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3fd1a35 | 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 | #!/usr/bin/env python3
"""Activation-diagonal weighted W4 scale search; unchanged per-row INT4 layout.
Calibration and held-out fixtures must be different. This is not GPTQ/AWQ:
no full Hessian compensation, channel rescaling, or optimizer training is used.
"""
import argparse
import hashlib
import json
import time
from pathlib import Path
import torch
from safetensors.torch import save_file
from transformers import AutoTokenizer
from quant_loss_reference import load_model, evaluate
def safe_rows(codes, splits):
"""Exact worst-case signed INT4 products, matching SplitAligned(K, splits, 32)."""
blocks = codes.shape[1] // 32
if codes.shape[1] % 32 or not 1 <= splits <= blocks:
raise ValueError('invalid aligned K partition')
size, extra = divmod(blocks, splits)
begin = 0
safe = torch.ones(codes.shape[0], dtype=torch.bool, device=codes.device)
for part in range(splits):
end = begin + (size + (part < extra)) * 32
q = codes[:,begin:end].float()
positive = q.clamp_min(0).sum(1)
negative = (-q).clamp_min(0).sum(1)
safe &= (7*positive+8*negative <= 32767) & (8*positive+7*negative <= 32768)
begin = end
return safe
def choose_scales(weight, diagonal, rows=256, k_splits=None):
"""Search clipping and least-squares scales; retain baseline if no improvement."""
selected = []
before, after = 0.0, 0.0
for offset in range(0, weight.shape[0], rows):
w = weight[offset:offset+rows].float()
base = torch.maximum(w.amax(1).clamp_min(0)/7, (-w.amin(1)).clamp_min(0)/8)
base = torch.where(base > 0, base, torch.ones_like(base))
def error(scale):
q = torch.round(w/scale[:, None]).clamp(-8, 7)
return ((w-q*scale[:, None]).square()*diagonal).sum(1), q
best_error, baseline_codes = error(base)
if k_splits is not None and not safe_rows(baseline_codes,k_splits).all():
raise ValueError('baseline scale is unsafe for required K partitions')
before += best_error.double().sum().item()
best = base.clone()
# Include the old solution exactly. Search never worsens this local objective.
for fraction in (1.0, .95, .9, .85, .8, .75, .7, .65, .6, .55, .5):
scale = base * fraction
for iteration in range(3):
loss, q = error(scale)
improve = loss < best_error
if k_splits is not None:
improve &= safe_rows(q,k_splits)
best = torch.where(improve, scale, best)
best_error = torch.where(improve, loss, best_error)
if iteration < 2:
scale = ((w*q*diagonal).sum(1) /
(q.square()*diagonal).sum(1).clamp_min(1e-30)).clamp_min(1e-20)
after += best_error.double().sum().item()
selected.append(best)
return torch.cat(selected), before, after
def main():
p = argparse.ArgumentParser(description=__doc__)
p.add_argument('--source', type=Path, required=True)
p.add_argument('--corpus', type=Path, required=True)
p.add_argument('--suite', type=Path, required=True)
p.add_argument('--output', type=Path, required=True)
p.add_argument('--max-tokens', type=int, default=512)
p.add_argument('--reuse-activations', action='store_true')
p.add_argument('--baseline-manifest', type=Path, help='Preserve baseline INT16-safe K partitions')
a = p.parse_args()
torch.set_num_threads(4)
torch.backends.cuda.matmul.allow_tf32 = False
a.output.mkdir(parents=True, exist_ok=True)
corpus = json.loads(a.corpus.read_text())
suite = json.loads(a.suite.read_text())
