INNER CODE UNIT · Python

base_mask

yxuansu/SimCTG · dialogue_generation/loss_func.py:28

    base_mask = torch.ones(seqlen, seqlen) - torch.eye(seqlen, seqlen)
    base_mask = base_mask.type(torch.FloatTensor)
    bsz = len(valid_len_list)
    for i in range(bsz):
        one_base_mask = base_mask.clone()
        one_valid_len = valid_len_list[i]
        one_base_mask[:,one_valid_len:] = 0.
        one_base_mask[one_valid_len:, :] = 0.
        if prefix_len > 0:
            one_base_mask[:prefix_len, :prefix_len] = 0.
        res_list.append(one_base_mask)
    res_mask = torch.stack(res_list, dim = 0)#torch.FloatTensor(res_list)
    #print (res_mask)
    assert res_mask.size() == torch.Size([bsz, seqlen, seqlen])
    return res_mask
        
def contrastive_loss(margin, score_matrix, input_ids, pad_token_id, prefix_len=0):
    '''

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