diff --git a/docs/source/environments.md b/docs/source/environments.md index a14564a1a..58f36c155 100644 --- a/docs/source/environments.md +++ b/docs/source/environments.md @@ -549,13 +549,13 @@ AgentWorldModel-1K — 1,000 synthetic MCP tool-use environments with 10,000 tas ``` ```` -````{grid-item-card} Opencode +````{grid-item-card} Coding Agent :class-card: sd-border-1 -`opencode_env` runs the OpenCode coding agent inside an isolated E2B sandbox against any OpenAI-compatible LLM endpoint, optionally capturing per-token logpr... +`coding_agent_env` runs coding-agent harnesses (currently OpenCode + Pi) inside an isolated E2B sandbox against any OpenAI-compatible LLM endpoint, optionally capturing per-token logpr... +++ -```{button-link} environments/opencode.html +```{button-link} environments/coding_agent.html :color: primary :outline: @@ -633,5 +633,5 @@ environments/tbench2 environments/unity environments/wildfire environments/agent_world_model -environments/opencode +environments/coding_agent ``` diff --git a/docs/source/environments/coding_agent.md b/docs/source/environments/coding_agent.md new file mode 100644 index 000000000..2903e2322 --- /dev/null +++ b/docs/source/environments/coding_agent.md @@ -0,0 +1,2 @@ +```{include} ../../../envs/coding_agent_env/README.md +``` diff --git a/docs/source/environments/opencode.md b/docs/source/environments/opencode.md deleted file mode 100644 index 9a93ebe33..000000000 --- a/docs/source/environments/opencode.md +++ /dev/null @@ -1,2 +0,0 @@ -```{include} ../../../envs/opencode_env/README.md -``` diff --git a/envs/opencode_env/.dockerignore b/envs/coding_agent_env/.dockerignore similarity index 100% rename from envs/opencode_env/.dockerignore rename to envs/coding_agent_env/.dockerignore diff --git a/envs/opencode_env/.gitignore b/envs/coding_agent_env/.gitignore similarity index 100% rename from envs/opencode_env/.gitignore rename to envs/coding_agent_env/.gitignore diff --git a/envs/opencode_env/README.md b/envs/coding_agent_env/README.md similarity index 61% rename from envs/opencode_env/README.md rename to envs/coding_agent_env/README.md index 79ebc6ed3..347afdd05 100644 --- a/envs/opencode_env/README.md +++ b/envs/coding_agent_env/README.md @@ -1,5 +1,5 @@ --- -title: OpenCode Environment Server +title: Coding Agent Environment Server emoji: 🛠️ colorFrom: indigo colorTo: purple @@ -9,23 +9,24 @@ app_port: 8000 base_path: /web tags: - openenv -short_description: OpenCode coding agent in an E2B sandbox with logprob capture +short_description: Multi-harness coding-agent env (OpenCode + Pi) in E2B --- -# OpenCode Environment for OpenEnv +# Coding Agent Environment for OpenEnv -`opencode_env` runs the [OpenCode](https://opencode.ai) coding agent inside -an isolated [E2B](https://e2b.dev) sandbox against any OpenAI-compatible -LLM endpoint, optionally capturing per-token logprobs for GRPO training. +`coding_agent_env` runs coding-agent harnesses (currently +[OpenCode](https://opencode.ai) and [Pi](https://github.com/badlogic/pi-mono)) +inside an isolated [E2B](https://e2b.dev) sandbox against any OpenAI-compatible +LLM endpoint with optional trainer-owned interception for RL training. -**🚀 Try it live**: [`AdithyaSK/opencode-env`](https://huggingface.co/spaces/AdithyaSK/opencode-env) +**🚀 Try it live**: [`AdithyaSK/coding-agent-env`](https://huggingface.co/spaces/AdithyaSK/coding-agent-env) The deployed Space exposes: -- **Web UI** at [`/web`](https://adithyask-opencode-env.hf.space/web) — pick endpoint, write task, hit Run, watch live phase log + reward + logprobs. -- **MCP tool API** at [`/mcp`](https://adithyask-opencode-env.hf.space/mcp) — programmatic `run_rollout` calls. -- **OpenAPI docs** at [`/docs`](https://adithyask-opencode-env.hf.space/docs). -- **Health** at [`/health`](https://adithyask-opencode-env.hf.space/health). +- **Web UI** at [`/web`](https://adithyask-coding-agent-env.hf.space/web) — pick endpoint, write task, hit Run, watch live phase log + reward. +- **MCP tool API** at [`/mcp`](https://adithyask-coding-agent-env.hf.space/mcp) — programmatic `run_rollout` calls. +- **OpenAPI docs** at [`/docs`](https://adithyask-coding-agent-env.hf.space/docs). +- **Health** at [`/health`](https://adithyask-coding-agent-env.hf.space/health). The env is **task-agnostic** — every rollout is configured at call-time with a uniform Task shape: @@ -47,20 +48,21 @@ a float to `/home/user/logs/verifier/reward.txt` (override). ```python import asyncio import os -from opencode_env import OpenCodeEnv -from opencode_env.client import _extract_text -from opencode_env.models import RolloutResult +from coding_agent_env import CodingAgentEnv +from coding_agent_env.client import _extract_text +from coding_agent_env.models import RolloutResult async def main(): - SPACE = "https://adithyask-opencode-env.hf.space" + SPACE = "https://adithyask-coding-agent-env.hf.space" - async with OpenCodeEnv(base_url=SPACE) as env: + async with CodingAgentEnv(base_url=SPACE) as env: await env.reset() # The MCP tool returns JSON; deserialize via the typed model. raw = await env.call_tool( "run_rollout", + agent="opencode", # opencode | pi endpoint="openai", # vllm | openai | hf_router api_key=os.environ["OPENAI_API_KEY"], # or set as a Space secret instruction=( @@ -75,13 +77,12 @@ async def main(): "import binary_search; " "assert binary_search.binary_search([1,2,3], 2) == 1; print('OK')\"", ], - template="opencode-rl", # prebaked E2B template + template="coding-agent-rl", # prebaked E2B template task_id="binary_search_v1", ) result = RolloutResult.model_validate_json(_extract_text(raw)) print("reward:", result.reward) - print("turns:", len(result.proxy_turns)) print("files:", list(result.files.keys())) print("wall:", result.wall_s, "s") @@ -93,7 +94,6 @@ Expected output (~20s with the prebaked template): ``` reward: 1.0 -turns: 3 files: ['/home/user/workdir/binary_search.py', ...] wall: 19.8 s ``` @@ -102,10 +102,10 @@ wall: 19.8 s ```python import os -from opencode_env import OpenCodeEnv +from coding_agent_env import CodingAgentEnv # .sync() returns a synchronous wrapper around the async client. -with OpenCodeEnv(base_url="https://adithyask-opencode-env.hf.space").sync() as env: +with CodingAgentEnv(base_url="https://adithyask-coding-agent-env.hf.space").sync() as env: env.reset() # MCP tools are reachable via env.call_tool(...) / env.step(...) sync-wrapped. # See the async example above for the full run_rollout signature. @@ -120,23 +120,22 @@ For trainers that want to drive a sandbox directly without an HTTP boundary: ```python import os -from opencode_env import ( - OpenCodeConfig, OpenCodeSessionFactory, OpenCodeTask, E2BSandboxBackend, +from coding_agent_env import ( + CodingAgentConfig, CodingAgentSessionFactory, CodingAgentTask, E2BSandboxBackend, ) -factory = OpenCodeSessionFactory( - config=OpenCodeConfig( +factory = CodingAgentSessionFactory( + config=CodingAgentConfig( provider="openai_compatible", base_url="https://api.openai.com/v1", api_key=os.environ["OPENAI_API_KEY"], model="gpt-4o-mini", ), sandbox_backend=E2BSandboxBackend(), - mode="transparent_proxy", # captures per-token logprobs + mode="interception_gate", # trainer-owned interception mode ) -session = factory.create(task=OpenCodeTask(instruction="...")) +session = factory.create(task=CodingAgentTask(instruction="...")) session.wait_for_completion() -turns = session.fetch_proxy_trace() # per-turn (tokens, logprobs) session.close() ``` @@ -146,22 +145,22 @@ The Dockerfile lives at `server/Dockerfile`. Use the `openenv` CLI from the env root: ```bash -cd envs/opencode_env +cd envs/coding_agent_env openenv validate # check pyproject.toml + openenv.yaml + server/app.py + uv.lock -openenv build -t opencode-env # builds the image (uses server/Dockerfile) +openenv build -t coding-agent-env # builds the image (uses server/Dockerfile) # run locally with E2B credentials -docker run -p 8000:8000 -e E2B_API_KEY=e2b_... opencode-env +docker run -p 8000:8000 -e E2B_API_KEY=e2b_... coding-agent-env # push to HF Spaces (Docker variant) -openenv push --repo-id /opencode-env +openenv push --repo-id /coding-agent-env ``` Or build directly without the CLI: ```bash -docker build -t opencode-env -f envs/opencode_env/server/Dockerfile envs/opencode_env +docker build -t coding-agent-env -f envs/coding_agent_env/server/Dockerfile envs/coding_agent_env ``` The image: @@ -174,7 +173,7 @@ The image: ## The MCP Tool: `run_rollout` -Single tool, two ways to specify the LLM endpoint: +Single tool, with an ``agent`` selector plus two ways to specify the LLM endpoint: **Option A — endpoint shorthand (recommended)**: pass `endpoint="vllm"` (or `"openai"` / `"hf_router"`). The server resolves @@ -186,29 +185,30 @@ directly. | Arg | Type | Default | Notes | |---|---|---|---| +| `agent` | `str` | `"opencode"` | Harness to run: `"opencode"` or `"pi"`. | | `endpoint` | `str` | `""` | One of `"vllm"` / `"openai"` / `"hf_router"`. | | `base_url` / `api_key` / `model` | `str` | `""` | Override / supply explicitly. | -| `instruction` | `str` | required | Prompt passed to `opencode run`. | +| `instruction` | `str` | required | Prompt passed to the selected harness CLI. | | `setup` | `list[str]` | `[]` | Bash commands run **before** the agent. | | `verify` | `list[str]` | `[]` | Bash commands run **after** the agent. | | `task_id` | `str` | `""` | Echoed back in result. | -| `mode` | `str` | `"transparent_proxy"` | Or `"black_box"` (no logprobs). | +| `mode` | `str` | `"black_box"` | Or `"interception_gate"` for trainer-owned generation. | | `disable_thinking` | `bool \| None` | `None` (catalog default) | Inject `chat_template_kwargs.enable_thinking=false`. | | `max_tokens_cap` | `int` | `4096` | Per-turn `max_tokens` clamp. | -| `top_logprobs` | `int` | `5` | HF Router cap is 5; OpenAI 0–20; vLLM unbounded. | -| `agent_timeout_s` | `float` | `600.0` | Hard wall budget for opencode. | -| `template` | `str` | `""` | E2B template name; `"opencode-rl"` skips ~2 min of install per rollout. | +| `top_logprobs` | `int` | `5` | Reserved for trainer-owned interception workflows. | +| `agent_timeout_s` | `float` | `600.0` | Hard wall budget for the selected harness. | +| `template` | `str` | `""` | E2B template name; `"coding-agent-rl"` skips ~2 min of install per rollout. | Returns `RolloutResult` JSON with: `reward`, `setup_results[]`, -`verify_results[]`, `proxy_turns[]`, `files{}`, `agent_log_tail`, -`proxy_log_tail`, `wall_s`, `agent_exit_code`, `sandbox_id`, `error`. +`verify_results[]`, `files{}`, `agent_log_tail`, `wall_s`, +`agent_exit_code`, `sandbox_id`, `error`. ## Two Operating Modes | Mode | What it does | Best for | |---|---|---| -| **`transparent_proxy`** (default) | In-sandbox proxy at `localhost:7000` forwards opencode's LLM calls to `base_url`, injects `logprobs=true`, captures per-turn `(messages, completion_tokens, logprobs)` to `proxy_trace.jsonl`. | GRPO / RL training, observability, top-k distillation. | -| **`black_box`** | No proxy. opencode talks straight to `base_url`. | Smoke tests, eval, SFT data collection. | +| **`black_box`** (default) | The selected harness talks directly to `base_url`. | Smoke tests, eval, SFT data collection. | +| **`interception_gate`** | Agent calls are routed through trainer-host interception endpoints. Trainer owns forward pass + trajectory capture. | RL training with trainer-owned generation. | ## Environment Variables @@ -227,34 +227,30 @@ sibling `.env` file; on HF Spaces, set them as **Space secrets**. | **OpenAI endpoint** | | | | `OPENAI_API_KEY` | required for `endpoint="openai"` | Standard OpenAI key. | | `OPENAI_BASE_URL` | no | Defaults to `https://api.openai.com/v1`. | -| `OPENAI_MODEL` | no | Defaults to `gpt-4o-mini` (gpt-5.x and o-series refuse logprobs). | +| `OPENAI_MODEL` | no | Defaults to `gpt-4o-mini`. | | **HF Router endpoint** | | | | `HF_ROUTER_API_KEY` | required for `endpoint="hf_router"` | HF user token. | | `HF_ROUTER_BASE_URL` | no | Defaults to `https://router.huggingface.co/v1`. | | `HF_ROUTER_MODEL` | no | Defaults to `Qwen/Qwen3-4B-Instruct-2507:nscale`. | -Pick `provider:` suffixes that actually return logprobs: -**Together / Nscale / Scaleway / SambaNova / Cerebras**. Avoid Novita / -Hyperbolic / Featherless (silent drop) and Groq (HTTP 400). ## Pre-baked E2B Template The first rollout in a fresh E2B sandbox spends ~2 min installing -opencode and the proxy's Python deps. Build a one-time template that -ships those pre-installed: +harness tooling. Build a one-time template that ships those pre-installed: ```bash -.venv/bin/python envs/opencode_env/sandbox/build_template.py -# → builds `opencode-rl` template in your E2B account (~1m20s, one-time) +.venv/bin/python envs/coding_agent_env/sandbox/build_template.py +# → builds `coding-agent-rl` template in your E2B account (~1m20s, one-time) ``` -After this, pass `template="opencode-rl"` on every `run_rollout` call — +After this, pass `template="coding-agent-rl"` on every `run_rollout` call — each rollout drops to ~20–30s end-to-end. ## Project Structure ``` -opencode_env/ +coding_agent_env/ ├── README.md # this file ├── openenv.yaml # OpenEnv space spec ├── pyproject.toml # deps + ``server`` entrypoint @@ -262,33 +258,39 @@ opencode_env/ ├── .gitignore / .dockerignore # excludes .env / __pycache__ ├── __init__.py # re-exports primitive + client + models │ -├── client.py # OpenCodeEnv(MCPToolClient) -├── models.py # RolloutResult / RolloutTurn / OpenCodeState +├── client.py # CodingAgentEnv(MCPToolClient) +├── models.py # RolloutResult / CodingAgentState │ -├── config.py # OpenCodeConfig (primitive) -├── harness.py # OpenCodeSession / OpenCodeSessionFactory (CLI-only) +├── config.py # CodingAgentConfig (primitive) +├── harness.py # CodingAgentSession / CodingAgentSessionFactory (CLI-only) ├── opencode_runtime.py # opencode.json builder + cmds -├── task.py # OpenCodeTask +├── task.py # CodingAgentTask │ ├── server/ │ ├── __init__.py │ ├── app.py # FastAPI factory; mounts Gradio at /web -│ ├── opencode_environment.py # MCPEnvironment with single ``run_rollout`` tool +│ ├── coding_environment.py # MCPEnvironment with single ``run_rollout`` tool │ ├── gradio_ui.py # the /web Gradio Blocks UI │ ├── catalog.py # endpoint shorthand resolver │ └── Dockerfile # multi-stage uv build (used by ``openenv build``) │ └── sandbox/ ├── __init__.py - ├── base.py # SandboxBackend / SandboxHandle Protocols - ├── e2b.py # E2B implementation - ├── interception.py # in-sandbox FastAPI proxy (logprob capture) └── build_template.py # one-time E2B template builder + +# Shared sandbox runtime (moved to core): +src/openenv/core/harness/sandbox/ +├── base.py # SandboxBackend / SandboxHandle protocols +├── e2b_backend.py # E2B implementation +├── docker_backend.py # local Docker backend +├── hf_backend.py # HF sandbox backend +└── _util.py # shared sandbox shell utilities ``` ## References - [OpenEnv docs](https://meta-pytorch.org/OpenEnv/) - [OpenCode CLI](https://opencode.ai/docs/cli/) +- [Pi](https://github.com/badlogic/pi-mono) - [E2B Python SDK](https://e2b.dev/docs) -- [HF Inference Providers logprob matrix](../../../DOCS/HF/hf_inference_providers_logprobs.md) + diff --git a/envs/coding_agent_env/__init__.py b/envs/coding_agent_env/__init__.py new file mode 100644 index 000000000..bc04e7236 --- /dev/null +++ b/envs/coding_agent_env/__init__.py @@ -0,0 +1,55 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Coding-agent environment for OpenEnv. + +Two layers in this package: + +1. **Harness primitive** -- :class:`CodingAgentSessionFactory` / + :class:`CodingAgentSession` / :class:`CodingAgentConfig` / + :class:`E2BSandboxBackend`. Built on the generic + :class:`CLIAgentDriver` from ``openenv.core.harness.agents``. + +2. **Deployable env** -- :class:`CodingAgentEnv` (MCP client) talks to the + FastAPI server at ``server/app.py`` over HTTP. Use this when the + sandbox + agent live behind an HTTP boundary (e.g. an HF Space). + See ``client.py`` and ``server/``. +""" + +from openenv.core.env_server.mcp_types import CallToolAction, ListToolsAction +from openenv.core.harness.sandbox import SandboxBackend, SandboxHandle + +from .client import CodingAgentEnv +from .config import CodingAgentConfig, Provider +from .harness import CodingAgentSession, CodingAgentSessionFactory +from .models import CommandResult, CodingAgentState, RolloutResult +from .task import CodingAgentTask + +try: + from openenv.core.harness.sandbox import E2BSandboxBackend +except ImportError: # e2b not installed + E2BSandboxBackend = None # type: ignore[assignment,misc] + +__all__ = [ + # Deployed-env client + "CodingAgentEnv", + "CallToolAction", + "ListToolsAction", + # HTTP API models + "CommandResult", + "CodingAgentState", + "RolloutResult", + # Harness primitive + "CodingAgentConfig", + "CodingAgentSession", + "CodingAgentSessionFactory", + "CodingAgentTask", + "Provider", + # Sandbox backend + "E2BSandboxBackend", + "SandboxBackend", + "SandboxHandle", +] diff --git a/envs/opencode_env/client.py b/envs/coding_agent_env/client.py similarity index 77% rename from envs/opencode_env/client.py rename to envs/coding_agent_env/client.py index a00afc4e1..492060a25 100644 --- a/envs/opencode_env/client.py +++ b/envs/coding_agent_env/client.py @@ -4,16 +4,17 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -"""Client for the deployed opencode_env server. +"""Client for the deployed coding_agent_env server. -The server exposes a single MCP tool ``run_rollout`` that runs one OpenCode -rollout in an E2B sandbox and returns a JSON-serialized :class:`RolloutResult`. +The server exposes a single MCP tool ``run_rollout`` that runs one coding-agent +rollout (OpenCode or Pi) in an E2B sandbox and returns a JSON-serialized +:class:`RolloutResult`. Example:: - from opencode_env import OpenCodeEnv + from coding_agent_env import CodingAgentEnv - with OpenCodeEnv(base_url="https://adithya-sk-opencode-env.hf.space") as env: + with CodingAgentEnv(base_url="https://your-space.hf.space") as env: env.reset() result = env.run_rollout( base_url="https://api.openai.com/v1", @@ -24,7 +25,7 @@ verify=["python /home/user/test.py"], task_id="binary_search_v1", ) - print(result.reward, len(result.proxy_turns)) + print(result.reward) """ from __future__ import annotations @@ -40,8 +41,8 @@ from models import RolloutResult # type: ignore -class OpenCodeEnv(MCPToolClient): - """Typed client for the opencode_env MCP server. +class CodingAgentEnv(MCPToolClient): + """Typed client for the coding_agent_env MCP server. Inherits ``reset`` / ``call_tool`` / ``list_tools`` / ``from_docker_image`` / context-manager semantics from :class:`MCPToolClient`. @@ -50,8 +51,9 @@ class OpenCodeEnv(MCPToolClient): def run_rollout( self, *, - # Endpoint — pass either the shorthand selector OR explicit fields. - endpoint: str = "", # "vllm" | "openai" | "hf_router" + # Agent + endpoint — pass either shorthand endpoint or explicit fields. + agent: str = "opencode", # "opencode" | "pi" + endpoint: str = "", # "vllm" | "openai" | "hf_router" base_url: str = "", api_key: str = "", model: str = "", @@ -61,49 +63,50 @@ def run_rollout( verify: list[str] | None = None, # Bookkeeping / tunables task_id: str = "", - mode: str = "transparent_proxy", + mode: str = "black_box", disable_thinking: bool | None = None, max_tokens_cap: int = 4096, top_logprobs: int = 5, agent_timeout_s: float = 600.0, template: str = "", ) -> RolloutResult: - """Run one OpenCode rollout and return the typed result. + """Run one coding-agent rollout and return the typed result. Args: + agent: Harness CLI to run in sandbox (``"opencode"`` or ``"pi"``). base_url: OpenAI-compatible LLM endpoint (with trailing /v1). api_key: Bearer token for the LLM. Use ``"intercepted"`` for vLLM if it doesn't enforce auth. model: Model id understood by the LLM endpoint (e.g. ``"gpt-4o-mini"``, ``"Qwen/Qwen3.5-4B"``, ``"Qwen/Qwen3-4B-Instruct-2507:nscale"``). - instruction: Prompt passed to ``opencode run``. + instruction: Prompt passed to the selected harness CLI. setup: Bash commands run sequentially **before** the agent starts. Each command runs in the sandbox; non-zero exit aborts setup. verify: Bash commands run sequentially **after** the agent exits. Reward = ``passed_count / total`` unless any command writes a float to ``/home/user/logs/verifier/reward.txt`` (override). task_id: Echoed back in the result for traceability. - mode: ``"transparent_proxy"`` (captures per-token logprobs via - an in-sandbox FastAPI proxy) or ``"black_box"`` (no proxy). + mode: ``"black_box"`` (agent talks directly to the LLM) or + ``"interception_gate"`` (LLM calls routed to trainer-side + InterceptionServer for trainer-owned generation). disable_thinking: Inject ``chat_template_kwargs.enable_thinking=false`` on forwarded requests. Needed for Qwen3.5 vLLM; harmless on Instruct variants; rejected by OpenAI direct. - max_tokens_cap: Clamp on per-turn ``max_tokens``. OpenCode asks - for ~32k by default; gpt-4o-mini caps at 16k. - top_logprobs: Top-k logprobs requested upstream. HF Router caps - at 5; OpenAI accepts up to 20; vLLM is unbounded. - agent_timeout_s: Hard wall-clock budget for one ``opencode run``. - template: E2B template name (e.g. ``"opencode-rl"``). Empty + max_tokens_cap: Clamp on per-turn ``max_tokens``. + top_logprobs: Reserved for trainer-owned interception workflows. + agent_timeout_s: Hard wall-clock budget for one agent run. + template: E2B template name (e.g. ``"coding-agent-rl"``). Empty string uses the default (slow) base image. Returns: - A :class:`RolloutResult` with reward, per-turn logprobs, file - outputs, setup/verify results, and diagnostic tails. + A :class:`RolloutResult` with reward, file outputs, + setup/verify results, and diagnostic tails. """ raw = self.call_tool( "run_rollout", + agent=agent, endpoint=endpoint, base_url=base_url, api_key=api_key, diff --git a/envs/opencode_env/config.py b/envs/coding_agent_env/config.py similarity index 66% rename from envs/opencode_env/config.py rename to envs/coding_agent_env/config.py index 57273b9eb..d70610542 100644 --- a/envs/opencode_env/config.py +++ b/envs/coding_agent_env/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -"""Configuration model for the OpenCode harness primitive.""" +"""Configuration model for the coding-agent harness primitive.""" from __future__ import annotations @@ -16,8 +16,8 @@ Provider = Literal["openai_compatible", "openai", "anthropic"] -class OpenCodeConfig(BaseModel): - """All configuration required to launch one OpenCode rollout in a sandbox. +class CodingAgentConfig(BaseModel): + """All configuration required to launch one coding-agent rollout in a sandbox. Field names are provider-agnostic. The primitive maps ``provider`` onto the correct ``opencode.json`` provider block (``@ai-sdk/openai-compatible``, @@ -34,9 +34,7 @@ class OpenCodeConfig(BaseModel): # --- OpenCode CLI --------------------------------------------------------- opencode_version: str = "latest" - disabled_tools: list[str] = Field( - default_factory=lambda: ["webfetch", "question"] - ) + disabled_tools: list[str] = Field(default_factory=lambda: ["webfetch", "question"]) enabled_tools: list[str] | None = None system_prompt: str | None = None extra_opencode_json: dict[str, Any] = Field(default_factory=dict) @@ -47,25 +45,16 @@ class OpenCodeConfig(BaseModel): extra_env: dict[str, str] = Field(default_factory=dict) extra_setup_shell: str | None = None + # --- Model behavior -------------------------------------------------------- + disable_thinking: bool = False + max_tokens_cap: int | None = None + # --- Sandbox paths -------------------------------------------------------- # Root directory inside the sandbox where the primitive writes config, # task files, and logs. E2B's default user is ``user`` with home # ``/home/user``. Override when using a root-privileged backend (Docker). sandbox_home: str = "/home/user" - # --- Transparent-proxy tuning -------------------------------------------- - # Cap ``max_tokens`` / ``max_completion_tokens`` on forwarded requests. - # OpenCode defaults to a very large number (~32000) which exceeds some - # provider limits (e.g. gpt-4o-mini = 16384). Only used in - # ``mode="transparent_proxy"``. ``None`` disables the cap. - proxy_max_tokens_cap: int | None = 16384 - # Per-turn top-k logprobs the proxy requests from the upstream. - proxy_top_logprobs: int = 5 - # Disable reasoning/thinking mode for Qwen3 / Qwen3.5 models. Proxy sets - # ``extra_body.chat_template_kwargs.enable_thinking=false`` on forwarded - # requests. Ignored by providers that don't support the field. - proxy_disable_thinking: bool = False - _PROVIDER_NPM = { "openai_compatible": "@ai-sdk/openai-compatible", diff --git a/envs/coding_agent_env/harness.py b/envs/coding_agent_env/harness.py new file mode 100644 index 000000000..de4ec91dd --- /dev/null +++ b/envs/coding_agent_env/harness.py @@ -0,0 +1,178 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Coding-agent session factory + session — backed by CLIAgentDriver.""" + +from __future__ import annotations + +import queue as _queue_mod +import uuid +from typing import Any, Literal + +from openenv.core.harness import ResourceSessionFactory +from openenv.core.harness.agents.cli_driver import ( + CLIAgentDriver, + CLIAgentSession, + Verifier, +) +from openenv.core.harness.agents.interception_server import InterceptionServer +from openenv.core.harness.agents.opencode import OPENCODE_SPEC +from openenv.core.harness.sandbox import SandboxBackend, SandboxHandle + +from .config import CodingAgentConfig +from .opencode_runtime import agent_log_path, build_env_vars, build_run_cmd +from .task import CodingAgentTask + + +class CodingAgentSession(CLIAgentSession): + def __init__( + self, + *, + sandbox: SandboxHandle, + config: CodingAgentConfig, + task: CodingAgentTask, + verifier: Verifier | None = None, + base_url_override: str | None = None, + interception_server: InterceptionServer | None = None, + interception_rollout_id: str | None = None, + interception_queue: _queue_mod.Queue[str] | None = None, + ) -> None: + super().__init__( + spec=OPENCODE_SPEC, + sandbox=sandbox, + task=task, + config=config, + verifier=verifier, + base_url_override=base_url_override, + interception_server=interception_server, + interception_rollout_id=interception_rollout_id, + interception_queue=interception_queue, + ) + + def fetch_trace(self) -> str: + return self.sandbox.read_text(agent_log_path(self.config)) + + def wait_for_completion(self, timeout_s: float | None = None) -> int: + budget = timeout_s if timeout_s is not None else self.config.agent_timeout_s + if self._agent_bg_job is None: + raise RuntimeError("Agent not started.") + return self._agent_bg_job.wait(timeout=budget) + + def start_agent(self) -> None: + if self._agent_bg_job is not None: + return + cmd = build_run_cmd(self.config) + envs = build_env_vars(self.config, base_url_override=self._base_url_override) + self._agent_bg_job = self.sandbox.start_bg(cmd, envs=envs) + + +class CodingAgentSessionFactory(ResourceSessionFactory): + def __init__( + self, + *, + config: CodingAgentConfig, + sandbox_backend: SandboxBackend, + mode: Literal["black_box", "interception_gate"] = "black_box", + verifier: Verifier | None = None, + install_timeout_s: int = 240, + setup_timeout_s: int = 300, + interception_server: InterceptionServer | None = None, + interception_base_url: str | None = None, + ) -> None: + if mode not in {"black_box", "interception_gate"}: + raise ValueError(f"Unknown mode: {mode!r}") + self._config = config + self._backend = sandbox_backend + self._verifier = verifier + self._driver = CLIAgentDriver( + spec=OPENCODE_SPEC, + sandbox_backend=sandbox_backend, + mode=mode, + install_timeout_s=install_timeout_s, + setup_timeout_s=setup_timeout_s, + interception_server=interception_server, + interception_base_url=interception_base_url, + ) + + def create( + self, + task: Any, + seed: int | None = None, + episode_id: str | None = None, + ) -> CodingAgentSession: + import logging + + _log = logging.getLogger(__name__) + oc_task = CodingAgentTask.coerce(task) + setup_parts: list[str] = [] + if self._config.extra_setup_shell: + setup_parts.append(self._config.extra_setup_shell) + if oc_task.setup_shell: + setup_parts.append(oc_task.setup_shell) + if setup_parts: + oc_task = oc_task.model_copy( + update={"setup_shell": "set -e\n" + "\n".join(setup_parts)} + ) + + sandbox_timeout = int(self._config.agent_timeout_s) + 300 + sandbox = self._backend.create( + timeout_s=sandbox_timeout, + metadata={"episode_id": episode_id} if episode_id else None, + ) + try: + self._bootstrap_sandbox(sandbox, oc_task) + except Exception as exc: + _log.error("factory.create: bootstrap failed: %r", exc) + sandbox.kill() + raise + + # Wire up interception_gate if the driver is configured for it + base_url_override: str | None = None + interception_rollout_id: str | None = None + interception_queue: _queue_mod.Queue[str] | None = None + + if self._driver.mode == "interception_gate": + interception_server = self._driver._interception_server + if interception_server is None: + raise RuntimeError( + "interception_gate mode requires an InterceptionServer" + ) + interception_base_url = self._driver._interception_base_url + if interception_base_url is None: + raise RuntimeError( + "interception_gate mode requires interception_base_url" + ) + rollout_id = episode_id or f"rollout_{uuid.uuid4().hex[:8]}" + interception_rollout_id = rollout_id + interception_queue = interception_server.register_rollout(rollout_id) + base_url_override = ( + f"{interception_base_url.rstrip('/')}/rollout/{rollout_id}/v1" + ) + + session = CodingAgentSession( + sandbox=sandbox, + config=self._config, + task=oc_task, + verifier=self._verifier, + base_url_override=base_url_override, + interception_server=self._driver._interception_server, + interception_rollout_id=interception_rollout_id, + interception_queue=interception_queue, + ) + + session.start_agent() + return session + + def _bootstrap_sandbox(self, sandbox: SandboxHandle, task: CodingAgentTask) -> None: + self._driver.bootstrap_sandbox(sandbox, task, self._config) + + +__all__ = [ + "CodingAgentSession", + "CodingAgentSessionFactory", + "CodingAgentTask", + "Verifier", +] diff --git a/envs/opencode_env/models.py b/envs/coding_agent_env/models.py similarity index 59% rename from envs/opencode_env/models.py rename to envs/coding_agent_env/models.py index b218c5f78..2bf19925e 100644 --- a/envs/opencode_env/models.py +++ b/envs/coding_agent_env/models.py @@ -4,52 +4,37 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -"""Pydantic models for the deployed opencode_env HTTP server. +"""Pydantic models for the deployed coding_agent_env HTTP server. The server exposes a single MCP tool ``run_rollout`` that takes a Task (instruction + setup commands + verify commands) plus an LLM endpoint -config, runs one OpenCode rollout end-to-end inside an E2B sandbox, and +config, runs one coding-agent rollout end-to-end inside an E2B sandbox, and returns a :class:`RolloutResult` JSON. """ from __future__ import annotations -from typing import Any - from openenv.core.env_server.types import State from pydantic import BaseModel, Field -class RolloutTurn(BaseModel): - """One intercepted LLM turn captured by the in-sandbox proxy (Mode B).""" - - turn: int - finish_reason: str | None = None - completion_tokens: list[str] = Field(default_factory=list) - completion_token_ids: list[int] = Field(default_factory=list) - per_token_logps: list[float] = Field(default_factory=list) - latency_s: float = 0.0 - timestamp: float = 0.0 - upstream_status: int | None = None - upstream_error: dict[str, Any] | None = None - - class CommandResult(BaseModel): - """Outcome of one bash command in setup/verify.""" + """Outcome of one bash command in setup/verify. + + When ``exit_code`` is ``None``, the command ran during sandbox bootstrap + and its individual exit code was not captured (bootstrap succeeds or fails + atomically). + """ cmd: str - exit_code: int + exit_code: int | None = None stdout: str = "" stderr: str = "" duration_s: float = 0.0 class RolloutResult(BaseModel): - """Full payload returned from one ``run_rollout`` invocation. - - The trainer (or any client) decodes this from the MCP tool result JSON - and feeds ``proxy_turns`` + ``reward`` into GRPO. - """ + """Full payload returned from one ``run_rollout`` invocation.""" # Identifiers task_id: str = "" @@ -59,29 +44,25 @@ class RolloutResult(BaseModel): reward: float | None = None agent_exit_code: int | None = None wall_s: float = 0.0 - mode: str = "transparent_proxy" + mode: str = "black_box" # Per-step results setup_results: list[CommandResult] = Field(default_factory=list) verify_results: list[CommandResult] = Field(default_factory=list) - # Per-turn LLM trajectory (empty in black_box mode) - proxy_turns: list[RolloutTurn] = Field(default_factory=list) - # Filesystem the agent produced (path -> contents, truncated) files: dict[str, str] = Field(default_factory=dict) files_extra: list[str] = Field(default_factory=list) # Diagnostic tails agent_log_tail: str = "" - proxy_log_tail: str = "" # Error surfacing error: str | None = None -class OpenCodeState(State): - """Per-session env state across calls to one OpenCodeEnvironment instance. +class CodingAgentState(State): + """Per-session env state across calls to one CodingAgentEnvironment instance. Each HTTP session gets its own env (``SUPPORTS_CONCURRENT_SESSIONS=True`` on the server class), so this state is per-session. diff --git a/envs/opencode_env/opencode_runtime.py b/envs/coding_agent_env/opencode_runtime.py similarity index 77% rename from envs/opencode_env/opencode_runtime.py rename to envs/coding_agent_env/opencode_runtime.py index 07fd5322d..31285556e 100644 --- a/envs/opencode_env/opencode_runtime.py +++ b/envs/coding_agent_env/opencode_runtime.py @@ -16,34 +16,34 @@ import json from typing import Any -from .config import OpenCodeConfig, provider_npm_package +from .config import CodingAgentConfig, provider_npm_package -def opencode_config_path(config: OpenCodeConfig) -> str: +def opencode_config_path(config: CodingAgentConfig) -> str: return f"{config.sandbox_home}/.config/opencode/opencode.json" -def instruction_path(config: OpenCodeConfig) -> str: +def instruction_path(config: CodingAgentConfig) -> str: return f"{config.sandbox_home}/task/instruction.md" -def agent_log_path(config: OpenCodeConfig) -> str: +def agent_log_path(config: CodingAgentConfig) -> str: return f"{config.sandbox_home}/logs/agent/opencode.jsonl" -def system_prompt_path(config: OpenCodeConfig) -> str: +def system_prompt_path(config: CodingAgentConfig) -> str: return f"{config.sandbox_home}/task/system.md" -def verifier_reward_path(config: OpenCodeConfig) -> str: +def verifier_reward_path(config: CodingAgentConfig) -> str: return f"{config.sandbox_home}/logs/verifier/reward.txt" -def workdir_path(config: OpenCodeConfig) -> str: +def workdir_path(config: CodingAgentConfig) -> str: return f"{config.sandbox_home}/workdir" -def build_opencode_json(config: OpenCodeConfig) -> str: +def build_opencode_json(config: CodingAgentConfig) -> str: """Return the serialized ``opencode.json`` the sandbox should install. Provider block is keyed by a stable internal name (``intercepted``) so the @@ -52,6 +52,12 @@ def build_opencode_json(config: OpenCodeConfig) -> str: """ provider_name = "intercepted" + model_key = config.model.split("/", 1)[-1] + + model_block: dict[str, Any] = {"name": "Intercepted Model"} + if config.max_tokens_cap is not None: + model_block["limit"] = {"output": config.max_tokens_cap} + provider_block: dict[str, Any] = { "npm": provider_npm_package(config.provider), "name": "Intercepted", @@ -61,16 +67,21 @@ def build_opencode_json(config: OpenCodeConfig) -> str: "timeout": config.request_timeout_ms, }, "models": { - config.model.split("/", 1)[-1]: {"name": "Intercepted Model"}, + model_key: model_block, }, } doc: dict[str, Any] = { "$schema": "https://opencode.ai/config.json", - "model": f"{provider_name}/{config.model.split('/', 1)[-1]}", + "model": f"{provider_name}/{model_key}", "provider": {provider_name: provider_block}, } + # Disable thinking/reasoning tokens when requested. AI SDK respects + # the top-level "reasoning" key to control reasoning token generation. + if config.disable_thinking: + doc["reasoning"] = "none" + tools = _build_tools_block(config) if tools: doc["tools"] = tools @@ -79,7 +90,7 @@ def build_opencode_json(config: OpenCodeConfig) -> str: return json.dumps(doc, indent=2) -def build_install_cmd(config: OpenCodeConfig) -> str: +def build_install_cmd(config: CodingAgentConfig) -> str: """Return the shell command that installs OpenCode + ensures PATH. The upstream installer honors ``OPENCODE_VERSION=x.y.z`` for pinning; @@ -99,7 +110,7 @@ def build_install_cmd(config: OpenCodeConfig) -> str: ) -def build_run_cmd(config: OpenCodeConfig) -> str: +def build_run_cmd(config: CodingAgentConfig) -> str: """Return the shell command that launches OpenCode against a task.""" format_flag = "--format json" if config.run_format == "json" else "" @@ -111,7 +122,9 @@ def build_run_cmd(config: OpenCodeConfig) -> str: ).strip() -def build_env_vars(config: OpenCodeConfig, *, base_url_override: str | None = None) -> dict[str, str]: +def build_env_vars( + config: CodingAgentConfig, *, base_url_override: str | None = None +) -> dict[str, str]: """Return env vars to set on the OpenCode process. When a proxy is wrapping ``config.base_url`` the factory passes the proxy's @@ -127,7 +140,7 @@ def build_env_vars(config: OpenCodeConfig, *, base_url_override: str | None = No return env -def _build_tools_block(config: OpenCodeConfig) -> dict[str, bool]: +def _build_tools_block(config: CodingAgentConfig) -> dict[str, bool]: """Translate enabled/disabled lists into opencode's ``tools`` map.""" if config.enabled_tools is not None: diff --git a/envs/opencode_env/openenv.yaml b/envs/coding_agent_env/openenv.yaml similarity index 76% rename from envs/opencode_env/openenv.yaml rename to envs/coding_agent_env/openenv.yaml index 2a534a088..be34c3a51 100644 --- a/envs/opencode_env/openenv.yaml +++ b/envs/coding_agent_env/openenv.yaml @@ -1,5 +1,5 @@ spec_version: 1 -name: opencode_env +name: coding_agent_env type: space runtime: fastapi app: server.app:app diff --git a/envs/opencode_env/pyproject.toml b/envs/coding_agent_env/pyproject.toml similarity index 64% rename from envs/opencode_env/pyproject.toml rename to envs/coding_agent_env/pyproject.toml index 50337baa2..d935a0bf5 100644 --- a/envs/opencode_env/pyproject.toml +++ b/envs/coding_agent_env/pyproject.toml @@ -9,9 +9,9 @@ requires = ["setuptools>=45", "wheel"] build-backend = "setuptools.build_meta" [project] -name = "openenv-opencode-env" +name = "openenv-coding-agent-env" version = "0.1.0" -description = "OpenCode coding-agent environment for OpenEnv — runs the OpenCode CLI in an E2B sandbox against any OpenAI-compatible LLM, optionally capturing per-token logprobs." +description = "Coding-agent environment for OpenEnv — runs OpenCode/Pi harnesses in an E2B sandbox against OpenAI-compatible LLM endpoints." requires-python = ">=3.10" dependencies = [ # Core OpenEnv (server + MCP). 0.3.0 ships the harness runtime. @@ -26,7 +26,7 @@ dependencies = [ # behavior drift on Space rebuilds. "gradio>=6.0.0", - # OpenCode harness primitive — sandbox + proxy + agent driver + # OpenCode harness primitive — sandbox + agent driver "httpx>=0.27.0", "e2b>=1.0.0", ] @@ -40,17 +40,16 @@ dev = [ [project.scripts] # Server entrypoint — enables ``uv run --project . server``. -server = "opencode_env.server.app:main" +server = "coding_agent_env.server.app:main" [tool.setuptools] include-package-data = true packages = [ - "opencode_env", - "opencode_env.sandbox", - "opencode_env.server", - "opencode_env.tests", + "coding_agent_env", + "coding_agent_env.sandbox", + "coding_agent_env.server", ] -package-dir = { "opencode_env" = ".", "opencode_env.sandbox" = "sandbox", "opencode_env.server" = "server", "opencode_env.tests" = "tests" } +package-dir = { "coding_agent_env" = ".", "coding_agent_env.sandbox" = "sandbox", "coding_agent_env.server" = "server" } [tool.setuptools.package-data] -opencode_env = ["**/*.md"] +coding_agent_env = ["**/*.md"] diff --git a/envs/coding_agent_env/sandbox/__init__.py b/envs/coding_agent_env/sandbox/__init__.py new file mode 100644 index 000000000..8a2477104 --- /dev/null +++ b/envs/coding_agent_env/sandbox/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Sandbox backends live in ``openenv.core.harness.sandbox``. + +This package exists only for the ``build_template`` helper used by E2B +template builds. Import sandbox protocols and backends from +``openenv.core.harness.sandbox`` directly. +""" diff --git a/envs/coding_agent_env/sandbox/build_template.py b/envs/coding_agent_env/sandbox/build_template.py new file mode 100644 index 000000000..01978b520 --- /dev/null +++ b/envs/coding_agent_env/sandbox/build_template.py @@ -0,0 +1,76 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Build a pre-baked E2B template with opencode already installed.""" + +from __future__ import annotations + +import argparse +import os +import sys +from pathlib import Path + +from e2b import default_build_logger, Template + +_REPO_ROOT = Path(__file__).resolve().parents[3] + + +def _load_env(path: Path) -> None: + if not path.exists(): + return + for raw in path.read_text().splitlines(): + line = raw.strip() + if not line or line.startswith("#") or "=" not in line: + continue + k, _, v = line.partition("=") + k = k.strip() + v = v.strip().strip('"').strip("'") + if k and k not in os.environ: + os.environ[k] = v + + +def build_template(name: str, *, skip_cache: bool = False) -> str: + template = ( + Template() + .from_python_image("3.12") + .set_user("user") + .run_cmd("curl -fsSL https://opencode.ai/install | bash") + .run_cmd("/home/user/.opencode/bin/opencode --version") + .make_dir("/home/user/.config/opencode") + .make_dir("/home/user/logs/agent") + .make_dir("/home/user/logs/verifier") + .make_dir("/home/user/task") + .make_dir("/home/user/workdir") + .set_workdir("/home/user/workdir") + ) + if skip_cache: + template = template.skip_cache() + info = Template.build( + template, + name, + cpu_count=2, + memory_mb=2048, + on_build_logs=default_build_logger(), + ) + return info.template_id if hasattr(info, "template_id") else str(info) + + +def main(argv: list[str] | None = None) -> int: + p = argparse.ArgumentParser(prog="build_e2b_template") + p.add_argument("--name", default="coding-agent-rl") + p.add_argument("--skip-cache", action="store_true") + args = p.parse_args(argv) + _load_env(_REPO_ROOT / "envs" / "coding_agent_env" / "sandbox" / ".env") + if not os.environ.get("E2B_API_KEY"): + print("ERROR: E2B_API_KEY required.", file=sys.stderr) + return 2 + template_id = build_template(args.name, skip_cache=args.skip_cache) + print(f"Built. Template id/name: {template_id}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/envs/opencode_env/server/Dockerfile b/envs/coding_agent_env/server/Dockerfile similarity index 91% rename from envs/opencode_env/server/Dockerfile rename to envs/coding_agent_env/server/Dockerfile index ad8319423..97e880343 100644 --- a/envs/opencode_env/server/Dockerfile +++ b/envs/coding_agent_env/server/Dockerfile @@ -4,14 +4,14 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. # -# opencode_env Dockerfile — mirrors the standard OpenEnv multi-stage uv +# coding_agent_env Dockerfile — mirrors the standard OpenEnv multi-stage uv # build used by echo_env / repl_env / jupyter_agent. # # Build: -# docker build -t opencode-env . +# docker build -t coding-agent-env . # # Run: -# docker run -p 8000:8000 -e E2B_API_KEY=e2b_... opencode-env +# docker run -p 8000:8000 -e E2B_API_KEY=e2b_... coding-agent-env ARG BASE_IMAGE=ghcr.io/meta-pytorch/openenv-base:latest FROM ${BASE_IMAGE} AS builder diff --git a/envs/coding_agent_env/server/__init__.py b/envs/coding_agent_env/server/__init__.py new file mode 100644 index 000000000..2eac4fb05 --- /dev/null +++ b/envs/coding_agent_env/server/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Server-side for the deployed coding_agent_env.""" diff --git a/envs/opencode_env/server/app.py b/envs/coding_agent_env/server/app.py similarity index 76% rename from envs/opencode_env/server/app.py rename to envs/coding_agent_env/server/app.py index 200c7f2d7..df40b507f 100644 --- a/envs/opencode_env/server/app.py +++ b/envs/coding_agent_env/server/app.py @@ -4,7 +4,7 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -"""FastAPI app for the opencode_env MCP server. +"""FastAPI app for the coding_agent_env MCP server. Mirrors the standard OpenEnv pattern (echo_env / repl_env / jupyter_agent) plus the custom Gradio UI mounted at ``/web`` per the @@ -16,7 +16,7 @@ E2B_API_KEY=... uvicorn server.app:app --host 0.0.0.0 --port 8000 # Docker: - docker run -p 8000:8000 -e E2B_API_KEY=... opencode-env + docker run -p 8000:8000 -e E2B_API_KEY=... coding-agent-env # HF Space: deploys via the root ``Dockerfile``. @@ -56,21 +56,15 @@ def _load_env_file() -> None: try: from openenv.core.env_server.http_server import create_app - from openenv.core.env_server.mcp_types import ( - CallToolAction, - CallToolObservation, - ) + from openenv.core.env_server.mcp_types import CallToolAction, CallToolObservation - from .gradio_ui import opencode_gradio_builder - from .opencode_environment import OpenCodeEnvironment + from .gradio_ui import coding_agent_gradio_builder + from .coding_environment import CodingAgentEnvironment except ImportError: # pragma: no cover from openenv.core.env_server.http_server import create_app - from openenv.core.env_server.mcp_types import ( - CallToolAction, - CallToolObservation, - ) - from server.gradio_ui import opencode_gradio_builder # type: ignore - from server.opencode_environment import OpenCodeEnvironment # type: ignore + from openenv.core.env_server.mcp_types import CallToolAction, CallToolObservation + from server.gradio_ui import coding_agent_gradio_builder # type: ignore + from server.coding_environment import CodingAgentEnvironment # type: ignore # Always expose the Gradio UI at /web. Set ENABLE_WEB_INTERFACE=false to @@ -86,22 +80,22 @@ def _custom_gradio_builder( title, quick_start_md, ): - """Hand off to ``server.gradio_ui.opencode_gradio_builder``.""" - return opencode_gradio_builder( + """Hand off to ``server.gradio_ui.coding_agent_gradio_builder``.""" + return coding_agent_gradio_builder( web_manager, action_fields, metadata, is_chat_env, - title or "opencode_env", + title or "coding_agent_env", quick_start_md, ) app = create_app( - OpenCodeEnvironment, + CodingAgentEnvironment, CallToolAction, CallToolObservation, - env_name="opencode_env", + env_name="coding_agent_env", max_concurrent_envs=int(os.getenv("MAX_CONCURRENT_ENVS", "4")), gradio_builder=_custom_gradio_builder, ) diff --git a/envs/opencode_env/server/catalog.py b/envs/coding_agent_env/server/catalog.py similarity index 100% rename from envs/opencode_env/server/catalog.py rename to envs/coding_agent_env/server/catalog.py diff --git a/envs/opencode_env/server/opencode_environment.py b/envs/coding_agent_env/server/coding_environment.py similarity index 61% rename from envs/opencode_env/server/opencode_environment.py rename to envs/coding_agent_env/server/coding_environment.py index 07f0d69ed..9174666e7 100644 --- a/envs/opencode_env/server/opencode_environment.py +++ b/envs/coding_agent_env/server/coding_environment.py @@ -4,30 +4,32 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -"""OpenCode MCP environment. +"""Coding-agent MCP environment. -Single MCP tool ``run_rollout`` that takes a uniform Task shape: +Single MCP tool ``run_rollout`` with a uniform task shape: - - ``instruction`` — prompt for the agent + - ``instruction`` — prompt for the selected agent - ``setup`` — bash commands run BEFORE the agent (in the sandbox) - ``verify`` — bash commands run AFTER the agent Reward = ``passed_verify_commands / total`` unless a verify command writes a float to ``/home/user/logs/verifier/reward.txt`` (override). -Returns a JSON-serialized :class:`RolloutResult` with reward + per-turn -logprobs (Mode B) + setup/verify command results + file outputs. +Returns a JSON-serialized :class:`RolloutResult` with reward, +setup/verify command results, and file outputs. """ from __future__ import annotations import json +import logging import os import time from typing import Any, Optional from uuid import uuid4 from fastmcp import FastMCP +from pydantic import BaseModel, Field try: from openenv.core.env_server.mcp_environment import MCPEnvironment @@ -40,7 +42,7 @@ from server.catalog import ENDPOINT_KINDS, resolve_endpoint # type: ignore -# One rollout (sandbox cold start + opencode install + opencode run + +# One rollout (sandbox cold start + harness install + agent run + # verifier) typically takes 30-180s; can spike to ~600s under load. Override # OpenEnv's 30s MCP-tool default so the server doesn't cut us off. _RUN_ROLLOUT_TIMEOUT_S = 900.0 @@ -49,13 +51,32 @@ HOME = "/home/user" WORKDIR = f"{HOME}/workdir" INSTRUCTION_PATH = f"{HOME}/task/instruction.md" +_log = logging.getLogger(__name__) + REWARD_FILE = f"{HOME}/logs/verifier/reward.txt" -PROXY_LOG = f"{HOME}/logs/agent/proxy.log" AGENT_LOG = f"{HOME}/logs/agent/opencode.jsonl" VERIFY_TIMEOUT_S = 120 +_SUPPORTED_AGENTS = ("opencode", "pi") +_AGENT_LOG_BY_AGENT: dict[str, str] = { + "opencode": f"{HOME}/logs/agent/opencode.jsonl", + "pi": f"{HOME}/logs/agent/pi.txt", +} + + +class _GenericAgentConfig(BaseModel): + """Minimal config shape for CLIAgentSessionFactory-backed agents.""" + base_url: str + api_key: str + model: str + agent_timeout_s: float = 600.0 + sandbox_home: str = HOME + provider: str | None = None + thinking: str | None = "off" + extra_env: dict[str, str] = Field(default_factory=dict) -class OpenCodeEnvironment(MCPEnvironment): + +class CodingAgentEnvironment(MCPEnvironment): """Per-session environment exposing a single ``run_rollout`` MCP tool.""" SUPPORTS_CONCURRENT_SESSIONS = True @@ -64,34 +85,38 @@ def __init__(self) -> None: # Lazy imports so module import stays cheap and so tests can patch. try: from ..models import ( + CodingAgentState, CommandResult, - OpenCodeState, RolloutResult, - RolloutTurn, ) except ImportError: # pragma: no cover from models import ( # type: ignore + CodingAgentState, CommandResult, - OpenCodeState, RolloutResult, - RolloutTurn, ) - from opencode_env import ( - E2BSandboxBackend, - OpenCodeConfig, - OpenCodeSessionFactory, - OpenCodeTask, - ) + from openenv.core.harness.agents import get_agent_spec + from openenv.core.harness.agents.cli_driver import CLIAgentSessionFactory + + from coding_agent_env.config import CodingAgentConfig + from coding_agent_env.harness import CodingAgentSessionFactory + from coding_agent_env.task import CodingAgentTask + + try: + from openenv.core.harness.sandbox import E2BSandboxBackend + except ImportError: + E2BSandboxBackend = None # type: ignore[assignment,misc] self._CommandResult = CommandResult self._RolloutResult = RolloutResult - self._RolloutTurn = RolloutTurn - self._OpenCodeState = OpenCodeState - self._OpenCodeConfig = OpenCodeConfig - self._OpenCodeSessionFactory = OpenCodeSessionFactory - self._OpenCodeTask = OpenCodeTask + self._CodingAgentState = CodingAgentState + self._CodingAgentConfig = CodingAgentConfig + self._CodingAgentSessionFactory = CodingAgentSessionFactory + self._CodingAgentTask = CodingAgentTask self._E2BSandboxBackend = E2BSandboxBackend + self._CLIAgentSessionFactory = CLIAgentSessionFactory + self._get_agent_spec = get_agent_spec # Don't raise on missing E2B_API_KEY here — OpenEnv's web-interface # layer instantiates the env at import time for schema introspection, @@ -99,12 +124,14 @@ def __init__(self) -> None: # just exploring. The real check happens lazily in # ``_run_rollout_impl`` (any rollout without creds fails fast there # with a clear error in the result payload). - self._state = self._OpenCodeState(episode_id=str(uuid4())) + self._state = self._CodingAgentState(episode_id=str(uuid4())) - mcp = FastMCP("opencode_env") + mcp = FastMCP("coding_agent_env") @mcp.tool def run_rollout( + # Agent + endpoint. + agent: str = "opencode", # Endpoint — either a shorthand (resolved from env vars + catalog # defaults) OR explicit base_url+api_key+model. Explicit fields # always win over the catalog. @@ -118,21 +145,24 @@ def run_rollout( verify: Optional[list[str]] = None, # Bookkeeping / tunables task_id: str = "", - mode: str = "transparent_proxy", + mode: str = "black_box", disable_thinking: Optional[bool] = None, max_tokens_cap: int = 4096, top_logprobs: int = 5, agent_timeout_s: float = 600.0, template: str = "", ) -> str: - """Run one OpenCode rollout end-to-end. + """Run one coding-agent rollout end-to-end. + + ``agent`` selects the harness CLI to run inside the sandbox. + Currently supported: ``"opencode"``, ``"pi"``. ``endpoint`` is the shorthand selector (one of ``"vllm"`` / ``"openai"`` / ``"hf_router"``) — the server resolves base_url / api_key / model from env vars + catalog defaults. Pass any of those explicitly to override. - See ``opencode_env.client.OpenCodeEnv.run_rollout`` for full + See ``coding_agent_env.client.CodingAgentEnv.run_rollout`` for full arg docs. Returns a JSON-serialized ``RolloutResult``. """ # Resolve via catalog when shorthand is provided. @@ -149,6 +179,11 @@ def run_rollout( if disable_thinking_resolved is None: disable_thinking_resolved = False + agent = (agent or "opencode").strip() + if agent not in _SUPPORTED_AGENTS: + raise ValueError( + f"unsupported agent {agent!r}; supported agents: {_SUPPORTED_AGENTS}" + ) if not (base_url and api_key and model): raise ValueError( "must provide either ``endpoint`` (one of " @@ -158,6 +193,7 @@ def run_rollout( raise ValueError("instruction is required") return self._run_rollout_impl( + agent=agent, base_url=base_url, api_key=api_key, model=model, @@ -183,14 +219,14 @@ def reset( episode_id: Optional[str] = None, **_: Any, ) -> Observation: - self._state = self._OpenCodeState(episode_id=episode_id or str(uuid4())) + self._state = self._CodingAgentState(episode_id=episode_id or str(uuid4())) return Observation( done=False, reward=None, metadata={ "status": "ready", "message": ( - "opencode_env ready. Call run_rollout(...) with a task." + "coding_agent_env ready. Call run_rollout(agent=..., ...) with a task." ), }, ) @@ -241,6 +277,7 @@ def state(self) -> Any: def _run_rollout_impl( self, *, + agent: str, base_url: str, api_key: str, model: str, @@ -281,80 +318,73 @@ def _emit(msg: str) -> None: _emit("error: E2B_API_KEY missing on server") return result.model_dump_json() - _emit(f"resolving config (model={model}, mode={mode})") + _emit(f"resolving config (agent={agent}, model={model}, mode={mode})") - # Build OpenCodeConfig + factory. We keep the proxy in charge of - # ``model_override`` / ``logprobs`` / ``max_tokens``-cap injection. - config = self._OpenCodeConfig( - provider="openai_compatible", - base_url=base_url.rstrip("/"), + config = self._build_agent_config( + agent=agent, + mode=mode, + base_url=base_url, api_key=api_key, model=model, agent_timeout_s=agent_timeout_s, - proxy_disable_thinking=disable_thinking, - proxy_top_logprobs=top_logprobs, - proxy_max_tokens_cap=max_tokens_cap if max_tokens_cap > 0 else None, - ) - - # Concatenate setup commands into a single ``set -e`` script and let - # the primitive run it as ``task.setup_shell`` before the agent - # starts. The per-command tracking happens here too — we re-run - # each command in a wrapper that captures exit/stdout/stderr. - # That way the primitive still aborts on setup failure AND we get - # observability in the response. - instruction_payload = instruction - opencode_task = self._OpenCodeTask( - instruction=instruction_payload, - metadata={"task_id": task_id}, + disable_thinking=disable_thinking, + top_logprobs=top_logprobs, + max_tokens_cap=max_tokens_cap, ) - backend_kwargs: dict[str, Any] = {} - if template: - backend_kwargs["template"] = template - - factory = self._OpenCodeSessionFactory( - config=config, - sandbox_backend=self._E2BSandboxBackend(**backend_kwargs), - mode=mode, - verifier=None, + # Concatenate setup commands into a single ``set -e`` script so the + # primitive runs them inside _bootstrap_sandbox BEFORE the agent + # starts. This avoids the race where the agent's first tool call + # depends on files or packages that setup is still installing. + setup_shell: str | None = None + if setup: + # ``set -e`` makes the script abort on the first failing command. + setup_shell = "set -e\n" + "\n".join(setup) + + rollout_task = self._CodingAgentTask( + instruction=instruction, + setup_shell=setup_shell, + metadata={"task_id": task_id, "agent": agent}, ) session = None try: + factory = self._build_session_factory( + agent=agent, + config=config, + mode=mode, + template=template, + disable_thinking=disable_thinking, + top_logprobs=top_logprobs, + max_tokens_cap=max_tokens_cap, + ) _emit( f"creating E2B sandbox (template={template or 'default'}) — " "this is the slow phase (~5–60s cold, ~5s with template)" ) - session = factory.create(task=opencode_task) + session = factory.create(task=rollout_task) result.sandbox_id = session.sandbox.sandbox_id - _emit( - f"sandbox ready: {result.sandbox_id} — agent started " - f"({'proxy on :7000, logprobs capturing' if mode == 'transparent_proxy' else 'direct LLM, no logprobs'})" - ) - - # Run setup commands one at a time, *before* the agent starts. - # The factory has already started the agent in start_agent() - # during create(); to keep the order "setup → agent → verify" - # we'd need to restructure. As a pragmatic compromise we run - # setup IMMEDIATELY after create(), which races with the agent - # for ~1-2s but is fine for typical pip/git/download work - # because opencode itself takes >=20s to make its first model - # call. - for i, cmd in enumerate(setup, 1): - _emit(f"setup [{i}/{len(setup)}]: {cmd[:80]}") - cr = self._exec_command(session.sandbox, cmd) - result.setup_results.append(cr) - if cr.exit_code != 0: - result.error = ( - f"setup command failed (exit {cr.exit_code}): {cmd[:120]}" + _emit(f"sandbox ready: {result.sandbox_id} — agent started (mode={mode})") + + # setup commands already ran atomically during sandbox bootstrap. + # Avoid re-running them here because many setup scripts are not + # idempotent (e.g., migrations, one-shot installs, destructive prep). + # We still surface per-command bookkeeping for callers. + for cmd in setup: + result.setup_results.append( + self._CommandResult( + cmd=cmd, + exit_code=None, + stdout="executed during bootstrap (individual exit code not captured)", + stderr="", + duration_s=0.0, ) - _emit(f"setup FAILED at [{i}]: exit={cr.exit_code}") - break + ) - # Block until the agent is done (or setup already failed). + # Block until the agent is done. if result.error is None: _emit( - f"agent running — opencode CLI in sandbox " + f"agent running — {agent} CLI in sandbox " f"(timeout {int(agent_timeout_s)}s)" ) try: @@ -384,23 +414,19 @@ def _emit(msg: str) -> None: else: result.reward = None - # Collect filesystem + proxy trace. - _emit("collecting workdir files + proxy trace + logs") + # Collect filesystem + agent log tail. + _emit("collecting workdir files + logs") result.files, result.files_extra = self._collect_files(session.sandbox) - result.proxy_turns = self._collect_proxy_turns(session) - result.proxy_log_tail = self._safe_read(session.sandbox, PROXY_LOG)[-2000:] - result.agent_log_tail = self._safe_read(session.sandbox, AGENT_LOG)[-2000:] + result.agent_log_tail = self._collect_agent_log_tail(session, agent) _emit( f"collected: {len(result.files)} file(s), " - f"{len(result.proxy_turns)} proxy turn(s), " f"reward={'%.2f' % result.reward if result.reward is not None else 'n/a'}" ) except Exception as exc: # noqa: BLE001 result.error = f"{type(exc).__name__}: {exc}" _emit(f"ERROR: {result.error}") if session is not None: - result.proxy_log_tail = self._safe_read(session.sandbox, PROXY_LOG)[-2000:] - result.agent_log_tail = self._safe_read(session.sandbox, AGENT_LOG)[-2000:] + result.agent_log_tail = self._collect_agent_log_tail(session, agent) finally: if session is not None: try: @@ -420,6 +446,110 @@ def _emit(msg: str) -> None: return result.model_dump_json() + def _build_agent_config( + self, + *, + agent: str, + mode: str, + base_url: str, + api_key: str, + model: str, + agent_timeout_s: float, + disable_thinking: bool, + top_logprobs: int, + max_tokens_cap: int, + ) -> Any: + if agent == "opencode": + if top_logprobs: + _log.warning( + "top_logprobs=%d is not supported for agent='opencode' " + "and will have no effect. Use interception_gate mode for " + "logprob capture.", + top_logprobs, + ) + return self._CodingAgentConfig( + provider="openai_compatible", + base_url=base_url.rstrip("/"), + api_key=api_key, + model=model, + agent_timeout_s=agent_timeout_s, + disable_thinking=disable_thinking, + max_tokens_cap=max_tokens_cap if max_tokens_cap > 0 else None, + ) + + provider = self._infer_pi_provider(base_url) + return _GenericAgentConfig( + base_url=base_url.rstrip("/"), + api_key=api_key, + model=model, + agent_timeout_s=agent_timeout_s, + provider=provider, + thinking="off" if disable_thinking else None, + ) + + def _build_session_factory( + self, + *, + agent: str, + config: Any, + mode: str, + template: str, + disable_thinking: bool, + top_logprobs: int, + max_tokens_cap: int, + ) -> Any: + if self._E2BSandboxBackend is None: + raise RuntimeError( + "E2BSandboxBackend unavailable: install optional dependency 'e2b'." + ) + + backend_kwargs: dict[str, Any] = {} + if template: + backend_kwargs["template"] = template + backend = self._E2BSandboxBackend(**backend_kwargs) + + if agent == "opencode": + return self._CodingAgentSessionFactory( + config=config, + sandbox_backend=backend, + mode=mode, + verifier=None, + ) + + spec = self._get_agent_spec(agent) + return self._CLIAgentSessionFactory( + spec=spec, + config=config, + sandbox_backend=backend, + mode=mode, + verifier=None, + ) + + @staticmethod + def _infer_pi_provider(base_url: str) -> str: + url = (base_url or "").lower() + if "router.huggingface.co" in url: + return "huggingface" + if "anthropic" in url: + return "anthropic" + if "googleapis.com" in url or "generativelanguage" in url: + return "gemini" + return "openai" + + def _collect_agent_log_tail(self, session: Any, agent: str) -> str: + if hasattr(session, "collect_artifacts"): + try: + artifacts = session.collect_artifacts() + if isinstance(artifacts, dict) and "agent_log" in artifacts: + val = artifacts["agent_log"] + if isinstance(val, str): + return val[-2000:] + return json.dumps(val, default=str)[-2000:] + except Exception: + pass + path = _AGENT_LOG_BY_AGENT.get(agent, AGENT_LOG) + return self._safe_read(session.sandbox, path)[-2000:] + # ── Helpers ──────────────────────────────────────────────────────────── def _exec_command(self, sandbox: Any, cmd: str) -> Any: @@ -450,9 +580,7 @@ def _read_reward(self, sandbox: Any) -> float | None: except ValueError: return None - def _collect_files( - self, sandbox: Any - ) -> tuple[dict[str, str], list[str]]: + def _collect_files(self, sandbox: Any) -> tuple[dict[str, str], list[str]]: listing = sandbox.exec( f"find {WORKDIR} -maxdepth 2 -type f -size -64k 2>/dev/null | head -32", timeout=10, @@ -469,39 +597,6 @@ def _collect_files( extras.append(path) return files, extras - def _collect_proxy_turns(self, session: Any) -> list[Any]: - turns: list[Any] = [] - proxy_trace_path = getattr(session, "_proxy_trace_path", None) - if not proxy_trace_path: - return turns - raw = self._safe_read(session.sandbox, proxy_trace_path) - for line in raw.splitlines(): - line = line.strip() - if not line: - continue - try: - rec = json.loads(line) - except Exception: - continue - response = rec.get("response") or {} - turns.append( - self._RolloutTurn( - turn=int(rec.get("turn") or 0), - finish_reason=rec.get("finish_reason"), - completion_tokens=list(rec.get("completion_tokens") or []), - completion_token_ids=list(rec.get("completion_token_ids") or []), - per_token_logps=[ - float(x) for x in (rec.get("per_token_logps") or []) - if x is not None - ], - latency_s=float(rec.get("latency_s") or 0.0), - timestamp=float(rec.get("timestamp") or 0.0), - upstream_status=response.get("upstream_status"), - upstream_error=response.get("upstream_error"), - ) - ) - return turns - @staticmethod def _safe_read(sandbox: Any, path: str) -> str: try: diff --git a/envs/opencode_env/server/gradio_ui.py b/envs/coding_agent_env/server/gradio_ui.py similarity index 76% rename from envs/opencode_env/server/gradio_ui.py rename to envs/coding_agent_env/server/gradio_ui.py index 79a696d75..ea9cdb81f 100644 --- a/envs/opencode_env/server/gradio_ui.py +++ b/envs/coding_agent_env/server/gradio_ui.py @@ -4,7 +4,7 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -"""Minimal Gradio UI for opencode_env. +"""Minimal Gradio UI for coding_agent_env. Mounts under the standard OpenEnv ``/web`` path via the ``gradio_builder=`` callback documented at @@ -19,8 +19,7 @@ agent_timeout_s, template). - Preset buttons for the ready-made example tasks. - Run button → result panel with reward, setup/verify per-command - results, file outputs, logprob stats, agent + proxy log tails, - and the raw RolloutResult JSON. + results, file outputs, agent log tail, and the raw RolloutResult JSON. """ from __future__ import annotations @@ -31,11 +30,15 @@ import gradio as gr try: - from .catalog import ENDPOINT_KINDS, catalog_summary, resolve_endpoint - from .opencode_environment import OpenCodeEnvironment + from .catalog import catalog_summary, ENDPOINT_KINDS, resolve_endpoint + from .coding_environment import CodingAgentEnvironment except ImportError: # pragma: no cover - from server.catalog import ENDPOINT_KINDS, catalog_summary, resolve_endpoint # type: ignore - from server.opencode_environment import OpenCodeEnvironment # type: ignore + from server.catalog import ( # type: ignore + catalog_summary, + ENDPOINT_KINDS, + resolve_endpoint, + ) + from server.coding_environment import CodingAgentEnvironment # type: ignore # ──────────────────────────────────────────────────────────────────────────── @@ -144,57 +147,16 @@ def _command_rows(items: list[dict[str, Any]]) -> list[list[str]]: cmd if len(cmd) <= 80 else cmd[:77] + "...", str(it.get("exit_code", "")), f"{it.get('duration_s', 0):.2f}s", - (it.get("stderr") or "").splitlines()[-1][:80] if it.get("exit_code") else "", + (it.get("stderr") or "").splitlines()[-1][:80] + if it.get("exit_code") + else "", ] ) return rows -def _logprobs_md(turns: list[dict[str, Any]]) -> str: - if not turns: - return "_No proxy turns captured._\n\nThis is normal in `black_box` mode. In `transparent_proxy` mode, an empty list usually means the agent never made an LLM call (check the agent log)." - n = len(turns) - productive = sum(1 for t in turns if t.get("completion_tokens")) - total_toks = sum(len(t.get("completion_tokens") or []) for t in turns) - all_lps = [ - float(x) - for t in turns - for x in (t.get("per_token_logps") or []) - if x is not None - ] - mean_lp = (sum(all_lps) / len(all_lps)) if all_lps else None - lines = [ - f"**turns**: `{n}` · **productive**: `{productive}` · " - f"**total_completion_tokens**: `{total_toks}`", - ] - if mean_lp is not None: - lines.append(f"**mean_logprob**: `{mean_lp:+.4f}`") - finishes: dict[str, int] = {} - for t in turns: - f = t.get("finish_reason") or "unknown" - finishes[f] = finishes.get(f, 0) + 1 - if finishes: - lines.append( - "**finish_reasons**: " + " ".join(f"`{k}={v}`" for k, v in finishes.items()) - ) - productive_rows = [t for t in turns if t.get("completion_tokens")] - if productive_rows: - first = productive_rows[0] - toks = first["completion_tokens"][:10] - lps = first.get("per_token_logps") or [] - lines.append( - f"\n**first productive turn (first 10 tokens)**\n\n" - f"```\n" - + "\n".join( - f" {tok!r:<14} {lp:+.3f}" if i < len(lps) else f" {tok!r:<14} -" - for i, (tok, lp) in enumerate(zip(toks, lps + [None] * len(toks))) - ) - + "\n```" - ) - return "\n\n".join(lines) - - def _live_status_md( + agent: str, endpoint_kind: str, model: str, mode: str, @@ -204,7 +166,7 @@ def _live_status_md( """Render a live phase log (latest at the bottom) with elapsed timestamps.""" head = ( f"### running… `elapsed={elapsed_s:.1f}s`\n\n" - f"_endpoint=`{endpoint_kind}` model=`{model}` mode=`{mode}`_\n\n" + f"_agent=`{agent}` endpoint=`{endpoint_kind}` model=`{model}` mode=`{mode}`_\n\n" ) if not lines: body = "_(waiting for first phase update…)_" @@ -248,24 +210,25 @@ def _catalog_banner() -> str: # ──────────────────────────────────────────────────────────────────────────── -def opencode_gradio_builder( - web_manager, # noqa: ARG001 (unused: we instantiate the env directly) - action_fields, # noqa: ARG001 - metadata, # noqa: ARG001 - is_chat_env, # noqa: ARG001 +def coding_agent_gradio_builder( + web_manager, # noqa: ARG001 (unused: we instantiate the env directly) + action_fields, # noqa: ARG001 + metadata, # noqa: ARG001 + is_chat_env, # noqa: ARG001 title, - quick_start_md, # noqa: ARG001 + quick_start_md, # noqa: ARG001 ) -> gr.Blocks: - """Build the opencode_env console. + """Build the coding_agent_env console. Compatible with ``create_app(..., gradio_builder=...)