38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
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# cp from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/transformer.py, modified by Puyuan Peng
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import torch
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def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
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"""
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Args:
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lengths:
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A 1-D tensor containing sentence lengths.
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max_len:
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The length of masks.
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Returns:
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Return a 2-D bool tensor, where masked positions
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are filled with `True` and non-masked positions are
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filled with `False`.
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>>> lengths = torch.tensor([1, 3, 2, 5])
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>>> make_pad_mask(lengths)
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tensor([[False, True, True, True, True],
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[False, False, False, True, True],
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[False, False, True, True, True],
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[False, False, False, False, False]])
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"""
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assert lengths.ndim == 1, lengths.ndim
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max_len = max(max_len, lengths.max())
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n = lengths.size(0)
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seq_range = torch.arange(0, max_len, device=lengths.device)
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expaned_lengths = seq_range.unsqueeze(0).expand(n, max_len)
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return expaned_lengths >= lengths.unsqueeze(-1)
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def generate_partial_autoregressive_mask(sz, start, end):
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mask = torch.zeros(sz, sz).bool()
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mask[start:end, start:end] = torch.triu(torch.ones(end-start, end-start,dtype=torch.bool), diagonal=1)
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mask[:start, start:end] = True
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mask[end:, start:end] = True
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return mask
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