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This draft implementation should now be considered completed. If you are interested in using Named Tensor check out the core PyTorch implementation:

Thanks for everyone who contributed to this version.

Named Tensor for Torch

Build Status Coverage Status


A proposal for a named tensor for Torch described here:

NamedTensor is an thin-wrapper on Torch tensor that makes three changes to the API:

  1. Naming: Dimension access and reduction use a named dim argument instead of an index. Constructing and adding dimensions use a name argument. Axis-based indexing [ ] is replaced by named indexing.
  2. Broadcasting: All functions broadcast based on set-operations not through heuristic ordering rules, e.g. if z = x + y then z has the union of the dimension in x and y.
  3. Lifting: Order-based functions can be lifted by providing name annotations through .spec methods. For instance, convolution requires the user to name the channel and kernel dims, e.g .conv2d.spec("channel", ("x", "y")). This provides dynamic checks, better error messages, and consistent documentation.


pip install git+


from namedtensor import ntorch

Building tensors.

All pytorch builders have an extra keyword argument names.

x = ntorch.randn(10, 10, 20, names=("batch", "h", "w"))
x = ntorch.ones(10, 10, 20, names=("batch", "h", "w"))

Standard functions

All functions that keep dimensionality work in the same way.

x = x.log()
x = x.float()
x = ntorch.exp(x)

Named Indexing

Indexing and masking operation work by name as opposed to absolute position.

first_batch = x[{"batch": 1}]
three_examples = x[{"batch": slice(1, 4)}]
masked = x[ x > 0.5 ]

Advanced indexing by named tensors.

select = ntorch.tensor([1, 4, 5], names=("rows",))
y = x[{"h": select}] 
# y shape ("batch", "rows", "w")

No view or unsqueeze

View, tranpose, and friends are deprecated in favor of named access and movement.

x = x.stack(("w", "h"), "stackdim")

# Roundtrip

x = x.split("stackdim", ("w", "h"), w=20)

There is no need to ever have unsqueeze since broadcasting is done by name overlap.

Similar notation can be used for setting values.

All methods take named args

Any function with a dim argument now can be accessed based on the dimension name.

x = x.narrow("w", 0, 10)
x = x.softmax("w")

This is true of reductions functions as well, where the named dimension is eliminated.

x = x.mean("w")
x, argmax = x.max("w")

Tensor contractions

Matrix operations also use the dimension arguments. We can replace einsum based on persistent names.

x = ntorch.randn(10, 10, 20, names=("batch", "h", "w"))
y = ntorch.randn(10, 20, 30, names=("batch", "w", "c"))"w", y)

This also makes indexing much easier to read.

x = ntorch.ones(10, 10, 20, names=("batch", "time", "vocab"))
y = ntorch.randn(20, 30, names=("vocab", "embsize"))
y.index_select("vocab", x)

Removed Functions

The following functions are removed from the stdlib.

  • view, expand, squeeze, unsqueeze, transpose

NN Modules

NN units no longer take ordered tensors. They now have a required additional method spec that lets the user set the the input and output dimensions of the object.


  conv = ntorch.nn.Conv1d(5, 10, 2).spec("input", "time", "output")
  n = ntorch.randn(20, 30, 5, names=("batch", "time", "input"))
  out = conv(n)
  drop = ntorch.nn.Dropout()
  n = ntorch.randn(4, 20, names=("batch", "target"))
  out = drop(n)
  loss = ntorch.nn.NLLLoss().spec("target")
  predict = ntorch.randn(20, 4, names=("target", "batch"))
  target = ntorch.tensor([2, 2, 3, 4], names=("batch",))
  out = loss(predict, target)


# Univariate
mu = ntorch.randn(10, names=("batch"))
sigma = ntorch.randn(10, names=("batch"))
dist = ntorch.distributions.Normal(mu, sigma)
sample = dist.sample((30, 40), names=("sample1", "sample2"))

# Discrete
params = ntorch.randn(10, 20, 30, names=("batch1", "batch2", "logits"))
dist = ntorch.distributions.Categorical(logits=params, logit_dim="logits")




(NamedTensor is being collectively developed by Harvard CS 287)

  • Yuntian Deng
  • Justin Chiu
  • Francisco Rivera
  • Jiafeng Chen
  • Celine Liang
  • Miro Furtado
  • Roshan Padaki
  • Mirac Suzgun
  • Belén Saldías
  • Jason Ren
  • Josh Feldman
  • Jambay Kinley
  • Ian Kivlichan
  • Sanyuan Chen
  • Simon Shen


Named Tensor implementation for Torch




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