These implementations have been tested on several datasets (see the examples) and should match the performances of the associated TensorFlow implementations (e.g. ~91 F1 on SQuAD for BERT, ~88 F1 on RocStories for OpenAI GPT and ~18.3 perplexity on WikiText 103 for the Transformer-XL). You can find more details in the [Examples](#examples) section below.
Here are some information on these models:
**BERT** was released together with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
This PyTorch implementation of BERT is provided with [Google's pre-trained models](https://github.com/google-research/bert), examples, notebooks and a command-line interface to load any pre-trained TensorFlow checkpoint for BERT is also provided.
**OpenAI GPT** was released together with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
This PyTorch implementation of OpenAI GPT is an adaptation of the [PyTorch implementation by HuggingFace](https://github.com/huggingface/pytorch-openai-transformer-lm) and is provided with [OpenAI's pre-trained model](https://github.com/openai/finetune-transformer-lm) and a command-line interface that was used to convert the pre-trained NumPy checkpoint in PyTorch.
**Google/CMU's Transformer-XL** was released together with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](http://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
This PyTorch implementation of Transformer-XL is an adaptation of the original [PyTorch implementation](https://github.com/kimiyoung/transformer-xl) which has been slightly modified to match the performances of the TensforFlow implementation and allow to re-use the pretrained weights. A command-line interface is provided to convert TensorFlow checkpoints in PyTorch models.
**OpenAI GPT-2** was released together with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, JeffreyWu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
This PyTorch implementation of OpenAI GPT-2 is an adaptation of the [OpenAI's implementation](https://github.com/openai/gpt-2) and is provided with [OpenAI's pre-trained model](https://github.com/openai/gpt-2 and a command-line interface that was used to convert the TensorFlow checkpoint in PyTorch.
If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install `ftfy` (limit to version 4.4.3 if you are using Python 2) and `SpaCy` :
If you don't install `ftfy` and `SpaCy`, the `OpenAI GPT` tokenizer will default to tokenize using BERT's `BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage, don't worry).
Here also, if you want to reproduce the original tokenization process of the `OpenAI GPT` model, you will need to install `ftfy` (limit to version 4.4.3 if you are using Python 2) and `SpaCy` :
Again, if you don't install `ftfy` and `SpaCy`, the `OpenAI GPT` tokenizer will default to tokenize using BERT's `BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage).
A series of tests is included in the [tests folder](https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/tests) and can be run using `pytest` (install pytest if needed: `pip install pytest`).
- [`BertModel`](./pytorch_pretrained_bert/modeling.py#L556) - raw BERT Transformer model (**fully pre-trained**),
- [`BertForMaskedLM`](./pytorch_pretrained_bert/modeling.py#L710) - BERT Transformer with the pre-trained masked language modeling head on top (**fully pre-trained**),
- [`BertForNextSentencePrediction`](./pytorch_pretrained_bert/modeling.py#L771) - BERT Transformer with the pre-trained next sentence prediction classifier on top (**fully pre-trained**),
- [`BertForPreTraining`](./pytorch_pretrained_bert/modeling.py#L639) - BERT Transformer with masked language modeling head and next sentence prediction classifier on top (**fully pre-trained**),
- [`BertForSequenceClassification`](./pytorch_pretrained_bert/modeling.py#L833) - BERT Transformer with a sequence classification head on top (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**),
- [`BertForMultipleChoice`](./pytorch_pretrained_bert/modeling.py#L899) - BERT Transformer with a multiple choice head on top (used for task like Swag) (BERT Transformer is **pre-trained**, the multiple choice classification head **is only initialized and has to be trained**),
- [`BertForTokenClassification`](./pytorch_pretrained_bert/modeling.py#L969) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**),
- [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L1034) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**).
- Three **OpenAI GPT** PyTorch models (`torch.nn.Module`) with pre-trained weights (in the [`modeling_openai.py`](./pytorch_pretrained_bert/modeling_openai.py) file):
- [`OpenAIGPTModel`](./pytorch_pretrained_bert/modeling_openai.py#L537) - raw OpenAI GPT Transformer model (**fully pre-trained**),
- [`OpenAIGPTLMHeadModel`](./pytorch_pretrained_bert/modeling_openai.py#L691) - OpenAI GPT Transformer with the tied language modeling head on top (**fully pre-trained**),
- [`OpenAIGPTDoubleHeadsModel`](./pytorch_pretrained_bert/modeling_openai.py#L752) - OpenAI GPT Transformer with the tied language modeling head and a multiple choice classification head on top (OpenAI GPT Transformer is **pre-trained**, the multiple choice classification head **is only initialized and has to be trained**),
- Two **Transformer-XL** PyTorch models (`torch.nn.Module`) with pre-trained weights (in the [`modeling_transfo_xl.py`](./pytorch_pretrained_bert/modeling_transfo_xl.py) file):
- [`TransfoXLModel`](./pytorch_pretrained_bert/modeling_transfo_xl.py#L974) - Transformer-XL model which outputs the last hidden state and memory cells (**fully pre-trained**),
- [`TransfoXLLMHeadModel`](./pytorch_pretrained_bert/modeling_transfo_xl.py#L1236) - Transformer-XL with the tied adaptive softmax head on top for language modeling which outputs the logits/loss and memory cells (**fully pre-trained**),
- Three **OpenAI GPT-2** PyTorch models (`torch.nn.Module`) with pre-trained weights (in the [`modeling_gpt2.py`](./pytorch_pretrained_bert/modeling_gpt2.py) file):
- [`GPT2Model`](./pytorch_pretrained_bert/modeling_gpt2.py#L537) - raw OpenAI GPT-2 Transformer model (**fully pre-trained**),
- [`GPT2LMHeadModel`](./pytorch_pretrained_bert/modeling_gpt2.py#L691) - OpenAI GPT-2 Transformer with the tied language modeling head on top (**fully pre-trained**),
- [`GPT2DoubleHeadsModel`](./pytorch_pretrained_bert/modeling_gpt2.py#L752) - OpenAI GPT-2 Transformer with the tied language modeling head and a multiple choice classification head on top (OpenAI GPT-2 Transformer is **pre-trained**, the multiple choice classification head **is only initialized and has to be trained**),
- Tokenizer for **Transformer-XL** (word tokens ordered by frequency for adaptive softmax) (in the [`tokenization_transfo_xl.py`](./pytorch_pretrained_bert/tokenization_transfo_xl.py) file):
-`OpenAIGPTTokenizer` - perform word tokenization and can order words by frequency in a corpus for use in an adaptive softmax.
- Tokenizer for **OpenAI GPT-2** (using byte-level Byte-Pair-Encoding) (in the [`tokenization_gpt2.py`](./pytorch_pretrained_bert/tokenization_gpt2.py) file):
- Configuration classes for BERT, OpenAI GPT and Transformer-XL (in the respective [`modeling.py`](./pytorch_pretrained_bert/modeling.py), [`modeling_openai.py`](./pytorch_pretrained_bert/modeling_openai.py), [`modeling_transfo_xl.py`](./pytorch_pretrained_bert/modeling_transfo_xl.py) files):
-`OpenAIGPTConfig` - Configuration class to store the configuration of a `OpenAIGPTModel` with utilities to read and write from JSON configuration files.
-`TransfoXLConfig` - Configuration class to store the configuration of a `TransfoXLModel` with utilities to read and write from JSON configuration files.
- One example on how to use **OpenAI GPT-2** in the unconditional and interactive mode (in the [`examples` folder](./examples)):
- [`run_gpt2.py`](./examples/run_gpt2.py) - Show how to use OpenAI GPT-2 an instance of `GPT2LMHeadModel` to generate text (same as the original OpenAI GPT-2 examples).
- Three notebooks that were used to check that the TensorFlow and PyTorch models behave identically (in the [`notebooks` folder](./notebooks)):
- [`Comparing-TF-and-PT-models.ipynb`](./notebooks/Comparing-TF-and-PT-models.ipynb) - Compare the hidden states predicted by `BertModel`,
- [`Comparing-TF-and-PT-models-SQuAD.ipynb`](./notebooks/Comparing-TF-and-PT-models-SQuAD.ipynb) - Compare the spans predicted by `BertForQuestionAnswering` instances,
- [`Comparing-TF-and-PT-models-MLM-NSP.ipynb`](./notebooks/Comparing-TF-and-PT-models-MLM-NSP.ipynb) - Compare the predictions of the `BertForPretraining` instances.
- A command-line interface to convert TensorFlow checkpoints (BERT, Transformer-XL) or NumPy checkpoint (OpenAI) in a PyTorch save of the associated PyTorch model:
Here is a quick-start example using `BertTokenizer`, `BertModel` and `BertForMaskedLM` class with Google AI's pre-trained `Bert base uncased` model. See the [doc section](#doc) below for all the details on these classes.
