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- import torch
-
- class TriangularCausalMask():
- def __init__(self, B, L, device="cpu"):
- mask_shape = [B, 1, L, L]
- with torch.no_grad():
- self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device)
-
- @property
- def mask(self):
- return self._mask
-
- class ProbMask():
- def __init__(self, B, H, L, index, scores, device="cpu"):
- _mask = torch.ones(L, scores.shape[-1], dtype=torch.bool).to(device).triu(1)
- _mask_ex = _mask[None, None, :].expand(B, H, L, scores.shape[-1])
- indicator = _mask_ex[torch.arange(B)[:, None, None],
- torch.arange(H)[None, :, None],
- index, :].to(device)
- self._mask = indicator.view(scores.shape).to(device)
-
- @property
- def mask(self):
- return self._mask
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