Audio-Deepfake-Detection
/
fairseq-a54021305d6b3c4c5959ac9395135f63202db8f1
/tests
/test_binaries.py
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import contextlib | |
| import json | |
| import logging | |
| import os | |
| import random | |
| import sys | |
| import tempfile | |
| import unittest | |
| from io import StringIO | |
| from typing import Dict, List | |
| import torch | |
| from fairseq import options | |
| from fairseq_cli import eval_lm, train | |
| from tests.utils import ( | |
| create_dummy_data, | |
| create_laser_data_and_config_json, | |
| generate_main, | |
| preprocess_lm_data, | |
| preprocess_summarization_data, | |
| preprocess_translation_data, | |
| train_language_model, | |
| train_translation_model, | |
| ) | |
| try: | |
| import transformers # noqa | |
| has_hf_transformers = True | |
| except ImportError: | |
| has_hf_transformers = False | |
| class TestTranslation(unittest.TestCase): | |
| def setUp(self): | |
| logging.disable(logging.CRITICAL) | |
| def tearDown(self): | |
| logging.disable(logging.NOTSET) | |
| 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) | |
| def test_raw(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_fconv_raw") as data_dir: | |
| create_dummy_data(data_dir) | |
| 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"]) | |
| 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) | |
| 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( | |
| "skip this example with --skip-invalid-size-inputs-valid-test" | |
| in str(context.exception) | |
| ) | |
| 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"]) | |
| 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", | |
| "--temperature", | |
| "2", | |
| "--beam", | |
| "2", | |
| "--nbest", | |
| "2", | |
| ], | |
| ) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--sampling", | |
| "--sampling-topk", | |
| "3", | |
| "--beam", | |
| "2", | |
| "--nbest", | |
| "2", | |
| ], | |
| ) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--sampling", | |
| "--sampling-topp", | |
| "0.2", | |
| "--beam", | |
| "2", | |
| "--nbest", | |
| "2", | |
| ], | |
| ) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--diversity-rate", | |
| "0.5", | |
| "--beam", | |
| "6", | |
| ], | |
| ) | |
| with self.assertRaises(ValueError): | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--diverse-beam-groups", | |
| "4", | |
| "--match-source-len", | |
| ], | |
| ) | |
| generate_main(data_dir, ["--prefix-size", "2"]) | |
| generate_main(data_dir, ["--retain-dropout"]) | |
| def test_eval_bleu(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_eval_bleu") as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_translation_data(data_dir) | |
| train_translation_model( | |
| data_dir, | |
| "fconv_iwslt_de_en", | |
| [ | |
| "--eval-bleu", | |
| "--eval-bleu-print-samples", | |
| "--eval-bleu-remove-bpe", | |
| "--eval-bleu-detok", | |
| "space", | |
| "--eval-bleu-args", | |
| '{"beam": 4, "min_len": 10}', | |
| ], | |
| ) | |
| 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) | |
| train_translation_model( | |
| data_dir, | |
| "lstm_wiseman_iwslt_de_en", | |
| [ | |
| "--encoder-layers", | |
| "2", | |
| "--decoder-layers", | |
| "2", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| "--decoder-out-embed-dim", | |
| "8", | |
| ], | |
| ) | |
| 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", | |
| "--encoder-hidden-size", | |
| "16", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| "--decoder-out-embed-dim", | |
| "8", | |
| "--decoder-layers", | |
| "2", | |
| ], | |
| ) | |
| 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) | |
| train_translation_model( | |
| data_dir, | |
| "transformer_iwslt_de_en", | |
| [ | |
| "--encoder-layers", | |
| "2", | |
| "--decoder-layers", | |
| "2", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| ], | |
| run_validation=True, | |
| ) | |
| generate_main(data_dir) | |
| def test_multilingual_transformer(self): | |
| # test with all combinations of encoder/decoder lang tokens | |
| encoder_langtok_flags = [ | |
| [], | |
| ["--encoder-langtok", "src"], | |
| ["--encoder-langtok", "tgt"], | |
| ] | |
| decoder_langtok_flags = [[], ["--decoder-langtok"]] | |
| with contextlib.redirect_stdout(StringIO()): | |
| for i in range(len(encoder_langtok_flags)): | |
| for j in range(len(decoder_langtok_flags)): | |
| enc_ltok_flag = encoder_langtok_flags[i] | |
| dec_ltok_flag = decoder_langtok_flags[j] | |
| with tempfile.TemporaryDirectory( | |
| f"test_multilingual_transformer_{i}_{j}" | |
| ) as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_translation_data(data_dir) | |
| train_translation_model( | |
| data_dir, | |
| arch="multilingual_transformer", | |
| task="multilingual_translation", | |
| extra_flags=[ | |
| "--encoder-layers", | |
| "2", | |
| "--decoder-layers", | |
| "2", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| ] | |
| + enc_ltok_flag | |
| + dec_ltok_flag, | |
| lang_flags=["--lang-pairs", "in-out,out-in"], | |
| run_validation=True, | |
| extra_valid_flags=enc_ltok_flag + dec_ltok_flag, | |
| ) | |
| generate_main( | |
| data_dir, | |
| extra_flags=[ | |
| "--task", | |
| "multilingual_translation", | |
| "--lang-pairs", | |
| "in-out,out-in", | |
| "--source-lang", | |
| "in", | |
| "--target-lang", | |
| "out", | |
| ] | |
| + enc_ltok_flag | |
| + dec_ltok_flag, | |
| ) | |
| def test_multilingual_translation_latent_depth(self): | |
| # test with latent depth in encoder, decoder, or both | |
| encoder_latent_layer = [[], ["--encoder-latent-layer"]] | |
| decoder_latent_layer = [[], ["--decoder-latent-layer"]] | |
| with contextlib.redirect_stdout(StringIO()): | |
| for i in range(len(encoder_latent_layer)): | |
| for j in range(len(decoder_latent_layer)): | |
| if i == 0 and j == 0: | |
| continue | |
| enc_ll_flag = encoder_latent_layer[i] | |
| dec_ll_flag = decoder_latent_layer[j] | |
| with tempfile.TemporaryDirectory( | |
| f"test_multilingual_translation_latent_depth_{i}_{j}" | |
| ) as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_translation_data( | |
| data_dir, extra_flags=["--joined-dictionary"] | |
| ) | |
| train_translation_model( | |
| data_dir, | |
| arch="latent_multilingual_transformer", | |
| task="multilingual_translation_latent_depth", | |
| extra_flags=[ | |
| "--user-dir", | |
| "examples/latent_depth/latent_depth_src", | |
| "--encoder-layers", | |
| "2", | |
| "--decoder-layers", | |
| "2", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| "--share-encoders", | |
| "--share-decoders", | |
| "--sparsity-weight", | |
| "0.1", | |
| ] | |
| + enc_ll_flag | |
| + dec_ll_flag, | |
| lang_flags=["--lang-pairs", "in-out,out-in"], | |
| run_validation=True, | |
| extra_valid_flags=[ | |
| "--user-dir", | |
| "examples/latent_depth/latent_depth_src", | |
| ] | |
| + enc_ll_flag | |
| + dec_ll_flag, | |
| ) | |
| generate_main( | |
| data_dir, | |
| extra_flags=[ | |
| "--user-dir", | |
| "examples/latent_depth/latent_depth_src", | |
| "--task", | |
| "multilingual_translation_latent_depth", | |
| "--lang-pairs", | |
| "in-out,out-in", | |
| "--source-lang", | |
| "in", | |
| "--target-lang", | |
| "out", | |
| ] | |
| + enc_ll_flag | |
| + dec_ll_flag, | |
| ) | |
| def test_translation_multi_simple_epoch(self): | |
| # test with all combinations of encoder/decoder lang tokens | |
| encoder_langtok_flags = [ | |
| [], | |
| ["--encoder-langtok", "src"], | |
| ["--encoder-langtok", "tgt"], | |
| ] | |
| decoder_langtok_flags = [[], ["--decoder-langtok"]] | |
| with contextlib.redirect_stdout(StringIO()): | |
| for i in range(len(encoder_langtok_flags)): | |
| for j in range(len(decoder_langtok_flags)): | |
| enc_ltok_flag = encoder_langtok_flags[i] | |
| dec_ltok_flag = decoder_langtok_flags[j] | |
| with tempfile.TemporaryDirectory( | |
| f"test_translation_multi_simple_epoch_{i}_{j}" | |
| ) as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_translation_data( | |
| data_dir, extra_flags=["--joined-dictionary"] | |
| ) | |
| train_translation_model( | |
| data_dir, | |
| arch="transformer", | |
| task="translation_multi_simple_epoch", | |
| extra_flags=[ | |
| "--encoder-layers", | |
| "2", | |
| "--decoder-layers", | |
| "2", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| "--sampling-method", | |
| "temperature", | |
| "--sampling-temperature", | |
| "1.5", | |
| "--virtual-epoch-size", | |
| "1000", | |
| ] | |
| + enc_ltok_flag | |
| + dec_ltok_flag, | |
| lang_flags=["--lang-pairs", "in-out,out-in"], | |
| run_validation=True, | |
| extra_valid_flags=enc_ltok_flag + dec_ltok_flag, | |
| ) | |
| generate_main( | |
| data_dir, | |
| extra_flags=[ | |
| "--task", | |
| "translation_multi_simple_epoch", | |
| "--lang-pairs", | |
| "in-out,out-in", | |
| "--source-lang", | |
| "in", | |
| "--target-lang", | |
| "out", | |
| ] | |
| + enc_ltok_flag | |
| + dec_ltok_flag, | |
| ) | |
| def test_translation_multi_simple_epoch_no_vepoch(self): | |
| # test with all combinations of encoder/decoder lang tokens | |
| with contextlib.redirect_stdout(StringIO()): | |
| enc_ltok_flag = ["--encoder-langtok", "src"] | |
| dec_ltok_flag = ["--decoder-langtok"] | |
| with tempfile.TemporaryDirectory( | |
| "test_translation_multi_simple_epoch_dict" | |
| ) as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_translation_data(data_dir, extra_flags=[]) | |
| train_translation_model( | |
| data_dir, | |
| arch="transformer", | |
| task="translation_multi_simple_epoch", | |
| extra_flags=[ | |
| "--encoder-layers", | |
| "2", | |
| "--decoder-layers", | |
| "2", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| "--sampling-method", | |
| "temperature", | |
| "--sampling-temperature", | |
| "1.5", | |
| ] | |
| + enc_ltok_flag | |
| + dec_ltok_flag, | |
