chore: import upstream snapshot with attribution

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

@@ -0,0 +1,104 @@
+#   Copyright (c) 2019 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
+
+from paddle.incubate.optimizer import RecomputeOptimizer as RO
+
+from .meta_optimizer_base import MetaOptimizerBase
+
+__all__ = []
+
+
+class RecomputeOptimizer(MetaOptimizerBase):
+    def __init__(self, optimizer):
+        super().__init__(optimizer)
+        self.inner_opt = optimizer
+        self.wrapped_opt = None
+        # we do not allow meta optimizer to be inner optimizer currently
+        self.meta_optimizers_white_list = [
+            "LarsOptimizer",
+            "LambOptimizer",
+            "DGCOptimizer",
+        ]
+        self.meta_optimizers_black_list = []
+
+    def _set_basic_info(
+        self, loss, role_maker, user_defined_optimizer, user_defined_strategy
+    ):
+        super()._set_basic_info(
+            loss, role_maker, user_defined_optimizer, user_defined_strategy
+        )
+
+    def _init_wrapped_opt(self):
+        if self.wrapped_opt is not None:
+            return
+
+        configs = self.user_defined_strategy.recompute_configs
+        self.wrapped_opt = RO(self.inner_opt)
+        self.wrapped_opt._set_checkpoints(list(configs["checkpoints"]))
+        if configs["enable_offload"]:
+            self.wrapped_opt._enable_offload()
+            # TODO(JZ-LIANG) might found a way to infer the checkpoint shape automatically
+            checkpoint_shapes = list(configs["checkpoint_shape"])
+            self.wrapped_opt.checkpoint_shape = checkpoint_shapes
+
+    def _can_apply(self):
+        if not self.role_maker._is_collective:
+            return False
+
+        if self.user_defined_strategy.recompute:
+            if (
+                len(self.user_defined_strategy.recompute_configs["checkpoints"])
+                == 0
+            ):
+                return False
+            else:
+                return True
+
+    def _disable_strategy(self, dist_strategy):
+        dist_strategy.recompute = False
+        dist_strategy.recompute_configs = {}
+
+    def _enable_strategy(self, dist_strategy, context):
+        # we do not support automatically recompute checkpoints currently
+        return
+
+    def backward(
+        self,
+        loss,
+        startup_program=None,
+        parameter_list=None,
+        no_grad_set=None,
+        callbacks=None,
+    ):
+        # maybe inner_opt of other meta optimizer
+        self._init_wrapped_opt()
+        return self.wrapped_opt.backward(
+            loss, startup_program, parameter_list, no_grad_set, callbacks
+        )
+
+    def apply_gradients(self, params_grads):
+        return self.wrapped_opt.apply_gradients(params_grads=params_grads)
+
+    def apply_optimize(self, loss, startup_program, params_grads):
+        return self.wrapped_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
+    ):
+        self._init_wrapped_opt()
+        optimize_ops, params_grads = self.wrapped_opt.minimize(
+            loss, startup_program, parameter_list, no_grad_set
+        )
+        return optimize_ops, params_grads