957 lines
42 KiB
Python
957 lines
42 KiB
Python
"""EvalHarness CLI. Zero third-party deps beyond the data layer (pydantic)."""
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import argparse
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import os
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import time
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import json
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import sys
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from concurrent.futures import ThreadPoolExecutor, as_completed
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def _overrides(args):
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"""Optional DatasetSpec field overrides shared by fetch/stats/show."""
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if getattr(args, 'hf_endpoint', None):
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os.environ['HF_ENDPOINT'] = args.hf_endpoint
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if getattr(args, 'cache_dir', None):
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from evalharness.data.dataset import set_cache_root
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set_cache_root(args.cache_dir)
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ov = {}
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for k in ('source', 'split', 'subset'):
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v = getattr(args, k, None)
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if v is not None:
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ov[k] = v
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return ov
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def _cmd_data_list(_args) -> int:
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from evalharness.data import list_datasets
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specs = list_datasets()
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if not specs:
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print('no datasets registered')
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return 0
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name_w = max(len(s.name) for s in specs)
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type_w = max(len(s.task_type) for s in specs)
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for s in specs:
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print(f'{s.name:<{name_w}} {s.task_type:<{type_w}} {s.source} {s.description}')
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print(f'\n{len(specs)} dataset(s) registered')
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return 0
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def _fetch_one(name: str, force: bool, overrides) -> str:
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from evalharness.data import get_dataset
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ds = get_dataset(name, **overrides)
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ds.materialize(force=force)
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origin = 'cache' if ds.lineage.get('from') == 'cache' else 'source'
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return f'{name}: {len(ds)} sample(s) [{origin}] -> {ds.cache_dir}'
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def _cmd_data_fetch(args) -> int:
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names = args.names
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if len(names) == 1:
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print(_fetch_one(names[0], args.force, _overrides(args)))
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return 0
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# Concurrent prefetch: downloads are I/O-bound, threads suffice.
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# Per-dataset file locks inside materialize() guard shared cache entries.
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ok = True
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with ThreadPoolExecutor(max_workers=args.workers) as pool:
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futures = {pool.submit(_fetch_one, n, args.force, _overrides(args)): n for n in names}
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for fut in as_completed(futures):
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try:
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print(fut.result())
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except Exception as e: # one failure must not block the rest
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ok = False
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print(f'{futures[fut]}: FAILED ({e})', file=sys.stderr)
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return 0 if ok else 1
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def _cmd_data_stats(args) -> int:
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from evalharness.data import get_dataset
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stats = get_dataset(args.name, **_overrides(args)).stats()
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print(json.dumps(stats, ensure_ascii=False, indent=2))
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return 0
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def _cmd_data_show(args) -> int:
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from evalharness.data import get_dataset
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ds = get_dataset(args.name, **_overrides(args))
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for s in ds[: args.n]:
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print(json.dumps(s.model_dump(), ensure_ascii=False, indent=2))
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print('---')
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return 0
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def _cmd_data_unload(args) -> int:
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from evalharness.data import get_dataset
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for name in args.names:
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ds = get_dataset(name, **_overrides(args))
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removed = ds.unload()
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print(f'{name}: cache {"removed" if removed else "not present (nothing to do)"} -> {ds.cache_dir}')
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return 0
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def _cmd_sandbox_prefetch(args) -> int:
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from evalharness.data import get_dataset
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from evalharness.sandbox import docker_available, images_for_dataset, prefetch_images
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if not docker_available():
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print('docker is not available on this host', file=sys.stderr)
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return 1
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ds = get_dataset(args.dataset, **_overrides(args))
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images = images_for_dataset(ds, limit=args.limit)
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if not images:
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print(f'{args.dataset}: no sandbox images declared by its samples')
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return 0
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prefetch_images(images, workers=args.workers)
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return 0
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def _add_override_flags(p: argparse.ArgumentParser) -> None:
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p.add_argument('--hf-endpoint', default='',
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help='HuggingFace endpoint override, e.g. https://hf-mirror.com '
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'(sets HF_ENDPOINT before any dataset download)')
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p.add_argument('--source', help='override DatasetSpec.source (e.g. a local dir)')
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p.add_argument('--split', help='override DatasetSpec.split')
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p.add_argument('--subset', help='override DatasetSpec.subset')
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p.add_argument('--cache-dir', help='cache root (default: $EVALHARNESS_CACHE or ~/.cache/evalharness)')
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def _cmd_eval_list(_args) -> int:
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from evalharness.eval import list_evals
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names = list_evals()
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print('\n'.join(names) if names else 'no eval recipes registered')
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print(f'\n{len(names)} eval recipe(s) registered')
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return 0
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def _print_run_progress(done, total, name='', status='running', started=None):
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"""Print one live progress line for a multi-benchmark run."""
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import time
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width = 28
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filled = int(width * done / max(total, 1))
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bar = '#' * filled + '-' * (width - filled)
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elapsed = time.time() - started if started else 0
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label = f'{done}/{total} [{bar}] {status}: {name}'
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print(f'\r{label} ({elapsed:.0f}s)', end='\n' if done >= total else '', flush=True)
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def _rich_console():
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"""Return a Rich console when installed; keep the CLI dependency-free."""
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try:
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from rich.console import Console
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return Console()
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except ImportError:
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return None
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def _print_run_plan(console, args, model_spec):
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"""Print the important run facts before any dataset work starts."""
