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Release Notes

Mar 18, 2019

first release
also available in github

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

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

Model architecture

The Transformer model uses standard NMT encoder-decoder architecture. This model unlike other NMT models, uses no recurrent connections and operates on fixed size context window.\nThe encoder stack is made up of N identical layers. Each layer is composed of the following sublayers:\n1. Self-attention layer\n2. Feedforward network (which is 2 fully-connected layers)\nLike the encoder stack, the decoder stack is made up of N identical layers. Each layer is composed of the sublayers:\n1. Self-attention layer\n2. Multi-headed attention layer combining encoder outputs with results from\nthe previous self-attention layer.\n3. Feedforward network (2 fully-connected layers)

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

\n \n
\n Figure 1. The architecture of a Transformer model.\n

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

Default configuration

The Transformer uses Byte Pair Encoding tokenization scheme using Moses decoder. This is a lossy compression method (we drop information about white spaces). Tokenization is applied over whole WMT14 en-de dataset including test set. Default vocabulary size is 33708, excluding all special tokens. Encoder and decoder are using shared embeddings.\nWe use 6 blocks in each encoder and decoder stacks. Self attention layer computes it's outputs according to the following formula $Attention(Q,K,V) = softmax(\\frac{QK^T}{\\sqrt{d_k}})V$. At each attention step, the model computes 16 different attention representations (which we will call attention heads) and concatenates them.\nWe trained the Transformer model using the Adam optimizer with betas (0.9, 0.997), epsilon 1e-9 and learning rate 6e-4. We used the inverse square root training schedule preceded with linear warmup of 4000 steps.\nThe implementation allows to perform training in mixed precision. We use dynamic loss scaling and custom mixed precision optimizer. Distributed multi-GPU and multi-Node is implemented with torch.distirbuted module with NCCL backend.\nFor inference, we use beam search with default beam size of 5. Model performance is evaluated with BLEU4 metrics. For clarity, we report internal (legacy) BLEU implementation as well as external SacreBleu score.

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\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Feature	Yes column
Multi-GPU training with Distributed Communication Package	Yes
Nvidia APEX	Yes
AMP	Yes
TorchScript	Yes

Features

\n
Multi-GPU training with Distributed Communication Package: Our model uses torch.distributed package to implement efficient multi-GPU training with NCCL.\nTo enable multi-GPU training with torch.distributed, you have to initialize your model identically in every process spawned by torch.distributed.launch. Distributed strategy is implemented with APEX's DistributedDataParallel.\nFor details, see example sources in this repo or see the pytorch tutorial
\n
\n
Nvidia APEX: The purpose of the APEX is to provide easy and intuitive framework for distributed training and mixed precision training. For details, see official APEX repository.
\n
\n
AMP: This implementation uses Apex's AMP to perform mixed precision training.
\n
\n
TorchScript: Transformer can be converted to TorchScript format offering ease of deployment on platforms without Python dependencies. For more information see official TorchScript documentation.
\n

Mixed precision training

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

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

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

For information about:

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

Enabling mixed precision

Mixed precision is enabled using the --amp option in the train.py script. The default is optimization level O2 but can be overriden with --amp-level $LVL option (for details see amp documentation). Forward and backward pass are computed with FP16 precision with exclusion of a loss function which is computed in FP32 precision. Default optimization level keeps a copy of a model in higher precision in order to perform accurate weight update. After the update FP32 weights are again copied to FP16 model. We use dynamic loss scaling with initial scale of 2^7 increasing it by a factor of 2 every 2000 successful iterations. Overflow is being checked after reducing gradients from all of the workers. If we encounter infs or nans the whole batch is dropped.

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.

Glossary

Attention layer - Layer that computes which elements of input sequence or it's hidden representation contribute the most to the currently considered output element.
\nBeam search - A heuristic search algorithm which at each step of predictions keeps N most possible outputs as a base to perform further prediction.
\nBPE - Binary Pair Encoding, compression algorithm that find most common pair of symbols in a data and replaces them with new symbol absent in the data.
\nEOS - End of a sentence.
\nSelf attention layer - Attention layer that computes hidden representation of input using the same tensor as query, key and value.
\nToken - A string that is representable within the model. We also refer to the token's position in the dictionary as a token. There are special non-string tokens: alphabet tokens (all characters in a dataset), EOS token, PAD token.
\nTokenizer - Object that converts raw strings to sequences of tokens.
\nVocabulary embedding - Layer that projects one-hot token representations to a high dimensional space which preserves some information about correlations between tokens.

