omeco 0.1.2

Tensor network contraction order optimization

One More Einsum Contraction Order (OMECO)

Tensor network contraction order optimization in Rust.

Ported from OMEinsumContractionOrders.jl.

Overview

When contracting multiple tensors together, the order of contractions significantly affects computational cost. Finding the optimal contraction order is NP-complete, but good heuristics can find near-optimal solutions quickly.

This library provides two optimization algorithms:

• GreedyMethod: Fast O(n² log n) greedy algorithm
• TreeSA: Simulated annealing for higher quality solutions

Installation

Python

pip install omeco

Rust

Add to your Cargo.toml:

[dependencies]
omeco = "0.1"

Python Quick Start

from omeco import optimize_greedy, contraction_complexity

# Matrix chain: A[0,1] × B[1,2] × C[2,3] → D[0,3]
ixs = [[0, 1], [1, 2], [2, 3]]
out = [0, 3]
sizes = {0: 100, 1: 200, 2: 50, 3: 100}

# Optimize contraction order
tree = optimize_greedy(ixs, out, sizes)

# Check complexity
complexity = contraction_complexity(tree, ixs, sizes)
print(f"Time: 2^{complexity.tc:.2f}, Space: 2^{complexity.sc:.2f}")

Using with PyTorch

The optimized contraction tree can be used with PyTorch to perform tensor contractions efficiently:

import torch
from omeco import optimize_greedy, contraction_complexity

# Step 1: Define einsum notation and input tensors
ixs = [[0, 1], [1, 2], [2, 3], [3, 4]]  # A(i,j) × B(j,k) × C(k,l) × D(l,m)
out = [0, 4]  # Output: (i,m)
dims = {0: 100, 1: 50, 2: 80, 3: 60, 4: 100}

tensors = [
    torch.randn(dims[0], dims[1]),
    torch.randn(dims[1], dims[2]),
    torch.randn(dims[2], dims[3]),
    torch.randn(dims[3], dims[4]),
]

# Step 2: Optimize contraction order
tree = optimize_greedy(ixs, out, dims)
print(f"Optimized tree: {tree}")

# Step 3: Contract using the optimized order
def einsum_int(ixs, iy, tensors):
    """Einsum with integer index labels."""
    labels = {l: chr(ord('a') + i) for i, l in enumerate(sorted(set(sum(ixs, []) + iy)))}
    eq = ",".join("".join(labels[l] for l in ix) for ix in ixs) + "->" + "".join(labels[l] for l in iy)
    return torch.einsum(eq, *tensors)

def contract(tree_dict, tensors):
    """Contract tensors according to the optimized tree."""
    if "tensor_index" in tree_dict:
        return tensors[tree_dict["tensor_index"]]
    args = [contract(arg, tensors) for arg in tree_dict["args"]]
    return einsum_int(tree_dict["eins"]["ixs"], tree_dict["eins"]["iy"], args)

result = contract(tree.to_dict(), tensors)

# Step 4: Verify against native einsum
expected = torch.einsum("ij,jk,kl,lm->im", *tensors)
print(f"Max difference: {torch.max(torch.abs(result - expected)):.2e}")

See examples/pytorch_tensor_network_example.py for a complete example.

Slicing to Reduce Memory

When space complexity is too high, use slice_code to automatically find indices to slice over. This trades time for space - each sliced index adds a loop but reduces peak memory.

from omeco import optimize_code, slice_code, contraction_complexity, sliced_complexity
from omeco import TreeSA, TreeSASlicer

ixs = [[0, 1], [1, 2], [2, 3]]
out = [0, 3]
sizes = {0: 100, 1: 200, 2: 50, 3: 100}

# Step 1: Optimize contraction order
tree = optimize_code(ixs, out, sizes, TreeSA.fast())
c = contraction_complexity(tree, ixs, sizes)
print(f"Original: tc=2^{c.tc:.2f}, sc=2^{c.sc:.2f}")

# Step 2: Slice to reduce memory (target sc=10 means ~1024 elements max)
slicer = TreeSASlicer.fast().with_sc_target(10.0)
sliced = slice_code(tree, ixs, sizes, slicer)

c_sliced = sliced_complexity(sliced, ixs, sizes)
print(f"Sliced:   tc=2^{c_sliced.tc:.2f}, sc=2^{c_sliced.sc:.2f}")
print(f"Indices to loop over: {sliced.slicing()}")

