""" 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 convert_fp8_to_int4.py --indir glm_tiny --outdir glm_tiny_i4 --ebits 4 --io-bits 4 # selftest del dequant fp8 (richiede torch) python3 convert_fp8_to_int4.py --selftest # reale: scarica+converte+cancella shard per shard python3 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): if name.endswith("_scale_inv"): return "consumed" # gestito col suo peso li = layer_idx(name) 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): 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) 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=4) # bit residenti: attn/dense-mlp/shared 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="scarica/converte SOLO la testa MTP (model.layers..*) -> out-mtp-*.safetensors") a = ap.parse_args() 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] errore relativo medio = {rel:.4f} " f"({'OK' if rel < 0.05 else 'ALTO'})") 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"convertito {len(shards)} shard -> {a.outdir}") return # reale: scarica shard per shard, converte, cancella # ROBUSTEZZA RETE (WSL: la scheda virtuale puo' bloccarsi): timeout sulle read cosi' un # download appeso FALLISCE invece di restare fermo per sempre, e retry con backoff. os.environ.setdefault("HF_HUB_DOWNLOAD_TIMEOUT", "30") os.environ.setdefault("HF_HUB_ETAG_TIMEOUT", "30") # hf_xet si blocca quando la rete WSL viene riavviata (connessioni zombie senza timeout): # forza la via HTTP classica, che curl ha dimostrato funzionare. (misurato 2026-07-02) os.environ["HF_HUB_DISABLE_XET"] = "1" from huggingface_hub import HfApi, hf_hub_download # lock anti-doppione: DUE convertitori sulla stessa outdir si corrompono a vicenda 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("ERRORE: un altro convertitore sta gia' lavorando su questa outdir. Esco."); return def download_retry(repo, fn, dest, tries=999): import time as _t for att in range(tries): try: return hf_hub_download(repo, fn, local_dir=dest) except KeyboardInterrupt: raise except Exception as ex: wait = min(60, 5 * (att + 1)) print(f" rete KO ({type(ex).__name__}): riprovo tra {wait}s " f"(tentativo {att+1})", flush=True) _t.sleep(wait) raise RuntimeError("download fallito dopo troppi tentativi") 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); break except KeyboardInterrupt: raise except Exception as ex: w = min(60, 5*(att+1)); print(f"repo_info KO ({type(ex).__name__}): riprovo tra {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] testa nel layer {a.n_layers}: {len(mtp_shards)} shard da processare: {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} gia' fatto"); continue print(f"[MTP {i+1}/{len(mtp_shards)}] scarico {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)} tensori)", flush=True) shutil.rmtree(tmp, ignore_errors=True); print("[MTP] FATTO."); return for i, sh in enumerate(shards): if free_gb(a.outdir) < a.min_free_gb: print(f"STOP: spazio libero < {a.min_free_gb} GB. Libera spazio e rilancia (riprende)."); 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)}] scarico {sh} (libero {free_gb(a.outdir):.0f} GB)...", 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("FATTO." if i == len(shards)-1 else "INTERROTTO (rilancia per riprendere).") if __name__ == "__main__": main()