train_dist_multi_qa.py

import argparse
import collections
import torch
import data_loader.data_loader as module_data
import model.loss as module_loss
import model.metric as module_metric
import model.model as module_arch
import utils.visualizer as module_vis
from utils.util import replace_nested_dict_item
from parse_config_dist_multi import ConfigParser
from trainer import Multi_ObjectQATrainer_dist
from sacred import Experiment
import transformers
import os

ex = Experiment('train')


@ex.main
def run():
    logger = config.get_logger('train')
    os.environ['TOKENIZERS_PARALLELISM'] = "false"
    os.environ['TRANSFORMERS_OFFLINE'] = "1"
    # TODO: improve Create identity (do nothing) visualiser?
    if config['visualizer']['type'] != "":
        visualizer = config.initialize(name='visualizer',
                                       module=module_vis,
                                       exp_name=config['name'],
                                       web_dir=config._web_log_dir)
    else:
        visualizer = None
    torch.cuda.set_device(args.local_rank)
    torch.distributed.init_process_group(backend='nccl',
                                         init_method='tcp://{}:{}'.format(
                                             args.master_address,
                                             args.master_port),
                                         rank=args.rank,
                                         world_size=args.world_size)
    device = torch.device(f'cuda:{args.local_rank}')
    print('world_size', args.world_size, flush=True)
    print('local_rank: ', args.local_rank, flush=True)
    # build tokenizer
    tokenizer = transformers.AutoTokenizer.from_pretrained(
        config['arch']['args']['text_params']['model'],
        TOKENIZERS_PARALLELISM=False)

    # setup data_loader instances
    data_loader, valid_data_loader = init_dataloaders(config, module_data)
    print('Train dataset: ', [x.n_samples for x in data_loader], ' samples')
    print('Val dataset: ', [x.n_samples for x in valid_data_loader],
          ' samples')
    # build model architecture, then print to console
    model = config.initialize('arch', module_arch)
    if args.local_rank == 0:
        logger.info(model)

    # get function handles of loss and metrics
    loss = config.initialize(name="loss", module=module_loss)
    metrics = [getattr(module_metric, met) for met in config['metrics']]
    trainable_params = filter(lambda p: p.requires_grad, model.parameters())
    for name, param in model.named_parameters():
        if param.requires_grad:
            print(name)
    optimizer = config.initialize('optimizer', transformers, trainable_params)
    lr_scheduler = None
    if 'lr_scheduler' in config._config:
        if hasattr(transformers, config._config['lr_scheduler']['type']):
            lr_scheduler = config.initialize('lr_scheduler', transformers,
                                             optimizer)
        else:
            print('lr scheduler not found')
    if config['trainer']['neptune']:
        writer = ex
    else:
        writer = None
    trainer = Multi_ObjectQATrainer_dist(
        args,
        model,
        loss,
        metrics,
        optimizer,
        config=config,
        data_loader=data_loader,
        valid_data_loader=valid_data_loader,
        lr_scheduler=lr_scheduler,
        visualizer=visualizer,
        writer=writer,
        tokenizer=tokenizer,
        max_samples_per_epoch=config['trainer']['max_samples_per_epoch'])
    trainer.train()


def init_dataloaders(config, module_data):
    """
    We need a way to change split from 'train' to 'val'.
    """
    if "type" in config["data_loader"] and "args" in config["data_loader"]:
        # then its a single dataloader
        data_loader = [config.initialize("data_loader", module_data)]
        config['data_loader']['args'] = replace_nested_dict_item(
            config['data_loader']['args'], 'split', 'test')
        if config['data_loader']['args']['dataset_name'].startswith(
                "Conceptual"):
            config['data_loader']['args'] = replace_nested_dict_item(
                config['data_loader']['args'], 'subsample', 0.01)
        # config['data_loader']['args'] = replace_nested_dict_item(config['data_loader']['args'], 'subsample', 0.01)
        valid_data_loader = [config.initialize("data_loader", module_data)]
    elif isinstance(config["data_loader"], list):
        data_loader = [
            config.initialize('data_loader', module_data, index=idx)
            for idx in range(len(config['data_loader']))
        ]
        new_cfg_li = []
        for dl_cfg in config['data_loader']:
            dl_cfg['args'] = replace_nested_dict_item(dl_cfg['args'], 'split',
                                                      'test')
            if dl_cfg['args']['dataset_name'].startswith("Conceptual"):
                dl_cfg['args'] = replace_nested_dict_item(
                    dl_cfg['args'], 'subsample', 0.1)
            new_cfg_li.append(dl_cfg)
        config._config['data_loader'] = new_cfg_li
        valid_data_loader = [
            config.initialize('data_loader', module_data, index=idx)
            for idx in range(len(config['data_loader']))
        ]
    else:
        raise ValueError("Check data_loader config, not correct format.")

    return data_loader, valid_data_loader


if __name__ == '__main__':
    args = argparse.ArgumentParser(description='PyTorch Template')
    args.add_argument('-c',
                      '--config',
                      default=None,
                      type=str,
                      help='config file path (default: None)')
    args.add_argument('-r',
                      '--resume',
                      default=None,
                      type=str,
                      help='path to latest checkpoint (default: None)')
    args.add_argument('-d',
                      '--device',
                      default=None,
                      type=str,
                      help='indices of GPUs to enable (default: all)')
    args.add_argument('-o',
                      '--observe',
                      action='store_true',
                      help='Whether to observe (neptune)')
    args.add_argument('-l',
                      '--launcher',
                      choices=['none', 'pytorch'],
                      default='none',
                      help='job launcher')
    args.add_argument('-k', '--local_rank', type=int, default=0)

    master_address = os.environ['MASTER_ADDR']
    master_port = int(os.environ['MASTER_PORT'])
    world_size = int(os.environ['WORLD_SIZE'])
    # world_size = int(torch.cuda.device_count())
    rank = int(os.environ['RANK'])
    args.local_rank = int(os.environ['LOCAL_RANK'])

    if torch.cuda.device_count() > 1:
        print("Let's use", torch.cuda.device_count(), "GPUs!")

    args.add_argument('-ma', '--master_address', default=master_address)
    args.add_argument('-mp', '--master_port', type=int, default=master_port)
    args.add_argument('-ws', '--world_size', type=int, default=world_size)
    args.add_argument('-rk', '--rank', type=int, default=rank)
    args.add_argument('-lr1', '--learning_rate1', type=float, default=2e-4)
    args.add_argument('-sc',
                      '--schedule',
                      type=int,
                      nargs='+',
                      default=[30, 40])
    args.add_argument('-le', '--linear_evaluation', default=False)

    CustomArgs = collections.namedtuple('CustomArgs', 'flags type target')
    options = [
        CustomArgs(['--lr', '--learning_rate'],
                   type=float,
                   target=('optimizer', 'args', 'lr')),
        CustomArgs(['--bs', '--batch_size'],
                   type=int,
                   target=('data_loader', 'args', 'batch_size')),
    ]
    config = ConfigParser(args, options)
    args = args.parse_args()
    ex.add_config(config._config)

    run()