add ai edited test 100 3.27 (#78509)

* add ai edit test 3.27

* add ai edit test 3.27

* add ai edit test 3.27 2

* add ai edit test 3.27 2

* add ai edit test 3.27 2

* add ai edit test 3.27 3

* add ai edit test 3.27 4

Author: liuhao2638

.coverage

@@ -8,6 +8,9 @@ string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 file(GLOB TEST_PY_FILES "${CMAKE_CURRENT_SOURCE_DIR}/test_*.py")
 file(COPY ${TEST_PY_FILES} DESTINATION ${CMAKE_CURRENT_BINARY_DIR})
 
-foreach(TEST_OP ${TEST_OPS})
-  py_test_modules(${TEST_OP} MODULES ${TEST_OP})
-endforeach()
+if(WITH_COVERAGE)
+  foreach(TEST_OP ${TEST_OPS})
+    py_test_modules(${TEST_OP} MODULES ${TEST_OP})
+    # set_tests_properties(${TEST_OP} PROPERTIES LABELS "RUN_TYPE=NIGHTLY")
+  endforeach()
+endif()

test/ai_edited_test/CMakeLists.txt

@@ -0,0 +1,163 @@
+# Copyright (c) 2026 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.
+
+"""
+混合精度训练高级测试 / Advanced Mixed Precision Training Tests
+
+测试目标 / Test Target:
+  paddle AMP (Automatic Mixed Precision) 功能
+
+覆盖的模块 / Covered Modules:
+  - paddle.amp.auto_cast: 自动混合精度上下文
+  - paddle.amp.GradScaler: 梯度缩放器
+  - paddle.amp.decorate: AMP装饰器
+
+作用 / Purpose:
+  补充混合精度训练API的测试，提升覆盖率。
+"""
+
+import unittest
+
+import paddle
+from paddle import nn
+
+paddle.disable_static()
+
+
+class TestAutocast(unittest.TestCase):
+    """测试自动类型转换 / Test auto casting"""
+
+    def test_autocast_basic(self):
+        """测试基本autocast / Test basic autocast"""
+        model = nn.Linear(4, 2)
+        x = paddle.randn([4, 4])
+        with paddle.amp.auto_cast():
+            output = model(x)
+        self.assertIsNotNone(output)
+
+    def test_autocast_disable(self):
+        """测试禁用autocast / Test disabled autocast"""
+        model = nn.Linear(4, 2)
+        x = paddle.randn([4, 4])
+        with paddle.amp.auto_cast(enable=False):
+            output = model(x)
+        self.assertEqual(output.dtype, paddle.float32)
+
+    def test_autocast_nested(self):
+        """测试嵌套autocast / Test nested autocast"""
+        model = nn.Linear(4, 2)
+        x = paddle.randn([4, 4])
+        with paddle.amp.auto_cast():
+            y = model(x)
+            with paddle.amp.auto_cast(enable=False):
+                z = model(x)
+        self.assertIsNotNone(y)
+        self.assertIsNotNone(z)
+
+
+class TestGradScaler(unittest.TestCase):
+    """测试梯度缩放器 / Test gradient scaler"""
+
+    def test_grad_scaler_basic(self):
+        """测试基本梯度缩放 / Test basic gradient scaling"""
+        model = nn.Linear(4, 2)
+        optimizer = paddle.optimizer.Adam(parameters=model.parameters())
+        scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
+
+        x = paddle.randn([4, 4])
+        y = paddle.randn([4, 2])
+
+        with paddle.amp.auto_cast():
+            output = model(x)
+            loss = nn.functional.mse_loss(output, y)
+
+        scaled_loss = scaler.scale(loss)
+        scaled_loss.backward()
+        scaler.step(optimizer)
+        scaler.update()
+
+    def test_grad_scaler_state(self):
+        """测试梯度缩放器状态 / Test grad scaler state"""
+        scaler = paddle.amp.GradScaler(init_loss_scaling=512)
+        state = scaler.state_dict()
+        self.assertIn('scale', state)
+
+    def test_grad_scaler_save_load(self):
+        """测试梯度缩放器保存加载 / Test grad scaler save/load"""
+        scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
+        state = scaler.state_dict()
+
+        new_scaler = paddle.amp.GradScaler(init_loss_scaling=512)
+        new_scaler.load_state_dict(state)
+        new_state = new_scaler.state_dict()
+        self.assertEqual(float(state['scale']), float(new_state['scale']))
+
+
+class TestAMPDecorate(unittest.TestCase):
+    """测试AMP装饰器 / Test AMP decorate"""
+
+    def test_decorate_model(self):
+        """测试模型AMP装饰 / Test model AMP decoration"""
+        model = nn.Linear(4, 2)
+        optimizer = paddle.optimizer.Adam(parameters=model.parameters())
+        model, optimizer = paddle.amp.decorate(
+            models=model, optimizers=optimizer, level='O1'
+        )
+        x = paddle.randn([4, 4])
+        with paddle.amp.auto_cast():
+            output = model(x)
+        self.assertIsNotNone(output)
+
+    def test_decorate_level_o1(self):
+        """测试O1级别AMP / Test O1 level AMP"""
+        model = nn.Sequential(
+            nn.Conv2D(3, 8, 3, padding=1), nn.ReLU(), nn.AdaptiveAvgPool2D(1)
+        )
+        optimizer = paddle.optimizer.Adam(parameters=model.parameters())
+        model, optimizer = paddle.amp.decorate(
+            models=model, optimizers=optimizer, level='O1'
+        )
+        x = paddle.randn([2, 3, 16, 16])
+        with paddle.amp.auto_cast():
+            output = model(x)
+        self.assertIsNotNone(output)
+
+
+class TestMixedPrecisionTraining(unittest.TestCase):
+    """测试混合精度训练 / Test mixed precision training"""
+
+    def test_full_amp_training_step(self):
+        """测试完整AMP训练步骤 / Test full AMP training step"""
+        model = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 2))
+        optimizer = paddle.optimizer.Adam(parameters=model.parameters())
+        scaler = paddle.amp.GradScaler()
+
+        x = paddle.randn([8, 4])
+        y = paddle.randn([8, 2])
+
+        with paddle.amp.auto_cast():
+            output = model(x)
+            loss = nn.functional.mse_loss(output, y)
+
+        scaled_loss = scaler.scale(loss)
+        scaled_loss.backward()
+        scaler.step(optimizer)
+        scaler.update()
+        optimizer.clear_grad()
+
+        self.assertIsNotNone(loss)
+
+
+if __name__ == '__main__':
+    unittest.main()