Zha0q1 commented on a change in pull request #19059:
URL: https://github.com/apache/incubator-mxnet/pull/19059#discussion_r489115433
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File path: tests/nightly/test_np_large_array.py
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@@ -335,30 +298,12 @@ def test_bincount():
assert B[-1] == INT_OVERFLOW - 1
B.backward()
assert A.grad.shape == (INT_OVERFLOW, )
- assert A.grad[0] == 0
-
-# broken
-# TODO add backward test after forward is fixed
-@use_np
[email protected](reason='Does not support large tensor; to be fixed')
-def test_bitwise_family():
- def batch_check(x1, x2, funcs):
- for f in funcs:
- y = f(x1, x2)
- one = np.ones((1), dtype='int32')
- assert y.shape == (INT_OVERFLOW, 2)
- assert y[0][0] == f(one, one)
- # test on broadcast input
- A = np.ones((INT_OVERFLOW, 1), dtype='int32')
- B = np.ones((INT_OVERFLOW, 2), dtype='int32')
- batch_check(A, B, [np.bitwise_and, np.bitwise_or, np.bitwise_xor])
- C = np.bitwise_not(A)
- assert C.shape == (INT_OVERFLOW, 1)
- assert C[0] == np.bitwise_not(np.ones((1), dtype='int32'))
+ assert A.grad[0] == 0
@use_np
def test_blackman():
A = np.blackman((INT_OVERFLOW))
+ npx.waitall()
Review comment:
This operator returns the blackman window of the given shape. Yes there
is a formula for each term, I will change waitall() into a value check
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