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# Transformer-XL For PyTorch
This repository provides a script and recipe to train the Transformer-XL model
to achieve state-of-the-art accuracy, and is tested and maintained by NVIDIA.
## Table Of Contents
<!-- TOC GFM -->
* [Model overview](#model-overview)
* [Model architecture](#model-architecture)
* [Default configuration](#default-configuration)
* [Feature support matrix](#feature-support-matrix)
* [Features](#features)
* [Mixed precision training](#mixed-precision-training)
* [Enabling mixed precision](#enabling-mixed-precision)
* [Enabling TF32](#enabling-tf32)
* [Setup](#setup)
* [Requirements](#requirements)
* [Quick Start Guide](#quick-start-guide)
* [Advanced](#advanced)
* [Scripts and sample code](#scripts-and-sample-code)
* [Parameters](#parameters)
* [Command-line options](#command-line-options)
* [Getting the data](#getting-the-data)
* [Dataset guidelines](#dataset-guidelines)
* [Multi-dataset](#multi-dataset)
* [Training process](#training-process)
* [Multi-node](#multi-node)
* [Inference process](#inference-process)
* [Performance](#performance)
* [Benchmarking](#benchmarking)
* [Training performance benchmark](#training-performance-benchmark)
* [Training performance benchmark for multi-node](#training-performance-benchmark-for-multi-node)
* [Inference performance benchmark](#inference-performance-benchmark)
* [Results](#results)
* [Training accuracy results](#training-accuracy-results)
* [Training accuracy: NVIDIA DGX A100 (8x A100 40GB)](#training-accuracy-nvidia-dgx-a100-8x-a100-40gb)
* [Base model](#base-model)
* [Large model](#large-model)
* [Training accuracy: NVIDIA DGX-1 (8x V100 16GB)](#training-accuracy-nvidia-dgx-1-8x-v100-16gb)
* [Base model](#base-model-1)
* [Large model](#large-model-1)
* [Training accuracy: NVIDIA DGX-2H (16x V100 32GB)](#training-accuracy-nvidia-dgx-2h-16x-v100-32gb)
* [Base model](#base-model-2)
* [Large model](#large-model-2)
* [Training accuracy: 8x NVIDIA DGX-2H (16x V100 32GB)](#training-accuracy-8x-nvidia-dgx-2h-16x-v100-32gb)
* [Large model](#large-model-3)
* [Training accuracy plots](#training-accuracy-plots)
* [Base model](#base-model-3)
* [Large model (single-node)](#large-model-single-node)
* [Large model (multi-node)](#large-model-multi-node)
* [Training stability test](#training-stability-test)
* [Base model](#base-model-4)
* [Large model (single-node)](#large-model-single-node-1)
* [Large model (multi-node)](#large-model-multi-node-1)
* [Training performance results](#training-performance-results)
* [Training performance: NVIDIA DGX A100 (8x A100 40GB)](#training-performance-nvidia-dgx-a100-8x-a100-40gb)
* [Base model](#base-model-5)
* [Large model](#large-model-4)
* [Training performance: NVIDIA DGX-1 (8x V100 16GB)](#training-performance-nvidia-dgx-1-8x-v100-16gb)
* [Base model](#base-model-6)
* [Large model](#large-model-5)
* [Training performance: NVIDIA DGX-2H (16x V100 32GB)](#training-performance-nvidia-dgx-2h-16x-v100-32gb)
* [Base model](#base-model-7)
* [Large model](#large-model-6)
* [Training performance: 8x NVIDIA DGX-2H (16x V100 32GB)](#training-performance-8x-nvidia-dgx-2h-16x-v100-32gb)
* [Large model](#large-model-7)
* [Inference performance results](#inference-performance-results)
* [Inference performance: NVIDIA DGX A100 (1x A100 40GB)](#inference-performance-nvidia-dgx-a100-1x-a100-40gb)
* [Base model](#base-model-8)
* [Large model](#large-model-8)
* [Inference performance: NVIDIA DGX-1 (1x V100 16GB)](#inference-performance-nvidia-dgx-1-1x-v100-16gb)
* [Base model](#base-model-9)
* [Large model](#large-model-9)
* [Inference performance: NVIDIA T4](#inference-performance-nvidia-t4)
* [Base model](#base-model-10)
* [Large model](#large-model-10)
* [Release notes](#release-notes)
* [Changelog](#changelog)
* [Known issues](#known-issues)
<!-- /TOC -->
## Model overview
This repository provides an implementation of the Transformer-XL model in
[PyTorch](https://pytorch.org) from the paper [Transformer-XL: Attentive
Language Models Beyond a Fixed-Length
Context](https://arxiv.org/abs/1901.02860). Transformer-XL is a
transformer-based language model with a segment-level recurrence and a novel
relative positional encoding. Enhancements introduced in Transformer-XL help
capture better long-term dependencies by attending to tokens from multiple
previous segments.
Our implementation is based on the
[codebase](https://github.com/kimiyoung/transformer-xl) published by the
authors of the Transformer-XL paper.
Our implementation uses a modified model architecture. Our
modifications were made to achieve better hardware utilization and to take
advantage of Tensor Cores. Similar modifications were also proposed in an
implementation available from
[github.com/cybertronai/transformer-xl](https://github.com/cybertronai/transformer-xl).
Refer to the [Model architecture](#model-architecture) section for more
details.
This model is trained with mixed precision using Tensor Cores on NVIDIA Volta
and the NVIDIA Ampere GPU architectures and evaluated on Volta, Turing and the
NVIDIA Ampere GPU architectures.
Therefore, researchers can get results up to 2.5x 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.
### 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 for 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 Transformer-XL "large" model for WikiText-103 dataset available in this
repository uses the original hyperparameters from the [reference
implementation](https://github.com/kimiyoung/transformer-xl).
The following table lists the hyperparameters for the large and the base
Transformer-XL models for WikiText-103 dataset available in this repository.
| **Hyperparameter** | **Description** | **Base model** | **Large model** |
| ------------------ | ---------------------------------------------------------------- | -------------: | ---------------: |
| `n_layer` | number of layers | 16 | 18 |
| `d_model` | hidden size | 512 | 1024 |
| `n_head` | number of attention heads | 8 | 16 |
| `d_head` | size of each attention head | 64 | 64 |
| `d_inner` | inner hidden size in fully-connected layers | 2048 | 4096 |
| `dropout` | dropout | 0.1 | 0.2 |
| `dropatt` | dropout after softmax in the attention | 0.0 | 0.2 |
| `lr` | base learning rate | 0.01 | 0.01 |
| `eta_min` | minimum learning rate (for cosine decay) | 0.001 | 0.0001 |
| `max_step` | number of training steps | 40,000 | 100,000 |
| `warmup_step` | number of learning rate warmup steps | 1,000 | 16,000 |
| `batch_size` | training batch size | 256 | 128 |
| `tgt_len` | number of tokens to predict during training | 192 | 384 |
| `mem_len` | number of tokens cached from previous iterations during training | 192 | 384 |
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 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](https://arxiv.org/abs/1901.02860) is shown below.
![model](pytorch/img/model.png)
### Default configuration
The following features were implemented in this model:
* general
* single-node or multi-node, data-parallel multi-GPU training
* training and inference with mixed precision using Tensor Cores
* mixed precision training implemented using
[Apex AMP](https://nvidia.github.io/apex/amp.html), with `O2` optimization
level and with a dynamic loss scaling
* model
* 16-layer base Transformer-XL model with hidden size 512, 8 attention heads,
each head with hidden size 64
* 18-layer large Transformer-XL model with hidden size 1024, 16 attention
heads, each head with hidden size 64
* the model trained on
[WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/)
dataset, using word-level vocabulary and
adaptive softmax
* embedding weights are tied with weights in the classifier
* training
* training with [LAMB](https://arxiv.org/abs/1904.00962) optimizer, the
implementation of the optimizer uses
[TorchScript](https://pytorch.org/docs/stable/jit.html), which enables
the fusion of elementwise operations and accelerates the training
* support for training with a gradient accumulation
* base model:
* linear learning rate warmup for 1,000 iterations, followed by the cosine
learning rate schedule, the initial learning rate is set to 0.01, and the final
learning rate is set to 0.001
* training for 40,000 steps, using a batch size of 256
* large model:
* single node:
* linear learning rate warmup for 16,000 iterations, followed by the cosine
learning rate schedule, the initial learning rate is set to 0.01, and the final
learning rate is set to 0.0001
* training for 100,000 steps, using a batch size of 128
* multi node:
* linear learning rate warmup for 16,000 iterations, followed by the cosine
learning rate schedule, the initial learning rate is set to 0.02, and the final
learning rate is set to 0.0002
* training for 25,000 steps, using a batch size of 512
* inference
* support for multi-gpu inference
* support for [TorchScript](https://pytorch.org/docs/stable/jit.html) and
pure Python inference
* each token is using the same size of the context from previous time steps.
* base model:
* target length is set to 64, length of memory is set to 640
* positional embeddings are clamped after 400 time steps
* large model:
* target length is set to 128, length of memory is set to 1,600
* positional embeddings are clamped after 1,000 time steps
### Feature support matrix
The following features are supported by this model:
| **Feature** | **Transformer-XL** |
|:------------|-------------------:|
|[Apex AMP](https://nvidia.github.io/apex/amp.html) | Yes |
|[PyTorch DistributedDataParallel](https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel) | Yes |
|[LAMB](https://arxiv.org/abs/1904.00962v3) | Yes |
| Inference with [TorchScript](https://pytorch.org/docs/stable/jit.html) | Yes |
| Multi-node training | Yes |
#### Features
[Apex AMP](https://nvidia.github.io/apex/amp.html) - a tool that enables Tensor
Core-accelerated training. Refer to the [Enabling mixed
precision](#enabling-mixed-precision) section for more details.
[PyTorch
DistributedDataParallel](https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel) - a module
wrapper that enables easy multiprocess distributed data-parallel
training.
[LAMB](https://arxiv.org/abs/1904.00962v3) - stands for Layerwise Adaptive
Moments Based optimizer, is a large batch optimization technique that helps
accelerate training of deep neural networks using large minibatches.
[TorchScript](https://pytorch.org/docs/stable/jit.html) - is a way to create
serializable and optimizable models from PyTorch code. Any TorchScript program
can be saved from a Python process and loaded in a process where there is no
Python dependency.
### Mixed precision training
Mixed precision is the combined use of different numerical precisions in a
computational method.
[Mixed precision](https://arxiv.org/abs/1710.03740) 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](https://developer.nvidia.com/tensor-cores) in Volta, and following with
both the Turing and Ampere architectures, 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 previously required two
steps:
1. Porting the model to use the FP16 data type where appropriate.
2. Manually 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](https://devblogs.nvidia.com/parallelforall/tag/fp16/) in the NVIDIA Deep
Learning SDK.
For information about:
* How to train using mixed precision, see the [Mixed Precision
Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed
Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html)
documentation.
* Techniques used for mixed precision training, see the [Mixed-Precision
Training of Deep Neural
Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/)
blog.
* APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy
Mixed-Precision Training in
PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/)
.
#### Enabling mixed precision
The `pytorch/train.py` training script launches mixed precision training
with Tensor Cores if the flag `--fp16` is set.
Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision
(AMP), library from [APEX](https://github.com/NVIDIA/apex) that casts variables
to half-precision upon retrieval, while storing variables in single-precision
format. Furthermore, to preserve small gradient magnitudes in backpropagation,
a [loss
scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling)
step must be included when applying gradients. In PyTorch, loss scaling can be
easily applied by using `scale_loss()` method provided by AMP. The scaling
value to be used can be
[dynamic](https://nvidia.github.io/apex/amp.html#apex.amp.initialize) or fixed.
For an in-depth walk through on AMP, check out sample usage
[here](https://nvidia.github.io/apex/amp.html#).
[APEX](https://github.com/NVIDIA/apex) is a PyTorch extension that contains
utility libraries, such as AMP, which require minimal network code changes to
leverage Tensor Cores performance.
The following steps were needed to enable mixed precision training in
Transformer-XL:
1. Import AMP from APEX:
```
from apex import amp
```
2. Initialize AMP and wrap the model and the optimizer before starting the
training:
```
model, optimizer = amp.initialize(
model,
optimizer,
opt_level='O2',
)
```
3. Apply `scale_loss` context manager:
```
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
```
4. Apply gradient clipping on single precision master weights:
```
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.clip)
```
#### Enabling TF32
TensorFloat-32 (TF32) is the new math mode in [NVIDIA
A100](https://www.nvidia.com/en-us/data-center/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](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/)
blog post.
TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by
default.
## Setup
The following section lists the requirements that you need to meet in order to
start training the Transformer-XL model.
### Requirements
This repository contains `Dockerfile` which extends the PyTorch NGC container
and encapsulates some dependencies. Aside from these dependencies, ensure you
have the following components:
* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
* [PyTorch 20.06-py3 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch)
* GPU architecture:
* [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
* [NVIDIA Turing](https://www.nvidia.com/en-us/geforce/turing/)
* [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)
For more information about how to get started with NGC containers, see the
following sections from the NVIDIA GPU Cloud Documentation and the Deep
Learning DGX Documentation:
* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html),
* [Accessing And Pulling From The NGC container registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry),
* [Running PyTorch](https://docs.nvidia.com/deeplearning/dgx/pytorch-release-notes/running.html#running).
For those unable to use the Pytorch NGC container, to set up the required
environment or create your own container, see the versioned [NVIDIA Container
Support
Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).
For multi-node, the sample provided in this repository requires
[Enroot](https://github.com/NVIDIA/enroot) and
[Pyxis](https://github.com/NVIDIA/pyxis) set up on a
[SLURM](https://slurm.schedmd.com) cluster.
## Quick Start Guide
To train your model using mixed or TF32 precision with Tensor Cores or using
FP32, perform the following steps using the default parameters of the
Transformer-XL base model on the
[WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/)
dataset.
For the specifics concerning training
and inference, see the [Advanced](#advanced) section.
1. Clone the repository.
```
git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/PyTorch/LanguageModeling/Transformer-XL
```
2. Download and preprocess the dataset.
```
bash getdata.sh
```
3. Build the Transformer-XL PyTorch NGC container.
```
bash pytorch/scripts/docker/build.sh
```
4. Start an interactive session in the NGC container to run training/inference.
```
bash pytorch/scripts/docker/interactive.sh
```
5. Start training.
This repository contains a number of predefined configurations to run the
training on NVIDIA DGX-1, NVIDIA DGX-2H or NVIDIA DGX A100 nodes.
To start the training on NVIDIA DGX-1 or NVIDIA DGX-2H, run:
```
bash run_wt103_{base,large}.sh train <#GPUs> --config {dgx1,dgx2}_<#GPUs>gpu_{fp16,fp32}
```
To start the training on NVIDIA DGX A100, run:
```
bash run_wt103_{base,large}.sh train <#GPUs> --config dgxa100_<#GPUs>gpu_{fp16,tf32}
```
* use the `run_wt103_base.sh` script to train the base model, and use the
`run_wt103_large.sh` script to train the large model
* the training is executed on `<#GPUs>` GPUs, supported values for `<#GPUs>`
for NVIDIA DGX-1 and NVIDIA DGX A100 are: 1, 2, 4, 8 and for NVIDIA DGX-2H:
1, 2, 4, 8, 16
* use configs with the `dgx1` prefix to run on a NVIDIA DGX-1, configs with the
`dgx2` prefix to run on a NVIDIA DGX-2H and configs with the `dgxa100` prefix
to run on a NVIDIA DGX A100
* configs with the `fp16` suffix are launching mixed precision training,
configs with the `fp32` suffix are launching FP32 training, configs with the
`tf32` suffix are launching TF32 training
Examples:
To launch TF32 training of the base Transformer-XL model on a NVIDIA DGX A100
using 8 GPUs, run:
```
bash run_wt103_base.sh train 8 --config dgxa100_8gpu_tf32
```
To launch FP32 training of the base Transformer-XL model on a NVIDIA DGX-1
using 8 GPUs, run:
```
bash run_wt103_base.sh train 8 --config dgx1_8gpu_fp32
```
To launch mixed precision training of the large Transformer-XL model on a
NVIDIA DGX-2H using 16 GPUs, run:
```
bash run_wt103_large.sh train 16 --config dgx2_16gpu_fp16
```
To launch mixed precision training of the large Transformer-XL model on a
NVIDIA DGX A100 using 8 GPUs, run:
```
bash run_wt103_large.sh train 8 --config dgxa100_8gpu_fp16
```
To run on multiple nodes, see the [Multi-node](#multi-node) section.
For more information on the available options, and for an explanation of what
happens at the end of training, refer to the [Training
process](#training-process) section.
6. Start evaluation.
To start inference on the test set using `<#GPUs>` GPUs, run:
```
bash run_wt103_{base,large}.sh eval <#GPUs> [--fp16] [--type {pytorch, torchscript}]
```
Select `run_wt103_base.sh` for the base Transformer-XL model and
`run_wt103_large.sh` for the large Transformer-XL model.
The `--fp16` flag is optional, however, if it's specified, then the script
launches mixed precision inference with Tensor Cores. If the flag is not
present, then the script launches FP32 inference on NVIDIA Volta and NVIDIA
Turing GPUs and TF32 inference on NVIDIA Ampere GPUs.
By default, the script is loading the checkpoint from
`LM-TFM/checkpoint_best.pt`, which contains the model corresponding to the
lowest value of the validation loss from the previous training run. Path to the
checkpoint can be customized by setting the `--model` flag.
Inference can use pure Python execution or TorchScript from using the `--type`
flag.
Supported values for `<#GPUs>` are: 1, 2, 4, 8 for NVIDIA DGX-1 and NVIDIA DGX
A100 and 1, 2, 4, 8, 16 for NVIDIA DGX-2H.
Additionally, one can pass the input text directly from the command-line using
the `--manual` flag. This mode of operation supports only 1 GPU and batch size
of 1. The script outputs average loss and perplexity for the provided input
text.
Examples:
```
bash run_wt103_base.sh eval 1 \
--model LM-TFM/checkpoint_best.pt \
--fp16 \
--manual "recognize speech"
===============================================================================
| test loss 6.20 | test ppl 494.291
===============================================================================
```
```
bash run_wt103_base.sh eval 1 \
--model LM-TFM/checkpoint_best.pt \
--fp16 \
--manual "wreck a nice beach"
===============================================================================
| test loss 8.04 | test ppl 3099.706
===============================================================================
```
For more information on the available options, refer to the [Inference
process](#inference-process) section.
## Advanced
The following sections provide greater details of the dataset, running training
and inference, and the training results.
### Scripts and sample code
In the root directory, the most important files are:
* `Dockerfile`: container with the basic set of dependencies to run
Transformer-XL
* `requirements.txt`: set of extra requirements for running Transformer-XL
* `getdata.sh`: script for downloading datasets
In the `pytorch` directory, the most important files are:
* `data_utils.py`: data loading utilities
* `eval.py`: serves as the entry point to launch the evaluation and inference
* `lamb.py`: implementation of [LAMB](https://arxiv.org/abs/1904.00962)
optimizer
* `mem_transformer.py`: implementation of the Transformer-XL model
* `train.py`: serves as the entry point to launch the training
* `run.sub`: Slurm batch script for launching multi-node training
The `pytorch/utils` directory contains the following additional modules:
* `adaptive_softmax.py`: implementation of adaptive softmax
* `data_parallel.py`: implementation of `BalancedDataParallel` class
* `distributed.py`: utility functions for running distributed training
* `exp_utils.py`: utility functions for running training and benchmarking
* `log_uniform_sampler.py`: implementation of log-uniform sampler
* `proj_adaptive_softmax.py`: implementation of projected adaptive softmax
* `vocabulary.py`: implementation of word-level vocabulary and BPE-based
vocabulary
The `pytorch/inference` directory contains modules optimized for running
inference with TorchScript:
* `mem_transformer_jit.py`: implementation of TorchScript-compatible
Transformer-XL model
* `proj_adaptive_softmax_jit.py`: implementation of TorchScript-compatible
projected adaptive softmax
### Parameters
**Training**
The complete list of available parameters for the `pytorch/train.py` training
script contains:
```
general setup:
--work_dir WORK_DIR Directory for the results
--append_dataset Automatically append dataset name to work_dir
--append_time Automatically append current time to work_dir
--cuda Run training on a GPU using CUDA
--fp16 Run training in fp16/mixed precision
--restart RESTART Restart training from the saved checkpoint
--debug Run in debug mode (do not create exp dir)
--log_all_ranks Enable logging from all distributed ranks
--dllog_file DLLOG_FILE
Name of the DLLogger output file
--txtlog_file TXTLOG_FILE
Name of the txt log file
--save_all Save all checkpoints
--no_env Do not print info on execution env
--no_eval Disable model evaluation
--log_interval LOG_INTERVAL
Report interval
--target_throughput TARGET_THROUGHPUT
Target training throughput (for benchmarking)
--target_perplexity TARGET_PERPLEXITY
Target validation perplexity (for benchmarking)
--amp_mode {O0,O1,O2,O3}
Optimization level for apex amp
dataset setup:
--data DATA Location of the data corpus
--dataset {wt103,lm1b,enwik8,text8}
Dataset name
--vocab {word,bpe} Type of vocabulary
model setup:
--n_layer N_LAYER Number of total layers
--n_head N_HEAD Number of heads
--d_head D_HEAD Head dimension
--d_embed D_EMBED Embedding dimension
--d_model D_MODEL Model dimension
--d_inner D_INNER Inner dimension in feedforward layer
--dropout DROPOUT Global dropout rate
--dropatt DROPATT Attention probability dropout rate
--pre_lnorm Apply LayerNorm to the input instead of the output
--attn_type ATTN_TYPE
Attention type. 0 for ours, 1 for Shaw et al,2 for
Vaswani et al, 3 for Al Rfou et al.
--not_tied Do not tie the word embedding and softmax weights
--clamp_len CLAMP_LEN
Use the same pos embeddings after clamp_len
--adaptive Use adaptive softmax
--div_val DIV_VAL Dividend value for adaptive input and softmax
--sample_softmax SAMPLE_SOFTMAX
Number of samples in sampled softmax
--init INIT Parameter initializer to use
--emb_init EMB_INIT Parameter initializer to use
--init_range INIT_RANGE
Parameters initialized by U(-init_range, init_range)
--emb_init_range EMB_INIT_RANGE
Parameters initialized by U(-init_range, init_range)
--init_std INIT_STD Parameters initialized by N(0, init_std)
--proj_init_std PROJ_INIT_STD
Parameters initialized by N(0, init_std)
optimizer setup:
--optim {adam,sgd,adagrad,lamb,jitlamb}
Optimizer to use
--lr LR Initial learning rate
--mom MOM Momentum for sgd
--scheduler {cosine,inv_sqrt,dev_perf,constant}
LR scheduler to use
--max_step_scheduler MAX_STEP_SCHEDULER
Max number of training steps for LR scheduler
--warmup_step WARMUP_STEP
Number of iterations for LR warmup
--decay_rate DECAY_RATE
Decay factor when ReduceLROnPlateau is used
--lr_min LR_MIN Minimum learning rate during annealing
--clip CLIP Gradient clipping
--weight_decay WEIGHT_DECAY
Weight decay for adam|lamb
--clip_nonemb Only clip the gradient of non-embedding params
--patience PATIENCE Patience
--eta_min ETA_MIN Min learning rate for cosine scheduler
training setup:
--max_step MAX_STEP Max number of training steps
--batch_size BATCH_SIZE
Global batch size
--local_batch_size LOCAL_BATCH_SIZE
Local (per-device) batch size, this setting overrides
global --batch_size and sets batch_size to
local_batch_size * world_size
--batch_chunk BATCH_CHUNK
Split batch into chunks and train with gradient
accumulation
--roll Enable random shifts within each data stream
--tgt_len TGT_LEN Number of tokens to predict
--ext_len EXT_LEN Length of the extended context
--mem_len MEM_LEN Length of the retained previous heads
--seed SEED Random seed
--multi_gpu {ddp,dp} Use multiple GPU
--gpu0_bsz GPU0_BSZ Batch size on gpu 0 (for "dp" backend)
--same_length Use the same attn length for all tokens
--varlen Use variable length
validation setup:
--eval_tgt_len EVAL_TGT_LEN
Number of tokens to predict for evaluation
--eval_batch_size EVAL_BATCH_SIZE
Eval batch size
--eval_max_steps EVAL_MAX_STEPS
Max eval steps
--eval_interval EVAL_INTERVAL
Evaluation interval
```
**Inference**
The complete list of available parameters for the `eval.py` inference
script contains:
```
--work_dir WORK_DIR experiment directory
--debug run in debug mode (do not create exp dir)
--data DATA location of the data corpus
--manual MANUAL [MANUAL ...]
run model on raw input data
--dataset {wt103,lm1b,enwik8,text8}
dataset name
--split {all,valid,test}
which split to evaluate
--type {pytorch,torchscript}
type of runtime to use
--batch_size BATCH_SIZE
batch size
--tgt_len TGT_LEN number of tokens to predict
--ext_len EXT_LEN length of the extended context
--mem_len MEM_LEN length of the retained previous heads
--seed SEED Random seed
--clamp_len CLAMP_LEN
max positional embedding index
--cuda Run evaluation on a GPU using CUDA
--model MODEL path to the checkpoint
--manual_config MANUAL_CONFIG
Manually specify config for the model
--manual_vocab {word,bpe}
Manually specify type of vocabulary
--fp16 Run training in fp16/mixed precision
--log_all_ranks Enable logging for all distributed ranks
--dllog_file DLLOG_FILE
Name of the DLLogger output file
--same_length set same length attention with masking
--no_env Do not print info on execution env
--log_interval LOG_INTERVAL
Report interval
--target_perplexity TARGET_PERPLEXITY
target perplexity
--target_throughput TARGET_THROUGHPUT
target throughput
--save_data save latency and throughput data to a file
--repeat REPEAT loop over the dataset REPEAT times
--max_size MAX_SIZE run inference on up to MAX_SIZE batches
--percentiles PERCENTILES [PERCENTILES ...]
percentiles for latency confidence intervals
--save_torchscript SAVE_TORCHSCRIPT
save torchscript model to a file
--load_torchscript LOAD_TORCHSCRIPT
load torchscript model from a file
```
### Command-line options
To see the full list of available options and their descriptions, use the `-h`
or `--help` command-line option. For example, for training:
```
python3 train.py --help
usage: train.py [-h] [--work_dir WORK_DIR] [--append_dataset] [--append_time]
[--cuda] [--fp16] [--restart RESTART] [--debug]
[--log_all_ranks] [--dllog_file DLLOG_FILE]
[--txtlog_file TXTLOG_FILE] [--save_all] [--no_env]
[--no_eval] [--log_interval LOG_INTERVAL]
[--target_throughput TARGET_THROUGHPUT]
[--target_perplexity TARGET_PERPLEXITY]
[--amp_mode {O0,O1,O2,O3}] [--data DATA]
[--dataset {wt103,lm1b,enwik8,text8}] [--vocab {word,bpe}]
[--n_layer N_LAYER] [--n_head N_HEAD] [--d_head D_HEAD]
[--d_embed D_EMBED] [--d_model D_MODEL] [--d_inner D_INNER]
[--dropout DROPOUT] [--dropatt DROPATT] [--pre_lnorm]
[--attn_type ATTN_TYPE] [--not_tied] [--clamp_len CLAMP_LEN]
[--adaptive] [--div_val DIV_VAL]
[--sample_softmax SAMPLE_SOFTMAX] [--init INIT]
[--emb_init EMB_INIT] [--init_range INIT_RANGE]
[--emb_init_range EMB_INIT_RANGE] [--init_std INIT_STD]
[--proj_init_std PROJ_INIT_STD]
[--optim {adam,sgd,adagrad,lamb,jitlamb}] [--lr LR]
[--mom MOM] [--scheduler {cosine,inv_sqrt,dev_perf,constant}]
[--max_step_scheduler MAX_STEP_SCHEDULER]
[--warmup_step WARMUP_STEP] [--decay_rate DECAY_RATE]
[--lr_min LR_MIN] [--clip CLIP] [--weight_decay WEIGHT_DECAY]
[--clip_nonemb] [--patience PATIENCE] [--eta_min ETA_MIN]
[--max_step MAX_STEP] [--batch_size BATCH_SIZE]
[--local_batch_size LOCAL_BATCH_SIZE]
[--batch_chunk BATCH_CHUNK] [--roll] [--tgt_len TGT_LEN]
[--ext_len EXT_LEN] [--mem_len MEM_LEN] [--seed SEED]
[--multi_gpu {ddp,dp}] [--gpu0_bsz GPU0_BSZ] [--same_length]
[--varlen] [--eval_tgt_len EVAL_TGT_LEN]
[--eval_batch_size EVAL_BATCH_SIZE]
[--eval_max_steps EVAL_MAX_STEPS]
[--eval_interval EVAL_INTERVAL] [--local_rank LOCAL_RANK]
```
For example, for inference:
```
python3 eval.py --help
usage: eval.py [-h] [--work_dir WORK_DIR] [--debug] [--data DATA]
[--manual MANUAL [MANUAL ...]]
[--dataset {wt103,lm1b,enwik8,text8}]
[--split {all,valid,test}] [--type {pytorch,torchscript}]
[--batch_size BATCH_SIZE] [--tgt_len TGT_LEN]
[--ext_len EXT_LEN] [--mem_len MEM_LEN] [--seed SEED]
[--clamp_len CLAMP_LEN] [--cuda] [--model MODEL]
[--manual_config MANUAL_CONFIG] [--manual_vocab {word,bpe}]
[--fp16] [--log_all_ranks] [--dllog_file DLLOG_FILE]
[--same_length] [--no_env] [--log_interval LOG_INTERVAL]
[--target_perplexity TARGET_PERPLEXITY]
[--target_throughput TARGET_THROUGHPUT] [--save_data]
[--repeat REPEAT] [--max_size MAX_SIZE]
[--percentiles PERCENTILES [PERCENTILES ...]]
[--save_torchscript SAVE_TORCHSCRIPT]
[--load_torchscript LOAD_TORCHSCRIPT] [--local_rank LOCAL_RANK]
```
### Getting the data
The Transformer-XL model was trained on the
[WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/)
dataset. The WikiText-103 dataset is a collection of over 100 million tokens
extracted from the set of verified
[Good](https://en.wikipedia.org/wiki/Wikipedia:Good_articles) and
[Featured](https://en.wikipedia.org/wiki/Wikipedia:Featured_articles) articles
on Wikipedia.
This repository contains the `getdata.sh` download script which
automatically downloads and extracts the training, validation and test
datasets. By default, data is downloaded to the `data` directory.
In order to test with other datasets, the script needs to be customized
accordingly.
#### Dataset guidelines
The WikiText-103 dataset was already pre-tokenized with word-level tokens. The
dataset features a large vocabulary of 267,735 tokens and retains the original
case, punctuation and numbers.
The `getdata.sh` script downloads the data, extracts the archive and renames
the training, validation, and test set to `train.txt`, `valid.txt`, `test.txt`
respectively.
#### Multi-dataset
Using other datasets requires changes in the following files:
* `pytorch/train.py`:
* the name of the new dataset should be added to the `dataset` argument in
the `parse_args()` function
* desired values of cutoffs for adaptive softmax should be added in the
`main()` function, after the section which builds train/valid/test data
iterators
* `pytorch/data_utils.py`:
* the support for the new dataset needs to be added to the `Corpus` class:
names of files containing training, validation and test data, options for
the tokenizer, and dataset iterator
The current codebase supports training with word-level vocabulary
(automatically generated based on the provided dataset) and with BPE vocabulary
(using pre-built vocabulary from pretrained GPT2 model imported from
[github.com/huggingface/transformers](https://github.com/huggingface/transformers).
Additionally, using other datasets may require changes in some hyperparameters
(for example, batch size, learning rate, number of training steps,
and the configuration of learning rate scheduler).
### Training process
The default training configuration can be launched by running the
`run_wt103_base.sh` or the `run_wt103_large.sh` script with the first argument
set to `train`. By default, the training results are saved to the `LM-TFM`
directory; this can be customized by setting the `--work_dir` parameter.
The training script launches a single-node data-parallel training with a fixed
global batch size of 256, optionally with gradient accumulation to allow
training on configurations with less than 8 GPUs. Logs from the training are
automatically saved to the `LM-TFM/train_log.log` file.
**Command-line**
You can launch training of the Transformer-XL base/large model on the
WikiText-103 dataset with the word-based vocabulary and adaptive softmax using
`<#GPUs>` GPUs. For example:
```
bash run_wt103_base.sh train <#GPUs> [--fp16] [--batch_chunk CHUNK]
```
and
```
bash run_wt103_large.sh train <#GPUs> [--fp16] [--batch_chunk CHUNK]
```
The `--fp16` flag is optional, however, if it's specified, then the script
launches mixed precision training with Tensor Cores; if the flag is not
present, then the script launches FP32 training on NVIDIA Volta GPUs and TF32
training on NVIDIA Ampere GPUs.
The `--batch_chunk CHUNK` parameter controls gradient accumulation. With
gradient accumulation, the batch size is split into `CHUNK` chunks of equal
size, the training script executes the forward and backward pass using each
chunk and then executes the optimizer using accumulated gradients.
**Examples**
You can launch mixed precision training of the Transformer-XL base model on the
WikiText-103 dataset using 16 GPUs. For example:
```
bash run_wt103_base.sh train 16 --fp16 --batch_chunk 1
```
The batch size per GPU is equal to the default global batch size of 256 divided
by the product of the number of GPUs times the number of chunks, in this case
batch size per GPU is equal to `256 / (16 * 1) = 16`.
You can launch FP32 training using 8 GPUs; the batch size per GPU is equal to
16 (`--batch_chunk` was set to `2` because a local batch size of 32 runs out of
memory on a NVIDIA DGX-1 with Tesla V100 16GB in FP32 training). For example:
```
bash run_wt103_base.sh train 8 --batch_chunk 2
```
A progress summary of the training progress is printed after every 10 training
iterations; this can be customized by setting the `--log_interval` parameter.
The summary is printed in the following format:
```
| epoch 18 step 36000 | batches 283 / 2101 | lr 1.220e-03 | ms/batch 185.1 | tok/s 265585 | loss 3.12 | ppl 22.71
```
which contains information about a current training epoch, current training
step, number of batches processed within the current epoch, current learning
rate, execution time in milliseconds per batch, throughput in tokens per
second, current training loss and training perplexity.
The script saves two checkpoints: `checkpoint_best.pt` which contains the model
corresponding to the lowest value of the validation loss and
`checkpoint_last.pt` which contains the model corresponding to the last
execution of the validation step. By default, the validation is executed every
5000 training steps, this can be customized by setting the `--eval_interval`
parameter. The summary of results on the validation dataset is printed in the
following format:
```
| Eval 7 at step 35000 | time: 1.37s | valid loss 3.14 | valid ppl 23.132
```
which contains information about the current epoch, current training step, time
needed to execute the validation, current validation loss, and validation
perplexity.
At the end of the training, the training script automatically runs evaluation
on the test dataset. This automatic evaluation is executed with values of
`mem_len` and `tgt_len` hyperparameters inherited from the training setup.
Evaluation (inference) benefits from longer attention sequences, therefore to
reproduce perplexity values reported in the [Transformer-XL
paper](https://arxiv.org/abs/1901.02860), it's necessary to run the final
evaluation with a dedicated inference script. Refer to the [Inference
process](#inference-process) section for more details.
#### Multi-node
Multi-node runs can be launched on a pyxis/enroot Slurm cluster (see
[Requirements](#requirements)). To launch a multi-node run, issue the
`run.sub` script with the following command for an 8-node DGX-2H training, for
example:
```
sbatch run.sub all
```
This repository contains a number of predefined configurations to run the
multi-node training on DGX-2H nodes. By default, `run.sub` launches 8-node
training.
To launch multi-node training on `<NODES>` DGX-2H nodes, run:
```
CONFIG=<NODES>dgx2_16gpu_{fp16,fp32} sbatch -N <NODES> run.sub all
```
* supported values for `<NODES>` parameter are: 1, 2, 4, 8
* configs with `fp16` suffix launch mixed precision training, configs with
`fp32` suffix launch FP32 training
Examples:
To launch 4-node mixed-precision training, run:
```
CONFIG=4dgx2_16gpu_fp16 sbatch -N 4 run.sub all
```
To launch 2-node FP32 training, run:
```
CONFIG=2dgx2_16gpu_fp32 sbatch -N 2 run.sub all
```
Note that the `run.sub` script is a starting point that has to be adapted
depending on the environment. In particular, variables such as `WORK_DIR`
handle the location of the workspace in the file system. The variable `CONT`
should point to the location of the Transformer-XL Docker container. It's
assumed that the Docker container built with the `scripts/docker/build.sh`
script was pushed to a Docker registry accessible from all compute nodes.
Refer to the contents of the file to see the full list of variables to adjust
for your system.
### Inference process
Inference can be run by launching the `run_wt103_base.sh` or the
`run_wt103_large.sh` script with the first argument set to `eval`. Running
inference requires a pre-trained model checkpoint.
The script supports single-node multi-GPU inference, each batch is split
equally among all GPUs running the inference and the loss is averaged over the
global batch. Logs from the inference are automatically saved to the
`LM-TFM/eval_log.log` file.
**Command-line**
You can launch inference of the Transformer-XL base/large model on the
WikiText-103 dataset with the word-based vocabulary and adaptive softmax using
`<#GPUs>` GPUs. For example:
```
bash run_wt103_base.sh eval <#GPUs> --model <PATH TO THE CHECKPOINT> [--fp16] [--type {pytorch, torchscript}]
```
and
```
bash run_wt103_large.sh eval <#GPUs> --model <PATH TO THE CHECKPOINT> [--fp16] [--type {pytorch, torchscript}]
```
The `--fp16` flag is optional, however, if it's specified, then the script
launches inference with Tensor Cores; if the flag is not present, then the
script launches FP32 inference on NVIDIA Volta and NVIDIA Turing GPUs and TF32
inference on NVIDIA Ampere GPUs.
The `--type` flag selects between pure Python PyTorch execution and TorchScript
execution.
Supported values for `<#GPUs>` are: 1, 2, 4, 8 for NVIDIA DGX-1 and NVIDIA DGX
A100 and 1, 2, 4, 8, 16 for NVIDIA DGX-2H.
**Examples**
To launch TorchScript mixed precision inference on 8 GPUs using a checkpoint
loaded from `LM-TFM/checkpoint_best.pt`, run:
```
bash run_wt103_base.sh eval 8 --model LM-TFM/checkpoint_best.pt --fp16 --type torchscript
```
To launch pure Python TF32/FP32 inference on a single GPU using a checkpoint loaded
from `LM-TFM/checkpoint_best.pt`, run:
```
bash run_wt103_base.sh eval 1 --model LM-TFM/checkpoint_best.pt --type pytorch
```
After the execution, the script prints a summary in the following format:
```
Evaluating with math fp16 type torchscript bsz 16 tgt_len 64 ext_len 0 mem_len 640 clamp_len 400
Time : 5.29s, 22.05ms/segment
====================================================================================================
| test loss 3.15 | test ppl 23.304
====================================================================================================
```
which contains information about runtime parameters, execution time, loss and
perplexity on the test dataset.
## Performance
The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA’s latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).
### Benchmarking
The following section shows how to run benchmarks measuring the model
performance in training and inference modes.
#### Training performance benchmark
To benchmark the training performance for a specific local (per-gpu) batch size
`<LBS>`, with a specific number of GPUs `<#GPUs>` for a specific number of
training iterations `<ITER>`, run:
```
bash run_wt103_{base,large}.sh train <#GPUs> --config trainbench --local_batch_size <LBS> --max_step <ITER> [--fp16]
```
* use the `run_wt103_base.sh` script to run the benchmark for the base model,
and use the `run_wt103_large.sh` script to run the benchmark for the large
model
* it's recommended to launch at least 500 training steps to get a reliable
estimate of training performace.
* the `--fp16` flag is optional, however, if it's specified, then the script
launches mixed precision training with Tensor Cores. If the flag is not
present, then the script launches FP32 training on NVIDIA Volta GPUs and TF32
training on NVIDIA Ampere GPUs.
For more information about the available options, refer to the [Training
process](#training-process) section.
The training script prints information in the following format:
```
(...)
| epoch 1 step 499 | batches 499 / 16802 | lr 4.990e-03 | ms/batch 219.9 | tok/s 27947 | loss 6.43 | ppl 620.80
| epoch 1 step 500 | batches 500 / 16802 | lr 5.000e-03 | ms/batch 221.4 | tok/s 27747 | loss 6.42 | ppl 611.70
-------------------------------------------------------------------------------
(...)
Training time: 1.81 minutes
Training throughput: 28508.91 tok/s
```
The last two lines contain information on the total training time and on the
average training throughput measured in tokens per second.
##### Training performance benchmark for multi-node
To benchmark the multi-node training performance of the large model on a
specific number of DGX-2H nodes `<NODES>` and a specific local batch size
`<LBS>`, run:
For mixed precision:
```
FP16=1 LOCAL_BATCH_SIZE=<LBS> CONFIG=trainbench_multinode sbatch -N <NODES> run.sub train
```
For FP32:
```
LOCAL_BATCH_SIZE=<LBS> CONFIG=trainbench_multinode sbatch -N <NODES> run.sub train
```
#### Inference performance benchmark
The inference performance and accuracy benchmarks require a checkpoint from a
trained model.
To benchmark the inference performance on a specific global batch size `<BS>`
with a specific number of GPUs `<#GPUs>`, run:
For the base model:
```
bash run_wt103_base.sh eval <#GPUs> --model <CHECKPOINT> --batch_size <BS> --save_data [--fp16] [--type {pytorch, torchscript}]
```
For the large model:
```
bash run_wt103_large.sh eval <#GPUs> --model <CHECKPOINT> --batch_size <BS> --save_data [--fp16] [--type {pytorch, torchscript}]
```
The inference script prints information in the following format:
```
Evaluating with math fp16 type torchscript bsz 16 tgt_len 64 ext_len 0 mem_len 640 clamp_len 400
Time : 5.25s, 21.88ms/segment
====================================================================================================
| test loss 3.15 | test ppl 23.304
====================================================================================================
Throughput Avg: 46316.64 tok/s
Latency Avg: 22.09 ms
Latency 90%: 22.22 ms
Latency 95%: 22.25 ms
Latency 99%: 22.37 ms
====================================================================================================
```
The output contains information on the achieved test loss and test perplexity,
average inference throughput (measured in tokens per second), average inference
latency and latency at 90%, 95% and 99% confidence intervals (measured in
milliseconds).
The `scripts/inference_benchmark.sh` benchmarking script is provided for
convenience, it automatically launches TF32/FP32 and FP16 inference for various
batch sizes.
### Results
The following sections provide details on how we achieved our performance and
accuracy in training and inference.
