From 7b58dbb8aa2bf6d3a93e66b3c8d552538c06fb92 Mon Sep 17 00:00:00 2001 From: luyd Date: Tue, 27 Oct 2026 11:41:50 +0800 Subject: [PATCH] Integrate sandbox deployment and configuration workflows Import the snapshot from 2773d45512fc1e9b9c492c9cc1b3c7e3f0188600 without source history, based on the existing local main. Include controller and rollout services, OpenSandbox and H-cluster adapters, configuration tools, local web console, and native train/inference consistency. Exclude the standalone Transformers math trainer, its OPD helper, reference gate, examples, tests, and guide. --- .github/workflows/ci.yml | 29 + .gitignore | 15 +- CONTRIBUTING.md | 19 + NOTICE.md | 4 + README.md | 22 +- README_zh.md | 11 + docs/configuration-wizard.md | 140 + docs/deployment.md | 110 + docs/sandbox-rollout.md | 72 + docs/train-inference-consistency.md | 79 + docs/web-console.md | 110 + examples/native/catalog.jsonl | 1 + examples/native/deployment-embedded.yaml | 45 + examples/native/deployment-service.yaml | 43 + examples/native/sandbox.yaml | 10 + examples/options/embedded-local.answers.yaml | 30 + examples/options/service-sandbox.answers.yaml | 34 + examples/options/service-ssh.answers.yaml | 44 + harborrl/algorithms/__init__.py | 1 + harborrl/algorithms/grpo.py | 18 + harborrl/backends/slime_v032/config.py | 125 + harborrl/backends/slime_v032/converter.py | 2 + harborrl/backends/slime_v032/launcher.py | 35 +- harborrl/backends/slime_v032/loss.py | 7 + harborrl/backends/slime_v032/postprocess.py | 31 +- harborrl/backends/slime_v032/rollout.py | 164 +- .../slime_v032/rollout_service_client.py | 210 ++ harborrl/cli.py | 52 +- harborrl/config/native.py | 183 +- harborrl/consistency/__init__.py | 1 + harborrl/consistency/actor.py | 88 + harborrl/consistency/config.py | 51 + harborrl/consistency/metrics.py | 67 + harborrl/consistency/probe.py | 35 + harborrl/consistency/report.py | 51 + harborrl/consistency/sglang.py | 92 + harborrl/consistency/slime.py | 74 + harborrl/consistency/train.py | 76 + harborrl/data/harbor/__main__.py | 1 + harborrl/data/harbor/inspector.py | 25 +- harborrl/data/harbor/materializer.py | 2 +- harborrl/data/harbor/native_inspector.py | 6 +- harborrl/data/harbor/probe.py | 9 +- harborrl/data/harbor/receipt.py | 2 +- harborrl/deployment/__init__.py | 1 + harborrl/deployment/controller.py | 207 ++ harborrl/deployment/preflight.py | 340 ++ harborrl/deployment/remote.py | 55 + harborrl/deployment/schema.py | 330 ++ harborrl/deployment/training_target.py | 217 ++ harborrl/gateway/messages.py | 24 +- harborrl/gateway/server.py | 2 +- harborrl/gateway/sglang_backend.py | 17 +- harborrl/gateway/trace.py | 20 + harborrl/harnesses/__init__.py | 1 + harborrl/harnesses/claude_code.py | 53 + harborrl/harnesses/offline_claude.py | 61 + harborrl/math_registry.py | 316 ++ harborrl/models/__init__.py | 1 + harborrl/models/qwen.py | 39 + harborrl/options/__init__.py | 11 + harborrl/options/catalog.py | 147 + harborrl/options/cli.py | 184 + harborrl/options/presets.py | 153 + harborrl/options/probe.py | 39 + harborrl/options/resources/datasets.yaml | 40 + harborrl/options/validation.py | 169 + harborrl/options/wizard.py | 68 + harborrl/platform/evidence.py | 126 + harborrl/platform/native_train.py | 281 +- harborrl/platform/policy_pool.py | 2 + harborrl/platform/preflight.py | 220 ++ harborrl/platform/slime_train.sh | 11 +- harborrl/rollout/exporters/native.py | 63 +- harborrl/rollout/harbor_job/__main__.py | 6 +- harborrl/rollout/harbor_job/bindings.py | 12 +- harborrl/rollout/harbor_job/coordinator.py | 34 +- .../harbor_job/environments/__init__.py | 1 + .../harbor_job/environments/opensandbox.py | 189 + harborrl/rollout/harbor_job/offline.py | 6 +- harborrl/rollout/harbor_job/runner.py | 38 +- harborrl/rollout/harbor_job/sandbox_config.py | 135 + harborrl/rollout/harbor_job/sandbox_probe.py | 63 + .../rollout/harbor_job/sandbox_transport.py | 27 + harborrl/rollout/native_generate.py | 272 +- harborrl/rollout/runtime.py | 350 ++ harborrl/rollout/service.py | 562 +++ harborrl/tasks/catalog.py | 8 + harborrl/tasks/cli.py | 6 +- harborrl/tasks/content.py | 26 + harborrl/tasks/jsonl_provider.py | 92 + harborrl/tasks/provider.py | 72 + harborrl/webui/__init__.py | 1 + harborrl/webui/cli.py | 67 + harborrl/webui/jobs.py | 173 + harborrl/webui/server.py | 189 + harborrl/webui/static/app.js | 884 +++++ harborrl/webui/static/brand.jpg | Bin 0 -> 186959 bytes harborrl/webui/static/index.html | 273 ++ harborrl/webui/static/lucide-LICENSE.txt | 43 + harborrl/webui/static/lucide.min.js | 12 + harborrl/webui/static/style.css | 1094 ++++++ practices/h_cluster/README.md | 100 + .../h_cluster/configs/deployment.example.yaml | 37 + .../h_cluster/configs/sandbox.example.yaml | 23 + practices/h_cluster/pyproject.toml | 16 + .../src/harborrl_h_cluster/__init__.py | 1 + .../src/harborrl_h_cluster/environment.py | 143 + .../h_cluster/src/harborrl_h_cluster/relay.py | 50 + .../src/harborrl_h_cluster/relay_server.py | 150 + .../src/harborrl_h_cluster/reverse_relay.py | 49 + .../src/harborrl_h_cluster/transport.py | 28 + practices/h_cluster/tests/test_environment.py | 69 + practices/h_cluster/tests/test_relay.py | 137 + practices/h_cluster/tests/test_transport.py | 66 + pyproject.toml | 8 + scripts/audit_public_tree.sh | 51 +- scripts/collect_service_evidence.py | 102 + scripts/readiness_dial.py | 91 + scripts/rollout_service_smoke.py | 263 ++ scripts/sandbox_sdk_contract.py | 60 + scripts/sandbox_smoke.py | 65 + scripts/sync_options_manifest.py | 22 + tests/harborrl/test_delivery_barrier.py | 41 + tests/harborrl/test_deployment_config.py | 176 + tests/harborrl/test_deployment_preflight.py | 241 ++ tests/harborrl/test_deployment_targets.py | 235 ++ tests/harborrl/test_math_interfaces.py | 126 + tests/harborrl/test_native_gateway.py | 65 +- tests/harborrl/test_native_lifecycle.py | 5 + tests/harborrl/test_native_terminal.py | 111 + tests/harborrl/test_native_training.py | 255 +- tests/harborrl/test_options.py | 308 ++ tests/harborrl/test_rollout_runtime.py | 321 ++ tests/harborrl/test_rollout_service.py | 415 +++ tests/harborrl/test_sandbox.py | 97 + .../test_sandbox_allocation_transport.py | 24 + tests/harborrl/test_sandbox_cleanup.py | 36 + tests/harborrl/test_sandbox_portability.py | 57 + tests/harborrl/test_sandbox_runtime.py | 92 + tests/harborrl/test_sandbox_scale.py | 36 + .../test_service_group_concurrency.py | 63 + tests/harborrl/test_slime_v032_exporter.py | 84 +- tests/harborrl/test_webui.py | 244 ++ tests/test_consistency.py | 81 + tests/test_consistency_torch.py | 31 + uv.lock | 3256 ++++++++++++++++- 147 files changed, 16964 insertions(+), 1026 deletions(-) create mode 100644 docs/configuration-wizard.md create mode 100644 docs/deployment.md create mode 100644 docs/sandbox-rollout.md create mode 100644 docs/train-inference-consistency.md create mode 100644 docs/web-console.md create mode 100644 examples/native/catalog.jsonl create mode 100644 examples/native/deployment-embedded.yaml create mode 100644 examples/native/deployment-service.yaml create mode 100644 examples/native/sandbox.yaml create mode 100644 examples/options/embedded-local.answers.yaml create mode 100644 examples/options/service-sandbox.answers.yaml create mode 100644 examples/options/service-ssh.answers.yaml create mode 100644 harborrl/algorithms/__init__.py create mode 100644 harborrl/algorithms/grpo.py create mode 100644 harborrl/backends/slime_v032/config.py create mode 100644 harborrl/backends/slime_v032/rollout_service_client.py create mode 100644 harborrl/consistency/__init__.py create mode 100644 harborrl/consistency/actor.py create mode 100644 harborrl/consistency/config.py create mode 100644 harborrl/consistency/metrics.py create mode 100644 harborrl/consistency/probe.py create mode 100644 harborrl/consistency/report.py create mode 100644 harborrl/consistency/sglang.py create mode 100644 harborrl/consistency/slime.py create mode 100644 harborrl/consistency/train.py create mode 100644 harborrl/deployment/__init__.py create mode 100644 harborrl/deployment/controller.py create mode 100644 harborrl/deployment/preflight.py create mode 100644 harborrl/deployment/remote.py create mode 100644 harborrl/deployment/schema.py create mode 100644 harborrl/deployment/training_target.py create mode 100644 harborrl/harnesses/__init__.py create mode 100644 harborrl/harnesses/claude_code.py create mode 100644 harborrl/harnesses/offline_claude.py create mode 100644 harborrl/math_registry.py create mode 100644 harborrl/models/__init__.py create mode 100644 harborrl/models/qwen.py create mode 100644 harborrl/options/__init__.py create mode 100644 harborrl/options/catalog.py create mode 100644 harborrl/options/cli.py create mode 100644 harborrl/options/presets.py create mode 100644 harborrl/options/probe.py create mode 100644 harborrl/options/resources/datasets.yaml create mode 100644 harborrl/options/validation.py create mode 100644 harborrl/options/wizard.py create mode 100644 harborrl/platform/evidence.py create mode 100644 harborrl/platform/preflight.py create mode 100644 harborrl/rollout/harbor_job/environments/__init__.py create mode 100644 harborrl/rollout/harbor_job/environments/opensandbox.py create mode 100644 harborrl/rollout/harbor_job/sandbox_config.py create mode 100644 harborrl/rollout/harbor_job/sandbox_probe.py create mode 100644 harborrl/rollout/harbor_job/sandbox_transport.py create mode 100644 harborrl/rollout/runtime.py create mode 100644 harborrl/rollout/service.py create mode 100644 harborrl/tasks/catalog.py create mode 100644 harborrl/tasks/content.py create mode 100644 harborrl/tasks/jsonl_provider.py create mode 100644 harborrl/tasks/provider.py create mode 100644 harborrl/webui/__init__.py create mode 100644 harborrl/webui/cli.py create mode 100644 harborrl/webui/jobs.py create mode 100644 harborrl/webui/server.py create mode 100644 harborrl/webui/static/app.js create mode 100644 harborrl/webui/static/brand.jpg create mode 100644 harborrl/webui/static/index.html create mode 100644 harborrl/webui/static/lucide-LICENSE.txt create mode 100644 harborrl/webui/static/lucide.min.js create mode 100644 harborrl/webui/static/style.css create mode 100644 practices/h_cluster/README.md create mode 100644 practices/h_cluster/configs/deployment.example.yaml create mode 100644 practices/h_cluster/configs/sandbox.example.yaml create mode 100644 practices/h_cluster/pyproject.toml create mode 100644 practices/h_cluster/src/harborrl_h_cluster/__init__.py create mode 100644 practices/h_cluster/src/harborrl_h_cluster/environment.py create mode 100644 practices/h_cluster/src/harborrl_h_cluster/relay.py create mode 100644 practices/h_cluster/src/harborrl_h_cluster/relay_server.py create mode 100644 practices/h_cluster/src/harborrl_h_cluster/reverse_relay.py create mode 100644 practices/h_cluster/src/harborrl_h_cluster/transport.py create mode 100644 practices/h_cluster/tests/test_environment.py create mode 100644 practices/h_cluster/tests/test_relay.py create mode 100644 practices/h_cluster/tests/test_transport.py create mode 100755 scripts/collect_service_evidence.py create mode 100755 scripts/readiness_dial.py create mode 100755 scripts/rollout_service_smoke.py create mode 100644 scripts/sandbox_sdk_contract.py create mode 100644 scripts/sandbox_smoke.py create mode 100644 scripts/sync_options_manifest.py create mode 100644 tests/harborrl/test_delivery_barrier.py create mode 100644 tests/harborrl/test_deployment_config.py create mode 100644 tests/harborrl/test_deployment_preflight.py create mode 100644 tests/harborrl/test_deployment_targets.py create mode 100644 tests/harborrl/test_math_interfaces.py create mode 100644 tests/harborrl/test_native_terminal.py create mode 100644 tests/harborrl/test_options.py create mode 100644 tests/harborrl/test_rollout_runtime.py create mode 100644 tests/harborrl/test_rollout_service.py create mode 100644 tests/harborrl/test_sandbox.py create mode 100644 tests/harborrl/test_sandbox_allocation_transport.py create mode 100644 tests/harborrl/test_sandbox_cleanup.py create mode 100644 tests/harborrl/test_sandbox_portability.py create mode 100644 tests/harborrl/test_sandbox_runtime.py create mode 100644 tests/harborrl/test_sandbox_scale.py create mode 100644 tests/harborrl/test_service_group_concurrency.py create mode 100644 tests/harborrl/test_webui.py create mode 100644 tests/test_consistency.py create mode 100644 tests/test_consistency_torch.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 280801786..c8c90eead 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -31,6 +31,35 @@ jobs: - name: Public tree audit run: bash scripts/audit_public_tree.sh + documentation-history: + if: github.event_name == 'pull_request' + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + - name: Audit documents in every new commit + env: + DOCUMENTATION_BASE: ${{ github.event.pull_request.base.sha }} + run: bash scripts/audit_public_tree.sh --history-base "$DOCUMENTATION_BASE" + + sandbox-contracts: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + - name: Install optional runtime and practice + run: python -m pip install -e '.[dev,sandbox]' -e practices/h_cluster + - name: SDK contracts without allocation + run: python scripts/sandbox_sdk_contract.py + - name: Optional lifecycle and practice tests + run: python -m pytest tests/harborrl/test_sandbox_cleanup.py practices/h_cluster/tests + package: runs-on: ubuntu-latest needs: cpu diff --git a/.gitignore b/.gitignore index 2f4e7f480..7755b672b 100644 --- a/.gitignore +++ b/.gitignore @@ -32,7 +32,20 @@ harbor_datasets/ .tools/ .worktrees/ benchmarks/harbor/0.5-mvp/ -docs/ +docs/* +!docs/agents/ +docs/agents/* +!docs/agents/environment-check.md +!docs/configuration-wizard.md +!docs/deployment.md +!docs/sandbox-rollout.md +!docs/train-inference-consistency.md +!docs/web-console.md +practices/**/VALIDATION.md +practices/**/HISTORICAL_VALIDATION.md +integration-report-*.md +integration-roadmap-*.md +*.bundle local/ configs/site/ examples/**/private* diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 98b03e866..46f7e2791 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -13,6 +13,25 @@ HarborRL accepts changes through pull requests from a public branch. 6. Submit a reviewable commit series and describe external versions, task provenance, and any acceptance evidence. +## Public documentation + +Commit documentation that helps users install, configure, run or troubleshoot +HarborRL. Keep development plans, integration reports, experiment logs and +machine-specific validation records outside the repository. Summarize current +support and limitations in the relevant user guide without development history. + +The public-document allowlist is maintained in `scripts/audit_public_tree.sh`. +Update it deliberately when adding a user guide. Check both the final tree and +every new commit before opening a pull request: + +```bash +bash scripts/audit_public_tree.sh --history-base origin/main +``` + +Deleting a private document in a later commit does not remove it from history. +Clean unpublished commits before pushing; do not merge a development branch +whose history contains private documents into a public branch. + Configuration and task fixtures must use generic placeholders. Absolute personal paths, internal DNS names, private registry references, and unexplained large files will fail review. diff --git a/NOTICE.md b/NOTICE.md index 4dc4f09ac..3ab8ecf6f 100644 --- a/NOTICE.md +++ b/NOTICE.md @@ -2,6 +2,10 @@ HarborRL is MIT-licensed by Princeton-AI. +The local web console bundles Lucide 1.61.0 (ISC license). Its unmodified browser +distribution and license are in `harborrl/webui/static/lucide.min.js` and +`harborrl/webui/static/lucide-LICENSE.txt`. No CDN connection is required. + The public package does not vendor Slime, Megatron-LM, SGLang, Harbor, Claude Code, or model weights. Operators are responsible for reviewing the licenses of the exact external versions they pin and for complying with model and dataset diff --git a/README.md b/README.md index cc82cddd7..1b1b8ac7d 100644 --- a/README.md +++ b/README.md @@ -2,9 +2,9 @@ HarborRL

- +

