Merge pull request #1127 from huggingface/dilbert

DilBERT

README.md

@@ -12,7 +12,9 @@ The library currently contains PyTorch implementations, pre-trained model weight
 4. **[Transformer-XL](https://github.com/kimiyoung/transformer-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 5. **[XLNet](https://github.com/zihangdai/xlnet/)** (from Google/CMU) released with the paper [​XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
 6. **[XLM](https://github.com/facebookresearch/XLM/)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
-7. **[RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta)** (from Facebook), a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du et al.
+7. **[RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
+8. **[DistilBERT](https://github.com/huggingface/pytorch-transformers/tree/master/examples/distillation)** (from HuggingFace), released together with the blogpost [Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT](https://medium.com/huggingface/distilbert-8cf3380435b5
+) by Victor Sanh, Lysandre Debut and Thomas Wolf.
 
 These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Peason R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/pytorch-transformers/examples.html).
 

docs/source/index.rst

@@ -48,3 +48,4 @@ The library currently contains PyTorch implementations, pre-trained model weight
     model_doc/xlm
     model_doc/xlnet
     model_doc/roberta
+    model_doc/distilbert

docs/source/model_doc/distilbert.rst

+DistilBERT
+----------------------------------------------------
+
+``DistilBertConfig``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.DistilBertConfig
+    :members:
+
+
+``DistilBertTokenizer``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.DistilBertTokenizer
+    :members:
+
+
+``DistilBertModel``
+~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.DistilBertModel
+    :members:
+
+
+``DistilBertForMaskedLM``
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.DistilBertForMaskedLM
+    :members:
+
+
+``DistilBertForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.DistilBertForSequenceClassification
+    :members:
+
+
+``DistilBertForQuestionAnswering``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.DistilBertForQuestionAnswering
+    :members:

docs/source/pretrained_models.rst

@@ -111,5 +111,13 @@ Here is the full list of the currently provided pretrained models together with
 |                   |                                                            | | ``roberta-large`` fine-tuned on `MNLI <http://www.nyu.edu/projects/bowman/multinli/>`__.                                            |
 |                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| DistilBERT        | ``distilbert-base-uncased``                                | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
+|                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint                                                   |
+|                   |                                                            | (see `details <https://medium.com/huggingface/distilbert-8cf3380435b5>`__)                                                                       |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``distilbert-base-uncased-distilled-squad``                | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
+|                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint, with an additional linear layer.                 |
+|                   |                                                            | (see `details <https://medium.com/huggingface/distilbert-8cf3380435b5>`__)                                                                       |
++-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 
 .. <https://huggingface.co/pytorch-transformers/examples.html>`__
\ No newline at end of file

examples/distillation/README.md

+# DistilBERT
+
+This folder contains the original code used to train DistilBERT as well as examples showcasing how to use DistilBERT.
+
+## What is DistilBERT
+
+DistilBERT stands for Distillated-BERT. DistilBERT is a small, fast, cheap and light Transformer model based on Bert architecture. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving over 95% of Bert's performances as measured on the GLUE language understanding benchmark. DistilBERT is trained using knowledge distillation, a technique to compress a large model called the teacher into a smaller model called the student. By distillating Bert, we obtain a smaller Transformer model that bears a lot of similarities with the original BERT model while being lighter, smaller and faster to run. DistilBERT is thus an interesting option to put large-scaled trained Transformer model into production.
+
+For more information on DistilBERT, please refer to our [detailed blog post](https://medium.com/huggingface/smaller-faster-cheaper-lighter-introducing-distilbert-a-distilled-version-of-bert-8cf3380435b5
+).
+
+## How to use DistilBERT
+
+PyTorch-Transformers includes two pre-trained DistilBERT models, currently only provided for English (we are investigating the possibility to train and release a multilingual version of DistilBERT):
+
+- `distilbert-base-uncased`: DistilBERT English language model pretrained on the same data used to pretrain Bert (concatenation of the Toronto Book Corpus and full English Wikipedia) using distillation with the supervision of the `bert-base-uncased` version of Bert. The model has 6 layers, 768 dimension and 12 heads, totalizing 66M parameters.
+- `distilbert-base-uncased-distilled-squad`: A finetuned version of `distilbert-base-uncased` finetuned using (a second step of) knwoledge distillation on SQuAD 1.0. This model reaches a F1 score of 86.2 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score).
+
+Using DistilBERT is very similar to using BERT. DistilBERT share the same tokenizer as BERT's `bert-base-uncased` even though we provide a link to this tokenizer under the `DistilBertTokenizer` name to have a consistent naming between the library models.
+
+```python
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+model = DistilBertModel.from_pretrained('distilbert-base-uncased')
+
+input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)
+outputs = model(input_ids)
+last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
+```
+
+## How to train DistilBERT
+
+In the following, we will explain how you can train your own compressed model.
+
+### A. Preparing the data
+
+The weights we release are trained using a concatenation of Toronto Book Corpus and English Wikipedia (same training data as the English version of BERT).
+
+To avoid processing the data several time, we do it once and for all before the training. From now on, will suppose that you have a text file `dump.txt` which contains one sequence per line (a sequence being composed of one of several coherent sentences).
+
+First, we will binarize the data, i.e. tokenize the data and convert each token in an index in our model's vocabulary.
+
+```bash
+python scripts/binarized_data.py \
+    --file_path data/dump.txt \
+    --bert_tokenizer bert-base-uncased \
+    --dump_file data/binarized_text
+```
+
+Our implementation of masked language modeling loss follows [XLM](https://github.com/facebookresearch/XLM)'s one and smoothes the probability of masking with a factor that put more emphasis on rare words. Thus we count the occurences of each tokens in the data:
+
+```bash
+python scripts/token_counts.py \
+    --data_file data/binarized_text.bert-base-uncased.pickle \
+    --token_counts_dump data/token_counts.bert-base-uncased.pickle
+```
+
+### B. Training
+
+Training with distillation is really simple once you have pre-processed the data:
+
+```bash
+python train.py \
+    --dump_path serialization_dir/my_first_training \
+    --data_file data/binarized_text.bert-base-uncased.pickle \
+    --token_counts data/token_counts.bert-base-uncased.pickle \
+    --force # overwrites the `dump_path` if it already exists.
+```
+
+By default, this will launch a training on a single GPU (even if more are available on the cluster). Other parameters are available in the command line, please look in `train.py` or run `python train.py --help` to list them.
+
+We highly encourage you to distributed training for training DistilBert as the training corpus is quite large. Here's an example that runs a distributed training on a single node having 4 GPUs:
+
+```bash
+export NODE_RANK=0
+export N_NODES=1
+
+export N_GPU_NODE=4
+export WORLD_SIZE=4
+export MASTER_PORT=<AN_OPEN_PORT>
+export MASTER_ADDR=<I.P.>
+
+pkill -f 'python -u train.py'
+
+python -m torch.distributed.launch \
+    --nproc_per_node=$N_GPU_NODE \
+    --nnodes=$N_NODES \
+    --node_rank $NODE_RANK \
+    --master_addr $MASTER_ADDR \
+    --master_port $MASTER_PORT \
+    train.py \
+        --force \
+        --n_gpu $WORLD_SIZE \
+        --data_file data/dump_concat_wiki_toronto_bk.bert-base-uncased.pickle \
+        --token_counts data/token_counts_concat_wiki_toronto_bk.bert-base-uncased.pickle \
+        --dump_path serialization_dir/with_transform/last_word
+```
+
+**Tips** Starting distillated training with good initialization of the model weights is crucial to reach decent performance. In our experiments, we initialized our model from a few layers of the teacher (Bert) itself! Please refer to `scripts/extract_for_distil.py` to create a valid initialization checkpoint and use `--from_pretrained_weights` and `--from_pretrained_config` arguments to use this initialization for the distilled training!
+
+Happy distillation!

examples/distillation/dataset.py

+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Dataloaders to train DistilBERT
+    adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
+"""
+from typing import List
+import math
+from itertools import chain
+from collections import Counter
+import numpy as np
+import torch
+
+from utils import logger
+
+class Dataset:
+    def __init__(self,
+                 params,
+                 data):
+        self.params = params
+        self.tokens_per_batch = params.tokens_per_batch
+        self.batch_size = params.batch_size
+        self.shuffle = params.shuffle
+        self.group_by_size = params.group_by_size
+
+        self.token_ids = np.array(data)
+        self.lengths = np.uint16([len(t) for t in data])
+
+        self.check()
+        self.remove_long_sequences()
+        self.remove_empty_sequences()
+        self.check()
+        self.print_statistics()
+
+    def __len__(self):
+        return len(self.lengths)
+
+    def check(self):
+        """
+        Some sanity checks
+        """
+        assert len(self.token_ids) == len(self.lengths)
+
+    def remove_long_sequences(self):
+        """
+        Sequences that are too long are splitted by chunk of max_position_embeddings.
+        """
+        indices = self.lengths >= self.params.max_position_embeddings
+        logger.info(f'Splitting {sum(indices)} too long sequences.')
+
+        def divide_chunks(l, n):
+            return [l[i:i + n] for i in range(0, len(l), n)]
+
+        new_tok_ids = []
+        new_lengths = []
+        cls_id, sep_id = self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
+        max_len = self.params.max_position_embeddings
+
+        for seq_, len_ in zip(self.token_ids, self.lengths):
+            if len_ <= max_len:
+                new_tok_ids.append(seq_)
+                new_lengths.append(len_)
+            else:
+                sub_seqs = []
+                for sub_s in divide_chunks(seq_, max_len-2):
+                    if sub_s[0] != cls_id:
+                        sub_s = np.insert(sub_s, 0, cls_id)
+                    if sub_s[-1] != sep_id:
+                        sub_s = np.insert(sub_s, len(sub_s), cls_id)
+                    assert len(sub_s) <= max_len
+                    sub_seqs.append(sub_s)
+
+                new_tok_ids.extend(sub_seqs)
+                new_lengths.extend([len(l) for l in sub_seqs])
+
+        self.token_ids = np.array(new_tok_ids)
+        self.lengths = np.array(new_lengths)
+
+    def remove_empty_sequences(self):
+        """
+        Too short sequences are simply removed. This could be tunedd.
+        """
+        init_size = len(self)
+        indices = self.lengths > 5
+        self.token_ids = self.token_ids[indices]
+        self.lengths = self.lengths[indices]
+        new_size = len(self)
+        logger.info(f'Remove {init_size - new_size} too short (<=5 tokens) sequences.')
+
+    def print_statistics(self):
+        """
+        Print some statistics on the corpus. Only the master process.
+        """
+        if not self.params.is_master:
+            return
+        logger.info(f'{len(self)} sequences')
+        # data_len = sum(self.lengths)
+        # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
+        # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
+
+        # unk_idx = self.params.special_tok_ids['unk_token']
+        # nb_unkown = sum([(t==unk_idx).sum() for t in self.token_ids])
+        # logger.info(f'{nb_unkown} unknown tokens (covering {100*nb_unkown/data_len:.2f}% of the data)')
+
+    def select_data(self, a: int, b: int):
+        """
+        Select a subportion of the data.
+        """
+        n_sequences = len(self)
+        assert 0 <= a < b <= n_sequences, ValueError(f'`0 <= a < b <= n_sequences` is not met with a={a} and b={b}')
+
+        logger.info(f'Selecting sequences from {a} to {b} (excluded).')
+        self.token_ids = self.token_ids[a:b]
+        self.lengths = self.lengths[a:b]
+
+        self.check()
+
+    def split(self):
+        """
+        Distributed training: split the data accross the processes.
+        """
+        assert self.params.n_gpu > 1
+        logger.info('Splitting the data accross the processuses.')
+        n_seq = len(self)
+        n_seq_per_procesus = n_seq // self.params.world_size
+        a = n_seq_per_procesus * self.params.global_rank
+        b = a + n_seq_per_procesus
+        self.select_data(a=a, b=b)
+
+    def batch_sequences(self,
+                        token_ids: List[List[int]],
+                        lengths: List[int]):
+        """
+        Do the padding and transform into torch.tensor.
+        """
+        assert len(token_ids) == len(lengths)
+
+        # Max for paddings
+        max_seq_len_ = max(lengths)
+
+        # Pad token ids
+        pad_idx = self.params.special_tok_ids['pad_token']
+        tk_ = [list(t.astype(int)) + [pad_idx]*(max_seq_len_-len(t)) for t in token_ids]
+        assert len(tk_) == len(token_ids)
+        assert all(len(t) == max_seq_len_ for t in tk_)
+
+        tk_t = torch.tensor(tk_)                  # (bs, max_seq_len_)
+        lg_t = torch.tensor(lengths.astype(int))  # (bs)
+        return tk_t, lg_t
+
+    def get_batches_iterator(self,
+                             batches):
+        """
+        Return an iterator over batches.
+        """
+        for sequences_ids in batches:
+            token_ids, lengths = self.batch_sequences(self.token_ids[sequences_ids],
+                                                    self.lengths[sequences_ids])
+            yield (token_ids, lengths)
+
+    def get_iterator(self,
+                     seed: int = None):
+        """
+        Return a data iterator.
+        """
+        rng = np.random.RandomState(seed)
+
+        n_sequences = len(self)
+        indices = np.arange(n_sequences)
+
+        if self.group_by_size:
+            indices = indices[np.argsort(self.lengths[indices], kind='mergesort')]
+
+        if self.tokens_per_batch == -1:
+            batches = np.array_split(indices, math.ceil(len(indices) * 1. / self.batch_size))
+        else:
+            assert self.tokens_per_batch > 0
+            batch_ids = np.cumsum(self.lengths[indices]) // self.tokens_per_batch
+            _, bounds = np.unique(batch_ids, return_index=True)
+            batches = [indices[bounds[i]:bounds[i + 1]] for i in range(len(bounds) - 1)]
+            if bounds[-1] < len(indices):
+                batches.append(indices[bounds[-1]:])
+
+        if self.shuffle:
+            rng.shuffle(batches)
+
+        assert n_sequences == sum([len(x) for x in batches])
+        assert self.lengths[indices].sum() == sum([self.lengths[x].sum() for x in batches])
+
+        return self.get_batches_iterator(batches=batches)

examples/distillation/distiller.py

+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" The distiller to distil DistilBERT
+    adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
+"""
+import os
+import math
+from tensorboardX import SummaryWriter
+from tqdm import trange, tqdm
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from pytorch_transformers import AdamW, WarmupLinearSchedule
+
+from utils import logger
+from dataset import Dataset
+
+class Distiller:
+    def __init__(self,
+                 params: dict,
+                 dataloader: Dataset,
+                 token_probs: torch.tensor,
+                 student: nn.Module,
+                 teacher: nn.Module):
+        logger.info('Initializing Distiller')
+        self.params = params
+        self.dump_path = params.dump_path
+        self.multi_gpu = params.multi_gpu
+        self.fp16 = params.fp16
+
+        self.student = student
+        self.teacher = teacher
+
+        self.dataloader = dataloader
+        if self.params.n_gpu > 1:
+            self.dataloader.split()
+        self.get_iterator(seed=params.seed)
+
+        self.temperature = params.temperature
+        assert self.temperature > 0.
+
+        self.alpha_ce = params.alpha_ce
+        self.alpha_mlm = params.alpha_mlm
+        self.alpha_mse = params.alpha_mse
+        assert self.alpha_ce >= 0.
+        assert self.alpha_mlm >= 0.
+        assert self.alpha_mse >= 0.
+        assert self.alpha_ce + self.alpha_mlm + self.alpha_mse > 0.
+
+        self.mlm_mask_prop = params.mlm_mask_prop
+        assert 0.0 <= self.mlm_mask_prop <= 1.0
+        assert params.word_mask + params.word_keep + params.word_rand == 1.0
+        self.pred_probs = torch.FloatTensor([params.word_mask, params.word_keep, params.word_rand])
+        self.pred_probs = self.pred_probs.to(f'cuda:{params.local_rank}') if params.n_gpu > 0 else self.pred_probs
+        self.token_probs = token_probs.to(f'cuda:{params.local_rank}') if params.n_gpu > 0 else token_probs
+        if self.fp16:
+            self.pred_probs = self.pred_probs.half()
+            self.token_probs = self.token_probs.half()
+
+        self.epoch = 0
+        self.n_iter = 0
+        self.n_total_iter = 0
+        self.n_sequences_epoch = 0
+        self.total_loss_epoch = 0
+        self.last_loss = 0
+        self.last_loss_ce = 0
+        self.last_loss_mlm = 0
+        self.last_loss_mse = 0
+
+        self.ce_loss_fct = nn.KLDivLoss(reduction='batchmean')
+        self.mlm_loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
+        self.mse_loss_fct = nn.MSELoss(reduction='sum')
+
+        logger.info('--- Initializing model optimizer')
+        assert params.gradient_accumulation_steps >= 1
+        self.num_steps_epoch = int(len(self.dataloader) / params.batch_size) + 1
+        num_train_optimization_steps = int(self.num_steps_epoch / params.gradient_accumulation_steps * params.n_epoch) + 1
+        warmup_steps = math.ceil(num_train_optimization_steps * params.warmup_prop)
+
+        no_decay = ['bias', 'LayerNorm.weight']
+        optimizer_grouped_parameters = [
+            {'params': [p for n, p in student.named_parameters() if not any(nd in n for nd in no_decay) and p.requires_grad], 'weight_decay': params.weight_decay},
+            {'params': [p for n, p in student.named_parameters() if any(nd in n for nd in no_decay) and p.requires_grad], 'weight_decay': 0.0}
+        ]
+        logger.info("------ Number of trainable parameters (student): %i" % sum([p.numel() for p in self.student.parameters() if p.requires_grad]))
+        logger.info("------ Number of parameters (student): %i" % sum([p.numel() for p in self.student.parameters()]))
+        self.optimizer = AdamW(optimizer_grouped_parameters,
+                               lr=params.learning_rate,
+                               eps=params.adam_epsilon,
+                               betas=(0.9, 0.98))
+        self.scheduler = WarmupLinearSchedule(self.optimizer,
+                                              warmup_steps=warmup_steps,
+                                              t_total=num_train_optimization_steps)
+
+        if self.fp16:
+            try:
+                from apex import amp
+            except ImportError:
+                raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
+            logger.info(f"Using fp16 training: {self.params.fp16_opt_level} level")
+            self.student, self.optimizer = amp.initialize(self.student,
+                                                          self.optimizer,
+                                                          opt_level=self.params.fp16_opt_level)
+            self.teacher = self.teacher.half()
+
+        if self.multi_gpu:
+            if self.fp16:
+                from apex.parallel import DistributedDataParallel
+                logger.info("Using apex.parallel.DistributedDataParallel for distributed training.")
+                self.student = DistributedDataParallel(self.student)
+            else:
+                from torch.nn.parallel import DistributedDataParallel
+                logger.info("Using nn.parallel.DistributedDataParallel for distributed training.")
+                self.student = DistributedDataParallel(self.student,
+                                                       device_ids=[params.local_rank],
+                                                       output_device=params.local_rank)
+
+        self.is_master = params.is_master
+        if self.is_master:
+            logger.info('--- Initializing Tensorboard')
+            self.tensorboard = SummaryWriter(log_dir=os.path.join(self.dump_path, 'log', 'train'))
+            self.tensorboard.add_text(tag='config', text_string=str(self.params), global_step=0)
+
+    def get_iterator(self,
+                     seed: int = None):
+        """
+        Initialize the data iterator.
+        Each process has its own data iterator (iterating on his own random portion of the dataset).
+
+        Input:
+        ------
+            seed: `int` - The random seed.
+        """
+        logger.info('--- Initializing Data Iterator')
+        self.data_iterator = self.dataloader.get_iterator(seed=seed)
+
+    def get_batch(self):
+        """
+        Call the data iterator to output a new batch.
+        If the data iterator went through the whole dataset, create a new iterator.
+        """
+        assert hasattr(self, 'data_iterator')
+        try:
+            x = next(self.data_iterator)
+        except StopIteration:
+            logger.warning('--- Went through the whole dataset. Creating new data iterator.')
+            self.data_iterator = self.dataloader.get_iterator()
+            x = next(self.data_iterator)
+        return x
+
+    def prepare_batch(self,
+                      batch):
+        """
+        Prepare the batch: from the token_ids and the lenghts, compute the attention mask and the masked label for MLM.
+
+        Input:
+        ------
+            batch: `Tuple`
+                token_ids: `torch.tensor(bs, seq_length)` - The token ids for each of the sequence. It is padded.
+                lengths: `torch.tensor(bs)` - The lengths of each of the sequences in the batch.
+
+        Output:
+        -------
+            token_ids: `torch.tensor(bs, seq_length)` - The token ids after the modifications for MLM.
+            attn_mask: `torch.tensor(bs, seq_length)` - The attention mask for the self-attention.
+            mlm_labels: `torch.tensor(bs, seq_length)` - The masked languge modeling labels. There is a -1 where there is nothing to predict.
+        """
+        token_ids, lengths = batch
+        token_ids, lengths = self.round_batch(x=token_ids, lengths=lengths)
+        assert token_ids.size(0) == lengths.size(0)
+
+        attn_mask = (torch.arange(token_ids.size(1), dtype=torch.long, device=lengths.device) < lengths[:, None])
+
+        bs, max_seq_len = token_ids.size()
+        mlm_labels = token_ids.new(token_ids.size()).copy_(token_ids)
+
+        x_prob = self.token_probs[token_ids.flatten()]
+        n_tgt = math.ceil(self.mlm_mask_prop * lengths.sum().item())
+        tgt_ids = torch.multinomial(x_prob / x_prob.sum(), n_tgt, replacement=False)
+        pred_mask = torch.zeros(bs * max_seq_len, dtype=torch.uint8, device=token_ids.device)
+        pred_mask[tgt_ids] = 1
+        pred_mask = pred_mask.view(bs, max_seq_len)
+
+        pred_mask[token_ids == self.params.special_tok_ids['pad_token']] = 0
+
+        # mask a number of words == 0 [8] (faster with fp16)
+        if self.fp16:
+            n1 = pred_mask.sum().item()
+            if n1 > 8:
+                pred_mask = pred_mask.view(-1)
+                n2 = max(n1 % 8, 8 * (n1 // 8))
+                if n2 != n1:
+                    pred_mask[torch.nonzero(pred_mask).view(-1)[:n1-n2]] = 0
+                pred_mask = pred_mask.view(bs, max_seq_len)
+                assert pred_mask.sum().item() % 8 == 0, pred_mask.sum().item()
+
+        _token_ids_real = token_ids[pred_mask]
+        _token_ids_rand = _token_ids_real.clone().random_(self.params.vocab_size)
+        _token_ids_mask = _token_ids_real.clone().fill_(self.params.special_tok_ids['mask_token'])
+        probs = torch.multinomial(self.pred_probs, len(_token_ids_real), replacement=True)
+        _token_ids = _token_ids_mask * (probs == 0).long() + _token_ids_real * (probs == 1).long() + _token_ids_rand * (probs == 2).long()
+        token_ids = token_ids.masked_scatter(pred_mask, _token_ids)
+
+        mlm_labels[1-pred_mask] = -1
+
+        return token_ids, attn_mask, mlm_labels
+
+    def round_batch(self,
+                    x: torch.tensor,
+                    lengths: torch.tensor):
+        """
+        For float16 only.
+        Sub-sample sentences in a batch, and add padding, so that each dimension is a multiple of 8.
+
+        Input:
+        ------
+            x: `torch.tensor(bs, seq_length)` - The token ids.
+            lengths: `torch.tensor(bs, seq_length)` - The lengths of each of the sequence in the batch.
+
+        Output:
+        -------
+            x:  `torch.tensor(new_bs, new_seq_length)` - The updated token ids.
+            lengths: `torch.tensor(new_bs, new_seq_length)` - The updated lengths.
+        """
+        if not self.fp16 or len(lengths) < 8:
+            return x, lengths
+
+        # number of sentences == 0 [8]
+        bs1 = len(lengths)
+        bs2 = 8 * (bs1 // 8)
+        assert bs2 > 0 and bs2 % 8 == 0
+        if bs1 != bs2:
+            idx = torch.randperm(bs1)[:bs2]
+            lengths = lengths[idx]
+            slen = lengths.max().item()
+            x = x[idx, :slen]
+        else:
+            idx = None
+
+        # sequence length == 0 [8]
+        ml1 = x.size(1)
+        if ml1 % 8 != 0:
+            pad = 8 - (ml1 % 8)
+            ml2 = ml1 + pad
+            pad_id = self.params.special_tok_ids['pad_token']
+            padding_tensor = torch.zeros(bs2, pad, dtype=torch.long, device=x.device).fill_(pad_id)
+            x = torch.cat([x, padding_tensor], 1)
+            assert x.size() == (bs2, ml2)
+
+        assert x.size(0) % 8 == 0
+        assert x.size(1) % 8 == 0
+        return x, lengths
+
+    def train(self):
+        """
+        The real training loop.
+        """
+        if self.is_master: logger.info('Starting training')
+        self.student.train()
+        self.teacher.eval()
+
+        for _ in range(self.params.n_epoch):
+            if self.is_master: logger.info(f'--- Starting epoch {self.epoch}/{self.params.n_epoch-1}')
+
+            iter_bar = trange(self.num_steps_epoch, desc="-Iter", disable=self.params.local_rank not in [-1, 0])
+            for __ in range(self.num_steps_epoch):
+                batch = self.get_batch()
+                if self.params.n_gpu > 0:
+                    batch = tuple(t.to(f'cuda:{self.params.local_rank}') for t in batch)
+                token_ids, attn_mask, mlm_labels = self.prepare_batch(batch=batch)
+
+                self.step(input_ids=token_ids, attention_mask=attn_mask, mlm_labels=mlm_labels)
+
+                iter_bar.update()
+                iter_bar.set_postfix({'Last_loss': f'{self.last_loss:.2f}',
+                                      'Avg_cum_loss': f'{self.total_loss_epoch/self.n_iter:.2f}'})
+            iter_bar.close()
+
+            if self.is_master: logger.info(f'--- Ending epoch {self.epoch}/{self.params.n_epoch-1}')
+            self.end_epoch()
+
+        if self.is_master: logger.info('Training is finished')
+
+    def step(self,
+             input_ids: torch.tensor,
+             attention_mask: torch.tensor,
+             mlm_labels: torch.tensor):
+        """
+        One optimization step: forward of student AND teacher, backward on the loss (for gradient accumulation),
+        and possibly a parameter update (depending on the gradient accumulation).
+
+        Input:
+        ------
+        input_ids: `torch.tensor(bs, seq_length)` - The token ids.
+        attention_mask: `torch.tensor(bs, seq_length)` - The attention mask for self attention.
+        mlm_labels: `torch.tensor(bs, seq_length)` - The masked language modeling labels.
+        """
+        s_logits = self.student(input_ids=input_ids, attention_mask=attention_mask)[0]     # (bs, seq_length, voc_size)
+        with torch.no_grad():
+            t_logits = self.teacher(input_ids=input_ids, attention_mask=attention_mask)[0] # (bs, seq_length, voc_size)
+        assert s_logits.size() == t_logits.size()
+
+        #https://github.com/peterliht/knowledge-distillation-pytorch/blob/master/model/net.py#L100
+        #https://github.com/peterliht/knowledge-distillation-pytorch/issues/2
+        if self.params.restrict_ce_to_mask:
+            mask = (mlm_labels>-1).unsqueeze(-1).expand_as(s_logits)   # (bs, seq_lenth, voc_size)
+        else:
+            mask = attention_mask.unsqueeze(-1).expand_as(s_logits)    # (bs, seq_lenth, voc_size)
+        s_logits_slct = torch.masked_select(s_logits, mask)            # (bs * seq_length * voc_size) modulo the 1s in mask
+        s_logits_slct = s_logits_slct.view(-1, s_logits.size(-1))      # (bs * seq_length, voc_size) modulo the 1s in mask
+        t_logits_slct = torch.masked_select(t_logits, mask)            # (bs * seq_length * voc_size) modulo the 1s in mask
+        t_logits_slct = t_logits_slct.view(-1, s_logits.size(-1))      # (bs * seq_length, voc_size) modulo the 1s in mask
+        assert t_logits_slct.size() == s_logits_slct.size()
+
+        loss_ce = self.ce_loss_fct(F.log_softmax(s_logits_slct/self.temperature, dim=-1),
+                                   F.softmax(t_logits_slct/self.temperature, dim=-1)) * (self.temperature)**2
+        loss = self.alpha_ce*loss_ce
+        if self.alpha_mlm > 0.:
+            loss_mlm = self.mlm_loss_fct(s_logits.view(-1, s_logits.size(-1)), mlm_labels.view(-1))
+            loss += self.alpha_mlm * loss_mlm
+        if self.alpha_mse > 0.:
+            loss_mse = self.mse_loss_fct(s_logits_slct, t_logits_slct)/s_logits_slct.size(0) # Reproducing batchmean reduction
+            loss += self.alpha_mse * loss_mse
+
+        self.total_loss_epoch += loss.item()
+        self.last_loss = loss.item()
+        self.last_loss_ce = loss_ce.item()
+        if self.alpha_mlm > 0.:
+            self.last_loss_mlm = loss_mlm.item()
+        if self.alpha_mse > 0.:
+            self.last_loss_mse = loss_mse.item()
+
+        self.optimize(loss)
+
+        self.n_sequences_epoch += input_ids.size(0)
+
+    def optimize(self,
+                 loss):
+        """
+        Normalization on the loss (gradient accumulation or distributed training), followed by
+        backward pass on the loss, possibly followed by a parameter update (depending on the gradient accumulation).
+        Also update the metrics for tensorboard.
+        """
+        # Check for NaN
+        if (loss != loss).data.any():
+            logger.error('NaN detected')
+            exit()
+
+        if self.multi_gpu:
+            loss = loss.mean()
+        if self.params.gradient_accumulation_steps > 1:
+            loss = loss / self.params.gradient_accumulation_steps
+
+        if self.fp16:
+            from apex import amp
+            with amp.scale_loss(loss, self.optimizer) as scaled_loss:
+                scaled_loss.backward()
+        else:
+            loss.backward()
+
+        self.iter()
+        if self.n_iter % self.params.gradient_accumulation_steps == 0:
+            if self.fp16:
+                torch.nn.utils.clip_grad_norm_(amp.master_params(self.optimizer), self.params.max_grad_norm)
+            else:
+                torch.nn.utils.clip_grad_norm_(self.student.parameters(), self.params.max_grad_norm)
+            self.scheduler.step()
+            self.optimizer.step()
+            self.optimizer.zero_grad()
+
+    def iter(self):
+        """
+        Update global counts, write to tensorboard and save checkpoint.
+        """
+        self.n_iter += 1
+        self.n_total_iter += 1
+
+        if self.n_total_iter % self.params.log_interval == 0:
+            self.log_tensorboard()
+        if self.n_total_iter % self.params.checkpoint_interval == 0:
+            self.save_checkpoint()
+
+    def log_tensorboard(self):
+        """
+        Log into tensorboard. Only by the master process.
+        """
+        if not self.is_master:
+            return
+
+        for param_name, param in self.student.named_parameters():
+            self.tensorboard.add_scalar(tag='parameter_mean/' + param_name, scalar_value=param.data.mean(), global_step=self.n_total_iter)
+            self.tensorboard.add_scalar(tag='parameter_std/' + param_name, scalar_value=param.data.std(), global_step=self.n_total_iter)
+            if param.grad is None:
+                continue
+            self.tensorboard.add_scalar(tag="grad_mean/" + param_name, scalar_value=param.grad.data.mean(),global_step=self.n_total_iter)
+            self.tensorboard.add_scalar(tag="grad_std/" + param_name, scalar_value=param.grad.data.std(), global_step=self.n_total_iter)
+
+        self.tensorboard.add_scalar(tag="losses/cum_avg_loss_epoch", scalar_value=self.total_loss_epoch/self.n_iter, global_step=self.n_total_iter)
+        self.tensorboard.add_scalar(tag="losses/loss", scalar_value=self.last_loss, global_step=self.n_total_iter)
+        self.tensorboard.add_scalar(tag="losses/loss_ce", scalar_value=self.last_loss_ce, global_step=self.n_total_iter)
+        if self.alpha_mlm > 0.:
+            self.tensorboard.add_scalar(tag="losses/loss_mlm", scalar_value=self.last_loss_mlm, global_step=self.n_total_iter)
+        if self.alpha_mse > 0.:
+            self.tensorboard.add_scalar(tag="losses/loss_mse", scalar_value=self.last_loss_mse, global_step=self.n_total_iter)
+        self.tensorboard.add_scalar(tag="learning_rate/lr", scalar_value=self.scheduler.get_lr()[0], global_step=self.n_total_iter)
+
+    def end_epoch(self):
+        """
+        Finally arrived at the end of epoch (full pass on dataset).
+        Do some tensorboard logging and checkpoint saving.
+        """
+        logger.info(f'{self.n_sequences_epoch} sequences have been trained during this epoch.')
+
+        if self.is_master:
+            self.save_checkpoint(checkpoint_name=f'model_epoch_{self.epoch}.pth')
+            self.tensorboard.add_scalar(tag='epoch/loss', scalar_value=self.total_loss_epoch/self.n_iter, global_step=self.epoch)
+
+        self.epoch += 1
+        self.n_sequences_epoch = 0
+        self.n_iter = 0
+        self.total_loss_epoch = 0
+
+    def save_checkpoint(self,
+                        checkpoint_name: str = 'checkpoint.pth'):
+        """
+        Save the current state. Only by the master process.
+        """
+        if not self.is_master:
+            return
+        mdl_to_save = self.student.module if hasattr(self.student, 'module') else self.student
+        mdl_to_save.config.save_pretrained(self.dump_path)
+        state_dict = mdl_to_save.state_dict()
+        torch.save(state_dict, os.path.join(self.dump_path, checkpoint_name))

