Files
colibri/c/download_fp8.py
T
woolcoxm 69f65d5173 Windows-dev: grouped quantization + mixed precision + expert budget + download tool
Consolidates all experiment branches into one Windows-dev branch:

1. Group-scaled int4 (fmt=4, gs=128) — glm.c
   - QT struct: added gs field
   - matmul_i4_grouped: AVX2 kernel, verified to 3e-08 vs f32
   - Format detection: auto-detects from .qs scale array size
   - expert_load: both mmap and slab+pread paths handle fmt=4
   - qt_bytes: fmt=4 case added

2. Per-tensor-type mixed precision — convert_fp8_to_int4.py
   - Split classify() into sh/o/kvb/attn/dmlp sub-types
   - New args: --shared-bits, --o-bits, --kvb-bits, --attn-bits, --dmlp-bits
   - Plan: shared expert + o_proj + kv_b_proj at int8, rest grouped int4
   - Only +5.3 GB RAM vs +0 for pure int4

3. EXPERT_BUDGET (miss-aware) — glm.c
   - Caps distinct experts per layer across batch-union
   - Always keeps cache hits, only drops misses
   - Up to 1.8x faster decode on low-RAM hosts

4. Two-step shared-expert prediction (PILOT_TWO) — glm.c
   - la_predict kind==2 + pilot_prefetch integration
   - +3.1% recall over baseline PILOT

5. FP8 download tool — download_fp8.py
   - ModelScope + HuggingFace dual-source
   - Parallel shard download with stall recovery

6. Tiny model generation — make_glm_oracle.py
   - Generated and tested locally for pipeline validation
2026-07-15 02:32:12 -04:00

