0.6.1 -> 0.6.2 (#577)

Summary:
Changelog:
- 998ba4f: Add language models from Baevski & Auli (2018)
- 4294c4f6: Add mixture of experts code from Shen et al. (2019)
- 00493490: Add example for multilingual training
- 48d9afbe: Speed improvements, including fused operators from apex
- 44d27e64: Add Tensorboard support
- d17fa851: Add Adadelta optimizer
- 9e1c880f: Add `FairseqEncoderModel`
- b65c579b: Add `FairseqTask.inference_step` to modularize generate.py
- 2ad1178e: Add back `--curriculum`
- Misc bug fixes and other features

Pull Request resolved: https://github.com/pytorch/fairseq/pull/577

Differential Revision: D14481233

Pulled By: myleott

fbshipit-source-id: 4ff8625ef1c0b24273fc65df7c5658e3c932e8b7

README.md

@@ -5,7 +5,7 @@ developers to train custom models for translation, summarization, language
 modeling and other text generation tasks. It provides reference implementations
 of various sequence-to-sequence models, including:
 - **Convolutional Neural Networks (CNN)**
-  - [Dauphin et al. (2017): Language Modeling with Gated Convolutional Networks](examples/conv_lm/README.md)
+  - [Dauphin et al. (2017): Language Modeling with Gated Convolutional Networks](examples/language_model/conv_lm/README.md)
   - [Gehring et al. (2017): Convolutional Sequence to Sequence Learning](examples/conv_seq2seq/README.md)
   - [Edunov et al. (2018): Classical Structured Prediction Losses for Sequence to Sequence Learning](https://github.com/pytorch/fairseq/tree/classic_seqlevel)
   - [Fan et al. (2018): Hierarchical Neural Story Generation](examples/stories/README.md)
@@ -18,7 +18,8 @@ of various sequence-to-sequence models, including:
   - [Vaswani et al. (2017): Attention Is All You Need](https://arxiv.org/abs/1706.03762)
   - [Ott et al. (2018): Scaling Neural Machine Translation](examples/scaling_nmt/README.md)
   - [Edunov et al. (2018): Understanding Back-Translation at Scale](examples/backtranslation/README.md)
-  - **_New_** [Shen et al. (2019) Mixture Models for Diverse Machine Translation: Tricks of the Trade](examples/translation_moe/README.md)
+  - **_New_** [Baevski and Auli (2018): Adaptive Input Representations for Neural Language Modeling](examples/language_model/transformer_lm/README.md)
+  - **_New_** [Shen et al. (2019): Mixture Models for Diverse Machine Translation: Tricks of the Trade](examples/translation_moe/README.md)
 
 Fairseq features:
 - multi-GPU (distributed) training on one machine or across multiple machines
@@ -88,7 +89,7 @@ We also have more detailed READMEs to reproduce results from specific papers:
 - [Fan et al. (2018): Hierarchical Neural Story Generation](examples/stories/README.md)
 - [Ott et al. (2018): Scaling Neural Machine Translation](examples/scaling_nmt/README.md)
 - [Gehring et al. (2017): Convolutional Sequence to Sequence Learning](examples/conv_seq2seq/README.md)
-- [Dauphin et al. (2017): Language Modeling with Gated Convolutional Networks](examples/conv_lm/README.md)
+- [Dauphin et al. (2017): Language Modeling with Gated Convolutional Networks](examples/language_model/conv_lm/README.md)
 
 # Join the fairseq community
 

docs/conf.py

@@ -60,9 +60,9 @@ github_doc_root = 'https://github.com/pytorch/fairseq/tree/master/docs/'
 # built documents.
 #
 # The short X.Y version.
-version = '0.6.1'
+version = '0.6.2'
 # The full version, including alpha/beta/rc tags.
-release = '0.6.1'
+release = '0.6.2'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

eval_lm.py

@@ -14,6 +14,7 @@ import numpy as np
 import torch
 
 from fairseq import options, progress_bar, tasks, utils
+from fairseq.data import LMContextWindowDataset
 from fairseq.meters import StopwatchMeter, TimeMeter
 from fairseq.sequence_scorer import SequenceScorer
 from fairseq.utils import import_user_module
@@ -65,11 +66,22 @@ def main(parsed_args):
     for arg in vars(parsed_args).keys():
         if arg not in {'self_target', 'future_target', 'past_target', 'tokens_per_sample', 'output_size_dictionary'}:
             setattr(args, arg, getattr(parsed_args, arg))
+
+    # reduce tokens per sample by the required context window size
+    args.tokens_per_sample -= args.context_window
     task = tasks.setup_task(args)
 
