├── README.md ├── fairseq_src ├── .gitignore ├── .gitmodules ├── LICENSE ├── README.md ├── examples │ ├── language_model_rdrop │ │ ├── README.md │ │ ├── language_model_rdrop_src │ │ │ ├── __init__.py │ │ │ ├── loss │ │ │ │ ├── reg_adaptive_loss.py │ │ │ │ └── reg_cross_entropy.py │ │ │ └── rdrop_lm.py │ │ └── script │ │ │ ├── evaluate_lm_wiki103.sh │ │ │ ├── prepare_data.sh │ │ │ ├── train_lm_adaptive_wiki103.sh │ │ │ ├── train_lm_adaptive_wiki103_rdrop.sh │ │ │ ├── train_lm_wiki103.sh │ │ │ └── train_lm_wiki103_rdrop.sh │ ├── roberta │ │ ├── README.md │ │ ├── inference.py │ │ ├── roberta_rdrop_src │ │ │ ├── loss │ │ │ │ └── reg_cross_entropy.py │ │ │ └── rdrop_sentence_prediction.py │ │ └── run.sh │ ├── summeration_rdrop │ │ ├── README.md │ │ ├── script │ │ │ ├── preprocess.sh │ │ │ ├── run_inference.py │ │ │ └── run_train.sh │ │ └── summeration_rdrop_src │ │ │ ├── __init__.py │ │ │ ├── loss │ │ │ └── rdrop_cross_entropy_loss.py │ │ │ └── rdrop_translation.py │ └── translation_rdrop │ │ ├── README.md │ │ ├── script │ │ ├── prepare-iwslt14.sh │ │ ├── run_binarize.sh │ │ ├── run_inference.sh │ │ └── run_train.sh │ │ └── translation_rdrop_src │ │ ├── __init__.py │ │ ├── loss │ │ └── reg_label_smooth_cross_entropy.py │ │ └── rdrop_translations.py ├── fairseq │ ├── __init__.py │ ├── benchmark │ │ ├── __init__.py │ │ ├── dummy_lm.py │ │ ├── dummy_masked_lm.py │ │ ├── dummy_model.py │ │ └── dummy_mt.py │ ├── binarizer.py │ ├── checkpoint_utils.py │ ├── clib │ │ ├── cuda │ │ │ ├── ngram_repeat_block_cuda.cpp │ │ │ └── ngram_repeat_block_cuda_kernel.cu │ │ ├── libbleu │ │ │ ├── libbleu.cpp │ │ │ └── module.cpp │ │ ├── libnat │ │ │ └── edit_dist.cpp │ │ └── libnat_cuda │ │ │ ├── binding.cpp │ │ │ ├── edit_dist.cu │ │ │ └── edit_dist.h │ ├── config │ │ ├── __init__.py │ │ ├── config.yaml │ │ └── model │ │ │ ├── transformer_lm │ │ │ ├── transformer_lm_baevski_gbw.yaml │ │ │ ├── transformer_lm_baevski_wiki103.yaml │ │ │ ├── transformer_lm_big.yaml │ │ │ ├── transformer_lm_gbw.yaml │ │ │ ├── transformer_lm_gpt.yaml │ │ │ ├── transformer_lm_gpt2_big.yaml │ │ │ ├── transformer_lm_gpt2_medium.yaml │ │ │ ├── transformer_lm_gpt2_small.yaml │ │ │ └── transformer_lm_wiki103.yaml │ │ │ ├── wav2vec │ │ │ └── vq_wav2vec_gumbel.yaml │ │ │ └── wav2vec2 │ │ │ ├── wav2vec2_base.yaml │ │ │ └── wav2vec2_large.yaml │ ├── criterions │ │ ├── __init__.py │ │ ├── adaptive_loss.py │ │ ├── composite_loss.py │ │ ├── cross_entropy.py │ │ ├── ctc.py │ │ ├── fairseq_criterion.py │ │ ├── label_smoothed_cross_entropy.py │ │ ├── label_smoothed_cross_entropy_with_alignment.py │ │ ├── legacy_masked_lm.py │ │ ├── masked_lm.py │ │ ├── model_criterion.py │ │ ├── nat_loss.py │ │ ├── sentence_prediction.py │ │ ├── sentence_ranking.py │ │ └── wav2vec_criterion.py │ ├── data │ │ ├── __init__.py │ │ ├── add_target_dataset.py │ │ ├── append_token_dataset.py │ │ ├── audio │ │ │ ├── __init__.py │ │ │ ├── audio_utils.py │ │ │ ├── feature_transforms │ │ │ │ ├── __init__.py │ │ │ │ ├── global_cmvn.py │ │ │ │ ├── specaugment.py │ │ │ │ └── utterance_cmvn.py │ │ │ ├── raw_audio_dataset.py │ │ │ └── speech_to_text_dataset.py │ │ ├── backtranslation_dataset.py │ │ ├── base_wrapper_dataset.py │ │ ├── bucket_pad_length_dataset.py │ │ ├── colorize_dataset.py │ │ ├── concat_dataset.py │ │ ├── concat_sentences_dataset.py │ │ ├── data_utils.py │ │ ├── data_utils_fast.cpp │ │ ├── data_utils_fast.pyx │ │ ├── denoising_dataset.py │ │ ├── dictionary.py │ │ ├── encoders │ │ │ ├── __init__.py │ │ │ ├── byte_bpe.py │ │ │ ├── byte_utils.py │ │ │ ├── bytes.py │ │ │ ├── characters.py │ │ │ ├── fastbpe.py │ │ │ ├── gpt2_bpe.py │ │ │ ├── gpt2_bpe_utils.py │ │ │ ├── hf_bert_bpe.py │ │ │ ├── hf_byte_bpe.py │ │ │ ├── moses_tokenizer.py │ │ │ ├── nltk_tokenizer.py │ │ │ ├── sentencepiece_bpe.py │ │ │ ├── space_tokenizer.py │ │ │ ├── subword_nmt_bpe.py │ │ │ └── utils.py │ │ ├── fairseq_dataset.py │ │ ├── fasta_dataset.py │ │ ├── id_dataset.py │ │ ├── indexed_dataset.py │ │ ├── iterators.py │ │ ├── language_pair_dataset.py │ │ ├── legacy │ │ │ ├── __init__.py │ │ │ ├── block_pair_dataset.py │ │ │ ├── masked_lm_dataset.py │ │ │ └── masked_lm_dictionary.py │ │ ├── list_dataset.py │ │ ├── lm_context_window_dataset.py │ │ ├── lru_cache_dataset.py │ │ ├── mask_tokens_dataset.py │ │ ├── monolingual_dataset.py │ │ ├── multi_corpus_dataset.py │ │ ├── multi_corpus_sampled_dataset.py │ │ ├── multilingual │ │ │ ├── __init__.py │ │ │ ├── multilingual_data_manager.py │ │ │ ├── multilingual_utils.py │ │ │ ├── sampled_multi_dataset.py │ │ │ ├── sampled_multi_epoch_dataset.py │ │ │ └── sampling_method.py │ │ ├── nested_dictionary_dataset.py │ │ ├── noising.py │ │ ├── num_samples_dataset.py │ │ ├── numel_dataset.py │ │ ├── offset_tokens_dataset.py │ │ ├── pad_dataset.py │ │ ├── plasma_utils.py │ │ ├── prepend_dataset.py │ │ ├── prepend_token_dataset.py │ │ ├── raw_label_dataset.py │ │ ├── replace_dataset.py │ │ ├── resampling_dataset.py │ │ ├── roll_dataset.py │ │ ├── round_robin_zip_datasets.py │ │ ├── shorten_dataset.py │ │ ├── sort_dataset.py │ │ ├── strip_token_dataset.py │ │ ├── subsample_dataset.py │ │ ├── token_block_dataset.py │ │ ├── token_block_utils_fast.cpp │ │ ├── token_block_utils_fast.pyx │ │ ├── transform_eos_dataset.py │ │ └── transform_eos_lang_pair_dataset.py │ ├── dataclass │ │ ├── __init__.py │ │ ├── configs.py │ │ ├── constants.py │ │ ├── initialize.py │ │ └── utils.py │ ├── distributed │ │ ├── __init__.py │ │ ├── distributed_timeout_wrapper.py │ │ ├── legacy_distributed_data_parallel.py │ │ ├── module_proxy_wrapper.py │ │ ├── tpu_distributed_data_parallel.py │ │ └── utils.py │ ├── file_io.py │ ├── file_utils.py │ ├── hub_utils.py │ ├── incremental_decoding_utils.py │ ├── iterative_refinement_generator.py │ ├── logging │ │ ├── __init__.py │ │ ├── meters.py │ │ ├── metrics.py │ │ └── progress_bar.py │ ├── model_parallel │ │ ├── __init__.py │ │ ├── criterions │ │ │ ├── __init__.py │ │ │ └── vocab_parallel_cross_entropy.py │ │ ├── megatron_trainer.py │ │ ├── models │ │ │ ├── __init__.py │ │ │ ├── pipeline_parallel_transformer │ │ │ │ ├── __init__.py │ │ │ │ ├── layers.py │ │ │ │ └── model.py │ │ │ ├── roberta │ │ │ │ ├── __init__.py │ │ │ │ └── model.py │ │ │ ├── transformer.py │ │ │ └── transformer_lm.py │ │ └── modules │ │ │ ├── __init__.py │ │ │ ├── multihead_attention.py │ │ │ ├── transformer_layer.py │ │ │ ├── transformer_sentence_encoder.py │ │ │ └── transformer_sentence_encoder_layer.py │ ├── models │ │ ├── __init__.py │ │ ├── bart │ │ │ ├── __init__.py │ │ │ ├── hub_interface.py │ │ │ └── model.py │ │ ├── composite_encoder.py │ │ ├── distributed_fairseq_model.py │ │ ├── fairseq_decoder.py │ │ ├── fairseq_encoder.py │ │ ├── fairseq_incremental_decoder.py │ │ ├── fairseq_model.py │ │ ├── fconv.py │ │ ├── fconv_lm.py │ │ ├── fconv_self_att.py │ │ ├── huggingface │ │ │ ├── __init__.py │ │ │ └── hf_gpt2.py │ │ ├── lightconv.py │ │ ├── lightconv_lm.py │ │ ├── lstm.py │ │ ├── lstm_lm.py │ │ ├── masked_lm.py │ │ ├── model_utils.py │ │ ├── multilingual_transformer.py │ │ ├── nat │ │ │ ├── __init__.py │ │ │ ├── cmlm_transformer.py │ │ │ ├── fairseq_nat_model.py │ │ │ ├── insertion_transformer.py │ │ │ ├── iterative_nonautoregressive_transformer.py │ │ │ ├── levenshtein_transformer.py │ │ │ ├── levenshtein_utils.py │ │ │ ├── nat_crf_transformer.py │ │ │ ├── nonautoregressive_ensembles.py │ │ │ └── nonautoregressive_transformer.py │ │ ├── roberta │ │ │ ├── __init__.py │ │ │ ├── alignment_utils.py │ │ │ ├── hub_interface.py │ │ │ ├── model.py │ │ │ ├── model_camembert.py │ │ │ ├── model_gottbert.py │ │ │ └── model_xlmr.py │ │ ├── speech_to_text │ │ │ ├── __init__.py │ │ │ ├── berard.py │ │ │ └── s2t_transformer.py │ │ ├── transformer.py │ │ ├── transformer_align.py │ │ ├── transformer_from_pretrained_xlm.py │ │ ├── transformer_lm.py │ │ └── wav2vec │ │ │ ├── __init__.py │ │ │ ├── wav2vec.py │ │ │ ├── wav2vec2.py │ │ │ └── wav2vec2_asr.py │ ├── modules │ │ ├── __init__.py │ │ ├── adaptive_input.py │ │ ├── adaptive_softmax.py │ │ ├── beamable_mm.py │ │ ├── character_token_embedder.py │ │ ├── checkpoint_activations.py │ │ ├── conv_tbc.py │ │ ├── cross_entropy.py │ │ ├── cuda_utils.cu │ │ ├── downsampled_multihead_attention.py │ │ ├── dynamic_convolution.py │ │ ├── dynamic_crf_layer.py │ │ ├── dynamicconv_layer │ │ │ ├── __init__.py │ │ │ ├── cuda_function_gen.py │ │ │ ├── dynamicconv_cuda.cpp │ │ │ ├── dynamicconv_cuda.cuh │ │ │ ├── dynamicconv_cuda_kernel.cu │ │ │ ├── dynamicconv_layer.py │ │ │ ├── dynamiconv_cpu.cpp │ │ │ └── setup.py │ │ ├── fairseq_dropout.py │ │ ├── fp32_group_norm.py │ │ ├── gelu.py │ │ ├── grad_multiply.py │ │ ├── gumbel_vector_quantizer.py │ │ ├── kmeans_vector_quantizer.py │ │ ├── layer_drop.py │ │ ├── layer_norm.py │ │ ├── learned_positional_embedding.py │ │ ├── lightconv_layer │ │ │ ├── __init__.py │ │ │ ├── cuda_function_gen.py │ │ │ ├── lightconv_cuda.cpp │ │ │ ├── lightconv_cuda.cuh │ │ │ ├── lightconv_cuda_kernel.cu │ │ │ ├── lightconv_layer.py │ │ │ └── setup.py │ │ ├── lightweight_convolution.py │ │ ├── linearized_convolution.py │ │ ├── multihead_attention.py │ │ ├── positional_embedding.py │ │ ├── quant_noise.py │ │ ├── quantization │ │ │ ├── __init__.py │ │ │ ├── pq │ │ │ │ ├── __init__.py │ │ │ │ ├── em.py │ │ │ │ ├── modules │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── qconv.py │ │ │ │ │ ├── qemb.py │ │ │ │ │ └── qlinear.py │ │ │ │ ├── pq.py │ │ │ │ └── utils.py │ │ │ ├── quantization_options.py │ │ │ └── scalar │ │ │ │ ├── __init__.py │ │ │ │ ├── modules │ │ │ │ ├── __init__.py │ │ │ │ ├── qact.py │ │ │ │ ├── qconv.py │ │ │ │ ├── qemb.py │ │ │ │ └── qlinear.py │ │ │ │ ├── ops.py │ │ │ │ └── utils.py │ │ ├── same_pad.py │ │ ├── scalar_bias.py │ │ ├── sinusoidal_positional_embedding.py │ │ ├── sparse_multihead_attention.py │ │ ├── sparse_transformer_sentence_encoder.py │ │ ├── sparse_transformer_sentence_encoder_layer.py │ │ ├── transformer_layer.py │ │ ├── transformer_sentence_encoder.py │ │ ├── transformer_sentence_encoder_layer.py │ │ ├── transpose_last.py │ │ ├── unfold.py │ │ └── vggblock.py │ ├── nan_detector.py │ ├── ngram_repeat_block.py │ ├── optim │ │ ├── __init__.py │ │ ├── adadelta.py │ │ ├── adafactor.py │ │ ├── adagrad.py │ │ ├── adam.py │ │ ├── adamax.py │ │ ├── bmuf.py │ │ ├── composite.py │ │ ├── cpu_adam.py │ │ ├── dynamic_loss_scaler.py │ │ ├── fairseq_optimizer.py │ │ ├── fp16_optimizer.py │ │ ├── fused_adam.py │ │ ├── fused_lamb.py │ │ ├── lr_scheduler │ │ │ ├── __init__.py │ │ │ ├── cosine_lr_scheduler.py │ │ │ ├── fairseq_lr_scheduler.py │ │ │ ├── fixed_schedule.py │ │ │ ├── inverse_square_root_schedule.py │ │ │ ├── manual_lr_scheduler.py │ │ │ ├── pass_through.py │ │ │ ├── polynomial_decay_schedule.py │ │ │ ├── reduce_lr_on_plateau.py │ │ │ ├── tri_stage_lr_scheduler.py │ │ │ └── triangular_lr_scheduler.py │ │ ├── nag.py │ │ ├── sgd.py │ │ └── shard.py │ ├── options.py │ ├── pdb.py │ ├── quantization_utils.py │ ├── registry.py │ ├── scoring │ │ ├── __init__.py │ │ ├── bleu.py │ │ ├── chrf.py │ │ ├── tokenizer.py │ │ └── wer.py │ ├── search.py │ ├── sequence_generator.py │ ├── sequence_scorer.py │ ├── tasks │ │ ├── __init__.py │ │ ├── audio_pretraining.py │ │ ├── cross_lingual_lm.py │ │ ├── denoising.py │ │ ├── fairseq_task.py │ │ ├── language_modeling.py │ │ ├── legacy_masked_lm.py │ │ ├── masked_lm.py │ │ ├── multilingual_denoising.py │ │ ├── multilingual_masked_lm.py │ │ ├── multilingual_translation.py │ │ ├── semisupervised_translation.py │ │ ├── sentence_prediction.py │ │ ├── sentence_ranking.py │ │ ├── speech_to_text.py │ │ ├── translation.py │ │ ├── translation_from_pretrained_bart.py │ │ ├── translation_from_pretrained_xlm.py │ │ ├── translation_lev.py │ │ └── translation_multi_simple_epoch.py │ ├── token_generation_constraints.py │ ├── tokenizer.py │ ├── trainer.py │ ├── utils.py │ ├── version.py │ └── version.txt ├── fairseq_cli │ ├── __init__.py │ ├── eval_lm.py │ ├── generate.py │ ├── hydra_train.py │ ├── interactive.py │ ├── preprocess.py │ ├── score.py │ ├── train.py │ └── validate.py ├── hubconf.py ├── pyproject.toml ├── scripts │ ├── __init__.py │ ├── average_checkpoints.py │ ├── build_sym_alignment.py │ ├── compare_namespaces.py │ ├── compound_split_bleu.sh │ ├── constraints │ │ ├── extract.py │ │ └── validate.py │ ├── convert_dictionary.lua │ ├── convert_model.lua │ ├── count_docs.py │ ├── read_binarized.py │ ├── rm_pt.py │ ├── sacrebleu.sh │ ├── shard_docs.py │ ├── split_train_valid_docs.py │ ├── spm_decode.py │ ├── spm_encode.py │ └── spm_train.py ├── setup.py └── train.py ├── huggingface_transformer_src ├── LICENSE ├── MANIFEST.in ├── Makefile ├── README.md ├── bert_rdrop │ ├── run.sh │ └── run_glue.py ├── pyproject.toml ├── setup.cfg ├── setup.py ├── src │ ├── transformers.egg-info │ │ ├── PKG-INFO │ │ ├── SOURCES.txt │ │ ├── dependency_links.txt │ │ ├── entry_points.txt │ │ ├── requires.txt │ │ └── top_level.txt │ └── transformers │ │ ├── __init__.py │ │ ├── __pycache__ │ │ ├── __init__.cpython-36.pyc │ │ ├── activations.cpython-36.pyc │ │ ├── configuration_utils.cpython-36.pyc │ │ ├── convert_slow_tokenizer.cpython-36.pyc │ │ ├── dependency_versions_check.cpython-36.pyc │ │ ├── dependency_versions_table.cpython-36.pyc │ │ ├── file_utils.cpython-36.pyc │ │ ├── generation_beam_search.cpython-36.pyc │ │ ├── generation_logits_process.cpython-36.pyc │ │ ├── generation_stopping_criteria.cpython-36.pyc │ │ ├── generation_utils.cpython-36.pyc │ │ ├── hf_api.cpython-36.pyc │ │ ├── hf_argparser.cpython-36.pyc │ │ ├── integrations.cpython-36.pyc │ │ ├── modeling_outputs.cpython-36.pyc │ │ ├── modeling_utils.cpython-36.pyc │ │ ├── optimization.cpython-36.pyc │ │ ├── tokenization_utils.cpython-36.pyc │ │ ├── tokenization_utils_base.cpython-36.pyc │ │ ├── tokenization_utils_fast.cpython-36.pyc │ │ ├── trainer.cpython-36.pyc │ │ ├── trainer_callback.cpython-36.pyc │ │ ├── trainer_pt_utils.cpython-36.pyc │ │ ├── trainer_utils.cpython-36.pyc │ │ └── training_args.cpython-36.pyc │ │ ├── activations.py │ │ ├── activations_tf.py │ │ ├── benchmark │ │ ├── __init__.py │ │ ├── benchmark.py │ │ ├── benchmark_args.py │ │ ├── benchmark_args_tf.py │ │ ├── benchmark_args_utils.py │ │ ├── benchmark_tf.py │ │ └── benchmark_utils.py │ │ ├── commands │ │ ├── __init__.py │ │ ├── add_new_model.py │ │ ├── convert.py │ │ ├── download.py │ │ ├── env.py │ │ ├── lfs.py │ │ ├── run.py │ │ ├── serving.py │ │ ├── train.py │ │ ├── transformers_cli.py │ │ └── user.py │ │ ├── configuration_utils.py │ │ ├── convert_graph_to_onnx.py │ │ ├── convert_pytorch_checkpoint_to_tf2.py │ │ ├── convert_slow_tokenizer.py │ │ ├── convert_slow_tokenizers_checkpoints_to_fast.py │ │ ├── convert_tf_hub_seq_to_seq_bert_to_pytorch.py │ │ ├── data │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-36.pyc │ │ │ └── data_collator.cpython-36.pyc │ │ ├── data_collator.py │ │ ├── datasets │ │ │ ├── __init__.py │ │ │ ├── glue.py │ │ │ ├── language_modeling.py │ │ │ └── squad.py │ │ ├── metrics │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ └── __init__.cpython-36.pyc │ │ │ └── squad_metrics.py │ │ ├── processors │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── glue.cpython-36.pyc │ │ │ │ ├── squad.cpython-36.pyc │ │ │ │ ├── utils.cpython-36.pyc │ │ │ │ └── xnli.cpython-36.pyc │ │ │ ├── glue.py │ │ │ ├── squad.py │ │ │ ├── utils.py │ │ │ └── xnli.py │ │ └── test_generation_utils.py │ │ ├── dependency_versions_check.py │ │ ├── dependency_versions_table.py │ │ ├── feature_extraction_sequence_utils.py │ │ ├── feature_extraction_utils.py │ │ ├── file_utils.py │ │ ├── generation_beam_search.py │ │ ├── generation_logits_process.py │ │ ├── generation_stopping_criteria.py │ │ ├── generation_tf_utils.py │ │ ├── generation_utils.py │ │ ├── hf_api.py │ │ ├── hf_argparser.py │ │ ├── image_utils.py │ │ ├── integrations.py │ │ ├── modelcard.py │ │ ├── modeling_flax_pytorch_utils.py │ │ ├── modeling_flax_utils.py │ │ ├── modeling_outputs.py │ │ ├── modeling_tf_outputs.py │ │ ├── modeling_tf_pytorch_utils.py │ │ ├── modeling_tf_utils.py │ │ ├── modeling_utils.py │ │ ├── models │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ └── __init__.cpython-36.pyc │ │ ├── albert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_albert.cpython-36.pyc │ │ │ │ ├── modeling_albert.cpython-36.pyc │ │ │ │ ├── tokenization_albert.cpython-36.pyc │ │ │ │ └── tokenization_albert_fast.cpython-36.pyc │ │ │ ├── configuration_albert.py │ │ │ ├── convert_albert_original_tf_checkpoint_to_pytorch.py │ │ │ ├── modeling_albert.py │ │ │ ├── modeling_tf_albert.py │ │ │ ├── tokenization_albert.py │ │ │ └── tokenization_albert_fast.py │ │ ├── auto │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── auto_factory.cpython-36.pyc │ │ │ │ ├── configuration_auto.cpython-36.pyc │ │ │ │ ├── modeling_auto.cpython-36.pyc │ │ │ │ └── tokenization_auto.cpython-36.pyc │ │ │ ├── auto_factory.py │ │ │ ├── configuration_auto.py │ │ │ ├── feature_extraction_auto.py │ │ │ ├── modeling_auto.py │ │ │ ├── modeling_flax_auto.py │ │ │ ├── modeling_tf_auto.py │ │ │ └── tokenization_auto.py │ │ ├── bart │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_bart.cpython-36.pyc │ │ │ │ ├── modeling_bart.cpython-36.pyc │ │ │ │ ├── tokenization_bart.cpython-36.pyc │ │ │ │ └── tokenization_bart_fast.cpython-36.pyc │ │ │ ├── configuration_bart.py │ │ │ ├── convert_bart_original_pytorch_checkpoint_to_pytorch.py │ │ │ ├── modeling_bart.py │ │ │ ├── modeling_tf_bart.py │ │ │ ├── tokenization_bart.py │ │ │ └── tokenization_bart_fast.py │ │ ├── barthez │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── tokenization_barthez.cpython-36.pyc │ │ │ │ └── tokenization_barthez_fast.cpython-36.pyc │ │ │ ├── tokenization_barthez.py │ │ │ └── tokenization_barthez_fast.py │ │ ├── bert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_bert.cpython-36.pyc │ │ │ │ ├── modeling_bert.cpython-36.pyc │ │ │ │ ├── tokenization_bert.cpython-36.pyc │ │ │ │ └── tokenization_bert_fast.cpython-36.pyc │ │ │ ├── configuration_bert.py │ │ │ ├── convert_bert_original_tf2_checkpoint_to_pytorch.py │ │ │ ├── convert_bert_original_tf_checkpoint_to_pytorch.py │ │ │ ├── convert_bert_pytorch_checkpoint_to_original_tf.py │ │ │ ├── modeling_bert copy.py │ │ │ ├── modeling_bert.py │ │ │ ├── modeling_flax_bert.py │ │ │ ├── modeling_tf_bert.py │ │ │ ├── tokenization_bert.py │ │ │ └── tokenization_bert_fast.py │ │ ├── bert_generation │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_bert_generation.cpython-36.pyc │ │ │ │ ├── modeling_bert_generation.cpython-36.pyc │ │ │ │ └── tokenization_bert_generation.cpython-36.pyc │ │ │ ├── configuration_bert_generation.py │ │ │ ├── modeling_bert_generation.py │ │ │ └── tokenization_bert_generation.py │ │ ├── bert_japanese │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ └── tokenization_bert_japanese.cpython-36.pyc │ │ │ └── tokenization_bert_japanese.py │ │ ├── bertweet │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ └── tokenization_bertweet.cpython-36.pyc │ │ │ └── tokenization_bertweet.py │ │ ├── big_bird │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_big_bird.cpython-36.pyc │ │ │ │ ├── modeling_big_bird.cpython-36.pyc │ │ │ │ └── tokenization_big_bird.cpython-36.pyc │ │ │ ├── configuration_big_bird.py │ │ │ ├── convert_bigbird_original_tf_checkpoint_to_pytorch.py │ │ │ ├── modeling_big_bird.py │ │ │ └── tokenization_big_bird.py │ │ ├── blenderbot │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_blenderbot.cpython-36.pyc │ │ │ │ ├── modeling_blenderbot.cpython-36.pyc │ │ │ │ └── tokenization_blenderbot.cpython-36.pyc │ │ │ ├── configuration_blenderbot.py │ │ │ ├── convert_blenderbot_original_pytorch_checkpoint_to_pytorch.py │ │ │ ├── modeling_blenderbot.py │ │ │ ├── modeling_tf_blenderbot.py │ │ │ └── tokenization_blenderbot.py │ │ ├── blenderbot_small │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_blenderbot_small.cpython-36.pyc │ │ │ │ ├── modeling_blenderbot_small.cpython-36.pyc │ │ │ │ └── tokenization_blenderbot_small.cpython-36.pyc │ │ │ ├── configuration_blenderbot_small.py │ │ │ ├── modeling_blenderbot_small.py │ │ │ ├── modeling_tf_blenderbot_small.py │ │ │ ├── tokenization_blenderbot_small.py │ │ │ └── tokenization_blenderbot_small_fast.py │ │ ├── bort │ │ │ └── convert_bort_original_gluonnlp_checkpoint_to_pytorch.py │ │ ├── camembert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_camembert.cpython-36.pyc │ │ │ │ ├── modeling_camembert.cpython-36.pyc │ │ │ │ ├── tokenization_camembert.cpython-36.pyc │ │ │ │ └── tokenization_camembert_fast.cpython-36.pyc │ │ │ ├── configuration_camembert.py │ │ │ ├── modeling_camembert.py │ │ │ ├── modeling_tf_camembert.py │ │ │ ├── tokenization_camembert.py │ │ │ └── tokenization_camembert_fast.py │ │ ├── convbert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_convbert.cpython-36.pyc │ │ │ │ ├── modeling_convbert.cpython-36.pyc │ │ │ │ ├── tokenization_convbert.cpython-36.pyc │ │ │ │ └── tokenization_convbert_fast.cpython-36.pyc │ │ │ ├── configuration_convbert.py │ │ │ ├── convert_convbert_original_tf1_checkpoint_to_pytorch_and_tf2.py │ │ │ ├── modeling_convbert.py │ │ │ ├── modeling_tf_convbert.py │ │ │ ├── tokenization_convbert.py │ │ │ └── tokenization_convbert_fast.py │ │ ├── cpm │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ └── tokenization_cpm.cpython-36.pyc │ │ │ └── tokenization_cpm.py │ │ ├── ctrl │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_ctrl.cpython-36.pyc │ │ │ │ ├── modeling_ctrl.cpython-36.pyc │ │ │ │ └── tokenization_ctrl.cpython-36.pyc │ │ │ ├── configuration_ctrl.py │ │ │ ├── modeling_ctrl.py │ │ │ ├── modeling_tf_ctrl.py │ │ │ └── tokenization_ctrl.py │ │ ├── deberta │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_deberta.cpython-36.pyc │ │ │ │ ├── modeling_deberta.cpython-36.pyc │ │ │ │ └── tokenization_deberta.cpython-36.pyc │ │ │ ├── configuration_deberta.py │ │ │ ├── modeling_deberta.py │ │ │ └── tokenization_deberta.py │ │ ├── deberta_v2 │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_deberta_v2.cpython-36.pyc │ │ │ │ ├── modeling_deberta_v2.cpython-36.pyc │ │ │ │ └── tokenization_deberta_v2.cpython-36.pyc │ │ │ ├── configuration_deberta_v2.py │ │ │ ├── modeling_deberta_v2.py │ │ │ └── tokenization_deberta_v2.py │ │ ├── deit │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_deit.cpython-36.pyc │ │ │ │ └── modeling_deit.cpython-36.pyc │ │ │ ├── configuration_deit.py │ │ │ ├── convert_deit_timm_to_pytorch.py │ │ │ ├── feature_extraction_deit.py │ │ │ └── modeling_deit.py │ │ ├── dialogpt │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ └── __init__.cpython-36.pyc │ │ │ └── convert_dialogpt_original_pytorch_checkpoint_to_pytorch.py │ │ ├── distilbert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_distilbert.cpython-36.pyc │ │ │ │ ├── modeling_distilbert.cpython-36.pyc │ │ │ │ ├── tokenization_distilbert.cpython-36.pyc │ │ │ │ └── tokenization_distilbert_fast.cpython-36.pyc │ │ │ ├── configuration_distilbert.py │ │ │ ├── modeling_distilbert.py │ │ │ ├── modeling_tf_distilbert.py │ │ │ ├── tokenization_distilbert.py │ │ │ └── tokenization_distilbert_fast.py │ │ ├── dpr │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_dpr.cpython-36.pyc │ │ │ │ ├── modeling_dpr.cpython-36.pyc │ │ │ │ ├── tokenization_dpr.cpython-36.pyc │ │ │ │ └── tokenization_dpr_fast.cpython-36.pyc │ │ │ ├── configuration_dpr.py │ │ │ ├── convert_dpr_original_checkpoint_to_pytorch.py │ │ │ ├── modeling_dpr.py │ │ │ ├── modeling_tf_dpr.py │ │ │ ├── tokenization_dpr.py │ │ │ └── tokenization_dpr_fast.py │ │ ├── electra │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_electra.cpython-36.pyc │ │ │ │ ├── modeling_electra.cpython-36.pyc │ │ │ │ ├── tokenization_electra.cpython-36.pyc │ │ │ │ └── tokenization_electra_fast.cpython-36.pyc │ │ │ ├── configuration_electra.py │ │ │ ├── convert_electra_original_tf_checkpoint_to_pytorch.py │ │ │ ├── modeling_electra.py │ │ │ ├── modeling_tf_electra.py │ │ │ ├── tokenization_electra.py │ │ │ └── tokenization_electra_fast.py │ │ ├── encoder_decoder │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_encoder_decoder.cpython-36.pyc │ │ │ │ └── modeling_encoder_decoder.cpython-36.pyc │ │ │ ├── configuration_encoder_decoder.py │ │ │ └── modeling_encoder_decoder.py │ │ ├── flaubert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_flaubert.cpython-36.pyc │ │ │ │ ├── modeling_flaubert.cpython-36.pyc │ │ │ │ └── tokenization_flaubert.cpython-36.pyc │ │ │ ├── configuration_flaubert.py │ │ │ ├── modeling_flaubert.py │ │ │ ├── modeling_tf_flaubert.py │ │ │ └── tokenization_flaubert.py │ │ ├── fsmt │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_fsmt.cpython-36.pyc │ │ │ │ ├── modeling_fsmt.cpython-36.pyc │ │ │ │ └── tokenization_fsmt.cpython-36.pyc │ │ │ ├── configuration_fsmt.py │ │ │ ├── convert_fsmt_original_pytorch_checkpoint_to_pytorch.py │ │ │ ├── modeling_fsmt.py │ │ │ └── tokenization_fsmt.py │ │ ├── funnel │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_funnel.cpython-36.pyc │ │ │ │ ├── modeling_funnel.cpython-36.pyc │ │ │ │ ├── tokenization_funnel.cpython-36.pyc │ │ │ │ └── tokenization_funnel_fast.cpython-36.pyc │ │ │ ├── configuration_funnel.py │ │ │ ├── convert_funnel_original_tf_checkpoint_to_pytorch.py │ │ │ ├── modeling_funnel.py │ │ │ ├── modeling_tf_funnel.py │ │ │ ├── tokenization_funnel.py │ │ │ └── tokenization_funnel_fast.py │ │ ├── gpt2 │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_gpt2.cpython-36.pyc │ │ │ │ ├── modeling_gpt2.cpython-36.pyc │ │ │ │ ├── tokenization_gpt2.cpython-36.pyc │ │ │ │ └── tokenization_gpt2_fast.cpython-36.pyc │ │ │ ├── configuration_gpt2.py │ │ │ ├── convert_gpt2_original_tf_checkpoint_to_pytorch.py │ │ │ ├── modeling_gpt2.py │ │ │ ├── modeling_tf_gpt2.py │ │ │ ├── tokenization_gpt2.py │ │ │ └── tokenization_gpt2_fast.py │ │ ├── gpt_neo │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_gpt_neo.cpython-36.pyc │ │ │ │ └── modeling_gpt_neo.cpython-36.pyc │ │ │ ├── configuration_gpt_neo.py │ │ │ ├── convert_gpt_neo_mesh_tf_to_pytorch.py │ │ │ └── modeling_gpt_neo.py │ │ ├── herbert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── tokenization_herbert.cpython-36.pyc │ │ │ │ └── tokenization_herbert_fast.cpython-36.pyc │ │ │ ├── tokenization_herbert.py │ │ │ └── tokenization_herbert_fast.py │ │ ├── ibert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_ibert.cpython-36.pyc │ │ │ │ ├── modeling_ibert.cpython-36.pyc │ │ │ │ └── quant_modules.cpython-36.pyc │ │ │ ├── configuration_ibert.py │ │ │ ├── modeling_ibert.py │ │ │ └── quant_modules.py │ │ ├── layoutlm │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_layoutlm.cpython-36.pyc │ │ │ │ ├── modeling_layoutlm.cpython-36.pyc │ │ │ │ ├── tokenization_layoutlm.cpython-36.pyc │ │ │ │ └── tokenization_layoutlm_fast.cpython-36.pyc │ │ │ ├── configuration_layoutlm.py │ │ │ ├── modeling_layoutlm.py │ │ │ ├── modeling_tf_layoutlm.py │ │ │ ├── tokenization_layoutlm.py │ │ │ └── tokenization_layoutlm_fast.py │ │ ├── led │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_led.cpython-36.pyc │ │ │ │ ├── modeling_led.cpython-36.pyc │ │ │ │ ├── tokenization_led.cpython-36.pyc │ │ │ │ └── tokenization_led_fast.cpython-36.pyc │ │ │ ├── configuration_led.py │ │ │ ├── modeling_led.py │ │ │ ├── modeling_tf_led.py │ │ │ ├── tokenization_led.py │ │ │ └── tokenization_led_fast.py │ │ ├── longformer │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_longformer.cpython-36.pyc │ │ │ │ ├── modeling_longformer.cpython-36.pyc │ │ │ │ ├── tokenization_longformer.cpython-36.pyc │ │ │ │ └── tokenization_longformer_fast.cpython-36.pyc │ │ │ ├── configuration_longformer.py │ │ │ ├── convert_longformer_original_pytorch_lightning_to_pytorch.py │ │ │ ├── modeling_longformer.py │ │ │ ├── modeling_tf_longformer.py │ │ │ ├── tokenization_longformer.py │ │ │ └── tokenization_longformer_fast.py │ │ ├── lxmert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_lxmert.cpython-36.pyc │ │ │ │ ├── modeling_lxmert.cpython-36.pyc │ │ │ │ ├── tokenization_lxmert.cpython-36.pyc │ │ │ │ └── tokenization_lxmert_fast.cpython-36.pyc │ │ │ ├── configuration_lxmert.py │ │ │ ├── convert_lxmert_original_tf_checkpoint_to_pytorch.py │ │ │ ├── modeling_lxmert.py │ │ │ ├── modeling_tf_lxmert.py │ │ │ ├── tokenization_lxmert.py │ │ │ └── tokenization_lxmert_fast.py │ │ ├── m2m_100 │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_m2m_100.cpython-36.pyc │ │ │ │ ├── modeling_m2m_100.cpython-36.pyc │ │ │ │ └── tokenization_m2m_100.cpython-36.pyc │ │ │ ├── configuration_m2m_100.py │ │ │ ├── convert_m2m100_original_checkpoint_to_pytorch.py │ │ │ ├── modeling_m2m_100.py │ │ │ └── tokenization_m2m_100.py │ │ ├── marian │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_marian.cpython-36.pyc │ │ │ │ ├── modeling_marian.cpython-36.pyc │ │ │ │ └── tokenization_marian.cpython-36.pyc │ │ │ ├── configuration_marian.py │ │ │ ├── convert_marian_tatoeba_to_pytorch.py │ │ │ ├── convert_marian_to_pytorch.py │ │ │ ├── modeling_marian.py │ │ │ ├── modeling_tf_marian.py │ │ │ └── tokenization_marian.py │ │ ├── mbart │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_mbart.cpython-36.pyc │ │ │ │ ├── modeling_mbart.cpython-36.pyc │ │ │ │ ├── tokenization_mbart.cpython-36.pyc │ │ │ │ ├── tokenization_mbart50.cpython-36.pyc │ │ │ │ ├── tokenization_mbart50_fast.cpython-36.pyc │ │ │ │ └── tokenization_mbart_fast.cpython-36.pyc │ │ │ ├── configuration_mbart.py │ │ │ ├── convert_mbart_original_checkpoint_to_pytorch.py │ │ │ ├── modeling_mbart.py │ │ │ ├── modeling_tf_mbart.py │ │ │ ├── tokenization_mbart.py │ │ │ ├── tokenization_mbart50.py │ │ │ ├── tokenization_mbart50_fast.py │ │ │ └── tokenization_mbart_fast.py │ │ ├── megatron_bert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_megatron_bert.cpython-36.pyc │ │ │ │ └── modeling_megatron_bert.cpython-36.pyc │ │ │ ├── configuration_megatron_bert.py │ │ │ ├── convert_megatron_bert_checkpoint.py │ │ │ └── modeling_megatron_bert.py │ │ ├── megatron_gpt2 │ │ │ └── convert_megatron_gpt2_checkpoint.py │ │ ├── mmbt │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ └── __init__.cpython-36.pyc │ │ │ ├── configuration_mmbt.py │ │ │ └── modeling_mmbt.py │ │ ├── mobilebert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_mobilebert.cpython-36.pyc │ │ │ │ ├── modeling_mobilebert.cpython-36.pyc │ │ │ │ ├── tokenization_mobilebert.cpython-36.pyc │ │ │ │ └── tokenization_mobilebert_fast.cpython-36.pyc │ │ │ ├── configuration_mobilebert.py │ │ │ ├── convert_mobilebert_original_tf_checkpoint_to_pytorch.py │ │ │ ├── modeling_mobilebert.py │ │ │ ├── modeling_tf_mobilebert.py │ │ │ ├── tokenization_mobilebert.py │ │ │ └── tokenization_mobilebert_fast.py │ │ ├── mpnet │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_mpnet.cpython-36.pyc │ │ │ │ ├── modeling_mpnet.cpython-36.pyc │ │ │ │ ├── tokenization_mpnet.cpython-36.pyc │ │ │ │ └── tokenization_mpnet_fast.cpython-36.pyc │ │ │ ├── configuration_mpnet.py │ │ │ ├── modeling_mpnet.py │ │ │ ├── modeling_tf_mpnet.py │ │ │ ├── tokenization_mpnet.py │ │ │ └── tokenization_mpnet_fast.py │ │ ├── mt5 │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_mt5.cpython-36.pyc │ │ │ │ └── modeling_mt5.cpython-36.pyc │ │ │ ├── configuration_mt5.py │ │ │ ├── modeling_mt5.py │ │ │ └── modeling_tf_mt5.py │ │ ├── openai │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_openai.cpython-36.pyc │ │ │ │ ├── modeling_openai.cpython-36.pyc │ │ │ │ ├── tokenization_openai.cpython-36.pyc │ │ │ │ └── tokenization_openai_fast.cpython-36.pyc │ │ │ ├── configuration_openai.py │ │ │ ├── convert_openai_original_tf_checkpoint_to_pytorch.py │ │ │ ├── modeling_openai.py │ │ │ ├── modeling_tf_openai.py │ │ │ ├── tokenization_openai.py │ │ │ └── tokenization_openai_fast.py │ │ ├── pegasus │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_pegasus.cpython-36.pyc │ │ │ │ ├── modeling_pegasus.cpython-36.pyc │ │ │ │ ├── tokenization_pegasus.cpython-36.pyc │ │ │ │ └── tokenization_pegasus_fast.cpython-36.pyc │ │ │ ├── configuration_pegasus.py │ │ │ ├── convert_pegasus_tf_to_pytorch.py │ │ │ ├── modeling_pegasus.py │ │ │ ├── modeling_tf_pegasus.py │ │ │ ├── tokenization_pegasus.py │ │ │ └── tokenization_pegasus_fast.py │ │ ├── phobert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ └── tokenization_phobert.cpython-36.pyc │ │ │ └── tokenization_phobert.py │ │ ├── prophetnet │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_prophetnet.cpython-36.pyc │ │ │ │ ├── modeling_prophetnet.cpython-36.pyc │ │ │ │ └── tokenization_prophetnet.cpython-36.pyc │ │ │ ├── configuration_prophetnet.py │ │ │ ├── convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py │ │ │ ├── modeling_prophetnet.py │ │ │ └── tokenization_prophetnet.py │ │ ├── rag │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_rag.cpython-36.pyc │ │ │ │ ├── modeling_rag.cpython-36.pyc │ │ │ │ ├── retrieval_rag.cpython-36.pyc │ │ │ │ └── tokenization_rag.cpython-36.pyc │ │ │ ├── configuration_rag.py │ │ │ ├── modeling_rag.py │ │ │ ├── modeling_tf_rag.py │ │ │ ├── retrieval_rag.py │ │ │ └── tokenization_rag.py │ │ ├── reformer │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_reformer.cpython-36.pyc │ │ │ │ ├── modeling_reformer.cpython-36.pyc │ │ │ │ ├── tokenization_reformer.cpython-36.pyc │ │ │ │ └── tokenization_reformer_fast.cpython-36.pyc │ │ │ ├── configuration_reformer.py │ │ │ ├── convert_reformer_trax_checkpoint_to_pytorch.py │ │ │ ├── modeling_reformer.py │ │ │ ├── tokenization_reformer.py │ │ │ └── tokenization_reformer_fast.py │ │ ├── retribert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_retribert.cpython-36.pyc │ │ │ │ ├── modeling_retribert.cpython-36.pyc │ │ │ │ ├── tokenization_retribert.cpython-36.pyc │ │ │ │ └── tokenization_retribert_fast.cpython-36.pyc │ │ │ ├── configuration_retribert.py │ │ │ ├── modeling_retribert.py │ │ │ ├── tokenization_retribert.py │ │ │ └── tokenization_retribert_fast.py │ │ ├── roberta │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_roberta.cpython-36.pyc │ │ │ │ ├── modeling_roberta.cpython-36.pyc │ │ │ │ ├── tokenization_roberta.cpython-36.pyc │ │ │ │ └── tokenization_roberta_fast.cpython-36.pyc │ │ │ ├── configuration_roberta.py │ │ │ ├── convert_roberta_original_pytorch_checkpoint_to_pytorch.py │ │ │ ├── modeling_flax_roberta.py │ │ │ ├── modeling_roberta.py │ │ │ ├── modeling_tf_roberta.py │ │ │ ├── tokenization_roberta.py │ │ │ └── tokenization_roberta_fast.py │ │ ├── speech_to_text │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_speech_to_text.cpython-36.pyc │ │ │ │ ├── modeling_speech_to_text.cpython-36.pyc │ │ │ │ └── tokenization_speech_to_text.cpython-36.pyc │ │ │ ├── configuration_speech_to_text.py │ │ │ ├── convert_s2t_fairseq_to_tfms.py │ │ │ ├── feature_extraction_speech_to_text.py │ │ │ ├── modeling_speech_to_text.py │ │ │ ├── processing_speech_to_text.py │ │ │ └── tokenization_speech_to_text.py │ │ ├── squeezebert │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_squeezebert.cpython-36.pyc │ │ │ │ ├── modeling_squeezebert.cpython-36.pyc │ │ │ │ ├── tokenization_squeezebert.cpython-36.pyc │ │ │ │ └── tokenization_squeezebert_fast.cpython-36.pyc │ │ │ ├── configuration_squeezebert.py │ │ │ ├── modeling_squeezebert.py │ │ │ ├── tokenization_squeezebert.py │ │ │ └── tokenization_squeezebert_fast.py │ │ ├── t5 │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_t5.cpython-36.pyc │ │ │ │ ├── modeling_t5.cpython-36.pyc │ │ │ │ ├── tokenization_t5.cpython-36.pyc │ │ │ │ └── tokenization_t5_fast.cpython-36.pyc │ │ │ ├── configuration_t5.py │ │ │ ├── convert_t5_original_tf_checkpoint_to_pytorch.py │ │ │ ├── modeling_t5.py │ │ │ ├── modeling_tf_t5.py │ │ │ ├── tokenization_t5.py │ │ │ └── tokenization_t5_fast.py │ │ ├── tapas │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_tapas.cpython-36.pyc │ │ │ │ ├── modeling_tapas.cpython-36.pyc │ │ │ │ └── tokenization_tapas.cpython-36.pyc │ │ │ ├── configuration_tapas.py │ │ │ ├── convert_tapas_original_tf_checkpoint_to_pytorch.py │ │ │ ├── modeling_tapas.py │ │ │ └── tokenization_tapas.py │ │ ├── transfo_xl │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_transfo_xl.cpython-36.pyc │ │ │ │ ├── modeling_transfo_xl.cpython-36.pyc │ │ │ │ ├── modeling_transfo_xl_utilities.cpython-36.pyc │ │ │ │ └── tokenization_transfo_xl.cpython-36.pyc │ │ │ ├── configuration_transfo_xl.py │ │ │ ├── convert_transfo_xl_original_tf_checkpoint_to_pytorch.py │ │ │ ├── modeling_tf_transfo_xl.py │ │ │ ├── modeling_tf_transfo_xl_utilities.py │ │ │ ├── modeling_transfo_xl.py │ │ │ ├── modeling_transfo_xl_utilities.py │ │ │ └── tokenization_transfo_xl.py │ │ ├── vit │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_vit.cpython-36.pyc │ │ │ │ └── modeling_vit.cpython-36.pyc │ │ │ ├── configuration_vit.py │ │ │ ├── convert_vit_timm_to_pytorch.py │ │ │ ├── feature_extraction_vit.py │ │ │ └── modeling_vit.py │ │ ├── wav2vec2 │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_wav2vec2.cpython-36.pyc │ │ │ │ ├── modeling_wav2vec2.cpython-36.pyc │ │ │ │ └── tokenization_wav2vec2.cpython-36.pyc │ │ │ ├── configuration_wav2vec2.py │ │ │ ├── convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py │ │ │ ├── feature_extraction_wav2vec2.py │ │ │ ├── modeling_wav2vec2.py │ │ │ ├── processing_wav2vec2.py │ │ │ └── tokenization_wav2vec2.py │ │ ├── xlm │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_xlm.cpython-36.pyc │ │ │ │ ├── modeling_xlm.cpython-36.pyc │ │ │ │ └── tokenization_xlm.cpython-36.pyc │ │ │ ├── configuration_xlm.py │ │ │ ├── convert_xlm_original_pytorch_checkpoint_to_pytorch.py │ │ │ ├── modeling_tf_xlm.py │ │ │ ├── modeling_xlm.py │ │ │ └── tokenization_xlm.py │ │ ├── xlm_prophetnet │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_xlm_prophetnet.cpython-36.pyc │ │ │ │ ├── modeling_xlm_prophetnet.cpython-36.pyc │ │ │ │ └── tokenization_xlm_prophetnet.cpython-36.pyc │ │ │ ├── configuration_xlm_prophetnet.py │ │ │ ├── modeling_xlm_prophetnet.py │ │ │ └── tokenization_xlm_prophetnet.py │ │ ├── xlm_roberta │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── configuration_xlm_roberta.cpython-36.pyc │ │ │ │ ├── modeling_xlm_roberta.cpython-36.pyc │ │ │ │ ├── tokenization_xlm_roberta.cpython-36.pyc │ │ │ │ └── tokenization_xlm_roberta_fast.cpython-36.pyc │ │ │ ├── configuration_xlm_roberta.py │ │ │ ├── modeling_tf_xlm_roberta.py │ │ │ ├── modeling_xlm_roberta.py │ │ │ ├── tokenization_xlm_roberta.py │ │ │ └── tokenization_xlm_roberta_fast.py │ │ └── xlnet │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── configuration_xlnet.cpython-36.pyc │ │ │ ├── modeling_xlnet.cpython-36.pyc │ │ │ ├── tokenization_xlnet.cpython-36.pyc │ │ │ └── tokenization_xlnet_fast.cpython-36.pyc │ │ │ ├── configuration_xlnet.py │ │ │ ├── convert_xlnet_original_tf_checkpoint_to_pytorch.py │ │ │ ├── modeling_tf_xlnet.py │ │ │ ├── modeling_xlnet.py │ │ │ ├── tokenization_xlnet.py │ │ │ └── tokenization_xlnet_fast.py │ │ ├── optimization.py │ │ ├── optimization_tf.py │ │ ├── pipelines │ │ ├── __init__.py │ │ ├── base.py │ │ ├── conversational.py │ │ ├── feature_extraction.py │ │ ├── fill_mask.py │ │ ├── question_answering.py │ │ ├── table_question_answering.py │ │ ├── text2text_generation.py │ │ ├── text_classification.py │ │ ├── text_generation.py │ │ ├── token_classification.py │ │ └── zero_shot_classification.py │ │ ├── sagemaker │ │ ├── __init__.py │ │ ├── trainer_sm.py │ │ └── training_args_sm.py │ │ ├── testing_utils.py │ │ ├── tokenization_utils.py │ │ ├── tokenization_utils_base.py │ │ ├── tokenization_utils_fast.py │ │ ├── trainer.py │ │ ├── trainer_callback.py │ │ ├── trainer_pt_utils.py │ │ ├── trainer_seq2seq.py │ │ ├── trainer_tf.py │ │ ├── trainer_utils.py │ │ ├── training_args.py │ │ ├── training_args_seq2seq.py │ │ ├── training_args_tf.py │ │ └── utils │ │ ├── __init__.py │ │ ├── __pycache__ │ │ ├── __init__.cpython-36.pyc │ │ ├── dummy_flax_objects.cpython-36.pyc │ │ ├── dummy_sentencepiece_and_speech_objects.cpython-36.pyc │ │ ├── dummy_sentencepiece_and_tokenizers_objects.cpython-36.pyc │ │ ├── dummy_sentencepiece_objects.cpython-36.pyc │ │ ├── dummy_tf_objects.cpython-36.pyc │ │ ├── logging.cpython-36.pyc │ │ ├── model_parallel_utils.cpython-36.pyc │ │ ├── modeling_auto_mapping.cpython-36.pyc │ │ └── versions.cpython-36.pyc │ │ ├── dummy_flax_objects.py │ │ ├── dummy_pt_objects.py │ │ ├── dummy_sentencepiece_and_speech_objects.py │ │ ├── dummy_sentencepiece_and_tokenizers_objects.py │ │ ├── dummy_sentencepiece_objects.py │ │ ├── dummy_speech_objects.py │ │ ├── dummy_tf_objects.py │ │ ├── dummy_tokenizers_objects.py │ │ ├── dummy_vision_objects.py │ │ ├── hp_naming.py │ │ ├── imagenet_classes.py │ │ ├── logging.py │ │ ├── model_parallel_utils.py │ │ ├── modeling_auto_mapping.py │ │ ├── notebook.py │ │ ├── sentencepiece_model_pb2.py │ │ └── versions.py ├── utils │ ├── check_copies.py │ ├── check_dummies.py │ ├── check_inits.py │ ├── check_repo.py │ ├── check_table.py │ ├── check_tf_ops.py │ ├── class_mapping_update.py │ ├── custom_init_isort.py │ ├── download_glue_data.py │ ├── get_modified_files.py │ ├── link_tester.py │ ├── notification_service.py │ ├── release.py │ ├── style_doc.py │ └── tf_ops │ │ └── onnx.json └── valohai.yaml └── vit_src ├── .DS_Store ├── .gitignore ├── README.md ├── models ├── configs.py ├── modeling.py └── modeling_resnet.py ├── requirements.txt ├── train.py └── utils ├── data_utils.py ├── dist_util.py └── scheduler.py /fairseq_src/.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "fairseq/model_parallel/megatron"] 2 | path = fairseq/model_parallel/megatron 3 | url = https://github.com/ngoyal2707/Megatron-LM 4 | branch = fairseq 5 | -------------------------------------------------------------------------------- /fairseq_src/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) Facebook, Inc. and its affiliates. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /fairseq_src/README.md: -------------------------------------------------------------------------------- 1 | # Introduction 2 | 3 | This Repo conduct three NLP task: Neural Machine Translation, Abstractive Summarization, and Language Modeling. 4 | 5 | # Requirements and Installation 6 | 7 | * [PyTorch](http://pytorch.org/) version == 1.8 8 | * Python version >= 3.6 9 | 10 | To install fairseq from source and develop locally: 11 | ``` 12 | git clone https://github.com/dropreg/R-Drop.git 13 | cd R-Drop/fairseq_src/ 14 | pip install --editable . 15 | ``` 16 | 17 | # Examples 18 | 19 | * [Nerual Translation Task](examples/translation_rdrop/README.md): R-Drop transformer models are available 20 | * [Abstractive Summarization Task](examples/summeration_rdrop/README.md): R-Drop transformer models are available 21 | * [Language Modeling Task](examples/language_model_rdrop/README.md): R-Drop transformer models are available -------------------------------------------------------------------------------- /fairseq_src/examples/language_model_rdrop/language_model_rdrop_src/__init__.py: -------------------------------------------------------------------------------- 1 | from . import rdrop_lm 2 | from .loss import reg_adaptive_loss, reg_cross_entropy 3 | -------------------------------------------------------------------------------- /fairseq_src/examples/language_model_rdrop/script/evaluate_lm_wiki103.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | set -x 3 | set -e 4 | #--------------------------------------- 5 | 6 | DATA=wikitext-103 7 | DATA_PATH=$PREFIX/${DATA}/data-bin 8 | 9 | MODEL_PATH=$PREFIX/lm_${ARCH}_${DATA}_ckpt 10 | mkdir -p ${MODEL_PATH} 11 | nvidia-smi 12 | 13 | python -c "import torch; print(torch.__version__)" 14 | 15 | export CUDA_VISIBLE_DEVICES=0 16 | 17 | fairseq-eval-lm $DATA_PATH \ 18 | --path $MODEL_PATH/checkpoint_best.pt \ 19 | --max-sentences 2 \ 20 | --tokens-per-sample 512 \ 21 | --context-window 400 22 | -------------------------------------------------------------------------------- /fairseq_src/examples/language_model_rdrop/script/prepare_data.sh: -------------------------------------------------------------------------------- 1 | TEXT=/data/lxb/wikitext-103 2 | fairseq-preprocess \ 3 | --only-source \ 4 | --trainpref $TEXT/wiki.train.tokens \ 5 | --validpref $TEXT/wiki.valid.tokens \ 6 | --testpref $TEXT/wiki.test.tokens \ 7 | --destdir /data/lxb/wikitext-103/data-bin \ 8 | --workers 20 -------------------------------------------------------------------------------- /fairseq_src/examples/language_model_rdrop/script/train_lm_adaptive_wiki103.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | set -x 3 | set -e 4 | #--------------------------------------- 5 | ARCH=${1:-"transformer_lm_wiki103"} 6 | PREFIX=~/ 7 | 8 | export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 9 | 10 | DATA=wikitext-103 11 | SEED=1 12 | LR=1e-4 13 | DATA_PATH=$PREFIX/data/lm_data/${DATA}_bin 14 | CODE_PATH=$PREFIX/fairseq 15 | nvidia-smi 16 | 17 | MODEL_PATH=$PREFIX/models/lm_ada_${ARCH}_${DATA} 18 | mkdir -p ${MODEL_PATH} 19 | 20 | python -c "import torch; print(torch.__version__)" 21 | 22 | python ${CODE_PATH}/train.py ${DATA_PATH} \ 23 | --task language_modeling \ 24 | --save-dir ${MODEL_PATH} --arch ${ARCH} \ 25 | --max-update 286000 --lr 1.0 --t-mult 2 --lr-period-updates 270000 \ 26 | --lr-scheduler cosine --lr-shrink 0.75 --warmup-updates 16000 --warmup-init-lr 1e-07 --stop-min-lr 1e-09 \ 27 | --optimizer nag --min-lr ${LR} --clip-norm 0.1 \ 28 | --criterion adaptive_loss \ 29 | --max-tokens 3072 --update-freq 3 --tokens-per-sample 3072 --seed ${SEED} \ 30 | --sample-break-mode none --skip-invalid-size-inputs-valid-test \ 31 | --ddp-backend=legacy_ddp \ 32 | --tensorboard-logdir $MODEL_PATH/tensorboard 33 | 34 | -------------------------------------------------------------------------------- /fairseq_src/examples/language_model_rdrop/script/train_lm_wiki103.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | set -x 3 | set -e 4 | #--------------------------------------- 5 | 6 | DATA=wikitext-103 7 | DATA_PATH=$PREFIX/${DATA}/data-bin 8 | 9 | 10 | MODEL_PATH=$PREFIX/lm_${ARCH}_${DATA}_ckpt 11 | mkdir -p ${MODEL_PATH} 12 | nvidia-smi 13 | 14 | python -c "import torch; print(torch.__version__)" 15 | 16 | export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 17 | 18 | fairseq-train $DATA_PATH \ 19 | --task language_modeling \ 20 | --arch transformer_lm_gpt --share-decoder-input-output-embed \ 21 | --dropout 0.1 \ 22 | --optimizer adam --adam-betas '(0.9, 0.98)' --weight-decay 0.01 --clip-norm 0.0 \ 23 | --lr 0.0005 --lr-scheduler inverse_sqrt --warmup-updates 4000 --warmup-init-lr 1e-07 \ 24 | --tokens-per-sample 512 --sample-break-mode none \ 25 | --max-tokens 2000 --update-freq 16 \ 26 | --fp16 --ddp-backend=no_c10d \ 27 | --max-update 50000 --skip-invalid-size-inputs-valid-test \ 28 | --tensorboard-logdir $MODEL_PATH/tensorboard \ 29 | --save-dir $MODEL_PATH | tee -a $MODEL_PATH/train.log \ 30 | -------------------------------------------------------------------------------- /fairseq_src/examples/language_model_rdrop/script/train_lm_wiki103_rdrop.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | set -x 3 | set -e 4 | #--------------------------------------- 5 | ARCH=${1:-"transformer_lm_gpt"} 6 | PREFIX=~/ 7 | 8 | DATA=wikitext-103 9 | SEED=1 10 | DATA_PATH=$PREFIX/data/lm_data/${DATA}_bin 11 | nvidia-smi 12 | 13 | 14 | MODEL_PATH=$PREFIX/models/lm_self_reg_${ARCH}_${DATA}_${KL_WEIGHT} 15 | mkdir -p ${MODEL_PATH} 16 | 17 | python -c "import torch; print(torch.__version__)" 18 | 19 | export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 20 | 21 | fairseq-train $DATA_PATH \ 22 | --ddp-backend=no_c10d \ 23 | --arch $ARCH --share-decoder-input-output-embed \ 24 | --save-dir $MODEL_PATH \ 25 | --user-dir /examples/language_model_rdrop/language_model_rdrop_src \ 26 | --dropout 0.1 \ 27 | --task rdrop_lm \ 28 | --criterion reg_cross_entropy \ 29 | --reg-alpha 1.0 \ 30 | --optimizer adam --adam-betas '(0.9, 0.98)' --weight-decay 0.01 --clip-norm 0.0 \ 31 | --lr 0.0005 --lr-scheduler inverse_sqrt --warmup-updates 4000 --warmup-init-lr 1e-07 \ 32 | --tokens-per-sample 512 --sample-break-mode none \ 33 | --max-tokens 2000 --update-freq 16 \ 34 | --fp16 \ 35 | --max-update 50000 --skip-invalid-size-inputs-valid-test \ 36 | --tensorboard-logdir $MODEL_PATH/tensorboard 37 | 38 | 39 | -------------------------------------------------------------------------------- /fairseq_src/examples/roberta/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dropreg/R-Drop/9dcc8302eb9b0f112c38230864bbee807bbe1aa7/fairseq_src/examples/roberta/README.md -------------------------------------------------------------------------------- /fairseq_src/examples/roberta/inference.py: -------------------------------------------------------------------------------- 1 | from fairseq.models.roberta import RobertaModel 2 | import torch 3 | 4 | roberta = RobertaModel.from_pretrained( 5 | '/data/roberta/MRPC/', 6 | checkpoint_file='checkpoint_best.pt', 7 | data_name_or_path='/data/MRPC-bin' 8 | ) 9 | 10 | label_fn = lambda label: roberta.task.label_dictionary.string( 11 | [label + roberta.task.label_dictionary.nspecial] 12 | ) 13 | 14 | for idx, l in enumerate(roberta.model.encoder.sentence_encoder.layers): 15 | l.dropout_module.p = 0.2 16 | l.dropout_module.apply_during_inference = True 17 | 18 | ncorrect, nsamples = 0, 0 19 | roberta.cuda() 20 | roberta.eval() 21 | with open('/data/MRPC/dev.tsv') as fin: 22 | fin.readline() 23 | for index, line in enumerate(fin): 24 | tokens = line.strip().split('\t') 25 | sent1, sent2, target = tokens[3], tokens[4], tokens[0] 26 | tokens = roberta.encode(sent1, sent2) 27 | prediction = roberta.predict('sentence_classification_head', tokens).argmax().item() 28 | prediction_label = label_fn(prediction) 29 | ncorrect += int(prediction_label == target) 30 | nsamples += 1 31 | print('| Accuracy: ', float(ncorrect)/float(nsamples)) -------------------------------------------------------------------------------- /fairseq_src/examples/roberta/roberta_rdrop_src/rdrop_sentence_prediction.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import logging 7 | import torch 8 | from fairseq.tasks.sentence_prediction import SentencePredictionTask 9 | from fairseq.tasks import register_task 10 | 11 | logger = logging.getLogger(__name__) 12 | 13 | 14 | @register_task("rdrop_sentence_prediction") 15 | class RDropSentencePredictionTask(SentencePredictionTask): 16 | 17 | def train_step( 18 | self, sample, model, criterion, optimizer, update_num, ignore_grad=False 19 | ): 20 | model.train() 21 | model.set_num_updates(update_num) 22 | with torch.autograd.profiler.record_function("forward"): 23 | loss, sample_size, logging_output = criterion.forward_reg(model, sample) 24 | if ignore_grad: 25 | loss *= 0 26 | with torch.autograd.profiler.record_function("backward"): 27 | optimizer.backward(loss) 28 | return loss, sample_size, logging_output -------------------------------------------------------------------------------- /fairseq_src/examples/summeration_rdrop/script/preprocess.sh: -------------------------------------------------------------------------------- 1 | wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' 2 | wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' 3 | wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt' 4 | 5 | TASK=cnn_dm 6 | for SPLIT in train val 7 | do 8 | for LANG in source target 9 | do 10 | python -m examples.roberta.multiprocessing_bpe_encoder \ 11 | --encoder-json encoder.json \ 12 | --vocab-bpe vocab.bpe \ 13 | --inputs "/data/lxb/cnndailymail_sum/cnn_dm/$SPLIT.$LANG" \ 14 | --outputs "/data/lxb/cnndailymail_sum/cnn_dm/$SPLIT.bpe.$LANG" \ 15 | --workers 60 \ 16 | --keep-empty; 17 | done 18 | done 19 | 20 | 21 | fairseq-preprocess \ 22 | --source-lang "source" \ 23 | --target-lang "target" \ 24 | --trainpref "/data/lxb/cnndailymail_sum/cnn_dm/train.bpe" \ 25 | --validpref "/data/lxb/cnndailymail_sum/cnn_dm/val.bpe" \ 26 | --destdir "/data/lxb/cnndailymail_sum/cnn_dm-bin/" \ 27 | --workers 60 \ 28 | --srcdict dict.txt \ 29 | --tgtdict dict.txt; -------------------------------------------------------------------------------- /fairseq_src/examples/summeration_rdrop/script/run_inference.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from fairseq.models.bart import BARTModel 3 | 4 | bart = BARTModel.from_pretrained( 5 | 'bart-large-checkpoints/', 6 | checkpoint_file='checkpoint_best.pt', 7 | data_name_or_path='bart-large-checkpoints/' 8 | ) 9 | 10 | bart.cuda() 11 | bart.eval() 12 | bart.half() 13 | count = 1 14 | bsz = 32 15 | with open('/data/cnn_dm/test.source') as source, open('/data/cnn_dm/test.hypo', 'w') as fout: 16 | sline = source.readline().strip() 17 | slines = [sline] 18 | for sline in source: 19 | if count % bsz == 0: 20 | with torch.no_grad(): 21 | hypotheses_batch = bart.sample(slines, beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3) 22 | 23 | for hypothesis in hypotheses_batch: 24 | fout.write(hypothesis + '\n') 25 | fout.flush() 26 | slines = [] 27 | 28 | slines.append(sline.strip()) 29 | count += 1 30 | if slines != []: 31 | hypotheses_batch = bart.sample(slines, beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3) 32 | for hypothesis in hypotheses_batch: 33 | fout.write(hypothesis + '\n') 34 | fout.flush() 35 | -------------------------------------------------------------------------------- /fairseq_src/examples/summeration_rdrop/summeration_rdrop_src/__init__.py: -------------------------------------------------------------------------------- 1 | from . import rdrop_translation 2 | from .loss import rdrop_cross_entropy_loss 3 | -------------------------------------------------------------------------------- /fairseq_src/examples/translation_rdrop/README.md: -------------------------------------------------------------------------------- 1 | 2 | # Neural Machine Translation 3 | 4 | This example contains instructions for training a new NMT model with R-Drop. 5 | 6 | # Prepare Data 7 | The following instructions can be used to train a Transformer model on the [IWSLT'14 German to English dataset](http://workshop2014.iwslt.org/downloads/proceeding.pdf). 8 | 9 | Download and preprocess the data: 10 | ```bash 11 | bash script/prepare-iwslt14.sh 12 | 13 | ``` 14 | 15 | Binarize the dataset 16 | ``` 17 | cd ../../ 18 | bash examples/translation_rdrop/script/run_binarize.sh 19 | ``` 20 | 21 | # Training Script 22 | 23 | Train a Transformer translation model with R-Drop over this data: 24 | ``` 25 | bash examples/translation_rdrop/script/run_train.sh 26 | ``` 27 | 28 | # Inference Script 29 | 30 | Evaluate R-Drop model over this data: 31 | ``` 32 | bash examples/translation_rdrop/script/run_inference.sh 33 | ``` 34 | -------------------------------------------------------------------------------- /fairseq_src/examples/translation_rdrop/script/run_binarize.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | TEXT=examples/translation_rdrop/iwslt14.tokenized.de-en 4 | fairseq-preprocess --source-lang de --target-lang en \ 5 | --joined-dictionary \ 6 | --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ 7 | --destdir data-bin/iwslt14.rdrop.tokenized.de-en \ 8 | --workers 20 9 | -------------------------------------------------------------------------------- /fairseq_src/examples/translation_rdrop/script/run_inference.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | set -x 3 | set -e 4 | #--------------------------------------- 5 | 6 | src=de 7 | tgt=en 8 | 9 | DATA_PATH=data-bin/iwslt14.rdrop.tokenized.de-en/ 10 | MODEL_PATH=iwslt14.rdrop.de-en-ckpt 11 | nvidia-smi 12 | 13 | python -c "import torch; print(torch.__version__)" 14 | 15 | export CUDA_VISIBLE_DEVICES=0 16 | 17 | python fairseq_cli/generate.py $DATA_PATH \ 18 | --path $MODEL_PATH/checkpoint_best.pt \ 19 | --beam 5 --remove-bpe >> $MODEL_PATH/result.gen 20 | 21 | bash scripts/compound_split_bleu.sh $MODEL_PATH/result.gen 22 | -------------------------------------------------------------------------------- /fairseq_src/examples/translation_rdrop/translation_rdrop_src/__init__.py: -------------------------------------------------------------------------------- 1 | from . import rdrop_translations 2 | from .loss import reg_label_smooth_cross_entropy 3 | -------------------------------------------------------------------------------- /fairseq_src/examples/translation_rdrop/translation_rdrop_src/rdrop_translations.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import logging 3 | from fairseq.tasks import register_task 4 | from fairseq.tasks.translation import TranslationTask 5 | 6 | 7 | logger = logging.getLogger(__name__) 8 | 9 | @register_task("rdrop_translation") 10 | class RDropTranslation(TranslationTask): 11 | 12 | @staticmethod 13 | def add_args(parser): 14 | TranslationTask.add_args(parser) 15 | parser.add_argument('--reg-alpha', default=0, type=int) 16 | 17 | def __init__(self, args, src_dict, tgt_dict): 18 | super().__init__(args, src_dict, tgt_dict) 19 | self.criterion_reg_alpha = getattr(args, 'reg_alpha', 0) 20 | 21 | def train_step(self, sample, model, criterion, optimizer, update_num, ignore_grad=False): 22 | model.train() 23 | model.set_num_updates(update_num) 24 | with torch.autograd.profiler.record_function("forward"): 25 | loss, sample_size, logging_output = criterion.forward_reg(model, sample, optimizer, self.criterion_reg_alpha, ignore_grad) 26 | return loss, sample_size, logging_output 27 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/benchmark/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | # import models/tasks to register them 7 | from . import dummy_lm, dummy_masked_lm, dummy_model, dummy_mt # noqa 8 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/clib/libbleu/module.cpp: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright 2017-present, Facebook, Inc. 3 | * All rights reserved. 4 | * 5 | * This source code is licensed under the license found in the 6 | * LICENSE file in the root directory of this source tree. 7 | */ 8 | 9 | #include 10 | 11 | 12 | static PyMethodDef method_def[] = { 13 | {NULL, NULL, 0, NULL} 14 | }; 15 | 16 | static struct PyModuleDef module_def = { 17 | PyModuleDef_HEAD_INIT, 18 | "libbleu", /* name of module */ 19 | NULL, /* module documentation, may be NULL */ 20 | -1, /* size of per-interpreter state of the module, 21 | or -1 if the module keeps state in global variables. */ 22 | method_def 23 | }; 24 | 25 | 26 | #if PY_MAJOR_VERSION == 2 27 | PyMODINIT_FUNC init_libbleu() 28 | #else 29 | PyMODINIT_FUNC PyInit_libbleu() 30 | #endif 31 | { 32 | PyObject *m = PyModule_Create(&module_def); 33 | if (!m) { 34 | return NULL; 35 | } 36 | return m; 37 | } 38 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/clib/libnat_cuda/edit_dist.h: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright 2017-present, Facebook, Inc. 3 | * All rights reserved. 4 | * 5 | * This source code is licensed under the license found in the 6 | * LICENSE file in the root directory of this source tree. 7 | */ 8 | 9 | #pragma once 10 | 11 | #include 12 | 13 | torch::Tensor LevenshteinDistanceCuda( 14 | torch::Tensor source, 15 | torch::Tensor target, 16 | torch::Tensor source_length, 17 | torch::Tensor target_length); 18 | 19 | torch::Tensor GenerateDeletionLabelCuda( 20 | torch::Tensor source, 21 | torch::Tensor operations); 22 | 23 | std::pair GenerateInsertionLabelCuda( 24 | torch::Tensor source, 25 | torch::Tensor operations); 26 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/config.yaml: -------------------------------------------------------------------------------- 1 | # @package _group_ 2 | 3 | hydra: 4 | run: 5 | dir: . 