``. We ignore - ``web_manager`` and instantiate :class:`OpenCodeEnvironment` ourselves - inside the run handler — opencode_env's run_rollout doesn't need any + ``web_manager`` and instantiate :class:`CodingAgentEnvironment` ourselves + inside the run handler — coding_agent_env's run_rollout doesn't need any per-session state beyond the env's own bookkeeping, and instantiating is cheap (no sandbox is created until the tool fires). """ def run( + agent: str, endpoint: str, model: str, base_url: str, @@ -283,9 +246,9 @@ def run( """Generator handler — yields incremental UI updates. Each ``yield`` is a tuple matching ``outputs=[...]``: - (summary_md, setup_table, verify_table, files_md, logprobs_md, - logs_md, raw_json). Early yields keep summary_md as a live phase - log while the rollout runs; the final yield populates everything. + (summary_md, setup_table, verify_table, files_md, logs_md, + raw_json). Early yields keep summary_md as a live phase log while + the rollout runs; the final yield populates everything. """ import queue import threading @@ -299,7 +262,7 @@ def run( ) except ValueError as exc: err = f"endpoint resolution failed: {exc}" - yield (f"### error\n\n```\n{err}\n```", [], [], "", "", "", {"error": err}) + yield (f"### error\n\n```\n{err}\n```", [], [], "", "", {"error": err}) return # Translate "auto" / "on" / "off" into bool / None. @@ -310,7 +273,7 @@ def run( else: dt = None - env = OpenCodeEnvironment() + env = CodingAgentEnvironment() # The worker fires _run_rollout_impl in a background thread and # streams progress messages into a queue; this generator polls the @@ -324,6 +287,7 @@ def _cb(msg: str) -> None: def _worker(): try: payload = env._run_rollout_impl( + agent=agent, base_url=resolved.base_url, api_key=resolved.api_key, model=resolved.model, @@ -354,8 +318,12 @@ def _worker(): # First yield: announce we've started. Empty result panels. yield ( - f"### running…\n\n_endpoint=`{resolved.kind}` model=`{resolved.model}` mode=`{mode}`_", - [], [], "", "", "", {}, + f"### running…\n\n_agent=`{agent}` endpoint=`{resolved.kind}` model=`{resolved.model}` mode=`{mode}`_", + [], + [], + "", + "", + {}, ) status_lines: list[tuple[float, str]] = [] @@ -374,8 +342,15 @@ def _worker(): # Render the live status pane. elapsed = time.time() - t_start - md = _live_status_md(resolved.kind, resolved.model, mode, elapsed, status_lines) - yield (md, [], [], "", "", "", {}) + md = _live_status_md( + agent, + resolved.kind, + resolved.model, + mode, + elapsed, + status_lines, + ) + yield (md, [], [], "", "", {}) # Drain any final messages still in the queue. while not status_q.empty(): @@ -390,9 +365,17 @@ def _worker(): err = result_holder.get("error", "unknown error") yield ( f"### error\n\n```\n{err}\n```", - [], [], "", "", - _live_status_md(resolved.kind, resolved.model, mode, - time.time() - t_start, status_lines), + [], + [], + "", + _live_status_md( + agent, + resolved.kind, + resolved.model, + mode, + time.time() - t_start, + status_lines, + ), {"error": err}, ) return @@ -403,13 +386,17 @@ def _worker(): _command_rows(result.get("setup_results") or []), _command_rows(result.get("verify_results") or []), _files_md(result.get("files") or {}), - _logprobs_md(result.get("proxy_turns") or []), ( - f"### live phase log\n\n" - + _live_status_md(resolved.kind, resolved.model, mode, - time.time() - t_start, status_lines) - + f"\n\n### agent log (tail)\n```\n{result.get('agent_log_tail', '')[:4000]}\n```\n\n" - f"### proxy log (tail)\n```\n{result.get('proxy_log_tail', '')[:4000]}\n```" + "### live phase log\n\n" + + _live_status_md( + agent, + resolved.kind, + resolved.model, + mode, + time.time() - t_start, + status_lines, + ) + + f"\n\n### agent log (tail)\n```\n{result.get('agent_log_tail', '')[:4000]}\n```" ), result, ) @@ -418,17 +405,23 @@ def apply_preset(name: str) -> tuple[str, str, str]: p = PRESETS.get(name) or {"instruction": "", "setup": "", "verify": ""} return p["instruction"], p["setup"], p["verify"] - with gr.Blocks(title=title or "opencode_env") as app: - gr.Markdown(f"# {title or 'opencode_env'}") + with gr.Blocks(title=title or "coding_agent_env") as app: + gr.Markdown(f"# {title or 'coding_agent_env'}") gr.Markdown( - "Run one OpenCode rollout in an E2B sandbox against your chosen " - "LLM endpoint. Pick an endpoint, write the task as `(instruction, " - "setup, verify)`, and inspect the reward + per-token logprobs." + "Run one coding-agent rollout in an E2B sandbox against your chosen " + "LLM endpoint. Pick an agent + endpoint, write the task as " + "`(instruction, setup, verify)`, and inspect reward + logs." ) gr.Markdown(_catalog_banner()) with gr.Row(): + agent = gr.Dropdown( + choices=["opencode", "pi"], + value="opencode", + label="Agent", + scale=1, + ) endpoint = gr.Dropdown( choices=list(ENDPOINT_KINDS), value="openai", @@ -436,21 +429,25 @@ def apply_preset(name: str) -> tuple[str, str, str]: scale=1, ) model = gr.Textbox( - label="Model (blank → catalog default)", placeholder="gpt-4o-mini", + label="Model (blank → catalog default)", + placeholder="gpt-4o-mini", scale=2, ) with gr.Row(): base_url = gr.Textbox( label="Base URL (blank → env / catalog default)", - placeholder="https://api.openai.com/v1", scale=2, + placeholder="https://api.openai.com/v1", + scale=2, ) api_key = gr.Textbox( label="API key (blank → server env var)", - placeholder="(server env)", type="password", scale=1, + placeholder="(server env)", + type="password", + scale=1, ) instruction = gr.Textbox( - label="Instruction (the prompt opencode runs)", + label="Instruction (the prompt the selected agent runs)", lines=4, value=PRESETS["binary_search"]["instruction"], ) @@ -475,8 +472,8 @@ def apply_preset(name: str) -> tuple[str, str, str]: with gr.Accordion("Tunables", open=False): with gr.Row(): mode = gr.Dropdown( - choices=["transparent_proxy", "black_box"], - value="transparent_proxy", + choices=["black_box", "interception_gate"], + value="black_box", label="mode", ) disable_thinking = gr.Dropdown( @@ -515,8 +512,6 @@ def apply_preset(name: str) -> tuple[str, str, str]: ) with gr.Tab("Files"): files_md = gr.Markdown("") - with gr.Tab("Logprobs"): - logprobs_md = gr.Markdown("") with gr.Tab("Logs"): logs_md = gr.Markdown("") with gr.Tab("Raw JSON"): @@ -536,14 +531,28 @@ def apply_preset(name: str) -> tuple[str, str, str]: run_btn.click( fn=run, inputs=[ - endpoint, model, base_url, api_key, - instruction, setup_text, verify_text, - mode, disable_thinking, template, - max_tokens_cap, top_logprobs, agent_timeout_s, + agent, + endpoint, + model, + base_url, + api_key, + instruction, + setup_text, + verify_text, + mode, + disable_thinking, + template, + max_tokens_cap, + top_logprobs, + agent_timeout_s, ], outputs=[ - summary_md, setup_table, verify_table, - files_md, logprobs_md, logs_md, raw_json, + summary_md, + setup_table, + verify_table, + files_md, + logs_md, + raw_json, ], ) diff --git a/envs/opencode_env/task.py b/envs/coding_agent_env/task.py similarity index 73% rename from envs/opencode_env/task.py rename to envs/coding_agent_env/task.py index f9d208d84..8633eb7aa 100644 --- a/envs/opencode_env/task.py +++ b/envs/coding_agent_env/task.py @@ -4,7 +4,7 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -"""Task payload accepted by :class:`OpenCodeSessionFactory`.""" +"""Task payload accepted by :class:`CodingAgentSessionFactory`.""" from __future__ import annotations @@ -13,8 +13,8 @@ from pydantic import BaseModel, Field -class OpenCodeTask(BaseModel): - """One task for an OpenCode rollout. +class CodingAgentTask(BaseModel): + """One task for a coding-agent rollout. The primitive only needs ``instruction`` (the prompt handed to ``opencode run``). Callers may attach ``setup_shell`` (run once inside the sandbox @@ -29,8 +29,8 @@ class OpenCodeTask(BaseModel): metadata: dict[str, Any] = Field(default_factory=dict) @classmethod - def coerce(cls, value: Any) -> "OpenCodeTask": - """Accept a bare string, a dict, or an existing ``OpenCodeTask``.""" + def coerce(cls, value: Any) -> "CodingAgentTask": + """Accept a bare string, a dict, or an existing ``CodingAgentTask``.""" if isinstance(value, cls): return value if isinstance(value, str): @@ -38,6 +38,6 @@ def coerce(cls, value: Any) -> "OpenCodeTask": if isinstance(value, dict): return cls(**value) raise TypeError( - f"Cannot coerce {type(value).__name__} to OpenCodeTask; " - "pass a str, dict, or OpenCodeTask." + f"Cannot coerce {type(value).__name__} to CodingAgentTask; " + "pass a str, dict, or CodingAgentTask." ) diff --git a/envs/opencode_env/uv.lock b/envs/coding_agent_env/uv.lock similarity index 99% rename from envs/opencode_env/uv.lock rename to envs/coding_agent_env/uv.lock index 80dd00ba0..aa35531cc 100644 --- a/envs/opencode_env/uv.lock +++ b/envs/coding_agent_env/uv.lock @@ -1664,38 +1664,7 @@ wheels = [ ] [[package]] -name = "openenv-core" -version = "0.2.3" -source = { git = "https://github.com/adithya-s-k/OpenEnv.git?rev=opencode-harness#aabcdbb9d52aa62a842ec69472b2a1106acb831a" } -dependencies = [ - { name = "fastapi" }, - { name = "fastmcp" }, - { name = "gradio" }, - { name = "httpx" }, - { name = "huggingface-hub" }, - { name = "openai" }, - { name = "pydantic" }, - { name = "pyyaml" }, - { name = "requests" }, - { name = "rich" }, - { name = "tomli" }, - { name = "tomli-w" }, - { name = "typer" }, - { name = "uvicorn" }, - { name = "websockets" }, -] - -[package.optional-dependencies] -core = [ - { name = "fastapi" }, - { name = "pydantic" }, - { name = "requests" }, - { name = "uvicorn" }, - { name = "websockets" }, -] - -[[package]] -name = "openenv-opencode-env" +name = "openenv-coding-agent-env" version = "0.1.0" source = { editable = "." } dependencies = [ @@ -1724,7 +1693,7 @@ requires-dist = [ { name = "fastmcp", specifier = ">=2.0.0" }, { name = "gradio", specifier = ">=6.0.0" }, { name = "httpx", specifier = ">=0.27.0" }, - { name = "openenv-core", extras = ["core"], git = "https://github.com/adithya-s-k/OpenEnv.git?rev=opencode-harness" }, + { name = "openenv-core", extras = ["core"], specifier = ">=0.3.0" }, { name = "pydantic", specifier = ">=2.0.0" }, { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.0.0" }, { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.23.0" }, @@ -1734,6 +1703,41 @@ requires-dist = [ ] provides-extras = ["dev"] +[[package]] +name = "openenv-core" +version = "0.3.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "fastapi" }, + { name = "fastmcp" }, + { name = "gradio" }, + { name = "httpx" }, + { name = "huggingface-hub" }, + { name = "openai" }, + { name = "pydantic" }, + { name = "pyyaml" }, + { name = "requests" }, + { name = "rich" }, + { name = "tomli" }, + { name = "tomli-w" }, + { name = "typer" }, + { name = "uvicorn" }, + { name = "websockets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/ce/d6/3bebe8afb55fcc3ea9251c4c2dfbab2879e31089bc91a8fe9696e5ce019b/openenv_core-0.3.0.tar.gz", hash = "sha256:c7fee2035badab5be497eb6f4afb2cb417de000f82cc19afd72fb5ec332c431d", size = 164720, upload-time = "2026-05-11T11:37:57.274Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f8/f5/aafa43138589bfd5d369a8d02ea365aae9d6fe55ac0b3894368d6d69bd03/openenv_core-0.3.0-py3-none-any.whl", hash = "sha256:859e875c9d5211b157c30fb9abc681606fcf0bf1b6ffcdf404678992823a1df0", size = 194313, upload-time = "2026-05-11T11:37:55.537Z" }, +] + +[package.optional-dependencies] +core = [ + { name = "fastapi" }, + { name = "pydantic" }, + { name = "requests" }, + { name = "uvicorn" }, + { name = "websockets" }, +] + [[package]] name = "opentelemetry-api" version = "1.41.1" diff --git a/envs/mini_swe_env/README.md b/envs/mini_swe_env/README.md new file mode 100644 index 000000000..948c7ee9c --- /dev/null +++ b/envs/mini_swe_env/README.md @@ -0,0 +1,137 @@ +# Mini SWE Environment + +A training environment for SWE tasks using OpenEnv. + +## Overview + +`mini_swe_env` provides an MCP-based environment that: + +1. **Provisions a sandbox** (Docker, E2B, or HF Sandbox) +2. **Clones a Git repository** at a specified base commit +3. **Runs setup commands** to prepare the environment +4. **Starts an in-sandbox MCP tool server** exposing a `terminal` tool +5. **Launches a coding agent** (Pi, OpenCode) that uses `terminal` to explore and fix code +6. **Runs verification commands** after the agent finishes +7. **Computes reward** (passed/total verify commands, or explicit override) + +## Architecture + +``` +┌─────────────────────────────────────────────────────┐ +│ Training Script / Client │ +│ env.run_swe_rollout(task=..., agent="pi", ...) │ +└─────────────┬───────────────────────────────────────┘ + │ MCP tool call + ▼ +┌─────────────────────────────────────────────────────┐ +│ SWEEnvironment (OpenEnv Server) │ +│ - Creates sandbox │ +│ - Clones repo at base_commit │ +│ - Runs setup commands │ +│ - Deploys in-sandbox MCP server │ +│ - Launches agent │ +│ - Runs verify → computes reward │ +└─────────────┬───────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────┐ +│ Sandbox (Docker / E2B / HF) │ +│ ┌─────────────────────────────────────────────┐ │ +│ │ sandbox_mcp_server.py (port 8765) │ │ +│ │ └─ terminal(command=...) → execute shell │ │ +│ │ └─ terminal(final_answer=...) → verify │ │ +│ └─────────────────┬───────────────────────────┘ │ +│ │ MCP tool calls │ +│ ┌─────────────────▼───────────────────────────┐ │ +│ │ Agent (Pi / OpenCode) │ │ +│ │ Explores code, makes changes, submits fix │ │ +│ └─────────────────────────────────────────────┘ │ +│ │ +│ /home/user/workdir/ ← cloned repo at base_commit │ +└─────────────────────────────────────────────────────┘ +``` + +## Quick Start + +### As a Client + +```python +from mini_swe_env import MiniSWEEnv + +with MiniSWEEnv(base_url="http://localhost:8000") as env: + env.reset() + result = env.run_swe_rollout( + instance_id="requests__requests-12345", + repo="psf/requests", + base_commit="abc123...", + instruction="Fix the redirect edge case in Session.send.", + verify=["python -m pytest tests/test_redirect.py -q"], + agent="pi", + base_url="https://api.openai.com/v1", + api_key="sk-...", + model="gpt-4o-mini", + ) + print(f"Reward: {result.reward}") + print(f"Files changed: {list(result.files.keys())}") +``` + +### Running the Server + +```bash +# Local development +PYTHONPATH=src:envs uvicorn mini_swe_env.server.app:app --host 0.0.0.0 --port 8000 + +# With Docker backend (default) +PYTHONPATH=src:envs python -m mini_swe_env.server.app +``` + +## Task Shape (SWETask) + +```python +@dataclass +class SWETask: + task_id: str # Unique identifier + source: str # Task source identifier + instance_id: str # SWE-bench / SWE-Gym instance id + repo: str # "org/repo" + base_commit: str # Git commit hash + instruction: str # Problem statement + setup: list[str] # Commands before agent starts + verify: list[str] # Commands after agent finishes + timeout_s: int = 1800 # Total timeout + sandbox_image: str | None # Optional custom image + metadata: dict # Additional info +``` + +## Terminal Tool + +The in-sandbox MCP server exposes a single `terminal` tool: + +### Execute Command +```json +{"command": "git diff HEAD"} +``` +Returns: `{"output": "...", "stderr": "...", "exit_code": 0, "done": false}` + +### Submit Final Answer +```json +{"final_answer": "I fixed the bug by changing line 42 in utils.py..."} +``` +Returns: `{"reward": 0.75, "verify_results": [...], "done": true}` + +## Reward Computation + +1. **Default**: `passed_verify_commands / total_verify_commands` +2. **Override**: Any verify command (or the agent) can write a float to + `/home/user/logs/verifier/reward.txt` to override the default. + +## Components + +| File | Purpose | +|------|---------| +| `models.py` | SWETask dataclass + Pydantic models (SWERolloutResult, SWEState) | +| `server/swe_environment.py` | Main environment (MCPEnvironment) | +| `server/sandbox_mcp_server.py` | In-sandbox terminal tool server | +| `server/app.py` | FastAPI application | +| `client.py` | Typed MiniSWEEnv client | +| `harness.py` | SWESession + SWESessionFactory for agent harness | diff --git a/envs/mini_swe_env/__init__.py b/envs/mini_swe_env/__init__.py new file mode 100644 index 000000000..9dfe7defd --- /dev/null +++ b/envs/mini_swe_env/__init__.py @@ -0,0 +1,68 @@ +"""Mini SWE environment — SWE-Gym task loader + grading + environment. + +Exports: + Task models: + SWEGymTask, SWETask, validate_swe_task, coerce_swe_task, ... + + Task loader (SWE-Gym): + load_swegym_tasks, validate_swegym_task, get_instance_image, ... + + Grading: + grade_from_case_results, GradeResult + + Environment + client: + SWEEnvironment, MiniSWEEnv, SWERolloutResult, ... +""" + +from .models import ( + SWECommandResult, + SWEGymTask, + SWERolloutResult, + SWEState, + SWETask, + SWETaskValidationError, + coerce_swe_task, + validate_swe_task, +) + +from .task_loader_swegym import ( + SWEGymLoadError, + get_instance_image, + load_swegym_tasks, + load_swegym_tasks_from_dicts, + swegym_task_to_swe_task, + validate_swegym_task, +) + +from .grading import ( + GradeResult, + GradingError, + grade_from_case_results, +) + +from .client import MiniSWEEnv + +__all__ = [ + # Task models + "SWEGymTask", + "SWETask", + "SWETaskValidationError", + "coerce_swe_task", + "validate_swe_task", + # Task loader + "SWEGymLoadError", + "get_instance_image", + "load_swegym_tasks", + "load_swegym_tasks_from_dicts", + "swegym_task_to_swe_task", + "validate_swegym_task", + # Grading + "GradeResult", + "GradingError", + "grade_from_case_results", + # Server models + "MiniSWEEnv", + "SWECommandResult", + "SWERolloutResult", + "SWEState", +] diff --git a/envs/mini_swe_env/client.py b/envs/mini_swe_env/client.py new file mode 100644 index 000000000..fe5f359bb --- /dev/null +++ b/envs/mini_swe_env/client.py @@ -0,0 +1,166 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Client for the Mini SWE Environment. + +Provides a typed interface for running SWE-bench rollouts through the +deployed MCP server. + +Example:: + + from mini_swe_env import MiniSWEEnv + + with MiniSWEEnv(base_url="http://localhost:8000") as env: + env.reset() + result = env.run_swe_rollout( + instance_id="requests__requests-12345", + repo="psf/requests", + base_commit="abc123...", + instruction="Fix the redirect edge case...", + verify=["python -m pytest tests/test_redirect.py -q"], + base_url="https://api.openai.com/v1", + api_key=os.environ["OPENAI_API_KEY"], + model="gpt-4o-mini", + agent="pi", + ) + print(f"Reward: {result.reward}") +""" + +from __future__ import annotations + +import json +from typing import Any + +from openenv.core.mcp_client import MCPToolClient + +try: + from .models import SWERolloutResult +except ImportError: # pragma: no cover + from models import SWERolloutResult # type: ignore + + +class MiniSWEEnv(MCPToolClient): + """Typed client for the mini_swe_env MCP server. + + Inherits ``reset`` / ``call_tool`` / ``list_tools`` / ``from_docker_image`` + / context-manager semantics from :class:`MCPToolClient`. + """ + + def run_swe_rollout( + self, + *, + # Task fields (SWETask shape). + instance_id: str = "", + repo: str = "", + base_commit: str = "", + instruction: str = "", + setup: list[str] | None = None, + verify: list[str] | None = None, + timeout_s: int = 1800, + # Agent config. + agent: str = "pi", + base_url: str = "", + api_key: str = "", + model: str = "", + agent_timeout_s: float = 600.0, + # Infrastructure. + sandbox_backend: str = "docker", + sandbox_image: str = "", + task_id: str = "", + task_json: str = "", + ) -> SWERolloutResult: + """Run one SWE rollout and return the typed result. + + Args: + instance_id: SWE-bench Lite instance id. + repo: GitHub ``org/repo`` to clone. + base_commit: Commit to reset the repo to. + instruction: Problem statement for the agent. + setup: Bash commands run before the agent starts. + verify: Bash commands run after the agent exits. + Reward = ``passed / total`` unless a command writes a float + to ``/home/user/logs/verifier/reward.txt`` (override). + timeout_s: Total timeout for the task. + agent: Harness CLI (``"pi"`` or ``"opencode"``). + base_url: OpenAI-compatible LLM endpoint. + api_key: Bearer token for the LLM. + model: Model id for the LLM endpoint. + agent_timeout_s: Wall-clock budget for the agent run. + sandbox_backend: ``"docker"`` / ``"e2b"`` / ``"hf"``. + sandbox_image: Docker image or E2B template. Empty = default. + task_id: Echoed back in the result for traceability. + task_json: Complete SWETask as JSON string (overrides individual fields). + + Returns: + A :class:`SWERolloutResult` with reward, verify results, + file outputs, and diagnostic tails. + """ + raw = self.call_tool( + "run_swe_rollout", + instance_id=instance_id, + repo=repo, + base_commit=base_commit, + instruction=instruction, + setup=list(setup or []), + verify=list(verify or []), + timeout_s=timeout_s, + agent=agent, + base_url=base_url, + api_key=api_key, + model=model, + agent_timeout_s=agent_timeout_s, + sandbox_backend=sandbox_backend, + sandbox_image=sandbox_image, + task_id=task_id, + task_json=task_json, + ) + return SWERolloutResult.model_validate_json(_extract_text(raw)) + + +def _extract_text(result: Any) -> str: + """Pull the JSON text out of whatever shape the MCP layer returns. + + Handles the three shapes :meth:`MCPToolClient.call_tool` may surface: + a raw string, a ``CallToolObservation``-like object with + ``.result.content[0].text``, or a dict with ``content[0]["text"]``. + """ + if isinstance(result, str): + return result + + inner = getattr(result, "result", None) + if inner is not None: + content = getattr(inner, "content", None) + if content: + first = content[0] + text = getattr(first, "text", None) + if isinstance(text, str): + return text + if isinstance(first, dict) and "text" in first: + return first["text"] + + if isinstance(result, dict): + content = result.get("content") + if isinstance(content, list) and content: + first = content[0] + if isinstance(first, dict) and "text" in first: + return first["text"] + nested = result.get("result") + if isinstance(nested, dict): + content = nested.get("content") + if isinstance(content, list) and content: + first = content[0] + if isinstance(first, dict) and "text" in first: + return first["text"] + return json.dumps(result, default=str) + + content = getattr(result, "content", None) + if content: + first = content[0] + text = getattr(first, "text", None) + if isinstance(text, str): + return text + + return str(result) diff --git a/envs/mini_swe_env/grading.py b/envs/mini_swe_env/grading.py new file mode 100644 index 000000000..d0c6c120d --- /dev/null +++ b/envs/mini_swe_env/grading.py @@ -0,0 +1,131 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Grading helpers for SWE-Gym tasks. + +Supports both the canonical binary SWE-Gym reward and an optional +case-fraction shaping mode for RL experiments: + +- ``binary``: reward = 1.0 iff every FAIL_TO_PASS test passes AND every + PASS_TO_PASS test still passes, else 0.0. +- ``case_fraction``: reward = passed_cases / total_cases, while + ``resolved`` still requires every FAIL_TO_PASS and PASS_TO_PASS case to + pass. +""" + +from __future__ import annotations + +from typing import Any + +from .models import SWEGymTask + + +class GradingError(RuntimeError): + """Raised when grading input is invalid.""" + + +class GradeResult: + """Result of grading a SWE-Gym submission.""" + + __slots__ = ( + "reward", + "case_fraction", + "resolved", + "patch_applied", + "tests_status", + "instance_id", + ) + + def __init__( + self, + *, + reward: float, + case_fraction: float, + resolved: bool, + patch_applied: bool, + tests_status: dict[str, Any] | None = None, + instance_id: str = "", + ): + self.reward = reward + self.case_fraction = case_fraction + self.resolved = resolved + self.patch_applied = patch_applied + self.tests_status = tests_status + self.instance_id = instance_id + + def __repr__(self) -> str: + return ( + f"GradeResult(reward={self.reward}, case_fraction={self.case_fraction}, " + f"resolved={self.resolved}, " + f"patch_applied={self.patch_applied}, instance_id={self.instance_id!r})" + ) + + +def grade_from_case_results( + task: SWEGymTask, + case_results: dict[str, bool], + *, + patch_applied: bool = True, + reward_mode: str = "binary", +) -> GradeResult: + """Grade directly from per-test-case outcomes. + + Args: + task: SWE-Gym task with FAIL_TO_PASS and PASS_TO_PASS lists. + case_results: Mapping ``test_case -> passed``. + patch_applied: Whether test patch was successfully applied. + reward_mode: ``"binary"`` or ``"case_fraction"``. + """ + + if not isinstance(case_results, dict): + raise GradingError("case_results must be a dict[str, bool]") + if reward_mode not in {"binary", "case_fraction"}: + raise GradingError( + f"Unknown reward_mode {reward_mode!r}; expected 'binary' or 'case_fraction'" + ) + + def _passed(case: str) -> bool: + return bool(case_results.get(case, False)) + + all_cases = list(dict.fromkeys([*task.FAIL_TO_PASS, *task.PASS_TO_PASS])) + passed_cases = sum(1 for case in all_cases if _passed(case)) + total_cases = len(all_cases) + case_fraction = (passed_cases / total_cases) if total_cases else 0.0 + + resolved = all(_passed(case) for case in task.FAIL_TO_PASS) and all( + _passed(case) for case in task.PASS_TO_PASS + ) + + tests_status = { + "FAIL_TO_PASS": { + case: "PASSED" if _passed(case) else "FAILED" + for case in task.FAIL_TO_PASS + }, + "PASS_TO_PASS": { + case: "PASSED" if _passed(case) else "FAILED" + for case in task.PASS_TO_PASS + }, + } + + reward = 1.0 if resolved else 0.0 + if reward_mode == "case_fraction": + reward = case_fraction + + return GradeResult( + reward=reward, + case_fraction=case_fraction, + resolved=resolved, + patch_applied=patch_applied, + tests_status=tests_status, + instance_id=task.instance_id, + ) + + +__all__ = [ + "GradeResult", + "GradingError", + "grade_from_case_results", +] diff --git a/envs/mini_swe_env/harness.py b/envs/mini_swe_env/harness.py new file mode 100644 index 000000000..595a521d4 --- /dev/null +++ b/envs/mini_swe_env/harness.py @@ -0,0 +1,985 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""SWE harness session and session factory. + +Integrates ``mini_swe_env`` with the ``CLIAgentDriver`` / ``ResourceSession`` +harness infrastructure. Pi runs in the sandbox with its built-in tools +(bash, edit, write, read, grep, find, ls) plus one host-side tool: +``answer``. + +Session lifecycle:: + + factory = SWESessionFactory(agent="pi", config=..., sandbox_backend=..., ...) + session = factory.create(task=swe_gym_task.to_swe_task()) + + # interception_gate mode: + request = await session.next_request() + await session.deliver(request, response_dict) + ... + # OR black_box mode: + session.wait_for_completion(timeout_s=600) + + vr = session.verify(transcript=[]) + print(vr.env_reward) # 1.0 or 0.0 (binary) + session.close() + +**Reward architecture**: The ``answer`` tool is a **host-side tool** +routed through the InterceptionServer's tool routing layer (``/vf/tools``). +When the agent calls ``answer()``, the request goes to the host, which +runs SWE-Gym-native grading (revert test files → apply test_patch → run +explicit FAIL_TO_PASS/PASS_TO_PASS tests), and returns the result to the +agent. +This is the same result ``verify()`` returns — one grading path, no +in-sandbox grading infrastructure. + +This matches SWE-Gym's architecture where ``answer`` is server-side code +on the OpenReward platform. + +**Requires core changes** — see ``CORE_CHANGES.md`` for the +InterceptionServer tool routing, models.json, workdir, and Docker +host-IP changes needed in ``openenv.core``. +""" + +from __future__ import annotations + +import asyncio +import json +import os +import queue as _queue_mod +import logging +import shlex +import time +import uuid +from dataclasses import dataclass, field +from typing import Any, Callable, Literal + +from openenv.core.harness import Message, ResourceSessionFactory, VerifyResult +from openenv.core.harness.agents import get_agent_spec +from openenv.core.harness.agents.cli_driver import ( + CLIAgentDriver, + CLIAgentSession, + build_interception_rollout_url, +) +from openenv.core.harness.agents.interception_server import InterceptionServer +from openenv.core.harness.sandbox import SandboxBackend, SandboxHandle + +from .grading import grade_from_case_results +from .models import SWEGymTask, SWETask, coerce_swe_task, validate_swe_task + + +_log = logging.getLogger(__name__) + +# ── Sandbox filesystem layout (SWE-Gym convention) ───────────────────────── + +HOME = "/home/user" +TESTBED = "/testbed" + +VERIFY_TIMEOUT_S = 300 +SETUP_TIMEOUT_S = 600 +DEFAULT_GIT_CHECKOUT_TIMEOUT_S = 300 + + +def _git_checkout_timeout_s() -> int: + """Read configurable git checkout timeout from environment.""" + for name in ("SWE_GIT_CHECKOUT_TIMEOUT_S", "SWE_LOCAL_GIT_CHECKOUT_TIMEOUT_S"): + value = os.environ.get(name) + if value is None: + continue + try: + timeout_s = int(value) + except ValueError: + _log.warning( + "%s must be an integer; using %ss", + name, + DEFAULT_GIT_CHECKOUT_TIMEOUT_S, + ) + return DEFAULT_GIT_CHECKOUT_TIMEOUT_S + if timeout_s > 0: + return timeout_s + _log.warning( + "%s must be positive; using %ss", + name, + DEFAULT_GIT_CHECKOUT_TIMEOUT_S, + ) + return DEFAULT_GIT_CHECKOUT_TIMEOUT_S + return DEFAULT_GIT_CHECKOUT_TIMEOUT_S + +_ANSWER_TOOL_DEFINITION = { + "type": "function", + "function": { + "name": "answer", + "description": "Submit your final answer for SWE grading.", + "parameters": { + "type": "object", + "properties": {}, + "additionalProperties": False, + }, + }, +} + + +# ── SWE instruction template ────────────────────────────────────────────── + +_SWE_INSTRUCTION_TEMPLATE = """ +Consider the following PR description: +{problem_statement} + + +{hints_block} + + +# Task Instructions + +## Overview +You're a software engineer working on a codebase at {workdir}. +Your task is to fix the issue described in the PR description above +by making changes to the source code (non-test files). + +## Important Boundaries +- MODIFY: Regular source code files in {workdir} +- DO NOT MODIFY: Tests, configuration files (pyproject.toml, setup.cfg, etc.) +- Test edits do not help. Grading may restore evaluation tests before running. +- Plain text does not execute anything. To inspect files, run commands, edit + code, or submit a fix, you must use your available tools. +- The repo environment is already bootstrapped. Do not create a new virtualenv + or reinstall the project unless a command clearly shows it is necessary. +- Do not use `git commit`, `git branch`, or `git push`. Grading only checks the + final working tree state. +- Each bash tool call runs in a fresh shell. If commands depend on shell state, + combine them into one bash invocation; `source` and shell variables do not + persist across separate tool calls. +- Do not claim a file was changed or a test passed unless you actually used a + tool and observed that result. + +## Recommended Workflow +1. Start with the maintainer hints or issue description. If they point to a + likely source file or function, inspect that before broad repo-wide searches. + If the issue mentions an identifier or exact error text, grep for that first. +2. Reproduce the issue with focused commands instead of exhaustive greps over + unrelated tests. +3. Edit the source code to resolve the issue. +4. Verify the fix with targeted commands, then check for obvious regressions. +5. If the `answer` tool is available, call it only after you have actually + changed source code and verified the result. + +{submission_block} +""" + + +def _bool_env(name: str, default: bool) -> bool: + raw = os.environ.get(name) + if raw is None: + return default + return raw.strip().lower() in {"1", "true", "yes", "on"} + + +def _answer_tool_enabled() -> bool: + return _bool_env("SWE_ENABLE_ANSWER_TOOL", True) + + +def _wrap_instruction( + problem_statement: str, + *, + hints_text: str = "", + workdir: str = TESTBED, + answer_tool_enabled: bool = True, +) -> str: + """Wrap a problem statement with SWE-Gym-style task instructions. + + Tells the agent about the workflow, boundaries, and crucially + about the ``answer`` tool for submission. + """ + hints = (hints_text or "").strip() + hints_block = "" + if hints: + hints_block = ( + "\n" + "Additional context from issue triage or maintainers:\n" + f"{hints}\n" + "" + ) + if answer_tool_enabled: + submission_block = ( + "## Submitting Your Answer\n" + "When you've completed your work and verified your fix, call the " + "`answer`\n" + "tool to submit your solution for grading. This runs the test suite and\n" + "returns whether the issue is resolved.\n\n" + "You cannot continue working after submitting — make sure your fix is\n" + "tested before calling `answer`." + ) + else: + submission_block = ( + "## Ending The Run\n" + "There is no `answer` tool in this run.\n" + "Keep working until you have made and checked the best source-code " + "fix you can.\n" + "Your final repo state will be graded automatically when the session " + "ends." + ) + return _SWE_INSTRUCTION_TEMPLATE.format( + problem_statement=problem_statement, + hints_block=hints_block, + workdir=workdir, + submission_block=submission_block, + ) + + +@dataclass +class SWEAgentConfig: + """Minimal config for the CLI agent driver.""" + + base_url: str = "" + api_key: str = "" + model: str = "" + agent_timeout_s: float = 600.0 + sandbox_home: str = HOME + workdir: str = TESTBED + provider: str = "" + thinking: str = "off" + + +@dataclass +class _SWEAgentTask: + """Internal task shape passed to CLIAgentDriver (not SWETask).""" + + instruction: str = "" + setup_shell: str | None = None + upload_files: dict[str, str] = field(default_factory=dict) + metadata: dict[str, Any] = field(default_factory=dict) + + +# ── SWE Session ──────────────────────────────────────────────────────────── + + +class SWESession(CLIAgentSession): + """Per-rollout session with SWE-specific verify and reward logic. + + Extends :class:`CLIAgentSession` with: + - ``verify()`` — returns the reward produced by the host-side + ``answer`` tool, or grades the repo state post-hoc if no answer + was called. + - SWE task metadata. + + **Reward architecture**: The ``answer`` tool is optional. If the + agent calls it, grading runs immediately and returns feedback. + If the agent never calls ``answer()`` (timeout, max turns, natural + exit), ``verify()`` grades the repo state post-hoc — the agent's + edits are evaluated regardless of explicit submission. + """ + + def __init__( + self, + *, + swe_task: SWETask, + verify_timeout_s: int = VERIFY_TIMEOUT_S, + grade_fn: Any | None = None, + **kwargs: Any, + ) -> None: + super().__init__(**kwargs) + self._swe_task = swe_task + self._verify_timeout_s = verify_timeout_s + self._answer_reward: float | None = None # set by host-side answer tool + self._answer_reward_source: str | None = None + self._answer_called = False + self._answer_bridged = False + # Callback for post-hoc grading: (sandbox, swe_task) -> (reward, resolved) + self._grade_fn: Callable[..., tuple[float, bool]] | None = grade_fn + + @property + def swe_task(self) -> SWETask: + return self._swe_task + + @property + def answer_reward(self) -> float | None: + return self._answer_reward + + @property + def answer_reward_source(self) -> str | None: + return self._answer_reward_source + + @property + def answer_called(self) -> bool: + return self._answer_called + + @property + def answer_bridged(self) -> bool: + return self._answer_bridged + + def mark_answer_called(self) -> None: + self._answer_called = True + + def mark_answer_bridged(self) -> None: + self._answer_bridged = True + + def set_answer_reward( + self, + reward: float, + *, + source: str = "host_answer_tool", + ) -> None: + """Called by the host-side answer tool handler to store the reward.""" + self._answer_reward = reward + self._answer_reward_source = source + + def initial_messages(self) -> list[Message]: + """Return the SWE instruction as the initial prompt.""" + return [{"role": "user", "content": self._swe_task.instruction}] + + def verify( + self, + transcript: list[Message], + final_state: Any | None = None, + ) -> VerifyResult: + """Return the reward computed by the host-side ``answer`` tool. + + If the agent called ``answer()`` during the rollout, the + InterceptionServer's tool handler already computed the reward + and stored it via ``set_answer_reward()``. + + If the agent never called ``answer()`` (timeout, crash), + falls back to verify commands (legacy) or defaults to 0.0. + """ + # 1. Primary: reward from host-side answer tool. + if self._answer_reward is not None: + return VerifyResult( + env_reward=self._answer_reward, + done=True, + metrics={ + "instance_id": self._swe_task.instance_id, + "reward_source": self._answer_reward_source + or "host_answer_tool", + "answer_called": self._answer_called, + "answer_bridged": self._answer_bridged, + }, + artifacts={ + "task_id": self._swe_task.task_id, + }, + ) + + # 2. Guardrail: answer was attempted but host-side reward not recorded. + if self._answer_called: + return VerifyResult( + env_reward=0.0, + done=True, + metrics={ + "instance_id": self._swe_task.instance_id, + "reward_source": "answer_called_missing_host_reward", + "answer_called": True, + "answer_bridged": self._answer_bridged, + }, + artifacts={ + "task_id": self._swe_task.task_id, + }, + ) + + # 3. Fallback: run verify commands (legacy tasks with shell commands). + if self._swe_task.verify: + passed = 0 + verify_details: list[dict[str, Any]] = [] + + for cmd in self._swe_task.verify: + t0 = time.time() + try: + r = self.sandbox.exec( + cmd, cwd=TESTBED, timeout=self._verify_timeout_s + ) + detail = { + "cmd": cmd, + "exit_code": r.exit_code, + "stdout_tail": (r.stdout or "")[-2000:], + "stderr_tail": (r.stderr or "")[-2000:], + "duration_s": round(time.time() - t0, 3), + } + if r.exit_code == 0: + passed += 1 + except Exception as exc: + detail = { + "cmd": cmd, + "exit_code": -1, + "error": f"{type(exc).__name__}: {exc}", + "duration_s": round(time.time() - t0, 3), + } + verify_details.append(detail) + + fallback_reward = passed / len(self._swe_task.verify) + return VerifyResult( + env_reward=fallback_reward, + done=True, + metrics={ + "verify_passed": passed, + "verify_total": len(self._swe_task.verify), + "instance_id": self._swe_task.instance_id, + "reward_source": "verify_commands", + "answer_called": False, + "answer_bridged": False, + }, + artifacts={ + "verify_details": verify_details, + "task_id": self._swe_task.task_id, + }, + ) + + # 4. Post-hoc grading: grade the repo state regardless of answer(). + # whether or not it explicitly submitted. + if self._grade_fn is not None: + try: + reward, resolved = self._grade_fn(self.sandbox, self._swe_task) + source = "post_hoc_grade" + return VerifyResult( + env_reward=float(reward), + done=True, + metrics={ + "instance_id": self._swe_task.instance_id, + "reward_source": source, + "resolved": resolved, + "answer_called": False, + "answer_bridged": False, + }, + artifacts={ + "task_id": self._swe_task.task_id, + }, + ) + except Exception: + _log.exception( + "post-hoc grading failed for %s", self._swe_task.instance_id + ) + + # 5. No grading possible (no grade_fn, no verify cmds, no answer). + return VerifyResult( + env_reward=0.0, + done=True, + metrics={ + "instance_id": self._swe_task.instance_id, + "reward_source": "default_no_answer", + "answer_called": False, + "answer_bridged": False, + }, + artifacts={ + "task_id": self._swe_task.task_id, + }, + ) + + +# ── Tool-call parsing (kept for backward compatibility) ──────────────────── + + +def parse_terminal_call(text: str) -> dict[str, Any] | None: + """Parse a terminal tool-call from text. + + Handles multiple formats: + - ``{"command": "..."}`` + - ``{"final_answer": "..."}`` + - ``terminal(command="...")`` (Python-style) + + Returns parsed arguments dict or None if not a terminal call. + """ + text = text.strip() + if not text: + return None + + if text.startswith("{"): + try: + data = json.loads(text) + if isinstance(data, dict) and ("command" in data or "final_answer" in data): + return data + except json.JSONDecodeError: + pass + + if "```" in text: + for block in text.split("```"): + block = block.strip() + if block.startswith("json"): + block = block[4:].strip() + if block.startswith("{"): + try: + data = json.loads(block) + if isinstance(data, dict) and ( + "command" in data or "final_answer" in data + ): + return data + except json.JSONDecodeError: + continue + + for key in ("command", "final_answer"): + prefix = f"terminal({key}=" + if prefix in text: + idx = text.index(prefix) + len(prefix) + rest = text[idx:] + if rest.startswith('"') or rest.startswith("'"): + quote = rest[0] + end = rest.find(quote, 1) + if end > 0: + return {key: rest[1:end]} + + return None + + +# ── SWE Session Factory ─────────────────────────────────────────────────── + + +class SWESessionFactory(ResourceSessionFactory): + """Creates isolated SWE sessions. + + The ``answer`` tool is registered as a host-side tool on the + InterceptionServer (via ``/vf/tools``). No in-sandbox grading + scripts or extensions are deployed. + + Compatible with :func:`build_harness_rollout_func`. + """ + + def __init__( + self, + *, + agent: str = "pi", + config: SWEAgentConfig, + sandbox_backend: SandboxBackend, + mode: Literal["black_box", "interception_gate"] = "black_box", + install_timeout_s: int = 300, + setup_timeout_s: int = SETUP_TIMEOUT_S, + verify_timeout_s: int = VERIFY_TIMEOUT_S, + interception_server: InterceptionServer | None = None, + interception_base_url: str | None = None, + ) -> None: + if mode not in {"black_box", "interception_gate"}: + raise ValueError(f"Unknown mode: {mode!r}") + if mode == "interception_gate": + if interception_server is None: + raise ValueError( + "interception_gate mode requires an InterceptionServer." + ) + if interception_base_url is None: + raise ValueError( + "interception_gate mode requires interception_base_url." + ) + + self._config = config + self._backend = sandbox_backend + self._mode = mode + self._verify_timeout_s = verify_timeout_s + self._interception_server = interception_server + self._interception_base_url = interception_base_url + + self._spec = get_agent_spec(agent) + self._driver = CLIAgentDriver( + spec=self._spec, + sandbox_backend=sandbox_backend, + mode=mode, + install_timeout_s=install_timeout_s, + setup_timeout_s=setup_timeout_s, + interception_server=interception_server, + interception_base_url=interception_base_url, + ) + + def create( + self, + task: Any, + seed: int | None = None, + episode_id: str | None = None, + ) -> SWESession: + """Create one SWE session. + + ``task`` can be an ``SWETask``, ``SWEGymTask``, or a dict. + """ + if isinstance(task, SWEGymTask): + swe_task = task.to_swe_task() + elif isinstance(task, SWETask): + swe_task = task + else: + swe_task = coerce_swe_task(task) + validate_swe_task(swe_task) + + sandbox_timeout = int(self._config.agent_timeout_s) + 600 + sandbox = self._backend.create( + timeout_s=sandbox_timeout, + metadata=( + {"episode_id": episode_id, "instance_id": swe_task.instance_id} + if episode_id + else {"instance_id": swe_task.instance_id} + ), + image=swe_task.sandbox_image, + ) + + try: + if not swe_task.sandbox_image: + self._prepare_repo(sandbox, swe_task) + + self._run_setup(sandbox, swe_task) + + agent_task = self._build_agent_task(swe_task) + self._driver._bootstrap_sandbox(sandbox, agent_task, self._config) + + except Exception as exc: + _log.error("SWESessionFactory.create: bootstrap failed: %r", exc) + sandbox.kill() + raise + + base_url_override: str | None = None + interception_rollout_id: str | None = None + interception_queue: _queue_mod.Queue[str] | None = None + + if self._mode == "interception_gate": + assert self._interception_server is not None + assert self._interception_base_url is not None + rollout_id = episode_id or f"rollout_{uuid.uuid4().hex[:8]}" + interception_rollout_id = rollout_id + interception_queue = self._interception_server.register_rollout(rollout_id) + base_url_override = build_interception_rollout_url( + self._interception_base_url, + rollout_id, + ) + + agent_task = self._build_agent_task(swe_task) + agent_bg = self._driver._start_agent( + sandbox, agent_task, self._config, base_url_override=base_url_override + ) + + session = SWESession( + swe_task=swe_task, + verify_timeout_s=self._verify_timeout_s, + grade_fn=self._grade_answer_submission, + spec=self._spec, + sandbox=sandbox, + task=agent_task, + config=self._config, + base_url_override=base_url_override, + agent_bg_job=agent_bg, + interception_server=self._interception_server, + interception_rollout_id=interception_rollout_id, + interception_queue=interception_queue, + ) + + if self._mode == "interception_gate" and _answer_tool_enabled(): + self._register_answer_tool(session) + + return session + + # ── Bootstrap helpers ────────────────────────────────────────────────── + + def _prepare_repo(self, sandbox: SandboxHandle, task: SWETask) -> None: + """Clone the repo and reset to base_commit.""" + sandbox.exec(f"mkdir -p {TESTBED}", timeout=10) + clone_url = f"https://github.com/{task.repo}.git" + r = sandbox.exec( + f"git clone --quiet {clone_url} {TESTBED}", + timeout=SETUP_TIMEOUT_S, + ) + if r.exit_code != 0: + raise RuntimeError( + f"git clone failed (exit {r.exit_code}): {r.stderr[:500]}" + ) + r = sandbox.exec( + f"git checkout --quiet {task.base_commit}", + cwd=TESTBED, + timeout=_git_checkout_timeout_s(), + ) + if r.exit_code != 0: + raise RuntimeError( + f"git checkout failed (exit {r.exit_code}): {r.stderr[:500]}" + ) + + def _run_setup(self, sandbox: SandboxHandle, task: SWETask) -> None: + """Run task setup commands in the workspace.""" + for cmd in task.setup: + r = sandbox.exec(cmd, cwd=TESTBED, timeout=SETUP_TIMEOUT_S) + if r.exit_code != 0: + raise RuntimeError( + f"Setup command failed (exit {r.exit_code}): " + f"{cmd[:120]}\nstderr: {(r.stderr or '')[:500]}" + ) + + def _build_agent_task(self, swe_task: SWETask) -> _SWEAgentTask: + """Convert SWETask into the shape CLIAgentDriver expects. + + Wraps the raw problem statement with SWE-Gym-style instructions + that tell the agent about the ``answer`` tool. + """ + answer_tool_enabled = _answer_tool_enabled() + return _SWEAgentTask( + instruction=_wrap_instruction( + swe_task.instruction, + hints_text=str((swe_task.metadata or {}).get("hints_text", "") or ""), + workdir=self._config.workdir, + answer_tool_enabled=answer_tool_enabled, + ), + setup_shell=None, + metadata={ + "task_id": swe_task.task_id, + "instance_id": swe_task.instance_id, + "repo": swe_task.repo, + }, + ) + + def _register_answer_tool(self, session: SWESession) -> None: + """Register the host-side ``answer`` tool for one interception rollout.""" + + async def _answer_handler(arguments: dict[str, Any]) -> dict[str, Any]: + del arguments + + session.mark_answer_called() + session.mark_answer_bridged() + + if session.answer_reward is not None: + resolved = session.answer_reward >= 1.0 + return { + "content": [ + { + "type": "text", + "text": f"✅ Resolved: {str(resolved).lower()}", + } + ] + } + + reward, resolved = await asyncio.to_thread( + self._grade_answer_submission, + session.sandbox, + session.swe_task, + ) + session.set_answer_reward(reward, source="host_answer_tool") + return { + "content": [ + { + "type": "text", + "text": f"✅ Resolved: {str(resolved).lower()}", + } + ] + } + + session.register_tool_handler( + "answer", + _answer_handler, + tool_definition=_ANSWER_TOOL_DEFINITION, + ) + + def _grade_answer_submission( + self, + sandbox: SandboxHandle, + swe_task: SWETask, + ) -> tuple[float, bool]: + """Compute answer-tool reward on host and return ``(reward, resolved)``.""" + try: + metadata = swe_task.metadata or {} + required = {"version", "patch", "test_patch", "FAIL_TO_PASS"} + if required.issubset(metadata): + return self._grade_with_swegym_metadata(sandbox, swe_task) + return self._grade_with_verify_commands(sandbox, swe_task) + except Exception: + _log.exception("answer-tool grading failed for %s", swe_task.instance_id) + return 0.0, False + + def _grade_with_swegym_metadata( + self, + sandbox: SandboxHandle, + swe_task: SWETask, + ) -> tuple[float, bool]: + """Grade SWE-Gym tasks directly from FAIL/PASS test-case outcomes.""" + metadata = swe_task.metadata + assert metadata is not None + + gym_task = SWEGymTask( + instance_id=swe_task.instance_id, + repo=swe_task.repo, + base_commit=swe_task.base_commit, + problem_statement=swe_task.instruction, + version=str(metadata["version"]), + patch=str(metadata["patch"]), + test_patch=str(metadata["test_patch"]), + FAIL_TO_PASS=[str(t) for t in metadata["FAIL_TO_PASS"]], + PASS_TO_PASS=[str(t) for t in metadata.get("PASS_TO_PASS", [])], + hints_text=str(metadata.get("hints_text", "")), + created_at=str(metadata.get("created_at", "")), + timeout_s=swe_task.timeout_s, + ) + + touched_files = self._extract_paths_from_test_patch(gym_task.test_patch) + # Also revert any test-like files the agent modified (anti-gaming). + changed_test_like_files = self._list_changed_test_paths(sandbox) + files_to_restore = sorted(set(touched_files) | set(changed_test_like_files)) + self._revert_test_files( + sandbox, + base_commit=swe_task.base_commit, + paths=files_to_restore, + strict=True, + ) + + self._apply_test_patch(sandbox, gym_task.test_patch) + case_results = self._run_swegym_case_tests(sandbox, gym_task) + grade = grade_from_case_results( + gym_task, + case_results, + reward_mode=os.environ.get("SWE_REWARD_MODE", "binary").strip().lower() + or "binary", + ) + + # Best-effort cleanup in case grading was interrupted. + self._revert_test_files( + sandbox, + base_commit=swe_task.base_commit, + paths=files_to_restore, + strict=False, + ) + + return float(grade.reward), bool(grade.resolved) + + def _apply_test_patch(self, sandbox: SandboxHandle, test_patch: str) -> None: + patch_path = f"{HOME}/.openenv_swe_test_patch.diff" + sandbox.write_text(patch_path, test_patch) + result = sandbox.exec( + f"git apply --whitespace=nowarn {shlex.quote(patch_path)}", + cwd=TESTBED, + timeout=30, + ) + if result.exit_code != 0: + raise RuntimeError( + "failed to apply SWE-Gym test_patch: " + f"{(result.stderr or result.stdout or '').strip()}" + ) + + def _run_swegym_case_tests( + self, + sandbox: SandboxHandle, + gym_task: SWEGymTask, + ) -> dict[str, bool]: + cases: list[str] = [] + seen: set[str] = set() + for case in [*gym_task.FAIL_TO_PASS, *gym_task.PASS_TO_PASS]: + if case in seen: + continue + seen.add(case) + cases.append(case) + + results: dict[str, bool] = {} + for case in cases: + cmd = f"python -m pytest -q --maxfail=1 {shlex.quote(case)}" + run = sandbox.exec(cmd, cwd=TESTBED, timeout=self._verify_timeout_s) + results[case] = run.exit_code == 0 + return results + + def _grade_with_verify_commands( + self, + sandbox: SandboxHandle, + swe_task: SWETask, + ) -> tuple[float, bool]: + """Legacy fallback for non-SWE-Gym tasks.""" + if not swe_task.verify: + return 0.0, False + passed = 0 + for cmd in swe_task.verify: + r = sandbox.exec(cmd, cwd=TESTBED, timeout=self._verify_timeout_s) + if r.exit_code == 0: + passed += 1 + reward = passed / len(swe_task.verify) + return reward, reward >= 1.0 + + @staticmethod + def _extract_paths_from_test_patch(test_patch: str) -> list[str]: + paths: list[str] = [] + for line in (test_patch or "").splitlines(): + if not line.startswith("+++ b/"): + continue + path = line[len("+++ b/") :].strip() + if not path or path == "/dev/null": + continue + paths.append(path) + return sorted(set(paths)) + + @staticmethod + def _is_test_like_path(path: str) -> bool: + """Return True if path looks like a test file. + + Used to detect agent-modified test files before grading so they + can be reverted — prevents agents from gaming reward by editing + test expectations. + """ + text = (path or "").strip().strip('"') + if not text: + return False + normalized = text.replace("\\", "/") + basename = normalized.rsplit("/", 1)[-1] + if basename == "conftest.py": + return True + if basename.startswith("test_") or basename.endswith("_test.py"): + return True + parts = normalized.split("/") + return any(part in {"test", "tests", "testing"} for part in parts[:-1]) + + @classmethod + def _list_changed_test_paths( + cls, + sandbox: SandboxHandle, + ) -> list[str]: + """Detect test-like files the agent modified or created. + + Checks both tracked modifications (git diff) and untracked new + files (git ls-files --others). Results are filtered through + ``_is_test_like_path`` to only revert test-related files. + """ + commands = ( + "git diff --name-only HEAD --", + "git ls-files --others --exclude-standard", + ) + paths: set[str] = set() + for cmd in commands: + result = sandbox.exec(cmd, cwd=TESTBED, timeout=10) + if result.exit_code != 0: + continue + for line in (result.stdout or "").splitlines(): + path = line.strip() + if cls._is_test_like_path(path): + paths.add(path) + return sorted(paths) + + def _revert_test_files( + self, + sandbox: SandboxHandle, + *, + base_commit: str, + paths: list[str], + strict: bool, + ) -> None: + if not paths: + return + + failures: list[str] = [] + for path in paths: + has_file = sandbox.exec( + f"git cat-file -e {shlex.quote(f'{base_commit}:{path}')}", + cwd=TESTBED, + timeout=10, + ) + if has_file.exit_code == 0: + cmd = ( + "git checkout --quiet " + f"{shlex.quote(base_commit)} -- {shlex.quote(path)}" + ) + else: + cmd = f"rm -f -- {shlex.quote(path)}" + + result = sandbox.exec(cmd, cwd=TESTBED, timeout=20) + if result.exit_code != 0: + failures.append( + f"{path}: {(result.stderr or result.stdout or '').strip()}" + ) + + if not failures: + return + + msg = "failed to revert test files before/after grading: " + "; ".join(failures) + if strict: + raise RuntimeError(msg) + _log.warning(msg) + + +__all__ = [ + "SWEAgentConfig", + "SWESession", + "SWESessionFactory", + "_wrap_instruction", + "parse_terminal_call", + "HOME", + "TESTBED", +] diff --git a/envs/mini_swe_env/models.py b/envs/mini_swe_env/models.py new file mode 100644 index 000000000..8c36e6fae --- /dev/null +++ b/envs/mini_swe_env/models.py @@ -0,0 +1,251 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Data models for the mini_swe_env. + +Contains: + - ``SWEGymTask`` — frozen dataclass mirroring the SWE-Gym HF dataset + schema (``instance_id``, ``repo``, ``patch``, ``test_patch``, etc.). + - ``SWETask`` — internal task shape shared across environment, harness, + and client layers. ``SWEGymTask.to_swe_task()`` converts between them. + - ``SWERolloutResult`` / ``SWECommandResult`` / ``SWEState`` — Pydantic + models for the server's MCP tool. +""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, field +from typing import Any + +from openenv.core.env_server.types import State +from pydantic import BaseModel, Field + + +DEFAULT_TIMEOUT_S = 1800 +SWEGYM_SOURCE = "swegym" + + +# ── SWEGymTask (matches HF dataset schema) ──────────────────────────────── + + +class SWETaskValidationError(ValueError): + """Raised when a task fails schema validation.""" + + +@dataclass(frozen=True) +class SWEGymTask: + """One SWE-Gym task, mirroring the HuggingFace dataset columns. + + Fields correspond 1:1 to the ``SWE-Gym/SWE-Gym`` dataset: + ``instance_id``, ``repo``, ``base_commit``, ``problem_statement``, + ``version``, ``patch`` (ground-truth), ``test_patch``, + ``FAIL_TO_PASS``, ``PASS_TO_PASS``, ``hints_text``, ``created_at``. + + The ``timeout_s`` field is not in the dataset; it is set by the loader. + """ + + instance_id: str + repo: str + base_commit: str + problem_statement: str + version: str + patch: str + test_patch: str + FAIL_TO_PASS: list[str] + PASS_TO_PASS: list[str] + hints_text: str = "" + created_at: str = "" + timeout_s: int = DEFAULT_TIMEOUT_S + + # ── Derived helpers ──────────────────────────────────────────────── + + @property + def instance_image(self) -> str: + """Docker image name for this task (SWE-Gym convention). + + Uses the ``xingyaoww/`` namespace with ``sweb.eval.x86_64.`` prefix. + Double underscores are replaced with ``_s_`` per SWE-Gym convention + (matching ``env-swe-gym/swegym.py``). + """ + sanitised = self.instance_id.lower().replace("__", "_s_") + return f"xingyaoww/sweb.eval.x86_64.{sanitised}:latest" + + def to_swe_task(self) -> SWETask: + """Convert to the internal ``SWETask`` used by the harness. + + The ``verify`` list is left empty because grading uses + SWE-Gym FAIL_TO_PASS/PASS_TO_PASS case outcomes, not shell commands. + The ``instruction`` is the ``problem_statement``. + """ + return SWETask( + task_id=f"{SWEGYM_SOURCE}::{self.instance_id}", + source=SWEGYM_SOURCE, + instance_id=self.instance_id, + repo=self.repo, + base_commit=self.base_commit, + instruction=self.problem_statement, + setup=[], + verify=[], # grading via SWE-Gym case outcomes, not shell commands + timeout_s=self.timeout_s, + sandbox_image=self.instance_image, + metadata={ + "version": self.version, + "patch": self.patch, + "test_patch": self.test_patch, + "FAIL_TO_PASS": list(self.FAIL_TO_PASS), + "PASS_TO_PASS": list(self.PASS_TO_PASS), + "hints_text": self.hints_text, + "created_at": self.created_at, + }, + ) + + def to_dict(self) -> dict[str, Any]: + """Serialize to a plain dictionary.""" + return asdict(self) + + +# ── SWETask (internal task shape) ────────────────────────────────────────── + + +@dataclass(frozen=True) +class SWETask: + """One SWE task (repo + commit + instruction + verify commands). + + This is the internal task shape shared across the environment, + harness, and client layers. It is backend-agnostic: both + SWE-Gym and any future task sources produce ``SWETask`` instances. + """ + + task_id: str + source: str + instance_id: str + repo: str + base_commit: str + instruction: str + setup: list[str] + verify: list[str] + timeout_s: int = DEFAULT_TIMEOUT_S + sandbox_image: str | None = None + metadata: dict[str, Any] = field(default_factory=dict) + + def to_dict(self) -> dict[str, Any]: + """Serialize to a JSONL-friendly dictionary.""" + return asdict(self) + + +def validate_swe_task(task: SWETask) -> None: + """Validate a ``SWETask`` and raise explicit schema errors.""" + errors: list[str] = [] + + for field_name in ( + "task_id", + "source", + "instance_id", + "repo", + "base_commit", + "instruction", + ): + value = getattr(task, field_name) + if not isinstance(value, str) or not value.strip(): + errors.append(f"{field_name} must be a non-empty string") + + if not isinstance(task.setup, list): + errors.append("setup must be a list[str]") + else: + for idx, command in enumerate(task.setup): + if not isinstance(command, str) or not command.strip(): + errors.append(f"setup[{idx}] must be a non-empty string") + + if not isinstance(task.verify, list): + errors.append("verify must be a list[str]") + + if not isinstance(task.timeout_s, int) or task.timeout_s <= 0: + errors.append("timeout_s must be a positive int") + + if task.sandbox_image is not None and ( + not isinstance(task.sandbox_image, str) or not task.sandbox_image.strip() + ): + errors.append("sandbox_image must be None or a non-empty string") + + if not isinstance(task.metadata, dict): + errors.append("metadata must be a dict") + + if errors: + raise SWETaskValidationError("; ".join(errors)) + + +def coerce_swe_task(value: SWETask | dict[str, Any]) -> SWETask: + """Coerce an input mapping into a validated ``SWETask``.""" + if isinstance(value, SWETask): + validate_swe_task(value) + return value + if not isinstance(value, dict): + raise SWETaskValidationError( + f"Expected SWETask or dict, got {type(value).__name__}" + ) + task = SWETask(**value) + validate_swe_task(task) + return task + + +# ── Pydantic models (server payloads) ───────────────────────────────────── + + +class SWECommandResult(BaseModel): + """Outcome of one shell command in setup or verify.""" + + cmd: str + exit_code: int + stdout: str = "" + stderr: str = "" + duration_s: float = 0.0 + + +class SWERolloutResult(BaseModel): + """Full payload returned from one ``run_swe_rollout`` invocation. + + The trainer (or any client) decodes this from the MCP tool result JSON + and feeds ``reward`` into GRPO. + """ + + # Identifiers + task_id: str = "" + instance_id: str = "" + sandbox_id: str = "" + + # Scalars — binary reward (1.0 resolved, 0.0 not resolved) + reward: float | None = None + resolved: bool | None = None + agent_exit_code: int | None = None + wall_s: float = 0.0 + + # Per-step results + setup_results: list[SWECommandResult] = Field(default_factory=list) + verify_results: list[SWECommandResult] = Field(default_factory=list) + + # Filesystem the agent produced (path -> contents, truncated) + files: dict[str, str] = Field(default_factory=dict) + files_extra: list[str] = Field(default_factory=list) + + # Diagnostic tails + agent_log_tail: str = "" + + # Error surfacing + error: str | None = None + + +class SWEState(State): + """Per-session environment state across calls to one SWEEnvironment instance. + + Each HTTP session gets its own env (``SUPPORTS_CONCURRENT_SESSIONS=True`` + on the server class), so this state is per-session. + """ + + rollouts_completed: int = 0 + last_reward: float | None = None + last_task_id: str | None = None + last_instance_id: str | None = None + last_sandbox_id: str | None = None diff --git a/envs/mini_swe_env/pyproject.toml b/envs/mini_swe_env/pyproject.toml new file mode 100644 index 000000000..b093fd303 --- /dev/null +++ b/envs/mini_swe_env/pyproject.toml @@ -0,0 +1,37 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +[build-system] +requires = ["setuptools>=45", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "openenv-mini-swe-env" +version = "0.1.0" +description = "Mini SWE environment for OpenEnv — SWE-Gym task execution and host-side grading." +requires-python = ">=3.10" +dependencies = [ + # Core OpenEnv (server + MCP). 0.3.0 ships the harness runtime. + "openenv-core[core]>=0.3.0", + "fastapi>=0.104.0", + "uvicorn[standard]>=0.24.0", + "pydantic>=2.0.0", + "fastmcp>=3.3.0", + "requests>=2.25.0", + "aiohttp>=3.13.5", + "datasets>=4.8.0", +] + +[project.optional-dependencies] +dev = ["pytest>=8.0.0", "pytest-asyncio>=0.23.0", "pytest-cov>=4.0.0"] + +[project.scripts] +server = "mini_swe_env.server.app:main" + +[tool.setuptools] +include-package-data = true +packages = ["mini_swe_env", "mini_swe_env.server"] +package-dir = { "mini_swe_env" = ".", "mini_swe_env.server" = "server" } diff --git a/envs/opencode_env/server/__init__.py b/envs/mini_swe_env/server/__init__.py similarity index 81% rename from envs/opencode_env/server/__init__.py rename to envs/mini_swe_env/server/__init__.py index 56363edaa..4422a48c2 100644 --- a/envs/opencode_env/server/__init__.py +++ b/envs/mini_swe_env/server/__init__.py @@ -4,4 +4,4 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -"""Server-side for the deployed opencode_env.""" +"""Mini SWE environment server package.""" diff --git a/envs/mini_swe_env/server/app.py b/envs/mini_swe_env/server/app.py new file mode 100644 index 000000000..1039602ac --- /dev/null +++ b/envs/mini_swe_env/server/app.py @@ -0,0 +1,52 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""FastAPI application for the Mini SWE Environment. + +Exposes the SWEEnvironment over HTTP and WebSocket endpoints, +compatible with MCPToolClient. + +Usage: + # Development: + PYTHONPATH=src:envs uvicorn mini_swe_env.server.app:app --reload --port 8000 + + # Production: + uvicorn mini_swe_env.server.app:app --host 0.0.0.0 --port 8000 +""" + +import os + +try: + from openenv.core.env_server.http_server import create_app + from openenv.core.env_server.mcp_types import CallToolAction, CallToolObservation + + from .swe_environment import SWEEnvironment +except ImportError: # pragma: no cover + from openenv.core.env_server.http_server import create_app + from openenv.core.env_server.mcp_types import CallToolAction, CallToolObservation + from server.swe_environment import SWEEnvironment # type: ignore + + +max_concurrent = int(os.getenv("MAX_CONCURRENT_ENVS", "4")) + +app = create_app( + SWEEnvironment, + CallToolAction, + CallToolObservation, + env_name="mini_swe_env", + max_concurrent_envs=max_concurrent, +) + + +def main() -> None: + """Entry point for ``uv run --project . server`` and direct invocation.""" + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8000) + + +if __name__ == "__main__": + main() diff --git a/envs/mini_swe_env/server/sandbox_mcp_server.py b/envs/mini_swe_env/server/sandbox_mcp_server.py new file mode 100644 index 000000000..2867b89bd --- /dev/null +++ b/envs/mini_swe_env/server/sandbox_mcp_server.py @@ -0,0 +1,425 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Lightweight MCP tool server deployed INSIDE the sandbox. + +This script is written into the sandbox by the SWE environment before the +agent launches. It exposes a single ``terminal`` tool over HTTP (streamable +HTTP MCP transport) that the agent discovers via its MCP config. + +The ``terminal`` tool supports two call shapes: + + terminal(command="git diff HEAD") + Execute a shell command in the workspace and return output. + + terminal(final_answer="I fixed the bug by ...") + Signal that the agent is done. The server runs the pre-configured + verify commands, computes a reward, writes it to the reward file, + and returns the verification outcome. + +Configuration is read from a JSON file whose path is given by the +``SWE_MCP_CONFIG`` environment variable (default: +``/home/user/.swe_mcp_config.json``). + +This file is stdlib-only so it works in bare sandbox images (no pip deps). +""" + +from __future__ import annotations + +import json +import os +import subprocess +import sys +import time +from http.server import BaseHTTPRequestHandler, HTTPServer +from typing import Any + +# ── Config ───────────────────────────────────────────────────────────────── + +DEFAULT_CONFIG_PATH = "/home/user/.swe_mcp_config.json" +DEFAULT_PORT = 8765 +DEFAULT_WORKSPACE = "/home/user/workdir" +DEFAULT_COMMAND_TIMEOUT = 300 +DEFAULT_OUTPUT_LIMIT = 16_000 # chars per command output +REWARD_FILE = "/home/user/logs/verifier/reward.txt" +FINAL_ANSWER_FILE = "/home/user/logs/agent/final_answer.txt" +DONE_MARKER = "/home/user/logs/agent/.done" + +_config: dict[str, Any] = {} +_done = False + + +def load_config() -> dict[str, Any]: + """Load config from the JSON file.""" + path = os.environ.get("SWE_MCP_CONFIG", DEFAULT_CONFIG_PATH) + if os.path.exists(path): + with open(path) as f: + return json.load(f) + return {} + + +def execute_command( + command: str, + workspace: str, + timeout: int, + output_limit: int, +) -> dict[str, Any]: + """Run a shell command and return structured result.""" + t0 = time.time() + try: + proc = subprocess.run( + command, + shell=True, + cwd=workspace, + capture_output=True, + text=True, + timeout=timeout, + ) + return { + "exit_code": proc.returncode, + "stdout": proc.stdout[-output_limit:] if proc.stdout else "", + "stderr": proc.stderr[-output_limit:] if proc.stderr else "", + "duration_s": round(time.time() - t0, 3), + "timed_out": False, + } + except subprocess.TimeoutExpired: + return { + "exit_code": -1, + "stdout": "", + "stderr": f"Command timed out after {timeout}s", + "duration_s": round(time.time() - t0, 3), + "timed_out": True, + } + except Exception as exc: + return { + "exit_code": -1, + "stdout": "", + "stderr": f"{type(exc).__name__}: {exc}", + "duration_s": round(time.time() - t0, 3), + "timed_out": False, + } + + +def run_verify( + verify_commands: list[str], + workspace: str, + timeout: int, + output_limit: int, +) -> tuple[float, list[dict[str, Any]]]: + """Run verify commands and compute reward. + + Returns (reward, results_list). + Reward = passed / total. An explicit reward.txt overrides. + """ + results = [] + passed = 0 + for cmd in verify_commands: + r = execute_command(cmd, workspace, timeout, output_limit) + results.append({"cmd": cmd, **r}) + if r["exit_code"] == 0: + passed += 1 + + # Check for explicit reward override + reward = passed / max(len(verify_commands), 1) + if os.path.exists(REWARD_FILE): + try: + with open(REWARD_FILE) as f: + reward = float(f.read().strip()) + except (ValueError, OSError): + pass + + return reward, results + + +def handle_terminal(arguments: dict[str, Any]) -> dict[str, Any]: + """Core terminal tool logic.""" + global _done + + command = arguments.get("command") + final_answer = arguments.get("final_answer") + + if _done: + return { + "error": "Session is already complete. No further commands accepted.", + "done": True, + } + + if command and final_answer: + return { + "error": "Provide exactly one of 'command' or 'final_answer', not both.", + "done": False, + } + + if not command and not final_answer: + return { + "error": "Provide either 'command' or 'final_answer'.", + "done": False, + } + + workspace = _config.get("workspace", DEFAULT_WORKSPACE) + timeout = _config.get("timeout_per_command_s", DEFAULT_COMMAND_TIMEOUT) + output_limit = _config.get("output_limit", DEFAULT_OUTPUT_LIMIT) + + if command: + result = execute_command(command, workspace, timeout, output_limit) + return { + "output": result["stdout"], + "stderr": result["stderr"], + "exit_code": result["exit_code"], + "timed_out": result["timed_out"], + "done": False, + } + + # final_answer path + _done = True + + # Write final answer + os.makedirs(os.path.dirname(FINAL_ANSWER_FILE), exist_ok=True) + with open(FINAL_ANSWER_FILE, "w") as f: + f.write(final_answer) + + # Write done marker + os.makedirs(os.path.dirname(DONE_MARKER), exist_ok=True) + with open(DONE_MARKER, "w") as f: + f.write("1") + + # Run verify commands + verify_commands = _config.get("verify_commands", []) + if verify_commands: + reward, verify_results = run_verify( + verify_commands, workspace, timeout, output_limit + ) + # Write reward + os.makedirs(os.path.dirname(REWARD_FILE), exist_ok=True) + with open(REWARD_FILE, "w") as f: + f.write(str(reward)) + + return { + "message": "Submission received. Verification complete.", + "reward": reward, + "verify_results": verify_results, + "done": True, + } + + return { + "message": "Submission received. No verify commands configured.", + "reward": None, + "done": True, + } + + +# ── MCP JSON-RPC ────────────────────────────────────────────────────────── + +TERMINAL_TOOL_SCHEMA = { + "name": "terminal", + "description": ( + "Execute a shell command in the repository workspace, or submit " + "your final answer.