkv}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}, {F_skip}, {F_sink}>;
template<>
float fmha_fwd_pagedkv_<trait_{F_idx}, {F_arch.tag}>(const ck_tile::stream_config& s, fmha_fwd_pagedkv_args a)
@@ -129,9 +130,9 @@ float fmha_fwd_pagedkv(fmha_fwd_pagedkv_traits& t, fmha_fwd_pagedkv_args& a, con
}}
"""
FMHA_FWD_API_INNER_DISPATCH = """{F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && (t.has_logits_soft_cap == {F_logits}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_lse == {F_lse}) && (t.use_pagedkv == {F_pagedkv}) && (t.do_fp8_static_quant == {F_squant}) && (t.skip_min_seqlen_q == {F_skip}) &&
FMHA_FWD_API_INNER_DISPATCH = """{F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && (t.has_logits_soft_cap == {F_logits}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_lse == {F_lse}) && (t.use_pagedkv == {F_pagedkv}) && (t.do_fp8_static_quant == {F_squant}) && (t.skip_min_seqlen_q == {F_skip}) && (t.has_sink == {F_sink}) &&
({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{
using trait_ = fmha_fwd_pagedkv_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_logits}, {F_mask}, {F_bias}, {F_lse}, {F_pagedkv}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}, {F_skip}>;
using trait_ = fmha_fwd_pagedkv_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_logits}, {F_mask}, {F_bias}, {F_lse}, {F_pagedkv}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}, {F_skip},{F_sink}>;
return fmha_fwd_pagedkv_<trait_, {F_arch.tag}>(s, a);
}}
"""
@@ -163,12 +164,13 @@ class FmhaFwdApiTrait:
dpad: str
dvpad: str
skip: str
sink: str
@property
def name(self) -> str:
return (
f"{self.hdim}-{self.dtype}-{self.mode}-{self.bm0}-{self.bn0}-{self.bk0}-{self.bn0}-{self.bk1}-{self.bk0max}-"
+ f"{self.vlayout}-{self.logits}-{self.mask}-{self.bias}-{self.lse}-{self.pagedkv}-{self.squant}-{self.spad}-{self.skpad}-{self.dpad}-{self.dvpad}-{self.skip}"
+ f"{self.vlayout}-{self.logits}-{self.mask}-{self.bias}-{self.lse}-{self.pagedkv}-{self.squant}-{self.spad}-{self.skpad}-{self.dpad}-{self.dvpad}-{self.skip}-{self.sink}"
)
@property
@@ -256,6 +258,7 @@ class FmhaFwdPipeline:
F_squant: str #
F_mask: str # value from MASK_MAP
F_skip: str # true/false
F_sink: str # true/false
@property
def name(self) -> str:
@@ -320,6 +323,10 @@ class FmhaFwdPipeline:
n += "_pagedkv"
else:
n += "_npagedkv"
if self.F_sink == "t":
n += "_sink"
else:
n += "_nsink"
return n
@@ -363,6 +370,7 @@ class FmhaFwdApiPool:
F_lse=BOOL_MAP[trait.lse],
F_pagedkv=BOOL_MAP[trait.pagedkv],
F_skip=BOOL_MAP[trait.skip],
F_sink=BOOL_MAP[trait.sink],
F_squant=BOOL_MAP[trait.squant],
F_scheck=trait.scheck,
F_skcheck=trait.skcheck,
@@ -480,6 +488,7 @@ class FmhaFwdKernel:
F_pagedkv=BOOL_MAP[self.F_pipeline.F_pagedkv],
F_squant=BOOL_MAP[self.F_pipeline.F_squant],
F_skip=BOOL_MAP[self.F_pipeline.F_skip],
F_sink=BOOL_MAP[self.F_pipeline.F_sink],
F_occupancy=self.F_tile.F_occupancy,
F_pipeline_enum=PIPELINE_ENUM_MAP[self.F_pipeline.tag],
F_mask=get_mask_map(self.mask_impl)[self.F_pipeline.F_mask],
@@ -526,6 +535,7 @@ class FmhaFwdKernel:
dpad=self.F_pipeline.F_dpad,
dvpad=self.F_pipeline.F_dvpad,
skip=self.F_pipeline.F_skip,
sink=self.F_pipeline.F_sink,
)
@@ -539,22 +549,23 @@ class KernelComponentFactoryBase:
squant = "t" if dtype == "fp8" else "f"
pipelines = []
if dtype in ["fp16", "bf16"]:
for logits, mask, bias, pagedkv, skip in itertools.product(
for logits, mask, bias, pagedkv, skip, sink in itertools.product(
["t", "f"],
get_mask_map(mask_impl).keys(),
BIAS_MAP.keys(),
["t"],
["f"],
["t", "f"],
):
pipelines.append(FmhaFwdPipeline("qr_pagedkv", "row", "t", "f", "f", "f", logits, bias, "f", pagedkv, squant, mask, skip)) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_pagedkv", "row", "t", "t", "f", "f", logits, bias, "f", pagedkv, squant, mask, skip)) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_pagedkv", "row", "t", "f", "f", "f", logits, bias, "f", pagedkv, squant, mask, skip, sink)) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_pagedkv", "row", "t", "t", "f", "f", logits, bias, "f", pagedkv, squant, mask, skip, sink)) # fmt: skip
elif dtype in ["fp8", "bf8"]:
# no need lse/dropout kernels
for logits, mask, bias in itertools.product(
["t", "f"], get_mask_map(mask_impl).keys(), BIAS_MAP.keys()
):
pipelines.append(FmhaFwdPipeline("qr_pagedkv", "row", "f", "f", "f", "f", logits, bias, "f", "t", squant, mask, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_pagedkv", "row", "t", "t", "f", "f", logits, bias, "f", "t", squant, mask, "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_pagedkv", "row", "f", "f", "f", "f", logits, bias, "f", "t", squant, mask, "f", "f")) # fmt: skip
pipelines.append(FmhaFwdPipeline("qr_pagedkv", "row", "t", "t", "f", "f", logits, bias, "f", "t", squant, mask, "f", "f")) # fmt: skip
elif dtype in ["fp8fp16", "fp8bf16"]:
pass # TODO
else:
@@ -678,6 +689,7 @@ def get_fwd_blobs(
cond &= pipeline.F_bias in ["no", "alibi"]
cond &= pipeline.F_squant == "f"
cond &= pipeline.F_skip == "f"
cond &= pipeline.F_sink == "f"
if not cond:
continue
# PyTorch integration
@@ -687,6 +699,7 @@ def get_fwd_blobs(
cond &= pipeline.F_bias in ["no", "bias"]
cond &= pipeline.F_squant == "f"
cond &= pipeline.F_skip == "f"
cond &= pipeline.F_sink == "f"
if not cond:
continue
# Aiter(mha_fwd) integration

25
example/ck_tile/01_fmha/fmha_fwd.hpp
View File

@@ -266,6 +266,7 @@ struct fmha_fwd_args
ck_tile::index_t window_size_left;
ck_tile::index_t window_size_right;
ck_tile::index_t sink_size;
ck_tile::index_t mask_type;
ck_tile::index_t min_seqlen_q;
@@ -352,6 +353,7 @@ struct fmha_fwd_pagedkv_args
ck_tile::index_t window_size_left;
ck_tile::index_t window_size_right;
ck_tile::index_t sink_size;
ck_tile::index_t mask_type;
ck_tile::index_t min_seqlen_q;
};
@@ -442,6 +444,7 @@ struct fmha_fwd_splitkv_args
ck_tile::index_t window_size_left;
ck_tile::index_t window_size_right;
ck_tile::index_t sink_size;
ck_tile::index_t mask_type;
};
@@ -561,6 +564,7 @@ struct fmha_batch_prefill_args
ck_tile::index_t window_size_left;
ck_tile::index_t window_size_right;
ck_tile::index_t sink_size;
ck_tile::index_t mask_type;
float p_drop;
@@ -613,6 +617,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
args.nhead_stride_o,
args.window_size_left,
args.window_size_right,
args.sink_size,
args.mask_type,
args.min_seqlen_q,
args.p_drop,
@@ -663,6 +668,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
args.batch_stride_o,
args.window_size_left,
args.window_size_right,
args.sink_size,
args.mask_type,
args.p_drop,
args.s_randval,
@@ -824,6 +830,7 @@ auto fmha_fwd_pagedkv_create_kargs_and_grids(fmha_fwd_pagedkv_args args)
args.batch_stride_v,
args.window_size_left,
args.window_size_right,
args.sink_size,
args.mask_type,
args.min_seqlen_q);
}
@@ -869,6 +876,7 @@ auto fmha_fwd_pagedkv_create_kargs_and_grids(fmha_fwd_pagedkv_args args)
args.batch_stride_o,
args.window_size_left,
args.window_size_right,
args.sink_size,
args.mask_type);
}
}();
@@ -935,6 +943,7 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args)
args.split_stride_o_acc,
args.window_size_left,
args.window_size_right,
args.sink_size,
args.mask_type);
}
else
@@ -982,6 +991,7 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args)
args.split_stride_o_acc,
args.window_size_left,
args.window_size_right,
args.sink_size,
args.mask_type);
}
}();
@@ -1142,6 +1152,7 @@ auto fmha_batch_prefill_create_kargs_and_grids(fmha_batch_prefill_args args)
args.batch_stride_v,
args.window_size_left,
args.window_size_right,
args.sink_size,
args.mask_type,
args.p_drop,
args.s_randval,
@@ -1194,6 +1205,7 @@ auto fmha_batch_prefill_create_kargs_and_grids(fmha_batch_prefill_args args)
args.batch_stride_o,
args.window_size_left,
args.window_size_right,
args.sink_size,
args.mask_type,
args.p_drop,
args.s_randval,
@@ -1228,7 +1240,8 @@ template <ck_tile::index_t HDim_,
bool kPadD_,
bool kPadDv_,
bool kUseTrLoad_,
bool kSkipMinSeqlenQ_ = false>
bool kSkipMinSeqlenQ_ = false,
bool kHasSink_ = false>
struct fmha_fwd_traits_
{
static constexpr ck_tile::index_t HDim = HDim_;
@@ -1254,6 +1267,7 @@ struct fmha_fwd_traits_
static constexpr bool kPadDv = kPadDv_;
static constexpr bool kUseTrLoad = kUseTrLoad_;
static constexpr bool kSkipMinSeqlenQ = kSkipMinSeqlenQ_;
static constexpr bool kHasSink = kHasSink_;
};
template <typename Traits_, typename Arch = void>
@@ -1280,7 +1294,8 @@ template <ck_tile::index_t HDim_,
bool kPadSK_,
bool kPadD_,
bool kPadDv_,
bool kSkipMinSeqlenQ_ = false>
bool kSkipMinSeqlenQ_ = false,
bool kHasSink_ = false>
struct fmha_fwd_pagedkv_traits_
{
static constexpr ck_tile::index_t HDim = HDim_;
@@ -1305,6 +1320,7 @@ struct fmha_fwd_pagedkv_traits_
static constexpr bool kPadD = kPadD_;
static constexpr bool kPadDv = kPadDv_;
static constexpr bool kSkipMinSeqlenQ = kSkipMinSeqlenQ_;
static constexpr bool kHasSink = kHasSink_;
};
template <typename Traits_, typename Arch = void>
@@ -1327,6 +1343,7 @@ template <ck_tile::index_t HDim_,
bool kStoreLse_,
bool kDoFp8StaticQuant_,
bool kIsPagedKV_,
bool kHasSink_,
bool kPadS_,
bool kPadSK_,
bool kPadD_,
@@ -1354,6 +1371,7 @@ struct fmha_fwd_splitkv_traits_
static constexpr bool kPadD = kPadD_;
static constexpr bool kPadDv = kPadDv_;
static constexpr bool kIsPagedKV = kIsPagedKV_;
static constexpr bool kHasSink = kHasSink_;
};
template <typename Traits_, typename Arch = void>
@@ -1440,6 +1458,7 @@ struct fmha_fwd_traits
bool has_dropout;
quant_scale_enum qscale_type;
bool skip_min_seqlen_q = false;
bool has_sink = false;
// TODO: padding check is inside this api
};
float fmha_fwd(fmha_fwd_traits, fmha_fwd_args, const ck_tile::stream_config&);
@@ -1458,6 +1477,7 @@ struct fmha_fwd_pagedkv_traits
bool use_pagedkv = true;
bool do_fp8_static_quant = false;
bool skip_min_seqlen_q = false;
bool has_sink = false;
// TODO: padding check is inside this api
};
@@ -1477,6 +1497,7 @@ struct fmha_fwd_splitkv_traits
bias_enum bias_type; // 0:no bias, 1:elementwise bias, 2:alibi. sync with BlockAttentionBiasEnum
bool has_lse;
bool do_fp8_static_quant;
bool has_sink = false;
// TODO: padding check is inside this api
};
float fmha_fwd_splitkv(fmha_fwd_splitkv_traits,

