Bugfix: fix broken of download models from modelscope (#5233)

Co-authored-by: mulin.lyh <mulin.lyh@taobao.com>

tests/test_regression.py

@@ -53,6 +53,27 @@ def test_gc():
     assert allocated < 50 * 1024 * 1024
 
 
+def test_model_from_modelscope(monkeypatch):
+    # model: https://modelscope.cn/models/qwen/Qwen1.5-0.5B-Chat/summary
+    MODELSCOPE_MODEL_NAME = "qwen/Qwen1.5-0.5B-Chat"
+    monkeypatch.setenv("VLLM_USE_MODELSCOPE", "True")
+    try:
+        llm = LLM(model=MODELSCOPE_MODEL_NAME)
+
+        prompts = [
+            "Hello, my name is",
+            "The president of the United States is",
+            "The capital of France is",
+            "The future of AI is",
+        ]
+        sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+        outputs = llm.generate(prompts, sampling_params)
+        assert len(outputs) == 4
+    finally:
+        monkeypatch.delenv("VLLM_USE_MODELSCOPE", raising=False)
+
+
 if __name__ == "__main__":
     import pytest
     pytest.main([__file__])

vllm/config.py

@@ -113,6 +113,10 @@ class ModelConfig:
         self.revision = revision
         self.code_revision = code_revision
         self.rope_scaling = rope_scaling
+        # The tokenizer version is consistent with the model version by default.
+        if tokenizer_revision is None:
+            self.tokenizer_revision = revision
+        else:
             self.tokenizer_revision = tokenizer_revision
         self.quantization = quantization
         self.quantization_param_path = quantization_param_path

vllm/transformers_utils/config.py

 from typing import Dict, Optional
 
-from transformers import AutoConfig, PretrainedConfig
+from transformers import PretrainedConfig
 
+from vllm.envs import VLLM_USE_MODELSCOPE
 from vllm.logger import init_logger
 from vllm.transformers_utils.configs import (ChatGLMConfig, DbrxConfig,
                                              JAISConfig, MPTConfig, RWConfig)
@@ -24,6 +25,10 @@ def get_config(model: str,
                code_revision: Optional[str] = None,
                rope_scaling: Optional[dict] = None) -> PretrainedConfig:
     try:
+        if VLLM_USE_MODELSCOPE:
+            from modelscope import AutoConfig
+        else:
+            from transformers import AutoConfig
         config = AutoConfig.from_pretrained(
             model,
             trust_remote_code=trust_remote_code,