I suggest you fix the code as follows to avoid the error.
Also, I recommend you to read this tutorial 278 about the mini batch training of RNN.
import torch
import torch.nn.functional as F
from torch import nn, optim
from torch.autograd import Variable
from numpy import *
from torch.utils.data import DataLoader

# from mydataset import MyDataset

CONTEXT_SIZE = 1
EMBEDDING_DIM = 10
HIDDEN_UNITS = 50

N_LAYERS = 2

test_sentence = """When forty winters shall besiege thy brow,
And dig deep trenches in thy beauty's field,
Thy youth's proud livery so gazed on now,
Will be a totter'd weed of small worth held:
Then being asked, where all thy beauty lies,
Where all the treasure of thy lusty days;
To say, within thine own deep sunken eyes,
Were an all-eating shame, and thriftless praise.
How much more praise deserv'd thy beauty's use,
If thou couldst answer 'This fair child of mine
Shall sum my count, and make my old excuse,'
Proving his beauty by succession thine!
This were to be new made when thou art old,
And see thy blood warm when thou feel'st it cold.""".split()

trigram = [(test_sentence[i], test_sentence[i + 1])
           for i in range(len(test_sentence) - 1)]

x_set = [test_sentence[i] for i in range(len(test_sentence)-1)]

y_set = [test_sentence[i+1] for i in range(len(test_sentence)-1)]

vocb = set(test_sentence)
word_to_idx = {word: i for i, word in enumerate(vocb)}
idx_to_word = {word_to_idx[word]: word for word in word_to_idx}

# dataset = MyDataset(x_set,y_set)
# train_loader = DataLoader(dataset,batch_size=5)


class RNNModel(nn.Module):
    def __init__(self, vocb_size,embdding_size,hidden_size):
        super(RNNModel, self).__init__()
        self.n_word = vocb_size
        self.embedding = nn.Embedding(self.n_word, embdding_size)
        #self.embedding = OneHot(vocb)
        
        # self.rnn = nn.RNN(embdding_size, 
        #     hidden_size,num_layers=2,nonlinearity='relu')
        self.rnn = nn.RNN(embdding_size, 
            hidden_size,num_layers=N_LAYERS,nonlinearity='relu')
        
        # self.linear = nn.Linear(hidden_size, embdding_size)
        self.linear = nn.Linear(hidden_size, self.n_word)
    
    def forward(self, x, hidden):
        x = self.embedding(x)
        
        # input = torch.cat((x.view(1,-1),hidden.view(1,-1)),dim=1)
        # output1, h_n = self.rnn(input)
        output1, h_n = self.rnn(x, hidden)
        
        # output2 = self.linear2(output1)
        output2 = self.linear(output1)
        
        log_prob = F.log_softmax(output2)
        return log_prob, h_n


rnnmodel = RNNModel(len(word_to_idx), EMBEDDING_DIM, HIDDEN_UNITS)

# It is better to use NLLLoss instead of CrossEntropy
# if using log_softmax
# http://pytorch.org/docs/master/nn.html#torch.nn.NLLLoss
# criterion = nn.CrossEntropyLoss()
criterion = nn.NLLLoss()

optimizer = optim.SGD(rnnmodel.parameters(), lr=1e-3)

for epoch in range(3):
    print('epoch: {}'.format(epoch + 1))
    print('*' * 10)
    running_loss = 0
    
    # input_hidden = torch.autograd.Variable(torch.randn(1, HIDDEN_UNITS))
    input_hidden = torch.autograd.Variable(torch.randn(N_LAYERS, 1, HIDDEN_UNITS))
    
    for data in trigram:
        word, label = data
        word = Variable(torch.LongTensor([word_to_idx[word]]))
        label = Variable(torch.LongTensor([word_to_idx[label]]))
        # forward
        out,input_hidden = rnnmodel(word,input_hidden)
        
        # loss = criterion(out, label)
        loss = criterion(out[0], label)
        
        running_loss += loss.data[0]
    
    # backward
    optimizer.zero_grad()
    
    # loss.backward(retain_graph=True)
    loss.backward()
    
    optimizer.step()
    
    print('Loss: {:.6f}'.format(running_loss / len(word_to_idx)))