Add nvidia megatron models (#10911)

* Add support for NVIDIA Megatron models

* Add support for NVIDIA Megatron GPT2 and BERT

Add the megatron_gpt2 model. That model reuses the existing GPT2 model. This
commit includes a script to convert a Megatron-GPT2 checkpoint downloaded
from NVIDIA GPU Cloud. See examples/megatron-models/README.md for details.

Add the megatron_bert model. That model is implemented as a modification of
the existing BERT model in Transformers. This commit includes a script to
convert a Megatron-BERT checkpoint downloaded from NVIDIA GPU Cloud. See
examples/megatron-models/README.md for details.

* Update src/transformers/models/megatron_bert/configuration_megatron_bert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/megatron_bert/configuration_megatron_bert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/megatron_bert/configuration_megatron_bert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Remove model.half in tests + add "# Copied ..."

Remove the model.half() instruction which makes tests fail on the CPU.

Add a comment "# Copied ..." before many classes in the model to enable automatic
tracking in CI between the new Megatron classes and the original Bert ones.

* Fix issues

* Fix Flax/TF tests

* Fix copyright

* Update src/transformers/models/megatron_bert/configuration_megatron_bert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/megatron_bert/configuration_megatron_bert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update docs/source/model_doc/megatron_bert.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update docs/source/model_doc/megatron_gpt2.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/megatron_bert/modeling_megatron_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Resolve most of 'sgugger' comments

* Fix conversion issue + Run make fix-copies/quality/docs

* Apply suggestions from code review

* Causal LM & merge

* Fix init

* Add CausalLM to last auto class
Co-authored-by: Julien Demouth <jdemouth@nvidia.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

README.md

@@ -223,6 +223,8 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 1. **[MarianMT](https://huggingface.co/transformers/model_doc/marian.html)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
 1. **[MBart](https://huggingface.co/transformers/model_doc/mbart.html)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
 1. **[MBart-50](https://huggingface.co/transformers/model_doc/mbart.html)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
+1. **[Megatron-BERT](https://huggingface.co/transformers/model_doc/megatron_bert.html)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
+1. **[Megatron-GPT2](https://huggingface.co/transformers/model_doc/megatron_gpt2.html)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
 1. **[MPNet](https://huggingface.co/transformers/model_doc/mpnet.html)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
 1. **[MT5](https://huggingface.co/transformers/model_doc/mt5.html)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
 1. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777)> by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.

docs/source/index.rst

@@ -178,58 +178,64 @@ and conversion utilities for the following models:
 32. :doc:`MBart-50 <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Translation with Extensible
     Multilingual Pretraining and Finetuning <https://arxiv.org/abs/2008.00401>`__ by Yuqing Tang, Chau Tran, Xian Li,
     Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
-33. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
+33. :doc:`Megatron-BERT <model_doc/megatron_bert>` (from NVIDIA) released with the paper `Megatron-LM: Training
+    Multi-Billion Parameter Language Models Using Model Parallelism <https://arxiv.org/abs/1909.08053>`__ by Mohammad
+    Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
+34. :doc:`Megatron-GPT2 <model_doc/megatron_gpt2>` (from NVIDIA) released with the paper `Megatron-LM: Training
+    Multi-Billion Parameter Language Models Using Model Parallelism <https://arxiv.org/abs/1909.08053>`__ by Mohammad
+    Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
+35. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
     Pre-training for Language Understanding <https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin,
     Jianfeng Lu, Tie-Yan Liu.
-34. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
+36. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
     text-to-text transformer <https://arxiv.org/abs/2010.11934>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir
     Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-35. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
+37. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
     Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__> by Jingqing Zhang, Yao Zhao,
     Mohammad Saleh and Peter J. Liu.
-36. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
+38. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
     Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
     Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-37. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
+39. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
     Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-38. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
+40. :doc:`RoBERTa <model_doc/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.
-39. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper
+41. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper
     `fairseq S2T: Fast Speech-to-Text Modeling with fairseq <https://arxiv.org/abs/2010.05171>`__ by Changhan Wang, Yun
     Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
-40. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
+42. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
     about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi
     Krishna, and Kurt W. Keutzer.
-41. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
+43. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
     Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
     Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-42. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
+44. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
     Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
     Francesco Piccinno and Julian Martin Eisenschlos.
-43. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+45. :doc:`Transformer-XL <model_doc/transformerxl>` (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.
-44. :doc:`Vision Transformer (ViT) <model_doc/vit>` (from Google AI) released with the paper `An Image is Worth 16x16
+46. :doc:`Vision Transformer (ViT) <model_doc/vit>` (from Google AI) released with the paper `An Image is Worth 16x16
     Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`__ by Alexey Dosovitskiy,
     Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias
     Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
-45. :doc:`Wav2Vec2 <model_doc/wav2vec2>` (from Facebook AI) released with the paper `wav2vec 2.0: A Framework for
+47. :doc:`Wav2Vec2 <model_doc/wav2vec2>` (from Facebook AI) released with the paper `wav2vec 2.0: A Framework for
     Self-Supervised Learning of Speech Representations <https://arxiv.org/abs/2006.11477>`__ by Alexei Baevski, Henry
     Zhou, Abdelrahman Mohamed, Michael Auli.
-46. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+48. :doc:`XLM <model_doc/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.
-47. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+49. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
     Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
     Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-48. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+50. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
     Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
     Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
     Zettlemoyer and Veselin Stoyanov.
-49. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
+51. :doc:`XLNet <model_doc/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.
-50. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised
+52. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised
     Cross-Lingual Representation Learning For Speech Recognition <https://arxiv.org/abs/2006.13979>`__ by Alexis
     Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
 
@@ -304,6 +310,8 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|        MegatronBert         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         OpenAI GPT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
@@ -449,6 +457,8 @@ TensorFlow and/or Flax.
     model_doc/marian
     model_doc/m2m_100
     model_doc/mbart
+    model_doc/megatron_bert
+    model_doc/megatron_gpt2
     model_doc/mobilebert
     model_doc/mpnet
     model_doc/mt5

docs/source/model_doc/megatron_bert.rst

+.. 
+    Copyright 2021 NVIDIA Corporation and The HuggingFace Team. All rights reserved.
+
+    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.
+
+MegatronBERT
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The MegatronBERT model was proposed in `Megatron-LM: Training Multi-Billion Parameter Language Models Using Model
+Parallelism <https://arxiv.org/abs/1909.08053>`__ by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley,
+Jared Casper and Bryan Catanzaro.
+
+The abstract from the paper is the following:
+
+*Recent work in language modeling demonstrates that training large transformer models advances the state of the art in
+Natural Language Processing applications. However, very large models can be quite difficult to train due to memory
+constraints. In this work, we present our techniques for training very large transformer models and implement a simple,
+efficient intra-layer model parallel approach that enables training transformer models with billions of parameters. Our
+approach does not require a new compiler or library changes, is orthogonal and complimentary to pipeline model
+parallelism, and can be fully implemented with the insertion of a few communication operations in native PyTorch. We
+illustrate this approach by converging transformer based models up to 8.3 billion parameters using 512 GPUs. We sustain
+15.1 PetaFLOPs across the entire application with 76% scaling efficiency when compared to a strong single GPU baseline
+that sustains 39 TeraFLOPs, which is 30% of peak FLOPs. To demonstrate that large language models can further advance
+the state of the art (SOTA), we train an 8.3 billion parameter transformer language model similar to GPT-2 and a 3.9
+billion parameter model similar to BERT. We show that careful attention to the placement of layer normalization in
+BERT-like models is critical to achieving increased performance as the model size grows. Using the GPT-2 model we
+achieve SOTA results on the WikiText103 (10.8 compared to SOTA perplexity of 15.8) and LAMBADA (66.5% compared to SOTA
+accuracy of 63.2%) datasets. Our BERT model achieves SOTA results on the RACE dataset (90.9% compared to SOTA accuracy
+of 89.4%).*
+
+Tips:
+
+We have provided pretrained `BERT-345M <https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m>`__ checkpoints
+for use to evaluate or finetuning downstream tasks.
+
+To access these checkpoints, first `sign up <https://ngc.nvidia.com/signup>`__ for and setup the NVIDIA GPU Cloud (NGC)
+Registry CLI. Further documentation for downloading models can be found in the `NGC documentation
+<https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1>`__.
+
+Alternatively, you can directly download the checkpoints using:
+
+BERT-345M-uncased::
+
+.. code-block:: bash
+
+  wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_uncased/zip
+  -O megatron_bert_345m_v0_1_uncased.zip
+
+BERT-345M-cased::
+
+.. code-block:: bash
+
+  wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_cased/zip -O
+  megatron_bert_345m_v0_1_cased.zip
+
+Once you have obtained the checkpoints from NVIDIA GPU Cloud (NGC), you have to convert them to a format that will
+easily be loaded by Hugging Face Transformers and our port of the BERT code.
+
+The following commands allow you to do the conversion. We assume that the folder ``models/megatron_bert`` contains
+``megatron_bert_345m_v0_1_{cased, uncased}.zip`` and that the commands are run from inside that folder::
+
+.. code-block:: bash
+
+  python3 $PATH_TO_TRANSFORMERS/models/megatron_bert/convert_megatron_bert_checkpoint.py megatron_bert_345m_v0_1_uncased.zip 
+
+.. code-block:: bash
+
+  python3 $PATH_TO_TRANSFORMERS/models/megatron_bert/convert_megatron_bert_checkpoint.py megatron_bert_345m_v0_1_cased.zip
+
+The original code can be found `here <https://github.com/NVIDIA/Megatron-LM>`__. That repository contains a multi-GPU
+and multi-node implementation of the Megatron Language models. In particular, it contains a hybrid model parallel
+approach using "tensor parallel" and "pipeline parallel" techniques.
+
+MegatronBertConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertConfig
+    :members:
+
+
+MegatronBertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertModel
+    :members: forward
+
+
+MegatronBertForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForMaskedLM
+    :members: forward
+
+
+MegatronBertForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForCausalLM
+    :members: forward
+
+
+MegatronBertForNextSentencePrediction
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForNextSentencePrediction
+    :members: forward
+
+
+MegatronBertForPreTraining
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForPreTraining
+    :members: forward
+
+
+MegatronBertForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForSequenceClassification
+    :members: forward
+
+
+MegatronBertForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForMultipleChoice
+    :members: forward
+
+
+MegatronBertForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForTokenClassification
+    :members: forward
+
+
+MegatronBertForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForQuestionAnswering
+    :members: forward
+
+