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title = 'EvalHarness · Run Plan'
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provider = getattr(args, 'provider', 'openai-chat') if model_spec else '—'
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api_url = getattr(args, 'api_url', '') or '—'
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model_name = getattr(args, 'model', '') or 'predictions file'
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# sampling summary: what the run will actually evaluate
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if getattr(args, 'limit', None):
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samples = f'up to {args.limit} total (--limit)'
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elif getattr(args, 'limit_per_task', None):
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samples = f'up to {args.limit_per_task} per subject (--limit-per-task)'
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else:
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samples = 'full dataset (counted when each loads)'
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if console is None:
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print(f'=== {title} ===')
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print(f'Provider: {provider}')
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print(f'API URL: {api_url}')
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print(f'Model: {model_name}')
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print(f'Benchmarks: {len(args.datasets)} -> {", ".join(args.datasets)}')
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print(f'Samples: {samples}')
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print(f'Concurrency: {args.concurrency} | Thinking: '
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f'{"enabled" if not args.disable_thinking else "disabled"} | '
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f'Performance: {"on" if args.perf else "off"}')
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print(f'Resume: {"on" if args.resume else "off"} | Output: {args.out_dir or "(none)"}')
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return
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from rich.panel import Panel
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from rich.table import Table
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table = Table(show_header=False, box=None, padding=(0, 1))
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table.add_column('Item', style='cyan', no_wrap=True)
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table.add_column('Value', style='white')
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table.add_row('Provider', provider)
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table.add_row('API URL', api_url)
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table.add_row('Model', model_name)
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table.add_row('Benchmarks', f'{len(args.datasets)} · {", ".join(args.datasets)}')
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table.add_row('Samples', samples)
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table.add_row('Concurrency', str(args.concurrency))
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table.add_row('Thinking', '[red]disabled[/red]' if args.disable_thinking else '[green]enabled[/green]')
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table.add_row('Performance', '[green]enabled[/green]' if args.perf else '[dim]disabled[/dim]')
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table.add_row('Checkpoint', '[green]resume[/green]' if args.resume else '[dim]new run[/dim]')
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table.add_row('Output', args.out_dir or '[dim](not specified)[/dim]')
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console.print(Panel(table, title=title, border_style='blue', expand=False),
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justify='center')
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def _narration(msg: str) -> str:
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"""Icon + path-highlighting for narration lines (visual separation at
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a glance; paths in cyan)."""
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icon = ''
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m = msg.lower()
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if m.startswith('loading/'):
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icon = '⬇ '
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elif 'dataset ready' in m or m.startswith('dataset ready'):
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icon = '📦 '
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elif 'few-shot' in m:
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icon = '✳ '
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elif 'checkpoint' in m or m.endswith('samples') or 'to generate' in m:
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icon = '◷ '
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elif 'generation skipped' in m:
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icon = '⏭ '
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elif 'generating' in m:
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icon = '🤖 '
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elif 'generation complete' in m:
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icon = '✓ '
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elif 'scoring' in m:
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icon = '★ '
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elif 'writing' in m:
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icon = '📝 '
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elif 'endpoint ok' in m:
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icon = '🔗 '
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# THE key fact: the score in 'scoring complete · <metric> <pct>%'
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# gets bold green -- it is what the eye should find first
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import re as _re1
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m = _re1.search(r'[:·] ([a-zA-Z_@]+ [0-9.]+%)$', msg)
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if m:
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return (f'{icon}{msg[:m.start()]}'
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f'[bold green]{m.group(1)}[/bold green]')
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# semantic PHRASES get color, not bare numbers: '164 samples from cache',
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# '4/4 predictions' -- a lone number is easy to lose. Runs AFTER the
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# score rule so scores keep their solid green.
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import re as _re0
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msg = _re0.sub(r'(?<![\w/%.])(\d+(?:/\d+)?(?:\s+[a-z-]+){0,4})(?=[\s,.]|$)',
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r'[bold yellow]\1[/bold yellow]', msg)
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# highlight any trailing path after '->'
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if '-> ' in msg:
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head, _, tail = msg.partition('-> ')
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return f'{icon}{head}-> [cyan]{tail}[/cyan]'
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return f'{icon}{msg}'
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def _phase_color(message: str) -> str:
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"""Narration line color. Currently PLAIN WHITE for everything (user
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preference); flip the returns to 'yellow'/'green' to restore the
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start/finish coloring scheme."""
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return ''
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m = message.lower() # unreachable, kept for quick restore
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if any(k in m for k in ('complete', 'ready', 'downloaded', 'parsed',
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'restored', 'ok (')):
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return 'green'
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return 'yellow'
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def _print_phase(console, index, total, name, message):
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# single-benchmark runs: the [1/1] tag is noise, drop it
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prefix = f'[{index}/{total}] ' if total > 1 else ''
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text = f'{prefix}{name}: {_narration(message)}'
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color = _phase_color(message)
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if console is not None:
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if color:
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console.print(f'[{color}]{text}[/{color}]', highlight=False)
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else:
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console.print(text, highlight=False)
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else:
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import re as _re0
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print(_re0.sub(r'\[/?[a-z ]+\]', '', text), flush=True)
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def _print_benchmark_result(console, index, total, name, status, elapsed):
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if console is not None:
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color = 'green' if status == 'done' else 'red'
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icon = '✓' if status == 'done' else '✗'
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console.print(f'[{color}]{icon}[/{color}] benchmark {index}/{total} '
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f'{name} · {status} · elapsed={elapsed:.0f}s')
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else:
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_print_run_progress(index, total, name, status, time.time() - elapsed)
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def _model_with_flags(model, args):
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"""Translate explicit CLI flags to the adapter's internal options."""
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for enabled, flag in ((getattr(args, 'disable_thinking', False), '!nothink'),
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(getattr(args, 'perf', False), '!perf'),
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(getattr(args, 'textools', False), '!textools')):
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if enabled and not model.endswith(flag):
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model += flag
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return model
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def _compose_model_spec(args):
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"""Build the internal model spec from separate provider fields."""
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model = args.model or ''
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api_url = getattr(args, 'api_url', '') or ''
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provider = getattr(args, 'provider', 'openai-chat') or 'openai-chat'
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# openai-chat is the public name; the current client implementation
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# remains registered as openai internally.
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internal_provider = {'openai-chat': 'openai',
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'openai-pool': 'openai-pool'}.get(provider, provider)
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if api_url:
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if not model:
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raise SystemExit('error: --api-url requires --model (model name)')
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model = f'{internal_provider}/{api_url.rstrip("/")}?{model}'
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return _model_with_flags(model, args)
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def _print_result_panel(console, rep, wall_s: float = 0.0):
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"""Rich result panel: metrics + throughput + health + top/bottom groups."""
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if console is None:
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return False
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from rich.panel import Panel
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from rich.table import Table
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from rich.text import Text
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def color(v):
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return 'green' if v >= 0.8 else ('yellow' if v >= 0.6 else 'red')
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metrics = [(m, v) for m, v in rep.metrics.items()
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if isinstance(v, (int, float))]
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info = rep.metric_groups.get('run_info', {}) or {}
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model_lat = sum(float((s.usage or {}).get('latency_s', 0) or 0)
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for s in rep.samples)
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tok_in = info.get('gen_input_tokens', 0) or 0
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tok_out = info.get('gen_output_tokens', 0) or 0
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tput = rep.num_samples / wall_s if wall_s > 0.5 else 0.0
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tps = tok_out / wall_s if (wall_s > 0.5 and tok_out) else 0.0
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import re as _re
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m = str(rep.model or '?').split('?')[-1]
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m = _re.sub(r'![a-z]+$', '', m).strip('/')
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m = m.rsplit('/', 1)[-1] if '/' in m else m
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title = f'{rep.dataset} · {m}'
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body = Table(show_header=False, box=None, padding=(0, 2))
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body.add_column('k', style='dim', no_wrap=True)
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body.add_column('v', overflow='fold')
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def score_bar(v, width=22):
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# rich-Bar-style glyphs with half-cell precision: ━━━╸╌╌
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filled = max(0.0, min(1.0, v)) * width
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full = int(filled)
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half = '╸' if filled - full >= 0.5 else ''
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return '━' * full + half + '╌' * (width - full - len(half))
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for m, v in metrics:
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if m == 'extraction_failure_rate':
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continue
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body.add_row(m, Text.assemble(
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(f'{v * 100:6.1f}% ', f'bold {color(v)}'),
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(score_bar(v), color(v))))
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stats = []
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stats.append(f'n={rep.num_samples}')
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if wall_s >= 1:
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stats.append(f'wall={wall_s:.0f}s')
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if model_lat >= 1:
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stats.append(f'model-time={model_lat:.0f}s')
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if tput:
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stats.append(f'{tput:.1f} samples/s')
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if tok_in or tok_out:
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stats.append(f'tokens in={tok_in:,} out={tok_out:,}')
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if tps:
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stats.append(f'{tps:.0f} tok/s out')
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if rep.num_failed_extractions:
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stats.append(f'[red]extract-fail={rep.num_failed_extractions}[/red]')
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body.add_row('run', ' '.join(stats))
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for gname, groups in rep.metric_groups.items():
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if gname in ('run_info', 'perf') or gname.startswith('agg_error'):
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continue
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numeric = {g: v for g, v in groups.items() if isinstance(v, (int, float))}
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if len(numeric) < 2:
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continue
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rank = sorted(numeric.items(), key=lambda kv: -kv[1])
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show = [f'[green]{g} {v * 100:.0f}%[/green]' for g, v in rank[:3]]
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show += [f'[red]{g} {v * 100:.0f}%[/red]' for g, v in rank[-2:]]
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body.add_row(gname, ' '.join(show) + f' (+{len(numeric) - 5} more)'
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if len(numeric) > 5 else ' '.join(show))
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perf = rep.metric_groups.get('perf') or {}
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if perf:
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keys = ('ttft_mean_s', 'ttft_p90_s', 'latency_mean_s', 'latency_p90_s',
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'output_tps', 'success_rate', 'retry_rate')
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pstats = [f'{k}={perf[k]:.2f}' if isinstance(perf.get(k), float) and perf[k] < 10
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else f'{k}={perf[k]}' for k in keys if perf.get(k) is not None]
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if pstats:
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body.add_row('perf', ' '.join(pstats))
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console.print(Panel(body, title=title, border_style='blue', expand=False),
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justify='center')
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return True
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def _compose_judge_spec(args):
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"""--judge accepts a bare model name (with --judge-api-url) or a full
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legacy spec; keep both working like the main model flags."""
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judge = getattr(args, 'judge', '') or ''
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url = getattr(args, 'judge_api_url', '') or ''
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provider = getattr(args, 'judge_provider', 'openai-chat') or 'openai-chat'
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internal = {'openai-chat': 'openai', 'openai-pool': 'openai-pool'}.get(provider, provider)
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if url and judge and '/' not in judge:
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judge = f'{internal}/{url.rstrip("/")}?{judge}'
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return judge or None
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def _print_result_panel(console, rep, wall_s: float = 0.0):
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"""Rich result panel: metrics + throughput + health + top/bottom groups."""
