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[pypi-ver-cpu]: https://img.shields.io/pypi/v/spconv
[pypi-ver-114]: https://img.shields.io/pypi/v/spconv-cu114
[pypi-ver-111]: https://img.shields.io/pypi/v/spconv-cu111
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[pypi-ver-120]: https://img.shields.io/pypi/v/spconv-cu120
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[pypi-url-102]: https://pypi.org/project/spconv-cu102/
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[pypi-url-111]: https://pypi.org/project/spconv-cu111/
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[pypi-url-cpu]: https://pypi.org/project/spconv/
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[pypi-url-118]: https://pypi.org/project/spconv-cu118/
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# SpConv: Spatially Sparse Convolution Library
[![Build Status](https://github.com/traveller59/spconv/workflows/build/badge.svg)](https://github.com/traveller59/spconv/actions?query=workflow%3Abuild) 
![pypi versions](https://img.shields.io/pypi/pyversions/spconv-cu117)


|                | PyPI   | Install  |Downloads  |
| -------------- |:---------------------:| ---------------------:| ---------------------:| 
| CPU (Linux Only) | [![PyPI Version][pypi-ver-cpu]][pypi-url-cpu] | ```pip install spconv``` | [![pypi monthly download][pypi-download-cpu]][pypi-url-cpu] | 
| CUDA 10.2 | [![PyPI Version][pypi-ver-102]][pypi-url-102] | ```pip install spconv-cu102```| [![pypi monthly download][pypi-download-102]][pypi-url-102]| 
| CUDA 11.3 | [![PyPI Version][pypi-ver-113]][pypi-url-113] | ```pip install spconv-cu113```| [![pypi monthly download][pypi-download-113]][pypi-url-113]| 
| CUDA 11.4 | [![PyPI Version][pypi-ver-114]][pypi-url-114] | ```pip install spconv-cu114```| [![pypi monthly download][pypi-download-114]][pypi-url-114]|
| CUDA 11.6 | [![PyPI Version][pypi-ver-116]][pypi-url-116] | ```pip install spconv-cu116```| [![pypi monthly download][pypi-download-116]][pypi-url-116]|
| CUDA 11.7 | [![PyPI Version][pypi-ver-117]][pypi-url-117] | ```pip install spconv-cu117```| [![pypi monthly download][pypi-download-117]][pypi-url-117]| 
| CUDA 11.8 | [![PyPI Version][pypi-ver-118]][pypi-url-118] | ```pip install spconv-cu118```| [![pypi monthly download][pypi-download-118]][pypi-url-118]| 
| CUDA 12.0 | [![PyPI Version][pypi-ver-120]][pypi-url-120] | ```pip install spconv-cu120```| [![pypi monthly download][pypi-download-120]][pypi-url-120]| 


```spconv``` is a project that provide heavily-optimized sparse convolution implementation with tensor core support. check [benchmark](docs/BENCHMARK.md) to see how fast spconv 2.x runs.

[Spconv 1.x code](https://github.com/traveller59/spconv/tree/v1.2.1). We won't provide any support for spconv 1.x since it's deprecated. use spconv 2.x if possible. <!--remove this message in spconv 2.2-->

Check [spconv 2.x algorithm introduction](docs/spconv2_algo.pdf) to understand sparse convolution algorithm in spconv 2.x!

## WARNING

Use spconv >= cu114 if possible. cuda 11.4 can compile greatly faster kernel in some situation.

Update Spconv: you **MUST UNINSTALL** all spconv/cumm/spconv-cuxxx/cumm-cuxxx first, use ```pip list | grep spconv``` and ```pip list | grep cumm``` to check all installed package. then use pip to install new spconv.

## NEWS

* spconv 2.3: int8 quantization support. see docs and examples for more details.

* spconv 2.2: ampere feature support (by [EvernightAurora](https://github.com/EvernightAurora)), pure c++ code generation, nvrtc, drop python 3.6

## Spconv 2.2 vs Spconv 2.1

* faster fp16 conv kernels (~5-30%) in ampere GPUs (tested in RTX 3090)
* greatly faster int8 conv kernels (~1.2x-2.7x) in ampere GPUs (tested in RTX 3090)
* drop python 3.6 support
* nvrtc support: kernel in old GPUs will be compiled in runtime.
* [libspconv](docs/PURE_CPP_BUILD.md): pure c++ build of all spconv ops. see [example](example/libspconv/run_build.sh)
* tf32 kernels, faster fp32 training, disabled by default. set ```import spconv as spconv_core; spconv_core.constants.SPCONV_ALLOW_TF32 = True``` to enable them.
* all weights are KRSC layout, some old model can't be loaded anymore.


## Spconv 2.1 vs Spconv 1.x

* spconv now can be installed by **pip**. see install section in readme for more details. Users don't need to build manually anymore!
* Microsoft Windows support (only windows 10 has been tested).
* fp32 (not tf32) training/inference speed is increased (+50~80%)
* fp16 training/inference speed is greatly increased when your layer support tensor core (channel size must be multiple of 8).
* int8 op is ready, but we still need some time to figure out how to run int8 in pytorch.
* [doesn't depend on pytorch binary](docs/FAQ.md#What-does-no-dependency-on-pytorch-mean), but you may need at least pytorch >= 1.5.0 to run spconv 2.x.
* since spconv 2.x doesn't depend on pytorch binary (never in future), it's impossible to support torch.jit/libtorch inference.

## Usage

Firstly you need to use ```import spconv.pytorch as spconv``` in spconv 2.x.

Then see [this](docs/USAGE.md).

Don't forget to check [performance guide](docs/PERFORMANCE_GUIDE.md).

### Common Solution for Some Bugs

see [common problems](docs/COMMON_PROBLEMS.md).

## Install

You need to install python >= 3.7 first to use spconv 2.x.

You need to install CUDA toolkit first before using prebuilt binaries or build from source.

You need at least CUDA 11.0 to build and run spconv 2.x. We won't offer any support for CUDA < 11.0.

### Prebuilt

We offer python 3.7-3.11 and cuda 10.2/11.3/11.4/11.7/12.0 prebuilt binaries for linux (manylinux).

We offer python 3.7-3.11 and cuda 10.2/11.4/11.7/12.0 prebuilt binaries for windows 10/11.

For Linux users, you need to install pip >= 20.3 first to install prebuilt.

**WARNING**: spconv-cu117 may require CUDA Driver >= 515.

