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