torch.nn.utils.rnn.pack_padded_sequence — PyTorch 2.7 documentation (original) (raw)
torch.nn.utils.rnn.pack_padded_sequence(input, lengths, batch_first=False, enforce_sorted=True)[source][source]¶
Packs a Tensor containing padded sequences of variable length.
input can be of size T x B x * (if batch_first is False) or B x T x * (if batch_first is True) where T is the length of the longest sequence, B is the batch size, and * is any number of dimensions (including 0).
For unsorted sequences, use enforce_sorted = False. If enforce_sorted isTrue, 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. enforce_sorted = True is only necessary for ONNX export.
It is an inverse operation to pad_packed_sequence(), and hence pad_packed_sequence()can be used to recover the underlying tensor packed in PackedSequence.
Note
This function accepts 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 Tensor can be retrieved from a PackedSequence object by accessing its .data attribute.
Parameters
- input (Tensor) – padded batch of variable length sequences.
- lengths (Tensor or list(int)) – list of sequence lengths of each batch element (must be on the CPU if provided as a tensor).
- batch_first (bool, optional) – if
True, the input is expected inB x T x *format,T x B x *otherwise. Default:False. - enforce_sorted (bool, optional) – if
True, the input is expected to contain sequences sorted by length in a decreasing order. IfFalse, the input will get sorted unconditionally. Default:True.
Return type
Warning
The dim of input tensor will be truncated if its length larger than correspond value in length.
Returns
a PackedSequence object
Return type