docs/source/model_doc/megatron_gpt2.rst

+.. 
+    Copyright 2021 NVIDIA Corporation and The HuggingFace Team. All rights reserved.
+
+    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.
+
+MegatronGPT2
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The MegatronGPT2 model was proposed in `Megatron-LM: Training Multi-Billion Parameter Language Models Using Model
+Parallelism <https://arxiv.org/abs/1909.08053>`__ by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley,
+Jared Casper and Bryan Catanzaro.
+
+The abstract from the paper is the following:
+
+*Recent work in language modeling demonstrates that training large transformer models advances the state of the art in
+Natural Language Processing applications. However, very large models can be quite difficult to train due to memory
+constraints. In this work, we present our techniques for training very large transformer models and implement a simple,
+efficient intra-layer model parallel approach that enables training transformer models with billions of parameters. Our
+approach does not require a new compiler or library changes, is orthogonal and complimentary to pipeline model
+parallelism, and can be fully implemented with the insertion of a few communication operations in native PyTorch. We
+illustrate this approach by converging transformer based models up to 8.3 billion parameters using 512 GPUs. We sustain
+15.1 PetaFLOPs across the entire application with 76% scaling efficiency when compared to a strong single GPU baseline
+that sustains 39 TeraFLOPs, which is 30% of peak FLOPs. To demonstrate that large language models can further advance
+the state of the art (SOTA), we train an 8.3 billion parameter transformer language model similar to GPT-2 and a 3.9
+billion parameter model similar to BERT. We show that careful attention to the placement of layer normalization in
+BERT-like models is critical to achieving increased performance as the model size grows. Using the GPT-2 model we
+achieve SOTA results on the WikiText103 (10.8 compared to SOTA perplexity of 15.8) and LAMBADA (66.5% compared to SOTA
+accuracy of 63.2%) datasets. Our BERT model achieves SOTA results on the RACE dataset (90.9% compared to SOTA accuracy
+of 89.4%).*
+
+Tips:
+
+We have provided pretrained `GPT2-345M <https://ngc.nvidia.com/catalog/models/nvidia:megatron_lm_345m>`__ checkpoints
+for use to evaluate or finetuning downstream tasks.
+
+To access these checkpoints, first `sign up <https://ngc.nvidia.com/signup>`__ for and setup the NVIDIA GPU Cloud (NGC)
+Registry CLI. Further documentation for downloading models can be found in the `NGC documentation
+<https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1>`__.
+
+Alternatively, you can directly download the checkpoints using::
+
+.. code-block:: bash
+
+  wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_lm_345m/versions/v0.0/zip -O
+  megatron_gpt2_345m_v0_0.zip
+
+Once you have obtained the checkpoint from NVIDIA GPU Cloud (NGC), you have to convert it to a format that will easily
+be loaded by Hugging Face Transformers GPT2 implementation.
+
+The following command allows you to do the conversion. We assume that the folder ``models/megatron_gpt2`` contains
+``megatron_gpt2_345m_v0_0.zip`` and that the command is run from that folder::
+
+.. code-block:: bash
+
+  python3 $PATH_TO_TRANSFORMERS/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py megatron_gpt2_345m_v0_0.zip
+
+The original code can be found `here <https://github.com/NVIDIA/Megatron-LM>`__. That repository contains a multi-GPU
+and multi-node implementation of the Megatron Language models. In particular, it contains a hybrid model parallel
+approach using "tensor parallel" and "pipeline parallel" techniques.
+

src/transformers/__init__.py

@@ -191,6 +191,7 @@ _import_structure = {
     "models.m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config"],
     "models.marian": ["MarianConfig"],
     "models.mbart": ["MBartConfig"],
+    "models.megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
     "models.mmbt": ["MMBTConfig"],
     "models.mobilebert": ["MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileBertConfig", "MobileBertTokenizer"],
     "models.mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig", "MPNetTokenizer"],
@@ -765,6 +766,20 @@ if is_torch_available():
             "MBartModel",
         ]
     )
+    _import_structure["models.megatron_bert"].extend(
+        [
+            "MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "MegatronBertForCausalLM",
+            "MegatronBertForMaskedLM",
+            "MegatronBertForMultipleChoice",
+            "MegatronBertForNextSentencePrediction",
+            "MegatronBertForPreTraining",
+            "MegatronBertForQuestionAnswering",
+            "MegatronBertForSequenceClassification",
+            "MegatronBertForTokenClassification",
+            "MegatronBertModel",
+        ]
+    )
     _import_structure["models.mmbt"].extend(["MMBTForClassification", "MMBTModel", "ModalEmbeddings"])
     _import_structure["models.mobilebert"].extend(
         [
@@ -1514,6 +1529,7 @@ if TYPE_CHECKING:
     from .models.m2m_100 import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, M2M100Config
     from .models.marian import MarianConfig
     from .models.mbart import MBartConfig
+    from .models.megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
     from .models.mmbt import MMBTConfig
     from .models.mobilebert import MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileBertConfig, MobileBertTokenizer
     from .models.mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig, MPNetTokenizer
@@ -1999,6 +2015,18 @@ if TYPE_CHECKING:
             MBartForSequenceClassification,
             MBartModel,
         )
+        from .models.megatron_bert import (
+            MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            MegatronBertForCausalLM,
+            MegatronBertForMaskedLM,
+            MegatronBertForMultipleChoice,
+            MegatronBertForNextSentencePrediction,
+            MegatronBertForPreTraining,
+            MegatronBertForQuestionAnswering,
+            MegatronBertForSequenceClassification,
+            MegatronBertForTokenClassification,
+            MegatronBertModel,
+        )
         from .models.mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
         from .models.mobilebert import (
             MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,

src/transformers/models/__init__.py

@@ -50,6 +50,7 @@ from . import (
     m2m_100,
     marian,
     mbart,
+    megatron_bert,
     mmbt,
     mobilebert,
     mpnet,

src/transformers/models/auto/configuration_auto.py

@@ -50,6 +50,7 @@ from ..lxmert.configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
 from ..m2m_100.configuration_m2m_100 import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, M2M100Config
 from ..marian.configuration_marian import MarianConfig
 from ..mbart.configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig
+from ..megatron_bert.configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
 from ..mobilebert.configuration_mobilebert import MobileBertConfig
 from ..mpnet.configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from ..mt5.configuration_mt5 import MT5Config
@@ -85,6 +86,7 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
         # Add archive maps here
         GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         VIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -155,6 +157,7 @@ CONFIG_MAPPING = OrderedDict(
         ("pegasus", PegasusConfig),
         ("marian", MarianConfig),
         ("mbart", MBartConfig),
+        ("megatron_bert", MegatronBertConfig),
         ("mpnet", MPNetConfig),
         ("bart", BartConfig),
         ("blenderbot", BlenderbotConfig),
@@ -211,6 +214,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
         ("blenderbot", "Blenderbot"),
         ("marian", "Marian"),
         ("mbart", "mBART"),
+        ("megatron_bert", "MegatronBert"),
         ("bart", "BART"),
         ("reformer", "Reformer"),
         ("longformer", "Longformer"),

