Add Model Revision Support (#1014)

Co-authored-by: Jasmond Loh <Jasmond.Loh@hotmail.com>
Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>

vllm/config.py

@@ -38,6 +38,9 @@ class ModelConfig:
             will use FP16 precision for FP32 and FP16 models, and BF16 precision
             for BF16 models.
         seed: Random seed for reproducibility.
+        revision: The specific model version to use. It can be a branch name,
+            a tag name, or a commit id. If unspecified, will use the default
+            version.
         max_model_len: Maximum length of a sequence (including prompt and
             output). If None, will be derived from the model.
     """
@@ -52,6 +55,7 @@ class ModelConfig:
         load_format: str,
         dtype: str,
         seed: int,
+        revision: Optional[str],
         max_model_len: Optional[int] = None,
     ) -> None:
         self.model = model
@@ -61,8 +65,9 @@ class ModelConfig:
         self.download_dir = download_dir
         self.load_format = load_format
         self.seed = seed
+        self.revision = revision
 
-        self.hf_config = get_config(model, trust_remote_code)
+        self.hf_config = get_config(model, trust_remote_code, revision)
         self.dtype = _get_and_verify_dtype(self.hf_config, dtype)
         self._verify_load_format()
         self._verify_tokenizer_mode()

vllm/engine/arg_utils.py

@@ -28,6 +28,7 @@ class EngineArgs:
     max_num_batched_tokens: int = 2560
     max_num_seqs: int = 256
     disable_log_stats: bool = False
+    revision: Optional[str] = None
 
     def __post_init__(self):
         if self.tokenizer is None:
@@ -49,6 +50,13 @@ class EngineArgs:
             type=str,
             default=EngineArgs.tokenizer,
             help='name or path of the huggingface tokenizer to use')
+        parser.add_argument(
+            '--revision',
+            type=str,
+            default=None,
+            help='the specific model version to use. It can be a branch '
+            'name, a tag name, or a commit id. If unspecified, will use '
+            'the default version.')
         parser.add_argument('--tokenizer-mode',
                             type=str,
                             default=EngineArgs.tokenizer_mode,
@@ -159,7 +167,8 @@ class EngineArgs:
         model_config = ModelConfig(self.model, self.tokenizer,
                                    self.tokenizer_mode, self.trust_remote_code,
                                    self.download_dir, self.load_format,
-                                   self.dtype, self.seed, self.max_model_len)
+                                   self.dtype, self.seed, self.revision,
+                                   self.max_model_len)
         cache_config = CacheConfig(self.block_size,
                                    self.gpu_memory_utilization,
                                    self.swap_space)

vllm/engine/llm_engine.py

@@ -74,6 +74,7 @@ class LLMEngine:
             f"model={model_config.model!r}, "
             f"tokenizer={model_config.tokenizer!r}, "
             f"tokenizer_mode={model_config.tokenizer_mode}, "
+            f"revision={model_config.revision}, "
             f"trust_remote_code={model_config.trust_remote_code}, "
             f"dtype={model_config.dtype}, "
             f"download_dir={model_config.download_dir!r}, "
@@ -92,7 +93,8 @@ class LLMEngine:
         self.tokenizer = get_tokenizer(
             model_config.tokenizer,
             tokenizer_mode=model_config.tokenizer_mode,
-            trust_remote_code=model_config.trust_remote_code)
+            trust_remote_code=model_config.trust_remote_code,
+            revision=model_config.revision)
         self.seq_counter = Counter()
 
         # Create the parallel GPU workers.

vllm/entrypoints/llm.py

@@ -38,6 +38,8 @@ class LLM:
             However, if the `torch_dtype` in the config is `float32`, we will
             use `float16` instead.
         seed: The seed to initialize the random number generator for sampling.
+        revision: The specific model version to use. It can be a branch name,
+            a tag name, or a commit id.
     """
 
     def __init__(

vllm/model_executor/model_loader.py

@@ -64,6 +64,6 @@ def get_model(model_config: ModelConfig) -> nn.Module:
         else:
             # Load the weights from the cached or downloaded files.
             model.load_weights(model_config.model, model_config.download_dir,
-                               model_config.load_format)
+                               model_config.load_format, model_config.revision)
             model = model.cuda()
     return model.eval()

vllm/model_executor/models/aquila.py

@@ -288,7 +288,8 @@ class AquilaForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tp_size = get_tensor_model_parallel_world_size()
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         q_proj_shard_size = (self.config.hidden_size // tp_size)
@@ -305,7 +306,7 @@ class AquilaForCausalLM(nn.Module):
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
 

vllm/model_executor/models/baichuan.py

@@ -303,13 +303,14 @@ class BaiChuanBaseForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tp_world_size = get_tensor_model_parallel_world_size()
         tp_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
 

vllm/model_executor/models/bloom.py

@@ -279,11 +279,12 @@ class BloomForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tp_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if name == "lm_head.weight":
                 # Since hidden_states are parallelized, we need to
                 # load lm_head.weight in parallel.

vllm/model_executor/models/falcon.py

@@ -420,7 +420,8 @@ class FalconForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tp_size = (get_tensor_model_parallel_world_size())
         tp_rank = get_tensor_model_parallel_rank()
 
@@ -452,7 +453,7 @@ class FalconForCausalLM(nn.Module):
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if "query_key_value" in name:
                 loaded_weight = convert_pyslice_to_tensor(loaded_weight)
                 loaded_weight_size = loaded_weight.size()

vllm/model_executor/models/gpt2.py

@@ -231,14 +231,15 @@ class GPT2LMHeadModel(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tensor_model_parallel_world_size = (
             get_tensor_model_parallel_world_size())
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if "lm_head.weight" in name:
                 # GPT-2 ties the weights of the embedding layer and the final
                 # linear layer.

