Fastseq 基于ONNXRUNTIME的文本生成加速框架

Overview

Fastseq

基于ONNXRUNTIME的文本生成加速框架

1. 环境配置

# 创建onnx conda环境
conda create -n onnx_py38 python=3.8
conda activate onnx_py38
conda install pytorch cudatoolkit=10.2 -c pytorch

# 安装onnxruntime-gpu(目前只有1.5.2版本测试成功)
pip install onnxruntime-gpu==1.5.2

# 安装transformers==3.1.0版本
pip install transformers==3.1.0

2. ONNX转换

# 将huggingface保存的 模型/checkpoint 转换为onnx格式。这里使用onnxruntime自带的转换工具。
python -m onnxruntime.transformers.convert_to_onnx \
    -m "path_to_checkpoint/model_name(gpt2)" \
    --model_class GPT2LMHeadModel \
    --output gpt2_fp32.onnx \
    -p fp32

3. DEMO测试

CUDA_VISIBLE_DEVICES=3 python demo.py \
    --onnx_model_path "./gpt2_fp32.onnx" \
    --model_name_or_path "path_to_checkpoint" \
    --prompt_text "here is an example of gpt2 model" \
    --do_sample_top_k 5

4. TODO

  • TopK Decoding
  • Beam Search Decoding
  • INT8 Support
  • TensorRT Provider
  • ONNXRUNTIME Framework for BERT
  • Benchmark test
  • RESTful server demo
  • ONNXRUNTIME 1.8.1 Support
Owner
Jun Gao
Le vent se lève, il faut tenter de vivre.
Jun Gao
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