def _init_device_info():
"""
INTERNAL USE ONLY!
As rank_id need to pass into deep layer for numa and device_queue.
One process work with only one rank_id, In standalone scenario,
rank_id may come from env 'CUDA_VISIBLE_DEVICES', For distribute
scenario, rank_id come from _get_global_rank()
"""
from mindspore import context
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.parallel._utils import _get_global_rank, _get_device_num
if context.get_context("device_target") == "GPU":
rank_id = _get_global_rank()
parallel_mode = auto_parallel_context().get_parallel_mode()
if parallel_mode == "stand_alone":
cuda_device_info = os.getenv("CUDA_VISIBLE_DEVICES")
if cuda_device_info:
cuda_id = int(cuda_device_info.split(",")[0].strip())
if cuda_id != rank_id:
rank_id = cuda_id
_config.set_rank_id(rank_id)
elif context.get_context("device_target") == "Ascend":
rank_id = _get_global_rank()
device_num = _get_device_num()
# Ascend only support multi-process scenario
if device_num > 1:
_config.set_rank_id(rank_id)
def _init_device_info():
"""
INTERNAL USE ONLY!
As rank_id need to pass into deep layer for numa and device_queue.
One process work with only one rank_id, In standalone scenario,
rank_id may come from env 'CUDA_VISIBLE_DEVICES', For distribute
scenario, rank_id come from _get_global_rank()
"""
from mindspore import context
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.parallel._utils import _get_global_rank
if context.get_context("device_target") == "GPU":
rank_id = _get_global_rank()
parallel_mode = auto_parallel_context().get_parallel_mode()
if parallel_mode == "stand_alone":
cuda_device_info = os.getenv("CUDA_VISIBLE_DEVICES")
if cuda_device_info:
cuda_id = int(cuda_device_info.split(",")[0].strip())
if cuda_id != rank_id:
rank_id = cuda_id
_config.set_rank_id(rank_id)
elif context.get_context("device_target") == "Ascend":
# Ascend is a special scenario, we'd better get rank info from env
env_rank_size = os.getenv("RANK_SIZE", None)
env_rank_id = os.getenv("RANK_ID", None)
if env_rank_size and env_rank_id:
# Ascend only support multi-process scenario
rank_size = int(env_rank_size.strip())
rank_id = int(env_rank_id.strip())
if rank_size > 1:
_config.set_rank_id(rank_id)
def __init__(self,
network,
loss_fn=None,
optimizer=None,
metrics=None,
eval_network=None,
eval_indexes=None,
amp_level="O0",
frequency=278,
stop_epoch=100,
**kwargs):
self._network = network
self._loss_fn = loss_fn
self._optimizer = optimizer
self._loss_scale_manager = None
self._loss_scale_manager_set = False
self._keep_bn_fp32 = True
self._check_kwargs(kwargs)
self._amp_level = amp_level
self._process_amp_args(kwargs)
self._parallel_mode = _get_parallel_mode()
self._device_number = _get_device_num()
self._global_rank = _get_global_rank()
self._parameter_broadcast = _get_parameter_broadcast()
self._frequency = frequency
self._stop_epoch = stop_epoch
self._train_network = self._build_train_network()
self._build_eval_network(metrics, eval_network, eval_indexes)
self._build_predict_network()
def _init_device_info():
"""
INTERNAL USE ONLY!
As rank_id need to pass into deep layer for numa and device_queue.
One process work with only one rank_id, In standalone scenario,
rank_id may come from env 'CUDA_VISIBLE_DEVICES', For distribute
scenario, rank_id come from _get_global_rank()
"""
from mindspore import context
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.parallel._utils import _get_global_rank
if context.get_context("device_target") == "GPU":
rank_id = _get_global_rank()
parallel_mode = auto_parallel_context().get_parallel_mode()
if parallel_mode == "stand_alone":
rank_id = context.get_context("device_id")
_config.set_rank_id(rank_id)
elif context.get_context("device_target") == "Ascend":
# Ascend is a special scenario, we'd better get rank info from env
env_rank_size = os.getenv("RANK_SIZE", None)
env_rank_id = os.getenv("RANK_ID", None)
if env_rank_size and env_rank_id:
# Ascend only support multi-process scenario
rank_size = int(env_rank_size.strip())
rank_id = int(env_rank_id.strip())
if rank_size > 1:
_config.set_rank_id(rank_id)
# Now single process under ascend mode doesn't support numa bind for performance consideration.
if _config.get_numa_enable() is True and rank_size == 1:
raise ValueError(
"single process under Ascend mode doesn't support numa bind for "
"performance consideration.")
def _init_device_info():
"""
INTERNAL USE ONLY!
