[Numpy] Add op fmax, fmin, fmod

benchmark/python/ffi/benchmark_ffi.py

@@ -77,6 +77,9 @@ def prepare_workloads():
     OpArgMngr.add_workload("ones_like", pool['2x2'])
     OpArgMngr.add_workload("random.uniform", low=0, high=1, size=1)
     OpArgMngr.add_workload("where", pool['2x3'], pool['2x3'], pool['2x1'])
+    OpArgMngr.add_workload("fmax", pool['2x2'], pool['2x2'])
+    OpArgMngr.add_workload("fmin", pool['2x2'], pool['2x2'])
+    OpArgMngr.add_workload("fmod", pool['2x2'], pool['2x2'])
     OpArgMngr.add_workload("may_share_memory", pool['2x3'][:0], pool['2x3'][:1])
 
 

python/mxnet/ndarray/numpy/_op.py

@@ -30,15 +30,16 @@
 
 
 __all__ = ['shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like', 'empty_like', 'invert', 'delete',
-           'add', 'broadcast_to', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'bitwise_not',
+           'add', 'broadcast_to', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'fmod',
+           'power', 'bitwise_not',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'insert', 'fabs',
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'matmul',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor', 'histogram',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'all', 'any', 'sort',
            'tensordot', 'eye', 'linspace',
            'logspace', 'expand_dims', 'tile', 'arange', 'array_split', 'split', 'hsplit', 'vsplit', 'dsplit',
            'concatenate', 'append', 'stack', 'vstack', 'row_stack', 'column_stack', 'hstack', 'dstack',
-           'average', 'mean', 'maximum', 'minimum', 'around', 'round', 'round_', 'flatnonzero',
+           'average', 'mean', 'maximum', 'fmax', 'minimum', 'fmin', 'around', 'round', 'round_', 'flatnonzero',
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index',
            'diag_indices_from', 'hanning', 'hamming', 'blackman', 'flip', 'flipud', 'fliplr',
            'hypot', 'bitwise_and', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
@@ -1169,6 +1170,35 @@ def mod(x1, x2, out=None, **kwargs):
     return _api_internal.mod(x1, x2, out)
 
 
+@set_module('mxnet.ndarray.numpy')
+@wrap_np_binary_func
+def fmod(x1, x2, out=None, **kwargs):
+    """
+    Return element-wise remainder of division.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Dividend array.
+
+    x2 : ndarray or scalar
+        Divisor array.
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        This is a scalar if both x1 and x2 are scalars.
+    """
+    if isinstance(x1, numeric_types) and isinstance(x2, numeric_types):
+        _np.fmod(x1, x2, out=out)
+    return _api_internal.fmod(x1, x2, out)
+
+
 @set_module('mxnet.ndarray.numpy')
 def delete(arr, obj, axis=None):
     """
@@ -4366,6 +4396,27 @@ def maximum(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.maximum, _np.maximum, _npi.maximum_scalar, None, out)
 
 
+@set_module('mxnet.ndarray.numpy')
+@wrap_np_binary_func
+def fmax(x1, x2, out=None, **kwargs):
+    """
+    Returns element-wise maximum of the input arrays with broadcasting. (Ignores NaNs)
+
+    Parameters
+    ----------
+    x1, x2 : scalar or mxnet.numpy.ndarray
+        The arrays holding the elements to be compared. They must have the same shape,
+        or shapes that can be broadcast to a single shape.
+
+    Returns
+    -------
+    out : mxnet.numpy.ndarray or scalar
+        The maximum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars."""
+    if isinstance(x1, numeric_types) and isinstance(x2, numeric_types):
+        _np.fmax(x1, x2, out=out)
+    return _api_internal.fmax(x1, x2, out)
+
+
 @set_module('mxnet.ndarray.numpy')
 @wrap_np_binary_func
 def minimum(x1, x2, out=None, **kwargs):
@@ -4385,6 +4436,27 @@ def minimum(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.minimum, _np.minimum, _npi.minimum_scalar, None, out)
 
 
+@set_module('mxnet.ndarray.numpy')
+@wrap_np_binary_func
+def fmin(x1, x2, out=None, **kwargs):
+    """
+    Returns element-wise minimum of the input arrays with broadcasting. (Ignores NaNs)
+
+    Parameters
+    ----------
+    x1, x2 : scalar or mxnet.numpy.ndarray
+        The arrays holding the elements to be compared. They must have the same shape,
+        or shapes that can be broadcast to a single shape.
+
+    Returns
+    -------
+    out : mxnet.numpy.ndarray or scalar
+        The minimum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars."""
+    if isinstance(x1, numeric_types) and isinstance(x2, numeric_types):
+        _np.fmin(x1, x2, out=out)
+    return _api_internal.fmin(x1, x2, out)
+
+
 @set_module('mxnet.ndarray.numpy')
 def swapaxes(a, axis1, axis2):
     """Interchange two axes of an array.

python/mxnet/numpy/multiarray.py

@@ -53,15 +53,17 @@
 
 __all__ = ['ndarray', 'empty', 'empty_like', 'array', 'shape',
            'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like', 'all', 'any', 'broadcast_to',
-           'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'bitwise_not', 'delete',
+           'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'fmod', 'power', 'bitwise_not',
+           'delete',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'invert',
            'sqrt', 'cbrt', 'abs', 'absolute', 'fabs', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'histogram',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'append', 'argsort',
            'sort', 'tensordot', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
            'array_split', 'split', 'hsplit', 'vsplit', 'dsplit', 'flatnonzero',
            'concatenate', 'stack', 'vstack', 'row_stack', 'column_stack', 'hstack', 'dstack',
-           'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'insert',
+           'average', 'mean', 'maximum', 'fmax', 'minimum', 'fmin',
+           'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'insert',
            'indices', 'copysign', 'ravel', 'unravel_index', 'diag_indices_from', 'hanning', 'hamming', 'blackman',
            'flip', 'flipud', 'fliplr', 'around', 'round', 'round_', 'arctan2', 'hypot',
            'bitwise_and', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad',
@@ -3147,6 +3149,38 @@ def mod(x1, x2, out=None, **kwargs):
     return _mx_nd_np.mod(x1, x2, out=out)
 
 
+@set_module('mxnet.numpy')
+@wrap_np_binary_func
+def fmod(x1, x2, out=None, **kwargs):
+    """
+    Return element-wise remainder of division.
+
+    Parameters
+    ----------
+    x1 : ndarray or scalar
+        Dividend array.
+
+    x2 : ndarray or scalar
+        Divisor array.
+
+    out : ndarray
+        A location into which the result is stored. If provided, it must have a shape
+        that the inputs broadcast to. If not provided or None, a freshly-allocated array
+        is returned.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        This is a scalar if both x1 and x2 are scalars.
+
+    Examples
+    --------
+    >>> np.fmod(np.arange(7), 5)
+    array([0., 1., 2., 3., 4., 0., 1.])
+    """
+    return _mx_nd_np.fmod(x1, x2, out=out)
+
+
 @set_module('mxnet.numpy')
 @wrap_np_binary_func
 def matmul(a, b, out=None, **kwargs):
@@ -6185,6 +6219,35 @@ def maximum(x1, x2, out=None, **kwargs):
     return _mx_nd_np.maximum(x1, x2, out=out)
 
 
+@set_module('mxnet.numpy')
+@wrap_np_binary_func
+def fmax(x1, x2, out=None, **kwargs):
+    """
+    Returns element-wise maximum of the input arrays with broadcasting. (Ignores NaNs)
+
+    Parameters
+    ----------
+    x1, x2 : scalar or mxnet.numpy.ndarray
+        The arrays holding the elements to be compared. They must have the same shape,
+        or shapes that can be broadcast to a single shape.
+
+    Returns
+    -------
+    out : mxnet.numpy.ndarray or scalar
+        The maximum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+
+    Examples
+    --------
+    >>> np.fmax(np.array([2, 3, 4]), np.array([1, 5, 2]))
+    array([2., 5., 4.])
+
+    >>> np.fmax(np.eye(2), np.array([0.5, 2])) # broadcasting
+    array([[1. , 2. ],
+           [0.5, 2. ]])
+    """
+    return _mx_nd_np.fmax(x1, x2, out=out)
+
+
 @set_module('mxnet.numpy')
 @wrap_np_binary_func
 def minimum(x1, x2, out=None, **kwargs):
@@ -6214,6 +6277,35 @@ def minimum(x1, x2, out=None, **kwargs):
     return _mx_nd_np.minimum(x1, x2, out=out)
 
 
+@set_module('mxnet.numpy')
+@wrap_np_binary_func
+def fmin(x1, x2, out=None, **kwargs):
+    """
+    Returns element-wise minimum of the input arrays with broadcasting. (Ignores NaNs)
+
+    Parameters
+    ----------
+    x1, x2 : scalar or mxnet.numpy.ndarray
+        The arrays holding the elements to be compared. They must have the same shape,
+        or shapes that can be broadcast to a single shape.
+
+    Returns
+    -------
+    out : mxnet.numpy.ndarray or scalar
+        The fmin of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
+
+    Examples
+    --------
+    >>> np.fmin(np.array([2, 3, 4]), np.array([1, 5, 2]))
+    array([1., 3., 2.])
+
+    >>> np.fmin(np.eye(2), np.array([0.5, 2])) # broadcasting
+    array([[0.5, 0. ],
+           [0. , 1. ]])
+    """
+    return _mx_nd_np.fmin(x1, x2, out=out)
+
+
 @set_module('mxnet.numpy')
 def swapaxes(a, axis1, axis2):
     """Interchange two axes of an array.

