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Feb. 23, 2019
Oct. 25, 2018

Tensor Shape Annotations Library (tsalib)

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Why tsalib?

Writing deep learning programs which manipulate multi-dim tensors (numpy, pytorch, keras, tensorflow, ...) requires you to carefully keep track of shapes of tensors. Carrying around the tensor shapes in your head gets increasingly hard as programs become more complex. For example, reshaping before a matmult, figuring out RNN output shapes, examining/modifying deep pre-trained architectures (resnet, densenet, elmo), designing new kinds of attention mechanisms (multi-head attention), and so on.

In absence of a principled way to name tensor dimensions and track shapes, most developers resort to writing adhoc shape comments embedded in code (see code from google-research/bert).

The tsalib library enables you to write

  • first-class, library-independent, shape annotations (TSAs) over named dimension variables (x: (B,T,D)),
  • defensive shape assertions using these named shapes (assert x.shape == (B,T,D)),
  • more fluent shape transformations and tensor operations using tensor shorthand notation (TSN). ('b,d,t').
  • avoid memorizing a laundry list of APIs (reshape,permute,stack, concat) -- use the one-stop warp operator for shape transformations. warp(x, '(btd)* -> btdl -> bdtl -> b,d//2,t*2,l', 'jpv')

TSAs expose the typically invisible tensor dimension names, which enhances code clarity, accelerates debugging and leads to improved productivity across the board.

The complete API for tsalib is illustrated in a notebook here. Quick start here. Detailed article here.


Developers benefit from shape annotations/assertions in many ways: Benefits: * Quickly verify the variable shapes when writing new transformations or modifying existing modules. * Assertions and annotations remain the same even if the actual dimension sizes change. * Faster *debugging*: if you annotate-as-you-go, the tensor variable shapes are explicit in code, readily available for a quick inspection. No more adhoc shape `print`ing when investigating obscure shape errors. * Do shape transformations using *shorthand* notation and avoid unwanted shape surgeries. * Use TSAs to improve code clarity everywhere, even in your machine learning data pipelines. * They serve as useful documentation to help others understand or extend your module.

Dimension Variables

Tensor shape annotations (TSAs) are constructed using dimension variables --B (Batch), C (Channels), D (EmbedDim) -- and arithmetic expressions (B*2, C+D) over them. Using tsalib, you can define dimension variables customized to your architecture/program. Even complex architectures need only a small number of named dimensions.

TSAs may be represented as tuples or shorthand strings:

  • a tuple (B,H,D) [long form]
  • a string 'b,h,d' (or simply 'bhd')
  • a string with anonymous dimensions (',h,' or _h_ is a 3-d tensor).

The tensor shorthand notation (TSN) is used extensively in tsalib.

Here is an example snippet which uses TSAs and TSN to define, transform and verify tensor shapes. tsalib is designed to work seamlessly with arbitrary backends: numpy, pytorch, keras, tensorflow, mxnet, etc.

from tsalib import dim_vars as dvs, size_assert
import tensorflow as tf
import torch

#declare dimension variables
B, C, H, W = dvs('Batch:32 Channels:3 Height:256 Width:256') 
# create tensors (pytorch) using dimension variables (interpret dim vars as integers)
x: (B, C, H, W)=torch.randn(B, C, H, W)
# or use shorthand labels
x: 'bchw'=tf.get_variable("x", shape=(B, C, H, W), initializer=tf.random_normal_initializer())

# perform tensor transformations
x: (B, C, H // 2, W // 2) = maxpool(x) 

# check symbolic assertions over TSAs
# assertions don't change even if dim sizes change
assert x.size() == (B, C, H // 2, W // 2)
#or, check selected dimensions
size_assert (x.size(), (B,C,H//2,W//2), dims=[1,2])

# super convenient reshapes (long form)!
x1 = x.view ((B, C, (H//2)*(W//2)))
assert x1.size() == (B, C, (H//2)*(W//2))

Note how TSAs are used as optional type annotations supported by Python >= 3.5. These annotations are optional and do not affect program performance.

Use TSN to write intuitive and crisp shape transformations.

from tsalib import permute_transform as pt

# permute: irrelevant dimensions are anonymous (underscores).
x: 'bchw'
x1 = x.permute(pt('_c__ -> ___c'))
assert x1.size() == (B, H, W, C)

# A powerful one-stop `warp` operator to compose multiple transforms inline
# here: a sequence of a permute ('p') and view ('v') transformations
y = warp(x1, 'bhwc -> bchw -> b*c,h,w', 'pv')
assert y.size() == (B*C,H,W)

#or, the same transformation sequence with anonymous dims
y = warp (x1, ['_hwc -> _chw', 'bc,, -> b*c,,'], 'pv')