Here is a quick-start example using `OpenAIGPTTokenizer`, `OpenAIGPTModel` and `OpenAIGPTLMHeadModel` class with OpenAI's pre-trained model. See the [doc section](#doc) below for all the details on these classes.
First let's prepare a tokenized input with `OpenAIGPTTokenizer`
```python
import torch
from pytorch_pretrained_bert import OpenAIGPTTokenizer, OpenAIGPTModel, OpenAIGPTLMHeadModel
Here is a quick-start example using `TransfoXLTokenizer`, `TransfoXLModel` and `TransfoXLModelLMHeadModel` class with the Transformer-XL model pre-trained on WikiText-103. See the [doc section](#doc) below for all the details on these classes.
Here is a quick-start example using `GPT2Tokenizer`, `GPT2Model` and `GPT2LMHeadModel` class with OpenAI's pre-trained model. See the [doc section](#doc) below for all the details on these classes.
First let's prepare a tokenized input with `GPT2Tokenizer`
```python
import torch
from pytorch_pretrained_bert import GPT2Tokenizer, GPT2Model, GPT2LMHeadModel
# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
import logging
logging.basicConfig(level=logging.INFO)
# Load pre-trained model tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# Encode input
text = "Who was Jim Henson ? Jim Henson was a puppeteer"
indexed_tokens = tokenizer.encode(text)
# Convert inputs to PyTorch tensors
tokens_tensor = torch.tensor([indexed_tokens])
```
Let's see how to use `GPT2Model` to get hidden states
| [Loading Google AI's/OpenAI's pre-trained weigths](#Loading-Google-AI-or-OpenAI-pre-trained-weigths-and-PyTorch-dump) | How to load Google AI/OpenAI's pre-trained weight or a PyTorch saved instance |
| [PyTorch models](#PyTorch-models) | API of the eight PyTorch model classes: `BertModel`, `BertForMaskedLM`, `BertForNextSentencePrediction`, `BertForPreTraining`, `BertForSequenceClassification`, `BertForMultipleChoice` or `BertForQuestionAnswering` |
To load one of Google AI's, OpenAI's pre-trained models or a PyTorch saved model (an instance of `BertForPreTraining` saved with `torch.save()`), the PyTorch model classes and the tokenizer can be instantiated as
-`BERT_CLASS` is either a tokenizer to load the vocabulary (`BertTokenizer` or `OpenAIGPTTokenizer` classes) or one of the eight BERT or three OpenAI GPT PyTorch model classes (to load the pre-trained weights): `BertModel`, `BertForMaskedLM`, `BertForNextSentencePrediction`, `BertForPreTraining`, `BertForSequenceClassification`, `BertForTokenClassification`, `BertForMultipleChoice`, `BertForQuestionAnswering`, `OpenAIGPTModel`, `OpenAIGPTLMHeadModel` or `OpenAIGPTDoubleHeadsModel`, and
-`pytorch_model.bin` a PyTorch dump of a pre-trained instance of `BertForPreTraining`, `OpenAIGPTModel`, `TransfoXLModel`, `GPT2LMHeadModel` (saved with the usual `torch.save()`)
If `PRE_TRAINED_MODEL_NAME_OR_PATH` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links [here](pytorch_pretrained_bert/modeling.py)) and stored in a cache folder to avoid future download (the cache folder can be found at `~/.pytorch_pretrained_bert/`).
-`cache_dir` can be an optional path to a specific directory to download and cache the pre-trained model weights. This option is useful in particular when you are using distributed training: to avoid concurrent access to the same weights you can set for example `cache_dir='./pretrained_model_{}'.format(args.local_rank)` (see the section on distributed training for more information).
`Uncased` means that the text has been lowercased before WordPiece tokenization, e.g., `John Smith` becomes `john smith`. The Uncased model also strips out any accent markers. `Cased` means that the true case and accent markers are preserved. Typically, the Uncased model is better unless you know that case information is important for your task (e.g., Named Entity Recognition or Part-of-Speech tagging). For information about the Multilingual and Chinese model, see the [Multilingual README](https://github.com/google-research/bert/blob/master/multilingual.md) or the original TensorFlow repository.
**When using an `uncased model`, make sure to pass `--do_lower_case` to the example training scripts (or pass `do_lower_case=True` to FullTokenizer if you're using your own script and loading the tokenizer your-self.).**
`BertModel` is the basic BERT Transformer model with a layer of summed token, position and sequence embeddings followed by a series of identical self-attention blocks (12 for BERT-base, 24 for BERT-large).
The inputs and output are **identical to the TensorFlow model inputs and outputs**.
-`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary (see the tokens preprocessing logic in the scripts [`extract_features.py`](./examples/extract_features.py), [`run_classifier.py`](./examples/run_classifier.py) and [`run_squad.py`](./examples/run_squad.py)), and
-`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
-`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if some input sequence lengths are smaller than the max input sequence length of the current batch. It's the mask that we typically use for attention when a batch has varying length sentences.
-`output_all_encoded_layers=True`: outputs a list of the encoded-hidden-states at the end of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
-`output_all_encoded_layers=False`: outputs only the encoded-hidden-states corresponding to the last attention block, i.e. a single torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
-`pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a classifier pretrained on top of the hidden state associated to the first character of the input (`CLF`) to train on the Next-Sentence task (see BERT's paper).
An example on how to use this class is given in the [`extract_features.py`](./examples/extract_features.py) script which can be used to extract the hidden states of the model for a given input.
-`masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss is only computed for the labels set in [0, ..., vocab_size]
-`next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size] with indices selected in [0, 1]. 0 => next sentence is the continuation, 1 => next sentence is a random sentence.
*Outputs*:
- if `masked_lm_labels` and `next_sentence_label` are not `None`: Outputs the total_loss which is the sum of the masked language modeling loss and the next sentence classification loss.
- if `masked_lm_labels` or `next_sentence_label` is `None`: Outputs a tuple comprising
An example on how to use this class is given in the [`run_lm_finetuning.py`](./examples/run_lm_finetuning.py) script which can be used to fine-tune the BERT language model on your specific different text corpus. This should improve model performance, if the language style is different from the original BERT training corpus (Wiki + BookCorpus).
-`masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss is only computed for the labels set in [0, ..., vocab_size]
*Outputs*:
- if `masked_lm_labels` is not `None`: Outputs the masked language modeling loss.
- if `masked_lm_labels` is `None`: Outputs the masked language modeling logits.
-`next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size] with indices selected in [0, 1]. 0 => next sentence is the continuation, 1 => next sentence is a random sentence.
*Outputs*:
- if `next_sentence_label` is not `None`: Outputs the next sentence classification loss.
- if `next_sentence_label` is `None`: Outputs the next sentence classification logits.
`BertForSequenceClassification` is a fine-tuning model that includes `BertModel` and a sequence-level (sequence or pair of sequences) classifier on top of the `BertModel`.
The sequence-level classifier is a linear layer that takes as input the last hidden state of the first character in the input sequence (see Figures 3a and 3b in the BERT paper).
An example on how to use this class is given in the [`run_classifier.py`](./examples/run_classifier.py) script which can be used to fine-tune a single sequence (or pair of sequence) classifier using BERT, for example for the MRPC task.
The linear layer outputs a single value for each choice of a multiple choice problem, then all the outputs corresponding to an instance are passed through a softmax to get the model choice.
This implementation is largely inspired by the work of OpenAI in [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) and the answer of Jacob Devlin in the following [issue](https://github.com/google-research/bert/issues/38).
An example on how to use this class is given in the [`run_swag.py`](./examples/run_swag.py) script which can be used to fine-tune a multiple choice classifier using BERT, for example for the Swag task.
`BertForQuestionAnswering` is a fine-tuning model that includes `BertModel` with a token-level classifiers on top of the full sequence of last hidden states.
The token-level classifier takes as input the full sequence of the last hidden state and compute several (e.g. two) scores for each tokens that can for example respectively be the score that a given token is a `start_span` and a `end_span` token (see Figures 3c and 3d in the BERT paper).
An example on how to use this class is given in the [`run_squad.py`](./examples/run_squad.py) script which can be used to fine-tune a token classifier using BERT, for example for the SQuAD task.
`OpenAIGPTModel` is the basic OpenAI GPT Transformer model with a layer of summed token and position embeddings followed by a series of 12 identical self-attention blocks.
-`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] (or more generally [d_1, ..., d_n, sequence_length] were d_1 ... d_n are arbitrary dimensions) with the word BPE token indices selected in the range [0, total_tokens_embeddings[
-`position_ids`: an optional torch.LongTensor with the same shape as input_ids
with the position indices (selected in the range [0, config.n_positions - 1[.
-`token_type_ids`: an optional torch.LongTensor with the same shape as input_ids
You can use it to add a third type of embedding to each input token in the sequence
(the previous two being the word and position embeddings). The input, position and token_type embeddings are summed inside the Transformer before the first self-attention block.