examples/distillation/requirements.txt

+gitpython==3.0.2

examples/distillation/scripts/binarized_data.py

+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Preprocessing script before training DistilBERT.
+"""
+import argparse
+import pickle
+import random
+import time
+import numpy as np
+from pytorch_transformers import BertTokenizer
+
+from ..utils import logger
+
+def main():
+    parser = argparse.ArgumentParser(description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).")
+    parser.add_argument('--file_path', type=str, default='data/dump.txt',
+                        help='The path to the data.')
+    parser.add_argument('--bert_tokenizer', type=str, default='bert-base-uncased',
+                        help="The tokenizer to use.")
+    parser.add_argument('--dump_file', type=str, default='data/dump',
+                        help='The dump file prefix.')
+    args = parser.parse_args()
+
+
+    logger.info(f'Loading Tokenizer ({args.bert_tokenizer})')
+    bert_tokenizer = BertTokenizer.from_pretrained(args.bert_tokenizer)
+
+
+    logger.info(f'Loading text from {args.file_path}')
+    with open(args.file_path, 'r', encoding='utf8') as fp:
+        data = fp.readlines()
+
+
+    logger.info(f'Start encoding')
+    logger.info(f'{len(data)} examples to process.')
+
+    rslt = []
+    iter = 0
+    interval = 10000
+    start = time.time()
+    for text in data:
+        text = f'[CLS] {text.strip()} [SEP]'
+        token_ids = bert_tokenizer.encode(text)
+        rslt.append(token_ids)
+
+        iter += 1
+        if iter % interval == 0:
+            end = time.time()
+            logger.info(f'{iter} examples processed. - {(end-start)/interval:.2f}s/expl')
+            start = time.time()
+    logger.info('Finished binarization')
+    logger.info(f'{len(data)} examples processed.')
+
+
+    dp_file = f'{args.dump_file}.{args.bert_tokenizer}.pickle'
+    rslt_ = [np.uint16(d) for d in rslt]
+    random.shuffle(rslt_)
+    logger.info(f'Dump to {dp_file}')
+    with open(dp_file, 'wb') as handle:
+        pickle.dump(rslt_, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file

examples/distillation/scripts/extract_for_distil.py

+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Preprocessing script before training DistilBERT.
+"""
+from pytorch_transformers import BertForPreTraining
+import torch
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="Extraction some layers of the full BertForPreTraining for Transfer Learned Distillation")
+    parser.add_argument("--bert_model", default='bert-base-uncased', type=str)
+    parser.add_argument("--dump_checkpoint", default='serialization_dir/transfer_learning_checkpoint_0247911.pth', type=str)
+    parser.add_argument("--vocab_transform", action='store_true')
+    args = parser.parse_args()
+
+
+    model = BertForPreTraining.from_pretrained(args.bert_model)
+
+    state_dict = model.state_dict()
+    compressed_sd = {}
+
+    for w in ['word_embeddings', 'position_embeddings']:
+        compressed_sd[f'distilbert.embeddings.{w}.weight'] = \
+            state_dict[f'bert.embeddings.{w}.weight']
+    for w in ['weight', 'bias']:
+        compressed_sd[f'distilbert.embeddings.LayerNorm.{w}'] = \
+            state_dict[f'bert.embeddings.LayerNorm.{w}']
+
+    std_idx = 0
+    for teacher_idx in [0, 2, 4, 7, 9, 11]:
+        for w in ['weight', 'bias']:
+            compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}'] = \
+                state_dict[f'bert.encoder.layer.{teacher_idx}.attention.self.query.{w}']
+            compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}'] = \
+                state_dict[f'bert.encoder.layer.{teacher_idx}.attention.self.key.{w}']
+            compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}'] = \
+                state_dict[f'bert.encoder.layer.{teacher_idx}.attention.self.value.{w}']
+
+            compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}'] = \
+                state_dict[f'bert.encoder.layer.{teacher_idx}.attention.output.dense.{w}']
+            compressed_sd[f'distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}'] = \
+                state_dict[f'bert.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}']
+
+            compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}'] = \
+                state_dict[f'bert.encoder.layer.{teacher_idx}.intermediate.dense.{w}']
+            compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}'] = \
+                state_dict[f'bert.encoder.layer.{teacher_idx}.output.dense.{w}']
+            compressed_sd[f'distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}'] = \
+                state_dict[f'bert.encoder.layer.{teacher_idx}.output.LayerNorm.{w}']
+        std_idx += 1
+
+    compressed_sd[f'vocab_projector.weight'] = state_dict[f'cls.predictions.decoder.weight']
+    compressed_sd[f'vocab_projector.bias'] = state_dict[f'cls.predictions.bias']
+    if args.vocab_transform:
+        for w in ['weight', 'bias']:
+            compressed_sd[f'vocab_transform.{w}'] = state_dict[f'cls.predictions.transform.dense.{w}']
+            compressed_sd[f'vocab_layer_norm.{w}'] = state_dict[f'cls.predictions.transform.LayerNorm.{w}']
+
+    print(f'N layers selected for distillation: {std_idx}')
+    print(f'Number of params transfered for distillation: {len(compressed_sd.keys())}')
+
+    print(f'Save transfered checkpoint to {args.dump_checkpoint}.')
+    torch.save(compressed_sd, args.dump_checkpoint)

examples/distillation/scripts/token_counts.py

+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Preprocessing script before training DistilBERT.
+"""
+from collections import Counter
+import argparse
+import pickle
+
+from utils import logger
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)")
+    parser.add_argument("--data_file", type=str, default="data/dump.bert-base-uncased.pickle",
+                        help="The binarized dataset."
+    parser.add_argument("--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle",
+                        help="The dump file.")
+    parser.add_argument("--vocab_size", default=30522, type=int)
+    args = parser.parse_args()
+
+    logger.info(f'Loading data from {args.data_file}')
+    with open(args.data_file, 'rb') as fp:
+        data = pickle.load(fp)
+
+    logger.info('Counting occurences for MLM.')
+    counter = Counter()
+    for tk_ids in data:
+        counter.update(tk_ids)
+    counts = [0]*args.vocab_size
+    for k, v in counter.items():
+        counts[k] = v
+
+    logger.info(f'Dump to {args.token_counts_dump}')
+    with open(args.token_counts_dump, 'wb') as handle:
+        pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)