6
example/ck_tile/01_fmha/fmha_fwd_runner.hpp
View File

@@ -879,6 +879,7 @@ fwd_result fmha_fwd_run(mode_enum mode,
traits.has_logits_soft_cap = 0.f < logits_soft_cap;
traits.mask_type = mask.type;
traits.bias_type = bias.type;
traits.has_sink = mask.sink > 0 ? true : false;
traits.has_lse = lse;
if constexpr(std::is_same_v<fmha_fwd_traits, std::decay_t<decltype(traits)>>)
@@ -1042,6 +1043,7 @@ fwd_result fmha_fwd_run(mode_enum mode,
args.window_size_left = mask.left;
args.window_size_right = mask.right;
args.sink_size = mask.sink;
args.mask_type = static_cast<ck_tile::index_t>(mask.type);
if constexpr(std::is_same_v<fmha_fwd_args, std::decay_t<decltype(args)>>)
@@ -1645,7 +1647,7 @@ fwd_result fmha_fwd_run(mode_enum mode,
ck_tile::reference_batched_masking<SaccDataType>(
s_host_ref,
ck_tile::make_generic_attention_mask_from_lr_window<FmhaMasks::GenericMask>(
mask.left, mask.right, real_seqlen_q, real_seqlen_k));
mask.left, mask.right, mask.sink, real_seqlen_q, real_seqlen_k));
}
else
{
@@ -1657,6 +1659,7 @@ fwd_result fmha_fwd_run(mode_enum mode,
ck_tile::make_generic_attention_mask_from_lr_window<FmhaMasks::CausalMask>(
mask.left,
mask.right,
mask.sink,
real_seqlen_q,
real_seqlen_k,
mask.type == mask_enum::mask_top_left));
@@ -1666,6 +1669,7 @@ fwd_result fmha_fwd_run(mode_enum mode,
ck_tile::make_generic_attention_mask_from_lr_window<FmhaMasks::GenericMask>(
mask.left,
mask.right,
mask.sink,
real_seqlen_q,
real_seqlen_k,
mask.type == mask_enum::mask_top_left));

42
example/ck_tile/01_fmha/mask.hpp
View File

@@ -25,6 +25,7 @@ struct mask_info
ck_tile::index_t seqlen_k;
ck_tile::index_t y, x;
ck_tile::index_t left, right; // FA style SWA left/right
ck_tile::index_t sink;
void serialize(std::ostream& os) const
{
@@ -58,13 +59,14 @@ struct mask_info
ck_tile::index_t window_size = std::stoi(v);
ck_tile::index_t left_size = -1;
ck_tile::index_t right_size = 0;
ck_tile::index_t sink_size = 0;
if(window_size > 0)
{
left_size = window_size / 2;
right_size = window_size - 1 - left_size;
}
auto r = ck_tile::make_generic_attention_mask_coordinates_from_lr_window(
left_size, right_size, y_total, x_total, t == "xt");
left_size, right_size, sink_size, y_total, x_total, t == "xt");
tmp.type = t == "xt" ? mask_enum::mask_top_left : mask_enum::mask_bottom_right;
tmp.y = r.at(ck_tile::number<0>{});
@@ -79,27 +81,54 @@ struct mask_info
{
throw std::invalid_argument("invalid mask value: " + str);
}
ck_tile::index_t v0 = std::stoi(v.substr(0, found_1));
ck_tile::index_t v1 = std::stoi(v.substr(found_1 + 1));
tmp.type = mask_enum::window_generic;
ck_tile::index_t v0 = atoi(v.substr(0, found_1).c_str());
auto found_2 = v.find(',', found_1 + 1);
ck_tile::index_t v1 = 0;
ck_tile::index_t sink = 0;
// ck_tile::index_t v1 = atoi(v.substr(found_1 + 1).c_str());
// TODO: some validation
if(t == "t")
{
if(found_2 != std::string::npos)
{
v1 = atoi(v.substr(found_1 + 1, found_2 - found_1 - 1).c_str());
sink = atoi(v.substr(found_2 + 1).c_str());
}
else
{
v1 = atoi(v.substr(found_1 + 1).c_str());
sink = 0;
}
tmp.type = mask_enum::mask_top_left;
auto r = ck_tile::make_generic_attention_mask_coordinates_from_lr_window(
v0, v1, y_total, x_total, true);
v0, v1, sink, y_total, x_total, true);
tmp.y = r.at(ck_tile::number<0>{});
tmp.x = r.at(ck_tile::number<1>{});
tmp.left = v0;
tmp.right = v1;
tmp.sink = sink;
}
else if(t == "b")
{
if(found_2 != std::string::npos)
{
v1 = atoi(v.substr(found_1 + 1, found_2 - found_1 - 1).c_str());
sink = atoi(v.substr(found_2 + 1).c_str());
}
else
{
v1 = atoi(v.substr(found_1 + 1).c_str());
sink = 0;
}
tmp.type = mask_enum::mask_bottom_right;
auto r = ck_tile::make_generic_attention_mask_coordinates_from_lr_window(
v0, v1, y_total, x_total, false);
v0, v1, sink, y_total, x_total, false);
tmp.y = r.at(ck_tile::number<0>{});
tmp.x = r.at(ck_tile::number<1>{});
tmp.left = v0;
tmp.right = v1;
tmp.sink = sink;
}
else if(t == "g")
{
@@ -108,6 +137,7 @@ struct mask_info
tmp.x = v1;
tmp.left = v0; // TODO: don't use this?
tmp.right = v1;
tmp.sink = 0;
}
}
else
@@ -126,6 +156,7 @@ struct mask_info
tmp.x = 1;
tmp.left = -1;
tmp.right = 0;
tmp.sink = 0;
}
else if(str == "2" || str == "b")
{
@@ -134,6 +165,7 @@ struct mask_info
tmp.x = seqlen_k - seqlen_q + 1;
tmp.left = -1;
tmp.right = 0;
tmp.sink = 0;
}
else
{

77
example/ck_tile/01_fmha/script/correct_test_fwd_sink.sh Normal file
View File

@@ -0,0 +1,77 @@
#!/bin/bash
# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
# SPDX-License-Identifier: MIT
# TODO: run this script from CK root or build directory
EXE="$(find . -name tile_example_fmha_fwd -type f | head -n 1)"
KNAME=1
export CK_WARMUP=0
export CK_REPEAT=1
COMMON_ARGS='-v=1 -warmup=0 -repeat=1'
# mode=0
# export HIP_VISIBLE_DEVICES=4
TEST_SPLITKV=0
TEST_APPENDKV=0
# options:
# -s: run splitkv tests
# -a: run appendkv tests
while getopts ":sa" opt; do
case "${opt}" in
s)
TEST_SPLITKV=1
;;
a)
TEST_APPENDKV=1
;;
*)
;;
esac
done
run_fp16_bf16_tests() {
local NUM_SPLITS="1"
local PAGE_BLOCK_SIZE="0"
local CACHE_BATCH_IDX="0"
if [ $TEST_SPLITKV -eq 1 ] ; then
NUM_SPLITS="$NUM_SPLITS 2 3"
PAGE_BLOCK_SIZE="$PAGE_BLOCK_SIZE 128"
CACHE_BATCH_IDX="$CACHE_BATCH_IDX 1"
fi
for prec in "fp16"; do
for mode in 1 0 ; do
for perm in 0 1 ; do
for vlayout in "r" "c" ; do
for batch in 1 4; do
for head in 1; do
for h_k in 1; do
for q_seq in 128 512 ; do
for kv_seq in 128 1024; do
for hdim in 32 64 128 256; do #256
for lse in 0 1 ; do
for bias in "e" ; do
for p_drop in 0.0 0.2; do # 0.0
for mask in "t:2,0,4" "b:1,0,2"; do
for num_splits in $NUM_SPLITS ; do
for page_block_size in $PAGE_BLOCK_SIZE ; do
for cache_batch_idx in $CACHE_BATCH_IDX ; do
# $EXE -prec=$prec -mode=$mode -b=1 -h=1 -d=$hdim -s=1024 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=$batch -h=$head -h_k=$h_k -d=16 -d_v=$hdim -s=$q_seq -s_k=$kv_seq -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS -mask=$mask
done ; done ; done ; done ; done
done ; done ; done ; done ; done
done ; done ; done ; done ; done
done ; done
}
set -x
run_fp16_bf16_tests
set +x

1
example/ck_tile/01_fmha/script/run_full_test.sh
View File

@@ -39,6 +39,7 @@ function print_log_header(){
#run verification tests
time example/ck_tile/01_fmha/script/smoke_test_fwd.sh
time example/ck_tile/01_fmha/script/smoke_test_bwd.sh
time example/ck_tile/01_fmha/script/smoke_test_fwd_sink.sh
#run performance benchmarks
export fmha_fwd_log="perf_fmha_fwd_$GPU_arch.log"

86
example/ck_tile/01_fmha/script/smoke_test_fwd_sink.sh Executable file
View File

@@ -0,0 +1,86 @@
#!/bin/bash
# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
# SPDX-License-Identifier: MIT
# TODO: run this script from CK root or build directory
#EXE="/code/composable_kernel/build/bin/tile_example_fmha_fwd"
set -euo pipefail
SCRIPT_DIR=$(cd $(dirname "${BASH_SOURCE[0]}") && pwd)
EXE_NAME=tile_example_fmha_fwd
EXE="$(find . -name $EXE_NAME -type f | head -n 1)"
KNAME=1
GPU_arch=$GPU_arch
if [ -z "$GPU_arch" ] ; then
GPU_arch=$(rocminfo | grep -E 'Name:\s+gfx' | head -n1 | awk '{print $2}')
fi
set -x
COMMON_ARGS='-v=1 -warmup=0 -repeat=1'
$EXE -prec=fp16 -mode=0 -b=1 -h=1 -d=128 -d_v=128 -s=512 -s_k=512 -bias=n -lse=0 -iperm=0 -operm=0 -vlayout=r -num_splits=1 -page_block_size=128 -cache_batch_idx=0 -kname=1 -v=1 -warmup=0 -repeat=1 -mask=t:2,0,2
# window_size[2,0], sink_size = 2
# x=1/y=3
# 1 * * * * * * * 1 * * * * * * *
# 1 1 * * * * * * 1 1 * * * * * *
# 1 1 1 * * * * * ----> 1 1 1 * * * * *
# * 1 1 1 * * * * 1 1 1 1 * * * *
# * * 1 1 1 * * * 1 1 1 1 1 * * *
# * * * 1 1 1 * * 1 1 * 1 1 1 * *
# * * * * 1 1 1 * 1 1 * * 1 1 1 *
# * * * * * 1 1 1 1 1 * * * 1 1 1
# l=2/r=0(tl) l=2/r=0/s=2(tl)
$EXE -prec=fp16 -mode=0 -b=1 -h=1 -d=128 -d_v=128 -s=1024 -s_k=1024 -bias=n -lse=0 -iperm=0 -operm=0 -vlayout=r -num_splits=1 -page_block_size=128 -cache_batch_idx=0 -kname=1 -v=1 -warmup=0 -repeat=1 -mask=t:0,3,2 #-mask=b:3,0,2
# x=4/y=1
# 1 1 1 1 * * * * 1 1 1 1 * * * *
# * 1 1 1 1 * * * 1 1 1 1 1 * * *
# * * 1 1 1 1 * * ----> 1 1 1 1 1 1 * *
# * * * 1 1 1 1 * 1 1 * 1 1 1 1 *
# * * * * 1 1 1 1 1 1 * * 1 1 1 1
# l=0/r=3(tl) l=0/r=3/s=2(tl)
# l=3/r=0(br) l=3/r=0/s=2(br)
$EXE -prec=fp16 -mode=0 -b=1 -h=1 -d=128 -d_v=128 -s=4096 -s_k=4096 -bias=n -lse=0 -iperm=0 -operm=0 -vlayout=r -num_splits=1 -page_block_size=128 -cache_batch_idx=0 -kname=1 -v=1 -warmup=0 -repeat=1 -mask=b:1,0,2
# x=4/y=-1
# * * 1 1 * * * * 1 1 1 1 * * * *
# * * * 1 1 * * * 1 1 * 1 1 * * *
# * * * * 1 1 * * ----> 1 1 * * 1 1 * *
# * * * * * 1 1 * 1 1 * * * 1 1 *
# * * * * * * 1 1 1 1 * * * * 1 1
# l=1/r=0(br) l=1/r=0/s=2(br)
$EXE -prec=fp16 -mode=1 -b=1 -h=1 -d=128 -d_v=128 -s=8192 -s_k=8192 -bias=n -lse=0 -iperm=0 -operm=0 -vlayout=r -num_splits=1 -page_block_size=128 -cache_batch_idx=0 -kname=1 -v=1 -warmup=0 -repeat=1 -mask=b:2,0,2
# x=-1/y=5
# * * * * * * * * * * * *
# * * * * * * * * * * * *
# 1 * * * * * 1 * * * * *
# 1 1 * * * * 1 1 * * * *
# 1 1 1 * * * ----> 1 1 1 * * *
# * 1 1 1 * * 1 1 1 1 * *
# * * 1 1 1 * 1 1 1 1 1 *
# * * * 1 1 1 1 1 * 1 1 1
# l=2/r=0(br) l=2/r=0/s=2(br)
$EXE -prec=fp16 -mode=1 -b=1 -h=1 -d=128 -d_v=128 -s=16384 -s_k=16384 -bias=n -lse=0 -iperm=0 -operm=0 -vlayout=r -num_splits=1 -page_block_size=128 -cache_batch_idx=0 -kname=1 -v=1 -warmup=0 -repeat=1 -mask=b:-1,1,2
# x=-1/y=8
# * * * * * * * * * *
# * * * * * * * * * *
# 1 * * * * ----> 1 * * * *
# 1 1 * * * 1 1 * * *
# 1 1 1 * * 1 1 1 * *
# 1 1 1 1 * 1 1 1 1 *
# 1 1 1 1 1 1 1 1 1 1
# 1 1 1 1 1 1 1 1 1 1
# l=2/r=0(br) l=2/r=0/s=2(br)

60
example/ck_tile/03_gemm/gemm_utils.hpp
View File

@@ -12,40 +12,6 @@
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/utility/json_dump.hpp"
template <typename PrecType, ck_tile::index_t M_Warp_Tile>
constexpr ck_tile::index_t get_k_warp_tile()
{
#if defined(CK_GFX950_SUPPORT)
constexpr bool is_8bit_float =
std::is_same_v<PrecType, ck_tile::fp8_t> || std::is_same_v<PrecType, ck_tile::bf8_t>;
if constexpr(M_Warp_Tile == 32)
return is_8bit_float ? 64 : 16;
else
return is_8bit_float ? 128 : 32;
#else
if constexpr(M_Warp_Tile == 32)
return 16;
else
return 32;
#endif
}
template <typename PrecType, ck_tile::index_t M_Warp_Tile>
constexpr ck_tile::index_t get_k_warp_tile_flatmm()
{
#if defined(CK_GFX950_SUPPORT)
if constexpr(M_Warp_Tile == 32)
return sizeof(PrecType) == 2 ? 16 : 64;
else
return sizeof(PrecType) == 2 ? 32 : 128;
#else
if constexpr(M_Warp_Tile == 32)
return sizeof(PrecType) == 2 ? 16 : 32;
else
return sizeof(PrecType) == 2 ? 32 : 64;
#endif
}
struct GemmConfigBase
{
static constexpr bool kPadM = false;
@@ -122,7 +88,8 @@ struct GemmConfigComputeV3 : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr bool DoubleSmemBuffer = false;
static constexpr ck_tile::GemmPipeline Pipeline = ck_tile::GemmPipeline::COMPUTE_V3;
@@ -141,7 +108,8 @@ struct GemmConfigComputeV3_1 : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 32;
static constexpr ck_tile::index_t N_Warp_Tile = 32;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr bool DoubleSmemBuffer = false;
static constexpr ck_tile::GemmPipeline Pipeline = ck_tile::GemmPipeline::COMPUTE_V3;
@@ -160,7 +128,8 @@ struct GemmConfigComputeV3_2 : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr bool DoubleSmemBuffer = false;
static constexpr ck_tile::GemmPipeline Pipeline = ck_tile::GemmPipeline::COMPUTE_V3;
@@ -204,7 +173,8 @@ struct GemmConfigComputeV4 : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 32;
static constexpr ck_tile::index_t N_Warp_Tile = 32;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr bool DoubleSmemBuffer = true;
static constexpr ck_tile::GemmPipeline Pipeline = ck_tile::GemmPipeline::COMPUTE_V4;
@@ -223,7 +193,8 @@ struct GemmConfigComputeV4_1 : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 32;
static constexpr ck_tile::index_t N_Warp_Tile = 32;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr bool DoubleSmemBuffer = true;
static constexpr ck_tile::GemmPipeline Pipeline = ck_tile::GemmPipeline::COMPUTE_V4;
@@ -242,7 +213,8 @@ struct GemmConfigComputeV5 : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 32;
static constexpr ck_tile::index_t N_Warp_Tile = 32;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr bool DoubleSmemBuffer = false;
static constexpr ck_tile::GemmPipeline Pipeline = ck_tile::GemmPipeline::COMPUTE_V5;
@@ -282,7 +254,8 @@ struct GemmConfigPreshuffleDecode : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile_flatmm<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile, true>();
static constexpr int kBlockPerCu = 1;
static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Default;
@@ -306,7 +279,8 @@ struct GemmConfigPreshufflePrefill : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile_flatmm<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile, true>();
static constexpr int kBlockPerCu = 2;
static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Default;
@@ -459,7 +433,7 @@ struct PipelineTypeTraits<ck_tile::GemmPipeline::PRESHUFFLE_V2>
ck_tile::BaseWeightPreshufflePipelineAGmemBGmemCRegV2<PipelineProblem>;
};
auto create_args()
inline auto create_args()
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("m", "3840", "m dimension")

