f2e923b3ca
* Add Axolotl 0.4.0, remove legacy 0.3.0 support. * replace is_torch_bf16_gpu_available * Add HF_HUB_OFFLINE=1 * Move transformers out of requirement * Refine readme and qlora.yml
83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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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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#
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# This file is copied from
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# https://github.com/OpenAccess-AI-Collective/axolotl/blob/v0.4.0/src/axolotl/cli/train.py
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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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from ipex_llm import llm_patch
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llm_patch(train=True)
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# The following is the original axolotl train code (without IPEX-LLM)
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"""
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CLI to run training on a model
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"""
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import logging
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from pathlib import Path
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from typing import Tuple
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import fire
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import transformers
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from transformers import PreTrainedModel, PreTrainedTokenizer
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from axolotl.cli import (
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check_accelerate_default_config,
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check_user_token,
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load_cfg,
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load_datasets,
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load_rl_datasets,
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print_axolotl_text_art,
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)
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from axolotl.common.cli import TrainerCliArgs
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from axolotl.train import train
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LOG = logging.getLogger("axolotl.cli.train")
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def do_cli(config: Path = Path("examples/"), **kwargs):
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# pylint: disable=duplicate-code
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parsed_cfg = load_cfg(config, **kwargs)
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parser = transformers.HfArgumentParser((TrainerCliArgs))
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parsed_cli_args, _ = parser.parse_args_into_dataclasses(
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return_remaining_strings=True
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)
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return do_train(parsed_cfg, parsed_cli_args)
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def do_train(cfg, cli_args) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
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print_axolotl_text_art()
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check_accelerate_default_config()
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check_user_token()
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if cfg.rl:
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dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
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else:
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
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return train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
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if __name__ == "__main__":
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fire.Fire(do_cli)
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