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

This repository provides an implementation of the Transformer-XL model in\nTensorFlow from the paper Transformer-XL: Attentive\nLanguage Models Beyond a Fixed-Length\nContext. Transformer-XL is a\ntransformer-based language model with a segment-level recurrence and a novel\nrelative positional encoding. Enhancements introduced in Transformer-XL help\ncapture better long-term dependencies by attending to tokens from multiple\nprevious segments.

Our implementation is based on the\ncodebase published by the\nauthors of the Transformer-XL paper.\nOur implementation uses a modified model architecture. Our\nmodifications were made to achieve better hardware utilization and to take\nadvantage of Tensor Cores. Similar modifications were also proposed in an\nimplementation available from\ngithub.com/cybertronai/transformer-xl.\nRefer to the Model architecture section for more\ndetails.

This model is trained with mixed precision using Tensor Cores on Volta, Turing,\nand the NVIDIA Ampere GPU architectures. Therefore, researchers can get\nresults up to 1.5x faster than training without Tensor Cores, while\nexperiencing the benefits of mixed precision training. This model is tested\nagainst each NGC monthly container release to ensure consistent accuracy and\nperformance over time.

Model architecture

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

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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

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

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

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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

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

An illustration of the recurrence mechanism taken from the Transformer-XL\npaper is shown below.\n $\"model\"$

Default configuration

The following features were implemented in this model:

general\n
- single-node, Horovod multi-GPU training
- training and inference with mixed precision using Tensor Cores
- automatic mixed precision training (AMP)
\n
model\n
- 16-layer base Transformer-XL model with hidden size 512, 8 attention heads,\neach head with hidden size 64
- the model trained on\nWikiText-103\ndataset, using word-level vocabulary and\nadaptive softmax
- embedding weights are tied with weights in the classifier
\n
training\n
- training with LAMB optimizer, the\nimplementation of the optimizer uses XLA, which enables\nthe fusion of elementwise operations and accelerates the training
- support for training with a gradient accumulation
- base model:\n
  - linear learning rate warmup for 1,000 iterations, followed by the cosine\nlearning rate schedule, the initial learning rate is set to 0.0, and the final\nlearning rate is set to 0.001 (min_lr_ratio * base_lr)
  - training for 40,000 steps, using a batch size of 256
  \n
\n
inference\n
- support for single-GPU inference
- each token is using the same size of the context from previous time steps.
- base model:\n
  - target length is set to 64, length of memory is set to 640
  - positional embeddings are clamped after 400 time steps
  \n
\n

Feature support matrix

The following features are supported by this model:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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Feature	Transformer-XL
Automatic mixed precision (AMP)	Yes
Horovod Multi-GPU (NCCL)	Yes
LAMB	Yes

Features

TF-AMP - a\ntool that enables Tensor Core-accelerated training. Refer to the Enabling\nmixed precision section for more details.

Horovod - Horovod\nis a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet.\nThe goal of Horovod is to make distributed deep learning fast and easy to use.\nFor more information about how to get started with Horovod, see the Horovod:\nOfficial repository.

Multi-GPU training with Horovod - our model\nuses Horovod to implement efficient multi-GPU training with NCCL. For details,\nsee example sources in this repository or see the TensorFlow\ntutorial.

LAMB - stands\nfor Layerwise Adaptive Moments Based optimizer, is a large batch optimization\ntechnique that helps accelerate training of deep neural networks using large\nminibatches.

Mixed precision training

Mixed precision is the combined use of different numerical precisions in a\ncomputational method.\nMixed precision training offers significant\ncomputational speedup by performing operations in half-precision format while\nstoring minimal information in single-precision to retain as much information\nas possible in critical parts of the network. Since the introduction of Tensor\nCores in Volta, and following with both the\nTuring and Ampere architectures, significant training speedups are experienced by switching to\nmixed precision -- up to 3x overall speedup on the most arithmetically intense\nmodel architectures. Using mixed precision training previously required two\nsteps:

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

This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full\nmixed precision methodology\nin your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically.\nThe TensorFlow framework code makes all necessary model changes internally.

In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16,\nand the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work\nwith the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment\nvariable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically\nrewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.

For information about:

How to train using mixed precision, see the Mixed Precision\nTraining paper and Training With Mixed\nPrecision\ndocumentation.
Techniques used for mixed precision training, see the Mixed-Precision\nTraining of Deep Neural\nNetworks\nblog.
How to access and enable AMP for TensorFlow, see Using\nTF-AMP\nfrom the TensorFlow User Guide.

Enabling mixed precision

Mixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP)\nextension which casts variables to half-precision upon retrieval, while storing variables\nin single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation,\na loss scaling\nstep must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by\nusing simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed\nprecision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations.\nFirst, programmers need not modify network model code, reducing development and maintenance effort.\nSecond, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.

To enable mixed precision, you can simply add the values to the environmental variables inside your training script:

Enable TF-AMP graph rewrite:

os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE\"] = \"1\"\n

Enable Automated Mixed Precision:

os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'\n

Enabling TF32

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.