\n\n" + "Use terminal(command='...') to run any shell command (git, " + "python, cat, grep, etc.).\n\n" + "When you are confident the issue is fixed, call " + "terminal(final_answer='...') to submit. This runs the " + "verification tests and reports the result." + ), + "inputSchema": { + "type": "object", + "properties": { + "command": { + "type": "string", + "description": "Shell command to execute in the workspace.", + }, + "final_answer": { + "type": "string", + "description": ( + "Submit your final answer. Explain what you changed " + "and why. This triggers verification." + ), + }, + }, + }, +} + + +def handle_jsonrpc(request: dict[str, Any]) -> dict[str, Any]: + """Handle one MCP JSON-RPC request.""" + request_id = request.get("id") + method = request.get("method", "") + params = request.get("params", {}) or {} + + if method == "initialize": + return _success( + { + "protocolVersion": "2025-03-26", + "capabilities": {"tools": {"listChanged": False}}, + "serverInfo": {"name": "swe-terminal", "version": "1.0.0"}, + }, + request_id, + ) + + if method == "notifications/initialized": + # Client acknowledgement, no response needed for notifications + return _success({}, request_id) + + if method == "tools/list": + return _success({"tools": [TERMINAL_TOOL_SCHEMA]}, request_id) + + if method == "tools/call": + tool_name = params.get("name", "") + arguments = params.get("arguments", {}) + + if tool_name != "terminal": + return _error(-32601, f"Unknown tool: {tool_name}", request_id) + + result = handle_terminal(arguments) + return _success( + { + "content": [ + {"type": "text", "text": json.dumps(result, indent=2)}, + ], + "isError": "error" in result, + }, + request_id, + ) + + if method == "ping": + return _success({}, request_id) + + return _error(-32601, f"Method not found: {method}", request_id) + + +def _success(result: Any, request_id: Any) -> dict[str, Any]: + return {"jsonrpc": "2.0", "id": request_id, "result": result} + + +def _error(code: int, message: str, request_id: Any) -> dict[str, Any]: + return { + "jsonrpc": "2.0", + "id": request_id, + "error": {"code": code, "message": message}, + } + + +# ── HTTP Server ──────────────────────────────────────────────────────────── + + +class MCPHandler(BaseHTTPRequestHandler): + """Minimal HTTP handler for MCP streamable HTTP transport.""" + + def do_POST(self): + content_length = int(self.headers.get("Content-Length", 0)) + body = self.rfile.read(content_length) + + try: + request = json.loads(body) + except json.JSONDecodeError: + self._send_json( + 400, + _error(-32700, "Parse error", None), + ) + return + + response = handle_jsonrpc(request) + self._send_json(200, response) + + def do_GET(self): + """Health check endpoint.""" + if self.path in ("/health", "/"): + self._send_json(200, {"status": "ok", "tool": "terminal"}) + else: + self.send_error(404) + + def _send_json(self, status: int, data: Any) -> None: + payload = json.dumps(data).encode("utf-8") + self.send_response(status) + self.send_header("Content-Type", "application/json") + self.send_header("Content-Length", str(len(payload))) + self.end_headers() + self.wfile.write(payload) + + def log_message(self, fmt, *args): + """Suppress noisy per-request logs; write to stderr only.""" + sys.stderr.write(f"[swe-mcp] {fmt % args}\n") + + +# ── Stdio transport ──────────────────────────────────────────────────────── + + +def run_stdio() -> None: + """Run as an MCP stdio server (read JSON-RPC from stdin, write to stdout). + + This is the preferred transport for agent MCP discovery (.mcp.json): + the agent launches this script as a subprocess and communicates over + stdin/stdout. Logs go to stderr. + """ + sys.stderr.write("[swe-mcp] terminal tool server (stdio mode)\n") + sys.stderr.flush() + + for raw_line in sys.stdin: + line = raw_line.strip() + if not line: + continue + try: + request = json.loads(line) + except json.JSONDecodeError: + response = _error(-32700, "Parse error", None) + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + continue + + # Notifications (no id) don't need a response. + if request.get("id") is None and request.get("method", "").startswith( + "notifications/" + ): + continue + + response = handle_jsonrpc(request) + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + + +def run_http() -> None: + """Run as an HTTP server (MCP streamable HTTP transport).""" + port = _config.get("port", DEFAULT_PORT) + port_env = os.environ.get("SWE_MCP_PORT") + if port_env: + port = int(port_env) + + server = HTTPServer(("127.0.0.1", port), MCPHandler) + sys.stderr.write(f"[swe-mcp] terminal tool server on http://127.0.0.1:{port}\n") + sys.stderr.flush() + + try: + server.serve_forever() + except KeyboardInterrupt: + pass + finally: + server.server_close() + + +def main() -> None: + """Entry point: load config and start in stdio or HTTP mode.""" + global _config + _config = load_config() + + # Ensure log directories exist (best-effort; may fail outside sandbox). + for d in [ + "/home/user/logs/verifier", + "/home/user/logs/agent", + ]: + try: + os.makedirs(d, exist_ok=True) + except OSError: + pass + + if "--stdio" in sys.argv: + run_stdio() + else: + run_http() + + +if __name__ == "__main__": + main() diff --git a/envs/mini_swe_env/server/swe_environment.py b/envs/mini_swe_env/server/swe_environment.py new file mode 100644 index 000000000..916d6f938 --- /dev/null +++ b/envs/mini_swe_env/server/swe_environment.py @@ -0,0 +1,828 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""SWE environment implementation with SWE-Gym-native grading. + +Single MCP tool ``run_swe_rollout`` with the ``SWEGymTask`` shape: + + - ``instance_id`` — SWE-Gym instance identifier + - ``repo`` — GitHub ``org/repo`` + - ``base_commit`` — commit hash in per-task Docker image + - ``problem_statement`` — agent instruction (problem text) + +**Reward** is binary via SWE-Gym test-case outcomes: + - ``1.0`` if resolved (all FAIL_TO_PASS pass, all PASS_TO_PASS still pass) + - ``0.0`` otherwise + +**Per-task Docker images** from SWE-Gym (``xingyaoww/...``): the repo is +pre-cloned at ``/testbed`` with all dependencies installed. No git clone +or dependency install at runtime. + +The ``terminal`` tool is delivered via an in-sandbox MCP server +(:mod:`sandbox_mcp_server`) started before the agent launches. +""" + +from __future__ import annotations + +import json +import logging +import shlex +import time +from pathlib import Path +from typing import Any, Optional +from uuid import uuid4 + +from fastmcp import FastMCP + +try: + from openenv.core.env_server.mcp_environment import MCPEnvironment + from openenv.core.env_server.types import Action, Observation + + from ..grading import GradeResult, grade_from_case_results + from ..models import ( + SWECommandResult, + SWEGymTask, + SWERolloutResult, + SWEState, + SWETask, + validate_swe_task, + ) + from ..task_loader_swegym import ( + get_instance_image, + validate_swegym_task, + ) +except ImportError: # pragma: no cover + from grading import GradeResult, grade_from_case_results # type: ignore + from models import ( # type: ignore + SWECommandResult, + SWEGymTask, + SWERolloutResult, + SWEState, + SWETask, + validate_swe_task, + ) + from openenv.core.env_server.mcp_environment import MCPEnvironment + from openenv.core.env_server.types import Action, Observation + from task_loader_swegym import get_instance_image, validate_swegym_task # type: ignore + +_log = logging.getLogger(__name__) + +# Long timeout for the single MCP tool (sandbox cold-start + agent run + +# verify can take 10-30 min for real SWE tasks). +_RUN_ROLLOUT_TIMEOUT_S = 2400.0 + +# Sandbox filesystem layout — SWE-Gym convention. +# Per-task images have the repo pre-cloned at /testbed. +TESTBED = "/testbed" +HOME = "/home/user" +MCP_CONFIG_PATH = f"{HOME}/.swe_mcp_config.json" +MCP_SERVER_PATH = f"{HOME}/.swe_mcp_server.py" +MCP_PORT = 8765 +VERIFY_TIMEOUT_S = 300 +SETUP_TIMEOUT_S = 600 + +# Path to the sandbox_mcp_server.py source alongside this module. +_SANDBOX_MCP_SERVER_SOURCE = Path(__file__).parent / "sandbox_mcp_server.py" + +_SUPPORTED_AGENTS = ("pi", "opencode") +_AGENT_LOG_PATHS: dict[str, str] = { + "pi": f"{HOME}/logs/agent/pi.txt", + "opencode": f"{HOME}/logs/agent/opencode.jsonl", +} + + +class SWEEnvironment(MCPEnvironment): + """Per-session SWE environment exposing ``run_swe_rollout`` MCP tool. + + Updated for SWE-Gym: + - Per-task Docker images (no git clone needed). + - Binary reward via SWE-Gym case outcomes. + - Working directory is ``/testbed``. + """ + + SUPPORTS_CONCURRENT_SESSIONS = True + + def __init__(self) -> None: + from openenv.core.harness.agents import get_agent_spec + from openenv.core.harness.agents.cli_driver import CLIAgentSessionFactory + + self._get_agent_spec = get_agent_spec + self._CLIAgentSessionFactory = CLIAgentSessionFactory + + self._state = SWEState(episode_id=str(uuid4())) + + mcp = FastMCP("mini_swe_env") + + @mcp.tool + def run_swe_rollout( + # Task fields (match SWEGymTask / SWETask shape). + instance_id: str = "", + repo: str = "", + base_commit: str = "", + instruction: str = "", + setup: Optional[list[str]] = None, + verify: Optional[list[str]] = None, + timeout_s: int = 1800, + # Agent config. + agent: str = "pi", + base_url: str = "", + api_key: str = "", + model: str = "", + agent_timeout_s: float = 600.0, + # Infrastructure. + sandbox_backend: str = "docker", + sandbox_image: str = "", + task_id: str = "", + task_json: str = "", + ) -> str: + """Run one SWE rollout end-to-end. + + Pass either individual fields (instance_id, repo, ...) or a + complete SWETask/SWEGymTask as ``task_json``. Returns a + JSON-serialized ``SWERolloutResult``. + """ + return self._run_swe_rollout_impl( + instance_id=instance_id, + repo=repo, + base_commit=base_commit, + instruction=instruction, + setup=list(setup or []), + verify=list(verify or []), + timeout_s=timeout_s, + agent=agent, + base_url=base_url, + api_key=api_key, + model=model, + agent_timeout_s=agent_timeout_s, + sandbox_backend=sandbox_backend, + sandbox_image=sandbox_image, + task_id=task_id, + task_json=task_json, + ) + + super().__init__(mcp) + + # ── OpenEnv lifecycle ────────────────────────────────────────────────── + + def reset( + self, + seed: Optional[int] = None, + episode_id: Optional[str] = None, + **_: Any, + ) -> Observation: + self._state = SWEState(episode_id=episode_id or str(uuid4())) + return Observation( + done=False, + reward=None, + metadata={ + "status": "ready", + "message": ( + "mini_swe_env ready. Call run_swe_rollout(...) with an SWE task." + ), + }, + ) + + def _step_impl( + self, + action: Action, + timeout_s: Optional[float] = None, + **_: Any, + ) -> Observation: + return Observation( + done=False, + reward=None, + metadata={ + "error": ( + f"Unknown action type: {type(action).__name__}. " + "Use CallToolAction(name='run_swe_rollout', ...)." + ), + }, + ) + + def step( + self, + action: Action, + timeout_s: Optional[float] = None, + **kwargs: Any, + ) -> Observation: + if timeout_s is None: + timeout_s = _RUN_ROLLOUT_TIMEOUT_S + return super().step(action, timeout_s=timeout_s, **kwargs) + + async def step_async( + self, + action: Action, + timeout_s: Optional[float] = None, + **kwargs: Any, + ) -> Observation: + if timeout_s is None: + timeout_s = _RUN_ROLLOUT_TIMEOUT_S + return await super().step_async(action, timeout_s=timeout_s, **kwargs) + + @property + def state(self) -> Any: + return self._state + + # ── Rollout orchestration ────────────────────────────────────────────── + + def _run_swe_rollout_impl( + self, + *, + instance_id: str, + repo: str, + base_commit: str, + instruction: str, + setup: list[str], + verify: list[str], + timeout_s: int, + agent: str, + base_url: str, + api_key: str, + model: str, + agent_timeout_s: float, + sandbox_backend: str, + sandbox_image: str, + task_id: str, + task_json: str, + ) -> str: + result = SWERolloutResult(task_id=task_id) + t0 = time.time() + + # ── Resolve task ────────────────────────────────────────────── + task = self._resolve_task( + instance_id=instance_id, + repo=repo, + base_commit=base_commit, + instruction=instruction, + setup=setup, + verify=verify, + timeout_s=timeout_s, + task_json=task_json, + task_id=task_id, + ) + if isinstance(task, str): + # Error string + result.error = task + result.wall_s = round(time.time() - t0, 3) + return result.model_dump_json() + + result.task_id = task.task_id + result.instance_id = task.instance_id + + # ── Validate agent + LLM config ─────────────────────────────── + agent = (agent or "pi").strip() + if agent not in _SUPPORTED_AGENTS: + result.error = ( + f"Unsupported agent {agent!r}; supported: {_SUPPORTED_AGENTS}" + ) + result.wall_s = round(time.time() - t0, 3) + return result.model_dump_json() + + if not (base_url and api_key and model): + result.error = "Must provide base_url, api_key, and model." + result.wall_s = round(time.time() - t0, 3) + return result.model_dump_json() + + # ── Create sandbox ──────────────────────────────────────────── + sandbox = None + session = None + try: + # Use per-task image if available (SWE-Gym convention). + image = sandbox_image or task.sandbox_image + backend = self._create_backend(sandbox_backend, image) + sandbox = backend.create( + timeout_s=int(agent_timeout_s) + 600, + ) + result.sandbox_id = sandbox.sandbox_id + + # ── Prepare repo (only if not using a per-task image) ───── + if not image: + self._prepare_repo(sandbox, task) + + # ── Run setup commands ──────────────────────────────────── + for cmd in task.setup: + cr = self._exec_command(sandbox, cmd, cwd=TESTBED) + result.setup_results.append(cr) + if cr.exit_code != 0: + result.error = f"Setup failed (exit {cr.exit_code}): {cmd[:120]}" + break + + if result.error is not None: + result.wall_s = round(time.time() - t0, 3) + return result.model_dump_json() + + # ── Start in-sandbox MCP server ─────────────────────────── + self._deploy_mcp_server(sandbox, task) + + # ── Launch agent ────────────────────────────────────────── + spec = self._get_agent_spec(agent) + config = self._build_agent_config( + agent=agent, + base_url=base_url, + api_key=api_key, + model=model, + agent_timeout_s=agent_timeout_s, + ) + rollout_task = self._build_agent_task(task) + + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + driver = CLIAgentDriver( + spec=spec, + sandbox_backend=backend, + mode="black_box", + ) + driver._bootstrap_sandbox(sandbox, rollout_task, config) + agent_bg = driver._start_agent(sandbox, rollout_task, config) + + from openenv.core.harness.agents.cli_driver import CLIAgentSession + + session = CLIAgentSession( + spec=spec, + sandbox=sandbox, + task=rollout_task, + config=config, + agent_bg_job=agent_bg, + ) + + # ── Wait for agent ──────────────────────────────────────── + try: + result.agent_exit_code = session.wait_for_completion( + timeout_s=agent_timeout_s + ) + except TimeoutError as exc: + result.error = f"Agent timeout: {exc}" + + # ── Grade submission ────────────────────────────────────── + grade_result = self._grade_submission(sandbox, task) + if grade_result is not None: + result.reward = grade_result.reward + result.resolved = grade_result.resolved + else: + # Fallback: legacy verify commands + result.reward, result.resolved = self._legacy_verify( + sandbox, task + ) + + # ── Collect artifacts ────────────────────────────────────── + result.files, result.files_extra = self._collect_files(sandbox) + result.agent_log_tail = self._collect_agent_log(sandbox, session, agent) + + except Exception as exc: # noqa: BLE001 + result.error = f"{type(exc).__name__}: {exc}" + if sandbox is not None: + result.agent_log_tail = self._safe_read( + sandbox, _AGENT_LOG_PATHS.get(agent, "") + )[-2000:] + finally: + if session is not None: + try: + session.close() + except Exception: + pass + elif sandbox is not None: + try: + sandbox.kill() + except Exception: + pass + + result.wall_s = round(time.time() - t0, 3) + + # ── Update state ────────────────────────────────────────────── + self._state.rollouts_completed += 1 + self._state.last_reward = result.reward + self._state.last_task_id = result.task_id or None + self._state.last_instance_id = result.instance_id or None + self._state.last_sandbox_id = result.sandbox_id or None + + return result.model_dump_json() + + # ── Task resolution ──────────────────────────────────────────────────── + + def _resolve_task( + self, + *, + instance_id: str, + repo: str, + base_commit: str, + instruction: str, + setup: list[str], + verify: list[str], + timeout_s: int, + task_json: str, + task_id: str, + ) -> SWETask | str: + """Build an SWETask from the provided arguments. + + Returns the task on success, or an error string on failure. + """ + if task_json: + try: + raw = json.loads(task_json) + # Try SWEGymTask first — it has richer metadata. + if "problem_statement" in raw and "patch" in raw: + gym_task = SWEGymTask(**raw) + validate_swegym_task(gym_task) + return gym_task.to_swe_task() + # Fall back to SWETask. + task = SWETask(**raw) + validate_swe_task(task) + return task + except Exception as exc: + return f"Invalid task_json: {exc}" + + if not instruction: + return "instruction is required" + if not repo: + return "repo is required" + if not base_commit: + return "base_commit is required" + if not instance_id: + instance_id = f"manual::{repo}::{base_commit[:12]}" + + # Derive per-task image. + image = get_instance_image(instance_id) if instance_id else None + + try: + task = SWETask( + task_id=task_id or f"swegym::{instance_id}", + source="swegym", + instance_id=instance_id, + repo=repo, + base_commit=base_commit, + instruction=instruction, + setup=setup, + verify=verify, + timeout_s=timeout_s, + sandbox_image=image, + ) + validate_swe_task(task) + return task + except Exception as exc: + return f"Task validation failed: {exc}" + + # ── Grading ──────────────────────────────────────────────────────────── + + def _grade_submission( + self, + sandbox: Any, + task: SWETask, + ) -> GradeResult | None: + """Run SWE-Gym-native grading when metadata includes test lists.""" + metadata = task.metadata + if not metadata: + return None + + required_keys = {"patch", "test_patch", "FAIL_TO_PASS", "version"} + if not required_keys.issubset(metadata.keys()): + return None + + try: + gym_task = SWEGymTask( + instance_id=task.instance_id, + repo=task.repo, + base_commit=task.base_commit, + problem_statement=task.instruction, + version=str(metadata["version"]), + patch=str(metadata["patch"]), + test_patch=str(metadata["test_patch"]), + FAIL_TO_PASS=list(metadata["FAIL_TO_PASS"]), + PASS_TO_PASS=list(metadata.get("PASS_TO_PASS", [])), + hints_text=str(metadata.get("hints_text", "")), + created_at=str(metadata.get("created_at", "")), + timeout_s=task.timeout_s, + ) + except Exception as exc: + _log.warning("Could not reconstruct SWEGymTask for grading: %s", exc) + return None + + touched_files = self._extract_paths_from_test_patch(gym_task.test_patch) + try: + self._revert_test_files( + sandbox, + base_commit=task.base_commit, + paths=touched_files, + strict=True, + ) + self._apply_test_patch(sandbox, gym_task.test_patch) + case_results = self._run_swegym_case_tests(sandbox, gym_task) + return grade_from_case_results(gym_task, case_results) + except Exception as exc: + _log.warning("SWE-Gym grading failed, falling back: %s", exc) + return None + finally: + self._revert_test_files( + sandbox, + base_commit=task.base_commit, + paths=touched_files, + strict=False, + ) + + def _apply_test_patch(self, sandbox: Any, test_patch: str) -> None: + patch_path = f"{HOME}/.openenv_swe_test_patch.diff" + sandbox.write_text(patch_path, test_patch) + r = sandbox.exec( + f"git apply --whitespace=nowarn {shlex.quote(patch_path)}", + cwd=TESTBED, + timeout=30, + ) + if r.exit_code != 0: + raise RuntimeError((r.stderr or r.stdout or "").strip()) + + def _run_swegym_case_tests( + self, + sandbox: Any, + task: SWEGymTask, + ) -> dict[str, bool]: + cases: list[str] = [] + seen: set[str] = set() + for case in [*task.FAIL_TO_PASS, *task.PASS_TO_PASS]: + if case in seen: + continue + seen.add(case) + cases.append(case) + + results: dict[str, bool] = {} + for case in cases: + r = sandbox.exec( + f"python -m pytest -q --maxfail=1 {shlex.quote(case)}", + cwd=TESTBED, + timeout=VERIFY_TIMEOUT_S, + ) + results[case] = r.exit_code == 0 + return results + + @staticmethod + def _extract_paths_from_test_patch(test_patch: str) -> list[str]: + paths: list[str] = [] + for line in (test_patch or "").splitlines(): + if not line.startswith("+++ b/"): + continue + path = line[len("+++ b/") :].strip() + if not path or path == "/dev/null": + continue + paths.append(path) + return sorted(set(paths)) + + def _revert_test_files( + self, + sandbox: Any, + *, + base_commit: str, + paths: list[str], + strict: bool, + ) -> None: + if not paths: + return + + failures: list[str] = [] + for path in paths: + has_file = sandbox.exec( + f"git cat-file -e {shlex.quote(f'{base_commit}:{path}')}", + cwd=TESTBED, + timeout=10, + ) + if has_file.exit_code == 0: + cmd = ( + "git checkout --quiet " + f"{shlex.quote(base_commit)} -- {shlex.quote(path)}" + ) + else: + cmd = f"rm -f -- {shlex.quote(path)}" + result = sandbox.exec(cmd, cwd=TESTBED, timeout=20) + if result.exit_code != 0: + failures.append( + f"{path}: {(result.stderr or result.stdout or '').strip()}" + ) + + if failures and strict: + raise RuntimeError("; ".join(failures)) + + def _legacy_verify( + self, + sandbox: Any, + task: SWETask, + ) -> tuple[float | None, bool | None]: + """Legacy verify: run verify commands, compute pass ratio.""" + verify_passed = 0 + for cmd in task.verify: + cr = self._exec_command(sandbox, cmd, cwd=TESTBED) + if cr.exit_code == 0: + verify_passed += 1 + + if task.verify: + ratio = verify_passed / len(task.verify) + return ratio, ratio == 1.0 + + return None, None + + # ── Sandbox helpers ──────────────────────────────────────────────────── + + def _create_backend(self, backend_name: str, image: str | None) -> Any: + """Create a sandbox backend by name.""" + from openenv.core.harness.sandbox import create_sandbox_backend + + kwargs: dict[str, Any] = {} + if image: + kwargs["image"] = image + return create_sandbox_backend(backend_name, **kwargs) + + def _prepare_repo(self, sandbox: Any, task: SWETask) -> None: + """Clone the repo and reset to base_commit in the sandbox. + + Only used when there is no per-task Docker image. SWE-Gym + per-task images already have the repo at ``/testbed``. + """ + sandbox.exec(f"mkdir -p {TESTBED}", timeout=10) + + clone_url = f"https://github.com/{task.repo}.git" + r = sandbox.exec( + f"git clone --quiet {clone_url} {TESTBED}", + timeout=SETUP_TIMEOUT_S, + ) + if r.exit_code != 0: + raise RuntimeError( + f"git clone failed (exit {r.exit_code}): {r.stderr[:500]}" + ) + + r = sandbox.exec( + f"git checkout --quiet {task.base_commit}", + cwd=TESTBED, + timeout=60, + ) + if r.exit_code != 0: + raise RuntimeError( + f"git checkout failed (exit {r.exit_code}): {r.stderr[:500]}" + ) + + def _deploy_mcp_server(self, sandbox: Any, task: SWETask) -> None: + """Write the MCP server script and config into the sandbox, then start it.""" + mcp_source = _SANDBOX_MCP_SERVER_SOURCE.read_text() + sandbox.write_text(MCP_SERVER_PATH, mcp_source) + + mcp_config = json.dumps( + { + "workspace": TESTBED, + "verify_commands": list(task.verify), + "timeout_per_command_s": VERIFY_TIMEOUT_S, + "output_limit": 16_000, + "port": MCP_PORT, + }, + indent=2, + ) + sandbox.write_text(MCP_CONFIG_PATH, mcp_config) + + sandbox.exec( + f"mkdir -p {HOME}/logs/verifier {HOME}/logs/agent", + timeout=10, + ) + + sandbox.start_bg( + f"python3 {MCP_SERVER_PATH}", + envs={"SWE_MCP_CONFIG": MCP_CONFIG_PATH}, + ) + + for attempt in range(10): + r = sandbox.exec( + f"curl -sf http://127.0.0.1:{MCP_PORT}/health 2>/dev/null || echo FAIL", + timeout=5, + ) + if "FAIL" not in (r.stdout or ""): + return + time.sleep(0.5) + + raise RuntimeError("In-sandbox MCP server did not start within 5s") + + def _build_agent_config( + self, + *, + agent: str, + base_url: str, + api_key: str, + model: str, + agent_timeout_s: float, + ) -> Any: + """Build the agent-specific config dataclass.""" + from dataclasses import dataclass + + @dataclass + class _AgentConfig: + base_url: str = base_url + api_key: str = api_key + model: str = model + agent_timeout_s: float = agent_timeout_s + sandbox_home: str = HOME + provider: str = "" + thinking: str = "off" + + config = _AgentConfig() + + if agent == "pi": + config.provider = self._infer_provider(base_url) + + return config + + def _build_agent_task(self, task: SWETask) -> Any: + """Build a task object compatible with CLIAgentDriver.""" + from dataclasses import dataclass, field as dc_field + + @dataclass + class _AgentTask: + instruction: str = task.instruction + setup_shell: str | None = None + upload_files: dict[str, str] = dc_field(default_factory=dict) + metadata: dict[str, Any] = dc_field(default_factory=dict) + + return _AgentTask( + metadata={ + "task_id": task.task_id, + "instance_id": task.instance_id, + "repo": task.repo, + }, + ) + + @staticmethod + def _infer_provider(base_url: str) -> str: + url = (base_url or "").lower() + if "router.huggingface.co" in url: + return "huggingface" + if "anthropic" in url: + return "anthropic" + if "googleapis.com" in url or "generativelanguage" in url: + return "gemini" + return "openai" + + def _exec_command( + self, sandbox: Any, cmd: str, cwd: str | None = None + ) -> SWECommandResult: + """Execute a command and return a structured result.""" + t = time.time() + try: + kwargs: dict[str, Any] = {"timeout": VERIFY_TIMEOUT_S} + if cwd: + kwargs["cwd"] = cwd + r = sandbox.exec(cmd, **kwargs) + return SWECommandResult( + cmd=cmd, + exit_code=int(r.exit_code), + stdout=(r.stdout or "")[-4000:], + stderr=(r.stderr or "")[-4000:], + duration_s=round(time.time() - t, 3), + ) + except Exception as exc: # noqa: BLE001 + return SWECommandResult( + cmd=cmd, + exit_code=-1, + stderr=f"{type(exc).__name__}: {exc}", + duration_s=round(time.time() - t, 3), + ) + + def _collect_files(self, sandbox: Any) -> tuple[dict[str, str], list[str]]: + """Collect modified files from the workspace.""" + listing = sandbox.exec( + f"cd {TESTBED} && git diff --name-only HEAD 2>/dev/null | head -32", + timeout=10, + ) + files: dict[str, str] = {} + extras: list[str] = [] + for line in (listing.stdout or "").splitlines(): + rel_path = line.strip() + if not rel_path: + continue + full_path = f"{TESTBED}/{rel_path}" + try: + content = sandbox.read_text(full_path) + if len(content) <= 16_000: + files[rel_path] = content + else: + files[rel_path] = content[:16_000] + "\n... [truncated]" + except Exception: + extras.append(rel_path) + return files, extras + + def _collect_agent_log(self, sandbox: Any, session: Any, agent: str) -> str: + """Collect agent log tail.""" + if session is not None and hasattr(session, "collect_artifacts"): + try: + artifacts = session.collect_artifacts() + if isinstance(artifacts, dict) and "agent_log" in artifacts: + val = artifacts["agent_log"] + if isinstance(val, str): + return val[-4000:] + return json.dumps(val, default=str)[-4000:] + except Exception: + pass + path = _AGENT_LOG_PATHS.get(agent, "") + return self._safe_read(sandbox, path)[-4000:] + + @staticmethod + def _safe_read(sandbox: Any, path: str) -> str: + if not path: + return "" + try: + return sandbox.read_text(path) or "" + except Exception: + return "" diff --git a/envs/mini_swe_env/task_loader_swegym.py b/envs/mini_swe_env/task_loader_swegym.py new file mode 100644 index 000000000..6f2e1631e --- /dev/null +++ b/envs/mini_swe_env/task_loader_swegym.py @@ -0,0 +1,243 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""SWE-Gym task loader. + +Loads tasks from the HuggingFace ``SWE-Gym/SWE-Gym`` and +``SWE-Gym/SWE-Gym-Lite`` datasets and converts them to +:class:`SWEGymTask` instances. + +Usage:: + + from mini_swe_env.task_loader_swegym import load_swegym_tasks + + # Full dataset (2,438 tasks) + tasks = load_swegym_tasks("full") + + # Lite subset (230 tasks) + tasks = load_swegym_tasks("lite") +""" + +from __future__ import annotations + +import json +import logging +from typing import Any, Sequence + +from .models import DEFAULT_TIMEOUT_S, SWEGymTask, SWETask + +_log = logging.getLogger(__name__) + +# HuggingFace dataset identifiers. +_HF_DATASETS: dict[str, str] = { + "full": "SWE-Gym/SWE-Gym", + "lite": "SWE-Gym/SWE-Gym-Lite", +} + +# All SWE-Gym datasets use the "train" split. +_HF_SPLIT = "train" + + +class SWEGymLoadError(ValueError): + """Raised when a SWE-Gym dataset cannot be loaded or a row is invalid.""" + + +# ── Public API ───────────────────────────────────────────────────────────── + + +def load_swegym_tasks( + variant: str = "lite", + *, + timeout_s: int = DEFAULT_TIMEOUT_S, +) -> list[SWEGymTask]: + """Load SWE-Gym tasks from HuggingFace. + + Args: + variant: ``"lite"`` (230 tasks) or ``"full"`` (2,438 tasks). + timeout_s: Per-task timeout override. + + Returns: + List of validated :class:`SWEGymTask` instances. + + Raises: + SWEGymLoadError: If the variant is unknown or the dataset fails + to load. + """ + dataset_name = _HF_DATASETS.get(variant) + if dataset_name is None: + raise SWEGymLoadError( + f"Unknown variant {variant!r}; choose from {sorted(_HF_DATASETS)}" + ) + + try: + from datasets import load_dataset + except ImportError as exc: + raise SWEGymLoadError( + "The 'datasets' package is required to load SWE-Gym tasks. " + "Install it with: pip install datasets" + ) from exc + + _log.info("Loading SWE-Gym dataset %s (split=%s) ...", dataset_name, _HF_SPLIT) + + try: + ds = load_dataset(dataset_name, split=_HF_SPLIT) + except Exception as exc: + raise SWEGymLoadError( + f"Failed to load HuggingFace dataset {dataset_name!r}: {exc}" + ) from exc + + tasks: list[SWEGymTask] = [] + for idx, row in enumerate(ds): + try: + task = _row_to_swegym_task(row, timeout_s=timeout_s) + validate_swegym_task(task) + tasks.append(task) + except Exception as exc: + _log.warning( + "Skipping row %d (%s): %s", + idx, + row.get("instance_id", "?"), + exc, + ) + + _log.info("Loaded %d SWE-Gym tasks from %s", len(tasks), dataset_name) + return tasks + + +def load_swegym_tasks_from_dicts( + rows: Sequence[dict[str, Any]], + *, + timeout_s: int = DEFAULT_TIMEOUT_S, +) -> list[SWEGymTask]: + """Convert raw dicts (e.g. from JSONL) to :class:`SWEGymTask` instances. + + Args: + rows: Iterable of dicts matching the SWE-Gym HF schema. + timeout_s: Per-task timeout override. + + Returns: + List of validated :class:`SWEGymTask` instances. + """ + tasks: list[SWEGymTask] = [] + for row in rows: + task = _row_to_swegym_task(row, timeout_s=timeout_s) + validate_swegym_task(task) + tasks.append(task) + return tasks + + +def swegym_task_to_swe_task(task: SWEGymTask) -> SWETask: + """Convert a :class:`SWEGymTask` to the internal :class:`SWETask`. + + Convenience wrapper around ``task.to_swe_task()``. + """ + return task.to_swe_task() + + +def validate_swegym_task(task: SWEGymTask) -> None: + """Validate a :class:`SWEGymTask` and raise on errors.""" + errors: list[str] = [] + + for field_name in ("instance_id", "repo", "base_commit", "problem_statement"): + value = getattr(task, field_name) + if not isinstance(value, str) or not value.strip(): + errors.append(f"{field_name} must be a non-empty string") + + if not isinstance(task.version, str): + errors.append("version must be a string") + + if not isinstance(task.patch, str) or not task.patch.strip(): + errors.append("patch (ground truth) must be a non-empty string") + + if not isinstance(task.test_patch, str) or not task.test_patch.strip(): + errors.append("test_patch must be a non-empty string") + + if not isinstance(task.FAIL_TO_PASS, list) or not task.FAIL_TO_PASS: + errors.append("FAIL_TO_PASS must be a non-empty list") + + if not isinstance(task.PASS_TO_PASS, list): + errors.append("PASS_TO_PASS must be a list") + + if not isinstance(task.timeout_s, int) or task.timeout_s <= 0: + errors.append("timeout_s must be a positive int") + + if errors: + raise SWEGymLoadError( + f"Invalid SWEGymTask {task.instance_id!r}: {'; '.join(errors)}" + ) + + +def get_instance_image(instance_id: str) -> str: + """Derive the per-task Docker image name from an instance id. + + SWE-Gym convention: + ``xingyaoww/sweb.eval.x86_64.:latest`` + where ``__`` in the instance id is replaced with ``_1776_``. + """ + sanitised = instance_id.lower().replace("__", "_s_") + return f"xingyaoww/sweb.eval.x86_64.{sanitised}:latest" + + +# ── Internal helpers ─────────────────────────────────────────────────────── + + +def _coerce_string_list(value: Any) -> list[str]: + """Coerce a value to ``list[str]``. + + Handles: ``list``, JSON-encoded string, ``None``. + """ + if value is None: + return [] + if isinstance(value, list): + return [str(item) for item in value if str(item).strip()] + if isinstance(value, str): + stripped = value.strip() + if not stripped: + return [] + try: + parsed = json.loads(stripped) + except json.JSONDecodeError: + return [stripped] + if isinstance(parsed, list): + return [str(item) for item in parsed if str(item).strip()] + return [str(parsed)] + return [str(value)] + + +def _row_to_swegym_task( + row: dict[str, Any], + *, + timeout_s: int = DEFAULT_TIMEOUT_S, +) -> SWEGymTask: + """Convert one HF dataset row to an :class:`SWEGymTask`.""" + instance_id = row.get("instance_id", "") + if not instance_id: + raise SWEGymLoadError("Row missing required field 'instance_id'") + + return SWEGymTask( + instance_id=str(instance_id), + repo=str(row.get("repo", "")), + base_commit=str(row.get("base_commit", "")), + problem_statement=str(row.get("problem_statement", "")), + version=str(row.get("version", "")), + patch=str(row.get("patch", "")), + test_patch=str(row.get("test_patch", "")), + FAIL_TO_PASS=_coerce_string_list(row.get("FAIL_TO_PASS")), + PASS_TO_PASS=_coerce_string_list(row.get("PASS_TO_PASS")), + hints_text=str(row.get("hints_text", "") or ""), + created_at=str(row.get("created_at", "") or ""), + timeout_s=timeout_s, + ) + + +__all__ = [ + "SWEGymLoadError", + "get_instance_image", + "load_swegym_tasks", + "load_swegym_tasks_from_dicts", + "swegym_task_to_swe_task", + "validate_swegym_task", +] diff --git a/envs/mini_swe_env/uv.lock b/envs/mini_swe_env/uv.lock new file mode 100644 index 000000000..efbdfbd75 --- /dev/null +++ b/envs/mini_swe_env/uv.lock @@ -0,0 +1,4139 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Used in-process to drive one rollout - inside an E2B sandbox. See ``harness.py``. - -2. **Deployable env** — :class:`OpenCodeEnv` (MCP client) talks to the - FastAPI server at ``server/app.py`` over HTTP. Use this when the - sandbox + agent live behind an HTTP boundary (e.g. an HF Space). - See ``client.py`` and ``server/``. -""" - -from openenv.core.env_server.mcp_types import CallToolAction, ListToolsAction - -from .client import OpenCodeEnv -from .config import OpenCodeConfig, Provider -from .harness import OpenCodeSession, OpenCodeSessionFactory -from .models import ( - CommandResult, - OpenCodeState, - RolloutResult, - RolloutTurn, -) -from .sandbox import E2BSandboxBackend, SandboxBackend, SandboxHandle -from .task import OpenCodeTask - -__all__ = [ - # Deployed-env client - "OpenCodeEnv", - "CallToolAction", - "ListToolsAction", - # HTTP API models - "CommandResult", - "OpenCodeState", - "RolloutResult", - "RolloutTurn", - # Harness primitive - "OpenCodeConfig", - "OpenCodeSession", - "OpenCodeSessionFactory", - "OpenCodeTask", - "Provider", - # Sandbox backend - "E2BSandboxBackend", - "SandboxBackend", - "SandboxHandle", -] diff --git a/envs/opencode_env/harness.py b/envs/opencode_env/harness.py deleted file mode 100644 index da4410dd4..000000000 --- a/envs/opencode_env/harness.py +++ /dev/null @@ -1,532 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD-style license found in the -# LICENSE file in the root directory of this source tree. - -"""OpenCode session factory + session implementation. - -Implements the :class:`ResourceSessionFactory` / :class:`ResourceSession` -contracts from ``openenv.core.harness`` (PR #471). The session wraps one -sandbox running the ``opencode`` CLI agent. - -Two operating modes: - - - ``mode="black_box"`` — opencode talks directly to ``config.base_url``. - No proxy, no logprob capture. Use for smoke tests / SFT / eval. - - ``mode="transparent_proxy"`` (default) — an in-sandbox FastAPI proxy - sits between opencode and the upstream LLM. It injects ``logprobs=true`` - on every request and writes per-turn ``(messages, completion_tokens, - per_token_logps)`` to ``proxy_trace.jsonl`` for GRPO consumption. - -Single driver path: opencode is started as a background subprocess via -``opencode run --format json --dangerously-skip-permissions ...`` and we -poll its exit code. The previous ``opencode serve`` driver was removed — -opencode CLI is the only path now. -""" - -from __future__ import annotations - -import json -import shlex -from pathlib import Path -from typing import Any, Callable, Literal - -from openenv.core.env_server.mcp_types import Tool -from openenv.core.harness import ( - Message, - ResourceSession, - ResourceSessionFactory, - ToolResult, - VerifyResult, -) - -from .config import OpenCodeConfig -from .opencode_runtime import ( - agent_log_path, - build_env_vars, - build_install_cmd, - build_opencode_json, - build_run_cmd, - instruction_path, - opencode_config_path, - system_prompt_path, -) -from .sandbox.base import BgJob, SandboxBackend, SandboxHandle -from .task import OpenCodeTask - - -# Inside-sandbox proxy paths (Mode B). -_PROXY_PORT = 7000 -_PROXY_TRACE_PATH = "/home/user/logs/agent/proxy_trace.jsonl" -_PROXY_LOG_PATH = "/home/user/logs/agent/proxy.log" - -# Where the proxy source lives on disk (in this repo). Uploaded into the -# sandbox at /home/user/proxy/interception.py before each rollout, unless -# the sandbox was created from a template that already has it baked in. -_PROXY_SOURCE_PATH = Path(__file__).parent / "sandbox" / "interception.py" - - -Verifier = Callable[[SandboxHandle, OpenCodeTask], VerifyResult] - - -class OpenCodeSession(ResourceSession): - """One live OpenCode rollout inside a sandbox. - - The session is created already-running: :meth:`OpenCodeSessionFactory.create` - calls :meth:`start_agent` before returning. Typical usage:: - - session = factory.create(task) - session.wait_for_completion() - result = session.verify([]) - session.close() - """ - - def __init__( - self, - *, - sandbox: SandboxHandle, - config: OpenCodeConfig, - task: OpenCodeTask, - verifier: Verifier | None = None, - base_url_override: str | None = None, - proxy_trace_path: str | None = None, - proxy_bg_job: BgJob | None = None, - ) -> None: - self.sandbox = sandbox - self.config = config - self.task = task - self._verifier = verifier - self._base_url_override = base_url_override - self._bg_job: BgJob | None = None - self._proxy_trace_path = proxy_trace_path - self._proxy_bg_job = proxy_bg_job - - # ------------------------------------------------------------------ - # ResourceSession contract (PR #471) - # ------------------------------------------------------------------ - def initial_messages(self) -> list[Message]: - return [{"role": "user", "content": self.task.instruction}] - - def list_tools(self) -> list[Tool]: - # OpenCode owns its own tool loop — none are exposed to the harness. - return [] - - def call_tool(self, name: str, arguments: dict[str, Any]) -> ToolResult: - return ToolResult( - error=( - "OpenCodeSession does not expose external tool calls; the " - "CLI agent owns its own tool loop." - ) - ) - - def verify( - self, - transcript: list[Message], - final_state: Any | None = None, - ) -> VerifyResult: - if self._verifier is None: - return VerifyResult(env_reward=None, done=True) - return self._verifier(self.sandbox, self.task) - - def close(self) -> None: - if self._bg_job is not None: - try: - self._bg_job.kill() - except Exception: - pass - self._bg_job = None - if self._proxy_bg_job is not None: - try: - self._proxy_bg_job.kill() - except Exception: - pass - self._proxy_bg_job = None - self.sandbox.kill() - - # ------------------------------------------------------------------ - # OpenCode-specific session API - # ------------------------------------------------------------------ - def start_agent(self) -> None: - """Launch ``opencode run`` as a background subprocess in the sandbox.""" - if self._bg_job is not None: - return - cmd = build_run_cmd(self.config) - envs = build_env_vars(self.config, base_url_override=self._base_url_override) - self._bg_job = self.sandbox.start_bg(cmd, envs=envs) - - def wait_for_completion(self, timeout_s: float | None = None) -> int: - """Block until the agent exits, returning its exit code.""" - budget = timeout_s if timeout_s is not None else self.config.agent_timeout_s - if self._bg_job is None: - raise RuntimeError("Agent not started; call start_agent() first.") - return self._bg_job.wait(timeout=budget) - - def fetch_trace(self) -> str: - """Return the raw ``opencode run`` log (JSON-lines when ``run_format=json``).""" - return self.sandbox.read_text(agent_log_path(self.config)) - - def fetch_proxy_trace(self) -> list[dict[str, Any]]: - """Return per-turn proxy-captured records (Mode B only). - - Each entry has ``request``, ``response``, ``completion_tokens``, - ``completion_token_ids``, ``per_token_logps``, ``finish_reason``, - and ``latency_s``. Returns ``[]`` in Mode A. - """ - if self._proxy_trace_path is None: - return [] - try: - content = self.sandbox.read_text(self._proxy_trace_path) - except Exception: - return [] - records: list[dict[str, Any]] = [] - for line in content.splitlines(): - line = line.strip() - if not line: - continue - records.append(json.loads(line)) - return records - - -class OpenCodeSessionFactory(ResourceSessionFactory): - """Produce isolated per-rollout :class:`OpenCodeSession` instances. - - The factory owns sandbox provisioning, opencode install, config injection, - and (Mode B) proxy startup. Each :meth:`create` call returns a fresh - sandbox with a running agent. - """ - - def __init__( - self, - *, - config: OpenCodeConfig, - sandbox_backend: SandboxBackend, - mode: Literal["black_box", "transparent_proxy"] = "black_box", - verifier: Verifier | None = None, - install_timeout_s: int = 240, - setup_timeout_s: int = 300, - ) -> None: - if mode not in {"black_box", "transparent_proxy"}: - raise ValueError(f"Unknown mode: {mode!r}") - self._config = config - self._backend = sandbox_backend - self._mode = mode - self._verifier = verifier - self._install_timeout_s = install_timeout_s - self._setup_timeout_s = setup_timeout_s - - def create( - self, - task: Any, - seed: int | None = None, - episode_id: str | None = None, - ) -> OpenCodeSession: - import logging - _log = logging.getLogger(__name__) - - oc_task = OpenCodeTask.coerce(task) - sandbox_timeout = int(self._config.agent_timeout_s) + 300 - - _log.info( - "factory.create: creating sandbox timeout=%ds mode=%s", - sandbox_timeout, self._mode, - ) - sandbox = self._backend.create( - timeout_s=sandbox_timeout, - metadata={"episode_id": episode_id} if episode_id else None, - ) - sid = ( - getattr(sandbox, "sandbox_id", None) - or getattr(getattr(sandbox, "raw", None), "sandbox_id", "?") - ) - _log.info("factory.create: sandbox=%s — bootstrapping…", sid) - try: - self._bootstrap_sandbox(sandbox, oc_task) - except Exception as exc: - _log.error("factory.create: bootstrap failed: %r", exc) - sandbox.kill() - raise - - base_url_override: str | None = None - proxy_trace_path: str | None = None - proxy_bg_job: BgJob | None = None - if self._mode == "transparent_proxy": - _log.info( - "factory.create: starting interception proxy on :%d → %s", - _PROXY_PORT, self._config.base_url, - ) - proxy_bg_job, base_url_override, proxy_trace_path = self._start_proxy( - sandbox - ) - _log.info("factory.create: proxy up at %s", base_url_override) - # Rewrite opencode.json so opencode points at the proxy. Force - # ``openai_compatible`` so opencode hits ``/v1/chat/completions`` - # (which the proxy serves) rather than provider-specific paths. - from .config import OpenCodeConfig as _OCC - - proxy_cfg = _OCC( - **{ - **self._config.model_dump(), - "provider": "openai_compatible", - "base_url": base_url_override, - } - ) - sandbox.write_text( - opencode_config_path(self._config), - build_opencode_json(proxy_cfg), - ) - - session = OpenCodeSession( - sandbox=sandbox, - config=self._config, - task=oc_task, - verifier=self._verifier, - base_url_override=base_url_override, - proxy_trace_path=proxy_trace_path, - proxy_bg_job=proxy_bg_job, - ) - session.start_agent() - return session - - # ------------------------------------------------------------------ - def _wait_for_sandbox_ready( - self, - sandbox: SandboxHandle, - *, - attempts: int = 15, - delay_s: float = 1.0, - ) -> None: - """Probe the sandbox until ``echo ok`` succeeds. - - E2B (and other backends) sometimes return the handle before the - guest is fully ready. Issue ``echo ok`` with short timeouts until - it succeeds. Returns silently on success; raises ``RuntimeError`` - on prolonged failure. - """ - import time - - last_err = "" - for _ in range(attempts): - try: - r = sandbox.exec("echo ok", timeout=5) - if r.exit_code == 0 and "ok" in (r.stdout or ""): - return - last_err = (r.stderr or r.stdout or "").strip() or f"exit={r.exit_code}" - except Exception as exc: # noqa: BLE001 - last_err = f"{type(exc).__name__}: {exc}" - time.sleep(delay_s) - raise RuntimeError( - f"sandbox did not become ready within {attempts * delay_s:.0f}s " - f"(last error: {last_err})" - ) - - def _exec_with_retry( - self, - sandbox: SandboxHandle, - cmd: str, - *, - timeout: float, - attempts: int = 3, - backoff_s: float = 3.0, - label: str = "cmd", - ): - """Run ``sandbox.exec`` with exponential backoff on transient failure. - - Transient = ``exit_code != 0`` AND empty stderr (SIGKILL / network - blip signature) OR an exception during exec. Final failure is raised - as ``RuntimeError`` carrying the last exit code + stderr. - """ - import time - - last_stdout = "" - last_stderr = "" - last_exit = 0 - for i in range(attempts): - try: - r = sandbox.exec(cmd, timeout=timeout) - if r.exit_code == 0: - return r - last_stdout = r.stdout or "" - last_stderr = r.stderr or "" - last_exit = r.exit_code - if last_stderr.strip(): - break - except Exception as exc: # noqa: BLE001 - last_stderr = f"{type(exc).__name__}: {exc}" - last_exit = -1 - if i + 1 < attempts: - time.sleep(backoff_s * (2**i)) - raise RuntimeError( - f"{label} failed after {attempts} attempts " - f"(exit={last_exit}, stderr={last_stderr!r}, stdout_tail={last_stdout[-400:]!r})" - ) - - def _opencode_already_installed(self, sandbox: SandboxHandle) -> bool: - """Cheap probe — returns True if opencode is on disk in the sandbox. - - Used to skip the slow ``curl install`` step when running against a - prebaked template that already ships opencode. - """ - try: - r = sandbox.exec( - "/home/user/.opencode/bin/opencode --version", - timeout=10, - ) - return r.exit_code == 0 - except Exception: - return False - - def _bootstrap_sandbox( - self, - sandbox: SandboxHandle, - task: OpenCodeTask, - ) -> None: - """Install opencode, write config + task files, run optional setup.""" - - # Stage 1: wait for the sandbox to be responsive. - self._wait_for_sandbox_ready(sandbox) - - # Stage 2: install opencode (skipped if a prebaked template already - # has it). curl|bash is flaky — retry with backoff. - if not self._opencode_already_installed(sandbox): - self._exec_with_retry( - sandbox, - build_install_cmd(self._config), - timeout=self._install_timeout_s, - attempts=3, - backoff_s=3.0, - label="opencode install", - ) - - sandbox.write_text( - opencode_config_path(self._config), - build_opencode_json(self._config), - ) - sandbox.write_text(instruction_path(self._config), task.instruction) - - if self._config.system_prompt: - sandbox.write_text( - system_prompt_path(self._config), - self._config.system_prompt, - ) - - for remote_path, content in task.upload_files.items(): - sandbox.write_text(remote_path, content) - - if self._config.extra_setup_shell: - self._exec_with_retry( - sandbox, - self._config.extra_setup_shell, - timeout=self._setup_timeout_s, - attempts=2, - backoff_s=2.0, - label="extra_setup_shell", - ) - - if task.setup_shell: - r = sandbox.exec(task.setup_shell, timeout=self._setup_timeout_s) - if r.exit_code != 0: - raise RuntimeError( - f"task.setup_shell failed ({r.exit_code}): {r.stderr}" - ) - - def _start_proxy( - self, - sandbox: SandboxHandle, - ) -> tuple[BgJob, str, str]: - """Install proxy deps + start the proxy as a bg job inside the sandbox. - - Returns ``(proxy_bg_job, base_url_override, proxy_trace_path)``. - Skips the pip install + source-upload steps when the prebaked - template already has them in place. - """ - proxy_already_present = sandbox.exists( - "/home/user/proxy/interception.py" - ) - - if not proxy_already_present: - # Install proxy deps (idempotent on retries). - self._exec_with_retry( - sandbox, - "pip install --quiet 'fastapi>=0.104' 'uvicorn[standard]>=0.24' " - "'httpx>=0.27' 2>&1 | tail -20", - timeout=180, - attempts=3, - backoff_s=2.0, - label="proxy deps install", - ) - # Upload the proxy module into the sandbox. - sandbox.write_text( - "/home/user/proxy/interception.py", - _PROXY_SOURCE_PATH.read_text(), - ) - sandbox.write_text("/home/user/proxy/__init__.py", "") - - proxy_args = [ - "python", - "interception.py", - "--upstream-url", - self._config.base_url, - "--trace", - _PROXY_TRACE_PATH, - "--port", - str(_PROXY_PORT), - "--top-logprobs", - str(self._config.proxy_top_logprobs), - ] - if self._config.proxy_max_tokens_cap is not None: - proxy_args.extend( - ["--max-tokens-cap", str(self._config.proxy_max_tokens_cap)] - ) - if self._config.proxy_disable_thinking: - proxy_args.append("--disable-thinking") - # Force the upstream model id on every forwarded request — opencode's - # internal title-gen call sometimes strips the provider prefix. - if self._config.model: - proxy_args.extend(["--model-override", self._config.model]) - - quoted_proxy_args = " ".join(shlex.quote(arg) for arg in proxy_args) - proxy_cmd = ( - "cd /home/user/proxy && " - f"{quoted_proxy_args} " - f"> {shlex.quote(_PROXY_LOG_PATH)} 2>&1" - ) - proxy_env = {"OPENCODE_UPSTREAM_API_KEY": self._config.api_key} - proxy_job = sandbox.start_bg(proxy_cmd, envs=proxy_env) - - # Wait for the proxy to start listening. Cold uvicorn boot inside - # E2B can take anywhere from <1s to ~30s depending on cache state. - import time - - attempts = 120 - interval_s = 0.5 - for _ in range(attempts): - r = sandbox.exec( - f"curl -sf http://127.0.0.1:{_PROXY_PORT}/healthz", - timeout=5, - ) - if r.exit_code == 0: - break - time.sleep(interval_s) - else: - log = "" - try: - log = sandbox.read_text(_PROXY_LOG_PATH) - except Exception: - pass - proxy_job.kill() - raise RuntimeError( - f"proxy did not start within {attempts * interval_s:.0f}s. " - f"log:\n{log[-2000:]}" - ) - - base_url_override = f"http://127.0.0.1:{_PROXY_PORT}/v1" - return proxy_job, base_url_override, _PROXY_TRACE_PATH - - -__all__ = [ - "OpenCodeSession", - "OpenCodeSessionFactory", - "OpenCodeTask", - "Verifier", -] diff --git a/envs/opencode_env/sandbox/__init__.py b/envs/opencode_env/sandbox/__init__.py deleted file mode 100644 index 321f81547..000000000 --- a/envs/opencode_env/sandbox/__init__.py +++ /dev/null @@ -1,53 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD-style license found in the -# LICENSE file in the root directory of this source tree. - -"""Sandbox backends for the OpenCode harness. - -The primitive ships with :class:`E2BSandboxBackend` as the default; any backend -that satisfies the :class:`SandboxBackend` / :class:`SandboxHandle` protocols -can be swapped in. - -The ``e2b`` import is wrapped in ``try/except`` so this package can be loaded -in environments where ``e2b`` isn't installed (CI smoke tests, lint runs). -Instantiating ``E2BSandboxBackend`` without ``e2b`` raises a clear error. -""" - -from .base import BgJob, ExecResult, SandboxBackend, SandboxHandle - -try: - from .e2b import E2BBgJob, E2BSandboxBackend, E2BSandboxHandle # noqa: F401 -except ImportError as _e2b_err: # pragma: no cover - - class _RequiresE2B: - """Stub raised when ``e2b`` is not installed. - - Lets the package import cleanly so unit tests, ``openenv validate``, - and the docs build can run without the heavy ``e2b`` dependency. - Actually constructing one of these classes raises a clear ImportError. - """ - - _e2b_import_error = _e2b_err - - def __init__(self, *_args, **_kwargs): - raise ImportError( - "e2b is not installed; install it via " - "`pip install 'openenv-opencode-env[dev]'` or " - "`pip install e2b` to use E2BSandboxBackend. " - f"Original import error: {self._e2b_import_error}" - ) - - E2BBgJob = E2BSandboxBackend = E2BSandboxHandle = _RequiresE2B # type: ignore[assignment] - - -__all__ = [ - "BgJob", - "ExecResult", - "SandboxBackend", - "SandboxHandle", - "E2BBgJob", - "E2BSandboxBackend", - "E2BSandboxHandle", -] diff --git a/envs/opencode_env/sandbox/build_template.py b/envs/opencode_env/sandbox/build_template.py deleted file mode 100644 index 01c32d537..000000000 --- a/envs/opencode_env/sandbox/build_template.py +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD-style license found in the -# LICENSE file in the root directory of this source tree. - -"""Build a pre-baked E2B template with opencode + proxy deps already installed. - -Run-time per rollout drops from ~3 min (cold install) to ~30s once the -template is built, because we skip: - - - ``curl https://opencode.ai/install | bash`` (~30-90s) - - ``pip install fastapi uvicorn httpx`` (~30-60s) - - directory layout setup - - copying the proxy source - -The template ships: - - - opencode CLI at ``/home/user/.opencode/bin/opencode`` - - Python deps for the in-sandbox proxy - - The proxy source at ``/home/user/proxy/interception.py`` - - Pre-created dirs: ``~/.config/opencode``, ``~/logs/{agent,verifier}``, - ``~/task``, ``~/workdir``, ``~/proxy`` - - Default workdir: ``/home/user/workdir`` - -Usage:: - - .venv/bin/python envs/opencode_env/tests/build_e2b_template.py - # → builds (or rebuilds) ``opencode-rl`` template, prints template id - -Then ``test_five_sorts_e2e.py`` will use it via ``--template opencode-rl``. - -Requires ``E2B_API_KEY`` in the environment. First build is ~3-8 min; -subsequent builds reuse the cache and can finish in <60s. -""" - -from __future__ import annotations - -import argparse -import os -import sys -from pathlib import Path - -from e2b import Template, default_build_logger - - -_ENV_DIR = Path(__file__).resolve().parent -_PROXY_SOURCE = _ENV_DIR / "interception.py" - - -def _load_env(path: Path) -> None: - if not path.exists(): - return - for raw in path.read_text().splitlines(): - line = raw.strip() - if not line or line.startswith("#") or "=" not in line: - continue - k, _, v = line.partition("=") - k = k.strip() - v = v.strip().strip('"').strip("'") - if k and k not in os.environ: - os.environ[k] = v - - -def build_template(name: str, *, skip_cache: bool = False) -> str: - if not _PROXY_SOURCE.exists(): - raise RuntimeError(f"proxy source missing at {_PROXY_SOURCE}") - - # Template.copy() resolves relative paths against the caller's source - # file directory. This script lives next to ``interception.py`` so the - # bare filename works. - - # Stage 1 (root): system-wide pip deps for the proxy. - # Stage 2 (user): opencode install + dir layout + proxy copy. - template = ( - Template() - .from_python_image("3.12") - .pip_install( - [ - "fastapi>=0.104", - "uvicorn[standard]>=0.24", - "httpx>=0.27", - ] - ) - .set_user("user") - .run_cmd("curl -fsSL https://opencode.ai/install | bash") - .run_cmd("/home/user/.opencode/bin/opencode --version") - .make_dir("/home/user/.config/opencode") - .make_dir("/home/user/logs/agent") - .make_dir("/home/user/logs/verifier") - .make_dir("/home/user/task") - .make_dir("/home/user/workdir") - .make_dir("/home/user/proxy") - .copy("interception.py", "/home/user/proxy/interception.py") - .set_workdir("/home/user/workdir") - ) - if skip_cache: - template = template.skip_cache() - - info = Template.build( - template, - name, - cpu_count=2, - memory_mb=2048, - on_build_logs=default_build_logger(), - ) - return info.template_id if hasattr(info, "template_id") else str(info) - - -def main(argv: list[str] | None = None) -> int: - p = argparse.ArgumentParser(prog="build_e2b_template") - p.add_argument( - "--name", - default="opencode-rl", - help="Template name (default: opencode-rl).", - ) - p.add_argument( - "--skip-cache", - action="store_true", - help="Force a clean rebuild, ignoring cache.", - ) - args = p.parse_args(argv) - - _load_env(_ENV_DIR / ".env") - if not os.environ.get("E2B_API_KEY"): - print("ERROR: E2B_API_KEY required.", file=sys.stderr) - return 2 - - print(f"Building template '{args.name}' " - f"(proxy source: {_PROXY_SOURCE})") - print(f"Skip cache: {args.skip_cache}") - print() - - template_id = build_template(args.name, skip_cache=args.skip_cache) - print() - print(f"Built. Template id/name: {template_id}") - print(f"Use in code: Sandbox.create(template='{args.name}')") - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/envs/opencode_env/sandbox/interception.py b/envs/opencode_env/sandbox/interception.py deleted file mode 100644 index 131d41024..000000000 --- a/envs/opencode_env/sandbox/interception.py +++ /dev/null @@ -1,661 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD-style license found in the -# LICENSE file in the root directory of this source tree. - -"""Transparent OpenAI-compatible forwarding proxy with logprob capture. - -The proxy is a small FastAPI app that OpenCode talks to instead of the upstream -LLM endpoint. It: - -1. Forwards every ``POST /v1/chat/completions`` request to the real upstream - URL, injecting ``logprobs=true`` and ``top_logprobs=N`` so the upstream - returns per-token logprobs. -2. Captures each ``(request, response, logprobs)`` triple to a JSON-lines - trace file. -3. Returns the upstream response to OpenCode verbatim (minus the ``logprobs`` - field, which we strip so the CLI never sees anything unexpected). - -The proxy is stateless beyond the trace file. One proxy instance runs per -session, normally inside the sandbox on ``localhost:7000``. - -Run standalone:: - - OPENCODE_UPSTREAM_API_KEY=... python -m opencode_env.interception \\ - --upstream-url https://vllm.example/v1 \\ - --trace /tmp/trace.jsonl \\ - --port 7000 -""" - -from __future__ import annotations - -import argparse -import asyncio -import copy -import json -import logging -import os -import socket -import threading -import time -from contextlib import asynccontextmanager, closing -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any - -import httpx -import uvicorn -from fastapi import FastAPI, Request, Response -from fastapi.responses import JSONResponse, StreamingResponse - - -CHAT_COMPLETIONS_PATH = "/v1/chat/completions" -_LOG = logging.getLogger(__name__) - - -@dataclass -class ProxyConfig: - """Runtime configuration for one :class:`InterceptionProxy`.""" - - upstream_url: str - upstream_api_key: str = "intercepted" - trace_path: str = "/tmp/opencode-proxy-trace.jsonl" - host: str = "127.0.0.1" - port: int = 7000 - top_logprobs: int = 5 - request_timeout_s: float = 600.0 - # Cap ``max_tokens`` before forwarding. OpenCode historically asks for very - # large values (e.g. 32000) that exceed gpt-4o-mini's 16384 cap; capping - # here avoids spurious upstream 400s without requiring the caller to know - # per-model limits. - max_tokens_cap: int | None = 16384 - # Disable Qwen-style reasoning/thinking by injecting - # ``chat_template_kwargs.enable_thinking=false`` into forwarded requests. - disable_thinking: bool = False - # Override the ``model`` field on every forwarded request. Some opencode - # builds emit a stripped model id (e.g. ``Qwen3.5-4B`` instead of the - # ``Qwen/Qwen3.5-4B`` the upstream serves) for their internal - # title-generation call. Setting this to the exact upstream model id - # bypasses that mismatch. - model_override: str | None = None - - -@dataclass -class TurnRecord: - """One intercepted turn, written to the trace file as JSON-lines.""" - - turn: int - request: dict[str, Any] - response: dict[str, Any] - logprobs: list[dict[str, Any]] | None - completion_tokens: list[str] - completion_token_ids: list[int] - per_token_logps: list[float] - finish_reason: str | None - latency_s: float - timestamp: float = field(default_factory=time.time) - - def to_json(self) -> str: - return json.dumps(self.__dict__, default=str) - - -def _build_app(cfg: ProxyConfig) -> FastAPI: - """Construct the FastAPI app that serves one proxy session.""" - - state: dict[str, Any] = {"turn": 0, "lock": asyncio.Lock()} - - # HTTP client reused across requests. ``None`` auth header — we let each - # request carry its own ``Authorization`` populated from ``upstream_api_key``. - client = httpx.AsyncClient(timeout=cfg.request_timeout_s) - trace_file = open(cfg.trace_path, "a", buffering=1) - - @asynccontextmanager - async def lifespan(_: FastAPI) -> Any: - try: - yield - finally: - await client.aclose() - trace_file.close() - - app = FastAPI(title="opencode-interception-proxy", lifespan=lifespan) - - @app.get("/healthz") - def healthz() -> dict[str, str]: - return {"status": "ok"} - - @app.post(CHAT_COMPLETIONS_PATH) - async def chat_completions(request: Request) -> Response: - raw_body = await request.body() - try: - body = json.loads(raw_body) - except json.JSONDecodeError: - return JSONResponse( - status_code=400, content={"error": "invalid json body"} - ) - - forwarded_body = _prepare_forwarded_body(body, cfg) - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {cfg.upstream_api_key}", - } - upstream_url = _resolve_upstream_url(cfg.upstream_url) - - async with state["lock"]: - state["turn"] += 1 - turn_idx = state["turn"] - - if forwarded_body.get("stream"): - return await _proxy_streaming( - client=client, - upstream_url=upstream_url, - headers=headers, - forwarded_body=forwarded_body, - original_body=body, - trace_file=trace_file, - turn_idx=turn_idx, - ) - return await _proxy_unary( - client=client, - upstream_url=upstream_url, - headers=headers, - forwarded_body=forwarded_body, - original_body=body, - trace_file=trace_file, - turn_idx=turn_idx, - ) - - return app - - -def _prepare_forwarded_body(body: dict[str, Any], cfg: ProxyConfig) -> dict[str, Any]: - """Return the body we actually send upstream. - - - Injects ``logprobs=true`` + ``top_logprobs`` so the upstream emits - per-token logprobs. - - Caps ``max_tokens`` / ``max_completion_tokens`` to ``max_tokens_cap``. - - For models that reject ``max_tokens`` (e.g. gpt-5.x), translates to - ``max_completion_tokens``. - """ - forwarded = copy.deepcopy(body) - forwarded.setdefault("logprobs", True) - forwarded.setdefault("top_logprobs", cfg.top_logprobs) - - # GPT-5.x and newer: ``max_tokens`` is rejected; must use - # ``max_completion_tokens``. Detect via model string so we don't break - # gpt-4.x or vLLM-hosted models that accept ``max_tokens``. - model = str(forwarded.get("model", "")) - needs_translation = _model_uses_max_completion_tokens(model) - if needs_translation and "max_tokens" in forwarded: - value = forwarded.pop("max_tokens") - forwarded.setdefault("max_completion_tokens", value) - - if cfg.max_tokens_cap is not None: - for key in ("max_tokens", "max_completion_tokens"): - value = forwarded.get(key) - if isinstance(value, int) and value > cfg.max_tokens_cap: - forwarded[key] = cfg.max_tokens_cap - - if cfg.disable_thinking: - # vLLM applies chat_template_kwargs to the tokenizer's chat template - # for Qwen3/Qwen3.5 models, turning off ... generation. - extra = forwarded.setdefault("chat_template_kwargs", {}) - extra.setdefault("enable_thinking", False) - - if cfg.model_override: - forwarded["model"] = cfg.model_override - - return forwarded - - -def _model_uses_max_completion_tokens(model: str) -> bool: - """Heuristic: ``True`` for models that reject ``max_tokens``.""" - # Strip a provider prefix opencode may have prepended (e.g. "intercepted/"). - bare = model.split("/", 1)[-1].lower() - return bare.startswith(("gpt-5", "o1", "o3", "o4")) - - -def _resolve_upstream_url(upstream: str) -> str: - """Build the fully qualified chat-completions URL from a base URL.""" - base = upstream.rstrip("/") - if base.endswith("/v1"): - return f"{base}/chat/completions" - return f"{base}{CHAT_COMPLETIONS_PATH}" - - -async def _proxy_unary( - *, - client: httpx.AsyncClient, - upstream_url: str, - headers: dict[str, str], - forwarded_body: dict[str, Any], - original_body: dict[str, Any], - trace_file: Any, - turn_idx: int, -) -> Response: - start = time.time() - upstream_response = await client.post( - upstream_url, content=json.dumps(forwarded_body), headers=headers - ) - latency = time.time() - start - try: - response_json = upstream_response.json() - except Exception: - return Response( - content=upstream_response.content, - status_code=upstream_response.status_code, - media_type=upstream_response.headers.get( - "content-type", "application/json" - ), - ) - - record = _build_turn_record( - turn_idx=turn_idx, - request_body=forwarded_body, - response_json=response_json, - latency_s=latency, - ) - trace_file.write(record.to_json() + "\n") - sanitized = _strip_logprobs(response_json) - return JSONResponse(content=sanitized, status_code=upstream_response.status_code) - - -async def _proxy_streaming( - *, - client: httpx.AsyncClient, - upstream_url: str, - headers: dict[str, str], - forwarded_body: dict[str, Any], - original_body: dict[str, Any], - trace_file: Any, - turn_idx: int, -) -> Response: - """Forward an SSE stream while accumulating the full response. - - Opens the upstream stream and inspects the status. On non-2xx, reads the - full body (an error JSON, not SSE) and returns it to the caller as a - regular JSON response — previously we silently emitted an empty - ``text/event-stream`` which opencode interpreted as an empty assistant - turn. Both the error body and the latency are written to the trace file - so debugging a broken rollout doesn't require another round-trip. - """ - - start = time.time() - - # Open the stream outside the generator so we can branch on status before - # committing to a streaming response shape. - upstream_cm = client.stream( - "POST", - upstream_url, - content=json.dumps(forwarded_body), - headers=headers, - ) - upstream = await upstream_cm.__aenter__() - - if upstream.status_code >= 400: - # Upstream responded with an error body (not SSE). Read it fully and - # return as a non-streaming JSON payload. - error_bytes = await upstream.aread() - await upstream_cm.__aexit__(None, None, None) - latency = time.time() - start - try: - error_json = json.loads(error_bytes.decode() or "{}") - except Exception: - error_json = {"error": error_bytes.decode(errors="replace")[:4000]} - record = _build_turn_record( - turn_idx=turn_idx, - request_body=forwarded_body, - response_json={ - "choices": [], - "usage": None, - "upstream_status": upstream.status_code, - "upstream_error": error_json, - }, - latency_s=latency, - ) - trace_file.write(record.to_json() + "\n") - _LOG.warning( - "proxy turn %s: upstream %s: %s", - turn_idx, - upstream.status_code, - str(error_json)[:400], - ) - return JSONResponse(content=error_json, status_code=upstream.status_code) - - async def _stream() -> Any: - accumulated: dict[str, Any] = { - "content_by_idx": {}, - "tool_calls_by_idx": {}, - "finish_by_idx": {}, - "logprobs_by_idx": {}, - } - last_chunk: dict[str, Any] = {} - try: - async for line in upstream.aiter_lines(): - if not line: - yield "\n" - continue - yield line + "\n" - if not line.startswith("data:"): - continue - data = line[len("data:"):].strip() - if data == "[DONE]": - continue - try: - chunk = json.loads(data) - except json.JSONDecodeError: - continue - last_chunk = chunk - _accumulate_stream_chunk(chunk, accumulated) - finally: - await upstream_cm.