opt-einsum-fx

library0.1.4pypypi✓ verified 84d ago

opt-einsum-fx is a Python library that leverages opt_einsum and PyTorch FX to optimize Einstein summation (einsum) expressions within PyTorch computation graphs. It aims to reduce the overall execution time and memory footprint of complex tensor contractions by intelligently reordering operations. The current version is 0.1.4, with the last release in November 2021, indicating a maintenance-level release cadence.

ai-ml data

pip install opt_einsum_fx

github ↗package ↗

INSTALL

IMPORT

SIG · OPT-EINSUM-FX

opt-einsum-fx

ai-mlpythonv0.1.4

Install

65.9s avg

Import

5334ms

Disk

4787MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.1.4 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 74.18s

py 3.11

✕ build_error

✓ 67.4s

py 3.12

✕ build_error

✓ 63.7s

py 3.13

✕ build_error

✓ 58.2s

py 3.9

✕ build_error

1/4 runs

4787MB installed

● package 4787MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

opt_einsum_fx

✓ import opt_einsum_fx

optimize_einsums_full

✓ from opt_einsum_fx import optimize_einsums_full

Main function for full graph optimization.

This quickstart demonstrates how to use `opt_einsum_fx` to optimize a PyTorch function containing an `einsum` operation. It involves symbolic tracing the function with `torch.fx.symbolic_trace`, providing example inputs for shape inference, and then applying `opt_einsum_fx.optimize_einsums_full` to get an optimized graph module. The outputs of the original and optimized graphs are compared to ensure correctness.

import torch import torch.fx import opt_einsum_fx def einmatvecmul(a, b, vec): """Batched matrix-matrix-vector product using einsum""" return torch.einsum("zij,zjk,zk->zi", a, b, vec) # 1. Create an FX graph module from the function graph_mod = torch.fx.symbolic_trace(einmatvecmul) # 2. Define example inputs for shape propagation and optimization # These shapes are used to determine the optimal contraction path. example_inputs = ( torch.randn(7, 4, 5), torch.randn(7, 5, 3), torch.randn(7, 3) ) # 3. Optimize the einsums within the FX graph graph_opt = opt_einsum_fx.optimize_einsums_full( model=graph_mod, example_inputs=example_inputs ) # 4. (Optional) Print the optimized code to see the changes print("Original code:\n", graph_mod.code) print("Optimized code:\n", graph_opt.code) # 5. Run the optimized graph and verify correctness output_original = graph_mod(*example_inputs) output_optimized = graph_opt(*example_inputs) assert torch.allclose(output_original, output_optimized) print("\nOptimization successful and outputs match!")

Debug

Known issues

gotchaThe latest release (v0.1.4) explicitly lists compatibility with PyTorch 1.9 and 1.10. While it might work with newer PyTorch versions (e.g., 2.x), direct compatibility with the latest PyTorch versions is not guaranteed and should be tested by the user.

fix

Thoroughly test `opt_einsum_fx` with your specific PyTorch version. Refer to the `opt_einsum_fx` GitHub repository for any community reports or updates on newer PyTorch compatibility.

affects: <=0.1.4

gotcha`opt_einsum_fx` relies on `torch.fx.symbolic_trace` to build computation graphs. `symbolic_trace` has limitations and may not correctly trace all Python language features or PyTorch operations. Functions with control flow, external data dependencies, or non-traceable operations will fail or produce incorrect graphs.

fix

Ensure the functions you intend to optimize are compatible with `torch.fx.symbolic_trace`. Simplify functions, move non-traceable logic outside, or use custom tracers if necessary. Consult PyTorch FX documentation for tracing limitations.

affects: *

gotchaThe underlying `opt_einsum` library, used by `opt_einsum_fx`, employs heuristic algorithms to find contraction paths because determining the truly optimal path for einsum expressions is an NP-hard problem. This means the generated 'optimized' path might not always be the absolute best, especially for very complex expressions.

fix

While `opt_einsum_fx` aims for significant improvements, be aware that the optimization is heuristic. For critical performance scenarios, consider benchmarking different inputs or manually inspecting the contraction path if `opt_einsum` exposes such functionality.

affects: *

gotchaInefficient einsum contraction orders can lead to the creation of extremely large intermediate tensors, potentially causing out-of-memory (OOM) errors. `opt_einsum_fx`'s `EfficientShapeProp` specifically avoids executing einsums during shape propagation to mitigate this, but if `opt_einsum_fx` fails to optimize an expression, or is not applied, such issues can arise.

fix

Always use `opt_einsum_fx` for complex einsum expressions to benefit from its optimization and shape propagation strategies. If OOM errors persist, analyze the einsum equation and input tensor shapes to identify potential intermediate tensor explosion, and simplify the expression if possible.

affects: *

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'opt_einsum_fx'

The `opt-einsum-fx` library or one of its dependencies is not installed, or the Python environment where it was installed is not active.

fix

Ensure the library is installed using pip: `pip install opt-einsum-fx`

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed.

This is a common warning from pip indicating conflicting dependencies in your Python environment, often when `opt-einsum-fx` is installed alongside other complex libraries like PyTorch, scikit-learn, or torchaudio.

fix

Carefully manage your Python environment using virtual environments (like `venv` or `conda`) and try to install `opt-einsum-fx` with its core dependencies first, then add other libraries, resolving conflicts as they arise. Consider pinning specific versions of conflicting packages.

RuntimeError: CUDA out of memory

Although `opt-einsum-fx` aims to optimize einsum operations to reduce memory footprint, inefficient or very large einsum expressions, even after optimization, can still exceed available GPU memory, especially when combined with other memory-intensive PyTorch operations.

fix

Review the einsum expressions and the overall computation graph. Try reducing batch sizes, breaking down very large einsum operations into smaller, sequential steps, or using PyTorch's `torch.cuda.empty_cache()` if memory fragmentation is suspected. Ensure `opt-einsum-fx.optimize_einsums` is being correctly applied to the relevant parts of your model.

AttributeError: module 'opt_einsum_fx' has no attribute 'optimize_einsums'

The `optimize_einsums` function is a key entry point in `opt-einsum-fx`, but this error occurs if the function is called incorrectly, or if an older/corrupted installation of the library is being used, or if the import statement is wrong.

fix

Ensure you are importing correctly (`from opt_einsum_fx import optimize_einsums`) and that `opt-einsum-fx` is installed in the current environment and up to date (`pip install --upgrade opt-einsum-fx`). Refer to the official documentation for the correct usage of `optimize_einsums`.

Upgrade

Version history

0.1.4latest on PyPI · released Nov 7, 2021

Audit

Dependencies

opt_einsumrequiredCore dependency for einsum optimization algorithms.

torchrequiredRequired for PyTorch FX graph rewriting and tensor operations.

packagingrequiredRuntime dependency added for PyTorch compatibility in v0.1.3.

Agent activity

4 hits · last 30 days

node

Resources

githubhttps://github.com/Linux-cpp-lisp/opt_einsum_fx ↗

packagehttps://pypi.org/project/opt-einsum-fx/ ↗