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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 | """Capture real per-token expert routing traces from OLMoE and derive the
statistics that drive the expert-cache model: activation-frequency skew,
temporal reuse, and cross-layer predictability.
"""
import json, os, sys, time
import numpy as np
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
sys.path.insert(0, os.path.dirname(__file__))
import data
MODEL = "allenai/OLMoE-1B-7B-0924"
DEV = "cuda"
RES = os.path.join(os.path.dirname(__file__), "..", "results")
@torch.no_grad()
def trace(nseq=24, seqlen=2048):
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
L = model.config.num_hidden_layers
E = model.config.num_experts
K = model.config.num_experts_per_tok
picks = {l: [] for l in range(L)}
hooks = []
def mk(l):
def fn(mod, inp, out):
logits = out[0] if isinstance(out, tuple) else out
top = logits.float().reshape(-1, E).topk(K, dim=-1).indices
picks[l].append(top.to(torch.int16).cpu())
return fn
for l, layer in enumerate(model.model.layers):
hooks.append(layer.mlp.gate.register_forward_hook(mk(l)))
tests = data.test_tokens(tok, seqlen)[:nseq]
model.model.embed_tokens.to(DEV); model.model.rotary_emb.to(DEV)
model.model.norm.to(DEV)
for i, b in enumerate(tests):
b = b.to(DEV)
hs = model.model.embed_tokens(b)
pos = torch.arange(seqlen, device=DEV).unsqueeze(0)
pe = model.model.rotary_emb(hs, pos)
for layer in model.model.layers:
layer.to(DEV)
hs = layer(hs, attention_mask=None, position_ids=pos,
position_embeddings=pe)
hs = hs[0] if isinstance(hs, tuple) else hs
layer.to("cpu")
torch.cuda.empty_cache()
print(f" seq {i+1}/{len(tests)}", flush=True)
for h in hooks:
h.remove()
T = torch.stack([torch.cat(picks[l]) for l in range(L)]) # [L, tokens, K]
np.save(os.path.join(RES, "routing_trace.npy"), T.numpy().astype(np.int16))
print("trace shape", tuple(T.shape))
return T.numpy().astype(np.int64), L, E, K
def analyse(T, L, E, K):
ntok = T.shape[1]
freq = np.zeros((L, E))
for l in range(L):
c = np.bincount(T[l].reshape(-1), minlength=E)
freq[l] = c / c.sum()
json.dump({str(l): freq[l].tolist() for l in range(L)},
open(os.path.join(RES, "routing_freq.json"), "w"))
srt = np.sort(freq, axis=1)[:, ::-1]
cum = np.cumsum(srt, axis=1)
out = {
"tokens": int(ntok), "layers": L, "experts": E, "topk": K,
"gini": [float(gini(freq[l])) for l in range(L)],
"mass_top25pct": float(cum[:, E // 4 - 1].mean()),
"mass_top50pct": float(cum[:, E // 2 - 1].mean()),
"cum_mean": cum.mean(0).tolist(),
}
# temporal reuse: probability an expert used at token t was also used at t-1
reuse = []
for l in range(L):
a = T[l][:-1]; b = T[l][1:]
m = np.zeros((len(a), E), dtype=bool)
m[np.arange(len(a))[:, None], a] = True
hit = m[np.arange(len(b))[:, None], b].sum(1) / K
reuse.append(float(hit.mean()))
out["reuse_prev_token"] = reuse
# working set: distinct experts over a window of W tokens
ws = {}
for W in [1, 4, 16, 64, 256, 1024]:
vals = []
for l in range(L):
n = min(len(T[l]) // W, 64)
for i in range(n):
vals.append(len(np.unique(T[l][i * W:(i + 1) * W])))
ws[W] = float(np.mean(vals))
out["working_set"] = ws
json.dump(out, open(os.path.join(RES, "routing_stats.json"), "w"), indent=2)
return out
def gini(p):
x = np.sort(p)
n = len(x)
return float((2 * np.arange(1, n + 1) - n - 1).dot(x) / (n * x.sum()))
def simulate_cache(T, L, E, K, expert_bytes, cache_bytes, freq=None,
policy="lru", pin_frac=0.0):
"""Byte-accurate expert cache simulation over the real trace.
Returns fraction of expert activations served from cache (hit rate) and
bytes fetched from storage per token.
"""
cap = int(cache_bytes // expert_bytes)
if cap <= 0:
return 0.0, K * L * expert_bytes
npin = int(cap * pin_frac)
pinned = set()
if npin and freq is not None:
flat = [(freq[l][e], (l, e)) for l in range(L) for e in range(E)]
flat.sort(reverse=True)
pinned = {k for _, k in flat[:npin]}
from collections import OrderedDict
cache = OrderedDict((k, True) for k in pinned)
hits = tot = 0
ntok = T.shape[1]
for t in range(ntok):
for l in range(L):
for e in T[l, t]:
key = (l, int(e))
tot += 1
if key in cache:
hits += 1
if key not in pinned:
cache.move_to_end(key)
else:
cache[key] = True
while len(cache) > cap:
k0, _ = next(iter(cache.items()))
if k0 in pinned:
cache.move_to_end(k0)
continue
cache.popitem(last=False)
hr = hits / tot
return hr, (1 - hr) * K * L * expert_bytes
if __name__ == "__main__":
nseq = int(sys.argv[1]) if len(sys.argv) > 1 else 24
p = os.path.join(RES, "routing_trace.npy")
if os.path.exists(p):
T = np.load(p).astype(np.int64)
L, E, K = T.shape[0], 64, T.shape[2]
else:
T, L, E, K = trace(nseq)
st = analyse(T, L, E, K)
print(json.dumps({k: v for k, v in st.items() if k != "cum_mean"}, indent=2))
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