"""Context of auto parallel"""
import threading
import mindspore.context as context
import mindspore.log as logger
from mindspore.parallel._dp_allreduce_fusion import _set_fusion_strategy_by_idx, _set_fusion_strategy_by_size
from mindspore.parallel._ps_context import _is_role_pserver
from mindspore._c_expression import AutoParallelContext
from mindspore._checkparam import args_type_check, Validator
_MAX_GROUP_NAME_LEN = 127
_DEFAULT_HCCL_FUSION_GROUP_NAME = "hccl_world_groupsum1"
_DEFAULT_NCCL_FUSION_GROUP_NAME = "nccl_world_groupsum1"
class _AutoParallelContext:
"""
_AutoParallelContext is the environment in which operations are executed
Note:
Create a context through instantiating Context object is not recommended.
Should use auto_parallel_context() to get the context since Context is singleton.
"""
_instance = None
_instance_lock = threading.Lock()
def __init__(self):
self._context_handle = AutoParallelContext.get_instance()
self._dataset_strategy_using_str = True
def __new__(cls):
if cls._instance is None:
cls._instance_lock.acquire()
cls._instance = object.__new__(cls)
cls._instance_lock.release()
return cls._instance
def check_context_handle(self):
"""
Check context handle.
Raises:
ValueError: If the context handle is none.
"""
if self._context_handle is None:
raise ValueError("Context handle is none in context!!!")
def set_device_num(self, device_num):
"""
Set device num for auto parallel.
Args:
device_num (int): The device number.
Raises:
ValueError: If the device num is not in [1, 4096].
"""
self.check_context_handle()
if device_num < 1 or device_num > 4096:
raise ValueError("Device num must be in [1, 4096], but got {}".format(device_num))
self._context_handle.set_device_num(device_num)
def get_device_num(self):
"""Get device num."""
self.check_context_handle()
return self._context_handle.get_device_num()
def set_global_rank(self, global_rank):
"""
Set global rank for auto parallel.
Args:
global_rank (int): The rank id of current rank.
Raises:
ValueError: If the global rank is not in [1, 4096].
"""
self.check_context_handle()
if global_rank < 0 or global_rank > 4095:
raise ValueError("Global rank must be in [0, 4095], but got {}".format(global_rank))
self._context_handle.set_global_rank(global_rank)
def get_global_rank(self):
"""Get current rank id."""
self.check_context_handle()
return self._context_handle.get_global_rank()
def set_pipeline_stages(self, stages):
"""Set the stages of the pipeline"""
if isinstance(stages, bool):
raise TypeError("The type of pipeline_stage_num must be int, but got bool.")
if not isinstance(stages, int):
raise TypeError("The type of pipeline_stage_num must be int.")
if stages < 1:
raise ValueError("pipeline_stage_num can't be less than 1.")
backend = context.get_context("device_target")
if backend == "GPU" and stages > 1:
raise RuntimeError("Now GPU don't support pipeline parallel.")
self.check_context_handle()
self._context_handle.set_pipeline_stage_split_num(stages)
def get_pipeline_stages(self):
"""Get the stages of the pipeline"""
self.check_context_handle()
return self._context_handle.get_pipeline_stage_split_num()
def set_gradients_mean(self, gradients_mean):
"""
Set gradients_mean flag.
Note:
If gradients_mean is true, it will insert a div operator after parameter gradients allreduce.
Args:
gradients_mean (bool): The gradients_mean flag.
"""
self.check_context_handle()
self._context_handle.set_gradients_mean(gradients_mean)
def get_gradients_mean(self):
"""Get gradients_mean flag."""
self.check_context_handle()
return self._context_handle.get_gradients_mean()
def set_gradient_fp32_sync(self, gradient_fp32_sync):
"""
Set gradient_fp32_sync.
Note:
If gradient_fp32_sync is true,
it will convert tensor type from fp16 to fp32 before parameter gradients allreduce.
Args:
gradient_fp32_sync (bool): The gradient_fp32_sync flag.
