INNER CODE UNIT · Python

gold_score

yxuansu/SimCTG · dialogue_generation/loss_func.py:53

    gold_score = torch.unsqueeze(gold_score, -1)
    assert gold_score.size() == torch.Size([bsz, seqlen, 1])
    difference_matrix = gold_score - score_matrix
    assert difference_matrix.size() == torch.Size([bsz, seqlen, seqlen])
    loss_matrix = margin - difference_matrix # bsz x seqlen x seqlen
    loss_matrix = torch.nn.functional.relu(loss_matrix)

    ### input mask
    input_mask = torch.ones_like(input_ids).type(torch.FloatTensor)
    if loss_matrix.is_cuda:
        input_mask = input_mask.cuda(loss_matrix.get_device())
    input_mask = input_mask.masked_fill(input_ids.eq(pad_token_id), 0.0)

    if loss_matrix.is_cuda:
        input_mask = input_mask.cuda(loss_matrix.get_device())

    valid_len_list = torch.sum(input_mask, dim = -1).tolist()
    loss_mask = build_mask_matrix(seqlen, [int(item) for item in valid_len_list], prefix_len)

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