Remove weight_scale.T special case for SM90 Block FP8 CUTLASS kernel (#28431)

Signed-off-by: mgoin <mgoin64@gmail.com>

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py

@@ -173,7 +173,7 @@ class CompressedTensorsW8A8Fp8(CompressedTensorsScheme):
             layer.input_scale = None
 
         if self.strategy == QuantizationStrategy.BLOCK:
-            maybe_post_process_fp8_weight_block(layer, self.cutlass_block_fp8_supported)
+            maybe_post_process_fp8_weight_block(layer)
 
     def apply_weights(
         self,

vllm/model_executor/layers/quantization/fp8.py

@@ -540,7 +540,7 @@ class Fp8LinearMethod(LinearMethodBase):
             return
 
         if self.block_quant:
-            maybe_post_process_fp8_weight_block(layer, self.cutlass_block_fp8_supported)
+            maybe_post_process_fp8_weight_block(layer)
 
     def apply(
         self,

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

@@ -55,17 +55,13 @@ def cutlass_scaled_mm(
     Bs: torch.Tensor,
     block_size: list[int],
     output_dtype: torch.dtype = torch.float16,
-    is_hopper: bool | None = None,
 ) -> torch.Tensor:
-    if is_hopper is None:
-        is_hopper = current_platform.is_device_capability(90)
     return ops.cutlass_scaled_mm(
         A,
         B.T,
         out_dtype=output_dtype,
         scale_a=As,
-        # SM90 block FP8 requires row-major scale_b, which we do ahead of time
-        scale_b=Bs if block_size is not None and is_hopper else Bs.T,
+        scale_b=Bs.T,
     )
 
 
@@ -130,7 +126,7 @@ def _padded_cutlass(
     padded_x_scale[0 : x_scale.shape[0], ...].copy_(x_scale)
 
     output = cutlass_scaled_mm(
-        padded_qx, weight, padded_x_scale, weight_scale, block_size, output_dtype, True
+        padded_qx, weight, padded_x_scale, weight_scale, block_size, output_dtype
     )
     return output[0 : qx.shape[0], ...]
 
@@ -303,7 +299,6 @@ class W8A8BlockFp8LinearOp:
                 weight_scale,
                 list(self.weight_group_shape),
                 input_2d.dtype,
-                False,
             )
 
     def _run_aiter(
@@ -1125,9 +1120,7 @@ def process_fp8_weight_block_strategy(
     return weight, weight_scale
 
 
-def maybe_post_process_fp8_weight_block(
-    layer: torch.nn.Module, cutlass_block_fp8_supported: bool
-):
+def maybe_post_process_fp8_weight_block(layer: torch.nn.Module):
     assert layer.weight_block_size is not None
 
     from vllm.utils.deep_gemm import (
@@ -1146,15 +1139,6 @@ def maybe_post_process_fp8_weight_block(
         requant_weight_ue8m0_inplace(
             layer.weight.data, layer.weight_scale.data, block_sz
         )
-    # SM90 Block FP8 CUTLASS requires row-major weight scales
-    elif (
-        current_platform.is_device_capability(90)
-        and cutlass_block_fp8_supported
-        and not should_use_deepgemm
-    ):
-        layer.weight_scale = torch.nn.Parameter(
-            layer.weight_scale.data.T.contiguous(), requires_grad=False
-        )
 
 
 def expert_weight_is_col_major(x: torch.Tensor) -> bool: