NanoSocrates/Project_Model/Libs/Transformer/Utils/attention_mask.py

import torch

def get_causal_attention_mask(seq_len: int) -> torch.Tensor:
    return torch.triu(torch.ones(seq_len, seq_len, dtype=torch.bool), diagonal=1)


# there is no need for this since MultiHeadAttention of torch does this internally
def get_causal_attention_mask_batched(seq_len: int, batch_size: int ) -> torch.Tensor:
    base_mask = get_causal_attention_mask(seq_len)
    return base_mask.unsqueeze(0).expand(batch_size, -1, -1)  # add another dimension at the beginning, big as batch_size   
    # the result is that z,x,y where x,y are repeated along z

def get_causal_attention_mask_with_prefix(seq_len, prefix):
    mask = torch.triu(torch.ones(seq_len, seq_len, dtype=torch.bool), diagonal=1)
    mask[:,:prefix] = False
    return mask

def get_prefix_causal_mask_from_padding_mask(seq_len:int, prefix_mask, att_heads:int=1):
    expanded_padding_mask = prefix_mask.unsqueeze(-1).repeat(1, 1, seq_len)         # B,T,T
    expanded_padding_mask = expanded_padding_mask.permute(0,2,1)                    # B,T,T
    mask = torch.triu(torch.ones(seq_len, seq_len, dtype=torch.bool), diagonal=1)   #   T,T
    tri_batched = mask.unsqueeze(0)                                                 # 1,T,T will broadcast over B
    prefix_causal_mask = expanded_padding_mask & tri_batched
    prefix_causal_mask = prefix_causal_mask.repeat_interleave(att_heads, dim=0)     # B*H,T,T
    return prefix_causal_mask