# Disallow accidental reuse of held-out questions or answers in calibration.
for c in corpus:
for e in suite['cases']:
assert c['prompt'] not in e['prompt_text'] and c['answer'] not in e['target_text']
model = load_model(a.source)
modules = {name: m for name, m in model.named_modules() if isinstance(m, torch.nn.Linear)}
partitions = {}
if a.baseline_manifest:
from quantize_model import source_linear_names
for item in json.loads(a.baseline_manifest.read_text())['tensors']:
if item['role'] == 'linear_weight':
for n in source_linear_names(item['name'].removesuffix('.weight')):
partitions[n.removesuffix('.weight')] = item['flags']
assert set(partitions) == set(modules)
stats_path = a.output/'activations.pt'
if a.reuse_activations:
stats = torch.load(stats_path, map_location='cuda', weights_only=True)
assert stats['corpus_sha256'] == hashlib.sha256(a.corpus.read_bytes()).hexdigest()
else:
sums, counts = {}, {}
def hook(name):
def accumulate(module, inputs):
x = inputs[0].detach().reshape(-1, inputs[0].shape[-1]).float()
if not x.shape[0]:
return
value = x.square().sum(0)
if name in sums:
sums[name].add_(value)
else:
sums[name] = value
counts[name] = counts.get(name, 0) + x.shape[0]
return accumulate
hooks = [m.register_forward_pre_hook(hook(n)) for n,m in modules.items()]
tokenizer = AutoTokenizer.from_pretrained(a.source, trust_remote_code=True, local_files_only=True)
lengths = []
with torch.inference_mode():
for i, c in enumerate(corpus):
for thinking in (False, True):
prompt = tokenizer.apply_chat_template([{'role':'user','content':c['prompt']}],
tokenize=False, add_generation_prompt=True, enable_thinking=thinking)
target = (c['reasoning']+'</think>' if thinking else '') + c['answer'] + tokenizer.eos_token
ids = tokenizer.encode(prompt+target, add_special_tokens=False)[:a.max_tokens]
output = model(input_ids=torch.tensor([ids],device='cuda'), use_cache=False)
del output
lengths.append(len(ids))
print(json.dumps({'calibration_cases':i+1, 'total_tokens':sum(lengths),
'matrices_observed':len(sums)}), flush=True)
for h in hooks:
h.remove()
stats = {'sum_squares':{k:v.cpu() for k,v in sums.items()},'counts':counts,
'lengths':lengths, 'corpus_sha256':hashlib.sha256(a.corpus.read_bytes()).hexdigest()}
torch.save(stats, stats_path)
del sums
scales, records = {}, []
start = time.monotonic()
with torch.inference_mode():
for i,(name,m) in enumerate(modules.items()):
count = stats['counts'].get(name,0)
diag = stats['sum_squares'].get(name)
if diag is None or count == 0:
# Unobserved experts retain baseline weights, not a guessed calibration.
w = m.weight.float()
scale = torch.maximum(w.amax(1).clamp_min(0)/7,(-w.amin(1)).clamp_min(0)/8)
scale = torch.where(scale>0,scale,torch.ones_like(scale))
old = new = None
else:
diag = diag.to('cuda') / count
mean = diag.mean().clamp_min(1e-20)
# Regularize rarely visited experts more strongly towards uniform weights.
strength = .05 + .95 * max(0., 1.-count/64.)
diag = (1-strength)*diag/mean + strength
scale, old, new = choose_scales(m.weight,diag,k_splits=partitions.get(name))
scales[name+'.weight'] = scale.cpu().contiguous()
records.append({'name':name,'observed_tokens':count,'weighted_error_before':old,
'weighted_error_after':new})
for row in range(0,m.weight.shape[0],1024):
w = m.weight[row:row+1024].float()
s = scale[row:row+1024,None]
m.weight[row:row+1024].copy_((torch.round(w/s).clamp(-8,7)*s).to(m.weight.dtype))
if (i+1)%200 == 0:
print(json.dumps({'calibrated_matrices':i+1,'seconds':time.monotonic()-start}),flush=True)
save_file(scales,str(a.output/'scales.safetensors'))
report = {'method':'activation_diagonal_weighted_scale_search_v1','corpus_sha256':stats['corpus_sha256'],
'calibration_sequences':len(stats['lengths']),'calibration_tokens':sum(stats['lengths']),
'matrices':records,'seconds':time.monotonic()-start,'max_tokens':a.max_tokens,
'baseline_manifest_sha256':hashlib.sha256(a.baseline_manifest.read_bytes()).hexdigest() if a.baseline_manifest else None,
'preserve_baseline_k_partitions':bool(a.baseline_manifest)}
(a.output/'calibration.json').write_text(json.dumps(report,indent=2)+'\n')
evaluate(model,suite['cases'],a.output,'calibrated_bf16')
print('PASS: calibrated scales and held-out logits exported',flush=True)
if __name__ == '__main__':
main()
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