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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2020/11/05 18:36:26 UTC
[GitHub] [incubator-mxnet] barry-jin commented on pull request #19457: [Numpy] Update fallback.py
barry-jin commented on pull request #19457:
URL: https://github.com/apache/incubator-mxnet/pull/19457#issuecomment-722562890
I have updated `test_numpy_interoperability.py`. But there will be segmentation fault when running test on operator `atleast_1d` (click toggle to see detailed test logs)
<details>
<summary>Test Logs</summary>
```
====================================================================== test session starts =======================================================================
platform darwin -- Python 3.8.5, pytest-6.0.1, py-1.9.0, pluggy-0.13.1
rootdir: /Users/zhenghuj/GitHub/incubator-mxnet, configfile: pytest.ini
plugins: env-0.6.2, flaky-3.7.0
collected 4 items
tests/python/unittest/test_numpy_interoperability.py .F.. [100%]
============================================================================ FAILURES ============================================================================
________________________________________________________________ test_np_array_function_protocol _________________________________________________________________
args = (), kwargs = {}
@functools.wraps(func)
def _run_with_array_func_proto(*args, **kwargs):
if cur_np_ver >= np_1_17_ver:
try:
> func(*args, **kwargs)
python/mxnet/numpy_dispatch_protocol.py:55:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
@use_np
@with_array_function_protocol
@pytest.mark.serial
def test_np_array_function_protocol():
> check_interoperability(_NUMPY_ARRAY_FUNCTION_LIST)
tests/python/unittest/test_numpy_interoperability.py:3330:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
op_list = ['all', 'any', 'sometrue', 'argmin', 'argmax', 'around', ...]
def check_interoperability(op_list):
OpArgMngr.randomize_workloads()
for name in op_list:
if name in _TVM_OPS and not is_op_runnable():
continue
if name in ['shares_memory', 'may_share_memory', 'empty_like',
'__version__', 'dtype', '_NoValue']: # skip list
continue
if name in ['delete']: # https://github.com/apache/incubator-mxnet/issues/18600
continue
if name in ['full_like', 'zeros_like', 'ones_like'] and \
StrictVersion(platform.python_version()) < StrictVersion('3.0.0'):
continue
default_tols = (1e-3, 1e-4)
tols = {'linalg.tensorinv': (1e-2, 5e-3),
'linalg.solve': (1e-3, 5e-2)}
(rel_tol, abs_tol) = tols.get(name, default_tols)
print('Dispatch test:', name)
workloads = OpArgMngr.get_workloads(name)
assert workloads is not None, 'Workloads for operator `{}` has not been ' \
'added for checking interoperability with ' \
'the official NumPy.'.format(name)
for workload in workloads:
> _check_interoperability_helper(name, rel_tol, abs_tol, *workload['args'], **workload['kwargs'])
tests/python/unittest/test_numpy_interoperability.py:3310:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
op_name = 'atleast_1d', rel_tol = 0.001, abs_tol = 0.0001
args = ([array([[1., 2.],
[1., 2.]]), array([[1., 2.],
[1., 2.]])], [array([[2., 3.],
[2., 3.]]), array([[2., 3.],
[2., 3.]])])
kwargs = {}, strs = ['atleast_1d'], onp_op = <function atleast_1d at 0x11a09fc10>, mxnp_op = <function atleast_1d at 0x1537e6dc0>
def _check_interoperability_helper(op_name, rel_tol, abs_tol, *args, **kwargs):
strs = op_name.split('.')
if len(strs) == 1:
onp_op = getattr(_np, op_name)
mxnp_op = getattr(np, op_name)
elif len(strs) == 2:
onp_op = getattr(getattr(_np, strs[0]), strs[1])
mxnp_op = getattr(getattr(np, strs[0]), strs[1])
else:
assert False
if not is_op_runnable():
return
> out = mxnp_op(*args, **kwargs)
tests/python/unittest/test_numpy_interoperability.py:3266:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
arys = ([array([[1., 2.],
[1., 2.]]), array([[1., 2.],
[1., 2.]])], [array([[2., 3.],
[2., 3.]]), array([[2., 3.],
[2., 3.]])])
