| 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 |
|
|
