File size: 9,675 Bytes
fc32096 | 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 | import argparse
import hashlib
import json
import math
from pathlib import Path
import shutil
import struct
import time
import numpy as np
import torch
from safetensors import safe_open
from safetensors.torch import save_file
from common import ROOT, inside, save_json, append, permit_compute
torch.set_grad_enabled(False)
FROZEN = ('embed_tokens', 'lm_head', 'model.language_model.norm.weight', 'model.norm.weight')
def headers(model):
result = {}
for file in sorted(inside(model).glob('*.safetensors')):
with file.open('rb') as f:
length = struct.unpack('<Q', f.read(8))[0]
head = json.loads(f.read(length))
for name, info in head.items():
if name != '__metadata__':
if name in result:
raise ValueError(f'Duplicate tensor: {name}')
result[name] = dict(info, file=str(file))
return result
def load(model, name):
info = headers(model)[name]
with safe_open(info['file'], framework='pt', device='cpu') as f:
return f.get_tensor(name)
def validate(name, original, value):
if not name.startswith('model.language_model.layers.') or any(x in name for x in FROZEN):
raise ValueError('Разрешены только внутренние слои языковой модели')
if value.shape != original.shape or not torch.isfinite(value).all():
raise ValueError('Неверная форма или нечисловые значения')
w, v = original.float(), value.float()
if torch.equal(w, v):
raise ValueError('Правка не меняет веса')
ratio = (w * v).sum() / w.square().sum().clamp_min(1e-30)
if (v - ratio * w).norm() <= 1e-6 * v.norm().clamp_min(1e-30):
raise ValueError('Масштабирование всего тензора запрещено')
cast = value.to(original.dtype).contiguous()
if not torch.isfinite(cast).all() or torch.equal(cast, original):
raise ValueError('Правка исчезла или стала нечисловой после приведения dtype')
return cast
def digest(tensor):
return hashlib.sha256(tensor.contiguous().view(torch.uint8).numpy().tobytes()).hexdigest()
def save_edit(model, directory, name, value, hypothesis):
permit_compute()
directory = inside(directory)
if not directory.is_relative_to(ROOT / 'edits'):
raise ValueError('Правки сохраняются только в edits/')
if not hypothesis.strip():
raise ValueError('Нужна техническая гипотеза')
original = load(model, name)
cast = validate(name, original, value)
directory.mkdir(parents=True, exist_ok=True)
file = directory / (name + '.safetensors')
save_file({name: cast}, str(file))
record = {'time': time.time(), 'source_model': str(inside(model)), 'tensor': name, 'hypothesis': hypothesis, 'status': 'unconfirmed',
'changed_entries': int((cast != original).sum()), 'tensor_entries': original.numel(),
'base_sha256': digest(original), 'edited_sha256': digest(cast),
'relative_frobenius': float((cast.float()-original.float()).norm()/original.float().norm())}
save_json(directory / (name + '.json'), record)
append(ROOT / 'logs/experiments.jsonl', record)
return record
def project_writers(w, direction, columns, alpha):
w = w.float()
u = torch.as_tensor(direction, dtype=torch.float32)
if not torch.isfinite(u).all() or u.norm() == 0:
raise ValueError('Некорректное направление')
u = u / u.norm()
coeff = u @ w
mask = torch.zeros_like(coeff)
mask[columns] = coeff[columns]
return w - float(alpha) * torch.outer(u, mask)
def lineage(model):
current, ancestors, inherited = inside(model), [], set()
while True:
if str(current) in ancestors:
raise ValueError('Цикл в происхождении модели')
ancestors.append(str(current))
manifest = current / 'wsp_manifest.json'
if not manifest.exists():
return current, ancestors, inherited
record = json.loads(manifest.read_text())
inherited.update(record['tensors'])
current = inside(record['source'])
def weight_fingerprint(model):
result = []
for path in sorted(inside(model).glob('*.safetensors')):
with path.open('rb') as stream:
result.append([path.name, hashlib.file_digest(stream, 'sha256').hexdigest()])
