Difference between revisions of "Eosc/pretraining"
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There are several established tokenization workflows for large pre-trained language models. | There are several established tokenization workflows for large pre-trained language models. | ||
We are describing them here. | We are describing them here. | ||
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| + | - ''ELMo'' does not use any sub-word splitting. | ||
| + | It splits tokens by white spaces, and then represents each token as a sequence of UTF-8 code units (max 50 by default, 8 bit each). | ||
| + | The final (non-contextual) token embedding is produced by running a simple CNN over this sequence. | ||
| + | This naturally handles OOV words, since they are composed of the same UTF-8 code units. | ||
= Design = | = Design = | ||
Revision as of 21:38, 15 September 2020
Contents
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.
Tokenization
There are several established tokenization workflows for large pre-trained language models. We are describing them here.
- ELMo does not use any sub-word splitting. It splits tokens by white spaces, and then represents each token as a sequence of UTF-8 code units (max 50 by default, 8 bit each). The final (non-contextual) token embedding is produced by running a simple CNN over this sequence. This naturally handles OOV words, since they are composed of the same UTF-8 code units.
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.
Developed by Google.
Multi-GPU training: Not officially supported, but supposedly can be achieved with Distributed training or with Horovod
Multi-node training: Not officially supported, but supposedly can be achieved with Distributed training or with Horovod
Training time: training on 3.3 billion words for 40 epochs takes "four days on 4 to 16 Cloud TPUs".
Can train either with TensorFlow or with PyTorch. Requirements: Python >=3.6, TensorFlow >= 2.0, PyTorch >=1.3.1.
Developed by HuggingFace (no corporations involved :)).
Multi-GPU training: Yes, PyTorch+NCCL
Multi-node training: Yes, PyTorch+NCCL
Training time: training on 160 million words for 2 epochs takes 8-9 days on 4 NVIDIA P100 GPUs.
Add multi-node, multi-gpu support and XLA and mixed precision; recommended by our role models. Requirements: Docker, tensorflow >= 1.11, networkx.
Developed by NVIDIA.
Multi-GPU training: Yes, TensorFlow+Horovod+NCCL
Multi-node training: Yes, TensorFlow+Horovod, requires Enroot and Pyxis.
Training time: training on 3.3 billion words for 40 epochs takes 3 days with 16 NVIDIA V100 GPUs or 12 days with 8 NVIDIA V100 GPUs.
Add multi-node, multi-gpu support and XLA and mixed precision. Requirements: Docker, PyTorch NGC container from Nvidia.
Developed by NVIDIA.
Multi-GPU training: Yes, PyTorch+NCCL
Multi-node training: Yes, PyTorch+NCCL, requires Enroot and Pyxis.
Training time: training on 3.3 billion words for 40 epochs takes 3 days with 16 NVIDIA V100 GPUs
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.
Developed (but not much maintained) by Allen AI.
Multi-GPU training: Yes (TensorFlow native support)
Multi-node training: unknown (arguably not required for ELMo)
Training time: one epoch over 100 million word tokens takes 3 hours with 2 NVIDIA P100 GPUs (batch size 192). 3 epochs already give reasonable performance in NLP tasks.
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.
Developed by UiO LTG.
Multi-GPU training: Yes (TensorFlow native support)
Multi-node training: unknown (arguably not required for ELMo)
Training time: one epoch over 100 million word tokens takes 3 hours with 2 NVIDIA P100 GPUs (batch size 192). 3 epochs already give reasonable performance in NLP tasks.
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.
Developed by Facebook.
Multi-GPU training: Yes, PyTorch + NCCL
Multi-node training: Yes, PyTorch + NCCL
Training time: not reported (essentially, they just recommend to train for as long as you can)
Can train either with TensorFlow or with PyTorch. Requirements: Python >=3.6, TensorFlow >= 2.0, PyTorch >=1.3.1.
Developed by HuggingFace.
Multi-GPU training: Yes, PyTorch + NCCL
Multi-node training: Yes, PyTorch + NCCL
Training time: unknown
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
Requirements: Python 3, 1.15 <= TensorFlow < 2.0.
Developed by Google.
Multi-GPU training: Not supported.
Multi-node training: Not officially supported, but supposedly can be achieved with Distributed training or with Horovod
Training time: training on 18 billion words takes 4 days on 1 NVIDIA V100 GPU.
Can train either with TensorFlow or with PyTorch. Requirements: Python >=3.6, TensorFlow >= 2.0, PyTorch >=1.3.1.
Developed by HuggingFace (well, strictly speaking it is still in development)
Multi-GPU training: Yes, PyTorch + NCCL
Multi-node training: Yes, PyTorch + NCCL
Training time: Should be approximately the same as the reference implementation, but not directly reported.