","resourceVersions",[231,232],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_210":28,"_211":-5,"_52":116,"_113":-5,"_117":212,"_119":-5,"_213":115,"_124":16,"_121":34,"_214":-5,"_120":34,"_215":216,"_217":90,"_218":219,"_220":90,"_221":222,"_223":90,"_224":225,"_226":90,"_227":228,"_229":90,"_122":230},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/dle/resources/transformerxl_tf1/-","-","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/dle/transformerxl_tf1:20.06.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/dle/resources/transformerxl_tf1/-/version-history","accessCheckResult",{"_209":90},"hasRelatedCollections","context",{"_205":206,"_207":208},1783956325954,[],[],[198,199,200,201,202,203,204],{"_48":62,"_50":73,"_52":64,"_54":65,"_56":74,"_58":59},{"_48":62,"_50":71,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":64,"_54":65,"_56":70,"_58":59},{"_48":62,"_50":67,"_52":64,"_54":65,"_56":68,"_58":59},{"_48":62,"_50":63,"_52":64,"_54":65,"_56":66,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":57,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"orgName","nvidia","teamName","dle","allowed","versionId","description",["D",1663857841055],"totalSizeInBytes","malwareScanStatus","releaseNotes","### Changelog\n\nJune 2020\n * upgrade the TensorFlow container to 20.06\n * update performance tables to include A100 results\n\n* April 2020\n * Initial release\n * Support for FP32 and mixed precision training on NVIDIA\n DGX-1, NVIDIA DGX-2, and inference on NVIDIA Tesla V100 16G\n and NVIDIA T4\n\n### Known issues\n\nThere are no known issues with this model.","hasReleaseNotes","setup","The following section lists the requirements that you need to meet in order to\nstart training the Transformer-XL model.\n\n### Requirements\n\nThis repository contains `Dockerfile` which extends the TensorFlow NGC container\nand encapsulates some dependencies. Aside from these dependencies, ensure you\nhave the following components:\n\n* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n* [TensorFlow 20.06-tf1-py3](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) NGC container\n* GPU-based architecture:\n * [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n * [NVIDIA Turing](https://www.nvidia.com/en-us/geforce/turing/)\n * [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)\n\nFor more information about how to get started with NGC containers, see the\nfollowing sections from the NVIDIA GPU Cloud Documentation and the Deep\nLearning DGX Documentation:\n\n* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html),\n* [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry),\n* [Running TensorFlow](https://docs.nvidia.com/deeplearning/frameworks/tensorflow-release-notes/running.html#running)\n\nFor those unable to use the TensorFlow NGC container, to set up the required environment or create your own container,\nsee the versioned [NVIDIA Container Support\nMatrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).","hasSetup","quickStartGuide","To train your model using mixed precision with Tensor Cores or using FP32,\nperform the following steps using the default parameters of the Transformer-XL\nbase model on the\n[WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/)\ndataset. \n\nFor the specifics concerning training\nand inference, see the [Advanced](#advanced) section.\n\n1. Clone the repository.\n\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/TensorFlow/LanguageModeling/Transformer-XL\n```\n\n2. Download and preprocess the dataset.\n\n```\nbash getdata.sh\n```\n\n3. Build the Transformer-XL TensorFlow NGC container.\n\n```\nbash tf/scripts/docker/build.sh\n```\n\n4. Start an interactive session in the NGC container to run training/inference.\n\n```\nbash tf/scripts/docker/interactive.sh\n```\n\n5. Create tfrecords before your first training/evaluation for a given batch size per GPU.\nUse same --batch_chunk and --training_batch_size flags as in the training.\n\nFor training on DGX-A100 without gradient accumulation:\n```\nbash run_wt103_base.sh train_data\n```\n\nFor training on DGX-1 with gradient accumulation in 2 steps:\n```\nbash run_wt103_base.sh train_data --batch_chunk 2\n```\n\nFor single GPU training with gradient accumulation in 16 steps:\n```\nbash run_wt103_base.sh train_data --batch_chunk 16\n```\n\nFor evaluation:\n```\nbash run_wt103_base.sh test_data\n```\n\n6. Start training.\n\nTo start TF32 training on 8 GPUs on DGX-A100, run:\n\n```\nbash run_wt103_base.sh train 8\n```\n\nTo start mixed precision training on 8 GPUs on DGX-1, run:\n\n```\nbash run_wt103_base.sh train 8 --amp --batch_chunk 2\n```\n\nTo start FP32 training on single GPU, run:\n\n```\nbash run_wt103_base.sh train 1 --batch_chunk 16\n```\n\nTo start mixed precision training on 16 GPUs on DGX-2, run:\n\n```\nbash run_wt103_base.sh train 16 --amp\n```\n\nTo start FP32 training on 16 GPUs on DGX-2, run:\n\n```\nbash run_wt103_base.sh train 16\n```\n\nFor more information on the available options, and for an explanation of what\nhappens at the end of training, refer to the [Training\nprocess](#training-process) section.\n\n7. Start evaluation.\n\nTo start mixed precision inference on the test set, run:\n\n```\nbash run_wt103_base.sh eval [--amp]\n```\n\nThe `--amp` flag is optional, however, if it's set, then the script\nlaunches mixed precision inference with Tensor Cores. If the flag is not\npresent, then the script launches FP32 inference.\nBy default, the script is loading the checkpoint from\n`LM-TFM/model.ckpt`, which contains the model corresponding to the\nlast checkpoint from the previous training run. The path to the\ncheckpoint can be customized by setting the `--model_dir` flag.\n\nFor more information on the available options, refer to the [Inference\nprocess](#inference-process) section.","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training\nand inference, and the training results.