File size: 7,045 Bytes
ed3aeeb | 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 | #!/usr/bin/env python3
"""Build the concise AD01 compiled-MLIR numerical and task-quality report."""
from __future__ import annotations
import argparse
import csv
import hashlib
import json
import os
import tempfile
from pathlib import Path
from typing import Any
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--repo-root", required=True, type=Path)
parser.add_argument("--result-dir", required=True, type=Path)
parser.add_argument("--report-csv", required=True, type=Path)
parser.add_argument("--report-md", required=True, type=Path)
return parser.parse_args()
def sha256_file(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()
def atomic_text(path: Path, value: str) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with tempfile.NamedTemporaryFile("w", encoding="utf-8", dir=path.parent, delete=False) as handle:
handle.write(value)
temporary = Path(handle.name)
os.replace(temporary, path)
def main() -> int:
args = parse_args()
root = args.repo_root.resolve()
results = args.result_dir.resolve()
fixed = json.loads((results / "compiled_output_comparison.json").read_text())
validation = json.loads((results / "validation.json").read_text())
q1_summaries = {
variant: json.loads((results / f"q1/{variant}/quality_summary.json").read_text())
for variant in ("fp32", "public_quantized")
}
metrics_path = results / "compiled_quality_metrics.csv"
with metrics_path.open(newline="") as handle:
metrics = list(csv.DictReader(handle))
lookup = {(row["variant"], row["machine_id"]): row for row in metrics}
rows: list[dict[str, Any]] = []
for variant, label in (
("fp32", "FP32"),
("public_quantized", "PUBLIC_INT8"),
):
compiler_key = (
"fp32_compiled_vs_onnxruntime"
if variant == "fp32"
else "public_quantized_compiled_vs_onnxruntime"
)
comparison = fixed["comparisons"][compiler_key]
q1_summary = q1_summaries[variant]
fidelity = q1_summary["row_level_fidelity"]
runtime_prefix = "fp32" if variant == "fp32" else "public_quantized"
ort_metric = lookup[(f"{runtime_prefix}_onnxruntime", "Average")]
compiled_metric = lookup[(f"{runtime_prefix}_compiled", "Average")]
rows.append(
{
"model_id": "AD01",
"variant": label,
"compiled_invoke": fixed["abi_runtime_checks"][variant]["status"],
"onnxruntime_vs_compiled": comparison["status"],
"official_dataset_fidelity": q1_summary["fidelity_status"],
"comparison_rule": "allclose(atol=1e-5,rtol=1e-5)" if variant == "fp32" else "raw_int8_exact",
"max_abs_error": comparison["max_abs_error"],
"mismatch_elements": comparison["mismatch_element_count"],
"official_matching_vectors": fidelity["matching_rows"],
"official_mismatching_vectors": fidelity["mismatching_rows"],
"official_max_abs_error": fidelity["max_abs_error"],
"official_mean_abs_error": fidelity["mean_abs_error"],
"official_files": 2459,
"official_vectors": 481964,
"onnxruntime_auc": ort_metric["auc"],
"compiled_auc": compiled_metric["auc"],
"compiled_minus_onnxruntime_auc": float(compiled_metric["auc"]) - float(ort_metric["auc"]),
"onnxruntime_pauc": ort_metric["pauc"],
"compiled_pauc": compiled_metric["pauc"],
"compiled_minus_onnxruntime_pauc": float(compiled_metric["pauc"]) - float(ort_metric["pauc"]),
"q1_acceptance": "THRESHOLD_UNDEFINED",
}
)
fp32_compiled_auc = float(next(row for row in rows if row["variant"] == "FP32")["compiled_auc"])
fp32_compiled_pauc = float(next(row for row in rows if row["variant"] == "FP32")["compiled_pauc"])
for row in rows:
row["compiled_minus_fp32_auc"] = float(row["compiled_auc"]) - fp32_compiled_auc
row["compiled_minus_fp32_pauc"] = float(row["compiled_pauc"]) - fp32_compiled_pauc
fieldnames = list(rows[0])
import io
csv_buffer = io.StringIO(newline="")
writer = csv.DictWriter(csv_buffer, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(rows)
atomic_text(args.report_csv.resolve(), csv_buffer.getvalue())
lines = [
"# AD01 ONNX-MLIR compiled accuracy validation",
"",
"## 결과",
"",
"| Variant | invoke | fixed ORT↔compiled | official vectors match/mismatch | official max abs | compiled AUC / pAUC |",
"|---|---|---|---:|---:|---:|",
]
for row in rows:
lines.append(
f"| {row['variant']} | {row['compiled_invoke']} | {row['onnxruntime_vs_compiled']} "
f"| {row['official_matching_vectors']}/{row['official_mismatching_vectors']} "
f"| {float(row['official_max_abs_error']):.10g} "
f"| {float(row['compiled_auc']):.10f} / {float(row['compiled_pauc']):.10f} |"
)
fp32_row = next(row for row in rows if row["variant"] == "FP32")
quantized_row = next(row for row in rows if row["variant"] == "PUBLIC_INT8")
quantized_minus_fp32_auc = float(quantized_row["compiled_auc"]) - float(fp32_row["compiled_auc"])
quantized_minus_fp32_pauc = float(quantized_row["compiled_pauc"]) - float(fp32_row["compiled_pauc"])
lines.extend(
[
"",
"공식 DCASE ToyCar test 2,459개 파일(481,964 feature vector)을 사용했다.",
"",
f"Compiled FP32 대비 PUBLIC_INT8의 task accuracy 변화는 AUC `{quantized_minus_fp32_auc:.10f}`, pAUC `{quantized_minus_fp32_pauc:.10f}`이다.",
"",
"고정 fixture ORT 비교는 FP32·PUBLIC_INT8 모두 통과했다. 공식 전체 입력에서는 FP32 47,620개, PUBLIC_INT8 4,080개 vector가 엄격 비교 기준을 벗어났다.",
"",
"## 무결성",
"",
f"- Independent validation: `{validation['status']}` ({validation['check_summary']['passed']}/{validation['check_summary']['total']})",
f"- Fixed comparison SHA-256: `{sha256_file(results / 'compiled_output_comparison.json')}`",
f"- Quality scores SHA-256: `{sha256_file(results / 'compiled_file_scores.csv')}`",
f"- Quality metrics SHA-256: `{sha256_file(metrics_path)}`",
"",
]
)
atomic_text(args.report_md.resolve(), "\n".join(lines))
print(json.dumps({"status": "PASS", "rows": len(rows), "report": str(args.report_md.resolve())}, sort_keys=True))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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