v11.0.0rc1

版本发布时间: 2022-06-30 14:53:52

cupy/cupy最新发布版本:v13.3.0(2024-08-22 15:42:45)

This is the release note of v11.0.0rc1. See here for the complete list of solved issues and merged PRs.

We are going to release v11.0.0 on July 28th. Please start testing your workload with this release candidate (pip install --pre cupy-cuda11x -f https://pip.cupy.dev/pre). See the Upgrade Guide for the list of possible breaking changes.

We are running a Gitter chat for general discussions and quick questions. Feel free to join the channel to talk with developers and users!

Highlights

Support CUDA 11.7 (#6767)

Full support for CUDA 11.7 has been added as of this release. Binary packages can be installed with the following command: pip install --pre cupy-cuda11x -f https://pip.cupy.dev/pre

Unified Binary Package for CUDA 11.2 or later (#6730)

CuPy v11 provides a unified binary package named cupy-cuda11x that supports all CUDA 11.2+ releases. This replaces per-CUDA version binary packages (cupy-cuda112, cupy-cuda113, …, cupy-cuda117) provided in CuPy v10 or earlier.

Note that CUDA 11.1 or earlier still requires per-CUDA version binary packages. cupy-cuda102, cupy-cuda110, and cupy-cuda111 will be provided for CUDA 10.2, 11.0, and 11.1, respectively.

Binary Package for Arm Platform (#6705)

CuPy v11 provides cupy-cuda11x binary package built for aarch64, which supports CUDA 11.2+ Arm SBSA and JetPack 5. These wheels are available through our Pip index: pip install --pre cupy-cuda11x -f https://pip.cupy.dev/aarch64

Support for `ndarray` subclassing (#6720, #6755)

This release allows users to subclass cupy.ndarray, using the same protocol as NumPy:

class C(cupy.ndarray):

    def __new__(cls, *args, info=None, **kwargs):
        obj = super().__new__(cls, *args, **kwargs)
        obj.info = info
        return obj

    def __array_finalize__(self, obj):
        if obj is None:
            return
        self.info = getattr(obj, 'info', None)

a = C([0, 1, 2, 3], info='information')
assert type(a) is C
assert issubclass(type(a), cupy.ndarray)
assert a.info == 'information'

Note that view casting and new from template mechanisms are also supported as described by the NumPy documentation.

Add Collective Communication APIs in `cupyx.distributed` for Sparse Matrices

All the collective calls implemented for dense matrices now support sparse matrices. Users interested in this feature should install mpi4py in order to perform an efficient metadata exchange.

Google Summer of Code 2022

We would like to give a warm welcome to @khushi-411 who will be working in adding support for the cupyx.scipy.interpolate APIs as part of her GSoC internship!

Changes without compatibility

Bump base Docker image to the latest supported one (#6802)

CuPy official Docker images have been upgraded. Users relying on these images may suffer from compatibility issues with preinstalled tools or libraries.

Changes

New Features

Add cupy.setxor1d (#6582)
Add initial cupyx.spatial.distance support from pylibraft (#6690)
Support cupy.ndarray subclassing - Part 2 - View casting (#6720)
Add sparse broadcast (#6758)
Add sparse reduce (#6761)
Add sparse all_reduce and minor fixes (#6762)
Add sparse all_to_all, reduce_scatter, send_recv (#6765)
Subclass cupy.ndarray subclassing - Part 3 - New from template (ufunc) (#6775)
Add cupyx.scipy.special.log_ndtr (#6776)
Add cupyx.scipy.special.expn (#6790)

Enhancements

Utilize CUDA Enhanced Compatibility (#6730)
Fix to return correct CUDA version when in CUDA Python mode (#6736)
Support CUDA 11.7 (#6767)
Make the warning for cupy.array_api say "cupy" instead of "numpy" (#6791)
Utilize CUDA Enhanced Compatibility in all wrappers (#6799)
Add support for cupy-cuda11x wheel (#6800)
Bump base Docker image to the latest supported one (#6802)
Remove CUPY_CUDA_VERSION as much as possible (#6810)
Raise UserWarning in cupy.cuda.compile_with_cache (#6818)
cupy-wheel: Use NVRTC to infer the toolkit version (#6819)
Support NumPy 1.23 (#6820)
Fix for NumPy 1.23 (#6807)

Performance Improvements

Improved integer matrix multiplication performance by modifying tuning parameters (#6703)
Use fast convolution algorithm in cupy.poly1d.__pow__ (#6770)