DeepLearningExamples/PyTorch/Translation/Transformer/train.py

#!/usr/bin/env python3 -u
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
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# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
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#-------------------------------------------------------------------------
#
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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import collections
import os
import math
import time
import ctypes

from copy import deepcopy

import torch
import sacrebleu
import dllogger as DLLogger

from fairseq import data, distributed_utils, options, utils, tokenizer
from fairseq.ddp_trainer import DDPTrainer
from fairseq.meters import StopwatchMeter
from fairseq.sequence_generator import SequenceGenerator
from fairseq.data import data_utils, load_dataset_splits
from fairseq.models import build_model
from fairseq.log_helper import setup_logger, reset_perf_meters

def main(args):

    print(args)
    setup_logger(args)

    if not torch.cuda.is_available():
        raise NotImplementedError('Training on CPU is not supported')
    torch.cuda.set_device(args.device_id)
    if args.distributed_world_size > 1:
        assert torch.distributed.is_initialized()
        torch.distributed.broadcast(torch.tensor([1], device="cuda"), 0)
        torch.cuda.synchronize()
    pValue = ctypes.cast((ctypes.c_int * 1)(), ctypes.POINTER(ctypes.c_int))
    ctypes.CDLL('libcudart.so').cudaDeviceSetLimit(ctypes.c_int(0x05), ctypes.c_int(128))
    ctypes.CDLL('libcudart.so').cudaDeviceGetLimit(pValue, ctypes.c_int(0x05))
    torch.manual_seed(args.seed)

    src_dict, tgt_dict = data_utils.load_dictionaries(args)
    add_extra_items_to_checkpoint({'src_dict': src_dict, 'tgt_dict': tgt_dict})
    datasets = load_dataset_splits(args, ['train', 'valid', 'test'], src_dict, tgt_dict)

    model = build_model(args)
    print('| num. model params: {}'.format(sum(p.numel() for p in model.parameters())))

    # Build trainer
    if torch.cuda.get_device_capability(0)[0] >= 7 and not args.amp:
        print('| NOTICE: your device may support faster training with --amp')
    trainer = DDPTrainer(args, model)
    print('| model {}, criterion {}'.format(args.arch, trainer.criterion.__class__.__name__))
    print('| training on {} GPUs'.format(args.distributed_world_size))
    print('| max tokens per GPU = {} and max sentences per GPU = {}'.format(
        args.max_tokens,
        args.max_sentences,
    ))

    epoch_itr = data.EpochBatchIterator(
        dataset=datasets[args.train_subset],
        max_tokens=args.max_tokens,
        max_sentences=args.max_sentences_valid,
        max_positions=args.max_positions,
        required_batch_size_multiple=8,
        seed=args.seed,
        num_shards=args.distributed_world_size,
        shard_id=args.distributed_rank,
    )
    # Load the latest checkpoint if one is available
    load_checkpoint(args, trainer, epoch_itr)

    # Send a dummy batch to warm the caching allocator
    dummy_batch = data_utils.get_dummy_batch(args.max_tokens, src_dict, tgt_dict)
    trainer.dummy_train_step(dummy_batch)

    # Sanity check
    if args.do_sanity_check:
        print('Performing sanity check...')
        sanity_score = score(args, trainer, datasets['test'], src_dict, tgt_dict, 'test.raw.de')
        DLLogger.log(step='SANITY_CHECK', data={'sanity_check_score': sanity_score}, verbosity=1)

    # Train until the learning rate gets too small or model reaches target score
    max_epoch = args.max_epoch or math.inf
    max_update = args.max_update or math.inf
    tgt_bleu = args.target_bleu or math.inf
    current_bleu = 0.0
    best_bleu = -1.0
    lr = trainer.get_lr()
    train_meter = StopwatchMeter()
    train_meter.start()
    valid_losses = [None]
    valid_subsets = args.valid_subset.split(',')
    run_summary = {'loss': float('inf'),
                   'val_loss': float('inf'),
                   'speed': 0,
                   'accuracy': 0}

