Difference between revisions of "Eosc/pretraining"

From Nordic Language Processing Laboratory
Jump to: navigation, search
(Design)
Line 29: Line 29:
 
* [https://github.com/google-research/bert Reference Google implementation in TensorFlow].  
 
* [https://github.com/google-research/bert Reference Google implementation in TensorFlow].  
 
Requirements: 1.11 <= TensorFlow < 2.0.
 
Requirements: 1.11 <= TensorFlow < 2.0.
 +
 +
''Multi-GPU training'':
 +
 +
''Multi-node training'':
 +
 +
''Training time'':
  
 
* [https://huggingface.co/transformers/model_doc/bert.html HuggingFace Transformers implementation].  
 
* [https://huggingface.co/transformers/model_doc/bert.html HuggingFace Transformers implementation].  
 
Can train either with TensorFlow or with PyTorch.  
 
Can train either with TensorFlow or with PyTorch.  
 
Requirements: Python >=3.6, TensorFlow >= 2.0, PyTorch >=1.3.1.
 
Requirements: Python >=3.6, TensorFlow >= 2.0, PyTorch >=1.3.1.
 +
 +
''Multi-GPU training'':
 +
 +
''Multi-node training'':
 +
 +
''Training time'':
  
 
* [https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT NVIDIA BERT for TF].   
 
* [https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT NVIDIA BERT for TF].   
 
Add multi-node, multi-gpu support and XLA and mixed precision; recommended by our [https://github.com/TurkuNLP/FinBERT/blob/master/nlpl_tutorial/training_bert.md role models].
 
Add multi-node, multi-gpu support and XLA and mixed precision; recommended by our [https://github.com/TurkuNLP/FinBERT/blob/master/nlpl_tutorial/training_bert.md role models].
 
Requirements: Docker, tensorflow >= 1.11, networkx, [https://github.com/NVIDIA/enroot Enroot] and [https://github.com/NVIDIA/pyxis Pyxis] for multi-node training.
 
Requirements: Docker, tensorflow >= 1.11, networkx, [https://github.com/NVIDIA/enroot Enroot] and [https://github.com/NVIDIA/pyxis Pyxis] for multi-node training.
 +
 +
''Multi-GPU training'':
 +
 +
''Multi-node training'':
 +
 +
''Training time'':
  
 
* [https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BERT NVIDIA BERT for PyTorch].   
 
* [https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BERT NVIDIA BERT for PyTorch].   
 
Add multi-node, multi-gpu support and XLA and mixed precision.
 
Add multi-node, multi-gpu support and XLA and mixed precision.
 
Requirements: Docker, PyTorch NGC container from Nvidia, [https://github.com/NVIDIA/enroot Enroot] and [https://github.com/NVIDIA/pyxis Pyxis] for multi-node training.
 
Requirements: Docker, PyTorch NGC container from Nvidia, [https://github.com/NVIDIA/enroot Enroot] and [https://github.com/NVIDIA/pyxis Pyxis] for multi-node training.
 +
 +
''Multi-GPU training'':
 +
 +
''Multi-node training'':
 +
 +
''Training time'':
  
 
* [https://github.com/soskek/bert-chainer Chainer implementation].  
 
* [https://github.com/soskek/bert-chainer Chainer implementation].  
Line 56: Line 80:
  
 
Created (but not much maintained) by [https://allenai.org/ Allen AI].
 
Created (but not much maintained) by [https://allenai.org/ Allen AI].
 +
 +
''Multi-GPU training'': Yes (TensorFlow native support)
  
 
''Multi-node training'': unknown
 
''Multi-node training'': unknown
Line 67: Line 93:
  
 
Created by [https://www.mn.uio.no/ifi/english/research/groups/ltg/ UiO LTG].
 
Created by [https://www.mn.uio.no/ifi/english/research/groups/ltg/ UiO LTG].
 +
 +
''Multi-GPU training'': Yes (TensorFlow native support)
  
 
''Multi-node training'': unknown
 
''Multi-node training'': unknown
Line 85: Line 113:
 
* [https://github.com/pytorch/fairseq/tree/master/examples/roberta Reference implementation in Fairseq].  
 
* [https://github.com/pytorch/fairseq/tree/master/examples/roberta Reference implementation in Fairseq].  
 
Requirements: Python >= 3.6, PyTorch >= 1.4, [https://github.com/NVIDIA/nccl NCCL].
 
Requirements: Python >= 3.6, PyTorch >= 1.4, [https://github.com/NVIDIA/nccl NCCL].
 +
 +
''Multi-GPU training'':
 +
 +
''Multi-node training'':
 +
 +
''Training time'':
  
 
* [https://huggingface.co/transformers/model_doc/roberta.html HuggingFace Transformers implementation].
 
* [https://huggingface.co/transformers/model_doc/roberta.html HuggingFace Transformers implementation].
 
Can train either with TensorFlow or with PyTorch.  
 
Can train either with TensorFlow or with PyTorch.  
 
Requirements: Python >=3.6, TensorFlow >= 2.0, PyTorch >=1.3.1.
 
Requirements: Python >=3.6, TensorFlow >= 2.0, PyTorch >=1.3.1.
 +
 +
''Multi-GPU training'':
 +
 +
''Multi-node training'':
 +
 +
''Training time'':
  
 
= ELECTRA =
 
= ELECTRA =
Line 98: Line 138:
 
Single-GPU training only.
 
