serialization.mdx

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# Export to ONNX

Deploying 🤗 Transformers models in production environments often requires, or can benefit from exporting the models into 
a serialized format that can be loaded and executed on specialized runtimes and hardware.

🤗 Optimum is an extension of Transformers that enables exporting models from PyTorch or TensorFlow to serialized formats 
such as ONNX and TFLite through its `exporters` module. 🤗 Optimum also provides a set of performance optimization tools to train 
and run models on targeted hardware with maximum efficiency.

This guide demonstrates how you can export 🤗 Transformers models to ONNX with 🤗 Optimum, for the guide on exporting models to TFLite, 
please refer to the [Export to TFLite page](tflite).

## Export to ONNX 

[ONNX (Open Neural Network eXchange)](http://onnx.ai) is an open standard that defines a common set of operators and a 
common file format to represent deep learning models in a wide variety of frameworks, including PyTorch and
TensorFlow. When a model is exported to the ONNX format, these operators are used to
construct a computational graph (often called an _intermediate representation_) which
represents the flow of data through the neural network.

By exposing a graph with standardized operators and data types, ONNX makes it easy to
switch between frameworks. For example, a model trained in PyTorch can be exported to
ONNX format and then imported in TensorFlow (and vice versa).

Once exported to ONNX format, a model can be:
- optimized for inference via techniques such as [graph optimization](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/optimization) and [quantization](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/quantization). 
- run with ONNX Runtime via [`ORTModelForXXX` classes](https://huggingface.co/docs/optimum/onnxruntime/package_reference/modeling_ort),
which follow the same `AutoModel` API as the one you are used to in 🤗 Transformers.
- run with [optimized inference pipelines](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/pipelines),
which has the same API as the [`pipeline`] function in 🤗 Transformers. 

🤗 Optimum provides support for the ONNX export by leveraging configuration objects. These configuration objects come 
ready-made for a number of model architectures, and are designed to be easily extendable to other architectures.

For the list of ready-made configurations, please refer to [🤗 Optimum documentation](https://huggingface.co/docs/optimum/exporters/onnx/overview).

There are two ways to export a 🤗 Transformers model to ONNX, here we show both:

- export with 🤗 Optimum via CLI.
- export with 🤗 Optimum with `optimum.onnxruntime`.

### Exporting a 🤗 Transformers model to ONNX with CLI

To export a 🤗 Transformers model to ONNX, first install an extra dependency:

```bash
pip install optimum[exporters]
```

To check out all available arguments, refer to the [🤗 Optimum docs](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli), 
or view help in command line:

```bash
optimum-cli export onnx --help
```

To export a model's checkpoint from the 🤗 Hub, for example, `distilbert-base-uncased-distilled-squad`, run the following command: 

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

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

```bash
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
```

The example above illustrates exporting a checkpoint from 🤗 Hub. When exporting a local model, first make sure that you 
saved both the model's weights and tokenizer files in the same directory (`local_path`). When using CLI, pass the 
`local_path` to the `model` argument instead of the checkpoint name on 🤗 Hub and provide the `--task` argument. 
You can review the list of supported tasks in the [🤗 Optimum documentation](https://huggingface.co/docs/optimum/exporters/task_manager).
If `task` argument is not provided, it will default to the model architecture without any task specific head.

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

The resulting `model.onnx` file can then be run on one of the [many
accelerators](https://onnx.ai/supported-tools.html#deployModel) that support the ONNX
standard. For example, we can load and run the model with [ONNX
Runtime](https://onnxruntime.ai/) as follows:

```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)
```

The process is identical for TensorFlow checkpoints on the Hub. For instance, here's how you would
export a pure TensorFlow checkpoint from the [Keras organization](https://huggingface.co/keras-io):

```bash
optimum-cli export onnx --model keras-io/transformers-qa distilbert_base_cased_squad_onnx/
```

### Exporting a 🤗 Transformers model to ONNX with `optimum.onnxruntime`

Alternative to CLI, you can export a 🤗 Transformers model to ONNX programmatically like so: 

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

>>> model_checkpoint = "distilbert_base_uncased_squad"
>>> save_directory = "onnx/"

>>> # Load a model from transformers and export it to ONNX
>>> ort_model = ORTModelForSequenceClassification.from_pretrained(model_checkpoint, export=True)
>>> tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

>>> # Save the onnx model and tokenizer
>>> ort_model.save_pretrained(save_directory)
>>> tokenizer.save_pretrained(save_directory)
```

### Exporting a model for an unsupported architecture

If you wish to contribute by adding support for a model that cannot be currently exported, you should first check if it is
supported in [`optimum.exporters.onnx`](https://huggingface.co/docs/optimum/exporters/onnx/overview),
and if it is not, [contribute to 🤗 Optimum](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/contribute)
directly.

### Exporting a model with `transformers.onnx`

<Tip warning={true}>

`tranformers.onnx` is no longer maintained, please export models with 🤗 Optimum as described above. This section will be removed in the future versions.

</Tip>

To export a 🤗 Transformers model to ONNX with `tranformers.onnx`, install extra dependencies:

```bash
pip install transformers[onnx]
```

Use `transformers.onnx` package as a Python module to export a checkpoint using a ready-made configuration:

```bash
python -m transformers.onnx --model=distilbert-base-uncased onnx/
```

This exports an ONNX graph of the checkpoint defined by the `--model` argument. Pass any checkpoint on the 🤗 Hub or one that's stored locally.
The resulting `model.onnx` file can then be run on one of the many accelerators that support the ONNX standard. For example, 
load and run the model with ONNX Runtime as follows:

```python
>>> from transformers import AutoTokenizer
>>> from onnxruntime import InferenceSession

>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
>>> session = InferenceSession("onnx/model.onnx")
>>> # ONNX Runtime expects NumPy arrays as input
>>> inputs = tokenizer("Using DistilBERT with ONNX Runtime!", return_tensors="np")
>>> outputs = session.run(output_names=["last_hidden_state"], input_feed=dict(inputs))
```

The required output names (like `["last_hidden_state"]`) can be obtained by taking a look at the ONNX configuration of 
each model. For example, for DistilBERT we have:

```python
>>> from transformers.models.distilbert import DistilBertConfig, DistilBertOnnxConfig

>>> config = DistilBertConfig()
>>> onnx_config = DistilBertOnnxConfig(config)
>>> print(list(onnx_config.outputs.keys()))
["last_hidden_state"]
```

The process is identical for TensorFlow checkpoints on the Hub. For example, export a pure TensorFlow checkpoint like so:

```bash
python -m transformers.onnx --model=keras-io/transformers-qa onnx/
```

To export a model that's stored locally, save the model's weights and tokenizer files in the same directory (e.g. `local-pt-checkpoint`), 
then export it to ONNX by pointing the `--model` argument of the `transformers.onnx` package to the desired directory:

```bash
python -m transformers.onnx --model=local-pt-checkpoint onnx/
```