src/transformers/models/auto/modeling_auto.py

@@ -174,6 +174,17 @@ from ..mbart.modeling_mbart import (
     MBartForSequenceClassification,
     MBartModel,
 )
+from ..megatron_bert.modeling_megatron_bert import (
+    MegatronBertForCausalLM,
+    MegatronBertForMaskedLM,
+    MegatronBertForMultipleChoice,
+    MegatronBertForNextSentencePrediction,
+    MegatronBertForPreTraining,
+    MegatronBertForQuestionAnswering,
+    MegatronBertForSequenceClassification,
+    MegatronBertForTokenClassification,
+    MegatronBertModel,
+)
 from ..mobilebert.modeling_mobilebert import (
     MobileBertForMaskedLM,
     MobileBertForMultipleChoice,
@@ -298,6 +309,7 @@ from .configuration_auto import (
     M2M100Config,
     MarianConfig,
     MBartConfig,
+    MegatronBertConfig,
     MobileBertConfig,
     MPNetConfig,
     MT5Config,
@@ -355,6 +367,7 @@ MODEL_MAPPING = OrderedDict(
         (BertConfig, BertModel),
         (OpenAIGPTConfig, OpenAIGPTModel),
         (GPT2Config, GPT2Model),
+        (MegatronBertConfig, MegatronBertModel),
         (MobileBertConfig, MobileBertModel),
         (TransfoXLConfig, TransfoXLModel),
         (XLNetConfig, XLNetModel),
@@ -398,6 +411,7 @@ MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
         (BigBirdConfig, BigBirdForPreTraining),
         (OpenAIGPTConfig, OpenAIGPTLMHeadModel),
         (GPT2Config, GPT2LMHeadModel),
+        (MegatronBertConfig, MegatronBertForPreTraining),
         (MobileBertConfig, MobileBertForPreTraining),
         (TransfoXLConfig, TransfoXLLMHeadModel),
         (XLNetConfig, XLNetLMHeadModel),
@@ -441,6 +455,7 @@ MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
         (BertConfig, BertForMaskedLM),
         (OpenAIGPTConfig, OpenAIGPTLMHeadModel),
         (GPT2Config, GPT2LMHeadModel),
+        (MegatronBertConfig, MegatronBertForMaskedLM),
         (MobileBertConfig, MobileBertForMaskedLM),
         (TransfoXLConfig, TransfoXLLMHeadModel),
         (XLNetConfig, XLNetLMHeadModel),
@@ -456,6 +471,7 @@ MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
         (DebertaConfig, DebertaForMaskedLM),
         (DebertaV2Config, DebertaV2ForMaskedLM),
         (IBertConfig, IBertForMaskedLM),
+        (MegatronBertConfig, MegatronBertForCausalLM),
     ]
 )
 
@@ -487,6 +503,7 @@ MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict(
         (MarianConfig, MarianForCausalLM),
         (BlenderbotConfig, BlenderbotForCausalLM),
         (BlenderbotSmallConfig, BlenderbotSmallForCausalLM),
+        (MegatronBertConfig, MegatronBertForCausalLM),
     ]
 )
 
@@ -514,6 +531,7 @@ MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
         (RobertaConfig, RobertaForMaskedLM),
         (SqueezeBertConfig, SqueezeBertForMaskedLM),
         (BertConfig, BertForMaskedLM),
+        (MegatronBertConfig, MegatronBertForMaskedLM),
         (MobileBertConfig, MobileBertForMaskedLM),
         (FlaubertConfig, FlaubertWithLMHeadModel),
         (XLMConfig, XLMWithLMHeadModel),
@@ -566,6 +584,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
         (LayoutLMConfig, LayoutLMForSequenceClassification),
         (BertConfig, BertForSequenceClassification),
         (XLNetConfig, XLNetForSequenceClassification),
+        (MegatronBertConfig, MegatronBertForSequenceClassification),
         (MobileBertConfig, MobileBertForSequenceClassification),
         (FlaubertConfig, FlaubertForSequenceClassification),
         (XLMConfig, XLMForSequenceClassification),
@@ -602,6 +621,7 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
         (BertConfig, BertForQuestionAnswering),
         (XLNetConfig, XLNetForQuestionAnsweringSimple),
         (FlaubertConfig, FlaubertForQuestionAnsweringSimple),
+        (MegatronBertConfig, MegatronBertForQuestionAnswering),
         (MobileBertConfig, MobileBertForQuestionAnswering),
         (XLMConfig, XLMForQuestionAnsweringSimple),
         (ElectraConfig, ElectraForQuestionAnswering),
@@ -637,6 +657,7 @@ MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
         (RobertaConfig, RobertaForTokenClassification),
         (SqueezeBertConfig, SqueezeBertForTokenClassification),
         (BertConfig, BertForTokenClassification),
+        (MegatronBertConfig, MegatronBertForTokenClassification),
         (MobileBertConfig, MobileBertForTokenClassification),
         (XLNetConfig, XLNetForTokenClassification),
         (AlbertConfig, AlbertForTokenClassification),
@@ -663,6 +684,7 @@ MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
         (SqueezeBertConfig, SqueezeBertForMultipleChoice),
         (BertConfig, BertForMultipleChoice),
         (DistilBertConfig, DistilBertForMultipleChoice),
+        (MegatronBertConfig, MegatronBertForMultipleChoice),
         (MobileBertConfig, MobileBertForMultipleChoice),
         (XLNetConfig, XLNetForMultipleChoice),
         (AlbertConfig, AlbertForMultipleChoice),
@@ -677,6 +699,7 @@ MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
 MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = OrderedDict(
     [
         (BertConfig, BertForNextSentencePrediction),
+        (MegatronBertConfig, MegatronBertForNextSentencePrediction),
         (MobileBertConfig, MobileBertForNextSentencePrediction),
     ]
 )

src/transformers/models/megatron_bert/__init__.py

+# flake8: noqa
+# There's no way to ignore "F401 '...' imported but unused" warnings in this
+# module, but to preserve other warnings. So, don't check this module at all.
+
+# Copyright 2021  NVIDIA Corporation and The HuggingFace Team. All rights reserved.
+#
+# 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.
+from typing import TYPE_CHECKING
+
+from ...file_utils import _BaseLazyModule, is_torch_available
+
+
+_import_structure = {
+    "configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
+}
+
+if is_torch_available():
+    _import_structure["modeling_megatron_bert"] = [
+        "MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "MegatronBertForCausalLM",
+        "MegatronBertForMaskedLM",
+        "MegatronBertForMultipleChoice",
+        "MegatronBertForNextSentencePrediction",
+        "MegatronBertForPreTraining",
+        "MegatronBertForQuestionAnswering",
+        "MegatronBertForSequenceClassification",
+        "MegatronBertForTokenClassification",
+        "MegatronBertModel",
+    ]
+
+if TYPE_CHECKING:
+    from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
+
+    if is_torch_available():
+        from .modeling_megatron_bert import (
+            MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            MegatronBertForCausalLM,
+            MegatronBertForMaskedLM,
+            MegatronBertForMultipleChoice,
+            MegatronBertForNextSentencePrediction,
+            MegatronBertForPreTraining,
+            MegatronBertForQuestionAnswering,
+            MegatronBertForSequenceClassification,
+            MegatronBertForTokenClassification,
+            MegatronBertModel,
+        )
+
+else:
+    import importlib
+    import os
+    import sys
+
+    class _LazyModule(_BaseLazyModule):
+        """
+        Module class that surfaces all objects but only performs associated imports when the objects are requested.
+        """
+
+        __file__ = globals()["__file__"]
+        __path__ = [os.path.dirname(__file__)]
+
+        def _get_module(self, module_name: str):
+            return importlib.import_module("." + module_name, self.__name__)
+
+    sys.modules[__name__] = _LazyModule(__name__, _import_structure)

src/transformers/models/megatron_bert/configuration_megatron_bert.py

+# coding=utf-8
+# Copyright 2021- NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+""" MEGATRON_BERT model configuration """
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
+}
+
+
+class MegatronBertConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a :class:`~transformers.MegatronBertModel`. It is
+    used to instantiate a MEGATRON_BERT model according to the specified arguments, defining the model architecture.
+    Instantiating a configuration with the defaults will yield a similar configuration to that of the MEGATRON_BERT
+    `megatron-bert-uncased-345m <https://huggingface.co/nvidia/megatron-bert-uncased-345m>`__ architecture.
+
+    Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
+    outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
+
+
+    Args:
+        vocab_size (:obj:`int`, `optional`, defaults to 29056):
+            Vocabulary size of the MEGATRON_BERT model. Defines the number of different tokens that can be represented
+            by the :obj:`inputs_ids` passed when calling :class:`~transformers.MegatronBertModel`.
+        hidden_size (:obj:`int`, `optional`, defaults to 1024):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (:obj:`int`, `optional`, defaults to 24):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (:obj:`int`, `optional`, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (:obj:`int`, `optional`, defaults to 4096):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string,
+            :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported.
+        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        type_vocab_size (:obj:`int`, `optional`, defaults to 2):
+            The vocabulary size of the :obj:`token_type_ids` passed when calling
+            :class:`~transformers.MegatronBertModel`.
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            If True, use gradient checkpointing to save memory at the expense of slower backward pass.
+        position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`):
+            Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`,
+            :obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on
+            :obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.)
+            <https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"relative_key_query"`, please refer to
+            `Method 4` in `Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)
+            <https://arxiv.org/abs/2009.13658>`__.
+        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if ``config.is_decoder=True``.
+
+    Examples::
+
+        >>> from transformers import MegatronBertModel, MegatronBertConfig
+
+        >>> # Initializing a MEGATRON_BERT bert-base-uncased style configuration
+        >>> configuration = MegatronBertConfig()
+
+        >>> # Initializing a model from the bert-base-uncased style configuration
+        >>> model = MegatronBertModel(configuration)
+
+        >>> # Accessing the model configuration
+        >>> configuration = model.config
+    """
+    model_type = "megatron-bert"
+
+    def __init__(
+        self,
+        vocab_size=29056,
+        hidden_size=1024,
+        num_hidden_layers=24,
+        num_attention_heads=16,
+        intermediate_size=4096,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=2,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        pad_token_id=0,
+        gradient_checkpointing=False,
+        position_embedding_type="absolute",
+        use_cache=True,
+        **kwargs
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.gradient_checkpointing = gradient_checkpointing
+        self.position_embedding_type = position_embedding_type
+        self.use_cache = use_cache

