N-Bit Precision¶

There are numerous benefits to using numerical formats with lower precision than the 32-bit floating-point or higher precision such as 64-bit floating-point.

Lower precision, such as the 16-bit floating-point, requires less memory enabling the training and deployment of large neural networks, enhances data transfer operations since they require less memory bandwidth and run batch operations much faster on GPUs that support Tensor Core. [1].

Higher precision, such as the 64-bit floating-point, can be used for highly sensitive use-cases.

Following are the precisions available in Lightning along with their supported Accelerator:

Precision with Accelerators¶
Precision	CPU	GPU	TPU	IPU	HPU
16	No	Yes	No	Yes	No
BFloat16	Yes	Yes	Yes	No	Yes
32	Yes	Yes	Yes	Yes	Yes
64	Yes	Yes	No	No	No

Mixed Precision¶

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].

Note

In some cases, it is essential to remain in FP32 for numerical stability, so keep this in mind when using mixed precision. For example, when running scatter operations during the forward (such as torchpoint3d), computation must remain in FP32.

Warning

Do not cast anything to other dtypes manually using torch.autocast or tensor.half() when using native precision because this can bring instability.

class LitModel(LightningModule):
    def training_step(self, batch, batch_idx):
        outs = self(batch)

        a_float32 = torch.rand((8, 8), device=self.device, dtype=self.dtype)
        b_float32 = torch.rand((8, 4), device=self.device, dtype=self.dtype)

        # casting to float16 manually
        with torch.autocast(device_type=self.device.type):
            c_float16 = torch.mm(a_float32, b_float32)
            target = self.layer(c_float16.flatten()[None])

        # here outs is of type float32 and target is of type float16
        loss = torch.mm(target @ outs).float()
        return loss


trainer = Trainer(accelerator="gpu", devices=1, precision=32)

FP16 Mixed Precision¶

In most cases, mixed precision uses FP16. Supported PyTorch operations automatically run in FP16, saving memory and improving throughput on the supported accelerators.

Note

When using TPUs, setting precision=16 will enable bfloat16, the only supported half precision type on TPUs.

Trainer(accelerator="gpu", devices=1, precision=16)

PyTorch Native¶

PyTorch 1.6 release introduced mixed precision functionality into their core as the AMP package, torch.cuda.amp. It is more flexible and intuitive compared to 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. Lightning uses native amp by default with precision=16|"bf16". You can also set it using:

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 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.

Trainer(accelerator="gpu", devices=1, amp_backend="apex", precision=16)

Set the NVIDIA optimization level via the trainer.

Trainer(accelerator="gpu", devices=1, amp_backend="apex", amp_level="O2", precision=16)

BFloat16 Mixed Precision¶

Warning

BFloat16 requires PyTorch 1.10 or later and is only supported with PyTorch Native AMP.

BFloat16 is also experimental and may not provide significant speedups or memory improvements, offering better numerical stability.

Do note for GPUs, the most significant benefits require Ampere 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.

Under the hood, we use torch.autocast with the dtype set to bfloat16, with no gradient scaling.

Trainer(accelerator="gpu", devices=1, precision="bf16")

It is also possible to use BFloat16 mixed precision on the CPU, relying on MKLDNN under the hood.

Trainer(precision="bf16")

Single Precision¶

PyTorch models train with 32-bit floating-point (FP32) arithmetic by default. Lightning uses 32-bit by default. You can also set it using:

Trainer(precision=32)

Double Precision¶

Lightning supports training models with double precision/64-bit. You can set it using:

Trainer(precision=64)

Note

Since in deep learning, memory is always a bottleneck, especially when dealing with a large volume of data and with limited resources. It is recommended using single precision for better speed. Although you can still use it if you want for your particular use-case.

Precision Plugins¶

You can also customize and pass your own Precision Plugin by subclassing the PrecisionPlugin class.

Perform pre and post backward/optimizer step operations such as scaling gradients.
Provide context managers for forward, training_step, etc.

class CustomPrecisionPlugin(PrecisionPlugin):
    precision = 16

    ...

trainer = Trainer(plugins=[CustomPrecisionPlugin()])

8-bit Optimizer¶

It is possible to further reduce the precision using third-party libraries like 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).