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Commit b74918bc authored by ThomasNing's avatar ThomasNing
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compiled version of cross gpu connection

parents 3fcad951 1c45ca35
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Showing with 620 additions and 442 deletions
example/59_grouped_gemm_multi_ABD/grouped_gemm_multi_abd_xdl_fixed_nk_bias_fp16.cpp
View file @ b74918bc
...@@ -184,9 +184,9 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co ...@@ -184,9 +184,9 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5}); b_tensors[i].GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
break; break;
default: default:
a0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{}); a0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<A0DataType, 0>{});
a1_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{}); a1_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<A1DataType, 0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{}); b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<B0DataType, 1>{});
} }
d0_tensors[i].GenerateTensorValue(GeneratorTensor_3<D0DataType>{-0.5, 0.5}); d0_tensors[i].GenerateTensorValue(GeneratorTensor_3<D0DataType>{-0.5, 0.5});
......
example/62_convnd_activ/convscale/convnd_fwd_convscale_common.hpp
View file @ b74918bc
...@@ -172,12 +172,13 @@ bool run_grouped_conv_fwd(bool do_verification, ...@@ -172,12 +172,13 @@ bool run_grouped_conv_fwd(bool do_verification,
{ {
case 0: break; case 0: break;
case 1: case 1:
in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}); // values generated: -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5
wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5}); in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 6});
wei.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-1.0, 1.0});
break; break;
default: default:
in.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0}); in.GenerateTensorValue(GeneratorTensor_3<InDataType>{-5.0, 5.0});
wei.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5}); wei.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-1.0, 1.0});
} }
DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpaceSize()); DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
......
example/65_gemm_multiply_multiply/gemm_multiply_multiply_xdl_fp8_ab_scale.cpp
View file @ b74918bc
...@@ -205,7 +205,6 @@ int main(int argc, char* argv[]) ...@@ -205,7 +205,6 @@ int main(int argc, char* argv[])
a1_device_buf.ToDevice(a1_m_k.mData.data()); a1_device_buf.ToDevice(a1_m_k.mData.data());
b0_device_buf.ToDevice(b0_k_n.mData.data()); b0_device_buf.ToDevice(b0_k_n.mData.data());
b1_device_buf.ToDevice(b1_k_n.mData.data()); b1_device_buf.ToDevice(b1_k_n.mData.data());
e_device_buf.ToDevice(e_m_n_device_result.mData.data());
auto a_element_op = AElementOp{}; auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{}; auto b_element_op = BElementOp{};
...@@ -253,8 +252,6 @@ int main(int argc, char* argv[]) ...@@ -253,8 +252,6 @@ int main(int argc, char* argv[])
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s" std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
<< std::endl; << std::endl;
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
if(do_verification) if(do_verification)
{ {
Tensor<AccDataType> c_m_n({M, N}); Tensor<AccDataType> c_m_n({M, N});
......
example/CMakeLists.txt
View file @ b74918bc
...@@ -54,6 +54,13 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME) ...@@ -54,6 +54,13 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
list(REMOVE_ITEM FILE_NAME "${source}") list(REMOVE_ITEM FILE_NAME "${source}")
endif() endif()
endforeach() endforeach()
#Do not build any DPP examples if DL_KERNELS not set
foreach(source IN LISTS FILE_NAME)
if(NOT DEFINED DL_KERNELS AND source MATCHES "_dpp")
message("removing dpp example ${source} ")
list(REMOVE_ITEM FILE_NAME "${source}")
endif()
endforeach()
#Do not build any XDL examples if gfx9 targets are not on the list #Do not build any XDL examples if gfx9 targets are not on the list
foreach(source IN LISTS FILE_NAME) foreach(source IN LISTS FILE_NAME)
if(NOT EX_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl") if(NOT EX_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl")
......
example/README.md 0 → 100644
View file @ b74918bc
[Back to the main page](../README.md)
# Composable Kernel examples
\ No newline at end of file
example/ck_tile/01_fmha/codegen/cpp_symbol_map.py
View file @ b74918bc
...@@ -2,10 +2,17 @@ ...@@ -2,10 +2,17 @@
# Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved. # Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
# generate kernel instances to speed up compilation # generate kernel instances to speed up compilation
DTYPE_MAP = { FWD_DTYPE_MAP = {
"fp16": "ck_tile::fp16_t", "fp16" : "FmhaFwdFp16",
"bf16": "ck_tile::bf16_t", "bf16" : "FmhaFwdBf16",
"fp8" : "ck_tile::fp8_t" "fp8" : "FmhaFwdFp8",
"fp8fp16": "FmhaFwdFp8Fp16",
"fp8bf16": "FmhaFwdFp8Bf16"
}
BWD_DTYPE_MAP = {
"fp16": "FmhaBwdFp16",
"bf16": "FmhaBwdBf16"
} }
MASK_IMPL = { MASK_IMPL = {
...@@ -112,6 +119,7 @@ PIPELINE_MAP = { ...@@ -112,6 +119,7 @@ PIPELINE_MAP = {
PIPELINE_ENUM_MAP = { PIPELINE_ENUM_MAP = {
"qr" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS", "qr" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS",
"qr_async" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS_ASYNC", "qr_async" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS_ASYNC",
"qr_nwarp_sshuffle" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS",
} }
BOOL_MAP = { BOOL_MAP = {
......
example/ck_tile/01_fmha/codegen/ops/fmha_bwd.py
View file @ b74918bc
...@@ -283,7 +283,7 @@ class FmhaBwdApiPool: ...@@ -283,7 +283,7 @@ class FmhaBwdApiPool:
inners = inners + FMHA_BWD_API_INNER_DISPATCH.format(F_if=if_k, F_mode=MODE_MAP[trait.mode], F_pipeline_enum=BWD_DQDKDV_PIPELINE_ENUM_MAP[trait.pipeline], inners = inners + FMHA_BWD_API_INNER_DISPATCH.format(F_if=if_k, F_mode=MODE_MAP[trait.mode], F_pipeline_enum=BWD_DQDKDV_PIPELINE_ENUM_MAP[trait.pipeline],
F_mask_check=get_mask_check_map(self.mask_impl)[trait.mask], F_mask=get_mask_map(self.mask_impl)[trait.mask], F_bias_check=BIAS_CHECK_MAP[trait.bias], F_mask_check=get_mask_check_map(self.mask_impl)[trait.mask], F_mask=get_mask_map(self.mask_impl)[trait.mask], F_bias_check=BIAS_CHECK_MAP[trait.bias],
F_bias=BIAS_MAP[trait.bias], F_dbias=BOOL_MAP[trait.dbias], F_dropout_check=DROPOUT_CHECK_MAP[trait.dropout], F_dropout=DROPOUT_MAP[trait.dropout], F_bias=BIAS_MAP[trait.bias], F_dbias=BOOL_MAP[trait.dbias], F_dropout_check=DROPOUT_CHECK_MAP[trait.dropout], F_dropout=DROPOUT_MAP[trait.dropout],
F_scheck=trait.scheck(spad1=spad1), F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_hdim=hdim, F_dtype=DTYPE_MAP[dtype], F_scheck=trait.scheck(spad1=spad1), F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_hdim=hdim, F_dtype=BWD_DTYPE_MAP[dtype],
F_spad0=BOOL_MAP[trait.spad], F_spad1=BOOL_MAP[spad1], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad], F_spad0=BOOL_MAP[trait.spad], F_spad1=BOOL_MAP[spad1], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
F_deterministic=BOOL_MAP[trait.deterministic]) F_deterministic=BOOL_MAP[trait.deterministic])
...@@ -360,7 +360,7 @@ class FmhaBwdDQDKDVKernel: ...@@ -360,7 +360,7 @@ class FmhaBwdDQDKDVKernel:
FMHA_BWD_DQ_DK_DV_KERNEL_BODY.format( FMHA_BWD_DQ_DK_DV_KERNEL_BODY.format(
F_idx = self.F_idx, F_idx = self.F_idx,
F_hdim = self.F_hdim, F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype], F_dtype = BWD_DTYPE_MAP[self.F_dtype],
F_bm0 = self.F_tile.F_bm0, F_bm0 = self.F_tile.F_bm0,
F_bn0 = self.F_tile.F_bn0, F_bn0 = self.F_tile.F_bn0,
F_bk0 = self.F_tile.F_bk0, F_bk0 = self.F_tile.F_bk0,
...@@ -469,7 +469,7 @@ def get_bwd_dq_dk_dv_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> ...@@ -469,7 +469,7 @@ def get_bwd_dq_dk_dv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
gen = list() gen = list()
api_pool = FmhaBwdApiPool(mask_impl) api_pool = FmhaBwdApiPool(mask_impl)
for dtype in DTYPE_MAP.keys(): for dtype in BWD_DTYPE_MAP.keys():
d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(dtype) d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(dtype)
if d == None: if d == None:
continue continue
...@@ -585,7 +585,7 @@ class FmhaBwdOGradDotOKernel: ...@@ -585,7 +585,7 @@ class FmhaBwdOGradDotOKernel:
FMHA_BWD_DOT_DO_O_KERNEL_BODY.format( FMHA_BWD_DOT_DO_O_KERNEL_BODY.format(
F_idx = self.F_idx, F_idx = self.F_idx,
F_hdim = self.F_hdim, F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype], F_dtype = BWD_DTYPE_MAP[self.F_dtype],
F_spad = BOOL_MAP[self.F_spad], F_spad = BOOL_MAP[self.F_spad],
F_dvpad = BOOL_MAP[self.F_dvpad], F_dvpad = BOOL_MAP[self.F_dvpad],
F_mode = MODE_MAP[self.F_mode], F_mode = MODE_MAP[self.F_mode],
...@@ -616,7 +616,7 @@ def get_bwd_dot_do_o_blobs() -> List[FmhaBwdOGradDotOKernel]: ...@@ -616,7 +616,7 @@ def get_bwd_dot_do_o_blobs() -> List[FmhaBwdOGradDotOKernel]:
gen = list() gen = list()
for dtype in DTYPE_MAP.keys(): for dtype in BWD_DTYPE_MAP.keys():
d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(dtype) d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(dtype)
if d == None: if d == None:
continue continue
...@@ -716,7 +716,7 @@ class FmhaBwdConvertQGradKernel: ...@@ -716,7 +716,7 @@ class FmhaBwdConvertQGradKernel:
FMHA_BWD_CONVERT_DQ_KERNEL_BODY.format( FMHA_BWD_CONVERT_DQ_KERNEL_BODY.format(
F_idx = self.F_idx, F_idx = self.F_idx,
F_hdim = self.F_hdim, F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype], F_dtype = BWD_DTYPE_MAP[self.F_dtype],
F_bm0 = self.F_bm0, F_bm0 = self.F_bm0,
F_bn0 = self.F_bn0, F_bn0 = self.F_bn0,
F_spad = BOOL_MAP[self.F_spad], F_spad = BOOL_MAP[self.F_spad],
...@@ -751,7 +751,7 @@ def get_bwd_convert_dq_blobs() -> List[FmhaBwdConvertQGradKernel]: ...@@ -751,7 +751,7 @@ def get_bwd_convert_dq_blobs() -> List[FmhaBwdConvertQGradKernel]:
gen = list() gen = list()
for dtype in DTYPE_MAP.keys(): for dtype in BWD_DTYPE_MAP.keys():
d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(dtype) d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(dtype)
if d == None: if d == None:
continue continue
......
