w_times_x = torch.bmm(batch_w_ih, x.unsqueeze(-1)) # [bs, 4*h_size, 1] RuntimeError: Expected batch2_sizes[0] == bs && batch2_sizes[1] == contraction_size to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)

这个错误通常是因为batch_w_ih和x的形状不兼容导致的。在这种情况下，batch_w_ih和x的维度应该分别为[bs, 4*h_size, h_size]和[bs, h_size]，其中bs是批量大小，h_size是隐藏状态的大小。

请确保您的输入张量具有正确的形状。如果batch_w_ih或x的形状不正确，您可以使用以下代码更改它们的形状：

batch_w_ih = batch_w_ih.view(bs, 4*h_size, h_size)
x = x.view(bs, h_size)

如果您的输入张量已经具有正确的形状，则可能是由于其他代码中的错误导致的。您可以尝试打印batch_w_ih和x的形状，以确定它们是否正确，并查看其他代码是否有问题。

相关问题

以下代码存在无法求导反向传播的bug，请你修改：# 反向LSTM input_backward = torch.flip(input, [0]) outputs_backward = [] for t in range(input_backward.size(0)): x = input_backward[t] i = torch.sigmoid(torch.matmul(x, self.W_i_backward) + torch.matmul(h_backward[-1], self.U_i_backward) + self.b_i_backward) f = torch.sigmoid(torch.matmul(x, self.W_f_backward) + torch.matmul(h_backward[-1], self.U_f_backward) + self.b_f_backward) c_tilde = torch.tanh(torch.matmul(x, self.W_c_backward) + torch.matmul(h_backward[-1], self.U_c_backward) + self.b_c_backward) c_backward[-1] = f * c_backward[-1] + i * c_tilde o = torch.matmul(x, self.W_o_backward) + torch.matmul(h_backward[-1], self.U_o_backward) + self.b_o_backward o = torch.sigmoid(o) h_backward[-1] = o * torch.tanh(c_backward[-1]) outputs_backward.append(h_backward[-1]) outputs_backward = torch.flip(torch.stack(outputs_backward), [0])

这段代码看起来没有明显的无法求导反向传播的 bug，但是如果存在问题的话可能是因为某些操作的输入没有进行 requires_grad=True 的设置。可以尝试添加以下代码，保证所有涉及到反向传播的张量都设置了 requires_grad=True：

input_backward.requires_grad_()
self.W_i_backward.requires_grad_()
self.U_i_backward.requires_grad_()
self.b_i_backward.requires_grad_()
self.W_f_backward.requires_grad_()
self.U_f_backward.requires_grad_()
self.b_f_backward.requires_grad_()
self.W_c_backward.requires_grad_()
self.U_c_backward.requires_grad_()
self.b_c_backward.requires_grad_()
self.W_o_backward.requires_grad_()
self.U_o_backward.requires_grad_()
self.b_o_backward.requires_grad_()

另外，如果在模型训练时发现该部分无法进行反向传播，可以尝试将该部分的代码放到 `torch.no_grad()` 中，避免该部分的梯度被累加。

with torch.no_grad():
    input_backward = torch.flip(input, [0])
    outputs_backward = []
    for t in range(input_backward.size(0)):
        x = input_backward[t]
        i = torch.sigmoid(torch.matmul(x, self.W_i_backward) + torch.matmul(h_backward[-1], self.U_i_backward) + self.b_i_backward)
        f = torch.sigmoid(torch.matmul(x, self.W_f_backward) + torch.matmul(h_backward[-1], self.U_f_backward) + self.b_f_backward)
        c_tilde = torch.tanh(torch.matmul(x, self.W_c_backward) + torch.matmul(h_backward[-1], self.U_c_backward) + self.b_c_backward)
        c_backward[-1] = f * c_backward[-1] + i * c_tilde
        o = torch.matmul(x, self.W_o_backward) + torch.matmul(h_backward[-1], self.U_o_backward) + self.b_o_backward
        o = torch.sigmoid(o)
        h_backward[-1] = o * torch.tanh(c_backward[-1])
        outputs_backward.append(h_backward[-1])
    outputs_backward = torch.flip(torch.stack(outputs_backward), [0])

以下代码存在不可以自动反向传播的bug，为什么： # 前向LSTM outputs_forward = [] for t in range(input.size(0)): x = input[t] i = torch.sigmoid(torch.matmul(x, self.W_i_forward) + torch.matmul(h_forward[-1], self.U_i_forward) + self.b_i_forward) f = torch.sigmoid(torch.matmul(x, self.W_f_forward) + torch.matmul(h_forward[-1], self.U_f_forward) + self.b_f_forward) c_tilde = torch.tanh(torch.matmul(x, self.W_c_forward) + torch.matmul(h_forward[-1], self.U_c_forward) + self.b_c_forward) c_forward[-1] = f * c_forward[-1] + i * c_tilde o = torch.matmul(x, self.W_o_forward) + torch.matmul(h_forward[-1], self.U_o_forward) + self.b_o_forward o = torch.sigmoid(o) h_forward[-1] = o * torch.tanh(c_forward[-1]) outputs_forward.append(h_forward[-1])

这段代码存在一个不能自动反向传播的bug，原因是在循环中对h_forward和c_forward的值进行了修改。在PyTorch中，只有被标记为requires_grad=True的张量才能够自动求梯度，而对张量进行in-place操作会导致梯度信息丢失，从而无法进行自动求梯度。

具体来说，在这段代码中，对h_forward和c_forward的修改会导致后续的梯度计算出现问题，从而无法进行自动反向传播。解决这个问题的方法是创建一个新的变量来保存h_forward和c_forward的值，而不是直接修改它们的值。修改后的代码如下：

# 前向LSTM
outputs_forward = []
h_forward_new = h_forward.clone()
c_forward_new = c_forward.clone()
for t in range(input.size(0)):
    x = input[t]
    i = torch.sigmoid(torch.matmul(x, self.W_i_forward) + torch.matmul(h_forward_new[-1], self.U_i_forward) + self.b_i_forward)
    f = torch.sigmoid(torch.matmul(x, self.W_f_forward) + torch.matmul(h_forward_new[-1], self.U_f_forward) + self.b_f_forward)
    c_tilde = torch.tanh(torch.matmul(x, self.W_c_forward) + torch.matmul(h_forward_new[-1], self.U_c_forward) + self.b_c_forward)
    c_forward_new[-1] = f * c_forward_new[-1] + i * c_tilde
    o = torch.matmul(x, self.W_o_forward) + torch.matmul(h_forward_new[-1], self.U_o_forward) + self.b_o_forward
    o = torch.sigmoid(o)
    h_forward_new[-1] = o * torch.tanh(c_forward_new[-1])
    outputs_forward.append(h_forward_new[-1])
    h_forward_new = torch.cat([h_forward_new[1:], h_forward_new[-1].unsqueeze(0)])
    c_forward_new = torch.cat([c_forward_new[1:], c_forward_new[-1].unsqueeze(0)])
h_forward = h_forward_new
c_forward = c_forward_new

在修改后的代码中，我们先创建了新的变量h_forward_new和c_forward_new来保存h_forward和c_forward的值，并使用clone()方法复制张量的值。然后，在循环中，我们使用h_forward_new和c_forward_new来进行前向计算，并将计算得到的输出保存到outputs_forward中。最后，我们使用torch.cat()方法来更新h_forward_new和c_forward_new的值，并将其赋值给h_forward和c_forward。这样就可以避免in-place操作导致的梯度丢失问题。