You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
441 lines
16 KiB
441 lines
16 KiB
# 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
|
|
# limitations under the License.
|
|
|
|
from __future__ import print_function
|
|
|
|
import paddle
|
|
from paddle.fluid import program_guard, layers, default_main_program
|
|
from .meta_optimizer_base import MetaOptimizerBase
|
|
from .common import OpRole, OP_ROLE_KEY, CollectiveHelper, is_update_op
|
|
|
|
|
|
class LocalSGDOptimizer(MetaOptimizerBase):
|
|
def __init__(self, optimizer):
|
|
super(LocalSGDOptimizer, self).__init__(optimizer)
|
|
self.inner_opt = optimizer
|
|
self.meta_optimizers_white_list = ['AMPOptimizer']
|
|
self.meta_optimizers_black_list = [
|
|
"GraphExecutionOptimizer",
|
|
"AdaptiveLocalSGDOptimizer",
|
|
]
|
|
self.snapshot_key = '@SNAPSHOT'
|
|
|
|
def _can_apply(self):
|
|
if not self.role_maker._is_collective:
|
|
return False
|
|
|
|
if not self.user_defined_strategy.localsgd:
|
|
return False
|
|
|
|
if self.role_maker._worker_num() <= 1:
|
|
return False
|
|
|
|
return isinstance(self.inner_opt, paddle.optimizer.momentum.Momentum) \
|
|
or isinstance(self.inner_opt, paddle.fluid.optimizer.Momentum) \
|
|
or isinstance(self.inner_opt, paddle.optimizer.sgd.SGD) \
|
|
or isinstance(self.inner_opt, paddle.fluid.optimizer.SGD)
|
|
|
|
def _disable_strategy(self, dist_strategy):
|
|
dist_strategy.localsgd = False
|
|
dist_strategy.localsgd_configs = {}
|
|
|
|
def _enable_strategy(self, dist_strategy, context):
|
|
dist_strategy.localsgd = True
|
|
dist_strategy.localsgd_configs = {"k_steps": 1, "begin_step": 1}
|
|
|
|
def snapshot_name(self, param_name):
|
|
return param_name + self.snapshot_key
|
|
|
|
def create_snapshot_vars(self, program):
|
|
block = program.global_block()
|
|
|
|
non_dist_params = []
|
|
for param in block.iter_parameters():
|
|
if not param.is_distributed:
|
|
non_dist_params.append(param)
|
|
|
|
p2s = []
|
|
for param in non_dist_params:
|
|
snapshot = block.create_var(
|
|
name=self.snapshot_name(param.name),
|
|
shape=param.shape,
|
|
persistable=True,
|
|
stop_gradient=True,
|
|
dtype=param.dtype)
|
|
p2s.append([param, snapshot])
|
|
return p2s
|
|
|
|
def init_snapshot_vars(self, startup_program, param2snapshot):
|
|
with program_guard(startup_program):
|
|
for param, snapshot in param2snapshot:
|
|
layers.assign(param, snapshot)
|
|
|
|
def minimize_impl(self,
|
|
loss,
|
|
startup_program=None,
|
|
parameter_list=None,
|
|
no_grad_set=None):
|
|
minimized = self.inner_opt.minimize(
|
|
loss, startup_program=startup_program)
|
|
|
|
k_steps_value = self.user_defined_strategy.localsgd_configs['k_steps']
|
|
begin_step_value = self.user_defined_strategy.localsgd_configs[
|
|
'begin_step']
|
|
|
|
if startup_program is None:
|
|
startup_program = default_startup_program()
|
|
main_block = loss.block
|
|
|
|
self.nrings = 2
|
|
collective_helper = CollectiveHelper(self.role_maker, self.nrings)
|
|
collective_helper.update_startup_program(startup_program)
|
|
p2s = self.create_snapshot_vars(startup_program)
|
|
self.init_snapshot_vars(startup_program, p2s)
