""" Convertitore GLM-5.2-FP8 -> nostro container int4 (STADIO B). Strategia DISK-SAFE (richiesta dell'utente): scarica UNO shard (~5 GB), lo converte in int4, lo CANCELLA, passa al prossimo. Il disco non si riempie mai: picco = 1 shard + l'output int4 che cresce fino a ~372 GB. Controllo di spazio che si ferma se manca margine. Cosa fa per ogni tensore: - pesi FP8 (e4m3) con `*.weight_scale_inv` -> dequant a blocchi 128x128 -> f32 - pesi BF16 (norme/embed/lm_head/...) -> f32 poi: - attn/mlp/shared/expert/embed/lm_head -> QUANTIZZATO int4 (o int8) con la STESSA matematica del motore C (np.rint = lrintf, stesse soglie, stesso packing dei nibble) -> token identici - norme / router (mlp.gate.weight) / bias / e_score_correction_bias -> tenuti F32 - indexer DSA / layer MTP (78) / shared_head / eh_proj / *norm dell'indexer -> SALTATI Output: una dir di safetensors leggibile dal motore C (per ogni peso quantizzato: `nome` U8 = dati impacchettati, `nome.qs` F32 = scale per riga). USO: # test locale (oracolo tiny, niente download): converte una dir gia' presente python3 tools/convert_fp8_to_int4.py --indir glm_tiny --outdir glm_tiny_i4 --ebits 4 --io-bits 4 # selftest del dequant fp8 (richiede torch) python3 tools/convert_fp8_to_int4.py --selftest # reale: scarica+converte+cancella shard per shard python3 tools/convert_fp8_to_int4.py --repo zai-org/GLM-5.2-FP8 --outdir /home/vincenzo/glm52_i4 """ import os, sys, glob, json, shutil, argparse import numpy as np # ---------- quantizzazione: identica al C (glm.c) ---------- def quant_int8(w, bits): # w: [O,I] f32 -> (qbytes U8 [O*I], scale f32 [O]) qmax = (1 << (bits - 1)) - 1 amax = np.abs(w).max(axis=1, keepdims=True) s = np.maximum(amax / qmax, 1e-8) q = np.clip(np.rint(w / s), -qmax - 1, qmax).astype(np.int8) return q.reshape(-1).view(np.uint8).copy(), s[:, 0].astype(np.float32) def quant_int4(w, bits): # -> (qbytes U8 [O*ceil(I/2)], scale f32 [O]) O, I = w.shape qmax = (1 << (bits - 1)) - 1 amax = np.abs(w).max(axis=1, keepdims=True) s = np.maximum(amax / qmax, 1e-8) q = np.clip(np.rint(w / s), -8, qmax).astype(np.int32) # nibble [-8,7] rb = (I + 1) // 2 out = np.zeros((O, rb), np.uint8) v0 = (q[:, 0::2] + 8).astype(np.uint8) out[:, :v0.shape[1]] = v0 if I > 1: v1 = (q[:, 1::2] + 8).astype(np.uint8) out[:, :v1.shape[1]] |= (v1 << 4) return out.reshape(-1), s[:, 0].astype(np.float32) def quant_int2(w, bits): # -> (qbytes U8 [O*ceil(I/4)], scale f32 [O]); 4/byte O, I = w.shape qmax = (1 << (bits - 1)) - 1 # bits=2 -> qmax=1, valori [-2,1] amax = np.abs(w).max(axis=1, keepdims=True) s = np.maximum(amax / qmax, 1e-8) q = np.clip(np.rint(w / s), -2, qmax).astype(np.int32) rb = (I + 3) // 4 out = np.zeros((O, rb), np.uint8) for k in range(4): # impacchetta 4 valori per byte (identico a pack_int2 in C) vk = q[:, k::4] out[:, :vk.shape[1]] |= ((vk + 2).astype(np.uint8) << (k * 2)) return out.reshape(-1), s[:, 0].astype(np.float32) # ---------- classificazione dei tensori ---------- def layer_idx(name): p = name.split(".") if len(p) > 2 and p[0] == "model" and p[1] == "layers": try: return int(p[2]) except ValueError: return -1 return -1 def classify(name, n_layers, keep_mtp=False, keep_idx=False): if name.endswith("_scale_inv"): return "consumed" # gestito col suo peso li = layer_idx(name) if keep_idx: # modalita' --indexer: SOLO i pesi del DSA lightning indexer dei layer principali if li < 0 or li >= n_layers or "indexer" not in name: return "skip" if name.endswith("norm.weight"): return "f32" return "q" # int8 consigliato (--ebits 8): pesi di scoring if