examples/distillation/train.py

+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Training DistilBERT.
+"""
+import os
+import argparse
+import pickle
+import json
+import shutil
+import numpy as np
+import torch
+
+from pytorch_transformers import BertTokenizer, BertForMaskedLM
+from pytorch_transformers import DistilBertForMaskedLM, DistilBertConfig
+
+from distiller import Distiller
+from utils import git_log, logger, init_gpu_params, set_seed
+from dataset import Dataset
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Training")
+
+    parser.add_argument("--dump_path", type=str, required=True,
+                        help="The output directory (log, checkpoints, parameters, etc.)")
+    parser.add_argument("--data_file", type=str, required=True,
+                        help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence.")
+    parser.add_argument("--token_counts", type=str, required=True,
+                        help="The token counts in the data_file for MLM.")
+    parser.add_argument("--force", action='store_true',
+                        help="Overwrite dump_path if it already exists.")
+
+    parser.add_argument("--vocab_size", default=30522, type=int,
+                        help="The vocabulary size.")
+    parser.add_argument("--max_position_embeddings", default=512, type=int,
+                        help="Maximum sequence length we can model (including [CLS] and [SEP]).")
+    parser.add_argument("--sinusoidal_pos_embds", action='store_false',
+                        help="If true, the position embeddings are simply fixed with sinusoidal embeddings.")
+    parser.add_argument("--n_layers", default=6, type=int,
+                        help="Number of Transformer blocks.")
+    parser.add_argument("--n_heads", default=12, type=int,
+                        help="Number of heads in the self-attention module.")
+    parser.add_argument("--dim", default=768, type=int,
+                        help="Dimension through the network. Must be divisible by n_heads")
+    parser.add_argument("--hidden_dim", default=3072, type=int,
+                        help="Intermediate dimension in the FFN.")
+    parser.add_argument("--dropout", default=0.1, type=float,
+                        help="Dropout.")
+    parser.add_argument("--attention_dropout", default=0.1, type=float,
+                        help="Dropout in self-attention.")
+    parser.add_argument("--activation", default='gelu', type=str,
+                        help="Activation to use in self-attention")
+    parser.add_argument("--tie_weights_", action='store_false',
+                        help="If true, we tie the embeddings matrix with the projection over the vocabulary matrix. Default is true.")
+
+    parser.add_argument("--from_pretrained_weights", default=None, type=str,
+                        help="Load student initialization checkpoint.")
+    parser.add_argument("--from_pretrained_config", default=None, type=str,
+                        help="Load student initialization architecture config.")
+    parser.add_argument("--bert_model", default='bert-base-uncased', type=str,
+                        help="The teacher BERT model.")
+
+    parser.add_argument("--temperature", default=2., type=float,
+                        help="Temperature for the softmax temperature.")
+    parser.add_argument("--alpha_ce", default=0.5, type=float,
+                        help="Linear weight for the distillation loss. Must be >=0.")
+    parser.add_argument("--alpha_mlm", default=0.5, type=float,
+                        help="Linear weight for the MLM loss. Must be >=0.")
+    parser.add_argument("--alpha_mse", default=0.0, type=float,
+                        help="Linear weight of the MSE loss. Must be >=0.")
+    parser.add_argument("--mlm_mask_prop", default=0.15, type=float,
+                        help="Proportion of tokens for which we need to make a prediction.")
+    parser.add_argument("--word_mask", default=0.8, type=float,
+                        help="Proportion of tokens to mask out.")
+    parser.add_argument("--word_keep", default=0.1, type=float,
+                        help="Proportion of tokens to keep.")
+    parser.add_argument("--word_rand", default=0.1, type=float,
+                        help="Proportion of tokens to randomly replace.")
+    parser.add_argument("--mlm_smoothing", default=0.7, type=float,
+                        help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).")
+    parser.add_argument("--restrict_ce_to_mask", action='store_true',
+                        help="If true, compute the distilation loss only the [MLM] prediction distribution.")
+
+    parser.add_argument("--n_epoch", type=int, default=3,
+                        help="Number of pass on the whole dataset.")
+    parser.add_argument("--batch_size", type=int, default=5,
+                        help="Batch size (for each process).")
+    parser.add_argument("--tokens_per_batch", type=int, default=-1,
+                        help="If specified, modify the batches so that they have approximately this number of tokens.")
+    parser.add_argument("--shuffle", action='store_false',
+                        help="If true, shuffle the sequence order. Default is true.")
+    parser.add_argument("--group_by_size", action='store_false',
+                        help="If true, group sequences that have similar length into the same batch. Default is true.")
+
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=50,
+                        help="Gradient accumulation for larger training batches.")
+    parser.add_argument("--warmup_prop", default=0.05, type=float,
+                        help="Linear warmup proportion.")
+    parser.add_argument("--weight_decay", default=0.0, type=float,
+                        help="Weight deay if we apply some.")
+    parser.add_argument("--learning_rate", default=5e-4, type=float,
+                        help="The initial learning rate for Adam.")
+    parser.add_argument("--adam_epsilon", default=1e-6, type=float,
+                        help="Epsilon for Adam optimizer.")
+    parser.add_argument("--max_grad_norm", default=5.0, type=float,
+                        help="Max gradient norm.")
+    parser.add_argument("--initializer_range", default=0.02, type=float,
+                        help="Random initialization range.")
+
+    parser.add_argument('--fp16', action='store_true',
+                        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
+    parser.add_argument('--fp16_opt_level', type=str, default='O1',
+                        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
+                             "See details at https://nvidia.github.io/apex/amp.html")
+    parser.add_argument("--n_gpu", type=int, default=1,
+                        help="Number of GPUs in the node.")
+    parser.add_argument("--local_rank", type=int, default=-1,
+                        help="Distributed training - Local rank")
+    parser.add_argument("--seed", type=int, default=56,
+                        help="Random seed")
+
+    parser.add_argument("--log_interval", type=int, default=500,
+                        help="Tensorboard logging interval.")
+    parser.add_argument("--checkpoint_interval", type=int, default=4000,
+                        help="Checkpoint interval.")
+    args = parser.parse_args()
+
+
+    ## ARGS ##
+    init_gpu_params(args)
+    set_seed(args)
+    if args.is_master:
+        if os.path.exists(args.dump_path):
+            if not args.force:
+                raise ValueError(f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite it'
+                                   'Use `--force` if you want to overwrite it')
+            else:
+                shutil.rmtree(args.dump_path)
+
+        if not os.path.exists(args.dump_path):
+            os.makedirs(args.dump_path)
+        logger.info(f'Experiment will be dumped and logged in {args.dump_path}')
+
+
+        ### SAVE PARAMS ###
+        logger.info(f'Param: {args}')
+        with open(os.path.join(args.dump_path, 'parameters.json'), 'w') as f:
+            json.dump(vars(args), f, indent=4)
+        git_log(args.dump_path)
+    assert (args.from_pretrained_weights is None and args.from_pretrained_config is None) or \
+           (args.from_pretrained_weights is not None and args.from_pretrained_config is not None)
+
+
+    ### TOKENIZER ###
+    bert_tokenizer = BertTokenizer.from_pretrained(args.bert_model)
+    special_tok_ids = {}
+    for tok_name, tok_symbol in bert_tokenizer.special_tokens_map.items():
+        idx = bert_tokenizer.all_special_tokens.index(tok_symbol)
+        special_tok_ids[tok_name] = bert_tokenizer.all_special_ids[idx]
+    logger.info(f'Special tokens {special_tok_ids}')
+    args.special_tok_ids = special_tok_ids
+
+
+    ## DATA LOADER ##
+    logger.info(f'Loading data from {args.data_file}')
+    with open(args.data_file, 'rb') as fp:
+        data = pickle.load(fp)
+
+
+    assert os.path.isfile(args.token_counts)
+    logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)')
+    with open(args.token_counts, 'rb') as fp:
+        counts = pickle.load(fp)
+        assert len(counts) == args.vocab_size
+    token_probs = np.maximum(counts, 1) ** -args.mlm_smoothing
+    for idx in special_tok_ids.values():
+        token_probs[idx] = 0.  # do not predict special tokens
+    token_probs = torch.from_numpy(token_probs)
+
+
+    train_dataloader = Dataset(params=args, data=data)
+    logger.info(f'Data loader created.')
+
+
+    ## STUDENT ##
+    if args.from_pretrained_weights is not None:
+        assert os.path.isfile(os.path.join(args.from_pretrained_weights))
+        assert os.path.isfile(os.path.join(args.from_pretrained_config))
+        logger.info(f'Loading pretrained weights from {args.from_pretrained_weights}')
+        logger.info(f'Loading pretrained config from {args.from_pretrained_config}')
+        stu_architecture_config = DistilBertConfig.from_json_file(args.from_pretrained_config)
+        student = DistilBertForMaskedLM.from_pretrained(args.from_pretrained_weights,
+                                                     config=stu_architecture_config)
+    else:
+        args.vocab_size_or_config_json_file = args.vocab_size
+        stu_architecture_config = DistilBertConfig(**vars(args))
+        student = DistilBertForMaskedLM(stu_architecture_config)
+
+
+    if args.n_gpu > 0:
+        student.to(f'cuda:{args.local_rank}')
+    logger.info(f'Student loaded.')
+
+
+    ## TEACHER ##
+    teacher = BertForMaskedLM.from_pretrained(args.bert_model)
+    if args.n_gpu > 0:
+        teacher.to(f'cuda:{args.local_rank}')
+    logger.info(f'Teacher loaded from {args.bert_model}.')
+
+    ## DISTILLER ##
+    torch.cuda.empty_cache()
+    distiller = Distiller(params=args,
+                          dataloader=train_dataloader,
+                          token_probs=token_probs,
+                          student=student,
+                          teacher=teacher)
+    distiller.train()
+    logger.info("Let's go get some drinks.")
+
+
+if __name__ == "__main__":
+    main()

