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Inputs are one raw browser sample per JSONL line. Each line must carry the
full per-sample schema plus a provenance record that describes the session
configuration. The tool performs strict validation before producing any
summary: identity duplicates, malformed types, non-finite timings, mixed
experimental conditions, and diffusion rows without noise hashes all cause
the tool to fail before the output path is touched.
Successful rows contribute to linearly interpolated p50/p95 percentiles per
timing stage. Failed rows are counted explicitly and retain nullable timing
slots. Heap maxima are derived only from actually-measured observations.
Measurement completeness (>= 5 independent session starts, >= 10 warmups
excluded per session, >= 100 timed rows total) is reported as a separate
flag without elevating partial runs to "complete". Because the page harness
performs in-page reloads only, "process cold" is never claimed, and the
requested provider is never represented as proven.
"""
from __future__ import annotations
import argparse
import hashlib
import json
import math
import re
from collections.abc import Iterator
from pathlib import Path
from typing import Any
TIMING_FIELDS: tuple[str, ...] = (
"tokenizeMs",
"prepareFeedMs",
"inferenceAndReadbackMs",
"postprocessMs",
"totalRequestMs",
)
HEAP_FIELDS: tuple[str, ...] = (
"js_heap_used_bytes_before",
"js_heap_used_bytes_after",
)
PROVIDERS = {"wasm", "webgpu"}
BACKENDS = {"direct", "diffusion"}
SHA256_PATTERN = re.compile(r"^[0-9a-f]{64}$")
REQUIRED_IDENTITY_FIELDS: tuple[str, ...] = ("run_id", "session_start", "repeat")
REQUIRED_TOP_FIELDS: tuple[str, ...] = (
"run_id",
"session_start",
"repeat",
"case_id",
"backend",
"requested_provider",
"observed_provider",
"bundle_hash",
"tokenizer_hash",
"input_hash",
"candidate_count",
"padded_option_slots",
"sequence_length",
"seed",
"ort_version",
"status",
)
REQUIRED_PROVENANCE_FIELDS: tuple[str, ...] = (
"environment",
"execution_provider_evidence",
"warmup_excluded_per_session",
"session_start_semantics",
)
CONDITION_KEYS: tuple[str, ...] = (
"run_id",
"backend",
"requested_provider",
"observed_provider",
"bundle_hash",
"tokenizer_hash",
"candidate_count",
"padded_option_slots",
"sequence_length",
"ort_version",
)
MAX_PADDED_OPTION_SLOTS = 32
MAX_SEQUENCE_LENGTH = 512
def _finite_number(
value: Any, label: str, *, allow_none: bool = False
) -> float | None:
if value is None and allow_none:
return None
if isinstance(value, bool) or not isinstance(value, (int, float)):
raise TypeError(f"{label} must be a finite number or null")
numeric = float(value)
if not math.isfinite(numeric) or numeric < 0:
raise ValueError(f"{label} must be a finite non-negative number")
return numeric
def _positive_integer(value: Any, label: str) -> int:
if isinstance(value, bool) or not isinstance(value, int) or value < 1:
raise ValueError(f"{label} must be a positive integer")
return value
def _nonnegative_integer(value: Any, label: str) -> int:
if isinstance(value, bool) or not isinstance(value, int) or value < 0:
raise ValueError(f"{label} must be a non-negative integer")
return value
def _sha256(value: Any, label: str, *, allow_none: bool = False) -> str | None:
if value is None and allow_none:
return None
if not isinstance(value, str) or SHA256_PATTERN.fullmatch(value) is None:
raise ValueError(f"{label} must be a lowercase SHA-256 hex string")
return value
def _nonempty_string(value: Any, label: str) -> str:
if not isinstance(value, str) or not value:
raise ValueError(f"{label} must be a non-empty string")
return value
def percentile(values: list[float], fraction: float) -> float | None:
if not values:
return None
if not 0.0 <= fraction <= 1.0:
raise ValueError("percentile fraction must be within [0, 1]")
ordered = sorted(values)
index = (len(ordered) - 1) * fraction
lower = math.floor(index)
