fast_trimul

Fused Triangle Multiplicative Update (AlphaFold2 / OpenFold) built on hand-written CUTLASS CuTe DSL kernels — a drop-in nn.Module for the structural-biology stacks (OpenFold, Boltz, Chai, Protenix).

Honest status. The kernels are fp16 and numerically correct (they match PyTorch fp16 to fp16 tolerance). On a fair comparison (torch.compile(..., mode="reduce-overhead") in fp16) they are slower than torch.compile above small N today — the GEMMs are not yet epilogue-fused. The wins are: correctness, a drop-in API, and (with full fusion, future work) lower memory. Full GEMM epilogue fusion and a FlashAttention-style megakernel are future work — see Limitations.

Install

pip install fast_trimul          # or: uv pip install fast_trimul

Requires a CUDA GPU, torch, nvidia-cutlass-dsl, and cuda-python. Kernels JIT-compile on first use (one-time cost, then cached in-process).

Quick start

import torch
from fast_trimul import FastTriangleMultiplication

module = FastTriangleMultiplication(d_z=128, d_c=128, mode="outgoing").cuda()
z = torch.randn(1, 256, 256, 128, device="cuda")          # (B, N, N, d_z)
mask = torch.ones(1, 256, 256, device="cuda")             # optional (B, N, N)
out = module(z, mask=mask)                                 # same dtype as z

Low-level functional API (FlashAttention style):

from fast_trimul import functional
out = functional.triangle_multiplication(z, module._impl, mask=mask)

Drop-in monkeypatch for the 4 target libraries

Each helper replaces the library's TriMul class with an adapter matching its constructor. Patch before building the model. See Limitations for the pretrained-weight caveat.

OpenFold

import fast_trimul.integrations as fti
fti.patch_openfold()          # patches Outgoing + Incoming
# ... now build your OpenFold model as usual ...

Equivalent manual form:

import openfold.model.triangular_multiplicative_update as of_tri
from fast_trimul.integrations import adapter
of_tri.TriangleMultiplicationOutgoing = adapter("outgoing")
of_tri.TriangleMultiplicationIncoming = adapter("incoming")

Boltz-1 / BoltzDesign

import fast_trimul.integrations as fti
fti.patch_boltz()

Manual form:

import boltz.model.layers.triangular_mult as b_tri
from fast_trimul.integrations import adapter
b_tri.TriangleMultiplicationOutgoing = adapter("outgoing")
b_tri.TriangleMultiplicationIncoming = adapter("incoming")

Protenix

import fast_trimul.integrations as fti
fti.patch_protenix()

Manual form:

import protenix.model.modules.pairformer as p_tri
from fast_trimul.integrations import adapter
p_tri.TriangleMultiplication = adapter("outgoing")

Chai-1

Chai's module path is version-dependent, so patch the attribute explicitly (replace the import path with the one in your installed version):

from fast_trimul.integrations import adapter
import chai_lab.model.<...>.triangle_mult as c_tri   # <- verify path for your version
c_tri.TriangleMultiplicationOutgoing = adapter("outgoing")
c_tri.TriangleMultiplicationIncoming = adapter("incoming")

API

  • fast_trimul.nn.FastTriangleMultiplication(d_z, d_c=None, mode="outgoing") — high-level module, forward(z, mask=None).
  • fast_trimul.functional.triangle_multiplication(z, params, mask=None) — low-level functional call.
  • fast_trimul.integrations.{patch_openfold, patch_boltz, patch_protenix, adapter} — monkeypatch helpers.

Limitations (read before relying on it)

  • Slower than torch.compile (fp16) above small N. Correctness and drop-in compatibility come first; speed parity needs the epilogue fusion / megakernel (future work).
  • fp16 only. bf16/fp32 inputs are cast to fp16 and back; keep the module in fp16 (do not call .float()/.bfloat16() on it).
  • Pretrained weights need name remapping. Each library names its projections/norms differently, so a strict checkpoint load will not line up. Patch-then-train, or supply a parameter remap. Loading pretrained checkpoints is not yet automated.
  • Mask semantics are approximate. The mask is applied to the pair tensor in and out; validate against each library's exact masking before production use.
  • Backward is correct but not fast (torch recompute), so it helps inference more than training throughput.
  • Ampere (sm80) tested. Hopper/Blackwell + fp8 are future work.
  • import fast_trimul needs a CUDA GPU (device properties are read at import).

License

MIT (this project). The GEMM core is derived from NVIDIA CUTLASS and is licensed under BSD 3-Clause — see NOTICE.

Project links

Key dates

PyPI data
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  • Released:
Newer release available (3.0.4)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for tiagomonteiro0715 from gravatar.com tiagomonteiro0715

Credits

Author: Tiago Monteiro

License

MIT

Requires

Python >=3.10

Provides Extra

test

Tags

alphafold cute cutlass gpu-kernel openfold triangle-multiplication

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