src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py

+####################################################################################################
+
+# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+
+####################################################################################################
+
+import argparse
+import json
+import os
+import re
+import zipfile
+
+import torch
+
+
+####################################################################################################
+
+
+def recursive_print(name, val, spaces=0):
+    # Format the message.
+    if name is None:
+        msg = None
+    else:
+        fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}"
+        msg = fmt.format(name)
+
+    # Print and recurse (if needed).
+    if isinstance(val, dict):
+        if msg is not None:
+            print(msg)
+        for k in val.keys():
+            recursive_print(k, val[k], spaces + 2)
+    elif isinstance(val, torch.Tensor):
+        print(msg, ":", val.size())
+    else:
+        print(msg, ":", val)
+
+
+####################################################################################################
+
+
+def convert_megatron_checkpoint(args, input_state_dict):
+    # The converted output model.
+    output_state_dict = {}
+
+    # The model.
+    model = input_state_dict["model"]
+    # The language model.
+    lm = model["language_model"]
+    # The embeddings.
+    embeddings = lm["embedding"]
+
+    # The word embeddings.
+    word_embeddings = embeddings["word_embeddings"]["weight"]
+    # Store the word embeddings.
+    output_state_dict["bert.embeddings.word_embeddings.weight"] = word_embeddings
+
+    # The position embeddings.
+    pos_embeddings = embeddings["position_embeddings"]["weight"]
+    # Trained for 512 x 1024.
+    assert pos_embeddings.size(0) == 512 and pos_embeddings.size(1) == 1024
+    # Store the position embeddings.
+    output_state_dict["bert.embeddings.position_embeddings.weight"] = pos_embeddings
+
+    # The token-type embeddings.
+    tokentype_embeddings = embeddings["tokentype_embeddings"]["weight"]
+    # Store the position embeddings.
+    output_state_dict["bert.embeddings.token_type_embeddings.weight"] = tokentype_embeddings
+
+    # The transformer.
+    transformer = lm["transformer"]
+
+    # The regex to extract layer names.
+    layer_re = re.compile("layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")
+
+    # The simple map of names for "automated" rules.
+    megatron_to_transformers = {
+        "attention.dense": ".attention.output.dense.",
+        "mlp.dense_h_to_4h": ".intermediate.dense.",
+        "mlp.dense_4h_to_h": ".output.dense.",
+    }
+
+    # Keep track of the attention/query/value tensor.
+    attention_qkv_weight = None
+
+    # Extract the layers.
+    for key, val in transformer.items():
+        # Match the name.
+        m = layer_re.match(key)
+
+        # Stop if that's not a layer
+        if m is None:
+            break
+
+        # The index of the layer.
+        layer_idx = int(m.group(1))
+        # The name of the operation.
+        op_name = m.group(2)
+        # Is it a weight or a bias?
+        weight_or_bias = m.group(3)
+
+        # The name of the layer.
+        layer_name = f"bert.encoder.layer.{layer_idx}"
+
+        # For layernorm(s), simply store the layer norm.
+        if op_name.endswith("layernorm"):
+
+            ln_name = "attention.ln" if op_name.startswith("input") else "ln"
+            output_state_dict[layer_name + "." + ln_name + "." + weight_or_bias] = val
+
+        # Transpose the QKV matrix.
+        elif op_name == "attention.query_key_value" and weight_or_bias == "weight":
+
+            # Make sure the QKV pointer is nil.
+            assert attention_qkv_weight is None, ""
+
+            # Store the tensor as we need the bias as well to interleave QKV and biases.
+            attention_qkv_weight = val
+
+        # Transpose the bias.
+        elif op_name == "attention.query_key_value" and weight_or_bias == "bias":
+
+            # Make sure we read the weight tensor.
+            assert attention_qkv_weight is not None, ""
+
+            # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
+            q = attention_qkv_weight[0 * 1024 : 1 * 1024, :]
+            k = attention_qkv_weight[1 * 1024 : 2 * 1024, :]
+            v = attention_qkv_weight[2 * 1024 : 3 * 1024, :]
+
+            # Split the bias.
+            q_bias = val[0 * 1024 : 1 * 1024]
+            k_bias = val[1 * 1024 : 2 * 1024]
+            v_bias = val[2 * 1024 : 3 * 1024]
+
+            # Store.
+            output_state_dict[f"{layer_name}.attention.self.query.weight"] = q
+            output_state_dict[f"{layer_name}.attention.self.query.bias"] = q_bias
+            output_state_dict[f"{layer_name}.attention.self.key.weight"] = k
+            output_state_dict[f"{layer_name}.attention.self.key.bias"] = k_bias
+            output_state_dict[f"{layer_name}.attention.self.value.weight"] = v
+            output_state_dict[f"{layer_name}.attention.self.value.bias"] = v_bias
+
+            # Clear the stored tensor.
+            attention_qkv_weight = None
+
+        # Copy weights and biases as is.
+        elif weight_or_bias in ["weight", "bias"]:
+
+            out_name = megatron_to_transformers[op_name]
+            output_state_dict[layer_name + out_name + weight_or_bias] = val
+
+    # The final layernorm.
+    output_state_dict["bert.encoder.ln.weight"] = transformer["final_layernorm.weight"]
+    output_state_dict["bert.encoder.ln.bias"] = transformer["final_layernorm.bias"]
+
+    # The config.
+    output_config = {
+        "vocab_size": word_embeddings.size(0),
+        "hidden_size": 1024,
+        "num_hidden_layers": 24,
+        "num_attention_heads": 16,
+        "hidden_act": "gelu_new",
+        "intermediate_size": 4096,
+        "hidden_dropout_prob": 0.1,
+        "attention_probs_dropout_prob": 0.1,
+        "max_position_embeddings": 512,
+        "type_vocab_size": 2,
+        "initializer_range": 0.2,
+        "layer_norm_eps": 1e-12,
+        "gradient_checkpointing": False,
+        "position_embedding_type": "absolute",
+        "use_cache": False,
+    }
+
+    # The pooler.
+    pooler = lm["pooler"]
+
+    # Store the matrix and the bias.
+    output_state_dict["bert.pooler.dense.weight"] = pooler["dense.weight"]
+    output_state_dict["bert.pooler.dense.bias"] = pooler["dense.bias"]
+
+    # The LM head from Megatron (for RACE).
+    lm_head = model["lm_head"]
+
+    # The transform matrix.
+    output_state_dict["cls.predictions.transform.dense.weight"] = lm_head["dense.weight"]
+    output_state_dict["cls.predictions.transform.dense.bias"] = lm_head["dense.bias"]
+
+    # The transform LN.
+    output_state_dict["cls.predictions.transform.LayerNorm.weight"] = lm_head["layernorm.weight"]
+    output_state_dict["cls.predictions.transform.LayerNorm.bias"] = lm_head["layernorm.bias"]
+
+    # For the decoder, we replicate the weights.
+    output_state_dict["cls.predictions.decoder.weight"] = word_embeddings
+    output_state_dict["cls.predictions.bias"] = lm_head["bias"]
+
+    # The classifier from Megatron (for MLNI).
+    binary_head = model["binary_head"]
+
+    # Store the classifier.
+    output_state_dict["cls.seq_relationship.weight"] = binary_head["weight"]
+    output_state_dict["cls.seq_relationship.bias"] = binary_head["bias"]
+
+    # It should be done!
+    return output_state_dict, output_config
+
+
+####################################################################################################
+
+
+def main():
+    # Create the argument parser.
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--print-checkpoint-structure", action="store_true")
+    parser.add_argument("path_to_checkpoint", type=str, help="Path to the ZIP file containing the checkpoint")
+    args = parser.parse_args()
+
+    # Extract the basename.
+    basename = os.path.dirname(args.path_to_checkpoint)
+
+    # Load the model.
+    print(f'Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"')
+    with zipfile.ZipFile(args.path_to_checkpoint, "r") as checkpoint:
+        with checkpoint.open("release/mp_rank_00/model_optim_rng.pt") as pytorch_dict:
+            input_state_dict = torch.load(pytorch_dict, map_location="cpu")
+
+    # Convert.
+    print("Converting")
+    output_state_dict, output_config = convert_megatron_checkpoint(args, input_state_dict)
+
+    # Print the structure of converted state dict.
+    if args.print_checkpoint_structure:
+        recursive_print(None, output_state_dict)
+
+    # Store the config to file.
+    output_config_file = os.path.join(basename, "config.json")
+    print(f'Saving config to "{output_config_file}"')
+    with open(output_config_file, "w") as f:
+        json.dump(output_config, f)
+
+    # Store the state_dict to file.
+    output_checkpoint_file = os.path.join(basename, "pytorch_model.bin")
+    print(f'Saving checkpoint to "{output_checkpoint_file}"')
+    torch.save(output_state_dict, output_checkpoint_file)
+
+
+####################################################################################################
+
+if __name__ == "__main__":
+    main()
+
+####################################################################################################

