[Fix][torch.compile] Enable custom ops by default when Inductor off

tests/model_executor/test_enabled_custom_ops.py

@@ -28,42 +28,49 @@ class Relu3(ReLUSquaredActivation):
 
 
 @pytest.mark.parametrize(
-    "env, torch_level, ops_enabled, default_on",
+    "env, torch_level, use_inductor, ops_enabled, default_on",
     [
         # Default values based on compile level
-        ("", 0, [True] * 4, True),
-        ("", 1, [True] * 4, True),
-        ("", 2, [True] * 4, True),  # All by default
-        ("", 3, [False] * 4, False),
-        ("", 4, [False] * 4, False),  # None by default
+        # - All by default (no Inductor compilation)
+        ("", 0, False, [True] * 4, True),
+        ("", 1, True, [True] * 4, True),
+        ("", 2, False, [True] * 4, True),
+        # - None by default (with Inductor)
+        ("", 3, True, [False] * 4, False),
+        ("", 4, True, [False] * 4, False),
+        # - All by default (without Inductor)
+        ("", 3, False, [True] * 4, True),
+        ("", 4, False, [True] * 4, True),
         # Explicitly enabling/disabling
         #
         # Default: all
         #
         # All but SiluAndMul
-        ("+rms_norm,-silu_and_mul", 0, [1, 0, 1, 1], True),
+        ("+rms_norm,-silu_and_mul", 0, True, [1, 0, 1, 1], True),
         # Only ReLU3
-        ("none,-rms_norm,+relu3", 0, [0, 0, 0, 1], False),
+        ("none,-rms_norm,+relu3", 1, False, [0, 0, 0, 1], False),
         # All but SiluAndMul
-        ("all,-silu_and_mul", 1, [1, 0, 1, 1], True),
+        ("all,-silu_and_mul", 2, True, [1, 0, 1, 1], True),
         # All but ReLU3 (even if ReLU2 is on)
-        ("-relu3,relu2", 1, [1, 1, 1, 0], True),
-        # GeluAndMul and SiluAndMul
-        ("none,-relu3,+gelu_and_mul,+silu_and_mul", 2, [0, 1, 1, 0], False),
+        ("-relu3,relu2", 3, False, [1, 1, 1, 0], True),
+        # RMSNorm and SiluAndMul
+        ("none,-relu3,+rms_norm,+silu_and_mul", 4, False, [1, 1, 0, 0], False),
         # All but RMSNorm
-        ("-rms_norm", 2, [0, 1, 1, 1], True),
+        ("-rms_norm", 3, False, [0, 1, 1, 1], True),
         #
         # Default: none
         #
         # Only ReLU3
-        ("-silu_and_mul,+relu3", 3, [0, 0, 0, 1], False),
+        ("-silu_and_mul,+relu3", 3, True, [0, 0, 0, 1], False),
         # All but RMSNorm
-        ("all,-rms_norm", 4, [0, 1, 1, 1], True),
+        ("all,-rms_norm", 4, True, [0, 1, 1, 1], True),
     ])
-def test_enabled_ops(env: str, torch_level: int, ops_enabled: list[int],
-                     default_on: bool):
-    vllm_config = VllmConfig(compilation_config=CompilationConfig(
-        level=torch_level, custom_ops=env.split(",")))
+def test_enabled_ops(env: str, torch_level: int, use_inductor: bool,
+                     ops_enabled: list[int], default_on: bool):
+    vllm_config = VllmConfig(
+        compilation_config=CompilationConfig(use_inductor=bool(use_inductor),
+                                             level=torch_level,
+                                             custom_ops=env.split(",")))
     with set_current_vllm_config(vllm_config):
         assert CustomOp.default_on() == default_on
 

vllm/config.py

@@ -3929,7 +3929,8 @@ class CompilationConfig:
     - 'none,+op1,+op2' to enable only op1 and op2
 
     By default, all custom ops are enabled when running without Inductor and
-    disabled when running with Inductor (compile_level >= Inductor)."""
+    disabled when running with Inductor: level>=PIECEWISE and use_inductor=True.
+    Inductor generates (fused) Triton kernels for disabled custom ops."""
     splitting_ops: list[str] = field(default_factory=list)
     """A list of ops to split the full graph into subgraphs, used in piecewise
     compilation."""
@@ -3938,10 +3939,13 @@ class CompilationConfig:
     use_inductor: bool = True
     """Whether to use inductor compilation:
 
-    - False: inductor compilation is not used. graph runs in eager.
-    - True: inductor compilation is used. one graph for symbolic shape
-        is compiled. In addition, compile for compile_sizes,
-        using configurations in inductor_compile_config."""
+    - False: inductor compilation is not used. graph runs in eager
+        (custom_ops enabled by default).
+    - True: inductor compilation is used (custom_ops disabled by default).
+        One graph for symbolic shape and one graph per size in compile_sizes
+        are compiled using configurations in inductor_compile_config.
+
+    This setting is ignored if level<PIECEWISE."""
     compile_sizes: Optional[list[Union[int, str]]] = None
     """Sizes to compile for inductor. In addition
     to integers, it also supports "cudagraph_capture_sizes" to
@@ -4469,19 +4473,6 @@ def __post_init__(self):
             self.compilation_config.level = CompilationLevel.PIECEWISE
             self.compilation_config.set_splitting_ops_for_v1()
 
-            # The behavior of custom ops with inductor depends on the config:
-            # - If use_inductor=True and custom_ops is empty:
-            #   Inductor generates Triton kernels for all registered custom ops
-            #   (default behavior)
-            # - If use_inductor=True and custom_ops is non-empty:
-            #   Custom CUDA kernels are used for specified ops while inductor
-            #   generates Triton kernels for remaining ops, including misc torch
-            #   ops in the model.
-            if (not self.compilation_config.custom_ops
-                    and self.compilation_config.use_inductor):
-                # Let inductor generate Triton kernels for the custom ops.
-                self.compilation_config.custom_ops = ["none"]
-
         self._set_cudagraph_sizes()
 
         if self.cache_config.cpu_offload_gb > 0 and \

vllm/model_executor/custom_op.py

@@ -141,16 +141,16 @@ def enabled(cls) -> bool:
     @staticmethod
     def default_on() -> bool:
         """
-        On by default if level < CompilationLevel.PIECEWISE
+        On by default if PyTorch Inductor is not used.
         Specifying 'all' or 'none' in custom_op takes precedence.
         """
         from vllm.config import CompilationLevel
         compilation_config = get_current_vllm_config().compilation_config
-        custom_ops = compilation_config.custom_ops
-        count_none = custom_ops.count("none")
-        count_all = custom_ops.count("all")
-        return compilation_config.level < CompilationLevel.PIECEWISE and \
-            not count_none > 0 or count_all > 0
+        default_on = (compilation_config.level < CompilationLevel.PIECEWISE
+                      or not compilation_config.use_inductor)
+        count_none = compilation_config.custom_ops.count("none")
+        count_all = compilation_config.custom_ops.count("all")
+        return default_on and not count_none > 0 or count_all > 0
 
     # Dictionary of all custom ops (classes, indexed by registered name).
     # To check if an op with a name is enabled, call .enabled() on the class.