JustVugg
f8e4dc95b5
Two operator-surprise fixes: openai_server.py (#382, LordMZTE): a request without max_tokens defaulted to min(256, limit) — so `coli serve --ngen 32768` still cut every answer at 256 tokens for clients that omit the field. The operator's configured budget IS the default now; generation still ends at EOS, so it's a cap, not a target. convert_fp8_to_int4.py (#383, bokiko): the resolved conversion plan (mode, source, ebits/io/x bits, grouped-vs-per-row) prints as a [PLAN] line before any work. The two traps in #383 — --mtp defaulting ebits to 8 with the grouped branch silently gated off, and --mtp appearing to ignore --indir — are already defused by the #355 fix (the --mtp pass emits ONLY head tensors on the local path), but a 3.5-hour job must show its plan at second 1, not in a post-hoc size sanity check. bokiko's exact invocation now prints: [PLAN] mode: MTP head only | source: local ./fp8 | experts 8-bit, ... | PER-ROW (grouped branch needs bits<=4; ebits=8 disables it) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
699 lines
41 KiB
Python
699 lines
41 KiB
Python
"""
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Convertitore GLM-5.2-FP8 -> nostro container int4 (STADIO B).
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Strategia DISK-SAFE (richiesta dell'utente): scarica UNO shard (~5 GB), lo converte in
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int4, lo CANCELLA, passa al prossimo. Il disco non si riempie mai: picco = 1 shard + l'output
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int4 che cresce fino a ~372 GB. Controllo di spazio che si ferma se manca margine.
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Cosa fa per ogni tensore:
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- pesi FP8 (e4m3) con `*.weight_scale_inv` -> dequant a blocchi 128x128 -> f32
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- pesi BF16 (norme/embed/lm_head/...) -> f32
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poi:
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- attn/mlp/shared/expert/embed/lm_head -> QUANTIZZATO int4 (o int8) con la STESSA matematica
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del motore C (np.rint = lrintf, stesse soglie, stesso packing dei nibble) -> token identici
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- norme / router (mlp.gate.weight) / bias / e_score_correction_bias -> tenuti F32
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- indexer DSA / layer MTP (78) / shared_head / eh_proj / *norm dell'indexer -> SALTATI
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Output: una dir di safetensors leggibile dal motore C (per ogni peso quantizzato: `nome` U8 =
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dati impacchettati, `nome.qs` F32 = scale per riga).
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USO:
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# test locale (oracolo tiny, niente download): converte una dir gia' presente
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python3 tools/convert_fp8_to_int4.py --indir glm_tiny --outdir glm_tiny_i4 --ebits 4 --io-bits 4
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# selftest del dequant fp8 (richiede torch)
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python3 tools/convert_fp8_to_int4.py --selftest
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# reale: scarica+converte+cancella shard per shard
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python3 tools/convert_fp8_to_int4.py --repo zai-org/GLM-5.2-FP8 --outdir /home/vincenzo/glm52_i4
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"""
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import os, sys, glob, json, shutil, argparse
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import numpy as np
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# ---------- quantizzazione: identica al C (glm.c) ----------
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def quant_int8(w, bits): # w: [O,I] f32 -> (qbytes U8 [O*I], scale f32 [O])
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qmax = (1 << (bits - 1)) - 1
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amax = np.abs(w).max(axis=1, keepdims=True)
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s = np.maximum(amax / qmax, 1e-8)
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q = np.clip(np.rint(w / s), -qmax - 1, qmax).astype(np.int8)
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return q.reshape(-1).view(np.uint8).copy(), s[:, 0].astype(np.float32)
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def quant_int4(w, bits): # -> (qbytes U8 [O*ceil(I/2)], scale f32 [O])
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O, I = w.shape
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qmax = (1 << (bits - 1)) - 1
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amax = np.abs(w).max(axis=1, keepdims=True)
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s = np.maximum(amax / qmax, 1e-8)
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q = np.clip(np.rint(w / s), -8, qmax).astype(np.int32) # nibble [-8,7]
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rb = (I + 1) // 2
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out = np.zeros((O, rb), np.uint8)
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v0 = (q[:, 0::2] + 8).astype(np.uint8)
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out[:, :v0.shape[1]] = v0
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if I > 1:
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v1 = (q[:, 1::2] + 8).astype(np.uint8)
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out[:, :v1.shape[1]] |= (v1 << 4)
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return out.reshape(-1), s[:, 0].astype(np.float32)
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def quant_int4_grouped(w, bits, gs=128):
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"""Group-scaled int4: one scale per group of `gs` elements along the input dim.
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Drastically reduces quantization error vs per-row scaling — matches the FP8
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source's 128x128 block-scale granularity. Output layout:
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qbytes: same packed nibble format as quant_int4
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scales: f32 [O * ngroups] where ngroups = ceil(I/gs), laid out as
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s[o * ngroups + g] = scale for row o, group g.
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The engine detects this format (fmt=4) by checking the .qs array size."""
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O, I = w.shape
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qmax = (1 << (bits - 1)) - 1
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ngroups = (I + gs - 1) // gs
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# pad I to a multiple of gs for clean reshape, then trim
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Ipad = ngroups * gs
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wpad = np.zeros((O, Ipad), np.float32)
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wpad[:, :I] = w
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wr = wpad.reshape(O, ngroups, gs) # [O, ngroups, gs]
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amax = np.abs(wr).max(axis=2, keepdims=True) # [O, ngroups, 1]
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s = np.maximum(amax / qmax, 1e-8) # [O, ngroups, 1]
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q = np.clip(np.rint(wr / s), -8, qmax).astype(np.int32) # [O, ngroups, gs]
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q = q.reshape(O, Ipad)[:, :I] # trim padding -> [O, I]
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# pack nibbles (identical to quant_int4)
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rb = (I + 1) // 2
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out = np.zeros((O, rb), np.uint8)
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v0 = (q[:, 0::2] + 8).astype(np.uint8)
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out[:, :v0.shape[1]] = v0
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if I > 1:
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v1 = (q[:, 1::2] + 8).astype(np.uint8)
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out[:, :v1.shape[1]] |= (v1 << 4)
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# scales: flatten [O, ngroups] -> [O * ngroups]
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s_flat = s[:, :, 0].astype(np.float32).reshape(-1)
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return out.reshape(-1), s_flat
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def quant_int2(w, bits): # -> (qbytes U8 [O*ceil(I/4)], scale f32 [O]); 4/byte
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O, I = w.shape
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qmax = (1 << (bits - 1)) - 1 # bits=2 -> qmax=1, valori [-2,1]
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amax = np.abs(w).max(axis=1, keepdims=True)
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s = np.maximum(amax / qmax, 1e-8)
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q = np.clip(np.rint(w / s), -2, qmax).astype(np.int32)
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rb = (I + 3) // 4
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out = np.zeros((O, rb), np.uint8)
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for k in range(4): # impacchetta 4 valori per byte (identico a pack_int2 in C)
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vk = q[:, k::4]
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out[:, :vk.shape[1]] |= ((vk + 2).astype(np.uint8) << (k * 2))
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return out.reshape(-1), s[:, 0].astype(np.float32)
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# ---------- NVFP4 (modelopt) : LUT e2m1 ----------
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# FP4 e2m1 = 1 sign + 2 exp + 1 mantissa. 16 codici, magnitudini {0,.5,1,1.5,2,3,4,6}.
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# Bit 3 = segno. Ordine impacchettato (compressed_tensors/vLLM): nibble BASSO = elemento
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# pari, nibble ALTO = elemento dispari. LUT verificata 1:1 con ml_dtypes.float4_e2m1fn.
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# EN: FP4 e2m1 = 1 sign + 2 exp + 1 mantissa. 16 codes, magnitudes {0,.5,1,1.5,2,3,4,6}.
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# EN: bit 3 = sign. Packed order (compressed_tensors/vLLM): LOW nibble = even element,
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# EN: HIGH nibble = odd element. LUT verified 1:1 against ml_dtypes.float4_e2m1fn.
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_E2M1 = [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0,
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-0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0]
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# ---------- classificazione dei tensori ----------
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def layer_idx(name):
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p = name.split(".")
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if len(p) > 2 and p[0] == "model" and p[1] == "layers":
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try: return int(p[2])
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except ValueError: return -1
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return -1
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def classify(name, n_layers, keep_mtp=False, keep_idx=False):
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if name.endswith("_scale_inv"): return "consumed" # FP8 base: gestito col suo peso
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# NVFP4 (modelopt): i sidecar delle scale sono consumati insieme al loro U8 .weight.
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# EN: NVFP4 (modelopt): scale sidecars are consumed together with their U8 .weight.
