DeepLearningExamples/PyTorch/Translation/GNMT/translate.py

#!/usr/bin/env python

# Copyright (c) 2017 Elad Hoffer
# Copyright (c) 2018-2020, NVIDIA CORPORATION. All rights reserved.
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import argparse
import itertools
import logging
import os
import sys
import warnings
from itertools import product

import dllogger
import numpy as np
import torch

import seq2seq.utils as utils
from seq2seq.data.dataset import RawTextDataset
from seq2seq.data.dataset import SyntheticDataset
from seq2seq.data.tokenizer import Tokenizer
from seq2seq.inference import tables
from seq2seq.inference.translator import Translator
from seq2seq.models.gnmt import GNMT


def parse_args():
    """
    Parse commandline arguments.
    """
    def exclusive_group(group, name, default, help):
        destname = name.replace('-', '_')
        subgroup = group.add_mutually_exclusive_group(required=False)
        subgroup.add_argument(f'--{name}', dest=f'{destname}',
                              action='store_true',
                              help=f'{help} (use \'--no-{name}\' to disable)')
        subgroup.add_argument(f'--no-{name}', dest=f'{destname}',
                              action='store_false', help=argparse.SUPPRESS)
        subgroup.set_defaults(**{destname: default})

    parser = argparse.ArgumentParser(
        description='GNMT Translate',
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)

    # dataset
    dataset = parser.add_argument_group('data setup')
    dataset.add_argument('-o', '--output', required=False,
                         help='full path to the output file \
                         if not specified, then the output will be printed')
    dataset.add_argument('-r', '--reference', default=None,
                         help='full path to the file with reference \
                         translations (for sacrebleu, raw text)')
    dataset.add_argument('-m', '--model', type=str, default=None,
                         help='full path to the model checkpoint file')

    dataset.add_argument('--synthetic', action='store_true',
                         help='use synthetic dataset')
    dataset.add_argument('--synthetic-batches', type=int, default=64,
                         help='number of synthetic batches to generate')
    dataset.add_argument('--synthetic-vocab', type=int, default=32320,
                         help='size of synthetic vocabulary')
    dataset.add_argument('--synthetic-len', type=int, default=50,
                         help='sequence length of synthetic samples')

    source = dataset.add_mutually_exclusive_group(required=False)
    source.add_argument('-i', '--input', required=False,
                        help='full path to the input file (raw text)')
    source.add_argument('-t', '--input-text', nargs='+', required=False,
                        help='raw input text')

    exclusive_group(group=dataset, name='sort', default=False,
                    help='sorts dataset by sequence length')

    # parameters
    params = parser.add_argument_group('inference setup')
    params.add_argument('--batch-size', nargs='+', default=[128], type=int,
                        help='batch size per GPU')
    params.add_argument('--beam-size', nargs='+', default=[5], type=int,
                        help='beam size')
    params.add_argument('--max-seq-len', default=80, type=int,
                        help='maximum generated sequence length')
    params.add_argument('--len-norm-factor', default=0.6, type=float,
                        help='length normalization factor')
    params.add_argument('--cov-penalty-factor', default=0.1, type=float,
                        help='coverage penalty factor')
    params.add_argument('--len-norm-const', default=5.0, type=float,
                        help='length normalization constant')
    # general setup
    general = parser.add_argument_group('general setup')
    general.add_argument('--math', nargs='+', default=['fp16'],
                         choices=['fp16', 'fp32', 'tf32'], help='precision')

    exclusive_group(group=general, name='env', default=False,
                    help='print info about execution env')
    exclusive_group(group=general, name='bleu', default=True,
                    help='compares with reference translation and computes \
                    BLEU')
    exclusive_group(group=general, name='cuda', default=True,
                    help='enables cuda')
    exclusive_group(group=general, name='cudnn', default=True,
                    help='enables cudnn')