| lang_flags=["--lang-pairs", "in-out"], | |
| run_validation=True, | |
| extra_valid_flags=enc_ltok_flag + dec_ltok_flag, | |
| ) | |
| generate_main( | |
| data_dir, | |
| extra_flags=[ | |
| "--task", | |
| "translation_multi_simple_epoch", | |
| "--lang-pairs", | |
| "in-out", | |
| "--source-lang", | |
| "in", | |
| "--target-lang", | |
| "out", | |
| ] | |
| + enc_ltok_flag | |
| + dec_ltok_flag, | |
| ) | |
| def test_translation_multi_simple_epoch_dicts(self): | |
| # test with all combinations of encoder/decoder lang tokens | |
| with contextlib.redirect_stdout(StringIO()): | |
| enc_ltok_flag = ["--encoder-langtok", "src"] | |
| dec_ltok_flag = ["--decoder-langtok"] | |
| with tempfile.TemporaryDirectory( | |
| "test_translation_multi_simple_epoch_dict" | |
| ) as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_translation_data(data_dir, extra_flags=[]) | |
| train_translation_model( | |
| data_dir, | |
| arch="transformer", | |
| task="translation_multi_simple_epoch", | |
| extra_flags=[ | |
| "--encoder-layers", | |
| "2", | |
| "--decoder-layers", | |
| "2", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| "--sampling-method", | |
| "temperature", | |
| "--sampling-temperature", | |
| "1.5", | |
| "--virtual-epoch-size", | |
| "1000", | |
| ] | |
| + enc_ltok_flag | |
| + dec_ltok_flag, | |
| lang_flags=["--lang-pairs", "in-out"], | |
| run_validation=True, | |
| extra_valid_flags=enc_ltok_flag + dec_ltok_flag, | |
| ) | |
| generate_main( | |
| data_dir, | |
| extra_flags=[ | |
| "--task", | |
| "translation_multi_simple_epoch", | |
| "--lang-pairs", | |
| "in-out", | |
| "--source-lang", | |
| "in", | |
| "--target-lang", | |
| "out", | |
| ] | |
| + enc_ltok_flag | |
| + dec_ltok_flag, | |
| ) | |
| def test_translation_multi_simple_epoch_src_tgt_dict_spec(self): | |
| # test the specification of explicit --src-dict and --tgt-dict | |
| with contextlib.redirect_stdout(StringIO()): | |
| enc_ltok_flag = ["--encoder-langtok", "src"] | |
| dec_ltok_flag = ["--decoder-langtok"] | |
| with tempfile.TemporaryDirectory( | |
| "test_translation_multi_simple_epoch_dict" | |
| ) as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_translation_data(data_dir, extra_flags=[]) | |
| train_translation_model( | |
| data_dir, | |
| arch="transformer", | |
| task="translation_multi_simple_epoch", | |
| extra_flags=[ | |
| "--source-dict", | |
| f"{data_dir}/dict.in.txt", | |
| "--target-dict", | |
| f"{data_dir}/dict.out.txt", | |
| "--encoder-layers", | |
| "2", | |
| "--decoder-layers", | |
| "2", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| "--sampling-method", | |
| "temperature", | |
| "--sampling-temperature", | |
| "1.5", | |
| "--virtual-epoch-size", | |
| "1000", | |
| ] | |
| + enc_ltok_flag | |
| + dec_ltok_flag, | |
| lang_flags=["--lang-pairs", "in-out"], | |
| run_validation=True, | |
| extra_valid_flags=enc_ltok_flag + dec_ltok_flag, | |
| ) | |
| generate_main( | |
| data_dir, | |
| extra_flags=[ | |
| "--task", | |
| "translation_multi_simple_epoch", | |
| "--lang-pairs", | |
| "in-out", | |
| "--source-lang", | |
| "in", | |
| "--target-lang", | |
| "out", | |
| ] | |
| + enc_ltok_flag | |
| + dec_ltok_flag, | |
| ) | |
| 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", | |
| ], | |
| run_validation=True, | |
| ) | |
| generate_main(data_dir, extra_flags=[]) | |
| def test_transformer_pointer_generator(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory( | |
| "test_transformer_pointer_generator" | |
| ) as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_summarization_data(data_dir) | |
| train_translation_model( | |
| data_dir, | |
| "transformer_pointer_generator", | |
| extra_flags=[ | |
| "--user-dir", | |
| "examples/pointer_generator/pointer_generator_src", | |
| "--encoder-layers", | |
| "2", | |
| "--decoder-layers", | |
| "2", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| "--alignment-layer", | |
| "-1", | |
| "--alignment-heads", | |
| "1", | |
| "--source-position-markers", | |
| "0", | |
| ], | |
| run_validation=True, | |
| extra_valid_flags=[ | |
| "--user-dir", | |
| "examples/pointer_generator/pointer_generator_src", | |
| ], | |
| ) | |
| generate_main( | |
| data_dir, | |
| extra_flags=[ | |
| "--user-dir", | |
| "examples/pointer_generator/pointer_generator_src", | |
| ], | |
| ) | |
| 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", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| ], | |
| ) | |
| 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", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| ], | |
| ) | |
| generate_main(data_dir) | |
| def test_cmlm_transformer(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_cmlm_transformer") as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_translation_data(data_dir, ["--joined-dictionary"]) | |
| train_translation_model( | |
| data_dir, | |
