[v1] EngineArgs for better config handling for v1

.buildkite/test-pipeline.yaml

@@ -171,7 +171,7 @@ steps:
     - vllm/
     - tests/v1
   commands:
-    - pytest -v -s v1
+    - VLLM_USE_V1=1 pytest -v -s v1
 
 - label: Examples Test # 15min
   working_dir: "/vllm-workspace/examples"

tests/v1/engine/test_async_llm.py

@@ -32,6 +32,9 @@ async def generate(engine: AsyncLLM, request_id: str,
 
 @pytest.mark.asyncio
 async def test_load(monkeypatch):
+    # TODO(rickyx): Remove monkeypatch once we have a better way to test V1
+    # so that in the future when we switch, we don't have to change all the
+    # tests.
     with monkeypatch.context() as m:
         m.setenv("VLLM_USE_V1", "1")
 

tests/v1/engine/test_engine_args.py

@@ -0,0 +1,42 @@
+import pytest
+
+from vllm import envs
+from vllm.config import VllmConfig
+from vllm.engine.arg_utils import EngineArgs
+from vllm.usage.usage_lib import UsageContext
+
+if not envs.VLLM_USE_V1:
+    pytest.skip(
+        "Skipping V1 tests. Rerun with `VLLM_USE_V1=1` to test.",
+        allow_module_level=True,
+    )
+
+
+def test_defaults():
+    engine_args = EngineArgs(model="facebook/opt-125m")
+
+    # Assert V1 defaults
+    assert (engine_args.enable_prefix_caching
+            ), "V1 turns on prefix caching by default"
+
+
+def test_defaults_with_usage_context():
+    engine_args = EngineArgs(model="facebook/opt-125m")
+    vllm_config: VllmConfig = engine_args.create_engine_config(
+        UsageContext.LLM_CLASS)
+
+    assert vllm_config.scheduler_config.max_num_seqs == 1024
+    assert vllm_config.scheduler_config.max_num_batched_tokens == 8192
+
+    engine_args = EngineArgs(model="facebook/opt-125m")
+    vllm_config = engine_args.create_engine_config(
+        UsageContext.OPENAI_API_SERVER)
+    assert vllm_config.scheduler_config.max_num_seqs == 1024
+    assert vllm_config.scheduler_config.max_num_batched_tokens == 2048
+
+
+def test_prefix_cache_disabled_with_multimodel():
+    engine_args = EngineArgs(model="llava-hf/llava-1.5-7b-hf")
+
+    vllm_config = engine_args.create_engine_config(UsageContext.LLM_CLASS)
+    assert not vllm_config.cache_config.enable_prefix_caching

tests/v1/engine/test_engine_core.py

@@ -43,7 +43,8 @@ def test_engine_core(monkeypatch):
         m.setenv("VLLM_USE_V1", "1")
         """Setup the EngineCore."""
         engine_args = EngineArgs(model=MODEL_NAME)
-        vllm_config = engine_args.create_engine_config()
+        vllm_config = engine_args.create_engine_config(
+            usage_context=UsageContext.UNKNOWN_CONTEXT)
         executor_class = AsyncLLM._get_executor_cls(vllm_config)
 
         engine_core = EngineCore(vllm_config=vllm_config,

tests/v1/engine/test_engine_core_client.py

@@ -82,7 +82,8 @@ def test_engine_core_client(monkeypatch, multiprocessing_mode: bool):
         m.setenv("VLLM_USE_V1", "1")
 
         engine_args = EngineArgs(model=MODEL_NAME)
-        vllm_config = engine_args.create_engine_config()
+        vllm_config = engine_args.create_engine_config(
+            UsageContext.UNKNOWN_CONTEXT)
         executor_class = AsyncLLM._get_executor_cls(vllm_config)
         client = EngineCoreClient.make_client(
             vllm_config,
@@ -153,7 +154,8 @@ async def test_engine_core_client_asyncio(monkeypatch):
         m.setenv("VLLM_USE_V1", "1")
 
