def __init__(self,
init_optimizer,
static_loss_scale=1.0,
dynamic_loss_scale=False,
dynamic_loss_args=None,
verbose=True,
dp_process_group=None,
partition_size=None,
mpu=None,
all_gather_partitions=True,
allgather_size=500000000,
clip_grad=0.0):
if dp_process_group is not None and partition_size is not None:
raise ValueError("Cannot specify both dp_process_group "
"and partition size")
if dp_process_group is None:
dp_process_group = _initialize_parameter_parallel_groups(
partition_size)
if not torch.cuda.is_available:
raise SystemError("Cannot use fp16 without CUDA.")
self.optimizer = init_optimizer
self.verbose = verbose
self.dp_process_group = dp_process_group
# TODO: automatically turn off if #params > some_limit
self.all_gather_partitions = all_gather_partitions
self.allgather_size = allgather_size
# param flattened by groups
self.fp16_groups = []
self.fp16_groups_flat = []
#param partitioned by data parallel degree
#this will contain a list of equal sized tensors
#each of which will be updated by a different process
self.parallel_partitioned_fp16_groups = []
#a single 32-bit partition of the parallel partitioned parameters
#that this process will update
self.single_partition_of_fp32_groups = []
#param partition info
#These are the parameters in each group that will not be updated by this process directly
self.params_not_in_partition = []
#These are the parameters that will be updated by this process directly
self.params_in_partition = []
#Offset from the first paramter in the the self.params_in_partition
#the parameter boundaries may not align with partition boundaries
#so we need to keep track of the offset
self.first_offset = []
#number of elements per partition in each group
self.partition_size = []
partition_id = dist.get_rank(group=self.dp_process_group)
# loop to deal with groups
for i, param_group in enumerate(self.optimizer.param_groups):
# push this group to list before modify
self.fp16_groups.append(param_group['params'])
self.fp16_groups_flat.append(
flatten_dense_tensors_aligned(
self.fp16_groups[i],
dist.get_world_size(group=self.dp_process_group),
self.dp_process_group))
# set model fp16 weight to slices of flattened buffer
updated_params = _unflatten_dense_tensors(self.fp16_groups_flat[i],
self.fp16_groups[i])
for p, q in zip(self.fp16_groups[i], updated_params):
p.data = q.data
#divide the flat weights into near equal paritition equal to the data parallel degree
#each process will compute on a different part of the partition
data_parallel_partitions = self.get_data_parallel_partitions(
self.fp16_groups_flat[i])
self.parallel_partitioned_fp16_groups.append(
data_parallel_partitions)
# a partition of the fp32 master weights that will be updated by this process
self.single_partition_of_fp32_groups.append(
self.parallel_partitioned_fp16_groups[i]
[partition_id].clone().float().detach())
# modify optimizer of have flat master weight
self.single_partition_of_fp32_groups[
i].requires_grad = True # keep this in case internal optimizer uses it
param_group['params'] = [self.single_partition_of_fp32_groups[i]]
partition_size = len(
self.fp16_groups_flat[i]) / dist.get_world_size(
group=self.dp_process_group)
params_in_partition, params_not_in_partition, first_offset = self.get_partition_info(
self.fp16_groups[i], partition_size, partition_id)
self.partition_size.append(partition_size)
self.params_in_partition.append(params_in_partition)
self.params_not_in_partition.append(params_not_in_partition)
self.first_offset.append(first_offset)
# we may have a way of fusing dynamic scale. Do not support for now
if dynamic_loss_scale:
self.dynamic_loss_scale = True
if dynamic_loss_args is None:
self.loss_scaler = DynamicLossScaler()
else:
self.loss_scaler = DynamicLossScaler(**dynamic_loss_args)
else:
self.dynamic_loss_scale = False
self.loss_scaler = LossScaler(scale=static_loss_scale)
self.cur_iter = 0
self.mpu = mpu
self.clip_grad = clip_grad
self.overflow = False
self.overflow_checker = CheckOverflow(self.fp16_groups, mpu=self.mpu)
def __init__(self,
init_optimizer,
static_loss_scale=1.0,
dynamic_loss_scale=False,
dynamic_loss_args=None,
verbose=True,
dp_process_group=None,
partition_size=None,
mpu=None,
all_gather_partitions=True,
allgather_size=500000000,
clip_grad=0.0,
max_elements_per_comm=5e8):
if dp_process_group is not None and partition_size is not None:
raise ValueError("Cannot specify both dp_process_group "
"and partition size")
if dp_process_group is None:
dp_process_group = _initialize_parameter_parallel_groups(
partition_size)
if not torch.cuda.is_available:
raise SystemError("Cannot use fp16 without CUDA.")