As rank_id need to pass into deep layer for numa and device_queue.
One process work with only one rank_id, In standalone scenario,
rank_id may come from env 'CUDA_VISIBLE_DEVICES', For distribute
scenario, rank_id come from _get_global_rank()
"""
from mindspore import context
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.parallel._utils import _get_global_rank
numa_enable = False
numa_enable_env = os.getenv("DATASET_ENABLE_NUMA", None)
if numa_enable_env and numa_enable_env.strip() == 'True':
numa_enable = True
if context.get_context("device_target") == "GPU":
rank_id = _get_global_rank()
parallel_mode = auto_parallel_context().get_parallel_mode()
if parallel_mode == "stand_alone":
rank_id = context.get_context("device_id")
if numa_enable:
_config.set_numa_enable(True)
_config.set_rank_id(rank_id)
elif context.get_context("device_target") == "Ascend":
# Ascend is a special scenario, we'd better get rank info from env
env_rank_size = os.getenv("RANK_SIZE", None)
env_rank_id = os.getenv("RANK_ID", None)
if env_rank_size and env_rank_id:
# Ascend only support multi-process scenario
rank_size = int(env_rank_size.strip())
rank_id = int(env_rank_id.strip())
if rank_size > 1:
if numa_enable:
_config.set_numa_enable(True)
_config.set_rank_id(rank_id)
def _use_parallel_optimizer(self):
"""Indicates whether to use automatic parallelism."""
if context.get_auto_parallel_context("enable_parallel_optimizer"):
if _get_parallel_mode() == ParallelMode.DATA_PARALLEL and context.get_context("device_target") == "Ascend":
self.use_parallel = True
elif _get_parallel_mode() == ParallelMode.DATA_PARALLEL \
and context.get_context("device_target") != "Ascend":
raise RuntimeError("Parallel optimizer only supports Ascend in data parallel mode.")
elif _get_parallel_mode() in (ParallelMode.STAND_ALONE, ParallelMode.HYBRID_PARALLEL):
raise RuntimeError("Parallel optimizer is not supported in {}.".format(_get_parallel_mode()))
else:
self.use_parallel = False
else:
self.use_parallel = False
if self.use_parallel:
if self.cls_name not in ["Lamb", "AdamWeightDecay"]:
raise RuntimeError("Parallel optimizer does not support optimizer {}".format(self.cls_name))
self.dev_num = _get_device_num()
if self.dev_num > self.param_length:
raise RuntimeError("Parallel optimizer can not be applied when the number of parameters {} is"
" less than the number of devices {}".format(self.param_length, self.dev_num))
self.param_rank = self._get_parameter_group_id()
self.optim_filter = tuple(map(lambda x: x == _get_global_rank(), self.param_rank))
self.param_names = []
for param in self.parameters:
self.param_names.append(param.name)
else:
self.optim_filter = (True,) * self.param_length
def __init__(self, dataset, first_order_order):
self.dataset = dataset
self.device_num = _get_device_num()
self.global_rank = _get_global_rank()
self.repeat_count = dataset.get_repeat_count()
self.repeat_ind = 0
self.loop_count = dataset.get_dataset_size()
self.ind = 0
parallel_mode = context.get_auto_parallel_context("parallel_mode")
self.need_to_full = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL,
ParallelMode.AUTO_PARALLEL)
def __init__(self,
learning_rate,
parameters,
weight_decay=0.0,
loss_scale=1.0):
super(Optimizer, self).__init__(auto_prefix=False)
if parameters is not None and not isinstance(parameters, list):
parameters = list(parameters)
if not parameters:
raise ValueError("Optimizer got an empty parameter list.")
