Note
Click here to download the full example code
Introduction to ONNX || Exporting a PyTorch model to ONNX || Extending the ONNX Registry
ONNX 介绍¶
Authors: Thiago Crepaldi,
Open Neural Network eXchange (ONNX) is an open standard
format for representing machine learning models. The torch.onnx
module provides APIs to
capture the computation graph from a native PyTorch torch.nn.Module
model and convert
it into an ONNX graph.
The exported model can be consumed by any of the many runtimes that support ONNX, including Microsoft’s ONNX Runtime.
Note
Currently, there are two flavors of ONNX exporter APIs,
but this tutorial will focus on the torch.onnx.dynamo_export
.
The TorchDynamo engine is leveraged to hook into Python’s frame evaluation API and dynamically rewrite its bytecode into an FX graph. The resulting FX Graph is polished before it is finally translated into an ONNX graph.
The main advantage of this approach is that the FX graph is captured using bytecode analysis that preserves the dynamic nature of the model instead of using traditional static tracing techniques.
Dependencies¶
PyTorch 2.1.0 or newer is required.
The ONNX exporter depends on extra Python packages:
ONNX standard library
ONNX Script library that enables developers to author ONNX operators, functions and models using a subset of Python in an expressive, and yet simple fashion.
They can be installed through pip:
pip install --upgrade onnx onnxscript
To validate the installation, run the following commands:
import torch
print(torch.__version__)
import onnxscript
print(onnxscript.__version__)
from onnxscript import opset18 # opset 18 is the latest (and only) supported version for now
import onnxruntime
print(onnxruntime.__version__)
Each import must succeed without any errors and the library versions must be printed out.
Further reading¶
The list below refers to tutorials that ranges from basic examples to advanced scenarios, not necessarily in the order they are listed. Feel free to jump directly to specific topics of your interest or sit tight and have fun going through all of them to learn all there is about the ONNX exporter.
Total running time of the script: ( 0 minutes 0.000 seconds)