docs/source/features/mixed_precision.rst

.. _mix_precision:

Mixed Precision Training
========================

Mixed precision training enhances computational efficiency by conducting operations in low-precision
format while selectively maintaining critical data in single-precision. NeMo supports FP16 and BF16
precision via PyTorch Lightning, in both mixed and true half-precision modes.

Precision Modes
---------------

PyTorch Lightning provides two categories of half-precision training:

**Mixed Precision** (``"bf16-mixed"`` / ``"16-mixed"``):
    Operations run in half-precision where safe, but model weights are kept in FP32.
    Gradients are computed in half-precision and accumulated in FP32. This is the safest
    option and generally a good default for ASR and TTS training.

**True Half Precision** (``"bf16-true"`` / ``"fp16-true"``):
    The entire model -- weights, activations, and gradients -- runs in half-precision.
    This uses less memory than mixed precision (no FP32 weight copy) and is faster,
    but requires the model to be numerically stable in half-precision.
    SpeechLM2 models use ``"bf16-true"`` by default for training.

Configuration
-------------

Set precision through the PyTorch Lightning trainer's ``precision`` argument.

In YAML (with Hydra):

.. code-block:: yaml

    trainer:
        precision: "bf16-mixed"    # BF16 mixed precision
        # precision: "16-mixed"    # FP16 mixed precision
        # precision: "bf16-true"   # True BF16 half precision
        # precision: "fp16-true"   # True FP16 half precision

In Python:

.. code-block:: python

    import lightning.pytorch as pl

    trainer = pl.Trainer(
        precision="bf16-mixed",
        devices=2,
        accelerator="gpu",
    )

Choosing a Precision Format
----------------------------

- **BF16** has the same dynamic range as FP32, which makes it more numerically stable and generally
  easier to use. It is the recommended choice for most Speech AI training workloads.
- **FP16** offers slightly higher throughput on some hardware but has a reduced dynamic range.
  In mixed precision mode, PyTorch Lightning handles loss scaling automatically.

HalfPrecisionForAudio
----------------------

Audio waveform tensors are sensitive to precision loss -- downcasting raw audio samples to half-precision
can degrade signal quality and hurt model accuracy. NeMo provides the ``HalfPrecisionForAudio`` plugin
(in ``nemo.utils.trainer_utils``) that extends Lightning's ``HalfPrecision`` plugin to preserve
full-precision for audio tensors while still casting all other inputs to half-precision.

Specifically, when the training mini-batch is a dictionary, any tensor whose key contains
the substring ``"audio"`` is kept in its original precision (typically FP32). All other floating-point
tensors are cast to the target half-precision dtype.

This plugin is used automatically when you launch training with NeMo's ``resolve_trainer_cfg``
utility (used by all NeMo example training scripts). When the trainer config specifies
``precision: "bf16-true"`` or ``precision: "fp16-true"``, ``resolve_trainer_cfg`` replaces
the precision setting with the ``HalfPrecisionForAudio`` plugin:

.. code-block:: python

    from nemo.utils.trainer_utils import resolve_trainer_cfg

    # In YAML: trainer.precision = "bf16-true"
    # resolve_trainer_cfg automatically installs HalfPrecisionForAudio
    trainer = pl.Trainer(**resolve_trainer_cfg(cfg.trainer))

If you construct the trainer manually, you can install the plugin directly:

.. code-block:: python

    from nemo.utils.trainer_utils import HalfPrecisionForAudio

    trainer = pl.Trainer(
        plugins=[HalfPrecisionForAudio("bf16-true")],
        devices=2,
        accelerator="gpu",
    )