    batch_first_parser = general.add_mutually_exclusive_group(required=False)
    batch_first_parser.add_argument('--batch-first', dest='batch_first',
                                    action='store_true',
                                    help='uses (batch, seq, feature) data \
                                    format for RNNs')
    batch_first_parser.add_argument('--seq-first', dest='batch_first',
                                    action='store_false',
                                    help='uses (seq, batch, feature) data \
                                    format for RNNs')
    batch_first_parser.set_defaults(batch_first=True)

    general.add_argument('--save-dir', default='gnmt',
                         help='path to directory with results, it will be \
                         automatically created if it does not exist')
    general.add_argument('--dllog-file', type=str, default='eval_log.json',
                         help='Name of the DLLogger output file')
    general.add_argument('--print-freq', '-p', default=1, type=int,
                         help='print log every PRINT_FREQ batches')

    # benchmarking
    benchmark = parser.add_argument_group('benchmark setup')
    benchmark.add_argument('--target-perf', default=None, type=float,
                           help='target inference performance (in tokens \
                           per second)')
    benchmark.add_argument('--target-bleu', default=None, type=float,
                           help='target accuracy')

    benchmark.add_argument('--repeat', nargs='+', default=[1], type=float,
                           help='loops over the dataset REPEAT times, flag \
                           accepts multiple arguments, one for each specified \
                           batch size')
    benchmark.add_argument('--warmup', default=0, type=int,
                           help='warmup iterations for performance counters')
    benchmark.add_argument('--percentiles', nargs='+', type=int,
                           default=(90, 95, 99),
                           help='Percentiles for confidence intervals for \
                           throughput/latency benchmarks')
    exclusive_group(group=benchmark, name='tables', default=False,
                    help='print accuracy, throughput and latency results in \
                    tables')

    # distributed
    distributed = parser.add_argument_group('distributed setup')
    distributed.add_argument('--local_rank',  type=int,
                             default=os.getenv('LOCAL_RANK', 0),
                             help='Used for multi-process training.')

    args = parser.parse_args()

    if args.input_text:
        args.bleu = False

    if args.bleu and args.reference is None:
        parser.error('--bleu requires --reference')

    if ('fp16' in args.math or 'tf32' in args.math) and not args.cuda:
        parser.error(f'--math {args.math} requires --cuda')

    if len(list(product(args.math, args.batch_size, args.beam_size))) > 1:
        args.target_bleu = None
        args.target_perf = None

    args.repeat = dict(itertools.zip_longest(args.batch_size,
                                             args.repeat,
                                             fillvalue=1))

    return args


def main():
    """
    Launches translation (inference).
    Inference is executed on a single GPU, implementation supports beam search
    with length normalization and coverage penalty.
    """
    args = parse_args()
    device = utils.set_device(args.cuda, args.local_rank)
    utils.init_distributed(args.cuda)
    args.rank = utils.get_rank()
    os.makedirs(args.save_dir, exist_ok=True)
    utils.setup_logging()

    dllog_file = os.path.join(args.save_dir, args.dllog_file)
    utils.setup_dllogger(enabled=True, filename=dllog_file)

    if args.env:
        utils.log_env_info()

    logging.info(f'Run arguments: {args}')
    dllogger.log(step='PARAMETER', data=vars(args))

    if not args.cuda and torch.cuda.is_available():
        warnings.warn('cuda is available but not enabled')
    if not args.cudnn:
        torch.backends.cudnn.enabled = False

    # load checkpoint and deserialize to CPU (to save GPU memory)
    if args.model:
        checkpoint = torch.load(args.model, map_location={'cuda:0': 'cpu'})

        # build GNMT model
        tokenizer = Tokenizer()
        tokenizer.set_state(checkpoint['tokenizer'])
        model_config = checkpoint['model_config']
        model_config['batch_first'] = args.batch_first
        model_config['vocab_size'] = tokenizer.vocab_size
        model = GNMT(**model_config)
        model.load_state_dict(checkpoint['state_dict'])
    elif args.synthetic:
        model = GNMT(args.synthetic_vocab, batch_first=args.batch_first)
        tokenizer = None
    else:
        raise RuntimeError('Specify model either with --synthetic or with --model flag')

