diff --git a/assets/lab/environments/AGENTS.md b/assets/lab/environments/AGENTS.md index b46955d9ca..862a1d555b 100644 --- a/assets/lab/environments/AGENTS.md +++ b/assets/lab/environments/AGENTS.md @@ -7,7 +7,7 @@ This file mirrors the "Environments" documentation page. --- -This guide walks through building environments in Verifiers, from simple single-turn tasks to complex multi-turn agents with tools. See [Overview](overview.md) for how to initialize a new environment template. For reusable taskset/harness environments, see [BYO Harness](byo-harness.md). +This guide walks through building environments in Verifiers, from simple single-turn tasks to complex multi-turn agents with tools. See [Overview](overview.md) for how to initialize a new environment template. ## Table of Contents @@ -35,7 +35,6 @@ This guide walks through building environments in Verifiers, from simple single- - [Cleanup and Teardown](#cleanup-and-teardown) - [Signaling Early Termination](#signaling-early-termination) - [Developing Environments](#developing-environments) - - [v1 Env Shape](#v1-env-shape) - [pyproject.toml](#pyprojecttoml) - [Managing Dependencies](#managing-dependencies) - [Installation](#installation) @@ -689,7 +688,6 @@ The `prime env init` command initializes a new environment project: ```bash prime env init my-env # v0 stub -prime env init my-env --v1 # v1 Taskset/Harness template ``` This creates the following structure: @@ -701,128 +699,6 @@ environments/my_env/ └── README.md # documentation template ``` -### v1 Env Shape - -The v1 template teaches the standard object layout: one taskset class, one -typed `load_taskset(config: MyTasksetConfig)` child factory, and a tiny -`load_environment(config: vf.EnvConfig)` root loader that delegates through -`vf.load_taskset(config=config.taskset)` and -`vf.load_harness(config=config.harness)`. The child factory annotation defines -the taskset config type for TOML, CLI, eval, GEPA, RL, and Hosted Training. - -After `prime env init my-env --v1`, edit the generated taskset class: - -1. Add task settings to `TasksetConfig`. -2. Return task records from `load_tasks(split=...)`. -3. Return task-owned tools from `load_toolsets` when needed. -4. Add lifecycle, metric, reward, and advantage methods with `@vf.*`. - -Add a harness config, harness class, and `load_harness(config: -MyHarnessConfig)` when the environment owns reusable rollout behavior. -Otherwise the generated root loader uses the base harness. - -`EnvConfig` is the lightweight envelope for the two child configs. Put -environment knobs on `TasksetConfig` or `HarnessConfig`. - -The taskset-only shape is: - -```python -import verifiers as vf - - -class MyTasksetConfig(vf.TasksetConfig): - system_prompt: vf.SystemPrompt = "Answer exactly." - - -class MyTaskset(vf.Taskset[MyTasksetConfig]): - def load_tasks(self, split: vf.TaskSplit = "train") -> vf.Tasks: - """Return serializable task records as a list, generator, or Dataset.""" - if split == "eval": - return [] - return [ - { - "prompt": [{"role": "user", "content": "Reverse abc."}], - "answer": "cba", - "max_turns": 1, - } - ] - - @vf.reward(weight=1.0) - async def correct_answer(self, task: vf.Task, state: vf.State) -> float: - messages = vf.get_messages(state.get("completion") or [], role="assistant") - if not messages: - return 0.0 - response = str(messages[-1].content or "").strip() - return float(response == task["answer"]) - - -def load_taskset(config: MyTasksetConfig) -> MyTaskset: - return MyTaskset(config=config) - - -def load_environment(config: vf.EnvConfig) -> vf.Env: - """Loader pattern for all Taskset/Harness environments.""" - return vf.Env( - taskset=vf.load_taskset(config=config.taskset), - harness=vf.load_harness(config=config.harness), - ) -``` - -With a reusable harness, keep the same explicit object boundary: - -```python -class MyHarnessConfig(vf.HarnessConfig): - max_turns: int = 20 - - -class MyHarness(vf.Harness[MyHarnessConfig]): - """Reusable execution behavior for this environment.""" - - -def load_taskset(config: MyTasksetConfig) -> MyTaskset: - return MyTaskset(config=config) - - -def load_harness(config: MyHarnessConfig) -> MyHarness: - return MyHarness(config=config) - - -def load_environment(config: vf.EnvConfig) -> vf.Env: - """Loader pattern for all Taskset/Harness environments.""" - return vf.Env( - taskset=vf.load_taskset(config=config.taskset), - harness=vf.load_harness(config=config.harness), - ) -``` - -Keep v1 dependencies behind the owning taskset or harness. Do not pass -already-instantiated resource objects through environment loaders. Bindings are -allowed wherever the owning taskset, toolset, user, program, or harness wires -callables. `objects` entries should be loader specs: prefer serializable import -paths in config, and use factory callables directly only for Python-only -construction when the dependency cannot be serialized. Required Taskset and -Toolset factory parameters must be supplied through bindings. - -Judge-style rewards should read endpoint details from the rollout state: - -```python -@vf.reward(weight=1.0) -async def judge_reward(task, state) -> float: - endpoint = state.get_endpoint_config(api="chat") - client = state.get_client(api="chat") - model = str(task.get("judge_model") or endpoint.model) - ... -``` - -Expose at most `judge_model: str | None = None` on the taskset config. Do not -add judge endpoint URL/API-key fields or read `os.environ` inside reward/update -handlers. - -For reusable tasksets and harnesses, [BYO Harness](byo-harness.md) is the -canonical v1 implementation guide. It covers ownership, configs, task controls, -system prompts, users, toolsets, programs, sandboxes, artifacts, nested -harnesses, package adapters, and TOML/CLI overrides. - ### pyproject.toml The `pyproject.toml` defines package metadata, dependencies, and evaluation defaults: @@ -1017,9 +893,8 @@ Supported third-party environment integrations include: - **`ReasoningGymEnv`** — wraps [reasoning-gym](https://github.com/open-thought/reasoning-gym) procedural datasets - **`BrowserEnv`** — unified browser automation via [Browserbase](https://browserbase.com) with DOM and CUA modes - **`OpenEnvEnv`** — wraps OpenEnv gym and MCP contracts using Prime Sandboxes with prebuilt images referenced from `.build.json` -- **`NeMoGymTaskset` / `NeMoGymHarness`** — packaged v1 taskset/harness adapters for NeMo Gym JSONL rows and rollout collection -These require additional dependencies installed via extras (e.g., `uv add 'verifiers[ta]'` for TextArena, `uv add 'verifiers[browser]'` for BrowserEnv, `uv add 'verifiers[openenv]'` for OpenEnvEnv, `uv add 'verifiers[nemogym]'` for NeMo Gym). The bundled OpenEnv project under `proj/` owns its server dependencies and must be built with `uv run vf-build ` before evaluation or training. +These require additional dependencies installed via extras (e.g., `uv add 'verifiers[ta]'` for TextArena, `uv add 'verifiers[browser]'` for BrowserEnv, `uv add 'verifiers[openenv]'` for OpenEnvEnv). The bundled OpenEnv project under `proj/` owns its server dependencies and must be built with `uv run vf-build ` before evaluation or training. Newer and more experimental environment classes include: diff --git a/environments/AGENTS.md b/environments/AGENTS.md index 05de2b893e..5eb0a62808 100644 --- a/environments/AGENTS.md +++ b/environments/AGENTS.md @@ -6,7 +6,7 @@ This file mirrors the "Environments" documentation page. --- -This guide walks through building environments in Verifiers, from simple single-turn tasks to complex multi-turn agents with tools. See [Overview](overview.md) for how to initialize a new environment template. For reusable taskset/harness environments, see [BYO Harness](byo-harness.md). +This guide walks through building environments in Verifiers, from simple single-turn tasks to complex multi-turn agents with tools. See [Overview](overview.md) for how to initialize a new environment template. ## Table of Contents @@ -34,7 +34,6 @@ This guide walks through building environments in Verifiers, from simple single- - [Cleanup and Teardown](#cleanup-and-teardown) - [Signaling Early Termination](#signaling-early-termination) - [Developing Environments](#developing-environments) - - [v1 Env Shape](#v1-env-shape) - [pyproject.toml](#pyprojecttoml) - [Managing Dependencies](#managing-dependencies) - [Installation](#installation) @@ -688,7 +687,6 @@ The `prime env init` command initializes a new environment project: ```bash prime env init my-env # v0 stub -prime env init my-env --v1 # v1 Taskset/Harness template ``` This creates the following structure: @@ -700,128 +698,6 @@ environments/my_env/ └── README.md # documentation template ``` -### v1 Env Shape - -The v1 template teaches the standard object layout: one taskset class, one -typed `load_taskset(config: MyTasksetConfig)` child factory, and a tiny -`load_environment(config: vf.EnvConfig)` root loader that delegates through -`vf.load_taskset(config=config.taskset)` and -`vf.load_harness(config=config.harness)`. The child factory annotation defines -the taskset config type for TOML, CLI, eval, GEPA, RL, and Hosted Training. - -After `prime env init my-env --v1`, edit the generated taskset class: - -1. Add task settings to `TasksetConfig`. -2. Return task records from `load_tasks(split=...)`. -3. Return task-owned tools from `load_toolsets` when needed. -4. Add lifecycle, metric, reward, and advantage methods with `@vf.*`. - -Add a harness config, harness class, and `load_harness(config: -MyHarnessConfig)` when the environment owns reusable rollout behavior. -Otherwise the generated root loader uses the base harness. - -`EnvConfig` is the lightweight envelope for the two child configs. Put -environment knobs on `TasksetConfig` or `HarnessConfig`. - -The taskset-only shape is: - -```python -import verifiers as vf - - -class MyTasksetConfig(vf.TasksetConfig): - system_prompt: vf.SystemPrompt = "Answer exactly." - - -class MyTaskset(vf.Taskset[MyTasksetConfig]): - def load_tasks(self, split: vf.TaskSplit = "train") -> vf.Tasks: - """Return serializable task records as a list, generator, or Dataset.""" - if split == "eval": - return [] - return [ - { - "prompt": [{"role": "user", "content": "Reverse abc."}], - "answer": "cba", - "max_turns": 1, - } - ] - - @vf.reward(weight=1.0) - async def correct_answer(self, task: vf.Task, state: vf.State) -> float: - messages = vf.get_messages(state.get("completion") or [], role="assistant") - if not messages: - return 0.0 - response = str(messages[-1].content or "").strip() - return float(response == task["answer"]) - - -def load_taskset(config: MyTasksetConfig) -> MyTaskset: - return MyTaskset(config=config) - - -def load_environment(config: vf.EnvConfig) -> vf.Env: - """Loader pattern for all Taskset/Harness environments.""" - return vf.Env( - taskset=vf.load_taskset(config=config.taskset), - harness=vf.load_harness(config=config.harness), - ) -``` - -With a reusable harness, keep the same explicit object boundary: - -```python -class MyHarnessConfig(vf.HarnessConfig): - max_turns: int = 20 - - -class MyHarness(vf.Harness[MyHarnessConfig]): - """Reusable execution behavior for this environment.""" - - -def load_taskset(config: MyTasksetConfig) -> MyTaskset: - return MyTaskset(config=config) - - -def load_harness(config: MyHarnessConfig) -> MyHarness: - return MyHarness(config=config) - - -def load_environment(config: vf.EnvConfig) -> vf.Env: - """Loader pattern for all Taskset/Harness environments.""" - return vf.Env( - taskset=vf.load_taskset(config=config.taskset), - harness=vf.load_harness(config=config.harness), - ) -``` - -Keep v1 dependencies behind the owning taskset or harness. Do not pass -already-instantiated resource objects through environment loaders. Bindings are -allowed wherever the owning taskset, toolset, user, program, or harness wires -callables. `objects` entries should be loader specs: prefer serializable import -paths in config, and use factory callables directly only for Python-only -construction when the dependency cannot be serialized. Required Taskset and -Toolset factory parameters must be