mmarco-mMiniLMv2-L12-H384-v1 / train_script.py
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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