from collections import namedtuple
import torch
from torch.autograd import Variable
import torch.onnx
from .._functions.packing import PackPadded
PackedSequence_ = namedtuple('PackedSequence', ['data', 'batch_sizes'])
[docs]class PackedSequence(PackedSequence_):
r"""Holds the data and list of batch_sizes of a packed sequence.
All RNN modules accept packed sequences as inputs.
Note:
Instances of this class should never be created manually. They are meant
to be instantiated by functions like :func:`pack_padded_sequence`.
Batch sizes represent the number elements at each sequence step in
the batch, not the varying sequence lengths passed to
:func:`pack_padded_sequence`. For instance, given data ``abc`` and `d`
the ``PackedSequence`` would be ``adbc`` with ``batch_sizes=[2,1,1]``.
Attributes:
data (Variable): Variable containing packed sequence
batch_sizes (Variable): Variable of integers holding
information about the batch size at each sequence step
"""
def __new__(cls, *args):
# support being called as `PackedSequence(data, batch_sizes)`
if len(args) == 2:
return super(PackedSequence, cls).__new__(cls, *args)
# support being called as `PackedSequence((data, batch_sizes))`
else:
assert len(args) == 1
return super(PackedSequence, cls).__new__(cls, *args[0])
def cuda(self, *args, **kwargs):
"""Returns a GPU copy if `self.data` not already on the GPU"""
if self.is_cuda:
return self
else:
return type(self)(self.data.cuda(*args, **kwargs), self.batch_sizes)
def cpu(self):
"""Returns a CPU copy if `self.data` not already on the CPU"""
if self.is_cuda:
return type(self)(self.data.cpu(), self.batch_sizes)
else:
return self
def double(self):
r"""Returns copy with `self.data` cast to double type"""
return type(self)(self.data.double(), self.batch_sizes)
def float(self):
r"""Returns copy with `self.data` cast to float type"""
return type(self)(self.data.float(), self.batch_sizes)
def half(self):
r"""Returns copy with `self.data` cast to half type"""
return type(self)(self.data.half(), self.batch_sizes)
def long(self):
r"""Returns copy with `self.data` cast to long type"""
return type(self)(self.data.long(), self.batch_sizes)
def int(self):
r"""Returns copy with `self.data` cast to int type"""
return type(self)(self.data.int(), self.batch_sizes)
def short(self):
r"""Returns copy with `self.data` cast to short type"""
return type(self)(self.data.short(), self.batch_sizes)
def char(self):
r"""Returns copy with `self.data` cast to char type"""
return type(self)(self.data.char(), self.batch_sizes)
def byte(self):
r"""Returns copy with `self.data` cast to byte type"""
return type(self)(self.data.byte(), self.batch_sizes)
@property
def is_cuda(self):
r"""Returns true if `self.data` stored on a gpu"""
return self.data.is_cuda
def _symbolic_pack_padded_sequence(g, input, lengths, batch_first=False):
if batch_first:
input = g.op('Transpose', input, perm_i=[1, 0, 2])
# There currently is no PackPadded operator in ONNX. We rely on an
# optimization pass to remove this later. It is an error if all
# PackPadded operators cannot be optimized out.
return g.op("prim::PackPadded", input, lengths, outputs=2)
[docs]@torch.onnx.symbolic_override_first_arg_based(_symbolic_pack_padded_sequence)
def pack_padded_sequence(input, lengths, batch_first=False):
r"""Packs a Variable containing padded sequences of variable length.
Input can be of size ``T x B x *`` where `T` is the length of the longest sequence
(equal to ``lengths[0]``), `B` is the batch size, and `*` is any number of
dimensions (including 0). If ``batch_first`` is True ``B x T x *`` inputs are
expected.
The sequences should be sorted by length in a decreasing order, i.e.
``input[:,0]`` should be the longest sequence, and ``input[:,B-1]`` the
shortest one.
Note:
This function accept any input that has at least two dimensions. You
can apply it to pack the labels, and use the output of the RNN with
them to compute the loss directly. A Variable can be retrieved from
a :class:`PackedSequence` object by accessing its ``.data`` attribute.
Arguments:
input (Variable): padded batch of variable length sequences.
lengths (Variable): list of sequences lengths of each batch element.
batch_first (bool, optional): if ``True``, the input is expected in ``B x T x *``
format.
Returns:
a :class:`PackedSequence` object
"""
if isinstance(lengths, list):
lengths = Variable(torch.LongTensor(lengths))
data, batch_sizes = PackPadded.apply(input, lengths, batch_first)
return PackedSequence(data, batch_sizes)
def _symbolic_pad_packed_sequence(g, input, batch_first=False, padding_value=0.0):
# See comment on _symbolic_pack_padded_sequence
data, lengths = g.op("prim::PadPacked", input.data, input.batch_sizes, outputs=2)
if batch_first:
data = g.op('Transpose', data, perm_i=[1, 0, 2])
return data, lengths
[docs]@torch.onnx.symbolic_override_packed_sequence_based(_symbolic_pad_packed_sequence)
def pad_packed_sequence(sequence, batch_first=False, padding_value=0):
r"""Pads a packed batch of variable length sequences.
