chore: import upstream snapshot with attribution

2026-07-13 12:40:42 +08:00
commit e25996e7db
15472 changed files with 3536181 additions and 0 deletions
@@ -0,0 +1,76 @@
+# Copyright (c) 2025 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
+
+import paddle
+
+
+class TestFromNumpy(unittest.TestCase):
+    def setUp(self):
+        self.shape = [3, 4, 5]
+        self.dtypes = [
+            "bool",
+            "float16",
+            "float32",
+            "float64",
+            "int8",
+            "int16",
+            "int32",
+            "int64",
+            "uint8",
+            "complex64",
+            "complex128",
+        ]
+        self.devices = ["cpu", paddle.CPUPlace()]
+        if paddle.base.is_compiled_with_cuda():
+            self.devices.append("gpu")
+            self.devices.append(paddle.CUDAPlace(0))
+        self.stop_gradients = [True, False]
+
+    def prepare_data(self, dtype):
+        if dtype == "bool":
+            return np.random.randint(0, 2, self.shape)
+        else:
+            return np.random.randn(*self.shape)
+
+    def test_base(self):
+        for dtype in self.dtypes:
+            np_data = self.prepare_data(dtype)
+            for device in self.devices:
+                for stop_gradient in self.stop_gradients:
+                    tensor = paddle.asarray(
+                        np_data,
+                        device=device,
+                        requires_grad=stop_gradient,
+                        dtype=dtype,
+                    )
+                    target_place = device
+                    if isinstance(target_place, str):
+                        target_place = (
+                            paddle.CPUPlace()
+                            if target_place == "cpu"
+                            else paddle.CUDAPlace(0)
+                        )
+                    self.assertEqual(tensor.stop_gradient, not stop_gradient)
+                    self.assertEqual(tensor.place, target_place)
+                    np.testing.assert_allclose(
+                        tensor.numpy(), np_data.astype(dtype)
+                    )
+
+
+if __name__ == "__main__":
+    unittest.main()