pytorch:GRU无法更新隐藏的_状态

Question

我在使用pytorch实现GRU网络时遇到问题：

我的代码如下：

import torch

class GRU_model(torch.nn.Module):
    def __init__(self, device):
        super(GRU_model, self).__init__()
        self.h = torch.randn((1,1,5), device=device, dtype=torch.float)

        self.GRU_1 = torch.nn.GRU(input_size=5, hidden_size=5)

    def forward(self, a):
        output, self.h = self.GRU_1(a, self.h)
        return output

if __name__ == '__main__':
    learn_rate=1e-4
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = GRU_model(device).to(device=device)
    optimizer = torch.optim.Adam(model.parameters(), lr=learn_rate)

    for i in range(10):
        a = torch.randn((1, 1, 5), device=device, dtype=torch.float)
        output = model(a)
        loss = (a - output).mean()

        optimizer.zero_grad()
        loss.backward(retain_graph=True)
        optimizer.step()

我犯了这样一个错误：

Traceback (most recent call last):
  File "C:/Users/Administrator_/Desktop/Graduation_Project/MIDI_Music_style_transfer/GRU_toy_in-place_hidden_states_change/main.py", line 40, in <module>
    loss.backward(retain_graph=True)
  File "C:\Users\Administrator_\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\tensor.py", line 245, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
  File "C:\Users\Administrator_\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\autograd\__init__.py", line 147, in backward
    allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [15, 5]] is at version 2; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

我只想在一个纪元后更新GRU中的隐藏状态，但它就是不起作用

如果你能帮我，我将不胜感激

Answer 1

在PyTorch中，为一个历元中的每次迭代创建计算图。在每次迭代中，我们执行前向传递，计算输出w.r.t对网络参数的导数，并更新参数以适合给定示例。执行向后传递后，将释放图形以节省内存。在下一次迭代中，将创建一个新的图形，并准备进行反向传播

由于默认情况下，第一次向后传递后将释放计算图，因此如果第二次尝试在同一个图上向后传递，将遇到错误。这就是为什么会弹出以下错误消息：

RuntimeError: Trying to backward through the graph a second time, but the buffers have already been freed. Specify retain_graph=True when calling backward the first time

source

在您的情况下，在指定retain_graph=True之后，您会看到：

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [15, 5]] is at version 2; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

当您尝试在正向传递中更新self.h时，会出现此问题。您没有修改它inplace，因为它是梯度计算所需要的source 这一条应该有效：

import torch


class GRU_model(torch.nn.Module):
    def __init__(self, device):
        super(GRU_model, self).__init__()
        self.GRU_1 = torch.nn.GRU(input_size=5, hidden_size=5)

    def forward(self, a, h):
        output, hh = self.GRU_1(a, h)
        return output, hh


if __name__ == '__main__':
    learn_rate = 1e-4
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = GRU_model(device).to(device=device)
    model.train()
    optimizer = torch.optim.Adam(model.parameters(), lr=learn_rate)

    def mse(output, target):
        loss = torch.mean((output - target)**2)
        return loss

    for i in range(10):
        a = torch.randn((1, 1, 5), device=device, dtype=torch.float, requires_grad=True)
        target = torch.randn((1, 1, 5), device=device)
        h = torch.randn((1, 1, 5), device=device)

        optimizer.zero_grad()
        output, h = model(a, h)
        loss = mse(output, target)

        loss.backward(retain_graph=True)
        optimizer.step()

Answer 2

真正的问题是隐藏态不应该参与梯度反向传播的计算。 因此，只需添加一行self.h = self.h.detach()，如下所示，这肯定会解决问题：

    def forward(self, a):
        output, self.h = self.GRU_1(a, self.h)
        self.h = self.h.detach()
        return output