Initial commit: BitFlow MoE - 微型中文 MoE 语言模型 for micro:bit
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"""
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PC端模拟器: 验证 bitflow_moe.py 的推理逻辑与 PyTorch 模型一致
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模拟 micro:bit 上的 int8 解量化 + GLA + MoE + 采样 全流程
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"""
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import struct, math, random, sys
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# ========== 加载 int8 权重 ==========
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with open("bitflow_moe_weights.bin", "rb") as f:
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nt = struct.unpack('<H', f.read(2))[0]
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LEN = list(struct.unpack('<%dH' % nt, f.read(2 * nt)))
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SC = list(struct.unpack('<%df' % nt, f.read(4 * nt)))
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W = f.read()
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OFF = []
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o = 0
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for l in LEN:
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OFF.append(o)
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o += l
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# ========== 参数 ==========
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VS = 149; D = 40; CT = 48; FD = 80; NE = 3; NL = 2; RD = 32
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RB = 2 + NE * 12 # 38
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NB = RB + 4 # 42
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HI = NB + 2 # 44
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# ========== 词表 ==========
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VOCAB_CHARS = (
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"猫狗鸟鱼猪牛马鸡虫羊兔鸭鹅"
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"坐跑飞游看吃睡走听来叫爬跳唱画读做"
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"在上里到"
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"的是好了"
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"吗什么怎哪"
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"你我他她们它"
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"蓝绿红黄白"
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"天地水火花草树木日月星云春夏秋冬"
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"大小多高远快"
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"有没会不"
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"人头脚手眼腿"
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"垫肉书子"
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"一三只两"
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"热冷落开"
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"自然动植物质色样颜得都"
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"你好吗谢谢再见早晚安静"
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"很棒爱喜欢乐伤心气"
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"名字叫谁和问答应"
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"聊天话今天说"
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"客用"
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"bit"
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"\n"
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)
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CH2I = {c: i for i, c in enumerate(VOCAB_CHARS)}
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I2CH = {i: c for i, c in enumerate(VOCAB_CHARS)}
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# ========== 预解量化 ==========
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def dqS(i):
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s = SC[i]; o = OFF[i]; n = LEN[i]; r = [0.0] * n
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for j in range(n):
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v = W[o + j]
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if v >= 128: v -= 256
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r[j] = v * s
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return r
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SMW = []
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for e in range(NE):
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b0 = 2 + e * 12
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SMW.append(dqS(b0)) # norm1.w
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SMW.append(dqS(b0 + 1)) # norm1.b
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SMW.append(dqS(b0 + 2)) # log_decay
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SMW.append(dqS(b0 + 8)) # norm2.w
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SMW.append(dqS(b0 + 9)) # norm2.b
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rf1b = dqS(RB + 1)
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rf2b = dqS(RB + 3)
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nfw = dqS(NB)
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nfb = dqS(NB + 1)
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# ========== 融合解量化+矩阵乘 ==========
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def dqmv(i, ni, no, x, out):
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s = SC[i]; o = OFF[i]
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for r in range(no):
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ss = 0.0; b = r * ni
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for c in range(ni):
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v = W[o + b + c]
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if v >= 128: v -= 256
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ss += v * s * x[c]
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out[r] = ss
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def mvW(w, ni, no, x, out):
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for r in range(no):
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ss = 0.0; b = r * ni
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for c in range(ni):
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ss += w[b + c] * x[c]
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out[r] = ss
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def ln(w, b, x, o):
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m = sum(x) / D
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v = sum((x[i] - m) ** 2 for i in range(D)) / D
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iv = 1.0 / math.sqrt(v + 1e-5)
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for i in range(D):
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o[i] = w[i] * (x[i] - m) * iv + b[i]
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def e1(x):
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return x + 1.0 if x >= 0 else math.exp(x)
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def sg(x):
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if x >= 0:
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e = math.exp(-x); return 1.0 / (1.0 + e)
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e = math.exp(x); return e / (1.0 + e)
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def ge(x):
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c = 0.7978845608
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return x * sg(2.0 * c * (x + 0.044715 * x * x * x))
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def sm(l, t=0.3):
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m = -1e9
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for i in range(VS):
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v = l[i] - bcnt[i] * 2.0
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if v > m: m = v
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s = 0.0
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for i in range(VS):
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v = l[i] - bcnt[i] * 2.0
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e = math.exp((v - m) / t)
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bpr[i] = e; s += e
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r = random.random() * s; c = 0.0
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for i in range(VS):
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c += bpr[i]
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if c >= r: return i
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return VS - 1
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# ========== 状态 ==========
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S = [[0.0] * (D * D) for _ in range(NL)]
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z = [[0.0] * D for _ in range(NL)]
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bx = [0.0] * D; bh = [0.0] * D
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bq = [0.0] * D; bk = [0.0] * D
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bv = [0.0] * D; bgd = [0.0] * D
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bnum = [0.0] * D; by = [0.0] * D
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bout = [0.0] * D; bff = [0.0] * FD
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bpr = [0.0] * VS; bcnt = [0] * VS
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brh = [0.0] * RD; brl = [0.0] * NE
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blg = [0.0] * VS
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bsum = [0.0] * D; brx = [0.0] * D; bct = [0]
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# ========== GLA ==========
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def gs(e, layer, h):
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b0 = 2 + e * 12; dec = SMW[e * 5 + 2]
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dqmv(b0 + 3, D, D, h, bq)
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dqmv(b0 + 4, D, D, h, bk)
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dqmv(b0 + 5, D, D, h, bv)
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dqmv(b0 + 6, D, D, h, bgd)
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for i in range(D):
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bq[i] = e1(bq[i]); bk[i] = e1(bk[i])
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for i in range(D):
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bgd[i] = sg(bgd[i]) * dec[i]
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sl = S[layer]; zl = z[layer]
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for i in range(D):
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si = i * D; gd = bgd[i]; ki = bk[i]
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for j in range(D):
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sl[si + j] = gd * sl[si + j] + ki * bv[j]
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for i in range(D):
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zl[i] = bgd[i] * zl[i] + bk[i]
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for j in range(D):
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bnum[j] = 0.0
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for i in range(D):
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si = i * D; qi = bq[i]
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for j in range(D):
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bnum[j] += qi * sl[si + j]
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den = 1e-6
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for i in range(D):
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den += bq[i] * zl[i]
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iv = 1.0 / den
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for j in range(D):
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by[j] = bnum[j] * iv
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dqmv(b0 + 7, D, D, by, bout)
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def bs(e, x, layer):
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b0 = 2 + e * 12
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ln(SMW[e * 5], SMW[e * 5 + 1], x, bh)
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gs(e, layer, bh)
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for i in range(D):
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x[i] += bout[i]
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ln(SMW[e * 5 + 3], SMW[e * 5 + 4], x, bh)
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dqmv(b0 + 10, D, FD, bh, bff)
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for i in range(FD):
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bff[i] = ge(bff[i])
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dqmv(b0 + 11, FD, D, bff, bout)
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for i in range(D):
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x[i] += bout[i]
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def route(x):
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dqmv(RB, D, RD, x, brh)
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for i in range(RD):
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brh[i] = max(0.0, brh[i] + rf1b[i])
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dqmv(RB + 2, RD, NE, brh, brl)
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for i in range(NE):
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brl[i] += rf2b[i]
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bi = 0; bv2 = brl[0]
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for i in range(1, NE):
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if brl[i] > bv2:
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bv2 = brl[i]; bi = i
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return bi, brl[:]
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def fw(ctx, gen_flag=True):
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t = min(len(ctx) - 1, CT - 1); ti = ctx[-1]
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s0 = SC[0]; o0 = OFF[0]; s1 = SC[1]; o1 = OFF[1]
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for i in range(D):
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v0 = W[o0 + ti * D + i]
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if v0 >= 128: v0 -= 256
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v1 = W[o1 + t * D + i]
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if v1 >= 128: v1 -= 256
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bx[i] = v0 * s0 + v1 * s1
