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2b6ab7c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 | #!/usr/bin/env python3
"""Recompute a whole paged MoE layer in NumPy and compare with sdq-chat's output.
`sdq-chat` with SDQ_MOE_DUMP=<prefix> SDQ_MOE_DUMP_LAYER=<il> writes, from inside
the custom MoE op:
int32 meta[6] = { n_embd, n_tok, n_expert_used, n_expert, 1, 2 }
float cur [n_tok][n_embd] (FFN input)
int32 ids [n_tok][n_expert_used] (routed experts)
float wts [n_tok][n_expert_used] (routed weights)
float out [n_tok][n_embd] (SDQ MoE output)
This script reads the same expert weights straight out of the GGUF, runs the
SiLU-gated expert forward in fp32 and diffs the result, so the whole
pager + custom kernel + weight-scaling path is checked at once.
"""
import importlib.util
import struct
import sys
import numpy as np
spec = importlib.util.spec_from_file_location('vl', str(__import__('pathlib').Path(__file__).with_name('verify_ling.py')))
vl = importlib.util.module_from_spec(spec)
spec.loader.exec_module(vl)
QK_K = 256
def dequant_q4_K_vec(raw, n_elem):
"""raw: uint8 [..., 144] -> float32 [..., 256]"""
raw = raw.reshape(-1, 144)
nb = raw.shape[0]
out = np.empty((nb, QK_K), dtype=np.float32)
d = raw[:, 0:2].copy().view(np.float16).astype(np.float32)[:, 0]
dmin = raw[:, 2:4].copy().view(np.float16).astype(np.float32)[:, 0]
b = np.ascontiguousarray(raw[:, 4:16]) # 12 packed 6-bit scale bytes
sc = np.empty((nb, 8), dtype=np.int32)
mn = np.empty((nb, 8), dtype=np.int32)
for j in range(4):
sc[:, j] = b[:, j] & 63
mn[:, j] = b[:, j + 4] & 63
for j in range(4, 8):
sc[:, j] = (b[:, j + 4] & 0xF) | ((b[:, j - 4] >> 6) << 4)
mn[:, j] = (b[:, j + 4] >> 4) | ((b[:, j] >> 6) << 4)
qs = raw[:, 16:144].reshape(nb, 4, 32).astype(np.int32)
q = np.empty((nb, QK_K), dtype=np.int32)
for j in range(4):
q[:, j * 64:j * 64 + 32] = qs[:, j, :] & 0xF
q[:, j * 64 + 32:j * 64 + 64] = qs[:, j, :] >> 4
gidx = np.repeat(np.arange(8), 32) # 32-value group -> (sc, mn) index
out[:] = d[:, None] * sc[:, gidx] * q - dmin[:, None] * mn[:, gidx]
return out
def dequant_q6_K_vec(raw, n_elem):
raw = raw.reshape(-1, 210)
nb = raw.shape[0]
out = np.empty((nb, QK_K), dtype=np.float32)
d = raw[:, 208:210].copy().view(np.float16).astype(np.float32)[:, 0]
ql = raw[:, 0:128].reshape(nb, 4, 32).astype(np.int32) # 4 x 32 low/high nibbles
qh = raw[:, 128:192].reshape(nb, 2, 32).astype(np.int32)
sc = raw[:, 192:208].copy().view(np.int8).astype(np.int32)
q = np.empty((nb, QK_K), dtype=np.int32)
sidx = np.empty(QK_K, dtype=np.int32)
for g in range(2):
for l in range(32):
is_ = l // 16
o = g * 128
a = ql[:, 2 * g, l] # ql[g*64 + l]
b_ = ql[:, 2 * g + 1, l] # ql[g*64 + l + 32]
h = qh[:, g, l]
q[:, o + l] = ((a & 0xF) | ((h & 3) << 4)) - 32
q[:, o + l + 32] = ((b_ & 0xF) | (((h >> 2) & 3) << 4)) - 32
q[:, o + l + 64] = ((a >> 4) | (((h >> 4) & 3) << 4)) - 32
