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license: other
license_name: sovereign-source-license-v2
library_name: custom
tags:
- code
- sovereign-compute
---
# Assembly Bite β ML Macromodel Corpus
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**Author:** Ahmad Ali Parr
**Trust:** Bel Esprit D'Accord Irrevocable Trust Β· EIN 42-697643
> Low-level pseudo-assembly language for representing ML models at the instruction level. Token matchers, tree matchers, full transformer macromodels with multiplicity β plus production SASS/PTX kernels for sm_89 (RTX 4090).
---
## What Is Assembly Bite
Assembly Bite is Ahmad's custom pseudo-assembly language for expressing ML model computations at the byte-code level. Four instruction types:
- `.DATA` β memory layout declarations (`.word`, `.repl`, `.float`)
- `.CODE` β instruction stream (`LOAD`, `STORE`, `CALL`, `MATMUL`, `ADD`, `SUB`, `CMP`, `JEQ`, etc.)
- Subroutine calls for primitive operations (`MATMUL`, `SOFTMAX`, `RELU`, `LAYER_NORM`, `ADD_BIAS`)
- No invented syntax β grounded in standard CS algorithms
---
## Contents
```
examples/
token-matcher/token_matcher.asm β Literal token ID sequence matcher (sliding window)
tree-matcher/tree_matcher.asm β DFS path matcher on [id, child, sibling] trees
transformer/transformer_macromodel.asm β Full transformer: 24L Γ 12H Γ 768D Γ M=4
python/
deberta_encoder.py β DeBERTa-v3 encoder wrapper + instruction token
gguf_dag_pipeline.py β GGUF load + networkx DAG pipeline
sass/
flash_attention.ptx β Flash attention paged (sm_89 Hopper)
dequant_q4k.ptx β GGUF Q4_K dequant PTX (sm_89)
dequant_q4k.sass β GGUF Q4_K dequant SASS
cuda_kernels.c β Host launch wrappers
mamba_bind.h β Mamba SSM + GGUF binding header
qemu_arm64_holyc.HC β HolyC QEMU ARM64 integration
```
---
## Transformer Macromodel β Multiplicity Architecture
The key insight in `transformer_macromodel.asm`: each neuron block has **M copies** (default M=4). The outer loop is `LΓAΓM` β layer Γ head Γ multiplicity. Each copy computes Q/K/V independently. Results are summed across copies before the next layer.
```
Parameters: L=24, A=12, D=768, H=3072, M=4
Weight layout: W_Q[L][A][M][D][D] = 24Γ12Γ4Γ768Γ768
Per copy: full attention + FFN + residual + layer norm
Aggregate: LAYER_ACC += LAYER_OUT for each M copy
```
This is distinct from standard multi-head attention β it's multiplicity within each head, not across heads.
---
## SASS/PTX Kernels β sm_89 (RTX 4090)
### flash_attention.ptx
Full paged flash attention with TMA async copy and WMMA tensor core tiles.
### dequant_q4k.ptx / dequant_q4k.sass
GGUF Q4_K block dequantization. 32-value blocks β FP16. Each thread processes 8 values (4 packed bytes). 36 bytes per block layout: `[32 bytes packed][2 bytes scale][2 bytes min]`.
```
Grid: ceil(num_blocks / 256) blocks
Block: 256 threads
Dequant: q_val * scale + min β FP16
```
---
## Build
```bash
# PTX β Cubin (requires CUDA 12.x + SM89 GPU)
ptxas -arch=sm_89 sass/dequant_q4k.ptx -o dequant_q4k.cubin
ptxas -arch=sm_89 sass/flash_attention.ptx -o flash_attention.cubin
# Host wrappers
nvcc -arch=sm_89 sass/cuda_kernels.c -o kernels.so
# Python
pip install torch transformers llama_cpp_python networkx
python python/deberta_encoder.py
```
---
Β© 2026 Bel Esprit D'Accord Irrevocable Trust Β· Patent Pending Β· ΞΈ = 89/2462
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