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"""BF16 teacher streamer for GLM-5.2 (phase-2 full-AQLM teacher).
Backs `get_expert(layer, expert, proj) -> torch.bfloat16` with HTTP range
reads of zai-org/GLM-5.2 (the original BF16 weights). The safetensors index
is fetched once; per-shard headers are cached on first touch under
/data/bf16-cache/headers/; fetched tensor byte ranges live in an on-disk LRU
blob cache under /data/bf16-cache/blobs/, capped at 400 GB with mtime-based
eviction.
Range-fetch pattern reused from tools/gguf_remote.py and
tools/make_hot_manifest.py (safetensors: 8-byte little-endian header length,
JSON header of {name: {dtype, shape, data_offsets:[b,e]}}, tensor bytes at
8 + header_len + b .. 8 + header_len + e).
Run directly to VERIFY against the NVFP4 teacher:
python tools/bf16_stream.py [--n 10]
prints cosine similarity for N random (layer, expert, proj) triples between
the streamed BF16 tensor and the NVFP4-dequantized teacher (regions in
/tmp/glm52-hot-dl2); all must exceed 0.98.
"""
import argparse
import hashlib
import json
import os
import struct
import time
import urllib.request
BASE = "https://huggingface.co/zai-org/GLM-5.2/resolve/main"
CACHE = "/data/bf16-cache"
CAP_BYTES = 400 * (1 << 30) # 400 GB LRU cap on the blob cache
LOCAL_TEACHER_LAYERS = {3, 4, 5, 8, 74, 75, 76, 77}
# canonical projection names + convenient aliases
_PROJ = {
"gate_proj": "gate_proj", "up_proj": "up_proj", "down_proj": "down_proj",
"gate": "gate_proj", "up": "up_proj", "down": "down_proj",
"w1": "gate_proj", "w3": "up_proj", "w2": "down_proj",
}
# safetensors dtype -> (torch dtype str, numpy dtype for raw view)
_ST_DT = {"BF16": "bfloat16", "F16": "float16", "F32": "float32"}
def _fetch(url, start=None, length=None, retries=6, timeout=120):
"""HTTP GET (optionally a byte range) with exponential backoff."""
last = None
for attempt in range(retries):
try:
req = urllib.request.Request(url)
if start is not None:
req.add_header("Range", f"bytes={start}-{start + length - 1}")
with urllib.request.urlopen(req, timeout=timeout) as r:
return r.read()
except Exception as e: # noqa: BLE001 - transient net errors
last = e
if attempt == retries - 1:
break
time.sleep(min(30, 2 ** attempt))
raise RuntimeError(f"fetch failed {url} [{start}:{length}]: {last}")
class Bf16Teacher:
def __init__(self, base=BASE, cache=CACHE, cap_bytes=CAP_BYTES):
self.base = base.rstrip("/")
self.cache = cache
self.cap = cap_bytes
self.hdr_dir = os.path.join(cache, "headers")
self.blob_dir = os.path.join(cache, "blobs")
os.makedirs(self.hdr_dir, exist_ok=True)
os.makedirs(self.blob_dir, exist_ok=True)
self._headers = {} # shard -> {name: info}
self._hdr_len = {} # shard -> header_len (bytes offset)
self._wm = None # weight_map (name -> shard)
# -------------------------------------------------- index / headers
def _index(self):
if self._wm is None:
p = os.path.join(self.cache, "index.json")
if os.path.exists(p):
idx = json.load(open(p))
else:
idx = json.loads(_fetch(f"{self.base}/model.safetensors.index.json"))
json.dump(idx, open(p, "w"))
self._wm = idx["weight_map"]
return self._wm
def _header(self, shard):
if shard in self._headers:
return self._headers[shard], self._hdr_len[shard]
hp = os.path.join(self.hdr_dir, shard + ".json")
if os.path.exists(hp):
d = json.load(open(hp))
hdr, hlen = d["header"], d["header_len"]
else:
url = f"{self.base}/{shard}"
hlen = struct.unpack("<Q", _fetch(url, 0, 8))[0]
