How to Handle “IndexError: index out of range in self” in PyTorch

The popular deep learning and natural language processing framework PyTorch is renowned for being user-friendly and adaptable, so what’s the deal with the IndexError: index out of range in self error?

This happens when a PyTorch embedding tensor attempts to access an index that is out of bounds. A lookup table that converts integers into vectors of real numbers is known as an embedding tensor. The valid indices for this tensor, which has a predetermined vocabulary size, run from 0 to vocabulary_size - 1.

What causes the “IndexError: index out of range in self” error?

This error is one of the most frequent in PyTorch and it mainly happens for two reasons.

1. Attempting to access out-of-range indices

The most frequent cause of this error is attempting to access an index that is larger than the embedding layer's permitted vocabulary size. For instance:

import torch

# Create an embedding layer with a vocabulary size of 10 and an embedding dimension of 5.
embedding = torch.nn.Embedding(10, 5)

# Create an input tensor with an index that is out of bounds.

input_tensor = torch.tensor([20])

# Try to access the embedding tensor using the out-of-bounds index.
output_tensor = embedding(input_tensor)

Output:

When the above code is run, you get the following error (the code was run in Google Colab):

—-----------------------------------------------------------------------
IndexError          Traceback (most recent call last)
<ipython-input-9-0fd79aa0642e> in <cell line: 11>()
    9
    10 # Try to access the embedding tensor using the out-of-bounds index.
—-->11 output_tensor = embedding(input_tensor)
_________________________________________ 2 frames_____________________________________________________________________

/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_byfreq, sparse)
    2208        # remove once script supports set_grad_enabled
    2209        _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
—-->2210  return torch.embedding(weight, input, padding_idx, scale_grad_by_fre, sparse)
    2211
    2212

IndexError: index out of range in self

2. Negative or incorrect indices

This error can also occur while trying to access non-integer or negative indices in your input tensor. For instance:

import torch

# Create an embedding layer with a vocabulary size of 5 and an embedding dimension of 3.
embedding = torch.nn.Embedding(5, 3)

# Create an input tensor with negative or non-integer indices.
input_tensor = torch.tensor([-1])

# Try to access the embedding tensor using the invalid indices.
output_tensor = embedding(input_tensor)

Output:

—-----------------------------------------------------------------------
IndexError          Traceback (most recent call last)
<ipython-input-23-17063d3c9301> in <cell line: 10>()
    8
    9 # Try to access the embedding tensor using the invalid indices.
—-->10 output_tensor = embedding(input_tensor)
_________________________________________ 2 frames________________________________________
/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_byfreq, sparse)
    2208        # remove once script supports set_grad_enabled
    2209        _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
—-->2210  return torch.embedding(weight, input, padding_idx, scale_grad_by_fre, sparse)
    2211
    2212

IndexError: index out of range in self

How to handle the “IndexError: index out of range in self” error in PyTorch

You can fix or even prevent the IndexError when working with PyTorch embedding tensors by following these three strategies:

1. validating the indices before you use them,
2. implementing a masking technique to filter out any indices in your input tensors that are out of bounds, or
3. using the torch.clamp() function to clamp the indices in your input tensor to the valid range.

Let’s delve into each.

1. Validate the indices

By validating the indices before you use them to access the embedding tensor and ensuring they fall within the valid range, you can avoid this error completely. For example:

import torch

embedding = torch.nn.Embedding(5, 3)

# Create an input tensor with valid indices.
valid_input_tensor = torch.tensor([1, 2], dtype=torch.long)  

# Verify that all indices in the input tensor are within the valid range.
if torch.all(valid_input_tensor >= 0) and torch.all(valid_input_tensor < embedding.num_embeddings):
    output_tensor = embedding(valid_input_tensor)
    print(output_tensor)
else:
    print("Invalid indices detected.")

Output:

tensor([[-1.0858, -1.5973,  0.1911],
        [ 0.6144,  1.7730,  0.1751]], grad_fn=<EmbeddingBackward0>)

2. Use a masking technique

You can implement a masking technique to filter out any indices in your input tensors that are out of bounds.

For example:

import torch
import torch.nn as nn

embedding = nn.Embedding(10, 5)

input_tensor = torch.tensor([1, 16, 7])

# a boolean mask to filter out indices
mask = (input_tensor < embedding.num_embeddings) & (input_tensor >= 0)

# Apply the mask the input tensor
valid_indices = input_tensor[mask]
    output_tensor = embedding(valid_indices)

print(output_tensor)

In this example, you create a mask that filters out invalid indices from the input_tensor by checking if each index is greater than or equal to 0 and less than the vocabulary size. You then use the valid_indices to access the embedding tensor.

Output:

tensor([[  0.7224,   0.2376,   -1.0406,  2.4378,  0.7870],
        [ -0.2450,  -0.8258,   -0.8410, -0.6145,  1.4647]],
        grad_fn=<EmbeddingBackward0>)

3. Use torch.clamp()

You can use the torch.clamp() function to clamp the indices in your input tensor to the valid range. This will ensure that your indices are always within the valid range, even if they are initially outside of the range.

For example:

import torch

embedding = torch.nn.Embedding(5, 3)

# Create an input tensor with potentially out-of-range indices.
input_tensor = torch.tensor([1, 6, -1, 2.5], dtype=torch.long)

# Using torch.clamp() to clamp the indices to the valid range.
input_tensor_clamped = torch.clamp(input_tensor, 0, embedding.num_embeddings - 1)

output_tensor = embedding(input_tensor_clamped)

# Print the output tensor.
print(output_tensor)

Output:

tensor([[  0.1202,   0.4567,   -2.3354],
        [ -2.2047,   0.6332,   -0.7470],
        [ -0.3353,   0.0924,   -0.9382],
        [  1.8913,   -1.0245,   0.9622]], grad_fn=<EmbeddingBackward0>)

By incorporating index validation, masking strategies, and stringent data validation in your PyTorch code, you can successfully prevent and gracefully handle the IndexError and ensure the stability of your NLP models.

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