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fairseq/fairseq.egg-info/PKG-INFO

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+Metadata-Version: 2.2
+Name: fairseq
+Version: 0.12.2
+Summary: Facebook AI Research Sequence-to-Sequence Toolkit
+Home-page: https://github.com/pytorch/fairseq
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: cffi
+Requires-Dist: cython
+Requires-Dist: hydra-core<1.1,>=1.0.7
+Requires-Dist: omegaconf<2.1
+Requires-Dist: numpy>=1.21.3
+Requires-Dist: regex
+Requires-Dist: sacrebleu>=1.4.12
+Requires-Dist: torch>=1.13
+Requires-Dist: tqdm
+Requires-Dist: bitarray
+Requires-Dist: torchaudio>=0.8.0
+Requires-Dist: scikit-learn
+Requires-Dist: packaging
+Provides-Extra: dev
+Requires-Dist: flake8; extra == "dev"
+Requires-Dist: pytest; extra == "dev"
+Requires-Dist: black==22.3.0; extra == "dev"
+Provides-Extra: docs
+Requires-Dist: sphinx; extra == "docs"
+Requires-Dist: sphinx-argparse; extra == "docs"
+Dynamic: classifier
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: provides-extra
+Dynamic: requires-dist
+Dynamic: summary
+
+<p align="center">
+  <img src="docs/fairseq_logo.png" width="150">
+  <br />
+  <br />
+  <a href="https://opensource.fb.com/support-ukraine"><img alt="Support Ukraine" src="https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB" /></a>
+  <a href="https://github.com/pytorch/fairseq/blob/main/LICENSE"><img alt="MIT License" src="https://img.shields.io/badge/license-MIT-blue.svg" /></a>
+  <a href="https://github.com/pytorch/fairseq/releases"><img alt="Latest Release" src="https://img.shields.io/github/release/pytorch/fairseq.svg" /></a>
+  <a href="https://github.com/pytorch/fairseq/actions?query=workflow:build"><img alt="Build Status" src="https://github.com/pytorch/fairseq/workflows/build/badge.svg" /></a>
+  <a href="https://fairseq.readthedocs.io/en/latest/?badge=latest"><img alt="Documentation Status" src="https://readthedocs.org/projects/fairseq/badge/?version=latest" /></a>
+  <a href="https://app.circleci.com/pipelines/github/facebookresearch/fairseq/"><img alt="CicleCI Status" src="https://circleci.com/gh/facebookresearch/fairseq.svg?style=shield" /></a>
+</p>
+
+--------------------------------------------------------------------------------
+
+Fairseq(-py) is a sequence modeling toolkit that allows researchers and
+developers to train custom models for translation, summarization, language
+modeling and other text generation tasks.
+
+We provide reference implementations of various sequence modeling papers:
+
+<details><summary>List of implemented papers</summary><p>
+
+* **Convolutional Neural Networks (CNN)**
+  + [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/conv_lm/README.md)
+  + [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md)
+  + [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel)
+  + [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md)
+  + [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md)
+* **LightConv and DynamicConv models**
+  + [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md)
+* **Long Short-Term Memory (LSTM) networks**
+  + Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015)
+* **Transformer (self-attention) networks**
+  + Attention Is All You Need (Vaswani et al., 2017)
+  + [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md)
+  + [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md)
+  + [Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)](examples/language_model/README.adaptive_inputs.md)
+  + [Lexically constrained decoding with dynamic beam allocation (Post & Vilar, 2018)](examples/constrained_decoding/README.md)
+  + [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context (Dai et al., 2019)](examples/truncated_bptt/README.md)
+  + [Adaptive Attention Span in Transformers (Sukhbaatar et al., 2019)](examples/adaptive_span/README.md)
+  + [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md)
+  + [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md)
+  + [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md)
+  + [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md )
+  + [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md)
+  + [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md)
+  + [Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)](examples/unsupervised_quality_estimation/README.md)
+  + [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](examples/wav2vec/README.md)
+  + [Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models (Enarvi et al., 2020)](examples/pointer_generator/README.md)
+  + [Linformer: Self-Attention with Linear Complexity (Wang et al., 2020)](examples/linformer/README.md)
+  + [Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)](examples/criss/README.md)
+  + [Deep Transformers with Latent Depth (Li et al., 2020)](examples/latent_depth/README.md)
+  + [Unsupervised Cross-lingual Representation Learning for Speech Recognition (Conneau et al., 2020)](https://arxiv.org/abs/2006.13979)
+  + [Self-training and Pre-training are Complementary for Speech Recognition (Xu et al., 2020)](https://arxiv.org/abs/2010.11430)
+  + [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (Hsu, et al., 2021)](https://arxiv.org/abs/2104.01027)
+  + [Unsupervised Speech Recognition (Baevski, et al., 2021)](https://arxiv.org/abs/2105.11084)
+  + [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition (Xu et al., 2021)](https://arxiv.org/abs/2109.11680)
+  + [VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding (Xu et. al., 2021)](https://arxiv.org/pdf/2109.14084.pdf)
+  + [VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding (Xu et. al., 2021)](https://aclanthology.org/2021.findings-acl.370.pdf)
+  + [NormFormer: Improved Transformer Pretraining with Extra Normalization (Shleifer et. al, 2021)](examples/normformer/README.md)
+* **Non-autoregressive Transformers**
+  + Non-Autoregressive Neural Machine Translation (Gu et al., 2017)
+  + Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. 2018)
+  + Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. 2019)
+  + Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019)
+  + [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md)
+* **Finetuning**
+  + [Better Fine-Tuning by Reducing Representational Collapse (Aghajanyan et al. 2020)](examples/rxf/README.md)
+
+</p></details>
+
+### What's New:
+* May 2023 [Released models for Scaling Speech Technology to 1,000+ Languages  (Pratap, et al., 2023)](examples/mms/README.md)
+* June 2022 [Released code for wav2vec-U 2.0 from Towards End-to-end Unsupervised Speech Recognition (Liu, et al., 2022)](examples/wav2vec/unsupervised/README.md)
+* May 2022 [Integration with xFormers](https://github.com/facebookresearch/xformers)
+* December 2021 [Released Direct speech-to-speech translation code](examples/speech_to_speech/README.md)
+* October 2021 [Released VideoCLIP and VLM models](examples/MMPT/README.md)
+* October 2021 [Released multilingual finetuned XLSR-53 model](examples/wav2vec/README.md)
+* September 2021 [`master` branch renamed to `main`](https://github.com/github/renaming).
+* July 2021 [Released DrNMT code](examples/discriminative_reranking_nmt/README.md)
+* July 2021 [Released Robust wav2vec 2.0 model](examples/wav2vec/README.md)
+* June 2021 [Released XLMR-XL and XLMR-XXL models](examples/xlmr/README.md)
+* May 2021 [Released Unsupervised Speech Recognition code](examples/wav2vec/unsupervised/README.md)
+* March 2021 [Added full parameter and optimizer state sharding + CPU offloading](examples/fully_sharded_data_parallel/README.md)
+* February 2021 [Added LASER training code](examples/laser/README.md)
+* December 2020: [Added Adaptive Attention Span code](examples/adaptive_span/README.md)
+* December 2020: [GottBERT model and code released](examples/gottbert/README.md)
+* November 2020: Adopted the [Hydra](https://github.com/facebookresearch/hydra) configuration framework
+  * [see documentation explaining how to use it for new and existing projects](docs/hydra_integration.md)
+* November 2020: [fairseq 0.10.0 released](https://github.com/pytorch/fairseq/releases/tag/v0.10.0)
+* October 2020: [Added R3F/R4F (Better Fine-Tuning) code](examples/rxf/README.md)
+* October 2020: [Deep Transformer with Latent Depth code released](examples/latent_depth/README.md)
+* October 2020: [Added CRISS models and code](examples/criss/README.md)
+
+<details><summary>Previous updates</summary><p>
+
+* September 2020: [Added Linformer code](examples/linformer/README.md)
+* September 2020: [Added pointer-generator networks](examples/pointer_generator/README.md)
+* August 2020: [Added lexically constrained decoding](examples/constrained_decoding/README.md)
+* August 2020: [wav2vec2 models and code released](examples/wav2vec/README.md)
+* July 2020: [Unsupervised Quality Estimation code released](examples/unsupervised_quality_estimation/README.md)
+* May 2020: [Follow fairseq on Twitter](https://twitter.com/fairseq)
+* April 2020: [Monotonic Multihead Attention code released](examples/simultaneous_translation/README.md)
+* April 2020: [Quant-Noise code released](examples/quant_noise/README.md)
+* April 2020: [Initial model parallel support and 11B parameters unidirectional LM released](examples/megatron_11b/README.md)
+* March 2020: [Byte-level BPE code released](examples/byte_level_bpe/README.md)
+* February 2020: [mBART model and code released](examples/mbart/README.md)
+* February 2020: [Added tutorial for back-translation](https://github.com/pytorch/fairseq/tree/main/examples/backtranslation#training-your-own-model-wmt18-english-german)
+* December 2019: [fairseq 0.9.0 released](https://github.com/pytorch/fairseq/releases/tag/v0.9.0)
+* November 2019: [VizSeq released (a visual analysis toolkit for evaluating fairseq models)](https://facebookresearch.github.io/vizseq/docs/getting_started/fairseq_example)
+* November 2019: [CamemBERT model and code released](examples/camembert/README.md)
+* November 2019: [BART model and code released](examples/bart/README.md)
+* November 2019: [XLM-R models and code released](examples/xlmr/README.md)
+* September 2019: [Nonautoregressive translation code released](examples/nonautoregressive_translation/README.md)
+* August 2019: [WMT'19 models released](examples/wmt19/README.md)
+* July 2019: fairseq relicensed under MIT license
+* July 2019: [RoBERTa models and code released](examples/roberta/README.md)
+* June 2019: [wav2vec models and code released](examples/wav2vec/README.md)
+
+</p></details>
+
+### Features:
+
+* multi-GPU training on one machine or across multiple machines (data and model parallel)
+* fast generation on both CPU and GPU with multiple search algorithms implemented:
+  + beam search
+  + Diverse Beam Search ([Vijayakumar et al., 2016](https://arxiv.org/abs/1610.02424))
+  + sampling (unconstrained, top-k and top-p/nucleus)
+  + [lexically constrained decoding](examples/constrained_decoding/README.md) (Post & Vilar, 2018)
+* [gradient accumulation](https://fairseq.readthedocs.io/en/latest/getting_started.html#large-mini-batch-training-with-delayed-updates) enables training with large mini-batches even on a single GPU
+* [mixed precision training](https://fairseq.readthedocs.io/en/latest/getting_started.html#training-with-half-precision-floating-point-fp16) (trains faster with less GPU memory on [NVIDIA tensor cores](https://developer.nvidia.com/tensor-cores))
+* [extensible](https://fairseq.readthedocs.io/en/latest/overview.html): easily register new models, criterions, tasks, optimizers and learning rate schedulers
+* [flexible configuration](docs/hydra_integration.md) based on [Hydra](https://github.com/facebookresearch/hydra) allowing a combination of code, command-line and file based configuration
+* [full parameter and optimizer state sharding](examples/fully_sharded_data_parallel/README.md)
+* [offloading parameters to CPU](examples/fully_sharded_data_parallel/README.md)
+
+We also provide [pre-trained models for translation and language modeling](#pre-trained-models-and-examples)
+with a convenient `torch.hub` interface:
+
+``` python
+en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model')
+en2de.translate('Hello world', beam=5)
+# 'Hallo Welt'
+```
+
+See the PyTorch Hub tutorials for [translation](https://pytorch.org/hub/pytorch_fairseq_translation/)
+and [RoBERTa](https://pytorch.org/hub/pytorch_fairseq_roberta/) for more examples.
+
+# Requirements and Installation
+
+* [PyTorch](http://pytorch.org/) version >= 1.10.0
+* Python version >= 3.8
+* For training new models, you'll also need an NVIDIA GPU and [NCCL](https://github.com/NVIDIA/nccl)
+* **To install fairseq** and develop locally:
+
+``` bash
+git clone https://github.com/pytorch/fairseq
+cd fairseq
+pip install --editable ./
+
+# on MacOS:
+# CFLAGS="-stdlib=libc++" pip install --editable ./
+
+# to install the latest stable release (0.10.x)
+# pip install fairseq
+```
+
+* **For faster training** install NVIDIA's [apex](https://github.com/NVIDIA/apex) library:
+
+``` bash
+git clone https://github.com/NVIDIA/apex
+cd apex
+pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" \
+  --global-option="--deprecated_fused_adam" --global-option="--xentropy" \
+  --global-option="--fast_multihead_attn" ./
+```
+
+* **For large datasets** install [PyArrow](https://arrow.apache.org/docs/python/install.html#using-pip): `pip install pyarrow`
+* If you use Docker make sure to increase the shared memory size either with `--ipc=host` or `--shm-size`
+ as command line options to `nvidia-docker run` .
+
+# Getting Started
+
+The [full documentation](https://fairseq.readthedocs.io/) contains instructions
+for getting started, training new models and extending fairseq with new model
+types and tasks.
+
+# Pre-trained models and examples
+
+We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below,
+as well as example training and evaluation commands.
+
+* [Translation](examples/translation/README.md): convolutional and transformer models are available
+* [Language Modeling](examples/language_model/README.md): convolutional and transformer models are available
+
+We also have more detailed READMEs to reproduce results from specific papers:
+
+* [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale (Babu et al., 2021)](examples/wav2vec/xlsr/README.md)
+* [Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)](examples/criss/README.md)
+* [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](examples/wav2vec/README.md)
+* [Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)](examples/unsupervised_quality_estimation/README.md)
+* [Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)](examples/quant_noise/README.md)
+* [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md)
+* [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md)
+* [Reducing Transformer Depth on Demand with Structured Dropout (Fan et al., 2019)](examples/layerdrop/README.md)
+* [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md)
+* [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md)
+* [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md)
+* [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md)
+* [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md)
+* [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md)
+* [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md)
+* [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md)
+* [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel)
+* [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md)
+* [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md)
+* [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md)
+* [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/README.conv.md)
+
+# Join the fairseq community
+
+* Twitter: https://twitter.com/fairseq
+* Facebook page: https://www.facebook.com/groups/fairseq.users
+* Google group: https://groups.google.com/forum/#!forum/fairseq-users
+
+# License
+
+fairseq(-py) is MIT-licensed.
+The license applies to the pre-trained models as well.
+
+# Citation
+
+Please cite as:
+
+``` bibtex
+@inproceedings{ott2019fairseq,
+  title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
+  author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
+  booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
+  year = {2019},
+}
+```

fairseq/fairseq.egg-info/SOURCES.txt

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+LICENSE
+MANIFEST.in
+README.md
+pyproject.toml
+setup.cfg
+setup.py
+examples/operators/alignment_train_cpu.cpp
+examples/operators/alignment_train_cuda.cpp
+examples/operators/alignment_train_kernel.cu
+fairseq/__init__.py
+fairseq/binarizer.py
+fairseq/checkpoint_utils.py
+fairseq/file_chunker_utils.py
+fairseq/file_io.py
+fairseq/file_utils.py
+fairseq/hub_utils.py
+fairseq/incremental_decoding_utils.py
+fairseq/iterative_refinement_generator.py
+fairseq/nan_detector.py
+fairseq/ngram_repeat_block.py
+fairseq/options.py
+fairseq/pdb.py
+fairseq/quantization_utils.py
+fairseq/registry.py
+fairseq/search.py
+fairseq/sequence_generator.py
+fairseq/sequence_scorer.py
+fairseq/speech_generator.py
+fairseq/token_generation_constraints.py
+fairseq/tokenizer.py
+fairseq/trainer.py
+fairseq/utils.py
+fairseq/version.py
+fairseq/version.txt
+fairseq.egg-info/PKG-INFO
+fairseq.egg-info/SOURCES.txt
+fairseq.egg-info/dependency_links.txt
+fairseq.egg-info/entry_points.txt
+fairseq.egg-info/not-zip-safe
+fairseq.egg-info/requires.txt
+fairseq.egg-info/top_level.txt
+fairseq/benchmark/__init__.py
+fairseq/benchmark/benchmark_multihead_attention.py
+fairseq/benchmark/dummy_dataset.py
+fairseq/benchmark/dummy_lm.py
+fairseq/benchmark/dummy_masked_lm.py
+fairseq/benchmark/dummy_model.py
+fairseq/benchmark/dummy_mt.py
+fairseq/clib/cuda/ngram_repeat_block_cuda.cpp
+fairseq/clib/cuda/ngram_repeat_block_cuda_kernel.cu
+fairseq/clib/libbase/balanced_assignment.cpp
+fairseq/clib/libbleu/libbleu.cpp
+fairseq/clib/libbleu/module.cpp
+fairseq/clib/libnat/edit_dist.cpp
+fairseq/clib/libnat_cuda/binding.cpp
+fairseq/clib/libnat_cuda/edit_dist.cu
+fairseq/config/__init__.py
+fairseq/config/config.yaml
+fairseq/config/fb_run_config/slurm.yaml
+fairseq/config/model/transformer_lm/transformer_lm_baevski_gbw.yaml
+fairseq/config/model/transformer_lm/transformer_lm_baevski_wiki103.yaml
+fairseq/config/model/transformer_lm/transformer_lm_big.yaml
+fairseq/config/model/transformer_lm/transformer_lm_gbw.yaml
+fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml
+fairseq/config/model/transformer_lm/transformer_lm_gpt2_big.yaml
+fairseq/config/model/transformer_lm/transformer_lm_gpt2_medium.yaml
+fairseq/config/model/transformer_lm/transformer_lm_gpt2_small.yaml
+fairseq/config/model/transformer_lm/transformer_lm_wiki103.yaml
+fairseq/config/model/wav2vec/vq_wav2vec_gumbel.yaml
+fairseq/config/model/wav2vec2/wav2vec2_base.yaml
+fairseq/config/model/wav2vec2/wav2vec2_large.yaml
+fairseq/criterions/__init__.py
+fairseq/criterions/adaptive_loss.py
+fairseq/criterions/composite_loss.py
+fairseq/criterions/cross_entropy.py
+fairseq/criterions/ctc.py
+fairseq/criterions/fairseq_criterion.py
+fairseq/criterions/fastspeech2_loss.py
+fairseq/criterions/hubert_criterion.py
+fairseq/criterions/label_smoothed_cross_entropy.py
+fairseq/criterions/label_smoothed_cross_entropy_latency_augmented.py
+fairseq/criterions/label_smoothed_cross_entropy_with_alignment.py
+fairseq/criterions/label_smoothed_cross_entropy_with_ctc.py
+fairseq/criterions/label_smoothed_cross_entropy_with_rdrop.py
+fairseq/criterions/legacy_masked_lm.py
+fairseq/criterions/masked_lm.py
+fairseq/criterions/model_criterion.py
+fairseq/criterions/nat_loss.py
+fairseq/criterions/sentence_prediction.py
+fairseq/criterions/sentence_prediction_adapters.py
+fairseq/criterions/sentence_ranking.py
+fairseq/criterions/speech_dlm_criterion.py
+fairseq/criterions/speech_to_speech_criterion.py
+fairseq/criterions/speech_ulm_criterion.py
+fairseq/criterions/tacotron2_loss.py
+fairseq/criterions/wav2vec_criterion.py
+fairseq/data/__init__.py
+fairseq/data/add_class_target_dataset.py
+fairseq/data/add_target_dataset.py
+fairseq/data/append_token_dataset.py
+fairseq/data/backtranslation_dataset.py
+fairseq/data/base_wrapper_dataset.py
+fairseq/data/bucket_pad_length_dataset.py
+fairseq/data/codedataset.py
+fairseq/data/colorize_dataset.py
+fairseq/data/concat_dataset.py
+fairseq/data/concat_sentences_dataset.py
+fairseq/data/data_utils.py
+fairseq/data/data_utils_fast.pyx
+fairseq/data/denoising_dataset.py
+fairseq/data/dictionary.py
+fairseq/data/fairseq_dataset.py
+fairseq/data/fasta_dataset.py
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+fairseq/tasks/translation_from_pretrained_xlm.py
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+fairseq_cli/__init__.py
+fairseq_cli/eval_lm.py
+fairseq_cli/generate.py
+fairseq_cli/hydra_train.py
+fairseq_cli/hydra_validate.py
+fairseq_cli/interactive.py
+fairseq_cli/preprocess.py
+fairseq_cli/score.py
+fairseq_cli/train.py
+fairseq_cli/validate.py
+tests/test_activation_checkpointing.py
+tests/test_amp_optimizer.py
+tests/test_average_checkpoints.py
+tests/test_backtranslation_dataset.py
+tests/test_binaries.py
+tests/test_binarizer.py
+tests/test_character_token_embedder.py
+tests/test_checkpoint_utils.py
+tests/test_checkpoint_utils_for_task_level_attributes.py
+tests/test_concat_dataset.py
+tests/test_constraints.py
+tests/test_convtbc.py
+tests/test_data_utils.py
+tests/test_dataclass_utils.py
+tests/test_dataset.py
+tests/test_dictionary.py
+tests/test_ema.py
+tests/test_espnet_multihead_attention.py
+tests/test_export.py
+tests/test_file_chunker_utils.py
+tests/test_file_io.py
+tests/test_fp16_optimizer.py
+tests/test_hf_hub.py
+tests/test_huffman.py
+tests/test_inference_dropout.py
+tests/test_iopath.py
+tests/test_iterators.py
+tests/test_label_smoothing.py
+tests/test_lm_context_window.py
+tests/test_lstm_jitable.py
+tests/test_memory_efficient_fp16.py
+tests/test_metrics.py
+tests/test_multi_corpus_dataset.py
+tests/test_multi_corpus_sampled_dataset.py
+tests/test_multihead_attention.py
+tests/test_noising.py
+tests/test_online_backtranslation.py
+tests/test_plasma_utils.py
+tests/test_positional_encoding.py
+tests/test_reproducibility.py
+tests/test_resampling_dataset.py
+tests/test_roberta.py
+tests/test_rotary_positional_embedding.py
+tests/test_sequence_generator.py
+tests/test_sequence_scorer.py
+tests/test_sparse_multihead_attention.py
+tests/test_token_block_dataset.py
+tests/test_train.py
+tests/test_transformer.py
+tests/test_utils.py
+tests/test_valid_subset_checks.py

