mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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05271756d2
Co-authored-by: Benjamin Feuer <penfever@gmail.com> Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>
94 lines
3.6 KiB
Python
94 lines
3.6 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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from types import MethodType
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from typing import TYPE_CHECKING, Optional
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import torch
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from transformers import Trainer
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from typing_extensions import override
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from ...extras.packages import is_transformers_version_greater_than
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from ..callbacks import SaveProcessorCallback
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from ..fp8_utils import configure_fp8_environment, verify_fp8_status
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from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
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if TYPE_CHECKING:
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from transformers import ProcessorMixin
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from ...hparams import FinetuningArguments, ModelArguments
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class CustomTrainer(Trainer):
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r"""Inherit Trainer for custom optimizer."""
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def __init__(
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self,
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finetuning_args: "FinetuningArguments",
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processor: Optional["ProcessorMixin"],
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model_args: Optional["ModelArguments"] = None,
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**kwargs,
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) -> None:
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# Configure FP8 environment if enabled
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if model_args is not None and model_args.fp8:
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configure_fp8_environment(model_args)
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if is_transformers_version_greater_than("4.46"):
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kwargs["processing_class"] = kwargs.pop("tokenizer")
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super().__init__(**kwargs)
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if processor is not None:
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# avoid wrong loss under gradient accumulation
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# https://github.com/huggingface/transformers/pull/36044#issuecomment-2746657112
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self.model_accepts_loss_kwargs = False
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self.finetuning_args = finetuning_args
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if processor is not None:
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self.add_callback(SaveProcessorCallback(processor))
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if finetuning_args.use_badam:
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from badam import BAdamCallback, clip_grad_norm_old_version # type: ignore
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self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
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self.add_callback(BAdamCallback)
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# Verify FP8 status after trainer initialization (accelerator should be available)
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if model_args is not None and model_args.fp8 and hasattr(self, "accelerator"):
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verify_fp8_status(self.accelerator, model_args)
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@override
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def create_optimizer(self) -> "torch.optim.Optimizer":
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if self.optimizer is None:
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self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args)
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return super().create_optimizer()
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@override
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def create_scheduler(
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self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
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) -> "torch.optim.lr_scheduler.LRScheduler":
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create_custom_scheduler(self.args, num_training_steps, optimizer)
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return super().create_scheduler(num_training_steps, optimizer)
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@override
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def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
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if self.finetuning_args.disable_shuffling:
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return torch.utils.data.SequentialSampler(self.train_dataset)
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return super()._get_train_sampler(*args, **kwargs)
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@override
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def compute_loss(self, model, inputs, *args, **kwargs):
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return super().compute_loss(model, inputs, *args, **kwargs)
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