[MoE Refactor] Oracle Select FP8+NVFP4 Kernels In Priority (#32414)

vllm/model_executor/warmup/deep_gemm_warmup.py

@@ -128,11 +128,15 @@ def _fp8_linear_may_use_deep_gemm(module: torch.nn.Module) -> bool:
     """
     Return True if the input module/layer could be processed with DeepGEMM.
     """
+
+    # FIXME: this logic is brittle and incorrect - since we
+    # could use DeepGEMM with for than just Fp8LinearMethod
     block_size = get_mk_alignment_for_contiguous_layout()[0]
     if not (
         isinstance(module, LinearBase)
         and isinstance(module.quant_method, Fp8LinearMethod)
         and module.quant_method.block_quant
+        and not module.quant_method.use_marlin
     ):
         return False
 

vllm/v1/attention/backends/flashinfer.py

@@ -29,7 +29,7 @@ from vllm.model_executor.layers.batch_invariant import (
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
     kFp8StaticTensorSym,
-    kNvfp4Quant,
+    kNvfp4Dynamic,
 )
 from vllm.platforms import current_platform
 from vllm.platforms.interface import DeviceCapability
@@ -1184,7 +1184,7 @@ class FlashInferImpl(AttentionImpl):
         return (
             self.support_trtllm_attn
             and self.kv_cache_dtype.startswith("fp8")
-            and quant_key in (kFp8StaticTensorSym, kNvfp4Quant)
+            and quant_key in (kFp8StaticTensorSym, kNvfp4Dynamic)
         )
 
     # FlashInfer requires attention sinks to be float32