[SciPy-Dev] Lapack alignment test in linalg failing

Warren Weckesser warren.weckesser@enthought....
Fri Apr 9 23:12:46 CDT 2010

I am getting a failure of an alignment test in test_decomp.py in the linalg
test suite.  I just made several changes in linalg, but this failure has
been occurring since before those changes.

Here is the error message:

ERROR: test_decomp.test_lapack_misaligned(<function solve at 0x4160c70>, 
(array([[  1.73394741e-255,   8.18880997e-217,   4.02522535e-178,
Traceback (most recent call last):
line 183, in runTest
line 1074, in check_lapack_misaligned
line 47, in solve
    a1, b1 = map(asarray_chkfinite,(a,b))
line 586, in asarray_chkfinite
    raise ValueError, "array must not contain infs or NaNs"
ValueError: array must not contain infs or NaNs


The file test_decomp.py in linalg/tests contains the following code:

def check_lapack_misaligned(func, args, kwargs):
    args = list(args)
    for i in range(len(args)):
        a = args[:]
        if isinstance(a[i],np.ndarray):
            # Try misaligning a[i]
            aa = np.zeros(a[i].size*a[i].dtype.itemsize+8, dtype=np.uint8)
            aa = np.frombuffer(aa.data, offset=4, count=a[i].size, 
            aa.shape = a[i].shape
            aa[...] = a[i]
            a[i] = aa
            if len(a[i].shape)>1:
                a[i] = a[i].T

def test_lapack_misaligned():
    M = np.eye(10,dtype=float)
    R = np.arange(100)
    R.shape = 10,10
    S = np.arange(20000,dtype=np.uint8)
    S = np.frombuffer(S.data, offset=4, count=100, dtype=np.float)
    S.shape = 10, 10
    b = np.ones(10)
    v = np.ones(3,dtype=float)
    LU, piv = lu_factor(S)
    for (func, args, kwargs) in [
            (eig,(S,),dict(overwrite_a=True)), # crash
            (eigvals,(S,),dict(overwrite_a=True)), # no crash
            (lu,(S,),dict(overwrite_a=True)), # no crash
            (lu_factor,(S,),dict(overwrite_a=True)), # no crash
            (svd,(M,),dict(overwrite_a=True)), # no crash
            (svd,(R,),dict(overwrite_a=True)), # no crash
            (svd,(S,),dict(overwrite_a=True)), # crash
            (svdvals,(S,),dict()), # no crash
            (svdvals,(S,),dict(overwrite_a=True)), #crash
            (cholesky,(M,),dict(overwrite_a=True)), # no crash
            (qr,(S,),dict(overwrite_a=True)), # crash
            (rq,(S,),dict(overwrite_a=True)), # crash
            (hessenberg,(S,),dict(overwrite_a=True)), # crash
            (schur,(S,),dict(overwrite_a=True)), # crash
        yield check_lapack_misaligned, func, args, kwargs

The error occurs when `solve` is called from within
check_lapack_misaligned.  Since `solve` has two array arguments,
check_lapack_misaligned will try to call `solve` four times.
The problem is that the first call results in NaNs in the arrays,
which causes the next call to to `solve` to fail.  Apparently the NaNs
result from the data in the array being random junk with exponents
ranging all over.

If I add these lines after setting `S.shape` in test_lapack_misaligned,
the error does not occur:

    S[...] = 0.0
    S[range(10),range(10)] = 1.0
In other words, keep S misaligned, but make it the identity matrix.

Is that a "saner" test?

The error can also be avoided by setting the overwrite arguments
to False.  However, I don't know if the values of these arguments
are an important part of the test.

I don't really understand the logic in these two functions.
test_lapack_misaligned misaligns `S`, but then check_lapack_misaligned
further misaligns its arguments.  Why do this twice?


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