if not isinstance(parameters[0], (dict, Parameter)):
raise TypeError(
"Only a list of Parameter or dict can be supported.")
if isinstance(loss_scale, int):
loss_scale = float(loss_scale)
validator.check_value_type("loss_scale", loss_scale, [float],
self.cls_name)
validator.check_positive_float(loss_scale, "loss_scale", self.cls_name)
self.loss_scale = loss_scale
weight_decay = self._preprocess_weight_decay(weight_decay)
self._unique = True
self._target = context.get_context("device_target")
self.dynamic_lr = False
self.assignadd = None
self.global_step = None
self.is_group = False
self.is_group_lr = False
self.is_group_params_ordered = False
learning_rate = self._preprocess_single_lr(learning_rate)
if isinstance(parameters[0], dict):
self.is_group = True
self.group_params = []
self.group_lr = []
self.group_weight_decay = []
self._init_group_params(parameters, learning_rate, weight_decay)
# The final value of dynamic_lr can be determined after the process of parse_single_lr and init_group_params
if self.dynamic_lr:
self.assignadd = P.AssignAdd()
self.global_step = Parameter(initializer(0, [1], mindspore.int32),
name='global_step')
if self.is_group_lr:
if self.dynamic_lr:
self.learning_rate = CellList(self.group_lr)
else:
self.learning_rate = ParameterTuple(self.group_lr)
else:
self.learning_rate = self._build_single_lr(learning_rate,
'learning_rate')
if self.is_group:
self.parameters = ParameterTuple(self.group_params)
self.weight_decay = tuple(self.group_weight_decay)
self.weight_decay_tensor_tuple = tuple(
Tensor(x, mstype.float32) for x in self.group_weight_decay)
decay_filter = lambda x: x > 0
self.decay_flags = tuple(
decay_filter(x) for x in self.weight_decay)
self.exec_weight_decay = any(self.decay_flags)
else:
self.parameters = ParameterTuple(parameters)
self.weight_decay = weight_decay * loss_scale
self.weight_decay_tensor = Tensor(self.weight_decay,
mstype.float32)
decay_filter = lambda x: 'beta' not in x.name and 'gamma' not in x.name
self.decay_flags = tuple(decay_filter(x) for x in self.parameters)
self.exec_weight_decay = self.weight_decay > 0
# when a parameter has been unique, there is no need do another unique in optimizer.
for param in self.parameters:
if param.unique:
self._unique = False
break
ps_filter = lambda x: x.is_param_ps
self.ps_parameters = tuple(ps_filter(x) for x in self.parameters)
ps_cache_filter = lambda x: x.cache_enable
self.cache_enable = tuple(ps_cache_filter(x) for x in self.parameters)
self.reciprocal_scale = Tensor(1.0 / loss_scale, mstype.float32)
self.need_scale = loss_scale != 1.0
self.global_step_increase_tensor = Tensor(1, mstype.int32)
self.param_length = len(self.parameters)
self.map_ = C.Map()
if context.get_auto_parallel_context("enable_parallel_optimizer"):
if _get_parallel_mode(
) == ParallelMode.DATA_PARALLEL and context.get_context(
"device_target") == "Ascend":
self.use_parallel = True
elif _get_parallel_mode() == ParallelMode.DATA_PARALLEL \
and context.get_context("device_target") != "Ascend":
raise RuntimeError(
"Parallel optimizer only supports Ascend in data parallel mode."
