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f0f7133 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | #!/usr/bin/env python3
"""Step 2 - GGUF packing (run LOCALLY, numpy + gguf-py only).
Reads slothe_tensors.npz / roles.json / slothe_config.json / syl_vocab.json and
writes slothe-t-25m.gguf:
- fp tensors -> F16
- ternary linears -> in-features padded to next multiple of 256, quantized to TQ2_0 (lossless on exact-ternary)
"""
import json, os, sys
GGUF_PY = "/tmp/slothing.cpp/gguf-py"
sys.path.insert(0, GGUF_PY)
import numpy as np
import gguf
from gguf import GGUFWriter, GGUFReader, quants
from gguf.constants import GGMLQuantizationType
HF = "/home/luigi/sloth-zhuyin-linux/model/hf_release"
NPZ = os.path.join(HF, "slothe_tensors.npz")
ROLES = os.path.join(HF, "roles.json")
CFG = os.path.join(HF, "slothe_config.json")
SYL = os.path.join(HF, "syl_vocab.json")
OUT = os.path.join(HF, "slothe-t-25m.gguf")
NAMES_MD = os.path.join(HF, "NAMES.md")
PAD_TO = 256
ARCH = "slothe"
def next_mult(n, m):
return ((n + m - 1) // m) * m
def ggml_name(name):
"""Map original checkpoint tensor name -> ggml tensor name."""
top = {
"embed.weight": "token_embd.weight",
"embed_norm.w": "token_embd_norm.weight",
"norm.w": "output_norm.weight",
"head.weight": "output.weight",
}
if name in top:
return top[name]
assert name.startswith("blocks."), name
parts = name.split(".")
i = int(parts[1])
rest = ".".join(parts[2:])
per_block = {
"n1.w": "attn_norm.weight",
"n2.w": "ffn_norm.weight",
"attn.qn.w": "attn_q_rmsnorm.weight",
"attn.kn.w": "attn_k_rmsnorm.weight",
"attn.q.weight": "attn_q.weight",
"attn.q.pre.w": "attn_q_subln.weight",
"attn.o.weight": "attn_output.weight",
"attn.o.pre.w": "attn_output_subln.weight",
"attn.k.weight": "attn_k.weight",
"attn.k.pre.w": "attn_k_subln.weight",
"attn.v.weight": "attn_v.weight",
"attn.v.pre.w": "attn_v_subln.weight",
"ffn.w1.weight": "ffn_gate.weight",
"ffn.w1.pre.w": "ffn_gate_subln.weight",
"ffn.w3.weight": "ffn_up.weight",
"ffn.w3.pre.w": "ffn_up_subln.weight",
"ffn.w2.weight": "ffn_down.weight",
"ffn.w2.pre.w": "ffn_down_subln.weight",
}
assert rest in per_block, f"unmapped: {name}"
return f"blk.{i}.{per_block[rest]}"
def main():
tensors = dict(np.load(NPZ))
roles = json.load(open(ROLES))
cfg = json.load(open(CFG))
syl = json.load(open(SYL))
# syllable vocab in index order (handle dict token->id or list)
if isinstance(syl, dict):
syl_tokens = [None] * len(syl)
for tok, idx in syl.items():
syl_tokens[int(idx)] = tok
assert all(t is not None for t in syl_tokens), "gap in syl vocab ids"
else:
syl_tokens = list(syl)
assert len(syl_tokens) == cfg["n_syl"], (len(syl_tokens), cfg["n_syl"])
writer = GGUFWriter(OUT, ARCH)
writer.add_architecture()
writer.add_name("slothe-t-25m-zhuyin")
writer.add_uint32("slothe.context_length", 512)
writer.add_uint32("slothe.embedding_length", cfg["dim"]) # 352
writer.add_uint32("slothe.block_count", cfg["depth"]) # 16
writer.add_uint32("slothe.feed_forward_length", cfg["ffn"]) # 960
writer.add_uint32("slothe.attention.head_count", cfg["heads"]) # 8
writer.add_uint32("slothe.attention.head_count_kv", cfg["kv"]) # 2
writer.add_uint32("slothe.attention.key_length", 44)
writer.add_uint32("slothe.attention.value_length", 44)
writer.add_float32("slothe.attention.layer_norm_rms_epsilon", 1e-6)
writer.add_uint32("slothe.fp_boundary", cfg["fp_boundary"]) # 1
writer.add_uint32("slothe.vocab_syl", cfg["n_syl"]) # 1539
writer.add_uint32("slothe.vocab_char", cfg["n_char"]) # 8342
writer.add_uint32("slothe.ternary_pad_to", PAD_TO) # 256
writer.add_array("slothe.syl_tokens", syl_tokens)
# deterministic order: embed/norm/head first, then blocks
order = sorted(tensors.keys(), key=lambda n: (not n.startswith("embed"),
not n.endswith("head.weight") and n in ("norm.w",),
n))
# simpler explicit order for readability of the reader table
def sort_key(n):
pri = {"embed.weight": 0, "embed_norm.w": 1, "norm.w": 2, "head.weight": 3}
if n in pri:
return (0, pri[n], "")
parts = n.split(".")
