[fix] resolve cutlass_scaled_mm inference error

vllm_kunlun/vllm_utils_wrapper.py

@@ -1646,7 +1646,9 @@ def cutlass_scaled_mm(
     bias: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
     out = torch.empty((a.shape[0], b.shape[1]), dtype=out_dtype, device=a.device)
-    torch.ops.xspeedgate_ops.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)
+    torch.ops.xspeedgate_ops.cutlass_scaled_mm(
+        out, a.contiguous(), b.contiguous(), scale_a, scale_b, bias
+    )
     return out
 
 
@@ -1660,7 +1662,9 @@ def cutlass_scaled_mm_cuda(
     bias: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
     out = torch.empty((a.shape[0], b.shape[1]), dtype=out_dtype, device=a.device)
-    torch.ops.xspeedgate_ops.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)
+    torch.ops.xspeedgate_ops.cutlass_scaled_mm(
+        out, a.contiguous(), b.contiguous(), scale_a, scale_b, bias
+    )
     return out
 
 
@@ -1694,7 +1698,7 @@ def cutlass_scaled_mm_azp(
 ) -> torch.Tensor:
     out = torch.empty((a.shape[0], b.shape[1]), dtype=out_dtype, device=a.device)
     torch.ops.xspeedgate_ops.cutlass_scaled_mm_azp(
-        out, a, b, scale_a, scale_b, azp_adj, azp, bias
+        out, a.contiguous(), b.contiguous(), scale_a, scale_b, azp_adj, azp, bias
     )
     return out
 
@@ -1712,7 +1716,7 @@ def cutlass_scaled_mm_azp_cuda(
 ) -> torch.Tensor:
     out = torch.empty((a.shape[0], b.shape[1]), dtype=out_dtype, device=a.device)
     torch.ops.xspeedgate_ops.cutlass_scaled_mm_azp(
-        out, a, b, scale_a, scale_b, azp_adj, azp, bias
+        out, a.contiguous(), b.contiguous(), scale_a, scale_b, azp_adj, azp, bias
     )
     return out