| 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) | |
| 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) | |
| def mask(self): | |
| return self._mask | |