A lightweight agentic reinforcement learning framework for everyone @@ -25,6 +25,8 @@ ## News +- **2026-10-26** Added an [offline configuration wizard](docs/configuration-wizard.md) and a [local Web console](docs/web-console.md) for configuration, validation, dry-runs, training launch, and logs. +- **2026-10-26** Added [controller/GPU deployment configuration](docs/deployment.md), optional [OpenSandbox execution](docs/sandbox-rollout.md), and opt-in [train/inference consistency checks](docs/train-inference-consistency.md). - **10/2026** 📣📣📣 HarborRL released ## About @@ -40,6 +42,14 @@ HarborRL supports flexible combinations of **Models × Algorithms × Tasks × Ha ## AI-assisted Configure +For the offline options catalog, interactive configuration, and answer-file setup, +see the [configuration CLI guide](docs/configuration-wizard.md). +Start with `harborrl options` or `harborrl init --help`. + +Run `harborrl web` to open the local browser console for configuration, validation, +dry-runs, training launch, and logs. See the [web console guide](docs/web-console.md) +for remote-controller access and lifecycle details. + If you want an AI agent to help deploy this repository and verify training, ask it to first read and strictly follow [docs/agents/environment-check.md](docs/agents/environment-check.md). @@ -78,6 +88,12 @@ Confirm the result yourself against this checklist: ## Quick Start +The default CPU install does not need a Sandbox SDK or cluster account. +For controller placement, see [deployment](docs/deployment.md). For optional +standard OpenSandbox execution, see [Sandbox rollout](docs/sandbox-rollout.md). +The independently installed [H-cluster practice](practices/h_cluster/README.md) +contains Brainbox-specific deployment and restricted-network relay examples. + ### Install Use Python 3.10 or newer. Runtime dependencies, development dependencies, and the `harborrl` command are locked by [pyproject.toml](pyproject.toml) and [uv.lock](uv.lock). The CPU development install does not require CUDA, Docker, model weights, Slime, Megatron-LM, or SGLang. @@ -128,7 +144,7 @@ The command style is a shorthand over `config.yaml` in the working directory (or ## Task Configuration -Training is configured with one YAML file that has a fixed section and field set. Relative paths resolve against the YAML file, and repeated `--set section.field=value` overrides are parsed as YAML values and validated again. Start site-specific configurations from [examples/native/train_qwen_native.yaml](examples/native/train_qwen_native.yaml). +Training is configured with one YAML file that has a fixed section and field set. Relative paths resolve against the YAML file, and repeated `--set section.field=value` overrides are parsed as YAML values and validated again. Start site-specific configurations from [examples/native/train_qwen_native.yaml](examples/native/train_qwen_native.yaml). Optional [train/inference consistency checks](docs/train-inference-consistency.md) compare frozen rollout scores with the pre-update trainer and probe synchronized inference engines. The configuration has three broad parts: `execution` is fixed to `harbor_job`; `harbor`, `gateway`, and `deployment` describe rollout workers, service endpoints, and GPU topology; `tasks`, `harness`, `model`, `training`, `sampling`, and `output` describe the experiment and its outputs. diff --git a/README_zh.md b/README_zh.md index 18a33b25c..f75e744a9 100644 --- a/README_zh.md +++ b/README_zh.md @@ -25,6 +25,8 @@ ## News +- **2026-10-26** 新增[离线配置向导](docs/configuration-wizard.md)和[本地 Web 控制台](docs/web-console.md)，支持配置、校验、dry-run、训练启动和日志查看。 +- **2026-10-26** 新增 [controller/GPU 部署配置](docs/deployment.md)、可选的 [OpenSandbox 执行能力](docs/sandbox-rollout.md)，以及按需启用的[训练与推理一致性检查](docs/train-inference-consistency.md)。 - **10/2026** 📣📣📣 HarborRL released ## 项目介绍 @@ -43,6 +45,11 @@ HarborRL 支持 **Models × Algorithms × Tasks × Harnesses (MATH)** 的灵活 如果你希望让 AI agent 辅助完成本 repo 的部署和训练验证，请让它先阅读并严格遵循 [docs/agents/environment-check.md](docs/agents/environment-check.md)。 +离线选项目录、交互式配置和 answer-file 配置方式见[配置 CLI 指南](docs/configuration-wizard.md)。 +可以从 `harborrl options` 或 `harborrl init --help` 开始。 +运行 `harborrl web` 可打开本地浏览器控制台，完成配置、校验、dry-run、训练启动和日志查看； +远程访问与生命周期说明见 [Web 控制台指南](docs/web-console.md)。 + 推荐的实际操作流程是把下面这段提示词交给 agent，并把尖括号内容替换成你的机器信息： ```text @@ -73,6 +80,10 @@ doctor 和 launch_native --dry-run。 ## 快速开始 +默认 CPU 安装不需要 Sandbox SDK 或集群账号。 +controller 与 GPU 的部署方式见[部署指南](docs/deployment.md)，可选的标准 OpenSandbox 执行方式见[Sandbox rollout 指南](docs/sandbox-rollout.md)。 +独立安装的 [H-cluster 实践包](practices/h_cluster/README.md)提供 Brainbox 部署与受限网络中继示例。 + ### 安装 使用 Python 3.10 或更新版本。Python 依赖、开发依赖和 `harborrl` 命令由 [pyproject.toml](pyproject.toml) 与 [uv.lock](uv.lock) 锁定。CPU 开发环境不需要 CUDA、Docker、模型权重、Slime、Megatron-LM 或 SGLang。 diff --git a/docs/configuration-wizard.md b/docs/configuration-wizard.md new file mode 100644 index 000000000..441a6138b --- /dev/null +++ b/docs/configuration-wizard.md @@ -0,0 +1,140 @@ +# Configuration CLI + +The configuration tools run with the core package (Python 3.10+ and PyYAML). +They enumerate implemented capabilities, check configuration offline, and create +consistent training schema 2 / deployment schema 1 files. They do not install +software, download tasks or models, connect over SSH, or allocate Sandbox/GPU jobs. + +## Browse and validate + +```bash +harborrl options +harborrl options deployments --json +harborrl options fields --json +harborrl validate site/config.yaml --deployment site/deployment.yaml --json +harborrl validate site/config.yaml --deployment site/deployment.yaml --check-files +``` + +Catalog dimensions are `models`, `algorithms`, `tasks` (task providers), +`harnesses`, `datasets`, `deployments`, `fields`, and `compat`. JSON keeps the +`catalog_version` and `schema_versions` envelope when selecting a dimension. +The catalog reflects registered implementations, not roadmap claims. Dataset +`status` describes support; `available` describes local task files. A wheel does +not include repository examples, and planned datasets are not ready to train. + +Validation defaults