DeepLearningExamples/TensorFlow/Translation/GNMT

GNMT v2 For TensorFlow

This repository provides a script and recipe to train GNMT v2 to achieve state of the art accuracy, and is tested and maintained by NVIDIA.

Table Of Contents

• The model
  • Default configuration
• Setup
  • Requirements
• Quick Start Guide
• Details
  • Command line arguments
  • Getting the data
  • Training process
  • Enabling mixed precision
  • Inference process
• Results
  • Training accuracy results
    • Training stability test
  • Training performance results
  • Inference performance results
• Changelog
• Known issues

The model

The GNMT v2 model is similar to the one discussed in the Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation paper.

The most important difference between the two models is in the attention mechanism. In our model, the output from the first LSTM layer of the decoder goes into the attention module, then the re-weighted context is concatenated with inputs to all subsequent LSTM layers in the decoder at the current timestep.

The same attention mechanism is also implemented in the default GNMT-like models from TensorFlow Neural Machine Translation Tutorial and NVIDIA OpenSeq2Seq Toolkit.

Default configuration

The following features were implemented in this model:

• general:
  • encoder and decoder are using shared embeddings
  • data-parallel multi-gpu training
  • dynamic loss scaling with backoff for Tensor Cores (mixed precision) training
  • trained with label smoothing loss (smoothing factor 0.1)
• encoder:
  • 4-layer LSTM, hidden size 1024, first layer is bidirectional, the rest are unidirectional
  • with residual connections starting from 3rd layer
  • dropout is applied on input to all LSTM layers, probability of dropout is set to 0.2
  • hidden state of LSTM layers is initialized with zeros
  • weights and bias of LSTM layers is initialized with uniform(-0.1, 0.1) distribution
• decoder:
  • 4-layer unidirectional LSTM with hidden size 1024 and fully-connected classifier
  • with residual connections starting from 3rd layer
  • dropout is applied on input to all LSTM layers, probability of dropout is set to 0.2
  • hidden state of LSTM layers is initialized with the last hidden state from encoder
  • weights and bias of LSTM layers is initialized with uniform(-0.1, 0.1) distribution
  • weights and bias of fully-connected classifier is initialized with uniform(-0.1, 0.1) distribution
• attention:
  • normalized Bahdanau attention
  • output from first LSTM layer of decoder goes into attention, then re-weighted context is concatenated with the input to all subsequent LSTM layers of the decoder at the current timestep
  • linear transform of keys and queries is initialized with uniform(-0.1, 0.1), normalization scalar is initialized with 1.0 / sqrt(1024), normalization bias is initialized with zero
• inference:
  • beam search with default beam size of 5
  • with coverage penalty and length normalization, coverage penalty factor is set to 0.1, length normalization factor is set to 0.6 and length normalization constant is set to 5.0
  • de-tokenized BLEU computed by SacreBLEU
  • motivation for choosing SacreBLEU

When comparing the BLEU score, there are various tokenization approaches and BLEU calculation methodologies; therefore, ensure you align similar metrics.

Code from this repository can be used to train a larger, 8-layer GNMT v2 model. Our experiments show that a 4-layer model is significantly faster to train and yields comparable accuracy on the public WMT16 English-German dataset. The number of LSTM layers is controlled by the --num_layers parameter in the nmt.py script.

Setup

The following section list the requirements in order to start training the GNMT v2 model.

Requirements

This repository contains Dockerfile which extends the TensorFlow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:

• NVIDIA Docker
• TensorFlow 19.03-py3 NGC container
• NVIDIA Volta based GPU

For more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning DGX Documentation:

• Getting Started Using NVIDIA GPU Cloud,
• Accessing And Pulling From The NGC container registry,
• Running TensorFlow.

Quick Start Guide

To train your model using mixed precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the GNMT v2 model on the WMT16 English German dataset.

1. Clone the repository.

git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/TensorFlow/Translation/GNMT

2. Build the GNMT v2 container.

bash scripts/docker/build.sh

3. Start an interactive session in the container to run training/inference.

bash scripts/docker/interactive.sh

4. Download and preprocess the dataset.

Data will be downloaded to the data directory (on the host). The data directory is mounted to the /workspace/gnmt/data location in the Docker container.

bash scripts/wmt16_en_de.sh

5. Start training.

All results and logs are saved to the results directory (on the host) or to the /workspace/gnmt/results directory (in the container). The training script saves checkpoint after every training epoch and after every 2000 training steps within each epoch. You can modify the results directory using --output_dir argument.