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if console is None:
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return False
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from rich.panel import Panel
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from rich.table import Table
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from rich.text import Text
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def color(v):
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return 'green' if v >= 0.8 else ('yellow' if v >= 0.6 else 'red')
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metrics = [(m, v) for m, v in rep.metrics.items()
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if isinstance(v, (int, float))]
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info = rep.metric_groups.get('run_info', {}) or {}
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model_lat = sum(float((s.usage or {}).get('latency_s', 0) or 0)
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for s in rep.samples)
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tok_in = info.get('gen_input_tokens', 0) or 0
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tok_out = info.get('gen_output_tokens', 0) or 0
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tput = rep.num_samples / wall_s if wall_s > 0.5 else 0.0
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tps = tok_out / wall_s if (wall_s > 0.5 and tok_out) else 0.0
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import re as _re
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m = str(rep.model or '?').split('?')[-1]
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m = _re.sub(r'![a-z]+$', '', m).strip('/')
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m = m.rsplit('/', 1)[-1] if '/' in m else m
|
|
title = f'{rep.dataset} · {m}'
|
|
body = Table(show_header=False, box=None, padding=(0, 2))
|
|
body.add_column('k', style='dim', no_wrap=True)
|
|
body.add_column('v', overflow='fold')
|
|
def score_bar(v, width=22):
|
|
# rich-Bar-style glyphs with half-cell precision: ━━━╸╌╌
|
|
filled = max(0.0, min(1.0, v)) * width
|
|
full = int(filled)
|
|
half = '╸' if filled - full >= 0.5 else ''
|
|
return '━' * full + half + '╌' * (width - full - len(half))
|
|
|
|
for m, v in metrics:
|
|
if m == 'extraction_failure_rate':
|
|
continue
|
|
body.add_row(m, Text.assemble(
|
|
(f'{v * 100:6.1f}% ', f'bold {color(v)}'),
|
|
(score_bar(v), color(v))))
|
|
stats = []
|
|
stats.append(f'n={rep.num_samples}')
|
|
if wall_s >= 1:
|
|
stats.append(f'wall={wall_s:.0f}s')
|
|
if model_lat >= 1:
|
|
stats.append(f'model-time={model_lat:.0f}s')
|
|
if tput:
|
|
stats.append(f'{tput:.1f} samples/s')
|
|
if tok_in or tok_out:
|
|
stats.append(f'tokens in={tok_in:,} out={tok_out:,}')
|
|
if tps:
|
|
stats.append(f'{tps:.0f} tok/s out')
|
|
if rep.num_failed_extractions:
|
|
stats.append(f'[red]extract-fail={rep.num_failed_extractions}[/red]')
|
|
body.add_row('run', ' '.join(stats))
|
|
for gname, groups in rep.metric_groups.items():
|
|
if gname in ('run_info', 'perf') or gname.startswith('agg_error'):
|
|
continue
|
|
numeric = {g: v for g, v in groups.items() if isinstance(v, (int, float))}
|
|
if len(numeric) < 2:
|
|
continue
|
|
rank = sorted(numeric.items(), key=lambda kv: -kv[1])
|
|
show = [f'[green]{g} {v * 100:.0f}%[/green]' for g, v in rank[:3]]
|
|
show += [f'[red]{g} {v * 100:.0f}%[/red]' for g, v in rank[-2:]]
|
|
body.add_row(gname, ' '.join(show) + f' (+{len(numeric) - 5} more)'
|
|
if len(numeric) > 5 else ' '.join(show))
|
|
perf = rep.metric_groups.get('perf') or {}
|
|
if perf:
|
|
keys = ('ttft_mean_s', 'ttft_p90_s', 'latency_mean_s', 'latency_p90_s',
|
|
'output_tps', 'success_rate', 'retry_rate')
|
|
pstats = [f'{k}={perf[k]:.2f}' if isinstance(perf.get(k), float) and perf[k] < 10
|
|
else f'{k}={perf[k]}' for k in keys if perf.get(k) is not None]
|
|
if pstats:
|
|
body.add_row('perf', ' '.join(pstats))
|
|
console.print(Panel(body, title=title, border_style='blue', expand=False),
|
|
justify='center')
|
|
return True
|
|
|
|
def _compose_judge_spec(args):
|
|
"""--judge accepts a bare model name (with --judge-api-url) or a full
|
|
legacy spec; both keep working, mirroring the main model flags."""
|
|
judge = getattr(args, 'judge', '') or ''
|
|
url = getattr(args, 'judge_api_url', '') or ''
|
|
provider = getattr(args, 'judge_provider', 'openai-chat') or 'openai-chat'
|
|
internal = {'openai-chat': 'openai', 'openai-pool': 'openai-pool'}.get(provider, provider)
|
|
if url and judge and '/' not in judge:
|
|
judge = f'{internal}/{url.rstrip("/")}?{judge}'
|
|
return judge or None
|
|
|
|
|
|
def _cmd_eval_run(args) -> int:
|
|
import asyncio
|
|
import time as _time
|
|
|
|
from evalharness.data import get_dataset
|
|
from evalharness.viz import render
|
|
|
|
comma = [d for d in getattr(args, 'datasets', []) if ',' in d]
|
|
if comma:
|
|
tip = ', '.join(comma)
|
|
fixed = ' '.join(tip.split(','))
|
|
raise SystemExit(
|
|
f'error: benchmark names must be SPACE-separated.\n'
|
|
f' got: evalharness eval run {tip}\n'
|
|
f' expected: evalharness eval run {fixed}')
|
|
overrides = _overrides(args)
|
|
out_dir = args.out_dir
|
|
if out_dir:
|
|
from pathlib import Path
|
|
|
|
Path(out_dir).mkdir(parents=True, exist_ok=True)
|
|
|
|
rows = []
|
|
all_reports = []
|
|
run_started = _time.time()
|
|
total_runs = len(args.datasets)
|
|
model_spec = _compose_model_spec(args)
|
|
if not out_dir and model_spec and not args.out:
|
|
# always persist results: default dir = evalharness-results/<stamp>-<model>/
|
|
import re as _re
|
|
|
|
stamp = _time.strftime('%Y%m%d-%H%M%S')
|
|
tag = _re.sub(r'[^A-Za-z0-9._-]+', '-', args.model or 'run')[:40].strip('-')
|
|
out_dir = f'evalharness-results/{stamp}-{tag}'
|
|
from pathlib import Path
|
|
|
|
Path(out_dir).mkdir(parents=True, exist_ok=True)
|
|
console = _rich_console()
|
|
_print_run_plan(console, args, model_spec)
|
|
_shared_reporter = None
|
|
for i, name in enumerate(args.datasets):
|
|
t0 = _time.time()
|
|
try:
|
|
if _shared_reporter is not None and total_runs > 1:
|
|
_shared_reporter.set_bench_tag(f'[{i + 1}/{total_runs}]')
|
|
|
|
def _emit(msg, _i=i, _n=name):
|
|
if _shared_reporter is not None:
|
|
_shared_reporter.log(
|
|
f'[{_i + 1}/{total_runs}] {_n}: {_narration(msg)}')
|
|
else:
|
|
_print_phase(console, _i + 1, total_runs, _n, msg)
|
|
|
|
_emit('Loading dataset (downloads on first use, cached afterwards)')
|
|
ds = get_dataset(name, **overrides)
|
|
if _shared_reporter is not None:
|
|
_shared_reporter.pause() # let hub tqdm print cleanly
|
|
sample_count = len(ds)
|
|
if _shared_reporter is not None:
|
|
_shared_reporter.resume()
|
|
origin = ds.lineage.get('from', 'unknown')
|
|
_emit(f'Dataset ready: {sample_count} samples from {origin}')
|
|
if model_spec: # generate + score in one go
|
|
from evalharness.model import run_eval
|
|
|
|
progress_reporter = None
|
|
if args.progress:
|
|
from evalharness.progress import RichTerminalProgress
|
|
|
|
if RichTerminalProgress is not None:
|
|
# share ONE console: phase lines printed by another
|
|
# writer during the live bar interleave incorrectly.
|
|
# One reporter for the WHOLE run: overall bar (which
|
|
# benchmark) + sample bar (which sample), reused per
|
|
# benchmark via reset_samples().
|
|
if _shared_reporter is None and console.is_terminal:
|
|
# live bars only on a real terminal: through pipes
|
|
# (| grep, > log) rich's refresh thread misbehaves
|
|
# and stalls the run -- plain phases instead
|
|
_shared_reporter = RichTerminalProgress(console=console)
|
|
_shared_reporter.owned_externally = True
|
|
progress_reporter = _shared_reporter
|
|
|
|
|
|
def status_callback(msg, _idx=i + 1, _name=name,
|
|
_reporter=progress_reporter,
|
|
_console=console):
|
|
_tag = f'[{_idx}/{total_runs}] ' if total_runs > 1 else ''
|
|
if _reporter is not None:
|
|
_reporter.log(f'{_tag}{_name}: {_narration(msg)}')
|
|
if 'scoring' in msg:
|
|
_reporter.set_phase('scoring')
|
|
elif 'generating model responses' in msg:
|
|
_reporter.set_phase('generating')
|
|
elif 'writing' in msg:
|
|
_reporter.set_phase('writing')
|
|
else:
|
|
_print_phase(_console, _idx, total_runs, _name, msg)
|
|
report = asyncio.run(run_eval(
|
|
ds, model_spec, concurrency=args.concurrency, limit=args.limit,
|
|
limit_per_task=args.limit_per_task,
|
|
checkpoint=args.resume,
|
|
judge_spec=_compose_judge_spec(args), env=args.env,
|
|
api_key=getattr(args, 'api_key', ''),
|
|
judge_api_key=getattr(args, 'judge_api_key', ''),
|
|
gen_profile=getattr(args, 'profile', ''),
|
|
progress_reporter=progress_reporter,
|
|
status_callback=status_callback))
|
|
else:
|
|
from evalharness.eval import evaluate
|
|
|
|
if not args.predictions:
|
|
raise SystemExit('error: provide --model or a predictions file')
|
|
_print_phase(console, i + 1, total_runs, name, 'loading predictions')
|
|
preds_path = args.predictions[i] if len(args.predictions) > i else args.predictions[0]
|
|
preds = [json.loads(line) for line in open(preds_path, encoding='utf-8') if line.strip()]
|
|
preds = [p.get('raw', p.get('prediction', '')) if isinstance(p, dict) else p
|
|
for p in preds]
|
|
report = evaluate(ds, preds, model=model_spec or 'preds')
|
|
_print_phase(console, i + 1, total_runs, name, 'scoring complete')
|
|
if args.out:
|
|
report.save(args.out)
|
|
if out_dir:
|
|
_emit(f'Writing results to {out_dir}/{name}/' if out_dir
|
|
else 'Writing results')
|
|
from pathlib import Path as _P
|
|
|
|
bench_dir = _P(out_dir) / name
|
|
bench_dir.mkdir(parents=True, exist_ok=True)
|
|
report.save(str(bench_dir / 'report.jsonl'))
|
|
with open(bench_dir / 'detail.md', 'w', encoding='utf-8') as f:
|
|
f.write(render(report, style='md'))
|
|
if args.verbose:
|
|
if not _print_result_panel(console, report, _time.time() - t0):
|
|
print(render(report, style=args.style))
|
|
elif len(args.datasets) == 1:
|
|
if console is None:
|
|
print(render(report, style=args.style))
|
|
all_reports.append(report)
|
|
primary = next(iter(report.metrics), '')
|
|
secs_total = sum(float((s.usage or {}).get('latency_s', 0) or 0)
|
|
for s in report.samples)
|
|
groups = {k: v for k, v in report.metric_groups.items()
|
|
if isinstance(v, dict) and k not in ('run_info',)
|
|
and not k.startswith('agg_error')}
|
|
info = report.metric_groups.get('run_info', {}) or {}
|
|
lats = sorted(float((s.usage or {}).get('latency_s', 0) or 0)
|
|
for s in report.samples
|
|
if float((s.usage or {}).get('latency_s', 0) or 0) > 0)
|
|
def _pct(q):
|
|
return lats[min(int(len(lats) * q), len(lats) - 1)] if lats else 0.0
|
|
fins = [(s.usage or {}).get('finish_reason', '')
|
|
for s in report.samples]
|
|
rows.append({'name': name, 'metric': primary, 'value': report.metrics.get(primary),
|
|
'n': report.num_samples,
|
|
'extract_fail': report.num_failed_extractions,
|