# Combinations of `alignto`, `dot` and broadcast
# Enables writing really compact code for similar patterns
ht: 'bd'; Wh: 'dd'; Y: 'bld'; WY: 'dd'

a: 'bd' = dot('_d.d_', ht, Wh) 
b: 'b,1,d' = alignto((a,'bd'), 'bld')
Mt: 'bld' = torch.tanh(dot('__d.d_', Y, WY) + b)
[Compare] Old Code vs New Code:
def merge_heads_old(x):
  x = x.permute(0, 2, 1, 3).contiguous()
  new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
  res = x.view(*new_x_shape)
def merge_heads_tsalib(x: 'bhtd'):
    res: 'b,t,h*d' = warp(x, 'bhtd -> bthd -> b,t,h*d', 'pcv')


pip install [--upgrade] tsalib

Documentation, Design Principles, Model Examples

This notebook serves as a working documentation for the tsalib library and illustrates the complete tsalib API. The shorthand notation is documented here.

The models directory contains tsalib annotations of a few well-known, complex neural architectures:

With TSAs, we can gain deeper and immediate insight into how the module works by scanning through the forward (or equivalent) function.

  • tsalib is designed to stay light and easy to incorporate into existing workflow with minimal code changes. Choose to use tsalib for tensor labels and shape asserts only, or, integrate deeply by using warp everywhere in your code.
  • The API includes both library-independent and dependent parts, giving developers flexibility in how they choose to incorporate tsalib in their workflow.
  • We've carefully avoided deeper integration into popular tensor libraries to keep tsalib light-weight and avoid backend-inflicted bugs.


from tsalib import dim_vars as dvs, get_dim_vars
import numpy as np


Declare Dimension Variables

#or declare dim vars with default integer values (optional)
B, C, D, H, W = dvs('Batch:48 Channels:3 EmbedDim:300 Height Width')
#or provide *shorthand* names and default values for dim vars [best practice]
B, C, D, H, W = dvs('Batch(b):48 Channels(c):3 EmbedDim(d):300 Height(h) Width(w)')

# switch from using config constants to using dimension vars
B, C, D = dvs('Batch(b):{0} Channels(c):{1} EmbedDim(d):{2}'.format(config.batch_size, config.num_channels, config.embed_dim))

Use Dimension Variables to declare Tensors

Instead of scalar variables batch_size, embed_dim, use dimension variables B, D uniformly throughout your code.

B, D = dvs('Batch(b):{batch_size} EmbedDim(d):{embed_dim}}')
#declare a 2-D tensor of shape(48, 300)
x = torch.randn(B, D)
#assertions over dimension variables (code unchanged even if dim sizes change)
assert x.size() == (B, D)

Use TSAs to annotate variables on-the-go

B, D = get_dim_vars('b d') #lookup pre-declared dim vars
a: (B, D) = np.array([[1., 2., 3.], [10., 9., 8.]]) #(Batch, EmbedDim): (2, 3)
b: (2, B, D) = np.stack([a, a]) #(2, Batch, EmbedDim): (2, 2, 3)

#or simply, use TSN strings as type labels
a: 'b,d'
b: '2bd'

Annotations are optional and do not affect program performance.

Arithmetic over dimension variables is supported. This enables easy tracking of shape changes across neural network layers.

B, C, H, W = get_dim_vars('b c h w') #lookup pre-declared dim vars
v: 'bchw' = torch.randn(B, C, h, w)
x : 'b,c*2,h//2,w//2' = torch.nn.conv2D(C, C*2, ...)(v) 

Shape and Tensor Transformations

Reshape, Permute/Transpose transformations

Avoid explicit shape computations for reshaping. The *_transform functions are backend-independent and work with arbitrary backends.

    #use dimension variables directly
    x = torch.ones(B, T, D)
    x = x.view(B, T, 4, D//4)
In general, use `tsalib.view_transform` to specify view changes declaratively. ...
    x = np.ones((B, T, D))
    from tsalib import view_transform as vt
    #or, compact form:
    y = x.reshape(vt('btd -> b,t,4,d//4', x.shape)) #(20, 10, 300) -> (20, 10, 4, 75)
    assert y.shape == (B, T, 4, D//4)
    #or, super-compact, using anonymous dimensions:
    y = x.reshape(vt(',,d -> ,,4,d//4', x.shape))
Similarly, use `tsalib.permute_transform` to compute permutation index order (no manual guess-n-check) from a declarative spec. ...
    from tsalib import permute_transform as pt

    x = np.ones ((B, T, D, K))
    perm_indices = pt('btdk -> dtbk') # (2, 1, 0, 3)
    y = x.transpose(perm_indices)
    assert y.shape == (D, T, B, K)

    #or, super-compact:
    y = x.transpose(pt('b,,d, -> d,,b,'))

One-stop shape transforms: warp operator

The warp operator enables squeezing in a sequence of shape transformations in a single line using TSN. The operator takes in an input tensor, a sequence of shape transformations, and the corresponding transform types (view transform -> 'v', permute transform -> 'p'). See docs for transform types here.

    x: 'btd' = torch.randn(B, T, D)
    y = warp(x, 'btd -> b,t,4,d//4 ->  b,4,t,d//4 ', 'vp') #(v)iew, then (p)ermute, transform
    assert(y.shape == (B,4,T,D//4))

Because it returns transformed tensors, the warp operator is backend library-dependent. Currently supported backends are numpy, tensorflow and pytorch. New backends can be added easily (see

See notebook for complete working examples.