example/ck_tile/01_fmha/codegen/ops/fmha_fwd.py
View file @ b74918bc
...@@ -44,13 +44,12 @@ FMHA_FWD_KERNEL_BODY=""" ...@@ -44,13 +44,12 @@ FMHA_FWD_KERNEL_BODY="""
using fmha_dtype_{F_idx} = {F_dtype}; using fmha_dtype_{F_idx} = {F_dtype};
using fmha_block_tile_{F_idx} = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}>; using fmha_block_tile_{F_idx} = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}>;
using fmha_warp_tile_{F_idx} = ck_tile::sequence<{F_wm}, {F_wn}, {F_wk}>;
using fmha_shape_{F_idx} = ck_tile::TileFmhaShape<fmha_block_tile_{F_idx}, using fmha_shape_{F_idx} = ck_tile::TileFmhaShape<fmha_block_tile_{F_idx},
ck_tile::sequence<{F_rm0}, {F_rn0}, {F_rk0}>, ck_tile::sequence<{F_rm0}, {F_rn0}, {F_rk0}>,
fmha_warp_tile_{F_idx}, ck_tile::sequence<{F_wm0}, {F_wn0}, {F_wk0}>,
ck_tile::sequence<{F_rm1}, {F_rn1}, {F_rk1}>, ck_tile::sequence<{F_rm1}, {F_rn1}, {F_rk1}>,
fmha_warp_tile_{F_idx}, ck_tile::sequence<{F_wm1}, {F_wn1}, {F_wk1}>,
{F_vlayout}>; {F_vlayout}>;
using fmha_trait_{F_idx} = ck_tile::TileFmhaTraits<{F_spad}, using fmha_trait_{F_idx} = ck_tile::TileFmhaTraits<{F_spad},
...@@ -282,7 +281,7 @@ class FmhaFwdApiPool: ...@@ -282,7 +281,7 @@ class FmhaFwdApiPool:
F_squant=BOOL_MAP[trait.squant], F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_squant=BOOL_MAP[trait.squant], F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck,
F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad], F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
F_bm0=trait.bm0, F_bn0=trait.bn0, F_bk0=trait.bk0, F_bn1=trait.bn1, F_bk1=trait.bk1, F_bk0max=trait.bk0max, F_bm0=trait.bm0, F_bn0=trait.bn0, F_bk0=trait.bk0, F_bn1=trait.bn1, F_bk1=trait.bk1, F_bk0max=trait.bk0max,
F_hdim=hdim, F_dtype=DTYPE_MAP[dtype]) F_hdim=hdim, F_dtype=FWD_DTYPE_MAP[dtype])
if_j = 'if' if j == 0 else 'else if' if_j = 'if' if j == 0 else 'else if'
per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners) per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners)
if_i = 'if' if i == 0 else 'else if' if_i = 'if' if i == 0 else 'else if'
...@@ -306,15 +305,19 @@ class FmhaFwdTileSize: ...@@ -306,15 +305,19 @@ class FmhaFwdTileSize:
F_rm1 : int # number of warps for gemm1 along q seqlen F_rm1 : int # number of warps for gemm1 along q seqlen
F_rn1 : int # number of warps for gemm1 along head dim v F_rn1 : int # number of warps for gemm1 along head dim v
F_rk1 : int # number of warps for gemm1 along k seqlen (not used) F_rk1 : int # number of warps for gemm1 along k seqlen (not used)
F_wm : int # warp size along m (warp size) F_wm0 : int # gemm0 warp size along m
F_wn : int # warp size along n F_wn0 : int # gemm0 warp size along n
F_wk : int # warp size along k F_wk0 : int # gemm0 warp size along k
F_wm1 : int # gemm1 warp size along m
F_wn1 : int # gemm1 warp size along n
F_wk1 : int # gemm1 warp size along k
F_occupancy : int # occupancy, -1 will let pipeline decide the occupancy, other value will overwrite occupancy F_occupancy : int # occupancy, -1 will let pipeline decide the occupancy, other value will overwrite occupancy
@property @property
def name(self) -> str: def name(self) -> str:
return f"b{self.F_bm0}x{self.F_bn0}x{self.F_bk0}x{self.F_bn1}x{self.F_bk1}x{self.F_bk0max}" +\ return f"b{self.F_bm0}x{self.F_bn0}x{self.F_bk0}x{self.F_bn1}x{self.F_bk1}x{self.F_bk0max}" +\
f"_r{self.F_rm0}x{self.F_rn0}x{self.F_rk0}_r{self.F_rm1}x{self.F_rn1}x{self.F_rk1}" +\ f"_r{self.F_rm0}x{self.F_rn0}x{self.F_rk0}_r{self.F_rm1}x{self.F_rn1}x{self.F_rk1}" +\
f"_w{self.F_wm}x{self.F_wn}x{self.F_wk}" + ("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}") f"_w{self.F_wm0}x{self.F_wn0}x{self.F_wk0}_w{self.F_wm1}x{self.F_wn1}x{self.F_wk1}" +\
("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}")
@dataclass @dataclass
class FmhaFwdKernel: class FmhaFwdKernel:
...@@ -339,7 +342,7 @@ class FmhaFwdKernel: ...@@ -339,7 +342,7 @@ class FmhaFwdKernel:
FMHA_FWD_KERNEL_BODY.format( FMHA_FWD_KERNEL_BODY.format(
F_idx = self.F_idx, F_idx = self.F_idx,
F_hdim = self.F_hdim, F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype], F_dtype = FWD_DTYPE_MAP[self.F_dtype],
F_bm0 = self.F_tile.F_bm0, F_bm0 = self.F_tile.F_bm0,
F_bn0 = self.F_tile.F_bn0, F_bn0 = self.F_tile.F_bn0,
F_bk0 = self.F_tile.F_bk0, F_bk0 = self.F_tile.F_bk0,
...@@ -352,9 +355,12 @@ class FmhaFwdKernel: ...@@ -352,9 +355,12 @@ class FmhaFwdKernel:
F_rm1 = self.F_tile.F_rm1, F_rm1 = self.F_tile.F_rm1,
F_rn1 = self.F_tile.F_rn1, F_rn1 = self.F_tile.F_rn1,
F_rk1 = self.F_tile.F_rk1, F_rk1 = self.F_tile.F_rk1,
F_wm = self.F_tile.F_wm, F_wm0 = self.F_tile.F_wm0,
F_wn = self.F_tile.F_wn, F_wn0 = self.F_tile.F_wn0,
F_wk = self.F_tile.F_wk, F_wk0 = self.F_tile.F_wk0,
F_wm1 = self.F_tile.F_wm1,
F_wn1 = self.F_tile.F_wn1,
F_wk1 = self.F_tile.F_wk1,
F_vlayout = LAYOUT_MAP[self.F_pipeline.F_vlayout], F_vlayout = LAYOUT_MAP[self.F_pipeline.F_vlayout],
F_spad = BOOL_MAP[self.F_pipeline.F_spad], F_spad = BOOL_MAP[self.F_pipeline.F_spad],
F_skpad = BOOL_MAP[self.F_pipeline.F_skpad], F_skpad = BOOL_MAP[self.F_pipeline.F_skpad],
...@@ -409,17 +415,17 @@ class FmhaFwdKernel: ...@@ -409,17 +415,17 @@ class FmhaFwdKernel:
def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]: def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
if dtype == 'fp16' or dtype == 'bf16': if dtype == 'fp16' or dtype == 'bf16':
return { return {
'32' : FmhaFwdTileSize(128, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 32, 32, 16, -1), '32' : FmhaFwdTileSize(128, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 32, 32, 16, 32, 32, 16, -1),
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 4, 1, 1, 4, 1, 1, 32, 32, 16, -1), '64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
## '96' : FmhaFwdTileSize(128, 128, 32, 128, 32, 96, 4, 1, 1, 4, 1, 1, 32, 32, 16, -1), ### '96' : FmhaFwdTileSize(128, 128, 32, 128, 32, 96, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 16, -1), '128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 16, -1), '256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
} }
elif dtype == 'fp8' or dtype == 'bf8': elif dtype == 'fp8' or dtype == 'bf8':
return { return {
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, -1), '64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, 32, 32, 32, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, -1), '128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, -1) '256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
} }
else: else:
return None return None
...@@ -462,6 +468,9 @@ def get_fwd_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[Fm ...@@ -462,6 +468,9 @@ def get_fwd_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[Fm
# no need lse/dropout kernels # no need lse/dropout kernels
for mask, bias in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys()): for mask, bias in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys()):
pipelines.append(FmhaFwdPipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 'f', 'f', squant, mask)) pipelines.append(FmhaFwdPipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 'f', 'f', squant, mask))
elif dtype in ['fp8fp16', 'fp8bf16']:
# TODO
None
else: else:
assert False assert False
return pipelines return pipelines
...@@ -469,7 +478,7 @@ def get_fwd_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[Fm ...@@ -469,7 +478,7 @@ def get_fwd_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[Fm
gen = list() gen = list()
api_pool = FmhaFwdApiPool(mask_impl) api_pool = FmhaFwdApiPool(mask_impl)
for dtype in DTYPE_MAP.keys(): for dtype in FWD_DTYPE_MAP.keys():
d = get_fmha_fwd_tile_dict_from_dtype(dtype) d = get_fmha_fwd_tile_dict_from_dtype(dtype)
if d == None: if d == None:
continue continue
......
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_appendkv.py
View file @ b74918bc
...@@ -181,7 +181,7 @@ class FmhaFwdAppendKVApiPool: ...@@ -181,7 +181,7 @@ class FmhaFwdAppendKVApiPool:
inners = inners + FMHA_FWD_APPENDKV_API_INNER_DISPATCH.format(F_if=if_k, F_vlayout=LAYOUT_MAP[trait.vlayout], inners = inners + FMHA_FWD_APPENDKV_API_INNER_DISPATCH.format(F_if=if_k, F_vlayout=LAYOUT_MAP[trait.vlayout],
F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_rope_check=ROPE_CHECK_MAP[trait.rope], F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_rope_check=ROPE_CHECK_MAP[trait.rope],
F_pagedkv=BOOL_MAP[trait.pagedkv], F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad], F_pagedkv=BOOL_MAP[trait.pagedkv], F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
F_rope=ROPE_MAP[trait.rope], F_bs=trait.bs, F_bsk=trait.bsk, F_bd=trait.bd, F_bdv=trait.bdv, F_hdim=hdim, F_dtype=DTYPE_MAP[dtype]) F_rope=ROPE_MAP[trait.rope], F_bs=trait.bs, F_bsk=trait.bsk, F_bd=trait.bd, F_bdv=trait.bdv, F_hdim=hdim, F_dtype=FWD_DTYPE_MAP[dtype])
if_j = 'if' if j == 0 else 'else if' if_j = 'if' if j == 0 else 'else if'
per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners) per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners)
if_i = 'if' if i == 0 else 'else if' if_i = 'if' if i == 0 else 'else if'
...@@ -216,7 +216,7 @@ class FmhaFwdAppendKVKernel: ...@@ -216,7 +216,7 @@ class FmhaFwdAppendKVKernel:
FMHA_FWD_APPENDKV_KERNEL_BODY.format( FMHA_FWD_APPENDKV_KERNEL_BODY.format(
F_idx = self.F_idx, F_idx = self.F_idx,
F_hdim = self.F_hdim, F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype], F_dtype = FWD_DTYPE_MAP[self.F_dtype],
F_bs = self.F_tile.F_bs, F_bs = self.F_tile.F_bs,
F_bsk = self.F_tile.F_bsk, F_bsk = self.F_tile.F_bsk,
F_bd = self.F_tile.F_bd, F_bd = self.F_tile.F_bd,
...@@ -301,6 +301,9 @@ def get_fwd_appendkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> ...@@ -301,6 +301,9 @@ def get_fwd_appendkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
elif dtype in ['fp8', 'bf8']: elif dtype in ['fp8', 'bf8']:
# rope/paged-kv is not supported # rope/paged-kv is not supported
pipelines.append(FmhaFwdAppendKVPipeline('col', 't', 't', 't', 't', 'no', 'f')) pipelines.append(FmhaFwdAppendKVPipeline('col', 't', 't', 't', 't', 'no', 'f'))
elif dtype in ['fp8fp16', 'fp8bf16']:
# TODO
None
else: else:
assert False assert False
return pipelines return pipelines
...@@ -308,7 +311,7 @@ def get_fwd_appendkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> ...@@ -308,7 +311,7 @@ def get_fwd_appendkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
gen = list() gen = list()
api_pool = FmhaFwdAppendKVApiPool(mask_impl) api_pool = FmhaFwdAppendKVApiPool(mask_impl)
for dtype in DTYPE_MAP.keys(): for dtype in FWD_DTYPE_MAP.keys():
d = get_fmha_fwd_appendkv_tile_dict_from_dtype(dtype) d = get_fmha_fwd_appendkv_tile_dict_from_dtype(dtype)
if d == None: if d == None:
continue continue
......