#### Training accuracy results
##### Training accuracy: NVIDIA DGX A100 (8x A100 40GB)
###### Base model
Our results were obtained by running the `pytorch/run_wt103_base.sh`
training script in the pytorch-20.06-py3 NGC container on NVIDIA DGX A100
with 8x A100 40GB GPUs.
|**GPUs**|**Batch Size / GPU**|**Accuracy - TF32 (perplexity)**|**Accuracy - Mixed precision (perplexity)**|**Time to Train - TF32 (minutes)**|**Time to Train - Mixed precision (minutes)**|**Time to Train Speedup (TF32 to Mixed precision)**|
|-------:|-------------------:|-------------------------------:|------------------------------------------:|---------------------------------:|--------------------------------------------:|--------------------------------------------------:|
| 8 | 32 | 23.24 | 23.24 | 110 | 76 | 1.45 |
###### Large model
Our results were obtained by running the `pytorch/run_wt103_large.sh`
training script in the pytorch-20.06-py3 NGC container on NVIDIA DGX A100
with 8x A100 40GB GPUs.
|**GPUs**|**Batch Size / GPU**|**Accuracy - TF32 (perplexity)**|**Accuracy - Mixed precision (perplexity)**|**Time to Train - TF32 (minutes)**|**Time to Train - Mixed precision (minutes)**|**Time to Train Speedup (TF32 to Mixed precision)**|
|-------:|-------------------:|-------------------------------:|------------------------------------------:|---------------------------------:|--------------------------------------------:|--------------------------------------------------:|
| 8 | 8 | 18.18 | 18.18 | 735 | 477 | 1.54 |
| 8 | 16 | N/A | 18.19 | N/A | 430 | 1.71 |
##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)
###### Base model
Our results were obtained by running the `pytorch/run_wt103_base.sh`
training script in the pytorch-20.06-py3 NGC container on NVIDIA DGX-1
with 8x V100 16GB GPUs.
|**GPUs**|**Batch Size / GPU**|**Accuracy - FP32 (perplexity)**|**Accuracy - Mixed precision (perplexity)**|**Time to Train - FP32 (minutes)**|**Time to Train - Mixed precision (minutes)**|**Time to Train Speedup (FP32 to Mixed precision)**|
|-------:|-------------------:|-------------------------------:|------------------------------------------:|---------------------------------:|--------------------------------------------:|--------------------------------------------------:|
| 1 | 16 | 23.12 | 23.13 | 2146 | 960 | 2.24 |
| 8 | 16 | 23.17 | 23.14 | 316 | 167 | 1.89 |
| 1 | 32 | N/A | 23.15 | N/A | 766 | 2.80 |
| 8 | 32 | N/A | 23.18 | N/A | 121 | 2.61 |
###### Large model
Our results were obtained by running the `pytorch/run_wt103_large.sh`
training script in the pytorch-20.06-py3 NGC container on NVIDIA DGX-1
with 8x V100 16GB GPUs.
|**GPUs**|**Batch Size / GPU**|**Accuracy - FP32 (perplexity)**|**Accuracy - Mixed precision (perplexity)**|**Time to Train - FP32 (minutes)**|**Time to Train - Mixed precision (minutes)**|**Time to Train Speedup (FP32 to Mixed precision)**|
|-------:|-------------------:|-------------------------------:|------------------------------------------:|---------------------------------:|--------------------------------------------:|--------------------------------------------------:|
| 8 | 2 | 18.22 | 18.20 | 2983 | 1480 | 2.01 |
| 8 | 4 | N/A | 18.17 | N/A | 984 | 3.03 |
##### Training accuracy: NVIDIA DGX-2H (16x V100 32GB)
###### Base model
Our results were obtained by running the `pytorch/run_wt103_base.sh`
training script in the pytorch-20.06-py3 NGC container on NVIDIA DGX-2H
with 16x V100 32GB GPUs.
|**GPUs**|**Batch Size / GPU**|**Accuracy - FP32 (perplexity)**|**Accuracy - Mixed precision (perplexity)**|**Time to Train - FP32 (minutes)**|**Time to Train - Mixed precision (minutes)**|**Time to Train Speedup (FP32 to Mixed precision)**|
|-------:|-------------------:|-------------------------------:|------------------------------------------:|---------------------------------:|--------------------------------------------:|--------------------------------------------------:|
| 16 | 16 | 23.22 | 23.22 | 149 | 80 | 1.86 |
###### Large model
Our results were obtained by running the `pytorch/run_wt103_large.sh`
training script in the pytorch-20.06-py3 NGC container on NVIDIA DGX-2H
with 16x V100 32GB GPUs.
|**GPUs**|**Batch Size / GPU**|**Accuracy - FP32 (perplexity)**|**Accuracy - Mixed precision (perplexity)**|**Time to Train - FP32 (minutes)**|**Time to Train - Mixed precision (minutes)**|**Time to Train Speedup (FP32 to Mixed precision)**|
|-------:|-------------------:|-------------------------------:|------------------------------------------:|---------------------------------:|--------------------------------------------:|--------------------------------------------------:|
| 16 | 8 | 18.21 | 18.20 | 1075 | 394 | 2.73 |
##### Training accuracy: 8x NVIDIA DGX-2H (16x V100 32GB)
###### Large model
Our results were obtained by running the `pytorch/run.sub`
training script in the pytorch-20.06-py3 NGC container on 8x NVIDIA DGX-2H
with 16x V100 32GB GPUs.
|**DGX System**|**Nodes**|**Batch Size / GPU**|**Accuracy - FP32 (perplexity)**|**Accuracy - Mixed precision (perplexity)**|**Time to Train - FP32 (minutes)**|**Time to Train - Mixed precision (minutes)**|**Time to Train Speedup (FP32 to Mixed precision)**|
|-------------:|--------:|-------------------:|-------------------------------:|------------------------------------------:|---------------------------------:|--------------------------------------------:|--------------------------------------------------:|
| DGX-2H | 8 | 4 | 18.27 | 18.28 | 156 | 74 | 2.11 |
##### Training accuracy plots
###### Base model
![TrainingLossBase](pytorch/img/training_loss_base.png)
###### Large model (single-node)
![TrainingLossLarge](pytorch/img/training_loss_large.png)
###### Large model (multi-node)
![TrainingLossLargeMultiNode](pytorch/img/training_loss_large_multinode.png)
##### Training stability test
###### Base model
The Transformer-XL base model was trained for 40,000 training steps, starting
from 16 different initial random seeds. After every 5,000 training steps, the
model was evaluated on the validation dataset and validation perplexity was
recorded. The training was performed in the pytorch-20.06-py3 NGC container on
NVIDIA DGX A100 with 8x A100 40GB GPUs. The following table summarizes the
perplexity of our validation dataset.
|**Training step**|**Average perplexity**|**Standard deviation**|**Minimum**|**Maximum**|**Median**|
|----------------:|----------:|---------------------:|----------:|----------:|---------:|
| 5000 | 42.62 | 0.27311 | 42.01 | 43.09 | 42.67 |
| 10000 | 32.31 | 0.12814 | 32.10 | 32.59 | 32.31 |
| 15000 | 28.38 | 0.10764 | 28.23 | 28.57 | 28.35 |
| 20000 | 26.14 | 0.10218 | 25.96 | 26.36 | 26.14 |
| 25000 | 24.59 | 0.09060 | 24.42 | 24.81 | 24.60 |
| 30000 | 23.71 | 0.07259 | 23.61 | 23.84 | 23.71 |
| 35000 | 23.15 | 0.04781 | 23.05 | 23.26 | 23.15 |
| 40000 | 22.93 | 0.05593 | 22.83 | 23.04 | 22.94 |
After training, the models were evaluated on the test dataset. The following
table summarizes the final perplexity on the test set.
|**Average perplexity**|**Standard deviation**|**Minimum**|**Maximum**|**Median**|
|----------:|---------------------:|----------:|----------:|---------:|
| 23.24| 0.07794| 23.11| 23.38| 23.25|
###### Large model (single-node)
The Transformer-XL large model was trained for 100,000 training steps, starting
from 16 different initial random seeds. After every 10,000 training steps, the
model was evaluated on the validation dataset and validation perplexity was
recorded. The training was performed in the pytorch-20.06-py3 NGC container on
NVIDIA DGX A100 with 8x A100 40GB GPUs. The following table summarizes the
perplexity of our validation dataset.
|**Training step**|**Average perplexity**|**Standard deviation**|**Minimum**|**Maximum**|**Median**|
|----------------:|----------:|---------------------:|----------:|----------:|---------:|
| 10000 | 32.63 | 0.20432 | 32.34 | 33.05 | 32.62 |
| 20000 | 24.08 | 0.10980 | 23.90 | 24.28 | 24.10 |
| 30000 | 21.52 | 0.09069 | 21.36 | 21.66 | 21.52 |
| 40000 | 20.17 | 0.06922 | 20.06 | 20.27 | 20.17 |
| 50000 | 19.23 | 0.05975 | 19.11 | 19.33 | 19.24 |
| 60000 | 18.57 | 0.06008 | 18.47 | 18.72 | 18.56 |
| 70000 | 18.17 | 0.06473 | 18.08 | 18.32 | 18.15 |
| 80000 | 17.95 | 0.06506 | 17.82 | 18.08 | 17.94 |
| 90000 | 17.80 | 0.04350 | 17.71 | 17.90 | 17.80 |
| 100000 | 17.80 | 0.03592 | 17.74 | 17.86 | 17.81 |
After training, the models were evaluated on the test dataset. The following
table summarizes the final perplexity on the test set.
|**Average perplexity**|**Standard deviation**|**Minimum**|**Maximum**|**Median**|
|---------------------:|---------------------:|----------:|----------:|---------:|
| 18.17 | 0.04016 | 18.09 | 18.24 | 18.17 |
###### Large model (multi-node)
The Transformer-XL large model was trained for 25,000 training steps, starting
from 10 different initial random seeds. After every 1,000 training steps, the
model was evaluated on the validation dataset and validation perplexity was
recorded. The training was performed in the pytorch-20.06-py3 NGC container on
8x NVIDIA DGX-2H with 16x V100 32GB GPUs. The following table summarizes the
perplexity of our validation dataset.
|**Training step**|**Average perplexity**|**Standard deviation**|**Minimum**|**Maximum**|**Median**|
|----------------:|----------:|---------------------:|----------:|----------:|---------:|
| 1000 | 608.09 | 3.80116 | 600.65 | 613.73 | 609.40 |
| 2000 | 142.75 | 0.94452 | 141.21 | 143.84 | 143.07 |
| 3000 | 62.19 | 0.44544 | 61.38 | 63.01 | 62.18 |
| 4000 | 40.22 | 0.16397 | 39.93 | 40.54 | 40.20 |
| 5000 | 32.00 | 0.15850 | 31.61 | 32.19 | 32.02 |
| 6000 | 28.05 | 0.17854 | 27.81 | 28.41 | 28.05 |
| 7000 | 25.65 | 0.10946 | 25.51 | 25.87 | 25.65 |
| 8000 | 24.20 | 0.11385 | 23.98 | 24.36 | 24.20 |
| 9000 | 23.18 | 0.14936 | 22.84 | 23.37 | 23.20 |
| 10000 | 22.88 | 0.22752 | 22.54 | 23.33 | 22.94 |
| 11000 | 21.99 | 0.16232 | 21.73 | 22.29 | 21.97 |
| 12000 | 21.69 | 0.10824 | 21.46 | 21.81 | 21.73 |
| 13000 | 21.42 | 0.09154 | 21.25 | 21.57 | 21.44 |
| 14000 | 21.33 | 0.13821 | 21.15 | 21.55 | 21.27 |
| 15000 | 21.24 | 0.15526 | 20.95 | 21.57 | 21.20 |
| 16000 | 21.19 | 0.10521 | 21.01 | 21.44 | 21.18 |
| 17000 | 20.89 | 0.18239 | 20.69 | 21.18 | 20.82 |
| 18000 | 20.36 | 0.10715 | 20.21 | 20.53 | 20.34 |
| 19000 | 19.74 | 0.12803 | 19.45 | 19.92 | 19.75 |
| 20000 | 19.18 | 0.10020 | 19.05 | 19.39 | 19.15 |
| 21000 | 18.49 | 0.06319 | 18.36 | 18.60 | 18.49 |
| 22000 | 18.17 | 0.03674 | 18.11 | 18.22 | 18.16 |
| 23000 | 17.98 | 0.03682 | 17.90 | 18.04 | 17.99 |
| 24000 | 17.88 | 0.02880 | 17.84 | 17.92 | 17.89 |
| 25000 | 17.85 | 0.02793 | 17.80 | 17.90 | 17.86 |
After training, the models were evaluated on the test dataset. The following
table summarizes the final perplexity on the test set.
