""" Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset. """ import logging import os import sys import datasets import evaluate import transformers from transformers import set_seed from transformers.trainer_callback import TrainerState from transformers.trainer_utils import get_last_checkpoint from .arguments import get_args from .data.data_collator import DataCollatorForCausalLMSeq2Seq from .data.data_utils import load_data from .data.postprocessors import postprocess_text_for_metric from .models.utils import load_model from .trainers.trainer_ar import ARTrainer # Will error if the minimal version of Transformers is not installed. Remove at your own risks. # check_min_version("4.40.0.dev0") # require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") logger = logging.getLogger(__name__) summarization_name_mapping = { "amazon_reviews_multi": ("review_body", "review_title"), "big_patent": ("description", "abstract"), "cnn_dailymail": ("article", "highlights"), "orange_sum": ("text", "summary"), "pn_summary": ("article", "summary"), "psc": ("extract_text", "summary_text"), "samsum": ("dialogue", "summary"), "thaisum": ("body", "summary"), "xglue": ("news_body", "news_title"), "xsum": ("document", "summary"), "wiki_summary": ("article", "highlights"), "multi_news": ("document", "summary"), } def main(): model_args, data_args, training_args, diffusion_args = get_args() # 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)], ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() 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: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # Detecting last checkpoint. last_checkpoint = None if ( os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir ): last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif ( last_checkpoint is not None and training_args.resume_from_checkpoint is None ): logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Set seed before initializing model. set_seed(training_args.seed) # load data raw_datasets = load_data(data_args, model_args) # load model tokenizer, model = load_model( model_args, data_args, training_args, diffusion_args, logger ) total_seq2seq_length = data_args.max_source_length + data_args.max_target_length if ( hasattr(model.config, "max_position_embeddings") and model.config.max_position_embeddings < total_seq2seq_length ): if model_args.resize_position_embeddings is None: logger.warning( "Increasing the model's number of position embedding vectors from" f" {model.config.max_position_embeddings} to {total_seq2seq_length}." ) # position_ids starts from `padding_idx + 1` (padding_index=1) and we therefore requires # 2 more position embeddings. model.resize_position_embeddings( total_seq2seq_length + 2, with_alternatation=model_args.resize_position_embeddings_alternatively, ) elif model_args.resize_position_embeddings: model.resize_position_embeddings( total_seq2seq_length + 2, with_alternatation=model_args.resize_position_embeddings_alternatively, ) else: raise ValueError( f"`max_source_length`+`max_target_length` is set to {total_seq2seq_length}, but the model only has" f" {model.config.max_position_embeddings} position encodings. Consider either reducing" f" `max_source_length`+`max_target_length` to {model.config.max_position_embeddings} or to automatically resize the" " model's position encodings by passing `--resize_position_embeddings`." ) # Preprocessing the datasets. if training_args.do_train: column_names = raw_datasets["train"].column_names elif training_args.do_eval: column_names = raw_datasets["validation"].column_names elif training_args.do_predict: column_names = raw_datasets["test"].column_names else: logger.info( "There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`." ) return # Get the column names for input/target. dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None) assert dataset_columns is not None, "You need to provide the columns names." text_column, summary_column = dataset_columns[0], dataset_columns[1] # Temporarily set max_target_length for training. max_target_length = data_args.max_target_length def preprocess_function(examples): # remove pairs where at least one record is None inputs, targets = [], [] for i in range(len(examples[text_column])): if examples[text_column][i] and examples[summary_column][i]: inputs.append(examples[text_column][i]) targets.append(examples[summary_column][i]) # TODO: we need to process first the target, then cut the inputs to the max_length-target length to use the # maximum number of tokens. model_inputs = tokenizer( inputs, max_length=data_args.max_source_length, padding=False, truncation=True, ) # Tokenize targets with the `text_target` keyword argument labels = tokenizer( text_target=targets, max_length=max_target_length, padding=False, truncation=True, ) model_inputs["labels"] = labels["input_ids"] return model_inputs 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"): train_dataset = train_dataset.map( preprocess_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on train dataset", ) if training_args.do_eval: max_target_length = data_args.val_max_target_length if "validation" not in raw_datasets: raise ValueError("--do_eval requires a validation dataset") eval_dataset = raw_datasets["validation"] 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" ): eval_dataset = eval_dataset.map( preprocess_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on validation dataset", ) if training_args.do_predict: max_target_length = data_args.val_max_target_length if "test" not in raw_datasets: raise ValueError("--do_predict requires a test dataset") test_dataset = raw_datasets["test"] if data_args.max_predict_samples is not None: max_predict_samples = min(len(test_dataset), data_args.max_predict_samples) test_dataset = test_dataset.select(range(max_predict_samples)) with training_args.main_process_first( desc="prediction dataset map pre-processing" ): test_dataset = test_dataset.map( preprocess_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on prediction dataset", ) # Data collator. To be consistent with the run_mlm.py we need to add `mode`. data_collator = lambda mode: DataCollatorForCausalLMSeq2Seq( # 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, ) # Metric metric = evaluate.load("rouge") def compute_metrics(eval_preds): import numpy as np preds, labels = eval_preds if isinstance(preds, tuple): preds = preds[0] # Replace -100s used for padding as we can't decode them preds = np.where(preds != -100, preds, tokenizer.pad_token_id) decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True) labels = np.where(labels != -100, labels, tokenizer.pad_token_id) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) # Some simple post-processing decoded_preds, decoded_labels = postprocess_text_for_metric( "rouge", decoded_preds, decoded_labels ) result = metric.compute( predictions=decoded_preds, references=decoded_labels, use_stemmer=True ) result = {k: round(v * 100, 4) for k, v in result.items()} prediction_lens = [ np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds ] result["gen_len"] = np.mean(prediction_lens) return result assert training_args.do_eval or training_args.do_predict # 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, # preprocess_logits_for_metrics=preprocess_logits_for_metrics # if (training_args.do_eval or training_args.do_predict) # else None, # data_args=data_args, ) # Training if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() # Saves the tokenizer too for easy upload 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.log_metrics("train", metrics) trainer.save_metrics("train", metrics) trainer.save_state() # We will load the best model here to avoid an issue when do_train is not set. if training_args.load_states_in_eval_from_model_path and not training_args.do_train: trainer.state = TrainerState.load_from_json( os.path.join(model_args.model_name_or_path, "trainer_state.json") ) if ( training_args.load_best_model_at_end and trainer.state.best_model_checkpoint is not None ): checkpoint_path = trainer.state.best_model_checkpoint else: checkpoint_path = model_args.model_name_or_path trainer._load_from_checkpoint(checkpoint_path) trainer._load_rng_state(checkpoint_path) # 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)) # try: # perplexity = math.exp(metrics["eval_loss"]) # except OverflowError: # perplexity = float("inf") # metrics["perplexity"] = perplexity trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) if training_args.do_predict: logger.info("*** Test ***") metrics = trainer.evaluate(test_dataset, metric_key_prefix="test") max_predict_samples = ( data_args.max_predict_samples if data_args.max_predict_samples is not None else len(test_dataset) ) metrics["test_samples"] = min(max_predict_samples, len(test_dataset)) trainer.log_metrics("test", metrics) trainer.save_metrics("test", metrics) if __name__ == "__main__": main()