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_splitkv.py
View file @ b74918bc
...@@ -39,6 +39,7 @@ K0_MAX_SUBMAX_MAP = { ...@@ -39,6 +39,7 @@ K0_MAX_SUBMAX_MAP = {
FMHA_FWD_SPLITKV_PIPELINE_MAP = { FMHA_FWD_SPLITKV_PIPELINE_MAP = {
"qr" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVS", "qr" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVS",
"qr_nwarp_sshuffle" : "ck_tile::BlockFmhaFwdSplitKVPipelineNWarpSShuffleQRKSVS",
"qr_async" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVSAsync", "qr_async" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVSAsync",
} }
...@@ -50,13 +51,12 @@ namespace {{ ...@@ -50,13 +51,12 @@ namespace {{
template <bool kHasUnevenSplits> template <bool kHasUnevenSplits>
struct kernel_runner {{ struct kernel_runner {{
using fmha_block_tile = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}>; using fmha_block_tile = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}>;
using fmha_warp_tile = ck_tile::sequence<{F_wm}, {F_wn}, {F_wk}>;
using fmha_shape = ck_tile::TileFmhaShape<fmha_block_tile, using fmha_shape = ck_tile::TileFmhaShape<fmha_block_tile,
ck_tile::sequence<{F_rm0}, {F_rn0}, {F_rk0}>, ck_tile::sequence<{F_rm0}, {F_rn0}, {F_rk0}>,
fmha_warp_tile, ck_tile::sequence<{F_wm0}, {F_wn0}, {F_wk0}>,
ck_tile::sequence<{F_rm1}, {F_rn1}, {F_rk1}>, ck_tile::sequence<{F_rm1}, {F_rn1}, {F_rk1}>,
fmha_warp_tile, ck_tile::sequence<{F_wm1}, {F_wn1}, {F_wk1}>,
{F_vlayout}>; {F_vlayout}>;
using fmha_trait = ck_tile::TileFmhaFwdSplitKVTraits<{F_spad}, using fmha_trait = ck_tile::TileFmhaFwdSplitKVTraits<{F_spad},
...@@ -161,9 +161,8 @@ using fmha_pipeline_problem = ck_tile::BlockFmhaSplitKVCombinePipelineProblem< ...@@ -161,9 +161,8 @@ using fmha_pipeline_problem = ck_tile::BlockFmhaSplitKVCombinePipelineProblem<
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::OaccDataType, typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::OaccDataType,
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::ODataType, typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::ODataType,
{F_hdim}, {F_hdim},
{F_bm0},
{F_bn1},
{F_mode}, {F_mode},
{F_bn1},
fmha_trait>; fmha_trait>;
using fmha_pipeline = ck_tile::BlockFmhaFwdSplitKVCombinePipeline< using fmha_pipeline = ck_tile::BlockFmhaFwdSplitKVCombinePipeline<
...@@ -177,7 +176,9 @@ using fmha_epilogue = ...@@ -177,7 +176,9 @@ using fmha_epilogue =
false, false>>; false, false>>;
using fmha_kernel = using fmha_kernel =
ck_tile::FmhaFwdSplitKVCombineKernel<ck_tile::FmhaFwdSplitKVCombineTilePartitioner<{F_bm0}, {F_bn1}>, ck_tile::FmhaFwdSplitKVCombineKernel<
ck_tile::FmhaFwdSplitKVCombineTilePartitioner<
fmha_pipeline_problem::kM0, fmha_pipeline_problem::kN1>,
fmha_pipeline, fmha_pipeline,
fmha_epilogue>; fmha_epilogue>;
...@@ -192,7 +193,7 @@ static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a) ...@@ -192,7 +193,7 @@ static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
}}; }};
}} }}
using trait_{F_idx} = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn1}, using trait_{F_idx} = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bn1},
{F_lse}, {F_squant}, {F_spad}, {F_dvpad}>; {F_lse}, {F_squant}, {F_spad}, {F_dvpad}>;
#include <iostream> #include <iostream>
...@@ -247,13 +248,32 @@ float fmha_fwd_splitkv(fmha_fwd_splitkv_traits t, fmha_fwd_splitkv_args a, const ...@@ -247,13 +248,32 @@ float fmha_fwd_splitkv(fmha_fwd_splitkv_traits t, fmha_fwd_splitkv_args a, const
}} }}
""" """
FMHA_FWD_SPLITKV_API_INNER_DISPATCH=""" {F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_lse == {F_lse}) && (t.do_fp8_static_quant == {F_squant}) && FMHA_FWD_SPLITKV_API_INNER_DISPATCH=""" {F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.do_fp8_static_quant == {F_squant}) &&
((a.block_table_ptr != nullptr) == {F_pagedkv}) && ({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{ ((a.block_table_ptr != nullptr) == {F_pagedkv}) && ({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{
using traits_ = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, {F_lse}, {F_squant}, {F_pagedkv}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>; using traits_ = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, true, {F_squant}, {F_pagedkv}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
using traits2_ = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}/2, {F_bn1}/2, {F_lse}, {F_squant}, {F_spad}, {F_dvpad}>;
// get combine kernel tile sizes
using OaccDataType = typename FmhaFwdTypeConfig<{F_dtype}>::OaccDataType;
constexpr ck_tile::index_t kM0 = ck_tile::BlockFmhaSplitKVCombinePipelineTileSizes<OaccDataType, /*F_bn1=*/32>::kM0;
// make sure we can reuse the padding flags in combine kernels
static_assert({F_bm0} % kM0 == 0);
static_assert({F_bn1} % 32 == 0);
if (t.has_lse) {{
if constexpr (std::is_same_v<{F_dtype}, ck_tile::fp8_t>) {{
return -1;
}} else {{
using traits2_ = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, /*F_bn1=*/32, true, {F_squant}, {F_spad}, {F_dvpad}>;
return fmha_fwd_splitkv_<traits_, traits2_>(s, a); return fmha_fwd_splitkv_<traits_, traits2_>(s, a);
}} }}
}} else {{
using traits2_ = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, /*F_bn1=*/32, false, {F_squant}, {F_spad}, {F_dvpad}>;
return fmha_fwd_splitkv_<traits_, traits2_>(s, a);
}}
}}
""" """
@dataclass @dataclass
...@@ -292,7 +312,7 @@ class FmhaFwdSplitKVApiTrait: ...@@ -292,7 +312,7 @@ class FmhaFwdSplitKVApiTrait:
if self.pipeline_tag == 'qr_async': if self.pipeline_tag == 'qr_async':
if self.spad == 't' : return 'true' # always support if self.spad == 't' : return 'true' # always support
else : return 'true' else : return 'true'
elif self.pipeline_tag in ['qr']: elif self.pipeline_tag in ['qr', 'qr_nwarp_sshuffle']:
if self.spad == 't' : return f'true /*a.seqlen_q % {self.bm0} != 0*/' # TODO: order of get_pipelines() matters! (ugly) if self.spad == 't' : return f'true /*a.seqlen_q % {self.bm0} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.seqlen_q % {self.bm0} == 0' else : return f'a.seqlen_q % {self.bm0} == 0'
else: assert False else: assert False
...@@ -303,7 +323,7 @@ class FmhaFwdSplitKVApiTrait: ...@@ -303,7 +323,7 @@ class FmhaFwdSplitKVApiTrait:
if self.pipeline_tag == 'qr_async': if self.pipeline_tag == 'qr_async':
if self.skpad == 't' : return f'a.seqlen_k == 0 || a.seqlen_k % {self.bn0} != 0' if self.skpad == 't' : return f'a.seqlen_k == 0 || a.seqlen_k % {self.bn0} != 0'
else : return f'a.seqlen_k != 0 && a.seqlen_k % {self.bn0} == 0' else : return f'a.seqlen_k != 0 && a.seqlen_k % {self.bn0} == 0'
elif self.pipeline_tag in ['qr', 'qr_fp8']: elif self.pipeline_tag in ['qr', 'qr_nwarp_sshuffle']:
if self.skpad == 't' : return f'true /*a.seqlen_k % {self.bn0} != 0*/' # TODO: order of get_pipelines() matters! (ugly) if self.skpad == 't' : return f'true /*a.seqlen_k % {self.bn0} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.seqlen_k % {self.bn0} == 0' else : return f'a.seqlen_k % {self.bn0} == 0'
else: assert False else: assert False
...@@ -314,7 +334,7 @@ class FmhaFwdSplitKVApiTrait: ...@@ -314,7 +334,7 @@ class FmhaFwdSplitKVApiTrait:
vec = int((32 * 4) / DTYPE_BITS[self.dtype]) vec = int((32 * 4) / DTYPE_BITS[self.dtype])
if self.dpad == 't': return f'a.hdim_q % {vec} == 0' if self.dpad == 't': return f'a.hdim_q % {vec} == 0'
else : assert False else : assert False
elif self.pipeline_tag in ['qr']: elif self.pipeline_tag in ['qr', 'qr_nwarp_sshuffle']:
bk0submax = K0_MAX_SUBMAX_MAP[self.bk0max] bk0submax = K0_MAX_SUBMAX_MAP[self.bk0max]
if self.dpad == 't': return f'true /*a.hdim_q % {bk0submax} != 0*/' # TODO: order of get_pipelines() matters! (ugly) if self.dpad == 't': return f'true /*a.hdim_q % {bk0submax} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_q % {bk0submax} == 0' else : return f'a.hdim_q % {bk0submax} == 0'
...@@ -326,7 +346,7 @@ class FmhaFwdSplitKVApiTrait: ...@@ -326,7 +346,7 @@ class FmhaFwdSplitKVApiTrait:
vec = int((32 * 4) / DTYPE_BITS[self.dtype]) vec = int((32 * 4) / DTYPE_BITS[self.dtype])
if self.dvpad == 't': return f'a.hdim_v % {vec} == 0' if self.dvpad == 't': return f'a.hdim_v % {vec} == 0'
else : assert False else : assert False
elif self.pipeline_tag in ['qr']: elif self.pipeline_tag in ['qr', 'qr_nwarp_sshuffle']:
bk0submax = K0_MAX_SUBMAX_MAP[self.bk0max] bk0submax = K0_MAX_SUBMAX_MAP[self.bk0max]
if self.dvpad == 't': return f'true /*a.hdim_v % {bk0submax} != 0*/' # TODO: order of get_pipelines() matters! (ugly) if self.dvpad == 't': return f'true /*a.hdim_v % {bk0submax} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_v % {bk0submax} == 0' else : return f'a.hdim_v % {bk0submax} == 0'
...@@ -425,7 +445,7 @@ class FmhaFwdSplitKVApiPool: ...@@ -425,7 +445,7 @@ class FmhaFwdSplitKVApiPool:
F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck,
F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad], F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
F_bm0=trait.bm0, F_bn0=trait.bn0, F_bk0=trait.bk0, F_bn1=trait.bn1, F_bk1=trait.bk1, F_bk0max=trait.bk0max, F_bm0=trait.bm0, F_bn0=trait.bn0, F_bk0=trait.bk0, F_bn1=trait.bn1, F_bk1=trait.bk1, F_bk0max=trait.bk0max,
F_hdim=hdim, F_dtype=DTYPE_MAP[dtype]) F_hdim=hdim, F_dtype=FWD_DTYPE_MAP[dtype])
if_j = 'if' if j == 0 else 'else if' if_j = 'if' if j == 0 else 'else if'
per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners) per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners)
if_i = 'if' if i == 0 else 'else if' if_i = 'if' if i == 0 else 'else if'
...@@ -437,12 +457,11 @@ class FmhaFwdSplitKVApiPool: ...@@ -437,12 +457,11 @@ class FmhaFwdSplitKVApiPool:
@dataclass @dataclass
class FmhaFwdSplitKVCombineTileSize: class FmhaFwdSplitKVCombineTileSize:
F_bm0 : int # tile size along q seqlen
F_bn1 : int # tile size along v head_dim F_bn1 : int # tile size along v head_dim
F_occupancy : int # occupancy, -1 will let pipeline decide the occupancy, other value will overwrite occupancy F_occupancy : int # occupancy, -1 will let pipeline decide the occupancy, other value will overwrite occupancy
@property @property
def name(self) -> str: def name(self) -> str:
return f"b{self.F_bm0}x{self.F_bn1}" +\ return f"b{self.F_bn1}" +\
("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}") ("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}")
@dataclass @dataclass
...@@ -462,7 +481,7 @@ class FmhaFwdSplitKVKernel: ...@@ -462,7 +481,7 @@ class FmhaFwdSplitKVKernel:
FMHA_FWD_SPLITKV_KERNEL_BODY.format( FMHA_FWD_SPLITKV_KERNEL_BODY.format(
F_idx = self.F_idx, F_idx = self.F_idx,
F_hdim = self.F_hdim, F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype], F_dtype = FWD_DTYPE_MAP[self.F_dtype],
F_bm0 = self.F_tile.F_bm0, F_bm0 = self.F_tile.F_bm0,
F_bn0 = self.F_tile.F_bn0, F_bn0 = self.F_tile.F_bn0,
F_bk0 = self.F_tile.F_bk0, F_bk0 = self.F_tile.F_bk0,
...@@ -475,9 +494,12 @@ class FmhaFwdSplitKVKernel: ...@@ -475,9 +494,12 @@ class FmhaFwdSplitKVKernel:
F_rm1 = self.F_tile.F_rm1, F_rm1 = self.F_tile.F_rm1,
F_rn1 = self.F_tile.F_rn1, F_rn1 = self.F_tile.F_rn1,
F_rk1 = self.F_tile.F_rk1, F_rk1 = self.F_tile.F_rk1,
F_wm = self.F_tile.F_wm, F_wm0 = self.F_tile.F_wm0,
F_wn = self.F_tile.F_wn, F_wn0 = self.F_tile.F_wn0,
F_wk = self.F_tile.F_wk, F_wk0 = self.F_tile.F_wk0,
F_wm1 = self.F_tile.F_wm1,
F_wn1 = self.F_tile.F_wn1,
F_wk1 = self.F_tile.F_wk1,
F_vlayout = LAYOUT_MAP[self.F_pipeline.F_vlayout], F_vlayout = LAYOUT_MAP[self.F_pipeline.F_vlayout],
F_spad = BOOL_MAP[self.F_pipeline.F_spad], F_spad = BOOL_MAP[self.F_pipeline.F_spad],
F_skpad = BOOL_MAP[self.F_pipeline.F_skpad], F_skpad = BOOL_MAP[self.F_pipeline.F_skpad],
...@@ -542,8 +564,7 @@ class FmhaFwdSplitKVCombineKernel: ...@@ -542,8 +564,7 @@ class FmhaFwdSplitKVCombineKernel:
FMHA_FWD_SPLITKV_COMBINE_KERNEL_BODY.format( FMHA_FWD_SPLITKV_COMBINE_KERNEL_BODY.format(
F_idx = self.F_idx, F_idx = self.F_idx,
