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82 lines
2.7 KiB
82 lines
2.7 KiB
# coding=utf-8
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# Implements parameter-efficient PPO training of fine-tuned models.
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# This code is inspired by:
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# https://github.com/lvwerra/trl/blob/main/examples/sentiment/scripts/gpt-neox-20b_peft/gpt-neo-20b_sentiment_peft.py
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import math
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from torch.optim import AdamW
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from transformers.optimization import get_scheduler
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from trl import PPOConfig
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from utils import (
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prepare_args,
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prepare_data,
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load_pretrained,
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preprocess_data,
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DynamicDataCollatorWithPadding,
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PPOPeftTrainer,
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LogCallback,
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plot_loss
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)
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def main():
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# Prepare pretrained model and dataset
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model_args, data_args, training_args, finetuning_args = prepare_args(stage="ppo")
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dataset = prepare_data(model_args, data_args)
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model, tokenizer = load_pretrained(model_args, finetuning_args, training_args.do_train, stage="ppo")
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dataset = preprocess_data(dataset, tokenizer, data_args, training_args, stage="ppo")
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data_collator = DynamicDataCollatorWithPadding(tokenizer, model.pretrained_model)
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ppo_config = PPOConfig(
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model_name=model_args.model_name_or_path,
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learning_rate=training_args.learning_rate,
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mini_batch_size=training_args.per_device_train_batch_size,
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batch_size=training_args.per_device_train_batch_size,
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gradient_accumulation_steps=training_args.gradient_accumulation_steps,
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ppo_epochs=1,
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max_grad_norm=training_args.max_grad_norm
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)
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optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=ppo_config.learning_rate)
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total_train_batch_size = \
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training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
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lr_scheduler = get_scheduler(
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training_args.lr_scheduler_type,
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optimizer=optimizer,
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num_warmup_steps=training_args.warmup_steps,
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num_training_steps=(training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size))
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)
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# Initialize our Trainer
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ppo_trainer = PPOPeftTrainer(
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training_args=training_args,
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finetuning_args=finetuning_args,
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callbacks=[LogCallback()],
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config=ppo_config,
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model=model,
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ref_model=None,
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tokenizer=tokenizer,
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dataset=dataset,
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data_collator=data_collator,
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optimizer=optimizer,
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lr_scheduler=lr_scheduler
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)
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ppo_trainer.ppo_train(max_target_length=data_args.max_target_length)
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ppo_trainer.save_model()
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ppo_trainer.save_state() # must be after save_model
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if ppo_trainer.is_world_process_zero() and model_args.plot_loss:
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plot_loss(training_args, keys=["loss", "reward"])
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def _mp_fn(index):
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# For xla_spawn (TPUs)
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main()
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if __name__ == "__main__":
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main()
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