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Tests of NumPy interoperability #16469

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Oct 16, 2019
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Tests of NumPy interoperability #16469

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44391b8
cumsum test
Oct 10, 2019
ee009b5
ravel test
Oct 10, 2019
4f0fe12
test reciprocal remainder repeat reshape
Oct 11, 2019
3c80250
test rint roll sign
Oct 11, 2019
90953ff
print test
Oct 15, 2019
0d912d0
print debug
Oct 15, 2019
7339369
life is short use pytorch
Oct 15, 2019
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2 changes: 2 additions & 0 deletions python/mxnet/numpy_dispatch_protocol.py
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Original file line number Diff line number Diff line change
Expand Up @@ -98,8 +98,10 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
'min',
'ones_like',
'prod',
'ravel',
'repeat',
'reshape',
'roll',
'split',
'squeeze',
'stack',
Expand Down
160 changes: 149 additions & 11 deletions tests/python/unittest/test_numpy_interoperability.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@
from mxnet.numpy_dispatch_protocol import with_array_function_protocol, with_array_ufunc_protocol
from mxnet.numpy_dispatch_protocol import _NUMPY_ARRAY_FUNCTION_LIST, _NUMPY_ARRAY_UFUNC_LIST

import itertools

class OpArgMngr(object):
"""Operator argument manager for storing operator workloads."""
Expand All @@ -49,6 +50,10 @@ def _prepare_workloads():
'1x1x0': np.array([[[]]])
}

dt_int = [np.int8, np.int32, np.int64, np.uint8]
dt_float = [np.float16, np.float32, np.float64]
dt = dt_int + dt_float

# workloads for array function protocol
OpArgMngr.add_workload('argmax', array_pool['4x1'])
OpArgMngr.add_workload('broadcast_arrays', array_pool['4x1'], array_pool['1x2'])
Expand All @@ -57,8 +62,14 @@ def _prepare_workloads():
OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']])
OpArgMngr.add_workload('concatenate', [array_pool['4x1'], array_pool['4x1']], axis=1)
OpArgMngr.add_workload('copy', array_pool['4x1'])
OpArgMngr.add_workload('cumsum', array_pool['4x1'])
OpArgMngr.add_workload('cumsum', array_pool['4x1'], axis=1)

for ctype in dt:
OpArgMngr.add_workload('cumsum', np.array([1, 2, 10, 11, 6, 5, 4], dtype=ctype))
OpArgMngr.add_workload('cumsum', np.array([[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]], dtype=ctype), axis=0)
OpArgMngr.add_workload('cumsum', np.array([[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]], dtype=ctype), axis=1)

OpArgMngr.add_workload('ravel', np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]))

OpArgMngr.add_workload('dot', array_pool['4x1'], array_pool['4x1'].T)
OpArgMngr.add_workload('expand_dims', array_pool['4x1'], -1)
OpArgMngr.add_workload('fix', array_pool['4x1'])
Expand All @@ -68,8 +79,72 @@ def _prepare_workloads():
OpArgMngr.add_workload('mean', array_pool['4x1'], axis=0, keepdims=True)
OpArgMngr.add_workload('ones_like', array_pool['4x1'])
OpArgMngr.add_workload('prod', array_pool['4x1'])

OpArgMngr.add_workload('repeat', array_pool['4x1'], 3)
OpArgMngr.add_workload('reshape', array_pool['4x1'], -1)
OpArgMngr.add_workload('repeat', np.array(_np.arange(12).reshape(4, 3)[:, 2]), 3)

m = _np.array([1, 2, 3, 4, 5, 6])
m_rect = m.reshape((2, 3))

# OpArgMngr.add_workload('repeat', np.array(m), [1, 3, 2, 1, 1, 2]) # Argument "repeats" only supports int
OpArgMngr.add_workload('repeat', np.array(m), 2)
B = np.array(m_rect)
# OpArgMngr.add_workload('repeat', B, [2, 1], axis=0) # Argument "repeats" only supports int
# OpArgMngr.add_workload('repeat', B, [1, 3, 2], axis=1) # Argument "repeats" only supports int
OpArgMngr.add_workload('repeat', B, 2, axis=0)
OpArgMngr.add_workload('repeat', B, 2, axis=1)

# test_repeat_broadcasting
a = _np.arange(60).reshape(3, 4, 5)
for axis in itertools.chain(range(-a.ndim, a.ndim), [None]):
OpArgMngr.add_workload('repeat', np.array(a), 2, axis=axis)
# OpArgMngr.add_workload('repeat', np.array(a), [2], axis=axis) # Argument "repeats" only supports int

