add op bitwise_or [numpy]

python/mxnet/ndarray/numpy/_op.py

@@ -36,11 +36,10 @@
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
-           'around', 'hypot', 'bitwise_xor', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
-           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
-           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff', 'resize',
-           'nan_to_num', 'where']
-
+           'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity',
+           'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
+           'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory',
+           'diff', 'resize', 'nan_to_num', 'where']
 
 @set_module('mxnet.ndarray.numpy')
 def shape(a):
@@ -4365,6 +4364,44 @@ def bitwise_xor(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.bitwise_xor, _np.bitwise_xor, _npi.bitwise_xor_scalar, None, out)
 
 
+@set_module('mxnet.ndarray.numpy')
+@wrap_np_binary_func
+def bitwise_or(x1, x2, out=None, **kwargs):
+    r"""
+    Compute the bit-wise OR of two arrays element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : ndarray or scalar
+        Only integer and boolean types are handled. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which becomes the shape of the output).
+    out : ndarray, optional
+        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
+        Result.
+
+    Examples
+    --------
+    >>> np.bitwise_or(13, 17)
+    29
+
+    >>> np.bitwise_or(31, 5)
+    31
+    >>> np.bitwise_or(np.array([31,3], dtype='int32'), 5)
+    array([31,  7])
+
+    >>> np.bitwise_or(np.array([31,3], dtype='int32'), np.array([5,6], dtype='int32'))
+    array([31,  7])
+    >>> np.bitwise_or(np.array([True, True], dtype='bool'), np.array([False, True], dtype='bool'))
+    array([ True, True])
+    """
+    return _ufunc_helper(x1, x2, _npi.bitwise_or, _np.bitwise_or, _npi.bitwise_or_scalar, None, out)
+
+
 @set_module('mxnet.ndarray.numpy')
 @wrap_np_binary_func
 def ldexp(x1, x2, out=None, **kwargs):

python/mxnet/numpy/multiarray.py

@@ -54,10 +54,10 @@
            'tensordot', 'histogram', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
            'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum',
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
-           'blackman', 'flip', 'around', 'arctan2', 'hypot', 'bitwise_xor', 'rad2deg', 'deg2rad', 'unique', 'lcm',
-           'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
-           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
-           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
+           'blackman', 'flip', 'around', 'arctan2', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad',
+           'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal',
+           'greater', 'less', 'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero',
+           'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -6277,6 +6277,44 @@ def bitwise_xor(x1, x2, out=None, **kwargs):
     return _mx_nd_np.bitwise_xor(x1, x2, out=out)
 
 
+@set_module('mxnet.numpy')
+@wrap_np_binary_func
+def bitwise_or(x1, x2, out=None, **kwargs):
+    r"""
+    Compute the bit-wise OR of two arrays element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : ndarray or scalar
+        Only integer and boolean types are handled. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which becomes the shape of the output).
+    out : ndarray, optional
+        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
+        Result.
+
+    Examples
+    --------
+    >>> np.bitwise_or(13, 17)
+    29
+
+    >>> np.bitwise_or(31, 5)
+    31
+    >>> np.bitwise_or(np.array([31,3], dtype=np.int32), 5)
+    array([31,  7])
+
+    >>> np.bitwise_or(np.array([31,3], dtype='int32'), np.array([5,6], dtype='int32'))
+    array([31,  7])
+    >>> np.bitwise_or(np.array([True, True], dtype='bool'), np.array([False, True], dtype='bool'))
+    array([ True, True])
+    """
+    return _mx_nd_np.bitwise_or(x1, x2, out=out)
+
+
 @set_module('mxnet.numpy')
 @wrap_np_binary_func
 def ldexp(x1, x2, out=None, **kwargs):

python/mxnet/numpy_dispatch_protocol.py

@@ -229,6 +229,7 @@ def _register_array_function():
     'trunc',
     'floor',
     'bitwise_xor',
+    'bitwise_or',
     'logical_not',
     'equal',
     'not_equal',

python/mxnet/symbol/numpy/_symbol.py

@@ -38,12 +38,11 @@
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
-           'around', 'hypot', 'bitwise_xor', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
-           'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
+           'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity',
+           'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
            'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory', 'may_share_memory', 'diff',
            'resize', 'nan_to_num', 'where']
 
