dlsa

E2E DLSA Pipeline

1. Introduction
2. Get Started

Introduction

DLSA is a representative E2E Fine-Tuning & Inference NLP workload with Sentiment Analysis Task on Intel® Extension for Transformers.

The DLSA workload code uses Intel® Extension for Transformers toolkit to run an end-to-end deep learning workload, which will analyze the sentiment of any input English sentence or paragraph.

It consists of load data, tokenization + feature extraction, load model, fine tuning, model quantization and inference.

Validated model list

Dataset	Model	Fine-tuning	Quantization	Inference
SST-2	distilbert-base-uncased	✔	✔	✔
SST-2	bert-large-uncased	TODO	TODO	TODO
IMDb	distilbert-base-uncased	✔	✔	✔
IMDb	bert-large-uncased	TODO	TODO	TODO

Get Started

Deploy the test environment

Create a new python environment

conda create -n <env name> python=3.10
conda activate <env name>

Install the ITREX

pip install intel-extension-for-transformers

Install package for examples

cd <intel_extension_for_transformers_folder>/workflows/dlsa
pip install -r requirements.txt

Running the E2E DLSA pipeline

The shell script run_dlsa.sh provides running E2E DLSA pipeline with one-click.

bash ./run_dlsa.sh

By default, it will launch one instance to run E2E DLSA pipeline which includes load data, tokenization + feature extraction, load model, fine-tuning, model quantization and inference, then print the elapsed time of each phase. It use SST-2 dataset and conduct inference for both default FP32 model and ITREX quantized INT8 model. You can also pass the parameters when running the script.

Below is the help message by using the command bash ./run_dlsa.sh -h:

Usage: run_dlsa.sh [OPTIONS]
OPTION includes:
   -l | --log_name - the log name of this round
   -d | --dataset - [imdb|sst2] whether to use imdb or sst2 DATASET
   -s | --sequence_len - max sequence length
   -o | --output_dir - output dir
   -m | --model - the input model name or path
   --train_batch_size - batch size for fine-tuning
   --eval_batch_size - batch size for inference
   -h | --help - displays this message