supplied through bindings. - -Judge-style rewards should read endpoint details from the rollout state: - -```python -@vf.reward(weight=1.0) -async def judge_reward(task, state) -> float: - endpoint = state.get_endpoint_config(api="chat") - client = state.get_client(api="chat") - model = str(task.get("judge_model") or endpoint.model) - ... -``` - -Expose at most `judge_model: str | None = None` on the taskset config. Do not -add judge endpoint URL/API-key fields or read `os.environ` inside reward/update -handlers. - -For reusable tasksets and harnesses, [BYO Harness](byo-harness.md) is the -canonical v1 implementation guide. It covers ownership, configs, task controls, -system prompts, users, toolsets, programs, sandboxes, artifacts, nested -harnesses, package adapters, and TOML/CLI overrides. - ### pyproject.toml The `pyproject.toml` defines package metadata, dependencies, and evaluation defaults: @@ -1016,9 +892,8 @@ Supported third-party environment integrations include: - **`ReasoningGymEnv`** — wraps [reasoning-gym](https://github.com/open-thought/reasoning-gym) procedural datasets - **`BrowserEnv`** — unified browser automation via [Browserbase](https://browserbase.com) with DOM and CUA modes - **`OpenEnvEnv`** — wraps OpenEnv gym and MCP contracts using Prime Sandboxes with prebuilt images referenced from `.build.json` -- **`NeMoGymTaskset` / `NeMoGymHarness`** — packaged v1 taskset/harness adapters for NeMo Gym JSONL rows and rollout collection -These require additional dependencies installed via extras (e.g., `uv add 'verifiers[ta]'` for TextArena, `uv add 'verifiers[browser]'` for BrowserEnv, `uv add 'verifiers[openenv]'` for OpenEnvEnv, `uv add 'verifiers[nemogym]'` for NeMo Gym). The bundled OpenEnv project under `proj/` owns its server dependencies and must be built with `uv run vf-build ` before evaluation or training. +These require additional dependencies installed via extras (e.g., `uv add 'verifiers[ta]'` for TextArena, `uv add 'verifiers[browser]'` for BrowserEnv, `uv add 'verifiers[openenv]'` for OpenEnvEnv). The bundled OpenEnv project under `proj/` owns its server dependencies and must be built with `uv run vf-build ` before evaluation or training. Newer and more experimental environment classes include: diff --git a/pyproject.toml b/pyproject.toml index dee4eda417..b08cffae11 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -66,6 +66,7 @@ dev = [ "pytest-xdist>=3.8.0", "ipykernel", "ipywidgets", + "nest-asyncio>=1.6.0", "reasoning-gym", "stagehand>=3.0.0", "aiohttp>=3.9.0", diff --git a/tests/v1/test_docker_runtime.py b/tests/v1/test_docker_runtime.py new file mode 100644 index 0000000000..909d7dd7d9 --- /dev/null +++ b/tests/v1/test_docker_runtime.py @@ -0,0 +1,56 @@ +import pytest + +from verifiers.v1.runtimes import DockerConfig +from verifiers.v1.runtimes import DockerMount +from verifiers.v1.runtimes import DockerRuntime +from verifiers.v1.runtimes import runtime_host_endpoint_url +from verifiers.v1.runtimes.base import ProgramResult + + +@pytest.mark.asyncio +async def test_docker_runtime_passes_configured_bind_mounts_to_docker_run( + monkeypatch: pytest.MonkeyPatch, +) -> None: + calls = [] + + async def fake_docker(*args: str) -> ProgramResult: + calls.append(args) + if args[:2] == ("version", "--format"): + return ProgramResult(exit_code=0, stdout="25.0.0\n", stderr="") + if args[:1] == ("run",): + return ProgramResult(exit_code=0, stdout="abc123def456\n", stderr="") + raise AssertionError(args) + + monkeypatch.setattr("verifiers.v1.runtimes.docker.docker", fake_docker) + runtime = DockerRuntime( + DockerConfig( + mounts=[ + DockerMount( + source="/host/library", + target="/workspace/library", + read_only=True, + ) + ] + ), + name="vf-test", + ) + + await runtime.start() + + assert ( + "--mount", + "type=bind,source=/host/library,target=/workspace/library,readonly", + ) in zip(calls[1], calls[1][1:]) + + +def test_docker_runtime_uses_docker_desktop_host_endpoint_on_macos( + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr("verifiers.v1.runtimes.docker.sys.platform", "darwin") + runtime = DockerRuntime(DockerConfig(), name="vf-test") + + assert runtime.host_endpoint_url(55521) == "http://host.docker.internal:55521" + assert ( + runtime_host_endpoint_url(DockerConfig(), 55521) + == "http://host.docker.internal:55521" + ) diff --git a/tests/v1/test_harbor_taskset.py b/tests/v1/test_harbor_taskset.py new file mode 100644 index 0000000000..9f4d8be2c8 --- /dev/null +++ b/tests/v1/test_harbor_taskset.py @@ -0,0 +1,591 @@ +import subprocess +import logging +from pathlib import Path +from types import SimpleNamespace +from collections.abc import Mapping + +import pytest + +from verifiers.v1.clients import Client, RolloutContext +from verifiers.v1.dialects import Dialect +from verifiers.v1.env import resolve_runtime_config +from verifiers.v1.graph import PendingTurn +from verifiers.v1.harness import Harness, HarnessConfig +from verifiers.v1.rollout import Rollout +from verifiers.v1.runtimes import DockerConfig +from verifiers.v1.runtimes.base import ProgramResult +from verifiers.v1.types import Response, SamplingConfig +from verifiers.v1.tasksets.harbor.taskset import ( + HarborConfig, + HarborTaskset, + parse_task, +) +from verifiers.v1.trace import Trace + + +def write_task_toml(task_dir: Path, content: str) -> None: + task_dir.mkdir(parents=True) + (task_dir / "task.toml").write_text(content.strip() + "\n") + + +class FakeRuntime: + def __init__(self, rewards: list[str]) -> None: + self.rewards = rewards + self.writes: list[str] = [] + self.commands: list[list[str]] = [] + + async def write(self, path: str, data: bytes) -> None: + self.writes.append(path) + + async def run(self, argv: list[str], env: dict[str, str]) -> ProgramResult: + self.commands.append(argv) + return ProgramResult(exit_code=0, stdout="", stderr="") + + async def read(self, path: str) -> bytes: + if path.endswith("reward.txt") and self.rewards: + return self.rewards.pop(0).encode() + raise FileNotFoundError(path) + + +class MissingRewardRuntime: + async def write(self, path: str, data: bytes) -> None: + return None + + async def run(self, argv: list[str], env: dict[str, str]) -> ProgramResult: + return ProgramResult(exit_code=0, stdout="", stderr="") + + async def read(self, path: str) -> bytes: + raise FileNotFoundError(path) + + +class DummyClient(Client): + async def get_response( + self, + dialect: Dialect, + body: dict, + model: str, + sampling_args: SamplingConfig, + session_id: str | None = None, + turn: PendingTurn | None = None, + headers: Mapping[str, str] | None = None, + ) -> Response: + raise AssertionError("the test harness should not call the model") + + +class HarborFixtureHarness(Harness[HarnessConfig]): + async def launch( + self, + ctx: RolloutContext, + trace: Trace, + runtime, + endpoint: str, + secret: str, + mcp_urls: dict[str, str], + ) -> ProgramResult: + return await runtime.run( + [ + "sh", + "-c", + "mkdir -p /workspace && " + "printf ok >/workspace/answer.txt && " + "printf ok >/workspace/step1.txt && " + "printf ok >/workspace/step2.txt && " + "mkdir -p /workspace/examples/step1 /workspace/examples/step2 && " + "printf saved >/workspace/examples/step1/main.py && " + "printf junk >/workspace/examples/step1/cache.tmp && " + "printf saved >/workspace/examples/step2/main.py && " + "printf junk >/workspace/examples/step2/cache.tmp", + ], + {}, + ) + + +class RecordingStepHarness: + def __init__(self) -> None: + self.prompts: list[str] = [] + + async def run( + self, + ctx: RolloutContext, + trace: Trace, + runtime, + endpoint: str, + secret: str, + mcp_urls: dict[str, str], + ) -> None: + self.prompts.append(trace.task.prompt) + trace.stop("agent_completed") + + +def docker_available() -> bool: + result = subprocess.run( + ["docker", "version", "--format", "{{.Server.Version}}"], + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + return result.returncode == 0 + + +async def run_harbor_rollout(taskset: HarborTaskset, task) -> Trace: + runtime_config = resolve_runtime_config(DockerConfig(), task) + rollout = Rollout( + task=task, + taskset=taskset, + harness=HarborFixtureHarness(HarnessConfig(id="harbor-fixture")), + ctx=RolloutContext( + model="unused", client=DummyClient(), sampling=SamplingConfig() + ), + runtime_config=runtime_config, + setup_timeout=60, + harness_timeout=60, + scoring_timeout=60, + ) + return await rollout.run() + + +def make_single_step_e2e_task(root: Path) -> Path: + task_dir = root / "single_step" + write_task_toml( + task_dir, + """ + [task] + name = "single_step" + + [environment] + docker_image = "python:3.11-slim" + workdir = "/workspace" + """, + ) + (task_dir / "instruction.md").write_text("Write ok to /workspace/answer.txt") + tests_dir = task_dir / "tests" + tests_dir.mkdir() + (tests_dir / "test.sh").write_text( + """ + set -eu + mkdir -p /logs/verifier + if [ "$(cat /workspace/answer.txt 2>/dev/null || true)" = ok ]; then + printf '{"reward": 1.0, "exact": 1.0}' >/logs/verifier/reward.json + else + printf '{"reward": 0.0, "exact": 0.0}' >/logs/verifier/reward.json + fi + """.strip() + + "\n" + ) + return task_dir + + +def make_multi_step_e2e_task(root: Path) -> Path: + task_dir = root / "phase_2" + write_task_toml( + task_dir, + """ + [environment] + docker_image = "python:3.11-slim" + workdir = "/workspace" + + [[steps]] + name = "01_step" + artifacts = [{source = "/workspace/examples/step1", destination = "step1", exclude = ["*.tmp"]}] + [steps.agent] + timeout_sec = 60.0 + [steps.verifier] + timeout_sec = 60.0 + + [[steps]] + name = "02_step" + artifacts = [{source = "/workspace/examples/step2", destination = "step2", exclude = ["*.tmp"]}] + [steps.agent] + timeout_sec = 60.0 + [steps.verifier] + timeout_sec = 60.0 + """, + ) + (task_dir / "tests").mkdir() + (task_dir / "tests" / "helpers.sh").write_text( + 'reward() { printf \'{"reward": %s, "%s": %s}\' "$1" "$2" "$1" >/logs/verifier/reward.json; }\n' + ) + for name, marker in [("01_step", "step1"), ("02_step", "step2")]: + step_dir = task_dir / "steps" / name + (step_dir / "tests").mkdir(parents=True) + (step_dir / "instruction.md").write_text(f"Write ok to /workspace/{marker}.txt") + (step_dir / "tests" / "test.sh").write_text( + f""" + set -eu + mkdir -p /logs/verifier + . /tests/helpers.sh + if [ "$(cat /workspace/{marker}.txt 2>/dev/null || true)" = ok ]; then + reward 1.0 {marker} + else + reward 0.0 {marker} + fi + """.strip() + + "\n" + ) + return task_dir + + +def make_multi_step_task(root: Path) -> Path: + task_dir = root / "phase_2" + write_task_toml( + task_dir, + """ + [environment] + workdir = "/workspace/examples" + memory_mb = 1024 + storage_mb = 2048 + + [[steps]] + name = "01_step" + [steps.agent] + timeout_sec = 60.0 + [steps.verifier] + timeout_sec = 60.0 + + [[steps]] + name = "02_step" + [steps.agent] + timeout_sec = 60.0 + [steps.verifier] + timeout_sec = 60.0 + """, + ) + (task_dir / "tests").mkdir() + (task_dir / "tests" / "helpers.sh").write_text("true\n") + for name in ["01_step", "02_step"]: + step_dir = task_dir / "steps" / name + (step_dir / "workdir").mkdir(parents=True) + (step_dir / "workdir" / "setup.sh").write_text("true\n") + (step_dir / "tests").mkdir() + (step_dir / "tests" / "test.sh").write_text("true\n") + (step_dir / "instruction.md").write_text(f"{name} instruction") + return task_dir + + +def test_harbor_loads_multi_step_task( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + dataset_root = tmp_path / "dataset" + make_multi_step_task(dataset_root) + monkeypatch.setattr( + "verifiers.v1.tasksets.harbor.taskset.dataset_dir", + lambda dataset: dataset_root, + ) + + taskset = HarborTaskset(HarborConfig(dataset="fixtures", ignore_dockerfile=True)) + task = taskset.load_tasks()[0] + + assert task.prompt.startswith("Complete this Harbor multi-step task.") + assert "01_step instruction" in task.prompt + assert "02_step instruction" in task.prompt + assert task.workdir == "/workspace/examples" + assert task.resources.memory == 1.0 + assert task.resources.disk == 2.0 + assert [step.name for step in task.steps] == ["01_step", "02_step"] + assert [step.harness_timeout for step in task.steps] == [60.0, 60.0] + assert [step.scoring_timeout for step in task.steps] == [60.0, 60.0] + assert task.timeout.harness == 120.0 + assert task.timeout.scoring == 120.0 + for step in task.steps: + assert (Path(step.task_dir) / "workdir" / "setup.sh").is_file() + assert step.prompt.strip() + + +@pytest.mark.asyncio +@pytest.mark.docker +async def test_harbor_v1_single_step_rollout_scores_in_real_docker_runtime( + tmp_path: Path, +) -> None: + if not docker_available(): + pytest.skip("docker daemon is not available") + make_single_step_e2e_task(tmp_path) + taskset = HarborTaskset( + HarborConfig(dataset=str(tmp_path), dockerfile_policy="ignore") + ) + task = taskset.load_tasks()[0] + + trace = await run_harbor_rollout(taskset, task) + + assert trace.errors == [] + assert trace.stop_condition == "agent_completed" + assert trace.reward == 1.0 + assert trace.rewards == {"solved": 1.0} + + +@pytest.mark.asyncio +@pytest.mark.docker +async def test_harbor_v1_multi_step_rollout_records_step_results_in_real_docker_runtime( + tmp_path: Path, +) -> None: + if not docker_available(): + pytest.skip("docker daemon is not available") + make_multi_step_e2e_task(tmp_path) + taskset = HarborTaskset( + HarborConfig(dataset=str(tmp_path), dockerfile_policy="ignore") + ) + task = taskset.load_tasks()[0] + + trace = await run_harbor_rollout(taskset, task) + + assert trace.errors == [] + assert trace.stop_condition == "agent_completed" + assert trace.reward == 1.0 + assert trace.info["harbor_steps"] == [ + {"name": "01_step", "rewards": {"reward": 1.0, "step1": 1.0}}, + {"name": "02_step", "rewards": {"reward": 1.0, "step2": 1.0}}, + ] + assert trace.info["harbor_multi_step_reward"] == { + "reward": 1.0, + "step1": 0.5, + "step2": 0.5, + } + + +@pytest.mark.asyncio +@pytest.mark.docker +async def test_harbor_v1_multi_step_rollout_records_declared_step_artifacts( + tmp_path: Path, +) -> None: + if not docker_available(): + pytest.skip("docker daemon is not available") + make_multi_step_e2e_task(tmp_path) + taskset = HarborTaskset( + HarborConfig(dataset=str(tmp_path), dockerfile_policy="ignore") + ) + task = taskset.load_tasks()[0] + + trace = await run_harbor_rollout(taskset, task) + + assert trace.errors == [] + assert trace.info["harbor_step_artifacts"] == [ + { + "step": "01_step", + "source": "/workspace/examples/step1", + "destination": "step1", + "files": [{"path": "main.py", "content": "c2F2ZWQ="}], + }, + { + "step": "02_step", + "source": "/workspace/examples/step2", + "destination": "step2", + "files": [{"path": "main.py", "content": "c2F2ZWQ="}], + }, + ] + + +def test_harbor_multi_step_timeout_is_unbounded_when_any_step_is_unbounded( + tmp_path: Path, +) -> None: + task_dir = make_multi_step_task(tmp_path) + config = ( + (task_dir / "task.toml") + .read_text() + .replace( + " [steps.verifier]\n timeout_sec = 60.0\n\n [[steps]]", + "\n [[steps]]", + 1, + ) + ) + (task_dir / "task.toml").write_text(config) + + task = parse_task(task_dir, 0, HarborConfig()) + + assert task.steps[0].scoring_timeout is None + assert task.timeout.harness == 120.0 + assert task.timeout.scoring is None + + +@pytest.mark.asyncio +async def test_harbor_multi_step_harness_runs_once_per_step_with_step_timeouts( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch, caplog: pytest.LogCaptureFixture +) -> None: + task_dir = make_multi_step_task(tmp_path) + taskset = HarborTaskset(HarborConfig(dataset=tmp_path.as_posix())) + task = parse_task(task_dir, 0, taskset.config) + trace = Trace(task=task) + harness = RecordingStepHarness() + timeouts: list[float | None] = [] + + async def recording_wait_for(awaitable, timeout): + timeouts.append(timeout) + return await awaitable + + monkeypatch.setattr( + "verifiers.v1.tasksets.harbor.taskset.asyncio.wait_for", + recording_wait_for, + ) + + caplog.set_level(logging.INFO, logger="verifiers.v1.tasksets.harbor") + await taskset.run_harness( + task, + trace, + FakeRuntime([]), + harness, + RolloutContext(model="unused", client=DummyClient(), sampling=SamplingConfig()), + "endpoint", + "secret", + {}, + ) + + assert harness.prompts == ["01_step instruction", "02_step instruction"] + assert timeouts == [60.0, 60.0] + assert trace.task is task + assert trace.stop_condition == "agent_completed" + assert [ + record.message + for record in caplog.records + if record.message.startswith("harbor step harness") + ] == [ + "harbor step harness start: task=phase_2 step=01_step index=1/2 timeout=60.0", + "harbor step harness end: task=phase_2 step=01_step index=1/2 stop=agent_completed", + "harbor step harness start: task=phase_2 step=02_step index=2/2 timeout=60.0", + "harbor step harness end: task=phase_2 step=02_step index=2/2 stop=agent_completed", + ] + + +def make_dockerfile_task(root: Path) -> Path: + task_dir = root / "docker_task" + write_task_toml( + task_dir, + """ + [task] + name = "Docker Task" + + [environment] + build_timeout_sec = 12.0 + workdir = "/workspace" + """, + ) + (task_dir / "instruction.md").write_text("Solve it") + tests_dir = task_dir / "tests" + tests_dir.mkdir() + (tests_dir / "test.sh").write_text("exit 0\n") + env_dir = task_dir / "environment" + env_dir.mkdir() + (env_dir / "Dockerfile").write_text("FROM python:3.12-slim\n") + return task_dir + + +def test_harbor_dockerfile_policy_defaults_to_ignore(tmp_path: Path) -> None: + task_dir = make_dockerfile_task(tmp_path) + + task = parse_task(task_dir, 0, HarborConfig()) + + assert task.image is None + assert task.environment.kind == "dockerfile" + + +def test_harbor_dockerfile_policy_error_rejects(tmp_path: Path) -> None: + task_dir = make_dockerfile_task(tmp_path) + + with pytest.raises(ValueError, match="dockerfile-policy build"): + parse_task(task_dir, 0, HarborConfig(dockerfile_policy="error")) + + +def test_harbor_dockerfile_policy_builds_cached_local_image( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + task_dir = make_dockerfile_task(tmp_path) + commands = [] + + def fake_which(name: str) -> str | None: + return "/usr/bin/docker" if name == "docker" else None + + def fake_run(argv, **kwargs): + commands.append((argv, kwargs)) + if argv[:3] == ["docker", "image", "inspect"]: + return SimpleNamespace(returncode=1, stdout="", stderr="") + return SimpleNamespace(returncode=0, stdout="", stderr="") + + monkeypatch.setattr("verifiers.v1.tasksets.harbor.taskset.shutil.which", fake_which) + monkeypatch.setattr("verifiers.v1.tasksets.harbor.taskset.subprocess.run", fake_run) + + task = parse_task(task_dir, 0, HarborConfig(dockerfile_policy="build")) + + assert task.image is not None + assert task.image.startswith("vf-harbor-docker_task:") + assert commands[-1][0] == [ + "/usr/bin/docker", + "build", + "-t", + task.image, + str(task_dir / "environment"), + ] + assert commands[-1][1]["timeout"] == 12.0 + + +@pytest.mark.asyncio +async def test_harbor_missing_reward_files_score_as_failure( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + dataset_root = tmp_path / "dataset" + task_dir = dataset_root / "missing_reward" + write_task_toml(task_dir, '[task]\nname = "missing_reward"') + (task_dir / "instruction.md").write_text("Solve it") + tests_dir = task_dir / "tests" + tests_dir.mkdir() + (tests_dir / "test.sh").write_text("exit 0\n") + monkeypatch.setattr( + "verifiers.v1.tasksets.harbor.taskset.dataset_dir", + lambda dataset: dataset_root, + ) + + taskset = HarborTaskset(HarborConfig(dataset="fixtures", ignore_dockerfile=True)) + task = taskset.load_tasks()[0] + runtime = MissingRewardRuntime() + rewards = await taskset.run_verifier( + runtime, tests_dir=tests_dir, solution_dir=task_dir / "solution" + ) + + assert rewards == {} + assert await taskset.solved(task, Trace(task=task), runtime) == 0.0 + + +@pytest.mark.asyncio +async def test_harbor_multi_step_solved_aggregates_step_rewards( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + dataset_root = tmp_path / "dataset" + make_multi_step_task(dataset_root) + monkeypatch.setattr( + "verifiers.v1.tasksets.harbor.taskset.dataset_dir", + lambda dataset: dataset_root, + ) + + taskset = HarborTaskset(HarborConfig(dataset="fixtures", ignore_dockerfile=True)) + task = taskset.load_tasks()[0] + trace = Trace(task=task) + runtime = FakeRuntime(['{"reward": 0.5, "style": 1.0}', '{"reward": 1.0}']) + + assert await taskset.solved(task, trace, runtime) == 0.75 + + assert trace.task is task + assert trace.info["harbor_multi_step_reward"] == {"reward": 0.75, "style": 0.5} + assert [record["name"] for record in trace.info["harbor_steps"]] == [ + "01_step", + "02_step", + ] + assert runtime.writes.count("/tmp/harbor_tests.tgz") == 2 + + +@pytest.mark.asyncio +async def test_harbor_missing_multi_step_reward_counts_as_zero( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + dataset_root = tmp_path / "dataset" + make_multi_step_task(dataset_root) + monkeypatch.setattr( + "verifiers.v1.tasksets.harbor.taskset.dataset_dir", + lambda dataset: dataset_root, + ) + + taskset = HarborTaskset(HarborConfig(dataset="fixtures", ignore_dockerfile=True)) + task = taskset.load_tasks()[0] + trace = Trace(task=task) + runtime = FakeRuntime(['{"reward": 1.0}']) + + assert await taskset.solved(task, trace, runtime) == 0.5 + assert trace.info["harbor_steps"] == [ + {"name": "01_step", "rewards": {"reward": 1.0}}, + {"name": "02_step", "rewards": {}}, + ] + assert trace.info["harbor_multi_step_reward"] == {"reward": 0.5} diff --git a/tests/v1/test_harbor_taskset_setup.py b/tests/v1/test_harbor_taskset_setup.py new file mode 100644 index 0000000000..c24c343b9a --- /dev/null +++ b/tests/v1/test_harbor_taskset_setup.py @@ -0,0 +1,132 @@ +from pathlib import Path + +import pytest + +from verifiers.v1.runtimes.base import ProgramResult +from verifiers.v1.tasksets.harbor.taskset import ( + HarborConfig, + HarborTaskset, + parse_task, +) + + +class RecordingRuntime: + def __init__(self) -> None: + self.writes: dict[str, bytes] = {} + self.commands: list[list[str]] = [] + + async def write(self, path: str, data: bytes) -> None: + self.writes[path] = data + + async def run(self, argv: list[str], env: dict[str, str]) -> ProgramResult: + self.commands.append(argv) + return ProgramResult(exit_code=0, stdout="", stderr="") + + +def write_task_toml(task_dir: Path, content: str) -> None: + task_dir.mkdir(parents=True) + (task_dir / "task.toml").write_text(content.strip() + "\n") + + +@pytest.mark.asyncio +async def test_harbor_setup_extracts_workspace_overlays(tmp_path: Path): + task_dir = tmp_path / "task" + write_task_toml( + task_dir, + """ + [environment] + workdir = "/workspace" + """, + ) + (task_dir / "instruction.md").write_text("Check restored artifacts.") + overlay = tmp_path / "overlay" + (overlay / "workspace" / "examples" / "demo").mkdir(parents=True) + (overlay / "workspace" / "examples" / "demo" / "main.py").write_text("print(1)") + config = HarborConfig( + id="harbor", + dataset=task_dir.as_posix(), + workspace_overlays=[{"source": overlay.as_posix()}], + ) + taskset = HarborTaskset(config) + task = parse_task(task_dir, 0, config) + runtime = RecordingRuntime() + + await taskset.setup(task, runtime) + + assert "/tmp/harbor_setup.tgz" in runtime.writes + assert runtime.commands == [ + [ + "sh", + "-c", + "mkdir -p / && tar -xzf /tmp/harbor_setup.tgz -C /", + ] + ] + + +@pytest.mark.asyncio +async def test_harbor_setup_runs_solution_entrypoints_in_task_workdir(tmp_path: Path): + task_dir = tmp_path / "task" + write_task_toml( + task_dir, + """ + [environment] + workdir = "/workspace" + + [agent] + timeout_sec = 12 + """, + ) + (task_dir / "instruction.md").write_text("Run the reference solution.") + solution = task_dir / "solution" + solution.mkdir() + (solution / "solve.sh").write_text("#!/usr/bin/env bash\n") + config = HarborConfig( + id="harbor", + dataset=task_dir.as_posix(), + run_solutions=True, + ) + taskset = HarborTaskset(config) + task = parse_task(task_dir, 0, config) + runtime = RecordingRuntime() + + await taskset.setup(task, runtime) + + assert runtime.commands[-1] == [ + "sh", + "-c", + "cd /workspace && timeout 12.0s bash /oracle/solve.sh", + ] + + +@pytest.mark.asyncio +async def test_harbor_setup_extracts_and_runs_step_workdir_setup(tmp_path: Path): + task_dir = tmp_path / "task" + write_task_toml( + task_dir, + """ + [environment] + workdir = "/workspace/examples" + + [[steps]] + name = "01_step" + """, + ) + step_dir = task_dir / "steps" / "01_step" + (step_dir / "workdir").mkdir(parents=True) + (step_dir / "workdir" / "setup.sh").write_text("#!/usr/bin/env bash\n") + (step_dir / "instruction.md").write_text("Use the workdir setup.") + config = HarborConfig(id="harbor", dataset=task_dir.as_posix()) + taskset = HarborTaskset(config) + task = parse_task(task_dir, 0, config) + runtime = RecordingRuntime() + + await taskset.setup(task, runtime) + + assert runtime.commands == [ + [ + "sh", + "-c", + "mkdir -p /workspace/examples && tar -xzf /tmp/harbor_setup.tgz -C /", + ], + ["sh", "-c", "cd /workspace/examples && bash setup.sh"], + ] diff --git a/tests/v1/test_mini_swe_agent_harness.py b/tests/v1/test_mini_swe_agent_harness.py new file mode 100644 index 0000000000..138325a015 --- /dev/null +++ b/tests/v1/test_mini_swe_agent_harness.py @@ -0,0 +1,43 @@ +import pytest + +from verifiers.v1.harnesses.mini_swe_agent.harness import ( + MiniSWEAgentHarness, + MiniSWEAgentHarnessConfig, +) + + +class RecordingRuntime: + def __init__(self) -> None: + self.envs: list[dict[str, str] | None] = [] + + async def prepare_uv_script( + self, script: str | bytes, env: dict[str, str] | None = None + ) -> list[str]: + self.envs.append(env) + return ["python", "/tmp/mini_swe_agent.py"] + + +@pytest.mark.asyncio +async def test_mini_swe_agent_setup_can_install_harness_dependencies_in_offline_tasks(): + harness = MiniSWEAgentHarness( + MiniSWEAgentHarnessConfig( + env={ + "UV_OFFLINE": "1", + "PIP_NO_INDEX": "1", + "PIP_CONFIG_FILE": "/etc/pip.conf", + "TASK_ENV": "kept", + } + ) + ) + runtime = RecordingRuntime() + + await harness.setup(runtime) + + assert runtime.envs == [ + { + "UV_OFFLINE": "false", + "PIP_NO_INDEX": "0", + "PIP_CONFIG_FILE": "/dev/null", + "TASK_ENV": "kept", + } + ] diff --git a/tests/v1/test_noop_harness.py b/tests/v1/test_noop_harness.py new file mode 100644 index 0000000000..fd939ce144 --- /dev/null +++ b/tests/v1/test_noop_harness.py @@ -0,0 +1,14 @@ +import pytest + +from verifiers.v1.harnesses.noop import NoopHarness, NoopHarnessConfig + + +@pytest.mark.asyncio +async def test_noop_harness_exits_without_model_or_runtime_calls(): + harness = NoopHarness(NoopHarnessConfig(id="noop")) + + result = await harness.launch(None, None, None, "", "", {}) + + assert result.exit_code == 0 + assert result.stdout == "" + assert result.stderr == "" diff --git a/uv.lock b/uv.lock index 9bf62c8c88..36696113b5 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 2 requires-python = ">=3.11, <3.14" resolution-markers = [ "python_full_version >= '3.13' and sys_platform == 'win32'", @@ -13,19 +13,9 @@ resolution-markers = [ "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'", ] -[options] -exclude-newer = "0001-01-01T00:00:00Z" # This has no effect and is included for backwards compatibility when using relative exclude-newer values. -exclude-newer-span = "P7D" - -[options.exclude-newer-package] -prime-tunnel = false -prime-sandboxes = false -prime-pydantic-config = false -renderers = false - [[package]] name = "accelerate" -version = "1.13.0" +version = "1.14.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "huggingface-hub" }, @@ -36,9 +26,9 @@ dependencies = [ { name = "safetensors" }, { name = "torch" }, ] -sdist = { url = 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missing task.toml or instruction.md" - ) + if not task_toml.exists(): + logger.warning(f"Skipping {task_dir.name}: missing task.toml") continue with open(task_toml, "rb") as f: config = load_toml(f) - instruction = instruction_md.read_text().strip() + try: + steps = load_harbor_steps(task_dir, config) + except (FileNotFoundError, ValueError) as e: + logger.warning(f"Skipping {task_dir.name}: invalid Harbor steps: {e}") + continue + if not instruction_md.exists() and not steps: + logger.warning( + f"Skipping {task_dir.name}: missing instruction.md or valid steps" + ) + continue + instruction = harbor_task_prompt(task_dir, steps) messages = [{"role": "user", "content": instruction}] task_entry = { @@ -85,6 +103,10 @@ def load_harbor_dataset(self) -> Dataset: "task_dir": str(task_dir), "docker_image": config.get("environment", {}).get("docker_image"), "config": config, + "steps": [harbor_step_info(step) for step in steps], + "multi_step_reward_strategy": config.get( + "multi_step_reward_strategy", "mean" + ), }, } @@ -151,6 +173,19 @@ async def prepare_harbor_task(self, sandbox_id: str, task_dir: Path) -> None: with tarfile.open(tar_path, "w:gz") as tar: if instruction_path.exists(): tar.add(instruction_path, arcname="task/instruction.md") + else: + if task_toml_path.exists(): + with open(task_toml_path, "rb") as f: + config = load_toml(f) + else: + config = {} + instruction = harbor_task_prompt( + task_dir, load_harbor_steps(task_dir, config) + ) + info = tarfile.TarInfo(name="task/instruction.md") + data = instruction.encode() + info.size = len(data) + tar.addfile(info, io.BytesIO(data)) if task_toml_path.exists(): tar.add(task_toml_path, arcname="task/task.toml") @@ -167,24 +202,32 @@ async def prepare_harbor_task(self, sandbox_id: str, task_dir: Path) -> None: finally: tar_path.unlink(missing_ok=True) - async def upload_test_assets(self, sandbox_id: str, task_dir: Path) -> None: - """Upload oracle/tests after agent completes, right before running tests.""" - solution_dir = task_dir / "solution" - tests_dir = task_dir / "tests" + async def upload_test_assets( + self, + sandbox_id: str, + task_dir: Path, + tests_dir: Path | None = None, + solution_dir: Path | None = None, + shared_tests_dir: Path | None = None, + ) -> None: + """Upload oracle/tests after agent completes, right before running tests. + + Multi-step Harbor tasks merge root tests first, then step tests so the + step-local test.sh overrides the shared fallback/helper files. + """ + tests_dir = task_dir / "tests" if tests_dir is None else tests_dir + solution_dir = task_dir / "solution" if solution_dir is None else solution_dir + + directories = [] + if shared_tests_dir is not None: + directories.append((shared_tests_dir, "tests")) + directories.extend([(tests_dir, "tests"), (solution_dir, "oracle")]) with tempfile.NamedTemporaryFile(suffix=".tar.gz", delete=False) as tmp_file: tar_path = Path(tmp_file.name) + tmp_file.write(make_harbor_tar(directories)) try: - with tarfile.open(tar_path, "w:gz") as tar: - if solution_dir.exists(): - for item in solution_dir.iterdir(): - tar.add(item, arcname=f"oracle/{item.name}") - - if tests_dir.exists(): - for item in tests_dir.iterdir(): - tar.add(item, arcname=f"tests/{item.name}") - remote_tar = "/tmp/harbor_tests.tar.gz" await self.sandbox_client.upload_file(sandbox_id, remote_tar, str(tar_path)) await self.sandbox_client.execute_command( @@ -209,6 +252,57 @@ async def post_rollout(self, state: vf.State): async def harbor_reward(self, state: vf.State, **kwargs) -> float: return state.get("reward", 0.0) + async def compute_step_reward( + self, + state: vf.State, + task_dir: Path, + verifier_dir: Path, + step: HarborStep | None, + ) -> dict[str, float]: + sandbox_id = state["sandbox_id"] + await self.with_retry(self.upload_test_assets)( + sandbox_id, + task_dir, + tests_dir=verifier_dir / "tests", + solution_dir=verifier_dir / "solution", + shared_tests_dir=task_dir / "tests" if verifier_dir != task_dir else None, + ) + + logger.info( + "Running Harbor tests for task %s step %s", + state.get("task"), + step.name if step is not None else "single", + ) + timeout = int((step.scoring_timeout if step is not None else None) or 300) + results = await self.run_background_job( + state, + "bash test.sh", + timeout=timeout, + working_dir="/tests", + poll_interval=5, + ) + if getattr(results, "exit_code", 0) != 0: + logger.warning( + f"Harbor tests exit_code={results.exit_code} " + f"stdout_len={len(getattr(results, 'stdout', '') or '')} " + f"stderr_len={len(getattr(results, 'stderr', '') or '')}" + ) + + reward_result = await self.with_retry(self.sandbox_client.execute_command)( + sandbox_id, + "if [ -s /logs/verifier/reward.txt ]; then cat /logs/verifier/reward.txt; " + "elif [ -s /logs/verifier/reward.json ]; then cat /logs/verifier/reward.json; fi", + working_dir=None, + ) + stdout_val = getattr(reward_result, "stdout", "") + if stdout_val is None: + reward_val = "" + elif isinstance(stdout_val, str): + reward_val = stdout_val.strip() + else: + reward_val = str(stdout_val).strip() + return parse_harbor_rewards(reward_val) + async def compute_reward(self, state: vf.State) -> float: """ Execute Harbor tests (tests/test.sh) inside the sandbox to compute reward. @@ -229,57 +323,35 @@ async def compute_reward(self, state: vf.State) -> float: logger.error(f"Task directory not found: {task_dir}") return 0.0 + task_info: dict[str, Any] = state.get("info") or {} + steps = [ + HarborStep.model_validate(step) for step in task_info.get("steps") or [] + ] try: - await self.with_retry(self.upload_test_assets)(sandbox_id, task_dir) - - logger.info(f"Running Harbor tests for task {state.get('task')}") - results = await self.run_background_job( - state, - "bash test.sh", - timeout=300, - working_dir="/tests", - poll_interval=5, - ) - if getattr(results, "exit_code", 0) != 0: - logger.warning( - f"Harbor tests exit_code={results.exit_code} " - f"stdout_len={len(getattr(results, 'stdout', '') or '')} " - f"stderr_len={len(getattr(results, 'stderr', '') or '')}" - ) - - reward_result = await self.with_retry(self.sandbox_client.execute_command)( - sandbox_id, - "if [ -s /logs/verifier/reward.txt ]; then cat /logs/verifier/reward.txt; " - "elif [ -s /logs/verifier/reward.json ]; then cat /logs/verifier/reward.json; fi", - working_dir=None, - ) + if steps: + + async def run_step(step: HarborStep) -> dict[str, float]: + return await self.compute_step_reward( + state, task_dir, step.task_dir, step + ) + + strategy = task_info.get("multi_step_reward_strategy", "mean") + step_results = await run_harbor_steps(steps, run_step) + step_records, aggregate = harbor_step_summary(step_results, strategy) + state["harbor_steps"] = step_records + state["harbor_multi_step_reward"] = aggregate + return float(aggregate.get("reward", 0.0)) + + rewards = await self.compute_step_reward(state, task_dir, task_dir, None) + if rewards: + value = float(rewards.get("reward", 0.0)) + logger.info(f"Reward from Harbor verifier: {value}") + return value except Exception as e: if state.get("error") is None: state["error"] = vf.SandboxError(str(e)) logger.error(f"Error computing Harbor reward: {e}") return 0.0 - stdout_val = getattr(reward_result, "stdout", "") - if stdout_val is None: - reward_val = "" - elif isinstance(stdout_val, str): - reward_val = stdout_val.strip() - else: - reward_val = str(stdout_val).strip() - if reward_val: - try: - value = float(reward_val) - logger.info(f"Reward from reward.txt: {value}") - return value - except ValueError: - try: - data = json.loads(reward_val) - except json.JSONDecodeError as e: - logger.error(f"Invalid reward.json: {e}") - return 0.0 - value = float(data.get("reward", 0.0)) - logger.info(f"Reward from reward.json: {value}") - return value - logger.warning("No reward.txt or reward.json produced by Harbor tests") return 0.0 diff --git a/verifiers/harbor.py b/verifiers/harbor.py new file mode 100644 index 0000000000..7bf87237f1 --- /dev/null +++ b/verifiers/harbor.py @@ -0,0 +1,266 @@ +"""Shared Harbor task parsing and reward helpers.""" + +import io +import json +import tarfile +import tomllib +from collections.abc import Awaitable, Callable, Iterable +from pathlib import Path +from typing import Annotated, Literal + +from pydantic import BaseModel, ConfigDict, Field + +RewardStrategy = Literal["mean", "final"] +MinReward = float | dict[str, float] + + +class HarborModel(BaseModel): + model_config = ConfigDict(extra="forbid") + + +class HarborImageEnvironment(HarborModel): + """A Harbor task environment backed by a pullable