keep_mtp: if li != n_layers: return "skip" # solo il layer MTP if "indexer" in name: return "skip" # il DSA indexer resta un no-op else: if li >= n_layers: return "skip" # layer MTP (78) if any(k in name for k in ["indexer", "indexers_proj", "eh_proj", "enorm", "hnorm", "shared_head"]): return "skip" if name.endswith("e_score_correction_bias"): return "f32" if name.endswith("mlp.gate.weight"): return "f32" # router (NON gate_proj) if name.endswith("norm.weight") or name == "model.norm.weight": return "f32" if name in ("model.embed_tokens.weight", "lm_head.weight"): return "io" if ".mlp.experts." in name and name.endswith(".weight"): return "x" # expert ROUTED (streaming) if name.endswith(".weight"): return "q" # attn/dense-mlp/shared (residente) return "f32" # ---------- dequant di un tensore (fp8+scale a blocchi / bf16 / f32) ---------- def dequant(f, name): import torch sl = f.get_slice(name); dt = sl.get_dtype() if dt in ("F8_E4M3", "float8_e4m3fn"): w = f.get_tensor(name).to(torch.float32) sc = f.get_tensor(name + "_scale_inv").to(torch.float32) # [ceil(O/128),ceil(I/128)] O, I = w.shape sc = sc.repeat_interleave(128, 0).repeat_interleave(128, 1)[:O, :I] return (w * sc).numpy() return f.get_tensor(name).to(torch.float32).numpy() def convert_shard(path, out_dict, n_layers, ebits, io_bits, xbits, keep_mtp=False, keep_idx=False): from safetensors import safe_open with safe_open(path, framework="pt") as f: for name in f.keys(): kind = classify(name, n_layers, keep_mtp, keep_idx) if kind in ("skip", "consumed"): continue w = dequant(f, name) if kind == "f32": out_dict[name] = w.astype(np.float32) else: bits = io_bits if kind == "io" else xbits if kind == "x" else ebits if w.ndim != 2: # es. bias 1D non previsto come 'q' -> tienilo f32 out_dict[name] = w.astype(np.float32); continue q, s = (quant_int2(w, bits) if bits <= 2 else quant_int4(w, bits) if bits <= 4 else quant_int8(w, bits)) out_dict[name] = q out_dict[name + ".qs"] = s def free_gb(p): return shutil.disk_usage(p).free / 1e9 def main(): ap = argparse.ArgumentParser() ap.add_argument("--repo", default=None) ap.add_argument("--indir", default=None) ap.add_argument("--outdir", required=False) ap.add_argument("--ebits", type=int, default=None) # bit residenti (default 4; 8 per --mtp/--indexer) ap.add_argument("--io-bits", type=int, default=8) # bit di embed/lm_head ap.add_argument("--xbits", type=int, default=None) # bit degli expert ROUTED (streaming); default=ebits ap.add_argument("--n-layers", type=int, default=78) ap.add_argument("--min-free-gb", type=float, default=20.0) ap.add_argument("--selftest", action="store_true") ap.add_argument("--mtp", action="store_true", help="download and convert ONLY the MTP head (model.layers..*) -> out-mtp-*.safetensors") ap.add_argument("--indexer", action="store_true", help="extract ONLY the DSA lightning-indexer weights -> out-idx-*.safetensors. WARNING: " "indexer tensors are spread across nearly every shard, so this re-downloads the whole " "repository (~756 GB of traffic) to retain only a few GB. Resumable per shard. " "Recommended: --ebits 8.") a = ap.parse_args() if a.ebits is None: # testa MTP a int4 = acceptance ~0-4% (misurato, issue #8): il draft sbaglia sempre # e la speculazione non parte mai. A int8: 39-59%, 2.2-2.8 token/forward. a.ebits = 8 if (a.mtp or a.indexer) else 4 if a.xbits is None: a.xbits = a.ebits if a.selftest: import torch w = (torch.randn(256, 256) * 0.3) O, I = w.shape; bs = 128 sc = torch.zeros(O // bs, I // bs) for bi in range(O // bs): for bj in range(I // bs): blk = w[bi*bs:(bi+1)*bs, bj*bs:(bj+1)*bs] sc[bi, bj] = blk.abs().max() / 448.0 q = (w / sc.repeat_interleave(bs,0).repeat_interleave(bs,1)).to(torch.float8_e4m3fn) deq = (q.to(torch.float32) * sc.repeat_interleave(bs,0).repeat_interleave(bs,1)) rel = (deq - w).abs().mean() / w.abs().mean() print(f"[selftest fp8 block-dequant] mean relative error = {rel:.4f} " f"({'OK' if rel < 0.05 else 'HIGH'})") return os.makedirs(a.outdir, exist_ok=True) if a.indir: # conversione locale (test) shards = sorted(glob.glob(os.path.join(a.indir, "*.safetensors"))) from safetensors.numpy import save_file for i, sp in enumerate(shards): out = {}; convert_shard(sp, out, a.n_layers, a.ebits, a.io_bits, a.xbits) save_file(out, os.path.join(a.outdir, f"out-{i:05d}.safetensors")) # copia config + tokenizer for fn in ["config.json"]: src = os.path.join(a.indir, fn) if os.path.exists(src): shutil.copy(src, a.outdir) print(f"converted {len(shards)} shards -> {a.outdir}") return # reale: scarica shard per shard, converte, cancella # EN: real: download shard by shard, convert, delete # # ROBUSTEZZA RETE: timeout brevi sulle read cosi' un download appeso FALLISCE invece # di restare fermo per sempre. 8s, non 30: "timeout" = ZERO byte ricevuti in quella # finestra; su un transfer vivo i chunk arrivano di continuo, quindi 8s e' sicuro e # uno stallo costa 8s invece di 30. # EN: NETWORK ROBUSTNESS: short read timeouts so a hung download FAILS instead of # EN: sitting there forever. 8s, not 30: a "timeout" means ZERO bytes received in that # EN: window; a live transfer delivers chunks constantly, so 8s is safe and a stall # EN: costs 8s instead of 30. os.environ.setdefault("HF_HUB_DOWNLOAD_TIMEOUT", "8") os.environ.setdefault("HF_HUB_ETAG_TIMEOUT", "15") # log con timestamp: i messaggi "Trying to resume" di hf_hub diventano databili. # EN: timestamped logs: hf_hub's "Trying to resume" messages become datable. import logging logging.basicConfig(format="%(asctime)s %(name)s: %(message)s", datefmt="%H:%M:%S") # hf_xet si blocca quando la rete si riavvia (connessioni zombie senza timeout): # forza la via HTTP classica, che curl ha dimostrato funzionare. (misurato 2026-07-02) # EN: hf_xet hangs when the network restarts (zombie connections with no timeout): # EN: force the classic HTTP path, which curl proved works (measured 2026-07-02). os.environ.setdefault("HF_HUB_DISABLE_XET", "1") # =0 per riabilitare xet / to re-enable xet from huggingface_hub import HfApi, hf_hub_download # lock anti-doppione: DUE convertitori sulla stessa outdir si corrompono a vicenda. # EN: anti-duplicate lock: TWO converters on the same outdir corrupt each other. import fcntl lock = open(os.path.join(a.outdir, ".convert.lock"), "w") try: fcntl.flock(lock, fcntl.LOCK_EX | fcntl.LOCK_NB) except OSError: print("ERROR: another converter is already using this output directory. Exiting."); return # dimensioni note dei file, riempite dopo repo_info: il downloader multi-stream le usa # per calcolare i confini dei segmenti e per sapere quando un file e' completo. # EN: known file sizes, filled after repo_info: the multi-stream downloader uses them # EN: to compute segment boundaries and to know when a file is complete. SIZES = {} def download_retry(repo, fn, dest, tries=999): """Downloader multi-stream con resume via Range. Apre N segmenti concorrenti (default 2, COLI_DL_STREAMS per cambiarli) e salva lo stato per-segmento in un sidecar .seg -> NESSUN byte perso comunque muoia la connessione. Un singolo stream HF e' limitato a ~2 MB/s (misurato); 2 stream ~ raddoppiano