-`hidden_states`: the encoded-hidden-states at the top of the model as a torch.FloatTensor of size [batch_size, sequence_length, hidden_size] (or more generally [d_1, ..., d_n, hidden_size] were d_1 ... d_n are the dimension of input_ids)
#### 10. `OpenAIGPTLMHeadModel`
`OpenAIGPTLMHeadModel` includes the `OpenAIGPTModel` Transformer followed by a language modeling head with weights tied to the input embeddings (no additional parameters).
*Inputs* are the same as the inputs of the [`OpenAIGPTModel`](#-9.-`OpenAIGPTModel`) class plus optional labels:
-`lm_labels`: optional language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss is only computed for the labels set in [0, ..., vocab_size].
Outputs `lm_logits`: the language modeling logits as a torch.FloatTensor of size [batch_size, sequence_length, total_tokens_embeddings] (or more generally [d_1, ..., d_n, total_tokens_embeddings] were d_1 ... d_n are the dimension of input_ids)
-`multiple_choice_token_ids`: a torch.LongTensor of shape [batch_size, num_choices] with the index of the token whose hidden state should be used as input for the multiple choice classifier (usually the [CLS] token for each choice).
-`lm_labels`: optional language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss is only computed for the labels set in [0, ..., vocab_size].
-`multiple_choice_labels`: optional multiple choice labels: torch.LongTensor of shape [batch_size] with indices selected in [0, ..., num_choices].
*Outputs*:
- if `lm_labels` and `multiple_choice_labels` are not `None`:
Outputs a tuple of losses with the language modeling loss and the multiple choice loss.
-`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the token indices selected in the range [0, self.config.n_token[
-`mems`: an optional memory of hidden states from previous forward passes as a list (num layers) of hidden states at the entry of each layer. Each hidden states has shape [self.config.mem_len, bsz, self.config.d_model]. Note that the first two dimensions are transposed in `mems` with regards to `input_ids`.
-`last_hidden_state`: the encoded-hidden-states at the top of the model as a torch.FloatTensor of size [batch_size, sequence_length, self.config.d_model]
-`new_mems`: list (num layers) of updated mem states at the entry of each layer each mem state is a torch.FloatTensor of size [self.config.mem_len, batch_size, self.config.d_model]. Note that the first two dimensions are transposed in `mems` with regards to `input_ids`.
##### Extracting a list of the hidden states at each layer of the Transformer-XL from `last_hidden_state` and `new_mems`:
The `new_mems` contain all the hidden states PLUS the output of the embeddings (`new_mems[0]`). `new_mems[-1]` is the output of the hidden state of the layer below the last layer and `last_hidden_state` is the output of the last layer (i.E. the input of the softmax when we have a language modeling head on top).
There are two differences between the shapes of `new_mems` and `last_hidden_state`: `new_mems` have transposed first dimensions and are longer (of size `self.config.mem_len`). Here is how to extract the full list of hidden states from the model output:
```python
hidden_states, mems = model(tokens_tensor)
seq_length = hidden_states.size(1)
lower_hidden_states = list(t[-seq_length:, ...].transpose(0, 1) for t in mems)
-`target`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the target token indices selected in the range [0, self.config.n_token[
- if target is None: Negative log likelihood of shape [batch_size, sequence_length]
- else: log probabilities of tokens, shape [batch_size, sequence_length, n_tokens]
-`new_mems`: list (num layers) of updated mem states at the entry of each layer each mem state is a torch.FloatTensor of size [self.config.mem_len, batch_size, self.config.d_model]. Note that the first two dimensions are transposed in `mems` with regards to `input_ids`.
`GPT2Model` is the OpenAI GPT-2 Transformer model with a layer of summed token and position embeddings followed by a series of 12 identical self-attention blocks.
The inputs and output are **identical to the TensorFlow model inputs and outputs**.
We detail them here. This model takes as *inputs*:
-`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] (or more generally [d_1, ..., d_n, sequence_length] were d_1 ... d_n are arbitrary dimensions) with the word BPE token indices selected in the range [0, vocab_size[
-`position_ids`: an optional torch.LongTensor with the same shape as input_ids
with the position indices (selected in the range [0, config.n_positions - 1[.
-`token_type_ids`: an optional torch.LongTensor with the same shape as input_ids
You can use it to add a third type of embedding to each input token in the sequence
(the previous two being the word and position embeddings). The input, position and token_type embeddings are summed inside the Transformer before the first self-attention block.
