[Bugfix][Kernel][Misc] Basic support for SmoothQuant, symmetric case

vllm/_custom_ops.py

@@ -1,6 +1,6 @@
 import contextlib
 import functools
-from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, List, Optional, Tuple, Type, Union
 
 import torch
 import torch.library
@@ -491,6 +491,22 @@ def cutlass_scaled_mm_supports_fp8(cuda_device_capability: int) -> bool:
     return torch.ops._C.cutlass_scaled_mm_supports_fp8(cuda_device_capability)
 
 
+def scaled_mm_torch(a: torch.Tensor,
+                    b: torch.Tensor,
+                    scale_a: torch.Tensor,
+                    scale_b: torch.Tensor,
+                    out_dtype: Type[torch.dtype],
+                    bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    out = torch.mm(a.to(torch.float32), b.to(torch.float32))
+    out = scale_a * out
+    out = scale_b.T * out
+    out = out.to(out_dtype)
+    if bias is not None:
+        out = out + bias
+
+    return out
+
+
 def cutlass_scaled_mm(a: torch.Tensor,
                       b: torch.Tensor,
                       scale_a: torch.Tensor,
@@ -504,9 +520,12 @@ def cutlass_scaled_mm(a: torch.Tensor,
 
     m = a.shape[0]
     n = b.shape[1]
-    out = torch.empty((m, n), dtype=out_dtype, device=a.device)
 
-    torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)
+    if is_hip():
+        return scaled_mm_torch(a, b, scale_a, scale_b, out_dtype, bias)
+    else:
+        out = torch.empty((m, n), dtype=out_dtype, device=a.device)
+        torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)
 
     return out