This is the release note of v9.0.0rc1. See here for the complete list of solved issues and merged PRs.

We are planning to release the final v9.0.0 on April 22th. Please start testing your workload with this release. 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

CuPy JIT (#4774)

Now creating raw kernels out of python functions is possible thanks to the introduction of the @cupyx.jit.rawkernel decorator.

from cupyx import jit

@jit.rawkernel()
def f(x, y, z, n):
    tid = jit.threadIdx.x + jit.blockIdx.x * jit.blockDim.x
    ntid = jit.blockDim.x * jit.gridDim.x
    for i in range(tid, n, ntid):
        z[i] = x[i] + y[i]

n = numpy.uint32(1024)
x = cupy.arange(n)
y = cupy.arange(n)
z = cupy.empty((n,), dtype='l')
f[16, 16](x, y, z, n)

Support for Generalized Universal Functions (#4675)

We have added an interface to support Generalized Universal Functions based on the one in Dask. Currently, it is used in matmul to ensure compatibility with __array_ufunc__ numpy dispatching.

cuTENSOR Support in Binary Packages (#4600)

cuTENSOR support is now enabled in wheel packages. To use cuTENSOR features you will need to install the shared library using python -m cupyx.tools.install_library --cuda 11.2 --library cutensor after installing wheels.

New Sphinx Theme in Documentation (#4351)

Following NumPy, we have adopted the pydata_sphinx_theme in our documentation site starting from this release.

CUDA 11.0 and 11.1 wheels not available yet in PyPI (#4971)

In the meantime they can be downloaded from the Assets section below. See #4971 for the detailed instructions.

Changes without compatibility

cupy.cuda.nccl is hidden by default (#4919)

NCCL wrapper is no longer imported in cupy/cuda/__init__.py requiring it to be explicitly imported from cupy.cuda.nccl.

Drop NCCL & cuDNN shared libraries from wheels (#4850, #4932)

NCCL and cuDNN shared libraries are no longer bundled in all wheels. To activate features using NCCL / cuDNN in CuPy v9, you will need to install these libraries using python -m cupyx.tools.install_library tool after installing CuPy wheels. See the Installation Guide for details.

By eliminating the default bundling of cuDNN & NCCL we have achieved further reductions in the wheel size averaging 5x.

Deprecate cupy.bool, cupy.int, cupy.float and cupy.complex (#4790)

Following NumPy 1.20 API, these aliases for the Python scalar types have been deprecated.
cupy.bool_, cupy.int_, cupy.float_ and cupy.complex_ should be used instead when required.

Docker image updated to CUDA 11.2 and Python 3.8

The official Docker image is now updated to use CUDA 11.2 and Python 3.8.

Changes

New Features

• LOBPCG solver - cupyx.scipy.sparse.linalg.lobpcg (#4281)
• Add diagonal and setdiag methods for COO sparse matrices (#4664)
• Support for Generalized Universal Functions (#4675)
• Support batched pinv (#4686)
• Add CuPy JIT Kernel definition (#4774)
• Add cupy.random.Generator.standard_normal (#4885)
• Support tuple in CuPy JIT (#4890)
• Add exponential distribution to random API (#4915)
• Support tuple indexing in CuPy JIT (#4939)
• Support __syncthreads() in CuPy JIT (#4941)

Enhancements

• Support nvrtcGetSupportedArchs (#4691)
• Update DLPack support (#4695)
• Bump cuDNN to v8.1.1 in library installer tool (#4780)
• Support norm='forward'/'backward' in cupy.fft functions (#4797)
• Fix for flake8 F541 (#4803)
• Complete build only when all of the essential modules are available (#4815)
• Support norm='forward'/'backward' in cupyx.scipy.fft functions (#4816)
• Support cuSparse functions for matrix conversion added in CUDA 11.2 (#4844)
• Add NCCL to library installer (#4848)
• Improve cuTENSOR installer (#4852)
• Support cupy.ndarray type shift in cupy.roll (#4884)
• Fix uniform random generation interval (#4894)
• Use NVCC --threads option when building CuPy (#4908)
• Bump headers to CUDA 11.2.2 (#4911)
• Update preload to look for lib directory to support cuTENSOR/NCCL (#4912)
• Move the NCCL module to cupy_backends.cuda.libs (#4919)
• Add cupy/cuda/cutensor.py (#4920)

Performance Improvements

• Improve batched SVD (#4731)
• Avoid evaluating PTDS environment variable every time (#4842)

