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
- Clone onnxruntime repository.
git clone https://github.com/microsoft/onnxruntime
cd onnxruntime
- Install required dependencies
python3 -m pip install -r onnxruntime/python/tools/transformers/models/llama/requirements-cuda.txt
- 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))
Model tree for microsoft/falcon-7B-onnx
Base model
tiiuae/falcon-7b