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, prefix_mask):


"""
print(get_causal_attention_mask_with_prefix(10,3))
seq_len = 10
prefix = 3
mask = torch.arange(seq_len) >= prefix
"""