Bug Fixes

• Fix dtypes in cupy.linalg (#4363)
• Fix: avoid redeclaring attributes (#4764)
• Windows: Fix compiler error for CUB block reduction kernels (#4771)
• Support int argument for Dirichlet shape (#4772)
• Windows: Fix histogram test failures (#4777)
• Windows: fix sparse matrix indexing type (#4778)
• Unify linux/windows randint with NumPy (#4808)
• Improve/fix csc/csr argmax/argmin (#4813)
• ROCm: Fix sorting bug (#4823)
• Fixed choice function for 0 samples from 0 candidates (#4830)
• Fix redeclaration of sparse warning classes (#4837)
• Fix cuFFT callback compilations - v2 (#4853)
• Solve UnboundLocalError on copy_from_host_async (#4900)
• Add out arg verifier in new random interface. (#4904)
• Fix compilation error due to invalid complex-to-real casting in _SimpleReductionKernel (#4909)
• Fix C++ compilation error (#4922)
• Fix cutensor import (#4933)
• Fix flaky CUDAarray tests (#4946)
• Declare CArray._indexing() only in CuPy JIT mode (#4951)

Code Fixes

• Rename submodules under cupy.testing package (#3868)
• Fix: code quality issues (#4587)
• Use newest versions of stylecheck packages (#4694)
• Clean-up sparse max/min argmax/argmin (#4860)

Documentation

• Use pydata_sphinx_theme in Sphinx (#4351)
• Remove cupy-cuda112 support from documentation (#4761)
• Revert "Remove cupy-cuda112 support from documentation" (#4785)
• Fix broken Stream docs (#4843)
• Reformat environment variables table (#4845)
• Revert memory back to reference (#4857)
• Update wheel list in README (#4910)
• Merge ROCm installation guide (#4928)
• Document that cuDNN and NCCL are no longer included (#4932)
• Update install docs (#4943)

Installation

• Support optional dependencies from Conda-Forge (#4873)
• Bump version to v9.0.0rc1 (#4953)
• Bump Docker image to use CUDA 11.2 (#4972)

Tests

• Show config on Windows CI (#4649)
• Windows: Fix test condition for CUB device kernels (#4776)
• Xfail some tests for cupyx.scipy.statistics.correlation under ROCm/HIP (#4781)
• Windows: fix vectorize tests (#4794)
• Windows: fix OOM errors in the CI (#4801)
• Windows: Fix sepfir2d tests (#4804)
• Windows: Fix cuTENSOR tests (#4806)
• Windows: Fix cuTENSOR tests (#4818)
• Remove AppVeyor configurations (#4836)
• Windows: Fix test_poly1d_pow_scalar (#4854)
• Fix for flake8 E741 (#4888)
• Windows: Skip failing cuDNN tests (#4893)
• Add names for workflows (#4913)
• Prioritize FlexCI daemon in Windows CI (#4916)
• Fix to work with scheduled FlexCI job (#4929)
• Change irfft tests tolerance (#4937)
• Xfail tests for ndarray indexing under HIP (#4653)
• Adjust tolerance of TestPolyArithmeticDiffTypes under HIP/ROCm (#4657)
• Xfail tests in polynomial roots (#4658)
• Xfail tests for manipulation dims under HIP/ROCm (#4662)
• Xfail TestPolyfitParametersCombinations when deg == 0 under ROCm/HIP (#4758)
• Xfail TestPolyfitCovMode when deg == 0 under ROCm/HIP (#4759)
• Xfail TestInvh under ROCm/HIP (#4760)
• ROCm: remove the need to set HCC_AMDGPU_TARGET at runtime (#4766)
• Assert MT19937 not implemented in hipRAND (#4769)
• Xfail chi-squared test for some random functions under ROCm/HIP (#4770)
• Remove duplicated typedef in example when HIP (#4782)
• Xfail cuDNN version check test under ROCm/HIP (#4791)
• Remove solved xfail mark for msort (#4792)
• Fix to test checking HIP version (#4859)
• Xfail test on sparse handle under ROCm/HIP (#4861)
• Xfail some tests under ROCm/HIP (#4868)
• Xfail some conditions of ndimage filter under ROCm/HIP (#4877)
• Xfail some conditions of ndimage interpolation tests under ROCm/HIP (#4878)
• Xfail some conditions of ndimage measurements under ROCm/HIP (#4879)
• Xfail some conditions of signal tests under ROCm/HIP (#4880)

Others

• Add CODEOWNERS file (#4757)
• Add GitHub Actions workflow for automatic backport (#4812)
• Fix pytest opts for Windows CI (#4820)
• Use access token for automated backport (#4833)
• Fix automated backport workflow (#4835)
• Use pull_request_target trigger in backport automation (#4841)

Contributors

The CuPy Team would like to thank all those who contributed to this release!

@anaruse @aryamccarthy @grlee77 @leofang @mattvend @povinsahu1909 @venkywonka @viantirreau @withshubh