6 | 7 | defaults: 8 | - task: null 9 | - model: null 10 | - criterion: cross_entropy 11 | - optimizer: null 12 | - lr_scheduler: fixed 13 | - bpe: null 14 | - tokenizer: null 15 | - scoring: null 16 | - generation: null 17 | - common_eval: null 18 | - eval_lm: null 19 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/model/transformer_lm/transformer_lm_baevski_gbw.yaml: -------------------------------------------------------------------------------- 1 | # @package _group_ 2 | activation_fn: "relu" 3 | dropout: 0.1 4 | attention_dropout: 0.1 5 | activation_dropout: 0.0 6 | relu_dropout: 0.0 7 | decoder_embed_dim: 512 8 | decoder_output_dim: 512 9 | decoder_input_dim: 512 10 | decoder_ffn_embed_dim: 4096 11 | decoder_layers: 12 12 | decoder_attention_heads: 16 13 | decoder_normalize_before: true 14 | no_decoder_final_norm: true 15 | adaptive_softmax_cutoff: null 16 | adaptive_softmax_dropout: 0 17 | adaptive_softmax_factor: 4 18 | no_token_positional_embeddings: false 19 | share_decoder_input_output_embed: false 20 | character_embeddings: false 21 | character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" 22 | character_embedding_dim: 4 23 | char_embedder_highway_layers: 2 24 | adaptive_input: false 25 | adaptive_input_factor: 4 26 | adaptive_input_cutoff: null 27 | tie_adaptive_weights: false 28 | tie_adaptive_proj: false 29 | decoder_learned_pos: false 30 | decoder_layerdrop: 0 31 | decoder_layers_to_keep: null 32 | layernorm_embedding: false 33 | no_scale_embedding: false 34 | quant_noise_pq: 0 35 | quant_noise_pq_block_size: 8 36 | quant_noise_scalar: 0 37 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/model/transformer_lm/transformer_lm_baevski_wiki103.yaml: -------------------------------------------------------------------------------- 1 | # @package _group_ 2 | activation_fn: "relu" 3 | dropout: 0.3 4 | attention_dropout: 0.1 5 | activation_dropout: 0.1 6 | relu_dropout: 0.1 7 | decoder_embed_dim: 1024 8 | decoder_output_dim: 1024 9 | decoder_input_dim: 1024 10 | decoder_ffn_embed_dim: 4096 11 | decoder_layers: 16 12 | decoder_attention_heads: 8 13 | decoder_normalize_before: true 14 | no_decoder_final_norm: true 15 | adaptive_softmax_cutoff: "20000,60000" 16 | adaptive_softmax_dropout: 0.2 17 | adaptive_softmax_factor: 4 18 | no_token_positional_embeddings: false 19 | share_decoder_input_output_embed: false 20 | character_embeddings: false 21 | character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" 22 | character_embedding_dim: 4 23 | char_embedder_highway_layers: 2 24 | adaptive_input: true 25 | adaptive_input_factor: 4 26 | adaptive_input_cutoff: "20000,60000" 27 | tie_adaptive_weights: true 28 | tie_adaptive_proj: true 29 | decoder_learned_pos: false 30 | decoder_layerdrop: 0 31 | decoder_layers_to_keep: null 32 | layernorm_embedding: false 33 | no_scale_embedding: false 34 | quant_noise_pq: 0 35 | quant_noise_pq_block_size: 8 36 | quant_noise_scalar: 0 37 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/model/transformer_lm/transformer_lm_big.yaml: -------------------------------------------------------------------------------- 1 | # @package _group_ 2 | activation_fn: "relu" 3 | dropout: 0.1 4 | attention_dropout: 0.0 5 | activation_dropout: 0.0 6 | relu_dropout: 0.0 7 | decoder_embed_dim: 1024 8 | decoder_output_dim: 1024 9 | decoder_input_dim: 1024 10 | decoder_ffn_embed_dim: 4096 11 | decoder_layers: 12 12 | decoder_attention_heads: 16 13 | decoder_normalize_before: true 14 | no_decoder_final_norm: false 15 | adaptive_softmax_cutoff: null 16 | adaptive_softmax_dropout: 0 17 | adaptive_softmax_factor: 4 18 | no_token_positional_embeddings: false 19 | share_decoder_input_output_embed: false 20 | character_embeddings: false 21 | character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" 22 | character_embedding_dim: 4 23 | char_embedder_highway_layers: 2 24 | adaptive_input: false 25 | adaptive_input_factor: 4 26 | adaptive_input_cutoff: null 27 | tie_adaptive_weights: false 28 | tie_adaptive_proj: false 29 | decoder_learned_pos: false 30 | decoder_layerdrop: 0 31 | decoder_layers_to_keep: null 32 | layernorm_embedding: false 33 | no_scale_embedding: false 34 | quant_noise_pq: 0 35 | quant_noise_pq_block_size: 8 36 | quant_noise_scalar: 0 37 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/model/transformer_lm/transformer_lm_gbw.yaml: -------------------------------------------------------------------------------- 1 | # @package _group_ 2 | activation_fn: "relu" 3 | dropout: 0.1 4 | attention_dropout: 0.1 5 | activation_dropout: 0.0 6 | relu_dropout: 0.0 7 | decoder_embed_dim: 512 8 | decoder_output_dim: 512 9 | decoder_input_dim: 512 10 | decoder_ffn_embed_dim: 4096 11 | decoder_layers: 12 12 | decoder_attention_heads: 16 13 | decoder_normalize_before: true 14 | no_decoder_final_norm: true 15 | adaptive_softmax_cutoff: null 16 | adaptive_softmax_dropout: 0 17 | adaptive_softmax_factor: 4 18 | no_token_positional_embeddings: false 19 | share_decoder_input_output_embed: false 20 | character_embeddings: false 21 | character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" 22 | character_embedding_dim: 4 23 | char_embedder_highway_layers: 2 24 | adaptive_input: false 25 | adaptive_input_factor: 4 26 | adaptive_input_cutoff: null 27 | tie_adaptive_weights: false 28 | tie_adaptive_proj: false 29 | decoder_learned_pos: false 30 | decoder_layerdrop: 0 31 | decoder_layers_to_keep: null 32 | layernorm_embedding: false 33 | no_scale_embedding: false 34 | quant_noise_pq: 0 35 | quant_noise_pq_block_size: 8 36 | quant_noise_scalar: 0 37 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml: -------------------------------------------------------------------------------- 1 | # @package _group_ 2 | activation_fn: "gelu" 3 | dropout: 0.1 4 | attention_dropout: 0.1 5 | activation_dropout: 0.0 6 | relu_dropout: 0.0 7 | decoder_embed_dim: 768 8 | decoder_output_dim: 768 9 | decoder_input_dim: 768 10 | decoder_ffn_embed_dim: 3072 11 | decoder_layers: 12 12 | decoder_attention_heads: 12 13 | decoder_normalize_before: true 14 | no_decoder_final_norm: false 15 | adaptive_softmax_cutoff: null 16 | adaptive_softmax_dropout: 0 17 | adaptive_softmax_factor: 4 18 | no_token_positional_embeddings: false 19 | share_decoder_input_output_embed: false 20 | character_embeddings: false 21 | character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" 22 | character_embedding_dim: 4 23 | char_embedder_highway_layers: 2 24 | adaptive_input: false 25 | adaptive_input_factor: 4 26 | adaptive_input_cutoff: null 27 | tie_adaptive_weights: false 28 | tie_adaptive_proj: false 29 | decoder_learned_pos: false 30 | decoder_layerdrop: 0 31 | decoder_layers_to_keep: null 32 | layernorm_embedding: false 33 | no_scale_embedding: false 34 | quant_noise_pq: 0 35 | quant_noise_pq_block_size: 8 36 | quant_noise_scalar: 0 37 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/model/transformer_lm/transformer_lm_gpt2_big.yaml: -------------------------------------------------------------------------------- 1 | # @package _group_ 2 | activation_fn: "gelu" 3 | dropout: 0.1 4 | attention_dropout: 0.1 5 | activation_dropout: 0.0 6 | relu_dropout: 0.0 7 | decoder_embed_dim: 1600 8 | decoder_output_dim: 1600 9 | decoder_input_dim: 1600 10 | decoder_ffn_embed_dim: 6400 11 | decoder_layers: 48 12 | decoder_attention_heads: 25 13 | decoder_normalize_before: true 14 | no_decoder_final_norm: false 15 | adaptive_softmax_cutoff: null 16 | adaptive_softmax_dropout: 0 17 | adaptive_softmax_factor: 4 18 | no_token_positional_embeddings: false 19 | share_decoder_input_output_embed: false 20 | character_embeddings: false 21 | character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" 22 | character_embedding_dim: 4 23 | char_embedder_highway_layers: 2 24 | adaptive_input: false 25 | adaptive_input_factor: 4 26 | adaptive_input_cutoff: null 27 | tie_adaptive_weights: false 28 | tie_adaptive_proj: false 29 | decoder_learned_pos: false 30 | decoder_layerdrop: 0 31 | decoder_layers_to_keep: null 32 | layernorm_embedding: false 33 | no_scale_embedding: false 34 | quant_noise_pq: 0 35 | quant_noise_pq_block_size: 8 36 | quant_noise_scalar: 0 37 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/model/transformer_lm/transformer_lm_gpt2_medium.yaml: -------------------------------------------------------------------------------- 1 | # @package _group_ 2 | activation_fn: "gelu" 3 | dropout: 0.1 4 | attention_dropout: 0.1 5 | activation_dropout: 0.0 6 | relu_dropout: 0.0 7 | decoder_embed_dim: 1280 8 | decoder_output_dim: 1280 9 | decoder_input_dim: 1280 10 | decoder_ffn_embed_dim: 5120 11 | decoder_layers: 36 12 | decoder_attention_heads: 20 13 | decoder_normalize_before: true 14 | no_decoder_final_norm: false 15 | adaptive_softmax_cutoff: null 16 | adaptive_softmax_dropout: 0 17 | adaptive_softmax_factor: 4 18 | no_token_positional_embeddings: false 19 | share_decoder_input_output_embed: false 20 | character_embeddings: false 21 | character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" 22 | character_embedding_dim: 4 23 | char_embedder_highway_layers: 2 24 | adaptive_input: false 25 | adaptive_input_factor: 4 26 | adaptive_input_cutoff: null 27 | tie_adaptive_weights: false 28 | tie_adaptive_proj: false 29 | decoder_learned_pos: false 30 | decoder_layerdrop: 0 31 | decoder_layers_to_keep: null 32 | layernorm_embedding: false 33 | no_scale_embedding: false 34 | quant_noise_pq: 0 35 | quant_noise_pq_block_size: 8 36 | quant_noise_scalar: 0 37 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/model/transformer_lm/transformer_lm_gpt2_small.yaml: -------------------------------------------------------------------------------- 1 | # @package _group_ 2 | activation_fn: "gelu" 3 | dropout: 0.1 4 | attention_dropout: 0.1 5 | activation_dropout: 0.0 6 | relu_dropout: 0.0 7 | decoder_embed_dim: 1024 8 | decoder_output_dim: 1024 9 | decoder_input_dim: 1024 10 | decoder_ffn_embed_dim: 4096 11 | decoder_layers: 24 12 | decoder_attention_heads: 16 13 | decoder_normalize_before: true 14 | no_decoder_final_norm: false 15 | adaptive_softmax_cutoff: null 16 | adaptive_softmax_dropout: 0 17 | adaptive_softmax_factor: 4 18 | no_token_positional_embeddings: false 19 | share_decoder_input_output_embed: false 20 | character_embeddings: false 21 | character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" 22 | character_embedding_dim: 4 23 | char_embedder_highway_layers: 2 24 | adaptive_input: false 25 | adaptive_input_factor: 4 26 | adaptive_input_cutoff: null 27 | tie_adaptive_weights: false 28 | tie_adaptive_proj: false 29 | decoder_learned_pos: false 30 | decoder_layerdrop: 0 31 | decoder_layers_to_keep: null 32 | layernorm_embedding: false 33 | no_scale_embedding: false 34 | quant_noise_pq: 0 35 | quant_noise_pq_block_size: 8 36 | quant_noise_scalar: 0 37 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/model/transformer_lm/transformer_lm_wiki103.yaml: -------------------------------------------------------------------------------- 1 | # @package _group_ 2 | activation_fn: "relu" 3 | dropout: 0.3 4 | attention_dropout: 0.1 5 | activation_dropout: 0.1 6 | relu_dropout: 0.1 7 | decoder_embed_dim: 1024 8 | decoder_output_dim: 1024 9 | decoder_input_dim: 1024 10 | decoder_ffn_embed_dim: 4096 11 | decoder_layers: 16 12 | decoder_attention_heads: 8 13 | decoder_normalize_before: true 14 | no_decoder_final_norm: true 15 | adaptive_softmax_cutoff: "20000,60000" 16 | adaptive_softmax_dropout: 0.2 17 | adaptive_softmax_factor: 4 18 | no_token_positional_embeddings: false 19 | share_decoder_input_output_embed: false 20 | character_embeddings: false 21 | character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" 22 | character_embedding_dim: 4 23 | char_embedder_highway_layers: 2 24 | adaptive_input: true 25 | adaptive_input_factor: 4 26 | adaptive_input_cutoff: "20000,60000" 27 | tie_adaptive_weights: true 28 | tie_adaptive_proj: true 29 | decoder_learned_pos: false 30 | decoder_layerdrop: 0 31 | decoder_layers_to_keep: null 32 | layernorm_embedding: false 33 | no_scale_embedding: false 34 | quant_noise_pq: 0 35 | quant_noise_pq_block_size: 8 36 | quant_noise_scalar: 0 37 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/model/wav2vec/vq_wav2vec_gumbel.yaml: -------------------------------------------------------------------------------- 1 | # @package _group_ 2 | activation: gelu 3 | vq_type: gumbel 4 | vq_depth: 2 5 | combine_groups: true 6 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/model/wav2vec2/wav2vec2_base.yaml: -------------------------------------------------------------------------------- 1 | # @package _group_ 2 | 3 | quantize_targets: true 4 | final_dim: 256 5 | encoder_layerdrop: 0.05 6 | dropout_input: 0.1 7 | dropout_features: 0.1 8 | feature_grad_mult: 0.1 9 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/config/model/wav2vec2/wav2vec2_large.yaml: -------------------------------------------------------------------------------- 1 | # @package _group_ 2 | 3 | quantize_targets: true 4 | extractor_mode: layer_norm 5 | layer_norm_first: true 6 | final_dim: 768 7 | latent_temp: [2.0,0.1,0.999995] 8 | encoder_layerdrop: 0.0 9 | dropout_input: 0.0 10 | dropout_features: 0.0 11 | dropout: 0.0 12 | attention_dropout: 0.0 13 | conv_bias: true 14 | 15 | encoder_layers: 24 16 | encoder_embed_dim: 1024 17 | encoder_ffn_embed_dim: 4096 18 | encoder_attention_heads: 16 19 | 20 | feature_grad_mult: 1.0 21 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/criterions/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | """isort:skip_file""" 6 | 7 | import importlib 8 | import os 9 | 10 | from fairseq import registry 11 | from fairseq.criterions.fairseq_criterion import ( # noqa 12 | FairseqCriterion, 13 | LegacyFairseqCriterion, 14 | ) 15 | from omegaconf import DictConfig 16 | 17 | 18 | ( 19 | build_criterion_, 20 | register_criterion, 21 | CRITERION_REGISTRY, 22 | CRITERION_DATACLASS_REGISTRY, 23 | ) = registry.setup_registry( 24 | "--criterion", base_class=FairseqCriterion, default="cross_entropy" 25 | ) 26 | 27 | 28 | def build_criterion(cfg: DictConfig, task): 29 | return build_criterion_(cfg, task) 30 | 31 | 32 | # automatically import any Python files in the criterions/ directory 33 | for file in os.listdir(os.path.dirname(__file__)): 34 | if file.endswith(".py") and not file.startswith("_"): 35 | file_name = file[: file.find(".py")] 36 | importlib.import_module("fairseq.criterions." + file_name) 37 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/append_token_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import numpy as np 7 | import torch 8 | 9 | from . import BaseWrapperDataset 10 | 11 | 12 | class AppendTokenDataset(BaseWrapperDataset): 13 | def __init__(self, dataset, token=None): 14 | super().__init__(dataset) 15 | self.token = token 16 | if token is not None: 17 | self._sizes = np.array(dataset.sizes) + 1 18 | else: 19 | self._sizes = dataset.sizes 20 | 21 | def __getitem__(self, idx): 22 | item = self.dataset[idx] 23 | if self.token is not None: 24 | item = torch.cat([item, item.new([self.token])]) 25 | return item 26 | 27 | @property 28 | def sizes(self): 29 | return self._sizes 30 | 31 | def num_tokens(self, index): 32 | n = self.dataset.num_tokens(index) 33 | if self.token is not None: 34 | n += 1 35 | return n 36 | 37 | def size(self, index): 38 | n = self.dataset.size(index) 39 | if self.token is not None: 40 | n += 1 41 | return n 42 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/audio/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dropreg/R-Drop/9dcc8302eb9b0f112c38230864bbee807bbe1aa7/fairseq_src/fairseq/data/audio/__init__.py -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/audio/feature_transforms/global_cmvn.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from fairseq.data.audio.feature_transforms import ( 3 | AudioFeatureTransform, 4 | register_audio_feature_transform, 5 | ) 6 | 7 | 8 | @register_audio_feature_transform("global_cmvn") 9 | class GlobalCMVN(AudioFeatureTransform): 10 | """Global CMVN (cepstral mean and variance normalization). The global mean 11 | and variance need to be pre-computed and stored in NumPy format (.npz).""" 12 | 13 | @classmethod 14 | def from_config_dict(cls, config=None): 15 | _config = {} if config is None else config 16 | return GlobalCMVN(_config.get("stats_npz_path")) 17 | 18 | def __init__(self, stats_npz_path): 19 | self.stats_npz_path = stats_npz_path 20 | stats = np.load(stats_npz_path) 21 | self.mean, self.std = stats["mean"], stats["std"] 22 | 23 | def __repr__(self): 24 | return self.