GitLab

* Test g5g runner

* Switch L4 to L40S runner; swap GitHub Linux T4 runner for AWS g4dn

* Run tests on last 2 pytorch stable releases

* Run tests on last 2 pytorch stable releases

* Improvements for testing suite

* Add workflow for macOS arm64 CPU tests

* Run unit tests on GH Actions

* fix

* fix

* trigger workflow

* Update

* Update

* Update

* Run tests nightly

* Disable paged optimizer test on Windows

* Skip unit tests on Windows for CUDA 12.x (driver on runner is too old)

* Additional 4bit CPU ops

* Additional 4bit CPU ops

* Implement additional device-agnostic ops and test updates

* More test fixes

* int8 tests passing

* Fix feature flag for multi_backend

* Include device support tags for transformers multi-backend compatability; add xpu() and cpu() to Params4bit

* Make test suite more device-agnostic

* Additional device agnostic tests

* Additional device agnosticism for tests

* Add BNB_TEST_DEVICE env var to manually select device for unit tests

* Include device support tags for transformers multi-backend compatability; add xpu() and cpu() to Params4bit

* Make test suite more device-agnostic

* Additional device agnostic tests

* Additional device agnosticism for tests

* Add BNB_TEST_DEVICE env var to manually select device for unit tests

* Small bugfix for int8 test

* Exclude backward() from code coverage reports

* Params4bit: don't try to quantize when moving to meta device

* Testing cleanup

* More test cleanup

* Additional deprecations/removals.

* Skip benchmark, deprecated, slow tests by default

* Drop Python 3.8 support.

* Formatting

* Sketch out first custom op registration

* Add note

* Initial int8 op registration

* Cleanup some deprecated functions.

* Int8 ops updates; tests

* Implement 4bit quant/dequant ops

* Fix nested quant

* cleanup

* Test improvements

* Clean up and improve tests

* Add higher level custom op for int8 matmul + dequant + bias

* Add gemv 4bit custom op

* Cleanup

* Implement out kwarg overloads for custom ops

* Update PyTorch minimum to 2.1

* Deprecation updates

* Deprecation updates

* Cleanup; rename int8_linear_dequant -> int8_scaled_mm

* Bump min pytorch to 2.2

* cleanup

* Test reorganization

* Remove deprecated supports_igemmlt

* More cleanup

* Cleanup obsolete C++/CUDA code

* Cleanup

* Create 'default' backend for fallback op implementations; initial CPU nf4 work

* Stub out for multi-platform

* Fix serialization tests for torch>=2.6.0

* Add example for torch.compile e2e inference

* Test update

---------
Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com>

* Start of int8 refactor: remove col32/col_ampere/col_turing transforms in new igemmlt implementation

* Fix unintended change

* New naive mm_dequant kernel for row-major; cleanup

* fix

* int8 refactor: initial sparse decomp, cleanup

* Int8 refactoring: remove separate NO_CUBLASLT build; more cleanup

* int8: inference optimizations, some cleanup

* int8: more tests passing, cleanup

* int8 - more cleanup, most tests passing

* int8: specify CUDA stream for int8 ops

* perf: reduce overhead from getting cudaStream ptr

* Mark some functions for deprecation.

* int8 sparse decomp: small perf improvement

* update setup.py

* Update bitsandbytes/autograd/_functions.py
Co-authored-by: Aarni Koskela <akx@iki.fi>

* Update bitsandbytes/functional.py
Co-authored-by: Aarni Koskela <akx@iki.fi>

* Update bitsandbytes/functional.py
Co-authored-by: Aarni Koskela <akx@iki.fi>

* Update bitsandbytes/research/autograd/_functions.py
Co-authored-by: Aarni Koskela <akx@iki.fi>

* int8 - perf improvement for sparse decomposition inference; deprecate get_tensor_stream() in favor of new private fn

* int8 cleanup

* Ignore ruff rule ISC001 (incompatible with formatter)

