Optimum-habana

Stable Diffusion

This release adds a new interface for the :hugs: Diffusers library which enables to support the Stable Diffusion pipeline for inference. Thus, you can now generate images from text on Gaudi relying on the user-friendliness of :hugs: Diffusers.

- Add support for Stable Diffusion 131

Check out the [documentation](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion) and [this example](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion) for more information.


Wav2Vec2

After text and image models, a third modality is now supported with the addition of Wav2Vec2.

- Add suport for Wav2Vec2 120

Check out the [audio classification](https://github.com/huggingface/optimum-habana/tree/main/examples/audio-classification) and [speech recognition](https://github.com/huggingface/optimum-habana/tree/main/examples/speech-recognition) examples to see how to use it.

DeepSpeed

This release brings support for DeepSpeed. It is now possible to train bigger models on Gaudi with Optimum Habana!
- Add support for DeepSpeed 93

Check the documentation [here](https://huggingface.co/docs/optimum/habana_deepspeed) to know how to use it.


Computer Vision Models

Two computer-vision models have been validated for performing image classification in both single- and multi-cards configurations:
- ViT 80
- Swin

You can see how to use them [in this example](https://github.com/huggingface/optimum-habana/tree/main/examples/image-classification).

This patch release fixes a bug where it is possible to initialize processes multiple times in distributed mode, leading to an error.

This patch release fixes a bug where the loss is equal to NaN from the first training iteration with Transformers 4.21.0.

GPT2

You can now train or fine-tune GPT2 for causal language modeling on up to 8 HPUs. An example of fine-tuning on WikiText-2 is provided [here](https://github.com/huggingface/optimum-habana/tree/main/examples/language-modeling).

- Add support for language modeling (GPT2) 52

You can also use GPT2 for text generation in lazy mode.

- Accelerate generation 61


T5

Encoder-decoder architectures are now supported. In particular, examples relying on T5 for the following tasks are available:

- summarization, with an [example](https://github.com/huggingface/optimum-habana/tree/main/examples/summarization) of fine-tuning T5 on the CNN/DailyMail dataset,
- translation, with an [example](https://github.com/huggingface/optimum-habana/tree/main/examples/translation) of fine-tuning T5 on the WMT16 dataset for translating English to Romanian.

You can also use T5 for text generation in lazy mode.

- Accelerate generation 61


Support for SynapseAI 1.5.0

The newly released SynapseAI 1.5.0 is now supported. You can find more information about it [here](https://docs.habana.ai/en/v1.5.0/Release_Notes/GAUDI_Release_Notes.html).

- Add support for SynapseAI 1.5.0 65

*This is a breaking change, you should update your version of SynapseAI as described [here](https://docs.habana.ai/en/latest/Installation_Guide/index.html) in order to use this new release.*


GaudiConfig instantiation is not mandatory anymore

If the name of your Gaudi configuration is given in the training arguments, you do not have to instantiate it and provide it to the trainer anymore. This will be automatically taken care of. You can still instantiate a Gaudi configuration and provide it to the trainer.

- Enable GaudiConfig instantiation from inside the trainer 55


Refined throughput computation in lazy mode

In lazy mode, the first two steps are warmup steps used for graph compilation. In order to discard them from the throughput computation, you can just add the following training argument: `--throughput_warmup_steps 2`.

- Add a new argument for taking warmup steps into account in throughput computation 48

With this release, we enable easy and fast deployment of models from the Transformers library on Habana Gaudi Processors (HPU).

- The class `GaudiTrainer` is built on top of the original class `Trainer` and enables to train and evaluate models from the Transformers library on HPUs.
- The class `GaudiTrainingArguments` is built on top of the original class `TrainingArguments` and adds 3 new arguments:
- `use_habana` to deploy on HPU
- `use_lazy_mode` to use lazy mode instead of eager mode
- `gaudi_config_name` to specify the name of or the path to the Gaudi configuration file
- The class `GaudiConfig` enables to specify a configuration for deployment on HPU, such as the use of Habana Mixed Precision, the use of custom ops,...
- Multi-card deployment is enabled
- Examples are provided for *question answering* and *text classification* in both single- and multi-card settings.
- The following models have been validated:
- BERT base/large
- RoBERTa base/large
- ALBERT large/XXL
- DistilBERT