hdr = json.loads(_fetch(url, 8, hlen))
hdr.pop("__metadata__", None)
json.dump({"header_len": hlen, "header": hdr}, open(hp, "w"))
self._headers[shard] = hdr
self._hdr_len[shard] = hlen
return hdr, hlen
# -------------------------------------------------- blob LRU cache
def _blob_path(self, key):
h = hashlib.sha1(key.encode()).hexdigest()
return os.path.join(self.blob_dir, h + ".bin")
def _evict_if_needed(self, incoming):
files = []
total = 0
with os.scandir(self.blob_dir) as it:
for e in it:
if e.name.endswith(".bin"):
st = e.stat()
files.append((st.st_mtime, e.path, st.st_size))
total += st.st_size
if total + incoming <= self.cap:
return
files.sort() # oldest mtime first
for _, path, size in files:
if total + incoming <= self.cap:
break
try:
os.remove(path)
total -= size
except OSError:
pass
def _get_bytes(self, name):
wm = self._index()
if name not in wm:
raise KeyError(name)
shard = wm[name]
hdr, hlen = self._header(shard)
info = hdr[name]
b, e = info["data_offsets"]
key = f"{shard}:{name}"
bp = self._blob_path(key)
if os.path.exists(bp):
os.utime(bp, None) # LRU touch
return open(bp, "rb").read(), info
length = e - b
buf = _fetch(f"{self.base}/{shard}", 8 + hlen + b, length)
self._evict_if_needed(len(buf))
tmp = bp + f".tmp{os.getpid()}"
with open(tmp, "wb") as f:
f.write(buf)
os.replace(tmp, bp)
return buf, info
# -------------------------------------------------- public API
def get_expert(self, layer: int, expert: int, proj: str):
import torch
pj = _PROJ.get(proj, proj)
name = f"model.layers.{layer}.mlp.experts.{expert}.{pj}.weight"
buf, info = self._get_bytes(name)
dt = _ST_DT.get(info["dtype"])
if dt is None:
raise ValueError(f"unexpected dtype {info['dtype']} for {name}")
t = torch.frombuffer(bytearray(buf), dtype=getattr(torch, dt))
return t.reshape(info["shape"]).to(torch.bfloat16)
# ------------------------------------------------------------- verification
def _verify(n):
import random
import torch
import sys
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from aqlm_converge import TeacherReader, dequant_teacher, FP4_LUT
teacher = Bf16Teacher()
tr = TeacherReader()
lut = torch.tensor(FP4_LUT, dtype=torch.float32)
# experts with actual NVFP4 region coverage, layers using the ranged path
avail = {}
for shard, h in tr.headers.items():
for nm in h["header"]:
if ".mlp.experts." in nm and nm.endswith("gate_proj.weight"):
p = nm.split(".")
li, e = int(p[2]), int(p[5])
if li not in LOCAL_TEACHER_LAYERS:
avail.setdefault(li, set()).add(e)
pool = [(li, e) for li, es in avail.items() for e in es]
rng = random.Random(1234)
rng.shuffle(pool)
cosines = []
tried = 0
for li, e in pool:
if len(cosines) >= n:
break
proj = rng.choice(["gate_proj", "up_proj", "down_proj"])
tried += 1
try:
nv = dequant_teacher(tr, li, e, proj, "cpu", lut) # fp32 [out,in]
except KeyError:
continue
bf = teacher.get_expert(li, e, proj).float()
a = nv.reshape(-1)
b = bf.reshape(-1)
cos = torch.dot(a, b) / (a.norm() * b.norm() + 1e-12)
cosines.append(cos.item())
print(f" L{li:>2} e{e:<3} {proj:<10} cos={cos.item():.5f}")
print(f"\n{len(cosines)} cosines (from {tried} tries); "
f"min={min(cosines):.5f} mean={sum(cosines)/len(cosines):.5f}")
assert all(c > 0.98 for c in cosines), "cosine <= 0.98 for some triple"
print("VERIFY PASS: all cosines > 0.98")
if __name__ == "__main__":
ap = argparse.ArgumentParser()
ap.add_argument("--n", type=int, default=10)
_verify(ap.parse_args().n)
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