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Ensure you have the following software:\n* [nvidia-docker](https://github.com/NVIDIA/nvidia-docker)\n* [PyTorch 19.01-py3 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch) or newer\n* [SacreBLEU 1.2.10](https://pypi.org/project/sacrebleu/1.2.10/)\n\nIf you use multiprocessing for multi-threaded data loaders, the default shared memory segment size that the container runs with may not be enough. Therefore, we recommend you to increase the shared memory size by issuing either: \n```\n--ipc=host\n```\nOr\n```\n--shm-size=\n```\nin the command line to `nvidia-docker run`. For more information,see [Setting The Shared Memory Flag](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#setincshmem) in the NVIDIA Container User Guide.\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 Learning\nDGX Documentation:\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 PyTorch](https://docs.nvidia.com/deeplearning/dgx/pytorch-release-notes/running.html#running)\n\n#### Training using mixed precision with Tensor Cores\nThe training script provided in this project takes advantage of Tensor Cores to speedup the time it takes to train the Transformer model (for a translation task in this example). Tensor Cores accelerate matrix multiplication math and are available on NVIDIA Volta and Turing based GPUs. For more information about how to use Tensor Cores, see the [Training With Mixed Precision Guide](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) to Mixed Precision Training on NVIDIA GPUs.\n\nAn additional resource for mixed precision training is NVIDIA's\n[Apex](https://github.com/NVIDIA/apex), a PyTorch extension, that contains\nutility libraries, such as AMP, which stands for Automatic Mixed Precision and enables the use of Tensor Cores with minimal code changes to existing PyTorch training scripts.","hasSetup","quickStartGuide","## Quick Start Guide\nPerform the following steps to train using provided default parameters of the Transformer model on the [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) dataset.\n### Build and launch Transformer Docker container\n```bash\ndocker build . -t your.repository:transformer\nnvidia-docker run -it --rm --ipc=host -v /path/to/your/dataset:/container/dataset/path your.repository:transformer bash\n```\n### Downloading and preprocessing dataset\nDownload and preprocess the WMT14 English-German dataset.\n```bash\n./run_preprocessing.sh\n```\n### Run training\nThe following command runs the training script that is distributed between 8 workers.\n```bash\npython -m torch.distributed.launch --nproc_per_node 8 /workspace/translation/train.py /workspace/data-bin/wmt14_en_de_joined_dict \\\n --arch transformer_wmt_en_de_big_t2t \\\n --share-all-embeddings \\\n --optimizer adam \\\n --adam-betas '(0.9, 0.997)' \\\n --adam-eps \"1e-9\" \\\n --clip-norm 0.0 \\\n --lr-scheduler inverse_sqrt \\\n --warmup-init-lr 0.0 \\\n --warmup-updates 4000 \\\n --lr 0.0006 \\\n --min-lr 0.0 \\\n --dropout 0.1 \\\n --weight-decay 0.0 \\\n --criterion label_smoothed_cross_entropy \\\n --label-smoothing 0.1 \\\n --max-tokens 5120 \\\n --seed 1 \\\n --target-bleu 28.3 \\\n --ignore-case \\\n --fp16 \\\n --save-dir /workspace/checkpoints \\\n --distributed-init-method env:// \n\n```\n**WARNING**: If you don't have access to sufficient disk space, use the --save-interval $N option. The checkpoints are ~2.5GB large. For example it takes the Transformer model 16 epochs to reach the BLEU score of 28 points. Default option is to save the latest checkpoint, the best checkpoint and a checkpoint for every epoch, which means (16+1+1)*2.5GB = 45GB of a disk space used. Specifying `--save-interval 5` you can reduce this to (16/5+1+1)*2.5GB = 12.5GB. \n\n### Details\n\n#### Getting the data\nThe Transformer model was trained on the [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) dataset. Concatenation of the *commoncrawl*, *europarl* and *news-commentary* is used as train and vaidation dataset and *newstest2014* is used as test dataset.
\nThis repository contains `run_preprocessing.sh` script which will automatically download and preprocess the training and test datasets. By default data will be stored in `/data/wmt14_en_de_joined_dict` directory.