     # Load dataset splits
     task.load_dataset(args.gen_subset)
-    print('| {} {} {} examples'.format(args.data, args.gen_subset, len(task.dataset(args.gen_subset))))
+    dataset = task.dataset(args.gen_subset)
+    if args.context_window > 0:
+        dataset = LMContextWindowDataset(
+            dataset=dataset,
+            tokens_per_sample=args.tokens_per_sample,
+            context_window=args.context_window,
+            pad_idx=task.source_dictionary.pad(),
+        )
+    print('| {} {} {} examples'.format(args.data, args.gen_subset, len(dataset)))
 
     # Optimize ensemble for generation and set the source and dest dicts on the model (required by scorer)
     for model in models:
@@ -84,7 +96,7 @@ def main(parsed_args):
     print('num. model params: {}'.format(sum(p.numel() for p in models[0].parameters())))
 
     itr = task.get_batch_iterator(
-        dataset=task.dataset(args.gen_subset),
+        dataset=dataset,
         max_tokens=args.max_tokens or 36000,
         max_sentences=args.max_sentences,
         max_positions=utils.resolve_max_positions(*[
@@ -97,7 +109,7 @@ def main(parsed_args):
     ).next_epoch_itr(shuffle=False)
 
     gen_timer = StopwatchMeter()
-    scorer = SequenceScorer(task.target_dictionary)
+    scorer = SequenceScorer(task.target_dictionary, args.softmax_batch)
 
     score_sum = 0.
     count = 0
@@ -107,7 +119,11 @@ def main(parsed_args):
             raise NotImplementedError
         else:
             bpe_cont = args.remove_bpe.rstrip()
-            bpe_toks = set(i for i in range(len(task.dictionary)) if task.dictionary[i].endswith(bpe_cont))
+            bpe_toks = set(
+                i
+                for i in range(len(task.source_dictionary))
+                if task.source_dictionary[i].endswith(bpe_cont)
+            )
         bpe_len = len(bpe_cont)
     else:
         bpe_toks = None
@@ -117,23 +133,28 @@ def main(parsed_args):
 
     with progress_bar.build_progress_bar(args, itr) as t:
         wps_meter = TimeMeter()
+
         for sample in t:
-            sample = utils.move_to_cuda(sample) if use_cuda else sample
             if 'net_input' not in sample:
                 continue
 
+            sample = utils.move_to_cuda(sample) if use_cuda else sample
+
             gen_timer.start()
             hypos = scorer.generate(models, sample)
             gen_timer.stop(sample['ntokens'])
 
             for hypos_i in hypos:
                 hypo = hypos_i[0]
-                pos_scores = hypo['positional_scores']
+
+                tokens = hypo['tokens']
+                tgt_len = tokens.numel()
+                pos_scores = hypo['positional_scores'].float()
 
                 skipped_toks = 0
                 if bpe_toks is not None:
-                    for i in range(len(hypo['tokens']) - 1):
-                        if hypo['tokens'][i].item() in bpe_toks:
+                    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
@@ -141,7 +162,7 @@ def main(parsed_args):
                 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()]))
+                          task.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
@@ -150,9 +171,9 @@ def main(parsed_args):
                     w = ''
                     word_prob = []
                     is_bpe = False
-                    for i in range(len(hypo['tokens'])):
-                        w_ind = hypo['tokens'][i].item()
-                        w += task.dictionary[w_ind]
+                    for i in range(len(tokens)):
+                        w_ind = tokens[i].item()
+                        w += task.source_dictionary[w_ind]
                         if bpe_toks is not None and w_ind in bpe_toks:
                             w = w[:-bpe_len]
                             is_bpe = True
@@ -161,7 +182,7 @@ def main(parsed_args):
 
