#
Quick tour

This quick tour is intended for developers who are ready to dive into the code and see examples of how to integrate 🤗 Optimum into their model training and inference workflows.

##
Accelerated inference

####
OpenVINO

To load a model and run inference with OpenVINO Runtime, you can just replace your `AutoModelForXxx` class with the corresponding `OVModelForXxx` class. If you want to load a PyTorch checkpoint, set `export=True` to convert your model to the OpenVINO IR (Intermediate Representation).

    - from transformers import AutoModelForSequenceClassification
    + from optimum.intel.openvino import OVModelForSequenceClassification
      from transformers import AutoTokenizer, pipeline

      # Download a tokenizer and model from the Hub and convert to OpenVINO format
      tokenizer = AutoTokenizer.from_pretrained(model_id)
      model_id = "distilbert-base-uncased-finetuned-sst-2-english"
    - model = AutoModelForSequenceClassification.from_pretrained(model_id)
    + model = OVModelForSequenceClassification.from_pretrained(model_id, export=True)

      # Run inference!
      classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
      results = classifier("He's a dreadful magician.")

You can find more examples in the documentation and in the examples.

####
ONNX Runtime

To accelerate inference with ONNX Runtime, 🤗 Optimum uses configuration objects to define parameters for graph optimization and quantization. These objects are then used to instantiate dedicated optimizers and quantizers.

Before applying quantization or optimization, first we need to load our model. To load a model and run inference with ONNX Runtime, you can just replace the canonical Transformers `AutoModelForXxx` class with the corresponding `ORTModelForXxx` class. If you want to load from a PyTorch checkpoint, set `export=True` to export your model to the ONNX format.

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

    >>> model_checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
    >>> save_directory = "tmp/onnx/"

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

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

Let’s see now how we can apply dynamic quantization with ONNX Runtime:

    >>> from optimum.onnxruntime.configuration import AutoQuantizationConfig
    >>> from optimum.onnxruntime import ORTQuantizer

    >>> # Define the quantization methodology
    >>> qconfig = AutoQuantizationConfig.arm64(is_static=False, per_channel=False)
    >>> quantizer = ORTQuantizer.from_pretrained(ort_model)

    >>> # Apply dynamic quantization on the model
    >>> quantizer.quantize(save_dir=save_directory, quantization_config=qconfig)

In this example, we’ve quantized a model from the Hugging Face Hub, in the same manner we can quantize a model hosted locally by providing the path to the directory containing the model weights. The result from applying the `quantize()` method is a `model_quantized.onnx` file that can be used to run inference. Here’s an example of how to load an ONNX Runtime model and generate predictions with it:

    >>> from optimum.onnxruntime import ORTModelForSequenceClassification
    >>> from transformers import pipeline, AutoTokenizer

    >>> model = ORTModelForSequenceClassification.from_pretrained(save_directory, file_name="model_quantized.onnx")
    >>> tokenizer = AutoTokenizer.from_pretrained(save_directory)
    >>> classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
    >>> results = classifier("I love burritos!")

You can find more examples in the documentation and in the examples.

##
Accelerated training

####
Habana

To train transformers on Habana’s Gaudi processors, 🤗 Optimum provides a `GaudiTrainer` that is very similar to the 🤗 Transformers Trainer. Here is a simple example:

    - from transformers import Trainer, TrainingArguments
    + from optimum.habana import GaudiTrainer, GaudiTrainingArguments

      # Download a pretrained model from the Hub
      model = AutoModelForXxx.from_pretrained("bert-base-uncased")

      # Define the training arguments
    - training_args = TrainingArguments(
    + training_args = GaudiTrainingArguments(
          output_dir="path/to/save/folder/",
    +     use_habana=True,
    +     use_lazy_mode=True,
    +     gaudi_config_name="Habana/bert-base-uncased",
          ...
      )

      # Initialize the trainer
    - trainer = Trainer(
    + trainer = GaudiTrainer(
          model=model,
          args=training_args,
          train_dataset=train_dataset,
          ...
      )

      # Use Habana Gaudi processor for training!
      trainer.train()

You can find more examples in the documentation and in the examples.

##
Out of the box ONNX export

The Optimum library handles out of the box the ONNX export of Transformers and Diffusers models!

Exporting a model to ONNX is as simple as

    optimum-cli export onnx --model gpt2 gpt2_onnx/

Check out the help for more options:

    optimum-cli export onnx --help

Check out the documentation for more.

##
torch.fx integration

Optimum integrates with `torch.fx`, providing as a one-liner several graph transformations. We aim at supporting a better management of quantization through `torch.fx`, both for quantization-aware training (QAT) and post-training quantization (PTQ).

Check out the documentation and reference for more!