fairseq/fairseq.egg-info/entry_points.txt

@@ -0,0 +1,9 @@
+[console_scripts]
+fairseq-eval-lm = fairseq_cli.eval_lm:cli_main
+fairseq-generate = fairseq_cli.generate:cli_main
+fairseq-hydra-train = fairseq_cli.hydra_train:cli_main
+fairseq-interactive = fairseq_cli.interactive:cli_main
+fairseq-preprocess = fairseq_cli.preprocess:cli_main
+fairseq-score = fairseq_cli.score:cli_main
+fairseq-train = fairseq_cli.train:cli_main
+fairseq-validate = fairseq_cli.validate:cli_main

fairseq/fairseq.egg-info/requires.txt

@@ -0,0 +1,22 @@
+cffi
+cython
+hydra-core<1.1,>=1.0.7
+omegaconf<2.1
+numpy>=1.21.3
+regex
+sacrebleu>=1.4.12
+torch>=1.13
+tqdm
+bitarray
+torchaudio>=0.8.0
+scikit-learn
+packaging
+
+[dev]
+flake8
+pytest
+black==22.3.0
+
+[docs]
+sphinx
+sphinx-argparse

fairseq/fairseq.egg-info/top_level.txt

@@ -0,0 +1,4 @@
+alignment_train_cpu_binding
+alignment_train_cuda_binding
+fairseq
+fairseq_cli

fairseq/fairseq_cli/eval_lm.py

@@ -0,0 +1,347 @@
+#!/usr/bin/env python3 -u
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Evaluate the perplexity of a trained language model.
+"""
+
+import logging
+import math
+import os
+import sys
+from argparse import Namespace
+from typing import Iterable, List, Optional
+
+import torch
+from omegaconf import DictConfig
+
+import fairseq
+from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
+from fairseq.dataclass.utils import convert_namespace_to_omegaconf
+from fairseq.logging import progress_bar
+from fairseq.logging.meters import StopwatchMeter
+from fairseq.sequence_scorer import SequenceScorer
+
+logging.basicConfig(
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=os.environ.get("LOGLEVEL", "INFO").upper(),
+    stream=sys.stdout,
+)
+logger = logging.getLogger("fairseq_cli.eval_lm")
+
+
+def eval_lm(
+    models: List[fairseq.models.FairseqModel],
+    source_dictionary: fairseq.data.Dictionary,
+    batch_iterator: Iterable,
+    post_process: Optional[str] = None,
+    output_word_probs: bool = False,
+    output_word_stats: bool = False,
+    target_dictionary: Optional[fairseq.data.Dictionary] = None,
+    softmax_batch: int = 0,
+    remove_bos_token: bool = False,
+    device: Optional[torch.device] = None,
+):
+    """
+    Args:
+        models (List[~fairseq.models.FairseqModel]): list of models to
+            evaluate. Models are essentially `nn.Module` instances, but
+            must be compatible with fairseq's `SequenceScorer`.
+        source_dictionary (~fairseq.data.Dictionary): dictionary for
+            applying any relevant post processing or outputing word
+            probs/stats.
+        batch_iterator (Iterable): yield batches of data
+        post_process (Optional[str]): post-process text by removing BPE,
+            letter segmentation, etc. Valid options can be found in
+            fairseq.data.utils.post_process, although not all options
+            are implemented here.
+        output_word_probs (Optional[bool]): output words and their
+            predicted log probabilities
+        output_word_stats (Optional[bool]): output word statistics such
+            as word count and average probability
+        target_dictionary (Optional[~fairseq.data.Dictionary]): output
+            dictionary (defaults to *source_dictionary*)
+        softmax_batch (Optional[bool]): if BxT is more than this, will
+            batch the softmax over vocab to this amount of tokens, in
+            order to fit into GPU memory
+        remove_bos_token (Optional[bool]): if True, confirm that the
+            first token is the beginning-of-sentence symbol (according
+            to the relevant dictionary) and remove it from the output
+        device (Optional[torch.device]): device to use for evaluation
+            (defaults to device of first model parameter)
+    """
+    if target_dictionary is None:
+        target_dictionary = source_dictionary
+    if device is None:
+        device = next(models[0].parameters()).device
+
+    gen_timer = StopwatchMeter()
+    scorer = SequenceScorer(target_dictionary, softmax_batch)
+
+    score_sum = 0.0
+    count = 0
+
+    if post_process is not None:
+        if post_process in {"subword_nmt", "@@ "}:
+            bpe_cont = post_process.rstrip()
+            bpe_toks = {
+                i
+                for i in range(len(source_dictionary))
+                if source_dictionary[i].endswith(bpe_cont)
+            }
+        else:
+            raise NotImplementedError(
+                f"--post-process={post_process} is not implemented"
+            )
+        bpe_len = len(bpe_cont)
+    else:
+        bpe_toks = None
+        bpe_len = 0
+
+    word_stats = dict()
+
+    for sample in batch_iterator:
+        if "net_input" not in sample:
+            continue
+
+        sample = utils.move_to_cuda(sample, device=device)
+
+        gen_timer.start()
+        hypos = scorer.generate(models, sample)
+        gen_timer.stop(sample["ntokens"])
+
+        for i, hypos_i in enumerate(hypos):
+            hypo = hypos_i[0]
+            sample_id = sample["id"][i]
+
+            tokens = hypo["tokens"]
+            tgt_len = tokens.numel()
+            pos_scores = hypo["positional_scores"].float()
+
+            if remove_bos_token:
+                assert hypo["tokens"][0].item() == target_dictionary.bos()
+                tokens = tokens[1:]
+                pos_scores = pos_scores[1:]
+
+            skipped_toks = 0
+            if bpe_toks is not None:
+                for i in range(tgt_len - 1):
+                    if tokens[i].item() in bpe_toks:
+                        skipped_toks += 1
+                        pos_scores[i + 1] += pos_scores[i]
+                        pos_scores[i] = 0
+
+            inf_scores = pos_scores.eq(float("inf")) | pos_scores.eq(float("-inf"))
+            if inf_scores.any():
+                logger.info(
+                    "skipping tokens with inf scores:",
+                    target_dictionary.string(tokens[inf_scores.nonzero()]),
+                )
+                pos_scores = pos_scores[(~inf_scores).nonzero()]
+            score_sum += pos_scores.sum().cpu()
+            count += pos_scores.numel() - skipped_toks
+
+            if output_word_probs or output_word_stats:
+                w = ""
+                word_prob = []
+                is_bpe = False
+                for i in range(len(tokens)):
+                    w_ind = tokens[i].item()
+                    w += source_dictionary[w_ind]
+                    if bpe_toks is not None and w_ind in bpe_toks:
+                        w = w[:-bpe_len]
+                        is_bpe = True
+                    else:
+                        word_prob.append((w, pos_scores[i].item()))
+
+                        next_prob = None
+                        ind = i + 1
+                        while ind < len(tokens):
+                            if pos_scores[ind].item() != 0:
+                                next_prob = pos_scores[ind]
+                                break
+                            ind += 1
+
+                        word_stats.setdefault(w, WordStat(w, is_bpe)).add(
+                            pos_scores[i].item(), next_prob
+                        )
+                        is_bpe = False
+                        w = ""
+                if output_word_probs:
+                    logger.info(
+                        str(int(sample_id))
+                        + " "
+                        + (
+                            "\t".join(
+                                "{} [{:2f}]".format(x[0], x[1]) for x in word_prob
+                            )
+                        )
+                    )
+
+    avg_nll_loss = (
+        -score_sum / count / math.log(2) if count > 0 else 0
+    )  # convert to base 2
+    logger.info(
+        "Evaluated {:,} tokens in {:.1f}s ({:.2f} tokens/s)".format(
+            gen_timer.n, gen_timer.sum, 1.0 / gen_timer.avg if gen_timer.avg > 0 else 0
+        )
+    )
+
+    if output_word_stats:
+        for ws in sorted(word_stats.values(), key=lambda x: x.count, reverse=True):
+            logger.info(ws)
+
+    return {
+        "loss": avg_nll_loss,
+        "perplexity": 2**avg_nll_loss,
+    }
+
+
+class WordStat(object):
+    def __init__(self, word, is_bpe):
+        self.word = word
+        self.is_bpe = is_bpe
+        self.log_prob = 0
+        self.next_word_prob = 0
+        self.count = 0
+        self.missing_next_words = 0
+
+    def add(self, log_prob, next_word_prob):
+        """increments counters for the sum of log probs of current word and next
+        word (given context ending at current word). Since the next word might be at the end of the example,
+        or it might be not counted because it is not an ending subword unit,
+        also keeps track of how many of those we have seen"""
+        if next_word_prob is not None:
+            self.next_word_prob += next_word_prob
+        else:
+            self.missing_next_words += 1
+        self.log_prob += log_prob
+        self.count += 1
+
+    def __str__(self):
+        return "{}\t{}\t{}\t{}\t{}\t{}".format(
+            self.word,
+            self.count,
+            self.log_prob,
+            self.is_bpe,
+            self.next_word_prob,
+            self.count - self.missing_next_words,
+        )
+
+
+def main(cfg: DictConfig, **unused_kwargs):
+    if isinstance(cfg, Namespace):
+        cfg = convert_namespace_to_omegaconf(cfg)
+
+    utils.import_user_module(cfg.common)
+
+    logger.info(cfg)
+
+    if cfg.eval_lm.context_window > 0:
+        # reduce tokens per sample by the required context window size
+        cfg.task.tokens_per_sample -= cfg.eval_lm.context_window
+
+    # Initialize the task using the current *cfg*
+    task = tasks.setup_task(cfg.task)
+
+    # Load ensemble
+    logger.info("loading model(s) from {}".format(cfg.common_eval.path))
+    models, model_args, task = checkpoint_utils.load_model_ensemble_and_task(
+        [cfg.common_eval.path],
+        arg_overrides=eval(cfg.common_eval.model_overrides),
+        suffix=cfg.checkpoint.checkpoint_suffix,
+        strict=(cfg.checkpoint.checkpoint_shard_count == 1),
+        num_shards=cfg.checkpoint.checkpoint_shard_count,
+        task=task,
+    )
+
+    use_fp16 = cfg.common.fp16
+    use_cuda = torch.cuda.is_available() and not cfg.common.cpu
+    if use_cuda:
+        torch.cuda.set_device(cfg.distributed_training.device_id)
+
+    # Optimize ensemble for generation and set the source and dest dicts on the model
+    # (required by scorer)
+    for model in models:
+        if use_fp16:
+            model.half()
+        if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
+            model.cuda()
+        model.prepare_for_inference_(cfg)
+
+    assert len(models) > 0
+
+    logger.info(
+        "num. model params: {:,}".format(sum(p.numel() for p in models[0].parameters()))
+    )
+
+    # Load dataset splits
+    task.load_dataset(cfg.dataset.gen_subset)
+    dataset = task.dataset(cfg.dataset.gen_subset)
+    logger.info(
+        "{} {} {:,} examples".format(
+            cfg.task.data, cfg.dataset.gen_subset, len(dataset)
+        )
+    )
+
+    itr = task.eval_lm_dataloader(
+        dataset=dataset,
+        max_tokens=cfg.dataset.max_tokens or 36000,
+        batch_size=cfg.dataset.batch_size,
+        max_positions=utils.resolve_max_positions(
+            *[model.max_positions() for model in models]
+        ),
+        num_shards=max(
+            cfg.dataset.num_shards,
+            cfg.distributed_training.distributed_world_size,
+        ),
+        shard_id=max(
+            cfg.dataset.shard_id,
+            cfg.distributed_training.distributed_rank,
+        ),
+        num_workers=cfg.dataset.num_workers,
+        data_buffer_size=cfg.dataset.data_buffer_size,
+        context_window=cfg.eval_lm.context_window,
+    )
+
+    itr = progress_bar.progress_bar(
+        itr,
+        log_format=cfg.common.log_format,
+        log_interval=cfg.common.log_interval,
+        default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
+    )
+
+    results = eval_lm(
+        models=models,
+        source_dictionary=task.source_dictionary,
+        batch_iterator=itr,
+        post_process=cfg.common_eval.post_process,
+        output_word_probs=cfg.eval_lm.output_word_probs,
+        output_word_stats=cfg.eval_lm.output_word_stats,
+        target_dictionary=task.target_dictionary,
+        softmax_batch=cfg.eval_lm.softmax_batch,
+        remove_bos_token=getattr(cfg.task, "add_bos_token", False),
+    )
+
+    logger.info(
+        "Loss (base 2): {:.4f}, Perplexity: {:.2f}".format(
+            results["loss"], results["perplexity"]
+        )
+    )
+
+    return results
+
+
+def cli_main():
+    parser = options.get_eval_lm_parser()
+    args = options.parse_args_and_arch(parser)
+
+    distributed_utils.call_main(convert_namespace_to_omegaconf(args), main)
+
+
+if __name__ == "__main__":
+    cli_main()

fairseq/fairseq_cli/generate.py

@@ -0,0 +1,417 @@
+#!/usr/bin/env python3 -u
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""
+Translate pre-processed data with a trained model.
+"""
+
+import ast
+import logging
+import math
+import os
+import sys
+from argparse import Namespace
+from itertools import chain
+
+import numpy as np
+import torch
+from omegaconf import DictConfig
+
+from fairseq import checkpoint_utils, options, scoring, tasks, utils
+from fairseq.dataclass.utils import convert_namespace_to_omegaconf
+from fairseq.logging import progress_bar
+from fairseq.logging.meters import StopwatchMeter, TimeMeter
+
+
+def main(cfg: DictConfig):
+
+    if isinstance(cfg, Namespace):
+        cfg = convert_namespace_to_omegaconf(cfg)
+
+    assert cfg.common_eval.path is not None, "--path required for generation!"
+    assert (
+        not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam
+    ), "--sampling requires --nbest to be equal to --beam"
+    assert (
+        cfg.generation.replace_unk is None or cfg.dataset.dataset_impl == "raw"
+    ), "--replace-unk requires a raw text dataset (--dataset-impl=raw)"
+
+    if cfg.common_eval.results_path is not None:
+        os.makedirs(cfg.common_eval.results_path, exist_ok=True)
+        output_path = os.path.join(
+            cfg.common_eval.results_path,
+            "generate-{}.txt".format(cfg.dataset.gen_subset),
+        )
+        with open(output_path, "w", buffering=1, encoding="utf-8") as h:
+            return _main(cfg, h)
+    else:
+        return _main(cfg, sys.stdout)
+
+
+def get_symbols_to_strip_from_output(generator):
+    if hasattr(generator, "symbols_to_strip_from_output"):
+        return generator.symbols_to_strip_from_output
+    else:
+        return {generator.eos}
+
+
+def _main(cfg: DictConfig, output_file):
+    logging.basicConfig(
+        format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+        level=os.environ.get("LOGLEVEL", "INFO").upper(),
+        stream=output_file,
+    )
+    logger = logging.getLogger("fairseq_cli.generate")
+
+    utils.import_user_module(cfg.common)
+
+    if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None:
+        cfg.dataset.max_tokens = 12000
+    logger.info(cfg)
+
+    # Fix seed for stochastic decoding
+    if cfg.common.seed is not None and not cfg.generation.no_seed_provided:
+        np.random.seed(cfg.common.seed)
+        utils.set_torch_seed(cfg.common.seed)
+
+    use_cuda = torch.cuda.is_available() and not cfg.common.cpu
+
+    # Load dataset splits
+    task = tasks.setup_task(cfg.task)
+
+    # Set dictionaries
+    try:
+        src_dict = getattr(task, "source_dictionary", None)
+    except NotImplementedError:
+        src_dict = None
+    tgt_dict = task.target_dictionary
+
+    overrides = ast.literal_eval(cfg.common_eval.model_overrides)
+
+    # Load ensemble
+    logger.info("loading model(s) from {}".format(cfg.common_eval.path))
+    models, saved_cfg = checkpoint_utils.load_model_ensemble(
+        utils.split_paths(cfg.common_eval.path),
+        arg_overrides=overrides,
+        task=task,
+        suffix=cfg.checkpoint.checkpoint_suffix,
+        strict=(cfg.checkpoint.checkpoint_shard_count == 1),
+        num_shards=cfg.checkpoint.checkpoint_shard_count,
+    )
+
+    # loading the dataset should happen after the checkpoint has been loaded so we can give it the saved task config
+    task.load_dataset(cfg.dataset.gen_subset, task_cfg=saved_cfg.task)
+
+    if cfg.generation.lm_path is not None:
+        overrides["data"] = cfg.task.data
+
+        try:
+            lms, _ = checkpoint_utils.load_model_ensemble(
+                [cfg.generation.lm_path], arg_overrides=overrides, task=None
+            )
+        except:
+            logger.warning(
+                f"Failed to load language model! Please make sure that the language model dict is the same "
+                f"as target dict and is located in the data dir ({cfg.task.data})"
+            )
+            raise
+
+        assert len(lms) == 1
+    else:
+        lms = [None]
+
+    # Optimize ensemble for generation
+    for model in chain(models, lms):
+        if model is None:
+            continue
+        if cfg.common.fp16:
+            model.half()
+        if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
+            model.cuda()
+        model.prepare_for_inference_(cfg)
+
+    # Load alignment dictionary for unknown word replacement
+    # (None if no unknown word replacement, empty if no path to align dictionary)
+    align_dict = utils.load_align_dict(cfg.generation.replace_unk)
+
+    # Load dataset (possibly sharded)
+    itr = task.get_batch_iterator(
+        dataset=task.dataset(cfg.dataset.gen_subset),
+        max_tokens=cfg.dataset.max_tokens,
+        max_sentences=cfg.dataset.batch_size,
+        max_positions=utils.resolve_max_positions(
+            task.max_positions(), *[m.max_positions() for m in models]
+        ),
+        ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
+        required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,
+        seed=cfg.common.seed,
+        num_shards=cfg.distributed_training.distributed_world_size,
+        shard_id=cfg.distributed_training.distributed_rank,
+        num_workers=cfg.dataset.num_workers,
+        data_buffer_size=cfg.dataset.data_buffer_size,
+    ).next_epoch_itr(shuffle=False)
+    progress = progress_bar.progress_bar(
+        itr,
+        log_format=cfg.common.log_format,
+        log_interval=cfg.common.log_interval,
+        default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
+    )
+
+    # Initialize generator
+    gen_timer = StopwatchMeter()
+
+    extra_gen_cls_kwargs = {"lm_model": lms[0], "lm_weight": cfg.generation.lm_weight}
+    generator = task.build_generator(
+        models, cfg.generation, extra_gen_cls_kwargs=extra_gen_cls_kwargs
+    )
+
+    # Handle tokenization and BPE
+    tokenizer = task.build_tokenizer(cfg.tokenizer)
+    bpe = task.build_bpe(cfg.bpe)
+
+    def decode_fn(x):
+        if bpe is not None:
+            x = bpe.decode(x)
+        if tokenizer is not None:
+            x = tokenizer.decode(x)
+        return x
+
+    scorer = scoring.build_scorer(cfg.scoring, tgt_dict)
+
+    num_sentences = 0
+    has_target = True
+    wps_meter = TimeMeter()
+    for sample in progress:
+        sample = utils.move_to_cuda(sample) if use_cuda else sample
+        if "net_input" not in sample:
+            continue
+
+        prefix_tokens = None
+        if cfg.generation.prefix_size > 0:
+            prefix_tokens = sample["target"][:, : cfg.generation.prefix_size]
+
+        constraints = None
+        if "constraints" in sample:
+            constraints = sample["constraints"]
+
+        gen_timer.start()
+        hypos = task.inference_step(
+            generator,
+            models,
+            sample,
+            prefix_tokens=prefix_tokens,
+            constraints=constraints,
+        )
+        num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
+        gen_timer.stop(num_generated_tokens)
+
+        for i, sample_id in enumerate(sample["id"].tolist()):
+            has_target = sample["target"] is not None
+
+            # Remove padding
+            if "src_tokens" in sample["net_input"]:
+                src_tokens = utils.strip_pad(
+                    sample["net_input"]["src_tokens"][i, :], tgt_dict.pad()
+                )
+            else:
+                src_tokens = None
+
+            target_tokens = None
+            if has_target:
+                target_tokens = (
+                    utils.strip_pad(sample["target"][i, :], tgt_dict.pad()).int().cpu()
+                )
+
+            # Either retrieve the original sentences or regenerate them from tokens.
+            if align_dict is not None:
+                src_str = task.dataset(cfg.dataset.gen_subset).src.get_original_text(
+                    sample_id
+                )
+                target_str = task.dataset(cfg.dataset.gen_subset).tgt.get_original_text(
+                    sample_id
+                )
+            else:
+                if src_dict is not None:
+                    src_str = src_dict.string(src_tokens, cfg.common_eval.post_process)
+                else:
+                    src_str = ""
+                if has_target:
+                    target_str = tgt_dict.string(
+                        target_tokens,
+                        cfg.common_eval.post_process,
+                        escape_unk=True,
+                        extra_symbols_to_ignore=get_symbols_to_strip_from_output(
+                            generator
+                        ),
+                    )
+
+            src_str = decode_fn(src_str)
+            if has_target:
+                target_str = decode_fn(target_str)
+
+            if not cfg.common_eval.quiet:
+                if src_dict is not None:
+                    print("S-{}\t{}".format(sample_id, src_str), file=output_file)
+                if has_target:
+                    print("T-{}\t{}".format(sample_id, target_str), file=output_file)
+
+            # Process top predictions
+            for j, hypo in enumerate(hypos[i][: cfg.generation.nbest]):
+                hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
+                    hypo_tokens=hypo["tokens"].int().cpu(),
+                    src_str=src_str,
+                    alignment=hypo["alignment"],
+                    align_dict=align_dict,
+                    tgt_dict=tgt_dict,
+                    remove_bpe=cfg.common_eval.post_process,
+                    extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator),
+                )
+                detok_hypo_str = decode_fn(hypo_str)
+                if not cfg.common_eval.quiet:
+                    score = hypo["score"] / math.log(2)  # convert to base 2
+                    # original hypothesis (after tokenization and BPE)
+                    print(
+                        "H-{}\t{}\t{}".format(sample_id, score, hypo_str),
+                        file=output_file,
+                    )
+                    # detokenized hypothesis
+                    print(
+                        "D-{}\t{}\t{}".format(sample_id, score, detok_hypo_str),
+                        file=output_file,
+                    )
+                    print(
+                        "P-{}\t{}".format(
+                            sample_id,
+                            " ".join(
+                                map(
+                                    lambda x: "{:.4f}".format(x),
+                                    # convert from base e to base 2
+                                    hypo["positional_scores"]
+                                    .div_(math.log(2))
+                                    .tolist(),
+                                )
+                            ),
+                        ),
+                        file=output_file,
+                    )
+
+                    if cfg.generation.print_alignment == "hard":
+                        print(
+                            "A-{}\t{}".format(
+                                sample_id,
+                                " ".join(
+                                    [
+                                        "{}-{}".format(src_idx, tgt_idx)
+                                        for src_idx, tgt_idx in alignment
+                                    ]
+                                ),
+                            ),
+                            file=output_file,
+                        )
+                    if cfg.generation.print_alignment == "soft":
+                        print(
+                            "A-{}\t{}".format(
+                                sample_id,
+                                " ".join(
+                                    [",".join(src_probs) for src_probs in alignment]
+                                ),
+                            ),
+                            file=output_file,
+                        )
+
+                    if cfg.generation.print_step:
+                        print(
+                            "I-{}\t{}".format(sample_id, hypo["steps"]),
+                            file=output_file,
+                        )
+
+                    if cfg.generation.retain_iter_history:
+                        for step, h in enumerate(hypo["history"]):
+                            _, h_str, _ = utils.post_process_prediction(
+                                hypo_tokens=h["tokens"].int().cpu(),
+                                src_str=src_str,
+                                alignment=None,
+                                align_dict=None,
+                                tgt_dict=tgt_dict,
+                                remove_bpe=None,
+                            )
+                            print(
+                                "E-{}_{}\t{}".format(sample_id, step, h_str),
+                                file=output_file,
+                            )
+
+                # Score only the top hypothesis
+                if has_target and j == 0:
+                    if (
+                        align_dict is not None
+                        or cfg.common_eval.post_process is not None
+                    ):
+                        # Convert back to tokens for evaluation with unk replacement and/or without BPE
+                        target_tokens = tgt_dict.encode_line(
+                            target_str, add_if_not_exist=True
+                        )
+                        hypo_tokens = tgt_dict.encode_line(
+                            detok_hypo_str, add_if_not_exist=True
+                        )
+                    if hasattr(scorer, "add_string"):
+                        scorer.add_string(target_str, detok_hypo_str)
+                    else:
+                        scorer.add(target_tokens, hypo_tokens)
+
+        wps_meter.update(num_generated_tokens)
+        progress.log({"wps": round(wps_meter.avg)})
+        num_sentences += (
+            sample["nsentences"] if "nsentences" in sample else sample["id"].numel()
+        )
+
+    logger.info("NOTE: hypothesis and token scores are output in base 2")
+    logger.info(
+        "Translated {:,} sentences ({:,} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)".format(
+            num_sentences,
+            gen_timer.n,
+            gen_timer.sum,
+            num_sentences / gen_timer.sum,
+            1.0 / gen_timer.avg,
+        )
+    )
+    if has_target:
+        if cfg.bpe and not cfg.generation.sacrebleu:
+            if cfg.common_eval.post_process:
+                logger.warning(
+                    "BLEU score is being computed by splitting detokenized string on spaces, this is probably not what you want. Use --sacrebleu for standard 13a BLEU tokenization"
+                )
+            else:
+                logger.warning(
+                    "If you are using BPE on the target side, the BLEU score is computed on BPE tokens, not on proper words.  Use --sacrebleu for standard 13a BLEU tokenization"
+                )
+        # use print to be consistent with other main outputs: S-, H-, T-, D- and so on
+        print(
+            "Generate {} with beam={}: {}".format(
+                cfg.dataset.gen_subset, cfg.generation.beam, scorer.result_string()
+            ),
+            file=output_file,
+        )
+
+    return scorer
+
+
+def cli_main():
+    parser = options.get_generation_parser()
+    # TODO: replace this workaround with refactoring of `AudioPretraining`
+    parser.add_argument(
+        "--arch",
+        "-a",
+        metavar="ARCH",
+        default="wav2vec2",
+        help="Model architecture. For constructing tasks that rely on "
+        "model args (e.g. `AudioPretraining`)",
+    )
+    args = options.parse_args_and_arch(parser)
+    main(args)
+
+
+if __name__ == "__main__":
+    cli_main()