It is an inverse operation to :func:`pack_padded_sequence`.
The returned Variable's data will be of size ``T x B x *``, where `T` is the length
of the longest sequence and `B` is the batch size. If ``batch_first`` is True,
the data will be transposed into ``B x T x *`` format.
Batch elements will be ordered decreasingly by their length.
Arguments:
sequence (PackedSequence): batch to pad
batch_first (bool, optional): if ``True``, the output will be in ``B x T x *``
format.
padding_value (float, optional): values for padded elements.
Returns:
Tuple of Variable containing the padded sequence, and Variable
containing the list of lengths of each sequence in the batch.
"""
var_data, batch_sizes = sequence
max_batch_size = int(batch_sizes[0])
output = var_data.data.new(len(batch_sizes), max_batch_size, *var_data.size()[1:]).fill_(padding_value)
output = Variable(output)
lengths = []
data_offset = 0
prev_batch_size = int(batch_sizes[0])
prev_i = 0
for i, batch_size in enumerate(batch_sizes.tolist() + [0]):
if batch_size != prev_batch_size:
l = prev_batch_size * (i - prev_i)
tmp = var_data[data_offset:data_offset + l]
output[prev_i:i, :prev_batch_size] = tmp.view(i - prev_i, prev_batch_size, *tmp.size()[1:])
data_offset += l
prev_i = i
dec = prev_batch_size - batch_size
if dec > 0:
lengths.extend((i,) * dec)
prev_batch_size = batch_size
lengths.reverse()
if batch_first:
output = output.transpose(0, 1)
# This Variable doesn't actually have any history (well,
# technically it does; it's just untracked), it is purely here to
# make ONNX export easier. That is to say, from an autodiff
# standpoint this doesn't make any sense.
return output, Variable(torch.LongTensor(lengths))
[docs]def pad_sequence(sequences, batch_first=False, padding_value=0):
r"""Pad a list of variable length Variables with zero
``pad_sequence`` stacks a list of Variables along a new dimension,
and padds them to equal length. For example, if the input is list of
sequences with size ``L x *`` and if batch_first is False, and ``T x B x *``
otherwise. The list of sequences should be sorted in the order of
decreasing length.
`B` is batch size. It's equal to the number of elements in ``sequences``.
`T` is length of the longest sequence.
`L` is length of the sequence.
`*` is any number of trailing dimensions, including none.
Example:
>>> from torch.nn.utils.rnn import pad_sequence
>>> a = Variable(torch.ones(25, 300))
>>> b = Variable(torch.ones(22, 300))
>>> c = Variable(torch.ones(15, 300))
>>> pad_sequence([a, b, c]).size()
torch.Size([25, 3, 300])
Note:
This function returns a Variable of size ``T x B x *`` or ``B x T x *`` where `T` is the
length of longest sequence.
Function assumes trailing dimensions and type of all the Variables
in sequences are same.
Arguments:
sequences (list[Variable]): list of variable length sequences.
batch_first (bool, optional): output will be in ``B x T x *`` if True, or in
``T x B x *`` otherwise
padding_value (float, optional): value for padded elements.
Returns:
Variable of size ``T x B x *`` if batch_first is False
Variable of size ``B x T x *`` otherwise
"""
# assuming trailing dimensions and type of all the Variables
# in sequences are same and fetching those from sequences[0]
max_size = sequences[0].size()
max_len, trailing_dims = max_size[0], max_size[1:]
prev_l = max_len
if batch_first:
out_dims = (len(sequences), max_len) + trailing_dims
else:
out_dims = (max_len, len(sequences)) + trailing_dims
out_variable = Variable(sequences[0].data.new(*out_dims).fill_(padding_value))
for i, variable in enumerate(sequences):
length = variable.size(0)
# temporary sort check, can be removed when we handle sorting internally
if prev_l < length:
raise ValueError("lengths array has to be sorted in decreasing order")
prev_l = length
# use index notation to prevent duplicate references to the variable
if batch_first:
out_variable[i, :length, ...] = variable
else:
out_variable[:length, i, ...] = variable
return out_variable
[docs]def pack_sequence(sequences):
r"""Packs a list of variable length Variables
``sequences`` should be a list of Variables of size ``L x *``, where `L` is
the length of a sequence and `*` is any number of trailing dimensions,
including zero. They should be sorted in the order of decreasing length.
Example:
>>> from torch.nn.utils.rnn import pack_sequence
>>> a = Variable(torch.Tensor([1,2,3]))
>>> b = Variable(torch.Tensor([4,5]))
>>> c = Variable(torch.Tensor([6]))
>>> pack_sequence([a, b, c]])
PackedSequence(data=
1
4
6
2
5
3
[torch.FloatTensor of size 6]
, batch_sizes=[3, 2, 1])
Arguments:
sequences (list[Variable]): A list of sequences of decreasing length.
Returns:
a :class:`PackedSequence` object
"""
return pack_padded_sequence(pad_sequence(sequences), [v.size(0) for v in sequences])