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