Add RoBERTa

Summary: Pull Request resolved: facebookresearch#916

Differential Revision: D16537774

Pulled By: myleott

fbshipit-source-id: 86bb7b1913a428ee4a21674cc3fc7b39264067ec

README.md

@@ -1,28 +1,37 @@
-# Introduction <img src="fairseq_logo.png" width="50"> 
+# <img src="fairseq_logo.png" width="30"> Introduction
 
 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. It provides reference implementations
-of various sequence-to-sequence models, including:
+modeling and other text generation tasks.
+
+### What's New:
+
+- July 2019: [RoBERTa models and code release](examples/roberta/README.md)
+- June 2019: [wav2vec models and code release](examples/wav2vec/README.md)
+- April 2019: [fairseq demo paper @ NAACL 2019](https://arxiv.org/abs/1904.01038)
+
+### Features:
+
+Fairseq 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/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)
   - **_New_** [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md)
 - **LightConv and DynamicConv models**
-  - **_New_** [Wu et al. (2019): Pay Less Attention with Lightweight and Dynamic Convolutions](examples/pay_less_attention_paper/README.md)
+  - [Wu et al. (2019): Pay Less Attention with Lightweight and Dynamic Convolutions](examples/pay_less_attention_paper/README.md)
 - **Long Short-Term Memory (LSTM) networks**
-  - [Luong et al. (2015): Effective Approaches to Attention-based Neural Machine Translation](https://arxiv.org/abs/1508.04025)
-  - [Wiseman and Rush (2016): Sequence-to-Sequence Learning as Beam-Search Optimization](https://arxiv.org/abs/1606.02960)
+  - Luong et al. (2015): Effective Approaches to Attention-based Neural Machine Translation
 - **Transformer (self-attention) networks**
-  - [Vaswani et al. (2017): Attention Is All You Need](https://arxiv.org/abs/1706.03762)
+  - Vaswani et al. (2017): Attention Is All You Need
   - [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_** [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)
+  - [Baevski and Auli (2018): Adaptive Input Representations for Neural Language Modeling](examples/language_model/transformer_lm/README.md)
+  - [Shen et al. (2019): Mixture Models for Diverse Machine Translation: Tricks of the Trade](examples/translation_moe/README.md)
+  - **_New_** [Liu et al. (2019): RoBERTa: A Robustly Optimized BERT Pretraining Approach](examples/roberta/README.md)
 
-Fairseq features:
+**Additionally:**
 - multi-GPU (distributed) training on one machine or across multiple machines
 - fast generation on both CPU and GPU with multiple search algorithms implemented:
   - beam search
@@ -83,6 +92,7 @@ as well as example training and evaluation commands.
 - [Language Modeling](examples/language_model/README.md): convolutional models are available
 
 We also have more detailed READMEs to reproduce results from specific papers:
+- [Liu et al. (2019): RoBERTa: A Robustly Optimized BERT Pretraining Approach](examples/roberta/README.md)
 - [Schneider et al. (2019): wav2vec: Unsupervised Pre-training for Speech Recognition](examples/wav2vec/README.md)
 - [Shen et al. (2019) Mixture Models for Diverse Machine Translation: Tricks of the Trade](examples/translation_moe/README.md)
 - [Wu et al. (2019): Pay Less Attention with Lightweight and Dynamic Convolutions](examples/pay_less_attention_paper/README.md)

