chore: import upstream snapshot with attribution

test/auto_parallel/test_dist_tensor.py

@@ -0,0 +1,148 @@
+# Copyright (c) 2023 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.distributed as dist
+from paddle.distributed import Replicate
+
+
+class TestDistTensor(unittest.TestCase):
+    def test_dist_tensor_creation(self):
+        shape = [10, 5]
+        mesh = dist.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"])
+        placements = [Replicate(), Replicate()]
+
+        # create dist tensor using numpy
+        dist_tensor_with_numpy = dist.shard_tensor(
+            np.ones(shape, dtype=np.float32), mesh, placements
+        )
+
+        # create dist tensor using tensor
+        dist_tensor_with_tensor = dist.shard_tensor(
+            paddle.ones(shape), mesh, placements
+        )
+
+        # create normal tensor
+        tensor = paddle.ones(shape)
+
+        # test dist tensor properties
+        self.assertEqual(dist_tensor_with_numpy.shape, shape)
+        self.assertEqual(dist_tensor_with_tensor.shape, shape)
+        self.assertEqual(dist_tensor_with_numpy.is_dist(), True)
+        self.assertEqual(dist_tensor_with_tensor.is_dist(), True)
+        self.assertEqual(tensor.is_dist(), False)
+        self.assertEqual(
+            str(dist_tensor_with_numpy), str(dist_tensor_with_tensor)
+        )
+        self.assertEqual(dist_tensor_with_numpy.placements, placements)
+        self.assertEqual(dist_tensor_with_tensor.placements, placements)
+
+    def test_dist_parameter(self):
+        mesh = dist.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"])
+        placements = [Replicate(), Replicate()]
+
+        dense_param = paddle.create_parameter(
+            [10, 5], name="linear_1.weight", dtype='float32'
+        )
+        dist_param = dist.shard_tensor(dense_param, mesh, placements)
+
+        self.assertEqual(dense_param.name + ".dist", dist_param.name)
+
+
+class TestDistTensorFromFn(unittest.TestCase):
+    def run_dtensor_from_fn(self):
+        # Create a dist_attr
+        mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
+        placements = [Replicate()]
+
+        # for static graph here.
+        dist_attr = dist.DistAttr(mesh=mesh, sharding_specs=[None])
+
+        # Call the function dtensor_from_fn with dist_attr parameter
+        result = dist.dtensor_from_fn(paddle.ones, mesh, placements, shape=[16])
+        # Verify the result
+        if paddle.in_dynamic_mode():
+            self.assertIsInstance(result, paddle.Tensor)
+            self.assertEqual(result.shape, [16])
+            self.assertEqual(result.placements, placements)
+        else:
+            dist_attr.dynamic_dims = [0]
+            dist_attr.chunk_id = 0
+            self.assertIsInstance(result, paddle.base.libpaddle.pir.Value)
+            self.assertEqual(result.shape, [16])
+            self.assertEqual(
+                result.dist_attr().dims_mapping, dist_attr.dims_mapping
+            )
+            self.assertEqual(
+                result.dist_attr().process_mesh, dist_attr.process_mesh
+            )
+
+        result_zeros = dist.dtensor_from_fn(
+            paddle.zeros, mesh, placements, shape=[16]
+        )
+        if paddle.in_dynamic_mode():
+            dist_attr.dynamic_dims = []
+            self.assertIsInstance(result_zeros, paddle.Tensor)
+            self.assertEqual(result_zeros.shape, [16])
+            self.assertEqual(result_zeros.placements, placements)
+        else:
+            dist_attr.dynamic_dims = [0]
+            dist_attr.chunk_id = 0
+            self.assertIsInstance(result_zeros, paddle.base.libpaddle.pir.Value)
+            self.assertEqual(result_zeros.shape, [16])
+            self.assertEqual(
+                result_zeros.dist_attr().dims_mapping, dist_attr.dims_mapping
+            )
+            self.assertEqual(
+                result_zeros.dist_attr().process_mesh, dist_attr.process_mesh
+            )
+
+        result_random = dist.dtensor_from_fn(
+            paddle.rand, mesh, placements, shape=[16]
+        )
+        if paddle.in_dynamic_mode():
+            dist_attr.dynamic_dims = []
+            self.assertIsInstance(result_random, paddle.Tensor)
+            self.assertEqual(result_random.shape, [16])
+            self.assertEqual(result_random.placements, placements)
+        else:
+            dist_attr.dynamic_dims = [0]
+            dist_attr.chunk_id = 0
+            self.assertIsInstance(
+                result_random, paddle.base.libpaddle.pir.Value
+            )
+            self.assertEqual(result_random.shape, [16])
+            self.assertEqual(
+                result_random.dist_attr().dims_mapping, dist_attr.dims_mapping
+            )
+            self.assertEqual(
+                result_random.dist_attr().process_mesh, dist_attr.process_mesh
+            )
+
+    def test_dynamic_mode(self):
+        self.run_dtensor_from_fn()
+
+    # Test exceptions when running in static mode
+    def test_static_mode(self):
+        paddle.enable_static()
+        self.run_dtensor_from_fn()
+        paddle.disable_static()
+
+
+if __name__ == "__main__":
+    unittest.main()