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# 累加运行均值 (匹配训练时router的x.mean(dim=1))
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for i in range(D): bsum[i] += bx[i]
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bct[0] += 1
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for i in range(D): brx[i] = bsum[i] / bct[0]
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ei, gate = route(brx)
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for layer in range(NL):
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bs(ei, bx, layer)
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ln(nfw, nfb, bx, bh)
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if gen_flag:
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dqmv(HI, D, VS, bh, blg)
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return blg, ei, gate
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return None, ei, gate
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def rs():
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for l in range(NL):
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for i in range(D * D):
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S[l][i] = 0.0
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for i in range(D):
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z[l][i] = 0.0
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for i in range(D):
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bsum[i] = 0.0
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bct[0] = 0
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def gen(ids, n):
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NL_ch = VS - 1
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ids = list(ids) + [NL_ch]
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sl = len(ids); c = list(ids); rs()
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for i in range(VS):
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bcnt[i] = 0
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# 种子处理
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for i in range(len(c) - 1):
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fw(c[:i + 1], False)
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# 生成
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expert_names = ["事实", "推理", "问答"]
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for _ in range(n):
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lg, ei, gate = fw(c)
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nx = sm(lg)
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bcnt[nx] += 1
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c.append(nx)
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gen_ids = c[sl:]
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gen = "".join(I2CH.get(i, '?') for i in gen_ids)
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answer = gen.split("\n")[0] if "\n" in gen else gen
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return answer, ei
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# ========== 测试 ==========
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seeds = [
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"你好", "你好吗", "谢谢", "再见", "你叫什么名字",
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"你喜欢什么", "你喜欢猫吗", "你好棒", "你是谁",
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"你会什么", "你开心吗", "早安", "今天好吗",
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"你在做什么", "猫好吗", "你喜欢什么颜色",
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"猫是什么", "猫在做什么", "猫吃什么", "天是什么颜色",
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"什么会飞", "什么会跑", "什么会游", "什么吃草",
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]
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print("=" * 60)
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print("PC模拟器验证 (模拟micro:bit int8推理)")
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print("=" * 60)
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correct = 0; total = 0
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for seed in seeds:
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ctx = [CH2I[c] for c in seed if c in CH2I]
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if not ctx:
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continue
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random.seed(42) # 固定随机种子便于复现
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answer, ei = gen(ctx, 20)
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expert_names = ["事实", "推理", "问答"]
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print(f"Q: {seed:12s} [E{ei}={expert_names[ei]}]")
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print(f"A: {answer}")
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print()
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total += 1
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# 简单验证: 答案非空且包含相关字符
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if answer and len(answer) > 0:
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correct += 1
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print(f"\n结果: {correct}/{total} 有效输出")
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# 内存估算
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mem_w = len(W)
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mem_S = NL * D * D * 4
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mem_z = NL * D * 4
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mem_buf = (9 * D + FD + VS + VS + RD + NE + VS + D + D) * 4 # +bsum, brx
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mem_SMW = NE * 5 * D * 4
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mem_small = (RD + NE + D + D) * 4 # rf1b, rf2b, nfw, nfb
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mem_lists = 3 * nt * 8 # LEN, SC, OFF as Python lists
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total_mem = mem_w + mem_S + mem_z + mem_buf + mem_SMW + mem_small + mem_lists
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print(f"\n内存估算 (micro:bit):")
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print(f" W (int8权重): {mem_w:>6d} B = {mem_w/1024:.1f}KB")
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print(f" S (GLA状态): {mem_S:>6d} B = {mem_S/1024:.1f}KB")
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print(f" z (归一化): {mem_z:>6d} B = {mem_z/1024:.1f}KB")
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print(f" 缓冲区: {mem_buf:>6d} B = {mem_buf/1024:.1f}KB")
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print(f" SMW (小权重): {mem_SMW:>6d} B = {mem_SMW/1024:.1f}KB")
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print(f" 路由/norm_f: {mem_small:>6d} B = {mem_small/1024:.1f}KB")
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print(f" LEN/SC/OFF: {mem_lists:>6d} B = {mem_lists/1024:.1f}KB")
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print(f" 总计: {total_mem:>6d} B = {total_mem/1024:.1f}KB")
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print(f" micro:bit可用: ~108KB (128KB-20KB解释器)")
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print(f" 余量: {108*1024 - total_mem:>6d} B = {(108*1024 - total_mem)/1024:.1f}KB")
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