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This implementation of Transformer model architecture is based on the optimized implementation in Fairseq NLP toolkit.

NVIDIA Deep Learning Examples

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22.06.0

November 4, 2022

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This resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from NGC

The Transformer is a Neural Machine Translation (NMT) model which uses attention mechanism to boost training speed and overall accuracy. The Transformer model was introduced in Attention Is All You Need and improved in Scaling Neural Machine Translation. This implementation is based on the optimized implementation in Facebook's Fairseq NLP toolkit, built on top of PyTorch.

This model is trained with mixed precision using Tensor Cores on NVIDIA Volta, Turing and Ampere GPU architectures. Therefore, researchers can get results 6.5x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

The Transformer model uses standard NMT encoder-decoder architecture. This model unlike other NMT models, uses no recurrent connections and operates on fixed size context window. The encoder stack is made up of N identical layers. Each layer is composed of the following sublayers: 1. Self-attention layer 2. Feedforward network (which is 2 fully-connected layers) Like the encoder stack, the decoder stack is made up of N identical layers. Each layer is composed of the sublayers: 1. Self-attention layer 2. Multi-headed attention layer combining encoder outputs with results from the previous self-attention layer. 3. Feedforward network (2 fully-connected layers)

The encoder uses self-attention to compute a representation of the input sequence. The decoder generates the output sequence one token at a time, taking the encoder output and previous decoder-outputted tokens as inputs. The model also applies embeddings on the input and output tokens, and adds a constant positional encoding. The positional encoding adds information about the position of each token.

Figure 1. The architecture of a Transformer model.

The complete description of the Transformer architecture can be found in Attention Is All You Need paper.

The Transformer uses Byte Pair Encoding tokenization scheme using Moses decoder. This is a lossy compression method (we drop information about white spaces). Tokenization is applied over whole WMT14 en-de dataset including test set. Default vocabulary size is 33708, excluding all special tokens. Encoder and decoder are using shared embeddings.
We use 6 blocks in each encoder and decoder stacks. Self attention layer computes it's outputs according to the following formula $`Attention(Q,K,V) = softmax(\frac{QK^T}{\sqrt{d_k}})V`

$. At each attention step, the model computes 16 different attention representations (which we will call attention heads) and concatenates them.
We trained the Transformer model using the Adam optimizer with betas `(0.9, 0.997)`

, epsilon `1e-9`

and learning rate `6e-4`

. We used the inverse square root training schedule preceded with linear warmup of 4000 steps.
The implementation allows to perform training in mixed precision. We use dynamic loss scaling and custom mixed precision optimizer. Distributed multi-GPU and multi-Node is implemented with `torch.distirbuted`

module with NCCL backend.
For inference, we use beam search with default beam size of 5. Model performance is evaluated with BLEU4 metrics. For clarity, we report internal (legacy) BLEU implementation as well as external SacreBleu score.

The following features are supported by this model.

Feature | Yes column |
---|---|

Multi-GPU training with Distributed Communication Package | Yes |

Nvidia APEX | Yes |

AMP | Yes |

TorchScript | Yes |

Multi-GPU training with Distributed Communication Package: Our model uses torch.distributed package to implement efficient multi-GPU training with NCCL. To enable multi-GPU training with torch.distributed, you have to initialize your model identically in every process spawned by torch.distributed.launch. Distributed strategy is implemented with APEX's DistributedDataParallel. For details, see example sources in this repo or see the pytorch tutorial

Nvidia APEX: The purpose of the APEX is to provide easy and intuitive framework for distributed training and mixed precision training. For details, see official APEX repository.

AMP: This implementation uses Apex's AMP to perform mixed precision training.

TorchScript: Transformer can be converted to TorchScript format offering ease of deployment on platforms without Python dependencies. For more information see official TorchScript documentation.

Mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of Tensor Cores in the Volta and Turing architecture, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:

- Porting the model to use the FP16 data type where appropriate.
- Adding loss scaling to preserve small gradient values.

The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in CUDA 8 in the NVIDIA Deep Learning SDK.

For information about:

- How to train using mixed precision, see the Mixed Precision Training paper and Training With Mixed Precision documentation.
- Techniques used for mixed precision training, see the Mixed-Precision Training of Deep Neural Networks blog.
- APEX tools for mixed precision training, see the NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch.

Mixed precision is enabled using the `--amp`

option in the `train.py`

script. The default is optimization level `O2`

but can be overriden with `--amp-level $LVL`

option (for details see amp documentation). Forward and backward pass are computed with FP16 precision with exclusion of a loss function which is computed in FP32 precision. Default optimization level keeps a copy of a model in higher precision in order to perform accurate weight update. After the update FP32 weights are again copied to FP16 model. We use dynamic loss scaling with initial scale of 2^7 increasing it by a factor of 2 every 2000 successful iterations. Overflow is being checked after reducing gradients from all of the workers. If we encounter infs or nans the whole batch is dropped.

TensorFloat-32 (TF32) is the new math mode in NVIDIA A100 GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs.

TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.

For more information, refer to the TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x blog post.

TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.

Attention layer - Layer that computes which elements of input sequence or it's hidden representation contribute the most to the currently considered output element.

Beam search - A heuristic search algorithm which at each step of predictions keeps N most possible outputs as a base to perform further prediction.

BPE - Binary Pair Encoding, compression algorithm that find most common pair of symbols in a data and replaces them with new symbol absent in the data.

EOS - End of a sentence.

Self attention layer - Attention layer that computes hidden representation of input using the same tensor as query, key and value.

Token - A string that is representable within the model. We also refer to the token's position in the dictionary as a token. There are special non-string tokens: alphabet tokens (all characters in a dataset), EOS token, PAD token.

Tokenizer - Object that converts raw strings to sequences of tokens.

Vocabulary embedding - Layer that projects one-hot token representations to a high dimensional space which preserves some information about correlations between tokens.