self.optimizer = init_optimizer
self.verbose = verbose
self.dp_process_group = dp_process_group
# TODO: automatically turn off if #params > some_limit
self.all_gather_partitions = all_gather_partitions
self.allgather_size = allgather_size
self.max_elements_per_comm = max_elements_per_comm
print("max_elements_per_comm={}".format(max_elements_per_comm))
# param flattened by groups
self.fp16_groups = []
self.fp16_groups_flat = []
# Setup bookkeeping data structures depending on partitioning type
# parallel_sub_partitioned_fp16_groups[group-idx] -> [comm-ids] -> [rank-ids]
self.parallel_sub_partitioned_fp16_groups = []
# same underlying data as above but viewed as: [groups] -> [rank-ids] -> [comm-ids]
self.parallel_comm_sub_partitioned_fp16_groups = []
# 32-bit sub-partitions of the parallel partitioned parameters
# that this process will update
self.local_sub_partitions_of_fp32_groups = []
# param partition info
# parameters in each group that will not be updated by this process directly
self.params_not_local = []
# parameters that will be updated by this process directly
self.params_in_rank_sub_partitions = []
# parameter offsets for parameters in sub-partitions. Parameter
# boundaries may not align with sub-partition boundaries
# so we need to keep track of the offsets
self.params_in_rank_sub_partitions_offsets = []
# number of elements per sub-partition in each group
self.sub_partition_sizes = []
# number of communication intervals for each group
self.num_comm_intervals_per_group = []
local_rank = dist.get_rank(group=self.dp_process_group)
# loop to deal with groups
for i, param_group in enumerate(self.optimizer.param_groups):
# push this group to list before modify
self.fp16_groups.append(param_group['params'])
# flattens all tensors into single 1d tensor aligned with sub-partition size for later dividing
# RS: create aligned sub-partitions
self.fp16_groups_flat.append(
flatten_dense_tensors_sub_partition_aligned(
tensor_list=self.fp16_groups[i],
dp=dist.get_world_size(group=self.dp_process_group),
max_elements_per_comm=self.max_elements_per_comm,
pg=self.dp_process_group))
# TODO: I don't think this does anything?
# set model fp16 weight to slices of flattened buffer
updated_params = _unflatten_dense_tensors(self.fp16_groups_flat[i],
self.fp16_groups[i])
for p, q in zip(self.fp16_groups[i], updated_params):
p.data = q.data
# divide the flat weights into near equal partition equal to the data parallel degree
# each process will compute on a different part of the partition
# RS: split into two layer list -> [comm-id] -> [sub-partitions per rank]
comm_partitions, dp_sub_partitions, element_intervals, sub_partition_size, num_comm_intervals = \
self.get_data_parallel_sub_partitions(
tensor=self.fp16_groups_flat[i],
max_elements_per_comm=self.max_elements_per_comm,
world_size=dist.get_world_size(
group=self.dp_process_group),
dp_process_group=self.dp_process_group
)
self.parallel_comm_sub_partitioned_fp16_groups.append(
comm_partitions) # comm -> rank
self.parallel_sub_partitioned_fp16_groups.append(
dp_sub_partitions) # rank -> comm
self.sub_partition_sizes.append(sub_partition_size)
self.num_comm_intervals_per_group.append(num_comm_intervals)
# data_parallel_partitions = self.get_data_parallel_partitions(self.fp16_groups_flat[i])
# self.parallel_partitioned_fp16_groups.append(data_parallel_partitions)
# a partition of the fp32 master weights that will be updated by this process
# RS: store/detach/cast our local sub-partitions
local_sub_partitions = []
for sub_partition in self.parallel_sub_partitioned_fp16_groups[i][
local_rank]:
fp32_sub_partition = sub_partition.clone().float().detach()
