train_script.py

from codecs import EncodedFile
from datetime import datetime
from typing import Optional

import datasets
import torch
from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything
from torch.utils.data import DataLoader
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    get_scheduler,
)
import torch
import sys
import os
from argparse import ArgumentParser
from datasets import load_dataset
import tqdm
import json
import gzip
import random
from pytorch_lightning.callbacks import ModelCheckpoint
import numpy as np
from shutil import copyfile
from pytorch_lightning.loggers import WandbLogger
import transformers


class MSMARCOData(LightningDataModule):
    def __init__(
        self,
        model_name: str,
        triplets_path: str,
        langs,
        max_seq_length: int = 250,
        train_batch_size: int = 32,
        eval_batch_size: int = 32,
        num_negs: int = 3,
        cross_lingual_chance: float = 0.0,
        **kwargs,
    ):
        super().__init__()
        self.model_name = model_name
        self.triplets_path = triplets_path
        self.max_seq_length = max_seq_length
        self.train_batch_size = train_batch_size
        self.eval_batch_size = eval_batch_size
        self.langs = langs
        self.num_negs = num_negs
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
        self.cross_lingual_chance = cross_lingual_chance  #Probability for cross-lingual batches

    #def setup(self, stage: str):
        print(f"!!!!!!!!!!!!!!!!!! SETUP {os.getpid()}  !!!!!!!!!!!!!!!")

        #Get the queries
        self.queries = {lang: {} for lang in self.langs}
    
        for lang in self.langs:
            for row in tqdm.tqdm(load_dataset('unicamp-dl/mmarco', f'queries-{lang}')['train'], desc=lang):
                self.queries[lang][row['id']] = row['text']

        #Get the passages
        self.collections = {lang: load_dataset('unicamp-dl/mmarco', f'collection-{lang}')['collection'] for lang in self.langs}

        #Get the triplets
        with gzip.open(self.triplets_path, 'rt') as fIn:
            self.triplets = [json.loads(line) for line in tqdm.tqdm(fIn, desc="triplets", total=502938)] 
            """
            self.triplets = []
            for line in tqdm.tqdm(fIn):
                self.triplets.append(json.loads(line))
                if len(self.triplets) >= 1000:
                    break
            """

    def collate_fn(self, batch):
        cross_lingual_batch = random.random() < self.cross_lingual_chance 

        #Create data for list-rank-loss
        query_doc_pairs = [[] for _ in  range(1+self.num_negs)]

        for row in batch:
            qid = row['qid']
            pos_id = random.choice(row['pos'])

            query_lang = random.choice(self.langs)
            query_text = self.queries[query_lang][qid]
            
            doc_lang = random.choice(self.langs) if cross_lingual_batch else query_lang 
            query_doc_pairs[0].append((query_text, self.collections[doc_lang][pos_id]['text']))

            dense_bm25_neg = list(set(row['dense_neg'] + row['bm25_neg']))
            neg_ids = random.sample(dense_bm25_neg, self.num_negs)

            for num_neg, neg_id in enumerate(neg_ids):
                doc_lang = random.choice(self.langs) if cross_lingual_batch else query_lang
                query_doc_pairs[1+num_neg].append((query_text, self.collections[doc_lang][neg_id]['text']))

        #Now tokenize the data
        features = [self.tokenizer(qd_pair, max_length=self.max_seq_length, padding=True, truncation='only_second', return_tensors="pt") for qd_pair in query_doc_pairs]
    
        return features

    def train_dataloader(self):
        return DataLoader(self.triplets, shuffle=True, batch_size=self.train_batch_size, num_workers=1, pin_memory=True, collate_fn=self.collate_fn)

    



class ListRankLoss(LightningModule):
    def __init__(
        self,
        model_name: str,
        learning_rate: float = 2e-5,
        warmup_steps: int = 1000,
        weight_decay: float = 0.01,
        train_batch_size: int = 32,
        eval_batch_size: int = 32,
        **kwargs,
    ):
        super().__init__()

        self.save_hyperparameters()
        print(self.hparams)

        self.config = AutoConfig.from_pretrained(model_name, num_labels=1)
        self.model = AutoModelForSequenceClassification.from_pretrained(model_name, config=self.config)
        self.loss_fct = torch.nn.CrossEntropyLoss()
        self.global_train_step = 0
        

    def forward(self, **inputs):
        return self.model(**inputs)

    def training_step(self, batch, batch_idx):
        pred_scores = []
        scores = torch.tensor([0] * len(batch[0]['input_ids']), device=self.model.device)
   
        for feature in batch:
            pred_scores.append(self(**feature).logits.squeeze())

        pred_scores = torch.stack(pred_scores, 1)
        loss_value = self.loss_fct(pred_scores, scores)
        self.global_train_step += 1
        self.log('global_train_step', self.global_train_step)
        self.log("train/loss", loss_value)

        return loss_value
     

    def setup(self, stage=None) -> None:
        if stage != "fit":
            return
        # Get dataloader by calling it - train_dataloader() is called after setup() by default
        train_loader = self.trainer.datamodule.train_dataloader()

        # Calculate total steps
        tb_size = self.hparams.train_batch_size * max(1, self.trainer.gpus)
        ab_size = self.trainer.accumulate_grad_batches
        self.total_steps = (len(train_loader) // ab_size) * self.trainer.max_epochs

        print(f"{tb_size=}")
        print(f"{ab_size=}")
        print(f"{len(train_loader)=}")
        print(f"{self.total_steps=}")
        

    def configure_optimizers(self):
        """Prepare optimizer and schedule (linear warmup and decay)"""
        model = self.model
        no_decay = ["bias", "LayerNorm.weight"]
        optimizer_grouped_parameters = [
            {
                "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
                "weight_decay": self.hparams.weight_decay,
            },
            {
                "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
                "weight_decay": 0.0,
            },
        ]
        optimizer =  torch.optim.AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate)

        lr_scheduler = get_scheduler(
            name="linear",
            optimizer=optimizer,
            num_warmup_steps=self.hparams.warmup_steps,
            num_training_steps=self.total_steps,
        )

        scheduler = {"scheduler": lr_scheduler, "interval": "step", "frequency": 1}
        return [optimizer], [scheduler]