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if name.endswith((".weight_scale", ".weight_scale_2", ".input_scale")): return "consumed"
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li = layer_idx(name)
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if keep_idx:
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# modalita' --indexer: SOLO i pesi del DSA lightning indexer dei layer principali
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if li < 0 or li >= n_layers or "indexer" not in name: return "skip"
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if name.endswith("norm.weight"): return "f32"
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return "q" # int8 consigliato (--ebits 8): pesi di scoring
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if keep_mtp:
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if li != n_layers: return "skip" # solo il layer MTP
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if "indexer" in name: return "skip" # il DSA indexer resta un no-op
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else:
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if li >= n_layers: return "skip" # layer MTP (78)
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if any(k in name for k in ["indexer", "indexers_proj", "eh_proj",
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"enorm", "hnorm", "shared_head"]): return "skip"
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if name.endswith("e_score_correction_bias"): return "f32"
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if name.endswith("mlp.gate.weight"): return "f32" # router (NON gate_proj)
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if name.endswith("norm.weight") or name == "model.norm.weight": return "f32"
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if name in ("model.embed_tokens.weight", "lm_head.weight"): return "io"
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if ".mlp.experts." in name and name.endswith(".weight"): return "x" # expert ROUTED (streaming)
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# Split resident weights by type for mixed-precision control:
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# "sh" = shared expert (fires on every token, highest sensitivity)
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# "o" = o_proj attention (reconstructs output, biggest attn tensor)
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# "kvb" = kv_b_proj (reconstructs KV cache on every decode step)
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# "attn" = other attention projections (q_a, q_b, kv_a)
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# "dmlp" = dense MLP (first 3 layers)
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if "shared_experts" in name: return "sh"
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if name.endswith("o_proj.weight"): return "o"
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if name.endswith("kv_b_proj.weight"): return "kvb"
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if any(name.endswith(k) for k in ("q_a_proj.weight", "q_b_proj.weight",
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"kv_a_proj_with_mqa.weight")): return "attn"
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if any(name.endswith(k) for k in ("mlp.gate_proj.weight", "mlp.up_proj.weight",
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"mlp.down_proj.weight")): return "dmlp"
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if name.endswith(".weight"): return "q" # fallback: other resident weights
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return "f32"
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# ---------- dequant NVFP4 (modelopt) di UN tensore expert -> f32 [O,I] ----------
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def dequant_nvfp4(f, name):
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"""NVFP4 di NVIDIA modelopt (quant_algo=NVFP4, quant_method=modelopt).
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- `name` U8 [O, I/2] : due nibble e2m1 per byte lungo la dim di
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contrazione (input); pari=nibble basso, dispari=alto.
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- `name.weight_scale` F8_E4M3 [O, I/16] : scala per-BLOCCO di 16 elementi (group_size=16),
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lungo la dim di input. Decodifica f8e4m3 -> f32.
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- `name.weight_scale_2` F32 [] : scala GLOBALE per-tensore, ~amax/(6*448) (piccola).
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Dequant (convenzione modelopt = MOLTIPLICA, NON dividere):
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W[o,i] = e2m1_lut[nibble] * f8_block_scale[o, i//16] * weight_scale_2
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FOOTGUN: llm-compressor/compressed-tensors memorizza il RECIPROCO (global grande) e DIVIDE;
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modelopt memorizza il valore piccolo e MOLTIPLICA. Questo checkpoint e' modelopt -> moltiplica.
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EN: NVIDIA modelopt NVFP4. LOW nibble=even elem, HIGH=odd. weight_scale = per-16-block FP8
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EN: (group_size 16) along the input dim; weight_scale_2 = per-tensor global FP32 (~amax/2688,
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EN: small). Dequant MULTIPLIES both scales. FOOTGUN: llm-compressor stores the reciprocal
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EN: (large global) and DIVIDES; modelopt stores the small value and MULTIPLIES."""
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import torch
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GS = 16 # NVFP4: block scale ogni 16 elementi
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packed = f.get_tensor(name) # uint8 [O, I/2]
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bscale = f.get_tensor(name + "_scale").to(torch.float32) # [O, ceil(I/16)] da f8e4m3
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gscale = f.get_tensor(name + "_scale_2").to(torch.float32) # scalare per-tensore
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O, Ih = packed.shape; I = Ih * 2
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# Convenzione: modelopt memorizza il global PICCOLO e MOLTIPLICA. Se e' >=1 e'
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# quasi certamente il reciproco di compressed-tensors (che DIVIDE) -> ci fermiamo
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# invece di corrompere silenziosamente ogni tensore. EN: guard modelopt-vs-CT.