|
|
|
|
p2s = self.create_snapshot_vars(main_block.program)
|
|
with program_guard(main_block.program, startup_program):
|
|
step = layers.autoincreased_step_counter(begin=1)
|
|
k_steps = layers.create_global_var(
|
|
name="k_steps",
|
|
shape=[1],
|
|
value=k_steps_value,
|
|
dtype='int64',
|
|
persistable=True)
|
|
|
|
begin_step = layers.create_global_var(
|
|
name="begin_step",
|
|
shape=[1],
|
|
value=begin_step_value,
|
|
dtype='int64',
|
|
persistable=True)
|
|
|
|
last_step = layers.create_global_var(
|
|
name="last_step",
|
|
shape=[1],
|
|
value=begin_step_value,
|
|
dtype='int64',
|
|
persistable=True)
|
|
|
|
def communicate():
|
|
sub_block = default_main_program().current_block()
|
|
ring_id = -1
|
|
for param, snapshot in p2s:
|
|
sub_block.append_op(
|
|
type='elementwise_sub',
|
|
inputs={'X': [snapshot],
|
|
'Y': [param]},
|
|
outputs={'Out': [param]},
|
|
attrs={OP_ROLE_KEY: OpRole.Optimize})
|
|
sub_block.append_op(
|
|
type='c_sync_calc_stream',
|
|
inputs={'X': param},
|
|
outputs={'Out': param},
|
|
attrs={OP_ROLE_KEY: OpRole.Optimize})
|
|
ring_id = (ring_id + 1) % self.nrings
|
|
sub_block.append_op(
|
|
type='c_allreduce_sum',
|
|
inputs={'X': [param]},
|
|
outputs={'Out': [param]},
|
|
attrs={
|
|
'ring_id': ring_id,
|
|
OP_ROLE_KEY: OpRole.Optimize
|
|
})
|
|
|
|
for ring_id in range(self.nrings):
|
|
sub_block.append_op(
|
|
type='c_sync_comm_stream',
|
|
inputs={'X': param},
|
|
outputs={'Out': param},
|
|
attrs={
|
|
'ring_id': ring_id,
|
|
OP_ROLE_KEY: OpRole.Optimize
|
|
})
|
|
|
|
for param, snapshot in p2s:
|
|
sub_block.append_op(
|
|
type='scale',
|
|
inputs={'X': [param]},
|
|
outputs={'Out': [param]},
|
|
attrs={
|
|
'scale': 1.0 / self.role_maker._worker_num(),
|
|
OP_ROLE_KEY: OpRole.Optimize
|
|
})
|
|
sub_block.append_op(
|
|
type='elementwise_sub',
|
|
inputs={'X': [snapshot],
|
|
'Y': [param]},
|
|
outputs={'Out': [param]},
|
|
attrs={OP_ROLE_KEY: OpRole.Optimize})
|
|
sub_block.append_op(
|
|
type='assign',
|
|
inputs={'X': [param]},
|
|
outputs={'Out': [snapshot]},
|
|
attrs={OP_ROLE_KEY: OpRole.Optimize})
|
|
layers.assign(step, last_step)
|
|
|
|
def begin_localsgd():
|
|
layers.cond(step - last_step == k_steps, communicate)
|
|
|
|
layers.cond(step > begin_step, begin_localsgd, communicate)
|
|
return minimized
|
|
|
|
|
|
class AdaptiveLocalSGDOptimizer(MetaOptimizerBase):
|
|
def __init__(self, optimizer):
|
|
super(AdaptiveLocalSGDOptimizer, self).__init__(optimizer)
|
|
self.inner_opt = optimizer
|
|
self.meta_optimizers_white_list = ['AMPOptimizer']
|
|
self.meta_optimizers_black_list = [
|
|
"GraphExecutionOptimizer", "LocalSGDOptimizer"
|
|
]
|
|
self.snapshot_key = '@SNAPSHOT'
|
|
|
|
def _can_apply(self):
|
|
if not self.role_maker._is_collective:
|
|
return False
|
|
|
|
if not self.user_defined_strategy.adaptive_localsgd:
|
|
return False
|
|
|
|
if self.role_maker._worker_num() <= 1:
|
|
return False
|
|
|
|
return isinstance(self.inner_opt, paddle.optimizer.momentum.Momentum) \
|
|
or isinstance(self.inner_opt, paddle.fluid.optimizer.Momentum) \
|
|
or isinstance(self.inner_opt, paddle.optimizer.sgd.SGD) \
|
|
or isinstance(self.inner_opt, paddle.fluid.optimizer.SGD)
|
|
|
|
def _disable_strategy(self, dist_strategy):
|
|
dist_strategy.adaptive_localsgd = False
|
|
dist_strategy.adaptive_localsgd_configs = {}
|
|
|
|
def _enable_strategy(self, dist_strategy, context):
|
|
dist_strategy.adaptive_localsgd = True
|
|
dist_strategy.adaptive_localsgd_configs = {
|
|
"init_k_steps": 1,
|
|
"begin_step": 1
|
|
}
|
|
|
|
def snapshot_name(self, param_name):
|
|
return param_name + self.snapshot_key
|
|
|
|
def create_snapshot_vars(self, program):
|
|
block = program.global_block()
|
|
|
|
non_dist_params = []
|
|
for param in block.iter_parameters():
|
|
if not param.is_distributed:
|
|
non_dist_params.append(param)
|
|
|
|
p2s = []
|
|
for param in non_dist_params:
|
|
snapshot = block.create_var(
|
|
name=self.snapshot_name(param.name),
|
|
shape=param.shape,
|
|
persistable=True,
|
|
stop_gradient=True,
|
|
dtype=param.dtype)
|
|
p2s.append([param, snapshot])
|
|
return p2s
|
|
|
|
def init_snapshot_vars(self, startup_program, param2snapshot):
|
|
with program_guard(startup_program):
|
|
for param, snapshot in param2snapshot:
|
|
layers.assign(param, snapshot)
|
|
|
|
def _generate_avg_loss(self, program_block, loss, avg_loss):
|
|
program_block.append_op(
|
|
type='c_allreduce_sum',
|
|
inputs={'X': [loss]},
|
|
outputs={'Out': [avg_loss]},
|
|
attrs={
|
|
'ring_id': 0,
|
|
OP_ROLE_KEY: OpRole.Optimize,
|
|
'use_calc_stream': True
|
|
})
|
|
program_block.append_op(
|
|
type='c_sync_calc_stream',
|
|
inputs={'X': [avg_loss]},
|
|
outputs={'Out': [avg_loss]},
|
|
attrs={OP_ROLE_KEY: OpRole.Optimize})
|
|
|
|
program_block.append_op(
|
|
type='scale',
|
|
inputs={'X': [avg_loss]},
|
|
outputs={'Out': [avg_loss]},
|
|
attrs={
|
|
'scale': 1.0 / self.role_maker._worker_num(),
|
|
OP_ROLE_KEY: OpRole.Optimize
|
|
})
|
|
|
|
def minimize_impl(self,
|
|
loss,
|
|
startup_program=None,
|
|
parameter_list=None,
|
|
no_grad_set=None):
|
|
minimized = self.inner_opt.minimize(
|
|
loss, startup_program=startup_program)
|
|
|
|
init_k_steps = self.user_defined_strategy.adaptive_localsgd_configs[
|
|
'init_k_steps']
|
|
begin_step_value = self.user_defined_strategy.adaptive_localsgd_configs[
|
|
'begin_step']
|
|
|
|
if startup_program is None:
|
|
startup_program = default_startup_program()
|
|
main_block = loss.block
|
|
|
|
self.nrings = 2
|
|
collective_helper = CollectiveHelper(self.role_maker, self.nrings)
|
|
collective_helper.update_startup_program(startup_program)
|
|
p2s = self.create_snapshot_vars(startup_program)
|
|
self.init_snapshot_vars(startup_program, p2s)
|
|
|
|
p2s = self.create_snapshot_vars(main_block.program)
|
|
with program_guard(main_block.program, startup_program):
|
|
step = layers.autoincreased_step_counter(begin=1)
|
|
|
|
k_steps = layers.create_global_var(
|
|
name="k_steps",
|
|
shape=[1],
|
|
value=int(init_k_steps),
|
|
dtype='int64',
|
|
persistable=True)
|
|
|
|
begin_step = layers.create_global_var(
|
|
name="begin_step",
|
|
shape=[1],
|
|
value=int(begin_step_value),
|
|
dtype='int64',
|
|
persistable=True)
|
|
|
|
last_step = layers.create_global_var(
