Change the FP8 per-tensor GEMM backend on SM120 to cuBLAS (#31961)

Co-authored-by: Brayden Zhong <brayden.zhong@radixark.ai>

python/sglang/srt/layers/quantization/fp8_utils.py

@@ -1710,7 +1710,7 @@ def apply_fp8_linear_bmm_flashinfer(
     input_scale: torch.Tensor,
     bias: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
-    """Per-tensor static fp8 linear via flashinfer bmm_fp8 (SM10X only)."""
+    """Per-tensor static fp8 linear via flashinfer bmm_fp8 (SM100/SM120 Blackwell)."""
     output_shape = [*input.shape[:-1], weight.shape[1]]
     input_2d = input.view(-1, input.shape[-1])
     qinput, x_scale = static_quant_fp8(input_2d, input_scale, repeat_scale=False)

python/sglang/srt/layers/quantization/modelopt_quant.py

@@ -499,7 +499,9 @@ class ModelOptFp8LinearMethod(LinearMethodBase):
         super().__init__()
         self.quant_config = quant_config
         self.cutlass_fp8_supported = cutlass_fp8_supported()
-        self.enable_flashinfer_bmm = is_sm100_supported() and is_flashinfer_available()
+        self.enable_flashinfer_bmm = (
+            is_sm100_supported() or is_sm120_supported()
+        ) and is_flashinfer_available()
 
     def create_weights(
         self,