| import os |
| from dataclasses import replace |
|
|
| import jax |
| import wandb |
| from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step |
| from datasets import load_dataset |
| from flax import jax_utils |
|
|
| from transformers import BigBirdTokenizerFast |
|
|
|
|
| if __name__ == "__main__": |
| print("#################### AVAILABLE DEVICES ####################") |
| print(jax.devices()) |
| print("###########################################################") |
|
|
| |
| args = Args() |
| logger = wandb.init(project="bigbird-natural-questions", config=args.__dict__) |
| wandb_args = dict(logger.config) |
| del wandb_args["batch_size"] |
| args = replace(args, **wandb_args) |
| base_dir = args.base_dir + "-" + wandb.run.id |
| args = replace(args, base_dir=base_dir) |
| print(args) |
|
|
| tr_dataset = load_dataset("json", data_files=args.tr_data_path)["train"] |
| val_dataset = load_dataset("json", data_files=args.val_data_path)["train"] |
|
|
| |
| indices = range(len(tr_dataset) - len(tr_dataset) % args.batch_size) |
| tr_dataset = tr_dataset.shuffle().select(indices) |
| indices = range(len(val_dataset) - len(val_dataset) % args.batch_size) |
| val_dataset = val_dataset.shuffle().select(indices) |
|
|
| if os.environ.get("TRAIN_ON_SMALL", "false") == "true": |
| tr_dataset = tr_dataset.shuffle().select(range(80000)) |
| val_dataset = val_dataset.shuffle().select(range(8000)) |
|
|
| print(tr_dataset) |
| print(val_dataset) |
|
|
| model = FlaxBigBirdForNaturalQuestions.from_pretrained( |
| args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks |
| ) |
| tokenizer = BigBirdTokenizerFast.from_pretrained(args.model_id) |
| data_collator = DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096) |
|
|
| tx_args = { |
| "lr": args.lr, |
| "init_lr": args.init_lr, |
| "warmup_steps": args.warmup_steps, |
| "num_train_steps": args.max_epochs * (len(tr_dataset) // args.batch_size), |
| "weight_decay": args.weight_decay, |
| } |
| tx, lr = build_tx(**tx_args) |
|
|
| trainer = Trainer( |
| args=args, |
| data_collator=data_collator, |
| model_save_fn=model.save_pretrained, |
| train_step_fn=train_step, |
| val_step_fn=val_step, |
| logger=logger, |
| scheduler_fn=lr, |
| ) |
|
|
| ckpt_dir = None |
| state = trainer.create_state(model, tx, num_train_steps=tx_args["num_train_steps"], ckpt_dir=ckpt_dir) |
| try: |
| trainer.train(state, tr_dataset, val_dataset) |
| except KeyboardInterrupt: |
| print("Oooops; TRAINING STOPPED UNFORTUNATELY") |
|
|
| print("SAVING WEIGHTS IN `final-weights`") |
| params = jax_utils.unreplicate(state.params) |
| model.save_pretrained(os.path.join(args.base_dir, "final-weights"), params=params) |
|
|
