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【Fix PIR Unittest No.125、147、481】Fix some 0D uts in PIR mode (part1) (P…
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test/deprecated/legacy_test/test_zero_dim_sundry_static_api_deprecated.py
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| # Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| # Note: | ||
| # 0D Tensor indicates that the tensor's dimension is 0 | ||
| # 0D Tensor's shape is always [], numel is 1 | ||
| # which can be created by paddle.rand([]) | ||
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| import unittest | ||
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| import numpy as np | ||
| from decorator_helper import prog_scope | ||
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| import paddle | ||
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| # Use to test zero-dim of Sundry API, which is unique and can not be classified | ||
| # with others. It can be implemented here flexibly. | ||
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| class TestSundryAPIStatic(unittest.TestCase): | ||
| def setUp(self): | ||
| paddle.enable_static() | ||
| self.exe = paddle.static.Executor() | ||
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| def assertShapeEqual(self, out, target_tuple): | ||
| if not paddle.framework.in_pir_mode(): | ||
| out_shape = list(out.shape) | ||
| else: | ||
| out_shape = out.shape | ||
| self.assertEqual(out_shape, target_tuple) | ||
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| @prog_scope() | ||
| def test_create_global_var(self): | ||
| zero_dim_var = paddle.static.create_global_var( | ||
| shape=[], value=0.5, dtype='float32' | ||
| ) | ||
| self.assertEqual(zero_dim_var.shape, ()) | ||
| prog = paddle.static.default_startup_program() | ||
| res = self.exe.run(prog, fetch_list=[zero_dim_var]) | ||
| self.assertEqual(res[0].shape, ()) | ||
| self.assertEqual(res[0], 0.5) | ||
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| @prog_scope() | ||
| def test_setitem(self): | ||
| # NOTE(zoooo0820): __setitem__ has gradient problem in static graph. | ||
| # To solve this, we may not support __setitem__ in static graph. | ||
| # These unit tests will delete soon. | ||
|
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| # case1: all axis have a scalar indice | ||
| x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5)) | ||
| x.stop_gradient = False | ||
| out = x * 2 | ||
| out = paddle.static.setitem(out, (1, 2, 3, 4), 10) | ||
| paddle.static.append_backward(out.sum()) | ||
| prog = paddle.static.default_main_program() | ||
| res = self.exe.run(prog, fetch_list=[out, x.grad_name]) | ||
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| self.assertEqual(out.shape, x.shape) | ||
| np.testing.assert_allclose(res[0][1, 2, 3, 4], np.array(10)) | ||
| self.assertEqual(res[1].shape, (2, 3, 4, 5)) | ||
| x_grad_expected = np.ones((2, 3, 4, 5)) * 2 | ||
| x_grad_expected[1, 2, 3, 4] = 0 | ||
| np.testing.assert_allclose(res[1], x_grad_expected) | ||
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| # case2: 0-D Tensor indice in some axis | ||
| # NOTE(zoooo0820): Now, int/slice with 0-D Tensor will still be | ||
| # treated as combined indexing, which is not support backward. | ||
| # There should have more test cases such as out[1, indice, :] = 0.5 when this | ||
| # problem is fixed. | ||
| x = paddle.randn((2, 3, 4, 5)) | ||
| x.stop_gradient = False | ||
| indice = paddle.full([], 1, dtype='int32') | ||
| out = x * 1 | ||
| out = paddle.static.setitem(out, (indice, indice), 0.5) | ||
| paddle.static.append_backward(out.sum()) | ||
| prog = paddle.static.default_main_program() | ||
| res = self.exe.run(prog, fetch_list=[out, x.grad_name]) | ||
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| self.assertEqual(out.shape, x.shape) | ||
| np.testing.assert_allclose(res[0][1, 1], np.ones((4, 5)) * 0.5) | ||
| x_grad_expected = np.ones((2, 3, 4, 5)) | ||
| x_grad_expected[1, 1] = 0 | ||
| np.testing.assert_allclose(res[1], x_grad_expected) | ||
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| # case3：0-D Tensor indice in some axis, value is a Tensor | ||
| # and there is broadcast | ||
| x = paddle.randn((2, 3, 4, 5)) | ||
| x.stop_gradient = False | ||
| v = paddle.ones((4, 5), dtype='float32') * 5 | ||
| v.stop_gradient = False | ||
| indice = paddle.full([], 1, dtype='int32') | ||
| out = x * 1 | ||
| out = paddle.static.setitem(out, indice, v) | ||
| paddle.static.append_backward(out.sum()) | ||
| prog = paddle.static.default_main_program() | ||
| res = self.exe.run(prog, fetch_list=[out, x.grad_name, v.grad_name]) | ||
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| self.assertEqual(out.shape, x.shape) | ||
| np.testing.assert_allclose(res[0][1], np.ones((3, 4, 5)) * 5) | ||
| x_grad_expected = np.ones((2, 3, 4, 5)) | ||
| x_grad_expected[1] = 0 | ||
| np.testing.assert_allclose(res[1], x_grad_expected) | ||
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| @prog_scope() | ||
| def test_static_auc(self): | ||
| x = paddle.full(shape=[3, 2], fill_value=0.25) | ||
| y = paddle.full(shape=[3], fill_value=1, dtype="int64") | ||
| out = paddle.static.auc(input=x, label=y)[0] | ||
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| prog = paddle.static.default_main_program() | ||
| res = self.exe.run( | ||
| prog, | ||
| fetch_list=[out], | ||
| ) | ||
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| self.assertEqual(res[0].shape, ()) | ||
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| @prog_scope() | ||
| def test_static_nn_prelu(self): | ||
| x1 = paddle.full([], 1.0, 'float32') | ||
| x1.stop_gradient = False | ||
| out1 = paddle.static.nn.prelu(x1, 'all') | ||
| grad_list = paddle.static.append_backward( | ||
| out1.sum(), parameter_list=[x1, out1] | ||
| ) | ||
| (_, x1_grad), (_, out1_grad) = grad_list | ||
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| prog = paddle.static.default_main_program() | ||
| self.exe.run(paddle.static.default_startup_program()) | ||
| res = self.exe.run( | ||
| prog, | ||
| fetch_list=[ | ||
| out1, | ||
| x1_grad, | ||
| out1_grad, | ||
| ], | ||
| ) | ||
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| self.assertEqual(res[0].shape, ()) | ||
| self.assertEqual(res[1].shape, ()) | ||
| self.assertEqual(res[2].shape, ()) | ||
| np.testing.assert_allclose(res[0], np.array(1)) | ||
| np.testing.assert_allclose(res[1], np.array(1)) | ||
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| if __name__ == "__main__": | ||
| unittest.main() |
2 changes: 1 addition & 1 deletion
2
..._test/test_zero_dim_sundry_dygraph_api.py → ..._test/test_zero_dim_sundry_dygraph_api.py
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