-`past`: an optional list of torch.LongTensor that contains pre-computed hidden-states (key and values in the attention blocks) to speed up sequential decoding (this is the `presents` output of the model, cf. below).
This model *outputs*:
-`hidden_states`: the encoded-hidden-states at the top of the model as a torch.FloatTensor of size [batch_size, sequence_length, hidden_size] (or more generally [d_1, ..., d_n, hidden_size] were d_1 ... d_n are the dimension of input_ids)
-`presents`: a list of pre-computed hidden-states (key and values in each attention blocks) as a torch.FloatTensors. They can be reused to speed up sequential decoding (see the `run_gpt2.py` example).
#### 15. `GPT2LMHeadModel`
`GPT2LMHeadModel` includes the `GPT2Model` Transformer followed by a language modeling head with weights tied to the input embeddings (no additional parameters).
*Inputs* are the same as the inputs of the [`GPT2Model`](#-14.-`GPT2Model`) class plus optional labels:
-`lm_labels`: optional language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss is only computed for the labels set in [0, ..., vocab_size].
*Outputs*:
- if `lm_labels` is not `None`:
Outputs the language modeling loss.
- else: a tupple of
-`lm_logits`: the language modeling logits as a torch.FloatTensor of size [batch_size, sequence_length, total_tokens_embeddings] (or more generally [d_1, ..., d_n, total_tokens_embeddings] were d_1 ... d_n are the dimension of input_ids)
-`presents`: a list of pre-computed hidden-states (key and values in each attention blocks) as a torch.FloatTensors. They can be reused to speed up sequential decoding (see the `run_gpt2.py` example).
#### 16. `GPT2DoubleHeadsModel`
`GPT2DoubleHeadsModel` includes the `GPT2Model` Transformer followed by two heads:
- a language modeling head with weights tied to the input embeddings (no additional parameters) and:
- a multiple choice classifier (linear layer that take as input a hidden state in a sequence to compute a score, see details in paper).
*Inputs* are the same as the inputs of the [`GPT2Model`](#-14.-`GPT2Model`) class plus a classification mask and two optional labels:
-`multiple_choice_token_ids`: a torch.LongTensor of shape [batch_size, num_choices] with the index of the token whose hidden state should be used as input for the multiple choice classifier (usually the [CLS] token for each choice).
-`lm_labels`: optional language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss is only computed for the labels set in [0, ..., vocab_size].
-`multiple_choice_labels`: optional multiple choice labels: torch.LongTensor of shape [batch_size] with indices selected in [0, ..., num_choices].
*Outputs*:
- if `lm_labels` and `multiple_choice_labels` are not `None`:
Outputs a tuple of losses with the language modeling loss and the multiple choice loss.
- else Outputs a tuple with:
-`lm_logits`: the language modeling logits as a torch.FloatTensor of size [batch_size, num_choices, sequence_length, total_tokens_embeddings]
-`multiple_choice_logits`: the multiple choice logits as a torch.FloatTensor of size [batch_size, num_choices]
-`presents`: a list of pre-computed hidden-states (key and values in each attention blocks) as a torch.FloatTensors. They can be reused to speed up sequential decoding (see the `run_gpt2.py` example).
Please refer to the doc strings and code in [`tokenization.py`](./pytorch_pretrained_bert/tokenization.py) for the details of the `BasicTokenizer` and `WordpieceTokenizer` classes. In general it is recommended to use `BertTokenizer` unless you know what you are doing.
-`max_len`: max length to filter the input of the Transformer. Default to pre-trained value for the model if `None`. **Default = None**
-`special_tokens`: a list of tokens to add to the vocabulary for fine-tuning. If SpaCy is not installed and BERT's `BasicTokenizer` is used as the pre-BPE tokenizer, these tokens are not split. **Default= None**
-`set_special_tokens(self, special_tokens)`: update the list of special tokens (see above arguments)
-`decode(ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)`: decode a list of `int` indices in a string and do some post-processing if needed: (i) remove special tokens from the output and (ii) clean up tokenization spaces.
Please refer to the doc strings and code in [`tokenization_openai.py`](./pytorch_pretrained_bert/tokenization_openai.py) for the details of the `OpenAIGPTTokenizer`.
`TransfoXLTokenizer` perform word tokenization. This tokenizer can be used for adaptive softmax and has utilities for counting tokens in a corpus to create a vocabulary ordered by toekn frequency (for adaptive softmax). See the adaptive softmax paper ([Efficient softmax approximation for GPUs](http://arxiv.org/abs/1609.04309)) for more details.
