einx 0.3.0
Universal Tensor Operations in Einstein-Inspired Notation for Python
License: MIT
Requires Python: >=3.8

# *einx* - Universal Tensor Operations in Einstein-Inspired Notation

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einx is a Python library that provides a universal interface to formulate tensor operations in frameworks such as Numpy, PyTorch, Jax and Tensorflow. The design is based on the following principles:

1. **Provide a set of elementary tensor operations** following Numpy-like naming: `einx.{sum|max|where|add|dot|flip|get_at|...}`
2. **Use einx notation to express vectorization of the elementary operations.** einx notation is inspired by [einops](https://github.com/arogozhnikov/einops), but introduces several novel concepts such as `[]`-bracket notation and full composability that allow using it as a universal language for tensor operations.

einx can be integrated and mixed with existing code seamlessly. All operations are [just-in-time compiled](https://einx.readthedocs.io/en/latest/more/jit.html) into regular Python functions using Python's [exec()](https://docs.python.org/3/library/functions.html#exec) and invoke operations from the respective framework.

**Getting started:**

* [Tutorial](https://einx.readthedocs.io/en/latest/gettingstarted/tutorial_overview.html)
* [Example: GPT-2 with einx](https://einx.readthedocs.io/en/latest/gettingstarted/gpt2.html)
* [How is einx different from einops?](https://einx.readthedocs.io/en/latest/faq/einops.html)
* [How is einx notation universal?](https://einx.readthedocs.io/en/latest/faq/universal.html)
* [API reference](https://einx.readthedocs.io/en/latest/api.html)

## Installation

```python
pip install einx
```

See [Installation](https://einx.readthedocs.io/en/latest/gettingstarted/installation.html) for more information.

## What does einx look like?

#### Tensor manipulation

```python
import einx
x = {np.asarray|torch.as_tensor|jnp.asarray|...}(...) # Create some tensor

einx.sum("a [b]", x)                              # Sum-reduction along second axis
einx.flip("... (g [c])", x, c=2)                  # Flip pairs of values along the last axis
einx.mean("b [s...] c", x)                        # Spatial mean-pooling
einx.sum("b (s [s2])... c", x, s2=2)              # Sum-pooling with kernel_size=stride=2
einx.add("a, b -> a b", x, y)                     # Outer sum

einx.get_at("b [h w] c, b i [2] -> b i c", x, y)  # Gather values at coordinates

einx.rearrange("b (q + k) -> b q, b k", x, q=2)   # Split
einx.rearrange("b c, 1 -> b (c + 1)", x, [42])    # Append number to each channel

                                                  # Apply custom operations:
einx.vmap("b [s...] c -> b c", x, op=np.mean)     # Spatial mean-pooling
einx.vmap("a [b], [b] c -> a c", x, y, op=np.dot) # Matmul
```

All einx functions simply forward computation to the respective backend, e.g. by internally calling `np.reshape`, `np.transpose`, `np.sum` with the appropriate arguments.

#### Common neural network operations

```python
# Layer normalization
mean = einx.mean("b... [c]", x, keepdims=True)
var = einx.var("b... [c]", x, keepdims=True)
x = (x - mean) * torch.rsqrt(var + epsilon)

# Prepend class token
einx.rearrange("b s... c, c -> b (1 + (s...)) c", x, cls_token)

# Multi-head attention
attn = einx.dot("b q (h c), b k (h c) -> b q k h", q, k, h=8)
attn = einx.softmax("b q [k] h", attn)
x = einx.dot("b q k h, b k (h c) -> b q (h c)", attn, v)

# Matmul in linear layers
einx.dot("b...      [c1->c2]",  x, w)              # - Regular
einx.dot("b...   (g [c1->c2])", x, w)              # - Grouped: Same weights per group
einx.dot("b... ([g c1->g c2])", x, w)              # - Grouped: Different weights per group
einx.dot("b  [s...->s2]  c",    x, w)              # - Spatial mixing as in MLP-mixer
```

See [Common neural network ops](https://einx.readthedocs.io/en/latest/gettingstarted/commonnnops.html) for more examples.

#### Optional: Deep learning modules

```python
import einx.nn.{torch|flax|haiku|equinox|keras} as einn

batchnorm       = einn.Norm("[b...] c", decay_rate=0.9)
layernorm       = einn.Norm("b... [c]") # as used in transformers
instancenorm    = einn.Norm("b [s...] c")
groupnorm       = einn.Norm("b [s...] (g [c])", g=8)
rmsnorm         = einn.Norm("b... [c]", mean=False, bias=False)

channel_mix     = einn.Linear("b... [c1->c2]", c2=64)
spatial_mix1    = einn.Linear("b [s...->s2] c", s2=64)
spatial_mix2    = einn.Linear("b [s2->s...] c", s=(64, 64))
patch_embed     = einn.Linear("b (s [s2->])... [c1->c2]", s2=4, c2=64)

dropout         = einn.Dropout("[...]",       drop_rate=0.2)
spatial_dropout = einn.Dropout("[b] ... [c]", drop_rate=0.2)
droppath        = einn.Dropout("[b] ...",     drop_rate=0.2)
```

See `examples/train_{torch|flax|haiku|equinox|keras}.py` for example trainings on CIFAR10, [GPT-2](https://einx.readthedocs.io/en/latest/gettingstarted/gpt2.html) and [Mamba](https://github.com/fferflo/weightbridge/blob/master/examples/mamba2flax.py) for working example implementations of language models using einx, and [Tutorial: Neural networks](https://einx.readthedocs.io/en/latest/gettingstarted/tutorial_neuralnetworks.html) for more details.

#### Just-in-time compilation

einx traces the required backend operations for a given call into graph representation and just-in-time compiles them into a regular Python function using Python's [`exec()`](https://docs.python.org/3/library/functions.html#exec). This reduces overhead to a single cache lookup and allows inspecting the generated function. For example:

```python
>>> x = np.zeros((3, 10, 10))
>>> graph = einx.sum("... (g [c])", x, g=2, graph=True)
>>> print(graph)
import numpy as np
def op0(i0):
    x0 = np.reshape(i0, (3, 10, 2, 5))
    x1 = np.sum(x0, axis=3)
    return x1
```

See [Just-in-time compilation](https://einx.readthedocs.io/en/latest/more/jit.html) for more details.