|**Average perplexity**|**Standard deviation**|**Minimum**|**Maximum**|**Median**|
|----------:|---------------------:|----------:|----------:|---------:|
| 18.30 | 0.02747 | 18.24 | 18.33 | 18.30 |
#### Training performance results
##### Training performance: NVIDIA DGX A100 (8x A100 40GB)
###### Base model
Our results were obtained by running the `pytorch/run_wt103_base.sh` training
script in the pytorch-20.06-py3 NGC container on NVIDIA DGX A100 with 8x A100
40GB GPUs. Performance numbers (in tokens per second) were averaged over 500
training iterations.
|**GPUs**|**Batch Size / GPU**|**Throughput - TF32 (tok/s)**|**Throughput - Mixed precision (tok/s)**|**Throughput speedup (TF32 to Mixed precision)**|**Weak Scaling - TF32**|**Weak Scaling - Mixed precision**|
|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|
| 1 | 32 | 41,527 | 59,961 | 1.444 | 1.000 | 1.000 |
| 2 | 32 | 77,625 | 113,238 | 1.459 | 1.869 | 1.889 |
| 4 | 32 | 153,945 | 225,609 | 1.466 | 3.707 | 3.763 |
| 8 | 32 | 305,933 | 449,890 | 1.471 | 7.367 | 7.503 |
To achieve these same results, follow the steps in the
[Quick Start Guide](#quick-start-guide) to download the dataset and setup
the container, and then proceed to the
[Training performance benchmark](#training-performance-benchmark) section for
instruction on how to launch the benchmark.
###### Large model
Our results were obtained by running the `pytorch/run_wt103_large.sh` training
script in the pytorch-20.06-py3 NGC container on NVIDIA DGX A100 with 8x A100
40GB GPUs. Performance numbers (in tokens per second) were averaged over 500
training iterations.
|**GPUs**|**Batch Size / GPU**|**Throughput - TF32 (tok/s)**|**Throughput - Mixed precision (tok/s)**|**Throughput speedup (TF32 to Mixed precision)**|**Weak Scaling - TF32**|**Weak Scaling - Mixed precision**|
|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|
| 1 | 8 | 14,497 | 21,554 | 1.487 | 1.000 | 1.000 |
| 2 | 8 | 27,304 | 40,222 | 1.473 | 1.883 | 1.866 |
| 4 | 8 | 53,756 | 80,226 | 1.492 | 3.708 | 3.722 |
| 8 | 8 | 106,651 | 159,185 | 1.493 | 7.357 | 7.385 |
| 1 | 16 | N/A | 25,084 | 1.730 | N/A | 1.000 |
| 2 | 16 | N/A | 48,562 | 1.779 | N/A | 1.936 |
| 4 | 16 | N/A | 95,997 | 1.786 | N/A | 3.827 |
| 8 | 16 | N/A | 191,148 | 1.792 | N/A | 7.620 |
To achieve these same results, follow the steps in the
[Quick Start Guide](#quick-start-guide) to download the dataset and setup
the container, and then proceed to the
[Training performance benchmark](#training-performance-benchmark) section for
instruction on how to launch the benchmark.
##### Training performance: NVIDIA DGX-1 (8x V100 16GB)
###### Base model
Our results were obtained by running the `pytorch/run_wt103_base.sh` training
script in the pytorch-20.06-py3 NGC container on NVIDIA DGX-1 with 8x V100 16GB
GPUs. Performance numbers (in tokens per second) were averaged over 500
training iterations.
|**GPUs**|**Batch Size / GPU**|**Throughput - FP32 (tok/s)**|**Throughput - Mixed precision (tok/s)**|**Throughput speedup (FP32 to Mixed precision)**|**Weak Scaling - FP32**|**Weak Scaling - Mixed precision**|
|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|
| 1 | 16 | 13,981 | 26,639 | 1.905 | 1.000 | 1.000 |
| 2 | 16 | 23,163 | 45,299 | 1.956 | 1.657 | 1.700 |
| 4 | 16 | 48,893 | 92,618 | 1.894 | 3.497 | 3.477 |
| 8 | 16 | 97,005 | 170,532 | 1.758 | 6.938 | 6.402 |
| 1 | 32 | N/A | 36,692 | 2.624 | N/A | 1.000 |
| 2 | 32 | N/A | 65,889 | 2.845 | N/A | 1.796 |
| 4 | 32 | N/A | 133,838 | 2.737 | N/A | 3.648 |
| 8 | 32 | N/A | 258,648 | 2.666 | N/A | 7.049 |
To achieve these same results, follow the steps in the
[Quick Start Guide](#quick-start-guide) to download the dataset and setup
the container, and then proceed to the
[Training performance benchmark](#training-performance-benchmark) section for
instruction on how to launch the benchmark.
###### Large model
Our results were obtained by running the `pytorch/run_wt103_large.sh` training
script in the pytorch-20.06-py3 NGC container on NVIDIA DGX-1 with 8x V100 16GB
GPUs. Performance numbers (in tokens per second) were averaged over 500
training iterations.
|**GPUs**|**Batch Size / GPU**|**Throughput - FP32 (tok/s)**|**Throughput - Mixed precision (tok/s)**|**Throughput speedup (FP32 to Mixed precision)**|**Weak Scaling - FP32**|**Weak Scaling - Mixed precision**|
|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|
| 1 | 2 | 3,558 | 6,907 | 1.941 | 1.000 | 1.000 |
| 2 | 2 | 6,153 | 11,272 | 1.832 | 1.729 | 1.632 |
| 4 | 2 | 12,492 | 22,530 | 1.804 | 3.511 | 3.262 |
| 8 | 2 | 24,595 | 40,920 | 1.664 | 6.913 | 5.925 |
| 1 | 4 | N/A | 10,210 | 2.870 | N/A | 1.000 |
| 2 | 4 | N/A | 17,984 | 2.923 | N/A | 1.761 |
| 4 | 4 | N/A | 36,340 | 2.909 | N/A | 3.559 |
| 8 | 4 | N/A | 66,716 | 2.713 | N/A | 6.535 |
To achieve these same results, follow the steps in the
[Quick Start Guide](#quick-start-guide) to download the dataset and setup
the container, and then proceed to the
[Training performance benchmark](#training-performance-benchmark) section for
instruction on how to launch the benchmark.
##### Training performance: NVIDIA DGX-2H (16x V100 32GB)
###### Base model
Our results were obtained by running the `pytorch/run_wt103_base.sh` training
script in the pytorch-20.06-py3 NGC container on NVIDIA DGX-2H with 16x V100
32GB GPUs. Performance numbers (in tokens per second) were averaged over 500
training iterations.
|**GPUs**|**Batch Size / GPU**|**Throughput - FP32 (tok/s)**|**Throughput - Mixed precision (tok/s)**|**Throughput speedup (FP32 to Mixed precision)**|**Weak Scaling - FP32**|**Weak Scaling - Mixed precision**|
|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|
| 1 | 16 | 16,150 | 32,875 | 2.036 | 1.000 | 1.000 |
| 2 | 16 | 29,712 | 59,058 | 1.988 | 1.840 | 1.796 |
| 4 | 16 | 58,011 | 113,985 | 1.965 | 3.592 | 3.467 |
| 8 | 16 | 114,655 | 223,907 | 1.953 | 7.099 | 6.811 |
| 16 | 16 | 222,920 | 414,994 | 1.862 | 13.803 | 12.623 |
To achieve these same results, follow the steps in the
[Quick Start Guide](#quick-start-guide) to download the dataset and setup
the container, and then proceed to the
[Training performance benchmark](#training-performance-benchmark) section for
instruction on how to launch the benchmark.
###### Large model
Our results were obtained by running the `pytorch/run_wt103_large.sh` training
script in the pytorch-20.06-py3 NGC container on NVIDIA DGX-2H with 16x V100
32GB GPUs. Performance numbers (in tokens per second) were averaged over 500
training iterations.
|**GPUs**|**Batch Size / GPU**|**Throughput - FP32 (tok/s)**|**Throughput - Mixed precision (tok/s)**|**Throughput speedup (FP32 to Mixed precision)**|**Weak Scaling - FP32**|**Weak Scaling - Mixed precision**|
|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|
| 1 | 8 | 5,169 | 14,787 | 2.861 | 1.000 | 1.000 |
| 2 | 8 | 9,977 | 27,710 | 2.777 | 1.930 | 1.874 |
| 4 | 8 | 19,691 | 54,207 | 2.753 | 3.810 | 3.666 |
| 8 | 8 | 39,157 | 107,073 | 2.734 | 7.576 | 7.241 |
| 16 | 8 | 77,568 | 211,387 | 2.725 | 15.008 | 14.296 |
To achieve these same results, follow the steps in the
[Quick Start Guide](#quick-start-guide) to download the dataset and setup
the container, and then proceed to the
[Training performance benchmark](#training-performance-benchmark) section for
instruction on how to launch the benchmark.
##### Training performance: 8x NVIDIA DGX-2H (16x V100 32GB)
Our results were obtained by running the `pytorch/run.sub` training script in
the pytorch-20.06-py3 NGC container. Performance numbers (in tokens per second)
were averaged over 500 training iterations.