Please refer to the doc strings and code in [`tokenization_transfo_xl.py`](./pytorch_pretrained_bert/tokenization_transfo_xl.py) for the details of these additional methods in `TransfoXLTokenizer`.
`BertAdam` is a `torch.optimizer` adapted to be closer to the optimizer used in the TensorFlow implementation of Bert. The differences with PyTorch Adam optimizer are the following:
| [Training large models: introduction, tools and examples](#Training-large-models-introduction,-tools-and-examples) | How to use gradient-accumulation, multi-gpu training, distributed training, optimize on CPU and 16-bits training to train Bert models |
| [Fine-tuning with BERT: running the examples](#Fine-tuning-with-BERT-running-the-examples) | Running the examples in [`./examples`](./examples/): `extract_classif.py`, `run_classifier.py`, `run_squad.py` and `run_lm_finetuning.py` |
BERT-base and BERT-large are respectively 110M and 340M parameters models and it can be difficult to fine-tune them on a single GPU with the recommended batch size for good performance (in most case a batch size of 32).
To help with fine-tuning these models, we have included several techniques that you can activate in the fine-tuning scripts [`run_classifier.py`](./examples/run_classifier.py) and [`run_squad.py`](./examples/run_squad.py): gradient-accumulation, multi-gpu training, distributed training and 16-bits training . For more details on how to use these techniques you can read [the tips on training large batches in PyTorch](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255) that I published earlier this month.
- **Gradient Accumulation**: Gradient accumulation can be used by supplying a integer greater than 1 to the `--gradient_accumulation_steps` argument. The batch at each step will be divided by this integer and gradient will be accumulated over `gradient_accumulation_steps` steps.
- **Multi-GPU**: Multi-GPU is automatically activated when several GPUs are detected and the batches are splitted over the GPUs.
- **Distributed training**: Distributed training can be activated by supplying an integer greater or equal to 0 to the `--local_rank` argument (see below).
- **16-bits training**: 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found [here](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) and a full documentation is [here](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html). In our scripts, this option can be activated by setting the `--fp16` flag and you can play with loss scaling using the `--loss_scale` flag (see the previously linked documentation for details on loss scaling). The loss scale can be zero in which case the scale is dynamically adjusted or a positive power of two in which case the scaling is static.
To use 16-bits training and distributed training, you need to install NVIDIA's apex extension [as detailed here](https://github.com/nvidia/apex). You will find more information regarding the internals of `apex` and how to use `apex` in [the doc and the associated repository](https://github.com/nvidia/apex). The results of the tests performed on pytorch-BERT by the NVIDIA team (and my trials at reproducing them) can be consulted in [the relevant PR of the present repository](https://github.com/huggingface/pytorch-pretrained-BERT/pull/116).
Note: To use *Distributed Training*, you will need to run one training script on each of your machines. This can be done for example by running the following command on each server (see [the above mentioned blog post]((https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255)) for more details):
python -m torch.distributed.launch --nproc_per_node=4 --nnodes=2 --node_rank=$THIS_MACHINE_INDEX --master_addr="192.168.1.1" --master_port=1234 run_classifier.py (--arg1 --arg2 --arg3 and all other arguments of the run_classifier script)
Where `$THIS_MACHINE_INDEX` is an sequential index assigned to each of your machine (0, 1, 2...) and the machine with rank 0 has an IP address `192.168.1.1` and an open port `1234`.
Our test ran on a few seeds with [the original implementation hyper-parameters](https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks) gave evaluation results between 84% and 88%.
The data should be a text file in the same format as [sample_text.txt](./samples/sample_text.txt) (one sentence per line, docs separated by empty line).
You can download an [exemplary training corpus](https://ext-bert-sample.obs.eu-de.otc.t-systems.com/small_wiki_sentence_corpus.txt) generated from wikipedia articles and splitted into ~500k sentences with spaCy.
Training one epoch on this corpus takes about 1:20h on 4 x NVIDIA Tesla P100 with `train_batch_size=200` and `max_seq_length=128`:
We provide three examples of scripts for OpenAI GPT, Transformer-XL and OpenAI GPT-2 based on (and extended from) the respective original implementations:
#### Fine-tuning OpenAI GPT on the RocStories dataset
This example code fine-tunes OpenAI GPT on the RocStories dataset.