upper = math.ceil(index)
if lower == upper:
return float(ordered[lower])
return float(
ordered[lower] + (ordered[upper] - ordered[lower]) * (index - lower)
)
def _provenance_condition_signature(provenance: dict[str, Any]) -> dict[str, Any]:
environment = provenance["environment"]
evidence = provenance["execution_provider_evidence"]
warmup = provenance["warmup_excluded_per_session"]
semantics = provenance["session_start_semantics"]
return {
"environment": environment,
"execution_provider_evidence": evidence,
"warmup_excluded_per_session": warmup,
"session_start_semantics": semantics,
}
def _validate_row(row: dict[str, Any], line_no: int) -> None:
if not isinstance(row, dict):
raise TypeError(f"line {line_no}: sample must be a JSON object")
provenance = row.get("provenance")
if not isinstance(provenance, dict):
raise TypeError(f"line {line_no}: provenance object is required")
for field in REQUIRED_TOP_FIELDS:
if field not in row:
raise ValueError(f"line {line_no}: missing required field {field}")
for field in REQUIRED_PROVENANCE_FIELDS:
if field not in provenance:
raise ValueError(
f"line {line_no}: provenance missing required field {field}"
)
_nonempty_string(row["run_id"], f"line {line_no}.run_id")
_positive_integer(row["session_start"], f"line {line_no}.session_start")
if isinstance(row["repeat"], bool) or not isinstance(row["repeat"], int) or row["repeat"] < 0:
raise ValueError(f"line {line_no}.repeat must be a non-negative integer")
_nonempty_string(row["case_id"], f"line {line_no}.case_id")
if row["backend"] not in BACKENDS:
raise ValueError(f"line {line_no}.backend must be direct or diffusion")
if row["requested_provider"] not in PROVIDERS:
raise ValueError(
f"line {line_no}.requested_provider must be wasm or webgpu"
)
observed = row["observed_provider"]
if observed is not None and observed not in PROVIDERS:
raise ValueError(
f"line {line_no}.observed_provider must be wasm, webgpu, or null"
)
_sha256(row["bundle_hash"], f"line {line_no}.bundle_hash")
_sha256(row["tokenizer_hash"], f"line {line_no}.tokenizer_hash")
_sha256(row["input_hash"], f"line {line_no}.input_hash")
noise_hash = _sha256(
row.get("noise_hash"), f"line {line_no}.noise_hash", allow_none=True
)
if row["backend"] == "diffusion" and noise_hash is None:
raise ValueError(
f"line {line_no}.noise_hash is required for diffusion backend"
)
_positive_integer(row["candidate_count"], f"line {line_no}.candidate_count")
_positive_integer(
row["padded_option_slots"], f"line {line_no}.padded_option_slots"
)
_positive_integer(row["sequence_length"], f"line {line_no}.sequence_length")
if "seed" not in row:
raise ValueError(f"line {line_no}: missing required field seed")
seed_value = row["seed"]
if seed_value is None:
if row["backend"] != "direct" or noise_hash is not None:
raise ValueError(
f"line {line_no}.seed may only be null for direct backend with "
"noise_hash=null (deterministic-direct; seed is not_applicable); "
"diffusion or direct-with-noise require a non-negative integer seed"
)
else:
_nonnegative_integer(seed_value, f"line {line_no}.seed")
if row["candidate_count"] > row["padded_option_slots"]:
raise ValueError(
f"line {line_no}: candidate_count must be <= padded_option_slots"
)
if row["padded_option_slots"] > MAX_PADDED_OPTION_SLOTS:
raise ValueError(
f"line {line_no}: padded_option_slots must be <= {MAX_PADDED_OPTION_SLOTS}"
)
if row["sequence_length"] > MAX_SEQUENCE_LENGTH:
raise ValueError(
f"line {line_no}: sequence_length must be <= {MAX_SEQUENCE_LENGTH}"
)
_nonempty_string(row["ort_version"], f"line {line_no}.ort_version")
status = row["status"]
if status not in {"ok", "error", "timeout"}:
raise ValueError(
f"line {line_no}.status must be ok, error, or timeout"
)
error = row.get("error")
if status in {"error", "timeout"}:
if error is None:
pass
elif not isinstance(error, str) or not error:
raise ValueError(
