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![](https://github.com/huggingface/optimum-habana/blob/main/readme_logo.png)


# Optimum Habana

🤗 Optimum Habana is the interface between the 🤗 Transformers and Diffusers libraries and [Habana's Gaudi processor (HPU)](https://docs.habana.ai/en/latest/index.html).
It provides a set of tools enabling easy model loading, training and inference on single- and multi-HPU settings for different downstream tasks.
The list of officially validated models and tasks is available [here](https://github.com/huggingface/optimum-habana#validated-models). Users can try other models and tasks with only few changes.


## What is a Habana Processing Unit (HPU)?

HPUs offer fast model training and inference as well as a great price-performance ratio.
Check out [this blog post about BERT pre-training](https://huggingface.co/blog/pretraining-bert) and [this article benchmarking Habana Gaudi2 versus Nvidia A100 GPUs](https://huggingface.co/blog/habana-gaudi-2-benchmark) for concrete examples.
If you are not familiar with HPUs and would like to know more about them, we recommend you take a look at [our conceptual guide](https://huggingface.co/docs/optimum/habana/concept_guides/hpu).


## Install
To install the latest release of this package:

```bash
pip install optimum[habana]
```

> To use DeepSpeed on HPUs, you also need to run the following command:
>```bash
>pip install git+https://github.com/HabanaAI/DeepSpeed.git@1.10.0
>```

Optimum Habana is a fast-moving project, and you may want to install it from source:

```bash
pip install git+https://github.com/huggingface/optimum-habana.git
```

Last but not least, don't forget to install the requirements for every example:

```bash
cd <example-folder>
pip install -r requirements.txt
```


## How to use it?

### Quick Start

🤗 Optimum Habana was designed with one goal in mind: **to make training and inference straightforward for any 🤗 Transformers and 🤗 Diffusers user while leveraging the complete power of Gaudi processors**.

#### Transformers Interface

There are two main classes one needs to know:
- [GaudiTrainer](https://huggingface.co/docs/optimum/habana/package_reference/trainer): the trainer class that takes care of compiling (lazy or eager mode) and distributing the model to run on HPUs, and performing training and evaluation.
- [GaudiConfig](https://huggingface.co/docs/optimum/habana/package_reference/gaudi_config): the class that enables to configure Habana Mixed Precision and to decide whether optimized operators and optimizers should be used or not.

The [GaudiTrainer](https://huggingface.co/docs/optimum/habana/package_reference/trainer) is very similar to the [🤗 Transformers Trainer](https://huggingface.co/docs/transformers/main_classes/trainer), and adapting a script using the Trainer to make it work with Gaudi will mostly consist in simply swapping the `Trainer` class for the `GaudiTrainer` one.
That's how most of the [example scripts](https://github.com/huggingface/optimum-habana/tree/main/examples) were adapted from their [original counterparts](https://github.com/huggingface/transformers/tree/main/examples/pytorch).

Here is an example:
```diff
- from transformers import Trainer, TrainingArguments
+ from optimum.habana import GaudiConfig, GaudiTrainer, GaudiTrainingArguments

- training_args = TrainingArguments(
+ training_args = GaudiTrainingArguments(
  # training arguments...
+ use_habana=True,
+ use_lazy_mode=True,  # whether to use lazy or eager mode
+ gaudi_config_name=path_to_gaudi_config,
)

# A lot of code here

# Initialize our Trainer
- trainer = Trainer(
+ trainer = GaudiTrainer(
    model=model,
    args=training_args,  # Original training arguments.
    train_dataset=train_dataset if training_args.do_train else None,
    eval_dataset=eval_dataset if training_args.do_eval else None,
    compute_metrics=compute_metrics,
    tokenizer=tokenizer,
    data_collator=data_collator,
)
```

where `gaudi_config_name` is the name of a model from the [Hub](https://huggingface.co/Habana) (Gaudi configurations are stored in model repositories) or a path to a local Gaudi configuration file (you can see [here](https://huggingface.co/docs/optimum/habana/package_reference/gaudi_config) how to write your own).


#### Diffusers Interface

You can generate images from prompts using Stable Diffusion on Gaudi using the [`GaudiStableDiffusionPipeline`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline) class and the [`GaudiDDIMScheduler`] which have been both optimized for HPUs. Here is how to use them and the differences with the 🤗 Diffusers library:

```diff
- from diffusers import DDIMScheduler, StableDiffusionPipeline
+ from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline


model_name = "runwayml/stable-diffusion-v1-5"

- scheduler = DDIMScheduler.from_pretrained(model_name, subfolder="scheduler")
+ scheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder="scheduler")

- pipeline = StableDiffusionPipeline.from_pretrained(
+ pipeline = GaudiStableDiffusionPipeline.from_pretrained(
    model_name,
    scheduler=scheduler,
+   use_habana=True,
+   use_hpu_graphs=True,
+   gaudi_config="Habana/stable-diffusion",
)

outputs = generator(
    ["An image of a squirrel in Picasso style"],
    num_images_per_prompt=16,
+   batch_size=4,
)
```


### Documentation

Check out [the documentation of Optimum Habana](https://huggingface.co/docs/optimum/habana/index) for more advanced usage.