q[:, o + l + 96] = ((b_ >> 4) | (((h >> 6) & 3) << 4)) - 32
sidx[o + l] = g * 8 + is_
sidx[o + l + 32] = g * 8 + is_ + 2
sidx[o + l + 64] = g * 8 + is_ + 4
sidx[o + l + 96] = g * 8 + is_ + 6
out[:] = d[:, None] * sc[:, sidx] * q
return out
def dequant_mat(raw, n_rows, ne0, tt):
nrow = vl.row_bytes(ne0, tt)
nblk = ne0 // QK_K
arr = np.frombuffer(raw[:n_rows * nrow], dtype=np.uint8).reshape(n_rows, nblk, nrow // nblk)
if tt == 12:
return dequant_q4_K_vec(arr, ne0).reshape(n_rows, ne0)
if tt == 14:
return dequant_q6_K_vec(arr, ne0).reshape(n_rows, ne0)
raise ValueError(tt)
def main():
dump = sys.argv[1]
il = int(sys.argv[2])
kv, ts = vl.parse_headers()
n_embd = int(kv['bailingmoe3.embedding_length'])
n_ff = int(kv['bailingmoe3.expert_feed_forward_length'])
n_expert = int(kv['bailingmoe3.expert_count'])
raw = open(dump, 'rb').read()
meta = struct.unpack('<6i', raw[:24])
n_tok, n_used = meta[1], meta[2]
off = 24
cur = np.frombuffer(raw, dtype=np.float32, count=n_tok * n_embd, offset=off).reshape(n_tok, n_embd)
off += 4 * n_tok * n_embd
ids = np.frombuffer(raw, dtype=np.int32, count=n_tok * n_used, offset=off).reshape(n_tok, n_used)
off += 4 * n_tok * n_used
wts = np.frombuffer(raw, dtype=np.float32, count=n_tok * n_used, offset=off).reshape(n_tok, n_used)
off += 4 * n_tok * n_used
got = np.frombuffer(raw, dtype=np.float32, count=n_tok * n_embd, offset=off).reshape(n_tok, n_embd)
print(f'layer {il}: n_tok={n_tok} n_expert_used={n_used} n_expert={n_expert} '
f'n_embd={n_embd} n_ff={n_ff}')
def seg(nm, ie):
si, off_t, dims, tt = ts[f'blk.{il}.{nm}']
nrow = vl.row_bytes(dims[0], tt)
nb = nrow * dims[1]
with open(vl.SHARDS[si], 'rb') as f:
f.seek(off_t + ie * nb)
return f.read(nb), nrow, tt, dims[0]
ref = np.zeros((n_tok, n_embd), dtype=np.float32)
experts = sorted(set(int(e) for e in ids.reshape(-1)))
print(f'distinct experts: {len(experts)}')
for ie in experts:
g_raw, g_nrow, g_t, g_ne0 = seg('ffn_gate_exps.weight', ie)
u_raw, _, u_t, u_ne0 = seg('ffn_up_exps.weight', ie)
d_raw, _, d_t, d_ne0 = seg('ffn_down_exps.weight', ie)
Wg = dequant_mat(g_raw, n_ff, g_ne0, g_t)
Wu = dequant_mat(u_raw, n_ff, u_ne0, u_t)
Wd = dequant_mat(d_raw, n_embd, d_ne0, d_t)
rows = [t for t in range(n_tok) if ie in ids[t]]
x = cur[rows].T # [n_embd, T]
w = np.array([wts[t][list(ids[t]).index(ie)] for t in rows], dtype=np.float32)
h = ((Wg @ x) / (1.0 + np.exp(-(Wg @ x)))) * (Wu @ x)
y = Wd @ h # [n_embd, T]
ref[rows] += (y * w).T
del Wg, Wu, Wd
denom = max(float(np.abs(ref).max()), 1e-9)
diff = np.abs(got - ref)
corr = float(np.corrcoef(got.reshape(-1), ref.reshape(-1))[0, 1])
print(f'max|got-ref| = {diff.max():.6g} max|ref| = {denom:.6g} '
f'max_rel = {diff.max() / denom:.6g} corr = {corr:.8f}')
ok = corr > 0.99 and diff.max() / denom < 0.05
print('PASS' if ok else 'FAIL')
return 0 if ok else 1
if __name__ == '__main__':
sys.exit(main())
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