CUDA 11 Test: `TestFftAllocate`

I built the latest master and fixed #3757 with #3775, and the only error I got from all FFT tests we have is this:

$ pytest tests/cupy_tests/fft_tests/test_fft.py
========================================================================= test session starts =========================================================================
platform linux -- Python 3.7.8, pytest-6.0.1, py-1.9.0, pluggy-0.13.1
rootdir: /home/leofang/cupy_cuda11, configfile: setup.cfg
collected 717 items                                                                                                                                                   

tests/cupy_tests/fft_tests/test_fft.py ........................................................................................................................ [ 16%]
............................................................................................................................................................... [ 38%]
............................................................................................................................................................... [ 61%]
............................................................................................................................................................... [ 83%]
.....................................................................................................................F..                                        [100%]

============================================================================== FAILURES ===============================================================================
__________________________________________________________________ TestFftAllocate.test_fft_allocate __________________________________________________________________

self = <cupy_tests.fft_tests.test_fft.TestFftAllocate testMethod=test_fft_allocate>

    def test_fft_allocate(self):
        # Check CuFFTError is not raised when the GPU memory is enough.
        # See https://github.com/cupy/cupy/issues/1063
        # TODO(mizuno): Simplify "a" after memory compaction is implemented.
        a = []
        for i in range(10):
            a.append(cupy.empty(100000000))
        del a
        b = cupy.empty(100000007, dtype=cupy.float32)
>       cupy.fft.fft(b)

tests/cupy_tests/fft_tests/test_fft.py:336: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
cupy/fft/fft.py:567: in fft
    return _fft(a, (n,), (axis,), norm, cupy.cuda.cufft.CUFFT_FORWARD)
cupy/fft/fft.py:182: in _fft
    a = _fft_c2c(a, direction, norm, axes, overwrite_x, plan=plan)
cupy/fft/fft.py:152: in _fft_c2c
    a = _exec_fft(a, direction, 'C2C', norm, axis, overwrite_x, plan=plan)
cupy/fft/fft.py:109: in _exec_fft
    plan = cufft.Plan1d(out_size, fft_type, batch, devices=devices)
cupy/cuda/cufft.pyx:277: in cupy.cuda.cufft.Plan1d.__init__
    self._single_gpu_get_plan(plan, nx, fft_type, batch)
cupy/cuda/cufft.pyx:306: in cupy.cuda.cufft.Plan1d._single_gpu_get_plan
    check_result(result)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

>   raise CuFFTError(result)
E   cupy.cuda.cufft.CuFFTError: CUFFT_INTERNAL_ERROR

cupy/cuda/cufft.pyx:147: CuFFTError
======================================================================= short test summary info =======================================================================
FAILED tests/cupy_tests/fft_tests/test_fft.py::TestFftAllocate::test_fft_allocate - cupy.cuda.cufft.CuFFTError: CUFFT_INTERNAL_ERROR
=================================================================== 1 failed, 716 passed in 13.16s ====================================================================

[pentschev]

I heard back from cuFFT developers, and the issue has been prioritized for the CUDA 11.2 release, I don’t have further details at this time though.

[leofang]

Yeah tried that too, still reproducible.