File size: 6,225 Bytes
c9e7d1f | 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 | """Challenger v1: the king with every PQ2_0 block's fp16 scale refit by least squares against the
teacher's rotated weights (codes untouched). Works entirely in the stored (rotated) layout, where
stored = W_hf 路 signs 路 H per block for both folded weights and latent embeddings.
.venv/bin/python scale_refit.py [--out /root/models/chal-v1]
"""
import argparse
import shutil
import sys
from pathlib import Path
sys.path.insert(0, "/root/ingot/src")
import numpy as np
from common.gguf import read_header
from common.gguf.dequant import hadamard, rotation
from common.gguf.types import PQ2_0, TYPES
from model_dedup.gguf_checkpoint import GgufCheckpoint, Heads
ap = argparse.ArgumentParser()
ap.add_argument("--king", default="/root/models/king-ix")
ap.add_argument("--teacher", default="/root/models/teacher")
ap.add_argument("--out", default="/root/models/chal-v1")
ap.add_argument("--max-tensors", type=int, default=0, help="limit for testing")
args = ap.parse_args()
ck = GgufCheckpoint(args.king, "cpu")
g = ck.g
heads = ck._heads
block_size = int(g.kv["prism.hadamard.block_size"].value)
H = hadamard(block_size)
print("block_size", block_size, "heads", heads)
teacher_shards = {}
import json
idx = json.loads((Path(args.teacher) / "model.safetensors.index.json").read_text())
weight_map = idx["weight_map"]
def teacher_tensor(hf_name: str) -> np.ndarray:
raw_name = (
hf_name
if hf_name == "lm_head.weight"
else "model.language_model." + hf_name[len("model.") :]
)
shard = weight_map[raw_name]
if shard not in teacher_shards:
import safetensors
teacher_shards[shard] = safetensors.safe_open(str(Path(args.teacher) / shard), "pt")
t = teacher_shards[shard].get_tensor(raw_name)
return t.to(dtype=torch.float32).numpy()
import torch # noqa: E402
def tile(w: np.ndarray, dim: int, head: int) -> np.ndarray:
"""Inverse of GgufCheckpoint._untile: HF grouped [n_k, rep, head] -> stored tiled [rep, n_k, head]."""
shape = list(w.shape)
x = w.reshape(*shape[:dim], heads.n_k, heads.rep, head, *shape[dim + 1 :])
return x.transpose(dim, dim + 1).reshape(shape)
def hf_to_stored(name: str, w: np.ndarray) -> np.ndarray:
"""Teacher tensor, HF layout -> the king's stored layout (row order, then rotation)."""
if name.startswith("layers.") and heads.rep > 1:
kind = name.split(".")[2]
if kind == "in_proj_qkv" and name.endswith("in_proj_qkv.weight"):
qk = 2 * heads.n_k * heads.k_dim
w = np.concatenate([w[:qk], tile(w[qk:], 0, heads.v_dim)])
elif kind == "in_proj_z":
w = tile(w, 0, heads.v_dim)
elif kind in ("in_proj_a", "in_proj_b"):
w = tile(w, 0, 1)
elif kind == "out_proj" and not (rot_signs(name) is not None and ck._grouped):
w = tile(w.T, 0, heads.v_dim).T
signs = rot_signs(name)
if signs is None:
return w
rows, width = w.shape
nblk = width // block_size
# stored = (W 路 signs) 路 H per col block, as get()'s inverse: W = (stored 路 H) 路 signs
w = (w.reshape(rows, nblk, block_size) * signs.reshape(nblk, block_size)) @ H
return w.reshape(rows, width)
def rot_signs(name: str) -> np.ndarray | None:
kv = g.kv
listed = [
kv["prism.hadamard." + key].value
for key in ("weight_names", "inverse_weight_names")
if "prism.hadamard." + key in kv
]
if not any(name in names for names in listed):
return None
widths = kv["prism.hadamard.sign_widths"].value
values = kv["prism.hadamard.sign_values"].value
width = g.tensors[name].shape[0]
at = 0
for w_ in widths:
if w_ == width:
return np.asarray(values[at : at + w_], np.float32)
at += w_
raise ValueError(f"no sign vector of width {width} for {name}")
# the gguf tensor name each HF name maps to (dedup canonical, with the .weight suffix)
from model_dedup.gguf_checkpoint import canonical as gguf_canonical # noqa: E402
out_dir = Path(args.out)
out_dir.mkdir(parents=True, exist_ok=True)
src = Path(args.king) / "Ingot-IX-27B-Q2.gguf"
dst = out_dir / "chal-v1.gguf"
shutil.copy(src, dst)
done = 0
with open(dst, "r+b") as fh:
for name, t in g.tensors.items():
if t.ggml_type != PQ2_0:
continue
if args.max_tensors and done >= args.max_tensors:
break
canon = gguf_canonical(name)
if canon is None:
print("skip (no hf name):", name)
continue
hf_name = "model." + canon if not canon.startswith(("lm_head", "norm.")) else canon
# canonical gives e.g. layers.0.self_attn.q_proj.weight; HF: model.layers.0...
w_t = teacher_tensor(hf_name)
w_t = hf_to_stored(name, w_t)
rows, width = w_t.shape
nblk = width // 128 # the PQ2_0 block is 128 wide; the Hadamard block is not
# read raw blocks
size = rows * nblk * 34
raw = np.memmap(dst, np.uint8, "r+", g.data_start + t.offset, (size,))
blocks = raw.reshape(-1, 34)
qs = blocks[:, 2:]
codes = np.stack([(qs >> (2 * k)) & 3 for k in range(4)], axis=-1).reshape(rows, nblk, 128)
if (codes == 3).any():
raise SystemExit(f"{name}: a code 3 in the king")
c_vals = codes.astype(np.float32) - 1.0
w_t_b = w_t.reshape(rows, nblk, 128)
d_orig = blocks[:, :2].copy().view("<f2").astype(np.float32).reshape(rows, nblk)
support = (c_vals * c_vals).sum(-1)
num = (c_vals * w_t_b).sum(-1)
# a block of mostly-zero codes has no stable least-squares scale: ridge + a support floor,
# and never more than double the king's own scale (its codes were fit with it)
d_new = num / (support + 4.0)
d_new = np.where(support >= 8, d_new, d_orig)
d_new = np.clip(d_new, -2.0 * np.abs(d_orig), 2.0 * np.abs(d_orig))
d_new = np.clip(d_new, -5000.0, 5000.0)
d_new = d_new.astype(np.float16)
blocks[:, :2] = d_new.astype("<f2").view(np.uint8).reshape(-1, 2)
done += 1
if done % 50 == 0:
print(f"refit {done} tensors ({name})", flush=True)
print(f"refit {done} tensors -> {dst}")
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