###### Large model
|**DGX System**|**Nodes**|**Batch Size / GPU**|**Throughput - FP32 (tok/s)**|**Throughput - Mixed precision (tok/s)**|**Throughput speedup (FP32 to Mixed precision)**|**Weak Scaling - FP32**|**Weak scaling - Mixed precision**|
|-------------:|--------:|-------------------:|-------------------------------:|------------------------------------------:|---------------------------------:|--------------------------------------------:|--------------------------------------------------:|
| DGX-2H | 1 | 4 | 69,070 | 154,950 | 2.24 | 1.00 | 1.00 |
| DGX-2H | 2 | 4 | 136,960 | 307,520 | 2.25 | 1.98 | 1.98 |
| DGX-2H | 4 | 4 | 270,120 | 605,530 | 2.24 | 3.91 | 3.91 |
| DGX-2H | 8 | 4 | 514,500 | 1,189,700 | 2.31 | 7.45 | 7.68 |
To achieve these same results, follow the steps in the
[Quick Start Guide](#quick-start-guide) to download the dataset and then
proceed to the
[Training performance benchmark for
multi-node](#training-performance-benchmark-for-multi-node) section for
instruction on how to launch the multi-node performance benchmark. The numbers
presented above were obtained with `LOCAL_BATCH_SIZE=4`.
#### Inference performance results
##### Inference performance: NVIDIA DGX A100 (1x A100 40GB)
###### Base model
Our results were obtained by running the
`pytorch/scripts/inference_benchmark.sh` inferencing benchmarking script in the
pytorch-20.06-py3 NGC container on NVIDIA DGX A100 with 1x A100 40GB GPU.
The command to launch the inference performance benchmark is provided in the
[Inference performance benchmark](#inference-performance-benchmark) section.
**FP16, pure Python**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 64 | 640 | 4,163.7 | 15.38 | 15.58 | 15.66 | 16.12 |
| 2 | 64 | 640 | 7,915.4 | 16.17 | 16.36 | 16.42 | 17.19 |
| 4 | 64 | 640 | 15,710.2 | 16.29 | 16.45 | 16.49 | 17.38 |
| 8 | 64 | 640 | 32,712.1 | 15.64 | 15.77 | 15.82 | 16.65 |
| 16 | 64 | 640 | 59,378.6 | 17.23 | 17.32 | 17.36 | 18.39 |
| 32 | 64 | 640 | 91,654.2 | 22.33 | 22.39 | 22.53 | 23.63 |
**FP16, TorchScript**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 64 | 640 | 6,935.9 | 9.231 | 9.388 | 9.445 | 9.534 |
| 2 | 64 | 640 | 12,649.4 | 10.120 | 10.253 | 10.294 | 10.945 |
| 4 | 64 | 640 | 25,029.5 | 10.223 | 10.346 | 10.381 | 10.475 |
| 8 | 64 | 640 | 52,666.3 | 9.716 | 9.808 | 9.851 | 10.540 |
| 16 | 64 | 640 | 90,767.8 | 11.274 | 11.321 | 11.334 | 11.800 |
| 32 | 64 | 640 | 107,082.4 | 19.109 | 19.138 | 19.162 | 19.608 |
**TF32, pure Python**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 64 | 640 | 4,003.8 | 15.99 | 16.26 | 16.36 | 16.58 |
| 2 | 64 | 640 | 7,499.2 | 17.07 | 17.32 | 17.39 | 17.86 |
| 4 | 64 | 640 | 14,835.4 | 17.25 | 17.46 | 17.50 | 18.34 |
| 8 | 64 | 640 | 30,001.5 | 17.06 | 17.22 | 17.28 | 18.40 |
| 16 | 64 | 640 | 50,189.7 | 20.39 | 20.48 | 20.52 | 21.41 |
| 32 | 64 | 640 | 63,660.5 | 32.14 | 32.17 | 32.29 | 33.19 |
**TF32, TorchScript**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 64 | 640 | 6,084.5 | 10.52 | 10.74 | 10.84 | 10.95 |
| 2 | 64 | 640 | 11,680.6 | 10.96 | 11.17 | 11.22 | 11.76 |
| 4 | 64 | 640 | 22,867.3 | 11.19 | 11.35 | 11.40 | 12.07 |
| 8 | 64 | 640 | 45,165.5 | 11.33 | 11.46 | 11.49 | 12.03 |
| 16 | 64 | 640 | 61,042.0 | 16.76 | 16.84 | 16.86 | 17.13 |
| 32 | 64 | 640 | 71,124.1 | 28.77 | 28.81 | 28.84 | 28.86 |
To achieve these same results, follow the steps in the
[Quick Start Guide](#quick-start-guide) to download the dataset and setup
the container, and then proceed to the
[Inference performance benchmark](#inference-performance-benchmark) section for
instruction on how to launch the benchmark.
###### Large model
Our results were obtained by running the
`pytorch/scripts/inference_benchmark.sh` inferencing benchmarking script in the
pytorch-20.06-py3 NGC container on NVIDIA DGX A100 with 1x A100 40GB GPU.
The command to launch the inference performance benchmark is provided in the
[Inference performance benchmark](#inference-performance-benchmark) section.
**FP16, pure Python**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 128 | 1,600 | 7,033.0 | 18.20 | 18.57 | 18.64 | 18.93 |
| 2 | 128 | 1,600 | 12,832.5 | 19.94 | 20.23 | 20.29 | 21.07 |
| 4 | 128 | 1,600 | 21,500.2 | 23.80 | 23.99 | 24.07 | 25.09 |
| 8 | 128 | 1,600 | 25,797.1 | 39.66 | 39.74 | 39.91 | 41.00 |
| 16 | 128 | 1,600 | 28,143.5 | 72.71 | 72.74 | 73.12 | 74.00 |
| 32 | 128 | 1,600 | 28,533.6 | 143.44 | 143.30 | 143.48 | 149.07 |
**FP16, TorchScript**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 128 | 1,600 | 11,068.2 | 11.57 | 11.83 | 11.88 | 12.42 |
| 2 | 128 | 1,600 | 19,847.0 | 12.89 | 13.09 | 13.11 | 13.27 |
| 4 | 128 | 1,600 | 24,450.7 | 20.92 | 21.08 | 21.10 | 21.15 |
| 8 | 128 | 1,600 | 27,938.4 | 36.62 | 36.72 | 36.75 | 36.86 |
| 16 | 128 | 1,600 | 30,783.0 | 66.48 | 66.54 | 66.59 | 66.98 |
| 32 | 128 | 1,600 | 32,161.6 | 127.26 | 127.19 | 127.34 | 131.64 |
**TF32, pure Python**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 128 | 1,600 | 6,558.8 | 19.52 | 19.87 | 19.95 | 20.44 |
| 2 | 128 | 1,600 | 10,658.4 | 24.00 | 24.28 | 24.36 | 25.17 |
| 4 | 128 | 1,600 | 14,769.6 | 34.64 | 34.82 | 34.89 | 35.74 |
| 8 | 128 | 1,600 | 16,852.6 | 60.71 | 60.82 | 61.05 | 62.17 |
| 16 | 128 | 1,600 | 18,071.8 | 113.23 | 113.28 | 113.37 | 114.64 |
| 32 | 128 | 1,600 | 17,619.2 | 234.04 | 229.98 | 239.30 | 328.15 |
**TF32, TorchScript**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 128 | 1,600 | 9,084.4 | 14.09 | 14.37 | 14.40 | 14.46 |
| 2 | 128 | 1,600 | 12,839.4 | 19.92 | 20.15 | 20.17 | 20.25 |
| 4 | 128 | 1,600 | 15,582.4 | 32.83 | 33.00 | 33.02 | 33.28 |
| 8 | 128 | 1,600 | 17,825.0 | 57.40 | 57.55 | 57.59 | 57.94 |
| 16 | 128 | 1,600 | 19,419.2 | 105.38 | 105.49 | 105.54 | 105.91 |
| 32 | 128 | 1,600 | 20,079.4 | 203.81 | 203.77 | 203.84 | 207.47 |
To achieve these same results, follow the steps in the
[Quick Start Guide](#quick-start-guide) to download the dataset and setup
the container, and then proceed to the
[Inference performance benchmark](#inference-performance-benchmark) section for
instruction on how to launch the benchmark.
##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)
###### Base model
Our results were obtained by running the
`pytorch/scripts/inference_benchmark.sh` inferencing benchmarking script in the
pytorch-20.06-py3 NGC container on NVIDIA DGX-1 with 1x V100 16GB GPU.
The command to launch the inference performance benchmark is provided in the
[Inference performance benchmark](#inference-performance-benchmark) section.
**FP16, pure Python**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 64 | 640 | 2,999.6 | 21.36 | 21.72 | 21.90 | 24.86 |
| 2 | 64 | 640 | 5,738.5 | 22.32 | 22.64 | 22.89 | 25.97 |
| 4 | 64 | 640 | 11,773.5 | 21.73 | 21.92 | 22.06 | 22.68 |
| 8 | 64 | 640 | 22,604.7 | 22.63 | 22.92 | 23.08 | 23.56 |
| 16 | 64 | 640 | 41,481.6 | 24.67 | 24.83 | 24.99 | 25.73 |
| 32 | 64 | 640 | 58,556.9 | 34.95 | 35.13 | 35.24 | 35.85 |
**FP16, TorchScript**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 64 | 640 | 5,199.9 | 12.31 | 12.59 | 12.65 | 12.98 |
| 2 | 64 | 640 | 9,802.5 | 13.06 | 13.30 | 13.42 | 13.82 |
| 4 | 64 | 640 | 19,609.4 | 13.05 | 13.17 | 13.24 | 13.94 |
| 8 | 64 | 640 | 37,598.7 | 13.61 | 13.71 | 13.77 | 14.62 |
| 16 | 64 | 640 | 57,840.2 | 17.69 | 17.73 | 17.76 | 18.36 |
| 32 | 64 | 640 | 66,955.9 | 30.57 | 30.78 | 30.86 | 30.96 |
**FP32, pure Python**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 64 | 640 | 2,940.0 | 21.79 | 22.23 | 22.42 | 25.52 |
| 2 | 64 | 640 | 5,652.0 | 22.66 | 23.00 | 23.20 | 26.86 |
| 4 | 64 | 640 | 10,526.0 | 24.30 | 24.62 | 24.72 | 25.03 |
| 8 | 64 | 640 | 15,767.2 | 32.45 | 32.67 | 32.78 | 33.32 |
| 16 | 64 | 640 | 20,303.2 | 50.39 | 50.82 | 50.89 | 51.07 |
| 32 | 64 | 640 | 21,707.1 | 94.26 | 94.76 | 94.94 | 95.26 |
**FP32, TorchScript**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 64 | 640 | 4,974.1 | 12.88 | 13.25 | 13.37 | 13.69 |
| 2 | 64 | 640 | 9,625.3 | 13.30 | 13.58 | 13.72 | 14.15 |
| 4 | 64 | 640 | 15,069.9 | 16.98 | 17.27 | 17.35 | 17.54 |
| 8 | 64 | 640 | 18,269.8 | 28.00 | 28.23 | 28.28 | 28.37 |
| 16 | 64 | 640 | 20,884.5 | 48.99 | 49.46 | 49.50 | 49.63 |
| 32 | 64 | 640 | 22,289.2 | 91.80 | 92.25 | 92.56 | 92.67 |
To achieve these same results, follow the steps in the
[Quick Start Guide](#quick-start-guide) to download the dataset and setup
the container, and then proceed to the
[Inference performance benchmark](#inference-performance-benchmark) section for
instruction on how to launch the benchmark.
