Support eos_token_id from generation_config.json (#4182)

vllm/engine/llm_engine.py

@@ -1,7 +1,7 @@
 import time
 from typing import Iterable, List, Optional, Type, Union
 
-from transformers import PreTrainedTokenizer
+from transformers import GenerationConfig, PreTrainedTokenizer
 
 import vllm
 from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, LoadConfig,
@@ -34,6 +34,17 @@ logger = init_logger(__name__)
 _LOCAL_LOGGING_INTERVAL_SEC = 5
 
 
+def _load_generation_config_dict(model_config: ModelConfig):
+    try:
+        return GenerationConfig.from_pretrained(
+            model_config.model,
+            revision=model_config.revision,
+        ).to_diff_dict()
+    except OSError:
+        # Not found.
+        return {}
+
+
 class LLMEngine:
     """An LLM engine that receives requests and generates texts.
 
@@ -124,6 +135,8 @@ class LLMEngine:
         self._init_tokenizer()
         self.detokenizer = Detokenizer(self.tokenizer)
         self.seq_counter = Counter()
+        self.generation_config_fields = _load_generation_config_dict(
+            model_config)
 
         self.model_executor = executor_class(
             model_config=model_config,
@@ -391,6 +404,8 @@ class LLMEngine:
         # inject the eos token id into the sampling_params to support min_tokens
         # processing
         sampling_params.eos_token_id = seq.eos_token_id
+        sampling_params.update_from_generation_config(
+            self.generation_config_fields)
 
         # Create the sequence group.
         seq_group = SequenceGroup(request_id, [seq], sampling_params,
@@ -435,7 +450,7 @@ class LLMEngine:
             scheduled_seq_groups: List[SequenceGroup],
             ignored_seq_groups: List[SequenceGroup]) -> List[RequestOutput]:
         """Apply the model output to the sequences in the scheduled seq groups.
-        
+
         Returns RequestOutputs that can be returned to the client.
         """
 

vllm/sampling_params.py

@@ -2,7 +2,7 @@
 import copy
 from enum import IntEnum
 from functools import cached_property
-from typing import Callable, List, Optional, Union
+from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
 from pydantic import Field
@@ -271,6 +271,18 @@ class SamplingParams:
             raise ValueError("best_of must be 1 when using greedy sampling."
                              f"Got {self.best_of}.")
 
+    def update_from_generation_config(
+            self, generation_config: Dict[str, Any]) -> None:
+        """Update if there are non-default values from generation_config"""
+        # Update eos_token_id for generation
+        if eos_ids := generation_config.get("eos_token_id"):
+            # it can be either int or list of int
+            if isinstance(eos_ids, int):
+                eos_ids = [eos_ids]
+            original_stop_token_ids = set(self.stop_token_ids)
+            original_stop_token_ids.update(eos_ids)
+            self.stop_token_ids = list(original_stop_token_ids)
+
     @cached_property
     def sampling_type(self) -> SamplingType:
         if self.use_beam_search: