# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import collections
import functools
import math
import numpy as np
[docs]class SamplerHelper(object):
"""
The class is to help construct iterable sampler used for
:class:`paddle.io.DataLoader`. It wraps a dataset and uses its
:meth:`__getitem__` method. Every subclass of :class:`SamplerHelper` has
to provide an :meth:`__iter__` method, providing a way to iterate over
indices of dataset elements, and a :meth:`__len__` method that returns the
length of the returned iterators.
The class also can be used as batch iterator instead of indices iterator
when `iterator` yield samples rather than indices by initializing `iterator`
with a iterable dataset.
.. note::
The :meth:`__len__` method isn't strictly required by
:class:`paddle.io.DataLoader`, but is expected in any calculation
involving the length of a :class:`paddle.io.DataLoader`.
Args:
dataset (Dataset): Input dataset for :class:`SamplerHelper`.
iterable (Iterable, optional): Iterator of dataset. Default: None.
"""
# chain sampler
def __init__(self, dataset, iterable=None):
self.data_source = dataset
self.iterable = iterable
if isinstance(dataset, collections.abc.Iterable) and iterable is None:
# iterable-style datasets
self.iterable = dataset
def __iter__(self):
if self.iterable is None:
return iter(range(len(self.data_source)))
elif isinstance(self.iterable, collections.abc.Iterable):
return iter(self.iterable)
elif callable(self.iterable):
return self.iterable()
else:
raise ValueError("`iterable` should be None, instance of Iterable or callable " "producing generator.")
def __len__(self):
# Allow some samplers have different length with `len(data_source)`,
# such as batch sampler.
if hasattr(self, "_length"):
return self._length
else:
return len(self.data_source)
@property
def length(self):
"""
Returns the length.
"""
# since `len()` only produce integer, use length property to get None
# for uncertain length. samplers can set length if necessary.
try:
length = len(self)
except Exception:
length = None
return length
@length.setter
def length(self, length):
self._length = length
def apply(self, fn):
# Transformation functions would be performed. It includes
# :meth:`shuffle`, :meth:`sort`, :meth:`fit` and :meth:`shard`.
# Args:
# fn (callable): Transformation functions to be performed.
# Returns:
# SamplerHelper: A new transformed :class:`SamplerHelper` object.
rs = fn(self)
if isinstance(rs, (list, tuple)):
iterable, data_source = rs
else:
iterable, data_source = rs, self.data_source
sampler = type(self)(data_source, iterable)
return sampler
[docs] def shuffle(self, buffer_size=-1, seed=None):
"""
Shuffles the dataset according to the given buffer size and random seed.
Args:
buffer_size (int, optional): Buffer size for shuffle. If
`buffer_size < 0` or more than the length of the dataset,
`buffer_size` is the length of the dataset. Default: -1.
seed (int, optional): Seed for the random. Default: None.
Returns:
SamplerHelper: A new shuffled :class:`SamplerHelper` object.
Example:
.. code-block:: python
from paddlenlp.data import SamplerHelper
from paddle.io import Dataset
class MyDataset(Dataset):
def __init__(self):
super(MyDataset, self).__init__()
self.data = [
[[1, 2, 3, 4], [1]],
[[5, 6, 7], [0]],
[[8, 9], [1]],
]
def __getitem__(self, index):
data = self.data[index][0]
label = self.data[index][1]
return data, label
def __len__(self):
return len(self.data)
dataset = MyDataset()
sampler = SamplerHelper(dataset)
print(list(sampler)) # indices of dataset elements
# [0, 1, 2]
sampler = sampler.shuffle(seed=2)
print(list(sampler)) # indices of dataset elements
# [2, 1, 0]
"""
if seed is not None:
random_generator = np.random.RandomState(seed)
else: # use the global random generator
random_generator = np.random
def _impl():
buf = []
for idx in iter(self):
buf.append(idx)
if buffer_size > 0 and len(buf) >= buffer_size:
random_generator.shuffle(buf)
for b in buf:
yield b
buf = []
if len(buf) > 0:
random_generator.shuffle(buf)
for b in buf:
yield b
return type(self)(self.data_source, _impl)
[docs] def sort(self, cmp=None, key=None, reverse=False, buffer_size=-1):
"""
Sorts the dataset according to given callable :meth:`cmp` or :meth:`key`.
Args:
cmp (callable, optional): The function of comparison. Default: None.
key (callable, optional): The function of key. Default: None.
reverse (bool, optional): Whether to reverse when sorting the data
samples. If True, it means in descending order, and False means
in ascending order. Default: False.
buffer_size (int, optional): Buffer size for sort. If
`buffer_size < 0` or `buffer_size` is more than the length
of the data, `buffer_size` will be set to the length of the data.
Default: -1.
Returns:
SamplerHelper: A new sorted :class:`SamplerHelper` object.