   

def main(args):
    dm = MSMARCOData(
        model_name=args.model,
        langs=args.langs,
        triplets_path='data/msmarco-hard-triplets.jsonl.gz',
        train_batch_size=args.batch_size,
        cross_lingual_chance=args.cross_lingual_chance,
        num_negs=args.num_negs
    )
    output_dir = f"output/{args.model.replace('/', '-')}-{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}"
    print("Output_dir:", output_dir)

    os.makedirs(output_dir, exist_ok=True)

    wandb_logger = WandbLogger(project="multilingual-cross-encoder", name=output_dir.split("/")[-1])

    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))

    
    # saves top-K checkpoints based on "val_loss" metric
    checkpoint_callback = ModelCheckpoint(
        every_n_train_steps=25000,
        save_top_k=5,
        monitor="global_train_step",
        mode="max",
        dirpath=output_dir,
        filename="ckpt-{global_train_step}",
    )


    model = ListRankLoss(model_name=args.model)

    trainer = Trainer(max_epochs=args.epochs, 
                      accelerator="gpu", 
                      devices=args.num_gpus, 
                      precision=args.precision, 
                      strategy=args.strategy,    
                      default_root_dir=output_dir,
                      callbacks=[checkpoint_callback],
                      logger=wandb_logger
                      )

    trainer.fit(model, datamodule=dm)

    #Save final HF model 
    final_path = os.path.join(output_dir, "final")
    dm.tokenizer.save_pretrained(final_path)
    model.model.save_pretrained(final_path)

  
def eval(args):
    import ir_datasets
    
 
    model = ListRankLoss.load_from_checkpoint(args.ckpt)
    hf_model = model.model.cuda()
    tokenizer = AutoTokenizer.from_pretrained(model.hparams.model_name)

    dev_qids = set()

    dev_queries = {}
    dev_rel_docs = {}
    needed_pids = set()
    needed_qids = set()

    corpus = {}
    retrieved_docs = {}

    dataset = ir_datasets.load("msmarco-passage/dev/small")
    for query in dataset.queries_iter():
        dev_qids.add(query.query_id)

    
    with open('data/qrels.dev.tsv') as fIn:
        for line in fIn:
            qid, _, pid, _ = line.strip().split('\t')

            if qid not in dev_qids:
                continue

            if qid not in dev_rel_docs:
                dev_rel_docs[qid] = set()
            dev_rel_docs[qid].add(pid)

            retrieved_docs[qid] = set()
            needed_qids.add(qid)
            needed_pids.add(pid)

    for query in dataset.queries_iter():
        qid = query.query_id
        if qid in needed_qids:
            dev_queries[qid] = query.text

    with open('data/top1000.dev', 'rt') as fIn:
        for line in fIn:
            qid, pid, query, passage = line.strip().split("\t")
            corpus[pid] = passage
            retrieved_docs[qid].add(pid)


    ## Run evaluator
    print("Queries: {}".format(len(dev_queries)))

    mrr_scores = []
    hf_model.eval()

    with torch.no_grad():
        for qid in tqdm.tqdm(dev_queries, total=len(dev_queries)):
            query = dev_queries[qid]
            top_pids = list(retrieved_docs[qid])
            cross_inp = [[query, corpus[pid]] for pid in top_pids]

            encoded = tokenizer(cross_inp, padding=True, truncation=True, return_tensors="pt").to('cuda')
            output = model(**encoded)
            bert_score = output.logits.detach().cpu().numpy()
            bert_score = np.squeeze(bert_score)
        
            argsort = np.argsort(-bert_score)

            rank_score = 0
            for rank, idx in enumerate(argsort[0:10]):
                pid = top_pids[idx]
                if pid in dev_rel_docs[qid]:
                    rank_score = 1/(rank+1)
                    break

            mrr_scores.append(rank_score)
        
            if len(mrr_scores) % 10 == 0:
                print("{} MRR@10: {:.2f}".format(len(mrr_scores), 100*np.mean(mrr_scores)))

    print("MRR@10: {:.2f}".format(np.mean(mrr_scores)*100))


if __name__ == '__main__':
    parser = ArgumentParser()
    parser.add_argument("--num_gpus", type=int, default=1)
    parser.add_argument("--batch_size", type=int, default=32)
    parser.add_argument("--epochs", type=int, default=10)
    parser.add_argument("--strategy", default=None)
    parser.add_argument("--model", default='microsoft/mdeberta-v3-base')
    parser.add_argument("--eval", action="store_true")
    parser.add_argument("--ckpt")
    parser.add_argument("--cross_lingual_chance", type=float, default=0.33)
    parser.add_argument("--precision", type=int, default=16)
    parser.add_argument("--num_negs", type=int, default=3)
    parser.add_argument("--langs", nargs="+", default=['english', 'chinese', 'french', 'german', 'indonesian', 'italian', 'portuguese', 'russian', 'spanish', 'arabic', 'dutch', 'hindi', 'japanese', 'vietnamese'])
    
    
    args = parser.parse_args()

    if args.eval:
        eval(args)
    else:
        main(args)
    

# Script was called via:
#python cross_mutlilingual.py --model nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large