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# ONNX

[ONNX](http://onnx.ai) is an open standard that defines a common set of operators and a file format to represent deep learning models in different frameworks, including PyTorch and TensorFlow. When a model is exported to ONNX, the operators construct a computational graph (or *intermediate representation*) which represents the flow of data through the model. Standardized operators and data types makes it easy to switch between frameworks.

The [Optimum](https://huggingface.co/docs/optimum/index) library exports a model to ONNX with configuration objects which are supported for [many architectures](https://huggingface.co/docs/optimum/exporters/onnx/overview) and can be easily extended. If a model isn't supported, feel free to make a [contribution](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/contribute) to Optimum.

The benefits of exporting to ONNX include the following.

- [Graph optimization](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/optimization) and [quantization](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/quantization) for improving inference.
- Use the [`~optimum.onnxruntime.ORTModel`] API to run a model with [ONNX Runtime](https://onnxruntime.ai/).
- Use [optimized inference pipelines](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/pipelines) for ONNX models.

Export a Transformers model to ONNX with the Optimum CLI or the `optimum.onnxruntime` module.

## Optimum CLI

Run the command below to install Optimum and the [exporters](https://huggingface.co/docs/optimum/exporters/overview) module.

```bash
pip install optimum-onnx
```

> [!TIP]
> Refer to the [Export a model to ONNX with optimum.exporters.onnx](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) guide for all available arguments or with the command below.
>
> ```bash
> optimum-cli export onnx --help
> ```

Set the `--model` argument to export a PyTorch model from the Hub.

```bash
optimum-cli export onnx --model distilbert/distilbert-base-uncased-distilled-squad distilbert_base_uncased_squad_onnx/
```

You should see logs indicating the progress and showing where the resulting `model.onnx` is saved.

```text
Validating ONNX model distilbert_base_uncased_squad_onnx/model.onnx...
	-[✓] ONNX model output names match reference model (start_logits, end_logits)
	- Validating ONNX Model output "start_logits":
		-[✓] (2, 16) matches (2, 16)
		-[✓] all values close (atol: 0.0001)
	- Validating ONNX Model output "end_logits":
		-[✓] (2, 16) matches (2, 16)
		-[✓] all values close (atol: 0.0001)
The ONNX export succeeded and the exported model was saved at: distilbert_base_uncased_squad_onnx
```

For local models, make sure the model weights and tokenizer files are saved in the same directory, for example `local_path`. Pass the directory to the `--model` argument and use `--task` to indicate the [task](https://huggingface.co/docs/optimum/exporters/task_manager) a model can perform. If `--task` isn't provided, the model architecture without a task-specific head is used.

```bash
optimum-cli export onnx --model local_path --task question-answering distilbert_base_uncased_squad_onnx/
```

The `model.onnx` file can be deployed with any [accelerator](https://onnx.ai/supported-tools.html#deployModel) that supports ONNX. The example below demonstrates loading and running a model with ONNX Runtime.

```python
>>> from transformers import AutoTokenizer
>>> from optimum.onnxruntime import ORTModelForQuestionAnswering

>>> tokenizer = AutoTokenizer.from_pretrained("distilbert_base_uncased_squad_onnx")
>>> model = ORTModelForQuestionAnswering.from_pretrained("distilbert_base_uncased_squad_onnx")
>>> inputs = tokenizer("What am I using?", "Using DistilBERT with ONNX Runtime!", return_tensors="pt")
>>> outputs = model(**inputs)
```

## optimum.onnxruntime

The `optimum.onnxruntime` module supports programmatically exporting a Transformers model. Instantiate a [`~optimum.onnxruntime.ORTModel`] for a task and set `export=True`. Use [`~OptimizedModel.save_pretrained`] to save the ONNX model.

```python
>>> from optimum.onnxruntime import ORTModelForSequenceClassification
>>> from transformers import AutoTokenizer

>>> model_checkpoint = "distilbert/distilbert-base-uncased-distilled-squad"
>>> save_directory = "onnx/"

>>> ort_model = ORTModelForSequenceClassification.from_pretrained(model_checkpoint, export=True)
>>> tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

>>> ort_model.save_pretrained(save_directory)
>>> tokenizer.save_pretrained(save_directory)
```
