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f36843f 728caeb f36843f 728caeb f36843f 728caeb f36843f 728caeb f36843f 728caeb f36843f 728caeb f36843f 728caeb f36843f 728caeb f36843f | 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 | """Synchronized paired comparison on real Qwen weights and upstream presets."""
import argparse
import gc
import json
import platform
import random
import statistics
import sys
import time
from pathlib import Path
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from tree_decode import FieldPlan, decode_fields, prefill
ROOT = Path(__file__).resolve().parent
SOURCE = ROOT / 'upstream' if (ROOT / 'upstream').exists() else ROOT
sys.path.insert(0, str(SOURCE))
from core.schema import StructuredSchema
def sync():
if torch.backends.mps.is_available():
torch.mps.synchronize()
def timed(fn):
sync()
start = time.perf_counter()
result = fn()
sync()
return result, (time.perf_counter() - start) * 1000
def summary(xs):
ordered = sorted(xs)
return dict(median_ms=statistics.median(xs), min_ms=min(xs),
p90_ms=ordered[min(len(xs)-1, int(len(xs)*0.9))], samples_ms=xs)
def speedup_interval(batch, tree):
rng = random.Random(83)
ratios = []
for _ in range(2000):
indices = [rng.randrange(len(batch)) for _ in batch]
ratios.append(statistics.median([batch[i] for i in indices]) /
statistics.median([tree[i] for i in indices]))
ratios.sort()
return [ratios[50], ratios[1949]]
def main():
ap = argparse.ArgumentParser()
ap.add_argument('--model', default='Qwen/Qwen2.5-1.5B-Instruct')
ap.add_argument('--presets', nargs='+', default=['fintech_fraud', 'support_triage', 'code_security'])
ap.add_argument('--fields', nargs='+', type=int, default=[4, 16, 28])
ap.add_argument('--steps', type=int, default=1)
ap.add_argument('--repeats', type=int, default=12)
ap.add_argument('--warmup', type=int, default=3)
ap.add_argument('--output', default='results.json')
ap.add_argument('--device', choices=['auto', 'mps', 'cpu'], default='auto')
ap.add_argument('--dtype', choices=['auto', 'float16', 'float32'], default='auto')
args = ap.parse_args()
if args.repeats < 2 or args.warmup < 1:
ap.error('Use at least two repeats and one warmup')
torch.set_num_threads(4)
random.seed(42)
device = args.device
if device == 'auto':
device = 'mps' if torch.backends.mps.is_available() else 'cpu'
dtype = ({'float16': torch.float16, 'float32': torch.float32}.get(args.dtype)
or (torch.float16 if device == 'mps' else torch.float32))
tokenizer = AutoTokenizer.from_pretrained(args.model)
model = AutoModelForCausalLM.from_pretrained(args.model, dtype=dtype, attn_implementation='sdpa').to(device).eval()
print(f'Loaded {args.model} on {device} ({dtype})', flush=True)
report = {'environment': {'model': args.model, 'revision': model.config._commit_hash,
'torch': torch.__version__, 'transformers': transformers.__version__,
'gpu': device if device == 'mps' else None,
'platform': platform.platform(), 'dtype': str(dtype), 'attention': 'sdpa'},
'arguments': vars(args), 'cases': []}
for preset_name in args.presets:
preset = json.loads((SOURCE / 'presets' / f'{preset_name}.json').read_text(encoding='utf-8'))
for count in args.fields:
if count > len(preset['schema']):
continue
schema = StructuredSchema(dict(list(preset['schema'].items())[:count]))
print(f'Checking {preset_name}, {count} fields...', flush=True)
meta = schema.compile_parallel_metadata(tokenizer)
suffixes = [list(map(int, row[:length])) for row, length in zip(meta['suffixes_batch'], meta['suffix_lengths'])]
prompt = (f'<|im_start|>system\nClassify JSON attributes:\n{schema.to_parallel_schema_str()}<|im_end|>\n'
f'<|im_start|>user\n{preset["context"]}<|im_end|>\n<|im_start|>assistant\n{{\n')
ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
plan = FieldPlan(suffixes, ids.shape[1], device, dtype, tokenizer.pad_token_id or 0)
cache = prefill(model, ids)
ref = decode_fields(model, cache, plan, 'batch', args.steps)
actual = decode_fields(model, cache, plan, 'tree', args.steps, ref.argmax(-1))
delta = (ref.float() - actual.float()).abs()
