tkonolige commented on a change in pull request #7435:
URL: https://github.com/apache/tvm/pull/7435#discussion_r578597721
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File path: python/tvm/relay/op/nn/nn.py
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@@ -2148,6 +2148,39 @@ def sparse_transpose(x):
return expr.TupleWrapper(_make.sparse_transpose(x[0], x[1], x[2]), 3)
+# pylint: disable=no-else-return,inconsistent-return-statements
+def sparse_add(dense_mat, sparse_mat):
+ r"""
+ Computes the matrix addition of `dense_mat` and `sparse_mat`, where
`dense_mat` is
+ a dense matrix and `sparse_mat` is a sparse (either BSR or CSR) namedtuple
with
+ fields `data`, `indices`, and `indptr`.
+
+ .. math::
+
+ \mbox{sparse_add}(dense_mat, sparse_mat)[m, n] =
\mbox{add}(\mbox{as_dense}(S), (D))[m, n]
+
+ where `as_dense` returns dense equivalent of the given S(sparse matrix)
+ while performing addition with given D(dense matrix).
+
+ Parameters
+ ----------
+ dense_mat : tvm.relay.Expr
+ The input dense matrix for the matrix multiplication
+
+ sparse_mat : Union[namedtuple, Tuple[ndarray, ndarray, ndarray]].
+ The input sparse matrix for the matrix multiplication.
+
+ Returns
+ -------
+ result: tvm.relay.Expr
+ The computed result.
Review comment:
Look in `python/tvm/relay/op/transform.py`.
Examples are useful for showing both new and experienced users how to use
the op. Especially in this case where the input formats maybe be a little
confusing.
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