"""
self.check_context_handle()
self._context_handle.set_gradient_fp32_sync(gradient_fp32_sync)
def get_gradient_fp32_sync(self):
"""Get gradient_fp32_sync flag."""
self.check_context_handle()
return self._context_handle.get_gradient_fp32_sync()
def set_loss_repeated_mean(self, loss_repeated_mean):
"""
Set loss_repeated_mean flag.
Note:
If loss_repeated_mean is true,
Distributed automatic differentiation will perform a mean operator
in backward in the case of repeated calculations.
Args:
loss_repeated_mean (bool): The loss_repeated_mean flag.
"""
if not isinstance(loss_repeated_mean, bool):
raise TypeError(f"The type of loss_repeated_mean must be bool, but got {type(loss_repeated_mean)}.")
self.check_context_handle()
self._context_handle.set_loss_repeated_mean(loss_repeated_mean)
def get_loss_repeated_mean(self):
"""Get loss_repeated_mean flag."""
self.check_context_handle()
return self._context_handle.get_loss_repeated_mean()
def set_parallel_mode(self, parallel_mode):
"""
Set parallel mode for auto parallel.
Args:
parallel_mode (str): The parallel mode of auto parallel.
Raises:
ValueError: If parallel mode is not supported.
"""
self.check_context_handle()
ret = self._context_handle.set_parallel_mode(parallel_mode)
if ret is False:
raise ValueError("Parallel mode does not support {}".format(parallel_mode))
def get_parallel_mode(self):
"""Get parallel mode."""
self.check_context_handle()
if _is_role_pserver():
return context.ParallelMode.STAND_ALONE
return self._context_handle.get_parallel_mode()
def set_strategy_search_mode(self, auto_parallel_search_mode):
"""
Set search mode of strategy.
Args:
auto_parallel_search_mode (str): The search mode of strategy.
"""
self.check_context_handle()
ret = self._context_handle.set_strategy_search_mode(auto_parallel_search_mode)
if ret is False:
raise ValueError("Strategy search mode does not support {}".format(auto_parallel_search_mode))
def get_strategy_search_mode(self):
"""Get search mode of strategy."""
self.check_context_handle()
return self._context_handle.get_strategy_search_mode()
def set_parameter_broadcast(self, parameter_broadcast):
"""
Set parameter broadcast.
Args:
parameter_broadcast (bool): Parameter broadcast or not.
"""
self.check_context_handle()
self._context_handle.set_parameter_broadcast(parameter_broadcast)
def get_parameter_broadcast(self):
"""Get parameter broadcast flag."""
self.check_context_handle()
return self._context_handle.get_parameter_broadcast()
def set_strategy_ckpt_load_file(self, strategy_ckpt_load_file):
"""
Set strategy checkpoint load path.
Args:
strategy_ckpt_load_file (str): Path to load parallel strategy checkpoint.
"""
self.check_context_handle()
self._context_handle.set_strategy_ckpt_load_file(strategy_ckpt_load_file)
def get_strategy_ckpt_load_file(self):
"""Get strategy checkpoint load path."""
self.check_context_handle()
return self._context_handle.get_strategy_ckpt_load_file()
def set_full_batch(self, full_batch):
"""
Set whether load full batch on each device.
Args:
full_batch (bool): True if load full batch on each device.
"""
self.check_context_handle()
self._context_handle.set_full_batch(full_batch)
def get_full_batch(self):
"""Get whether load full batch on each device."""
self.check_context_handle()
if _is_role_pserver():
return False
return self._context_handle.get_full_batch()
def set_dataset_strategy(self, dataset_strategy):
"""
Set dataset sharding strategy.
Args:
dataset_strategy (str or tuple(tuple)): The dataset sharding strategy.
"""
self.check_context_handle()
if isinstance(dataset_strategy, str):
if dataset_strategy not in ("full_batch", "data_parallel"):
raise ValueError("The dataset_strategy string should be 'full_batch' or 'data_parallel', "
"otherwise, incoming tuple(tuple) type strategy")
self._context_handle.set_full_batch(dataset_strategy == "full_batch")
self._dataset_strategy_using_str = True
return
if not isinstance(dataset_strategy, tuple):
raise TypeError(f'strategy must be str or tuple type, but got:{type(dataset_strategy)}')
for ele in dataset_strategy:
if not isinstance(ele, tuple):
raise TypeError(f'The element of strategy must be tuple type, but got:{type(ele)}')
for dim in ele:
if not isinstance(dim, int):
raise TypeError(f'The dim of each strategy value must be int type, but got:{type(dim)}')
self._dataset_strategy_using_str = False
self._context_handle.set_dataset_strategy(dataset_strategy)
def get_dataset_strategy(self):
"""Get dataset sharding strategy."""
self.check_context_handle()
if self._dataset_strategy_using_str:
if self._context_handle.get_full_batch():
return "full_batch"
return "data_parallel"
return self._context_handle.get_dataset_strategy()
def set_grad_accumulation_step(self, grad_accumulation_step):
"""
Set grad accumulation step.
Args:
grad_accumulation_step (int): The grad accumulation step.
"""
self.check_context_handle()
Validator.check_positive_int(grad_accumulation_step)
self._context_handle.set_grad_accumulation_step(grad_accumulation_step)
def get_grad_accumulation_step(self):
"""Get grad accumulation step."""
self.check_context_handle()
return self._context_handle.get_grad_accumulation_step()
def set_strategy_ckpt_save_file(self, strategy_ckpt_save_file):
"""
Set strategy checkpoint save path.
Args:
strategy_ckpt_save_file (bool): Path to save parallel strategy checkpoint.
"""
self.check_context_handle()
import os
dir_path = os.path.dirname(strategy_ckpt_save_file)
if dir_path and not os.path.exists(dir_path):
os.makedirs(dir_path)
self._context_handle.set_strategy_ckpt_save_file(strategy_ckpt_save_file)
def get_strategy_ckpt_save_file(self):
"""Get strategy checkpoint save path."""
self.check_context_handle()
return self._context_handle.get_strategy_ckpt_save_file()
def set_group_ckpt_save_file(self, group_ckpt_save_file):
"""Set group checkpoint save path."""
self.check_context_handle()
import os
dir_path = os.path.dirname(group_ckpt_save_file)
if dir_path and not os.path.exists(dir_path):
os.makedirs(dir_path)
self._context_handle.set_group_ckpt_save_file(group_ckpt_save_file)
def get_parameter_broadcast_is_set(self):
"""Get parameter broadcast is set or not."""
self.check_context_handle()
return self._context_handle.get_parameter_broadcast_is_set()
def set_all_reduce_fusion_split_indices(self, indices, group=""):
"""
Set allreduce fusion strategy by parameters indices.
Args:
indices (list): Indices list.
group (str): The communication group of hccl/nccl.
Raises:
TypeError: If type of indices item is not int.
TypeError: If group is not a python str.
"""
self.check_context_handle()
if not indices:
raise ValueError('indices can not be empty')
if isinstance(indices, (list)):
for index in indices:
if not isinstance(index, int) or isinstance(index, bool):
raise TypeError(f"The type of index must be int, but got {type(index)}.")
else:
raise TypeError('indices must be a python list')
if len(set(indices)) != len(indices):
raise ValueError('indices has duplicate elements')
if sorted(indices) != indices:
raise ValueError('elements in indices must be sorted in ascending order')
new_group = self._check_and_default_group(group)
self._context_handle.set_all_reduce_fusion_split_indices(indices, new_group)
if context.get_context("device_target") == "Ascend" and context.get_context("enable_ge"):
_set_fusion_strategy_by_idx(indices)
def get_all_reduce_fusion_split_indices(self, group=""):
"""
Get allreduce fusion split indices.
Args:
group (str): The communication group of hccl/nccl.
Returns:
Return split sizes list according to the group.
Raises:
TypeError: If group is not a python str.
"""
self.check_context_handle()
new_group = self._check_and_default_group(group)
return self._context_handle.get_all_reduce_fusion_split_indices(new_group)
def set_all_reduce_fusion_split_sizes(self, sizes, group=""):
"""
Set allreduce fusion strategy by parameters data sizes.
Args:
sizes (list): Sizes list.
group (str): The communication group of hccl/nccl.
Raises:
TypeError: If type of sizes item is not int.
