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refine docs for multi-backend alpha release (#1380)
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* refine docs for multi-backend alpha release

* docs: further tweaks to multi-backend alpha docs

* docs: further tweaks to multi-backend alpha docs

* docs: further tweaks to multi-backend alpha docs

* docs: add multi-backend feedback links

* docs: add request for contributions

* docs: small fixes

* docs: small fixes

* docs: add info about `main` continuous build

* docs: further tweaks to multi-backend alpha docs

* docs: further tweaks to multi-backend alpha docs
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Titus-von-Koeller authored Sep 30, 2024
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224 changes: 181 additions & 43 deletions docs/source/installation.mdx
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# Installation
# Installation Guide

## CUDA
Welcome to the installation guide for the `bitsandbytes` library! This document provides step-by-step instructions to install `bitsandbytes` across various platforms and hardware configurations. The library primarily supports CUDA-based GPUs, but the team is actively working on enabling support for additional backends like AMD ROCm, Intel, and Apple Silicon.

bitsandbytes is only supported on CUDA GPUs for CUDA versions **11.0 - 12.5**. However, there's a multi-backend effort under way which is currently in alpha release, check [the respective section below in case you're interested to help us with early feedback](#multi-backend).
> [!TIP]
> For a high-level overview of backend support and compatibility, see the [Multi-backend Support](#multi-backend) section.
The latest version of bitsandbytes builds on:
## Table of Contents

| OS | CUDA | Compiler |
|---|---|---|
| Linux | 11.7 - 12.3 | GCC 11.4 |
| | 12.4+ | GCC 13.2 |
| Windows | 11.7 - 12.4 | MSVC 19.38+ (VS2022 17.8.0+) |
- [CUDA](#cuda)
- [Installation via PyPI](#cuda-pip)
- [Compile from Source](#cuda-compile)
- [Multi-backend Support (Alpha Release)](#multi-backend)
- [Supported Backends](#multi-backend-supported-backends)
- [Pre-requisites](#multi-backend-pre-requisites)
- [Installation](#multi-backend-pip)
- [Compile from Source](#multi-backend-compile)
- [PyTorch CUDA Versions](#pytorch-cuda-versions)

> [!TIP]
> MacOS support is still a work in progress! Subscribe to this [issue](https://github.com/TimDettmers/bitsandbytes/issues/1020) to get notified about discussions and to track the integration progress.
## CUDA[[cuda]]

For Linux systems, make sure your hardware meets the following requirements to use bitsandbytes features.
`bitsandbytes` is currently only supported on CUDA GPUs for CUDA versions **11.0 - 12.5**. However, there's an ongoing multi-backend effort under development, which is currently in alpha. If you're interested in providing feedback or testing, check out [the multi-backend section below](#multi-backend).

| **Feature** | **Hardware requirement** |
|---|---|
| LLM.int8() | NVIDIA Turing (RTX 20 series, T4) or Ampere (RTX 30 series, A4-A100) GPUs |
| 8-bit optimizers/quantization | NVIDIA Kepler (GTX 780 or newer) |
### Supported CUDA Configurations[[cuda-pip]]

The latest version of `bitsandbytes` builds on the following configurations:

| **OS** | **CUDA Version** | **Compiler** |
|-------------|------------------|----------------------|
| **Linux** | 11.7 - 12.3 | GCC 11.4 |
| | 12.4+ | GCC 13.2 |
| **Windows** | 11.7 - 12.4 | MSVC 19.38+ (VS2022) |

For Linux systems, ensure your hardware meets the following requirements:

| **Feature** | **Hardware Requirement** |
|---------------------------------|--------------------------------------------------------------------|
| LLM.int8() | NVIDIA Turing (RTX 20 series, T4) or Ampere (RTX 30 series, A4-A100) GPUs |
| 8-bit optimizers/quantization | NVIDIA Kepler (GTX 780 or newer) |

> [!WARNING]
> bitsandbytes >= 0.39.1 no longer includes Kepler binaries in pip installations. This requires manual compilation, and you should follow the general steps and use `cuda11x_nomatmul_kepler` for Kepler-targeted compilation.
> `bitsandbytes >= 0.39.1` no longer includes Kepler binaries in pip installations. This requires [manual compilation using](#cuda-compile) the `cuda11x_nomatmul_kepler` configuration.
To install from PyPI.

```bash
pip install bitsandbytes
```