|
'secs': round(secs_total, 1),
|
|
'wall': round(_time.time() - t0, 1),
|
|
'hours': round(secs_total / 3600, 2),
|
|
'tok_in': info.get('gen_input_tokens', 0) or 0,
|
|
'tok_out': info.get('gen_output_tokens', 0) or 0,
|
|
'tokens': (info.get('gen_input_tokens', 0) or 0)
|
|
+ (info.get('gen_output_tokens', 0) or 0),
|
|
'lat_p50': _pct(0.50), 'lat_p90': _pct(0.90),
|
|
'trunc': sum(1 for f in fins if f == 'length'),
|
|
'groups': groups, 'ok': True})
|
|
if progress_reporter is not None:
|
|
progress_reporter.advance_overall()
|
|
_print_benchmark_result(console, i + 1, total_runs, name,
|
|
'done', _time.time() - t0)
|
|
except Exception as e:
|
|
rows.append({'name': name, 'metric': '-', 'value': None,
|
|
'secs': round(_time.time() - t0, 1), 'ok': False,
|
|
'err': f'{type(e).__name__}: {str(e)[:100]}'})
|
|
print(f'{name}: FAILED {type(e).__name__}: {str(e)[:160]}', file=sys.stderr)
|
|
if console is not None:
|
|
from rich.panel import Panel
|
|
|
|
console.print(Panel(
|
|
f'{type(e).__name__}: {e}',
|
|
title=f'[bold red]✗ {name} FAILED[/bold red]',
|
|
border_style='red', expand=False), justify='center')
|
|
_print_benchmark_result(console, i + 1, total_runs, name,
|
|
'failed', _time.time() - t0)
|
|
|
|
if _shared_reporter is not None:
|
|
_shared_reporter.close()
|
|
if all_reports and out_dir:
|
|
try:
|
|
from evalharness.viz import render as _render
|
|
|
|
xb = _render(all_reports, style='excel',
|
|
out=f'{out_dir}/summary.xlsx')
|
|
print(f'excel -> {xb}', flush=True)
|
|
except Exception as e:
|
|
print(f'excel export skipped: {type(e).__name__}: {str(e)[:80]}',
|
|
file=sys.stderr)
|
|
if rows:
|
|
_print_summary_table(console, rows)
|
|
if out_dir:
|
|
import csv as _csv
|
|
|
|
with open(f'{out_dir}/summary.csv', 'w', newline='', encoding='utf-8') as f:
|
|
w = _csv.writer(f)
|
|
w.writerow(['benchmark', 'score', 'metric', 'num_samples',
|
|
'time_h', 'time_s', 'extract_fail',
|
|
'success_rate', 'latency_mean_s', 'output_tps', 'request_qps',
|
|
'input_tokens_mean', 'output_tokens_mean', 'total_tokens',
|
|
'ttft_mean_s', 'ttft_p90_s', 'ttft_p99_s',
|
|
'tpot_mean_s', 'tpot_p90_s', 'tpot_p99_s',
|
|
'categories'])
|
|
for r in rows:
|
|
perf = (r.get('groups') or {}).get('perf') or {}
|
|
cats = '; '.join(f'{g}={_f3(v)}'
|
|
for gname, gv in (r.get('groups') or {}).items()
|
|
if gname != 'perf'
|
|
for g, v in (gv or {}).items()
|
|
if isinstance(v, (int, float)))[:2000]
|
|
w.writerow([r['name'], _f3(r.get('value')), r['metric'], r.get('n', ''),
|
|
r.get('hours', ''), r.get('secs', ''),
|
|
r.get('extract_fail', 0)] +
|
|
[perf.get(k, '') for k in (
|
|
'success_rate', 'latency_mean_s', 'output_tps', 'request_qps',
|
|
'input_tokens_mean', 'output_tokens_mean', 'total_tokens',
|
|
'ttft_mean_s', 'ttft_p90_s', 'ttft_p99_s',
|
|
'tpot_mean_s', 'tpot_p90_s', 'tpot_p99_s')] +
|
|
[cats])
|
|
|
|
# artifacts notice: tell the user where everything landed (or how to save);
|
|
# rich terminals get clickable file:// links (iTerm2/kitty/WezTerm/WT...)
|
|
def _notice(label, *paths):
|
|
print(f'\n{label} -> ' + ' · '.join(os.path.abspath(p) for p in paths))
|
|
|
|
ok_n = sum(1 for r in rows if r['ok'])
|
|
if out_dir:
|
|
ap = os.path.abspath(out_dir)
|
|
mark = '[green]✓[/green]' if ok_n == len(rows) else '[yellow]◐[/yellow]'
|
|
if console is not None:
|
|
console.print(
|
|
f'\n{mark} [bold]运行结束[/bold] · {ok_n}/{len(rows)} benchmarks ok'
|
|
f' · 结果 {ap}')
|
|
else:
|
|
print(f'\n运行结束 · {ok_n}/{len(rows)} benchmarks ok · 结果 {ap}')
|
|
elif rows and rows[0]['ok'] and args.out:
|
|
_notice('运行结束 · 结果已保存', args.out)
|
|
print(f'{ok_n}/{len(rows)} benchmarks ok')
|
|
return 0 if all(r['ok'] for r in rows) else 1
|
|
|
|
|
|
def _fmt_score(v):
|
|
"""Unified score format: fractions render as percentages everywhere."""