fairseq/fairseq_cli/hydra_train.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python3 -u
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+
+import hydra
+import torch
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import OmegaConf, open_dict
+
+from fairseq import distributed_utils, metrics
+from fairseq.dataclass.configs import FairseqConfig
+from fairseq.dataclass.initialize import add_defaults, hydra_init
+from fairseq.dataclass.utils import omegaconf_no_object_check
+from fairseq.utils import reset_logging
+from fairseq_cli.train import main as pre_main
+
+logger = logging.getLogger("fairseq_cli.hydra_train")
+
+
+@hydra.main(config_path=os.path.join("..", "fairseq", "config"), config_name="config")
+def hydra_main(cfg: FairseqConfig) -> float:
+    _hydra_main(cfg)
+
+
+def _hydra_main(cfg: FairseqConfig, **kwargs) -> float:
+    add_defaults(cfg)
+
+    if cfg.common.reset_logging:
+        reset_logging()  # Hydra hijacks logging, fix that
+    else:
+        # check if directly called or called through hydra_main
+        if HydraConfig.initialized():
+            with open_dict(cfg):
+                # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126)
+                cfg.job_logging_cfg = OmegaConf.to_container(
+                    HydraConfig.get().job_logging, resolve=True
+                )
+
+    with omegaconf_no_object_check():
+        cfg = OmegaConf.create(
+            OmegaConf.to_container(cfg, resolve=True, enum_to_str=True)
+        )
+    OmegaConf.set_struct(cfg, True)
+
+    try:
+        if cfg.common.profile:
+            with torch.cuda.profiler.profile():
+                with torch.autograd.profiler.emit_nvtx():
+                    distributed_utils.call_main(cfg, pre_main, **kwargs)
+        else:
+            distributed_utils.call_main(cfg, pre_main, **kwargs)
+    except BaseException as e:
+        if not cfg.common.suppress_crashes:
+            raise
+        else:
+            logger.error("Crashed! " + str(e))
+
+    # get best val and return - useful for sweepers
+    try:
+        best_val = metrics.get_smoothed_value(
+            "valid", cfg.checkpoint.best_checkpoint_metric
+        )
+    except:
+        best_val = None
+
+    if best_val is None:
+        best_val = float("inf")
+
+    return best_val
+
+
+def cli_main():
+    try:
+        from hydra._internal.utils import get_args
+
+        cfg_name = get_args().config_name or "config"
+    except:
+        logger.warning("Failed to get config name from hydra args")
+        cfg_name = "config"
+
+    hydra_init(cfg_name)
+    hydra_main()
+
+
+if __name__ == "__main__":
+    cli_main()

fairseq/fairseq_cli/hydra_validate.py

@@ -0,0 +1,188 @@
+#!/usr/bin/env python3 -u
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import sys
+from itertools import chain
+
+import torch
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import OmegaConf, open_dict
+import hydra
+
+from fairseq import checkpoint_utils, distributed_utils, utils
+from fairseq.dataclass.configs import FairseqConfig
+from fairseq.dataclass.initialize import add_defaults, hydra_init
+from fairseq.dataclass.utils import omegaconf_no_object_check
+from fairseq.distributed import utils as distributed_utils
+from fairseq.logging import metrics, progress_bar
+from fairseq.utils import reset_logging
+
+logging.basicConfig(
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=os.environ.get("LOGLEVEL", "INFO").upper(),
+    stream=sys.stdout,
+)
+logger = logging.getLogger("fairseq_cli.validate")
+
+
+@hydra.main(config_path=os.path.join("..", "fairseq", "config"), config_name="config")
+def hydra_main(cfg: FairseqConfig) -> float:
+    return _hydra_main(cfg)
+
+
+def _hydra_main(cfg: FairseqConfig, **kwargs) -> float:
+    add_defaults(cfg)
+
+    if cfg.common.reset_logging:
+        reset_logging()  # Hydra hijacks logging, fix that
+    else:
+        # check if directly called or called through hydra_main
+        if HydraConfig.initialized():
+            with open_dict(cfg):
+                # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126)
+                cfg.job_logging_cfg = OmegaConf.to_container(
+                    HydraConfig.get().job_logging, resolve=True
+                )
+
+    with omegaconf_no_object_check():
+        cfg = OmegaConf.create(
+            OmegaConf.to_container(cfg, resolve=True, enum_to_str=True)
+        )
+    OmegaConf.set_struct(cfg, True)
+
+    assert (
+        cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None
+    ), "Must specify batch size either with --max-tokens or --batch-size"
+
+    distributed_utils.call_main(cfg, validate, **kwargs)
+
+
+def validate(cfg):
+    utils.import_user_module(cfg.common)
+
+    use_fp16 = cfg.common.fp16
+    use_cuda = torch.cuda.is_available() and not cfg.common.cpu
+
+    if use_cuda:
+        torch.cuda.set_device(cfg.distributed_training.device_id)
+
+    if cfg.distributed_training.distributed_world_size > 1:
+        data_parallel_world_size = distributed_utils.get_data_parallel_world_size()
+        data_parallel_rank = distributed_utils.get_data_parallel_rank()
+    else:
+        data_parallel_world_size = 1
+        data_parallel_rank = 0
+
+    overrides = {"task": {"data": cfg.task.data}}
+
+    # Load ensemble
+    logger.info("loading model(s) from {}".format(cfg.common_eval.path))
+    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+        [cfg.common_eval.path],
+        arg_overrides=overrides,
+        suffix=cfg.checkpoint.checkpoint_suffix,
+    )
+    model = models[0]
+
+    # Move models to GPU
+    for model in models:
+        model.eval()
+        if use_fp16:
+            model.half()
+        if use_cuda:
+            model.cuda()
+
+    # Print args
+    logger.info(saved_cfg)
+
+    # Build criterion
+    criterion = task.build_criterion(saved_cfg.criterion, from_checkpoint=True)
+    criterion.eval()
+
+    for subset in cfg.dataset.valid_subset.split(","):
+        try:
+            task.load_dataset(subset, combine=False, epoch=1, task_cfg=saved_cfg.task)
+            dataset = task.dataset(subset)
+        except KeyError:
+            raise Exception("Cannot find dataset: " + subset)
+
+        # Initialize data iterator
+        itr = task.get_batch_iterator(
+            dataset=dataset,
+            max_tokens=cfg.dataset.max_tokens,
+            max_sentences=cfg.dataset.batch_size,
+            max_positions=utils.resolve_max_positions(
+                task.max_positions(),
+                *[m.max_positions() for m in models],
+            ),
+            ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
+            required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,
+            seed=cfg.common.seed,
+            num_shards=data_parallel_world_size,
+            shard_id=data_parallel_rank,
+            num_workers=cfg.dataset.num_workers,
+            data_buffer_size=cfg.dataset.data_buffer_size,
+        ).next_epoch_itr(shuffle=False)
+        progress = progress_bar.progress_bar(
+            itr,
+            log_format=cfg.common.log_format,
+            log_interval=cfg.common.log_interval,
+            prefix=f"valid on '{subset}' subset",
+            default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
+        )
+
+        def apply_half(t):
+            if t.dtype is torch.float32:
+                return t.to(dtype=torch.half)
+            return t
+
+        log_outputs = []
+        for i, sample in enumerate(progress):
+            sample = utils.move_to_cuda(sample) if use_cuda else sample
+
+            if use_fp16:
+                sample = utils.apply_to_sample(apply_half, sample)
+
+            _loss, _sample_size, log_output = task.valid_step(sample, model, criterion)
+            with metrics.aggregate() as agg:
+                task.reduce_metrics([log_output], criterion)
+                progress.log(agg.get_smoothed_values(), step=i)
+            # progress.log(log_output, step=i) from vision
+            log_outputs.append(log_output)
+
+        if data_parallel_world_size > 1:
+            log_outputs = distributed_utils.all_gather_list(
+                log_outputs,
+                max_size=cfg.common.all_gather_list_size,
+                group=distributed_utils.get_data_parallel_group(),
+            )
+            log_outputs = list(chain.from_iterable(log_outputs))
+
+        with metrics.aggregate() as agg:
+            task.reduce_metrics(log_outputs, criterion)
+            log_output = agg.get_smoothed_values()
+
+        progress.print(log_output, tag=subset, step=i)
+
+
+def cli_main():
+    try:
+        from hydra._internal.utils import get_args
+
+        cfg_name = get_args().config_name or "config"
+    except:
+        logger.warning("Failed to get config name from hydra args")
+        cfg_name = "config"
+
+    hydra_init(cfg_name)
+    hydra_main()
+
+
+if __name__ == "__main__":
+    cli_main()

fairseq/fairseq_cli/interactive.py

@@ -0,0 +1,317 @@
+#!/usr/bin/env python3 -u
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""
+Translate raw text with a trained model. Batches data on-the-fly.
+"""
+
+import ast
+import fileinput
+import logging
+import math
+import os
+import sys
+import time
+from argparse import Namespace
+from collections import namedtuple
+
+import numpy as np
+import torch
+
+from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
+from fairseq.dataclass.configs import FairseqConfig
+from fairseq.dataclass.utils import convert_namespace_to_omegaconf
+from fairseq.token_generation_constraints import pack_constraints, unpack_constraints
+from fairseq_cli.generate import get_symbols_to_strip_from_output
+
+logging.basicConfig(
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=os.environ.get("LOGLEVEL", "INFO").upper(),
+    stream=sys.stdout,
+)
+logger = logging.getLogger("fairseq_cli.interactive")
+
+
+Batch = namedtuple("Batch", "ids src_tokens src_lengths constraints")
+Translation = namedtuple("Translation", "src_str hypos pos_scores alignments")
+
+
+def buffered_read(input, buffer_size):
+    buffer = []
+    with fileinput.input(files=[input], openhook=fileinput.hook_encoded("utf-8")) as h:
+        for src_str in h:
+            buffer.append(src_str.strip())
+            if len(buffer) >= buffer_size:
+                yield buffer
+                buffer = []
+
+    if len(buffer) > 0:
+        yield buffer
+
+
+def make_batches(lines, cfg, task, max_positions, encode_fn):
+    def encode_fn_target(x):
+        return encode_fn(x)
+
+    if cfg.generation.constraints:
+        # Strip (tab-delimited) contraints, if present, from input lines,
+        # store them in batch_constraints
+        batch_constraints = [list() for _ in lines]
+        for i, line in enumerate(lines):
+            if "\t" in line:
+                lines[i], *batch_constraints[i] = line.split("\t")
+
+        # Convert each List[str] to List[Tensor]
+        for i, constraint_list in enumerate(batch_constraints):
+            batch_constraints[i] = [
+                task.target_dictionary.encode_line(
+                    encode_fn_target(constraint),
+                    append_eos=False,
+                    add_if_not_exist=False,
+                )
+                for constraint in constraint_list
+            ]
+
+    if cfg.generation.constraints:
+        constraints_tensor = pack_constraints(batch_constraints)
+    else:
+        constraints_tensor = None
+
+    tokens, lengths = task.get_interactive_tokens_and_lengths(lines, encode_fn)
+
+    itr = task.get_batch_iterator(
+        dataset=task.build_dataset_for_inference(
+            tokens, lengths, constraints=constraints_tensor
+        ),
+        max_tokens=cfg.dataset.max_tokens,
+        max_sentences=cfg.dataset.batch_size,
+        max_positions=max_positions,
+        ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
+    ).next_epoch_itr(shuffle=False)
+    for batch in itr:
+        ids = batch["id"]
+        src_tokens = batch["net_input"]["src_tokens"]
+        src_lengths = batch["net_input"]["src_lengths"]
+        constraints = batch.get("constraints", None)
+
+        yield Batch(
+            ids=ids,
+            src_tokens=src_tokens,
+            src_lengths=src_lengths,
+            constraints=constraints,
+        )
+
+
+def main(cfg: FairseqConfig):
+    if isinstance(cfg, Namespace):
+        cfg = convert_namespace_to_omegaconf(cfg)
+
+    start_time = time.time()
+    total_translate_time = 0
+
+    utils.import_user_module(cfg.common)
+
+    if cfg.interactive.buffer_size < 1:
+        cfg.interactive.buffer_size = 1
+    if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None:
+        cfg.dataset.batch_size = 1
+
+    assert (
+        not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam
+    ), "--sampling requires --nbest to be equal to --beam"
+    assert (
+        not cfg.dataset.batch_size
+        or cfg.dataset.batch_size <= cfg.interactive.buffer_size
+    ), "--batch-size cannot be larger than --buffer-size"
+
+    logger.info(cfg)
+
+    # Fix seed for stochastic decoding
+    if cfg.common.seed is not None and not cfg.generation.no_seed_provided:
+        np.random.seed(cfg.common.seed)
+        utils.set_torch_seed(cfg.common.seed)
+
+    use_cuda = torch.cuda.is_available() and not cfg.common.cpu
+
+    # Setup task, e.g., translation
+    task = tasks.setup_task(cfg.task)
+
+    # Load ensemble
+    overrides = ast.literal_eval(cfg.common_eval.model_overrides)
+    logger.info("loading model(s) from {}".format(cfg.common_eval.path))
+    models, _model_args = checkpoint_utils.load_model_ensemble(
+        utils.split_paths(cfg.common_eval.path),
+        arg_overrides=overrides,
+        task=task,
+        suffix=cfg.checkpoint.checkpoint_suffix,
+        strict=(cfg.checkpoint.checkpoint_shard_count == 1),
+        num_shards=cfg.checkpoint.checkpoint_shard_count,
+    )
+
+    # Set dictionaries
+    src_dict = task.source_dictionary
+    tgt_dict = task.target_dictionary
+
+    # Optimize ensemble for generation
+    for model in models:
+        if model is None:
+            continue
+        if cfg.common.fp16:
+            model.half()
+        if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
+            model.cuda()
+        model.prepare_for_inference_(cfg)
+
+    # Initialize generator
+    generator = task.build_generator(models, cfg.generation)
+
+    # Handle tokenization and BPE
+    tokenizer = task.build_tokenizer(cfg.tokenizer)
+    bpe = task.build_bpe(cfg.bpe)
+
+    def encode_fn(x):
+        if tokenizer is not None:
+            x = tokenizer.encode(x)
+        if bpe is not None:
+            x = bpe.encode(x)
+        return x
+
+    def decode_fn(x):
+        if bpe is not None:
+            x = bpe.decode(x)
+        if tokenizer is not None:
+            x = tokenizer.decode(x)
+        return x
+
+    # Load alignment dictionary for unknown word replacement
+    # (None if no unknown word replacement, empty if no path to align dictionary)
+    align_dict = utils.load_align_dict(cfg.generation.replace_unk)
+
+    max_positions = utils.resolve_max_positions(
+        task.max_positions(), *[model.max_positions() for model in models]
+    )
+
+    if cfg.generation.constraints:
+        logger.warning(
+            "NOTE: Constrained decoding currently assumes a shared subword vocabulary."
+        )
+
+    if cfg.interactive.buffer_size > 1:
+        logger.info("Sentence buffer size: %s", cfg.interactive.buffer_size)
+    logger.info("NOTE: hypothesis and token scores are output in base 2")
+    logger.info("Type the input sentence and press return:")
+    start_id = 0
+    for inputs in buffered_read(cfg.interactive.input, cfg.interactive.buffer_size):
+        results = []
+        for batch in make_batches(inputs, cfg, task, max_positions, encode_fn):
+            bsz = batch.src_tokens.size(0)
+            src_tokens = batch.src_tokens
+            src_lengths = batch.src_lengths
+            constraints = batch.constraints
+            if use_cuda:
+                src_tokens = src_tokens.cuda()
+                src_lengths = src_lengths.cuda()
+                if constraints is not None:
+                    constraints = constraints.cuda()
+
+            sample = {
+                "net_input": {
+                    "src_tokens": src_tokens,
+                    "src_lengths": src_lengths,
+                },
+            }
+            translate_start_time = time.time()
+            translations = task.inference_step(
+                generator, models, sample, constraints=constraints
+            )
+            translate_time = time.time() - translate_start_time
+            total_translate_time += translate_time
+            list_constraints = [[] for _ in range(bsz)]
+            if cfg.generation.constraints:
+                list_constraints = [unpack_constraints(c) for c in constraints]
+            for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)):
+                src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad())
+                constraints = list_constraints[i]
+                results.append(
+                    (
+                        start_id + id,
+                        src_tokens_i,
+                        hypos,
+                        {
+                            "constraints": constraints,
+                            "time": translate_time / len(translations),
+                        },
+                    )
+                )
+
+        # sort output to match input order
+        for id_, src_tokens, hypos, info in sorted(results, key=lambda x: x[0]):
+            src_str = ""
+            if src_dict is not None:
+                src_str = src_dict.string(src_tokens, cfg.common_eval.post_process)
+                print("S-{}\t{}".format(id_, src_str))
+                print("W-{}\t{:.3f}\tseconds".format(id_, info["time"]))
+                for constraint in info["constraints"]:
+                    print(
+                        "C-{}\t{}".format(
+                            id_,
+                            tgt_dict.string(constraint, cfg.common_eval.post_process),
+                        )
+                    )
+
+            # Process top predictions
+            for hypo in hypos[: min(len(hypos), cfg.generation.nbest)]:
+                hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
+                    hypo_tokens=hypo["tokens"].int().cpu(),
+                    src_str=src_str,
+                    alignment=hypo["alignment"],
+                    align_dict=align_dict,
+                    tgt_dict=tgt_dict,
+                    remove_bpe=cfg.common_eval.post_process,
+                    extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator),
+                )
+                detok_hypo_str = decode_fn(hypo_str)
+                score = hypo["score"] / math.log(2)  # convert to base 2
+                # original hypothesis (after tokenization and BPE)
+                print("H-{}\t{}\t{}".format(id_, score, hypo_str))
+                # detokenized hypothesis
+                print("D-{}\t{}\t{}".format(id_, score, detok_hypo_str))
+                print(
+                    "P-{}\t{}".format(
+                        id_,
+                        " ".join(
+                            map(
+                                lambda x: "{:.4f}".format(x),
+                                # convert from base e to base 2
+                                hypo["positional_scores"].div_(math.log(2)).tolist(),
+                            )
+                        ),
+                    )
+                )
+                if cfg.generation.print_alignment:
+                    alignment_str = " ".join(
+                        ["{}-{}".format(src, tgt) for src, tgt in alignment]
+                    )
+                    print("A-{}\t{}".format(id_, alignment_str))
+
+        # update running id_ counter
+        start_id += len(inputs)
+
+    logger.info(
+        "Total time: {:.3f} seconds; translation time: {:.3f}".format(
+            time.time() - start_time, total_translate_time
+        )
+    )
+
+
+def cli_main():
+    parser = options.get_interactive_generation_parser()
+    args = options.parse_args_and_arch(parser)
+    distributed_utils.call_main(convert_namespace_to_omegaconf(args), main)
+
+
+if __name__ == "__main__":
+    cli_main()

