disable graph partition in custom op

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -49,6 +49,7 @@
 from vllm.model_executor.layers.quantization.utils.mxfp4_utils import dequant_mxfp4
 from vllm.model_executor.layers.quantization.utils.mxfp6_utils import dequant_mxfp6
 from vllm.model_executor.layers.quantization.utils.ocp_mx_utils import OCP_MX_Scheme
+from vllm.model_executor.utils import maybe_disable_graph_partition
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils import direct_register_custom_op, is_torch_equal_or_newer
@@ -1145,7 +1146,11 @@ def fused_topk_bias(
 
 
 # This is used by the Deepseek-V2 and Deepseek-V3 model
-@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
+@torch.compile(
+    dynamic=True,
+    backend=current_platform.simple_compile_backend,
+    options=maybe_disable_graph_partition(current_platform.simple_compile_backend),
+)
 def grouped_topk(
     hidden_states: torch.Tensor,
     gating_output: torch.Tensor,

vllm/model_executor/utils.py

@@ -7,6 +7,8 @@
 
 import torch
 
+from vllm.utils import is_torch_equal_or_newer
+
 
 def set_random_seed(seed: int) -> None:
     from vllm.platforms import current_platform
@@ -83,3 +85,10 @@ def get_moe_expert_mapping(
             if child_map is not None:
                 return child_map()
         return []
+
+
+def maybe_disable_graph_partition(current_backend: str) -> dict[str, bool]:
+    if current_backend == "inductor" and is_torch_equal_or_newer("2.9.0.dev"):
+        return {"graph_partition": False}
+    else:
+        return {}