Transformer-XL Base TensorFlow checkpoint (AMP, Base)

Model Overview

Transformer-XL is a transformer-based language model with a segment-level recurrence and a novel relative positional encoding.

Model Architecture

The Transformer-XL "base" model for WikiText-103 dataset available in this repository was modified to use the following hyperparameter values:

Hyperparameter	Description	Original setting for the base model	Our modification to the base model
d_model	hidden size	410	512
n_head	number of attention heads	10	8
d_head	size of each attention head	41	64
d_inner	hidden size in fully-connected layers	2100	2048
tgt_len	number of tokens to predict during training	150	192
mem_len	number of tokens cached from previous iterations during training	150	192

Changes described above were made to align certain hyperparameters with powers of two, with this modification, the model is able to achieve better hardware utilization, and therefore higher training throughput.

The following table lists the hyperparameters for the base Transformer-XL model for WikiText-103 dataset available in this repository.

Hyperparameter	Description	Base model
n_layer	number of layers	16
d_model	hidden size	512
n_head	number of attention heads	8
d_head	size of each attention head	64
d_inner	inner hidden size in fully-connected layers	2048
dropout	dropout	0.1
dropatt	dropout after softmax in the attention	0.0
lr	base learning rate	0.01
min_lr_ratio	minimum ratio learning rate (for cosine decay)	0.1
max_step	number of training steps	40,000
warmup_step	number of learning rate warmup steps	1,000
batch_size	training batch size	256
tgt_len	number of tokens to predict during training	192
mem_len	number of tokens cached from previous iterations during training	192

The Transformer-XL model addresses the limitations of vanilla transformer-based language models, which are only able to use relatively short context, bounded by the segment length. The Transformer-XL introduces a recurrence mechanism, which is able to use a cached hidden state from previous segments. During training, the context consists of a concatenation of the current segment's hidden state and cached states from previous iterations. Gradients are backpropagated only through the current segment, although the model is able to take advantage of the extra information stored in the cache and therefore is able to model long-term dependencies.

An illustration of the recurrence mechanism taken from the Transformer-XL paper is shown below.

Training

This model was trained using script available on NGC and in GitHub repo.

Dataset

The following datasets were used to train this model:

• WikiText-103 - A collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia.

Performance

Performance numbers for this model are available in NGC.

References

• Original paper
• NVIDIA model implementation in NGC
• NVIDIA model implementation on GitHub

License

This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.