nwgrad
Differentiable sequence alignment. nwgrad runs Needleman–Wunsch (global) and Smith–Waterman (local) alignment and returns not just the score but its gradient with respect to the substitution matrix and the gap penalties — so alignment parameters can be learned by gradient descent, inside any ML pipeline.
score, grad = nwgrad.SeqPair("PLEASANTLY", "MEANLY", params).score_and_grad()
params = params + 0.01 * grad # every matrix cell and all four gap costs move together
Why nwgrad
- Gradients, not just scores. The hard gradient counts substitution pairs and gap positions along the optimal path (a subgradient of the Viterbi score). The soft gradient is the exact gradient of the log-partition function over all alignments, computed by forward–backward (in scaled probability space by default, no logarithm per cell; log space on request). Matrix and gap fields share one sign convention, so a single update rule moves them all.
- Fast. Striped SIMD kernels for SSE2, AVX2, AVX-512 and NEON ship in one binary and are chosen at runtime. Every one is bit-exact with the scalar reference: the instruction set is a speed knob, never a correctness one. Batches run on all cores; measured at 6.4 billion DP cells per second on a 16-thread laptop CPU (Intel i7-11850H, AVX-512, float32).
- Linear memory for long sequences. Affine global alignment defaults to Hirschberg (Myers–Miller) traceback, which keeps O(m+n) state instead of O(m×n) tables: titin (35,991 residues) aligned against itself needs about 0.9 MB of DP memory per thread rather than 3.9 GB.
- Built for training loops. Sequences are validated and encoded once and reused across steps. After a parameter update, banded re-alignment around the previous path costs O(length × bandwidth) instead of a full O(m × n) DP.
- Any alphabet. Protein, DNA, RNA or your own symbol set; asymmetric matrices and asymmetric gap costs; BLOSUM, PAM, VTML and NUC44 included.
- Python and C++. nanobind bindings with numpy interop (matrices and gradients are copied in and out as numpy arrays) over a header-only C++20 core.
Installation
pip install nwgrad
Prebuilt wheels cover CPython 3.10–3.14 on Linux x86_64 / aarch64 and macOS arm64.
Anywhere else pip builds from source, which needs a C++20 compiler with libstdc++'s
<experimental/simd>: GCC 11+, or Clang 13+ built against libstdc++. Apple's
system clang (libc++) and MSVC lack that header — on macOS use Homebrew gcc;
Windows is not supported.
Quick start
import nwgrad
from nwgrad.matrices import BLOSUM62 # ready-made SubstMatrix, alphabet included
params = nwgrad.AlignParams(BLOSUM62,
gap_open_a=11.0, gap_extend_a=1.0,
gap_open_b=11.0, gap_extend_b=1.0)
sp = nwgrad.SeqPair("PLEASANTLY", "MEANLY", params,
gap_model="affine", # or "linear"
mode="global", # or "local"
grad_mode="hard") # or "soft", or "none"
score, grad = sp.score_and_grad()
print(score)
print(sp.formatted()) # the alignment, ready to read
g = grad.to_dict() # numpy matrix + the four gap fields
print(g["matrix"].shape, g["gap_open_a"], g["gap_extend_b"])
A gradient is itself an AlignParams, so params + lr * grad is a complete update
step. Gap penalties are costs: a gap of length k costs gap_extend * k (linear) or
gap_open + gap_extend * k (affine), matching BioPython; _a applies to gaps in
sequence A, _b to gaps in sequence B.
Learning a substitution matrix
Any differentiable objective over alignment scores works. Here, a contrastive one: make a set of pairs you believe are homologous score higher than a set of decoys.
pos = nwgrad.SeqPairBatch(grad_mode="soft") # all cores by default
neg = nwgrad.SeqPairBatch(grad_mode="soft") # one problem type per batch
pos.add_many(homologs_a, homologs_b, params)
neg.add_many(decoys_a, decoys_b, params)
for step in range(100):
margin = pos.score_and_grad() - neg.score_and_grad() # sums of log Z
grad = pos.compute_grad() - neg.compute_grad() # d margin / d params
params = params + 1e-3 * grad
pos.set_params(params)
neg.set_params(params)
add_many() encodes the pairs in parallel straight into the batch's arrays — a few
hundred bytes per pair, no per-pair objects — so batches of millions of pairs are cheap
to set up and to keep. Per-pair scores and gradients are
available too (batch.scores(), batch.grads(), batch.weighted_grad(weights), batch[i]), for
objectives that weight each pair differently.
