[compile] Split compile/warmup monitoring (#36098)

vllm/compilation/caching.py

@@ -189,13 +189,13 @@ class VllmSerializableFunction(SerializableCallable):  # type: ignore[misc]
         self.shape_env = None
         self.vllm_backend = vllm_backend
         self.sym_tensor_indices = sym_tensor_indices
+        self._fake_mode: Any | None = None
 
         import torch._functorch.config as functorch_config
 
         self.aot_autograd_config = (
             aot_autograd_config or functorch_config.save_config_portable()
         )
-
         sym_input = next(
             (i for i in self.example_inputs if isinstance(i, torch.SymInt)), None
         )
@@ -217,6 +217,7 @@ class VllmSerializableFunction(SerializableCallable):  # type: ignore[misc]
         state.pop("optimized_call")
         state.pop("shape_env")
         state.pop("vllm_backend", None)
+        state.pop("_fake_mode", None)
         for node in state["graph_module"].graph.nodes:
             node.meta.pop("source_fn_stack", None)
             node.meta.pop("nn_module_stack", None)
@@ -351,8 +352,31 @@ class VllmSerializableFunction(SerializableCallable):  # type: ignore[misc]
             return fn.optimized_call(*example_inputs)
 
         fn = cls(**state, optimized_call=optimized_call)
+        fn._fake_mode = fake_mode
         return fn
 
+    def finalize_loading(self, vllm_config: VllmConfig) -> None:
+        """Eagerly initialize the compiled backend and perform all loading.
+
+        Must be called after _verify_source_unchanged has populated
+        compilation_config.traced_files, which is needed for cache dir
+        computation.
+        """
+        if self._fake_mode is None:
+            return  # Already finalized, or mega path (no _fake_mode set)
+
+        from torch._guards import TracingContext, tracing
+
+        from vllm.compilation.backends import VllmBackend
+
+        vllm_backend = VllmBackend(vllm_config, self.prefix, self.is_encoder)
+        with tracing(TracingContext(self._fake_mode)):
+            result = vllm_backend(self.graph_module, list(self.example_inputs))
+            self.optimized_call = result.optimized_call
+            self.vllm_backend = vllm_backend
+
+        self._fake_mode = None
+
     @property
     def co_name(self) -> Literal["VllmSerializableFunction"]:
         """

vllm/compilation/decorators.py

@@ -30,7 +30,7 @@ from vllm.sequence import IntermediateTensors
 from vllm.utils.import_utils import resolve_obj_by_qualname
 from vllm.utils.torch_utils import is_torch_equal_or_newer
 
-from .monitor import start_monitoring_torch_compile
+from .monitor import monitor_profiling_run, monitor_torch_compile
 
 if TYPE_CHECKING:
     # Only added on nightly/2.10 so wrap
@@ -434,17 +434,24 @@ def _support_torch_compile(
             cache_dir = os.path.join(cache_dir, f"rank_{rank}_{dp_rank}")
             aot_compilation_path = os.path.join(cache_dir, "model")
             try:
+                with monitor_torch_compile(self.vllm_config):
                     with (
                         set_current_vllm_config(self.vllm_config),
                         open(aot_compilation_path, "rb") as f,
                     ):
-                    start_monitoring_torch_compile(self.vllm_config)
                         loaded_fn = torch.compiler.load_compiled_function(
                             f, f_globals=self.forward.__globals__
                         )
                     _verify_source_unchanged(loaded_fn.source_info(), self.vllm_config)
-                if not self.compilation_config.dynamic_shapes_config.evaluate_guards:
+                    ds_config = self.compilation_config.dynamic_shapes_config
+                    if not ds_config.evaluate_guards:
                         loaded_fn.disable_guard_check()
+                    # Eagerly load compiled artifacts now that traced_files
+                    # is populated by _verify_source_unchanged.
+                    with maybe_use_cudagraph_partition_wrapper(self.vllm_config):
+                        loaded_fn._artifacts.compiled_fn.finalize_loading(
+                            self.vllm_config
+                        )
                 self.aot_compiled_fn = loaded_fn
                 self.was_aot_compile_fn_loaded_from_disk = True
             except Exception as e:
@@ -465,12 +472,11 @@ def _support_torch_compile(
                 logger.info(
                     "Directly load AOT compilation from path %s", aot_compilation_path
                 )
-                # Apply partition wrapper context for proper CUDA graph capture
-                from .monitor import end_monitoring_torch_compile
-
-                with maybe_use_cudagraph_partition_wrapper(self.vllm_config):
+                with (
+                    monitor_profiling_run(),
+                    maybe_use_cudagraph_partition_wrapper(self.vllm_config),
+                ):
                     output = self.aot_compiled_fn(self, *args, **kwargs)
-                end_monitoring_torch_compile(self.vllm_config)
                 return output
 
         if self.compiled:
@@ -489,8 +495,6 @@ def _support_torch_compile(
             **kwargs,
         )
 
