不能向后传递分类变压器模型中的两个损失

Question

对于我的模型，我使用了 roberta transformer 模型和 Huggingface transformer 库中的 Trainer。

我计算了两个损失： lloss 是交叉熵损失，dloss 计算层级之间的损失。

总损失是lloss和dloss之和。（基于 this）

然而，当调用 total_loss.backwards() 时，出现错误：

RuntimeError: Trying to backward through the graph a second time, but the buffers have already been freed

知道为什么会这样吗？我可以强制它只向后调用一次吗？下面是损失计算部分：

dloss = calculate_dloss(prediction, labels, 3)
lloss = calculate_lloss(predeiction, labels, 3)
total_loss = lloss + dloss 
total_loss.backward()

def calculate_lloss(predictions, true_labels, total_level):
    '''Calculates the layer loss.
    '''

    loss_fct = nn.CrossEntropyLoss()

    lloss = 0
    for l in range(total_level):

        lloss += loss_fct(predictions[l], true_labels[l])

    return self.alpha * lloss

def calculate_dloss(predictions, true_labels, total_level):
    '''Calculate the dependence loss.
    '''

    dloss = 0
    for l in range(1, total_level):

        current_lvl_pred = torch.argmax(nn.Softmax(dim=1)(predictions[l]), dim=1)
        prev_lvl_pred = torch.argmax(nn.Softmax(dim=1)(predictions[l-1]), dim=1)

        D_l = self.check_hierarchy(current_lvl_pred, prev_lvl_pred, l)  #just a boolean tensor

        l_prev = torch.where(prev_lvl_pred == true_labels[l-1], torch.FloatTensor([0]).to(self.device), torch.FloatTensor([1]).to(self.device))
        l_curr = torch.where(current_lvl_pred == true_labels[l], torch.FloatTensor([0]).to(self.device), torch.FloatTensor([1]).to(self.device))

        dloss += torch.sum(torch.pow(self.p_loss, D_l*l_prev)*torch.pow(self.p_loss, D_l*l_curr) - 1)

    return self.beta * dloss

Answer 1

损失是两个单独损失的总和并没有错，这里有一个小的原理证明改编from the docs：

import torch
import numpy
from sklearn.datasets import make_blobs

class Feedforward(torch.nn.Module):
    def __init__(self, input_size, hidden_size):
        super(Feedforward, self).__init__()
        self.input_size = input_size
        self.hidden_size  = hidden_size
        self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)
        self.relu = torch.nn.ReLU()
        self.fc2 = torch.nn.Linear(self.hidden_size, 1)
        self.sigmoid = torch.nn.Sigmoid()
    def forward(self, x):
        hidden = self.fc1(x)
        relu = self.relu(hidden)
        output = self.fc2(relu)
        output = self.sigmoid(output)
        return output

def blob_label(y, label, loc): # assign labels
    target = numpy.copy(y)
    for l in loc:
        target[y == l] = label
    return target

x_train, y_train = make_blobs(n_samples=40, n_features=2, cluster_std=1.5, shuffle=True)
x_train = torch.FloatTensor(x_train)
y_train = torch.FloatTensor(blob_label(y_train, 0, [0]))
y_train = torch.FloatTensor(blob_label(y_train, 1, [1,2,3]))

x_test, y_test = make_blobs(n_samples=10, n_features=2, cluster_std=1.5, shuffle=True)
x_test = torch.FloatTensor(x_test)
y_test = torch.FloatTensor(blob_label(y_test, 0, [0]))
y_test = torch.FloatTensor(blob_label(y_test, 1, [1,2,3]))


model = Feedforward(2, 10)
criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)


model.eval()
y_pred = model(x_test)
before_train = criterion(y_pred.squeeze(), y_test)
print('Test loss before training' , before_train.item())

model.train()
epoch = 20
for epoch in range(epoch):
    optimizer.zero_grad()    # Forward pass
    y_pred = model(x_train)    # Compute Loss
    lossCE= criterion(y_pred.squeeze(), y_train)
    lossSQD = (y_pred.squeeze()-y_train).pow(2).mean()
    loss=lossCE+lossSQD
    print('Epoch {}: train loss: {}'.format(epoch, loss.item()))    # Backward pass
    loss.backward()
    optimizer.step()

必须有第二次直接或间接调用 backward 某些变量，然后遍历您的图形。在这里要求完整的代码有点过分，只有你可以检查这个或者至少将它简化为一个最小的例子（这样做时，你可能已经发现了问题）。除此之外，我会开始检查：

它是否已经出现在第一次迭代训练中？如果不是：您是否在没有 detach 的情况下为第二次迭代重用任何计算结果？
当你分别对你的损失进行 backward 时 lloss.backward() 然后 dloss.backward() （这与首先将它们加在一起的效果相同，因为梯度是累积的）：会发生什么？这将让您追踪错误发生的两个损失中的哪一个。

Answer 2

在 backward() 之后你的补偿。图被释放，因此对于第二个向后，您需要通过再次提供输入来创建一个新图。如果你想在向后重复相同的图形（出于某种原因），你需要将向后的 retain_graph 标志指定为 True。见 retain_graph here.

P.S。由于张量的求和是自动可微的，因此求和损失不会导致向后的任何问题。

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