il throughput senza saturare una linea domestica. File piccoli, COLI_DL_STREAMS=1 o un vecchio .part legacy -> percorso a stream singolo (_download_single). EN: multi-stream Range-resume downloader. Opens N concurrent segments (default 2, EN: COLI_DL_STREAMS to change) and saves per-segment state in a .seg sidecar -> NO EN: byte is lost however the connection dies. A single HF stream is paced at EN: ~2 MB/s (measured); 2 streams roughly double throughput without saturating a EN: home line. Small files, COLI_DL_STREAMS=1 or a legacy .part -> single-stream EN: path (_download_single).""" import time as _t, threading, urllib.request, urllib.error url = f"https://huggingface.co/{repo}/resolve/main/{fn}" out = os.path.join(dest, fn); part = out + ".part"; side = part + ".seg" os.makedirs(dest, exist_ok=True) expected = SIZES.get(fn) if os.path.exists(out) and (expected is None or os.path.getsize(out) == expected): return out NS = max(1, min(8, int(os.environ.get("COLI_DL_STREAMS", "2")))) # un .part senza sidecar l'ha scritto una versione precedente a stream singolo. # EN: a .part without a sidecar was written by an older single-stream version. legacy = os.path.exists(part) and not os.path.exists(side) if expected is None or expected < (256 << 20) or NS == 1 or legacy: return _download_single(url, fn, out, part, expected) # ---- multi-stream ---- segs = [(expected * t // NS, expected * (t + 1) // NS) for t in range(NS)] done = [0] * NS # riprendi lo stato dei segmenti se il sidecar combacia (stesso N, stessa size). # EN: resume per-segment progress if the sidecar matches (same N, same size). if os.path.exists(side): try: st = json.loads(open(side).read()) if st.get("n") == NS and st.get("size") == expected: done = st["done"] except Exception: pass if not os.path.exists(part): with open(part, "wb") as f: f.truncate(expected) # file sparse / sparse file fd = os.open(part, os.O_WRONLY) t0 = _t.time(); nres = [0]; log_lock = threading.Lock(); stopfail = [] def worker(t): s0, s1 = segs[t] while done[t] < s1 - s0 and not stopfail: pos = s0 + done[t] req = urllib.request.Request(url, headers={"User-Agent": "colibri-convert", "Range": f"bytes={pos}-{s1-1}"}) try: with urllib.request.urlopen(req, timeout=8) as r: if r.status != 206: # Range ignorato: multi-stream impossibile stopfail.append(t); return # EN: Range ignored: multi-stream impossible while done[t] < s1 - s0: chunk = r.read(1 << 20) if not chunk: break rem = (s1 - s0) - done[t] # mai oltre il segmento / never past the segment if len(chunk) > rem: chunk = chunk[:rem] os.pwrite(fd, chunk, s0 + done[t]) done[t] += len(chunk) except KeyboardInterrupt: raise except Exception as ex: with log_lock: nres[0] += 1 print(f" [dl] s{t}: {type(ex).__name__} at {(s0+done[t])/1e9:.2f} GB: " f"resuming (#{nres[0]})", flush=True) _t.sleep(min(15, 1 + nres[0] // NS)) th = [threading.Thread(target=worker, args=(t,), daemon=True) for t in range(NS)] for x in th: x.start() print(f" [dl {_t.strftime('%H:%M:%S')}] connected: {NS} streams, " f"{sum(done)/1e9:.2f} of {expected/1e9:.2f} GB", flush=True) mark = sum(done); tmark = t0 while any(x.is_alive() for x in th): _t.sleep(5) have = sum(done) tmpside = side + ".tmp" # checkpoint atomico / atomic checkpoint open(tmpside, "w").write(json.dumps({"n": NS, "size": expected, "done": list(done)})) os.replace(tmpside, side) now = _t.time() if now - tmark >= 30: print(f" [dl {_t.strftime('%H:%M:%S')}] {have/1e9:5.2f} GB " f"({(have-mark)/max(now-tmark,1e-9)/1e6:5.1f} MB/s, {NS} stream)", flush=True) mark = have; tmark = now os.close(fd) if stopfail: # il server non onora il Range: fallback for f2 in (part, side): # EN: server won't honor Range: fall back if os.path.exists(f2): os.remove(f2) return _download_single(url, fn, out, part, expected) assert sum(done) == expected if os.path.exists(side): os.remove(side) os.replace(part, out) dt = max(_t.time() - t0, 1e-9) print(f" [dl] {fn}: {expected/1e9:.2f} GB in {dt/60:.1f} min " f"({expected/dt/1e6:.1f} MB/s avg, {NS} streams, {nres[0]} resumes)", flush=True) return out def _download_single(url, fn, out, part, expected): """Percorso a stream singolo con resume via Range (file piccoli / .part legacy / COLI_DL_STREAMS=1). Un EOF corto ma pulito conta come ripresa; se non arriva NESSUN byte nuovo, backoff invece di girare a vuoto. EN: single-stream path with Range resume (small files / legacy .part / EN: COLI_DL_STREAMS=1). A clean short EOF counts as a resume; if NO new byte EN: arrives, back off instead of spinning.""" import time as _t, urllib.request, urllib.error t0 = _t.time(); nres = 0; mark = 0; tmark = t0 while True: have = os.path.getsize(part) if os.path.exists(part) else 0 if expected is not None and have >= expected: break have0 = have req = urllib.request.Request(url, headers={"User-Agent": "colibri-convert"}) if have: req.add_header("Range", f"bytes={have}-") try: with urllib.request.urlopen(req, timeout=8) as r: if have and r.status == 200: # server ha ignorato il Range: riparti pulito have = 0 # EN: server ignored Range: restart clean if expected is None: cl = r.headers.get("Content-Length") if cl: expected = have + int(cl) if have == 0 or nres: # segnale di vita subito / immediate sign of life print(f" [dl {_t.strftime('%H:%M:%S')}] connected" f"{f' @ {have/1e9:.2f} GB' if have else ''}" f"{f' of {expected/1e9:.2f} GB' if expected else ''}", flush=True) with open(part, "ab" if have else "wb") as f: if not have: f.truncate(0) while True: chunk = r.read(1 << 20) if not chunk: break f.write(chunk); have += len(chunk) if have - mark >= 512 * 1024 * 1024 or _t.time() - tmark >= 30: now = _t.time() print(f" [dl {_t.strftime('%H:%M:%S')}] {have/1e9:5.2f} GB " f"({(have-mark)/max(now-tmark,1e-9)/1e6:5.1f} MB/s)", flush=True) mark = have; tmark = now if expected is None: break # lunghezza ignota: passata singola / unknown length if have < expected: # EOF corto ma pulito: conta come ripresa nres += 1 # EN: clean short EOF: counts as a resume if have == have0: _t.sleep(min(15, 1 + nres)) # zero progresso -> backoff / zero progress -> back off except KeyboardInterrupt: raise except urllib.error.HTTPError as ex: if ex.code == 416: break # gia' completo / already complete nres += 1 print(f" [dl] HTTP {ex.code} at {have/1e9:.2f} GB: resuming (#{nres})", flush=True) _t.sleep(min(15, 1 + nres)) except Exception as ex: nres += 1 print(f" [dl] {type(ex).__name__} at {have/1e9:.2f} GB: resuming (#{nres})", flush=True) _t.sleep(min(15, 1 + nres)) os.replace(part, out) dt = max(_t.time() - t0, 1e-9); sz = os.path.getsize(out) print(f" [dl] {fn}: {sz/1e9:.2f} GB in {dt/60:.1f} min " f"({sz/dt/1e6:.1f} MB/s avg, {nres} resumes)", flush=True) return out from safetensors.numpy import save_file import time as _t for att in range(999): try: info = HfApi().repo_info(a.repo, files_metadata=True) # dimensioni note dallo store: abilitano il download multi-stream a segmenti. # EN: sizes known from the store: enable segmented multi-stream download. SIZES.update({s.rfilename: s.size for s in info.siblings if s.size}) break except KeyboardInterrupt: raise except Exception as ex: w = min(60, 5*(att+1)); print(f"repo_info failed ({type(ex).