F_hdim = self.F_hdim, F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype], F_dtype = FWD_DTYPE_MAP[self.F_dtype],
F_bm0 = self.F_tile.F_bm0,
F_bn1 = self.F_tile.F_bn1, F_bn1 = self.F_tile.F_bn1,
F_spad = BOOL_MAP[self.F_pipeline.F_spad], F_spad = BOOL_MAP[self.F_pipeline.F_spad],
F_dvpad = BOOL_MAP[self.F_pipeline.F_dvpad], F_dvpad = BOOL_MAP[self.F_pipeline.F_dvpad],
...@@ -567,17 +588,17 @@ class FmhaFwdSplitKVCombineKernel: ...@@ -567,17 +588,17 @@ class FmhaFwdSplitKVCombineKernel:
def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]: def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
if dtype == 'fp16' or dtype == 'bf16': if dtype == 'fp16' or dtype == 'bf16':
return { return {
'32' : FmhaFwdTileSize(32, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 16, 16, 16, -1), '32' : FmhaFwdTileSize(32, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 16, 16, 16, 16, 16, 16, -1),
'64' : FmhaFwdTileSize(64, 64, 32, 64, 32, 64, 4, 1, 1, 4, 1, 1, 16, 16, 16, -1), '64' : FmhaFwdTileSize(64, 64, 32, 64, 32, 64, 4, 1, 1, 4, 1, 1, 16, 16, 16, 16, 16, 16, -1),
## '96' : FmhaFwdTileSize(64, 128, 32, 128, 32, 96, 4, 1, 1, 4, 1, 1, 16, 16, 16, -1), ### '96' : FmhaFwdTileSize(64, 128, 32, 128, 32, 96, 4, 1, 1, 4, 1, 1, 16, 16, 16, 16, 16, 16, -1),
'128' : FmhaFwdTileSize(64, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 16, 16, 16, -1), '128' : FmhaFwdTileSize(64, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 16, 16, 16, 16, 16, 16, -1),
'256' : FmhaFwdTileSize(64, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 16, 16, 16, -1), '256' : FmhaFwdTileSize(64, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 16, 16, 16, 16, 16, 16, -1),
} }
elif dtype == 'fp8' or dtype == 'bf8': elif dtype == 'fp8' or dtype == 'bf8':
return { return {
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, -1), '64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, 32, 32, 32, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, -1), '128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, -1) '256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
} }
else: else:
return None return None
...@@ -585,17 +606,17 @@ def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]: ...@@ -585,17 +606,17 @@ def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
def get_fmha_fwd_splitkv_combine_tile_dict_from_dtype(dtype : str) -> Optional[dict]: def get_fmha_fwd_splitkv_combine_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
if dtype == 'fp16' or dtype == 'bf16': if dtype == 'fp16' or dtype == 'bf16':
return { return {
'32' : FmhaFwdSplitKVCombineTileSize(16, 16, -1), '32' : FmhaFwdSplitKVCombineTileSize(32, -1),
'64' : FmhaFwdSplitKVCombineTileSize(32, 32, -1), '64' : FmhaFwdSplitKVCombineTileSize(32, -1),
## '96' : FmhaFwdSplitKVCombineTileSize(32, 64, -1), ### '96' : FmhaFwdSplitKVCombineTileSize(32, -1),
'128' : FmhaFwdSplitKVCombineTileSize(32, 64, -1), '128' : FmhaFwdSplitKVCombineTileSize(32, -1),
'256' : FmhaFwdSplitKVCombineTileSize(32, 128, -1), '256' : FmhaFwdSplitKVCombineTileSize(32, -1),
} }
elif dtype == 'fp8' or dtype == 'bf8': elif dtype == 'fp8' or dtype == 'bf8':
return { return {
'64' : FmhaFwdSplitKVCombineTileSize(64, 32, -1), '64' : FmhaFwdSplitKVCombineTileSize(32, -1),
'128' : FmhaFwdSplitKVCombineTileSize(64, 64, -1), '128' : FmhaFwdSplitKVCombineTileSize(32, -1),
'256' : FmhaFwdSplitKVCombineTileSize(64, 128, -1), '256' : FmhaFwdSplitKVCombineTileSize(32, -1),
} }
else: else:
return None return None
...@@ -614,27 +635,29 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> ...@@ -614,27 +635,29 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
squant = 't' if dtype == 'fp8' else 'f' squant = 't' if dtype == 'fp8' else 'f'
pipelines = [] pipelines = []
if dtype in ['fp16', 'bf16']: if dtype in ['fp16', 'bf16']:
for mask, bias, lse, pagedkv in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"], ["t", "f"]): for mask, bias, pagedkv in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"]):
# TODO: use async pipeline when compiler is more stable # TODO: use async pipeline when compiler is more stable
if hdim == 256 or hdim in [32, 64, 128]: ### [32, 64, 96, 128]: if hdim == 256 or hdim in [32, 64, 128]: ### [32, 64, 96, 128]:
# if True: # if True:
pipelines.append(Pipeline('qr', 'row', 'f', 't', 'f', 'f', bias, lse, squant, pagedkv, mask)) pipelines.append(Pipeline('qr', 'row', 'f', 't', 'f', 'f', bias, 't', squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'col', 'f', 't', 'f', 'f', bias, lse, squant, pagedkv, mask)) pipelines.append(Pipeline('qr', 'col', 'f', 't', 'f', 'f', bias, 't', squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask)) pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, 't', squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'col', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask)) pipelines.append(Pipeline('qr', 'col', 't', 't', 't', 't', bias, 't', squant, pagedkv, mask))
else: else:
pipelines.append(Pipeline('qr_async', 'row', 't', 'f', 't', 't', bias, lse, squant, pagedkv, mask)) pipelines.append(Pipeline('qr_async', 'row', 't', 'f', 't', 't', bias, 't', squant, pagedkv, mask))
pipelines.append(Pipeline('qr_async', 'row', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask)) pipelines.append(Pipeline('qr_async', 'row', 't', 't', 't', 't', bias, 't', squant, pagedkv, mask))
pipelines.append(Pipeline('qr_async', 'col', 't', 'f', 't', 't', bias, lse, squant, pagedkv, mask)) pipelines.append(Pipeline('qr_async', 'col', 't', 'f', 't', 't', bias, 't', squant, pagedkv, mask))
pipelines.append(Pipeline('qr_async', 'col', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask)) pipelines.append(Pipeline('qr_async', 'col', 't', 't', 't', 't', bias, 't', squant, pagedkv, mask))
if receipt == 1: if receipt == 1:
pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask)) # TODO: cover arbitraty hdim pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, 't', squant, pagedkv, mask)) # TODO: cover arbitraty hdim
pipelines.append(Pipeline('qr', 'col', 't', 'f', 't', 't', bias, lse, squant, pagedkv, mask)) # TODO: cover arbitraty hdim pipelines.append(Pipeline('qr', 'col', 't', 'f', 't', 't', bias, 't', squant, pagedkv, mask)) # TODO: cover arbitraty hdim
elif dtype in ['fp8', 'bf8']: elif dtype in ['fp8', 'bf8']:
# no need lse/paged-kv kernels
for mask, bias in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys()): for mask, bias in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys()):
pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 'f', squant, 'f', mask)) pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 't', squant, 'f', mask))
elif dtype in ['fp8fp16', 'fp8bf16']:
# TODO
None
else: else:
assert False assert False
return pipelines return pipelines
...@@ -642,7 +665,7 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> ...@@ -642,7 +665,7 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
gen = list() gen = list()
api_pool = FmhaFwdSplitKVApiPool(mask_impl) api_pool = FmhaFwdSplitKVApiPool(mask_impl)
for dtype in DTYPE_MAP.keys(): for dtype in FWD_DTYPE_MAP.keys():
d = get_fmha_fwd_tile_dict_from_dtype(dtype) d = get_fmha_fwd_tile_dict_from_dtype(dtype)
if d == None: if d == None:
continue continue
...@@ -655,9 +678,6 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> ...@@ -655,9 +678,6 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
if pipeline.F_spad != 't' or pipeline.F_skpad != 't': if pipeline.F_spad != 't' or pipeline.F_skpad != 't':
# in group mode, spad/skpad must be true, since we can't predict if seqlen of current batch need pad or not # in group mode, spad/skpad must be true, since we can't predict if seqlen of current batch need pad or not
continue continue
if pipeline.F_pagedkv == 't':
# we only use batch mode kernels to handle (paged-) kvcache problems
continue
k = Kernel(F_idx=0, k = Kernel(F_idx=0,
F_hdim=hdim, F_hdim=hdim,
F_dtype=dtype, F_dtype=dtype,
...@@ -705,7 +725,7 @@ def get_fwd_splitkv_combine_blobs(kernel_filter : Optional[str], receipt) -> Lis ...@@ -705,7 +725,7 @@ def get_fwd_splitkv_combine_blobs(kernel_filter : Optional[str], receipt) -> Lis
gen = list() gen = list()
for dtype in DTYPE_MAP.keys(): for dtype in FWD_DTYPE_MAP.keys():
d = get_fmha_fwd_splitkv_combine_tile_dict_from_dtype(dtype) d = get_fmha_fwd_splitkv_combine_tile_dict_from_dtype(dtype)
if d == None: if d == None:
continue continue
......
example/ck_tile/01_fmha/fmha_bwd.cpp
View file @ b74918bc
...@@ -101,7 +101,7 @@ auto create_args(int argc, char* argv[]) ...@@ -101,7 +101,7 @@ auto create_args(int argc, char* argv[])
} }
// different threshold for different dtype // different threshold for different dtype
template <typename DataType> template <typename DataTypeConfig>
auto get_elimit(ck_tile::index_t /*hdim_q*/, ck_tile::index_t /*hdim_v*/) auto get_elimit(ck_tile::index_t /*hdim_q*/, ck_tile::index_t /*hdim_v*/)
{ {
double rtol = 1e-2; double rtol = 1e-2;
...@@ -110,7 +110,7 @@ auto get_elimit(ck_tile::index_t /*hdim_q*/, ck_tile::index_t /*hdim_v*/) ...@@ -110,7 +110,7 @@ auto get_elimit(ck_tile::index_t /*hdim_q*/, ck_tile::index_t /*hdim_v*/)
} }
template <> template <>
auto get_elimit<ck_tile::bf16_t>(ck_tile::index_t hdim_q, ck_tile::index_t hdim_v) auto get_elimit<FmhaBwdBf16>(ck_tile::index_t hdim_q, ck_tile::index_t hdim_v)
{ {
double rtol = 1e-2; double rtol = 1e-2;
double atol = 1e-2; double atol = 1e-2;
...@@ -122,7 +122,7 @@ auto get_elimit<ck_tile::bf16_t>(ck_tile::index_t hdim_q, ck_tile::index_t hdim_ ...@@ -122,7 +122,7 @@ auto get_elimit<ck_tile::bf16_t>(ck_tile::index_t hdim_q, ck_tile::index_t hdim_
return ck_tile::make_tuple(rtol, atol); return ck_tile::make_tuple(rtol, atol);
} }
template <typename DataType> template <typename DataTypeConfig>
bool run(const ck_tile::ArgParser& arg_parser) bool run(const ck_tile::ArgParser& arg_parser)
{ {
std::string data_type = arg_parser.get_str("prec"); std::string data_type = arg_parser.get_str("prec");
...@@ -209,7 +209,7 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -209,7 +209,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
const auto seqstart_q_host = generate_seqstarts(mode, batch, seqlen_q); const auto seqstart_q_host = generate_seqstarts(mode, batch, seqlen_q);
const auto seqstart_k_host = generate_seqstarts(mode, batch, seqlen_k); const auto seqstart_k_host = generate_seqstarts(mode, batch, seqlen_k);
using TypeConfig = FmhaBwdTypeConfig<DataType>; using TypeConfig = FmhaBwdTypeConfig<DataTypeConfig>;
using QDataType = typename TypeConfig::QDataType; using QDataType = typename TypeConfig::QDataType;
using KDataType = typename TypeConfig::KDataType; using KDataType = typename TypeConfig::KDataType;
...@@ -933,7 +933,7 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -933,7 +933,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
} }
// clang-format on // clang-format on
auto [rtol, atol] = get_elimit<DataType>(hdim_q, hdim_v); auto [rtol, atol] = get_elimit<DataTypeConfig>(hdim_q, hdim_v);
bool dq_cur_pass = ck_tile::check_err(dq_host_result, bool dq_cur_pass = ck_tile::check_err(dq_host_result,
dq_host_ref, dq_host_ref,
std::string("Error: QGrad Incorrect results!"), std::string("Error: QGrad Incorrect results!"),
...@@ -986,11 +986,11 @@ int main(int argc, char* argv[]) ...@@ -986,11 +986,11 @@ int main(int argc, char* argv[])
const std::string data_type = arg_parser.get_str("prec"); const std::string data_type = arg_parser.get_str("prec");
if(data_type == "fp16") if(data_type == "fp16")
{ {
return run<ck_tile::half_t>(arg_parser) ? 0 : -2; return run<FmhaBwdFp16>(arg_parser) ? 0 : -2;
} }
else if(data_type == "bf16") else if(data_type == "bf16")
{ {
return run<ck_tile::bf16_t>(arg_parser) ? 0 : -2; return run<FmhaBwdBf16>(arg_parser) ? 0 : -2;
} }
return -3; return -3;
......
