chore: import upstream snapshot with attribution

test/auto_parallel/dtensor_from_local_api.py

@@ -0,0 +1,77 @@
+# Copyright (c) 2024 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 os
+
+import paddle
+import paddle.distributed as dist
+from paddle.distributed.auto_parallel.api import dtensor_from_local
+
+
+class TestDtensorFromLocalAPI:
+    def __init__(self):
+        self._dtype = os.getenv("dtype")
+        self._seeds = eval(os.getenv("seeds"))
+        self._backend = os.getenv("backend")
+        self._shard = eval(os.getenv("shard"))
+        self._mesh = dist.ProcessMesh([[0, 1]], dim_names=["x", "y"])
+
+    def run_test_cases(self):
+        self.test_case_forward_backward()
+
+    def test_case_forward_backward(self):
+        a = paddle.ones([10, 20])
+        a.stop_gradient = False
+
+        tensor1 = a + 3
+        assert not tensor1.is_dist()
+        tensor1.register_hook(self.check_grad_mesh(None, None))
+
+        mesh = self._mesh
+        tensor2 = dtensor_from_local(
+            tensor1, mesh, [dist.Shard(0), dist.Replicate()]
+        )
+
+        assert tensor2.is_dist()
+        assert tensor2.process_mesh == mesh
+        assert tensor2.placements == [dist.Shard(0), dist.Replicate()]
+        tensor2.register_hook(
+            self.check_grad_mesh(mesh, [dist.Shard(0), dist.Replicate()])
+        )
+
+        tensor3 = tensor2 * 3
+        tensor3.register_hook(
+            self.check_grad_mesh(mesh, [dist.Shard(0), dist.Replicate()])
+        )
+        tensor4 = tensor3 + 4
+
+        tensor4.backward()
+
+    def check_grad_mesh(self, mesh, placements):
+        def _check_mesh(grad):
+            if mesh is None and placements is None:
+                assert not grad.is_dist(), "grad.is_dist() is not False"
+            else:
+                assert grad.process_mesh == mesh, (
+                    "grad.process_mesh is not equal to mesh"
+                )
+                assert grad.placements == placements, (
+                    "grad.placements is not equal to placements"
+                )
+
+        return _check_mesh
+
+
+if __name__ == '__main__':
+    TestDtensorFromLocalAPI().run_test_cases()