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Changelog: - 97b58b4: add Transformer model from Vaswani et al. (2017) - b2374e5: faster Transformer inference with improved caching - 2d27ae0: simulate large mini-batch training with delayed updates (`--update-freq`) - 7ee1d28: add FP16 training support (`--fp16`) - 2a84f46: faster inference by removing completed sentences from the batch - 663fd80: batched interactive generation - 4c2ef2d: add language modeling / gated convolutional model from Dauphin et al. (2017) - b59815b: add Hierarchical Neural Story Generation model from Fan et al. (2018) - ff68a9e: add FairseqTask to modularize task definitions (e.g., translation, language modeling)
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| #!/usr/bin/env python3 -u | ||
| # Copyright (c) 2017-present, Facebook, Inc. | ||
| # All rights reserved. | ||
| # | ||
| # This source code is licensed under the license found in the LICENSE file in | ||
| # the root directory of this source tree. An additional grant of patent rights | ||
| # can be found in the PATENTS file in the same directory. | ||
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| import numpy as np | ||
| import torch | ||
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| from fairseq import data, options, progress_bar, tasks, utils | ||
| from fairseq.meters import StopwatchMeter, TimeMeter | ||
| from fairseq.sequence_scorer import SequenceScorer | ||
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| def main(args): | ||
| assert args.path is not None, '--path required for evaluation!' | ||
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| if args.tokens_per_sample is None: | ||
| args.tokens_per_sample = 1024 | ||
| print(args) | ||
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| use_cuda = torch.cuda.is_available() and not args.cpu | ||
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| # Load dataset splits | ||
| task = tasks.setup_task(args) | ||
| task.load_dataset(args.gen_subset) | ||
| print('| {} {} {} examples'.format(args.data, args.gen_subset, len(task.dataset(args.gen_subset)))) | ||
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| # Load ensemble | ||
| print('| loading model(s) from {}'.format(args.path)) | ||
| models, _ = utils.load_ensemble_for_inference(args.path.split(':'), task) | ||
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| # Optimize ensemble for generation and set the source and dest dicts on the model (required by scorer) | ||
| for model in models: | ||
| model.make_generation_fast_() | ||
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| itr = data.EpochBatchIterator( | ||
| dataset=task.dataset(args.gen_subset), | ||
| max_sentences=args.max_sentences or 4, | ||
| max_positions=model.max_positions(), | ||
| num_shards=args.num_shards, | ||
| shard_id=args.shard_id, | ||
| ).next_epoch_itr(shuffle=False) | ||
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| gen_timer = StopwatchMeter() | ||
| scorer = SequenceScorer(models, task.target_dictionary) | ||
| if use_cuda: | ||
| scorer.cuda() | ||
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| score_sum = 0. | ||
| count = 0 | ||
| with progress_bar.build_progress_bar(args, itr) as t: | ||
| results = scorer.score_batched_itr(t, cuda=use_cuda, timer=gen_timer) | ||
| wps_meter = TimeMeter() | ||
| for _, src_tokens, __, hypos in results: | ||
| for hypo in hypos: | ||
| pos_scores = hypo['positional_scores'] | ||
| inf_scores = pos_scores.eq(float('inf')) | pos_scores.eq(float('-inf')) | ||
| if inf_scores.any(): | ||
| print('| Skipping tokens with inf scores:', | ||
| task.target_dictionary.string(hypo['tokens'][inf_scores.nonzero()])) | ||
| pos_scores = pos_scores[(~inf_scores).nonzero()] | ||
| score_sum += pos_scores.sum() | ||
| count += pos_scores.numel() | ||
| wps_meter.update(src_tokens.size(0)) | ||
| t.log({'wps': round(wps_meter.avg)}) | ||
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| avg_nll_loss = -score_sum / count | ||
| print('| Evaluated {} tokens in {:.1f}s ({:.2f} tokens/s)'.format(gen_timer.n, gen_timer.sum, 1. / gen_timer.avg)) | ||
| print('| Loss: {:.4f}, Perplexity: {:.2f}'.format(avg_nll_loss, np.exp(avg_nll_loss))) | ||
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| if __name__ == '__main__': | ||
| parser = options.get_eval_lm_parser() | ||
| args = options.parse_args_and_arch(parser) | ||
| main(args) |
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| Sample data processing scripts for the FAIR Sequence-to-Sequence Toolkit | ||
