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• Bfloat16 is currently an optimal tradeoff between range and relative error for deep networks.
• Bfloat16 can be used quite efficiently on Nvidia GPUs with Ampere architecture (A100, A10, A30, RTX3090...)

However, neither AMP in pytorch is ready for bfloat16, nor optimizers.

If you just convert all weights and inputs to bfloat16, you're likely to run into an issue of stale weights: updates are too small to modify bfloat16 weight (see gopher paper, section C2 for a large-scale example).

There are two possible remedies:

• keep weights in float32 (precise) and bfloat16 (approximate)
• keep weights in bfloat16, and keep correction term in bfloat16

As recent study has shown, both options are completely competitive in quality to float32 training.

Install:

pip install git+https://github.com/arogozhnikov/adamw_bfloat16.git

Use as a drop-in replacement for pytorch's AdamW:

import torch
from adamw_bfloat16 import LR, AdamW_BF16
model = model.to(torch.bfloat16)

# default preheat and decay
optimizer = AdamW_BF16(model.parameters())

# configure LR schedule. Use built-in scheduling opportunity
optimizer = AdamW_BF16(model.parameters(), lr_function=LR(lr=1e-4, preheat_steps=5000, decay_power=-0.25))