---
id: opt-einsum-fx
version: "0.1.4"
license: MIT
license_treatment: permissive
maintenance: abandoned
---

# opt-einsum-fx — Einsum optimization using opt_einsum and PyTorch FX

License: permissive · Maintenance: abandoned · Downloads: 504.3K/mo

## What it is and what it does

opt_einsum_fx wraps PyTorch's FX graph tracing to automatically optimize einsum operations within traced functions. It uses opt_einsum to reorder tensor contractions for better computational efficiency, then rewrites the traced graph with the optimized contraction sequence. The package is designed for developers working with batched tensor operations, matrix products, and other multi-tensor contractions expressed via einsum notation.

The typical workflow is to define a function containing einsum calls, trace it with torch.fx.symbolic_trace, pass the traced module to optimize_einsums_full with example inputs, and receive an optimized module with reordered contractions. The fact sheet shows a concrete example achieving roughly 2x speedup on CPU for a batched matrix-vector product, though gains depend on tensor shapes and hardware.

Use it for:

- Accelerate batched matrix operations in neural networks by reordering einsum contraction sequences.
- Optimize custom tensor algebra kernels expressed via einsum without rewriting the contraction logic.
- Profile and improve performance of multi-tensor operations in research code before deployment.
- Automatically find better contraction orders for complex tensor expressions in scientific computing.
- Reduce computational cost of attention mechanisms or other einsum-heavy layers in transformer models.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Optimizes PyTorch einsum operations and functions containing them by applying opt_einsum strategies through PyTorch FX compute graphs.

No. The package is abandoned (last commit 2022-03-17, no releases since 2021-11-07) and likely incompatible with current PyTorch and opt_einsum versions. While the idea is sound and the MIT license is permissive, the lack of maintenance means you would need to fork and patch it yourself to use it with modern libraries. For new projects, consider using opt_einsum directly or PyTorch's built-in optimization passes instead.

## Install

pip install opt-einsum-fx
uv add opt-einsum-fx
poetry add opt-einsum-fx

## Installing opt-einsum-fx

Before you install: Low install friction with a pure-Python wheel. However, the package is abandoned—last commit was 2022-03-17 and no releases since 2021-11-07. It may not be compatible with recent PyTorch or opt_einsum versions without manual updates.

License in practice: MIT license permits commercial and private use with minimal restrictions, requiring only license and copyright notice preservation.

Quickstart:

import torch
import torch.fx
import opt_einsum_fx

def einmatvecmul(a, b, vec):
    return torch.einsum("zij,zjk,zk->zi", a, b, vec)

graph_mod = torch.fx.symbolic_trace(einmatvecmul)
graph_opt = opt_einsum_fx.optimize_einsums_full(
    model=graph_mod,
    example_inputs=(torch.randn(7, 4, 5), torch.randn(7, 5, 3), torch.randn(7, 3))
)

Requires PyTorch with FX support and opt_einsum installed; symbolic_trace requires the function to be traceable (no dynamic control flow).

Verify before relying:

- Compatibility with PyTorch versions released after 2022; FX API may have evolved.
- Whether the package works with current opt_einsum releases without modification.
- Performance gains on modern hardware (GPU, recent CPU architectures).

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: abandoned
- Downloads: 504.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

einsum optimization pytorch, torch fx graph optimization, tensor contraction optimization, pytorch performance tuning, einsum reordering, compute graph optimization, tensor-optimization, pytorch-fx, performance

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