Add trust-remote-code flag to handle remote tokenizers (#364)

vllm/config.py

@@ -20,6 +20,8 @@ class ModelConfig:
         tokenizer: Name or path of the huggingface tokenizer to use.
         tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
             available, and "slow" will always use the slow tokenizer.
+        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
+            downloading the model and tokenizer.
         download_dir: Directory to download and load the weights, default to the
             default cache directory of huggingface.
         use_np_weights: Save a numpy copy of model weights for faster loading.
@@ -36,6 +38,7 @@ class ModelConfig:
         model: str,
         tokenizer: str,
         tokenizer_mode: str,
+        trust_remote_code: bool,
         download_dir: Optional[str],
         use_np_weights: bool,
         use_dummy_weights: bool,
@@ -45,6 +48,7 @@ class ModelConfig:
         self.model = model
         self.tokenizer = tokenizer
         self.tokenizer_mode = tokenizer_mode
+        self.trust_remote_code = trust_remote_code
         self.download_dir = download_dir
         self.use_np_weights = use_np_weights
         self.use_dummy_weights = use_dummy_weights

vllm/engine/arg_utils.py

@@ -13,6 +13,7 @@ class EngineArgs:
     model: str
     tokenizer: Optional[str] = None
     tokenizer_mode: str = 'auto'
+    trust_remote_code: bool = False
     download_dir: Optional[str] = None
     use_np_weights: bool = False
     use_dummy_weights: bool = False
@@ -55,6 +56,9 @@ class EngineArgs:
                             help='tokenizer mode. "auto" will use the fast '
                             'tokenizer if available, and "slow" will '
                             'always use the slow tokenizer.')
+        parser.add_argument('--trust-remote-code',
+                            action='store_true',
+                            help='trust remote code from huggingface')
         parser.add_argument('--download-dir',
                             type=str,
                             default=EngineArgs.download_dir,
@@ -141,9 +145,10 @@ class EngineArgs:
     ) -> Tuple[ModelConfig, CacheConfig, ParallelConfig, SchedulerConfig]:
         # Initialize the configs.
         model_config = ModelConfig(self.model, self.tokenizer,
-                                   self.tokenizer_mode, self.download_dir,
-                                   self.use_np_weights, self.use_dummy_weights,
-                                   self.dtype, self.seed)
+                                   self.tokenizer_mode, self.trust_remote_code,
+                                   self.download_dir, self.use_np_weights,
+                                   self.use_dummy_weights, self.dtype,
+                                   self.seed)
         cache_config = CacheConfig(self.block_size,
                                    self.gpu_memory_utilization,
                                    self.swap_space)

vllm/engine/llm_engine.py

@@ -62,6 +62,7 @@ class LLMEngine:
             f"model={model_config.model!r}, "
             f"tokenizer={model_config.tokenizer!r}, "
             f"tokenizer_mode={model_config.tokenizer_mode}, "
+            f"trust_remote_code={model_config.trust_remote_code}, "
             f"dtype={model_config.dtype}, "
             f"use_dummy_weights={model_config.use_dummy_weights}, "
             f"download_dir={model_config.download_dir!r}, "
@@ -78,7 +79,9 @@ class LLMEngine:
         self._verify_args()
 
         self.tokenizer = get_tokenizer(
-            model_config.tokenizer, tokenizer_mode=model_config.tokenizer_mode)
+            model_config.tokenizer,
+            tokenizer_mode=model_config.tokenizer_mode,
+            trust_remote_code=model_config.trust_remote_code)
         self.seq_counter = Counter()
 
         # Create the parallel GPU workers.

vllm/entrypoints/llm.py

@@ -28,6 +28,8 @@ class LLM:
         tokenizer: The name or path of a HuggingFace Transformers tokenizer.
         tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer
             if available, and "slow" will always use the slow tokenizer.
+        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
+            downloading the model and tokenizer.
         tensor_parallel_size: The number of GPUs to use for distributed
             execution with tensor parallelism.
         dtype: The data type for the model weights and activations. Currently,
@@ -43,6 +45,7 @@ class LLM:
         model: str,
         tokenizer: Optional[str] = None,
         tokenizer_mode: str = "auto",
+        trust_remote_code: bool = False,
         tensor_parallel_size: int = 1,
         dtype: str = "auto",
         seed: int = 0,
@@ -54,6 +57,7 @@ class LLM:
             model=model,
             tokenizer=tokenizer,
             tokenizer_mode=tokenizer_mode,
+            trust_remote_code=trust_remote_code,
             tensor_parallel_size=tensor_parallel_size,
             dtype=dtype,
             seed=seed,

vllm/transformers_utils/tokenizer.py

@@ -15,6 +15,7 @@ def get_tokenizer(
     tokenizer_name: str,
     *args,
     tokenizer_mode: str = "auto",
+    trust_remote_code: bool = False,
     **kwargs,
 ) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
     """Gets a tokenizer for the given model name via Huggingface."""
@@ -31,8 +32,11 @@ def get_tokenizer(
             f"using '{_FAST_LLAMA_TOKENIZER}' instead of the original "
             "tokenizer.")
     try:
-        tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, *args,
-                                                  **kwargs)
+        tokenizer = AutoTokenizer.from_pretrained(
+            tokenizer_name,
+            trust_remote_code=trust_remote_code,
+            *args,
+            **kwargs)
     except TypeError as e:
         # The LLaMA tokenizer causes a protobuf error in some environments.
         err_msg = (
@@ -40,6 +44,19 @@ def get_tokenizer(
             f"model, use '{_FAST_LLAMA_TOKENIZER}' instead of the original "
             "tokenizer.")
         raise RuntimeError(err_msg) from e
+    except ValueError as e:
+        # If the error pertains to the tokenizer class not existing or not
+        # currently being imported, suggest using the --trust-remote-code flag.
+        if (e is not None and
+            ("does not exist or is not currently imported." in str(e)
+             or "requires you to execute the tokenizer file" in str(e))):
+            err_msg = (
+                "Failed to load the tokenizer. If the tokenizer is a custom "
+                "tokenizer not yet available in the HuggingFace transformers "
+                "library, consider using the --trust-remote-code flag.")
+            raise RuntimeError(err_msg) from e
+        else:
+            raise e
 
     if not isinstance(tokenizer, PreTrainedTokenizerFast):
         logger.warning(