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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import time
import sys
import torch
import logging
import json
import numpy as np
import random
import pickle
import torch.distributed as dist
from torch.utils.data import DataLoader, RandomSampler
from src.options import Options
from src import data, beir_utils, slurm, dist_utils, utils
from src import moco, inbatch
logger = logging.getLogger(__name__)
def train(opt, model, optimizer, scheduler, step):
run_stats = utils.WeightedAvgStats()
tb_logger = utils.init_tb_logger(opt.output_dir)
logger.info("Data loading")
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
tokenizer = model.module.tokenizer
else:
tokenizer = model.tokenizer
collator = data.Collator(opt=opt)
train_dataset = data.load_data(opt, tokenizer)
logger.warning(f"Data loading finished for rank {dist_utils.get_rank()}")
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(
train_dataset,
sampler=train_sampler,
batch_size=opt.per_gpu_batch_size,
drop_last=True,
num_workers=opt.num_workers,
collate_fn=collator,
)
epoch = 1
model.train()
while step < opt.total_steps:
train_dataset.generate_offset()
logger.info(f"Start epoch {epoch}")
for i, batch in enumerate(train_dataloader):
step += 1
batch = {key: value.cuda() if isinstance(value, torch.Tensor) else value for key, value in batch.items()}
train_loss, iter_stats = model(**batch, stats_prefix="train")
train_loss.backward()
optimizer.step()
scheduler.step()
model.zero_grad()
run_stats.update(iter_stats)
if step % opt.log_freq == 0:
log = f"{step} / {opt.total_steps}"
for k, v in sorted(run_stats.average_stats.items()):
log += f" | {k}: {v:.3f}"
if tb_logger:
tb_logger.add_scalar(k, v, step)
log += f" | lr: {scheduler.get_last_lr()[0]:0.3g}"
log += f" | Memory: {torch.cuda.max_memory_allocated()//1e9} GiB"
logger.info(log)
run_stats.reset()
if step % opt.eval_freq == 0:
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
encoder = model.module.get_encoder()
else:
encoder = model.get_encoder()
eval_model(
opt, query_encoder=encoder, doc_encoder=encoder, tokenizer=tokenizer, tb_logger=tb_logger, step=step
)
if dist_utils.is_main():
utils.save(model, optimizer, scheduler, step, opt, opt.output_dir, f"lastlog")
model.train()
if dist_utils.is_main() and step % opt.save_freq == 0:
utils.save(model, optimizer, scheduler, step, opt, opt.output_dir, f"step-{step}")
if step > opt.total_steps:
break
epoch += 1
def eval_model(opt, query_encoder, doc_encoder, tokenizer, tb_logger, step):
for datasetname in opt.eval_datasets:
metrics = beir_utils.evaluate_model(
query_encoder,
doc_encoder,
tokenizer,
dataset=datasetname,
batch_size=opt.per_gpu_eval_batch_size,
norm_doc=opt.norm_doc,
norm_query=opt.norm_query,
beir_dir=opt.eval_datasets_dir,
score_function=opt.score_function,
lower_case=opt.lower_case,
normalize_text=opt.eval_normalize_text,
)
message = []
if dist_utils.is_main():
for metric in ["NDCG@10", "Recall@10", "Recall@100"]:
message.append(f"{datasetname}/{metric}: {metrics[metric]:.2f}")
if tb_logger is not None:
tb_logger.add_scalar(f"{datasetname}/{metric}", metrics[metric], step)
logger.info(" | ".join(message))
if __name__ == "__main__":
logger.info("Start")
options = Options()
opt = options.parse()
torch.manual_seed(opt.seed)
slurm.init_distributed_mode(opt)
slurm.init_signal_handler()
directory_exists = os.path.isdir(opt.output_dir)
if dist.is_initialized():
dist.barrier()
os.makedirs(opt.output_dir, exist_ok=True)
if not directory_exists and dist_utils.is_main():
options.print_options(opt)
if dist.is_initialized():
dist.barrier()
utils.init_logger(opt)
os.environ["TOKENIZERS_PARALLELISM"] = "false"
if opt.contrastive_mode == "moco":
model_class = moco.MoCo
elif opt.contrastive_mode == "inbatch":
model_class = inbatch.InBatch
else:
raise ValueError(f"contrastive mode: {opt.contrastive_mode} not recognised")
if not directory_exists and opt.model_path == "none":
model = model_class(opt)
model = model.cuda()
optimizer, scheduler = utils.set_optim(opt, model)
step = 0
elif directory_exists:
model_path = os.path.join(opt.output_dir, "checkpoint", "latest")
model, optimizer, scheduler, opt_checkpoint, step = utils.load(
model_class,
model_path,
opt,
reset_params=False,
)
logger.info(f"Model loaded from {opt.output_dir}")
else:
model, optimizer, scheduler, opt_checkpoint, step = utils.load(
model_class,
opt.model_path,
opt,
reset_params=False if opt.continue_training else True,
)
if not opt.continue_training:
step = 0
logger.info(f"Model loaded from {opt.model_path}")
logger.info(utils.get_parameters(model))
if dist.is_initialized():
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[opt.local_rank],
output_device=opt.local_rank,
find_unused_parameters=False,
)
dist.barrier()
logger.info("Start training")
train(opt, model, optimizer, scheduler, step)
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