                             next_prob = None
                             ind = i + 1
-                            while ind < len(hypo['tokens']):
+                            while ind < len(tokens):
                                 if pos_scores[ind].item() != 0:
                                     next_prob = pos_scores[ind]
                                     break

examples/.gitignore

-*/*
 !*/*.sh
 !*/*.md

examples/language_model/README.md

@@ -2,10 +2,10 @@
 
 ## Pre-trained models
 
-Description | Dataset | Model | Test set(s)
----|---|---|---
-Convolutional <br> ([Dauphin et al., 2017](https://arxiv.org/abs/1612.08083)) | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/gbw_fconv_lm.tar.bz2) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/gbw_test_lm.tar.bz2)
-Convolutional <br> ([Dauphin et al., 2017](https://arxiv.org/abs/1612.08083)) | [WikiText-103](https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wiki103_fconv_lm.tar.bz2) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wiki103_test_lm.tar.bz2)
+Description | Parameters | Dataset | Model and Test set(s)
+---|---:|---|---
+Adaptive Inputs <br> ([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 1026M | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.bz2)
+Adaptive Inputs <br> ([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 247M | [WikiText-103](https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.bz2)
 
 ## Example usage
 
@@ -16,6 +16,8 @@ These scripts provide an example of pre-processing data for the Language Modelin
 Provides an example of pre-processing for [WikiText-103 language modeling task](https://www.salesforce.com/products/einstein/ai-research/the-wikitext-dependency-language-modeling-dataset/):
 
 Example usage:
+
+Prepare data:
 ```
 $ cd examples/language_model/
 $ bash prepare-wikitext-103.sh
@@ -27,17 +29,39 @@ $ TEXT=examples/language_model/wikitext-103
 $ fairseq-preprocess --only-source \
   --trainpref $TEXT/wiki.train.tokens --validpref $TEXT/wiki.valid.tokens --testpref $TEXT/wiki.test.tokens \ 
   --destdir data-bin/wikitext-103
+```
+
+Train a transformer language model with adaptive inputs ([Baevski and Auli (2018): Adaptive Input Representations for Neural Language Modeling](transformer_lm/README.md)):
+```
+# If it runs out of memory, try to reduce max-tokens and tokens-per-sample
+$ mkdir -p checkpoints/transformer_wikitext-103
+$ fairseq-train --task language_modeling data-bin/wikitext-103 \
+  --save-dir checkpoints/transformer_wikitext-103 --arch transformer_lm_wiki103 \
+  --max-update 286000 --max-lr 1.0 --t-mult 2 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 \
+  --warmup-updates 16000 --warmup-init-lr 1e-07 --min-lr 1e-09 --optimizer nag --lr 0.0001 --clip-norm 0.1 \
+  --criterion adaptive_loss --max-tokens 3072 --update-freq 4 --tokens-per-sample 3072 --seed 1 \
+  --sample-break-mode none --skip-invalid-size-inputs-valid-test --ddp-backend=no_c10d
 
-# Train the model:
-# If it runs out of memory, try to reduce max-tokens and max-target-positions
-$ mkdir -p checkpoints/wikitext-103
+# Evaluate:
+$ fairseq-eval-lm data-bin/wikitext-103 --path 'checkpoints/transformer_wiki103/checkpoint_best.pt' \
+  --sample-break-mode complete --max-tokens 3072 --context-window 2560 --softmax-batch 1024
+
+```
+
+
+Train a convolutional language model ([Dauphin et al. (2017): Language Modeling with Gated Convolutional Networks](conv_lm/README.md)):
+```
+# If it runs out of memory, try to reduce max-tokens and tokens-per-sample
+$ mkdir -p checkpoints/fconv_wikitext-103
 $ fairseq-train --task language_modeling data-bin/wikitext-103 \
+  --save-dir checkpoints/fconv_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
+  --ddp-backend=no_c10d
 
 # Evaluate:
-$ fairseq-eval-lm data-bin/wikitext-103 --path 'checkpoints/wiki103/checkpoint_best.pt'
+$ fairseq-eval-lm data-bin/wikitext-103 --path 'checkpoints/fconv_wiki103/checkpoint_best.pt'
 