fp32_sub_partition.requires_grad = True
local_sub_partitions.append(fp32_sub_partition)
self.local_sub_partitions_of_fp32_groups.append(
local_sub_partitions)
# modify optimizer of have flat master weight
# self.single_partition_of_fp32_groups[i].requires_grad = True # keep this in case internal optimizer uses it
param_group['params'] = self.local_sub_partitions_of_fp32_groups[i]
# RS: divide up the sub-partitions and keep track of offsets for each param
# partition_size = len(self.fp16_groups_flat[i]) / dist.get_world_size(group=self.dp_process_group)
params_in_rank_sub_partition, params_in_rank_sub_partitions_offsets, \
params_not_local = self.get_all_sub_partition_info(
tensor_list=self.fp16_groups[i],
all_element_intervals=element_intervals,
local_rank=local_rank,
world_size=dist.get_world_size(group=self.dp_process_group)
)
self.params_in_rank_sub_partitions.append(
params_in_rank_sub_partition)
self.params_not_local.append(params_not_local)
self.params_in_rank_sub_partitions_offsets.append(
params_in_rank_sub_partitions_offsets)
# we may have a way of fusing dynamic scale. Do not support for now
if dynamic_loss_scale:
if dynamic_loss_args is None:
self.loss_scaler = DynamicLossScaler()
else:
self.loss_scaler = DynamicLossScaler(**dynamic_loss_args)
self.dynamic_loss_scale = True
else:
self.dynamic_loss_scale = False
self.loss_scaler = LossScaler(scale=static_loss_scale)
self.cur_iter = 0
self.mpu = mpu
self.clip_grad = clip_grad
self.overflow = False
self.overflow_checker = CheckOverflow(self.fp16_groups,
mpu=self.mpu,
zero_reduce_scatter=True)
class FP16_DeepSpeedZeroOptimizer(object):
"""
DeepSpeedZeroOptimizer designed to reduce the memory footprint
required for training large deep learning models.
For more details please see ZeRO: Memory Optimization Towards Training A Trillion Parameter Models
https://arxiv.org/abs/1910.02054
For usage examples, refer to TODO: DeepSpeed V2 Tutorial
"""
def __init__(self,
init_optimizer,
static_loss_scale=1.0,
dynamic_loss_scale=False,
dynamic_loss_args=None,
verbose=True,
dp_process_group=None,
partition_size=None,
mpu=None,
all_gather_partitions=True,
allgather_size=500000000,
clip_grad=0.0):
if dp_process_group is not None and partition_size is not None:
raise ValueError("Cannot specify both dp_process_group "
"and partition size")
if dp_process_group is None:
dp_process_group = _initialize_parameter_parallel_groups(
partition_size)
if not torch.cuda.is_available:
raise SystemError("Cannot use fp16 without CUDA.")
self.optimizer = init_optimizer
self.verbose = verbose
self.dp_process_group = dp_process_group
# TODO: automatically turn off if #params > some_limit
self.all_gather_partitions = all_gather_partitions
self.allgather_size = allgather_size
# param flattened by groups
self.fp16_groups = []
self.fp16_groups_flat = []
#param partitioned by data parallel degree
#this will contain a list of equal sized tensors
#each of which will be updated by a different process
self.parallel_partitioned_fp16_groups = []
#a single 32-bit partition of the parallel partitioned parameters
#that this process will update
self.single_partition_of_fp32_groups = []
#param partition info
#These are the parameters in each group that will not be updated by this process directly
self.params_not_in_partition = []
#These are the parameters that will be updated by this process directly
self.params_in_partition = []
#Offset from the first paramter in the the self.params_in_partition
#the parameter boundaries may not align with partition boundaries
#so we need to keep track of the offset
self.first_offset = []
#number of elements per partition in each group
self.partition_size = []
