index_select

Documentation

treetensor.torch.index_select(input, dim, index, *args, **kwargs)

Returns a new tensor which indexes the input tensor along dimension dim using the entries in index which is a LongTensor.

Examples:

>>> import torch
>>> import treetensor.torch as ttorch
>>> t = torch.randn(3, 4)
>>> t
tensor([[ 0.2247, -0.1441, -1.2249, -0.2738],
        [-0.1496, -0.4883, -1.2442,  0.6374],
        [ 0.8017,  1.1220, -2.1013, -0.5951]])
>>> ttorch.index_select(t, 1, torch.tensor([1, 2]))
tensor([[-0.1441, -1.2249],
        [-0.4883, -1.2442],
        [ 1.1220, -2.1013]])

>>> tt = ttorch.randn({
...     'a': (3, 4),
...     'b': {'x': (5, 6)},
... })
>>> tt
<Tensor 0x7f6b636c1cf8>
├── a --> tensor([[ 3.9724e-05, -3.3134e-01, -1.0441e+00,  7.9233e-01],
│                 [-1.0035e-01,  2.3422e+00,  1.9307e+00, -1.7215e-01],
│                 [ 1.9069e+00,  1.1852e+00, -1.0672e+00,  1.3463e+00]])
└── b --> <Tensor 0x7f6b636c1be0>
    └── x --> tensor([[ 0.5200, -0.3595, -1.4235, -0.2655,  0.9504, -1.7564],
                      [-1.6577, -0.5516,  0.1660, -2.3273, -0.9811, -0.4677],
                      [ 0.7047, -1.6920,  0.3139,  0.6220,  0.4758, -1.2637],
                      [-0.3945, -2.1694,  0.8404, -0.4224, -1.4819,  0.3998],
                      [-0.0308,  0.9777, -0.7776, -0.0101, -1.0446, -1.1500]])
>>> ttorch.index_select(tt, 1, torch.tensor([1, 2]))
<Tensor 0x7f6b636c1f28>
├── a --> tensor([[-0.3313, -1.0441],
│                 [ 2.3422,  1.9307],
│                 [ 1.1852, -1.0672]])
└── b --> <Tensor 0x7f6b636c1e80>
    └── x --> tensor([[-0.3595, -1.4235],
                      [-0.5516,  0.1660],
                      [-1.6920,  0.3139],
                      [-2.1694,  0.8404],
                      [ 0.9777, -0.7776]])

Note

If you need to select different indices in the tensors, just do like this.

>>> ttorch.index_select(tt, 1, ttorch.tensor({'a': [1, 2], 'b': {'x': [1, 3, 5]}}))
<Tensor 0x7f6b636dbf60>
├── a --> tensor([[-0.3313, -1.0441],
│                 [ 2.3422,  1.9307],
│                 [ 1.1852, -1.0672]])
└── b --> <Tensor 0x7f6b636dbe80>
    └── x --> tensor([[-0.3595, -0.2655, -1.7564],
                      [-0.5516, -2.3273, -0.4677],
                      [-1.6920,  0.6220, -1.2637],
                      [-2.1694, -0.4224,  0.3998],
                      [ 0.9777, -0.0101, -1.1500]])

Torch Version Related

This documentation is based on torch.index_select in torch v2.4.1+cu121. Its arguments’ arrangements depend on the version of pytorch you installed.

If some arguments listed here are not working properly, please check your pytorch’s version with the following command and find its documentation.

python -c 'import torch;print(torch.__version__)'

The arguments and keyword arguments supported in torch v2.4.1+cu121 is listed below.

Description From Torch v2.4.1+cu121

torch.index_select(input, dim, index, *, out=None) → Tensor

Returns a new tensor which indexes the input tensor along dimension dim using the entries in index which is a LongTensor.

The returned tensor has the same number of dimensions as the original tensor (input). The dimth dimension has the same size as the length of index; other dimensions have the same size as in the original tensor.

Note

The returned tensor does not use the same storage as the original tensor. If out has a different shape than expected, we silently change it to the correct shape, reallocating the underlying storage if necessary.

Args:

input (Tensor): the input tensor. dim (int): the dimension in which we index index (IntTensor or LongTensor): the 1-D tensor containing the indices to index

Keyword args:

out (Tensor, optional): the output tensor.

Example:

>>> x = torch.randn(3, 4)
>>> x
tensor([[ 0.1427,  0.0231, -0.5414, -1.0009],
        [-0.4664,  0.2647, -0.1228, -1.1068],
        [-1.1734, -0.6571,  0.7230, -0.6004]])
>>> indices = torch.tensor([0, 2])
>>> torch.index_select(x, 0, indices)
tensor([[ 0.1427,  0.0231, -0.5414, -1.0009],
        [-1.1734, -0.6571,  0.7230, -0.6004]])
>>> torch.index_select(x, 1, indices)
tensor([[ 0.1427, -0.5414],
        [-0.4664, -0.1228],
        [-1.1734,  0.7230]])