chore: import upstream snapshot with attribution

python/paddle/distributed/fleet/base/meta_optimizer_factory.py

@@ -0,0 +1,41 @@
+#   Copyright (c) 2020 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.
+
+from ..meta_optimizers import *  # noqa: F403
+
+__all__ = []
+
+meta_optimizer_names = list(
+    filter(lambda name: name.endswith("Optimizer"), dir())
+)
+
+# Because HybridParallelOptimizer is dygraph optimizer, it
+# should be removed
+meta_optimizer_names.remove("HybridParallelOptimizer")
+meta_optimizer_names.remove("HeterParallelOptimizer")
+meta_optimizer_names.remove("DGCMomentumOptimizer")
+meta_optimizer_names.remove("MuonShardingOptimizer")
+
+
+class MetaOptimizerFactory:
+    def __init__(self):
+        pass
+
+    def _get_valid_meta_optimizers(self, user_defined_optimizer, skip_names=[]):
+        opt_list = []
+        for opt_name in meta_optimizer_names:
+            if opt_name in skip_names:
+                continue
+            opt_list.append(globals()[opt_name](user_defined_optimizer))
+        return opt_list