[AutoParallel] fix dist_split (#46505)

* [AutoParallel] fix dist_split

* add unittest

* update cmakelist

python/paddle/distributed/auto_parallel/operators/dist_split.py

@@ -101,8 +101,12 @@ class DistributedSplitImpl(DistributedOperatorImpl):
         return changed
 
     def is_auto_compatible(self, dist_op):
-        raise NotImplementedError(
-            "Auto Search is not supported by dist split yet.")
+        if (not self.is_input_compatible(dist_op)) or \
+            (not self.is_output_compatible(dist_op)) or \
+            (not self.is_compatible(dist_op)):
+            return False
+
+        return True
 
     @staticmethod
     def forward(ctx, *args, **kwargs):

python/paddle/fluid/tests/unittests/auto_parallel/CMakeLists.txt

@@ -78,6 +78,7 @@ if(WITH_DISTRIBUTE AND WITH_GPU)
   py_test_modules(test_dist_embedding MODULES test_dist_embedding ENVS
                   ${dist_ENVS})
   py_test_modules(test_dist_slice MODULES test_dist_slice ENVS ${dist_ENVS})
+  py_test_modules(test_dist_split MODULES test_dist_split ENVS ${dist_ENVS})
   py_test_modules(test_cluster MODULES test_cluster ENVS ${dist_ENVS})
   py_test_modules(test_comm_cost MODULES test_comm_cost ENVS ${dist_ENVS})
   py_test_modules(test_comp_cost MODULES test_comp_cost ENVS ${dist_ENVS})

python/paddle/fluid/tests/unittests/auto_parallel/test_dist_split.py

+# 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
+
+from paddle.fluid import program_guard
+from paddle.distributed.auto_parallel.utils import print_program_with_dist_attr
+
+paddle.enable_static()
+
+
+def make_program_dp2():
+    main_program = paddle.fluid.Program()
+    start_program = paddle.fluid.Program()
+    with paddle.static.program_guard(main_program, start_program):
+        x = paddle.static.data(name='x', shape=[4, 12, 16], dtype='float32')
+        x.stop_gradient = False
+        auto.shard_tensor(x, auto.ProcessMesh([0, 1], dim_names=["x"]),
+                          ["x", None, None])
+        out0, out1, out2 = paddle.split(x, num_or_sections=3, axis=1)
+    return main_program, start_program
+
+
+def parallelizer(program_func, rank):
+    from paddle.distributed.auto_parallel.completion import Completer
+    from paddle.distributed.auto_parallel.partitioner import Partitioner
+    from paddle.distributed.auto_parallel.dist_context import DistributedContext
+
+    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 TestDistSplit(unittest.TestCase):
+
+    def test_dist_split_dp2(self):
+
+        for rank in range(2):
+            dist_main_prog, dist_context = parallelizer(make_program_dp2, rank)
+            ops = dist_main_prog.global_block().ops
+            op_dist_attr = dist_context.get_op_dist_attr_for_program(ops[0])
+            assert op_dist_attr.impl_type == "split"
+            assert op_dist_attr.impl_idx == 0
+
+
+if __name__ == "__main__":
+    unittest.main()