example/ck_tile/01_fmha/fmha_bwd.hpp
View file @ b74918bc
...@@ -14,11 +14,19 @@ ...@@ -14,11 +14,19 @@
#include <utility> #include <utility>
#include <variant> #include <variant>
struct FmhaBwdFp16
{
};
struct FmhaBwdBf16
{
};
template <typename DataType> template <typename DataType>
struct FmhaBwdTypeConfig; struct FmhaBwdTypeConfig;
template <> template <>
struct FmhaBwdTypeConfig<ck_tile::half_t> struct FmhaBwdTypeConfig<FmhaBwdFp16>
{ {
using QDataType = ck_tile::half_t; using QDataType = ck_tile::half_t;
using KDataType = ck_tile::half_t; using KDataType = ck_tile::half_t;
...@@ -38,7 +46,7 @@ struct FmhaBwdTypeConfig<ck_tile::half_t> ...@@ -38,7 +46,7 @@ struct FmhaBwdTypeConfig<ck_tile::half_t>
}; };
template <> template <>
struct FmhaBwdTypeConfig<ck_tile::bf16_t> struct FmhaBwdTypeConfig<FmhaBwdBf16>
{ {
using QDataType = ck_tile::bf16_t; using QDataType = ck_tile::bf16_t;
using KDataType = ck_tile::bf16_t; using KDataType = ck_tile::bf16_t;
...@@ -150,7 +158,7 @@ auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args) ...@@ -150,7 +158,7 @@ auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
// create group mode kernel arguments // create group mode kernel arguments
if constexpr(FmhaBwdDQDKDVKernel::kIsGroupMode) if constexpr(FmhaBwdDQDKDVKernel::kIsGroupMode)
{ {
return FmhaBwdDQDKDVKernel::MakeKargs(args.q_ptr, return FmhaBwdDQDKDVKernel::MakeKargsImpl(args.q_ptr,
args.k_ptr, args.k_ptr,
args.v_ptr, args.v_ptr,
args.bias_ptr, args.bias_ptr,
...@@ -200,7 +208,7 @@ auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args) ...@@ -200,7 +208,7 @@ auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
} }
else else
{ // create batch mode kernel arguments { // create batch mode kernel arguments
return FmhaBwdDQDKDVKernel::MakeKargs(args.q_ptr, return FmhaBwdDQDKDVKernel::MakeKargsImpl(args.q_ptr,
args.k_ptr, args.k_ptr,
args.v_ptr, args.v_ptr,
args.bias_ptr, args.bias_ptr,
......
example/ck_tile/01_fmha/fmha_fwd.cpp
View file @ b74918bc
...@@ -3,6 +3,7 @@ ...@@ -3,6 +3,7 @@
#include "fmha_fwd.hpp" #include "fmha_fwd.hpp"
#include "ck_tile/host.hpp" #include "ck_tile/host.hpp"
#include "ck_tile/ref/naive_attention.hpp"
#include "mask.hpp" #include "mask.hpp"
#include "rotary.hpp" #include "rotary.hpp"
#include "utils.hpp" #include "utils.hpp"
...@@ -41,7 +42,7 @@ std::ostream& operator<<(std::ostream& os, const std::vector<T>& v) ...@@ -41,7 +42,7 @@ std::ostream& operator<<(std::ostream& os, const std::vector<T>& v)
auto create_args(int argc, char* argv[]) auto create_args(int argc, char* argv[])
{ {
ck_tile::ArgParser arg_parser; ck_tile::ArgParser arg_parser;
arg_parser.insert("v", "1", "weather do CPU validation or not") arg_parser.insert("v", "1", "0:no validation, 2:cpu validation, 2:gpu validation(experimental)")
.insert("mode", "0", "kernel mode. 0:batch, 1:group") .insert("mode", "0", "kernel mode. 0:batch, 1:group")
.insert("b", "2", "batch size") .insert("b", "2", "batch size")
.insert("h", "8", "num of head, for q") .insert("h", "8", "num of head, for q")
...@@ -62,7 +63,7 @@ auto create_args(int argc, char* argv[]) ...@@ -62,7 +63,7 @@ auto create_args(int argc, char* argv[])
"-1 to choose s_knew in [1, s] randomly.") "-1 to choose s_knew in [1, s] randomly.")
.insert("s_kpad", .insert("s_kpad",
"-1", "-1",
"seqlen_k stride between 2 tokens, currently used in group-mode only\n" "seqlen_k stride between 2 batches, currently used in group-mode only\n"
"for kv-cache case, each batch [1,s,h,d]/[1,h,s,d] can have a stride\n" "for kv-cache case, each batch [1,s,h,d]/[1,h,s,d] can have a stride\n"
"along seqlen, instead of packed. same as xformer kv_padding") "along seqlen, instead of packed. same as xformer kv_padding")
.insert("d", "128", "head dim for q, k") .insert("d", "128", "head dim for q, k")
...@@ -142,7 +143,7 @@ auto create_args(int argc, char* argv[]) ...@@ -142,7 +143,7 @@ auto create_args(int argc, char* argv[])
} }
// different threshold for different dtype // different threshold for different dtype
template <typename DataType> template <typename DataTypeConfig>
auto get_elimit(std::string /*init_method*/) auto get_elimit(std::string /*init_method*/)
{ {
double rtol = 1e-3; double rtol = 1e-3;
...@@ -151,7 +152,7 @@ auto get_elimit(std::string /*init_method*/) ...@@ -151,7 +152,7 @@ auto get_elimit(std::string /*init_method*/)
} }
template <> template <>
auto get_elimit<ck_tile::bf16_t>(std::string /*init_method*/) auto get_elimit<FmhaFwdBf16>(std::string /*init_method*/)
{ {
double rtol = 1e-2; double rtol = 1e-2;
double atol = 1e-2; double atol = 1e-2;
...@@ -159,7 +160,7 @@ auto get_elimit<ck_tile::bf16_t>(std::string /*init_method*/) ...@@ -159,7 +160,7 @@ auto get_elimit<ck_tile::bf16_t>(std::string /*init_method*/)
} }
template <> template <>
auto get_elimit<ck_tile::fp8_t>(std::string init_method) auto get_elimit<FmhaFwdFp8>(std::string init_method)
{ {
if(init_method == "ui" || init_method == "ni") if(init_method == "ui" || init_method == "ni")
{ {
...@@ -261,7 +262,7 @@ int override_num_splits_if_necessary( ...@@ -261,7 +262,7 @@ int override_num_splits_if_necessary(
return num_splits; return num_splits;
} }
template <typename DataType> template <typename DataTypeConfig>
bool run(const ck_tile::ArgParser& arg_parser) bool run(const ck_tile::ArgParser& arg_parser)
{ {
std::string data_type = arg_parser.get_str("prec"); std::string data_type = arg_parser.get_str("prec");
...@@ -294,7 +295,8 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -294,7 +295,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
#if !CK_TILE_FMHA_FWD_APPENDKV_API #if !CK_TILE_FMHA_FWD_APPENDKV_API
if(seqlen_knew != 0) if(seqlen_knew != 0)
{ {
std::cerr << "kvcache is not supported. ignoring the 's_knew' option" << std::endl; std::cerr << "fmha_fwd_appendkv() is not enabled. ignoring the 's_knew' option"
<< std::endl;
seqlen_knew = 0; seqlen_knew = 0;
} }
#endif #endif
...@@ -304,8 +306,8 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -304,8 +306,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
} }
ck_tile::index_t rotary_dim = arg_parser.get_int("rotary_dim"); ck_tile::index_t rotary_dim = arg_parser.get_int("rotary_dim");
if constexpr(!(std::is_same_v<DataType, ck_tile::fp16_t> || if constexpr(!(std::is_same_v<DataTypeConfig, FmhaFwdFp16> ||
std::is_same_v<DataType, ck_tile::bf16_t>)) std::is_same_v<DataTypeConfig, FmhaFwdBf16>))
{ {
if(0 < rotary_dim) if(0 < rotary_dim)
{ {
...@@ -321,6 +323,13 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -321,6 +323,13 @@ bool run(const ck_tile::ArgParser& arg_parser)
rotary_dim = 0; rotary_dim = 0;
} }
#endif #endif
// to use fmha_fwd_appendkv(), make sure it's in batch mode
const bool need_append_kvcache = (0 < seqlen_knew || 0 < rotary_dim);
if(need_append_kvcache && mode == mode_enum::group)
{
std::cerr << "fmha_fwd_appendkv() will be invoked. ignoring the 'mode' option" << std::endl;
mode = mode_enum::batch;
}
if(!(rotary_dim <= hdim_q)) if(!(rotary_dim <= hdim_q))
{ {
std::cerr << "rotary_dim should be less than or equal to head dim for q" << std::endl; std::cerr << "rotary_dim should be less than or equal to head dim for q" << std::endl;
...@@ -356,22 +365,26 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -356,22 +365,26 @@ bool run(const ck_tile::ArgParser& arg_parser)
<< std::endl; << std::endl;
use_cache_batch_idx = false; use_cache_batch_idx = false;
} }
#endif #else
if(0 < page_block_size && use_cache_batch_idx) if(use_cache_batch_idx)
{
if(0 < page_block_size)
{ {
std::cerr << "paged-kvcache does not support cache_batch_idx. ignoring the " std::cerr << "paged-kvcache does not support cache_batch_idx. ignoring the "
"'cache_batch_idx' option" "'cache_batch_idx' option"
<< std::endl; << std::endl;
use_cache_batch_idx = false; use_cache_batch_idx = false;
} }
// the input tensor layout for kvcache is same as batch mode else if(mode == mode_enum::group)
const bool need_append_kvcache = (0 < seqlen_knew || 0 < rotary_dim);
const bool use_kvcache = (need_append_kvcache || use_cache_batch_idx || 0 < page_block_size);
if(use_kvcache && mode != mode_enum::batch)
{ {
std::cerr << "kvcache enabled. ignoring the 'mode' option" << std::endl; std::cerr << "group mode will not use cache_batch_idx. ignoring the "
mode = mode_enum::batch; "'cache_batch_idx' option"
<< std::endl;
use_cache_batch_idx = false;
} }
}
#endif
const bool use_kvcache = (need_append_kvcache || use_cache_batch_idx || 0 < page_block_size);
auto [seqlen_qs, seqlen_ks, seqlen_kpads] = auto [seqlen_qs, seqlen_ks, seqlen_kpads] =
decode_seqlen(mode, decode_seqlen(mode,
...@@ -380,7 +393,7 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -380,7 +393,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
arg_parser.get_str("s_k"), arg_parser.get_str("s_k"),
arg_parser.get_str("s_kpad"), arg_parser.get_str("s_kpad"),
/*seqlen_k_min=*/0 < seqlen_knew ? seqlen_knew : 0, /*seqlen_k_min=*/0 < seqlen_knew ? seqlen_knew : 0,
use_kvcache); need_append_kvcache);
// compute kvcache seqlen_k (before appending knew/vnew) // compute kvcache seqlen_k (before appending knew/vnew)
auto cache_seqlen_ks = seqlen_ks; auto cache_seqlen_ks = seqlen_ks;
std::transform(cache_seqlen_ks.begin(), std::transform(cache_seqlen_ks.begin(),
...@@ -416,25 +429,6 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -416,25 +429,6 @@ bool run(const ck_tile::ArgParser& arg_parser)
return atoi(squant_str.c_str()) != 0 ? true : false; return atoi(squant_str.c_str()) != 0 ? true : false;
}(); }();
float range_q = arg_parser.get_float("range_q");
float range_k = arg_parser.get_float("range_k");
float range_v = arg_parser.get_float("range_v");