__aexit__(None, None, None) - - latency = time.time() - start - response_json = _assemble_streamed_response(last_chunk, accumulated) - record = _build_turn_record( - turn_idx=turn_idx, - request_body=forwarded_body, - response_json=response_json, - latency_s=latency, - ) - trace_file.write(record.to_json() + "\n") - - return StreamingResponse(_stream(), media_type="text/event-stream") - - -def _accumulate_stream_chunk(chunk: dict[str, Any], acc: dict[str, Any]) -> None: - for choice in chunk.get("choices", []) or []: - idx = choice.get("index", 0) - delta = choice.get("delta") or {} - content = delta.get("content") - if content: - acc["content_by_idx"].setdefault(idx, []).append(content) - # HF-Router's Qwen thinking mode streams the chain-of-thought under a - # separate ``reasoning`` field (per Together/Scaleway). Accumulate it - # so the assembled response surfaces it — otherwise it's dropped and - # proxy_turn observability is lost for thinking-mode rollouts. - reasoning = delta.get("reasoning") - if reasoning: - acc.setdefault("reasoning_by_idx", {}).setdefault(idx, []).append(reasoning) - for tc in delta.get("tool_calls") or []: - tc_idx = tc.get("index", 0) - bucket = acc["tool_calls_by_idx"].setdefault( - (idx, tc_idx), - {"id": None, "type": "function", "function": {"name": "", "arguments": ""}}, - ) - if tc.get("id"): - bucket["id"] = tc["id"] - fn = tc.get("function") or {} - if fn.get("name"): - bucket["function"]["name"] += fn["name"] - if fn.get("arguments"): - bucket["function"]["arguments"] += fn["arguments"] - if choice.get("finish_reason"): - acc["finish_by_idx"][idx] = choice["finish_reason"] - lp = choice.get("logprobs") or {} - content_lp = lp.get("content") - if content_lp: - acc["logprobs_by_idx"].setdefault(idx, []).extend(content_lp) - - -def _assemble_streamed_response( - last_chunk: dict[str, Any], acc: dict[str, Any] -) -> dict[str, Any]: - indices = sorted( - set(acc["content_by_idx"]) - | set(acc["finish_by_idx"]) - | {k[0] for k in acc["tool_calls_by_idx"]} - | set(acc["logprobs_by_idx"]) - | {0} - ) - choices: list[dict[str, Any]] = [] - for idx in indices: - tool_calls = [ - acc["tool_calls_by_idx"][k] - for k in sorted(acc["tool_calls_by_idx"]) - if k[0] == idx - ] - message: dict[str, Any] = {"role": "assistant"} - content = "".join(acc["content_by_idx"].get(idx, [])) - if content: - message["content"] = content - reasoning = "".join((acc.get("reasoning_by_idx") or {}).get(idx, [])) - if reasoning: - message["reasoning"] = reasoning - if tool_calls: - message["tool_calls"] = tool_calls - choice: dict[str, Any] = { - "index": idx, - "message": message, - "finish_reason": acc["finish_by_idx"].get(idx), - } - if acc["logprobs_by_idx"].get(idx): - choice["logprobs"] = {"content": acc["logprobs_by_idx"][idx]} - choices.append(choice) - return { - "id": last_chunk.get("id", ""), - "object": "chat.completion", - "model": last_chunk.get("model", ""), - "choices": choices, - "usage": last_chunk.get("usage"), - } - - -def _build_turn_record( - *, - turn_idx: int, - request_body: dict[str, Any], - response_json: dict[str, Any], - latency_s: float, -) -> TurnRecord: - """Extract per-token logprobs into a normalized :class:`TurnRecord`.""" - - choice = (response_json.get("choices") or [{}])[0] - logprobs_field = choice.get("logprobs") or {} - content_lp = logprobs_field.get("content") or [] - - tokens: list[str] = [] - token_ids: list[int] = [] - per_token_logps: list[float] = [] - for entry in content_lp: - tokens.append(entry.get("token", "")) - # OpenAI returns no raw token ids; vLLM returns them as ``token_id``. - token_id = entry.get("token_id") - if token_id is not None: - token_ids.append(int(token_id)) - lp = entry.get("logprob") - if lp is not None: - per_token_logps.append(float(lp)) - - return TurnRecord( - turn=turn_idx, - request=request_body, - response=response_json, - logprobs=content_lp, - completion_tokens=tokens, - completion_token_ids=token_ids, - per_token_logps=per_token_logps, - finish_reason=choice.get("finish_reason"), - latency_s=latency_s, - ) - - -def _strip_logprobs(response_json: dict[str, Any]) -> dict[str, Any]: - """Return a copy of the response with ``choices[*].logprobs`` removed.""" - - out = dict(response_json) - choices = out.get("choices") - if isinstance(choices, list): - out["choices"] = [ - {k: v for k, v in (ch or {}).items() if k != "logprobs"} - for ch in choices - ] - return out - - -# --------------------------------------------------------------------------- -# Standalone runner (used inside the sandbox) -# --------------------------------------------------------------------------- - - -def serve(cfg: ProxyConfig) -> None: - """Start the proxy and block (for use as the sandbox-side entry point).""" - - app = _build_app(cfg) - uvicorn.run(app, host=cfg.host, port=cfg.port, log_level="warning") - - -class InterceptionProxy: - """Thread-backed controller for running the proxy locally. - - Used by unit tests and by any in-process driver that wants a short-lived - proxy on the local machine. Inside a sandbox we invoke :func:`serve` - directly via ``python -m opencode_env.interception``. - """ - - def __init__(self, cfg: ProxyConfig) -> None: - self._cfg = cfg - self._server: uvicorn.Server | None = None - self._thread: threading.Thread | None = None - self._ready = threading.Event() - - @property - def url(self) -> str: - return f"http://{self._cfg.host}:{self._cfg.port}/v1" - - @property - def config(self) -> ProxyConfig: - return self._cfg - - def start(self) -> None: - app = _build_app(self._cfg) - config = uvicorn.Config( - app, - host=self._cfg.host, - port=self._cfg.port, - log_level="warning", - lifespan="on", - ) - self._server = uvicorn.Server(config) - self._thread = threading.Thread( - target=self._run_server, daemon=True - ) - self._thread.start() - # Wait for the server to accept connections. - deadline = time.time() + 10 - while time.time() < deadline: - if _port_open(self._cfg.host, self._cfg.port): - self._ready.set() - return - time.sleep(0.05) - raise RuntimeError("InterceptionProxy failed to start within 10s") - - def _run_server(self) -> None: - assert self._server is not None - self._server.run() - - def stop(self) -> None: - if self._server is None: - return - self._server.should_exit = True - if self._thread is not None: - self._thread.join(timeout=5) - self._server = None - self._thread = None - - def __enter__(self) -> "InterceptionProxy": - self.start() - return self - - def __exit__(self, *exc) -> None: - self.stop() - - -def _port_open(host: str, port: int) -> bool: - with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: - s.settimeout(0.2) - return s.connect_ex((host, port)) == 0 - - -# --------------------------------------------------------------------------- -# Trace reader (used by the session to pull captured turns back) -# --------------------------------------------------------------------------- - - -def read_trace(path: str | os.PathLike) -> list[dict[str, Any]]: - """Read a proxy trace file into a list of dicts.""" - - trace: list[dict[str, Any]] = [] - p = Path(path) - if not p.exists(): - return trace - for line in p.read_text().splitlines(): - line = line.strip() - if not line: - continue - trace.append(json.loads(line)) - return trace - - -# --------------------------------------------------------------------------- -# CLI entry point -# --------------------------------------------------------------------------- - - -def main() -> None: - parser = argparse.ArgumentParser(prog="opencode_env.interception") - parser.add_argument("--upstream-url", required=True) - parser.add_argument( - "--upstream-api-key", - default=None, - help=( - "Upstream API key. Prefer OPENCODE_UPSTREAM_API_KEY so the key " - "does not appear in process argv." - ), - ) - parser.add_argument("--trace", default="/tmp/opencode-proxy-trace.jsonl") - parser.add_argument("--host", default="127.0.0.1") - parser.add_argument("--port", type=int, default=7000) - parser.add_argument("--top-logprobs", type=int, default=5) - parser.add_argument("--request-timeout", type=float, default=600.0) - parser.add_argument( - "--max-tokens-cap", - type=int, - default=None, - help="Clamp max_tokens/max_completion_tokens on forwarded requests.", - ) - parser.add_argument( - "--disable-thinking", - action="store_true", - help="Inject chat_template_kwargs.enable_thinking=false (Qwen3/Qwen3.5).", - ) - parser.add_argument( - "--model-override", - default=None, - help="Rewrite the `model` field on every forwarded request.", - ) - args = parser.parse_args() - upstream_api_key = ( - args.upstream_api_key - or os.environ.get("OPENCODE_UPSTREAM_API_KEY") - or os.environ.get("UPSTREAM_API_KEY") - or "intercepted" - ) - - cfg = ProxyConfig( - upstream_url=args.upstream_url, - upstream_api_key=upstream_api_key, - trace_path=args.trace, - host=args.host, - port=args.port, - top_logprobs=args.top_logprobs, - request_timeout_s=args.request_timeout, - max_tokens_cap=args.max_tokens_cap, - disable_thinking=args.disable_thinking, - model_override=args.model_override, - ) - serve(cfg) - - -if __name__ == "__main__": - main() diff --git a/examples/opencode_env_simple.py b/examples/coding_agent_env_simple.py similarity index 66% rename from examples/opencode_env_simple.py rename to examples/coding_agent_env_simple.py index 1713880fb..caf81bad8 100644 --- a/examples/opencode_env_simple.py +++ b/examples/coding_agent_env_simple.py @@ -5,21 +5,18 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -"""End-to-end opencode_env example: write binary_search.py and verify it. +"""End-to-end coding_agent_env example: write binary_search.py and verify it. -Hits the deployed HF Space ``AdithyaSK/opencode-env`` (override via -``OPENCODE_ENV_SPACE`` env var to point at your own Space or a local +Hits the deployed HF Space ``AdithyaSK/coding-agent-env`` (override via +``CODING_AGENT_ENV_SPACE`` env var to point at your own Space or a local container). The single MCP tool ``run_rollout`` does: - 1. Spawns a fresh E2B sandbox (using the prebaked ``opencode-rl`` + 1. Spawns a fresh E2B sandbox (using the prebaked ``coding-agent-rl`` template — falls back to a cold install if the template isn't present in your E2B account). - 2. Bootstraps an in-sandbox FastAPI proxy that captures per-token - logprobs (``mode="transparent_proxy"``). - 3. Runs ``opencode run`` with the instruction. - 4. Executes the verify bash commands; reward = passed / total. - 5. Returns a ``RolloutResult`` with reward + per-turn logprobs + - the file contents the agent produced. + 2. Runs the selected harness CLI with the instruction. + 3. Executes the verify bash commands; reward = passed / total. + 4. Returns a ``RolloutResult`` with reward + produced file contents. Prerequisites ------------- @@ -29,12 +26,11 @@ Usage:: - PYTHONPATH=src:envs uv run python examples/opencode_env_simple.py + PYTHONPATH=src:envs uv run python examples/coding_agent_env_simple.py Expected output (~20s with the prebaked template):: reward: 1.0 - turns: 3 files: ['/home/user/workdir/binary_search.py', ...] wall: 19.8 s """ @@ -49,12 +45,14 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "envs")) -from opencode_env import OpenCodeEnv # noqa: E402 -from opencode_env.client import _extract_text # noqa: E402 -from opencode_env.models import RolloutResult # noqa: E402 +from coding_agent_env import CodingAgentEnv # noqa: E402 +from coding_agent_env.client import _extract_text # noqa: E402 +from coding_agent_env.models import RolloutResult # noqa: E402 -SPACE = os.environ.get("OPENCODE_ENV_SPACE", "https://adithyask-opencode-env.hf.space") +SPACE = os.environ.get( + "CODING_AGENT_ENV_SPACE", "https://adithyask-coding-agent-env.hf.space" +) INSTRUCTION = ( "Create a single Python file named `binary_search.py` in the current " @@ -91,7 +89,7 @@ async def main() -> int: print(f"Instruction: {INSTRUCTION.splitlines()[0]} ...") print() - async with OpenCodeEnv(base_url=SPACE) as env: + async with CodingAgentEnv(base_url=SPACE) as env: await env.reset() raw = await env.call_tool( "run_rollout", @@ -101,7 +99,7 @@ async def main() -> int: instruction=INSTRUCTION, setup=[], # no setup commands verify=VERIFY, - template="opencode-rl", # prebaked E2B template + template="coding-agent-rl", # prebaked E2B template task_id="binary_search_simple", agent_timeout_s=600, ) @@ -109,8 +107,6 @@ async def main() -> int: print("--- result ---") print(f"reward: {result.reward}") - print(f"turns: {len(result.proxy_turns)}") - print(f"tokens: {sum(len(t.completion_tokens) for t in result.proxy_turns)}") print(f"sandbox: {result.sandbox_id}") print(f"wall_s: {result.wall_s}") print(f"files: {sorted(result.files)}") @@ -118,16 +114,6 @@ async def main() -> int: if result.error: print(f"error: {result.error}") - if result.proxy_turns: - first = next((t for t in result.proxy_turns if t.completion_tokens), None) - if first: - print() - print("--- first productive turn (first 8 tokens with logprobs) ---") - toks = first.completion_tokens[:8] - lps = first.per_token_logps[:8] - for tok, lp in zip(toks, lps): - print(f" {tok!r:<14} {lp:+.3f}") - return 0 if result.reward == 1.0 else 1 diff --git a/examples/mini_swe_env/README.md b/examples/mini_swe_env/README.md new file mode 100644 index 000000000..141353791 --- /dev/null +++ b/examples/mini_swe_env/README.md @@ -0,0 +1,145 @@ +# SWE-Gym Training Example + +End-to-end training pipeline: SWE-Gym tasks → Pi agent → InterceptionServer → GRPO. + +## Overview + +This example demonstrates how to train a coding agent on real-world +software engineering tasks from [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym) +using the OpenEnv harness infrastructure. + +### Architecture + +``` +┌─────────────────────────────────────────────────────────────┐ +│ train_swe_async_grpo.py │ +│ │ +│ 1. Load SWE-Gym tasks from HuggingFace │ +│ 2. Start InterceptionServer on trainer host │ +│ 3. For each batch: │ +│ a. SWESessionFactory.create(task) → sandbox + Pi │ +│ b. Pi makes LLM call → intercepted by server │ +│ c. Training loop runs vLLM forward, captures logprobs │ +│ d. Delivers response back to Pi │ +│ e. Repeat until Pi exits or calls answer() │ +│ f. session.verify() → host-side SWE-Gym grading │ +│ 4. AsyncGRPOTrainer consumes queue + updates policy │ +└─────────────────────────────────────────────────────────────┘ +``` + +### Reward Integrity + +The training reward is computed **host-side** by `SWESession.verify()`: + +1. Reverts any test files the agent modified (anti-reward-hacking) +2. Applies the known-good `test_patch` +3. Runs the explicit `FAIL_TO_PASS` and `PASS_TO_PASS` pytest cases +4. Computes binary reward from those case outcomes +5. Returns binary reward: `1.0` (resolved) or `0.0` (not resolved) + +The agent **cannot** influence the training reward. The in-sandbox +`answer` tool only gives the agent a feedback signal ("Resolved: true/false"). + +## Files + +| File | Purpose | +|------|---------| +| `run_swe_sample.py` | Real end-to-end interception rollout with a live LLM | +| `train_swe_async_grpo.py` | AsyncGRPO trainer entrypoint wired to SWE custom rollout worker | +| `examples/mini_swe_env/async_grpo/` | Control plane, worker, and trainer wiring modules | + +## Quick Start + +### 1. Real end-to-end interception rollout (live LLM) + +```bash +SWE_LLM_BASE_URL=https://api.openai.com/v1 \ +SWE_LLM_API_KEY=... \ +SWE_LLM_MODEL=gpt-4o-mini \ +PYTHONPATH=src:envs python examples/mini_swe_env/run_swe_sample.py \ + --task-variant lite --task-index 16 --assert-host-answer +``` + +Notes: +- Uses `interception_gate` mode and forwards every intercepted request to the + configured OpenAI-compatible endpoint. +- If `--assert-host-answer` is set, the script fails unless reward comes from + the host-side `answer` tool path (`reward_source=host_answer_tool`). + +### 2. AsyncGRPO training (Track D wiring) + +```bash +SWE_ASYNC_MODEL=Qwen/Qwen3-8B \ +SWE_LLM_BASE_URL=http://127.0.0.1:8000/v1 \ +SWE_LLM_API_KEY=test \ +SWE_LLM_MODEL=Qwen/Qwen3-8B \ +INTERCEPTION_AUTH_TOKEN=... \ +INTERCEPTION_BASE_URL=https://.hf.space \ +PYTHONPATH=src:envs uv run python examples/mini_swe_env/train_swe_async_grpo.py \ + --task-variant lite --max-tasks 1 --sandbox-backend hf --max-steps 2 +``` + +Notes: +- Use separate GPUs for vLLM and trainer, per TRL async docs. +- `INTERCEPTION_BASE_URL` must be reachable from HF sandboxes. + +## Configuration + +Main env vars for `train_swe_async_grpo.py`: + +| Env Variable | Default | Description | +|-------------|---------|-------------| +| `SWE_ASYNC_MODEL` | *(required)* | Policy model id for `AsyncGRPOTrainer`. | +| `SWE_LLM_BASE_URL` | *(required)* | OpenAI-compatible generation endpoint (typically vLLM `/v1`). | +| `SWE_LLM_API_KEY` | *(required)* | Bearer token for the generation endpoint. | +| `SWE_LLM_MODEL` | *(required)* | Model id exposed by the generation endpoint. | +| `INTERCEPTION_AUTH_TOKEN` | *(required)* | Auth token for interception server + sandbox client wiring. | +| `INTERCEPTION_BASE_URL` | *(required for remote sandboxes)* | Public URL reachable from HF/E2B sandboxes. | +| `SWE_ASYNC_MAX_STALENESS` | `2` | Async sample staleness bound. | +| `SWE_ASYNC_WEIGHT_SYNC_STEPS` | `1` | Weight sync interval. | +| `SWE_ASYNC_MAX_INFLIGHT_TASKS` | `2` | Inflight rollout concurrency. | +| `SWE_ASYNC_QUEUE_MAXSIZE` | `64` | Rollout queue size cap. | +| `SWE_ASYNC_MAX_STEPS` | `10` | Trainer step budget (override with CLI `--max-steps`). | +| `HF_TOKEN` | *(optional)* | Needed when calling private Space URLs from trainer-side answer bridge. | + +## Sandbox Connectivity + +The InterceptionServer runs on the trainer host. The sandbox must be +able to reach it: + +- **Docker (same host)**: Automatic — uses `http://host.docker.internal:` +- **Remote sandbox (E2B, HF)**: You must provide a tunnel/public URL via + `INTERCEPTION_BASE_URL`. Options: [bore](https://github.com/ekzhang/bore), + [ngrok](https://ngrok.com/), [frp](https://github.com/fatedier/frp), + or a public IP. + +## Task Source + +Tasks are loaded at runtime from HuggingFace: + +- **Lite** (230 tasks): `SWE-Gym/SWE-Gym-Lite` — good for development +- **Full** (2,438 tasks): `SWE-Gym/SWE-Gym` — for production training + +Each task comes with a per-task Docker image (`xingyaoww/sweb.eval.x86_64.*`) +that has the repo pre-cloned at `/testbed` with all dependencies installed. + +## What the Model Learns + +During training, the model sees Pi's system prompt, tool calls, and +results — the exact format it will use at inference time: + +``` +[user] + +[assistant] Let me investigate... +[tool_call] bash(command="cd /testbed && python -m pytest tests/test_foo.py -x -q") +[tool_result] FAILED tests/test_foo.py::test_bar - AssertionError... + +[assistant] I see the bug. +[tool_call] edit(path="/testbed/src/foo.py", edits=[{oldText: "if x > 0", newText: "if x >= 0"}]) +[tool_result] ✅ Applied 1 edit + +[assistant] Fix verified. Submitting. +[tool_call] answer() +[tool_result] ✅ Resolved: true +``` diff --git a/examples/mini_swe_env/async_grpo/__init__.py b/examples/mini_swe_env/async_grpo/__init__.py new file mode 100644 index 000000000..ae01b5912 --- /dev/null +++ b/examples/mini_swe_env/async_grpo/__init__.py @@ -0,0 +1,27 @@ +"""Async GRPO training for Mini SWE with Pi agent. + +Architecture: Pi runs in an HF Sandbox, its LLM calls are intercepted +by :class:`SWEAsyncControlPlane`, and :class:`SWERolloutWorker` forwards +them to vLLM ``/v1/completions`` to get exact token IDs and logprobs. + +The worker implements TRL's ``RolloutWorkerProtocol`` and plugs into +``AsyncGRPOTrainer(rollout_worker=...)``. +""" + +from .control_plane import ( + SWEAsyncControlPlane, + SWEAsyncControlPlaneConfig, +) +from .rollout_worker import ( + RolloutSample, + SWERolloutWorker, + WorkerConfig, +) + +__all__ = [ + "RolloutSample", + "SWEAsyncControlPlane", + "SWEAsyncControlPlaneConfig", + "SWERolloutWorker", + "WorkerConfig", +] diff --git a/examples/mini_swe_env/async_grpo/control_plane.py b/examples/mini_swe_env/async_grpo/control_plane.py new file mode 100644 index 000000000..719404426 --- /dev/null +++ b/examples/mini_swe_env/async_grpo/control_plane.py @@ -0,0 +1,169 @@ +from __future__ import annotations + +import contextlib +import queue as _queue_mod +import logging +import os +from dataclasses import dataclass +from typing import Any, Mapping + +from openenv.core.harness.agents.interception_server import InterceptionServer + + +_log = logging.getLogger(__name__) + +_DEFAULT_INTERCEPTION_HOST = "0.0.0.0" +_DEFAULT_INTERCEPTION_PORT = 8765 + + +def build_hf_space_base_url(space_id: str) -> str: + """Return canonical public Space URL from ``owner/name``.""" + owner, sep, name = (space_id or "").strip().partition("/") + if not sep or not owner or not name: + raise ValueError( + "SPACE_ID must be in 'owner/name' format to derive interception base URL" + ) + return f"https://{owner}-{name}.hf.space" + + +@dataclass(frozen=True) +class SWEAsyncControlPlaneConfig: + """Runtime config for the Space-hosted interception control plane.""" + + host: str + port: int + auth_token: str + interception_base_url: str + + @classmethod + def from_env( + cls, + *, + env: Mapping[str, str] | None = None, + require_auth_token: bool = True, + ) -> "SWEAsyncControlPlaneConfig": + source = env or os.environ + + host = source.get("INTERCEPTION_HOST", _DEFAULT_INTERCEPTION_HOST).strip() + port_raw = source.get("INTERCEPTION_PORT") or source.get("PORT") + port = int(port_raw) if port_raw else _DEFAULT_INTERCEPTION_PORT + + token = ( + source.get("INTERCEPTION_AUTH_TOKEN") + or source.get("INTERCEPTION_SECRET") + or "" + ).strip() + if require_auth_token and not token: + raise ValueError( + "Missing interception auth token. Set INTERCEPTION_AUTH_TOKEN in Space secrets." + ) + + base_url = (source.get("INTERCEPTION_BASE_URL") or "").strip().rstrip("/") + if not base_url: + space_host = (source.get("SPACE_HOST") or "").strip().rstrip("/") + if space_host: + if "http" not in space_host: + space_host = f"https://{space_host}" + base_url = space_host.rstrip("/") + else: + space_id = (source.get("SPACE_ID") or "").strip() + if space_id: + base_url = build_hf_space_base_url(space_id) + + if not base_url: + raise ValueError( + "Could not resolve interception_base_url. Set INTERCEPTION_BASE_URL, " + "SPACE_HOST, or SPACE_ID." + ) + + return cls( + host=host, + port=port, + auth_token=token, + interception_base_url=base_url, + ) + + +class SWEAsyncControlPlane: + """Owns the interception server and rollout registration state. + + Trainer-side async rollout workers should use this object directly for + ``register_rollout/get_intercept/unregister_rollout`` so rollout queues and + intercept state remain in-process with the trainer. + """ + + def __init__( + self, + *, + config: SWEAsyncControlPlaneConfig, + server: InterceptionServer | None = None, + ) -> None: + self.config = config + self.server = server or InterceptionServer( + host=config.host, + port=config.port, + secret=config.auth_token, + tool_name_allowlist={"answer"}, + ) + + @property + def interception_base_url(self) -> str: + return self.config.interception_base_url + + @property + def auth_token(self) -> str: + return self.config.auth_token + + async def start(self) -> None: + await self.server.start() + # If port was 0 in config, InterceptionServer resolves it after bind. + _log.info( + "swe_async_control_plane_started base_url=%s port=%d", + self.interception_base_url, + self.server.port, + ) + + async def stop(self) -> None: + await self.server.stop() + _log.info("swe_async_control_plane_stopped") + + def register_rollout( + self, + rollout_id: str, + *, + state: dict[str, Any] | None = None, + ) -> _queue_mod.Queue[str]: + queue = self.server.register_rollout(rollout_id, state=state) + stats = self.stats() + _log.info( + "swe_async_rollout_registered rollout_id=%s active_rollouts=%d", + rollout_id, + stats["active_rollouts"], + ) + return queue + + def unregister_rollout(self, rollout_id: str) -> None: + self.server.unregister_rollout(rollout_id) + stats = self.stats() + _log.info( + "swe_async_rollout_unregistered rollout_id=%s active_rollouts=%d", + rollout_id, + stats["active_rollouts"], + ) + + @contextlib.contextmanager + def rollout( + self, + rollout_id: str, + *, + state: dict[str, Any] | None = None, + ): + """Leak-safe context manager for one rollout registration.""" + queue = self.register_rollout(rollout_id, state=state) + try: + yield queue + finally: + self.unregister_rollout(rollout_id) + + def stats(self) -> dict[str, int]: + return self.server.stats() diff --git a/examples/mini_swe_env/async_grpo/rollout_worker.py b/examples/mini_swe_env/async_grpo/rollout_worker.py new file mode 100644 index 000000000..7c52964e1 --- /dev/null +++ b/examples/mini_swe_env/async_grpo/rollout_worker.py @@ -0,0 +1,1443 @@ +"""Custom rollout worker for Pi-in-sandbox SWE training. + +Implements ``RolloutWorkerProtocol`` from TRL's ``AsyncGRPOTrainer``. + +Architecture: + Pi (sandbox) → InterceptionServer → this worker → vLLM /v1/completions + ← chat response back to Pi + +Pi drives the generation loop inside the sandbox. This worker: + +1. Dequeues each intercepted LLM request from Pi. +2. Tokenizes the messages with ``apply_chat_template``. +3. Calls vLLM ``/v1/completions`` with ``prompt=token_ids``, + ``return_token_ids=True``, ``logprobs=0`` — same as TRL's own + ``AsyncRolloutWorker._generate_one_turn``. +4. Gets exact ``completion_ids`` and ``completion_logprobs`` from vLLM. +5. Wraps the completion text as a chat response and delivers it back to Pi. +6. Tracks multi-turn token sequences matching TRL's pattern: + ``input_ids = initial_prompt_ids + [turn_ids + suffix_ids]*N`` + ``completion_mask = [0]*prompt + [1]*turn + [0]*suffix + ...`` +7. On ``answer()``, bridges to host-side grading. +8. Assembles the final ``RolloutSample`` and pushes to ``rollout_buffer``. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import queue +import threading +import time +import uuid +from dataclasses import dataclass, field +from typing import Any, Callable, Iterator, Sequence, cast + +import requests + +try: + from trl.chat_template_utils import ( + add_response_schema, + get_training_chat_template, + is_chat_template_prefix_preserving, + parse_response, + ) +except Exception: # pragma: no cover - defensive for older TRL versions + add_response_schema = None + get_training_chat_template = None + is_chat_template_prefix_preserving = None + parse_response = None + +try: + from vllm.distributed.weight_transfer.nccl_engine import ( + NCCLTrainerSendWeightsArgs, + NCCLWeightTransferEngine, + ) + from vllm.utils.network_utils import get_ip, get_open_port +except Exception: # pragma: no cover - optional at import time + NCCLTrainerSendWeightsArgs = None + NCCLWeightTransferEngine = None + get_ip = None + get_open_port = None + +from mini_swe_env.models import SWETask + +_log = logging.getLogger(__name__) + + + +# ── Sample dataclass ─────────────────────────────────────────────────── + + +@dataclass +class RolloutSample: + """Matches the fields TRL's ``RolloutQueueDataset`` reads. + + Note: TRL's own RolloutSample also has ``prompt`` and ``completion`` + (message-level) but those are only used by TRL's built-in reward_funcs. + We compute rewards via ``session.verify()`` so they're unnecessary. + """ + + input_ids: list[int] + completion_mask: list[int] + old_log_probs: list[float] + advantage: float + model_version: int + metrics: dict[str, Any] = field(default_factory=dict) + + +# ── Config ───────────────────────────────────────────────────────────── + + +@dataclass(frozen=True) +class WorkerConfig: + max_inflight: int = 2 + queue_maxsize: int = 64 + request_timeout_s: float = 600.0 + max_turns: int = 50 + max_completion_tokens: int = 2048 + temperature: float = 1.0 + # Number of rollouts per prompt for group-relative advantage + # normalization (true GRPO). Polar used 16. + num_generations: int = 16 + # After returning a terminal plain-text response (finish_reason=stop, + # no tool_calls), wait briefly for a follow-up request before treating + # the rollout as complete. This avoids 600s stalls when agent exit + # detection is delayed on remote backends. + post_response_grace_s: float = 10.0 + stop_on_idle_terminal_response: bool = True + idle_backoff_s: float = 0.5 + # vLLM max_model_len for pre-generation guard. If the prompt + + # max_completion_tokens exceeds this, the rollout terminates early + # with reward=0 rather than crashing the engine. Set to match + # vLLM's --max-model-len. + max_model_len: int = 40960 + # Number of context overflow failures before a task is blacklisted + # for the remainder of the training run. Prevents infinite retry + # loops on tasks whose prompts inherently exceed the context window. + max_context_overflow_per_task: int = 3 + # Message truncation settings for context window management. + # Instead of crashing on overflow, progressively truncate long tool + # outputs and assistant messages to fit within context budget. + max_tool_message_chars: int = 6000 + min_tool_message_chars: int = 256 + max_assistant_message_chars: int = 4000 + min_assistant_message_chars: int = 256 + # Retry config for transient sandbox/network errors. + max_rollout_attempts: int = 4 + failure_backoff_s: float = 30.0 + + +# ── Worker ───────────────────────────────────────────────────────────── + + +class SWERolloutWorker: + """Background rollout producer for Pi + InterceptionServer + vLLM. + + Implements ``RolloutWorkerProtocol`` so it plugs into + ``AsyncGRPOTrainer(rollout_worker=...)``. + """ + + def __init__( + self, + *, + session_factory: Any, + tasks: Sequence[SWETask], + tokenizer: Any, + vllm_base_url: str, + vllm_api_key: str, + vllm_model: str, + config: WorkerConfig | None = None, + ) -> None: + self._factory = session_factory + self._tasks = list(tasks) + self._tokenizer = tokenizer + if add_response_schema is not None: + try: + self._tokenizer = add_response_schema(self._tokenizer) + except Exception as exc: + _log.debug("add_response_schema unavailable for tokenizer: %s", exc) + + # Use TRL's prefix-preserving training template if available. + # The stock Qwen3.5 template is NOT prefix-preserving (it conditionally + # renders tags based on message position), which breaks our + # suffix computation. The training template fixes this. + self._chat_template: str | None = None + if get_training_chat_template is not None: + try: + self._chat_template = get_training_chat_template(self._tokenizer) + if self._chat_template: + _log.info("using TRL prefix-preserving training chat template") + except (ValueError, TypeError) as exc: + _log.debug("get_training_chat_template failed: %s", exc) + self._vllm_base_url = vllm_base_url.rstrip("/") + self._vllm_api_key = vllm_api_key + self._vllm_model = vllm_model + self._cfg = config or WorkerConfig() + + self.rollout_buffer: queue.Queue[RolloutSample] = queue.Queue( + maxsize=self._cfg.queue_maxsize, + ) + + self._stop = threading.Event() + self._pause = threading.Event() + self._lock = threading.Lock() + self._weight_sync_lock = threading.Lock() + self._threads: list[threading.Thread] = [] + self._task_idx = 0 + self._model_version = 0 + self._started = False + self._model_update_group: Any | None = None + + # ── Group-relative advantage (GRPO) ────────────────────── + # Accumulate rollout samples by task until num_generations are + # collected, then normalize advantages within the group and push + # all samples to the rollout buffer. + self._group_lock = threading.Lock() + # task_id → list of (RolloutSample with raw reward in .advantage) + self._pending_groups: dict[str, list[RolloutSample]] = {} + + # ── Context overflow tracking ──────────────────────────── + # Track tasks that repeatedly exceed context to avoid infinite + # retry loops. Failed sessions get reward=0 and are excluded + # from group normalization in _score_and_push_group. + self._overflow_counts: dict[str, int] = {} + self._overflow_lock = threading.Lock() + + # Prefix to prepend to trainer parameter names so they match vLLM's + # model architecture. For VLM models like Qwen3_5ForConditionalGeneration + # served with --language-model-only, vLLM parameters are at + # "language_model.model.layers.X" but the trainer (AutoModelForCausalLM) + # produces "model.layers.X". With is_checkpoint_format=True (vLLM 0.21+), + # vLLM's load_weights() handles this remapping automatically — no manual + # prefix is needed. + + self._init_weight_transfer() + + # ── RolloutWorkerProtocol ────────────────────────────────────── + + def start(self) -> None: + with self._lock: + if self._started: + return + self._started = True + self._stop.clear() + for i in range(max(1, self._cfg.max_inflight)): + t = threading.Thread( + target=self._loop, + args=(i,), + daemon=True, + name=f"swe-rollout-{i}", + ) + t.start() + self._threads.append(t) + _log.info("worker started threads=%d", len(self._threads)) + + def stop(self) -> None: + self._stop.set() + for t in self._threads: + t.join(timeout=5.0) + with self._lock: + self._started = False + self._threads = [] + self._destroy_model_update_group() + + def pause(self) -> None: + self._pause.set() + if self._model_update_group is None: + return + self._post_json( + "/pause", + timeout=60, + params={"mode": "keep"}, + ) + + def resume(self) -> None: + if self._model_update_group is not None: + self._post_json("/resume", timeout=60) + self._pause.clear() + + def send_weights(self, iterator: Iterator[tuple[str, Any]]) -> None: + # Materialize once so we can derive metadata and send the same + # tensors through NCCL. + items = list(iterator) + if not items: + return + + if self._model_update_group is None: + _log.warning( + "weight sync disabled: NCCL weight-transfer group not initialized" + ) + return + + names = [name for name, _ in items] + + # With is_checkpoint_format=True (vLLM 0.21+), vLLM's load_weights() + # handles the name remapping internally (e.g. model.layers.X → + # language_model.model.layers.X for VLM architectures like Qwen3.5). + # Send HuggingFace checkpoint-format names as-is. + + dtype_names = [ + str(getattr(tensor, "dtype", "float32")).split(".")[-1] + for _, tensor in items + ] + shapes = [list(getattr(tensor, "shape", [])) for _, tensor in items] + update_info = { + "names": names, + "dtype_names": dtype_names, + "shapes": shapes, + "packed": True, + } + + with self._weight_sync_lock: + # vLLM 0.21+ four-phase protocol: + # start_weight_update(is_checkpoint_format=True) → update_weights → finish_weight_update + # is_checkpoint_format=True tells vLLM to use model.load_weights() which + # handles parameter name remapping (e.g. CausalLM → VLM prefixes). + self._post_json( + "/start_weight_update", + timeout=60, + json_body={"is_checkpoint_format": True}, + ) + + post_error: list[Exception] = [] + + def _post_update() -> None: + try: + self._post_json( + "/update_weights", + timeout=1800, + json_body={"update_info": update_info}, + ) + except Exception as exc: # noqa: BLE001 + post_error.append(exc) + + t_update = threading.Thread(target=_post_update, daemon=True) + t_update.start() + + assert NCCLWeightTransferEngine is not None + assert NCCLTrainerSendWeightsArgs is not None + NCCLWeightTransferEngine.trainer_send_weights( + iterator=iter(items), + trainer_args=NCCLTrainerSendWeightsArgs( + group=self._model_update_group, + packed=True, + ), + ) + + t_update.join(timeout=1800) + if t_update.is_alive(): + raise TimeoutError("Timed out waiting for vLLM /update_weights") + if post_error: + raise RuntimeError( + f"vLLM /update_weights failed: {post_error[0]}" + ) from post_error[0] + + # Finalize layerwise reload / quantization. + self._post_json("/finish_weight_update", timeout=120) + + def update_model_version(self, version: int) -> None: + with self._lock: + self._model_version = version + + def check_health(self, stale_after_s: float) -> None: + """Health check called by the trainer when the queue is empty. + + Required by TRL's RolloutWorkerProtocol. Can be used to detect + stuck workers. Currently a no-op — our workers self-recover via + retry logic and idle backoff. + """ + pass + + def _post_json( + self, + path: str, + *, + timeout: float, + json_body: dict[str, Any] | None = None, + params: dict[str, Any] | None = None, + ) -> requests.Response: + response = requests.post( + f"{self._vllm_base_url}{path}", + headers={"Authorization": f"Bearer {self._vllm_api_key}"}, + json=json_body, + params=params, + timeout=timeout, + ) + if response.status_code != 200: + raise RuntimeError( + f"{path} returned {response.status_code}: {response.text[:400]}" + ) + return response + + def _init_weight_transfer(self) -> None: + if ( + NCCLWeightTransferEngine is None + or NCCLTrainerSendWeightsArgs is None + or get_ip is None + or get_open_port is None + ): + _log.warning( + "vLLM NCCL weight-transfer modules unavailable; disabling sync" + ) + return + + response = requests.get( + f"{self._vllm_base_url}/get_world_size", + headers={"Authorization": f"Bearer {self._vllm_api_key}"}, + timeout=10, + ) + if response.status_code != 200: + raise RuntimeError( + "vLLM weight sync requires /get_world_size endpoint; " + "start vLLM with VLLM_SERVER_DEV_MODE=1 and " + '--weight-transfer-config \'{"backend":"nccl"}\'' + ) + + inference_world_size = int(response.json()["world_size"]) + world_size = inference_world_size + 1 + master_address = get_ip() + master_port = get_open_port() + + init_info = { + "master_address": master_address, + "master_port": master_port, + "rank_offset": 1, + "world_size": world_size, + } + + post_error: list[Exception] = [] + + def _post_init() -> None: + try: + self._post_json( + "/init_weight_transfer_engine", + timeout=120, + json_body={"init_info": init_info}, + ) + except Exception as exc: # noqa: BLE001 + post_error.append(exc) + + t_init = threading.Thread(target=_post_init, daemon=True) + t_init.start() + self._model_update_group = NCCLWeightTransferEngine.trainer_init( + { + "master_address": master_address, + "master_port": master_port, + "world_size": world_size, + } + ) + t_init.join(timeout=120) + if t_init.is_alive(): + raise TimeoutError("Timed out waiting for vLLM init_weight_transfer_engine") + if post_error: + raise RuntimeError( + f"vLLM init_weight_transfer_engine failed: {post_error[0]}" + ) from post_error[0] + + _log.info("initialized NCCL weight-transfer group with vLLM") + + def _destroy_model_update_group(self) -> None: + group = self._model_update_group + if group is None: + return + try: + group.group.store = None + group.group.socket = None + except Exception: + pass + self._model_update_group = None + + # ── Internal ─────────────────────────────────────────────────── + + def _next_task(self) -> SWETask: + with self._lock: + t = self._tasks[self._task_idx % len(self._tasks)] + self._task_idx += 1 + return t + + def _model_ver(self) -> int: + with self._lock: + return self._model_version + + def _is_task_blacklisted(self, task_id: str) -> bool: + """Check if a task has been blacklisted due to repeated context overflow.""" + with self._overflow_lock: + return ( + self._overflow_counts.get(task_id, 0) + >= self._cfg.max_context_overflow_per_task + ) + + def _record_context_overflow(self, task_id: str) -> None: + """Record a context overflow for a task. After max attempts, + abandon the group with reward=0 for all pending samples.""" + abandon_group = False + with self._overflow_lock: + self._overflow_counts[task_id] = ( + self._overflow_counts.get(task_id, 0) + 1 + ) + count = self._overflow_counts[task_id] + if count >= self._cfg.max_context_overflow_per_task: + _log.warning( + "task %s blacklisted after %d context overflows — " + "abandoning group with reward=0", + task_id, + count, + ) + abandon_group = True + + if abandon_group: + # Flush the partial group: failed sessions get reward=0 + # and are excluded from group normalization. + with self._group_lock: + partial = self._pending_groups.pop(task_id, None) + if partial: + for sample in partial: + sample.advantage = 0.0 + sample.metrics["context_overflow_abandoned"] = 1.0 + try: + self.rollout_buffer.put(sample, timeout=1.0) + except queue.Full: + pass + + def _next_group_task(self) -> SWETask: + """Pick a task that needs more rollouts to complete its group. + + Prioritizes tasks that already have partial groups (some rollouts + done but < num_generations). Falls back to a fresh task. + Skips tasks blacklisted due to repeated context overflow. + """ + with self._group_lock: + # Find a task with a partial group that needs more rollouts + for task_id, samples in self._pending_groups.items(): + if len(samples) < self._cfg.num_generations: + if self._is_task_blacklisted(task_id): + continue + # Return the corresponding task + for t in self._tasks: + if t.instance_id == task_id: + return t + # No partial groups — start a fresh task (skip blacklisted) + for _ in range(len(self._tasks)): + task = self._next_task() + if not self._is_task_blacklisted(task.instance_id): + return task + # All tasks blacklisted — fatal (shouldn't happen with 293 tasks) + return self._next_task() + + def _submit_to_group(self, task: SWETask, sample: RolloutSample) -> None: + """Add a completed rollout to its task's group. + + When the group reaches num_generations, normalize advantages + (GRPO-style) and push all samples to the rollout buffer. + """ + completed_group: list[RolloutSample] | None = None + + with self._group_lock: + group = self._pending_groups.setdefault(task.instance_id, []) + group.append(sample) + + if len(group) >= self._cfg.num_generations: + completed_group = self._pending_groups.pop(task.instance_id) + + if completed_group is not None: + self._score_and_push_group(task.instance_id, completed_group) + + def _score_and_push_group( + self, task_id: str, group: list[RolloutSample] + ) -> None: + """Compute group-relative advantages (GRPO) and push to buffer. + + Failed sessions (context_overflow) are excluded from the group + mean/std and get advantage=0, matching Polar's reward_post_process + which zeros FAILED/ABORTED trajectories. + """ + rewards = [s.advantage for s in group] # raw rewards stored here + n = len(rewards) + + # Exclude failed sessions from the baseline computation. + valid_mask = [ + s.metrics.get("context_overflow", 0.0) == 0.0 for s in group + ] + valid_rewards = [r for r, v in zip(rewards, valid_mask) if v] + + if not valid_rewards: + # All sessions failed — no signal. + for sample in group: + sample.advantage = 0.0 + sample.metrics["group_reward_mean"] = 0.0 + sample.metrics["group_reward_std"] = 0.0 + sample.metrics["group_size"] = float(n) + try: + self.rollout_buffer.put(sample, timeout=2.0) + except queue.Full: + _log.warning("queue full, dropping sample from group %s", task_id) + _log.info( + "group complete (all failed): task=%s n=%d", task_id, n + ) + return + + mean_r = sum(valid_rewards) / len(valid_rewards) + var_r = sum((r - mean_r) ** 2 for r in valid_rewards) / len(valid_rewards) + std_r = var_r**0.5 + + for i, sample in enumerate(group): + if not valid_mask[i]: + # Failed session: zero advantage, zero gradient. + sample.advantage = 0.0 + elif std_r > 1e-8: + sample.advantage = (rewards[i] - mean_r) / std_r + else: + # All valid rewards identical — zero advantage + sample.advantage = 0.0 + + sample.metrics["group_reward_mean"] = mean_r + sample.metrics["group_reward_std"] = std_r + sample.metrics["group_size"] = float(n) + + try: + self.rollout_buffer.put(sample, timeout=2.0) + except queue.Full: + _log.warning( + "queue full, dropping sample from group %s", task_id + ) + + _log.info( + "group complete: task=%s n=%d valid=%d reward_mean=%.3f reward_std=%.3f", + task_id, n, len(valid_rewards), mean_r, std_r, + ) + + def _loop(self, idx: int) -> None: + while not self._stop.is_set(): + while self._pause.is_set() and not self._stop.is_set(): + time.sleep(0.05) + if self._stop.is_set(): + return + + task = self._next_group_task() + eid = f"swe-{idx}-{uuid.uuid4().hex[:8]}" + try: + sample = asyncio.run(self._rollout(task, eid)) + except SWERolloutWorker.ContextOverflowError as exc: + # Record the overflow and submit a zero-reward sample so + # the group can still progress. After max_context_overflow_per_task + # failures the task is blacklisted. + _log.warning( + "context overflow worker=%d task=%s: %s", + idx, + task.instance_id, + exc, + ) + self._record_context_overflow(task.instance_id) + pad = getattr(self._tokenizer, "pad_token_id", 0) or 0 + overflow_sample = RolloutSample( + input_ids=[pad], + completion_mask=[1], + old_log_probs=[0.0], + advantage=0.0, + model_version=self._model_ver(), + metrics={ + "reward": 0.0, + "turns": 0.0, + "context_overflow": 1.0, + "answer_called": 0.0, + }, + ) + self._submit_to_group(task, overflow_sample) + time.sleep(self._cfg.idle_backoff_s) + continue + except Exception: + _log.exception("rollout failed worker=%d id=%s", idx, task.instance_id) + time.sleep(self._cfg.idle_backoff_s) + continue + + if sample is None: + time.sleep(self._cfg.idle_backoff_s) + continue + + self._submit_to_group(task, sample) + + # ── Single rollout ───────────────────────────────────────────── + + async def _rollout(self, task: SWETask, episode_id: str) -> RolloutSample | None: + session = self._factory.create(task=task, episode_id=episode_id) + + # Accumulate the full token sequence across turns, matching TRL's + # _generate_one pattern: + # input_ids = initial_prompt_ids + turn1_ids + suffix1_ids + turn2_ids + ... + # completion_mask = [0]*prompt + [1]*turn1 + [0]*suffix1 + [1]*turn2 + ... + # old_log_probs = [0.0]*prompt + lp1 + [0.0]*suf1 + lp2 + ... + all_ids: list[int] = [] + all_mask: list[int] = [] + all_lps: list[float] = [] + + initial_prompt_ids: list[int] | None = None + # prev_prompt_ids: the FULL prompt (with add_generation_prompt=True) + # used for the most recently completed turn. Used for EOT-based + # interstitial extraction following Polar's prefix_merging approach + # (arXiv:2605.24220 §3.4.2). The next turn's canonical_tail = + # next_prompt[len(prev_prompt_ids):] contains the canonical response + # copy + interstitial; we split at EOT to get only the interstitial. + prev_prompt_ids: list[int] | None = None + # The raw turn_ids from the most recent generation, needed to detect + # whether the response already ends with EOT (for correct slicing). + prev_turn_ids: list[int] | None = None + + turns = 0 + answer_called = False + pending_intercept: dict[str, Any] | None = None + turn_limit: int | None = self._cfg.max_turns if self._cfg.max_turns > 0 else None + rollout_stop_reason = "max_turns" if turn_limit is not None else "running" + t0 = time.time() + + try: + while (turn_limit is None or turns < turn_limit) and not self._stop.is_set(): + if pending_intercept is not None: + intercept = pending_intercept + pending_intercept = None + else: + intercept = await session.next_request( + timeout_s=self._cfg.request_timeout_s, + ) + if intercept is None: + rollout_stop_reason = "agent_exit_detected" + break + + # ── Tokenize this turn's full prompt ────────────── + messages = _get_messages(intercept) + tools = _get_tools(intercept) + + # Trim messages to fit within context budget before tokenizing. + messages, _ = _fit_messages_to_context_window( + messages=messages, + tools=tools, + render_prompt_ids=self._render_prompt_ids, + requested_completion_tokens=self._cfg.max_completion_tokens, + max_model_len=self._cfg.max_model_len, + max_tool_message_chars=self._cfg.max_tool_message_chars, + min_tool_message_chars=self._cfg.min_tool_message_chars, + max_assistant_message_chars=self._cfg.max_assistant_message_chars, + min_assistant_message_chars=self._cfg.min_assistant_message_chars, + ) + + current_prompt_ids = self._render_prompt_ids(messages, tools) + + if initial_prompt_ids is None: + # First turn: the entire prompt is non-completion tokens. + initial_prompt_ids = current_prompt_ids + all_ids.extend(current_prompt_ids) + all_mask.extend([0] * len(current_prompt_ids)) + all_lps.extend([0.0] * len(current_prompt_ids)) + elif prev_prompt_ids is not None: + # Subsequent turns: compute the interstitial (tool-result + # suffix) between the last generation and this turn's prompt. + # + # Following Polar's prefix_merging (arXiv:2605.24220 §3.4.2) + # and TRL's _get_tool_suffix_ids approach: + # + # canonical_tail = next_prompt[len(prev_prompt):] contains: + # 1. Canonical copy of prev response (may differ from raw + # turn_ids due to BPE non-canonicality) + # 2. EOT token (<|im_end|>) + # 3. True interstitial (tool result, user msg, gen prompt) + # + # We split at EOT to extract only the interstitial (#3), + # avoiding the ~2× sequence bloat from duplicating responses. + # + # If the prefix breaks (due to message truncation changing + # earlier content between turns), we find the longest common + # prefix first, then EOT-split the remainder. + prev_len = len(prev_prompt_ids) + if ( + len(current_prompt_ids) >= prev_len + and current_prompt_ids[:prev_len] == prev_prompt_ids + ): + canonical_tail = current_prompt_ids[prev_len:] + else: + # Message truncation changed earlier content — find + # the actual divergence point. + common_len = 0 + for a, b in zip(current_prompt_ids, prev_prompt_ids): + if a != b: + break + common_len += 1 + canonical_tail = current_prompt_ids[common_len:] + if common_len < prev_len: + _log.debug( + "prefix diverged at turn %d: expected %d, " + "common=%d (likely truncation drift)", + turns, prev_len, common_len, + ) + + suffix_ids = _extract_interstitial_after_eot( + canonical_tail=canonical_tail, + prev_turn_ids=prev_turn_ids, + eot_id=self._tokenizer.eos_token_id, + ) + + all_ids.extend(suffix_ids) + all_mask.extend([0] * len(suffix_ids)) + all_lps.extend([0.0] * len(suffix_ids)) + + turns += 1 + + # ── Generate via /v1/completions ────────────────── + # If context overflows on turn 1, let it propagate to _loop() + # which emits a zero-reward placeholder. On later turns, + # terminate the rollout with the partial trajectory (reward=0) + # so the session still contributes to the group. + try: + turn_ids, turn_lps, text, finish_reason = self._generate( + current_prompt_ids + ) + except SWERolloutWorker.ContextOverflowError: + if turns <= 1: + raise + _log.warning( + "context overflow at turn %d for task=%s — " + "terminating with partial trajectory (%d tokens)", + turns, + task.instance_id, + len(all_ids), + ) + rollout_stop_reason = "context_overflow" + self._record_context_overflow(task.instance_id) + break + + all_ids.extend(turn_ids) + all_mask.extend([1] * len(turn_ids)) + all_lps.extend(turn_lps) + + # For next turn's suffix computation: save this turn's full + # prompt (with add_generation_prompt=True) as the reference. + # The next turn's prompt will extend this prefix (same messages + # plus the response + tool result), so canonical_tail = + # next_prompt[len(prev_prompt):] gives us the new content. + # + # This is Polar/TRL's approach: comparing canonical-vs-canonical + # server tokenizations avoids BPE drift from raw tokens. + prev_prompt_ids = current_prompt_ids + prev_turn_ids = turn_ids + + # ── Build chat response for Pi ──────────────────── + assistant_message = _parse_assistant_message( + tokenizer=self._tokenizer, + completion_ids=turn_ids, + fallback_text=text, + ) + chat_resp = _make_chat_response( + assistant_message, + self._vllm_model, + finish_reason=finish_reason, + ) + + # ── Check for answer tool call ──────────────────── + if _has_answer_call(chat_resp): + answer_called = True + + await session.deliver(intercept, chat_resp) + + # Host-side answer handler marks this as soon as Pi executes answer(). + if bool(getattr(session, "answer_called", False)): + answer_called = True + + if answer_called: + rollout_stop_reason = "answer_called" + break + + # Semantic completion fast-path (especially important on HF + # Sandbox, where process-exit detection can be delayed). + if ( + self._cfg.stop_on_idle_terminal_response + and self._cfg.post_response_grace_s > 0 + and _is_terminal_non_tool_response(chat_resp) + ): + try: + maybe_next = await session.next_request( + timeout_s=self._cfg.post_response_grace_s, + ) + except TimeoutError: + rollout_stop_reason = "idle_after_terminal_stop" + _log.info( + "rollout terminal idle-stop: instance_id=%s turns=%d", + task.instance_id, + turns, + ) + break + + if maybe_next is None: + rollout_stop_reason = "agent_exit_after_terminal_stop" + break + pending_intercept = maybe_next + + # ── Reward ──────────────────────────────────────────── + # Context overflow means the agent didn't finish; assign reward=0. + if rollout_stop_reason == "context_overflow": + reward = 0.0 + else: + vr = session.verify(transcript=[]) + reward = float(getattr(vr, "env_reward", 0.0) or 0.0) + + if not all_lps: + pad = getattr(self._tokenizer, "pad_token_id", 0) or 0 + all_ids = [pad] + all_mask = [1] + all_lps = [0.0] + + return RolloutSample( + input_ids=all_ids, + completion_mask=all_mask, + old_log_probs=all_lps, + advantage=reward, + model_version=self._model_ver(), + metrics={ + "reward": reward, + "turns": float(turns), + "answer_called": float(answer_called), + "context_overflow": float( + rollout_stop_reason == "context_overflow" + ), + "terminal_idle_stop": float( + rollout_stop_reason + in { + "idle_after_terminal_stop", + "agent_exit_after_terminal_stop", + } + ), + "request_idle_timeout": float( + rollout_stop_reason == "request_idle_timeout" + ), + "wall_s": round(time.time() - t0, 3), + "n_tokens": float(len(all_ids)), + }, + ) + finally: + session.close() + + # ── vLLM call (matches TRL's _generate_one_turn exactly) ────── + + def _render_prompt_ids( + self, + messages: list[dict[str, Any]], + tools: list[dict[str, Any]] | None, + ) -> list[int]: + kwargs: dict[str, Any] = { + "add_generation_prompt": True, + "return_dict": False, + } + # ``tools=[]`` can trigger unwanted boilerplate in some templates. + if tools: + kwargs["tools"] = tools + # Use TRL's prefix-preserving training template when available. + if self._chat_template: + kwargs["chat_template"] = self._chat_template + + # Qwen3.5's chat template iterates tool_call.arguments with |items, + # expecting a dict. The OpenAI API spec (and our _normalize_tool_calls) + # stores arguments as a JSON *string*. Parse them into dicts here so + # the Jinja template can iterate key-value pairs. + messages = _ensure_tool_call_arguments_parsed(messages) + + try: + ids = self._tokenizer.apply_chat_template(messages, **kwargs) + except TypeError: + kwargs.pop("tools", None) + ids = self._tokenizer.apply_chat_template(messages, **kwargs) + return cast(list[int], ids) + + class ContextOverflowError(RuntimeError): + """Raised when prompt + max_tokens exceeds max_model_len. + + Caught in _loop() to terminate the session gracefully with + reward=0, avoiding the infinite retry loop. + """ + + def _generate( + self, + prompt_ids: list[int], + ) -> tuple[list[int], list[float], str, str | None]: + """POST /v1/completions with token IDs. + + Returns: ``(token_ids, token_logprobs, text, finish_reason)``. + + Raises ContextOverflowError if the prompt would exceed + max_model_len. This is caught in _loop() for graceful + termination with reward=0. + """ + # Pre-generation guard: check before sending to vLLM so we don't + # waste a round trip and risk the engine going idle. + total_needed = len(prompt_ids) + self._cfg.max_completion_tokens + if total_needed > self._cfg.max_model_len: + raise SWERolloutWorker.ContextOverflowError( + f"prompt ({len(prompt_ids)}) + max_tokens " + f"({self._cfg.max_completion_tokens}) = {total_needed} > " + f"max_model_len ({self._cfg.max_model_len})" + ) + + body = { + "model": self._vllm_model, + "prompt": prompt_ids, + "max_tokens": self._cfg.max_completion_tokens, + "temperature": self._cfg.temperature, + "n": 1, + "return_token_ids": True, + "logprobs": 0, + } + resp = requests.post( + f"{self._vllm_base_url}/v1/completions", + headers={ + "Authorization": f"Bearer {self._vllm_api_key}", + "Content-Type": "application/json", + }, + json=body, + timeout=self._cfg.request_timeout_s, + ) + if resp.status_code != 200: + error_text = resp.text[:400] + # Detect context overflow from vLLM's 400 response + if resp.status_code == 400 and "max_model_len" in error_text: + raise SWERolloutWorker.ContextOverflowError( + f"vLLM rejected: {error_text}" + ) + raise RuntimeError(f"vllm {resp.status_code}: {error_text}") + + choice = resp.json()["choices"][0] + return ( + choice["token_ids"], + choice["logprobs"]["token_logprobs"], + choice.get("text", ""), + choice.get("finish_reason"), + ) + + +# ── Helpers ──────────────────────────────────────────────────────────── + + +def _extract_interstitial_after_eot( + *, + canonical_tail: list[int], + prev_turn_ids: list[int] | None, + eot_id: int | None, +) -> list[int]: + """Extract the true interstitial from a canonical tail using EOT splitting. + + Following Polar's ``PrefixMergingBuilder._slice_interstitial`` and TRL's + ``_get_tool_suffix_ids`` EOS-trimming approach: + + ``canonical_tail`` = next_prompt[len(prev_prompt):] contains: + 1. Canonical re-rendering of the previous assistant response + 2. EOT token (``<|im_end|>`` for Qwen/ChatML) + 3. True interstitial (tool result, user turn, gen prompt) + + We split at the first EOT to skip the canonical response copy (#1-#2) + and return only the interstitial (#3). + + If ``prev_turn_ids`` already ends with EOT (natural stop), the EOT in + the canonical tail is a duplicate — skip it. Otherwise (truncation), + include it so the stream closes the assistant turn. + + Parameters + ---------- + canonical_tail: + Tokens from next_prompt that come after prev_prompt. + prev_turn_ids: + The raw response token IDs from the previous generation. + eot_id: + The end-of-turn token ID (e.g. ``<|im_end|>`` / eos_token_id). + + Returns + ------- + list[int] + Only the interstitial tokens (tool results, user messages, + generation prompt for the next turn). + """ + if not canonical_tail: + return canonical_tail + + if eot_id is None: + raise ValueError( + "Cannot extract interstitial without eot_id (tokenizer.eos_token_id). " + "This would duplicate the previous response in the training stream." + ) + + # Find the first EOT in the canonical tail. This marks the end of the + # canonical copy of the previous response. + try: + eot_pos = canonical_tail.index(eot_id) + except ValueError: + # No EOT found — edge case (e.g. truncated response without stop token). + # Return full tail as interstitial (conservative). + return canonical_tail + + # If the raw response already ended with EOT (model emitted stop token), + # the EOT in canonical_tail is a duplicate — skip past it. + # Otherwise (response was truncated), include the EOT to properly close + # the assistant turn in the training stream. + if prev_turn_ids and prev_turn_ids[-1] == eot_id: + return canonical_tail[eot_pos + 1:] + else: + return canonical_tail[eot_pos:] + + +def _get_messages(intercept: dict[str, Any]) -> list[dict[str, Any]]: + msgs = intercept.get("messages") + if isinstance(msgs, list) and msgs: + return msgs + body = intercept.get("body") or {} + msgs = body.get("messages") + if isinstance(msgs, list) and msgs: + return msgs + raise RuntimeError("intercept has no messages") + + +def _get_tools(intercept: dict[str, Any]) -> list[dict[str, Any]] | None: + tools = intercept.get("tools") + if isinstance(tools, list): + return [t for t in tools if isinstance(t, dict)] + body = intercept.get("body") or {} + tools = body.get("tools") + if isinstance(tools, list): + return [t for t in tools if isinstance(t, dict)] + return None + + +def _ensure_tool_call_arguments_parsed( + messages: list[dict[str, Any]], +) -> list[dict[str, Any]]: + """Deep-copy messages, parsing tool_call arguments from JSON strings to dicts. + + Qwen3.5's chat template iterates ``tool_call.arguments|items`` expecting a + mapping. The OpenAI API (and our interception layer) stores arguments as a + JSON string. This bridges the gap. + """ + out: list[dict[str, Any]] = [] + for msg in messages: + tool_calls = msg.get("tool_calls") + if not tool_calls or not isinstance(tool_calls, list): + out.append(msg) + continue + new_tcs: list[dict[str, Any]] = [] + for tc in tool_calls: + if not isinstance(tc, dict): + new_tcs.append(tc) + continue + fn = tc.get("function") + if not isinstance(fn, dict): + new_tcs.append(tc) + continue + args = fn.get("arguments") + if isinstance(args, str): + try: + args = json.loads(args) + except (json.JSONDecodeError, TypeError): + args = {} + new_tcs.append( + {**tc, "function": {**fn, "arguments": args if isinstance(args, dict) else {}}} + ) + out.append({**msg, "tool_calls": new_tcs}) + return out + + +def _normalize_tool_calls(raw_tool_calls: Any) -> list[dict[str, Any]]: + normalized: list[dict[str, Any]] = [] + if not isinstance(raw_tool_calls, list): + return normalized + + for raw_call in raw_tool_calls: + if not isinstance(raw_call, dict): + continue + function = raw_call.get("function") + if not isinstance(function, dict): + continue + name = function.get("name") + if not isinstance(name, str) or not name: + continue + + arguments = function.get("arguments", {}) + if isinstance(arguments, str): + arguments_str = arguments + else: + arguments_str = json.dumps(arguments or {}, ensure_ascii=False) + + normalized.append( + { + "id": str(raw_call.get("id") or f"call_{uuid.uuid4().hex[:8]}"), + "type": "function", + "function": { + "name": name, + "arguments": arguments_str, + }, + } + ) + + return normalized + + +def _parse_assistant_message( + *, + tokenizer: Any, + completion_ids: list[int], + fallback_text: str, +) -> dict[str, Any]: + parsed: dict[str, Any] | None = None + if parse_response is not None: + try: + maybe = parse_response(tokenizer, completion_ids) + if isinstance(maybe, dict): + parsed = maybe + except Exception: + parsed = None + + if parsed is None: + return {"role": "assistant", "content": fallback_text} + + content = parsed.get("content") + if content is None: + content = "" + if not isinstance(content, str): + content = str(content) + + message: dict[str, Any] = { + "role": "assistant", + "content": content, + } + tool_calls = _normalize_tool_calls(parsed.get("tool_calls")) + if tool_calls: + message["tool_calls"] = tool_calls + return message + + +def _make_chat_response( + assistant_message: dict[str, Any], + model: str, + *, + finish_reason: str | None = "stop", +) -> dict[str, Any]: + return { + "id": f"chatcmpl-{uuid.uuid4().hex[:8]}", + "object": "chat.completion", + "model": model, + "choices": [ + { + "index": 0, + "message": assistant_message, + "finish_reason": finish_reason or "stop", + } + ], + } + + +def _is_terminal_non_tool_response(resp: dict[str, Any]) -> bool: + for choice in resp.get("choices") or []: + message = (choice or {}).get("message") or {} + if message.get("tool_calls") or []: + return False + if (choice or {}).get("finish_reason") == "stop": + return True + return False + + +def _has_answer_call(resp: dict[str, Any]) -> bool: + for choice in resp.get("choices") or []: + for tc in ((choice or {}).get("message") or {}).get("tool_calls") or []: + if ((tc or {}).get("function") or {}).get("name") == "answer": + return True + return False + + +# ── Context window message trimming ──────────────────────────────────── + +_TRUNCATION_MARKER = "\n...[truncated]...\n" +_OMITTED_TOOL_OUTPUT_MARKER = "[tool output omitted]" +_OMITTED_ASSISTANT_TEXT_MARKER = "[omitted]" + + +def _truncate_text_middle( + text: str, + *, + max_chars: int, + marker: str = _TRUNCATION_MARKER, +) -> tuple[str, bool]: + """Truncate text in the middle, preserving head and tail.""" + if max_chars <= 0 or len(text) <= max_chars: + return text, False + + usable = max_chars - len(marker) + if usable <= 8: + return marker[: max(1, max_chars)], True + + head = max(1, int(usable * 0.75)) + tail = max(1, usable - head) + return text[:head].rstrip() + marker + text[-tail:].lstrip(), True + + +def _truncate_messages_for_prompt_budget( + messages: Sequence[dict[str, Any]], + *, + max_tool_message_chars: int, + max_assistant_message_chars: int, +) -> tuple[list[dict[str, Any]], int, int]: + """Truncate long tool/assistant messages in-place.""" + truncated_messages: list[dict[str, Any]] = [] + tool_truncations = 0 + assistant_truncations = 0 + + for message in messages: + updated = dict(message) + content = updated.get("content") + if not isinstance(content, str): + truncated_messages.append(updated) + continue + + role = str(updated.get("role") or "") + if role == "tool": + content, changed = _truncate_text_middle( + content, + max_chars=max_tool_message_chars, + ) + if changed: + tool_truncations += 1 + elif role == "assistant": + content, changed = _truncate_text_middle( + content, + max_chars=max_assistant_message_chars, + ) + if changed: + assistant_truncations += 1 + + updated["content"] = content + truncated_messages.append(updated) + + return truncated_messages, tool_truncations, assistant_truncations + + +def _replace_oldest_message_content( + messages: Sequence[dict[str, Any]], + *, + role: str, + replacement: str, +) -> tuple[list[dict[str, Any]], bool]: + """Replace the oldest message of a given role with a short placeholder.""" + updated_messages = [dict(message) for message in messages] + for idx, message in enumerate(updated_messages): + if message.get("role") != role: + continue + content = message.get("content") + if not isinstance(content, str) or content == replacement: + continue + if len(replacement) >= len(content): + continue + message["content"] = replacement + updated_messages[idx] = message + return updated_messages, True + return updated_messages, False + + +def _fit_messages_to_context_window( + *, + messages: Sequence[dict[str, Any]], + tools: list[dict[str, Any]] | None, + render_prompt_ids: Callable[ + [list[dict[str, Any]], list[dict[str, Any]] | None], + list[int], + ], + requested_completion_tokens: int, + max_model_len: int, + max_tool_message_chars: int, + min_tool_message_chars: int, + max_assistant_message_chars: int, + min_assistant_message_chars: int, + safety_margin: int = 16, +) -> tuple[list[dict[str, Any]], list[int]]: + """Progressively truncate messages to fit prompt within context budget. + + Strategy: + 1. Truncate tool outputs to max_tool_message_chars, halving until min. + 2. Truncate assistant messages similarly. + 3. Replace oldest tool messages entirely with a placeholder. + 4. Replace oldest assistant messages entirely with a placeholder. + + Returns (trimmed_messages, prompt_token_ids). + """ + prompt_budget = max( + 1, + max_model_len - max(1, int(requested_completion_tokens)) - safety_margin, + ) + tool_char_budget = max(1, int(max_tool_message_chars)) + assistant_char_budget = max(1, int(max_assistant_message_chars)) + min_tool_chars = max(1, int(min_tool_message_chars)) + min_assistant_chars = max(1, int(min_assistant_message_chars)) + + base_messages = [dict(message) for message in messages] + prepared_messages = base_messages + prompt_ids = render_prompt_ids(prepared_messages, tools) + tool_truncations = 0 + assistant_truncations = 0 + + # Phase 1: progressive truncation + while True: + prepared_messages, tool_truncations, assistant_truncations = ( + _truncate_messages_for_prompt_budget( + base_messages, + max_tool_message_chars=tool_char_budget, + max_assistant_message_chars=assistant_char_budget, + ) + ) + prompt_ids = render_prompt_ids(prepared_messages, tools) + if len(prompt_ids) <= prompt_budget: + break + if tool_char_budget > min_tool_chars: + tool_char_budget = max(min_tool_chars, tool_char_budget // 2) + continue + if assistant_char_budget > min_assistant_chars: + assistant_char_budget = max( + min_assistant_chars, + assistant_char_budget // 2, + ) + continue + break + + # Phase 2: replace entire oldest messages with placeholders + omitted_tool_messages = 0 + omitted_assistant_messages = 0 + while len(prompt_ids) > prompt_budget: + prepared_messages, changed = _replace_oldest_message_content( + prepared_messages, + role="tool", + replacement=_OMITTED_TOOL_OUTPUT_MARKER, + ) + if changed: + omitted_tool_messages += 1 + prompt_ids = render_prompt_ids(prepared_messages, tools) + continue + + prepared_messages, changed = _replace_oldest_message_content( + prepared_messages, + role="assistant", + replacement=_OMITTED_ASSISTANT_TEXT_MARKER, + ) + if not changed: + break + omitted_assistant_messages += 1 + prompt_ids = render_prompt_ids(prepared_messages, tools) + + if ( + tool_truncations + or assistant_truncations + or omitted_tool_messages + or omitted_assistant_messages + ): + _log.info( + "trimmed intercepted prompt: prompt_tokens=%d/%d tool_truncations=%d " + "assistant_truncations=%d omitted_tool_messages=%d " + "omitted_assistant_messages=%d", + len(prompt_ids), + prompt_budget, + tool_truncations, + assistant_truncations, + omitted_tool_messages, + omitted_assistant_messages, + ) + + return prepared_messages, prompt_ids diff --git a/examples/mini_swe_env/async_grpo/space_app/Dockerfile b/examples/mini_swe_env/async_grpo/space_app/Dockerfile new file mode 100644 index 000000000..32e224a4a --- /dev/null +++ b/examples/mini_swe_env/async_grpo/space_app/Dockerfile @@ -0,0 +1,68 @@ +# vLLM 0.21.0 — supports Qwen3_5ForCausalLM (text-only), transformers >=5.0, +# and the four-phase weight-transfer protocol (init/start/update/finish). +# TRL AsyncGRPO uses vLLM as an external HTTP server; its optional-dep pin +# (<=0.18.0) does not apply when vLLM is installed independently. +FROM vllm/vllm-openai:v0.21.0 + +# System deps + Docker CLI (for Docker sandbox backend) +RUN apt-get update -qq && \ + apt-get install -y -qq --no-install-recommends curl ca-certificates docker.io git && \ + update-ca-certificates && \ + rm -rf /var/lib/apt/lists/* + +# uv +COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv + +# HF Spaces requires uid 1000 +RUN useradd -m -u 1000 user && usermod -aG docker user +ENV HOME=/home/user \ + PATH=/home/user/.local/bin:$PATH + +# Pre-create HF cache dir (writable by user) +RUN mkdir -p /home/user/.cache/huggingface && chown -R user:user /home/user/.cache + +# Copy repo +WORKDIR $HOME/app +COPY --chown=user . . + +# Install deps: +# 1. Force-upgrade huggingface-hub (resolves vLLM vs hf-sandbox conflict). +# 2. Force-install transformers >=5.2.0 (required by TRL v1.6.0 AsyncGRPO, +# and for native Qwen3.5-4B model type support). --no-deps avoids +# pulling vLLM's bundled transformers which may conflict. +# 3. Install Qwen3.5 fast-path kernel deps (latest known): +# - flash-linear-attention: provides `fla.ops.gated_delta_rule` +# NOTE: we intentionally do NOT install `causal-conv1d` in Spaces because +# its CUDA extension currently fails to load there (`libcudart.so.12`), +# which crashes model import. Qwen3.5 will fall back for conv1d. +# 4. TRL emits a warning about vLLM >0.18 but functions correctly — +# the version check is advisory, not blocking. +RUN uv pip install --system --no-cache "huggingface-hub>=1.5" && \ + uv pip install --system --no-cache --no-deps \ + -e "." \ + -e "envs/mini_swe_env" && \ + uv pip install --system --no-cache \ + "trl @ git+https://github.com/rycerzes/trl@feat/async-grpo-parity" \ + "peft>=0.15.0" \ + "accelerate>=1.13.0" \ + "hf-sandbox[named-tunnels] @ git+https://github.com/rycerzes/hf-sandbox@feat/named-tunnels" \ + "datasets>=4.8.0" \ + "aiohttp>=3.13.5" \ + "fastmcp>=3.3.0" \ + "trackio>=0.25.0" \ + "bitsandbytes>=0.43.0" \ + "flash-linear-attention>=0.5.0" && \ + uv pip install --system --no-cache --no-deps "transformers>=5.2.0" && \ + uv pip uninstall --system causal-conv1d || true + +# Runtime defaults +ENV PYTHONPATH=$HOME/app/src:$HOME/app/envs \ + PYTHONUNBUFFERED=1 \ + INTERCEPTION_HOST=0.0.0.0 \ + INTERCEPTION_PORT=7860 + +USER user +EXPOSE 7860 + +ENTRYPOINT [] +CMD ["bash", "start.sh"] diff --git a/examples/mini_swe_env/async_grpo/space_app/README.md b/examples/mini_swe_env/async_grpo/space_app/README.md new file mode 100644 index 000000000..3cbece430 --- /dev/null +++ b/examples/mini_swe_env/async_grpo/space_app/README.md @@ -0,0 +1,47 @@ +--- +title: SWE Async GRPO Training +emoji: 🔧 +colorFrom: blue +colorTo: green +sdk: docker +app_port: 7860 +suggested_hardware: a10g-largex4 +startup_duration_timeout: 30m +preload_from_hub: + - Qwen/Qwen3.5-4B +--- + +# SWE Async GRPO Training + +Trains a language model on SWE-Gym tasks using TRL's AsyncGRPOTrainer +with Pi as the coding agent. + +**Architecture**: Pi runs in HF Sandboxes, its LLM calls are intercepted +and forwarded to a co-located vLLM server for generation with exact +token IDs and logprobs. The trainer runs GRPO updates on the remaining +three GPUs (default `TRAINER_GPU=1,2,3`). + +See `SWE_ASYNC_GRPO_SPACE_DEPLOYMENT.md` in the repo for full details. + +## Required Secrets + +Set these in the Space Settings tab: + +| Secret | Description | +|--------|-------------| +| `HF_TOKEN` | HF token for sandbox creation, model downloads, and checkpoint uploads | +| `INTERCEPTION_AUTH_TOKEN` | Shared auth token for Pi ↔ InterceptionServer | +| `SWE_MODEL` | Model ID to serve and train (e.g. `Qwen/Qwen3.5-4B`) | + +## Optional Variables (recommended) + +| Variable | Description | +|----------|-------------| +| `TRACKIO_SPACE_ID` | Trackio dashboard Space for metrics (e.g. `user/swe-grpo-dashboard`) | +| `SWE_REWARD_MODE` | Reward mode (`binary` recommended, default set by deploy script) | +| `SWE_CHECKPOINT_TO_HUB` | Enable checkpoint upload to HF Hub (`1`/`0`, default `1` on Spaces) | +| `SWE_HUB_MODEL_ID` | Hub model repo used as checkpoint bucket (e.g. `user/swe-async-grpo-checkpoints`) | +| `SWE_RESUME_FROM_CHECKPOINT` | `auto` (default) to resume from `last-checkpoint`, or explicit path/name | +| `SWE_CHECKPOINT_SAVE_STEPS` | Save/upload frequency in trainer steps (default `2`) | +| `SWE_CHECKPOINT_SAVE_TOTAL_LIMIT` | Number of local checkpoints to keep (default `2`) | +| `SWE_HUB_PRIVATE_REPO` | Create/use private checkpoint repo (`1`/`0`, default `1`) | diff --git a/examples/mini_swe_env/async_grpo/space_app/deploy_hf_space.sh b/examples/mini_swe_env/async_grpo/space_app/deploy_hf_space.sh new file mode 100755 index 000000000..0f95634ab --- /dev/null +++ b/examples/mini_swe_env/async_grpo/space_app/deploy_hf_space.sh @@ -0,0 +1,413 @@ +#!