File size: 8,096 Bytes
c83c617 | 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 | """Collect V4.2 Mac gate, task and measurement records into one acceptance status file."""
import argparse
import json
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(ROOT / "src"))
from vimeml.benchmarks.comparison import paired
from vimeml.training.data import file_sha
OUT = ROOT / "outputs/deployment"
TRACKS = ("standard-development", "legacy-regression", "ajimee", "historical-development")
EXPECTED_TOP1 = {"standard-development": 523, "legacy-regression": 370, "ajimee": 139, "historical-development": 123}
V21_TOP1 = {"standard-development": 505, "legacy-regression": 374, "ajimee": 144, "historical-development": 122}
RUNS = {
"cuda": ROOT / "outputs/evaluation-v42-20261020",
"mac_pytorch": OUT / "v42-mac-pytorch-baseline-v1/tracks",
"coreml_fp32": OUT / "v42-coreml-fp32-tracks-v1",
"coreml_int8": OUT / "v42-coreml-int8-tracks-v1",
"kv_fp32": OUT / "v42-coreml-kv-fp32-tracks-v1",
"kv_int8": OUT / "v42-coreml-kv-int8-tracks-v1",
"kvchunk_fp32": OUT / "v42-coreml-kvchunk-fp32-tracks-v1",
"kvchunk_int8": OUT / "v42-coreml-kvchunk-int8-tracks-v1",
"v21_coreml_fp32": OUT / "v42-v21-coreml-fp32-tracks-v1",
"v21_coreml_int8": OUT / "v42-v21-coreml-int8-tracks-v1",
}
METHODS = ("lm_context_sum", "lm_context_mean_secondary", "lm_no_context_sum")
def read(path):
return json.loads(Path(path).read_text(encoding="utf-8"))
def rows(run, track):
return [json.loads(line) for line in (RUNS[run] / track / "scores.jsonl").read_text(encoding="utf-8").splitlines() if line]
def all_method_orders(left, right):
"""Per-case order differences for every LM method, not only the primary sum."""
result = {}
for track in TRACKS:
a, b = rows(left, track), rows(right, track)
assert [r["id"] for r in a] == [r["id"] for r in b]
result[track] = {method: [x["id"] for x, y in zip(a, b) if x["orders"][method] != y["orders"][method]]
for method in METHODS}
return result
def top1(run):
out = {}
for track in TRACKS:
data = rows(run, track)
out[track] = sum(bool(r["orders"]["lm_context_sum"]) and r["orders"]["lm_context_sum"][0] in r["answers"] for r in data)
return out
def track_comparison(run):
summary = read(RUNS[run] / "summary.json")
return {track: summary["tracks"][track]["comparison"] for track in TRACKS}
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--measurements", type=Path, required=True)
parser.add_argument("--resources", type=Path)
parser.add_argument("--output", type=Path, required=True)
args = parser.parse_args()
if args.output.exists():
parser.error("Fresh output required.")
counts = {run: top1(run) for run in RUNS}
fixtures = read(OUT / "v42-mac-pytorch-baseline-v1/fixtures.json")
gates = {name: read(OUT / f"{name}/alignment.json") for name in (
"v42-fp32-alignment-v1", "v42-kv-fp32-alignment-v1", "v42-int8-alignment-v1",
"v42-kv-int8-vs-stateless-int8-v1", "v42-kv-int8-vs-fp32-fixtures-v1",
"v42-kvchunk-fp32-alignment-v1", "v42-kvchunk-int8-vs-stateless-int8-v1",
"v42-kvchunk-int8-vs-fp32-fixtures-v1")}
orders = {f"{a}_vs_{b}": all_method_orders(a, b) for a, b in (
("cuda", "mac_pytorch"), ("mac_pytorch", "coreml_fp32"), ("mac_pytorch", "kv_fp32"),
("coreml_fp32", "coreml_int8"), ("coreml_int8", "kv_int8"),
("coreml_fp32", "kvchunk_fp32"), ("coreml_int8", "kvchunk_int8"))}
no_changes = lambda key: all(not ids for track in orders[key].values() for ids in track.values())
int8 = track_comparison("coreml_int8")
measured = read(args.measurements)
checks = {
"1_mac_pytorch_fixtures_within_3e-4": fixtures["passed"],
"1_mac_pytorch_top1_equals_recorded_cuda": counts["mac_pytorch"] == EXPECTED_TOP1,
"1_mac_pytorch_orders_equal_cuda_all_methods": no_changes("cuda_vs_mac_pytorch"),