float range_p = arg_parser.get_float("range_p");
float range_o = arg_parser.get_float("range_o");
float dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<DataType>::max());
float scale_p = 1.f;
float scale_o = 1.f;
if(squant)
{
scale_s = scale_s * (range_q / dtype_max) * (range_k / dtype_max);
scale_p = dtype_max / range_p;
// scale_p = [max(fp8_t)/range_o] * [range_p/max(fp8_t)] * [range_v/max(fp8_t)]
scale_o = range_p * range_v / range_o / dtype_max;
}
std::string vlayout = arg_parser.get_str("vlayout"); std::string vlayout = arg_parser.get_str("vlayout");
bool lse = arg_parser.get_bool("lse"); bool lse = arg_parser.get_bool("lse");
...@@ -454,7 +448,7 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -454,7 +448,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
} }
bool s_randval = false; bool s_randval = false;
if(p_drop > 0.0f && do_validation) if(p_drop > 0.0f && do_validation != 0)
{ {
s_randval = true; s_randval = true;
} }
...@@ -487,7 +481,7 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -487,7 +481,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
const auto seqstart_k_host = to_seqstarts(seqlen_ks); const auto seqstart_k_host = to_seqstarts(seqlen_ks);
const auto seqstart_k_with_padding_host = to_seqstarts(seqlen_kpads); const auto seqstart_k_with_padding_host = to_seqstarts(seqlen_kpads);
using TypeConfig = FmhaFwdTypeConfig<DataType>; using TypeConfig = FmhaFwdTypeConfig<DataTypeConfig>;
using QDataType = typename TypeConfig::QDataType; using QDataType = typename TypeConfig::QDataType;
using KDataType = typename TypeConfig::KDataType; using KDataType = typename TypeConfig::KDataType;
...@@ -501,6 +495,28 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -501,6 +495,28 @@ bool run(const ck_tile::ArgParser& arg_parser)
using OaccDataType = typename TypeConfig::OaccDataType; using OaccDataType = typename TypeConfig::OaccDataType;
using ODataType = typename TypeConfig::ODataType; using ODataType = typename TypeConfig::ODataType;
float range_q = arg_parser.get_float("range_q");
float range_k = arg_parser.get_float("range_k");
float range_v = arg_parser.get_float("range_v");
float range_p = arg_parser.get_float("range_p");
float range_o = arg_parser.get_float("range_o");
float q_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<QDataType>::max());
float k_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<KDataType>::max());
float v_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<VDataType>::max());
float p_dtype_max = v_dtype_max; // assume p and v is the same type
float o_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<ODataType>::max());
float scale_p = 1.f;
float scale_o = 1.f;
if(squant)
{
scale_s = scale_s * (range_q / q_dtype_max) * (range_k / k_dtype_max);
scale_p = p_dtype_max / range_p;
scale_o = (o_dtype_max / range_o) * (range_p / p_dtype_max) * (range_v / v_dtype_max);
}
// accumulation numbers for performance evaluation // accumulation numbers for performance evaluation
std::size_t flop = 0, num_byte = 0; std::size_t flop = 0, num_byte = 0;
auto max_seqlen_q = auto max_seqlen_q =
...@@ -697,14 +713,14 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -697,14 +713,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
else if(init_method == "ufq" || init_method == "uf:q" || else if(init_method == "ufq" || init_method == "uf:q" ||
init_method == "3") // suitable for fp8 quantization init_method == "3") // suitable for fp8 quantization
{ {
ck_tile::FillUniformDistribution<QDataType>{-dtype_max, dtype_max, seed}(q_host); ck_tile::FillUniformDistribution<QDataType>{-q_dtype_max, q_dtype_max, seed}(q_host);
ck_tile::FillUniformDistribution<KDataType>{-dtype_max, dtype_max, seed}(k_host); ck_tile::FillUniformDistribution<KDataType>{-k_dtype_max, k_dtype_max, seed}(k_host);
ck_tile::FillUniformDistribution<KDataType>{-dtype_max, dtype_max, seed}(knew_host); ck_tile::FillUniformDistribution<KDataType>{-k_dtype_max, k_dtype_max, seed}(knew_host);
ck_tile::FillUniformDistribution<VDataType>{-dtype_max, dtype_max, seed}(v_host); ck_tile::FillUniformDistribution<VDataType>{-v_dtype_max, v_dtype_max, seed}(v_host);
ck_tile::FillUniformDistribution<VDataType>{-dtype_max, dtype_max, seed}(vnew_host); ck_tile::FillUniformDistribution<VDataType>{-v_dtype_max, v_dtype_max, seed}(vnew_host);
// bias_fp8 = qscale_bias * bias_fp32 // bias_fp8 = qscale_bias * bias_fp32
float qscale_bias = (dtype_max / range_q) * (dtype_max / range_k); float qscale_bias = (q_dtype_max / range_q) * (k_dtype_max / range_k);
// Assume bias is in [-1.f, 1.f] in original fp32 // Assume bias is in [-1.f, 1.f] in original fp32
ck_tile::FillUniformDistribution<BiasDataType>{-qscale_bias, qscale_bias, seed}(bias_host); ck_tile::FillUniformDistribution<BiasDataType>{-qscale_bias, qscale_bias, seed}(bias_host);
} }
...@@ -741,8 +757,10 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -741,8 +757,10 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::DeviceMem o_buf(o_host.get_element_space_size_in_bytes()); ck_tile::DeviceMem o_buf(o_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem seqstart_q(seqstart_q_host.size() * sizeof(int32_t)); ck_tile::DeviceMem seqstart_q(seqstart_q_host.size() * sizeof(int32_t));
ck_tile::DeviceMem seqstart_k(seqstart_k_host.size() * sizeof(int32_t)); ck_tile::DeviceMem seqstart_k(seqstart_k_host.size() * sizeof(int32_t));
ck_tile::DeviceMem seqlen_k_buf( ck_tile::DeviceMem seqlen_k_buf((mode == mode_enum::batch && use_kvcache) ||
use_kvcache || 0 <= seqlen_kpads[0] ? seqlen_ks.size() * sizeof(int32_t) : 0); 0 <= seqlen_kpads[0]
? seqlen_ks.size() * sizeof(int32_t)
: 0);
ck_tile::DeviceMem cache_seqlen_k_buf( ck_tile::DeviceMem cache_seqlen_k_buf(
need_append_kvcache ? cache_seqlen_ks.size() * sizeof(int32_t) : 0); need_append_kvcache ? cache_seqlen_ks.size() * sizeof(int32_t) : 0);
ck_tile::DeviceMem rotary_cos_buf(rotary_cos_host.get_element_space_size_in_bytes()); ck_tile::DeviceMem rotary_cos_buf(rotary_cos_host.get_element_space_size_in_bytes());
...@@ -763,7 +781,9 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -763,7 +781,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
seqstart_q.ToDevice(seqstart_q_host.data()); seqstart_q.ToDevice(seqstart_q_host.data());
seqstart_k.ToDevice(seqlen_kpads[0] < 0 ? seqstart_k_host.data() seqstart_k.ToDevice(seqlen_kpads[0] < 0 ? seqstart_k_host.data()
: seqstart_k_with_padding_host.data()); : seqstart_k_with_padding_host.data());
seqlen_k_buf.ToDevice(use_kvcache || 0 <= seqlen_kpads[0] ? seqlen_ks.data() : nullptr); seqlen_k_buf.ToDevice((mode == mode_enum::batch && use_kvcache) || 0 <= seqlen_kpads[0]
? seqlen_ks.data()
: nullptr);
cache_seqlen_k_buf.ToDevice(need_append_kvcache ? cache_seqlen_ks.data() : nullptr); cache_seqlen_k_buf.ToDevice(need_append_kvcache ? cache_seqlen_ks.data() : nullptr);
rotary_cos_buf.ToDevice(rotary_cos_host.data()); rotary_cos_buf.ToDevice(rotary_cos_host.data());
rotary_sin_buf.ToDevice(rotary_sin_host.data()); rotary_sin_buf.ToDevice(rotary_sin_host.data());
...@@ -976,8 +996,9 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -976,8 +996,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
(mode == mode_enum::group ? seqstart_q.GetDeviceBuffer() : nullptr); (mode == mode_enum::group ? seqstart_q.GetDeviceBuffer() : nullptr);
args.seqstart_k_ptr = args.seqstart_k_ptr =
(mode == mode_enum::group ? seqstart_k.GetDeviceBuffer() : nullptr); (mode == mode_enum::group ? seqstart_k.GetDeviceBuffer() : nullptr);
args.seqlen_k_ptr = args.seqlen_k_ptr = ((mode == mode_enum::batch && use_kvcache) || 0 <= k_paddings_[0]
(use_kvcache || 0 <= k_paddings_[0] ? seqlen_k_buf.GetDeviceBuffer() : nullptr); ? seqlen_k_buf.GetDeviceBuffer()
: nullptr);
args.seqlen_k = shape_seqlen_k; // unused in group mode (or kvcache enabled) args.seqlen_k = shape_seqlen_k; // unused in group mode (or kvcache enabled)
args.max_seqlen_q = max_seqlen_q; args.max_seqlen_q = max_seqlen_q;
...@@ -1029,6 +1050,7 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -1029,6 +1050,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
(0 < page_block_size ? block_table_buf.GetDeviceBuffer() : nullptr); (0 < page_block_size ? block_table_buf.GetDeviceBuffer() : nullptr);
args.batch_stride_block_table = batch_stride_block_table; args.batch_stride_block_table = batch_stride_block_table;
args.page_block_size = page_block_size; args.page_block_size = page_block_size;
args.is_gappy = false; // use 'false' for flash-attention integration
args.cache_batch_idx = args.cache_batch_idx =
(use_cache_batch_idx ? cache_batch_idx_buf.GetDeviceBuffer() : nullptr); (use_cache_batch_idx ? cache_batch_idx_buf.GetDeviceBuffer() : nullptr);
...@@ -1100,25 +1122,75 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -1100,25 +1122,75 @@ bool run(const ck_tile::ArgParser& arg_parser)
<< std::setprecision(2) << tflops << " TFlops, " << std::setprecision(2) << gb_per_sec << std::setprecision(2) << tflops << " TFlops, " << std::setprecision(2) << gb_per_sec
<< " GB/s" << std::flush; << " GB/s" << std::flush;
if(!do_validation) if(do_validation == 0)
{ {
std::cout << std::flush << std::endl; std::cout << std::flush << std::endl;
return true; return true;
} }
if(do_validation == 2)
{
// NOTE: use gpu to do validation
ck_tile::naive_attention_fwd_traits naive_t;
naive_t.q_type = data_type;
naive_t.k_type = data_type;
naive_t.v_type = data_type;
naive_t.o_type = data_type;
naive_t.q_layout = i_perm == 1 ? "bhsd" : "bshd";
naive_t.k_layout = i_perm == 1 ? "bhsd" : "bshd";
naive_t.v_layout = i_perm == 1 ? "bhsd" : "bshd";
naive_t.o_layout = o_perm == 1 ? "bhsd" : "bshd";
naive_t.variation = 0; // TODO?