return (1, int(parts[1]), n)
order = sorted(tensors.keys(), key=sort_key)
names_rows = [] # (ggml_name, orig_name, role, orig_shape, stored_dtype, padded_in)
packed_cache = {} # ggml_name -> (packed, eff_padded, orig_shape) for validation
for name in order:
arr = np.asarray(tensors[name], dtype=np.float32)
gname = ggml_name(name)
role = roles[name]
orig_shape = tuple(int(x) for x in arr.shape)
if role == "fp":
writer.add_tensor(gname, arr.astype(np.float16))
names_rows.append((gname, name, role, orig_shape, "F16", ""))
else: # ternary linear -> pad in-features -> TQ2_0
out_f, in_f = arr.shape
pad_in = next_mult(in_f, PAD_TO)
padded = np.zeros((out_f, pad_in), dtype=np.float32)
padded[:, :in_f] = arr
packed = quants.quantize(padded, GGMLQuantizationType.TQ2_0)
writer.add_tensor(gname, packed, raw_dtype=GGMLQuantizationType.TQ2_0)
names_rows.append((gname, name, role, orig_shape, "TQ2_0", pad_in))
packed_cache[gname] = (packed, padded, orig_shape, in_f)
# ---- VALIDATION 1: round-trip on 2 sample ternary tensors (pre-write) ----
print("== VALIDATION 1: TQ2_0 round-trip on exact-ternary weights ==")
samples = [g for g in ("blk.1.attn_q.weight", "blk.7.ffn_down.weight") if g in packed_cache]
for g in samples:
packed, padded, orig_shape, in_f = packed_cache[g]
deq = quants.dequantize(packed, GGMLQuantizationType.TQ2_0).reshape(padded.shape)
deq_stripped = deq[:, :in_f]
eff = padded[:, :in_f]
maxerr = float(np.abs(deq_stripped - eff).max())
print(f" {g}: orig={orig_shape} padded={padded.shape} maxerr={maxerr:.3e}")
if maxerr >= 1e-3:
print(f" FATAL: {g} round-trip maxerr {maxerr} >= 1e-3 -- lossy TQ2_0, aborting")
sys.exit(1)
print(" round-trip OK (lossless).")
writer.write_header_to_file()
writer.write_kv_data_to_file()
writer.write_tensors_to_file()
writer.close()
# ---- NAMES.md ----
with open(NAMES_MD, "w") as f:
f.write("# slothe GGUF tensor name map\n\n")
f.write(f"Architecture: `{ARCH}` | ternary in-features padded to multiple of {PAD_TO} "
f"(recorded as `slothe.ternary_pad_to`).\n\n")
f.write("Ternary linears (blocks 1..14 q/o/k/v/w1/w2/w3) are stored TQ2_0 with the "
"in-features zero-padded to the next multiple of 256 (352->512, 960->1024). "
"Strip padding using the original in-features listed below. All other tensors are F16.\n\n")
f.write("| ggml name | original name | role | original shape | stored dtype | padded in-features |\n")
f.write("|---|---|---|---|---|---|\n")
for gname, oname, role, oshape, dt, pin in names_rows:
f.write(f"| `{gname}` | `{oname}` | {role} | {oshape} | {dt} | {pin} |\n")
# ---- VALIDATION 2: reload GGUF and print reader table ----
print("\n== VALIDATION 2: GGUFReader reload ==")
r = GGUFReader(OUT)
arch_field = r.fields.get("general.architecture")
arch_val = None
if arch_field is not None:
arch_val = bytes(arch_field.parts[arch_field.data[0]]).decode("utf-8")
print(f" architecture: {arch_val}")
print(f" tensor count: {len(r.tensors)}")
print(f" {'name':40s} {'shape':20s} {'ggml_type'}")
for t in r.tensors[:12]:
shp = "x".join(str(int(d)) for d in t.shape)
print(f" {t.name:40s} {shp:20s} {t.tensor_type.name}")
# ---- VALIDATION 3: file size ----
sz = os.path.getsize(OUT)
print(f"\n== VALIDATION 3: file size ==")
print(f" {OUT}: {sz} bytes ({sz/1024/1024:.2f} MiB)")
if __name__ == "__main__":
main()
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