I have seen other people have this error and I try to follow the steps to resolve, but continue to receive this error. "RuntimeError: Input and parameter tensors are not at the same device, found input tensor at cpu and parameter tensor at cuda:0"
I run both model.to(device) and input_seq.to(device). Error says it found an input tensor on CPU, but all input data should be on GPU with input_seq.to(device). Below is fill code
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()))