 ```

examples/conv_lm/README.md → examples/language_model/conv_lm/README.md

 # Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)
 
-## Pre-trained models
-
-Description | Dataset | Model | Test set(s)
----|---|---|---
-Convolutional <br> ([Dauphin et al., 2017](https://arxiv.org/abs/1612.08083)) | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/gbw_fconv_lm.tar.bz2) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/gbw_test_lm.tar.bz2)
-Convolutional <br> ([Dauphin et al., 2017](https://arxiv.org/abs/1612.08083)) | [WikiText-103](https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wiki103_fconv_lm.tar.bz2) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wiki103_test_lm.tar.bz2)
-
 ## Example usage
 
-See the [language modeling README](../language_model/README.md) for instructions on reproducing results for WikiText-103
+See the [language modeling README](../README.md) for instructions on reproducing results for WikiText-103
 using the `fconv_lm_dauphin_wikitext103` model architecture.
 
 ## Citation

examples/language_model/prepare-wikitext-103.sh

@@ -21,13 +21,13 @@ for ((i=0;i<${#URLS[@]};++i)); do
             echo "$url not successfully downloaded."
             exit -1
         fi
-    fi
-    if [ ${file: -4} == ".tgz" ]; then
-        tar zxvf $file
-    elif [ ${file: -4} == ".tar" ]; then
-        tar xvf $file
-    elif [ ${file: -4} == ".zip" ]; then
-        unzip $file
+        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 ..

examples/language_model/transformer_lm/README.md

+# Adaptive Input Representations for Neural Language Modeling (Baevski and Auli; 2018)
+
+## Pre-trained models
+
+Description | Parameters | Dataset | Model and Test set(s)
+---|---:|---|---
+Adaptive Inputs <br> ([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 1026M | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.bz2)
+Adaptive Inputs <br> ([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 247M | [WikiText-103](https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.bz2)
+
+## Example usage
+
+See the [language modeling README](../language_model/README.md) for instructions on reproducing results for WikiText-103
+using the `transformer_lm_wiki103` model architecture.
+
+## Citation
+
+```bibtex
+@inproceedings{
+    baevski2018adaptive,
+    title={Adaptive Input Representations for Neural Language Modeling},
+    author={Alexei Baevski and Michael Auli},
+    booktitle={International Conference on Learning Representations},
+    year={2019},
+    url={https://openreview.net/forum?id=ByxZX20qFQ},
+}
+```

fairseq/__init__.py

@@ -6,7 +6,7 @@
 # can be found in the PATENTS file in the same directory.
 
 __all__ = ['pdb']
-__version__ = '0.6.1'
+__version__ = '0.6.2'
 
 import fairseq.criterions
 import fairseq.models

fairseq/data/__init__.py

@@ -11,6 +11,7 @@ from .backtranslation_dataset import BacktranslationDataset
 from .concat_dataset import ConcatDataset
 from .indexed_dataset import IndexedCachedDataset, IndexedDataset, IndexedRawTextDataset
 from .language_pair_dataset import LanguagePairDataset
+from .lm_context_window_dataset import LMContextWindowDataset
 from .monolingual_dataset import MonolingualDataset
 from .round_robin_zip_datasets import RoundRobinZipDatasets
 from .token_block_dataset import TokenBlockDataset
@@ -35,6 +36,7 @@ __all__ = [
     'IndexedDataset',
     'IndexedRawTextDataset',
     'LanguagePairDataset',
+    'LMContextWindowDataset',
     'MonolingualDataset',
     'RoundRobinZipDatasets',
     'ShardedIterator',