# FP16 kernels have different reduction orders for these shapes.
torch.testing.assert_close(actual, ref, atol=0.15 if dtype == torch.float16 else 1e-4,
rtol=0.01 if dtype == torch.float16 else 1e-4)
candidate_mismatches = []
for i, candidate_ids in enumerate(meta['cands_per_field']):
batch_choice = ref[:, i, candidate_ids].argmax(-1)
tree_choice = actual[:, i, candidate_ids].argmax(-1)
if not torch.equal(batch_choice, tree_choice):
candidate_mismatches.append(meta['field_items'][i][0])
candidates_equal = not candidate_mismatches
check = {'max_abs_logit_error': delta.max().item(), 'mean_abs_logit_error': delta.mean().item(),
'full_vocab_argmax_agreement': (ref.argmax(-1) == actual.argmax(-1)).float().mean().item(),
'candidate_decisions_equal': candidates_equal,
'candidate_decision_mismatches': candidate_mismatches,
'candidate_collision_fields': [name for (name, _), collision in zip(meta['field_items'], meta['has_collisions']) if collision]}
print(f'Correctness passed: {check}', flush=True)
del ref, actual, delta
funcs = {mode: (lambda m=mode: decode_fields(model, cache, plan, m, args.steps)) for mode in ('batch', 'tree')}
for _ in range(args.warmup):
for fn in funcs.values():
fn()
samples = {mode: [] for mode in funcs}
peaks = {}
for _ in range(args.repeats):
order = list(funcs)
random.shuffle(order)
for mode in order:
value, ms = timed(funcs[mode])
samples[mode].append(ms)
del value
print(f'Decode medians: batch={statistics.median(samples["batch"]):.1f} ms, tree={statistics.median(samples["tree"]):.1f} ms', flush=True)
for mode, fn in funcs.items():
if device == 'mps':
torch.mps.empty_cache()
baseline = torch.mps.driver_allocated_memory()
value, _ = timed(fn)
high_water = torch.mps.driver_allocated_memory()
peaks[mode] = {'driver_high_water_mib': high_water/2**20,
'extra_driver_mib': (high_water-baseline)/2**20}
del value
# Complete model request includes a fresh common prefill for each mode.
end_to_end = {mode: [] for mode in funcs}
del cache
for _ in range(args.repeats):
order = list(funcs)
random.shuffle(order)
for mode in order:
def request():
prefix = prefill(model, ids)
return decode_fields(model, prefix, plan, mode, args.steps)
value, ms = timed(request)
end_to_end[mode].append(ms)
del value
case = dict(preset=preset_name, fields=count, prefix_tokens=ids.shape[1],
suffix_tokens=sum(map(len, suffixes)), batch_padded_tokens=count*plan.width,
correctness=check, decode={m:summary(v) for m,v in samples.items()},
end_to_end={m:summary(v) for m,v in end_to_end.items()}, memory=peaks)
case['decode_speedup'] = statistics.median(samples['batch'])/statistics.median(samples['tree'])
case['end_to_end_speedup'] = statistics.median(end_to_end['batch'])/statistics.median(end_to_end['tree'])
case['decode_speedup_ci95'] = speedup_interval(samples['batch'], samples['tree'])
case['end_to_end_speedup_ci95'] = speedup_interval(end_to_end['batch'], end_to_end['tree'])
report['cases'].append(case)
Path(args.output).write_text(json.dumps(report, indent=2), encoding='utf-8')
print(f'{preset_name} {count} fields: decode {case["decode_speedup"]:.3f}x, end-to-end {case["end_to_end_speedup"]:.3f}x; {check}', flush=True)
gc.collect()
print(f'Results saved to {args.output}', flush=True)
if __name__ == '__main__':
main()
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