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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 | #!/usr/bin/env python3
"""Validate the SDQ paged MoE kernel for Ling-3.0-flash (bailingmoe3) with pure NumPy.
The weights are read straight out of the sharded GGUF, dequantized with ggml's
definitions, and the SiLU-gated expert forward pass is recomputed in fp32. The
result is compared against `sdq-chat --selftest IL:IE:SEED:OUT.bin`.
Ling-3.0-flash expert tensors (Q4_K_M):
ffn_gate_exps [n_embd=2560, n_ff_exp=768, n_expert=512] q4_K
ffn_up_exps [2560, 768, 512] q4_K
ffn_down_exps [768, 2560, 512] q4_K or q6_K
"""
import os
import struct
import sys
import numpy as np
QK_K = 256
MODEL = os.environ.get('MODEL', '/root/app/ling/tiny/models/Ling-3.0-tiny-Q4_K_M.gguf')
SHARDS = [MODEL.replace('00001-of', f'{i:05d}-of') for i in (1, 2)]
TS = {0: 1, 1: 1, 2: 2, 3: 2, 4: 4, 5: 4, 6: 4, 7: 1, 10: 8, 11: 8, 12: 8}
BLK = {12: (256, 144), 13: (256, 176), 14: (256, 210), 7: (32, 24), 8: (32, 34),
2: (32, 18), 3: (32, 20), 6: (32, 22), 0: (1, 4), 1: (1, 2)}
FMT = {0: '<B', 1: '<b', 2: '<H', 3: '<h', 4: '<I', 5: '<i', 6: '<f', 7: '<B',
10: '<Q', 11: '<q', 12: '<d'}
NAME = {12: 'q4_K', 13: 'q5_K', 14: 'q6_K', 7: 'q5_1', 8: 'q8_0', 6: 'q5_0', 2: 'q4_0'}
def rstr(f):
n = struct.unpack('<Q', f.read(8))[0]
return f.read(n)
def read_val(f, t):
if t == 8:
return rstr(f).decode('utf-8', 'replace')
if t == 9:
et = struct.unpack('<I', f.read(4))[0]
n = struct.unpack('<Q', f.read(8))[0]
if et == 8:
return [rstr(f).decode('utf-8', 'replace') for _ in range(n)]
buf = f.read(n * TS[et])
return np.frombuffer(buf, dtype=np.dtype(FMT[et])).tolist()
buf = f.read(TS[t])
return np.frombuffer(buf, dtype=np.dtype(FMT[t]))[0].item()
def parse_headers():
kv = {}
tensors = {}
for si, path in enumerate(SHARDS):
with open(path, 'rb') as f:
assert f.read(4) == b'GGUF'
f.read(4)
n_tensors = struct.unpack('<Q', f.read(8))[0]
n_kv = struct.unpack('<Q', f.read(8))[0]
for _ in range(n_kv):
k = rstr(f).decode()
kv[k] = read_val(f, struct.unpack('<I', f.read(4))[0])
align = kv.get('general.alignment', 32)
pending = []
for _ in range(n_tensors):
name = rstr(f).decode()
nd = struct.unpack('<I', f.read(4))[0]
dims = struct.unpack(f'<{nd}Q', f.read(8 * nd))
tt = struct.unpack('<I', f.read(4))[0]
off = struct.unpack('<Q', f.read(8))[0]
pending.append((name, dims, tt, off))
# data_start is only known after the whole tensor table has been read
data_start = (f.tell() + align - 1) // align * align
for name, dims, tt, off in pending:
tensors[name] = (si, data_start + off, dims, tt)
return kv, tensors
def fp16(x):
return np.frombuffer(x, dtype=np.float16)[0].astype(np.float32)
def dequant_q4_K(raw, n):
nb = n // QK_K
out = np.empty(n, dtype=np.float32)
for i in range(nb):
b = raw[i * 144:(i + 1) * 144]
d = fp16(b[0:2])
dmin = fp16(b[2:4])
sc = np.frombuffer(b, dtype=np.uint8, count=12, offset=4)
qs = np.frombuffer(b, dtype=np.uint8, count=128, offset=16)
base = i * QK_K
is_ = 0
for j in range(0, QK_K, 64):
if is_ < 4:
sc1, m1 = int(sc[is_] & 63), int(sc[is_ + 4] & 63)
sc2, m2 = int(sc[is_ + 1] & 63), int(sc[is_ + 5] & 63)
else:
sc1 = int((sc[is_ + 4] & 0xF) | ((sc[is_ - 4] >> 6) << 4))
m1 = int((sc[is_ + 4] >> 4) | ((sc[is_] >> 6) << 4))
sc2 = int((sc[is_ + 5] & 0xF) | ((sc[is_ - 3] >> 6) << 4))
m2 = int((sc[is_ + 5] >> 4) | ((sc[is_ + 1] >> 6) << 4))
q = qs[j // 2: j // 2 + 32]
out[base + j: base + j + 32] = d * sc1 * (q & 0xF) - dmin * m1
out[base + j + 32: base + j + 64] = d * sc2 * (q >> 4) - dmin * m2
