[9/N] torch.compile LLM usage (#10552)

Signed-off-by: youkaichao <youkaichao@gmail.com>

tests/tpu/test_compilation.py

@@ -4,7 +4,7 @@ import tempfile
 
 import depyf
 
-from vllm.config import CompilationConfig, CompilationLevel
+from vllm.config import CompilationLevel
 
 temp_dir = tempfile.mkdtemp()
 with depyf.prepare_debug(temp_dir):
@@ -34,8 +34,7 @@ with depyf.prepare_debug(temp_dir):
     # all the control
     llm = LLM(model="google/gemma-2b",
               enforce_eager=True,
-              compilation_config=CompilationConfig(
-                  level=CompilationLevel.DYNAMO_AS_IS))
+              compilation_config={"level": CompilationLevel.DYNAMO_AS_IS})
     outputs = llm.generate(prompts, sampling_params)
     for output, answer in zip(outputs, answers):
         prompt = output.prompt

vllm/entrypoints/llm.py

 import itertools
+import json
 import warnings
 from contextlib import contextmanager
 from typing import (Any, ClassVar, Dict, List, Optional, Sequence, Tuple, Type,
@@ -9,6 +10,7 @@ from tqdm import tqdm
 from vllm import envs
 from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput,
                               BeamSearchSequence, get_beam_search_score)
+from vllm.config import CompilationConfig
 from vllm.engine.arg_utils import (EngineArgs, HfOverrides, PoolerConfig,
                                    TaskOption)
 from vllm.engine.llm_engine import LLMEngine
@@ -107,13 +109,16 @@ class LLM:
         hf_overrides: If a dictionary, contains arguments to be forwarded to the
             HuggingFace config. If a callable, it is called to update the
             HuggingFace config.
+        compilation_config: Either an integer or a dictionary. If it is an integer,
+            it is used as the level of compilation optimization. If it is a dictionary,
+            it can specify the full compilation configuration.
         **kwargs: Arguments for :class:`~vllm.EngineArgs`. (See
             :ref:`engine_args`)
 
     Note:
         This class is intended to be used for offline inference. For online
         serving, use the :class:`~vllm.AsyncLLMEngine` class instead.
-    """
+    """ # noqa
 
     DEPRECATE_LEGACY: ClassVar[bool] = False
     """A flag to toggle whether to deprecate the legacy generate/encode API."""
@@ -166,6 +171,7 @@ class LLM:
         # After positional args are removed, move this right below `model`
         task: TaskOption = "auto",
         override_pooler_config: Optional[PoolerConfig] = None,
+        compilation_config: Optional[Union[int, Dict[str, Any]]] = None,
         **kwargs,
     ) -> None:
         '''
@@ -178,6 +184,12 @@ class LLM:
         if "disable_log_stats" not in kwargs:
             kwargs["disable_log_stats"] = True
 
+        if compilation_config is not None:
+            compilation_config_instance = CompilationConfig.from_cli(
+                json.dumps(compilation_config))
+        else:
+            compilation_config_instance = None
+
         engine_args = EngineArgs(
             model=model,
             task=task,
@@ -202,6 +214,7 @@ class LLM:
             hf_overrides=hf_overrides,
             mm_processor_kwargs=mm_processor_kwargs,
             override_pooler_config=override_pooler_config,
+            compilation_config=compilation_config_instance,
             **kwargs,
         )
         # Logic to switch between engines is done at runtime instead of import