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Changelog

March 2019

Initial release

Known issues

There are no known issues with this model.

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BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding paper. NVIDIA's BERT is an optimized version of Google's official implementation, leveraging mixed precision arithmetic and Tensor Cores on A100, V100 and T4 GPUs for faster training times while maintaining target accuracy.

Other publicly available implementations of BERT include:

This model is trained with mixed precision using Tensor Cores on NVIDIA Volta, Ampere and Turing GPUs. Therefore, researchers can get results up to 4x 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

BERT's model architecture is a multi-layer bidirectional Transformer encoder. Based on the model size, we have the following two default configurations of BERT:

\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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Model	Hidden layers	Hidden unit size	Attention heads	Feedforward filter size	Max sequence length	Parameters
BERTBASE	12 encoder	768	12	4 x 768	512	110M
BERTLARGE	24 encoder	1024	16	4 x 1024	512	330M

BERT training consists of two steps, pre-training the language model in an unsupervised fashion on vast amounts of unannotated datasets, and then using this pre-trained model for fine-tuning for various NLP tasks, such as question and answer, sentence classification, or sentiment analysis. Fine-tuning typically adds an extra layer or two for the specific task and further trains the model using a task-specific annotated dataset, starting from the pre-trained backbone weights. The end-to-end process in depicted in the following image:

$\"\"$

Figure 1: BERT Pipeline

Default configuration

This repository contains scripts to interactively launch data download, training, benchmarking and inference routines in a Docker container for both pre-training and fine tuning for Question Answering. The major differences between the official implementation of the paper and our version of BERT are as follows:

Mixed precision support with TensorFlow Automatic Mixed Precision (TF-AMP), which enables mixed precision training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an environmental variable.
Scripts to download dataset for:\n
- Pre-training - Wikipedia, BookCorpus
- Fine tuning - SQuAD (Stanford Question Answering Dataset)
- Fine tuning - GLUE (The General Language Understanding Evaluation benchmark)
- Pretrained weights from Google
\n
Custom fused CUDA kernels for faster computations
Multi-GPU/Multi-node support using Horovod

The following performance optimizations were implemented in this model:

XLA support (experimental).

These techniques and optimizations improve model performance and reduce training time, allowing you to perform various NLP tasks with no additional effort.

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

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Feature	BERT
Horovod Multi-GPU	Yes
Horovod Multi-Node	Yes
Automatic mixed precision (AMP)	Yes
LAMB	Yes

Features

Multi-GPU training with Horovod - Our model uses Horovod to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the TensorFlow tutorial

LAMB stands for Layerwise Adaptive Moments based optimizer, is a large batch optimization technique that helps accelerate training of deep neural networks using large minibatches. It allows using a global batch size of 65536 and 32768 on sequence lengths 128 and 512 respectively, compared to a batch size of 256 for Adam. The optimized implementation accumulates 1024 gradient batches in phase 1 and 4096 steps in phase 2 before updating weights once. This results in 27% training speedup on a single DGX2 node. On multi-node systems, LAMB allows scaling up to 1024 GPUs resulting in training speedups of up to 17x in comparison to Adam. Adam has limitations on the learning rate that can be used since it is applied globally on all parameters whereas LAMB follows a layerwise learning rate strategy.

NVLAMB adds necessary tweaks to LAMB version 1, to ensure correct convergence. A guide to implementating the LAMB optimizer can be found in our article on Medium.com. The algorithm is as follows:\n $\"NVLAMB\"$

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 Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training previously required two steps:

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 mixed precision methodology in your existing TensorFlow model code. AMP enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically. The 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, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting 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 Training paper and Training With Mixed Precision documentation.
Techniques used for mixed precision training, see the Mixed-Precision Training of Deep Neural Networks blog.
How to access and enable AMP for TensorFlow, see Using TF-AMP from the TensorFlow User Guide.
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 in TensorFlow by using the Automatic Mixed Precision (TF-AMP) extension which casts variables to half-precision upon retrieval, while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a loss scaling step must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by using simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed precision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations. First, programmers need not modify network model code, reducing development and maintenance effort. Second, 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.

Glossary

Fine-tuning\nTraining an already pretrained model further using a task specific dataset for subject-specific refinements, by adding task-specific layers on top if required.

Language Model\nAssigns a probability distribution over a sequence of words. Given a sequence of words, it assigns a probability to the whole sequence.

Pre-training\nTraining a model on vast amounts of data on the same (or different) task to build general understandings.

Transformer\nThe paper Attention Is All You Need introduces a novel architecture called Transformer that uses an attention mechanism and transforms one sequence into another.