To launch mixed precision training on 1 GPU, run:

python nmt.py --output_dir=results --batch_size=192 --learning_rate=8e-4

To launch mixed precision training on 8 GPUs, run:

python nmt.py --output_dir=results --batch_size=1536 --num_gpus=8 --learning_rate=2e-3

To launch FP32 training on 1 GPU, run:

python nmt.py --output_dir=results --batch_size=128 --learning_rate=5e-4 --use_amp=false

To launch FP32 training on 8 GPUs, run:

python nmt.py --output_dir=results --batch_size=1024 --num_gpus=8 --learning_rate=2e-3 --use_amp=false

6. Start evaluation.

The training process automatically runs evaluation and outputs the BLEU score after each training epoch. Additionally, after the training is done, you can manually run inference on test dataset with the checkpoint saved during the training.

To launch mixed precision inference on 1 GPU, run:

python nmt.py --output_dir=results --infer_batch_size=128 --mode=infer

To launch FP32 inference on 1 GPU, run:

python nmt.py --output_dir=results --infer_batch_size=128 --use_amp=false --mode=infer

Details

The following sections provide greater details of the dataset, running training and inference, and the training results.

Command line arguments

To see the full list of available options and their descriptions, use the -h or --help command line option, for example:

python nmt.py --help

To summarize, the most useful arguments are as follows:

  --learning_rate LEARNING_RATE
                        Learning rate.
  --max_train_epochs MAX_TRAIN_EPOCHS
                        Max number of epochs.
  --data_dir DATA_DIR   Training/eval data directory.
  --output_dir OUTPUT_DIR
                        Store log/model files.
  --batch_size BATCH_SIZE
                        Total batch size.
  --log_step_count_steps LOG_STEP_COUNT_STEPS
                        The frequency, in number of global steps, that the
                        global step and the loss will be logged during training
  --num_gpus NUM_GPUS   Number of gpus in each worker.
  --random_seed RANDOM_SEED
                        Random seed (>0, set a specific seed).
  --ckpt CKPT           Checkpoint file to load a model for inference.
                        (defaults to newest checkpoint)
  --infer_batch_size INFER_BATCH_SIZE
                        Batch size for inference mode.
  --beam_width BEAM_WIDTH
                        beam width when using beam search decoder. If 0,
                        use standard decoder with greedy helper.
  --use_amp USE_AMP     use_amp for training and inference
  --mode {train_and_eval,infer}

Getting the data

The GNMT v2 model was trained on the WMT16 English-German dataset and newstest2014 is used as a testing dataset.

This repository contains the scripts/wmt16_en_de.sh download script which automatically downloads and preprocesses the training and test datasets. By default, data is downloaded to the data directory.

Our download script is very similar to the wmt16_en_de.sh script from the tensorflow/nmt repository. Our download script contains an extra preprocessing step, which discards all pairs of sentences which can't be decoded by latin-1 encoder. The scripts/wmt16_en_de.sh script uses the subword-nmt package to segment text into subword units (Byte Pair Encodings - BPE). By default, the script builds the shared vocabulary of 32,000 tokens.

In order to test with other datasets, scripts need to be customized accordingly.

Training process

The training configuration can be launched by running the nmt.py script. By default, the training script saves the checkpoint after every training epoch and after every 2000 training steps within each epoch. Results are stored in the results directory.

The training script launches data-parallel training on multiple GPUs. We have tested reliance on up to 8 GPUs on a single node.

After each training epoch, the script runs an evaluation and outputs a BLEU score on the test dataset (newstest2014). BLEU is computed by the SacreBLEU package. Logs from the training and evaluation are saved to the results directory.

The training script automatically runs testing after each training epoch. The results from the testing are printed to the standard output and saved to the log files.

The summary after each training epoch is printed in the following format:

training time for epoch 1: 29.37 mins (2918.36 sent/sec, 139640.48 tokens/sec)
[...]
bleu is 20.50000
eval time for epoch 1: 1.57 mins (78.48 sent/sec, 4283.88 tokens/sec)

The BLEU score is computed on the test dataset. Performance is reported in total sentences per second and in total tokens per second. The performance result is averaged over an entire training epoch and summed over all GPUs participating in the training.

To view all available options for training, run python nmt.py --help.

Enabling mixed precision

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

1. Porting the model to use the FP16 data type where appropriate.
2. Manually adding loss scaling to preserve small gradient values.

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 and Turing GPUs 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.
• How to access and enable AMP for TensorFlow, see Using TF-AMP from the TensorFlow User Guide.
• Techniques used for mixed precision training, see the Mixed-Precision Training of Deep Neural Networks blog.

Inference process

Inference can be run by launching the nmt.py script, although, it requires a pre-trained model checkpoint and tokenized input.

The script, nmt.py, supports batched inference (--mode=infer flag). By default, it launches beam search with beam size of 5, coverage penalty term and length normalization term. Greedy decoding can be enabled by setting the --beam_width=1 flag for the nmt.py inference script. To control the batch size use the --infer_batch_size flag.

To view all available options for inference, run python nmt.py --help.

Results

The following sections provide details on how we achieved our performance and accuracy in training and inference.

Training accuracy results

Our results were obtained by running the nmt.py script in the tensorflow-19.03-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs.

Commands to launch the training:

for 1 GPUs in mixed precision:
python nmt.py --output_dir=results --batch_size=192 --learning_rate=8e-4

for 8 GPUs in mixed precision:
python nmt.py --output_dir=results --batch_size=1536 --num_gpus=8 --learning_rate=2e-3

for 1 GPUs in FP32:
python nmt.py --output_dir=results --batch_size=128 --learning_rate=5e-4 --use_amp=false

for 8 GPUs in FP32:
python nmt.py --output_dir=results --batch_size=1024 --num_gpus=8 --learning_rate=2e-3 --use_amp=false

Number of GPUs	Mixed precision batch size/GPU	FP32 batch size/GPU	Mixed precision BLEU	FP32 BLEU	Mixed precision training time	FP32 training time
1	192	128	24.58	24.64	789 min	1375 min
8	192	128	24.46	24.51	184 min	262 min

In the following plot, the BLEU scores after each training epoch for different configurations are displayed.

Training stability test

The GNMT v2 model was trained for 6 epochs, starting from 6 different initial random seeds. After each training epoch, the model was evaluated on the test dataset and the BLEU score was recorded. The training was performed in the tensorflow-19.03-py3 NGC container on NVIDIA DGX-1 with 8 Tesla V100 16G GPUs.

In the following table, the BLEU scores after each training epoch for different initial random seeds are displayed.

Epoch	Average	Standard deviation	Minimum	Maximum	Median
1	19.943	0.240	19.670	20.290	19.855
2	21.750	0.197	21.550	22.110	21.690
3	22.408	0.150	22.160	22.630	22.430
4	23.057	0.219	22.770	23.440	22.985
5	23.897	0.142	23.700	24.080	23.915
6	24.243	0.174	24.030	24.460	24.235

Training performance results

Our results were obtained by running the nmt.py 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.

Number of GPUs	Mixed precision batch size/GPU	FP32 batch size/GPU	Mixed precision tokens/s	FP32 tokens/s	Mixed precision speedup	Mixed precision multi-gpu weak scaling	FP32 multi-gpu weak scaling
1	192	128	22 297	12 767	1.74	1.00	1.00
8	192	128	133 992	83 337	1.61	6.01	6.53

To achieve these same results, follow the Quick Start Guide outlined above.

Inference performance results

Our results were obtained by running the scripts/translate.py script in the tensorflow-19.03-py3 NGC container on NVIDIA DGX-1 with a single V100 16G GPUs. The benchmark requires a checkpoint from a fully trained model.

To launch the inference benchmark in mixed precision on 1 GPU, run:

python scripts/inference_benchmark.py --output_dir=/path/to/trained/model --beam_width 1,2,5,10 --infer_batch_size 32,128,512

To launch the inference benchmark in FP32 on 1 GPU, run:

python scripts/inference_benchmark.py --output_dir=/path/to/trained/model --beam_width 1,2,5,10 --infer_batch_size 32,128,512 --use_amp=false

Batch size	Beam size	Mixed precision BLEU	FP32 BLEU	Mixed precision tokens/s	FP32 tokens/s
32	1	23.43	23.47	8180	7555
32	2	24.14	24.13	6870	6359
32	5	24.43	24.47	4729	3991
32	10	24.29	24.29	2344	2495
128	1	23.43	23.47	19343	14810
128	2	24.15	24.13	10267	9956
128	5	24.45	24.47	7009	5224
128	10	24.30	24.29	3873	2855
512	1	23.45	23.47	32325	20782
512	2	24.17	24.13	18917	13702
512	5	24.46	24.47	8789	6036
512	10	24.29	24.29	4684	3149

To achieve these same results, follow the Quick Start Guide outlined above.

Changelog

1. Mar 18, 2019

• Initial release

Known issues

There are no known issues in this release.