[LoRA] Change lora_tokenizers capacity (#10796)

Signed-off-by: Xin Yang <xyang19@gmail.com>

tests/lora/test_tokenizer_group.py

@@ -17,6 +17,7 @@ async def test_tokenizer_group_lora(sql_lora_files, tokenizer_group_type):
         tokenizer_id="gpt2",
         enable_lora=True,
         max_num_seqs=1,
+        max_loras=1,
         max_input_length=None,
     )
     lora_request = LoRARequest("1", 1, sql_lora_files)
@@ -53,3 +54,22 @@ def test_get_lora_tokenizer(sql_lora_files, tmp_path):
     lora_request = LoRARequest("1", 1, str(tmp_path))
     tokenizer = get_lora_tokenizer(lora_request)
     assert not tokenizer
+
+
+@pytest.mark.parametrize("enable_lora", [True, False])
+@pytest.mark.parametrize("max_num_seqs", [1, 2])
+@pytest.mark.parametrize("max_loras", [1, 2])
+def test_lora_tokenizers(enable_lora, max_num_seqs, max_loras):
+    tokenizer_group = get_tokenizer_group(
+        get_tokenizer_pool_config(None),
+        tokenizer_id="gpt2",
+        enable_lora=enable_lora,
+        max_num_seqs=max_num_seqs,
+        max_loras=max_loras,
+        max_input_length=None,
+    )
+    if enable_lora:
+        assert tokenizer_group.lora_tokenizers.capacity == max(
+            max_num_seqs, max_loras)
+    else:
+        assert tokenizer_group.lora_tokenizers.capacity == 0

vllm/engine/llm_engine.py

@@ -620,7 +620,7 @@ class LLMEngine:
             model_config=self.model_config,
             scheduler_config=self.scheduler_config,
             parallel_config=self.parallel_config,
-            enable_lora=bool(self.lora_config))
+            lora_config=self.lora_config)
 
     def _verify_args(self) -> None:
         self.model_config.verify_with_parallel_config(self.parallel_config)

vllm/engine/multiprocessing/client.py

@@ -94,8 +94,7 @@ class MQLLMEngineClient(EngineClient):
             model_config=self.model_config,
             scheduler_config=engine_config.scheduler_config,
             parallel_config=engine_config.parallel_config,
-            enable_lora=bool(engine_config.lora_config),
-        )
+            lora_config=engine_config.lora_config)
         self.input_preprocessor = InputPreprocessor(self.model_config,
                                                     self.tokenizer)
 

vllm/transformers_utils/tokenizer_group/__init__.py

@@ -1,7 +1,7 @@
 from typing import Optional, Type
 
-from vllm.config import (ModelConfig, ParallelConfig, SchedulerConfig,
-                         TokenizerPoolConfig)
+from vllm.config import (LoRAConfig, ModelConfig, ParallelConfig,
+                         SchedulerConfig, TokenizerPoolConfig)
 from vllm.executor.ray_utils import ray
 
 from .base_tokenizer_group import AnyTokenizer, BaseTokenizerGroup
@@ -16,10 +16,11 @@ else:
 def init_tokenizer_from_configs(model_config: ModelConfig,
                                 scheduler_config: SchedulerConfig,
                                 parallel_config: ParallelConfig,
-                                enable_lora: bool):
+                                lora_config: LoRAConfig):
     init_kwargs = dict(tokenizer_id=model_config.tokenizer,
-                       enable_lora=enable_lora,
+                       enable_lora=bool(lora_config),
                        max_num_seqs=scheduler_config.max_num_seqs,
+                       max_loras=lora_config.max_loras if lora_config else 0,
                        max_input_length=None,
                        tokenizer_mode=model_config.tokenizer_mode,
                        trust_remote_code=model_config.trust_remote_code,

vllm/transformers_utils/tokenizer_group/tokenizer_group.py

@@ -21,8 +21,9 @@ class TokenizerGroup(BaseTokenizerGroup):
         self.enable_lora = enable_lora
         self.max_input_length = max_input_length
         self.tokenizer = get_tokenizer(self.tokenizer_id, **tokenizer_config)
+        max_loras = tokenizer_config.get("max_loras", 0)
         self.lora_tokenizers = LRUCache[AnyTokenizer](
-            capacity=max_num_seqs if enable_lora else 0)
+            capacity=max(max_loras, max_num_seqs) if enable_lora else 0)
 
     @classmethod
     def from_config(cls, tokenizer_pool_config: Optional[TokenizerPoolConfig],

vllm/v1/engine/async_llm.py

@@ -51,7 +51,7 @@ class AsyncLLM(EngineClient):
             model_config=vllm_config.model_config,
             scheduler_config=vllm_config.scheduler_config,
             parallel_config=vllm_config.parallel_config,
-            enable_lora=bool(vllm_config.lora_config))
+            lora_config=vllm_config.lora_config)
         self.tokenizer.ping()
 
         # Request streams (map of request_id -> AsyncStream).

vllm/v1/engine/llm_engine.py

@@ -46,7 +46,7 @@ class LLMEngine:
             model_config=vllm_config.model_config,
             scheduler_config=vllm_config.scheduler_config,
             parallel_config=vllm_config.parallel_config,
-            enable_lora=bool(vllm_config.lora_config))
+            lora_config=vllm_config.lora_config)
         self.tokenizer.ping()
 
         # Processor (convert Inputs --> EngineCoreRequests)