fairseq/data/lm_context_window_dataset.py

+# 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.
+
+import numpy as np
+import torch
+
+from fairseq.data.monolingual_dataset import MonolingualDataset
+
+from . import FairseqDataset
+
+
+class LMContextWindowDataset(FairseqDataset):
+    """Wraps a MonolingualDataset and provides more context for evaluation."""
+
+    def __init__(self, dataset, tokens_per_sample, context_window, pad_idx):
+        assert isinstance(dataset, MonolingualDataset)
+        assert context_window > 0
+        self.dataset = dataset
+        self.tokens_per_sample = tokens_per_sample
+        self.context_window = context_window
+        self.pad_idx = pad_idx
+        self.prev_tokens = np.empty([0])
+
+    def __getitem__(self, index):
+        return self.dataset[index]
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def collater(self, samples):
+        sample = self.dataset.collater(samples)
+
+        pad = self.pad_idx
+        max_sample_len = self.tokens_per_sample + self.context_window
+
+        bsz, tsz = sample['net_input']['src_tokens'].shape
+        start_idxs = [0] * bsz
+        toks = sample['net_input']['src_tokens']
+        lengths = sample['net_input']['src_lengths']
+        tgt = sample['target']
+        new_toks = np.empty([bsz, tsz + self.context_window], dtype=np.int64)
+        new_tgt = np.full([bsz, tsz + self.context_window], pad, dtype=np.int64)
+        sample_lens = toks.ne(pad).long().sum(dim=1).cpu()
+        for i in range(bsz):
+            sample_len = sample_lens[i]
+            extra = len(self.prev_tokens) + sample_len - max_sample_len
+            if extra > 0:
+                self.prev_tokens = self.prev_tokens[extra:]
+            pads = np.full(self.context_window - len(self.prev_tokens), pad)
+            new_toks[i] = np.concatenate([self.prev_tokens, toks[i].numpy(), pads])
+            new_tgt[i, len(self.prev_tokens):len(self.prev_tokens) + len(tgt[i])] = tgt[i]
+            start_idxs[i] = len(self.prev_tokens)
+            lengths[i] += len(self.prev_tokens)
+            self.prev_tokens = new_toks[i][new_toks[i] != pad][-self.context_window:]
+        sample['net_input']['src_tokens'] = torch.from_numpy(new_toks)
+        sample['target'] = torch.from_numpy(new_tgt)
+        sample['start_indices'] = start_idxs
+
+        return sample
+
+    def get_dummy_batch(self, *args, **kwargs):
+        return self.dataset.get_dummy_batch(*args, **kwargs)
+
+    def num_tokens(self, index):
+        return self.dataset.num_tokens(index)
+
+    def size(self, index):
+        return self.dataset.size(index)
+
+    def ordered_indices(self):
+        # NOTE we don't shuffle the data to retain access to the previous dataset elements
+        return np.arange(len(self.dataset))
+
+    @property
+    def supports_prefetch(self):
+        return getattr(self.dataset, 'supports_prefetch', False)
+
+    def prefetch(self, indices):
+        return self.dataset.prefetch(indices)

fairseq/models/transformer.py

@@ -196,7 +196,7 @@ class TransformerLanguageModel(FairseqLanguageModel):
                             help='size of character embeddings')
         parser.add_argument('--char-embedder-highway-layers', type=int, metavar='N', default=2,
                             help='number of highway layers for character token embeddder')
-        parser.add_argument('--adaptive-input', default=False, action='store_true',
+        parser.add_argument('--adaptive-input', action='store_true',
                             help='if set, uses adaptive input')
         parser.add_argument('--adaptive-input-factor', type=float, metavar='N',
                             help='adaptive input factor')
@@ -811,6 +811,7 @@ def base_lm_architecture(args):
 
 @register_model_architecture('transformer_lm', 'transformer_lm_big')
 def transformer_lm_big(args):
+    args.decoder_layers = getattr(args, 'decoder_layers', 12)
     args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024)
     args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096)
     args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
@@ -819,7 +820,16 @@ def transformer_lm_big(args):
 
