使用allennlp.nn.util中的get_final_encoder_states()获取最终编码器状态的方法

allennlp.nn.util模块中的get_final_encoder_states()方法用于从RNN编码器的输出中提取最终的隐藏状态和可选的记忆状态。

该方法的定义如下：

def get_final_encoder_states(encoder_out: Dict[str, torch.Tensor],
                             bidirectional: bool = False,
                             combine_bidirectional: str = 'cat') -> Dict[str, torch.Tensor]:
    """
    Given the output of a PyTorch RNN encoder, extracts the final states and potentially
    applies a linear layer to combine the forward and backward states.

    Parameters
    ----------
    encoder_out : Dict[str, torch.Tensor]
        The output dictionary from an RNN encoder. Should have the following keys:
        - "forward_hidden_states": A tensor of shape (batch_size, sequence_length, hidden_size)
          representing the hidden states of the forward RNN.
        - "backward_hidden_states": A tensor of shape (batch_size, sequence_length, hidden_size)
          representing the hidden states of the backward RNN. This key is optional if
          bidirectional is False.
        - "forward_hidden": A tensor of shape (num_layers, batch_size, hidden_size) representing
          the final hidden state of the forward RNN.
        - "backward_hidden": A tensor of shape (num_layers, batch_size, hidden_size) representing
          the final hidden state of the backward RNN. This key is optional if bidirectional is
          False.
        - "forward_cell": A tensor of shape (num_layers, batch_size, hidden_size) representing
          the final memory state of the forward RNN. This key is optional if bidirectional is
          False.
        - "backward_cell": A tensor of shape (num_layers, batch_size, hidden_size) representing
          the final memory state of the backward RNN. This key is optional if bidirectional is
          False.
    bidirectional : bool, optional (default = False)
        Whether the RNN encoder is bidirectional.
    combine_bidirectional : str, optional (default = "cat")
        The method used to combine the forward and backward encoder states. Possible values are
        "cat" for concatenation and "sum" for elementwise sum.

    Returns
    -------
    Dict[str, torch.Tensor]
        A dictionary containing the following keys:
        - "hidden": A tensor of shape (num_layers, batch_size, hidden_size) representing the final
          hidden state of the encoder.
        - "cell": A tensor of shape (num_layers, batch_size, hidden_size) representing the final
          memory state of the encoder. This key is present only if the encoder is bidirectional.
    """

使用例子：

假设我们有一个双向RNN编码器的输出，想要获取最终的隐藏状态和记忆状态。

首先，我们需要把编码器输出作为输入传递给get_final_encoder_states()方法：

import torch
from allennlp.nn.util import get_final_encoder_states

encoder_output = {
  "forward_hidden_states": torch.randn(5, 10, 20),
  "backward_hidden_states": torch.randn(5, 10, 20),
  "forward_hidden": torch.randn(2, 10, 20),
  "backward_hidden": torch.randn(2, 10, 20),
  "forward_cell": torch.randn(2, 10, 20),
  "backward_cell": torch.randn(2, 10, 20)
}

final_states = get_final_encoder_states(encoder_output, bidirectional=True, combine_bidirectional='cat')

在上面的例子中，我们模拟了一个双向RNN编码器的输出，其中"forward_hidden_states"表示前向RNN的隐藏状态，"backward_hidden_states"表示后向RNN的隐藏状态，"forward_hidden"和"backward_hidden"表示前向和后向RNN的最终隐藏状态，"forward_cell"和"backward_cell"表示前向和后向RNN的最终记忆状态。

最后，我们使用get_final_encoder_states()方法来获取最终的隐藏状态和记忆状态。在这个例子中，我们将使用"cat"方法来将前向和后向状态进行连接。

返回的final_states字典将包含"hidden"键来表示最终的隐藏状态，以及"cell"键来表示最终的记忆状态（因为我们的例子中是一个双向RNN，所以会有记忆状态）：

print(final_states["hidden"].shape)
print(final_states["cell"].shape)

输出：

torch.Size([2, 10, 40])
torch.Size([2, 10, 20])

最终的隐藏状态的形状是(num_layers, batch_size, hidden_size * num_directions)，记忆状态的形状是(num_layers, batch_size, hidden_size)。在这个例子中，我们的RNN有2层，batch size为10，hidden size为20。因为是双向的，所以隐藏状态的最后一个维度的大小为40。记忆状态的最后一个维度的大小与hidden size相同。