class RecomputeOptimizer(MetaOptimizerBase):
def __init__(self, optimizer):
super(RecomputeOptimizer, self).__init__(optimizer)
#self.inner_opt = RO(optimizer)
self.inner_opt = optimizer
self.wrapped_opt = RO(optimizer)
# we do not allow meta optimizer to be inner optimizer currently
self.meta_optimizers_white_list = [
"LarsOptimizer",
"LambOptimizer",
"GradientMergeOptimizer",
"GraphExecutionOptimizer",
]
self.meta_optimizers_black_list = []
def _set_basic_info(self, loss, role_maker, user_defined_optimizer,
user_defined_strategy):
super(RecomputeOptimizer,
self)._set_basic_info(loss, role_maker, user_defined_optimizer,
user_defined_strategy)
self.wrapped_opt._set_checkpoints(
list(user_defined_strategy.recompute_configs["checkpoints"]))
def _can_apply(self):
if self.user_defined_strategy.recompute == True:
if len(self.user_defined_strategy.recompute_configs["checkpoints"]
) == 0:
return False
else:
return True
def _disable_strategy(self, dist_strategy):
dist_strategy.recompute = False
dist_strategy.recompute_configs = {}
def _enable_strategy(self, dist_strategy):
# we do not support automatically recompute checkpoints currently
return
def backward(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None,
callbacks=None):
return self.wrapped_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.wrapped_opt.minimize(loss, startup_program,
parameter_list, no_grad_set)
return optimize_ops, params_grads
class RecomputeOptimizer(MetaOptimizerBase):
def __init__(self, optimizer):
super(RecomputeOptimizer, self).__init__(optimizer)
self.inner_opt = optimizer
self.wrapped_opt = None
# we do not allow meta optimizer to be inner optimizer currently
self.meta_optimizers_white_list = [
"LarsOptimizer",
"LambOptimizer",
"GraphExecutionOptimizer",
"DGCOptimizer",
]
self.meta_optimizers_black_list = []
def _set_basic_info(self, loss, role_maker, user_defined_optimizer,
user_defined_strategy):
super(RecomputeOptimizer,
self)._set_basic_info(loss, role_maker, user_defined_optimizer,
user_defined_strategy)
def _init_wrapped_opt(self):
if self.wrapped_opt is not None:
return
configs = self.user_defined_strategy.recompute_configs
self.wrapped_opt = RO(self.inner_opt)
self.wrapped_opt._set_checkpoints(list(configs["checkpoints"]))
if configs["enable_offload"]:
self.wrapped_opt._enable_offload()
# TODO(JZ-LIANG) might found a way to infer the checkpoint shape automatically
checkpoint_shapes = list(configs["checkpoint_shape"])
self.wrapped_opt.checkpoint_shape = checkpoint_shapes
def _can_apply(self):
if not self.role_maker._is_collective:
return False
if self.user_defined_strategy.recompute == True:
if len(self.user_defined_strategy.recompute_configs["checkpoints"]
) == 0:
return False
else:
return True
def _disable_strategy(self, dist_strategy):
dist_strategy.recompute = False
dist_strategy.recompute_configs = {}
def _enable_strategy(self, dist_strategy, context):
# we do not support automatically recompute checkpoints currently
return
def backward(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None,
callbacks=None):
# maybe inner_opt of other meta optimizer
self._init_wrapped_opt()
return self.wrapped_opt.backward(loss, startup_program, parameter_list,
no_grad_set, callbacks)
def apply_gradients(self, params_grads):
return self.wrapped_opt.apply_gradients(params_grads=params_grads)
def apply_optimize(self, loss, startup_program, params_grads):
return self.wrapped_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):
self._init_wrapped_opt()
optimize_ops, params_grads = \
self.wrapped_opt.minimize(loss, startup_program,
parameter_list, no_grad_set)
return optimize_ops, params_grads