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assert float(gscale) < 1.0, (
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f"{name}: weight_scale_2={float(gscale):.4g} >= 1 sembra il reciproco "
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"(compressed-tensors, che DIVIDE); questo path assume modelopt (MOLTIPLICA)")
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# Il layout deve essere lo scale per-blocco piatto di modelopt: una colonna ogni
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# 16 elementi di input (niente swizzle cutlass/TensorRT). Verifichiamo, non deduciamo:
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# dedurre gs = I // ncol misallinea in silenzio su layout paddati/swizzati.
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nb = (I + GS - 1) // GS
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assert bscale.shape[1] == nb, (
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f"{name}: weight_scale ha {bscale.shape[1]} colonne, attese {nb} = ceil({I}/{GS}); "
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"layout scale inatteso (swizzled/paddato?), rifiuto per non corrompere")
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lut = torch.tensor(_E2M1, dtype=torch.float32)
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nib = torch.empty((O, I), dtype=torch.long)
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nib[:, 0::2] = (packed & 0x0F).to(torch.long) # elemento pari = nibble basso
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nib[:, 1::2] = ((packed >> 4) & 0x0F).to(torch.long) # elemento dispari = nibble alto
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w4 = lut[nib] # [O, I] valori e2m1
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sc = bscale.repeat_interleave(GS, dim=1)[:, :I] # blocco parziale di coda: slice a I
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return (w4 * sc * gscale).numpy()
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# ---------- dequant di un tensore (nvfp4 / fp8+scale a blocchi / bf16 / f32) ----------
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def dequant(f, name, keys):
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import torch
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sl = f.get_slice(name); dt = sl.get_dtype()
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# NVFP4 (modelopt): pesi expert U8 con sidecar `.weight_scale`. In questo checkpoint gli
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# UNICI tensori U8 sono gli expert NVFP4, ma richiediamo comunque il sidecar (keys e'
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# obbligatorio: senza, un qualunque U8 verrebbe decodificato come NVFP4).
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# EN: NVFP4 expert weights are U8 with a `.weight_scale` sidecar; require the sidecar.
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if dt in ("U8", "uint8") and (name + "_scale") in keys:
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return dequant_nvfp4(f, name)
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if dt in ("F8_E4M3", "float8_e4m3fn"):
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w = f.get_tensor(name).to(torch.float32)
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sc = f.get_tensor(name + "_scale_inv").to(torch.float32) # [ceil(O/128),ceil(I/128)]
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O, I = w.shape
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sc = sc.repeat_interleave(128, 0).repeat_interleave(128, 1)[:O, :I]
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return (w * sc).numpy()
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return f.get_tensor(name).to(torch.float32).numpy()
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def convert_shard(path, out_dict, n_layers, ebits, io_bits, xbits,
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keep_mtp=False, keep_idx=False, group_size=0, bits_map=None):
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from safetensors import safe_open
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with safe_open(path, framework="pt") as f:
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keys = set(f.keys())
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for name in f.keys():
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kind = classify(name, n_layers, keep_mtp, keep_idx)
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if kind in ("skip", "consumed"): continue
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w = dequant(f, name, keys)
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if kind == "f32":
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out_dict[name] = w.astype(np.float32)
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else:
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# Resolve bits for this tensor type: use bits_map override if provided,
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# otherwise fall back to the classic ebits/xbits/io_bits scheme.
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if bits_map and kind in bits_map:
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bits = bits_map[kind]
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else:
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bits = io_bits if kind == "io" else xbits if kind == "x" else ebits
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# Any unknown kind that fell through classify as "q"
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if bits_map and kind not in bits_map and kind not in ("io", "x", "sh", "o", "kvb", "attn", "dmlp"):
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bits = ebits
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if w.ndim != 2: # es. bias 1D non previsto come 'q' -> tienilo f32
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out_dict[name] = w.astype(np.float32); continue
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if group_size > 0 and bits <= 4:
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q, s = quant_int4_grouped(w, bits, group_size)
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else:
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q, s = (quant_int2(w, bits) if bits <= 2 else
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quant_int4(w, bits) if bits <= 4 else quant_int8(w, bits))
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out_dict[name] = q
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out_dict[name + ".qs"] = s
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def free_gb(p): return shutil.disk_usage(p).free / 1e9
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--repo", default=None)
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ap.add_argument("--indir", default=None)
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ap.add_argument("--outdir", required=False)
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ap.add_argument("--ebits", type=int, default=None) # bit residenti (default 4; 8 per --mtp/--indexer)
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ap.add_argument("--io-bits", type=int, default=8) # bit di embed/lm_head
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ap.add_argument("--xbits", type=int, default=None) # bit degli expert ROUTED (streaming); default=ebits
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# Mixed-precision: per-tensor-type bit overrides. Default = ebits (all same).