TypeError: If group is not a python str.
"""
self.check_context_handle()
if isinstance(sizes, (list)):
for size in sizes:
if not isinstance(size, int) or isinstance(size, bool):
raise TypeError(f"The type of size must be int, but got {type(size)}.")
else:
raise TypeError('sizes must be a python list')
new_group = self._check_and_default_group(group)
self._context_handle.set_all_reduce_fusion_split_sizes(sizes, new_group)
if context.get_context("device_target") == "Ascend":
_set_fusion_strategy_by_size(sizes)
def get_all_reduce_fusion_split_sizes(self, group=""):
"""
Get allreduce fusion split sizes.
Args:
group (str): The communication group of hccl/nccl.
Returns:
Return split sizes list according to the group.
Raises:
TypeError: If group is not a python str.
"""
self.check_context_handle()
new_group = self._check_and_default_group(group)
return self._context_handle.get_all_reduce_fusion_split_sizes(new_group)
def set_enable_all_reduce_fusion(self, enable_all_reduce_fusion):
"""
Set enable/disable all reduce fusion.
Args:
enable_all_reduce_fusion (bool): Enable/disable all reduce fusion.
"""
self.check_context_handle()
if not isinstance(enable_all_reduce_fusion, bool):
raise TypeError('enable_all_reduce_fusion is invalid type')
self._context_handle.set_enable_all_reduce_fusion(enable_all_reduce_fusion)
def get_enable_all_reduce_fusion(self):
"""Get all reduce fusion flag."""
self.check_context_handle()
return self._context_handle.get_enable_all_reduce_fusion()
def get_device_num_is_set(self):
"""Get device number is set or not."""
self.check_context_handle()
return self._context_handle.get_device_num_is_set()
def get_global_rank_is_set(self):
"""Get global rank is set or not."""
self.check_context_handle()
return self._context_handle.get_global_rank_is_set()
def set_enable_parallel_optimizer(self, enable_parallel_optimizer):
"""
Set enable/disable parallel optimizer.
Args:
set_enable_parallel_optimizer (bool): Enable/disable parallel optimizer.
"""
self.check_context_handle()
if not isinstance(enable_parallel_optimizer, bool):
raise TypeError('enable_parallel_optimizer is invalid type')
self._context_handle.set_enable_parallel_optimizer(enable_parallel_optimizer)
def get_enable_parallel_optimizer(self):
"""Get parallel optimizer flag."""
self.check_context_handle()
return self._context_handle.get_enable_parallel_optimizer()
def set_sharding_propagation(self, sharding_propagation):
"""
Set the value of sharding strategy propagation in AUTO_PARALLEL mode. If True, the strategy-configured operators
will propagate the strategies to other operators with minimum redistribution cost; otherwise, the algorithm
will search the desired strategies.
Default: False.
Args:
sharding_propagation (bool): Enable/disable strategy propagation.
"""
self.check_context_handle()
if not isinstance(sharding_propagation, bool):
raise TypeError("'sharding_propagation' is an invalid type.")
self._context_handle.set_sharding_propagation(sharding_propagation)
def get_sharding_propagation(self):
"""Get the value of sharding strategy propagation."""
self.check_context_handle()
return self._context_handle.get_sharding_propagation()
def set_enable_alltoall(self, enable_a2a):
"""
Set the value of enabling AllToAll. If False, AllGather and Split are used to circumvent AllToAll.
Default: False.
Args:
enable_a2a (bool): Enable/disable AllToAll.
"""
self.check_context_handle()
if not isinstance(enable_a2a, bool):
raise TypeError("'enable_a2a' is an invalid type.")
self._context_handle.set_enable_alltoall(enable_a2a)
def get_enable_alltoall(self):
"""Get the value of enabling AllToAll."""
self.check_context_handle()
return self._context_handle.get_enable_alltoall()
def set_communi_parallel_mode(self, communi_parallel_mode):
"""
Set communication parallel mode.
Args:
communi_parallel_mode (str): The communication parallel mode.
Raises:
ValueError: If parallel mode is not supported.
