[Numpy] Add op linalg.matrix_rank

benchmark/python/ffi/benchmark_ffi.py

@@ -65,6 +65,7 @@ def prepare_workloads():
     OpArgMngr.add_workload("linalg.eigh", pool['3x3'])
     OpArgMngr.add_workload("linalg.det", pool['3x3'])
     OpArgMngr.add_workload("linalg.slogdet", pool['3x3'])
+    OpArgMngr.add_workload("linalg.matrix_rank", pool['3x3'], pool['1'], hermitian=False)
     OpArgMngr.add_workload("linalg.svd", pool['3x3'])
     OpArgMngr.add_workload("linalg.cholesky", pool['1x1'])
     OpArgMngr.add_workload("linalg.eigvals", pool['1x1'])
@@ -124,10 +125,10 @@ def prepare_workloads():
                            out=dnp.array([False, False], dtype=bool), keepdims=False)
     OpArgMngr.add_workload("roll", pool["2x2"], 1, axis=0)
     OpArgMngr.add_workload("rot90", pool["2x2"], 2)
-    OpArgMngr.add_workload("array_split", pool['2X2'], 2, axis=1)
-    OpArgMngr.add_workload("vsplit", pool['2X2'], 2)
-    OpArgMngr.add_workload("hsplit", pool['2X2'], 2)
-    OpArgMngr.add_workload("dsplit", pool['2X2x2'], 2)
+    OpArgMngr.add_workload("array_split", pool['2x2'], 2, axis=1)
+    OpArgMngr.add_workload("vsplit", pool['2x2'], 2)
+    OpArgMngr.add_workload("hsplit", pool['2x2'], 2)
+    OpArgMngr.add_workload("dsplit", pool['2x2x2'], 2)
     OpArgMngr.add_workload("arange", 10)
     OpArgMngr.add_workload("concatenate", (pool['1x2'], pool['1x2'], pool['1x2']), axis=0)
     OpArgMngr.add_workload("append", pool['2x2'], pool['1x2'], axis=0)

python/mxnet/ndarray/numpy/linalg.py

@@ -23,7 +23,52 @@
 from . import _api_internal
 
 __all__ = ['norm', 'svd', 'cholesky', 'qr', 'inv', 'det', 'slogdet', 'solve', 'tensorinv', 'tensorsolve',
-           'pinv', 'eigvals', 'eig', 'eigvalsh', 'eigh', 'lstsq']
+           'pinv', 'eigvals', 'eig', 'eigvalsh', 'eigh', 'lstsq', 'matrix_rank']
+
+
+def matrix_rank(M, tol=None, hermitian=False):
+    """
+    Return matrix rank of array using SVD method
+
+    Rank of the array is the number of singular values of the array that are
+    greater than `tol`.
+
+    Parameters
+    M : {(M,), (..., M, N)} ndarray
+        Input vector or stack of matrices.
+    tol : (...) ndarray, float, optional
+        Threshold below which SVD values are considered zero. If `tol` is
+        None, and ``S`` is an array with singular values for `M`, and
+        ``eps`` is the epsilon value for datatype of ``S``, then `tol` is
+        set to ``S.max() * max(M.shape) * eps``.
+    hermitian : bool, optional
+        If True, `M` is assumed to be Hermitian (symmetric if real-valued),
+        enabling a more efficient method for finding singular values.
+        Defaults to False.
+
+    Returns
+    -------
+    rank : (...) ndarray
+        Rank of M.
+
+    Examples
+    --------
+    >>> from mxnet import np
+    >>> np.matrix_rank(np.eye(4)) # Full rank matrix
+    4
+    >>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix
+    >>> np.matrix_rank(I)
+    3
+    >>> np.matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0
+    1
+    >>> np.matrix_rank(np.zeros((4,)))
+    0
+    """
+    finfo_eps_32 = _np.finfo(_np.float32).eps
+    finfo_eps_64 = _np.finfo(_np.float64).eps
+    if hermitian is True:
+        raise NotImplementedError("hermitian is not supported yet...")
+    return _api_internal.matrix_rank(M, tol, hermitian, finfo_eps_32, finfo_eps_64)
 
 
 def lstsq(a, b, rcond='warn'):

python/mxnet/numpy/fallback_linalg.py

@@ -24,11 +24,9 @@
 __all__ = [
     'cond',
     'matrix_power',
-    'matrix_rank',
     'multi_dot'
 ]
 
 cond = onp.linalg.cond
 matrix_power = onp.linalg.matrix_power
-matrix_rank = onp.linalg.matrix_rank
 multi_dot = onp.linalg.multi_dot

python/mxnet/numpy/linalg.py

@@ -22,10 +22,51 @@
 from . import fallback_linalg
 
 __all__ = ['norm', 'svd', 'cholesky', 'qr', 'inv', 'det', 'slogdet', 'solve', 'tensorinv', 'tensorsolve',
-           'pinv', 'eigvals', 'eig', 'eigvalsh', 'eigh', 'lstsq']
+           'pinv', 'eigvals', 'eig', 'eigvalsh', 'eigh', 'lstsq', 'matrix_rank']
 __all__ += fallback_linalg.__all__
 
 
+def matrix_rank(M, tol=None, hermitian=False):
+    """
+    Return matrix rank of array using SVD method
+
+    Rank of the array is the number of singular values of the array that are
+    greater than `tol`.
+
+    Parameters
+    M : {(M,), (..., M, N)} ndarray
+        Input vector or stack of matrices.
+    tol : (...) ndarray, float, optional
+        Threshold below which SVD values are considered zero. If `tol` is
+        None, and ``S`` is an array with singular values for `M`, and
+        ``eps`` is the epsilon value for datatype of ``S``, then `tol` is
+        set to ``S.max() * max(M.shape) * eps``.
+    hermitian : bool, optional
+        If True, `M` is assumed to be Hermitian (symmetric if real-valued),
+        enabling a more efficient method for finding singular values.
+        Defaults to False.
+
+    Returns
+    -------
+    rank : (...) ndarray
+        Rank of M.
+
+    Examples
+    --------
+    >>> from mxnet import np
+    >>> np.matrix_rank(np.eye(4)) # Full rank matrix
+    4
+    >>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix
+    >>> np.matrix_rank(I)
+    3
+    >>> np.matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0
+    1
+    >>> np.matrix_rank(np.zeros((4,)))
+    0
+    """
+    return _mx_nd_np.linalg.matrix_rank(M, tol, hermitian)
+
+
 def lstsq(a, b, rcond='warn'):
     r"""
     Return the least-squares solution to a linear matrix equation.

python/mxnet/numpy_dispatch_protocol.py

@@ -166,6 +166,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'linalg.eigvalsh',
     'linalg.eigh',
     'linalg.qr',
+    'linalg.matrix_rank',
     'shape',
     'trace',
     'tril',

python/mxnet/symbol/numpy/linalg.py

@@ -23,7 +23,40 @@
 from . import _internal as _npi
 
 __all__ = ['norm', 'svd', 'cholesky', 'qr', 'inv', 'det', 'slogdet', 'solve', 'tensorinv', 'tensorsolve',
-           'pinv', 'eigvals', 'eig', 'eigvalsh', 'eigh', 'lstsq']
+           'pinv', 'eigvals', 'eig', 'eigvalsh', 'eigh', 'lstsq', 'matrix_rank']
+
+
+def matrix_rank(M, tol=None, hermitian=False):
+    """
+    Return matrix rank of array using SVD method
+
+    Rank of the array is the number of singular values of the array that are
+    greater than `tol`.
+
+    Parameters
+    M : {(M,), (..., M, N)} _Symbol
+        Input vector or stack of matrices.
+    tol : (...) _Symbol, float, optional
+        Threshold below which SVD values are considered zero. If `tol` is
+        None, and ``S`` is an array with singular values for `M`, and
+        ``eps`` is the epsilon value for datatype of ``S``, then `tol` is
+        set to ``S.max() * max(M.shape) * eps``.
+    hermitian : bool, optional
+        If True, `M` is assumed to be Hermitian (symmetric if real-valued),
+        enabling a more efficient method for finding singular values.
+        Defaults to False.
+
+    Returns
+    -------
+    rank : (...) _Symbol
+        Rank of M.
+    """
+    finfo_eps_32 = _np.finfo(_np.float32).eps
+    finfo_eps_64 = _np.finfo(_np.float64).eps
+    if tol is None:
+        return _npi.matrix_rank_none_tol(M, finfo_eps_32, finfo_eps_64, hermitian)
+    else:
+        return _npi.matrix_rank(M, tol, hermitian)
 
 
 def lstsq(a, b, rcond='warn'):

src/api/operator/numpy/linalg/np_matrix_rank.cc

@@ -0,0 +1,76 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ * 
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_pinv.cc
+ * \brief Implementation of the API of functions in src/operator/numpy/linalg/np_matrix_rank.cc
+ */
+#include <mxnet/api_registry.h>
+#include <mxnet/runtime/packed_func.h>
+#include "../../utils.h"
+#include "../../../../operator/numpy/linalg/np_matrix_rank-inl.h"
+
+namespace mxnet {
+
+inline static void _npi_matrix_rank_none_tol(runtime::MXNetArgs args,
+                                             runtime::MXNetRetValue* ret) {
+  using namespace runtime;
+  const nnvm::Op* op = Op::Get("_npi_matrix_rank_none_tol");
+  op::MatrixRankNoneTolParam param;
+  nnvm::NodeAttrs attrs;
+  param.hermitian = args[2].operator bool();
+  param.finfoEps32 = args[3].operator double();
+  param.finfoEps64 = args[4].operator double();
+  attrs.parsed = param;
+  attrs.op = op;
+  SetAttrDict<op::MatrixRankNoneTolParam>(&attrs);
+  int num_inputs = 1;
+  int num_outputs = 0;
+  NDArray* inputs[] = {args[0].operator mxnet::NDArray*()};
+  auto ndoutputs = Invoke(op, &attrs, num_inputs, inputs, &num_outputs, nullptr);
+  *ret = reinterpret_cast<mxnet::NDArray*>(ndoutputs[0]);
+}
+
+inline static void _npi_matrix_rank(runtime::MXNetArgs args,
+                                    runtime::MXNetRetValue* ret) {
+  using namespace runtime;
+  const nnvm::Op* op = Op::Get("_npi_matrix_rank");
+  op::MatrixRankParam param;
+  nnvm::NodeAttrs attrs;
+  param.hermitian = args[2].operator bool();
+  attrs.parsed = param;
+  attrs.op = op;
+  SetAttrDict<op::MatrixRankParam>(&attrs);
+  int num_inputs = 2;
+  int num_outputs = 0;
+  NDArray* inputs[] = {args[0].operator mxnet::NDArray*(), args[1].operator mxnet::NDArray*()};
+  auto ndoutputs = Invoke(op, &attrs, num_inputs, inputs, &num_outputs, nullptr);
+  *ret = reinterpret_cast<mxnet::NDArray*>(ndoutputs[0]);
+}
+
+MXNET_REGISTER_API("_npi.matrix_rank")
+.set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
+  if (args[1].type_code() == kNull) {
+    _npi_matrix_rank_none_tol(args, ret);
+  } else {
+    _npi_matrix_rank(args, ret);
+  }
+});
+
+}  // namespace mxnet