| "cmlm_transformer", | |
| [ | |
| "--apply-bert-init", | |
| "--criterion", | |
| "nat_loss", | |
| "--noise", | |
| "full_mask", | |
| "--pred-length-offset", | |
| "--length-loss-factor", | |
| "0.1", | |
| ], | |
| task="translation_lev", | |
| ) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--task", | |
| "translation_lev", | |
| "--iter-decode-max-iter", | |
| "9", | |
| "--iter-decode-eos-penalty", | |
| "0", | |
| "--print-step", | |
| ], | |
| ) | |
| 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, ["--joined-dictionary"]) | |
| 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, | |
| [ | |
| "--task", | |
| "translation_lev", | |
| "--iter-decode-max-iter", | |
| "0", | |
| "--iter-decode-eos-penalty", | |
| "0", | |
| "--print-step", | |
| ], | |
| ) | |
| # def test_nat_crf_transformer(self): | |
| # with contextlib.redirect_stdout(StringIO()): | |
| # with tempfile.TemporaryDirectory('test_nat_crf_transformer') as data_dir: | |
| # create_dummy_data(data_dir) | |
| # preprocess_translation_data(data_dir, ['--joined-dictionary']) | |
| # train_translation_model(data_dir, 'nacrf_transformer', [ | |
| # '--apply-bert-init', '--criterion', | |
| # 'nat_loss', '--noise', 'full_mask', '--pred-length-offset', | |
| # '--length-loss-factor', '0.1', | |
| # '--word-ins-loss-factor', '0.5', | |
| # '--crf-lowrank-approx', '1', | |
| # '--crf-beam-approx', '1' | |
| # ], task='translation_lev') | |
| # generate_main(data_dir, [ | |
| # '--task', 'translation_lev', | |
| # '--iter-decode-max-iter', '0', | |
| # '--iter-decode-eos-penalty', '0', | |
| # '--print-step', | |
| # ]) | |
| 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, ["--joined-dictionary"]) | |
| 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, | |
| [ | |
| "--task", | |
| "translation_lev", | |
| "--iter-decode-max-iter", | |
| "9", | |
| "--iter-decode-eos-penalty", | |
| "0", | |
| "--print-step", | |
| ], | |
| ) | |
| 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, ["--joined-dictionary"]) | |
| train_translation_model( | |
| data_dir, | |
| "insertion_transformer", | |
| [ | |
| "--apply-bert-init", | |
| "--criterion", | |
| "nat_loss", | |
| "--noise", | |
| "random_mask", | |
| ], | |
| task="translation_lev", | |
| ) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--task", | |
| "translation_lev", | |
| "--iter-decode-max-iter", | |
| "9", | |
| "--iter-decode-eos-penalty", | |
| "0", | |
| "--print-step", | |
| ], | |
| ) | |
| 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", | |
| "--user-dir", | |
| "examples/translation_moe/translation_moe_src", | |
| "--method", | |
| "hMoElp", | |
| "--mean-pool-gating-network", | |
| "--num-experts", | |
| "3", | |
| "--encoder-layers", | |
| "2", | |
| "--decoder-layers", | |
| "2", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| ], | |
| ) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--task", | |
| "translation_moe", | |
| "--user-dir", | |
| "examples/translation_moe/translation_moe_src", | |
| "--method", | |
| "hMoElp", | |
| "--mean-pool-gating-network", | |
| "--num-experts", | |
| "3", | |
| "--gen-expert", | |
| "0", | |
| ], | |
| ) | |
| 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) | |
| def test_laser_lstm(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_laser_lstm") as data_dir: | |
| laser_config_file = create_laser_data_and_config_json(data_dir) | |
| train_translation_model( | |
| laser_config_file.name, | |
| "laser_lstm", | |
| [ | |
| "--user-dir", | |
| "examples/laser/laser_src", | |
| "--weighting-alpha", | |
| "0.3", | |
| "--encoder-bidirectional", | |
| "--encoder-hidden-size", | |
| "512", | |
| "--encoder-layers", | |
| "5", | |
| "--decoder-layers", | |
| "1", | |
| "--encoder-embed-dim", | |
| "320", | |
| "--decoder-embed-dim", | |
| "320", | |
| "--decoder-lang-embed-dim", | |
| "32", | |
| "--save-dir", | |
| data_dir, | |
| "--disable-validation", | |
| ], | |
| task="laser", | |
| lang_flags=[], | |
| ) | |
| def test_laser_transformer(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_laser_transformer") as data_dir: | |
| laser_config_file = create_laser_data_and_config_json(data_dir) | |
| train_translation_model( | |
| laser_config_file.name, | |
| "laser_transformer", | |
| [ | |
| "--user-dir", | |
| "examples/laser/laser_src", | |
| "--weighting-alpha", | |
| "0.3", | |
| "--encoder-embed-dim", | |
| "320", | |
| "--decoder-embed-dim", | |
| "320", | |
| "--decoder-lang-embed-dim", | |
| "32", | |
| "--save-dir", | |
| data_dir, | |
| "--disable-validation", | |
| ], | |
| task="laser", | |
| lang_flags=[], | |
| ) | |
| def test_alignment_full_context(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", | |
| "--full-context-alignment", | |
| ], | |
| run_validation=True, | |
| ) | |
| generate_main(data_dir) | |