src/transformers/models/megatron_bert/modeling_megatron_bert.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018-2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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 MegatronBERT model. """
+
+
+import math
+import os
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...file_utils import (
+    ModelOutput,
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+from ...modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    CausalLMOutputWithCrossAttentions,
+    MaskedLMOutput,
+    MultipleChoiceModelOutput,
+    NextSentencePredictorOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from ...modeling_utils import (
+    PreTrainedModel,
+    apply_chunking_to_forward,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
+from ...utils import logging
+from .configuration_megatron_bert import MegatronBertConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "MegatronBertConfig"
+_TOKENIZER_FOR_DOC = "BertTokenizer"
+_CHECKPOINT_FOR_DOC = "nvidia/megatron-bert-cased-345m"
+
+MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "nvidia/megatron-bert-cased-345m",
+    # See all MegatronBERT models at https://huggingface.co/models?filter=megatron_bert
+]
+
+
+def load_tf_weights_in_megatron_bert(model, config, tf_checkpoint_path):
+    """Load tf checkpoints in a pytorch model."""
+    try:
+        import re
+
+        import numpy as np
+        import tensorflow as tf
+    except ImportError:
+        logger.error(
+            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
+            "https://www.tensorflow.org/install/ for installation instructions."
+        )
+        raise
+    tf_path = os.path.abspath(tf_checkpoint_path)
+    logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
+    # Load weights from TF model
+    init_vars = tf.train.list_variables(tf_path)
+    names = []
+    arrays = []
+    for name, shape in init_vars:
+        logger.info(f"Loading TF weight {name} with shape {shape}")
+        array = tf.train.load_variable(tf_path, name)
+        names.append(name)
+        arrays.append(array)
+
+    for name, array in zip(names, arrays):
+        name = name.split("/")
+        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
+        # which are not required for using pretrained model
+        if any(
+            n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
+            for n in name
+        ):
+            logger.info(f"Skipping {'/'.join(name)}")
+            continue
+        pointer = model
+        for m_name in name:
+            if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
+                scope_names = re.split(r"_(\d+)", m_name)
+            else:
+                scope_names = [m_name]
+            if scope_names[0] == "kernel" or scope_names[0] == "gamma":
+                pointer = getattr(pointer, "weight")
+            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
+                pointer = getattr(pointer, "bias")
+            elif scope_names[0] == "output_weights":
+                pointer = getattr(pointer, "weight")
+            elif scope_names[0] == "squad":
+                pointer = getattr(pointer, "classifier")
+            else:
+                try:
+                    pointer = getattr(pointer, scope_names[0])
+                except AttributeError:
+                    logger.info(f"Skipping {'/'.join(name)}")
+                    continue
+            if len(scope_names) >= 2:
+                num = int(scope_names[1])
+                pointer = pointer[num]
+        if m_name[-11:] == "_embeddings":
+            pointer = getattr(pointer, "weight")
+        elif m_name == "kernel":
+            array = np.transpose(array)
+        try:
+            assert (
+                pointer.shape == array.shape
+            ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
+        except AssertionError as e:
+            e.args += (pointer.shape, array.shape)
+            raise
+        logger.info("Initialize PyTorch weight {}".format(name))
+        pointer.data = torch.from_numpy(array)
+    return model
+
+
+class MegatronBertEmbeddings(nn.Module):
+    """Construct the embeddings from word, position and token_type embeddings."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+
+        # In Megatron, layer-norm is applied after the 1st dropout.
+        # self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+
+    def forward(
+        self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
+    ):
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
+
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = inputs_embeds + token_type_embeddings
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings += position_embeddings
+
+        # Megatron BERT moves that layer norm after the drop-out (and to each layer).
+        # embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->MegatronBert
+class MegatronBertSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            self.max_position_embeddings = config.max_position_embeddings
+            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
+
+        self.is_decoder = config.is_decoder
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        mixed_query_layer = self.query(hidden_states)
+
+        # If this is instantiated as a cross-attention module, the keys
+        # and values come from an encoder; the attention mask needs to be
+        # such that the encoder's padding tokens are not attended to.
+        is_cross_attention = encoder_hidden_states is not None
+
+        if is_cross_attention and past_key_value is not None:
+            # reuse k,v, cross_attentions
+            key_layer = past_key_value[0]
+            value_layer = past_key_value[1]
+            attention_mask = encoder_attention_mask
+        elif is_cross_attention:
+            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
+            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
+            attention_mask = encoder_attention_mask
+        elif past_key_value is not None:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+        else:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        if self.is_decoder:
+            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
+            # Further calls to cross_attention layer can then reuse all cross-attention
+            # key/value_states (first "if" case)
+            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
+            # all previous decoder key/value_states. Further calls to uni-directional self-attention
+            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+            # if encoder bi-directional self-attention `past_key_value` is always `None`
+            past_key_value = (key_layer, value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            seq_length = hidden_states.size()[1]
+            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
+            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
+            distance = position_ids_l - position_ids_r
+            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
+            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
+
+            if self.position_embedding_type == "relative_key":
+                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+                attention_scores = attention_scores + relative_position_scores
+            elif self.position_embedding_type == "relative_key_query":
+                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
+                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
+
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in MegatronBertModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+        if self.is_decoder:
+            outputs = outputs + (past_key_value,)
+        return outputs
+
+
+# Based transformers.models.bert.modeling_bert.BertSelfOutput. Moved LayerNorm to MegatronBertAttention below.
+class MegatronBertSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, residual):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return residual + hidden_states
+
+
+# Based transformers.models.bert.modeling_bert.BertAttention. Added LayerNorm.
+class MegatronBertAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.self = MegatronBertSelfAttention(config)
+        self.output = MegatronBertSelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        ln_outputs = self.ln(hidden_states)
+        self_outputs = self.self(
+            ln_outputs,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            past_key_value,
+            output_attentions,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->MegatronBert
+class MegatronBertIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+# Based on transformers.models.bert.modeling_bert.BertOutput. Moved LayerNorm to MegatronBertLayer below.
+class MegatronBertOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return input_tensor + hidden_states
+
+
+# Based on transformers.models.bert.modeling_bert.BertLayer. Added LayerNorm.
+class MegatronBertLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = MegatronBertAttention(config)
+        self.is_decoder = config.is_decoder
+        self.add_cross_attention = config.add_cross_attention
+        if self.add_cross_attention:
+            assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
+            self.crossattention = MegatronBertAttention(config)
+        self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.intermediate = MegatronBertIntermediate(config)
+        self.output = MegatronBertOutput(config)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
+        self_attention_outputs = self.attention(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            output_attentions=output_attentions,
+            past_key_value=self_attn_past_key_value,
+        )
+        attention_output = self_attention_outputs[0]
+
+        # if decoder, the last output is tuple of self-attn cache
+        if self.is_decoder:
+            outputs = self_attention_outputs[1:-1]
+            present_key_value = self_attention_outputs[-1]
+        else:
+            outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
+
+        cross_attn_present_key_value = None
+        if self.is_decoder and encoder_hidden_states is not None:
+            assert hasattr(
+                self, "crossattention"
+            ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
+
+            # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
+            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
+            cross_attention_outputs = self.crossattention(
+                attention_output,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                cross_attn_past_key_value,
+                output_attentions,
+            )
+            attention_output = cross_attention_outputs[0]
+            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights
+
+            # add cross-attn cache to positions 3,4 of present_key_value tuple
+            cross_attn_present_key_value = cross_attention_outputs[-1]
+            present_key_value = present_key_value + cross_attn_present_key_value
+
+        layer_output = apply_chunking_to_forward(
+            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+        )
+        outputs = (layer_output,) + outputs
+
+        # if decoder, return the attn key/values as the last output
+        if self.is_decoder:
+            outputs = outputs + (present_key_value,)
+
+        return outputs
+
+    def feed_forward_chunk(self, attention_output):
+        ln_output = self.ln(attention_output)
+        intermediate_output = self.intermediate(ln_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+class MegatronBertEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([MegatronBertLayer(config) for _ in range(config.num_hidden_layers)])
+
+        # The final layer norm. We removed the 1st LN, moved LN to each hidden layer and this one
+        # is simply the final LN (Transformer's BERT has it attached to each hidden layer).
+        self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        return_dict=True,
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
+
+        next_decoder_cache = () if use_cache else None
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            past_key_value = past_key_values[i] if past_key_values is not None else None
+
+            if getattr(self.config, "gradient_checkpointing", False) and self.training:
+
+                if use_cache:
+                    logger.warn(
+                        "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
+                        "`use_cache=False`..."
+                    )
+                    use_cache = False
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, past_key_value, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                )
+
+            # Because we moved the layer-norm at the end of the hidden layer, we have non-normali-
+            # zed data here. If that's really needed, we must apply LN to match Transformer's BERT.
+
+            hidden_states = layer_outputs[0]
+            if use_cache:
+                next_decoder_cache += (layer_outputs[-1],)
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+                if self.config.add_cross_attention:
+                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
+
+        # Finalize the hidden states.
+        hidden_states = self.ln(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    next_decoder_cache,
+                    all_hidden_states,
+                    all_self_attentions,
+                    all_cross_attentions,
+                ]
+                if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=next_decoder_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->MegatronBert
+class MegatronBertPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->MegatronBert
+class MegatronBertPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        if isinstance(config.hidden_act, str):
+            self.transform_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.transform_act_fn = config.hidden_act
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->MegatronBert
+class MegatronBertLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = MegatronBertPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->MegatronBert
+class MegatronBertOnlyMLMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = MegatronBertLMPredictionHead(config)
+
+    def forward(self, sequence_output):
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert->MegatronBert
+class MegatronBertOnlyNSPHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, pooled_output):
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return seq_relationship_score
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->MegatronBert
+class MegatronBertPreTrainingHeads(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = MegatronBertLMPredictionHead(config)
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, sequence_output, pooled_output):
+        prediction_scores = self.predictions(sequence_output)
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return prediction_scores, seq_relationship_score
+
+
+class MegatronBertPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = MegatronBertConfig
+    load_tf_weights = load_tf_weights_in_megatron_bert
+    base_model_prefix = "bert"
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+    def _init_weights(self, module):
+        """ Initialize the weights """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            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_()
+
+
+@dataclass
+# Copied from transformers.models.bert.modeling_bert.BertForPreTrainingOutput with Bert->MegatronBert
+class MegatronBertForPreTrainingOutput(ModelOutput):
+    """
+    Output type of :class:`~transformers.MegatronBertForPreTraining`.
+
+    Args:
+        loss (`optional`, returned when ``labels`` is provided, ``torch.FloatTensor`` of shape :obj:`(1,)`):
+            Total loss as the sum of the masked language modeling loss and the next sequence prediction
+            (classification) loss.
+        prediction_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        seq_relationship_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
+            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
+            before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(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.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    prediction_logits: torch.FloatTensor = None
+    seq_relationship_logits: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+MEGATRON_BERT_START_DOCSTRING = r"""
+
+    This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
+    methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
+    pruning heads etc.)
+
+    This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
+    subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
+    general usage and behavior.
+
+    Parameters:
+        config (:class:`~transformers.MegatronBertConfig`): 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:`~transformers.PreTrainedModel.from_pretrained` method to load the model
+            weights.
+"""
+
+MEGATRON_BERT_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using :class:`~transformers.BertTokenizer`. See
+            :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
+            details.
+
+            `What are input IDs? <../glossary.html#input-ids>`__
+        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`):
