Memory efficient backprop

This release introduces memory-efficient backprop through frozen weights where the gradient is calculated from the 8-bit weights but is computed in fp16. This is useful for creating Low-rank (LoRa) Adapters for fine-tuning large models.

This is a feature contributed by @dbaranchuk and @justheuristic.

0.34.0

Bug fixes and memory-efficient backprop

Features:

• Linear8bitLt layer now supports memory_efficient_backward=True which enables backprop of gradients through frozen weights.

Bug fixes:

• fixed an issue where too many threads were created in blockwise quantization on the CPU for large tensors

0.33.0: Various bug fixes

0.33.0

Various bug fixes

Features:

• CPU quantization now supports a variable blocksize variable to enhance quantization speed or precision. 19a7adc

Bug fixes:

• fixed an issue in CPU quantization where tensors with more than 2^31 elements would fail 19a7adc
• fixed a bug where cpu binaries would fail if no GPU would be detected eab4d82
• fixed an issue where cpu binaries cause additional stdout messages 92a3363
• fixed an import of bnb.utils 2e630b5

We thank @mryab, @mbrukman, @chessgecko, @dbaranchuk for pull request with bug fixes and new features.