)
elif _get_parallel_mode() in (ParallelMode.STAND_ALONE,
ParallelMode.HYBRID_PARALLEL):
raise RuntimeError(
"Parallel optimizer is not supported in {}.".format(
_get_parallel_mode()))
else:
self.use_parallel = False
else:
self.use_parallel = False
if self.use_parallel:
if self.cls_name not in ["Lamb", "AdamWeightDecay"]:
raise RuntimeError(
"Parallel optimizer does not support optimizer {}".format(
self.cls_name))
self.dev_num = _get_device_num()
if self.dev_num > self.param_length:
raise RuntimeError(
"Parallel optimizer can not be applied when the number of parameters {} is"
" less than the number of devices {}".format(
self.param_length, self.dev_num))
self.param_rank = self._get_parameter_group_id()
self.optim_filter = tuple(
map(lambda x: x == _get_global_rank(), self.param_rank))
self.param_names = []
for param in self.parameters:
self.param_names.append(param.name)
else:
self.optim_filter = (True, ) * self.param_length
def __init__(self, learning_rate, parameters, weight_decay=0.0, loss_scale=1.0):
super(Optimizer, self).__init__(auto_prefix=False)
if parameters is not None and not isinstance(parameters, list):
parameters = list(parameters)
if not parameters:
raise ValueError("Optimizer got an empty parameter list.")
if not isinstance(parameters[0], (dict, Parameter)):
raise TypeError("Only a list of Parameter or dict can be supported.")
if isinstance(loss_scale, int):
loss_scale = float(loss_scale)
validator.check_value_type("loss_scale", loss_scale, [float], self.cls_name)
validator.check_number_range("loss_scale", loss_scale, 0.0, float("inf"), Rel.INC_NEITHER, self.cls_name)
self.loss_scale = loss_scale
weight_decay = self._preprocess_weight_decay(weight_decay)
self.dynamic_lr = False
self.assignadd = None
self.global_step = None
self.is_group = False
self.is_group_lr = False
self.is_group_params_ordered = False
learning_rate = self._preprocess_single_lr(learning_rate)
if isinstance(parameters[0], dict):
self.is_group = True
self.group_params = []
self.group_lr = []
self.group_weight_decay = []
self._init_group_params(parameters, learning_rate, weight_decay)
# The final value of dynamic_lr can be determined after the process of parse_single_lr and init_group_params
if self.dynamic_lr:
self.assignadd = P.AssignAdd()
self.global_step = Parameter(initializer(0, [1], mindspore.int32), name='global_step')
if self.is_group_lr:
if self.dynamic_lr:
self.learning_rate = CellList(self.group_lr)
else:
self.learning_rate = ParameterTuple(self.group_lr)
else:
self.learning_rate = self._build_single_lr(learning_rate, 'learning_rate')
if self.is_group:
self.parameters = ParameterTuple(self.group_params)
self.weight_decay = tuple(self.group_weight_decay)
decay_filter = lambda x: x > 0
self.decay_flags = tuple(decay_filter(x) for x in self.weight_decay)
self.exec_weight_decay = any(self.decay_flags)
else:
self.parameters = ParameterTuple(parameters)
self.weight_decay = weight_decay * loss_scale
decay_filter = lambda x: 'beta' not in x.name and 'gamma' not in x.name
self.decay_flags = tuple(decay_filter(x) for x in self.parameters)
self.exec_weight_decay = self.weight_decay > 0
ps_filter = lambda x: x.is_param_ps
self.ps_parameters = tuple(ps_filter(x) for x in self.parameters)
self.reciprocal_scale = 1.0 / loss_scale
self.param_length = len(self.parameters)
self.map_ = C.Map()
use_parallel = context.get_auto_parallel_context("enable_parallel_optimizer")
self.use_parallel = use_parallel
if use_parallel:
if self.cls_name not in ["Lamb", "AdamWeightDecay"]:
raise RuntimeError("Optimizer segmentation does not support optimizer {}".format(self.cls_name))
if _get_parallel_mode() != ParallelMode.DATA_PARALLEL:
raise RuntimeError("Optimizer segmentation does not support parallel mode {}".format
(_get_parallel_mode()))
self.dev_num = _get_device_num()
if self.dev_num > self.param_length:
raise RuntimeError("Optimizer segmentation can not be applied when the number of parameters {} is"
" less than the number of devices {}".format(self.param_length, self.dev_num))
self.param_rank = self._get_parameter_group_id()
self.optim_filter = tuple(map(lambda x: x == _get_global_rank(), self.param_rank))
self.param_names = []
for param in self.parameters:
self.param_names.append(param.name)
else:
self.optim_filter = (True,) * self.param_length