partition_id = dist.get_rank(group=self.dp_process_group)
# loop to deal with groups
for i, param_group in enumerate(self.optimizer.param_groups):
# push this group to list before modify
self.fp16_groups.append(param_group['params'])
self.fp16_groups_flat.append(
flatten_dense_tensors_aligned(
self.fp16_groups[i],
dist.get_world_size(group=self.dp_process_group),
self.dp_process_group))
# set model fp16 weight to slices of flattened buffer
updated_params = _unflatten_dense_tensors(self.fp16_groups_flat[i],
self.fp16_groups[i])
for p, q in zip(self.fp16_groups[i], updated_params):
p.data = q.data
#divide the flat weights into near equal paritition equal to the data parallel degree
#each process will compute on a different part of the partition
data_parallel_partitions = self.get_data_parallel_partitions(
self.fp16_groups_flat[i])
self.parallel_partitioned_fp16_groups.append(
data_parallel_partitions)
# a partition of the fp32 master weights that will be updated by this process
self.single_partition_of_fp32_groups.append(
self.parallel_partitioned_fp16_groups[i]
[partition_id].clone().float().detach())
# modify optimizer of have flat master weight
self.single_partition_of_fp32_groups[
i].requires_grad = True # keep this in case internal optimizer uses it
param_group['params'] = [self.single_partition_of_fp32_groups[i]]
partition_size = len(
self.fp16_groups_flat[i]) / dist.get_world_size(
group=self.dp_process_group)
params_in_partition, params_not_in_partition, first_offset = self.get_partition_info(
self.fp16_groups[i], partition_size, partition_id)
self.partition_size.append(partition_size)
self.params_in_partition.append(params_in_partition)
self.params_not_in_partition.append(params_not_in_partition)
self.first_offset.append(first_offset)
# we may have a way of fusing dynamic scale. Do not support for now
if dynamic_loss_scale:
if dynamic_loss_args is None:
self.loss_scaler = DynamicLossScaler()
else:
self.loss_scaler = DynamicLossScaler(**dynamic_loss_args)
self.dynamic_loss_scale = True
else:
self.dynamic_loss_scale = False
self.cur_iter = 0
self.mpu = mpu
self.clip_grad = clip_grad
self.overflow = False
self.overflow_checker = CheckOverflow(self.fp16_groups, mpu=self.mpu)
#views the tensor as multiple partitions and returns
#those partitions
def get_data_parallel_partitions(self, tensor):
partitions = []
dp = dist.get_world_size(group=self.dp_process_group)
total_num_elements = tensor.numel()
base_size = total_num_elements // dp
remaining = total_num_elements % dp
start = 0
for id in range(dp):
partition_size = base_size
if id < remaining:
partition_size = partition_size + 1
partitions.append(tensor.narrow(0, start, partition_size))
start = start + partition_size
return partitions
def get_partition_info(self, tensor_list, partition_size, partition_id):
params_in_partition = []
params_not_in_partition = []
start_index = partition_size * partition_id
end_index = partition_size * (partition_id + 1)
current_index = 0
first_offset = 0
for tensor in tensor_list:
tensor_size = tensor.numel()
if (current_index >= start_index and current_index < end_index):
params_in_partition.append(tensor)
elif start_index > current_index and start_index < (current_index +
tensor_size):
params_in_partition.append(tensor)
assert (
first_offset == 0
), "This can happen either zero or only once as this must be the first tensor in the partition"
first_offset = start_index - current_index
else:
params_not_in_partition.append(tensor)
current_index = current_index + tensor_size
return params_in_partition, params_not_in_partition, first_offset
def zero_grad(self, set_grads_to_None=True):
"""
Zero FP16 parameter grads.
"""