\n\n### Scripts and sample code\n\n* `Dockerfile`: a container with the basic set of dependencies to run\n Transformer-XL\n\nIn the `tf` directory, the most important files are:\n\n* `data_utils.py`: data loading utilities\n* `exp_utils.py`: utility functions for running training and benchmarking\n* `lamb.py`: implementation of [LAMB](https://arxiv.org/abs/1904.00962)\n optimizer\n* `main.py`: serves as the entry point to launch the training and inference\n* `model.py`: implementation of the Transformer-XL model\n* `vocabulary.py`: implementation of word-level vocabulary\n\n### Parameters\n\nThe complete list of available parameters for the `tf/main.py` script contains:\n\n```\n --batch_chunk: Number of accumulation steps.\n (default: '1')\n (an integer)\n --clamp_len: Clamp length\n (default: '-1')\n (an integer)\n --clip: Gradient clipping value.\n (default: '0.25')\n (a number)\n --corpus_info_path: Path to corpus-info.json file.\n (default: '')\n --d_embed: Dimension of the embeddings.\n (default: '512')\n (an integer)\n --d_head: Dimension of each attention head.\n (default: '64')\n (an integer)\n --d_inner: Dimension of inner hidden size in positionwise feed-forward.\n (default: '2048')\n (an integer)\n --d_model: Dimension of the model.\n (default: '512')\n (an integer)\n --data_dir: Path to tf-records directory.\n (default: '')\n --div_val: Divide the embedding size by this val for each bin\n (default: '1')\n (an integer)\n --[no]do_eval: Whether to run eval on the dev set.\n (default: 'false')\n --[no]do_train: Whether to run training.\n (default: 'true')\n --dropatt: Attention dropout rate.\n (default: '0.0')\n (a number)\n --dropout: Dropout rate.\n (default: '0.1')\n (a number)\n --eval_batch_size: Size of valid batch.\n (default: '16')\n (an integer)\n --eval_ckpt_path: Checkpoint path for do_test evaluation.If set, model_dir will be ignored.If unset, will use the latest ckpt in model_dir.\n --eval_split: Which data split to evaluate.\n (default: 'valid')\n --amp: Whether to enable AMP ops.\n (default: 'false')\n --init: : Initialization method.\n (default: 'normal')\n --init_range: Initialization std when init is uniform.\n (default: '0.1')\n (a number)\n --init_std: Initialization std when init is normal.\n (default: '0.02')\n (a number)\n --learning_rate: Maximum learning rate.\n (default: '0.01')\n (a number)\n --log_interval: Number of iterations per repeat loop.\n (default: '100')\n (an integer)\n --max_eval_batch: Set -1 to turn off. Only used in test mode.\n (default: '-1')\n (an integer)\n --mem_len: Number of steps to cache\n (default: '192')\n (an integer)\n --min_lr_ratio: Minimum ratio learning rate.\n (default: '0.1')\n (a number)\n --model_dir: Estimator model_dir.\n (default: 'LM-TFM')\n --n_head: Number of attention heads.\n (default: '8')\n (an integer)\n --n_layer: Number of layers.\n (default: '16')\n (an integer)\n --num_core_per_host: Number of cores per host\n (default: '8')\n (an integer)\n --percentiles: percentiles for latency confidence intervals\n (default: '90,95,99')\n (a comma separated list)\n --proj_init_std: Initialization std for embedding projection.\n (default: '0.01')\n (a number)\n --[no]proj_same_dim: Project the bin with the same dimension.\n (default: 'true')\n --[no]proj_share_all_but_first: True to share all but first projs, False not to share.\n (default: 'false')\n --record_info_dir: Path to local directory containing filenames.txt.\n (default: '')\n --[no]same_length: Same length attention\n (default: 'false')\n --save_steps: number of steps for model checkpointing.\n (default: '5000')\n (an integer)\n --tgt_len: Number of steps to predict\n (default: '192')\n (an integer)\n --[no]tie_weight: Tie embedding and softmax weight.\n (default: 'true')\n --train_batch_size: Size of train batch.\n (default: '256')\n (an integer)\n --train_steps: Total number of training steps.\n (default: '40000')\n (an integer)\n --[no]untie_r: untie r_w_bias and r_r_bias\n (default: 'false')\n --warmup_steps: Number of steps for linear lr warmup.\n (default: '1000')\n (an integer)\n```\n\n### Command-line options\n\nTo see the full list of available options and their descriptions, use the `--help` command-line option.\nFor example:\n\n```\npython3 main.py --help\n```\n\n### Getting the data\n\nThe Transformer-XL model was trained on the\n[WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/)\ndataset. The WikiText-103 dataset is a collection of over 100 million tokens\nextracted from the set of verified\n[Good](https://en.wikipedia.org/wiki/Wikipedia:Good_articles) and\n[Featured](https://en.wikipedia.org/wiki/Wikipedia:Featured_articles) articles\non Wikipedia.\n\nThis repository contains the `getdata.sh` download script which\nautomatically downloads and extracts the training, validation and test\ndatasets. By default, data is downloaded to the `data` directory.\n\nIn order to test with other datasets, the script needs to be customized\naccordingly.\n\n#### Dataset guidelines\n\nThe WikiText-103 dataset was already pre-tokenized with word-level tokens. The\ndataset features a large vocabulary of 267,735 tokens and retains the original\ncase, punctuation and numbers.\n\nThe `getdata.sh` script downloads the data, extracts the archive and renames\nthe training, validation, and test set to `train.txt`, `valid.txt`, `test.txt`\nrespectively.\n\n#### Multi-dataset\n\nUsing other datasets requires changes in the `tf/data_utils.py` file:\n* the name of the new dataset should be added to the `dataset` flag\n* the support for the new dataset needs to be added to the `Corpus` class:\n names of files containing training, validation and test data, options for\n the tokenizer, dataset iterator and desired values of cutoffs for adaptive softmax\n\nThe current codebase supports training with word-level vocabulary\n(automatically generated based on the provided dataset)\n\nAdditionally, using other datasets may require changes in some hyperparameters\n(for example, batch size, learning rate, number of training steps,\nand the configuration of learning rate scheduler). \n\n### Training process\n\nThe default training configuration can be launched by running the\n`run_wt103_base.sh` script with the first argument\nset to `train`. By default, the training results are saved to `tf/LM-TFM` directory,\nand map to your container's `/workspace/transformer-x/tf/LM-TFM` directory;\nthis can be customized by setting the `--model_dir` parameter.\n\nThe training script launches a single-node data-parallel training with a fixed\nglobal batch size of 256, optionally with gradient accumulation to allow\ntraining on configurations with less than 16 GPUs.\n\n**Command-line**\n\nYou can launch training of the Transformer-XL base model on the\nWikiText-103 dataset with the word-based vocabulary and adaptive softmax using\n`<#GPUs>` GPUs. For example:\n\n```\nbash run_wt103_base.sh train <#GPUs> [--amp] [--batch_chunk CHUNK]\n```\n\nThe `--amp` flag is optional, however, if it's set, then the script\nlaunches mixed precision training with Tensor Cores; if the flag is not\npresent, then the script launches FP32 training.\n\nThe `--batch_chunk CHUNK` parameter controls gradient accumulation. With\ngradient accumulation, the batch size is split into `CHUNK` chunks of equal\nsize, the training script executes the forward and backward pass using each\nchunk and then executes the optimizer using accumulated gradients.\n\nThe `--train batch_size GBS` parameter can be used to change batch size.\nEquation below shows the relation between parameters:\n```\nglobal batch size (--train_batch_size) = <#GPU> * gradient accumulation steps (--batch_chunk) * local batch size\n```\n\n**Examples**\n\nYou can launch mixed precision training of the Transformer-XL base model on the\nWikiText-103 dataset using 16 GPUs. For example:\n\n```\nbash run_wt103_base.sh train 16 --amp --batch_chunk 1\n```\n\nThe batch size per GPU is equal to the default global batch size of 256\ndivided by the product of the number of GPUs times the number of chunks. In this\ncase, batch size per GPU is equal to `256 / (16 * 1) = 16`.\n\nYou can launch FP32 training using 8 GPUs; the batch size per GPU is equal to 16\n(`--batch_chunk` was set to `2` because a local batch size of 32 runs out\nof memory on a DGX-1 with Tesla V100 16G in FP32 training). For example:\n\n```\nbash run_wt103_base.sh train 8 --batch_chunk 2\n```\n\nA summary of the training progress is printed after every 100 training\niterations; this can be customized by setting the `--log_interval` parameter.\nThe summary is printed in the following format:\n\n```\nstep 1300 | lr 0.009998686 | loss 5.09 | pplx 162.70, bpc 7.3461, tok/s 138037\n```\n\nwhich contains information about a current training\nstep, current learning rate, current training loss,\ntraining [perplexity](https://en.wikipedia.org/wiki/Perplexity#Perplexity_per_word),\nbits per character and throughput in tokens per second.\n\nThe script saves one checkpoint: `model.ckpt` which contains the last saved model.\nBy default, model saving is executed every\n5000 training steps, this can be customized by setting the `--save_steps`\nparameter.\n\nEvaluation (inference) benefits from longer attention sequences, therefore to\nreproduce perplexity values reported in the [Transformer-XL\npaper](https://arxiv.org/abs/1901.02860), it's necessary to run the final\nevaluation with a dedicated inference script. Refer to the [Inference\nprocess](#inference-process) section for more details.\n\n### Inference process\n\nInference can be run by launching the `run_wt103_base.sh` script\nwith the first argument set to `eval`. Running\ninference requires a pre-trained model checkpoint.\n\nThe script supports only single-GPU inference.\n\n**Command-line**\n\nYou can launch inference of the Transformer-XL base model on the\nWikiText-103 dataset with the word-based vocabulary and adaptive softmax.\n\nFor example:\n\n```\nbash run_wt103_base.sh eval --model_dir [--amp]\n```\n\nThe `--amp` flag is optional, however, if it's specified, then the script\nlaunches inference with Tensor Cores; if the flag is not present, then the\nscript launches FP32 inference.\n\n**Examples**\n\nTo launch mixed precision inference on a single GPU using a checkpoint\nloaded from `LM-TFM/model.ckpt*`, run:\n\n```\nbash run_wt103_base.sh eval --model_dir LM-TFM --amp\n```\n\nTo launch FP32 inference on a single GPU using a checkpoint loaded\nfrom `LM-TFM/model.ckpt*`, run:\n\n```\nbash run_wt103_base.sh eval --model_dir LM-TFM\n```\n\nAfter the execution, the script prints a summary in the following format:\n\n```\nI0109 13:02:31.304439 139903273469760 main.py:440] Evaluating with: math fp16\nINFO:tensorflow:| loss 3.15 | pplx 23.32, bpc 4.5432, tok/s 9946, ms/batch 102.84\n```\n\nwhich contains information about loss, perplexity and execution performance on the test dataset.","hasAdvanced","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).\n\n### Benchmarking\n\nThe following section shows how to run benchmarks measuring the model\nperformance in training and inference modes.\n\n#### Training performance benchmark\n\nTo benchmark the training performance on a specific global batch size ``,\nwith a specific number of GPUs `<#GPUs>` for a specific number of training\niterations `` run:\n\nFor the base model:\n\n```\nbash run_wt103_base.sh train <#GPUs> --train_batch_size --train_steps --log_interval 1 [--amp] [--batch_chunk CHUNK]\n```\n\nIt's recommended to launch at least 1500 training steps to get a reliable\nestimate of training performance. For more information about the available\noptions, refer to the [Training process](#training-process) section.\n\nThe training script prints information in the following format:\n\n```\n(...)\n[1,0]:INFO:tensorflow:step 99 | lr 0.000990000 | loss 9.22 | pplx 10069.60, bpc 13.2977, tok/s 136092\n[1,0]:I0109 12:18:41.333325 140403024426816 main.py:333] step 99 | lr 0.000990000 | loss 9.22 | pplx 10069.60,\nbpc 13.2977, tok/s 136092\n[1,0]:INFO:tensorflow:step 100 | lr 0.001000000 | loss 9.21 | pplx 9981.87, bpc 13.2851, tok/s 135309\n[1,0]:I0109 12:18:41.696926 140403024426816 main.py:333] step 100 | lr 0.001000000 | loss 9.21 | pplx 9981.87,\nbpc 13.2851, tok/s 135309\n(...)\n[1,0]:INFO:tensorflow:Training throughput: 135959 tok/s\n```\n\nThe last two lines contain information on the\naverage training throughput measured in tokens per second.\n\n#### Inference performance benchmark\n\nThe inference performance and accuracy benchmarks require a checkpoint from a\ntrained model.\n\nTo benchmark the inference performance on a specific global batch size ``, run:\n\n```\nbash run_wt103_base.sh eval --model_dir --eval_batch_size [--amp]\n```\n\nThe inference script prints information in the following format:\n\n```\nI0109 13:02:31.304439 139903273469760 main.py:440] Evaluating with: math fp16\nINFO:tensorflow:| loss 3.15 | pplx 23.32, bpc 4.5432, tok/s 9946, ms/batch 102.84\n```\n\nThe output contains information on the achieved test loss and test perplexity,\naverage inference throughput (measured in tokens per second), average inference\nlatency (measured in milliseconds).\n\n### Results\n\nThe following sections provide details on how we achieved our performance and\naccuracy in training and inference.\n\n#### Training accuracy results\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 40GB)\n\n###### Base model\nOur results were obtained by running the `tf/run_wt103_base.sh`\ntraining script in the tensorflow:20.06-tf1-py3 NGC container on NVIDIA DGX A100\nwith8x A100 40GB GPUs.\n\n|**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)**|\n|-------:|-------------------:|-------------------------------:|------------------------------------------:|---------------------------------:|--------------------------------------------:|--------------------------------------------------:|\n| 1 | 16 | 23.53 | 23.50 | 960 | 880 | 1.09 |\n| 8 | 16 | 23.45 | 23.48 | 150 | 142 | 1.06 |\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\n###### Base model\nOur results were obtained by running the `tf/run_wt103_base.sh`\ntraining script in the tensorflow:20.06-tf1-py3 NGC container on NVIDIA DGX-1\nwith 8x V100 16G GPUs.\n\n|**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)**|\n|-------:|-------------------:|-------------------------------:|------------------------------------------:|---------------------------------:|--------------------------------------------:|--------------------------------------------------:|\n| 1 | 16 | 23.64 | 23.58 | 2949 | 2021 | 1.46 |\n| 8 | 16 | 23.35 | 23.34 | 459 | 343 | 1.34 |\n\n##### Training accuracy: NVIDIA DGX-2 (16x V100 32GB)\n\n###### Base model\n\nOur results were obtained by running the `tf/run_wt103_base.sh`\ntraining script in the tensorflow:20.06-tf1-py3 NGC container on NVIDIA DGX-2\nwith 16x V100 32G GPUs.\n\n|**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)**|\n|-------:|-------------------:|-------------------------------:|------------------------------------------:|---------------------------------:|--------------------------------------------:|--------------------------------------------------:|\n| 16 | 16 | 23.39 | 23.37 | 202 | 161 | 1.25 |\n| 8 | 32 | 23.33 | 23.40 | 330 | 227 | 1.46 |\n\n##### Training loss plot\n\n###### Base model\n\n![TrainingLossBase](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/TensorFlow/LanguageModeling/Transformer-XL//tf/img/training_loss_base.png)\n\n\n##### Training stability test\n\n###### Base model\nThe Transformer-XL base model was trained for 40,000 training steps, starting\nfrom 20 different initial random seeds. The training was performed in the tensorflow:20.06-tf1-py3 NGC container on\nNVIDIA DGX-1 with 8x V100 16G GPUs.\nAfter training, the models were evaluated on the test dataset. The following\ntable summarizes the final perplexity on the test set.\n\n|**Average perplexity**|**Standard deviation**|**Minimum**|**Maximum**|**Median**|\n|---------------------:|---------------------:|----------:|----------:|---------:|\n| 23.38 | 0.0879 | 23.24 | 23.58 | 23.39 |\n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 40GB)\n\nOur results were obtained by running the `tf/run_wt103_base.sh`\ntraining script in the tensorflow:20.06-tf1-py3 NGC container on NVIDIA DGX A100\nwith 8x A100 40GB GPUs. Performance numbers (in tokens per second) were averaged over 2000\ntraining iterations.\n\n|**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**|\n|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|\n| 1 | 16 | 25,127 | 26,130 | 1.040 | 1.000 | 1.000 |\n| 1 | 32 | 30,958 | 33,117 | 1.070 | 1.000 | 1.000 |\n| 1 | 64 | 34,244 | 36,455 | 1.065 | 1.000 | 1.000 |\n| 8 | 16 | 157,538 | 155,656 | 0.988 | 6.270 | 5.957 |\n| 8 | 32 | 224,474 | 227,502 | 1.013 | 7.251 | 6.870 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Training performance: NVIDIA DGX-1 (8x V100 16GB)\n\n###### Base model\n\nOur results were obtained by running the `tf/run_wt103_base.sh`\ntraining script in the tensorflow:20.06-tf1-py3 NGC container on NVIDIA DGX-1 with 8x\nV100 16G GPUs. Performance numbers (in tokens per second) were averaged over 2000\ntraining iterations.\n\n|**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**|\n|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|\n| 1 | 16 | 9,104 | 13,004 | 1.428 | 1.000 | 1.000 |\n| 2 | 16 | 18,169 | 23,856 | 1.313 | 1.996 | 1.835 |\n| 4 | 16 | 38,876 | 50,310 | 1.294 | 4.270 | 3.869 |\n| 8 | 16 | 78,626 | 101,954 | 1.297 | 8.636 | 7.840 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Training performance: NVIDIA DGX-2 (16x V100 32GB)\n\n###### Base model\n\nOur results were obtained by running the `tf/run_wt103_base.sh` training\nscript in the tensorflow:20.06-tf1-py3 NGC container on NVIDIA DGX-2 with 16x V100 32G\nGPUs. Performance numbers (in tokens per second) were averaged over 2000\ntraining iterations.\n\n|**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**|\n|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|\n| 1 | 16 | 9,891 | 13,791 | 1.394 | 1.000 | 1.000 |\n| 2 | 16 | 21,550 | 28,306 | 1.314 | 2.179 | 2.052 |\n| 4 | 16 | 42,616 | 55,430 | 1.301 | 4.309 | 4.019 |\n| 8 | 16 | 83,932 | 107,999 | 1.287 | 8.486 | 7.831 |\n| 16 | 16 | 164,675 | 206,906 | 1.256 | 16.649 | 15.003 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n#### Inference performance results\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 40GB)\n\n###### Base model\n\nOur results were obtained by running the\n`tf/scripts/inference_benchmark.sh` inferencing benchmarking script in the\ntensorflow:20.06-tf1-py3 NGC container on NVIDIA DGX A100 (1x A100 40GB) GPU.\n\nThe command to launch the inference performance benchmark is provided in the\n[Inference performance benchmark](#inference-performance-benchmark) section.\n\n**FP16**\n\n|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|\n|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|\n| 1 | 64 | 640 | 2592.8 | 24.71 | 25.72 | 26.12 | 26.68 |\n| 2 | 64 | 640 | 5060.4 | 25.32 | 26.58 | 26.93 | 27.71 |\n| 4 | 64 | 640 | 8910.2 | 28.73 | 29.74 | 30.06 | 30.58 |\n| 8 | 64 | 640 | 13844.1 | 36.96 | 37.62 | 37.80 | 38.34 |\n| 16 | 64 | 640 | 18313.1 | 55.92 | 56.46 | 56.69 | 57.39 |\n| 32 | 64 | 640 | 21854.7 | 93.63 | 94.37 | 94.74 | 94.92 |\n\n**TF32**\n\n|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|\n|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|\n| 1 | 64 | 640 | 2587.6 | 24.75 | 25.63 | 25.92 | 26.44 |\n| 2 | 64 | 640 | 5177.8 | 24.73 | 25.71 | 26.03 | 26.56 |\n| 4 | 64 | 640 | 9113.6 | 28.09 | 29.40 | 29.71 | 30.07 |\n| 8 | 64 | 640 | 13371.7 | 38.27 | 38.95 | 39.34 | 40.07 |\n| 16 | 64 | 640 | 16971.0 | 60.29 | 60.88 | 61.13 | 61.73 |\n| 32 | 64 | 640 | 19434.5 | 105.29 | 106.00 | 106.19 | 106.79 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)\n\n###### Base model\n\nOur results were obtained by running the\n`tf/scripts/inference_benchmark.sh` inferencing benchmarking script in the\ntensorflow:20.06-tf1-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPU.\n\nThe command to launch the inference performance benchmark is provided in the\n[Inference performance benchmark](#inference-performance-benchmark) section.\n\n**FP16**\n\n|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|\n|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|\n| 1 | 64 | 640 | 1823.8 | 35.17 | 37.27 | 38.22 | 41.28 |\n| 2 | 64 | 640 | 3337.6 | 38.46 | 41.09 | 41.94 | 43.91 |\n| 4 | 64 | 640 | 5354.0 | 47.83 | 49.74 | 50.54 | 53.08 |\n| 8 | 64 | 640 | 7779.7 | 65.79 | 67.71 | 68.37 | 69.71 |\n| 16 | 64 | 640 | 9796.5 | 104.46 | 107.22 | 108.07 | 108.69 |\n| 32 | 64 | 640 | 11215.5 | 182.45 | 184.11 | 184.49 | 186.92 |\n\n**FP32**\n\n|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|\n|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|\n| 1 | 64 | 640 | 1912.7 | 33.56 | 35.98 | 36.84 | 38.96 |\n| 2 | 64 | 640 | 3497.0 | 36.66 | 39.07 | 39.85 | 41.28 |\n| 4 | 64 | 640 | 4732.9 | 54.10 | 56.32 | 57.10 | 58.14 |\n| 8 | 64 | 640 | 6303.7 | 81.19 | 83.32 | 84.02 | 88.12 |\n| 16 | 64 | 640 | 7676.3 | 133.29 | 134.84 | 135.33 | 136.70 |\n| 32 | 64 | 640 | 8555.6 | 239.15 | 240.02 | 240.20 | 240.48 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Inference performance: NVIDIA T4\n\n###### Base model\n\nOur results were obtained by running the\n`tf/scripts/inference_benchmark.sh` inferencing benchmarking script in the\ntensorflow:20.06-tf1-py3 NGC container on NVIDIA T4.\n\nThe command to launch the inference performance benchmark is provided in the\n[Inference performance benchmark](#inference-performance-benchmark) section.\n\n**FP16**\n\n|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|\n|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|\n| 1 | 64 | 640 | 1228.6 | 52.21 | 55.14 | 56.32 | 59.77 |\n| 2 | 64 | 640 | 2108.5 | 60.78 | 63.62 | 64.68 | 67.07 |\n| 4 | 64 | 640 | 3376.7 | 75.83 | 78.77 | 79.63 | 82.80 |\n| 8 | 64 | 640 | 4666.3 | 109.69 | 112.58 | 113.88 | 117.35 |\n| 16 | 64 | 640 | 5557.0 | 184.14 | 186.51 | 187.20 | 189.64 |\n| 32 | 64 | 640 | 6174.3 | 331.41 | 333.67 | 334.94 | 336.90 |\n\n**FP32**\n\n|**Batch size**|**Sequence length**|**Memory length**|**Throughput Avg (tok/s)**|**Latency Avg (ms)**|**Latency 90% (ms)**|**Latency 95% (ms)**|**Latency 99% (ms)**|\n|-------------:|------------------:|----------------:|-------------------------:|-------------------:|-------------------:|-------------------:|-------------------:|\n| 1 | 64 | 640 | 1029.7 | 62.28 | 65.82 | 66.93 | 70.22 |\n| 2 | 64 | 640 | 1667.7 | 76.81 | 79.80 | 80.71 | 84.35 |\n| 4 | 64 | 640 | 2302.3 | 111.13 | 113.75 | 114.85 | 118.57 |\n| 8 | 64 | 640 | 2756.9 | 185.58 | 188.16 | 189.38 | 192.68 |\n| 16 | 64 | 640 | 3188.8 | 320.86 | 324.24 | 325.63 | 327.76 |\n| 32 | 64 | 640 | 3439.1 | 594.96 | 599.13 | 599.89 | 602.59 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).","hasPerformance",[],{"_85":182,"_112":234,"_113":-5,"_121":34,"_120":34,"_122":233,"_217":90},{"_85":28,"_112":28,"_113":-5,"_121":34,"_120":34,"_122":233,"_217":90},[],"Latest (20.06.0)","nvidia/dle/transformerxl_tf1","key","nvidia/dle/resources/transformerxl_tf1","resourceId","transformerxl_tf1","shortDescription","Transformer-XL is a transformer-based language model with a segment-level recurrence and a novel relative positional encoding.","This resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/transformerxl-tf1)\n\n\nThis repository provides an implementation of the Transformer-XL model in\n[TensorFlow](https://www.tensorflow.org) from the paper [Transformer-XL: Attentive\nLanguage Models Beyond a Fixed-Length\nContext](https://arxiv.org/abs/1901.02860). Transformer-XL is a\ntransformer-based language model with a segment-level recurrence and a novel\nrelative positional encoding. Enhancements introduced in Transformer-XL help\ncapture better long-term dependencies by attending to tokens from multiple\nprevious segments.\n\nOur implementation is based on the\n[codebase](https://github.com/kimiyoung/transformer-xl) published by the\nauthors of the Transformer-XL paper.\nOur implementation uses a modified model architecture. Our\nmodifications were made to achieve better hardware utilization and to take\nadvantage of Tensor Cores. Similar modifications were also proposed in an\nimplementation available from\n[github.com/cybertronai/transformer-xl](https://github.com/cybertronai/transformer-xl).\nRefer to the [Model architecture](#model-architecture) section for more\ndetails.\n\nThis model is trained with mixed precision using Tensor Cores on Volta, Turing,\nand the NVIDIA Ampere GPU architectures. Therefore, researchers can get\nresults up to 1.5x faster than training without Tensor Cores, while\nexperiencing the benefits of mixed precision training. This model is tested\nagainst each NGC monthly container release to ensure consistent accuracy and\nperformance over time.\n\n### Model architecture\n\nThe Transformer-XL \"base\" model for WikiText-103 dataset available in this\nrepository was modified to use the following hyperparameter values:\n\n|**Hyperparameter**|**Description**|**Original setting for the base model**|**Our modification to the base model**|\n|------------------|---------------|--------------------------------------:|--------------------------------------:|\n| `d_model` | hidden size | 410 | 512 |\n| `n_head` | number of attention heads | 10 | 8 |\n| `d_head` | size of each attention head | 41 | 64 |\n| `d_inner` | hidden size in fully-connected layers | 2100 | 2048 |\n| `tgt_len` | number of tokens to predict during training | 150 | 192 |\n| `mem_len` | number of tokens cached from previous iterations during training | 150 | 192 |\n\nChanges described above were made to align certain hyperparameters with powers\nof two, with this modification, the model is able to achieve better hardware\nutilization, and therefore higher training throughput.\n\nThe following table lists the hyperparameters for the base\nTransformer-XL model for WikiText-103 dataset available in this repository.\n\n| **Hyperparameter** | **Description** | **Base model** |\n| ------------------ | ---------------------------------------------------------------- | -------------: |\n| `n_layer` | number of layers | 16 |\n| `d_model` | hidden size | 512 |\n| `n_head` | number of attention heads | 8 |\n| `d_head` | size of each attention head | 64 |\n| `d_inner` | inner hidden size in fully-connected layers | 2048 |\n| `dropout` | dropout | 0.1 |\n| `dropatt` | dropout after softmax in the attention | 0.0 |\n| `lr` | base learning rate | 0.01 |\n| `min_lr_ratio` | minimum ratio learning rate (for cosine decay) | 0.1 |\n| `max_step` | number of training steps | 40,000 |\n| `warmup_step` | number of learning rate warmup steps | 1,000 |\n| `batch_size` | training batch size | 256 |\n| `tgt_len` | number of tokens to predict during training | 192 |\n| `mem_len` | number of tokens cached from previous iterations during training | 192 |\n\nThe Transformer-XL model addresses the limitations of vanilla transformer-based\nlanguage models, which are only able to use relatively short context, bounded\nby the segment length. The Transformer-XL introduces a recurrence mechanism,\nwhich is able to use a cached hidden state from previous segments. During\ntraining, the context consists of a concatenation of the current segment's hidden\nstate and cached states from previous iterations. Gradients are backpropagated\nonly through the current segment, although the model is able to take advantage\nof the extra information stored in the cache and therefore is able to model\nlong-term dependencies.\n\nAn illustration of the recurrence mechanism taken from the [Transformer-XL\npaper](https://arxiv.org/abs/1901.02860) is shown below.\n![model](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/TensorFlow/LanguageModeling/Transformer-XL//tf/img/model.png)\n\n\n### Default configuration\n\nThe following features were implemented in this model:\n\n* general\n * single-node, Horovod multi-GPU training\n * training and inference with mixed precision using Tensor Cores\n * automatic mixed precision training (AMP)\n* model\n * 16-layer base Transformer-XL model with hidden size 512, 8 attention heads,\n each head with hidden size 64\n * the model trained on\n [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/)\n dataset, using word-level vocabulary and\n adaptive softmax\n * embedding weights are tied with weights in the classifier\n* training\n * training with [LAMB](https://arxiv.org/abs/1904.00962) optimizer, the\n implementation of the optimizer uses [XLA](https://www.tensorflow.org/xla), which enables\n the fusion of elementwise operations and accelerates the training\n * support for training with a gradient accumulation\n * base model:\n * linear learning rate warmup for 1,000 iterations, followed by the cosine\n learning rate schedule, the initial learning rate is set to 0.0, and the final\n learning rate is set to 0.001 (min_lr_ratio * base_lr)\n * training for 40,000 steps, using a batch size of 256\n* inference\n * support for single-GPU inference\n * each token is using the same size of the context from previous time steps.\n * base model:\n * target length is set to 64, length of memory is set to 640\n * positional embeddings are clamped after 400 time steps\n\n### Feature support matrix\n\nThe following features are supported by this model:\n\n| **Feature** | **Transformer-XL** |\n|:------------|-------------------:|\n|[Automatic mixed precision (AMP)](https://nvidia.github.io/apex/amp.html) | Yes |\n|[Horovod Multi-GPU (NCCL)](https://github.com/horovod/horovod) | Yes |\n|[LAMB](https://arxiv.org/abs/1904.00962v3) | Yes |\n\n#### Features\n\n[TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) - a \ntool that enables Tensor Core-accelerated training. Refer to the [Enabling\nmixed precision](#enabling-mixed-precision) section for more details.\n\n[Horovod](https://github.com/horovod/horovod) - Horovod \nis a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet.\nThe goal of Horovod is to make distributed deep learning fast and easy to use.\nFor more information about how to get started with Horovod, see the [Horovod:\nOfficial repository](https://github.com/horovod/horovod).\n\n[Multi-GPU training with Horovod](https://github.com/horovod/horovod/#usage) - our model \nuses Horovod to implement efficient multi-GPU training with NCCL. For details,\nsee example sources in this repository or see the [TensorFlow\ntutorial](https://github.com/horovod/horovod/#usage).\n\n[LAMB](https://arxiv.org/abs/1904.00962v3) - stands \nfor Layerwise Adaptive Moments Based optimizer, is a large batch optimization\ntechnique that helps accelerate training of deep neural networks using large\nminibatches.\n\n### Mixed precision training\n\nMixed precision is the combined use of different numerical precisions in a\ncomputational method.