\nOur download script utilizes [Moses decoder](https://github.com/moses-smt/mosesdecoder) to perform tokenization of the dataset and [subword-nmt](https://github.com/rsennrich/subword-nmt) to segment text into subword units (BPE). By default, script builds shared vocabulary of 33708 tokens, which is constistent withbuilds shared vocabulary of 33708 tokens, which is constistent with [Scaling Neural Machine Translation](https://arxiv.org/abs/1806.00187).\n\n#### Running training\nThe default training configuration can be launched ny running the `train.py` training script. By default, the script saves one checkpoint evety epoch in addition to the latest and the best ones. The best chckpoint is considered the one with the lowest value of loss, not the one with the highest BLEU score. To override this behavior use the `--save-interval $N` option to save epoch checkpoints every N epoch or `--no-epoch-checkpoints` to disable them entirely (with this option latest and the best checkpoints still will be saved). Specify save directory with `--save-dir` option.
\nIn order to run multi-GPU training launch the training script with `python -m torch.distributed.launch --nproc_per_node $N` prepended, where N is the number of GPUs.\nWe have tested reliance on up to 16 GPUs on a single node.
\nAfter each training epoch, the script runs a loss validation on the validation split of the dataset and outputs validation loss. By default the evaluation after each epoch is disabled. To enable it use `--online-eval` option or to use BLEU score value as training stopping condition use `--target-bleu $TGT` option. In order to compute case insensitive BLEU score use flag `--ignore-case` along with previous ones. BLEU is computed by the internal fairseq algorithm which implementation can be found in `fairseq/bleu.py` script.
\nBy default, the `train.py` script will launch fp32 training without Tensor Cores. To use mixed precision with Tensor Cores use `--fp16` option.
\nTo view all available options for training, run `python train.py --help`.\n\n#### Running inference\nInference on a raw input can be performed by launching `interactive.py` inference script. It requires pre-trained model checkpoint,BPE codes file and dictionary file (both are produced by `run_preprocessing.sh` script and can be found in the dataset directory).
\nTo enhance speed of the inference on large input files it is recommended to preprocess them the same way as the dataset and run inference on a binarized input with the `generate.py` script.
\nBoth scripts run inference with a default beam size of 4 and give tokenized output. To remove BPE codes use `--remove-bpe` option.
\nTo view all available options for training, run `python interactive.py --help`.\n\n#### Testing\nComputing BLEU score is contained inside the training script and can be used to determine when the script should stop the training. To disable this feature replace `--target-bleu $BLEU$` and `--ignore-case` options with `--max-epoch $N`, where `N` is number of training epochs. By default, evaluation of the Transformer model is then performed on the binarized test split of the dataset by default. To evaluate the model, issue:\n```bash\npython generate.py /path/to/dataset/wmt14_en_de_joined_dict \\\n --path /path/to/your/checkpoint.pt \\\n --beam 4 --remove-bpe\n```\nIn order to use [SacreBLEU](https://pypi.org/project/sacrebleu/1.2.10/) for evaluation, run:\n```bash\nsacrebleu -t wmt14 -l en-de --echo src > wmt14-en-de.src\npython interactive.py --buffer-size 1 --fp16 --path /path/to/your/checkpoint.pt --max-tokens 128 \\\n --fuse-dropout-add --remove-bpe --bpe-codes /path/to/code/file \\\n /path/to/dataset/wmt14_en_de_joined_dict/ < wmt14-en-de.src > wmt14.detok\ngrep ^H wmt14.detok | cut -f3- > wmt14.translated\ncat wmt14.translated | sacrebleu -t wmt14 -lc -l en-de\n```\nSacrebleu test set is a subset of test set used during a course of training thus score obtained with sacreBLEU can slightly differ from the one computed during training.","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training and inference, and the training results.\n\n### Scripts and sample code\n\nThe `preprocess.py` script performs binarization of the dataset obtained and tokenized by the `examples/translation/prepare-wmt14en2de.sh` script. The `train.py` script contains training loop as well as statistics gathering code. Steps performed in single training step can be found in `fairseq/ddp_trainer.py`. Model definition is placed in the file `fairseq/models/transformer.py`. Model specific modules including multiheaded attention and sinusoidal positional embedding are inside the `fairseq/modules/` directory. Finally, the data wrappers are placed inside the `fairseq/data/` directory.\n\n### Parameters\n\nIn this section we give a user friendly description of the most common options used in the `train.py` script.\n### Command-line options\n`--arch` - select the specific configuration for the model. You can select between various predefined hyper parameters values like number of encoder/decoder blocks, dropout value or size of hidden state representation.