|
|
try:
|
|
v = float(v)
|
|
except (TypeError, ValueError):
|
|
return 'ERR'
|
|
return f'{v * 100:.1f}%' if 0.0 <= v <= 1.0 else f'{v:g}'
|
|
|
|
|
|
def _print_summary_table(console, rows):
|
|
"""Rich multi-benchmark summary (falls back to aligned plain text)."""
|
|
if console is not None:
|
|
from rich.table import Table
|
|
|
|
t = Table(title='Run Summary', header_style='bold cyan',
|
|
title_style='bold', expand=False)
|
|
for col, just in (('benchmark', 'left'), ('metric', 'left'),
|
|
('score', 'right'), ('n', 'right'), ('time', 'right'),
|
|
('tok in', 'right'), ('tok out', 'right'),
|
|
('in/s', 'right'), ('out/s', 'right')):
|
|
t.add_column(col, justify=just)
|
|
for r in rows:
|
|
v = _fmt_score(r.get('value')) if r['ok'] else 'ERR'
|
|
wall = r.get('wall') or r.get('secs') or 0
|
|
tm = f'{wall / 3600:.2f}h' if wall >= 3600 else f'{wall:.0f}s'
|
|
ti, to = r.get('tok_in', 0), r.get('tok_out', 0)
|
|
tin = f'{ti:,}' if ti else '—'
|
|
tout = f'{to:,}' if to else '—'
|
|
tis = f'{ti / wall:.0f}' if (wall > 1 and ti) else '—'
|
|
tos = f'{to / wall:.0f}' if (wall > 1 and to) else '—'
|
|
t.add_row(r['name'], r['metric'], v, str(r.get('n', '')), tm,
|
|
tin, tout, tis, tos,
|
|
style='green' if r['ok'] else 'red')
|
|
wall_all = sum(r.get('wall') or r.get('secs') or 0 for r in rows)
|
|
ti_all = sum(r.get('tok_in', 0) for r in rows)
|
|
to_all = sum(r.get('tok_out', 0) for r in rows)
|
|
n_all = sum(r.get('n', 0) or 0 for r in rows if isinstance(r.get('n'), int))
|
|
tm_all = f'{wall_all / 3600:.2f}h' if wall_all >= 3600 else f'{wall_all:.0f}s'
|
|
t.add_section()
|
|
tis_all = f'{ti_all / wall_all:.0f}' if wall_all > 1 else ''
|
|
tos_all = f'{to_all / wall_all:.0f}' if wall_all > 1 else ''
|
|
t.add_row(f'[bold]{len(rows)} benchmarks[/bold]', '',
|
|
f'{sum(1 for r in rows if r["ok"])}/{len(rows)} ok',
|
|
str(n_all), tm_all, f'{ti_all:,}', f'{to_all:,}',
|
|
tis_all, tos_all)
|
|
console.print(t, justify='center')
|
|
return
|
|
print(f'\n{"benchmark":<20} {"metric":<16} {"score":>8} {"n":>6} {"time":>8}')
|
|
print('-' * 64)
|
|
for r in rows:
|
|
v = _fmt_score(r.get('value')) if r['ok'] else 'ERR'
|
|
h = r.get('hours') or 0
|
|
tm = f'{h:.2f}h' if h >= 0.995 else f"{r.get('secs', 0):.0f}s"
|
|
err = f" {r.get('err', '')}" if not r['ok'] else ''
|
|
print(f"{r['name']:<20} {r['metric']:<16} {v:>8} "
|
|
f"{str(r.get('n', '')):>6} {tm:>8}{err}")
|
|
|
|
|
|
def _f3(v):
|
|
try:
|
|
return round(float(v), 4)
|
|
except (TypeError, ValueError):
|
|
return v
|
|
|
|
|
|
def _cmd_viz_show(args) -> int:
|
|
from evalharness.viz import render
|
|
|
|
if args.style == 'excel':
|
|
from evalharness.eval.record import EvalReport
|
|
|
|
reps = [EvalReport.load(p) for p in args.reports]
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out = render(reps, style='excel',
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**({'n': args.n} if args.n else {}),
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**({'out': args.out} if args.out else {}))
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print(f'excel -> {out}')
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return 0
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print(render([*args.reports], style=args.style, **({'n': args.n} if args.n else {})))
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return 0
|
|
|
|
|
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def build_parser() -> argparse.ArgumentParser:
|
|
parser = argparse.ArgumentParser(prog='evalharness', description='EvalHarness CLI')
|
|
sub = parser.add_subparsers(dest='command', required=True)
|
|
|
|
data = sub.add_parser('data', help='dataset plugin commands')
|
|
dsub = data.add_subparsers(dest='data_command', required=True)
|
|
|
|
p = dsub.add_parser('list', help='list registered datasets (no download)')
|
|
p.set_defaults(func=_cmd_data_list)
|
|
|
|
p = dsub.add_parser('fetch', help='materialize dataset(s) into the cache')
|
|
p.add_argument('names', nargs='+')
|
|
p.add_argument('--force', action='store_true', help='re-download and rebuild the cache')
|
|
p.add_argument('--workers', type=int, default=8, help='concurrent downloads (default 8)')
|
|
_add_override_flags(p)
|
|
p.set_defaults(func=_cmd_data_fetch)
|
|
|
|
p = dsub.add_parser('unload', help='drop cache entries (raw + samples); images belong to the sandbox layer')
|
|
p.add_argument('names', nargs='+')
|
|
_add_override_flags(p)
|
|
p.set_defaults(func=_cmd_data_unload)
|
|
|
|
p = dsub.add_parser('stats', help='materialize and show dataset statistics')
|
|
p.add_argument('name')
|
|
_add_override_flags(p)
|
|
p.set_defaults(func=_cmd_data_stats)
|
|
|
|
p = dsub.add_parser('show', help='print the first N samples')
|
|
p.add_argument('name')
|
|
p.add_argument('-n', type=int, default=2)
|
|
_add_override_flags(p)
|
|
p.set_defaults(func=_cmd_data_show)
|
|
|
|
# ---- eval ----
|
|
ev = sub.add_parser('eval', help='evaluation recipes & runs')
