test_binaries.py 27.9 KB
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
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import contextlib
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from io import StringIO
import os
import random
import sys
import tempfile
import unittest

import torch

from fairseq import options

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import preprocess
import train
import generate
import interactive
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import eval_lm
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import validate
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class TestTranslation(unittest.TestCase):

    def test_fconv(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_fconv') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'fconv_iwslt_de_en')
                generate_main(data_dir)

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    def test_raw(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_fconv_raw') as data_dir:
                create_dummy_data(data_dir)
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                preprocess_translation_data(data_dir, ['--dataset-impl', 'raw'])
                train_translation_model(data_dir, 'fconv_iwslt_de_en', ['--dataset-impl', 'raw'])
                generate_main(data_dir, ['--dataset-impl', 'raw'])
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    def test_fp16(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_fp16') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'fconv_iwslt_de_en', ['--fp16'])
                generate_main(data_dir)

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    def test_memory_efficient_fp16(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_memory_efficient_fp16') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'fconv_iwslt_de_en', ['--memory-efficient-fp16'])
                generate_main(data_dir)

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    def test_update_freq(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_update_freq') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'fconv_iwslt_de_en', ['--update-freq', '3'])
                generate_main(data_dir)

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    def test_max_positions(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_max_positions') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                with self.assertRaises(Exception) as context:
                    train_translation_model(
                        data_dir, 'fconv_iwslt_de_en', ['--max-target-positions', '5'],
                    )
                self.assertTrue(
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                    'skip this example with --skip-invalid-size-inputs-valid-test' in str(context.exception)
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                )
                train_translation_model(
                    data_dir, 'fconv_iwslt_de_en',
                    ['--max-target-positions', '5', '--skip-invalid-size-inputs-valid-test'],
                )
                with self.assertRaises(Exception) as context:
                    generate_main(data_dir)
                generate_main(data_dir, ['--skip-invalid-size-inputs-valid-test'])

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    def test_generation(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_sampling') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'fconv_iwslt_de_en')
                generate_main(data_dir, [
                    '--sampling',
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                    '--temperature', '2',
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                    '--beam', '2',
                    '--nbest', '2',
                ])
                generate_main(data_dir, [
                    '--sampling',
                    '--sampling-topk', '3',
                    '--beam', '2',
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                    '--nbest', '2',
                ])
                generate_main(data_dir, [
                    '--sampling',
                    '--sampling-topp', '0.2',
                    '--beam', '2',
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                    '--nbest', '2',
                ])
                generate_main(data_dir, ['--prefix-size', '2'])

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    def test_lstm(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_lstm') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
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                train_translation_model(data_dir, 'lstm_wiseman_iwslt_de_en', [
                    '--encoder-layers', '2',
                    '--decoder-layers', '2',
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                    '--encoder-embed-dim', '8',
                    '--decoder-embed-dim', '8',
                    '--decoder-out-embed-dim', '8',
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                ])
                generate_main(data_dir)

    def test_lstm_bidirectional(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_lstm_bidirectional') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'lstm', [
                    '--encoder-layers', '2',
                    '--encoder-bidirectional',
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                    '--encoder-hidden-size', '16',
                    '--encoder-embed-dim', '8',
                    '--decoder-embed-dim', '8',
                    '--decoder-out-embed-dim', '8',
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                    '--decoder-layers', '2',
                ])
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                generate_main(data_dir)

    def test_transformer(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_transformer') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
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                train_translation_model(data_dir, 'transformer_iwslt_de_en', [
                    '--encoder-layers', '2',
                    '--decoder-layers', '2',
                    '--encoder-embed-dim', '8',
                    '--decoder-embed-dim', '8',
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                ], run_validation=True)
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                generate_main(data_dir)

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    def test_transformer_cross_self_attention(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_transformer_cross_self_attention') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'transformer_iwslt_de_en', [
                    '--encoder-layers', '2',
                    '--decoder-layers', '2',
                    '--encoder-embed-dim', '8',
                    '--decoder-embed-dim', '8',
                    '--decoder-embed-dim', '8',
                    '--no-cross-attention',
                    '--cross-self-attention',
                    '--layer-wise-attention',
                ], run_validation=True)
                generate_main(data_dir, extra_flags=[])