-
 def _num_outputs(sym):
     return len(sym.as_nd_ndarray())
 
@@ -4103,6 +4102,30 @@ def bitwise_xor(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.bitwise_xor, _np.bitwise_xor, _npi.bitwise_xor_scalar, None, out)
 
 
+@set_module('mxnet.symbol.numpy')
+@wrap_np_binary_func
+def bitwise_or(x1, x2, out=None, **kwargs):
+    r"""
+    Compute the bit-wise OR of two arrays element-wise.
+
+    Parameters
+    ----------
+    x1, x2 : _Symbol or scalar
+        Only integer and boolean types are handled. If x1.shape != x2.shape,
+        they must be broadcastable to a common shape (which becomes the shape of the output).
+    out : _Symbol or None, optional
+        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 : _Symbol or scalar
+        Result.
+    """
+    return _ufunc_helper(x1, x2, _npi.bitwise_or, _np.bitwise_or, _npi.bitwise_or_scalar, None, out)
+
+
 @set_module('mxnet.symbol.numpy')
 def unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None):
     """

src/operator/mshadow_op.h

@@ -562,6 +562,8 @@ MXNET_BINARY_MATH_OP(logical_xor, (a || b) && !(a && b) ? DType(1) : DType(0));
 
 MXNET_BINARY_MATH_OP(bitwise_xor, static_cast<int64_t>(a) ^ static_cast<int64_t>(b));
 
+MXNET_BINARY_MATH_OP(bitwise_or, static_cast<int64_t>(a) | static_cast<int64_t>(b));
+
 MXNET_UNARY_MATH_OP(square_root, math::sqrt(a));
 
 MXNET_UNARY_MATH_OP(square_root_grad, 0.5f / math::id(a));

src/operator/numpy/np_elemwise_broadcast_op.cc

@@ -161,6 +161,7 @@ MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_mod)
 MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_power)
 .set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastCompute<cpu, mshadow_op::power>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_broadcast_power"});
+
 MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_add_scalar)
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::plus>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"});

src/operator/numpy/np_elemwise_broadcast_op_extended.cc

@@ -118,6 +118,24 @@ NNVM_REGISTER_OP(_npi_bitwise_xor)
 .add_argument("lhs", "NDArray-or-Symbol", "First input to the function")
 .add_argument("rhs", "NDArray-or-Symbol", "Second input to the function");
 
+NNVM_REGISTER_OP(_npi_bitwise_or)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+[](const NodeAttrs& attrs) {
+     return std::vector<std::string>{"lhs", "rhs"};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", BinaryBroadcastShape)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<2, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+[](const NodeAttrs& attrs){
+     return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
+})
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.set_attr<FCompute>("FCompute<cpu>", BinaryBroadcastIntCompute<cpu, mshadow_op::bitwise_or>)
+.add_argument("lhs", "NDArray-or-Symbol", "First input to the function")
+.add_argument("rhs", "NDArray-or-Symbol", "Second input to the function");
+
 NNVM_REGISTER_OP(_npi_bitwise_xor_scalar)
 .set_num_inputs(1)
 .set_num_outputs(1)
@@ -135,6 +153,23 @@ NNVM_REGISTER_OP(_npi_bitwise_xor_scalar)
 .add_argument("scalar", "int", "scalar input")
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::ComputeInt<cpu, mshadow_op::bitwise_xor>);
 
+NNVM_REGISTER_OP(_npi_bitwise_or_scalar)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser([](NodeAttrs* attrs) {
+    attrs->parsed = std::stod(attrs->dict["scalar"]);
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("data", "NDArray-or-Symbol", "source input")
+.add_argument("scalar", "int", "scalar input")
+.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::ComputeInt<cpu, mshadow_op::bitwise_or>);
+
 MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_copysign_scalar)
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::copysign>)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_npi_copysign_scalar"});

src/operator/numpy/np_elemwise_broadcast_op_extended.cu

@@ -37,6 +37,9 @@ NNVM_REGISTER_OP(_npi_lcm)
 NNVM_REGISTER_OP(_npi_bitwise_xor)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastIntCompute<gpu, mshadow_op::bitwise_xor>);
 
