add numpy operator remainder

Author: gyshi

python/mxnet/ndarray/numpy/_op.py

@@ -33,7 +33,7 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var']
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'remainder']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -2338,3 +2338,28 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint:
     0.2025
     """
     return _npi.var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def remainder(x1, x2, out=None):
+    """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.
+    """
+    return _ufunc_helper(x1, x2, _npi.remainder, _np.remainder, _npi.remainder_scalar, _npi.rremainder_scalar, out)

python/mxnet/numpy/multiarray.py

@@ -52,7 +52,8 @@
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
-           'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var']
+           'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var',
+           'remainder']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -3779,3 +3780,28 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=None):
     0.2025
     """
     return _npi.var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
+
+
+@set_module('mxnet.numpy')
+def remainder(x1, x2, out=None):
+    """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.
+    """
+    return _mx_nd_np.remainder(x1, x2, out=out)

python/mxnet/symbol/numpy/_symbol.py

@@ -35,7 +35,7 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var']
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'remainder']
 
 
 def _num_outputs(sym):
@@ -1066,7 +1066,6 @@ def divide(x1, x2, out=None):
 def mod(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.mod, _np.mod, _npi.mod_scalar, _npi.rmod_scalar, out)
 
-
 @set_module('mxnet.symbol.numpy')
 def power(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.power, _np.power, _npi.power_scalar, _npi.rpower_scalar, out)
@@ -2669,4 +2668,26 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint:
     return _npi.var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
 
 
+@set_module('mxnet.symbol.numpy')
+def remainder(x1, x2, out=None):
+    """Return element-wise remainder of division.
+
+    Parameters
+    ----------
+    x1 : _Symbol or scalar
+        Dividend array.
+
+    x2 : _Symbol or scalar
+        Divisor array.
+
+    out : _Symbol, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+
+    Returns
+    -------
+    out : _Symbol or scalar
+        This is a scalar if both x1 and x2 are scalars.
+    """
+    return _ufunc_helper(x1, x2, _npi.remainder, _np.remainder, _npi.remainder_scalar, _npi.rremainder_scalar, out)
+
 _set_np_symbol_class(_Symbol)

src/operator/numpy/np_elemwise_broadcast_op.cc

@@ -69,6 +69,7 @@ MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_multiply)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_mul"});
 
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_mod)
+.add_alias("_npi_remainder")
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::mod>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_mod"});
 
@@ -93,10 +94,12 @@ MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_multiply_scalar)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_mul_scalar"});
 
 MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_mod_scalar)
+.add_alias("_npi_remainder_scalar")
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::mod>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_mod_scalar"});
 
 MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rmod_scalar)
+.add_alias("_npi_rremainder_scalar")
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rmod>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_rmod_scalar"});
 

src/operator/numpy/np_elemwise_broadcast_op.cu

@@ -37,6 +37,7 @@ NNVM_REGISTER_OP(_npi_multiply)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, op::mshadow_op::mul>);
 
 NNVM_REGISTER_OP(_npi_mod)
+.add_alias("_npi_remainder")
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::mod>);
 
 NNVM_REGISTER_OP(_npi_power)
@@ -55,9 +56,11 @@ NNVM_REGISTER_OP(_npi_multiply_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, op::mshadow_op::mul>);
 
 NNVM_REGISTER_OP(_npi_mod_scalar)
+.add_alias("_npi_remainder_scalar")
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::mod>);
 
 NNVM_REGISTER_OP(_npi_rmod_scalar)
+.add_alias("_npi_rremainder_scalar")
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::Compute<gpu, mshadow_op::rmod>);
 
 NNVM_REGISTER_OP(_npi_power_scalar)

tests/python/unittest/test_numpy_ndarray.py

@@ -155,6 +155,7 @@ def test_ndarray_binary_element_wise_ops():
         '*': _np.multiply,
         '-': _np.subtract,
         '/': _np.divide,
+        'remainder': _np.remainder,
         'mod': _np.mod,
         'pow': _np.power,
         '==': _np.equal,
@@ -201,6 +202,11 @@ def hybrid_forward(self, F, x, *args):
                     return x % self._scalar if not self._reverse else self._scalar % x
                 else:
                     return x % args[0] if not self._reverse else args[0] % x
+            elif self._op == 'remainder':
+                if self._scalar is not None:
+                    return x % self._scalar if not self._reverse else self._scalar % x
+                else:
+                    return x % args[0] if not self._reverse else args[0] % x
             elif self._op == 'pow':
                 if self._scalar is not None:
                     return x ** self._scalar if not self._reverse else self._scalar ** x