Deep learning operations reinvented (for pytorch, tensorflow, jax and others)

Project README

https://user-images.githubusercontent.com/6318811/177030658-66f0eb5d-e136-44d8-99c9-86ae298ead5b.mp4

Flexible and powerful tensor operations for readable and reliable code.

Supports numpy, pytorch, tensorflow, jax, and others.

- 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)
- flax and oneflow backend added
- torch.jit.script is supported for pytorch layers
- powerful EinMix added to einops. Einmix tutorial notebook

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

Writing better code with PyTorch and einops 👌 Andrej Karpathy, AI at Tesla

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, MILA (Montreal)

Watch a 15-minute talk focused on main problems of standard tensor manipulation methods, and how einops improves this process.

- Installation
- Documentation
- Tutorial
- API micro-reference
- Why using einops
- Supported frameworks
- Repository and discussions

Plain and simple:

```
pip install einops
```

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

`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)
```

And two corresponding layers (`einops`

keeps a separate version for each framework) with the same API.

```
from einops.layers.torch import Rearrange, Reduce
from einops.layers.tensorflow import Rearrange, Reduce
from einops.layers.flax import Rearrange, Reduce
from einops.layers.gluon import Rearrange, Reduce
from einops.layers.keras import Rearrange, Reduce
from einops.layers.chainer import Rearrange, Reduce
```

Layers behave similarly to operations and have the same parameters (with the exception of the first argument, which is passed during call).

Example of using layers within a model:

```
# 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),
)
```

Later additions to the family are `einsum`

, `pack`

and `unpack`

functions:

```
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')
# 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')
# Pack/Unpack are more convenient than concat and split, see tutorial
```

Last, but not the least `EinMix`

layer is available!

`EinMix`

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

`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).

`einops`

notation?! ```
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.

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)
```

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.

```
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)
```

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, cupy, chainer, pytorch:
`(60,)`

- keras, tensorflow.layers, gluon:
`(3, 20)`

`einops`

works the same way in all frameworks.

Example: `tile`

vs `repeat`

causes lots of confusion. To copy image along width:

```
np.tile(image, (1, 2)) # in numpy
image.repeat(1, 2) # pytorch's repeat ~ numpy's tile
```

With einops you don't need to decipher which axis was repeated:

```
repeat(image, 'h w -> h (tile w)', tile=2) # in numpy
repeat(image, 'h w -> h (tile w)', tile=2) # in pytorch
repeat(image, 'h w -> h (tile w)', tile=2) # in tf
repeat(image, 'h w -> h (tile w)', tile=2) # in jax
repeat(image, 'h w -> h (tile w)', tile=2) # in cupy
... (etc.)
```

Testimonials provide user's perspective on the same question.

Einops works with ...

Please use the following bibtex record

```
@inproceedings{
rogozhnikov2022einops,
title={Einops: Clear and Reliable Tensor Manipulations with Einstein-like Notation},
author={Alex Rogozhnikov},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=oapKSVM2bcj}
}
```

`einops`

works with python 3.7 or later.

Open Source Agenda is not affiliated with "Einops" Project. README Source: arogozhnikov/einops

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