    while lr >= args.min_lr and epoch_itr.epoch < max_epoch and trainer.get_num_updates() < max_update and current_bleu < tgt_bleu:
        DLLogger.log(step=trainer.get_num_updates()+1, data={'epoch': epoch_itr.epoch}, verbosity=0)
        # train for one epoch
        train(args, trainer, epoch_itr)
        DLLogger.log(step=trainer.get_num_updates(), data={'walltime': train_meter.sum}, verbosity=1)
        DLLogger.log(step=trainer.get_num_updates(),
                     data={'avg_epoch_loss': trainer.avg_loss_meter.avg}, verbosity=1)

        if epoch_itr.epoch % args.validate_interval == 0:
            valid_losses = validate(args, trainer, datasets, valid_subsets)
            valid_bleu = score(args, trainer, datasets[valid_subsets[0]], src_dict, tgt_dict, 'valid.raw.de')
            DLLogger.log(step=trainer.get_num_updates(),
                         data={'val_loss': valid_losses[0], 'val_bleu': valid_bleu}, verbosity=1)

        # Eval BLEU score
        if args.online_eval or (tgt_bleu is not math.inf):
            current_bleu = score(args, trainer, datasets[args.gen_subset], src_dict, tgt_dict, 'test.raw.de')
            DLLogger.log(step=trainer.get_num_updates(), data={'test_bleu': current_bleu}, verbosity=1)
            best_bleu = max(best_bleu, current_bleu)

        run_summary['val_loss'] = min(run_summary['val_loss'], valid_losses[0])
        run_summary['accuracy'] = best_bleu if best_bleu >= 0 else valid_bleu
        run_summary['loss'] = valid_losses[0]
        run_summary['speed'] = trainer.throughput_meter.u_avg

        # Only use first validation loss to update the learning rate
        lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0])

        save_checkpoint(args, trainer, epoch_itr, valid_losses[0])

    train_meter.stop()
    run_summary['walltime'] = train_meter.sum
    DLLogger.log(step=(), data=run_summary, verbosity=0)
    print('| done training in {:.1f} seconds'.format(train_meter.sum))

def train(args, trainer, epoch_itr):
    """Train the model for one epoch."""

    # Initialize data iterator
    itr = epoch_itr.next_epoch_itr()

    # update parameters every N batches
    if epoch_itr.epoch <= len(args.update_freq):
        update_freq = args.update_freq[epoch_itr.epoch - 1]
    else:
        update_freq = args.update_freq[-1]

    max_update = args.max_update or math.inf
    num_batches = len(epoch_itr)
    begin = time.time()

    # reset meters
    DLLogger.flush()
    trainer.get_throughput_meter().reset()

    for i, sample in enumerate(itr):
        if i < num_batches - 1 and (i + 1) % update_freq > 0:
            # buffer updates according to --update-freq
            trainer.train_step(sample, update_params=False, last_step=(i == len(itr)-1))
            continue
        else:
            trainer.train_step(sample, update_params=True, last_step=(i == len(itr)-1))

        # ignore the first mini-batch in words-per-second calculation
        if i == 0:
            trainer.get_throughput_meter().reset()
            reset_perf_meters()

        if (i+1) % args.log_interval == 0:
            DLLogger.flush()

        if trainer.get_num_updates() >= max_update:
            break

    print('Epoch time:', time.time() - begin)

    # Print epoch stats and reset training meters
    DLLogger.log(step=trainer.get_num_updates(),
                 data={'speed': trainer.get_throughput_meter().avg}, verbosity=0)
    DLLogger.flush()

def validate(args, trainer, datasets, subsets):
    """Evaluate the model on the validation set(s) and return the losses."""

    valid_losses = []
    for subset in subsets:

        if len(subsets) > 1:
            print('Validating on \'{}\' subset'.format(subset))

        # Initialize data iterator
        itr = data.EpochBatchIterator(
            dataset=datasets[subset],
            max_tokens=args.max_tokens,
            max_sentences=args.max_sentences_valid,
            max_positions=args.max_positions,
            required_batch_size_multiple=8,
            seed=args.seed,
            num_shards=args.distributed_world_size,
            shard_id=args.distributed_rank,
        ).next_epoch_itr(shuffle=False)

