biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Kernel] Delegate construction of FusedMoEQuantConfig to FusedMoEMethodBase subclasses (#22537)

Browse Source 
Signed-off-by: Bill Nell <bnell@redhat.com>
This commit is contained in:
bnellnm
2025-09-17 19:43:31 -04:00
committed by GitHub
parent e6585ddb45
commit 5963b98b46
68 changed files with 2698 additions and 2526 deletions
Download Patch File Download Diff File Expand all files Collapse all files

40
tests/kernels/moe/test_modular_kernel_combinations.py
View File

@@ -12,7 +12,6 @@ import torch
import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from vllm.config import VllmConfig, current_platform, set_current_vllm_config
from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
from vllm.utils import has_deep_ep, has_deep_gemm, has_pplx
from vllm.utils.flashinfer import has_flashinfer_cutlass_fused_moe
@@ -22,7 +21,8 @@ from .modular_kernel_tools.common import (Config, RankTensors, WeightTensors,
run_modular_kernel)
from .modular_kernel_tools.mk_objects import (
MK_FUSED_EXPERT_TYPES, MK_MULTI_GPU_PREPARE_FINALIZE_TYPES,
MK_QUANT_CONFIGS, MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES, expert_info)
MK_QUANT_CONFIGS, MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES, TestMoEQuantConfig,
expert_info)
from .modular_kernel_tools.parallel_utils import (ProcessGroupInfo,
parallel_launch_with_config)
@@ -55,7 +55,7 @@ def rank_worker(
pgi: ProcessGroupInfo,
vllm_config: VllmConfig,
cpu_group,
config: Config,
base_config: Config,
weights: WeightTensors,
verbose: bool,
):
@@ -63,42 +63,44 @@ def rank_worker(
# sanity check
from vllm import envs
if config.fused_moe_chunk_size is not None:
assert (config.fused_moe_chunk_size == envs.VLLM_FUSED_MOE_CHUNK_SIZE)
if base_config.fused_moe_chunk_size is not None:
assert (
base_config.fused_moe_chunk_size == envs.VLLM_FUSED_MOE_CHUNK_SIZE)
# get weights to this device
weights.to_current_device()
Ms = config.Ms
Ms = base_config.Ms
assert isinstance(Ms, list)
TOPKs = config.topks
TOPKs = base_config.topks
assert isinstance(TOPKs, list)
exceptions = []
count = 0
for m, topk in product(Ms, TOPKs):
# override m and topk
config = copy.deepcopy(base_config)
config.Ms = m
config.topks = topk
try:
print(f"Running[{pgi.rank}]: m={m}, topk={topk} ...")
count = count + 1
# override m and topk
cfgx = copy.deepcopy(config)
cfgx.Ms = m
cfgx.topks = topk
# inputs for rank
rank_tensors = RankTensors.make(cfgx, pgi)
rank_tensors = RankTensors.make(config, pgi)
# modular kernel out
mk_out = run_modular_kernel(pgi, vllm_config, cfgx, weights,
mk_out = run_modular_kernel(pgi, vllm_config, config, weights,
rank_tensors)
with set_current_vllm_config(vllm_config):
ref_out = reference_moe_impl(cfgx, weights, rank_tensors)
ref_out = reference_moe_impl(config, weights, rank_tensors)
if config.quant_dtype == "nvfp4":
atol = 1e-1
rtol = 1e-1
atol = 1e-1 if config.K < 4096 else 2e-1
rtol = 1e-1 if config.K < 4096 else 2e-1
else:
atol = 3e-2
rtol = 3e-2
@@ -132,7 +134,7 @@ Ms = [32, 64]
# hidden sizes, making this too large will cause fp4 tests to fail.
# Also needs to be a multiple of 1024 for deep_gemm.
Ks = [2048]
Ns = [2048]
Ns = [1024]
TOPKs = [4, 1]
Es = [32]
DTYPEs = [torch.bfloat16]
@@ -167,7 +169,7 @@ def is_nyi_config(config: Config) -> bool:
@meets_multi_gpu_requirements
def test_modular_kernel_combinations_multigpu(
k: int, n: int, e: int, dtype: torch.dtype,
quant_config: Optional[FusedMoEQuantConfig],
quant_config: Optional[TestMoEQuantConfig],
combination: tuple[mk.FusedMoEPrepareAndFinalize,
mk.FusedMoEPermuteExpertsUnpermute],
fused_moe_chunk_size: Optional[int], world_size: int, pytestconfig):
@@ -208,7 +210,7 @@ def test_modular_kernel_combinations_multigpu(
@pytest.mark.parametrize("world_size", [1])
def test_modular_kernel_combinations_singlegpu(
k: int, n: int, e: int, dtype: torch.dtype,
quant_config: Optional[FusedMoEQuantConfig],
quant_config: Optional[TestMoEQuantConfig],
combination: tuple[mk.FusedMoEPrepareAndFinalize,
mk.FusedMoEPermuteExpertsUnpermute],
fused_moe_chunk_size: Optional[int], world_size: int, pytestconfig):