+            Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            `What are attention masks? <../glossary.html#attention-mask>`__
+        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
+            Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
+            1]``:
+
+            - 0 corresponds to a `sentence A` token,
+            - 1 corresponds to a `sentence B` token.
+
+            `What are token type IDs? <../glossary.html#token-type-ids>`_
+        position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
+            config.max_position_embeddings - 1]``.
+
+            `What are position IDs? <../glossary.html#position-ids>`_
+        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
+            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**.
+
+        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
+            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
+            This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
+            vectors than the model's internal embedding lookup matrix.
+        output_attentions (:obj:`bool`, `optional`):
+            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
+            tensors for more detail.
+        output_hidden_states (:obj:`bool`, `optional`):
+            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
+            more detail.
+        return_dict (:obj:`bool`, `optional`):
+            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare MegatronBert Model transformer outputting raw hidden-states without any specific head on top.",
+    MEGATRON_BERT_START_DOCSTRING,
+)
+class MegatronBertModel(MegatronBertPreTrainedModel):
+    """
+
+    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
+    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
+    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+
+    To behave as an decoder the model needs to be initialized with the :obj:`is_decoder` argument of the configuration
+    set to :obj:`True`. To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder`
+    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
+    input to the forward pass.
+    """
+
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = MegatronBertEmbeddings(config)
+        self.encoder = MegatronBertEncoder(config)
+
+        self.pooler = MegatronBertPooler(config) if add_pooling_layer else None
+
+        self.init_weights()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    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.encoder.layer[layer].attention.prune_heads(heads)
+
+    @add_start_docstrings_to_model_forward(MEGATRON_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutputWithPoolingAndCrossAttentions,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.config.is_decoder:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+            batch_size, seq_length = input_shape
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            batch_size, seq_length = input_shape
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        # past_key_values_length
+        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
+
+        if attention_mask is None:
+            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if self.config.is_decoder and encoder_hidden_states is not None:
+            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+        else:
+            encoder_extended_attention_mask = None
+
+        # 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]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+            past_key_values_length=past_key_values_length,
+        )
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    MegatronBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
+    `next sentence prediction (classification)` head.
+    """,
+    MEGATRON_BERT_START_DOCSTRING,
+)
+class MegatronBertForPreTraining(MegatronBertPreTrainedModel):
+    def __init__(self, config, add_binary_head=True):
+        super().__init__(config)
+
+        self.bert = MegatronBertModel(config)
+        self.cls = MegatronBertPreTrainingHeads(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    @add_start_docstrings_to_model_forward(MEGATRON_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=MegatronBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        next_sentence_label=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape ``(batch_size, sequence_length)``, `optional`):
+            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
+            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
+            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
+        next_sentence_label (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`):
+            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
+            (see :obj:`input_ids` docstring) Indices should be in ``[0, 1]``:
+
+            - 0 indicates sequence B is a continuation of sequence A,
+            - 1 indicates sequence B is a random sequence.
+        kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
+            Used to hide legacy arguments that have been deprecated.
+
+        Returns:
+
+        Example::
+
+            >>> from transformers import BertTokenizer, MegatronBertForPreTraining
+            >>> import torch
+
+            >>> tokenizer = BertTokenizer.from_pretrained('nvidia/megatron-bert-cased-345m')
+            >>> model = MegatronBertForPreTraining.from_pretrained('nvidia/megatron-bert-cased-345m')
+
+            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+            >>> outputs = model(**inputs)
+
+            >>> prediction_logits = outputs.prediction_logits
+            >>> seq_relationship_logits = outputs.seq_relationship_logits
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output, pooled_output = outputs[:2]
+        prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
+
+        total_loss = None
+        if labels is not None and next_sentence_label is not None:
+            loss_fct = CrossEntropyLoss()
+            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
+            total_loss = masked_lm_loss + next_sentence_loss
+
+        if not return_dict:
+            output = (prediction_scores, seq_relationship_score) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return MegatronBertForPreTrainingOutput(
+            loss=total_loss,
+            prediction_logits=prediction_scores,
+            seq_relationship_logits=seq_relationship_score,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """MegatronBert Model with a `language modeling` head on top for CLM fine-tuning. """,
+    MEGATRON_BERT_START_DOCSTRING,
+)
+class MegatronBertForCausalLM(MegatronBertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        if not config.is_decoder:
+            logger.warning("If you want to use `MegatronBertForCausalLM` as a standalone, add `is_decoder=True.`")
+
+        self.bert = MegatronBertModel(config, add_pooling_layer=False)
+        self.cls = MegatronBertOnlyMLMHead(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    @add_start_docstrings_to_model_forward(MEGATRON_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        labels=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
+            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
+            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+
+        Returns:
+
+        Example::
+
+            >>> from transformers import BertTokenizer, MegatronBertForCausalLM, MegatronBertConfig
+            >>> import torch
+
+            >>> tokenizer = BertTokenizer.from_pretrained('nvidia/megatron-bert-cased-345m')
+            >>> model = MegatronBertLMHeadModel.from_pretrained('nvidia/megatron-bert-cased-345m', is_decoder=True)
+
+            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+            >>> outputs = model(**inputs)
+
+            >>> prediction_logits = outputs.logits
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        if labels is not None:
+            use_cache = False
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+        prediction_scores = self.cls(sequence_output)
+
+        lm_loss = None
+        if labels is not None:
+            # we are doing next-token prediction; shift prediction scores and input ids by one
+            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
+            labels = labels[:, 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return ((lm_loss,) + output) if lm_loss is not None else output
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=lm_loss,
+            logits=prediction_scores,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            cross_attentions=outputs.cross_attentions,
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
+        input_shape = input_ids.shape
+        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+        if attention_mask is None:
+            attention_mask = input_ids.new_ones(input_shape)
+
+        # cut decoder_input_ids if past is used
+        if past is not None:
+            input_ids = input_ids[:, -1:]
+
+        return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}
+
+    def _reorder_cache(self, past, beam_idx):
+        reordered_past = ()
+        for layer_past in past:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+        return reordered_past
+
+
+@add_start_docstrings("""MegatronBert Model with a `language modeling` head on top. """, MEGATRON_BERT_START_DOCSTRING)
+class MegatronBertForMaskedLM(MegatronBertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler", r"seq_relationship"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        if config.is_decoder:
+            logger.warning(
+                "If you want to use `MegatronBertForMaskedLM` make sure `config.is_decoder=False` for "
+                "bi-directional self-attention."
+            )
+
+        self.bert = MegatronBertModel(config, add_pooling_layer=False)
+        self.cls = MegatronBertOnlyMLMHead(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    @add_start_docstrings_to_model_forward(MEGATRON_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=MaskedLMOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
+            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
+            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+        prediction_scores = self.cls(sequence_output)
+
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()  # -100 index = padding token
+            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
+        input_shape = input_ids.shape
+        effective_batch_size = input_shape[0]
+
+        #  add a dummy token
+        assert self.config.pad_token_id is not None, "The PAD token should be defined for generation"
+        attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
+        dummy_token = torch.full(
+            (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
+        )
+        input_ids = torch.cat([input_ids, dummy_token], dim=1)
+
+        return {"input_ids": input_ids, "attention_mask": attention_mask}
+
+
+@add_start_docstrings(
+    """MegatronBert Model with a `next sentence prediction (classification)` head on top. """,
+    MEGATRON_BERT_START_DOCSTRING,
+)
+class MegatronBertForNextSentencePrediction(MegatronBertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"predictions"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = MegatronBertModel(config)
+        self.cls = MegatronBertOnlyNSPHead(config)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(MEGATRON_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
+            (see ``input_ids`` docstring). Indices should be in ``[0, 1]``:
+
+            - 0 indicates sequence B is a continuation of sequence A,
+            - 1 indicates sequence B is a random sequence.
+
+        Returns:
+
+        Example::
+
+            >>> from transformers import BertTokenizer, MegatronBertForNextSentencePrediction
+            >>> import torch
+
+            >>> tokenizer = BertTokenizer.from_pretrained('nvidia/megatron-bert-cased-345m')
+            >>> model = MegatronBertForNextSentencePrediction.from_pretrained('nvidia/megatron-bert-cased-345m')
+
+            >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+            >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
+            >>> encoding = tokenizer(prompt, next_sentence, return_tensors='pt')
+
+            >>> outputs = model(**encoding, labels=torch.LongTensor([1]))
+            >>> logits = outputs.logits
+            >>> assert logits[0, 0] < logits[0, 1] # next sentence was random
+        """
+
+        if "next_sentence_label" in kwargs:
+            warnings.warn(
+                "The `next_sentence_label` argument is deprecated and will be removed in a future version, use `labels` instead.",
+                FutureWarning,
+            )
+            labels = kwargs.pop("next_sentence_label")
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = outputs[1]
+
+        seq_relationship_scores = self.cls(pooled_output)
+
+        next_sentence_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
+
+        if not return_dict:
+            output = (seq_relationship_scores,) + outputs[2:]
+            return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
+
+        return NextSentencePredictorOutput(
+            loss=next_sentence_loss,
+            logits=seq_relationship_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    MegatronBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+    pooled output) e.g. for GLUE tasks.
+    """,
+    MEGATRON_BERT_START_DOCSTRING,
+)
+class MegatronBertForSequenceClassification(MegatronBertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.bert = MegatronBertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(MEGATRON_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=SequenceClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.num_labels == 1:
+                #  We are doing regression
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1), labels.view(-1))
+            else:
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    MegatronBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output
+    and a softmax) e.g. for RocStories/SWAG tasks.
+    """,
+    MEGATRON_BERT_START_DOCSTRING,
+)
+class MegatronBertForMultipleChoice(MegatronBertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = MegatronBertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, 1)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(
+        MEGATRON_BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
+    )
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=MultipleChoiceModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
+            num_choices-1]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
+            :obj:`input_ids` above)
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
+        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+        inputs_embeds = (
+            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+            if inputs_embeds is not None
+            else None
+        )
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = logits.view(-1, num_choices)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+
+        if not return_dict:
+            output = (reshaped_logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return MultipleChoiceModelOutput(
+            loss=loss,
+            logits=reshaped_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    MegatronBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
+    for Named-Entity-Recognition (NER) tasks.
+    """,
+    MEGATRON_BERT_START_DOCSTRING,
+)
+class MegatronBertForTokenClassification(MegatronBertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.bert = MegatronBertModel(config, add_pooling_layer=False)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(MEGATRON_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
+            1]``.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            # Only keep active parts of the loss
+            if attention_mask is not None:
+                active_loss = attention_mask.view(-1) == 1
+                active_logits = logits.view(-1, self.num_labels)
+                active_labels = torch.where(
+                    active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
+                )
+                loss = loss_fct(active_logits, active_labels)
+            else:
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    MegatronBert 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`).
+    """,
+    MEGATRON_BERT_START_DOCSTRING,
+)
+class MegatronBertForQuestionAnswering(MegatronBertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.bert = MegatronBertModel(config, add_pooling_layer=False)
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(MEGATRON_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=QuestionAnsweringModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        start_positions=None,
+        end_positions=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            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 (:obj:`sequence_length`). Position outside of the
+            sequence are not taken into account for computing the loss.
+        end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            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 (:obj:`sequence_length`). Position outside of the
+            sequence are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1)
+
+        total_loss = None
+        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 = 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
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py