## Validated Models

The following model architectures, tasks and device distributions have been validated for 🤗 Optimum Habana:

<div align="center">

| Architecture | Single Card | Multi Card | DeepSpeed | <center>Tasks</center> |
|--------------|:-----------:|:----------:|:---------:|------------------------|
| BERT         | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | <li>[text classification](https://github.com/huggingface/optimum-habana/tree/main/examples/text-classification)</li><li>[question answering](https://github.com/huggingface/optimum-habana/tree/main/examples/question-answering)</li><li>[language modeling](https://github.com/huggingface/optimum-habana/tree/main/examples/language-modeling)</li> |
| RoBERTa | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | <li>[question answering](https://github.com/huggingface/optimum-habana/tree/main/examples/question-answering)</li><li>[language modeling](https://github.com/huggingface/optimum-habana/tree/main/examples/language-modeling)</li> |
| ALBERT | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | <li>[question answering](https://github.com/huggingface/optimum-habana/tree/main/examples/question-answering)</li><li>[language modeling](https://github.com/huggingface/optimum-habana/tree/main/examples/language-modeling)</li> |
| DistilBERT |:heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | <li>[question answering](https://github.com/huggingface/optimum-habana/tree/main/examples/question-answering)</li><li>[language modeling](https://github.com/huggingface/optimum-habana/tree/main/examples/language-modeling)</li> |
| GPT2             | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | <li>[language modeling](https://github.com/huggingface/optimum-habana/tree/main/examples/language-modeling)</li><li>[text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation)</li> |
| T5 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | <li>[summarization](https://github.com/huggingface/optimum-habana/tree/main/examples/summarization)</li><li>[translation](https://github.com/huggingface/optimum-habana/tree/main/examples/translation)</li><li>[question answering](https://github.com/huggingface/optimum-habana/tree/main/examples/question-answering#fine-tuning-t5-on-squad20)</li> |
| ViT | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | <li>[image classification](https://github.com/huggingface/optimum-habana/tree/main/examples/image-classification)</li> |
| Swin | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | <li>[image classification](https://github.com/huggingface/optimum-habana/tree/main/examples/image-classification)</li> |
| Wav2Vec2 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | <li>[audio classification](https://github.com/huggingface/optimum-habana/tree/main/examples/audio-classification)</li><li>[speech recognition](https://github.com/huggingface/optimum-habana/tree/main/examples/speech-recognition)</li> |
| Stable Diffusion | :heavy_check_mark: | :x: | :x: | <li>[text-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion)</li> |
| CLIP | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | <li>[contrastive image-text training](https://github.com/huggingface/optimum-habana/tree/main/examples/contrastive-image-text)</li> |
| BLOOM(Z) | :x: | :x: | :heavy_check_mark: | <li>[text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation)</li> |
| StarCoder | :heavy_check_mark: | :x: | :heavy_check_mark: | <li>[text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation)</li> |
| ESMFold | :heavy_check_mark: | :x: | :x: | <li>[protein folding](https://github.com/huggingface/optimum-habana/tree/main/examples/protein-folding)</li> |
| GPT-J | :heavy_check_mark: | :x: | :heavy_check_mark: | <li>[text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation)</li> |
| GPT-NeoX | :heavy_check_mark: | :x: | :heavy_check_mark: | <li>[text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation)</li> |
| OPT | :heavy_check_mark: | :x: | :heavy_check_mark: | <li>[text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation)</li> |
| BridgeTower | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | <li>[contrastive image-text training](https://github.com/huggingface/optimum-habana/tree/main/examples/contrastive-image-text)</li> |

</div>

Other models and tasks supported by the 🤗 Transformers library may also work. You can refer to this [section](https://github.com/huggingface/optimum-habana#how-to-use-it) for using them with 🤗 Optimum Habana. Besides, [this page](https://github.com/huggingface/optimum-habana/tree/main/examples) explains how to modify any [example](https://github.com/huggingface/transformers/tree/main/examples/pytorch) from the 🤗 Transformers library to make it work with 🤗 Optimum Habana.

If you find any issue while using those, please open an issue or a pull request.


## Gaudi Setup

Please refer to Habana Gaudi's official [installation guide](https://docs.habana.ai/en/latest/Installation_Guide/index.html).

> Tests should be run in a Docker container based on Habana Docker images.
>
> The current version has been validated for SynapseAI 1.10.


## Development

Check the [contributor guide](https://github.com/huggingface/optimum/blob/main/CONTRIBUTING.md) for instructions.