"2_fp32_stateless_and_kv_converted": True,
"3_fp32_stateless_alignment_passed": gates["v42-fp32-alignment-v1"]["passed"],
"3_fp32_kv_alignment_passed": gates["v42-kv-fp32-alignment-v1"]["passed"],
"3_coreml_fp32_top1_equals_mac_pytorch": counts["coreml_fp32"] == counts["mac_pytorch"],
"3_coreml_fp32_orders_equal_mac_pytorch_all_methods": no_changes("mac_pytorch_vs_coreml_fp32"),
"3_kv_fp32_top1_equals_mac_pytorch": counts["kv_fp32"] == counts["mac_pytorch"],
"3_kv_fp32_orders_equal_mac_pytorch_all_methods": no_changes("mac_pytorch_vs_kv_fp32"),
"4_int8_strict_logits_alignment_passed (expected false)": gates["v42-int8-alignment-v1"]["passed"],
"4_int8_kv_vs_same_weight_stateless_int8_passed": gates["v42-kv-int8-vs-stateless-int8-v1"]["passed"],
"4_kv_int8_top1_equals_stateless_int8": counts["kv_int8"] == counts["coreml_int8"],
"4_kv_int8_orders_equal_stateless_int8_all_methods": no_changes("coreml_int8_vs_kv_int8"),
"7_kvchunk_fp32_alignment_passed": gates["v42-kvchunk-fp32-alignment-v1"]["passed"],
"7_kvchunk_int8_vs_same_weight_stateless_int8_passed": gates["v42-kvchunk-int8-vs-stateless-int8-v1"]["passed"],
"7_kvchunk_int8_strict_vs_fp32_fixtures_passed (expected false)": gates["v42-kvchunk-int8-vs-fp32-fixtures-v1"]["passed"],
"7_kvchunk_fp32_top1_equals_stateless_fp32": counts["kvchunk_fp32"] == counts["coreml_fp32"],
"7_kvchunk_fp32_orders_equal_stateless_fp32_all_methods": no_changes("coreml_fp32_vs_kvchunk_fp32"),
"7_kvchunk_int8_top1_equals_stateless_int8": counts["kvchunk_int8"] == counts["coreml_int8"],
"7_kvchunk_int8_orders_equal_stateless_int8_all_methods": no_changes("coreml_int8_vs_kvchunk_int8"),
"8_v21_coreml_fp32_top1_equals_recorded": counts["v21_coreml_fp32"] == V21_TOP1,
"5_measurements_recorded": set(measured["models"]) >= {"v42-fp32", "v42-int8", "v21-fp32", "v21-int8"},
"6_ios_resources_prepared": bool(args.resources and (args.resources / "manifest.json").is_file()),
}
status = {"format": "vimeml_v42_mac_acceptance_v1", "date": "2026-10-20", "checks": checks,
"top1": counts, "recorded_v21_fp32_top1": V21_TOP1,
"int8_vs_coreml_fp32": {track: {"fp32": int8[track]["reference_top1"], "int8": counts["coreml_int8"][track],
**int8[track]["paired"], "improved_ids": int8[track]["improved_ids"], "regressed_ids": int8[track]["regressed_ids"],
"order_changed_cases": int8[track]["order_changed_cases"], "top1_changed_cases": int8[track]["top1_changed_cases"],
"max_score_sum_abs": int8[track]["max_score_sum_abs"], "mean_score_sum_abs": int8[track]["mean_score_sum_abs"]}
for track in TRACKS},
"v21_int8_vs_v42_int8": {track: {k: v for k, v in paired(rows("v21_coreml_int8", track),
rows("coreml_int8", track))["paired"].items() if k != "significant_at_0_05"}
for track in TRACKS},
"v21_fp32_vs_v42_fp32": {track: {k: v for k, v in paired(rows("v21_coreml_fp32", track),
rows("coreml_fp32", track))["paired"].items() if k != "significant_at_0_05"}
for track in TRACKS},
"v21_int8_vs_v21_fp32": {track: track_comparison("v21_coreml_int8")[track]["paired"] for track in TRACKS},
"all_method_order_differences": orders,
"alignment_summaries": {name: {"passed": g["passed"], **g["summary"]} for name, g in gates.items()},
"inputs_sha256": {"measurements": file_sha(args.measurements)},
"policy": "Failed gates are results. No retraining, checkpoint selection, pool/reference change or scoring tuning."}
args.output.mkdir(parents=True)
(args.output / "status.json").write_text(json.dumps(status, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
print(json.dumps({"checks": checks, "top1": counts}, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
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