__class__.__name__ + f'(stats_npz_path="{self.stats_npz_path}")' 25 | 26 | def __call__(self, x): 27 | x = np.subtract(x, self.mean) 28 | x = np.divide(x, self.std) 29 | return x 30 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/colorize_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | from . import BaseWrapperDataset 9 | 10 | 11 | class ColorizeDataset(BaseWrapperDataset): 12 | """ Adds 'colors' property to net input that is obtained from the provided color getter for use by models """ 13 | 14 | def __init__(self, dataset, color_getter): 15 | super().__init__(dataset) 16 | self.color_getter = color_getter 17 | 18 | def collater(self, samples): 19 | base_collate = super().collater(samples) 20 | if len(base_collate) > 0: 21 | base_collate["net_input"]["colors"] = torch.tensor( 22 | list(self.color_getter(self.dataset, s["id"]) for s in samples), 23 | dtype=torch.long, 24 | ) 25 | return base_collate 26 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/encoders/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | 7 | import importlib 8 | import os 9 | 10 | from fairseq import registry 11 | 12 | 13 | build_tokenizer, register_tokenizer, TOKENIZER_REGISTRY, _ = registry.setup_registry( 14 | "--tokenizer", 15 | default=None, 16 | ) 17 | 18 | 19 | build_bpe, register_bpe, BPE_REGISTRY, _ = registry.setup_registry( 20 | "--bpe", 21 | default=None, 22 | ) 23 | 24 | 25 | # automatically import any Python files in the encoders/ directory 26 | for file in os.listdir(os.path.dirname(__file__)): 27 | if file.endswith(".py") and not file.startswith("_"): 28 | module = file[: file.find(".py")] 29 | importlib.import_module("fairseq.data.encoders." + module) 30 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/encoders/bytes.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | 7 | from fairseq.data.encoders import register_bpe 8 | from fairseq.data.encoders.byte_utils import ( 9 | SPACE, 10 | SPACE_ESCAPE, 11 | byte_encode, 12 | smart_byte_decode, 13 | ) 14 | 15 | 16 | @register_bpe("bytes") 17 | class Bytes(object): 18 | def __init__(self, *unused): 19 | pass 20 | 21 | @staticmethod 22 | def add_args(parser): 23 | pass 24 | 25 | @staticmethod 26 | def encode(x: str) -> str: 27 | encoded = byte_encode(x) 28 | escaped = encoded.replace(SPACE, SPACE_ESCAPE) 29 | return SPACE.join(list(escaped)) 30 | 31 | @staticmethod 32 | def decode(x: str) -> str: 33 | unescaped = x.replace(SPACE, "").replace(SPACE_ESCAPE, SPACE) 34 | return smart_byte_decode(unescaped) 35 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/encoders/characters.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | 7 | from fairseq.data.encoders import register_bpe 8 | 9 | 10 | SPACE = chr(32) 11 | SPACE_ESCAPE = chr(9601) 12 | 13 | 14 | @register_bpe("characters") 15 | class Characters(object): 16 | def __init__(self, *unused): 17 | pass 18 | 19 | @staticmethod 20 | def add_args(parser): 21 | pass 22 | 23 | @staticmethod 24 | def encode(x: str) -> str: 25 | escaped = x.replace(SPACE, SPACE_ESCAPE) 26 | return SPACE.join(list(escaped)) 27 | 28 | @staticmethod 29 | def decode(x: str) -> str: 30 | return x.replace(SPACE, "").replace(SPACE_ESCAPE, SPACE) 31 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/encoders/nltk_tokenizer.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq.data.encoders import register_tokenizer 7 | from fairseq.dataclass import FairseqDataclass 8 | 9 | 10 | @register_tokenizer("nltk", dataclass=FairseqDataclass) 11 | class NLTKTokenizer(object): 12 | def __init__(self, *unused): 13 | try: 14 | from nltk.tokenize import word_tokenize 15 | 16 | self.word_tokenize = word_tokenize 17 | except ImportError: 18 | raise ImportError("Please install nltk with: pip install nltk") 19 | 20 | def encode(self, x: str) -> str: 21 | return " ".join(self.word_tokenize(x)) 22 | 23 | def decode(self, x: str) -> str: 24 | return x 25 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/encoders/space_tokenizer.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import re 7 | 8 | from fairseq.data.encoders import register_tokenizer 9 | from fairseq.dataclass import FairseqDataclass 10 | 11 | 12 | @register_tokenizer("space", dataclass=FairseqDataclass) 13 | class SpaceTokenizer(object): 14 | def __init__(self, *unused): 15 | self.space_tok = re.compile(r"\s+") 16 | 17 | def encode(self, x: str) -> str: 18 | return self.space_tok.sub(" ", x) 19 | 20 | def decode(self, x: str) -> str: 21 | return x 22 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/encoders/utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | from fairseq.data import encoders 8 | 9 | 10 | def get_whole_word_mask(args, dictionary): 11 | bpe = encoders.build_bpe(args) 12 | if bpe is not None: 13 | 14 | def is_beginning_of_word(i): 15 | if i < dictionary.nspecial: 16 | # special elements are always considered beginnings 17 | return True 18 | tok = dictionary[i] 19 | if tok.startswith("madeupword"): 20 | return True 21 | try: 22 | return bpe.is_beginning_of_word(tok) 23 | except ValueError: 24 | return True 25 | 26 | mask_whole_words = torch.ByteTensor( 27 | list(map(is_beginning_of_word, range(len(dictionary)))) 28 | ) 29 | return mask_whole_words 30 | return None 31 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/id_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | from . import FairseqDataset 9 | 10 | 11 | class IdDataset(FairseqDataset): 12 | def __getitem__(self, index): 13 | return index 14 | 15 | def __len__(self): 16 | return 0 17 | 18 | def collater(self, samples): 19 | return torch.tensor(samples) 20 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/legacy/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .block_pair_dataset import BlockPairDataset 7 | from .masked_lm_dataset import MaskedLMDataset 8 | from .masked_lm_dictionary import BertDictionary, MaskedLMDictionary 9 | 10 | 11 | __all__ = [ 12 | "BertDictionary", 13 | "BlockPairDataset", 14 | "MaskedLMDataset", 15 | "MaskedLMDictionary", 16 | ] 17 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/list_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from . import BaseWrapperDataset 7 | 8 | 9 | class ListDataset(BaseWrapperDataset): 10 | def __init__(self, dataset, sizes=None): 11 | super().__init__(dataset) 12 | self._sizes = sizes 13 | 14 | def __iter__(self): 15 | for x in self.dataset: 16 | yield x 17 | 18 | def collater(self, samples): 19 | return samples 20 | 21 | @property 22 | def sizes(self): 23 | return self._sizes 24 | 25 | def num_tokens(self, index): 26 | return self.sizes[index] 27 | 28 | def size(self, index): 29 | return self.sizes[index] 30 | 31 | def set_epoch(self, epoch): 32 | pass 33 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/lru_cache_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from functools import lru_cache 7 | 8 | from . import BaseWrapperDataset 9 | 10 | 11 | class LRUCacheDataset(BaseWrapperDataset): 12 | def __init__(self, dataset, token=None): 13 | super().__init__(dataset) 14 | 15 | @lru_cache(maxsize=8) 16 | def __getitem__(self, index): 17 | return self.dataset[index] 18 | 19 | @lru_cache(maxsize=8) 20 | def collater(self, samples): 21 | return self.dataset.collater(samples) 22 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/multilingual/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/num_samples_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from . import FairseqDataset 7 | 8 | 9 | class NumSamplesDataset(FairseqDataset): 10 | def __getitem__(self, index): 11 | return 1 12 | 13 | def __len__(self): 14 | return 0 15 | 16 | def collater(self, samples): 17 | return sum(samples) 18 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/numel_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import numpy as np 7 | import torch 8 | 9 | from . import BaseWrapperDataset 10 | 11 | 12 | class NumelDataset(BaseWrapperDataset): 13 | def __init__(self, dataset, reduce=False): 14 | super().__init__(dataset) 15 | self.reduce = reduce 16 | 17 | def __getitem__(self, index): 18 | item = self.dataset[index] 19 | if torch.is_tensor(item): 20 | return torch.numel(item) 21 | else: 22 | return np.size(item) 23 | 24 | def __len__(self): 25 | return len(self.dataset) 26 | 27 | def collater(self, samples): 28 | if self.reduce: 29 | return sum(samples) 30 | else: 31 | return torch.tensor(samples) 32 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/offset_tokens_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from . import BaseWrapperDataset 7 | 8 | 9 | class OffsetTokensDataset(BaseWrapperDataset): 10 | def __init__(self, dataset, offset): 11 | super().__init__(dataset) 12 | self.offset = offset 13 | 14 | def __getitem__(self, idx): 15 | return self.dataset[idx] + self.offset 16 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/pad_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq.data import data_utils 7 | 8 | from . import BaseWrapperDataset 9 | 10 | 11 | class PadDataset(BaseWrapperDataset): 12 | def __init__(self, dataset, pad_idx, left_pad): 13 | super().__init__(dataset) 14 | self.pad_idx = pad_idx 15 | self.left_pad = left_pad 16 | 17 | def collater(self, samples): 18 | return data_utils.collate_tokens(samples, self.pad_idx, left_pad=self.left_pad) 19 | 20 | 21 | class LeftPadDataset(PadDataset): 22 | def __init__(self, dataset, pad_idx): 23 | super().__init__(dataset, pad_idx, left_pad=True) 24 | 25 | 26 | class RightPadDataset(PadDataset): 27 | def __init__(self, dataset, pad_idx): 28 | super().__init__(dataset, pad_idx, left_pad=False) 29 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/prepend_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import numpy as np 7 | import torch 8 | 9 | from . import BaseWrapperDataset 10 | 11 | 12 | class PrependDataset(BaseWrapperDataset): 13 | def __init__(self, dataset, prepend_getter, ensure_first_token_is=None): 14 | super().__init__(dataset) 15 | self.prepend_getter = prepend_getter 16 | self.ensure_first_token = ensure_first_token_is 17 | 18 | def __getitem__(self, idx): 19 | item = self.dataset[idx] 20 | is_tuple = isinstance(item, tuple) 21 | src = item[0] if is_tuple else item 22 | 23 | assert self.ensure_first_token is None or src[0] == self.ensure_first_token 24 | prepend_idx = self.prepend_getter(self.dataset, idx) 25 | assert isinstance(prepend_idx, int) 26 | src[0] = prepend_idx 27 | item = tuple((src,) + item[1:]) if is_tuple else src 28 | return item 29 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/prepend_token_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import numpy as np 7 | import torch 8 | 9 | from . import BaseWrapperDataset 10 | 11 | 12 | class PrependTokenDataset(BaseWrapperDataset): 13 | def __init__(self, dataset, token=None): 14 | super().__init__(dataset) 15 | self.token = token 16 | if token is not None: 17 | self._sizes = np.array(dataset.sizes) + 1 18 | else: 19 | self._sizes = dataset.sizes 20 | 21 | def __getitem__(self, idx): 22 | item = self.dataset[idx] 23 | if self.token is not None: 24 | item = torch.cat([item.new([self.token]), item]) 25 | return item 26 | 27 | @property 28 | def sizes(self): 29 | return self._sizes 30 | 31 | def num_tokens(self, index): 32 | n = self.dataset.num_tokens(index) 33 | if self.token is not None: 34 | n += 1 35 | return n 36 | 37 | def size(self, index): 38 | n = self.dataset.size(index) 39 | if self.token is not None: 40 | n += 1 41 | return n 42 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/raw_label_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | from . import FairseqDataset 9 | 10 | 11 | class RawLabelDataset(FairseqDataset): 12 | def __init__(self, labels): 13 | super().__init__() 14 | self.labels = labels 15 | 16 | def __getitem__(self, index): 17 | return self.labels[index] 18 | 19 | def __len__(self): 20 | return len(self.labels) 21 | 22 | def collater(self, samples): 23 | return torch.tensor(samples) 24 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/roll_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | from . import BaseWrapperDataset 9 | 10 | 11 | class RollDataset(BaseWrapperDataset): 12 | def __init__(self, dataset, shifts): 13 | super().__init__(dataset) 14 | self.shifts = shifts 15 | 16 | def __getitem__(self, index): 17 | item = self.dataset[index] 18 | return torch.roll(item, self.shifts) 19 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/sort_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import numpy as np 7 | 8 | from . import BaseWrapperDataset 9 | 10 | 11 | class SortDataset(BaseWrapperDataset): 12 | def __init__(self, dataset, sort_order): 13 | super().__init__(dataset) 14 | if not isinstance(sort_order, (list, tuple)): 15 | sort_order = [sort_order] 16 | self.sort_order = sort_order 17 | 18 | assert all(len(so) == len(dataset) for so in sort_order) 19 | 20 | def ordered_indices(self): 21 | return np.lexsort(self.sort_order) 22 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/data/strip_token_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from . import BaseWrapperDataset 7 | 8 | 9 | class StripTokenDataset(BaseWrapperDataset): 10 | def __init__(self, dataset, id_to_strip): 11 | super().__init__(dataset) 12 | self.id_to_strip = id_to_strip 13 | 14 | def __getitem__(self, index): 15 | item = self.dataset[index] 16 | while len(item) > 0 and item[-1] == self.id_to_strip: 17 | item = item[:-1] 18 | while len(item) > 0 and item[0] == self.id_to_strip: 19 | item = item[1:] 20 | return item 21 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/dataclass/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .configs import FairseqDataclass 7 | from .constants import ChoiceEnum 8 | 9 | 10 | __all__ = [ 11 | "FairseqDataclass", 12 | "ChoiceEnum", 13 | ] 14 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/distributed/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .distributed_timeout_wrapper import DistributedTimeoutWrapper 7 | from .legacy_distributed_data_parallel import LegacyDistributedDataParallel 8 | from .module_proxy_wrapper import ModuleProxyWrapper 9 | from .tpu_distributed_data_parallel import TPUDistributedDataParallel 10 | 11 | 12 | __all__ = [ 13 | "DistributedTimeoutWrapper", 14 | "LegacyDistributedDataParallel", 15 | "ModuleProxyWrapper", 16 | "TPUDistributedDataParallel", 17 | ] 18 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/logging/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dropreg/R-Drop/9dcc8302eb9b0f112c38230864bbee807bbe1aa7/fairseq_src/fairseq/logging/__init__.py -------------------------------------------------------------------------------- /fairseq_src/fairseq/model_parallel/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from . import criterions, models, modules # noqa 7 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/model_parallel/criterions/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import importlib 7 | import os 8 | 9 | 10 | # automatically import any Python files in the criterions/ directory 11 | for file in os.listdir(os.path.dirname(__file__)): 12 | if file.endswith(".py") and not file.startswith("_"): 13 | module = file[: file.find(".py")] 14 | importlib.import_module("fairseq.model_parallel.criterions." + module) 15 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/model_parallel/models/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import importlib 7 | import os 8 | 9 | 10 | # automatically import any Python files in the models/ directory 11 | models_dir = os.path.dirname(__file__) 12 | for file in os.listdir(models_dir): 13 | path = os.path.join(models_dir, file) 14 | if ( 15 | not file.startswith("_") 16 | and not file.startswith(".") 