ck_tile::DeviceMem o_naive_buf(o_host.get_element_space_size_in_bytes());
ck_tile::naive_attention_fwd_args naive_a;
naive_a.q_ptr = q_buf.GetDeviceBuffer();
naive_a.k_ptr = k_buf.GetDeviceBuffer();
naive_a.v_ptr = v_buf.GetDeviceBuffer();
naive_a.o_ptr = o_naive_buf.GetDeviceBuffer();
naive_a.scale_s = scale_s;
naive_a.context_len_ptr = nullptr; // used when seqlen kv come from a pointer
naive_a.page_table_ptr =
nullptr; // [batch, num_blocks] seqlen_kv is in different block(paged attn)
naive_a.hdim = hdim_q;
naive_a.hdim_v = hdim_v; // could be cross-attn, where V and Q/K hdim are different
naive_a.batch_q = batch;
naive_a.batch_kv = batch;
naive_a.batch_ratio_kv = 1; // batch_q / batch_kv
naive_a.seqlen_q = seqlen_qs[0];
naive_a.seqlen_kv = seqlen_ks[0]; // if context_len_ptr is not nullptr, ignore this field
naive_a.nhead_q = nhead;
naive_a.nhead_kv = nhead_k;
naive_a.nhead_ratio_kv = naive_a.nhead_q / naive_a.nhead_kv; // nhead_q / nhead_kv
naive_a.page_size = 0; // if paged, the seqlen-kv for each block
ck_tile::stream_config naive_s{};
naive_attention_fwd(naive_t, naive_a, naive_s);
auto o_naive_ref = o_naive_buf.ToHost<ODataType>();
o_buf.FromDevice(o_host.data()); // TODO: ugly
auto [rtol_, atol_] = get_elimit<DataTypeConfig>(init_method);
bool pass_ = ck_tile::check_err(
o_host, o_naive_ref, std::string("OUT Error: Incorrect results!"), rtol_, atol_);
std::cout << ", valid:" << (pass_ ? "y" : "n") << std::flush << std::endl;
return pass_;
}
o_buf.FromDevice(o_host.data()); o_buf.FromDevice(o_host.data());
lse_buf.FromDevice(lse_host.data()); lse_buf.FromDevice(lse_host.data());
randval_buf.FromDevice(randval_host.data()); randval_buf.FromDevice(randval_host.data());
auto p_compute_element_func = [&]() { auto p_compute_element_func = [&]() {
if constexpr(std::is_same_v<DataType, ck_tile::fp8_t>) if constexpr(std::is_same_v<DataTypeConfig, ck_tile::fp8_t>)
return ck_tile::scales{scale_p}; return ck_tile::scales{scale_p};
else else
return ck_tile::identity{}; return ck_tile::identity{};
}(); }();
auto oacc_element_func = [&]() { auto oacc_element_func = [&]() {
if constexpr(std::is_same_v<DataType, ck_tile::fp8_t>) if constexpr(std::is_same_v<DataTypeConfig, ck_tile::fp8_t>)
return ck_tile::composes(ck_tile::saturates<ck_tile::fp8_t>{}, return ck_tile::composes(ck_tile::saturates<ck_tile::fp8_t>{},
ck_tile::scales{scale_o}); ck_tile::scales{scale_o});
else else
...@@ -1440,7 +1512,7 @@ bool run(const ck_tile::ArgParser& arg_parser) ...@@ -1440,7 +1512,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
else o_host_result.ForEach([&](auto& self, auto idx) { self(idx) = o_host(b_idx, idx[1] + query_offset, idx[0], idx[2]); }); else o_host_result.ForEach([&](auto& self, auto idx) { self(idx) = o_host(b_idx, idx[1] + query_offset, idx[0], idx[2]); });
// clang-format on // clang-format on
auto [rtol, atol] = get_elimit<DataType>(init_method); auto [rtol, atol] = get_elimit<DataTypeConfig>(init_method);
bool cur_pass = ck_tile::check_err( bool cur_pass = ck_tile::check_err(
o_host_result, o_host_ref, std::string("OUT Error: Incorrect results!"), rtol, atol); o_host_result, o_host_ref, std::string("OUT Error: Incorrect results!"), rtol, atol);
pass &= cur_pass; pass &= cur_pass;
...@@ -1497,15 +1569,15 @@ int main(int argc, char* argv[]) ...@@ -1497,15 +1569,15 @@ int main(int argc, char* argv[])
const std::string data_type = arg_parser.get_str("prec"); const std::string data_type = arg_parser.get_str("prec");
if(data_type == "fp16") if(data_type == "fp16")
{ {
return run<ck_tile::half_t>(arg_parser) ? 0 : -2; return run<FmhaFwdFp16>(arg_parser) ? 0 : -2;
} }
else if(data_type == "bf16") else if(data_type == "bf16")
{ {
return run<ck_tile::bf16_t>(arg_parser) ? 0 : -2; return run<FmhaFwdBf16>(arg_parser) ? 0 : -2;
} }
else if(data_type == "fp8") else if(data_type == "fp8")
{ {
return run<ck_tile::fp8_t>(arg_parser) ? 0 : -2; return run<FmhaFwdFp8>(arg_parser) ? 0 : -2;
} }
return -3; return -3;
......
example/ck_tile/01_fmha/fmha_fwd.hpp
View file @ b74918bc
...@@ -16,11 +16,35 @@ ...@@ -16,11 +16,35 @@
#include <utility> #include <utility>
#include <variant> #include <variant>
struct FmhaFwdFp16
{
};
struct FmhaFwdBf16
{
};
struct FmhaFwdFp8
{
};
struct FmhaFwdBf8
{
};
struct FmhaFwdFp8Fp16
{
};
struct FmhaFwdFp8Bf16
{
};
template <typename DataType> template <typename DataType>
struct FmhaFwdTypeConfig; struct FmhaFwdTypeConfig;
template <> template <>
struct FmhaFwdTypeConfig<ck_tile::half_t> struct FmhaFwdTypeConfig<FmhaFwdFp16>
{ {
using QDataType = ck_tile::half_t; using QDataType = ck_tile::half_t;
using KDataType = ck_tile::half_t; using KDataType = ck_tile::half_t;
...@@ -36,7 +60,7 @@ struct FmhaFwdTypeConfig<ck_tile::half_t> ...@@ -36,7 +60,7 @@ struct FmhaFwdTypeConfig<ck_tile::half_t>
}; };
template <> template <>
struct FmhaFwdTypeConfig<ck_tile::bf16_t> struct FmhaFwdTypeConfig<FmhaFwdBf16>
{ {
using QDataType = ck_tile::bf16_t; using QDataType = ck_tile::bf16_t;
using KDataType = ck_tile::bf16_t; using KDataType = ck_tile::bf16_t;
...@@ -52,7 +76,7 @@ struct FmhaFwdTypeConfig<ck_tile::bf16_t> ...@@ -52,7 +76,7 @@ struct FmhaFwdTypeConfig<ck_tile::bf16_t>
}; };
template <> template <>
struct FmhaFwdTypeConfig<ck_tile::fp8_t> struct FmhaFwdTypeConfig<FmhaFwdFp8>
{ {
using QDataType = ck_tile::fp8_t; using QDataType = ck_tile::fp8_t;
using KDataType = ck_tile::fp8_t; using KDataType = ck_tile::fp8_t;
...@@ -68,7 +92,7 @@ struct FmhaFwdTypeConfig<ck_tile::fp8_t> ...@@ -68,7 +92,7 @@ struct FmhaFwdTypeConfig<ck_tile::fp8_t>
}; };
template <> template <>
struct FmhaFwdTypeConfig<ck_tile::bf8_t> struct FmhaFwdTypeConfig<FmhaFwdBf8>
{ {
using QDataType = ck_tile::bf8_t; using QDataType = ck_tile::bf8_t;
using KDataType = ck_tile::bf8_t; using KDataType = ck_tile::bf8_t;
...@@ -165,6 +189,8 @@ struct fmha_fwd_splitkv_args ...@@ -165,6 +189,8 @@ struct fmha_fwd_splitkv_args
void* block_table_ptr; void* block_table_ptr;
ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr
ck_tile::index_t page_block_size; // only used if 'block_table_ptr' is not nullptr ck_tile::index_t page_block_size; // only used if 'block_table_ptr' is not nullptr
bool is_gappy; // differentiate seqstart_k_ptr usage. only used if 'block_table_ptr' is not
// nullptr.
const void* cache_batch_idx; const void* cache_batch_idx;
...@@ -173,9 +199,21 @@ struct fmha_fwd_splitkv_args ...@@ -173,9 +199,21 @@ struct fmha_fwd_splitkv_args
// seqlen_k = kargs.seqlen_k // seqlen_k = kargs.seqlen_k
// group mode: seqlen_q = kargs.seqstart_q_ptr[b + 1] - kargs.seqstart_q_ptr[b] // group mode: seqlen_q = kargs.seqstart_q_ptr[b + 1] - kargs.seqstart_q_ptr[b]
// seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b] // seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b]
// kvcache mode (use same kernel as batch mode): // or kargs.seqlen_k_ptr[b]
//
// batch mode (kvcache):
// seqlen_q = kargs.seqlen_q // seqlen_q = kargs.seqlen_q
// seqlen_k = kargs.seqlen_k_ptr[b]
// group mode (kvcache):
// seqlen_q = kargs.seqstart_q_ptr[b + 1] - kargs.seqstart_q_ptr[b]
//
// when is_gappy=true:
// seqlen_k = kargs.seqlen_k_ptr[b]
// seqstart_k_ptr[b] now store local offset of each batch
//
// when is_gappy=false:
// seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b] // seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b]
// or kargs.seqlen_k_ptr[b]
const void* seqstart_q_ptr; const void* seqstart_q_ptr;
const void* seqstart_k_ptr; const void* seqstart_k_ptr;
const void* seqlen_k_ptr; const void* seqlen_k_ptr;
...@@ -251,7 +289,7 @@ struct fmha_fwd_appendkv_args ...@@ -251,7 +289,7 @@ struct fmha_fwd_appendkv_args
ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr
ck_tile::index_t page_block_size; // only used if 'block_table_ptr' is not nullptr ck_tile::index_t page_block_size; // only used if 'block_table_ptr' is not nullptr
const void* cache_batch_idx; const void* cache_batch_idx; // only used if block_table_ptr is nullptr -> batch mode (kvcache)
ck_tile::index_t stride_q; ck_tile::index_t stride_q;
ck_tile::index_t stride_k; ck_tile::index_t stride_k;
...@@ -278,7 +316,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args) ...@@ -278,7 +316,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
// create group mode kernel arguments // create group mode kernel arguments
if constexpr(FmhaKernel::kIsGroupMode) if constexpr(FmhaKernel::kIsGroupMode)
{ {
return FmhaKernel::MakeKargs(args.q_ptr, return FmhaKernel::MakeKargsImpl(args.q_ptr,
args.k_ptr, args.k_ptr,
args.v_ptr, args.v_ptr,
args.bias_ptr, args.bias_ptr,
...@@ -317,7 +355,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args) ...@@ -317,7 +355,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
} }
else else
{ // create batch mode kernel arguments { // create batch mode kernel arguments
return FmhaKernel::MakeKargs(args.q_ptr, return FmhaKernel::MakeKargsImpl(args.q_ptr,
args.k_ptr, args.k_ptr,
args.v_ptr, args.v_ptr,
args.bias_ptr, args.bias_ptr,
...@@ -389,6 +427,10 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args) ...@@ -389,6 +427,10 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args)
args.nhead_q, args.nhead_q,
args.nhead_q / args.nhead_k, args.nhead_q / args.nhead_k,
args.num_splits, args.num_splits,
args.block_table_ptr,
args.batch_stride_block_table,
args.page_block_size,
args.is_gappy,
args.scale_s, args.scale_s,
args.scale_p, args.scale_p,
args.stride_q, args.stride_q,
...@@ -667,7 +709,6 @@ std::string fmha_fwd_splitkv_get_name_(); ...@@ -667,7 +709,6 @@ std::string fmha_fwd_splitkv_get_name_();
template <ck_tile::index_t HDim_, template <ck_tile::index_t HDim_,
typename DataType_, typename DataType_,
bool kIsGroupMode_, bool kIsGroupMode_,
ck_tile::index_t kM0_,
ck_tile::index_t kN1_, ck_tile::index_t kN1_,
bool kStoreLse_, bool kStoreLse_,
bool kDoFp8StaticQuant_, bool kDoFp8StaticQuant_,
...@@ -678,7 +719,6 @@ struct fmha_fwd_splitkv_combine_traits_ ...@@ -678,7 +719,6 @@ struct fmha_fwd_splitkv_combine_traits_
static constexpr ck_tile::index_t HDim = HDim_; static constexpr ck_tile::index_t HDim = HDim_;
using DataType = ck_tile::remove_cvref_t<DataType_>; using DataType = ck_tile::remove_cvref_t<DataType_>;
static constexpr bool kIsGroupMode = kIsGroupMode_; static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr ck_tile::index_t kM0 = kM0_;
static constexpr ck_tile::index_t kN1 = kN1_; static constexpr ck_tile::index_t kN1 = kN1_;
static constexpr bool kStoreLse = kStoreLse_; static constexpr bool kStoreLse = kStoreLse_;
static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_; static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
......
example/ck_tile/01_fmha/utils.hpp
View file @ b74918bc
...@@ -145,7 +145,7 @@ decode_seqlen(mode_enum mode, ...@@ -145,7 +145,7 @@ decode_seqlen(mode_enum mode,
std::string k_val, std::string k_val,
std::string k_pad_val, std::string k_pad_val,
ck_tile::index_t seqlen_k_min = 0, ck_tile::index_t seqlen_k_min = 0,
bool use_kvcache = false, bool need_append_kvcache = false,
std::optional<unsigned> seed = std::nullopt) std::optional<unsigned> seed = std::nullopt)
{ {
#define _S2I_(str_) static_cast<ck_tile::index_t>(std::atoi((str_).c_str())) #define _S2I_(str_) static_cast<ck_tile::index_t>(std::atoi((str_).c_str()))
...@@ -159,7 +159,7 @@ decode_seqlen(mode_enum mode, ...@@ -159,7 +159,7 @@ decode_seqlen(mode_enum mode,
const ck_tile::index_t seqlen_k_max = (k < 0 ? q : k); const ck_tile::index_t seqlen_k_max = (k < 0 ? q : k);
std::vector<ck_tile::index_t> seqlen_ks(batch, seqlen_k_max); std::vector<ck_tile::index_t> seqlen_ks(batch, seqlen_k_max);
if(1 < batch && use_kvcache) if(1 < batch && need_append_kvcache)
{ {
// to keep the original s_k value, we always use seqlen_k_max in first batch // to keep the original s_k value, we always use seqlen_k_max in first batch
randints(std::next(seqlen_ks.begin()), randints(std::next(seqlen_ks.begin()),
......
