ESFT/train.py

import argparse
import json
import yaml
import os
import random
import torch
from torch.utils.data import TensorDataset
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, logging

from benchmarks import *
from utils import get_formatted_input_and_target, get_examples_from_buffer_pad
from esft import to_esft
from deepseek.modeling_deepseek import DeepseekV2ForCausalLM


os.environ["TOKENIZERS_PARALLELISM"] = "false"


def main():
    parser = argparse.ArgumentParser()

    parser.add_argument("--base_model_path", type=str, required=True)
    parser.add_argument("--expert_config", type=str, required=True)
    parser.add_argument("--train_dataset", type=str, required=True)
    parser.add_argument("--output_dir", type=str, required=True)
    parser.add_argument("--train_config", type=str, required=True)
    parser.add_argument("--wandb_api_key", type=str, required=False)
    args = parser.parse_args()

    expert_config = json.load(open(args.expert_config))
    output_dir = args.output_dir
    base_model_path = args.base_model_path
    config = yaml.safe_load(open(args.train_config))
    os.makedirs(args.output_dir, exist_ok=True)

    seed = config['seed']
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    random.seed(seed)

    if args.wandb_api_key is not None:
        import wandb
        wandb.login(key=args.wandb_api_key)

    # Prepare data
    tokenizer =  AutoTokenizer.from_pretrained(base_model_path)
    samples = [json.loads(i) for i in open(f"datasets/train/{args.train_dataset}.jsonl").readlines()]
    buffer = []
    for instance in samples:
        input_ids, target_ids = get_formatted_input_and_target(instance['messages'], tokenizer, -100)
        buffer.append((input_ids, target_ids))
    seq_length = config['seq_length']
    random_concat_ratio = config['random_concat_ratio']
    concated_examples = get_examples_from_buffer_pad(buffer, seq_length, tokenizer, random_concat_ratio)

    dataset = TensorDataset(concated_examples['input_ids'], concated_examples['labels'])
    train_dataset, valid_dataset = torch.utils.data.random_split(dataset, [int(len(dataset) * 0.98), len(dataset) - int(len(dataset) * 0.98)])

    # Training arguments
    training_args = TrainingArguments(
        output_dir=output_dir,
        max_steps=config['steps'],
        per_device_train_batch_size=config['per_device_batch_size'],
        per_device_eval_batch_size=config['per_device_batch_size'],
        warmup_steps=config['warmup_steps'],
        weight_decay=config['weight_decay'],
        logging_dir=f"{output_dir}/logs",
        logging_steps=config['logging_steps'],
        save_steps=config['save_steps'],
        eval_strategy="steps",
        eval_steps=config['eval_steps'],
        gradient_accumulation_steps=config['gradient_accumulation_steps'],
        load_best_model_at_end=True,
        metric_for_best_model="loss",
        greater_is_better=False,
        bf16=True,
        lr_scheduler_type='constant',
        save_total_limit=5,
        learning_rate=config['learning_rate'],
        optim=config['optim'],
        adam_beta1=config['adam_beta1'],
        adam_beta2=config['adam_beta2'],
        gradient_checkpointing=config['gradient_checkpointing'],
        gradient_checkpointing_kwargs={"use_reentrant": False} if config['gradient_checkpointing'] else {}, # if set to True, backward will raise bug
    )

    def data_collator(data):
        input_ids = torch.stack([item[0] for item in data])
        labels = torch.stack([item[1] for item in data])
        return {"input_ids": input_ids, "labels": labels}


    model = DeepseekV2ForCausalLM.from_pretrained(base_model_path, trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2")
    to_esft(model, expert_config)

    # Initialize Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=valid_dataset,
        data_collator=data_collator,
    )
    # Training
    if os.path.exists(output_dir) and len(os.listdir(output_dir)) > 1: # has checkpoints already
        trainer.train(resume_from_checkpoint=True)
    else:
        trainer.train()

    # Save the model and tokenizer
    trainer.save_model(output_dir + "/last_checkpoint")
    tokenizer.save_pretrained(output_dir + "/last_checkpoint")

    print("Training complete")

if __name__ == "__main__":
    main()