# FP32 grad should never exist.
# For speed, set model fp16 grad to None by default
for group in self.fp16_groups:
for p in group:
if set_grads_to_None:
p.grad = None
else:
if p.grad is not None:
p.grad.detach_()
p.grad.zero_()
#creates a flat fused tensor from the tensor list starting at the first_offset
#in the first tensor of the list. If there are not enough elements in the tensor
#list then the flat tensor will be padded with zeros
def get_flat_partition(self,
tensor_list,
first_offset,
partition_size,
dtype=None):
flat_tensor_list = []
current_size = 0
if dtype is None:
dtype = tensor_list[0].dtype
for i, tensor in enumerate(tensor_list):
if tensor.grad is None:
tensor.grad = torch.zeros(tensor.size(),
dtype=tensor.dtype,
device=tensor.device)
tensor = tensor.grad
num_elements = tensor.numel()
tensor_offset = 0
#we need to offset to get to the right element
if i == 0 and first_offset > 0:
tensor_offset = first_offset
num_elements = num_elements - tensor_offset
#we dont need all elements of the tensor
if num_elements > (partition_size - current_size):
num_elements = partition_size - current_size
#we need a narrow view of the tensor based on the tensor offset and number of elements that
#we need from this tensor
if tensor_offset > 0 or num_elements < tensor.numel():
flat_tensor_list.append(tensor.contiguous().view(-1).narrow(
0, int(tensor_offset), int(num_elements)).to(dtype))
else:
flat_tensor_list.append(tensor.to(dtype))
current_size = current_size + num_elements
#this means its the last partition and does not align with the dp boundary. We need to pad before flattening
if current_size < partition_size:
flat_tensor_list.append(
torch.zeros(int(partition_size - current_size),
dtype=dtype,
device=tensor_list[0].device))
return _flatten_dense_tensors(flat_tensor_list)
def free_grad_in_param_list(self, param_list):
for p in param_list:
p.grad = None
def see_memory_usage(self):
print("Memory Allocated ",
torch.cuda.memory_allocated() / (1024 * 1024 * 1024),
"GigaBytes")
print("Max Memory Allocated ",
torch.cuda.max_memory_allocated() / (1024 * 1024 * 1024),
"GigaBytes")
print("Cache Allocated ",
torch.cuda.memory_cached() / (1024 * 1024 * 1024), "GigaBytes")
print("Max cache Allocated ",
torch.cuda.max_memory_cached() / (1024 * 1024 * 1024),
"GigaBytes")
def print_first_n(self, caption, tensor, n=10):
if tensor is not None:
print(
caption,
tensor.data.contiguous().view(-1).narrow(0, 0,
n).cpu().numpy())
else:
print(caption, None)
def step(self, closure=None):
"""
Not supporting closure.
"""
# First compute norm for all group so we know if there is overflow
self.overflow = self.overflow_checker.check()
prev_scale = self.loss_scale
self._update_scale(self.overflow)
if self.overflow:
self.zero_grad()
if self.verbose:
print("[deepspeed] OVERFLOW! Skipping step. Attempted loss "
"scale: {}, reducing to {}".format(
prev_scale, self.loss_scale))
return self.overflow
norm_groups = []
single_partition_grad_groups = []
partition_id = dist.get_rank(group=self.dp_process_group)
for i, group in enumerate(self.fp16_groups):
norm_groups.append(get_grad_norm(group, mpu=self.mpu))
#free gradients for all the parameters that are not updated by this process
self.free_grad_in_param_list(self.params_not_in_partition[i])
#create a flat gradients for parameters updated by this process
single_grad_partition = self.get_flat_partition(
self.params_in_partition[i],
self.first_offset[i],
self.partition_size[i],
dtype=self.single_partition_of_fp32_groups[i].dtype)
self.single_partition_of_fp32_groups[
i].grad = single_grad_partition
#release all the gradient since we have already created a necessary copy in dp_grad_partition
self.free_grad_in_param_list(self.params_in_partition[i])
single_partition_grad_groups.append(single_grad_partition)
self.unscale_and_clip_grads(single_partition_grad_groups, norm_groups)
self.optimizer.step()
#get rid of the fp32 gradients. Not needed anymore
for group in self.single_partition_of_fp32_groups:
group.grad = None
for i in range(len(norm_groups)):
for fp16_partitions, fp32_partition in zip(
self.parallel_partitioned_fp16_groups,
self.single_partition_of_fp32_groups):
fp16_partitions[partition_id].data.copy_(fp32_partition.data)
dp_world_size = dist.get_world_size(group=self.dp_process_group)
#gather the updated weights from everyone
for _, partitioned_params in enumerate(
self.parallel_partitioned_fp16_groups):
if self.all_gather_partitions:
# controllable memory-time tradeoff
num_shards = max(
1, partitioned_params[partition_id].numel() *