vllm/model_executor/models/gpt_bigcode.py

@@ -259,14 +259,15 @@ class GPTBigCodeForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tensor_model_parallel_world_size = (
             get_tensor_model_parallel_world_size())
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if "lm_head.weight" in name:
                 # GPT-2 ties the weights of the embedding layer and the final
                 # linear layer.

vllm/model_executor/models/gpt_j.py

@@ -222,11 +222,12 @@ class GPTJForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tp_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if "attn.bias" in name or "attn.masked_bias" in name:
                 continue
 

vllm/model_executor/models/gpt_neox.py

@@ -231,11 +231,12 @@ class GPTNeoXForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if ("attention.bias" in name or "attention.masked_bias" in name
                     or "rotary_emb.inv_freq" in name):
                 continue

vllm/model_executor/models/internlm.py

@@ -233,12 +233,13 @@ class InternLMForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
 

vllm/model_executor/models/llama.py

@@ -271,7 +271,8 @@ class LlamaForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tp_size = get_tensor_model_parallel_world_size()
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         q_proj_shard_size = (self.config.hidden_size // tp_size)
@@ -288,7 +289,7 @@ class LlamaForCausalLM(nn.Module):
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
 

vllm/model_executor/models/mpt.py

@@ -244,12 +244,13 @@ class MPTForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tp_world_size = get_tensor_model_parallel_world_size()
         tp_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if "Wqkv" in name:
                 # NOTE(woosuk): MPT's fused QKV has the shape of
                 # [3 * num_heads * head_size, hidden_size].

vllm/model_executor/models/opt.py

@@ -297,12 +297,13 @@ class OPTForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if "lm_head.weight" in name:
                 continue
 

vllm/model_executor/models/qwen.py

@@ -251,13 +251,14 @@ class QWenLMHeadModel(nn.Module):
         model_name_or_path: str,
         cache_dir: Optional[str] = None,
         load_format: str = "auto",
+        revision: Optional[str] = None,
     ):
         tp_world_size = get_tensor_model_parallel_world_size()
         tp_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
 

vllm/model_executor/weight_utils.py

@@ -83,6 +83,7 @@ def prepare_hf_model_weights(
     cache_dir: Optional[str] = None,
     use_safetensors: bool = False,
     fall_back_to_pt: bool = True,
+    revision: Optional[str] = None,
 ):
     # Download model weights from huggingface.
     is_local = os.path.isdir(model_name_or_path)
@@ -94,7 +95,8 @@ def prepare_hf_model_weights(
             hf_folder = snapshot_download(model_name_or_path,
                                           allow_patterns=allow_patterns,
                                           cache_dir=cache_dir,
-                                          tqdm_class=Disabledtqdm)
+                                          tqdm_class=Disabledtqdm,
+                                          revision=revision)
     else:
         hf_folder = model_name_or_path
     hf_weights_files = glob.glob(os.path.join(hf_folder, allow_patterns))
@@ -107,7 +109,8 @@ def prepare_hf_model_weights(
         return prepare_hf_model_weights(model_name_or_path,
                                         cache_dir=cache_dir,
                                         use_safetensors=False,
-                                        fall_back_to_pt=False)
+                                        fall_back_to_pt=False,
+                                        revision=revision)
 
     if len(hf_weights_files) == 0:
         raise RuntimeError(
@@ -120,6 +123,7 @@ def hf_model_weights_iterator(
     model_name_or_path: str,
     cache_dir: Optional[str] = None,
     load_format: str = "auto",
+    revision: Optional[str] = None,
 ) -> Iterator[Tuple[str, torch.Tensor]]:
     use_safetensors = False
     use_np_cache = False
@@ -140,7 +144,8 @@ def hf_model_weights_iterator(
         model_name_or_path,
         cache_dir=cache_dir,
         use_safetensors=use_safetensors,
-        fall_back_to_pt=fall_back_to_pt)
+        fall_back_to_pt=fall_back_to_pt,
+        revision=revision)
 
     if use_np_cache:
         # Currently np_cache only support *.bin checkpoints

vllm/transformers_utils/config.py

+from typing import Optional
+
 from transformers import AutoConfig, PretrainedConfig
 
 from vllm.transformers_utils.configs import *  # pylint: disable=wildcard-import
@@ -12,10 +14,12 @@ _CONFIG_REGISTRY = {
 }
 
 
-def get_config(model: str, trust_remote_code: bool) -> PretrainedConfig:
+def get_config(model: str,
+               trust_remote_code: bool,
+               revision: Optional[str] = None) -> PretrainedConfig:
     try:
         config = AutoConfig.from_pretrained(
-            model, trust_remote_code=trust_remote_code)
+            model, trust_remote_code=trust_remote_code, revision=revision)
     except ValueError as e:
         if (not trust_remote_code and
                 "requires you to execute the configuration file" in str(e)):
@@ -29,5 +33,5 @@ def get_config(model: str, trust_remote_code: bool) -> PretrainedConfig:
             raise e
     if config.model_type in _CONFIG_REGISTRY:
         config_class = _CONFIG_REGISTRY[config.model_type]
-        config = config_class.from_pretrained(model)
+        config = config_class.from_pretrained(model, revision=revision)
     return config