"""
if not isinstance(communi_parallel_mode, str):
raise TypeError(f"The type of communi_parallel_mode must be str, \
but got {type(communi_parallel_mode)}.")
self.check_context_handle()
ret = self._context_handle.set_communi_parallel_mode(communi_parallel_mode)
if ret is False:
raise ValueError("Communication parallel mode does not support {}".format(communi_parallel_mode))
def get_communi_parallel_mode(self):
"""Get communication parallel mode."""
self.check_context_handle()
return self._context_handle.get_communi_parallel_mode()
def set_optimizer_weight_shard_size(self, optimizer_weight_shard_size):
"""
Set optimizer_weight_shard_size.
Args:
optimizer_weight_shard_size (int): Opt shard group size when not globally use parallel
optimizer across devices.
"""
self.check_context_handle()
if not isinstance(optimizer_weight_shard_size, int) or isinstance(optimizer_weight_shard_size, bool):
raise TypeError(f"The type of optimizer_weight_shard_size must be int, \
but got {type(optimizer_weight_shard_size)}.")
if optimizer_weight_shard_size <= 1:
logger.warning("The setting 'optimizer_weight_shard_size' is invalid. "
"Please use the integer larger than 1.")
return
self._context_handle.set_optimizer_weight_shard_size(optimizer_weight_shard_size)
def get_optimizer_weight_shard_size(self):
"""Get optimizer_weight_shard_size."""
self.check_context_handle()
return self._context_handle.get_optimizer_weight_shard_size()
def set_optimizer_weight_shard_aggregated_save(self, optimizer_weight_shard_aggregated_save):
"""
Set optimizer_weight_shard_aggregated_save.
Args:
optimizer_weight_shard_aggregated_save (bool): Whether to integrated save weight shard when
enable parallel optimizer.
"""
self.check_context_handle()
if not isinstance(optimizer_weight_shard_aggregated_save, bool):
raise TypeError('optimizer_weight_shard_aggregated_save is invalid type')
self._context_handle.set_optimizer_weight_shard_aggregated_save(optimizer_weight_shard_aggregated_save)
def get_optimizer_weight_shard_aggregated_save(self):
"""Get optimizer_weight_shard_size."""
self.check_context_handle()
return self._context_handle.get_optimizer_weight_shard_aggregated_save()
def reset(self):
"""Reset all settings."""
self.check_context_handle()
self._context_handle.reset()
def _check_and_default_group(self, group):
"""Validate the given group, if group is empty, returns a default fusion group"""
if isinstance(group, (str)):
group_len = len(group)
if group_len > _MAX_GROUP_NAME_LEN:
raise ValueError('Group name len is out of range {_MAX_GROUP_NAME_LEN}')
else:
raise TypeError('Group must be a python str')
if group == "":
if context.get_context("device_target") == "Ascend":
group = _DEFAULT_HCCL_FUSION_GROUP_NAME
else:
group = _DEFAULT_NCCL_FUSION_GROUP_NAME
return group
_auto_parallel_context = None
def auto_parallel_context():
"""
Get the global _auto_parallel_context, if it is not created, create a new one.
Returns:
_AutoParallelContext, the global auto parallel context.
