train/train_qwen.py

import sys
sys.path.append("..")

import os
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling
from utils_qwen import PERTURBATIONS, BABYLM_SPLITS, BABYLM_DATA_PATH, \
    GENRES, MARKER_TOKEN_IDS, marker_sg_token, marker_pl_token, marker_rev_token, write_file
import argparse
# import wandb

# Setup for Weights & Biases
# wandb.init(project="kallini", group="babylm-perturbation-experiments", name=run_id)

if __name__ == "__main__":

    # === CONFIGURATION SETTINGS ===
    parser = argparse.ArgumentParser(description="Training configuration.")

    parser.add_argument('--perturbation', type=str, default='hop_tokens4', help='Type of perturbation to use.')
    parser.add_argument('--train_set', type=str, default='10M', help='Dataset size for training.')
    parser.add_argument('--batch_size', type=int, default=4, help='Batch size for training.')
    parser.add_argument('--epoch', type=int, default=20, help='train epoch')
    parser.add_argument('--seed', type=int, default=0, help='Random seed.')

    args = parser.parse_args()

    # no_pos_encodings_underscore = ""  # Ex: "_nopos" if needed
    ckpt_path = "./checkpoints"
    # effective_bsz = 512

    model_name = "Qwen/Qwen2.5-7B"

    model_save_name = "Qwen2.5-7B"
    # === FILE PATHS BASED ON CONFIGURATION ===
    run_id = f"babylm_{args.perturbation}_{args.train_set}_seed{args.seed}"
    cache_dir = os.path.join(ckpt_path, f"{model_save_name}", run_id, "artifacts")
    run_dir = os.path.join(ckpt_path, f"{model_save_name}", run_id, "runs")
    os.makedirs(cache_dir, exist_ok=True)
    os.makedirs(run_dir, exist_ok=True)

    # === DATASET LOADING ===
    dataset_name = f"babylm_{args.perturbation}_{args.train_set}_seed{args.seed}"
    dataset = load_dataset('babylm_dataset.py', name=dataset_name, trust_remote_code=True)
    train_dataset = dataset['train']

    # === TOKENIZER & MODEL LOADING ===
    # model_name = f"gpt2{'' if no_pos_encodings_underscore == '' else '-no-pos'}-small-{perturbation}-{paren_model}"
 
    # tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir)
    tokenizer = PERTURBATIONS[args.perturbation]['qwen_tokenizer']
    model = AutoModelForCausalLM.from_pretrained(model_name, 
                                                device_map="auto",  # Place different layers of the model on different GPUs
                                                cache_dir=cache_dir)

    # print("model:", model)
    # === TOKENIZATION ===
    def tokenize_function(examples):
        return tokenizer(examples['text'], padding="max_length", truncation=True, max_length=1024)
    tokenized_train = train_dataset.map(tokenize_function, batched=True, remove_columns=["text"])

    # === DATA COLLATOR ===
    data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)

    # === TRAINING ARGUMENTS ===
    training_args = TrainingArguments(
        output_dir=run_dir,
        # evaluation_strategy="steps",
        evaluation_strategy="no",
        # per_device_train_batch_size=int(effective_bsz / 1),  # Assuming 1 GPU for this example
        per_device_train_batch_size=args.batch_size,  # Assuming 1 GPU for this example
        logging_dir='./logs',
        logging_steps=1000,
        save_steps=1000,
        # save_total_limit=5,
        learning_rate=2e-5,
        num_train_epochs=args.epoch,
        seed=args.seed,
        # load_best_model_at_end=True,
        gradient_accumulation_steps=1, # help reduce gpu memory
        fp16 = True, # Enable mixed precision training
        # report_to="wandb"
    )

    # === TRAINER ===
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_train,
        tokenizer=tokenizer,
        data_collator=data_collator
    )
    
    # === TRAIN MODEL ===
    trainer.train()
    # End logging
    # wandb.finish()