.index_reduce_()

In PyTorch, .index_reduce_() performs an in-place reduction operation (such as sum, product, or mean) on a tensor along a specified dimension. It uses an index tensor to map input elements to positions in the output tensor, effectively aggregating values with the same index.

Syntax

Tensor.index_reduce_(dim, index, source, reduce, *, include_self=True)

• dim: The axis of the tensor along which the reduction is performed.
• index: A 1D tensor containing indices that map the elements in the source tensor to specific positions in the current tensor.
• source: The tensor whose values are reduced and added to the current tensor at positions specified by index.
• reduce: Specifies the reduction operation to apply. Possible values include:
  • 'prod': Product of elements with the same index.
  • 'mean': Mean of elements with the same index.
  • 'amax': Maximum of elements with the same index.
  • 'amin': Minimum of elements with the same index.
• include_self (Optional): Determines whether the existing values in the current tensor are included in the reduction operation.
  • If True, the values already present in the tensor are included. If no value is provided for the parameter, include_self defaults to True.
  • If False, only the source tensor values contribute to the reduction.

Example

The following example demonstrates the usage of the .index_reduce() method:

import torch

# Define the target tensor
target = torch.zeros(2)

# Source tensor
source = torch.tensor([1.0, 2.0, 3.0, 4.0])

# Indices mapping source to target
index = torch.tensor([0, 1, 0, 1], dtype=torch.long)  # Ensure index tensor is of type 'long'

# Perform in-place reduction using 'mean' along the 0th dimension (rows)
target.index_reduce_(dim=0, index=index, source=source, reduce='mean')
print(target)

The above code produces the following output:

tensor([1.3333, 2.0000])

This code reduces the source tensor along dimension 0 by averaging ('mean' reduce) the values mapped to the same indices in the index tensor, updating the target tensor in place.