Single-GPU training only.
 
Requirements: Python 3, 1.15 <= TensorFlow < 2.0.
 
Requirements: Python 3, 1.15 <= TensorFlow < 2.0.
 +
 +
''Multi-GPU training'':
 +
 +
''Multi-node training'':
 +
 +
''Training time'':
  
 
* [https://huggingface.co/transformers/model_doc/electra.html HuggingFace Transformers implementation].
 
* [https://huggingface.co/transformers/model_doc/electra.html HuggingFace Transformers implementation].
 
Can train either with TensorFlow or with PyTorch.  
 
Can train either with TensorFlow or with PyTorch.  
 
Requirements: Python >=3.6, TensorFlow >= 2.0, PyTorch >=1.3.1.
 
Requirements: Python >=3.6, TensorFlow >= 2.0, PyTorch >=1.3.1.
 +
 +
''Multi-GPU training'':
 +
 +
''Multi-node training'':
 +
 +
''Training time'':

Revision as of 16:36, 2 September 2020

Background

This page provides an informal, technically-oriented survey over available (and commonly used) architectures and implementations for large-scale pre-training (and fine-tuning) of contextualized neural language models.

The NLPL use case, will install, validate, and maintain a selection of these implementations, in an automated and uniform manner, on multiple HPC systems.

Design

Which systems to target? At least two of the following would seem desirable: Saga, Puhti, eX3, the quad-V100 Power9 node at UiO.

Inclusion of Saga would allow comparison to (older) P100 cards; they do support half-precision operations and XLA. The Power9 node may be interesting because of its non-Intel cpu architecture.

BERT

Bidirectional Encoder Representations from Transformers (BERT) is a deep language model jointly conditioned on both left and right context in all layers. It is based on the Transformer neural architecture (Devlin et al 2019).

The de-facto standard for contextualized representations in modern NLP.

Available implementations

Requirements: 1.11 <= TensorFlow < 2.0.

Multi-GPU training:

Multi-node training:

Training time:

Can train either with TensorFlow or with PyTorch. Requirements: Python >=3.6, TensorFlow >= 2.0, PyTorch >=1.3.1.

Multi-GPU training:

Multi-node training:

Training time:

Add multi-node, multi-gpu support and XLA and mixed precision; recommended by our role models. Requirements: Docker, tensorflow >= 1.11, networkx, Enroot and Pyxis for multi-node training.

Multi-GPU training:

Multi-node training:

Training time:

Add multi-node, multi-gpu support and XLA and mixed precision. Requirements: Docker, PyTorch NGC container from Nvidia, Enroot and Pyxis for multi-node training.

Multi-GPU training:

Multi-node training:

Training time:

Not much interesting to us, since it does not support training, only inference.

ELMo

Embeddings from Language Models (ELMo) use bidirectional LSTM language models to produce contextualized word token representations (Peters et al 2018).

The only architecture in the list to use recurrent neural networks, not Transformers. Despite being much less computationally demanding, often performs on par with BERT.

Available implementations

Requirements: Python >=3.5, 1.2 < TensorFlow < 1.13 (later versions produce too many deprecation warnings), h5py.

Created (but not much maintained) by Allen AI.

Multi-GPU training: Yes (TensorFlow native support)

Multi-node training: unknown

Training time: one epoch over 100 million word tokens takes 3 hours with 2 NVIDIA P100 GPUs (batch size 192)

Based on the reference implementation, but with improved data loading, hyper-parameter handling, and the code updated to more recent versions of TensorFlow. Requirements: Python >=3.5, 1.15 <= TensorFlow < 2.0 (2.0 version is planned), h5py, smart_open. Tutorial is available. A PyPi module is planned.

Created by UiO LTG.

Multi-GPU training: Yes (TensorFlow native support)

Multi-node training: unknown

Training time: one epoch over 100 million word tokens takes 3 hours with 2 NVIDIA P100 GPUs (batch size 192)

Not much interesting to us, since it does not support training, only inference. Requirements: Python >= 3.6, 1.6 <= PyTorch < 1.7.

RoBERTa

Robustly Optimized BERT (RoBERTa) is a BERT variation by Facebook. The most important changes are removing the next sentence prediction objective and dynamically changing the masking pattern applied to the training data. Otherwise, it is just BERT on steroids (training longer, bigger batches, longer sequences). Interestingly, the RoBERTa paper was rejected by ICLR 2020.

Available implementations

Requirements: Python >= 3.6, PyTorch >= 1.4, NCCL.

Multi-GPU training:

Multi-node training:

Training time:

Can train either with TensorFlow or with PyTorch. Requirements: Python >=3.6, TensorFlow >= 2.0, PyTorch >=1.3.1.

Multi-GPU training:

Multi-node training:

Training time:

ELECTRA

In ELECTRA, a discriminator model tries to detect which tokens in the input were replaced by a small generator language model. It is claimed to be computationally efficient in comparison to other Transformer models (Clark et al 2019).

Available implementations

Single-GPU training only. Requirements: Python 3, 1.15 <= TensorFlow < 2.0.

Multi-GPU training:

Multi-node training:

Training time:

Can train either with TensorFlow or with PyTorch. Requirements: Python >=3.6, TensorFlow >= 2.0, PyTorch >=1.3.1.

Multi-GPU training:

Multi-node training:

Training time: