diff --git a/config.example.yaml b/config.example.yaml index 38f9f55..575e6e3 100644 --- a/config.example.yaml +++ b/config.example.yaml @@ -144,6 +144,11 @@ output: # These files are excluded from resume detection, conversion, README generation, and uploads. failures_dir: ./failures + # Harbor bench-mode intermediates (downloaded sources, raw trials, normalized sessions). + # Defaults to a `bench` dir beside traces_dir (parallel to sandbox/failures), never inside + # the dataset. Set a path to override. + bench_dir: null + # Used in the generated trace README. pretty_name: "My Agent Traces" @@ -154,6 +159,25 @@ publish: hf_token: null private: false +# Benchmark mode (`teich generate --mode bench`). Each source is run by a backend selected +# by `type`: `harbor` (the harbor package; needs teich[harbor], Python 3.12+) or `swe-bench` +# (the swebench package; needs teich[swe]). teich runs the configured agent on each task/ +# instance, harvests the plain native trace into output/{passed,failed,borderline}/, and +# writes scores + provenance to output/metadata/.json. Intermediates live under +# output.bench_dir (a sibling `bench` dir by default), outside the dataset/uploads. +# `--resume` skips already-harvested tasks; `--refresh` re-downloads remote harbor sources +# (no effect on swe-bench, which uses Hugging Face's own dataset cache). +# Keep bench its own project: dedicated output.traces_dir + publish.repo_id. +bench: + sources: [] + # - { type: harbor, source: terminal-bench@2.0 } # registry spec + # - { type: harbor, source: ./local-tasks } # local task dir / dir of task dirs + # - { type: swe-bench, source: SWE-bench/SWE-bench_Verified, split: test } + # - { type: swe-bench, source: SWE-bench/SWE-bench_Lite, instances: [django__django-12345] } + # - { type: swe-bench, source: ./my-instances.jsonl, namespace: null } # build custom images locally + # # optional per-source: repo (git/HF registry), version, instances [..], backend (harbor); + # # swe-bench namespace (default "swebench" pulls published images; null builds locally) + # Number of prompts to run in parallel. max_concurrency: 1 diff --git a/pyproject.toml b/pyproject.toml index 394d41e..528be09 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ dependencies = [ "rich>=13.0", "datasets>=2.19.0", "huggingface_hub>=0.23.0", + "jinja2>=3.1", "fastapi>=0.110", "uvicorn>=0.29", "websockets>=12", @@ -28,6 +29,10 @@ dependencies = [ [project.optional-dependencies] studio = ["fastapi>=0.110", "uvicorn>=0.29", "websockets>=12"] dev = ["pytest>=8.0", "ruff>=0.4", "jinja2>=3.1", "httpx>=0.27"] +# Per-backend bench extras (install only what you run); `bench` enables both. +harbor = ["harbor>=0.15.0 ; python_full_version >= '3.12'"] +swe = ["swebench>=4.1.0", "jinja2>=3.1"] +bench = ["teich[harbor,swe]"] [project.scripts] teich = "teich.cli:main" diff --git a/src/teich/agent_cfg.py b/src/teich/agent_cfg.py new file mode 100644 index 0000000..420612b --- /dev/null +++ b/src/teich/agent_cfg.py @@ -0,0 +1,110 @@ +"""Shared, cfg-only agent-configuration helpers. + +Prompt mode (``runner.py``) and bench mode (``bench/backends/*``) both need to turn a ``Config`` +into the credentials / model name / base URL an agent CLI consumes. This module is the single +source of truth for the *pure* parts (no ``self``, no Docker), so bench stops re-deriving a +thinner, diverging copy (which previously only knew openai/openrouter/anthropic and dropped the +localhost -> host.docker.internal rewrite). + +Note: agent-specific *config files* (codex ``config.toml``, pi ``models.json``/``settings.json``) +are threaded separately by each backend; this module owns the shared env/model/url derivation. +""" + +from __future__ import annotations + +import re +from typing import TYPE_CHECKING +from urllib.parse import urlsplit, urlunsplit + +if TYPE_CHECKING: + from .config import Config + +# api.provider -> the ENV var its CLI reads for the key. Mirrors runner.ExternalCliRunner +# (the full map, incl. zai->GLM_API_KEY, deepseek/xai/google/...); unknown providers fall back +# to ``_API_KEY``. +_PROVIDER_ENV_KEYS = { + "anthropic": "ANTHROPIC_API_KEY", + "claude": "ANTHROPIC_API_KEY", + "claude_code": "ANTHROPIC_API_KEY", + "claude-code": "ANTHROPIC_API_KEY", + "openai": "OPENAI_API_KEY", + "openrouter": "OPENROUTER_API_KEY", + "nous": "NOUS_API_KEY", + "nous_portal": "NOUS_API_KEY", + "google": "GOOGLE_API_KEY", + "gemini": "GEMINI_API_KEY", + "deepseek": "DEEPSEEK_API_KEY", + "xai": "XAI_API_KEY", + "grok": "XAI_API_KEY", + "zai": "GLM_API_KEY", + "z_ai": "GLM_API_KEY", + "glm": "GLM_API_KEY", +} + +# api.wire_api values that mean "chat completions" (vs the OpenAI Responses API default). +CHAT_WIRE_APIS = {"completions", "chat_completions", "chat-completions", "openai-completions"} + + +def provider_env_key(provider: str) -> str: + """The ENV var name an agent reads for ``provider``'s API key.""" + normalized = re.sub(r"[^a-zA-Z0-9_]+", "_", provider.strip().lower()) + return _PROVIDER_ENV_KEYS.get(normalized, f"{normalized.upper() or 'TEICH'}_API_KEY") + + +def container_base_url(base_url: str | None) -> str | None: + """Rewrite a host-local base URL so a container can reach it (localhost -> host.docker.internal).""" + if not base_url: + return None + parsed = urlsplit(base_url) + hostname = parsed.hostname or "" + if hostname not in {"localhost", "127.0.0.1"}: + return base_url + netloc = parsed.netloc.replace(hostname, "host.docker.internal", 1) + return urlunsplit((parsed.scheme, netloc, parsed.path, parsed.query, parsed.fragment)) + + +def pi_prefixed_model(cfg: Config) -> str: + """Model for a bench agent; pi picks its provider from a ``/`` prefix. + + ``model: z-ai/glm-5.2`` + ``api.provider: openrouter`` -> ``openrouter/z-ai/glm-5.2``. Only the + pi agent needs the prefix; other agents get the model unchanged. + """ + model = cfg.get_effective_model().strip() + if not model: + return model + provider = (cfg.api.provider or "").strip() + if cfg.get_agent_provider() == "pi" and provider and not model.startswith(f"{provider}/"): + return f"{provider}/{model}" + return model + + +def bench_auth_env(cfg: Config) -> dict[str, str]: + """Credential + base-URL env for a bench agent container. + + Sets the provider-specific key from the full provider map (so ``provider: zai`` -> ``GLM_API_KEY``, + ``deepseek`` -> ``DEEPSEEK_API_KEY``, ...) plus the OpenAI/OpenRouter/Anthropic compat vars the + CLIs actually read, and routes ``base_url`` through :func:`container_base_url` so a host-local + endpoint is reachable from inside the container. + """ + env: dict[str, str] = {} + provider = cfg.api.provider.strip().lower() + api_key = cfg.get_api_key() + if api_key: + env["TEICH_API_KEY"] = api_key + env[provider_env_key(provider)] = api_key # the correct var for zai/deepseek/xai/... + if provider == "anthropic": + env["ANTHROPIC_API_KEY"] = api_key + else: + # codex/claude-code read OPENAI_API_KEY against the configured base_url; pi/hermes on + # OpenRouter also read OPENROUTER_API_KEY. + env["OPENAI_API_KEY"] = api_key + if provider == "openrouter": + env["OPENROUTER_API_KEY"] = api_key + base_url = container_base_url(cfg.get_base_url()) + if base_url: + env["TEICH_BASE_URL"] = base_url + if provider == "anthropic": + env["ANTHROPIC_BASE_URL"] = base_url + else: + env["OPENAI_BASE_URL"] = base_url + return env diff --git a/src/teich/bench/__init__.py b/src/teich/bench/__init__.py new file mode 100644 index 0000000..ea1257c --- /dev/null +++ b/src/teich/bench/__init__.py @@ -0,0 +1,5 @@ +"""Benchmark (Harbor-format) task generation for ``teich generate --mode bench``.""" + +from .runner import run_bench + +__all__ = ["run_bench"] diff --git a/src/teich/bench/backends/__init__.py b/src/teich/bench/backends/__init__.py new file mode 100644 index 0000000..4eff601 --- /dev/null +++ b/src/teich/bench/backends/__init__.py @@ -0,0 +1,50 @@ +"""Bench backends: a registry mapping a source ``type`` to its backend implementation.""" + +from __future__ import annotations + +from .base import ( + BENCH_SPLITS, + BenchBackend, + BenchRun, + BenchTask, + bench_root, + bench_stem, + existing_output, + harvest, + primary_score, + route_split, + source_id, +) +from .harbor import HarborBackend +from .swebench import SweBenchBackend + +# type -> backend factory. +_BACKENDS: dict[str, type] = { + HarborBackend.type: HarborBackend, + SweBenchBackend.type: SweBenchBackend, +} + + +def get_backend(source_type: str) -> BenchBackend: + """Instantiate the backend for a source ``type``; clear error for an unknown type.""" + cls = _BACKENDS.get(source_type) + if cls is None: + supported = ", ".join(sorted(_BACKENDS)) + raise RuntimeError(f"Unknown bench source type {source_type!r}; supported: {supported}.") + return cls() + + +__all__ = [ + "BENCH_SPLITS", + "BenchBackend", + "BenchRun", + "BenchTask", + "bench_root", + "bench_stem", + "existing_output", + "get_backend", + "harvest", + "primary_score", + "route_split", + "source_id", +] diff --git a/src/teich/bench/backends/base.py b/src/teich/bench/backends/base.py new file mode 100644 index 0000000..054ce0c --- /dev/null +++ b/src/teich/bench/backends/base.py @@ -0,0 +1,227 @@ +"""Pluggable bench-backend abstraction. + +A benchmark *source* (declared in ``bench.sources`` with a ``type``) is executed by a +backend (harbor, swe-bench). Each backend turns a task into a ``BenchRun`` — the agent's +plain native trace plus a rewards dict — and the *harvest* here (route by score into +``passed``/``failed``/``borderline`` + a per-task ``metadata/`` sidecar) is backend-agnostic. + +Trace/metadata filenames are namespaced by source (``bench--``) so two sources +in one dataset can never collide; an unfinished task writes nothing and is retried on resume. +""" + +from __future__ import annotations + +import hashlib +import json +import re +from dataclasses import dataclass, field +from pathlib import Path +from typing import TYPE_CHECKING, Any, Iterable, Protocol + +from ...converter import convert_traces_to_training_data + +if TYPE_CHECKING: + from ...config import BenchSource, Config + +BENCH_SPLITS = ("passed", "failed", "borderline") + + +@dataclass +class BenchTask: + """One unit of work within a source (a harbor task dir, a swe-bench instance, ...).""" + + id: str + raw: Any = None + + +@dataclass +class BenchRun: + """A backend's result for one task: the native trace + scores + provenance metadata.""" + + native_lines: list[str] + rewards: dict[str, float] | None = None + metadata: dict[str, Any] = field(default_factory=dict) + + +class BenchBackend(Protocol): + """What a benchmark backend must provide. The driver + harvest are shared.""" + + type: str + + def require(self) -> None: + """Raise RuntimeError with an install hint if the backend's extra is missing.""" + + def tasks(self, cfg: Config, source: BenchSource, *, refresh: bool = False) -> Iterable[BenchTask]: + """Resolve a source into its tasks/instances (``refresh`` re-fetches remote data).""" + + def run(self, cfg: Config, source: BenchSource, task: BenchTask) -> BenchRun: + """Run the agent on one task in its environment and return its trace + rewards.""" + + +def bench_root(cfg: Config) -> Path: + """Working dir for backends' intermediates (downloads, sessions, trials). + + A ``bench`` dir beside ``traces_dir`` (parallel to sandbox/failures, outside the + dataset); overridable via ``output.bench_dir``. Re-checked here (not just at config + load) so a ``--output`` override can't leave ``bench_dir`` inside the dataset. + """ + root = Path(cfg.output.bench_dir) if cfg.output.bench_dir is not None else cfg.output.traces_dir.parent / "bench" + # Guard both the explicit override and the computed default: a bench root at/under traces_dir + # (e.g. output.traces_dir named ``bench``, so the sibling default collides with it) would get + # raw trials/sessions uploaded and misclassified as dataset rows. + traces = cfg.output.traces_dir.resolve() + if traces == root.resolve() or traces in root.resolve().parents: + raise RuntimeError( + f"bench working dir ({root}) must be outside output.traces_dir " + f"({cfg.output.traces_dir}); raw trials/sessions there would be uploaded and " + "misclassified as dataset rows. Set output.bench_dir explicitly or rename traces_dir." + ) + return root + + +def slug(value: str) -> str: + """Filesystem-safe slug (``terminal-bench@2.0`` -> ``terminal-bench-2.0``).""" + return re.sub(r"[^A-Za-z0-9._-]+", "-", value).strip("-") or "x" + + +def _instances_key(instances: list[str] | None) -> str | None: + """A bounded, order-independent discriminator for an ``instances`` subset. + + The full comma-joined list would otherwise flow through ``source_id`` into per-task + filenames, Docker tags, and container/env-file names; a dozen ~22-char SWE-bench IDs + blow past the 255-byte path-component and 128-char Docker-tag limits and crash the run + before any result is written. Short lists stay readable; longer ones collapse to a + stable hash (sorted, so listing order doesn't spawn a distinct id). + """ + if not instances: + return None + joined = ",".join(sorted(instances)) + if len(joined) <= 40: + return joined + return "i" + hashlib.sha1(joined.encode("utf-8")).hexdigest()[:8] + + +def source_id(source: BenchSource) -> str: + """Stable identifier for a source, used to namespace its output files. + + Keyed on the discriminating knobs (not just ``source``): two sources that share a spec + but differ in ``repo``/``version``/``split``/``instances``/``backend`` get distinct ids, + so they can't overwrite each other's traces or wrongly resume-skip. A suffix is appended + only when a field diverges from its default, so the common single-field source keeps a + clean, stable id. ``type`` is intentionally excluded (it is recorded in ``metadata`` and + the existing namespacing contract is type-independent). + """ + extras = [ + slug(part) + for part in ( + source.repo, + source.version, + source.split, + _instances_key(source.instances), + source.backend if source.backend != "docker" else None, + ) + if part + ] + base_id = slug(source.source) + return base_id if not extras else f"{base_id}-{slug('-'.join(extras))}" + + +def bench_stem(source: BenchSource, task_id: str) -> str: + """Per-task dataset stem, namespaced by source (the co-mingle guard keys on ``bench-``).""" + return f"bench-{source_id(source)}-{slug(task_id)}" + + +def existing_output(cfg: Config, stem: str) -> Path | None: + """The harvested trace for ``stem`` (in any split), or None — used for ``--resume``.""" + for split in BENCH_SPLITS: + path = cfg.output.traces_dir / split / f"{stem}.jsonl" + if path.is_file() and path.stat().st_size > 0: + return path + return None + + +def numeric(value: Any) -> float | None: + """A real number (bools excluded) as float, else None.""" + if isinstance(value, (int, float)) and not isinstance(value, bool): + return float(value) + return None + + +def rewards_from_mapping(data: Any) -> dict[str, float] | None: + """The full dict of numeric scores (no clamping). Accepts ``{"rewards": {...}}`` or flat.""" + if not isinstance(data, dict): + return None + rewards = data.get("rewards") if isinstance(data.get("rewards"), dict) else data + scores = {key: numeric(val) for key, val in rewards.items() if numeric(val) is not None} + return scores or None + + +def primary_score(rewards: dict[str, float] | None) -> float | None: + """The scalar used for routing: the ``reward`` key, else the first numeric value.""" + if not rewards: + return None + primary = numeric(rewards.get("reward")) + if primary is not None: + return primary + for value in rewards.values(): + primary = numeric(value) + if primary is not None: + return primary + return None + + +def route_split(primary: float | None) -> str: + """passed = score 1, failed = score 0 / unscored, borderline = any other value.""" + if primary is None or primary == 0: + return "failed" + if primary == 1: + return "passed" + return "borderline" + + +def trace_metadata(native_dir: Path | None) -> dict[str, Any]: + """Metadata teich's converter recovers from a native trace (model, session, cwd, ...).""" + if native_dir is None: + return {} + try: + rows = convert_traces_to_training_data(native_dir) + except Exception: # metadata is best-effort; never fail the harvest over it + return {} + return rows[0].get("metadata", {}) if rows else {} + + +def harvest(cfg: Config, source: BenchSource, task: BenchTask, run: BenchRun) -> tuple[list[Path], str]: + """Write a backend's native trace (routed by score) + a per-task metadata sidecar. + + Returns (written paths, split). The trace is written verbatim (plain agent-trace data); + scores + provenance go to ``output/metadata/.json``. + """ + primary = primary_score(run.rewards) + split = route_split(primary) + stem = bench_stem(source, task.id) + + trace_path = cfg.output.traces_dir / split / f"{stem}.jsonl" + # A re-run (without --resume) whose score crosses a routing boundary would otherwise leave a + # stale copy in the old split; the dataset scanners read every split, so a task would appear + # twice with contradictory labels. Drop any sibling copy before writing the new one. + for other in BENCH_SPLITS: + if other != split: + (cfg.output.traces_dir / other / f"{stem}.jsonl").unlink(missing_ok=True) + trace_path.parent.mkdir(parents=True, exist_ok=True) + trace_path.write_text("\n".join(run.native_lines) + "\n", encoding="utf-8") + + metadata: dict[str, Any] = { + "task": task.id, + "source": source_id(source), + "type": source.type, + "split": split, + "reward": primary, + "rewards": run.rewards or {}, + "agent": cfg.get_agent_provider(), + "trace_file": f"{split}/{stem}.jsonl", + **run.metadata, + } + meta_path = cfg.output.traces_dir / "metadata" / f"{stem}.json" + meta_path.parent.mkdir(parents=True, exist_ok=True) + meta_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8") + return [trace_path], split diff --git a/src/teich/bench/backends/harbor.py b/src/teich/bench/backends/harbor.py new file mode 100644 index 0000000..0985001 --- /dev/null +++ b/src/teich/bench/backends/harbor.py @@ -0,0 +1,435 @@ +"""Harbor bench backend. + +Drives the optional ``harbor`` package over Harbor-format tasks: resolve a source +(local dir, registry spec, or git/HF registry) into task dirs, run each in its own +environment image via harbor's built-in agent, and return the native trace + the task +verifier's reward dict. harbor is imported lazily (the ``teich[harbor]`` extra, py>=3.12). +""" + +from __future__ import annotations + +import asyncio +import json +import os +import re +import shutil +import subprocess +import sys +from collections import Counter +from pathlib import Path +from typing import TYPE_CHECKING, Any + +from ... import agent_cfg +from . import base + +if TYPE_CHECKING: + from ...config import BenchSource, Config + +HARBOR_INSTALL_HINT = ( + "The harbor bench backend needs the optional 'harbor' extra: install with " + "`pip install 'teich[harbor]'` (requires Python 3.12+)." +) + +# teich agent provider -> harbor AgentName value. +_PROVIDER_TO_AGENT: dict[str, str] = { + "codex": "codex", + "claude": "claude-code", + "claude-code": "claude-code", + "claude_code": "claude-code", + "pi": "pi", + "hermes": "hermes", +} + + +def _agent_name_for(provider: str) -> str: + name = _PROVIDER_TO_AGENT.get(provider.strip().lower()) + if not name: + raise RuntimeError( + f"Bench mode does not support agent provider {provider!r}; " + "use one of: codex, claude-code, pi, hermes." + ) + return name + + +def _agent_auth_env(cfg: Config) -> dict[str, str]: + """Model credentials + base URL for the in-container agent — shared ``agent_cfg`` derivation + (the same one prompt mode uses), so every provider gets the right key var (not just + openai/openrouter/anthropic) and a host-local base_url reaches the container.""" + return agent_cfg.bench_auth_env(cfg) + + +def _bench_model_name(cfg: Config) -> str: + """Model name for the in-container agent; pi requires a ``/`` prefix so it + splits on the first ``/`` to pick the credential env var (shared ``agent_cfg`` helper).""" + return agent_cfg.pi_prefixed_model(cfg) + + +def _resolve_task_dirs(root: str | Path) -> list[Path]: + """Resolve a local Harbor task root to one or more task dirs (single-task or dir-of-tasks).""" + path = Path(root).expanduser() + if not path.exists(): + raise RuntimeError(f"bench task directory not found: {path}.") + if (path / "task.toml").is_file(): + return [path] + tasks = sorted(d for d in path.iterdir() if d.is_dir() and (d / "task.toml").is_file()) + if not tasks: + raise RuntimeError(f"No Harbor tasks (a task.toml) found under {path}.") + return tasks + + +def _classify_remote_source(source: str, repo: str | None, version: str | None) -> tuple[str, str]: + """Map a remote source to (client kind, harbor dataset ref), mirroring ``harbor download``.""" + has_version = "@" in source + name = source.split("@", 1)[0] + + def _ref(default_version: str | None = None) -> str: + if has_version: + return source + if version: + return f"{source}@{version}" + return f"{source}@{default_version}" if default_version else source + + if repo: + return "repo", _ref() + if "/" in name: + return "package", _ref(default_version="latest") + return "registry", _ref() + + +def _source_slug(source: str, version: str | None, repo: str | None = None) -> str: + raw = source if "@" in source else (f"{source}@{version}" if version else source) + # ``repo`` selects the download origin in ``_download_async`` and is a discriminating + # knob in ``source_id``; fold it into the cache key too, else two sources with the same + # spec/version but different repos share a cache dir and the second reuses the first's tasks. + if repo: + raw = f"{repo}#{raw}" + return re.sub(r"[^A-Za-z0-9._-]+", "-", raw).strip("-") or "source" + + +def _task_root(cache_dir: Path) -> Path | None: + """The common parent of every exported ``task.toml`` (harbor exports ``//``).""" + parents = sorted({toml.parent for toml in cache_dir.rglob("task.toml")}) + if not parents: + return None + if len(parents) == 1: + return parents[0] + return Path(os.path.commonpath([str(p) for p in parents])) + + +async def _download_async(source: BenchSource, cache_dir: Path) -> None: + kind, ref = _classify_remote_source(source.source.strip(), source.repo, source.version) + if kind == "repo": + from harbor.registry.client.factory import RegistryClientFactory + + client = RegistryClientFactory.create(repo=source.repo) + elif kind == "package": + from harbor.registry.client.package import PackageDatasetClient + + client = PackageDatasetClient() + else: + from harbor.registry.client.factory import RegistryClientFactory + + client = RegistryClientFactory.create() + cache_dir.mkdir(parents=True, exist_ok=True) + await client.download_dataset(ref, overwrite=True, output_dir=cache_dir, export=True) + + +def _fetch_remote(source: BenchSource, cache_dir: Path) -> None: + try: + asyncio.run(_download_async(source, cache_dir)) + except Exception as exc: # network / unknown dataset / auth -> a clean bench error + raise RuntimeError( + f"Failed to download bench source {source.source!r}: {type(exc).__name__}: {exc}. " + "Check the spec/version, network, and (for HF/private registries) HF_TOKEN." + ) from exc + + +def _resolve_source(cfg: Config, source: BenchSource, *, refresh: bool) -> Path: + """Return a local task root: a local path as-is, else download the remote spec.""" + spec = source.source.strip() + local = Path(spec).expanduser() + if local.exists(): + return local + cache_dir = base.bench_root(cfg) / "sources" / _source_slug(spec, source.version, source.repo) + root = None if refresh else _task_root(cache_dir) + if root is None: + if refresh and cache_dir.exists(): + shutil.rmtree(cache_dir, ignore_errors=True) + _fetch_remote(source, cache_dir) + root = _task_root(cache_dir) + if root is None: + raise RuntimeError(f"bench source {spec!r}: no Harbor tasks found after download into {cache_dir}.") + return root + + +def _build_trial_config(cfg: Config, source: BenchSource, task_dir: Path, trials_dir: Path) -> Any: + from harbor.models.agent.name import AgentName + from harbor.models.environment_type import EnvironmentType + from harbor.models.trial.config import TaskConfig, TrialConfig + + config = TrialConfig(task=TaskConfig(path=task_dir), trials_dir=trials_dir) + config.agent.name = AgentName(_agent_name_for(cfg.get_agent_provider())) + model = _bench_model_name(cfg) + if model: + config.agent.model_name = model + config.agent.env.update(_agent_auth_env(cfg)) + try: + config.environment.type = EnvironmentType(source.backend) + except ValueError as exc: + supported = ", ".join(t.value for t in EnvironmentType) + raise RuntimeError( + f"Unknown backend {source.backend!r}; harbor supports: {supported}." + ) from exc + return config + + +async def _create_and_run(config: Any) -> tuple[Any, Any]: + from harbor.trial.trial import Trial + + trial = await Trial.create(config) + result = await trial.run() + return trial, result + + +def _agent_dir(trial: Any) -> Path | None: + paths = getattr(trial, "paths", None) + agent_dir = getattr(paths, "agent_dir", None) + return Path(agent_dir) if agent_dir else None + + +def _pi_stream_to_session_events(pi_txt: Path) -> list[dict[str, Any]]: + """Normalize pi's ``--mode json`` stream into pi session events. + + harbor runs pi with ``--no-session`` (only a streaming log + a leading non-JSON + ``Warning:`` line). Keep the ``session`` header, turn each ``message_end`` into a + ``message``, and synthesize a ``model_change`` from the first assistant message. + """ + events: list[dict[str, Any]] = [] + model_change_added = False + for raw in pi_txt.read_text(encoding="utf-8").splitlines(): + raw = raw.strip() + if not raw.startswith("{"): + continue + try: + event = json.loads(raw) + except json.JSONDecodeError: + continue + if not isinstance(event, dict): + continue + event_type = event.get("type") + if event_type == "session": + events.append(event) + elif event_type == "message_end": + message = event.get("message") + if not isinstance(message, dict) or not message.get("role"): + continue + if not model_change_added and message.get("role") == "assistant": + provider, model_id = message.get("provider"), message.get("model") + if isinstance(provider, str) and isinstance(model_id, str): + events.append({"type": "model_change", "provider": provider, "modelId": model_id}) + model_change_added = True + events.append({"type": "message", "message": message}) + return events + + +def _native_trace( + cfg: Config, source: BenchSource, agent_dir: Path, task_id: str +) -> tuple[list[str], Path | None]: + """The agent's plain native trace as JSONL lines + an isolated dir (for metadata recovery).""" + sessions = agent_dir / "sessions" # codex / claude-code export a native session dir + if sessions.is_dir() and any(sessions.glob("*.jsonl")): + lines = [ + line + for path in sorted(sessions.glob("*.jsonl")) + for line in path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + return lines, sessions + pi_txt = agent_dir / "pi.txt" # pi runs --no-session: normalize its --mode json stream + if pi_txt.is_file(): + events = _pi_stream_to_session_events(pi_txt) + if events: + # Namespace by source so concurrent same-named tasks from different sources can't race. + norm_dir = base.bench_root(cfg) / "sessions" / base.bench_stem(source, task_id) + norm_dir.mkdir(parents=True, exist_ok=True) + lines = [json.dumps(event, ensure_ascii=False) for event in events] + (norm_dir / "pi.jsonl").write_text("\n".join(lines) + "\n", encoding="utf-8") + return lines, norm_dir + jsonls = sorted(agent_dir.rglob("*.jsonl")) # last resort: anything the agent left + if jsonls: + lines = [ + line + for path in jsonls + for line in path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + return lines, agent_dir + return [], None + + +def _rewards_from_result(result: Any) -> dict[str, float] | None: + verifier = getattr(result, "verifier_result", None) + if isinstance(verifier, dict): + return base.rewards_from_mapping(verifier) + return base.rewards_from_mapping(getattr(verifier, "rewards", None)) + + +def _rewards_from_files(base_dir: Path | None) -> dict[str, float] | None: + if base_dir is None: + return None + for name in ("rewards.json", "reward.json"): + for path in sorted(base_dir.rglob(name)): + try: + data = json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError): + continue + scores = base.rewards_from_mapping(data) + if scores is not None: + return scores + for path in sorted(base_dir.rglob("reward.txt")): + try: + value = base.numeric(float(path.read_text(encoding="utf-8").strip())) + except (OSError, ValueError): + continue + if value is not None: + return {"reward": value} + return None + + +def _sanitize_hb_image(name: str) -> str: + """Replicate harbor's docker image-name sanitizer (environments/docker/docker.py).""" + name = name.lower() + if not re.match(r"^[a-z0-9]", name): + name = "0" + name + return re.sub(r"[^a-z0-9._-]", "-", name) + + +def _task_prebuilt_images(task_dir: Path | None) -> list[str]: + """The task's prebuilt image name(s) from task.toml: ``[environment].docker_image`` + plus a separate ``[verifier.environment].docker_image`` when declared. + + A task that declares one runs in harbor's prebuilt mode: compose pulls that image and + runs it directly — no ``hb__`` image is ever built — and harbor's teardown + (``down --rmi local``) never removes a pulled tagged image. All terminal-bench 2.0 + tasks are prebuilt, so without these names the purge is a no-op and each task leaks + its (often multi-GB) image. + """ + if task_dir is None: + return [] + try: + import tomllib # py3.11+; the harbor backend already requires 3.12 + + with (Path(task_dir) / "task.toml").open("rb") as fh: + data = tomllib.load(fh) + candidates = [ + data.get("environment", {}).get("docker_image"), + data.get("verifier", {}).get("environment", {}).get("docker_image"), + ] + except Exception: # missing/unparseable task.toml — stay best-effort like the purge itself + return [] + return [image for image in candidates if isinstance(image, str) and image.strip()] + + +def _harbor_image_names(trial: Any, task_id: str, task_dir: Path | None = None) -> list[str]: + """Per-task image name(s) harbor leaves behind after ``compose down --rmi local``: + the custom-tagged ``hb__`` image (built mode) and/or the pulled prebuilt + image(s) declared in task.toml, so we remove them ourselves. + + Prefers the actual name off the trial's environments; always includes the deterministic + ``sanitize("hb__" + short_name)`` (short_name == the task dir name == task_id) and the + task.toml prebuilt names, which also cover the error/interrupt path where the trial + object was never returned. + """ + names: set[str] = set() + for attr in ("agent_environment", "verifier_environment"): + env = getattr(trial, attr, None) if trial is not None else None + try: + value = getattr(env, "_main_image_name", None) + except Exception: + value = None + if isinstance(value, str) and value: + names.add(value) + short = getattr(getattr(trial, "task", None), "short_name", None) if trial is not None else None + names.add(_sanitize_hb_image(f"hb__{short or task_id}")) + names.update(_task_prebuilt_images(task_dir)) + return sorted(names) + + +def _purge_images(names: list[str]) -> None: + """Best-effort ``docker rmi -f`` of the given images (ignore not-found / in-use / no docker).""" + for name in names: + try: + # Short timeout: this also runs in the finally after a Ctrl-C, so a slow/unhappy + # docker must not stall the exit for 2 minutes per image. + subprocess.run(["docker", "rmi", "-f", name], capture_output=True, timeout=30) + except Exception: + pass + + +class HarborBackend: + """Runs Harbor-format tasks via the harbor package (one container per task).""" + + type = "harbor" + + def __init__(self) -> None: + # Prebuilt images declared by more than one task of this source (see tasks()). + # rmi'ing a shared image after one task can race a concurrent sibling's + # `compose up` (No such image -> that task fails) and forces a re-pull per task, + # so those are kept; only images unique to a task are purged after it. + self._shared_prebuilt: frozenset[str] = frozenset() + + def require(self) -> None: + if sys.version_info < (3, 12): + raise RuntimeError( + "The harbor bench backend requires Python 3.12+ " + f"(current: {sys.version_info.major}.{sys.version_info.minor}). {HARBOR_INSTALL_HINT}" + ) + try: + import harbor # noqa: F401 + except ImportError as exc: # pragma: no cover - only without the extra + raise RuntimeError(HARBOR_INSTALL_HINT) from exc + + def tasks(self, cfg: Config, source: BenchSource, *, refresh: bool = False) -> list[base.BenchTask]: + root = _resolve_source(cfg, source, refresh=refresh) + task_dirs = _resolve_task_dirs(root) + # Computed once, before any run() is dispatched (runner threads only call run()). + # Deduped per task: one task using the same image for agent + verifier isn't sharing. + counts = Counter(image for task_dir in task_dirs for image in set(_task_prebuilt_images(task_dir))) + self._shared_prebuilt = frozenset(image for image, n in counts.items() if n > 1) + return [base.BenchTask(id=task_dir.name, raw=task_dir) for task_dir in task_dirs] + + def run(self, cfg: Config, source: BenchSource, task: base.BenchTask) -> base.BenchRun: + trials_dir = base.bench_root(cfg) / "trials" + trials_dir.mkdir(parents=True, exist_ok=True) + config = _build_trial_config(cfg, source, task.raw, trials_dir) + trial: Any = None + try: + trial, result = asyncio.run(_create_and_run(config)) + + exc_info = getattr(result, "exception_info", None) + exception: str | None = None + if exc_info: + exception = ( + exc_info.get("exception_type") if isinstance(exc_info, dict) + else getattr(exc_info, "exception_type", None) + ) or "agent error" + + agent_dir = _agent_dir(trial) + lines: list[str] = [] + native_dir: Path | None = None + if agent_dir is not None and agent_dir.exists(): + lines, native_dir = _native_trace(cfg, source, agent_dir, task.id) + + rewards = _rewards_from_result(result) or _rewards_from_files( + agent_dir.parent if agent_dir else None + ) + metadata = {"exception": exception, **base.trace_metadata(native_dir)} + return base.BenchRun(native_lines=lines, rewards=rewards, metadata=metadata) + finally: + # harbor's `down --rmi local` leaves the `hb__` image (built mode) and the + # pulled task image(s) (prebuilt mode); remove them on every outcome + # (passed/failed/error/Ctrl-C) so per-task images don't pile up and fill the + # disk. Images shared with sibling tasks are kept (see __init__). + if not cfg.output.keep_bench_images: + names = _harbor_image_names(trial, task.id, task.raw) + _purge_images([name for name in names if name not in self._shared_prebuilt]) diff --git a/src/teich/bench/backends/swebench.py b/src/teich/bench/backends/swebench.py new file mode 100644 index 0000000..c37f79d --- /dev/null +++ b/src/teich/bench/backends/swebench.py @@ -0,0 +1,445 @@ +"""SWE-bench bench backend. + +SWE-bench ships an *evaluation* harness (it grades a patch against held-out tests); it +does not run agents. So teich supplies the agent: load instances from a SWE-bench dataset, +run teich's agent against the clean repo @ base_commit inside swebench's prebuilt instance +image (with a thin Jinja-rendered agent layer on top), take the agent's ``git diff`` as the +candidate patch, and hand that to swebench's ``run_instance`` for grading. The ``resolved`` +verdict becomes the reward. swebench is imported lazily (the ``teich[swe]`` extra, py>=3.10). + +Two seams here are inherently Docker-bound and validated by an integration run rather than +unit tests: building the agent image (``_build_image``) + running the agent (``_run_agent``), +and grading (``_grade``). Everything else — dataset loading, the Dockerfile render, and the +reward mapping — is plain logic with unit coverage. +""" + +from __future__ import annotations + +import json +import os +import shlex +import shutil +import subprocess +import sys +from dataclasses import dataclass, field +from pathlib import Path +from typing import TYPE_CHECKING, Any + +from ... import agent_cfg +from . import base + +if TYPE_CHECKING: + from ...config import BenchSource, Config + +SWE_INSTALL_HINT = ( + "The swe-bench bench backend needs the optional 'swe' extra: install with " + "`pip install 'teich[swe]'` (requires Python 3.10+)." +) + +# SWE-bench publishes prebuilt instance images under the "swebench" Docker namespace (the +# per-source default): we pull one image and add a thin agent layer instead of rebuilding +# base+env+instance. A source with ``namespace: null`` builds instance images locally instead. +TESTBED = "/testbed" # swebench DOCKER_WORKDIR: the repo @ base_commit lives here +CAPTURE = "/teich-out" # mounted host dir for the prompt, the agent session, and the diff + +_TEMPLATE_DIR = Path(__file__).parent.parent / "templates" + + +# --------------------------------------------------------------------------- agent layer + +# Installs Node.js (for the JS agent CLIs) onto a swebench instance image (Debian + conda). +_NODE_RUNTIME = ( + "RUN apt-get update && apt-get install -y --no-install-recommends curl ca-certificates git && \\\n" + " (command -v node >/dev/null 2>&1 || \\\n" + " (curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \\\n" + " apt-get install -y --no-install-recommends nodejs)) && \\\n" + " rm -rf /var/lib/apt/lists/*" +) + + +@dataclass(frozen=True) +class AgentLayer: + """How to install and invoke one agent inside the rendered image. + + ``run`` is the in-container command; it reads the task prompt from ``CAPTURE/prompt.txt`` + and runs non-interactively in ``/testbed``. ``env`` points the agent's session/home at + ``CAPTURE`` so the native trace lands on the mounted host dir; ``session_glob`` finds it. + ``model_flag`` is the CLI flag used to pin teich's configured model (all current agents + use ``--model``); ``_run_command`` appends `` `` so the container runs + teich's model rather than each CLI's own default. + """ + + provider: str + runtime_install: str + agent_install: str + env: dict[str, str] = field(default_factory=dict) + session_glob: str = "**/*.jsonl" + run: str = "" + model_flag: str = "--model" + + +_PROMPT = f"$(cat {CAPTURE}/prompt.txt)" + +# Per-provider recipes mirror teich's own non-interactive agent invocations. The exact CLI +# flags + session locations are what the Docker integration pass validates. +_LAYERS: dict[str, AgentLayer] = { + "pi": AgentLayer( + provider="pi", + runtime_install=_NODE_RUNTIME, + agent_install="RUN npm install -g @mariozechner/pi-coding-agent", + env={"PI_SESSION_DIR": f"{CAPTURE}/sessions"}, + session_glob="sessions/**/*.jsonl", + run=f'pi --yolo --print "{_PROMPT}"', + ), + "codex": AgentLayer( + provider="codex", + runtime_install=_NODE_RUNTIME, + agent_install="RUN npm install -g @openai/codex", + env={"CODEX_HOME": f"{CAPTURE}/codex"}, + session_glob="codex/sessions/**/*.jsonl", + run=f'codex exec --skip-git-repo-check --dangerously-bypass-approvals-and-sandbox "{_PROMPT}"', + ), + "claude-code": AgentLayer( + provider="claude-code", + runtime_install=_NODE_RUNTIME, + agent_install="RUN npm install -g @anthropic-ai/claude-code", + env={"CLAUDE_CONFIG_DIR": f"{CAPTURE}/claude"}, + session_glob="claude/projects/**/*.jsonl", + run=f'claude -p "{_PROMPT}" --output-format stream-json --verbose --dangerously-skip-permissions', + ), +} + +# teich provider aliases -> the swe-bench layer key. +_PROVIDER_ALIASES = {"claude": "claude-code", "claude_code": "claude-code"} + + +def _agent_layer(cfg: Config) -> AgentLayer: + provider = cfg.get_agent_provider().strip().lower() + key = _PROVIDER_ALIASES.get(provider, provider) + layer = _LAYERS.get(key) + if layer is None: + raise RuntimeError( + f"The swe-bench backend does not support agent provider {provider!r}; " + f"use one of: {', '.join(sorted(_LAYERS))}." + ) + # The claude-code layer runs the Anthropic ``claude`` CLI, which authenticates via + # ``ANTHROPIC_*``; ``_auth_env`` only seeds those for an ``anthropic`` project. With any other + # api.provider the container has no usable Claude credentials, so the run would silently + # produce empty traces — fail fast instead. (Routing Claude through an OpenRouter/OpenAI proxy + # is the larger prompt-mode-parity follow-up.) + if layer.provider == "claude-code" and cfg.api.provider != "anthropic": + raise RuntimeError( + "The swe-bench claude-code agent runs the Anthropic `claude` CLI, which needs an " + f"anthropic-provider config; api.provider is {cfg.api.provider!r}. Set api.provider: " + "anthropic for swe-bench claude-code runs, or use a different agent provider." + ) + return layer + + +def _model_name(cfg: Config) -> str: + """Model for the in-container agent (pi gets a ``/`` prefix). Shared helper.""" + return agent_cfg.pi_prefixed_model(cfg) + + +def _run_command(cfg: Config, layer: AgentLayer) -> str: + """The agent's in-container command, with teich's configured model pinned when set.""" + model = _model_name(cfg) + if not model: + return layer.run + return f"{layer.run} {layer.model_flag} {shlex.quote(model)}" + + +def _auth_env(cfg: Config) -> dict[str, str]: + """Model credentials + base URL for the in-container agent — the same derivation prompt mode + uses (shared ``agent_cfg``), so every provider (zai/deepseek/xai/... not just + openai/openrouter/anthropic) gets the right key var and a host-local base_url is rewritten + to host.docker.internal.""" + return agent_cfg.bench_auth_env(cfg) + + +def render_agent_dockerfile( + base_image: str, layer: AgentLayer, *, langfuse: bool = False, langfuse_install: str = "" +) -> str: + """Render the agent-layer Dockerfile (FROM the instance image) from the Jinja template.""" + from jinja2 import Environment, FileSystemLoader, StrictUndefined + + env = Environment( + loader=FileSystemLoader(str(_TEMPLATE_DIR)), + undefined=StrictUndefined, + keep_trailing_newline=True, + ) + template = env.get_template("agent.dockerfile.j2") + return template.render( + base_image=base_image, + provider=layer.provider, + runtime_install=layer.runtime_install, + agent_install=layer.agent_install, + langfuse=langfuse, + langfuse_install=langfuse_install, + ) + + +# --------------------------------------------------------------------------- dataset + + +def _load_instances(source: BenchSource) -> list[dict[str, Any]]: + """Load SWE-bench instances for a source (HF dataset id or a local json/jsonl path).""" + from swebench.harness.utils import load_swebench_dataset + + split = source.split or "test" + instance_ids = list(source.instances) if source.instances else None + try: + dataset = load_swebench_dataset(source.source, split, instance_ids) + except Exception as exc: # bad name/split/ids, network, HF auth -> a clean bench error + raise RuntimeError( + f"Failed to load swe-bench dataset {source.source!r} (split {split!r}): " + f"{type(exc).__name__}: {exc}." + ) from exc + return [dict(instance) for instance in dataset] + + +# --------------------------------------------------------------------------- rewards + + +def _rewards_from_report(entry: dict[str, Any]) -> dict[str, float]: + """Map one swebench report entry to a rewards dict (``reward`` = resolved, for routing).""" + resolved = 1.0 if entry.get("resolved") else 0.0 + rewards: dict[str, float] = { + "reward": resolved, + "resolved": resolved, + "patch_applied": 1.0 if entry.get("patch_successfully_applied") else 0.0, + } + tests = entry.get("tests_status") + if isinstance(tests, dict): + for key, name in (("FAIL_TO_PASS", "fail_to_pass"), ("PASS_TO_PASS", "pass_to_pass")): + group = tests.get(key) + if isinstance(group, dict): + passed = len(group.get("success") or []) + failed = len(group.get("failure") or []) + if passed + failed: + rewards[name] = passed / (passed + failed) + return rewards + + +# ----------------------------------------------------------------- docker seam (integration) + + +def _docker(args: list[str], *, timeout: int | None = None, check: bool = True) -> subprocess.CompletedProcess: + return subprocess.run( + ["docker", *args], capture_output=True, text=True, timeout=timeout, check=check + ) + + +def _ensure_instance_image(spec: Any, *, namespace: str | None, timeout: int | None = None) -> None: + """Make the swebench instance image available locally (pull remote, else build).""" + image = spec.instance_image_key + if _docker(["images", "-q", image], check=False).stdout.strip(): + return + if namespace: # remote (published) image -> pull it + _docker(["pull", image], timeout=timeout) + return + import docker # local build path + from swebench.harness.docker_build import build_instance_images + + build_instance_images(docker.from_env(), [spec], namespace=None, max_workers=1) + + +def _build_image(dockerfile: str, tag: str, *, timeout: int | None = None) -> None: + # Build from stdin (no Dockerfile written into the repo/context); capture logs and bound the + # build by the run timeout, surfacing the tail on failure instead of flooding stdout. + try: + subprocess.run( + ["docker", "build", "-t", tag, "-"], + input=dockerfile, text=True, capture_output=True, check=True, timeout=timeout, + ) + except subprocess.CalledProcessError as exc: + tail = (exc.stderr or exc.stdout or "")[-2000:] + raise RuntimeError(f"docker build for {tag} failed:\n{tail}") from exc + + +def _native_trace(capture_dir: Path, session_glob: str) -> tuple[list[str], Path | None]: + """Collect the agent's native session JSONL from the mounted capture dir.""" + files = sorted(capture_dir.glob(session_glob)) + lines = [ + line + for path in files + if path.is_file() + for line in path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + if not lines: + return [], None + return lines, files[0].parent + + +def _run_agent( + cfg: Config, source: BenchSource, instance: dict[str, Any], spec: Any, + layer: AgentLayer, capture_dir: Path, +) -> tuple[list[str], Path | None, str]: + """Build the agent layer, run the agent on the problem, return (trace, dir, model_patch).""" + # Reset the per-task capture dir: it's a stable path (keyed by source+task.id), so a rerun + # that aborts before writing a fresh session/model.patch would otherwise harvest and grade + # the previous attempt's stale artifacts. + if capture_dir.exists(): + shutil.rmtree(capture_dir) + capture_dir.mkdir(parents=True, exist_ok=True) + (capture_dir / "prompt.txt").write_text(instance.get("problem_statement") or "", encoding="utf-8") + + # Namespace all scratch (image tag, container, env-file) by source so two sources that + # contain the same instance can't collide under concurrency; lowercased for Docker. + key = base.slug(f"{base.source_id(source)}-{instance['instance_id']}").lower() + # Langfuse is intentionally not wired here (guarded against in tasks()); render it off so + # the image never carries a phantom tracing block. + dockerfile = render_agent_dockerfile(spec.instance_image_key, layer) + agent_image = f"teich-swe-{key}:latest" + timeout = cfg.timeout_seconds if cfg.timeout_seconds and cfg.timeout_seconds > 0 else None + _build_image(dockerfile, agent_image, timeout=timeout) + + # Pass credentials via an env-file (host-only, outside the mounted dir, mode 0600) rather + # than `-e KEY=VALUE`, so keys aren't visible in the host process list. Note: they still + # become container env vars (readable via `docker inspect` / inside the container), so this + # guards the host's `ps`, not a hostile Docker-socket user. + env = {**layer.env, **_auth_env(cfg)} + env_file = capture_dir.parent / f"{key}.env" + fd = os.open(env_file, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o600) + os.fchmod(fd, 0o600) # O_CREAT's mode only applies on create (and is umask-masked); enforce 0600 either way + with os.fdopen(fd, "w", encoding="utf-8") as handle: + handle.write("".join(f"{name}={value}\n" for name, value in env.items())) + + container = f"teich-swe-{key}" + capture = f"{CAPTURE}/model.patch" + run = _run_command(cfg, layer) + shell = ( + f"cd {TESTBED} && ({run} || true) && git -C {TESTBED} add -A && " + f"git -C {TESTBED} diff --cached --no-color > {capture} 2>/dev/null || true; " + # The container runs as root and writes the agent's session dir into the bind-mounted + # capture tree. On native-Linux Docker those files land root-owned, so the next no-resume + # run's shutil.rmtree(capture_dir) fails; hand the tree back to the host UID before exit. + f"chmod -R a+rwX {CAPTURE} 2>/dev/null || true" + ) + cmd = [ + "docker", "run", "--rm", "--name", container, "--env-file", str(env_file), + "-v", f"{capture_dir}:{CAPTURE}", + agent_image, "bash", "-lc", shell, + ] + try: + result = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout, check=False) + except subprocess.TimeoutExpired: + _docker(["rm", "-f", container], check=False) # reap the named container, then fail the task + raise + finally: + env_file.unlink(missing_ok=True) + # Remove the per-task agent-layer image so it doesn't accumulate (the shared swebench + # instance image is left in place — it's reused and expensive to re-pull). Best-effort: + # check=False only swallows non-zero exits, so a missing docker / hang must not mask the + # real task result during this finally. + if not cfg.output.keep_bench_images: + try: + _docker(["rmi", "-f", agent_image], timeout=30, check=False) + except Exception: + pass + if result.returncode != 0: + # The shell masks the *agent's* exit with `|| true`, so a non-zero exit here is a Docker/ + # infra failure — fail the task rather than grade an empty patch as a real result. + raise RuntimeError( + f"docker run failed for {container} (exit {result.returncode}): " + f"{(result.stderr or result.stdout or '').strip()[-1000:]}" + ) + + patch_file = capture_dir / "model.patch" + model_patch = patch_file.read_text(encoding="utf-8") if patch_file.is_file() else "" + lines, native_dir = _native_trace(capture_dir, layer.session_glob) + return lines, native_dir, model_patch + + +def _grade(cfg: Config, instance: dict[str, Any], spec: Any, model_patch: str) -> dict[str, Any]: + """Grade a candidate patch with swebench's harness; return the report entry for the instance.""" + import docker + from swebench.harness.constants import RUN_EVALUATION_LOG_DIR + from swebench.harness.run_evaluation import run_instance + + instance_id = instance["instance_id"] + model_name = "teich" + run_id = f"teich-{base.slug(instance_id)}" + prediction = { + "instance_id": instance_id, + "model_name_or_path": model_name, + "model_patch": model_patch, + } + timeout = cfg.timeout_seconds if cfg.timeout_seconds and cfg.timeout_seconds > 0 else None + report_path = ( + Path(RUN_EVALUATION_LOG_DIR) / run_id / model_name / instance_id / "report.json" + ) + # run_id is deterministic, so drop any prior report first: if this rerun fails before + # writing a fresh one, we must not grade off a stale verdict from a previous attempt. + report_path.unlink(missing_ok=True) + run_instance( + spec, prediction, rm_image=False, force_rebuild=False, + client=docker.from_env(), run_id=run_id, timeout=timeout, + ) + if not report_path.is_file(): + return {"resolved": False, "patch_successfully_applied": bool(model_patch)} + report = json.loads(report_path.read_text(encoding="utf-8")) + return report.get(instance_id, {"resolved": False}) + + +# --------------------------------------------------------------------------- backend + + +class SweBenchBackend: + """Runs teich's agent against SWE-bench instances and grades the diff with swebench.""" + + type = "swe-bench" + + def require(self) -> None: + if sys.version_info < (3, 10): + raise RuntimeError( + "The swe-bench bench backend requires Python 3.10+ " + f"(current: {sys.version_info.major}.{sys.version_info.minor}). {SWE_INSTALL_HINT}" + ) + try: + import swebench # noqa: F401 + except ImportError as exc: # pragma: no cover - only without the extra + raise RuntimeError(SWE_INSTALL_HINT) from exc + + def tasks(self, cfg: Config, source: BenchSource, *, refresh: bool = False) -> list[base.BenchTask]: + # refresh is a no-op here: HF datasets handle their own cache. + if cfg.agent.langfuse.enabled: + # The swe-bench backend does not wire Langfuse into the agent container (no + # creds in the env-file, no install layer), so tracing would silently no-op. + # Fail loudly rather than pretend support. + raise RuntimeError( + "The swe-bench bench backend does not support Langfuse tracing; " + "set agent.langfuse.enabled = false for swe-bench sources." + ) + return [ + base.BenchTask(id=instance["instance_id"], raw=instance) + for instance in _load_instances(source) + ] + + def run(self, cfg: Config, source: BenchSource, task: base.BenchTask) -> base.BenchRun: + from swebench.harness.test_spec.test_spec import make_test_spec + + instance = task.raw + layer = _agent_layer(cfg) # fail fast on an unsupported provider, before any Docker work + # Default "swebench" pulls the published instance image; namespace=None builds it locally + # (make_test_spec + _ensure_instance_image both honor it), needed for custom instances. + namespace = source.namespace + spec = make_test_spec(instance, namespace=namespace) + timeout = cfg.timeout_seconds if cfg.timeout_seconds and cfg.timeout_seconds > 0 else None + _ensure_instance_image(spec, namespace=namespace, timeout=timeout) + + capture_dir = base.bench_root(cfg) / "swe" / base.bench_stem(source, task.id) + lines, native_dir, model_patch = _run_agent(cfg, source, instance, spec, layer, capture_dir) + + entry = _grade(cfg, instance, spec, model_patch) + rewards = _rewards_from_report(entry) + metadata = { + "instance_id": task.id, + "repo": instance.get("repo"), + "base_commit": instance.get("base_commit"), + "model_patch": model_patch, + "patch_applied": bool(entry.get("patch_successfully_applied")), + **base.trace_metadata(native_dir), + } + return base.BenchRun(native_lines=lines, rewards=rewards, metadata=metadata) diff --git a/src/teich/bench/runner.py b/src/teich/bench/runner.py new file mode 100644 index 0000000..cc319eb --- /dev/null +++ b/src/teich/bench/runner.py @@ -0,0 +1,250 @@ +"""Drive ``teich generate --mode bench``. + +A thin loop over ``cfg.bench.sources``: each source declares a ``type`` (harbor, +swe-bench), resolved to a backend that turns tasks into native traces + rewards; the +shared harvest (``backends.base.harvest``) routes each into passed/failed/borderline and +writes a per-task ``metadata/`` sidecar. Backends are the only thing that differs per type. +""" + +from __future__ import annotations + +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path +from typing import TYPE_CHECKING, Any + +from .backends import BenchTask, base, get_backend + +if TYPE_CHECKING: + from ..config import BenchSource, Config + +# api.wire_api values that mean "chat completions" (mirrors runner._pi_provider_api); anything +# else (e.g. "responses") is the OpenAI Responses API, which the bench agents default to. +_CHAT_WIRE_APIS = {"completions", "chat_completions", "chat-completions", "openai-completions"} + + +class _BenchProgress: + """Live per-source progress bar for bench runs (rich); a no-op when not a terminal. + + Bench tasks are coarse (one container per task, minutes each), so this shows a per-source + bar with an M/N count and a running passed/failed/borderline tally rather than prompt mode's + per-session table. Counts are tracked even when the bar is disabled (non-terminal / no + console) so the logic is testable; printing via the caller's ``out`` interleaves above the + live bars. It never installs a signal handler, so the Ctrl-C handling in ``run_bench`` is + unaffected. + """ + + def __init__(self, console: Any): + self._console = console + self.enabled = bool(console is not None and getattr(console, "is_terminal", False)) + self._progress: Any = None + self.passed = self.failed = self.borderline = self.errored = 0 + + def __enter__(self) -> _BenchProgress: + if self.enabled: + from rich.progress import ( + BarColumn, + MofNCompleteColumn, + Progress, + SpinnerColumn, + TextColumn, + TimeElapsedColumn, + ) + + self._progress = Progress( + SpinnerColumn(), + TextColumn("[bold blue]{task.description}"), + BarColumn(), + MofNCompleteColumn(), + TextColumn("{task.fields[tally]}"), + TimeElapsedColumn(), + console=self._console, + ) + self._progress.__enter__() + return self + + def __exit__(self, *exc: Any) -> None: + if self._progress is not None: + self._progress.__exit__(*exc) + self._progress = None + + def add_source(self, label: str, total: int) -> Any: + """Start a bar for one source; returns an opaque handle (None when disabled).""" + return self._progress.add_task(label, total=total, tally="") if self._progress else None + + def _tally(self) -> str: + parts = [] + if self.passed: + parts.append(f"[green]{self.passed}✓[/green]") + if self.failed: + parts.append(f"[red]{self.failed}✗[/red]") + if self.borderline: + parts.append(f"[yellow]{self.borderline}~[/yellow]") + if self.errored: + parts.append(f"[red]{self.errored}![/red]") + return " ".join(parts) + + def advance(self, bar: Any, *, split: str | None = None, errored: bool = False) -> None: + """Mark one task done: bump the split/error tally and advance its source bar.""" + if errored: + self.errored += 1 + elif split in ("passed", "failed", "borderline"): + setattr(self, split, getattr(self, split) + 1) + if self._progress is not None and bar is not None: + self._progress.update(bar, advance=1, tally=self._tally()) + + +def run_bench( + cfg: Config, *, console: Any = None, resume: bool = False, refresh: bool = False +) -> list[Path]: + """Run every configured bench source through its backend and harvest reward-labeled traces. + + Tasks within a source run through a bounded pool of size ``cfg.max_concurrency`` (default 1); + each task is isolated (its own container + per-task output files), the resume-skip is checked + before dispatch, and the harvest runs on the main thread as results complete. Backends honor + ``cfg.timeout_seconds`` on their own container runs. + """ + sources = cfg.bench.sources + if not sources: + raise RuntimeError( + "--mode bench requires at least one entry in bench.sources, e.g.\n" + " bench:\n" + " sources:\n" + " - { type: harbor, source: terminal-bench@2.0 }\n" + " - { type: swe-bench, source: SWE-bench/SWE-bench_Verified }" + ) + + # Bench backends don't yet seed the Codex host-auth snapshot / token broker into the task + # containers (unlike prompt mode). Fail fast rather than launch a silently-unauthenticated + # run when host auth is the only credential configured. + if cfg.get_agent_provider() == "codex" and cfg.agent.codex.use_host_auth and not cfg.get_api_key(): + raise RuntimeError( + "bench mode does not yet support Codex host auth (agent.codex.use_host_auth). " + "Configure an API key for the run, or disable use_host_auth." + ) + + # Unlike prompt mode, bench doesn't thread api.wire_api into the agent (no codex config, no pi + # provider_settings), so a chat-completions-only endpoint isn't honored: pi/codex default to the + # Responses API and hit /responses on e.g. api.z.ai, which silently fails. openrouter is exempt + # (its own model-prefix routing already selects completions). Fail fast instead of ignoring it. + _provider = cfg.api.provider.strip().lower() + if ( + cfg.get_agent_provider() in ("codex", "pi") + and _provider != "openrouter" + and cfg.api.wire_api.strip().lower() in _CHAT_WIRE_APIS + ): + raise RuntimeError( + "bench mode does not yet thread api.wire_api into the agent, so a chat-completions-only " + f"endpoint won't be honored (api.wire_api={cfg.api.wire_api!r}, api.provider={_provider!r}, " + f"agent={cfg.get_agent_provider()!r}); the agent would default to the Responses API. " + "Use api.provider: openrouter, or api.wire_api: responses against a Responses-API endpoint." + ) + + def out(message: str) -> None: + if console is not None: + console.print(message) + + max_workers = max(1, int(cfg.max_concurrency)) + written: list[Path] = [] + attempted = 0 + interrupt_hint = "Re-run with --resume to continue from where it stopped." + + with _BenchProgress(console) as progress: + + def record(task: BenchTask, run: base.BenchRun, src: BenchSource) -> str | None: + """Harvest a task's trace; return its split (None if nothing was harvested).""" + if not run.native_lines: + out(f"[yellow]bench: no trace harvested for {task.id}[/yellow]") + return None + paths, split = base.harvest(cfg, src, task, run) + written.extend(paths) + if not progress.enabled: # the live bar's tally already conveys per-task outcomes + primary = base.primary_score(run.rewards) + score = f"reward={primary:g}" if primary is not None else "unscored" + out(f"[green]bench: {task.id}: {split} ({score})[/green]") + return split + + for source in sources: + backend = get_backend(source.type) + backend.require() + # Source-level errors (bad spec, download failure) abort the run. + tasks = list(backend.tasks(cfg, source, refresh=refresh)) + + pending: list[BenchTask] = [] + for task in tasks: + if resume: + existing = base.existing_output(cfg, base.bench_stem(source, task.id)) + if existing is not None: + out(f"[yellow]bench: skipping {task.id} (already harvested)[/yellow]") + written.append(existing) + continue + pending.append(task) + out( + f"[blue]bench[{source.type}]: {source.source} -> {len(pending)} task(s) " + f"(concurrency {max_workers})[/blue]" + ) + if not pending: + continue + attempted += len(pending) + bar = progress.add_source(f"{source.type}: {source.source}", len(pending)) + + # Bind src + the bound run method as defaults so the closure doesn't capture the + # loop variables by reference (ruff B023). + def _run( + task: BenchTask, src: BenchSource = source, run=backend.run + ) -> tuple[BenchTask, base.BenchRun]: + return task, run(cfg, src, task) + + if max_workers == 1: + # Single worker: run inline on the main thread so Ctrl-C propagates straight into + # the agent/Docker call and stops it. A thread pool would move the work off the + # main thread, where SIGINT can't reach it, so the container would keep running. + try: + for task in pending: + try: + _, run = _run(task) + except Exception as exc: # one task's failure (docker/agent/grade) — skip it + progress.advance(bar, errored=True) + out(f"[red]bench: {task.id}: failed ({type(exc).__name__}: {exc})[/red]") + continue + progress.advance(bar, split=record(task, run, source)) + except KeyboardInterrupt: + out(f"[red]bench: interrupted. {interrupt_hint}[/red]") + raise + continue + + # Bounded concurrency: harvest on the main thread as results complete. On Ctrl-C, drop + # the queued tasks immediately (cancel_futures); workers already inside a Docker call + # can't be signalled, so those few finish before the process exits. + pool = ThreadPoolExecutor(max_workers=max_workers) + interrupted = False + try: + futures = {pool.submit(_run, task): task for task in pending} + for future in as_completed(futures): + task = futures[future] + try: + _, run = future.result() + except Exception as exc: # one task's failure (docker/agent/grade) — skip it + progress.advance(bar, errored=True) + out(f"[red]bench: {task.id}: failed ({type(exc).__name__}: {exc})[/red]") + continue + progress.advance(bar, split=record(task, run, source)) + except KeyboardInterrupt: + interrupted = True + out( + "[red]bench: interrupt — cancelling queued tasks; in-flight containers will " + f"finish before exit. {interrupt_hint}[/red]" + ) + raise + finally: + pool.shutdown(wait=not interrupted, cancel_futures=interrupted) + + # Per-task failures are swallowed (skipped) above; if every dispatched task failed we'd + # otherwise return an empty list and the CLI would exit 0 — a misconfigured run then looks + # like a successful empty benchmark in automation. Distinguish it from a legitimately empty + # run (nothing dispatched / all resume-skipped, which keeps `written` populated). + if attempted and not written: + raise RuntimeError( + f"bench: all {attempted} attempted task(s) failed; no rows were harvested " + "(see the per-task errors above)." + ) + return written diff --git a/src/teich/bench/templates/agent.dockerfile.j2 b/src/teich/bench/templates/agent.dockerfile.j2 new file mode 100644 index 0000000..ef824b2 --- /dev/null +++ b/src/teich/bench/templates/agent.dockerfile.j2 @@ -0,0 +1,26 @@ +{#- Teich SWE-bench agent layer. + + Rendered on top of a SWE-bench *instance* image (which already has the repo + checked out at base_commit in /testbed with its deps installed) so a teich agent + can edit /testbed. The agent's git diff becomes the candidate patch, which swebench + then grades. Generated file — do not edit by hand; change agent.dockerfile.j2. + + Vars: base_image, provider, runtime_install, agent_install, langfuse (+ langfuse_install). + Whitespace is trimmed after this comment so the syntax directive stays the first line. +-#} +# syntax=docker/dockerfile:1 +FROM {{ base_image }} + +USER root +ENV DEBIAN_FRONTEND=noninteractive + +# --- runtime ({{ provider }}) --- +{{ runtime_install }} + +# --- {{ provider }} agent CLI --- +{{ agent_install }} +{% if langfuse %} +# --- Langfuse tracing (side-channel: reads transcripts, fails open) --- +{{ langfuse_install }} +{% endif %} +WORKDIR /testbed diff --git a/src/teich/cli.py b/src/teich/cli.py index 3aa0a8a..078f2b1 100644 --- a/src/teich/cli.py +++ b/src/teich/cli.py @@ -19,7 +19,7 @@ from .anonymize import anonymize_path from .config import Config -from .converter import convert_traces_to_training_data +from .converter import NON_DATA_TRACE_DIR_NAMES, convert_traces_to_training_data from .extract import CURSOR_EXTRACTION_NOTICE, ExtractProvider, extract_local_sessions from .runner import ( ChatRunner, @@ -177,8 +177,7 @@ def _help_group(name: str, extra_help: str) -> type[ExtraHelpGroup]: cls=_help_group("PoolHelpGroup", POOL_EXTRA_HELP), ) app.add_typer(pool_app, name="pool") -NON_DATA_TRACE_DIR_NAMES = {"partials", "failures"} -UPLOAD_IGNORE_PATTERNS = ["partials/**", "failures/**"] +UPLOAD_IGNORE_PATTERNS = ["partials/**", "failures/**", "bench/**"] UPLOAD_METADATA_PATTERNS = ["README.md", "tools.json"] @@ -211,6 +210,25 @@ def _has_non_empty_trace_outputs(traces_dir: Path) -> bool: return False +def _existing_dataset_modes(traces_dir: Path) -> set[str]: + """Which generation modes already wrote dataset rows into ``traces_dir``. + + bench rows are ``bench-.jsonl``; anything else (incl. routed + ``passed/``|``failed/`` traces) is prompts. Used to keep bench and prompts as + separate, homogeneous datasets — a mixed dir is unpleasant to consume. + """ + modes: set[str] = set() + if not traces_dir.exists(): + return modes + for path in traces_dir.rglob("*.jsonl"): + if not path.is_file() or path.stat().st_size == 0: + continue + if NON_DATA_TRACE_DIR_NAMES.intersection(path.relative_to(traces_dir).parts): + continue + modes.add("bench" if path.name.startswith("bench-") else "prompts") + return modes + + def _write_output_metadata(cfg: Config) -> Path: return write_traces_readme( cfg.output.traces_dir, @@ -220,6 +238,9 @@ def _write_output_metadata(cfg: Config) -> Path: repo_id=cfg.get_publish_repo_id(), tools=snapshot_configured_tools(cfg), excluded_dirs=[cfg.output.failures_dir], + license=cfg.output.license, + card_extra=cfg.output.card_extra, + readme_template=cfg.output.readme_template, ) @@ -382,6 +403,9 @@ def _write_extract_readme( model_id=None, repo_id=cfg.get_publish_repo_id(), extraction_provider=provider, + license=cfg.output.license, + card_extra=cfg.output.card_extra, + readme_template=cfg.output.readme_template, ) @@ -652,8 +676,18 @@ def generate( "--resume", help="Skip prompts that already have completed answers in the output directory", ), + mode: str = typer.Option( + "prompts", + "--mode", + help="Generation mode: 'prompts' (default) or 'bench' (run benchmark tasks from bench.sources).", + ), + refresh: bool = typer.Option( + False, + "--refresh", + help="Bench mode (harbor sources only): re-download a remote harbor bench source even if it's already cached. No effect on swe-bench, which uses Hugging Face's own dataset cache.", + ), ) -> None: - """Generate training traces from prompts.""" + """Generate agent traces: plain traces from prompts (--mode prompts), or reward-labeled benchmark traces from bench.sources (--mode bench).""" console.print(Panel.fit("Teich", style="bold blue")) if not config.exists(): @@ -666,6 +700,45 @@ def generate( if concurrency is not None: cfg.max_concurrency = concurrency + if mode not in {"prompts", "bench"}: + console.print(f"[red]Unknown --mode '{mode}'. Use 'prompts' or 'bench'.[/red]") + raise typer.Exit(1) + conflicting_modes = _existing_dataset_modes(cfg.output.traces_dir) - {mode} + if conflicting_modes: + other = ", ".join(sorted(conflicting_modes)) + console.print( + f"[red]{cfg.output.traces_dir} already contains {other}-mode data; " + f"refusing to mix it with {mode}-mode output.[/red]" + ) + console.print( + "[yellow]Keep bench and prompts as separate datasets (a mixed dataset is unpleasant " + "to consume). Point --output at a fresh directory, or use a separate config/project " + "with its own output.traces_dir and publish.repo_id.[/yellow]" + ) + raise typer.Exit(1) + if mode == "bench": + from .bench import run_bench # lazy: harbor is an optional extra + + try: + written = run_bench(cfg, console=console, resume=resume, refresh=refresh) + except RuntimeError as exc: + console.print(f"[red]{exc}[/red]") + # An earlier source may have harvested rows before a later one failed; write the card + # for that partial dataset (like prompt mode does on error). Don't auto-publish. + readme_path = _try_write_completed_output_metadata(cfg) + if readme_path: + console.print(f"[green]Wrote README: {readme_path}[/green]") + raise typer.Exit(1) from exc + if written: + readme_path = _write_output_metadata(cfg) + console.print(f"[green]Wrote README: {readme_path}[/green]") + if cfg.get_publish_repo_id(): + repo_url = _publish_dataset_to_hub(cfg) + console.print(f"[green]Published dataset: {repo_url}[/green]") + else: + console.print("[yellow]bench: no rows written; skipping dataset card.[/yellow]") + return + prompt_inputs = unique_prompt_inputs_by_completion_key(cfg.get_prompt_inputs()) if not prompt_inputs: console.print("[red]No prompts configured. Add prompts in config.yaml or prompts.jsonl.[/red]") @@ -1169,6 +1242,11 @@ def init( # These files are excluded from resume detection, conversion, README generation, and uploads. failures_dir: ./failures + # Harbor bench-mode intermediates (downloaded sources, raw trials, normalized sessions). + # Defaults to a `bench` dir beside traces_dir (parallel to sandbox/failures), never inside + # the dataset. Set a path to override. + bench_dir: null + # Used in the generated trace README. pretty_name: "My Agent Traces" @@ -1179,6 +1257,25 @@ def init( hf_token: null private: false +# Benchmark mode (`teich generate --mode bench`). Each source is run by a backend selected +# by `type`: `harbor` (the harbor package; needs teich[harbor], Python 3.12+) or `swe-bench` +# (the swebench package; needs teich[swe]). teich runs the configured agent on each task/ +# instance, harvests the plain native trace into output/{passed,failed,borderline}/, and +# writes scores + provenance to output/metadata/.json. Intermediates live under +# output.bench_dir (a sibling `bench` dir by default), outside the dataset/uploads. +# `--resume` skips already-harvested tasks; `--refresh` re-downloads remote harbor sources +# (no effect on swe-bench, which uses Hugging Face's own dataset cache). +# Keep bench its own project: dedicated output.traces_dir + publish.repo_id. +bench: + sources: [] + # - { type: harbor, source: terminal-bench@2.0 } # registry spec + # - { type: harbor, source: ./local-tasks } # local task dir / dir of task dirs + # - { type: swe-bench, source: SWE-bench/SWE-bench_Verified, split: test } + # - { type: swe-bench, source: SWE-bench/SWE-bench_Lite, instances: [django__django-12345] } + # - { type: swe-bench, source: ./my-instances.jsonl, namespace: null } # build custom images locally + # # optional per-source: repo (git/HF registry), version, instances [..], backend (harbor); + # # swe-bench namespace (default "swebench" pulls published images; null builds locally) + # Number of prompts to run in parallel. max_concurrency: 1 diff --git a/src/teich/config.py b/src/teich/config.py index 4917124..5c10b35 100644 --- a/src/teich/config.py +++ b/src/teich/config.py @@ -185,7 +185,32 @@ class OutputConfig(BaseModel): traces_dir: Path = Field(default=Path("./output")) sandbox_dir: Path = Field(default=Path("./sandbox")) failures_dir: Path = Field(default=Path("./failures")) + # Harbor bench intermediates (trials/sources/sessions). None -> a sibling ``bench`` + # dir next to traces_dir, parallel to sandbox/failures (never inside the dataset). + bench_dir: Path | None = None + # Remove each task's per-task Docker image after the bench run (harbor's ``hb__`` + # image or the pulled prebuilt task image, the swe-bench agent layer) so they don't + # accumulate and fill the disk. Set True to keep them (e.g. for debugging, or to avoid + # rebuilds/re-pulls across runs). + keep_bench_images: bool = False pretty_name: str = "Agentic Training Traces" + # Dataset-card customization (all optional; the default card is unchanged when unset). + # ``license`` adds an SPDX id to the card frontmatter; ``card_extra`` merges arbitrary + # extra frontmatter keys; ``readme_template`` points at a user Jinja2 template that + # replaces the shipped default (rendered with the same context). + license: str | None = None + card_extra: dict[str, Any] = Field(default_factory=dict) + readme_template: Path | None = None + + @field_validator("readme_template") + @classmethod + def validate_readme_template(cls, value: Path | None) -> Path | None: + if value is None: + return None + path = Path(value).expanduser() + if not path.is_file(): + raise ValueError(f"output.readme_template not found: {path}") + return path class PublishConfig(BaseModel): @@ -277,6 +302,35 @@ def turn_prompts(self) -> list[str]: return [self.prompt, *self.follow_up_prompts] +class BenchSource(BaseModel): + """One benchmark source for ``generate --mode bench``. + + ``type`` selects the backend (``harbor`` | ``swe-bench``). ``source`` is the dataset + spec for that backend: harbor → a local task dir, a registry spec (``name@version`` or + ``org/name@ref``), or (with ``repo``) a dataset name in a git/HF registry; swe-bench → + a Hugging Face dataset id (or a local json/jsonl path). ``version``/``repo`` are harbor + knobs; ``split``/``instances`` select swe-bench rows; ``backend`` is harbor's + EnvironmentType. Remote data is fetched into ``/`` and run locally. + + ``namespace`` (swe-bench only) is the Docker image namespace for instance images: the + default ``"swebench"`` pulls the published ``swebench/...`` images; set it to ``null`` to + build instance images locally, which is required for custom/unpublished instances. + """ + type: str = "harbor" + source: str + repo: str | None = None + version: str | None = None + split: str | None = None + instances: list[str] | None = None + backend: str = "docker" + namespace: str | None = "swebench" + + +class BenchConfig(BaseModel): + """Bench-mode settings: an array of benchmark sources, each run by its backend.""" + sources: list[BenchSource] = Field(default_factory=list) + + class Config(BaseModel): """Main configuration.""" agent: AgentConfig = Field(default_factory=AgentConfig) @@ -287,6 +341,7 @@ class Config(BaseModel): prompts_file: Path | None = None output: OutputConfig = Field(default_factory=OutputConfig) publish: PublishConfig = Field(default_factory=PublishConfig) + bench: BenchConfig = Field(default_factory=BenchConfig) max_concurrency: int = Field(default=1, ge=1) timeout_seconds: int = 600 openai_api_key: str | None = None @@ -345,6 +400,35 @@ def from_yaml(cls, path: Path) -> Config: if not Path(prompts_file_path).is_absolute(): data["prompts_file"] = (path.parent / prompts_file_path).resolve() + # Resolve a relative output.readme_template against the config file, like prompts_file + # above — otherwise it's validated against the process CWD and `teich -c project/x.yaml` + # from elsewhere fails even when the template sits next to the config. + output_section = data.get("output") + if isinstance(output_section, dict): + template = output_section.get("readme_template") + if isinstance(template, str) and template.strip(): + template_path = Path(template).expanduser() + if not template_path.is_absolute(): + output_section["readme_template"] = (path.parent / template_path).resolve() + + # Resolve a relative *local* bench source (./tasks, ../x, ~/x) against the config dir, like + # prompts_file — otherwise it's tried against the process CWD and falls through to the + # remote-download path. A registry spec (name@version, org/name) never starts with those. + bench_section = data.get("bench") + if isinstance(bench_section, dict): + for bench_source in bench_section.get("sources") or []: + if not isinstance(bench_source, dict): + continue + spec = bench_source.get("source") + if isinstance(spec, str) and spec.strip(): + spec_str = spec.strip() + candidate = path.parent / Path(spec_str).expanduser() + # Rewrite an explicit ./ ../ ~ path, or a bare relative that actually exists + # beside the config (data/tasks, instances.jsonl). A registry/HF spec + # (name@version, org/name) has no such local path, so it's left untouched. + if spec_str.startswith(("./", "../", "~")) or candidate.exists(): + bench_source["source"] = str(candidate.resolve()) + # Apply environment variable overrides if model_env := _get_env_alias("TEICH_MODEL"): data.setdefault("model", {})["model"] = model_env diff --git a/src/teich/converter.py b/src/teich/converter.py index 1061903..85eef31 100644 --- a/src/teich/converter.py +++ b/src/teich/converter.py @@ -18,6 +18,15 @@ ) _INLINE_THINKING_BLOCK_PATTERN = re.compile(r"<(think|thinking)>(.*?)", re.DOTALL) +# Output subdirectories that hold non-dataset artifacts (partial/failed runs, verifier +# sidecars, sandbox checkouts, harbor bench intermediates). Dataset scanners, the README +# builder, and the mode guard skip these so the dataset view stays clean. (Publish keeps +# its own UPLOAD_IGNORE_PATTERNS in cli.py, which drops partials/failures/bench but still +# uploads verifier sidecars as dataset provenance.) The bench intermediates normally live +# in a sibling ``bench`` dir; the name is listed here only to defend a nested override. +NON_DATA_TRACE_DIR_NAMES = frozenset( + {"partials", "failures", "verification", "sandbox", "__pycache__", "bench"} +) FIRST_MESSAGE_TIMESTAMP_METADATA_KEY = "first_message_timestamp" PI_SYSTEM_PROMPT_CUSTOM_TYPE = "teich-system-prompt" TEICH_AVAILABLE_TOOLS_CUSTOM_TYPE = "teich-available-tools" @@ -3933,7 +3942,8 @@ def _jsonl_files(source: Path) -> list[Path]: return sorted( path for path in source.rglob("*.jsonl") - if path.is_file() and not {"partials", "failures"}.intersection(path.relative_to(source).parts) + if path.is_file() + and not NON_DATA_TRACE_DIR_NAMES.intersection(path.relative_to(source).parts) ) @@ -3979,5 +3989,6 @@ def convert_traces_to_training_data(traces_dir: Path | str, *, skip_invalid_line trace_files = _jsonl_files(source) rows: list[dict[str, Any]] = [] for path in trace_files: - rows.extend(_convert_jsonl_file_to_training_rows(path, skip_invalid_lines=skip_invalid_lines)) + file_rows = _convert_jsonl_file_to_training_rows(path, skip_invalid_lines=skip_invalid_lines) + rows.extend(file_rows) return rows diff --git a/src/teich/runner.py b/src/teich/runner.py index 3a38933..c510aed 100644 --- a/src/teich/runner.py +++ b/src/teich/runner.py @@ -27,11 +27,13 @@ if TYPE_CHECKING: from .config import Config +from . import agent_cfg from .config import PromptInput from .codex_auth_broker import CodexTokenBroker from .converter import ( + NON_DATA_TRACE_DIR_NAMES, TEICH_AVAILABLE_TOOLS_CUSTOM_TYPE, convert_trace_to_training_example, convert_traces_to_training_data, @@ -69,7 +71,6 @@ HERMES_TRACE_WRITE_LOCK = threading.Lock() CHAT_REQUEST_MAX_ATTEMPTS = 3 AGENT_TURN_RETRY_LIMIT = 3 -NON_DATA_TRACE_DIR_NAMES = {"partials", "failures"} def _make_tree_world_writable(path: Path) -> None: @@ -1045,14 +1046,7 @@ def _build_image(self) -> None: @staticmethod def _container_base_url(base_url: str | None) -> str | None: - if not base_url: - return None - parsed = urlsplit(base_url) - hostname = parsed.hostname or "" - if hostname not in {"localhost", "127.0.0.1"}: - return base_url - netloc = parsed.netloc.replace(hostname, "host.docker.internal", 1) - return urlunsplit((parsed.scheme, netloc, parsed.path, parsed.query, parsed.fragment)) + return agent_cfg.container_base_url(base_url) def _langfuse_container_base_url(self) -> str | None: return self._container_base_url(self.config.agent.langfuse.base_url) @@ -2864,26 +2858,7 @@ class ExternalCliRunner(DockerRuntimeRunner): @staticmethod def _provider_env_key(provider: str) -> str: - normalized = re.sub(r"[^a-zA-Z0-9_]+", "_", provider.strip().lower()) - aliases = { - "anthropic": "ANTHROPIC_API_KEY", - "claude": "ANTHROPIC_API_KEY", - "claude_code": "ANTHROPIC_API_KEY", - "claude-code": "ANTHROPIC_API_KEY", - "openai": "OPENAI_API_KEY", - "openrouter": "OPENROUTER_API_KEY", - "nous": "NOUS_API_KEY", - "nous_portal": "NOUS_API_KEY", - "google": "GOOGLE_API_KEY", - "gemini": "GEMINI_API_KEY", - "deepseek": "DEEPSEEK_API_KEY", - "xai": "XAI_API_KEY", - "grok": "XAI_API_KEY", - "zai": "GLM_API_KEY", - "z_ai": "GLM_API_KEY", - "glm": "GLM_API_KEY", - } - return aliases.get(normalized, f"{normalized.upper() or 'TEICH'}_API_KEY") + return agent_cfg.provider_env_key(provider) def _api_env_items(self) -> list[tuple[str, str]]: api_key = self.config.get_api_key() or "" diff --git a/src/teich/studio/extraction.py b/src/teich/studio/extraction.py index 2d828b0..d945af7 100644 --- a/src/teich/studio/extraction.py +++ b/src/teich/studio/extraction.py @@ -173,6 +173,9 @@ def _write_readme(self) -> None: model_id=None, repo_id=cfg.get_publish_repo_id(), extraction_provider=self.provider, + license=cfg.output.license, + card_extra=cfg.output.card_extra, + readme_template=cfg.output.readme_template, ) self.events.append({"kind": "extract_readme", "text": f"Wrote {readme_path.name}.", "path": str(readme_path)}) diff --git a/src/teich/studio/generation.py b/src/teich/studio/generation.py index f1b426d..f86ef9f 100644 --- a/src/teich/studio/generation.py +++ b/src/teich/studio/generation.py @@ -185,6 +185,9 @@ def _write_readme(self) -> None: repo_id=self.config.get_publish_repo_id(), tools=snapshot_configured_tools(self.config), excluded_dirs=[self.config.output.failures_dir], + license=self.config.output.license, + card_extra=self.config.output.card_extra, + readme_template=self.config.output.readme_template, ) except Exception: pass diff --git a/src/teich/studio/server.py b/src/teich/studio/server.py index e026b95..72f33a7 100644 --- a/src/teich/studio/server.py +++ b/src/teich/studio/server.py @@ -844,6 +844,9 @@ def upload_dataset(payload: DatasetUploadRequest) -> dict[str, Any]: repo_id=publish.repo_id, extraction_provider=extraction_provider, excluded_dirs=[cfg.output.failures_dir], + license=cfg.output.license, + card_extra=cfg.output.card_extra, + readme_template=cfg.output.readme_template, ) repo_url = _upload_dataset_folder( folder_path=cfg.output.traces_dir, diff --git a/src/teich/templates/dataset_card.md.j2 b/src/teich/templates/dataset_card.md.j2 new file mode 100644 index 0000000..298f8ae --- /dev/null +++ b/src/teich/templates/dataset_card.md.j2 @@ -0,0 +1,140 @@ +--- +pretty_name: "{{ pretty_name }}" +task_categories: +- text-generation +{% if tags %} +tags: +{% for tag in tags %} +- "{{ tag }}" +{% endfor %} +{% endif %} +configs: +- config_name: default + data_files: +{% for split, path in data_files %} + - split: {{ split }} + path: "{{ path }}" +{% endfor %} +{% if license %} +license: {{ license }} +{% endif %} +{% if size_categories %} +size_categories: +{% for size in size_categories %} +- {{ size }} +{% endfor %} +{% endif %} +{{ card_extra_yaml }}--- + +This dataset was generated using [teich](https://github.com/TeichAI/teich) by [TeichAI](https://huggingface.co/TeichAI) + +# {{ pretty_name }} + +{{ intro_line }} + +{{ rows_line }} + +{% if model_id %} +Model metadata: `{{ model_id }}` + +{% endif %} +{% if reward_stats %} +## Reward labels + +This is a verifiable dataset: rows carry the task verifier's outcome so it can be used for reward-based training and filtering. + +- `passed` (bool): whether the task verifier succeeded after the agent's edits +- `reward` (float): the verifier's own score when it reports one, else the binary `1.0`/`0.0` implied by `passed` + +Verified tasks: {{ reward_stats.total }} ({{ reward_stats.passed }} passed / {{ reward_stats.failed }} failed{% if reward_stats.borderline %} / {{ reward_stats.borderline }} borderline{% endif %}). +Each reward comes from the task verifier run after the agent's attempt. +{% if reward_stats.numeric %} +{{ reward_stats.numeric }} of {{ reward_stats.total }} carry an explicit numeric score (the rest are binary pass/fail). +{% endif %} + +{% endif %} +{% if dataset_tools %} +## Training-ready tools + +Generated agent traces carry configured or recovered tool schemas so tools remain available for training even when a session did not call them. +Native Claude Code imports recover schemas for Claude Code and Claude Desktop built-ins, plus conservative name-derived MCP schemas, when the raw transcript only records tool names or calls. +{% if externalize_tools %} +A complete dataset-level `tools` schema snapshot is stored in `tools.json` to keep this dataset card upload-safe. +{% else %} +A complete dataset-level `tools` schema snapshot is embedded in the collapsed section at the bottom of this README. +{% endif %} +`load_traces` applies the dataset snapshot to each loaded example as a fallback `tools` field. + +{% endif %} +{% if extraction_provider %} +## Create A Similar Dataset + +This dataset was staged from existing local agent sessions with `teich extract`. +To build your own local dataset, install Teich and point it at one of the supported providers: + +```bash +teich extract {{ extraction_provider }} --out data +``` + +Use `--sessions-dir /path/to/store` when your sessions are not in the default location, +and `--model ` when you only want sessions whose model metadata matches a value. + +{% endif %} +## Format + +{% if structured_dataset %} +Each file is newline-delimited JSON where every line is already a training example. +Chat-only datasets include `messages` plus convenience fields like optional `system`, `prompt`, `follow_up_prompts`, `thinking`, `response`, and `responses`. +Tool datasets can include the same normalized `messages` structure together with a `tools` field. + +{% elif agent_trace_rows %} +Each file is newline-delimited JSON where every line is one preserved agent session. +Rows use `id`, `task`, `traces`, `tools`, and `metadata`: + +- `id`: provider session id +- `task`: first user prompt extracted from the trace +- `traces`: native conversation messages in `from` / `value` form +- `tools`: the tool schema available to that session, including tools that were not called +- `metadata`: provider, model, first-message timestamp, token, cost, parent-session, and export details +- `metadata.first_message_timestamp`: first timestamp-bearing source user message when the trace format provides one + +{% else %} +Each file is newline-delimited JSON representing a single captured agent session. +The trace schema is designed for upload-first preservation so you can keep the original session history and convert it later for training. +Teich normalizes split assistant fragments during trace copy and conversion so the semantic order is reasoning first, optional assistant text second, and tool calls last. +Native Claude Code conversion also preserves runtime context such as skills, MCP instructions, hook context, permission state, date changes, and session recaps as masked `system` messages when the raw transcript provides them. + +Common top-level event groups: + +- `session_meta` +- `turn_context` +- `event_msg` +- `response_item` +- `session` +- `message` +- `session_info` +- `model_change` +- `thinking_level_change` +- `external_session_meta` +- `external_message` +- `external_stderr` + +{% endif %} +{% if sample_block %} +## Example + +```json +{{ sample_block }} +``` + +{% endif %} +## Training + +Use this dataset as `{{ dataset_reference }}` with Teich's data preparation and training utilities. +If you do not want Teich to handle chat-template formatting or masking, run `teich convert` to write standalone OpenAI-style JSONL rows with `prompt`, `messages`, `tools`, and `metadata`. +Training setup details evolve over time, so the maintained guide lives in the [Teich training docs]({{ training_docs_url }}). +For loading, mixing, converting, and validating Teich datasets, see [Preparing Data]({{ prepare_docs_url }}). +{% if dataset_tools %} + +{{ tools_details_block }} +{% endif %} diff --git a/src/teich/trace_readme.py b/src/teich/trace_readme.py index ed39e88..d888bf4 100644 --- a/src/teich/trace_readme.py +++ b/src/teich/trace_readme.py @@ -5,7 +5,7 @@ from pathlib import Path from typing import Any, Iterable -from .converter import convert_traces_to_training_data +from .converter import NON_DATA_TRACE_DIR_NAMES, convert_traces_to_training_data README_SAMPLE_MAX_CHARS = 4_000 README_SAMPLE_STRING_MAX_CHARS = 600 @@ -15,7 +15,40 @@ TEICH_TRAINING_DOCS_URL = "https://github.com/TeichAI/teich/blob/main/docs/training.md" TEICH_PREPARE_DOCS_URL = "https://github.com/TeichAI/teich/blob/main/docs/prepare-data.md" EXTRACTION_PROVIDERS = {"claude", "codex", "cursor", "hermes", "pi"} -NON_DATA_README_SCAN_DIR_NAMES = {"failures", "partials", "sandbox", "__pycache__"} + +_TEMPLATE_DIR = Path(__file__).parent / "templates" +_DEFAULT_CARD_TEMPLATE = "dataset_card.md.j2" +# Frontmatter keys the card owns; a user's ``card_extra`` may not shadow them. +_RESERVED_CARD_KEYS = frozenset( + {"pretty_name", "task_categories", "tags", "configs", "size_categories", "license"} +) +# Hugging Face dataset-card ``size_categories`` buckets, ascending by upper bound. +_SIZE_BUCKETS: tuple[tuple[int, str], ...] = ( + (1_000, "n<1K"), + (10_000, "1K str | None: + """Map a dataset row count to its Hugging Face ``size_categories`` bucket. + + Returns ``None`` for an empty/unknown dataset (``row_count <= 0``) so the card + omits the key rather than advertising a bogus size. + """ + if row_count <= 0: + return None + for upper, label in _SIZE_BUCKETS: + if row_count < upper: + return label + return "n>1T" def normalize_extraction_provider(provider: str | None) -> str | None: @@ -61,7 +94,7 @@ def extraction_provider_from_dataset_rows(traces_dir: Path) -> str | None: relative_parts = trace_file.relative_to(traces_dir).parts except ValueError: relative_parts = trace_file.parts - if any(part in NON_DATA_README_SCAN_DIR_NAMES for part in relative_parts): + if any(part in NON_DATA_TRACE_DIR_NAMES for part in relative_parts): continue try: with trace_file.open("r", encoding="utf-8") as handle: @@ -182,29 +215,45 @@ def _dataset_tools(trace_files: Iterable[Path]) -> list[dict[str, Any]]: return [merged_by_name[name] for name in sorted(merged_by_name)] -def _frontmatter(pretty_name: str, tags: list[str]) -> str: - lines = [ - "---", - f'pretty_name: "{pretty_name}"', - "task_categories:", - "- text-generation", +def _split_data_files(traces_dir: Path, excluded_dirs: Iterable[Path] = ()) -> list[tuple[str, str]]: + """Dataset-card split -> file glob, reflecting the actual routing folders. + + When routed split folders (passed/failed/borderline) hold data, expose them as + HF splits. Otherwise a single ``train`` split: the top-level ``*.jsonl`` files + plus a recursive ``/**/*.jsonl`` for each data-bearing subdir that isn't a + known non-data dir (partials/failures/bench/verification/...). Nested extractions + (e.g. Cursor's project-relative transcripts) live only in subdirs, so a bare + top-level ``*.jsonl`` would advertise an empty dataset while the card still counts + those rows — HF's ``data_files`` config must reach them. + + ``excluded_dirs`` (e.g. a custom ``output.failures_dir`` under ``traces_dir``) are + skipped too — they hold ignored/failed runs and their basename may not be in the + reserved non-data set, so they must not be advertised as a train split. + """ + routing = ("passed", "failed", "borderline") + splits = [ + (name, f"{name}/*.jsonl") + for name in routing + if (traces_dir / name).is_dir() and any((traces_dir / name).glob("*.jsonl")) ] - if tags: - lines.append("tags:") - for tag in tags: - lines.append(f'- "{tag}"') - lines.extend( - [ - "configs:", - "- config_name: default", - " data_files:", - " - split: train", - ' path: "**/*.jsonl"', - "---", - "", - ] - ) - return "\n".join(lines) + if splits: + return splits + traces_resolved = traces_dir.resolve() + excluded_names = { + excluded.resolve().relative_to(traces_resolved).parts[0] + for excluded in excluded_dirs + if excluded.resolve() != traces_resolved and _path_is_relative_to(excluded, traces_dir) + } + patterns = ["*.jsonl"] + if traces_dir.is_dir(): + for child in sorted(traces_dir.iterdir()): + if not child.is_dir() or child.name in NON_DATA_TRACE_DIR_NAMES or child.name in routing: + continue + if child.name in excluded_names: + continue + if any(child.rglob("*.jsonl")): + patterns.append(f"{child.name}/**/*.jsonl") + return [("train", pattern) for pattern in patterns] def _truncate_text(value: str, max_chars: int) -> str: @@ -369,22 +418,105 @@ def _tools_details_block(tools: list[dict[str, Any]]) -> list[str]: ] -def _extraction_snippet(provider: str) -> list[str]: - provider = normalize_extraction_provider(provider) or provider.strip().lower().replace("_", "-") or "claude" - return [ - "## Create A Similar Dataset", - "", - "This dataset was staged from existing local agent sessions with `teich extract`.", - "To build your own local dataset, install Teich and point it at one of the supported providers:", - "", - "```bash", - f"teich extract {provider} --out data", - "```", - "", - "Use `--sessions-dir /path/to/store` when your sessions are not in the default location,", - "and `--model ` when you only want sessions whose model metadata matches a value.", - "", - ] +def _reward_stats(traces_dir: Path, trace_files: Iterable[Path]) -> dict[str, int] | None: + """Summarize verifier outcomes for the dataset card. + + Two reward-labeled paths write sidecars in different shapes: the prompt-mode seed + verifier writes ``verification/.json`` with a ``passed`` bool, while bench + harvest writes ``metadata/.json`` with a ``split`` (passed/failed/borderline) + and a numeric ``reward``. Scan both so the counts describe an all-prompts, all-bench, + or mixed dataset. Only sidecars whose stem matches a live dataset row are counted (so + a stale/orphaned sidecar can't inflate the count), and each stem is counted once. + Returns None when nothing is verified. + """ + live_stems = {path.stem for path in trace_files} + counted: set[str] = set() + passed = failed = borderline = numeric = 0 + + def _numeric(reward: Any) -> bool: + return isinstance(reward, (int, float)) and not isinstance(reward, bool) + + verification_dir = traces_dir / "verification" + if verification_dir.is_dir(): + for sidecar in sorted(verification_dir.glob("*.json")): + if sidecar.stem not in live_stems or sidecar.stem in counted: + continue + try: + data = json.loads(sidecar.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError): + continue + if not isinstance(data, dict) or not isinstance(data.get("passed"), bool): + continue + counted.add(sidecar.stem) + passed += 1 if data["passed"] else 0 + failed += 0 if data["passed"] else 1 + numeric += 1 if _numeric(data.get("reward")) else 0 + + metadata_dir = traces_dir / "metadata" + if metadata_dir.is_dir(): + for sidecar in sorted(metadata_dir.glob("*.json")): + if sidecar.stem not in live_stems or sidecar.stem in counted: + continue + try: + data = json.loads(sidecar.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError): + continue + if not isinstance(data, dict): + continue + split = data.get("split") + if split == "passed": + passed += 1 + elif split == "failed": + failed += 1 + elif split == "borderline": + borderline += 1 # a partial score (0 dict[str, Any]: + """Drop reserved frontmatter keys the card owns; keep user keys in order.""" + if not card_extra: + return {} + return {key: value for key, value in card_extra.items() if key not in _RESERVED_CARD_KEYS} + + +def _card_extra_yaml(card_extra: dict[str, Any] | None) -> str: + """Serialize ``card_extra`` to YAML frontmatter lines (empty dict -> "").""" + sanitized = _sanitize_card_extra(card_extra) + if not sanitized: + return "" + import yaml + + return yaml.safe_dump(sanitized, sort_keys=False, default_flow_style=False) + + +def _render_card(context: dict[str, Any], template_path: Path | None = None) -> str: + """Render the dataset card from ``context`` via Jinja2 (default or user template).""" + from jinja2 import Environment, FileSystemLoader, StrictUndefined + + if template_path is not None: + loader_dir = str(template_path.parent) + template_name = template_path.name + else: + loader_dir = str(_TEMPLATE_DIR) + template_name = _DEFAULT_CARD_TEMPLATE + env = Environment( + loader=FileSystemLoader(loader_dir), + undefined=StrictUndefined, + trim_blocks=True, + lstrip_blocks=True, + keep_trailing_newline=True, + ) + template = env.get_template(template_name) + return template.render(**context) def build_traces_readme( @@ -396,6 +528,11 @@ def build_traces_readme( repo_id: str | None = None, tools: list[dict[str, Any]] | None = None, extraction_provider: str | None = None, + reward_stats: dict[str, int] | None = None, + data_files: list[tuple[str, str]] | None = None, + license: str | None = None, + card_extra: dict[str, Any] | None = None, + readme_template: Path | None = None, ) -> str: structured_dataset = _is_structured_dataset(trace_files) agent_trace_rows = _is_agent_trace_row_dataset(trace_files) @@ -404,113 +541,48 @@ def build_traces_readme( row_count = _row_count(trace_files) sample_lines = _sample_lines(trace_files) sample_block = "\n".join(sample_lines) - lines = [ - _frontmatter(pretty_name, tags), - 'This dataset was generated using [teich](https://github.com/TeichAI/teich) by [TeichAI](https://huggingface.co/TeichAI) ', - "", - f"# {pretty_name}", - "", - ( + effective_tags = list(tags) + routed = any(split != "train" for split, _ in data_files or []) + if (reward_stats or routed) and "reward-labeled" not in effective_tags: + effective_tags.append("reward-labeled") + normalized_provider = ( + normalize_extraction_provider(extraction_provider) + or extraction_provider.strip().lower().replace("_", "-") + or "claude" + if extraction_provider + else None + ) + size = size_category(row_count) + is_row_dataset = structured_dataset or agent_trace_rows + context: dict[str, Any] = { + "pretty_name": pretty_name, + "tags": effective_tags, + "data_files": list(data_files or [("train", "**/*.jsonl")]), + "license": license, + "size_categories": [size] if size else None, + "card_extra_yaml": _card_extra_yaml(card_extra), + "model_id": model_id, + "reward_stats": reward_stats, + "dataset_tools": dataset_tools, + "externalize_tools": _should_externalize_tools(dataset_tools), + "tools_details_block": "\n".join(_tools_details_block(dataset_tools)).rstrip("\n") + if dataset_tools + else "", + "extraction_provider": normalized_provider, + "structured_dataset": structured_dataset, + "agent_trace_rows": agent_trace_rows, + "sample_block": sample_block, + "dataset_reference": dataset_reference, + "training_docs_url": TEICH_TRAINING_DOCS_URL, + "prepare_docs_url": TEICH_PREPARE_DOCS_URL, + "intro_line": ( "This directory contains newline-delimited JSON training examples generated by teich." if structured_dataset else "This directory contains raw agent trace files generated by teich." ), - "", - f"Rows: {row_count}" if structured_dataset or agent_trace_rows else f"JSONL files: {len(trace_files)}", - "", - ] - if model_id: - lines.extend([f"Model metadata: `{model_id}`", ""]) - if dataset_tools: - lines.extend( - [ - "## Training-ready tools", - "", - "Generated agent traces carry configured or recovered tool schemas so tools remain available for training even when a session did not call them.", - "Native Claude Code imports recover schemas for Claude Code and Claude Desktop built-ins, plus conservative name-derived MCP schemas, when the raw transcript only records tool names or calls.", - ( - "A complete dataset-level `tools` schema snapshot is embedded in the collapsed section at the bottom of this README." - if not _should_externalize_tools(dataset_tools) - else "A complete dataset-level `tools` schema snapshot is stored in `tools.json` to keep this dataset card upload-safe." - ), - "`load_traces` applies the dataset snapshot to each loaded example as a fallback `tools` field.", - "", - ] - ) - if extraction_provider: - lines.extend(_extraction_snippet(extraction_provider)) - lines.extend( - [ - "## Format", - "", - ] - ) - if structured_dataset: - lines.extend( - [ - "Each file is newline-delimited JSON where every line is already a training example.", - "Chat-only datasets include `messages` plus convenience fields like optional `system`, `prompt`, `follow_up_prompts`, `thinking`, `response`, and `responses`.", - "Tool datasets can include the same normalized `messages` structure together with a `tools` field.", - "", - ] - ) - elif agent_trace_rows: - lines.extend( - [ - "Each file is newline-delimited JSON where every line is one preserved agent session.", - "Rows use `id`, `task`, `traces`, `tools`, and `metadata`:", - "", - "- `id`: provider session id", - "- `task`: first user prompt extracted from the trace", - "- `traces`: native conversation messages in `from` / `value` form", - "- `tools`: the tool schema available to that session, including tools that were not called", - "- `metadata`: provider, model, first-message timestamp, token, cost, parent-session, and export details", - "- `metadata.first_message_timestamp`: first timestamp-bearing source user message when the trace format provides one", - "", - ] - ) - else: - lines.extend( - [ - "Each file is newline-delimited JSON representing a single captured agent session.", - "The trace schema is designed for upload-first preservation so you can keep the original session history and convert it later for training.", - "Teich normalizes split assistant fragments during trace copy and conversion so the semantic order is reasoning first, optional assistant text second, and tool calls last.", - "Native Claude Code conversion also preserves runtime context such as skills, MCP instructions, hook context, permission state, date changes, and session recaps as masked `system` messages when the raw transcript provides them.", - "", - "Common top-level event groups:", - "", - "- `session_meta`", - "- `turn_context`", - "- `event_msg`", - "- `response_item`", - "- `session`", - "- `message`", - "- `session_info`", - "- `model_change`", - "- `thinking_level_change`", - "- `external_session_meta`", - "- `external_message`", - "- `external_stderr`", - "", - ] - ) - if sample_block: - lines.extend(["## Example", "", "```json", sample_block, "```", ""]) - lines.extend( - [ - "## Training", - "", - f"Use this dataset as `{dataset_reference}` with Teich's data preparation and training utilities.", - "If you do not want Teich to handle chat-template formatting or masking, run `teich convert` " - "to write standalone OpenAI-style JSONL rows with `prompt`, `messages`, `tools`, and `metadata`.", - f"Training setup details evolve over time, so the maintained guide lives in the [Teich training docs]({TEICH_TRAINING_DOCS_URL}).", - f"For loading, mixing, converting, and validating Teich datasets, see [Preparing Data]({TEICH_PREPARE_DOCS_URL}).", - "", - ] - ) - if dataset_tools: - lines.extend(_tools_details_block(dataset_tools)) - return "\n".join(lines) + "rows_line": f"Rows: {row_count}" if is_row_dataset else f"JSONL files: {len(trace_files)}", + } + return _render_card(context, readme_template) def write_traces_readme( @@ -523,12 +595,15 @@ def write_traces_readme( tools: list[dict[str, Any]] | None = None, excluded_dirs: list[Path] | None = None, extraction_provider: str | None = None, + license: str | None = None, + card_extra: dict[str, Any] | None = None, + readme_template: Path | None = None, ) -> Path: trace_files = sorted( path for path in traces_dir.rglob("*.jsonl") if path.is_file() - and not {"partials", "failures"}.intersection(path.relative_to(traces_dir).parts) + and not NON_DATA_TRACE_DIR_NAMES.intersection(path.relative_to(traces_dir).parts) and not any(_path_is_relative_to(path, excluded_dir) for excluded_dir in excluded_dirs or []) ) dataset_tools = tools if tools is not None else _dataset_tools(trace_files) @@ -542,6 +617,11 @@ def write_traces_readme( repo_id=repo_id, tools=dataset_tools, extraction_provider=extraction_provider, + reward_stats=_reward_stats(traces_dir, trace_files), + data_files=_split_data_files(traces_dir, excluded_dirs or []), + license=license, + card_extra=card_extra, + readme_template=readme_template, ), encoding="utf-8", ) diff --git a/tests/test_agent_cfg.py b/tests/test_agent_cfg.py new file mode 100644 index 0000000..7dfcbe7 --- /dev/null +++ b/tests/test_agent_cfg.py @@ -0,0 +1,45 @@ +"""Tests for the shared agent-config helpers (used by both prompt mode and bench).""" + +from teich import agent_cfg +from teich.config import Config + + +def test_provider_env_key_covers_all_providers(): + # The full provider map — previously bench only knew openai/openrouter/anthropic. + assert agent_cfg.provider_env_key("zai") == "GLM_API_KEY" + assert agent_cfg.provider_env_key("z-ai") == "GLM_API_KEY" + assert agent_cfg.provider_env_key("deepseek") == "DEEPSEEK_API_KEY" + assert agent_cfg.provider_env_key("xai") == "XAI_API_KEY" + assert agent_cfg.provider_env_key("grok") == "XAI_API_KEY" + assert agent_cfg.provider_env_key("google") == "GOOGLE_API_KEY" + assert agent_cfg.provider_env_key("openrouter") == "OPENROUTER_API_KEY" + assert agent_cfg.provider_env_key("anthropic") == "ANTHROPIC_API_KEY" + assert agent_cfg.provider_env_key("mystery") == "MYSTERY_API_KEY" # fallback + + +def test_container_base_url_rewrites_host_local(): + assert agent_cfg.container_base_url("http://localhost:8080/v1") == "http://host.docker.internal:8080/v1" + assert agent_cfg.container_base_url("http://127.0.0.1:1234") == "http://host.docker.internal:1234" + assert agent_cfg.container_base_url("https://api.z.ai/api/paas/v4") == "https://api.z.ai/api/paas/v4" + assert agent_cfg.container_base_url(None) is None + + +def test_bench_auth_env_sets_provider_specific_key_and_rewrites_localhost(monkeypatch): + for var in ("TEICH_API_KEY", "OPENAI_API_KEY", "OPENROUTER_API_KEY", "ANTHROPIC_API_KEY", "GLM_API_KEY"): + monkeypatch.delenv(var, raising=False) + cfg = Config( + agent={"provider": "pi"}, + api={"provider": "zai", "base_url": "http://localhost:9000/v1", "api_key": "glm-key"}, + ) + env = agent_cfg.bench_auth_env(cfg) + assert env["GLM_API_KEY"] == "glm-key" # the correct var for zai (was dropped before) + assert env["TEICH_API_KEY"] == "glm-key" + assert env["OPENAI_API_KEY"] == "glm-key" # compat var for OpenAI-family CLIs + assert env["OPENAI_BASE_URL"] == "http://host.docker.internal:9000/v1" # host-local rewritten + + +def test_pi_prefixed_model_only_prefixes_pi(): + pi = Config(agent={"provider": "pi"}, api={"provider": "openrouter"}, model={"model": "z-ai/glm-5.2"}) + assert agent_cfg.pi_prefixed_model(pi) == "openrouter/z-ai/glm-5.2" + codex = Config(agent={"provider": "codex"}, api={"provider": "openai"}, model={"model": "gpt-5"}) + assert agent_cfg.pi_prefixed_model(codex) == "gpt-5" # non-pi agents get the model unchanged diff --git a/tests/test_bench.py b/tests/test_bench.py new file mode 100644 index 0000000..3a7d397 --- /dev/null +++ b/tests/test_bench.py @@ -0,0 +1,731 @@ +"""Tests for bench mode: config (sources