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    def test_lightconv(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_lightconv') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'lightconv_iwslt_de_en', [
                    '--encoder-conv-type', 'lightweight',
                    '--decoder-conv-type', 'lightweight',
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                    '--encoder-embed-dim', '8',
                    '--decoder-embed-dim', '8',
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                ])
                generate_main(data_dir)

    def test_dynamicconv(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_dynamicconv') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'lightconv_iwslt_de_en', [
                    '--encoder-conv-type', 'dynamic',
                    '--decoder-conv-type', 'dynamic',
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                    '--encoder-embed-dim', '8',
                    '--decoder-embed-dim', '8',
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                ])
                generate_main(data_dir)

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    def test_levenshtein_transformer(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_levenshtein_transformer') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'levenshtein_transformer', [
                    '--apply-bert-init', '--early-exit', '6,6,6',
                    '--criterion', 'nat_loss'
                ], task='translation_lev')
                generate_main(data_dir)

    def test_nonautoregressive_transformer(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_nonautoregressive_transformer') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'nonautoregressive_transformer', [
                    '--apply-bert-init', '--src-embedding-copy', '--criterion',
                    'nat_loss', '--noise', 'full_mask', '--pred-length-offset',
                    '--length-loss-factor', '0.1'
                ], task='translation_lev')
                generate_main(data_dir)

    def test_iterative_nonautoregressive_transformer(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_iterative_nonautoregressive_transformer') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'iterative_nonautoregressive_transformer', [
                    '--apply-bert-init', '--src-embedding-copy', '--criterion',
                    'nat_loss', '--noise', 'full_mask', '--stochastic-approx',
                    '--dae-ratio', '0.5', '--train-step', '3'
                ], task='translation_lev')
                generate_main(data_dir)

    def test_insertion_transformer(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_insertion_transformer') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'insertion_transformer', [
                    '--apply-bert-init', '--criterion', 'nat_loss', '--noise',
                    'random_mask'
                ], task='translation_lev')
                generate_main(data_dir)

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    def test_mixture_of_experts(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_moe') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                train_translation_model(data_dir, 'transformer_iwslt_de_en', [
                    '--task', 'translation_moe',
                    '--method', 'hMoElp',
                    '--mean-pool-gating-network',
                    '--num-experts', '3',
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                    '--encoder-layers', '2',
                    '--decoder-layers', '2',
                    '--encoder-embed-dim', '8',
                    '--decoder-embed-dim', '8',
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                ])
                generate_main(data_dir, [
                    '--task', 'translation_moe',
                    '--method', 'hMoElp',
                    '--mean-pool-gating-network',
                    '--num-experts', '3',
                    '--gen-expert', '0'
                ])

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    def test_alignment(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_alignment') as data_dir:
                create_dummy_data(data_dir, alignment=True)
                preprocess_translation_data(data_dir, ['--align-suffix', 'align'])
                train_translation_model(
                    data_dir,
                    'transformer_align',
                    [
                        '--encoder-layers', '2',
                        '--decoder-layers', '2',
                        '--encoder-embed-dim', '8',
                        '--decoder-embed-dim', '8',
                        '--load-alignments',
                        '--alignment-layer', '1',
                        '--criterion', 'label_smoothed_cross_entropy_with_alignment'
                    ],
                    run_validation=True,
                )
                generate_main(data_dir)

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class TestStories(unittest.TestCase):

    def test_fconv_self_att_wp(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_fconv_self_att_wp') as data_dir:
                create_dummy_data(data_dir)
                preprocess_translation_data(data_dir)
                config = [
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                    '--encoder-layers', '[(128, 3)] * 2',
                    '--decoder-layers', '[(128, 3)] * 2',
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                    '--decoder-attention', 'True',
                    '--encoder-attention', 'False',
                    '--gated-attention', 'True',
                    '--self-attention', 'True',
                    '--project-input', 'True',
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                    '--encoder-embed-dim', '8',
                    '--decoder-embed-dim', '8',
                    '--decoder-out-embed-dim', '8',
                    '--multihead-self-attention-nheads', '2'
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                ]
                train_translation_model(data_dir, 'fconv_self_att_wp', config)
                generate_main(data_dir)

