Revert #26113 "[Frontend] CompilationConfig overhaul (#20283): deprecate use_inductor in favor of backend, simplify custom_ops"

tests/compile/piecewise/test_toy_llama.py

@@ -258,13 +258,13 @@ def tractable_computation(
 
 @torch.inference_mode
 def run_model(
-    llama_config, use_compile: bool, backend: str, split_attn: bool = False
+    llama_config, use_compile: bool, use_inductor: bool, split_attn: bool = False
 ) -> torch.Tensor:
     if use_compile:
         compilation_config = CompilationConfig(
             level=CompilationLevel.PIECEWISE,
             use_cudagraph=True,
-            backend=backend,
+            use_inductor=use_inductor,
             cudagraph_capture_sizes=[1, 2],
         )
         if split_attn:
@@ -338,8 +338,8 @@ def run_model(
             return output.cpu()
 
 
-@pytest.mark.parametrize("backend", ["inductor", "eager"])
-def test_toy_llama(backend: str):
+@pytest.mark.parametrize("use_inductor", [True, False])
+def test_toy_llama(use_inductor: bool):
     # compare output with and without piecewise compilation
 
     llama_config = LlamaConfig(
@@ -358,10 +358,10 @@ def test_toy_llama(backend: str):
         num_backend_compilations=0,
         num_cudagraph_captured=0,
     ):
-        outputs.append(run_model(llama_config, backend="eager", use_compile=False))
-    run_model(tractable_config, backend="eager", use_compile=False)
+        outputs.append(run_model(llama_config, use_inductor=False, use_compile=False))
+    run_model(tractable_config, use_inductor=False, use_compile=False)
 
-    if backend == "inductor":
+    if use_inductor:
         kwargs = {"num_inductor_compiles": 1, "num_eager_compiles": 0}
     else:
         kwargs = {"num_eager_compiles": 1, "num_inductor_compiles": 0}
@@ -377,8 +377,10 @@ def test_toy_llama(backend: str):
         num_cudagraph_captured=2,
         **kwargs,
     ):
-        outputs.append(run_model(llama_config, backend=backend, use_compile=True))
-    run_model(tractable_config, backend=backend, use_compile=True)
+        outputs.append(
+            run_model(llama_config, use_inductor=use_inductor, use_compile=True)
+        )
+    run_model(tractable_config, use_inductor=use_inductor, use_compile=True)
 
     with compilation_counter.expect(
         num_graphs_seen=1,  # one graph for the model
@@ -393,9 +395,16 @@ def test_toy_llama(backend: str):
         ),  # num_cudagraph_sizes * num_piecewise_capturable_graphs_seen
     ):
         outputs.append(
-            run_model(llama_config, backend=backend, use_compile=True, split_attn=True)
+            run_model(
+                llama_config,
+                use_inductor=use_inductor,
+                use_compile=True,
+                split_attn=True,
+            )
         )
-    run_model(tractable_config, backend=backend, use_compile=True, split_attn=True)
+    run_model(
+        tractable_config, use_inductor=use_inductor, use_compile=True, split_attn=True
+    )
 
     for i in range(1, len(outputs)):
         assert torch.allclose(outputs[0], outputs[i])

tests/model_executor/test_enabled_custom_ops.py

@@ -37,59 +37,57 @@ class Relu3(ReLUSquaredActivation):
 
