45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
import json
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import os
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from types import MethodType
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
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import numpy as np
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import torch
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from transformers import Seq2SeqTrainer, Trainer
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from ...extras.constants import IGNORE_INDEX
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from ...extras.logging import get_logger
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from ..utils import create_custom_optimzer, create_custom_scheduler
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if TYPE_CHECKING:
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from transformers.trainer import PredictionOutput
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from peft import PeftModelForCausalLM
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from ...hparams import FinetuningArguments
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logger = get_logger(__name__)
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class CustomSeq2SeqTrainer(Seq2SeqTrainer):
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r"""
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Inherits Seq2SeqTrainer to compute generative metrics such as BLEU and ROUGE.
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"""
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def __init__(self, finetuning_args: "FinetuningArguments", **kwargs) -> None:
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super().__init__(**kwargs)
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self.finetuning_args = finetuning_args
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if finetuning_args.use_badam:
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from badam import clip_grad_norm_for_sparse_tensor
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self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_for_sparse_tensor, self.accelerator)
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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_optimzer(self.model, self.args, self.finetuning_args)
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return super().create_optimizer()
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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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