                # fusion model
                os.rename(os.path.join(data_dir, 'checkpoint_last.pt'), os.path.join(data_dir, 'pretrained.pt'))
                config.extend([
                    '--pretrained', 'True',
                    '--pretrained-checkpoint', os.path.join(data_dir, 'pretrained.pt'),
                    '--save-dir', os.path.join(data_dir, 'fusion_model'),
                ])
                train_translation_model(data_dir, 'fconv_self_att_wp', config)


class TestLanguageModeling(unittest.TestCase):

    def test_fconv_lm(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_fconv_lm') as data_dir:
                create_dummy_data(data_dir)
                preprocess_lm_data(data_dir)
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                train_language_model(data_dir, 'fconv_lm', [
                    '--decoder-layers', '[(850, 3)] * 2 + [(1024,4)]',
                    '--decoder-embed-dim', '280',
                    '--optimizer', 'nag',
                    '--lr', '0.1',
                ])
                eval_lm_main(data_dir)

    def test_transformer_lm(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_transformer_lm') as data_dir:
                create_dummy_data(data_dir)
                preprocess_lm_data(data_dir)
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                train_language_model(
                    data_dir, 'transformer_lm', ['--add-bos-token'], run_validation=True,
                )
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                eval_lm_main(data_dir)


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class TestMaskedLanguageModel(unittest.TestCase):
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    def test_legacy_masked_lm(self):
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        with contextlib.redirect_stdout(StringIO()):
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            with tempfile.TemporaryDirectory("test_legacy_mlm") as data_dir:
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                create_dummy_data(data_dir)
                preprocess_lm_data(data_dir)
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                train_legacy_masked_language_model(data_dir, "masked_lm")
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    def _test_pretrained_masked_lm_for_translation(self, learned_pos_emb, encoder_only):
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        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory("test_mlm") as data_dir:
                create_dummy_data(data_dir)
                preprocess_lm_data(data_dir)
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                train_legacy_masked_language_model(
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                    data_dir,
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                    arch="masked_lm",
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                    extra_args=('--encoder-learned-pos',) if learned_pos_emb else ()
                )
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                with tempfile.TemporaryDirectory(
                    "test_mlm_translation"
                ) as translation_dir:
                    create_dummy_data(translation_dir)
                    preprocess_translation_data(
                        translation_dir, extra_flags=["--joined-dictionary"]
                    )
                    # Train transformer with data_dir/checkpoint_last.pt
                    train_translation_model(
                        translation_dir,
                        arch="transformer_from_pretrained_xlm",
                        extra_flags=[
                            "--decoder-layers",
                            "1",
                            "--decoder-embed-dim",
                            "32",
                            "--decoder-attention-heads",
                            "1",
                            "--decoder-ffn-embed-dim",
                            "32",
                            "--encoder-layers",
                            "1",
                            "--encoder-embed-dim",
                            "32",
                            "--encoder-attention-heads",
                            "1",
                            "--encoder-ffn-embed-dim",
                            "32",
                            "--pretrained-xlm-checkpoint",
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                            "{}/checkpoint_last.pt".format(data_dir),
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                            "--activation-fn",
                            "gelu",
                            "--max-source-positions",
                            "500",
                            "--max-target-positions",
                            "500",
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                        ] + (
                            ["--encoder-learned-pos", "--decoder-learned-pos"]
                            if learned_pos_emb else []
                        ) + (['--init-encoder-only'] if encoder_only else []),
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                        task="translation_from_pretrained_xlm",
                    )

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    def test_pretrained_masked_lm_for_translation_learned_pos_emb(self):
        self._test_pretrained_masked_lm_for_translation(True, False)

    def test_pretrained_masked_lm_for_translation_sinusoidal_pos_emb(self):
        self._test_pretrained_masked_lm_for_translation(False, False)

