codeislife99 commented on a change in pull request #7442:
URL: https://github.com/apache/tvm/pull/7442#discussion_r577141409
##########
File path: tests/python/relay/dyn/test_dynamic_op_level3.py
##########
@@ -202,5 +209,152 @@ def verify_sparse_to_dense(sparse_indices, sparse_values,
default_value, output_
verify_sparse_to_dense(1, 3, None, [5], [0, 3, 0, 0, 0]) # default value
not specified
[email protected](
+ "sparse_indices, sparse_values, dense_shape, default_value",
+ [
+ (
+ np.array([[0, 1], [0, 3], [2, 0], [3, 1]], dtype=np.int64),
+ np.array([1, 2, 3, 4], dtype=np.int64),
+ np.array([5, 6], dtype=np.int64),
+ np.array([10], dtype=np.int64),
+ ),
+ (
+ np.array([[1, 1, 1], [1, 3, 1], [2, 0, 5], [3, 1, 6]],
dtype=np.int64),
+ np.array([1, 2, 3, 4], dtype=np.int64),
+ np.array([7, 7, 7], dtype=np.int64),
+ np.array([5], dtype=np.int64),
+ ),
+ (
+ np.array([[1], [2]], dtype=np.int64),
+ np.array([7, 8], dtype=np.int64),
+ np.array([5], dtype=np.int64),
+ np.array([4], dtype=np.int64),
+ ),
+ (
+ np.ones((0, 1), dtype=np.int64),
+ np.array([], dtype=np.int64),
+ np.array([5], dtype=np.int64),
+ np.array([4], dtype=np.int64),
+ ),
+ (
+ np.ones((0, 3), dtype=np.int64),
+ np.array([], dtype=np.int64),
+ np.array([9, 3, 7], dtype=np.int64),
+ np.array([100], dtype=np.int64),
+ ),
+ ],
+)
[email protected]("dtype", [np.int32, np.int64])
[email protected]("use_dyn", [True, False])
+def test_sparse_fill_empty_rows(
+ sparse_indices, sparse_values, dense_shape, default_value, dtype, use_dyn
+):
+ def ref_sparse_fill_empty_rows(
+ sparse_indices: np.ndarray,
+ sparse_values: np.ndarray,
+ dense_shape: np.ndarray,
+ default_value: np.ndarray,
+ ) -> None:
+ """
+ This function calculates the expected output of sparse_fill_empty_rows
operator given the
+ inputs.
+ """
+
+ def check_add_rows(current_idx, limit_idx):
+ while current_idx < limit_idx:
+ new_sparse_indices.append([current_idx] + [0] * (num_cols - 1))
+ new_sparse_values.append(default_value[0])
+ empty_row_indicator[current_idx] = 1
+ current_idx += 1
+
+ return current_idx
+
+ current_idx = 0
+ new_sparse_indices = []
+ new_sparse_values = []
+ empty_row_indicator = [0 for _ in range(dense_shape[0])]
+ num_cols = sparse_indices.shape[1]
+ for sparse_row, sparse_value in zip(sparse_indices, sparse_values):
+ limit_idx = sparse_row[0]
+ current_idx = check_add_rows(current_idx, limit_idx)
+ new_sparse_indices.append(list(sparse_row))
+ new_sparse_values.append(sparse_value)
+ current_idx = limit_idx + 1
+
+ check_add_rows(current_idx, dense_shape[0])
+ return new_sparse_indices, new_sparse_values, empty_row_indicator
+
+ def verify_sparse_fill_empty_rows(
+ sparse_indices_np: np.ndarray,
+ sparse_values_np: np.ndarray,
+ dense_shape_np: np.ndarray,
+ default_value_np: np.ndarray,
+ ) -> None:
+ """
+ This function verifies the relay output of sparse_fill_empty_rows with
its expected output.
+ """
+ if use_dyn:
+ sparse_indices = relay.var(
+ "sparse_indices",
+ shape=[relay.Any(), relay.Any()],
+ dtype=str(sparse_indices_np.dtype),
+ )
+ sparse_values = relay.var(
+ "sparse_values",
+ shape=[relay.Any()],
+ dtype=str(sparse_values_np.dtype),
+ )
+ dense_shape = relay.var(
+ "dense_shape",
+ shape=[relay.Any()],
+ dtype=str(dense_shape_np.dtype),
+ )
+ default_value = relay.var(
+ "default_value",
+ shape=[relay.Any()],
+ dtype=str(default_value_np.dtype),
+ )
+ else:
+ sparse_indices = relay.var(
+ "sparse_indices",
+ relay.TensorType(sparse_indices_np.shape,
str(sparse_indices_np.dtype)),
+ )
+ sparse_values = relay.var(
+ "sparse_values",
+ relay.TensorType(sparse_values_np.shape,
str(sparse_values_np.dtype)),
+ )
+ dense_shape = relay.var(
+ "dense_shape",
+ relay.TensorType(dense_shape_np.shape,
str(dense_shape_np.dtype)),
+ )
+ default_value = relay.var(
+ "default_value",
+ relay.TensorType(default_value_np.shape,
str(default_value_np.dtype)),
+ )
+ z = relay.sparse_fill_empty_rows(
+ sparse_indices, sparse_values, dense_shape, default_value
+ ).astuple()
+ func = relay.Function([sparse_indices, sparse_values, dense_shape,
default_value], z)
+ ref_res = ref_sparse_fill_empty_rows(
+ sparse_indices_np,
+ sparse_values_np,
+ dense_shape_np,
+ default_value_np,
+ )
+ verify_func(
Review comment:
I have set the output dtype to int64 irrespective of input . Is this an
issue ?
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