+####################################################################################################
+
+# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+
+####################################################################################################
+
+import argparse
+import json
+import os
+import re
+import zipfile
+
+import torch
+
+
+####################################################################################################
+
+
+def recursive_print(name, val, spaces=0):
+    # Format the message.
+    if name is None:
+        msg = None
+    else:
+        fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}"
+        msg = fmt.format(name)
+
+    # Print and recurse (if needed).
+    if isinstance(val, dict):
+        if msg is not None:
+            print(msg)
+        for k in val.keys():
+            recursive_print(k, val[k], spaces + 2)
+    elif isinstance(val, torch.Tensor):
+        print(msg, ":", val.size())
+    else:
+        print(msg, ":", val)
+
+
+####################################################################################################
+
+
+def convert_megatron_checkpoint(args, input_state_dict):
+    # The converted output model.
+    output_state_dict = {}
+
+    # The number of heads.
+    heads = 16
+    # The hidden_size per head.
+    hidden_size_per_head = 64
+
+    # The model.
+    model = input_state_dict["model"]
+    # The language model.
+    lm = model["language_model"]
+    # The embeddings.
+    embeddings = lm["embedding"]
+
+    # The word embeddings.
+    word_embeddings = embeddings["word_embeddings"]["weight"]
+    # Truncate the embedding table to 50257 rows.
+    word_embeddings = word_embeddings[:50257, :]
+    # Truncate the embedding table to 50257 rows.
+    output_state_dict["transformer.wte.weight"] = word_embeddings
+
+    # The position embeddings.
+    pos_embeddings = embeddings["position_embeddings"]["weight"]
+    # Read the hidden dimension.
+    hidden_size = pos_embeddings.size(0)
+    # DEBUG.
+    assert hidden_size == heads * hidden_size_per_head
+    # Store the position embeddings.
+    output_state_dict["transformer.wpe.weight"] = pos_embeddings
+
+    # The transformer.
+    transformer = lm["transformer"]
+
+    # The regex to extract layer names.
+    layer_re = re.compile("layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")
+
+    # The simple map of names for "automated" rules.
+    megatron_to_transformers = {
+        "attention.dense": ".attn.c_proj.",
+        "mlp.dense_h_to_4h": ".mlp.c_fc.",
+        "mlp.dense_4h_to_h": ".mlp.c_proj.",
+    }
+
+    # Extract the layers.
+    for key, val in transformer.items():
+        # Match the name.
+        m = layer_re.match(key)
+
+        # Stop if that's not a layer
+        if m is None:
+            break
+
+        # The index of the layer.
+        layer_idx = int(m.group(1))
+        # The name of the operation.
+        op_name = m.group(2)
+        # Is it a weight or a bias?
+        weight_or_bias = m.group(3)
+
+        # The name of the layer.
+        layer_name = f"transformer.h.{layer_idx}"
+
+        # For layernorm(s), simply store the layer norm.
+        if op_name.endswith("layernorm"):
+
+            ln_name = "ln_1" if op_name.startswith("input") else "ln_2"
+            output_state_dict[layer_name + "." + ln_name + "." + weight_or_bias] = val
+
+        # Transpose the QKV matrix.
+        elif op_name == "attention.query_key_value" and weight_or_bias == "weight":
+
+            # Insert a tensor of 1x1xDxD bias.
+            zeros = torch.ones(1, 1, hidden_size, hidden_size)
+            output_state_dict[layer_name + ".attn.bias"] = zeros
+
+            # Insert a "dummy" tensor for masked_bias.
+            masked_bias = torch.tensor(-1e4)
+            output_state_dict[layer_name + ".attn.masked_bias"] = masked_bias
+
+            # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
+            out_val = val.transpose(0, 1)
+            # Store.
+            output_state_dict[layer_name + ".attn.c_attn.weight"] = out_val
+
+        # Transpose the bias.
+        elif op_name == "attention.query_key_value" and weight_or_bias == "bias":
+
+            # Store. No change of shape.
+            output_state_dict[layer_name + ".attn.c_attn.bias"] = val
+
+        # Transpose the weights.
+        elif weight_or_bias == "weight":
+
+            out_name = megatron_to_transformers[op_name]
+            output_state_dict[layer_name + out_name + "weight"] = val.transpose(0, 1)
+
+        # Copy the bias.
+        elif weight_or_bias == "bias":
+
+            out_name = megatron_to_transformers[op_name]
+            output_state_dict[layer_name + out_name + "bias"] = val
+
+    # The final layernorm.
+    output_state_dict["transformer.ln_f.weight"] = transformer["final_layernorm.weight"]
+    output_state_dict["transformer.ln_f.bias"] = transformer["final_layernorm.bias"]
+
+    # For LM head, transformers' wants the matrix to weight embeddings.
+    output_state_dict["lm_head.weight"] = word_embeddings
+
+    # The config.
+    output_config = {
+        "activation_function": "gelu_new",
+        "architectures": ["GPT2LMHeadModel"],
+        "attn_pdrop": 0.1,
+        "bos_token_id": 50256,
+        "embd_pdrop": 0.1,
+        "eos_token_id": 50256,
+        "initializer_range": 0.02,
+        "layer_norm_epsilon": 1e-05,
+        "model_type": "gpt2",
+        "n_ctx": 1024,
+        "n_embd": 1024,
+        "n_head": 16,
+        "n_layer": 24,
+        "n_positions": 1024,
+        "resid_pdrop": 0.1,
+        "summary_activation": None,
+        "summary_first_dropout": 0.1,
+        "summary_proj_to_labels": True,
+        "summary_type": "cls_index",
+        "summary_use_proj": True,
+        "vocab_size": 50257,
+    }
+
+    # It should be done!
+    return output_state_dict, output_config
+
+
+####################################################################################################
+
+
+def main():
+    # Create the argument parser.
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--print-checkpoint-structure", action="store_true")
+    parser.add_argument("path_to_checkpoint", type=str, help="Path to the ZIP file containing the checkpoint")
+    args = parser.parse_args()
+
+    # Extract the basename.
+    basename = os.path.dirname(args.path_to_checkpoint)
+
+    # Load the model.
+    print('Extracting PyTorch state dictionary from "{}"'.format(args.path_to_checkpoint))
+    with zipfile.ZipFile(args.path_to_checkpoint, "r") as checkpoint:
+        with checkpoint.open("release/mp_rank_00/model_optim_rng.pt") as pytorch_dict:
+            input_state_dict = torch.load(pytorch_dict, map_location="cpu")
+
+    # Convert.
+    print("Converting")
+    output_state_dict, output_config = convert_megatron_checkpoint(args, input_state_dict)
+
+    # Print the structure of converted state dict.
+    if args.print_checkpoint_structure:
+        recursive_print(None, output_state_dict)
+
+    # Store the config to file.
+    output_config_file = os.path.join(basename, "config.json")
+    print(f'Saving config to "{output_config_file}"')
+    with open(output_config_file, "w") as f:
+        json.dump(output_config, f)
+
+    # Store the state_dict to file.
+    output_checkpoint_file = os.path.join(basename, "pytorch_model.bin")
+    print(f'Saving checkpoint to "{output_checkpoint_file}"')
+    torch.save(output_state_dict, output_checkpoint_file)
+
+
+####################################################################################################
+
+if __name__ == "__main__":
+    main()
+
+####################################################################################################

