Support NumPy-compatible bitwise_and

python/mxnet/ndarray/numpy/_op.py

@@ -38,10 +38,11 @@
            'stack', 'vstack', 'row_stack', 'column_stack', 'hstack', 'dstack',
            'average', 'mean', 'maximum', 'minimum',
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index',
-           'hanning', 'hamming', 'blackman', 'flip', 'flipud', 'fliplr', 'around', 'round', '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', 'quantile', 'percentile', 'shares_memory', 'may_share_memory',
+           'hanning', 'hamming', 'blackman', 'flip', 'flipud', 'fliplr', 'around', 'round', 'hypot',
+           'bitwise_and', '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', 'quantile', 'percentile', 'shares_memory', 'may_share_memory',
            'diff', 'resize', 'nan_to_num', 'where', 'bincount']
 
 
@@ -5254,6 +5255,46 @@ def hypot(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.hypot, _np.hypot, _npi.hypot_scalar, None, out)
 
 
+@set_module('mxnet.ndarray.numpy')
+@wrap_np_binary_func
+def bitwise_and(x1, x2, out=None, **kwargs):
+    r"""
+    Compute the bit-wise XOR 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_and(13, 17)
+    1
+
+    >>> np.bitwise_and(14, 13)
+    12
+    >>> np.bitwise_and(np.array([14,3], dtype='int32'), 13)
+    array([12,  1], dtype=int32)
+
+    >>> np.bitwise_and(np.array([11,7], dtype='int32'), np.array([4,25], dtype='int32'))
+    array([0, 1], dtype=int32)
+    >>> np.bitwise_and(np.array([2,5,255], dtype='int32'), np.array([3,14,16], dtype='int32'))
+    array([ 2,  4, 16], dtype=int32)
+    >>> np.bitwise_and(np.array([True, True], dtype='bool'), np.array([False, True], dtype='bool'))
+    array([False,  True])
+    """
+    return _ufunc_helper(x1, x2, _npi.bitwise_and, _np.bitwise_and, _npi.bitwise_and_scalar, None, out)
+
+
 @set_module('mxnet.ndarray.numpy')
 @wrap_np_binary_func
 def bitwise_xor(x1, x2, out=None, **kwargs):

python/mxnet/numpy/multiarray.py

@@ -60,14 +60,16 @@
            'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'row_stack', 'column_stack', 'hstack', 'dstack',
            'average', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var',
            'indices', 'copysign', 'ravel', 'unravel_index', 'hanning', 'hamming', 'blackman', 'flip', 'flipud',
-           'fliplr', 'around', 'round', 'arctan2', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad',
+           'fliplr', 'around', 'round', 'arctan2', 'hypot',
+           'bitwise_and', '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',
            'quantile', 'percentile', 'shares_memory', 'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where',
            'bincount']
 
 __all__ += fallback.__all__
 
+
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
 _NDARRAY_BASIC_INDEXING = 0
@@ -7094,6 +7096,46 @@ def hypot(x1, x2, out=None, **kwargs):
     return _mx_nd_np.hypot(x1, x2, out=out)
 
 
+@set_module('mxnet.numpy')
+@wrap_np_binary_func
+def bitwise_and(x1, x2, out=None, **kwargs):
+    r"""
+    Compute the bit-wise XOR 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_and(13, 17)
+    1
+
+    >>> np.bitwise_and(14, 13)
+    12
+    >>> np.bitwise_and(np.array([14,3], dtype='int32'), 13)
+    array([26,  5], dtype=int32)
+
+    >>> np.bitwise_and(np.array([11,7], dtype='int32'), np.array([4,25], dtype='int32'))
+    array([0, 1], dtype=int32)
+    >>> np.bitwise_and(np.array([2,5,255], dtype='int32'), np.array([3,14,16], dtype='int32'))
+    array([ 2,  4, 16], dtype=int32)
+    >>> np.bitwise_and(np.array([True, True], dtype='bool'), np.array([False, True], dtype='bool'))
+    array([False,  True])
+    """
+    return _mx_nd_np.bitwise_and(x1, x2, out=out)
+
+
 @set_module('mxnet.numpy')
 @wrap_np_binary_func
 def bitwise_xor(x1, x2, out=None, **kwargs):
@@ -7122,10 +7164,10 @@ def bitwise_xor(x1, x2, out=None, **kwargs):
     >>> np.bitwise_xor(31, 5)
     26
     >>> np.bitwise_xor(np.array([31,3], dtype=np.int32), 5)
-    array([26,  6])
+    array([26,  6], dtype=int32)
 
     >>> np.bitwise_xor(np.array([31,3], dtype='int32'), np.array([5,6], dtype='int32'))
-    array([26,  5])
+    array([26,  5], dtype=int32)
     >>> np.bitwise_xor(np.array([True, True], dtype='bool'), np.array([False, True], dtype='bool'))
     array([ True, False])
     """

python/mxnet/numpy_dispatch_protocol.py

@@ -257,6 +257,7 @@ def _register_array_function():
     'ceil',
     'trunc',
     'floor',
+    'bitwise_and',
     'bitwise_xor',
     'bitwise_or',
     'logical_not',

python/mxnet/symbol/numpy/_symbol.py

@@ -46,10 +46,11 @@
            'stack', 'vstack', 'row_stack', 'column_stack', 'hstack', 'dstack',
            'average', 'mean', 'maximum', 'minimum',
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'unravel_index',
-           'hanning', 'hamming', 'blackman', 'flip', 'flipud', 'fliplr', 'around', 'round', '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', 'quantile', 'percentile', 'shares_memory', 'may_share_memory', 'diff',
+           'hanning', 'hamming', 'blackman', 'flip', 'flipud', 'fliplr', 'around', 'round', 'hypot',
+           'bitwise_and', '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', 'quantile', 'percentile', 'shares_memory', 'may_share_memory', 'diff',
            'resize', 'nan_to_num', 'where', 'bincount']
 
 
@@ -4896,6 +4897,30 @@ def hypot(x1, x2, out=None, **kwargs):
     return _ufunc_helper(x1, x2, _npi.hypot, _np.hypot, _npi.hypot_scalar, None, out)
 
 
+@set_module('mxnet.symbol.numpy')
+@wrap_np_binary_func
+def bitwise_and(x1, x2, out=None, **kwargs):
+    r"""
+    Compute the bit-wise XOR 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_and, _np.bitwise_and, _npi.bitwise_and_scalar, None, out)
+
+
 @set_module('mxnet.symbol.numpy')
 @wrap_np_binary_func
 def bitwise_xor(x1, x2, out=None, **kwargs):

src/operator/mshadow_op.h

@@ -618,6 +618,8 @@ MXNET_BINARY_MATH_OP(logical_or, a || b ? DType(1) : DType(0));
 
 MXNET_BINARY_MATH_OP(logical_xor, (a || b) && !(a && b) ? DType(1) : DType(0));
 
+MXNET_BINARY_MATH_OP(bitwise_and, static_cast<int64_t>(a) & static_cast<int64_t>(b));
+
 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));

src/operator/numpy/np_elemwise_broadcast_op_extended.cc

@@ -100,6 +100,41 @@ NNVM_REGISTER_OP(_npi_lcm_scalar)
 .add_argument("scalar", "int", "scalar input")
 .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::lcm>);
 
+NNVM_REGISTER_OP(_npi_bitwise_and)
+.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_and>)
+.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_and_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_and>);
+
 NNVM_REGISTER_OP(_npi_bitwise_xor)
 .set_num_inputs(2)
 .set_num_outputs(1)

src/operator/numpy/np_elemwise_broadcast_op_extended.cu

@@ -34,6 +34,9 @@ NNVM_REGISTER_OP(_npi_copysign)
 NNVM_REGISTER_OP(_npi_lcm)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastIntCompute<gpu, mshadow_op::lcm>);
 
+NNVM_REGISTER_OP(_npi_bitwise_and)
+.set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastIntCompute<gpu, mshadow_op::bitwise_and>);
+
 NNVM_REGISTER_OP(_npi_bitwise_xor)
 .set_attr<FCompute>("FCompute<gpu>", BinaryBroadcastIntCompute<gpu, mshadow_op::bitwise_xor>);
 
@@ -85,6 +88,9 @@ NNVM_REGISTER_OP(_backward_npi_rarctan2_scalar)
 NNVM_REGISTER_OP(_npi_lcm_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::ComputeInt<gpu, mshadow_op::lcm>);
 
+NNVM_REGISTER_OP(_npi_bitwise_and_scalar)
+.set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::ComputeInt<gpu, mshadow_op::bitwise_and>);
+
 NNVM_REGISTER_OP(_npi_bitwise_xor_scalar)
 .set_attr<FCompute>("FCompute<gpu>", BinaryScalarOp::ComputeInt<gpu, mshadow_op::bitwise_xor>);
 

src/operator/operator_tune.cc

@@ -397,6 +397,7 @@ IMPLEMENT_BINARY_WORKLOAD_FWD(mxnet::op::mshadow_op::logical_or);  // NOLINT()
 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_and);  // 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()

tests/python/unittest/test_numpy_interoperability.py

@@ -1229,6 +1229,18 @@ def _add_workload_bitwise_or():
         OpArgMngr.add_workload('bitwise_or', ones, ones)
 
 
+def _add_workload_bitwise_and():
+    OpArgMngr.add_workload('bitwise_and', 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_and', zeros, zeros)
+        OpArgMngr.add_workload('bitwise_and', ones, zeros)
+        OpArgMngr.add_workload('bitwise_and', zeros, ones)
+        OpArgMngr.add_workload('bitwise_and', ones, ones)
+
+
 def _add_workload_bitwise_xor():
     OpArgMngr.add_workload('bitwise_xor', np.array([False, False, True, True], dtype=np.bool),
                            np.array([False, True, False, True], dtype=np.bool))
@@ -1816,6 +1828,7 @@ def _prepare_workloads():
     _add_workload_inner()
     _add_workload_hypot()
     _add_workload_lcm()
+    _add_workload_bitwise_and()
     _add_workload_bitwise_xor()
     _add_workload_bitwise_or()
     _add_workload_ldexp()

tests/python/unittest/test_numpy_op.py

@@ -2063,6 +2063,7 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
         'power': (1.0, 3.0, [lambda y, x1, x2: _np.power(x1, x2 - 1.0) * x2],
                              [lambda y, x1, x2: _np.power(x1, x2) * _np.log(x1)]),
         'lcm': (-100, 100, [None], None, [[_np.int32]]),
+        'bitwise_and': (-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)],