362 lines
12 KiB
Python
362 lines
12 KiB
Python
from codecs import EncodedFile
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from datetime import datetime
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from typing import Optional
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import datasets
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import torch
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from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything
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from torch.utils.data import DataLoader
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from transformers import (
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AutoConfig,
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AutoModelForSequenceClassification,
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AutoTokenizer,
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get_linear_schedule_with_warmup,
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get_scheduler,
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)
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import torch
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import sys
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import os
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from argparse import ArgumentParser
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from datasets import load_dataset
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import tqdm
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import json
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import gzip
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import random
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from pytorch_lightning.callbacks import ModelCheckpoint
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import numpy as np
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from shutil import copyfile
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from pytorch_lightning.loggers import WandbLogger
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import transformers
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class MSMARCOData(LightningDataModule):
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def __init__(
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self,
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model_name: str,
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triplets_path: str,
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langs,
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max_seq_length: int = 250,
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train_batch_size: int = 32,
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eval_batch_size: int = 32,
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num_negs: int = 3,
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cross_lingual_chance: float = 0.0,
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**kwargs,
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):
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super().__init__()
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self.model_name = model_name
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self.triplets_path = triplets_path
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self.max_seq_length = max_seq_length
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self.train_batch_size = train_batch_size
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self.eval_batch_size = eval_batch_size
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self.langs = langs
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self.num_negs = num_negs
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
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self.cross_lingual_chance = cross_lingual_chance #Probability for cross-lingual batches
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#def setup(self, stage: str):
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print(f"!!!!!!!!!!!!!!!!!! SETUP {os.getpid()} !!!!!!!!!!!!!!!")
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#Get the queries
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self.queries = {lang: {} for lang in self.langs}
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for lang in self.langs:
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for row in tqdm.tqdm(load_dataset('unicamp-dl/mmarco', f'queries-{lang}')['train'], desc=lang):
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self.queries[lang][row['id']] = row['text']
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#Get the passages
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self.collections = {lang: load_dataset('unicamp-dl/mmarco', f'collection-{lang}')['collection'] for lang in self.langs}
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#Get the triplets
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with gzip.open(self.triplets_path, 'rt') as fIn:
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self.triplets = [json.loads(line) for line in tqdm.tqdm(fIn, desc="triplets", total=502938)]
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"""
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self.triplets = []
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for line in tqdm.tqdm(fIn):
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self.triplets.append(json.loads(line))
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if len(self.triplets) >= 1000:
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break
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"""
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def collate_fn(self, batch):
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cross_lingual_batch = random.random() < self.cross_lingual_chance
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#Create data for list-rank-loss
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query_doc_pairs = [[] for _ in range(1+self.num_negs)]
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for row in batch:
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qid = row['qid']
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pos_id = random.choice(row['pos'])
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query_lang = random.choice(self.langs)
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query_text = self.queries[query_lang][qid]
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doc_lang = random.choice(self.langs) if cross_lingual_batch else query_lang
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query_doc_pairs[0].append((query_text, self.collections[doc_lang][pos_id]['text']))
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dense_bm25_neg = list(set(row['dense_neg'] + row['bm25_neg']))
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neg_ids = random.sample(dense_bm25_neg, self.num_negs)
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for num_neg, neg_id in enumerate(neg_ids):
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doc_lang = random.choice(self.langs) if cross_lingual_batch else query_lang
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query_doc_pairs[1+num_neg].append((query_text, self.collections[doc_lang][neg_id]['text']))
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#Now tokenize the data
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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]
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return features
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def train_dataloader(self):
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return DataLoader(self.triplets, shuffle=True, batch_size=self.train_batch_size, num_workers=1, pin_memory=True, collate_fn=self.collate_fn)
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class ListRankLoss(LightningModule):
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def __init__(
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self,
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model_name: str,
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learning_rate: float = 2e-5,
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warmup_steps: int = 1000,
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weight_decay: float = 0.01,
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train_batch_size: int = 32,
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eval_batch_size: int = 32,
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**kwargs,
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):
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super().__init__()
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self.save_hyperparameters()
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print(self.hparams)
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self.config = AutoConfig.from_pretrained(model_name, num_labels=1)
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self.model = AutoModelForSequenceClassification.from_pretrained(model_name, config=self.config)
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self.loss_fct = torch.nn.CrossEntropyLoss()
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self.global_train_step = 0
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def forward(self, **inputs):
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return self.model(**inputs)
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def training_step(self, batch, batch_idx):
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pred_scores = []
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scores = torch.tensor([0] * len(batch[0]['input_ids']), device=self.model.device)
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for feature in batch:
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pred_scores.append(self(**feature).logits.squeeze())
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pred_scores = torch.stack(pred_scores, 1)
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loss_value = self.loss_fct(pred_scores, scores)
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self.global_train_step += 1
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self.log('global_train_step', self.global_train_step)
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self.log("train/loss", loss_value)
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return loss_value
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def setup(self, stage=None) -> None:
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if stage != "fit":
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return
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# Get dataloader by calling it - train_dataloader() is called after setup() by default
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train_loader = self.trainer.datamodule.train_dataloader()
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# Calculate total steps
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tb_size = self.hparams.train_batch_size * max(1, self.trainer.gpus)
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ab_size = self.trainer.accumulate_grad_batches
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self.total_steps = (len(train_loader) // ab_size) * self.trainer.max_epochs
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print(f"{tb_size=}")
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print(f"{ab_size=}")
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print(f"{len(train_loader)=}")
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print(f"{self.total_steps=}")
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def configure_optimizers(self):
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"""Prepare optimizer and schedule (linear warmup and decay)"""
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model = self.model
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no_decay = ["bias", "LayerNorm.weight"]
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optimizer_grouped_parameters = [
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{
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"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
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"weight_decay": self.hparams.weight_decay,
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},
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{
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"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
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"weight_decay": 0.0,
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},
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]
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optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate)
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lr_scheduler = get_scheduler(
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name="linear",
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optimizer=optimizer,
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num_warmup_steps=self.hparams.warmup_steps,
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num_training_steps=self.total_steps,
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)
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scheduler = {"scheduler": lr_scheduler, "interval": "step", "frequency": 1}
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return [optimizer], [scheduler]
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def main(args):
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dm = MSMARCOData(
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model_name=args.model,
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langs=args.langs,
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triplets_path='data/msmarco-hard-triplets.jsonl.gz',
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train_batch_size=args.batch_size,
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cross_lingual_chance=args.cross_lingual_chance,
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num_negs=args.num_negs
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)
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output_dir = f"output/{args.model.replace('/', '-')}-{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}"
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print("Output_dir:", output_dir)
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os.makedirs(output_dir, exist_ok=True)
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wandb_logger = WandbLogger(project="multilingual-cross-encoder", name=output_dir.split("/")[-1])
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train_script_path = os.path.join(output_dir, 'train_script.py')
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copyfile(__file__, train_script_path)
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with open(train_script_path, 'a') as fOut:
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fOut.write("\n\n# Script was called via:\n#python " + " ".join(sys.argv))
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# saves top-K checkpoints based on "val_loss" metric
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checkpoint_callback = ModelCheckpoint(
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every_n_train_steps=25000,
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save_top_k=5,
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monitor="global_train_step",
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mode="max",
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dirpath=output_dir,
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filename="ckpt-{global_train_step}",
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)
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model = ListRankLoss(model_name=args.model)
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trainer = Trainer(max_epochs=args.epochs,
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accelerator="gpu",
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devices=args.num_gpus,
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precision=args.precision,
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strategy=args.strategy,
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default_root_dir=output_dir,
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callbacks=[checkpoint_callback],
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logger=wandb_logger
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)
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trainer.fit(model, datamodule=dm)
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#Save final HF model
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final_path = os.path.join(output_dir, "final")
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dm.tokenizer.save_pretrained(final_path)
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model.model.save_pretrained(final_path)
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def eval(args):
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import ir_datasets
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model = ListRankLoss.load_from_checkpoint(args.ckpt)
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hf_model = model.model.cuda()
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tokenizer = AutoTokenizer.from_pretrained(model.hparams.model_name)
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dev_qids = set()
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dev_queries = {}
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dev_rel_docs = {}
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needed_pids = set()
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needed_qids = set()
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corpus = {}
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retrieved_docs = {}
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dataset = ir_datasets.load("msmarco-passage/dev/small")
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for query in dataset.queries_iter():
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dev_qids.add(query.query_id)
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with open('data/qrels.dev.tsv') as fIn:
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for line in fIn:
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qid, _, pid, _ = line.strip().split('\t')
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if qid not in dev_qids:
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continue
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if qid not in dev_rel_docs:
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dev_rel_docs[qid] = set()
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dev_rel_docs[qid].add(pid)
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retrieved_docs[qid] = set()
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needed_qids.add(qid)
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needed_pids.add(pid)
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for query in dataset.queries_iter():
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qid = query.query_id
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if qid in needed_qids:
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dev_queries[qid] = query.text
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with open('data/top1000.dev', 'rt') as fIn:
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for line in fIn:
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qid, pid, query, passage = line.strip().split("\t")
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corpus[pid] = passage
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retrieved_docs[qid].add(pid)
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## Run evaluator
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print("Queries: {}".format(len(dev_queries)))
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mrr_scores = []
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hf_model.eval()
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with torch.no_grad():
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for qid in tqdm.tqdm(dev_queries, total=len(dev_queries)):
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query = dev_queries[qid]
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top_pids = list(retrieved_docs[qid])
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cross_inp = [[query, corpus[pid]] for pid in top_pids]
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encoded = tokenizer(cross_inp, padding=True, truncation=True, return_tensors="pt").to('cuda')
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output = model(**encoded)
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bert_score = output.logits.detach().cpu().numpy()
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bert_score = np.squeeze(bert_score)
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argsort = np.argsort(-bert_score)
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rank_score = 0
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for rank, idx in enumerate(argsort[0:10]):
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pid = top_pids[idx]
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if pid in dev_rel_docs[qid]:
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rank_score = 1/(rank+1)
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break
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mrr_scores.append(rank_score)
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if len(mrr_scores) % 10 == 0:
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print("{} MRR@10: {:.2f}".format(len(mrr_scores), 100*np.mean(mrr_scores)))
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print("MRR@10: {:.2f}".format(np.mean(mrr_scores)*100))
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if __name__ == '__main__':
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parser = ArgumentParser()
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parser.add_argument("--num_gpus", type=int, default=1)
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parser.add_argument("--batch_size", type=int, default=32)
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parser.add_argument("--epochs", type=int, default=10)
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parser.add_argument("--strategy", default=None)
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parser.add_argument("--model", default='microsoft/mdeberta-v3-base')
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parser.add_argument("--eval", action="store_true")
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parser.add_argument("--ckpt")
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parser.add_argument("--cross_lingual_chance", type=float, default=0.33)
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parser.add_argument("--precision", type=int, default=16)
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parser.add_argument("--num_negs", type=int, default=3)
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parser.add_argument("--langs", nargs="+", default=['english', 'chinese', 'french', 'german', 'indonesian', 'italian', 'portuguese', 'russian', 'spanish', 'arabic', 'dutch', 'hindi', 'japanese', 'vietnamese'])
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args = parser.parse_args()
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if args.eval:
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eval(args)
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else:
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main(args)
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# Script was called via:
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#python cross_mutlilingual.py --model nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large |