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Aside from these dependencies, ensure you have the following components:\n- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n- [TensorFlow 19.03-py3](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) NGC container\n- [NVIDIA Volta based GPU](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n\n\nFor more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning 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 TensorFlow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running)","hasSetup","quickStartGuide","## Quick Start Guide\nTo pretrain or fine tune your model for Question Answering using mixed precision with tensor cores or using FP32, perform the following steps using the default parameters of the BERT model.\n\n### 1. Clone the repository.\n\n```bash\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/TensorFlow/LanguageModeling/BERT\n```\n\n### 2. Build the BERT TensorFlow NGC container.\n\n```bash\nbash scripts/docker/build.sh\n```\n\n### 3. Download and preprocess the dataset.\nThis repository provides scripts to download, verify and extract the SQuaD dataset+pretrained weights for fine tuning as well as Wikipedia + BookCorpus dataset for pretraining.\n\nTo download, verify, and extract required datasets: \n\n```bash\nbash scripts/data_download.sh \n```\n\nThe script launches a docker container with current directory mounted and downloads datasets to `data/` folder on the host. \n\n### 4. Start an interactive session in the NGC container to run training/inference.\nAfter you build the container image and download the data, you can start an interactive CLI session as follows: \n\n```bash\nbash scripts/docker/launch.sh\n```\n\nThe `launch.sh` script assumes that the datasets are in the following locations by default after downloading data. \n- SQuaD v1.1 - `data/squad/v1.1`\n- BERT - `data/pretrained_models_google/uncased_L-24_H-1024_A-16`\n- Wikipedia - `data/wikipedia_corpus/final_tfrecords_sharded`\n- BookCorpus - `data/bookcorpus/final_tfrecords_sharded`\n\n\n### 5. Start pre-training.\nBERT is designed to pre-train deep bidirectional representations for language representations. The following scripts are to replicate pretraining on Wikipedia+Book Corpus from the [paper](https://arxiv.org/pdf/1810.04805.pdf). These scripts are general and can be used for pretraining language representations on any corpus of choice.\n\nFrom within the container, you can use the following script to run pre-training.\n```bash\nbash scripts/run_pretraining.sh \n```\n\nFor FP16 training with XLA using a DGX-1 V100 32G, run:\n```bash\nbash scripts/run_pretraining.sh 14 8 5e-5 fp16_xla 8 5000 2285000 5000 true\n```\n\nFor FP32 training without XLA using a DGX-1 V100 32G, run:\n```bash\nbash scripts/run_pretraining.sh 6 6 2e-5 fp32 8 2000 5333333 5000 true\n```\n\n### 6. Start fine tuning.\nThe above pretrained BERT representations can be fine tuned with just one additional output layer for a state-of-the-art Question Answering system. From within the container, you can use the following script to run fine-training for SQuaD.\n\n```bash\nbash scripts/run_squad.sh \n```\n\nFor FP16 training with XLA using a DGX-1 V100 32G, run:\n```bash\nbash scripts/run_squad.sh 10 5e-6 fp16 true 8 /bert/bert_model.ckpt\n```\n\nFor FP32 training without XLA using a DGX-1 V100 32G, run:\n```bash\nbash scripts/run_squad.sh 5 5e-6 fp32 false 8 /bert/bert_model.ckpt\n```\n\n### 7. Start validation/evaluation.\nThe `run_squad_inference.sh` script runs inference on a checkpoint fine tuned for SQuaD and evaluates the goodness of predictions on the basis of exact match and F1 score.\n\n```bash\nbash scripts/run_squad_inference.sh \n```\n\nFor FP16 inference with XLA using a DGX-1 V100 32G, run:\n```bash\nbash scripts/run_squad_inference.sh /results/model.ckpt 8 fp16 true \n```\n\nFor FP32 inference without XLA using a DGX-1 V100 32G, run:\n```bash\nbash scripts/run_squad_inference.sh /results/model.ckpt 8 fp32 false\n```\n\n### Details\nThe following sections provide greater details of the dataset, running training and inference, and the training results.\n\n#### Command line options\nTo see the full list of available options and their descriptions, use the -h or --help command line option, for example: \n```bash\npython run_pretraining.py --help\npython run_squad.py --help \n```\n\nAside from options to set hyperparameters, the relevant options to control the behaviour of the `run_pretraining.py` script are: \n```bash\n --[no]amp: Whether to enable AMP ops.(default: 'false')\n --[no]amp_fastmath: Whether to enable AMP fasthmath ops.(default: 'false')\n --bert_config_file: The config json file corresponding to the pre-trained BERT model. This specifies the model architecture.\n --[no]do_eval: Whether to run evaluation on the dev set.(default: 'false')\n --[no]do_train: Whether to run training.(default: 'false')\n --eval_batch_size: Total batch size for evaluation.(default: '8')(an integer)\n --[no]fastmath: Whether to enable loss scaler for fasthmath ops.(default: 'false')\n --[no]horovod: Whether to use Horovod for multi-gpu runs(default: 'false')\n --init_checkpoint: Initial checkpoint (usually from a pre-trained BERT model).\n --input_file: Input TF example files (can be a glob or comma separated).\n --iterations_per_loop: How many steps to make in each estimator call.(default: '1000')\n```\n\nAside from options to set hyperparameters, some relevant options to control the behaviour of the run_squad.py script are: \n```bash\n --bert_config_file: The config json file corresponding to the pre-trained BERT model. This specifies the model architecture.\n --[no]do_predict: Whether to run evaluation on the dev set. (default: 'false')\n --[no]do_train: Whether to run training. (default: 'false')\n --learning_rate: The initial learning rate for Adam.(default: '5e-06')(a number)\n --max_answer_length: The maximum length of an answer that can be generated. This is needed because the start and end predictions are not conditioned on one another.(default: '30')(an integer)\n --max_query_length: The maximum number of tokens for the question. Questions longer than this will be truncated to this length.(default: '64')(an integer)\n --max_seq_length: The maximum total input sequence length after WordPiece tokenization. Sequences longer than this will be truncated, and sequences shorter than this will be padded.(default: '384')(an integer)\n --predict_batch_size: Total batch size for predictions.(default: '8')(an integer)\n --train_batch_size: Total batch size for training.(default: '8')(an integer)\n --[no]use_fp16: Whether to use fp32 or fp16 arithmetic on GPU.(default: 'false')\n --[no]use_xla: Whether to enable XLA JIT compilation.(default: 'false')\n --[no]verbose_logging: If true, all of the warnings related to data processing will be printed. A number of warnings are expected for a normal SQuAD evaluation.(default: 'false')\n --[no]version_2_with_negative: If true, the SQuAD examples contain some that do not have an answer.(default: 'false')\n```\n\n#### Getting the data\nFor pre-training BERT, we use the concatenation of Wikipedia (2500M words) as well as Book Corpus (800M words). For Wikipedia, we extract only the text passages from [here](ftp://ftpmirror.your.org/pub/wikimedia/dumps/enwiki/20190301/enwiki-20190301-pages-articles-multistream.xml.bz2) and ignore headers list and tables. It is structured as a document level corpus rather than a shuffled sentence level corpus because it is critical to extract long contiguous sentences. The next step is to run `create_pretraining_data.py` with the document level corpus as input, which generates input data and labels for the masked language modeling and next sentence prediction tasks. Pre-training can also be performed on any corpus of your choice. The collection of data generation scripts are intended to be modular to allow modifications for additional preprocessing steps or to use additional data.\n\nWe can use a pre-trained BERT model for other fine tuning tasks like Question Answering. We use SQuaD for this task. SQuaD v1.1 has 100,000+ question-answer pairs on 500+ articles. SQuaD v2.0 combines v1.1 with an additional 50,000 new unanswerable questions and must not only answer questions but also determine when that is not possible. \n\n#### Training process\nThe training process consists of two steps: pre-training and fine tuning.\n\n##### Pre-training\nPre-training is performed using the `run_pretraining.py` script along with parameters defined in the `scripts/run_pretraining.sh`.\n\n\nThe `run_pretraining.sh` script runs a job on a single node that trains the BERT-large model from scratch using the Wikipedia and Book corpus datasets as training data. By default, the training script:\n- Runs on 8 GPUs with training batch size of 14 and evaluation batch size of 8 per GPU.\n- Has FP16 precision enabled.\n- Is XLA enabled.\n- Trains with default learning rate of 1e-4 for 1144000 steps with 10000 warm-up steps.\n- Saves a checkpoint every 5000 iterations.\n- Creates a log file containing all the output.\n- Evaluates the model at the end of training. To skip evaluation, modify `--do_eval` to `False`.\n\nThese parameters will train Wikipedia + BooksCorpus to reasonable accuracy on a DGX1 with 32GB V100 cards. If you want to match google’s best results from the BERT paper, you should either train for twice as many steps (2,288,000 steps) on a DGX1, or train on 16 GPUs on a DGX2. The DGX2 having 16 GPUs will be able to fit a batch size twice as large as a DGX1 (224 vs 112), hence the DGX2 can finish in half as many steps. \n\n\nFor example:\n```bash\nrun_pretraining.sh

