fix #3347 #3387

src/llmtuner/chat/vllm_engine.py

@@ -2,9 +2,9 @@ import uuid
 from typing import TYPE_CHECKING, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence
 
 from ..data import get_template_and_fix_tokenizer
-from ..extras.misc import get_device_count
+from ..extras.misc import get_device_count, infer_optim_dtype
 from ..extras.packages import is_vllm_available
-from ..model import load_tokenizer
+from ..model import load_config, load_tokenizer
 from .base_engine import BaseEngine, Response
 
 
@@ -23,10 +23,20 @@ class VllmEngine(BaseEngine):
         finetuning_args: "FinetuningArguments",
         generating_args: "GeneratingArguments",
     ) -> None:
+        config = load_config(model_args)  # may download model from ms hub
+        load_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))
+
         self.can_generate = finetuning_args.stage == "sft"
+        self.tokenizer = load_tokenizer(model_args)
+        self.tokenizer.padding_side = "left"
+        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
+        self.generating_args = generating_args.to_dict()
+
         engine_args = AsyncEngineArgs(
             model=model_args.model_name_or_path,
             trust_remote_code=True,
+            download_dir=model_args.cache_dir,
+            dtype=str(load_dtype).split(".")[-1],
             max_model_len=model_args.vllm_maxlen,
             tensor_parallel_size=get_device_count() or 1,
             gpu_memory_utilization=model_args.vllm_gpu_util,
@@ -35,10 +45,6 @@ class VllmEngine(BaseEngine):
             enforce_eager=model_args.vllm_enforce_eager,
         )
         self.model = AsyncLLMEngine.from_engine_args(engine_args)
-        self.tokenizer = load_tokenizer(model_args)
-        self.tokenizer.padding_side = "left"
-        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
-        self.generating_args = generating_args.to_dict()
 
     async def _generate(
         self,

src/llmtuner/model/__init__.py

@@ -1,8 +1,9 @@
-from .loader import load_model, load_tokenizer
+from .loader import load_config, load_model, load_tokenizer
 from .utils import find_all_linear_modules, load_valuehead_params
 
 
 __all__ = [
+    "load_config",
     "load_model",
     "load_tokenizer",
     "load_valuehead_params",

src/llmtuner/model/loader.py

@@ -12,7 +12,7 @@ from .utils import load_valuehead_params, register_autoclass
 
 
 if TYPE_CHECKING:
-    from transformers import PreTrainedModel, PreTrainedTokenizer
+    from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
 
     from ..hparams import FinetuningArguments, ModelArguments
 
@@ -21,6 +21,11 @@ logger = get_logger(__name__)
 
 
 def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
+    r"""
+    Gets arguments to load config/tokenizer/model.
+
+    Note: including inplace operation of model_args.
+    """
     model_args.model_name_or_path = try_download_model_from_ms(model_args)
     return {
         "trust_remote_code": True,
@@ -32,9 +37,7 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
 
 def load_tokenizer(model_args: "ModelArguments") -> "PreTrainedTokenizer":
     r"""
-    Loads pretrained tokenizer. Must before load_model.
-
-    Note: including inplace operation of model_args.
+    Loads pretrained tokenizer.
     """
     init_kwargs = _get_init_kwargs(model_args)
     try:
@@ -57,6 +60,14 @@ def load_tokenizer(model_args: "ModelArguments") -> "PreTrainedTokenizer":
     return tokenizer
 
 
+def load_config(model_args: "ModelArguments") -> "PretrainedConfig":
+    r"""
+    Loads model config.
+    """
+    init_kwargs = _get_init_kwargs(model_args)
+    return AutoConfig.from_pretrained(model_args.model_name_or_path, **init_kwargs)
+
+
 def load_model(
     tokenizer: "PreTrainedTokenizer",
     model_args: "ModelArguments",
@@ -65,10 +76,10 @@ def load_model(
     add_valuehead: bool = False,
 ) -> "PreTrainedModel":
     r"""
-    Loads pretrained model. Must after load_tokenizer.
+    Loads pretrained model.
     """
     init_kwargs = _get_init_kwargs(model_args)
-    config = AutoConfig.from_pretrained(model_args.model_name_or_path, **init_kwargs)
+    config = load_config(model_args)
     patch_config(config, tokenizer, model_args, init_kwargs, is_trainable)
 
     model = None