Refactor CustomOp to avoid confusing bugs (#5382)

python/sglang/srt/custom_op.py

@@ -1,6 +1,3 @@
-from typing import Optional
-
-import torch
 from torch import nn
 
 from sglang.srt.utils import is_cuda, is_hip
@@ -14,6 +11,26 @@ class CustomOp(nn.Module):
         super().__init__()
         self._forward_method = self.dispatch_forward()
 
+    def enter_torch_compile(self, num_tokens: int):
+        # NOTE: Temporarily workaround MoE
+        if "FusedMoE" in self.__class__.__name__:
+            if num_tokens == 1:
+                from sglang.srt.layers.moe.fused_moe_native import (
+                    fused_moe_forward_native,
+                )
+
+                # The performance of torch.compile on this layer is not always good when bs > 1,
+                # so we decide to only use torch.compile when bs =1
+                self._forward_method = fused_moe_forward_native
+        else:
+            self._forward_method = self.forward_native
+        self.is_torch_compile = True
+
+    def leave_torch_compile(self):
+        self._forward_method = self.forward_cuda
+        self.is_torch_compile = False
+
+    # Please do not override this method, because `self._forward_method` can change when in torch compile mode
     def forward(self, *args, **kwargs):
         return self._forward_method(*args, **kwargs)
 

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -28,7 +28,6 @@ from sglang.srt.custom_op import CustomOp
 from sglang.srt.distributed import get_tensor_model_parallel_rank
 from sglang.srt.distributed.parallel_state import GroupCoordinator, graph_capture
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
-from sglang.srt.layers.moe.fused_moe_native import fused_moe_forward_native
 from sglang.srt.layers.torchao_utils import save_gemlite_cache
 from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import (
@@ -60,18 +59,9 @@ def _to_torch(model: torch.nn.Module, reverse: bool, num_tokens: int):
     for sub in model._modules.values():
         if isinstance(sub, CustomOp):
             if reverse:
-                sub._forward_method = sub.forward_cuda
-                setattr(sub, "is_torch_compile", False)
+                sub.leave_torch_compile()
             else:
-                # NOTE: Temporarily workaround MoE
-                if "FusedMoE" in sub.__class__.__name__:
-                    if num_tokens == 1:
-                        # The performance of torch.compile on this layer is not always good when bs > 1,
-                        # so we decide to only use torch.compile when bs =1
-                        sub._forward_method = fused_moe_forward_native
-                else:
-                    sub._forward_method = sub.forward_native
-                setattr(sub, "is_torch_compile", True)
+                sub.enter_torch_compile(num_tokens=num_tokens)
         if isinstance(sub, torch.nn.Module):
             _to_torch(sub, reverse, num_tokens)