wehub-resource-sync
495 lines
17 KiB
Python
495 lines
17 KiB
Python
# 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 paddle
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from paddle.static import (
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default_main_program,
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default_startup_program,
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program_guard,
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)
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from .common import OP_ROLE_KEY, CollectiveHelper, OpRole
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from .meta_optimizer_base import MetaOptimizerBase
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__all__ = []
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class LocalSGDOptimizer(MetaOptimizerBase):
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def __init__(self, optimizer):
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super().__init__(optimizer)
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self.inner_opt = optimizer
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self.meta_optimizers_white_list = ['AMPOptimizer']
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self.meta_optimizers_black_list = [
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"AdaptiveLocalSGDOptimizer",
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]
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self.snapshot_key = '@SNAPSHOT'
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def _can_apply(self):
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if not self.role_maker._is_collective:
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return False
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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(
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self.inner_opt,
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(
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paddle.optimizer.momentum.Momentum,
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paddle.optimizer.sgd.SGD,
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),
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)
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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 = {}
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def _enable_strategy(self, dist_strategy, context):
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dist_strategy.localsgd = True
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dist_strategy.localsgd_configs = {"k_steps": 1, "begin_step": 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 create_snapshot_vars(self, program):
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block = program.global_block()
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non_dist_params = []
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for param in block.iter_parameters():
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if not param.is_distributed:
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non_dist_params.append(param)
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p2s = []
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for param in non_dist_params:
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snapshot = 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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)
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p2s.append([param, snapshot])
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return p2s
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def init_snapshot_vars(self, startup_program, param2snapshot):
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with program_guard(startup_program):
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for param, snapshot in param2snapshot:
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paddle.assign(param, snapshot)
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def minimize_impl(
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self, loss, startup_program=None, parameter_list=None, no_grad_set=None
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):
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minimized = self.inner_opt.minimize(
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loss, startup_program=startup_program
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)
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k_steps_value = self.user_defined_strategy.localsgd_configs['k_steps']
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begin_step_value = self.user_defined_strategy.localsgd_configs[
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'begin_step'
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]
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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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p2s = self.create_snapshot_vars(startup_program)
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self.init_snapshot_vars(startup_program, p2s)
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p2s = self.create_snapshot_vars(main_block.program)
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with program_guard(main_block.program, startup_program):
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step = paddle.optimizer.lr.autoincreased_step_counter(begin=1)
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k_steps = paddle.static.create_global_var(
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name="k_steps",
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shape=[1],
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value=k_steps_value,
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dtype='int64',
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persistable=True,
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)
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begin_step = paddle.static.create_global_var(
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name="begin_step",
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shape=[1],
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value=begin_step_value,
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dtype='int64',
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persistable=True,
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)
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last_step = paddle.static.create_global_var(
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name="last_step",
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shape=[1],
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value=begin_step_value,
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dtype='int64',
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persistable=True,
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)
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def communicate():
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sub_block = default_main_program().current_block()
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ring_id = -1
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for param, snapshot in p2s:
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sub_block.append_op(
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type='elementwise_sub',
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inputs={'X': [snapshot], 'Y': [param]},
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outputs={'Out': [param]},
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attrs={OP_ROLE_KEY: OpRole.Optimize},
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)
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sub_block.append_op(
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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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)
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ring_id = (ring_id + 1) % self.nrings
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sub_block.append_op(
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type='all_reduce',
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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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'reduce_type': paddle.distributed.ReduceOp.SUM,
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OP_ROLE_KEY: OpRole.Optimize,
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},
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)
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for ring_id in range(self.nrings):
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sub_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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)
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for param, snapshot in p2s:
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sub_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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)
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sub_block.append_op(
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type='elementwise_sub',
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inputs={'X': [snapshot], 'Y': [param]},
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outputs={'Out': [param]},
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attrs={OP_ROLE_KEY: OpRole.Optimize},
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)
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sub_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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)
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paddle.assign(step, last_step)
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def begin_localsgd():
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paddle.static.nn.cond(step - last_step == k_steps, communicate)
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paddle.static.nn.cond(
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step > begin_step, begin_localsgd, communicate
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)
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return minimized
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class AdaptiveLocalSGDOptimizer(MetaOptimizerBase):
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def __init__(self, optimizer):
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super().__init__(optimizer)
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self.inner_opt = optimizer
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self.meta_optimizers_white_list = ['AMPOptimizer']
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self.meta_optimizers_black_list = [
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"LocalSGDOptimizer",
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]
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self.snapshot_key = '@SNAPSHOT'
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def _can_apply(self):
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if not self.role_maker._is_collective:
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return False
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if not self.user_defined_strategy.adaptive_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(
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self.inner_opt,
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(
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paddle.optimizer.Momentum,
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paddle.optimizer.sgd.SGD,
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),
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)
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def _disable_strategy(self, dist_strategy):
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dist_strategy.adaptive_localsgd = False
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dist_strategy.adaptive_localsgd_configs = {}
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def _enable_strategy(self, dist_strategy, context):
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dist_strategy.adaptive_localsgd = True
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dist_strategy.adaptive_localsgd_configs = {
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"init_k_steps": 1,
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"begin_step": 1,
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}
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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 create_snapshot_vars(self, program):
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block = program.global_block()
