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[Numpy] More numpy dispatch tests #16426

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Oct 18, 2019
Merged

[Numpy] More numpy dispatch tests #16426

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c560fa4
tests added
xidulu Oct 10, 2019
8ad774f
remove not equal
xidulu Oct 15, 2019
10630ff
fix tiny bug
xidulu Oct 15, 2019
1a8d5c3
Merge remote-tracking branch 'upstream/master' into np_dispatch_test
xidulu Oct 15, 2019
919e524
remove meshgrid test
xidulu Oct 16, 2019
b90f24a
modify meshgrid return type, add test
xidulu Oct 16, 2019
166a899
Update from master
xidulu Oct 17, 2019
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2 changes: 1 addition & 1 deletion python/mxnet/numpy/stride_tricks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,6 @@ def broadcast_arrays(*args):

if all(array.shape == shape for array in args):
# Common case where nothing needs to be broadcasted.
return args
return list(args)

return [_mx_np_op.broadcast_to(array, shape) for array in args]
3 changes: 3 additions & 0 deletions python/mxnet/numpy_dispatch_protocol.py
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Expand Up @@ -113,6 +113,8 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
'transpose',
'var',
'zeros_like',
'meshgrid',
'outer'
]


Expand Down Expand Up @@ -196,6 +198,7 @@ def _register_array_function():
'ceil',
'trunc',
'floor',
'logical_not',
]


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21 changes: 21 additions & 0 deletions tests/python/unittest/test_numpy_interoperability.py
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Expand Up @@ -77,6 +77,9 @@ def _prepare_workloads():
OpArgMngr.add_workload('min', array_pool['4x1'])
OpArgMngr.add_workload('mean', array_pool['4x1'])
OpArgMngr.add_workload('mean', array_pool['4x1'], axis=0, keepdims=True)
OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]))
OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]), axis=0)
OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]), axis=1)
OpArgMngr.add_workload('ones_like', array_pool['4x1'])
OpArgMngr.add_workload('prod', array_pool['4x1'])

Expand Down Expand Up @@ -157,6 +160,10 @@ def _prepare_workloads():
OpArgMngr.add_workload('transpose', array_pool['4x1'])
OpArgMngr.add_workload('var', array_pool['4x1'])
OpArgMngr.add_workload('zeros_like', array_pool['4x1'])
OpArgMngr.add_workload('outer', np.ones((5)), np.ones((2)))
OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]))
OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]), np.array([4, 5, 6, 7]))
OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]), np.array([4, 5, 6, 7]), indexing='ij')

# workloads for array ufunc protocol
OpArgMngr.add_workload('add', array_pool['4x1'], array_pool['1x2'])
Expand All @@ -175,6 +182,9 @@ def _prepare_workloads():
OpArgMngr.add_workload('power', array_pool['4x1'], 2)
OpArgMngr.add_workload('power', 2, array_pool['4x1'])
OpArgMngr.add_workload('power', array_pool['4x1'], array_pool['1x1x0'])
OpArgMngr.add_workload('power', np.array([1, 2, 3], np.int32), 2.00001)
OpArgMngr.add_workload('power', np.array([15, 15], np.int64), np.array([15, 15], np.int64))
OpArgMngr.add_workload('power', 0, np.arange(1, 10))
OpArgMngr.add_workload('mod', array_pool['4x1'], array_pool['1x2'])
OpArgMngr.add_workload('mod', array_pool['4x1'], 2)
OpArgMngr.add_workload('mod', 2, array_pool['4x1'])
Expand Down Expand Up @@ -256,6 +266,12 @@ def _signs(dt):
OpArgMngr.add_workload('exp', array_pool['4x1'])
OpArgMngr.add_workload('log', array_pool['4x1'])
OpArgMngr.add_workload('log2', array_pool['4x1'])
OpArgMngr.add_workload('log2', np.array(2.**65))
OpArgMngr.add_workload('log2', np.array(np.inf))
OpArgMngr.add_workload('log2', np.array(1.))
OpArgMngr.add_workload('log1p', np.array(-1.))
OpArgMngr.add_workload('log1p', np.array(np.inf))
OpArgMngr.add_workload('log1p', np.array(1e-6))
OpArgMngr.add_workload('log10', array_pool['4x1'])
OpArgMngr.add_workload('expm1', array_pool['4x1'])
OpArgMngr.add_workload('sqrt', array_pool['4x1'])
Expand All @@ -282,6 +298,11 @@ def _signs(dt):
OpArgMngr.add_workload('ceil', array_pool['4x1'])
OpArgMngr.add_workload('trunc', array_pool['4x1'])
OpArgMngr.add_workload('floor', array_pool['4x1'])
OpArgMngr.add_workload('logical_not', np.ones(10, dtype=np.int32))
OpArgMngr.add_workload('logical_not', array_pool['4x1'])
OpArgMngr.add_workload('logical_not', np.array([True, False, True, False], dtype=np.bool))




_prepare_workloads()
Expand Down