- Linear8bitLt now supports for pre-turing GPUs by temporarily upcasting quantized weights.
- added a test for linear8bitlt accuracy with the new fallback, the accuracy is similar than the real thing, (slightly better due to non-quantized A)
- performance is roughly halfway between the default mode and memory_efficient_backward
Alternatives considered:
- cupy - slow, casting to float internally
- triton - fast but unstable af. every 3rd attempt to matmul is a segfault
- bnb.functional.igemm (no lt) - "CuBLAS Error 8" on old GPUs
Co-authored-by: Aleksandr Borzunov <borzunov.alexander@gmail.com>
A patch to bitsandbytes 0.34.0 that introduces an option to run backward pass in default (fast) matrix layout.
Authors: cxb inversion by @borzunov, original 8bit code by @timdettmers
* optimized layout inversion code by @borzunov ([original code](https://colab.research.google.com/drive/1EJ0MKifajXSSVq7O2_QGwtb0l6gRAGrh?usp=sharing)) to use less forward calls
* implemented CustomLinear8bitLt, a child of Linear8bitLt that can do backward without CB
* added exact match tests for layouts and linear layers: see tests/test_linear8bitlt.py
* switched petals to the new layer type
Core idea: layouts apply the same permutation to every tile in the matrix. We can treat this as (batched) gather ops.
Reshape input tensor so that ij-th gather operation op will apply to ij-th elements in each tile.
Prototype:
Layout info: https://github.com/TimDettmers/bitsandbytes/blob/main/csrc/kernels.cu#L2130-L2136
Co-authored-by: Alexander Borzunov <hxrussia@gmail.com>
Co-authored-by: Aleksandr Borzunov <borzunov.alexander@gmail.com>
Co-authored-by: Tim Dettmers <tim.dettmers@gmail.com>