fairseq/fairseq_cli/preprocess.py

@@ -0,0 +1,393 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""
+Data pre-processing: build vocabularies and binarize training data.
+"""
+
+import logging
+import os
+import shutil
+import sys
+import typing as tp
+from argparse import Namespace
+from itertools import zip_longest
+
+from fairseq import options, tasks, utils
+from fairseq.binarizer import (
+    AlignmentDatasetBinarizer,
+    FileBinarizer,
+    VocabularyDatasetBinarizer,
+)
+from fairseq.data import Dictionary
+
+logging.basicConfig(
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=os.environ.get("LOGLEVEL", "INFO").upper(),
+    stream=sys.stdout,
+)
+logger = logging.getLogger("fairseq_cli.preprocess")
+
+#####################################################################
+# file name tools
+#####################################################################
+
+
+def _train_path(lang, trainpref):
+    return "{}{}".format(trainpref, ("." + lang) if lang else "")
+
+
+def _file_name(prefix, lang):
+    fname = prefix
+    if lang is not None:
+        fname += ".{lang}".format(lang=lang)
+    return fname
+
+
+def _dest_path(prefix, lang, destdir):
+    return os.path.join(destdir, _file_name(prefix, lang))
+
+
+def _dict_path(lang, destdir):
+    return _dest_path("dict", lang, destdir) + ".txt"
+
+
+def dataset_dest_prefix(args, output_prefix, lang):
+    base = os.path.join(args.destdir, output_prefix)
+    if lang is not None:
+        lang_part = f".{args.source_lang}-{args.target_lang}.{lang}"
+    elif args.only_source:
+        lang_part = ""
+    else:
+        lang_part = f".{args.source_lang}-{args.target_lang}"
+
+    return "{}{}".format(base, lang_part)
+
+
+def dataset_dest_file(args, output_prefix, lang, extension):
+    return "{}.{}".format(dataset_dest_prefix(args, output_prefix, lang), extension)
+
+
+#####################################################################
+# dictionary tools
+#####################################################################
+
+
+def _build_dictionary(
+    filenames,
+    task,
+    args,
+    src=False,
+    tgt=False,
+):
+    assert src ^ tgt
+    return task.build_dictionary(
+        filenames,
+        workers=args.workers,
+        threshold=args.thresholdsrc if src else args.thresholdtgt,
+        nwords=args.nwordssrc if src else args.nwordstgt,
+        padding_factor=args.padding_factor,
+    )
+
+
+#####################################################################
+# bin file creation logic
+#####################################################################
+
+
+def _make_binary_dataset(
+    vocab: Dictionary,
+    input_prefix: str,
+    output_prefix: str,
+    lang: tp.Optional[str],
+    num_workers: int,
+    args: Namespace,
+):
+    logger.info("[{}] Dictionary: {} types".format(lang, len(vocab)))
+
+    binarizer = VocabularyDatasetBinarizer(
+        vocab,
+        append_eos=True,
+    )
+
+    input_file = "{}{}".format(input_prefix, ("." + lang) if lang is not None else "")
+    full_output_prefix = dataset_dest_prefix(args, output_prefix, lang)
+
+    final_summary = FileBinarizer.multiprocess_dataset(
+        input_file,
+        args.dataset_impl,
+        binarizer,
+        full_output_prefix,
+        vocab_size=len(vocab),
+        num_workers=num_workers,
+    )
+
+    logger.info(f"[{lang}] {input_file}: {final_summary} (by {vocab.unk_word})")
+
+
+def _make_binary_alignment_dataset(
+    input_prefix: str, output_prefix: str, num_workers: int, args: Namespace
+):
+
+    binarizer = AlignmentDatasetBinarizer(utils.parse_alignment)
+
+    input_file = input_prefix
+    full_output_prefix = dataset_dest_prefix(args, output_prefix, lang=None)
+
+    final_summary = FileBinarizer.multiprocess_dataset(
+        input_file,
+        args.dataset_impl,
+        binarizer,
+        full_output_prefix,
+        vocab_size=None,
+        num_workers=num_workers,
+    )
+
+    logger.info(
+        "[alignments] {}: parsed {} alignments".format(
+            input_file, final_summary.num_seq
+        )
+    )
+
+
+#####################################################################
+# routing logic
+#####################################################################
+
+
+def _make_dataset(
+    vocab: Dictionary,
+    input_prefix: str,
+    output_prefix: str,
+    lang: tp.Optional[str],
+    args: Namespace,
+    num_workers: int,
+):
+    if args.dataset_impl == "raw":
+        # Copy original text file to destination folder
+        output_text_file = _dest_path(
+            output_prefix + ".{}-{}".format(args.source_lang, args.target_lang),
+            lang,
+            args.destdir,
+        )
+        shutil.copyfile(_file_name(input_prefix, lang), output_text_file)
+    else:
+        _make_binary_dataset(
+            vocab, input_prefix, output_prefix, lang, num_workers, args
+        )
+
+
+def _make_all(lang, vocab, args):
+    if args.trainpref:
+        _make_dataset(
+            vocab, args.trainpref, "train", lang, args=args, num_workers=args.workers
+        )
+    if args.validpref:
+        for k, validpref in enumerate(args.validpref.split(",")):
+            outprefix = "valid{}".format(k) if k > 0 else "valid"
+            _make_dataset(
+                vocab, validpref, outprefix, lang, args=args, num_workers=args.workers
+            )
+    if args.testpref:
+        for k, testpref in enumerate(args.testpref.split(",")):
+            outprefix = "test{}".format(k) if k > 0 else "test"
+            _make_dataset(
+                vocab, testpref, outprefix, lang, args=args, num_workers=args.workers
+            )
+
+
+def _make_all_alignments(args):
+    if args.trainpref and os.path.exists(args.trainpref + "." + args.align_suffix):
+        _make_binary_alignment_dataset(
+            args.trainpref + "." + args.align_suffix,
+            "train.align",
+            num_workers=args.workers,
+            args=args,
+        )
+    if args.validpref and os.path.exists(args.validpref + "." + args.align_suffix):
+        _make_binary_alignment_dataset(
+            args.validpref + "." + args.align_suffix,
+            "valid.align",
+            num_workers=args.workers,
+            args=args,
+        )
+    if args.testpref and os.path.exists(args.testpref + "." + args.align_suffix):
+        _make_binary_alignment_dataset(
+            args.testpref + "." + args.align_suffix,
+            "test.align",
+            num_workers=args.workers,
+            args=args,
+        )
+
+
+#####################################################################
+# align
+#####################################################################
+
+
+def _align_files(args, src_dict, tgt_dict):
+    assert args.trainpref, "--trainpref must be set if --alignfile is specified"
+    src_file_name = _train_path(args.source_lang, args.trainpref)
+    tgt_file_name = _train_path(args.target_lang, args.trainpref)
+    freq_map = {}
+    with open(args.alignfile, "r", encoding="utf-8") as align_file:
+        with open(src_file_name, "r", encoding="utf-8") as src_file:
+            with open(tgt_file_name, "r", encoding="utf-8") as tgt_file:
+                for a, s, t in zip_longest(align_file, src_file, tgt_file):
+                    si = src_dict.encode_line(s, add_if_not_exist=False)
+                    ti = tgt_dict.encode_line(t, add_if_not_exist=False)
+                    ai = list(map(lambda x: tuple(x.split("-")), a.split()))
+                    for sai, tai in ai:
+                        srcidx = si[int(sai)]
+                        tgtidx = ti[int(tai)]
+                        if srcidx != src_dict.unk() and tgtidx != tgt_dict.unk():
+                            assert srcidx != src_dict.pad()
+                            assert srcidx != src_dict.eos()
+                            assert tgtidx != tgt_dict.pad()
+                            assert tgtidx != tgt_dict.eos()
+                            if srcidx not in freq_map:
+                                freq_map[srcidx] = {}
+                            if tgtidx not in freq_map[srcidx]:
+                                freq_map[srcidx][tgtidx] = 1
+                            else:
+                                freq_map[srcidx][tgtidx] += 1
+    align_dict = {}
+    for srcidx in freq_map.keys():
+        align_dict[srcidx] = max(freq_map[srcidx], key=freq_map[srcidx].get)
+    with open(
+        os.path.join(
+            args.destdir,
+            "alignment.{}-{}.txt".format(args.source_lang, args.target_lang),
+        ),
+        "w",
+        encoding="utf-8",
+    ) as f:
+        for k, v in align_dict.items():
+            print("{} {}".format(src_dict[k], tgt_dict[v]), file=f)
+
+
+#####################################################################
+# MAIN
+#####################################################################
+
+
+def main(args):
+    # setup some basic things
+    utils.import_user_module(args)
+
+    os.makedirs(args.destdir, exist_ok=True)
+
+    logger.addHandler(
+        logging.FileHandler(
+            filename=os.path.join(args.destdir, "preprocess.log"),
+        )
+    )
+    logger.info(args)
+
+    assert (
+        args.dataset_impl != "huffman"
+    ), "preprocessing.py doesn't support Huffman yet, use HuffmanCodeBuilder directly."
+
+    # build dictionaries
+
+    target = not args.only_source
+
+    if not args.srcdict and os.path.exists(_dict_path(args.source_lang, args.destdir)):
+        raise FileExistsError(_dict_path(args.source_lang, args.destdir))
+
+    if (
+        target
+        and not args.tgtdict
+        and os.path.exists(_dict_path(args.target_lang, args.destdir))
+    ):
+        raise FileExistsError(_dict_path(args.target_lang, args.destdir))
+
+    task = tasks.get_task(args.task)
+
+    if args.joined_dictionary:
+        assert (
+            not args.srcdict or not args.tgtdict
+        ), "cannot use both --srcdict and --tgtdict with --joined-dictionary"
+
+        if args.srcdict:
+            src_dict = task.load_dictionary(args.srcdict)
+        elif args.tgtdict:
+            src_dict = task.load_dictionary(args.tgtdict)
+        else:
+            assert (
+                args.trainpref
+            ), "--trainpref must be set if --srcdict is not specified"
+            src_dict = _build_dictionary(
+                {
+                    _train_path(lang, args.trainpref)
+                    for lang in [args.source_lang, args.target_lang]
+                },
+                task=task,
+                args=args,
+                src=True,
+            )
+        tgt_dict = src_dict
+    else:
+        if args.srcdict:
+            src_dict = task.load_dictionary(args.srcdict)
+        else:
+            assert (
+                args.trainpref
+            ), "--trainpref must be set if --srcdict is not specified"
+            src_dict = _build_dictionary(
+                [_train_path(args.source_lang, args.trainpref)],
+                task=task,
+                args=args,
+                src=True,
+            )
+
+        if target:
+            if args.tgtdict:
+                tgt_dict = task.load_dictionary(args.tgtdict)
+            else:
+                assert (
+                    args.trainpref
+                ), "--trainpref must be set if --tgtdict is not specified"
+                tgt_dict = _build_dictionary(
+                    [_train_path(args.target_lang, args.trainpref)],
+                    task=task,
+                    args=args,
+                    tgt=True,
+                )
+        else:
+            tgt_dict = None
+
+    # save dictionaries
+
+    src_dict.save(_dict_path(args.source_lang, args.destdir))
+    if target and tgt_dict is not None:
+        tgt_dict.save(_dict_path(args.target_lang, args.destdir))
+
+    if args.dict_only:
+        return
+
+    _make_all(args.source_lang, src_dict, args)
+    if target:
+        _make_all(args.target_lang, tgt_dict, args)
+
+    # align the datasets if needed
+    if args.align_suffix:
+        _make_all_alignments(args)
+
+    logger.info("Wrote preprocessed data to {}".format(args.destdir))
+
+    if args.alignfile:
+        _align_files(args, src_dict=src_dict, tgt_dict=tgt_dict)
+
+
+def cli_main():
+    parser = options.get_preprocessing_parser()
+    args = parser.parse_args()
+    main(args)
+
+
+if __name__ == "__main__":
+    cli_main()

fairseq/fairseq_cli/score.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""
+BLEU scoring of generated translations against reference translations.
+"""
+
+import argparse
+import os
+import sys
+
+from fairseq.data import dictionary
+from fairseq.scoring import bleu
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        description="Command-line script for BLEU scoring."
+    )
+    # fmt: off
+    parser.add_argument('-s', '--sys', default='-', help='system output')
+    parser.add_argument('-r', '--ref', required=True, help='references')
+    parser.add_argument('-o', '--order', default=4, metavar='N',
+                        type=int, help='consider ngrams up to this order')
+    parser.add_argument('--ignore-case', action='store_true',
+                        help='case-insensitive scoring')
+    parser.add_argument('--sacrebleu', action='store_true',
+                        help='score with sacrebleu')
+    parser.add_argument('--sentence-bleu', action='store_true',
+                        help='report sentence-level BLEUs (i.e., with +1 smoothing)')
+    # fmt: on
+    return parser
+
+
+def cli_main():
+    parser = get_parser()
+    args = parser.parse_args()
+    print(args)
+
+    assert args.sys == "-" or os.path.exists(
+        args.sys
+    ), "System output file {} does not exist".format(args.sys)
+    assert os.path.exists(args.ref), "Reference file {} does not exist".format(args.ref)
+
+    dict = dictionary.Dictionary()
+
+    def readlines(fd):
+        for line in fd.readlines():
+            if args.ignore_case:
+                yield line.lower()
+            else:
+                yield line
+
+    if args.sacrebleu:
+        import sacrebleu
+
+        def score(fdsys):
+            with open(args.ref) as fdref:
+                print(sacrebleu.corpus_bleu(fdsys, [fdref]).format())
+
+    elif args.sentence_bleu:
+
+        def score(fdsys):
+            with open(args.ref) as fdref:
+                scorer = bleu.Scorer(dict.pad(), dict.eos(), dict.unk())
+                for i, (sys_tok, ref_tok) in enumerate(
+                    zip(readlines(fdsys), readlines(fdref))
+                ):
+                    scorer.reset(one_init=True)
+                    sys_tok = dict.encode_line(sys_tok)
+                    ref_tok = dict.encode_line(ref_tok)
+                    scorer.add(ref_tok, sys_tok)
+                    print(i, scorer.result_string(args.order))
+
+    else:
+
+        def score(fdsys):
+            with open(args.ref) as fdref:
+                scorer = bleu.Scorer(
+                    bleu.BleuConfig(
+                        pad=dict.pad(),
+                        eos=dict.eos(),
+                        unk=dict.unk(),
+                    )
+                )
+                for sys_tok, ref_tok in zip(readlines(fdsys), readlines(fdref)):
+                    sys_tok = dict.encode_line(sys_tok)
+                    ref_tok = dict.encode_line(ref_tok)
+                    scorer.add(ref_tok, sys_tok)
+                print(scorer.result_string(args.order))
+
+    if args.sys == "-":
+        score(sys.stdin)
+    else:
+        with open(args.sys, "r") as f:
+            score(f)
+
+
+if __name__ == "__main__":
+    cli_main()

