# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Dataset help for minddata dataset"""
import math
import os
from mindspore._checkparam import check_bool, check_int
from .. import context
from ._utils import _exec_datagraph, _get_types_and_shapes, _to_tensor, \
_construct_tensor_list, _to_full_shapes, _to_full_tensor
from ..nn.wrap import GetNextSingleOp
from ..parallel._utils import _get_device_num, _get_global_rank, _need_to_full
def _send_data(dataset, epoch_num):
"""Engine dataset to write data to tdt queue."""
if not hasattr(dataset, '__has_sent__'):
exec_dataset = dataset.__TRANSFER_DATASET__
exec_dataset.send(epoch_num)
dataset.__has_sent__ = True
def _send_data_no_flag(dataset, epoch_num):
"""Engine dataset to write data to tdt queue directly."""
exec_dataset = dataset.__TRANSFER_DATASET__
exec_dataset.send(epoch_num)
[docs]class DatasetHelper:
"""
Help function to use the Minddata dataset.
According to different context, change the iter of dataset, to use the same for loop in different context.
Note:
The iter of DatasetHelper will give one epoch data.
Args:
dataset (DataSet): The training dataset iterator.
dataset_sink_mode (bool): If true use GetNext to fetch the data, or else feed the data from host. Default: True.
sink_size (int): Control the amount of data each sink.
If sink_size=-1, sink the complete dataset each epoch.
If sink_size>0, sink sink_size data each epoch. Default: -1.
epoch_num (int): Control the number of epoch data to send.
Examples:
>>> dataset_helper = DatasetHelper(dataset)
>>> for inputs in dataset_helper:
>>> outputs = network(*inputs)
"""
def __init__(self, dataset, dataset_sink_mode=True, sink_size=-1, epoch_num=1):
check_bool(dataset_sink_mode)
check_int(sink_size)
if sink_size < -1 or sink_size == 0:
raise ValueError("The sink_size must be -1 or positive, but got sink_size {}.".format(sink_size))
if dataset_sink_mode:
if context.get_context("enable_ge"):
iterclass = _DatasetIterGE
else:
if context.get_context("device_target") == "Ascend":
iterclass = _DatasetIterMSLoopSink
elif context.get_context("device_target") == "GPU":
ms_role = os.getenv("MS_ROLE")
if ms_role in ("MS_PSERVER", "MS_SCHED"):
iterclass = _DatasetIterPSLite
else:
iterclass = _DatasetIterMS
elif context.get_context("device_target") == "CPU":
raise RuntimeError("Currently dataset sink mode is not supported when the device target is CPU.")
self.iter = iterclass(dataset, sink_size, epoch_num)
else:
iterclass = _DatasetIterNormal
self.iter = iterclass(dataset)
def __iter__(self):
return self.iter.__iter__()
# A temp solution for loop sink. Delete later
[docs] def types_shapes(self):
"""Get the types and shapes from dataset on current config."""
return self.iter.types_shapes()
[docs] def sink_size(self):
"""Get sink_size for every iteration."""
return self.iter.get_sink_size()
[docs] def stop_send(self):
"""Free up resources about data sink."""
self.iter.stop_send()
class _DatasetIter:
"""Base iter for dataset helper"""
def __init__(self, dataset, sink_size, epoch_num):
self.dataset = dataset
self.sink_size = sink_size
self.sink_count = 1
if not hasattr(dataset, '__TRANSFER_DATASET__'):
if hasattr(dataset, '__loop_size__'):
self.sink_size = dataset.__loop_size__
dataset.__TRANSFER_DATASET__ = _exec_datagraph(dataset, self.sink_size)
dataset.__ME_INITED__ = dataset.__TRANSFER_DATASET__.queue_name
if not hasattr(dataset, '__no_send__'):
_send_data(dataset, epoch_num)
else:
_send_data_no_flag(dataset, epoch_num)
self.stop_send = dataset.__TRANSFER_DATASET__.stop_send
self.dataset_types, self.dataset_shapes = _get_types_and_shapes(dataset)
def __iter__(self):
self.index = 0
return self
def __next__(self):
if self.index >= self.sink_count:
raise StopIteration()
self.index += 1
return self.op()
def types_shapes(self):
return self.dataset_types, self.dataset_shapes
def get_sink_count(self, dataset):
sink_count = 1
if hasattr(dataset, '__loop_size__'):
loop_size = dataset.__loop_size__
if loop_size <= dataset.get_dataset_size() and dataset.get_dataset_size() % loop_size != 0:
raise ValueError(f'Dataset size {dataset.get_dataset_size()} and '
f'sink_size {loop_size} are not matched.')
