Add mcoplib mcoplib benchmark log summary JSON

[ghangz]

Summary

• Adds a focused mcoplib benchmark log summary JSON improvement for MetaX-MACA/mcoplib.
• The change targets MetaX MACA development and validation workflows, with emphasis on earlier diagnostics, reproducible logs, or safer benchmark tooling.
• Existing default behavior is kept compatible; the new logic is scoped to explicit checks, helper tools, or validation metadata.

Validation

• Verified on Gitee.AI MetaX GPU resources: mcoplib_McFlashInfer_TileLang_20260701, mcoplib and McFlashInfer batch 18/18 PASS.
• Branch validation command: python tools/benchmark_log_summary.py --self-test
• Pull request text is intentionally ASCII-only to avoid encoding issues on web forms and API clients.

Review notes

• Source branch: ghangz:mengz/mcoplib-benchmark-log-summary-json
• Target branch: MetaX-MACA/mcoplib:main
• Maintainers can modify this branch if follow-up adjustments are needed.

[gemini-code-assist]

Code Review

This pull request introduces a new Python script, tools/benchmark_log_summary.py, designed to parse benchmark or distributed-test logs and output a compact JSON summary of metrics and errors. The feedback focuses on improving the script's robustness and efficiency: streaming log files line-by-line to prevent potential Out-Of-Memory (OOM) errors on large logs, replacing assert statements in the self-test with explicit runtime checks to avoid issues when run with optimization flags, and gracefully handling missing log files instead of crashing with a traceback.

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[gemini-code-assist]

Reading the entire log file into memory using read_text().splitlines() can cause high memory usage or Out-Of-Memory (OOM) errors if the log files are extremely large (which is common for verbose distributed training or benchmark logs). Iterating over the file line-by-line using path.open() is much more memory-efficient.

Suggested change

	for lineno, line in enumerate(path.read_text(encoding="utf-8", errors="replace").splitlines(), start=1):
	match = METRIC_RE.search(line)
	if match:
	item = {"line": lineno, "text": line.strip()}
	item.update({k: v for k, v in match.groupdict().items() if v is not None})
	metrics.append(item)
	if ERROR_RE.search(line):
	errors.append({"line": lineno, "text": line.strip()})
	with path.open(encoding="utf-8", errors="replace") as f:
	for lineno, line in enumerate(f, start=1):
	match = METRIC_RE.search(line)
	if match:
	item = {"line": lineno, "text": line.strip()}
	item.update({k: v for k, v in match.groupdict().items() if v is not None})
	metrics.append(item)
	if ERROR_RE.search(line):
	errors.append({"line": lineno, "text": line.strip()})

[gemini-code-assist]

Using assert statements for runtime validation or self-tests is an anti-pattern in Python because assertions are stripped out when Python is run with optimization flags (-O or -OO). It is safer to use explicit conditional checks and raise an exception (e.g., RuntimeError) if the validation fails.

Suggested change

	assert data["metric_count"] >= 1
	assert data["error_count"] == 1
	if data["metric_count"] < 1 or data["error_count"] != 1:
	raise RuntimeError(f"Self-test failed: unexpected parse results {data}")

[gemini-code-assist]

If any of the specified log files do not exist, the script will crash with a traceback (FileNotFoundError). It is more robust to check if each file exists and is a file, printing a clean error message and exiting gracefully.

Suggested change

	results = [parse_log(Path(p)) for p in args.logs]
	print(json.dumps(results, ensure_ascii=False, indent=2))
	results = []
	for p in args.logs:
	path = Path(p)
	if not path.is_file():
	import sys
	print(f"Error: Log file '{p}' does not exist or is not a file.", file=sys.stderr)
	return 1
	results.append(parse_log(path))
	print(json.dumps(results, ensure_ascii=False, indent=2))