fairseq/fairseq_cli/train.py

@@ -0,0 +1,581 @@
+#!/usr/bin/env python3 -u
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""
+Train a new model on one or across multiple GPUs.
+"""
+
+import argparse
+import logging
+import math
+import os
+import sys
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+# We need to setup root logger before importing any fairseq libraries.
+logging.basicConfig(
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=os.environ.get("LOGLEVEL", "INFO").upper(),
+    stream=sys.stdout,
+)
+logger = logging.getLogger("fairseq_cli.train")
+
+import numpy as np
+import torch
+from omegaconf import DictConfig, OmegaConf
+
+from fairseq import checkpoint_utils, options, quantization_utils, tasks, utils
+from fairseq.data import data_utils, iterators
+from fairseq.data.plasma_utils import PlasmaStore
+from fairseq.dataclass.configs import FairseqConfig
+from fairseq.dataclass.initialize import add_defaults
+from fairseq.dataclass.utils import convert_namespace_to_omegaconf
+from fairseq.distributed import fsdp_enable_wrap, fsdp_wrap
+from fairseq.distributed import utils as distributed_utils
+from fairseq.file_io import PathManager
+from fairseq.logging import meters, metrics, progress_bar
+from fairseq.model_parallel.megatron_trainer import MegatronTrainer
+from fairseq.trainer import Trainer
+
+
+def main(cfg: FairseqConfig) -> None:
+    if isinstance(cfg, argparse.Namespace):
+        cfg = convert_namespace_to_omegaconf(cfg)
+
+    utils.import_user_module(cfg.common)
+    add_defaults(cfg)
+
+    if (
+        distributed_utils.is_master(cfg.distributed_training)
+        and "job_logging_cfg" in cfg
+    ):
+        # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126)
+        logging.config.dictConfig(OmegaConf.to_container(cfg.job_logging_cfg))
+
+    assert (
+        cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None
+    ), "Must specify batch size either with --max-tokens or --batch-size"
+    metrics.reset()
+
+    if cfg.common.log_file is not None:
+        handler = logging.FileHandler(filename=cfg.common.log_file)
+        logger.addHandler(handler)
+
+    np.random.seed(cfg.common.seed)
+    utils.set_torch_seed(cfg.common.seed)
+
+    if distributed_utils.is_master(cfg.distributed_training):
+        checkpoint_utils.verify_checkpoint_directory(cfg.checkpoint.save_dir)
+
+    # Print args
+    logger.info(cfg)
+
+    if cfg.checkpoint.write_checkpoints_asynchronously:
+        try:
+            import iopath  # noqa: F401
+        except ImportError:
+            logging.exception(
+                "Asynchronous checkpoint writing is specified but iopath is "
+                "not installed: `pip install iopath`"
+            )
+            return
+
+    # Setup task, e.g., translation, language modeling, etc.
+    task = tasks.setup_task(cfg.task)
+
+    assert cfg.criterion, "Please specify criterion to train a model"
+
+    # Build model and criterion
+    if cfg.distributed_training.ddp_backend == "fully_sharded":
+        with fsdp_enable_wrap(cfg.distributed_training):
+            model = fsdp_wrap(task.build_model(cfg.model))
+    else:
+        model = task.build_model(cfg.model)
+    criterion = task.build_criterion(cfg.criterion)
+    logger.info(model)
+    logger.info("task: {}".format(task.__class__.__name__))
+    logger.info("model: {}".format(model.__class__.__name__))
+    logger.info("criterion: {}".format(criterion.__class__.__name__))
+    logger.info(
+        "num. shared model params: {:,} (num. trained: {:,})".format(
+            sum(
+                p.numel() for p in model.parameters() if not getattr(p, "expert", False)
+            ),
+            sum(
+                p.numel()
+                for p in model.parameters()
+                if not getattr(p, "expert", False) and p.requires_grad
+            ),
+        )
+    )
+
+    logger.info(
+        "num. expert model params: {} (num. trained: {})".format(
+            sum(p.numel() for p in model.parameters() if getattr(p, "expert", False)),
+            sum(
+                p.numel()
+                for p in model.parameters()
+                if getattr(p, "expert", False) and p.requires_grad
+            ),
+        )
+    )
+
+    # Load valid dataset (we load training data below, based on the latest checkpoint)
+    # We load the valid dataset AFTER building the model
+    if not cfg.dataset.disable_validation:
+        data_utils.raise_if_valid_subsets_unintentionally_ignored(cfg)
+        if cfg.dataset.combine_valid_subsets:
+            task.load_dataset("valid", combine=True, epoch=1)
+        else:
+            for valid_sub_split in cfg.dataset.valid_subset.split(","):
+                task.load_dataset(valid_sub_split, combine=False, epoch=1)
+
+    # (optionally) Configure quantization
+    if cfg.common.quantization_config_path is not None:
+        quantizer = quantization_utils.Quantizer(
+            config_path=cfg.common.quantization_config_path,
+            max_epoch=cfg.optimization.max_epoch,
+            max_update=cfg.optimization.max_update,
+        )
+    else:
+        quantizer = None
+
+    # Build trainer
+    if cfg.common.model_parallel_size == 1:
+        trainer = Trainer(cfg, task, model, criterion, quantizer)
+    else:
+        trainer = MegatronTrainer(cfg, task, model, criterion)
+    logger.info(
+        "training on {} devices (GPUs/TPUs)".format(
+            cfg.distributed_training.distributed_world_size
+        )
+    )
+    logger.info(
+        "max tokens per device = {} and max sentences per device = {}".format(
+            cfg.dataset.max_tokens,
+            cfg.dataset.batch_size,
+        )
+    )
+
+    # Load the latest checkpoint if one is available and restore the
+    # corresponding train iterator
+    extra_state, epoch_itr = checkpoint_utils.load_checkpoint(
+        cfg.checkpoint,
+        trainer,
+        # don't cache epoch iterators for sharded datasets
+        disable_iterator_cache=task.has_sharded_data("train"),
+    )
+    if cfg.common.tpu:
+        import torch_xla.core.xla_model as xm
+
+        xm.rendezvous("load_checkpoint")  # wait for all workers
+
+    max_epoch = cfg.optimization.max_epoch or math.inf
+    lr = trainer.get_lr()
+
+    # TODO: a dry run on validation set to pin the memory
+    valid_subsets = cfg.dataset.valid_subset.split(",")
+    if not cfg.dataset.disable_validation:
+        for subset in valid_subsets:
+            logger.info('begin dry-run validation on "{}" subset'.format(subset))
+            itr = trainer.get_valid_iterator(subset).next_epoch_itr(
+                shuffle=False, set_dataset_epoch=False  # use a fixed valid set
+            )
+            if cfg.common.tpu:
+                itr = utils.tpu_data_loader(itr)
+            for _ in itr:
+                pass
+    # TODO: end of dry run section
+
+    train_meter = meters.StopwatchMeter()
+    train_meter.start()
+    while epoch_itr.next_epoch_idx <= max_epoch:
+        if lr <= cfg.optimization.stop_min_lr:
+            logger.info(
+                f"stopping training because current learning rate ({lr}) is smaller "
+                "than or equal to minimum learning rate "
+                f"(--stop-min-lr={cfg.optimization.stop_min_lr})"
+            )
+            break
+
+        # train for one epoch
+        valid_losses, should_stop = train(cfg, trainer, task, epoch_itr)
+        if should_stop:
+            break
+
+        # only use first validation loss to update the learning rate
+        lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0])
+
+        epoch_itr = trainer.get_train_iterator(
+            epoch_itr.next_epoch_idx,
+            # sharded data: get train iterator for next epoch
+            load_dataset=task.has_sharded_data("train"),
+            # don't cache epoch iterators for sharded datasets
+            disable_iterator_cache=task.has_sharded_data("train"),
+        )
+    train_meter.stop()
+    logger.info("done training in {:.1f} seconds".format(train_meter.sum))
+
+    # ioPath implementation to wait for all asynchronous file writes to complete.
+    if cfg.checkpoint.write_checkpoints_asynchronously:
+        logger.info(
+            "ioPath PathManager waiting for all asynchronous checkpoint "
+            "writes to finish."
+        )
+        PathManager.async_close()
+        logger.info("ioPath PathManager finished waiting.")
+
+
+def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool:
+    # skip check if no validation was done in the current epoch
+    if valid_loss is None:
+        return False
+    if cfg.checkpoint.patience <= 0:
+        return False
+
+    def is_better(a, b):
+        return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b
+
+    prev_best = getattr(should_stop_early, "best", None)
+    if prev_best is None or is_better(valid_loss, prev_best):
+        should_stop_early.best = valid_loss
+        should_stop_early.num_runs = 0
+        return False
+    else:
+        should_stop_early.num_runs += 1
+        if should_stop_early.num_runs >= cfg.checkpoint.patience:
+            logger.info(
+                "early stop since valid performance hasn't improved for last {} runs".format(
+                    cfg.checkpoint.patience
+                )
+            )
+            return True
+        else:
+            return False
+
+
+@metrics.aggregate("train")
+def train(
+    cfg: DictConfig, trainer: Trainer, task: tasks.FairseqTask, epoch_itr
+) -> Tuple[List[Optional[float]], bool]:
+    """Train the model for one epoch and return validation losses."""
+    # Initialize data iterator
+    itr = epoch_itr.next_epoch_itr(
+        fix_batches_to_gpus=cfg.distributed_training.fix_batches_to_gpus,
+        shuffle=(epoch_itr.next_epoch_idx > cfg.dataset.curriculum),
+    )
+    update_freq = (
+        cfg.optimization.update_freq[epoch_itr.epoch - 1]
+        if epoch_itr.epoch <= len(cfg.optimization.update_freq)
+        else cfg.optimization.update_freq[-1]
+    )
+    itr = iterators.GroupedIterator(
+        itr,
+        update_freq,
+        skip_remainder_batch=cfg.optimization.skip_remainder_batch,
+    )
+    if cfg.common.tpu:
+        itr = utils.tpu_data_loader(itr)
+    progress = progress_bar.progress_bar(
+        itr,
+        log_format=cfg.common.log_format,
+        log_file=cfg.common.log_file,
+        log_interval=cfg.common.log_interval,
+        epoch=epoch_itr.epoch,
+        aim_repo=(
+            cfg.common.aim_repo
+            if distributed_utils.is_master(cfg.distributed_training)
+            else None
+        ),
+        aim_run_hash=(
+            cfg.common.aim_run_hash
+            if distributed_utils.is_master(cfg.distributed_training)
+            else None
+        ),
+        aim_param_checkpoint_dir=cfg.checkpoint.save_dir,
+        tensorboard_logdir=(
+            cfg.common.tensorboard_logdir
+            if distributed_utils.is_master(cfg.distributed_training)
+            else None
+        ),
+        default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
+        wandb_project=(
+            cfg.common.wandb_project
+            if distributed_utils.is_master(cfg.distributed_training)
+            else None
+        ),
+        wandb_run_name=os.environ.get(
+            "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
+        ),
+        azureml_logging=(
+            cfg.common.azureml_logging
+            if distributed_utils.is_master(cfg.distributed_training)
+            else False
+        ),
+    )
+    progress.update_config(_flatten_config(cfg))
+
+    trainer.begin_epoch(epoch_itr.epoch)
+
+    valid_subsets = cfg.dataset.valid_subset.split(",")
+    should_stop = False
+    num_updates = trainer.get_num_updates()
+    logger.info("Start iterating over samples")
+    for i, samples in enumerate(progress):
+        with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function(
+            "train_step-%d" % i
+        ):
+            log_output = trainer.train_step(samples)
+
+        if log_output is not None:  # not OOM, overflow, ...
+            # log mid-epoch stats
+            num_updates = trainer.get_num_updates()
+            if num_updates % cfg.common.log_interval == 0:
+                stats = get_training_stats(metrics.get_smoothed_values("train_inner"))
+                progress.log(stats, tag="train_inner", step=num_updates)
+
+                # reset mid-epoch stats after each log interval
+                # the end-of-epoch stats will still be preserved
+                metrics.reset_meters("train_inner")
+
+        end_of_epoch = not itr.has_next()
+        valid_losses, should_stop = validate_and_save(
+            cfg, trainer, task, epoch_itr, valid_subsets, end_of_epoch
+        )
+
+        if should_stop:
+            break
+
+    # log end-of-epoch stats
+    logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch))
+    stats = get_training_stats(metrics.get_smoothed_values("train"))
+    progress.print(stats, tag="train", step=num_updates)
+
+    # reset epoch-level meters
+    metrics.reset_meters("train")
+    return valid_losses, should_stop
+
+
+def _flatten_config(cfg: DictConfig):
+    config = OmegaConf.to_container(cfg)
+    # remove any legacy Namespaces and replace with a single "args"
+    namespace = None
+    for k, v in list(config.items()):
+        if isinstance(v, argparse.Namespace):
+            namespace = v
+            del config[k]
+    if namespace is not None:
+        config["args"] = vars(namespace)
+    return config
+
+
+def validate_and_save(
+    cfg: DictConfig,
+    trainer: Trainer,
+    task: tasks.FairseqTask,
+    epoch_itr,
+    valid_subsets: List[str],
+    end_of_epoch: bool,
+) -> Tuple[List[Optional[float]], bool]:
+    num_updates = trainer.get_num_updates()
+    max_update = cfg.optimization.max_update or math.inf
+
+    # Stopping conditions (and an additional one based on validation loss later
+    # on)
+    should_stop = False
+    if num_updates >= max_update:
+        should_stop = True
+        logger.info(
+            f"Stopping training due to "
+            f"num_updates: {num_updates} >= max_update: {max_update}"
+        )
+
+    training_time_hours = trainer.cumulative_training_time() / (60 * 60)
+    if (
+        cfg.optimization.stop_time_hours > 0
+        and training_time_hours > cfg.optimization.stop_time_hours
+    ):
+        should_stop = True
+        logger.info(
+            f"Stopping training due to "
+            f"cumulative_training_time: {training_time_hours} > "
+            f"stop_time_hours: {cfg.optimization.stop_time_hours} hour(s)"
+        )
+
+    do_save = (
+        (end_of_epoch and epoch_itr.epoch % cfg.checkpoint.save_interval == 0)
+        or should_stop
+        or (
+            cfg.checkpoint.save_interval_updates > 0
+            and num_updates > 0
+            and num_updates % cfg.checkpoint.save_interval_updates == 0
+            and num_updates >= cfg.dataset.validate_after_updates
+        )
+    )
+    do_validate = (
+        (
+            (not end_of_epoch and do_save)  # validate during mid-epoch saves
+            or (end_of_epoch and epoch_itr.epoch % cfg.dataset.validate_interval == 0)
+            or should_stop
+            or (
+                cfg.dataset.validate_interval_updates > 0
+                and num_updates > 0
+                and num_updates % cfg.dataset.validate_interval_updates == 0
+            )
+        )
+        and not cfg.dataset.disable_validation
+        and num_updates >= cfg.dataset.validate_after_updates
+    )
+
+    # Validate
+    valid_losses = [None]
+    if do_validate:
+        valid_losses = validate(cfg, trainer, task, epoch_itr, valid_subsets)
+
+    should_stop |= should_stop_early(cfg, valid_losses[0])
+
+    # Save checkpoint
+    if do_save or should_stop:
+        cp_path = checkpoint_utils.save_checkpoint(
+            cfg.checkpoint, trainer, epoch_itr, valid_losses[0]
+        )
+        if cp_path is not None and hasattr(task, "post_save"):
+            task.post_save(cp_path, num_updates)
+
+    return valid_losses, should_stop
+
+
+def get_training_stats(stats: Dict[str, Any]) -> Dict[str, Any]:
+    stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0)
+    return stats
+
+
+def validate(
+    cfg: DictConfig,
+    trainer: Trainer,
+    task: tasks.FairseqTask,
+    epoch_itr,
+    subsets: List[str],
+) -> List[Optional[float]]:
+    """Evaluate the model on the validation set(s) and return the losses."""
+
+    if cfg.dataset.fixed_validation_seed is not None:
+        # set fixed seed for every validation
+        utils.set_torch_seed(cfg.dataset.fixed_validation_seed)
+
+    trainer.begin_valid_epoch(epoch_itr.epoch)
+    valid_losses = []
+    for subset_idx, subset in enumerate(subsets):
+        logger.info('begin validation on "{}" subset'.format(subset))
+
+        # Initialize data iterator
+        itr = trainer.get_valid_iterator(subset).next_epoch_itr(
+            shuffle=False, set_dataset_epoch=False  # use a fixed valid set
+        )
+        if cfg.common.tpu:
+            itr = utils.tpu_data_loader(itr)
+        progress = progress_bar.progress_bar(
+            itr,
+            log_format=cfg.common.log_format,
+            log_interval=cfg.common.log_interval,
+            epoch=epoch_itr.epoch,
+            prefix=f"valid on '{subset}' subset",
+            aim_repo=(
+                cfg.common.aim_repo
+                if distributed_utils.is_master(cfg.distributed_training)
+                else None
+            ),
+            aim_run_hash=(
+                cfg.common.aim_run_hash
+                if distributed_utils.is_master(cfg.distributed_training)
+                else None
+            ),
+            aim_param_checkpoint_dir=cfg.checkpoint.save_dir,
+            tensorboard_logdir=(
+                cfg.common.tensorboard_logdir
+                if distributed_utils.is_master(cfg.distributed_training)
+                else None
+            ),
+            default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
+            wandb_project=(
+                cfg.common.wandb_project
+                if distributed_utils.is_master(cfg.distributed_training)
+                else None
+            ),
+            wandb_run_name=os.environ.get(
+                "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
+            ),
+        )
+
+        # create a new root metrics aggregator so validation metrics
+        # don't pollute other aggregators (e.g., train meters)
+        with metrics.aggregate(new_root=True) as agg:
+            for i, sample in enumerate(progress):
+                if (
+                    cfg.dataset.max_valid_steps is not None
+                    and i > cfg.dataset.max_valid_steps
+                ):
+                    break
+                trainer.valid_step(sample)
+
+        # log validation stats
+        # only tracking the best metric on the 1st validation subset
+        tracking_best = subset_idx == 0
+        stats = get_valid_stats(cfg, trainer, agg.get_smoothed_values(), tracking_best)
+
+        if hasattr(task, "post_validate"):
+            task.post_validate(trainer.get_model(), stats, agg)
+
+        progress.print(stats, tag=subset, step=trainer.get_num_updates())
+
+        valid_losses.append(stats[cfg.checkpoint.best_checkpoint_metric])
+    return valid_losses
+
+
+def get_valid_stats(
+    cfg: DictConfig,
+    trainer: Trainer,
+    stats: Dict[str, Any],
+    tracking_best: bool,
+) -> Dict[str, Any]:
+    stats["num_updates"] = trainer.get_num_updates()
+    if tracking_best and hasattr(checkpoint_utils.save_checkpoint, "best"):
+        key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric)
+        best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min
+        stats[key] = best_function(
+            checkpoint_utils.save_checkpoint.best,
+            stats[cfg.checkpoint.best_checkpoint_metric],
+        )
+    return stats
+
+
+def cli_main(
+    modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None
+) -> None:
+    parser = options.get_training_parser()
+    args = options.parse_args_and_arch(parser, modify_parser=modify_parser)
+
+    cfg = convert_namespace_to_omegaconf(args)
+
+    if cfg.common.use_plasma_view:
+        server = PlasmaStore(path=cfg.common.plasma_path)
+        logger.info(
+            f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}"
+        )
+
+    if args.profile:
+        with torch.cuda.profiler.profile():
+            with torch.autograd.profiler.emit_nvtx():
+                distributed_utils.call_main(cfg, main)
+    else:
+        distributed_utils.call_main(cfg, main)
+
+    # if cfg.common.use_plasma_view:
+    #     server.server.kill()
+
+
+if __name__ == "__main__":
+    cli_main()

fairseq/fairseq_cli/validate.py

@@ -0,0 +1,153 @@
+#!/usr/bin/env python3 -u
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import sys
+from argparse import Namespace
+from itertools import chain
+
+import torch
+from omegaconf import DictConfig
+
+from fairseq import checkpoint_utils, distributed_utils, options, utils
+from fairseq.dataclass.utils import convert_namespace_to_omegaconf
+from fairseq.logging import metrics, progress_bar
+from fairseq.utils import reset_logging
+
+logging.basicConfig(
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=os.environ.get("LOGLEVEL", "INFO").upper(),
+    stream=sys.stdout,
+)
+logger = logging.getLogger("fairseq_cli.validate")
+
+
+def main(cfg: DictConfig, override_args=None):
+    if isinstance(cfg, Namespace):
+        cfg = convert_namespace_to_omegaconf(cfg)
+
+    utils.import_user_module(cfg.common)
+
+    reset_logging()
+
+    assert (
+        cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None
+    ), "Must specify batch size either with --max-tokens or --batch-size"
+
+    use_fp16 = cfg.common.fp16
+    use_cuda = torch.cuda.is_available() and not cfg.common.cpu
+
+    if use_cuda:
+        torch.cuda.set_device(cfg.distributed_training.device_id)
+
+    if cfg.distributed_training.distributed_world_size > 1:
+        data_parallel_world_size = distributed_utils.get_data_parallel_world_size()
+        data_parallel_rank = distributed_utils.get_data_parallel_rank()
+    else:
+        data_parallel_world_size = 1
+        data_parallel_rank = 0
+
+    if override_args is not None:
+        overrides = vars(override_args)
+        overrides.update(eval(getattr(override_args, "model_overrides", "{}")))
+    else:
+        overrides = None
+
+    # Load ensemble
+    logger.info("loading model(s) from {}".format(cfg.common_eval.path))
+    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+        [cfg.common_eval.path],
+        arg_overrides=overrides,
+        suffix=cfg.checkpoint.checkpoint_suffix,
+    )
+    model = models[0]
+
+    # Move models to GPU
+    for model in models:
+        model.eval()
+        if use_fp16:
+            model.half()
+        if use_cuda:
+            model.cuda()
+
+    # Print args
+    logger.info(saved_cfg)
+
+    # Build criterion
+    criterion = task.build_criterion(saved_cfg.criterion)
+    criterion.eval()
+
+    for subset in cfg.dataset.valid_subset.split(","):
+        try:
+            task.load_dataset(subset, combine=False, epoch=1, task_cfg=saved_cfg.task)
+            dataset = task.dataset(subset)
+        except KeyError:
+            raise Exception("Cannot find dataset: " + subset)
+
+        # Initialize data iterator
+        itr = task.get_batch_iterator(
+            dataset=dataset,
+            max_tokens=cfg.dataset.max_tokens,
+            max_sentences=cfg.dataset.batch_size,
+            max_positions=utils.resolve_max_positions(
+                task.max_positions(),
+                *[m.max_positions() for m in models],
+            ),
+            ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
+            required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,
+            seed=cfg.common.seed,
+            num_shards=data_parallel_world_size,
+            shard_id=data_parallel_rank,
+            num_workers=cfg.dataset.num_workers,
+            data_buffer_size=cfg.dataset.data_buffer_size,
+        ).next_epoch_itr(shuffle=False)
+        progress = progress_bar.progress_bar(
+            itr,
+            log_format=cfg.common.log_format,
+            log_interval=cfg.common.log_interval,
+            prefix=f"valid on '{subset}' subset",
+            default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
+        )
+
+        log_outputs = []
+        for i, sample in enumerate(progress):
+            sample = utils.move_to_cuda(sample) if use_cuda else sample
+            _loss, _sample_size, log_output = task.valid_step(sample, model, criterion)
+            progress.log(log_output, step=i)
+            log_outputs.append(log_output)
+
+        if data_parallel_world_size > 1:
+            log_outputs = distributed_utils.all_gather_list(
+                log_outputs,
+                max_size=cfg.common.all_gather_list_size,
+                group=distributed_utils.get_data_parallel_group(),
+            )
+            log_outputs = list(chain.from_iterable(log_outputs))
+
+        with metrics.aggregate() as agg:
+            task.reduce_metrics(log_outputs, criterion)
+            log_output = agg.get_smoothed_values()
+
+        progress.print(log_output, tag=subset, step=i)
+
+
+def cli_main():
+    parser = options.get_validation_parser()
+    args = options.parse_args_and_arch(parser)
+
+    # only override args that are explicitly given on the command line
+    override_parser = options.get_validation_parser()
+    override_args = options.parse_args_and_arch(override_parser, suppress_defaults=True)
+
+    distributed_utils.call_main(
+        convert_namespace_to_omegaconf(args), main, override_args=override_args
+    )
+
+
+if __name__ == "__main__":
+    cli_main()

fairseq/hydra_plugins/dependency_submitit_launcher/hydra_plugins/dependency_submitit_launcher/__init__.py

@@ -0,0 +1,3 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+__version__ = "0.1"

fairseq/hydra_plugins/dependency_submitit_launcher/hydra_plugins/dependency_submitit_launcher/config.py