\n```\n\nWhere:\n- per-gpu batch size used for training. Batch size varies with , larger batch sizes run more efficiently, but require more memory.\n\n- per-gpu batch size used for evaluation after training. Default rate of 1e-4 is good for global batch size 256.\n\n- Type of math in your model, can be either fp32, fp16, fp16_xla, fastmath, amp_fm, amp_fm_xla, amp or amp_xla. The options mean:\n\n - fp32 32 bit IEEE single precision floats.\n\n - fp16 Hand-coded mixed precision 16 and 32 bit floats.\n\n - fp16 Hand-coded mixed precision floats, JIT compiled with XLA.\n\n - fastmath Matmuls done by tensor cores in mixed precision, the rest is done in FP32.\n\n - amp_fm Alternative FastMath implementation that works by manipulating TensorFlow’s compute graph.\n\n - amp_fm_xla The amp_fm flag plus XLA JIT compilation.\n\n - amp Automatic rewrite of TensorFlow compute graph to take advantage of 16 bit arithmetic whenever that is safe.\n\n - amp_xla The amp flag plus XLA JIT compilation.\n\n- Number of GPUs to use for training. Must be equal to or smaller than the number of GPUs attached to your node.\n\n- Number of warm-up steps at the start of training.\n\n- Total number of training steps.\n\n- Controls how often checkpoints are saved. Default is 5000 steps.\n\n- Flag indicating if output should be written to a logfile or not (acceptable values are ‘true’ or ‘false’, true indicates output should be saved to a logfile.)\n\n\nFor example:\n```bash\nbert_tf/scripts/run_pretraining.sh 14 8 1e-4 fp16_xla 16 10000 1144000 5000 true\n```\n\nTrains BERT-large from scratch on a single DGX-2 using FP16 arithmetic. This will take around 156 hours / 6.5 days. Checkpoints are written out every 5000 steps and all printouts are saved to a logfile.\n\n##### Fine tuning\nFine tuning is performed using the `run_squad.py` script along with parameters defined in `scripts/run_squad.sh`.\n\nThe `run_squad.sh` script trains a model and performs evaluation on the SQuaD v1.1 dataset. By default, the training script: \n- Uses 8 GPUs and batch size of 10 on each GPU.\n- Has FP16 precision enabled.\n- Is XLA enabled.\n- Runs for 2 epochs.\n- Saves a checkpoint every 1000 iterations (keeps only the latest checkpoint) and at the end of training. All checkpoints, evaluation results and training logs are saved to the `/results` directory (in the container which can be mounted to a local directory).\n- Evaluation is done at the end of training. To skip evaluation, modify `--do_predict` to `False`.\n\nThis script outputs checkpoints to the `/results` directory, by default, inside the container. Mount point of `/results` can be changed in the `scripts/docker/launch.sh` file. The training log contains information about:\n- Loss for the final step\n- Training and evaluation performance\n- F1 and exact match score on the Dev Set of SQuaD after evaluation. \n\nThe summary after training is printed in the following format:\n```bash\nI0312 23:10:45.137036 140287431493376 run_squad.py:1332] 0 Total Training Time = 3007.00 Training Time W/O start up overhead = 2855.92 Sentences processed = 175176\nI0312 23:10:45.137243 140287431493376 run_squad.py:1333] 0 Training Performance = 61.3378 sentences/sec\nI0312 23:14:00.550846 140287431493376 run_squad.py:1396] 0 Total Inference Time = 145.46 Inference Time W/O start up overhead = 131.86 Sentences processed = 10840\nI0312 23:14:00.550973 140287431493376 run_squad.py:1397] 0 Inference Performance = 82.2095 sentences/sec\n{\"exact_match\": 83.69914853358561, \"f1\": 90.8477003317459}\n```\n\nMulti-gpu training is enabled with the Horovod TensorFlow module. The following example runs training on 8 GPUs: \n```bash\nmpi_command=\"mpirun -np 8 -H localhost:8 \\\n --allow-run-as-root -bind-to none -map-by slot \\\n -x NCCL_DEBUG=INFO \\\n -x LD_LIBRARY_PATH \\\n -x PATH -mca pml ob1 -mca btl ^openib\" \\\n python run_squad.py --horovod\n```\n\n#### Enabling mixed precision\n[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 architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using [mixed precision training](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) previously required two steps:\n1. Porting the model to use the FP16 data type where appropriate.\n2. Manually adding loss scaling to preserve small gradient values. \nThis can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically. The 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, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing `tf.contrib` loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\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- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.\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\n#### Inference process\nInference on a fine tuned Question Answering system is performed using the `run_squad.py` script along with parameters defined in the `scripts/run_squad_inference.sh`. Inference is supported on single GPU at this moment.\n\nThe `run_squad_inference.sh` script trains a model and performs evaluation on the SQuaD v1.1 dataset. By default, the inferencing script: \n- Has FP16 precision enabled\n- Is XLA enabled\n- Evaluates the latest checkpoint present in `/results` with a batch size of 8\n\nThis script outputs predictions file to `/results/predictions.json` and computes F1 score and exact match score using SQuaD's `evaluate-v1.1.py`. Mount point of `/results` can be changed in the `scripts/docker/launch.sh` file. \n\nThe output log contains information about:\n- Evaluation performance\n- F1 and exact match score on the Dev Set of SQuaD after evaluation. \n\nThe summary after inference is printed in the following format:\n```bash\nI0312 23:14:00.550846 140287431493376 run_squad.py:1396] 0 Total Inference Time = 145.46 Inference Time W/O start up overhead = 131.86 Sentences processed = 10840\nI0312 23:14:00.550973 140287431493376 run_squad.py:1397] 0 Inference Performance = 82.2095 sentences/sec\n{\"exact_match\": 83.69914853358561, \"f1\": 90.8477003317459}\n```","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\nIn the root directory, the most important files are:\n* `run_pretraining.py` - Serves as entry point for pre-training\n* `run_squad.py` - Serves as entry point for SQuAD training\n* `run_classifier.py` - Serves as entry point for GLUE training\n* `Dockerfile` - Container with the basic set of dependencies to run BERT\n\nThe `scripts/` folder encapsulates all the one-click scripts required for running various functionalities supported such as:\n* `run_squad.sh` - Runs SQuAD training and inference using `run_squad.py` file\n* `run_glue.sh` - Runs GLUE training and inference using the `run_classifier.py` file\n* `run_pretraining_adam.sh` - Runs pre-training with Adam optimizer using the `run_pretraining.py` file\n* `run_pretraining_lamb.sh` - Runs pre-training with LAMB optimizer using the `run_pretraining.py` file in two phases. Phase 1 does 90% of training with sequence length = 128. In phase 2, the remaining 10% of the training is done with sequence length = 512.