to schema, semantics, and cross-file consistency. It does +not open referenced profiles or tasks until `--check-files` is supplied. Run file +checks on the controller: its source/interpreter, profile, task catalog and +Sandbox configuration belong there. Local GPU and runner paths are checked +locally; SSH target paths are deferred. The shared root may be created by a later +run and is not required to exist during configuration. Credentials are never read. + +Checks are `passed`, `failed`, or `deferred`. The report is `invalid` if a check +fails, otherwise `valid_with_deferred_checks` while live checks remain. Exit codes +are 0 for no failed checks, 1 for validation/creation failure, and 2 for CLI usage +errors. `--json` writes one JSON object to stdout. Schema validators may report +only their first error. Without `--deployment`, the existing CLI discovery of +`./deployment.yaml` and embedded fallback is preserved and reported. + +GPU memory fit, model loading, live tokenizer fingerprints, package versions, +connectivity and platform capacity are not established by offline validation. +Sandbox image bindings are checked locally; trusted site adapters and credentials +are checked later by runner preflight. Sandbox installation/profile timing +compatibility is additionally checked by the existing training dry-run. + +## Prepare inputs + +1. Obtain Harbor-format tasks and run `harborrl hub inspect --tasks-dir /path/tasks + --dataset my-tasks --revision pinned-revision --run-dir /path/inspection`. + The resulting JSONL is an inspection report, not a training catalog. Build a + JSON list (or JSONL records with a `.jsonl` suffix) containing exactly `id`, + `revision`, `path`, `task_digest`, and `reward_profile` for each selected task. + Copy `path` and `task_digest` from a `NEEDS_PROBE` inspection record, assign a + unique stable `id`, retain the pinned revision, and explicitly set the verifier's + reward contract (for example `reward_profile: {key: reward}`). See + [the catalog example](../examples/native/catalog.jsonl). `INVALID` and + `UNSUPPORTED` inspection results cannot be made trainable by changing labels. + Do not replace computed digests with placeholder values. +2. Prepare a pinned Claude Code profile. See + [the native examples](../examples/native) for profile structure. It must match + the agent and task requirements of your deployment. +3. Stage checkpoint and reference directories on the GPU target. In its existing + training environment, load the actual local tokenizer with the same options as + the runtime and call `harborrl.rollout.runtime.tokenizer_fingerprint(tokenizer)`. + Its return values are `tokenizer_digest, template_digest`. This is an explicit + preparation step, not an operation performed by the wizard. Hashing a filename + or guessing an arbitrary 64-character value does not establish compatibility. +4. Identify controller/GPU/runner Python executables, checkout paths, SSH aliases + and shared storage. Service origins must include their declared ports and be + reachable by the participating machines. +5. For Sandbox, prepare the existing platform configuration and image bindings. + H-cluster users follow [the optional H practice](../practices/h_cluster/README.md) + and reference its site file. The core CLI never asks for H project/environment + identifiers or embeds private endpoints or credentials. + +## Generate + +```bash +harborrl init --out-dir ./site-new +harborrl init --answers my-answers.yaml --out-dir ./site-new +``` + +Interactive mode asks for deployment roles, existing inputs, model fingerprints, +GPU layout and a few sampling settings, then previews both configurations and +requires `yes` to publish. Entering no or EOF cancels. Non-terminal use requires +`--answers`; missing required answers never fall back to prompting. + +Start from one of these complete answer templates and replace every `REPLACE_*` +value and example path before running: + +- [embedded-local](../examples/options/embedded-local.answers.yaml): one machine, + local Harbor runner, colocated training/serving by default. +- [service-ssh](../examples/options/service-ssh.answers.yaml): controller, SSH GPU + target and explicit SSH Docker workers. +- [service-sandbox-local-runner](../examples/options/service-sandbox.answers.yaml): + controller and runner here, SSH GPU target, Sandbox task execution. + +Templates deliberately contain invalid digest placeholders. They cannot claim +training readiness without your real inputs. Default one-GPU colocate is a +configuration starting point, not a Qwen model memory-fit recommendation. + +Answers allow only `answers_version: 1`, `preset`, `training`, `deployment`, and +optional `sandbox_config`. Overrides use the existing schema section/field names; +unknown keys fail. No expressions or shell commands are evaluated. Gateway fields, +shared run root, GPU budget and worker destinations are derived when only one +side is provided. Contradictory explicit values fail. Supply matching origin URLs +when changing ports. Paths in training overrides are relative to the **final +output directory**, not the answers file. Deployment machine paths are absolute. + +The Sandbox preset can also describe remote runners through an answers file: +set `training.harbor.runner_transport: ssh` and provide deployment rollout workers +with `docker: false`. The builder derives training worker destinations and the +first worker's runner Python. All configured interpreters still require preflight. + +`--probe-local` explicitly enables bounded local `nvidia-smi` UUID and current +Python version queries. It also reads explicit SSH Host aliases (no wildcard, +Match execution or Include expansion) and gives a parent-directory access hint. +Missing tools and failed commands return `unknown`, not zero GPUs. Suggestions +never silently change answers; no remote probe or write permission test occurs. + +The output directory must not exist, and its parent must exist. Publication uses +an exclusive per-target lock, same-parent staging and a directory rename. Existing +files are never overwritten. Failures clean up only this invocation's staging and +lock. After an abrupt process termination, inspect any remaining `.NAME.init.lock` +and staging directory before removing them; automatic stale-lock recovery is not +attempted. The lock coordinates wizard processes, not arbitrary external writers. + +The bundle contains `config.yaml`, `deployment.yaml`, and `setup-manifest.json`. +The manifest records schema versions, configuration digests, references and the +offline validation scope, including deferred checks. It does not copy credentials. + +The CLI prints these next commands without executing them: + +```bash +harborrl validate site-new/config.yaml --deployment site-new/deployment.yaml --check-files +harborrl train site-new/config.yaml --deployment site-new/deployment.yaml --dry-run +harborrl doctor site-new/config.yaml --deployment site-new/deployment.yaml +``` + +`doctor` performs live environment/connectivity checks. Actual training remains a +separate action. Command examples use POSIX shell quoting. diff --git a/docs/deployment.md b/docs/deployment.md new file mode 100644 index 000000000..2f35a8501 --- /dev/null +++ b/docs/deployment.md @@ -0,0 +1,110 @@ +# Deployment configuration and the controller placement + +A `deployment.yaml` +describes where the manager, GPU training target and rollout workers live and +how they reach each other; the training YAML keeps every training-side field +(learning rate, sampling, task catalog) and is unchanged. + +```bash +harborrl train train.yaml --deployment deployment.yaml +``` + +Without `--deployment`, a `deployment.yaml` in the working directory is picked +up; when no file exists the compatible embedded-local topology is used and the +legacy same-machine behavior is preserved bit-for-bit (recorded in the plan as +`deployment.source: embedded-local`). + +## What the schema covers (v1) + +- `manager` — the HarborRL controller node: `embedded` or `service` mode, bind + address, control/gateway ports and the URLs other machines use to reach them. + In `service` mode both URLs must be plain origins that match their ports and + must not be loopback. +- `gpu_target` — the Slime training machine: `local` or `ssh` transport, the + checkout (`source_dir`), training interpreter (`train_python`), an optional + controlled `environment_file` (a JSON variable map, never a shell), and + `expected_gpus`. +- `storage` — `shared_fs` mode with the shared `run_root` and writability + expectations per role. +- `rollout` — `ssh_docker` workers: SSH destination, runner interpreter, + Docker requirement and task roots. The `embedded` provider keeps worker + discovery in the training config. +- `connectivity` — timeouts and which control-loop hops are required. + +Secrets are rejected by the schema: no passwords, keys or API tokens. SSH +destinations resolve through the invoking user's SSH config, agent or a +restricted identity file. Paths are interpreted in target-machine semantics — +the manager never assumes its own `sys.executable` works on the GPU or workers. +See `examples/native/deployment-embedded.yaml` and +`examples/native/deployment-service.yaml`. + +## Preflight graph + +`train` and `doctor` share one check graph, executed at the earliest launch +stage. Each check records the role and machine it ran against, the outcome, +timing, and a user-executable fix. Any failed check exits nonzero before a run +directory or training process is created. + +Layer order: manager local state (run root, token file, ports) → GPU target +(SSH reachability, paths, model paths, visible GPU budget) → rollout workers +(SSH, runner Python >= 3.12, pinned Harbor version, Docker, task roots) → +control-loop addresses (DNS resolution now, live health dials after components +start). `--dry-run` parses both configs and prints the resolved plan without +any remote probing or side effects. + +When the GPU target is remote, machine-local dependency probes (Ray, Torch, +Slime imports, SGLang image, `nvidia-smi`) do not run on the manager: they are +enforced fail-closed by `slime_train.sh` at GPU job startup. The manager CPU +needs no GPU dependencies. + +## TrainingTarget + +`harborrl/deployment/training_target.py` launches one Slime job either as a +local child (legacy path) or over SSH (service mode). The SSH target writes a +generated `gpu-launcher.sh` into the shared run directory, starts it detached +(`setsid`) on the GPU machine, records the launcher pid, polls the shared +`exit-code` record, and cancels by signalling the remote process group +(INT → TERM → KILL). It never resubmits or restarts a job. + +## RolloutService (manager mode) + +`harborrl/rollout/service.py` is the manager-side control plane: HTTP JSON +with run-scoped tokens. + +- `GET /healthz`, `GET /readyz` +- `POST /runs/{run_id}/register` — idempotent by manifest digest; the same id + with a different manifest is a 409. +- `POST /runs/{run_id}/groups` — idempotent group submission. Same group id + and content returns the existing state; same id with