class LarsOptimizer(MetaOptimizerBase):
def __init__(self, optimizer):
super(LarsOptimizer, self).__init__(optimizer)
self.inner_opt = optimizer
self.lars_opt = None
# we do not allow meta optimizer to be inner optimizer currently
self.meta_optimizers_white_list = ["GraphExecutionOptimizer"]
self.meta_optimizers_black_list = []
def _set_basic_info(self, loss, role_maker, user_defined_optimizer,
user_defined_strategy):
super(LarsOptimizer,
self)._set_basic_info(loss, role_maker, user_defined_optimizer,
user_defined_strategy)
opt = self.inner_opt
if not isinstance(opt, Momentum):
return
configs = self.user_defined_strategy.lars_configs
self.lars_opt = LarsMomentumOptimizer(
learning_rate=opt._learning_rate,
momentum=opt._momentum,
lars_coeff=configs['lars_coeff'],
lars_weight_decay=configs['lars_weight_decay'],
parameter_list=opt._parameter_list,
regularization=opt.regularization,
grad_clip=opt._grad_clip,
name=opt._name,
exclude_from_weight_decay=configs['exclude_from_weight_decay'],
epsilon=configs['epsilon'])
def _can_apply(self):
if not self.role_maker._is_collective:
return False
if self.user_defined_strategy.lars:
if not isinstance(self.inner_opt, Momentum):
logging.warn(
"lars need the inner optimizer to be Momentum optimizer but got {}."
.format(self.inner_opt.type))
return False
return True
return False
def _disable_strategy(self, dist_strategy):
dist_strategy.lars = False
dist_strategy.lars_configs = {}
def _enable_strategy(self, dist_strategy, context):
dist_strategy.lars = True
dist_strategy.lars_configs = {
"lars_coeff": 0.01,
"lars_weight_decay": 0.0005,
}
def backward(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None,
callbacks=None):
return self.lars_opt.backward(loss, startup_program, parameter_list,
no_grad_set, callbacks)
# the following function will be used by AMP if both LARS and AMP are turn on together.
def apply_gradients(self, params_grads):
return self.lars_opt.apply_gradients(params_grads=params_grads)
def apply_optimize(self, loss, startup_program, params_grads):
return self.lars_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):
optimize_ops, params_grads = \
self.lars_opt.minimize(loss, startup_program,
parameter_list, no_grad_set)
return optimize_ops, params_grads
class LarsOptimizer(MetaOptimizerBase):
def __init__(self, optimizer):
super(LarsOptimizer, self).__init__(optimizer)
self.inner_opt = optimizer
self.lars_opt = None
# we do not allow meta optimizer to be inner optimizer currently
self.meta_optimizers_white_list = ["GraphExecutionOptimizer"]
self.meta_optimizers_black_list = []
def _set_basic_info(self, loss, role_maker, user_defined_optimizer,
user_defined_strategy):
super(LarsOptimizer,
self)._set_basic_info(loss, role_maker, user_defined_optimizer,
user_defined_strategy)
opt = self.inner_opt
if not isinstance(opt, Momentum):
return
configs = self.user_defined_strategy.lars_configs
self.lars_opt = LarsMomentumOptimizer(
learning_rate=opt._learning_rate,
momentum=opt._momentum,
lars_coeff=configs['lars_coeff'],
lars_weight_decay=configs['lars_weight_decay'],
parameter_list=opt._parameter_list,
regularization=opt.regularization,
grad_clip=opt._grad_clip,
name=opt._name)
def _can_apply(self):
if self.user_defined_strategy.lars:
if not isinstance(self.inner_opt, Momentum):
logging.warn(
"lars need the inner optimizer to be Momentum optimizer.")
return False
return True
return False
def _disable_strategy(self, dist_strategy):
dist_strategy.lars = False
dist_strategy.lars_configs = {}
def _enable_strategy(self, dist_strategy):
dist_strategy.lars = True
dist_strategy.lars_configs = {
"lars_coeff": 0.01,
"lars_weight_decay": 0.0005,
}
def backward(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None,
callbacks=None):
return self.lars_opt.backward(loss, startup_program, parameter_list,
no_grad_set, callbacks)
def minimize_impl(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None):
optimize_ops, params_grads = \
self.lars_opt.minimize(loss, startup_program,
parameter_list, no_grad_set)
return optimize_ops, params_grads