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Fix windows flakiness (#16415)
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* Revert "Numpy det and slogdet operators (#15861)"

This reverts commit 295fc14.

* Delete non-windows test

* Revert "Numpy Operators: Inner, Outer, vdot (#15846)"

This reverts commit 2df3282.

* Revert "Revert "Numpy Operators: Inner, Outer, vdot (#15846)""

This reverts commit 79ceaa7.

* Revert "Delete non-windows test"

This reverts commit c915877.
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reminisce authored Oct 10, 2019
1 parent 1d0d1e6 commit 9ff644b
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122 changes: 0 additions & 122 deletions python/mxnet/_numpy_op_doc.py
Original file line number Diff line number Diff line change
Expand Up @@ -20,128 +20,6 @@
"""Doc placeholder for numpy ops with prefix _np."""


def _np__linalg_det(a):
"""
det(a)
Compute the determinant of an array.
Parameters
----------
a : (..., M, M) ndarray
Input array to compute determinants for.
Returns
-------
det : (...) ndarray
Determinant of `a`.
See Also
--------
slogdet : Another way to represent the determinant, more suitable
for large matrices where underflow/overflow may occur.
Notes
-----
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
The determinant is computed via LU factorization using the LAPACK
routine z/dgetrf.
Examples
--------
The determinant of a 2-D array [[a, b], [c, d]] is ad - bc:
>>> a = np.array([[1, 2], [3, 4]])
>>> np.linalg.det(a)
-2.0
Computing determinants for a stack of matrices:
>>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
>>> a.shape
(3, 2, 2)
>>> np.linalg.det(a)
array([-2., -3., -8.])
"""
pass


def _np__linalg_slogdet(a):
"""
slogdet(a)
Compute the sign and (natural) logarithm of the determinant of an array.
If an array has a very small or very large determinant, then a call to
`det` may overflow or underflow. This routine is more robust against such
issues, because it computes the logarithm of the determinant rather than
the determinant itself.
Parameters
----------
a : (..., M, M) ndarray
Input array, has to be a square 2-D array.
Returns
-------
sign : (...) ndarray
A number representing the sign of the determinant. For a real matrix,
this is 1, 0, or -1.
logdet : (...) array_like
The natural log of the absolute value of the determinant.
If the determinant is zero, then `sign` will be 0 and `logdet` will be
-Inf. In all cases, the determinant is equal to ``sign * np.exp(logdet)``.
See Also
--------
det
Notes
-----
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
The determinant is computed via LU factorization using the LAPACK
routine z/dgetrf.
Examples
--------
The determinant of a 2-D array ``[[a, b], [c, d]]`` is ``ad - bc``:
>>> a = np.array([[1, 2], [3, 4]])
>>> (sign, logdet) = np.linalg.slogdet(a)
>>> (sign, logdet)
(-1., 0.69314718055994529)
>>> sign * np.exp(logdet)
-2.0
Computing log-determinants for a stack of matrices:
>>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
>>> a.shape
(3, 2, 2)
>>> sign, logdet = np.linalg.slogdet(a)
>>> (sign, logdet)
(array([-1., -1., -1.]), array([ 0.69314718, 1.09861229, 2.07944154]))
>>> sign * np.exp(logdet)
array([-2., -3., -8.])
This routine succeeds where ordinary `det` does not:
>>> np.linalg.det(np.eye(500) * 0.1)
0.0
>>> np.linalg.slogdet(np.eye(500) * 0.1)
(1., -1151.2925464970228)
"""
pass


def _np_ones_like(a):
"""
Return an array of ones with the same shape and type as a given array.
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2 changes: 0 additions & 2 deletions src/operator/tensor/la_op.cc
Original file line number Diff line number Diff line change
Expand Up @@ -941,7 +941,6 @@ NNVM_REGISTER_OP(_backward_linalg_inverse)

NNVM_REGISTER_OP(_linalg_det)
.add_alias("linalg_det")
.add_alias("_np__linalg_det")
.describe(R"code(Compute the determinant of a matrix.
Input is a tensor *A* of dimension *n >= 2*.
Expand Down Expand Up @@ -992,7 +991,6 @@ NNVM_REGISTER_OP(_backward_linalg_det)
.set_attr<FCompute>("FCompute<cpu>", LaOpDetBackward<cpu, 1, det_backward>);

NNVM_REGISTER_OP(_linalg_slogdet)
.add_alias("_np__linalg_slogdet")
.add_alias("linalg_slogdet")
.describe(R"code(Compute the sign and log of the determinant of a matrix.
Input is a tensor *A* of dimension *n >= 2*.
Expand Down
2 changes: 0 additions & 2 deletions src/operator/tensor/la_op.cu
Original file line number Diff line number Diff line change
Expand Up @@ -100,14 +100,12 @@ NNVM_REGISTER_OP(_backward_linalg_inverse)
.set_attr<FCompute>("FCompute<gpu>", LaOpBackward<gpu, 2, 2, 2, 1, inverse_backward>);

NNVM_REGISTER_OP(_linalg_det)
.add_alias("_np__linalg_det")
.set_attr<FCompute>("FCompute<gpu>", LaOpDetForward<gpu, 1, det>);

NNVM_REGISTER_OP(_backward_linalg_det)
.set_attr<FCompute>("FCompute<gpu>", LaOpDetBackward<gpu, 1, det_backward>);

NNVM_REGISTER_OP(_linalg_slogdet)
.add_alias("_np__linalg_slogdet")
.set_attr<FCompute>("FCompute<gpu>", LaOpDetForward<gpu, 2, slogdet>);

NNVM_REGISTER_OP(_backward_linalg_slogdet)
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7 changes: 1 addition & 6 deletions src/operator/tensor/la_op.h
Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,6 @@
#define MXNET_OPERATOR_TENSOR_LA_OP_H_

#include <mxnet/operator_util.h>
#include <mxnet/imperative.h>
#include <vector>
#include <algorithm>
#include "../mshadow_op.h"
Expand Down Expand Up @@ -429,11 +428,7 @@ inline bool DetShape(const nnvm::NodeAttrs& attrs,
CHECK_EQ(in[ndim-2], in[ndim-1]) << "Input A's last two dimension must be equal";
mxnet::TShape out;
if (ndim == 2) {
if (Imperative::Get()->is_np_shape()) {
out = mxnet::TShape(0, 1);
} else {
out = mxnet::TShape(1, 1);
}
out = mxnet::TShape(1, 1);
} else {
out = mxnet::TShape(in.begin(), in.end() - 2);
}
Expand Down
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