f"line {line_no}.error must be a non-empty string for status {status}"
)
elif status == "ok" and error is not None:
raise ValueError(
f"line {line_no}.error must be null for successful samples"
)
for field in TIMING_FIELDS:
timing = _finite_number(
row.get(field), f"line {line_no}.{field}", allow_none=True
)
if status == "ok" and timing is None:
raise ValueError(
f"line {line_no}: successful sample requires {field}"
)
for field in HEAP_FIELDS:
_finite_number(row.get(field), f"line {line_no}.{field}", allow_none=True)
environment = provenance["environment"]
if not isinstance(environment, dict):
raise TypeError(f"line {line_no}.provenance.environment must be an object")
_nonempty_string(
provenance["execution_provider_evidence"],
f"line {line_no}.provenance.execution_provider_evidence",
)
_positive_integer(
provenance["warmup_excluded_per_session"],
f"line {line_no}.provenance.warmup_excluded_per_session",
)
_nonempty_string(
provenance["session_start_semantics"],
f"line {line_no}.provenance.session_start_semantics",
)
def _iter_jsonl(path: Path) -> Iterator[tuple[int, dict[str, Any]]]:
with path.open("r", encoding="utf-8") as handle:
line_no = 0
for raw in handle:
line_no += 1
stripped = raw.strip()
if not stripped:
continue
yield line_no, json.loads(stripped)
def _condition_tuple(row: dict[str, Any]) -> tuple[Any, ...]:
provenance_sig = _provenance_condition_signature(row["provenance"])
return (
tuple(row[key] for key in CONDITION_KEYS),
provenance_sig["warmup_excluded_per_session"],
provenance_sig["session_start_semantics"],
json.dumps(provenance_sig["environment"], sort_keys=True),
provenance_sig["execution_provider_evidence"],
)
def validate_and_aggregate(input_path: Path) -> dict[str, Any]:
if not input_path.exists():
raise FileNotFoundError(f"input file not found: {input_path}")
rows: list[dict[str, Any]] = []
seen_identities: set[tuple[str, int, int]] = set()
first_condition: tuple[Any, ...] | None = None
provenance_reference: dict[str, Any] | None = None
session_samples: dict[int, int] = {}
success_count = 0
failure_count = 0
total_rows = 0
stage_timings: dict[str, list[float]] = {field: [] for field in TIMING_FIELDS}
heap_values: dict[str, list[float]] = {field: [] for field in HEAP_FIELDS}
for line_no, row in _iter_jsonl(input_path):
_validate_row(row, line_no)
total_rows += 1
identity = (row["run_id"], row["session_start"], row["repeat"])
if identity in seen_identities:
raise ValueError(
f"duplicate (run_id, session_start, repeat) identity at line {line_no}: {identity}"
)
seen_identities.add(identity)
condition = _condition_tuple(row)
if first_condition is None:
first_condition = condition
provenance_reference = _provenance_condition_signature(
row["provenance"]
)
elif condition != first_condition:
raise ValueError(
f"line {line_no}: mixed experimental conditions detected; "
"bundle, tokenizer, shape, provider, runtime, environment, "
"and config provenance fields must be homogeneous across all rows"
)
session_start = row["session_start"]
session_samples[session_start] = session_samples.get(session_start, 0) + 1
status = row["status"]
if status == "ok":
success_count += 1
for field in TIMING_FIELDS:
stage_timings[field].append(float(row[field]))
for field in HEAP_FIELDS:
value = row.get(field)
if value is not None:
heap_values[field].append(float(value))
else:
failure_count += 1
rows.append(row)
if total_rows == 0:
raise ValueError("input JSONL contains no samples")
return _build_summary(
rows=rows,
input_path=input_path,
provenance_reference=provenance_reference,
first_condition=first_condition,
session_samples=session_samples,
success_count=success_count,
failure_count=failure_count,
total_rows=total_rows,
stage_timings=stage_timings,
heap_values=heap_values,
)
def _build_summary(
*,
rows: list[dict[str, Any]],
input_path: Path,
provenance_reference: dict[str, Any] | None,
first_condition: tuple[Any, ...] | None,
session_samples: dict[int, int],
