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