[Bugfix] Better FP8 supported defaults

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

@@ -15,7 +15,7 @@
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     _normalize_quant_group_shape, scaled_dequantize)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
-    apply_fp8_linear)
+    CUTLASS_BLOCK_FP8_SUPPORTED, CUTLASS_FP8_SUPPORTED, apply_fp8_linear)
 from vllm.platforms import current_platform
 
 logger = init_logger(__name__)
@@ -38,7 +38,7 @@ def apply_w8a8_block_fp8_linear(
     weight_scale: torch.Tensor,
     input_scale: Optional[torch.Tensor] = None,
     bias: Optional[torch.Tensor] = None,
-    cutlass_block_fp8_supported: bool = True,
+    cutlass_block_fp8_supported: bool = CUTLASS_BLOCK_FP8_SUPPORTED,
 ) -> torch.Tensor:
     assert input_scale is None
     # View input as 2D matrix for fp8 methods
@@ -85,12 +85,14 @@ def apply_w8a8_block_fp8_linear(
 # `apply_fp8_linear`
 # NOTE(lucas): this is quite messy, we should think through this more formally
 def apply_fp8_linear_generic(
-        input: torch.Tensor,
-        weight: torch.Tensor,
-        weight_scale: torch.Tensor,
-        input_group_shape: Tuple[int, int],
-        weight_group_shape: Tuple[int, int],
-        input_scale: Optional[torch.Tensor] = None,  # static scale if one
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    weight_scale: torch.Tensor,
+    input_group_shape: Tuple[int, int],
+    weight_group_shape: Tuple[int, int],
+    input_scale: Optional[torch.Tensor] = None,  # static scale if one
+    cutlass_fp8_supported: bool = CUTLASS_FP8_SUPPORTED,
+    cutlass_block_fp8_supported: bool = CUTLASS_BLOCK_FP8_SUPPORTED,
 ) -> torch.Tensor:
     # View input as 2D matrix for fp8 methods
     input = input.view(-1, input.shape[-1])
@@ -105,14 +107,18 @@ def is_dim_blocked(dim, shape, group_shape):
     if is_dim_blocked(0, weight.shape, weight_group_shape[0])\
      and is_dim_blocked(1, weight.shape, weight_group_shape[1]) and\
      input_group_shape == (1, weight_group_shape[1]):
-        return apply_w8a8_block_fp8_linear(input, weight,
-                                           list(weight_group_shape),
-                                           weight_scale)
+        return apply_w8a8_block_fp8_linear(
+            input,
+            weight,
+            list(weight_group_shape),
+            weight_scale,
+            cutlass_block_fp8_supported=cutlass_block_fp8_supported)
     else:
         # Despite having linear in the it doesn't conform to
         # `torch.nn.functional.linear` which is defined as `input @ weight.T`
         # so we explicitly transpose the weight matrix here
         return apply_fp8_linear(input, weight.T, weight_scale.T,
+                    cutlass_fp8_supported=cutlass_fp8_supported,
                          use_per_token_if_dynamic=\
                              (input_group_shape == (1, input.shape[1])))
 

vllm/model_executor/layers/quantization/utils/w8a8_utils.py

@@ -42,6 +42,10 @@ def cutlass_block_fp8_supported() -> bool:
     return ops.cutlass_scaled_mm_supports_block_fp8(capability)
 
 
+CUTLASS_FP8_SUPPORTED = cutlass_fp8_supported()
+CUTLASS_BLOCK_FP8_SUPPORTED = cutlass_block_fp8_supported()
+
+
 def per_tensor_dequantize(
         tensor: torch.Tensor, inv_scale: Union[float,
                                                torch.Tensor]) -> torch.Tensor:
@@ -109,7 +113,7 @@ def apply_fp8_linear(
     input_scale: Optional[torch.Tensor] = None,
     input_scale_ub: Optional[torch.Tensor] = None,
     bias: Optional[torch.Tensor] = None,
-    cutlass_fp8_supported: bool = True,
+    cutlass_fp8_supported: bool = CUTLASS_BLOCK_FP8_SUPPORTED,
     use_per_token_if_dynamic: bool = False,
 ) -> torch.Tensor:
     # ops.scaled_fp8_quant supports both dynamic and static quant.