chore: import upstream snapshot with attribution

test/auto_parallel/test_dist_scale.py

@@ -0,0 +1,76 @@
+# Copyright (c) 2022 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 paddle
+from paddle.distributed.fleet import auto
+
+paddle.enable_static()
+
+
+def make_program():
+    main_program = paddle.base.Program()
+    start_program = paddle.base.Program()
+    with paddle.static.program_guard(main_program, start_program):
+        x = paddle.static.data(name='x', shape=[4, 4, 8], dtype='float32')
+        x.stop_gradient = False
+        auto.shard_tensor(
+            x, auto.ProcessMesh([0, 1], dim_names=["x"]), [None, "x", None]
+        )
+        res = paddle.scale(x, scale=2.0, bias=1.0)
+    return main_program, start_program
+
+
+def parallelizer(program_func, rank):
+    from paddle.distributed.auto_parallel.static.completion import Completer
+    from paddle.distributed.auto_parallel.static.dist_context import (
+        DistributedContext,
+    )
+    from paddle.distributed.auto_parallel.static.partitioner import Partitioner
+
+    main_program, start_program = program_func()
+
+    dist_context = DistributedContext()
+    completer = Completer(dist_context)
+    completer.complete_forward_annotation(main_program)
+    dist_context.block_state.parse_forward_blocks(main_program)
+
+    partitioner = Partitioner(dist_context, rank)
+    dist_main_prog, _, _ = partitioner.partition(
+        main_program, start_program, []
+    )
+
+    return dist_main_prog, dist_context
+
+
+class TestDistScale(unittest.TestCase):
+    def test_dist_scale(self):
+        dist_main_prog, dist_context = parallelizer(make_program, 0)
+        ops = dist_main_prog.global_block().ops
+        scale_op = ops[0]
+        dist_op = dist_context.get_dist_op_for_program(scale_op)
+        assert dist_op.dist_attr.impl_type == "scale"
+        assert dist_op.dist_attr.impl_idx == 0
+
+        in_name = scale_op.input_arg_names[0]
+        out_name = scale_op.output_arg_names[0]
+        in_dims_mapping = dist_op.dist_attr.get_input_dims_mapping(in_name)
+        out_dims_mapping = dist_op.dist_attr.get_output_dims_mapping(out_name)
+
+        assert in_dims_mapping == out_dims_mapping
+
+
+if __name__ == "__main__":
+    unittest.main()