+NNVM_REGISTER_OP(_npi_bitwise_or)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastIntCompute<gpu, mshadow_op::bitwise_or>);
+
 NNVM_REGISTER_OP(_backward_npi_copysign)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastBackwardUseIn<gpu, mshadow_op::copysign_grad,
                                                                   mshadow_op::copysign_rgrad>);
@@ -85,6 +88,9 @@ NNVM_REGISTER_OP(_npi_lcm_scalar)
 NNVM_REGISTER_OP(_npi_bitwise_xor_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::ComputeInt<gpu, mshadow_op::bitwise_xor>);
 
+NNVM_REGISTER_OP(_npi_bitwise_or_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::ComputeInt<gpu, mshadow_op::bitwise_or>);
+
 NNVM_REGISTER_OP(_npi_ldexp)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastCompute<gpu, mshadow_op::ldexp>);
 

src/operator/operator_tune.cc

@@ -397,6 +397,7 @@ IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::logical_or);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::logical_xor);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::logical_xor);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::bitwise_xor);  // NOLINT()
+IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::bitwise_or);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::smooth_l1_loss);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_BWD(mxnet::op::mshadow_op::smooth_l1_gradient);  // NOLINT()
 IMPLEMENT_BINARY_WORKLOAD_FWD_WITH_BOOL(mxnet::op::mshadow_op::lcm);  // NOLINT()

tests/python/unittest/test_numpy_interoperability.py

@@ -845,6 +845,16 @@ def _add_workload_lcm():
     OpArgMngr.add_workload('lcm', np.array([12, 120], dtype=np.uint8), np.array([20, 200], dtype=np.uint8))
     OpArgMngr.add_workload('lcm', np.array(195225786*2, dtype=np.int32), np.array(195225786*5, dtype=np.int32))
 
+def _add_workload_bitwise_or():
+    OpArgMngr.add_workload('bitwise_or', np.array([False, False, True, True], dtype=np.bool),
+                           np.array([False, True, False, True], dtype=np.bool))
+    for dtype in [np.int8, np.int32, np.int64]:
+        zeros = np.array([0], dtype=dtype)
+        ones = np.array([-1], dtype=dtype)
+        OpArgMngr.add_workload('bitwise_or', zeros, zeros)
+        OpArgMngr.add_workload('bitwise_or', ones, zeros)
+        OpArgMngr.add_workload('bitwise_or', zeros, ones)
+        OpArgMngr.add_workload('bitwise_or', ones, ones)
 
 def _add_workload_bitwise_xor():
     OpArgMngr.add_workload('bitwise_xor', np.array([False, False, True, True], dtype=np.bool),
@@ -1308,6 +1318,7 @@ def _prepare_workloads():
     _add_workload_hypot()
     _add_workload_lcm()
     _add_workload_bitwise_xor()
+    _add_workload_bitwise_or()
     _add_workload_ldexp()
     _add_workload_subtract(array_pool)
     _add_workload_multiply(array_pool)

tests/python/unittest/test_numpy_op.py

@@ -1671,6 +1671,7 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
                              [lambda y, x1, x2: _np.power(x1, x2) * _np.log(x1)]),
         'lcm': (-100, 100, [None], None, [[_np.int32]]),
         'bitwise_xor': (-100, 100, [None], None, [[_np.int32]]),
+        'bitwise_or': (-100, 100, [None], None, [[_np.int32]]),
         'maximum': (-1, 1, [lambda y, x1, x2: _np.ones(y.shape) * (x1 >= x2)],
                            [lambda y, x1, x2: _np.ones(y.shape) * (x1 < x2)]),
         'minimum': (-1, 1, [lambda y, x1, x2: _np.ones(y.shape) * (x1 <= x2)],