[RL] fix register the same ops multiple times (#9564)

python/sglang/srt/layers/quantization/mxfp4.py

@@ -146,27 +146,21 @@ def _quant_dequant_mxfp4_fake(
     return torch.empty_like(x)
 
 
-try:
-    direct_register_custom_op(
+direct_register_custom_op(
     op_name="dequant_mxfp4",
     op_func=_dequant_mxfp4,
     mutates_args=[],
     fake_impl=_dequant_mxfp4_fake,
-    )
-    dequant_mxfp4 = torch.ops.sglang.dequant_mxfp4
-except AttributeError as error:
-    raise error
+)
+dequant_mxfp4 = torch.ops.sglang.dequant_mxfp4
 
-try:
-    direct_register_custom_op(
+direct_register_custom_op(
     op_name="quant_dequant_mxfp4",
     op_func=_quant_dequant_mxfp4,
     mutates_args=[],
     fake_impl=_quant_dequant_mxfp4_fake,
-    )
-    quant_dequant_mxfp4 = torch.ops.sglang.quant_dequant_mxfp4
-except AttributeError as error:
-    raise error
+)
+quant_dequant_mxfp4 = torch.ops.sglang.quant_dequant_mxfp4
 
 
 class Mxfp4Config(QuantizationConfig):

python/sglang/srt/utils.py

@@ -1665,9 +1665,29 @@ def direct_register_custom_op(
     IMPORTANT: the lifetime of the operator is tied to the lifetime of the
     library object. If you want to bind the operator to a different library,
     make sure the library object is alive when the operator is used.
+
+    Note: This function will silently skip registration if the operator
+    with the same name is already registered to avoid RuntimeError in
+    multi-engine scenarios (e.g., VERL framework).
     """
     import torch.library
 
+    my_lib = target_lib or sglang_lib
+
+    # Check if operator is already registered to avoid duplicate registration
+    # This is important for scenarios where multiple SGLang engines run in the same process
+    try:
+        # Try to access the operator to see if it's already registered
+        lib_name = my_lib.m.name if hasattr(my_lib.m, "name") else "sglang"
+        if hasattr(torch.ops, lib_name) and hasattr(
+            getattr(torch.ops, lib_name), op_name
+        ):
+            # Operator already exists, skip registration
+            return
+    except (AttributeError, RuntimeError):
+        # Operator doesn't exist, proceed with registration
+        pass
+
     if hasattr(torch.library, "infer_schema"):
         schema_str = torch.library.infer_schema(op_func, mutates_args=mutates_args)
     else:
@@ -1676,11 +1696,22 @@ def direct_register_custom_op(
 
         schema_str = torch._custom_op.impl.infer_schema(op_func, mutates_args)
 
-    my_lib = target_lib or sglang_lib
+    try:
         my_lib.define(op_name + schema_str)
         my_lib.impl(op_name, op_func, "CUDA")
         if fake_impl is not None:
             my_lib._register_fake(op_name, fake_impl)
+    except RuntimeError as error:
+        if "Tried to register an operator" in str(e) and "multiple times" in str(e):
+            # Silently ignore duplicate registration errors
+            # This can happen in multi-engine scenarios
+            pass
+        else:
+            # Re-raise other RuntimeErrors
+            raise error
+    except AttributeError as error:
+        # Always re-raise AttributeError as it indicates missing dependencies
+        raise error
 
 
 def set_gpu_proc_affinity(