[Refactor] Refactor for DeepGemmQuantScaleFMT using cache

vllm/model_executor/layers/quantization/utils/fp8_utils.py

@@ -31,6 +31,7 @@
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils.deep_gemm import (
+    DeepGemmQuantScaleFMT,
     fp8_gemm_nt,
     is_deep_gemm_e8m0_used,
     is_deep_gemm_supported,
@@ -247,7 +248,6 @@ def __init__(
         self.act_quant_group_shape = act_quant_group_shape
         self.is_deep_gemm_supported = is_deep_gemm_supported()
         self.is_hopper = current_platform.is_device_capability(90)
-        self.is_blackwell = current_platform.is_device_capability_family(100)
         self.use_deep_gemm_e8m0 = is_deep_gemm_e8m0_used()
 
         # Get the correct blockscale mul and input quant operations.
@@ -303,7 +303,7 @@ def _run_deepgemm(
         weight: torch.Tensor,
         weight_scale: torch.Tensor,
     ) -> torch.Tensor:
-        if self.use_deep_gemm_e8m0 and self.is_blackwell:
+        if DeepGemmQuantScaleFMT.from_oracle() == DeepGemmQuantScaleFMT.UE8M0:
             q_input, input_scale = per_token_group_quant_fp8_packed_for_deepgemm(
                 input_2d,
                 group_size=self.act_quant_group_shape.col,

vllm/utils/deep_gemm.py

@@ -32,16 +32,35 @@ class DeepGemmQuantScaleFMT(Enum):
     # element contains 4 scale values.
     UE8M0 = 2
 
-    @staticmethod
-    def from_oracle() -> "DeepGemmQuantScaleFMT":
-        if not is_deep_gemm_e8m0_used():
-            return DeepGemmQuantScaleFMT.FLOAT32
-        return (
-            DeepGemmQuantScaleFMT.UE8M0
+    @classmethod
+    def init_oracle_cache(cls) -> None:
+        """Initialize the oracle decision and store it in the class cache"""
+        cached = getattr(cls, "_oracle_cache", None)
+        if cached is not None:
+            return
+
+        use_e8m0 = (
+            envs.VLLM_USE_DEEP_GEMM_E8M0
+            and is_deep_gemm_supported()
+            and (_fp8_gemm_nt_impl is not None)
+        )
+        if not use_e8m0:
+            cls._oracle_cache = cls.FLOAT32  # type: ignore
+            return
+
+        cls._oracle_cache = (  # type: ignore
+            cls.UE8M0
             if current_platform.is_device_capability_family(100)
-            else DeepGemmQuantScaleFMT.FLOAT32_CEIL_UE8M0
+            else cls.FLOAT32_CEIL_UE8M0
         )
 
+    @classmethod
+    def from_oracle(cls) -> "DeepGemmQuantScaleFMT":
+        """Return the pre-initialized oracle decision"""
+        cached = getattr(cls, "_oracle_cache", None)
+        assert cached is not None, "DeepGemmQuantScaleFMT oracle cache not initialized"
+        return cached
+
 
 @functools.cache
 def is_deep_gemm_supported() -> bool:
@@ -149,6 +168,7 @@ def _lazy_init() -> None:
     _transform_sf_into_required_layout_impl = getattr(
         _dg, "transform_sf_into_required_layout", None
     )
+    DeepGemmQuantScaleFMT.init_oracle_cache()
 
 
 def get_num_sms() -> int: