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[CK_TILE] FMHA Support hdim_v to as a Multiple of 32 (#2114)

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* 160+192

* Add splitkv d160

* cleanup

* fix

* Add change log

* Fix CHANGELOG

* Use static_cast

* Update ignored instance

---------

Co-authored-by: asleepzzz <hanwen.chang@amd.com>

[ROCm/composable_kernel commit: b8212864cf]
This commit is contained in:
Yi DING
2025-06-24 01:33:31 +08:00
committed by GitHub
parent 470608bdcb
commit cba904aeff
7 changed files with 89 additions and 68 deletions
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1
example/ck_tile/01_fmha/README.md
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@@ -71,6 +71,7 @@ args:
-drop_seed seed for random number generator (default:1)
-drop_offset offset for random number generator (default:0)
-drop_prefs seed and offset values are present on GPU; 0 - host, 1 - device/GPU (default:0)
-num_splits number of splits for key/value. 0 to determine actual number by heuristic (default:1)
-warmup number of iterations before benchmark the kernel (default:5)
-repeat number of iterations to benchmark the kernel (default:20)
```

45
example/ck_tile/01_fmha/codegen/ops/fmha_fwd.py
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@@ -282,18 +282,19 @@ class FmhaFwdApiPool:
# TODO: do we need to check duplication?
if trait.dtype not in self.pool.keys():
self.pool[trait.dtype] = dict()
if trait.hdim not in self.pool[trait.dtype].keys():
self.pool[trait.dtype][trait.hdim] = list()
hdim = trait.hdim, trait.bn1
if hdim not in self.pool[trait.dtype].keys():
self.pool[trait.dtype][hdim] = list()
self.pool[trait.dtype][trait.hdim].append(copy.copy(trait))
self.pool[trait.dtype][hdim].append(copy.copy(trait))
@property
def api(self) -> str:
per_dtypes=str()
for i, dtype in enumerate(self.pool.keys()):
per_hdim_case=str()
for j, hdim in enumerate(self.pool[dtype].keys()):
traits=self.pool[dtype][hdim]
for j, (hdim, hdim_v) in enumerate(self.pool[dtype].keys()):
traits=self.pool[dtype][(hdim, hdim_v)]
inners=str()
for k, trait in enumerate(traits):
if_k = 'if' if k == 0 else 'else if'
@@ -306,7 +307,7 @@ class FmhaFwdApiPool:
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=FWD_DTYPE_MAP[dtype])
if_j = 'if' if j == 0 else 'else if'
per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_hdim_v=trait.bn1, 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_hdim_v=hdim_v, F_inner_dispatch=inners)
if_i = 'if' if i == 0 else 'else if'
per_dtypes = per_dtypes + FMHA_FWD_API_PER_DTYPE.format(F_if=if_i, F_dtype=dtype, F_hdim_case=per_hdim_case)
if not per_dtypes:
@@ -435,18 +436,20 @@ class FmhaFwdKernel:
def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
if dtype == 'fp16' or dtype == 'bf16':
return {
'32' : FmhaFwdTileSize(128, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 32, 32, 16, 32, 32, 16, -1),
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
### '96' : FmhaFwdTileSize(128, 128, 32, 128, 32, 96, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
'192' : FmhaFwdTileSize(128, 128, 32, 128, 32, 192, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
(32, 32) : FmhaFwdTileSize(128, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 32, 32, 16, 32, 32, 16, -1),
(64, 64) : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
### (96, 128) : FmhaFwdTileSize(128, 128, 32, 128, 32, 96, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
(128,128) : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
### (160,160) : FmhaFwdTileSize(128, 128, 32, 160, 32, 160, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, 1),
(192,128) : FmhaFwdTileSize(128, 128, 32, 128, 32, 192, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
