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pytorch embedding index out of range

I'm following this tutorial here https://cs230-stanford.github.io/pytorch-nlp.html. In there a neural model is created, using nn.Module, with an embedding layer, which is initialized here

self.embedding = nn.Embedding(params['vocab_size'], params['embedding_dim'])

vocab_size is the total number of training samples, which is 4000. embedding_dim is 50. The relevant piece of the forward method is below

def forward(self, s):
        # apply the embedding layer that maps each token to its embedding
        s = self.embedding(s)   # dim: batch_size x batch_max_len x embedding_dim

I get this exception when passing a batch to the model like so model(train_batch) train_batch is a numpy array of dimension batch_sizexbatch_max_len. Each sample is a sentence, and each sentence is padded so that it has the length of the longest sentence in the batch.

File "/Users/liam_adams/Documents/cs512/research_project/custom/model.py", line 34, in forward s = self.embedding(s) # dim: batch_size x batch_max_len x embedding_dim File "/Users/liam_adams/Documents/cs512/venv_research/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in call result = self.forward(*input, **kwargs) File "/Users/liam_adams/Documents/cs512/venv_research/lib/python3.7/site-packages/torch/nn/modules/sparse.py", line 117, in forward self.norm_type, self.scale_grad_by_freq, self.sparse) File "/Users/liam_adams/Documents/cs512/venv_research/lib/python3.7/site-packages/torch/nn/functional.py", line 1506, in embedding return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) RuntimeError: index out of range at ../aten/src/TH/generic/THTensorEvenMoreMath.cpp:193

Is the problem here that the embedding is initialized with different dimensions than those of my batch array? My batch_size will be constant but batch_max_len will change with every batch. This is how its done in the tutorial.

Solution

  • Found the answer here https://discuss.pytorch.org/t/embeddings-index-out-of-range-error/12582

    I'm converting words to indexes, but I had the indexes based off the total number of words, not vocab_size which is a smaller set of the most frequent words.