### Compile from source[[compile]]
### `pip install` pre-built wheel from latest `main` commit

If you would like to use new feature even before they are officially released and help us test them, feel free to install the wheel directly from our CI (*the wheel links will remain stable!*):

<hfoptions id="OS">
<hfoption id="Linux">

```
# Note, if you don't want to reinstall BNBs dependencies, append the `--no-deps` flag!
pip install --force-reinstall 'https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_main/bitsandbytes-0.44.2.dev0-py3-none-manylinux_2_24_x86_64.whl'
```

</hfoption>
<hfoption id="Windows">

```
# Note, if you don't want to reinstall BNBs dependencies, append the `--no-deps` flag!
pip install --force-reinstall 'https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_multi-backend-refactor/bitsandbytes-0.44.1.dev0-py3-none-macosx_13_1_arm64.whl'
```
</hfoption>
</hfoptions>

### Compile from source[[cuda-compile]]

> [!TIP]
> Don't hesitate to compile from source! The process is pretty straight forward and resilient. This might be needed for older CUDA versions or other less common configurations, which we don't support out of the box due to package size.
For Linux and Windows systems, you can compile bitsandbytes from source. Installing from source allows for more build options with different CMake configurations.
For Linux and Windows systems, compiling from source allows you to customize the build configurations. See below for detailed platform-specific instructions (see the `CMakeLists.txt` if you want to check the specifics and explore some additional options):

<hfoptions id="source">
<hfoption id="Linux">

To compile from source, you need CMake >= **3.22.1** and Python >= **3.8** installed. Make sure you have a compiler installed to compile C++ (gcc, make, headers, etc.). For example, to install a compiler and CMake on Ubuntu:
To compile from source, you need CMake >= **3.22.1** and Python >= **3.8** installed. Make sure you have a compiler installed to compile C++ (`gcc`, `make`, headers, etc.).

For example, to install a compiler and CMake on Ubuntu:

```bash
apt-get install -y build-essential cmake
Expand All @@ -48,16 +91,16 @@ You should also install CUDA Toolkit by following the [NVIDIA CUDA Installation

Refer to the following table if you're using another CUDA Toolkit version.

| CUDA Toolkit | GCC |
|---|---|
| >= 11.4.1 | >= 11 |
| >= 12.0 | >= 12 |
| >= 12.4 | >= 13 |
| CUDA Toolkit | GCC |
|--------------|-------|
| >= 11.4.1 | >= 11 |
| >= 12.0 | >= 12 |
| >= 12.4 | >= 13 |

Now to install the bitsandbytes package from source, run the following commands:

```bash
git clone https://github.com/TimDettmers/bitsandbytes.git && cd bitsandbytes/
git clone https://github.com/bitsandbytes-foundation/bitsandbytes.git && cd bitsandbytes/
pip install -r requirements-dev.txt
cmake -DCOMPUTE_BACKEND=cuda -S .
make
Expand All @@ -81,7 +124,7 @@ Refer to the following table if you're using another CUDA Toolkit version.
| >= 11.6 | 19.30+ (VS2022) |

```bash
git clone https://github.com/TimDettmers/bitsandbytes.git && cd bitsandbytes/
git clone https://github.com/bitsandbytes-foundation/bitsandbytes.git && cd bitsandbytes/
pip install -r requirements-dev.txt
cmake -DCOMPUTE_BACKEND=cuda -S .
cmake --build . --config Release
Expand All @@ -93,7 +136,7 @@ Big thanks to [wkpark](https://github.com/wkpark), [Jamezo97](https://github.com
</hfoption>
</hfoptions>

### PyTorch CUDA versions
### PyTorch CUDA versions[[pytorch-cuda-versions]]

Some bitsandbytes features may need a newer CUDA version than the one currently supported by PyTorch binaries from Conda and pip. In this case, you should follow these instructions to load a precompiled bitsandbytes binary.

Expand All @@ -105,7 +148,7 @@ Some bitsandbytes features may need a newer CUDA version than the one currently
Then locally install the CUDA version you need with this script from bitsandbytes:

```bash
wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/install_cuda.sh
wget https://raw.githubusercontent.com/bitsandbytes-foundation/bitsandbytes/main/install_cuda.sh
# Syntax cuda_install CUDA_VERSION INSTALL_PREFIX EXPORT_TO_BASH
# CUDA_VERSION in {110, 111, 112, 113, 114, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125}
# EXPORT_TO_BASH in {0, 1} with 0=False and 1=True
Expand Down Expand Up @@ -134,28 +177,62 @@ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/YOUR_USERNAME/local/cuda-11.7

3. Now when you launch bitsandbytes with these environment variables, the PyTorch CUDA version is overridden by the new CUDA version (in this example, version 11.7) and a different bitsandbytes library is loaded.

