leezu opened a new issue #19071:
URL: https://github.com/apache/incubator-mxnet/issues/19071
```
[2020-09-01T19:26:55.175Z] =================================== FAILURES
===================================
[2020-09-01T19:26:55.175Z] ____
test_np_average[null-False-True-True-float32-a_shape1-w_shape1-axes1] _____
[2020-09-01T19:26:55.175Z] [gw2] linux -- Python 3.6.9
/opt/rh/rh-python36/root/usr/bin/python3
[2020-09-01T19:26:55.175Z]
[2020-09-01T19:26:55.175Z] a_shape = (4, 5, 6), w_shape = (4, 5, 6), axes =
(0, 2), is_weighted = True
[2020-09-01T19:26:55.175Z] req_a = 'null', hybridize = True, returned =
False, dtype = 'float32'
[2020-09-01T19:26:55.175Z]
[2020-09-01T19:26:55.175Z] @with_seed()
[2020-09-01T19:26:55.175Z] @use_np
[2020-09-01T19:26:55.175Z]
@pytest.mark.parametrize('a_shape,w_shape,axes', [
[2020-09-01T19:26:55.175Z] ((3, 5), (3, 5), None),
[2020-09-01T19:26:55.175Z] ((4, 5, 6), (4, 5, 6), (0, 2)),
[2020-09-01T19:26:55.175Z] ((3,), (3,), 0),
[2020-09-01T19:26:55.175Z] ((2, 3), (3,), 1),
[2020-09-01T19:26:55.175Z] ((2, 3, 4), (2,), 0),
[2020-09-01T19:26:55.175Z] ((2, 3, 4), (3,), 1),
[2020-09-01T19:26:55.175Z] ((2, 3, 4), (4,), -1),
[2020-09-01T19:26:55.175Z] ((2, 3, 4, 5), (5,), 3)
[2020-09-01T19:26:55.175Z] ])
[2020-09-01T19:26:55.175Z] @pytest.mark.parametrize('dtype', ['float32',
'float64'])
[2020-09-01T19:26:55.175Z] @pytest.mark.parametrize('hybridize', [True,
False])
[2020-09-01T19:26:55.175Z] @pytest.mark.parametrize('is_weighted',
[True, False])
[2020-09-01T19:26:55.175Z] @pytest.mark.parametrize('returned', [True,
False])
[2020-09-01T19:26:55.175Z] @pytest.mark.parametrize('req_a', ['null',
'add', 'write'])
[2020-09-01T19:26:55.175Z] def test_np_average(a_shape, w_shape, axes,
is_weighted, req_a,
[2020-09-01T19:26:55.175Z] hybridize, returned,
dtype):
[2020-09-01T19:26:55.175Z] class TestAverage(HybridBlock):
[2020-09-01T19:26:55.175Z] def __init__(self, axis=None,
returned=False):
[2020-09-01T19:26:55.175Z] super(TestAverage,
self).__init__()
[2020-09-01T19:26:55.175Z] # necessary initializations
[2020-09-01T19:26:55.175Z] self._axis = axis
[2020-09-01T19:26:55.175Z] self._returned = returned
[2020-09-01T19:26:55.175Z]
[2020-09-01T19:26:55.175Z] def hybrid_forward(self, F, a,
weights):
[2020-09-01T19:26:55.175Z] return F.np.average(a,
weights=weights, axis=self._axis, returned=self._returned)
[2020-09-01T19:26:55.175Z]
[2020-09-01T19:26:55.175Z] def avg_backward(a, w, avg, axes,
init_a_grad=None, init_w_grad=None):
[2020-09-01T19:26:55.175Z] # avg = sum(a * w) / sum(w)
[2020-09-01T19:26:55.175Z] if axes is not None and not
isinstance(axes, tuple) and axes < 0:
[2020-09-01T19:26:55.175Z] axes += a.ndim
[2020-09-01T19:26:55.175Z] if w is None:
[2020-09-01T19:26:55.175Z] a_grad = _np.ones(shape=a.shape,
dtype=a.dtype)/(a.size/avg.size)
[2020-09-01T19:26:55.175Z] if init_a_grad is not None:
[2020-09-01T19:26:55.175Z] a_grad +=
init_a_grad.asnumpy()
[2020-09-01T19:26:55.175Z] return [a_grad, None]
[2020-09-01T19:26:55.175Z] onedim = a.ndim != w.ndim
[2020-09-01T19:26:55.175Z] if onedim:
[2020-09-01T19:26:55.175Z] new_shape = [a.shape[i] if i ==
axes else 1 for i in range(a.ndim)]