Example:
.. code-block:: python
from paddlenlp.data import SamplerHelper
from paddle.io import Dataset
class MyDataset(Dataset):
def __init__(self):
super(MyDataset, self).__init__()
self.data = [
[[1, 2, 3, 4], [1]],
[[5, 6, 7], [0]],
[[8, 9], [1]],
]
def __getitem__(self, index):
data = self.data[index][0]
label = self.data[index][1]
return data, label
def __len__(self):
return len(self.data)
dataset = MyDataset()
sampler = SamplerHelper(dataset)
print(list(sampler)) # indices of dataset elements
# [0, 1, 2]
# Sorted in ascending order by the length of the first field
# of the sample
key = (lambda x, data_source: len(data_source[x][0]))
sampler = sampler.sort(key=key)
print(list(sampler)) # indices of dataset elements
# [2, 1, 0]
"""
if key:
key_wrapper = lambda x: key(x, self.data_source)
elif cmp:
key_wrapper = functools.cmp_to_key(lambda x, y: cmp(x, y, self.data_source))
else:
key_wrapper = lambda x: len(self.data_source[x])
def _impl():
buf = []
for idx in iter(self):
buf.append(idx)
if buffer_size > 0 and len(buf) >= buffer_size:
buf = sorted(buf, key=key_wrapper, reverse=reverse)
for b in buf:
yield b
buf = []
if len(buf) > 0:
buf = sorted(buf, key=key_wrapper, reverse=reverse)
for b in buf:
yield b
return type(self)(self.data_source, _impl)
[docs] def batch(self, batch_size, drop_last=False, batch_size_fn=None, key=None):
"""
Batches the dataset according to given `batch_size`.
Args:
batch_size (int): The batch size.
drop_last (bool, optional): Whether to drop the last mini batch.
Default: False.
batch_size_fn (callable, optional): It accepts four arguments:
index of data source, the length of minibatch, the size of
minibatch so far and data source, and it returns the size of
mini batch so far. Actually, the returned value can be anything
and would used as argument `size_so_far` in `key`. If None, it
would return the length of mini match. Default: None.
key (callable, optional): The function of key. It accepts the size of minibatch so far
and the length of minibatch, and returns what to be compared
with `batch_size`. If None, only the size of mini batch so far
would be compared with `batch_size`. Default: None.
Returns:
SamplerHelper: A new batched :class:`SamplerHelper` object.
Example:
.. code-block:: python
from paddlenlp.data import SamplerHelper
from paddle.io import Dataset
class MyDataset(Dataset):
def __init__(self):
super(MyDataset, self).__init__()
self.data = [
[[1, 2, 3, 4], [1]],
[[5, 6, 7], [0]],
[[8, 9], [1]],
]
def __getitem__(self, index):
data = self.data[index][0]
label = self.data[index][1]
return data, label
def __len__(self):
return len(self.data)
dataset = MyDataset()
sampler = SamplerHelper(dataset)
print(list(sampler)) # indices of dataset elements
# [0, 1, 2]
sampler = sampler.batch(batch_size=2)
print(list(sampler)) # indices of dataset elements
# [[0, 1], [2]]
"""
_key = lambda size_so_far, minibatch_len: size_so_far
ori_batch_size_fn = batch_size_fn
if batch_size_fn is None:
batch_size_fn = lambda new, count, sofar, data_source: count
key = _key if key is None else key
def _impl():
data_source = self.data_source
minibatch, size_so_far = [], 0
for idx in iter(self):
minibatch.append(idx)
size_so_far = batch_size_fn(idx, len(minibatch), size_so_far, data_source)
if key(size_so_far, len(minibatch)) == batch_size:
yield minibatch
minibatch, size_so_far = [], 0
elif key(size_so_far, len(minibatch)) > batch_size:
if len(minibatch) == 1:
raise ValueError(
"Please increase the value of `batch_size`, or limit the max length of batch."
)
yield minibatch[:-1]
minibatch, size_so_far = minibatch[-1:], batch_size_fn(idx, 1, 0, data_source)
if minibatch and not drop_last:
yield minibatch
sampler = type(self)(self.data_source, _impl)
if ori_batch_size_fn is None and self.length is not None:
sampler.length = (self.length + int(not drop_last) * (batch_size - 1)) // batch_size
else:
sampler.length = None
return sampler
[docs] def shard(self, num_replicas=None, rank=None):
"""
Slices the dataset for multi GPU training.
Args:
num_replicas (int, optional): The number of training process, and
is also the number of GPU cards used in training. If None, it
will be set by :meth:`paddle.distributed.get_world_size` method.
Default: None.
rank (int, optional): The id of current training process. Equal
to the value of the environment variable PADDLE_TRAINER_ID. If
None, it will be initialized by :meth:`paddle.distributed.get_rank`
method. Default: None.
Returns:
SamplerHelper: A new sliced :class:`SamplerHelper` object.
Example:
.. code-block:: python
from paddlenlp.data import SamplerHelper
from paddle.io import Dataset
class MyDataset(Dataset):
def __init__(self):
super(MyDataset, self).__init__()
self.data = [
[[1, 2, 3, 4], [1]],
[[5, 6, 7], [0]],
[[8, 9], [1]],
]
def __getitem__(self, index):
data = self.data[index][0]
label = self.data[index][1]
return data, label
def __len__(self):
return len(self.data)
dataset = MyDataset()
sampler = SamplerHelper(dataset)
print(list(sampler)) # indices of dataset elements
# [0, 1, 2]
sampler = sampler.shard(num_replicas=2)
print(list(sampler)) # indices of dataset elements
# [0, 2]
"""
import paddle.distributed as dist
if num_replicas is None:
num_replicas = dist.get_world_size()
if rank is None:
rank = dist.get_rank()
def _impl():
for i, idx in enumerate(self):
if i % num_replicas == rank:
yield idx
if i % num_replicas != num_replicas - 1 and rank > i % num_replicas:
# use last samples to make it evenly divisible
yield idx
sampler = type(self)(self.data_source, _impl)
if self.length is not None:
sampler.length = int(math.ceil(self.length * 1.0 / num_replicas))
else:
sampler.length = None
return sampler
def list(self):
# Produce a sampler with a `listiterator` when calling `iter`. Since
# `list` would fetch all contents at time, thus it can get accurate
# length.
def _impl():
indices = list(iter(self))
self.length = len(indices)
return iter(indices)
return type(self)(self.data_source, _impl)