4
example/ck_tile/03_gemm/run_gemm_example.inc
View File

@@ -197,8 +197,8 @@ bool do_verify(const ck_tile::HostTensor<CDataType>& c_m_n_dev_result,
return pass;
}
std::tuple<ck_tile::index_t, ck_tile::index_t, ck_tile::index_t>
parse_gemm_size(ck_tile::ArgParser& arg_parser)
std::tuple<ck_tile::index_t, ck_tile::index_t, ck_tile::index_t> inline parse_gemm_size(
ck_tile::ArgParser& arg_parser)
{
ck_tile::index_t M = arg_parser.get_int("m");
ck_tile::index_t N = arg_parser.get_int("n");

43
example/ck_tile/03_gemm/universal_gemm_invoker.hpp
View File

@@ -63,25 +63,30 @@ struct UniversalInvoker
const auto Run = [&](const auto memory_operation_) {
constexpr auto memory_operation = memory_operation_.value;
using GemmEpilogue = ck_tile::CShuffleEpilogue<
ck_tile::CShuffleEpilogueProblem<ADataType,
BDataType,
DsDataType,
AccDataType,
CDataType,
DsLayout,
ELayout,
CDEElementWise,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock,
GemmConfig::M_Warp,
GemmConfig::N_Warp,
GemmConfig::M_Warp_Tile,
GemmConfig::N_Warp_Tile,
GemmConfig::K_Warp_Tile,
UniversalGemmProblem::TransposeC,
memory_operation,
GemmConfig::NumWaveGroups>>;
using GemmEpilogue = ck_tile::CShuffleEpilogue<ck_tile::CShuffleEpilogueProblem<
ADataType,
BDataType,
DsDataType,
AccDataType,
CDataType,
DsLayout,
ELayout,
CDEElementWise,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock,
GemmConfig::M_Warp,
GemmConfig::N_Warp,
GemmConfig::M_Warp_Tile,
GemmConfig::N_Warp_Tile,
GemmConfig::K_Warp_Tile,
UniversalGemmProblem::TransposeC,
memory_operation,
GemmConfig::NumWaveGroups,
false, /*FixedVectorSize_*/
1, /*VectorSizeC_*/
false, /*TiledMMAPermuteN_*/
1, /*BlockedXDLN_PerWarp_*/
GemmConfig::DoubleSmemBuffer /*DoubleSmemBuffer*/>>;
using Kernel = ck_tile::GemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
auto kargs = Kernel::MakeKernelArgs(args);

49
example/ck_tile/17_grouped_gemm/grouped_gemm.hpp
View File

@@ -11,40 +11,6 @@
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/utility/json_dump.hpp"
template <typename PrecType, ck_tile::index_t M_Warp_Tile>
constexpr ck_tile::index_t get_k_warp_tile()
{
#if defined(CK_GFX950_SUPPORT)
constexpr bool is_8bit_float =
std::is_same_v<PrecType, ck_tile::fp8_t> || std::is_same_v<PrecType, ck_tile::bf8_t>;
if constexpr(M_Warp_Tile == 32)
return is_8bit_float ? 64 : 16;
else
return is_8bit_float ? 128 : 32;
#else
if constexpr(M_Warp_Tile == 32)
return 16;
else
return 32;
#endif
}
template <typename PrecType, ck_tile::index_t M_Warp_Tile>
constexpr ck_tile::index_t get_k_warp_tile_flatmm()
{
#if defined(CK_GFX950_SUPPORT)
if constexpr(M_Warp_Tile == 32)
return sizeof(PrecType) == 2 ? 16 : 64;
else
return sizeof(PrecType) == 2 ? 32 : 128;
#else
if constexpr(M_Warp_Tile == 32)
return sizeof(PrecType) == 2 ? 16 : 32;
else
return sizeof(PrecType) == 2 ? 32 : 64;
#endif
}
template <typename DataType>
struct GemmTypeConfig;
@@ -111,7 +77,8 @@ struct GemmConfigComputeV3_2 : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 32;
static constexpr ck_tile::index_t N_Warp_Tile = 32;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr bool DoubleSmemBuffer = false;
static constexpr ck_tile::GemmPipeline Pipeline = ck_tile::GemmPipeline::COMPUTE_V3;
@@ -134,7 +101,8 @@ struct GemmConfigComputeV4 : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 32;
static constexpr ck_tile::index_t N_Warp_Tile = 32;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr bool DoubleSmemBuffer = true;
static constexpr ck_tile::GemmPipeline Pipeline = ck_tile::GemmPipeline::COMPUTE_V4;
@@ -157,7 +125,8 @@ struct GemmConfigComputeV4_V2 : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr bool DoubleSmemBuffer = true;
static constexpr ck_tile::GemmPipeline Pipeline = ck_tile::GemmPipeline::COMPUTE_V4;
@@ -178,7 +147,8 @@ struct GemmConfigPreshuffleDecode : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile_flatmm<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile, true>();
static constexpr bool kPadK = true;
@@ -203,7 +173,8 @@ struct GemmConfigPreshufflePrefill : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile_flatmm<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile, true>();
static constexpr int kBlockPerCu = 2;
static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Default;

18
example/ck_tile/17_grouped_gemm/grouped_gemm_multi_d.hpp
View File

@@ -11,24 +11,6 @@
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/utility/json_dump.hpp"
template <typename PrecType, ck_tile::index_t M_Warp_Tile>
constexpr ck_tile::index_t get_k_warp_tile()
{
#if defined(CK_GFX950_SUPPORT)
constexpr bool is_8bit_float =
std::is_same_v<PrecType, ck_tile::fp8_t> || std::is_same_v<PrecType, ck_tile::bf8_t>;
if constexpr(M_Warp_Tile == 32)
return is_8bit_float ? 64 : 16;
else
return is_8bit_float ? 128 : 32;
#else
if constexpr(M_Warp_Tile == 32)
return 16;
else
return 32;
#endif
}
struct GemmConfigBase
{
static constexpr bool kPadM = false;

39
example/ck_tile/17_grouped_gemm/quant_grouped_gemm.hpp
View File

@@ -10,40 +10,6 @@
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/ops/elementwise/unary_element_wise_operation.hpp"
template <typename PrecType, ck_tile::index_t M_Warp_Tile>
constexpr ck_tile::index_t get_k_warp_tile()
{
#if defined(CK_GFX950_SUPPORT)
constexpr bool is_8bit_float =
std::is_same_v<PrecType, ck_tile::fp8_t> || std::is_same_v<PrecType, ck_tile::bf8_t>;
if constexpr(M_Warp_Tile == 32)
return is_8bit_float ? 64 : 16;
else
return is_8bit_float ? 128 : 32;
#else
if constexpr(M_Warp_Tile == 32)
return 16;
else
return 32;
#endif
}
template <typename PrecType, ck_tile::index_t M_Warp_Tile>
constexpr ck_tile::index_t get_k_from_preshuffled_warp_tile()
{
#if defined(CK_GFX950_SUPPORT)
if constexpr(M_Warp_Tile == 32)
return sizeof(PrecType) == 2 ? 16 : 64;
else
return sizeof(PrecType) == 2 ? 32 : 128;
#else
if constexpr(M_Warp_Tile == 32)
return sizeof(PrecType) == 2 ? 16 : 32;
else
return sizeof(PrecType) == 2 ? 32 : 64;
#endif
}
template <typename DataType>
struct GemmTypeConfig;
@@ -100,7 +66,8 @@ struct GemmConfigComputeV3_2 : public GemmConfigBase<Persistent>
static constexpr ck_tile::index_t M_Warp_Tile = 32;
static constexpr ck_tile::index_t N_Warp_Tile = 32;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
};
template <typename PrecType, bool Persistent>
@@ -117,7 +84,7 @@ struct GemmConfigPreshuffleB_Bquant_prefill : public GemmConfigBase<Persistent>
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile =
get_k_from_preshuffled_warp_tile<PrecType, M_Warp_Tile>();
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile, true>();
static constexpr bool PreshuffleB = true;
static constexpr bool DoubleSmemBuffer = true;

50
example/ck_tile/38_block_scale_gemm/gemm_utils.hpp
View File

@@ -24,39 +24,6 @@ inline size_t hash_multiple_strings(const std::vector<std::string>& inputs)
return combined_hash;
}
template <typename PrecType, ck_tile::index_t M_Warp_Tile>
constexpr ck_tile::index_t get_k_warp_tile()
{
#if defined(CK_GFX950_SUPPORT)
constexpr bool is_8bit_float =
std::is_same_v<PrecType, ck_tile::fp8_t> || std::is_same_v<PrecType, ck_tile::bf8_t>;
if constexpr(M_Warp_Tile == 32)
return is_8bit_float ? 64 : 16;
else
return is_8bit_float ? 128 : 32;
#else
if constexpr(M_Warp_Tile == 32)
return 16;
else
return 32;
#endif
}
template <typename PrecType, ck_tile::index_t M_Warp_Tile>
constexpr ck_tile::index_t get_k_from_preshuffled_warp_tile()
{
#if defined(CK_GFX950_SUPPORT)
if constexpr(M_Warp_Tile == 32)
return sizeof(PrecType) == 2 ? 16 : 64;
else
return sizeof(PrecType) == 2 ? 32 : 128;
#else
if constexpr(M_Warp_Tile == 32)
return sizeof(PrecType) == 2 ? 16 : 32;
else
return sizeof(PrecType) == 2 ? 32 : 64;
#endif
}
template <typename Layout>
static constexpr inline auto is_row_major(Layout layout_)
{
@@ -91,7 +58,7 @@ struct GemmConfigBase
{
static constexpr bool kPadM = false;
static constexpr bool kPadN = false;
static constexpr bool kPadK = false;
static constexpr bool kPadK = true;
static constexpr bool PermuteA = false;
static constexpr bool PermuteB = false;
@@ -124,7 +91,8 @@ struct GemmConfigQuantDecode : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
};
template <typename PrecType>
@@ -140,7 +108,8 @@ struct GemmConfigRowColQuant : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
};
template <typename PrecType>
@@ -157,7 +126,7 @@ struct GemmConfigPreshuffleQuantDecode : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile =
get_k_from_preshuffled_warp_tile<PrecType, M_Warp_Tile>();
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile, true>();
static constexpr bool PreshuffleQuant = true;
};
@@ -176,7 +145,7 @@ struct GemmConfigPreshuffleB_BQuant_Decode : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile =
get_k_from_preshuffled_warp_tile<PrecType, M_Warp_Tile>();
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile, true>();
static constexpr bool PreshuffleB = true;
static constexpr bool DoubleSmemBuffer = true;
@@ -206,7 +175,7 @@ struct GemmConfigPreshuffleB_BQuant_Prefill : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile =
get_k_from_preshuffled_warp_tile<PrecType, M_Warp_Tile>();
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile, true>();
static constexpr bool PreshuffleB = true;
static constexpr bool DoubleSmemBuffer = true;
@@ -236,7 +205,8 @@ struct GemmConfigQuantPrefill : public GemmConfigBase
static constexpr ck_tile::index_t M_Warp_Tile = 16;
static constexpr ck_tile::index_t N_Warp_Tile = 16;
static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
static constexpr ck_tile::index_t K_Warp_Tile =
ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile>();
};
template <typename PrecType>

