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modify and remove ScatterUpdate gpu testcases that do not meet the requirement of inrepetitive indices

This commit is contained in:
zhengzuohe 2023-02-27 21:26:40 +08:00
parent b7f0ceeabb
commit dfb8775e20
1 changed files with 18 additions and 50 deletions
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68
tests/st/ops/gpu/test_scatter_func_op.py
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@ -421,14 +421,11 @@ def test_scatter_func_input_less_than_1_float32():
updates = Tensor(np.arange(34, 70).reshape((2, 2, 3, 3)).astype(np.float32))
# update
output = scatter_func_net("update", inputx, indices, updates)
indices_unique = Tensor(np.array([[[1, 0, 2]]]).astype(np.int32))
updates_unique = Tensor(np.arange(34, 43).reshape((1, 1, 3, 3)).astype(np.float32))
output = scatter_func_net("update", inputx, indices_unique, updates_unique)
expected = np.array(
[
[37.0, 38.0, 39.0],
[34.0, 35.0, 66.0],
[67.0, 68.0, 69.0],
],
dtype=np.float32,
[[37.0, 38.0, 39.0], [34.0, 35.0, 36.0], [40.0, 41.0, 42.0]], dtype=np.float32,
)
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
@ -517,10 +514,12 @@ def test_scatter_func_large_float16():
updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.float16))
# update
output = scatter_func_net("update", inputx, indices, updates)
indices_unique = Tensor(np.array([[1, 0]]).astype(np.int32))
updates_unique = Tensor(np.arange(87, 111).reshape((1, 2, 3, 4)).astype(np.float16))
output = scatter_func_net("update", inputx, indices_unique, updates_unique)
expected = np.array([
[[63.0, 64.0, 65.0, 66.0], [67.0, 68.0, 69.0, 70.0], [71.0, 72.0, 73.0, 74.0]],
[[99.0, 100.0, 101.0, 102.0], [103.0, 104.0, 105.0, 106.0], [95.0, 96.0, 97.0, 98.0]],
[[99.0, 100.0, 101.0, 102.0], [103.0, 104.0, 105.0, 106.0], [107.0, 108.0, 109.0, 110.0]],
[[87.0, 88.0, 89.0, 90.0], [91.0, 92.0, 93.0, 94.0], [95.0, 96.0, 97.0, 98.0]],
])
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
@ -570,13 +569,6 @@ def test_scatter_func_disordered_float16():
indices = Tensor(np.array([[[0, 1, 2], [2, 1, 0]], [[0, 0, 0], [2, 2, 2]]]).astype(np.int32))
updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.float16))
# update
output = scatter_func_net("update", inputx, indices, updates)
expected = np.array(
[[95.0, 96.0, 97.0, 98.0], [67.0, 68.0, 69.0, 70.0], [99.0, 100.0, 101.0, 102.0]]
)
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
# add
output = scatter_func_net("add", inputx, indices, updates)
expected = np.array(
@ -621,10 +613,12 @@ def test_scatter_func_large_int32():
updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.int32))
# update
output = scatter_func_net("update", inputx, indices, updates)
indices_unique = Tensor(np.array([[1, 0]]).astype(np.int32))
updates_unique = Tensor(np.arange(87, 111).reshape((1, 2, 3, 4)).astype(np.int32))
output = scatter_func_net("update", inputx, indices_unique, updates_unique)
expected = np.array([
[[63.0, 64.0, 65.0, 66.0], [67.0, 68.0, 69.0, 70.0], [71.0, 72.0, 73.0, 74.0]],
[[99.0, 100.0, 101.0, 102.0], [103.0, 104.0, 105.0, 106.0], [95.0, 96.0, 97.0, 98.0]],
[[99.0, 100.0, 101.0, 102.0], [103.0, 104.0, 105.0, 106.0], [107.0, 108.0, 109.0, 110.0]],
[[87.0, 88.0, 89.0, 90.0], [91.0, 92.0, 93.0, 94.0], [95.0, 96.0, 97.0, 98.0]],
]).astype(np.int32)
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
@ -674,13 +668,6 @@ def test_scatter_func_disordered_int32():
indices = Tensor(np.array([[[0, 1, 2], [2, 1, 0]], [[0, 0, 0], [2, 2, 2]]]).astype(np.int32))
updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.int32))
# update
output = scatter_func_net("update", inputx, indices, updates)
expected = np.array(
[[95.0, 96.0, 97.0, 98.0], [67.0, 68.0, 69.0, 70.0], [99.0, 100.0, 101.0, 102.0]]
).astype(np.int32)
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
# add
output = scatter_func_net("add", inputx, indices, updates)
expected = np.array(
@ -724,13 +711,6 @@ def test_scatter_func_disordered_dynamic_int32():
indices = Tensor(np.array([[[0, 1, 2], [2, 1, 0]], [[0, 0, 0], [2, 2, 2]]]).astype(np.int32))
updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.int32))
# update
output = scatter_func_d_net("update", inputx, indices, updates)
expected = np.array(
[[95.0, 96.0, 97.0, 98.0], [67.0, 68.0, 69.0, 70.0], [99.0, 100.0, 101.0, 102.0]]
).astype(np.int32)
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
# add
output = scatter_func_d_net("add", inputx, indices, updates)
expected = np.array(
@ -770,13 +750,6 @@ def test_scatter_func_disordered_dynamic_int8():
indices = Tensor(np.array([[[0, 1, 2], [2, 1, 0]], [[0, 0, 0], [2, 2, 2]]]).astype(np.int32))
updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.int8))
# update
output = scatter_func_d_net("update", inputx, indices, updates)
expected = np.array(
[[95.0, 96.0, 97.0, 98.0], [67.0, 68.0, 69.0, 70.0], [99.0, 100.0, 101.0, 102.0]]
).astype(np.int8)
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
# add
output = scatter_func_d_net("add", inputx, indices, updates)
expected = np.array(
@ -804,13 +777,6 @@ def test_scatter_func_disordered_dynamic_uint8():
indices = Tensor(np.array([[[0, 1, 2], [2, 1, 0]], [[0, 0, 0], [2, 2, 2]]]).astype(np.int32))
updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.uint8))
# update
output = scatter_func_d_net("update", inputx, indices, updates)
expected = np.array(
[[95.0, 96.0, 97.0, 98.0], [67.0, 68.0, 69.0, 70.0], [99.0, 100.0, 101.0, 102.0]]
).astype(np.uint8)
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
# add
output = scatter_func_d_net("add", inputx, indices, updates)
expected = np.array(
@ -842,9 +808,11 @@ def test_scatter_func_input_less_than_1_dynamic_float32():
updates = Tensor(np.arange(34, 70).reshape((2, 2, 3, 3)).astype(np.float32))
# update
output = scatter_func_d_net("update", inputx, indices, updates)
indices_unique = Tensor(np.array([[[1, 0, 2]]]).astype(np.int32))
updates_unique = Tensor(np.arange(34, 43).reshape((1, 1, 3, 3)).astype(np.float32))
output = scatter_func_d_net("update", inputx, indices_unique, updates_unique)
expected = np.array(
[[37.0, 38.0, 39.0], [34.0, 35.0, 66.0], [67.0, 68.0, 69.0]], dtype=np.float32,
[[37.0, 38.0, 39.0], [34.0, 35.0, 36.0], [40.0, 41.0, 42.0]], dtype=np.float32,
)
np.testing.assert_array_almost_equal(output.asnumpy(), expected)