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# Set these higher to protect sensitive tensors from quantization error.
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ap.add_argument("--shared-bits", type=int, default=None,
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help="bits for shared expert (fires on every token, highest sensitivity). Default=ebits")
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ap.add_argument("--o-bits", type=int, default=None,
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help="bits for o_proj attention (reconstructs output, biggest attn tensor). Default=ebits")
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ap.add_argument("--kvb-bits", type=int, default=None,
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help="bits for kv_b_proj (reconstructs KV cache on every decode). Default=ebits")
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ap.add_argument("--attn-bits", type=int, default=None,
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help="bits for other attention projections (q_a, q_b, kv_a). Default=ebits")
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ap.add_argument("--dmlp-bits", type=int, default=None,
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help="bits for dense MLP (first 3 layers). Default=ebits")
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ap.add_argument("--group-size", type=int, default=0, # 0 = per-row (backward compat); 128 = group-scaled
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help="group size for int4 scales: 0=per-row (default), 128=one scale per 128 elements (much better quality)")
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ap.add_argument("--n-layers", type=int, default=78)
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ap.add_argument("--min-free-gb", type=float, default=20.0)
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ap.add_argument("--selftest", action="store_true")
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ap.add_argument("--selftest-nvfp4", action="store_true",
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help="unit-test del dequant NVFP4 (LUT e2m1 + round-trip), nessun download / no network")
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ap.add_argument("--mtp", action="store_true",
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help="download and convert ONLY the MTP head (model.layers.<n_layers>.*) -> out-mtp-*.safetensors")
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ap.add_argument("--indexer", action="store_true",
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help="extract ONLY the DSA lightning-indexer weights -> out-idx-*.safetensors. WARNING: "
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"indexer tensors are spread across nearly every shard, so this re-downloads the whole "
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"repository (~756 GB of traffic) to retain only a few GB. Resumable per shard. "
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"Recommended: --ebits 8.")
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a = ap.parse_args()
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if a.ebits is None:
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# testa MTP a int4 = acceptance ~0-4% (misurato, issue #8): il draft sbaglia sempre
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# e la speculazione non parte mai. A int8: 39-59%, 2.2-2.8 token/forward.
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a.ebits = 8 if (a.mtp or a.indexer) else 4
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if a.xbits is None: a.xbits = a.ebits
|
|
|
|
# Build per-type bits map. If a type-specific arg is set, use it; otherwise the
|
|
# converter falls back to ebits for that type.
|
|
bits_map = {}
|
|
if a.shared_bits is not None: bits_map["sh"] = a.shared_bits
|
|
if a.o_bits is not None: bits_map["o"] = a.o_bits
|
|
if a.kvb_bits is not None: bits_map["kvb"] = a.kvb_bits
|
|
if a.attn_bits is not None: bits_map["attn"] = a.attn_bits
|
|
if a.dmlp_bits is not None: bits_map["dmlp"] = a.dmlp_bits
|
|
if bits_map:
|
|
print(f"[MIXED] precision map: " + ", ".join(f"{k}={v}bit" for k,v in sorted(bits_map.items())))