 
 @pytest.mark.parametrize(
-    "env, torch_level, backend, ops_enabled, default_on",
+    "env, torch_level, use_inductor, ops_enabled, default_on",
     [
         # Default values based on compile level
         # - All by default (no Inductor compilation)
-        (None, 0, "eager", [True] * 4, True),
-        (None, 1, "eager", [True] * 4, True),
-        (None, 2, "eager", [True] * 4, True),
-        (None, 3, "eager", [True] * 4, True),
+        (None, 0, False, [True] * 4, True),
+        (None, 1, True, [True] * 4, True),
+        (None, 2, False, [True] * 4, True),
         # - None by default (with Inductor)
-        (None, 0, "inductor", [True] * 4, True),
-        # - None by default (with Inductor)
-        (None, 1, "inductor", [False] * 4, False),
-        (None, 2, "inductor", [False] * 4, False),
-        (None, 3, "inductor", [False] * 4, False),
+        (None, 3, True, [False] * 4, False),
+        (None, 4, True, [False] * 4, False),
+        # - All by default (without Inductor)
+        (None, 3, False, [True] * 4, True),
+        (None, 4, False, [True] * 4, True),
         # Explicitly enabling/disabling
         #
         # Default: all
         #
         # All but SiluAndMul
-        ("+rms_norm,-silu_and_mul", 0, "inductor", [1, 0, 1, 1], True),
+        ("+rms_norm,-silu_and_mul", 0, True, [1, 0, 1, 1], True),
         # Only ReLU3
-        ("none,-rms_norm,+relu3", 1, "eager", [0, 0, 0, 1], False),
+        ("none,-rms_norm,+relu3", 1, False, [0, 0, 0, 1], False),
         # All but SiluAndMul
-        ("all,-silu_and_mul", 2, "inductor", [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", 3, "eager", [1, 1, 1, 0], True),
+        ("-relu3,+relu2", 3, False, [1, 1, 1, 0], True),
         # RMSNorm and SiluAndMul
-        ("none,-relu3,+rms_norm,+silu_and_mul", 3, "eager", [1, 1, 0, 0], False),
+        ("none,-relu3,+rms_norm,+silu_and_mul", 4, False, [1, 1, 0, 0], False),
         # All but RMSNorm
-        ("-rms_norm", 3, "eager", [0, 1, 1, 1], True),
+        ("-rms_norm", 3, False, [0, 1, 1, 1], True),
         #
         # Default: none
         #
         # Only ReLU3
-        ("none,+relu3", 3, "inductor", [0, 0, 0, 1], False),
+        ("-silu_and_mul,+relu3", 3, True, [0, 0, 0, 1], False),
         # All but RMSNorm
-        ("all,-rms_norm", 3, "inductor", [0, 1, 1, 1], True),
+        ("all,-rms_norm", 4, True, [0, 1, 1, 1], True),
     ],
 )
 def test_enabled_ops(
     env: Optional[str],
     torch_level: int,
-    backend: str,
+    use_inductor: bool,
     ops_enabled: list[int],
     default_on: bool,
 ):
     custom_ops = env.split(",") if env else []
     vllm_config = VllmConfig(
         compilation_config=CompilationConfig(
-            backend=backend, level=torch_level, custom_ops=custom_ops
+            use_inductor=bool(use_inductor), level=torch_level, custom_ops=custom_ops
         )
     )
-    # breakpoint()
     with set_current_vllm_config(vllm_config):
         assert CustomOp.default_on() == default_on
 

vllm/compilation/backends.py

@@ -34,7 +34,7 @@
 
 
 def make_compiler(compilation_config: CompilationConfig) -> CompilerInterface:
-    if compilation_config.backend == "inductor":
+    if compilation_config.use_inductor:
         # Use standalone compile only if requested, version is new enough,
         # and the symbol actually exists in this PyTorch build.
         if (
@@ -48,10 +48,6 @@ def make_compiler(compilation_config: CompilationConfig) -> CompilerInterface:
             logger.debug("Using InductorAdaptor")
             return InductorAdaptor()
     else:
-        assert compilation_config.backend == "eager", (
-            "Custom backends not supported with CompilationLevel.PIECEWISE"
-        )
-
         logger.debug("Using EagerAdaptor")
         return EagerAdaptor()
 

vllm/config/compilation.py

@@ -180,11 +180,10 @@ class CompilationConfig:
     """The directory to store the compiled graph, to accelerate Inductor
     compilation. By default, it will use model-related information to generate
     a cache directory."""
-    backend: str = "inductor"
+    backend: str = ""
     """The backend for compilation. It needs to be a string:
 
-    - "" (empty string): use the default backend ("inductor" on CUDA-alike
-    platforms).
+    - "" (empty string): use the default backend.
     - "eager"/"openxla"/...: use the specified backend registered in PyTorch.
     - "full.module.name": a qualified name which can be used to import the
 
@@ -193,11 +192,7 @@ class CompilationConfig:
     distributed setting. When the compilation level is 1 or 2, the backend is
     used for the compilation directly (it sees the whole graph). When the
     compilation level is 3, the backend is used for the piecewise compilation
-    (it sees a part of the graph). The backend can not be custom for compilation
-    level 3. Furthermore, compilation is only piecewise if splitting ops is set
-    accordingly and use_inductor_cudagraphs_partition is off. Note that the
-    default options for splitting ops are sufficient for piecewise compilation.
-    """
+    (it sees a part of the graph)."""
     custom_ops: list[str] = field(default_factory=list)
     """Fine-grained control over which custom ops to enable/disable. Use 'all'
     to enable all, 'none' to disable all. Also specify a list of custom op
@@ -215,24 +210,16 @@ class CompilationConfig:
     compilation."""
 
