Einops: A New Flavor of Deep Learning Operations

0.8.2 verified Tue May 12 auth: no python install: verified

Einops (Einstein operations) is a Python library that provides a flexible and powerful way to reshape and manipulate tensors in deep learning frameworks like PyTorch, TensorFlow, JAX, and NumPy. It simplifies complex tensor operations using a human-readable notation, often replacing verbose permutations, transpositions, and reshaping operations. The library is actively maintained with frequent releases, currently at version 0.8.2.

pip install einops

Common errors

error ModuleNotFoundError: No module named 'einops' ↓

cause The 'einops' library is not installed in the Python environment you are currently using, or there's an issue with your Python environment's path.

fix

Install the 'einops' library using pip: pip install einops or, if using a specific Python interpreter, python -m pip install einops.

error EinopsError: Error while processing rearrange-reduction pattern "..." Input tensor shape: ... Additional info: ... Shape mismatch, X != Y. ↓

cause The dimensions specified in the `einops` pattern do not match the actual shape of the input tensor. This often happens when composite dimensions (e.g., `(b1 b2)`) don't multiply to the corresponding input dimension size, or named dimensions provided in `**axes_lengths` don't align.

fix

Review your einops pattern and the actual shape of your input tensor. Ensure that all decomposed dimensions in parentheses (e.g., (h w)) correctly multiply to the size of the corresponding dimension in the input tensor, and that any explicitly provided axes_lengths match the tensor's dimensions.

error EinopsError: Undefined axis name '...' for rearrange/reduce/repeat ↓

cause You have used an axis name in your `einops` pattern (e.g., `rearrange(tensor, 'b h w c -> (b x) h w c')`) that is not present in the input tensor and has not been provided as an explicit length argument (e.g., `x=2`).

fix

Ensure all axis names used in the pattern are either derived from the input tensor's dimensions or explicitly defined with their lengths as keyword arguments to the rearrange, reduce, or repeat function.

error einops.layers.torch.Rearrange does not accept a list[torch.Tensor] as an input ↓

cause The `einops.layers.torch.Rearrange` layer expects a single tensor as input, but it received a list of tensors, which it cannot directly process for concatenation or other operations that `einops.rearrange` can handle when given multiple inputs.

fix

If you intend to concatenate multiple tensors, use the functional einops.rearrange directly with a list of tensors, or concatenate the tensors using torch.cat (or equivalent) *before* passing a single resulting tensor to einops.layers.torch.Rearrange.

Warnings

breaking TensorFlow layers in `einops` were updated in v0.8.0 to align with TF 2.16+ and are no longer compatible with older TensorFlow versions (e.g., TF 2.13). ↓

fix Upgrade TensorFlow to 2.16 or newer, or stick to `einops < 0.8.0` for older TensorFlow versions.

breaking Support for Python 3.7 was officially dropped in `einops` v0.7.0. The minimum required Python version is now 3.9. ↓

fix Upgrade your Python environment to 3.9 or newer.

breaking As of v0.8.2, the minimum required Python version for `einops` is Python 3.9. ↓

fix Ensure your Python environment is 3.9 or newer before upgrading to `einops >= 0.8.2`.

deprecated Support for the Gluon (MXNet) backend was dropped in v0.6.1 and confirmed removed in v0.7.0. Operations with MXNet tensors are no longer supported directly by `einops`. ↓

fix Migrate to a supported backend like PyTorch, TensorFlow, JAX, or NumPy.

gotcha The integration with `torch.compile` has evolved across versions. In `einops < 0.7.0`, explicit registration via `einops._torch_specific.allow_ops_in_compiled_graph()` was required. From `v0.7.0` onwards, `torch.compile` integration became largely automatic. With `einops >= 0.8.2` and `torch >= 2.8`, `torch.compile` natively handles `einops` operations without any specific `einops` hints or registrations. ↓

fix For `einops < 0.7.0`, ensure `einops._torch_specific.allow_ops_in_compiled_graph()` is called. For `einops >= 0.7.0`, no explicit action is usually needed, but ensure your PyTorch version is recent enough (especially `torch >= 2.8` for `einops >= 0.8.2`) to benefit from the latest native compilation.

gotcha Ellipsis (`...`) support was added to `EinMix` layers in v0.8.1, allowing for more flexible input patterns in mixed-precision operations. ↓

fix Upgrade to `einops >= 0.8.1` to utilize ellipsis in `EinMix` patterns.

breaking The test environment is missing the `numpy` package, which is a required dependency for using `einops` with the NumPy backend. This prevents the script from executing. ↓

fix Ensure `numpy` is installed in your Python environment (e.g., `pip install numpy`).

gotcha Using `einops` with NumPy arrays requires `numpy` to be explicitly installed in your environment. While `einops` supports multiple backends, `numpy` is a common prerequisite for many usage patterns and examples. ↓

fix Ensure `numpy` is installed in your environment using `pip install numpy`.

Install compatibility verified last tested: 2026-05-12

python os / libc status wheel install import disk

3.10 alpine (musl) wheel - 0.01s 18.3M

3.10 alpine (musl) - - 0.01s 18.3M

3.10 slim (glibc) wheel 1.6s 0.01s 19M

3.10 slim (glibc) - - 0.01s 19M

3.11 alpine (musl) wheel - 0.02s 20.3M

3.11 alpine (musl) - - 0.03s 20.3M

3.11 slim (glibc) wheel 1.6s 0.02s 21M

3.11 slim (glibc) - - 0.02s 21M

3.12 alpine (musl) wheel - 0.02s 12.1M

3.12 alpine (musl) - - 0.02s 12.1M

3.12 slim (glibc) wheel 1.5s 0.02s 13M

3.12 slim (glibc) - - 0.02s 13M

3.13 alpine (musl) wheel - 0.02s 11.8M

3.13 alpine (musl) - - 0.02s 11.7M

3.13 slim (glibc) wheel 1.5s 0.02s 12M

3.13 slim (glibc) - - 0.02s 12M

3.9 alpine (musl) wheel - 0.01s 17.8M

3.9 alpine (musl) - - 0.01s 17.8M

3.9 slim (glibc) wheel 1.8s 0.01s 18M

3.9 slim (glibc) - - 0.01s 18M

Imports

rearrange
```
from einops import rearrange
```
reduce
```
from einops import reduce
```
repeat
```
from einops import repeat
```
einsum
```
from einops import einsum
```
pack
```
from einops import pack
```
unpack
```
from einops import unpack
```
EinMix
```
from einops.layers.torch import EinMix
```
Import path for layers depends on the specific deep learning framework (e.g., `einops.layers.tf`, `einops.layers.jax`).

Quickstart last tested: 2026-04-24

This quickstart demonstrates the core `rearrange` operation using NumPy. It shows how to combine dimensions (e.g., batch and height) and flatten dimensions (e.g., width and channel) using a concise, readable pattern string.

import numpy as np
from einops import rearrange

# Suppose we have a batch of 6 images, each 96x96 with 3 color channels
images = np.random.randn(6, 96, 96, 3)
print(f"Original shape: {images.shape}")

# Rearrange to stack images vertically (batch and height become one dimension)
stacked_images = rearrange(images, 'b h w c -> (b h) w c')
print(f"Stacked shape: {stacked_images.shape}")

# Alternatively, flatten width and channel for a 2D representation
flattened_data = rearrange(images, 'b h w c -> b h (w c)')
print(f"Flattened shape: {flattened_data.shape}")