14
example/ck_tile/38_block_scale_gemm/run_gemm_quant_example.inc
View File

@@ -391,20 +391,12 @@ int run_gemm_example_with_layouts(const ck_tile::ArgParser& arg_parser,
ck_tile::index_t N = arg_parser.get_int("n");
ck_tile::index_t K = arg_parser.get_int("k");
if constexpr(QuantMode == ck_tile::QuantType::AQuantGrouped ||
QuantMode == ck_tile::QuantType::BQuantGrouped)
{
if(K % QuantGroupSize::kK != 0)
{
throw std::runtime_error(
"K must be aligned with QuantGroupSize for AQuantGrouped/BQuantGrouped mode");
}
}
ck_tile::index_t AQK, BQK, BQN = 0;
if constexpr(QuantMode == ck_tile::QuantType::AQuantGrouped)
{
AQK = K / QuantGroupSize::kK; // Group quantization: AQK = K / GroupSize
BQK = 0; // No B quantization
AQK = ck_tile::integer_divide_ceil(
K, QuantGroupSize::kK); // Group quantization: AQK = K / GroupSize
BQK = 0; // No B quantization
}
else if constexpr(QuantMode == ck_tile::QuantType::BQuantGrouped)
{

56
example/ck_tile/40_streamk_gemm/gemm_utils.hpp
View File

@@ -7,46 +7,46 @@
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"
struct GemmConfigBase
struct GemmConfigurationBase
{
static constexpr bool kPadM = true;
static constexpr bool kPadN = true;
static constexpr bool kPadK = true;
static constexpr bool PAD_M = true;
static constexpr bool PAD_N = true;
static constexpr bool PAD_K = true;
static constexpr bool PermuteA = false;
static constexpr bool PermuteB = false;
static constexpr bool PERMUTE_A = false;
static constexpr bool PERMUTE_B = false;
static constexpr bool TransposeC = false;
static constexpr bool UseStructuredSparsity = false;
static constexpr bool TRANSPOSE_C = false;
static constexpr bool USE_STRUCTURED_SPARSITY = false;
static constexpr int kBlockPerCu = 1;
static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Intrawave;
static constexpr ck_tile::index_t NumWaveGroups = 1;
static constexpr bool Preshuffle = false;
static constexpr bool DoubleSmemBuffer = false;
static constexpr int BLOCK_PER_CU = 1;
static constexpr auto SCHEDULER = ck_tile::GemmPipelineScheduler::Intrawave;
static constexpr ck_tile::index_t NUM_WAVE_GROUPS = 1;
static constexpr bool PRESHUFFLE = false;
static constexpr bool DOUBLE_SMEM_BUFFER = false;
};
template <typename PrecType, bool Persistent_>
struct GemmConfigMemoryInterwave : public GemmConfigBase
template <typename PrecisionType, bool IsPersistent>
struct GemmConfigurationMemoryInterwave : public GemmConfigurationBase
{
static constexpr ck_tile::index_t M_Tile = 256;
static constexpr ck_tile::index_t N_Tile = 256;
static constexpr ck_tile::index_t K_Tile = 16;
static constexpr ck_tile::index_t M_TILE = 256;
static constexpr ck_tile::index_t N_TILE = 256;
static constexpr ck_tile::index_t K_TILE = 16;
static constexpr ck_tile::index_t M_Warp = 2;
static constexpr ck_tile::index_t N_Warp = 2;
static constexpr ck_tile::index_t K_Warp = 1;
static constexpr ck_tile::index_t M_WARP = 2;
static constexpr ck_tile::index_t N_WARP = 2;
static constexpr ck_tile::index_t K_WARP = 1;
static constexpr ck_tile::index_t M_Warp_Tile = 32;
static constexpr ck_tile::index_t N_Warp_Tile = 32;
static constexpr ck_tile::index_t K_Warp_Tile = sizeof(PrecType) == 2 ? 8 : 16;
static constexpr ck_tile::index_t M_WARP_TILE = 32;
static constexpr ck_tile::index_t N_WARP_TILE = 32;
static constexpr ck_tile::index_t K_WARP_TILE = sizeof(PrecisionType) == 2 ? 8 : 16;
static constexpr bool Persistent = Persistent_;
static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Intrawave;
static constexpr bool PERSISTENT = IsPersistent;
static constexpr auto SCHEDULER = ck_tile::GemmPipelineScheduler::Intrawave;
};
template <typename ADataType_, typename BDataType_ = ADataType_, typename CDataType_ = ADataType_>
struct StreamKGemmTypeConfig
struct StreamKGemmTypeConfiguration
{
using ADataType = ADataType_;
using BDataType = BDataType_;
@@ -54,7 +54,7 @@ struct StreamKGemmTypeConfig
using CDataType = CDataType_;
};
auto create_args(int argc, char* argv[])
auto createArgs(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("m", "512", "m dimension")

