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[Bug] Fix fp8 trtllm MoE modular kernel supported routing methods (#37346)

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Signed-off-by: wzhao18 <wzhao18.sz@gmail.com>
This commit is contained in:
Wei Zhao
2026-03-19 11:43:06 -04:00
committed by GitHub
parent 40b8363b45
commit e27b8ba3d1
1 changed files with 16 additions and 59 deletions
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75
vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py
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@@ -69,54 +69,11 @@ class TrtLlmFp8ExpertsBase:
"""Does not support non-gated MoE (i.e. Nanotron-3-Nano)."""
return True
@staticmethod
def _supports_quant_scheme(
weight_key: QuantKey | None,
activation_key: QuantKey | None,
) -> bool:
"""Supports Fp8 per-tensor, Fp8 block, and MXFP8."""
SUPPORTED_W_A = [
(kFp8Static128BlockSym, kFp8Dynamic128Sym),
(kFp8StaticTensorSym, kFp8StaticTensorSym),
(kMxfp8Static, kMxfp8Dynamic),
]
return (weight_key, activation_key) in SUPPORTED_W_A
@staticmethod
def _supports_activation(activation: MoEActivation) -> bool:
"""Supports only SiLU and RELU^2 non-gated activation."""
return activation in [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL]
@staticmethod
def _supports_routing_method(
routing_method: RoutingMethodType,
weight_key: QuantKey | None,
activation_key: QuantKey | None,
) -> bool:
"""Monolithic kernels need to express router support."""
# NOTE(dbari): TopK routing could also be enabled, but need to validate models
# NOTE(dbari): Default is not implemented and should not be enabled until it is
if (weight_key, activation_key) in [
(kFp8Static128BlockSym, kFp8Dynamic128Sym),
(kMxfp8Static, kMxfp8Dynamic),
]:
# NOTE(rob): potentially allow others here. This is a conservative list.
return routing_method in [
RoutingMethodType.DeepSeekV3,
RoutingMethodType.Renormalize,
RoutingMethodType.RenormalizeNaive,
]
elif (weight_key, activation_key) == (kFp8StaticTensorSym, kFp8StaticTensorSym):
# NOTE(dbari): as above, potentially allow others here.
return routing_method in [
RoutingMethodType.DeepSeekV3,
RoutingMethodType.Llama4,
RoutingMethodType.Renormalize,
RoutingMethodType.RenormalizeNaive,
]
else:
raise ValueError("Unsupported quantization scheme.")
@staticmethod
def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:
"""Monolithic kernel so only use with naive DP/EP and TP."""
@@ -125,22 +82,6 @@ class TrtLlmFp8ExpertsBase:
or moe_parallel_config.use_naive_all2all_kernels
) and not moe_parallel_config.enable_eplb
@staticmethod
def _supports_router_logits_dtype(
router_logits_dtype: torch.dtype | None,
routing_method: RoutingMethodType,
) -> bool:
"""
The FlashInfer TRTLLM FP8 kernel expects bfloat16 router_logits by default.
Only DeepSeekV3 routing supports float32 router_logits (which is converted
internally in the kernel).
"""
if router_logits_dtype == torch.float32:
# Only DeepSeekV3 routing handles float32 logits
# https://github.com/flashinfer-ai/flashinfer/issues/2469
return routing_method == RoutingMethodType.DeepSeekV3
return True
def supports_chunking(self) -> bool:
return False
@@ -306,6 +247,22 @@ class TrtLlmFp8ExpertsMonolithic(TrtLlmFp8ExpertsBase, mk.FusedMoEExpertsMonolit
]
return (weight_key, activation_key) in SUPPORTED_W_A
@staticmethod
def _supports_router_logits_dtype(
router_logits_dtype: torch.dtype | None,
routing_method: RoutingMethodType,
) -> bool:
"""
The FlashInfer TRTLLM FP8 kernel expects bfloat16 router_logits by default.
Only DeepSeekV3 routing supports float32 router_logits (which is converted
internally in the kernel).
"""
if router_logits_dtype == torch.float32:
# Only DeepSeekV3 routing handles float32 logits
# https://github.com/flashinfer-ai/flashinfer/issues/2469
return routing_method == RoutingMethodType.DeepSeekV3
return True
@staticmethod
def _supports_routing_method(
routing_method: RoutingMethodType,