|
|
esub = ev.add_subparsers(dest='eval_command', required=True)
|
|
|
|
p = esub.add_parser('list', help='list registered eval recipes')
|
|
p.set_defaults(func=_cmd_eval_list)
|
|
|
|
p = esub.add_parser('run', help='score predictions (file) or generate+score (--model); multiple datasets OK')
|
|
p.add_argument('datasets', nargs='+', help='dataset name(s) (recipe auto-resolved)')
|
|
p.add_argument('predictions', nargs='?', help='jsonl: one raw string or {"raw": ...} per sample')
|
|
p.add_argument('--model', default='',
|
|
help='served model name when --api-url is used; or full legacy model spec')
|
|
p.add_argument('--api-url', default='',
|
|
help='API base URL when --model is only the served model name')
|
|
p.add_argument('--provider', default='openai-chat',
|
|
choices=('openai-chat', 'openai-pool'),
|
|
help='API protocol/provider (default: openai-chat)')
|
|
p.add_argument('--judge-model', '--judge', dest='judge', default='',
|
|
help='judge model name with --judge-api-url, or full spec')
|
|
p.add_argument('--judge-api-url', default='',
|
|
help='judge API base URL when --judge is only the model name')
|
|
p.add_argument('--api-key', default='',
|
|
help='explicit API key for the model endpoint (overrides '
|
|
'env-based resolution; never written into reports)')
|
|
p.add_argument('--judge-api-key', default='',
|
|
help='explicit API key for the judge endpoint')
|
|
p.add_argument('--judge-provider', default='openai-chat',
|
|
help='judge protocol/provider (default openai-chat; '
|
|
'openai-pool for multi-endpoint judges)')
|
|
p.add_argument('--profile', default='',
|
|
help='named gen-params profile (dp4-nothink | qwen3-es-parity | t1-short '
|
|
'or any @register_gen_profile name); layers: plugin default < '
|
|
"profile.default < profile['<bench>'] < explicit kwargs")
|
|
p.add_argument('--disable-thinking', action='store_true',
|
|
help='send enable_thinking=false to the OpenAI-compatible model')
|
|
p.add_argument('--perf', action='store_true',
|
|
help='collect streaming TTFT and ITL metrics')
|
|
p.add_argument('--textools', action='store_true',
|
|
help='send tools as text instead of native tool calls')
|
|
p.add_argument('--env', default='', help="agent environment (e.g. 'bfcl_mock') -> message pump")
|
|
p.add_argument('--concurrency', type=int, default=32, help='parallel model calls (default 32)')
|
|
p.add_argument('--progress', action='store_true', default=True,
|
|
help='show per-sample Rich terminal progress (default: on)')
|
|
p.add_argument('--no-progress', dest='progress', action='store_false',
|
|
help='disable per-sample Rich terminal progress')
|
|
p.add_argument('--limit', type=int, help='evaluate only the first N samples total')
|
|
p.add_argument('--resume', nargs='?', const=True, default=False,
|
|
help='resume from per-sample checkpoint (default path auto-derived; '
|
|
'pass a path to override)')
|
|
p.add_argument('--limit-per-task', type=int,
|
|
help='first N samples PER subset/category (evalscope --limit semantics); '
|
|
'composable with --limit (intersection)')
|
|
p.add_argument('--out', help='save the EvalReport json here (single dataset)')
|
|
p.add_argument('--out-dir', help='output directory: summary.xlsx/csv + one dir per benchmark')
|
|
p.add_argument('--style', default='text', help='result render style (text/md/radar/errors)')
|
|
p.add_argument('--verbose', action='store_true', help='print full render for every dataset')
|
|
_add_override_flags(p)
|
|
p.set_defaults(func=_cmd_eval_run)
|
|
|
|
# ---- sandbox ----
|
|
sb = sub.add_parser('sandbox', help='execution environment management')
|
|
bsub = sb.add_subparsers(dest='sandbox_command', required=True)
|
|
|
|
p = bsub.add_parser('prefetch', help='parallel docker pull of a dataset\'s sandbox images')
|
|
p.add_argument('dataset', help='dataset whose samples declare images (e.g. swe_bench_verified)')
|
|
p.add_argument('--workers', type=int, default=8, help='concurrent pulls (default 8)')
|
|
p.add_argument('--limit', type=int, default=0, help='only first N samples (0=all)')
|
|
_add_override_flags(p)
|
|
p.set_defaults(func=_cmd_sandbox_prefetch)
|
|
|
|
# ---- viz ----
|
|
vz = sub.add_parser('viz', help='render saved EvalReport artifacts')
|
|
zsub = vz.add_subparsers(dest='viz_command', required=True)
|
|
|
|
p = zsub.add_parser('show', help='render report file(s)')
|
|
p.add_argument('reports', nargs='+')
|
|
p.add_argument('--style', default='text',
|
|
help='text | md | md_compare | radar | errors | excel (writes .xlsx)')
|
|
p.add_argument('--out', help='excel output path (default ./evalharness_report.xlsx)')
|
|
p.add_argument('-n', type=int, help='for errors style: how many samples')
|
|
p.set_defaults(func=_cmd_viz_show)
|
|
|
|
return parser
|
|
|
|
|
|
def main(argv=None) -> int:
|
|
args = build_parser().parse_args(argv)
|
|
return args.func(args)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
sys.exit(main())
|