|
|
name="last_step",
|
|
shape=[1],
|
|
value=int(0),
|
|
dtype='int64',
|
|
persistable=True)
|
|
|
|
avg_loss = layers.create_global_var(
|
|
name="avg_loss",
|
|
shape=[1],
|
|
value=float(0),
|
|
dtype=loss.dtype,
|
|
persistable=True)
|
|
|
|
lr_0 = layers.create_global_var(
|
|
name="lr_0",
|
|
shape=[1],
|
|
value=float(0),
|
|
dtype='float32',
|
|
persistable=True)
|
|
|
|
loss_0 = layers.create_global_var(
|
|
name="loss_0",
|
|
shape=[1],
|
|
value=float(0),
|
|
dtype='float32',
|
|
persistable=True)
|
|
|
|
global_lr = self.inner_opt._global_learning_rate()
|
|
|
|
def initialize():
|
|
self._generate_avg_loss(main_block, loss, avg_loss)
|
|
layers.assign(avg_loss, loss_0)
|
|
layers.assign(global_lr, lr_0)
|
|
|
|
layers.cond(step == 1, initialize)
|
|
|
|
def communicate():
|
|
sub_block = default_main_program().current_block()
|
|
ring_id = -1
|
|
for param, snapshot in p2s:
|
|
sub_block.append_op(
|
|
type='elementwise_sub',
|
|
inputs={'X': [snapshot],
|
|
'Y': [param]},
|
|
outputs={'Out': [param]},
|
|
attrs={OP_ROLE_KEY: OpRole.Optimize})
|
|
sub_block.append_op(
|
|
type='c_sync_calc_stream',
|
|
inputs={'X': param},
|
|
outputs={'Out': param},
|
|
attrs={OP_ROLE_KEY: OpRole.Optimize})
|
|
ring_id = (ring_id + 1) % self.nrings
|
|
sub_block.append_op(
|
|
type='c_allreduce_sum',
|
|
inputs={'X': [param]},
|
|
outputs={'Out': [param]},
|
|
attrs={
|
|
'ring_id': ring_id,
|
|
OP_ROLE_KEY: OpRole.Optimize
|
|
})
|
|
|
|
for ring_id in range(self.nrings):
|
|
sub_block.append_op(
|
|
type='c_sync_comm_stream',
|
|
inputs={'X': param},
|
|
outputs={'Out': param},
|
|
attrs={
|
|
'ring_id': ring_id,
|
|
OP_ROLE_KEY: OpRole.Optimize
|
|
})
|
|
|
|
for param, snapshot in p2s:
|
|
sub_block.append_op(
|
|
type='scale',
|
|
inputs={'X': [param]},
|
|
outputs={'Out': [param]},
|
|
attrs={
|
|
'scale': 1.0 / self.role_maker._worker_num(),
|
|
OP_ROLE_KEY: OpRole.Optimize
|
|
})
|
|
sub_block.append_op(
|
|
type='elementwise_sub',
|
|
inputs={'X': [snapshot],
|
|
'Y': [param]},
|
|
outputs={'Out': [param]},
|
|
attrs={OP_ROLE_KEY: OpRole.Optimize})
|
|
sub_block.append_op(
|
|
type='assign',
|
|
inputs={'X': [param]},
|
|
outputs={'Out': [snapshot]},
|
|
attrs={OP_ROLE_KEY: OpRole.Optimize})
|
|
layers.assign(step, last_step)
|
|
|
|
def communicate_avg_loss():
|
|
communicate()
|
|
self._generate_avg_loss(main_block, loss, avg_loss)
|
|
next_local_steps = layers.cast(
|
|
layers.ceil(
|
|
layers.sqrt(lr_0 * avg_loss / (global_lr * loss_0) *
|
|
float(init_k_steps))),
|
|
dtype='int64')
|
|
max_local_steps = layers.fill_constant(
|
|
shape=[1], dtype='int64', value=16)
|
|
min_local_steps = layers.fill_constant(
|
|
shape=[1], dtype='int64', value=1)
|
|
next_local_steps = layers.elementwise_min(next_local_steps,
|
|
max_local_steps)
|
|
next_local_steps = layers.elementwise_max(next_local_steps,
|
|
min_local_steps)
|
|
layers.assign(next_local_steps, k_steps)
|
|
|
|
def begin_localsgd():
|
|
layers.cond(step - last_step == k_steps, communicate_avg_loss)
|
|
|
|
layers.cond(step > begin_step, begin_localsgd, communicate)
|
|
|
|
return minimized
|