wehub-resource-sync
102 lines
4.0 KiB
Python
102 lines
4.0 KiB
Python
"""
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LLM Fine-tuning Entry Point
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Standard RDLoop entry point for LLM fine-tuning, consistent with data science implementation.
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"""
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import asyncio
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from typing import Optional, cast
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import fire
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from rdagent.app.finetune.llm.conf import FT_RD_SETTING
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from rdagent.log import rdagent_logger as logger
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from rdagent.scenarios.finetune.loop import LLMFinetuneRDLoop
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def main(
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path: Optional[str] = None,
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checkout: bool = True,
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user_target_scenario: Optional[str] = None,
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benchmark: Optional[str] = None,
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benchmark_description: Optional[str] = None,
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dataset: Optional[str] = None,
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base_model: Optional[str] = None,
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upper_data_size_limit: Optional[int] = None,
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step_n: Optional[int] = None,
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loop_n: Optional[int] = None,
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timeout: Optional[str] = None,
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):
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"""
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LLM fine-tuning entry point
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Parameters
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----------
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path :
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A path like `$LOG_PATH/__session__/1/0_propose`. This indicates that we restore the state after finishing step 0 in loop 1.
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checkout :
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Used to control the log session path. Boolean type, default is True.
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- If True, the new loop will use the existing folder and clear logs for sessions after the one corresponding to the given path.
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- If False, the new loop will use the existing folder but keep the logs for sessions after the one corresponding to the given path.
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dataset : str
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Dataset name for fine-tuning (e.g., 'shibing624/alpaca-zh')
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base_model : str, optional
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Model name for fine-tuning (e.g., 'Qwen/Qwen2.5-1.5B-Instruct').
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If not provided, auto-selects optimal model based on hardware and dataset.
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step_n : int, optional
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Number of steps to run; if None, runs indefinitely until completion or error
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loop_n : int, optional
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Number of loops to run; if None, runs indefinitely until completion or error
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timeout : str, optional
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Maximum duration for the entire process
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Examples:
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.. code-block:: bash
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dotenv run -- python rdagent/app/finetune/llm/loop.py --dataset shibing624/alpaca-zh --base-model Qwen/Qwen2.5-1.5B-Instruct
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dotenv run -- python rdagent/app/finetune/llm/loop.py --dataset shibing624/alpaca-zh # TODO: not enabled yet
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"""
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if user_target_scenario:
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FT_RD_SETTING.user_target_scenario = user_target_scenario
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assert (
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FT_RD_SETTING.user_target_scenario is None
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), "user_target_scenario is not yet supported, please specify via benchmark and benchmark_description"
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if upper_data_size_limit:
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FT_RD_SETTING.upper_data_size_limit = upper_data_size_limit
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logger.info(f"Set upper_data_size_limit to {FT_RD_SETTING.upper_data_size_limit}")
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if benchmark:
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FT_RD_SETTING.target_benchmark = benchmark
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if benchmark_description:
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FT_RD_SETTING.benchmark_description = benchmark_description
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assert FT_RD_SETTING.user_target_scenario or (
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FT_RD_SETTING.target_benchmark and FT_RD_SETTING.benchmark_description
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), "Either user_target_scenario or target_benchmark must be specified for LLM fine-tuning."
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# Update configuration with provided parameters
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if dataset:
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FT_RD_SETTING.dataset = dataset
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if base_model:
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FT_RD_SETTING.base_model = base_model
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# Create and run LLM fine-tuning loop
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data_set_target = FT_RD_SETTING.dataset if FT_RD_SETTING.dataset else "auto generated dataset"
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model_target = FT_RD_SETTING.base_model if FT_RD_SETTING.base_model else "auto selected model"
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# Temporary assertion until auto-selection is implemented
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assert (
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FT_RD_SETTING.base_model is not None
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), "Base model auto selection not yet supported, please specify via --base-model"
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logger.info(f"Starting LLM fine-tuning on dataset='{data_set_target}' with model='{model_target}'")
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if path is None:
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loop = LLMFinetuneRDLoop(FT_RD_SETTING)
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else:
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loop = cast(LLMFinetuneRDLoop, LLMFinetuneRDLoop.load(str(path), checkout=checkout))
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asyncio.run(loop.run(step_n=step_n, loop_n=loop_n, all_duration=timeout))
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if __name__ == "__main__":
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fire.Fire(main)
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