"""
global _auto_parallel_context
if _auto_parallel_context is None:
_auto_parallel_context = _AutoParallelContext()
return _auto_parallel_context
_set_auto_parallel_context_func_map = {
"device_num": auto_parallel_context().set_device_num,
"global_rank": auto_parallel_context().set_global_rank,
"gradients_mean": auto_parallel_context().set_gradients_mean,
"gradient_fp32_sync": auto_parallel_context().set_gradient_fp32_sync,
"loss_repeated_mean": auto_parallel_context().set_loss_repeated_mean,
"pipeline_stages": auto_parallel_context().set_pipeline_stages,
"parallel_mode": auto_parallel_context().set_parallel_mode,
"auto_parallel_search_mode": auto_parallel_context().set_strategy_search_mode,
"parameter_broadcast": auto_parallel_context().set_parameter_broadcast,
"strategy_ckpt_load_file": auto_parallel_context().set_strategy_ckpt_load_file,
"strategy_ckpt_save_file": auto_parallel_context().set_strategy_ckpt_save_file,
"group_ckpt_save_file": auto_parallel_context().set_group_ckpt_save_file,
"full_batch": auto_parallel_context().set_full_batch,
"dataset_strategy": auto_parallel_context().set_dataset_strategy,
"enable_parallel_optimizer": auto_parallel_context().set_enable_parallel_optimizer,
"grad_accumulation_step": auto_parallel_context().set_grad_accumulation_step,
"all_reduce_fusion_config": auto_parallel_context().set_all_reduce_fusion_split_indices,
"communi_parallel_mode": auto_parallel_context().set_communi_parallel_mode,
"optimizer_weight_shard_size": auto_parallel_context().set_optimizer_weight_shard_size,
"optimizer_weight_shard_aggregated_save": auto_parallel_context().set_optimizer_weight_shard_aggregated_save,
"sharding_propagation": auto_parallel_context().set_sharding_propagation,
"enable_alltoall": auto_parallel_context().set_enable_alltoall}
_get_auto_parallel_context_func_map = {
"device_num": auto_parallel_context().get_device_num,
"global_rank": auto_parallel_context().get_global_rank,
"gradients_mean": auto_parallel_context().get_gradients_mean,
"gradient_fp32_sync": auto_parallel_context().get_gradient_fp32_sync,
"loss_repeated_mean": auto_parallel_context().get_loss_repeated_mean,
"pipeline_stages": auto_parallel_context().get_pipeline_stages,
"parallel_mode": auto_parallel_context().get_parallel_mode,
"auto_parallel_search_mode": auto_parallel_context().get_strategy_search_mode,
"parameter_broadcast": auto_parallel_context().get_parameter_broadcast,
"strategy_ckpt_load_file": auto_parallel_context().get_strategy_ckpt_load_file,
"strategy_ckpt_save_file": auto_parallel_context().get_strategy_ckpt_save_file,
"full_batch": auto_parallel_context().get_full_batch,
"dataset_strategy": auto_parallel_context().get_dataset_strategy,
"enable_parallel_optimizer": auto_parallel_context().get_enable_parallel_optimizer,
"grad_accumulation_step": auto_parallel_context().get_grad_accumulation_step,
"all_reduce_fusion_config": auto_parallel_context().get_all_reduce_fusion_split_indices,
"communi_parallel_mode": auto_parallel_context().get_communi_parallel_mode,
"optimizer_weight_shard_size": auto_parallel_context().get_optimizer_weight_shard_size,
"optimizer_weight_shard_aggregated_save": auto_parallel_context().get_optimizer_weight_shard_aggregated_save,
"sharding_propagation": auto_parallel_context().get_sharding_propagation,
"enable_alltoall": auto_parallel_context().get_enable_alltoall}
@args_type_check(device_num=int, global_rank=int, gradients_mean=bool, gradient_fp32_sync=bool,
loss_repeated_mean=bool, parallel_mode=str, auto_parallel_search_mode=str,
parameter_broadcast=bool, strategy_ckpt_load_file=str,
strategy_ckpt_save_file=str, full_batch=bool, enable_parallel_optimizer=bool,
grad_accumulation_step=int, all_reduce_fusion_config=list, group_ckpt_save_file=str,
communi_parallel_mode=str, optimizer_weight_shard_size=int,
optimizer_weight_shard_aggregated_save=bool,
sharding_propagation=bool, enable_alltoall=bool)
def _set_auto_parallel_context(**kwargs):
"""
Set auto parallel context.
Note:
Attribute name is required for setting attributes.
Args:
device_num (int): Available device number, the value must be in [1, 4096]. Default: 1.
global_rank (int): Global rank id, the value must be in [0, 4095]. Default: 0.
gradients_mean (bool): Whether to perform mean operator after all-reduce of mirror. Default: False.
loss_repeated_mean (bool): Whether to perform mean operator in backward in the case of repeated
calculations. Default: True.
gradient_fp32_sync (bool): Gradients allreduce by fp32 even though gradients is fp16 if this flag is True.
Default: True.
parallel_mode (str): There are five kinds of parallel modes, "stand_alone", "data_parallel",
"hybrid_parallel", "semi_auto_parallel" and "auto_parallel". Default: "stand_alone".