return result
def export(model, edits, out):
permit_compute()
model, edits, out = inside(model), inside(edits), inside(out)
if out.exists():
raise ValueError('Каталог назначения должен быть новым')
if not out.is_relative_to(ROOT / 'models') or out == ROOT / 'models/original':
raise ValueError('Недопустимый каталог модели')
metadata = headers(model)
changes = {}
for file in edits.glob('*.safetensors'):
with safe_open(str(file), framework='pt', device='cpu') as f:
for name in f.keys():
if name in changes:
raise ValueError('Повторная правка тензора')
base = load(model, name)
changes[name] = validate(name, base, f.get_tensor(name))
manifest = json.loads((edits / (name + '.json')).read_text())
if manifest['base_sha256'] != digest(base) or manifest['edited_sha256'] != digest(changes[name]):
raise ValueError('Хеш правки не совпал')
if not changes:
raise ValueError('Нет правок для экспорта')
original, ancestors, inherited = lineage(model)
original_metadata = headers(original)
if metadata.keys() != original_metadata.keys() or any(
metadata[name]['shape'] != original_metadata[name]['shape'] or
metadata[name]['dtype'] != original_metadata[name]['dtype'] for name in metadata):
raise ValueError('Архитектура родителя отличается от исходной модели')
cumulative = {}
for name in sorted(inherited | changes.keys()):
value = changes[name] if name in changes else load(model, name)
cumulative[name] = int((value != load(original, name)).sum())
parent_fingerprint = weight_fingerprint(model)
original_fingerprint = parent_fingerprint if original == model else weight_fingerprint(original)
staging = out.with_name(out.name + '.building')
if staging.exists():
raise ValueError('Обнаружен незавершённый экспорт')
staging.mkdir(parents=True)
for file in model.iterdir():
if file.is_file() and file.suffix != '.safetensors':
shutil.copyfile(file, staging / file.name)
for shard in sorted({x['file'] for x in metadata.values()}):
with safe_open(shard, framework='pt', device='cpu') as f:
tensors = {name: changes[name] if name in changes else f.get_tensor(name) for name in f.keys()}
save_file(tensors, str(staging / Path(shard).name), metadata={'format': 'pt'})
del tensors
total = sum(math.prod(x['shape']) for x in metadata.values() if x['dtype'].startswith(('BF', 'F')))
language_total = sum(math.prod(x['shape']) for name, x in metadata.items()
if name.startswith('model.language_model.') and x['dtype'].startswith(('BF', 'F')))
changed = sum(int((value != load(model, name)).sum()) for name, value in changes.items())
cumulative_changed = sum(cumulative.values())
save_json(staging / 'wsp_manifest.json', {'schema_version': 2,
'source': str(model), 'edits': str(edits), 'tensors': list(changes),
'changed_entries': changed, 'stored_parameter_entries': total,
'changed_percent': 100 * changed / total,
'language_parameter_entries': language_total,
'changed_percent_language': 100 * changed / language_total,
'changed_entries_reference': 'immediate_parent',
'parent_weight_fingerprint': parent_fingerprint,
'original_source': str(original), 'ancestor_chain_parent_first': ancestors,
'original_weight_fingerprint': original_fingerprint,
'changed_entries_vs_original': cumulative_changed,
'changed_percent_vs_original': 100 * cumulative_changed / total,
'changed_percent_language_vs_original': 100 * cumulative_changed / language_total,
'changed_entries_vs_original_by_tensor': cumulative,
'status': 'unconfirmed'})
staging.rename(out)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('command', choices=['inventory', 'export'])
parser.add_argument('--model', default=str(ROOT / 'models/original'))
parser.add_argument('--edits')
parser.add_argument('--out')
args = parser.parse_args()
if args.command == 'inventory':
save_json(ROOT / 'reports/tensor_inventory.json', headers(args.model))
else:
export(args.model, args.edits, args.out)
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