Before running this example you should download the
[RocStories dataset](https://github.com/snigdhac/StoryComprehension_EMNLP/tree/master/Dataset/RoCStories) and unpack it to some directory `$ROC_STORIES_DIR`.
This command runs in about 10 min on a single K-80 an gives an evaluation accuracy of about 86.4% (the authors report a median accuracy with the TensorFlow code of 85.8% and the OpenAI GPT paper reports a best single run accuracy of 86.5%).
This command runs in about 1 min on a V100 and gives an evaluation perplexity of 18.22 on WikiText-103 (the authors report a perplexity of about 18.3 on this dataset with the TensorFlow code).
For example, fine-tuning BERT-large on SQuAD can be done on a server with 4 k-80 (these are pretty old now) in 18 hours. Our results are similar to the TensorFlow implementation results (actually slightly higher):
We include [three Jupyter Notebooks](https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/notebooks) that can be used to check that the predictions of the PyTorch model are identical to the predictions of the original TensorFlow model.
- The first NoteBook ([Comparing-TF-and-PT-models.ipynb](./notebooks/Comparing-TF-and-PT-models.ipynb)) extracts the hidden states of a full sequence on each layers of the TensorFlow and the PyTorch models and computes the standard deviation between them. In the given example, we get a standard deviation of 1.5e-7 to 9e-7 on the various hidden state of the models.
- The second NoteBook ([Comparing-TF-and-PT-models-SQuAD.ipynb](./notebooks/Comparing-TF-and-PT-models-SQuAD.ipynb)) compares the loss computed by the TensorFlow and the PyTorch models for identical initialization of the fine-tuning layer of the `BertForQuestionAnswering` and computes the standard deviation between them. In the given example, we get a standard deviation of 2.5e-7 between the models.
- The third NoteBook ([Comparing-TF-and-PT-models-MLM-NSP.ipynb](./notebooks/Comparing-TF-and-PT-models-MLM-NSP.ipynb)) compares the predictions computed by the TensorFlow and the PyTorch models for masked token language modeling using the pre-trained masked language modeling model.
A command-line interface is provided to convert a TensorFlow checkpoint in a PyTorch dump of the `BertForPreTraining` class (for BERT) or NumPy checkpoint in a PyTorch dump of the `OpenAIGPTModel` class (for OpenAI GPT).
You can convert any TensorFlow checkpoint for BERT (in particular [the pre-trained models released by Google](https://github.com/google-research/bert#pre-trained-models)) in a PyTorch save file by using the [`./pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py`](convert_tf_checkpoint_to_pytorch.py) script.
This CLI takes as input a TensorFlow checkpoint (three files starting with `bert_model.ckpt`) and the associated configuration file (`bert_config.json`), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using `torch.load()` (see examples in [`extract_features.py`](./examples/extract_features.py), [`run_classifier.py`](./examples/run_classifier.py) and [`run_squad.py`]((./examples/run_squad.py))).
You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow checkpoint (the three files starting with `bert_model.ckpt`) but be sure to keep the configuration file (`bert_config.json`) and the vocabulary file (`vocab.txt`) as these are needed for the PyTorch model too.
To run this specific conversion script you will need to have TensorFlow and PyTorch installed (`pip install tensorflow`). The rest of the repository only requires PyTorch.
Here is an example of the conversion process for a pre-trained `BERT-Base Uncased` model:
Here is an example of the conversion process for a pre-trained OpenAI GPT model, assuming that your NumPy checkpoint save as the same format than OpenAI pretrained model (see [here](https://github.com/openai/finetune-transformer-lm))
Here is an example of the conversion process for a pre-trained Transformer-XL model (see [here](https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models))
TPU are not supported by the current stable release of PyTorch (0.4.1). However, the next version of PyTorch (v1.0) should support training on TPU and is expected to be released soon (see the recent [official announcement](https://cloud.google.com/blog/products/ai-machine-learning/introducing-pytorch-across-google-cloud)).
We will add TPU support when this next release is published.
The original TensorFlow code further comprises two scripts for pre-training BERT: [create_pretraining_data.py](https://github.com/google-research/bert/blob/master/create_pretraining_data.py) and [run_pretraining.py](https://github.com/google-research/bert/blob/master/run_pretraining.py).
Since, pre-training BERT is a particularly expensive operation that basically requires one or several TPUs to be completed in a reasonable amout of time (see details [here](https://github.com/google-research/bert#pre-training-with-bert)) we have decided to wait for the inclusion of TPU support in PyTorch to convert these pre-training scripts.
