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einops

Flexible and powerful tensor operations for readable and reliable code. <br /> Supports numpy, pytorch, jax, mlx and others.

Recent updates:

• einops playground can run 2 of 4 example notebooks right in your browser
• 0.8.2: MLX backend added
• 0.8.0: tinygrad backend added, small fixes
• 0.7.0: no-hassle torch.compile, support of array api standard and more
• 10'000🎉: github reports that more than 10k project use einops
• einops 0.6.1: paddle backend added
• einops 0.6 introduces packing and unpacking
• einops 0.5: einsum is now a part of einops
• Einops paper is accepted for oral presentation at ICLR 2022 (yes, it worth reading). Talk recordings are available

<details markdown="1"> <summary>Previous updates</summary> - flax and oneflow backend added - torch.jit.script is supported for pytorch layers - powerful EinMix added to einops. [Einmix tutorial notebook](https://github.com/arogozhnikov/einops/blob/main/docs/3-einmix-layer.ipynb) </details> <!--<div align="center"> <img src="http://arogozhnikov.github.io/images/einops/einops_logo_350x350.png" alt="einops package logo" width="250" height="250" /> <br><br> </div> -->

Tweets

In case you need convincing arguments for setting aside time to learn about einsum and einops... Tim Rocktäschel

Writing better code with PyTorch and einops 👌 Andrej Karpathy

Slowly but surely, einops is seeping in to every nook and cranny of my code. If you find yourself shuffling around bazillion dimensional tensors, this might change your life Nasim Rahaman

More testimonials

Contents

• Installation
• Documentation
• Tutorial
• API micro-reference
• Why use einops
• Supported frameworks
• Citing
• Repository and discussions

Installation <a name="Installation"></a>

Plain and simple:

pip install einops

(uv pip install einops works as well)

Tutorials <a name="Tutorials"></a>

Tutorials are the most convenient way to see einops in action

• part 1: einops fundamentals
• part 2: einops for deep learning
• part 3: packing and unpacking
• part 4: improve pytorch code with einops

Kapil Sachdeva recorded a small intro to einops.

API <a name="API"></a>

einops has a minimalistic yet powerful API.

Three core operations provided (einops tutorial shows those cover stacking, reshape, transposition, squeeze/unsqueeze, repeat, tile, concatenate, view and numerous reductions)

from einops import rearrange, reduce, repeat
# rearrange elements according to the pattern
output_tensor = rearrange(input_tensor, 't b c -> b c t')
# combine rearrangement and reduction
output_tensor = reduce(input_tensor, 'b c (h h2) (w w2) -> b h w c', 'mean', h2=2, w2=2)
# copy along a new axis
output_tensor = repeat(input_tensor, 'h w -> h w c', c=3)

Later additions to the family are pack and unpack functions (better than stack/split/concatenate):

from einops import pack, unpack
# pack and unpack allow reversibly 'packing' multiple tensors into one.
# Packed tensors may be of different dimensionality:
packed,  ps = pack([class_token_bc, image_tokens_bhwc, text_tokens_btc], 'b * c')
class_emb_bc, image_emb_bhwc, text_emb_btc = unpack(transformer(packed), ps, 'b * c')

Finally, einops provides einsum with a support of multi-lettered names:

from einops import einsum, pack, unpack
# einsum is like ... einsum, generic and flexible dot-product
# but 1) axes can be multi-lettered  2) pattern goes last 3) works with multiple frameworks
C = einsum(A, B, 'b t1 head c, b t2 head c -> b head t1 t2')

EinMix

EinMix is a generic linear layer, perfect for MLP Mixers and similar architectures.

Layers

Einops provides layers (einops keeps a separate version for each framework) that reflect corresponding functions

from einops.layers.torch      import Rearrange, Reduce
from einops.layers.tensorflow import Rearrange, Reduce
from einops.layers.flax       import Rearrange, Reduce
from einops.layers.paddle     import Rearrange, Reduce

<details markdown="1"> <summary>Example of using layers within a pytorch model</summary> Example given for pytorch, but code in other frameworks is almost identical

from torch.nn import Sequential, Conv2d, MaxPool2d, Linear, ReLU
from einops.layers.torch import Rearrange

model = Sequential(
    ...,
    Conv2d(6, 16, kernel_size=5),
    MaxPool2d(kernel_size=2),
    # flattening without need to write forward
    Rearrange('b c h w -> b (c h w)'),
    Linear(16*5*5, 120),
    ReLU(),
    Linear(120, 10),
)

No more flatten needed!

Additionally, torch layers as those are script-able and compile-able. Operations are torch.compile-able, but not script-able due to limitations of torch.jit.script.

</details>

Naming <a name="Naming"></a>

einops stands for Einstein-Inspired Notation for operations (though "Einstein operations" is more attractive and easier to remember).

Notation was loosely inspired by Einstein summation (in particular by numpy.einsum operation).

Why use einops notation?! <a name="Why-use-einops-notation"></a>

Semantic information (being verbose in expectations)

y = x.view(x.shape[0], -1)
y = rearrange(x, 'b c h w -> b (c h w)')

While these two lines are doing the same job in some context, the second one provides information about the input and output. In other words, einops focuses on interface: what is the input and output, not how the output is computed.

The next operation looks similar:

y = rearrange(x, 'time c h w -> time (c h w)')

but it gives the reader a hint: this is not an independent batch of images we are processing, but rather a sequence (video).

Semantic information makes the code easier to read and maintain.

Convenient checks

Reconsider the same example:

y = x.view(x.shape[0], -1) # x: (batch, 256, 19, 19)
y = rearrange(x, 'b c h w -> b (c h w)')

The second line checks that the input has four dimensions, but you can also specify particular dimensions. That's opposed to just writing comments about shapes since comments don't prevent mistakes, not tested, and without code review tend to be outdated

y = x.view(x.shape[0], -1) # x: (batch, 256, 19, 19)
y = rearrange(x, 'b c h w -> b (c h w)', c=256, h=19, w=19)

Result is strictly determined

Below we have at least two ways to define the depth-to-space operation

# depth-to-space
rearrange(x, 'b c (h h2) (w w2) -> b (c h2 w2) h w', h2=2, w2=2)
rearrange(x, 'b c (h h2) (w w2) -> b (h2 w2 c) h w', h2=2, w2=2)

There are at least four more ways to do it. Which one is used by the framework?

These details are ignored, since usually it makes no difference, but it can make a big difference (e.g. if you use grouped convolutions in the next stage), and you'd like to specify this in your code.

Uniformity

reduce(x, 'b c (x dx) -> b c x', 'max', dx=2)
reduce(x, 'b c (x dx) (y dy) -> b c x y', 'max', dx=2, dy=3)
reduce(x, 'b c (x dx) (y dy) (z dz) -> b c x y z', 'max', dx=2, dy=3, dz=4)

These examples demonstrated that we don't use separate operations for 1d/2d/3d pooling, those are all defined in a uniform way.

Space-to-depth and depth-to space are defined in many frameworks but how about width-to-height? Here you go:

rearrange(x, 'b c h (w w2) -> b c (h w2) w', w2=2)

Framework independent behavior

Even simple functions are defined differently by different frameworks

y = x.flatten() # or flatten(x)

Suppose x's shape was (3, 4, 5), then y has shape ...

• numpy, pytorch, cupy, chainer, jax: `(