8000 ENH: Make it possible to NpyIter_RemoveAxis an empty dimension by seberg · Pull Request #3861 · numpy/numpy · GitHub
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ENH: Make it possible to NpyIter_RemoveAxis an empty dimension #3861

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Merged
merged 3 commits into from
Apr 29, 2017
Merged

ENH: Make it possible to NpyIter_RemoveAxis an empty dimension #3861

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bfb41d6
ENH: Allow removal of 0-sized axes from nditer and empty gufunc calls
seberg Mar 26, 2017
668fb78
MAINT: Remove python side empty array handling from linalg
seberg Mar 26, 2017
1c4e0d4
BUG: Fix matrix multiply test gufunc for empty matrices
seberg Mar 26, 2017
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BUG: Fix matrix multiply test gufunc for empty matrices 
The case where the result is not empty was missing the zeroing out
now that it is legal to use that input.
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@seberg
seberg committed Apr 29, 2017
commit 1c4e0d4d1b8e365111b70c559e6b66dd34c35d06
14 changes: 14 additions & 0 deletions numpy/core/src/umath/umath_tests.c.src
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Original file line number Diff line number Diff line change
Expand Up @@ -155,6 +155,20 @@ static void
npy_intp ib2_n = is2_n*dn;
npy_intp ib2_p = is2_p*dp;
npy_intp ob_p = os_p *dp;
if (dn == 0) {
/* No operand, need to zero the output */
BEGIN_OUTER_LOOP_3
char *op=args[2];
for (m = 0; m < dm; m++) {
for (p = 0; p < dp; p++) {
*(@typ@ *)op = 0;
op += os_p;
}
op += os_m - ob_p;
}
END_OUTER_LOOP
return;
}
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@eric-wieser eric-wieser Mar 26, 2017 •
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Wouldn't a better fix be to just reassign the pointers, and run the below code?

Something like

if (dn == 0) {
     @typ@ ip_val = 0;
     @typ@ ip2_val = 0;
      ip1 = ip2 = &ip_val;
      // set strides to 0
      dn = 1
}

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Nice hack, didn't think about it much.

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Hmm, no, B935 the char *ip1=args[0], *ip2=args[1], *op=args[2]; line is important where it is, since the macro will manipulate args

BEGIN_OUTER_LOOP_3
char *ip1=args[0], *ip2=args[1], *op=args[2];
for (m = 0; m < dm; m++) {
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6 changes: 6 additions & 0 deletions numpy/core/tests/test_ufunc.py
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Expand Up @@ -542,6 +542,12 @@ def test_matrix_multiply(self):
self.compare_matrix_multiply_results(np.long)
self.compare_matrix_multiply_results(np.double)

def test_matrix_multiply_umath_empty(self):
res = umt.matrix_multiply(np.ones((0, 10)), np.ones((10, 0)))
assert_array_equal(res, np.zeros((0, 0)))
res = umt.matrix_multiply(np.ones((10, 0)), np.ones((0, 10)))
assert_array_equal(res, np.zeros((10, 10)))

def compare_matrix_multiply_results(self, tp):
d1 = np.array(np.random.rand(2, 3, 4), dtype=tp)
d2 = np.array(np.random.rand(2, 3, 4), dtype=tp)
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