-        # here, it is the starting point of the `torch.compile` process
-        start_monitoring_torch_compile(self.vllm_config)
         original_code_object = self.original_code_object()
         logger.debug("Start compiling function %s", original_code_object)
 
@@ -559,16 +563,26 @@ def _support_torch_compile(
                 # store the path for saving after warmup
                 self._aot_compilation_path = aot_compilation_path
                 self._aot_cache_dir = cache_dir
+                with monitor_torch_compile(self.vllm_config):
                     self.aot_compiled_fn = self.aot_compile(*args, **kwargs)
-                # All compilation is done at this point, save the AOT artifact.
+                    # All compilation is done at this point, save the
+                    # AOT artifact.
                     self.save_aot_compiled_function()
+
+                with monitor_profiling_run():
                     output = self.aot_compiled_fn(self, *args, **kwargs)
             else:
-                output = TorchCompileWithNoGuardsWrapper.__call__(self, *args, **kwargs)  # type: ignore[arg-type]
-
-        from .monitor import end_monitoring_torch_compile
+                with monitor_torch_compile(
+                    self.vllm_config,
+                    "torch.compile and initial profiling/warmup "
+                    "run together took %.2f s in total",
+                ):
+                    output = TorchCompileWithNoGuardsWrapper.__call__(
+                        self,  # type: ignore[arg-type]
+                        *args,
+                        **kwargs,
+                    )
 
-        end_monitoring_torch_compile(self.vllm_config)
         self.compiled = True
         return output
 

vllm/compilation/monitor.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import contextlib
 import time
+from collections.abc import Generator
 
-from vllm.config import CompilationConfig, CompilationMode, VllmConfig
+from vllm.config import CompilationMode, VllmConfig
 from vllm.logger import init_logger
 
 logger = init_logger(__name__)
 
-context_manager = None
+# Shared global so backends.py can read the start time for Dynamo timing.
 torch_compile_start_time: float = 0.0
 
 
-def start_monitoring_torch_compile(vllm_config: VllmConfig) -> None:
+@contextlib.contextmanager
+def monitor_torch_compile(
+    vllm_config: VllmConfig,
+    message: str = "torch.compile took %.2f s in total",
+) -> Generator[None, None, None]:
+    """Context manager that times torch.compile and manages depyf debugging.
+
+    On normal exit: logs the compile time and exits depyf.
+    On exception: cleans up depyf without logging (compilation failed).
+    """
     global torch_compile_start_time
     torch_compile_start_time = time.perf_counter()
 
-    compilation_config: CompilationConfig = vllm_config.compilation_config
+    compilation_config = vllm_config.compilation_config
+    depyf_cm = None
     path = vllm_config.compile_debug_dump_path()
     if compilation_config.mode == CompilationMode.VLLM_COMPILE and path:
         import depyf
 
         path.mkdir(parents=True, exist_ok=True)
         logger.debug("Dumping depyf output to %s", path)
-        global context_manager
-        context_manager = depyf.prepare_debug(path.as_posix())
-        context_manager.__enter__()
-
-
-def end_monitoring_torch_compile(vllm_config: VllmConfig) -> None:
-    compilation_config: CompilationConfig = vllm_config.compilation_config
-    total_compile_time: float = time.perf_counter() - torch_compile_start_time
+        depyf_cm = depyf.prepare_debug(path.as_posix())
+        depyf_cm.__enter__()
+
+    try:
+        yield
+    except Exception:
+        raise
+    else:
+        total_compile_time = time.perf_counter() - torch_compile_start_time
         if compilation_config.mode == CompilationMode.VLLM_COMPILE:
+            logger.info_once(message, total_compile_time, scope="local")
+    finally:
+        if depyf_cm is not None:
+            try:
+                depyf_cm.__exit__(None, None, None)
+            except Exception:
+                logger.warning("Exception during depyf cleanup.", exc_info=True)
+
+
+@contextlib.contextmanager
+def monitor_profiling_run() -> Generator[None, None, None]:
+    """Context manager that times the initial profiling run.
+
+    Asserts that no backend compilation occurs during the profiling run
+    (all compilation should have completed before this point).
+    """
+    from vllm.compilation.counter import compilation_counter
+
+    backend_compilations_before = compilation_counter.num_backend_compilations
+    start = time.perf_counter()
+    yield
+    elapsed = time.perf_counter() - start
+    assert (
+        compilation_counter.num_backend_compilations == backend_compilations_before
+    ), (
+        "backend compilation occurred during the initial profiling run; "
+        "all compilation should be complete before the profiling run starts."
+    )
     logger.info_once(
-            "torch.compile and initial profiling run took %.2f s in total",
-            total_compile_time,
+        "Initial profiling/warmup run took %.2f s",
+        elapsed,
         scope="local",
     )
-        global context_manager
-        if context_manager is not None:
-            context_manager.__exit__(None, None, None)
-            context_manager = None
 
 
 cudagraph_capturing_enabled: bool = True