 @register_model_architecture('transformer_lm', 'transformer_lm_wiki103')
 def transformer_lm_wiki103(args):
+    args.decoder_layers = getattr(args, 'decoder_layers', 16)
+    args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
     args.dropout = getattr(args, 'dropout', 0.3)
+    args.adaptive_input = getattr(args, 'adaptive_input', True)
+    args.tie_adaptive_weights = getattr(args, 'tie_adaptive_weights', True)
+    args.adaptive_input_cutoff = getattr(args, 'adaptive_input_cutoff', '20000,60000')
+    args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '20000,60000')
+    args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0.2)
+    args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
+    args.relu_dropout = getattr(args, 'relu_dropout', 0.1)
     transformer_lm_big(args)
 
 
@@ -855,6 +865,7 @@ def base_architecture(args):
     args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
     args.share_all_embeddings = getattr(args, 'share_all_embeddings', False)
     args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)
+    args.adaptive_input = getattr(args, 'adaptive_input', False)
 
     args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
     args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)

fairseq/modules/adaptive_softmax.py

@@ -80,7 +80,7 @@ class AdaptiveSoftmax(nn.Module):
         else:
             self.head = nn.Linear(input_dim, output_dim, bias=False)
 
-        self._make_tail(True, adaptive_inputs, tie_proj)
+        self._make_tail(adaptive_inputs, tie_proj)
 
         def init_weights(m):
             if hasattr(m, 'weight') and not isinstance(m, TiedLinear) and not isinstance(m, TiedHeadModule):
@@ -89,15 +89,11 @@ class AdaptiveSoftmax(nn.Module):
         self.apply(init_weights)
 
         self.register_buffer('version', torch.LongTensor([1]))
-        # versions prior to 1 had a bug that offset indices on the head by 1
-        self.buggy_offset = 0
-
-    def _make_tail(self, fix_exponent, adaptive_inputs=None, tie_proj=False):
-        extra_denom = 1 if fix_exponent else 0
 
+    def _make_tail(self, adaptive_inputs=None, tie_proj=False):
         self.tail = nn.ModuleList()
         for i in range(len(self.cutoff) - 1):
-            dim = int(self.input_dim // self.factor ** (i + extra_denom))
+            dim = int(self.input_dim // self.factor ** (i + 1))
 
             tied_emb, tied_proj = adaptive_inputs.weights_for_band(i + 1) \
                 if adaptive_inputs is not None else (None, None)
@@ -123,9 +119,7 @@ class AdaptiveSoftmax(nn.Module):
     def upgrade_state_dict_named(self, state_dict, name):
         version_name = name + '.version'
         if version_name not in state_dict:
-            self.buggy_offset = 1
-            self._make_tail(False)
-            state_dict[version_name] = torch.LongTensor([1])
+            raise Exception('This version of the model is no longer supported')
 
     def adapt_target(self, target):
         """
@@ -141,7 +135,7 @@ class AdaptiveSoftmax(nn.Module):
 
         for i in range(len(self.cutoff) - 1):
             mask = target.ge(self.cutoff[i]).mul(target.lt(self.cutoff[i + 1]))
-            new_target[0][mask] = self.cutoff[0] + i - self.buggy_offset
+            new_target[0][mask] = self.cutoff[0] + i
 
             if mask.any():
                 target_idxs.append(mask.nonzero().squeeze(1))
@@ -194,7 +188,7 @@ class AdaptiveSoftmax(nn.Module):
 
         head_sz = self.cutoff[0] + len(self.tail)
         log_probs[:, :head_sz] = self.lsm(head_y)
-        tail_priors = log_probs[:, self.cutoff[0] - self.buggy_offset: head_sz - self.buggy_offset].clone()
+        tail_priors = log_probs[:, self.cutoff[0]: head_sz].clone()
 
         for i in range(len(self.tail)):
             start = self.cutoff[i]

fairseq/optim/lr_scheduler/cosine_lr_scheduler.py

@@ -54,6 +54,10 @@ class CosineSchedule(FairseqLRScheduler):
         self.t_mult = args.t_mult
         self.period = args.lr_period_updates
 
