MHAF-GPU — drop-in self-optimizing compute stack

[u/Mikey-506]

A portable runtime that attaches to an existing PyTorch module or callable, selects a device, autotunes precision and compilation tactics, and writes an append-only evidence ledger with explicit null baselines and pass/fail gates.

This repository implements the systems layer that the MHAF v2.0 note asks for (typed operators, measurable proxies, null hypotheses, a ledger). It does not treat the note’s consciousness ontology, microtubule claims, or 99.99999 % efficiency target as established science. Those claims remain unvalidated. What is implemented here is testable performance engineering.

What “seamless” means

from mhaf_gpu import wrap
import torch.nn as nn

model = wrap(existing_nn_module)   # same forward(...) signature
y = model(x)                       # first call calibrates, later calls reuse the winner

No change to optimizer, dataloader, or loss. Tensors are moved to the selected device; mixed precision is applied only when the device supports it.

Process-wide attachment:

from mhaf_gpu import AdaptiveRuntime
rt = AdaptiveRuntime.attach()
model = rt.wrap(model)
x = rt.to_device(x)

Operator mapping (engineering, not ontology)

| Symbol | MHAF name | Implementation in this stack | |--------|-----------|------------------------------| | S | Self-reference | Graph / parameter fingerprint of the hosted module | | T | Information–entropy | Activation bit-volume and Landauer diagnostic | | G | Geometry | Shape, stride, channels-last layout | | C | Computational closure | torch.compile when the backend supports it | | P | Participatory collapse | Argmax over measured tactic scores | | A | Autopoietic feedback | Closed-loop use of the previous trial’s throughput | | K | Causal ordering | Device synchronize around timed regions |

Ablating an operator (OperatorSet.ablate("C")) disables that pass so the Minimal Basis Conjecture can be tested as a systems experiment: does removing compilation, selection, or feedback reduce generative capacity (throughput × numeric fidelity)?

Sophia score

The paper’s weighting is used as an efficiency scalar, not as a claim about cognition:

s = w1·coherence + w2·compression + w3·prediction + w4·reproducibility − w5·ambiguity

where coherence is a squash of the throughput ratio versus the eager null, compression is a squash of the inverse peak-memory ratio, prediction is a squash of inverse error versus a reference output, and ambiguity is the coefficient of variation across repeats. The 0.65 threshold is recorded as a reference line, not as a discovered constant of nature.

Falsification gates (systems)

• G-throughput: tuned tactic must not fall more than 5 % below the eager null (speedup ≥ 0.95).
• G-numeric: mean-square deviation from the reference remains finite.

Additional gates from the note (BPMI quadratic law, 130 Hz / 9 Hz coupling, colchicine / microtubule correlation) are not implemented, because they require EEG / pharmacology protocols that this runtime does not perform.

Layout

src/mhaf_gpu/
  device.py      hardware plan (CUDA / ROCm / MPS / CPU)
  adapters.py    wrap() for modules and callables
  tuner.py       closed-loop tactic search
  engine.py      executable MHAFEngine with Sophia RG step
  metrics.py     Sophia score + EvidenceLedger
  operators.py   S,T,G,C,P,A,K as runtime passes
  kernels.py     portable fused ops (SDP, RMSNorm, GELU-linear)
  runtime.py     AdaptiveRuntime façade
  cli.py         mhaf-gpu-bench

Install and run

pip install -e .
python examples/drop_in.py
python -m mhaf_gpu.cli --batch 64 --dim 256
python -m pytest tests/test_stack.py -q

CUDA is used when torch.cuda.is_available() is true. Otherwise the identical API runs on CPU. Compilation tactics that fail on a given backend are skipped; the eager null always remains in the ledger.

Honest limits

• There is no holographic bulk, no Gödel tensor on the GPU, and no Orch-OR hardware path. Those objects are not defined at the level of CUDA.
• torch.compile speedups depend on the vendor compiler and appear after warmup. The first calibration call is therefore slower than subsequent calls.
• Landauer’s principle is reported as a thermodynamic lower bound on irreversible bit erasure, not as measured GPU wall-plug energy.
• Multi-node collectives (NCCL / RCCL) are out of scope of v1.0; attach this runtime inside each rank of an existing DDP/FSDP job.

Version

mhaf-gpu 1.0.0 — Apache-2.0.

https://github.com/TaoishTechy/Drops/blob/main/mhaf-gpu-stack.zip