tkonolige commented on a change in pull request #7477:
URL: https://github.com/apache/tvm/pull/7477#discussion_r582400531
##########
File path: tests/python/relay/test_op_level3.py
##########
@@ -1311,6 +1311,232 @@ def verify_sparse_to_dense(sparse_indices,
sparse_values, default_value, output_
# verify_sparse_to_dense([[[[0, 1, 4], [0, 2, 4]]]], [[[[3.1, 3.1,
3.1]]]], 3.5, [5], [3.1, 3.1, 3.5, 3.5, 3.1])
[email protected]_gpu
[email protected](
+ "sparse_indices_np, sparse_values_np, prev_shape_np, new_shape_np",
+ [
+ (
+ np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]],
dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([2, 3, 6], dtype=np.int64),
+ np.array([9, -1], dtype=np.int64),
+ ),
+ (
+ np.array(
+ [[0, 0, 0, 0], [0, 0, 1, 2], [0, 1, 0, 3], [1, 0, 0, 4], [1,
2, 3, 6]],
+ dtype=np.int64,
+ ),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([2, 3, 6, 7], dtype=np.int64),
+ np.array([9, -1, 7], dtype=np.int64),
+ ),
+ (
+ np.array(
+ [
+ [0, 0, 0, 0, 0],
+ [0, 0, 1, 2, 3],
+ [0, 1, 0, 3, 5],
+ [1, 0, 0, 4, 6],
+ [1, 2, 3, 6, 8],
+ ],
+ dtype=np.int64,
+ ),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([2, 3, 6, 7, 9], dtype=np.int64),
+ np.array([9, -1, 7], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 0], [0, 1], [3, 4], [4, 3], [7, 3]], dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([9, 4], dtype=np.int64),
+ np.array([2, -1, 6], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 0], [0, 1], [3, 4], [4, 3], [7, 3]], dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([9, 4], dtype=np.int64),
+ np.array([-1], dtype=np.int64),
+ ),
+ (
+ np.array([[0], [5], [10], [20], [24]], dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([25], dtype=np.int64),
+ np.array([5, 5], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]],
dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([500, 20], dtype=np.int64),
+ np.array([500, 20], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]],
dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([500, 20], dtype=np.int64),
+ np.array([500, -1], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]],
dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([500, 20], dtype=np.int64),
+ np.array([250, 40], dtype=np.int64),
+ ),
+ (
+ np.ones((0, 1), dtype=np.int64),
+ np.array([], dtype=np.int64),
+ np.array([4], dtype=np.int64),
+ np.array([2, -1], dtype=np.int64),
+ ),
+ (
+ np.ones((0, 1), dtype=np.int64),
+ np.array([], dtype=np.int64),
+ np.array([4], dtype=np.int64),
+ np.array([2, 2], dtype=np.int64),
+ ),
+ (
+ np.ones((0, 2), dtype=np.int64),
+ np.array([], dtype=np.int64),
+ np.array([3, 6], dtype=np.int64),
+ np.array([-1, 2], dtype=np.int64),
+ ),
+ ],
+)
[email protected]("dtype", [np.int32, np.int64])
[email protected]("use_dyn", [True, False])
+def test_sparse_reshape(
Review comment:
This is a lot of tests. It takes about a minute on my machine (which is
decently beefy). Can you not parameterize dtype and instead vary the dtype in
the input arrays?
##########
File path: tests/python/relay/test_op_level3.py
##########
@@ -1311,6 +1311,232 @@ def verify_sparse_to_dense(sparse_indices,
sparse_values, default_value, output_
# verify_sparse_to_dense([[[[0, 1, 4], [0, 2, 4]]]], [[[[3.1, 3.1,
3.1]]]], 3.5, [5], [3.1, 3.1, 3.5, 3.5, 3.1])
[email protected]_gpu
[email protected](
+ "sparse_indices_np, sparse_values_np, prev_shape_np, new_shape_np",
+ [
+ (
+ np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]],
dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([2, 3, 6], dtype=np.int64),
+ np.array([9, -1], dtype=np.int64),
+ ),
+ (
+ np.array(
+ [[0, 0, 0, 0], [0, 0, 1, 2], [0, 1, 0, 3], [1, 0, 0, 4], [1,
2, 3, 6]],
+ dtype=np.int64,
+ ),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([2, 3, 6, 7], dtype=np.int64),
+ np.array([9, -1, 7], dtype=np.int64),
+ ),
+ (
+ np.array(
+ [
+ [0, 0, 0, 0, 0],
+ [0, 0, 1, 2, 3],
+ [0, 1, 0, 3, 5],
+ [1, 0, 0, 4, 6],
+ [1, 2, 3, 6, 8],
+ ],
+ dtype=np.int64,
+ ),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([2, 3, 6, 7, 9], dtype=np.int64),
+ np.array([9, -1, 7], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 0], [0, 1], [3, 4], [4, 3], [7, 3]], dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([9, 4], dtype=np.int64),
+ np.array([2, -1, 6], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 0], [0, 1], [3, 4], [4, 3], [7, 3]], dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([9, 4], dtype=np.int64),
+ np.array([-1], dtype=np.int64),
+ ),
+ (
+ np.array([[0], [5], [10], [20], [24]], dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([25], dtype=np.int64),
+ np.array([5, 5], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]],
dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([500, 20], dtype=np.int64),
+ np.array([500, 20], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]],
dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([500, 20], dtype=np.int64),
+ np.array([500, -1], dtype=np.int64),
+ ),
+ (
+ np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]],
dtype=np.int64),
+ np.array([7, 5, 6, 3, 9], dtype=np.int64),
+ np.array([500, 20], dtype=np.int64),
+ np.array([250, 40], dtype=np.int64),
+ ),
+ (
+ np.ones((0, 1), dtype=np.int64),
+ np.array([], dtype=np.int64),
+ np.array([4], dtype=np.int64),
+ np.array([2, -1], dtype=np.int64),
+ ),
+ (
+ np.ones((0, 1), dtype=np.int64),
+ np.array([], dtype=np.int64),
+ np.array([4], dtype=np.int64),
+ np.array([2, 2], dtype=np.int64),
+ ),
+ (
+ np.ones((0, 2), dtype=np.int64),
+ np.array([], dtype=np.int64),
+ np.array([3, 6], dtype=np.int64),
+ np.array([-1, 2], dtype=np.int64),
+ ),
+ ],
+)
[email protected]("dtype", [np.int32, np.int64])
[email protected]("use_dyn", [True, False])
+def test_sparse_reshape(
+ sparse_indices_np, sparse_values_np, prev_shape_np, new_shape_np, dtype,
use_dyn
+):
+ def ref_sparse_reshape(
Review comment:
This is a lot of code and I'm not sure it is correct. Could you add a
test where you compare to a known-good output?
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