17 | and (file.endswith(".py") or os.path.isdir(path)) 18 | ): 19 | model_name = file[: file.find(".py")] if file.endswith(".py") else file 20 | module = importlib.import_module("fairseq.model_parallel.models." + model_name) 21 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/model_parallel/models/pipeline_parallel_transformer/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .model import * # noqa 7 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/model_parallel/models/roberta/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .model import * # noqa 7 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/model_parallel/modules/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | """isort:skip_file""" 6 | 7 | from .multihead_attention import ModelParallelMultiheadAttention 8 | from .transformer_layer import ( 9 | ModelParallelTransformerEncoderLayer, 10 | ModelParallelTransformerDecoderLayer, 11 | ) 12 | from .transformer_sentence_encoder_layer import ( 13 | ModelParallelTransformerSentenceEncoderLayer, 14 | ) 15 | from .transformer_sentence_encoder import ModelParallelTransformerSentenceEncoder 16 | 17 | __all__ = [ 18 | "ModelParallelMultiheadAttention", 19 | "ModelParallelTransformerEncoderLayer", 20 | "ModelParallelTransformerDecoderLayer", 21 | "ModelParallelTransformerSentenceEncoder", 22 | "ModelParallelTransformerSentenceEncoderLayer", 23 | ] 24 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/models/bart/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .hub_interface import * # noqa 7 | from .model import * # noqa 8 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/models/huggingface/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import importlib 7 | import os 8 | 9 | 10 | # automatically import any Python files in the models/huggingface/ directory 11 | models_dir = os.path.dirname(__file__) 12 | for file in os.listdir(models_dir): 13 | path = os.path.join(models_dir, file) 14 | if ( 15 | not file.startswith("_") 16 | and not file.startswith(".") 17 | and (file.endswith(".py") or os.path.isdir(path)) 18 | ): 19 | model_name = file[: file.find(".py")] if file.endswith(".py") else file 20 | module = importlib.import_module("fairseq.models.huggingface." + model_name) 21 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/models/nat/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | """isort:skip_file""" 6 | 7 | from .fairseq_nat_model import * 8 | from .nonautoregressive_transformer import * 9 | from .nat_crf_transformer import * 10 | from .iterative_nonautoregressive_transformer import * 11 | from .cmlm_transformer import * 12 | from .levenshtein_transformer import * 13 | from .insertion_transformer import * 14 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/models/roberta/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .hub_interface import * # noqa 7 | from .model import * # noqa 8 | from .model_camembert import * # noqa 9 | from .model_gottbert import * # noqa 10 | from .model_xlmr import * # noqa 11 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/models/speech_to_text/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .berard import * # noqa 7 | from .s2t_transformer import * # noqa 8 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/models/wav2vec/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .wav2vec import * # noqa 7 | from .wav2vec2 import * # noqa 8 | from .wav2vec2_asr import * # noqa 9 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/dynamicconv_layer/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .dynamicconv_layer import DynamicconvLayer # noqa 7 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/dynamicconv_layer/dynamiconv_cpu.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | std::vector dynamicconv_cpu_forward( 5 | float* input, 6 | float* filters, 7 | int padding_l); 8 | 9 | std::vector dynamicconv_cpu_backward( 10 | float* gradOutput, 11 | int padding_l, 12 | float* input, 13 | float* filters); 14 | 15 | std::vector dynamicconv_forward( 16 | float* input, 17 | float* filters, 18 | int padding_l) { 19 | 20 | return dynamicconv_cpu_forward(input, filters, padding_l); 21 | } 22 | 23 | std::vector dynamicconv_backward( 24 | float* gradOutput, 25 | int padding_l, 26 | float* input, 27 | float* filters) { 28 | 29 | return dynamicconv_cpu_backward(gradOutput, padding_l, input, filters); 30 | } 31 | 32 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 33 | m.def("forward", &dynamicconv_forward, "dynamicconv forward (CPU)"); 34 | m.def("backward", &dynamicconv_backward, "dynamicconv backward (CPU)"); 35 | } 36 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/dynamicconv_layer/setup.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from setuptools import setup 8 | from torch.utils.cpp_extension import BuildExtension, CUDAExtension 9 | 10 | 11 | setup( 12 | name="dynamicconv_layer", 13 | ext_modules=[ 14 | CUDAExtension( 15 | name="dynamicconv_cuda", 16 | sources=[ 17 | "dynamicconv_cuda.cpp", 18 | "dynamicconv_cuda_kernel.cu", 19 | ], 20 | ), 21 | ], 22 | cmdclass={"build_ext": BuildExtension}, 23 | ) 24 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/fp32_group_norm.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | """ 6 | Layer norm done in fp32 (for fp16 training) 7 | """ 8 | 9 | import torch.nn as nn 10 | import torch.nn.functional as F 11 | 12 | 13 | class Fp32GroupNorm(nn.GroupNorm): 14 | def __init__(self, *args, **kwargs): 15 | super().__init__(*args, **kwargs) 16 | 17 | def forward(self, input): 18 | output = F.group_norm( 19 | input.float(), 20 | self.num_groups, 21 | self.weight.float() if self.weight is not None else None, 22 | self.bias.float() if self.bias is not None else None, 23 | self.eps, 24 | ) 25 | return output.type_as(input) 26 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/gelu.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | """ 6 | See "Gaussian Error Linear Units (GELUs)" by Dan Hendrycks and Kevin Gimpel with 7 | the corresponding GitHub repo: https://github.com/hendrycks/GELUs 8 | """ 9 | 10 | import math 11 | 12 | import torch 13 | import torch.nn as nn 14 | 15 | 16 | def gelu_accurate(x): 17 | if not hasattr(gelu_accurate, "_a"): 18 | gelu_accurate._a = math.sqrt(2 / math.pi) 19 | return ( 20 | 0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3)))) 21 | ) 22 | 23 | 24 | def gelu(x: torch.Tensor) -> torch.Tensor: 25 | return torch.nn.functional.gelu(x.float()).type_as(x) 26 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/grad_multiply.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | 9 | class GradMultiply(torch.autograd.Function): 10 | @staticmethod 11 | def forward(ctx, x, scale): 12 | ctx.scale = scale 13 | res = x.new(x) 14 | return res 15 | 16 | @staticmethod 17 | def backward(ctx, grad): 18 | return grad * ctx.scale, None 19 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/lightconv_layer/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .lightconv_layer import LightconvLayer # noqa 7 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/lightconv_layer/setup.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from setuptools import setup 8 | from torch.utils.cpp_extension import BuildExtension, CUDAExtension 9 | 10 | 11 | setup( 12 | name="lightconv_layer", 13 | ext_modules=[ 14 | CUDAExtension( 15 | "lightconv_cuda", 16 | [ 17 | "lightconv_cuda.cpp", 18 | "lightconv_cuda_kernel.cu", 19 | ], 20 | ), 21 | ], 22 | cmdclass={"build_ext": BuildExtension}, 23 | ) 24 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/quantization/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dropreg/R-Drop/9dcc8302eb9b0f112c38230864bbee807bbe1aa7/fairseq_src/fairseq/modules/quantization/__init__.py -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/quantization/pq/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .utils import SizeTracker, quantize_model_ # NOQA 7 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/quantization/pq/modules/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .qconv import PQConv2d # NOQA 7 | from .qemb import PQEmbedding # NOQA 8 | from .qlinear import PQLinear # NOQA 9 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/quantization/scalar/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .utils import quantize_model_ # NOQA 7 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/quantization/scalar/modules/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .qact import ActivationQuantizer # NOQA 7 | from .qconv import IntConv2d # NOQA 8 | from .qemb import IntEmbedding # NOQA 9 | from .qlinear import IntLinear # NOQA 10 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/same_pad.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | 7 | from torch import nn 8 | 9 | 10 | class SamePad(nn.Module): 11 | def __init__(self, kernel_size, causal=False): 12 | super().__init__() 13 | if causal: 14 | self.remove = kernel_size - 1 15 | else: 16 | self.remove = 1 if kernel_size % 2 == 0 else 0 17 | 18 | def forward(self, x): 19 | if self.remove > 0: 20 | x = x[:, :, : -self.remove] 21 | return x 22 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/scalar_bias.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | # 6 | 7 | import torch 8 | 9 | 10 | class ScalarBias(torch.autograd.Function): 11 | """ 12 | Adds a vector of scalars, used in self-attention mechanism to allow 13 | the model to optionally attend to this vector instead of the past 14 | """ 15 | 16 | @staticmethod 17 | def forward(ctx, input, dim, bias_init): 18 | size = list(input.size()) 19 | size[dim] += 1 20 | output = input.new(*size).fill_(bias_init) 21 | output.narrow(dim, 1, size[dim] - 1).copy_(input) 22 | ctx.dim = dim 23 | return output 24 | 25 | @staticmethod 26 | def backward(ctx, grad): 27 | return grad.narrow(ctx.dim, 1, grad.size(ctx.dim) - 1), None, None 28 | 29 | 30 | def scalar_bias(input, dim, bias_init=0): 31 | return ScalarBias.apply(input, dim, bias_init) 32 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/transpose_last.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | """ 6 | transpose last 2 dimensions of the input 7 | """ 8 | 9 | import torch.nn as nn 10 | 11 | 12 | class TransposeLast(nn.Module): 13 | def __init__(self, deconstruct_idx=None): 14 | super().__init__() 15 | self.deconstruct_idx = deconstruct_idx 16 | 17 | def forward(self, x): 18 | if self.deconstruct_idx is not None: 19 | x = x[self.deconstruct_idx] 20 | return x.transpose(-2, -1) 21 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/modules/unfold.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch.nn.functional as F 7 | 8 | 9 | def unfold1d(x, kernel_size, padding_l, pad_value=0): 10 | """unfold T x B x C to T x B x C x K""" 11 | if kernel_size > 1: 12 | T, B, C = x.size() 13 | x = F.pad( 14 | x, (0, 0, 0, 0, padding_l, kernel_size - 1 - padding_l), value=pad_value 15 | ) 16 | x = x.as_strided((T, B, C, kernel_size), (B * C, C, 1, B * C)) 17 | else: 18 | x = x.unsqueeze(3) 19 | return x 20 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/optim/lr_scheduler/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | """isort:skip_file""" 6 | 7 | import importlib 8 | import os 9 | 10 | from fairseq import registry 11 | from fairseq.optim.lr_scheduler.fairseq_lr_scheduler import ( # noqa 12 | FairseqLRScheduler, 13 | LegacyFairseqLRScheduler, 14 | ) 15 | from omegaconf import DictConfig 16 | 17 | 18 | ( 19 | build_lr_scheduler_, 20 | register_lr_scheduler, 21 | LR_SCHEDULER_REGISTRY, 22 | LR_SCHEDULER_DATACLASS_REGISTRY, 23 | ) = registry.setup_registry( 24 | "--lr-scheduler", base_class=FairseqLRScheduler, default="fixed" 25 | ) 26 | 27 | 28 | def build_lr_scheduler(cfg: DictConfig, optimizer): 29 | return build_lr_scheduler_(cfg, optimizer) 30 | 31 | 32 | # automatically import any Python files in the optim/lr_scheduler/ directory 33 | for file in os.listdir(os.path.dirname(__file__)): 34 | if file.endswith(".py") and not file.startswith("_"): 35 | file_name = file[: file.find(".py")] 36 | importlib.import_module("fairseq.optim.lr_scheduler." + file_name) 37 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/scoring/chrf.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq.scoring import BaseScorer, register_scorer 7 | 8 | 9 | @register_scorer("chrf") 10 | class ChrFScorer(BaseScorer): 11 | def __init__(self, args): 12 | super(ChrFScorer, self).__init__(args) 13 | import sacrebleu 14 | 15 | self.sacrebleu = sacrebleu 16 | 17 | def add_string(self, ref, pred): 18 | self.ref.append(ref) 19 | self.pred.append(pred) 20 | 21 | def score(self, order=4): 22 | return self.result_string(order).score 23 | 24 | def result_string(self, order=4): 25 | if order != 4: 26 | raise NotImplementedError 27 | return self.sacrebleu.corpus_chrf(self.pred, [self.ref]).format() 28 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/tokenizer.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import re 7 | 8 | 9 | SPACE_NORMALIZER = re.compile(r"\s+") 10 | 11 | 12 | def tokenize_line(line): 13 | line = SPACE_NORMALIZER.sub(" ", line) 14 | line = line.strip() 15 | return line.split() 16 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/version.py: -------------------------------------------------------------------------------- 1 | __version__ = "1.0.0a0+b8ab305" 2 | -------------------------------------------------------------------------------- /fairseq_src/fairseq/version.txt: -------------------------------------------------------------------------------- 1 | 1.0.0a0 2 | -------------------------------------------------------------------------------- /fairseq_src/fairseq_cli/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dropreg/R-Drop/9dcc8302eb9b0f112c38230864bbee807bbe1aa7/fairseq_src/fairseq_cli/__init__.py -------------------------------------------------------------------------------- /fairseq_src/pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | requires = ["setuptools", "wheel", "cython"] 3 | build-backend = "setuptools.build_meta" 4 | -------------------------------------------------------------------------------- /fairseq_src/scripts/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dropreg/R-Drop/9dcc8302eb9b0f112c38230864bbee807bbe1aa7/fairseq_src/scripts/__init__.py -------------------------------------------------------------------------------- /fairseq_src/scripts/compound_split_bleu.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | if [ $# -ne 1 ]; then 4 | echo "usage: $0 GENERATE_PY_OUTPUT" 5 | exit 1 6 | fi 7 | 8 | GEN=$1 9 | 10 | SYS=$GEN.sys 11 | REF=$GEN.ref 12 | 13 | if [ $(tail -n 1 $GEN | grep BLEU | wc -l) -ne 1 ]; then 14 | echo "not done generating" 15 | exit 16 | fi 17 | 18 | grep ^H $GEN | awk -F '\t' '{print $NF}' | perl -ple 's{(\S)-(\S)}{$1 ##AT##-##AT## $2}g' > $SYS 19 | grep ^T $GEN | cut -f2- | perl -ple 's{(\S)-(\S)}{$1 ##AT##-##AT## $2}g' > $REF 20 | fairseq-score --sys $SYS --ref $REF 21 | -------------------------------------------------------------------------------- /fairseq_src/scripts/constraints/validate.