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import torch
from torch import Tensor
def pad_sequences(sequences: list[Tensor], padding_value: int) -> Tensor:
"""
Stack 1d tensors (sequences) into a single 2d tensor so that each sequence is padded on the
right.
"""
return torch.nn.utils.rnn.pad_sequence(sequences, padding_value=padding_value, batch_first=True)
def _build_condition_mask(sentences: list[list[str]], condition_fn: callable, device) -> Tensor:
masks = [
torch.tensor([condition_fn(word) for word in sentence], dtype=bool, device=device)
for sentence in sentences
]
return pad_sequences(masks, padding_value=False)
def build_padding_mask(sentences: list[list[str]], device) -> Tensor:
return _build_condition_mask(sentences, condition_fn=lambda word: True, device=device)
def build_null_mask(sentences: list[list[str]], device) -> Tensor:
return _build_condition_mask(sentences, condition_fn=lambda word: word != "#NULL", device=device)
def pairwise_mask(masks1d: Tensor) -> Tensor:
"""
Calculate an outer product of a mask, i.e. masks2d[:, i, j] = masks1d[:, i] & masks1d[:, j].
"""
return masks1d[:, None, :] & masks1d[:, :, None]
# Credits: https://docs.allennlp.org/main/api/nn/util/#replace_masked_values
def replace_masked_values(tensor: Tensor, mask: Tensor, replace_with: float):
"""
Replace all masked values in tensor with `replace_with`.
"""
assert tensor.dim() == mask.dim(), "tensor.dim() of {tensor.dim()} != mask.dim() of {mask.dim()}"
tensor.masked_fill_(~mask, replace_with)
def prepend_cls(sentences: list[list[str]]) -> list[list[str]]:
"""
Return a copy of sentences with [CLS] token prepended.
"""
return [["[CLS]", *sentence] for sentence in sentences]
def remove_nulls(sentences: list[list[str]]) -> list[list[str]]:
"""
Return a copy of sentences with nulls removed.
"""
return [[word for word in sentence if word != "#NULL"] for sentence in sentences]
def add_nulls(sentences: list[list[str]], counting_mask) -> list[list[str]]:
"""
Return a copy of sentences with nulls restored according to counting masks.
"""
sentences_with_nulls = []
for sentence, counting_mask in zip(sentences, counting_mask, strict=True):
sentence_with_nulls = []
assert 0 < len(counting_mask)
# Account for leading (CLS) auxiliary token.
sentence_with_nulls.extend(["#NULL"] * counting_mask[0])
for word, n_nulls_to_insert in zip(sentence, counting_mask[1:], strict=True):
sentence_with_nulls.append(word)
sentence_with_nulls.extend(["#NULL"] * n_nulls_to_insert)
sentences_with_nulls.append(sentence_with_nulls)
return sentences_with_nulls |