\n* `data_download.sh` - Downloads datasets using files in the `data/` folder\n* `finetune_train_benchmark.sh` - Captures performance metrics of training for multiple configurations\n* `finetune_inference_benchmark.sh` - Captures performance metrics of inference for multiple configurations\n\nOther folders included in the root directory are:\n* `data/` - Necessary folders and scripts to download datasets required for fine tuning and pre-training BERT.\n* `utils/` - Necessary files for preprocessing data before feeding into BERT and hooks for obtaining performance metrics from BERT.\n\n### Parameters\n\nAside from the options to set hyperparameters, the relevant options to control the behaviour of the `run_pretraining.py` script are:\n\n```\n --bert_config_file: The config json file corresponding to the pre-trained BERT model. This specifies the model architecture.\n --init_checkpoint: Initial checkpoint (usually from a pre-trained BERT model).\n --[no]do_eval: Whether to run evaluation on the dev set.(default: 'false')\n --[no]do_train: Whether to run training.(evaluation: 'false')\n --eval_batch_size: Total batch size for eval.(default: '8')(an integer)\n --[no]horovod: Whether to use Horovod for multi-gpu runs(default: 'false')\n --[no]amp: Whether to enable AMP ops. When false, uses TF32 on A100 and FP32 on V100 GPUS.(default: 'True')\n --[no]use_xla: Whether to enable XLA JIT compilation.(default: 'True')\n --input_files_dir: Input TF example files (can be a dir or comma separated).\n --output_dir: The output directory where the model checkpoints will be written.\n --optimizer_type: Optimizer used for training - LAMB or ADAM\n --num_accumulation_steps: Number of accumulation steps before gradient update. Global batch size = num_accumulation_steps * train_batch_size\n --allreduce_post_accumulation: Whether to all reduce after accumulation of N steps or after each step\n```\n\nAside from the options to set hyperparameters, some relevant options to control the behaviour of the `run_squad.py` script are:\n\n```\n --bert_config_file: The config json file corresponding to the pre-trained BERT model. This specifies the model architecture.\n --output_dir: The output directory where the model checkpoints will be written.\n --[no]do_predict: Whether to run evaluation on the dev set. (default: 'false')\n --[no]do_train: Whether to run training. (default: 'false')\n --learning_rate: The initial learning rate for Adam.(default: '5e-06')(a number)\n --max_answer_length: The maximum length of an answer that can be generated. This is needed because the start and end predictions are not conditioned on one another.(default: '30')(an integer)\n --max_query_length: The maximum number of tokens for the question. Questions longer than this will be truncated to this length.(default: '64')(an integer)\n --max_seq_length: The maximum total input sequence length after WordPiece tokenization. Sequences longer than this will be truncated, and sequences shorter than this will be padded.(default: '384')(an integer)\n --predict_batch_size: Total batch size for predictions.(default: '8')(an integer)\n --train_batch_size: Total batch size for training.(default: '8')(an integer)\n --[no]amp: Whether to enable AMP ops. When false, uses TF32 on A100 and FP32 on V100 GPUS.(default: 'True')\n --[no]use_xla: Whether to enable XLA JIT compilation.(default: 'True')\n --[no]version_2_with_negative: If true, the SQuAD examples contain some that do not have an answer.(default: 'false')\n```\n\nAside from the options to set hyperparameters, some relevant options to control the behaviour of the `run_classifier.py` script are:\n\n```\n --bert_config_file: The config json file corresponding to the pre-trained BERT model. This specifies the model architecture.\n --data_dir: The input data dir. Should contain the .tsv files (or other data files) for the task.\n --[no]do_eval: Whether to run eval on the dev set.\n (default: 'false')\n --[no]do_predict: Whether to run the model in inference mode on the test set.(default: 'false')\n --[no]do_train: Whether to run training.(default: 'false')\n --[no]horovod: Whether to use Horovod for multi-gpu runs(default: 'false')\n --init_checkpoint: Initial checkpoint (usually from a pre-trained BERT model).\n --max_seq_length: The maximum total input sequence length after WordPiece tokenization. Sequences longer than this will be truncated, and sequences shorter than this will be padded.(default: '128')(an integer)\n --num_train_epochs: Total number of training epochs to perform.(default: '3.0')(a number)\n --output_dir: The output directory where the model checkpoints will be written.\n --task_name: The name of the task to train.\n --train_batch_size: Total batch size for training.(default: '32')(an integer)\n --[no]amp: Whether to enable AMP ops. When false, uses TF32 on A100 and FP32 on V100 GPUS.(default: 'True')\n --[no]use_xla: Whether to enable XLA JIT compilation.(default: 'True')\n --vocab_file: The vocabulary file that the BERT model was trained on.\n --warmup_proportion: Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10% of training.(default: '0.1')(a number)\n```\n\nNote: When initializing from a checkpoint using `--init_checkpoint` and a corpus of your choice, keep in mind that `bert_config_file` and `vocab_file` should remain unchanged.\n\n### Command-line options\n\nTo see the full list of available options and their descriptions, use the `-h` or `--help` command-line option with the Python file, for example:\n\n```bash\npython run_pretraining.py --help\npython run_squad.py --help\npython run_classifier.py --help\n```\n\n### Getting the data\n\nFor pre-training BERT, we use the concatenation of Wikipedia (2500M words) as well as BookCorpus (800M words). For Wikipedia, we extract only the text passages from [here](ftp://ftpmirror.your.org/pub/wikimedia/dumps/enwiki/latest/enwiki-latest-pages-articles-multistream.xml.bz2) and ignore headers list and tables. It is structured as a document level corpus rather than a shuffled sentence level corpus because it is critical to extract long contiguous sentences.\n\nThe next step is to run `create_pretraining_data.py` with the document level corpus as input, which generates input data and labels for the masked language modeling and next sentence prediction tasks. Pre-training can also be performed on any corpus of your choice. The collection of data generation scripts are intended to be modular to allow modifications for additional preprocessing steps or to use additional data. They can hence easily be modified for an arbitrary corpus.\n\nThe preparation of an individual pre-training dataset is described in the `create_datasets_from_start.sh` script found in the `data/` folder. The component steps to prepare the datasets are as follows:\n\n1. Data download and extract - the dataset is downloaded and extracted.\n2. Clean and format - document tags, etc. are removed from the dataset. The end result of this step is a `{dataset_name_one_article_per_line}.txt` file that contains the entire corpus. Each line in the text file contains an entire document from the corpus. One file per dataset is created in the `formatted_one_article_per_line` folder.\n3. Sharding - the sentence segmented corpus file is split into a number of smaller text documents. The sharding is configured so that a document will not be split between two shards. Sentence segmentation is performed at this time using NLTK.\n4. TFRecord file creation - each text file shard is processed by the `create_pretraining_data.py` script to produce a corresponding TFRecord file. The script generates input data and labels for masked language modeling and sentence prediction tasks for the input text shard.\n\nFor fine tuning BERT for the task of Question Answering, we use SQuAD and GLUE. SQuAD v1.1 has 100,000+ question-answer pairs on 500+ articles. SQuAD v2.0 combines v1.1 with an additional 50,000 new unanswerable questions and must not only answer questions but also determine when that is not possible. GLUE consists of single-sentence tasks, similarity and paraphrase tasks and inference tasks. We support one of each: CoLA, MNLI and MRPC.\n\n#### Dataset guidelines\n\nThe procedure to prepare a text corpus for pre-training is described in the previous section. This section provides additional insight into how exactly raw text is processed so that it is ready for pre-training.\n\nFirst, raw text is tokenized using [WordPiece tokenization](https://arxiv.org/pdf/1609.08144.pdf). A [CLS] token is inserted at the start of every sequence, and the two sentences in the sequence are separated by a [SEP] token.\n\nNote: BERT pre-training looks at pairs of sentences at a time. A sentence embedding token [A] is added to the first sentence and token [B] to the next.\n\nBERT pre-training optimizes for two unsupervised classification tasks. The first is Masked Language Modelling (Masked LM). One training instance of Masked LM is a single modified sentence. Each token in the sentence has a 15% chance of being replaced by a [MASK] token. The chosen token is replaced with [MASK] 80% of the time, 10% with another random token and the remaining 10% with the same token. The task is then to predict the original token.\n\nThe second task is next sentence prediction. One training instance of BERT pre-training is two sentences (a sentence pair). A sentence pair may be constructed by simply taking two adjacent sentences from a single document, or by pairing up two random sentences with equal probability. The goal of this task is to predict whether or not the second sentence followed the first in the original document.\n\nThe `create_pretraining_data.py` script takes in raw text and creates training instances for both pre-training tasks.\n\n#### Multi-dataset\n\nWe are able to combine multiple datasets into a single dataset for pre-training on a diverse text corpus. Once TFRecords have been created for each component dataset, you can create a combined dataset by adding the directory to `SOURCES` in `run_pretraining_*.sh`. This will feed all matching files to the input pipeline in `run_pretraining.py`. However, in the training process, only one TFRecord file is consumed at a time, therefore, the training instances of any given training batch will all belong to the same source dataset.\n\n### Training process\n\nThe training process consists of two steps: pre-training and fine tuning.\n\n#### Pre-training\n\nPre-training is performed using the `run_pretraining.py` script along with parameters defined in the `scripts/run_pretraining_lamb.sh`.\n\nThe `run_pretraining_lamb.sh` script runs a job on a single node that trains the BERT-large model from scratch using the Wikipedia and BookCorpus datasets as training data. By default, the training script:\n- Runs on 8 GPUs.\n- Has FP16 precision enabled.\n- Is XLA enabled.\n- Creates a log file containing all the output.\n- Saves a checkpoint every 100 iterations (keeps only the latest checkpoint) and at the end of training. All checkpoints, evaluation results and training logs are saved to the `/results` directory (in the container which can be mounted to a local directory).\n- Evaluates the model at the end of each phase.\n\n- Phase 1\n - Runs 7038 steps with 2000 warmup steps\n - Sets Maximum sequence length as 128\n - Sets Global Batch size as 64K\n\n- Phase 2\n - Runs 1564 steps with 200 warm-up steps\n - Sets Maximum sequence length as 512\n - Sets Global Batch size as 32K\n - Starts from Phase1's final checkpoint\n\nThese parameters train Wikipedia and BookCorpus with reasonable accuracy on a DGX-1 with 32GB V100 cards.\n\nFor example:\n```bash\nscripts/run_pretraining_lamb.sh \n```\n\nWhere:\n- `` is per-GPU batch size used for training in the respective phase. Batch size varies with precision, larger batch sizes run more efficiently, but require more memory.\n\n- `` is per-GPU batch size used for evaluation after training.\n\n- `` is the default rate of 1e-4 is good for global batch size 256.\n\n- `` is the default rate of 1e-4 is good for global batch size 256.\n\n- `` is the type of math in your model, can be either `fp32` or `fp16`. Specifically:\n\n - `fp32` is 32-bit IEEE single precision floats. Is enabled by default on V100.\n - `fp16` is Automatic rewrite of TensorFlow compute graph to take advantage of 16-bit arithmetic whenever it is safe.\n - `tf32` uses same 10 bit mantissa as fp16 and 8 bit exponent as fp32. Is enabled by default on A100.\n\n- `` is the number of GPUs to use for training. Must be equal to or smaller than the number of GPUs attached to your node.\n\n- `` is the number of warm-up steps at the start of training in the respective phase.\n\n- `` is the total number of training steps in both phases combined.\n\n- `` controls how often checkpoints are saved. Default is 100 steps.\n\n- `` is used to mimic higher batch sizes in the respective phase by accumulating gradients N times before weight update.\n\n- `` is used to indicate whether to pretrain BERT Large or BERT Base model\n\nThe following sample code trains BERT-large from scratch on a single DGX-2 using FP16 arithmetic. This will take around 4.5 days.\n\n```bash\nbert_tf/scripts/run_pretraining_lamb.sh 32 8 8 3.75e-4 2.5e-4 fp16 true 16 2000 200 7820 100 128 256 large\n```\n\n#### Fine tuning\n\nFine tuning is performed using the `run_squad.py` script along with parameters defined in `scripts/run_squad.sh`.\n\nThe `run_squad.sh` script trains a model and performs evaluation on the SQuAD dataset. By default, the training script:\n\n- Trains for SQuAD v1.1 dataset.\n- Trains on BERT Large Model.\n- Uses 8 GPUs and batch size of 10 on each GPU.\n- Has FP16 precision enabled.\n- Is XLA enabled.\n- Runs for 2 epochs.\n- Saves a checkpoint every 1000 iterations (keeps only the latest checkpoint) and at the end of training. All checkpoints, evaluation results and training logs are saved to the `/results` directory (in the container which can be mounted to a local directory).\n- Evaluation is done at the end of training. To skip evaluation, modify `--do_predict` to `False`.\n\nThis script outputs checkpoints to the `/results` directory, by default, inside the container. Mount point of `/results` can be changed in the `scripts/docker/launch.sh` file. The training log contains information about:\n- Loss for the final step\n- Training and evaluation performance\n- F1 and exact match score on the Dev Set of SQuAD after evaluation.\n\nThe summary after training is printed in the following format:\n```bash\nI0312 23:10:45.137036 140287431493376 run_squad.py:1332] 0 Total Training Time = 3007.00 Training Time W/O start up overhead = 2855.92 Sentences processed = 175176\nI0312 23:10:45.137243 140287431493376 run_squad.py:1333] 0 Training Performance = 61.3378 sentences/sec\nI0312 23:14:00.550846 140287431493376 run_squad.py:1396] 0 Total Inference Time = 145.46 Inference Time W/O start up overhead = 131.86 Sentences processed = 10840\nI0312 23:14:00.550973 140287431493376 run_squad.py:1397] 0 Inference Performance = 82.2095 sentences/sec\n{\"exact_match\": 83.69914853358561, \"f1\": 90.8477003317459}\n```\n\nMulti-GPU training is enabled with the Horovod TensorFlow module. The following example runs fine tuning on 8 GPUs:\n\n```bash\nBERT_DIR=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16\nSQUAD_DIR=data/download/squad/v1.1\nmpi_command=\"mpirun -np 8 -H localhost:8 \\\n --allow-run-as-root -bind-to none -map-by slot \\\n -x NCCL_DEBUG=INFO \\\n -x LD_LIBRARY_PATH \\\n -x PATH -mca pml ob1 -mca btl ^openib\" \\\n python run_squad.py --horovod --vocab_file=$BERT_DIR/vocab.txt \\\n --bert_config_file=$BERT_DIR/bert_config.json \\\n --output_dir=/results --do_train --train_file=$SQUAD_DIR/train-v1.1.json\n```\n\n#### Multi-node\n\nMulti-node runs can be launched on a pyxis/enroot Slurm cluster (see [Requirements](#requirements)) with the `run.sub` script with the following command for a 4-node DGX1 example for both phase 1 and phase 2:\n```\nBATCHSIZE=16 LEARNING_RATE='1.875e-4' NUM_ACCUMULATION_STEPS=128 PHASE=1 sbatch -N4 --ntasks-per-node=8 run.sub\nBATCHSIZE=2 LEARNING_RATE='1.25e-4' NUM_ACCUMULATION_STEPS=512 PHASE=1 sbatch -N4 --ntasks-per-node=8 run.sub\n```\n\nCheckpoint after phase 1 will be saved in `checkpointdir` specified in `run.sub`. The checkpoint will be automatically picked up to resume training on phase 2. Note that phase 2 should be run after phase 1.\n\nVariables to re-run the [Training performance results](#training-performance-results) are available in the `scripts/configs/configurations.yml` file.\n\nThe batch variables `BATCHSIZE`, `LEARNING_RATE`, `NUM_ACCUMULATION_STEPS` refer to the Python arguments `train_batch_size`, `learning_rate`, `num_accumulation_steps` respectively.\nThe variable `PHASE` refers to phase specific arguments available in `run.sub`.\n\nNote that the `run.sub` script is a starting point that has to be adapted depending on the environment. In particular, variables such as `datadir` handle the location of the files for each phase.\n\nRefer to the files contents to see the full list of variables to adjust for your system.\n\n### Inference process\n\nInference on a fine tuned Question Answering system is performed using the `run_squad.py` script along with parameters defined in `scripts/run_squad_inference.sh`. Inference is supported on a single GPU.\n\nThe `run_squad_inference.sh` script trains a model and performs evaluation on the SQuAD dataset. By default, the inferencing script:\n\n- Uses SQuAD v1.1 dataset\n- Has FP16 precision enabled\n- Is XLA enabled\n- Evaluates the latest checkpoint present in `/results` with a batch size of 8\n\nThis script outputs predictions file to `/results/predictions.json` and computes F1 score and exact match score using SQuAD's evaluate file. Mount point of `/results` can be changed in the `scripts/docker/launch.sh` file.\n\nThe output log contains information about:\nInference performance\nInference Accuracy (F1 and exact match scores) on the Dev Set of SQuAD after evaluation.\n\nThe summary after inference is printed in the following format:\n```bash\nI0312 23:14:00.550846 140287431493376 run_squad.py:1396] 0 Total Inference Time = 145.46 Inference Time W/O start up overhead = 131.86 Sentences processed = 10840\nI0312 23:14:00.550973 140287431493376 run_squad.py:1397] 0 Inference Performance = 82.2095 sentences/sec\n{\"exact_match\": 83.69914853358561, \"f1\": 90.8477003317459}\n```\n\n### Inference Process With TensorRT\nNVIDIA TensorRT is a platform for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications. More information on how to perform inference using TensorRT can be found in the subfolder [./https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT/trt/README.md](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT/trt/README.md)\n\n### Deploying the BERT model using Triton Inference Server\n\nThe [NVIDIA Triton Inference Server](https://github.com/NVIDIA/triton-inference-server) provides a datacenter and cloud inferencing solution optimized for NVIDIA GPUs. The server provides an inference service via an HTTP or gRPC endpoint, allowing remote clients to request inferencing for any number of GPU or CPU models being managed by the server. More information on how to perform inference using `Triton Inference Server` can be found in the subfolder `./triton/README.md`.\n\n### BioBERT\n\nMany works, including [BioBERT](https://arxiv.org/pdf/1901.08746.pdf), [SciBERT](https://arxiv.org/pdf/1903.10676.pdf), [NCBI-BERT](https://arxiv.org/pdf/1906.05474.pdf), [ClinicalBERT (MIT)](https://arxiv.org/pdf/1904.03323.pdf), [ClinicalBERT (NYU, Princeton)](https://arxiv.org/pdf/1904.05342.pdf), and others at [BioNLP'19 workshop](https://aclweb.org/aclwiki/BioNLP_Workshop), show that pre-training of BERT on large biomedical text corpus such as [PubMed](https://www.ncbi.nlm.nih.gov/pubmed/) results in better performance in biomedical text-mining tasks.\n\nMore information on how to download a biomedical corpus and pre-train as well as finetune for biomedical tasks can be found in the subfolder `./biobert/README.md`.","hasAdvanced","performance","## Performance\n\n### Benchmarking\nThe following section shows how to run benchmarks measuring the model performance in training and inference modes.\n\nBenchmarking can be performed for both training and inference. Both scripts run the BERT model for fine tuning. You can specify whether benchmarking is performed in FP16 or FP32 by specifying it as an argument to the benchmarking scripts. \n\nBoth of these benchmarking scripts enable you to run a number of epochs and extract performance numbers.\n\n#### Training performance benchmark\nTraining benchmarking can be performed by running the script: \n```bash\nscripts/finetune_train_benchmark.sh squad \n```\n\n#### Inference performance benchmark\nInference benchmarking can be performed by running the script: \n```bash\nscripts/finetune_inference_benchmark.sh squad

\n```\n\n### Results\nThe following sections provide details on how we achieved our performance and accuracy in training and inference for Question Answering fine tuning.\n#### Training accuracy results\nOur results were obtained by running the `run_squad.py` training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-1 with 8x V100 32G GPUs.\n\n\n| **Number of GPUs** | **Batch size per GPU** | **Training time with FP16 (Hrs)** | **Training time with FP32 (Hrs)** |\n|:---:|:---:|:----:|:----:|\n| 8 | 4 |0.51|0.77|\n\n##### Training stability test\nThe following tables compare `F1` scores across 5 different training runs with different seeds, for both FP16 and FP32 respectively. The runs showcase consistent convergence on all 5 seeds with very little deviation.\n\n| **FP16, 8x GPUs** | **seed #1** | **seed #2** | **seed #3** | **seed #4** | **seed #5** | **mean** | **std** |\n|:-----------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|\n|F1 |91.16|90.69|90.99|90.94|91.17|90.99|0.196|\n|Exact match|84.2 |83.68|84.14|83.95|84.34|84.06|0.255|\n\n| **FP32, 8x GPUs** | **seed #1** | **seed #2** | **seed #3** | **seed #4** | **seed #5** | **mean** | **std** |\n|:-----------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|\n|F1 |90.67|90.8 |90.94|90.83|90.93|90.83|0.11 |\n|Exact match|83.56|83.96|83.99|83.95|84.12|83.92|0.21 |\n\n\n#### Training performance results\nOur results were obtained by running batch sizes up to 3x GPUs on a 16GB V100 and up to 10x GPUs on a 32G V100 with mixed precision.\n\n##### NVIDIA DGX-1 (8x V100 16G)\nOur results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. Performance numbers (in tokens per second) were averaged over an entire training epoch.\n\n\n| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |\n|:---:|:---:|:------:|:-----:|:----:|:----:|:----:|\n| 1 | 2 | 7.41 |11.86|1.6 |1.0 |1.0 |\n| 4 | 2 |23.699|35.34|1.49|3.2 |2.98|\n| 8 | 2 |44.29 |64.96|1.47|5.98|5.48|\n\n\n| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |\n|:---:|:---:|:-----:|:-----:|:---:|:---:|:----:|\n| 1 | 3 | - |14.86| - | - |1.0 |\n| 4 | 3 | - |44.17| - | - |2.97|\n| 8 | 3 | - |78.89| - | - |5.3 |\n\nNote: The respective values for FP32 runs that use a batch size of 3 are not available due to out of memory errors that arise. Batch size of 3 is only available on using FP16.\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.\n\n##### NVIDIA DGX-1 (8x V100 32G)\nOur results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-1 with 8x V100 32G GPUs. Performance numbers (in sentences per second) were averaged over an entire training epochs.\n\n\n| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |\n|---|---|-----|-----|----|----|----|\n| 1 | 4 | 8.55|18.14|2.12|1.0 |1.0 |\n| 4 | 4 |32.13|52.85|1.64|3.76|2.91|\n| 8 | 4 |62.83|95.28|1.51|7.35|5.25|\n\n\n| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |\n|---|---|-----|-------|---|---|----|\n| 1 | 10| - | 27.69 | - | - |1.0 |\n| 4 | 10| - | 85.193| - | - |3.07|\n| 8 | 10| - |142.69 | - | - |5.1 |\n\n\nNote: The respective values for FP32 runs that use a batch size of 10 are not available due to out of memory errors that arise. Batch size of 10 is only available on using FP16.\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. \n\n##### NVIDIA DGX-2 (16x V100 32G)\nOur results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-2 with 16x V100 32G GPUs. Performance numbers (in sentences per second) were averaged over an entire training epoch.\n\n\n| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |\n|---|---|------|------|----|-----|----|\n| 1| 4 | 8.80| 17.43|1.98| 1.0 |1.0 |\n| 4| 4 | 33.22| 56.87|1.71| 3.78|3.26|\n| 8| 4 | 64.46|100.58|1.56| 7.33|5.77|\n| 16| 4 |117.83|162.29|1.38|13.39|9.31|\n\n\n| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |\n|---|---|---|------|---|---|----|\n| 1| 10| - | 28.72| - | - |1.0 |\n| 4| 10| - | 92.73| - | - |3.22|\n| 8| 10| - |155.78| - | - |5.42|\n| 16| 10| - |228.75| - | - |7.96|\n\n\nNote: The respective values for FP32 runs that use a batch size of 10 are not available due to out of memory errors that arise. Batch size of 10 is only available on using FP16.\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. \n#### Inference performance results\n\n##### NVIDIA DGX-1 16G (1x V100 16G)\nOur results were obtained by running the `scripts/run_squad_inference.sh` training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPUs. Performance numbers (in sentences per second) were averaged over an entire training epoch.\n\n| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speedup** |\n|---|---|-----|------|----|\n| 1 | 8 |41.04|112.55|2.74|\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.\n\n\n##### NVIDIA DGX-1 32G (1x V100 32G)\nOur results were obtained by running the `scripts/run_squad_inference.sh` training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-1 with 1x V100 32G GPUs. Performance numbers (in sentences per second) were averaged over an entire training epoch.\n\n| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speedup** |\n|---|---|-----|------|----|\n| 1 | 8 |36.78|118.54|3.22|\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.\n\n##### NVIDIA DGX-2 32G (1x V100 32G)\nOur results were obtained by running the `scripts/run_squad_inference.sh` training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-2 with 1x V100 32G GPUs. Performance numbers (in sentences per second) were averaged over an entire training epoch.\n\n| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speedup** |\n|---|---|-----|------|----|\n| 1 | 8 |33.95|108.45|3.19|\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.","hasPerformance","policies",[],{"_195":246,"_196":247,"_144":-5,"_151":26,"_153":26,"_167":245,"_154":64},{"_195":244,"_196":244,"_144":-5,"_151":26,"_153":26,"_167":245,"_154":64},{"_195":242,"_196":242,"_144":-5,"_151":26,"_153":26,"_167":243,"_154":64},{"_195":240,"_196":240,"_144":-5,"_151":26,"_153":26,"_167":241,"_154":64},{"_195":238,"_196":238,"_144":-5,"_151":26,"_153":26,"_167":239,"_154":64},{"_195":236,"_196":236,"_144":-5,"_151":26,"_153":26,"_167":237,"_154":64},{"_195":234,"_196":234,"_144":-5,"_151":26,"_153":26,"_167":235,"_154":64},{"_195":232,"_196":232,"_144":-5,"_151":26,"_153":26,"_167":233,"_154":64},{"_195":230,"_196":230,"_144":-5,"_151":26,"_153":26,"_167":231,"_154":64},{"_195":228,"_196":228,"_144":-5,"_151":26,"_153":26,"_167":229,"_154":64},{"_195":226,"_196":226,"_144":-5,"_151":26,"_153":26,"_167":227,"_154":64},{"_195":224,"_196":224,"_144":-5,"_151":26,"_153":26,"_167":225,"_154":64},{"_195":222,"_196":222,"_144":-5,"_151":26,"_153":26,"_167":223,"_154":64},{"_195":220,"_196":220,"_144":-5,"_151":26,"_153":26,"_167":221,"_154":64},{"_195":218,"_196":218,"_144":-5,"_151":26,"_153":26,"_167":219,"_154":64},{"_195":216,"_196":216,"_144":-5,"_151":26,"_153":26,"_167":217,"_154":64},{"_195":214,"_196":214,"_144":-5,"_151":26,"_153":26,"_167":215,"_154":64},{"_195":212,"_196":212,"_144":-5,"_151":26,"_153":26,"_167":213,"_154":64},{"_195":210,"_196":210,"_144":-5,"_151":26,"_153":26,"_167":211,"_154":64},{"_195":208,"_196":208,"_144":-5,"_151":26,"_153":26,"_167":209,"_154":64},{"_195":206,"_196":206,"_144":-5,"_151":26,"_153":26,"_167":207,"_154":64},{"_195":204,"_196":204,"_144":-5,"_151":26,"_153":26,"_167":205,"_154":64},{"_195":202,"_196":202,"_144":-5,"_151":26,"_153":26,"_167":203,"_154":64},{"_195":200,"_196":200,"_144":-5,"_151":26,"_153":26,"_167":201,"_154":64},{"_195":198,"_196":198,"_144":-5,"_151":26,"_153":26,"_167":199,"_154":64},{"_195":14,"_196":14,"_144":-5,"_151":26,"_153":26,"_167":197,"_154":64},"id","name",[],"2",[],"3",[],"4",[],"5",[],"6",[],"7",[],"20.03.0",[],"20.06.0",[],"20.06.1",[],"20.06.2",[],"20.06.3",[],"20.06.5",[],"20.06.4",[],"20.06.6",[],"20.06.7",[],"20.06.8",[],"20.06.9",[],"20.06.10",[],"20.06.11",[],"20.06.12",[],"20.06.14",[],"20.06.15",[],"20.06.16",[],"20.06.17",[],"-","Latest (20.06.17)","nvidia/bert_for_tensorflow","key","nvidia/resources/bert_for_tensorflow","resourceId","bert_for_tensorflow","shortDescription","BERT is a method of pre-training language representations which obtains state-of-the-art results on a wide array of NLP tasks.","BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) paper. NVIDIA's BERT is an optimized version of [Google's official implementation](https://github.com/google-research/bert), leveraging mixed precision arithmetic and Tensor Cores on A100, V100 and T4 GPUs for faster training times while maintaining target accuracy.\n\nOther publicly available implementations of BERT include:\n1. [NVIDIA PyTorch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BERT)\n2. [Hugging Face](https://github.com/huggingface/pytorch-pretrained-BERT)\n3. [codertimo](https://github.com/codertimo/BERT-pytorch)\n4. [gluon-nlp](https://github.com/dmlc/gluon-nlp/tree/v0.10.x/scripts/bert)\n5. [Google's official implementation](https://github.com/google-research/bert)\n\nThis model is trained with mixed precision using Tensor Cores on NVIDIA Volta, Ampere and Turing GPUs. Therefore, researchers can get results up to 4x 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\nBERT's model architecture is a multi-layer bidirectional Transformer encoder. Based on the model size, we have the following two default configurations of BERT:\n\n| **Model** | **Hidden layers** | **Hidden unit size** | **Attention heads** | **Feedforward filter size** | **Max sequence length** | **Parameters** |\n|:---------:|:----------:|:----:|:---:|:--------:|:---:|:----:|\n|BERTBASE |12 encoder| 768| 12|4 x 768|512|110M|\n|BERTLARGE|24 encoder|1024| 16|4 x 1024|512|330M|\n\nBERT training consists of two steps, pre-training the language model in an unsupervised fashion on vast amounts of unannotated datasets, and then using this pre-trained model for fine-tuning for various NLP tasks, such as question and answer, sentence classification, or sentiment analysis. Fine-tuning typically adds an extra layer or two for the specific task and further trains the model using a task-specific annotated dataset, starting from the pre-trained backbone weights. The end-to-end process in depicted in the following image:\n\n![](https://github.com/NVIDIA/DeepLearningExamples/raw/master/TensorFlow/LanguageModeling/BERT/data/images/bert_pipeline.png?raw=true)\n\n\nFigure 1: BERT Pipeline\n\n### Default configuration\n\nThis repository contains scripts to interactively launch data download, training, benchmarking and inference routines in a Docker container for both pre-training and fine tuning for Question Answering. The major differences between the official implementation of the paper and our version of BERT are as follows:\n\n- Mixed precision support with TensorFlow Automatic Mixed Precision (TF-AMP), which enables mixed precision training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an environmental variable.\n- Scripts to download dataset for:\n - Pre-training - [Wikipedia](https://dumps.wikimedia.org/), [BookCorpus](http://yknzhu.wixsite.com/mbweb)\n - Fine tuning - [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) (Stanford Question Answering Dataset)\n - Fine tuning - [GLUE](https://gluebenchmark.com/) (The General Language Understanding Evaluation benchmark)\n - Pretrained weights from Google\n- Custom fused CUDA kernels for faster computations\n- Multi-GPU/Multi-node support using Horovod\n\nThe following performance optimizations were implemented in this model:\n- [XLA](https://www.tensorflow.org/xla) support (experimental).\n\nThese techniques and optimizations improve model performance and reduce training time, allowing you to perform various NLP tasks with no additional effort.\n\n### Feature support matrix\n\nThe following features are supported by this model.\n\n| **Feature** | **BERT** |\n|:-----------------------:|:--------------------------:|\n| Horovod Multi-GPU | Yes |\n| Horovod Multi-Node | Yes |\n| Automatic mixed precision (AMP) | Yes |\n| LAMB | Yes |\n\n#### Features\n\nMulti-GPU training with Horovod - Our model uses Horovod to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the [TensorFlow tutorial](https://github.com/horovod/horovod/#usage)\n\n[LAMB](https://arxiv.org/pdf/1904.00962.pdf) stands for Layerwise Adaptive Moments based optimizer, is a large batch optimization technique that helps accelerate training of deep neural networks using large minibatches. It allows using a global batch size of 65536 and 32768 on sequence lengths 128 and 512 respectively, compared to a batch size of 256 for Adam. The optimized implementation accumulates 1024 gradient batches in phase 1 and 4096 steps in phase 2 before updating weights once. This results in 27% training speedup on a single DGX2 node. On multi-node systems, LAMB allows scaling up to 1024 GPUs resulting in training speedups of up to 17x in comparison to [Adam](https://arxiv.org/pdf/1412.6980.pdf). Adam has limitations on the learning rate that can be used since it is applied globally on all parameters whereas LAMB follows a layerwise learning rate strategy.\n\nNVLAMB adds necessary tweaks to [LAMB version 1](https://arxiv.org/abs/1904.00962v1), to ensure correct convergence. A guide to implementating the LAMB optimizer can be found in our [article](https://medium.com/@NvidiaAI/a-guide-to-optimizer-implementation-for-bert-at-scale-8338cc7f45fd) on Medium.com. The algorithm is as follows:\n ![NVLAMB](https://github.com/NVIDIA/DeepLearningExamples/raw/master/TensorFlow/LanguageModeling/BERT/data/images/images_nvlamb.png)\n\n\n### Mixed precision training\n\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 Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using [mixed precision training](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) previously required two steps:\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 [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code. AMP enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically. The 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, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\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/performance/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- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.\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 in TensorFlow by using the Automatic Mixed Precision (TF-AMP) extension which casts variables to half-precision upon retrieval, while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling) step must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by using simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed precision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations. First, programmers need not modify network model code, reducing development and maintenance effort. Second, 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#### 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.\n\n### Glossary\n\n**Fine-tuning**\nTraining an already pretrained model further using a task specific dataset for subject-specific refinements, by adding task-specific layers on top if required.\n\n**Language Model**\nAssigns a probability distribution over a sequence of words. 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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.","linkedin.com",[486],{"_196":487,"_441":484,"_442":26,"_142":488},"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. 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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.","demdex.net",[507],{"_196":508,"_441":505,"_442":26,"_142":509},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[512],{"_196":513,"_441":510,"_442":26,"_142":514},"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",[517],{"_196":518,"_441":515,"_442":26,"_142":478},"R","6sc.co",[521],{"_196":522,"_441":519,"_442":26,"_142":523},"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 may store a unique ID so that our system will remember you when you return. 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