realign/train/train.py

import random
import sys
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1,2,3,4,5,6'

import fire
import torch
torch.autograd.set_detect_anomaly(True)
import transformers
from transformers import set_seed
set_seed(15)
from utils.datasets.nyt10_dataset import NYT10FullDataset, NYT10StylishDataset
from trainers import FSDPTrainingArguments, FSDPTrainer
from transformers import AutoTokenizer, AutoConfig
from transformers import AutoModelForCausalLM, LlamaForCausalLM
from llama import Method_1


def train(
    # model/data params
    base_model: str = '/home/tushilong/hf/models/Llama-2-7b-hf',
    data_path: str = '../data/nyt10/nyt10_train.txt',
    output_dir: str = '../ckpts/stylish',
    
    # training hyperparams
    do_train: bool = True,
    micro_batch_size: int = 2,
    gradient_accumulation_steps: int = 1,
    gradient_checkpointing: bool = True,
    num_epochs: int = 1,
    save_steps: int = 500,
    learning_rate: float = 2e-5,
    lr_scheduler_type: str = 'cosine',
    weight_decay: float = 1e-4,
    warmup_ratio: float = 0.06,
    deepspeed_config: str = None,
    fsdp: str = 'shard_grad_op auto_wrap offload',
    fsdp_config: str = './configs/fsdp/llama2_fsdp_config.json',
    smart_embedding: bool = False,

    # evaluating hyperparams
    do_eval: bool = False,
    val_set_size: int = 1000,
    eval_batch_size: int = 4,
    
    # wandb params
    wandb_project: str = "",
    wandb_run_name: str = "",
    wandb_watch: str = "",  # options: false | gradients | all
    wandb_log_model: str = "",  # options: false | true
    resume_from_checkpoint: str = None,  # either training checkpoint or final adapter
):
    if int(os.environ.get("LOCAL_RANK", 0)) == 0:
        print(
            f"Training model with params:\n"
            f"base_model: {base_model}\n"
            f"output_dir: {output_dir}\n"
            f"micro_batch_size: {micro_batch_size}\n"
            f"gradient_accumulation_steps: {gradient_accumulation_steps}\n"
            f"train_batch_size: {micro_batch_size * gradient_accumulation_steps}\n"
            f"gradient_checkpointing: {gradient_checkpointing}\n"
            f"num_epochs: {num_epochs}\n"
            f"learning_rate: {learning_rate}\n"
            f"weight_decay: {weight_decay}\n"
            f"warmup_ratio: {warmup_ratio}\n"
            f"deepspeed_config: {deepspeed_config}\n"
            f"fsdp: {fsdp}\n"
            f"fsdp_config: {fsdp_config}\n"
            f"smart_embedding: {smart_embedding}\n"
            f"wandb_project: {wandb_project}\n"
            f"wandb_run_name: {wandb_run_name}\n"
            f"wandb_watch: {wandb_watch}\n"
            f"wandb_log_model: {wandb_log_model}\n"
            f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
        )
    assert (
        not (deepspeed_config and fsdp)
    ), "Can not specified both deepspeed_config and fsdp_config"
    
    # training arguments
    bf16 = True # torch.cuda.get_device_capability()[0] >= 8
    fp16 = not bf16
    
    tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
    tokenizer.pad_token_id = tokenizer.eos_token_id

    # model = AutoModelForCausalLM.from_pretrained(base_model, trust_remote_code=True)
    # model_dev_id = int(os.environ.get("LOCAL_RANK", 0))

    model = Method_1.from_pretrained(base_model)



    # Check if parameter passed or if set within environ
    # use_wandb = len(wandb_project) > 0 or (
    #     "WANDB_PROJECT" in os.environ and len(os.environ["WANDB_PROJECT"]) > 0
    # )
    use_wandb = False
    # Only overwrite environ if wandb param passed
    # if len(wandb_project) > 0:
    #     os.environ["WANDB_PROJECT"] = wandb_project
    # if len(wandb_watch) > 0:
    #     os.environ["WANDB_WATCH"] = wandb_watch
    # if len(wandb_log_model) > 0:
    #     os.environ["WANDB_LOG_MODEL"] = wandb_log_model
    
    
    # train_data = NYT10FullDataset(data_path, tokenizer)
    # train_data = NYT10StylishDataset(data_path, tokenizer, 30)
    train_data = NYT10StylishDataset(data_path, tokenizer, 1000)
    val_data = None
        
    training_args = FSDPTrainingArguments(
        use_ffd_sampler=True,
        output_dir=output_dir,
        no_cuda=not torch.cuda.is_available(),
        seed=15,
        data_seed=15,
        do_train=do_train,
        num_train_epochs=num_epochs,
        optim="adamw_torch",
        learning_rate=learning_rate,
        lr_scheduler_type=lr_scheduler_type,
        per_device_train_batch_size=micro_batch_size,
        gradient_accumulation_steps=gradient_accumulation_steps,
        warmup_ratio=warmup_ratio,
        weight_decay=weight_decay,
        half_precision_backend="auto",
        fp16=fp16,
        bf16=bf16,
        adam_beta1=0.9,
        adam_beta2=0.95,
        save_strategy="steps",
        save_steps=save_steps,
        save_total_limit=2,
        logging_steps=1,
        report_to= "none", # "wandb" if use_wandb else None,
        run_name=None, #wandb_run_name if use_wandb else None,
        deepspeed=deepspeed_config,
        fsdp=fsdp,
        fsdp_config=fsdp_config,
        gradient_checkpointing=gradient_checkpointing,
        do_eval=do_eval and val_set_size > 0,
        evaluation_strategy="steps" if do_eval and val_set_size > 0 else "no",
        eval_steps=save_steps,
        per_device_eval_batch_size=eval_batch_size,
        # group_by_length=True,
    ) 
    
    trainer = FSDPTrainer(
        model=model,
        args=training_args,
        train_dataset=train_data,
        eval_dataset=val_data,
        data_collator=transformers.DataCollatorForSeq2Seq(
            tokenizer, pad_to_multiple_of=8, return_tensors='pt', padding=True,
        )
    )

    trainer.train(resume_from_checkpoint=resume_from_checkpoint)

    trainer.save_model(output_dir)
    tokenizer.save_pretrained(output_dir)
    

if __name__ == "__main__":
    fire.Fire(train)