```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.
...
...
@@ -52,15 +56,19 @@
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.
## NEWS
* 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 kernels (~5-30%) in ampere GPUs (tested in RTX 3090)
* greatly faster int8 kernels (~1.2x~2.7x) in ampere GPUs (tested in RTX 3090)
* no python 3.6 support
* 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.
...
...
@@ -84,6 +92,10 @@ 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.
...
...
@@ -94,9 +106,9 @@ You need at least CUDA 11.0 to build and run spconv 2.x. We won't offer any supp
### Prebuilt
We offer python 3.7-3.11 and cuda 10.2/11.1/11.3/11.4/12.0 prebuilt binaries for linux (manylinux).
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 10.2/11.1/11.4/12.0 prebuilt binaries for windows 10/11.
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.
...
...
@@ -104,12 +116,12 @@ For Linux users, you need to install pip >= 20.3 first to install prebuilt.
```pip install spconv-cu102``` for CUDA 10.2
```pip install spconv-cu111``` for CUDA 11.1
```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.
* spconv 2.x in Windows 10 is 1.5x~2x slower than Linux. use Linux if possible.
* If you train with float32 and ampere or later GPUs, you can set ```spconv.constants.SPCONV_ALLOW_TF32``` to enable faster fp32 training.
See [benchmark](BENCHMARK.md) for more performance details of different algorithms.
* Different CUDA version of spconv may have different performance. Use newest cuda version if possible. For example, spconv-cu117 is faster than spconv-cu114, spconv-cu114 is faster than spconv-cu111.