     # Inductor capture
-    use_inductor: Optional[bool] = None
-    """
-    Whether to use inductor compilation.
-
-    This flag is deprecated and will be removed.
-    Please use the 'backend' option instead.
+    use_inductor: bool = True
+    """Whether to use inductor compilation:
 
     - 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.
-
-    For future compatibility:
-    If use_inductor is True, backend="inductor" otherwise backend="eager".
-    """
+    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
@@ -538,59 +525,23 @@ def __post_init__(self) -> None:
                     "(where 'op' is the registered op name)"
                 )
 
-        # Currently only eager and inductor backend are supported.
-        # for piecewise compilation. Custom backends are not suppported for
-        # piecewise compilation. Update when more backends are supported.
-        if self.level == CompilationLevel.PIECEWISE and self.backend not in [
-            "",
-            "eager",
-            "inductor",
-        ]:
-            raise ValueError(
-                f"Invalid backend for piecewise compilation: {self.backend}"
-            )
-
-        if self.use_inductor is not None:
-            logger.warning_once(
-                "The 'use_inductor' flag is deprecated and will be\
-                    removed in a future release."
-                "Please use the 'backend' option instead.",
-            )
-            self.backend = "inductor" if self.use_inductor else "eager"
-
-        if self.backend == "":
-            self.backend = "inductor"
-
     def init_backend(self, vllm_config: "VllmConfig") -> Union[str, Callable]:
-        """
-        Initialize the backend for the compilation config from a vllm config.
-        Arguments:
-            vllm_config: The vllm config to initialize the backend from.
-        Returns:
-            The backend for the compilation config.
-        """
-        if self.level is None:
-            raise ValueError(
-                "No compilation level is set. This method should only be \
-                called via vllm config where the level is set if none is \
-                provided."
-            )
         if self.level == CompilationLevel.NO_COMPILATION:
             raise ValueError("No compilation level is set.")
 
         from torch._dynamo.backends.registry import list_backends
 
         torch_backends = list_backends(exclude_tags=tuple())
         if self.level in [CompilationLevel.DYNAMO_AS_IS, CompilationLevel.DYNAMO_ONCE]:
+            if self.backend == "":
+                return "eager"
             if self.backend in torch_backends:
                 return self.backend
             return resolve_obj_by_qualname(self.backend)
 
+        # TODO: pass user-specified backend to piecewise compilation
+        # merge with the config use_inductor
         assert self.level == CompilationLevel.PIECEWISE
-        if self.backend not in ["eager", "inductor"]:
-            raise ValueError(
-                f"Invalid backend for piecewise compilation: {self.backend}"
-            )
 
         from vllm.compilation.backends import VllmBackend
 
@@ -743,7 +694,7 @@ def is_attention_compiled_piecewise(self) -> bool:
         )
 
         inductor_used = (
-            self.level == CompilationLevel.PIECEWISE and self.backend == "inductor"
+            self.level == CompilationLevel.PIECEWISE and self.use_inductor
         ) or (
             self.level >= CompilationLevel.DYNAMO_AS_IS and self.backend == "inductor"
         )

vllm/config/vllm.py

@@ -318,25 +318,6 @@ def __post_init__(self):
                 # NB: Passing both --enforce-eager and a compilation level
                 # in V0 means the compilation level wins out.
                 self.compilation_config.level = CompilationLevel.NO_COMPILATION
-        else:
-            assert self.compilation_config.level >= CompilationLevel.NO_COMPILATION
-            assert self.compilation_config.level <= CompilationLevel.PIECEWISE
-            assert self.compilation_config.level <= 3
-
-        # If user does not set custom ops via none or all set it here based on
-        # compilation level and backend.
-        if (
-            self.compilation_config.custom_ops.count("none")
-            + self.compilation_config.custom_ops.count("all")
-            == 0
-        ):
-            if (
-                self.compilation_config.level > 0
-                and self.compilation_config.backend != "eager"
-            ):
-                self.compilation_config.custom_ops.append("none")
-            else:
-                self.compilation_config.custom_ops.append("all")
 
         # async tp is built on top of sequence parallelism
         # and requires it to be enabled.

vllm/model_executor/custom_op.py

@@ -114,9 +114,7 @@ def enabled(cls) -> bool:
         custom_ops = compilation_config.custom_ops
         if not hasattr(cls, "name"):
             logger.warning_once(
-                "Custom op %s was not registered, which means it won't appear\
-                 in the op registry. It will be enabled/disabled based on the\
-                 global settings.",  # noqa: E501
+                "Custom op %s was not registered, which means it won't appear in the op registry. It will be enabled/disabled based on the global settings.",  # noqa: E501
                 cls.__name__,
             )
             return CustomOp.default_on()
@@ -130,17 +128,19 @@ def enabled(cls) -> bool:
     @staticmethod
     def default_on() -> bool:
         """
-        Behavior controlled by `CompilationConfig.custom_ops`: On by default if
-        'all', off by default if 'none'.
-        When PyTorch Inductor is used, 'none' is the default value,
-        otherwise 'all'.
+        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_cached_compilation_config()
+        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")
-        assert count_none + count_all == 1
-
-        return not count_none > 0 or count_all > 0
+        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.

vllm/platforms/cpu.py

@@ -274,6 +274,8 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
                     "epilogue_fusion": True,
                 }
             )
+            if compilation_config.use_inductor:
+                compilation_config.custom_ops = ["none"]
 
         if vllm_config.lora_config is not None:
             compilation_config.level = CompilationLevel.NO_COMPILATION