mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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93 lines
3.2 KiB
Python
93 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 os
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from dataclasses import dataclass, field
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from uuid import uuid4
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from .arg_utils import BatchingStrategy, PluginConfig, get_plugin_config
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@dataclass
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class TrainingArguments:
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output_dir: str = field(
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default=os.path.join("outputs", str(uuid4().hex)),
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metadata={"help": "Path to the output directory."},
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)
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micro_batch_size: int = field(
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default=1,
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metadata={"help": "Micro batch size for training."},
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)
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global_batch_size: int | None = field(
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default=None,
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metadata={"help": "Global batch size for training, default to DP size * micro batch size."},
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)
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cutoff_len: int = field(
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default=2048,
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metadata={"help": "Maximum sequence length for training."},
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)
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learning_rate: float = field(
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default=1e-4,
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metadata={"help": "Learning rate for training."},
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)
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num_train_epochs: int = field(
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default=3,
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metadata={"help": "Number of training epochs."},
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)
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max_steps: int | None = field(
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default=None,
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metadata={"help": "Maximum number of training steps. If set, overrides num_train_epochs."},
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)
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max_grad_norm: float = field(
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default=1.0,
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metadata={"help": "Maximum gradient norm for training."},
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)
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bf16: bool = field(
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default=False,
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metadata={"help": "Use bf16 for training."},
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)
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batching_strategy: BatchingStrategy = field(
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default=BatchingStrategy.NORMAL,
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metadata={"help": "Batching strategy for training."},
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)
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batching_workers: int = field(
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default=16,
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metadata={"help": "Number of workers for batching."},
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)
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enable_activation_checkpointing: bool = field(
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default=False,
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metadata={"help": "Enable activation checkpointing for training."},
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)
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dist_config: PluginConfig | None = field(
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default=None,
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metadata={"help": "Distribution configuration for training."},
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)
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optim_config: PluginConfig | None = field(
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default=None,
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metadata={"help": "Optimizer configuration for training."},
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)
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lr_scheduler_config: PluginConfig | None = field(
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default=None,
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metadata={"help": "Learning rate scheduler configuration for training."},
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)
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seed: int = field(
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default=42,
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metadata={"help": "Random seed that will be set at the beginning of training."},
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)
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def __post_init__(self) -> None:
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self.dist_config = get_plugin_config(self.dist_config)
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self.optim_config = get_plugin_config(self.optim_config)
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self.lr_scheduler_config = get_plugin_config(self.lr_scheduler_config)
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