PyTorch, like most deep learning frameworks, trains on 32-bit floating-point (FP32) arithmetic by default. However, many deep learning models do not require this to reach complete accuracy. By conducting
operations in half-precision format while keeping minimum information in single-precision to maintain as much information as possible in crucial areas of the network, mixed precision training delivers
significant computational speedup. Switching to mixed precision has resulted in considerable training speedups since the introduction of Tensor Cores in the Volta and Turing architectures. It combines
FP32 and lower-bit floating-points (such as FP16) to reduce memory footprint and increase performance during model training and evaluation. It accomplishes this by recognizing the steps that require
complete accuracy and employing a 32-bit floating-point for those steps only, while using a 16-bit floating-point for the rest. When compared to complete precision training, mixed precision training
delivers all of these benefits while ensuring that no task-specific accuracy is lost. [`2 <https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html>`_].
In most cases, mixed precision uses FP16. Supported `PyTorch operations <https://pytorch.org/docs/stable/amp.html#op-specific-behavior>`__ automatically run in FP16, saving memory and improving throughput on the supported accelerators.
PyTorch 1.6 release introduced mixed precision functionality into their core as the AMP package, `torch.cuda.amp <https://pytorch.org/docs/stable/amp.html>`__. It is more flexible and intuitive compared to `NVIDIA APEX <https://github.com/NVIDIA/apex>`__.
Since computation happens in FP16, there is a chance of numerical instability during training. This is handled internally by a dynamic grad scaler which skips invalid steps and adjusts the scaler to ensure subsequent steps fall within a finite range. For more information `see the autocast docs <https://pytorch.org/docs/stable/amp.html#gradient-scaling>`__.
Lightning uses native amp by default with ``precision=16|"bf16"``. You can also set it using:
..testcode::
Trainer(precision=16, amp_backend="native")
NVIDIA APEX
-----------
..warning::
We strongly recommend using the above native mixed precision rather than NVIDIA APEX unless you require more refined control.
`NVIDIA APEX <https://github.com/NVIDIA/apex>`__ offers additional flexibility in setting mixed precision. This can be useful when trying out different precision configurations, such as keeping most of your weights in FP16 and running computation in FP16.
..testcode::
:skipif:not _APEX_AVAILABLE or not torch.cuda.is_available()
Do note for GPUs, the most significant benefits require `Ampere <https://en.wikipedia.org/wiki/Ampere_(microarchitecture)>`__ based GPUs, such as A100s or 3090s.
BFloat16 Mixed precision is similar to FP16 mixed precision, however, it maintains more of the "dynamic range" that FP32 offers. This means it is able to improve numerical stability than FP16 mixed precision. For more information, see `this TPU performance blogpost <https://cloud.google.com/blog/products/ai-machine-learning/bfloat16-the-secret-to-high-performance-on-cloud-tpus>`__.
It is possible to further reduce the precision using third-party libraries like `bitsandbytes <https://github.com/facebookresearch/bitsandbytes>`_. Although,
Lightning doesn't support it out of the box yet but you can still use it by configuring it in your LightningModule and setting ``Trainer(precision=32)``.