@set_module('mxnet.numpy')
def atleast_1d(*arys):
"""
Convert inputs to arrays with at least one dimension.
Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved.
Parameters
----------
arys1, arys2, ... : ndarray
One or more input arrays.
Returns
-------
ret : ndarray
An array, or list of arrays, each with a.ndim >= 1. Copies are made only if necessary.
See also
--------
atleast_2d, atleast_3d
Examples
--------
>>> np.atleast_1d(1.0)
array([1.])
>>> x = np.arange(9.0).reshape(3,3)
>>> np.atleast_1d(x)
array([[0., 1., 2.],
[3., 4., 5.],
[6., 7., 8.]])
>>> np.atleast_1d(np.array(1), np.array([3, 4]))
[array([1.]), array([3., 4.])]
"""
> return _mx_nd_np.atleast_1d(*arys)
python/mxnet/numpy/multiarray.py:11385:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
arys = ([array([[1., 2.],
[1., 2.]]), array([[1., 2.],
[1., 2.]])], [array([[2., 3.],
[2., 3.]]), array([[2., 3.],
[2., 3.]])])
@set_module('mxnet.ndarray.numpy')
def atleast_1d(*arys):
"""
Convert inputs to arrays with at least one dimension.
Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved.
Parameters
----------
arys1, arys2, ... : ndarray
One or more input arrays.
Returns
-------
ret : ndarray
An array, or list of arrays, each with a.ndim >= 1. Copies are made only if necessary.
See also
--------
atleast_2d, atleast_3d
Examples
--------
>>> np.atleast_1d(1.0)
array([1.])
>>> x = np.arange(9.0).reshape(3,3)
>>> np.atleast_1d(x)
array([[0., 1., 2.],
[3., 4., 5.],
[6., 7., 8.]])
>>> np.atleast_1d(np.array(1), np.array([3, 4]))
[array([1.]), array([3., 4.])]
"""
if len(arys) == 1:
return _api_internal.atleast_1d(*arys)[0]
> return list(_api_internal.atleast_1d(*arys))
python/mxnet/ndarray/numpy/_op.py:8901:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
mxnet/_ffi/_cython/./function.pxi:188:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
mxnet/_ffi/_cython/./function.pxi:120:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
mxnet/_ffi/_cython/./function.pxi:107:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
mxnet/_ffi/_cython/./function.pxi:36:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
mxnet/_ffi/_cython/./convert.pxi:75:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
mxnet/_ffi/_cython/./convert.pxi:63:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
E TypeError: Don't know how to convert type <class 'mxnet.numpy.ndarray'>
mxnet/_ffi/_cython/./convert.pxi:81: TypeError
During handling of the above exception, another exception occurred:
args = (), kwargs = {}
@functools.wraps(func)
def _with_np_shape(*args, **kwargs):
with np_shape(active=True):
> return func(*args, **kwargs)
python/mxnet/util.py:299:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
python/mxnet/util.py:480: in _with_np_array
return func(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
args = (), kwargs = {}
@functools.wraps(func)
def _run_with_array_func_proto(*args, **kwargs):
if cur_np_ver >= np_1_17_ver:
try:
func(*args, **kwargs)
except Exception as e:
> raise RuntimeError('Running function {} with NumPy array function protocol failed'
' with exception {}'
.format(func.__name__, str(e)))
E RuntimeError: Running function test_np_array_function_protocol with NumPy array function protocol failed with exception Don't know how to convert type <class 'mxnet.numpy.ndarray'>
python/mxnet/numpy_dispatch_protocol.py:57: RuntimeError
```
</details>
Segmentation Fault can be reproduced as follows
```
>>> from mxnet import np
>>> import numpy as _np
>>> x = np.arange(4.0).reshape(2,2)
>>> p = (x, x, x, x)
>>> _np.atleast_1d(p)
array([[[0., 1.],
[2., 3.]],
[[0., 1.],
[2., 3.]],
[[0., 1.],
[2., 3.]],
[[0., 1.],
[2., 3.]]])
>>> np.atleast_1d(p)
Fatal Error: Segmentation fault: 11
Stack trace:
Segmentation fault: 11
```
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