Fix windows flakiness

python/mxnet/_numpy_op_doc.py

@@ -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.

src/operator/tensor/la_op.cc

@@ -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*.
 
@@ -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*.

src/operator/tensor/la_op.cu

@@ -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)

src/operator/tensor/la_op.h

@@ -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"
@@ -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);
   }