oneflow.nn.utils.rnn.PackedSequence

class oneflow.nn.utils.rnn.PackedSequence(data: oneflow.Tensor, batch_sizes: Optional[oneflow.Tensor] = None, sorted_indices: Optional[oneflow.Tensor] = None, unsorted_indices: Optional[oneflow.Tensor] = None)

The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.utils.rnn.PackedSequence.html.

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 pack_padded_sequence().

Batch sizes represent the number elements at each sequence step in the batch, not the varying sequence lengths passed to pack_padded_sequence(). For instance, given data abc and x the PackedSequence would contain data axbc with batch_sizes=[2,1,1].

data

Tensor containing packed sequence

Type

Tensor

batch_sizes

Tensor of integers holding information about the batch size at each sequence step

Type

Tensor

sorted_indices

Tensor of integers holding how this PackedSequence is constructed from sequences.

Type

Tensor, optional

unsorted_indices

Tensor of integers holding how this to recover the original sequences with correct order.

Type

Tensor, optional

Note

data can be on arbitrary device and of arbitrary dtype. sorted_indices and unsorted_indices must be oneflow.int64 tensors on the same device as data.

However, batch_sizes should always be a CPU oneflow.int64 tensor.

This invariant is maintained throughout PackedSequence class, and all functions that construct a :class:PackedSequence in PyTorch (i.e., they only pass in tensors conforming to this constraint).

__init__(data: oneflow.Tensor, batch_sizes: Optional[oneflow.Tensor] = None, sorted_indices: Optional[oneflow.Tensor] = None, unsorted_indices: Optional[oneflow.Tensor] = None)

Initialize self. See help(type(self)) for accurate signature.

Methods

__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init__(data[, batch_sizes, …])	Initialize self.
__init_subclass__	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(**kwargs)	Create and return a new object.
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
byte()
char()
cpu(*args, **kwargs)
cuda(*args, **kwargs)
double()
float()
half()
int()
is_pinned()	Returns true if self.data stored on in pinned memory
long()
pin_memory()
short()
to(*args, **kwargs)	Performs dtype and/or device conversion on self.data.

Attributes

is_cuda

Returns true if self.data stored on a gpu