[2020-09-01T19:26:55.175Z] w = w.reshape(new_shape)
[2020-09-01T19:26:55.175Z] w = _np.broadcast_to(w, a.shape)
[2020-09-01T19:26:55.175Z]
[2020-09-01T19:26:55.175Z] # partial a = w / sum(w)
[2020-09-01T19:26:55.175Z] # partial w = (a*sum(w) - sum(a*w)) /
(sum(w) * sum(w))
[2020-09-01T19:26:55.175Z] scl = _np.sum(w, axis=axes,
keepdims=True)
[2020-09-01T19:26:55.175Z] a_grad = _np.divide(w, scl)
[2020-09-01T19:26:55.175Z] w_grad =
_np.divide(a*scl-_np.sum(a*w, axis=axes, keepdims=True), scl*scl)
[2020-09-01T19:26:55.175Z]
[2020-09-01T19:26:55.175Z] if onedim:
[2020-09-01T19:26:55.175Z] axis = list(range(a.ndim))
[2020-09-01T19:26:55.175Z] axis.remove(axes)
[2020-09-01T19:26:55.175Z] w_grad = _np.sum(w_grad,
axis=tuple(axis))
[2020-09-01T19:26:55.175Z] if init_a_grad is not None:
[2020-09-01T19:26:55.175Z] a_grad += init_a_grad.asnumpy()
[2020-09-01T19:26:55.175Z] if init_w_grad is not None:
[2020-09-01T19:26:55.175Z] w_grad += init_w_grad.asnumpy()
[2020-09-01T19:26:55.175Z] return [a_grad, w_grad]
[2020-09-01T19:26:55.175Z]
[2020-09-01T19:26:55.175Z] if req_a == 'null' and not is_weighted:
[2020-09-01T19:26:55.175Z] return
[2020-09-01T19:26:55.175Z] rtol, atol = 1e-3, 1e-4
[2020-09-01T19:26:55.175Z] test_average = TestAverage(axes, returned)
[2020-09-01T19:26:55.175Z] if hybridize:
[2020-09-01T19:26:55.175Z] test_average.hybridize()
[2020-09-01T19:26:55.175Z] a = np.random.uniform(-1.0, 1.0,
size=a_shape, dtype=dtype)
[2020-09-01T19:26:55.175Z] a.attach_grad(req_a)
[2020-09-01T19:26:55.176Z] init_a_grad = np.random.uniform(-1.0,
1.0, size=a_shape, dtype=dtype) if req_a == 'add' else None
[2020-09-01T19:26:55.176Z] init_w_grad = None
[2020-09-01T19:26:55.176Z] req_w = req_a
[2020-09-01T19:26:55.176Z] w, np_w = None, None
[2020-09-01T19:26:55.176Z] if is_weighted:
[2020-09-01T19:26:55.176Z] w = np.random.uniform(-1.0, 1.0,
size=w_shape, dtype=dtype)
[2020-09-01T19:26:55.176Z] if req_a == 'null':
[2020-09-01T19:26:55.176Z] req_w = random.choice(['add',
'write'])
[2020-09-01T19:26:55.176Z] w.attach_grad(req_w)
[2020-09-01T19:26:55.176Z] if req_w == 'add':
[2020-09-01T19:26:55.176Z] init_w_grad =
np.random.uniform(-1.0, 1.0, size=w_shape, dtype=dtype)
[2020-09-01T19:26:55.176Z] np_w = w.asnumpy()
[2020-09-01T19:26:55.176Z] np_out = _np.average(a.asnumpy(),
axis=axes, weights=np_w, returned=returned)
[2020-09-01T19:26:55.176Z] with mx.autograd.record():
[2020-09-01T19:26:55.176Z] mx_out = test_average(a, w)
[2020-09-01T19:26:55.176Z] if returned:
[2020-09-01T19:26:55.176Z] np_out, np_sum_of_weights = np_out
[2020-09-01T19:26:55.176Z] mx_out, mx_sum_of_weights = mx_out
[2020-09-01T19:26:55.176Z]
assert_almost_equal(mx_sum_of_weights.asnumpy(), np_sum_of_weights, rtol=rtol,
atol=atol)
[2020-09-01T19:26:55.176Z] assert mx_out.shape == np_out.shape
[2020-09-01T19:26:55.176Z] > assert_almost_equal(mx_out.asnumpy(),
np_out, rtol=rtol, atol=atol)
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] tests/python/unittest/test_numpy_op.py:941:
[2020-09-01T19:26:55.176Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] a = array([ -0.7383507, -0.892482 ,
-28541.63 , -0.5654906,
[2020-09-01T19:26:55.176Z] 11.051897 ], dtype=float32)