232 lines
9.1 KiB
Python

"""Download GLM-5.2-FP8 from ModelScope (fast, no HF throttling) with
HuggingFace fallback. Parallel shard download, clean progress display.
Usage: python download_fp8.py
python download_fp8.py --parallel 4
python download_fp8.py --source hf (force HuggingFace)
"""
import os, sys, time, threading, argparse, subprocess
REPO_MS = "ZhipuAI/GLM-5.2-FP8" # ModelScope
REPO_HF = "zai-org/GLM-5.2-FP8" # HuggingFace
DEST = r"I:\glm52_fp8"
# ── ANSI colors ──
class C:
dim="\033[2m"; grn="\033[32m"; yel="\033[33m"; cyn="\033[36m"; b="\033[1m"; r="\033[0m"
def fmt_bytes(n):
if n>=1e9: return f"{n/1e9:.2f} GB"
if n>=1e6: return f"{n/1e6:.1f} MB"
return f"{n/1e3:.0f} KB"
def fmt_time(s):
if s<60: return f"{s:.0f}s"
if s<3600: return f"{s/60:.0f}m"
return f"{s/3600:.1f}h"
def bar(cur, total, width=24):
if total<=0: return "["+" "*width+"]"
pct=min(cur/total,1.0); filled=int(width*pct)
return "["+""*filled+""*(width-filled)+f"] {pct*100:4.0f}%"
def get_shard_list_hf():
from huggingface_hub import HfApi
info=HfApi().repo_info(REPO_HF, files_metadata=True)
shards=sorted(s.rfilename for s in info.siblings if s.rfilename.endswith(".safetensors"))
sizes={s.rfilename:s.size for s in info.siblings if s.rfilename.endswith(".safetensors")}
return shards, sizes
def get_shard_list_ms():
"""Get shard list from ModelScope API."""
import requests
# ModelScope API: list files
r = requests.get(f"https://modelscope.cn/api/v1/models/{REPO_MS}/repo/files?Revision=master&Root=", timeout=30)
data = r.json()["Data"]["Files"]
shards = sorted(f["Path"] for f in data if f["Path"].endswith(".safetensors"))
sizes = {f["Path"]: f.get("Size", 0) for f in data if f["Path"].endswith(".safetensors")}
return shards, sizes
def download_file_ms(fn):
"""Download a single file from ModelScope using their CDN."""
from modelscope.hub.file_download import model_file_download
model_file_download(
model_id=REPO_MS,
file_path=fn,
local_dir=DEST,
revision="master",
)
def download_file_hf(fn):
"""Download a single file from HuggingFace with hf_transfer."""
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
from huggingface_hub import hf_hub_download
hf_hub_download(REPO_HF, fn, local_dir=DEST)
def download_file_curl(fn, base_url, expected_size):
"""Fallback: download with curl to a .part file with resume."""
outpath = os.path.join(DEST, fn)
partpath = outpath + ".part"
if os.path.exists(outpath) and os.path.getsize(outpath) == expected_size:
return True
url = f"{base_url}/{fn}"
cmd = ["curl", "-L", "-C", "-", "--retry", "999", "--retry-delay", "5",
"--connect-timeout", "15", "--speed-time", "30", "--speed-limit", "1000",
"-o", partpath, "-H", "User-Agent: colibri-download/1.0", url]
subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
if os.path.exists(partpath) and os.path.getsize(partpath) >= expected_size:
os.replace(partpath, outpath)
return True
return False
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--parallel", type=int, default=3)
ap.add_argument("--source", choices=["auto", "ms", "hf"], default="auto",
help="auto (try ModelScope first), ms (ModelScope only), hf (HuggingFace only)")
args = ap.parse_args()
os.makedirs(DEST, exist_ok=True)
# Determine source and get shard list
use_ms = False
shards, sizes = [], {}
if args.source in ("auto", "ms"):
try:
print(f"{C.dim}Trying ModelScope...{C.r}", end=" ", flush=True)
shards, sizes = get_shard_list_ms()
use_ms = True
print(f"{C.grn}{C.r} {len(shards)} shards found")
except Exception as e:
print(f"{C.yel}failed ({e}){C.r}")
if args.source == "ms":
print("ModelScope failed and --source ms was set. Exiting."); return
if not shards:
print(f"{C.dim}Using HuggingFace...{C.r}", end=" ", flush=True)
shards, sizes = get_shard_list_hf()
print(f"{C.grn}{C.r} {len(shards)} shards found")
total = len(shards)
total_bytes = sum(sizes.values())
source_name = "ModelScope" if use_ms else "HuggingFace"
# Download metadata files
meta_files = ["config.json", "tokenizer.json", "tokenizer_config.json",
"generation_config.json", "model.safetensors.index.json"]
for fn in meta_files:
out = os.path.join(DEST, fn)
if not os.path.exists(out):
try:
if use_ms: download_file_ms(fn)
else: download_file_hf(fn)
except Exception: pass
# Build work queue
todo = []
done_set = set()
for fn in shards:
outpath = os.path.join(DEST, fn)
if os.path.exists(outpath) and os.path.getsize(outpath) == sizes.get(fn, 0):
done_set.add(fn)
else:
todo.append(fn)
existing = len(done_set)
print(f"\n{C.b}GLM-5.2-FP8 Download ({source_name}){C.r}")
print(f" {C.dim}{total} shards · {total_bytes/1e9:.0f} GB · {existing}/{total} complete{C.r}")
print(f" {C.dim}{len(todo)} to download · {args.parallel} parallel{C.r}")
if todo:
remaining = sum(sizes[fn] for fn in todo)
print(f" {C.dim}Remaining: {remaining/1e9:.0f} GB{C.r}")
print()
if not todo:
print(f"{C.grn}✓ All shards already downloaded!{C.r}\n"); return
lock = threading.Lock()
completed = list(done_set)
t0 = time.time()
qidx = [0]
def worker(wid):
while True:
with lock:
if qidx[0] >= len(todo): return
idx = qidx[0]; qidx[0] += 1
fn = todo[idx]
expected = sizes.get(fn, 0)
shard_num = existing + idx + 1
print(f" {C.cyn}[{shard_num}/{total}]{C.r} {C.dim}{fn}{C.r}")
success = False
for attempt in range(3):
try:
if use_ms:
download_file_ms(fn)
else:
download_file_hf(fn)
outpath = os.path.join(DEST, fn)
# ModelScope downloads to a cache dir, need to check
# if the file exists at our expected path
if not os.path.exists(outpath):
# Try to find it in ModelScope's cache structure
# and copy/symlink it
pass
if os.path.exists(outpath):
actual = os.path.getsize(outpath)
if expected == 0 or actual == expected:
success = True; break
# Size mismatch — might be in cache
# If we got here, file downloaded but not at expected path
# ModelScope puts it in local_dir; check again
if os.path.exists(outpath):
success = True; break
# Retry with curl fallback
if use_ms:
base = f"https://modelscope.cn/api/v1/models/{REPO_MS}/repo?Revision=master&FilePath="
else:
base = f"https://huggingface.co/{REPO_HF}/resolve/main"
if download_file_curl(fn, base, expected):
success = True; break
except Exception as e:
if attempt < 2:
print(f" {C.yel}retry {attempt+1}: {e}{C.r}")
time.sleep(3)
else:
print(f" {C.yel}✗ failed: {e}{C.r}")
with lock:
if success:
completed.append(fn)
elapsed = time.time() - t0
have = sum(sizes.get(f,0) for f in completed)
pct = 100.0 * have / total_bytes
speed = (have - sum(sizes[f] for f in done_set)) / max(elapsed, 1)
eta = (total_bytes - have) / speed if speed > 0 else 0
print(f" {C.grn}{C.r} {fn} {C.dim}{len(completed)}/{total} · "
f"{pct:.1f}% · {fmt_time(elapsed)} · ETA {fmt_time(eta)}{C.r}")
else:
print(f" {C.yel}✗ GIVE UP: {fn}{C.r}")
threads = [threading.Thread(target=worker, args=(i,), daemon=True) for i in range(args.parallel)]
for t in threads: t.start()
for t in threads: t.join()
print()
final = sum(1 for fn in shards
if os.path.exists(os.path.join(DEST, fn))
and os.path.getsize(os.path.join(DEST, fn)) == sizes.get(fn, 0))
if final == total:
print(f"{C.grn}{'='*50}")
print(f" ✓ All {total} shards downloaded!{C.r}\n")
else:
print(f"{C.yel} {final}/{total} complete, {total-final} remaining{C.r}")
print(f" Re-run to resume.\n")
if __name__ == "__main__":
try: main()
except KeyboardInterrupt:
print(f"\n\n{C.yel}Interrupted. Re-run to resume — no data lost.{C.r}\n")