eric-haibin-lin commented on a change in pull request #10208: [MXNET-117]
Sparse operator broadcast_mul/div(csr, dense) = csr
URL: https://github.com/apache/incubator-mxnet/pull/10208#discussion_r178997359
##########
File path: python/mxnet/ndarray/sparse.py
##########
@@ -1159,6 +1186,322 @@ def _ndarray_cls(handle, writable=True,
stype=_STORAGE_TYPE_UNDEFINED):
_set_ndarray_class(_ndarray_cls)
+def add(lhs, rhs):
+ """Returns element-wise sum of the input arrays with broadcasting.
+
+ Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and
+ ``mx.nd.broadcast_plus(lhs, rhs)`` when shapes of lhs and rhs do not
+ match. If lhs.shape == rhs.shape, this is equivalent to
+ ``mx.nd.elemwise_add(lhs, rhs)``
+
+ .. note::
+
+ If the corresponding dimensions of two arrays have the same size or
one of them has size 1,
+ then the arrays are broadcastable to a common shape.abs
+
+ Parameters
+ ----------
+ lhs : scalar or array
+ First array to be added.
+ rhs : scalar or array
+ Second array to be added.
+ If ``lhs.shape != rhs.shape``, they must be
+ broadcastable to a common shape.
+
+ Returns
+ -------
+ NDArray
+ The element-wise sum of the input arrays.
+
+ Examples
+ --------
+ >>> x = mx.nd.ones((2,3)).tostype('csr')
+ >>> y = mx.nd.arange(2).reshape((2,1))
+ >>> z = mx.nd.arange(2).reshape((1,2))
+ >>> x.asnumpy()
+ array([[ 1., 1., 1.],
+ [ 1., 1., 1.]], dtype=float32)
+ >>> y.asnumpy()
+ array([[ 0.],
+ [ 1.]], dtype=float32)
+ >>> z.asnumpy()
+ array([[ 0., 1.]], dtype=float32)
+ >>> (x+2).asnumpy()
+ array([[ 3., 3., 3.],
+ [ 3., 3., 3.]], dtype=float32)
+ >>> (x+y).asnumpy()
+ array([[ 1., 1., 1.],
+ [ 2., 2., 2.]], dtype=float32)
+ >>> mx.nd.add(x,y).asnumpy()
+ array([[ 1., 1., 1.],
+ [ 2., 2., 2.]], dtype=float32)
+ >>> (z + y).asnumpy()
+ array([[ 0., 1.],
+ [ 1., 2.]], dtype=float32)
+ """
+ # pylint: disable= no-member, protected-access
+ if isinstance(lhs, NDArray) and isinstance(rhs, NDArray) and lhs.shape ==
rhs.shape:
+ return _ufunc_helper(
+ lhs,
+ rhs,
+ op.elemwise_add,
+ operator.add,
+ _internal._plus_scalar,
+ None)
+
+ return _ufunc_helper(
+ lhs,
+ rhs,
+ op.broadcast_add,
+ operator.add,
+ _internal._plus_scalar,
+ None)
+ # pylint: enable= no-member, protected-access
+
+
+def subtract(lhs, rhs):
+ """Returns element-wise difference of the input arrays with broadcasting.
+
+ Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and
+ ``mx.nd.broadcast_minus(lhs, rhs)`` when shapes of lhs and rhs do not
+ match. If lhs.shape == rhs.shape, this is equivalent to
+ ``mx.nd.elemwise_sub(lhs, rhs)``
+
+ .. note::
+
+ If the corresponding dimensions of two arrays have the same size or
one of them has size 1,
+ then the arrays are broadcastable to a common shape.
+
+ Parameters
+ ----------
+ lhs : scalar or array
+ First array to be subtracted.
+ rhs : scalar or array
+ Second array to be subtracted.
+ If ``lhs.shape != rhs.shape``, they must be
+ broadcastable to a common shape.__spec__
+
+ Returns
+ -------
+ NDArray
+ The element-wise difference of the input arrays.
+
+ Examples
+ --------
+ >>> x = mx.nd.ones((2,3)).tostype('csr')
+ >>> y = mx.nd.arange(2).reshape((2,1))
Review comment:
csr - dense will fallback..
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