import argparse import logging from torch.utils.data import Dataset, IterableDataset import gzip import json from transformers import Seq2SeqTrainer, AutoModelForSeq2SeqLM, AutoTokenizer, Seq2SeqTrainingArguments import sys from datetime import datetime import torch import random from shutil import copyfile import os import wandb import random import re from datasets import load_dataset import tqdm logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) parser = argparse.ArgumentParser() parser.add_argument("--lang", required=True) parser.add_argument("--model_name", default="google/mt5-base") parser.add_argument("--epochs", default=4, type=int) parser.add_argument("--batch_size", default=32, type=int) parser.add_argument("--max_source_length", default=320, type=int) parser.add_argument("--max_target_length", default=64, type=int) parser.add_argument("--eval_size", default=1000, type=int) #parser.add_argument("--fp16", default=False, action='store_true') args = parser.parse_args() wandb.init(project="doc2query", name=f"{args.lang}-{args.model_name}") def main(): ############ Load dataset queries = {} for row in tqdm.tqdm(load_dataset('unicamp-dl/mmarco', f'queries-{args.lang}')['train']): queries[row['id']] = row['text'] """ collection = {} for row in tqdm.tqdm(load_dataset('unicamp-dl/mmarco', f'collection-{args.lang}')['collection']): collection[row['id']] = row['text'] """ collection = load_dataset('unicamp-dl/mmarco', f'collection-{args.lang}')['collection'] train_pairs = [] eval_pairs = [] with open('qrels.train.tsv') as fIn: for line in fIn: qid, _, did, _ = line.strip().split("\t") qid = int(qid) did = int(did) assert did == collection[did]['id'] text = collection[did]['text'] pair = (queries[qid], text) if len(eval_pairs) < args.eval_size: eval_pairs.append(pair) else: train_pairs.append(pair) print(f"Train pairs: {len(train_pairs)}") ############ Model model = AutoModelForSeq2SeqLM.from_pretrained(args.model_name) tokenizer = AutoTokenizer.from_pretrained(args.model_name) save_steps = 1000 output_dir = 'output/'+args.lang+'-'+args.model_name.replace("/", "-")+'-'+datetime.now().strftime("%Y-%m-%d_%H-%M-%S") print("Output dir:", output_dir) # Write self to path os.makedirs(output_dir, exist_ok=True) train_script_path = os.path.join(output_dir, 'train_script.py') copyfile(__file__, train_script_path) with open(train_script_path, 'a') as fOut: fOut.write("\n\n# Script was called via:\n#python " + " ".join(sys.argv)) #### training_args = Seq2SeqTrainingArguments( output_dir=output_dir, bf16=True, per_device_train_batch_size=args.batch_size, evaluation_strategy="steps", save_steps=save_steps, logging_steps=100, eval_steps=save_steps, #logging_steps, warmup_steps=1000, save_total_limit=1, num_train_epochs=args.epochs, report_to="wandb", ) ############ Arguments ############ Load datasets print("Input:", train_pairs[0][1]) print("Target:", train_pairs[0][0]) print("Input:", eval_pairs[0][1]) print("Target:", eval_pairs[0][0]) def data_collator(examples): targets = [row[0] for row in examples] inputs = [row[1] for row in examples] label_pad_token_id = -100 model_inputs = tokenizer(inputs, max_length=args.max_source_length, padding=True, truncation=True, return_tensors='pt', pad_to_multiple_of=8 if training_args.fp16 else None) # Setup the tokenizer for targets with tokenizer.as_target_tokenizer(): labels = tokenizer(targets, max_length=args.max_target_length, padding=True, truncation=True, pad_to_multiple_of=8 if training_args.fp16 else None) # replace all tokenizer.pad_token_id in the labels by -100 to ignore padding in the loss. labels["input_ids"] = [ [(l if l != tokenizer.pad_token_id else label_pad_token_id) for l in label] for label in labels["input_ids"] ] model_inputs["labels"] = torch.tensor(labels["input_ids"]) return model_inputs ## Define the trainer trainer = Seq2SeqTrainer( model=model, args=training_args, train_dataset=train_pairs, eval_dataset=eval_pairs, tokenizer=tokenizer, data_collator=data_collator ) ### Save the model train_result = trainer.train() trainer.save_model() if __name__ == "__main__": main() # Script was called via: #python train_hf_trainer_multilingual.py --lang italian