array), the shared backend base/harvest, the +harbor backend, and the driver loop. Live harbor/Docker runs stay integration-only; here we +cover config, reward/route/harvest, harbor resolution/normalization, and driver behavior.""" + +import json + +import pytest + +from teich.bench import run_bench +from teich.bench import runner as bench_runner +from teich.bench.backends import base, get_backend +from teich.bench.backends import harbor as hb +from teich.config import BenchSource, Config + + +# --------------------------------------------------------------------------- config + +def test_bench_config_defaults(): + assert Config().bench.sources == [] + + +def test_bench_sources_from_yaml(tmp_path): + config_file = tmp_path / "config.yaml" + config_file.write_text( + "bench:\n" + " sources:\n" + " - { type: harbor, source: terminal-bench@2.0 }\n" + " - { type: swe-bench, source: SWE-bench/SWE-bench_Verified, split: test }\n", + encoding="utf-8", + ) + cfg = Config.from_yaml(config_file) + assert [s.type for s in cfg.bench.sources] == ["harbor", "swe-bench"] + assert cfg.bench.sources[0].source == "terminal-bench@2.0" + assert cfg.bench.sources[0].backend == "docker" # default + assert cfg.bench.sources[1].split == "test" + + +def test_run_bench_requires_sources(): + with pytest.raises(RuntimeError, match="bench.sources"): + run_bench(Config()) + + +def test_get_backend(): + assert get_backend("harbor").type == "harbor" + with pytest.raises(RuntimeError, match="Unknown bench source type"): + get_backend("nope") + + +# ------------------------------------------------------------------- base: scoring/routing + +def test_numeric_primary_route(): + assert base.numeric(1) == 1.0 and base.numeric(True) is None and base.numeric("x") is None + assert base.primary_score({"reward": 1.0, "sub": 0.5}) == 1.0 # 'reward' preferred + assert base.primary_score({"score": 0.6}) == 0.6 # else first numeric + assert base.primary_score(None) is None + assert base.route_split(1.0) == "passed" + assert base.route_split(0.0) == "failed" + assert base.route_split(None) == "failed" + assert base.route_split(0.6) == "borderline" + + +def test_rewards_from_mapping_keeps_full_dict(): + assert base.rewards_from_mapping({"rewards": {"reward": 1.0, "sub": 0.5, "bad": "x"}}) == { + "reward": 1.0, + "sub": 0.5, + } + assert base.rewards_from_mapping({"rewards": {}}) is None + assert base.rewards_from_mapping(None) is None + + +def test_bench_stem_namespaced_by_source(): + s = BenchSource(type="harbor", source="terminal-bench@2.0") + assert base.bench_stem(s, "task-a") == "bench-terminal-bench-2.0-task-a" + s2 = BenchSource(type="swe-bench", source="SWE-bench/SWE-bench_Verified") + assert base.bench_stem(s2, "astropy__astropy-12907") == ( + "bench-SWE-bench-SWE-bench_Verified-astropy__astropy-12907" + ) + + +def test_source_id_distinguishes_by_knobs(): + # Same `source` but a differing knob (split) must yield distinct ids/stems so the two + # sources can't overwrite each other or wrongly resume-skip; the plain case is unchanged. + a = BenchSource(type="swe-bench", source="ds", split="train") + b = BenchSource(type="swe-bench", source="ds", split="test") + assert base.source_id(a) != base.source_id(b) + assert base.bench_stem(a, "t") != base.bench_stem(b, "t") + assert base.source_id(BenchSource(type="swe-bench", source="ds")) == "ds" + + +def test_source_id_bounds_large_instance_list(): + # A big instances subset must not leak its full comma-joined list into source_id (and thus + # into filenames / Docker tags / container names past OS/Docker length limits). + many = [f"repo__proj-{i:04d}" for i in range(30)] + src = BenchSource(type="swe-bench", source="ds", instances=many) + sid = base.source_id(src) + assert len(sid) < 60 and "repo__proj-0029" not in sid + # order-independent (listing order shouldn't spawn a distinct id / dataset) + assert base.source_id(BenchSource(type="swe-bench", source="ds", instances=list(reversed(many)))) == sid + # distinct instance sets stay distinct + assert base.source_id(BenchSource(type="swe-bench", source="ds", instances=many[:15])) != sid + # a single instance stays human-readable + assert "django__django-1" in base.source_id( + BenchSource(type="swe-bench", source="ds", instances=["django__django-1"]) + ) + + +def test_bench_source_namespace_field(): + # swe-bench image namespace: default "swebench" pulls published images; null builds custom + # instances locally. (The pull/build execution itself is Docker/integration-only.) + assert BenchSource(type="swe-bench", source="ds").namespace == "swebench" + assert BenchSource(type="swe-bench", source="ds", namespace=None).namespace is None + + +def test_bench_progress_tracks_counts_when_disabled(): + # No terminal -> no live bar, but the pass/fail/borderline/error tally is still tracked. + p = bench_runner._BenchProgress(console=None) + assert not p.enabled + with p: + bar = p.add_source("swe: ds", 4) + assert bar is None + p.advance(bar, split="passed") + p.advance(bar, split="borderline") + p.advance(bar, split="failed") + p.advance(bar, errored=True) + assert (p.passed, p.failed, p.borderline, p.errored) == (1, 1, 1, 1) + + +def test_bench_progress_renders_with_terminal_console(): + # A forced-terminal console builds the rich Progress; exercises the column/field wiring + # (e.g. the `tally` field must be supplied to add_task) without asserting rendered output. + from rich.console import Console + + p = bench_runner._BenchProgress(Console(force_terminal=True)) + assert p.enabled + with p: + bar = p.add_source("swe: ds", 2) + assert bar is not None + p.advance(bar, split="passed") + p.advance(bar, errored=True) + assert (p.passed, p.errored) == (1, 1) + + +def test_harbor_source_slug_keys_on_repo(): + # Same spec/version but different repo must not share a download cache dir (else the second + # source silently reuses the first repo's tasks). Absent repo, the key is unchanged. + assert hb._source_slug("ds", "1.0") == hb._source_slug("ds", "1.0", None) + assert hb._source_slug("ds", "1.0", "orgA/reg") != hb._source_slug("ds", "1.0", "orgB/reg") + assert hb._source_slug("ds", "1.0", "orgA/reg") != hb._source_slug("ds", "1.0") + + +def test_run_bench_rejects_codex_host_auth(tmp_path, monkeypatch): + # Bench backends don't wire Codex host auth into the task container; fail fast rather than + # launch a silently-unauthenticated run when host auth is the only configured credential. + for var in ("TEICH_API_KEY", "OPENAI_API_KEY", "OPENROUTER_API_KEY", "ANTHROPIC_API_KEY"): + monkeypatch.delenv(var, raising=False) + cfg = Config( + agent={"provider": "codex", "codex": {"use_host_auth": True}}, + bench={"sources": [{"type": "harbor", "source": "terminal-bench@2.0"}]}, + output={"traces_dir": tmp_path / "o"}, + ) + assert cfg.get_api_key() is None + with pytest.raises(RuntimeError, match="does not yet support Codex host auth"): + run_bench(cfg) + + +def test_run_bench_rejects_chat_wire_api_on_custom_endpoint(tmp_path): + # Bench doesn't thread api.wire_api into pi/codex, so a chat-completions-only endpoint (e.g. + # z.ai via provider: openai + base_url) would silently be hit on /responses. Fail fast. + def _cfg(**api): + return Config( + agent={"provider": "pi"}, + api=api, + bench={"sources": [{"type": "harbor", "source": "S"}]}, + output={"traces_dir": tmp_path / "o"}, + ) + + with pytest.raises(RuntimeError, match="does not yet thread api.wire_api"): + run_bench(_cfg(provider="openai", base_url="https://api.z.ai/api/paas/v4", + wire_api="chat_completions", api_key="k")) + # openrouter is exempt (its prefix routing already uses completions); responses is fine. + # (these get past the guard and fail later for unrelated reasons — assert the guard message is absent) + for ok in (dict(provider="openrouter", wire_api="chat_completions", api_key="k"), + dict(provider="openai", wire_api="responses", api_key="k")): + try: + run_bench(_cfg(**ok)) + except RuntimeError as exc: + assert "does not yet thread api.wire_api" not in str(exc) + + +def test_existing_output_across_splits(tmp_path): + cfg = Config(output={"traces_dir": tmp_path / "output"}) + assert base.existing_output(cfg, "bench-x") is None + routed = tmp_path / "output" / "borderline" / "bench-x.jsonl" + routed.parent.mkdir(parents=True) + routed.write_text("{}\n", encoding="utf-8") + assert base.existing_output(cfg, "bench-x") == routed + + +def test_bench_root_sibling_of_output(tmp_path): + cfg = Config(output={"traces_dir": tmp_path / "out"}) + assert base.bench_root(cfg) == tmp_path / "bench" + cfg2 = Config(output={"traces_dir": tmp_path / "out", "bench_dir": tmp_path / "custom"}) + assert base.bench_root(cfg2) == tmp_path / "custom" + + +def test_bench_root_rejects_in_tree_bench_dir(tmp_path): + # A bench_dir inside traces_dir would get uploaded + misclassified as dataset rows. + with pytest.raises(RuntimeError, match="must be outside"): + base.bench_root(Config(output={"traces_dir": tmp_path / "out", "bench_dir": tmp_path / "out" / "bench"})) + with pytest.raises(RuntimeError, match="must be outside"): + base.bench_root(Config(output={"traces_dir": tmp_path / "out", "bench_dir": tmp_path / "out"})) + + +def test_bench_root_default_rejects_output_named_bench(tmp_path): + # --output bench makes the computed default sibling (traces_dir.parent/"bench") collide with + # the dataset dir; the default must be guarded too, not just an explicit output.bench_dir. + with pytest.raises(RuntimeError, match="must be outside"): + base.bench_root(Config(output={"traces_dir": tmp_path / "bench"})) + + +# ------------------------------------------------------------------- base: harvest + +def test_harvest_writes_native_trace_and_metadata(tmp_path): + cfg = Config(agent={"provider": "pi"}, output={"traces_dir": tmp_path / "output"}) + source = BenchSource(type="harbor", source="terminal-bench@2.0") + task = base.BenchTask(id="add-bug") + run = base.BenchRun( + native_lines=['{"type":"session","id":"s"}', '{"type":"message","message":{"role":"user","content":[]}}'], + rewards={"reward": 0.6, "tests": 0.8}, + metadata={"model": "z-ai/glm-5.2", "exception": None}, + ) + paths, split = base.harvest(cfg, source, task, run) + stem = base.bench_stem(source, "add-bug") + assert split == "borderline" + assert paths == [tmp_path / "output" / "borderline" / f"{stem}.jsonl"] + rows = [json.loads(line) for line in paths[0].read_text(encoding="utf-8").splitlines() if line.strip()] + assert {row.get("type") for row in rows} == {"session", "message"} # native, not converted + meta = json.loads((tmp_path / "output" / "metadata" / f"{stem}.json").read_text(encoding="utf-8")) + assert meta["split"] == "borderline" and meta["reward"] == 0.6 + assert meta["rewards"] == {"reward": 0.6, "tests": 0.8} # full dict, no clamping + assert meta["source"] == "terminal-bench-2.0" and meta["type"] == "harbor" + assert meta["agent"] == "pi" and meta["model"] == "z-ai/glm-5.2" + + +def test_harvest_reroute_removes_stale_split_copy(tmp_path): + # Re-harvesting a task (no --resume) whose score crosses a routing boundary must not leave a + # stale copy in the old split — the dataset scanners read every split. + cfg = Config(agent={"provider": "pi"}, output={"traces_dir": tmp_path / "output"}) + source = BenchSource(type="harbor", source="ds") + task = base.BenchTask(id="t") + stem = base.bench_stem(source, "t") + base.harvest(cfg, source, task, base.BenchRun(native_lines=['{"type":"session"}'], rewards={"reward": 0.0})) + assert (tmp_path / "output" / "failed" / f"{stem}.jsonl").is_file() + + paths, split = base.harvest( + cfg, source, task, base.BenchRun(native_lines=['{"type":"session"}'], rewards={"reward": 1.0}) + ) + assert split == "passed" + assert (tmp_path / "output" / "passed" / f"{stem}.jsonl").is_file() + assert not (tmp_path / "output" / "failed" / f"{stem}.jsonl").exists() + meta = json.loads((tmp_path / "output" / "metadata" / f"{stem}.json").read_text(encoding="utf-8")) + assert meta["split"] == "passed" + + +# ------------------------------------------------------------------- harbor backend helpers + +def test_harbor_agent_name_mapping(): + assert hb._agent_name_for("codex") == "codex" + assert hb._agent_name_for("claude-code") == "claude-code" + assert hb._agent_name_for("claude") == "claude-code" + assert hb._agent_name_for("pi") == "pi" + assert hb._agent_name_for("hermes") == "hermes" + with pytest.raises(RuntimeError, match="does not support agent provider"): + hb._agent_name_for("chat") + + +def test_harbor_auth_env(): + env = hb._agent_auth_env( + Config(api={"provider": "openrouter", "base_url": "https://openrouter.ai/api/v1", "api_key": "sk"}) + ) + assert env["OPENAI_API_KEY"] == "sk" and env["OPENROUTER_API_KEY"] == "sk" + assert env["OPENAI_BASE_URL"] == "https://openrouter.ai/api/v1" + env2 = hb._agent_auth_env(Config(api={"provider": "openai", "api_key": "sk-o"})) + assert env2["OPENAI_API_KEY"] == "sk-o" and "OPENROUTER_API_KEY" not in env2 + + +def test_harbor_model_prefix(): + pi_or = Config(agent={"provider": "pi"}, model={"model": "z-ai/glm-5.2"}, api={"provider": "openrouter"}) + assert hb._bench_model_name(pi_or) == "openrouter/z-ai/glm-5.2" + already = Config(agent={"provider": "pi"}, model={"model": "openrouter/z-ai/glm-5.2"}, api={"provider": "openrouter"}) + assert hb._bench_model_name(already) == "openrouter/z-ai/glm-5.2" + codex = Config(agent={"provider": "codex"}, model={"model": "z-ai/glm-5.2"}, api={"provider": "openrouter"}) + assert hb._bench_model_name(codex) == "z-ai/glm-5.2" + + +def test_harbor_classify_remote_source(): + assert hb._classify_remote_source("terminal-bench@2.0", None, None) == ("registry", "terminal-bench@2.0") + assert hb._classify_remote_source("terminal-bench", None, "2.0") == ("registry", "terminal-bench@2.0") + assert hb._classify_remote_source("org/name", None, None) == ("package", "org/name@latest") + assert hb._classify_remote_source("org/name@ref", None, None) == ("package", "org/name@ref") + assert hb._classify_remote_source("ds", "https://github.com/o/r", None) == ("repo", "ds") + + +def test_harbor_image_names_computed_and_from_trial(): + import types as _t + + # No trial (error path): the deterministic sanitize("hb__" + task id), lowercased. + assert hb._harbor_image_names(None, "Adaptive-Rejection-Sampler") == ["hb__adaptive-rejection-sampler"] + # With a trial: prefer the actual env image name(s) AND include the computed one. + trial = _t.SimpleNamespace( + agent_environment=_t.SimpleNamespace(_main_image_name="hb__custom"), + verifier_environment=None, + task=_t.SimpleNamespace(short_name="my-task"), + ) + names = hb._harbor_image_names(trial, "ignored") + assert "hb__custom" in names and "hb__my-task" in names + + +def test_harbor_image_names_include_prebuilt_docker_image(tmp_path): + """A task with ``[environment].docker_image`` runs in harbor's prebuilt mode: no hb__ + image is ever built, and harbor's ``down --rmi local`` teardown never removes a pulled + tagged image — so the purge list must carry the task.toml name (with or without a trial).""" + pytest.importorskip("tomllib") # py3.11+; the harbor backend itself requires 3.12 + import types as _t + + task_dir = tmp_path / "fix-ocaml-gc" + task_dir.mkdir() + (task_dir / "task.toml").write_text( + '[environment]\ndocker_image = "alexgshaw/fix-ocaml-gc:20251031"\n' + '[verifier.environment]\ndocker_image = "alexgshaw/verifier:v1"\n', + encoding="utf-8", + ) + names = hb._harbor_image_names(None, "fix-ocaml-gc", task_dir) + assert "alexgshaw/fix-ocaml-gc:20251031" in names + assert "alexgshaw/verifier:v1" in names # a separate verifier env's prebuilt image too + assert "hb__fix-ocaml-gc" in names # deterministic hb__ fallback still present + + trial = _t.SimpleNamespace( + agent_environment=_t.SimpleNamespace(_main_image_name="hb__custom"), + verifier_environment=None, + task=_t.SimpleNamespace(short_name="fix-ocaml-gc"), + ) + assert "alexgshaw/fix-ocaml-gc:20251031" in hb._harbor_image_names(trial, "fix-ocaml-gc", task_dir) + + +def test_harbor_image_names_tolerate_missing_or_bad_task_toml(tmp_path): + assert hb._harbor_image_names(None, "t", tmp_path) == ["hb__t"] # no task.toml + (tmp_path / "task.toml").write_text("not = valid [ toml", encoding="utf-8") + assert hb._harbor_image_names(None, "t", tmp_path) == ["hb__t"] # unparseable -> best-effort + assert hb._harbor_image_names(None, "t", None) == ["hb__t"] # no task dir at all + + +def test_purge_images_invokes_docker_rmi(monkeypatch): + calls = [] + monkeypatch.setattr(hb.subprocess, "run", lambda args, **kw: calls.append(args)) + hb._purge_images(["hb__a", "hb__b"]) + assert calls == [["docker", "rmi", "-f", "hb__a"], ["docker", "rmi", "-f", "hb__b"]] + + +def test_purge_images_swallows_errors(monkeypatch): + def boom(*a, **k): + raise OSError("no docker") + + monkeypatch.setattr(hb.subprocess, "run", boom) + hb._purge_images(["hb__a"]) # best-effort: must not raise + + +def test_harbor_run_purges_image_unless_kept(monkeypatch, tmp_path): + import types as _t + + fake_trial = _t.SimpleNamespace( + agent_environment=_t.SimpleNamespace(_main_image_name="hb__t1"), + verifier_environment=None, + task=_t.SimpleNamespace(short_name="t1"), + ) + fake_result = _t.SimpleNamespace(exception_info=None, verifier_result={"rewards": {"reward": 1.0}}) + monkeypatch.setattr(hb, "_build_trial_config", lambda *_a, **_k: object()) + monkeypatch.setattr(hb, "_create_and_run", lambda _config: "coro") + monkeypatch.setattr(hb.asyncio, "run", lambda _coro: (fake_trial, fake_result)) + monkeypatch.setattr(hb, "_agent_dir", lambda _trial: None) + purged: list[str] = [] + monkeypatch.setattr(hb, "_purge_images", purged.extend) + src = BenchSource(type="harbor", source="S") + + hb.HarborBackend().run(Config(output={"traces_dir": tmp_path / "o"}), src, base.BenchTask(id="t1", raw=tmp_path)) + assert "hb__t1" in purged + + purged.clear() + kept = Config(output={"traces_dir": tmp_path / "o2", "keep_bench_images": True}) + hb.HarborBackend().run(kept, src, base.BenchTask(id="t1", raw=tmp_path)) + assert purged == [] # keep_bench_images -> no purge + + +def test_harbor_run_purges_fallback_image_when_trial_fails(monkeypatch, tmp_path): + """When _create_and_run blows up before returning a trial, the finally still purges the + deterministic sanitize("hb__" + task_id) fallback so a failed setup can't leak its image.""" + monkeypatch.setattr(hb, "_build_trial_config", lambda *_a, **_k: object()) + + def _explode(_config): + raise RuntimeError("setup failed before any trial") + + # _create_and_run(config) raises while building asyncio.run's argument, so trial stays None. + monkeypatch.setattr(hb, "_create_and_run", _explode) + purged: list[str] = [] + monkeypatch.setattr(hb, "_purge_images", purged.extend) + src = BenchSource(type="harbor", source="S") + + with pytest.raises(RuntimeError, match="setup failed"): + hb.HarborBackend().run( + Config(output={"traces_dir": tmp_path / "o"}), src, base.BenchTask(id="my_task", raw=tmp_path) + ) + assert purged == ["hb__my_task"] # trial=None -> deterministic fallback (underscore preserved) + + +def test_harbor_run_purges_prebuilt_image_on_interrupt(monkeypatch, tmp_path): + """Ctrl-C mid-task (KeyboardInterrupt, no trial yet): the finally must still purge the + prebuilt task image — terminal-bench tasks are all prebuilt, so leaving it leaks GBs.""" + pytest.importorskip("tomllib") + task_dir = tmp_path / "t1" + task_dir.mkdir() + (task_dir / "task.toml").write_text('[environment]\ndocker_image = "example/t1:v1"\n', encoding="utf-8") + monkeypatch.setattr(hb, "_build_trial_config", lambda *_a, **_k: object()) + + def _interrupt(_config): + raise KeyboardInterrupt + + monkeypatch.setattr(hb, "_create_and_run", _interrupt) + purged: list[str] = [] + monkeypatch.setattr(hb, "_purge_images", purged.extend) + + with pytest.raises(KeyboardInterrupt): + hb.HarborBackend().run( + Config(output={"traces_dir": tmp_path / "o"}), + BenchSource(type="harbor", source="S"), + base.BenchTask(id="t1", raw=task_dir), + ) + assert "example/t1:v1" in purged + assert "hb__t1" in purged + + +def test_harbor_run_keeps_prebuilt_image_shared_across_tasks(monkeypatch, tmp_path): + """A prebuilt image declared by more than one task in the source must NOT be purged + per-task: rmi'ing it can race a concurrent sibling's `compose up` (No such image) and + forces a re-pull per task. Only images unique to the task are removed.""" + pytest.importorskip("tomllib") + root = tmp_path / "tasks" + for name, image in (("t1", "example/shared:v1"), ("t2", "example/shared:v1"), ("t3", "example/solo:v1")): + d = root / name + d.mkdir(parents=True) + (d / "task.toml").write_text(f'[environment]\ndocker_image = "{image}"\n', encoding="utf-8") + monkeypatch.setattr(hb, "_build_trial_config", lambda *_a, **_k: object()) + + def _explode(_config): + raise RuntimeError("boom") + + monkeypatch.setattr(hb, "_create_and_run", _explode) + purged: list[str] = [] + monkeypatch.setattr(hb, "_purge_images", purged.extend) + + cfg = Config(output={"traces_dir": tmp_path / "o"}) + src = BenchSource(type="harbor", source=str(root)) + backend = hb.HarborBackend() + tasks = {t.id: t for t in backend.tasks(cfg, src)} + + with pytest.raises(RuntimeError): + backend.run(cfg, src, tasks["t1"]) + assert "example/shared:v1" not in purged # t2 declares the same image + assert "hb__t1" in purged # per-task hb__ name still purged + + purged.clear() + with pytest.raises(RuntimeError): + backend.run(cfg, src, tasks["t3"]) + assert "example/solo:v1" in purged # unique to t3 -> purged + + +def test_harbor_run_purges_image_duplicated_within_one_task(monkeypatch, tmp_path): + """One task declaring the same image for both its agent and verifier environments is + NOT sharing across tasks: the image is still unique to the task and must be purged.""" + pytest.importorskip("tomllib") + root = tmp_path / "tasks" + d1, d2 = root / "t1", root / "t2" + d1.mkdir(parents=True) + (d1 / "task.toml").write_text( + '[environment]\ndocker_image = "example/both:v1"\n' + '[verifier.environment]\ndocker_image = "example/both:v1"\n', + encoding="utf-8", + ) + d2.mkdir() + (d2 / "task.toml").write_text('[environment]\ndocker_image = "example/other:v1"\n', encoding="utf-8") + monkeypatch.setattr(hb, "_build_trial_config", lambda *_a, **_k: object()) + + def _explode(_config): + raise RuntimeError("boom") + + monkeypatch.setattr(hb, "_create_and_run", _explode) + purged: list[str] = [] + monkeypatch.setattr(hb, "_purge_images", purged.extend) + + cfg = Config(output={"traces_dir": tmp_path / "o"}) + src = BenchSource(type="harbor", source=str(root)) + backend = hb.HarborBackend() + tasks = {t.id: t for t in backend.tasks(cfg, src)} + + with pytest.raises(RuntimeError): + backend.run(cfg, src, tasks["t1"]) + assert "example/both:v1" in purged # duplicated within t1 only -> still unique -> purged + + +def test_harbor_resolve_task_dirs(tmp_path): + single = tmp_path / "one" + single.mkdir() + (single / "task.toml").write_text("", encoding="utf-8") + assert hb._resolve_task_dirs(single) == [single] + coll = tmp_path / "many" + (coll / "a").mkdir(parents=True) + (coll / "a" / "task.toml").write_text("", encoding="utf-8") + (coll / "b").mkdir() + (coll / "b" / "task.toml").write_text("", encoding="utf-8") + assert hb._resolve_task_dirs(coll) == [coll / "a", coll / "b"] + with pytest.raises(RuntimeError, match="No Harbor tasks"): + empty = tmp_path / "empty" + empty.mkdir() + hb._resolve_task_dirs(empty) + + +# A minimal pi `--mode json` stream (as harbor's --no-session pi run emits to pi.txt). +_PI_STREAM_LINES = [ + 'Warning: Model "z-ai/glm-5.2" not found for provider "openrouter". Using custom model id.', + json.dumps({"type": "session", "version": 3, "id": "abc", "cwd": "/app"}), + json.dumps({"type": "agent_start"}), + json.dumps({"type": "message_end", "message": {"role": "user", "content": [{"type": "text", "text": "Fix add()"}]}}), + json.dumps({"type": "message_end", "message": {"role": "assistant", "provider": "openrouter", + "model": "z-ai/glm-5.2", "content": [{"type": "text", "text": "Fixed."