chore: import upstream snapshot with attribution

test/sot/test_numpy.py

@@ -0,0 +1,113 @@
+# Copyright (c) 2023 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.
+
+import unittest
+
+import numpy as np
+from test_case_base import (
+    TestCaseBase,
+    test_instruction_translator_cache_context,
+)
+
+import paddle
+from paddle.jit.sot.psdb import check_no_breakgraph
+from paddle.jit.sot.utils import strict_mode_guard
+
+
+def numpy_add(x, y):
+    out = paddle.to_tensor(x.numpy() + y.numpy())
+    return out
+
+
+def tensor_add_numpy(x, y):
+    ret = x + y
+    return ret
+
+
+def large_numpy_array_to_tensor(x):
+    return paddle.to_tensor(x)
+
+
+def normal_numpy_array_to_tensor(x):
+    return paddle.to_tensor(x)
+
+
+@check_no_breakgraph
+def numpy_api_with_number_calculation(t):
+    a = np.log(2)
+    b = np.exp(3)
+    c = np.sqrt(4)
+    d = np.ceil(5.1)
+    e = np.add(1, 2)
+    f = a + 1
+    g = 1 - b
+    h = c * 2
+    i = int(a)
+    j = float(b)
+    k = c.item()
+    l = t + d
+    return a, b, c, d, e, f, g, h, i, j, k, l
+
+
+@check_no_breakgraph
+def numpy_bool(x: np.number):
+    return bool(x == 1)
+
+
+class TestNumPy(TestCaseBase):
+    @strict_mode_guard(False)
+    def test_numpy_add(self):
+        x = paddle.to_tensor([2])
+        y = paddle.to_tensor([3])
+        self.assert_results(numpy_add, x, y)
+
+    def test_tensor_add_numpy_number(self):
+        x = paddle.to_tensor([1.0])
+        y = np.int64(2)
+        self.assert_results(tensor_add_numpy, x, y)
+        self.assert_results(tensor_add_numpy, y, x)
+
+    @strict_mode_guard(False)
+    def test_tensor_add_numpy_array(self):
+        x = paddle.to_tensor([1.0])
+        y = np.array(2.0)
+        self.assert_results(tensor_add_numpy, x, y)
+        self.assert_results(tensor_add_numpy, y, x)
+
+    def test_large_numpy_array_to_tensor(self):
+        # size should be larger than 1024*1024, because we throw an exception
+        # when the size is larger than 1024*1024 in assign API (to_tensor static branch)
+        x = np.random.rand(1024, 1024, 2).astype(np.float32)
+        self.assert_results(large_numpy_array_to_tensor, x)
+
+    def test_numpy_array_guard(self):
+        x = np.array([1.0, 2.0])
+        with test_instruction_translator_cache_context() as ctx:
+            self.assertEqual(ctx.translate_count, 0)
+            self.assert_results(normal_numpy_array_to_tensor, x)
+            self.assertEqual(ctx.translate_count, 1)
+            self.assert_results(normal_numpy_array_to_tensor, x)
+            self.assertEqual(ctx.translate_count, 1)
+
+    def test_numpy_api_with_number_calculation(self):
+        t = paddle.to_tensor([1.0])
+        self.assert_results(numpy_api_with_number_calculation, t)
+
+    def test_numpy_bool(self):
+        x = np.float32(1.0)
+        self.assert_results(numpy_bool, x)
+
+
+if __name__ == "__main__":
+    unittest.main()