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
OpArgMngr.add_workload('reshape', arr, (2, 6))
OpArgMngr.add_workload('reshape', arr, (3, 4))
# OpArgMngr.add_workload('reshape', arr, (3, 4), order='F') # Items are not equal with order='F'
OpArgMngr.add_workload('reshape', arr, (3, 4), order='C')
OpArgMngr.add_workload('reshape', np.array(_np.ones(100)), (100, 1, 1))

# test_reshape_order
a = np.array(_np.arange(6))
# OpArgMngr.add_workload('reshape', a, (2, 3), order='F') # Items are not equal with order='F'
a = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
b = a[:, 1]
# OpArgMngr.add_workload('reshape', b, (2, 2), order='F') # Items are not equal with order='F'

a = np.array(_np.ones((0, 2)))
OpArgMngr.add_workload('reshape', a, -1, 2)

OpArgMngr.add_workload('rint', np.array(4607998452777363968))
OpArgMngr.add_workload('rint', array_pool['4x1'])

# test_roll1d(self)
OpArgMngr.add_workload('roll', np.array(_np.arange(10)), 2)

# test_roll2d(self)
x2 = np.array(_np.reshape(_np.arange(10), (2, 5)))
OpArgMngr.add_workload('roll', x2, 1)
OpArgMngr.add_workload('roll', x2, 1, axis=0)
OpArgMngr.add_workload('roll', x2, 1, axis=1)
# # Roll multiple axes at once.
OpArgMngr.add_workload('roll', x2, 1, axis=(0, 1))
OpArgMngr.add_workload('roll', x2, (1, 0), axis=(0, 1))
OpArgMngr.add_workload('roll', x2, (-1, 0), axis=(0, 1))
OpArgMngr.add_workload('roll', x2, (0, 1), axis=(0, 1))
OpArgMngr.add_workload('roll', x2, (0, -1), axis=(0, 1))
OpArgMngr.add_workload('roll', x2, (1, 1), axis=(0, 1))
OpArgMngr.add_workload('roll', x2, (-1, -1), axis=(0, 1))
# # Roll the same axis multiple times.
# OpArgMngr.add_workload('roll', x2, 1, axis=(0, 0)) # Check failed: axes[i - 1] < axes[i] (0 vs. 0) : axes have duplicates [0,0]
# OpArgMngr.add_workload('roll', x2, 1, axis=(1, 1)) # Check failed: axes[i - 1] < axes[i] (1 vs. 1) : axes have duplicates [1,1]
# # Roll more than one turn in either direction.
OpArgMngr.add_workload('roll', x2, 6, axis=1)
OpArgMngr.add_workload('roll', x2, -4, axis=1)
# # test_roll_empty
OpArgMngr.add_workload('roll', np.array([]), 1)

OpArgMngr.add_workload('split', array_pool['4x1'], 2)
OpArgMngr.add_workload('squeeze', array_pool['4x1'])
OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2)
Expand Down Expand Up @@ -104,10 +179,64 @@ def _prepare_workloads():
OpArgMngr.add_workload('mod', array_pool['4x1'], 2)
OpArgMngr.add_workload('mod', 2, array_pool['4x1'])
OpArgMngr.add_workload('mod', array_pool['4x1'], array_pool['1x1x0'])
OpArgMngr.add_workload('remainder', array_pool['4x1'], array_pool['1x2'])
OpArgMngr.add_workload('remainder', array_pool['4x1'], 2)
OpArgMngr.add_workload('remainder', 2, array_pool['4x1'])
OpArgMngr.add_workload('remainder', array_pool['4x1'], array_pool['1x1x0'])

# test remainder basic
OpArgMngr.add_workload('remainder', np.array([0, 1, 2, 4, 2], dtype=np.float16),
np.array([-2, 5, 1, 4, 3], dtype=np.float16))

def _signs(dt):
if dt in [np.uint8]:
return (+1,)
else:
return (+1, -1)

for ct in dt:
for sg1, sg2 in itertools.product(_signs(ct), _signs(ct)):
a = np.array(sg1*71, dtype=ct)
b = np.array(sg2*19, dtype=ct)
OpArgMngr.add_workload('remainder', a, b)