examples/distillation/utils.py

+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Utils to train DistilBERT
+    adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
+"""
+import git
+import json
+import os
+import socket
+import torch
+import numpy as np
+
+import logging
+logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s',
+                    datefmt = '%m/%d/%Y %H:%M:%S',
+                    level = logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def git_log(folder_path: str):
+    """
+    Log commit info.
+    """
+    repo = git.Repo(search_parent_directories=True)
+    repo_infos = {
+        'repo_id': str(repo),
+        'repo_sha': str(repo.head.object.hexsha),
+        'repo_branch': str(repo.active_branch)
+    }
+
+    with open(os.path.join(folder_path, 'git_log.json'), 'w') as f:
+        json.dump(repo_infos, f, indent=4)
+
+
+def init_gpu_params(params):
+    """
+    Handle single and multi-GPU / multi-node.
+    """
+    if params.n_gpu <= 0:
+        params.local_rank = 0
+        params.master_port = -1
+        params.is_master = True
+        params.multi_gpu = False
+        return
+
+    assert torch.cuda.is_available()
+
+    logger.info('Initializing GPUs')
+    if params.n_gpu > 1:
+        assert params.local_rank != -1
+
+        params.world_size = int(os.environ['WORLD_SIZE'])
+        params.n_gpu_per_node = int(os.environ['N_GPU_NODE'])
+        params.global_rank = int(os.environ['RANK'])
+
+        # number of nodes / node ID
+        params.n_nodes = params.world_size // params.n_gpu_per_node
+        params.node_id = params.global_rank // params.n_gpu_per_node
+        params.multi_gpu = True
+
+        assert params.n_nodes == int(os.environ['N_NODES'])
+        assert params.node_id == int(os.environ['NODE_RANK'])
+
+    # local job (single GPU)
+    else:
+        assert params.local_rank == -1
+
+        params.n_nodes = 1
+        params.node_id = 0
+        params.local_rank = 0
+        params.global_rank = 0
+        params.world_size = 1
+        params.n_gpu_per_node = 1
+        params.multi_gpu = False
+
+    # sanity checks
+    assert params.n_nodes >= 1
+    assert 0 <= params.node_id < params.n_nodes
+    assert 0 <= params.local_rank <= params.global_rank < params.world_size
+    assert params.world_size == params.n_nodes * params.n_gpu_per_node
+
+    # define whether this is the master process / if we are in multi-node distributed mode
+    params.is_master = params.node_id == 0 and params.local_rank == 0
+    params.multi_node = params.n_nodes > 1
+
+    # summary
+    PREFIX = f"--- Global rank: {params.global_rank} - "
+    logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes)
+    logger.info(PREFIX + "Node ID        : %i" % params.node_id)
+    logger.info(PREFIX + "Local rank     : %i" % params.local_rank)
+    logger.info(PREFIX + "World size     : %i" % params.world_size)
+    logger.info(PREFIX + "GPUs per node  : %i" % params.n_gpu_per_node)
+    logger.info(PREFIX + "Master         : %s" % str(params.is_master))
+    logger.info(PREFIX + "Multi-node     : %s" % str(params.multi_node))
+    logger.info(PREFIX + "Multi-GPU      : %s" % str(params.multi_gpu))
+    logger.info(PREFIX + "Hostname       : %s" % socket.gethostname())
+
+    # set GPU device
+    torch.cuda.set_device(params.local_rank)
+
+    # initialize multi-GPU
+    if params.multi_gpu:
+        logger.info("Initializing PyTorch distributed")
+        torch.distributed.init_process_group(
+            init_method='env://',
+            backend='nccl',
+        )
+
+
+def set_seed(args):
+    """
+    Set the random seed.
+    """
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if args.n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)

pytorch_transformers/__init__.py

@@ -7,6 +7,7 @@ from .tokenization_gpt2 import GPT2Tokenizer
 from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE
 from .tokenization_xlm import XLMTokenizer
 from .tokenization_roberta import RobertaTokenizer
+from .tokenization_distilbert import DistilBertTokenizer
 
 from .tokenization_utils import (PreTrainedTokenizer)
 
@@ -40,6 +41,9 @@ from .modeling_xlm import (XLMConfig, XLMPreTrainedModel , XLMModel,
                            XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
 from .modeling_roberta import (RobertaConfig, RobertaForMaskedLM, RobertaModel, RobertaForSequenceClassification,
                                ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
+from .modeling_distilbert import (DistilBertConfig, DistilBertForMaskedLM, DistilBertModel,
+                               DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
+                               DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
 from .modeling_utils import (WEIGHTS_NAME, CONFIG_NAME, TF_WEIGHTS_NAME,
                           PretrainedConfig, PreTrainedModel, prune_layer, Conv1D)
 

pytorch_transformers/modeling_auto.py

@@ -30,6 +30,7 @@ from .modeling_transfo_xl import TransfoXLConfig, TransfoXLModel
 from .modeling_xlnet import XLNetConfig, XLNetModel
 from .modeling_xlm import XLMConfig, XLMModel
 from .modeling_roberta import RobertaConfig, RobertaModel
+from .modeling_distilbert import DistilBertConfig, DistilBertModel
 
 from .modeling_utils import PreTrainedModel, SequenceSummary
 
@@ -110,7 +111,9 @@ class AutoConfig(object):
             assert unused_kwargs == {'foo': False}
 
         """
-        if 'roberta' in pretrained_model_name_or_path:
+        if 'distilbert' in pretrained_model_name_or_path:
+            return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        elif 'roberta' in pretrained_model_name_or_path:
             return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
         elif 'bert' in pretrained_model_name_or_path:
             return BertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
@@ -225,7 +228,9 @@ class AutoModel(object):
             model = AutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
 
         """
-        if 'roberta' in pretrained_model_name_or_path:
+        if 'distilbert' in pretrained_model_name_or_path:
+            return DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+        elif 'roberta' in pretrained_model_name_or_path:
             return RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'bert' in pretrained_model_name_or_path:
             return BertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)

pytorch_transformers/modeling_bert.py

@@ -216,7 +216,7 @@ class BertConfig(PretrainedConfig):
             self.layer_norm_eps = layer_norm_eps
         else:
             raise ValueError("First argument must be either a vocabulary size (int)"
-                             "or the path to a pretrained model config file (str)")
+                             " or the path to a pretrained model config file (str)")
 
 
 
@@ -449,7 +449,7 @@ class BertEncoder(nn.Module):
             outputs = outputs + (all_hidden_states,)
         if self.output_attentions:
             outputs = outputs + (all_attentions,)
-        return outputs  # outputs, (hidden states), (attentions)
+        return outputs  # last-layer hidden state, (all hidden states), (all attentions)
 
 
 class BertPooler(nn.Module):

