[BugFix][AMD][Quantization] Fix torch.compile issue where wvSplitKQ not being...

[BugFix][AMD][Quantization] Fix torch.compile issue where wvSplitKQ not being called when it should when using quantized FP8 model (#22281)
Signed-off-by: Randall Smith <Randall.Smith@amd.com>

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

@@ -13,6 +13,7 @@ from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     GroupShape)
 from vllm.platforms import current_platform
+from vllm.utils import direct_register_custom_op
 
 # Input scaling factors are no longer optional in _scaled_mm starting
 # from pytorch 2.5. Allocating a dummy tensor to pass as input_scale
@@ -156,13 +157,10 @@ def cutlass_w8a8_scaled_mm(*, qinput: torch.Tensor, weight: torch.Tensor,
     return output.view(*output_shape)
 
 
-def rocm_per_tensor_w8a8_scaled_mm(*, qinput: torch.Tensor,
-                                   weight: torch.Tensor,
-                                   out_dtype: torch.dtype,
-                                   scale_a: torch.Tensor,
-                                   scale_b: torch.Tensor, bias: torch.Tensor,
-                                   input_2d: torch.Tensor,
-                                   output_shape: list) -> torch.Tensor:
+def rocm_per_tensor_w8a8_scaled_mm_impl(
+        qinput: torch.Tensor, weight: torch.Tensor, out_dtype: torch.dtype,
+        scale_a: torch.Tensor, scale_b: torch.Tensor, bias: torch.Tensor,
+        input_2d: torch.Tensor) -> torch.Tensor:
     from vllm.platforms.rocm import on_mi3xx
     if envs.VLLM_ROCM_USE_SKINNY_GEMM and on_mi3xx(
     ) and qinput.shape[0] == 1 and qinput.shape[1] % 16 == 0:
@@ -175,10 +173,38 @@ def rocm_per_tensor_w8a8_scaled_mm(*, qinput: torch.Tensor,
                                   scale_a=scale_a,
                                   scale_b=scale_b,
                                   bias=bias)
+    return output
+
+
+def rocm_per_tensor_w8a8_scaled_mm_fake(
+        qinput: torch.Tensor, weight: torch.Tensor, out_dtype: torch.dtype,
+        scale_a: torch.Tensor, scale_b: torch.Tensor, bias: torch.Tensor,
+        input_2d: torch.Tensor) -> torch.Tensor:
+    return qinput.new_empty((*qinput.shape[:-1], weight.shape[1]),
+                            dtype=out_dtype)
 
+
+def rocm_per_tensor_w8a8_scaled_mm(*, qinput: torch.Tensor,
+                                   weight: torch.Tensor,
+                                   out_dtype: torch.dtype,
+                                   scale_a: torch.Tensor,
+                                   scale_b: torch.Tensor, bias: torch.Tensor,
+                                   input_2d: torch.Tensor,
+                                   output_shape: list) -> torch.Tensor:
+    output = torch.ops.vllm.rocm_per_tensor_w8a8_scaled_mm_impl(
+        qinput, weight, out_dtype, scale_a, scale_b, bias, input_2d)
     return torch.narrow(output, 0, 0, input_2d.shape[0]).view(*output_shape)
 
 
+direct_register_custom_op(
+    op_name="rocm_per_tensor_w8a8_scaled_mm_impl",
+    op_func=rocm_per_tensor_w8a8_scaled_mm_impl,
+    mutates_args=[],
+    fake_impl=rocm_per_tensor_w8a8_scaled_mm_fake,
+    dispatch_key=current_platform.dispatch_key,
+)
+
+
 def torch_per_tensor_w8a8_scaled_mm(*, qinput: torch.Tensor,
                                     weight: torch.Tensor,
                                     out_dtype: torch.dtype,