\n`--share-all-embeddings` - use the same set of weights for encoder and decoder words embedding.
\n`--optimizer` - choose optimization algorithm.
\n`--clip-norm` - set a value that gradients will be clipped to.
\n`--lr-scheduler` - choose learning rate change strategy.
\n`--warmup-init-lr` - start linear warmup with a learning rate at this value.
\n`--warmup-updates` - set number of optimization steps after which linear warmup will end.
\n`--lr` - set learning rate.
\n`--min-lr` - prevent learning rate to fall below this value using arbitrary learning rate schedule.
\n`--dropout` - set dropout value.
\n`--weight-decay` - set weight decay value.
\n`--criterion` - select loss function.
\n`--label-smoothing` - distribute value of one-hot labels between all entries of a dictionary. Value set by this option will be a value subtracted from one-hot label.
\n`--max-tokens` - set batch size in terms of tokens.
\n`--max-sentences` - set batch size in terms of sentences. Note that then the actual batchsize will vary a lot more than when using `--max-tokens` option.
\n`--seed` - set random seed for NumPy and PyTorch RNGs.
\n`--max-epochs` - set the maximum number of epochs.
\n`--online-eval` - perform inference on test set and then compute BLEU score after every epoch.
\n`--target-bleu` - works like `--online-eval` and sets a BLEU score threshold which after being attained will cause training to stop.
\n`--amp` - use mixed precision.
\n`--save-dir` - set directory for saving checkpoints.
\n`--distributed-init-method` - method for initializing torch.distributed package. You can either provide addresses with the `tcp` method or use the envionment variables initialization with `env` method
\n`--update-freq` - use gradient accumulation. Set number of training steps across which gradient will be accumulated.
\n\nTo see the full list of available options and their descriptions, use the `-h` or `--help` command line option, for example:\n```\npython train.py --help\n```\n\nThe following (partial) output is printed when running the sample:\n```\nusage: train.py [-h] [--no-progress-bar] [--log-interval N]\n [--log-format {json,none,simple,tqdm}] [--seed N] [--fp16]\n [--task TASK] [--skip-invalid-size-inputs-valid-test] [--max-tokens N]\n [--max-sentences N] [--sentencepiece] [--train-subset SPLIT]\n [--valid-subset SPLIT] [--max-sentences-valid N]\n [--gen-subset SPLIT] [--num-shards N] [--shard-id ID]\n [--distributed-world-size N]\n [--distributed-rank DISTRIBUTED_RANK]\n [--local_rank LOCAL_RANK]\n [--distributed-backend DISTRIBUTED_BACKEND]\n [--distributed-init-method DISTRIBUTED_INIT_METHOD]\n [--distributed-port DISTRIBUTED_PORT] [--device-id DEVICE_ID]\n --arch ARCH [--criterion CRIT] [--max-epoch N]\n [--max-update N] [--target-bleu TARGET] [--clip-norm NORM]\n [--sentence-avg] [--update-freq N] [--optimizer OPT]\n [--lr LR_1,LR_2,...,LR_N] [--momentum M] [--weight-decay WD]\n [--lr-scheduler LR_SCHEDULER] [--lr-shrink LS] [--min-lr LR]\n [--min-loss-scale D] [--enable-parallel-backward-allred-opt]\n [--parallel-backward-allred-opt-threshold N]\n [--enable-parallel-backward-allred-opt-correctness-check]\n [--save-dir DIR] [--restore-file RESTORE_FILE]\n [--save-interval N] [--save-interval-updates N]\n [--keep-interval-updates N] [--no-save]\n [--no-epoch-checkpoints] [--validate-interval N] [--path FILE]\n [--remove-bpe [REMOVE_BPE]] [--cpu] [--quiet] [--beam N]\n [--nbest N] [--max-len-a N] [--max-len-b N] [--min-len N]\n [--no-early-stop] [--unnormalized] [--no-beamable-mm]\n [--lenpen LENPEN] [--unkpen UNKPEN]\n [--replace-unk [REPLACE_UNK]] [--score-reference]\n [--prefix-size PS] [--sampling] [--sampling-topk PS]\n [--sampling-temperature N] [--print-alignment]\n [--model-overrides DICT] [--online-eval] \n [--bpe-codes CODES] [--fuse-dropout-add] [--fuse-relu-dropout]\n```\n\n### Getting the data\n\nThe Transformer model was trained on the [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) dataset. Concatenation of the *commoncrawl*, *europarl* and *news-commentary* is used as train and validation dataset and *newstest2014* is used as test dataset.
\nThis repository contains the `run_preprocessing.sh` script which will automatically downloads and preprocesses the training and test datasets. By default, data will be stored in the `/data/wmt14_en_de_joined_dict` directory.
