docs/source/usage_guides/low_precision_training.md

# Low Precision Training Methods

🤗 Accelerate provides integrations to train on lower precision methods using specified supported hardware through the `TransformersEngine` and `MS-AMP` packages. This documentation will help guide you through what hardware is supported, how to configure your [`Accelerator`] to leverage the low precision methods, and what you can expect when training. 

## What training on FP8 means

To explore more of the nitty-gritty in traninig in FP8 with PyTorch and 🤗 Accelerate, check out the [concept_guide](../concept_guides/low_precision_training.md) on why this can be difficult. But essentially rather than training in BF16, some (or all) aspects of training a model can be performed using 8 bits instead of 16. The challenge is doing so without degrading final performance. 

This is only enabled on specific NVIDIA hardware, namely:

* Anything after the 3000 series consumer graphics cards (such as the 4090)
* Hopper-based GPU architectures (such as the `H100` and `H200`)

What this will result in is some gain in the memory used (as we've cut the needed memory in half for some parts of training) and an increase in throughput *should* be seen as well for larger models that can replace certain layers with FP8-enabled ones.

## Configuring the Accelerator

Currently two different backends for FP8 are supported (`TransformersEngine` and `MS-AMP`), each with different capabilities and configurations. 

To use either, the same core API is used. Just pass `mixed_precision="fp8"` to either the [`Accelerator`], during `accelerate config` when prompted about mixed precision, or as part of your `config.yaml` file in the `mixed_precision` key:

```{python}
from accelerate import Accelerator
accelerator = Accelerator(mixed_precision="fp8")
```

By default, if `MS-AMP` is available in your environment, 🤗 Accelerate will automatically utilize it as a backend. To specify it yourself (and customize other parts of the FP8 mixed precision setup), you can utilize the [`utils.FP8RecipeKwargs`]:

```{python}
from accelerate import Accelerator
from accelerate.utils import FP8RecipeKwargs
kwargs = [FP8RecipeKwargs(backend="msamp")]
# Or to specify the backend as `TransformersEngine` even if MS-AMP is installed
# kwargs = [FP8RecipeKwargs(backend="te")]
accelerator = Accelerator(mixed_precision="fp8", kwarg_handlers=kwargs)
```

## Configuring MS-AMP

Of the two, `MS-AMP` is traditionally the easier one to configure as there is only a single argument: the optimization level. 

Currently two levels of optimization are supported in the 🤗 Accelerate integration, `"O1"` and `"O2"` (using the letter 'o', not zero). 

* `"O1"` will cast the weight gradients and `all_reduce` communications to happen in 8-bit, while the rest are done in 16 bit. This reduces the general GPU memory usage and speeds up communication bandwidths.
* `"O2"` will also cast first-order optimizer states into 8 bit, while the second order states are in FP16. (Currently just the `Adam` optimizer is supported). This tries it's best to minimize final accuracy degredation and will save the highest potential memory.

To specify an optimization level, pass it to the `FP8KwargsHandler` by setting the `optimization_level` argument:

```{python}
from accelerate import Accelerator
from accelerate.utils import FP8RecipeKwargs
kwargs = [FP8RecipeKwargs(backend="msamp", optimization_level="O2")]
accelerator = Accelerator(mixed_precision="fp8", kwarg_handlers=kwargs)
```

## Configuring TransformersEngine

TransformersEngine has much more available for customizing how and what FP8 calculations are performed. A full list of supported arguments and what they mean are available in [NVIDIA's documentation](https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/api/common.html), however they are restated as part of [`FP8KwargsHandler`]'s docstring for your convience. 

🤗 Accelerate tries to set sensible defaults, but exploring and tweaking the various parameters yourself can lead to better performance potentially.

To use it, specify `backend="te"` and modify any of the arguments you want as part of your kwarg handler:

```{python}
from accelerate import Accelerator
from accelerate.utils import FP8RecipeKwargs
kwargs = [FP8RecipeKwargs(backend="te", ...)]
accelerator = Accelerator(mixed_precision="fp8", kwarg_handlers=kwargs)
```

## Futher Reading

To learn more about training in FP8 please check out the following resources:

* [Our concept guide](../concept_guides/low_precision_training.md) detailing into more about both TransformersEngine and MS-AMP
* [The `transformers-engine` documentation](https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/api/common.html)
* [The `MS-AMP` documentation](https://azure.github.io/MS-AMP/docs/)