### (192,192) : FmhaFwdTileSize(128, 128, 32, 192, 32, 192, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, 1),
(256,256) : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1),
}
elif dtype == 'fp8' or dtype == 'bf8':
return {
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, 32, 32, 32, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
(64,64 ) : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, 32, 32, 32, -1),
(128,128) : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
(256,256) : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
}
else:
return None
@@ -454,7 +457,7 @@ def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
def get_fwd_blobs(kernel_filter : Optional[str], receipt, optdim_list, mask_impl) -> Tuple[FmhaFwdApiPool, List[FmhaFwdKernel]]:
# TODO: we don't support tuning yet, so pick up one value for vlayout/pipeline/pad
# support this in future
def get_pipelines(dtype, hdim) -> List[FmhaFwdPipeline]:
def get_pipelines(dtype, hdim, hdim_v) -> List[FmhaFwdPipeline]:
# this function will populate a list possible pipelines
# TODO: the order of List matters! the later in this list will be also be checked later
# TODO: currently for qr pipeline, let 't' padding to appear later!!
@@ -463,7 +466,7 @@ def get_fwd_blobs(kernel_filter : Optional[str], receipt, optdim_list, mask_impl
pipelines = []
if dtype in ['fp16', 'bf16']:
for logits, mask, bias, lse, dropout, skip in itertools.product(["t", "f"], get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"], ["t", "f"], ["t", "f"]):
if hdim == 256:
if hdim == 256 and hdim_v == 256:
# if True:
pipelines.append(FmhaFwdPipeline('qr', 'row', 'f', 'f', 'f', 'f', logits, bias, lse, dropout, squant, mask, skip))
pipelines.append(FmhaFwdPipeline('qr', 'col', 'f', 'f', 'f', 'f', logits, bias, lse, dropout, squant, mask, skip))
@@ -507,15 +510,13 @@ def get_fwd_blobs(kernel_filter : Optional[str], receipt, optdim_list, mask_impl
if d == None:
continue
#for hdim_str, mode, mask, bias, lse in itertools.product(d.keys(), MODE_MAP.keys(), MASK_MAP.keys(), ["t", "f"], ["t", "f"]):
for hdim_str, mode in itertools.product(d.keys(), MODE_MAP.keys()):
tile = d[hdim_str]
hdim = int(hdim_str)
for pipeline in get_pipelines(dtype, hdim):
for ((hdim, hdim_v), tile), mode in itertools.product(d.items(), MODE_MAP.keys()):
for pipeline in get_pipelines(dtype, hdim, hdim_v):
if mode == "group":
if pipeline.F_spad != 't' or pipeline.F_skpad != 't':
# in group mode, spad/skpad must be true, since we can't predict if seqlen of current batch need pad or not
continue
if hdim == 192 and tile.F_bn1 == 128:
if (hdim, hdim_v) == (192, 128) or hdim == 160:
# NOTE: this is used to speedup deepseek prefill case, we don't gen training
if pipeline.F_bias != 'no' or pipeline.F_dropout == 't':
continue

5
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_splitkv.py
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@@ -34,6 +34,7 @@ K0_MAX_SUBMAX_MAP = {
64 : 64,
96 : 128,
128: 128,
# 160: 160,
256: 256
}
@@ -638,6 +639,7 @@ def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
'64' : FmhaFwdTileSize(64, 64, 32, 64, 32, 64, 4, 1, 1, 4, 1, 1, 16, 16, 16, 16, 16, 16, -1),
### '96' : FmhaFwdTileSize(64, 128, 32, 128, 32, 96, 4, 1, 1, 4, 1, 1, 16, 16, 16, 16, 16, 16, -1),
'128' : FmhaFwdTileSize(64, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 16, 16, 16, 16, 16, 16, -1),
### '160' : FmhaFwdTileSize(64, 128, 32, 160, 32, 160, 4, 1, 1, 4, 1, 1, 16, 16, 16, 16, 16, 16, -1),
'256' : FmhaFwdTileSize(64, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 16, 16, 16, 16, 16, 16, -1),
}
elif dtype == 'fp8' or dtype == 'bf8':
@@ -656,6 +658,7 @@ def get_fmha_fwd_splitkv_combine_tile_dict_from_dtype(dtype : str) -> Optional[d