        # reset validation loss meters
        DLLogger.flush()

        subset_losses = []
        for sample in itr:
            loss = trainer.valid_step(sample)
            subset_losses.append(loss)
        subset_loss = sum(subset_losses)/len(subset_losses)

        DLLogger.flush()

        valid_losses.append(subset_loss)
        print(f'Validation loss on subset {subset}: {subset_loss}')

    return valid_losses

def score(args, trainer, dataset, src_dict, tgt_dict, ref_file):

    begin = time.time()

    src_dict = deepcopy(src_dict)  # This is necessary, generation of translations
    tgt_dict = deepcopy(tgt_dict)  # alters target dictionary messing up with the rest of training

    model = trainer.get_model()

    # Initialize data iterator
    itr = data.EpochBatchIterator(
        dataset=dataset,
        max_tokens=None,
        max_sentences=max(8, min(math.ceil(1024/args.distributed_world_size), 128)),
        max_positions=args.max_positions,
        required_batch_size_multiple=8,
        num_shards=args.distributed_world_size,
        shard_id=args.distributed_rank,
        ).next_epoch_itr(shuffle=False)

    # Initialize generator
    gen_timer = StopwatchMeter()
    translator = SequenceGenerator(
	[model],
        tgt_dict.get_metadata(),
        maxlen=args.max_target_positions - 1,  # do not include EOS token
        beam_size=args.beam,
	stop_early=(not args.no_early_stop), normalize_scores=(not args.unnormalized),
	len_penalty=args.lenpen, unk_penalty=args.unkpen,
	sampling=args.sampling, sampling_topk=args.sampling_topk, minlen=args.min_len,
        use_amp=args.amp,
        )
    # Generate and compute BLEU
    predictions = []
    translations = translator.generate_batched_itr(
            itr, maxlen_a=args.max_len_a, maxlen_b=args.max_len_b,
            cuda=True, timer=gen_timer, prefix_size=args.prefix_size,
            )

    for sample_id, src_tokens, _, hypos in translations:
        # Process input and grount truth
        src_str = src_dict.string(src_tokens, args.remove_bpe)

        # Process top predictions
        for i, hypo in enumerate(hypos[:min(len(hypos), args.nbest)]):
            _, hypo_str, _ = utils.post_process_prediction(
                hypo_tokens=hypo['tokens'].int().cpu(),
                src_str=src_str,
                alignment=hypo['alignment'].int().cpu() if hypo['alignment'] is not None else None,
                align_dict=None,
                tgt_dict=tgt_dict,
                remove_bpe=args.remove_bpe
                )

            # Score only the top hypothesis
            if i == 0:
                hypo_str = tokenizer.Tokenizer.detokenize(hypo_str, 'de')
                predictions.append('{}\t{}'.format(sample_id, hypo_str))

    if args.distributed_world_size > 1:
        predictions = _all_gather_predictions(predictions)

    with open(os.path.join(args.data, ref_file), 'r') as reference:
        refs = [reference.readlines()]
    # reducing indexed predictions as strings is more memory efficient than reducing tuples
    predictions = [tuple(item.split('\t')) for item in predictions]
    predictions = [(int(item[0]), item[1]) for item in predictions]
    predictions.sort(key=lambda tup: tup[0])
    predictions = [hypo[1] + ('\n' if hypo[1][-1] != '\n' else '') for hypo in predictions]
    sacrebleu_score = sacrebleu.corpus_bleu(predictions, refs, lowercase=not args.test_cased_bleu).score

    if args.save_predictions:
        os.makedirs(os.path.join(args.save_dir, 'predictions'), exist_ok=True)
        fname = ref_file + '.pred.update_{}'.format(trainer.get_num_updates())
        save_path = os.path.join(args.save_dir, 'predictions', fname)
        with open(save_path, 'w') as f:
            f.write(''.join(predictions))