|
|
|
|
# Il PIANO risolto, PRIMA di toccare qualunque cosa (#383): --mtp/--indexer cambiano il
|
|
# default di ebits a 8 (testa int4 = acceptance ~0%, issue #8) e il ramo grouped e'
|
|
# gated su bits<=4 — combinazioni sorprendenti devono mostrarsi al secondo 1 di un job
|
|
# da ore, non nel size-check dopo. EN: print the RESOLVED plan before doing anything.
|
|
mode = "MTP head only" if a.mtp else "DSA indexer only" if a.indexer else "main model"
|
|
grp = f"grouped gs={a.group_size} (fmt=4)" if (a.group_size and a.ebits <= 4) else \
|
|
(f"PER-ROW (grouped branch needs bits<=4; ebits={a.ebits} disables it)" if a.group_size else "per-row")
|
|
print(f"[PLAN] mode: {mode} | source: {'local ' + a.indir if a.indir else 'download ' + a.repo} | "
|
|
f"experts {a.ebits}-bit, embed/lm_head {a.io_bits}-bit, x {a.xbits}-bit | {grp}")
|
|
|
|
if a.selftest_nvfp4:
|
|
import torch
|
|
# 1) LUT e2m1: i 16 codici devono decodificare esattamente ai valori attesi.
|
|
lut = torch.tensor(_E2M1, dtype=torch.float32)
|
|
expect = [0.0,0.5,1.0,1.5,2.0,3.0,4.0,6.0,-0.0,-0.5,-1.0,-1.5,-2.0,-3.0,-4.0,-6.0]
|
|
assert lut.tolist() == expect, "LUT e2m1 errata"
|
|
print("[nvfp4] LUT e2m1: 16/16 codici OK")
|
|
# 2) round-trip: costruisco un tensore ai SOLI valori rappresentabili (scala nota per
|
|
# blocco+globale), impacchetto come modelopt, poi dequant deve tornare ESATTO.
|
|
import numpy as np, io
|
|
from safetensors.torch import save as st_save
|
|
from safetensors import safe_open
|
|
rng = np.random.default_rng(0); O, I, GS = 8, 64, 16
|
|
codes = rng.integers(0, 16, size=(O, I)).astype(np.uint8) # nibble e2m1 casuali
|
|
w4 = np.array(_E2M1, np.float32)[codes] # [O,I]
|
|
# scale per-blocco (rappresentabili in f8e4m3) + globale piccola (stile modelopt)
|
|
blk = rng.choice([0.5,1.0,2.0,4.0,8.0], size=(O, I//GS)).astype(np.float32)
|
|
gscale = np.float32(3.9e-5)
|
|
W = w4 * np.repeat(blk, GS, axis=1) * gscale # riferimento esatto
|
|
# impacchetto: pari->nibble basso, dispari->alto
|
|
packed = (codes[:, 0::2] | (codes[:, 1::2] << 4)).astype(np.uint8)
|
|
import ml_dtypes # solo per il test: encode f8e4m3 delle scale di blocco
|
|
tens = {name: torch.from_numpy(arr) for name, arr in {
|
|
"w.weight": packed,
|
|
"w.weight_scale": blk.astype(ml_dtypes.float8_e4m3fn).view(np.uint8), # placeholder
|
|
}.items()}
|
|
# torch non ha un costruttore da bytes f8: passo via file safetensors scritto a mano.
|
|
# piu' semplice: uso direttamente dequant_nvfp4 su un finto 'f' in-memory.
|
|
class _F:
|
|
def __init__(s, d): s.d = d
|
|
def get_tensor(s, n): return s.d[n]
|
|
def get_slice(s, n): return None
|
|
blk_f8 = blk.astype(ml_dtypes.float8_e4m3fn) # quantizza le scale a f8
|
|
f = _F({"w.weight": torch.from_numpy(packed),
|
|
"w.weight_scale": torch.from_numpy(blk_f8.view(np.uint8)).view(torch.float8_e4m3fn),
|
|
"w.weight_scale_2": torch.tensor(gscale)})
|
|
got = dequant_nvfp4(f, "w.weight")
|
|
# riferimento con scale gia' quantizzate a f8 (per confronto esatto)
|
|
Wq = w4 * np.repeat(blk_f8.astype(np.float32), GS, axis=1) * gscale
|
|
maxerr = float(np.abs(got - Wq).max())
|
|
print(f"[nvfp4] round-trip encode->dequant: max abs err = {maxerr:.3e} "
|
|
f"({'OK' if maxerr < 1e-9 else 'FAIL'})")
|
|
assert maxerr < 1e-9
|
|
# 3) requant colibri int4 su valori dequantati -> errore piccolo atteso
|
|
q, s = quant_int4(got.astype(np.float32), 4)
|
|
rb = (I + 1)//2; qb = q.reshape(O, rb)
|
|
lo = (qb & 0x0F).astype(np.int32) - 8; hi = ((qb >> 4) & 0x0F).astype(np.int32) - 8
|
|
deq = np.empty((O, I), np.float32); deq[:, 0::2] = lo; deq[:, 1::2] = hi[:, :I-I//2]
|
|
deq = deq * s[:, None]
|
|
rel = np.abs(deq - got).mean() / (np.abs(got).mean() + 1e-12)