[2020-09-01T19:26:55.176Z] b = array([ -0.7383507, -0.8924821,
-28500.564 , -0.5654906,
[2020-09-01T19:26:55.176Z] 11.0519 ], dtype=float32)
[2020-09-01T19:26:55.176Z] rtol = 0.001, atol = 0.0001, names = ('a', 'b'),
equal_nan = False
[2020-09-01T19:26:55.176Z] use_broadcast = True, mismatches = (10, 10)
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] def assert_almost_equal(a, b, rtol=None,
atol=None, names=('a', 'b'), equal_nan=False,
[2020-09-01T19:26:55.176Z] use_broadcast=True,
mismatches=(10, 10)):
[2020-09-01T19:26:55.176Z] """Test that two numpy arrays are almost
equal. Raise exception message if not.
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] Parameters
[2020-09-01T19:26:55.176Z] ----------
[2020-09-01T19:26:55.176Z] a : np.ndarray or mx.nd.array
[2020-09-01T19:26:55.176Z] b : np.ndarray or mx.nd.array
[2020-09-01T19:26:55.176Z] rtol : None or float or dict of dtype ->
float
[2020-09-01T19:26:55.176Z] The relative threshold. Default
threshold will be used if set to ``None``.
[2020-09-01T19:26:55.176Z] atol : None or float or dict of dtype ->
float
[2020-09-01T19:26:55.176Z] The absolute threshold. Default
threshold will be used if set to ``None``.
[2020-09-01T19:26:55.176Z] names : tuple of names, optional
[2020-09-01T19:26:55.176Z] The names used in error message when
an exception occurs
[2020-09-01T19:26:55.176Z] equal_nan : boolean, optional
[2020-09-01T19:26:55.176Z] The flag determining how to treat NAN
values in comparison
[2020-09-01T19:26:55.176Z] mismatches : tuple of mismatches
[2020-09-01T19:26:55.176Z] Maximum number of mismatches to be
printed (mismatches[0]) and determine (mismatches[1])
[2020-09-01T19:26:55.176Z] """
[2020-09-01T19:26:55.176Z] if not use_broadcast:
[2020-09-01T19:26:55.176Z] checkShapes(a, b)
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] rtol, atol = get_tols(a, b, rtol, atol)
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] if isinstance(a, mx.numpy.ndarray):
[2020-09-01T19:26:55.176Z] a = a.asnumpy()
[2020-09-01T19:26:55.176Z] if isinstance(b, mx.numpy.ndarray):
[2020-09-01T19:26:55.176Z] b = b.asnumpy()
[2020-09-01T19:26:55.176Z] use_np_allclose = isinstance(a,
np.ndarray) and isinstance(b, np.ndarray)
[2020-09-01T19:26:55.176Z] if not use_np_allclose:
[2020-09-01T19:26:55.176Z] if not (hasattr(a, 'ctx') and
hasattr(b, 'ctx') and a.ctx == b.ctx and a.dtype == b.dtype):
[2020-09-01T19:26:55.176Z] use_np_allclose = True
[2020-09-01T19:26:55.176Z] if isinstance(a, mx.nd.NDArray):
[2020-09-01T19:26:55.176Z] a = a.asnumpy()
[2020-09-01T19:26:55.176Z] if isinstance(b, mx.nd.NDArray):
[2020-09-01T19:26:55.176Z] b = b.asnumpy()
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] if use_np_allclose:
[2020-09-01T19:26:55.176Z] if hasattr(a, 'dtype') and a.dtype ==
np.bool_ and hasattr(b, 'dtype') and b.dtype == np.bool_:
[2020-09-01T19:26:55.176Z] np.testing.assert_equal(a, b)
[2020-09-01T19:26:55.176Z] return
[2020-09-01T19:26:55.176Z] if almost_equal(a, b, rtol, atol,
equal_nan=equal_nan):
[2020-09-01T19:26:55.176Z] return
[2020-09-01T19:26:55.176Z] else:
[2020-09-01T19:26:55.176Z] output = mx.nd.contrib.allclose(a, b,