| def test_transformer_layerdrop(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_transformer_layerdrop") as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_translation_data(data_dir) | |
| train_translation_model( | |
| data_dir, | |
| "transformer_iwslt_de_en", | |
| [ | |
| "--encoder-layers", | |
| "3", | |
| "--decoder-layers", | |
| "3", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| "--encoder-layerdrop", | |
| "0.01", | |
| "--decoder-layerdrop", | |
| "0.01", | |
| ], | |
| ) | |
| generate_main(data_dir) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--model-overrides", | |
| "{'encoder_layers_to_keep':'0,2','decoder_layers_to_keep':'1'}", | |
| ], | |
| ) | |
| class TestStories(unittest.TestCase): | |
| def setUp(self): | |
| logging.disable(logging.CRITICAL) | |
| def tearDown(self): | |
| logging.disable(logging.NOTSET) | |
| 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 = [ | |
| "--encoder-layers", | |
| "[(128, 3)] * 2", | |
| "--decoder-layers", | |
| "[(128, 3)] * 2", | |
| "--decoder-attention", | |
| "True", | |
| "--encoder-attention", | |
| "False", | |
| "--gated-attention", | |
| "True", | |
| "--self-attention", | |
| "True", | |
| "--project-input", | |
| "True", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| "--decoder-out-embed-dim", | |
| "8", | |
| "--multihead-self-attention-nheads", | |
| "2", | |
| ] | |
| 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 setUp(self): | |
| logging.disable(logging.CRITICAL) | |
| def tearDown(self): | |
| logging.disable(logging.NOTSET) | |
| 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) | |
| 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) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--task", | |
| "language_modeling", | |
| "--sample-break-mode", | |
| "eos", | |
| "--tokens-per-sample", | |
| "500", | |
| ], | |
| ) | |
| 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) | |
| train_language_model( | |
| data_dir, | |
| "transformer_lm", | |
| ["--add-bos-token", "--nval", "1"], | |
| run_validation=True, | |
| ) | |
| eval_lm_main(data_dir) | |
| eval_lm_main(data_dir, extra_flags=["--context-window", "25"]) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--task", | |
| "language_modeling", | |
| "--sample-break-mode", | |
| "eos", | |
| "--tokens-per-sample", | |
| "500", | |
| ], | |
| ) | |
| def test_normformer_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) | |
| train_language_model( | |
| data_dir, | |
| "transformer_lm", | |
| [ | |
| "--add-bos-token", | |
| "--nval", | |
| "1", | |
| "--scale-fc", | |
| "--scale-heads", | |
| "--scale-attn", | |
| "--scale-fc", | |
| ], | |
| run_validation=True, | |
| ) | |
| eval_lm_main(data_dir) | |
| eval_lm_main(data_dir, extra_flags=["--context-window", "25"]) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--task", | |
| "language_modeling", | |
| "--sample-break-mode", | |
| "eos", | |
| "--tokens-per-sample", | |
| "500", | |
| ], | |
| ) | |
| def test_transformer_lm_with_adaptive_softmax(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory( | |
| "test_transformer_lm_with_adaptive_softmax" | |
| ) as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_lm_data(data_dir) | |
| train_language_model( | |
| data_dir, | |
| "transformer_lm", | |
| [ | |
| "--add-bos-token", | |
| "--criterion", | |
| "adaptive_loss", | |
| "--adaptive-softmax-cutoff", | |
| "5,10,15", | |
| ], | |
| run_validation=True, | |
| ) | |
| eval_lm_main(data_dir) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--task", | |
| "language_modeling", | |
| "--sample-break-mode", | |
| "eos", | |
| "--tokens-per-sample", | |
| "500", | |
| ], | |
| ) | |
| def test_lightconv_lm(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_lightconv_lm") as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_lm_data(data_dir) | |
| train_language_model( | |
| data_dir, | |
| "lightconv_lm", | |
| ["--add-bos-token"], | |
| run_validation=True, | |
| ) | |
| eval_lm_main(data_dir) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--task", | |
| "language_modeling", | |
| "--sample-break-mode", | |
| "eos", | |
| "--tokens-per-sample", | |
| "500", | |
| ], | |
| ) | |
| def test_lstm_lm(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_lstm_lm") as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_lm_data(data_dir) | |
| train_language_model( | |
| data_dir, | |
| "lstm_lm", | |
| ["--add-bos-token"], | |
| run_validation=True, | |
| ) | |
| eval_lm_main(data_dir) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--task", | |
| "language_modeling", | |
| "--sample-break-mode", | |
| "eos", | |
| "--tokens-per-sample", | |
| "500", | |
| ], | |
| ) | |
| def test_lstm_lm_residuals(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_lstm_lm_residuals") as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_lm_data(data_dir) | |
| train_language_model( | |
| data_dir, | |
