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6ec9472 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 | """Sequential layer-wise quantisation of OLMoE-1B-7B with Hessian-aware
sub-2-bit residual VQ, plus frequency-conditioned bit allocation over experts.
Runs on a 4 GB GPU by moving one decoder layer at a time onto the device.
"""
import argparse, gc, json, os, sys, time
import torch
import torch.nn as nn
from transformers import AutoModelForCausalLM, AutoTokenizer
sys.path.insert(0, os.path.dirname(__file__))
import codec, data
MODEL = "allenai/OLMoE-1B-7B-0924"
DEV = "cuda"
def hkey_of(name):
"""Linears sharing an input share one Hessian."""
if name.endswith(("q_proj", "k_proj", "v_proj")):
return "attn.in"
if name.endswith("o_proj"):
return "attn.o"
if ".experts." in name:
e = name.split(".experts.")[1].split(".")[0]
if name.endswith("down_proj"):
return f"e{e}.mid"
return f"e{e}.in"
return None
def find_linears(layer):
out = {}
for n, m in layer.named_modules():
if isinstance(m, nn.Linear) and hkey_of(n) is not None:
out[n] = m
return out
class Catcher(nn.Module):
def __init__(self, mod, store):
super().__init__()
self.mod, self.store = mod, store
def forward(self, hs, **kw):
self.store["inps"].append(hs.detach().to("cpu"))
if "kw" not in self.store:
self.store["kw"] = {k: v for k, v in kw.items()
if k not in ("past_key_value", "past_key_values")}
raise RuntimeError("caught")
@torch.no_grad()
def capture_inputs(model, batches):
store = {"inps": []}
model.model.embed_tokens.to(DEV)
model.model.rotary_emb.to(DEV)
layers = model.model.layers
layers[0] = Catcher(layers[0], store)
for b in batches:
try:
model(b.to(DEV))
except RuntimeError as e:
if "caught" not in str(e):
raise
layers[0] = layers[0].mod
model.model.embed_tokens.to("cpu")
torch.cuda.empty_cache()
return store["inps"], store["kw"]
@torch.no_grad()
def run_layer(layer, inps, kw, out=None):
res = out if out is not None else [None] * len(inps)
for j, x in enumerate(inps):
y = layer(x.to(DEV), **kw)
y = y[0] if isinstance(y, tuple) else y
res[j] = y.detach().to("cpu")
return res
def alloc_stages(freq, base_stages, spread, n_experts):
"""Frequency-conditioned bit allocation.
Experts are ranked by measured activation frequency; the top third get
+`spread` stages, the bottom third -`spread`, keeping the mean rate equal to
`base_stages` so the comparison against uniform allocation is rate-matched.
"""
order = sorted(range(n_experts), key=lambda i: -freq[i])
st = [base_stages] * n_experts
k = n_experts // 3
for i in order[:k]:
st[i] = base_stages + spread
for i in order[-k:]:
st[i] = max(1, base_stages - spread)
return st
@torch.no_grad()
def quantize_model(stages, nsamples=32, seqlen=2048, rht_on=True, ldlq_on=True,
alloc="uniform", spread=1, refine=0, tag="", rtn_bits=None):
tok = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True)
model.eval()
model.config.use_cache = False
cbs = codec.build_codebooks(8, device=DEV)
batches = data.calib_batches(tok, nsamples, seqlen)
inps, kw = capture_inputs(model, batches)
outs = [None] * len(inps)
layers = model.model.layers
n_exp = model.config.num_experts
freq = load_freq(n_exp, len(layers))
log = {"layers": [], "bits": [], "params": []}
t_start = time.time()
for li, layer in enumerate(layers):
t0 = time.time()
layer.to(DEV)
lin = find_linears(layer)
H, cnt = {}, {}
reps = {}
for n, m in lin.items():
k = hkey_of(n)
reps.setdefault(k, (n, m))
hooks = []
need_hess = ldlq_on and rtn_bits is None
def mk(k, insize):
H[k] = torch.zeros(insize, insize, device=DEV, dtype=torch.float32)
cnt[k] = 0
def fn(mod, inp, out):
x = inp[0].detach().reshape(-1, insize).float()
if x.shape[0]:
H[k] += x.t() @ x
cnt[k] += x.shape[0]
return fn
if need_hess:
for k, (n, m) in reps.items():
hooks.append(m.register_forward_hook(mk(k, m.in_features)))
run_layer(layer, inps, kw)
for h in hooks:
h.remove()
if alloc == "freq":
st_e = alloc_stages(freq[li], stages, spread, n_exp)
else:
st_e = [stages] * n_exp
fact = {}
lbits, lparams = 0.0, 0
for n, m in lin.items():
k = hkey_of(n)
s = stages
if ".experts." in n:
s = st_e[int(n.split(".experts.")[1].split(".")[0])]
W = m.weight.data.to(DEV).float()
