fix run error

vllm/compilation/fix_functionalization.py

@@ -65,9 +65,9 @@ class FixFunctionalizationPass(VllmInductorPass):
             # elif at_target == torch.ops._C.fused_add_rms_norm_static_fp8_quant.default:  # noqa: E501
             #     mutated_args = {1: 'result', 2: 'residual'}
             #     self.defunctionalize(graph, node, mutated_args)
-            # elif at_target == torch.ops._C.rms_norm_dynamic_per_token_quant.default:  # noqa: E501
-            #     mutated_args = {1: 'result', 2: 'scale', 3: 'residual'}
-            #     self.defunctionalize(graph, node, mutated_args)
+            elif at_target == torch.ops._C.rms_norm_dynamic_per_token_quant.default:  # noqa: E501
+                mutated_args = {1: 'result', 2: 'scale', 3: 'residual'}
+                self.defunctionalize(graph, node, mutated_args)
             elif at_target in [
                     torch.ops._C.rms_norm.default,
                     # torch.ops._C.rms_norm_static_fp8_quant.default,

vllm/compilation/fusion.py

@@ -82,9 +82,9 @@ class QuantKey(NamedTuple):
                 f"{'a' if not self.symmetric else ''}symmetric)")
 
 
-kFp8StaticTensorSym = QuantKey(FP8_DTYPE, True, GroupShape.PER_TENSOR, True)
-kFp8DynamicTensorSym = QuantKey(FP8_DTYPE, False, GroupShape.PER_TENSOR, True)
-kFp8DynamicTokenSym = QuantKey(FP8_DTYPE, False, GroupShape.PER_TOKEN, True)
+# kFp8StaticTensorSym = QuantKey(FP8_DTYPE, True, GroupShape.PER_TENSOR, True)
+# kFp8DynamicTensorSym = QuantKey(FP8_DTYPE, False, GroupShape.PER_TENSOR, True)
+# kFp8DynamicTokenSym = QuantKey(FP8_DTYPE, False, GroupShape.PER_TOKEN, True)
 
 QUANT_OPS: dict[QuantKey, OpOverload] = {
     # kFp8StaticTensorSym:

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -686,7 +686,7 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
         EM = min(sorted_token_ids.size(0),
                  A.size(0) * top_k * config['BLOCK_SIZE_M'])
     grid = lambda META: (triton.cdiv(EM, META['BLOCK_SIZE_M']) * triton.cdiv(
-        B.size(1) if not use_nn_moe else B.size[2], META['BLOCK_SIZE_N']), )
+        B.size(1) if not use_nn_moe else B.size(2), META['BLOCK_SIZE_N']), )
 
     if (use_int8_w8a16 or use_int4_w4a16) and \
             block_shape is not None and block_shape[1] > 0:
@@ -813,7 +813,7 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
             sorted_token_ids,
             expert_ids,
             num_tokens_post_padded,
-            B.size(1) if not use_nn_moe else B.size[2],
+            B.size(1) if not use_nn_moe else B.size(2),
             A.size(1),
             EM,
             num_tokens,
@@ -1485,7 +1485,7 @@ def fused_experts_impl(
         assert hidden_states.size(1) // 2 == w1.size(2), (
             "Hidden size mismatch")
     elif use_nn_moe:
-        assert hidden_states.size[1] == w1.size[1], "Hidden size mismatch"
+        assert hidden_states.size(1) == w1.size(1), "Hidden size mismatch"
     else:
         assert hidden_states.size(1) == w1.size(2), (
             f"Hidden size mismatch {hidden_states.size(1)} != {w1.size(2)}")
@@ -1500,7 +1500,7 @@ def fused_experts_impl(
 
     num_tokens = hidden_states.size(0)
     if use_nn_moe:
-        E, _, N = w1.size
+        E, _, N = w1.size()
     else:
         E, N, _ = w1.size()
     K = w2.size(1)

vllm/model_executor/layers/fused_moe/layer.py

@@ -58,11 +58,10 @@ else:
     FusedMoEPermuteExpertsUnpermute = None  # type: ignore
     FusedMoEPrepareAndFinalize = None  # type: ignore
 
-if is_rocm_aiter_moe_enabled():
+# if is_rocm_aiter_moe_enabled():
     # from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (  # noqa: E501
     #     rocm_aiter_grouped_topk as grouped_topk)
-    from vllm.model_executor.layers.fused_moe.fused_moe import grouped_topk
-elif current_platform.is_cpu():
+if current_platform.is_cpu():
     pass
 else:
     from vllm.model_executor.layers.fused_moe.fused_moe import grouped_topk