pytorch_transformers/modeling_distilbert.py

+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch DistilBERT model
+    adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
+    and in part from HuggingFace PyTorch version of Google AI Bert model (https://github.com/google-research/bert)
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+import math
+import copy
+import sys
+from io import open
+
+import itertools
+import numpy as np
+
+import torch
+import torch.nn as nn
+
+from pytorch_transformers.modeling_utils import PretrainedConfig, PreTrainedModel, add_start_docstrings, prune_linear_layer
+
+import logging
+logger = logging.getLogger(__name__)
+
+
+DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
+    'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-pytorch_model.bin",
+    'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-pytorch_model.bin"
+}
+
+DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json",
+    'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json"
+}
+
+
+class DistilBertConfig(PretrainedConfig):
+    pretrained_config_archive_map = DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
+
+    def __init__(self,
+                 vocab_size_or_config_json_file=30522,
+                 max_position_embeddings=512,
+                 sinusoidal_pos_embds=True,
+                 n_layers=6,
+                 n_heads=12,
+                 dim=768,
+                 hidden_dim=4*768,
+                 dropout=0.1,
+                 attention_dropout=0.1,
+                 activation='gelu',
+                 initializer_range=0.02,
+                 tie_weights_=True,
+                 qa_dropout=0.1,
+                 seq_classif_dropout=0.2,
+                 **kwargs):
+        super(DistilBertConfig, self).__init__(**kwargs)
+
+        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
+                        and isinstance(vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
+                json_config = json.loads(reader.read())
+            for key, value in json_config.items():
+                self.__dict__[key] = value
+        elif isinstance(vocab_size_or_config_json_file, int):
+            self.vocab_size = vocab_size_or_config_json_file
+            self.max_position_embeddings = max_position_embeddings
+            self.sinusoidal_pos_embds = sinusoidal_pos_embds
+            self.n_layers = n_layers
+            self.n_heads = n_heads
+            self.dim = dim
+            self.hidden_dim = hidden_dim
+            self.dropout = dropout
+            self.attention_dropout = attention_dropout
+            self.activation = activation
+            self.initializer_range = initializer_range
+            self.tie_weights_ = tie_weights_
+            self.qa_dropout = qa_dropout
+            self.seq_classif_dropout = seq_classif_dropout
+        else:
+            raise ValueError("First argument must be either a vocabulary size (int)"
+                             " or the path to a pretrained model config file (str)")
+    @property
+    def hidden_size(self):
+        return self.hidden_dim
+
+    @property
+    def num_attention_heads(self):
+        return self.n_heads
+
+    @property
+    def num_hidden_layers(self):
+        return self.n_layers
+
+
+### UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE ###
+def gelu(x):
+    return 0.5 * x * (1.0 + torch.erf(x / math.sqrt(2.0)))
+
+def create_sinusoidal_embeddings(n_pos, dim, out):
+    position_enc = np.array([
+        [pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)]
+        for pos in range(n_pos)
+    ])
+    out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
+    out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
+    out.detach_()
+    out.requires_grad = False
+
+class Embeddings(nn.Module):
+    def __init__(self,
+                 config):
+        super(Embeddings, self).__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.dim, padding_idx=0)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.dim)
+        if config.sinusoidal_pos_embds:
+            create_sinusoidal_embeddings(n_pos=config.max_position_embeddings,
+                                         dim=config.dim,
+                                         out=self.position_embeddings.weight)
+
+        self.LayerNorm = nn.LayerNorm(config.dim, eps=1e-12)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, input_ids):
+        """
+        Parameters
+        ----------
+        input_ids: torch.tensor(bs, max_seq_length)
+            The token ids to embed.
+
+        Outputs
+        -------
+        embeddings: torch.tensor(bs, max_seq_length, dim)
+            The embedded tokens (plus position embeddings, no token_type embeddings)
+        """
+        seq_length = input_ids.size(1)
+        position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) # (max_seq_length)
+        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)                      # (bs, max_seq_length)
+
+        word_embeddings = self.word_embeddings(input_ids)                   # (bs, max_seq_length, dim)
+        position_embeddings = self.position_embeddings(position_ids)        # (bs, max_seq_length, dim)
+
+        embeddings = word_embeddings + position_embeddings  # (bs, max_seq_length, dim)
+        embeddings = self.LayerNorm(embeddings)             # (bs, max_seq_length, dim)
+        embeddings = self.dropout(embeddings)               # (bs, max_seq_length, dim)
+        return embeddings
+
+class MultiHeadSelfAttention(nn.Module):
+    def __init__(self, config):
+        super(MultiHeadSelfAttention, self).__init__()
+
+        self.n_heads = config.n_heads
+        self.dim = config.dim
+        self.dropout = nn.Dropout(p=config.attention_dropout)
+        self.output_attentions = config.output_attentions
+
+        assert self.dim % self.n_heads == 0
+
+        self.q_lin = nn.Linear(in_features=config.dim, out_features=config.dim)
+        self.k_lin = nn.Linear(in_features=config.dim, out_features=config.dim)
+        self.v_lin = nn.Linear(in_features=config.dim, out_features=config.dim)
+        self.out_lin = nn.Linear(in_features=config.dim, out_features=config.dim)
+
+    def prune_heads(self, heads):
+        attention_head_size = self.dim // self.n_heads
+        if len(heads) == 0:
+            return
+        mask = torch.ones(self.n_heads, attention_head_size)
+        for head in heads:
+            mask[head] = 0
+        mask = mask.view(-1).contiguous().eq(1)
+        index = torch.arange(len(mask))[mask].long()
+        # Prune linear layers
+        self.q_lin = prune_linear_layer(self.q_lin, index)
+        self.k_lin = prune_linear_layer(self.k_lin, index)
+        self.v_lin = prune_linear_layer(self.v_lin, index)
+        self.out_lin = prune_linear_layer(self.out_lin, index, dim=1)
+        # Update hyper params
+        self.n_heads = self.n_heads - len(heads)
+        self.dim = attention_head_size * self.n_heads
+
+    def forward(self, query, key, value, mask, head_mask = None):
+        """
+        Parameters
+        ----------
+        query: torch.tensor(bs, seq_length, dim)
+        key: torch.tensor(bs, seq_length, dim)
+        value: torch.tensor(bs, seq_length, dim)
+        mask: torch.tensor(bs, seq_length)
+
+        Outputs
+        -------
+        weights: torch.tensor(bs, n_heads, seq_length, seq_length)
+            Attention weights
+        context: torch.tensor(bs, seq_length, dim)
+            Contextualized layer. Optional: only if `output_attentions=True`
+        """
+        bs, q_length, dim = query.size()
+        k_length = key.size(1)
+        # assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim)
+        # assert key.size() == value.size()
+
+        dim_per_head = self.dim // self.n_heads
+
+        assert 2 <= mask.dim() <= 3
+        causal = (mask.dim() == 3)
+        mask_reshp = (bs, 1, 1, k_length)
+
+        def shape(x):
+            """ separate heads """
+            return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2)
+
+        def unshape(x):
+            """ group heads """
+            return x.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * dim_per_head)
+
+        q = shape(self.q_lin(query))           # (bs, n_heads, q_length, dim_per_head)
+        k = shape(self.k_lin(key))             # (bs, n_heads, k_length, dim_per_head)
+        v = shape(self.v_lin(value))           # (bs, n_heads, k_length, dim_per_head)
+
+        q = q / math.sqrt(dim_per_head)                     # (bs, n_heads, q_length, dim_per_head)
+        scores = torch.matmul(q, k.transpose(2,3))          # (bs, n_heads, q_length, k_length)
+        mask = (mask==0).view(mask_reshp).expand_as(scores) # (bs, n_heads, q_length, k_length)
+        scores.masked_fill_(mask, -float('inf'))            # (bs, n_heads, q_length, k_length)
+
+        weights = nn.Softmax(dim=-1)(scores)   # (bs, n_heads, q_length, k_length)
+        weights = self.dropout(weights)        # (bs, n_heads, q_length, k_length)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            weights = weights * head_mask
+
+        context = torch.matmul(weights, v)     # (bs, n_heads, q_length, dim_per_head)
+        context = unshape(context)             # (bs, q_length, dim)
+        context = self.out_lin(context)        # (bs, q_length, dim)
+
+        if self.output_attentions:
+            return (context, weights)
+        else:
+            return (context,)
+
+class FFN(nn.Module):
+    def __init__(self, config):
+        super(FFN, self).__init__()
+        self.dropout = nn.Dropout(p=config.dropout)
+        self.lin1 = nn.Linear(in_features=config.dim, out_features=config.hidden_dim)
+        self.lin2 = nn.Linear(in_features=config.hidden_dim, out_features=config.dim)
+        assert config.activation in ['relu', 'gelu'], "activation ({}) must be in ['relu', 'gelu']".format(config.activation)
+        self.activation = gelu if config.activation == 'gelu' else nn.ReLU()
+
+    def forward(self, input):
+        x = self.lin1(input)
+        x = self.activation(x)
+        x = self.lin2(x)
+        x = self.dropout(x)
+        return x
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config):
+        super(TransformerBlock, self).__init__()
+
+        self.n_heads = config.n_heads
+        self.dim = config.dim
+        self.hidden_dim = config.hidden_dim
+        self.dropout = nn.Dropout(p=config.dropout)
+        self.activation = config.activation
+        self.output_attentions = config.output_attentions
+
+        assert config.dim % config.n_heads == 0
+
+        self.attention = MultiHeadSelfAttention(config)
+        self.sa_layer_norm = nn.LayerNorm(normalized_shape=config.dim, eps=1e-12)
+
+        self.ffn = FFN(config)
+        self.output_layer_norm = nn.LayerNorm(normalized_shape=config.dim, eps=1e-12)
+
+    def forward(self, x, attn_mask=None, head_mask=None):
+        """
+        Parameters
+        ----------
+        x: torch.tensor(bs, seq_length, dim)
+        attn_mask: torch.tensor(bs, seq_length)
+
+        Outputs
+        -------
+        sa_weights: torch.tensor(bs, n_heads, seq_length, seq_length)
+            The attention weights
+        ffn_output: torch.tensor(bs, seq_length, dim)
+            The output of the transformer block contextualization.
+        """
+        # Self-Attention
+        sa_output = self.attention(query=x, key=x, value=x, mask=attn_mask, head_mask=head_mask)
+        if self.output_attentions:
+            sa_output, sa_weights = sa_output                  # (bs, seq_length, dim), (bs, n_heads, seq_length, seq_length)
+        else: # To handle these `output_attention` or `output_hidden_states` cases returning tuples
+            assert type(sa_output) == tuple
+            sa_output = sa_output[0]
+        sa_output = self.sa_layer_norm(sa_output + x)          # (bs, seq_length, dim)
+
+        # Feed Forward Network
+        ffn_output = self.ffn(sa_output)                             # (bs, seq_length, dim)
+        ffn_output = self.output_layer_norm(ffn_output + sa_output)  # (bs, seq_length, dim)
+
+        output = (ffn_output,)
+        if self.output_attentions:
+            output = (sa_weights,) + output
+        return output
+
+
+class Transformer(nn.Module):
+    def __init__(self, config):
+        super(Transformer, self).__init__()
+        self.n_layers = config.n_layers
+        self.output_attentions = config.output_attentions
+        self.output_hidden_states = config.output_hidden_states
+
+        layer = TransformerBlock(config)
+        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.n_layers)])
+
+    def forward(self, x, attn_mask=None, head_mask=None):
+        """
+        Parameters
+        ----------
+        x: torch.tensor(bs, seq_length, dim)
+            Input sequence embedded.
+        attn_mask: torch.tensor(bs, seq_length)
+            Attention mask on the sequence.
+
+        Outputs
+        -------
+        hidden_state: torch.tensor(bs, seq_length, dim)
+            Sequence of hiddens states in the last (top) layer
+        all_hidden_states: Tuple[torch.tensor(bs, seq_length, dim)]
+            Tuple of length n_layers with the hidden states from each layer.
+            Optional: only if output_hidden_states=True
+        all_attentions: Tuple[torch.tensor(bs, n_heads, seq_length, seq_length)]
+            Tuple of length n_layers with the attention weights from each layer
+            Optional: only if output_attentions=True
+        """
+        all_hidden_states = ()
+        all_attentions = ()
+
+        hidden_state = x
+        for i, layer_module in enumerate(self.layer):
+            if self.output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_state,)
+
+            layer_outputs = layer_module(x=hidden_state,
+                                         attn_mask=attn_mask,
+                                         head_mask=head_mask[i])
+            hidden_state = layer_outputs[-1]
+
+            if self.output_attentions:
+                assert len(layer_outputs) == 2
+                attentions = layer_outputs[0]
+                all_attentions = all_attentions + (attentions,)
+            else:
+                assert len(layer_outputs) == 1
+
+        # Add last layer
+        if self.output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_state,)
+
+        outputs = (hidden_state,)
+        if self.output_hidden_states:
+            outputs = outputs + (all_hidden_states,)
+        if self.output_attentions:
+            outputs = outputs + (all_attentions,)
+        return outputs  # last-layer hidden state, (all hidden states), (all attentions)
+
+
+### INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL ###
+class DistilBertPreTrainedModel(PreTrainedModel):
+    """ An abstract class to handle weights initialization and
+        a simple interface for downloading and loading pretrained models.
+    """
+    config_class = DistilBertConfig
+    pretrained_model_archive_map = DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+    load_tf_weights = None
+    base_model_prefix = "distilbert"
+
+    def __init__(self, *inputs, **kwargs):
+        super(DistilBertPreTrainedModel, self).__init__(*inputs, **kwargs)
+    
+    def init_weights(self, module):
+        """ Initialize the weights.
+        """
+        if isinstance(module, nn.Embedding):
+            if module.weight.requires_grad:
+                module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+
+DISTILBERT_START_DOCSTRING = r"""
+    DistilBERT is a small, fast, cheap and light Transformer model
+    trained by distilling Bert base. It has 40% less parameters than
+    `bert-base-uncased`, runs 60% faster while preserving over 95% of
+    Bert's performances as measured on the GLUE language understanding benchmark.
+
+    Here are the differences between the interface of Bert and DistilBert:
+
+    - DistilBert doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`)
+    - DistilBert doesn't have options to select the input positions (`position_ids` input). This could be added if necessary though, just let's us know if you need this option.