success_count: int,
failure_count: int,
total_rows: int,
stage_timings: dict[str, list[float]],
heap_values: dict[str, list[float]],
) -> dict[str, Any]:
assert provenance_reference is not None
assert first_condition is not None
assert rows
first_row = rows[0]
unique_sessions = len(session_samples)
warmup_per_session = provenance_reference["warmup_excluded_per_session"]
timed_requests_total = success_count + failure_count
complete = (
unique_sessions >= 5
and warmup_per_session >= 10
and timed_requests_total >= 100
)
incomplete_reasons: list[str] = []
if unique_sessions < 5:
incomplete_reasons.append(
f"only {unique_sessions} independent session starts observed (>= 5 required)"
)
if warmup_per_session < 10:
incomplete_reasons.append(
f"warmup_excluded_per_session={warmup_per_session} (>= 10 required)"
)
if timed_requests_total < 100:
incomplete_reasons.append(
f"only {timed_requests_total} timed requests total (>= 100 required)"
)
process_cold_claimable = False
requested_provider = first_row["requested_provider"]
observed_provider = first_row["observed_provider"]
requested_is_proven = False
summary_timings: dict[str, dict[str, float | None]] = {}
for field in TIMING_FIELDS:
values = stage_timings[field]
summary_timings[field] = {
"p50": percentile(values, 0.5),
"p95": percentile(values, 0.95),
}
heap_maxima: dict[str, float | None] = {}
for field in HEAP_FIELDS:
values = heap_values[field]
heap_maxima[field] = max(values) if values else None
per_session_samples: dict[str, int] = {
str(session_start): count
for session_start, count in sorted(session_samples.items())
}
summary: dict[str, Any] = {
"schema": "vons.browser-benchmark-summary/v1",
"generated_from": str(input_path),
"input_sha256": _file_sha256(input_path),
"experimental_condition": {
"run_id": first_row["run_id"],
"backend": first_row["backend"],
"requested_provider": requested_provider,
"observed_provider": observed_provider,
"bundle_hash": first_row["bundle_hash"],
"tokenizer_hash": first_row["tokenizer_hash"],
"candidate_count": first_row["candidate_count"],
"padded_option_slots": first_row["padded_option_slots"],
"sequence_length": first_row["sequence_length"],
"seed": first_row["seed"],
"ort_version": first_row["ort_version"],
"provenance": provenance_reference,
},
"provider_claim_note": (
"requested_provider is not represented as proven in this v1 summary. "
"observed_provider preserves the browser-reported value and "
"execution_provider_evidence preserves the raw evidence string, "
"but no structured verified execution artifact exists to elevate "
"a match to a proven claim; requested_is_proven is always False in v1."
),
"process_cold_note": (
"process-level cold start identity is not verifiable from page-only "
"browser observations in this v1 schema; session_start_semantics is "
"preserved as a raw label but process_cold_claimable is always False "
"regardless of semantics string content."
),
"sample_counts": {
"total_rows": total_rows,
"successful": success_count,
"failed": failure_count,
"unique_session_starts": unique_sessions,
"per_session_samples": per_session_samples,
},
"stage_percentiles_ms": summary_timings,
"heap_maxima_bytes": heap_maxima,
"measurement_completeness": {
"complete": complete,
"required_min_session_starts": 5,
"required_min_warmup_excluded_per_session": 10,
"required_min_timed_requests_total": 100,
"actual_session_starts": unique_sessions,
"actual_warmup_excluded_per_session": warmup_per_session,
"actual_timed_requests_total": timed_requests_total,
"incomplete_reasons": incomplete_reasons,
},
"process_cold_claimable": bool(process_cold_claimable),
"requested_is_proven": bool(requested_is_proven),
}
return summary
def _iter_report_samples(
report: dict[str, Any], report_path: Path
) -> tuple[dict[str, Any], list[dict[str, Any]]]:
"""Return (top_provenance, normalized_rows) from a v1 browser-benchmark report.
Normalizes current report field names to the JSONL schema used by the
validator: live_candidates -> candidate_count, allocated_candidates ->
padded_option_slots, sequence_length preferred with sequence_tokens as
fallback alias. Never invents missing values. Raises ValueError on any
required field that cannot be resolved.
"""
if not isinstance(report, dict):
raise TypeError("browser benchmark report must be a JSON object")
if report.get("schema") != "vons.browser-benchmark/v1":
raise ValueError("unexpected browser benchmark schema")
_nonempty_string(report.get("run_id"), f"{report_path.name}.run_id")
backend = report.get("backend")
if backend not in BACKENDS:
raise ValueError(f"{report_path.name}.backend must be direct or diffusion")
requested_provider = report.get("requested_provider")
if requested_provider not in PROVIDERS:
raise ValueError(
f"{report_path.name}.requested_provider must be wasm or webgpu"
)
environment = report.get("environment")
if not isinstance(environment, dict):
raise TypeError(
f"{report_path.name}.environment is required as provenance input"
)
execution_provider_evidence = _nonempty_string(
report.get("execution_provider_evidence"),
f"{report_path.name}.execution_provider_evidence",
)
warmup_excluded_per_session = _positive_integer(
report.get("warmup_excluded_per_session"),
f"{report_path.name}.warmup_excluded_per_session",
)
session_start_semantics_raw = report.get("session_start_semantics")
if session_start_semantics_raw is None:
raise ValueError(
f"{report_path.name}.session_start_semantics is required to build "
"per-row provenance; do not invent missing values."
)
session_start_semantics = _nonempty_string(
session_start_semantics_raw,
f"{report_path.name}.session_start_semantics",
)
top_provenance = {
"environment": environment,
"execution_provider_evidence": execution_provider_evidence,
"warmup_excluded_per_session": warmup_excluded_per_session,
"session_start_semantics": session_start_semantics,
}
samples = report.get("samples")
if not isinstance(samples, list):
raise TypeError(f"{report_path.name}.samples must be a list")
if not samples:
raise ValueError(f"{report_path.name}.samples contains no rows")
ort_version = report.get("ort_version")
ort_version_is_str = isinstance(ort_version, str) and bool(ort_version)
if not ort_version_is_str:
runtime_info = report.get("runtime_info")
if isinstance(runtime_info, dict):
rt_ort = runtime_info.get("ortVersion")
if isinstance(rt_ort, str) and rt_ort:
ort_version = rt_ort
ort_version_is_str = True
normalized: list[dict[str, Any]] = []
for idx, sample in enumerate(samples, start=1):
if not isinstance(sample, dict):
raise TypeError(f"{report_path.name} sample {idx} must be an object")
live_candidates = sample.get("live_candidates")
if live_candidates is None:
raise ValueError(
f"{report_path.name} sample {idx} is missing live_candidates "
"(source for candidate_count normalization)"
)
allocated_candidates = sample.get("allocated_candidates")
if allocated_candidates is None:
raise ValueError(
f"{report_path.name} sample {idx} is missing allocated_candidates "
"(source for padded_option_slots normalization)"
)
sequence_length = sample.get("sequence_length")
if sequence_length is None:
sequence_length = sample.get("sequence_tokens")
if sequence_length is None:
raise ValueError(
f"{report_path.name} sample {idx} is missing sequence_length "
"(sequence_tokens not present as alias fallback)"
)
sample_ort_version = sample.get("ort_version")
if not (isinstance(sample_ort_version, str) and sample_ort_version):
if ort_version_is_str:
sample_ort_version = ort_version
else:
raise ValueError(
f"{report_path.name} sample {idx} is missing ort_version and "
"report does not provide a top-level ort_version fallback "
"(checked both ort_version and runtime_info.ortVersion)"
)
sample_backend = sample.get("backend")
noise_hash = sample.get("noise_hash")
sample_seed = sample.get("seed")
if sample_seed is None and sample_backend == "direct" and noise_hash is None:
resolved_seed = None
else:
if "seed" not in sample:
raise ValueError(
f"{report_path.name} sample {idx} is missing seed; "
"null seed is only permitted for direct backend with "