###### Large model
Our results were obtained by running the
`pytorch/scripts/inference_benchmark.sh` inferencing benchmarking script in the
pytorch-20.06-py3 NGC container on NVIDIA DGX-1 with 1x V100 16GB GPU.
The command to launch the inference performance benchmark is provided in the
[Inference performance benchmark](#inference-performance-benchmark) section.
**FP16, pure Python**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 128 | 1,600 | 5,119.6 | 25.00 | 25.47 | 25.66 | 26.12 |
| 2 | 128 | 1,600 | 8,676.1 | 29.49 | 29.81 | 29.94 | 30.88 |
| 4 | 128 | 1,600 | 12,960.9 | 39.47 | 39.84 | 39.91 | 40.69 |
| 8 | 128 | 1,600 | 14,870.6 | 68.81 | 69.28 | 69.42 | 69.76 |
| 16 | 128 | 1,600 | 15,528.5 | 131.78 | 132.74 | 132.86 | 133.07 |
| 32 | 128 | 1,600 | 15,649.4 | 261.54 | 262.45 | 262.99 | 271.10 |
**FP16, TorchScript**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 128 | 1,600 | 8,718.2 | 14.68 | 15.01 | 15.07 | 15.50 |
| 2 | 128 | 1,600 | 12,157.8 | 21.04 | 21.29 | 21.31 | 21.38 |
| 4 | 128 | 1,600 | 14,534.8 | 35.20 | 35.48 | 35.53 | 35.93 |
| 8 | 128 | 1,600 | 15,863.8 | 64.50 | 64.90 | 65.15 | 65.31 |
| 16 | 128 | 1,600 | 16,674.0 | 122.73 | 123.34 | 123.66 | 123.92 |
| 32 | 128 | 1,600 | 17,154.1 | 238.60 | 239.48 | 239.73 | 247.48 |
**FP32, pure Python**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 128 | 1,600 | 3,009.8 | 42.52 | 43.01 | 43.09 | 43.53 |
| 2 | 128 | 1,600 | 3,838.4 | 66.64 | 67.24 | 67.45 | 67.83 |
| 4 | 128 | 1,600 | 4,265.3 | 119.94 | 120.87 | 121.00 | 121.39 |
| 8 | 128 | 1,600 | 4,646.5 | 220.19 | 221.30 | 221.50 | 221.68 |
| 16 | 128 | 1,600 | 4,805.4 | 426.39 | 426.25 | 426.47 | 427.25 |
| 32 | 128 | 1,600 | 4,787.4 | 855.09 | 854.95 | 855.46 | 912.05 |
**FP32, TorchScript**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 128 | 1,600 | 3,319.0 | 38.56 | 38.91 | 39.01 | 39.19 |
| 2 | 128 | 1,600 | 3,925.2 | 65.16 | 65.74 | 65.89 | 66.12 |
| 4 | 128 | 1,600 | 4,344.1 | 117.76 | 118.46 | 118.55 | 118.69 |
| 8 | 128 | 1,600 | 4,716.2 | 216.94 | 217.99 | 218.27 | 218.69 |
| 16 | 128 | 1,600 | 4,922.1 | 415.72 | 417.16 | 417.32 | 417.59 |
| 32 | 128 | 1,600 | 4,965.2 | 824.98 | 821.79 | 831.71 | 952.47 |
To achieve these same results, follow the steps in the
[Quick Start Guide](#quick-start-guide) to download the dataset and setup
the container, and then proceed to the
[Inference performance benchmark](#inference-performance-benchmark) section for
instruction on how to launch the benchmark.
##### Inference performance: NVIDIA T4
###### Base model
Our results were obtained by running the
`pytorch/scripts/inference_benchmark.sh` inferencing benchmarking script in the
pytorch-20.06-py3 NGC container on NVIDIA T4.
The command to launch the inference performance benchmark is provided in the
[Inference performance benchmark](#inference-performance-benchmark) section.
**FP16, pure Python**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 64 | 640 | 3,775.3 | 16.97 | 17.51 | 17.84 | 18.18 |
| 2 | 64 | 640 | 6,417.4 | 19.96 | 20.49 | 20.56 | 21.52 |
| 4 | 64 | 640 | 9,988.6 | 25.64 | 26.07 | 26.14 | 27.32 |
| 8 | 64 | 640 | 11,878.9 | 43.07 | 43.42 | 43.46 | 44.24 |
| 16 | 64 | 640 | 13,630.0 | 75.07 | 75.26 | 75.32 | 76.07 |
| 32 | 64 | 640 | 14,511.2 | 141.01 | 141.38 | 141.41 | 142.16 |
**FP16, TorchScript**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 64 | 640 | 6,132.5 | 10.47 | 10.93 | 11.31 | 11.45 |
| 2 | 64 | 640 | 8,319.4 | 15.39 | 15.89 | 15.92 | 16.10 |
| 4 | 64 | 640 | 11,259.1 | 22.74 | 23.16 | 23.23 | 23.30 |
| 8 | 64 | 640 | 13,120.3 | 38.99 | 39.35 | 39.37 | 39.42 |
| 16 | 64 | 640 | 15,120.0 | 67.67 | 67.90 | 67.94 | 68.06 |
| 32 | 64 | 640 | 16,158.1 | 126.65 | 126.97 | 127.03 | 127.18 |
**FP32, pure Python**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 64 | 640 | 2,323.1 | 27.59 | 29.39 | 29.56 | 29.86 |
| 2 | 64 | 640 | 3,094.8 | 41.39 | 42.49 | 42.78 | 43.47 |
| 4 | 64 | 640 | 3,889.8 | 65.82 | 66.60 | 66.71 | 67.57 |
| 8 | 64 | 640 | 4,270.1 | 119.80 | 120.61 | 120.68 | 120.89 |
| 16 | 64 | 640 | 4,765.7 | 214.68 | 215.87 | 216.01 | 216.14 |
| 32 | 64 | 640 | 4,985.2 | 410.43 | 413.58 | 413.67 | 413.92 |
**FP32, TorchScript**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 64 | 640 | 2,486.3 | 25.78 | 27.52 | 27.66 | 27.92 |
| 2 | 64 | 640 | 3,260.7 | 39.28 | 40.32 | 40.49 | 40.84 |
| 4 | 64 | 640 | 4,033.3 | 63.48 | 64.28 | 64.35 | 64.56 |
| 8 | 64 | 640 | 4,411.4 | 115.96 | 116.74 | 116.85 | 116.89 |
| 16 | 64 | 640 | 4,924.9 | 207.74 | 208.91 | 209.04 | 209.21 |
| 32 | 64 | 640 | 5,163.1 | 396.29 | 399.42 | 399.50 | 399.70 |
To achieve these same results, follow the steps in the
[Quick Start Guide](#quick-start-guide) to download the dataset and setup
the container, and then proceed to the
[Inference performance benchmark](#inference-performance-benchmark) section for
instruction on how to launch the benchmark.
###### Large model
Our results were obtained by running the
`pytorch/scripts/inference_benchmark.sh` inferencing benchmarking script in the
pytorch-20.06-py3 NGC container on NVIDIA T4.
The command to launch the inference performance benchmark is provided in the
[Inference performance benchmark](#inference-performance-benchmark) section.
**FP16, pure Python**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 128 | 1,600 | 2,978.0 | 42.99 | 43.40 | 43.44 | 44.40 |
| 2 | 128 | 1,600 | 3,161.4 | 80.98 | 81.38 | 81.45 | 81.75 |
| 4 | 128 | 1,600 | 3,459.3 | 147.89 | 148.11 | 148.14 | 148.49 |
| 8 | 128 | 1,600 | 3,657.8 | 279.74 | 279.82 | 279.86 | 280.48 |
| 16 | 128 | 1,600 | 3,762.9 | 543.92 | 543.48 | 543.55 | 544.43 |
| 32 | 128 | 1,600 | 3,794.4 | 1079.15 | 1076.23 | 1076.37 | 1158.93 |
**FP16, TorchScript**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 128 | 1,600 | 3,066.4 | 41.74 | 42.08 | 42.12 | 42.19 |
| 2 | 128 | 1,600 | 3,399.2 | 75.31 | 75.54 | 75.57 | 75.64 |
| 4 | 128 | 1,600 | 3,721.5 | 137.47 | 137.65 | 137.70 | 137.82 |
| 8 | 128 | 1,600 | 3,932.9 | 260.19 | 260.23 | 260.29 | 260.50 |
| 16 | 128 | 1,600 | 4,057.9 | 504.43 | 503.97 | 504.01 | 504.14 |
| 32 | 128 | 1,600 | 4,117.8 | 994.54 | 991.40 | 991.46 | 1079.17 |
**FP32, pure Python**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 128 | 1,600 | 786.9 | 162.7 | 163.2 | 163.3 | 163.9 |
| 2 | 128 | 1,600 | 889.6 | 287.8 | 288.1 | 288.2 | 288.4 |
| 4 | 128 | 1,600 | 992.1 | 515.6 | 516.0 | 516.0 | 516.5 |
| 8 | 128 | 1,600 | 1,047.0 | 977.2 | 977.6 | 977.6 | 977.8 |
| 16 | 128 | 1,600 | 1,069.3 | 1913.5 | 1914.7 | 1914.7 | 1915.0 |
| 32 | 128 | 1,600 | 1,069.5 | 3826.3 | 3823.7 | 3823.8 | 3915.8 |
**FP32, TorchScript**
|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|
|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 1 | 128 | 1,600 | 792.5 | 161.5 | 161.9 | 162.0 | 162.2 |
| 2 | 128 | 1,600 | 904.7 | 283.0 | 283.3 | 283.3 | 283.4 |
| 4 | 128 | 1,600 | 1,009.0 | 507.0 | 507.3 | 507.4 | 507.5 |
| 8 | 128 | 1,600 | 1,065.0 | 960.7 | 961.1 | 961.1 | 961.2 |
| 16 | 128 | 1,600 | 1,088.6 | 1879.7 | 1880.9 | 1881.0 | 1881.1 |
| 32 | 128 | 1,600 | 1,102.0 | 3713.7 | 3710.0 | 3718.1 | 3819.0 |
To achieve these same results, follow the steps in the
[Quick Start Guide](#quick-start-guide) to download the dataset and setup
the container, and then proceed to the
[Inference performance benchmark](#inference-performance-benchmark) section for
instruction on how to launch the benchmark.
## Release notes
### Changelog
* June 2020
* Added support for NVIDIA DGX A100
* Updated default NGC container to pytorch-20.06-py3
* December 2019
* Added support for the large Transformer-XL model trained on WikiText-103
dataset, the large model was trained on NVIDIA DGX-1, NVIDIA DGX-2 and on
8x NVIDIA DGX-2H (multi-node training)
* Updated default NGC container to pytorch-19.11-py3
* Added support for inference with TorchScript
* October 2019
* Initial release
* Support for FP32 and mixed precision training on NVIDIA
DGX-1, NVIDIA DGX-2, and inference on NVIDIA Tesla V100 16GB
and NVIDIA T4
### Known issues
There are no known issues with this model.
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