\nOur download script utilizes [Moses decoder](https://github.com/moses-smt/mosesdecoder) to perform tokenization of the dataset and [subword-nmt](https://github.com/rsennrich/subword-nmt) to segment text into subword units (BPE). By default, the script builds a shared vocabulary of 33708 tokens, which is consistent with [Scaling Neural Machine Translation](https://arxiv.org/abs/1806.00187).\n\n#### Dataset guidelines\n\nThe Transformer model works with a fixed sized vocabulary. Prior to the training, we need to learn a data representation that allows us to store the entire dataset as a sequence of tokens. To achieve this we use Binary Pair Encoding. This algorithm builds a vocabulary by iterating over a dataset, looking for the most frequent pair of symbols and replacing them with a new symbol, yet absent in the dataset. After identifying the desired number of encodings (new symbols can also be merged together) it outputs a code file that is used as an input for the `Dictionary` class.\nThis approach does not minimize the length of the encoded dataset, however this is allowed using [SentencePiece](https://github.com/google/sentencepiece/) to tokenize the dataset with the unigram model. This approach tries to find encoding that is close to the theoretical entropy limit.\nData is then sorted by length (in terms of tokens) and examples with similar length are batched together, padded if necessary.\n\n#### Multi-dataset\n\nThe model has been tested oni the [wmt14 en-fr](http://www.statmt.org/wmt14/translation-task.html) dataset. Achieving state of the art accuracy of 41.4 BLEU.\n\n### Training process\n\nThe default training configuration can be launched by running the `train.py` training script. By default, the script saves one checkpoint every epoch in addition to the latest and the best ones. The best checkpoint is considered the one with the lowest value of loss, not the one with the highest BLEU score. To override this behavior use the `--save-interval $N` option to save epoch checkpoints every N epoch or `--no-epoch-checkpoints` to disable them entirely (with this option the latest and the best checkpoints still will be saved). Specify save the directory with `--save-dir` option.
\nIn order to run multi-GPU training, launch the training script with `python -m torch.distributed.launch --nproc_per_node $N` prepended, where N is the number of GPUs.\nWe have tested reliance on up to 16 GPUs on a single node.
\nAfter each training epoch, the script runs a loss validation on the validation split of the dataset and outputs the validation loss. By default the evaluation after each epoch is disabled. To enable it, use the `--online-eval` option or to use the BLEU score value as the training stopping condition use the `--target-bleu $TGT` option. The BLEU scores computed are case insensitive. The BLEU is computed by the internal fairseq algorithm which implementation can be found in the `fairseq/bleu.py` script.
\nBy default, the `train.py` script will launch FP32 training without Tensor Cores. To use mixed precision with Tensor Cores use the `--fp16` option.
\n\nTo reach the BLEU score reported in [Scaling Neural Machine Translation](https://arxiv.org/abs/1806.00187) research paper, we used mixed precision training with a batch size of 5120 per GPU and learning rate of 6e-4 on a DGX-1V system with 8 Tesla V100s 16G. If you use a different setup, we recommend you scale your hyperparameters by applying the following rules:\n1. To use FP32, reduce the batch size to 2560 and set the `--update-freq 2` option.\n2. To train on a fewer GPUs, multiply `--update-freq` by the reciprocal of the scaling factor.\n\nFor example, when training in FP32 mode on 4 GPUs, use the `--update-freq=4` option.\n\n### Inference process\n\nInference on a raw input can be performed by piping file to be translated into the `inference.py` script. It requires a pre-trained model checkpoint, BPE codes file and dictionary file (both are produced by the `run_preprocessing.sh` script and can be found in the dataset directory).
\nIn order to run interactive inference, run command:\n```\npython inference.py --path /path/to/your/checkpoint.pt --fuse-dropout-add --remove-bpe --bpe-codes /path/to/code/file\n```\nThe `--buffer-size` option allows the batching of input sentences up to `--max_token` length.\n\nTo test model checkpoint accuracy on wmt14 test set run following command:\n\n```bash\nsacrebleu -t wmt14/full -l en-de --echo src | python inference.py --buffer-size 5000 --path /path/to/your/checkpoint.pt --max-tokens 10240 --fuse-dropout-add --remove-bpe --bpe-codes /data/code --fp16 | sacrebleu -t wmt14/full -l en-de -lc\n```","hasAdvanced","performance","## Performance\n\n### Results\n\n#### Training Accuracy Results\nIn order to test accuracy of our implementation we have run experiments with different seeds for 100 epochs with batch size 5120 per GPU and learining rate 6e-4 in the pytorch-18.12-py3 Docker container. Plot below shows BLEU score changes.
