71 lines
2.9 KiB
Python
71 lines
2.9 KiB
Python
# 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 trl import PPOConfig
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from torch.optim import AdamW
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from typing import Optional, List
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from transformers import DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, TrainerCallback
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from transformers.optimization import get_scheduler
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from llmtuner.dsets import get_dataset, preprocess_dataset
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from llmtuner.extras.callbacks import LogCallback
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from llmtuner.extras.ploting import plot_loss
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from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
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from llmtuner.tuner.core import load_model_and_tokenizer
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from llmtuner.tuner.ppo.trainer import PPOPeftTrainer
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def run_ppo(
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model_args: ModelArguments,
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data_args: DataArguments,
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training_args: Seq2SeqTrainingArguments,
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finetuning_args: FinetuningArguments,
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callbacks: Optional[List[TrainerCallback]] = [LogCallback()]
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):
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dataset = get_dataset(model_args, data_args)
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model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="ppo")
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dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="ppo")
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data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, label_pad_token_id=tokenizer.pad_token_id)
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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 * training_args.gradient_accumulation_steps,
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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=callbacks,
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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.output_dir, keys=["loss", "reward"])
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