dp_world_size // self.allgather_size)
shard_size = partitioned_params[partition_id].numel(
) // num_shards
num_elements = shard_size
for shard_id in range(num_shards + 1):
if shard_id == num_shards:
if shard_size * num_shards >= partitioned_params[
partition_id].numel():
break
else:
num_elements = partitioned_params[
partition_id].numel() - shard_id * shard_size
shard_list = []
for dp_id in range(dp_world_size):
curr_shard = partitioned_params[dp_id].narrow(
0, shard_id * shard_size, num_elements)
shard_list.append(curr_shard)
dist.all_gather(shard_list,
shard_list[partition_id],
group=self.dp_process_group)
else:
#this should require less memory but should be faster
for src, partitioned_param in enumerate(partitioned_params):
global_src = _get_global_rank(self.dp_process_group, src)
dist.broadcast(partitioned_param,
global_src,
group=self.dp_process_group)
# TODO: we probably don't need this? just to be safe
for i in range(len(norm_groups)):
updated_params = _unflatten_dense_tensors(self.fp16_groups_flat[i],
self.fp16_groups[i])
for p, q in zip(self.fp16_groups[i], updated_params):
p.data = q.data
return self.overflow
def unscale_and_clip_grads(self, grad_groups_flat, norm_groups):
total_norm = 0.0
for norm in norm_groups:
total_norm += norm**2.0
total_norm = math.sqrt(total_norm)
# compute combined scale factor for this group
combined_scale = self.loss_scale
if self.clip_grad > 0.:
# norm is in fact norm*scale
clip = ((total_norm / self.loss_scale) + 1e-6) / self.clip_grad
if clip > 1:
combined_scale = clip * self.loss_scale
for grad in grad_groups_flat:
grad.data.mul_(1. / combined_scale)
def backward(self, loss, retain_graph=False):
self.loss_scaler.backward(loss.float(), retain_graph=retain_graph)
def _update_scale(self, has_overflow=False):
self.loss_scaler.update_scale(has_overflow)
# Promote state so it can be retrieved or set via "fp16_optimizer_instance.state"
def _get_state(self):
return self.optimizer.state
def _set_state(self, value):
self.optimizer.state = value
state = property(_get_state, _set_state)
# Promote param_groups so it can be retrieved or set via "fp16_optimizer_instance.param_groups"
# (for example, to adjust the learning rate)
def _get_param_groups(self):
return self.optimizer.param_groups
def _set_param_groups(self, value):
self.optimizer.param_groups = value
param_groups = property(_get_param_groups, _set_param_groups)
# Promote loss scale so it can be retrieved or set via "fp16_optimizer_instance.loss_scale"
def _get_loss_scale(self):
return self.loss_scaler.loss_scale
def _set_loss_scale(self, value):
self.loss_scaler.cur_scale = value
loss_scale = property(_get_loss_scale, _set_loss_scale)
cur_scale = property(_get_loss_scale, _set_loss_scale)
def state_dict(self):
"""
Returns a dict containing the current state of this :class:`FP16_Optimizer` instance.
This dict contains attributes of :class:`FP16_Optimizer`, as well as the state_dict
of the contained Pytorch optimizer.
Example::
checkpoint = {}
checkpoint['model'] = model.state_dict()
checkpoint['optimizer'] = optimizer.state_dict()
torch.save(checkpoint, "saved.pth")
"""
state_dict = {}
state_dict['loss_scaler'] = self.loss_scaler
state_dict['dynamic_loss_scale'] = self.dynamic_loss_scale
state_dict['overflow'] = self.overflow
state_dict['optimizer_state_dict'] = self.optimizer.state_dict()
state_dict[
'single_partition_of_fp32_groups'] = self.single_partition_of_fp32_groups
return state_dict
def load_state_dict(self, state_dict, load_optimizer_states=True):
"""
Loads a state_dict created by an earlier call to state_dict().
If ``fp16_optimizer_instance`` was constructed from some ``init_optimizer``,
whose parameters in turn came from ``model``, it is expected that the user
will call ``model.load_state_dict()`` before
``fp16_optimizer_instance.load_state_dict()`` is called.
Example::
model = torch.nn.Linear(D_in, D_out).cuda().half()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
optimizer = FP16_Optimizer(optimizer, static_loss_scale = 128.0)
...
checkpoint = torch.load("saved.pth")
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
"""
# I think it should actually be ok to reload the optimizer before the model.
self.loss_scaler = state_dict['loss_scaler']
self.dynamic_loss_scale = state_dict['dynamic_loss_scale']
self.overflow = state_dict['overflow']
if load_optimizer_states:
self.optimizer.load_state_dict(state_dict['optimizer_state_dict'])
for current, saved in zip(
self.single_partition_of_fp32_groups,
state_dict['single_partition_of_fp32_groups']):
current.data.copy_(saved.data)