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non_dist_params = []
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for param in block.iter_parameters():
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if not param.is_distributed:
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non_dist_params.append(param)
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p2s = []
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for param in non_dist_params:
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snapshot = 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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)
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p2s.append([param, snapshot])
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return p2s
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def init_snapshot_vars(self, startup_program, param2snapshot):
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with program_guard(startup_program):
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for param, snapshot in param2snapshot:
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paddle.assign(param, snapshot)
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def _generate_avg_loss(self, program_block, loss, avg_loss):
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program_block.append_op(
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type='all_reduce',
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inputs={'x': [loss]},
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outputs={'out': [avg_loss]},
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attrs={
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'ring_id': 0,
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OP_ROLE_KEY: OpRole.Optimize,
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'reduce_type': paddle.distributed.ReduceOp.SUM,
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},
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)
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program_block.append_op(
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type='c_sync_calc_stream',
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inputs={'X': [avg_loss]},
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outputs={'Out': [avg_loss]},
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attrs={OP_ROLE_KEY: OpRole.Optimize},
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)
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program_block.append_op(
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type='scale',
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inputs={'X': [avg_loss]},
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outputs={'Out': [avg_loss]},
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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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)
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def minimize_impl(
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self, loss, startup_program=None, parameter_list=None, no_grad_set=None
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):
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minimized = self.inner_opt.minimize(
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loss, startup_program=startup_program
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)
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init_k_steps = self.user_defined_strategy.adaptive_localsgd_configs[
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'init_k_steps'
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]
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begin_step_value = self.user_defined_strategy.adaptive_localsgd_configs[
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'begin_step'
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]
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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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p2s = self.create_snapshot_vars(startup_program)
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self.init_snapshot_vars(startup_program, p2s)
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p2s = self.create_snapshot_vars(main_block.program)
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with program_guard(main_block.program, startup_program):
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step = paddle.optimizer.lr.autoincreased_step_counter(begin=1)
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k_steps = paddle.static.create_global_var(
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name="k_steps",
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shape=[1],
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value=int(init_k_steps),
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dtype='int64',
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persistable=True,
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)
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begin_step = paddle.static.create_global_var(
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name="begin_step",
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shape=[1],
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value=int(begin_step_value),
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dtype='int64',
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persistable=True,
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)
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last_step = paddle.static.create_global_var(
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name="last_step",
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shape=[1],
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value=0,
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dtype='int64',
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persistable=True,
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)
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avg_loss = paddle.static.create_global_var(
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name="avg_loss",
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shape=[1],
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value=float(0),
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dtype=loss.dtype,
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persistable=True,
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)
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lr_0 = paddle.static.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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)
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loss_0 = paddle.static.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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)
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global_lr = self.inner_opt._global_learning_rate()
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def initialize():
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self._generate_avg_loss(main_block, loss, avg_loss)
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paddle.assign(avg_loss, loss_0)
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paddle.assign(global_lr, lr_0)
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paddle.static.nn.cond(step == 1, initialize)
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def communicate():
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sub_block = default_main_program().current_block()
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ring_id = -1
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for param, snapshot in p2s:
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sub_block.append_op(
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type='elementwise_sub',
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inputs={'X': [snapshot], 'Y': [param]},
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outputs={'Out': [param]},
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attrs={OP_ROLE_KEY: OpRole.Optimize},
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)
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sub_block.append_op(
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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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)
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ring_id = (ring_id + 1) % self.nrings
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sub_block.append_op(
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type='all_reduce',
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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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'reduce_type': paddle.distributed.ReduceOp.SUM,
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OP_ROLE_KEY: OpRole.Optimize,
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},
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)
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for ring_id in range(self.nrings):
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sub_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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)
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for param, snapshot in p2s:
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sub_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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)
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sub_block.append_op(
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type='elementwise_sub',
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inputs={'X': [snapshot], 'Y': [param]},
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outputs={'Out': [param]},
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attrs={OP_ROLE_KEY: OpRole.Optimize},
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)
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sub_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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)
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paddle.assign(step, last_step)
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def communicate_avg_loss():
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communicate()
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self._generate_avg_loss(main_block, loss, avg_loss)
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next_local_steps = paddle.cast(
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paddle.ceil(
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paddle.sqrt(
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lr_0
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* avg_loss
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/ (global_lr * loss_0)
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* float(init_k_steps)
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)
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),
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dtype='int64',
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)
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max_local_steps = paddle.full(
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shape=[1], dtype='int64', fill_value=16
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)
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min_local_steps = paddle.full(
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shape=[1], dtype='int64', fill_value=1
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)
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next_local_steps = paddle.minimum(
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next_local_steps, max_local_steps
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)
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next_local_steps = paddle.maximum(
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next_local_steps, min_local_steps
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)
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paddle.assign(next_local_steps, k_steps)
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def begin_localsgd():
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paddle.static.nn.cond(
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step - last_step == k_steps, communicate_avg_loss
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)
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paddle.static.nn.cond(
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step > begin_step, begin_localsgd, communicate
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)
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return minimized
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