__name__}); retrying in {w}s", flush=True); _t.sleep(w) shards = sorted(s.rfilename for s in info.siblings if s.rfilename.endswith(".safetensors")) for fn in ["config.json", "tokenizer.json", "tokenizer_config.json", "generation_config.json"]: try: shutil.copy(hf_hub_download(a.repo, fn, local_dir=a.outdir+"/_meta"), a.outdir) except Exception: pass tmp = os.path.join(a.outdir, "_inflight"); os.makedirs(tmp, exist_ok=True) if a.mtp: import urllib.request idx = json.loads(urllib.request.urlopen( f"https://huggingface.co/{a.repo}/resolve/main/model.safetensors.index.json", timeout=30).read())["weight_map"] pref = f"model.layers.{a.n_layers}." mtp_shards = sorted(set(v for k, v in idx.items() if k.startswith(pref))) print(f"[MTP] head at layer {a.n_layers}: {len(mtp_shards)} shards to process: {mtp_shards}") for i, sh in enumerate(mtp_shards): outp = os.path.join(a.outdir, f"out-mtp-{i:05d}.safetensors") if os.path.exists(outp): print(f"[MTP] {outp} already done"); continue print(f"[MTP {i+1}/{len(mtp_shards)}] downloading {sh}...", flush=True) p = download_retry(a.repo, sh, tmp) out = {}; convert_shard(p, out, a.n_layers, a.ebits, a.io_bits, a.xbits, keep_mtp=True) save_file(out, outp) os.remove(p) for blob in glob.glob(os.path.join(tmp, "**", "*"), recursive=True): if os.path.isfile(blob): os.remove(blob) print(f" -> {os.path.basename(outp)} ({os.path.getsize(outp)/1e9:.2f} GB, {len(out)} tensors)", flush=True) shutil.rmtree(tmp, ignore_errors=True); print("[MTP] DONE."); return if a.indexer: import urllib.request idx = json.loads(urllib.request.urlopen( f"https://huggingface.co/{a.repo}/resolve/main/model.safetensors.index.json", timeout=30).read())["weight_map"] idx_shards = sorted(set(v for k, v in idx.items() if "indexer" in k and 0 <= layer_idx(k) < a.n_layers)) tot_gb = len(idx_shards) * 5.4 print(f"[IDX] indexer weights across {len(idx_shards)} shards (~{tot_gb:.0f} GB total download, resumable)") for i, sh in enumerate(idx_shards): outp = os.path.join(a.outdir, f"out-idx-{i:05d}.safetensors") if os.path.exists(outp): continue # gia' fatto -> ripartibile print(f"[IDX {i+1}/{len(idx_shards)}] downloading {sh}...", flush=True) p = download_retry(a.repo, sh, tmp) out = {}; convert_shard(p, out, a.n_layers, a.ebits, a.io_bits, a.xbits, keep_idx=True) if out: save_file(out, outp) os.remove(p) for blob in glob.glob(os.path.join(tmp, "**", "*"), recursive=True): if os.path.isfile(blob): os.remove(blob) print(f" -> {os.path.basename(outp)} ({len(out)} tensors)", flush=True) shutil.rmtree(tmp, ignore_errors=True); print("[IDX] DONE."); return for i, sh in enumerate(shards): if free_gb(a.outdir) < a.min_free_gb: print(f"STOP: free space is below {a.min_free_gb} GB. Free space and rerun to resume."); break outp = os.path.join(a.outdir, f"out-{i:05d}.safetensors") if os.path.exists(outp): continue # gia' fatto -> ripartibile print(f"[{i+1}/{len(shards)}] downloading {sh} ({free_gb(a.outdir):.0f} GB free)...", flush=True) p = download_retry(a.repo, sh, tmp) out = {}; convert_shard(p, out, a.n_layers, a.ebits, a.io_bits, a.xbits) save_file(out, outp) os.remove(p) # <-- cancella subito lo shard fp8 for blob in glob.glob(os.path.join(tmp, "**", "*"), recursive=True): if os.path.isfile(blob): os.remove(blob) print(f" -> {os.path.basename(outp)} ({os.path.getsize(outp)/1e9:.2f} GB)", flush=True) shutil.rmtree(tmp, ignore_errors=True) print("DONE." if i == len(shards)-1 else "INTERRUPTED (rerun to resume).") if __name__ == "__main__": main()