Add more user-friendly CLI (#541)

* add

* import fire in main

* wrap to speed up fire cli

* update

* update docs

* update docs

* fix

* resolve commennts

* resolve confict and add test for cli

lmdeploy/lite/apis/kv_qparams.py

@@ -2,7 +2,6 @@
 from pathlib import Path
 from typing import Union
 
-import fire
 import numpy as np
 import torch
 
@@ -120,5 +119,6 @@ def main(work_dir: str,
 
 
 if __name__ == '__main__':
+    import fire
 
     fire.Fire(main)

lmdeploy/model.py

@@ -654,4 +654,5 @@ def main(model_name: str = 'test'):
 
 if __name__ == '__main__':
     import fire
+
     fire.Fire(main)

lmdeploy/pytorch/chat.py

@@ -51,7 +51,6 @@ import itertools
 import logging
 from typing import Optional
 
-import fire
 import torch
 from transformers import GenerationConfig, PreTrainedModel
 
@@ -205,6 +204,8 @@ def main(
 
 
 def cli():
+    import fire
+
     fire.Fire(main)
 
 

lmdeploy/serve/client.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import os
 
-import fire
-
 from lmdeploy.serve.turbomind.chatbot import Chatbot
 
 
@@ -66,4 +64,6 @@ def main(tritonserver_addr: str,
 
 
 if __name__ == '__main__':
+    import fire
+
     fire.Fire(main)

lmdeploy/serve/gradio/app.py

@@ -5,7 +5,6 @@ import time
 from functools import partial
 from typing import Sequence
 
-import fire
 import gradio as gr
 
 from lmdeploy.serve.async_engine import AsyncEngine
@@ -525,7 +524,7 @@ def run(model_path_or_server: str,
         server_port (int): the port of gradio server
         batch_size (int): batch size for running Turbomind directly
         tp (int): tensor parallel for Turbomind
-        restufl_api (bool): a flag for model_path_or_server
+        restful_api (bool): a flag for model_path_or_server
     """
     if ':' in model_path_or_server:
         if restful_api:
@@ -539,4 +538,6 @@ def run(model_path_or_server: str,
 
 
 if __name__ == '__main__':
+    import fire
+
     fire.Fire(run)

lmdeploy/serve/openai/api_client.py

@@ -2,7 +2,6 @@
 import json
 from typing import Iterable, List
 
-import fire
 import requests
 
 
@@ -89,4 +88,6 @@ def main(restful_api_url: str, session_id: int = 0):
 
 
 if __name__ == '__main__':
+    import fire
+
     fire.Fire(main)

lmdeploy/serve/openai/api_server.py

@@ -4,7 +4,6 @@ import time
 from http import HTTPStatus
 from typing import AsyncGenerator, List, Optional
 
-import fire
 import uvicorn
 from fastapi import FastAPI, Request
 from fastapi.middleware.cors import CORSMiddleware
@@ -357,4 +356,6 @@ def main(model_path: str,
 
 
 if __name__ == '__main__':
+    import fire
+
     fire.Fire(main)

lmdeploy/serve/turbomind/deploy.py

@@ -8,7 +8,6 @@ import shutil
 import sys
 from pathlib import Path
 
-import fire
 import safetensors
 import torch
 from safetensors.torch import load_file
@@ -1043,4 +1042,6 @@ def main(model_name: str,
 
 
 if __name__ == '__main__':
+    import fire
+
     fire.Fire(main)

lmdeploy/turbomind/chat.py

@@ -4,11 +4,7 @@ import os
 import os.path as osp
 import random
 
-import fire
-
-from lmdeploy import turbomind as tm
 from lmdeploy.model import MODELS
-from lmdeploy.tokenizer import Tokenizer
 
 os.environ['TM_LOG_LEVEL'] = 'ERROR'
 
@@ -88,6 +84,9 @@ def main(model_path,
         stream_output (bool): indicator for streaming output or not
         **kwarg (dict): other arguments for initializing model's chat template
     """
+    from lmdeploy import turbomind as tm
+    from lmdeploy.tokenizer import Tokenizer
+
     tokenizer_model_path = osp.join(model_path, 'triton_models', 'tokenizer')
     tokenizer = Tokenizer(tokenizer_model_path)
     tm_model = tm.TurboMind(model_path, eos_id=tokenizer.eos_token_id, tp=tp)
@@ -157,4 +156,6 @@ def main(model_path,
 
 
 if __name__ == '__main__':
+    import fire
+
     fire.Fire(main)

lmdeploy/turbomind/decode.py

@@ -2,7 +2,6 @@
 import os
 import os.path as osp
 
-import fire
 import torch
 
 from lmdeploy import turbomind as tm
@@ -37,4 +36,6 @@ def main(model_path, inputs):
 