rtol, atol, equal_nan)
[2020-09-01T19:26:55.176Z] if output.asnumpy() == 1:
[2020-09-01T19:26:55.176Z] return
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] a = a.asnumpy()
[2020-09-01T19:26:55.176Z] b = b.asnumpy()
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] index, rel = _find_max_violation(a, b,
rtol, atol)
[2020-09-01T19:26:55.176Z] if index != ():
[2020-09-01T19:26:55.176Z] # a, b are the numpy arrays
[2020-09-01T19:26:55.176Z] indexErr = index
[2020-09-01T19:26:55.176Z] relErr = rel
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] print('\n*** Maximum errors for
vector of size {}: rtol={}, atol={}\n'.format(a.size, rtol, atol))
[2020-09-01T19:26:55.176Z] aTmp = a.copy()
[2020-09-01T19:26:55.176Z] bTmp = b.copy()
[2020-09-01T19:26:55.176Z] i = 1
[2020-09-01T19:26:55.176Z] while i <= a.size:
[2020-09-01T19:26:55.176Z] if i <= mismatches[0]:
[2020-09-01T19:26:55.176Z] print("%3d: Error %f %s"
%(i, rel, locationError(a, b, index, names)))
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] aTmp[index] = bTmp[index] = 0
[2020-09-01T19:26:55.176Z] if almost_equal(aTmp, bTmp, rtol,
atol, equal_nan=equal_nan):
[2020-09-01T19:26:55.176Z] break
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] i += 1
[2020-09-01T19:26:55.176Z] if i <= mismatches[1] or
mismatches[1] <= 0:
[2020-09-01T19:26:55.176Z] index, rel =
_find_max_violation(aTmp, bTmp, rtol, atol)
[2020-09-01T19:26:55.176Z] else:
[2020-09-01T19:26:55.176Z] break
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] mismatchDegree = "at least " if
mismatches[1] > 0 and i > mismatches[1] else ""
[2020-09-01T19:26:55.176Z] errMsg = "Error %f exceeds tolerance
rtol=%e, atol=%e (mismatch %s%f%%).\n%s" % \
[2020-09-01T19:26:55.176Z] (relErr, rtol, atol,
mismatchDegree, 100*i/a.size, \
[2020-09-01T19:26:55.176Z] locationError(a, b,
indexErr, names, maxError=True))
[2020-09-01T19:26:55.176Z] else:
[2020-09-01T19:26:55.176Z] errMsg = "Error %f exceeds tolerance
rtol=%e, atol=%e.\n" % (rel, rtol, atol)
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] np.set_printoptions(threshold=4,
suppress=True)
[2020-09-01T19:26:55.176Z] msg = npt.build_err_msg([a, b],
err_msg=errMsg)
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] > raise AssertionError(msg)
[2020-09-01T19:26:55.176Z] E AssertionError:
[2020-09-01T19:26:55.176Z] E Items are not equal:
[2020-09-01T19:26:55.176Z] E Error 1.440893 exceeds tolerance
rtol=1.000000e-03, atol=1.000000e-04 (mismatch 20.000000%).
[2020-09-01T19:26:55.176Z] E Location of maximum error: (2,),
a=-28541.63085938, b=-28500.56445312
[2020-09-01T19:26:55.176Z] E ACTUAL: array([ -0.7383507,
-0.892482 , -28541.63 , -0.5654906,
[2020-09-01T19:26:55.176Z] E 11.051897 ], dtype=float32)
[2020-09-01T19:26:55.176Z] E DESIRED: array([ -0.7383507,
-0.8924821, -28500.564 , -0.5654906,
[2020-09-01T19:26:55.176Z] E 11.0519 ], dtype=float32)
[2020-09-01T19:26:55.176Z]
[2020-09-01T19:26:55.176Z] python/mxnet/test_utils.py:735: AssertionError
```
https://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Fcentos-gpu/detail/PR-19057/2/pipeline/
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