TreeSASlicer Parameters:

• sc_target: Target space complexity (log2 scale). Default: 30.0
• ntrials: Number of parallel optimization trials. Default: 10
• niters: Iterations per temperature level. Default: 10
• optimization_ratio: Controls iteration count for slicing. Default: 2.0

Presets:

• TreeSASlicer() - Default configuration
• TreeSASlicer.fast() - Faster but potentially lower quality

Rust Quick Start

Two core features are exposed in the quick start below: optimizing contraction orders and slicing for lower peak memory.

use omeco::{
    EinCode, GreedyMethod, SlicedEinsum, contraction_complexity, optimize_code, sliced_complexity,
};
use std::collections::HashMap;

// Matrix chain: A[i,j] * B[j,k] * C[k,l] -> D[i,l]
let code = EinCode::new(
    vec![vec!['i', 'j'], vec!['j', 'k'], vec!['k', 'l']],
    vec!['i', 'l']
);

// Define tensor dimensions
let mut sizes = HashMap::new();
sizes.insert('i', 100);
sizes.insert('j', 200);
sizes.insert('k', 50);
sizes.insert('l', 100);

// 1) Optimize contraction order
let optimized = optimize_code(&code, &sizes, &GreedyMethod::default()).unwrap();

let complexity = contraction_complexity(&optimized, &sizes, &code.ixs);
println!("Time complexity: 2^{:.2}", complexity.tc);
println!("Space complexity: 2^{:.2}", complexity.sc);

// 2) Slice to reduce memory (trade time for space)
let sliced = SlicedEinsum::new(vec!['j'], optimized);
let sliced_complexity = sliced_complexity(&sliced, &sizes, &code.ixs);
println!("Sliced space complexity: 2^{:.2}", sliced_complexity.sc);

Documentation

API documentation is available via cargo doc --open or at docs.rs/omeco.

Algorithms

GreedyMethod

Iteratively contracts the tensor pair with minimum cost:

use omeco::{EinCode, optimize_code, GreedyMethod};

let code = EinCode::new(
    vec![vec!['a', 'b'], vec!['b', 'c'], vec!['c', 'd']],
    vec!['a', 'd']
);
let sizes = omeco::uniform_size_dict(&code, 10);

// Default: deterministic greedy
let result = optimize_code(&code, &sizes, &GreedyMethod::default());

// Stochastic greedy with temperature
let stochastic = GreedyMethod::new(0.0, 1.0);
let result = optimize_code(&code, &sizes, &stochastic);

Parameters:

• alpha: Balances output size vs input size reduction (0.0 to 1.0)
• temperature: Enables Boltzmann sampling (0.0 = deterministic)

TreeSA

Simulated annealing with local tree mutations:

use omeco::{EinCode, optimize_code, TreeSA, ScoreFunction};

let code = EinCode::new(
    vec![vec!['i', 'j'], vec!['j', 'k'], vec!['k', 'l'], vec!['l', 'm']],
    vec!['i', 'm']
);
let sizes = omeco::uniform_size_dict(&code, 10);

// Fast configuration (fewer iterations)
let result = optimize_code(&code, &sizes, &TreeSA::fast());

// Default configuration (higher quality)
let result = optimize_code(&code, &sizes, &TreeSA::default());

// Custom configuration with space constraint
let custom = TreeSA::default()
    .with_sc_target(15.0)  // Target space complexity
    .with_ntrials(20);     // More parallel trials
let result = optimize_code(&code, &sizes, &custom);

Parameters:

• betas: Inverse temperature schedule
• ntrials: Number of parallel trials (uses rayon)
• niters: Iterations per temperature level
• score: Scoring function with complexity weights

Complexity Metrics

Three complexity metrics are computed (all in log2 scale):

use omeco::{EinCode, optimize_code, GreedyMethod, contraction_complexity};

let code = EinCode::new(
    vec![vec!['i', 'j'], vec!['j', 'k']],
    vec!['i', 'k']
);
let mut sizes = std::collections::HashMap::new();
sizes.insert('i', 100);
sizes.insert('j', 200);
sizes.insert('k', 100);

let optimized = optimize_code(&code, &sizes, &GreedyMethod::default()).unwrap();
let c = contraction_complexity(&optimized, &sizes, &code.ixs);

println!("Time complexity (FLOPs): 2^{:.2} = {:.2e}", c.tc, c.flops());
println!("Space complexity (memory): 2^{:.2} = {:.2e}", c.sc, c.peak_memory());
println!("Read-write complexity: 2^{:.2}", c.rwc);