 
 if __name__ == '__main__':
+    import fire
+
     fire.Fire(main)

lmdeploy/turbomind/generate_gemm_config.py

@@ -2,8 +2,6 @@
 
 import subprocess
 
-import fire
-
 
 def get_llama_gemm():
     import os.path as osp
@@ -30,4 +28,6 @@ def main(head_num: int = 32,
 
 
 if __name__ == '__main__':
+    import fire
+
     fire.Fire(main)

setup.py

@@ -121,26 +121,29 @@ def parse_requirements(fname='requirements.txt', with_version=True):
 
 if __name__ == '__main__':
     lmdeploy_package_data = ['lmdeploy/bin/llama_gemm']
-    setup(name='lmdeploy',
-          version=get_version(),
-          description='A toolset for compressing, deploying and serving LLM',
-          long_description=readme(),
-          long_description_content_type='text/markdown',
-          author='OpenMMLab',
-          author_email='openmmlab@gmail.com',
-          packages=find_packages(exclude=()),
-          package_data={
-              'lmdeploy': lmdeploy_package_data,
-          },
-          include_package_data=True,
-          install_requires=parse_requirements('requirements.txt'),
-          has_ext_modules=check_ext_modules,
-          classifiers=[
-              'Programming Language :: Python :: 3.8',
-              'Programming Language :: Python :: 3.9',
-              'Programming Language :: Python :: 3.10',
-              'Programming Language :: Python :: 3.11',
-              'Intended Audience :: Developers',
-              'Intended Audience :: Education',
-              'Intended Audience :: Science/Research',
-          ])
+    setup(
+        name='lmdeploy',
+        version=get_version(),
+        description='A toolset for compressing, deploying and serving LLM',
+        long_description=readme(),
+        long_description_content_type='text/markdown',
+        author='OpenMMLab',
+        author_email='openmmlab@gmail.com',
+        packages=find_packages(exclude=()),
+        package_data={
+            'lmdeploy': lmdeploy_package_data,
+        },
+        include_package_data=True,
+        install_requires=parse_requirements('requirements.txt'),
+        has_ext_modules=check_ext_modules,
+        classifiers=[
+            'Programming Language :: Python :: 3.8',
+            'Programming Language :: Python :: 3.9',
+            'Programming Language :: Python :: 3.10',
+            'Programming Language :: Python :: 3.11',
+            'Intended Audience :: Developers',
+            'Intended Audience :: Education',
+            'Intended Audience :: Science/Research',
+        ],
+        entry_points={'console_scripts': ['lmdeploy = lmdeploy.cli:run']},
+    )

tests/test_lmdeploy/test_cli.py

+import inspect
+
+
+def compare_func(class_method, function):
+    """Compare if a class method has same arguments as a function."""
+
+    argspec_cls = inspect.getfullargspec(class_method)
+    argspec_func = inspect.getfullargspec(function)
+    assert argspec_cls.args[1:] == argspec_func.args
+    assert argspec_cls.defaults == argspec_func.defaults
+    assert argspec_cls.annotations == argspec_func.annotations
+
+
+def test_cli():
+
+    from lmdeploy.cli.cli import CLI
+    from lmdeploy.serve.turbomind.deploy import main as convert
+    compare_func(CLI.convert, convert)
+
+
+def test_subcli_chat():
+    from lmdeploy.cli.chat import SubCliChat
+    from lmdeploy.pytorch.chat import main as run_torch_model
+    from lmdeploy.turbomind.chat import main as run_turbomind_model
+
+    compare_func(SubCliChat.torch, run_torch_model)
+    compare_func(SubCliChat.turbomind, run_turbomind_model)
+
+
+def test_subcli_lite():
+    from lmdeploy.cli.lite import SubCliLite
+    from lmdeploy.lite.apis.auto_awq import auto_awq
+    from lmdeploy.lite.apis.calibrate import calibrate
+    from lmdeploy.lite.apis.kv_qparams import main as run_kv_qparams
+
+    compare_func(SubCliLite.auto_awq, auto_awq)
+    compare_func(SubCliLite.calibrate, calibrate)
+    compare_func(SubCliLite.kv_qparams, run_kv_qparams)
+
+
+def test_subcli_serve():
+    from lmdeploy.cli.serve import SubCliServe
+    from lmdeploy.serve.client import main as run_triton_client
+    from lmdeploy.serve.gradio.app import run as run_gradio
+    from lmdeploy.serve.openai.api_client import main as run_api_client
+    from lmdeploy.serve.openai.api_server import main as run_api_server
+
+    compare_func(SubCliServe.gradio, run_gradio)
+    compare_func(SubCliServe.api_server, run_api_server)
+    compare_func(SubCliServe.api_client, run_api_client)
+    compare_func(SubCliServe.triton_client, run_triton_client)