different content is a + 409; submitting against a non-admitted policy version is a 409. On timeout + the response says `running` and points at the status endpoint — the client + must query, never resubmit. +- `GET /runs/{run_id}/groups/{group_id}`, `POST .../cancel` +- `POST /runs/{run_id}/policy/activate` — validates the whole serving pool is + healthy on the expected version, drains old admission, then opens the new + version and publishes `policy-pool.json` into the shared run directory. +- `POST /runs/{run_id}/serving/announce` — the GPU job publishes its inference + endpoint; the manager never hand-configures it. +- `POST /runs/{run_id}/drain`, `POST /runs/{run_id}/cancel` + +Bulky data (trajectory manifests, token ids, logprobs, loss masks, group +export) is written to the shared run directory. HTTP responses carry paths +plus SHA-256 digests; `GroupResult` also carries per-slot identity, finish +reason, reward, trace seal status and verifier digest. + +The GPU-side adapter (`harborrl/backends/slime_v032/rollout_service_client.py`) +recomputes every manifest digest, checks identities against the request, +requires a complete group of at least two slots on the locked policy version, +and only then converts trajectories into Slime samples. It is selected by +`HARBORRL_ROLLOUT_SERVICE_URL` in the GPU job environment. + +## Operational limits + +Service mode requires shared storage visible at the configured paths on the +controller, GPU target and runners. A successful dry run checks configuration; +it does not establish network reachability, available capacity or training +quality. Check connectivity with doctor and inspect run artifacts after launch. + +Choose worker and group concurrency for the target deployment. Instance +readiness does not imply that an agent is running, and a completed checkpoint +does not establish learning improvement. diff --git a/docs/sandbox-rollout.md b/docs/sandbox-rollout.md new file mode 100644 index 000000000..6fdc9d19a --- /dev/null +++ b/docs/sandbox-rollout.md @@ -0,0 +1,72 @@ +# Sandbox rollout + +Sandbox is an optional execution environment under the common Controller, +RolloutService, Runtime and Harbor Runner. Docker execution remains the default. +The core does not import the SDK until an explicitly selected runner needs it. + +## Standard OpenSandbox + +Use Python 3.12 or newer for the runner and install `pip install -e '.[sandbox]'`. +Start with `examples/native/sandbox.yaml`. Set the service origin (without a +trailing /v1), a private credential-file reference and exact task image bindings. +Standard credentials are opaque tokens, not a platform-specific AK:SK format. + +Select the environment in your training config: + +```yaml +harbor: + python: /opt/harborrl/runner/bin/python + version: 0.23.0 + runner_transport: local + workers: [] + environment: + runtime: sandbox + config: /srv/harborrl/site/sandbox.yaml +``` + +For service placement, use `rollout.provider: sandbox` in the deployment file. +A local runner requires an empty deployment worker list. SSH runners require +explicit workers, the same shared task/evidence paths and the adapter installed +on each runner. Runner placement is independent of the environment provider. +The target and route must be validated before claiming that an SSH/Sandbox +deployment works on a real platform. + +The standard adapter uses Harbor's OpenSandbox implementation and adds durable +allocation tracking, bounded lifecycle operations and cleanup-debt records. +Custom trusted adapters subclass ManagedOpenSandboxEnvironment and are selected +through `adapter: module:Class`; arbitrary task input cannot select code. +Provider settings belong in `options` and per-image binding `options`. +They must not contain inline secrets. Core plan construction does not import +the adapter, read credentials, connect to a platform or allocate instances. + +The optional `installation` block supports a pinned offline Claude binary: +`mode: offline_binary`, `version`, `binary`, and its 64-character `sha256`. +Without it, Harbor performs normal agent setup, which can need network access. + +## Concurrency and evidence + +`max_live_instances` bounds all active instances; each image binding has its +own limit. Binding capacity is acquired before global capacity to avoid +starvation. `startup_concurrency` separately bounds simultaneous SDK startup +using process-shared locks. Shared-filesystem flock support is required when +runners use the same lock directory across hosts. + +Groups use the same identity, policy barrier, verifier and consumption audit +as Docker. Failed slots cancel their group siblings; submitted independent +groups finish before a partial batch is rejected. Unconfirmed cleanup blocks +new admission. Production export never accepts synthetic evidence. + +## Checks + +`python scripts/sandbox_sdk_contract.py` checks installed APIs and fake lifecycle +cleanup without contacting a platform. `sandbox_probe` checks the selected +adapter, SDK, credential-file permissions and optional binary locally. +Its explicit `--network` mode is adapter-specific and reports skipped checks. + +`python scripts/sandbox_smoke.py CONFIG --output NEW_DIRECTORY --gateway URL` +is a live allocation test and consumes platform resources. A successful CPU +test or dependency probe does not establish live training acceptance. + +For Brainbox and developer-mediated networking, see the independently +installable [H-cluster practice](../practices/h_cluster/README.md). H-specific +configuration from the experimental branch