chore: import upstream snapshot with attribution

test/sot/test_numpy_array.py

@@ -0,0 +1,218 @@
+# 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.
+
+from __future__ import annotations
+
+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
+
+
+@check_no_breakgraph
+def unary_api(func, x: np.ndarray, *args, **kwargs):
+    return func(x, *args, **kwargs)
+
+
+@check_no_breakgraph
+def binary_api(
+    func: np.ufunc,
+    x: np.ndarray | list,
+    y: np.ndarray | list,
+    *args,
+    **kwargs,
+):
+    return func(x, y, *args, **kwargs)
+
+
+@check_no_breakgraph
+def binary_operator(op: str, x: np.ndarray, y: np.ndarray):
+    if op == "+":
+        return x + y
+    elif op == "-":
+        return x - y
+    elif op == "*":
+        return x * y
+    elif op == "/":
+        return x / y
+
+
+@check_no_breakgraph
+def get_item(x: np.ndarray, index: int | tuple | np.ndarray):
+    return x[index]
+
+
+@check_no_breakgraph
+def set_item(
+    x: np.ndarray,
+    index: int | list | np.ndarray,
+    value: int | list | np.ndarray,
+):
+    x[index] = value
+    return x
+
+
+@check_no_breakgraph
+def grad_fn(pd_x, np_y, np_y2):
+    pd_y = paddle.to_tensor(np_y)
+    pd_z = paddle.add(pd_x, pd_y)
+    pd_y2 = paddle.to_tensor(np_y2).astype("float32")
+    pd_z2 = paddle.matmul(pd_z, pd_y2)
+    return pd_z2
+
+
+@check_no_breakgraph
+def demo(pd_x):
+    np_y = np.array([[1, 2, 3], [4, 5, 6]])
+    np_y2 = np.transpose(np_y)
+    pd_y2 = paddle.to_tensor(np_y2)
+    pd_z = paddle.matmul(pd_x, pd_y2)
+    np_z = pd_z.numpy()
+    np_z2 = np.mean(np_z)
+    np_z3 = np.add(pd_x.numpy(), np_z2)
+    np_z4 = np_z * np_z3[:, :2]
+    pd_z2 = paddle.subtract(pd_z, paddle.to_tensor(np_z4))
+    return pd_z2
+
+
+def absolute(x):
+    return np.absolute(x)
+
+
+class TestNumPyArray(TestCaseBase):
+    def test_guard(self):
+        with test_instruction_translator_cache_context() as ctx:
+            self.assertEqual(ctx.translate_count, 0)
+            self.assert_results(
+                binary_api, np.add, np.array([1, 2]), np.array([3, 4])
+            )
+            self.assertEqual(ctx.translate_count, 1)
+            self.assert_results(
+                binary_api,
+                np.add,
+                np.array([1, 2], dtype=np.int32),
+                np.array([3, 4], dtype=np.int32),
+            )
+            self.assertEqual(ctx.translate_count, 2)
+            self.assert_results(
+                binary_api, np.add, np.array([1]), np.array([3])
+            )
+            self.assertEqual(ctx.translate_count, 3)
+            self.assert_results(
+                binary_api, np.add, np.array([4, 3]), np.array([2, 1])
+            )
+            self.assertEqual(ctx.translate_count, 3)
+
+    def test_gradient(self):
+        pd_x = paddle.randn([2, 3], dtype="float32")
+        pd_x.stop_gradient = False
+        self.assert_results_with_grad(
+            pd_x,
+            grad_fn,
+            pd_x,
+            np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32),
+            np.ones(shape=(3, 2)),
+        )
+
+    def test_add_01(self):
+        x = np.array([1, 2])
+        y = np.array([3, 4])
+        self.assert_results(binary_api, np.add, x, y)
+
+    @unittest.skip("Not supported yet")
+    def test_add_02(self):
+        x = [1, 2]
+        y = [3, 4]
+        self.assert_results(binary_api, np.add, x, y)
+
+    @unittest.skip("Not supported yet")
+    def test_sub(self):
+        x = np.array([1, 2])
+        y = np.array([3, 4])
+        self.assert_results(binary_api, np.subtract, x, y)
+        self.assert_results(binary_api, np.subtract, [1, 2], [3, 4])
+
+    @unittest.skip("Not supported yet")
+    def test_mul(self):
+        x = np.array([1, 2])
+        y = np.array([3, 4])
+        self.assert_results(binary_api, np.multiply, x, y)
+        self.assert_results(binary_api, np.multiply, [1, 2], [3, 4])
+        self.assert_results_with_grad(binary_api, np.multiply, [1, 2], [3, 4])
+
+    @unittest.skip("Not supported yet")
+    def test_div(self):
+        x = np.array([1, 2])
+        y = np.array([3, 4])
+        self.assert_results(binary_api, np.divide, x, y)
+        self.assert_results(binary_api, np.divide, [1, 2], [3, 4])
+
+    @unittest.skip("Not supported yet")
+    def test_operator(self):
+        x = np.array([1, 2])
+        y = np.array([3, 4])
+        self.assert_results(binary_operator, '+', x, y)
+        self.assert_results(binary_operator, '-', x, y)
+        self.assert_results(binary_operator, '*', x, y)
+        self.assert_results(binary_operator, '/', x, y)
+
+    @unittest.skip("Not supported yet")
+    def test_argmax(self):
+        x = np.array([[1, 2], [3, 4]])
+        self.assert_results(unary_api, np.argmax, x)
+        self.assert_results(unary_api, np.argmax, x, axis=0)
+
+    @unittest.skip("Not supported yet")
+    def test_sum(self):
+        x = np.array([[1, 2], [3, 4]])
+        self.assert_results(unary_api, np.sum, x)
+        self.assert_results(unary_api, np.sum, x, axis=0)
+
+    @unittest.skip("Not supported yet")
+    def test_getitem(self):
+        x = np.array([[1, 2], [3, 4]])
+        self.assert_results(get_item, x, 0)
+        i = tuple(0, 0)
+        self.assert_results(get_item, x, i)
+        i2 = np.array([0, 1])
+        self.assert_results(get_item, x, i2)
+
+    @unittest.skip("Not supported yet")
+    def test_setitem(self):
+        x = np.array([[1, 2], [3, 4]])
+        y = np.array([5, 6])
+        self.assert_results(set_item, x, 0, y)
+        i = np.array([0])
+        self.assert_results(set_item, x, i, y)
+        self.assert_results(set_item, x, [0], [5, 6])
+
+    @unittest.skip("Not supported yet")
+    def test_demo(self):
+        pd_x = paddle.randn([2, 3], dtype="float32")
+        pd_x.stop_gradient = False
+        self.assert_results_with_grad(pd_x, demo, pd_x)
+
+    def test_absolute(self):
+        x = np.array([[1, -2], [-3, 4]])
+        self.assert_results(absolute, x)
+
+
+if __name__ == "__main__":
+    unittest.main()