examples/roberta/README.md

@@ -1,6 +1,6 @@
 # RoBERTa: A Robustly Optimized BERT Pretraining Approach
 
-*Pre-print coming 7/28*
+https://arxiv.org/abs/1907.11692
 
 ## Introduction
 
@@ -144,6 +144,7 @@ A more detailed tutorial is coming soon.
   author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and
             Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and
             Luke Zettlemoyer and Veselin Stoyanov},
+  journal={arXiv preprint arXiv:1907.11692},
   year = {2019},
 }
 ```

fairseq/models/__init__.py

@@ -123,8 +123,8 @@ def register_model_arch_fn(fn):
 
 # automatically import any Python files in the models/ directory
 for file in os.listdir(os.path.dirname(__file__)):
-    if file.endswith('.py') and not file.startswith('_'):
-        model_name = file[:file.find('.py')]
+    if not file.startswith('_'):
+        model_name = file[:file.find('.py')] if file.endswith('.py') else file
         module = importlib.import_module('fairseq.models.' + model_name)
 
         # extra `model_parser` for sphinx

fairseq/models/roberta.py → fairseq/models/roberta/model.py

@@ -13,7 +13,6 @@
 import torch.nn.functional as F
 
 from fairseq import utils
-from fairseq.data import encoders
 from fairseq.models import (
     FairseqDecoder,
     FairseqLanguageModel,
@@ -26,113 +25,7 @@
 )
 from fairseq.modules.transformer_sentence_encoder import init_bert_params
 
-
-class RobertaHubInterface(nn.Module):
-    """A simple PyTorch Hub interface to RoBERTa.
-
-    Load RoBERTa::
-
-        >>> roberta = torch.hub.load('pytorch/fairseq', 'roberta.large')
-        >>> roberta.eval()  # disable dropout (or leave in train mode to finetune)
-
-    Apply Byte-Pair Encoding (BPE) to input text::
-
-        >>> tokens = roberta.encode('Hello world!')
-        >>> tokens
-        tensor([    0, 31414,   232,   328,     2])
-
-    Extract features from RoBERTa::
-
-        >>> last_layer_features = roberta.extract_features(tokens)
-        >>> last_layer_features.size()
-        torch.Size([1, 5, 1024])
-
-        >>> all_layers = roberta.extract_features(tokens, return_all_hiddens=True)
-        >>> len(all_layers)
-        25
-        >>> torch.all(all_layers[-1] == last_layer_features)
-        tensor(1, dtype=torch.uint8)
-
-    Use RoBERTa for sentence-pair classification tasks::
-
-        >>> roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')  # already finetuned
-        >>> roberta.eval()  # disable dropout for evaluation
-
-        >>> tokens = roberta.encode(
-        ...   'Roberta is a heavily optimized version of BERT.',
-        ...   'Roberta is not very optimized.'
-        ... )
-        >>> roberta.predict('mnli', tokens).argmax()
-        tensor(0)  # contradiction
-
-        >>> tokens = roberta.encode(
-        ...   'Roberta is a heavily optimized version of BERT.',
-        ...   'Roberta is based on BERT.'
-        ... )
-        >>> roberta.predict('mnli', tokens).argmax()
-        tensor(2)  # entailment
-
-    Register a new (randomly initialized) classification head::
-
-        >>> roberta.register_classification_head('new_task', num_classes=3)
-        >>> roberta.predict('new_task', tokens)
-        tensor([[-1.1050, -1.0672, -1.1245]], grad_fn=<LogSoftmaxBackward>)
-
-    Using the GPU::
-
-        >>> roberta.cuda()
-        >>> roberta.predict('new_task', tokens)
-        tensor([[-1.1050, -1.0672, -1.1245]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)
-    """
-
-    def __init__(self, args, task, model):
-        super().__init__()
-        self.args = args
-        self.task = task
-        self.model = model
-
-        self.bpe = encoders.build_bpe(args)
-
-        # this is useful for determining the device
-        self.register_buffer('_float_tensor', torch.tensor([0], dtype=torch.float))
-
-    @property
-    def device(self):
-        return self._float_tensor.device
-
-    def encode(self, sentence: str, *addl_sentences) -> torch.LongTensor:
-        bpe_sentence = '<s> ' + self.bpe.encode(sentence) + ' </s>'
-        for s in addl_sentences:
-            bpe_sentence += ' </s> ' + self.bpe.encode(s)
-        tokens = self.task.source_dictionary.encode_line(bpe_sentence, append_eos=True)
-        return tokens.long()
-
-    def extract_features(self, tokens: torch.LongTensor, return_all_hiddens=False) -> torch.Tensor:
-        if tokens.dim() == 1:
-            tokens = tokens.unsqueeze(0)
-        features, extra = self.model(
-            tokens.to(device=self.device),
-            features_only=True,
-            return_all_hiddens=return_all_hiddens,
-        )
-        if return_all_hiddens:
-            # convert from T x B x C -> B x T x C
-            inner_states = extra['inner_states']
-            return [inner_state.transpose(0, 1) for inner_state in inner_states]
-        else:
-            return features  # just the last layer's features
-
-    def register_classification_head(
-        self, name: str, num_classes: int = None, embedding_size: int = None, **kwargs
-    ):
-        self.model.register_classification_head(
-            name, num_classes=num_classes, embedding_size=embedding_size, **kwargs
-        )
-
-    def predict(self, head: str, tokens: torch.LongTensor):
-        features = self.extract_features(tokens)
-        logits = self.model.classification_heads[head](features)
-        return F.log_softmax(logits, dim=-1)
+from .hub_interface import RobertaHubInterface
 
 
 @register_model('roberta')