How modern machine learning works, one mechanism at a time.

Finite JavaScript numbers before display rounding; −0 is retained where supplied. Tiny allowed shares can underflow to zero; that is not a mask.

q = [2, 1]
A: key=[-1, 2]; value=[2, 0]; dot=0; score=0; share=0.14002924504337802; contribution=[0.28005849008675604, 0]
B: key=[1, 0]; value=[0, 2]; dot=2; score=1.414213562373095; share=0.575975345215362; contribution=[0, 1.151950690430724]
C: key=[0, 1]; value=[-1, -1]; dot=1; score=0.7071067811865475; share=0.28399540974126003; contribution=[-0.28399540974126003, -0.28399540974126003]
m=1.414213562373095; stable denominator=1.736185425829454
output=[-0.00393691965450399, 0.8679552806894639]

This runnable Python witness uses the current controls and the same labels. No NumPy or random inputs are needed.

from math import exp, sqrt

labels = ["A","B","C"]
q = [2,1]
keys = [[-1,2],[1,0],[0,1]]
values = [[2,0],[0,2],[-1,-1]]

d_k = len(q)  # key dimension, NOT number of sources or value width
raw_scores = [sum(qj * kj for qj, kj in zip(q, k)) for k in keys]
scores = [s / sqrt(d_k) for s in raw_scores]
exps = [exp(s - max(scores)) for s in scores]
weights = [e / sum(exps) for e in exps]
output = [sum(w * v[j] for w, v in zip(weights, values))
          for j in range(len(values[0]))]

for label, raw, score, weight in zip(labels, raw_scores, scores, weights):
    print(label, round(raw, 3), round(score, 3), round(weight, 3))
print("output", [round(x, 3) for x in output])

Expected output: [-0.004, 0.868] (rounded to three decimals).