Custom Scoring

Control the trade-off between time and space complexity:

use omeco::{ScoreFunction, TreeSA};

// Optimize primarily for time (ignore space)
let time_score = ScoreFunction::time_optimized();

// Optimize for space with target of 2^15 elements
let space_score = ScoreFunction::space_optimized(15.0);

// Custom weights
let custom_score = ScoreFunction::new(
    1.0,   // tc_weight
    2.0,   // sc_weight (penalize space more)
    0.0,   // rw_weight
    20.0,  // sc_target
);

let config = TreeSA {
    score: custom_score,
    ..TreeSA::default()
};

Working with NestedEinsum

The optimized result is a NestedEinsum representing the contraction tree:

use omeco::{EinCode, NestedEinsum, optimize_code, GreedyMethod};

let code = EinCode::new(
    vec![vec!['i', 'j'], vec!['j', 'k'], vec!['k', 'l']],
    vec!['i', 'l']
);
let sizes = omeco::uniform_size_dict(&code, 10);

let tree = optimize_code(&code, &sizes, &GreedyMethod::default()).unwrap();

// Inspect the tree
println!("Is binary tree: {}", tree.is_binary());
println!("Number of tensors: {}", tree.leaf_count());
println!("Tree depth: {}", tree.depth());

// Get contraction order (leaf indices)
let order = tree.leaf_indices();
println!("Contraction involves tensors: {:?}", order);

Sliced Contractions

For memory-constrained scenarios, use SlicedEinsum:

use omeco::{NestedEinsum, SlicedEinsum, sliced_complexity};

// Assume we have an optimized tree
let leaf0 = NestedEinsum::<char>::leaf(0);
let leaf1 = NestedEinsum::<char>::leaf(1);
let eins = omeco::EinCode::new(
    vec![vec!['i', 'j'], vec!['j', 'k']],
    vec!['i', 'k']
);
let tree = NestedEinsum::node(vec![leaf0, leaf1], eins.clone());

// Slice over index 'j' to reduce memory
let sliced = SlicedEinsum::new(vec!['j'], tree);

println!("Number of slices: {}", sliced.num_slices());

Integer Labels

For programmatic use, integer labels are often more convenient:

use omeco::{EinCode, optimize_code, GreedyMethod};
use std::collections::HashMap;

// Using usize labels instead of char
let code: EinCode<usize> = EinCode::new(
    vec![vec![0, 1], vec![1, 2], vec![2, 3]],
    vec![0, 3]
);

let mut sizes = HashMap::new();
sizes.insert(0, 100);
sizes.insert(1, 200);
sizes.insert(2, 200);
sizes.insert(3, 100);

let result = optimize_code(&code, &sizes, &GreedyMethod::default());

Performance Tips

1. Use TreeSA::fast() for quick results - Fewer iterations, single trial
2. Increase ntrials for large problems - More parallel exploration
3. Set sc_target based on available memory - Constrains space complexity
4. Use GreedyMethod for very large networks - O(n² log n) vs O(n² × iterations)

Benchmarks

We benchmark TreeSA performance by comparing the Rust implementation (exposed to Python via PyO3) against the original Julia implementation (OMEinsumContractionOrders.jl).

Hardware: Intel Xeon Gold 6226R @ 2.90GHz

Configuration: ntrials=1, niters=50-100, βs=0.01:0.05:15.0

TreeSA Results

Problem	Tensors	Indices	Rust (ms)	Julia (ms)	Rust tc	Julia tc	Speedup
chain_10	10	11	22.9	31.6	23.10	23.10	1.38x
grid_4x4	16	24	132.4	150.7	9.18	9.26	1.14x
grid_5x5	25	40	264.0	297.7	10.96	10.96	1.13x
reg3_250	250	372	4547	5099	48.01	47.17	1.12x

Key observations:

• Rust is 10-40% faster than Julia for TreeSA optimization
• Both implementations find solutions with comparable time complexity (tc)
• The reg3_250 benchmark (random 3-regular graph with 250 nodes) shows TreeSA reduces tc from ~66 (greedy) to ~48, a 27% improvement

To run the benchmarks yourself:

# Rust (via Python)
cd benchmarks && python benchmark_python.py

# Julia
cd benchmarks && julia --project=. benchmark_julia.jl

License

MIT