""" Finetuning the library models for sequence classification on GLUE.""" import logging import sys import alpaca_eval import datasets import transformers from datasets import load_dataset from transformers import AutoTokenizer, set_seed from .arguments import get_args from .data.data_collator import DataCollatorForCausalMultiTurnSeq2Seq from .data.data_utils import load_data from .models import load_model from .run_tulu import encode_with_messages_format_v1 from .trainers.trainer_ar import ARTrainer from .utils import ( get_last_checkpoint_with_beaker_preemption, resolve_last_checkpoint_vs_resume_from_checkpoint, ) logger = logging.getLogger(__name__) def main(): # parse args model_args, data_args, training_args, diffusion_args = get_args() assert data_args.dataset_name is not None data_args.dataset_name = data_args.dataset_name.lower() # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) log_level = training_args.get_process_log_level() logger.setLevel(log_level) datasets.utils.logging.set_verbosity(log_level) transformers.utils.logging.set_verbosity(log_level) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # Detecting last checkpoint. last_checkpoint = get_last_checkpoint_with_beaker_preemption(training_args) # load dataset raw_datasets = load_data(data_args, model_args) eval_dataset = load_dataset("tatsu-lab/alpaca_eval")["eval"] # Set seed before initializing model. set_seed(training_args.seed) # load tokenizer early tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, padding_side=model_args.tokenizer_padding_side, ) # load model tokenizer, model = load_model( model_args, data_args, training_args, diffusion_args, logger ) # Preprocessing the datasets. # We need to tokenize inputs and targets. if training_args.do_train: train_column_names = raw_datasets["train"].column_names # if training_args.do_eval: # eval_column_names = eval_dataset.column_names # Temporarily set max_target_length for training. max_target_length = data_args.max_seq_length if training_args.do_train: if "train" not in raw_datasets: raise ValueError("--do_train requires a train dataset") train_dataset = raw_datasets["train"] if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) with training_args.main_process_first(desc="train dataset map pre-processing"): # we assume the data is in the tulu format train_dataset = train_dataset.map( lambda x: encode_with_messages_format_v1( x, tokenizer, max_target_length ), batched=False, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, remove_columns=train_column_names, desc="Running tokenizer on train dataset", ) train_dataset.set_format("pt") train_dataset = train_dataset.filter(lambda x: (x["labels"] != -100).any()) if training_args.do_eval: logger.warn( "Running evaluation. This calls GPT-4, so PLEASE MAKE SURE YOU ARE NOT RUNNING IT A TONNE" ) max_target_length = data_args.max_seq_length # put the dataset into the correct format eval_dataset = eval_dataset.map( lambda x: {"messages": [{"role": "user", "content": x["instruction"]}]} ) if data_args.max_eval_samples is not None: max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) eval_dataset = eval_dataset.select(range(max_eval_samples)) with training_args.main_process_first( desc="validation dataset map pre-processing" ): prompt_function = lambda x: encode_with_messages_format_v1( # noqa: E731 x, tokenizer, max_target_length, add_generation_prompt=True ) # prompting eval_dataset = eval_dataset.map( prompt_function, batched=False, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, remove_columns=[ "instruction", "dataset", "generator", "messages", "output", ], desc="Running tokenizer on validation dataset", ) eval_dataset.set_format("pt") eval_dataset.remove_columns(["labels"]) if data_args.max_seq_length > tokenizer.model_max_length: logger.warning( f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) # Metric def compute_metrics(results): metrics = {} eval_data = [ tokenizer.decode(x, skip_special_tokens=True) .replace("<|user|>\n", "") .replace("<|assistant|>\n", "") .strip() for x in results.inputs ] # assume we stopped at eos decoded_preds = [] for prediction in results.predictions: # sometimes we get out of range somehow?? guard against it. prediction = [x for x in prediction if x > 0 and x < tokenizer.vocab_size] decoded_preds.append(tokenizer.decode(prediction, skip_special_tokens=True)) # for each decoded sample, format into alpacaeval setup decoded_preds = [ {"output": y, "instruction": x, "generator": "tess2"} for x, y in zip(eval_data, decoded_preds) ] df_leaderboard, _ = alpaca_eval.evaluate( model_outputs=decoded_preds, is_overwrite_leaderboard=True, is_return_instead_of_print=True, ) # grab tess2 results key_metrics = df_leaderboard.loc["tess2"].to_dict() metrics.update(key_metrics) return metrics # Data collator. To be consistent with the run_mlm.py we need to add `mode`. data_collator = lambda mode: DataCollatorForCausalMultiTurnSeq2Seq( # noqa: E731 tokenizer, # Note that if you do not use `pad_to_max_length`, this becomes very slow on multi-gpus. padding="max_length" if data_args.pad_to_max_length else True, max_length=data_args.max_seq_length, pad_to_multiple_of=8 if training_args.fp16 else None, ) # Initialize our Trainer trainer = ARTrainer( model=model, args=training_args, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics if (training_args.do_eval or training_args.do_predict) else None, ) # Training if training_args.do_train: checkpoint = resolve_last_checkpoint_vs_resume_from_checkpoint( last_checkpoint, training_args.resume_from_checkpoint, ) train_result = trainer.train(resume_from_checkpoint=checkpoint) metrics = train_result.metrics max_train_samples = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset) ) metrics["train_samples"] = min(max_train_samples, len(train_dataset)) trainer.save_model() # Saves the tokenizer too for easy upload trainer.log_metrics("train", metrics) trainer.save_metrics("train", metrics) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("*** Evaluate ***") metrics = trainer.evaluate() max_eval_samples = ( data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset) ) metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset)) trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) if __name__ == "__main__": main()