More useful operators: join, alignto, reduce_dims ...

more ..

Unified stack/concat using join. Join together sequence of tensors into a single tensor in different ways using the same join operator. join is backend-dependent.

    # xi : (B, T, D)
    # "concatenate" along the 'T' dimension: "(b,t,d)* -> (b,3*t,d)"
    x = tsalib.join([x1, x2, x3], ',*,') 
    assert x.shape == (B, 3*T, D)

    # "stack": join by adding a new dimension to the front: "(b,t,d)* -> (^,b,t,d)"
    x = join([x1, x2, x3], '^') 
    assert x.shape == (3, B, T, D)

Align one tensor to the rank of another tensor using alignto.

    x1 = np.random.randn(D,D)
    x2 = np.random.randn(B,D,T,D)

    x1_aligned = alignto((x1, 'dd'), 'bdtd')
    assert x1_aligned.shape == (1,D,1,D)
    x1_aligned = alignto((x1, 'dd'), 'bdtd', tile=True)
    assert x1_aligned.shape == (B,D,T,D)

Use dimension names instead of cryptic indices in reduction (mean, max, ...) operations.

    from tsalib import reduce_dims as rd
    b: (2, B, D)
    c: (D,) = np.mean(b, axis=rd('2bd -> d')) #axis = (0,1)

Simplified dot operator

Easy matmult specification when

  • exactly a single dimension is common between the operands and
  • the order of dimensions preserved in the output.
    x = torch.randn(B, C, T)
    y = torch.randn(C, D)
    z = dot('_c_.c_', x, y)
    assert z.size() == (B, T, D)


sympy. A library for building symbolic expressions in Python is the only dependency.

Tested with Python 3.6. Core API should work with Python 2. Contributions welcome.

For writing type annotations inline, Python >= 3.5 is required which allows optional type annotations for variables. These annotations do not affect the program performance in any way.

Best Practices

  • tsalib is designed for progressive adoption with your current deep learning models and pipelines. You can start off only with declaring dimension variables, labeling statements with TSAs and writing shape assertions. This already brings tremendous improvement in productivity and code readability. Once comfortable, move on to using the advanced features of tsalib: shorthand shapes (TSN), warp, join, etc.
  • Convert all relevant config parameters into dimension variables. Use only latter in your code.
  • Define all dimension variables upfront -- this requires some discipline. Use get_dim_vars to lookup pre-defined dimension variables by their shorthand names in any function context.
  • Avoid using reshape : use view and transpose together. An inadvertent reshape may not preserve your dimensions (axes). Using view to change shape protects against this: it throws an error if the dimensions being manipulated are not contiguous.
  • Shape Annotations vs Assertions. Shape labels (x: (B,T,D) or x: 'btd') ease shape recall during coding. Shape assertions (assert x.shape === (B,T,D)) enable catching inadvertent shape bugs at runtime. Pick either or both to work with.


  • Blog article introducing TSA.
  • A proposal for designing a tensor library with named dimensions from ground-up. The TSA library takes care of some use cases, without requiring any change in the tensor libraries.
  • Pytorch Issue on Names Axes here.
  • Using einsum for tensor operations improves productivity and code readability. blog
  • The Tile DSL uses indices ranging over dimension variables to write compact, library-independent tensor operations.
  • The datashape library introduces a generic type system and grammar for structure data. tsalib focuses on shapes of homogeneous tensor data types only, with arithmetic support.
  • The xarray library.
  • The einops library.
  • The namedtensor library.


Change Log

The library is in its early phases. Contributions/feedback welcome!

  • [5 Feb 2019] Added dot operator.
  • [4 Feb 2019] Added fully annotated and adapted BERT model. More illustrative pytorch and tensorflow snippets.
  • [31 Jan 2019] Added alignto operator.
  • [18 Dec 2018] Added the join operator. warp takes a list of (shorthand) transformations.
  • [28- Nov 2018] Added get_dim_vars to lookup dim vars declared earlier. Shorthand notation docs.
  • [21 Nov 2018] Added documentation notebook.
  • [18 Nov 2018] Support for warp, reduce_dims. Backend modules for numpy, tensorflow and torch added.
  • [9 Nov 2018] Support for shorthand notation in view/permute/expand transforms.
  • [9 Nov 2018] Support for using TSA in assertions and tensor constructors (cast to integers).
  • [25 Oct 2018] Initial Release