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    def test_pretrained_masked_lm_for_translation_encoder_only(self):
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        self._test_pretrained_masked_lm_for_translation(True, True)
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def train_legacy_masked_language_model(data_dir, arch, extra_args=()):
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    train_parser = options.get_training_parser()
    # TODO: langs should be in and out right?
    train_args = options.parse_args_and_arch(
        train_parser,
        [
            "--task",
            "cross_lingual_lm",
            data_dir,
            "--arch",
            arch,
            # Optimizer args
            "--optimizer",
            "adam",
            "--lr-scheduler",
            "reduce_lr_on_plateau",
            "--lr-shrink",
            "0.5",
            "--lr",
            "0.0001",
            "--min-lr",
            "1e-09",
            # dropout, attention args
            "--dropout",
            "0.1",
            "--attention-dropout",
            "0.1",
            # MLM args
            "--criterion",
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            "legacy_masked_lm_loss",
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            "--masked-lm-only",
            "--monolingual-langs",
            "in,out",
            "--num-segment",
            "5",
            # Transformer args: use a small transformer model for fast training
            "--encoder-layers",
            "1",
            "--encoder-embed-dim",
            "32",
            "--encoder-attention-heads",
            "1",
            "--encoder-ffn-embed-dim",
            "32",
            # Other training args
            "--max-tokens",
            "500",
            "--tokens-per-sample",
            "500",
            "--save-dir",
            data_dir,
            "--max-epoch",
            "1",
            "--no-progress-bar",
            "--distributed-world-size",
            "1",
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            "--dataset-impl",
            "raw",
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        ] + list(extra_args),
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    )
    train.main(train_args)


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class TestCommonOptions(unittest.TestCase):

    def test_optimizers(self):
        with contextlib.redirect_stdout(StringIO()):
            with tempfile.TemporaryDirectory('test_optimizers') as data_dir:
                # Use just a bit of data and tiny model to keep this test runtime reasonable
                create_dummy_data(data_dir, num_examples=10, maxlen=5)
                preprocess_translation_data(data_dir)
                optimizers = ['adafactor', 'adam', 'nag', 'adagrad', 'sgd', 'adadelta']
                last_checkpoint = os.path.join(data_dir, 'checkpoint_last.pt')
                for optimizer in optimizers:
                    if os.path.exists(last_checkpoint):
                        os.remove(last_checkpoint)
                    train_translation_model(data_dir, 'lstm', [
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                        '--required-batch-size-multiple', '1',
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                        '--encoder-layers', '1',
                        '--encoder-hidden-size', '32',
                        '--decoder-layers', '1',
                        '--optimizer', optimizer,
                    ])
                    generate_main(data_dir)


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def create_dummy_data(data_dir, num_examples=1000, maxlen=20, alignment=False):
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    def _create_dummy_data(filename):
        data = torch.rand(num_examples * maxlen)
        data = 97 + torch.floor(26 * data).int()
        with open(os.path.join(data_dir, filename), 'w') as h:
            offset = 0
            for _ in range(num_examples):
                ex_len = random.randint(1, maxlen)
                ex_str = ' '.join(map(chr, data[offset:offset+ex_len]))
                print(ex_str, file=h)
                offset += ex_len

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    def _create_dummy_alignment_data(filename_src, filename_tgt, filename):
        with open(os.path.join(data_dir, filename_src), 'r') as src_f, \
             open(os.path.join(data_dir, filename_tgt), 'r') as tgt_f, \
             open(os.path.join(data_dir, filename), 'w') as h:
                    for src, tgt in zip(src_f, tgt_f):
                        src_len = len(src.split())
                        tgt_len = len(tgt.split())
                        avg_len = (src_len + tgt_len) // 2
                        num_alignments = random.randint(avg_len // 2, 2 * avg_len)
                        src_indices = torch.floor(torch.rand(num_alignments) * src_len).int()
                        tgt_indices = torch.floor(torch.rand(num_alignments) * tgt_len).int()
                        ex_str = ' '.join(["{}-{}".format(src, tgt) for src, tgt in zip(src_indices, tgt_indices)])
                        print(ex_str, file=h)

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    _create_dummy_data('train.in')
    _create_dummy_data('train.out')
    _create_dummy_data('valid.in')
    _create_dummy_data('valid.out')
    _create_dummy_data('test.in')
    _create_dummy_data('test.out')

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    if alignment:
        _create_dummy_alignment_data('train.in', 'train.out', 'train.align')
        _create_dummy_alignment_data('valid.in', 'valid.out', 'valid.align')
        _create_dummy_alignment_data('test.in', 'test.out', 'test.align')
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def preprocess_translation_data(data_dir, extra_flags=None):
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    preprocess_parser = options.get_preprocessing_parser()
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    preprocess_args = preprocess_parser.parse_args(
        [
            '--source-lang', 'in',
            '--target-lang', 'out',
            '--trainpref', os.path.join(data_dir, 'train'),
            '--validpref', os.path.join(data_dir, 'valid'),
            '--testpref', os.path.join(data_dir, 'test'),
            '--thresholdtgt', '0',
            '--thresholdsrc', '0',
            '--destdir', data_dir,
        ] + (extra_flags or []),
    )
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    preprocess.main(preprocess_args)


