python - `for` 循环到PyTorch中的多维数组

Question

我想实现带有注意力机制的问答系统。我有两个输入； context和query哪些形状是 (batch_size, context_seq_len, embd_size)和(batch_size, query_seq_len, embd_size) .
我正在关注下面的论文。 使用 Match-LSTM 和答案指针进行机器理解。 https://arxiv.org/abs/1608.07905

然后，我想获得一个形状为(batch_size, context_seq_len, query_seq_len, embd_size)的注意力矩阵。在论文中，他们计算每一行的值(这意味着论文中的每个上下文单词，G_i，alpha_i)。

我的代码如下并且正在运行。但我不确定我的方法好不好。例如，我使用 for loop用于生成序列数据( for i in range(T): )。为了获取每一行，我使用就地运算符，如 G[:,i,:,:] , embd_context[:,i,:].clone()是pytorch中的好方法吗？如果不是，我应该在哪里更改代码？

如果您注意到其他要点，请告诉我。我是这个领域和 pytorch 的新手。抱歉我的问题含糊不清。

class MatchLSTM(nn.Module):
    def __init__(self, args):
        super(MatchLSTM, self).__init__()
        self.embd_size = args.embd_size
        d = self.embd_size
        self.answer_token_len = args.answer_token_len

        self.embd = WordEmbedding(args)
        self.ctx_rnn   = nn.GRU(d, d, dropout = 0.2)
        self.query_rnn = nn.GRU(d, d, dropout = 0.2)

        self.ptr_net = PointerNetwork(d, d, self.answer_token_len) # TBD

        self.w  = nn.Parameter(torch.rand(1, d, 1).type(torch.FloatTensor), requires_grad=True) # (1, 1, d)
        self.Wq = nn.Parameter(torch.rand(1, d, d).type(torch.FloatTensor), requires_grad=True) # (1, d, d)
        self.Wp = nn.Parameter(torch.rand(1, d, d).type(torch.FloatTensor), requires_grad=True) # (1, d, d)
        self.Wr = nn.Parameter(torch.rand(1, d, d).type(torch.FloatTensor), requires_grad=True) # (1, d, d)

        self.match_lstm_cell = nn.LSTMCell(2*d, d)

    def forward(self, context, query):
        # params
        d = self.embd_size
        bs = context.size(0) # batch size
        T = context.size(1)  # context length 
        J = query.size(1)    # query length

        # LSTM Preprocessing Layer
        shape = (bs, T, J, d)
        embd_context     = self.embd(context)         # (N, T, d)
        embd_context, _h = self.ctx_rnn(embd_context) # (N, T, d)
        embd_context_ex  = embd_context.unsqueeze(2).expand(shape).contiguous() # (N, T, J, d)
        embd_query       = self.embd(query)           # (N, J, d)
        embd_query, _h   = self.query_rnn(embd_query) # (N, J, d)
        embd_query_ex  = embd_query.unsqueeze(1).expand(shape).contiguous() # (N, T, J, d)

        # Match-LSTM layer
        G = to_var(torch.zeros(bs, T, J, d)) # (N, T, J, d)

        wh_q = torch.bmm(embd_query, self.Wq.expand(bs, d, d)) # (N, J, d) = (N, J, d)(N, d, d)

        hidden     = to_var(torch.randn([bs, d])) # (N, d)
        cell_state = to_var(torch.randn([bs, d])) # (N, d)
        # TODO bidirectional
        H_r = [hidden]
        for i in range(T):
            wh_p_i = torch.bmm(embd_context[:,i,:].clone().unsqueeze(1), self.Wp.expand(bs, d, d)).squeeze() # (N, 1, d) -> (N, d)
            wh_r_i = torch.bmm(hidden.unsqueeze(1), self.Wr.expand(bs, d, d)).squeeze() # (N, 1, d) -> (N, d)
            sec_elm = (wh_p_i + wh_r_i).unsqueeze(1).expand(bs, J, d) # (N, J, d)

            G[:,i,:,:] = F.tanh( (wh_q + sec_elm).view(-1, d) ).view(bs, J, d) # (N, J, d) # TODO bias

            attn_i = torch.bmm(G[:,i,:,:].clone(), self.w.expand(bs, d, 1)).squeeze() # (N, J)
            attn_query = torch.bmm(attn_i.unsqueeze(1), embd_query).squeeze() # (N, d) 
            z = torch.cat((embd_context[:,i,:], attn_query), 1) # (N, 2d)

            hidden, cell_state = self.match_lstm_cell(z, (hidden, cell_state)) # (N, d), (N, d)
            H_r.append(hidden)
        H_r = torch.stack(H_r, dim=1) # (N, T, d)

        indices = self.ptr_net(H_r) # (N, M, T) , M means (start, end)
        return indices

Answer 1

我认为你的代码很好。你无法避免循环: for i in range(T): 因为在论文 ( https://openreview.net/pdf?id=B1-q5Pqxl ) 的方程 (2) 中，有一个来自 Match-LSTM 单元的隐藏状态参与计算 G_i 和 alpha_i 向量，它们用于计算 Match-LSTM 下一个时间步的输入。因此，您需要为 Match-LSTM 的每个时间步运行循环，无论如何我都没有看到避免 for 循环的替代方法。