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # 3 | # Copyright (c) Facebook, Inc. and its affiliates. 4 | # 5 | # This source code is licensed under the MIT license found in the 6 | # LICENSE file in the root directory of this source tree. 7 | 8 | import sys 9 | 10 | 11 | """Reads in a fairseq output file, and verifies that the constraints 12 | (C- lines) are present in the output (the first H- line). Assumes that 13 | constraints are listed prior to the first hypothesis. 14 | """ 15 | 16 | constraints = [] 17 | found = 0 18 | total = 0 19 | for line in sys.stdin: 20 | if line.startswith("C-"): 21 | constraints.append(line.rstrip().split("\t")[1]) 22 | elif line.startswith("H-"): 23 | text = line.split("\t")[2] 24 | 25 | for constraint in constraints: 26 | total += 1 27 | if constraint in text: 28 | found += 1 29 | else: 30 | print(f"No {constraint} in {text}", file=sys.stderr) 31 | 32 | constraints = [] 33 | 34 | print(f"Found {found} / {total} = {100 * found / total:.1f}%") 35 | -------------------------------------------------------------------------------- /fairseq_src/scripts/convert_dictionary.lua: -------------------------------------------------------------------------------- 1 | -- Copyright (c) Facebook, Inc. and its affiliates. 2 | -- 3 | -- This source code is licensed under the MIT license found in the 4 | -- LICENSE file in the root directory of this source tree. 5 | -- 6 | -- Usage: convert_dictionary.lua 7 | require 'fairseq' 8 | require 'torch' 9 | require 'paths' 10 | 11 | if #arg < 1 then 12 | print('usage: convert_dictionary.lua ') 13 | os.exit(1) 14 | end 15 | if not paths.filep(arg[1]) then 16 | print('error: file does not exit: ' .. arg[1]) 17 | os.exit(1) 18 | end 19 | 20 | dict = torch.load(arg[1]) 21 | dst = paths.basename(arg[1]):gsub('.th7', '.txt') 22 | assert(dst:match('.txt$')) 23 | 24 | f = io.open(dst, 'w') 25 | for idx, symbol in ipairs(dict.index_to_symbol) do 26 | if idx > dict.cutoff then 27 | break 28 | end 29 | f:write(symbol) 30 | f:write(' ') 31 | f:write(dict.index_to_freq[idx]) 32 | f:write('\n') 33 | end 34 | f:close() 35 | -------------------------------------------------------------------------------- /fairseq_src/scripts/sacrebleu.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | if [ $# -ne 4 ]; then 4 | echo "usage: $0 TESTSET SRCLANG TGTLANG GEN" 5 | exit 1 6 | fi 7 | 8 | TESTSET=$1 9 | SRCLANG=$2 10 | TGTLANG=$3 11 | 12 | GEN=$4 13 | 14 | if ! command -v sacremoses &> /dev/null 15 | then 16 | echo "sacremoses could not be found, please install with: pip install sacremoses" 17 | exit 18 | fi 19 | 20 | grep ^H $GEN \ 21 | | sed 's/^H\-//' \ 22 | | sort -n -k 1 \ 23 | | cut -f 3 \ 24 | | sacremoses detokenize \ 25 | > $GEN.sorted.detok 26 | 27 | sacrebleu --test-set $TESTSET --language-pair "${SRCLANG}-${TGTLANG}" < $GEN.sorted.detok 28 | -------------------------------------------------------------------------------- /fairseq_src/scripts/spm_train.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # All rights reserved. 4 | # 5 | # This source code is licensed under the license found in the 6 | # LICENSE file in the root directory of this source tree. 7 | 8 | from __future__ import absolute_import, division, print_function, unicode_literals 9 | 10 | import sys 11 | 12 | import sentencepiece as spm 13 | 14 | 15 | if __name__ == "__main__": 16 | spm.SentencePieceTrainer.Train(" ".join(sys.argv[1:])) 17 | -------------------------------------------------------------------------------- /fairseq_src/train.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 -u 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | """ 7 | Legacy entry point. Use fairseq_cli/train.py or fairseq-train instead. 8 | """ 9 | 10 | from fairseq_cli.train import cli_main 11 | 12 | 13 | if __name__ == "__main__": 14 | cli_main() 15 | -------------------------------------------------------------------------------- /huggingface_transformer_src/MANIFEST.in: -------------------------------------------------------------------------------- 1 | include LICENSE 2 | -------------------------------------------------------------------------------- /huggingface_transformer_src/README.md: -------------------------------------------------------------------------------- 1 | # Introduction 2 | 3 | This Repo conduct language understanding task. 4 | 5 | # Requirements and Installation 6 | 7 | * [PyTorch](http://pytorch.org/) version == 1.8 8 | * Python version >= 3.6 9 | 10 | To install fairseq from source and develop locally: 11 | ``` 12 | git clone https://github.com/dropreg/R-Drop.git 13 | cd R-Drop/huggingface_transformer/ 14 | pip install --editable . 15 | ``` 16 | 17 | # Fine-tuning on GLUE task: 18 | Example fine-tuning cmd for `MRPC` task 19 | 20 | ```bash 21 | cd bert_rdrop/ 22 | bash run.sh 23 | ``` 24 | 25 | You can run it on Roberta by simply switching hyper-parameter: model_name_or_path 26 | 27 | ```bash 28 | --model_name_or_path roberta-base \ 29 | ``` 30 | 31 | For each of the GLUE task, you will need to use following cmd-line arguments: 32 | 33 | Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B 34 | ---|---|---|---|---|---|---|---|--- 35 | `--lr` | 1e-5 | 1e-5 | 1e-5 | 1e-5 | 1e-5 | 1e-5 | 1e-5 | 1e-5 36 | `--batch-size` | 32 | 32 | 32 | 8 | 32 | 16 | 16 | 16 37 | `--total-num-update` | 123873 | 33112 | 113272 | 2036 | 20935 | 2296 | 5336 | 3598 38 | `--warmup-updates` | 7432 | 1986 | 28318 | 122 | 1256 | 137 | 320 | 214 39 | -------------------------------------------------------------------------------- /huggingface_transformer_src/bert_rdrop/run.sh: -------------------------------------------------------------------------------- 1 | 2 | TOTAL_NUM_UPDATES=2296 3 | WARMUP_UPDATES=137 4 | LR=1e-5 5 | MAX_SENTENCES=16 6 | TASK=MRPC 7 | 8 | # --save_strategy no \ 9 | CUDA_VISIBLE_DEVICES=0 python run_glue.py \ 10 | --model_name_or_path bert-base-cased \ 11 | --task_name $TASK \ 12 | --do_train \ 13 | --do_eval \ 14 | --adam_beta2 0.98 \ 15 | --adam_epsilon 1e-6 \ 16 | --per_device_train_batch_size $MAX_SENTENCES \ 17 | --learning_rate $LR \ 18 | --evaluation_strategy epoch \ 19 | --fp16 \ 20 | --weight_decay 0.01 \ 21 | --lr_scheduler_type polynomial \ 22 | --max_steps $TOTAL_NUM_UPDATES \ 23 | --warmup_steps $WARMUP_UPDATES \ 24 | --output_dir "temp" \ 25 | --seed 5 \ 26 | --overwrite_output_dir \ 27 | -------------------------------------------------------------------------------- /huggingface_transformer_src/pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.black] 2 | line-length = 119 3 | target-version = ['py35'] 4 | -------------------------------------------------------------------------------- /huggingface_transformer_src/setup.cfg: -------------------------------------------------------------------------------- 1 | [isort] 2 | default_section = FIRSTPARTY 3 | ensure_newline_before_comments = True 4 | force_grid_wrap = 0 5 | include_trailing_comma = True 6 | known_first_party = transformers 7 | known_third_party = 8 | absl 9 | conllu 10 | datasets 11 | elasticsearch 12 | fairseq 13 | faiss-cpu 14 | fastprogress 15 | fire 16 | fugashi 17 | git 18 | h5py 19 | matplotlib 20 | nltk 21 | numpy 22 | packaging 23 | pandas 24 | PIL 25 | psutil 26 | pytest 27 | pytorch_lightning 28 | rouge_score 29 | sacrebleu 30 | seqeval 31 | sklearn 32 | streamlit 33 | tensorboardX 34 | tensorflow 35 | tensorflow_datasets 36 | timeout_decorator 37 | torch 38 | torchaudio 39 | torchtext 40 | torchvision 41 | torch_xla 42 | tqdm 43 | 44 | line_length = 119 45 | lines_after_imports = 2 46 | multi_line_output = 3 47 | use_parentheses = True 48 | 49 | [flake8] 50 | ignore = E203, E501, E741, W503, W605 51 | max-line-length = 119 52 | -------------------------------------------------------------------------------- /huggingface_transformer_src/src/transformers.egg-info/dependency_links.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /huggingface_transformer_src/src/transformers.egg-info/entry_points.txt: -------------------------------------------------------------------------------- 1 | [console_scripts] 2 | transformers-cli = transformers.commands.transformers_cli:main 3 | 4 | -------------------------------------------------------------------------------- /huggingface_transformer_src/src/transformers.egg-info/top_level.txt: -------------------------------------------------------------------------------- 1 | transformers 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All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from abc import ABC, abstractmethod 16 | from argparse import ArgumentParser 17 | 18 | 19 | class BaseTransformersCLICommand(ABC): 20 | @staticmethod 21 | @abstractmethod 22 | def register_subcommand(parser: ArgumentParser): 23 | raise NotImplementedError() 24 | 25 | @abstractmethod 26 | def run(self): 27 | raise NotImplementedError() 28 | -------------------------------------------------------------------------------- /huggingface_transformer_src/src/transformers/data/__pycache__/__init__.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dropreg/R-Drop/9dcc8302eb9b0f112c38230864bbee807bbe1aa7/huggingface_transformer_src/src/transformers/data/__pycache__/__init__.cpython-36.pyc -------------------------------------------------------------------------------- /huggingface_transformer_src/src/transformers/data/__pycache__/data_collator.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dropreg/R-Drop/9dcc8302eb9b0f112c38230864bbee807bbe1aa7/huggingface_transformer_src/src/transformers/data/__pycache__/data_collator.cpython-36.pyc -------------------------------------------------------------------------------- /huggingface_transformer_src/src/transformers/data/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | # flake8: noqa 2 | # There's no way to ignore "F401 '...' imported but unused" warnings in this 3 | # module, but to preserve other warnings. So, don't check this module at all. 4 | 5 | # Copyright 2020 The HuggingFace Team. All rights reserved. 6 | # 7 | # Licensed under the Apache License, Version 2.0 (the "License"); 8 | # you may not use this file except in compliance with the License. 9 | # You may obtain a copy of the License at 10 | # 11 | # http://www.apache.org/licenses/LICENSE-2.0 12 | # 13 | # Unless required by applicable law or agreed to in writing, software 14 | # distributed under the License is distributed on an "AS IS" BASIS, 15 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 16 | # See the License for the specific language governing permissions and 17 | # limitations under the License. 18 | 19 | from .glue import GlueDataset, GlueDataTrainingArguments 20 | from .language_modeling import ( 21 | LineByLineTextDataset, 22 | LineByLineWithRefDataset, 23 | LineByLineWithSOPTextDataset, 24 | TextDataset, 25 | TextDatasetForNextSentencePrediction, 26 | ) 27 | from .squad import SquadDataset, SquadDataTrainingArguments 28 | 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So, don't check this module at all. 4 | 5 | # Copyright 2021 The HuggingFace Team. All rights reserved. 6 | # 7 | # Licensed under the Apache License, Version 2.0 (the "License"); 8 | # you may not use this file except in compliance with the License. 9 | # You may obtain a copy of the License at 10 | # 11 | # http://www.apache.org/licenses/LICENSE-2.0 12 | # 13 | # Unless required by applicable law or agreed to in writing, software 14 | # distributed under the License is distributed on an "AS IS" BASIS, 15 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 16 | # See the License for the specific language governing permissions and 17 | # limitations under the License. 18 | 19 | from .trainer_sm import SageMakerTrainer 20 | from .training_args_sm import SageMakerTrainingArguments, is_sagemaker_dp_enabled 21 | -------------------------------------------------------------------------------- /huggingface_transformer_src/src/transformers/utils/__pycache__/__init__.cpython-36.pyc: 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requires_backends 3 | 4 | 5 | class Speech2TextProcessor: 6 | def __init__(self, *args, **kwargs): 7 | requires_backends(self, ["sentencepiece", "speech"]) 8 | -------------------------------------------------------------------------------- /huggingface_transformer_src/src/transformers/utils/dummy_sentencepiece_and_tokenizers_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..file_utils import requires_backends 3 | 4 | 5 | SLOW_TO_FAST_CONVERTERS = None 6 | 7 | 8 | def convert_slow_tokenizer(*args, **kwargs): 9 | requires_backends(convert_slow_tokenizer, ["sentencepiece", "tokenizers"]) 10 | -------------------------------------------------------------------------------- /huggingface_transformer_src/src/transformers/utils/dummy_speech_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..file_utils import requires_backends 3 | 4 | 5 | class Speech2TextFeatureExtractor: 6 | def __init__(self, *args, **kwargs): 7 | requires_backends(self, ["speech"]) 8 | -------------------------------------------------------------------------------- /huggingface_transformer_src/src/transformers/utils/dummy_vision_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..file_utils import requires_backends 3 | 4 | 5 | class ImageFeatureExtractionMixin: 6 | def __init__(self, *args, **kwargs): 7 | requires_backends(self, ["vision"]) 8 | 9 | 10 | class DeiTFeatureExtractor: 11 | def __init__(self, *args, **kwargs): 12 | requires_backends(self, ["vision"]) 13 | 14 | 15 | class ViTFeatureExtractor: 16 | def __init__(self, *args, **kwargs): 17 | requires_backends(self, ["vision"]) 18 | -------------------------------------------------------------------------------- /vit_src/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dropreg/R-Drop/9dcc8302eb9b0f112c38230864bbee807bbe1aa7/vit_src/.DS_Store -------------------------------------------------------------------------------- /vit_src/requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | numpy 3 | tqdm 4 | tensorboard 5 | ml-collections 6 | -------------------------------------------------------------------------------- /vit_src/utils/dist_util.py: -------------------------------------------------------------------------------- 1 | import torch.distributed as dist 2 | 3 | def get_rank(): 4 | if not dist.is_available(): 5 | return 0 6 | if not dist.is_initialized(): 7 | return 0 8 | return dist.get_rank() 9 | 10 | def get_world_size(): 11 | if not dist.is_available(): 12 | return 1 13 | if not dist.is_initialized(): 14 | return 1 15 | return dist.get_world_size() 16 | 17 | def is_main_process(): 18 | return get_rank() == 0 19 | 20 | def format_step(step): 21 | if isinstance(step, str): 22 | return step 23 | s = "" 24 | if len(step) > 0: 25 | s += "Training Epoch: {} ".format(step[0]) 26 | if len(step) > 1: 27 | s += "Training Iteration: {} ".format(step[1]) 28 | if len(step) > 2: 29 | s += "Validation Iteration: {} ".format(step[2]) 30 | return s 31 | --------------------------------------------------------------------------------