    DLLogger.log(step=trainer.get_num_updates(),
                 data={'inference tokens/s': float(args.distributed_world_size) / gen_timer.avg},
                 verbosity=0)
    DLLogger.flush()
    if gen_timer.sum != 0:
        print('| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'.format(
            len(predictions),
            gen_timer.n,
            gen_timer.sum,
            len(predictions) / gen_timer.sum,
            float(args.distributed_world_size)/gen_timer.avg
            ))

    print('| Eval completed in: {:.2f}s | {}CASED BLEU {:.2f}'.format(
        time.time()-begin,
        '' if args.test_cased_bleu else 'UN',
        sacrebleu_score
        ))

    return sacrebleu_score

def _all_gather_predictions(predictions):
    ready = False
    all_ready = False
    reduced_predictions = []
    max_size = 65000
    while not all_ready:
        lst_len = len(predictions)
        size = 2000     # some extra space for python stuff
        n = 0
        while n < lst_len:
            str_len = len(predictions[n].encode('utf8')) + 8  # per string pickle overhead
            if size + str_len >= max_size:
                break
            size += str_len
            n += 1
        chunk = predictions[:n]
        predictions = predictions[n:]
        if not predictions:
            ready = True
        chunk = (ready, chunk)
        torch.cuda.synchronize()
        gathered = distributed_utils.all_gather_list(chunk, max_size=65000)
        torch.cuda.synchronize()
        reduced_predictions += [t[1] for t in gathered]
        all_ready = all([t[0] for t in gathered])

    reduced_predictions = [item for sublist in reduced_predictions for item in sublist]

    return reduced_predictions


def save_checkpoint(args, trainer, epoch_itr, val_loss):
    if epoch_itr.epoch % args.save_interval != 0:
        return
    if args.no_save or not distributed_utils.is_master(args):
        return
    epoch = epoch_itr.epoch
    end_of_epoch = epoch_itr.end_of_epoch()

    checkpoint_conds = collections.OrderedDict()
    checkpoint_conds['checkpoint{}.pt'.format(epoch)] = end_of_epoch and not args.no_epoch_checkpoints
    checkpoint_conds['checkpoint_best.pt'] = (
            val_loss is not None and
            (not hasattr(save_checkpoint, 'best') or val_loss < save_checkpoint.best)
    )
    checkpoint_conds['checkpoint_last.pt'] = True  # keep this last so that it's a symlink

    prev_best = getattr(save_checkpoint, 'best', val_loss)
    if val_loss is not None:
        save_checkpoint.best = min(val_loss, prev_best)
    extra_state = {
        'best': save_checkpoint.best,
        'train_iterator': epoch_itr.state_dict(),
        'val_loss': val_loss,
    }
    extra_state.update(save_checkpoint.extra_items)

    checkpoints = [os.path.join(args.save_dir, 'checkpoints', fn)
                   for fn, cond in checkpoint_conds.items() if cond]
    if checkpoints:
        for cp in checkpoints:
            trainer.save_checkpoint(cp, extra_state)


def add_extra_items_to_checkpoint(items):
    if not hasattr(save_checkpoint, 'extra_items'):
        save_checkpoint.extra_items = {}
    save_checkpoint.extra_items.update(items)

def load_checkpoint(args, trainer, epoch_itr):
    """Load a checkpoint and replay dataloader to match."""
    os.makedirs(os.path.join(args.save_dir, 'checkpoints'), exist_ok=True)
    checkpoint_path = os.path.join(args.save_dir, 'checkpoints', args.restore_file)
    if os.path.isfile(checkpoint_path):
        extra_state = trainer.load_checkpoint(checkpoint_path)
        if extra_state is not None:
            # replay train iterator to match checkpoint
            epoch_itr.load_state_dict(extra_state['train_iterator'])

            print('| loaded checkpoint {} (epoch {} @ {} updates)'.format(
                checkpoint_path, epoch_itr.epoch, trainer.get_num_updates()))

            trainer.lr_step(epoch_itr.epoch)
            trainer.lr_step_update(trainer.get_num_updates())
            if 'best' in extra_state:
                save_checkpoint.best = extra_state['best']


if __name__ == '__main__':
    parser = options.get_training_parser()
    ARGS = options.parse_args_and_arch(parser)

    if ARGS.distributed_world_size > 1:
        distributed_utils.distributed_init(ARGS)

    main(ARGS)