trntensor 0.1.2

Tensor contractions for AWS Trainium via NKI

trntensor

Tensor contractions for AWS Trainium via NKI.

Einstein summation with contraction planning, CP and Tucker decompositions. Expresses scientific tensor workloads naturally instead of decomposing to GEMM. Part of the trnsci scientific computing suite (github.com/trnsci).

Install

pip install trntensor

Usage

import torch
import trntensor

# Einsum — drop-in for torch.einsum with contraction planning
C = trntensor.einsum("ij,jk->ik", A, B)           # matmul
T = trntensor.einsum("ap,bp->ab", B_i, B_j)       # DF-MP2 contraction
X = trntensor.einsum("mi,mnP->inP", C_occ, eri)   # AO→MO transform

# Contraction planning
plan = trntensor.plan_contraction("ij,jk->ik", A, B)
flops = trntensor.estimate_flops("ij,jk->ik", A, B)

# CP decomposition (tensor hypercontraction)
factors, weights = trntensor.cp_decompose(tensor, rank=10)
reconstructed = trntensor.cp_reconstruct(factors, weights)

# Tucker decomposition (HOSVD)
core, factors = trntensor.tucker_decompose(tensor, ranks=(5, 5, 5))

Operations

Category	Operation	Description
Contraction	einsum	General tensor contraction
Contraction	multi_einsum	Multiple contractions (fusion-ready)
Planning	plan_contraction	Analyze and select strategy
Planning	estimate_flops	FLOPs for a contraction
Decomposition	cp_decompose	CP/PARAFAC via ALS
Decomposition	tucker_decompose	Tucker via HOSVD

Status

• Einsum with matmul/bmm/torch dispatch
• Contraction planner
• CP decomposition (ALS)
• Tucker decomposition (HOSVD)
• DF-MP2 einsum example
• NKI fused contraction kernels
• Multi-contraction fusion
• Optimal contraction ordering (like opt_einsum)

Related Projects

Project	What
trnfft	FFT + complex ops
trnblas	BLAS operations
trnsolver	Linear solvers
trnrand	Random number generation
trnsparse	Sparse operations

License

Apache 2.0 — Copyright 2026 Scott Friedman