@@ -0,0 +1,23 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+from dataclasses import dataclass, field
+
+from hydra.core.config_store import ConfigStore
+
+from hydra_plugins.hydra_submitit_launcher.config import SlurmQueueConf
+
+
+@dataclass
+class DependencySubmititConf(SlurmQueueConf):
+    """Slurm configuration overrides and specific parameters"""
+
+    _target_: str = (
+        "hydra_plugins.dependency_submitit_launcher.launcher.DependencySubmititLauncher"
+    )
+
+
+ConfigStore.instance().store(
+    group="hydra/launcher",
+    name="dependency_submitit_slurm",
+    node=DependencySubmititConf(),
+    provider="dependency_submitit_slurm",
+)

fairseq/hydra_plugins/dependency_submitit_launcher/hydra_plugins/dependency_submitit_launcher/launcher.py

@@ -0,0 +1,121 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+import logging
+import os
+import subprocess
+from pathlib import Path
+from typing import Any, List, Sequence
+
+from hydra.core.singleton import Singleton
+from hydra.core.utils import JobReturn, filter_overrides
+from omegaconf import OmegaConf
+
+log = logging.getLogger(__name__)
+
+from .config import DependencySubmititConf
+from hydra_plugins.hydra_submitit_launcher.submitit_launcher import BaseSubmititLauncher
+
+
+class DependencySubmititLauncher(BaseSubmititLauncher):
+    _EXECUTOR = "slurm"
+
+    def launch(
+        self, job_overrides: Sequence[Sequence[str]], initial_job_idx: int
+    ) -> Sequence[JobReturn]:
+
+        # lazy import to ensure plugin discovery remains fast
+        import submitit
+
+        assert self.config is not None
+
+        num_jobs = len(job_overrides)
+        assert num_jobs > 0
+
+        next_script = None
+
+        for jo in job_overrides:
+            if next_script is None:
+                for item in jo:
+                    if "next_script=" in item:
+                        next_script = item
+                        break
+            assert (
+                next_script is not None
+            ), "job overrides must contain +next_script=path/to/next/script"
+            jo.remove(next_script)
+
+        idx = next_script.find("=")
+        next_script = next_script[idx + 1 :]
+
+        params = self.params
+        # build executor
+        init_params = {"folder": self.params["submitit_folder"]}
+        specific_init_keys = {"max_num_timeout"}
+
+        init_params.update(
+            **{
+                f"{self._EXECUTOR}_{x}": y
+                for x, y in params.items()
+                if x in specific_init_keys
+            }
+        )
+        init_keys = specific_init_keys | {"submitit_folder"}
+        executor = submitit.AutoExecutor(cluster=self._EXECUTOR, **init_params)
+
+        # specify resources/parameters
+        baseparams = set(OmegaConf.structured(DependencySubmititConf).keys())
+        params = {
+            x if x in baseparams else f"{self._EXECUTOR}_{x}": y
+            for x, y in params.items()
+            if x not in init_keys
+        }
+        executor.update_parameters(**params)
+
+        log.info(
+            f"Submitit '{self._EXECUTOR}' sweep output dir : "
+            f"{self.config.hydra.sweep.dir}"
+        )
+        sweep_dir = Path(str(self.config.hydra.sweep.dir))
+        sweep_dir.mkdir(parents=True, exist_ok=True)
+        if "mode" in self.config.hydra.sweep:
+            mode = int(str(self.config.hydra.sweep.mode), 8)
+            os.chmod(sweep_dir, mode=mode)
+
+        job_params: List[Any] = []
+        for idx, overrides in enumerate(job_overrides):
+            idx = initial_job_idx + idx
+            lst = " ".join(filter_overrides(overrides))
+            log.info(f"\t#{idx} : {lst}")
+            job_params.append(
+                (
+                    list(overrides),
+                    "hydra.sweep.dir",
+                    idx,
+                    f"job_id_for_{idx}",
+                    Singleton.get_state(),
+                )
+            )
+
+        jobs = executor.map_array(self, *zip(*job_params))
+
+        for j, jp in zip(jobs, job_params):
+            job_id = str(j.job_id)
+            task_id = "0" if "_" not in job_id else job_id.split("_")[1]
+            sweep_config = self.config_loader.load_sweep_config(self.config, jp[0])
+            dir = sweep_config.hydra.sweep.dir
+
+            dir = (
+                dir.replace("[", "")
+                .replace("]", "")
+                .replace("{", "")
+                .replace("}", "")
+                .replace(",", "_")
+                .replace("'", "")
+                .replace('"', "")
+            )
+
+            subprocess.call(
+                [next_script, job_id, task_id, dir],
+                shell=False,
+            )
+
+        return [j.results()[0] for j in jobs]

fairseq/hydra_plugins/dependency_submitit_launcher/setup.py

@@ -0,0 +1,29 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+# type: ignore
+from pathlib import Path
+
+from read_version import read_version
+from setuptools import find_namespace_packages, setup
+
+setup(
+    name="dependency-submitit-launcher",
+    version=read_version("hydra_plugins/dependency_submitit_launcher", "__init__.py"),
+    author="Alexei Baevski",
+    author_email="abaevski@fb.com",
+    description="Dependency-supporting Submitit Launcher for Hydra apps",
+    packages=find_namespace_packages(include=["hydra_plugins.*"]),
+    classifiers=[
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Operating System :: MacOS",
+        "Operating System :: POSIX :: Linux",
+        "Development Status :: 4 - Beta",
+    ],
+    install_requires=[
+        "hydra-core>=1.0.4",
+        "submitit>=1.0.0",
+    ],
+    include_package_data=True,
+)

fairseq/scripts/average_checkpoints.py

@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import collections
+import os
+import re
+
+import torch
+
+from fairseq.file_io import PathManager
+
+
+def average_checkpoints(inputs):
+    """Loads checkpoints from inputs and returns a model with averaged weights.
+
+    Args:
+      inputs: An iterable of string paths of checkpoints to load from.
+
+    Returns:
+      A dict of string keys mapping to various values. The 'model' key
+      from the returned dict should correspond to an OrderedDict mapping
+      string parameter names to torch Tensors.
+    """
+    params_dict = collections.OrderedDict()
+    params_keys = None
+    new_state = None
+    num_models = len(inputs)
+
+    for fpath in inputs:
+        with PathManager.open(fpath, "rb") as f:
+            state = torch.load(
+                f,
+                map_location=(
+                    lambda s, _: torch.serialization.default_restore_location(s, "cpu")
+                ),
+            )
+        # Copies over the settings from the first checkpoint
+        if new_state is None:
+            new_state = state
+
+        model_params = state["model"]
+
+        model_params_keys = list(model_params.keys())
+        if params_keys is None:
+            params_keys = model_params_keys
+        elif params_keys != model_params_keys:
+            raise KeyError(
+                "For checkpoint {}, expected list of params: {}, "
+                "but found: {}".format(f, params_keys, model_params_keys)
+            )
+
+        for k in params_keys:
+            p = model_params[k]
+            if isinstance(p, torch.HalfTensor):
+                p = p.float()
+            if k not in params_dict:
+                params_dict[k] = p.clone()
+                # NOTE: clone() is needed in case of p is a shared parameter
+            else:
+                params_dict[k] += p
+
+    averaged_params = collections.OrderedDict()
+    for k, v in params_dict.items():
+        averaged_params[k] = v
+        if averaged_params[k].is_floating_point():
+            averaged_params[k].div_(num_models)
+        else:
+            averaged_params[k] //= num_models
+    new_state["model"] = averaged_params
+    return new_state
+
+
+def last_n_checkpoints(paths, n, update_based, upper_bound=None):
+    assert len(paths) == 1
+    path = paths[0]
+    if update_based:
+        pt_regexp = re.compile(r"checkpoint_\d+_(\d+)\.pt")
+    else:
+        pt_regexp = re.compile(r"checkpoint(\d+)\.pt")
+    files = PathManager.ls(path)
+
+    entries = []
+    for f in files:
+        m = pt_regexp.fullmatch(f)
+        if m is not None:
+            sort_key = int(m.group(1))
+            if upper_bound is None or sort_key <= upper_bound:
+                entries.append((sort_key, m.group(0)))
+    if len(entries) < n:
+        raise Exception(
+            "Found {} checkpoint files but need at least {}", len(entries), n
+        )
+    return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)[:n]]
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Tool to average the params of input checkpoints to "
+        "produce a new checkpoint",
+    )
+    # fmt: off
+    parser.add_argument('--inputs', required=True, nargs='+',
+                        help='Input checkpoint file paths.')
+    parser.add_argument('--output', required=True, metavar='FILE',
+                        help='Write the new checkpoint containing the averaged weights to this path.')
+    num_group = parser.add_mutually_exclusive_group()
+    num_group.add_argument('--num-epoch-checkpoints', type=int,
+                           help='if set, will try to find checkpoints with names checkpoint_xx.pt in the '
+                           'path specified by input, and average last this many of them.')
+    num_group.add_argument('--num-update-checkpoints', type=int,
+                           help='if set, will try to find checkpoints with names checkpoint_ee_xx.pt in the path specified by'
+                           ' input, and average last this many of them.')
+    num_group.add_argument('--num-best-checkpoints', type=int, default=0,
+                           help='if set, will try to find checkpoints with names checkpoint_best_ee_xx.pt in the path specified by'
+                           ' input, and average last this many of them.')
+    parser.add_argument('--checkpoint-upper-bound', type=int,
+                        help='when using --num-epoch-checkpoints, this will set an upper bound on which epoch to use, '
+                        'when using --num-update-checkpoints, this will set an upper bound on which update to use'
+                        'e.g., with --num-epoch-checkpoints=10 --checkpoint-upper-bound=50, checkpoints 41-50 would be'
+                        ' averaged.'
+                        'e.g., with --num-update-checkpoints=10 --checkpoint-upper-bound=50000, checkpoints 40500-50000 would'
+                        ' be averaged assuming --save-interval-updates 500'
+                        )
+    # fmt: on
+    args = parser.parse_args()
+    print(args)
+
+    num = None
+    is_update_based = False
+    if args.num_update_checkpoints is not None:
+        num = args.num_update_checkpoints
+        is_update_based = True
+    elif args.num_epoch_checkpoints is not None:
+        num = args.num_epoch_checkpoints
+
+    assert args.checkpoint_upper_bound is None or (
+        args.num_epoch_checkpoints is not None
+        or args.num_update_checkpoints is not None
+    ), "--checkpoint-upper-bound requires --num-epoch-checkpoints or --num-update-checkpoints"
+    assert (
+        args.num_epoch_checkpoints is None or args.num_update_checkpoints is None
+    ), "Cannot combine --num-epoch-checkpoints and --num-update-checkpoints"
+
+    if num is not None:
+        args.inputs = last_n_checkpoints(
+            args.inputs,
+            num,
+            is_update_based,
+            upper_bound=args.checkpoint_upper_bound,
+        )
+        print("averaging checkpoints: ", args.inputs)
+
+    if args.num_best_checkpoints > 0:
+        args.inputs = list(
+            sorted(
+                args.inputs,
+                key=lambda x: float(
+                    os.path.basename(x).split("_")[-1].replace(".pt", "")
+                ),
+            )
+        )
+        args.inputs = args.inputs[: args.num_best_checkpoints]
+        for path in args.inputs:
+            print(os.path.basename(path))
+    new_state = average_checkpoints(args.inputs)
+    with PathManager.open(args.output, "wb") as f:
+        torch.save(new_state, f)
+    print("Finished writing averaged checkpoint to {}".format(args.output))
+
+
+if __name__ == "__main__":
+    main()

fairseq/scripts/build_sym_alignment.py

@@ -0,0 +1,97 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""
+Use this script in order to build symmetric alignments for your translation
+dataset.
+This script depends on fast_align and mosesdecoder tools. You will need to
+build those before running the script.
+fast_align:
+    github: http://github.com/clab/fast_align
+    instructions: follow the instructions in README.md
+mosesdecoder:
+    github: http://github.com/moses-smt/mosesdecoder
+    instructions: http://www.statmt.org/moses/?n=Development.GetStarted
+The script produces the following files under --output_dir:
+    text.joined - concatenation of lines from the source_file and the
+    target_file.
+    align.forward - forward pass of fast_align.
+    align.backward - backward pass of fast_align.
+    aligned.sym_heuristic - symmetrized alignment.
+"""
+
+import argparse
+import os
+from itertools import zip_longest
+
+
+def main():
+    parser = argparse.ArgumentParser(description="symmetric alignment builer")
+    # fmt: off
+    parser.add_argument('--fast_align_dir',
+                        help='path to fast_align build directory')
+    parser.add_argument('--mosesdecoder_dir',
+                        help='path to mosesdecoder root directory')
+    parser.add_argument('--sym_heuristic',
+                        help='heuristic to use for symmetrization',
+                        default='grow-diag-final-and')
+    parser.add_argument('--source_file',
+                        help='path to a file with sentences '
+                             'in the source language')
+    parser.add_argument('--target_file',
+                        help='path to a file with sentences '
+                             'in the target language')
+    parser.add_argument('--output_dir',
+                        help='output directory')
+    # fmt: on
+    args = parser.parse_args()
+
+    fast_align_bin = os.path.join(args.fast_align_dir, "fast_align")
+    symal_bin = os.path.join(args.mosesdecoder_dir, "bin", "symal")
+    sym_fast_align_bin = os.path.join(
+        args.mosesdecoder_dir, "scripts", "ems", "support", "symmetrize-fast-align.perl"
+    )
+
+    # create joined file
+    joined_file = os.path.join(args.output_dir, "text.joined")
+    with open(args.source_file, "r", encoding="utf-8") as src, open(
+        args.target_file, "r", encoding="utf-8"
+    ) as tgt:
+        with open(joined_file, "w", encoding="utf-8") as joined:
+            for s, t in zip_longest(src, tgt):
+                print("{} ||| {}".format(s.strip(), t.strip()), file=joined)
+
+    bwd_align_file = os.path.join(args.output_dir, "align.backward")
+
+    # run forward alignment
+    fwd_align_file = os.path.join(args.output_dir, "align.forward")
+    fwd_fast_align_cmd = "{FASTALIGN} -i {JOINED} -d -o -v > {FWD}".format(
+        FASTALIGN=fast_align_bin, JOINED=joined_file, FWD=fwd_align_file
+    )
+    assert os.system(fwd_fast_align_cmd) == 0
+
+    # run backward alignment
+    bwd_align_file = os.path.join(args.output_dir, "align.backward")
+    bwd_fast_align_cmd = "{FASTALIGN} -i {JOINED} -d -o -v -r > {BWD}".format(
+        FASTALIGN=fast_align_bin, JOINED=joined_file, BWD=bwd_align_file
+    )
+    assert os.system(bwd_fast_align_cmd) == 0
+
+    # run symmetrization
+    sym_out_file = os.path.join(args.output_dir, "aligned")
+    sym_cmd = "{SYMFASTALIGN} {FWD} {BWD} {SRC} {TGT} {OUT} {HEURISTIC} {SYMAL}".format(
+        SYMFASTALIGN=sym_fast_align_bin,
+        FWD=fwd_align_file,
+        BWD=bwd_align_file,
+        SRC=args.source_file,
+        TGT=args.target_file,
+        OUT=sym_out_file,
+        HEURISTIC=args.sym_heuristic,
+        SYMAL=symal_bin,
+    )
+    assert os.system(sym_cmd) == 0
+
+
+if __name__ == "__main__":
+    main()

fairseq/scripts/check_installation.py

@@ -0,0 +1,36 @@
+from pathlib import Path
+import os
+
+cwd = Path(".").resolve()
+print("running 'check_installation.py' from:", cwd)
+
+# Old versions of numpy/torch can prevent loading the .so files
+import torch
+
+print("torch:", torch.__version__)
+import numpy
+
+print("numpy:", numpy.__version__)
+
+import fairseq
+
+print("Fairseq installed at:", fairseq.__file__)
+import fairseq.criterions
+import fairseq.dataclass.configs
+
+import _imp
+
+print("Should load following .so suffixes:", _imp.extension_suffixes())
+
+so_files = list(Path(fairseq.__file__).parent.glob("*.so"))
+so_files.extend(Path(fairseq.__file__).parent.glob("data/*.so"))
+print("Found following .so files:")
+for so_file in so_files:
+    print(f"- {so_file}")
+
+from fairseq import libbleu
+
+print("Found libbleu at", libbleu.__file__)
+from fairseq.data import data_utils_fast
+
+print("Found data_utils_fast at", data_utils_fast.__file__)

fairseq/scripts/compare_namespaces.py

@@ -0,0 +1,46 @@
+#!/usr/bin/env python
+"""Helper script to compare two argparse.Namespace objects."""
+
+from argparse import Namespace  # noqa
+
+
+def main():
+
+    ns1 = eval(input("Namespace 1: "))
+    ns2 = eval(input("Namespace 2: "))
+
+    def keys(ns):
+        ks = set()
+        for k in dir(ns):
+            if not k.startswith("_"):
+                ks.add(k)
+        return ks
+
+    k1 = keys(ns1)
+    k2 = keys(ns2)
+
+    def print_keys(ks, ns1, ns2=None):
+        for k in ks:
+            if ns2 is None:
+                print("{}\t{}".format(k, getattr(ns1, k, None)))
+            else:
+                print(
+                    "{}\t{}\t{}".format(k, getattr(ns1, k, None), getattr(ns2, k, None))
+                )
+
+    print("Keys unique to namespace 1:")
+    print_keys(k1 - k2, ns1)
+    print()
+
+    print("Keys unique to namespace 2:")
+    print_keys(k2 - k1, ns2)
+    print()
+
+    print("Overlapping keys with different values:")
+    ks = [k for k in k1 & k2 if getattr(ns1, k, "None") != getattr(ns2, k, "None")]
+    print_keys(ks, ns1, ns2)
+    print()
+
+
+if __name__ == "__main__":
+    main()

fairseq/scripts/compound_split_bleu.sh

@@ -0,0 +1,20 @@
+#!/bin/bash
+
+if [ $# -ne 1 ]; then
+    echo "usage: $0 GENERATE_PY_OUTPUT"
+    exit 1
+fi
+
+GEN=$1
+
+SYS=$GEN.sys
+REF=$GEN.ref
+
+if [ $(tail -n 1 $GEN | grep BLEU | wc -l) -ne 1 ]; then
+    echo "not done generating"
+    exit
+fi
+
+grep ^H $GEN | awk -F '\t' '{print $NF}' | perl -ple 's{(\S)-(\S)}{$1 ##AT##-##AT## $2}g' > $SYS
+grep ^T $GEN | cut -f2- | perl -ple 's{(\S)-(\S)}{$1 ##AT##-##AT## $2}g' > $REF
+fairseq-score --sys $SYS --ref $REF

fairseq/scripts/constraints/extract.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Extracts random constraints from reference files."""
+
+import argparse
+import random
+import sys
+
+
+def get_phrase(words, index, length):
+    assert index < len(words) - length + 1
+    phr = " ".join(words[index : index + length])
+    for i in range(index, index + length):
+        words.pop(index)
+    return phr
+
+
+def main(args):
+
+    if args.seed:
+        random.seed(args.seed)
+
+    for line in sys.stdin:
+        constraints = []
+
+        def add_constraint(constraint):
+            constraints.append(constraint)
+
+        source = line.rstrip()
+        if "\t" in line:
+            source, target = line.split("\t")
+            if args.add_sos:
+                target = f"<s> {target}"
+            if args.add_eos:
+                target = f"{target} </s>"
+
+            if len(target.split()) >= args.len:
+                words = [target]
+
+                num = args.number
+
+                choices = {}
+                for i in range(num):
+                    if len(words) == 0:
+                        break
+                    segmentno = random.choice(range(len(words)))
+                    segment = words.pop(segmentno)
+                    tokens = segment.split()
+                    phrase_index = random.choice(range(len(tokens)))
+                    choice = " ".join(
+                        tokens[phrase_index : min(len(tokens), phrase_index + args.len)]
+                    )
+                    for j in range(
+                        phrase_index, min(len(tokens), phrase_index + args.len)
+                    ):
+                        tokens.pop(phrase_index)
+                    if phrase_index > 0:
+                        words.append(" ".join(tokens[0:phrase_index]))
+                    if phrase_index + 1 < len(tokens):
+                        words.append(" ".join(tokens[phrase_index:]))
+                    choices[target.find(choice)] = choice
+
+                    # mask out with spaces
+                    target = target.replace(choice, " " * len(choice), 1)
+
+                for key in sorted(choices.keys()):
+                    add_constraint(choices[key])
+
+        print(source, *constraints, sep="\t")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--number", "-n", type=int, default=1, help="number of phrases")
+    parser.add_argument("--len", "-l", type=int, default=1, help="phrase length")
+    parser.add_argument(
+        "--add-sos", default=False, action="store_true", help="add <s> token"
+    )
+    parser.add_argument(
+        "--add-eos", default=False, action="store_true", help="add </s> token"
+    )
+    parser.add_argument("--seed", "-s", default=0, type=int)
+    args = parser.parse_args()
+
+    main(args)

fairseq/scripts/constraints/validate.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import sys
+
+
+"""Reads in a fairseq output file, and verifies that the constraints
+(C- lines) are present in the output (the first H- line). Assumes that
+constraints are listed prior to the first hypothesis.
+"""
+
+constraints = []
+found = 0
+total = 0
+for line in sys.stdin:
+    if line.startswith("C-"):
+        constraints.append(line.rstrip().split("\t")[1])
+    elif line.startswith("H-"):
+        text = line.split("\t")[2]
+
+        for constraint in constraints:
+            total += 1
+            if constraint in text:
+                found += 1
+            else:
+                print(f"No {constraint} in {text}", file=sys.stderr)
+
+        constraints = []
+
+print(f"Found {found} / {total} = {100 * found / total:.1f}%")

