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Lewis Tunstall 2022-01-11 16:07:56 +01:00
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@ -228,9 +228,9 @@ Let's start with the ONNX configuration object. We provide three abstract
classes that you should inherit from, depending on the type of model
architecture you wish to export:
* Encoder-based models inherit from [`OnnxConfig`](https://github.com/huggingface/transformers/blob/c4fa908fa98c3d538462c537d29b7613dd71306e/src/transformers/onnx/config.py#L52)
* Decoder-based models inherit from [`OnnxConfigWithPast`](https://github.com/huggingface/transformers/blob/c4fa908fa98c3d538462c537d29b7613dd71306e/src/transformers/onnx/config.py#L264)
* Encoder-decoder models inherit from [`OnnxSeq2SeqConfigWithPast`](https://github.com/huggingface/transformers/blob/c4fa908fa98c3d538462c537d29b7613dd71306e/src/transformers/onnx/config.py#L399)
* Encoder-based models inherit from [`OnnxConfig`]
* Decoder-based models inherit from [`OnnxConfigWithPast`]
* Encoder-decoder models inherit from [`OnnxSeq2SeqConfigWithPast`]
<Tip>
@ -401,7 +401,7 @@ to the library, you will need to:
* Implement the ONNX configuration in the corresponding `configuration_<model_name>.py`
file
* Include the model architecture and corresponding features in
[`onnx.features.FeatureManager`](https://github.com/huggingface/transformers/blob/c4fa908fa98c3d538462c537d29b7613dd71306e/src/transformers/onnx/features.py#L71)
[`~onnx.features.FeatureManager`]
* Add your model architecture to the tests in
`test_onnx_v2.py`