| "lstm_lm", | |
| ["--add-bos-token", "--residuals"], | |
| run_validation=True, | |
| ) | |
| eval_lm_main(data_dir) | |
| generate_main( | |
| data_dir, | |
| [ | |
| "--task", | |
| "language_modeling", | |
| "--sample-break-mode", | |
| "eos", | |
| "--tokens-per-sample", | |
| "500", | |
| ], | |
| ) | |
| def test_transformer_xl_bptt_lm(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_transformer_xl_bptt_lm") as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_lm_data(data_dir) | |
| task_flags = [ | |
| "--user-dir", | |
| "examples/truncated_bptt", | |
| "--task", | |
| "truncated_bptt_lm", | |
| "--batch-size", | |
| "2", | |
| "--tokens-per-sample", | |
| "50", | |
| ] | |
| train_language_model( | |
| data_dir=data_dir, | |
| arch="transformer_xl", | |
| extra_flags=task_flags | |
| + [ | |
| "--n-layer", | |
| "2", | |
| ], | |
| task="truncated_bptt_lm", | |
| run_validation=True, | |
| extra_valid_flags=task_flags, | |
| ) | |
| eval_lm_main(data_dir, extra_flags=task_flags) | |
| # Train with activation offloading | |
| train_language_model( | |
| data_dir=data_dir, | |
| arch="transformer_xl", | |
| extra_flags=task_flags | |
| + [ | |
| "--n-layer", | |
| "2", | |
| "--offload-activations", | |
| ], | |
| task="truncated_bptt_lm", | |
| run_validation=True, | |
| extra_valid_flags=task_flags, | |
| ) | |
| class TestMaskedLanguageModel(unittest.TestCase): | |
| def setUp(self): | |
| logging.disable(logging.CRITICAL) | |
| def tearDown(self): | |
| logging.disable(logging.NOTSET) | |
| def test_legacy_masked_lm(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_legacy_mlm") as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_lm_data(data_dir) | |
| train_legacy_masked_language_model(data_dir, "masked_lm") | |
| def test_roberta_masked_lm(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_roberta_mlm") as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_lm_data(data_dir) | |
| train_masked_lm( | |
| data_dir, "roberta_base", extra_flags=["--encoder-layers", "2"] | |
| ) | |
| def test_roberta_sentence_prediction(self): | |
| num_classes = 3 | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_roberta_head") as data_dir: | |
| create_dummy_roberta_head_data(data_dir, num_classes=num_classes) | |
| preprocess_lm_data(os.path.join(data_dir, "input0")) | |
| preprocess_lm_data(os.path.join(data_dir, "label")) | |
| train_roberta_head(data_dir, "roberta_base", num_classes=num_classes) | |
| def test_roberta_regression_single(self): | |
| num_classes = 1 | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory( | |
| "test_roberta_regression_single" | |
| ) as data_dir: | |
| create_dummy_roberta_head_data( | |
| data_dir, num_classes=num_classes, regression=True | |
| ) | |
| preprocess_lm_data(os.path.join(data_dir, "input0")) | |
| train_roberta_head( | |
| data_dir, | |
| "roberta_base", | |
| num_classes=num_classes, | |
| extra_flags=["--regression-target"], | |
| ) | |
| def test_roberta_regression_multiple(self): | |
| num_classes = 3 | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory( | |
| "test_roberta_regression_multiple" | |
| ) as data_dir: | |
| create_dummy_roberta_head_data( | |
| data_dir, num_classes=num_classes, regression=True | |
| ) | |
| preprocess_lm_data(os.path.join(data_dir, "input0")) | |
| train_roberta_head( | |
| data_dir, | |
| "roberta_base", | |
| num_classes=num_classes, | |
| extra_flags=["--regression-target"], | |
| ) | |
| def test_linformer_roberta_masked_lm(self): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_linformer_roberta_mlm") as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_lm_data(data_dir) | |
| train_masked_lm( | |
| data_dir, | |
| "linformer_roberta_base", | |
| extra_flags=[ | |
| "--user-dir", | |
| "examples/linformer/linformer_src", | |
| "--encoder-layers", | |
| "2", | |
| ], | |
| ) | |
| def test_linformer_roberta_sentence_prediction(self): | |
| num_classes = 3 | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_linformer_roberta_head") as data_dir: | |
| create_dummy_roberta_head_data(data_dir, num_classes=num_classes) | |
| preprocess_lm_data(os.path.join(data_dir, "input0")) | |
| preprocess_lm_data(os.path.join(data_dir, "label")) | |
| train_roberta_head( | |
| data_dir, | |
| "linformer_roberta_base", | |
| num_classes=num_classes, | |
| extra_flags=["--user-dir", "examples/linformer/linformer_src"], | |
| ) | |
| def test_linformer_roberta_regression_single(self): | |
| num_classes = 1 | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory( | |
| "test_linformer_roberta_regression_single" | |
| ) as data_dir: | |
| create_dummy_roberta_head_data( | |
| data_dir, num_classes=num_classes, regression=True | |
| ) | |
| preprocess_lm_data(os.path.join(data_dir, "input0")) | |
| train_roberta_head( | |
| data_dir, | |
| "linformer_roberta_base", | |
| num_classes=num_classes, | |