/bin/bash +# deploy_hf_space.sh — Deploy SWE Async GRPO training to HF Space + HF Sandbox +# +# This script: +# 1. Prepares the Space directory (minimal repo subset) +# 2. Creates/updates the HF Space +# 3. Configures secrets and environment variables +# 4. Sets hardware (a10g-largex4) +# 5. Pushes code and triggers build +# 6. Monitors build/startup +# +# Prerequisites: +# - hf CLI installed and authenticated (hf auth login) +# - HF_TOKEN set or in ~/.cache/huggingface/token +# - Python with huggingface_hub installed in repo .venv (preferred), or uv/python3 available +# +# Usage: +# bash examples/mini_swe_env/async_grpo/space_app/deploy_hf_space.sh [OPTIONS] +# +# Options: +# --space-id OWNER/NAME Space ID (default: $HF_SPACE_ID or rycerzes/swe-async-grpo-train) +# --model MODEL_ID Model to train (default: Qwen/Qwen3.5-4B) +# --max-tasks N Number of SWE tasks (default: 230) +# --max-steps N Training steps (default: 230) +# --max-turns N Max agent turns per rollout (default: 0, i.e. unlimited) +# --num-generations N Samples per prompt (default: 16) +# --hardware HW Hardware tier (default: a10g-largex4) +# --sandbox-backend BACKEND Backend for agent (default: hf) +# --skip-build Only configure, don't push code +# --monitor Monitor logs after deploy +# --pause Pause the Space (stop billing) +# --resume Resume a paused Space +# +set -euo pipefail + +# ── Defaults ──────────────────────────────────────────────────────────── +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "$SCRIPT_DIR/../../../.." && pwd)" + +SPACE_ID="${HF_SPACE_ID:-rycerzes/swe-async-grpo-train}" +# Default deployment profile for SWE-Gym Lite: +# - Base checkpoint: Qwen/Qwen3.5-4B +# - Samples per prompt: 16 +# - One full pass over lite split (230 tasks): max_tasks=230, max_steps=230 +# - Unlimited turns (max_turns=0) +MODEL="${SWE_MODEL:-Qwen/Qwen3.5-4B}" +MAX_TASKS=230 +MAX_STEPS=230 +MAX_TURNS=0 +NUM_GENERATIONS=16 +HARDWARE="a10g-largex4" +SANDBOX_BACKEND="hf" +SKIP_BUILD=false +MONITOR=false +PAUSE=false +RESUME=false + +# ── Tooling prerequisites ─────────────────────────────────────────────── +# Prefer repo-local venv Python, then uv, then system python3. +if [ -x "$REPO_ROOT/.venv/bin/python" ]; then + PYTHON_CMD=("$REPO_ROOT/.venv/bin/python") +elif [ -x "$REPO_ROOT/.venv/Scripts/python.exe" ]; then + PYTHON_CMD=("$REPO_ROOT/.venv/Scripts/python.exe") +elif command -v uv >/dev/null 2>&1; then + PYTHON_CMD=(uv run python) +elif command -v python3 >/dev/null 2>&1; then + PYTHON_CMD=("$(command -v python3)") +else + echo "ERROR: Python interpreter not found." + echo "Expected one of:" + echo " - $REPO_ROOT/.venv/bin/python" + echo " - $REPO_ROOT/.venv/Scripts/python.exe" + echo " - uv run python" + echo " - python3" + exit 1 +fi + +if ! command -v hf >/dev/null 2>&1; then + echo "ERROR: hf CLI not found. Install huggingface_hub CLI and run 'hf auth login'." + exit 1 +fi + +# Ensure Python subprocesses use UTF-8 output (avoids cp1252 emoji crashes on Windows). +export PYTHONUTF8=1 + +# ── Parse args ────────────────────────────────────────────────────────── +while [[ $# -gt 0 ]]; do + case "$1" in + --space-id) SPACE_ID="$2"; shift 2 ;; + --model) MODEL="$2"; shift 2 ;; + --max-tasks) MAX_TASKS="$2"; shift 2 ;; + --max-steps) MAX_STEPS="$2"; shift 2 ;; + --max-turns) MAX_TURNS="$2"; shift 2 ;; + --num-generations) NUM_GENERATIONS="$2"; shift 2 ;; + --hardware) HARDWARE="$2"; shift 2 ;; + --sandbox-backend) SANDBOX_BACKEND="$2"; shift 2 ;; + --skip-build) SKIP_BUILD=true; shift ;; + --monitor) MONITOR=true; shift ;; + --pause) PAUSE=true; shift ;; + --resume) RESUME=true; shift ;; + *) echo "Unknown option: $1"; exit 1 ;; + esac +done + +# ── Resolve HF_TOKEN ──────────────────────────────────────────────────── +if [ -z "${HF_TOKEN:-}" ]; then + if [ -f "$HOME/.cache/huggingface/token" ]; then + HF_TOKEN=$(cat "$HOME/.cache/huggingface/token") + else + echo "ERROR: HF_TOKEN not set and not found in ~/.cache/huggingface/token" + echo "Run: hf auth login" + exit 1 + fi +fi +export HF_TOKEN + +# ── Pause/Resume shortcuts ────────────────────────────────────────────── +if [ "$PAUSE" = true ]; then + echo "⏸ Pausing Space $SPACE_ID..." + "${PYTHON_CMD[@]}" -c " +from huggingface_hub import HfApi +api = HfApi(token='$HF_TOKEN') +api.pause_space('$SPACE_ID') +print('Space paused.') +" + exit 0 +fi + +if [ "$RESUME" = true ]; then + echo "▶ Resuming Space $SPACE_ID..." + "${PYTHON_CMD[@]}" -c " +from huggingface_hub import HfApi +api = HfApi(token='$HF_TOKEN') +api.restart_space('$SPACE_ID', factory_reboot=True) +print('Space restarted.') +" + exit 0 +fi + +# ── Print config ──────────────────────────────────────────────────────── +echo "╔══════════════════════════════════════════════════════════╗" +echo "║ SWE Async GRPO — HF Space Deployment ║" +echo "╠══════════════════════════════════════════════════════════╣" +printf "║ %-54s ║\n" "Space: $SPACE_ID" +printf "║ %-54s ║\n" "Model: $MODEL" +printf "║ %-54s ║\n" "Hardware: $HARDWARE" +printf "║ %-54s ║\n" "Tasks: $MAX_TASKS" +printf "║ %-54s ║\n" "Steps: $MAX_STEPS" +printf "║ %-54s ║\n" "Turns: $MAX_TURNS" +printf "║ %-54s ║\n" "Gens: $NUM_GENERATIONS" +printf "║ %-54s ║\n" "Sandbox: $SANDBOX_BACKEND" +echo "╚══════════════════════════════════════════════════════════╝" +echo "" + +# ── Step 1: Create Space (idempotent) ────────────────────────────────── +echo "[1/6] Creating Space (if needed)..." +hf repo create "$SPACE_ID" --repo-type space --space-sdk docker --no-private --exist-ok >/dev/null +echo " ✓ Space exists: https://huggingface.co/spaces/$SPACE_ID" + +# ── Step 2: Configure secrets ────────────────────────────────────────── +echo "[2/6] Configuring secrets and variables..." +"${PYTHON_CMD[@]}" << PYEOF +from huggingface_hub import HfApi +import os, secrets + +api = HfApi(token=os.environ['HF_TOKEN']) +space_id = "$SPACE_ID" +checkpoint_repo = f"{space_id}-checkpoints" + +# Create/ensure checkpoint bucket repo for restart-safe training state. +try: + api.create_repo(repo_id=checkpoint_repo, repo_type="model", private=True, exist_ok=True) + print(f" ✓ Checkpoint repo: {checkpoint_repo}") +except Exception as e: + print(f" ⚠ Checkpoint repo {checkpoint_repo}: {e}") + +# Secrets (sensitive) +space_secrets = { + "HF_TOKEN": os.environ["HF_TOKEN"], + "INTERCEPTION_AUTH_TOKEN": secrets.token_urlsafe(32), + "SWE_MODEL": "$MODEL", + "VLLM_API_KEY": "token", +} + +# named tunnel secrets (optional — enables reliable sandbox connectivity) +cf_secrets = { + "CF_API_TOKEN": os.environ.get("CF_API_TOKEN", ""), + "CF_ACCOUNT_ID": os.environ.get("CF_ACCOUNT_ID", ""), + "CF_ZONE_ID": os.environ.get("CF_ZONE_ID", ""), + "CF_DOMAIN": os.environ.get("CF_DOMAIN", ""), +} +for key, value in cf_secrets.items(): + if value: + space_secrets[key] = value +for key, value in space_secrets.items(): + try: + api.add_space_secret(space_id, key, value) + print(f" ✓ Secret: {key}") + except Exception as e: + print(f" ⚠ Secret {key}: {e}") + +# Variables (non-sensitive) +variables = { + "MAX_MODEL_LEN": "40960", + "GPU_MEMORY_UTILIZATION": "0.75", + "VLLM_GPU": "0", + "TRAINER_GPU": os.environ.get("TRAINER_GPU", "1,2,3"), + "INTERCEPTION_HOST": "0.0.0.0", + "INTERCEPTION_PORT": "7860", + "TRL_EXPERIMENTAL_SILENCE": "1", + "TRACKIO_SPACE_ID": "rycerzes/swe-grpo-dashboard", + "SWE_REWARD_MODE": "binary", + "SWE_CHECKPOINT_TO_HUB": "1", + "SWE_HUB_MODEL_ID": checkpoint_repo, + "SWE_RESUME_FROM_CHECKPOINT": "none", + "SWE_CHECKPOINT_SAVE_STEPS": "2", + "SWE_CHECKPOINT_SAVE_TOTAL_LIMIT": "2", + "SWE_HUB_PRIVATE_REPO": "1", +} +for key, value in variables.items(): + try: + api.add_space_variable(space_id, key, value) + print(f" ✓ Var: {key}={value}") + except Exception as e: + print(f" ⚠ Var {key}: {e}") +PYEOF + +# ── Step 3: Set hardware ─────────────────────────────────────────────── +echo "[3/6] Setting hardware to $HARDWARE..." +"${PYTHON_CMD[@]}" -c " +from huggingface_hub import HfApi +import os +api = HfApi(token=os.environ['HF_TOKEN']) +api.request_space_hardware('$SPACE_ID', '$HARDWARE') +print(' ✓ Hardware: $HARDWARE') +" + +# ── Step 4: Prepare staging directory ────────────────────────────────── +if [ "$SKIP_BUILD" = true ]; then + echo "[4/6] Skipping build (--skip-build)" + echo "[5/6] Skipping upload" +else + echo "[4/6] Preparing Space directory..." + STAGE_DIR=$(mktemp -d) + trap "rm -rf $STAGE_DIR" EXIT + + cd "$REPO_ROOT" + + # Copy essential files only (Python-based to avoid rsync shell/path quirks on Windows). + export REPO_ROOT STAGE_DIR + "${PYTHON_CMD[@]}" << 'PYEOF' +import fnmatch +import os +import shutil +from pathlib import Path + +repo = Path(os.environ["REPO_ROOT"]) +stage = Path(os.environ["STAGE_DIR"]) + + +def _ignore(patterns: list[str]): + def _inner(_dir: str, names: list[str]) -> set[str]: + ignored: set[str] = set() + for name in names: + for pat in patterns: + if fnmatch.fnmatch(name, pat): + ignored.add(name) + break + return ignored + + return _inner + + +def copy_tree(src_rel: str, dst_rel: str, patterns: list[str]) -> None: + src = repo / src_rel + dst = stage / dst_rel + if not src.exists(): + raise FileNotFoundError(f"Missing source path: {src}") + shutil.copytree(src, dst, dirs_exist_ok=True, ignore=_ignore(patterns)) + + +common_ignores = [ + "__pycache__", + "*.pyc", + ".venv", + ".tox", + ".mypy_cache", + ".pytest_cache", + ".ruff_cache", + "*.egg-info", + "uv.lock", +] + +copy_tree("src", "src", common_ignores) +copy_tree("envs/mini_swe_env", "envs/mini_swe_env", common_ignores) +copy_tree("examples/mini_swe_env", "examples/mini_swe_env", ["__pycache__", "*.pyc"]) +PYEOF + + cp pyproject.toml "$STAGE_DIR/" + cp LICENSE "$STAGE_DIR/" + cp .gitignore "$STAGE_DIR/" + + # Space-specific files at root + cp examples/mini_swe_env/async_grpo/space_app/Dockerfile "$STAGE_DIR/Dockerfile" + cp examples/mini_swe_env/async_grpo/space_app/start.sh "$STAGE_DIR/start.sh" + + # Space README (metadata) + cat > "$STAGE_DIR/README.md" << EOF +--- +title: SWE Async GRPO Training +emoji: 🔧 +colorFrom: blue +colorTo: green +sdk: docker +app_port: 7860 +suggested_hardware: $HARDWARE +startup_duration_timeout: 30m +preload_from_hub: + - $MODEL +--- + +# SWE Async GRPO Training + +Model: \`$MODEL\` | Tasks: $MAX_TASKS | Steps: $MAX_STEPS | Backend: $SANDBOX_BACKEND +EOF + + # Patch start.sh to pass our training args. + # NOTE: Multi-GPU branch uses line continuations, so patching only the + # `exec ...train_swe_async_grpo.py` line misses it. Instead, inject at the + # shared `--vllm-url ...` anchor used by both single- and multi-GPU paths. + TRAIN_ARGS="--sandbox-backend $SANDBOX_BACKEND --max-tasks $MAX_TASKS --max-steps $MAX_STEPS --max-turns $MAX_TURNS --num-generations $NUM_GENERATIONS" + cat >> "$STAGE_DIR/start.sh" << EOF + +# Auto-generated training args from deploy script +# $TRAIN_ARGS +EOF + + TRAIN_ARGS="$TRAIN_ARGS" "${PYTHON_CMD[@]}" << 'PYEOF' +from pathlib import Path +import os + +path = Path(os.environ["STAGE_DIR"]) / "start.sh" +text = path.read_text() +anchor = '--vllm-url "http://127.0.0.1:${VLLM_PORT}" \\\n' +replacement = f'{os.environ["TRAIN_ARGS"]} --vllm-url "http://127.0.0.1:${{VLLM_PORT}}" \\\n' +count = text.count(anchor) +if count == 0: + raise RuntimeError("Could not find trainer arg anchor in start.sh") +text = text.replace(anchor, replacement) +path.write_text(text) +print(f" ✓ Injected deploy args at {count} trainer command site(s)") +PYEOF + + FILE_COUNT=$(find "$STAGE_DIR" -type f | wc -l) + echo " ✓ Staged $FILE_COUNT files" + + # ── Step 5: Upload to Space ──────────────────────────────────────── + echo "[5/6] Uploading to Space..." + cd "$STAGE_DIR" + hf upload "$SPACE_ID" . --repo-type space \ + --commit-message "Deploy: $MODEL, $MAX_TASKS tasks, $MAX_STEPS steps" 2>&1 | tail -3 + echo " ✓ Upload complete" +fi + +# ── Step 6: Monitor ──────────────────────────────────────────────────── +echo "[6/6] Deployment triggered." +echo "" +echo " 🔗 Space: https://huggingface.co/spaces/$SPACE_ID" +echo " 🔗 Logs: https://huggingface.co/spaces/$SPACE_ID?logs=container" +echo "" + +if [ "$MONITOR" = true ]; then + echo "Monitoring build (Ctrl+C to stop)..." + "${PYTHON_CMD[@]}" << PYEOF +from huggingface_hub import HfApi +import time, os + +api = HfApi(token=os.environ['HF_TOKEN']) +space_id = "$SPACE_ID" +start = time.time() +last_stage = "" + +while True: + info = api.space_info(space_id) + stage = info.runtime.stage if info.runtime else "UNKNOWN" + elapsed = int(time.time() - start) + + if stage != last_stage: + print(f" [{elapsed:>4}s] {last_stage} → {stage}") + last_stage = stage + + if stage == "RUNNING": + print(f"\n ✅ Space RUNNING after {elapsed}s") + print(f" Health: https://{space_id.replace('/', '-')}.hf.space/health") + break + elif stage in ("RUNTIME_ERROR", "BUILD_ERROR", "CONFIG_ERROR"): + print(f"\n ❌ Failed: {stage}") + print(f" Check logs: https://huggingface.co/spaces/{space_id}?logs=build") + break + + time.sleep(15) +PYEOF +fi + +echo "" +echo "Done. Useful commands:" +echo " # Pause (stop billing):" +echo " bash $0 --pause" +echo " # Resume:" +echo " bash $0 --resume" +echo " # Monitor logs:" +echo " curl -N -H 'Authorization: Bearer \$HF_TOKEN' \\" +echo " 'https://huggingface.co/api/spaces/$SPACE_ID/logs/run'" diff --git a/examples/mini_swe_env/async_grpo/space_app/start.sh b/examples/mini_swe_env/async_grpo/space_app/start.sh new file mode 100755 index 000000000..5f28ade13 --- /dev/null +++ b/examples/mini_swe_env/async_grpo/space_app/start.sh @@ -0,0 +1,116 @@ +#!/bin/bash +# Startup script for SWE Async GRPO training. +# +# Used both in HF Spaces and local Docker testing. +# +# Process 1 (background): vLLM server +# Process 2 (foreground): Trainer + InterceptionServer +# +# HF Spaces monitors app_port (7860) for health. The InterceptionServer +# binds to 7860 and serves /health, so the Space is marked Running once +# the trainer starts. +set -e + +MODEL="${SWE_MODEL:?ERROR: Set SWE_MODEL (e.g. Qwen/Qwen3-1.7B)}" +VLLM_PORT="${VLLM_PORT:-8000}" +VLLM_KEY="${VLLM_API_KEY:-token}" +MAX_MODEL_LEN="${MAX_MODEL_LEN:-49152}" +GPU_MEM_UTIL="${GPU_MEMORY_UTILIZATION:-0.9}" + +# Prefer repo-local venv Python for local/dev runs; fall back to image python. +PYTHON_BIN="${PYTHON_BIN:-python3}" +if [ -x "$HOME/app/.venv/bin/python" ]; then + PYTHON_BIN="$HOME/app/.venv/bin/python" +elif [ -x "$HOME/app/.venv/Scripts/python.exe" ]; then + PYTHON_BIN="$HOME/app/.venv/Scripts/python.exe" +fi + +# GPU assignment. On 2-GPU Spaces: vLLM=0, trainer=1. +# On single GPU: both empty (share GPU 0). +VLLM_GPU="${VLLM_GPU:-0}" +TRAINER_GPU="${TRAINER_GPU:-1}" + +echo "========================================" +echo "SWE Async GRPO Training" +echo "Model: $MODEL" +echo "vLLM port: $VLLM_PORT" +echo "Max model len: $MAX_MODEL_LEN" +echo "GPU mem util: $GPU_MEM_UTIL" +echo "vLLM GPU: $VLLM_GPU" +echo "Trainer GPU: $TRAINER_GPU" +echo "Checkpointing: ${SWE_CHECKPOINT_TO_HUB:-auto}" +echo "Checkpoint repo: ${SWE_HUB_MODEL_ID:-}" +echo "========================================" + +# ── 1. Start vLLM ───────────────────────────────────────────── +echo "[start.sh] Starting vLLM on GPU $VLLM_GPU..." +# Async GRPO weight sync requires vLLM dev mode + NCCL transfer endpoints. +# Qwen3.5 uses Gated DeltaNet (Mamba-like), which requires one Mamba cache +# block per concurrent sequence. --max-num-seqs 64 prevents exceeding cache. +# vLLM 0.21+ registers Qwen3_5ForCausalLM natively; --language-model-only skips +# vision encoder to save memory. Weight sync uses is_checkpoint_format=True +# which routes through model.load_weights() for proper name remapping. +CUDA_VISIBLE_DEVICES="$VLLM_GPU" VLLM_SERVER_DEV_MODE=1 vllm serve "$MODEL" \ + --tensor-parallel-size 1 \ + --max-model-len "$MAX_MODEL_LEN" \ + --max-num-seqs 64 \ + --host 127.0.0.1 \ + --port "$VLLM_PORT" \ + --api-key "$VLLM_KEY" \ + --gpu-memory-utilization "$GPU_MEM_UTIL" \ + --logprobs-mode processed_logprobs \ + --weight-transfer-config '{"backend":"nccl"}' \ + --language-model-only \ + --enable-auto-tool-choice \ + --tool-call-parser qwen3_coder \ + --reasoning-parser qwen3 \ + --default-chat-template-kwargs '{"enable_thinking": false}' \ + 2>&1 | tee /tmp/vllm.log & + +VLLM_PID=$! +echo "[start.sh] vLLM PID=$VLLM_PID" + +# ── 2. Wait for vLLM health ─────────────────────────────────── +echo "[start.sh] Waiting for vLLM to be ready..." +WAITED=0 +MAX_WAIT=600 +while [ $WAITED -lt $MAX_WAIT ]; do + if curl -sf "http://127.0.0.1:${VLLM_PORT}/health" > /dev/null 2>&1; then + echo "[start.sh] vLLM ready after ${WAITED}s" + break + fi + if ! kill -0 $VLLM_PID 2>/dev/null; then + echo "[start.sh] ERROR: vLLM process died. Log tail:" + tail -50 /tmp/vllm.log + exit 1 + fi + sleep 1 + WAITED=$((WAITED + 1)) +done + +if [ $WAITED -ge $MAX_WAIT ]; then + echo "[start.sh] ERROR: vLLM did not become ready within ${MAX_WAIT}s. Log tail:" + tail -50 /tmp/vllm.log + exit 1 +fi + +# ── 3. Start trainer (foreground) ────────────────────────────── +echo "[start.sh] Starting trainer on GPU $TRAINER_GPU..." + +# Count trainer GPUs for multi-GPU support +IFS=',' read -ra TRAINER_GPUS <<< "$TRAINER_GPU" +NUM_TRAINER_GPUS=${#TRAINER_GPUS[@]} + +if [ "$NUM_TRAINER_GPUS" -gt 1 ]; then + echo "[start.sh] Multi-GPU trainer: $NUM_TRAINER_GPUS GPUs ($TRAINER_GPU)" + CUDA_VISIBLE_DEVICES="$TRAINER_GPU" exec "$PYTHON_BIN" -m accelerate.commands.launch \ + --num_processes "$NUM_TRAINER_GPUS" \ + --mixed_precision bf16 \ + examples/mini_swe_env/train_swe_async_grpo.py \ + --vllm-url "http://127.0.0.1:${VLLM_PORT}" \ + "$@" +else + CUDA_VISIBLE_DEVICES="$TRAINER_GPU" exec "$PYTHON_BIN" examples/mini_swe_env/train_swe_async_grpo.py \ + --vllm-url "http://127.0.0.1:${VLLM_PORT}" \ + "$@" +fi diff --git a/examples/mini_swe_env/collect_rollouts_best_of_n.py b/examples/mini_swe_env/collect_rollouts_best_of_n.py new file mode 100644 index 000000000..a6e43335f --- /dev/null +++ b/examples/mini_swe_env/collect_rollouts_best_of_n.py @@ -0,0 +1,879 @@ +#!/usr/bin/env python3 +"""Teacher trajectory collection via Best-of-N rollouts. + +Runs a teacher model (e.g. Qwen3.6-27B) through the Pi scaffold on +SWE-Gym tasks, collecting N rollouts per task with full trajectory +capture for downstream SFT distillation. + +Architecture: + HF Sandbox (SWE-Gym image) → Pi agent + → InterceptionServer (this machine, public URL) + → forward to teacher vLLM endpoint + ← response back to Pi + On answer() → host-side grading (FAIL_TO_PASS / PASS_TO_PASS) + +Required env vars: + SWE_LLM_BASE_URL Teacher vLLM endpoint (e.g. https://your-api.com/v1) + SWE_LLM_API_KEY Bearer token for the endpoint + SWE_LLM_MODEL Model name (e.g. Qwen/Qwen3.6-27B) + INTERCEPTION_BASE_URL Public URL where HF sandboxes can reach this machine + INTERCEPTION_AUTH_TOKEN Auth token for interception server + +Optional: + HF_TOKEN HuggingFace token (for sandbox creation) + +Example: + SWE_LLM_BASE_URL=https://your-vllm.com/v1 \ + SWE_LLM_API_KEY=sk-... \ + SWE_LLM_MODEL=Qwen/Qwen3.6-27B \ + INTERCEPTION_BASE_URL=https://your-public-url.example.com \ + INTERCEPTION_AUTH_TOKEN=secret123 \ + PYTHONPATH=src:envs python examples/mini_swe_env/collect_rollouts_best_of_n.py \ + --n-rollouts 4 --max-concurrent 3 --output-dir trajectories/teacher_27b +""" + +from __future__ import annotations + +import argparse +import asyncio +import json +import logging +import os +import sys +import time +import uuid +from collections import defaultdict +from pathlib import Path +from typing import Any + +import httpx + +_root = Path(__file__).resolve().parent.parent.parent +for _p in (_root / "src", _root / "envs"): + if str(_p) not in sys.path: + sys.path.insert(0, str(_p)) + +from mini_swe_env.harness import SWEAgentConfig, SWESessionFactory +from mini_swe_env.task_loader_swegym import load_swegym_tasks +from openenv.core.harness.agents.interception_server import InterceptionServer +from openenv.core.harness.sandbox import create_sandbox_backend + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from trajectory_store import TrajectoryRecord, TrajectoryStore + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(name)s %(levelname)s %(message)s", + datefmt="%H:%M:%S", +) +_log = logging.getLogger("collect-teacher") + + +# ── Rate Limiter ─────────────────────────────────────────────────────────── + + +class TokenBucketRateLimiter: + """Async token-bucket rate limiter with exponential backoff on 429s. + + Allows `rate` requests per `per` seconds. On rate-limit hits (429), + applies exponential backoff before retrying. + """ + + def __init__(self, rate: float, per: float = 60.0) -> None: + self._rate = rate + self._per = per + self._tokens = rate + self._last_refill = time.monotonic() + self._lock = asyncio.Lock() + self._backoff_until = 0.0 # monotonic time until which we're backing off + self._consecutive_429s = 0 + + async def acquire(self) -> None: + """Wait until a token is available.""" + while True: + async with self._lock: + now = time.monotonic() + + # Respect backoff + if now < self._backoff_until: + wait = self._backoff_until - now + _log.debug("rate limiter backing off %.1fs", wait) + else: + # Refill tokens + elapsed = now - self._last_refill + self._tokens = min( + self._rate, self._tokens + elapsed * (self._rate / self._per) + ) + self._last_refill = now + + if self._tokens >= 1.0: + self._tokens -= 1.0 + self._consecutive_429s = 0 + return + wait = (1.0 - self._tokens) * (self._per / self._rate) + + await asyncio.sleep(wait) + + def report_429(self) -> None: + """Report a 429 response to trigger exponential backoff.""" + self._consecutive_429s += 1 + backoff = min(2 ** self._consecutive_429s, 120.0) # cap at 2 min + self._backoff_until = time.monotonic() + backoff + _log.warning( + "429 rate limit hit (consecutive=%d), backing off %.1fs", + self._consecutive_429s, + backoff, + ) + + def report_success(self) -> None: + """Report a successful request to reset backoff state.""" + if self._consecutive_429s > 0: + self._consecutive_429s = 0 + _log.debug("rate limiter backoff reset after success") + + +# ── CLI Args ─────────────────────────────────────────────────────────────── + + +def _build_parser() -> argparse.ArgumentParser: + p = argparse.ArgumentParser( + description="Collect Best-of-N teacher trajectories for SFT distillation" + ) + p.add_argument( + "--task-variant", + default="lite", + choices=["lite", "full"], + help="SWE-Gym variant (default: lite, 230 tasks)", + ) + p.add_argument( + "--n-rollouts", + type=int, + default=4, + help="Number of rollouts per task (default: 4)", + ) + p.add_argument( + "--max-concurrent", + type=int, + default=3, + help="Max concurrent rollouts (default: 3)", + ) + p.add_argument( + "--max-turns", + type=int, + default=0, + help="Max agent turns per rollout (default: 0 = unlimited, agent runs until exit or timeout)", + ) + p.add_argument( + "--agent-timeout-s", + type=float, + default=1800.0, + help="Agent timeout in seconds (default: 1800)", + ) + p.add_argument( + "--request-timeout-s", + type=float, + default=180.0, + help="Per-LLM-request timeout (default: 180s)", + ) + p.add_argument( + "--rate-limit", + type=float, + default=30.0, + help="Max LLM requests per minute (default: 30)", + ) + p.add_argument( + "--output-dir", + default="trajectories/teacher_27b", + help="Output directory for trajectories (default: trajectories/teacher_27b)", + ) + p.add_argument( + "--interception-port", + type=int, + default=9090, + help="InterceptionServer port (default: 9090)", + ) + p.add_argument( + "--interception-host", + default="0.0.0.0", + help="InterceptionServer bind host (default: 0.0.0.0)", + ) + p.add_argument( + "--hf-flavor", + default="cpu-basic", + help="HF Sandbox flavor (default: cpu-basic)", + ) + p.add_argument( + "--max-tasks", + type=int, + default=None, + help="Limit to first N tasks (for testing)", + ) + p.add_argument( + "--start-task", + type=int, + default=0, + help="Start from task index (for resuming partial runs)", + ) + p.add_argument( + "--max-retries", + type=int, + default=3, + help="Max retries per rollout on infra failure (default: 3)", + ) + p.add_argument( + "--export-sft", + action="store_true", + help="Export SFT-ready JSONL after collection", + ) + p.add_argument( + "--hub-repo-id", + default=os.environ.get("TRAJECTORY_HUB_REPO", ""), + help="HF Dataset repo for trajectory persistence (default: $TRAJECTORY_HUB_REPO)", + ) + p.add_argument( + "--hub-upload-every", + type=int, + default=5, + help="Upload to Hub every N trajectories (default: 5)", + ) + return p + + +# ── Env Var Helpers ──────────────────────────────────────────────────────── + + +def _must_env(name: str) -> str: + value = os.environ.get(name, "").strip() + if not value: + raise RuntimeError(f"Missing required env var: {name}") + return value + + +# ── Single Rollout ───────────────────────────────────────────────────────── + + +async def _run_one_rollout( + *, + gym_task: Any, + rollout_index: int, + factory: SWESessionFactory, + server: InterceptionServer, + client: httpx.AsyncClient, + llm_base_url: str, + llm_api_key: str, + llm_model: str, + interception_base_url: str, + rate_limiter: TokenBucketRateLimiter, + max_turns: int, + request_timeout_s: float, + agent_timeout_s: float, +) -> TrajectoryRecord | None: + """Run one complete rollout, returning a TrajectoryRecord or None on failure.""" + + swe_task = gym_task.to_swe_task() + episode_id = f"collect-{gym_task.instance_id}-r{rollout_index}-{uuid.uuid4().hex[:6]}" + + _log.info( + "rollout_start instance_id=%s rollout=%d episode=%s", + gym_task.instance_id, + rollout_index, + episode_id, + ) + + t0 = time.time() + session = factory.create(task=swe_task, episode_id=episode_id) + + turns = 0 + answer_called = False + answer_bridged = False + messages_captured: list[dict[str, Any]] = [] + tool_calls_log: list[dict[str, Any]] = [] + completions: list[dict[str, Any]] = [] # Per-turn completion records + + try: + while max_turns == 0 or turns < max_turns: + intercept = await session.next_request(timeout_s=agent_timeout_s) + if intercept is None: + _log.info( + "agent_exited instance_id=%s turns=%d", + gym_task.instance_id, + turns, + ) + break + + turns += 1 + + # Capture the request messages + body = intercept.get("body") or {} + req_messages = body.get("messages") or intercept.get("messages") or [] + + # Each intercept contains the full conversation so far + # (system + user + assistant + tool results from all previous turns). + # Always update to the latest — on the final turn this gives us + # the complete multi-turn trajectory. + messages_captured = list(req_messages) + + # ── Rate-limited LLM call with exponential backoff ── + await rate_limiter.acquire() + + llm_resp = await _forward_to_llm( + client, + intercept=intercept, + base_url=llm_base_url, + api_key=llm_api_key, + model=llm_model, + timeout_s=request_timeout_s, + rate_limiter=rate_limiter, + ) + + choice0 = (llm_resp.get("choices") or [{}])[0] + msg = choice0.get("message") or {} + tool_calls = msg.get("tool_calls") or [] + + # Store per-completion record + # Captures the full request + response per turn for RL reconstruction + completions.append({ + "completion_id": f"{episode_id}-t{turns}", + "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "request": { + "messages": list(req_messages), + "tools": body.get("tools") or intercept.get("tools") or [], + "model": llm_model, + }, + "response": llm_resp, + }) + + # Capture assistant message + assistant_msg: dict[str, Any] = {"role": "assistant"} + if msg.get("content"): + assistant_msg["content"] = msg["content"] + if tool_calls: + assistant_msg["tool_calls"] = tool_calls + messages_captured.append(assistant_msg) + + # Log tool calls + for tc in tool_calls: + fn = tc.get("function") or {} + tool_calls_log.append( + { + "turn": turns, + "name": fn.get("name", ""), + "arguments": fn.get("arguments", ""), + "id": tc.get("id", ""), + } + ) + + # Check for answer tool call + if any( + (tc.get("function") or {}).get("name") == "answer" + for tc in tool_calls + ): + answer_called = True + + # Invoke host-side grading directly (like run_swe_sample.py) + # Pi can't call the tool endpoint itself after we break. + rollout_id = intercept.get("rollout_id", "") + try: + answer_resp = await client.post( + f"http://127.0.0.1:{server.port}/rollout/{rollout_id}/v1/tools/answer", + headers={"Authorization": f"Bearer {server.secret}"}, + json={"arguments": {}}, + timeout=300.0, + ) + if answer_resp.status_code == 200: + answer_bridged = True + _log.info( + "answer_bridged instance_id=%s result=%s", + gym_task.instance_id, + answer_resp.text[:200], + ) + else: + _log.warning( + "answer_bridge_failed instance_id=%s status=%d", + gym_task.instance_id, + answer_resp.status_code, + ) + except Exception as exc: + _log.warning( + "answer_bridge_error instance_id=%s: %s", + gym_task.instance_id, + str(exc)[:150], + ) + + # Replace tool-call response with plain text so Pi stops cleanly + llm_resp = dict(llm_resp) + choices = list(llm_resp.get("choices") or []) + if choices: + choice0_mod = dict(choices[0]) + msg_mod = dict(choice0_mod.get("message") or {}) + msg_mod.pop("tool_calls", None) + msg_mod["content"] = ( + (msg_mod.get("content") or "") + + "\nSubmission received and graded on host." + ).strip() + choice0_mod["message"] = msg_mod + choice0_mod["finish_reason"] = "stop" + choices[0] = choice0_mod + llm_resp["choices"] = choices + + await session.deliver(intercept, llm_resp) + + if answer_called: + break + + _log.debug( + "turn=%d instance=%s tools=%s", + turns, + gym_task.instance_id, + [fn.get("name", "?") for tc in tool_calls for fn in [tc.get("function") or {}]], + ) + + # ── Grade ───────────────────────────────────────────────── + # session.verify() handles both cases: + # - answer was called → uses stored reward from host_answer_tool + # - answer not called → runs post-hoc grading via grade_fn + vr = session.verify(transcript=[]) + reward = float(getattr(vr, "env_reward", 0.0) or 0.0) + resolved = reward >= 1.0 + reward_source = (vr.metrics or {}).get("reward_source", "unknown") + test_outcomes = (vr.artifacts or {}).get("verify_details", {}) + wall_s = round(time.time() - t0, 2) + + _log.info( + "rollout_done instance_id=%s rollout=%d resolved=%s reward=%.2f " + "turns=%d wall_s=%.1f source=%s", + gym_task.instance_id, + rollout_index, + resolved, + reward, + turns, + wall_s, + reward_source, + ) + + # Capture agent log if zero turns (agent never called LLM) + if turns == 0: + try: + artifacts = session.collect_artifacts() + agent_log = artifacts.get("agent_log", "") if isinstance(artifacts, dict) else "" + if isinstance(agent_log, str) and agent_log.strip(): + _log.warning("zero_turns agent_log tail:\n%s", agent_log[-3000:]) + except Exception as log_exc: + _log.warning("failed to collect agent log: %s", log_exc) + + record = TrajectoryRecord( + trajectory_id=episode_id, + instance_id=gym_task.instance_id, + task_id=f"swegym::{gym_task.instance_id}", + repo=gym_task.repo, + teacher_model=llm_model, + rollout_index=rollout_index, + resolved=resolved, + reward=reward, + reward_source=reward_source, + turns=turns, + wall_s=wall_s, + answer_called=answer_called, + tool_calls_count=len(tool_calls_log), + messages=messages_captured, + tool_calls=tool_calls_log, + completions=completions, + test_outcomes=test_outcomes if isinstance(test_outcomes, dict) else {}, + metadata={ + "answer_bridged": answer_bridged, + "episode_id": episode_id, + }, + ) + return record + + except Exception as exc: + wall_s = round(time.time() - t0, 2) + _log.error( + "rollout_error instance_id=%s rollout=%d error=%s wall_s=%.1f", + gym_task.instance_id, + rollout_index, + str(exc)[:200], + wall_s, + ) + # Attempt to capture agent log for debugging + try: + artifacts = session.collect_artifacts() + agent_log = artifacts.get("agent_log", "") if isinstance(artifacts, dict) else "" + if isinstance(agent_log, str) and agent_log.strip(): + _log.error("agent_log tail:\n%s", agent_log[-3000:]) + else: + _log.warning("no agent_log captured") + except Exception as log_exc: + _log.warning("failed to collect agent log: %s", log_exc) + return None + finally: + session.close() + + +async def _forward_to_llm( + client: httpx.AsyncClient, + *, + intercept: dict[str, Any], + base_url: str, + api_key: str, + model: str, + timeout_s: float, + rate_limiter: TokenBucketRateLimiter, + max_retries: int = 5, +) -> dict[str, Any]: + """Forward intercepted request to the teacher LLM with retry + backoff.""" + body = dict(intercept.get("body") or {}) + body["model"] = model + # Request logprobs for RL-readiness (token-faithful reconstruction) + body["logprobs"] = True + body["top_logprobs"] = 1 + body.pop("stream", None) + body.pop("stream_options", None) + # Disable thinking mode — get direct content, not reasoning tokens + body.setdefault("chat_template_kwargs", {"enable_thinking": False}) + + for attempt in range(max_retries): + try: + r = await client.post( + f"{base_url.rstrip('/')}/chat/completions", + headers={ + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json", + }, + json=body, + timeout=timeout_s, + ) + + if r.status_code == 429: + rate_limiter.report_429() + # Wait for backoff then retry + backoff = min(2 ** (attempt + 1), 120.0) + _log.warning( + "LLM 429 (attempt %d/%d), backing off %.1fs", + attempt + 1, + max_retries, + backoff, + ) + await asyncio.sleep(backoff) + # Re-acquire token after backoff + await rate_limiter.acquire() + continue + + if r.status_code != 200: + raise RuntimeError(f"LLM error {r.status_code}: {r.text[:500]}") + + rate_limiter.report_success() + return r.json() + + except httpx.TimeoutException: + if attempt + 1 < max_retries: + backoff = min(2 ** (attempt + 1), 60.0) + _log.warning( + "LLM timeout (attempt %d/%d), retrying in %.1fs", + attempt + 1, + max_retries, + backoff, + ) + await asyncio.sleep(backoff) + continue + raise + + raise RuntimeError(f"LLM request failed after {max_retries} retries (429s)") + + +# ── Task Runner (manages N rollouts for one task) ────────────────────────── + + +async def _collect_task_rollouts( + *, + gym_task: Any, + n_rollouts: int, + existing_count: int, + factory: SWESessionFactory, + server: InterceptionServer, + client: httpx.AsyncClient, + llm_base_url: str, + llm_api_key: str, + llm_model: str, + interception_base_url: str, + rate_limiter: TokenBucketRateLimiter, + max_turns: int, + request_timeout_s: float, + agent_timeout_s: float, + max_retries: int, + semaphore: asyncio.Semaphore, + store: TrajectoryStore, +) -> list[TrajectoryRecord]: + """Collect all N rollouts for a single task, respecting concurrency.""" + records: list[TrajectoryRecord] = [] + start_idx = existing_count + + for rollout_idx in range(start_idx, n_rollouts): + async with semaphore: + record = None + for retry in range(max_retries): + try: + record = await _run_one_rollout( + gym_task=gym_task, + rollout_index=rollout_idx, + factory=factory, + server=server, + client=client, + llm_base_url=llm_base_url, + llm_api_key=llm_api_key, + llm_model=llm_model, + interception_base_url=interception_base_url, + rate_limiter=rate_limiter, + max_turns=max_turns, + request_timeout_s=request_timeout_s, + agent_timeout_s=agent_timeout_s, + ) + if record is not None: + break + except Exception as exc: + backoff = min(2 ** (retry + 1), 60.0) + _log.warning( + "infra_retry instance_id=%s rollout=%d retry=%d/%d " + "error=%s backoff=%.1fs", + gym_task.instance_id, + rollout_idx, + retry + 1, + max_retries, + str(exc)[:150], + backoff, + ) + await asyncio.sleep(backoff) + + if record is not None: + store.append(record) + store.flush() + records.append(record) + else: + _log.error( + "rollout_abandoned instance_id=%s rollout=%d after %d retries", + gym_task.instance_id, + rollout_idx, + max_retries, + ) + + return records + + +# ── Main ─────────────────────────────────────────────────────────────────── + + +async def _run(args: argparse.Namespace) -> int: + llm_base_url = _must_env("SWE_LLM_BASE_URL") + llm_api_key = _must_env("SWE_LLM_API_KEY") + llm_model = _must_env("SWE_LLM_MODEL") + interception_base_url = _must_env("INTERCEPTION_BASE_URL").rstrip("/") + interception_token = _must_env("INTERCEPTION_AUTH_TOKEN") + + # ── Load tasks ──────────────────────────────────────────────── + _log.info("Loading SWE-Gym %s tasks...", args.task_variant) + tasks = load_swegym_tasks(args.task_variant) + _log.info("Loaded %d tasks", len(tasks)) + + # Apply slicing + tasks = tasks[args.start_task:] + if args.max_tasks is not None: + tasks = tasks[: args.max_tasks] + _log.info( + "Will process %d tasks (start=%d, max=%s)", + len(tasks), + args.start_task, + args.max_tasks, + ) + + # ── Load/create trajectory store ────────────────────────────── + hub_repo = args.hub_repo_id.strip() if args.hub_repo_id else None + store = TrajectoryStore( + args.output_dir, + hub_repo_id=hub_repo or None, + hub_upload_every=args.hub_upload_every, + ) + _log.info("Trajectory store: %s (%d existing)", store.filepath, len(store)) + if hub_repo: + _log.info("Hub persistence: %s (every %d)", hub_repo, args.hub_upload_every) + + # Count existing rollouts per task for resume + existing_counts: dict[str, int] = defaultdict(int) + for rec in store.records: + existing_counts[rec.instance_id] += 1 + + tasks_needing_work = [ + t for t in tasks if existing_counts[t.instance_id] < args.n_rollouts + ] + _log.info( + "Tasks needing work: %d / %d (others already have %d rollouts)", + len(tasks_needing_work), + len(tasks), + args.n_rollouts, + ) + + if not tasks_needing_work: + _log.info("All tasks complete! Nothing to do.") + _print_summary(store, args) + return 0 + + # ── Start InterceptionServer ────────────────────────────────── + server = InterceptionServer( + port=args.interception_port, + host=args.interception_host, + secret=interception_token, + tool_name_allowlist={"answer"}, + ) + await server.start() + _log.info( + "InterceptionServer started on %s:%d (public: %s)", + args.interception_host, + server.port, + interception_base_url, + ) + + # ── Create sandbox backend + session factory ────────────────── + backend = create_sandbox_backend("hf", flavor=args.hf_flavor) + cfg = SWEAgentConfig( + base_url=interception_base_url, + api_key=interception_token, + model=llm_model, + agent_timeout_s=args.agent_timeout_s, + ) + factory = SWESessionFactory( + agent="pi", + config=cfg, + sandbox_backend=backend, + mode="interception_gate", + interception_server=server, + interception_base_url=interception_base_url, + ) + + # ── Rate limiter + concurrency ──────────────────────────────── + rate_limiter = TokenBucketRateLimiter(rate=args.rate_limit, per=60.0) + semaphore = asyncio.Semaphore(args.max_concurrent) + + # ── Run collection ──────────────────────────────────────────── + total_start = time.time() + completed_tasks = 0 + total_resolved = 0 + total_rollouts = 0 + + try: + async with httpx.AsyncClient() as client: + # Process tasks with bounded concurrency. + # We launch all tasks as coroutines but the semaphore inside + # _collect_task_rollouts gates actual sandbox creation. + coros = [] + for gym_task in tasks_needing_work: + existing = existing_counts[gym_task.instance_id] + coros.append( + _collect_task_rollouts( + gym_task=gym_task, + n_rollouts=args.n_rollouts, + existing_count=existing, + factory=factory, + server=server, + client=client, + llm_base_url=llm_base_url, + llm_api_key=llm_api_key, + llm_model=llm_model, + interception_base_url=interception_base_url, + rate_limiter=rate_limiter, + max_turns=args.max_turns, + request_timeout_s=args.request_timeout_s, + agent_timeout_s=args.agent_timeout_s, + max_retries=args.max_retries, + semaphore=semaphore, + store=store, + ) + ) + + # Use gather to run tasks — semaphore controls actual concurrency + results = await asyncio.gather(*coros, return_exceptions=True) + + for i, result in enumerate(results): + if isinstance(result, Exception): + _log.error( + "task_failed instance_id=%s error=%s", + tasks_needing_work[i].instance_id, + str(result)[:200], + ) + elif isinstance(result, list): + completed_tasks += 1 + total_rollouts += len(result) + total_resolved += sum(1 for r in result if r.resolved) + + # Periodic progress + if completed_tasks % 10 == 0: + elapsed = time.time() - total_start + _log.info( + "progress: %d/%d tasks done, %d rollouts " + "(%d resolved), %.0fs elapsed", + completed_tasks, + len(tasks_needing_work), + total_rollouts, + total_resolved, + elapsed, + ) + + finally: + await server.stop() + + # ── Final summary ───────────────────────────────────────────── + total_elapsed = time.time() - total_start + _log.info( + "collection_complete tasks=%d rollouts=%d resolved=%d " + "elapsed=%.1fs (%.1f min)", + completed_tasks, + total_rollouts, + total_resolved, + total_elapsed, + total_elapsed / 60, + ) + + _print_summary(store, args) + + # ── Final hub upload ─────────────────────────────────────── + store.upload_now() + + # ── Export SFT data if requested ────────────────────────────── + if args.export_sft: + sft_path = Path(args.output_dir) / "sft_export.jsonl" + count = store.export_for_sft(sft_path, resolved_only=True, max_turns=15) + _log.info("Exported %d trajectories for SFT to %s", count, sft_path) + + return 0 + + +def _print_summary(store: TrajectoryStore, args: argparse.Namespace) -> None: + """Print final statistics.""" + print("\n" + "=" * 68) + print("COLLECTION SUMMARY") + print("=" * 68) + print(store.summary()) + + stats = store.pass_at_k_stats(k_values=[1, 4, 8]) + print(f"\n Total tasks attempted: {stats['total_tasks']}") + print(f" Total trajectories: {stats['total_trajectories']}") + print(f" Overall resolve rate: {stats['resolve_rate']*100:.1f}%") + for k in (1, 4, 8): + key = f"pass@{k}" + if key in stats: + print(f" {key}: {stats[key]*100:.1f}%") + print("=" * 68) + + # Write stats to file + stats_path = Path(args.output_dir) / "stats.json" + # Remove per_task for cleaner top-level file + stats_summary = {k: v for k, v in stats.items() if k != "per_task"} + stats_path.write_text(json.dumps(stats_summary, indent=2)) + _log.info("Stats written to %s", stats_path) + + +def main() -> None: + args = _build_parser().parse_args() + raise SystemExit(asyncio.run(_run(args))) + + +if __name__ == "__main__": + main() diff --git a/examples/mini_swe_env/deploy_collect_space.sh b/examples/mini_swe_env/deploy_collect_space.sh new file mode 100755 index 000000000..6b2edc979 --- /dev/null +++ b/examples/mini_swe_env/deploy_collect_space.sh @@ -0,0 +1,497 @@ +#!/bin/bash +# deploy_collect_space.sh — Deploy teacher trajectory collection to a CPU-only HF Space +# +# This script: +# 1. Creates a CPU-only HF Space +# 2. Configures secrets (LLM API key, interception auth token) +# 3. Prepares a minimal Docker image with the collection script +# 4. Pushes code and triggers build +# 5. The Space runs collect_rollouts_best_of_n.py autonomously +# +# The Space itself IS the interception server — HF sandboxes call back to the +# Space URL directly (no external tunnel needed). +# +# Prerequisites: +# - hf CLI installed and authenticated (hf auth login) +# - HF_TOKEN set or in ~/.cache/huggingface/token +# +# Usage: +# bash examples/mini_swe_env/deploy_collect_space.sh [OPTIONS] +# +# Options: +# --space-id OWNER/NAME Space ID (default: rycerzes/swe-teacher-collect) +# --model MODEL_NAME Model name on vLLM endpoint (default: qwen-3.6-27b) +# --api-url URL vLLM API base URL (default: https://api.siemens.com/llm/v1) +# --api-key KEY vLLM API key (required, or set SWE_LLM_API_KEY) +# --cf-api-token TOKEN cf API token (or set CF_API_TOKEN) +# --cf-account-id ID cf account ID (or set CF_ACCOUNT_ID) +# --cf-zone-id ID cf zone ID (or set CF_ZONE_ID) +# --cf-domain DOMAIN Domain for sandbox tunnels (or set CF_DOMAIN) +# --n-rollouts N Rollouts per task (default: 4) +# --max-concurrent N Concurrent rollouts (default: 3) +# --max-turns N Max agent turns (default: 50) +# --max-tasks N Limit tasks for testing (default: all 230 Lite) +# --rate-limit N Max LLM requests/min (default: 30) +# --hardware HW Hardware tier (default: cpu-basic) +# --skip-build Only configure, don't push code +# --monitor Monitor logs after deploy +# --pause Pause the Space +# --resume Resume a paused Space +# +set -euo pipefail + +# ── Defaults ──────────────────────────────────────────────────────────── +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)" +export REPO_ROOT + +SPACE_ID="${HF_SPACE_ID:-rycerzes/swe-teacher-collect}" +MODEL="${SWE_LLM_MODEL:-qwen-3.6-27b}" +API_URL="${SWE_LLM_BASE_URL:-https://api.siemens.com/llm/v1}" +API_KEY="${SWE_LLM_API_KEY:-}" +CF_API_TOKEN="${CF_API_TOKEN:-}" +CF_ACCOUNT_ID="${CF_ACCOUNT_ID:-}" +CF_ZONE_ID="${CF_ZONE_ID:-}" +CF_DOMAIN="${CF_DOMAIN:-}" +N_ROLLOUTS=4 +MAX_CONCURRENT=3 +MAX_TURNS=50 +MAX_TASKS="" +RATE_LIMIT=30 +HARDWARE="cpu-basic" +SKIP_BUILD=false +MONITOR=false +PAUSE=false +RESUME=false + +# ── Python detection ──────────────────────────────────────────────────── +if [ -x "$REPO_ROOT/.venv/bin/python" ]; then + PYTHON_CMD=("$REPO_ROOT/.venv/bin/python") +elif command -v uv >/dev/null 2>&1; then + PYTHON_CMD=(uv run python) +elif command -v python3 >/dev/null 2>&1; then + PYTHON_CMD=("$(command -v python3)") +else + echo "ERROR: Python interpreter not found." + exit 1 +fi + +if ! command -v hf >/dev/null 2>&1; then + echo "ERROR: hf CLI not found. Install huggingface_hub CLI and run 'hf auth login'." + exit 1 +fi + +export PYTHONUTF8=1 + +# ── Parse args ────────────────────────────────────────────────────────── +while [[ $# -gt 0 ]]; do + case "$1" in + --space-id) SPACE_ID="$2"; shift 2 ;; + --model) MODEL="$2"; shift 2 ;; + --api-url) API_URL="$2"; shift 2 ;; + --api-key) API_KEY="$2"; shift 2 ;; + --cf-api-token) CF_API_TOKEN="$2"; shift 2 ;; + --cf-account-id) CF_ACCOUNT_ID="$2"; shift 2 ;; + --cf-zone-id) CF_ZONE_ID="$2"; shift 2 ;; + --cf-domain) CF_DOMAIN="$2"; shift 2 ;; + --n-rollouts) N_ROLLOUTS="$2"; shift 2 ;; + --max-concurrent) MAX_CONCURRENT="$2"; shift 2 ;; + --max-turns) MAX_TURNS="$2"; shift 2 ;; + --max-tasks) MAX_TASKS="$2"; shift 2 ;; + --rate-limit) RATE_LIMIT="$2"; shift 2 ;; + --hardware) HARDWARE="$2"; shift 2 ;; + --skip-build) SKIP_BUILD=true; shift ;; + --monitor) MONITOR=true; shift ;; + --pause) PAUSE=true; shift ;; + --resume) RESUME=true; shift ;; + *) echo "Unknown option: $1"; exit 1 ;; + esac +done + +# ── Validate ──────────────────────────────────────────────────────────── +if [ -z "$API_KEY" ] && [ "$PAUSE" = false ] && [ "$RESUME" = false ]; then + echo "ERROR: API key required. Set SWE_LLM_API_KEY or pass --api-key" + exit 1 +fi + +# ── Resolve HF_TOKEN ──────────────────────────────────────────────────── +if [ -z "${HF_TOKEN:-}" ]; then + if [ -f "$HOME/.cache/huggingface/token" ]; then + HF_TOKEN=$(cat "$HOME/.cache/huggingface/token") + else + echo "ERROR: HF_TOKEN not set and not found in ~/.cache/huggingface/token" + echo "Run: hf auth login" + exit 1 + fi +fi +export HF_TOKEN + +# ── Pause/Resume shortcuts ────────────────────────────────────────────── +if [ "$PAUSE" = true ]; then + echo "⏸ Pausing Space $SPACE_ID..." + "${PYTHON_CMD[@]}" -c " +from huggingface_hub import HfApi +import os +api = HfApi(token=os.environ['HF_TOKEN']) +api.pause_space('$SPACE_ID') +print('Space paused.') +" + exit 0 +fi + +if [ "$RESUME" = true ]; then + echo "▶ Resuming Space $SPACE_ID..." + "${PYTHON_CMD[@]}" -c " +from huggingface_hub import HfApi +import os +api = HfApi(token=os.environ['HF_TOKEN']) +api.restart_space('$SPACE_ID', factory_reboot=True) +print('Space restarted.') +" + exit 0 +fi + +# ── Print config ──────────────────────────────────────────────────────── +echo "╔══════════════════════════════════════════════════════════╗" +echo "║ SWE Teacher Trajectory Collection — HF Space Deploy ║" +echo "╠══════════════════════════════════════════════════════════╣" +printf "║ %-54s ║\n" "Space: $SPACE_ID" +printf "║ %-54s ║\n" "Model: $MODEL" +printf "║ %-54s ║\n" "API URL: $API_URL" +printf "║ %-54s ║\n" "Hardware: $HARDWARE" +printf "║ %-54s ║\n" "N Rollouts: $N_ROLLOUTS" +printf "║ %-54s ║\n" "Concurrent: $MAX_CONCURRENT" +printf "║ %-54s ║\n" "Max Turns: $MAX_TURNS" +printf "║ %-54s ║\n" "Max Tasks: ${MAX_TASKS:-all (230 Lite)}" +printf "║ %-54s ║\n" "Rate Limit: $RATE_LIMIT req/min" +if [ -n "$CF_API_TOKEN" ]; then +printf "║ %-54s ║\n" "CF Tunnel: named (${CF_DOMAIN})" +else +printf "║ %-54s ║\n" "CF Tunnel: quick (auto-generated URL)" +fi +echo "╚══════════════════════════════════════════════════════════╝" +echo "" + +# ── Step 1: Create Space ──────────────────────────────────────────────── +echo "[1/6] Creating Space (if needed)..." +hf repo create "$SPACE_ID" --repo-type space --space-sdk docker --no-private --exist-ok >/dev/null +echo " ✓ Space exists: https://huggingface.co/spaces/$SPACE_ID" + +# ── Step 2: Configure secrets ─────────────────────────────────────────── +echo "[2/6] Configuring secrets and variables..." +"${PYTHON_CMD[@]}" << PYEOF +from huggingface_hub import HfApi +import os, secrets + +api = HfApi(token=os.environ['HF_TOKEN']) +space_id = "$SPACE_ID" + +# Secrets (sensitive) +space_secrets = { + "HF_TOKEN": os.environ["HF_TOKEN"], + "SWE_LLM_API_KEY": "$API_KEY", + "INTERCEPTION_AUTH_TOKEN": secrets.token_urlsafe(32), +} + +# Named tunnel secrets (optional — enables reliable sandbox connectivity) +cf_secrets = { + "CF_API_TOKEN": "$CF_API_TOKEN", + "CF_ACCOUNT_ID": "$CF_ACCOUNT_ID", + "CF_ZONE_ID": "$CF_ZONE_ID", + "CF_DOMAIN": "$CF_DOMAIN", +} +for key, value in cf_secrets.items(): + if value: + space_secrets[key] = value +for key, value in space_secrets.items(): + try: + api.add_space_secret(space_id, key, value) + print(f" ✓ Secret: {key}") + except Exception as e: + print(f" ⚠ Secret {key}: {e}") + +# Variables (non-sensitive) +variables = { + "SWE_LLM_BASE_URL": "$API_URL", + "SWE_LLM_MODEL": "$MODEL", + "N_ROLLOUTS": "$N_ROLLOUTS", + "MAX_CONCURRENT": "$MAX_CONCURRENT", + "MAX_TURNS": "$MAX_TURNS", + "MAX_TASKS": "$MAX_TASKS", + "RATE_LIMIT": "$RATE_LIMIT", + "TASK_VARIANT": "lite", + "HF_SANDBOX_FLAVOR": "cpu-basic", + "TRAJECTORY_HUB_REPO": f"{space_id.split('/')[0]}/swe-teacher-trajectories", + "HUB_UPLOAD_EVERY": "5", +} +for key, value in variables.items(): + if not value: + # Remove variable if empty (e.g. MAX_TASKS not set = use all tasks) + try: + api.delete_space_variable(space_id, key) + print(f" ✓ Var removed: {key} (using default)") + except Exception: + pass + continue + try: + api.add_space_variable(space_id, key, value) + print(f" ✓ Var: {key}={value}") + except Exception as e: + print(f" ⚠ Var {key}: {e}") +PYEOF + +# ── Step 3: Set hardware ─────────────────────────────────────────────── +echo "[3/6] Setting hardware to $HARDWARE..." +"${PYTHON_CMD[@]}" -c " +from huggingface_hub import HfApi +import os +api = HfApi(token=os.environ['HF_TOKEN']) +api.request_space_hardware('$SPACE_ID', '$HARDWARE') +print(' ✓ Hardware: $HARDWARE') +" + +# ── Step 4: Prepare staging directory ────────────────────────────────── +if [ "$SKIP_BUILD" = true ]; then + echo "[4/6] Skipping build (--skip-build)" + echo "[5/6] Skipping upload" +else + echo "[4/6] Preparing Space directory..." + STAGE_DIR=$(mktemp -d) + export STAGE_DIR + trap "rm -rf $STAGE_DIR" EXIT + + cd "$REPO_ROOT" + + # Copy essential files + "${PYTHON_CMD[@]}" << 'PYEOF' +import fnmatch +import os +import shutil +from pathlib import Path + +repo = Path(os.environ["REPO_ROOT"]) +stage = Path(os.environ["STAGE_DIR"]) + +def _ignore(patterns): + def _inner(_dir, names): + ignored = set() + for name in names: + for pat in patterns: + if fnmatch.fnmatch(name, pat): + ignored.add(name) + break + return ignored + return _inner + +common_ignores = [ + "__pycache__", "*.pyc", ".venv", ".tox", ".mypy_cache", + ".pytest_cache", ".ruff_cache", "*.egg-info", "uv.lock", +] + +shutil.copytree(repo / "src", stage / "src", dirs_exist_ok=True, ignore=_ignore(common_ignores)) +shutil.copytree(repo / "envs/mini_swe_env", stage / "envs/mini_swe_env", dirs_exist_ok=True, ignore=_ignore(common_ignores)) + +# Only copy the collection-related files from examples +examples_dst = stage / "examples/mini_swe_env" +examples_dst.mkdir(parents=True, exist_ok=True) +for f in ["collect_rollouts_best_of_n.py", "trajectory_store.py"]: + src = repo / "examples/mini_swe_env" / f + if src.exists(): + shutil.copy2(src, examples_dst / f) + +PYEOF + + cp pyproject.toml "$STAGE_DIR/" + [ -f LICENSE ] && cp LICENSE "$STAGE_DIR/" + + # ── Dockerfile (CPU-only, lightweight) ────────────────────────────── + cat > "$STAGE_DIR/Dockerfile" << 'DOCKERFILE' +FROM python:3.12-slim + +WORKDIR /app + +# System deps for sandbox operations +RUN apt-get update -qq && \ + apt-get install -y -qq --no-install-recommends \ + git curl ca-certificates && \ + rm -rf /var/lib/apt/lists/* + +# Python deps +COPY pyproject.toml . +COPY src/ src/ +RUN pip install --no-cache-dir -e ".[core]" && \ + pip install --no-cache-dir \ + httpx \ + aiohttp \ + datasets \ + huggingface_hub \ + "hf-sandbox[named-tunnels] @ git+https://github.com/rycerzes/hf-sandbox@feat/named-tunnels" \ + fastmcp + +# Copy remaining source +COPY envs/ envs/ +COPY examples/ examples/ + +# The start script runs the collection +COPY start.sh /app/start.sh +RUN chmod +x /app/start.sh + +EXPOSE 7860 + +CMD ["/app/start.sh"] +DOCKERFILE + + # ── start.sh ──────────────────────────────────────────────────────── + cat > "$STAGE_DIR/start.sh" << 'STARTSH' +#!/bin/bash +# Teacher trajectory collection — runs inside HF Space. +# +# The Space URL is automatically the INTERCEPTION_BASE_URL since the +# InterceptionServer binds to the app port (7860). +set -e + +echo "========================================" +echo "SWE Teacher Trajectory Collection" +echo "Model: ${SWE_LLM_MODEL}" +echo "API URL: ${SWE_LLM_BASE_URL}" +echo "Variant: ${TASK_VARIANT:-lite}" +echo "N Rollouts: ${N_ROLLOUTS:-4}" +echo "Concurrent: ${MAX_CONCURRENT:-3}" +echo "Max Turns: ${MAX_TURNS:-50}" +echo "Max Tasks: ${MAX_TASKS:-all}" +echo "Rate Limit: ${RATE_LIMIT:-30} req/min" +echo "========================================" + +# Resolve INTERCEPTION_BASE_URL from Space environment +if [ -z "${INTERCEPTION_BASE_URL:-}" ]; then + if [ -n "${SPACE_HOST:-}" ]; then + INTERCEPTION_BASE_URL="https://${SPACE_HOST}" + elif [ -n "${SPACE_ID:-}" ]; then + # Convert owner/name → owner-name.hf.space + OWNER="${SPACE_ID%%/*}" + NAME="${SPACE_ID##*/}" + INTERCEPTION_BASE_URL="https://${OWNER}-${NAME}.hf.space" + else + echo "ERROR: Cannot determine INTERCEPTION_BASE_URL." + echo "Set INTERCEPTION_BASE_URL, SPACE_HOST, or SPACE_ID." + exit 1 + fi +fi +export INTERCEPTION_BASE_URL + +echo "Interception URL: ${INTERCEPTION_BASE_URL}" +echo "" + +# Build args +ARGS=( + --task-variant "${TASK_VARIANT:-lite}" + --n-rollouts "${N_ROLLOUTS:-4}" + --max-concurrent "${MAX_CONCURRENT:-3}" + --max-turns "${MAX_TURNS:-50}" + --rate-limit "${RATE_LIMIT:-30}" + --output-dir /app/trajectories + --hf-flavor "${HF_SANDBOX_FLAVOR:-cpu-basic}" + --max-retries 3 + --interception-port 7860 + --interception-host 0.0.0.0 + --agent-timeout-s 1800 + --export-sft + --hub-repo-id "${TRAJECTORY_HUB_REPO:-}" + --hub-upload-every "${HUB_UPLOAD_EVERY:-5}" +) + +# Optional: limit tasks +if [ -n "${MAX_TASKS:-}" ]; then + ARGS+=(--max-tasks "${MAX_TASKS}") +fi + +echo "Starting collection..." +export PYTHONPATH=/app/src:/app/envs:/app/examples/mini_swe_env +exec python examples/mini_swe_env/collect_rollouts_best_of_n.py "${ARGS[@]}" +STARTSH + + # ── Space README ──────────────────────────────────────────────────── + cat > "$STAGE_DIR/README.md" << EOF +--- +title: SWE Teacher Trajectory Collection +emoji: 📚 +colorFrom: green +colorTo: blue +sdk: docker +app_port: 7860 +suggested_hardware: $HARDWARE +startup_duration_timeout: 10m +--- + +# SWE Teacher Trajectory Collection + +Collects Best-of-N teacher trajectories from \`$MODEL\` on SWE-Gym Lite (230 tasks). + +- **Model:** \`$MODEL\` via \`$API_URL\` +- **Rollouts/task:** $N_ROLLOUTS +- **Concurrent:** $MAX_CONCURRENT +- **Max turns:** $MAX_TURNS +- **Rate limit:** $RATE_LIMIT req/min + +Trajectories are stored at \`/app/trajectories/\` inside the container. +EOF + + FILE_COUNT=$(find "$STAGE_DIR" -type f | wc -l) + echo " ✓ Staged $FILE_COUNT files" + + # ── Step 5: Upload to Space ──────────────────────────────────────── + echo "[5/6] Uploading to Space..." + cd "$STAGE_DIR" + hf upload "$SPACE_ID" . --repo-type space \ + --commit-message "Deploy: teacher collection $MODEL, $N_ROLLOUTS rollouts, $MAX_CONCURRENT concurrent" 2>&1 | tail -3 + echo " ✓ Upload complete" +fi + +# ── Step 6: Monitor ──────────────────────────────────────────────────── +echo "[6/6] Deployment triggered." +echo "" +echo " 🔗 Space: https://huggingface.co/spaces/$SPACE_ID" +echo " 🔗 Logs: https://huggingface.co/spaces/$SPACE_ID?logs=container" +echo "" + +if [ "$MONITOR" = true ]; then + echo "Monitoring build (Ctrl+C to stop)..." + "${PYTHON_CMD[@]}" << PYEOF +from huggingface_hub import HfApi +import time, os + +api = HfApi(token=os.environ['HF_TOKEN']) +space_id = "$SPACE_ID" +start = time.time() +last_stage = "" + +while True: + info = api.space_info(space_id) + stage = info.runtime.stage if info.runtime else "UNKNOWN" + elapsed = int(time.time() - start) + + if stage != last_stage: + print(f" [{elapsed:>4}s] {last_stage} -> {stage}") + last_stage = stage + + if stage == "RUNNING": + print(f"\n ✅ Space RUNNING after {elapsed}s") + print(f" Logs: https://huggingface.co/spaces/{space_id}?logs=container") + break + elif stage in ("RUNTIME_ERROR", "BUILD_ERROR", "CONFIG_ERROR"): + print(f"\n ❌ Failed: {stage}") + print(f" Check logs: https://huggingface.co/spaces/{space_id}?logs=build") + break + + time.sleep(15) +PYEOF +fi + +echo "" +echo "Done. Useful commands:" +echo " # Pause (stop billing):" +echo " bash $0 --pause" +echo " # Resume:" +echo " bash $0 --resume" +echo " # Download trajectories:" +echo " hf download $SPACE_ID trajectories/ --repo-type space --local-dir ./trajectories_download" diff --git a/examples/mini_swe_env/pyproject.toml b/examples/mini_swe_env/pyproject.toml new file mode 100644 index 000000000..49697ae83 --- /dev/null +++ b/examples/mini_swe_env/pyproject.toml @@ -0,0 +1,70 @@ +# Training dependencies for mini_swe_env async GRPO example. +# +# Install: +# cd examples/mini_swe_env +# uv sync +# +# Or from repo root: +# uv sync --project examples/mini_swe_env +# +# This installs everything needed to run train_swe_async_grpo.py locally +# on hosted compute (Slurm, bare-metal, cloud VMs) with HF Sandbox for +# agent execution. + +[project] +name = "mini-swe-train" +version = "0.1.0" +description = "SWE Async GRPO training — local/hosted compute launcher" +requires-python = ">=3.12" +dependencies = [ + "openenv-core>=0.3.0", + "openenv-mini-swe-env>=0.1.0", + # TRL fork with AsyncGRPOTrainer + async rollout parity + "trl @ git+https://github.com/rycerzes/trl@feat/async-grpo-parity", + # transformers >=5.2.0 required for native Qwen3.5 model support. + # Installed --no-deps to avoid pulling conflicting transitive deps. + "transformers>=5.2.0", + "accelerate>=1.13.0", + "peft>=0.15.0", + "datasets>=4.8.0", + "bitsandbytes>=0.43.0", + + # vLLM 0.21+ for Qwen3.5 support + NCCL weight transfer protocol. + # NOTE: on systems where vllm is installed via pip (not the Docker + # image), uncomment the line below or install separately: + # "vllm>=0.21.0", + + "hf-sandbox @ git+https://github.com/rycerzes/hf-sandbox@feat/named-tunnels", + + "aiohttp>=3.13.5", + "fastmcp>=3.3.0", + "requests>=2.25.0", + "huggingface-hub>=1.5", + + "trackio>=0.25.0", + + "flash-linear-attention>=0.5.0", +] + +[project.optional-dependencies] +# vLLM is large and often installed system-wide or via container. +# Include explicitly when needed. +vllm = ["vllm>=0.21.0"] + +dev = [ + "pytest>=8.0.0", + "pytest-asyncio>=0.23.0", + "ruff>=0.14.0", +] + +[build-system] +requires = ["setuptools>=45", "wheel"] +build-backend = "setuptools.build_meta" + +[tool.uv] +# Local path dependencies — resolved relative to this file. +# openenv-core from repo root, mini_swe_env from envs/. + +[tool.uv.sources] +openenv-core = { path = "../../", editable = true } +openenv-mini-swe-env = { path = "../../envs/mini_swe_env", editable = true } \ No newline at end of file diff --git a/examples/mini_swe_env/run_swe_sample.py b/examples/mini_swe_env/run_swe_sample.py new file mode 100644 index 000000000..64ce7b66f --- /dev/null +++ b/examples/mini_swe_env/run_swe_sample.py @@ -0,0 +1,307 @@ +#!/usr/bin/env python3 +"""Run one real SWE-Gym interception rollout against an actual LLM. + +This is a true end-to-end harness test: + + SWE-Gym task -> SWESessionFactory -> Pi in sandbox + -> InterceptionServer -> real OpenAI-compatible LLM + -> host-side answer tool grading -> session.verify() + +Required env vars: + SWE_LLM_BASE_URL e.g. https://api.openai.com/v1 + SWE_LLM_API_KEY bearer token + SWE_LLM_MODEL e.g. gpt-4o-mini + +Example: + SWE_LLM_BASE_URL=https://api.openai.com/v1 \ + SWE_LLM_API_KEY=... \ + SWE_LLM_MODEL=gpt-4o-mini \ + PYTHONPATH=src:envs python examples/mini_swe_env/run_swe_sample.py +""" + +from __future__ import annotations + +import argparse +import asyncio +import json +import logging +import os +import sys +import time +import uuid +from pathlib import Path + +import httpx + +_root = Path(__file__).resolve().parent.parent.parent +for _p in (_root / "src", _root / "envs"): + if str(_p) not in sys.path: + sys.path.insert(0, str(_p)) + +from mini_swe_env.harness import SWEAgentConfig, SWESessionFactory +from mini_swe_env.task_loader_swegym import load_swegym_tasks +from openenv.core.harness.agents.interception_server import InterceptionServer +from openenv.core.harness.sandbox import create_sandbox_backend + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(name)s %(levelname)s %(message)s", + datefmt="%H:%M:%S", +) +_log = logging.getLogger("swe-e2e") + +# Known easy SWE-Gym Lite task that has repeatedly produced reward=1.0 +# in local end-to-end validation with qwen-3.6-27b. +DEFAULT_TASK_INDEX = 16 # getmoto__moto-5699 + + +def _arg_parser() -> argparse.ArgumentParser: + p = argparse.ArgumentParser(description="Run one real SWE interception rollout") + p.add_argument("--task-variant", default="lite", choices=["lite", "full"]) + p.add_argument( + "--task-index", + type=int, + default=DEFAULT_TASK_INDEX, + help=f"Task index in variant split (default: {DEFAULT_TASK_INDEX}, getmoto__moto-5699)", + ) + p.add_argument("--sandbox-backend", default="docker", choices=["docker", "e2b", "hf"]) + p.add_argument("--interception-port", type=int, default=9090) + p.add_argument("--interception-host", default="0.0.0.0") + p.add_argument("--interception-base-url", default="") + p.add_argument("--agent-timeout-s", type=float, default=1800.0) + p.add_argument("--request-timeout-s", type=float, default=180.0) + p.add_argument("--max-turns", type=int, default=50) + p.add_argument("--assert-host-answer", action="store_true") + return p + + +def _must_env(name: str) -> str: + value = os.environ.get(name, "").strip() + if not value: + raise RuntimeError(f"Missing required env var: {name}") + return value + + +async def _forward_to_llm( + client: httpx.AsyncClient, + *, + intercept: dict, + base_url: str, + api_key: str, + model: str, + timeout_s: float, +) -> dict: + body = dict(intercept.get("body") or {}) + body["model"] = model + body["logprobs"] = True + body["top_logprobs"] = 5 + body.pop("stream", None) + body.pop("stream_options", None) + + r = await client.post( + f"{base_url.rstrip('/')}/chat/completions", + headers={ + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json", + }, + json=body, + timeout=timeout_s, + ) + if r.status_code != 200: + raise RuntimeError(f"LLM error {r.status_code}: {r.text[:500]}") + return r.json() + + +async def _invoke_answer_tool( + client: httpx.AsyncClient, + *, + server_port: int, + secret: str, + rollout_id: str, +) -> dict: + r = await client.post( + f"http://127.0.0.1:{server_port}/rollout/{rollout_id}/v1/tools/answer", + headers={"Authorization": f"Bearer {secret}"}, + json={"arguments": {}}, + timeout=180.0, + ) + if r.status_code != 200: + raise RuntimeError(f"answer tool error {r.status_code}: {r.text[:500]}") + return r.json() + + +async def _run(args: argparse.Namespace) -> int: + llm_base_url = _must_env("SWE_LLM_BASE_URL") + llm_api_key = _must_env("SWE_LLM_API_KEY") + llm_model = _must_env("SWE_LLM_MODEL") + + tasks = load_swegym_tasks(args.task_variant) + if not (0 <= args.task_index < len(tasks)): + raise RuntimeError( + f"task-index {args.task_index} out of range [0, {len(tasks) - 1}]" + ) + gym_task = tasks[args.task_index] + swe_task = gym_task.to_swe_task() + + server = InterceptionServer( + port=args.interception_port, + host=args.interception_host, + tool_name_allowlist={"answer"}, + ) + await server.start() + + try: + interception_base_url = args.interception_base_url.strip() + if not interception_base_url: + if args.sandbox_backend != "docker": + raise RuntimeError( + "For non-docker backends, pass --interception-base-url " + "that sandboxes can reach (tunnel/public URL)." + ) + interception_base_url = f"http://host.docker.internal:{server.port}" + + _log.info("Task: %s (%s)", gym_task.instance_id, gym_task.repo) + _log.info("Sandbox image: %s", gym_task.instance_image) + _log.info("Interception server: %s", interception_base_url) + + backend = create_sandbox_backend(args.sandbox_backend) + cfg = SWEAgentConfig( + base_url=interception_base_url, + api_key=server.secret, + model=llm_model, + agent_timeout_s=args.agent_timeout_s, + ) + factory = SWESessionFactory( + agent="pi", + config=cfg, + sandbox_backend=backend, + mode="interception_gate", + interception_server=server, + interception_base_url=interception_base_url, + ) + + episode_id = f"swe-e2e-{uuid.uuid4().hex[:8]}" + t0 = time.time() + session = factory.create(task=swe_task, episode_id=episode_id) + _log.info("Session created in %.1fs", time.time() - t0) + + turns = 0 + answer_requested = False + answer_bridge_invoked = False + logprob_tokens = 0 + + try: + async with httpx.AsyncClient() as client: + while turns < args.max_turns: + intercept = await session.next_request(timeout_s=args.agent_timeout_s) + if intercept is None: + _log.info("Agent exited") + break + + turns += 1 + llm_resp = await _forward_to_llm( + client, + intercept=intercept, + base_url=llm_base_url, + api_key=llm_api_key, + model=llm_model, + timeout_s=args.request_timeout_s, + ) + + choice0 = (llm_resp.get("choices") or [{}])[0] + msg = choice0.get("message") or {} + tool_calls = msg.get("tool_calls") or [] + if any( + (tc.get("function") or {}).get("name") == "answer" + for tc in tool_calls + ): + answer_requested = True + answer_resp = await _invoke_answer_tool( + client, + server_port=server.port, + secret=server.secret, + rollout_id=str(intercept.get("rollout_id", "")), + ) + answer_bridge_invoked = True + _log.info("host answer bridge response: %s", json.dumps(answer_resp, default=str)) + + # Replace tool-call response with plain assistant text so + # Pi doesn't need native /v1/tools client support. + llm_resp = dict(llm_resp) + choices = list(llm_resp.get("choices") or []) + if choices: + choice0 = dict(choices[0]) + msg0 = dict(choice0.get("message") or {}) + msg0.pop("tool_calls", None) + msg0["content"] = ( + (msg0.get("content") or "") + + "\nSubmission received and graded on host." + ).strip() + choice0["message"] = msg0 + choice0["finish_reason"] = "stop" + choices[0] = choice0 + llm_resp["choices"] = choices + + lp = (choice0.get("logprobs") or {}).get("content") or [] + logprob_tokens += len(lp) + + _log.info( + "turn=%d finish=%s tools=%s lp_tokens=%d", + turns, + choice0.get("finish_reason"), + [ + (tc.get("function") or {}).get("name", "?") + for tc in tool_calls + ], + len(lp), + ) + + await session.deliver(intercept, llm_resp) + + if answer_bridge_invoked: + break + + vr = session.verify(transcript=[]) + _log.info("verify reward=%s metrics=%s", vr.env_reward, json.dumps(vr.metrics, default=str)) + _log.info("turns=%d logprob_tokens=%d", turns, logprob_tokens) + + if turns == 0: + try: + artifacts = session.collect_artifacts() + agent_log = artifacts.get("agent_log", "") if isinstance(artifacts, dict) else "" + if isinstance(agent_log, str) and agent_log.strip(): + _log.warning("agent_log tail:\n%s", agent_log[-2000:]) + except Exception as exc: + _log.warning("Failed to collect agent log: %s", exc) + + if args.assert_host_answer: + src = (vr.metrics or {}).get("reward_source") + if src != "host_answer_tool": + raise RuntimeError( + "Expected reward_source=host_answer_tool, got " + f"{src!r}; model may not have called answer()." + ) + + print("\n" + "=" * 68) + print(f"Task : {gym_task.instance_id}") + print(f"Turns : {turns}") + print(f"Logprob tokens : {logprob_tokens}") + print(f"Answer called : {answer_requested}") + print(f"Answer bridged : {answer_bridge_invoked}") + print(f"Reward : {vr.env_reward}") + print(f"Reward source : {(vr.metrics or {}).get('reward_source')}") + print("=" * 68) + return 0 + finally: + session.close() + finally: + await server.stop() + + +def main() -> None: + args = _arg_parser().parse_args() + raise SystemExit(asyncio.run(_run(args))) + + +if __name__ == "__main__": + main() diff --git a/examples/mini_swe_env/test_version_compat.py b/examples/mini_swe_env/test_version_compat.py new file mode 100644 index 000000000..73eb2279d --- /dev/null +++ b/examples/mini_swe_env/test_version_compat.py @@ -0,0 +1,296 @@ +#!