chore: import upstream snapshot with attribution

test/dygraph_to_static/test_parameters_persistent_mode.py

@@ -0,0 +1,81 @@
+# 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
+from dygraph_to_static_utils import (
+    Dy2StTestBase,
+    test_phi_only,
+    test_sot_mgs0_only,
+)
+
+import paddle
+
+# Enable persistent mode for parameters in dy2st
+paddle.set_flags({'FLAGS_parameters_persistent_mode_in_dy2st': True})
+
+
+class NetWithParameters(paddle.nn.Layer):
+    def __init__(self, in_size, out_size):
+        super().__init__()
+        self.weight = self.create_parameter([in_size, out_size])
+        self.bias = self.create_parameter([out_size], is_bias=True)
+
+    def forward(self, x):
+        out = paddle.matmul(x, self.weight)
+        out = paddle.add(out, self.bias)
+        out = paddle.tanh(out)
+        return out
+
+
+class TestParametersPersistentMode(Dy2StTestBase):
+    def setUp(self):
+        paddle.seed(1127)
+        np.random.seed(1127)
+
+    def run_forward(self, net, inputs):
+        outs = []
+        for data in inputs:
+            outs.append(net(data))
+        return outs
+
+    def test_persistent_mode(self):
+        net = NetWithParameters(10, 3)
+        net.eval()
+        inputs = [paddle.randn([2, 10], dtype='float32') for _ in range(5)]
+        dy_outs = self.run_forward(net, inputs)
+        st_net = paddle.jit.to_static(net)
+        st_outs = self.run_forward(st_net, inputs)
+        for dy_out, st_out in zip(dy_outs, st_outs):
+            np.testing.assert_allclose(
+                dy_out.numpy(), st_out.numpy(), rtol=1e-05, atol=1e-05
+            )
+
+    @test_sot_mgs0_only
+    @test_phi_only
+    def test_training_mode_error(self):
+        net = NetWithParameters(10, 3)
+        net.train()
+        inputs = [paddle.randn([2, 10], dtype='float32')]
+        st_net = paddle.jit.to_static(net)
+        with self.assertRaisesRegex(
+            RuntimeError,
+            "Currently parameters persistent mode only support forward process",
+        ):
+            self.run_forward(st_net, inputs)
+
+
+if __name__ == "__main__":
+    unittest.main()