[Bugfix] [torch.compile] Add Dynamo metrics context during compilation (#15639)

Signed-off-by: luka <luka@neuralmagic.com>

tests/compile/test_full_graph.py

@@ -2,21 +2,20 @@
 
 from __future__ import annotations
 
-from typing import Any
+from typing import Any, Union
 
 import pytest
 import torch
 
 from tests.quantization.utils import is_quant_method_supported
 from vllm import LLM, SamplingParams
-from vllm.config import CompilationLevel
+from vllm.config import CompilationConfig, CompilationLevel
 from vllm.platforms import current_platform
 
 from ..utils import create_new_process_for_each_test
 
 
-@pytest.fixture(params=None, name="model_info")
-def models_list_fixture(request):
+def models_list(all: bool):
     TEST_MODELS: list[tuple[str, dict[str, Any]]] = [
         ("facebook/opt-125m", {}),
         ("nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change", {
@@ -33,6 +32,9 @@ def models_list_fixture(request):
         ("meta-llama/Llama-3.2-1B-Instruct", {}),
     ]
 
+    if not all:
+        return TEST_MODELS
+
     if is_quant_method_supported("aqlm"):
         TEST_MODELS.append(("ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf", {
             "quantization": "aqlm"
@@ -77,7 +79,7 @@ def models_list_fixture(request):
     "optimization_level",
     [CompilationLevel.DYNAMO_ONCE, CompilationLevel.PIECEWISE],
 )
-@pytest.mark.parametrize("model_info", "", indirect=True)
+@pytest.mark.parametrize("model_info", models_list(all=True))
 @create_new_process_for_each_test()
 def test_full_graph(
     monkeypatch: pytest.MonkeyPatch,
@@ -91,25 +93,50 @@ def test_full_graph(
         m.setenv("VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE", "1")
         print(f"MODEL={model}")
 
-        prompts = [
-            "Hello, my name is",
-            "The president of the United States is",
-            "The capital of France is",
-            "The future of AI is",
-        ]
-        sampling_params = SamplingParams(temperature=0)
-        llm = LLM(
-            model=model,
-            enforce_eager=True,
-            tensor_parallel_size=1,
-            disable_custom_all_reduce=True,
-            compilation_config=optimization_level,
-            **model_kwargs,
-        )
-        outputs = llm.generate(prompts, sampling_params)
+        run_model(optimization_level, model, model_kwargs)
 
-        # Print the outputs.
-        for output in outputs:
-            prompt = output.prompt
-            generated_text = output.outputs[0].text
-            print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+# TODO(luka) add other supported compilation config scenarios here
+@pytest.mark.parametrize(
+    "compilation_config",
+    # additional compile sizes
+    [
+        CompilationConfig(level=CompilationLevel.PIECEWISE,
+                          compile_sizes=[1, 2])
+    ])
+# only test some of the models
+@pytest.mark.parametrize("model_info", models_list(all=False))
+@create_new_process_for_each_test()
+def test_custom_compile_config(
+    model_info: tuple[str, dict[str, Any]],
+    compilation_config: CompilationConfig,
+):
+    model, model_kwargs = model_info
+    print(f"MODEL={model}")
+    run_model(compilation_config, model, model_kwargs)
+
+
+def run_model(compile_config: Union[int, CompilationConfig], model: str,
+              model_kwargs: dict[str, Any]):
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+    sampling_params = SamplingParams(temperature=0)
+    llm = LLM(
+        model=model,
+        enforce_eager=True,
+        tensor_parallel_size=1,
+        disable_custom_all_reduce=True,
+        compilation_config=compile_config,
+        **model_kwargs,
+    )
+    outputs = llm.generate(prompts, sampling_params)
+
+    # Print the outputs.
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

vllm/compilation/compiler_interface.py

@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
+import contextlib
 import copy
 import hashlib
+import importlib.metadata
 import os
 from contextlib import ExitStack
 from typing import Any, Callable, Dict, List, Optional, Tuple
@@ -9,6 +11,7 @@ from unittest.mock import patch
 import torch
 import torch._inductor.compile_fx
 import torch.fx as fx
+from packaging.version import Version
 
 from vllm.config import VllmConfig
 
@@ -285,6 +288,9 @@ class InductorAdaptor(CompilerInterface):
                     "torch._inductor.codecache.FxGraphCache._check_can_cache",
                     _check_can_cache))
 
+            # Dynamo metrics context, see method for more details.
+            stack.enter_context(self.metrics_context())
+
             compiled_graph = compile_fx(
                 graph,
                 example_inputs,
@@ -309,8 +315,14 @@ class InductorAdaptor(CompilerInterface):
         hash_str = handle[0]
 
         from torch._inductor.codecache import FxGraphCache
-        with patch("torch._inductor.codecache.FxGraphCache._get_shape_env",
-                   lambda *args, **kwargs: AlwaysHitShapeEnv()):
+        with ExitStack() as exit_stack:
+            exit_stack.enter_context(
+                patch("torch._inductor.codecache.FxGraphCache._get_shape_env",
+                      lambda *args, **kwargs: AlwaysHitShapeEnv()))
+
+            # Dynamo metrics context, see method for more details.
+            exit_stack.enter_context(self.metrics_context())
+
             if torch.__version__.startswith("2.5"):
                 inductor_compiled_graph = FxGraphCache._lookup_graph(
                     hash_str, example_inputs, True, False)
@@ -351,6 +363,28 @@ class InductorAdaptor(CompilerInterface):
 
         return compiled_graph
 
+    def metrics_context(self) -> contextlib.AbstractContextManager:
+        """
+        This method returns the Dynamo metrics context (if it exists,
+        otherwise a null context). It is used by various compile components.
+        Present in torch>=2.6, it's used inside FxGraphCache in
+        torch==2.6 (but not after). It might also be used in various other
+        torch.compile internal functions.
+
+        Because it is re-entrant, we always set it (even if entering via Dynamo
+        and the context was already entered). We might want to revisit if it
+        should be set at a different level of compilation.
+
+        This is likely a bug in PyTorch: public APIs should not rely on
+        manually setting up internal contexts. But we also rely on non-public
+        APIs which might not provide these guarantees.
+        """
+        if Version(importlib.metadata.version('torch')) >= Version("2.6"):
+            import torch._dynamo.utils
+            return torch._dynamo.utils.get_metrics_context()
+        else:
+            return contextlib.nullcontext()
+
 
 class EagerAdaptor(CompilerInterface):
     name = "eager"