if rtn_bits is not None:
Wq, info = codec.rtn(W, rtn_bits)
elif ldlq_on:
if k not in fact:
fact[k] = codec.prepare_hessian(H[k], 1234, rht_on=rht_on)
L, dead = fact[k]
Wq, info = codec.ldlq_quantize(W, L, dead, cbs, s,
rht_on=rht_on, refine=refine)
else:
Wq, info = codec.quantize(W, cbs, s, rht_on=rht_on, refine=refine)
m.weight.data = Wq.to(torch.bfloat16)
lbits += info["bits"] * W.numel()
lparams += W.numel()
del W, Wq
H.clear(); fact.clear()
torch.cuda.empty_cache()
run_layer(layer, inps, kw, outs)
layer.to("cpu")
inps, outs = outs, inps
gc.collect(); torch.cuda.empty_cache()
log["layers"].append(li)
log["bits"].append(lbits / lparams)
log["params"].append(lparams)
print(f"[{tag}] layer {li:2d} {lbits/lparams:.3f} bits/w "
f"({time.time()-t0:.0f}s, total {time.time()-t_start:.0f}s)", flush=True)
avg_bits = sum(log["bits"][i] * log["params"][i] for i in range(len(log["bits"]))) \
/ sum(log["params"])
log["avg_bits"] = avg_bits
log["quantized_params"] = sum(log["params"])
model.config.use_cache = False
return model, tok, log
def load_freq(n_exp, n_layers):
p = os.path.join(os.path.dirname(__file__), "..", "results", "routing_freq.json")
if os.path.exists(p):
f = json.load(open(p))
return [f[str(l)] for l in range(n_layers)]
return [[1.0] * n_exp for _ in range(n_layers)]
@torch.no_grad()
def perplexity(model, tok, seqlen=2048, limit=None):
"""Layer-sequential evaluation: each layer is moved to the GPU once and all
sequences are streamed through it, rather than paging layers per sequence."""
tests = data.test_tokens(tok, seqlen)
if limit:
tests = tests[:limit]
pos = torch.arange(seqlen, device=DEV).unsqueeze(0)
model.model.embed_tokens.to(DEV); model.model.rotary_emb.to(DEV)
hs = [model.model.embed_tokens(b.to(DEV)).cpu() for b in tests]
pe = model.model.rotary_emb(hs[0].to(DEV), pos)
model.model.embed_tokens.to("cpu"); torch.cuda.empty_cache()
for layer in model.model.layers:
layer.to(DEV)
for j in range(len(hs)):
y = layer(hs[j].to(DEV), attention_mask=None, position_ids=pos,
position_embeddings=pe)
hs[j] = (y[0] if isinstance(y, tuple) else y).cpu()
layer.to("cpu"); torch.cuda.empty_cache()
model.model.norm.to(DEV); model.lm_head.to(DEV)
nll, ntok = 0.0, 0
for j, b in enumerate(tests):
logits = model.lm_head(model.model.norm(hs[j].to(DEV))).float()
loss = torch.nn.functional.cross_entropy(
logits[:, :-1].reshape(-1, logits.shape[-1]),
b.to(DEV)[:, 1:].reshape(-1))
nll += loss.item() * (seqlen - 1)
ntok += seqlen - 1
del logits
torch.cuda.empty_cache()
return float(torch.exp(torch.tensor(nll / ntok)))
if __name__ == "__main__":
ap = argparse.ArgumentParser()
ap.add_argument("--stages", type=int, default=3)
ap.add_argument("--nsamples", type=int, default=32)
ap.add_argument("--seqlen", type=int, default=2048)
ap.add_argument("--no-rht", action="store_true")
ap.add_argument("--no-ldlq", action="store_true")
ap.add_argument("--alloc", default="uniform")
ap.add_argument("--spread", type=int, default=1)
ap.add_argument("--ppl-limit", type=int, default=24)
ap.add_argument("--rtn", type=int, default=None)
ap.add_argument("--fp16", action="store_true")
ap.add_argument("--tag", default="run")
a = ap.parse_args()
if a.fp16:
tok = AutoTokenizer.from_pretrained(MODEL)
m = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True)
m.eval(); m.config.use_cache = False
ppl = perplexity(m, tok, a.seqlen, a.ppl_limit)
print(f"[fp16] wikitext2 ppl={ppl:.4f}", flush=True)
json.dump({"ppl": ppl, "seqlen": a.seqlen, "limit": a.ppl_limit},
open(os.path.join(os.path.dirname(__file__), "..", "results",
"fp16_ppl.json"), "w"), indent=2)
sys.exit(0)
m, tok, log = quantize_model(a.stages, a.nsamples, a.seqlen,
rht_on=not a.no_rht, ldlq_on=not a.no_ldlq,
alloc=a.alloc, spread=a.spread, tag=a.tag,
rtn_bits=a.rtn)
t0 = time.time()
ppl = perplexity(m, tok, a.seqlen, a.ppl_limit)
log.update(ppl=ppl, config=vars(a), ppl_secs=time.time() - t0)
print(f"[{a.tag}] avg_bits={log['avg_bits']:.3f} wikitext2 ppl={ppl:.3f}", flush=True)
out = os.path.join(os.path.dirname(__file__), "..", "results", f"quant_{a.tag}.json")
json.dump(log, open(out, "w"), indent=2)
print("saved", out)
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