XLM-R code and model release (#900)

Summary:
TODO:
1) Need to update bibtex entry
2) Need to upload models, spm_vocab and dict.txt to public s3 location.

For Future:

1) I will probably add instructions to finetune on XNLI and NER, POS etc. but currently no timeline for that.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/900

Reviewed By: myleott

Differential Revision: D18333076

Pulled By: myleott

fbshipit-source-id: 3f3d3716fcc41c78d2dd4525f60b519abbd0459c

README.md

@@ -6,6 +6,7 @@ modeling and other text generation tasks.
 
 ### What's New:
 
+- 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

examples/roberta/README.md

@@ -8,6 +8,7 @@ RoBERTa iterates on BERT's pretraining procedure, including training the model l
 
 ### What's New:
 
+- November 2019: Multilingual encoder (XLM-RoBERTa) is available [XLM-R](https://github.com/pytorch/fairseq/examples/xlmr).
 - September 2019: TensorFlow and TPU support via the [transformers library](https://github.com/huggingface/transformers).
 - August 2019: RoBERTa is now supported in the [pytorch-transformers library](https://github.com/huggingface/pytorch-transformers).
 - August 2019: Added [tutorial for finetuning on WinoGrande](https://github.com/pytorch/fairseq/tree/master/examples/roberta/wsc#roberta-training-on-winogrande-dataset).

examples/xlmr/README.md

+# Unsupervised Cross-lingual Representation Learning at Scale (XLM-RoBERTa)
+
+## Introduction
+
+XLM-R (XLM-RoBERTa) is scaled cross lingual sentence encoder. It is trained on `2.5T` of data across `100` languages data filtered from Common Crawl. XLM-R achieves state-of-the-arts results on multiple cross lingual benchmarks.
+
+## Pre-trained models
+
+Model | Description | # params | Download
+---|---|---|---
+`xlmr.base.v0` | XLM-R using the BERT-base architecture | 250M | [xlm.base.v0.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.v0.tar.gz)
+`xlmr.large.v0` | XLM-R using the BERT-large architecture | 560M | [xlm.large.v0.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.v0.tar.gz)
+
+(Note: The above models are still under training, we will update the weights, once fully trained, the results are based on the above checkpoints.)
+
+## Results
+
+**[XNLI (Conneau et al., 2018)](https://arxiv.org/abs/1809.05053)**
+
+Model | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur
+---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
+`roberta.large.mnli` _(TRANSLATE-TEST)_ | 91.3 | 82.9 | 84.3 | 81.24 | 81.74 | 83.13 | 78.28 | 76.79 | 76.64 | 74.17 | 74.05 | 77.5 | 70.9 | 66.65 | 66.81
+`xlmr.large.v0` _(TRANSLATE-TRAIN-ALL)_ | 88.7 | 85.2 | 85.6 | 84.6 | 83.6 | 85.5 | 82.4 | 81.6 | 80.9 | 83.4 | 80.9 | 83.3 | 79.8 | 75.9 | 74.3
+
+## Example usage
+
+##### Load XLM-R from torch.hub (PyTorch >= 1.1):
+```python
+import torch
+xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large.v0')
+xlmr.eval()  # disable dropout (or leave in train mode to finetune)
+```
+
+##### Load XLM-R (for PyTorch 1.0 or custom models):
+```python
+# Download xlmr.large model
+wget https://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.v0.tar.gz
+tar -xzvf xlmr.large.v0.tar.gz
+
+# Load the model in fairseq
+from fairseq.models.roberta import XLMRModel
+xlmr = XLMRModel.from_pretrained('/path/to/xlmr.large.v0', checkpoint_file='model.pt')
+xlmr.eval()  # disable dropout (or leave in train mode to finetune)
+```
+
+##### Apply Byte-Pair Encoding (BPE) to input text:
+```python
+tokens = xlmr.encode('Hello world!')
+assert tokens.tolist() == [    0, 35378,  8999,    38,     2]
+xlmr.decode(tokens)  # 'Hello world!'
+```
+
+##### Extract features from XLM-R:
+```python
+# Extract the last layer's features
+last_layer_features = xlmr.extract_features(tokens)
+assert last_layer_features.size() == torch.Size([1, 5, 1024])
+
+# Extract all layer's features (layer 0 is the embedding layer)
+all_layers = xlmr.extract_features(tokens, return_all_hiddens=True)
+assert len(all_layers) == 25
+assert torch.all(all_layers[-1] == last_layer_features)
+```
+
+## Citation
+
+```bibtex
+@article{,
+    title = {Unsupervised Cross-lingual Representation Learning at Scale},
+    author = {Alexis Conneau and Kartikay Khandelwal and Naman Goyal
+        and Vishrav Chaudhary and Guillaume Wenzek and Francisco Guzm\'an
+        and Edouard Grave and Myle Ott and Luke Zettlemoyer and Veselin Stoyanov
+    },
+    journal={},
+    year = {2019},
+}
+```

fairseq/models/roberta/model.py

@@ -194,6 +194,30 @@ class RobertaModel(FairseqLanguageModel):
                     state_dict[prefix + 'classification_heads.' + k] = v
 
 
+@register_model('xlmr')
+class XLMRModel(RobertaModel):
+    @classmethod
+    def hub_models(cls):
+        return {
+            'xlmr.base.v0': 'http://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.v0.tar.gz',
+            'xlmr.large.v0': 'http://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.v0.tar.gz',
+        }
+
+    @classmethod
+    def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_name_or_path='.', bpe='sentencepiece', **kwargs):
+        from fairseq import hub_utils
+        x = hub_utils.from_pretrained(
+            model_name_or_path,
+            checkpoint_file,
+            data_name_or_path,
+            archive_map=cls.hub_models(),
+            bpe=bpe,
+            load_checkpoint_heads=True,
+            **kwargs,
+        )
+        return RobertaHubInterface(x['args'], x['task'], x['models'][0])
+
+
 class RobertaLMHead(nn.Module):
     """Head for masked language modeling."""