Transformer-LT MLPerf FP32 Training TensorFlow* Model Package

Download Command

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/transformer-mlperf-fp32-training.tar.gz

Description

This document has instructions to run a Transformer Language FP32 training in MLPerf* benchmark suite using Intel® Optimization for TensorFlow*. Detailed information on MLPerf benchmark can be found in mlperf/training.

Datasets

Decide the problem you want to run to get the appropriate dataset. We will get the training data of it as an example:

Download dataset for computing BLEU score.

export DATASET_DIR=/home/<user>/transformer_data
mkdir $DATASET_DIR && cd $DATASET_DIR
wget https://nlp.stanford.edu/projects/nmt/data/wmt14.en-de/newstest2014.en
wget https://nlp.stanford.edu/projects/nmt/data/wmt14.en-de/newstest2014.de

For the training dataset, download and untar the model package.

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/transformer-mlperf-fp32-training.tar.gz
tar -xzf transformer-mlperf-fp32-training.tar.gz

export PYTHONPATH=$PYTHONPATH:/home/<user>/transformer-mlperf-fp32-training/models/common/tensorflow
export DATASET_DIR=/home/<user>/transformer_data
    
cd /home/<user>/transformer-mlperf-fp32-training/models/language_translation/tensorflow/transformer_mlperf/training/fp32/transformer
python data_download.py --data_dir=$DATASET_DIR

Running python data_download.py --data_dir=$DATASET_DIR assumes you have a Python* environment similar to what the intel/intel-optimized-tensorflow:2.4.0-ubuntu-18.04 container provides. One option would be to run the above within the intel/intel-optimized-tensorflow:2.4.0-ubuntu-18.04 container eg: docker run -u $(id -u):$(id -g) --privileged --entrypoint /bin/bash -v /home/:/home/ -it intel/intel-optimized-tensorflow:2.4.0-ubuntu-18.04

Quick Start Scripts

Transformer Language in MLPerf benchmark can run with full training or fewer training steps. During training we can control if it will do the evaluation or not.

Bare Metal

To run on bare metal, the following prerequisites must be installed in your environment:

• Python* 3
• intel-tensorflow
• numactl

After installing the prerequisites, download and untar the model package. Set environment variables for the path to your DATASET_DIR and an OUTPUT_DIR where log files will be written, then run a quickstart script.

DATASET_DIR=<path to the dataset>
OUTPUT_DIR=<directory where log files will be written>

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/transformer-mlperf-fp32-training.tar.gz
tar -xzf transformer-mlperf-fp32-training.tar.gz
cd transformer-mlperf-fp32-training

quickstart/fp32_training.sh (or quickstart/fp32_training_demo.sh)

For training in multi-instance mode (2 sockets in a single node for example) in evaluation mode, where we are "saving checkpoints" and "doing the evaluation", the following prerequisites must be installed in your environment:

• gcc-8
• g++-8
• libopenmpi-dev
• Open MPI
• OpenSSH
• Horovod*

Set environment variables for the path to your DATASET_DIR and an OUTPUT_DIR where log files will be written, then run a quickstart script.

DATASET_DIR=<path to the dataset>
OUTPUT_DIR=<directory where log files will be written>

cd transformer-mlperf-fp32-training
quickstart/fp32_training_mpirun.sh

Documentation and Sources

Get Started​
Main GitHub*
Readme
Release Notes
Get Started Guide

Code Sources
Report Issue

License Agreement

LEGAL NOTICE: By accessing, downloading or using this software and any required dependent software (the “Software Package”), you agree to the terms and conditions of the software license agreements for the Software Package, which may also include notices, disclaimers, or license terms for third party software included with the Software Package. Please refer to the license file for additional details.

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Transformer-LT MLPerf FP32 Training TensorFlow* Container