Yi Liu
5 years ago
committed by
GitHub
parent
2d24f56a7a
commit
427c5529ea
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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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from __future__ import print_function
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import paddle.fluid as fluid
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from paddle.fluid import core, unique_name
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from ..base.private_helper_function import wait_server_ready
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OpRole = core.op_proto_and_checker_maker.OpRole
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OP_ROLE_KEY = core.op_proto_and_checker_maker.kOpRoleAttrName()
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OP_ROLE_VAR_KEY = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
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def is_update_op(op):
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return 'Param' in op.input_names and 'Grad' in op.input_names and \
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"LearningRate" in op.input_names
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def is_loss_grad_op(op):
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if OP_ROLE_KEY not in op.attr_names:
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return False
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op_role = int(op.all_attrs()[OP_ROLE_KEY])
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return op_role & int(OpRole.Backward) and op_role & int(OpRole.Loss)
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def is_backward_op(op):
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return OP_ROLE_KEY in op.attr_names and \
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int(op.all_attrs()[OP_ROLE_KEY]) & int(OpRole.Backward)
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def is_optimizer_op(op):
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return OP_ROLE_KEY in op.attr_names and \
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int(op.all_attrs()[OP_ROLE_KEY]) & int(OpRole.Optimize)
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class CollectiveHelper(object):
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def __init__(self, role_maker, nrings=1, wait_port='6174'):
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self.nrings = nrings
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self.wait_port = wait_port
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self.role_maker = role_maker
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def update_startup_program(self, startup_program=None):
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self.startup_program = startup_program
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if startup_program is None:
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self.startup_program = fluid.default_startup_program()
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endpoints = self.role_maker.get_trainer_endpoints()
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current_endpoint = endpoints[self.role_maker.worker_index()]
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for ring_id in range(self.nrings):
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self._init_communicator(
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self.startup_program, current_endpoint, endpoints,
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self.role_maker.worker_index(), ring_id, self.wait_port)
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self._broadcast_params()
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def _init_communicator(self, program, current_endpoint, endpoints, rank,
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ring_id, wait_port):
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nranks = len(endpoints)
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other_endpoints = endpoints[:]
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other_endpoints.remove(current_endpoint)
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if rank == 0 and wait_port:
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wait_server_ready(other_endpoints)
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block = program.global_block()
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nccl_id_var = block.create_var(
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name=unique_name.generate('nccl_id'),
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persistable=True,
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type=core.VarDesc.VarType.RAW)
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block.append_op(
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type='c_gen_nccl_id',
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inputs={},
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outputs={'Out': nccl_id_var},
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attrs={
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'rank': rank,
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'endpoint': current_endpoint,
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'other_endpoints': other_endpoints,
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OP_ROLE_KEY: OpRole.Forward
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})
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block.append_op(
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type='c_comm_init',
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inputs={'X': nccl_id_var},
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outputs={},
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attrs={
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'nranks': nranks,
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'rank': rank,
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'ring_id': ring_id,
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OP_ROLE_KEY: OpRole.Forward
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})
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def _broadcast_params(self):
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block = self.startup_program.global_block()
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ring_id = -1
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for param in block.iter_parameters():
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if param.is_distributed:
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continue
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ring_id = (ring_id + 1) % self.nrings
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block.append_op(
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type='c_broadcast',
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inputs={'X': param},
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outputs={'Out': param},
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attrs={
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'ring_id': ring_id,
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'root': 0,
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OP_ROLE_KEY: OpRole.Forward
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})
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for ring_id in range(self.nrings):
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block.append_op(
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type='c_sync_comm_stream',
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inputs={'X': param},
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outputs={'Out': param},
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attrs={'ring_id': ring_id,
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OP_ROLE_KEY: OpRole.Forward})
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@ -0,0 +1,193 @@
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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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from __future__ import print_function
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from paddle.fluid import program_guard, layers
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from paddle.fluid.optimizer import Momentum, SGD
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from .meta_optimizer_base import MetaOptimizerBase
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from .common import OpRole, OP_ROLE_KEY, CollectiveHelper, is_update_op
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class LocalSGDOptimizer(MetaOptimizerBase):
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def __init__(self, optimizer):
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super(LocalSGDOptimizer, self).__init__(optimizer)
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self.inner_opt = optimizer
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self.meta_optimizers_white_list = []
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self.snapshot_key = '@SNAPSHOT'
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def _can_apply(self):
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if not self.user_defined_strategy.localsgd:
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return False
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if self.role_maker.worker_num() <= 1:
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return False
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return isinstance(self.inner_opt, Momentum) \
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or isinstance(self.inner_opt, SGD)
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def _disable_strategy(self, dist_strategy):
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dist_strategy.localsgd = False
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dist_strategy.localsgd_configs = {'k_steps': 1}
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def snapshot_name(self, param_name):
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return param_name + self.snapshot_key
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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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minimized = self.inner_opt.minimize(
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loss, startup_program=startup_program)
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init_k_steps = self.user_defined_strategy.localsgd_configs['k_steps']
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auto_steps = self.user_defined_strategy.auto
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if startup_program is None:
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startup_program = default_startup_program()
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main_block = loss.block
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self.nrings = 2
