File size: 8,496 Bytes
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"""Assemble the per-expert hybrid NVFP4+AQLM GLM-5.2 checkpoint.
Sources:
- /tmp/glm52-dl/nvfp4-full: all non-expert tensors verbatim; per-expert
NVFP4 tensors for the whole-NVFP4 layers AND for hot experts of hybrid
layers (compacted into fused arrays)
- /data/glm52-aqlm-parts/layer_N.pt: AQLM codes for base/cold experts
(sliced per assignment; cold w2 = book-0 slice of the 2-book codes)
- /data/glm52-expert-assignment.json: per-layer hot/cold expert ids
Output: /data/glm52-v3 (swap into /data/glm52 after validation).
"""
import json
import os
import shutil
import torch
from safetensors import safe_open
from safetensors.torch import save_file
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
SRC = "/tmp/glm52-dl/nvfp4-full"
PARTS = "/data/glm52-aqlm-parts"
ASSIGN = "/data/glm52-expert-assignment.json"
DST = "/data/glm52-v3"
SHARD_BYTES = 4 << 30
plan = json.load(open(os.path.join(ROOT, "hybrid_plan.json")))
NVFP4_LAYERS = set(plan["nvfp4_layers"]) # whole-layer NVFP4 (incl MTP)
assignment = {int(k): v for k, v in json.load(open(ASSIGN)).items()}
HYBRID_LAYERS = sorted(assignment)
import re
_EXPERT_RE = re.compile(r"model\.layers\.(\d+)\.mlp\.experts\.(\d+)\.")
def keep(name: str) -> bool:
m = _EXPERT_RE.match(name)
if m:
return int(m.group(1)) in NVFP4_LAYERS
return True
class ShardWriter:
def __init__(self, dst: str):
self.dst = dst
self.cur: dict[str, torch.Tensor] = {}
self.cur_bytes = 0
self.n = 0
self.weight_map: dict[str, str] = {}
self.total = 0
self.files: list[str] = []
def add(self, name: str, tensor: torch.Tensor):
nb = tensor.numel() * tensor.element_size()
if self.cur_bytes + nb > SHARD_BYTES and self.cur:
self.flush()
self.cur[name] = tensor
self.cur_bytes += nb
self.total += nb
def flush(self):
if not self.cur:
return
self.n += 1
fname = f"model-{self.n:05d}.safetensors"
save_file(self.cur, os.path.join(self.dst, fname))
for k in self.cur:
self.weight_map[k] = fname
print(f" wrote {fname} ({self.cur_bytes/1e9:.2f} GB)", flush=True)
self.files.append(fname)
self.cur = {}
self.cur_bytes = 0
def finalize(self):
self.flush()
total_n = self.n
wm = {}
for i, fname in enumerate(self.files, 1):
new = f"model-{i:05d}-of-{total_n:05d}.safetensors"
os.rename(os.path.join(self.dst, fname), os.path.join(self.dst, new))
for k, v in self.weight_map.items():
if v == fname:
wm[k] = new
json.dump(
{"metadata": {"total_size": self.total}, "weight_map": wm},
open(os.path.join(self.dst, "model.safetensors.index.json"), "w"),
indent=0,
)
print(f"index: {len(wm)} tensors, {self.total/1e9:.1f} GB total")
class ShardReader:
def __init__(self, repo_dir: str):
idx = json.load(open(os.path.join(repo_dir, "model.safetensors.index.json")))
self.weight_map = idx["weight_map"]
self.repo_dir = repo_dir
self._open = {}
def get(self, name: str):
shard = self.weight_map[name]
if shard not in self._open:
self._open[shard] = safe_open(
os.path.join(self.repo_dir, shard), framework="pt"
)
return self._open[shard].get_tensor(name)
def build_hybrid_layer(li: int, reader: ShardReader, writer: ShardWriter):
n_exp, inter, hidden = 256, 2048, 6144
hot = sorted(assignment[li]["hot"])
cold = sorted(assignment[li]["cold"])
hot_s, cold_s = set(hot), set(cold)
base = [e for e in range(n_exp) if e not in hot_s and e not in cold_s]
b_all = sorted(set(base) | cold_s) # AQLM w13 group, ascending
kind = torch.ones(n_exp, dtype=torch.int8)
for e in hot:
kind[e] = 0
for e in cold:
kind[e] = 2
part = torch.load(
os.path.join(PARTS, f"layer_{li}.pt"), map_location="cpu",
weights_only=True,
)
assert part["books"]["w2"] == 2, f"layer {li} part lacks 2-book w2"
p = f"model.layers.{li}.mlp.experts"
