intel-extension-for-transformers 1.1

Repository of Intel® Intel Extension for Transformers

Intel® Extension for Transformers

An innovative toolkit to accelerate Transformer-based models on Intel platforms

Architecture   |   NeuralChat   |   Cpp inference   |   Examples   |   Documentations

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Intel® Extension for Transformers is an innovative toolkit to accelerate Transformer-based models on Intel platforms, in particular effective on 4th Intel Xeon Scalable processor Sapphire Rapids (codenamed Sapphire Rapids). The toolkit provides the below key features and examples:

• Seamless user experience of model compressions on Transformer-based models by extending Hugging Face transformers APIs and leveraging Intel® Neural Compressor

• Advanced software optimizations and unique compression-aware runtime (released with NeurIPS 2022's paper Fast Distilbert on CPUs and QuaLA-MiniLM: a Quantized Length Adaptive MiniLM, and NeurIPS 2021's paper Prune Once for All: Sparse Pre-Trained Language Models)

• Optimized Transformer-based model packages such as Stable Diffusion, GPT-J-6B, GPT-NEOX, BLOOM-176B, T5, Flan-T5 and end-to-end workflows such as SetFit-based text classification and document level sentiment analysis (DLSA)

• NeuralChat, a custom Chatbot trained on Intel CPUs through parameter-efficient fine-tuning PEFT on domain knowledge

• CPP Inference, inference of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels. It already enabled GPT-NEOX, LLAMA-7B, MPT-7B and FALCON-7B

Installation

Install from Pypi

pip install intel-extension-for-transformers

For more installation method, please refer to Installation Page

Getting Started

Sentiment Analysis with Quantization

Prepare Dataset

from datasets import load_dataset, load_metric
from transformers import AutoConfig,AutoModelForSequenceClassification,AutoTokenizer

raw_datasets = load_dataset("glue", "sst2")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
raw_datasets = raw_datasets.map(lambda e: tokenizer(e['sentence'], truncation=True, padding='max_length', max_length=128), batched=True)

Quantization

from intel_extension_for_transformers.optimization import QuantizationConfig, metrics, objectives
from intel_extension_for_transformers.optimization.trainer import NLPTrainer

config = AutoConfig.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english",num_labels=2)
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english",config=config)
model.config.label2id = {0: 0, 1: 1}
model.config.id2label = {0: 'NEGATIVE', 1: 'POSITIVE'}
# Replace transformers.Trainer with NLPTrainer
# trainer = transformers.Trainer(...)
trainer = NLPTrainer(model=model, 
    train_dataset=raw_datasets["train"], 
    eval_dataset=raw_datasets["validation"],
    tokenizer=tokenizer
)
q_config = QuantizationConfig(metrics=[metrics.Metric(name="eval_loss", greater_is_better=False)])
model = trainer.quantize(quant_config=q_config)

input = tokenizer("I like Intel Extension for Transformers", return_tensors="pt")
output = model(**input).logits.argmax().item()

For more quick samples, please refer to Get Started Page. For more validated examples, please refer to Support Model Matrix

Validated Performance

Model	FP32	BF16	INT8
EleutherAI/gpt-j-6B	4163.67 (ms)	1879.61 (ms)	1612.24 (ms)
CompVis/stable-diffusion-v1-4	10.33 (s)	3.02 (s)	N/A

Note*: GPT-J-6B software/hardware configuration please refer to text-generation. Stable-diffusion software/hardware configuration please refer to text-to-image

Documentation

OVERVIEW
Model Compression		NeuralChat		Neural Engine		Kernel Libraries
MODEL COMPRESSION
Quantization		Pruning		Distillation		Orchestration
Neural Architecture Search		Export		Metrics/Objectives		Pipeline
NEURAL ENGINE
Model Compilation		Custom Pattern		Deployment		Profiling
KERNEL LIBRARIES
Sparse GEMM Kernels		Custom INT8 Kernels		Profiling		Benchmark
ALGORITHMS
Length Adaptive				Data Augmentation
TUTORIALS AND RESULTS
Tutorials		Supported Models		Model Performance		Kernel Performance

Selected Publications/Events

• Arxiv: An Efficient Sparse Inference Software Accelerator for Transformer-based Language Models on CPUs (June 2023)
• Blog published on Medium: Simplify Your Custom Chatbot Deployment (June 2023)
• Blog published on Medium: Create Your Own Custom Chatbot (April 2023)
• Blog of Tech-Innovation Artificial-Intelligence(AI): Intel® Xeon® Processors Are Still the Only CPU With MLPerf Results, Raising the Bar By 5x - Intel Communities (April 2023)
• Blog published on Medium: MLefficiency — Optimizing transformer models for efficiency (Dec 2022)
• NeurIPS'2022: Fast Distilbert on CPUs (Nov 2022)
• NeurIPS'2022: QuaLA-MiniLM: a Quantized Length Adaptive MiniLM (Nov 2022)
• Blog published by Cohere: Top NLP Papers—November 2022 (Nov 2022)
• Blog published by Alibaba: Deep learning inference optimization for Address Purification (Aug 2022)
• NeurIPS'2021: Prune Once for All: Sparse Pre-Trained Language Models (Nov 2021)

Additional Content

• Release Information
• Contribution Guidelines
• Legal Information
• Security Policy

Research Collaborations

Welcome to raise any interesting research ideas on model compression techniques and feel free to reach us (maintainers). Look forward to our collaborations on Intel Extension for Transformers!