## Multi-backend[[multi-backend]]
## Multi-backend Support (Alpha Release)[[multi-backend]]

> [!TIP]
> This functionality is currently in preview and therefore not yet production-ready! Please reference [this guide](./non_cuda_backends) for more in-depth information about the different backends and their current status.
> This functionality is currently in preview and not yet production-ready. We very much welcome community feedback, contributions and leadership on topics like Apple Silicon as well as other less common accellerators! For more information, see [this guide on multi-backend support](./non_cuda_backends).
**Link to give us feedback** (bugs, install issues, perf results, requests, etc.)**:**

<hfoptions id="platform">
<hfoption id="ROCm">

[**Multi-backend refactor: Alpha release (AMD ROCm ONLY)**](https://github.com/bitsandbytes-foundation/bitsandbytes/discussions/1339)

</hfoption>
<hfoption id="Intel CPU+GPU">

[**Multi-backend refactor: Alpha release (INTEL ONLY)**](https://github.com/bitsandbytes-foundation/bitsandbytes/discussions/1338)

</hfoption>
<hfoption id="Apple Silicon / Metal (MPS)">

Please follow these steps to install bitsandbytes with device-specific backend support other than CUDA:
[**Github Discussion space on coordinating the kickoff of MPS backend development**](https://github.com/bitsandbytes-foundation/bitsandbytes/discussions/1340)

### Pip install the pre-built wheel (recommended for most)
</hfoption>
</hfoptions>

WIP (will be added in the coming days)
### Supported Backends[[multi-backend-supported-backends]]

### Compilation
| **Backend** | **Supported Versions** | **Python versions** | **Architecture Support** | **Status** |
|-------------|------------------------|---------------------------|-------------------------|------------|
| **AMD ROCm** | 6.1+ | 3.10+ | minimum CDNA - `gfx90a`, RDNA - `gfx1100` | Alpha |
| **Apple Silicon (MPS)** | WIP | 3.10+ | M1/M2 chips | Planned |
| **Intel CPU** | v2.4.0+ (`ipex`) | 3.10+ | Intel CPU | Alpha |
| **Intel GPU** | v2.4.0+ (`ipex`) | 3.10+ | Intel GPU | Experimental |

For each supported backend, follow the respective instructions below:

### Pre-requisites[[multi-backend-pre-requisites]]

To use bitsandbytes non-CUDA backends, be sure to install:

```
pip install "transformers>=4.45.1"
```

<hfoptions id="backend">
<hfoption id="AMD ROCm">

#### AMD GPU

bitsandbytes is fully supported from ROCm 6.1 onwards (currently in alpha release).
> [!WARNING]
> Pre-compiled binaries are only built for ROCm versions `6.1.0`/`6.1.1`/`6.1.2`/`6.2.0` and `gfx90a`, `gfx942`, `gfx1100` GPU architectures. [Find the pip install instructions here](#multi-backend-pip).
>
> Other supported versions that don't come with pre-compiled binaries [can be compiled for with these instructions](#multi-backend-compile).
>
> **Windows is not supported for the ROCm backend**; also not WSL2 to our knowledge.
> [!TIP]
> If you would like to install ROCm and PyTorch on bare metal, skip Docker steps and refer to our official guides at [ROCm installation overview](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/install-overview.html#rocm-install-overview) and [Installing PyTorch for ROCm](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/3rd-party/pytorch-install.html#using-wheels-package) (Step 3 of wheels build for quick installation). Please make sure to get PyTorch wheel for the installed ROCm version.
> If you would like to install ROCm and PyTorch on bare metal, skip the Docker steps and refer to ROCm's official guides at [ROCm installation overview](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/install-overview.html#rocm-install-overview) and [Installing PyTorch for ROCm](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/3rd-party/pytorch-install.html#using-wheels-package) (Step 3 of wheels build for quick installation). Special note: please make sure to get the respective ROCm-specific PyTorch wheel for the installed ROCm version, e.g. `https://download.pytorch.org/whl/nightly/rocm6.2/`!
```bash
# Create a docker container with latest ROCm image, which includes ROCm libraries
Expand All @@ -165,9 +242,70 @@ apt-get update && apt-get install -y git && cd home

# Install pytorch compatible with above ROCm version
pip install torch --index-url https://download.pytorch.org/whl/rocm6.1/
```

# Install bitsandbytes from PyPI
# (This is supported on Ubuntu 22.04, Python 3.10, ROCm 6.1.0/6.1.1/6.1.2/6.2.0 and gpu arch - gfx90a, gfx942, gfx1100
</hfoption>
<hfoption id="Intel CPU + GPU">

Compatible hardware and functioning `import intel_extension_for_pytorch as ipex` capable environment with Python `3.10` as the minimum requirement.