}]}}), +] + + +def test_pi_stream_to_session_events(tmp_path): + pi_txt = tmp_path / "pi.txt" + pi_txt.write_text("\n".join(_PI_STREAM_LINES) + "\n", encoding="utf-8") + events = hb._pi_stream_to_session_events(pi_txt) + assert [e["type"] for e in events] == ["session", "message", "model_change", "message"] + mc = next(e for e in events if e["type"] == "model_change") + assert mc == {"type": "model_change", "provider": "openrouter", "modelId": "z-ai/glm-5.2"} + + +def test_native_trace_pi_stream(tmp_path): + cfg = Config(output={"traces_dir": tmp_path / "output"}) + source = BenchSource(type="harbor", source="S") + agent_dir = tmp_path / "agent" + agent_dir.mkdir() + (agent_dir / "pi.txt").write_text("\n".join(_PI_STREAM_LINES) + "\n", encoding="utf-8") + lines, native_dir = hb._native_trace(cfg, source, agent_dir, "add-bug") + # session dir is namespaced by source so concurrent same-named tasks can't race + assert lines and native_dir == tmp_path / "bench" / "sessions" / base.bench_stem(source, "add-bug") + assert (native_dir / "pi.jsonl").is_file() + types = {json.loads(line)["type"] for line in lines} + assert "session" in types and "message" in types + + +def test_rewards_from_result_and_files(tmp_path): + class R: + verifier_result = {"rewards": {"reward": 1.0, "sub": 0.5}} + assert hb._rewards_from_result(R()) == {"reward": 1.0, "sub": 0.5} + + class N: + verifier_result = None + assert hb._rewards_from_result(N()) is None + + d = tmp_path / "trial" + d.mkdir() + (d / "reward.txt").write_text("0.0\n", encoding="utf-8") + assert hb._rewards_from_files(d) == {"reward": 0.0} + + +# ------------------------------------------------------------------- harbor backend run/tasks + +def test_harbor_tasks_local(tmp_path): + pytest.importorskip("harbor") + tasks_dir = tmp_path / "tasks" + (tasks_dir / "add-bug").mkdir(parents=True) + (tasks_dir / "add-bug" / "task.toml").write_text("", encoding="utf-8") + cfg = Config(output={"traces_dir": tmp_path / "output"}) + source = BenchSource(type="harbor", source=str(tasks_dir)) + tasks = list(hb.HarborBackend().tasks(cfg, source)) + assert [t.id for t in tasks] == ["add-bug"] + + +def test_harbor_run_builds_benchrun_from_trial(tmp_path, monkeypatch): + pytest.importorskip("harbor") + agent_dir = tmp_path / "trial" / "agent" + agent_dir.mkdir(parents=True) + (agent_dir / "pi.txt").write_text("\n".join(_PI_STREAM_LINES) + "\n", encoding="utf-8") + + class _Trial: + paths = type("P", (), {"agent_dir": agent_dir})() + + class _Result: + verifier_result = {"rewards": {"reward": 1.0}} + exception_info = None + + async def _fake_create_and_run(config): + return _Trial(), _Result() + + monkeypatch.setattr(hb, "_create_and_run", _fake_create_and_run) + monkeypatch.setattr(hb, "_build_trial_config", lambda cfg, source, task_dir, trials_dir: object()) + cfg = Config(agent={"provider": "pi"}, output={"traces_dir": tmp_path / "output"}) + source = BenchSource(type="harbor", source="terminal-bench@2.0") + run = hb.HarborBackend().run(cfg, source, base.BenchTask(id="add-bug", raw=tmp_path / "task")) + assert run.rewards == {"reward": 1.0} + assert run.native_lines and run.metadata.get("exception") is None + + +# ------------------------------------------------------------------- driver + +class _FakeBackend: + type = "fake" + + def __init__(self, runs): + self.runs = runs + self.ran: list[str] = [] + + def require(self): + pass + + def tasks(self, cfg, source, *, refresh=False): + return [base.BenchTask(id=task_id) for task_id in self.runs] + + def run(self, cfg, source, task): + self.ran.append(task.id) + result = self.runs[task.id] + if isinstance(result, Exception): + raise result + return result + + +def _bench_cfg(tmp_path): + return Config( + agent={"provider": "pi"}, + bench={"sources": [{"type": "fake", "source": "S"}]}, + output={"traces_dir": tmp_path / "output"}, + ) + + +def test_run_bench_drives_sources_and_harvests(tmp_path, monkeypatch): + fake = _FakeBackend({ + "t1": base.BenchRun(native_lines=['{"type":"session"}'], rewards={"reward": 1.0}), + "t2": base.BenchRun(native_lines=['{"type":"session"}'], rewards={"reward": 0.0}), + }) + monkeypatch.setattr(bench_runner, "get_backend", lambda t: fake) + written = run_bench(_bench_cfg(tmp_path)) + assert len(written) == 2 and fake.ran == ["t1", "t2"] + s = BenchSource(type="fake", source="S") + assert (tmp_path / "output" / "passed" / f"{base.bench_stem(s, 't1')}.jsonl").is_file() + assert (tmp_path / "output" / "failed" / f"{base.bench_stem(s, 't2')}.jsonl").is_file() + + +def test_run_bench_raises_when_all_tasks_fail(tmp_path, monkeypatch): + # Per-task errors are swallowed; if EVERY dispatched task fails, run_bench must raise (so the + # CLI exits non-zero) rather than return empty and look like a successful empty benchmark. + fake = _FakeBackend({"t1": RuntimeError("docker boom"), "t2": RuntimeError("docker boom")}) + monkeypatch.setattr(bench_runner, "get_backend", lambda t: fake) + with pytest.raises(RuntimeError, match="all 2 attempted task"): + run_bench(_bench_cfg(tmp_path)) + + +def test_run_bench_resume_skips_and_failure_continues(tmp_path, monkeypatch): + s = BenchSource(type="fake", source="S") + # Pre-harvest t1 so resume skips it; t2 raises (skipped); t3 succeeds. + done = tmp_path / "output" / "passed" / f"{base.bench_stem(s, 't1')}.jsonl" + done.parent.mkdir(parents=True) + done.write_text('{"type":"session"}\n', encoding="utf-8") + fake = _FakeBackend({ + "t1": base.BenchRun(native_lines=['{"x":1}'], rewards={"reward": 1.0}), + "t2": RuntimeError("docker boom"), + "t3": base.BenchRun(native_lines=['{"type":"session"}'], rewards={"reward": 1.0}), + }) + monkeypatch.setattr(bench_runner, "get_backend", lambda t: fake) + run_bench(_bench_cfg(tmp_path), resume=True) + assert "t1" not in fake.ran # skipped via resume + assert fake.ran == ["t2", "t3"] + assert (tmp_path / "output" / "passed" / f"{base.bench_stem(s, 't3')}.jsonl").is_file() + + +def test_run_bench_unknown_type_aborts(tmp_path): + # An unregistered source type -> a clear unknown-type error (harbor + swe-bench are known). + cfg = Config(bench={"sources": [{"type": "nope", "source": "x"}]}, output={"traces_dir": tmp_path / "o"}) + with pytest.raises(RuntimeError, match="Unknown bench source type"): + run_bench(cfg) + + +def test_run_bench_honors_max_concurrency(tmp_path, monkeypatch): + import threading + + max_workers = 3 + # A barrier makes "the pool parallelizes up to the cap" deterministic instead of + # sleep-timing-dependent: exactly max_workers tasks must be in-flight to release each wave. + barrier = threading.Barrier(max_workers, timeout=10) + state = {"current": 0, "peak": 0} + lock = threading.Lock() + + class _ConcBackend: + type = "fake" + + def require(self): + pass + + def tasks(self, cfg, source, *, refresh=False): + return [base.BenchTask(id=f"t{i}") for i in range(6)] + + def run(self, cfg, source, task): + with lock: + state["current"] += 1 + state["peak"] = max(state["peak"], state["current"]) + try: + barrier.wait() # blocks until max_workers are concurrently in-flight + except threading.BrokenBarrierError: + pass + with lock: + state["current"] -= 1 + return base.BenchRun(native_lines=['{"type":"session"}'], rewards={"reward": 1.0}) + + monkeypatch.setattr(bench_runner, "get_backend", lambda t: _ConcBackend()) + cfg = Config( + agent={"provider": "pi"}, + bench={"sources": [{"type": "fake", "source": "S"}]}, + output={"traces_dir": tmp_path / "output"}, + max_concurrency=max_workers, + ) + written = run_bench(cfg) + assert len(written) == 6 + assert state["peak"] == max_workers # pool parallelizes exactly up to the cap diff --git a/tests/test_cli.py b/tests/test_cli.py index 6d7ea8b..53580b4 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -51,6 +51,87 @@ def test_generate_command_missing_config(): assert "not found" in result.output +def test_generate_rejects_unknown_mode(tmp_path: Path): + config_file = tmp_path / "config.yaml" + config_file.write_text("prompts:\n - hello\n", encoding="utf-8") + result = runner.invoke(app, ["generate", "-c", str(config_file), "--mode", "wat"]) + assert result.exit_code == 1 + assert "Unknown --mode" in result.output + + +def test_generate_bench_mode_requires_source(tmp_path: Path): + config_file = tmp_path / "config.yaml" + config_file.write_text("agent:\n provider: codex\n", encoding="utf-8") # no bench.sources + result = runner.invoke(app, ["generate", "-c", str(config_file), "--mode", "bench"]) + assert result.exit_code == 1 + assert "bench.sources" in result.output + + +def test_generate_bench_refuses_to_mix_with_prompts_output(tmp_path: Path): + output = tmp_path / "output" + output.mkdir() + (output / "some-trace.jsonl").write_text('{"messages": []}\n', encoding="utf-8") # prompts data + config_file = tmp_path / "config.yaml" + config_file.write_text( + f"agent:\n provider: pi\nbench:\n sources:\n - {{type: harbor, source: {tmp_path}/tasks}}\noutput:\n traces_dir: {output}\n", + encoding="utf-8", + ) + result = runner.invoke(app, ["generate", "-c", str(config_file), "--mode", "bench"]) + assert result.exit_code == 1 + assert "already contains prompts-mode data" in " ".join(result.output.split()) + + +def test_generate_bench_writes_readme_for_partial_dataset_on_failure(tmp_path: Path, monkeypatch): + # An earlier bench source may harvest rows before a later one fails; the partial dataset must + # still get a README (like prompt mode) even though the CLI exits non-zero. + output = tmp_path / "output" + config_file = tmp_path / "config.yaml" + config_file.write_text( + f"agent:\n provider: pi\nbench:\n sources:\n - {{type: harbor, source: {tmp_path}/tasks}}\noutput:\n traces_dir: {output}\n", + encoding="utf-8", + ) + + def _partial_then_fail(cfg, **kwargs): + row = cfg.output.traces_dir / "passed" / "bench-x.jsonl" + row.parent.mkdir(parents=True, exist_ok=True) + row.write_text('{"messages": []}\n', encoding="utf-8") + raise RuntimeError("a later bench source blew up") + + monkeypatch.setattr("teich.bench.run_bench", _partial_then_fail) + result = runner.invoke(app, ["generate", "-c", str(config_file), "--mode", "bench"]) + assert result.exit_code == 1 + assert (output / "README.md").exists() # partial dataset still documented + + +def test_generate_prompts_refuses_to_mix_with_bench_output(tmp_path: Path): + output = tmp_path / "output" + output.mkdir() + (output / "bench-add-bug.jsonl").write_text('{"messages": []}\n', encoding="utf-8") # bench data + config_file = tmp_path / "config.yaml" + config_file.write_text( + f"agent:\n provider: chat\nprompts:\n - hello\noutput:\n traces_dir: {output}\n", + encoding="utf-8", + ) + result = runner.invoke(app, ["generate", "-c", str(config_file)]) + assert result.exit_code == 1 + assert "already contains bench-mode data" in " ".join(result.output.split()) + + +def test_existing_dataset_modes_classifies_rows(tmp_path: Path): + from teich.cli import _existing_dataset_modes + + (tmp_path / "bench-x.jsonl").write_text('{"messages": []}\n', encoding="utf-8") + (tmp_path / "organic.jsonl").write_text('{"messages": []}\n', encoding="utf-8") + (tmp_path / "passed").mkdir() + (tmp_path / "passed" / "routed.jsonl").write_text('{"messages": []}\n', encoding="utf-8") + # Intermediates / empties must not count as dataset rows (a nested `bench` dir is excluded + # by name; normally bench_dir is a sibling of output and never under traces_dir at all). + (tmp_path / "bench" / "sessions").mkdir(parents=True) + (tmp_path / "bench" / "sessions" / "pi.jsonl").write_text('{"type":"session"}\n', encoding="utf-8") + (tmp_path / "empty.jsonl").write_text("", encoding="utf-8") + assert _existing_dataset_modes(tmp_path) == {"bench", "prompts"} + + def test_convert_command_writes_openai_style_training_jsonl(tmp_path: Path): traces_dir = tmp_path / "traces" traces_dir.mkdir() @@ -465,7 +546,7 @@ def test_generate_command_publishes_dataset_when_publish_repo_is_configured(tmp_ folder_path=str(output_dir), repo_type="dataset", private=True, - ignore_patterns=["partials/**", "failures/**", "README.md", "tools.json"], + ignore_patterns=["partials/**", "failures/**", "bench/**", "README.md", "tools.json"], ) mock_api.upload_folder.assert_called_once_with( folder_path=str(output_dir), @@ -473,7 +554,7 @@ def test_generate_command_publishes_dataset_when_publish_repo_is_configured(tmp_ repo_type="dataset", commit_message="Upload teich dataset metadata", allow_patterns=["README.md"], - ignore_patterns=["partials/**", "failures/**"], + ignore_patterns=["partials/**", "failures/**", "bench/**"], ) @@ -621,7 +702,7 @@ def test_generate_command_can_publish_completed_outputs_after_failure(tmp_path: folder_path=str(output_dir), repo_type="dataset", private=False, - ignore_patterns=["partials/**", "failures/**", "README.md", "tools.json"], + ignore_patterns=["partials/**", "failures/**", "bench/**", "README.md", "tools.json"], ) mock_api.upload_folder.assert_called_once_with( folder_path=str(output_dir), @@ -629,7 +710,7 @@ def test_generate_command_can_publish_completed_outputs_after_failure(tmp_path: repo_type="dataset", commit_message="Upload teich dataset metadata", allow_patterns=["README.md"], - ignore_patterns=["partials/**", "failures/**"], + ignore_patterns=["partials/**", "failures/**", "bench/**"], ) diff --git a/tests/test_config.py b/tests/test_config.py index 6d8b7d3..d3f5c69 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -162,6 +162,65 @@ def test_config_generates_chat_dataset_tags(): assert config.get_dataset_tags() == ["conversational", "distillation", "teich", "gpt-4.1-mini"] +def test_config_readme_template_resolves_relative_to_config_dir(tmp_path: Path, monkeypatch): + """A relative output.readme_template resolves against the config file, like prompts_file — + not the process CWD, so `teich -c project/config.yaml` works from another directory.""" + project = tmp_path / "project" + project.mkdir() + template = project / "card.md.j2" + template.write_text("# {{ pretty_name }}\n", encoding="utf-8") + config_file = project / "config.yaml" + config_file.write_text("output:\n readme_template: card.md.j2\n", encoding="utf-8") + + elsewhere = tmp_path / "elsewhere" + elsewhere.mkdir() + monkeypatch.chdir(elsewhere) # load from a different working directory + + config = Config.from_yaml(config_file) + assert config.output.readme_template == template.resolve() + + +def test_config_bench_source_resolves_relative_to_config_dir(tmp_path: Path, monkeypatch): + """A relative *local* bench source (./local-tasks) resolves against the config file, not CWD; + a registry spec (name@version) is left untouched.""" + project = tmp_path / "project" + (project / "local-tasks").mkdir(parents=True) + config_file = project / "config.yaml" + config_file.write_text( + "bench:\n sources:\n - { type: harbor, source: ./local-tasks }\n", encoding="utf-8" + ) + elsewhere = tmp_path / "elsewhere" + elsewhere.mkdir() + monkeypatch.chdir(elsewhere) # load from a different working directory + + config = Config.from_yaml(config_file) + assert config.bench.sources[0].source == str((project / "local-tasks").resolve()) + + config_file.write_text( + "bench:\n sources:\n - { type: harbor, source: terminal-bench@2.0 }\n", encoding="utf-8" + ) + assert Config.from_yaml(config_file).bench.sources[0].source == "terminal-bench@2.0" + + +def test_config_bench_source_resolves_bare_relative_local_path(tmp_path: Path, monkeypatch): + """A bare relative bench source that exists beside the config (data/tasks) resolves against the + config dir too; a registry/HF spec with no matching local path is left untouched.""" + project = tmp_path / "project" + (project / "data" / "tasks").mkdir(parents=True) + config_file = project / "config.yaml" + config_file.write_text( + "bench:\n sources:\n" + " - { type: harbor, source: data/tasks }\n" + " - { type: swe-bench, source: SWE-bench/SWE-bench_Verified }\n", + encoding="utf-8", + ) + monkeypatch.chdir(tmp_path) # not the config dir + + sources = Config.from_yaml(config_file).bench.sources + assert sources[0].source == str((project / "data" / "tasks").resolve()) # bare local resolved + assert sources[1].source == "SWE-bench/SWE-bench_Verified" # HF id untouched + + def test_config_prompts_file(tmp_path: Path): """Test loading structured prompts from CSV file.""" prompts_file = tmp_path / "prompts.csv" diff --git a/tests/test_converter.py b/tests/test_converter.py index 15cec1c..e85c4a8 100644 --- a/tests/test_converter.py +++ b/tests/test_converter.py @@ -2875,6 +2875,20 @@ def test_convert_pi_trace_uses_thinking_blocks_and_tool_results(tmp_path: Path): assert bash_tool["function"]["parameters"]["properties"]["command"] == {"type": "string"} +def _write_structured_trace(path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + row = {"messages": [{"role": "user", "content": "hi"}, {"role": "assistant", "content": "ok"}], "prompt": "hi"} + path.write_text(json.dumps(row) + "\n", encoding="utf-8") + + +def test_convert_without_sidecar_has_no_reward(tmp_path: Path): + out = tmp_path / "output" + _write_structured_trace(out / "codex-z.jsonl") + rows = convert_traces_to_training_data(out) + assert "reward" not in rows[0] + assert "passed" not in rows[0] + + def test_codex_skips_injected_runtime_context(tmp_path: Path): """Codex injects / as leading user messages; they must not become the prompt (which also breaks --resume).""" diff --git a/tests/test_extract_anonymize_cli.py b/tests/test_extract_anonymize_cli.py index 01de554..b6e5110 100644 --- a/tests/test_extract_anonymize_cli.py +++ b/tests/test_extract_anonymize_cli.py @@ -1512,7 +1512,7 @@ def test_extract_can_upload_staged_anonymized_output_to_huggingface(tmp_path: Pa folder_path=str(output_dir), repo_type="dataset", private=False, - ignore_patterns=["partials/**", "failures/**", "README.md", "tools.json"], + ignore_patterns=["partials/**", "failures/**", "bench/**", "README.md", "tools.json"], ) mock_api.upload_folder.assert_called_once_with( folder_path=str(output_dir), @@ -1520,11 +1520,11 @@ def test_extract_can_upload_staged_anonymized_output_to_huggingface(tmp_path: Pa repo_type="dataset", commit_message="Upload teich dataset metadata", allow_patterns=["README.md"], - ignore_patterns=["partials/**", "failures/**"], + ignore_patterns=["partials/**", "failures/**", "bench/**"], ) readme = (output_dir / "README.md").read_text(encoding="utf-8") assert "armand0e/fable-traces" in readme - assert 'path: "**/*.jsonl"' in readme + assert 'path: "*.jsonl"' in readme # top-level glob (excludes non-data subdirs) assert '- "codex"' in readme assert '- "pi"' not in readme assert '- "fable-5"' not in readme diff --git a/tests/test_swebench.py b/tests/test_swebench.py new file mode 100644 index 0000000..b5c7af1 --- /dev/null +++ b/tests/test_swebench.py @@ -0,0 +1,272 @@ +"""Unit tests for the swe-bench bench backend (the Docker-free surface). + +The agent-run + grading seams need real Docker + the swebench package and are covered by an +opt-in integration pass; here we test dataset loading, the Dockerfile render, the per-provider +agent recipes, auth env, reward mapping, registration, and the install-hint guard. +""" + +from __future__ import annotations + +import sys +import types + +import pytest + +from teich.bench.backends import get_backend +from