chore: import upstream snapshot with attribution

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

@@ -0,0 +1,139 @@
+#   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
+
+import paddle.static.amp as mixed_precision
+
+from .meta_optimizer_base import MetaOptimizerBase
+
+__all__ = []
+
+
+class AMPOptimizer(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",
+            "RecomputeOptimizer",
+        ]
+        self.meta_optimizers_black_list = ["DGCOptimizer"]
+
+    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
+
+        config = self.user_defined_strategy.amp_configs
+
+        custom_white_list = set(config['custom_white_list'])
+        custom_black_list = set(config['custom_black_list'])
+        custom_black_varnames = set(config['custom_black_varnames'])
+        amp_lists = mixed_precision.AutoMixedPrecisionLists(
+            custom_white_list, custom_black_list, custom_black_varnames
+        )
+
+        self.wrapped_opt = mixed_precision.decorate(
+            self.inner_opt,
+            amp_lists,
+            config['init_loss_scaling'],
+            config['incr_every_n_steps'],
+            config['decr_every_n_nan_or_inf'],
+            config['incr_ratio'],
+            config['decr_ratio'],
+            config['use_dynamic_loss_scaling'],
+            config['use_pure_fp16'],
+            config['use_fp16_guard'],
+        )
+
+        # if worker_num > 1, all cards will communication with each other,
+        # add is_distributed to optimize amp, overlap communication and
+        # computation by split the check_finite_and_unscale op.
+        is_distributed = self.role_maker._worker_num() > 1
+        if self.user_defined_strategy.sharding:
+            # FIXME(wangxi). sharding failed when split check_finite_and_unscale
+            # FIXME(JZ-LIANG). To support Sharding-Megatron-AMP, Megatron should follow Sharding's behavior that to disable is_distributed.
+            is_distributed = False
+        self.wrapped_opt._set_distributed(is_distributed)
+
+    def _can_apply(self):
+        if not self.role_maker._is_collective:
+            return False
+
+        if self.user_defined_strategy.amp:
+            return True
+        return False
+
+    def _disable_strategy(self, dist_strategy):
+        dist_strategy.amp = False
+        dist_strategy.amp_configs = {}
+
+    def _enable_strategy(self, dist_strategy, context):
+        dist_strategy.amp = True
+        dist_strategy.amp_configs = {
+            "init_loss_scaling": 32768.0,
+            "incr_every_n_steps": 1000,
+            "decr_every_n_nan_or_inf": 2,
+            "incr_ratio": 2.0,
+            "decr_ratio": 0.8,
+            "use_dynamic_loss_scaling": True,
+        }
+
+    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
+
+    def amp_init(
+        self, place, scope=None, test_program=None, use_fp16_test=False
+    ):
+        return self.wrapped_opt.amp_init(
+            place, scope, test_program, use_fp16_test
+        )
+
+    def get_loss_scaling(self):
+        return self.wrapped_opt.get_loss_scaling()