+
+    For more information on DistilBERT, please refer to our
+    `detailed blog post`_
+    
+    .. _`detailed blog post`:
+        https://medium.com/huggingface/distilbert-8cf3380435b5
+
+    Parameters:
+        config (:class:`~pytorch_transformers.DistilBertConfig`): Model configuration class with all the parameters of the model. 
+            Initializing with a config file does not load the weights associated with the model, only the configuration.
+            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+"""
+
+DISTILBERT_INPUTS_DOCSTRING = r"""
+    Inputs:
+        **input_ids** ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
+            Indices of input sequence tokens in the vocabulary.
+            The input sequences should start with `[CLS]` and end with `[SEP]` tokens.
+            
+            For now, ONLY BertTokenizer(`bert-base-uncased`) is supported and you should use this tokenizer when using DistilBERT.
+        **attention_mask**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
+            Mask to avoid performing attention on padding token indices.
+            Mask values selected in ``[0, 1]``:
+            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
+        **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
+            Mask to nullify selected heads of the self-attention modules.
+            Mask values selected in ``[0, 1]``:
+            ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
+"""
+
+@add_start_docstrings("The bare DistilBERT encoder/transformer outputing raw hidden-states without any specific head on top.",
+                      DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
+class DistilBertModel(DistilBertPreTrainedModel):
+    r"""
+    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
+        **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
+            Sequence of hidden-states at the output of the last layer of the model.
+        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
+            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
+            of shape ``(batch_size, sequence_length, hidden_size)``:
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
+
+    Examples::
+
+        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+        model = DistilBertModel.from_pretrained('distilbert-base-uncased')
+        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
+        outputs = model(input_ids)
+        last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
+
+    """
+    def __init__(self, config):
+        super(DistilBertModel, self).__init__(config)
+
+        self.embeddings = Embeddings(config)   # Embeddings
+        self.transformer = Transformer(config) # Encoder
+
+        self.apply(self.init_weights)
+
+    def _resize_token_embeddings(self, new_num_tokens):
+        old_embeddings = self.embeddings.word_embeddings
+        new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
+        self.embeddings.word_embeddings = new_embeddings
+        return self.embeddings.word_embeddings
+
+    def _prune_heads(self, heads_to_prune):
+        """ Prunes heads of the model.
+            heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
+            See base class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.transformer.layer[layer].attention.prune_heads(heads)
+
+    def forward(self,
+                input_ids, attention_mask=None, head_mask=None):
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids) # (bs, seq_length)
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        if head_mask is not None:
+            if head_mask.dim() == 1:
+                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+                head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
+            elif head_mask.dim() == 2:
+                head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)  # We can specify head_mask for each layer
+            head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
+        else:
+            head_mask = [None] * self.config.num_hidden_layers
+
+        embedding_output = self.embeddings(input_ids)   # (bs, seq_length, dim)
+        tfmr_output = self.transformer(x=embedding_output,
+                                       attn_mask=attention_mask,
+                                       head_mask=head_mask)
+        hidden_state = tfmr_output[0]
+        output = (hidden_state, ) + tfmr_output[1:]
+
+        return output # last-layer hidden-state, (all hidden_states), (all attentions)
+
+
+@add_start_docstrings("""DistilBert Model with a `masked language modeling` head on top. """,
+                      DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
+class DistilBertForMaskedLM(DistilBertPreTrainedModel):
+    r"""
+        **masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
+            Labels for computing the masked language modeling loss.
+            Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
+            Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels
+            in ``[0, ..., config.vocab_size]``
+
+    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
+        **loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
+            Masked language modeling loss.
+        **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
+            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
+            of shape ``(batch_size, sequence_length, hidden_size)``:
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
+
+    Examples::
+
+        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+        model = DistilBertForMaskedLM.from_pretrained('distilbert-base-uncased')
+        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
+        outputs = model(input_ids, masked_lm_labels=input_ids)
+        loss, prediction_scores = outputs[:2]
+
+    """
+    def __init__(self, config):
+        super(DistilBertForMaskedLM, self).__init__(config)
+        self.output_attentions = config.output_attentions
+        self.output_hidden_states = config.output_hidden_states
+
+        self.distilbert = DistilBertModel(config)
+        self.vocab_transform = nn.Linear(config.dim, config.dim)
+        self.vocab_layer_norm = nn.LayerNorm(config.dim, eps=1e-12)
+        self.vocab_projector = nn.Linear(config.dim, config.vocab_size)
+
+        self.apply(self.init_weights)
+        self.tie_weights()
+
+        self.mlm_loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
+
+    def tie_weights(self):
+        """ Make sure we are sharing the input and output embeddings.
+            Export to TorchScript can't handle parameter sharing so we are cloning them instead.
+        """
+        self._tie_or_clone_weights(self.vocab_projector,
+                                   self.distilbert.embeddings.word_embeddings)
+
+    def forward(self, input_ids, attention_mask=None, masked_lm_labels=None, head_mask=None):
+        dlbrt_output = self.distilbert(input_ids=input_ids,
+                                    attention_mask=attention_mask,
+                                    head_mask=head_mask)
+        hidden_states = dlbrt_output[0]                              # (bs, seq_length, dim)
+        prediction_logits = self.vocab_transform(hidden_states)      # (bs, seq_length, dim)
+        prediction_logits = gelu(prediction_logits)                  # (bs, seq_length, dim)
+        prediction_logits = self.vocab_layer_norm(prediction_logits) # (bs, seq_length, dim)
+        prediction_logits = self.vocab_projector(prediction_logits)  # (bs, seq_length, vocab_size)
+
+        outputs = (prediction_logits, ) + dlbrt_output[1:]
+        if masked_lm_labels is not None:
+            mlm_loss = self.mlm_loss_fct(prediction_logits.view(-1, prediction_logits.size(-1)),
+                                         masked_lm_labels.view(-1))
+            outputs = (mlm_loss,) + outputs     
+
+        return outputs # (mlm_loss), prediction_logits, (all hidden_states), (all attentions)
+
+
+@add_start_docstrings("""DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of
+                         the pooled output) e.g. for GLUE tasks. """,
+                      DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
+class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
+    r"""
+        **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
+            Labels for computing the sequence classification/regression loss.
+            Indices should be in ``[0, ..., config.num_labels - 1]``.
+            If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
+            If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
+
+    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
+        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
+            Classification (or regression if config.num_labels==1) loss.
+        **logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
+            Classification (or regression if config.num_labels==1) scores (before SoftMax).
+        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
+            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
+            of shape ``(batch_size, sequence_length, hidden_size)``:
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
+
+    Examples::
+
+        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+        model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
+        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
+        labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
+        outputs = model(input_ids, labels=labels)
+        loss, logits = outputs[:2]
+
+    """
+    def __init__(self, config):
+        super(DistilBertForSequenceClassification, self).__init__(config)
+        self.num_labels = config.num_labels
+
+        self.distilbert = DistilBertModel(config)
+        self.pre_classifier = nn.Linear(config.dim, config.dim)
+        self.classifier = nn.Linear(config.dim, config.num_labels)
+        self.dropout = nn.Dropout(config.seq_classif_dropout)
+
+        self.apply(self.init_weights)
+
+    def forward(self, input_ids,  attention_mask=None, labels=None, head_mask=None):
+        distilbert_output = self.distilbert(input_ids=input_ids,
+                                      attention_mask=attention_mask,
+                                      head_mask=head_mask)
+        hidden_state = distilbert_output[0]                    # (bs, seq_len, dim)
+        pooled_output = hidden_state[:, 0]                    # (bs, dim)
+        pooled_output = self.pre_classifier(pooled_output)   # (bs, dim)
+        pooled_output = nn.ReLU()(pooled_output)             # (bs, dim)
+        pooled_output = self.dropout(pooled_output)         # (bs, dim)
+        logits = self.classifier(pooled_output)              # (bs, dim)
+
+        outputs = (logits,) + distilbert_output[1:]
+        if labels is not None:
+            if self.num_labels == 1:
+                loss_fct = nn.MSELoss()
+                loss = loss_fct(logits.view(-1), labels.view(-1))
+            else:
+                loss_fct = nn.CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            outputs = (loss,) + outputs
+
+        return outputs  # (loss), logits, (hidden_states), (attentions)
+
+
+@add_start_docstrings("""DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
+                         the hidden-states output to compute `span start logits` and `span end logits`). """,
+                      DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
+class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
+    r"""
+        **start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`).
+            Position outside of the sequence are not taken into account for computing the loss.
+        **end_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`).
+            Position outside of the sequence are not taken into account for computing the loss.
+
+    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
+        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
+            Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
+        **start_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
+            Span-start scores (before SoftMax).
+        **end_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
+            Span-end scores (before SoftMax).
+        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
+            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
+            of shape ``(batch_size, sequence_length, hidden_size)``:
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
+
+    Examples::
+
+        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+        model = DistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased')
+        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
+        start_positions = torch.tensor([1])
+        end_positions = torch.tensor([3])
+        outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
+        loss, start_scores, end_scores = outputs[:2]
+
+    """
+    def __init__(self, config):
+        super(DistilBertForQuestionAnswering, self).__init__(config)
+
+        self.distilbert = DistilBertModel(config)
+        self.qa_outputs = nn.Linear(config.dim, config.num_labels)
+        assert config.num_labels == 2
+        self.dropout = nn.Dropout(config.qa_dropout)
+
+        self.apply(self.init_weights)
+        
+    def forward(self, input_ids, attention_mask=None, start_positions=None, end_positions=None, head_mask=None):
+        distilbert_output = self.distilbert(input_ids=input_ids,
+                                      attention_mask=attention_mask,
+                                      head_mask=head_mask)
+        hidden_states = distilbert_output[0]                                 # (bs, max_query_len, dim)
+
+        hidden_states = self.dropout(hidden_states)                       # (bs, max_query_len, dim)
+        logits = self.qa_outputs(hidden_states)                           # (bs, max_query_len, 2)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1)                           # (bs, max_query_len)
+        end_logits = end_logits.squeeze(-1)                               # (bs, max_query_len)
+
+        outputs = (start_logits, end_logits,) + distilbert_output[1:]
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions.clamp_(0, ignored_index)
+            end_positions.clamp_(0, ignored_index)
+
+            loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+            outputs = (total_loss,) + outputs
+
+        return outputs  # (loss), start_logits, end_logits, (hidden_states), (attentions)

pytorch_transformers/modeling_transfo_xl.py

@@ -285,7 +285,7 @@ class TransfoXLConfig(PretrainedConfig):
             self.init_std = init_std
         else:
             raise ValueError("First argument must be either a vocabulary size (int)"
-                             "or the path to a pretrained model config file (str)")
+                             " or the path to a pretrained model config file (str)")
 
     @property
     def max_position_embeddings(self):

pytorch_transformers/modeling_xlm.py

@@ -178,7 +178,7 @@ class XLMConfig(PretrainedConfig):
             self.end_n_top = end_n_top
         else:
             raise ValueError("First argument must be either a vocabulary size (int)"
-                             "or the path to a pretrained model config file (str)")
+                             " or the path to a pretrained model config file (str)")
 
     @property
     def vocab_size(self):

pytorch_transformers/modeling_xlnet.py

@@ -306,7 +306,7 @@ class XLNetConfig(PretrainedConfig):
             self.end_n_top = end_n_top
         else:
             raise ValueError("First argument must be either a vocabulary size (int)"
-                             "or the path to a pretrained model config file (str)")
+                             " or the path to a pretrained model config file (str)")
 
     @property
     def max_position_embeddings(self):