mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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101 lines
3.2 KiB
Python
101 lines
3.2 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 json
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from dataclasses import dataclass, field
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from transformers import Seq2SeqTrainingArguments
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from transformers.training_args import _convert_str_dict
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from ..extras.misc import is_env_enabled, use_ray
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from ..extras.packages import is_mcore_adapter_available
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if is_env_enabled("USE_MCA"):
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if not is_mcore_adapter_available():
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raise ImportError(
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"mcore_adapter is required when USE_MCA=1. Please install `mcore_adapter` and its dependencies."
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)
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from mcore_adapter import Seq2SeqTrainingArguments as McaSeq2SeqTrainingArguments
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BaseTrainingArguments = McaSeq2SeqTrainingArguments
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else:
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BaseTrainingArguments = Seq2SeqTrainingArguments
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@dataclass
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class RayArguments:
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r"""Arguments pertaining to the Ray training."""
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ray_num_workers: int = field(
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default=1,
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metadata={"help": "The number of workers for Ray training. Default is 1 worker."},
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)
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ray_init_kwargs: dict | str | None = field(
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default=None,
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metadata={"help": "The arguments to pass to ray.init for Ray training. Default is None."},
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)
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master_addr: str | None = field(
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default=None,
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metadata={"help": "The master address for init_process_group"},
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)
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master_port: str | None = field(
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default=None,
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metadata={"help": "The master port for init_process_group"},
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)
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def __post_init__(self):
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self.use_ray = use_ray()
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if isinstance(self.ray_init_kwargs, str) and self.ray_init_kwargs.startswith("{"):
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self.ray_init_kwargs = _convert_str_dict(json.loads(self.ray_init_kwargs))
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@dataclass
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class Fp8Arguments:
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r"""Arguments pertaining to the FP8 training."""
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fp8: bool = field(
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default=False,
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metadata={
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"help": "Enable FP8 mixed precision training via HuggingFace Accelerate. "
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"Requires PyTorch 2.7+ and Hopper architecture GPUs."
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},
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)
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fp8_backend: str = field(
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default="auto",
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metadata={
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"help": "FP8 backend to use ('auto', 'torchao', 'te', 'msamp'). 'auto' selects best available backend."
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},
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)
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fp8_enable_fsdp_float8_all_gather: bool = field(
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default=False,
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metadata={"help": "Enable FP8 optimizations for FSDP2 all-gather operations."},
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)
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@dataclass
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class TrainingArguments(Fp8Arguments, RayArguments, BaseTrainingArguments):
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r"""Arguments pertaining to the trainer."""
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overwrite_output_dir: bool = field(
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default=False,
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metadata={"help": "deprecated"},
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)
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def __post_init__(self):
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RayArguments.__post_init__(self)
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BaseTrainingArguments.__post_init__(self)
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