+        if self.period <= 0:
+            assert args.max_update >= 0, 'Either --max_update or --lr-period-updates must be set'
+            self.period = args.max_update - args.warmup_updates
+
         if args.warmup_updates > 0:
             # linearly warmup for the first args.warmup_updates
             self.lr_step = (warmup_end_lr - args.warmup_init_lr) / args.warmup_updates
@@ -79,7 +83,7 @@ class CosineSchedule(FairseqLRScheduler):
                             help='max learning rate, must be more than args.lr')
         parser.add_argument('--t-mult', default=1, type=float, metavar='LR',
                             help='factor to grow the length of each period')
-        parser.add_argument('--lr-period-updates', default=5000, type=float, metavar='LR',
+        parser.add_argument('--lr-period-updates', default=-1, type=float, metavar='LR',
                             help='initial number of updates per period')
         # fmt: on
 

fairseq/options.py

@@ -8,6 +8,7 @@
 import argparse
 
 import torch
+import sys
 
 from fairseq.criterions import CRITERION_REGISTRY
 from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_CONFIG_REGISTRY
@@ -140,7 +141,7 @@ def get_parser(desc, default_task='translation'):
                         choices=['json', 'none', 'simple', 'tqdm'])
     parser.add_argument('--tensorboard-logdir', metavar='DIR', default='',
                         help='path to save logs for tensorboard, should match --logdir '
-                        'of running tensorboard (default: no tensorboard logging)')
+                             'of running tensorboard (default: no tensorboard logging)')
     parser.add_argument('--seed', default=1, type=int, metavar='N',
                         help='pseudo random number generator seed')
     parser.add_argument('--cpu', action='store_true', help='use CPU instead of CUDA')
@@ -374,6 +375,12 @@ def add_eval_lm_args(parser):
                        help='if set, outputs words and their predicted log probabilities to standard output')
     group.add_argument('--output-word-stats', action='store_true',
                        help='if set, outputs word statistics such as word count, average probability, etc')
+    group.add_argument('--context-window', default=0, type=int, metavar='N',
+                       help='ensures that every evaluated token has access to a context of at least this size,'
+                            ' if possible')
+    group.add_argument('--softmax-batch', default=sys.maxsize, type=int, metavar='N',
+                       help='if BxT is more than this, will batch the softmax over vocab to this amount of tokens'
+                            ' in order to fit into GPU memory')
     # fmt: on
 
 

fairseq/sequence_scorer.py

@@ -6,6 +6,7 @@
 # can be found in the PATENTS file in the same directory.
 
 import torch
+import sys
 
 from fairseq import utils
 
@@ -13,14 +14,40 @@ from fairseq import utils
 class SequenceScorer(object):
     """Scores the target for a given source sentence."""
 
-    def __init__(self, tgt_dict):
+    def __init__(self, tgt_dict, softmax_batch=None):
         self.pad = tgt_dict.pad()
+        self.softmax_batch = softmax_batch or sys.maxsize
+        assert self.softmax_batch > 0
 
     @torch.no_grad()
     def generate(self, models, sample, **kwargs):
         """Score a batch of translations."""
         net_input = sample['net_input']
 
+        def batch_for_softmax(dec_out, target):
+            # assumes decoder_out[0] is the only thing needed (may not be correct for future models!)
+            first, rest = dec_out[0], dec_out[1:]
+            bsz, tsz, dim = first.shape
+            if bsz * tsz < self.softmax_batch:
+                yield dec_out, target, True
+            else:
+                flat = first.contiguous().view(1, -1, dim)
+                flat_tgt = target.contiguous().view(flat.shape[:-1])
+                s = 0
+                while s < flat.size(1):
+                    e = s + self.softmax_batch
+                    yield (flat[:, s:e],) + rest, flat_tgt[:, s:e], False
+                    s = e
+
+        def gather_target_probs(probs, target):
+            probs = probs.gather(
+                dim=2,
+                index=target.unsqueeze(-1),
+            )
+            return probs
+
+        orig_target = sample['target']
+
         # compute scores for each model in the ensemble
         avg_probs = None
         avg_attn = None
@@ -29,7 +56,25 @@ class SequenceScorer(object):
             decoder_out = model.forward(**net_input)
             attn = decoder_out[1]
 