# test remainder exact
nlst = list(range(-127, 0))
plst = list(range(1, 128))
dividend = nlst + [0] + plst
divisor = nlst + plst
arg = list(itertools.product(dividend, divisor))
tgt = list(divmod(*t) for t in arg)
a, b = np.array(arg, dtype=int).T
# convert exact integer results from Python to float so that
# signed zero can be used, it is checked.
for dt in [np.float16, np.float32, np.float64]:
fa = a.astype(dt)
fb = b.astype(dt)
OpArgMngr.add_workload('remainder', fa, fb)

# test_float_remainder_roundoff
for ct in dt_float:
for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
a = np.array(sg1*78*6e-8, dtype=ct)
b = np.array(sg2*6e-8, dtype=ct)
OpArgMngr.add_workload('remainder', a, b)

# test_float_remainder_corner_cases
# Check remainder magnitude.
for ct in dt_float:
b = _np.array(1.0)
a = np.array(_np.nextafter(_np.array(0.0), -b), dtype=ct)
b = np.array(b, dtype=ct)
OpArgMngr.add_workload('remainder', a, b)
OpArgMngr.add_workload('remainder', -a, -b)

# Check nans, inf
for ct in [np.float16, np.float32, np.float64]:
fone = np.array(1.0, dtype=ct)
fzer = np.array(0.0, dtype=ct)
finf = np.array(np.inf, dtype=ct)
fnan = np.array(np.nan, dtype=ct)
# OpArgMngr.add_workload('remainder', fone, fzer) # failed
OpArgMngr.add_workload('remainder', fone, fnan)
OpArgMngr.add_workload('remainder', finf, fone)

OpArgMngr.add_workload('maximum', array_pool['4x1'], array_pool['1x2'])
OpArgMngr.add_workload('maximum', array_pool['4x1'], 2)
OpArgMngr.add_workload('maximum', 2, array_pool['4x1'])
Expand All @@ -118,8 +247,12 @@ def _prepare_workloads():
OpArgMngr.add_workload('minimum', array_pool['4x1'], array_pool['1x1x0'])
OpArgMngr.add_workload('negative', array_pool['4x1'])
OpArgMngr.add_workload('absolute', array_pool['4x1'])
OpArgMngr.add_workload('rint', array_pool['4x1'])

OpArgMngr.add_workload('sign', array_pool['4x1'])
OpArgMngr.add_workload('sign', np.array([-2, 5, 1, 4, 3], dtype=np.float16))
OpArgMngr.add_workload('sign', np.array([-.1, 0, .1]))
# OpArgMngr.add_workload('sign', np.array(_np.array([_np.nan]))) # failed

OpArgMngr.add_workload('exp', array_pool['4x1'])
OpArgMngr.add_workload('log', array_pool['4x1'])
OpArgMngr.add_workload('log2', array_pool['4x1'])
Expand All @@ -128,7 +261,12 @@ def _prepare_workloads():
OpArgMngr.add_workload('sqrt', array_pool['4x1'])
OpArgMngr.add_workload('square', array_pool['4x1'])
OpArgMngr.add_workload('cbrt', array_pool['4x1'])
OpArgMngr.add_workload('reciprocal', array_pool['4x1'])

for ctype in [np.float16, np.float32, np.float64]:
OpArgMngr.add_workload('reciprocal', np.array([-2, 5, 1, 4, 3], dtype=ctype))
OpArgMngr.add_workload('reciprocal', np.array([-2, 0, 1, 0, 3], dtype=ctype))
OpArgMngr.add_workload('reciprocal', np.array([0], dtype=ctype))

OpArgMngr.add_workload('sin', array_pool['4x1'])
OpArgMngr.add_workload('cos', array_pool['4x1'])
OpArgMngr.add_workload('tan', array_pool['4x1'])
Expand Down Expand Up @@ -176,10 +314,10 @@ def _check_interoperability_helper(op_name, *args, **kwargs):
assert isinstance(arr, np.ndarray)
for arr, expected_arr in zip(out, expected_out):
assert isinstance(arr, np.ndarray)
assert_almost_equal(arr.asnumpy(), expected_arr, rtol=1e-3, atol=1e-4, use_broadcast=False)
assert_almost_equal(arr.asnumpy(), expected_arr, rtol=1e-3, atol=1e-4, use_broadcast=False, equal_nan=True)
else:
assert isinstance(out, np.ndarray)
assert_almost_equal(out.asnumpy(), expected_out, rtol=1e-3, atol=1e-4, use_broadcast=False)
assert_almost_equal(out.asnumpy(), expected_out, rtol=1e-3, atol=1e-4, use_broadcast=False, equal_nan=True)


def check_interoperability(op_list):
Expand Down