microsoft/falcon-7B-onnx

This is an optimized version of the Falcon 7B model, available on this repository: https://huggingface.co/tiiuae/falcon-7b and under the license on such repository. Microsoft permits you to use, modify, redistribute and create derivatives of Microsoft's contributions to the optimized version subject to the restrictions and disclaimers of warranty and liability in license agreement.

falcon-7b for ONNX Runtime

Introduction

This repository hosts the optimized version of falcon-7b to accelerate inference with ONNX Runtime CUDA execution provider.

See the usage instructions for how to inference this model with the ONNX files hosted in this repository.

Model Description

• Developed by: TIIUAE
• Model type: Pretrained generative text model
• License: Apache 2.0 License
• Model Description: This is a conversion of the falcon-7b for ONNX Runtime inference with CUDA execution provider.

Performance Comparison

Latency for token generation

Below is average latency of generating a token using a prompt of varying size using NVIDIA A100-SXM4-80GB GPU:

Prompt Length	Batch Size	PyTorch 2.1 torch.compile	ONNX Runtime CUDA
32	1	53.64ms	15.68ms
256	1	59.55ms	26.05ms
1024	1	89.82ms	99.05ms
2048	1	208.0ms	227.0ms
32	4	70.8ms	19.62ms
256	4	78.6ms	81.29ms
1024	4	373.7ms	369.6ms
2048	4	N/A	879.2ms

Usage Example

1. Clone onnxruntime repository.

git clone https://github.com/microsoft/onnxruntime
cd onnxruntime

2. Install required dependencies

python3 -m pip install -r onnxruntime/python/tools/transformers/models/llama/requirements-cuda.txt

5. Inference using custom model API, or use Hugging Face's ORTModelForCausalLM

from optimum.onnxruntime import ORTModelForCausalLM
from onnxruntime import InferenceSession
from transformers import AutoConfig, AutoTokenizer

sess = InferenceSession("falcon-7b.onnx", providers = ["CUDAExecutionProvider"])
config = AutoConfig.from_pretrained("tiiuae/falcon-7b")

model = ORTFalconForCausalLM(sess, config, use_cache = True, use_io_binding = True)

tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b")

inputs = tokenizer("Instruct: What is a fermi paradox?\nOutput:", return_tensors="pt")

outputs = model.generate(**inputs)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))