BLAS operations for AWS Trainium via NKI (Neuron Kernel Interface).
Trainium ships no BLAS library. trnblas provides Level 1-3 BLAS operations with NKI kernel acceleration on the Tensor Engine, targeting scientific computing workloads that are GEMM-dominated.
Part of the trnsci scientific computing suite (github.com/trnsci).
trnblas follows the trnsci 5-phase roadmap. Active work is tracked in phase-labeled GitHub issues:
- Phase 1 — correctness: complete as of v0.4.0 (GEMM, SYRK, MP2 energy reduction kernels hardware-validated on trn1; end-to-end DF-MP2 validated against PySCF at nanohartree tolerance).
- Phase 2 — precision (next): double-double FP64 GEMM for chemistry workloads. Unblocks trnsolver#27 and trntensor#28.
- Phase 3 — perf: tile sweeps, fused DF-MP2 kernels, true 3D batched GEMM, NEFF cache reuse.
- Phase 4 — multi-chip: tensor-parallel GEMM across NeuronCores.
- Phase 5 — generation: trn2 FP16-accumulate GEMM path.
Suite-wide tracker: trnsci/trnsci#1.
NVIDIA has cuBLAS with 152 optimized routines. Trainium has torch.matmul. That's fine for ML training but insufficient for scientific computing codes that need TRSM, SYRK, SYMM, and batched GEMM with specific transpose/scaling semantics.
trnblas closes this gap — same BLAS API surface, NKI-accelerated GEMM on Trainium, PyTorch fallback everywhere else.
pip install trnblas
# With Neuron hardware support
pip install trnblas[neuron]import torch
import trnblas
# Level 3 — Matrix multiply (the hot path)
C = trnblas.gemm(alpha=1.0, A=A, B=B, beta=0.5, C=C_init, transA=True)
# Batched GEMM (DF-MP2 tensor contractions)
C = trnblas.batched_gemm(1.0, A_batch, B_batch)
# Symmetric matrix multiply (Fock builds)
F = trnblas.symm(1.0, density, H_core, side="left")
# Triangular solve (Cholesky-based density fitting)
X = trnblas.trsm(1.0, L, B, uplo="lower")
# Symmetric rank-k update (metric construction)
J = trnblas.syrk(1.0, integrals, trans=True)
# Level 2 — Matrix-vector
y = trnblas.gemv(1.0, A, x, beta=1.0, y=y)
# Level 1 — Vector operations
y = trnblas.axpy(alpha, x, y)
d = trnblas.dot(x, y)
n = trnblas.nrm2(x)# Run the density-fitted MP2 example
python examples/df_mp2.py --demo
python examples/df_mp2.py --nbasis 100 --nocc 20The example demonstrates all core BLAS operations in a realistic quantum chemistry workflow: Cholesky factorization, triangular solve, half-transform GEMMs, metric contraction, and energy evaluation.
pip install trnblas[pyscf]
python examples/df_mp2_pyscf.py # H2O / STO-3G
python examples/df_mp2_pyscf.py --mol ch4 --basis cc-pvdzRuns SCF + density fitting via PySCF, feeds the integrals through trnblas, and compares to PySCF's own DF-MP2 reference energy. Matches to < 10⁻⁷ Hartree on H2O, CH4, NH3 at cc-pvdz.
| Level | Operation | Description |
|---|---|---|
| 1 | axpy |
y = αx + y |
| 1 | dot |
x^T y |
| 1 | nrm2 |
‖x‖₂ |
| 1 | scal |
x = αx |
| 1 | asum |
Σ|xᵢ| |
| 1 | iamax |
argmax |xᵢ| |
| 2 | gemv |
y = α op(A) x + βy |
| 2 | symv |
y = α A x + βy (A symmetric) |
| 2 | trmv |
x = op(A) x (A triangular) |
| 2 | ger |
A = α x yᵀ + A |
| 3 | gemm |
C = α op(A) op(B) + βC |
| 3 | batched_gemm |
Batched GEMM |
| 3 | symm |
C = α A B + βC (A symmetric) |
| 3 | syrk |
C = α A Aᵀ + βC |
| 3 | trsm |
Solve op(A) X = αB |
| 3 | trmm |
B = α op(A) B |
- Level 1-3 BLAS with PyTorch backend
- GEMM with NKI dispatch stub
- DF-MP2 example
- NKI GEMM kernel validation on trn1/trn2
- NKI GEMM with stationary tile reuse
- Batched GEMM NKI kernel
- Double-double FP64 emulation
- Benchmarks vs cuBLAS
| Project | What |
|---|---|
| trnfft | FFT + complex ops for Trainium |
| trnrand | Random number generation (Philox/Sobol) for Trainium |
| trnsolver | Linear solvers and eigendecomposition |
Apache 2.0 — Copyright 2026 Scott Friedman
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