- stand_alone: Only one processor working.
- data_parallel: Distributing the data across different processors.
- hybrid_parallel: Achieving data parallelism and model parallelism manually.
- semi_auto_parallel: Achieving data parallelism and model parallelism by
setting parallel strategies.
- auto_parallel: Achieving parallelism automatically.
auto_parallel_search_mode (str): There are two kinds of search modes, "recursive_programming"
and "dynamic_programming". Default: "dynamic_programming".
- recursive_programming: Recursive programming search mode.
- dynamic_programming: Dynamic programming search mode.
parameter_broadcast (bool): Indicating whether to broadcast parameters before training.
"stand_alone", "semi_auto_parallel" and "auto_parallel" do not support parameter
broadcast. Default: False.
strategy_ckpt_load_file (str): The path to load parallel strategy checkpoint. Default: ''
strategy_ckpt_save_file (str): The path to save parallel strategy checkpoint. Default: ''
group_ckpt_save_file (str): The path to save parallel group checkpoint. Default: ''
full_batch (bool): Whether to load the whole batch on each device. Default: False.
dataset_strategy Union[str, tuple]: Dataset sharding strategy. Default: "data_parallel".
enable_parallel_optimizer (bool): Enable using optimizer segmentation or not. Default: False.
all_reduce_fusion_config (list): Set allreduce fusion strategy by parameters indices.
pipeline_stages (int): Set the stage information for pipeline parallel. This indicates how
the devices are distributed alone the pipeline. The total devices will be divided into
'pipeline_stags' stages. This currently could only be used when
parallel mode semi_auto_parallel is enabled. Default: 0
communi_parallel_mode (str): There are tree kinds of communication parallel modes, "all_group_parallel",
"same_server_group_parallel" and "no_group_parallel". Default: "all_group_parallel".
- all_group_parallel: All communication groups are in parallel.
- same_server_group_parallel: Only the communication groups within the same server are parallel.
- no_group_parallel: All communication groups are not parallel.
optimizer_weight_shard_size (int): Set optimizer shard group size when not fully use parallel optimizer.
It should be larger than one and less than or equal with the data parallel size.
Default: -1, which means fully use parallel optimizer in data parallel dimension.
optimizer_weight_shard_aggregated_save (bool): Whether to integrated save weight shard when enable parallel
optimizer. Default: False.
sharding_propagation (bool): Set the value of sharding strategy propagation in AUTO_PARALLEL mode. If True,
the strategy-configured operators will propagate the strategies to other
operators with minimum redistribution cost; otherwise, the algorithm will
search the desired strategies. Default: False.
enable_alltoall (bool): Set the value of enabling AllToAll. If False, AllGather and Split are used to
circumvent AllToAll. Default: False.
Raises:
ValueError: If input key is not attribute in auto parallel context.
"""
for key, value in kwargs.items():
if key not in _set_auto_parallel_context_func_map:
raise ValueError("Set context keyword %s is not recognized!" % key)
set_func = _set_auto_parallel_context_func_map[key]
set_func(value)
def _get_auto_parallel_context(attr_key):
"""
Get auto parallel context attribute value according to the key.
Args:
attr_key (str): The key of the attribute.
Returns:
Return attribute value according to the key.
Raises:
ValueError: If input key is not attribute in auto parallel context.
"""
if attr_key not in _get_auto_parallel_context_func_map:
raise ValueError("Get context keyword %s is not recognized!" % attr_key)
get_func = _get_auto_parallel_context_func_map[attr_key]
return get_func()
def _reset_auto_parallel_context():
"""
Reset auto parallel context attributes to the default values:
- device_num: 1.
- global_rank: 0.
- gradients_mean: False.
- gradient_fp32_sync: True.
- parallel_mode: "stand_alone".
- parameter_broadcast: False.
- strategy_ckpt_load_file: ""
- strategy_ckpt_save_file: ""
- enable_parallel_optimizer: False
- auto_parallel_search_mode: dynamic_programming
- pipeline_stages: 0
"""
auto_parallel_context().reset()