Good to know
- Precision. The plain names (
SeqPair,SeqPairBatch,nw_score, …) run the DP in float32;SeqPairDouble,SeqPairBatchDouble,nw_score_double, … run it in float64. Inputs and outputs are float64 either way. - Many short pairs. For millions of short pairs (e.g. miRNA × target site), a
batch's default
fill="interpair"aligns several pairs per vector instruction — 2.5–5× the per-pair"striped"fill on AVX2 — with bit-identical results; seeSeqPairBatch.fill. - Scoring without keeping pairs.
batch.align(seqs_a, seqs_b, params)returns the scores and the summed gradient and stores nothing per pair. - Stateless one-offs. Twelve functions (
nw_score,sw_affine_grad,nw_affine_soft_grad, …) align a single pair without keeping any state. - C++.
python -m nwgrad --includeprints the path to the header-only library.
Documentation
- Python API reference —
Alphabet,SubstMatrix,AlignParams,SeqPair,SeqPairBatchand the convenience functions, with every argument and contract, and a guide to migrating from 0.5. - Precision, traceback modes and SIMD kernels — what the defaults are, when to change them, and what each choice costs.
- C++ library and building from source — using the headers directly, running the C++ and sanitizer test suites.
License
MIT — see LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for nwgrad 0.6.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| nwgrad-0.6.0.tar.gz | 187.4 kB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| nwgrad-0.6.0-cp315-cp315t-musllinux_1_2_x86_64.whl | CPython 3.15 | CPython 3.15 free-threading | Linux musl 1.2+ x86-64 | Details |
| nwgrad-0.6.0-cp315-cp315t-musllinux_1_2_aarch64.whl | CPython 3.15 | CPython 3.15 free-threading | Linux musl 1.2+ ARM64 | Details |
| nwgrad-0.6.0-cp315-cp315t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.15 | CPython 3.15 free-threading | Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 | Details |
| nwgrad-0.6.0-cp315-cp315t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl | CPython 3.15 | CPython 3.15 free-threading | Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64 | Details |
| nwgrad-0.6.0-cp315-cp315t-macosx_11_0_arm64.whl | CPython 3.15 | CPython 3.15 free-threading | macOS 11.0+ ARM64 | Details |
| nwgrad-0.6.0-cp315-cp315-musllinux_1_2_x86_64.whl | CPython 3.15 | CPython 3.15 | Linux musl 1.2+ x86-64 | Details |
| nwgrad-0.6.0-cp315-cp315-musllinux_1_2_aarch64.whl | CPython 3.15 | CPython 3.15 | Linux musl 1.2+ ARM64 | Details |
| nwgrad-0.6.0-cp315-cp315-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.15 | CPython 3.15 | Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 | Details |
| nwgrad-0.6.0-cp315-cp315-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl | CPython 3.15 | CPython 3.15 | Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64 | Details |
| nwgrad-0.6.0-cp315-cp315-macosx_11_0_arm64.whl | CPython 3.15 | CPython 3.15 | macOS 11.0+ ARM64 | Details |
| nwgrad-0.6.0-cp314-cp314t-musllinux_1_2_x86_64.whl | CPython 3.14 | CPython 3.14 free-threading | Linux musl 1.2+ x86-64 | Details |
| nwgrad-0.6.0-cp314-cp314t-musllinux_1_2_aarch64.whl | CPython 3.14 | CPython 3.14 free-threading | Linux musl 1.2+ ARM64 | Details |
| nwgrad-0.6.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.14 | CPython 3.14 free-threading | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| nwgrad-0.6.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl | CPython 3.14 | CPython 3.14 free-threading | Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64 | Details |