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| These scripts provide an example of pre-processing data for the Language Modeling task. | ||
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| # prepare-wikitext-103.sh | ||
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| Provides an example of pre-processing for [WikiText-103 language modeling task](https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset): | ||
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| Example usage: | ||
| ``` | ||
| $ cd examples/language_model/ | ||
| $ bash prepare-wikitext-103.sh | ||
| $ cd ../.. | ||
| # Binarize the dataset: | ||
| $ TEXT=examples/language_model/wikitext-103 | ||
| $ python preprocess.py --only-source \ | ||
| --trainpref $TEXT/wiki.train.tokens --validpref $TEXT/wiki.valid.tokens --testpref $TEXT/wiki.test.tokens \ | ||
| --destdir data-bin/wikitext-103 | ||
| # Train the model: | ||
| # If it runs out of memory, try to reduce max-tokens and max-target-positions | ||
| $ mkdir -p checkpoints/wikitext-103 | ||
| $ python train.py --task language_modeling data-bin/wikitext-103 \ | ||
| --max-epoch 35 --arch fconv_lm_dauphin_wikitext103 --optimizer nag \ | ||
| --lr 1.0 --lr-scheduler reduce_lr_on_plateau --lr-shrink 0.5 \ | ||
| --clip-norm 0.1 --dropout 0.2 --weight-decay 5e-06 --criterion adaptive_loss \ | ||
| --adaptive-softmax-cutoff 10000,20000,200000 --max-tokens 1024 --tokens-per-sample 1024 | ||
| # Evaluate: | ||
| $ python eval_lm.py data-bin/wikitext-103 --path 'checkpoints/wiki103/checkpoint_best.pt' | ||
| ``` |
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| #!/bin/bash | ||
| # Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh | ||
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| URLS=( | ||
| "https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip" | ||
| ) | ||
| FILES=( | ||
| "wikitext-103-v1.zip" | ||
| ) | ||
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| for ((i=0;i<${#URLS[@]};++i)); do | ||
| file=${FILES[i]} | ||
| if [ -f $file ]; then | ||
| echo "$file already exists, skipping download" | ||
| else | ||
| url=${URLS[i]} | ||
| wget "$url" | ||
| if [ -f $file ]; then | ||
| echo "$url successfully downloaded." | ||
| else | ||
| echo "$url not successfully downloaded." | ||
| exit -1 | ||
| fi | ||
| if [ ${file: -4} == ".tgz" ]; then | ||
| tar zxvf $file | ||
| elif [ ${file: -4} == ".tar" ]; then | ||
| tar xvf $file | ||
| elif [ ${file: -4} == ".zip" ]; then | ||
| unzip $file | ||
| fi | ||
| fi | ||
| done | ||
| cd .. |
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| FAIR Sequence-to-Sequence Toolkit for Story Generation | ||
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| The following commands provide an example of pre-processing data, training a model, and generating text for story generation with the WritingPrompts dataset. | ||
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| The dataset can be downloaded like this: | ||
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| ``` | ||
| curl https://s3.amazonaws.com/fairseq-py/data/writingPrompts.tar.gz | tar xvjf - | ||
| ``` | ||
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| and contains a train, test, and valid split. The dataset is described here: https://arxiv.org/abs/1805.04833, where only the first 1000 words of each story are modeled. | ||
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| Example usage: | ||
| ``` | ||
| # Binarize the dataset: | ||
| $ TEXT=examples/stories/writingPrompts | ||
| $ python preprocess.py --source-lang wp_source --target-lang wp_target \ | ||
| --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ | ||
| --destdir data-bin/writingPrompts --thresholdtgt 10 --thresholdsrc 10 | ||
| # Train the model: | ||
| $ python train.py data-bin/writingPrompts -a fconv_self_att_wp --lr 0.25 --clip-norm 0.1 --max-tokens 1500 --lr-scheduler reduce_lr_on_plateau --decoder-attention True --encoder-attention False --criterion label_smoothed_cross_entropy --weight-decay .0000001 --label-smoothing 0 --source-lang wp_source --target-lang wp_target --gated-attention True --self-attention True --project-input True --pretrained False | ||
| # Train a fusion model: | ||
| # add the arguments: --pretrained True --pretrained-checkpoint path/to/checkpoint | ||
| # Generate: | ||
| $ python generate.py data-bin/writingPrompts --path /path/to/trained/model/checkpoint_best.pt --batch-size 32 --beam 1 --sampling --sampling-topk 10 --sampling-temperature 0.8 --nbest 1 | ||
| ``` |
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