codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r550161068
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File path: python/tvm/relay/op/transform.py
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@@ -1320,3 +1320,84 @@ def adv_index(inputs):
Output tensor.
"""
return _make.adv_index(Tuple(inputs))
+
+
+def sparsefillemptyrows(sparse_indices, sparse_values, dense_shape,
default_value):
+ """
+ Fill first column of the empty rows with default values for a sparse array.
+
+ Parameters
+ ----------
+ sparse_indices : relay.Expr
+ A 2-D tensor[N, n_dim] of integers containing location of sparse
values, where N is the
+ number of sparse values and n_dim is the number of dimensions of the
dense_shape
+
+ sparse_values : relay.Expr
+ A 1-D tensor[N] containing the sparse values for the sparse indices.
+
+ dense_shape : relay.Expr
+ A list of integers. Shape of the dense output tensor.
+
+ default_value : relay.Expr
+ A 0-D tensor containing the default value for the remaining locations.
+ Defaults to 0.
+
+ Returns
+ -------
+ TupleWrapper with the following four outputs
+
+ new_sparse_indices : relay.Expr
+ A 2-D tensor[N + dense_shape[0], n_dim] of integers containing
location of new sparse
+ indices where N is the number of sparse values. It is filled with -1
at to_be_discarded
+ indices.
+
+ empty_row_indicator : relay.Expr
+ A 1-D Boolean tensor[dense_shape[0]] indicating whether the particular
row is empty
+
+ new_sparse_values : relay.Expr
+ A 1-D tensor[dense_shape[0]] containing the sparse values for the
sparse indices. It is
+ filled with -1 at to_be_discarded indices.
+
+ slice_element_index : relay.Expr
+ A 1-D tensor containing the amount of elements in the sparse_indices
and new_sparse_values
+ expression to be sliced in a future op discarding non-useful elements
in new_sparse_indices
+ and new_sparse_values
+
+ Examples
+ -------
+
+ .. code-block:: python
+
+ sparse_indices = [[0, 1],
+ [0, 3],
+ [2, 0],
+ [3, 1]]
+ sparse_values = [1, 2, 3, 4]
+ default_value = [10]
+ dense_shape = [5, 6]
+ new_sparse_indices, empty_row_indicator, new_sparse_values,
slice_element_index =
+ relay.sparsereshape(
+ sparse_indices,
+ sparse_values,
+ prev_shape,
+ new_shape)
+ new_sparse_indices = [[0, 1],
+ [0, 3],
+ [2, 0],
+ [3, 1],
+ [1, 0],
+ [4, 0],
+ [-1, -1],
Review comment:
Resolving this conversation since based on our offline discussion, we
will start off with static shape
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