|
|
# Informativo, NON un test di uguaglianza: requantizzare int4 per-riga dati che
|
|
# spaziano 16x per il block-scale costa ~0.17 di errore relativo di suo. La soglia
|
|
# larga becca solo una corruzione grossolana, non e' un bound di precisione.
|
|
# EN: informational — per-row int4 requant of 16x-block-range data inherently ~0.17.
|
|
print(f"[nvfp4] dequant->colibri int4->dequant: errore rel medio = {rel:.4f} "
|
|
f"(atteso ~0.17; {'OK' if rel < 0.30 else 'ANOMALO'})")
|
|
assert rel < 0.30, f"requant rel err {rel:.3f} troppo alto: dequant probabilmente corrotto"
|
|
print("[nvfp4] SELFTEST OK")
|
|
return
|
|
|
|
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
|
|
# BUG #355: questo ramo ignorava --mtp/--indexer. Con --mtp scriveva
|
|
# out-NNNNN (gli STESSI nomi di una conversione normale) in ebits=8 e
|
|
# keep_mtp=False -> il "secondo passaggio MTP" nella stessa outdir
|
|
# SOVRASCRIVEVA il container gia' finito con una riconversione int8
|
|
# completa, in silenzio (137/141 shard distrutti prima di accorgersene).
|
|
# Ora il ramo locale rispecchia il download path: prefisso corretto,
|
|
# flag passate, shard vuoti saltati.
|
|
prefix = "out-mtp-" if a.mtp else "out-idx-" if a.indexer else "out-"
|
|
n = 0
|
|
for i, sp in enumerate(shards):
|
|
out = {}
|
|
convert_shard(sp, out, a.n_layers, a.ebits, a.io_bits, a.xbits,
|
|
keep_mtp=a.mtp, keep_idx=a.indexer,
|
|
group_size=a.group_size, bits_map=bits_map)
|
|
if not out: # shard senza MTP/idx: niente file (come il download path)
|
|
continue
|
|
save_file(out, os.path.join(a.outdir, f"{prefix}{n:05d}.safetensors"))
|
|
n += 1
|
|
# config/tokenizer solo per la conversione principale — i passaggi mtp/idx
|
|
# vanno nella stessa outdir di un container gia' completo di metadati.
|
|
if not a.mtp and not a.indexer:
|
|
for fn in ["config.json"]:
|
|
src = os.path.join(a.indir, fn)
|
|
if os.path.exists(src): shutil.copy(src, a.outdir)
|
|
tag = "MTP" if a.mtp else "indexer" if a.indexer else "main"
|
|
print(f"converted {n} {tag} shard(s) -> {a.outdir} ({prefix}NNNNN)")
|
|
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.
|
|
# fcntl is Unix-only; on Windows use msvcrt or skip locking.
|
|
lock = open(os.path.join(a.outdir, ".convert.lock"), "w")
|
|
try:
|
|
import fcntl
|
|
try: fcntl.flock(lock, fcntl.LOCK_EX | fcntl.LOCK_NB)
|
|
except OSError:
|
|
print("ERROR: another converter is already using this output directory. Exiting."); return
|
|
except ImportError:
|
|
try:
|
|
import msvcrt
|
|
try: msvcrt.locking(lock.fileno(), msvcrt.LK_NBLCK, 1)
|
|
except OSError:
|
|
print("ERROR: another converter is already using this output directory. Exiting."); return
|
|
except ImportError:
|
|
pass # no locking available — single-user converter, acceptable
|
|
|
|
# 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
|
|
info = None
|
|
for att in range(10):
|
|
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)
|
|
if info is None:
|
|
print("ERROR: could not reach the repository after 10 retries. Check your network and repo name.", flush=True)
|
|
return
|
|
shards = sorted(s.rfilename for s in info.siblings if s.rfilename.endswith(".safetensors"))
|
|
if not shards:
|
|
print("ERROR: no .safetensors shards found in this repository.", flush=True)
|
|
return
|
|
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, group_size=a.group_size, bits_map=bits_map)
|
|
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, group_size=a.group_size, bits_map=bits_map)
|
|
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, group_size=a.group_size, bits_map=bits_map)
|
|
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()
|