teich.bench.backends.swebench import ( + SweBenchBackend, + _agent_layer, + _auth_env, + _load_instances, + _model_name, + _rewards_from_report, + _run_command, + render_agent_dockerfile, +) +from teich.config import BenchSource, Config + + +def _cfg(provider="pi", api=None): + return Config(agent={"provider": provider}, api=api or {}) + + +# --------------------------------------------------------------------------- registration + + +def test_get_backend_returns_swebench(): + backend = get_backend("swe-bench") + assert isinstance(backend, SweBenchBackend) + assert backend.type == "swe-bench" + + +def test_require_hint_without_swebench(): + try: + import swebench # noqa: F401 + + pytest.skip("swebench is installed; the missing-extra path can't be exercised") + except ImportError: + pass + with pytest.raises(RuntimeError, match=r"teich\[swe\]"): + SweBenchBackend().require() + + +# --------------------------------------------------------------------------- dataset + + +def _inject_fake_swebench(monkeypatch, loader): + swebench = types.ModuleType("swebench") + harness = types.ModuleType("swebench.harness") + utils = types.ModuleType("swebench.harness.utils") + utils.load_swebench_dataset = loader + monkeypatch.setitem(sys.modules, "swebench", swebench) + monkeypatch.setitem(sys.modules, "swebench.harness", harness) + monkeypatch.setitem(sys.modules, "swebench.harness.utils", utils) + + +def test_load_instances_passes_split_and_ids(monkeypatch): + seen = {} + + def loader(name, split, instance_ids): + seen.update(name=name, split=split, instance_ids=instance_ids) + return [{"instance_id": "django__django-1", "problem_statement": "fix it"}] + + _inject_fake_swebench(monkeypatch, loader) + source = BenchSource( + type="swe-bench", source="SWE-bench/SWE-bench_Lite", split="dev", instances=["django__django-1"] + ) + out = _load_instances(source) + + assert out == [{"instance_id": "django__django-1", "problem_statement": "fix it"}] + assert seen == {"name": "SWE-bench/SWE-bench_Lite", "split": "dev", "instance_ids": ["django__django-1"]} + + +def test_load_instances_defaults_split_to_test(monkeypatch): + seen = {} + + def loader(name, split, instance_ids): + seen.update(split=split, instance_ids=instance_ids) + return [] + + _inject_fake_swebench(monkeypatch, loader) + _load_instances(BenchSource(type="swe-bench", source="x.jsonl")) + assert seen == {"split": "test", "instance_ids": None} + + +def test_load_instances_wraps_errors(monkeypatch): + def loader(name, split, instance_ids): + raise KeyError("nope") + + _inject_fake_swebench(monkeypatch, loader) + with pytest.raises(RuntimeError, match="Failed to load swe-bench dataset"): + _load_instances(BenchSource(type="swe-bench", source="bad")) + + +def test_tasks_maps_instances_to_bench_tasks(monkeypatch): + import teich.bench.backends.swebench as mod + + instances = [{"instance_id": "a__b-1"}, {"instance_id": "a__b-2"}] + monkeypatch.setattr(mod, "_load_instances", lambda source: instances) + tasks = SweBenchBackend().tasks(_cfg(), BenchSource(type="swe-bench", source="ds")) + + assert [t.id for t in tasks] == ["a__b-1", "a__b-2"] + assert tasks[0].raw is instances[0] + + +def test_tasks_rejects_langfuse_enabled(): + # swe-bench doesn't wire Langfuse into the agent container, so it fails loudly rather than + # silently no-op. The guard fires before any dataset/Docker work, so no mocks are needed. + cfg = Config(agent={ + "provider": "pi", + "langfuse": {"enabled": True, "public_key": "pk", "secret_key": "sk", "base_url": "https://lf"}, + }) + with pytest.raises(RuntimeError, match="does not support Langfuse"): + SweBenchBackend().tasks(cfg, BenchSource(type="swe-bench", source="ds")) + + +# --------------------------------------------------------------------------- agent layer + + +def test_agent_layer_for_known_providers(): + assert _agent_layer(_cfg("pi")).provider == "pi" + assert _agent_layer(_cfg("codex")).provider == "codex" + # claude alias maps to claude-code (needs an anthropic project, see the guard test below) + assert _agent_layer(_cfg("claude", api={"provider": "anthropic"})).provider == "claude-code" + + +def test_agent_layer_rejects_unsupported_provider(): + with pytest.raises(RuntimeError, match="does not support agent provider 'chat'"): + _agent_layer(_cfg("chat")) + + +def test_agent_layer_rejects_claude_code_without_anthropic_provider(): + # claude-code runs the Anthropic `claude` CLI; a non-anthropic project has no usable Claude + # creds in the container, so fail fast rather than emit empty traces. + with pytest.raises(RuntimeError, match="needs an anthropic-provider config"): + _agent_layer(_cfg("claude", api={"provider": "openrouter", "api_key": "sk-or"})) + assert _agent_layer(_cfg("claude", api={"provider": "anthropic", "api_key": "sk-ant"})).provider == "claude-code" + + +def test_auth_env_openrouter_sets_both_keys(): + cfg = _cfg("pi", api={"provider": "openrouter", "base_url": "https://openrouter.ai/api/v1", "api_key": "sk-or"}) + env = _auth_env(cfg) + assert env["OPENAI_API_KEY"] == "sk-or" + assert env["OPENROUTER_API_KEY"] == "sk-or" + assert env["OPENAI_BASE_URL"] == "https://openrouter.ai/api/v1" + + +def test_auth_env_openai_only_sets_openai_key(): + cfg = _cfg("codex", api={"provider": "openai", "api_key": "sk-oai"}) + env = _auth_env(cfg) + assert env["OPENAI_API_KEY"] == "sk-oai" + assert "OPENROUTER_API_KEY" not in env + + +def test_auth_env_anthropic_sets_anthropic_key_only(): + # The anthropic key must not be shadowed under OPENAI_API_KEY (would break OpenAI agents). + cfg = _cfg("claude", api={"provider": "anthropic", "api_key": "sk-ant"}) + env = _auth_env(cfg) + assert env["ANTHROPIC_API_KEY"] == "sk-ant" + assert "OPENAI_API_KEY" not in env + assert "OPENROUTER_API_KEY" not in env + + +# --------------------------------------------------------------------------- run command (model) + + +def test_run_command_pins_configured_model(): + cfg = Config(agent={"provider": "codex"}, model={"model": "gpt-5-codex"}, + api={"provider": "openai", "api_key": "sk"}) + assert _run_command(cfg, _agent_layer(cfg)).endswith("--model gpt-5-codex") + + +def test_run_command_pi_prefixes_provider(): + cfg = Config(agent={"provider": "pi"}, model={"model": "z-ai/glm-5.2"}, + api={"provider": "openrouter"}) + assert _run_command(cfg, _agent_layer(cfg)).endswith("--model openrouter/z-ai/glm-5.2") + + +def test_run_command_no_model_is_unchanged(): + # model.model defaults to a non-empty value, so the no-flag path needs an explicit empty model. + cfg = Config(agent={"provider": "codex"}, model={"model": ""}) + layer = _agent_layer(cfg) + assert _model_name(cfg) == "" + assert _run_command(cfg, layer) == layer.run + + +def test_ensure_instance_image_pull_bounds_timeout(monkeypatch): + # The remote-image pull must be bounded by the run timeout, not block forever. + import teich.bench.backends.swebench as mod + + calls = [] + + class _CP: + stdout = "" # empty -> the image isn't present locally -> take the pull path + + def fake_docker(args, *, timeout=None, check=True): + calls.append((args, timeout)) + return _CP() + + monkeypatch.setattr(mod, "_docker", fake_docker) + spec = types.SimpleNamespace(instance_image_key="swebench/x:latest") + mod._ensure_instance_image(spec, namespace="swebench", timeout=42) + pull_timeout = next(t for a, t in calls if a[0] == "pull") + assert pull_timeout == 42 + + +# --------------------------------------------------------------------------- dockerfile render + + +def test_render_dockerfile_from_instance_image_with_agent(): + df = render_agent_dockerfile("swebench/sweb.eval.x86_64.django__django-1:latest", _agent_layer(_cfg("pi"))) + lines = df.splitlines() + # The buildkit syntax directive must be the very first line, FROM the next directive. + assert lines[0] == "# syntax=docker/dockerfile:1" + directives = [ln for ln in lines if ln.strip() and not ln.startswith("#")] + assert directives[0] == "FROM swebench/sweb.eval.x86_64.django__django-1:latest" + assert "npm install -g @mariozechner/pi-coding-agent" in df + assert "WORKDIR /testbed" in df + assert "deb.nodesource.com" in df # node runtime layer + assert "Langfuse" not in df # off by default + + +def test_render_dockerfile_includes_langfuse_block_when_enabled(): + layer = _agent_layer(_cfg("codex")) + df = render_agent_dockerfile("base:img", layer, langfuse=True, langfuse_install="RUN echo lf") + assert "Langfuse" in df + assert "RUN echo lf" in df + + +# --------------------------------------------------------------------------- rewards + + +def test_rewards_resolved(): + rewards = _rewards_from_report( + { + "resolved": True, + "patch_successfully_applied": True, + "tests_status": { + "FAIL_TO_PASS": {"success": ["t1", "t2"], "failure": []}, + "PASS_TO_PASS": {"success": ["t3"], "failure": ["t4"]}, + }, + } + ) + assert rewards["reward"] == 1.0 + assert rewards["resolved"] == 1.0 + assert rewards["patch_applied"] == 1.0 + assert rewards["fail_to_pass"] == 1.0 + assert rewards["pass_to_pass"] == 0.5 + + +def test_rewards_unresolved_routes_to_failed(): + from teich.bench.backends import base + + rewards = _rewards_from_report({"resolved": False, "patch_successfully_applied": False}) + assert rewards["reward"] == 0.0 + assert base.route_split(base.primary_score(rewards)) == "failed" + + +def test_rewards_resolved_routes_to_passed(): + from teich.bench.backends import base + + rewards = _rewards_from_report({"resolved": True, "patch_successfully_applied": True}) + assert base.route_split(base.primary_score(rewards)) == "passed" diff --git a/tests/test_trace_readme.py b/tests/test_trace_readme.py index 3e81423..ac14217 100644 --- a/tests/test_trace_readme.py +++ b/tests/test_trace_readme.py @@ -1,7 +1,14 @@ from pathlib import Path import json -from teich.trace_readme import README_INLINE_TOOLS_MAX_CHARS, build_traces_readme, write_traces_readme +import pytest + +from teich.trace_readme import ( + README_INLINE_TOOLS_MAX_CHARS, + build_traces_readme, + size_category, + write_traces_readme, +) def test_build_traces_readme_includes_model_and_references_tools(tmp_path: Path): @@ -322,3 +329,301 @@ def test_write_traces_readme_uses_configured_tools_and_repo_id(tmp_path: Path): assert not (tmp_path / "tools.json").exists() assert '"name": "unobserved"' in readme assert '"description": "Available but not called"' in readme + + +def _write_structured_row(path: Path, **extra) -> None: + row = {"prompt": "hi", "messages": [{"role": "assistant", "content": "ok"}], **extra} + path.write_text(json.dumps(row) + "\n", encoding="utf-8") + + +def test_readme_is_reward_aware_from_verification_sidecars(tmp_path: Path): + # Two verified rows with sidecars; the card summarizes their pass/fail counts. + _write_structured_row(tmp_path / "task-a.jsonl", passed=True, reward=1.0) + _write_structured_row(tmp_path / "task-b.jsonl", passed=False, reward=0.0) + verification = tmp_path / "verification" + verification.mkdir() + (verification / "task-a.json").write_text(json.dumps({"passed": True}), encoding="utf-8") + (verification / "task-b.json").write_text( + json.dumps({"passed": False, "reward": 0.0}), encoding="utf-8" + ) + # A harbor intermediate that must NOT be treated as data or pollute the card (a nested + # `bench` dir is excluded by name; by default bench_dir is a sibling, never under output). + bench_sessions = tmp_path / "bench" / "sessions" / "add-bug" + bench_sessions.mkdir(parents=True) + (bench_sessions / "pi.jsonl").write_text('{"type":"session","id":"x"}\n', encoding="utf-8") + + readme_path = write_traces_readme( + tmp_path, pretty_name="Verifiable Traces", tags=["agent-traces"] + ) + readme = readme_path.read_text(encoding="utf-8") + assert "## Reward labels" in readme + assert '- "reward-labeled"' in readme + assert "Verified tasks: 2 (1 passed / 1 failed)." in readme + assert "1 of 2 carry an explicit numeric score" in readme # task-b has reward 0.0 + assert "Rows: 2" in readme # the bench/ session file is excluded from the row count + # The train split is a top-level glob that excludes the nested bench/ session. + assert "- split: train" in readme + assert 'path: "*.jsonl"' in readme + assert "**/*.jsonl" not in readme # top-level glob, not recursive + assert "bench/sessions/add-bug/pi.jsonl" not in readme + + +def test_card_data_files_reach_nested_extractions(tmp_path: Path): + # Cursor extraction writes transcripts under nested project-relative dirs. A bare top-level + # `*.jsonl` would advertise an empty dataset while the card still counts these rows, so the + # HF data_files config must include a recursive glob for each data-bearing subdir. + nested = tmp_path / "c-Users-test-project" / "agent-transcripts" / "session-1" + nested.mkdir(parents=True) + _write_structured_row(nested / "session-1.jsonl") + # a non-data dir (excluded by name) must NOT be advertised + (tmp_path / "failures").mkdir() + _write_structured_row(tmp_path / "failures" / "oops.jsonl") + + readme = write_traces_readme(tmp_path, pretty_name="Cursor Traces", tags=["agent-traces"]).read_text( + encoding="utf-8" + ) + assert "- split: train" in readme + assert 'path: "*.jsonl"' in readme # top-level files still advertised + assert 'path: "c-Users-test-project/**/*.jsonl"' in readme # nested extraction reached + assert "failures/" not in readme # non-data dir not advertised + assert "Rows: 1" in readme # the nested row is counted (and now reachable) + + +def test_reward_stats_from_bench_metadata_sidecars(tmp_path: Path): + # Bench harvest writes metadata/.json (split + numeric reward), not verification/. + # The card's reward summary must read those so bench datasets show pass/fail counts. + for split, stem in (("passed", "bench-ds-a"), ("failed", "bench-ds-b")): + (tmp_path / split).mkdir(exist_ok=True) + _write_structured_row(tmp_path / split / f"{stem}.jsonl") + metadata = tmp_path / "metadata" + metadata.mkdir() + (metadata / "bench-ds-a.json").write_text( + json.dumps({"task": "a", "split": "passed", "reward": 1.0}), encoding="utf-8" + ) + (metadata / "bench-ds-b.json").write_text( + json.dumps({"task": "b", "split": "failed", "reward": 0.0}), encoding="utf-8" + ) + readme = write_traces_readme(tmp_path, pretty_name="Bench", tags=["agent-traces"]).read_text( + encoding="utf-8" + ) + assert "## Reward labels" in readme + assert "Verified tasks: 2 (1 passed / 1 failed)." in readme + assert "2 of 2 carry an explicit numeric score" in readme + + +def test_reward_stats_counts_borderline_bench_tasks(tmp_path: Path): + # A partial score (0 single top-level train glob (not the recursive **/*.jsonl). + assert "- split: train" in readme and 'path: "*.jsonl"' in readme + assert "**/*.jsonl" not in readme + + +def test_card_splits_reflect_routing_folders(tmp_path: Path): + for split in ("passed", "failed", "borderline"): + (tmp_path / split).mkdir() + _write_structured_row(tmp_path / split / f"bench-{split}.jsonl") + readme = write_traces_readme(tmp_path, pretty_name="Bench", tags=["agent-traces"]).read_text(encoding="utf-8") + for split in ("passed", "failed", "borderline"): + assert f"- split: {split}" in readme + assert f'path: "{split}/*.jsonl"' in readme + assert "split: train" not in readme + assert '- "reward-labeled"' in readme # routed datasets are reward-labeled + + +def test_card_omits_empty_routing_splits(tmp_path: Path): + # Only passed/ has files -> only a passed split is declared (no empty failed/borderline). + (tmp_path / "passed").mkdir() + _write_structured_row(tmp_path / "passed" / "bench-a.jsonl") + readme = write_traces_readme(tmp_path, pretty_name="Bench", tags=["x"]).read_text(encoding="utf-8") + assert "- split: passed" in readme + assert "- split: failed" not in readme and "- split: borderline" not in readme + + +# Golden byte-for-byte lock for the Jinja-rendered card. The body below is identical to the +# pre-refactor (hand-assembled) build_traces_readme output; the only structural addition is the +# auto-populated ``size_categories`` block (always-on now). Any unintended drift in the template +# or context wiring will fail this exact-equality check. +_GOLDEN_CHAT_CARD = '''--- +pretty_name: "Chat Dataset" +task_categories: +- text-generation +tags: +- "chat" +configs: +- config_name: default + data_files: + - split: train + path: "*.jsonl" +size_categories: +- n<1K +--- + +This dataset was generated using [teich](https://github.com/TeichAI/teich) by [TeichAI](https://huggingface.co/TeichAI) + +# Chat Dataset + +This directory contains newline-delimited JSON training examples generated by teich. + +Rows: 1 + +Model metadata: `gpt-4.1-mini` + +## Format + +Each file is newline-delimited JSON where every line is already a training example. +Chat-only datasets include `messages` plus convenience fields like optional `system`, `prompt`, `follow_up_prompts`, `thinking`, `response`, and `responses`. +Tool datasets can include the same normalized `messages` structure together with a `tools` field. + +## Example + +```json +{"messages": [{"role": "system", "content": "You are a helpful assistant", "thinking": null}, {"role": "user", "content": "Hello", "thinking": null}, {"role": "assistant", "content": "Hi!", "thinking": "I should greet the user."}], "system": "You are a helpful assistant", "prompt": "Hello", "thinking": "I should greet the user.", "response": "Hi!", "model": "gpt-4.1-mini"} +``` + +## Training + +Use this dataset as `username/repo` with Teich's data preparation and training utilities. +If you do not want Teich to handle chat-template formatting or masking, run `teich convert` to write standalone OpenAI-style JSONL rows with `prompt`, `messages`, `tools`, and `metadata`. +Training setup details evolve over time, so the maintained guide lives in the [Teich training docs](https://github.com/TeichAI/teich/blob/main/docs/training.md). +For loading, mixing, converting, and validating Teich datasets, see [Preparing Data](https://github.com/TeichAI/teich/blob/main/docs/prepare-data.md). +''' + + +def test_rendered_card_matches_golden_byte_for_byte(tmp_path: Path): + trace_file = tmp_path / "chat.jsonl" + trace_file.write_text( + '{"messages":[{"role":"system","content":"You are a helpful assistant","thinking":null},' + '{"role":"user","content":"Hello","thinking":null},' + '{"role":"assistant","content":"Hi!","thinking":"I should greet the user."}],' + '"system":"You are a helpful assistant","prompt":"Hello",' + '"thinking":"I should greet the user.","response":"Hi!","model":"gpt-4.1-mini"}\n', + encoding="utf-8", + ) + readme = write_traces_readme( + tmp_path, pretty_name="Chat Dataset", tags=["chat"], model_id="gpt-4.1-mini" + ).read_text(encoding="utf-8") + assert readme == _GOLDEN_CHAT_CARD + + +@pytest.mark.parametrize( + "row_count, expected", + [ + (0, None), + (-5, None), + (999, "n<1K"), + (1000, "1K1T"), + (5_000_000_000_000, "n>1T"), + ], +) +def test_size_category_bucket_boundaries(row_count, expected): + assert size_category(row_count) == expected + + +def test_card_includes_license_when_set(tmp_path: Path): + _write_structured_row(tmp_path / "row.jsonl") + readme = write_traces_readme( + tmp_path, pretty_name="Licensed", tags=["x"], license="apache-2.0" + ).read_text(encoding="utf-8") + assert "license: apache-2.0\n" in readme + # Placed inside the frontmatter block, before the closing fence. + frontmatter = readme.split("---", 2)[1] + assert "license: apache-2.0" in frontmatter + + +def test_card_omits_license_when_unset(tmp_path: Path): + _write_structured_row(tmp_path / "row.jsonl") + readme = write_traces_readme(tmp_path, pretty_name="Unlicensed", tags=["x"]).read_text( + encoding="utf-8" + ) + assert "license:" not in readme + + +def test_card_extra_keys_appear_and_reserved_keys_dropped(tmp_path: Path): + _write_structured_row(tmp_path / "row.jsonl") + readme = write_traces_readme( + tmp_path, + pretty_name="Extra", + tags=["x"], + card_extra={ + "annotations_creators": ["machine-generated"], + "language": ["en"], + # Reserved keys the card owns must be dropped, not allowed to shadow. + "tags": ["should-not-appear"], + "pretty_name": "should-not-appear", + "size_categories": ["should-not-appear"], + "license": "should-not-appear", + }, + ).read_text(encoding="utf-8") + frontmatter = readme.split("---", 2)[1] + assert "annotations_creators:" in frontmatter + assert "machine-generated" in frontmatter + assert "language:" in frontmatter + assert "should-not-appear" not in readme + + +def test_card_extra_empty_dict_adds_no_lines(tmp_path: Path): + _write_structured_row(tmp_path / "row.jsonl") + baseline = write_traces_readme(tmp_path, pretty_name="Plain", tags=["x"]).read_text( + encoding="utf-8" + ) + with_empty = write_traces_readme( + tmp_path, pretty_name="Plain", tags=["x"], card_extra={} + ).read_text(encoding="utf-8") + assert baseline == with_empty + + +def test_custom_readme_template_override_renders(tmp_path: Path): + template = tmp_path / "card.md.j2" + template.write_text( + "---\npretty_name: \"{{ pretty_name }}\"\n---\nCustom card for {{ pretty_name }}, rows {{ rows_line }}.\n", + encoding="utf-8", + ) + _write_structured_row(tmp_path / "row.jsonl") + readme = write_traces_readme( + tmp_path, pretty_name="Override", tags=["x"], readme_template=template + ).read_text(encoding="utf-8") + assert readme == '---\npretty_name: "Override"\n---\nCustom card for Override, rows Rows: 1.\n' diff --git a/uv.lock b/uv.lock index 16e45d5..17cc698 100644 --- a/uv.lock +++ b/uv.lock @@ -5,9 +5,12 @@ resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", "python_full_version >= '3.14' and sys_platform == 'emscripten'", "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.11' and python_full_version < 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