| extra_flags=[ | |
| "--regression-target", | |
| "--user-dir", | |
| "examples/linformer/linformer_src", | |
| ], | |
| ) | |
| def test_linformer_roberta_regression_multiple(self): | |
| num_classes = 3 | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory( | |
| "test_linformer_roberta_regression_multiple" | |
| ) as data_dir: | |
| create_dummy_roberta_head_data( | |
| data_dir, num_classes=num_classes, regression=True | |
| ) | |
| preprocess_lm_data(os.path.join(data_dir, "input0")) | |
| train_roberta_head( | |
| data_dir, | |
| "linformer_roberta_base", | |
| num_classes=num_classes, | |
| extra_flags=[ | |
| "--regression-target", | |
| "--user-dir", | |
| "examples/linformer/linformer_src", | |
| ], | |
| ) | |
| def _test_pretrained_masked_lm_for_translation(self, learned_pos_emb, encoder_only): | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_mlm") as data_dir: | |
| create_dummy_data(data_dir) | |
| preprocess_lm_data(data_dir) | |
| train_legacy_masked_language_model( | |
| data_dir, | |
| arch="masked_lm", | |
| extra_args=("--encoder-learned-pos",) if learned_pos_emb else (), | |
| ) | |
| 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", | |
| "{}/checkpoint_last.pt".format(data_dir), | |
| "--activation-fn", | |
| "gelu", | |
| "--max-source-positions", | |
| "500", | |
| "--max-target-positions", | |
| "500", | |
| ] | |
| + ( | |
| ["--encoder-learned-pos", "--decoder-learned-pos"] | |
| if learned_pos_emb | |
| else [] | |
| ) | |
| + (["--init-encoder-only"] if encoder_only else []), | |
| task="translation_from_pretrained_xlm", | |
| ) | |
| 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) | |
| def test_pretrained_masked_lm_for_translation_encoder_only(self): | |
| self._test_pretrained_masked_lm_for_translation(True, True) | |
| def test_r4f_roberta(self): | |
| num_classes = 3 | |
| with contextlib.redirect_stdout(StringIO()): | |
| with tempfile.TemporaryDirectory("test_r4f_roberta_head") as data_dir: | |
| create_dummy_roberta_head_data(data_dir, num_classes=num_classes) | |
| preprocess_lm_data(os.path.join(data_dir, "input0")) | |
| preprocess_lm_data(os.path.join(data_dir, "label")) | |
| train_roberta_head( | |
| data_dir, | |
| "roberta_base", | |
| num_classes=num_classes, | |
| extra_flags=[ | |
| "--user-dir", | |
| "examples/rxf/rxf_src", | |
| "--criterion", | |
| "sentence_prediction_r3f", | |
| "--spectral-norm-classification-head", | |
| ], | |
| ) | |
| def train_legacy_masked_language_model(data_dir, arch, extra_args=()): | |
| 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", | |
| "--stop-min-lr", | |
| "1e-09", | |
| # dropout, attention args | |
| "--dropout", | |
| "0.1", | |
| "--attention-dropout", | |
| "0.1", | |
| # MLM args | |
| "--criterion", | |
| "legacy_masked_lm_loss", | |
| "--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", | |
| "--dataset-impl", | |
| "raw", | |
| "--num-workers", | |
| "0", | |
| ] | |
| + list(extra_args), | |
| ) | |
| train.main(train_args) | |
| class TestOptimizers(unittest.TestCase): | |
| def setUp(self): | |
| logging.disable(logging.CRITICAL) | |
| def tearDown(self): | |
| logging.disable(logging.NOTSET) | |
| 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", | |
| [ | |
| "--required-batch-size-multiple", | |
| "1", | |
| "--encoder-layers", | |
| "1", | |
| "--encoder-hidden-size", | |
| "32", | |
| "--decoder-layers", | |
| "1", | |
| "--optimizer", | |
| optimizer, | |
| ], | |
| ) | |
| generate_main(data_dir) | |
| def read_last_log_entry( | |
| logs: List[logging.LogRecord], logger_name: str | |
| ) -> Dict[str, float]: | |
| for x in reversed(logs): | |
| if x.name == logger_name: | |
| return json.loads(x.message) | |
| raise ValueError(f"No entries from {logger_name} found in captured logs") | |
| class TestActivationCheckpointing(unittest.TestCase): | |
| base_flags = [ | |
| "--encoder-layers", | |
| "2", | |
| "--decoder-layers", | |
| "2", | |
| "--encoder-embed-dim", | |
| "8", | |
| "--decoder-embed-dim", | |
| "8", | |
| "--restore-file", | |
| "x.pt", | |
| "--log-format", | |
| "json", | |
| "--log-interval", | |
| "1", | |
| "--max-update", | |
| "2", | |
| ] | |
| def _train(self, data_dir, extra_flags): | |
| with self.assertLogs() as logs: | |
| train_translation_model( | |
| data_dir, | |
| "transformer_iwslt_de_en", | |
| self.base_flags + extra_flags, | |
| run_validation=True, | |
| extra_valid_flags=["--log-format", "json"], | |
| ) | |
| return logs.records | |
| def test_activation_offloading_does_not_change_metrics(self): | |
| """Neither ----checkpoint-activations nor --offload-activations should change loss""" | |
| with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir: | |
| with self.assertLogs(): | |
| create_dummy_data(data_dir, num_examples=20) | |
| preprocess_translation_data(data_dir) | |
