# Copyright 2022 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.
# ============================================================================
"""Data sink help for minddata dataset"""
from functools import wraps
import mindspore.ops as ops
from mindspore import context
from mindspore.common.dtype import pytype_to_dtype
from mindspore.common.api import jit
from mindspore.train._utils import _exec_datagraph, _get_types_and_shapes
from mindspore.train.dataset_helper import _has_dynamic_shape, _check_inputs
import mindspore.dataset as ds
from mindspore._c_expression import _set_dataset_mode_config
from mindspore.parallel._utils import _get_device_num, _need_to_full, _to_full_shapes, _get_pipeline_stages
from mindspore import _checkparam as Validator
def _init_sink_dataset(dataset, sink_size, input_signature, create_info):
"""
Initialize data sinking
"""
if hasattr(dataset, '__transfer_dataset__'):
raise ValueError(f"The dataset has been used with network.")
dataset_size = dataset.get_dataset_size()
dataset_types, dataset_shapes = _get_types_and_shapes(dataset)
dynamic_shape = _has_dynamic_shape(dataset_shapes) or ds.config.get_dynamic_shape()
# create transfer_dataset
is_info_queue = (create_info and sink_size == 1 and dataset_size != 1 and
input_signature is None and not dynamic_shape and
context.get_context('device_target') == 'Ascend' and not context.get_context("enable_ge"))
transfer_dataset = _exec_datagraph(dataset, sink_size, create_data_info_queue=is_info_queue)
dataset.__transfer_dataset__ = transfer_dataset
# send data
transfer_dataset.send(-1)
# create GetNext op
if input_signature is not None:
_check_inputs(input_signature, dataset_shapes, dataset_types)
queue_name = transfer_dataset.queue_name
if _need_to_full() and context.get_context('mode') == context.GRAPH_MODE:
device_num = _get_device_num() // _get_pipeline_stages()
dataset_shapes = _to_full_shapes(dataset_shapes, device_num)
next_op = ops.GetNext(dataset_types, dataset_shapes, len(dataset_types), queue_name)
_set_dataset_mode_config('sink')
dataset.__transfer_dataset__ = transfer_dataset
return next_op, is_info_queue
class _DataSinkAux:
@staticmethod
def __deepcopy__(memodict):
return
def _get_next_op(dataset, ori_next_op, is_info_queue):
"""
get the next operation.
"""
if not is_info_queue:
return ori_next_op, ''
if not hasattr(dataset, '__sink_aux__'):
dataset.__sink_aux__ = _DataSinkAux()
dataset.__sink_aux__.next_ops = {}
dataset.__sink_aux__.sink_funcs = {}
queue_name = dataset.__transfer_dataset__.queue_name
dataset_types, dataset_shapes = dataset.__transfer_dataset__.get_data_info()
dataset_types = [pytype_to_dtype(x) for x in dataset_types]
key = str(dataset_types) + str(dataset_shapes)
if key in dataset.__sink_aux__.next_ops:
next_op = dataset.__sink_aux__.next_ops[key]
else:
if _need_to_full() and context.get_context('mode') == context.GRAPH_MODE:
device_num = _get_device_num() // _get_pipeline_stages()
dataset_shapes = _to_full_shapes(dataset_shapes, device_num)
next_op = ops.GetNext(dataset_types, dataset_shapes, len(dataset_types), queue_name)
return next_op, (key, dataset_shapes, dataset_types)
def _get_sink_fun(sink_fun, key_info, is_info_queue, dataset, jit_config):
"""
get the sink function.
"""
if not is_info_queue:
if not hasattr(dataset, '__sink_fun__'):
if jit_config is None:
dst_sink_fun = sink_fun
else:
dst_sink_fun = jit(sink_fun, jit_config=jit_config)
dataset.__sink_fun__ = dst_sink_fun
return dataset.__sink_fun__
key = key_info[0]
if key in dataset.__sink_aux__.sink_funcs:
dst_sink_fun = dataset.__sink_aux__.sink_funcs[key]
else:
if jit_config is None:
dst_sink_fun = sink_fun
else:
dst_sink_fun = jit(sink_fun, jit_config=jit_config)
dataset.__sink_aux__.sink_funcs[key] = dst_sink_fun
return dst_sink_fun
[文档]def data_sink(fn, dataset, sink_size=1, jit_config=None, input_signature=None):
"""
A wrapper function to generate a function for the input function.
Note:
When using data sinking, the dataset will automatically cycle. At this time,
only the total number of training steps (total_step) and the number of steps for each sinking (sink_size)
need to be considered. When switching from training by rounds (epochs) to data sinking,
the formula is as follows:
total_step = epochs * dataset_size
train_sink_step = total_step / sink_size
After transforming from `mindspore.data_sink`, you need to execute `train_sink_step` step for training.
Args:
fn (Function): The Python function that will be run with dataset.
dataset (Dataset): The dataset iterator. The dataset can be generated by dataset generator API in
:class:`mindspore.dataset`, such as :class:`mindspore.dataset.ImageFolderDataset`.
sink_size (int): Control the amount of data in each sink. `sink_size` must be positive integer. Default: 1.
jit_config (JitConfig): Controls the execution mode(Graph mode/PyNative mode) of the generated function, and Jit
config for compile. Default: None, means running in PyNative mode.
input_signature (Union[Tensor, List or Tuple of Tensors]): The Tensor which describes the input arguments.
The shape and dtype of the Tensor will be supplied to this function. If input_signature is specified,
each input to `fn` must be a `Tensor`. And the input parameters of `fn` cannot accept `**kwargs`. The shape
and dtype of actual inputs should keep the same as input_signature. Otherwise, TypeError will be raised.
Default: None.
Returns:
Function, the generated function will be executed in data sinking mode.
Raises:
ValueError: If `sink_size` is not positive integer.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import dataset as ds
>>>
>>> data = {"x": np.ones((1,), dtype=np.int32), "y": np.ones((1,), dtype=np.int32)}
>>> dataset = ds.NumpySlicesDataset(data=data)
>>>
>>> def func_net(x, y):
... out = x + y
... return out
>>>
>>> sink_process = ms.train.data_sink(func_net, dataset, sink_size=1)
>>> for _ in range(2):
... out = sink_process()
... print(out)
2
2
"""
Validator.check_value_type("sink_size", sink_size, int, "Data sink")
if sink_size <= 0:
raise ValueError(
f"The 'sink_size' must be positive, but got sink_size {sink_size}.")
if context.get_context('device_target') not in ('Ascend', 'GPU'):
raise ValueError(
f"Data sinking supports ascend or gpu device target, "
f"but device target is {context.get_context('device_target')}.")
loop = sink_size
create_info = True
if jit_config is None:
create_info = (loop == 1)
loop = 1
ori_next_op, is_info_queue = _init_sink_dataset(dataset, loop, input_signature, create_info)
@wraps(fn)
def sink_process(*args, **kwargs):
next_op, key_info = _get_next_op(dataset, ori_next_op, is_info_queue)
def sink_fun():
data = next_op()
out = fn(*data)
return out
real_sink_fun = _get_sink_fun(sink_fun, key_info, is_info_queue, dataset, jit_config)
loop = sink_size
if jit_config is not None:
loop = 1
out = None
for _ in range(loop):
out = real_sink_fun()
return out
return sink_process