:orphan:

.. _hpu_intermediate:

Accelerator: HPU training
=========================
**Audience:** Gaudi chip users looking to save memory and scale models with mixed-precision training.

----

Enable Mixed Precision
----------------------

Lightning also allows mixed precision training with HPUs.
By default, HPU training will use 32-bit precision. To enable mixed precision, set the ``precision`` flag.

.. code-block:: python

    trainer = Trainer(devices=1, accelerator="hpu", precision=16)

----

Customize Mixed Precision
-------------------------

Internally, :class:`~pytorch_lightning.plugins.precision.hpu.HPUPrecisionPlugin` uses the Habana Mixed Precision (HMP) package to enable mixed precision training.

You can execute the ops in FP32 or BF16 precision. The HMP package modifies the Python operators to add the appropriate cast operations for the arguments before execution.
The default settings enable users to enable mixed precision training with minimal code easily.

In addition to the default settings in HMP, users also have the option of overriding these defaults and providing their
BF16 and FP32 operator lists by passing them as parameter to :class:`~pytorch_lightning.plugins.precision.hpu.HPUPrecisionPlugin`.

The below snippet shows an example model using MNIST with a single Habana Gaudi device and making use of HMP by overriding the default parameters.
This enables advanced users to provide their own BF16 and FP32 operator list instead of using the HMP defaults.

.. code-block:: python

    import pytorch_lightning as pl
    from pytorch_lightning.plugins import HPUPrecisionPlugin

    # Initialize a trainer with HPU accelerator for HPU strategy for single device,
    # with mixed precision using overidden HMP settings
    trainer = pl.Trainer(
        accelerator="hpu",
        devices=1,
        # Optional Habana mixed precision params to be set
        # Checkout `pl_examples/hpu_examples/simple_mnist/ops_bf16_mnist.txt` for the format
        plugins=[
            HPUPrecisionPlugin(
                precision=16,
                opt_level="O1",
                verbose=False,
                bf16_file_path="ops_bf16_mnist.txt",
                fp32_file_path="ops_fp32_mnist.txt",
            )
        ],
    )

    # Init our model
    model = LitClassifier()
    # Init the data
    dm = MNISTDataModule(batch_size=batch_size)

    # Train the model ⚡
    trainer.fit(model, datamodule=dm)

For more details, please refer to `PyTorch Mixed Precision Training on Gaudi <https://docs.habana.ai/en/latest/PyTorch_User_Guide/PyTorch_User_Guide.html#pytorch-mixed-precision-training-on-gaudi>`__.