| offload_logs = self._train(data_dir, ["--offload-activations"]) | |
| baseline_logs = self._train(data_dir, []) | |
| assert len(baseline_logs) == len(offload_logs) | |
| baseline_valid_stats = read_last_log_entry(baseline_logs, "valid") | |
| offload_valid_stats = read_last_log_entry(offload_logs, "valid") | |
| baseline_train_stats = read_last_log_entry(baseline_logs, "train") | |
| offload_train_stats = read_last_log_entry(offload_logs, "train") | |
| assert ( | |
| baseline_train_stats["train_loss"] == offload_train_stats["train_loss"] | |
| ) | |
| assert ( | |
| baseline_valid_stats["valid_loss"] == offload_valid_stats["valid_loss"] | |
| ) | |
| def test_activation_checkpointing_does_not_change_metrics(self): | |
| """--checkpoint-activations should not change loss""" | |
| with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir: | |
| with self.assertLogs(): | |
| create_dummy_data(data_dir, num_examples=20) | |
| preprocess_translation_data(data_dir) | |
| ckpt_logs = self._train(data_dir, ["--checkpoint-activations"]) | |
| baseline_logs = self._train(data_dir, []) | |
| assert len(baseline_logs) == len(ckpt_logs) | |
| baseline_train_stats = read_last_log_entry(baseline_logs, "train") | |
| ckpt_train_stats = read_last_log_entry(ckpt_logs, "train") | |
| assert baseline_train_stats["train_loss"] == ckpt_train_stats["train_loss"] | |
| baseline_valid_stats = read_last_log_entry(baseline_logs, "valid") | |
| ckpt_valid_stats = read_last_log_entry(ckpt_logs, "valid") | |
| assert baseline_valid_stats["valid_loss"] == ckpt_valid_stats["valid_loss"] | |
| def create_dummy_roberta_head_data( | |
| data_dir, num_examples=100, maxlen=10, num_classes=2, regression=False | |
| ): | |
| input_dir = "input0" | |
| def _create_dummy_data(filename): | |
| random_data = torch.rand(num_examples * maxlen) | |
| input_data = 97 + torch.floor(26 * random_data).int() | |
| if regression: | |
| output_data = torch.rand((num_examples, num_classes)) | |
| else: | |
| output_data = 1 + torch.floor(num_classes * torch.rand(num_examples)).int() | |
| with open(os.path.join(data_dir, input_dir, filename + ".out"), "w") as f_in: | |
| label_filename = filename + ".label" if regression else filename + ".out" | |
| with open(os.path.join(data_dir, "label", label_filename), "w") as f_out: | |
| offset = 0 | |
| for i in range(num_examples): | |
| # write example input | |
| ex_len = random.randint(1, maxlen) | |
| ex_str = " ".join(map(chr, input_data[offset : offset + ex_len])) | |
| print(ex_str, file=f_in) | |
| # write example label | |
| if regression: | |
| class_str = " ".join(map(str, output_data[i].numpy())) | |
| print(class_str, file=f_out) | |
| else: | |
| class_str = "class{}".format(output_data[i]) | |
| print(class_str, file=f_out) | |
| offset += ex_len | |
| os.mkdir(os.path.join(data_dir, input_dir)) | |
| os.mkdir(os.path.join(data_dir, "label")) | |
| _create_dummy_data("train") | |
| _create_dummy_data("valid") | |
| _create_dummy_data("test") | |
| def train_masked_lm(data_dir, arch, extra_flags=None): | |
| train_parser = options.get_training_parser() | |
| train_args = options.parse_args_and_arch( | |
| train_parser, | |
| [ | |
| "--task", | |
| "masked_lm", | |
| data_dir, | |
| "--arch", | |
| arch, | |
| "--optimizer", | |
| "adam", | |
| "--lr", | |
| "0.0001", | |
| "--criterion", | |
| "masked_lm", | |
| "--batch-size", | |
| "500", | |
| "--save-dir", | |
| data_dir, | |
| "--max-epoch", | |
| "1", | |
| "--no-progress-bar", | |
| "--distributed-world-size", | |
| "1", | |
| "--ddp-backend", | |
| "no_c10d", | |
| "--num-workers", | |
| "0", | |
| ] | |
| + (extra_flags or []), | |
| ) | |
| train.main(train_args) | |
| def train_roberta_head(data_dir, arch, num_classes=2, extra_flags=None): | |
| train_parser = options.get_training_parser() | |
| train_args = options.parse_args_and_arch( | |
| train_parser, | |
| [ | |
| "--task", | |
| "sentence_prediction", | |
| data_dir, | |
| "--arch", | |
| arch, | |
| "--encoder-layers", | |
| "2", | |
| "--num-classes", | |
| str(num_classes), | |
| "--optimizer", | |
| "adam", | |
| "--lr", | |
| "0.0001", | |
| "--criterion", | |
| "sentence_prediction", | |
| "--max-tokens", | |
| "500", | |
| "--max-positions", | |
| "500", | |
| "--batch-size", | |
| "500", | |
| "--save-dir", | |
| data_dir, | |
| "--max-epoch", | |
| "1", | |
| "--no-progress-bar", | |
| "--distributed-world-size", | |
| "1", | |
| "--ddp-backend", | |
| "no_c10d", | |
| "--num-workers", | |
| "0", | |
| ] | |
| + (extra_flags or []), | |
| ) | |
| train.main(train_args) | |
| def eval_lm_main(data_dir, extra_flags=None): | |
| eval_lm_parser = options.get_eval_lm_parser() | |
| 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", | |
| "--num-workers", | |
| "0", | |
| ] | |
| + (extra_flags or []), | |
| ) | |
| eval_lm.main(eval_lm_args) | |
| if __name__ == "__main__": | |
| unittest.main() | |