example/ck_tile/03_gemm/CMakeLists.txt
View file @ b74918bc
add_executable(tile_example_gemm_basic EXCLUDE_FROM_ALL gemm_basic.cpp) add_executable(tile_example_gemm_basic EXCLUDE_FROM_ALL gemm_basic.cpp)
add_executable(tile_example_gemm_mem_pipeline EXCLUDE_FROM_ALL gemm_mem_pipeline.cpp) add_executable(tile_example_universal_gemm EXCLUDE_FROM_ALL universal_gemm.cpp)
example/ck_tile/03_gemm/README.md
View file @ b74918bc
...@@ -8,7 +8,10 @@ This folder contains example for GEMM using ck_tile tile-programming implementat ...@@ -8,7 +8,10 @@ This folder contains example for GEMM using ck_tile tile-programming implementat
mkdir build && cd build mkdir build && cd build
# you can replace <arch> with the appropriate architecture (for example gfx90a or gfx942) or leave it blank # you can replace <arch> with the appropriate architecture (for example gfx90a or gfx942) or leave it blank
sh ../script/cmake-ck-dev.sh ../ <arch> sh ../script/cmake-ck-dev.sh ../ <arch>
# The basic pipeline method on the gemm calculation
make tile_example_gemm_basic -j make tile_example_gemm_basic -j
# The memory bound pipeline on the gemm calculation
make tile_example_gemm_mem_pipeline -j
``` ```
This will result in an executable `build/bin/tile_example_gemm_basic` This will result in an executable `build/bin/tile_example_gemm_basic`
......
example/ck_tile/03_gemm/gemm_basic.cpp
View file @ b74918bc
...@@ -15,12 +15,13 @@ ...@@ -15,12 +15,13 @@
#include "gemm_basic.hpp" #include "gemm_basic.hpp"
template <typename ALayout, typename BLayout, typename CLayout> template <typename ALayout, typename BLayout, typename CLayout>
float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s) float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config& s)
{ {
// The kPadA, kPadB, kPadC & kBlockPerCu should also come from the Codegen part. // The kPadM, kPadN, kPadK & kBlockPerCu should also come from the Codegen part.
constexpr bool kPadA = true; constexpr bool kPadM = false;
constexpr bool kPadB = true; constexpr bool kPadN = false;
constexpr bool kPadC = true; constexpr bool kPadK = false;
constexpr bool kTilePermute = false; constexpr bool kTilePermute = false;
// The rank and permutation will also be generate out by the CodeGen part. // The rank and permutation will also be generate out by the CodeGen part.
constexpr ck_tile::index_t kOutputRank = 2; constexpr ck_tile::index_t kOutputRank = 2;
...@@ -56,8 +57,8 @@ float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s) ...@@ -56,8 +57,8 @@ float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s)
CShuffleEpilogue, CShuffleEpilogue,
ck_tile::CShuffleEpilogue<ck_tile::CShuffleEpilogueProblem<AccDataType, ck_tile::CShuffleEpilogue<ck_tile::CShuffleEpilogueProblem<AccDataType,
CDataType, CDataType,
kPadA, kPadM,
kPadB, kPadN,
kTilePermute, kTilePermute,
kOutputRank, kOutputRank,
1, 1,
...@@ -65,32 +66,29 @@ float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s) ...@@ -65,32 +66,29 @@ float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s)
TilePartitioner::kM, TilePartitioner::kM,
TilePartitioner::kN>>, TilePartitioner::kN>>,
ck_tile::Default2DEpilogue< ck_tile::Default2DEpilogue<
ck_tile::Default2DEpilogueProblem<AccDataType, CDataType, kPadA, kPadB>>>; ck_tile::Default2DEpilogueProblem<AccDataType, CDataType, kPadM, kPadN>>>;
using CodegenGemmTraits = using CodegenGemmTraits =
ck_tile::TileGemmTraits<kPadA, kPadB, kPadC, ALayout, BLayout, CLayout>; ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
using CodegenPipelineProblem = ck_tile:: using CodegenPipelineProblem = ck_tile::
GemmPipelineProblem<ADataType, BDataType, AccDataType, CodegenGemmShape, CodegenGemmTraits>; GemmPipelineProblem<ADataType, BDataType, AccDataType, CodegenGemmShape, CodegenGemmTraits>;
using CodegenGemmPolicy = ck_tile::UniversalGemmPipelineAgBgCrPolicy<ALayout, BLayout, CLayout>; using CodegenGemmPolicy = ck_tile::UniversalGemmPipelineAgBgCrPolicy;
using CodegenGemmPipeline = using CodegenGemmPipeline =
ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem, CodegenGemmPolicy>; ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem, CodegenGemmPolicy>;
// ToDo: Will add the codegen part to test different pipeline policies in GEMM. // ToDo: Will add the codegen part to test different pipeline policies in GEMM.
// Now we only use the BlockGemmASmemBSmemCRegV1DefaultPolicy. // Now we only use the BlockGemmASmemBSmemCRegV1DefaultPolicy.
using Kernel = ck_tile::GemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>; using Kernel = ck_tile::GemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
auto kargs = Kernel::MakeKargs(args.p_a, auto kargs = Kernel::MakeKernelArgs(args);
args.p_b,
args.p_c, const dim3 grids = Kernel::GridSize(args.M, args.N, args.k_batch);
args.M,
args.N,
args.K,
args.stride_A,
args.stride_B,
args.stride_C);
const dim3 grids = Kernel::GridSize(args.M, args.N, args.kbatch);
constexpr dim3 blocks = Kernel::BlockSize(); constexpr dim3 blocks = Kernel::BlockSize();
if(!Kernel::IsSupportedArgument(kargs))
{
throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
}
if(s.log_level_ > 0) if(s.log_level_ > 0)
{ {
std::cout << "Launching kernel with args:" std::cout << "Launching kernel with args:"
......
example/ck_tile/03_gemm/gemm_basic.hpp
View file @ b74918bc
...@@ -51,20 +51,6 @@ using BDataType = Types::BDataType; ...@@ -51,20 +51,6 @@ using BDataType = Types::BDataType;
using AccDataType = Types::AccDataType; using AccDataType = Types::AccDataType;
using CDataType = Types::CDataType; using CDataType = Types::CDataType;
struct gemm_basic_args
{
const void* p_a;
const void* p_b;
void* p_c;
ck_tile::index_t kbatch;
ck_tile::index_t M;
ck_tile::index_t N;
ck_tile::index_t K;
ck_tile::index_t stride_A;
ck_tile::index_t stride_B;
ck_tile::index_t stride_C;
};
auto create_args(int argc, char* argv[]) auto create_args(int argc, char* argv[])
{ {
ck_tile::ArgParser arg_parser; ck_tile::ArgParser arg_parser;
...@@ -89,4 +75,4 @@ auto create_args(int argc, char* argv[]) ...@@ -89,4 +75,4 @@ auto create_args(int argc, char* argv[])
} }
// host API // host API
float gemm_calc(gemm_basic_args args, const ck_tile::stream_config& s); float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config& s);
example/ck_tile/03_gemm/run_gemm_example.inc
View file @ b74918bc
...@@ -16,11 +16,11 @@ float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf, ...@@ -16,11 +16,11 @@ float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
int n_warmup, int n_warmup,
int n_repeat) int n_repeat)
{ {
gemm_basic_args args; ck_tile::GemmHostArgs args;
args.p_a = a_m_k_dev_buf.GetDeviceBuffer(); args.a_ptr = a_m_k_dev_buf.GetDeviceBuffer();
args.p_b = b_k_n_dev_buf.GetDeviceBuffer(); args.b_ptr = b_k_n_dev_buf.GetDeviceBuffer();
args.p_c = c_m_n_dev_buf.GetDeviceBuffer(); args.c_ptr = c_m_n_dev_buf.GetDeviceBuffer();
args.kbatch = kbatch; args.k_batch = kbatch;
args.M = M; args.M = M;
args.N = N; args.N = N;
args.K = K; args.K = K;
...@@ -31,15 +31,13 @@ float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf, ...@@ -31,15 +31,13 @@ float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
float ave_time = gemm_calc<ALayout, BLayout, CLayout>( float ave_time = gemm_calc<ALayout, BLayout, CLayout>(
args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat}); args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat});
std::string op_name{"Gemm{MemBoundPipeline}"};
std::size_t flop = std::size_t(2) * M * N * K; std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_byte = std::size_t num_byte =
sizeof(ADataType) * M * K + sizeof(BDataType) * N * K + sizeof(CDataType) * M * N; sizeof(ADataType) * M * K + sizeof(BDataType) * N * K + sizeof(CDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time; float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_byte / 1.E6 / ave_time; float gb_per_sec = num_byte / 1.E6 / ave_time;
std::cout << "Run " << op_name << "kernel with M =" << M << " N =" << N << " K =" << K std::cout << "Run Gemm kernel with M =" << M << " N =" << N << " K =" << K
<< " StrideA =" << stride_A << " StrideB =" << stride_B << " StrideC =" << stride_C << " StrideA =" << stride_A << " StrideB =" << stride_B << " StrideC =" << stride_C
<< " : " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, " << " : " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< std::endl; << std::endl;
...@@ -114,7 +112,6 @@ int run_gemm_example_with_layouts(int argc, ...@@ -114,7 +112,6 @@ int run_gemm_example_with_layouts(int argc,
f_host_tensor_descriptor(M, N, stride_C, CLayout{})); f_host_tensor_descriptor(M, N, stride_C, CLayout{}));
// TODO: add different init types // TODO: add different init types
ck_tile::FillUniformDistribution<ADataType>{-5.f, 5.f}(a_m_k); 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<BDataType>{-5.f, 5.f}(b_k_n);
...@@ -164,14 +161,39 @@ int run_gemm_example_with_layouts(int argc, ...@@ -164,14 +161,39 @@ int run_gemm_example_with_layouts(int argc,
c_m_n_gpu_ref.SetZero(); c_m_n_gpu_ref.SetZero();
c_m_n_gpu_buf_ref.SetZero(); c_m_n_gpu_buf_ref.SetZero();
ADataType* d_A;
BDataType* d_B;
CDataType* d_C;
ck_tile::hip_check_error(hipMalloc(&d_A, M * K * sizeof(ADataType)));
ck_tile::hip_check_error(hipMalloc(&d_B, N * K * sizeof(BDataType)));
ck_tile::hip_check_error(hipMalloc(&d_C, M * N * sizeof(CDataType)));
ck_tile::hip_check_error(hipMemcpy(d_A,
a_m_k_dev_buf.GetDeviceBuffer(),
M * K * sizeof(ADataType),
hipMemcpyHostToDevice));
ck_tile::hip_check_error(hipMemcpy(d_B,
b_k_n_dev_buf.GetDeviceBuffer(),
N * K * sizeof(BDataType),
hipMemcpyHostToDevice));
ck_tile::reference_gemm_gpu<ADataType, ck_tile::reference_gemm_gpu<ADataType,
BDataType, BDataType,
AccDataType, AccDataType,
CDataType, CDataType,
ALayout, ALayout,
BLayout, BLayout,
CLayout>( CLayout>(d_A, d_B, d_C, M, N, K, stride_A, stride_B, stride_C);
a_m_k_dev_buf, b_k_n_dev_buf, c_m_n_gpu_buf_ref, M, N, K, stride_A, stride_B, stride_C);
ck_tile::hip_check_error(hipMemcpy(c_m_n_gpu_buf_ref.GetDeviceBuffer(),
d_C,
M * N * sizeof(CDataType),
hipMemcpyDeviceToHost));
ck_tile::hip_check_error(hipFree(d_A));
ck_tile::hip_check_error(hipFree(d_B));
ck_tile::hip_check_error(hipFree(d_C));
c_m_n_gpu_buf_ref.FromDevice(c_m_n_gpu_ref.data()); c_m_n_gpu_buf_ref.FromDevice(c_m_n_gpu_ref.data());
pass = ck_tile::check_err(c_m_n_dev_result, c_m_n_gpu_ref); pass = ck_tile::check_err(c_m_n_dev_result, c_m_n_gpu_ref);
...@@ -202,14 +224,16 @@ int run_gemm_example(int argc, char* argv[]) ...@@ -202,14 +224,16 @@ int run_gemm_example(int argc, char* argv[])
{ {
return run_gemm_example_with_layouts(argc, argv, Row{}, Col{}, Row{}); return run_gemm_example_with_layouts(argc, argv, Row{}, Col{}, Row{});
} }
else if(a_layout == "C" && b_layout == "C") // TODO: Fixme: with latest changes to GemmPipelineAGmemBGmemCRegV1DefaultPolicy below do not
{ // work.
return run_gemm_example_with_layouts(argc, argv, Col{}, Col{}, Row{}); // else if(a_layout == "C" && b_layout == "C")
} // {
else if(a_layout == "C" && b_layout == "R") // return run_gemm_example_with_layouts(argc, argv, Col{}, Col{}, Row{});
{ // }
return run_gemm_example_with_layouts(argc, argv, Col{}, Row{}, Row{}); // else if(a_layout == "C" && b_layout == "R")
} // {
// return run_gemm_example_with_layouts(argc, argv, Col{}, Row{}, Row{});
// }
else else
{ {
throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!"); throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!");
......
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