fairseq/scripts/convert_dictionary.lua

@@ -0,0 +1,34 @@
+-- Copyright (c) Facebook, Inc. and its affiliates.
+--
+-- This source code is licensed under the MIT license found in the
+-- LICENSE file in the root directory of this source tree.
+--
+-- Usage: convert_dictionary.lua <dict.th7>
+require 'fairseq'
+require 'torch'
+require 'paths'
+
+if #arg < 1 then
+   print('usage: convert_dictionary.lua <dict.th7>')
+   os.exit(1)
+end
+if not paths.filep(arg[1]) then
+   print('error: file does not exit: ' .. arg[1])
+   os.exit(1)
+end
+
+dict = torch.load(arg[1])
+dst = paths.basename(arg[1]):gsub('.th7', '.txt')
+assert(dst:match('.txt$'))
+
+f = io.open(dst, 'w')
+for idx, symbol in ipairs(dict.index_to_symbol) do
+  if idx > dict.cutoff then
+    break
+  end
+  f:write(symbol)
+  f:write(' ')
+  f:write(dict.index_to_freq[idx])
+  f:write('\n')
+end
+f:close()

fairseq/scripts/convert_model.lua

@@ -0,0 +1,108 @@
+-- Copyright (c) Facebook, Inc. and its affiliates.
+--
+-- This source code is licensed under the MIT license found in the
+-- LICENSE file in the root directory of this source tree.
+--
+-- Usage: convert_model.lua <model_epoch1.th7>
+require 'torch'
+local fairseq = require 'fairseq'
+
+model = torch.load(arg[1])
+
+function find_weight_norm(container, module)
+  for _, wn in ipairs(container:listModules()) do
+    if torch.type(wn) == 'nn.WeightNorm' and wn.modules[1] == module then
+      return wn
+    end
+  end
+end
+
+function push_state(dict, key, module)
+  if torch.type(module) == 'nn.Linear' then
+    local wn = find_weight_norm(model.module, module)
+    assert(wn)
+    dict[key .. '.weight_v'] = wn.v:float()
+    dict[key .. '.weight_g'] = wn.g:float()
+  elseif torch.type(module) == 'nn.TemporalConvolutionTBC' then
+    local wn = find_weight_norm(model.module, module)
+    assert(wn)
+    local v = wn.v:float():view(wn.viewOut):transpose(2, 3)
+    dict[key .. '.weight_v'] = v
+    dict[key .. '.weight_g'] = wn.g:float():view(module.weight:size(3), 1, 1)
+  else
+    dict[key .. '.weight'] = module.weight:float()
+  end
+  if module.bias then
+    dict[key .. '.bias'] = module.bias:float()
+  end
+end
+
+encoder_dict = {}
+decoder_dict = {}
+combined_dict = {}
+
+function encoder_state(encoder)
+  luts = encoder:findModules('nn.LookupTable')
+  push_state(encoder_dict, 'embed_tokens', luts[1])
+  push_state(encoder_dict, 'embed_positions', luts[2])
+
+  fcs = encoder:findModules('nn.Linear')
+  assert(#fcs >= 2)
+  local nInputPlane = fcs[1].weight:size(1)
+  push_state(encoder_dict, 'fc1', table.remove(fcs, 1))
+  push_state(encoder_dict, 'fc2', table.remove(fcs, #fcs))
+
+  for i, module in ipairs(encoder:findModules('nn.TemporalConvolutionTBC')) do
+    push_state(encoder_dict, 'convolutions.' .. tostring(i - 1), module)
+    if nInputPlane ~= module.weight:size(3) / 2 then
+      push_state(encoder_dict, 'projections.' .. tostring(i - 1), table.remove(fcs, 1))
+    end
+    nInputPlane = module.weight:size(3) / 2
+  end
+  assert(#fcs == 0)
+end
+
+function decoder_state(decoder)
+  luts = decoder:findModules('nn.LookupTable')
+  push_state(decoder_dict, 'embed_tokens', luts[1])
+  push_state(decoder_dict, 'embed_positions', luts[2])
+
+  fcs = decoder:findModules('nn.Linear')
+  local nInputPlane = fcs[1].weight:size(1)
+  push_state(decoder_dict, 'fc1', table.remove(fcs, 1))
+  push_state(decoder_dict, 'fc2', fcs[#fcs - 1])
+  push_state(decoder_dict, 'fc3', fcs[#fcs])
+
+  table.remove(fcs, #fcs)
+  table.remove(fcs, #fcs)
+
+  for i, module in ipairs(decoder:findModules('nn.TemporalConvolutionTBC')) do
+    if nInputPlane ~= module.weight:size(3) / 2 then
+      push_state(decoder_dict, 'projections.' .. tostring(i - 1), table.remove(fcs, 1))
+    end
+    nInputPlane = module.weight:size(3) / 2
+
+    local prefix = 'attention.' .. tostring(i - 1)
+    push_state(decoder_dict, prefix .. '.in_projection', table.remove(fcs, 1))
+    push_state(decoder_dict, prefix .. '.out_projection', table.remove(fcs, 1))
+    push_state(decoder_dict, 'convolutions.' .. tostring(i - 1), module)
+  end
+  assert(#fcs == 0)
+end
+
+
+_encoder = model.module.modules[2]
+_decoder = model.module.modules[3]
+
+encoder_state(_encoder)
+decoder_state(_decoder)
+
+for k, v in pairs(encoder_dict) do
+  combined_dict['encoder.' .. k] = v
+end
+for k, v in pairs(decoder_dict) do
+  combined_dict['decoder.' .. k] = v
+end
+
+
+torch.save('state_dict.t7', combined_dict)

fairseq/scripts/count_docs.py

@@ -0,0 +1,58 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""
+Count the number of documents and average number of lines and tokens per
+document in a large file. Documents should be separated by a single empty line.
+"""
+
+import argparse
+import gzip
+import sys
+
+import numpy as np
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("input")
+    parser.add_argument("--gzip", action="store_true")
+    args = parser.parse_args()
+
+    def gopen():
+        if args.gzip:
+            return gzip.open(args.input, "r")
+        else:
+            return open(args.input, "r", encoding="utf-8")
+
+    num_lines = []
+    num_toks = []
+    with gopen() as h:
+        num_docs = 1
+        num_lines_in_doc = 0
+        num_toks_in_doc = 0
+        for i, line in enumerate(h):
+            if len(line.strip()) == 0:  # empty line indicates new document
+                num_docs += 1
+                num_lines.append(num_lines_in_doc)
+                num_toks.append(num_toks_in_doc)
+                num_lines_in_doc = 0
+                num_toks_in_doc = 0
+            else:
+                num_lines_in_doc += 1
+                num_toks_in_doc += len(line.rstrip().split())
+            if i % 1000000 == 0:
+                print(i, file=sys.stderr, end="", flush=True)
+            elif i % 100000 == 0:
+                print(".", file=sys.stderr, end="", flush=True)
+        print(file=sys.stderr, flush=True)
+
+    print("found {} docs".format(num_docs))
+    print("average num lines per doc: {}".format(np.mean(num_lines)))
+    print("average num toks per doc: {}".format(np.mean(num_toks)))
+
+
+if __name__ == "__main__":
+    main()

fairseq/scripts/read_binarized.py

@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+
+from fairseq.data import Dictionary, data_utils, indexed_dataset
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        description="writes text from binarized file to stdout"
+    )
+    # fmt: off
+    parser.add_argument('--dataset-impl', help='dataset implementation',
+                        choices=indexed_dataset.get_available_dataset_impl())
+    parser.add_argument('--dict', metavar='FP', help='dictionary containing known words', default=None)
+    parser.add_argument('--input', metavar='FP', required=True, help='binarized file to read')
+    # fmt: on
+
+    return parser
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    dictionary = Dictionary.load(args.dict) if args.dict is not None else None
+    dataset = data_utils.load_indexed_dataset(
+        args.input,
+        dictionary,
+        dataset_impl=args.dataset_impl,
+        default="lazy",
+    )
+
+    for tensor_line in dataset:
+        if dictionary is None:
+            line = " ".join([str(int(x)) for x in tensor_line])
+        else:
+            line = dictionary.string(tensor_line)
+
+        print(line)
+
+
+if __name__ == "__main__":
+    main()

fairseq/scripts/rm_pt.py

@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import os
+import re
+import shutil
+import sys
+
+
+pt_regexp = re.compile(r"checkpoint(\d+|_\d+_\d+|_[a-z]+)\.pt")
+pt_regexp_epoch_based = re.compile(r"checkpoint(\d+)\.pt")
+pt_regexp_update_based = re.compile(r"checkpoint_\d+_(\d+)\.pt")
+
+
+def parse_checkpoints(files):
+    entries = []
+    for f in files:
+        m = pt_regexp_epoch_based.fullmatch(f)
+        if m is not None:
+            entries.append((int(m.group(1)), m.group(0)))
+        else:
+            m = pt_regexp_update_based.fullmatch(f)
+            if m is not None:
+                entries.append((int(m.group(1)), m.group(0)))
+    return entries
+
+
+def last_n_checkpoints(files, n):
+    entries = parse_checkpoints(files)
+    return [x[1] for x in sorted(entries, reverse=True)[:n]]
+
+
+def every_n_checkpoints(files, n):
+    entries = parse_checkpoints(files)
+    return [x[1] for x in sorted(sorted(entries)[::-n])]
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=(
+            "Recursively delete checkpoint files from `root_dir`, "
+            "but preserve checkpoint_best.pt and checkpoint_last.pt"
+        )
+    )
+    parser.add_argument("root_dirs", nargs="*")
+    parser.add_argument(
+        "--save-last", type=int, default=0, help="number of last checkpoints to save"
+    )
+    parser.add_argument(
+        "--save-every", type=int, default=0, help="interval of checkpoints to save"
+    )
+    parser.add_argument(
+        "--preserve-test",
+        action="store_true",
+        help="preserve checkpoints in dirs that start with test_ prefix (default: delete them)",
+    )
+    parser.add_argument(
+        "--delete-best", action="store_true", help="delete checkpoint_best.pt"
+    )
+    parser.add_argument(
+        "--delete-last", action="store_true", help="delete checkpoint_last.pt"
+    )
+    parser.add_argument(
+        "--no-dereference", action="store_true", help="don't dereference symlinks"
+    )
+    args = parser.parse_args()
+
+    files_to_desymlink = []
+    files_to_preserve = []
+    files_to_delete = []
+    for root_dir in args.root_dirs:
+        for root, _subdirs, files in os.walk(root_dir):
+            if args.save_last > 0:
+                to_save = last_n_checkpoints(files, args.save_last)
+            else:
+                to_save = []
+            if args.save_every > 0:
+                to_save += every_n_checkpoints(files, args.save_every)
+            for file in files:
+                if not pt_regexp.fullmatch(file):
+                    continue
+                full_path = os.path.join(root, file)
+                if (
+                    not os.path.basename(root).startswith("test_") or args.preserve_test
+                ) and (
+                    (file == "checkpoint_last.pt" and not args.delete_last)
+                    or (file == "checkpoint_best.pt" and not args.delete_best)
+                    or file in to_save
+                ):
+                    if os.path.islink(full_path) and not args.no_dereference:
+                        files_to_desymlink.append(full_path)
+                    else:
+                        files_to_preserve.append(full_path)
+                else:
+                    files_to_delete.append(full_path)
+
+    if len(files_to_desymlink) == 0 and len(files_to_delete) == 0:
+        print("Nothing to do.")
+        sys.exit(0)
+
+    files_to_desymlink = sorted(files_to_desymlink)
+    files_to_preserve = sorted(files_to_preserve)
+    files_to_delete = sorted(files_to_delete)
+
+    print("Operations to perform (in order):")
+    if len(files_to_desymlink) > 0:
+        for file in files_to_desymlink:
+            print(" - preserve (and dereference symlink): " + file)
+    if len(files_to_preserve) > 0:
+        for file in files_to_preserve:
+            print(" - preserve: " + file)
+    if len(files_to_delete) > 0:
+        for file in files_to_delete:
+            print(" - delete: " + file)
+    while True:
+        resp = input("Continue? (Y/N): ")
+        if resp.strip().lower() == "y":
+            break
+        elif resp.strip().lower() == "n":
+            sys.exit(0)
+
+    print("Executing...")
+    if len(files_to_desymlink) > 0:
+        for file in files_to_desymlink:
+            realpath = os.path.realpath(file)
+            print("rm " + file)
+            os.remove(file)
+            print("cp {} {}".format(realpath, file))
+            shutil.copyfile(realpath, file)
+    if len(files_to_delete) > 0:
+        for file in files_to_delete:
+            print("rm " + file)
+            os.remove(file)
+
+
+if __name__ == "__main__":
+    main()

fairseq/scripts/sacrebleu.sh

@@ -0,0 +1,27 @@
+#!/bin/bash
+
+if [ $# -ne 4 ]; then
+    echo "usage: $0 TESTSET SRCLANG TGTLANG GEN"
+    exit 1
+fi
+
+TESTSET=$1
+SRCLANG=$2
+TGTLANG=$3
+
+GEN=$4
+
+if ! command -v sacremoses &> /dev/null
+then
+    echo "sacremoses could not be found, please install with: pip install sacremoses"
+    exit
+fi
+
+grep ^H $GEN \
+| sed 's/^H\-//' \
+| sort -n -k 1 \
+| cut -f 3 \
+| sacremoses detokenize \
+> $GEN.sorted.detok
+
+sacrebleu --test-set $TESTSET --language-pair "${SRCLANG}-${TGTLANG}" < $GEN.sorted.detok

fairseq/scripts/shard_docs.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""
+Split a large file into shards while respecting document boundaries. Documents
+should be separated by a single empty line.
+"""
+
+import argparse
+import contextlib
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("input")
+    parser.add_argument("--num-shards", type=int)
+    args = parser.parse_args()
+
+    assert args.num_shards is not None and args.num_shards > 1
+
+    with open(args.input, "r", encoding="utf-8") as h:
+        with contextlib.ExitStack() as stack:
+            outputs = [
+                stack.enter_context(
+                    open(args.input + ".shard" + str(i), "w", encoding="utf-8")
+                )
+                for i in range(args.num_shards)
+            ]
+
+            doc = []
+            first_doc = [True] * args.num_shards
+
+            def output_doc(i):
+                if not first_doc[i]:
+                    outputs[i].write("\n")
+                first_doc[i] = False
+                for line in doc:
+                    outputs[i].write(line)
+                doc.clear()
+
+            num_docs = 0
+            for line in h:
+                if line.strip() == "":  # empty line indicates new document
+                    output_doc(num_docs % args.num_shards)
+                    num_docs += 1
+                else:
+                    doc.append(line)
+            output_doc(num_docs % args.num_shards)
+
+
+if __name__ == "__main__":
+    main()

fairseq/scripts/split_train_valid_docs.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""
+Split a large file into a train and valid set while respecting document
+boundaries. Documents should be separated by a single empty line.
+"""
+
+import argparse
+import random
+import sys
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("input")
+    parser.add_argument("sample_output", help="train output file")
+    parser.add_argument("remainder_output", help="valid output file")
+    parser.add_argument("-k", type=int, help="remainder size")
+    parser.add_argument(
+        "--lines", action="store_true", help="split lines instead of docs"
+    )
+    args = parser.parse_args()
+
+    assert args.k is not None
+
+    sample = []
+    remainder = []
+    num_docs = [0]
+
+    def update_sample(doc):
+        if len(sample) < args.k:
+            sample.append(doc.copy())
+        else:
+            i = num_docs[0]
+            j = random.randrange(i + 1)
+            if j < args.k:
+                remainder.append(sample[j])
+                sample[j] = doc.copy()
+            else:
+                remainder.append(doc.copy())
+        num_docs[0] += 1
+        doc.clear()
+
+    with open(args.input, "r", encoding="utf-8") as h:
+        doc = []
+        for i, line in enumerate(h):
+            if line.strip() == "":  # empty line indicates new document
+                update_sample(doc)
+            else:
+                doc.append(line)
+            if args.lines:
+                update_sample(doc)
+            if i % 1000000 == 0:
+                print(i, file=sys.stderr, end="", flush=True)
+            elif i % 100000 == 0:
+                print(".", file=sys.stderr, end="", flush=True)
+        if len(doc) > 0:
+            update_sample(doc)
+    print(file=sys.stderr, flush=True)
+
+    assert len(sample) == args.k
+
+    with open(args.sample_output, "w", encoding="utf-8") as out:
+        first = True
+        for doc in sample:
+            if not first and not args.lines:
+                out.write("\n")
+            first = False
+            for line in doc:
+                out.write(line)
+
+    with open(args.remainder_output, "w", encoding="utf-8") as out:
+        first = True
+        for doc in remainder:
+            if not first and not args.lines:
+                out.write("\n")
+            first = False
+            for line in doc:
+                out.write(line)
+
+
+if __name__ == "__main__":
+    main()

fairseq/scripts/spm_decode.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import argparse
+
+import sentencepiece as spm
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="sentencepiece model to use for decoding"
+    )
+    parser.add_argument("--input", required=True, help="input file to decode")
+    parser.add_argument("--input_format", choices=["piece", "id"], default="piece")
+    args = parser.parse_args()
+
+    sp = spm.SentencePieceProcessor()
+    sp.Load(args.model)
+
+    if args.input_format == "piece":
+
+        def decode(input):
+            return "".join(sp.DecodePieces(input))
+
+    elif args.input_format == "id":
+
+        def decode(input):
+            return "".join(sp.DecodeIds(input))
+
+    else:
+        raise NotImplementedError
+
+    def tok2int(tok):
+        # remap reference-side <unk> (represented as <<unk>>) to 0
+        return int(tok) if tok != "<<unk>>" else 0
+
+    with open(args.input, "r", encoding="utf-8") as h:
+        for line in h:
+            if args.input_format == "id":
+                print(decode(list(map(tok2int, line.rstrip().split()))))
+            elif args.input_format == "piece":
+                print(decode(line.rstrip().split()))
+
+
+if __name__ == "__main__":
+    main()

fairseq/scripts/spm_encode.py

@@ -0,0 +1,119 @@
+#!/usr/bin/env python
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import argparse
+import contextlib
+import sys
+
+import sentencepiece as spm
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="sentencepiece model to use for encoding"
+    )
+    parser.add_argument(
+        "--inputs", nargs="+", default=["-"], help="input files to filter/encode"
+    )
+    parser.add_argument(
+        "--outputs", nargs="+", default=["-"], help="path to save encoded outputs"
+    )
+    parser.add_argument("--output_format", choices=["piece", "id"], default="piece")
+    parser.add_argument(
+        "--min-len",
+        type=int,
+        metavar="N",
+        help="filter sentence pairs with fewer than N tokens",
+    )
+    parser.add_argument(
+        "--max-len",
+        type=int,
+        metavar="N",
+        help="filter sentence pairs with more than N tokens",
+    )
+    args = parser.parse_args()
+
+    assert len(args.inputs) == len(
+        args.outputs
+    ), "number of input and output paths should match"
+
+    sp = spm.SentencePieceProcessor()
+    sp.Load(args.model)
+
+    if args.output_format == "piece":
+
+        def encode(input):
+            return sp.EncodeAsPieces(input)
+
+    elif args.output_format == "id":
+
+        def encode(input):
+            return list(map(str, sp.EncodeAsIds(input)))
+
+    else:
+        raise NotImplementedError
+
+    if args.min_len is not None or args.max_len is not None:
+
+        def valid(line):
+            return (args.min_len is None or len(line) >= args.min_len) and (
+                args.max_len is None or len(line) <= args.max_len
+            )
+
+    else:
+
+        def valid(lines):
+            return True
+
+    with contextlib.ExitStack() as stack:
+        inputs = [
+            stack.enter_context(open(input, "r", encoding="utf-8"))
+            if input != "-"
+            else sys.stdin
+            for input in args.inputs
+        ]
+        outputs = [
+            stack.enter_context(open(output, "w", encoding="utf-8"))
+            if output != "-"
+            else sys.stdout
+            for output in args.outputs
+        ]
+
+        stats = {
+            "num_empty": 0,
+            "num_filtered": 0,
+        }
+
+        def encode_line(line):
+            line = line.strip()
+            if len(line) > 0:
+                line = encode(line)
+                if valid(line):
+                    return line
+                else:
+                    stats["num_filtered"] += 1
+            else:
+                stats["num_empty"] += 1
+            return None
+
+        for i, lines in enumerate(zip(*inputs), start=1):
+            enc_lines = list(map(encode_line, lines))
+            if not any(enc_line is None for enc_line in enc_lines):
+                for enc_line, output_h in zip(enc_lines, outputs):
+                    print(" ".join(enc_line), file=output_h)
+            if i % 10000 == 0:
+                print("processed {} lines".format(i), file=sys.stderr)
+
+        print("skipped {} empty lines".format(stats["num_empty"]), file=sys.stderr)
+        print("filtered {} lines".format(stats["num_filtered"]), file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()

fairseq/scripts/spm_train.py

@@ -0,0 +1,16 @@
+#!/usr/bin/env python
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import sys
+
+import sentencepiece as spm
+
+
+if __name__ == "__main__":
+    spm.SentencePieceTrainer.Train(" ".join(sys.argv[1:]))