python/mxnet/numpy_dispatch_protocol.py

@@ -248,6 +248,7 @@ def _register_array_function():
     'negative',
     'power',
     'mod',
+    'fmod',
     'matmul',
     'absolute',
     'rint',
@@ -277,7 +278,9 @@ def _register_array_function():
     'arccosh',
     'arctanh',
     'maximum',
+    'fmax',
     'minimum',
+    'fmin',
     'ceil',
     'trunc',
     'floor',

python/mxnet/symbol/numpy/_symbol.py

@@ -36,14 +36,16 @@
     from builtins import slice as py_slice
 
 __all__ = ['zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like', 'empty_like', 'bitwise_not', 'invert',
-           'delete', 'add', 'broadcast_to', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power', 'arctan2',
+           'delete', 'add', 'broadcast_to', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'fmod',
+           'power', 'arctan2',
            'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs', 'absolute', 'fabs', 'exp',
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p', 'matmul',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor', 'histogram', 'insert',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'argsort', 'sort', 'tensordot', 'eye', 'linspace',
            'logspace', 'expand_dims', 'tile', 'arange', 'array_split', 'split', 'hsplit', 'vsplit', 'dsplit',
            'concatenate', 'append', 'stack', 'vstack', 'row_stack', 'column_stack', 'hstack', 'dstack',
-           'average', 'mean', 'maximum', 'minimum', 'any', 'all', 'around', 'round', 'round_', 'flatnonzero',
+           'average', 'mean', 'maximum', 'fmax', 'minimum', 'fmin', 'any', 'all', 'around', 'round', 'round_',
+           'flatnonzero',
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index',
            'diag_indices_from', 'hanning', 'hamming', 'blackman', 'flip', 'flipud', 'fliplr',
            'hypot', 'bitwise_and', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
@@ -1620,6 +1622,12 @@ def mod(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.mod, _np.mod, _npi.mod_scalar, _npi.rmod_scalar, out)
 
 
+@set_module('mxnet.symbol.numpy')
+@wrap_np_binary_func
+def fmod(x1, x2, out=None, **kwargs):
+    return _ufunc_helper(x1, x2, _npi.fmod, _np.fmod, _npi.fmod_scalar, _npi.rfmod_scalar, out)
+
+
 @set_module('mxnet.symbol.numpy')
 @wrap_np_binary_func
 def remainder(x1, x2, out=None, **kwargs):
@@ -4127,12 +4135,24 @@ def maximum(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.maximum, _np.maximum, _npi.maximum_scalar, None, out)
 
 
+@set_module('mxnet.symbol.numpy')
+@wrap_np_binary_func
+def fmax(x1, x2, out=None, **kwargs):
+    return _ufunc_helper(x1, x2, _npi.fmax, _np.fmax, _npi.fmax_scalar, None, out)
+
+
 @set_module('mxnet.symbol.numpy')
 @wrap_np_binary_func
 def minimum(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.minimum, _np.minimum, _npi.minimum_scalar, None, out)
 
 
+@set_module('mxnet.symbol.numpy')
+@wrap_np_binary_func
+def fmin(x1, x2, out=None, **kwargs):
+    return _ufunc_helper(x1, x2, _npi.fmin, _np.fmin, _npi.fmin_scalar, None, out)
+
+
 @set_module('mxnet.symbol.numpy')
 def all(a, axis=None, out=None, keepdims=False):
     """

src/api/operator/numpy/np_elemwise_broadcast_op_extended_sec.cc

@@ -0,0 +1,56 @@
+/*
+ * 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_elemwise_broadcast_op_extended_sec.cc
+ * \brief Implementation of the API of functions in src/operator/numpy/np_elemwise_broadcast_op_extended_sec.cc
+ */
+#include <mxnet/api_registry.h>
+#include <mxnet/runtime/packed_func.h>
+#include "../utils.h"
+#include "../ufunc_helper.h"
+
+namespace mxnet {
+
+MXNET_REGISTER_API("_npi.fmax")
+.set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
+  using namespace runtime;
+  const nnvm::Op* op = Op::Get("_npi_fmax");
+  const nnvm::Op* op_scalar = Op::Get("_npi_fmax_scalar");
+  UFuncHelper(args, ret, op, op_scalar, nullptr);
+});
+
+MXNET_REGISTER_API("_npi.fmin")
+.set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
+  using namespace runtime;
+  const nnvm::Op* op = Op::Get("_npi_fmin");
+  const nnvm::Op* op_scalar = Op::Get("_npi_fmin_scalar");
+  UFuncHelper(args, ret, op, op_scalar, nullptr);
+});
+
+MXNET_REGISTER_API("_npi.fmod")
+.set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
+  using namespace runtime;
+  const nnvm::Op* op = Op::Get("_npi_fmod");
+  const nnvm::Op* op_scalar = Op::Get("_npi_fmod_scalar");
+  const nnvm::Op* op_rscalar = Op::Get("_npi_rfmod_scalar");
+  UFuncHelper(args, ret, op, op_scalar, op_rscalar);
+});
+
+}  // namespace mxnet