trnsparse 0.6.0

Sparse matrix operations for AWS Trainium via NKI

trnsparse

Sparse matrix operations for AWS Trainium via NKI.

CSR/COO formats, SpMV, SpMM, and integral screening for sparse scientific computing on Trainium. Part of the trnsci scientific computing suite (github.com/trnsci).

Current phase

trnsparse follows the trnsci 5-phase roadmap. Active work is tracked in phase-labeled GitHub issues:

• Phase 1 — correctness ✅ v0.2.0: NKI SpMM validated on trn1 via densify-then-GEMM (see trnsci/trnsci#3).
• v0.3.0 ✅ BSRMatrix — Trainium-native 128×128 block-sparse format; bsr_spmm NKI kernel. CSR becomes interop; BSR is the compute path.
• v0.3.2 ✅ cg_bsr, power_iteration_bsr — iterative solvers over BSR (Python loop; fused kernel gated on NKI capability).
• v0.4.0 ✅ screened_spmm — fused Schwarz-screened SpMM in one NKI dispatch.
• v0.4.2 ✅ Block-sparse attention — BSRMatrix + bsr_spmm as the primitive; examples/block_sparse_attention.py + docs/sparse_attention.md.
• v0.4.3 ✅ Architecture-friendly alternatives: chebyshev_bsr / richardson_bsr (fixed-K solvers, no inner products); block_sparse_attention_tiled (two-pass, no O(seq²) intermediate).
• Phase 3 — perf: nnz-bucketing, NKI attention kernel pair — parked on NKI indirect DMA gather / hardware validation.
• Phase 4 — multi-chip: sharded BSR across NeuronCores.
• Phase 5 — generation: trn2 DMA bandwidth exploitation.

(No Phase 2 for trnsparse — precision inherited from trnblas.)

Suite-wide tracker: trnsci/trnsci#1.

Install

pip install trnsparse

Usage

import torch
import trnsparse

# Dense → sparse
A = torch.randn(100, 100)
A[torch.abs(A) < 1.0] = 0.0
csr = trnsparse.from_dense(A)

# SpMV: y = A @ x
y = trnsparse.spmv(csr, x, alpha=2.0)

# SpMM: C = A @ B
C = trnsparse.spmm(csr, B)

# Integral screening
Q = trnsparse.schwarz_bounds(diagonal_integrals)
mask = trnsparse.screen_quartets(Q, threshold=1e-10)
stats = trnsparse.sparsity_stats(Q)

Operations

Operation	Description
spmv	Sparse × dense vector (CSR)
spmm	Sparse × dense matrix (CSR, PyTorch fallback)
bsr_spmm	Block-sparse × dense (BSR-native, Tensor Engine)
screened_spmm	Fused Schwarz-screened matmul (one NKI dispatch)
spmv_symmetric	Symmetric SpMV (half storage)
sparse_add	C = αA + βB
sparse_scale	B = αA
sparse_transpose	Aᵀ
cg_bsr	Conjugate Gradient on BSR matrix
chebyshev_bsr	Fixed-K Chebyshev semi-iteration (no inner products)
richardson_bsr	Fixed-K Richardson iteration
power_iteration_bsr	Dominant eigenpair via power iteration
jacobi_preconditioner_bsr	Diagonal preconditioner for cg_bsr
bsr_diagonal	Extract main diagonal from BSR matrix
block_sparse_attention_tiled	Two-pass sparse attention, no O(seq²) intermediate
schwarz_bounds	Schwarz screening bounds
screen_quartets	Shell quartet significance mask
density_screen	Density-weighted screening

License

Apache 2.0 — Copyright 2026 Scott Friedman

Disclaimer

trnsci is an independent open-source project. It is not sponsored by, endorsed by, or affiliated with Amazon.com, Inc., Amazon Web Services, Inc., or Annapurna Labs Ltd.

"AWS", "Amazon", "Trainium", "Inferentia", "NeuronCore", "Neuron SDK", and related identifiers are trademarks of their respective owners and are used here solely for descriptive and interoperability purposes. Use does not imply endorsement, partnership, or any other relationship.

All work, opinions, analyses, benchmark results, architectural commentary, and editorial judgments in this repository and on trnsci.dev are those of the project's contributors. They do not represent the views, positions, or commitments of Amazon, AWS, or Annapurna Labs.

Feedback directed at the Neuron SDK or Trainium hardware is good-faith ecosystem commentary from independent users. It is not privileged information, is not pre-reviewed by AWS, and should not be read as authoritative about product roadmap, behavior, or quality.

For official AWS guidance, see aws-neuron documentation and the AWS Trainium product page.