| nwgrad-0.6.0-cp314-cp314t-macosx_11_0_arm64.whl | CPython 3.14 | CPython 3.14 free-threading | macOS 11.0+ ARM64 | Details |
| nwgrad-0.6.0-cp314-cp314-musllinux_1_2_x86_64.whl | CPython 3.14 | CPython 3.14 | Linux musl 1.2+ x86-64 | Details |
| nwgrad-0.6.0-cp314-cp314-musllinux_1_2_aarch64.whl | CPython 3.14 | CPython 3.14 | Linux musl 1.2+ ARM64 | Details |
| nwgrad-0.6.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.14 | CPython 3.14 | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| nwgrad-0.6.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl | CPython 3.14 | CPython 3.14 | Linux glibc 2.28+ ARM64, Linux glibc 2.26+ ARM64 | Details |
| nwgrad-0.6.0-cp314-cp314-macosx_11_0_arm64.whl | CPython 3.14 | CPython 3.14 | macOS 11.0+ ARM64 | Details |
| nwgrad-0.6.0-cp313-cp313-musllinux_1_2_x86_64.whl | CPython 3.13 | CPython 3.13 | Linux musl 1.2+ x86-64 | Details |
| nwgrad-0.6.0-cp313-cp313-musllinux_1_2_aarch64.whl | CPython 3.13 | CPython 3.13 | Linux musl 1.2+ ARM64 | Details |
| nwgrad-0.6.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.13 | CPython 3.13 | Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 | Details |
| nwgrad-0.6.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl | CPython 3.13 | CPython 3.13 | Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64 | Details |
| nwgrad-0.6.0-cp313-cp313-macosx_11_0_arm64.whl | CPython 3.13 | CPython 3.13 | macOS 11.0+ ARM64 | Details |
| nwgrad-0.6.0-cp312-cp312-musllinux_1_2_x86_64.whl | CPython 3.12 | CPython 3.12 | Linux musl 1.2+ x86-64 | Details |
| nwgrad-0.6.0-cp312-cp312-musllinux_1_2_aarch64.whl | CPython 3.12 | CPython 3.12 | Linux musl 1.2+ ARM64 | Details |
| nwgrad-0.6.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 | Details |
| nwgrad-0.6.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64 | Details |
| nwgrad-0.6.0-cp312-cp312-macosx_11_0_arm64.whl | CPython 3.12 | CPython 3.12 | macOS 11.0+ ARM64 | Details |
| nwgrad-0.6.0-cp311-cp311-musllinux_1_2_x86_64.whl | CPython 3.11 | CPython 3.11 | Linux musl 1.2+ x86-64 | Details |
| nwgrad-0.6.0-cp311-cp311-musllinux_1_2_aarch64.whl | CPython 3.11 | CPython 3.11 | Linux musl 1.2+ ARM64 | Details |
| nwgrad-0.6.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.11 | CPython 3.11 | Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 | Details |
| nwgrad-0.6.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl | CPython 3.11 | CPython 3.11 | Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64 | Details |
| nwgrad-0.6.0-cp311-cp311-macosx_11_0_arm64.whl | CPython 3.11 | CPython 3.11 | macOS 11.0+ ARM64 | Details |
| nwgrad-0.6.0-cp310-cp310-musllinux_1_2_x86_64.whl | CPython 3.10 | CPython 3.10 | Linux musl 1.2+ x86-64 | Details |
| nwgrad-0.6.0-cp310-cp310-musllinux_1_2_aarch64.whl | CPython 3.10 | CPython 3.10 | Linux musl 1.2+ ARM64 | Details |
| nwgrad-0.6.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.10 | CPython 3.10 | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| nwgrad-0.6.0-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl | CPython 3.10 | CPython 3.10 | Linux glibc 2.28+ ARM64, Linux glibc 2.26+ ARM64 | Details |
| nwgrad-0.6.0-cp310-cp310-macosx_11_0_arm64.whl | CPython 3.10 | CPython 3.10 | macOS 11.0+ ARM64 | Details |
Total release size: 68.2 MB
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