[Misc] Enable chunked prefill by default for long context models (#6666)

Author: WoosukKwon

vllm/engine/arg_utils.py

@@ -10,13 +10,16 @@
                          PromptAdapterConfig, SchedulerConfig,
                          SpeculativeConfig, TokenizerPoolConfig)
 from vllm.executor.executor_base import ExecutorBase
+from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.utils import FlexibleArgumentParser
 
 if TYPE_CHECKING:
     from vllm.transformers_utils.tokenizer_group.base_tokenizer_group import (
         BaseTokenizerGroup)
 
+logger = init_logger(__name__)
+
 
 def nullable_str(val: str):
     if not val or val == "None":
@@ -95,7 +98,7 @@ class EngineArgs:
     preemption_mode: Optional[str] = None
 
     scheduler_delay_factor: float = 0.0
-    enable_chunked_prefill: bool = False
+    enable_chunked_prefill: Optional[bool] = None
 
     guided_decoding_backend: str = 'outlines'
     # Speculative decoding configuration.
@@ -508,7 +511,10 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             'prompt latency) before scheduling next prompt.')
         parser.add_argument(
             '--enable-chunked-prefill',
-            action='store_true',
+            action=StoreBoolean,
+            default=EngineArgs.enable_chunked_prefill,
+            nargs="?",
+            const="True",
             help='If set, the prefill requests can be chunked based on the '
             'max_num_batched_tokens.')
 
@@ -728,6 +734,38 @@ def create_engine_config(self, ) -> EngineConfig:
             ray_workers_use_nsight=self.ray_workers_use_nsight,
             distributed_executor_backend=self.distributed_executor_backend)
 
+        max_model_len = model_config.max_model_len
+        use_long_context = max_model_len > 32768
+        if self.enable_chunked_prefill is None:
+            # If not explicitly set, enable chunked prefill by default for
+            # long context (> 32K) models. This is to avoid OOM errors in the
+            # initial memory profiling phase.
+            if use_long_context:
+                is_gpu = device_config.device_type == "cuda"
+                use_sliding_window = (model_config.get_sliding_window()
+                                      is not None)
+                use_spec_decode = self.speculative_model is not None
+                if (is_gpu and not use_sliding_window and not use_spec_decode
+                        and not self.enable_lora
+                        and not self.enable_prompt_adapter
+                        and not self.enable_prefix_caching):
+                    self.enable_chunked_prefill = True
+                    logger.warning(
+                        "Chunked prefill is enabled by default for models with "
+                        "max_model_len > 32K. Currently, chunked prefill might "
+                        "not work with some features or models. If you "
+                        "encounter any issues, please disable chunked prefill "
+                        "by setting --enable-chunked-prefill=False.")
+            if self.enable_chunked_prefill is None:
+                self.enable_chunked_prefill = False
+
+        if not self.enable_chunked_prefill and use_long_context:
+            logger.warning(
+                "The model has a long context length (%s). This may cause OOM "
+                "errors during the initial memory profiling phase, or result "
+                "in low performance due to small KV cache space. Consider "
+                "setting --max-model-len to a smaller value.", max_model_len)
+
         speculative_config = SpeculativeConfig.maybe_create_spec_config(
             target_model_config=model_config,
             target_parallel_config=parallel_config,
@@ -843,6 +881,18 @@ def add_cli_args(parser: FlexibleArgumentParser,
         return parser
 
 
+class StoreBoolean(argparse.Action):
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        if values.lower() == "true":
+            setattr(namespace, self.dest, True)
+        elif values.lower() == "false":
+            setattr(namespace, self.dest, False)
+        else:
+            raise ValueError(f"Invalid boolean value: {values}. "
+                             "Expected 'true' or 'false'.")
+
+
 # These functions are used by sphinx to build the documentation
 def _engine_args_parser():
     return EngineArgs.add_cli_args(FlexibleArgumentParser())