"noise_hash=null (deterministic-direct, not_applicable)"
)
resolved_seed = sample_seed
error_value = sample.get("error")
observed_provider = sample.get("observed_provider")
mapped: dict[str, Any] = {
"run_id": sample["run_id"],
"session_start": sample["session_start"],
"repeat": sample["repeat"],
"case_id": sample["case_id"],
"backend": sample_backend,
"requested_provider": sample["requested_provider"],
"observed_provider": observed_provider,
"bundle_hash": sample["bundle_hash"],
"tokenizer_hash": sample["tokenizer_hash"],
"input_hash": sample["input_hash"],
"noise_hash": noise_hash,
"candidate_count": live_candidates,
"padded_option_slots": allocated_candidates,
"sequence_length": sequence_length,
"seed": resolved_seed,
"ort_version": sample_ort_version,
"status": sample["status"],
"error": error_value,
"provenance": top_provenance,
}
for field in TIMING_FIELDS:
mapped[field] = sample.get(field)
for field in HEAP_FIELDS:
if field in sample:
mapped[field] = sample.get(field)
normalized.append(mapped)
return top_provenance, normalized
def validate_and_aggregate_report(report_path: Path) -> dict[str, Any]:
"""Entry point for --report-input: loads an existing v1 browser-benchmark
JSON report, normalizes its samples to the JSONL schema, then runs the
same validation and aggregation used by validate_and_aggregate.
"""
if not report_path.exists():
raise FileNotFoundError(f"report file not found: {report_path}")
report = json.loads(report_path.read_text(encoding="utf-8"))
provenance_reference, normalized_rows = _iter_report_samples(report, report_path)
total_rows = 0
seen_identities: set[tuple[str, int, int]] = set()
first_condition: tuple[Any, ...] | None = None
session_samples: dict[int, int] = {}
success_count = 0
failure_count = 0
stage_timings: dict[str, list[float]] = {field: [] for field in TIMING_FIELDS}
heap_values: dict[str, list[float]] = {field: [] for field in HEAP_FIELDS}
accepted_rows: list[dict[str, Any]] = []
for line_no, row in enumerate(normalized_rows, start=1):
_validate_row(row, line_no)
total_rows += 1
identity = (row["run_id"], row["session_start"], row["repeat"])
if identity in seen_identities:
raise ValueError(
f"duplicate (run_id, session_start, repeat) identity in sample {line_no}: {identity}"
)
seen_identities.add(identity)
condition = _condition_tuple(row)
if first_condition is None:
first_condition = condition
elif condition != first_condition:
raise ValueError(
f"sample {line_no}: mixed experimental conditions detected in report"
)
session_start = row["session_start"]
session_samples[session_start] = session_samples.get(session_start, 0) + 1
if row["status"] == "ok":
success_count += 1
for field in TIMING_FIELDS:
stage_timings[field].append(float(row[field]))
for field in HEAP_FIELDS:
value = row.get(field)
if value is not None:
heap_values[field].append(float(value))
else:
failure_count += 1
accepted_rows.append(row)
if total_rows == 0:
raise ValueError("report samples yielded no rows after normalization")
return _build_summary(
rows=accepted_rows,
input_path=report_path,
provenance_reference=provenance_reference,
first_condition=first_condition,
session_samples=session_samples,
success_count=success_count,
failure_count=failure_count,
total_rows=total_rows,
stage_timings=stage_timings,
heap_values=heap_values,
)
def _file_sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for chunk in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument("--input", type=Path,
help="JSONL file of raw browser benchmark samples")
group.add_argument("--report-input", type=Path,
help="Existing vons.browser-benchmark/v1 JSON report; "
"normalizes live_candidates/allocated_candidates/"
"sequence_tokens to JSONL schema and carries top-level provenance")
parser.add_argument("--output", type=Path, required=True,
help="destination path for the summary JSON")
args = parser.parse_args()
if args.input is not None:
summary = validate_and_aggregate(args.input)
else:
summary = validate_and_aggregate_report(args.report_input)
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(
json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8"
)
if __name__ == "__main__":
main()
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