Intel® Extension for Transformers

An Innovative Transformer-based Toolkit to Accelerate GenAI/LLM Everywhere

🏭Architecture   |   💬NeuralChat   |   😃Inference   |   💻Examples   |   📖Documentations

🏃Installation

Quick Install from Pypi

pip install intel-extension-for-transformers

For more installation methods, please refer to Installation Page

🌟Introduction

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 customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of plugins Knowledge Retrieval, Speech Interaction, Query Caching, Security Guardrail.

• Inference of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels, supporting GPT-NEOX, LLAMA, MPT, FALCON, BLOOM-7B, OPT, ChatGLM2-6B, GPT-J-6B and Dolly-v2-3B

🌱Getting Started

Below are the sample code to enable weight-only low precision inference. See more examples.

INT4 Inference

from transformers import AutoTokenizer
from intel_extension_for_transformers.transformers import AutoModel, WeightOnlyQuantConfig

model_name = "EleutherAI/gpt-j-6B"
config = WeightOnlyQuantConfig(compute_dtype="int8", weight_dtype="int4")
prompt = "Once upon a time, a little girl"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
inputs = tokenizer(prompt, return_tensors="pt").input_ids

model = AutoModel.from_pretrained(model_name, quantization_config=config)
gen_tokens = model.generate(inputs, max_new_tokens=300)
gen_text = tokenizer.batch_decode(gen_tokens)

INT8 Inference

from transformers import AutoTokenizer
from intel_extension_for_transformers.transformers import AutoModel, WeightOnlyQuantConfig

model_name = "EleutherAI/gpt-j-6B" 
config = WeightOnlyQuantConfig(compute_dtype="bf16", weight_dtype="int8")
prompt = "Once upon a time, a little girl"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
inputs = tokenizer(prompt, return_tensors="pt").input_ids

model = AutoModel.from_pretrained(model_name, quantization_config=config)
gen_tokens = model.generate(inputs, max_new_tokens=300)
gen_text = tokenizer.batch_decode(gen_tokens)

🎯Validated Models

Here is the average accuracy of validated models on Lambada (OpenAI), HellaSwag, Winogrande, PIQA, and WikiText. The next token latency is based on 32 input tokens and greedy search on Intel's 4th Generation Xeon Scalable Sapphire Rapids processor.

Find other models like ChatGLM, ChatGLM2, StarCoder... in LLM Runtime

📖Documentation

📃Selected Publications/Events

• Intel Innovation'23 Keynote: Intel Innovation 2023 Keynote by Greg Lavender (Sep 2023)
• Blog on Intel Community: NeuralChat: A Customizable Chatbot Framework (Sep 2023)
• Blog published on Medium: NeuralChat: A Customizable Chatbot Framework (Sep 2023)
• Blog published on Medium: Faster Stable Diffusion Inference with Intel Extension for Transformers (July 2023)
• Blog of Intel Developer News: The Moat Is Trust, Or Maybe Just Responsible AI (July 2023)
• Blog of Intel Developer News: Create Your Own Custom Chatbot (July 2023)
• Blog of Intel Developer News: Accelerate Llama 2 with Intel AI Hardware and Software Optimizations (July 2023)
• 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)

View Full Publication List.

Additional Content

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

Acknowledgements

• Excellent open-source projects: bitsandbytes, FastChat, fastRAG, ggml, gptq, llama.cpp, lm-evauation-harness, peft, trl, and many others.

💁Collaborations

Welcome to raise any interesting ideas on model compression techniques and LLM-based chatbot development! Feel free to reach us and look forward to our collaborations on Intel Extension for Transformers!