         engine_args = EngineArgs(model=MODEL_NAME)
-        vllm_config = engine_args.create_engine_config()
+        vllm_config = engine_args.create_engine_config(
+            usage_context=UsageContext.UNKNOWN_CONTEXT)
         executor_class = AsyncLLM._get_executor_cls(vllm_config)
         client = EngineCoreClient.make_client(
             vllm_config,

vllm/engine/arg_utils.py

@@ -20,6 +20,7 @@
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.platforms import current_platform
 from vllm.transformers_utils.utils import check_gguf_file
+from vllm.usage.usage_lib import UsageContext
 from vllm.utils import FlexibleArgumentParser, StoreBoolean
 
 if TYPE_CHECKING:
@@ -113,7 +114,7 @@ class EngineArgs:
     # NOTE(kzawora): default block size for Gaudi should be 128
     # smaller sizes still work, but very inefficiently
     block_size: int = 16 if not current_platform.is_hpu() else 128
-    enable_prefix_caching: bool = False
+    enable_prefix_caching: Optional[bool] = None
     disable_sliding_window: bool = False
     use_v2_block_manager: bool = True
     swap_space: float = 4  # GiB
@@ -196,6 +197,11 @@ def __post_init__(self):
         if not self.tokenizer:
             self.tokenizer = self.model
 
+        # Override the default value of enable_prefix_caching if it's not set
+        # by user.
+        if self.enable_prefix_caching is None:
+            self.enable_prefix_caching = bool(envs.VLLM_USE_V1)
+
         # Setup plugins
         from vllm.plugins import load_general_plugins
         load_general_plugins()
@@ -936,7 +942,12 @@ def create_load_config(self) -> LoadConfig:
             ignore_patterns=self.ignore_patterns,
         )
 
-    def create_engine_config(self) -> VllmConfig:
+    def create_engine_config(self,
+                             usage_context: Optional[UsageContext] = None
+                             ) -> VllmConfig:
+        if envs.VLLM_USE_V1:
+            self._override_v1_engine_args(usage_context)
+
         # gguf file needs a specific model loader and doesn't use hf_repo
         if check_gguf_file(self.model):
             self.quantization = self.load_format = "gguf"
@@ -1146,7 +1157,7 @@ def create_engine_config(self) -> VllmConfig:
             or "all" in detailed_trace_modules,
         )
 
-        return VllmConfig(
+        config = VllmConfig(
             model_config=model_config,
             cache_config=cache_config,
             parallel_config=parallel_config,
@@ -1161,6 +1172,42 @@ def create_engine_config(self) -> VllmConfig:
             compilation_config=self.compilation_config,
         )
 
+        if envs.VLLM_USE_V1:
+            self._override_v1_engine_config(config)
+        return config
+
+    def _override_v1_engine_args(self, usage_context: UsageContext) -> None:
+        """
+        Override the EngineArgs's args based on the usage context for V1.
+        """
+        assert envs.VLLM_USE_V1, "V1 is not enabled"
+
+        if self.max_num_batched_tokens is None:
+            # When no user override, set the default values based on the
+            # usage context.
+            if usage_context == UsageContext.LLM_CLASS:
+                logger.warning("Setting max_num_batched_tokens to 8192 "
+                               "for LLM_CLASS usage context.")
+                self.max_num_seqs = 1024
+                self.max_num_batched_tokens = 8192
+            elif usage_context == UsageContext.OPENAI_API_SERVER:
+                logger.warning("Setting max_num_batched_tokens to 2048 "
+                               "for OPENAI_API_SERVER usage context.")
+                self.max_num_seqs = 1024
+                self.max_num_batched_tokens = 2048
+
+    def _override_v1_engine_config(self, engine_config: VllmConfig) -> None:
+        """
+        Override the EngineConfig's configs based on the usage context for V1.
+        """
+        assert envs.VLLM_USE_V1, "V1 is not enabled"
+        # TODO (ywang96): Enable APC by default when VLM supports it.
+        if engine_config.model_config.is_multimodal_model:
+            logger.warning(
+                "Prefix caching is currently not supported for multimodal "
+                "models and has been disabled.")
+            engine_config.cache_config.enable_prefix_caching = False
+
 