\n[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant\ncomputational speedup by performing operations in half-precision format while\nstoring minimal information in single-precision to retain as much information\nas possible in critical parts of the network. Since the introduction of [Tensor\nCores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the\nTuring and Ampere architectures, significant training speedups are experienced by switching to\nmixed precision -- up to 3x overall speedup on the most arithmetically intense\nmodel architectures. Using mixed precision training previously required two\nsteps:\n\n1. Porting the model to use the FP16 data type where appropriate. \n2. Adding loss scaling to preserve small gradient values.\n\nThis can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full\n[mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow)\nin your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically.\nThe TensorFlow framework code makes all necessary model changes internally.\n\nIn TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16,\nand the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work\nwith the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment\nvariable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically\nrewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\n\nFor information about:\n\n* How to train using mixed precision, see the [Mixed Precision\n Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed\n Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html)\n documentation.\n* Techniques used for mixed precision training, see the [Mixed-Precision\n Training of Deep Neural\n Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/)\n blog.\n* How to access and enable AMP for TensorFlow, see [Using\n TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp)\n from the TensorFlow User Guide. \n\n#### Enabling mixed precision\n\nMixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP)\nextension which casts variables to half-precision upon retrieval, while storing variables\nin single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation,\na [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling)\nstep must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by\nusing simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed\nprecision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations.\nFirst, programmers need not modify network model code, reducing development and maintenance effort.\nSecond, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.\n\nTo enable mixed precision, you can simply add the values to the environmental variables inside your training script:\n- Enable TF-AMP graph rewrite:\n ```\n os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE\"] = \"1\"\n ```\n \n- Enable Automated Mixed Precision:\n ```\n os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'\n ```\n\n#### Enabling TF32\n\nTensorFloat-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. \n\nTF32 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.\n\nFor 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.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.","displayName","Transformer-XL for TensorFlow1",["D",1680632744670],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/transformerxl_tf1","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[274],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_243":275,"_276":277,"_278":279,"_280":87},"Natural Language Understanding","value","uscs_natural_language_understanding","filter",{"_281":282,"_276":277},"category","field","application",{"_151":1458,"_153":1459},"ngcApps",[1411],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1392":1393,"_1394":1395,"_1396":1397,"_1398":1399,"_1400":294,"_109":318,"_1401":1402,"_1403":59,"_1404":90,"_1405":90,"_1406":1407,"_1408":90,"_1409":1410},"amplitude",{"_1389":1390,"_991":-7,"_1391":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1383":1384,"_1385":1386,"_1387":1388},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1266":398,"_1267":1268,"_1269":1270,"_1271":1272},"announcements",[],"consentState",{"_355":1256,"_1257":90,"_1258":34,"_1259":34,"_1260":1261},"consentConfig",{"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356},"showContactAdmin",1783956325836,[],[],[332,333,334,335,336],{"_48":62,"_50":73,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":57,"_58":59},{"_48":49,"_50":51,"_52":146,"_54":-5,"_56":-5,"_58":59},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[436],{"_112":437,"_403":434,"_404":34,"_211":438},"sp","Testing","google.com",[441],{"_112":442,"_403":439,"_404":34,"_211":443},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[446,447,448,449],{"_112":454,"_403":444,"_404":34,"_211":451},{"_112":453,"_403":444,"_404":34,"_211":451},{"_112":452,"_403":444,"_404":34,"_211":451},{"_112":450,"_403":444,"_404":34,"_211":451},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[457],{"_112":458,"_403":455,"_404":34,"_211":459},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[462,463,464,465],{"_112":471,"_403":460,"_404":34,"_211":467},{"_112":469,"_403":460,"_404":34,"_211":470},{"_112":468,"_403":460,"_404":34,"_211":467},{"_112":466,"_403":460,"_404":34,"_211":467},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. 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YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[486],{"_112":487,"_403":484,"_404":34,"_211":488},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[491],{"_112":492,"_403":489,"_404":34,"_211":493},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[496],{"_112":497,"_403":494,"_404":34,"_211":438},"R","6sc.co",[500],{"_112":501,"_403":498,"_404":34,"_211":502},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650],[595,596,597,598,599],{"_396":489,"_243":489,"_211":398,"_369":621},{"_396":455,"_243":455,"_211":398,"_369":616},{"_396":609,"_243":609,"_211":398,"_369":610},{"_396":605,"_243":605,"_211":398,"_369":606},{"_396":600,"_243":600,"_211":398,"_369":601},"nvcrm.my.site.com",[602],{"_112":603,"_403":600,"_404":34,"_211":604},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[607],{"_112":608,"_403":605,"_404":34,"_211":438},"edge","github.com",[611,612],{"_112":615,"_403":609,"_404":34,"_211":614},{"_112":613,"_403":609,"_404":34,"_211":614},"logged_in","This domain is owned by Github. 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