380
example/ck_tile/40_streamk_gemm/run_gemm_example.inc
View File

@@ -12,31 +12,35 @@ static constexpr inline auto is_row_major(Layout)
}
template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
auto calculate_rtol_atol(const ck_tile::index_t K,
const ck_tile::index_t kbatch,
const float max_accumulated_value)
auto calculateRtolAtol(const ck_tile::index_t k_dim,
const ck_tile::index_t k_batch,
const float max_accumulated_value)
{
using ComputeType =
std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
// Calculate thresholds
const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
ck_tile::integer_divide_ceil(K, kbatch));
const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
const auto relative_tolerance =
ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
ck_tile::integer_divide_ceil(k_dim, k_batch));
const auto absolute_tolerance =
ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
max_accumulated_value / k_batch, ck_tile::integer_divide_ceil(k_dim, k_batch));
// Calculate error due to multiple WGs working in the same C macro tile
const auto rtol_split_k =
ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
max_accumulated_value, kbatch);
const auto relative_tolerance_split_k =
ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(k_batch);
const auto absolute_tolerance_split_k =
ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(max_accumulated_value,
k_batch);
// Use higher threshold
return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
return ck_tile::make_tuple(std::max(relative_tolerance, relative_tolerance_split_k),
std::max(absolute_tolerance, absolute_tolerance_split_k));
}
template <typename GemmConfig,
template <typename GemmConfiguration,
typename ADataType,
typename BDataType,
typename DsDataType,
typename AccDataType,
typename AccumulatorDataType,
typename CDataType,
typename ALayout,
typename BLayout,
@@ -45,102 +49,107 @@ template <typename GemmConfig,
typename CDEElementWise = ck_tile::element_wise::PassThrough,
ck_tile::StreamKReductionStrategy ReductionStrategy>
std::tuple<float, ck_tile::index_t> gemm(const ck_tile::StreamKHostArgs& args,
const ck_tile::stream_config& s);
const ck_tile::stream_config& stream_config);
template <typename GemmConfig,
template <typename GemmConfiguration,
typename ADataType,
typename BDataType,
typename DsDataType,
typename AccDataType,
typename AccumulatorDataType,
typename CDataType,
typename ALayout,
typename BLayout,
typename DsLayout,
typename CLayout,
typename CDEElementWise = ck_tile::element_wise::PassThrough>
std::tuple<float, ck_tile::index_t>
invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
ck_tile::DeviceMem& b_k_n_dev_buf,
ck_tile::DeviceMem& c_m_n_dev_buf,
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,
int n_warmup,
int n_repeat,
bool flush_cache,
ck_tile::StreamKReductionStrategy reduction_strategy)
std::tuple<float, ck_tile::index_t> invokeGemm(ck_tile::DeviceMem& a_m_k_device_memory,
ck_tile::DeviceMem& b_k_n_device_memory,
ck_tile::DeviceMem& c_m_n_device_memory,
ck_tile::index_t m_dim,
ck_tile::index_t n_dim,
ck_tile::index_t k_dim,
ck_tile::index_t stride_a,
ck_tile::index_t stride_b,
ck_tile::index_t stride_c,
int warmup_iterations,
int repeat_iterations,
bool flush_cache,
ck_tile::StreamKReductionStrategy reduction_strategy)
{
ck_tile::StreamKHostArgs args{a_m_k_dev_buf.GetDeviceBuffer(),
b_k_n_dev_buf.GetDeviceBuffer(),
c_m_n_dev_buf.GetDeviceBuffer(),
M,
N,
K,
stride_A,
stride_B,
stride_C};
ck_tile::StreamKHostArgs args{a_m_k_device_memory.GetDeviceBuffer(),
b_k_n_device_memory.GetDeviceBuffer(),
c_m_n_device_memory.GetDeviceBuffer(),
m_dim,
n_dim,
k_dim,
stride_a,
stride_b,
stride_c};
std::tuple<float, ck_tile::index_t> ave_time_and_batch;
std::tuple<float, ck_tile::index_t> average_time_and_batch;
if(reduction_strategy == ck_tile::StreamKReductionStrategy::Atomic)
{
ave_time_and_batch = gemm<GemmConfig,
ADataType,
BDataType,
DsDataType,
AccDataType,
CDataType,
ALayout,
BLayout,
DsLayout,
CLayout,
CDEElementWise,
ck_tile::StreamKReductionStrategy::Atomic>(
args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat, true, flush_cache});
average_time_and_batch = gemm<GemmConfiguration,
ADataType,
BDataType,
DsDataType,
AccumulatorDataType,
CDataType,
ALayout,
BLayout,
DsLayout,
CLayout,
CDEElementWise,
ck_tile::StreamKReductionStrategy::Atomic>(
args,
ck_tile::stream_config{
nullptr, true, 1, warmup_iterations, repeat_iterations, true, flush_cache});
}
else /*Reduction*/
{
ave_time_and_batch = gemm<GemmConfig,
ADataType,
BDataType,
DsDataType,
AccDataType,
CDataType,
ALayout,
BLayout,
DsLayout,
CLayout,
CDEElementWise,
ck_tile::StreamKReductionStrategy::Reduction>(
args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat, true, flush_cache});
average_time_and_batch = gemm<GemmConfiguration,
ADataType,
BDataType,
DsDataType,
AccumulatorDataType,
CDataType,
ALayout,
BLayout,
DsLayout,
CLayout,
CDEElementWise,
ck_tile::StreamKReductionStrategy::Reduction>(
args,
ck_tile::stream_config{
nullptr, true, 1, warmup_iterations, repeat_iterations, true, flush_cache});
}
return ave_time_and_batch;
return average_time_and_batch;
}
template <typename CDataType>
bool do_verify(const ck_tile::HostTensor<CDataType>& c_m_n_dev_result,
const ck_tile::HostTensor<CDataType>& c_m_n_ref,
const ck_tile::tuple<double, double>& rtol_atol,
const char* variant)
bool doVerify(const ck_tile::HostTensor<CDataType>& c_m_n_device_result,
const ck_tile::HostTensor<CDataType>& c_m_n_reference,
const ck_tile::tuple<double, double>& relative_absolute_tolerances,
const char* variant)
{
bool pass = ck_tile::check_err(c_m_n_dev_result,
c_m_n_ref,
bool pass = ck_tile::check_err(c_m_n_device_result,
c_m_n_reference,
"Error: Incorrect results!",
rtol_atol.at(ck_tile::number<0>{}),
rtol_atol.at(ck_tile::number<1>{}));
relative_absolute_tolerances.at(ck_tile::number<0>{}),
relative_absolute_tolerances.at(ck_tile::number<1>{}));
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{}) << std::endl;
std::cout << "Relative error threshold: "
<< relative_absolute_tolerances.at(ck_tile::number<0>{})
<< " Absolute error threshold: "
<< relative_absolute_tolerances.at(ck_tile::number<1>{}) << std::endl;
std::cout << "The " << variant << " verification result is:" << (pass ? "correct" : "fail")
<< std::endl;
return pass;
}
ck_tile::StreamKReductionStrategy get_reduction_strategy_value(const std::string& strategy)
ck_tile::StreamKReductionStrategy getReductionStrategyValue(const std::string& strategy)
{
if(strategy == "atomic")
{
@@ -156,172 +165,169 @@ ck_tile::StreamKReductionStrategy get_reduction_strategy_value(const std::string
}
}
template <typename GemmConfig,
typename TypeConfig,
template <typename GemmConfiguration,
typename TypeConfiguration,
typename ALayout,
typename BLayout,
typename CLayout>
int run_gemm_example_with_layouts(int argc,
char* argv[],
const ALayout a_layout = ALayout{},
const BLayout b_layout = BLayout{},
[[maybe_unused]] const CLayout c_layout = CLayout{})
int runGemmExampleWithLayouts(int argc,
char* argv[],
const ALayout a_layout = ALayout{},
const BLayout b_layout = BLayout{},
[[maybe_unused]] const CLayout c_layout = CLayout{})
{
auto [result, arg_parser] = create_args(argc, argv);
auto [result, arg_parser] = createArgs(argc, argv);
if(!result)
return -1;
static_assert(!GemmConfig::Preshuffle, "Not implemented");
static_assert(!GemmConfig::UseStructuredSparsity, "Not implemented");
static_assert(!GemmConfig::PermuteA, "Not implemented");
static_assert(!GemmConfig::PermuteB, "Not implemented");
static_assert(!GemmConfiguration::PRESHUFFLE, "Not implemented");
static_assert(!GemmConfiguration::USE_STRUCTURED_SPARSITY, "Not implemented");
static_assert(!GemmConfiguration::PERMUTE_A, "Not implemented");
static_assert(!GemmConfiguration::PERMUTE_B, "Not implemented");
using ADataType = typename TypeConfig::ADataType;
using BDataType = typename TypeConfig::BDataType;
using AccDataType = typename TypeConfig::AccDataType;
using CDataType = typename TypeConfig::CDataType;
using ADataType = typename TypeConfiguration::ADataType;
using BDataType = typename TypeConfiguration::BDataType;
using AccumulatorDataType = typename TypeConfiguration::AccDataType;
using CDataType = typename TypeConfiguration::CDataType;
ck_tile::index_t M = arg_parser.get_int("m");
ck_tile::index_t N = arg_parser.get_int("n");
ck_tile::index_t K = arg_parser.get_int("k");
ck_tile::index_t m_dim = arg_parser.get_int("m");
ck_tile::index_t n_dim = arg_parser.get_int("n");
ck_tile::index_t k_dim = arg_parser.get_int("k");
ck_tile::index_t stride_A = arg_parser.get_int("stride_a");
ck_tile::index_t stride_B = arg_parser.get_int("stride_b");
ck_tile::index_t stride_C = arg_parser.get_int("stride_c");
int n_warmup = arg_parser.get_int("warmup");
int n_repeat = arg_parser.get_int("repeat");
ck_tile::index_t stride_a = arg_parser.get_int("stride_a");
ck_tile::index_t stride_b = arg_parser.get_int("stride_b");
ck_tile::index_t stride_c = arg_parser.get_int("stride_c");
int warmup_iterations = arg_parser.get_int("warmup");
int repeat_iterations = arg_parser.get_int("repeat");
ck_tile::index_t init_method = arg_parser.get_int("init");
bool flush_cache = arg_parser.get_bool("flush_cache");
ck_tile::StreamKReductionStrategy reduction_strategy =
get_reduction_strategy_value(arg_parser.get_str("reduction_strategy"));
getReductionStrategyValue(arg_parser.get_str("reduction_strategy"));
stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));
stride_a = ck_tile::get_default_stride(m_dim, k_dim, stride_a, is_row_major(a_layout));
stride_b = ck_tile::get_default_stride(k_dim, n_dim, stride_b, is_row_major(b_layout));
stride_c = ck_tile::get_default_stride(m_dim, n_dim, stride_c, is_row_major(CLayout{}));
ck_tile::HostTensor<ADataType> a_m_k(
ck_tile::host_tensor_descriptor(M, K, stride_A, is_row_major(a_layout)));
ck_tile::HostTensor<BDataType> b_k_n(
ck_tile::host_tensor_descriptor(K, N, stride_B, is_row_major(b_layout)));
ck_tile::HostTensor<CDataType> c_m_n_dev_result(
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
ck_tile::HostTensor<ADataType> a_m_k_host(
ck_tile::host_tensor_descriptor(m_dim, k_dim, stride_a, is_row_major(a_layout)));
ck_tile::HostTensor<BDataType> b_k_n_host(
ck_tile::host_tensor_descriptor(k_dim, n_dim, stride_b, is_row_major(b_layout)));
ck_tile::HostTensor<CDataType> c_m_n_device_result(
ck_tile::host_tensor_descriptor(m_dim, n_dim, stride_c, is_row_major(CLayout{})));
if(init_method == 0)
{
ck_tile::FillUniformDistribution<ADataType>{-5.f, 5.f}(a_m_k);
ck_tile::FillUniformDistribution<BDataType>{-5.f, 5.f}(b_k_n);
ck_tile::FillUniformDistribution<ADataType>{-5.f, 5.f}(a_m_k_host);
ck_tile::FillUniformDistribution<BDataType>{-5.f, 5.f}(b_k_n_host);
}
else if(init_method == 1)
{
ck_tile::FillMonotonicSeq<ADataType>{}(a_m_k);
ck_tile::FillMonotonicSeq<BDataType>{}(b_k_n);
ck_tile::FillMonotonicSeq<ADataType>{}(a_m_k_host);
ck_tile::FillMonotonicSeq<BDataType>{}(b_k_n_host);
}
else if(init_method == 2)
{
ck_tile::FillUniformDistribution<ADataType>{1.f, 1.f}(a_m_k);
ck_tile::FillUniformDistribution<BDataType>{1.f, 1.f}(b_k_n);
ck_tile::FillUniformDistribution<ADataType>{1.f, 1.f}(a_m_k_host);
ck_tile::FillUniformDistribution<BDataType>{1.f, 1.f}(b_k_n_host);
}
else
{
a_m_k.SetZero();
b_k_n.SetZero();
a_m_k_host.SetZero();
b_k_n_host.SetZero();
}
ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.get_element_space_size_in_bytes());
ck_tile::DeviceMem b_k_n_dev_buf(b_k_n.get_element_space_size_in_bytes());
ck_tile::DeviceMem c_m_n_dev_buf(c_m_n_dev_result.get_element_space_size_in_bytes());
ck_tile::DeviceMem a_m_k_device_memory(a_m_k_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem b_k_n_device_memory(b_k_n_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem c_m_n_device_memory(c_m_n_device_result.get_element_space_size_in_bytes());
a_m_k_dev_buf.ToDevice(a_m_k.data());
b_k_n_dev_buf.ToDevice(b_k_n.data());
c_m_n_dev_buf.SetZero();
c_m_n_dev_result.SetZero();
a_m_k_device_memory.ToDevice(a_m_k_host.data());
b_k_n_device_memory.ToDevice(b_k_n_host.data());
c_m_n_device_memory.SetZero();
c_m_n_device_result.SetZero();
auto [average_time, num_wgs_per_tile] = invokeGemm<GemmConfiguration,
ADataType,
BDataType,
ck_tile::tuple<>,
AccumulatorDataType,
CDataType,
ALayout,
BLayout,
ck_tile::tuple<>,
CLayout>(a_m_k_device_memory,
b_k_n_device_memory,
c_m_n_device_memory,
m_dim,
n_dim,
k_dim,
stride_a,
stride_b,
stride_c,
warmup_iterations,
repeat_iterations,
flush_cache,
reduction_strategy);
auto [ave_time, num_wgs_per_tile] = invoke_gemm<GemmConfig,
ADataType,
BDataType,
ck_tile::tuple<>,
AccDataType,
CDataType,
ALayout,
BLayout,
ck_tile::tuple<>,
CLayout>(a_m_k_dev_buf,
b_k_n_dev_buf,
c_m_n_dev_buf,
M,
N,
K,
stride_A,
stride_B,
stride_C,
n_warmup,
n_repeat,
flush_cache,
reduction_strategy);
c_m_n_device_memory.FromDevice(c_m_n_device_result.data());
c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_byte =
sizeof(ADataType) * M * K + sizeof(BDataType) * N * K + sizeof(CDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_byte / 1.E6 / ave_time;
std::cout << "Run Gemm kernel with M=" << M << " N=" << N << " K=" << K
<< " StrideA=" << stride_A << " StrideB=" << stride_B << " StrideC=" << stride_C
std::size_t flop = std::size_t(2) * m_dim * n_dim * k_dim;
std::size_t num_byte = sizeof(ADataType) * m_dim * k_dim + sizeof(BDataType) * n_dim * k_dim +
sizeof(CDataType) * m_dim * n_dim;
float tflops = static_cast<float>(flop) / 1.E9 / average_time;
float gb_per_sec = num_byte / 1.E6 / average_time;
std::cout << "Run Gemm kernel with M=" << m_dim << " N=" << n_dim << " K=" << k_dim
<< " StrideA=" << stride_a << " StrideB=" << stride_b << " StrideC=" << stride_c
<< " A_Layout=" << ALayout::name << " B_Layout=" << BLayout::name
<< " C_Layout=" << CLayout::name
<< " A_Type=" << ck_tile::DataTypeTraits<ADataType>::name
<< " B_Type=" << ck_tile::DataTypeTraits<BDataType>::name
<< " C_Type=" << ck_tile::DataTypeTraits<CDataType>::name
<< " reduction_strategy=" << arg_parser.get_str("reduction_strategy") << " "
<< " persistent_dp=" << arg_parser.get_str("persistent_dp") << " " << ave_time
<< " persistent_dp=" << arg_parser.get_str("persistent_dp") << " " << average_time
<< " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, " << std::endl;
bool pass = false;
// Memory on host to store gpu reference result
ck_tile::HostTensor<CDataType> c_m_n_ref(
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
c_m_n_ref.SetZero();
ck_tile::HostTensor<CDataType> c_m_n_reference(
ck_tile::host_tensor_descriptor(m_dim, n_dim, stride_c, is_row_major(CLayout{})));
c_m_n_reference.SetZero();
if(arg_parser.get_int("v") == 1) // Validate on the CPU
{
ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
a_m_k, b_k_n, c_m_n_ref);
ck_tile::reference_gemm<ADataType, BDataType, AccumulatorDataType, CDataType>(
a_m_k_host, b_k_n_host, c_m_n_reference);
const float max_accumulated_value =
*std::max_element(c_m_n_ref.mData.begin(), c_m_n_ref.mData.end());
const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
K, num_wgs_per_tile, max_accumulated_value);
pass = do_verify(c_m_n_dev_result, c_m_n_ref, rtol_atol, "CPU");
*std::max_element(c_m_n_reference.mData.begin(), c_m_n_reference.mData.end());
const auto relative_absolute_tolerances =
calculateRtolAtol<ADataType, BDataType, AccumulatorDataType, CDataType>(
k_dim, num_wgs_per_tile, max_accumulated_value);
pass = doVerify(c_m_n_device_result, c_m_n_reference, relative_absolute_tolerances, "CPU");
}
else if(arg_parser.get_int("v") == 2) // Validate on the GPU
{
// Memory on device to store gpu reference result
ck_tile::DeviceMem c_m_n_gpu_buf_ref(c_m_n_ref.get_element_space_size_in_bytes());
c_m_n_gpu_buf_ref.SetZero();
ADataType* d_A = static_cast<ADataType*>(a_m_k_dev_buf.GetDeviceBuffer());
BDataType* d_B = static_cast<BDataType*>(b_k_n_dev_buf.GetDeviceBuffer());
CDataType* d_C = static_cast<CDataType*>(c_m_n_gpu_buf_ref.GetDeviceBuffer());
ck_tile::DeviceMem c_m_n_gpu_buffer_reference(
c_m_n_reference.get_element_space_size_in_bytes());
c_m_n_gpu_buffer_reference.SetZero();
ADataType* d_A = static_cast<ADataType*>(a_m_k_device_memory.GetDeviceBuffer());
BDataType* d_B = static_cast<BDataType*>(b_k_n_device_memory.GetDeviceBuffer());
CDataType* d_C = static_cast<CDataType*>(c_m_n_gpu_buffer_reference.GetDeviceBuffer());
ck_tile::reference_gemm_gpu<ADataType,
BDataType,
AccDataType,
AccumulatorDataType,
CDataType,
ALayout,
BLayout,
CLayout>(d_A, d_B, d_C, M, N, K, stride_A, stride_B, stride_C);
c_m_n_gpu_buf_ref.FromDevice(c_m_n_ref.data());
CLayout>(
d_A, d_B, d_C, m_dim, n_dim, k_dim, stride_a, stride_b, stride_c);
c_m_n_gpu_buffer_reference.FromDevice(c_m_n_reference.data());
const float max_accumulated_value =
*std::max_element(c_m_n_ref.mData.begin(), c_m_n_ref.mData.end());
const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
K, num_wgs_per_tile, max_accumulated_value);
pass = do_verify(c_m_n_dev_result, c_m_n_ref, rtol_atol, "GPU");
*std::max_element(c_m_n_reference.mData.begin(), c_m_n_reference.mData.end());
const auto relative_absolute_tolerances =
calculateRtolAtol<ADataType, BDataType, AccumulatorDataType, CDataType>(
k_dim, num_wgs_per_tile, max_accumulated_value);
pass = doVerify(c_m_n_device_result, c_m_n_reference, relative_absolute_tolerances, "GPU");
}
return pass;

