0.26.0

This release has important bug fixes for the StableEmbedding layer and it introduces new optimizers AdaGrad, and AdamW. The 0.26.0 release also features a new, lightweight embedding class, bnb.nn.Embedding which uses 32-bit optimizers but no layer norm. This layer allows for the easy use of pretrained models that do not use embedding layer norms. Now available on pip.

Changelog

Features:

• Added Adagrad (without grad clipping) as 32-bit and 8-bit block-wise optimizer.
• Added AdamW (copy of Adam with weight decay init 1e-2). #10
• Introduced ModuleConfig overrides which can be seamlessly be used at initialization time of a module.
• Added bnb.nn.Embedding layer which runs at 32-bit but without the layernorm. This works well if you need to fine-tune pretrained models that do not have a embedding layer norm. #19

Bug fixes:

• Fixed a bug where weight decay was incorrectly applied to 32-bit Adam. #13
• Fixed an unsafe use of eval. #8
• Fixed a bug where the StableEmbedding layer 32-bit optimizer override would not work without registering the whole model first (bnb.optim.GlobalOptimManager.get_instance().register_parameters(model.parameters())). #13 #15

Docs:

• Added instructions how to solve "__fatbinwrap_" errors.