sink_count = math.ceil(dataset.get_dataset_size() / loop_size)
return sink_count
def get_sink_size(self):
"""get sink_size to device"""
sink_size = 1
if hasattr(self.dataset, '__loop_size__'):
sink_size = self.dataset.__loop_size__
else:
if context.get_context("enable_ge") or context.get_context("device_target") == "Ascend":
if self.sink_size > 0:
sink_size = self.sink_size
else:
sink_size = self.dataset.get_dataset_size()
return sink_size
class _DatasetIterGE(_DatasetIter):
"""Iter for GE."""
def __init__(self, dataset, sink_size, epoch_num):
super().__init__(dataset, sink_size, epoch_num)
self.sink_count = self.get_sink_count(dataset)
batch_expand_num = 1
if _need_to_full():
batch_expand_num = _get_device_num()
tensor_list_run = _construct_tensor_list(self.dataset_types, self.dataset_shapes, batch_expand_num)
def op():
return tensor_list_run
self.op = op
class _DatasetIterMSLoopSink(_DatasetIter):
"""Iter for context (device_target=Ascend)"""
def __init__(self, dataset, sink_size, epoch_num):
super().__init__(dataset, sink_size, epoch_num)
self.sink_count = self.get_sink_count(dataset)
ms_role = os.getenv("MS_ROLE")
if ms_role in ("MS_PSERVER", "MS_SCHED"):
self.sink_count = 1
# for self._parallel_mode equal to semi_auto_parallel or auto_parallel, and not using full_batch,
# use a complete tensor to compile, and slice tensor to run. The batch dimension of tensors for
# compile is device_number times the batch dimension of tensors for run. Now only support LoopSink.
if _need_to_full():
device_num = _get_device_num()
self.dataset_shapes = _to_full_shapes(self.dataset_shapes, device_num)
def op():
return tuple()
self.op = op
class _DatasetIterMS(_DatasetIter):
"""Iter for MS(enable_loop_sink=False)."""
def __init__(self, dataset, sink_size, epoch_num):
super().__init__(dataset, sink_size, epoch_num)
if sink_size > 0:
self.sink_count = sink_size
else:
self.sink_count = dataset.get_dataset_size()
queue_name = dataset.__ME_INITED__
self.op = GetNextSingleOp(self.dataset_types, self.dataset_shapes, queue_name)
class _DatasetIterPSLite(_DatasetIter):
"""Iter for context (device_target=GPU) on MS_PSERVER or MS_SCHED"""
def __init__(self, dataset, sink_size, epoch_num):
super().__init__(dataset, sink_size, epoch_num)
self.sink_count = 1
self.sink_size = 1
self.op = None
def op():
return _construct_tensor_list(self.dataset_types, self.dataset_shapes, batch_expand_num=1)
self.op = op
class _DatasetIterNormal:
"""Iter for normal(non sink) mode, feed the data from host."""
def __init__(self, dataset):
self.dataset = dataset
self.device_num = _get_device_num()
self.global_rank = _get_global_rank()
self.iter = self.dataset.create_tuple_iterator()
def __iter__(self):
return self
def __next__(self):
data = self.iter.__next__()
if _need_to_full():
return _to_full_tensor(data, self.device_num, self.global_rank)
return _to_tensor(data)