chore: import upstream snapshot with attribution

test/legacy_test/test_op_function_generator.py

@@ -0,0 +1,123 @@
+#   Copyright (c) 2018 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
+import paddle.nn.functional as F
+from paddle import _legacy_C_ops, base
+
+
+class TestVariable(unittest.TestCase):
+    def setUp(self):
+        self.shape = [512, 768]
+        self.dtype = np.float32
+        self.array = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
+
+    def test_elementwise_add(self):
+        with base.dygraph.guard():
+            a = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
+            b = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
+            x = paddle.to_tensor(a)
+            y = paddle.to_tensor(b)
+            x.stop_gradient = False
+
+            res1 = paddle.add(x, y)
+            res2 = _legacy_C_ops.elementwise_add(x, y)
+
+            np.testing.assert_array_equal(res1.numpy(), res2.numpy())
+
+    def test_elementwise_mul(self):
+        with base.dygraph.guard():
+            a = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
+            b = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
+            x = paddle.to_tensor(a)
+            y = paddle.to_tensor(b)
+
+            res1 = paddle.multiply(x, y)
+            res2 = _legacy_C_ops.elementwise_mul(x, y)
+
+            np.testing.assert_array_equal(res1.numpy(), res2.numpy())
+
+    def test_relu(self):
+        with base.dygraph.guard():
+            a = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
+            x = paddle.to_tensor(a)
+
+            res1 = F.relu(x)
+            res2 = _legacy_C_ops.relu(x)
+
+            np.testing.assert_array_equal(res1.numpy(), res2.numpy())
+
+    def test_trace_backward(self):
+        with base.dygraph.guard():
+            a = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
+            b = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
+            x = paddle.to_tensor(a)
+            y = paddle.to_tensor(b)
+            x.stop_gradient = False
+            y.stop_gradient = False
+            x.retain_grads()
+            y.retain_grads()
+
+            loss = _legacy_C_ops.elementwise_mul(x, y)
+            loss.retain_grads()
+
+            loss.backward()
+            x_grad = x.gradient()
+            y_grad = y.gradient()
+
+            np.testing.assert_array_equal(x_grad, loss.gradient() * b)
+            np.testing.assert_array_equal(y_grad, loss.gradient() * a)
+
+    def test_retain_grad(self):
+        """Test retain_grad() for both leaf nodes and intermediate nodes (new API)"""
+        with base.dygraph.guard():
+            # Prepare input data
+            a = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
+            b = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
+            x = paddle.to_tensor(a)
+            y = paddle.to_tensor(b)
+            x.stop_gradient = False
+            y.stop_gradient = False
+
+            # ===== Test leaf nodes (x, y) =====
+            # Create scalar loss for leaf nodes (must be scalar)
+            loss_leaf = paddle.sum(_legacy_C_ops.elementwise_mul(x, y))
+            x.retain_grad()
+            y.retain_grad()
+            loss_leaf.backward()
+
+            # Verify leaf node gradients (x.grad = y, y.grad = x)
+            np.testing.assert_array_equal(x.gradient(), b)
+            np.testing.assert_array_equal(y.gradient(), a)
+
+            # ===== Test intermediate node (z = x * y) =====
+            # Create intermediate node z
+            z = _legacy_C_ops.elementwise_mul(x, y)
+            z.retain_grad()  # Retain gradient for intermediate node
+
+            # Create scalar loss for intermediate node
+            loss_mid = paddle.sum(z)
+            loss_mid.backward()
+
+            # Verify intermediate node gradient (d(loss_mid)/dz = 1)
+            expected_z_grad = np.ones_like(a)
+            np.testing.assert_array_equal(z.gradient(), expected_z_grad)
+
+
+if __name__ == '__main__':
+    unittest.main()