mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-02 03:32:50 +08:00
126 lines
4.5 KiB
Python
126 lines
4.5 KiB
Python
# Copyright 2025 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import subprocess
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import sys
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from copy import deepcopy
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from functools import partial
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USAGE = (
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"-" * 70
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+ "\n"
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+ "| Usage: |\n"
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+ "| llamafactory-cli api -h: launch an OpenAI-style API server |\n"
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+ "| llamafactory-cli chat -h: launch a chat interface in CLI |\n"
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+ "| llamafactory-cli eval -h: evaluate models |\n"
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+ "| llamafactory-cli export -h: merge LoRA adapters and export model |\n"
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+ "| llamafactory-cli train -h: train models |\n"
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+ "| llamafactory-cli webchat -h: launch a chat interface in Web UI |\n"
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+ "| llamafactory-cli webui: launch LlamaBoard |\n"
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+ "| llamafactory-cli version: show version info |\n"
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+ "-" * 70
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)
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def main():
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from . import launcher
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from .api.app import run_api
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from .chat.chat_model import run_chat
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from .eval.evaluator import run_eval
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from .extras import logging
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from .extras.env import VERSION, print_env
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from .extras.misc import find_available_port, get_device_count, is_env_enabled, use_ray
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from .train.tuner import export_model, run_exp
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from .webui.interface import run_web_demo, run_web_ui
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logger = logging.get_logger(__name__)
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WELCOME = (
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"-" * 58
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+ "\n"
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+ f"| Welcome to LLaMA Factory, version {VERSION}"
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+ " " * (21 - len(VERSION))
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+ "|\n|"
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+ " " * 56
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+ "|\n"
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+ "| Project page: https://github.com/hiyouga/LLaMA-Factory |\n"
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+ "-" * 58
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)
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COMMAND_MAP = {
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"api": run_api,
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"chat": run_chat,
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"env": print_env,
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"eval": run_eval,
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"export": export_model,
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"train": run_exp,
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"webchat": run_web_demo,
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"webui": run_web_ui,
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"version": partial(print, WELCOME),
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"help": partial(print, USAGE),
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}
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command = sys.argv.pop(1) if len(sys.argv) >= 1 else "help"
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if command == "train" and (is_env_enabled("FORCE_TORCHRUN") or (get_device_count() > 1 and not use_ray())):
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# launch distributed training
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nnodes = os.getenv("NNODES", "1")
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node_rank = os.getenv("NODE_RANK", "0")
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nproc_per_node = os.getenv("NPROC_PER_NODE", str(get_device_count()))
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master_addr = os.getenv("MASTER_ADDR", "127.0.0.1")
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master_port = os.getenv("MASTER_PORT", str(find_available_port()))
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logger.info_rank0(f"Initializing {nproc_per_node} distributed tasks at: {master_addr}:{master_port}")
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if int(nnodes) > 1:
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print(f"Multi-node training enabled: num nodes: {nnodes}, node rank: {node_rank}")
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env = deepcopy(os.environ)
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if is_env_enabled("OPTIM_TORCH", "1"):
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# optimize DDP, see https://zhuanlan.zhihu.com/p/671834539
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env["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
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env["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
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# NOTE: DO NOT USE shell=True to avoid security risk
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process = subprocess.run(
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(
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"torchrun --nnodes {nnodes} --node_rank {node_rank} --nproc_per_node {nproc_per_node} "
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"--master_addr {master_addr} --master_port {master_port} {file_name} {args}"
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)
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.format(
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nnodes=nnodes,
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node_rank=node_rank,
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nproc_per_node=nproc_per_node,
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master_addr=master_addr,
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master_port=master_port,
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file_name=launcher.__file__,
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args=" ".join(sys.argv[1:]),
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)
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.split(),
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env=env,
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check=True,
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)
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sys.exit(process.returncode)
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elif command in COMMAND_MAP:
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COMMAND_MAP[command]()
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else:
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print(f"Unknown command: {command}.\n{USAGE}")
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if __name__ == "__main__":
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from multiprocessing import freeze_support
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freeze_support()
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main()
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