/usr/bin/env python3 +"""Gate 0.3 — Version compatibility smoke test for TRL + vLLM + Qwen3.5-4B. + +Validates that the installed combination of TRL, vLLM, and transformers can: + 1. Import TRL's AsyncGRPOTrainer and AsyncGRPOConfig without errors. + 2. Import vLLM's NCCL weight-transfer modules. + 3. Load a Qwen3.5-4B tokenizer via transformers (proves native model support). + 4. (If vLLM server is running) Send a /v1/completions request and get a response. + 5. (If vLLM server is running) Validate weight-transfer endpoints exist. + +Usage: + # Minimal (no GPU / no running vLLM server): + python examples/mini_swe_env/test_version_compat.py + + # Full (requires vLLM serving Qwen3.5-4B on localhost:8000): + VLLM_URL=http://localhost:8000 python examples/mini_swe_env/test_version_compat.py + +Environment variables: + VLLM_URL Base URL for vLLM server (default: http://localhost:8000) + VLLM_API_KEY API key for vLLM server (default: token) + STUDENT_MODEL Model ID for Qwen3.5-4B (default: unsloth/Qwen3.5-4B) +""" + +from __future__ import annotations + +import importlib +import os +import warnings + +# ── Helpers ──────────────────────────────────────────────────────────── + + +def _check(label: str, passed: bool, detail: str = "") -> bool: + status = "✅ PASS" if passed else "❌ FAIL" + suffix = f" ({detail})" if detail else "" + print(f" {status} {label}{suffix}") + return passed + + +def _version(pkg: str) -> str: + try: + mod = importlib.import_module(pkg) + return getattr(mod, "__version__", "unknown") + except ImportError: + return "NOT INSTALLED" + + +# ── Gate checks ──────────────────────────────────────────────────────── + + +def check_imports() -> bool: + """Check 1: TRL AsyncGRPO imports.""" + print("\n[1/5] TRL AsyncGRPO imports") + ok = True + try: + from trl.experimental.async_grpo import ( # noqa: F401 + AsyncGRPOConfig, + AsyncGRPOTrainer, + ) + + ok = _check("AsyncGRPOConfig import", True) and ok + ok = _check("AsyncGRPOTrainer import", True) and ok + except Exception as exc: + ok = _check("TRL AsyncGRPO imports", False, str(exc)) and ok + return ok + + +def check_vllm_modules() -> bool: + """Check 2: vLLM NCCL weight-transfer modules.""" + print("\n[2/5] vLLM weight-transfer modules") + ok = True + try: + from vllm.distributed.weight_transfer.nccl_engine import ( # noqa: F401 + NCCLTrainerSendWeightsArgs, + NCCLWeightTransferEngine, + ) + + ok = _check("NCCLWeightTransferEngine import", True) and ok + ok = _check("NCCLTrainerSendWeightsArgs import", True) and ok + except Exception as exc: + ok = _check("NCCL engine imports", False, str(exc)) and ok + + try: + from vllm.utils.network_utils import get_ip, get_open_port # noqa: F401 + + ok = _check("network_utils import", True) and ok + except Exception as exc: + ok = _check("network_utils import", False, str(exc)) and ok + return ok + + +def check_tokenizer() -> bool: + """Check 3: Qwen3.5-4B tokenizer loads via transformers.""" + print("\n[3/5] Qwen3.5-4B tokenizer (transformers native support)") + model_id = os.environ.get("STUDENT_MODEL", "unsloth/Qwen3.5-4B") + try: + from transformers import AutoTokenizer + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=False) + return _check( + f"AutoTokenizer.from_pretrained('{model_id}')", + True, + f"vocab_size={tok.vocab_size}", + ) + except Exception as exc: + return _check(f"tokenizer load for {model_id}", False, str(exc)) + + +def check_vllm_completions() -> bool: + """Check 4: vLLM /v1/completions responds (requires running server).""" + print("\n[4/5] vLLM /v1/completions endpoint") + vllm_url = os.environ.get("VLLM_URL", "http://localhost:8000").rstrip("/") + api_key = os.environ.get("VLLM_API_KEY", "token") + model_id = os.environ.get("STUDENT_MODEL", "unsloth/Qwen3.5-4B") + + try: + import requests + + resp = requests.get(f"{vllm_url}/health", timeout=5) + if resp.status_code != 200: + return _check( + "/health", + False, + f"status={resp.status_code} — is vLLM running at {vllm_url}?", + ) + except Exception: + return _check( + "vLLM server reachable", + False, + f"Cannot connect to {vllm_url} — set VLLM_URL or start vLLM. Skipping.", + ) + + try: + import requests + + body = { + "model": model_id, + "prompt": "Hello", + "max_tokens": 8, + "temperature": 0.0, + } + resp = requests.post( + f"{vllm_url}/v1/completions", + headers={ + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json", + }, + json=body, + timeout=30, + ) + if resp.status_code != 200: + return _check( + "/v1/completions", + False, + f"status={resp.status_code}: {resp.text[:200]}", + ) + data = resp.json() + text = data["choices"][0].get("text", "") + return _check("/v1/completions", True, f"generated {len(text)} chars") + except Exception as exc: + return _check("/v1/completions", False, str(exc)) + + +def check_weight_transfer_endpoints() -> bool: + """Check 5: Weight-transfer HTTP endpoints exist (requires running server).""" + print("\n[5/5] Weight-transfer HTTP endpoints (VLLM_SERVER_DEV_MODE=1 required)") + vllm_url = os.environ.get("VLLM_URL", "http://localhost:8000").rstrip("/") + api_key = os.environ.get("VLLM_API_KEY", "token") + + try: + import requests + + resp = requests.get(f"{vllm_url}/health", timeout=5) + if resp.status_code != 200: + return _check("vLLM reachable", False, "skipping endpoint checks") + except Exception: + return _check("vLLM server reachable", False, "skipping endpoint checks") + + ok = True + headers = {"Authorization": f"Bearer {api_key}"} + + # Check /get_world_size + try: + resp = requests.get(f"{vllm_url}/get_world_size", headers=headers, timeout=5) + ws = resp.json().get("world_size", "?") if resp.status_code == 200 else "?" + ok = ( + _check( + "GET /get_world_size", + resp.status_code == 200, + f"world_size={ws}" + if resp.status_code == 200 + else f"status={resp.status_code}", + ) + and ok + ) + except Exception as exc: + ok = _check("GET /get_world_size", False, str(exc)) and ok + + # Check that /pause and /resume exist (OPTIONS or try POST) + for endpoint in ["/pause", "/resume"]: + try: + # Use POST with no body — will either work or return 422 (missing params) + # but NOT 404 if the endpoint exists. + resp = requests.post( + f"{vllm_url}{endpoint}", + headers=headers, + timeout=5, + params={"mode": "keep"} if endpoint == "/pause" else None, + ) + exists = resp.status_code != 404 + ok = ( + _check( + f"POST {endpoint}", + exists, + f"status={resp.status_code}" if exists else "404 Not Found", + ) + and ok + ) + except Exception as exc: + ok = _check(f"POST {endpoint}", False, str(exc)) and ok + + # Check vLLM >= 0.20 lifecycle endpoints + for endpoint in ["/start_weight_update", "/finish_weight_update"]: + try: + resp = requests.post( + f"{vllm_url}{endpoint}", + headers=headers, + json={} if endpoint == "/start_weight_update" else None, + timeout=5, + ) + exists = resp.status_code != 404 + ok = ( + _check( + f"POST {endpoint} (vLLM >=0.20)", + exists, + f"status={resp.status_code}" if exists else "404 — pre-0.20 vLLM?", + ) + and ok + ) + except Exception as exc: + ok = _check(f"POST {endpoint}", False, str(exc)) and ok + + return ok + + +# ── Main ─────────────────────────────────────────────────────────────── + + +def main() -> int: + print("=" * 60) + print("Gate 0.3 — TRL + vLLM + Transformers Version Compatibility") + print("=" * 60) + + # Print installed versions + print("\nInstalled versions:") + for pkg in ["trl", "vllm", "transformers", "torch", "accelerate"]: + print(f" {pkg:20s} {_version(pkg)}") + + # Run checks + results: list[bool] = [] + results.append(check_imports()) + results.append(check_vllm_modules()) + results.append(check_tokenizer()) + results.append(check_vllm_completions()) + results.append(check_weight_transfer_endpoints()) + + # Summary + passed = sum(results) + total = len(results) + print(f"\n{'=' * 60}") + print(f"Gate 0.3 result: {passed}/{total} check groups passed") + + if all(results): + print("✅ Gate 0.3 PASSED — version combination is compatible") + return 0 + else: + # Checks 4 and 5 (server-dependent) are soft — they require a running vLLM + core_ok = all(results[:3]) + if core_ok: + print( + "⚠️ Gate 0.3 PARTIAL — core imports OK, " + "server-dependent checks skipped/failed" + ) + print( + " Start vLLM with VLLM_SERVER_DEV_MODE=1 and re-run for full validation." + ) + return 0 + else: + print("❌ Gate 0.3 FAILED — fix import/version errors above") + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/examples/mini_swe_env/train_swe_async_grpo.py b/examples/mini_swe_env/train_swe_async_grpo.py new file mode 100644 index 000000000..492e71fc4 --- /dev/null +++ b/examples/mini_swe_env/train_swe_async_grpo.py @@ -0,0 +1,507 @@ +#!/usr/bin/env python3 +"""Train SWE with AsyncGRPOTrainer + Pi agent + InterceptionServer. + +Architecture: + Pi (HF Sandbox) → InterceptionServer → SWERolloutWorker → vLLM /v1/completions + ← chat response back to Pi + +vLLM runs on a separate GPU. The trainer, interception server, and rollout +worker share a process on the training GPU. + +The InterceptionServer runs in a background thread with its own asyncio +event loop so it stays alive while the synchronous trainer.train() runs +on the main thread. + +Prerequisites: + CUDA_VISIBLE_DEVICES=0 vllm serve Qwen/Qwen3-1.7B \\ + --tensor-parallel-size 1 --max-model-len 40960 + + CUDA_VISIBLE_DEVICES=1 \\ + SWE_MODEL=Qwen/Qwen3-1.7B \\ + INTERCEPTION_AUTH_TOKEN=secret123 \\ + INTERCEPTION_BASE_URL=http://localhost:8765 \\ + PYTHONPATH=src:envs python examples/mini_swe_env/train_swe_async_grpo.py +""" + +from __future__ import annotations + +import argparse +import asyncio +import inspect +import logging +import os +import sys +import threading +from importlib import metadata as importlib_metadata +from pathlib import Path +from typing import Any + +_root = Path(__file__).resolve().parent.parent.parent +for _p in (_root, _root / "src", _root / "envs"): + if str(_p) not in sys.path: + sys.path.insert(0, str(_p)) + +from datasets import Dataset # noqa: E402 +from transformers import AutoTokenizer # noqa: E402 +from transformers.utils.import_utils import ( # noqa: E402 + is_causal_conv1d_available, + is_flash_linear_attention_available, +) +from trl.experimental.async_grpo import AsyncGRPOConfig, AsyncGRPOTrainer # noqa: E402 + +try: + from peft import LoraConfig, TaskType # noqa: E402 +except ImportError: + LoraConfig = None # type: ignore[assignment,misc] + TaskType = None # type: ignore[assignment,misc] + +from examples.mini_swe_env.async_grpo.control_plane import ( # noqa: E402 + SWEAsyncControlPlane, + SWEAsyncControlPlaneConfig, +) +from examples.mini_swe_env.async_grpo.rollout_worker import ( # noqa: E402 + SWERolloutWorker, + WorkerConfig, +) +from mini_swe_env.harness import SWEAgentConfig, SWESessionFactory # noqa: E402 +from mini_swe_env.task_loader_swegym import load_swegym_tasks # noqa: E402 +from openenv.core.harness.sandbox import create_sandbox_backend # noqa: E402 + + +_log = logging.getLogger("swe-async-grpo") + + +# ── InterceptionServer background thread ────────────────────────── + + +def _run_event_loop(loop: asyncio.AbstractEventLoop) -> None: + """Run an asyncio event loop forever in the current thread.""" + asyncio.set_event_loop(loop) + loop.run_forever() + + +def start_interception_server( + control_plane: SWEAsyncControlPlane, +) -> tuple[asyncio.AbstractEventLoop, threading.Thread]: + """Start the InterceptionServer in a daemon thread. + + Returns the event loop and the thread so the caller can shut it down. + The aiohttp server runs on this loop and stays alive as long as the + thread is running. + """ + loop = asyncio.new_event_loop() + # Start the server on the new loop. + future = asyncio.run_coroutine_threadsafe(control_plane.start(), loop) + # The loop must be running for the coroutine to execute. + thread = threading.Thread( + target=_run_event_loop, args=(loop,), daemon=True, name="interception-server" + ) + thread.start() + # Wait for start() to complete. + future.result(timeout=30) + return loop, thread + + +def stop_interception_server( + control_plane: SWEAsyncControlPlane, + loop: asyncio.AbstractEventLoop, + thread: threading.Thread, +) -> None: + """Shut down the InterceptionServer and its event loop.""" + future = asyncio.run_coroutine_threadsafe(control_plane.stop(), loop) + try: + future.result(timeout=10) + except Exception: + pass + loop.call_soon_threadsafe(loop.stop) + thread.join(timeout=5) + + +# ── CLI ──────────────────────────────────────────────────────────── + + +def _args() -> argparse.Namespace: + p = argparse.ArgumentParser() + # Defaults target a full pass over SWE-Gym Lite. + # 230 tasks + 230 steps approximates one epoch on the lite split. + p.add_argument("--task-variant", default="lite", choices=["lite", "full"]) + p.add_argument("--max-tasks", type=int, default=230) + p.add_argument("--max-steps", type=int, default=230) + p.add_argument( + "--max-turns", + type=int, + default=0, + help="Max agent turns per rollout (0 means unlimited).", + ) + p.add_argument("--sandbox-backend", default="hf", choices=["docker", "e2b", "hf"]) + p.add_argument("--vllm-url", default="http://localhost:8000") + p.add_argument("--agent", default="pi", choices=["pi", "opencode"]) + p.add_argument( + "--num-generations", type=int, default=16, + help="Rollouts per prompt for group-relative advantage (GRPO).", + ) + return p.parse_args() + + +def _env(name: str) -> str: + v = os.environ.get(name, "").strip() + if not v: + raise RuntimeError(f"Missing env var: {name}") + return v + + +def _bool_env(name: str, default: bool) -> bool: + raw = os.environ.get(name) + if raw is None: + return default + return raw.strip().lower() in {"1", "true", "yes", "on"} + + +def _int_env(name: str, default: int, *, min_value: int = 1) -> int: + raw = os.environ.get(name) + if raw is None or not raw.strip(): + return default + value = int(raw) + if value < min_value: + raise ValueError(f"{name} must be >= {min_value}, got {value}") + return value + + +def _pkg_version(dist_name: str) -> str: + try: + return importlib_metadata.version(dist_name) + except importlib_metadata.PackageNotFoundError: + return "not-installed" + + +def _is_running_in_space() -> bool: + """Detect if we're running inside an HF Space (vs local/Slurm/cloud).""" + return bool( + os.environ.get("SPACE_HOST") + or os.environ.get("HF_SPACE_ID") + or os.environ.get("SPACE_ID") + ) + + +def _derive_checkpoint_repo_id() -> str | None: + explicit = os.environ.get("SWE_HUB_MODEL_ID", "").strip() + if explicit: + return explicit + space_id = ( + os.environ.get("HF_SPACE_ID", "").strip() + or os.environ.get("SPACE_ID", "").strip() + ) + if "/" not in space_id: + return None + return f"{space_id}-checkpoints" + + +def _build_checkpoint_args() -> tuple[dict[str, Any], str | None, bool]: + enabled = _bool_env("SWE_CHECKPOINT_TO_HUB", default=_is_running_in_space()) + if not enabled: + return {}, None, False + + repo_id = _derive_checkpoint_repo_id() + if not repo_id: + _log.warning( + "checkpointing requested, but SWE_HUB_MODEL_ID/HF_SPACE_ID missing; " + "disabling hub checkpointing" + ) + return {}, None, False + + save_steps = _int_env("SWE_CHECKPOINT_SAVE_STEPS", default=2) + save_total_limit = _int_env("SWE_CHECKPOINT_SAVE_TOTAL_LIMIT", default=2) + + checkpoint_args = { + "save_strategy": "steps", + "save_steps": save_steps, + "save_total_limit": save_total_limit, + "push_to_hub": True, + "hub_model_id": repo_id, + "hub_strategy": "checkpoint", + "hub_private_repo": _bool_env("SWE_HUB_PRIVATE_REPO", default=True), + "hub_token": os.environ.get("HF_TOKEN", "").strip() or None, + } + + resume_pref = os.environ.get("SWE_RESUME_FROM_CHECKPOINT", "auto").strip().lower() + if resume_pref in {"", "0", "false", "off", "none"}: + resume_from_checkpoint: str | None = None + elif resume_pref == "auto": + resume_from_checkpoint = "last-checkpoint" + else: + resume_from_checkpoint = os.environ.get( + "SWE_RESUME_FROM_CHECKPOINT", "" + ).strip() + + return checkpoint_args, resume_from_checkpoint, True + + +def _filter_async_grpo_kwargs(values: dict[str, Any]) -> dict[str, Any]: + sig = inspect.signature(AsyncGRPOConfig) + return {k: v for k, v in values.items() if k in sig.parameters} + + +def _is_missing_checkpoint_error(exc: Exception) -> bool: + msg = str(exc).lower() + if "last-checkpoint" not in msg: + return False + hints = ( + "no valid checkpoint", + "can't find", + "cannot find", + "does not exist", + "not found", + "404", + ) + return any(hint in msg for hint in hints) + + +def main() -> int: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(name)s %(levelname)s %(message)s", + datefmt="%H:%M:%S", + ) + args = _args() + model = _env("SWE_MODEL") + vllm_url = args.vllm_url + vllm_key = os.environ.get("VLLM_API_KEY", "token").strip() + + _log.info( + "qwen3.5 kernel deps: fla_available=%s causal_conv1d_available=%s fla_ver=%s causal_conv1d_ver=%s flash_attn_ver=%s", + is_flash_linear_attention_available(), + is_causal_conv1d_available(), + _pkg_version("flash-linear-attention"), + _pkg_version("causal-conv1d"), + _pkg_version("flash-attn"), + ) + + # ── Load tasks ──────────────────────────────────────────────── + gym_tasks = load_swegym_tasks(args.task_variant)[: args.max_tasks] + swe_tasks = [t.to_swe_task() for t in gym_tasks] + _log.info("loaded %d tasks", len(swe_tasks)) + + # ── Dataset (prompt per task) ───────────────────────────────── + dataset = Dataset.from_list( + [ + { + "prompt": [{"role": "user", "content": t.instruction}], + "instance_id": t.instance_id, + } + for t in swe_tasks + ] + ) + + # ── Tokenizer ───────────────────────────────────────────────── + tokenizer = AutoTokenizer.from_pretrained(model) + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + + # ── Interception control plane (background thread) ──────────── + # Only rank 0 owns the interception server, session factory, and rollout + # worker. Other ranks only participate in gradient computation. + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + is_main = local_rank == 0 + + control_plane: SWEAsyncControlPlane | None = None + server_loop = None + server_thread = None + worker: SWERolloutWorker | None = None + + if is_main: + control_cfg = SWEAsyncControlPlaneConfig.from_env() + control_plane = SWEAsyncControlPlane(config=control_cfg) + server_loop, server_thread = start_interception_server(control_plane) + _log.info("InterceptionServer running in background thread") + + try: + # ── Session factory + rollout worker (rank 0 only) ──────── + if is_main: + assert control_plane is not None + backend = create_sandbox_backend(args.sandbox_backend) + session_factory = SWESessionFactory( + agent=args.agent, + config=SWEAgentConfig( + base_url=control_plane.interception_base_url, + api_key=control_plane.auth_token, + model=model, + agent_timeout_s=1800.0, + ), + sandbox_backend=backend, + mode="interception_gate", + interception_server=control_plane.server, + interception_base_url=control_plane.interception_base_url, + ) + + worker = SWERolloutWorker( + session_factory=session_factory, + tasks=swe_tasks, + tokenizer=tokenizer, + vllm_base_url=vllm_url, + vllm_api_key=vllm_key, + vllm_model=model, + config=WorkerConfig( + max_inflight=2, + max_turns=args.max_turns, + num_generations=args.num_generations, + ), + ) + + # ── Trainer ─────────────────────────────────────────────── + def _noop_reward(**kwargs: Any) -> list[float]: + """Unused — rewards come from rollout_worker.advantage.""" + prompts = kwargs.get("prompts", []) + return [0.0] * len(prompts) + + checkpoint_args, resume_from_checkpoint, checkpoint_requested = ( + _build_checkpoint_args() + ) + async_grpo_args: dict[str, Any] = { + "output_dir": os.path.join( + os.environ.get("HOME", "/tmp"), "outputs/swe_async_grpo" + ), + "vllm_server_base_url": vllm_url, + "vllm_server_timeout": 2400.0, + "model_init_kwargs": {"dtype": "bfloat16"}, + "max_completion_length": 2048, + "max_steps": args.max_steps, + # Effective train batch is 4 via gradient accumulation. + "per_device_train_batch_size": 1, + "gradient_accumulation_steps": 4, + "num_generations": args.num_generations, + # Conservative optimizer settings for stability. + "learning_rate": 1e-6, + "weight_decay": 0.1, + "temperature": 1.0, + # bnb 8-bit optimizer is not yet compatible with FSDP2 DTensor step path. + # Use torch AdamW for multi-GPU FSDP2 stability. + "optim": "adamw_torch", + "bf16": True, + "gradient_checkpointing": True, + # FSDP2: make layer wrapping explicit so transformer class targeting is applied. + # Also force reshard_after_forward=True to avoid keeping full params resident. + "fsdp": "full_shard auto_wrap", + "fsdp_config": { + "fsdp_version": 2, + "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP", + "fsdp_transformer_layer_cls_to_wrap": "Qwen3_5DecoderLayer", + "fsdp_reshard_after_forward": True, + }, + "ddp_timeout": 7200, + "max_staleness": 4, + "weight_sync_steps": 1, + "max_inflight_tasks": 2, + "logging_steps": 1, + "report_to": "trackio", + "run_name": f"swe-grpo-{model.split('/')[-1]}", + "trackio_space_id": os.environ.get("TRACKIO_SPACE_ID", "").strip() or None, + } + filtered_checkpoint_args = _filter_async_grpo_kwargs(checkpoint_args) + async_grpo_args.update(filtered_checkpoint_args) + + checkpoint_enabled = bool(filtered_checkpoint_args.get("push_to_hub")) + if checkpoint_requested and not checkpoint_enabled: + _log.warning( + "checkpointing requested, but AsyncGRPOConfig does not expose hub args; " + "continuing without hub checkpointing" + ) + resume_from_checkpoint = None + + # ── LoRA config (Unsloth-recommended hyperparams for Qwen3.5) ── + peft_config = None + if LoraConfig is not None: + peft_config = LoraConfig( + task_type=TaskType.CAUSAL_LM, + r=16, + lora_alpha=16, # alpha == r recommended for Qwen3.5 + lora_dropout=0, + target_modules=[ + "q_proj", "k_proj", "v_proj", "o_proj", + "gate_proj", "up_proj", "down_proj", + ], + bias="none", + ) + _log.info( + "LoRA config: r=%d alpha=%d targets=%s", + peft_config.r, + peft_config.lora_alpha, + peft_config.target_modules, + ) + + trainer = AsyncGRPOTrainer( + model=model, + reward_funcs=_noop_reward, + train_dataset=dataset, + processing_class=tokenizer, + rollout_worker=worker, + peft_config=peft_config, + args=AsyncGRPOConfig(**async_grpo_args), + ) + + _log.info( + "starting training: model=%s tasks=%d checkpointing=%s resume=%s", + model, + len(swe_tasks), + checkpoint_enabled, + resume_from_checkpoint or "none", + ) + if resume_from_checkpoint is None: + trainer.train() + else: + try: + trainer.train(resume_from_checkpoint=resume_from_checkpoint) + except Exception as exc: + if ( + resume_from_checkpoint == "last-checkpoint" + and _is_missing_checkpoint_error(exc) + ): + _log.warning( + "No hub checkpoint found at 'last-checkpoint'; starting from scratch" + ) + trainer.train() + else: + raise + + if checkpoint_enabled and hasattr(trainer, "push_to_hub"): + trainer.push_to_hub( + commit_message=f"Final checkpoint at step {getattr(trainer.state, 'global_step', '?')}" + ) + + _log.info("done: step=%s", getattr(trainer.state, "global_step", "?")) + return 0 + finally: + if control_plane is not None and server_loop is not None and server_thread is not None: + stop_interception_server(control_plane, server_loop, server_thread) + + +if __name__ == "__main__": + _exit_code: int | None = None + try: + raise SystemExit(main()) + except SystemExit as exc: + _exit_code = exc.code # type: ignore[assignment] + if exc.code == 0: + _log.info("training completed successfully") + else: + _log.error("training failed with exit code %s", exc.code) + except Exception: + _exit_code = 1 + _log.exception("unhandled exception in training") + + # When running inside an HF Space, pause it to stop billing and + # prevent restart loops. On local/Slurm runs this is a no-op. + if _is_running_in_space(): + try: + from huggingface_hub import HfApi as _HfApi + + _space_id = os.environ.get("SPACE_ID", "rycerzes/swe-async-grpo-train") + _HfApi().pause_space(_space_id) + _log.info("paused Space %s", _space_id) + except Exception: + _log.warning("failed to pause Space; sleeping to preserve logs") + import time as _t + + while True: + _t.sleep(3600) + + raise SystemExit(_exit_code or 0) diff --git a/examples/mini_swe_env/trajectory_store.py b/examples/mini_swe_env/trajectory_store.py new file mode 100644 index 000000000..6c322e63e --- /dev/null +++ b/examples/mini_swe_env/trajectory_store.py @@ -0,0 +1,450 @@ +"""Trajectory storage and filtering for teacher rollout collection. + +Stores full multi-turn trajectories as JSONL with rich metadata for +downstream SFT/RFT training. + +Storage format (one JSON object per line): + + { + "trajectory_id": "...", + "instance_id": "...", + "task_id": "...", + "repo": "...", + "teacher_model": "Qwen/Qwen3.6-27B", + "rollout_index": 0, # which attempt (0..N-1) for this task + "resolved": true, + "reward": 1.0, + "reward_source": "host_answer_tool", + "turns": 12, + "wall_s": 245.3, + "answer_called": true, + "tool_calls_count": 15, + "messages": [...], # full conversation (system + user + assistant + tool) + "tool_calls": [...], # extracted tool call log + "test_outcomes": {...}, # FAIL_TO_PASS / PASS_TO_PASS results + "timestamp_utc": "2026-05-24T...", + "metadata": {...}, # additional context + } + +Usage:: + + store = TrajectoryStore("trajectories/teacher_27b") + store.append(trajectory_record) + store.flush() + + # Filter resolved trajectories for SFT + resolved = store.filter(resolved=True, max_turns=15) + + # Compute pass@k + stats = store.pass_at_k_stats(k_values=[1, 4, 8]) +""" + +from __future__ import annotations + +import json +import logging +import math +import os +import time +from collections import defaultdict +from dataclasses import asdict, dataclass, field +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Iterator, Sequence + +_log = logging.getLogger(__name__) + + +@dataclass +class TrajectoryRecord: + """One complete rollout trajectory with metadata.""" + + # Identifiers + trajectory_id: str + instance_id: str + task_id: str + repo: str + + # Teacher info + teacher_model: str + + # Rollout metadata + rollout_index: int # which attempt for this task (0..N-1) + resolved: bool + reward: float + reward_source: str + + # Conversation + turns: int + wall_s: float + answer_called: bool + tool_calls_count: int + messages: list[dict[str, Any]] # full conversation history + tool_calls: list[dict[str, Any]] # extracted tool calls log + + # Grading details + test_outcomes: dict[str, Any] = field(default_factory=dict) + + # Per-turn completion records + # Each entry: {completion_id, timestamp, request: {messages, tools, model}, response: {...}} + completions: list[dict[str, Any]] = field(default_factory=list) + + # Timestamps + timestamp_utc: str = "" + + # Catch-all metadata + metadata: dict[str, Any] = field(default_factory=dict) + + def __post_init__(self) -> None: + if not self.timestamp_utc: + self.timestamp_utc = datetime.now(timezone.utc).isoformat() + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + @classmethod + def from_dict(cls, d: dict[str, Any]) -> "TrajectoryRecord": + # Handle any extra keys gracefully + known_fields = {f.name for f in cls.__dataclass_fields__.values()} + filtered = {k: v for k, v in d.items() if k in known_fields} + return cls(**filtered) + + +class TrajectoryStore: + """JSONL-backed trajectory store with in-memory index. + + Supports: + - Append-only writes (crash-safe with periodic flush) + - Filtering by resolved, turn count, etc. + - pass@k statistics computation + - Loading from existing JSONL files + - Periodic upload to HF Hub dataset repo (crash-safe persistence) + """ + + def __init__( + self, + output_dir: str | Path, + *, + hub_repo_id: str | None = None, + hub_upload_every: int = 5, + ) -> None: + self._dir = Path(output_dir) + self._dir.mkdir(parents=True, exist_ok=True) + self._filepath = self._dir / "trajectories.jsonl" + self._records: list[TrajectoryRecord] = [] + self._pending_writes: list[TrajectoryRecord] = [] + + # HF Hub persistence + self._hub_repo_id = hub_repo_id + self._hub_upload_every = max(1, hub_upload_every) + self._since_last_upload = 0 + self._hub_api: Any = None + if hub_repo_id: + self._init_hub() + + # Load existing records if file exists + if self._filepath.exists(): + self._load_existing() + + @property + def filepath(self) -> Path: + return self._filepath + + @property + def records(self) -> list[TrajectoryRecord]: + return list(self._records) + + def __len__(self) -> int: + return len(self._records) + + def append(self, record: TrajectoryRecord) -> None: + """Add a trajectory record (buffered, call flush() to persist).""" + self._records.append(record) + self._pending_writes.append(record) + + def flush(self) -> None: + """Write pending records to disk and upload to Hub if configured.""" + if not self._pending_writes: + return + with open(self._filepath, "a") as f: + for rec in self._pending_writes: + f.write(json.dumps(rec.to_dict(), ensure_ascii=False) + "\n") + count = len(self._pending_writes) + self._pending_writes = [] + _log.debug("flushed %d trajectories to %s", count, self._filepath) + + # Periodic upload to HF Hub + if self._hub_repo_id: + self._since_last_upload += count + if self._since_last_upload >= self._hub_upload_every: + self._upload_to_hub() + self._since_last_upload = 0 + + def upload_now(self) -> None: + """Force an immediate upload to HF Hub (call at end of run).""" + if self._hub_repo_id: + self._upload_to_hub() + + def _init_hub(self) -> None: + """Initialize HF Hub repo for trajectory persistence.""" + try: + from huggingface_hub import HfApi + self._hub_api = HfApi() + self._hub_api.create_repo( + repo_id=self._hub_repo_id, + repo_type="dataset", + private=True, + exist_ok=True, + ) + _log.info("hub persistence enabled: %s", self._hub_repo_id) + + # Try to download existing trajectories on startup (resume support) + try: + from huggingface_hub import hf_hub_download + local_path = hf_hub_download( + repo_id=self._hub_repo_id, + filename="trajectories.jsonl", + repo_type="dataset", + local_dir=str(self._dir), + ) + _log.info("downloaded existing trajectories from hub") + except Exception: + _log.debug("no existing trajectories on hub (fresh start)") + + except ImportError: + _log.warning("huggingface_hub not installed, hub persistence disabled") + self._hub_repo_id = None + except Exception as exc: + _log.warning("hub init failed: %s (persistence disabled)", exc) + self._hub_repo_id = None + + def _upload_to_hub(self) -> None: + """Upload trajectories.jsonl + stats.json to HF Hub.""" + if not self._hub_api or not self._filepath.exists(): + return + try: + self._hub_api.upload_file( + path_or_fileobj=str(self._filepath), + path_in_repo="trajectories.jsonl", + repo_id=self._hub_repo_id, + repo_type="dataset", + commit_message=f"Update trajectories ({len(self._records)} total)", + ) + # Also upload stats + stats_path = self._dir / "stats.json" + stats = self.pass_at_k_stats() + stats_summary = {k: v for k, v in stats.items() if k != "per_task"} + stats_path.write_text(json.dumps(stats_summary, indent=2)) + self._hub_api.upload_file( + path_or_fileobj=str(stats_path), + path_in_repo="stats.json", + repo_id=self._hub_repo_id, + repo_type="dataset", + commit_message=f"Update stats ({len(self._records)} trajectories)", + ) + _log.info( + "hub_upload complete: %d trajectories to %s", + len(self._records), + self._hub_repo_id, + ) + except Exception as exc: + _log.warning("hub upload failed (will retry next flush): %s", exc) + + def filter( + self, + *, + resolved: bool | None = None, + max_turns: int | None = None, + min_turns: int | None = None, + teacher_model: str | None = None, + instance_ids: set[str] | None = None, + ) -> list[TrajectoryRecord]: + """Filter trajectories by criteria.""" + results = [] + for rec in self._records: + if resolved is not None and rec.resolved != resolved: + continue + if max_turns is not None and rec.turns > max_turns: + continue + if min_turns is not None and rec.turns < min_turns: + continue + if teacher_model is not None and rec.teacher_model != teacher_model: + continue + if instance_ids is not None and rec.instance_id not in instance_ids: + continue + results.append(rec) + return results + + def resolved_trajectories(self, max_turns: int | None = None) -> list[TrajectoryRecord]: + """Convenience: get all resolved trajectories, optionally capped by turn count.""" + return self.filter(resolved=True, max_turns=max_turns) + + def pass_at_k_stats( + self, + k_values: Sequence[int] = (1, 4, 8), + ) -> dict[str, Any]: + """Compute pass@k statistics across all tasks. + + Uses the unbiased estimator: + pass@k = 1 - C(n-c, k) / C(n, k) + where n = total rollouts for the task, c = number resolved. + + Returns dict with per-k rates and per-task details. + """ + # Group by instance_id + by_task: dict[str, list[TrajectoryRecord]] = defaultdict(list) + for rec in self._records: + by_task[rec.instance_id].append(rec) + + stats: dict[str, Any] = { + "total_tasks": len(by_task), + "total_trajectories": len(self._records), + "total_resolved": sum(1 for r in self._records if r.resolved), + "resolve_rate": ( + sum(1 for r in self._records if r.resolved) / len(self._records) + if self._records + else 0.0 + ), + } + + for k in k_values: + pass_rates: list[float] = [] + for instance_id, recs in by_task.items(): + n = len(recs) + c = sum(1 for r in recs if r.resolved) + if n < k: + # Not enough rollouts — use empirical rate + pass_rates.append(1.0 if c > 0 else 0.0) + else: + # Unbiased estimator: pass@k = 1 - C(n-c, k) / C(n, k) + pass_rates.append(_pass_at_k(n, c, k)) + + rate = sum(pass_rates) / len(pass_rates) if pass_rates else 0.0 + stats[f"pass@{k}"] = round(rate, 4) + + # Per-task breakdown + per_task: dict[str, dict[str, Any]] = {} + for instance_id, recs in by_task.items(): + n = len(recs) + c = sum(1 for r in recs if r.resolved) + per_task[instance_id] = { + "n_rollouts": n, + "n_resolved": c, + "resolve_rate": round(c / n, 4) if n > 0 else 0.0, + "avg_turns": round(sum(r.turns for r in recs) / n, 1) if n > 0 else 0, + "avg_wall_s": round(sum(r.wall_s for r in recs) / n, 1) if n > 0 else 0, + } + stats["per_task"] = per_task + + return stats + + def summary(self) -> str: + """Human-readable summary of the store.""" + if not self._records: + return "TrajectoryStore: empty" + + resolved = sum(1 for r in self._records if r.resolved) + n_tasks = len(set(r.instance_id for r in self._records)) + avg_turns = sum(r.turns for r in self._records) / len(self._records) + avg_wall = sum(r.wall_s for r in self._records) / len(self._records) + + lines = [ + f"TrajectoryStore: {self._filepath}", + f" Total trajectories: {len(self._records)}", + f" Unique tasks: {n_tasks}", + f" Resolved: {resolved}/{len(self._records)} ({100*resolved/len(self._records):.1f}%)", + f" Avg turns: {avg_turns:.1f}", + f" Avg wall time: {avg_wall:.1f}s", + ] + + stats = self.pass_at_k_stats() + for k in (1, 4, 8): + key = f"pass@{k}" + if key in stats: + lines.append(f" {key}: {stats[key]*100:.1f}%") + + return "\n".join(lines) + + def export_for_sft( + self, + output_path: str | Path, + *, + resolved_only: bool = True, + max_turns: int | None = 15, + deduplicate: bool = True, + ) -> int: + """Export filtered trajectories in chat-format JSONL for SFT training. + + Each line is: {"messages": [...], "instance_id": "...", "teacher_model": "..."} + + Returns the number of exported trajectories. + """ + candidates = self.filter(resolved=resolved_only, max_turns=max_turns) + + if deduplicate: + # Keep shortest successful trajectory per task + best_per_task: dict[str, TrajectoryRecord] = {} + for rec in candidates: + existing = best_per_task.get(rec.instance_id) + if existing is None or rec.turns < existing.turns: + best_per_task[rec.instance_id] = rec + candidates = list(best_per_task.values()) + + output_path = Path(output_path) + output_path.parent.mkdir(parents=True, exist_ok=True) + + count = 0 + with open(output_path, "w") as f: + for rec in candidates: + entry = { + "messages": rec.messages, + "instance_id": rec.instance_id, + "teacher_model": rec.teacher_model, + "turns": rec.turns, + "trajectory_id": rec.trajectory_id, + } + f.write(json.dumps(entry, ensure_ascii=False) + "\n") + count += 1 + + _log.info("exported %d trajectories for SFT to %s", count, output_path) + return count + + def _load_existing(self) -> None: + """Load records from existing JSONL file.""" + count = 0 + with open(self._filepath) as f: + for line_num, line in enumerate(f, 1): + line = line.strip() + if not line: + continue + try: + d = json.loads(line) + self._records.append(TrajectoryRecord.from_dict(d)) + count += 1 + except (json.JSONDecodeError, TypeError) as exc: + _log.warning("skipping invalid line %d: %s", line_num, exc) + if count: + _log.info("loaded %d existing trajectories from %s", count, self._filepath) + + +def _pass_at_k(n: int, c: int, k: int) -> float: + """Unbiased pass@k estimator. + + pass@k = 1 - C(n-c, k) / C(n, k) + + Uses log-space computation for numerical stability. + """ + if n - c < k: + return 1.0 + if c == 0: + return 0.0 + # log(C(n-c, k)) - log(C(n, k)) + log_num = sum(math.log(n - c - i) for i in range(k)) + log_den = sum(math.log(n - i) for i in range(k)) + return 1.0 - math.exp(log_num - log_den) + + +__all__ = [ + "TrajectoryRecord", + "TrajectoryStore", +] diff --git a/examples/mini_swe_env/uv.lock b/examples/mini_swe_env/uv.lock new file mode 100644 index 000000000..cca57a93d --- /dev/null +++ b/examples/mini_swe_env/uv.lock @@ -0,0 +1,6054 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" 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[project.optional-dependencies] diff --git a/src/openenv/core/harness/agents/__init__.py b/src/openenv/core/harness/agents/__init__.py new file mode 100644 index 000000000..b715582a4 --- /dev/null +++ b/src/openenv/core/harness/agents/__init__.py @@ -0,0 +1,111 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Agent registry and public API for CLI-based agentic harnesses. + +The registry maps agent names (``"opencode"``, ``"claude-code"``, etc.) to +their :class:`CLIAgentSpec` declarations. Each agent module registers itself +via :func:`register_agent` at import time. + +Usage:: + + from openenv.core.harness.agents import get_agent_spec, list_agents + + spec = get_agent_spec("opencode") + print(list_agents()) # ["opencode"] +""" + +from __future__ import annotations + +from .base import ( + AgentConfig, + AgentEvent, + AgentTask, + ArtifactSpec, + CLIAgentSpec, + MCPConfigSpec, +) +from .interception_server import deliver_response, InterceptionServer + +# Registry + +_REGISTRY: dict[str, CLIAgentSpec] = {} + + +def register_agent(spec: CLIAgentSpec) -> None: + """Register a :class:`CLIAgentSpec` under ``spec.name``. + + Raises :class:`ValueError` if the name is already registered with a + *different* spec object (re-registering the same object is a no-op, + which makes ``importlib.reload`` safe). + """ + existing = _REGISTRY.get(spec.name) + if existing is not None and existing is not spec: + raise ValueError( + f"Agent {spec.name!r} is already registered. " + "Use a unique name or call unregister_agent() first." + ) + _REGISTRY[spec.name] = spec + + +def unregister_agent(name: str) -> CLIAgentSpec | None: + """Remove a registered agent spec, returning it (or ``None``).""" + return _REGISTRY.pop(name, None) + + +def get_agent_spec(name: str) -> CLIAgentSpec: + """Look up a registered agent spec by name. + + Raises :class:`KeyError` if not found. To trigger auto-registration of + built-in agents, import the specific module first (e.g. + ``import openenv.core.harness.agents.opencode``). + """ + if name not in _REGISTRY: + # Auto-import built-in agent modules to trigger registration. + _auto_import(name) + try: + return _REGISTRY[name] + except KeyError: + available = ", ".join(sorted(_REGISTRY)) or "(none)" + raise KeyError( + f"Unknown agent {name!r}. Registered agents: {available}" + ) from None + + +def list_agents() -> list[str]: + """Return sorted names of all registered agents.""" + return sorted(_REGISTRY) + + +def _auto_import(name: str) -> None: + """Try to import the built-in module for ``name`` to trigger registration.""" + # Map agent names to module names (handles hyphens). + module_name = name.replace("-", "_") + try: + __import__(f"openenv.core.harness.agents.{module_name}", fromlist=["_"]) + except ImportError: + pass + + +# Convenience re-exports + +__all__ = [ + # Registry + "get_agent_spec", + "list_agents", + "register_agent", + "unregister_agent", + # Base types + "AgentConfig", + "AgentEvent", + "AgentTask", + "ArtifactSpec", + "CLIAgentSpec", + "MCPConfigSpec", + # Interception gate + "InterceptionServer", + "deliver_response", +] diff --git a/src/openenv/core/harness/agents/base.py b/src/openenv/core/harness/agents/base.py new file mode 100644 index 000000000..4ec1c297a --- /dev/null +++ b/src/openenv/core/harness/agents/base.py @@ -0,0 +1,256 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Agent spec and event protocols for CLI-based agentic harnesses. + +Defines the declarative :class:`CLIAgentSpec` data model that captures +*everything* a CLI harness needs — install commands, file uploads, MCP +config format, environment variables, artifacts to collect, and three +small callables (command builder, MCP config builder, event parser). + +The :class:`CLIAgentDriver` reads these fields mechanically without knowing +anything about the specific agent. Adding a new agent is filling in a +dataclass, not writing driver code. + +Pattern borrowed from `verifiers `_ +(Prime Intellect), where OpenCode, MiniSWEAgent, Pi, and RLM all express +their differences through constructor data passed to ``CLIHarness.__init__()``. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Callable, Literal, Protocol + + +# MCP config injection + + +@dataclass(frozen=True) +class MCPConfigSpec: + """How a harness discovers MCP tools. + + ``method`` controls how the driver injects MCP server configuration: + + - ``"config_file"`` — write a JSON file at ``path_template`` (e.g. + ``"{workdir}/mcp.json"``). The template receives ``{workdir}`` + and ``{home}`` substitutions at runtime. + - ``"cli_flags"`` — the driver passes MCP configuration via CLI + flags built by :attr:`CLIAgentSpec.build_command`. + - ``"settings_file"`` — write into a global settings file (e.g. + e.g. ``~/.config/agent/settings.json``). + """ + + method: Literal["config_file", "cli_flags", "settings_file"] + path_template: str | None = None + + +# Artifacts + + +@dataclass(frozen=True) +class ArtifactSpec: + """Declares a file to collect from the sandbox after the agent exits. + + The driver iterates :attr:`CLIAgentSpec.artifacts` and calls + ``sandbox.read_text(spec.path)`` for each entry. No per-agent collection + methods needed — the spec declares *what* to collect, the driver collects + it. + """ + + path: str + format: Literal["text", "json", "jsonl"] = "text" + optional: bool = True + + +# Agent events (normalized across harnesses) + + +@dataclass +class AgentEvent: + """Normalized event from any CLI harness's stdout. + + The :attr:`CLIAgentSpec.parse_events` callable converts raw JSONL lines + into these events so the driver can log and observe the agent's progress + without knowing which agent is running. + """ + + type: Literal[ + "assistant", + "tool_call", + "tool_result", + "reasoning", + "error", + "done", + ] + data: dict[str, Any] = field(default_factory=dict) + raw: str = "" + + +# Task protocol + + +class AgentTask(Protocol): + """Minimal interface a task must satisfy for the CLI agent driver.""" + + @property + def instruction(self) -> str: ... + + @property + def setup_shell(self) -> str | None: ... + + @property + def upload_files(self) -> dict[str, str]: ... + + @property + def metadata(self) -> dict[str, Any]: ... + + +# Agent config protocol + + +class AgentConfig(Protocol): + """Minimal interface a config must satisfy for the CLI agent driver. + + This is intentionally thin — concrete configs like :class:`OpenCodeConfig` + carry much more, but the generic driver only accesses these. + """ + + @property + def base_url(self) -> str: ... + + @property + def api_key(self) -> str: ... + + @property + def model(self) -> str: ... + + @property + def agent_timeout_s(self) -> float: ... + + +# CLIAgentSpec — the core declarative data model + + +@dataclass +class CLIAgentSpec: + """Declarative specification for a CLI-based agentic harness. + + Following the pattern established by verifiers' ``CLIHarness`` (Prime + Intellect), as much per-agent knowledge as possible is expressed as + *data* rather than imperative code. The :class:`CLIAgentDriver` + iterates these fields mechanically — it never needs to know what + ``"pi"`` or ``"claude-code"`` means. + + Three callables cover the remaining agent-specific logic that can't + be expressed as pure data: + + - :attr:`build_command` — constructs the CLI argv + - :attr:`build_mcp_config` — serializes MCP server configuration + - :attr:`parse_events` — converts raw stdout lines to :class:`AgentEvent` + + Everything else — file uploads, env vars, install scripts, artifact + collection — is pure data. + """ + + name: str + """Unique identifier: ``"opencode"``, ``"claude-code"``, ``"codex"``, etc.""" + + install_check_cmd: list[str] + """Command to probe whether the agent is already installed. + + Example: ``["claude", "--version"]`` + """ + + base_command: list[str] + """Base CLI invocation (before task-specific flags). + + Example: ``["claude", "--print", "--output-format", "stream-json"]`` + """ + + mcp_config: MCPConfigSpec + """How MCP tool configuration is injected.""" + + default_timeout_s: float = 600.0 + """Default per-rollout timeout in seconds.""" + + setup: str | list[str] | None = None + """Shell command(s) to install the agent CLI inside the sandbox. + + Run once after the sandbox is created, before any files are written. + Skipped when ``install_check_cmd`` succeeds (pre-baked template). + Can be a single string or a list of strings executed in order. + """ + + files: dict[str, str | Callable] | None = None + """Files to upload into the sandbox before the agent starts. + + Keys are absolute sandbox paths. Values are either literal strings or + callables ``(task, config) -> str`` resolved at rollout time. + """ + + artifacts: dict[str, ArtifactSpec] | None = None + """Files to collect from the sandbox after the agent exits. + + The driver iterates this dict and calls ``sandbox.read_text(spec.path)`` + for each entry. + """ + + env: dict[str, str] | None = None + """Environment variables for the agent process. + + Values can contain ``{model}``, ``{base_url}``, ``{api_key}`` placeholders + resolved from the rollout config at runtime. + """ + + extension_dir_template: str | None = None + """Optional extension install directory template. + + Receives ``{home}`` substitution at runtime (e.g. + ``"{home}/.pi/agent/extensions"``). Drivers may use this to create + extension directories in the correct sandbox user home. + """ + + build_command: Callable[..., str] | None = None + """``(spec, config, task, mcp_config_path) -> str`` + + Build the full shell command line for launching the agent. Returns a + string (not a list) because sandbox ``start_bg`` / ``exec`` take shell + strings. + """ + + build_mcp_config: Callable[..., str] | None = None + """``(spec, tools, workdir) -> str`` + + Serialize MCP server configuration in the format the agent expects. + Returns the file content (for ``config_file``/``settings_file`` methods) + or empty string (for ``cli_flags``, where the command builder handles it). + """ + + parse_events: Callable[[str], AgentEvent | None] | None = None + """``(line: str) -> AgentEvent | None`` + + Parse one line of the agent's stdout into a normalized event. + Return ``None`` for lines that are not parseable events. + """ + + build_env_vars: Callable[..., dict[str, str]] | None = None + """``(spec, config) -> dict[str, str]`` + + Optional override for env var construction. When provided, this is + called *instead of* resolving placeholders in :attr:`env`. Prefer + the declarative :attr:`env` dict for new agents. + """ + + +__all__ = [ + "AgentConfig", + "AgentEvent", + "AgentTask", + "ArtifactSpec", + "CLIAgentSpec", + "MCPConfigSpec", +] diff --git a/src/openenv/core/harness/agents/cli_driver.py b/src/openenv/core/harness/agents/cli_driver.py new file mode 100644 index 000000000..aedaec555 --- /dev/null +++ b/src/openenv/core/harness/agents/cli_driver.py @@ -0,0 +1,663 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Shared CLI agent driver, session, and session factory. + +Two modes are supported: + +- ``black_box`` — the agent talks directly to the upstream LLM. No logprob + capture. For eval and demos. +- ``interception_gate`` — the agent's LLM calls are routed to an + :class:`InterceptionServer` running on the trainer host. The training + loop owns the forward pass and delivers responses back. For RL training. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import queue as _queue_mod +import shlex +import time +import uuid +from typing import Any, Callable, Literal + +from openenv.core.env_server.mcp_types import Tool +from openenv.core.harness import ( + Message, + ResourceSession, + ResourceSessionFactory, + ToolResult, + VerifyResult, +) +from openenv.core.harness.sandbox import BgJob, SandboxBackend, SandboxHandle + +from .base import CLIAgentSpec +from .interception_server import deliver_response, InterceptionServer, ToolHandler + + +_log = logging.getLogger(__name__) + +Verifier = Callable[..., VerifyResult] + + +def build_interception_rollout_url(base_url: str, rollout_id: str) -> str: + """Build OpenAI-compatible interception endpoint for one rollout.""" + return f"{base_url.rstrip('/')}/rollout/{rollout_id}/v1" + + +class _ConfigOverrideView: + """Read-only attribute view with optional overrides.""" + + def __init__(self, base: Any, **overrides: Any) -> None: + self._base = base + self._overrides = overrides + + def __getattr__(self, name: str) -> Any: + if name in self._overrides: + return self._overrides[name] + return getattr(self._base, name) + + +class CLIAgentSession(ResourceSession): + """Per-rollout session wrapping one sandbox with one running agent CLI.""" + + def __init__( + self, + *, + spec: CLIAgentSpec, + sandbox: SandboxHandle, + task: Any, + config: Any, + verifier: Verifier | None = None, + base_url_override: str | None = None, + agent_bg_job: BgJob | None = None, + interception_server: InterceptionServer | None = None, + interception_rollout_id: str | None = None, + interception_queue: _queue_mod.Queue[str] | None = None, + ) -> None: + self.spec = spec + self.sandbox = sandbox + self.task = task + self.config = config + self._verifier = verifier + self._base_url_override = base_url_override + self._agent_bg_job = agent_bg_job + self._interception_server = interception_server + self._interception_rollout_id = interception_rollout_id + self._interception_queue = interception_queue + + def initial_messages(self) -> list[Message]: + instruction = ( + self.task.instruction + if hasattr(self.task, "instruction") + else str(self.task) + ) + return [{"role": "user", "content": instruction}] + + def list_tools(self) -> list[Tool]: + return [] + + def call_tool(self, name: str, arguments: dict[str, Any]) -> ToolResult: + return ToolResult( + error=( + f"{self.spec.name} session does not expose external tool calls; " + "the CLI agent owns its own tool loop." + ) + ) + + def verify( + self, + transcript: list[Message], + final_state: Any | None = None, + ) -> VerifyResult: + if self._verifier is None: + return VerifyResult(env_reward=None, done=True) + return self._verifier(self.sandbox, self.task) + + def close(self) -> None: + if self._agent_bg_job is not None: + try: + self._agent_bg_job.kill() + except Exception: + pass + self._agent_bg_job = None + if ( + self._interception_server is not None + and self._interception_rollout_id is not None + ): + self._interception_server.unregister_rollout(self._interception_rollout_id) + self.sandbox.kill() + + def wait_for_completion(self, timeout_s: float | None = None) -> int: + """Block until the agent exits, returning its exit code.""" + if self._agent_bg_job is None: + raise RuntimeError("Agent not started.") + default_timeout = ( + self.config.agent_timeout_s + if hasattr(self.config, "agent_timeout_s") + else self.spec.default_timeout_s + ) + budget = timeout_s if timeout_s is not None else default_timeout + return self._agent_bg_job.wait(timeout=budget) + + def collect_artifacts(self) -> dict[str, Any]: + """Collect all artifacts declared in ``spec.artifacts`` from the sandbox.""" + result: dict[str, Any] = {} + if not self.spec.artifacts: + return result + for name, artifact_spec in self.spec.artifacts.items(): + try: + content = self.sandbox.read_text(artifact_spec.path) + if artifact_spec.format == "json": + result[name] = json.loads(content) + elif artifact_spec.format == "jsonl": + records = [] + for line in content.splitlines(): + line = line.strip() + if not line: + continue + try: + records.append(json.loads(line)) + except json.JSONDecodeError: + _log.debug( + "Skipping non-JSON line in %s: %s", + artifact_spec.path, + line[:120], + ) + result[name] = records + else: + result[name] = content + except Exception: + if not artifact_spec.optional: + raise + _log.debug( + "Optional artifact %r (%s) not found, skipping", + name, + artifact_spec.path, + ) + return result + + # interception_gate API + + async def next_request( + self, timeout_s: float | None = None + ) -> dict[str, Any] | None: + """Await the next LLM request from the agent (interception_gate only). + + Returns the intercept dict, or ``None`` when the agent has exited. + """ + if self._interception_queue is None: + raise RuntimeError( + "next_request() is only available in interception_gate mode." + ) + server = self._interception_server + assert server is not None + + deadline = time.time() + (timeout_s or self.spec.default_timeout_s) + while True: + remaining = deadline - time.time() + if remaining <= 0: + raise TimeoutError( + f"{self.spec.name} interception_gate: no request within timeout" + ) + try: + request_id = await asyncio.to_thread( + self._interception_queue.get, + timeout=min(remaining, 1.0), + ) + # None sentinel = agent process exited (sent by /exit endpoint) + if request_id is None: + return None + intercept = server.get_intercept(request_id) + if intercept is not None: + return intercept + except _queue_mod.Empty: + pass + + if self._agent_bg_job is not None: + try: + self._agent_bg_job.wait(timeout=0) + return None + except TimeoutError: + pass + continue + + async def deliver( + self, intercept: dict[str, Any], response_dict: dict[str, Any] + ) -> None: + """Return a trainer-generated response to the waiting agent.""" + await deliver_response(intercept, response_dict) + + def register_tool_handler( + self, + tool_name: str, + handler: ToolHandler, + *, + tool_definition: dict[str, Any] | None = None, + ) -> None: + """Register a host-side interception tool for this rollout.""" + if self._interception_server is None or self._interception_rollout_id is None: + raise RuntimeError( + "register_tool_handler() is only available in interception_gate mode." + ) + self._interception_server.register_tool_handler( + self._interception_rollout_id, + tool_name, + handler, + tool_definition=tool_definition, + ) + + +class CLIAgentDriver: + """Shared driver for all CLI-based agentic harnesses.""" + + def __init__( + self, + spec: CLIAgentSpec, + sandbox_backend: SandboxBackend, + mode: Literal["black_box", "interception_gate"] = "black_box", + *, + install_timeout_s: int = 240, + setup_timeout_s: int = 300, + interception_server: InterceptionServer | None = None, + interception_base_url: str | None = None, + ) -> None: + if mode not in {"black_box", "interception_gate"}: + raise ValueError(f"Unknown mode: {mode!r}") + if mode == "interception_gate": + if interception_server is None: + raise ValueError( + "interception_gate mode requires an InterceptionServer instance." + ) + if interception_base_url is None: + raise ValueError( + "interception_gate mode requires interception_base_url." + ) + self.spec = spec + self.sandbox_backend = sandbox_backend + self.mode = mode + self._install_timeout_s = install_timeout_s + self._setup_timeout_s = setup_timeout_s + self._interception_server = interception_server + self._interception_base_url = interception_base_url + + def bootstrap_sandbox(self, sandbox: SandboxHandle, task: Any, config: Any) -> None: + """Public bootstrap hook used by external wrappers. + + Runs readiness checks, optional install, file upload, MCP config write, + and task setup shell execution. + """ + self._bootstrap_sandbox(sandbox, task, config) + + def create_session( + self, + task: Any, + config: Any, + *, + verifier: Verifier | None = None, + seed: int | None = None, + episode_id: str | None = None, + ) -> CLIAgentSession: + timeout_s = ( + config.agent_timeout_s + if hasattr(config, "agent_timeout_s") + else self.spec.default_timeout_s + ) + sandbox_timeout = int(timeout_s) + 300 + sandbox = self.sandbox_backend.create( + timeout_s=sandbox_timeout, + metadata={"episode_id": episode_id} if episode_id else None, + ) + try: + self._bootstrap_sandbox(sandbox, task, config) + except Exception as exc: + _log.error("%s driver: bootstrap failed: %r", self.spec.name, exc) + sandbox.kill() + raise + + base_url_override: str | None = None + interception_rollout_id: str | None = None + interception_queue: _queue_mod.Queue[str] | None = None + + if self.mode == "interception_gate": + assert self._interception_server is not None + assert self._interception_base_url is not None + rollout_id = episode_id or f"rollout_{uuid.uuid4().hex[:8]}" + interception_rollout_id = rollout_id + interception_queue = self._interception_server.register_rollout(rollout_id) + base_url_override = build_interception_rollout_url( + self._interception_base_url, + rollout_id, + ) + + agent_bg_job = self._start_agent( + sandbox, task, config, base_url_override=base_url_override + ) + + return CLIAgentSession( + spec=self.spec, + sandbox=sandbox, + task=task, + config=config, + verifier=verifier, + base_url_override=base_url_override, + agent_bg_job=agent_bg_job, + interception_server=self._interception_server, + interception_rollout_id=interception_rollout_id, + interception_queue=interception_queue, + ) + + def _bootstrap_sandbox( + self, sandbox: SandboxHandle, task: Any, config: Any + ) -> None: + self._wait_for_sandbox_ready(sandbox) + if not self._agent_already_installed(sandbox): + self._install_agent(sandbox) + self._ensure_extension_dir(sandbox, config) + self._upload_files(sandbox, task, config) + self._write_mcp_config(sandbox, config) + setup_shell = task.setup_shell if hasattr(task, "setup_shell") else None + if setup_shell: + r = sandbox.exec(setup_shell, timeout=self._setup_timeout_s) + if r.exit_code != 0: + raise RuntimeError( + f"task.setup_shell failed ({r.exit_code}): {r.stderr}" + ) + + def _wait_for_sandbox_ready( + self, sandbox: SandboxHandle, *, attempts: int = 15, delay_s: float = 1.0 + ) -> None: + last_err = "" + for _ in range(attempts): + try: + r = sandbox.exec("echo ok", timeout=5) + if r.exit_code == 0 and "ok" in (r.stdout or ""): + return + last_err = (r.stderr or r.stdout or "").strip() or f"exit={r.exit_code}" + except Exception as exc: + last_err = f"{type(exc).__name__}: {exc}" + time.sleep(delay_s) + raise RuntimeError( + f"sandbox did not become ready within {attempts * delay_s:.0f}s " + f"(last error: {last_err})" + ) + + def _agent_already_installed(self, sandbox: SandboxHandle) -> bool: + cmd = " ".join(shlex.quote(c) for c in self.spec.install_check_cmd) + try: + r = sandbox.exec(cmd, timeout=10) + return r.exit_code == 0 + except Exception: + return False + + def _install_agent(self, sandbox: SandboxHandle) -> None: + if self.spec.setup is None: + raise RuntimeError( + f"Agent {self.spec.name!r} is not installed and no setup commands provided." + ) + commands = ( + [self.spec.setup] if isinstance(self.spec.setup, str) else self.spec.setup + ) + for cmd in commands: + self._exec_with_retry( + sandbox, + cmd, + timeout=self._install_timeout_s, + attempts=3, + backoff_s=3.0, + label=f"{self.spec.name} install", + ) + + def _resolve_sandbox_home(self, sandbox: SandboxHandle, config: Any) -> str: + configured = getattr(config, "sandbox_home", None) + if isinstance(configured, str) and configured.strip(): + return configured + try: + result = sandbox.exec('printf %s "$HOME"', timeout=5) + candidate = (result.stdout or "").strip() + if result.exit_code == 0 and candidate: + return candidate + except Exception: + pass + return "/home/user" + + def _ensure_extension_dir(self, sandbox: SandboxHandle, config: Any) -> None: + template = self.spec.extension_dir_template + if not template: + return + home = self._resolve_sandbox_home(sandbox, config) + extension_dir = template.format(home=home) + result = sandbox.exec(f"mkdir -p {shlex.quote(extension_dir)}", timeout=10) + if result.exit_code != 0: + raise RuntimeError( + f"failed to create extension dir {extension_dir!r}: {result.stderr}" + ) + + def _upload_files(self, sandbox: SandboxHandle, task: Any, config: Any) -> None: + if not self.spec.files: + return + for path, content_or_fn in self.spec.files.items(): + content = ( + content_or_fn(task, config) + if callable(content_or_fn) + else content_or_fn + ) + if content is not None: + sandbox.write_text(path, content) + upload_files = task.upload_files if hasattr(task, "upload_files") else {} + for path, content in upload_files.items(): + sandbox.write_text(path, content) + + def _write_mcp_config(self, sandbox: SandboxHandle, config: Any) -> None: + if self.spec.build_mcp_config is None: + return + if ( + self.spec.mcp_config.method == "config_file" + and self.spec.mcp_config.path_template + ): + home = ( + config.sandbox_home if hasattr(config, "sandbox_home") else "/home/user" + ) + workdir = ( + config.workdir + if hasattr(config, "workdir") and getattr(config, "workdir") + else f"{home}/workdir" + ) + mcp_path = self.spec.mcp_config.path_template.format( + workdir=workdir, home=home + ) + mcp_content = self.spec.build_mcp_config(self.spec, [], workdir) + if mcp_content: + sandbox.write_text(mcp_path, mcp_content) + + def _start_agent( + self, + sandbox: SandboxHandle, + task: Any, + config: Any, + *, + base_url_override: str | None = None, + ) -> BgJob: + command_config = config + if ( + self.mode == "interception_gate" + and self._interception_server is not None + and self.spec.name == "pi" + and base_url_override + ): + self._write_pi_models_config( + sandbox, + config, + rollout_url=base_url_override, + api_key=self._interception_server.secret, + ) + command_config = _ConfigOverrideView(config, provider="openenv") + + if self.spec.build_command is not None: + cmd = self.spec.build_command(self.spec, command_config, task, None) + else: + cmd = " ".join(shlex.quote(c) for c in self.spec.base_command) + envs = self._resolve_env_vars(config, base_url_override=base_url_override) + if self.spec.name == "pi": + home = self._resolve_sandbox_home(sandbox, config) + # Make pi config discovery independent of the runtime user's $HOME. + envs["PI_CODING_AGENT_DIR"] = f"{home}/.pi/agent" + if self.mode == "interception_gate" and self._interception_server is not None: + envs["OPENAI_API_KEY"] = self._interception_server.secret + envs["ANTHROPIC_API_KEY"] = self._interception_server.secret + + # Append an exit notification so the InterceptionServer detects + # agent exit immediately instead of waiting for the full timeout. + # The /exit endpoint enqueues a None sentinel on the request queue, + # causing next_request() to return None. + if base_url_override: + exit_url = f"{base_url_override.rstrip('/')}/exit" + api_key = self._interception_server.secret + cmd = ( + f"{{ {cmd} ; }} ; " + f'curl -sf -X POST -H "Authorization: Bearer {api_key}" ' + f'"{exit_url}" || true' + ) + + return sandbox.start_bg(cmd, envs=envs) + + def _write_pi_models_config( + self, + sandbox: SandboxHandle, + config: Any, + *, + rollout_url: str, + api_key: str, + ) -> None: + home = self._resolve_sandbox_home(sandbox, config) + model = config.model if hasattr(config, "model") else "model" + content = json.dumps( + { + "providers": { + "openenv": { + "baseUrl": rollout_url, + "api": "openai-completions", + "apiKey": api_key, + "compat": { + "supportsDeveloperRole": False, + "supportsReasoningEffort": False, + }, + "models": [{"id": model, "reasoning": False}], + } + } + }, + indent=2, + ) + sandbox.write_text(f"{home}/.pi/agent/models.json", content) + + def _resolve_env_vars( + self, + config: Any, + *, + base_url_override: str | None = None, + ) -> dict[str, str]: + if self.spec.build_env_vars is not None: + return self.spec.build_env_vars(self.spec, config) + if not self.spec.env: + return {} + base_url = base_url_override or ( + config.base_url if hasattr(config, "base_url") else "" + ) + api_key = config.api_key if hasattr(config, "api_key") else "intercepted" + model = config.model if hasattr(config, "model") else "" + substitutions = {"base_url": base_url, "api_key": api_key, "model": model} + resolved: dict[str, str] = {} + for key, value in self.spec.env.items(): + try: + resolved[key] = value.format(**substitutions) + except KeyError: + resolved[key] = value + return resolved + + def _exec_with_retry( + self, + sandbox: SandboxHandle, + cmd: str, + *, + timeout: float, + attempts: int = 3, + backoff_s: float = 3.0, + label: str = "cmd", + ) -> Any: + last_stdout = "" + last_stderr = "" + last_exit = 0 + for i in range(attempts): + try: + r = sandbox.exec(cmd, timeout=timeout) + if r.exit_code == 0: + return r + last_stdout = r.stdout or "" + last_stderr = r.stderr or "" + last_exit = r.exit_code + except Exception as exc: + last_stderr = f"{type(exc).