wehub-resource-sync
114 lines
3.2 KiB
Python
114 lines
3.2 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from test_case_base import (
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TestCaseBase,
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test_instruction_translator_cache_context,
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)
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import paddle
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from paddle.jit.sot.psdb import check_no_breakgraph
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from paddle.jit.sot.utils import strict_mode_guard
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def numpy_add(x, y):
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out = paddle.to_tensor(x.numpy() + y.numpy())
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return out
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def tensor_add_numpy(x, y):
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ret = x + y
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return ret
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def large_numpy_array_to_tensor(x):
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return paddle.to_tensor(x)
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def normal_numpy_array_to_tensor(x):
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return paddle.to_tensor(x)
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@check_no_breakgraph
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def numpy_api_with_number_calculation(t):
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a = np.log(2)
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b = np.exp(3)
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c = np.sqrt(4)
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d = np.ceil(5.1)
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e = np.add(1, 2)
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f = a + 1
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g = 1 - b
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h = c * 2
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i = int(a)
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j = float(b)
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k = c.item()
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l = t + d
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return a, b, c, d, e, f, g, h, i, j, k, l
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@check_no_breakgraph
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def numpy_bool(x: np.number):
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return bool(x == 1)
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class TestNumPy(TestCaseBase):
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@strict_mode_guard(False)
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def test_numpy_add(self):
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x = paddle.to_tensor([2])
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y = paddle.to_tensor([3])
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self.assert_results(numpy_add, x, y)
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def test_tensor_add_numpy_number(self):
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x = paddle.to_tensor([1.0])
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y = np.int64(2)
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self.assert_results(tensor_add_numpy, x, y)
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self.assert_results(tensor_add_numpy, y, x)
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@strict_mode_guard(False)
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def test_tensor_add_numpy_array(self):
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x = paddle.to_tensor([1.0])
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y = np.array(2.0)
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self.assert_results(tensor_add_numpy, x, y)
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self.assert_results(tensor_add_numpy, y, x)
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def test_large_numpy_array_to_tensor(self):
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# size should be larger than 1024*1024, because we throw an exception
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# when the size is larger than 1024*1024 in assign API (to_tensor static branch)
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x = np.random.rand(1024, 1024, 2).astype(np.float32)
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self.assert_results(large_numpy_array_to_tensor, x)
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def test_numpy_array_guard(self):
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x = np.array([1.0, 2.0])
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with test_instruction_translator_cache_context() as ctx:
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self.assertEqual(ctx.translate_count, 0)
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self.assert_results(normal_numpy_array_to_tensor, x)
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self.assertEqual(ctx.translate_count, 1)
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self.assert_results(normal_numpy_array_to_tensor, x)
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self.assertEqual(ctx.translate_count, 1)
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def test_numpy_api_with_number_calculation(self):
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t = paddle.to_tensor([1.0])
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self.assert_results(numpy_api_with_number_calculation, t)
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def test_numpy_bool(self):
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x = np.float32(1.0)
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self.assert_results(numpy_bool, x)
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if __name__ == "__main__":
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unittest.main()
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