    # construct the dataset
    if args.input:
        data = RawTextDataset(raw_datafile=args.input,
                              tokenizer=tokenizer,
                              sort=args.sort,
                              )
    elif args.input_text:
        data = RawTextDataset(raw_data=args.input_text,
                              tokenizer=tokenizer,
                              sort=args.sort,
                              )
    elif args.synthetic:
        data = SyntheticDataset(args.synthetic_vocab, args.synthetic_len, args.batch_size[0] * args.synthetic_batches)

    latency_table = tables.LatencyTable(args.percentiles)
    throughput_table = tables.ThroughputTable(args.percentiles)
    accuracy_table = tables.AccuracyTable('BLEU')

    dtype = {
        'fp32': torch.FloatTensor,
        'tf32': torch.FloatTensor,
        'fp16': torch.HalfTensor
    }

    for (math, batch_size, beam_size) in product(args.math, args.batch_size,
                                                 args.beam_size):
        logging.info(f'math: {math}, batch size: {batch_size}, '
                     f'beam size: {beam_size}')

        model.type(dtype[math])
        model = model.to(device)
        model.eval()

        # build the data loader
        loader = data.get_loader(
            batch_size=batch_size,
            batch_first=args.batch_first,
            pad=True,
            repeat=args.repeat[batch_size],
            num_workers=0,
            )

        # build the translator object
        translator = Translator(
            model=model,
            tokenizer=tokenizer,
            loader=loader,
            beam_size=beam_size,
            max_seq_len=args.max_seq_len,
            len_norm_factor=args.len_norm_factor,
            len_norm_const=args.len_norm_const,
            cov_penalty_factor=args.cov_penalty_factor,
            print_freq=args.print_freq,
            )

        # execute the inference
        output, stats = translator.run(
            calc_bleu=args.bleu,
            eval_path=args.output,
            summary=True,
            warmup=args.warmup,
            reference_path=args.reference,
            )

        # print translated outputs
        if not args.synthetic and (not args.output and args.rank == 0):
            logging.info(f'Translated output:')
            for out in output:
                print(out)

        key = (batch_size, beam_size)
        latency_table.add(key, {math: stats['runtimes']})
        throughput_table.add(key, {math: stats['throughputs']})
        accuracy_table.add(key, {math: stats['bleu']})

    if args.tables:
        accuracy_table.write('Inference accuracy', args.math)

        if 'fp16' in args.math and 'fp32' in args.math:
            relative = 'fp32'
        elif 'fp16' in args.math and 'tf32' in args.math:
            relative = 'tf32'
        else:
            relative = None

        if 'fp32' in args.math:
            throughput_table.write('Inference throughput', 'fp32')
        if 'tf32' in args.math:
            throughput_table.write('Inference throughput', 'tf32')
        if 'fp16' in args.math:
            throughput_table.write('Inference throughput', 'fp16',
                                   relative=relative)

        if 'fp32' in args.math:
            latency_table.write('Inference latency', 'fp32')
        if 'tf32' in args.math:
            latency_table.write('Inference latency', 'tf32')
        if 'fp16' in args.math:
            latency_table.write('Inference latency', 'fp16',
                                relative=relative, reverse_speedup=True)

    avg_throughput = np.array(stats['throughputs']).mean()
    avg_latency = np.array(stats['runtimes']).mean()
    summary = {
        'eval_throughput': avg_throughput,
        'eval_bleu': stats['bleu'],
        'eval_avg_latency': avg_latency,
        }
    for p in args.percentiles:
        summary[f'eval_{p}%_latency'] = 1000 * np.percentile(stats['runtimes'], p)

    dllogger.log(step=tuple(), data=summary)

    passed = utils.benchmark(stats['bleu'], args.target_bleu,
                             stats['tokens_per_sec'], args.target_perf)
    return passed


if __name__ == '__main__':
    passed = main()
    if not passed:
        sys.exit(1)