[AMD][BugFix] Fix omission of wvSplitK kernel for small batch sizes (1-4) due...

[AMD][BugFix] Fix omission  of wvSplitK kernel for small batch sizes (1-4) due to torch.compile (#21350)
Signed-off-by: Randall Smith <Randall.Smith@amd.com>

vllm/model_executor/layers/utils.py

@@ -8,6 +8,7 @@ import torch
 from vllm import _custom_ops as ops
 from vllm import envs
 from vllm.platforms import current_platform
+from vllm.utils import direct_register_custom_op
 
 
 def get_token_bin_counts_and_mask(
@@ -70,10 +71,10 @@ def default_unquantized_gemm(layer: torch.nn.Module,
     return torch.nn.functional.linear(x, weight, bias)
 
 
-def rocm_unquantized_gemm(layer: torch.nn.Module,
-                          x: torch.Tensor,
-                          weight: torch.Tensor,
-                          bias: Optional[torch.Tensor] = None):
+def rocm_unquantized_gemm_impl(
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        bias: Optional[torch.Tensor] = None) -> torch.Tensor:
     from vllm.platforms.rocm import on_gfx9
     k = weight.shape[1]
     use_skinny = (envs.VLLM_ROCM_USE_SKINNY_GEMM and on_gfx9() and \
@@ -97,6 +98,29 @@ def rocm_unquantized_gemm(layer: torch.nn.Module,
     return torch.nn.functional.linear(x, weight, bias)
 
 
+def rocm_unquantized_gemm_impl_fake(
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    return x.new_empty((*x.shape[:-1], weight.shape[0]))
+
+
+def rocm_unquantized_gemm(layer: torch.nn.Module,
+                          x: torch.Tensor,
+                          weight: torch.Tensor,
+                          bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    return torch.ops.vllm.rocm_unquantized_gemm_impl(x, weight, bias)
+
+
+direct_register_custom_op(
+    op_name="rocm_unquantized_gemm_impl",
+    op_func=rocm_unquantized_gemm_impl,
+    mutates_args=[],
+    fake_impl=rocm_unquantized_gemm_impl_fake,
+    dispatch_key=current_platform.dispatch_key,
+)
+
+
 def cpu_unquantized_gemm(layer: torch.nn.Module,
                          x: torch.Tensor,
                          weight: torch.Tensor,