convert model with hf repo_id (#774)

lmdeploy/cli/cli.py

@@ -23,13 +23,15 @@ class CLI(object):
                 dst_path: str = './workspace',
                 tp: int = 1,
                 quant_path: str = None,
-                group_size: int = 0):
+                group_size: int = 0,
+                **kwargs):
         """Convert LLMs to lmdeploy format.
 
         Args:
             model_name (str): The name of the to-be-deployed model, such as
                 llama-7b, llama-13b, vicuna-7b and etc.
-            model_path (str): The directory path of the model
+            model_path (str): The directory path of the model or huggingface
+                repo_id like 'internlm/internlm-chat-20b'
             model_format (str): the format of the model, should choose from
                 ['llama', 'hf', 'awq', None]. 'llama' stands for META's llama
                 format, 'hf' means huggingface llama format, and 'awq' means
@@ -43,6 +45,7 @@ class CLI(object):
             quant_path (str): Path of the quantized model, which can be None.
             group_size (int): A parameter used in AWQ to quantize fp16 weights
                 to 4 bits.
+            kwargs (dict): other params for convert
         """
         from lmdeploy.turbomind.deploy.converter import main as convert
 
@@ -53,7 +56,8 @@ class CLI(object):
                 dst_path=dst_path,
                 tp=tp,
                 quant_path=quant_path,
-                group_size=group_size)
+                group_size=group_size,
+                **kwargs)
 
     def list(self, engine: str = 'turbomind'):
         """List supported model names.

lmdeploy/turbomind/deploy/converter.py

@@ -6,8 +6,10 @@ import shutil
 from pathlib import Path
 
 import fire
+from huggingface_hub import snapshot_download
 
 from lmdeploy.model import MODELS
+from lmdeploy.turbomind.utils import create_hf_download_args
 
 from .source_model.base import INPUT_MODELS
 from .target_model.base import OUTPUT_MODELS, TurbomindModelConfig
@@ -143,7 +145,8 @@ def main(model_name: str,
          dst_path: str = 'workspace',
          tp: int = 1,
          quant_path: str = None,
-         group_size: int = 0):
+         group_size: int = 0,
+         **kwargs):
     """deploy llama family models via turbomind.
 
     Args:
@@ -162,6 +165,7 @@ def main(model_name: str,
         quant_path (str): Path of the quantized model, which can be None.
         group_size (int): a parameter used in AWQ to quantize fp16 weights
             to 4 bits
+        kwargs (dict): other params for convert
     """
 
     assert model_name in MODELS.module_dict.keys(), \
@@ -184,6 +188,13 @@ def main(model_name: str,
               f'which is not in supported list {supported_keys}')
         exit(-1)
 
+    if not os.path.exists(model_path):
+        print(f'can\'t find model from local_path {model_path}, '
+              'try to download from huggingface')
+        download_kwargs = create_hf_download_args(**kwargs)
+        model_path = snapshot_download(model_path, **download_kwargs)
+        print(f'load model from {model_path}')
+
     # get tokenizer path
     tokenizer_path = get_tokenizer_path(model_path, tokenizer_path)