__name__}: {exc}" + last_exit = -1 + if i + 1 < attempts: + time.sleep(backoff_s * (2**i)) + raise RuntimeError( + f"{label} failed after {attempts} attempts " + f"(exit={last_exit}, stderr={last_stderr!r}, " + f"stdout_tail={last_stdout[-400:]!r})" + ) + + +class CLIAgentSessionFactory(ResourceSessionFactory): + def __init__( + self, + *, + spec: CLIAgentSpec, + config: Any, + sandbox_backend: SandboxBackend, + mode: Literal["black_box", "interception_gate"] = "black_box", + verifier: Verifier | None = None, + install_timeout_s: int = 240, + setup_timeout_s: int = 300, + interception_server: InterceptionServer | None = None, + interception_base_url: str | None = None, + ) -> None: + self._spec = spec + self._config = config + self._verifier = verifier + self._driver = CLIAgentDriver( + spec=spec, + sandbox_backend=sandbox_backend, + mode=mode, + install_timeout_s=install_timeout_s, + setup_timeout_s=setup_timeout_s, + interception_server=interception_server, + interception_base_url=interception_base_url, + ) + + def create( + self, + task: Any, + seed: int | None = None, + episode_id: str | None = None, + ) -> CLIAgentSession: + return self._driver.create_session( + task=task, + config=self._config, + verifier=self._verifier, + seed=seed, + episode_id=episode_id, + ) + + +__all__ = [ + "CLIAgentDriver", + "CLIAgentSession", + "CLIAgentSessionFactory", + "Verifier", + "build_interception_rollout_url", +] diff --git a/src/openenv/core/harness/agents/interception_server.py b/src/openenv/core/harness/agents/interception_server.py new file mode 100644 index 000000000..3e62c90e9 --- /dev/null +++ b/src/openenv/core/harness/agents/interception_server.py @@ -0,0 +1,674 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Host-side interception server for trainer-owned generation. + +The :class:`InterceptionServer` runs on the trainer node, outside any +sandbox. Each sandbox's agent is pointed at:: + + http://:/rollout//v1 + +When the agent makes an LLM call it blocks at this server. The training +loop calls :meth:`~InterceptionServer.register_rollout` to get a queue, +``await queue.get()`` to dequeue the pending request, runs its own vLLM +forward pass, then calls :func:`deliver_response` to unblock the agent. + +The caller is responsible for making the server reachable from the sandbox. +For Docker sandboxes on the same machine, ``host.docker.internal:`` +works. For remote sandboxes (E2B, HF Sandbox), set up your own tunnel +(ngrok, frp, public IP, VPN) and pass the URL as +``interception_base_url``. + +Usage — training loop:: + + server = InterceptionServer(port=8765, tool_name_allowlist={"answer"}) + await server.start() + + # Make the server reachable — your responsibility. + # Docker: base_url = f"http://host.docker.internal:{server.port}" + # Remote: base_url = your_tunnel_or_public_url + + request_queue = server.register_rollout(rollout_id) + # Agent runs with OPENAI_BASE_URL = f"{base_url}/rollout/{rollout_id}/v1" + + while True: + request_id = await asyncio.to_thread(request_queue.get, timeout=...) + intercept = server.get_intercept(request_id) + if intercept is None: + continue + response = await vllm.generate(intercept["messages"], ...) + await deliver_response(intercept, response) + + server.unregister_rollout(rollout_id) + await server.stop() +""" + +from __future__ import annotations + +import asyncio +import hmac +import json +import logging +import queue as _queue_mod +import re +import secrets +import threading +import time +import uuid +from typing import Any, Awaitable, Callable + +from openenv.core.env_server.mcp_types import RESERVED_TOOL_NAMES + +from aiohttp import web + + +_log = logging.getLogger(__name__) + +_KEEPALIVE_INTERVAL_S = 3.0 +_MAX_REQUEST_BODY = 16 * 1024 * 1024 +_TOOL_NAME_RE = re.compile(r"^[A-Za-z0-9_-]{1,64}$") + +ToolHandler = Callable[[dict[str, Any]], Awaitable[dict[str, Any]]] + + +class InterceptionServer: + """Async HTTP server that gates every LLM call from sandboxed agents. + + One shared instance handles all concurrent rollouts. Each rollout is + identified by a ``rollout_id`` in the URL path. + """ + + def __init__( + self, + port: int = 0, + secret: str | None = None, + host: str = "127.0.0.1", + tool_name_allowlist: set[str] | None = None, + ) -> None: + self.port = port + self.host = host + self.secret = secret or secrets.token_urlsafe(32) + if not self.secret.strip(): + raise ValueError("InterceptionServer secret must not be blank.") + normalized_allowlist: set[str] = set() + for raw_name in tool_name_allowlist or set(): + name = raw_name.strip() + if not name: + raise ValueError("tool_name_allowlist must not include blank names") + if not _TOOL_NAME_RE.fullmatch(name): + raise ValueError( + "tool_name_allowlist entries must match " + f"^[A-Za-z0-9_-]{{1,64}}$ (got {raw_name!r})" + ) + normalized_allowlist.add(name) + self._tool_name_allowlist = frozenset(normalized_allowlist) + self._app: web.Application | None = None + self._runner: web.AppRunner | None = None + self._site: web.TCPSite | None = None + self._lock = asyncio.Lock() + self._state_lock = threading.RLock() + self.active_rollouts: dict[str, dict[str, Any]] = {} + self.intercepts: dict[str, dict[str, Any]] = {} + + async def start(self) -> None: + async with self._lock: + if self._app is not None: + return + app = web.Application(client_max_size=_MAX_REQUEST_BODY) + app.router.add_post( + "/rollout/{rollout_id}/v1/chat/completions", + self._handle_chat_completions, + ) + app.router.add_post( + "/rollout/{rollout_id}/v1/tools/{tool_name}", + self._handle_tool_call, + ) + app.router.add_post( + "/rollout/{rollout_id}/exit", + self._handle_exit, + ) + app.router.add_post( + "/rollout/{rollout_id}/v1/exit", + self._handle_exit, + ) + app.router.add_get("/health", self._handle_health) + runner = web.AppRunner(app) + await runner.setup() + if self.host == "0.0.0.0": + _log.warning("InterceptionServer exposed on all interfaces (0.0.0.0).") + site = web.TCPSite(runner, self.host, self.port) + await site.start() + if self.port == 0: + server = getattr(site, "_server", None) + sockets = getattr(server, "sockets", None) if server else None + if sockets: + self.port = sockets[0].getsockname()[1] + if self.port == 0: + raise RuntimeError("Failed to resolve OS-assigned port") + self._app = app + self._runner = runner + self._site = site + _log.info("InterceptionServer listening on :%d", self.port) + + async def stop(self) -> None: + async with self._lock: + if self._runner is None: + return + with self._state_lock: + intercepts = list(self.intercepts.values()) + self.intercepts.clear() + self.active_rollouts.clear() + for intercept in intercepts: + fut: asyncio.Future | None = intercept.get("response_future") + if fut and not fut.done(): + fut.cancel() + cq: asyncio.Queue | None = intercept.get("chunk_queue") + if cq is not None: + try: + cq.put_nowait(None) + except asyncio.QueueFull: + pass + try: + await self._runner.cleanup() + except RuntimeError: + pass + self._runner = None + self._site = None + self._app = None + + def register_rollout( + self, + rollout_id: str, + state: dict[str, Any] | None = None, + ) -> _queue_mod.Queue[str]: + request_queue: _queue_mod.Queue[str] = _queue_mod.Queue() + with self._state_lock: + self.active_rollouts[rollout_id] = { + "request_id_queue": request_queue, + "state": state, + "tool_handlers": {}, + "tool_defs": {}, + } + active = len(self.active_rollouts) + _log.info( + "interception_rollout_registered rollout_id=%s active_rollouts=%d", + rollout_id, + active, + ) + return request_queue + + def unregister_rollout(self, rollout_id: str) -> None: + with self._state_lock: + matching_ids = [ + request_id + for request_id, intercept in self.intercepts.items() + if intercept.get("rollout_id") == rollout_id + ] + matching_intercepts = [self.intercepts[i] for i in matching_ids] + for request_id in matching_ids: + del self.intercepts[request_id] + removed = self.active_rollouts.pop(rollout_id, None) is not None + active = len(self.active_rollouts) + pending = len(self.intercepts) + + for intercept in matching_intercepts: + fut: asyncio.Future | None = intercept.get("response_future") + if fut and not fut.done(): + fut.cancel() + cq: asyncio.Queue | None = intercept.get("chunk_queue") + if cq is not None: + try: + cq.put_nowait(None) + except asyncio.QueueFull: + pass + + _log.info( + "interception_rollout_unregistered rollout_id=%s removed=%s " + "active_rollouts=%d pending_intercepts=%d", + rollout_id, + removed, + active, + pending, + ) + + def get_intercept(self, request_id: str) -> dict[str, Any] | None: + with self._state_lock: + return self.intercepts.get(request_id) + + def stats(self) -> dict[str, int]: + """Return lightweight runtime counters for health/debug views.""" + with self._state_lock: + return { + "active_rollouts": len(self.active_rollouts), + "pending_intercepts": len(self.intercepts), + } + + def register_tool_handler( + self, + rollout_id: str, + tool_name: str, + handler: ToolHandler, + *, + tool_definition: dict[str, Any] | None = None, + ) -> None: + """Register a host-side tool handler for a rollout. + + The handler is called by ``POST /rollout/{rollout_id}/v1/tools/{tool_name}`` + with a JSON payload containing ``arguments``. + + Optionally provide ``tool_definition`` (OpenAI tool schema). Registered + schemas are injected into intercepted chat-completion requests for the + rollout when the incoming request does not already include the tool. + + Only tool names explicitly configured in ``tool_name_allowlist`` are + accepted. Control-plane names (``reset``, ``step``, ``state``, + ``close``) are always rejected to preserve the dual API boundary. + """ + normalized_name = self._validate_tool_registration( + tool_name, + tool_definition=tool_definition, + ) + + with self._state_lock: + context = self.active_rollouts.get(rollout_id) + if context is None: + raise KeyError(f"rollout not found: {rollout_id}") + handlers: dict[str, ToolHandler] = context["tool_handlers"] + handlers[normalized_name] = handler + if tool_definition is not None: + tool_defs: dict[str, dict[str, Any]] = context["tool_defs"] + tool_defs[normalized_name] = tool_definition + + def unregister_tool_handler(self, rollout_id: str, tool_name: str) -> None: + with self._state_lock: + context = self.active_rollouts.get(rollout_id) + if context is None: + return + handlers: dict[str, ToolHandler] = context.get("tool_handlers", {}) + handlers.pop(tool_name, None) + tool_defs: dict[str, dict[str, Any]] = context.get("tool_defs", {}) + tool_defs.pop(tool_name, None) + + @staticmethod + def _tool_name(tool: dict[str, Any]) -> str | None: + if not isinstance(tool, dict): + return None + function = tool.get("function") + if not isinstance(function, dict): + return None + name = function.get("name") + return name if isinstance(name, str) and name else None + + def _validate_tool_registration( + self, + tool_name: str, + *, + tool_definition: dict[str, Any] | None, + ) -> str: + normalized = tool_name.strip() + if not normalized: + raise ValueError("tool_name must not be blank") + if not _TOOL_NAME_RE.fullmatch(normalized): + raise ValueError( + f"tool_name must match ^[A-Za-z0-9_-]{{1,64}}$ (got {tool_name!r})" + ) + if normalized.lower() in RESERVED_TOOL_NAMES: + raise ValueError( + "Interception tool name is reserved for infrastructure/control " + f"APIs: {normalized!r}" + ) + if normalized not in self._tool_name_allowlist: + raise ValueError( + "Interception tool name is not in the configured allowlist: " + f"{normalized!r}" + ) + + if tool_definition is not None: + definition_name = self._tool_name(tool_definition) + if definition_name is None: + raise ValueError( + "tool_definition must be an OpenAI tool schema with function.name" + ) + if definition_name != normalized: + raise ValueError( + "tool_definition.function.name must exactly match tool_name " + f"({definition_name!r} != {normalized!r})" + ) + + return normalized + + def _merge_rollout_tools( + self, + tools: Any, + tool_defs: dict[str, dict[str, Any]], + ) -> list[dict[str, Any]] | None: + merged: list[dict[str, Any]] = [] + if isinstance(tools, list): + for tool in tools: + if isinstance(tool, dict): + merged.append(tool) + + existing = { + name for item in merged if (name := self._tool_name(item)) is not None + } + for name, tool in tool_defs.items(): + if name in existing: + continue + merged.append(tool) + + return merged or None + + def _authorized(self, request: web.Request) -> bool: + auth = request.headers.get("Authorization", "") + api_key = request.headers.get("x-api-key", "") + return hmac.compare_digest( + auth, f"Bearer {self.secret}" + ) or hmac.compare_digest(api_key, self.secret) + + async def _handle_health(self, request: web.Request) -> web.Response: + return web.json_response({"status": "ok", **self.stats()}) + + async def _handle_exit(self, request: web.Request) -> web.Response: + """Handle agent process exit notification. + + Called by the sandbox entrypoint after the agent process exits. + Enqueues a sentinel ``None`` on the rollout's request queue so that + ``next_request()`` returns immediately instead of waiting for the + full timeout. + """ + rollout_id = request.match_info["rollout_id"] + with self._state_lock: + rollout = self.active_rollouts.get(rollout_id) + if rollout is None: + return web.json_response({"status": "ignored", "reason": "unknown rollout_id"}) + + queue = rollout.get("request_id_queue") + if queue is not None: + try: + queue.put_nowait(None) # sentinel: signals "agent exited" + except Exception: + pass + + _log.info( + "interception_exit_signal rollout_id=%s", + rollout_id, + ) + return web.json_response({"status": "ok"}) + + async def _handle_tool_call(self, request: web.Request) -> web.Response: + if not self._authorized(request): + return web.json_response({"error": "Unauthorized"}, status=401) + + rollout_id = request.match_info["rollout_id"] + tool_name = request.match_info["tool_name"] + with self._state_lock: + context = self.active_rollouts.get(rollout_id) + if context is None: + return web.json_response({"error": "rollout not found"}, status=404) + handlers: dict[str, ToolHandler] = context.get("tool_handlers", {}) + handler = handlers.get(tool_name) + if handler is None: + return web.json_response({"error": "tool not found"}, status=404) + + try: + body = await request.json() + except Exception as exc: + return web.json_response({"error": f"invalid JSON: {exc}"}, status=400) + + arguments_raw: Any + if isinstance(body, dict) and "arguments" in body: + arguments_raw = body.get("arguments") + else: + arguments_raw = body + + if arguments_raw is None: + arguments = {} + elif isinstance(arguments_raw, dict): + arguments = arguments_raw + else: + return web.json_response( + {"error": "tool arguments must be a JSON object"}, + status=400, + ) + + try: + response = await handler(arguments) + except Exception: + _log.exception( + "tool handler failed (rollout=%s, tool=%s)", + rollout_id, + tool_name, + ) + return web.json_response({"error": "tool execution failed"}, status=500) + + if not isinstance(response, dict): + return web.json_response( + {"error": "tool handler must return a JSON object"}, + status=500, + ) + return web.json_response(response) + + async def _handle_chat_completions( + self, request: web.Request + ) -> web.StreamResponse | web.Response: + if not self._authorized(request): + return web.json_response({"error": "Unauthorized"}, status=401) + + rollout_id = request.match_info["rollout_id"] + with self._state_lock: + context = self.active_rollouts.get(rollout_id) + if not context: + return web.json_response({"error": "rollout not found"}, status=404) + + try: + body = await request.json() + except Exception as exc: + return web.json_response({"error": f"invalid JSON: {exc}"}, status=400) + + tool_defs: dict[str, dict[str, Any]] = dict(context.get("tool_defs", {})) + merged_tools = self._merge_rollout_tools(body.get("tools"), tool_defs) + if merged_tools is not None: + body["tools"] = merged_tools + + is_streaming = bool(body.get("stream")) + request_id = f"req_{uuid.uuid4().hex[:8]}" + chunk_queue: asyncio.Queue | None = asyncio.Queue() if is_streaming else None + + intercept: dict[str, Any] = { + "request_id": request_id, + "rollout_id": rollout_id, + "messages": body.get("messages"), + "model": body.get("model"), + "tools": body.get("tools"), + "stream": is_streaming, + "chunk_queue": chunk_queue, + "response_future": asyncio.get_running_loop().create_future(), + "body": body, + } + with self._state_lock: + context = self.active_rollouts.get(rollout_id) + if context is None: + return web.json_response({"error": "rollout not found"}, status=404) + self.intercepts[request_id] = intercept + request_queue: _queue_mod.Queue[str] = context["request_id_queue"] + request_queue.put_nowait(request_id) + + if is_streaming: + return await self._stream_response(request, intercept) + + try: + response_dict = await intercept["response_future"] + except asyncio.CancelledError: + return web.json_response({"error": "rollout cancelled"}, status=499) + except Exception: + _log.exception("interception request %s failed", request_id) + return web.json_response({"error": "internal error"}, status=500) + finally: + with self._state_lock: + self.intercepts.pop(request_id, None) + + return web.json_response(response_dict) + + async def _stream_response( + self, request: web.Request, intercept: dict[str, Any] + ) -> web.StreamResponse: + chunk_queue: asyncio.Queue = intercept["chunk_queue"] + resp = web.StreamResponse( + status=200, + headers={ + "Content-Type": "text/event-stream", + "Cache-Control": "no-cache", + "Connection": "keep-alive", + }, + ) + await resp.prepare(request) + get_task: asyncio.Task | None = None + try: + while True: + if get_task is None: + get_task = asyncio.create_task(chunk_queue.get()) + done, _ = await asyncio.wait({get_task}, timeout=_KEEPALIVE_INTERVAL_S) + if get_task not in done: + await resp.write(b": keepalive\n\n") + continue + chunk = get_task.result() + get_task = None + if chunk is None: + await resp.write(b"data: [DONE]\n\n") + break + await resp.write(f"data: {json.dumps(chunk)}\n\n".encode()) + await asyncio.sleep(0) + except (asyncio.CancelledError, ConnectionResetError): + pass + finally: + if get_task and not get_task.done(): + get_task.cancel() + fut: asyncio.Future | None = intercept.get("response_future") + if fut and not fut.done(): + fut.cancel() + request_id = intercept.get("request_id") + if isinstance(request_id, str): + with self._state_lock: + self.intercepts.pop(request_id, None) + try: + await resp.write_eof() + except Exception: + pass + return resp + + +def _resolve_future_threadsafe(future: asyncio.Future, value: Any) -> None: + """Set a future's result from any thread. + + ``asyncio.Future`` is not thread-safe: calling ``set_result`` from a + thread that is not running the future's event loop can silently fail + to wake the coroutine awaiting it. This helper detects cross-loop + calls and uses ``call_soon_threadsafe`` to schedule the resolution on + the correct loop. + """ + if future.done(): + return + loop = future.get_loop() + try: + running = asyncio.get_running_loop() + except RuntimeError: + running = None + if running is loop: + future.set_result(value) + else: + loop.call_soon_threadsafe(future.set_result, value) + + +def _put_queue_threadsafe(q: asyncio.Queue, item: Any) -> None: + """Put an item on an asyncio.Queue from any thread.""" + loop = getattr(q, "_loop", None) + if loop is None: + # Fallback: put_nowait which is simpler. Let QueueFull propagate — + # silently dropping items would cause hard-to-debug streaming issues. + q.put_nowait(item) + return + try: + running = asyncio.get_running_loop() + except RuntimeError: + running = None + if running is loop: + q.put_nowait(item) + else: + loop.call_soon_threadsafe(q.put_nowait, item) + + +async def deliver_response( + intercept: dict[str, Any], response_dict: dict[str, Any] +) -> None: + """Unblock the agent's HTTP handler with ``response_dict``. + + For non-streaming requests, resolves the future directly. + For streaming requests, synthesizes SSE chunks from the complete + response and signals EOF. + + Thread-safe: can be called from any thread, not just the event loop + that owns the future/queue. This is required because the rollout + worker may run ``deliver_response`` from its own ``asyncio.run()`` + in a daemon thread while the ``InterceptionServer``'s aiohttp + handler awaits the future on a different loop. + """ + is_streaming = intercept.get("stream", False) + chunk_queue: asyncio.Queue | None = intercept.get("chunk_queue") + future: asyncio.Future | None = intercept.get("response_future") + + if not is_streaming: + if future: + _resolve_future_threadsafe(future, response_dict) + return + + if chunk_queue is None: + raise RuntimeError("chunk_queue missing on streaming intercept") + + choices = response_dict.get("choices") or [] + for choice in choices: + msg = choice.get("message") or {} + content_chunk = { + "id": response_dict.get("id", ""), + "object": "chat.completion.chunk", + "created": response_dict.get("created", int(time.time())), + "model": response_dict.get("model", ""), + "choices": [ + { + "index": choice.get("index", 0), + "delta": { + "role": "assistant", + "content": msg.get("content"), + "tool_calls": msg.get("tool_calls"), + }, + "finish_reason": None, + } + ], + } + _put_queue_threadsafe(chunk_queue, content_chunk) + finish_chunk = { + "id": response_dict.get("id", ""), + "object": "chat.completion.chunk", + "created": response_dict.get("created", int(time.time())), + "model": response_dict.get("model", ""), + "choices": [ + { + "index": choice.get("index", 0), + "delta": {}, + "finish_reason": choice.get("finish_reason"), + } + ], + } + _put_queue_threadsafe(chunk_queue, finish_chunk) + + _put_queue_threadsafe(chunk_queue, None) + if future: + _resolve_future_threadsafe(future, response_dict) + + +__all__ = [ + "InterceptionServer", + "deliver_response", +] diff --git a/src/openenv/core/harness/agents/opencode.py b/src/openenv/core/harness/agents/opencode.py new file mode 100644 index 000000000..9a829c3e2 --- /dev/null +++ b/src/openenv/core/harness/agents/opencode.py @@ -0,0 +1,220 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""OpenCode agent adapter. + +Expresses the OpenCode harness as a purely declarative :class:`CLIAgentSpec`. +All builders (command construction, config generation, env var resolution) +are self-contained with no imports from any environment package. + +Registered on import:: + + import openenv.core.harness.agents.opencode + # OPENCODE_SPEC is now in the registry +""" + +from __future__ import annotations + +import json +import shlex +from typing import Any + +from . import register_agent +from .base import AgentEvent, ArtifactSpec, CLIAgentSpec, MCPConfigSpec + + +def _build_opencode_command( + spec: CLIAgentSpec, + config: Any, + task: Any, + mcp_config_path: str | None, +) -> str: + """Build the ``opencode run`` shell command.""" + home = config.sandbox_home if hasattr(config, "sandbox_home") else "/home/user" + run_format = config.run_format if hasattr(config, "run_format") else "json" + format_flag = "--format json" if run_format == "json" else "" + instruction_file = f"{home}/task/instruction.md" + log_file = f"{home}/logs/agent/opencode.jsonl" + workdir = f"{home}/workdir" + + workdir_q = shlex.quote(workdir) + instruction_q = shlex.quote(instruction_file) + log_q = shlex.quote(log_file) + + return ( + f'export PATH="$HOME/.opencode/bin:$PATH" && ' + f"cd {workdir_q} && git init -q 2>/dev/null; " + f'opencode run {format_flag} "$(cat {instruction_q})" ' + f"2>&1 | tee {log_q}" + ).strip() + + +def _build_opencode_mcp_config( + spec: CLIAgentSpec, + tools: list[Any], + workdir: str, +) -> str: + """Build ``opencode.json`` content. + + Returns an empty string so the driver skips writing this file. + The actual config is written via ``spec.files`` using + ``_build_opencode_config_file`` which has access to the rollout + config (base_url, api_key, model). + """ + return "" + + +def _build_opencode_config_file(task: Any, config: Any) -> str: + """Build the full ``opencode.json`` dynamically from config fields.""" + base_url = ( + config.base_url if hasattr(config, "base_url") else "http://127.0.0.1:7000/v1" + ) + api_key = config.api_key if hasattr(config, "api_key") else "intercepted" + model = config.model if hasattr(config, "model") else "model" + timeout = ( + int(config.agent_timeout_s * 1000) + if hasattr(config, "agent_timeout_s") + else 600000 + ) + + # Split model into provider_name/model_id for the opencode config format. + # e.g. "zai-org/GLM-5.1:zai-org" becomes provider "hf", model_id as-is. + provider_name = "default" + model_id = model + if hasattr(config, "provider_name") and config.provider_name: + provider_name = config.provider_name + + return json.dumps( + { + "$schema": "https://opencode.ai/config.json", + "model": f"{provider_name}/{model_id}", + "provider": { + provider_name: { + "npm": "@ai-sdk/openai-compatible", + "name": provider_name.title(), + "options": { + "baseURL": base_url, + "apiKey": api_key, + "timeout": timeout, + }, + "models": { + model_id: { + "name": model_id, + "id": model_id, + }, + }, + } + }, + }, + indent=2, + ) + + +def _build_opencode_env_vars( + spec: CLIAgentSpec, + config: Any, +) -> dict[str, str]: + """Build env vars for the OpenCode process.""" + home = config.sandbox_home if hasattr(config, "sandbox_home") else "/home/user" + base_url = config.base_url if hasattr(config, "base_url") else "" + api_key = config.api_key if hasattr(config, "api_key") else "intercepted" + extra_env = config.extra_env if hasattr(config, "extra_env") else {} + + env = dict(extra_env) + env["OPENAI_BASE_URL"] = base_url + env["OPENAI_API_KEY"] = api_key + env["OPENCODE_CONFIG"] = f"{home}/.config/opencode/opencode.json" + return env + + +def _parse_opencode_event(line: str) -> AgentEvent | None: + """Parse one line of OpenCode's JSONL stdout.""" + line = line.strip() + if not line: + return None + try: + data = json.loads(line) + except json.JSONDecodeError: + return None + + event_type = data.get("type", "") + if event_type in ("assistant", "message", "text"): + return AgentEvent(type="assistant", data=data, raw=line) + elif event_type in ("tool_call", "tool_use"): + return AgentEvent(type="tool_call", data=data, raw=line) + elif event_type in ("tool_result", "tool_response"): + return AgentEvent(type="tool_result", data=data, raw=line) + elif event_type in ("step_start",): + return AgentEvent(type="assistant", data=data, raw=line) + elif event_type in ("step_finish",): + return AgentEvent(type="done", data=data, raw=line) + elif event_type == "error": + return AgentEvent(type="error", data=data, raw=line) + elif event_type in ("done", "complete", "end"): + return AgentEvent(type="done", data=data, raw=line) + return AgentEvent(type="assistant", data=data, raw=line) + + +def _instruction_file_content(task: Any, config: Any) -> str: + return task.instruction if hasattr(task, "instruction") else str(task) + + +def _system_prompt_content(task: Any, config: Any) -> str | None: + if hasattr(config, "system_prompt") and config.system_prompt: + return config.system_prompt + return None + + +OPENCODE_SPEC = CLIAgentSpec( + name="opencode", + install_check_cmd=["/home/user/.opencode/bin/opencode", "--version"], + base_command=[ + "opencode", + "run", + "--format", + "json", + "--dangerously-skip-permissions", + ], + mcp_config=MCPConfigSpec( + method="config_file", + path_template="{home}/.config/opencode/opencode.json", + ), + default_timeout_s=900.0, + setup=( + "set -e && " + "curl -fsSL https://opencode.ai/install | bash && " + "mkdir -p /home/user/.config/opencode /home/user/logs/agent " + "/home/user/logs/verifier /home/user/task /home/user/workdir && " + 'export PATH="$HOME/.opencode/bin:$PATH" && ' + "opencode --version" + ), + files={ + "/home/user/task/instruction.md": _instruction_file_content, + "/home/user/task/system.md": _system_prompt_content, + "/home/user/.config/opencode/opencode.json": _build_opencode_config_file, + }, + artifacts={ + "agent_log": ArtifactSpec( + path="/home/user/logs/agent/opencode.jsonl", + format="jsonl", + ), + }, + env={ + "PATH": "/home/user/.opencode/bin:$PATH", + "OPENAI_BASE_URL": "{base_url}", + "OPENAI_API_KEY": "{api_key}", + }, + build_command=_build_opencode_command, + build_mcp_config=_build_opencode_mcp_config, + parse_events=_parse_opencode_event, + build_env_vars=_build_opencode_env_vars, +) + +register_agent(OPENCODE_SPEC) + +__all__ = [ + "OPENCODE_SPEC", +] diff --git a/src/openenv/core/harness/agents/pi.py b/src/openenv/core/harness/agents/pi.py new file mode 100644 index 000000000..a2fdd7537 --- /dev/null +++ b/src/openenv/core/harness/agents/pi.py @@ -0,0 +1,193 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Pi coding agent adapter. + +Pi runs in print mode for non-interactive harness usage:: + + pi --no-session --no-context-files --provider

--model --thinking off \\ + -p @/home/user/task/instruction.txt 2>&1 | tee /home/user/logs/agent/pi.txt + +The provider and model are passed as CLI flags. Provider-specific credentials +are exported via ``build_env_vars`` according to Pi's provider docs +(``HF_TOKEN`` for ``huggingface``, ``OPENAI_API_KEY`` for ``openai``, etc.). + +Registered on import:: + + import openenv.core.harness.agents.pi + # PI_SPEC is now in the registry +""" + +from __future__ import annotations + +import json +import shlex +from typing import Any + +from . import register_agent +from .base import AgentEvent, ArtifactSpec, CLIAgentSpec, MCPConfigSpec + + +def _instruction(task: Any, config: Any) -> str: + return task.instruction if hasattr(task, "instruction") else str(task) + + +def _system_prompt(task: Any, config: Any) -> str | None: + if hasattr(config, "system_prompt") and config.system_prompt: + return config.system_prompt + return None + + +def _build_command( + spec: CLIAgentSpec, + config: Any, + task: Any, + mcp_config_path: str | None, +) -> str: + home = config.sandbox_home if hasattr(config, "sandbox_home") else "/home/user" + instruction_file = f"{home}/task/instruction.txt" + log_file = f"{home}/logs/agent/pi.txt" + workdir = ( + config.workdir + if hasattr(config, "workdir") and getattr(config, "workdir") + else f"{home}/workdir" + ) + + provider = "" + if hasattr(config, "provider") and config.provider: + provider = f" --provider {shlex.quote(config.provider)}" + model = "" + if hasattr(config, "model") and config.model: + model = f" --model {shlex.quote(config.model)}" + thinking = " --thinking off" + if hasattr(config, "thinking") and config.thinking: + thinking = f" --thinking {shlex.quote(config.thinking)}" + + workdir_q = shlex.quote(workdir) + instruction_q = shlex.quote(instruction_file) + log_q = shlex.quote(log_file) + + return ( + f"cd {workdir_q} && git init -q 2>/dev/null; " + f"pi --no-session --no-context-files" + f"{provider}{model}{thinking}" + f" -p @{instruction_q}" + f" 2>&1 | tee {log_q}" + ) + + +def _build_mcp_config( + spec: CLIAgentSpec, + tools: list[Any], + workdir: str, +) -> str: + return json.dumps({"mcpServers": {}}, indent=2) + + +def _parse_events(line: str) -> AgentEvent | None: + line = line.strip() + if not line: + return None + try: + data = json.loads(line) + except json.JSONDecodeError: + return AgentEvent(type="assistant", data={"text": line}, raw=line) + + event_type = data.get("type", "") + if event_type in ("assistant", "message", "response"): + return AgentEvent(type="assistant", data=data, raw=line) + if event_type in ("tool_call", "tool_use", "function_call"): + return AgentEvent(type="tool_call", data=data, raw=line) + if event_type in ("tool_result", "tool_response"): + return AgentEvent(type="tool_result", data=data, raw=line) + if event_type in ("thinking", "reasoning"): + return AgentEvent(type="reasoning", data=data, raw=line) + if event_type == "error": + return AgentEvent(type="error", data=data, raw=line) + if event_type in ("done", "complete", "end"): + return AgentEvent(type="done", data=data, raw=line) + return AgentEvent(type="assistant", data=data, raw=line) + + +def _provider_api_key_env(provider: str) -> str: + provider_key = provider.strip().lower() + env_by_provider = { + # https://github.com/earendil-works/pi/tree/main/packages/coding-agent#providers--models + "openai": "OPENAI_API_KEY", + "openenv": "OPENAI_API_KEY", + "huggingface": "HF_TOKEN", + "anthropic": "ANTHROPIC_API_KEY", + "gemini": "GEMINI_API_KEY", + "google": "GEMINI_API_KEY", + } + env_name = env_by_provider.get(provider_key) + if env_name is None: + raise ValueError( + f"Unsupported pi provider {provider!r}; expected one of " + f"{sorted(env_by_provider)}" + ) + return env_name + + +def _build_env_vars(spec: CLIAgentSpec, config: Any) -> dict[str, str]: + provider = config.provider if hasattr(config, "provider") else "openai" + if not isinstance(provider, str) or not provider.strip(): + provider = "openai" + api_key = config.api_key if hasattr(config, "api_key") else "" + base_url = config.base_url if hasattr(config, "base_url") else "" + extra_env = config.extra_env if hasattr(config, "extra_env") else {} + + env = dict(extra_env) + env["PI_SKIP_VERSION_CHECK"] = "1" + env["PI_TELEMETRY"] = "0" + + if base_url: + env["OPENAI_BASE_URL"] = base_url + + key_env_var = _provider_api_key_env(provider) + if api_key: + env[key_env_var] = api_key + + return env + + +PI_SPEC = CLIAgentSpec( + name="pi", + install_check_cmd=["pi", "--version"], + base_command=["pi", "--no-session", "--no-context-files"], + mcp_config=MCPConfigSpec( + method="config_file", + path_template="{workdir}/.mcp.json", + ), + default_timeout_s=600.0, + setup=( + "set -e && " + "apt-get update -qq && apt-get install -y -qq curl ca-certificates gnupg && " + "curl -fsSL https://deb.nodesource.com/setup_22.x | bash - && " + "apt-get install -y -qq nodejs && " + "curl -fsSL https://pi.dev/install.sh | sh && " + "mkdir -p /home/user/logs/agent /home/user/task /home/user/workdir && " + 'export PATH="$HOME/.local/bin:$HOME/.pi/bin:$PATH" && ' + "pi --version" + ), + files={ + "/home/user/task/instruction.txt": _instruction, + "/home/user/task/system.txt": _system_prompt, + }, + artifacts={ + "agent_log": ArtifactSpec(path="/home/user/logs/agent/pi.txt"), + }, + env=None, + extension_dir_template="{home}/.pi/agent/extensions", + build_command=_build_command, + build_mcp_config=_build_mcp_config, + parse_events=_parse_events, + build_env_vars=_build_env_vars, +) + +register_agent(PI_SPEC) + +__all__ = ["PI_SPEC"] diff --git a/src/openenv/core/harness/sandbox/__init__.py b/src/openenv/core/harness/sandbox/__init__.py new file mode 100644 index 000000000..208fe54d5 --- /dev/null +++ b/src/openenv/core/harness/sandbox/__init__.py @@ -0,0 +1,73 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Sandbox backends for harness-driven rollouts. + +Provides the :class:`SandboxBackend` / :class:`SandboxHandle` protocols and +concrete implementations. Any harness adapter can use any backend -- the +sandbox layer is orthogonal to the agent CLI choice. + +Optional backend imports are wrapped in ``try/except`` so this package +loads cleanly when dependencies aren't installed (CI smoke tests, lint). +""" + +from typing import Any, Literal + +from .base import BgJob, ExecResult, SandboxBackend, SandboxHandle +from .docker_backend import DockerBgJob, DockerSandboxBackend, DockerSandboxHandle + +__all__ = [ + "BgJob", + "DockerBgJob", + "DockerSandboxBackend", + "DockerSandboxHandle", + "ExecResult", + "SandboxBackend", + "SandboxHandle", + "create_sandbox_backend", +] + +try: + from .e2b_backend import E2BBgJob, E2BSandboxBackend, E2BSandboxHandle # noqa: F401 + + __all__.extend(["E2BBgJob", "E2BSandboxBackend", "E2BSandboxHandle"]) +except ImportError: + pass # e2b not installed + +try: + from .hf_backend import HFBgJob, HFSandboxBackend, HFSandboxHandle # noqa: F401 + + __all__.extend(["HFBgJob", "HFSandboxBackend", "HFSandboxHandle"]) +except ImportError: + pass # hf-sandbox not installed + + +def create_sandbox_backend( + backend: Literal["e2b", "docker", "hf"] = "e2b", + **kwargs: Any, +) -> SandboxBackend: + """Create a sandbox backend by name. + + For ``"e2b"``: works with both E2B cloud and CubeSandbox + (set ``E2B_API_URL``). + + For ``"docker"``: local Docker, no external dependencies. + + For ``"hf"``: Hugging Face Jobs via ``hf-sandbox``. + """ + if backend == "e2b": + from .e2b_backend import E2BSandboxBackend + + return E2BSandboxBackend(**kwargs) + elif backend == "docker": + return DockerSandboxBackend(**kwargs) + elif backend == "hf": + from .hf_backend import HFSandboxBackend + + return HFSandboxBackend(**kwargs) + raise ValueError( + f"Unknown sandbox backend: {backend!r}. Use 'e2b', 'docker', or 'hf'." + ) diff --git a/src/openenv/core/harness/sandbox/_util.py b/src/openenv/core/harness/sandbox/_util.py new file mode 100644 index 000000000..6291b0fb3 --- /dev/null +++ b/src/openenv/core/harness/sandbox/_util.py @@ -0,0 +1,12 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from __future__ import annotations + + +def shell_quote(s: str) -> str: + """Single-quote a string for shell, escaping embedded single quotes.""" + return "'" + s.replace("'", "'\\''") + "'" diff --git a/envs/opencode_env/sandbox/base.py b/src/openenv/core/harness/sandbox/base.py similarity index 79% rename from envs/opencode_env/sandbox/base.py rename to src/openenv/core/harness/sandbox/base.py index 76869149a..22f096310 100644 --- a/envs/opencode_env/sandbox/base.py +++ b/src/openenv/core/harness/sandbox/base.py @@ -6,18 +6,18 @@ """Sandbox backend protocol. -A ``SandboxBackend`` produces ``SandboxHandle`` instances that the harness uses -to stage files, run the OpenCode install, launch the agent as a background -process, and later tear the sandbox down. +A ``SandboxBackend`` produces ``SandboxHandle`` instances that harnesses use +to stage files, install agent CLIs, launch the agent as a background process, +and later tear the sandbox down. -Backends can be implemented against any provider (E2B, Docker, Modal, Prime) -as long as they satisfy the Protocols defined here. +Backends can be implemented against any provider (E2B, CubeSandbox, Docker, +Modal) as long as they satisfy the Protocols defined here. """ from __future__ import annotations from dataclasses import dataclass -from typing import Any, Protocol, runtime_checkable +from typing import Protocol, runtime_checkable @dataclass @@ -96,5 +96,10 @@ def create( timeout_s: int = 900, envs: dict[str, str] | None = None, metadata: dict[str, str] | None = None, + image: str | None = None, ) -> SandboxHandle: - """Create and return a new, ready-to-use sandbox.""" + """Create and return a new, ready-to-use sandbox. + + ``image`` is backend-specific and may be ignored by providers that do + not support per-sandbox image selection. + """ diff --git a/src/openenv/core/harness/sandbox/docker_backend.py b/src/openenv/core/harness/sandbox/docker_backend.py new file mode 100644 index 000000000..120fb9a11 --- /dev/null +++ b/src/openenv/core/harness/sandbox/docker_backend.py @@ -0,0 +1,373 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Docker implementation of :class:`SandboxBackend`. + +Runs each sandbox as a ``docker run -d`` container on the local machine. +Commands execute via ``docker exec``, files transfer via ``docker exec`` +with stdin piping. Suitable for CI, local dev, and environments without +KVM or cloud sandbox credentials. + +Usage:: + + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create() + result = sandbox.exec("echo hello") + print(result.stdout) # "hello" + sandbox.kill() +""" + +from __future__ import annotations + +import logging +import subprocess +import threading +import time +import uuid +from pathlib import PurePosixPath + +from openenv.core.harness.sandbox._util import shell_quote +from openenv.core.harness.sandbox.base import BgJob, ExecResult + +_log = logging.getLogger(__name__) + + +class DockerBgJob: + """Handle to a background process running inside a Docker container. + + Launches the command via ``docker exec -d`` and tracks the wrapper + shell PID. Completion is detected by polling whether the PID is still + alive inside the container. + """ + + def __init__( + self, + container_id: str, + pid: int, + poll_thread: threading.Thread | None = None, + ) -> None: + self._container_id = container_id + self._pid = pid + self._exit_code: int | None = None + self._done = threading.Event() + self._poll_thread = poll_thread + + @property + def pid(self) -> int: + return self._pid + + def wait(self, timeout: float | None = None) -> int: + if not self._done.wait(timeout=timeout): + raise TimeoutError( + f"Background command (pid={self._pid}) did not exit within {timeout}s" + ) + return self._exit_code if self._exit_code is not None else 0 + + def kill(self) -> None: + try: + subprocess.run( + ["docker", "exec", self._container_id, "kill", "-9", str(self._pid)], + capture_output=True, + timeout=5, + ) + except Exception: + pass + self._done.set() + + +class DockerSandboxHandle: + """Wraps a running Docker container to satisfy :class:`SandboxHandle`.""" + + def __init__(self, container_id: str, *, user: str | None = None) -> None: + self._container_id = container_id + self._user = user + self._bg_jobs: list[DockerBgJob] = [] + + @property + def sandbox_id(self) -> str: + return self._container_id[:12] + + def exec( + self, + cmd: str, + *, + envs: dict[str, str] | None = None, + cwd: str | None = None, + timeout: float | None = 60, + ) -> ExecResult: + docker_cmd = self._build_exec_cmd(envs=envs, cwd=cwd) + docker_cmd.extend(["bash", "-c", cmd]) + try: + result = subprocess.run( + docker_cmd, + capture_output=True, + text=True, + timeout=timeout, + ) + return ExecResult( + exit_code=result.returncode, + stdout=result.stdout, + stderr=result.stderr, + ) + except subprocess.TimeoutExpired: + return ExecResult( + exit_code=-1, stdout="", stderr=f"Command timed out after {timeout}s" + ) + except Exception as exc: + return ExecResult(exit_code=-1, stdout="", stderr=str(exc)) + + def start_bg( + self, + cmd: str, + *, + envs: dict[str, str] | None = None, + cwd: str | None = None, + ) -> BgJob: + marker = f"/tmp/.bg_{uuid.uuid4().hex}" + wrapped = f"bash -c {shell_quote(cmd + f'; echo $? > {marker}')} &\necho $!" + docker_cmd = self._build_exec_cmd(envs=envs, cwd=cwd) + docker_cmd.extend(["bash", "-c", wrapped]) + result = subprocess.run(docker_cmd, capture_output=True, text=True, timeout=10) + if result.returncode != 0: + raise RuntimeError(f"Failed to start background command: {result.stderr}") + # Extract PID from the last numeric-only line (commands may print + # banners like "Database migration complete." before the PID). + pid_line = None + for line in reversed(result.stdout.strip().splitlines()): + if line.strip().isdigit(): + pid_line = line.strip() + break + if pid_line is None: + raise RuntimeError( + f"Could not extract PID from start_bg output: {result.stdout!r}" + ) + pid = int(pid_line) + + job = DockerBgJob(self._container_id, pid) + poll_thread = threading.Thread( + target=self._poll_bg_job, + args=(job, marker), + daemon=True, + ) + job._poll_thread = poll_thread + self._bg_jobs.append(job) + poll_thread.start() + return job + + def write_text(self, path: str, content: str) -> None: + parent = str(PurePosixPath(path).parent) + if parent not in ("", "/"): + mkdir_result = subprocess.run( + ["docker", "exec", self._container_id, "mkdir", "-p", parent], + capture_output=True, + timeout=10, + ) + if mkdir_result.returncode != 0: + raise RuntimeError( + f"Failed to create directory {parent!r} in container " + f"{self._container_id}: {mkdir_result.stderr.decode(errors='replace')}" + ) + write_result = subprocess.run( + [ + "docker", + "exec", + "-i", + self._container_id, + "bash", + "-c", + f"cat > {shell_quote(path)}", + ], + input=content.encode(), + capture_output=True, + timeout=30, + ) + if write_result.returncode != 0: + raise RuntimeError( + f"Failed to write file {path!r} in container " + f"{self._container_id}: {write_result.stderr.decode(errors='replace')}" + ) + + def read_text(self, path: str) -> str: + result = subprocess.run( + ["docker", "exec", self._container_id, "cat", path], + capture_output=True, + text=True, + timeout=30, + ) + if result.returncode != 0: + raise FileNotFoundError(f"No such file in container: {path}") + return result.stdout + + def exists(self, path: str) -> bool: + result = subprocess.run( + ["docker", "exec", self._container_id, "test", "-e", path], + capture_output=True, + timeout=10, + ) + return result.returncode == 0 + + def kill(self) -> None: + for job in self._bg_jobs: + try: + job.kill() + except Exception: + pass + self._bg_jobs.clear() + try: + subprocess.run( + ["docker", "rm", "-f", self._container_id], + capture_output=True, + timeout=15, + ) + except Exception: + pass + + def _build_exec_cmd( + self, + *, + envs: dict[str, str] | None = None, + cwd: str | None = None, + ) -> list[str]: + cmd = ["docker", "exec"] + if self._user: + cmd.extend(["-u", self._user]) + if cwd: + cmd.extend(["-w", cwd]) + for k, v in (envs or {}).items(): + cmd.extend(["-e", f"{k}={v}"]) + cmd.append(self._container_id) + return cmd + + def _poll_bg_job(self, job: DockerBgJob, marker: str) -> None: + consecutive_failures = 0 + while not job._done.is_set(): + try: + result = subprocess.run( + ["docker", "exec", self._container_id, "cat", marker], + capture_output=True, + text=True, + timeout=5, + ) + if result.returncode == 0 and result.stdout.strip(): + job._exit_code = int(result.stdout.strip()) + job._done.set() + return + if "No such container" in (result.stderr or ""): + job._exit_code = 1 + job._done.set() + return + except Exception: + consecutive_failures += 1 + else: + consecutive_failures = 0 + + # Also check if PID is gone (crash without writing marker). + try: + check = subprocess.run( + ["docker", "exec", self._container_id, "kill", "-0", str(job._pid)], + capture_output=True, + text=True, + timeout=5, + ) + if check.returncode != 0: + job._exit_code = 1 + job._done.set() + return + if "No such container" in (check.stderr or ""): + job._exit_code = 1 + job._done.set() + return + except Exception: + consecutive_failures += 1 + + if consecutive_failures >= 10: + job._exit_code = 1 + job._done.set() + return + + time.sleep(0.5) + + +class DockerSandboxBackend: + """Creates Docker container sandboxes. + + Each :meth:`create` call spawns a fresh ``docker run -d`` container + that stays alive until :meth:`SandboxHandle.kill` is called or the + container's ``timeout_s`` sleep expires. + """ + + def __init__( + self, + *, + image: str = "ubuntu:22.04", + docker_args: list[str] | None = None, + user: str | None = None, + ) -> None: + self._image = image + self._docker_args = list(docker_args or []) + self._user = user + + # Linux Docker Engine does not auto-resolve host.docker.internal + # unless we explicitly map it. + if "host.docker.internal:host-gateway" not in self._docker_args: + self._docker_args.extend( + ["--add-host", "host.docker.internal:host-gateway"] + ) + + try: + subprocess.run( + ["docker", "version"], + capture_output=True, + check=True, + timeout=5, + ) + except ( + subprocess.CalledProcessError, + FileNotFoundError, + subprocess.TimeoutExpired, + ) as exc: + raise RuntimeError( + "DockerSandboxBackend requires a running Docker daemon." + ) from exc + + def create( + self, + *, + timeout_s: int = 900, + envs: dict[str, str] | None = None, + metadata: dict[str, str] | None = None, + image: str | None = None, + ) -> DockerSandboxHandle: + cmd = [ + "docker", + "run", + "-d", + "--label", + "openenv.sandbox=true", + ] + if metadata: + for k, v in metadata.items(): + cmd.extend(["--label", f"openenv.{k}={v}"]) + for k, v in (envs or {}).items(): + cmd.extend(["-e", f"{k}={v}"]) + cmd.extend(self._docker_args) + effective_image = image or self._image + cmd.extend([effective_image, "sleep", str(timeout_s)]) + + result = subprocess.run(cmd, capture_output=True, text=True, timeout=30) + if result.returncode != 0: + raise RuntimeError( + f"Failed to create Docker sandbox: {result.stderr.strip()}" + ) + container_id = result.stdout.strip() + _log.info( + "Docker sandbox created: %s (image=%s)", + container_id[:12], + effective_image, + ) + return DockerSandboxHandle(container_id, user=self._user) diff --git a/envs/opencode_env/sandbox/e2b.py b/src/openenv/core/harness/sandbox/e2b_backend.py similarity index 92% rename from envs/opencode_env/sandbox/e2b.py rename to src/openenv/core/harness/sandbox/e2b_backend.py index b567a9e65..c0cbf75ba 100644 --- a/envs/opencode_env/sandbox/e2b.py +++ b/src/openenv/core/harness/sandbox/e2b_backend.py @@ -4,7 +4,14 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -"""E2B implementation of :class:`SandboxBackend`.""" +"""E2B implementation of :class:`SandboxBackend`. + +Works with both E2B cloud (api.e2b.dev) and self-hosted E2B-compatible +backends like CubeSandbox. For CubeSandbox, set:: + + E2B_API_URL=http://your-cubesandbox:3000 + E2B_API_KEY=dummy # any non-empty string +""" from __future__ import annotations @@ -14,8 +21,7 @@ from e2b import Sandbox from e2b.sandbox_sync.commands.command_handle import CommandHandle - -from .base import BgJob, ExecResult, SandboxBackend, SandboxHandle +from openenv.core.harness.sandbox.base import BgJob, ExecResult, SandboxHandle class E2BBgJob: @@ -46,9 +52,7 @@ def pid(self) -> int: def wait(self, timeout: float | None = None) -> int: self._thread.join(timeout) if self._thread.is_alive(): - raise TimeoutError( - f"Background command did not exit within {timeout}s" - ) + raise TimeoutError(f"Background command did not exit within {timeout}s") if self._error is not None: # E2B raises CommandExitException on non-zero; treat as exit code. code = getattr(self._error, "exit_code", None) @@ -180,7 +184,9 @@ def create( timeout_s: int = 900, envs: dict[str, str] | None = None, metadata: dict[str, str] | None = None, + image: str | None = None, ) -> SandboxHandle: + del image sbx = Sandbox.create( template=self._template, timeout=timeout_s, diff --git a/src/openenv/core/harness/sandbox/hf_backend.py b/src/openenv/core/harness/sandbox/hf_backend.py new file mode 100644 index 000000000..3b7b060b5 --- /dev/null +++ b/src/openenv/core/harness/sandbox/hf_backend.py @@ -0,0 +1,308 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Hugging Face Sandbox implementation of :class:`SandboxBackend`. + +Wraps `hf-sandbox` (https://github.com/huggingface/hf-sandbox) so OpenEnv +harnesses can use it through the same protocol. +""" + +from __future__ import annotations + +import re +import time +import uuid +from pathlib import PurePosixPath +from threading import Event +from typing import Any + +from hf_sandbox import Sandbox +from openenv.core.harness.sandbox._util import shell_quote +from openenv.core.harness.sandbox.base import BgJob, ExecResult, SandboxHandle + +_ENV_KEY_RE = re.compile(r"^[A-Za-z_][A-Za-z0-9_]*$") + + +class HFSandboxError(RuntimeError): + """Base class for HF sandbox backend errors.""" + + +class HFSandboxCreateError(HFSandboxError): + """Raised when backend cannot create a sandbox.""" + + +class HFBgJob: + """Background process handle for :class:`HFSandboxHandle`.""" + + def __init__( + self, + sandbox: "HFSandboxHandle", + *, + pid: int, + marker_path: str, + poll_interval_s: float = 0.5, + ) -> None: + self._sandbox = sandbox + self._pid = pid + self._marker_path = marker_path + self._poll_interval_s = poll_interval_s + self._done = Event() + self._exit_code: int | None = None + + @property + def pid(self) -> int: + return self._pid + + def wait(self, timeout: float | None = None) -> int: + deadline = None if timeout is None else (time.monotonic() + timeout) + while True: + if self._done.is_set(): + return self._exit_code if self._exit_code is not None else 0 + if deadline is not None and time.monotonic() > deadline: + raise TimeoutError( + f"Background command (pid={self._pid}) " + f"did not exit within {timeout}s" + ) + + marker = self._sandbox.exec( + f"cat {shell_quote(self._marker_path)}", + timeout=10, + ) + if marker.exit_code == 0 and marker.stdout.strip(): + self._exit_code = _parse_exit_code(marker.stdout.strip(), default=0) + self._done.set() + return self._exit_code + + alive = self._sandbox.exec(f"kill -0 {self._pid}", timeout=10) + if alive.exit_code != 0: + self._exit_code = 1 + self._done.set() + return self._exit_code + + time.sleep(self._poll_interval_s) + + def kill(self) -> None: + if self._done.is_set(): + return + try: + self._sandbox.exec(f"kill -9 {self._pid}", timeout=5) + except Exception: + pass + self._exit_code = 137 + self._done.set() + + +class HFSandboxHandle: + """Wraps a live ``hf_sandbox.Sandbox`` to satisfy :class:`SandboxHandle`.""" + + def __init__( + self, + sandbox: Any, + *, + default_envs: dict[str, str] | None = None, + ) -> None: + self._sbx = sandbox + self._default_envs = dict(default_envs or {}) + self._bg_jobs: list[HFBgJob] = [] + + @property + def sandbox_id(self) -> str: + return str(getattr(self._sbx, "job_id", "hf-sandbox")) + + @property + def raw(self) -> Any: + """Escape hatch for callers that need the underlying SDK object.""" + return self._sbx + + def exec( + self, + cmd: str, + *, + envs: dict[str, str] | None = None, + cwd: str | None = None, + timeout: float | None = 60, + ) -> ExecResult: + merged_envs = dict(self._default_envs) + merged_envs.update(envs or {}) + shell_cmd = _with_env_prefix(cmd, merged_envs) + timeout_s = _normalize_exec_timeout(timeout) + try: + result = self._sbx.exec( + "bash", + "-lc", + shell_cmd, + workdir=cwd, + timeout=timeout_s, + ) + return ExecResult( + exit_code=int(getattr(result, "returncode", 1)), + stdout=str(getattr(result, "stdout", "") or ""), + stderr=str(getattr(result, "stderr", "") or ""), + ) + except Exception as exc: + return ExecResult(exit_code=-1, stdout="", stderr=str(exc)) + + def start_bg( + self, + cmd: str, + *, + envs: dict[str, str] | None = None, + cwd: str | None = None, + ) -> BgJob: + marker_path = f"/tmp/.openenv_bg_{uuid.uuid4().hex[:12]}.exit" + wrapped = f"{cmd}; rc=$?; echo $rc > {shell_quote(marker_path)}" + launch_cmd = f"nohup bash -lc {shell_quote(wrapped)} >/dev/null 2>&1 & echo $!" + + result = self.exec(launch_cmd, envs=envs, cwd=cwd, timeout=30) + if result.exit_code != 0: + raise RuntimeError( + f"Failed to start background command: {result.stderr or result.stdout}" + ) + + pid = _parse_pid(result.stdout) + if pid is None: + raise RuntimeError( + f"Could not extract PID from start_bg output: {result.stdout!r}" + ) + + job = HFBgJob(self, pid=pid, marker_path=marker_path) + self._bg_jobs.append(job) + return job + + def write_text(self, path: str, content: str) -> None: + parent = str(PurePosixPath(path).parent) + if parent not in ("", "/"): + r = self.exec(f"mkdir -p {shell_quote(parent)}", timeout=10) + if r.exit_code != 0: + raise RuntimeError( + f"Failed to create parent directory {parent!r}: {r.stderr}" + ) + self._sbx.write_file(path, content) + + def read_text(self, path: str) -> str: + return str(self._sbx.read_file(path, text=True)) + + def exists(self, path: str) -> bool: + r = self.exec(f"test -e {shell_quote(path)}", timeout=10) + return r.exit_code == 0 + + def kill(self) -> None: + for job in self._bg_jobs: + try: + job.kill() + except Exception: + pass + self._bg_jobs.clear() + try: + self._sbx.terminate() + except Exception: + pass + + +class HFSandboxBackend: + """Creates HF sandboxes for harness rollouts via ``hf-sandbox``.""" + + def __init__( + self, + *, + image: str = "python:3.12", + flavor: str = "cpu-basic", + timeout: str | None = None, + forward_hf_token: bool = False, + create_retries: int = 3, + create_backoff_s: float = 2.0, + ) -> None: + self._image = image + self._flavor = flavor + self._timeout = timeout + self._forward_hf_token = forward_hf_token + self._create_retries = max(1, int(create_retries)) + self._create_backoff_s = max(0.0, float(create_backoff_s)) + + def create( + self, + *, + timeout_s: int = 900, + envs: dict[str, str] | None = None, + metadata: dict[str, str] | None = None, + image: str | None = None, + ) -> SandboxHandle: + # `hf-sandbox` does not support metadata at create-time yet. + del metadata + + timeout = self._timeout or _format_timeout(timeout_s) + effective_image = image or self._image + last_error: Exception | None = None + + for attempt in range(self._create_retries): + try: + sbx = Sandbox.create( + image=effective_image, + flavor=self._flavor, + timeout=timeout, + forward_hf_token=self._forward_hf_token, + ) + return HFSandboxHandle(sbx, default_envs=envs) + except Exception as exc: # noqa: BLE001 + last_error = exc + if attempt + 1 < self._create_retries: + time.sleep(self._create_backoff_s * (2**attempt)) + + assert last_error is not None + raise HFSandboxCreateError( + f"Failed to create HF sandbox after {self._create_retries} attempts " + f"({type(last_error).__name__})." + ) from last_error + + +def _with_env_prefix(cmd: str, envs: dict[str, str]) -> str: + if not envs: + return cmd + parts: list[str] = [] + for key, value in envs.items(): + if not _ENV_KEY_RE.match(key): + raise ValueError(f"Invalid environment variable name: {key!r}") + parts.append(f"export {key}={shell_quote(str(value))};") + return " ".join(parts) + f" {cmd}" + + +def _normalize_exec_timeout(timeout: float | None) -> int: + if timeout is None: + return 24 * 60 * 60 + return max(1, int(timeout)) + + +def _format_timeout(timeout_s: int) -> str: + timeout_s = max(1, int(timeout_s)) + if timeout_s % 3600 == 0: + return f"{timeout_s // 3600}h" + if timeout_s % 60 == 0: + return f"{timeout_s // 60}m" + return f"{timeout_s}s" + + +def _parse_pid(stdout: str) -> int | None: + for line in reversed(stdout.strip().splitlines()): + raw = line.strip() + if raw.isdigit(): + return int(raw) + return None + + +def _parse_exit_code(raw: str, *, default: int) -> int: + try: + return int(raw.splitlines()[-1].strip()) + except Exception: + return default + + +__all__ = [ + "HFBgJob", + "HFSandboxBackend", + "HFSandboxCreateError", + "HFSandboxError", + "HFSandboxHandle", +] diff --git a/tests/core/test_cli_agent_driver.py b/tests/core/test_cli_agent_driver.py new file mode 100644 index 000000000..7338fc323 --- /dev/null +++ b/tests/core/test_cli_agent_driver.py @@ -0,0 +1,1317 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Tests for the CLI agent driver abstraction (Phase 2). + +Covers: + - Agent spec + event protocols (base.py) + - Agent registry (__init__.py) + - CLIAgentDriver / CLIAgentSession / CLIAgentSessionFactory (cli_driver.py) + - OpenCode adapter spec (opencode.py) + +All tests run without external dependencies (no E2B, no LLM, no network). +""" + +from __future__ import annotations + +import asyncio +import json +import queue as _queue_mod +import threading +import time +from dataclasses import dataclass, field +from typing import Any + +import pytest +from openenv.core.harness.sandbox.base import ExecResult, SandboxHandle + + +# Fake sandbox infrastructure (mirrors test_coding_agent_env.py pattern) + + +@dataclass +class FakeBgJob: + cmd: str = "" + envs: dict[str, str] | None = None + _exit_code: int = 0 + + @property + def pid(self) -> int: + return 12345 + + def wait(self, timeout: float | None = None) -> int: + return self._exit_code + + def kill(self) -> None: + pass + + +class FakeSandbox: + """In-memory sandbox for unit testing.""" + + def __init__( + self, + *, + install_check_succeeds: bool = False, + healthz_succeeds: bool = True, + ) -> None: + self.sandbox_id = "fake-sandbox-001" + self.written: dict[str, str] = {} + self.executed: list[str] = [] + self.bg_commands: list[tuple[str, dict[str, str] | None]] = [] + self._install_check_succeeds = install_check_succeeds + self._healthz_succeeds = healthz_succeeds + self._killed = False + + def exec( + self, + cmd: str, + *, + envs: dict[str, str] | None = None, + cwd: str | None = None, + timeout: float | None = 60, + ) -> ExecResult: + self.executed.append(cmd) + if cmd == "echo ok": + return ExecResult(exit_code=0, stdout="ok", stderr="") + # install check — only standalone version-check commands (short, just + # binary + --version) should be treated as install probes. Multi-part + # setup scripts that happen to end with --version should succeed. + if "--version" in cmd and len(cmd) < 80 and "&&" not in cmd: + if self._install_check_succeeds: + return ExecResult(exit_code=0, stdout="1.0.0", stderr="") + return ExecResult(exit_code=127, stdout="", stderr="not found") + # healthz check + if "healthz" in cmd: + if self._healthz_succeeds: + return ExecResult(exit_code=0, stdout='{"status":"ok"}', stderr="") + return ExecResult(exit_code=7, stdout="", stderr="connection refused") + # All other commands succeed + return ExecResult(exit_code=0, stdout="", stderr="") + + def start_bg( + self, + cmd: str, + *, + envs: dict[str, str] | None = None, + cwd: str | None = None, + ) -> FakeBgJob: + self.bg_commands.append((cmd, envs)) + return FakeBgJob(cmd=cmd, envs=envs) + + def write_text(self, path: str, content: str) -> None: + self.written[path] = content + + def read_text(self, path: str) -> str: + if path not in self.written: + raise FileNotFoundError(f"No such file: {path}") + return self.written[path] + + def exists(self, path: str) -> bool: + return path in self.written + + def kill(self) -> None: + self._killed = True + + +class FakeSandboxBackend: + """Backend that returns FakeSandbox instances.""" + + def __init__( + self, + *, + install_check_succeeds: bool = False, + healthz_succeeds: bool = True, + ) -> None: + self._install_check_succeeds = install_check_succeeds + self._healthz_succeeds = healthz_succeeds + self.created: list[FakeSandbox] = [] + + def create( + self, + *, + timeout_s: int = 900, + envs: dict[str, str] | None = None, + metadata: dict[str, str] | None = None, + ) -> SandboxHandle: + sbx = FakeSandbox( + install_check_succeeds=self._install_check_succeeds, + healthz_succeeds=self._healthz_succeeds, + ) + self.created.append(sbx) + return sbx + + +@dataclass +class FakeTask: + instruction: str = "Write hello.py" + setup_shell: str | None = None + upload_files: dict[str, str] = field(default_factory=dict) + metadata: dict[str, Any] = field(default_factory=dict) + + +@dataclass +class FakeConfig: + base_url: str = "https://api.example.com/v1" + api_key: str = "sk-test-key" + model: str = "test-model" + agent_timeout_s: float = 300.0 + sandbox_home: str = "/home/user" + workdir: str | None = None + extra_env: dict[str, str] = field(default_factory=dict) + + +# PR 2.1: Agent Spec and Event Parser Protocols + + +class TestAgentSpecProtocols: + """Tests for base.py data models.""" + + def test_mcp_config_spec_frozen(self): + from openenv.core.harness.agents.base import MCPConfigSpec + + spec = MCPConfigSpec(method="config_file", path_template="{workdir}/mcp.json") + assert spec.method == "config_file" + assert spec.path_template == "{workdir}/mcp.json" + with pytest.raises(AttributeError): + spec.method = "cli_flags" # type: ignore[misc] + + def test_artifact_spec_defaults(self): + from openenv.core.harness.agents.base import ArtifactSpec + + a = ArtifactSpec(path="/logs/agent/out.log") + assert a.format == "text" + assert a.optional is True + + def test_artifact_spec_json(self): + from openenv.core.harness.agents.base import ArtifactSpec + + a = ArtifactSpec(path="/data/traj.json", format="json", optional=False) + assert a.format == "json" + assert a.optional is False + + def test_agent_event_creation(self): + from openenv.core.harness.agents.base import AgentEvent + + e = AgentEvent( + type="tool_call", data={"name": "bash"}, raw='{"type":"tool_call"}' + ) + assert e.type == "tool_call" + assert e.data["name"] == "bash" + + def test_cli_agent_spec_minimal(self): + from openenv.core.harness.agents.base import CLIAgentSpec, MCPConfigSpec + + spec = CLIAgentSpec( + name="test-agent", + install_check_cmd=["test-agent", "--version"], + base_command=["test-agent", "run"], + mcp_config=MCPConfigSpec(method="cli_flags"), + ) + assert spec.name == "test-agent" + assert spec.default_timeout_s == 600.0 + assert spec.setup is None + assert spec.files is None + assert spec.artifacts is None + assert spec.env is None + assert spec.extension_dir_template is None + assert spec.build_command is None + + def test_cli_agent_spec_full(self): + from openenv.core.harness.agents.base import ( + ArtifactSpec, + CLIAgentSpec, + MCPConfigSpec, + ) + + spec = CLIAgentSpec( + name="full-agent", + install_check_cmd=["full-agent", "--version"], + base_command=["full-agent", "exec"], + mcp_config=MCPConfigSpec( + method="config_file", path_template="{workdir}/mcp.json" + ), + default_timeout_s=900.0, + setup="npm install -g full-agent", + files={ + "/task.txt": "hello", + "/dynamic.txt": lambda task, config: task.instruction, + }, + artifacts={ + "log": ArtifactSpec(path="/logs/out.log"), + "traj": ArtifactSpec(path="/logs/traj.json", format="json"), + }, + env={"API_KEY": "{api_key}", "MODEL": "{model}"}, + build_command=lambda spec, config, task, mcp: "full-agent exec", + build_mcp_config=lambda spec, tools, workdir: "{}", + parse_events=lambda line: None, + ) + assert spec.name == "full-agent" + assert spec.artifacts is not None + assert len(spec.artifacts) == 2 + assert spec.files is not None + assert callable(spec.files["/dynamic.txt"]) + + +# PR 2.2: Agent Registry + + +class TestAgentRegistry: + """Tests for the agent registry.""" + + def test_register_and_lookup(self): + from openenv.core.harness.agents import ( + get_agent_spec, + list_agents, + register_agent, + unregister_agent, + ) + from openenv.core.harness.agents.base import CLIAgentSpec, MCPConfigSpec + + spec = CLIAgentSpec( + name="test-registry-agent", + install_check_cmd=["tra", "--version"], + base_command=["tra", "run"], + mcp_config=MCPConfigSpec(method="cli_flags"), + ) + try: + register_agent(spec) + assert "test-registry-agent" in list_agents() + assert get_agent_spec("test-registry-agent") is spec + finally: + unregister_agent("test-registry-agent") + + def test_duplicate_registration_same_object_ok(self): + from openenv.core.harness.agents import register_agent, unregister_agent + from openenv.core.harness.agents.base import CLIAgentSpec, MCPConfigSpec + + spec = CLIAgentSpec( + name="test-dup-ok", + install_check_cmd=["x"], + base_command=["x"], + mcp_config=MCPConfigSpec(method="cli_flags"), + ) + try: + register_agent(spec) + register_agent(spec) # same object — should be fine + finally: + unregister_agent("test-dup-ok") + + def test_duplicate_registration_different_object_raises(self): + from openenv.core.harness.agents import register_agent, unregister_agent + from openenv.core.harness.agents.base import CLIAgentSpec, MCPConfigSpec + + spec1 = CLIAgentSpec( + name="test-dup-fail", + install_check_cmd=["x"], + base_command=["x"], + mcp_config=MCPConfigSpec(method="cli_flags"), + ) + spec2 = CLIAgentSpec( + name="test-dup-fail", + install_check_cmd=["y"], + base_command=["y"], + mcp_config=MCPConfigSpec(method="cli_flags"), + ) + try: + register_agent(spec1) + with pytest.raises(ValueError, match="already registered"): + register_agent(spec2) + finally: + unregister_agent("test-dup-fail") + + def test_unknown_agent_raises_keyerror(self): + from openenv.core.harness.agents import get_agent_spec + + with pytest.raises(KeyError, match="Unknown agent"): + get_agent_spec("nonexistent-agent-xyz") + + def test_unregister_returns_spec(self): + from openenv.core.harness.agents import register_agent, unregister_agent + from openenv.core.harness.agents.base import CLIAgentSpec, MCPConfigSpec + + spec = CLIAgentSpec( + name="test-unreg", + install_check_cmd=["x"], + base_command=["x"], + mcp_config=MCPConfigSpec(method="cli_flags"), + ) + register_agent(spec) + removed = unregister_agent("test-unreg") + assert removed is spec + assert unregister_agent("test-unreg") is None + + def test_auto_import_opencode(self): + """Auto-import triggers registration of built-in agents.""" + from openenv.core.harness.agents import get_agent_spec + + spec = get_agent_spec("opencode") + assert spec.name == "opencode" + + +# PR 2.3: CLIAgentDriver / CLIAgentSession / CLIAgentSessionFactory + + +def _make_test_spec(**overrides: Any): + from openenv.core.harness.agents.base import ( + ArtifactSpec, + CLIAgentSpec, + MCPConfigSpec, + ) + + defaults: dict[str, Any] = dict( + name="test-agent", + install_check_cmd=["test-agent", "--version"], + base_command=["test-agent", "run", "--json"], + mcp_config=MCPConfigSpec( + method="config_file", path_template="{workdir}/mcp.json" + ), + setup="apt-get install -y test-agent", + files={ + "/home/user/task/instruction.txt": lambda task, config: task.instruction, + }, + artifacts={ + "agent_log": ArtifactSpec(path="/home/user/logs/agent.log"), + }, + env={ + "API_KEY": "{api_key}", + "BASE_URL": "{base_url}", + "MODEL": "{model}", + }, + build_command=lambda spec, config, task, mcp: ( + f"test-agent run --json '{task.instruction}' 2>&1 | tee /home/user/logs/agent.log" + ), + build_mcp_config=lambda spec, tools, workdir: json.dumps({"tools": []}), + parse_events=lambda line: None, + ) + defaults.update(overrides) + return CLIAgentSpec(**defaults) + + +class TestCLIAgentDriver: + """Tests for the shared CLI agent driver.""" + + def test_create_session_full_lifecycle(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec() + backend = FakeSandboxBackend() + driver = CLIAgentDriver(spec=spec, sandbox_backend=backend, mode="black_box") + + task = FakeTask(instruction="Write hello.py") + config = FakeConfig() + session = driver.create_session(task=task, config=config) + + # Verify sandbox was created + assert len(backend.created) == 1 + sbx = backend.created[0] + + # Verify sandbox readiness was probed + assert "echo ok" in sbx.executed + + # Verify install was attempted (agent not pre-installed) + assert any("apt-get install" in cmd for cmd in sbx.executed) + + # Verify files were uploaded + assert "/home/user/task/instruction.txt" in sbx.written + assert sbx.written["/home/user/task/instruction.txt"] == "Write hello.py" + + # Verify MCP config was written + assert "/home/user/workdir/mcp.json" in sbx.written + + # Verify agent was launched as bg process + assert len(sbx.bg_commands) == 1 + bg_cmd, bg_envs = sbx.bg_commands[0] + assert "test-agent run" in bg_cmd + + # Verify env vars were resolved + assert bg_envs is not None + assert bg_envs["API_KEY"] == "sk-test-key" + assert bg_envs["BASE_URL"] == "https://api.example.com/v1" + assert bg_envs["MODEL"] == "test-model" + + # Session API + assert session.initial_messages() == [ + {"role": "user", "content": "Write hello.py"} + ] + assert session.list_tools() == [] + assert session.call_tool("x", {}).error is not None + assert session.wait_for_completion() == 0 + + session.close() + assert sbx._killed + + def test_create_session_honors_configured_workdir_for_mcp_file(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec() + backend = FakeSandboxBackend() + driver = CLIAgentDriver(spec=spec, sandbox_backend=backend, mode="black_box") + + config = FakeConfig(workdir="/testbed") + session = driver.create_session(task=FakeTask(), config=config) + + sbx = backend.created[0] + assert "/testbed/mcp.json" in sbx.written + session.close() + + def test_create_session_creates_extension_dir_when_spec_declares_one(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec(extension_dir_template="{home}/.agent/extensions") + backend = FakeSandboxBackend() + driver = CLIAgentDriver(spec=spec, sandbox_backend=backend, mode="black_box") + + session = driver.create_session(task=FakeTask(), config=FakeConfig()) + sbx = backend.created[0] + assert any( + cmd.startswith("mkdir -p /home/user/.agent/extensions") + for cmd in sbx.executed + ) + session.close() + + def test_create_session_skips_install_when_prebaked(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec() + backend = FakeSandboxBackend(install_check_succeeds=True) + driver = CLIAgentDriver(spec=spec, sandbox_backend=backend, mode="black_box") + + session = driver.create_session( + task=FakeTask(), + config=FakeConfig(), + ) + + sbx = backend.created[0] + # install should have been skipped + assert not any("apt-get install" in cmd for cmd in sbx.executed) + session.close() + + def test_create_session_interception_gate_requires_server(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec() + with pytest.raises(ValueError, match="InterceptionServer"): + CLIAgentDriver( + spec=spec, + sandbox_backend=FakeSandboxBackend(), + mode="interception_gate", + ) + + def test_create_session_uploads_task_files(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec() + backend = FakeSandboxBackend() + driver = CLIAgentDriver(spec=spec, sandbox_backend=backend, mode="black_box") + + task = FakeTask( + instruction="Write code", + upload_files={"/extra/data.json": '{"key": "value"}'}, + ) + session = driver.create_session(task=task, config=FakeConfig()) + + sbx = backend.created[0] + assert sbx.written["/extra/data.json"] == '{"key": "value"}' + session.close() + + def test_opencode_black_box_api_key_stays_out_of_command_argv(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + secret = "sk-test '$(leak)" + config = FakeConfig(api_key=secret) + backend = FakeSandboxBackend() + driver = CLIAgentDriver( + spec=OPENCODE_SPEC, + sandbox_backend=backend, + mode="black_box", + ) + + session = driver.create_session(task=FakeTask(), config=config) + sbx = backend.created[0] + cmd, envs = sbx.bg_commands[-1] + assert secret not in cmd + assert envs is not None + assert envs["OPENAI_API_KEY"] == secret + session.close() + + def test_opencode_interception_gate_uses_server_secret_not_user_key(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + from openenv.core.harness.agents.interception_server import InterceptionServer + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + secret = "sk-test '$(leak)" + config = FakeConfig(api_key=secret) + backend = FakeSandboxBackend() + server = InterceptionServer(port=0, secret="gate-secret") + driver = CLIAgentDriver( + spec=OPENCODE_SPEC, + sandbox_backend=backend, + mode="interception_gate", + interception_server=server, + interception_base_url="http://127.0.0.1:8765", + ) + + session = driver.create_session(task=FakeTask(), config=config) + sbx = backend.created[0] + cmd, envs = sbx.bg_commands[-1] + assert secret not in cmd + assert envs is not None + assert envs["OPENAI_API_KEY"] == "gate-secret" + session.close() + + def test_pi_interception_gate_writes_models_json_and_uses_openenv_provider(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + from openenv.core.harness.agents.interception_server import InterceptionServer + from openenv.core.harness.agents.pi import PI_SPEC + + backend = FakeSandboxBackend() + server = InterceptionServer(port=0, secret="gate-secret") + driver = CLIAgentDriver( + spec=PI_SPEC, + sandbox_backend=backend, + mode="interception_gate", + interception_server=server, + interception_base_url="http://127.0.0.1:8765", + ) + + session = driver.create_session(task=FakeTask(), config=FakeConfig()) + sbx = backend.created[0] + + # Command should force the custom provider backed by models.json. + cmd, envs = sbx.bg_commands[-1] + assert "--provider openenv" in cmd + assert envs is not None + assert envs["PI_CODING_AGENT_DIR"] == "/home/user/.pi/agent" + + home_models = "/home/user/.pi/agent/models.json" + root_models = "/root/.pi/agent/models.json" + assert home_models in sbx.written + assert root_models not in sbx.written + + cfg = json.loads(sbx.written[home_models]) + provider = cfg["providers"]["openenv"] + assert provider["api"] == "openai-completions" + assert provider["apiKey"] == "gate-secret" + assert provider["models"][0]["id"] == "test-model" + assert "/rollout/" in provider["baseUrl"] + assert provider["baseUrl"].endswith("/v1") + + session.close() + + def test_pi_interception_gate_uses_explicit_pi_config_dir(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + from openenv.core.harness.agents.interception_server import InterceptionServer + from openenv.core.harness.agents.pi import PI_SPEC + + backend = FakeSandboxBackend() + server = InterceptionServer(port=0, secret="gate-secret") + driver = CLIAgentDriver( + spec=PI_SPEC, + sandbox_backend=backend, + mode="interception_gate", + interception_server=server, + interception_base_url="http://127.0.0.1:8765", + ) + + config = FakeConfig(sandbox_home="/custom/home") + session = driver.create_session(task=FakeTask(), config=config) + sbx = backend.created[0] + + _cmd, envs = sbx.bg_commands[-1] + assert envs is not None + assert envs["PI_CODING_AGENT_DIR"] == "/custom/home/.pi/agent" + assert "/custom/home/.pi/agent/models.json" in sbx.written + assert "/root/.pi/agent/models.json" not in sbx.written + + session.close() + + def test_create_session_runs_task_setup_shell(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec() + backend = FakeSandboxBackend() + driver = CLIAgentDriver(spec=spec, sandbox_backend=backend, mode="black_box") + + task = FakeTask( + instruction="Write code", + setup_shell="pip install pandas", + ) + session = driver.create_session(task=task, config=FakeConfig()) + + sbx = backend.created[0] + assert "pip install pandas" in sbx.executed + session.close() + + def test_create_session_with_verifier(self): + from openenv.core.harness import VerifyResult + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec() + backend = FakeSandboxBackend() + driver = CLIAgentDriver(spec=spec, sandbox_backend=backend, mode="black_box") + + def verifier(sandbox, task): + return VerifyResult(env_reward=1.0, done=True, metrics={"correct": True}) + + session = driver.create_session( + task=FakeTask(), + config=FakeConfig(), + verifier=verifier, + ) + + result = session.verify([]) + assert result.env_reward == 1.0 + assert result.metrics["correct"] is True + session.close() + + def test_session_verify_without_verifier(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec() + backend = FakeSandboxBackend() + driver = CLIAgentDriver(spec=spec, sandbox_backend=backend, mode="black_box") + + session = driver.create_session(task=FakeTask(), config=FakeConfig()) + + result = session.verify([]) + assert result.env_reward is None + assert result.done is True + session.close() + + def test_invalid_mode_raises(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec() + with pytest.raises(ValueError, match="Unknown mode"): + CLIAgentDriver( + spec=spec, + sandbox_backend=FakeSandboxBackend(), + mode="invalid", # type: ignore[arg-type] + ) + + +class TestCLIAgentSession: + """Tests for CLIAgentSession.""" + + def test_collect_artifacts_text(self): + from openenv.core.harness.agents.base import ArtifactSpec + from openenv.core.harness.agents.cli_driver import CLIAgentSession + + spec = _make_test_spec( + artifacts={ + "log": ArtifactSpec(path="/logs/out.log"), + }, + ) + sbx = FakeSandbox() + sbx.written["/logs/out.log"] = "line1\nline2\n" + + session = CLIAgentSession( + spec=spec, + sandbox=sbx, + task=FakeTask(), + config=FakeConfig(), + ) + arts = session.collect_artifacts() + assert arts["log"] == "line1\nline2\n" + + def test_collect_artifacts_json(self): + from openenv.core.harness.agents.base import ArtifactSpec + from openenv.core.harness.agents.cli_driver import CLIAgentSession + + spec = _make_test_spec( + artifacts={ + "traj": ArtifactSpec(path="/logs/traj.json", format="json"), + }, + ) + sbx = FakeSandbox() + sbx.written["/logs/traj.json"] = json.dumps({"steps": [1, 2, 3]}) + + session = CLIAgentSession( + spec=spec, + sandbox=sbx, + task=FakeTask(), + config=FakeConfig(), + ) + arts = session.collect_artifacts() + assert arts["traj"] == {"steps": [1, 2, 3]} + + def test_collect_artifacts_jsonl(self): + from openenv.core.harness.agents.base import ArtifactSpec + from openenv.core.harness.agents.cli_driver import CLIAgentSession + + spec = _make_test_spec( + artifacts={ + "events": ArtifactSpec(path="/logs/events.jsonl", format="jsonl"), + }, + ) + sbx = FakeSandbox() + sbx.written["/logs/events.jsonl"] = '{"a":1}\n{"b":2}\n' + + session = CLIAgentSession( + spec=spec, + sandbox=sbx, + task=FakeTask(), + config=FakeConfig(), + ) + arts = session.collect_artifacts() + assert arts["events"] == [{"a": 1}, {"b": 2}] + + def test_collect_artifacts_missing_optional(self): + from openenv.core.harness.agents.base import ArtifactSpec + from openenv.core.harness.agents.cli_driver import CLIAgentSession + + spec = _make_test_spec( + artifacts={ + "log": ArtifactSpec(path="/missing/file.log", optional=True), + }, + ) + sbx = FakeSandbox() + session = CLIAgentSession( + spec=spec, + sandbox=sbx, + task=FakeTask(), + config=FakeConfig(), + ) + arts = session.collect_artifacts() + assert "log" not in arts + + def test_collect_artifacts_missing_required_raises(self): + from openenv.core.harness.agents.base import ArtifactSpec + from openenv.core.harness.agents.cli_driver import CLIAgentSession + + spec = _make_test_spec( + artifacts={ + "log": ArtifactSpec(path="/missing/file.log", optional=False), + }, + ) + sbx = FakeSandbox() + session = CLIAgentSession( + spec=spec, + sandbox=sbx, + task=FakeTask(), + config=FakeConfig(), + ) + with pytest.raises(FileNotFoundError): + session.collect_artifacts() + + def test_close_kills_sandbox_and_jobs(self): + from openenv.core.harness.agents.cli_driver import CLIAgentSession + + spec = _make_test_spec() + sbx = FakeSandbox() + agent_job = FakeBgJob() + + session = CLIAgentSession( + spec=spec, + sandbox=sbx, + task=FakeTask(), + config=FakeConfig(), + agent_bg_job=agent_job, + ) + session.close() + assert sbx._killed + assert session._agent_bg_job is None + + @pytest.mark.asyncio + async def test_next_request_handles_missing_intercept_without_keyerror(self): + from openenv.core.harness.agents.cli_driver import CLIAgentSession + from openenv.core.harness.agents.interception_server import InterceptionServer + + spec = _make_test_spec() + sbx = FakeSandbox() + q: _queue_mod.Queue[str] = _queue_mod.Queue() + q.put("req_missing") + + session = CLIAgentSession( + spec=spec, + sandbox=sbx, + task=FakeTask(), + config=FakeConfig(), + agent_bg_job=FakeBgJob(), + interception_server=InterceptionServer(secret="s"), + interception_rollout_id="rollout-1", + interception_queue=q, + ) + + # Missing request IDs can happen if unregister_rollout races with queue.get(). + assert await session.next_request(timeout_s=0.2) is None + + def test_next_request_soak_cross_loop_queue_get(self): + """Soak test cross-loop request dequeueing via queue.Queue. + + Exercises the worker pattern that used to be unsafe with asyncio.Queue: + repeatedly call next_request() from fresh event loops (asyncio.run) + while request IDs are pushed from another thread. + """ + from openenv.core.harness.agents.cli_driver import CLIAgentSession + from openenv.core.harness.agents.interception_server import InterceptionServer + + spec = _make_test_spec() + sbx = FakeSandbox() + server = InterceptionServer(secret="s") + request_queue = server.register_rollout("rollout-soak") + + session = CLIAgentSession( + spec=spec, + sandbox=sbx, + task=FakeTask(), + config=FakeConfig(), + interception_server=server, + interception_rollout_id="rollout-soak", + interception_queue=request_queue, + ) + + total_requests = 200 + consumed: list[str] = [] + failures: list[BaseException] = [] + + def _consumer() -> None: + try: + for _ in range(total_requests): + intercept = asyncio.run(session.next_request(timeout_s=2.0)) + assert intercept is not None + request_id = intercept["request_id"] + consumed.append(request_id) + with server._state_lock: + server.intercepts.pop(request_id, None) + except BaseException as exc: # pragma: no cover - assertion path + failures.append(exc) + + def _producer() -> None: + try: + for i in range(total_requests): + request_id = f"req_soak_{i:04d}" + with server._state_lock: + server.intercepts[request_id] = { + "request_id": request_id, + "messages": [{"role": "user", "content": "ping"}], + } + request_queue.put_nowait(request_id) + if i % 10 == 0: + time.sleep(0.001) + except BaseException as exc: # pragma: no cover - unexpected + failures.append(exc) + + consumer_t = threading.Thread(target=_consumer, name="soak-consumer") + producer_t = threading.Thread(target=_producer, name="soak-producer") + + consumer_t.start() + producer_t.start() + + producer_t.join(timeout=10) + consumer_t.join(timeout=15) + + assert not producer_t.is_alive(), "producer thread hung" + assert not consumer_t.is_alive(), "consumer thread hung" + assert not failures + assert len(consumed) == total_requests + assert len(set(consumed)) == total_requests + + session.close() + + +class TestCLIAgentSessionFactory: + """Tests for the ResourceSessionFactory wrapper.""" + + def test_factory_creates_sessions(self): + from openenv.core.harness.agents.cli_driver import CLIAgentSessionFactory + + spec = _make_test_spec() + backend = FakeSandboxBackend() + + factory = CLIAgentSessionFactory( + spec=spec, + config=FakeConfig(), + sandbox_backend=backend, + mode="black_box", + ) + + session = factory.create(task=FakeTask()) + assert len(backend.created) == 1 + assert session.initial_messages()[0]["content"] == "Write hello.py" + session.close() + + def test_factory_with_verifier(self): + from openenv.core.harness import VerifyResult + from openenv.core.harness.agents.cli_driver import CLIAgentSessionFactory + + spec = _make_test_spec() + backend = FakeSandboxBackend() + + def verifier(sandbox, task): + return VerifyResult(env_reward=0.5, done=True) + + factory = CLIAgentSessionFactory( + spec=spec, + config=FakeConfig(), + sandbox_backend=backend, + mode="black_box", + verifier=verifier, + ) + + session = factory.create(task=FakeTask()) + result = session.verify([]) + assert result.env_reward == 0.5 + session.close() + + def test_factory_implements_resource_session_factory(self): + from openenv.core.harness import ResourceSessionFactory + from openenv.core.harness.agents.cli_driver import CLIAgentSessionFactory + + assert issubclass(CLIAgentSessionFactory, ResourceSessionFactory) + + def test_session_implements_resource_session(self): + from openenv.core.harness import ResourceSession + from openenv.core.harness.agents.cli_driver import CLIAgentSession + + assert issubclass(CLIAgentSession, ResourceSession) + + +# PR 2.4: OpenCode Adapter Spec + + +class TestOpenCodeSpec: + """Tests for the OpenCode declarative spec.""" + + def test_spec_is_registered(self): + from openenv.core.harness.agents import get_agent_spec + + spec = get_agent_spec("opencode") + assert spec.name == "opencode" + + def test_spec_fields(self): + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + assert OPENCODE_SPEC.name == "opencode" + assert OPENCODE_SPEC.install_check_cmd == [ + "/home/user/.opencode/bin/opencode", + "--version", + ] + assert OPENCODE_SPEC.default_timeout_s == 900.0 + assert OPENCODE_SPEC.mcp_config.method == "config_file" + assert OPENCODE_SPEC.mcp_config.path_template is not None + assert "{home}" in OPENCODE_SPEC.mcp_config.path_template + assert OPENCODE_SPEC.artifacts is not None + assert "agent_log" in OPENCODE_SPEC.artifacts + assert OPENCODE_SPEC.artifacts["agent_log"].format == "jsonl" + + def test_build_command(self): + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + @dataclass + class OcConfig: + sandbox_home: str = "/home/user" + run_format: str = "json" + + assert OPENCODE_SPEC.build_command is not None + cmd = OPENCODE_SPEC.build_command( + OPENCODE_SPEC, + OcConfig(), + FakeTask(instruction="Write hello.py"), + None, + ) + assert "opencode run" in cmd + assert "--format json" in cmd + assert "/home/user/task/instruction.md" in cmd + + def test_build_command_quotes_paths(self): + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + @dataclass + class OcConfig: + sandbox_home: str = "/home/user with space" + run_format: str = "json" + + assert OPENCODE_SPEC.build_command is not None + cmd = OPENCODE_SPEC.build_command( + OPENCODE_SPEC, + OcConfig(), + FakeTask(instruction="Write hello.py"), + None, + ) + assert "cd '/home/user with space/workdir'" in cmd + assert "cat '/home/user with space/task/instruction.md'" in cmd + assert "tee '/home/user with space/logs/agent/opencode.jsonl'" in cmd + + def test_build_mcp_config(self): + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + assert OPENCODE_SPEC.build_mcp_config is not None + config_str = OPENCODE_SPEC.build_mcp_config( + OPENCODE_SPEC, + [], + "/home/user/workdir", + ) + # OpenCode returns empty string because the config is written + # via spec.files using _build_opencode_config_file instead. + assert config_str == "" + + def test_parse_events_assistant(self): + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + assert OPENCODE_SPEC.parse_events is not None + line = json.dumps({"type": "assistant", "content": "hello"}) + event = OPENCODE_SPEC.parse_events(line) + assert event is not None + assert event.type == "assistant" + + def test_parse_events_tool_call(self): + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + assert OPENCODE_SPEC.parse_events is not None + line = json.dumps({"type": "tool_call", "name": "bash", "args": {}}) + event = OPENCODE_SPEC.parse_events(line) + assert event is not None + assert event.type == "tool_call" + + def test_parse_events_error(self): + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + assert OPENCODE_SPEC.parse_events is not None + line = json.dumps({"type": "error", "message": "boom"}) + event = OPENCODE_SPEC.parse_events(line) + assert event is not None + assert event.type == "error" + + def test_parse_events_done(self): + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + assert OPENCODE_SPEC.parse_events is not None + line = json.dumps({"type": "done"}) + event = OPENCODE_SPEC.parse_events(line) + assert event is not None + assert event.type == "done" + + def test_parse_events_invalid_json(self): + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + assert OPENCODE_SPEC.parse_events is not None + assert OPENCODE_SPEC.parse_events("not json") is None + assert OPENCODE_SPEC.parse_events("") is None + + def test_build_env_vars(self): + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + config = FakeConfig() + config.extra_env = {"EXTRA": "val"} + assert OPENCODE_SPEC.build_env_vars is not None + envs = OPENCODE_SPEC.build_env_vars(OPENCODE_SPEC, config) + assert envs["OPENAI_BASE_URL"] == "https://api.example.com/v1" + assert envs["OPENAI_API_KEY"] == "sk-test-key" + assert envs["OPENCODE_CONFIG"] == "/home/user/.config/opencode/opencode.json" + assert envs["EXTRA"] == "val" + + def test_files_instruction_resolver(self): + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + task = FakeTask(instruction="Build a REST API") + config = FakeConfig() + assert OPENCODE_SPEC.files is not None + instruction_fn = OPENCODE_SPEC.files["/home/user/task/instruction.md"] + assert callable(instruction_fn) + assert instruction_fn(task, config) == "Build a REST API" + + def test_files_system_prompt_resolver(self): + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + task = FakeTask() + config = FakeConfig() + assert OPENCODE_SPEC.files is not None + system_fn = OPENCODE_SPEC.files["/home/user/task/system.md"] + assert callable(system_fn) + # No system prompt on FakeConfig → returns None + assert system_fn(task, config) is None + + def test_opencode_driver_integration(self): + """End-to-end: create a session using the OpenCode spec via the driver.""" + from openenv.core.harness.agents.cli_driver import CLIAgentSessionFactory + from openenv.core.harness.agents.opencode import OPENCODE_SPEC + + backend = FakeSandboxBackend() + factory = CLIAgentSessionFactory( + spec=OPENCODE_SPEC, + config=FakeConfig(), + sandbox_backend=backend, + mode="black_box", + ) + + session = factory.create(task=FakeTask(instruction="Hello")) + assert session.spec.name == "opencode" + assert session.initial_messages()[0]["content"] == "Hello" + + sbx = backend.created[0] + # Instruction file should have been written + assert sbx.written.get("/home/user/task/instruction.md") == "Hello" + + session.close() + + +class TestPiSpec: + def test_build_command_quotes_paths(self): + from openenv.core.harness.agents.pi import PI_SPEC + + @dataclass + class PiConfig: + sandbox_home: str = "/home/user with space" + provider: str = "openai" + model: str = "model/name" + thinking: str = "off" + + assert PI_SPEC.build_command is not None + cmd = PI_SPEC.build_command( + PI_SPEC, + PiConfig(), + FakeTask(instruction="Write hello.py"), + None, + ) + assert "cd '/home/user with space/workdir'" in cmd + assert "-p @'/home/user with space/task/instruction.txt'" in cmd + assert "tee '/home/user with space/logs/agent/pi.txt'" in cmd + + def test_build_command_uses_config_workdir_when_present(self): + from openenv.core.harness.agents.pi import PI_SPEC + + @dataclass + class PiConfig: + sandbox_home: str = "/home/user" + workdir: str = "/testbed" + provider: str = "openai" + model: str = "model/name" + thinking: str = "off" + + assert PI_SPEC.build_command is not None + cmd = PI_SPEC.build_command( + PI_SPEC, + PiConfig(), + FakeTask(instruction="Write hello.py"), + None, + ) + assert "cd /testbed" in cmd + + def test_spec_declares_extension_dir_template(self): + from openenv.core.harness.agents.pi import PI_SPEC + + assert PI_SPEC.extension_dir_template == "{home}/.pi/agent/extensions" + + +# Env var resolution + + +class TestEnvVarResolution: + """Tests for environment variable placeholder resolution.""" + + def test_resolve_placeholders(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec( + env={ + "KEY": "{api_key}", + "URL": "{base_url}", + "MDL": "{model}", + "STATIC": "fixed_value", + }, + build_env_vars=None, # use placeholder resolution + ) + driver = CLIAgentDriver( + spec=spec, + sandbox_backend=FakeSandboxBackend(), + mode="black_box", + ) + envs = driver._resolve_env_vars(FakeConfig()) + assert envs["KEY"] == "sk-test-key" + assert envs["URL"] == "https://api.example.com/v1" + assert envs["MDL"] == "test-model" + assert envs["STATIC"] == "fixed_value" + + def test_resolve_with_proxy_override(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec( + env={"URL": "{base_url}"}, + build_env_vars=None, + ) + driver = CLIAgentDriver( + spec=spec, + sandbox_backend=FakeSandboxBackend(), + mode="black_box", + ) + envs = driver._resolve_env_vars( + FakeConfig(), + base_url_override="http://127.0.0.1:7000/v1", + ) + assert envs["URL"] == "http://127.0.0.1:7000/v1" + + def test_build_env_vars_hook_takes_precedence(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + def custom_env(spec, config): + return {"CUSTOM": "yes", "MODEL": config.model} + + spec = _make_test_spec( + env={"SHOULD_NOT": "appear"}, + build_env_vars=custom_env, + ) + driver = CLIAgentDriver( + spec=spec, + sandbox_backend=FakeSandboxBackend(), + mode="black_box", + ) + envs = driver._resolve_env_vars(FakeConfig()) + assert envs == {"CUSTOM": "yes", "MODEL": "test-model"} + assert "SHOULD_NOT" not in envs + + def test_empty_env_dict(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec(env=None, build_env_vars=None) + driver = CLIAgentDriver( + spec=spec, + sandbox_backend=FakeSandboxBackend(), + mode="black_box", + ) + envs = driver._resolve_env_vars(FakeConfig()) + assert envs == {} + + +# Multiple setup commands + + +class TestMultiStepSetup: + """Tests for specs with multi-step setup commands.""" + + def test_list_of_setup_commands(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec( + setup=[ + "apt-get update", + "apt-get install -y nodejs", + "npm install -g test-agent", + ], + ) + backend = FakeSandboxBackend() + driver = CLIAgentDriver(spec=spec, sandbox_backend=backend, mode="black_box") + + session = driver.create_session(task=FakeTask(), config=FakeConfig()) + sbx = backend.created[0] + + # All three setup commands should have been executed + assert any("apt-get update" in cmd for cmd in sbx.executed) + assert any("apt-get install" in cmd for cmd in sbx.executed) + assert any("npm install" in cmd for cmd in sbx.executed) + session.close() + + def test_no_setup_and_not_installed_raises(self): + from openenv.core.harness.agents.cli_driver import CLIAgentDriver + + spec = _make_test_spec(setup=None) + backend = FakeSandboxBackend(install_check_succeeds=False) + driver = CLIAgentDriver(spec=spec, sandbox_backend=backend, mode="black_box") + + with pytest.raises(RuntimeError, match="not installed"): + driver.create_session(task=FakeTask(), config=FakeConfig()) diff --git a/tests/core/test_docker_sandbox_backend.py b/tests/core/test_docker_sandbox_backend.py new file mode 100644 index 000000000..c309e63b3 --- /dev/null +++ b/tests/core/test_docker_sandbox_backend.py @@ -0,0 +1,366 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Tests for the Docker sandbox backend. + +Tests marked ``@pytest.mark.docker`` require a running Docker daemon and +are skipped in CI when Docker is unavailable. They exercise the real +``docker run`` / ``docker exec`` / ``docker rm`` lifecycle. +""" + +from __future__ import annotations + +import subprocess +import time + +import pytest + +_DOCKER_AVAILABLE = False +try: + subprocess.run( + ["docker", "version"], + capture_output=True, + check=True, + timeout=5, + ) + _DOCKER_AVAILABLE = True +except Exception: + pass + +docker = pytest.mark.skipif(not _DOCKER_AVAILABLE, reason="Docker not available") + + +class TestDockerSandboxBackendUnit: + """Unit tests that don't require Docker.""" + + def test_import(self): + from openenv.core.harness.sandbox.docker_backend import ( + DockerBgJob, + DockerSandboxBackend, + DockerSandboxHandle, + ) + + assert DockerSandboxBackend is not None + assert DockerSandboxHandle is not None + assert DockerBgJob is not None + + def test_exported_from_package(self): + from openenv.core.harness.sandbox import ( + DockerBgJob, + DockerSandboxBackend, + DockerSandboxHandle, + ) + + assert DockerSandboxBackend is not None + assert DockerSandboxHandle is not None + assert DockerBgJob is not None + + def test_create_sandbox_backend_factory(self): + from openenv.core.harness.sandbox import create_sandbox_backend + + assert callable(create_sandbox_backend) + + def test_create_sandbox_backend_unknown_raises(self): + from openenv.core.harness.sandbox import create_sandbox_backend + + with pytest.raises(ValueError, match="Unknown sandbox backend"): + create_sandbox_backend("bogus") # type: ignore[arg-type] + + def test_create_adds_host_gateway_and_supports_image_override(self, monkeypatch): + import openenv.core.harness.sandbox.docker_backend as docker_backend + + calls: list[list[str]] = [] + + def _fake_run(cmd, *args, **kwargs): + calls.append(list(cmd)) + if cmd[:2] == ["docker", "version"]: + return subprocess.CompletedProcess(cmd, 0, "", "") + if cmd[:2] == ["docker", "run"]: + return subprocess.CompletedProcess( + cmd, + 0, + "1234567890abcdef\n", + "", + ) + return subprocess.CompletedProcess(cmd, 0, "", "") + + monkeypatch.setattr(docker_backend.subprocess, "run", _fake_run) + + backend = docker_backend.DockerSandboxBackend(image="base:latest") + handle = backend.create(image="override:latest") + assert handle.sandbox_id == "1234567890ab" + + run_cmds = [cmd for cmd in calls if cmd[:2] == ["docker", "run"]] + assert len(run_cmds) == 1 + run_cmd = run_cmds[0] + assert "--add-host" in run_cmd + assert "host.docker.internal:host-gateway" in run_cmd + assert "override:latest" in run_cmd + + @pytest.mark.skipif(_DOCKER_AVAILABLE, reason="Only test error when Docker missing") + def test_backend_raises_without_docker(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + with pytest.raises(RuntimeError, match="Docker daemon"): + DockerSandboxBackend() + + +@docker +class TestDockerSandboxBackendIntegration: + """Integration tests against a real Docker daemon.""" + + def test_create_and_kill(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + assert sandbox.sandbox_id + assert len(sandbox.sandbox_id) == 12 + finally: + sandbox.kill() + + def test_exec_echo(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + result = sandbox.exec("echo hello world") + assert result.exit_code == 0 + assert "hello world" in result.stdout + finally: + sandbox.kill() + + def test_exec_nonzero_exit(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + result = sandbox.exec("exit 42") + assert result.exit_code == 42 + finally: + sandbox.kill() + + def test_exec_with_env(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + result = sandbox.exec("echo $MY_VAR", envs={"MY_VAR": "test123"}) + assert result.exit_code == 0 + assert "test123" in result.stdout + finally: + sandbox.kill() + + def test_exec_with_cwd(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + result = sandbox.exec("pwd", cwd="/tmp") + assert result.exit_code == 0 + assert "/tmp" in result.stdout + finally: + sandbox.kill() + + def test_write_and_read_text(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + sandbox.write_text("/tmp/test.txt", "hello from test") + content = sandbox.read_text("/tmp/test.txt") + assert content == "hello from test" + finally: + sandbox.kill() + + def test_write_creates_parent_dirs(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + sandbox.write_text("/home/user/deep/nested/file.txt", "nested content") + content = sandbox.read_text("/home/user/deep/nested/file.txt") + assert content == "nested content" + finally: + sandbox.kill() + + def test_write_special_chars(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + text = "line1\nline2\n'quotes' and \"doubles\" and $vars" + sandbox.write_text("/tmp/special.txt", text) + content = sandbox.read_text("/tmp/special.txt") + assert content == text + finally: + sandbox.kill() + + def test_read_missing_file_raises(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + with pytest.raises(FileNotFoundError): + sandbox.read_text("/nonexistent/path.txt") + finally: + sandbox.kill() + + def test_exists(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + assert not sandbox.exists("/tmp/check_me.txt") + sandbox.write_text("/tmp/check_me.txt", "exists") + assert sandbox.exists("/tmp/check_me.txt") + finally: + sandbox.kill() + + def test_start_bg_and_wait(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + job = sandbox.start_bg("sleep 1 && echo done > /tmp/bg_out.txt") + exit_code = job.wait(timeout=10) + assert exit_code == 0 + content = sandbox.read_text("/tmp/bg_out.txt") + assert "done" in content + finally: + sandbox.kill() + + def test_start_bg_kill(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + job = sandbox.start_bg("sleep 300") + time.sleep(0.5) + job.kill() + # Should be able to wait without hanging + exit_code = job.wait(timeout=5) + # Exit code after kill is implementation-defined + assert isinstance(exit_code, int) + finally: + sandbox.kill() + + def test_start_bg_timeout(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + job = sandbox.start_bg("sleep 300") + with pytest.raises(TimeoutError): + job.wait(timeout=1) + job.kill() + finally: + sandbox.kill() + + def test_create_with_envs(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60, envs={"INIT_VAR": "from_create"}) + try: + result = sandbox.exec("echo $INIT_VAR") + assert "from_create" in result.stdout + finally: + sandbox.kill() + + def test_create_with_metadata(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create( + timeout_s=60, + metadata={"episode_id": "ep-123"}, + ) + try: + result = subprocess.run( + [ + "docker", + "inspect", + "--format", + '{{index .Config.Labels "openenv.episode_id"}}', + sandbox._container_id, + ], + capture_output=True, + text=True, + ) + assert "ep-123" in result.stdout + finally: + sandbox.kill() + + def test_factory_creates_docker_backend(self): + from openenv.core.harness.sandbox import create_sandbox_backend + + backend = create_sandbox_backend("docker", image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + result = sandbox.exec("echo ok") + assert result.exit_code == 0 + finally: + sandbox.kill() + + def test_satisfies_sandbox_handle_protocol(self): + from openenv.core.harness.sandbox import SandboxHandle + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + assert isinstance(sandbox, SandboxHandle) + assert hasattr(sandbox, "sandbox_id") + assert hasattr(sandbox, "exec") + assert hasattr(sandbox, "start_bg") + assert hasattr(sandbox, "write_text") + assert hasattr(sandbox, "read_text") + assert hasattr(sandbox, "exists") + assert hasattr(sandbox, "kill") + finally: + sandbox.kill() + + def test_satisfies_sandbox_backend_protocol(self): + from openenv.core.harness.sandbox import SandboxBackend + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + assert issubclass(DockerSandboxBackend, SandboxBackend) + + def test_satisfies_bg_job_protocol(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + try: + job = sandbox.start_bg("sleep 1") + assert hasattr(job, "pid") + assert hasattr(job, "wait") + assert hasattr(job, "kill") + job.kill() + finally: + sandbox.kill() + + def test_kill_is_idempotent(self): + from openenv.core.harness.sandbox.docker_backend import DockerSandboxBackend + + backend = DockerSandboxBackend(image="ubuntu:22.04") + sandbox = backend.create(timeout_s=60) + sandbox.kill() + sandbox.kill() # should not raise diff --git a/tests/core/test_harness_adapters.py b/tests/core/test_harness_adapters.py new file mode 100644 index 000000000..1766b8ad4 --- /dev/null +++ b/tests/core/test_harness_adapters.py @@ -0,0 +1,125 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Tests for currently implemented harness adapters (OpenCode + Pi).""" + +from __future__ import annotations + +import json +from dataclasses import dataclass, field +from typing import Any + +import pytest + + +@dataclass +class FakeTask: + instruction: str = "Write hello.py" + setup_shell: str | None = None + upload_files: dict[str, str] = field(default_factory=dict) + metadata: dict[str, Any] = field(default_factory=dict) + + +@dataclass +class FakeConfig: + base_url: str = "https://api.example.com/v1" + api_key: str = "sk-test" + model: str = "test-model" + agent_timeout_s: float = 300.0 + sandbox_home: str = "/home/user" + system_prompt: str | None = None + + +class TestPiSpec: + def test_registered(self): + from openenv.core.harness.agents import get_agent_spec + + spec = get_agent_spec("pi") + assert spec.name == "pi" + + def test_fields(self): + from openenv.core.harness.agents.pi import PI_SPEC + + assert PI_SPEC.install_check_cmd == ["pi", "--version"] + assert PI_SPEC.mcp_config.method == "config_file" + assert PI_SPEC.mcp_config.path_template is not None + assert ".mcp.json" in PI_SPEC.mcp_config.path_template + assert PI_SPEC.build_env_vars is not None + + def test_build_env_vars_provider_specific_api_key(self): + from openenv.core.harness.agents.pi import PI_SPEC + + @dataclass + class PiConfig: + provider: str + api_key: str = "secret" + base_url: str = "https://api.example.com/v1" + extra_env: dict[str, str] = field(default_factory=dict) + + assert PI_SPEC.build_env_vars is not None + + hf_env = PI_SPEC.build_env_vars(PI_SPEC, PiConfig(provider="huggingface")) + assert hf_env["HF_TOKEN"] == "secret" + assert "OPENAI_API_KEY" not in hf_env + + oa_env = PI_SPEC.build_env_vars(PI_SPEC, PiConfig(provider="openai")) + assert oa_env["OPENAI_API_KEY"] == "secret" + assert "HF_TOKEN" not in oa_env + + def test_build_env_vars_rejects_unknown_provider(self): + from openenv.core.harness.agents.pi import PI_SPEC + + @dataclass + class PiConfig: + provider: str = "unknown" + api_key: str = "secret" + base_url: str = "https://api.example.com/v1" + extra_env: dict[str, str] = field(default_factory=dict) + + assert PI_SPEC.build_env_vars is not None + with pytest.raises(ValueError, match="Unsupported pi provider"): + PI_SPEC.build_env_vars(PI_SPEC, PiConfig()) + + def test_build_command(self): + from openenv.core.harness.agents.pi import PI_SPEC + + assert PI_SPEC.build_command is not None + cmd = PI_SPEC.build_command(PI_SPEC, FakeConfig(), FakeTask(), None) + assert "pi --no-session" in cmd + assert "--no-context-files" in cmd + + def test_build_mcp_config(self): + from openenv.core.harness.agents.pi import PI_SPEC + + assert PI_SPEC.build_mcp_config is not None + content = PI_SPEC.build_mcp_config(PI_SPEC, [], "/workdir") + assert "mcpServers" in json.loads(content) + + +class TestOpenCodeSpec: + def test_registered(self): + from openenv.core.harness.agents import get_agent_spec + + spec = get_agent_spec("opencode") + assert spec.name == "opencode" + + +class TestRegistryAutoImport: + @pytest.mark.parametrize("name", ["pi", "opencode"]) + def test_auto_import(self, name): + from openenv.core.harness.agents import get_agent_spec + + spec = get_agent_spec(name) + assert spec.name == name + + def test_list_agents_includes_current(self): + import openenv.core.harness.agents.opencode # noqa: F401 + import openenv.core.harness.agents.pi # noqa: F401 + from openenv.core.harness.agents import list_agents + + agents = list_agents() + for name in ["opencode", "pi"]: + assert name in agents, f"{name} not in {agents}" diff --git a/tests/core/test_hf_sandbox_backend.py b/tests/core/test_hf_sandbox_backend.py new file mode 100644 index 000000000..6ad8e3fb2 --- /dev/null +++ b/tests/core/test_hf_sandbox_backend.py @@ -0,0 +1,231 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Unit tests for the HF sandbox backend. + +These tests mock ``hf-sandbox`` so they run without network or HF credentials. +""" + +from __future__ import annotations + +import importlib +import re +import subprocess +import sys +import types +from dataclasses import dataclass, field + +import pytest + + +@dataclass +class _FakeSandbox: + job_id: str + files: dict[str, str] = field(default_factory=dict) + marker_files: dict[str, str] = field(default_factory=dict) + bg_jobs: dict[int, dict] = field(default_factory=dict) + next_pid: int = 1000 + terminated: bool = False + + def exec( + self, + *cmd: str, + workdir: str | None = None, + stdin: str | None = None, + timeout: int = 600, + ) -> subprocess.CompletedProcess: + del workdir, stdin, timeout + if len(cmd) < 3: + return subprocess.CompletedProcess(cmd, 1, "", "invalid command") + script = cmd[2] + + if "ok_cmd" in script: + return subprocess.CompletedProcess(cmd, 0, "ok\n", "") + if "fail_cmd" in script: + return subprocess.CompletedProcess(cmd, 42, "", "failed") + if "timeout_cmd" in script: + return subprocess.CompletedProcess(cmd, -1, "", "timeout") + + if "mkdir -p" in script: + return subprocess.CompletedProcess(cmd, 0, "", "") + + if "test -e " in script: + match = re.search(r"test -e '([^']+)'", script) + assert match is not None + path = match.group(1) + exists = path in self.files or path in self.marker_files + return subprocess.CompletedProcess(cmd, 0 if exists else 1, "", "") + + if "cat '/tmp/.openenv_bg_" in script: + match = re.search(r"cat '([^']+)'", script) + assert match is not None + marker = match.group(1) + if marker in self.marker_files: + return subprocess.CompletedProcess( + cmd, + 0, + f"{self.marker_files[marker]}\n", + "", + ) + return subprocess.CompletedProcess(cmd, 1, "", "missing") + + if script.strip().startswith("kill -0 "): + pid = int(script.strip().split()[2]) + alive = self.bg_jobs.get(pid, {}).get("alive", False) + return subprocess.CompletedProcess(cmd, 0 if alive else 1, "", "") + + if script.strip().startswith("kill -9 "): + pid = int(script.strip().split()[2]) + if pid in self.bg_jobs: + self.bg_jobs[pid]["alive"] = False + marker = self.bg_jobs[pid]["marker"] + self.marker_files[marker] = "137" + return subprocess.CompletedProcess(cmd, 0, "", "") + + if "echo $!" in script: + marker_match = re.search(r"(/tmp/\.openenv_bg_[A-Za-z0-9]+\.exit)", script) + assert marker_match is not None + marker = marker_match.group(1) + pid = self.next_pid + self.next_pid += 1 + long_running = "sleep 300" in script + self.bg_jobs[pid] = { + "marker": marker, + "alive": long_running, + } + if not long_running: + self.marker_files[marker] = "0" + return subprocess.CompletedProcess(cmd, 0, f"{pid}\n", "") + + return subprocess.CompletedProcess(cmd, 0, "", "") + + def write_file( + self, + path: str, + content: str | bytes | bytearray | memoryview, + ) -> None: + if isinstance(content, str): + normalized = content + else: + normalized = bytes(content).decode("utf-8", "replace") + self.files[path] = normalized + + def read_file(self, path: str, text: bool = True) -> str | bytes: + if path not in self.files: + raise FileNotFoundError(path) + return self.files[path] if text else self.files[path].encode() + + def terminate(self) -> None: + self.terminated = True + + +class _FakeSandboxAPI: + calls: list[dict] = [] + + @classmethod + def create( + cls, + image: str, + flavor: str, + timeout: str, + forward_hf_token: bool, + ) -> _FakeSandbox: + cls.calls.append( + { + "image": image, + "flavor": flavor, + "timeout": timeout, + "forward_hf_token": forward_hf_token, + } + ) + return _FakeSandbox(job_id="job-123") + + +def _install_fake_hf_sandbox(monkeypatch) -> None: + fake_module = types.ModuleType("hf_sandbox") + setattr(fake_module, "Sandbox", _FakeSandboxAPI) + monkeypatch.setitem(sys.modules, "hf_sandbox", fake_module) + + +@pytest.fixture(autouse=True) +def _reset_fake_hf_calls() -> None: + _FakeSandboxAPI.calls.clear() + + +class TestHFSandboxBackend: + def test_exported_from_package(self, monkeypatch): + _install_fake_hf_sandbox(monkeypatch) + + import openenv.core.harness.sandbox as sandbox_pkg + + importlib.reload(sandbox_pkg) + assert hasattr(sandbox_pkg, "HFSandboxBackend") + assert hasattr(sandbox_pkg, "HFSandboxHandle") + assert hasattr(sandbox_pkg, "HFBgJob") + + def test_create_exec_write_read_exists_bg_and_kill(self, monkeypatch): + import openenv.core.harness.sandbox.hf_backend as hf_backend + + _install_fake_hf_sandbox(monkeypatch) + importlib.reload(hf_backend) + + monkeypatch.setattr(hf_backend, "Sandbox", _FakeSandboxAPI) + + backend = hf_backend.HFSandboxBackend( + image="python:3.12", + flavor="cpu-basic", + forward_hf_token=True, + ) + sandbox = backend.create(timeout_s=120, envs={"GLOBAL_ENV": "on"}) + + assert sandbox.sandbox_id == "job-123" + assert _FakeSandboxAPI.calls[-1]["timeout"] == "2m" + + ok = sandbox.exec("ok_cmd") + assert ok.exit_code == 0 + + failed = sandbox.exec("fail_cmd") + assert failed.exit_code == 42 + + timed = sandbox.exec("timeout_cmd") + assert timed.exit_code == -1 + + sandbox.write_text("/tmp/hello.txt", "hello") + assert sandbox.exists("/tmp/hello.txt") + assert sandbox.read_text("/tmp/hello.txt") == "hello" + + short_job = sandbox.start_bg("echo done > /tmp/bg.txt") + assert short_job.wait(timeout=2) == 0 + + # timeout=0 should still perform one probe and detect completed jobs. + short_job_nonblocking = sandbox.start_bg("echo done > /tmp/bg_nowait.txt") + assert short_job_nonblocking.wait(timeout=0) == 0 + + long_job = sandbox.start_bg("sleep 300") + with pytest.raises(TimeoutError): + long_job.wait(timeout=0) + with pytest.raises(TimeoutError): + long_job.wait(timeout=0.1) + long_job.kill() + assert isinstance(long_job.wait(timeout=2), int) + + sandbox.kill() + raw = getattr(sandbox, "raw", None) + assert raw is not None + assert raw.terminated is True + + def test_factory_creates_hf_backend(self, monkeypatch): + _install_fake_hf_sandbox(monkeypatch) + + import openenv.core.harness.sandbox as sandbox_pkg + import openenv.core.harness.sandbox.hf_backend as hf_backend + + importlib.reload(hf_backend) + importlib.reload(sandbox_pkg) + + monkeypatch.setattr(hf_backend, "Sandbox", _FakeSandboxAPI) + backend = sandbox_pkg.create_sandbox_backend("hf", image="python:3.12") + assert isinstance(backend, hf_backend.HFSandboxBackend) diff --git a/tests/core/test_interception_server.py b/tests/core/test_interception_server.py new file mode 100644 index 000000000..41ef38fe5 --- /dev/null +++ b/tests/core/test_interception_server.py @@ -0,0 +1,315 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from __future__ import annotations + +import asyncio + +import aiohttp +import pytest + +from openenv.core.harness.agents.interception_server import ( + InterceptionServer, + deliver_response, +) + + +_ANSWER_TOOL = { + "type": "function", + "function": { + "name": "answer", + "description": "Submit final answer for grading", + "parameters": { + "type": "object", + "properties": { + "answer": {"type": "string"}, + }, + "required": ["answer"], + }, + }, +} + + +@pytest.mark.asyncio +async def test_interception_server_rejects_unauthorized_requests() -> None: + server = InterceptionServer(port=0, secret="secret-token") + await server.start() + try: + async with aiohttp.ClientSession() as client: + resp = await client.post( + f"http://127.0.0.1:{server.port}/rollout/r1/v1/chat/completions", + json={"messages": []}, + ) + assert resp.status == 401 + finally: + await server.stop() + + +@pytest.mark.asyncio +async def test_interception_server_returns_404_for_unknown_rollout() -> None: + server = InterceptionServer(port=0, secret="secret-token") + await server.start() + try: + async with aiohttp.ClientSession() as client: + resp = await client.post( + f"http://127.0.0.1:{server.port}/rollout/missing/v1/chat/completions", + headers={"Authorization": "Bearer secret-token"}, + json={"messages": []}, + ) + assert resp.status == 404 + finally: + await server.stop() + + +@pytest.mark.asyncio +async def test_interception_server_non_stream_roundtrip_cleans_intercept() -> None: + server = InterceptionServer(port=0, secret="secret-token") + await server.start() + queue = server.register_rollout("r1") + try: + async with aiohttp.ClientSession() as client: + request_task = asyncio.create_task( + client.post( + f"http://127.0.0.1:{server.port}/rollout/r1/v1/chat/completions", + headers={"Authorization": "Bearer secret-token"}, + json={ + "messages": [{"role": "user", "content": "hi"}], + "stream": False, + }, + ) + ) + request_id = await asyncio.to_thread(queue.get, timeout=1.0) + intercept = server.get_intercept(request_id) + assert intercept is not None + + await deliver_response( + intercept, + { + "id": "resp-1", + "model": "test-model", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "hello"}, + "finish_reason": "stop", + } + ], + }, + ) + + resp = await request_task + assert resp.status == 200 + payload = await resp.json() + assert payload["id"] == "resp-1" + + # Request entries should not leak after completion. + assert server.get_intercept(request_id) is None + finally: + server.unregister_rollout("r1") + await server.stop() + + +@pytest.mark.asyncio +async def test_interception_server_unregister_rollout_cancels_pending_request() -> None: + server = InterceptionServer(port=0, secret="secret-token") + await server.start() + queue = server.register_rollout("r1") + try: + async with aiohttp.ClientSession() as client: + request_task = asyncio.create_task( + client.post( + f"http://127.0.0.1:{server.port}/rollout/r1/v1/chat/completions", + headers={"Authorization": "Bearer secret-token"}, + json={ + "messages": [{"role": "user", "content": "hi"}], + "stream": False, + }, + ) + ) + _request_id = await asyncio.to_thread(queue.get, timeout=1.0) + server.unregister_rollout("r1") + + resp = await request_task + assert resp.status == 499 + payload = await resp.json() + assert payload["error"] == "rollout cancelled" + finally: + await server.stop() + + +@pytest.mark.asyncio +async def test_interception_server_tool_endpoint_executes_registered_handler() -> None: + server = InterceptionServer( + port=0, + secret="secret-token", + tool_name_allowlist={"answer"}, + ) + await server.start() + server.register_rollout("r1") + seen: dict[str, object] = {} + + async def _handler(arguments: dict) -> dict: + seen["arguments"] = arguments + return {"content": [{"type": "text", "text": "✅"}]} + + server.register_tool_handler("r1", "answer", _handler) + try: + async with aiohttp.ClientSession() as client: + resp = await client.post( + f"http://127.0.0.1:{server.port}/rollout/r1/v1/tools/answer", + headers={"Authorization": "Bearer secret-token"}, + json={"arguments": {"answer": "42"}}, + ) + assert resp.status == 200 + payload = await resp.json() + assert payload["content"][0]["text"] == "✅" + assert seen["arguments"] == {"answer": "42"} + finally: + server.unregister_rollout("r1") + await server.stop() + + +@pytest.mark.asyncio +async def test_interception_server_tool_endpoint_returns_404_for_unknown_tool() -> None: + server = InterceptionServer(port=0, secret="secret-token") + await server.start() + server.register_rollout("r1") + try: + async with aiohttp.ClientSession() as client: + resp = await client.post( + f"http://127.0.0.1:{server.port}/rollout/r1/v1/tools/missing", + headers={"Authorization": "Bearer secret-token"}, + json={"arguments": {}}, + ) + assert resp.status == 404 + finally: + server.unregister_rollout("r1") + await server.stop() + + +def test_interception_server_rejects_reserved_tool_name_registration() -> None: + server = InterceptionServer( + port=0, + secret="secret-token", + tool_name_allowlist={"reset"}, + ) + server.register_rollout("r1") + + async def _handler(arguments: dict) -> dict: + return {"ok": True} + + with pytest.raises(ValueError, match="reserved"): + server.register_tool_handler("r1", "reset", _handler) + + +def test_interception_server_rejects_tool_definition_name_mismatch() -> None: + server = InterceptionServer( + port=0, + secret="secret-token", + tool_name_allowlist={"answer"}, + ) + server.register_rollout("r1") + + async def _handler(arguments: dict) -> dict: + return {"ok": True} + + mismatched = { + "type": "function", + "function": { + "name": "not_answer", + "description": "Mismatch", + "parameters": {"type": "object", "properties": {}}, + }, + } + + with pytest.raises(ValueError, match="must exactly match"): + server.register_tool_handler( + "r1", + "answer", + _handler, + tool_definition=mismatched, + ) + + +def test_interception_server_rejects_tool_not_in_allowlist() -> None: + server = InterceptionServer( + port=0, + secret="secret-token", + tool_name_allowlist={"answer"}, + ) + server.register_rollout("r1") + + async def _handler(arguments: dict) -> dict: + return {"ok": True} + + with pytest.raises(ValueError, match="allowlist"): + server.register_tool_handler("r1", "search", _handler) + + +@pytest.mark.asyncio +async def test_interception_server_injects_registered_tool_defs_into_intercept() -> ( + None +): + server = InterceptionServer( + port=0, + secret="secret-token", + tool_name_allowlist={"answer"}, + ) + await server.start() + queue = server.register_rollout("r1") + + async def _handler(arguments: dict) -> dict: + return {"content": [{"type": "text", "text": str(arguments)}]} + + server.register_tool_handler( + "r1", + "answer", + _handler, + tool_definition=_ANSWER_TOOL, + ) + + try: + async with aiohttp.ClientSession() as client: + request_task = asyncio.create_task( + client.post( + f"http://127.0.0.1:{server.port}/rollout/r1/v1/chat/completions", + headers={"Authorization": "Bearer secret-token"}, + json={ + "messages": [{"role": "user", "content": "grade this"}], + "stream": False, + }, + ) + ) + request_id = await asyncio.to_thread(queue.get, timeout=1.0) + intercept = server.get_intercept(request_id) + assert intercept is not None + tool_names = { + tool["function"]["name"] + for tool in intercept.get("tools", []) + if isinstance(tool, dict) and isinstance(tool.get("function"), dict) + } + assert "answer" in tool_names + + await deliver_response( + intercept, + { + "id": "resp-1", + "model": "test-model", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "done"}, + "finish_reason": "stop", + } + ], + }, + ) + + resp = await request_task + assert resp.status == 200 + finally: + server.unregister_rollout("r1") + await server.stop() diff --git a/tests/envs/test_opencode_env.py b/tests/envs/test_coding_agent_env.py similarity index 58% rename from tests/envs/test_opencode_env.py rename to tests/envs/test_coding_agent_env.py index 812ade194..fa3dcae79 100644 --- a/tests/envs/test_opencode_env.py +++ b/tests/envs/test_coding_agent_env.py @@ -4,7 +4,7 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -"""Smoke tests for ``opencode_env``. +"""Smoke tests for ``coding_agent_env``. The default suite runs in CI without any external dependencies (no E2B, no LLM, no network). It covers: @@ -13,7 +13,7 @@ - The endpoint catalog (`vllm` / `openai` / `hf_router`) resolves explicit + env-var + default-value precedence correctly. - Pydantic models accept their expected shapes. - - The `OpenCodeTask` coercion helper handles str / dict / `OpenCodeTask`. + - The `CodingAgentTask` coercion helper handles str / dict / `CodingAgentTask`. A second class is marked ``@pytest.mark.integration`` and exercises the deployed Space end-to-end. It only runs when ``E2B_API_KEY`` and at least @@ -24,7 +24,6 @@ from __future__ import annotations import os -import shlex import sys import pytest @@ -45,18 +44,17 @@ def test_public_api_imports() -> None: """Top-level package re-exports the documented surface.""" - from opencode_env import ( # noqa: F401 + from coding_agent_env import ( # noqa: F401 + CodingAgentConfig, + CodingAgentEnv, + CodingAgentSession, + CodingAgentSessionFactory, + CodingAgentState, + CodingAgentTask, CommandResult, E2BSandboxBackend, - OpenCodeConfig, - OpenCodeEnv, - OpenCodeSession, - OpenCodeSessionFactory, - OpenCodeState, - OpenCodeTask, Provider, RolloutResult, - RolloutTurn, SandboxBackend, SandboxHandle, ) @@ -64,14 +62,14 @@ def test_public_api_imports() -> None: def test_server_modules_import() -> None: """Server-side modules (FastAPI app, MCP env, catalog) import cleanly.""" - from opencode_env.server.app import app # noqa: F401 - from opencode_env.server.catalog import ( # noqa: F401 + from coding_agent_env.server.app import app # noqa: F401 + from coding_agent_env.server.catalog import ( # noqa: F401 catalog_summary, ENDPOINT_KINDS, resolve_endpoint, ) - from opencode_env.server.opencode_environment import ( # noqa: F401 - OpenCodeEnvironment, + from coding_agent_env.server.coding_environment import ( # noqa: F401 + CodingAgentEnvironment, ) @@ -81,14 +79,14 @@ def test_server_modules_import() -> None: def test_catalog_kinds() -> None: - from opencode_env.server.catalog import ENDPOINT_KINDS + from coding_agent_env.server.catalog import ENDPOINT_KINDS assert ENDPOINT_KINDS == ("vllm", "openai", "hf_router") def test_resolve_endpoint_explicit_args_win(monkeypatch: pytest.MonkeyPatch) -> None: """Explicit args beat env vars beat catalog defaults.""" - from opencode_env.server.catalog import resolve_endpoint + from coding_agent_env.server.catalog import resolve_endpoint monkeypatch.setenv("OPENAI_API_KEY", "from-env") r = resolve_endpoint( @@ -107,7 +105,7 @@ def test_resolve_endpoint_explicit_args_win(monkeypatch: pytest.MonkeyPatch) -> def test_resolve_endpoint_env_var_used_when_arg_missing( monkeypatch: pytest.MonkeyPatch, ) -> None: - from opencode_env.server.catalog import resolve_endpoint + from coding_agent_env.server.catalog import resolve_endpoint monkeypatch.setenv("OPENAI_API_KEY", "key-from-env") monkeypatch.setenv("OPENAI_MODEL", "gpt-4o") @@ -121,7 +119,7 @@ def test_resolve_endpoint_normalizes_v1_suffix( monkeypatch: pytest.MonkeyPatch, ) -> None: """Base URL gets ``/v1`` appended if missing, otherwise left alone.""" - from opencode_env.server.catalog import resolve_endpoint + from coding_agent_env.server.catalog import resolve_endpoint monkeypatch.setenv("VLLM_URL", "https://my-vllm.example/") monkeypatch.setenv("VLLM_API_KEY", "x") @@ -134,7 +132,7 @@ def test_resolve_endpoint_normalizes_v1_suffix( def test_resolve_endpoint_unknown_kind_raises() -> None: - from opencode_env.server.catalog import resolve_endpoint + from coding_agent_env.server.catalog import resolve_endpoint with pytest.raises(ValueError, match="unknown endpoint kind"): resolve_endpoint("bogus", base_url="x", api_key="y", model="z") @@ -143,7 +141,7 @@ def test_resolve_endpoint_unknown_kind_raises() -> None: def test_resolve_endpoint_missing_creds_raises( monkeypatch: pytest.MonkeyPatch, ) -> None: - from opencode_env.server.catalog import resolve_endpoint + from coding_agent_env.server.catalog import resolve_endpoint # Strip any inherited env vars. for k in ("OPENAI_API_KEY", "OPENAI_BASE_URL", "OPENAI_MODEL"): @@ -153,7 +151,7 @@ def test_resolve_endpoint_missing_creds_raises( def test_catalog_summary_shape() -> None: - from opencode_env.server.catalog import catalog_summary + from coding_agent_env.server.catalog import catalog_summary summary = catalog_summary() assert {entry["kind"] for entry in summary} == {"vllm", "openai", "hf_router"} @@ -166,13 +164,122 @@ def test_catalog_summary_shape() -> None: } <= entry.keys() +def test_build_agent_config_opencode() -> None: + from coding_agent_env.server.coding_environment import CodingAgentEnvironment + + env = CodingAgentEnvironment() + cfg = env._build_agent_config( + agent="opencode", + mode="black_box", + base_url="https://api.openai.com/v1", + api_key="sk-test", + model="gpt-4o-mini", + agent_timeout_s=123.0, + disable_thinking=True, + top_logprobs=7, + max_tokens_cap=2048, + ) + assert isinstance(cfg, env._CodingAgentConfig) + assert cfg.model == "gpt-4o-mini" + assert cfg.agent_timeout_s == 123.0 + assert cfg.max_tokens_cap == 2048 + + cfg_4096 = env._build_agent_config( + agent="opencode", + mode="black_box", + base_url="https://api.openai.com/v1", + api_key="sk-test", + model="gpt-4o-mini", + agent_timeout_s=123.0, + disable_thinking=True, + top_logprobs=7, + max_tokens_cap=4096, + ) + assert cfg_4096.max_tokens_cap == 4096 + + cfg_uncapped = env._build_agent_config( + agent="opencode", + mode="black_box", + base_url="https://api.openai.com/v1", + api_key="sk-test", + model="gpt-4o-mini", + agent_timeout_s=123.0, + disable_thinking=True, + top_logprobs=7, + max_tokens_cap=0, + ) + assert cfg_uncapped.max_tokens_cap is None + + +def test_build_agent_config_pi() -> None: + from coding_agent_env.server.coding_environment import CodingAgentEnvironment + + env = CodingAgentEnvironment() + cfg = env._build_agent_config( + agent="pi", + mode="black_box", + base_url="https://router.huggingface.co/v1", + api_key="hf_xxx", + model="zai-org/GLM-5.1", + agent_timeout_s=180.0, + disable_thinking=True, + top_logprobs=5, + max_tokens_cap=4096, + ) + assert cfg.provider == "huggingface" + assert cfg.thinking == "off" + assert cfg.model == "zai-org/GLM-5.1" + + cfg_gate = env._build_agent_config( + agent="pi", + mode="interception_gate", + base_url="https://router.huggingface.co/v1", + api_key="hf_xxx", + model="zai-org/GLM-5.1", + agent_timeout_s=180.0, + disable_thinking=False, + top_logprobs=5, + max_tokens_cap=4096, + ) + assert cfg_gate.provider == "huggingface" + + +def test_build_session_factory_requires_e2b_dependency() -> None: + from coding_agent_env.server.coding_environment import CodingAgentEnvironment + + env = CodingAgentEnvironment() + env._E2BSandboxBackend = None + cfg = env._build_agent_config( + agent="pi", + mode="black_box", + base_url="https://router.huggingface.co/v1", + api_key="hf_xxx", + model="zai-org/GLM-5.1", + agent_timeout_s=180.0, + disable_thinking=False, + top_logprobs=5, + max_tokens_cap=4096, + ) + + with pytest.raises(RuntimeError, match="E2BSandboxBackend unavailable"): + env._build_session_factory( + agent="pi", + config=cfg, + mode="black_box", + template="", + disable_thinking=False, + top_logprobs=5, + max_tokens_cap=4096, + ) + + # --------------------------------------------------------------------------- # Models + task coercion # --------------------------------------------------------------------------- def test_rollout_result_serializes_round_trip() -> None: - from opencode_env import CommandResult, RolloutResult, RolloutTurn + from coding_agent_env import CommandResult, RolloutResult r = RolloutResult( task_id="t1", @@ -180,157 +287,48 @@ def test_rollout_result_serializes_round_trip() -> None: reward=0.75, agent_exit_code=0, wall_s=12.5, - mode="transparent_proxy", + mode="black_box", setup_results=[CommandResult(cmd="pip install pandas", exit_code=0)], verify_results=[CommandResult(cmd="pytest", exit_code=1, stderr="boom")], - proxy_turns=[ - RolloutTurn( - turn=1, - finish_reason="stop", - completion_tokens=["hi"], - per_token_logps=[-0.1], - latency_s=0.2, - ) - ], files={"/home/user/workdir/x.py": "print('x')"}, ) blob = r.model_dump_json() rebuilt = RolloutResult.model_validate_json(blob) assert rebuilt.reward == 0.75 assert rebuilt.verify_results[0].exit_code == 1 - assert rebuilt.proxy_turns[0].completion_tokens == ["hi"] -def test_opencode_task_coerce_str() -> None: - from opencode_env import OpenCodeTask +def test_coding_agent_task_coerce_str() -> None: + from coding_agent_env import CodingAgentTask - t = OpenCodeTask.coerce("write fizzbuzz.py") + t = CodingAgentTask.coerce("write fizzbuzz.py") assert t.instruction == "write fizzbuzz.py" assert t.setup_shell is None assert t.upload_files == {} -def test_opencode_task_coerce_dict() -> None: - from opencode_env import OpenCodeTask +def test_coding_agent_task_coerce_dict() -> None: + from coding_agent_env import CodingAgentTask - t = OpenCodeTask.coerce({"instruction": "x", "setup_shell": "pip install pandas"}) + t = CodingAgentTask.coerce( + {"instruction": "x", "setup_shell": "pip install pandas"} + ) assert t.instruction == "x" assert t.setup_shell == "pip install pandas" -def test_opencode_task_coerce_existing_passthrough() -> None: - from opencode_env import OpenCodeTask +def test_coding_agent_task_coerce_existing_passthrough() -> None: + from coding_agent_env import CodingAgentTask - src = OpenCodeTask(instruction="y") - assert OpenCodeTask.coerce(src) is src + src = CodingAgentTask(instruction="y") + assert CodingAgentTask.coerce(src) is src -def test_opencode_task_coerce_rejects_unknown_type() -> None: - from opencode_env import OpenCodeTask +def test_coding_agent_task_coerce_rejects_unknown_type() -> None: + from coding_agent_env import CodingAgentTask with pytest.raises(TypeError, match="Cannot coerce"): - OpenCodeTask.coerce(42) # type: ignore[arg-type] - - -def test_start_proxy_keeps_upstream_key_out_of_command() -> None: - """The proxy API key must be passed via env, not shell argv.""" - from opencode_env import OpenCodeConfig, OpenCodeSessionFactory - - class FakeExecResult: - exit_code = 0 - stdout = "ok" - stderr = "" - - class FakeBgJob: - def wait(self, timeout: float | None = None) -> int: - return 0 - - def kill(self) -> None: - pass - - class FakeSandbox: - sandbox_id = "fake-sandbox" - - def __init__(self) -> None: - self.started_cmd: str | None = None - self.started_envs: dict[str, str] | None = None - self.written: dict[str, str] = {} - - def exec(self, *args, **kwargs) -> FakeExecResult: - return FakeExecResult() - - def start_bg(self, cmd: str, *, envs=None, cwd=None) -> FakeBgJob: - self.started_cmd = cmd - self.started_envs = envs - return FakeBgJob() - - def write_text(self, path: str, content: str) -> None: - self.written[path] = content - - def read_text(self, path: str) -> str: - return "" - - def exists(self, path: str) -> bool: - return path in self.written - - def kill(self) -> None: - pass - - class NoopInstallFactory(OpenCodeSessionFactory): - def _exec_with_retry(self, *args, **kwargs): - return FakeExecResult() - - secret = "sk-test '$(leak)" - model = "provider/model'; touch /tmp/pwn #" - config = OpenCodeConfig( - base_url="https://example.test/v1?x='y", - api_key=secret, - model=model, - ) - sandbox = FakeSandbox() - factory = NoopInstallFactory( - config=config, - sandbox_backend=object(), # unused by this protected-method test - mode="transparent_proxy", - ) - - factory._start_proxy(sandbox) - - assert sandbox.started_cmd is not None - assert sandbox.started_envs == {"OPENCODE_UPSTREAM_API_KEY": secret} - assert secret not in sandbox.started_cmd - assert "--upstream-api-key" not in sandbox.started_cmd - - argv = shlex.split(sandbox.started_cmd.split("&&", 1)[1].split(">", 1)[0].strip()) - assert argv[argv.index("--upstream-url") + 1] == config.base_url - assert argv[argv.index("--model-override") + 1] == model - - -def test_interception_cli_reads_upstream_key_from_env( - monkeypatch: pytest.MonkeyPatch, -) -> None: - from opencode_env.sandbox import interception - - captured = {} - - def fake_serve(cfg) -> None: - captured["cfg"] = cfg - - monkeypatch.setattr(interception, "serve", fake_serve) - monkeypatch.setenv("OPENCODE_UPSTREAM_API_KEY", "sk-from-env") - monkeypatch.setattr( - sys, - "argv", - [ - "interception.py", - "--upstream-url", - "https://example.test/v1", - ], - ) - - interception.main() - - assert captured["cfg"].upstream_api_key == "sk-from-env" + CodingAgentTask.coerce(42) # type: ignore[arg-type] # --------------------------------------------------------------------------- @@ -356,16 +354,16 @@ def test_run_rollout_e2e_via_deployed_space() -> None: import asyncio - from opencode_env import OpenCodeEnv - from opencode_env.client import _extract_text - from opencode_env.models import RolloutResult + from coding_agent_env import CodingAgentEnv + from coding_agent_env.client import _extract_text + from coding_agent_env.models import RolloutResult SPACE = os.environ.get( - "OPENCODE_ENV_SPACE", "https://adithyask-opencode-env.hf.space" + "CODING_AGENT_ENV_SPACE", "https://adithyask-coding-agent-env.hf.space" ) async def _go() -> RolloutResult: - async with OpenCodeEnv(base_url=SPACE) as env: + async with CodingAgentEnv(base_url=SPACE) as env: await env.reset() raw = await env.call_tool( "run_rollout", @@ -384,7 +382,7 @@ async def _go() -> RolloutResult: "import binary_search; " "assert binary_search.binary_search([1,2,3,4,5], 3) == 2; print('OK')\"", ], - template="opencode-rl", + template="coding-agent-rl", agent_timeout_s=600, ) return RolloutResult.model_validate_json(_extract_text(raw)) @@ -393,7 +391,6 @@ async def _go() -> RolloutResult: assert result.reward == 1.0, ( f"expected reward=1.0 got {result.reward}: {result.error}" ) - assert result.proxy_turns, "expected at least one captured LLM turn" assert any(f.endswith("/binary_search.py") for f in result.files), ( f"expected binary_search.py in workdir, got {list(result.files)}" ) diff --git a/tests/envs/test_swe_async_rollout_worker.py b/tests/envs/test_swe_async_rollout_worker.py new file mode 100644 index 000000000..a8bb0e04c --- /dev/null +++ b/tests/envs/test_swe_async_rollout_worker.py @@ -0,0 +1,70 @@ +import sys +from pathlib import Path + +_ROOT = Path(__file__).resolve().parents[2] +if str(_ROOT) not in sys.path: + sys.path.insert(0, str(_ROOT)) + +from examples.mini_swe_env.async_grpo.rollout_worker import ( + _get_tools, + _has_answer_call, + _is_terminal_non_tool_response, + _make_chat_response, + _normalize_tool_calls, +) + + +def test_normalize_tool_calls_serializes_arguments_to_json_string() -> None: + calls = _normalize_tool_calls( + [ + { + "id": "call_1", + "type": "function", + "function": {"name": "answer", "arguments": {}}, + } + ] + ) + assert len(calls) == 1 + assert calls[0]["function"]["name"] == "answer" + assert calls[0]["function"]["arguments"] == "{}" + + +def test_terminal_detection_requires_stop_and_no_tool_calls() -> None: + terminal = _make_chat_response( + {"role": "assistant", "content": "Done."}, + model="qwen", + finish_reason="stop", + ) + assert _is_terminal_non_tool_response(terminal) is True + + with_tools = _make_chat_response( + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "id": "call_1", + "type": "function", + "function": {"name": "answer", "arguments": "{}"}, + } + ], + }, + model="qwen", + finish_reason="stop", + ) + assert _is_terminal_non_tool_response(with_tools) is False + assert _has_answer_call(with_tools) is True + + +def test_get_tools_from_intercept_or_body() -> None: + tool_schema = { + "type": "function", + "function": { + "name": "answer", + "description": "submit", + "parameters": {"type": "object", "properties": {}}, + }, + } + assert _get_tools({"tools": [tool_schema]}) == [tool_schema] + assert _get_tools({"body": {"tools": [tool_schema]}}) == [tool_schema] + assert _get_tools({}) is None