如何修复输入和参数张量不在同一设备上？

Question

我看到其他人也遇到此错误，我尝试按照步骤解决，但仍然收到此错误。“运行时错误：输入和参数张量不在同一设备上，在 cpu 处找到输入张量，在 cuda:0 处找到参数张量”

我运行 model.to(device) 和 input_seq.to(device)。错误表示它在 CPU 上找到了输入张量，但所有输入数据都应位于具有 input_seq.to(device) 的 GPU 上。下面是填写代码

text = ['hey how are you','good i am fine','have a nice day']

# Join all the sentences together and extract the unique characters from the combined sentences
chars = set(''.join(text))

# Creating a dictionary that maps integers to the characters
int2char = dict(enumerate(chars))

# Creating another dictionary that maps characters to integers
char2int = {char: ind for ind, char in int2char.items()}

# Finding the length of the longest string in our data
maxlen = len(max(text, key=len))

# Padding

# A simple loop that loops through the list of sentences and adds a ' ' whitespace until the length of
# the sentence matches the length of the longest sentence
for i in range(len(text)):
  while len(text[i])<maxlen:
      text[i] += ' '

# Creating lists that will hold our input and target sequences
input_seq = []
target_seq = []

for i in range(len(text)):
    # Remove last character for input sequence
  input_seq.append(text[i][:-1])
    
    # Remove first character for target sequence
  target_seq.append(text[i][1:])
  print("Input Sequence: {}\nTarget Sequence: {}".format(input_seq[i], target_seq[i]))
  
for i in range(len(text)):
    input_seq[i] = [char2int[character] for character in input_seq[i]]
    target_seq[i] = [char2int[character] for character in target_seq[i]]
    
dict_size = len(char2int)
seq_len = maxlen - 1
batch_size = len(text)

def one_hot_encode(sequence, dict_size, seq_len, batch_size):
    # Creating a multi-dimensional array of zeros with the desired output shape
    features = np.zeros((batch_size, seq_len, dict_size), dtype=np.float32)
    
    # Replacing the 0 at the relevant character index with a 1 to represent that character
    for i in range(batch_size):
        for u in range(seq_len):
            features[i, u, sequence[i][u]] = 1
    return features

# Input shape --> (Batch Size, Sequence Length, One-Hot Encoding Size)
input_seq = one_hot_encode(input_seq, dict_size, seq_len, batch_size)


input_seq = torch.from_numpy(input_seq)
target_seq = torch.Tensor(target_seq)

# torch.cuda.is_available() checks and returns a Boolean True if a GPU is available, else it'll return False
is_cuda = torch.cuda.is_available()

# If we have a GPU available, we'll set our device to GPU. We'll use this device variable later in our code.
if is_cuda:
    device = torch.device("cuda")
    print("GPU is available")
else:
    device = torch.device("cpu")
    print("GPU not available, CPU used")

class Model(nn.Module):
    def __init__(self, input_size, output_size, hidden_dim, n_layers):
        super(Model, self).__init__()

        # Defining some parameters
        self.hidden_dim = hidden_dim
        self.n_layers = n_layers

        #Defining the layers
        # RNN Layer
        self.rnn = nn.RNN(input_size, hidden_dim, n_layers, batch_first=True)   
        # Fully connected layer
        self.fc = nn.Linear(hidden_dim, output_size)
    
    def forward(self, x):
        
        batch_size = x.size(0)

        # Initializing hidden state for first input using method defined below
        hidden = self.init_hidden(batch_size)

        # Passing in the input and hidden state into the model and obtaining outputs
        out, hidden = self.rnn(x, hidden)
        
        # Reshaping the outputs such that it can be fit into the fully connected layer
        out = out.contiguous().view(-1, self.hidden_dim)
        out = self.fc(out)
        
        return out, hidden
    
    def init_hidden(self, batch_size):
        # This method generates the first hidden state of zeros which we'll use in the forward pass
        # We'll send the tensor holding the hidden state to the device we specified earlier as well
        hidden = torch.zeros(self.n_layers, batch_size, self.hidden_dim)
        return hidden



# Instantiate the model with hyperparameters
model = Model(input_size=dict_size, output_size=dict_size, hidden_dim=12, n_layers=1)
# We'll also set the model to the device that we defined earlier (default is CPU)
model.to(device)

# Define hyperparameters
n_epochs = 100
lr=0.01

# Define Loss, Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)


# Training Run
for epoch in range(1, n_epochs + 1):
    optimizer.zero_grad() # Clears existing gradients from previous epoch
    input_seq.to(device)
    target_seq.to(device)
    output, hidden = model(input_seq)
    loss = criterion(output, target_seq.view(-1).long())
    loss.backward() # Does backpropagation and calculates gradients
    optimizer.step() # Updates the weights accordingly
    
    if epoch%10 == 0:
        print('Epoch: {}/{}.............'.format(epoch, n_epochs), end=' ')
        print("Loss: {:.4f}".format(loss.item()))

Answer 1

to与 s上可用的方法不同，nn.Module例如您的model. tos上的方法不是Tensor就地操作！如文档页面所述：

此方法 [ nn.Module.to] 就地修改模块。

与Tensor.to：

[...] 返回的张量是self 的副本，具有所需的 [...] torch.device。

换句话说，您需要重新分配张量才能有效地将它们发送到设备。

input_seq = input_seq.to(device)
target_seq = target_seq.to(device)

虽然nn.Module不需要这种治疗：

model.to(device)

为了清楚地理解这里发生的情况，请看这个例子：

>>> x = torch.zeros(1)  # on cpu
>>> y = x.cuda()        # y is a copy of x

>>> y.device            # placed on cuda device
'cuda:0'

>>> x.device            # but x remains on the original device
'cpu'