fairseq/scripts/test_fsdp.sh

@@ -0,0 +1,24 @@
+#!/usr/bin/env bash
+rm -rf fsdp_dummy
+mkdir -p fsdp_dummy
+CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train /private/home/sshleifer/data-bin/stories_mmap \
+    --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \
+    --cpu-offload --checkpoint-activations \
+    --task language_modeling --tokens-per-sample 256 --batch-size 8 \
+    --arch transformer_lm_gpt2_tiny \
+    --optimizer cpu_adam --adam-betas "(0.9,0.98)" \
+    --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \
+    --max-update 5 --log-format json --log-interval 1 \
+    --save-interval-updates 5 --save-dir fsdp_dummy --disable-validation \
+    --restore-file x.pt "$@"
+
+# Now we try to load the checkpoint
+CUDA_VISIBLE_DEVICES=0,1 fairseq-train /private/home/sshleifer/data-bin/stories_mmap \
+    --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \
+    --cpu-offload --checkpoint-activations \
+    --task language_modeling --tokens-per-sample 256 --batch-size 8 \
+    --arch transformer_lm_gpt2_tiny \
+    --optimizer cpu_adam --adam-betas "(0.9,0.98)" \
+    --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \
+    --max-update 2 --log-format json --log-interval 1 \
+    --save-interval-updates 2 --save-dir fsdp_dummy

fairseq/tests/tasks/test_masked_lm.py

@@ -0,0 +1,78 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import unittest
+from tempfile import TemporaryDirectory
+
+from fairseq.binarizer import FileBinarizer, VocabularyDatasetBinarizer
+from fairseq.tasks.masked_lm import MaskedLMConfig, MaskedLMTask
+from tests.utils import build_vocab, make_data
+
+
+class TestMaskedLM(unittest.TestCase):
+    def test_masks_tokens(self):
+        with TemporaryDirectory() as dirname:
+
+            # prep input file
+            raw_file = os.path.join(dirname, "raw")
+            data = make_data(out_file=raw_file)
+            vocab = build_vocab(data)
+
+            # binarize
+            binarizer = VocabularyDatasetBinarizer(vocab, append_eos=False)
+            split = "train"
+            bin_file = os.path.join(dirname, split)
+            FileBinarizer.multiprocess_dataset(
+                input_file=raw_file,
+                binarizer=binarizer,
+                dataset_impl="mmap",
+                vocab_size=len(vocab),
+                output_prefix=bin_file,
+            )
+
+            # setup task
+            cfg = MaskedLMConfig(
+                data=dirname,
+                seed=42,
+                mask_prob=0.5,  # increasing the odds of masking
+                random_token_prob=0,  # avoiding random tokens for exact match
+                leave_unmasked_prob=0,  # always masking for exact match
+            )
+            task = MaskedLMTask(cfg, binarizer.dict)
+
+            original_dataset = task._load_dataset_split(bin_file, 1, False)
+
+            # load datasets
+            task.load_dataset(split)
+            masked_dataset = task.dataset(split)
+
+            mask_index = task.source_dictionary.index("<mask>")
+            iterator = task.get_batch_iterator(
+                dataset=masked_dataset,
+                max_tokens=65_536,
+                max_positions=4_096,
+            ).next_epoch_itr(shuffle=False)
+            for batch in iterator:
+                for sample in range(len(batch)):
+                    net_input = batch["net_input"]
+                    masked_src_tokens = net_input["src_tokens"][sample]
+                    masked_src_length = net_input["src_lengths"][sample]
+                    masked_tgt_tokens = batch["target"][sample]
+
+                    sample_id = batch["id"][sample]
+                    original_tokens = original_dataset[sample_id]
+                    original_tokens = original_tokens.masked_select(
+                        masked_src_tokens[:masked_src_length] == mask_index
+                    )
+                    masked_tokens = masked_tgt_tokens.masked_select(
+                        masked_tgt_tokens != task.source_dictionary.pad()
+                    )
+
+                    assert masked_tokens.equal(original_tokens)
+
+
+if __name__ == "__main__":
+    unittest.main()

fairseq/tests/tasks/test_span_masked_lm.py

@@ -0,0 +1,106 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import unittest
+from tempfile import TemporaryDirectory
+
+from fairseq import options
+from fairseq.binarizer import FileBinarizer, VocabularyDatasetBinarizer
+from fairseq.dataclass.utils import convert_namespace_to_omegaconf
+from fairseq.tasks.span_masked_lm import SpanMaskedLMTask
+from tests.utils import build_vocab, make_data
+
+
+class TestSpanMaskedLM(unittest.TestCase):
+    def test_masks_token_spans(self):
+        with TemporaryDirectory() as dirname:
+
+            # prep input file
+            raw_file = os.path.join(dirname, "raw")
+            data = make_data(out_file=raw_file)
+            vocab = build_vocab(data)
+
+            # binarize
+            binarizer = VocabularyDatasetBinarizer(vocab, append_eos=False)
+            split = "train"
+            bin_file = os.path.join(dirname, split)
+            dataset_impl = "mmap"
+
+            FileBinarizer.multiprocess_dataset(
+                input_file=raw_file,
+                binarizer=binarizer,
+                dataset_impl=dataset_impl,
+                vocab_size=len(vocab),
+                output_prefix=bin_file,
+            )
+
+            # adding sentinel tokens
+            for i in range(100):
+                vocab.add_symbol(f"<extra_id_{i}>")
+
+            # setup task
+            train_args = options.parse_args_and_arch(
+                options.get_training_parser(),
+                [
+                    "--task",
+                    "span_masked_lm",
+                    "--arch",
+                    "bart_base",
+                    "--seed",
+                    "42",
+                    dirname,
+                ],
+            )
+            cfg = convert_namespace_to_omegaconf(train_args)
+            task = SpanMaskedLMTask(cfg.task, binarizer.dict)
+
+            # load datasets
+            original_dataset = task._load_dataset_split(bin_file, 1, False)
+            task.load_dataset(split)
+            masked_dataset = task.dataset(split)
+
+            iterator = task.get_batch_iterator(
+                dataset=masked_dataset,
+                max_tokens=65_536,
+                max_positions=4_096,
+            ).next_epoch_itr(shuffle=False)
+            num_tokens = len(vocab)
+            for batch in iterator:
+                for sample in range(len(batch)):
+                    sample_id = batch["id"][sample]
+                    original_tokens = original_dataset[sample_id]
+                    masked_src_tokens = batch["net_input"]["src_tokens"][sample]
+                    masked_src_length = batch["net_input"]["src_lengths"][sample]
+                    masked_tgt_tokens = batch["target"][sample]
+
+                    original_offset = 0
+                    masked_tgt_offset = 0
+                    extra_id_token = len(vocab) - 1
+                    for masked_src_token in masked_src_tokens[:masked_src_length]:
+                        if masked_src_token == extra_id_token:
+                            assert (
+                                masked_src_token == masked_tgt_tokens[masked_tgt_offset]
+                            )
+                            extra_id_token -= 1
+                            masked_tgt_offset += 1
+                            while (
+                                original_offset < len(original_tokens)
+                                and masked_tgt_tokens[masked_tgt_offset]
+                                != extra_id_token
+                            ):
+                                assert (
+                                    original_tokens[original_offset]
+                                    == masked_tgt_tokens[masked_tgt_offset]
+                                )
+                                original_offset += 1
+                                masked_tgt_offset += 1
+                        else:
+                            assert original_tokens[original_offset] == masked_src_token
+                            original_offset += 1
+
+
+if __name__ == "__main__":
+    unittest.main()

fairseq/tests/test_activation_checkpointing.py

@@ -0,0 +1,79 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import unittest
+
+import torch
+import torch.nn as nn
+from fairseq.modules.checkpoint_activations import checkpoint_wrapper
+from torch.utils.checkpoint import checkpoint
+
+
+class Model(nn.Module):
+    def __init__(
+        self, use_pytorch_checkpoint=False, use_fairseq_checkpoint=False, **kwargs
+    ):
+        super().__init__()
+        torch.manual_seed(0)
+        self.use_pytorch_checkpoint = use_pytorch_checkpoint
+        self.ffn = nn.Sequential(
+            nn.Linear(32, 128),
+            # add a Dropout layer to test RNG save/restore
+            nn.Dropout(p=0.5),
+            nn.Linear(128, 32),
+        )
+        if use_fairseq_checkpoint:
+            self.ffn = checkpoint_wrapper(self.ffn, **kwargs)
+        self.out = nn.Linear(32, 1)
+
+    def forward(self, x):
+        if self.use_pytorch_checkpoint:
+            x = checkpoint(self.ffn, x)
+        else:
+            x = self.ffn(x)
+        return self.out(x)
+
+
+class TestActivationCheckpointing(unittest.TestCase):
+    def _test_checkpoint_wrapper(self, device, log_memory_usage=False):
+        def get_loss_and_gnorm(model):
+            torch.manual_seed(1)
+            input = torch.rand(2, 16, 32).requires_grad_(True).to(device)
+            model.zero_grad()
+            loss = model(input).sum()
+            loss.backward()
+            gnorm = torch.norm(
+                torch.stack([torch.norm(p.grad.detach()) for p in model.parameters()])
+            )
+            return {"loss": loss, "gnorm": gnorm}
+
+        model = Model().to(device)
+        no_cpt = get_loss_and_gnorm(model)
+
+        model = Model(use_pytorch_checkpoint=True).to(device)
+        pyt_cpt = get_loss_and_gnorm(model)
+        torch.testing.assert_allclose(no_cpt["loss"], pyt_cpt["loss"])
+        torch.testing.assert_allclose(no_cpt["gnorm"], pyt_cpt["gnorm"])
+
+        model = Model(use_fairseq_checkpoint=True).to(device)
+        fairseq_cpt = get_loss_and_gnorm(model)
+        torch.testing.assert_allclose(no_cpt["loss"], fairseq_cpt["loss"])
+        torch.testing.assert_allclose(no_cpt["gnorm"], fairseq_cpt["gnorm"])
+
+        model = Model(use_fairseq_checkpoint=True, offload_to_cpu=True).to(device)
+        fairseq_cpt_offload = get_loss_and_gnorm(model)
+        torch.testing.assert_allclose(no_cpt["loss"], fairseq_cpt_offload["loss"])
+        torch.testing.assert_allclose(no_cpt["gnorm"], fairseq_cpt_offload["gnorm"])
+
+    def test_checkpoint_wrapper_cpu(self):
+        self._test_checkpoint_wrapper(device=torch.device("cpu"))
+
+    @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
+    def test_checkpoint_wrapper_cuda(self):
+        self._test_checkpoint_wrapper(device=torch.device("cuda"))
+
+
+if __name__ == "__main__":
+    unittest.main()

fairseq/tests/test_amp_optimizer.py

@@ -0,0 +1,75 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import copy
+import unittest
+
+import torch
+from torch.cuda.amp import GradScaler, autocast
+
+from fairseq.optim import build_optimizer
+
+
+@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
+class TestGradientScalingAMP(unittest.TestCase):
+    def setUp(self):
+        self.x = torch.tensor([2.0]).cuda().half()
+        weight = 3.0
+        bias = 5.0
+        self.error = 1.0
+        self.target = torch.tensor([self.x * weight + bias + self.error]).cuda()
+        self.loss_fn = torch.nn.L1Loss()
+
+        self.model = torch.nn.Linear(1, 1)
+        self.model.weight.data = torch.tensor([[weight]])
+        self.model.bias.data = torch.tensor([bias])
+        self.model.cuda()
+        self.params = list(self.model.parameters())
+
+        self.namespace_dls = argparse.Namespace(
+            optimizer="adam",
+            lr=[0.1],
+            adam_betas="(0.9, 0.999)",
+            adam_eps=1e-8,
+            weight_decay=0.0,
+            threshold_loss_scale=1,
+            min_loss_scale=1e-4,
+        )
+        self.scaler = GradScaler(
+            init_scale=1,
+            growth_interval=1,
+        )
+
+    def run_iter(self, model, params, optimizer):
+        optimizer.zero_grad()
+        with autocast():
+            y = model(self.x)
+            loss = self.loss_fn(y, self.target)
+        self.scaler.scale(loss).backward()
+        self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16))
+
+        self.scaler.unscale_(optimizer)
+        grad_norm = optimizer.clip_grad_norm(0)
+        self.assertAlmostEqual(grad_norm.item(), 2.2361, 4)
+
+        self.scaler.step(optimizer)
+        self.scaler.update()
+        self.assertEqual(
+            model.weight,
+            torch.tensor([[3.1]], device="cuda:0", requires_grad=True),
+        )
+        self.assertEqual(
+            model.bias,
+            torch.tensor([5.1], device="cuda:0", requires_grad=True),
+        )
+        self.assertEqual(self.scaler.get_scale(), 2.0)
+
+    def test_automatic_mixed_precision(self):
+        model = copy.deepcopy(self.model)
+        params = list(model.parameters())
+        optimizer = build_optimizer(self.namespace_dls, params)
+
+        self.run_iter(model, params, optimizer)

fairseq/tests/test_average_checkpoints.py

@@ -0,0 +1,134 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import collections
+import os
+import shutil
+import tempfile
+import unittest
+
+import numpy as np
+import torch
+from scripts.average_checkpoints import average_checkpoints
+from torch import nn
+
+
+class ModelWithSharedParameter(nn.Module):
+    def __init__(self):
+        super(ModelWithSharedParameter, self).__init__()
+        self.embedding = nn.Embedding(1000, 200)
+        self.FC1 = nn.Linear(200, 200)
+        self.FC2 = nn.Linear(200, 200)
+        # tie weight in FC2 to FC1
+        self.FC2.weight = nn.Parameter(self.FC1.weight)
+        self.FC2.bias = nn.Parameter(self.FC1.bias)
+
+        self.relu = nn.ReLU()
+
+    def forward(self, input):
+        return self.FC2(self.ReLU(self.FC1(input))) + self.FC1(input)
+
+
+class TestAverageCheckpoints(unittest.TestCase):
+    def test_average_checkpoints(self):
+        params_0 = collections.OrderedDict(
+            [
+                ("a", torch.DoubleTensor([100.0])),
+                ("b", torch.FloatTensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])),
+                ("c", torch.IntTensor([7, 8, 9])),
+            ]
+        )
+        params_1 = collections.OrderedDict(
+            [
+                ("a", torch.DoubleTensor([1.0])),
+                ("b", torch.FloatTensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])),
+                ("c", torch.IntTensor([2, 2, 2])),
+            ]
+        )
+        params_avg = collections.OrderedDict(
+            [
+                ("a", torch.DoubleTensor([50.5])),
+                ("b", torch.FloatTensor([[1.0, 1.5, 2.0], [2.5, 3.0, 3.5]])),
+                # We expect truncation for integer division
+                ("c", torch.IntTensor([4, 5, 5])),
+            ]
+        )
+
+        fd_0, path_0 = tempfile.mkstemp()
+        fd_1, path_1 = tempfile.mkstemp()
+        torch.save(collections.OrderedDict([("model", params_0)]), path_0)
+        torch.save(collections.OrderedDict([("model", params_1)]), path_1)
+
+        output = average_checkpoints([path_0, path_1])["model"]
+
+        os.close(fd_0)
+        os.remove(path_0)
+        os.close(fd_1)
+        os.remove(path_1)
+
+        for (k_expected, v_expected), (k_out, v_out) in zip(
+            params_avg.items(), output.items()
+        ):
+            self.assertEqual(
+                k_expected,
+                k_out,
+                "Key mismatch - expected {} but found {}. "
+                "(Expected list of keys: {} vs actual list of keys: {})".format(
+                    k_expected, k_out, params_avg.keys(), output.keys()
+                ),
+            )
+            np.testing.assert_allclose(
+                v_expected.numpy(),
+                v_out.numpy(),
+                err_msg="Tensor value mismatch for key {}".format(k_expected),
+            )
+
+    def test_average_checkpoints_with_shared_parameters(self):
+        def _construct_model_with_shared_parameters(path, value):
+            m = ModelWithSharedParameter()
+            nn.init.constant_(m.FC1.weight, value)
+            torch.save({"model": m.state_dict()}, path)
+            return m
+
+        tmpdir = tempfile.mkdtemp()
+        paths = []
+        path = os.path.join(tmpdir, "m1.pt")
+        m1 = _construct_model_with_shared_parameters(path, 1.0)
+        paths.append(path)
+
+        path = os.path.join(tmpdir, "m2.pt")
+        m2 = _construct_model_with_shared_parameters(path, 2.0)
+        paths.append(path)
+
+        path = os.path.join(tmpdir, "m3.pt")
+        m3 = _construct_model_with_shared_parameters(path, 3.0)
+        paths.append(path)
+
+        new_model = average_checkpoints(paths)
+        self.assertTrue(
+            torch.equal(
+                new_model["model"]["embedding.weight"],
+                (m1.embedding.weight + m2.embedding.weight + m3.embedding.weight) / 3.0,
+            )
+        )
+
+        self.assertTrue(
+            torch.equal(
+                new_model["model"]["FC1.weight"],
+                (m1.FC1.weight + m2.FC1.weight + m3.FC1.weight) / 3.0,
+            )
+        )
+
+        self.assertTrue(
+            torch.equal(
+                new_model["model"]["FC2.weight"],
+                (m1.FC2.weight + m2.FC2.weight + m3.FC2.weight) / 3.0,
+            )
+        )
+        shutil.rmtree(tmpdir)
+
+
+if __name__ == "__main__":
+    unittest.main()

fairseq/tests/test_backtranslation_dataset.py

@@ -0,0 +1,123 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import unittest
+
+import tests.utils as test_utils
+import torch
+from fairseq.data import (
+    BacktranslationDataset,
+    LanguagePairDataset,
+    TransformEosDataset,
+)
+from fairseq.sequence_generator import SequenceGenerator
+
+
+class TestBacktranslationDataset(unittest.TestCase):
+    def setUp(self):
+        (
+            self.tgt_dict,
+            self.w1,
+            self.w2,
+            self.src_tokens,
+            self.src_lengths,
+            self.model,
+        ) = test_utils.sequence_generator_setup()
+
+        dummy_src_samples = self.src_tokens
+
+        self.tgt_dataset = test_utils.TestDataset(data=dummy_src_samples)
+        self.cuda = torch.cuda.is_available()
+
+    def _backtranslation_dataset_helper(
+        self,
+        remove_eos_from_input_src,
+        remove_eos_from_output_src,
+    ):
+        tgt_dataset = LanguagePairDataset(
+            src=self.tgt_dataset,
+            src_sizes=self.tgt_dataset.sizes,
+            src_dict=self.tgt_dict,
+            tgt=None,
+            tgt_sizes=None,
+            tgt_dict=None,
+        )
+
+        generator = SequenceGenerator(
+            [self.model],
+            tgt_dict=self.tgt_dict,
+            max_len_a=0,
+            max_len_b=200,
+            beam_size=2,
+            unk_penalty=0,
+        )
+
+        backtranslation_dataset = BacktranslationDataset(
+            tgt_dataset=TransformEosDataset(
+                dataset=tgt_dataset,
+                eos=self.tgt_dict.eos(),
+                # remove eos from the input src
+                remove_eos_from_src=remove_eos_from_input_src,
+            ),
+            src_dict=self.tgt_dict,
+            backtranslation_fn=(
+                lambda sample: generator.generate([self.model], sample)
+            ),
+            output_collater=TransformEosDataset(
+                dataset=tgt_dataset,
+                eos=self.tgt_dict.eos(),
+                # if we remove eos from the input src, then we need to add it
+                # back to the output tgt
+                append_eos_to_tgt=remove_eos_from_input_src,
+                remove_eos_from_src=remove_eos_from_output_src,
+            ).collater,
+            cuda=self.cuda,
+        )
+        dataloader = torch.utils.data.DataLoader(
+            backtranslation_dataset,
+            batch_size=2,
+            collate_fn=backtranslation_dataset.collater,
+        )
+        backtranslation_batch_result = next(iter(dataloader))
+
+        eos, pad, w1, w2 = self.tgt_dict.eos(), self.tgt_dict.pad(), self.w1, self.w2
+
+        # Note that we sort by src_lengths and add left padding, so actually
+        # ids will look like: [1, 0]
+        expected_src = torch.LongTensor([[w1, w2, w1, eos], [pad, pad, w1, eos]])
+        if remove_eos_from_output_src:
+            expected_src = expected_src[:, :-1]
+        expected_tgt = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
+        generated_src = backtranslation_batch_result["net_input"]["src_tokens"]
+        tgt_tokens = backtranslation_batch_result["target"]
+
+        self.assertTensorEqual(expected_src, generated_src)
+        self.assertTensorEqual(expected_tgt, tgt_tokens)
+
+    def test_backtranslation_dataset_no_eos_in_output_src(self):
+        self._backtranslation_dataset_helper(
+            remove_eos_from_input_src=False,
+            remove_eos_from_output_src=True,
+        )
+
+    def test_backtranslation_dataset_with_eos_in_output_src(self):
+        self._backtranslation_dataset_helper(
+            remove_eos_from_input_src=False,
+            remove_eos_from_output_src=False,
+        )
+
+    def test_backtranslation_dataset_no_eos_in_input_src(self):
+        self._backtranslation_dataset_helper(
+            remove_eos_from_input_src=True,
+            remove_eos_from_output_src=False,
+        )
+
+    def assertTensorEqual(self, t1, t2):
+        self.assertEqual(t1.size(), t2.size(), "size mismatch")
+        self.assertEqual(t1.ne(t2).long().sum(), 0)
+
+
+if __name__ == "__main__":
+    unittest.main()