```pip install spconv``` for CPU only (**Linux Only**). you should only use this for debug usage, the performance isn't optimized due to manylinux limit (no omp support).

```pip install spconv-cu102``` for CUDA 10.2

```pip install spconv-cu113``` for CUDA 11.3 (**Linux Only**)

```pip install spconv-cu114``` for CUDA 11.4

```pip install spconv-cu117``` for CUDA 11.7

```pip install spconv-cu120``` for CUDA 12.0

**NOTE** It's safe to have different **minor** cuda version between system and conda (pytorch) in **CUDA >= 11.0** because of [CUDA Minor Version Compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/#minor-version-compatibility). For example, you can use spconv-cu114 with anaconda version of pytorch cuda 11.1 in a OS with CUDA 11.2 installed.

**NOTE** In Linux, you can install spconv-cuxxx without install CUDA to system! only suitable NVIDIA driver is required. for CUDA 11, we need driver >= 450.82. You may need newer driver if you use newer CUDA. for cuda 11.8, you need to have driver >= 520 installed.

#### Prebuilt GPU Support Matrix

See [this page](https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/) to check supported GPU names by arch.

If you use a GPU architecture that isn't compiled in prebuilt, spconv will use NVRTC to compile a slightly slower kernel.

| CUDA version | GPU Arch List  |
| -------------- |:---------------------:|
| 11.1~11.7       | 52,60,61,70,75,80,86     | 
| 11.8+       | 60,70,75,80,86,89,90     | 

### Build from source for development (JIT, recommend)

The c++ code will be built automatically when you change c++ code in project.

For NVIDIA Embedded Platforms, you need to specify cuda arch before build: ```export CUMM_CUDA_ARCH_LIST="7.2"``` for xavier, ```export CUMM_CUDA_ARCH_LIST="6.2"``` for TX2, ```export CUMM_CUDA_ARCH_LIST="8.7"``` for orin.

You need to remove ```cumm``` in ```requires``` section in pyproject.toml after install editable ```cumm``` and before install spconv due to pyproject limit (can't find editable installed ```cumm```).

You need to ensure ```pip list | grep spconv``` and ```pip list | grep cumm``` show nothing before install editable spconv/cumm.

#### Linux

0. uninstall spconv and cumm installed by pip
1. install build-essential, install CUDA
2. ```git clone https://github.com/FindDefinition/cumm```, ```cd ./cumm```, ```pip install -e .```
3. ```git clone https://github.com/traveller59/spconv```, ```cd ./spconv```, ```pip install -e .```
4. in python, ```import spconv``` and wait for build finish.

#### Windows
0. uninstall spconv and cumm installed by pip
1. install visual studio 2019 or newer. make sure C++ development component is installed. install CUDA
2. set [powershell script execution policy](https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.core/about/about_execution_policies?view=powershell-7.1)
3. start a new powershell, run ```tools/msvc_setup.ps1```
4. ```git clone https://github.com/FindDefinition/cumm```, ```cd ./cumm```, ```pip install -e .```
5. ```git clone https://github.com/traveller59/spconv```, ```cd ./spconv```, ```pip install -e .```
6. in python, ```import spconv``` and wait for build finish.

### Build wheel from source (not recommend, this is done in CI.)

You need to rebuild ```cumm``` first if you are build along a CUDA version that not provided in prebuilts.

#### Linux

1. install build-essential, install CUDA
2. run ```export SPCONV_DISABLE_JIT="1"```
3. run ```pip install pccm cumm wheel```
4. run ```python setup.py bdist_wheel```+```pip install dists/xxx.whl```

#### Windows

1. install visual studio 2019 or newer. make sure C++ development component is installed. install CUDA
2. set [powershell script execution policy](https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.core/about/about_execution_policies?view=powershell-7.1)
3. start a new powershell, run ```tools/msvc_setup.ps1```
4. run ```$Env:SPCONV_DISABLE_JIT = "1"```
5. run ```pip install pccm cumm wheel```
6. run ```python setup.py bdist_wheel```+```pip install dists/xxx.whl```

## Citation

If you find this project useful in your research, please consider cite:

```latex
@misc{spconv2022,
    title={Spconv: Spatially Sparse Convolution Library},
    author={Spconv Contributors},
    howpublished = {\url{https://github.com/traveller59/spconv}},
    year={2022}
}
```
## Contributers

* [EvernightAurora](https://github.com/EvernightAurora): add ampere feature.

## Note

The work is done when the author is an employee at [Tusimple](https://www.tusimple.com/).

## LICENSE

Apache 2.0