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collective_helper = CollectiveHelper(self.role_maker, self.nrings)
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collective_helper.update_startup_program(startup_program)
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with program_guard(main_block.program):
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step = layers.autoincreased_step_counter(begin=0)
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k_steps = layers.create_global_var(
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name="k_steps",
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shape=[1],
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value=init_k_steps,
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dtype='int64',
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persistable=True)
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last_step = layers.create_global_var(
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name="last_step",
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shape=[1],
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value=int(0),
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dtype='int64',
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persistable=True)
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if auto_steps:
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lr_0 = layers.create_global_var(
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name="lr_0",
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shape=[1],
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value=float(0),
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dtype='float32',
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persistable=True)
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loss_0 = layers.create_global_var(
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name="loss_0",
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shape=[1],
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value=float(0),
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dtype='float32',
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persistable=True)
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global_lr = self.inner_opt._global_learning_rate()
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def initialize():
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layers.assign(loss, loss_0)
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layers.assign(global_lr, lr_0)
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layers.cond(step == 0, initialize)
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def communicate():
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ordered_param_snapshot = []
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ring_id = -1
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for idx, op in reversed(list(enumerate(main_block.ops))):
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if is_update_op(op):
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param = main_block.vars[op.input('Param')[0]]
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if param.is_distributed:
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continue
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snapshot = main_block.create_var(
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name=self.snapshot_name(param.name),
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shape=param.shape,
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persistable=True,
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stop_gradient=True,
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dtype=param.dtype)
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main_block._insert_op(
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idx + 1,
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type='elementwise_sub',
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inputs={'X': [snapshot],
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'Y': [param]},
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outputs={'Out': [param]},
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attrs={OP_ROLE_KEY: OpRole.Optimize})
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main_block._insert_op(
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idx + 2,
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type='c_sync_calc_stream',
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inputs={'X': param},
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outputs={'Out': param},
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attrs={OP_ROLE_KEY: OpRole.Optimize})
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ring_id = (ring_id + 1) % self.nrings
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main_block._insert_op(
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idx + 3,
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type='c_allreduce_sum',
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inputs={'X': [param]},
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outputs={'Out': [param]},
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attrs={
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'ring_id': ring_id,
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OP_ROLE_KEY: OpRole.Optimize
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})
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ordered_param_snapshot.append((param, snapshot))
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for ring_id in range(self.nrings):
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main_block.append_op(
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type='c_sync_comm_stream',
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inputs={'X': param},
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outputs={'Out': param},
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attrs={
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'ring_id': ring_id,
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OP_ROLE_KEY: OpRole.Optimize
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})
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for param_snapshot in reversed(ordered_param_snapshot):
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param = param_snapshot[0]
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snapshot = param_snapshot[1]
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main_block.append_op(
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type='scale',
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inputs={'X': [param]},
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outputs={'Out': [param]},
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attrs={
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'scale': 1.0 / self.role_maker.worker_num(),
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OP_ROLE_KEY: OpRole.Optimize
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})
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main_block.append_op(
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type='elementwise_sub',
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inputs={'X': [snapshot],
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'Y': [param]},
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outputs={'Out': [param]},
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attrs={OP_ROLE_KEY: OpRole.Optimize})
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main_block.append_op(
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type='assign',
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inputs={'X': [param]},
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outputs={'Out': [snapshot]},
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attrs={OP_ROLE_KEY: OpRole.Optimize})
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if auto_steps:
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next_local_steps = layers.cast(
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layers.ceil(
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layers.sqrt(lr_0 * loss / (global_lr * loss_0) *
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float(init_k_steps))),
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dtype='int64')
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max_local_steps = layers.fill_constant(
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shape=[1], dtype='int64', value=16)
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next_local_steps = layers.elementwise_min(next_local_steps,
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max_local_steps)
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layers.assign(next_local_steps, k_steps)
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layers.assign(step, last_step)
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layers.cond(step - last_step == k_steps, communicate)
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return minimized
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@ -0,0 +1,55 @@
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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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import paddle.fleet as fleet
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import paddle.fluid.incubate.fleet.base.role_maker as role_maker
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class TestFleetLocalSGDMetaOptimizer(unittest.TestCase):
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def setUp(self):
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os.environ["PADDLE_TRAINER_ID"] = "1"
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os.environ[
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"PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001,127.0.0.1:36002"
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def test_localsgd_optimizer(self):
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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 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
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prediction = paddle.fluid.layers.fc(input=[fc], 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.localsgd = True
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strategy.auto = True
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config = strategy.localsgd_configs
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config['k_steps'] = 1
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strategy.localsgd_configs = config
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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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if __name__ == "__main__":
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unittest.main()
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