\n![Accuracy plot](https://github.com/NVIDIA/DeepLearningExamples/raw/master/PyTorch/Translation/Transformer/BLEU.png)\n\n#### Training Performance Results\n\nRunning this code with the provided hyperparameters will allow you to achieve the following results. Our setup is a DGX-1 with 8x Tesla V100 16GB. We've verified our results after training 32 epochs to obtain multi-GPU and mixed precision scaling results.\n\n GPU count | Mixed precision BLEU | fp32 BLEU | Mixed precision training time | fp32 training time\n---|---|---|---|---\n 8 | 28.69 | 28.43 | 446 min | 1896 min\n 4 | 28.35 | 28.31 | 834 min | 3733 min\n\nIn some cases we can train further with the same setup to achieve slightly better results. \n\nGPU count |Precision | BLEU score | Epochs to train | Training time\n---|---|---|---|---\n 4 |fp16 | 28.67 | 74 | 1925 min\n 4 |fp32 | 28.40 | 47 | 5478 min\n\nResults here are the best we achieved. We've observed a large variance in BLEU, while using random seed. Nearly all setups reach 28.4 BLEU, although the time it takes also varies between setups.\nWe also observed a good rate of week scaling. We measured performance in tokens (words) per second.\n\nGPU count | Mixed precision | FP32 | FP32/Mixed speedup | Mixed precision week scaling | FP32 week scaling\n---|---|---|---|---|---\n1 | 37650 | 8630 | 4.36 | 1.0 | 1.0\n4 | 132700 | 30500 | 4.35 | 3.52 | 3.53\n8 | 260000 | 61000 | 4.26 | 6.91 | 7.07\n\n#### Inference performance results\nAll results were obtained by `generate.py` inference script in the pytorch-19.01-py3 Docker container. Inference was run on a single GPU.\n\nGPU | Mixed precision | FP32 | FP16/Mixed speedup\n---|---|---|---\nTesla V100 | 5129.34 | 3396.09 | 1.51","hasPerformance","policies",[],{"_180":199,"_181":200,"_144":-5,"_152":26,"_154":26,"_168":198,"_155":64},{"_180":197,"_181":197,"_144":-5,"_152":26,"_154":26,"_168":198,"_155":64},{"_180":195,"_181":195,"_144":-5,"_152":26,"_154":26,"_168":196,"_155":64},{"_180":193,"_181":193,"_144":-5,"_152":26,"_154":26,"_168":194,"_155":64},{"_180":191,"_181":191,"_144":-5,"_152":26,"_154":26,"_168":192,"_155":64},{"_180":189,"_181":189,"_144":-5,"_152":26,"_154":26,"_168":190,"_155":64},{"_180":187,"_181":187,"_144":-5,"_152":26,"_154":26,"_168":188,"_155":64},{"_180":185,"_181":185,"_144":-5,"_152":26,"_154":26,"_168":186,"_155":64},{"_180":183,"_181":183,"_144":-5,"_152":26,"_154":26,"_168":184,"_155":64},{"_180":14,"_181":14,"_144":-5,"_152":26,"_154":26,"_168":182,"_155":64},"id","name",[],"2",[],"20.06.0",[],"20.06.1",[],"21.05.1",[],"21.05.2",[],"21.05.0",[],"21.05.4",[],"21.05.5",[],"-","Latest (21.05.5)","nvidia/transformer_for_pytorch","key","nvidia/resources/transformer_for_pytorch","resourceId","transformer_for_pytorch","shortDescription","This implementation of Transformer model architecture is based on the optimized implementation in Fairseq NLP toolkit.","The Transformer is a Neural Machine Translation (NMT) model which uses attention mechanism to boost training speed and overall accuracy. The Transformer model was introduced in [Attention Is All You Need](https://arxiv.org/abs/1706.03762) and improved in [Scaling Neural Machine Translation](https://arxiv.org/abs/1806.00187).\nThis implementation is based on the optimized implementation in [Facebook's Fairseq NLP toolkit](https://github.com/pytorch/fairseq), built on top of PyTorch.\n\nThis model is trained with mixed precision using Tensor Cores on NVIDIA Volta, Turing and Ampere GPU architectures. Therefore, researchers can get results 6.5x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n\n### Model architecture\n\nThe Transformer model uses standard NMT encoder-decoder architecture. This model unlike other NMT models, uses no recurrent connections and operates on fixed size context window.\nThe encoder stack is made up of N identical layers. Each layer is composed of the following sublayers:\n 1. Self-attention layer\n 2. Feedforward network (which is 2 fully-connected layers)\nLike the encoder stack, the decoder stack is made up of N identical layers. Each layer is composed of the sublayers:\n 1. Self-attention layer\n 2. Multi-headed attention layer combining encoder outputs with results from\n the previous self-attention layer.\n 3. Feedforward network (2 fully-connected layers)\n\nThe encoder uses self-attention to compute a representation of the input sequence. The decoder generates the output sequence one token at a time, taking the encoder output and previous decoder-outputted tokens as inputs.\nThe model also applies embeddings on the input and output tokens, and adds a constant positional encoding. The positional encoding adds information about the position of each token.\n\n

