File size: 4,920 Bytes
9d4fc2b | 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 | """GPU scale refit: per PQ2_0 block, d* = <c, teacher_rotated>/(support + 4), capped at 2x the
king's own scale, written back to a copy of the GGUF. All block math on CUDA."""
import argparse
import json
import shutil
import sys
from pathlib import Path
sys.path.insert(0, "/root/ingot/src")
import numpy as np
import torch
from common.gguf import read_header
from common.gguf.dequant import hadamard, rotation
from common.gguf.types import PQ2_0
from model_dedup.gguf_checkpoint import GgufCheckpoint, canonical
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("--device", default="cuda")
args = ap.parse_args()
dev = args.device
ck = GgufCheckpoint(args.king, "cpu")
g = ck.g
hd = ck._heads
block_size = int(g.kv["prism.hadamard.block_size"].value)
H = torch.from_numpy(hadamard(block_size)).float().to(dev)
idx = json.loads((Path(args.teacher) / "model.safetensors.index.json").read_text())
weight_map = idx["weight_map"]
import safetensors # noqa: E402
_shards = {}
def teacher_tensor(hf_name):
raw_name = "lm_head.weight" if hf_name == "lm_head.weight" else "model.language_model." + hf_name[len("model.") :]
shard = weight_map[raw_name]
if shard not in _shards:
_shards[shard] = safetensors.safe_open(str(Path(args.teacher) / shard), "pt")
t = _shards[shard].get_tensor(raw_name)
return t.float().to(dev)
def tile(w, dim, head):
shape = list(w.shape)
x = w.reshape(*shape[:dim], hd.n_k, hd.rep, head, *shape[dim + 1 :])
return x.transpose(dim, dim + 1).reshape(shape)
def rot_signs(name):
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 torch.from_numpy(np.asarray(values[at : at + w_], np.float32)).to(dev)
at += w_
raise ValueError(f"no sign vector of width {width} for {name}")
def hf_to_stored(name, w):
if name.startswith("layers.") and hd.rep > 1:
kind = name.split(".")[2]
if kind == "in_proj_qkv" and name.endswith("in_proj_qkv.weight"):
qk = 2 * hd.n_k * hd.k_dim
w = torch.cat([w[:qk], tile(w[qk:], 0, hd.v_dim)])
elif kind == "in_proj_z":
w = tile(w, 0, hd.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, hd.v_dim).T
signs = rot_signs(name)
if signs is None:
return w
rows, width = w.shape
nblk = width // block_size
w = (w.reshape(rows, nblk, block_size) * signs.reshape(nblk, block_size)) @ H
return w.reshape(rows, width)
out_dir = Path(args.out)
out_dir.mkdir(parents=True, exist_ok=True)
dst = out_dir / "chal-v1.gguf"
shutil.copy(Path(args.king) / "Ingot-IX-27B-Q2.gguf", dst)
done = 0
with open(dst, "r+b") as fh:
for name, t in g.tensors.items():
if t.ggml_type != PQ2_0:
continue
canon = canonical(name)
if canon is None:
continue
hf_name = canon if canon.startswith(("lm_head", "norm.")) else "model." + canon
w_t = hf_to_stored(name, teacher_tensor(hf_name))
rows, width = w_t.shape
nblk = rows // 128 # blocks tile the numpy rows (the gguf column axis)
size = rows * width // 128 * 34
raw = np.memmap(dst, np.uint8, "r+", g.data_start + t.offset, (size,))
blocks = raw.reshape(-1, 34)
qs = torch.from_numpy(blocks[:, 2:].copy()).to(dev)
codes = torch.stack([(qs >> (2 * k)) & 3 for k in range(4)], -1).reshape(-1, 128)
c_vals = (codes.float() - 1.0).reshape(width, nblk, 128).permute(1, 0, 2)
d_orig = torch.from_numpy(blocks[:, :2].copy().view("<f2").astype(np.float32)).to(dev)
d_orig = d_orig.reshape(width, nblk).T
w_b = w_t.reshape(nblk, 128, width).permute(0, 2, 1)
support = (c_vals * c_vals).sum(-1)
num = (c_vals * w_b).sum(-1)
d_new = num / (support + 4.0)
d_new = torch.where(support >= 8, d_new, d_orig)
cap = 2.0 * d_orig.abs()
d_new = torch.clamp(d_new, -cap, cap).clamp(-5000.0, 5000.0)
blocks[:, :2] = d_new.T.reshape(-1).cpu().numpy().astype(np.float16).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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