src/transformers/utils/dummy_pt_objects.py

@@ -1840,6 +1840,78 @@ class MBartModel:
         requires_backends(self, ["torch"])
 
 
+MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class MegatronBertForCausalLM:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class MegatronBertForMaskedLM:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class MegatronBertForMultipleChoice:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class MegatronBertForNextSentencePrediction:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class MegatronBertForPreTraining:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class MegatronBertForQuestionAnswering:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class MegatronBertForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class MegatronBertForTokenClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class MegatronBertModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class MMBTForClassification:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])

src/transformers/utils/modeling_auto_mapping.py

@@ -21,6 +21,7 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
         ("BertConfig", "BertForQuestionAnswering"),
         ("XLNetConfig", "XLNetForQuestionAnsweringSimple"),
         ("FlaubertConfig", "FlaubertForQuestionAnsweringSimple"),
+        ("MegatronBertConfig", "MegatronBertForQuestionAnswering"),
         ("MobileBertConfig", "MobileBertForQuestionAnswering"),
         ("XLMConfig", "XLMForQuestionAnsweringSimple"),
         ("ElectraConfig", "ElectraForQuestionAnswering"),

tests/test_modeling_megatron_bert.py

+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+# Copyright 2021 NVIDIA Corporation. All rights reserved.
+#
+# 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.
+""" Testing suite for the PyTorch MegatronBERT model. """
+
+
+import math
+import os
+import unittest
+
+from transformers import is_torch_available
+from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
+
+from .test_configuration_common import ConfigTester
+from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        MODEL_FOR_PRETRAINING_MAPPING,
+        MegatronBertConfig,
+        MegatronBertForCausalLM,
+        MegatronBertForMaskedLM,
+        MegatronBertForMultipleChoice,
+        MegatronBertForNextSentencePrediction,
+        MegatronBertForPreTraining,
+        MegatronBertForQuestionAnswering,
+        MegatronBertForSequenceClassification,
+        MegatronBertForTokenClassification,
+        MegatronBertModel,
+    )
+
+
+class MegatronBertModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=64,
+        embedding_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_token_type_ids = use_token_type_ids
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.embedding_size = embedding_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = MegatronBertConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            embedding_size=self.embedding_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+        )
+
+        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def create_and_check_megatron_bert_model(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = MegatronBertModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
+        result = model(input_ids, token_type_ids=token_type_ids)
+        result = model(input_ids)
+
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def create_and_check_megatron_bert_for_masked_lm(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = MegatronBertForMaskedLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_for_causal_lm(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = MegatronBertForCausalLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_megatron_bert_for_next_sequence_prediction(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = MegatronBertForNextSentencePrediction(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            labels=sequence_labels,
+        )
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
+
+    def create_and_check_megatron_bert_for_pretraining(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = MegatronBertForPreTraining(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            labels=token_labels,
+            next_sentence_label=sequence_labels,
+        )
+        self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+        self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
+
+    def create_and_check_megatron_bert_for_question_answering(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = MegatronBertForQuestionAnswering(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            start_positions=sequence_labels,
+            end_positions=sequence_labels,
+        )
+        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
+        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
+
+    def create_and_check_megatron_bert_for_sequence_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = MegatronBertForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_megatron_bert_for_token_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = MegatronBertForTokenClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
+    def create_and_check_megatron_bert_for_multiple_choice(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_choices = self.num_choices
+        model = MegatronBertForMultipleChoice(config=config)
+        model.to(torch_device)
+        model.eval()
+        multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        result = model(
+            multiple_choice_inputs_ids,
+            attention_mask=multiple_choice_input_mask,
+            token_type_ids=multiple_choice_token_type_ids,
+            labels=choice_labels,
+        )
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class MegatronBertModelTest(ModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (
+            MegatronBertModel,
+            MegatronBertForMaskedLM,
+            MegatronBertForCausalLM,
+            MegatronBertForMultipleChoice,
+            MegatronBertForNextSentencePrediction,
+            MegatronBertForPreTraining,
+            MegatronBertForQuestionAnswering,
+            MegatronBertForSequenceClassification,
+            MegatronBertForTokenClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+
+    # test_resize_embeddings = False
+    test_head_masking = False
+
+    # special case for ForPreTraining model
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+
+        if return_labels:
+            if model_class in MODEL_FOR_PRETRAINING_MAPPING.values():
+                inputs_dict["labels"] = torch.zeros(
+                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
+                )
+                inputs_dict["next_sentence_label"] = torch.zeros(
+                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
+                )
+        return inputs_dict
+
+    def setUp(self):
+        self.model_tester = MegatronBertModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=MegatronBertConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_megatron_bert_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_megatron_bert_model(*config_and_inputs)
+
+    def test_for_masked_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_megatron_bert_for_masked_lm(*config_and_inputs)
+
+    def test_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*config_and_inputs)
+
+    def test_for_next_sequence_prediction(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*config_and_inputs)
+
+    def test_for_pretraining(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_megatron_bert_for_pretraining(*config_and_inputs)
+
+    def test_for_question_answering(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_megatron_bert_for_question_answering(*config_and_inputs)
+
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*config_and_inputs)
+
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_megatron_bert_for_token_classification(*config_and_inputs)
+
+
+def _long_tensor(tok_lst):
+    return torch.tensor(
+        tok_lst,
+        dtype=torch.long,
+        device=torch_device,
+    )
+
+
+TOLERANCE = 1e-4
+
+
+@require_torch
+@require_sentencepiece
+@require_tokenizers
+class MegatronBertModelIntegrationTests(unittest.TestCase):
+    @slow
+    def test_inference_no_head(self):
+        directory = "nvidia/megatron-bert-uncased-345m"
+        if "MYDIR" in os.environ:
+            directory = os.path.join(os.environ["MYDIR"], directory)
+        model = MegatronBertModel.from_pretrained(directory)
+        model.to(torch_device)
+        model.half()
+        input_ids = _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]])
+        with torch.no_grad():
+            output = model(input_ids)[0]
+        expected_shape = torch.Size((1, 9, 1024))
+        self.assertEqual(output.shape, expected_shape)
+
+        expected = [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
+        for ii in range(3):
+            for jj in range(3):
+                a = output[0, ii, jj]
+                b = expected[3 * ii + jj]
+                msg = "ii={} jj={} a={} b={}".format(ii, jj, a, b)
+                self.assertTrue(math.isclose(a, b, rel_tol=TOLERANCE, abs_tol=TOLERANCE), msg=msg)

utils/check_repo.py

@@ -45,6 +45,10 @@ IGNORE_NON_TESTED = [
     "BlenderbotDecoderWrapper",  # Building part of bigger (tested) model.
     "MBartEncoder",  # Building part of bigger (tested) model.
     "MBartDecoderWrapper",  # Building part of bigger (tested) model.
+    "MegatronBertLMHeadModel",  # Building part of bigger (tested) model.
+    "MegatronBertEncoder",  # Building part of bigger (tested) model.
+    "MegatronBertDecoder",  # Building part of bigger (tested) model.
+    "MegatronBertDecoderWrapper",  # Building part of bigger (tested) model.
     "PegasusEncoder",  # Building part of bigger (tested) model.
     "PegasusDecoderWrapper",  # Building part of bigger (tested) model.
     "DPREncoder",  # Building part of bigger (tested) model.