\n \n
\n Figure 1. The architecture of a Transformer model.\n

\n\nThe complete description of the Transformer architecture can be found in [Attention Is All You Need](https://arxiv.org/abs/1706.03762) paper.\n### Default configuration\n\nThe Transformer uses Byte Pair Encoding tokenization scheme using [Moses decoder](https://github.com/moses-smt/mosesdecoder). This is a lossy compression method (we drop information about white spaces). Tokenization is applied over whole [WMT14](http://statmt.org/wmt14/translation-task.html#Download) en-de dataset including test set. Default vocabulary size is 33708, excluding all special tokens. Encoder and decoder are using shared embeddings.\nWe use 6 blocks in each encoder and decoder stacks. Self attention layer computes it's outputs according to the following formula $`Attention(Q,K,V) = softmax(\\frac{QK^T}{\\sqrt{d_k}})V`$. At each attention step, the model computes 16 different attention representations (which we will call attention heads) and concatenates them.\nWe trained the Transformer model using the Adam optimizer with betas `(0.9, 0.997)`, epsilon `1e-9` and learning rate `6e-4`. We used the inverse square root training schedule preceded with linear warmup of 4000 steps.\nThe implementation allows to perform training in mixed precision. We use dynamic loss scaling and custom mixed precision optimizer. Distributed multi-GPU and multi-Node is implemented with `torch.distirbuted` module with NCCL backend.\nFor inference, we use beam search with default beam size of 5. Model performance is evaluated with BLEU4 metrics. For clarity, we report internal (legacy) BLEU implementation as well as external [SacreBleu](https://github.com/mjpost/sacreBLEU) score.\n\n### Feature support matrix\n\nThe following features are supported by this model.
\n\n| Feature | Yes column \n|--------------------------|--------------------------\n| Multi-GPU training with [Distributed Communication Package](https://pytorch.org/docs/stable/distributed.html) | Yes \n| Nvidia APEX | Yes \n| AMP | Yes\n| TorchScript | Yes\n\n#### Features\n\n* Multi-GPU training with [Distributed Communication Package](https://pytorch.org/docs/stable/distributed.html): Our model uses torch.distributed package to implement efficient multi-GPU training with NCCL.\nTo enable multi-GPU training with torch.distributed, you have to initialize your model identically in every process spawned by torch.distributed.launch. Distributed strategy is implemented with APEX's DistributedDataParallel.\nFor details, see example sources in this repo or see the [pytorch tutorial](https://pytorch.org/docs/stable/distributed.html)\n\n* Nvidia APEX: The purpose of the APEX is to provide easy and intuitive framework for distributed training and mixed precision training. For details, see official [APEX repository](https://github.com/NVIDIA/apex).\n\n* AMP: This implementation uses Apex's AMP to perform mixed precision training.\n\n* TorchScript: Transformer can be converted to TorchScript format offering ease of deployment on platforms without Python dependencies. For more information see official [TorchScript](https://pytorch.org/docs/stable/jit.html) documentation.\n\n### Mixed precision training\nMixed 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 the Volta and Turing architecture, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:\n1. Porting the model to use the FP16 data type where appropriate. \n2. Adding loss scaling to preserve small gradient values.\n\nThe 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.\n\nFor information about:\n- 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.\n- 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.\n- 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/).\n\n#### Enabling mixed precision\n\nMixed precision is enabled using the `--amp` option in the `train.py` script. The default is optimization level `O2` but can be overriden with `--amp-level $LVL` option (for details see [amp documentation](https://nvidia.github.io/apex/amp.html)). Forward and backward pass are computed with FP16 precision with exclusion of a loss function which is computed in FP32 precision. Default optimization level keeps a copy of a model in higher precision in order to perform accurate weight update. After the update FP32 weights are again copied to FP16 model. We use dynamic loss scaling with initial scale of 2^7 increasing it by a factor of 2 every 2000 successful iterations. Overflow is being checked after reducing gradients from all of the workers. If we encounter infs or nans the whole batch is dropped.\n\n#### Enabling TF32\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.\n\n### Glossary\n\nAttention layer - Layer that computes which elements of input sequence or it's hidden representation contribute the most to the currently considered output element. \nBeam search - A heuristic search algorithm which at each step of predictions keeps N most possible outputs as a base to perform further prediction. \nBPE - Binary Pair Encoding, compression algorithm that find most common pair of symbols in a data and replaces them with new symbol absent in the data. \nEOS - End of a sentence. \nSelf attention layer - Attention layer that computes hidden representation of input using the same tensor as query, key and value. \nToken - A string that is representable within the model. We also refer to the token's position in the dictionary as a token. There are special non-string tokens: alphabet tokens (all characters in a dataset), EOS token, PAD token. \nTokenizer - Object that converts raw strings to sequences of tokens. \nVocabulary embedding - Layer that projects one-hot token representations to a high dimensional space which preserves some information about correlations between tokens.","displayName","Transformer for PyTorch",["D",1680631765672],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/transformer_for_pytorch","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes",865878,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[242,243,244,245,246],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_209":267,"_248":268,"_250":269,"_252":253},{"_209":263,"_248":264,"_250":265,"_252":253},{"_209":259,"_248":260,"_250":261,"_252":253},{"_209":256,"_248":257,"_250":258,"_252":253},{"_209":247,"_248":249,"_250":251,"_252":253},"Natural Language Understanding","value","uscs_natural_language_understanding","filter",{"_254":255,"_248":249},"category","label","field","application","Natural Language Processing","uscs_natural_language_processing",{"_254":255,"_248":257},"Deep Learning Examples","pltfm_deep_learning_examples",{"_254":262,"_248":260},"platform","Conversational AI","soln_conversational_ai",{"_254":266,"_248":264},"technology","AI","soln_ai",{"_254":266,"_248":268},{"_85":1446,"_87":1447},"ngcApps",[1399],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1380":1381,"_1382":1383,"_1384":1385,"_1386":1387,"_1388":281,"_116":305,"_1389":1390,"_1391":50,"_1392":64,"_1393":64,"_1394":1395,"_1396":64,"_1397":1398},"amplitude",{"_1377":1378,"_980":-7,"_1379":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1371":1372,"_1373":1374,"_1375":1376},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.2","defaultSearchSuggestions",{"_1255":386,"_1256":1257,"_1258":1259,"_1260":1261},"announcements",[],"consentState",{"_343":1245,"_1246":64,"_1247":26,"_1248":26,"_1249":1250},"consentConfig",{"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344},"showContactAdmin",1783546502557,[],[],[319,320,321,322,323,324],{"_39":53,"_41":61,"_43":80,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":60,"_43":80,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":58,"_43":80,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":54,"_43":80,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":80,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":80,"_45":-5,"_47":-5,"_49":50},"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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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529],[367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383],{"_384":491,"_209":491,"_142":386,"_357":492},{"_384":486,"_209":486,"_142":386,"_357":487},{"_384":482,"_209":482,"_142":386,"_357":483},{"_384":477,"_209":477,"_142":386,"_357":478},{"_384":472,"_209":472,"_142":386,"_357":473},{"_384":460,"_209":460,"_142":386,"_357":461},{"_384":448,"_209":448,"_142":386,"_357":449},{"_384":443,"_209":443,"_142":386,"_357":444},{"_384":432,"_209":432,"_142":386,"_357":433},{"_384":427,"_209":427,"_142":386,"_357":428},{"_384":422,"_209":422,"_142":386,"_357":423},{"_384":417,"_209":417,"_142":386,"_357":418},{"_384":412,"_209":412,"_142":386,"_357":413},{"_384":407,"_209":407,"_142":386,"_357":408},{"_384":402,"_209":402,"_142":386,"_357":403},{"_384":395,"_209":395,"_142":386,"_357":396},{"_384":385,"_209":385,"_142":386,"_357":387},"hostName","amazon-adsystem.com","",[388,389],{"_181":394,"_391":385,"_392":26,"_142":393},{"_181":390,"_391":385,"_392":26,"_142":393},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[397,398],{"_181":401,"_391":395,"_392":26,"_142":400},{"_181":399,"_391":395,"_392":26,"_142":400},"IDE","This domain is owned by Doubleclick (Google). The main business activity is: Doubleclick is Googles real time bidding advertising exchange","test_cookie","bing.com",[404],{"_181":405,"_391":402,"_392":26,"_142":406},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[409],{"_181":410,"_391":407,"_392":26,"_142":411},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","If you know anything about this host and its uses please add your contributions here. ","tiktok.com",[414],{"_181":415,"_391":412,"_392":26,"_142":416},"_ttp","TikTok is a social media platform owned by ByteDance, known for short-form video sharing. The service is used to create and share videos, and it utilizes cookies for various purposes including advertising and analytics.","t.co",[419],{"_181":420,"_391":417,"_392":26,"_142":421},"muc_ads","This domain is owned by Twitter. It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[424],{"_181":425,"_391":422,"_392":26,"_142":426},"sp","Testing","google.com",[429],{"_181":430,"_391":427,"_392":26,"_142":431},"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",[434,435,436,437],{"_181":442,"_391":432,"_392":26,"_142":439},{"_181":441,"_391":432,"_392":26,"_142":439},{"_181":440,"_391":432,"_392":26,"_142":439},{"_181":438,"_391":432,"_392":26,"_142":439},"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",[445],{"_181":446,"_391":443,"_392":26,"_142":447},"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",[450,451,452,453],{"_181":459,"_391":448,"_392":26,"_142":455},{"_181":457,"_391":448,"_392":26,"_142":458},{"_181":456,"_391":448,"_392":26,"_142":455},{"_181":454,"_391":448,"_392":26,"_142":455},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[462,463,464,465],{"_181":471,"_391":460,"_392":26,"_142":469},{"_181":470,"_391":460,"_392":26,"_142":469},{"_181":468,"_391":460,"_392":26,"_142":469},{"_181":466,"_391":460,"_392":26,"_142":467},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. 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",[474],{"_181":475,"_391":472,"_392":26,"_142":476},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[479],{"_181":480,"_391":477,"_392":26,"_142":481},"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",[484],{"_181":485,"_391":482,"_392":26,"_142":426},"R","6sc.co",[488],{"_181":489,"_391":486,"_392":26,"_142":490},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. 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,[618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638],[583,584,585,586,587],{"_384":477,"_209":477,"_142":386,"_357":609},{"_384":443,"_209":443,"_142":386,"_357":604},{"_384":597,"_209":597,"_142":386,"_357":598},{"_384":593,"_209":593,"_142":386,"_357":594},{"_384":588,"_209":588,"_142":386,"_357":589},"nvcrm.my.site.com",[590],{"_181":591,"_391":588,"_392":26,"_142":592},"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",[595],{"_181":596,"_391":593,"_392":26,"_142":426},"edge","github.com",[599,600],{"_181":603,"_391":597,"_392":26,"_142":602},{"_181":601,"_391":597,"_392":26,"_142":602},"logged_in","This domain is owned by Github. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website 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