bitsandbytes-rocm/CHANGELOG.md

0.0.21

• Ampere, RTX 30 series GPUs now compatible with the library.

0.0.22:

• Fixed an error where a reset_parameters() call on the StableEmbedding would lead to an error in older PyTorch versions (from 1.7.0).

0.0.23:

Bugs:

• Unified quantization API: each quantization function now returns Q, S where Q is the quantized tensor and S the quantization state which may hold absolute max values, a quantization map or more. For dequantization all functions now accept the inputs Q, S so that Q is dequantized with the quantization state S.
• Fixed an issue where the CUDA 11.1 binary was not compiled with the right headers

API changes:

• Block-wise quantization for optimizers now enabled by default

Features:

• Block-wise quantization routines now support CPU Tensors.

0.0.24:

• Fixed a bug where a float/half conversion led to a compilation error for CUDA 11.1 on Turning GPUs.
• removed Apex dependency for bnb LAMB

0.0.25:

Features:

• Added skip_zeros for block-wise and 32-bit optimizers. This ensures correct updates for sparse gradients and sparse models.
• Added support for Kepler GPUs. (#4)
• Added Analysis Adam to track 8-bit vs 32-bit quantization errors over time.
• Make compilation more user friendly.

Bug fixes:

• fixed "undefined symbol: __fatbinwrap_38" error for P100 GPUs on CUDA 10.1 (#5)

Docs:

• Added docs with instructions to compile from source.

0.26.0:

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.

0.30.0

8-bit Inference Update

Features:

• Added 8-bit matrix multiplication form cuBLAS, and cuBLASLt as well as multiple GEMM kernels (GEMM, GEMMEx, GEMMLt)
• Added 8-bit Linear layers with 8-bit Params that perform memory efficient inference with an option for 8-bit mixed precision matrix decomposition for inference without performance degradation
• Added quantization methods for "fake" quantization as well as optimized kernels vector-wise quantization and equalization as well as optimized cuBLASLt transformations
• CPU only build now available (Thank you, @mryab)

Deprecated:

• Pre-compiled release for CUDA 9.2, 10.0, 10.2 no longer available

0.31.0

8-bit Inference and Packaging Update

Features:

• added direct outlier extraction. This enables outlier extraction without fp16 weights without performance degradation.
• Added automatic CUDA SETUP procedure and packaging all binaries into a single bitsandbytes package.

0.32.0

8-bit Inference Performance Enhancements

We added performance enhancements for small models. This makes small models about 2x faster for LLM.int8() inference.

Features:

• Int32 dequantization now supports fused biases.
• Linear8bitLt now uses a fused bias implementation.
• Change .data.storage().data_ptr() to .data.data_ptr() to enhance inference performance.

Bug fixes:

• Now throws and error if LLM.int8() is used on a GPU that is not supported.
• Enhances error messaging if CUDA SETUP fails.