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def train_translation_model(data_dir, arch, extra_flags=None, task='translation', run_validation=False):
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    train_parser = options.get_training_parser()
    train_args = options.parse_args_and_arch(
        train_parser,
        [
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            '--task', task,
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            data_dir,
            '--save-dir', data_dir,
            '--arch', arch,
            '--lr', '0.05',
            '--max-tokens', '500',
            '--max-epoch', '1',
            '--no-progress-bar',
            '--distributed-world-size', '1',
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            '--source-lang', 'in',
            '--target-lang', 'out',
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        ] + (extra_flags or []),
    )
    train.main(train_args)

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    if run_validation:
        # test validation
        validate_parser = options.get_validation_parser()
        validate_args = options.parse_args_and_arch(
            validate_parser,
            [
                '--task', task,
                data_dir,
                '--path', os.path.join(data_dir, 'checkpoint_last.pt'),
                '--valid-subset', 'valid',
                '--max-tokens', '500',
                '--no-progress-bar',
            ]
        )
        validate.main(validate_args)
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def generate_main(data_dir, extra_flags=None):
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    if extra_flags is None:
        extra_flags = [
            '--print-alignment',
        ]
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    generate_parser = options.get_generation_parser()
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    generate_args = options.parse_args_and_arch(
        generate_parser,
        [
            data_dir,
            '--path', os.path.join(data_dir, 'checkpoint_last.pt'),
            '--beam', '3',
            '--batch-size', '64',
            '--max-len-b', '5',
            '--gen-subset', 'valid',
            '--no-progress-bar',
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        ] + (extra_flags or []),
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    )
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    # evaluate model in batch mode
    generate.main(generate_args)

    # evaluate model interactively
    generate_args.buffer_size = 0
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    generate_args.input = '-'
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    generate_args.max_sentences = None
    orig_stdin = sys.stdin
    sys.stdin = StringIO('h e l l o\n')
    interactive.main(generate_args)
    sys.stdin = orig_stdin


def preprocess_lm_data(data_dir):
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    preprocess_parser = options.get_preprocessing_parser()
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    preprocess_args = preprocess_parser.parse_args([
        '--only-source',
        '--trainpref', os.path.join(data_dir, 'train.out'),
        '--validpref', os.path.join(data_dir, 'valid.out'),
        '--testpref', os.path.join(data_dir, 'test.out'),
        '--destdir', data_dir,
    ])
    preprocess.main(preprocess_args)


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def train_language_model(data_dir, arch, extra_flags=None, run_validation=False):
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    train_parser = options.get_training_parser()
    train_args = options.parse_args_and_arch(
        train_parser,
        [
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            '--task', 'language_modeling',
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            data_dir,
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            '--arch', arch,
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            '--optimizer', 'adam',
            '--lr', '0.0001',
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            '--criterion', 'adaptive_loss',
            '--adaptive-softmax-cutoff', '5,10,15',
            '--max-tokens', '500',
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            '--tokens-per-sample', '500',
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            '--save-dir', data_dir,
            '--max-epoch', '1',
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            '--no-progress-bar',
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            '--distributed-world-size', '1',
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            '--ddp-backend', 'no_c10d',
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        ] + (extra_flags or []),
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    )
    train.main(train_args)

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    if run_validation:
        # test validation
        validate_parser = options.get_validation_parser()
        validate_args = options.parse_args_and_arch(
            validate_parser,
            [
                '--task', 'language_modeling',
                data_dir,
                '--path', os.path.join(data_dir, 'checkpoint_last.pt'),
                '--valid-subset', 'valid',
                '--max-tokens', '500',
                '--no-progress-bar',
            ]
        )
        validate.main(validate_args)
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def eval_lm_main(data_dir):
    eval_lm_parser = options.get_eval_lm_parser()
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    eval_lm_args = options.parse_args_and_arch(
        eval_lm_parser,
        [
            data_dir,
            '--path', os.path.join(data_dir, 'checkpoint_last.pt'),
            '--no-progress-bar',
        ],
    )
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    eval_lm.main(eval_lm_args)
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if __name__ == '__main__':
    unittest.main()