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[Bugfix][LoRA][FusedMoE] Select MxFP4 Backend based on LoRA Enablement (#27487)

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Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com>
Co-authored-by: Varun Sundar Rabindranath <vsundarr@redhat.com>
This commit is contained in:
Varun Sundar Rabindranath
2025-10-27 10:32:50 -04:00
committed by GitHub
parent 4f882be4a0
commit 5d3be3ba4c
3 changed files with 34 additions and 6 deletions
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23
vllm/model_executor/layers/quantization/mxfp4.py
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@@ -73,8 +73,24 @@ class Mxfp4Backend(Enum):
TRITON = 6
def get_mxfp4_backend():
def get_mxfp4_backend_with_lora() -> Mxfp4Backend:
"""
Not all MXFP4 backends support LoRA. Select backends that are known to
have LoRA support.
"""
if not current_platform.is_cuda():
return Mxfp4Backend.NONE
logger.info_once("[get_mxfp4_backend_with_lora] Using Marlin backend")
return Mxfp4Backend.MARLIN
def get_mxfp4_backend(with_lora_support: bool) -> Mxfp4Backend:
# Backend Selection
if with_lora_support:
return get_mxfp4_backend_with_lora()
if current_platform.is_cuda():
if (
current_platform.is_device_capability(90)
@@ -183,13 +199,14 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
super().__init__(moe)
self.topk_indices_dtype = None
self.moe = moe
self.mxfp4_backend = get_mxfp4_backend()
self.mxfp4_backend = get_mxfp4_backend(moe.is_lora_enabled)
self.max_capture_size = (
get_current_vllm_config().compilation_config.max_cudagraph_capture_size
)
assert self.mxfp4_backend != Mxfp4Backend.NONE, (
"No MXFP4 MoE backend (FlashInfer/Marlin/Triton) available."
f"get_mxfp4_backend(with_lora_support={moe.is_lora_enabled}) found"
"no compatible MXFP4 MoE backend (FlashInfer/Marlin/Triton)."
"Please check your environment and try again."
)
self._cache_permute_indices: dict[torch.Size, torch.Tensor] = {}