container image.""" + + kind: Literal["image"] = "image" + image: str + + +class HarborDockerfileEnvironment(HarborModel): + """A Harbor task environment backed by an environment/Dockerfile build context.""" + + kind: Literal["dockerfile"] = "dockerfile" + context: Path = Field(exclude=True) + build_timeout: float | None = None + + +class HarborDefaultEnvironment(HarborModel): + """A Harbor task with no declared environment; use the runtime default image.""" + + kind: Literal["default"] = "default" + + +HarborEnvironment = Annotated[ + HarborImageEnvironment | HarborDockerfileEnvironment | HarborDefaultEnvironment, + Field(discriminator="kind"), +] + + +class HarborStep(HarborModel): + """One ordered `[[steps]]` entry from a Harbor multi-step task.""" + + name: str + prompt: str + task_dir: Path = Field(exclude=True) + harness_timeout: float | None = None + scoring_timeout: float | None = None + min_reward: MinReward | None = None + verifier_env: dict[str, str] = Field(default_factory=dict) + artifacts: list["HarborArtifact"] = Field(default_factory=list) + + +class HarborArtifact(HarborModel): + source: str + destination: str | None = None + exclude: list[str] = Field(default_factory=list) + + +class HarborStepResult(HarborModel): + name: str + rewards: dict[str, float] = Field(default_factory=dict) + + +def load_harbor_config(task_dir: Path) -> dict: + return tomllib.loads((task_dir / "task.toml").read_text()) + + +def scaled_timeout(value: float | int | None, multiplier: float = 1.0) -> float | None: + return float(value) * multiplier if value is not None else None + + +def sum_timeouts(values: Iterable[float | None]) -> float | None: + resolved = list(values) + if not resolved or any(value is None for value in resolved): + return None + return sum(resolved) + + +def load_harbor_environment( + task_dir: Path, config: dict, timeout_multiplier: float = 1.0 +) -> HarborEnvironment: + env_config = config.get("environment", {}) + declared = env_config.get("docker_image") + if declared: + return HarborImageEnvironment(image=declared) + context = task_dir / "environment" + if (context / "Dockerfile").is_file(): + return HarborDockerfileEnvironment( + context=context, + build_timeout=scaled_timeout( + env_config.get("build_timeout_sec"), timeout_multiplier + ), + ) + return HarborDefaultEnvironment() + + +def load_harbor_steps( + task_dir: Path, config: dict, timeout_multiplier: float = 1.0 +) -> list[HarborStep]: + root_agent_timeout = config.get("agent", {}).get("timeout_sec") + root_scoring_timeout = config.get("verifier", {}).get("timeout_sec") + steps = [] + for raw_step in config.get("steps") or []: + if not isinstance(raw_step, dict): + raise ValueError(f"{task_dir.name}: Harbor step must be a table") + name = raw_step.get("name") + if not isinstance(name, str) or not name: + raise ValueError(f"{task_dir.name}: Harbor step missing non-empty name") + step_dir = task_dir / "steps" / name + instruction_path = step_dir / "instruction.md" + if not instruction_path.is_file(): + raise FileNotFoundError(f"{name}: missing instruction.md") + agent_timeout = raw_step.get("agent", {}).get("timeout_sec", root_agent_timeout) + scoring_timeout = raw_step.get("verifier", {}).get( + "timeout_sec", root_scoring_timeout + ) + steps.append( + HarborStep( + name=name, + prompt=instruction_path.read_text().strip(), + task_dir=step_dir, + harness_timeout=scaled_timeout(agent_timeout, timeout_multiplier), + scoring_timeout=scaled_timeout(scoring_timeout, timeout_multiplier), + min_reward=raw_step.get("min_reward"), + verifier_env=raw_step.get("verifier", {}).get("env", {}), + artifacts=raw_step.get("artifacts", []), + ) + ) + return steps + + +def harbor_task_prompt(task_dir: Path, steps: list[HarborStep]) -> str: + instruction = task_dir / "instruction.md" + if not steps: + if instruction.is_file(): + return instruction.read_text().strip() + return "Complete this Harbor task." + step_names = ", ".join(step.name for step in steps) + header = ( + instruction.read_text().strip() + if instruction.is_file() + else f"Complete this Harbor multi-step task. Steps: {step_names}." + ) + step_sections = [f"## Step {step.name}\n\n{step.prompt.strip()}" for step in steps] + return "\n\n".join([header, *step_sections]) + + +def valid_harbor_task_dir(task_dir: Path) -> bool: + try: + config = load_harbor_config(task_dir) + steps = load_harbor_steps(task_dir, config) + except (OSError, tomllib.TOMLDecodeError, ValueError, FileNotFoundError): + return False + return (task_dir / "instruction.md").is_file() or bool(steps) + + +def harbor_step_info(step: HarborStep) -> dict: + info = step.model_dump(mode="json") + info["task_dir"] = str(step.task_dir) + return info + + +def harbor_step_result_info(result: HarborStepResult) -> dict: + return result.model_dump(mode="json") + + +def harbor_step_summary( + results: list[HarborStepResult], strategy: RewardStrategy +) -> tuple[list[dict], dict[str, float]]: + return [ + harbor_step_result_info(result) for result in results + ], aggregate_step_rewards(results, strategy) + + +async def run_harbor_steps( + steps: Iterable[HarborStep], + run_step: Callable[[HarborStep], Awaitable[dict[str, float]]], +) -> list[HarborStepResult]: + results = [] + for step in steps: + result = HarborStepResult(name=step.name, rewards=await run_step(step)) + results.append(result) + if should_stop_after_step(result, step.min_reward): + break + return results + + +def make_harbor_tar(directories: Iterable[tuple[Path, str]]) -> bytes: + """Tar directory contents under target prefixes in order. + + Later entries with the same archive path intentionally override earlier ones + on extraction, matching Harbor's root-tests then step-tests merge semantics. + """ + buffer = io.BytesIO() + with tarfile.open(fileobj=buffer, mode="w:gz", dereference=True) as tar: + for directory, prefix in directories: + if not directory.exists(): + continue + for item in sorted(directory.iterdir()): + arcname = f"{prefix.rstrip('/')}/{item.name}" if prefix else item.name + tar.add(item, arcname=arcname) + return buffer.getvalue() + + +def parse_harbor_rewards(value: str) -> dict[str, float]: + stripped = value.strip() + if not stripped: + return {} + try: + data = json.loads(stripped) + except json.JSONDecodeError: + try: + return {"reward": float(stripped)} + except ValueError: + return {} + if isinstance(data, int | float) and not isinstance(data, bool): + return {"reward": float(data)} + if not isinstance(data, dict): + return {} + return { + key: float(raw) + for key, raw in data.items() + if isinstance(raw, int | float) and not isinstance(raw, bool) + } + + +def min_reward_failed(rewards: dict[str, float], min_reward: MinReward | None) -> bool: + if min_reward is None: + return False + thresholds = ( + {"reward": min_reward} if isinstance(min_reward, int | float) else min_reward + ) + return any( + rewards.get(key, float("-inf")) < value for key, value in thresholds.items() + ) + + +def should_stop_after_step( + result: HarborStepResult, min_reward: MinReward | None +) -> bool: + return not result.rewards or min_reward_failed(result.rewards, min_reward) + + +def aggregate_step_rewards( + results: list[HarborStepResult], strategy: RewardStrategy +) -> dict[str, float]: + if not results: + return {} + if strategy == "final": + return dict(results[-1].rewards) + keys = {key for result in results for key in result.rewards} + if not keys: + return {} + count = len(results) + return { + key: sum(result.rewards.get(key, 0.0) for result in results) / count + for key in keys + } diff --git a/verifiers/v1/README.md b/verifiers/v1/README.md index feb6907532..16b37dd0bd 100644 --- a/verifiers/v1/README.md +++ b/verifiers/v1/README.md @@ -100,10 +100,10 @@ filesystem, so run it under a containerized runtime: `docker` locally, or a remo `modal` sandbox (not the default `subprocess`). ```bash -uv run eval harbor -n 1 --taskset.ignore-dockerfile --harness.runtime.type docker --harness.id bash # bash-only agent -uv run eval harbor -n 1 --taskset.ignore-dockerfile --harness.runtime.type docker --harness.id mini-swe-agent # the mini-swe-agent CLI -uv run eval harbor -n 1 --taskset.ignore-dockerfile --harness.runtime.type docker --harness.id rlm # the rlm CLI agent -uv run eval harbor -n 1 --taskset.ignore-dockerfile --harness.runtime.type docker --harness.id codex # the codex CLI agent +uv run eval harbor -n 1 --harness.runtime.type docker --harness.id bash # bash-only agent +uv run eval harbor -n 1 --harness.runtime.type docker --harness.id mini-swe-agent # the mini-swe-agent CLI +uv run eval harbor -n 1 --harness.runtime.type docker --harness.id rlm # the rlm CLI agent +uv run eval harbor -n 1 --harness.runtime.type docker --harness.id codex # the codex CLI agent ``` ### Swappable runtime @@ -228,8 +228,11 @@ uv run eval gsm8k-v1 -n 1 --retries.rollout.max-retries 3 --retries.rollout.incl Some tasksets wrap a whole benchmark family rather than a single task — shipped, installed by default. For example, `textarena` (TextArena games) and `harbor` (the agentic- -benchmark registry). Harbor is the showcase: it pulls tasks straight from the Harbor registry, -each in its own declared, pullable container image — e.g. Terminal-Bench 2: +benchmark registry). Harbor is the showcase: it pulls tasks straight from the Harbor registry. +Pullable `[environment].docker_image` tasks run directly; Dockerfile-only Harbor environments +use the harness runtime image by default. Use `--taskset.dockerfile-policy build` to build a +deterministic local Docker image, or `--taskset.dockerfile-policy error` to require prebuilt +images. For example, Terminal-Bench 2: ```bash uv run eval harbor --taskset.dataset terminal-bench/terminal-bench-2 -n 10 --harness.id rlm diff --git a/verifiers/v1/cli/dashboard/eval.py b/verifiers/v1/cli/dashboard/eval.py index 465375a19b..d9213a188d 100644 --- a/verifiers/v1/cli/dashboard/eval.py +++ b/verifiers/v1/cli/dashboard/eval.py @@ -189,10 +189,15 @@ def _breakdown(done: list[Trace]) -> Table | None: names.extend(n for n in getattr(trace, source) if n not in names) if not names: continue - segments = [ - f"{name} {format_mean(done, lambda t, n=name, s=source: getattr(t, s).get(n, 0.0))}" - for name in names - ] + segments = [] + for name in names: + value = format_mean( + done, + lambda trace, key=name, source=source: getattr(trace, source).get( + key, 0.0 + ), + ) + segments.append(f"{name} {value}") grid.add_row(label, " · ".join(segments)) # Resource use over every completed rollout (errored ones still spent tokens/time): tokens and diff --git a/verifiers/v1/harnesses/mini_swe_agent/harness.py b/verifiers/v1/harnesses/mini_swe_agent/harness.py index d68fbe6b18..6e646c435e 100644 --- a/verifiers/v1/harnesses/mini_swe_agent/harness.py +++ b/verifiers/v1/harnesses/mini_swe_agent/harness.py @@ -8,6 +8,11 @@ from verifiers.v1.trace import Trace PROGRAM_SOURCE = (Path(__file__).resolve().parent / "program.py").read_text() +SCRIPT_SETUP_ENV = { + "UV_OFFLINE": "false", + "PIP_NO_INDEX": "0", + "PIP_CONFIG_FILE": "/dev/null", +} class MiniSWEAgentHarnessConfig(HarnessConfig): @@ -23,7 +28,7 @@ class MiniSWEAgentHarness(Harness[MiniSWEAgentHarnessConfig]): async def setup(self, runtime: Runtime) -> None: source = PROGRAM_SOURCE.replace("{version}", self.config.version) - await runtime.prepare_uv_script(source, self.config.env) + await runtime.prepare_uv_script(source, self.config.env | SCRIPT_SETUP_ENV) async def launch( self, @@ -70,5 +75,7 @@ async def launch( "MSWEA_CONFIGURED": "true", "MSWEA_SILENT_STARTUP": "true", } - program = await runtime.prepare_uv_script(source, self.config.env) + program = await runtime.prepare_uv_script( + source, self.config.env | SCRIPT_SETUP_ENV + ) return await runtime.run_program([*program, *args], env) diff --git a/verifiers/v1/harnesses/noop/__init__.py b/verifiers/v1/harnesses/noop/__init__.py new file mode 100644 index 0000000000..568fc84b50 --- /dev/null +++ b/verifiers/v1/harnesses/noop/__init__.py @@ -0,0 +1,5 @@ +"""noop — a built-in harness for verifier-only rollouts.""" + +from verifiers.v1.harnesses.noop.harness import