b_idx = torch.tensor(b_all, dtype=torch.long)
base_idx = torch.tensor(base, dtype=torch.long)
cold_idx = torch.tensor(cold, dtype=torch.long)
writer.add(f"{p}.hyb_kind", kind)
writer.add(f"{p}.w13_codes", part["w13_codes"][b_idx].contiguous())
writer.add(f"{p}.w13_codebooks", part["w13_codebooks"])
writer.add(f"{p}.w13_scales", part["w13_scales"][b_idx].contiguous())
writer.add(f"{p}.w2m_codes", part["w2_codes"][base_idx].contiguous())
writer.add(f"{p}.w2m_codebooks", part["w2_codebooks"])
writer.add(f"{p}.w2m_scales", part["w2_scales"][base_idx].contiguous())
# cold: book-0 slice of the 2-book residual encoding
writer.add(f"{p}.w2c_codes", part["w2_codes"][cold_idx, :1].clone())
writer.add(f"{p}.w2c_codebooks", part["w2_codebooks"][:1].clone())
writer.add(f"{p}.w2c_scales", part["w2_scales"][cold_idx].contiguous())
# hot experts: compact NVFP4 arrays from the source repo
na = len(hot)
w13_packed = torch.empty(na, 2 * inter, hidden // 2, dtype=torch.uint8)
w13_bscale = torch.empty(na, 2 * inter, hidden // 16, dtype=torch.uint8)
w13_scale2 = torch.empty(na, 2, dtype=torch.float32)
w2_packed = torch.empty(na, hidden, inter // 2, dtype=torch.uint8)
w2_bscale = torch.empty(na, hidden, inter // 16, dtype=torch.uint8)
w2_scale2 = torch.empty(na, 1, dtype=torch.float32)
for j, e in enumerate(hot):
ep = f"model.layers.{li}.mlp.experts.{e}"
g_w = reader.get(f"{ep}.gate_proj.weight")
u_w = reader.get(f"{ep}.up_proj.weight")
d_w = reader.get(f"{ep}.down_proj.weight")
w13_packed[j, :inter] = g_w
w13_packed[j, inter:] = u_w
w2_packed[j] = d_w
w13_bscale[j, :inter] = reader.get(
f"{ep}.gate_proj.weight_scale").view(torch.uint8)
w13_bscale[j, inter:] = reader.get(
f"{ep}.up_proj.weight_scale").view(torch.uint8)
w2_bscale[j] = reader.get(
f"{ep}.down_proj.weight_scale").view(torch.uint8)
w13_scale2[j, 0] = reader.get(f"{ep}.gate_proj.weight_scale_2").float()
w13_scale2[j, 1] = reader.get(f"{ep}.up_proj.weight_scale_2").float()
w2_scale2[j, 0] = reader.get(f"{ep}.down_proj.weight_scale_2").float()
writer.add(f"{p}.nvfp4_w13_packed", w13_packed)
writer.add(f"{p}.nvfp4_w13_bscale", w13_bscale)
writer.add(f"{p}.nvfp4_w13_scale2", w13_scale2)
writer.add(f"{p}.nvfp4_w2_packed", w2_packed)
writer.add(f"{p}.nvfp4_w2_bscale", w2_bscale)
writer.add(f"{p}.nvfp4_w2_scale2", w2_scale2)
print(f"layer {li}: hot={na} base={len(base)} cold={len(cold)}", flush=True)
return {"n_nvfp4": na, "n_base": len(base), "n_cold": len(cold)}
def main():
os.makedirs(DST, exist_ok=True)
reader = ShardReader(SRC)
writer = ShardWriter(DST)
# 1. non-expert tensors + whole-NVFP4 layers, streamed shard by shard
shards = sorted(set(reader.weight_map.values()))
for shard in shards:
with safe_open(os.path.join(SRC, shard), framework="pt") as f:
names = [n for n in f.keys() if keep(n)]
if not names:
continue
for n in names:
writer.add(n, f.get_tensor(n))
print(f"{shard}: kept {len(names)}", flush=True)
# 2. hybrid layers
layer_books = {}
for li in HYBRID_LAYERS:
layer_books[str(li)] = build_hybrid_layer(li, reader, writer)
writer.finalize()
# 3. config
cfg = json.load(open(os.path.join(SRC, "config.json")))
cfg["quantization_config"] = {
"quant_method": "nvfp4_aqlm_hybrid",
"nvfp4": cfg["quantization_config"],
"aqlm": {"entries": 65536, "group_size": 8},
"aqlm_layer_books": layer_books,
}
json.dump(cfg, open(os.path.join(DST, "config.json"), "w"), indent=2)
# 4. aux files
for f in os.listdir(SRC):
if (
f.endswith(".json")
and f not in ("config.json", "model.safetensors.index.json")
or f.endswith((".txt", ".jinja", ".py", ".md"))
):
shutil.copy2(os.path.join(SRC, f), os.path.join(DST, f))
print("DONE:", DST)
if __name__ == "__main__":
main()
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