3218a3987ef9f83d1ad5efd6f96d6f6c741c3cfa,python/eight_mile/pytorch/layers.py,,viterbi,#Any#Any#Any#Any#Any#Any#,776

Before Change

    
    seq_len, batch_size, tag_size = unary.size()
    min_length = torch.min(lengths)
    batch_range = torch.arange(batch_size, dtype=torch.int64)
    backpointers = []

    // Alphas: [B, 1, N]
    alphas = torch.Tensor(batch_size, 1, tag_size).fill_(-1e4).to(unary.device)
    alphas[:, 0, start_idx] = 0
    alphas = F.log_softmax(alphas, dim=-1) if norm else alphas

    for i, unary_t in enumerate(unary):
        next_tag_var = alphas + trans
        viterbi, best_tag_ids = torch.max(next_tag_var, 2)
        backpointers.append(best_tag_ids.data)
        new_alphas = viterbi + unary_t
        new_alphas.unsqueeze_(1)
        if i >= min_length:
            mask = (i < lengths).view(-1, 1, 1)
            alphas = alphas.masked_fill(mask, 0) + new_alphas.masked_fill(mask == 0, 0)
        else:
            alphas = new_alphas

    // Add end tag
    terminal_var = alphas.squeeze(1) + trans[:, end_idx]
    _, best_tag_id = torch.max(terminal_var, 1)
    path_score = terminal_var[batch_range, best_tag_id]  // Select best_tag from each batch

    best_path = [best_tag_id]
    // Flip lengths
    rev_len = seq_len - lengths - 1
    for i, backpointer_t in enumerate(reversed(backpointers)):
        // Get new best tag candidate
        new_best_tag_id = backpointer_t[batch_range, best_tag_id]
        // We are going backwards now, if you passed your flipped length then you aren"t in your real results yet
        mask = (i > rev_len)
        best_tag_id = best_tag_id.masked_fill(mask, 0) + new_best_tag_id.masked_fill(mask == 0, 0)
        best_path.append(best_tag_id)
    _ = best_path.pop()
    best_path.reverse()
    best_path = torch.stack(best_path)
    // Return list of paths
    paths = []
    best_path = best_path.transpose(0, 1)
    for path, length in zip(best_path, lengths):
        paths.append(path[:length])
    return paths, path_score.squeeze(0)


class TaggerGreedyDecoder(nn.Module):

After Change

    best_path = torch.stack(best_path)
    // Mask out the extra tags (This might be pointless given that anything that
    // will use this as a dense tensor downstream will mask it itself?)
    seq_mask = sequence_mask(lengths).to(best_path.device).transpose(0, 1)
    best_path = best_path.masked_fill(seq_mask == 0, 0)
    return best_path, path_score


class TaggerGreedyDecoder(nn.Module):

Instances