chore: import upstream snapshot with attribution

python/paddle/distributed/fleet/meta_optimizers/meta_optimizer_base.py

@@ -0,0 +1,106 @@
+#   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 paddle.optimizer import Optimizer
+
+__all__ = []
+
+
+class MetaOptimizerBase(Optimizer):
+    def __init__(self, optimizer):
+        self.inner_opt = optimizer
+        self._learning_rate = self.inner_opt._learning_rate
+        self._learning_rate_map = self.inner_opt._learning_rate_map
+        self.meta_optimizers_white_list = []
+        self.meta_optimizers_black_list = []
+
+    def _set_auxiliary_var(self, key, val):
+        super()._set_auxiliary_var(key, val)
+        self.inner_opt._set_auxiliary_var(key, val)
+
+    def _set_basic_info(
+        self, loss, role_maker, user_defined_optimizer, user_defined_strategy
+    ):
+        self.loss = loss
+        self.role_maker = role_maker
+        self.user_defined_optimizer = user_defined_optimizer
+        self.user_defined_strategy = user_defined_strategy
+
+    def _update_inner_optimizer(self, optimizer):
+        self.inner_opt = optimizer
+
+    def _can_apply(self):
+        return False
+
+    def _is_graph_out(self):
+        return False
+
+    def _can_update(self, optimizer):
+        if str(optimizer.__class__.__name__) in self.meta_optimizers_white_list:
+            return True
+        return False
+
+    def _disable_strategy(self, dist_strategy):
+        raise NotImplementedError(
+            f"you should implement disable strategy in {type(self).__name__}"
+        )
+
+    def _enable_strategy(self, dist_strategy, context=None):
+        raise NotImplementedError(
+            f"you should implement enable strategy in {type(self).__name__}"
+        )
+
+    def apply_gradients(self, params_grads):
+        return self.inner_opt.apply_gradients(params_grads=params_grads)
+
+    def backward(
+        self,
+        loss,
+        startup_program=None,
+        parameter_list=None,
+        no_grad_set=None,
+        callbacks=None,
+    ):
+        return self.inner_opt.backward(
+            loss, startup_program, parameter_list, no_grad_set, callbacks
+        )
+
+    def apply_optimize(self, loss, startup_program, params_grads):
+        return self.inner_opt._apply_optimize(
+            loss, startup_program=startup_program, params_grads=params_grads
+        )
+
+    def minimize_impl(
+        self, loss, startup_program=None, parameter_list=None, no_grad_set=None
+    ):
+        params_grads = self.backward(
+            loss,
+            startup_program=startup_program,
+            parameter_list=parameter_list,
+            no_grad_set=no_grad_set,
+        )
+
+        optimize_ops = self.apply_optimize(
+            loss, startup_program=startup_program, params_grads=params_grads
+        )
+
+        return optimize_ops, params_grads
+
+    def minimize(
+        self, loss, startup_program=None, parameter_list=None, no_grad_set=None
+    ):
+        optimize_ops, params_grads = self.minimize_impl(
+            loss, startup_program, parameter_list, no_grad_set
+        )
+        return optimize_ops, params_grads