is_ += 2
return out
def dequant_q6_K(raw, n):
nb = n // QK_K
out = np.empty(n, dtype=np.float32)
for i in range(nb):
b = raw[i * 210:(i + 1) * 210]
d = fp16(b[208:210])
ql = np.frombuffer(b, dtype=np.uint8, count=128, offset=0)
qh = np.frombuffer(b, dtype=np.uint8, count=64, offset=128)
sc = np.frombuffer(b, dtype=np.int8, count=16, offset=192)
base = i * QK_K
for g in range(2): # two 128-element groups per block
for l in range(32):
is_ = l // 16
q1 = ((ql[g * 64 + l] & 0xF) | (((qh[g * 32 + l] >> 0) & 3) << 4)).astype(np.int16) - 32
q2 = ((ql[g * 64 + l + 32] & 0xF) | (((qh[g * 32 + l] >> 2) & 3) << 4)).astype(np.int16) - 32
q3 = ((ql[g * 64 + l] >> 4) | (((qh[g * 32 + l] >> 4) & 3) << 4)).astype(np.int16) - 32
q4 = ((ql[g * 64 + l + 32] >> 4) | (((qh[g * 32 + l] >> 6) & 3) << 4)).astype(np.int16) - 32
off0 = base + g * 128
out[off0 + l] = d * sc[g * 8 + is_] * q1
out[off0 + l + 32] = d * sc[g * 8 + is_ + 2] * q2
out[off0 + l + 64] = d * sc[g * 8 + is_ + 4] * q3
out[off0 + l + 96] = d * sc[g * 8 + is_ + 6] * q4
return out
def dequant(raw, n, tt):
if tt == 12:
return dequant_q4_K(raw, n)
if tt == 14:
return dequant_q6_K(raw, n)
raise ValueError(f'unsupported type {tt} ({NAME.get(tt, tt)})')
def row_bytes(n, tt):
bs, ts = BLK[tt]
return n // bs * ts
def read_expert(tensors, il, ie):
def seg(name):
si, off, dims, tt = tensors[f'blk.{il}.{name}']
nrow = row_bytes(dims[0], tt)
n = dims[1] * nrow
with open(SHARDS[si], 'rb') as f:
f.seek(off + ie * n)
return f.read(n), nrow, tt, dims[0]
g = seg('ffn_gate_exps.weight')
u = seg('ffn_up_exps.weight')
d = seg('ffn_down_exps.weight')
return g, u, d
def load_matrix(seg, n_rows):
raw, nrow, tt, ne0 = seg
return np.stack([dequant(raw[r * nrow:(r + 1) * nrow], ne0, tt) for r in range(n_rows)])
def main():
cases = []
for path in sys.argv[1:]:
# st_l<IL>e<IE>.bin
base = os.path.basename(path)
body = base[len('st_l'):-len('.bin')]
il, ie = body.split('e')
cases.append((int(il), int(ie), path))
if not cases:
print('usage: verify_ling.py st_l<IL>e<IE>.bin ...')
return 1
kv, tensors = parse_headers()
n_embd = int(kv['bailingmoe3.embedding_length'])
n_ff = int(kv['bailingmoe3.expert_feed_forward_length'])
print(f"arch=bailingmoe3 n_expert={int(kv['bailingmoe3.expert_count'])} "
f"n_expert_used={int(kv['bailingmoe3.expert_used_count'])} "
f"n_embd={n_embd} n_ff_exp={n_ff}")
all_ok = True
for il, ie, path in cases:
raw = open(path, 'rb').read()
n_tok = len(raw) // 4 // (2 * n_embd)
got = np.frombuffer(raw[:n_tok * n_embd * 4], dtype=np.float32).reshape(n_tok, n_embd)
x = np.frombuffer(raw[n_tok * n_embd * 4:], dtype=np.float32).reshape(n_tok, n_embd)
g_seg, u_seg, d_seg = read_expert(tensors, il, ie)
Wg = load_matrix(g_seg, n_ff)
Wu = load_matrix(u_seg, n_ff)
Wd = load_matrix(d_seg, n_embd)
rels, corrs = [], []
for t in range(n_tok):
xt = x[t]
g = Wg @ xt
u = Wu @ xt
h = (g / (1.0 + np.exp(-g))) * u
ref = Wd @ h
denom = max(float(np.abs(ref).max()), 1e-9)
rels.append(float(np.abs(got[t] - ref).max()) / denom)
corrs.append(float(np.corrcoef(got[t], ref)[0, 1]))
rel = max(rels)
corr = min(corrs)
ok = rel < 0.05 and corr > 0.99
all_ok &= ok
print(f'L{il} E{ie}: gate={NAME[g_seg[2]]} up={NAME[u_seg[2]]} down={NAME[d_seg[2]]}'
f' | max_rel_err={rel:.4f} corr={corr:.6f} | {"PASS" if ok else "FAIL"}')
print('\nALL PASS' if all_ok else '\nSOME FAILED')
return 0 if all_ok else 1
if __name__ == '__main__':
sys.exit(main())
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