多头注意力机制里的valid_lens是什么意思？有什么作用？

Question

class MultiHeadAttention(nn.Module):
    """Multi-head attention."""
    def __init__(self, key_size, query_size, value_size, num_hiddens,
                 num_heads, dropout, bias=False, **kwargs):
        super(MultiHeadAttention, self).__init__(**kwargs)
        self.num_heads = num_heads
        self.attention = d2l.DotProductAttention(dropout)
        self.W_q = nn.Linear(query_size, num_hiddens, bias=bias)
        self.W_k = nn.Linear(key_size, num_hiddens, bias=bias)
        self.W_v = nn.Linear(value_size, num_hiddens, bias=bias)
        self.W_o = nn.Linear(num_hiddens, num_hiddens, bias=bias)

    def forward(self, queries, keys, values, valid_lens):
        # Shape of `queries`, `keys`, or `values`:
        # (`batch_size`, no. of queries or key-value pairs, `num_hiddens`)
        # Shape of `valid_lens`:
        # (`batch_size`,) or (`batch_size`, no. of queries)
        # After transposing, shape of output `queries`, `keys`, or `values`:
        # (`batch_size` * `num_heads`, no. of queries or key-value pairs,
        # `num_hiddens` / `num_heads`)
        queries = transpose_qkv(self.W_q(queries), self.num_heads)
        keys = transpose_qkv(self.W_k(keys), self.num_heads)
        values = transpose_qkv(self.W_v(values), self.num_heads)

        if valid_lens is not None:
            # On axis 0, copy the first item (scalar or vector) for
            # `num_heads` times, then copy the next item, and so on
            valid_lens = torch.repeat_interleave(valid_lens,
                                                 repeats=self.num_heads,
                                                 dim=0)

        # Shape of `output`: (`batch_size` * `num_heads`, no. of queries,
        # `num_hiddens` / `num_heads`)
        output = self.attention(queries, keys, values, valid_lens)

        # Shape of `output_concat`:
        # (`batch_size`, no. of queries, `num_hiddens`)
        output_concat = transpose_output(output, self.num_heads)
        return self.W_o(output_concat)

valid_lens在多头注意力机制中的作用是什么呢？

Answer 1

假设X的维度： torch.Size([64, 10, 32]) batchsz=64,seq_len=10,dim=32。
X的维度是[64, 10, 32]以valid_lens要mask它，所以，肯定是（64,10），现在 裂变成4个head，所以就是(256,10)。