Add amp to fleet meta optimizer, test=develop (#25770)
WangXi
5 years ago
committed by
GitHub
parent
9e9a569dae
commit
a6c87fd091
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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import paddle.fluid.contrib.mixed_precision as mixed_precision
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from .meta_optimizer_base import MetaOptimizerBase
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__all__ = ["AMPOptimizer"]
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class AMPOptimizer(MetaOptimizerBase):
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def __init__(self, optimizer):
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super(AMPOptimizer, self).__init__(optimizer)
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self.inner_opt = optimizer
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self.amp_opt = None
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# we do not allow meta optimizer to be inner optimizer currently
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self.meta_optimizers_white_list = []
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def _set_basic_info(self, loss, role_maker, user_defined_optimizer,
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user_defined_strategy):
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super(AMPOptimizer, self)._set_basic_info(
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loss, role_maker, user_defined_optimizer, user_defined_strategy)
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def _can_apply(self):
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if self.user_defined_strategy.amp:
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return True
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return False
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def _disable_strategy(self, dist_strategy):
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dist_strategy.amp = False
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def minimize_impl(self,
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loss,
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startup_program=None,
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parameter_list=None,
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no_grad_set=None):
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if self.amp_opt is None:
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config = self.user_defined_strategy.amp_configs
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custom_white_list = set(config['custom_white_list'])
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custom_black_list = set(config['custom_black_list'])
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custom_black_varnames = set(config['custom_black_varnames'])
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amp_lists = mixed_precision.AutoMixedPrecisionLists(
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custom_white_list, custom_black_list, custom_black_varnames)
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self.amp_opt = mixed_precision.decorate(
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self.inner_opt, amp_lists, config['init_loss_scaling'],
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config['incr_every_n_steps'], config['decr_every_n_nan_or_inf'],
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config['incr_ratio'], config['decr_ratio'],
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config['use_dynamic_loss_scaling'])
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optimize_ops, params_grads = \
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self.amp_opt.minimize(loss, startup_program,
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parameter_list, no_grad_set)
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return optimize_ops, params_grads
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@ -0,0 +1,64 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import paddle
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import os
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class TestFleetAMPOptimizer(unittest.TestCase):
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def setUp(self):
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os.environ["PADDLE_TRAINER_ID"] = "0"
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os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
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def test_amp_optimizer(self):
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import paddle.fleet as fleet
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import paddle.fluid.incubate.fleet.base.role_maker as role_maker
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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input_x = paddle.fluid.layers.data(
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name="x", shape=[32], dtype='float32')
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input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')
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fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
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fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
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prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
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cost = paddle.fluid.layers.cross_entropy(
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input=prediction, label=input_y)
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avg_cost = paddle.fluid.layers.mean(x=cost)
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strategy = paddle.fleet.DistributedStrategy()
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strategy.amp = True
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strategy.amp_configs = {
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"init_loss_scaling": 32768,
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"decr_every_n_nan_or_inf": 2,
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"incr_every_n_steps": 1000,
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"incr_ratio": 2.0,
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"use_dynamic_loss_scaling": True,
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"decr_ratio": 0.5,
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"custom_white_list": ['softmax'],
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"custom_black_list": ['tanh'],
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}
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optimizer = paddle.optimizer.SGD(learning_rate=0.01)
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optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
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optimizer.minimize(avg_cost)
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ops = [op.type for op in avg_cost.block.ops]
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self.assertIn('cast', ops)
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self.assertIn('isfinite', ops)
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if __name__ == "__main__":
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unittest.main()
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