Please refer to [the official Intel installations instructions](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=cpu&version=v2.4.0%2bcpu&os=linux%2fwsl2) for guidance on how to pip install the necessary `intel_extension_for_pytorch` dependency.

</hfoption>
<hfoption id="Apple Silicon (MPS)">

> [!TIP]
> Apple Silicon support is still a WIP. Please visit and write us in [this Github Discussion space on coordinating the kickoff of MPS backend development](https://github.com/bitsandbytes-foundation/bitsandbytes/discussions/1340) and coordinate a community-led effort to implement this backend.
</hfoption>
</hfoptions>

### Installation

You can install the pre-built wheels for each backend, or compile from source for custom configurations.

#### Pre-built Wheel Installation (recommended)[[multi-backend-pip]]

<hfoptions id="platform">
<hfoption id="Linux">

```
# Note, if you don't want to reinstall BNBs dependencies, append the `--no-deps` flag!
pip install --force-reinstall 'https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_multi-backend-refactor/bitsandbytes-0.44.1.dev0-py3-none-manylinux_2_24_x86_64.whl'
```

</hfoption>
<hfoption id="Windows">

```
# Note, if you don't want to reinstall BNBs dependencies, append the `--no-deps` flag!
pip install --force-reinstall 'https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_multi-backend-refactor/bitsandbytes-0.44.1.dev0-py3-none-win_amd64.whl'
```

</hfoption>
<hfoption id="Mac">

> [!WARNING]
> bitsandbytes does not yet support Apple Silicon / Metal with a dedicated backend. However, the build infrastructure is in place and the below pip install will eventually provide Apple Silicon support as it becomes available on the `multi-backend-refactor` branch based on community contributions.
```
# Note, if you don't want to reinstall BNBs dependencies, append the `--no-deps` flag!
pip install --force-reinstall 'https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_multi-backend-refactor/bitsandbytes-0.44.1.dev0-py3-none-macosx_13_1_arm64.whl'
```

</hfoption>
</hfoptions>

#### Compile from Source[[multi-backend-compile]]

<hfoptions id="backend">
<hfoption id="AMD ROCm">

#### AMD GPU

bitsandbytes is fully supported from ROCm 6.1 onwards (currently in alpha release).

```bash
# Please install from source if your configuration doesn't match with these)
pip install bitsandbytes

Expand Down Expand Up @@ -195,10 +333,10 @@ pip install -e . # `-e` for "editable" install, when developing BNB (otherwise
Similar to the CUDA case, you can compile bitsandbytes from source for Linux and Windows systems.

The below commands are for Linux. For installing on Windows, please adapt the below commands according to the same pattern as described [the section above on compiling from source under the Windows tab](#compile).
The below commands are for Linux. For installing on Windows, please adapt the below commands according to the same pattern as described [the section above on compiling from source under the Windows tab](#cuda-compile).

```
git clone --depth 1 -b multi-backend-refactor https://github.com/TimDettmers/bitsandbytes.git && cd bitsandbytes/
git clone --depth 1 -b multi-backend-refactor https://github.com/bitsandbytes-foundation/bitsandbytes.git && cd bitsandbytes/
pip install intel_extension_for_pytorch
pip install -r requirements-dev.txt
cmake -DCOMPUTE_BACKEND=cpu -S .
Expand Down
3 changes: 3 additions & 0 deletions docs/source/non_cuda_backends.mdx
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# Multi-backend support (non-CUDA backends)

> [!Tip]
> If you feel these docs need some additional info, please consider submitting a PR or respectfully request the missing info in one of the below mentioned Github discussion spaces.
As part of a recent refactoring effort, we will soon offer official multi-backend support. Currently, this feature is available in a preview alpha release, allowing us to gather early feedback from users to improve the functionality and identify any bugs.

At present, the Intel CPU and AMD ROCm backends are considered fully functional. The Intel XPU backend has limited functionality and is less mature.
Expand Down

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