majiayu000 LLM-as-Computer: Compiled Transformer Stack Machine
A working computer built from transformer primitives. Every instruction fetch and stack read is a parabolic attention head (dot-product → argmax → value extraction). The transformer's weights ARE the interpreter — compiled analytically, not trained.
What This Proves
Attention is lookup; feed-forward is routing. A vanilla transformer with compiled weights can execute arbitrary programs: loops, recursion, arithmetic, memory access. 55 opcodes covering WASM i32 semantics. 21M+ steps/second via the Mojo executor.
Setup (once per session)
cd /mnt/skills/user/llm-as-computer/src && bash setup.sh
This installs Mojo (20s) and compiles the executor binary (6s). If Mojo is unavailable,
the skill falls back to a pure-Python executor (slower but functional).
Usage
import sys
sys.path.insert(0, '/mnt/skills/user/llm-as-computer/src')
from programs import make_fibonacci, make_factorial, make_gcd, make_multiply
from runner import run, setup
# Ensure Mojo is compiled (idempotent)
setup()
# Run a program — shows instructions, trace, result
prog, expected = make_fibonacci(10)
print(run(prog))
# Benchmark mode — measures throughput
print(run(prog, benchmark=True, repeat=200))
Available Programs
From programs.py — all return (program, expected_result):
| Generator | Description | Example |
|---|---|---|
make_fibonacci(n) |
Iterative fib via SWAP+OVER+ADD+ROT | fib(10)=55, 111 steps |
make_multiply(a, b) |
Repeated addition | mul(7,8)=56 |
make_factorial(n) |
Loop with MUL | fact(8)=40320 |
make_gcd(a, b) |
Euclidean algorithm | gcd(48,18)=6 |
make_power_of_2(n) |
Repeated doubling | 2^7=128 |
make_sum_1_to_n(n) |
Accumulation loop | sum(15)=120 |
make_is_even(n) |
Parity check | is_even(7)=0 |
make_native_multiply(a,b) |
Single MUL opcode | |
make_native_divmod(a,b) |
DIV_S + REM_S | |
make_compare_binary(op,a,b) |
eq/ne/lt_s/gt_s/le_s/ge_s | |
make_bitwise_binary(op,a,b) |
and/or/xor/shl/shr_u/rotl/rotr | |
make_select(a,b,c) |
Conditional select |
Writing Custom Programs
from isa_lite import program
# Assembly tuples
prog = program(
('PUSH', 10),
('PUSH', 20),
('ADD',),
('DUP',),
('ADD',), # (10+20)*2 = 60
('HALT',),
)
print(run(prog))
# Loops: countdown from 5
prog = program(
('PUSH', 5), # 0: counter
('PUSH', 1), # 1: decrement
('SUB',), # 2: counter - 1
('DUP',), # 3: copy for JNZ test
('JNZ', 1), # 4: loop if non-zero
('HALT',), # 5: done, top = 0
)
print(run(prog))
ISA Reference (55 opcodes)
Stack: PUSH n, POP, DUP, SWAP, OVER, ROT Arithmetic: ADD, SUB, MUL, DIV_S, DIV_U, REM_S, REM_U Comparison: EQZ, EQ, NE, LT_S/U, GT_S/U, LE_S/U, GE_S/U Bitwise: AND, OR, XOR, SHL, SHR_S/U, ROTL, ROTR Unary: CLZ, CTZ, POPCNT, ABS, NEG, SELECT Control: JZ addr, JNZ addr, CALL addr, RETURN, HALT, NOP Locals: LOCAL.GET idx, LOCAL.SET idx, LOCAL.TEE idx Memory: I32.LOAD, I32.STORE, I32.LOAD8_U/S, I32.LOAD16_U/S, I32.STORE8, I32.STORE16
All arithmetic is 32-bit signed with WASM i32 semantics (wrap on overflow).
Architecture
The key insight: parabolic encoding k = (2j, -j²) makes dot-product attention peak
sharply at a target position. Same encoding addresses program memory, stack, locals,
and heap without interference. Each attention head is a compiled W_Q @ state → query,
W_K @ memory → keys, scores = K @ q, output = V[argmax(scores)].
Updating from Repo
To pull latest source from the repository:
cd /mnt/skills/user/llm-as-computer/src
GH_TOKEN=$(grep GH_TOKEN /mnt/project/GitHub.env 2>/dev/null | cut -d= -f2)
for f in executor.mojo; do
curl -sL -H "Authorization: token $GH_TOKEN" -H "Accept: application/vnd.github.v3.raw" \
"https://api.github.com/repos/oaustegard/llm-as-computer/contents/src/$f?ref=main" > $f
done
for f in isa_lite.py programs.py runner.py; do
curl -sL -H "Authorization: token $GH_TOKEN" -H "Accept: application/vnd.github.v3.raw" \
"https://api.github.com/repos/oaustegard/llm-as-computer/contents/skill/src/$f?ref=main" > $f
done
rm -f percepta_exec # force recompile
bash setup.sh