'64' : FmhaFwdSplitKVCombineTileSize(32, -1),
### '96' : FmhaFwdSplitKVCombineTileSize(32, -1),
'128' : FmhaFwdSplitKVCombineTileSize(32, -1),
### '160' : FmhaFwdSplitKVCombineTileSize(32, -1),
'256' : FmhaFwdSplitKVCombineTileSize(32, -1),
}
elif dtype == 'fp8' or dtype == 'bf8':
@@ -683,7 +686,7 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
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"]):
# 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, 160]:
# if True:
pipelines.append(Pipeline('qr', 'row', 'f', 't', 'f', 'f', logits, bias, 't', squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'col', 'f', 't', 'f', 'f', logits, bias, 't', squant, pagedkv, mask))

43
example/ck_tile/01_fmha/fmha_fwd.cpp
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@@ -1,5 +1,5 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#include "fmha_fwd.hpp"
#include "ck_tile/host.hpp"
@@ -178,50 +178,30 @@ auto get_elimit<FmhaFwdFp8>(std::string init_method)
}
}
int num_splits_heuristic(int batch_nhead_mblocks, int num_SMs, int num_n_blocks, int max_splits)
int num_splits_heuristic(int batch_nhead_mblocks, int num_SMs, int max_splits)
{
// If we have enough to almost fill the SMs, then just use 1 split
if(batch_nhead_mblocks >= 0.8f * num_SMs)
{
return 1;
}
max_splits = std::min({max_splits, num_SMs, num_n_blocks});
max_splits = std::min({max_splits, num_SMs});
float max_efficiency = 0.f;
std::vector<float> efficiency;
efficiency.reserve(max_splits);
auto ceildiv = [](int a, int b) { return (a + b - 1) / b; };
// Some splits are not eligible. For example, if we have 64 blocks and choose 11 splits,
// we'll have 6 * 10 + 4 blocks. If we choose 12 splits, we'll have 6 * 11 + (-2) blocks
// (i.e. it's 11 splits anyway).
// So we check if the number of blocks per split is the same as the previous num_splits.
auto is_split_eligible = [&ceildiv, &num_n_blocks](int num_splits) {
return num_splits == 1 ||
ceildiv(num_n_blocks, num_splits) != ceildiv(num_n_blocks, num_splits - 1);
};
for(int num_splits = 1; num_splits <= max_splits; num_splits++)
{
if(!is_split_eligible(num_splits))
float n_waves = float(batch_nhead_mblocks * num_splits) / num_SMs;
float eff = n_waves / ceil(n_waves);
// printf("num_splits = %d, eff = %f\n", num_splits, eff);
if(eff > max_efficiency)
{
efficiency.push_back(0.f);
}
else
{
float n_waves = float(batch_nhead_mblocks * num_splits) / num_SMs;
float eff = n_waves / ceil(n_waves);
// printf("num_splits = %d, eff = %f\n", num_splits, eff);
if(eff > max_efficiency)
{
max_efficiency = eff;
}
efficiency.push_back(eff);
max_efficiency = eff;
}
efficiency.push_back(eff);
}
for(int num_splits = 1; num_splits <= max_splits; num_splits++)
{
if(!is_split_eligible(num_splits))
{
continue;
}
if(efficiency[num_splits - 1] >= 0.85 * max_efficiency)
{
// printf("num_splits chosen = %d\n", num_splits);
@@ -234,6 +214,7 @@ int num_splits_heuristic(int batch_nhead_mblocks, int num_SMs, int num_n_blocks,
int override_num_splits_if_necessary(
int batch, int nhead, int max_seqlen_q, int hdim_v, float p_drop, int num_splits)
{
(void)hdim_v;
int device;
auto status = hipGetDevice(&device);
if(status != hipSuccess)
@@ -250,15 +231,13 @@ int override_num_splits_if_necessary(
// tile size should match the generate.py
const int kM0 = 64;
const int kN1 = hdim_v;
const int num_m_blocks = ck_tile::integer_divide_ceil(max_seqlen_q, kM0);
const int num_n_blocks = ck_tile::integer_divide_ceil(hdim_v, kN1);
if(num_splits < 1 && p_drop == 0.0f)
{
return num_splits_heuristic(
batch * nhead * num_m_blocks, props.multiProcessorCount * 2, num_n_blocks, 128);
batch * nhead * num_m_blocks, props.multiProcessorCount * 2, 128);
}
return num_splits;