-            probs = model.get_normalized_probs(decoder_out, log_probs=len(models) == 1, sample=sample)
+            batched = batch_for_softmax(decoder_out, orig_target)
+            probs, idx = None, 0
+            for bd, tgt, is_single in batched:
+                sample['target'] = tgt
+                curr_prob = model.get_normalized_probs(bd, log_probs=len(models) == 1, sample=sample).data
+                if is_single:
+                    probs = gather_target_probs(curr_prob, orig_target)
+                else:
+                    if probs is None:
+                        probs = curr_prob.new(orig_target.numel())
+                    step = curr_prob.size(0) * curr_prob.size(1)
+                    end = step + idx
+                    tgt_probs = gather_target_probs(curr_prob.view(tgt.shape + (curr_prob.size(-1),)), tgt)
+                    probs[idx:end] = tgt_probs.view(-1)
+                    idx = end
+                sample['target'] = orig_target
+
+            probs = probs.view(sample['target'].shape)
+
             if avg_probs is None:
                 avg_probs = probs
             else:
@@ -45,20 +90,19 @@ class SequenceScorer(object):
             avg_probs.log_()
             if avg_attn is not None:
                 avg_attn.div_(len(models))
-        avg_probs = avg_probs.gather(
-            dim=2,
-            index=sample['target'].unsqueeze(-1),
-        ).squeeze(2)
 
+        bsz = avg_probs.size(0)
         hypos = []
-        for i in range(avg_probs.size(0)):
+        start_idxs = sample['start_indices'] if 'start_indices' in sample else [0] * bsz
+        for i in range(bsz):
             # remove padding from ref
-            ref = utils.strip_pad(sample['target'][i, :], self.pad) if sample['target'] is not None else None
+            ref = utils.strip_pad(sample['target'][i, start_idxs[i]:], self.pad) \
+                if sample['target'] is not None else None
             tgt_len = ref.numel()
-            avg_probs_i = avg_probs[i][:tgt_len]
+            avg_probs_i = avg_probs[i][start_idxs[i]:start_idxs[i] + tgt_len]
             score_i = avg_probs_i.sum() / tgt_len
             if avg_attn is not None:
-                avg_attn_i = avg_attn[i]
+                avg_attn_i = avg_attn[i, start_idxs[i]:]
                 _, alignment = avg_attn_i.max(dim=0)
             else:
                 avg_attn_i = alignment = None

fairseq/tasks/language_modeling.py

@@ -223,7 +223,7 @@ class LanguageModelingTask(FairseqTask):
     def source_dictionary(self):
         """Return the :class:`~fairseq.data.Dictionary` for the language
         model."""
-        return self.output_dictionary
+        return self.dictionary
 
     @property
     def target_dictionary(self):

fairseq/tasks/translation_moe.py

@@ -212,13 +212,14 @@ class TranslationMoETask(TranslationTask):
             loss, sample_size, logging_output = self._get_loss(sample, model, criterion)
         return loss, sample_size, logging_output
 
-    def inference_step(self, generator, models, sample, prefix_tokens=None):
+    def inference_step(self, generator, models, sample, prefix_tokens=None, expert=None):
+        expert = expert or self.args.gen_expert
         with torch.no_grad():
             return generator.generate(
                 models,
                 sample,
                 prefix_tokens=prefix_tokens,
-                bos_token=self.expert_index(self.args.gen_expert),
+                bos_token=self.expert_index(expert),
             )
 
     def aggregate_logging_outputs(self, logging_outputs, criterion):

setup.py

@@ -29,7 +29,7 @@ bleu = Extension(
 
 setup(
     name='fairseq',
-    version='0.6.1',
+    version='0.6.2',
     description='Facebook AI Research Sequence-to-Sequence Toolkit',
     url='https://github.com/pytorch/fairseq',
     classifiers=[