NoopHarness, NoopHarnessConfig + +__all__ = ["NoopHarness", "NoopHarnessConfig"] diff --git a/verifiers/v1/harnesses/noop/harness.py b/verifiers/v1/harnesses/noop/harness.py new file mode 100644 index 0000000000..ace2677368 --- /dev/null +++ b/verifiers/v1/harnesses/noop/harness.py @@ -0,0 +1,25 @@ +"""A verifier-only harness that does not call a model or mutate the runtime.""" + +from verifiers.v1.clients import RolloutContext +from verifiers.v1.harness import Harness, HarnessConfig +from verifiers.v1.runtimes import ProgramResult, Runtime +from verifiers.v1.trace import Trace + + +class NoopHarnessConfig(HarnessConfig): + """The no-op harness.""" + + +class NoopHarness(Harness[NoopHarnessConfig]): + SUPPORTS_MCP = False + + async def launch( + self, + ctx: RolloutContext, + trace: Trace, + runtime: Runtime, + endpoint: str, + secret: str, + mcp_urls: dict[str, str], + ) -> ProgramResult: + return ProgramResult(exit_code=0, stdout="", stderr="") diff --git a/verifiers/v1/interception/pool.py b/verifiers/v1/interception/pool.py index 3b25d6e3f3..a47a792bad 100644 --- a/verifiers/v1/interception/pool.py +++ b/verifiers/v1/interception/pool.py @@ -17,7 +17,13 @@ from dataclasses import dataclass from verifiers.v1.interception.server import InterceptionServer, RolloutSession -from verifiers.v1.runtimes import HOST, RuntimeConfig, reachable_url, runtime_is_local +from verifiers.v1.runtimes import ( + HOST, + RuntimeConfig, + reachable_url, + runtime_host_endpoint_url, + runtime_is_local, +) logger = logging.getLogger(__name__) @@ -39,6 +45,7 @@ def __init__(self, runtime_config: RuntimeConfig, multiplex: int) -> None: # to the interception port, a local one is reached at localhost. Read off the runtime # class (no provisioning) — the pool never runs a sandbox. self.runtime_type = runtime_config.type + self.runtime_config = runtime_config self.is_local = runtime_is_local(runtime_config) self.multiplex = max(1, multiplex) self._servers: list[PooledServer] = [] @@ -63,9 +70,11 @@ async def _entry(self) -> PooledServer: await self._stack.enter_async_context(server) # The interception server is a HOST service the harness reaches: localhost for a local # harness runtime, a tunnel for a remote one. Owned by the pool's stack, torn down with it. - url = await self._stack.enter_async_context( - reachable_url(HOST, server.port, consumer_is_local=self.is_local) - ) + url = runtime_host_endpoint_url(self.runtime_config, server.port) + if url is None: + url = await self._stack.enter_async_context( + reachable_url(HOST, server.port, consumer_is_local=self.is_local) + ) entry = PooledServer(server, url) self._servers.append(entry) logger.info( diff --git a/verifiers/v1/rollout.py b/verifiers/v1/rollout.py index f7b56364d8..695c9810f1 100644 --- a/verifiers/v1/rollout.py +++ b/verifiers/v1/rollout.py @@ -218,8 +218,15 @@ async def run(self) -> Trace: # (like max_turns), even if its last call had failed. try: await asyncio.wait_for( - self.harness.run( - ctx, trace, runtime, endpoint, secret, urls + self.taskset.run_harness( + self.task, + trace, + runtime, + self.harness, + ctx, + endpoint, + secret, + urls, ), self.harness_timeout, ) diff --git a/verifiers/v1/runtimes/__init__.py b/verifiers/v1/runtimes/__init__.py index 7b0eb5e755..f728fdd879 100644 --- a/verifiers/v1/runtimes/__init__.py +++ b/verifiers/v1/runtimes/__init__.py @@ -19,7 +19,12 @@ reachable_url, register, ) -from verifiers.v1.runtimes.docker import DockerConfig, DockerRuntime +from verifiers.v1.runtimes.docker import ( + DockerConfig, + DockerMount, + DockerRuntime, + docker_host_endpoint_url, +) from verifiers.v1.runtimes.modal import ModalConfig, ModalRuntime from verifiers.v1.runtimes.prime import PrimeConfig, PrimeRuntime from verifiers.v1.runtimes.subprocess import SubprocessConfig, SubprocessRuntime @@ -54,18 +59,26 @@ def runtime_is_local(config: RuntimeConfig) -> bool: return _runtime_cls(config).is_local +def runtime_host_endpoint_url(config: RuntimeConfig, port: int) -> str | None: + if isinstance(config, DockerConfig): + return docker_host_endpoint_url(config, port) + return None + + __all__ = [ "ProgramResult", "Runtime", "RuntimeConfig", "make_runtime", "runtime_is_local", + "runtime_host_endpoint_url", "host_endpoint", "reachable_url", "HOST", "SubprocessConfig", "SubprocessRuntime", "DockerConfig", + "DockerMount", "DockerRuntime", "PrimeConfig", "PrimeRuntime", diff --git a/verifiers/v1/runtimes/base.py b/verifiers/v1/runtimes/base.py index 08bb038666..3a21b22dc8 100644 --- a/verifiers/v1/runtimes/base.py +++ b/verifiers/v1/runtimes/base.py @@ -277,6 +277,10 @@ async def expose(self, port: int) -> str | None: (which reaches a host port from inside a runtime).""" return None + def host_endpoint_url(self, port: int) -> str | None: + """Return a runtime-specific URL for reaching a host service, if needed.""" + return None + TunnelT = TypeVar("TunnelT") @@ -376,6 +380,13 @@ async def reachable_url( service is not HOST and not service.is_local ): # in a sandbox → it publishes its own port yield await service.expose(port) + elif service is HOST and consumer is not None: + url = consumer.host_endpoint_url(port) + if url is not None: + yield url + else: + async with host_endpoint(port, is_local) as url: + yield url else: # on the host network → reach it from wherever the consumer runs async with host_endpoint(port, is_local) as url: yield url diff --git a/verifiers/v1/runtimes/docker.py b/verifiers/v1/runtimes/docker.py index 75fc3fe7e7..6357044938 100644 --- a/verifiers/v1/runtimes/docker.py +++ b/verifiers/v1/runtimes/docker.py @@ -9,9 +9,11 @@ import logging import shlex import subprocess +import sys from pathlib import PurePosixPath from typing import Literal +from pydantic import Field from pydantic_config import BaseConfig from verifiers.v1.errors import SandboxError @@ -24,6 +26,8 @@ class DockerConfig(BaseConfig): type: Literal["docker"] = "docker" image: str = "python:3.11-slim" workdir: str = "/app" + mounts: list["DockerMount"] = Field(default_factory=list) + """Bind mounts to attach to the container before the harness runs.""" # TaskResources in Modal's units (also settable per-task via Task.resources). cpu: float | None = None """Pin the container to this many CPU cores (docker `--cpus`). None = unlimited.""" @@ -35,6 +39,32 @@ class DockerConfig(BaseConfig): disk: float | None = None """Advisory disk request in GB. Docker has no portable per-container size limit, so this is accepted (so a task can declare it without a warning) but not enforced.""" + host_address: str | None = None + """Host address visible inside the container. Defaults to Docker Desktop's host alias + on macOS and loopback elsewhere.""" + + +class DockerMount(BaseConfig): + source: str + target: str + read_only: bool = True + + +def mount_args(mounts: list[DockerMount]) -> list[str]: + args = [] + for mount in mounts: + value = f"type=bind,source={mount.source},target={mount.target}" + if mount.read_only: + value = f"{value},readonly" + args.extend(["--mount", value]) + return args + + +def docker_host_endpoint_url(config: DockerConfig, port: int) -> str: + host = config.host_address + if host is None: + host = "host.docker.internal" if sys.platform == "darwin" else "127.0.0.1" + return f"http://{host}:{port}" async def docker(*args: str) -> ProgramResult: @@ -67,6 +97,9 @@ def __init__(self, config: DockerConfig, name: str | None = None) -> None: def descriptor(self) -> str | None: return self._container_id + def host_endpoint_url(self, port: int) -> str | None: + return docker_host_endpoint_url(self.config, port) + async def start(self) -> None: try: version = await docker("version", "--format", "{{.Server.Version}}") @@ -95,12 +128,14 @@ async def start(self) -> None: _, gpu_count = parse_gpu(self.config.gpu) if gpu_count: limits += ["--gpus", str(gpu_count)] + mounts = mount_args(self.config.mounts) run = await docker( "run", "--detach", "--network", "host", *limits, + *mounts, "--workdir", self.config.workdir, "--name", diff --git a/verifiers/v1/taskset.py b/verifiers/v1/taskset.py index 716a23cfd8..7282e8453e 100644 --- a/verifiers/v1/taskset.py +++ b/verifiers/v1/taskset.py @@ -86,6 +86,25 @@ async def setup(self, task: TaskT, runtime: Runtime) -> None: a SWE row checking out its base commit). Errors propagate and fail the rollout.""" return None + async def run_harness( + self, + task: TaskT, + trace: Trace, + runtime: Runtime, + harness, + ctx, + endpoint: str, + secret: str, + mcp_urls: dict[str, str], + ) -> None: + """Drive the harness for this task. + + Most tasksets run one harness trajectory. Tasksets with internal execution + units, such as Harbor multi-step tasks, can override this while preserving + the same live runtime and scoring lifecycle. + """ + await harness.run(ctx, trace, runtime, endpoint, secret, mcp_urls) + async def finalize(self, task: TaskT, trace: Trace, runtime: Runtime) -> None: """Post-process the live runtime after the harness finishes, before scoring. No-op by default; override to do per-rollout work the rewards depend on — apply/commit the diff --git a/verifiers/v1/tasksets/harbor/__init__.py b/verifiers/v1/tasksets/harbor/__init__.py index e6293883d5..376e2bed1e 100644 --- a/verifiers/v1/tasksets/harbor/__init__.py +++ b/verifiers/v1/tasksets/harbor/__init__.py @@ -1,7 +1,25 @@ +from verifiers.harbor import ( + HarborDefaultEnvironment, + HarborDockerfileEnvironment, + HarborEnvironment, + HarborImageEnvironment, + HarborStep, +) from verifiers.v1.tasksets.harbor.taskset import ( + DockerfilePolicy, HarborConfig, HarborTask, HarborTaskset, ) -__all__ = ["HarborConfig", "HarborTask", "HarborTaskset"] +__all__ = [ + "DockerfilePolicy", + "HarborConfig", + "HarborDefaultEnvironment", + "HarborDockerfileEnvironment", + "HarborEnvironment", + "HarborImageEnvironment", + "HarborStep", + "HarborTask", + "HarborTaskset", +] diff --git a/verifiers/v1/tasksets/harbor/taskset.py b/verifiers/v1/tasksets/harbor/taskset.py index 6ea29ac11f..465661f8df 100644 --- a/verifiers/v1/tasksets/harbor/taskset.py +++ b/verifiers/v1/tasksets/harbor/taskset.py @@ -1,37 +1,66 @@ """harbor: run a Harbor (Terminal-Bench) dataset. `dataset` is a Harbor Hub package id (e.g. "org/name" or "org/name@ref"), -downloaded directly from the registry and cached on first use. Each task dir -ships task.toml + instruction.md (+ tests/, solution/, environment/). Defaults -to the registry `harbor/hello-world` dataset. +downloaded directly from the registry and cached on first use, or a local path to +Harbor task directories. Each task dir ships task.toml plus either a single root +instruction/tests pair or a Harbor multi-step `steps/` layout. Defaults to the +registry `harbor/hello-world` dataset. -The harness runs in a container and edits /app; then the task's verifier -(tests/test.sh) runs in the SAME container and the reward it writes to -/logs/verifier/reward.txt becomes the score. The verification lives on the -taskset (the `solved` reward), so a harbor task runs under ANY harness. +The harness runs in a container and edits the task's declared workdir; then the task's +verifier (tests/test.sh, or each step's tests/test.sh) runs in the SAME container and the +reward it writes to /logs/verifier/reward.txt or reward.json becomes the score. +The verification lives on the taskset, so a harbor task runs under any harness. A task's declared [environment].docker_image becomes a first-class `Task.image` the Environment injects into the runtime (docker/prime both pull it). A task whose -environment is only a `Dockerfile` has no pullable image — we don't build Dockerfiles -(a locally-built image isn't pullable by a remote sandbox) — so it's rejected unless -`ignore_dockerfile`, which runs it on the harness runtime's image instead. A task with -no environment at all also runs on that image, unless `require_image`. +environment is only a `Dockerfile` uses the harness runtime image by default. +`dockerfile_policy` can switch that behavior to a deterministic local Docker build or a +hard error. A task with no environment at all runs on the runtime image, unless +`require_image`. """ +import asyncio +import base64 +import hashlib import io import json +import logging import os +import re +import shlex +import shutil +import subprocess import tarfile import tempfile -import tomllib from concurrent.futures import ThreadPoolExecutor -from functools import lru_cache from pathlib import Path +from pathlib import PurePosixPath +from typing import Literal from urllib.parse import urlencode from urllib.request import Request, urlopen -from pydantic import Field - +from pydantic import Field, model_validator + +from verifiers.harbor import ( + HarborDockerfileEnvironment, + HarborEnvironment, + HarborArtifact, + HarborImageEnvironment, + HarborStep, + HarborStepResult, + RewardStrategy, + harbor_step_summary, + harbor_task_prompt, + load_harbor_config, + load_harbor_environment, + load_harbor_steps, + make_harbor_tar, + parse_harbor_rewards, + run_harbor_steps, + scaled_timeout, + sum_timeouts, + valid_harbor_task_dir, +) from verifiers.v1.decorators import reward from verifiers.v1.errors import SandboxError from verifiers.v1.runtimes import Runtime @@ -43,12 +72,18 @@ CACHE = Path.home() / ".cache" / "harbor" HARBOR_SUPABASE_URL = "https://ofhuhcpkvzjlejydnvyd.supabase.co" HARBOR_SUPABASE_PUBLISHABLE_KEY = "sb_publishable_Z-vuQbpvpG-PStjbh4yE0Q_e-d3MTIH" +TESTS_TAR = "/tmp/harbor_tests.tgz" +ARTIFACT_TAR = "/tmp/harbor_artifact.tgz" +SETUP_TAR = "/tmp/harbor_setup.tgz" +logger = logging.getLogger("verifiers.v1.tasksets.harbor") + +DockerfilePolicy = Literal["build", "ignore", "error"] class HarborConfig(TasksetConfig): dataset: str = "harbor/hello-world" """A Harbor Hub package id ("org/name" or "org/name@ref"), where ref is a - tag, integer revision, or sha256 digest. Downloaded directly and cached.""" + tag, integer revision, or sha256 digest; or a local directory of Harbor task dirs.""" tasks: list[str] | None = None """Optional subset of task names to load (None = all).""" timeout_multiplier: float = Field(1.0, gt=0) @@ -57,14 +92,32 @@ class HarborConfig(TasksetConfig): """Scale each task's CPU, memory, and disk requests. GPU requests are unchanged.""" require_image: bool = False """For a task with NO declared environment at all (no docker_image, no Dockerfile), - whether to reject it (True) or run it on the runtime's default image (False). A task - whose environment is a `Dockerfile` is rejected too (building Dockerfiles isn't - supported), unless `ignore_dockerfile`.""" + whether to reject it (True) or run it on the runtime's default image (False).""" + dockerfile_policy: DockerfilePolicy = "ignore" + """How to handle a task whose environment is declared by `environment/Dockerfile` but not + `[environment].docker_image`: ignore the Dockerfile and use the runtime image (default), + build a local deterministic Docker image, or raise an error.""" ignore_dockerfile: bool = False - """Run a task whose environment is only a `Dockerfile` on the harness runtime's image - instead of rejecting it. The Dockerfile is NOT built, so the task scores against the - harness image rather than its declared environment — only correct when that image already - has what the task needs (e.g. you've pointed the runtime at the right image).""" + """Legacy alias for `dockerfile_policy = "ignore"`. Explicit `dockerfile_policy` wins when + both are set.""" + workspace_overlays: list["HarborWorkspaceOverlay"] = Field(default_factory=list) + """Host directories to extract into the runtime before the harness runs. Intended for + verifier-only artifact replay; each directory's contents land under `target`.""" + run_solutions: bool = False + """Run checked-in Harbor `solution/solve.sh` scripts before the harness. Intended for + oracle-style verifier-only runs.""" + + @model_validator(mode="before") + @classmethod + def _legacy_ignore_dockerfile(cls, data): + if ( + isinstance(data, dict) + and data.get("ignore_dockerfile") + and "dockerfile_policy" not in data + ): + data = dict(data) + data["dockerfile_policy"] = "ignore" + return data class Author(StrictBaseModel): @@ -72,26 +125,37 @@ class Author(StrictBaseModel): email: str | None = None +class HarborWorkspaceOverlay(StrictBaseModel): + source: str + target: str = "/" + + class HarborTask(Task): - """A Harbor task. The base fields carry instruction.md (`prompt`), the - resolved container `image`, the `harness_timeout`/`scoring_timeout`/`resources` - (from task.toml's [harness]/[verifier]/[environment]), and [task].name/description; - the rest mirror [metadata].""" + """A Harbor task. The base fields carry instruction text (`prompt`), the + resolved container `image`, total task timeouts/resources, and [task] + name/description. `steps` is populated for Harbor multi-step tasks.""" - keywords: list[str] = [] - authors: list[Author] = [] + keywords: list[str] = Field(default_factory=list) + authors: list[Author] = Field(default_factory=list) difficulty: str | None = None category: str | None = None - tags: list[str] = [] + tags: list[str] = Field(default_factory=list) task_dir: str = Field(exclude=True) """Host path to the task dir; used to stage tests/ to verify, not serialized.""" + environment: HarborEnvironment = Field(exclude=True) + """Parsed Harbor environment spec, kept for diagnostics and policy-specific tests.""" + multi_step_reward_strategy: RewardStrategy = "mean" + steps: list[HarborStep] = Field(default_factory=list) def dataset_dir(dataset: str) -> Path: - """Download a Harbor Hub task or dataset package, cached on first use. + """Resolve a local Harbor dataset path or download a Harbor Hub package. Harbor refs are tags, integer revisions, or ``sha256:`` digests. """ + local = Path(dataset).expanduser() + if local.is_dir(): + return local out = CACHE / dataset.replace("/", "_").replace("@", "_") if out.is_dir(): return out @@ -215,32 +279,90 @@ def extract(item: tuple[Path, str]) -> None: return out +def dockerfile_context_digest(context: Path) -> str: + """Content hash for a Docker build context, independent of mtimes.""" + digest = hashlib.sha256() + for path in sorted(context.rglob("*")): + if not path.is_file(): + continue + relative = path.relative_to(context).as_posix() + digest.update(relative.encode()) + digest.update(b"\0") + digest.update(path.read_bytes()) + digest.update(b"\0") + return digest.hexdigest() + + +def docker_tag_component(value: str) -> str: + component = re.sub(r"[^a-z0-9_.-]+", "-", value.lower()).strip(".-") + return component or "task" + + +def docker_image_exists(tag: str) -> bool: + result = subprocess.run( + ["docker", "image", "inspect", tag], + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + return result.returncode == 0 + + +def build_dockerfile_image( + environment: HarborDockerfileEnvironment, task_name: str +) -> str: + """Build a Harbor `environment/Dockerfile` into a deterministic local image tag.""" + context = environment.context + dockerfile = context / "Dockerfile" + if not dockerfile.is_file(): + raise FileNotFoundError(dockerfile) + digest = dockerfile_context_digest(context)[:16] + tag = f"vf-harbor-{docker_tag_component(task_name)}:{digest}" + docker_bin = shutil.which("docker") + if docker_bin is None: + raise RuntimeError( + f"{task_name}: Dockerfile environment requires the `docker` CLI to build. " + "Install Docker, publish the image and set [environment].docker_image, or pass " + "--taskset.dockerfile-policy ignore to run on the harness runtime image." + ) + if docker_image_exists(tag): + logger.info("harbor: using cached Dockerfile image %s", tag) + return tag + logger.info("harbor: building Dockerfile image %s from %s", tag, context) + try: + result = subprocess.run( + [docker_bin, "build", "-t", tag, str(context)], + capture_output=True, + text=True, + timeout=environment.build_timeout, + ) + except subprocess.TimeoutExpired as e: + timeout = environment.build_timeout + detail = f" after {timeout:.0f}s" if timeout is not None else "" + raise RuntimeError(f"{task_name}: Dockerfile build timed out{detail}") from e + if result.returncode != 0: + detail = (result.stderr or result.stdout).strip()[-2000:] + raise RuntimeError(f"{task_name}: Dockerfile build failed: {detail}") + return tag + + def resolve_image( - task_dir: Path, - config: dict, - require_image: bool, - ignore_dockerfile: bool = False, + task_dir: Path, environment: HarborEnvironment, harbor_config: HarborConfig ) -> str | None: - """The task's declared registry image (usable by docker or prime). A pullable - `[environment].docker_image` is used directly. A task whose environment is a - `Dockerfile` is rejected — we don't build Dockerfiles, and running it on the default - image would silently score against the wrong environment (e.g. SWE-bench's `/testbed` - repo would be missing) — unless `ignore_dockerfile`, which returns None to run it on the - harness runtime's image. A task with no environment at all runs on that image too, unless - `require_image`. None means "use the runtime's own image".""" - declared = config.get("environment", {}).get("docker_image") - if declared: - return declared - if (task_dir / "environment" / "Dockerfile").exists(): - if ignore_dockerfile: + """Resolve the task image within Harbor loading, keeping Dockerfile policy local to Harbor.""" + if isinstance(environment, HarborImageEnvironment): + return environment.image + if isinstance(environment, HarborDockerfileEnvironment): + if harbor_config.dockerfile_policy == "ignore": return None - raise ValueError( - f"{task_dir.name}: environment is a Dockerfile, not a pullable " - "[environment].docker_image — building Dockerfiles isn't supported, so this " - "task can't run (it would otherwise score against the wrong default image). " - "Pass --taskset.ignore-dockerfile to run it on the harness runtime's image instead." - ) - if require_image: + if harbor_config.dockerfile_policy == "error": + raise ValueError( + f"{task_dir.name}: environment is a Dockerfile, not a pullable " + "[environment].docker_image. Set --taskset.dockerfile-policy build to build " + "it locally, publish an image and set [environment].docker_image, or pass " + "--taskset.dockerfile-policy ignore to run on the harness runtime image." + ) + return build_dockerfile_image(environment, task_dir.name) + if harbor_config.require_image: raise ValueError( f"{task_dir.name}: no [environment].docker_image and require_image=True" ) @@ -257,35 +379,70 @@ def parse_resources(env: dict, multiplier: float = 1.0) -> TaskResources: ) +def artifact_destination(artifact: HarborArtifact) -> str: + """Return the Harbor-compatible relative destination for an artifact entry.""" + if artifact.destination is not None: + return artifact.destination + parts = [ + part + for part in PurePosixPath(artifact.source).parts + if part not in ("", "/", "..") + ] + return PurePosixPath(*parts).as_posix() if parts else "." + + +def tar_exclude_args(patterns: list[str]) -> str: + return " ".join(f"--exclude={shlex.quote(pattern)}" for pattern in patterns) + + +def tar_file_records(data: bytes) -> list[dict[str, str]]: + records = [] + with tarfile.open(fileobj=io.BytesIO(data), mode="r:gz") as tar: + for member in sorted(tar.getmembers(), key=lambda item: item.name): + if not member.isfile(): + continue + extracted = tar.extractfile(member) + if extracted is None: + continue + path = member.name.removeprefix("./") + records.append( + { + "path": path, + "content": base64.b64encode(extracted.read()).decode("ascii"), + } + ) + return records + + def parse_task(task_dir: Path, idx: int, harbor_config: HarborConfig) -> HarborTask: - """Read a harbor task dir (task.toml + instruction.md) into a typed task, - handling both the [task].authors and legacy [metadata].author_name layouts.""" - config = tomllib.loads((task_dir / "task.toml").read_text()) + """Read a Harbor task dir into a typed task, supporting both single-step + (`instruction.md` + `tests/`) and multi-step (`[[steps]]` + `steps//`) layouts.""" + config = load_harbor_config(task_dir) task, meta = config.get("task", {}), config.get("metadata", {}) authors = [Author(**a) for a in task.get("authors", [])] if not authors and meta.get("author_name"): authors = [Author(name=meta["author_name"], email=meta.get("author_email"))] - harness_timeout = config.get("agent", {}).get("timeout_sec") - scoring_timeout = config.get("verifier", {}).get("timeout_sec") + steps = load_harbor_steps(task_dir, config, harbor_config.timeout_multiplier) + environment = load_harbor_environment( + task_dir, config, harbor_config.timeout_multiplier + ) + harness_timeout = scaled_timeout( + config.get("agent", {}).get("timeout_sec"), harbor_config.timeout_multiplier + ) + scoring_timeout = scaled_timeout( + config.get("verifier", {}).get("timeout_sec"), harbor_config.timeout_multiplier + ) + if steps: + harness_timeout = sum_timeouts(step.harness_timeout for step in steps) + scoring_timeout = sum_timeouts(step.scoring_timeout for step in steps) return HarborTask( idx=idx, name=task.get("name") or task_dir.name, description=task.get("description"), - prompt=(task_dir / "instruction.md").read_text().strip(), - image=resolve_image( - task_dir, - config, - harbor_config.require_image, - harbor_config.ignore_dockerfile, - ), - timeout=TaskTimeout( - harness=harness_timeout * harbor_config.timeout_multiplier - if harness_timeout is not None - else None, - scoring=scoring_timeout * harbor_config.timeout_multiplier - if scoring_timeout is not None - else None, - ), + prompt=harbor_task_prompt(task_dir, steps), + image=resolve_image(task_dir, environment, harbor_config), + workdir=config.get("environment", {}).get("workdir"), + timeout=TaskTimeout(harness=harness_timeout, scoring=scoring_timeout), resources=parse_resources( config.get("environment", {}), harbor_config.resource_multiplier ), @@ -295,28 +452,19 @@ def parse_task(task_dir: Path, idx: int, harbor_config: HarborConfig) -> HarborT category=meta.get("category"), tags=meta.get("tags", []), task_dir=str(task_dir), + environment=environment, + multi_step_reward_strategy=config.get("multi_step_reward_strategy") or "mean", + steps=steps, ) -# Harbor test directories are immutable after download, so repeated rollouts can reuse -# the latest archive. One entry bounds retained memory to one task. -@lru_cache(maxsize=1) -def make_tar(directory: Path) -> bytes: - """Tar a directory's contents (flat) into a gzipped tarball.""" - buffer = io.BytesIO() - with tarfile.open(fileobj=buffer, mode="w:gz") as tar: - for item in sorted(directory.iterdir()): - tar.add(item, arcname=item.name) - return buffer.getvalue() - - class HarborTaskset(Taskset[HarborTask, HarborConfig]): def load_tasks(self) -> list[HarborTask]: root = dataset_dir(self.config.dataset) task_dirs = [ toml_path.parent for toml_path in sorted(root.rglob("task.toml")) - if (toml_path.parent / "instruction.md").is_file() + if valid_harbor_task_dir(toml_path.parent) and ( self.config.tasks is None or toml_path.parent.name in self.config.tasks ) @@ -328,23 +476,287 @@ def load_tasks(self) -> list[HarborTask]: for idx, task_dir in enumerate(task_dirs) ] - @reward(weight=1.0) - async def solved(self, task: HarborTask, trace: Trace, runtime: Runtime) -> float: - # Stage the task's tests into the live runtime, run its harbor verifier, and - # read back the reward it writes — runtime-opaque (write/run/read hide whether - # that's the host fs or across a container boundary), so it scores under any harness. - await runtime.write("/tmp/tests.tgz", make_tar(Path(task.task_dir) / "tests")) + async def setup(self, task: HarborTask, runtime: Runtime) -> None: + await self.materialize_workdirs(task, runtime) + for overlay in self.config.workspace_overlays: + await self.extract_workspace_overlay(runtime, overlay) + if self.config.run_solutions: + await self.run_task_solutions(task, runtime) + + async def materialize_workdirs(self, task: HarborTask, runtime: Runtime) -> None: + workdirs = [Path(task.task_dir) / "workdir"] + workdirs.extend(step.task_dir / "workdir" for step in task.steps) + for workdir in workdirs: + if workdir.is_dir(): + await self.extract_workdir(runtime, workdir, task.workdir or "/") + + async def extract_workdir( + self, runtime: Runtime, source: Path, target: str + ) -> None: + target_path = target.strip("/") + await runtime.write(SETUP_TAR, make_harbor_tar([(source, target_path)])) + target_arg = shlex.quote("/" if not target_path else f"/{target_path}") + result = await runtime.run( + [ + "sh", + "-c", + f"mkdir -p {target_arg} && tar -xzf {SETUP_TAR} -C /", + ], + {}, + ) + if result.exit_code != 0: + raise SandboxError(f"extract workdir {source}: {result.stderr.strip()}") + setup = source / "setup.sh" + if setup.is_file(): + result = await runtime.run( + ["sh", "-c", f"cd {target_arg} && bash setup.sh"], + {}, + ) + if result.exit_code != 0: + output = (result.stderr or result.stdout).strip() + raise SandboxError(f"workdir setup failed: {output}") + + async def extract_workspace_overlay( + self, runtime: Runtime, overlay: HarborWorkspaceOverlay + ) -> None: + source = Path(overlay.source).expanduser().resolve() + if not source.is_dir(): + raise FileNotFoundError( + f"workspace overlay source is not a directory: {source}" + ) + target = overlay.target.strip("/") + await runtime.write(SETUP_TAR, make_harbor_tar([(source, target)])) + target_arg = shlex.quote("/" if not target else f"/{target}") + result = await runtime.run( + [ + "sh", + "-c", + f"mkdir -p {target_arg} && tar -xzf {SETUP_TAR} -C /", + ], + {}, + ) + if result.exit_code != 0: + raise SandboxError( + f"extract workspace overlay {source}: {result.stderr.strip()}" + ) + + async def run_task_solutions(self, task: HarborTask, runtime: Runtime) -> None: + if task.steps: + for step in task.steps: + await self.run_solution( + task, + runtime, + solution_dir=step.task_dir / "solution", + timeout=step.harness_timeout, + ) + return + await self.run_solution( + task, + runtime, + solution_dir=Path(task.task_dir) / "solution", + timeout=task.timeout.harness, + ) + + async def run_solution( + self, + task: HarborTask, + runtime: Runtime, + *, + solution_dir: Path, + timeout: float | None, + ) -> None: + solve = solution_dir / "solve.sh" + if not solve.is_file(): + raise FileNotFoundError(f"missing Harbor solution entrypoint: {solve}") + await runtime.write(SETUP_TAR, make_harbor_tar([(solution_dir, "oracle")])) await runtime.run( [ "sh", "-c", - "mkdir -p /logs/verifier /tests && tar -xzf /tmp/tests.tgz -C /tests", + f"rm -rf /oracle && mkdir -p /oracle && tar -xzf {SETUP_TAR} -C /", ], {}, ) - await runtime.run(["sh", "-c", "cd /tests && bash test.sh"], {}) + workdir = shlex.quote(task.workdir or "/") + command = "bash /oracle/solve.sh" + if timeout is not None: + command = f"timeout {shlex.quote(f'{timeout}s')} {command}" + result = await runtime.run(["sh", "-c", f"cd {workdir} && {command}"], {}) + if result.exit_code != 0: + output = (result.stderr or result.stdout).strip() + raise SandboxError(f"Harbor solution failed: {output}") + + async def run_harness( + self, + task: HarborTask, + trace: Trace, + runtime: Runtime, + harness, + ctx, + endpoint: str, + secret: str, + mcp_urls: dict[str, str], + ) -> None: + if ( + not task.steps + or self.config.run_solutions + or self.config.workspace_overlays + ): + await harness.run(ctx, trace, runtime, endpoint, secret, mcp_urls) + return + + original_task = trace.task try: - reward = (await runtime.read("/logs/verifier/reward.txt")).decode().strip() - return float(reward or 0) - except (SandboxError, OSError, ValueError): - return 0.0 + for index, step in enumerate(task.steps): + logger.info( + "harbor step harness start: task=%s step=%s index=%d/%d timeout=%s", + task.name or Path(task.task_dir).name, + step.name, + index + 1, + len(task.steps), + step.harness_timeout, + ) + trace.task = task.model_copy( + update={ + "name": f"{task.name or Path(task.task_dir).name}/{step.name}", + "prompt": step.prompt, + "timeout": TaskTimeout( + harness=step.harness_timeout, + scoring=step.scoring_timeout, + ), + "steps": [], + } + ) + await asyncio.wait_for( + harness.run(ctx, trace, runtime, endpoint, secret, mcp_urls), + step.harness_timeout, + ) + logger.info( + "harbor step harness end: task=%s step=%s index=%d/%d stop=%s", + task.name or Path(task.task_dir).name, + step.name, + index + 1, + len(task.steps), + trace.stop_condition, + ) + if index < len(task.steps) - 1: + if trace.stop_condition != "agent_completed": + return + trace.stop_condition = None + trace.is_completed = False + finally: + trace.task = original_task + + async def finalize(self, task: HarborTask, trace: Trace, runtime: Runtime) -> None: + records = [] + for step in task.steps: + for artifact in step.artifacts: + files = await self.collect_artifact_files(runtime, artifact) + records.append( + { + "step": step.name, + "source": artifact.source, + "destination": artifact_destination(artifact), + "files": files, + } + ) + if records: + trace.info["harbor_step_artifacts"] = records + + async def collect_artifact_files( + self, runtime: Runtime, artifact: HarborArtifact + ) -> list[dict[str, str]]: + source = PurePosixPath(artifact.source) + if source.name in ("", ".", ".."): + raise ValueError(f"Artifact source must name a file or directory: {source}") + excludes = tar_exclude_args(artifact.exclude) + archive = shlex.quote(ARTIFACT_TAR) + source_arg = shlex.quote(artifact.source) + parent_arg = shlex.quote(source.parent.as_posix()) + name_arg = shlex.quote(source.name) + command = ( + f"rm -f {archive}; " + f"if [ -d {source_arg} ]; then " + f"tar {excludes} -czf {archive} -C {source_arg} .; " + f"elif [ -f {source_arg} ]; then " + f"tar {excludes} -czf {archive} -C {parent_arg} {name_arg}; " + "else " + f"tar -czf {archive} -T /dev/null; " + "fi" + ) + result = await runtime.run(["sh", "-c", command], {}) + if result.exit_code != 0: + raise SandboxError( + f"collect artifact {artifact.source!r}: {result.stderr.strip()}" + ) + return tar_file_records(await runtime.read(ARTIFACT_TAR)) + + def record_step_results( + self, trace: Trace, task: HarborTask, results: list[HarborStepResult] + ) -> None: + steps, aggregate = harbor_step_summary(results, task.multi_step_reward_strategy) + trace.info["harbor_steps"] = steps + trace.info["harbor_multi_step_reward"] = aggregate + + async def run_verifier( + self, + runtime: Runtime, + tests_dir: Path, + solution_dir: Path, + *, + shared_tests_dir: Path | None = None, + verifier_env: dict[str, str] | None = None, + timeout: float | None = None, + ) -> dict[str, float]: + directories = [] + if shared_tests_dir is not None: + directories.append((shared_tests_dir, "tests")) + directories.extend([(tests_dir, "tests"), (solution_dir, "oracle")]) + await runtime.write(TESTS_TAR, make_harbor_tar(directories)) + await runtime.run( + [ + "sh", + "-c", + "rm -rf /tests /logs/verifier; " + f"mkdir -p /tests /oracle /logs/verifier && tar -xzf {TESTS_TAR} -C /", + ], + {}, + ) + command = "cd /tests && bash test.sh" + if timeout is not None: + command = f"cd /tests && timeout {shlex.quote(f'{timeout}s')} bash test.sh" + await runtime.run(["sh", "-c", command], verifier_env or {}) + return parse_harbor_rewards(await self.read_reward(runtime)) + + async def read_reward(self, runtime: Runtime) -> str: + for path in ("/logs/verifier/reward.txt", "/logs/verifier/reward.json"): + try: + return (await runtime.read(path)).decode().strip() + except (SandboxError, OSError, RuntimeError, FileNotFoundError): + continue + return "" + + @reward(weight=1.0) + async def solved(self, task: HarborTask, trace: Trace, runtime: Runtime) -> float: + if task.steps: + + async def run_step(step: HarborStep) -> dict[str, float]: + return await self.run_verifier( + runtime, + tests_dir=step.task_dir / "tests", + solution_dir=step.task_dir / "solution", + shared_tests_dir=Path(task.task_dir) / "tests", + verifier_env=step.verifier_env, + timeout=step.scoring_timeout, + ) + + results = await run_harbor_steps(task.steps, run_step) + self.record_step_results(trace, task, results) + rewards = trace.info["harbor_multi_step_reward"] + return float(rewards.get("reward", 0.0)) + rewards = await self.run_verifier( + runtime, + tests_dir=Path(task.task_dir) / "tests", + solution_dir=Path(task.task_dir) / "solution", + ) + return float(rewards.get("reward", 0.0))