204
example/ck_tile/40_streamk_gemm/streamk_gemm_basic.cpp
View File

@@ -4,11 +4,11 @@
#include "gemm_utils.hpp"
#include "ck_tile/ops/common.hpp"
template <typename GemmConfig,
template <typename GemmConfiguration,
typename ADataType,
typename BDataType,
typename DsDataType,
typename AccDataType,
typename AccumulatorDataType,
typename CDataType,
typename ALayout,
typename BLayout,
@@ -17,43 +17,49 @@ template <typename GemmConfig,
typename CDEElementWise,
ck_tile::StreamKReductionStrategy ReductionStrategy>
std::tuple<float, ck_tile::index_t> gemm(const ck_tile::StreamKHostArgs& args,
const ck_tile::stream_config& s)
const ck_tile::stream_config& stream_config)
{
using GemmShape = ck_tile::TileGemmShape<
ck_tile::sequence<GemmConfig::M_Tile, GemmConfig::N_Tile, GemmConfig::K_Tile>,
ck_tile::sequence<GemmConfig::M_Warp, GemmConfig::N_Warp, GemmConfig::K_Warp>,
ck_tile::
sequence<GemmConfig::M_Warp_Tile, GemmConfig::N_Warp_Tile, GemmConfig::K_Warp_Tile>,
GemmConfig::PermuteA,
GemmConfig::PermuteB>;
using GemmShape = ck_tile::TileGemmShape<ck_tile::sequence<GemmConfiguration::M_TILE,
GemmConfiguration::N_TILE,
GemmConfiguration::K_TILE>,
ck_tile::sequence<GemmConfiguration::M_WARP,
GemmConfiguration::N_WARP,
GemmConfiguration::K_WARP>,
ck_tile::sequence<GemmConfiguration::M_WARP_TILE,
GemmConfiguration::N_WARP_TILE,
GemmConfiguration::K_WARP_TILE>,
GemmConfiguration::PERMUTE_A,
GemmConfiguration::PERMUTE_B>;
using TilePartitioner =
ck_tile::StreamKTilePartitioner<GemmShape, ReductionStrategy, GemmConfig::Persistent>;
using TilePartitioner = ck_tile::
StreamKTilePartitioner<GemmShape, ReductionStrategy, GemmConfiguration::PERSISTENT>;
using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<GemmConfig::kPadM,
GemmConfig::kPadN,
GemmConfig::kPadK,
GemmConfig::DoubleSmemBuffer,
ALayout,
BLayout,
ELayout,
GemmConfig::TransposeC,
GemmConfig::UseStructuredSparsity,
GemmConfig::Persistent,
GemmConfig::NumWaveGroups,
GemmConfig::Preshuffle>;
using GemmUniversalTraits =
ck_tile::TileGemmUniversalTraits<GemmConfiguration::PAD_M,
GemmConfiguration::PAD_N,
GemmConfiguration::PAD_K,
GemmConfiguration::DOUBLE_SMEM_BUFFER,
ALayout,
BLayout,
ELayout,
GemmConfiguration::TRANSPOSE_C,
GemmConfiguration::USE_STRUCTURED_SPARSITY,
GemmConfiguration::PERSISTENT,
GemmConfiguration::NUM_WAVE_GROUPS,
GemmConfiguration::PRESHUFFLE>;
const auto Run = [&](const auto memory_operation) -> std::tuple<float, ck_tile::index_t> {
const auto runKernel = [&](const auto memory_operation) -> std::tuple<float, ck_tile::index_t> {
// We create the GEMM pipeline without specifying has_hot_loop or tail_num.
// This is because num_loop can vary (a) per WG and (b) per iteration of the Stream-K
// while loop. Instead, has_hot_loop and tail_num are determined in the Stream-K
// Kernel's RunGemm function. This is a similar pattern used by grouped GEMM.
using UniversalGemmProblem = ck_tile::UniversalGemmPipelineProblem<ADataType,
BDataType,
AccDataType,
GemmShape,
GemmUniversalTraits,
GemmConfig::Scheduler>;
using UniversalGemmProblem =
ck_tile::UniversalGemmPipelineProblem<ADataType,
BDataType,
AccumulatorDataType,
GemmShape,
GemmUniversalTraits,
GemmConfiguration::SCHEDULER>;
using GemmPipeline = ck_tile::GemmPipelineAgBgCrCompV3<UniversalGemmProblem>;
@@ -61,39 +67,39 @@ std::tuple<float, ck_tile::index_t> gemm(const ck_tile::StreamKHostArgs& args,
ck_tile::CShuffleEpilogueProblem<ADataType,
BDataType,
DsDataType,
AccDataType,
AccumulatorDataType,
CDataType,
DsLayout,
ELayout,
CDEElementWise,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock,
GemmConfig::M_Warp,
GemmConfig::N_Warp,
GemmConfig::M_Warp_Tile,
GemmConfig::N_Warp_Tile,
GemmConfig::K_Warp_Tile,
GemmConfiguration::M_WARP,
GemmConfiguration::N_WARP,
GemmConfiguration::M_WARP_TILE,
GemmConfiguration::N_WARP_TILE,
GemmConfiguration::K_WARP_TILE,
UniversalGemmProblem::TransposeC,
memory_operation.value,
GemmConfig::NumWaveGroups>>;
GemmConfiguration::NUM_WAVE_GROUPS>>;
using Kernel = ck_tile::StreamKKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
auto kargs = Kernel::MakeKernelArgs(args);
const auto workspace_size = Kernel::GetWorkSpaceSize(kargs);
auto kernel_args = Kernel::MakeKernelArgs(args);
const auto workspace_size = Kernel::GetWorkSpaceSize(kernel_args);
ck_tile::DeviceMem workspace_data(workspace_size);
workspace_data.SetZero();
kargs.workspace_ptr = workspace_data.GetDeviceBuffer();
kernel_args.workspace_ptr = workspace_data.GetDeviceBuffer();
dim3 grids = Kernel::GridSize(kargs.tile_partitioner);
dim3 grids = Kernel::GridSize(kernel_args.tile_partitioner);
dim3 blocks = Kernel::BlockSize();
if(!Kernel::IsSupportedArgument(kargs))
if(!Kernel::IsSupportedArgument(kernel_args))
{
throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
}
if(s.log_level_ > 0)
if(stream_config.log_level_ > 0)
{
std::cout << "Launching kernel with args: " << Kernel::GetName() << '\n'
<< "shape: " << GemmShape::GetName() << '\n'
@@ -109,7 +115,7 @@ std::tuple<float, ck_tile::index_t> gemm(const ck_tile::StreamKHostArgs& args,
{
// Clear the output C tensor results after each repetition of the kernel
hipGetErrorString(hipMemsetAsync(
args.e_ptr, 0, args.M * args.N * sizeof(CDataType), s.stream_id_));
args.e_ptr, 0, args.M * args.N * sizeof(CDataType), stream_config.stream_id_));
}
else if constexpr(ReductionStrategy == ck_tile::StreamKReductionStrategy::Reduction)
{
@@ -120,45 +126,47 @@ std::tuple<float, ck_tile::index_t> gemm(const ck_tile::StreamKHostArgs& args,
std::function<void()> preprocess = reset_data_buffers;
float ave_time = ck_tile::launch_kernel_time_mask(
s,
preprocess,
ck_tile::make_kernel<GemmConfig::kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
float average_time =
ck_tile::launch_kernel_time_mask(stream_config,
preprocess,
ck_tile::make_kernel<GemmConfiguration::BLOCK_PER_CU>(
Kernel{}, grids, blocks, 0, kernel_args));
ck_tile::index_t num_wgs_per_tile = kargs.tile_partitioner.estimate_num_wgs_per_tile();
return std::tuple{ave_time, num_wgs_per_tile};
ck_tile::index_t num_wgs_per_tile =
kernel_args.tile_partitioner.estimate_num_wgs_per_tile();
return std::tuple{average_time, num_wgs_per_tile};
};
if constexpr(ck_tile::StreamKReductionStrategy::Atomic == ReductionStrategy)
{
return Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
// Since we are doing stream K, in the case of
// atomics, multiple workgroups may write to the same
// output tile in the C tensor, so we must atomic add
// the results (not set)
ck_tile::memory_operation_enum::atomic_add>{});
return runKernel(ck_tile::integral_constant<ck_tile::memory_operation_enum,
// Since we are doing stream K, in the case of
// atomics, multiple workgroups may write to the
// same output tile in the C tensor, so we must
// atomic add the results (not set)
ck_tile::memory_operation_enum::atomic_add>{});
}
else // We are using ck_tile::StreamKReductionStrategy::Reduction
{
return Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
// In this case, there is only ever 1 WG writing final
// results to each macro tile in the C tensor, so we
// can do a set.
ck_tile::memory_operation_enum::set>{});
return runKernel(ck_tile::integral_constant<ck_tile::memory_operation_enum,
// In this case, there is only ever 1 WG writing
// final results to each macro tile in the C
// tensor, so we can do a set.
ck_tile::memory_operation_enum::set>{});
}
}
#include "run_gemm_example.inc"
template <typename GemmConfig, typename TypeConfig>
int run_gemm_example_prec_type(std::string a_layout, std::string b_layout, int argc, char* argv[])
template <typename GemmConfiguration, typename TypeConfiguration>
int runGemmExamplePrecisionType(std::string a_layout, std::string b_layout, int argc, char* argv[])
{
using Row = ck_tile::tensor_layout::gemm::RowMajor;
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
if(a_layout == "R" && b_layout == "C")
{
return run_gemm_example_with_layouts<GemmConfig, TypeConfig>(
return runGemmExampleWithLayouts<GemmConfiguration, TypeConfiguration>(
argc, argv, Row{}, Col{}, Row{});
}
else
@@ -169,72 +177,74 @@ int run_gemm_example_prec_type(std::string a_layout, std::string b_layout, int a
return 0;
}
template <template <typename PreType, bool Persistent_> typename GemmConfig>
int run_gemm_example(int argc, char* argv[])
template <template <typename PrecisionType, bool IsPersistent> typename GemmConfiguration>
int runGemmExample(int argc, char* argv[])
{
auto [result, arg_parser] = create_args(argc, argv);
auto [result, arg_parser] = createArgs(argc, argv);
if(!result)
return -1;
std::string data_type = arg_parser.get_str("prec");
std::string a_layout = arg_parser.get_str("a_layout");
std::string b_layout = arg_parser.get_str("b_layout");
auto persistent_dp = arg_parser.get_bool("persistent_dp");
std::string data_type = arg_parser.get_str("prec");
std::string a_layout = arg_parser.get_str("a_layout");
std::string b_layout = arg_parser.get_str("b_layout");
auto persistent_data_parallel = arg_parser.get_bool("persistent_dp");
if(data_type == "bf16")
{
using TypeConfig = StreamKGemmTypeConfig<ck_tile::bf16_t>;
if(persistent_dp)
using TypeConfiguration = StreamKGemmTypeConfiguration<ck_tile::bf16_t>;
if(persistent_data_parallel)
{
return run_gemm_example_prec_type<GemmConfig<ck_tile::bf16_t, true>, TypeConfig>(
a_layout, b_layout, argc, argv);
return runGemmExamplePrecisionType<GemmConfiguration<ck_tile::bf16_t, true>,
TypeConfiguration>(a_layout, b_layout, argc, argv);
}
else
{
return run_gemm_example_prec_type<GemmConfig<ck_tile::bf16_t, false>, TypeConfig>(
a_layout, b_layout, argc, argv);
return runGemmExamplePrecisionType<GemmConfiguration<ck_tile::bf16_t, false>,
TypeConfiguration>(a_layout, b_layout, argc, argv);
}
}
else if(data_type == "fp16")
{
using TypeConfig = StreamKGemmTypeConfig<ck_tile::half_t>;
if(persistent_dp)
using TypeConfiguration = StreamKGemmTypeConfiguration<ck_tile::half_t>;
if(persistent_data_parallel)
{
return run_gemm_example_prec_type<GemmConfig<ck_tile::half_t, true>, TypeConfig>(
a_layout, b_layout, argc, argv);
return runGemmExamplePrecisionType<GemmConfiguration<ck_tile::half_t, true>,
TypeConfiguration>(a_layout, b_layout, argc, argv);
}
else
{
return run_gemm_example_prec_type<GemmConfig<ck_tile::half_t, false>, TypeConfig>(
a_layout, b_layout, argc, argv);
return runGemmExamplePrecisionType<GemmConfiguration<ck_tile::half_t, false>,
TypeConfiguration>(a_layout, b_layout, argc, argv);
}
}
else if(data_type == "fp8")
{
using TypeConfig = StreamKGemmTypeConfig<ck_tile::fp8_t, ck_tile::fp8_t, ck_tile::half_t>;
if(persistent_dp)
using TypeConfiguration =
StreamKGemmTypeConfiguration<ck_tile::fp8_t, ck_tile::fp8_t, ck_tile::half_t>;
if(persistent_data_parallel)
{
return run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t, true>, TypeConfig>(
a_layout, b_layout, argc, argv);
return runGemmExamplePrecisionType<GemmConfiguration<ck_tile::fp8_t, true>,
TypeConfiguration>(a_layout, b_layout, argc, argv);
}
else
{
return run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t, false>, TypeConfig>(
a_layout, b_layout, argc, argv);
return runGemmExamplePrecisionType<GemmConfiguration<ck_tile::fp8_t, false>,
TypeConfiguration>(a_layout, b_layout, argc, argv);
}
}
else if(data_type == "bf8")
{
using TypeConfig = StreamKGemmTypeConfig<ck_tile::bf8_t, ck_tile::bf8_t, ck_tile::half_t>;
if(persistent_dp)
using TypeConfiguration =
StreamKGemmTypeConfiguration<ck_tile::bf8_t, ck_tile::bf8_t, ck_tile::half_t>;
if(persistent_data_parallel)
{
return run_gemm_example_prec_type<GemmConfig<ck_tile::bf8_t, true>, TypeConfig>(
a_layout, b_layout, argc, argv);
return runGemmExamplePrecisionType<GemmConfiguration<ck_tile::bf8_t, true>,
TypeConfiguration>(a_layout, b_layout, argc, argv);
}
else
{
return run_gemm_example_prec_type<GemmConfig<ck_tile::bf8_t, false>, TypeConfig>(
a_layout, b_layout, argc, argv);
return runGemmExamplePrecisionType<GemmConfiguration<ck_tile::bf8_t, false>,
TypeConfiguration>(a_layout, b_layout, argc, argv);
}
}
else
@@ -247,5 +257,5 @@ int run_gemm_example(int argc, char* argv[])
int main(int argc, char* argv[])
{
return !run_gemm_example<GemmConfigMemoryInterwave>(argc, argv);
return !runGemmExample<GemmConfigurationMemoryInterwave>(argc, argv);
}