* add comment

* int8 more cleanup

* Update bitsandbytes/functional.py
Co-authored-by: Aarni Koskela <akx@iki.fi>

* int8: rename / deprecate old fn signatures

* Update bitsandbytes/functional.py
Co-authored-by: Aarni Koskela <akx@iki.fi>

* type annotation

* format update

* Update bitsandbytes/research/autograd/_functions.py
Co-authored-by: Aarni Koskela <akx@iki.fi>

* cleanup

* Add comment to explain division optimization

* more cleanup

* Update bitsandbytes/functional.py
Co-authored-by: Aarni Koskela <akx@iki.fi>

* Update bitsandbytes/functional.py
Co-authored-by: Aarni Koskela <akx@iki.fi>

* Update bitsandbytes/functional.py
Co-authored-by: Aarni Koskela <akx@iki.fi>

* cleanup

* Type annotations, cleanup

* remove unused kernels; improved type annotations

* small perf optimization for single-GPU systems

* small perf optimization for single-GPU systems

* update docstrings

* Improve docs and tests

* Update docstring

* Update test

* add benchmarking script

* test cleanup: add deprecated marker, move benchmarks out

* Add int8 dequant function; misc improvements

* int8 matmul fallback for inner dims not divisible by 4

* improve register usage of kInt8VectorQuant - especially for A100/H100

* disable fail-fast for package build

* maxwell compat

* ptxas verbose

* docs update

* doc update

* backward fix

* Bugfix sparse decomp

* Int8 fix for PEFT OLoRA init

* Fix test for deprecated spmm_coo

* test improvement

* doc update

* typo

* doc cleanup

* docs

* add inference benchmark script

* Add benchmarks, doc update

---------
Co-authored-by: Aarni Koskela <akx@iki.fi>

* test_nvidia_transform: fix variable reference

`out_order` is the global parametrization list, not the test fixture argument

* Make `parametrize` use more idiomatic

* Use a more deterministic helper for `dim*` determination

* Convert NO_CUBLASLT errors into skips too

* Mark slow and benchmark tests as such (allows `-k "not benchmark"`)

`out_order` is the global parametrization list, not the test fixture argument

* Adjust Ruff configuration

* do not autofix always
* be less strict around tests and benchmarks
* adjust ignores for now

* Ruff: autofix I and F401

* Apply ruff autofixes

* Fix RUF013 complaint

* Fix mutable default in replace_linear

* Don't use bare except

* Wrap bitsandbytes.__main__ entrypoint in function; fix "sensible" typo

* Fix ruff B008 (function call in arguments)

* Add ruff noqas as suitable

* Fix RUF005 (splat instead of concatenating)

* Fix B018 (useless expression)

* Add pre-commit configuration + GitHub Actions lint workflow

* Fix unused `e` in bitsandbytes/__main__.py

* fix merge conflict resolution error

* run pre-commit hook

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Co-authored-by: Titus <9048635+Titus-von-Koeller@users.noreply.github.com>

* implicitly skip any test that implicitly uses CUDA on a non-CUDA box
* add a `requires_cuda` fixture

This PR adds initial FSDP support for training QLoRA models. It enables basic FSDP and CPU Offload support, with low memory training via FSDP.sync_module_states option unsupported.

This PR builds off of #840 commit 8278fca and BNB FSDP by @TimDettmers and @Titus-von-Koeller.

An example of using this PR to finetune QLoRA models with FSDP can be found in the demo repo: AnswerDotAi/fsdp_qlora.

* Minimal changes for fp32 4bit storage from BNB commit 8278fca

* Params4bit with selectable storage dtype

* possible fix for double quantizing linear weight & quant storage dtype

* minor fixes in Params4bit for peft tests

* remove redundant

* add float16

* update test

* Remove float16 quant cast as there are fp32, bf16, & fp16 quant kernels

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Co-authored-by: Kerem Turgutlu <keremturgutlu@gmail.com>