 @dataclass
 class AsyncEngineArgs(EngineArgs):

vllm/engine/async_llm_engine.py

@@ -680,7 +680,7 @@ def from_engine_args(
         """Creates an async LLM engine from the engine arguments."""
         # Create the engine configs.
         if engine_config is None:
-            engine_config = engine_args.create_engine_config()
+            engine_config = engine_args.create_engine_config(usage_context)
 
         executor_class = cls._get_executor_cls(engine_config)
 

vllm/engine/llm_engine.py

@@ -579,7 +579,7 @@ def from_engine_args(
     ) -> "LLMEngine":
         """Creates an LLM engine from the engine arguments."""
         # Create the engine configs.
-        engine_config = engine_args.create_engine_config()
+        engine_config = engine_args.create_engine_config(usage_context)
         executor_class = cls._get_executor_cls(engine_config)
         # Create the LLM engine.
         engine = cls(

vllm/engine/multiprocessing/engine.py

@@ -111,7 +111,7 @@ def from_engine_args(cls, engine_args: AsyncEngineArgs,
         from vllm.plugins import load_general_plugins
         load_general_plugins()
 
-        engine_config = engine_args.create_engine_config()
+        engine_config = engine_args.create_engine_config(usage_context)
         executor_class = LLMEngine._get_executor_cls(engine_config)
 
         use_async_sockets = engine_config.model_config.use_async_output_proc

vllm/entrypoints/openai/api_server.py

@@ -133,8 +133,8 @@ async def build_async_engine_client_from_engine_args(
     # TODO: fill out feature matrix.
     if (MQLLMEngineClient.is_unsupported_config(engine_args)
             or envs.VLLM_USE_V1 or disable_frontend_multiprocessing):
-
-        engine_config = engine_args.create_engine_config()
+        engine_config = engine_args.create_engine_config(
+            UsageContext.OPENAI_API_SERVER)
         uses_ray = getattr(AsyncLLMEngine._get_executor_cls(engine_config),
                            "uses_ray", False)
 

vllm/v1/engine/async_llm.py

@@ -94,7 +94,7 @@ def from_engine_args(
 
         # Create the engine configs.
         if engine_config is None:
-            vllm_config = engine_args.create_engine_config()
+            vllm_config = engine_args.create_engine_config(usage_context)
         else:
             vllm_config = engine_config
 

vllm/v1/engine/core.py

@@ -39,19 +39,6 @@ def __init__(
         executor_class: Type[GPUExecutor],
         usage_context: UsageContext,
     ):
-        # Override the configs for V1.
-        # FIXME
-        if usage_context == UsageContext.LLM_CLASS:
-            vllm_config.scheduler_config.max_num_seqs = 1024
-            vllm_config.scheduler_config.max_num_batched_tokens = 8192
-        elif usage_context == UsageContext.OPENAI_API_SERVER:
-            vllm_config.scheduler_config.max_num_seqs = 1024
-            vllm_config.scheduler_config.max_num_batched_tokens = 2048
-
-        # TODO (ywang96): Enable APC by default when VLM supports it.
-        if not vllm_config.model_config.is_multimodal_model:
-            vllm_config.cache_config.enable_prefix_caching = True
-
         assert vllm_config.model_config.task != "embedding"
 
         logger.info("Initializing an LLM engine (v%s) with config: %s",

vllm/v1/engine/llm_engine.py

@@ -82,7 +82,7 @@ def from_engine_args(
         """Creates an LLM engine from the engine arguments."""
 
         # Create the engine configs.
-        vllm_config = engine_args.create_engine_config()
+        vllm_config = engine_args.create_engine_config(usage_context)
         executor_class = cls._get_executor_cls(vllm_config)
 
         if VLLM_ENABLE_V1_MULTIPROCESSING: