File size: 2,596 Bytes
88325ca a44ba12 88325ca a44ba12 cceea9d a44ba12 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
license: apache-2.0
base_model: tiiuae/falcon-7b
language:
- en
tags:
- falcon-7b
- falcon
- onnxruntime
- onnx
- llm
---
# 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](#usage-example) 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](https://huggingface.co/tiiuae/falcon-7b) for [ONNX Runtime](https://github.com/microsoft/onnxruntime) 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.
```shell
git clone https://github.com/microsoft/onnxruntime
cd onnxruntime
```
2. Install required dependencies
```shell
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
```python
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))
```
|