must be migrated as described there. diff --git a/docs/train-inference-consistency.md b/docs/train-inference-consistency.md new file mode 100644 index 000000000..f4bd4d2d2 --- /dev/null +++ b/docs/train-inference-consistency.md @@ -0,0 +1,79 @@ +# Train / inference consistency + +Consistency checks compare log probabilities of the **same response tokens at +the same prefix**, before an optimizer update. This measures backend drift; +it does not measure model quality or full-distribution KL divergence. + +The three policies are the observed behavior `q`, frozen pre-update trainer +`p0`, and current trainer `pθ`. Backend drift is `log p0 - log q`; optimization +drift is `log pθ - log p0`. The Native loss remains unchanged. No TIS, clipping +correction, or new training objective is introduced. + +## Configuration + +Native schema 2 accepts an optional `consistency` mapping. Its default mode is +`off`. Existing configurations remain valid. `audit` records numerical violations; +`enforce` rejects violations before +training or serving admission. Invalid evidence always rejects, including in +audit mode. Enforce requires an explicit `max_abs_delta`. Calibrate tolerances +for the actual model, precision, parallel layout and kernels. + +```yaml +consistency: + mode: audit + max_abs_delta: null + max_mean_abs_delta: null + bad_token_delta: null + max_bad_fraction: 0.0 + worst_tokens: 8 +``` + +Native overrides use the existing `--set consistency.mode=audit` interface. +For enforce, configure a deployment-specific `max_abs_delta` in YAML before +loading the configuration. The configuration is preserved in launch evidence. + +## Native Megatron / SGLang + +The enabled launcher runs `harborrl.consistency.train`, which uses the pinned +Slime training loop and its supported actor factory. It never edits upstream +Slime. The existing pre-update forward supplies actual packed-batch `p0` values. +Immutable serving sidecars bind token IDs, float32-normalized behavior scores, +response masks, policy version, tokenizer and template identity. Missing or +mismatched sidecars reject the batch. + +Before each weight synchronization, the actor evaluates two fixed token +fixtures using Megatron. Every synchronized SGLang engine then teacher-forces +those exact sequences. Generation-time and post-probe versions must both match. +Each engine must satisfy the numerical limits independently. Checks also cover +the final sync, even when no subsequent rollout occurs. Probe records are reused +only for the same pool, configuration and immutable reference. A fixed canary +is a smoke check; actual rollout-batch checks provide broader coverage. + +Supported initial scope: dense models, synchronous full NCCL synchronization, +PP=CP=1, with TP and DP. Temperature and top-p must be 1; top-k must be disabled. +Critic, old-actor queues, MoE, release-train and routing replay are rejected. +Transformed sampling would require a separate definition of behavior scores. + +`RUN_DIR/consistency` contains sync references, pool admissions, serving-turn +sidecars and `batch-*.json` reports. References include a bounded sampled +parameter fingerprint per rank; this is **not** a full weight checksum. Reports +contain valid-token-weighted absolute/signed errors, log-ratio range, bad-token +fraction, bounded worst positions and histogram percentile **upper bounds**. +They do not copy raw prompt or response text. TP duplicate counts are excluded. +Distributed validation collects rank errors before all ranks reject. + +## Validation and limitations + +Run `pytest -q tests/test_consistency.py` for the dependency-free checks. With +Torch installed, run `pytest -q tests/test_consistency_torch.py` for two-rank +failure propagation. These tests do not run a separate training loop. + +GPU acceptance must additionally exercise the locked Megatron / SGLang versions +on the target hardware: initial sync, actual rollout tokens, optimizer update, +final sync and checkpoint resume. Record dtype, attention backend, TP/DP, model +revision and measured error distribution. CPU tests and mocked HTTP responses +cannot certify SGLang input-logprob behavior or CUDA numerical tolerances. +This implementation has not yet passed that live GPU acceptance gate. + +H-cluster relay topology remains a deployment practice. No H-cluster hostnames, +private paths or machine-specific numerical thresholds belong in this module. diff --git a/docs/web-console.md b/docs/web-console.md new file mode 100644 index 000000000..3d9603ccc --- /dev/null +++ b/docs/web-console.md @@ -0,0 +1,110 @@ +# Local web console + +```bash +harborrl web --workspace ./harborrl-web +``` + +This opens a browser on an available loopback port. Keep the terminal running. +`--port 8765` chooses a fixed port; `--no-browser` prints the URL without opening +it. The workspace stores saved configuration bundles and execution records. +One console process owns a workspace at a time. The web console uses the core +Python dependencies; Node, a frontend build and CDN access are not required. + +## Configure and launch + +1. Select Local, SSH workers, or Sandbox. Fill controller/GPU/runner paths and + service origins. Source and interpreter paths refer to the selected machine. + The controller source directory and Python executable select the checkout and + environment used by the launched CLI. +2. Provide existing checkpoint/reference directories, a pinned task catalog, + harness profile and real tokenizer/template fingerprints. The + [configuration CLI guide](configuration-wizard.md) describes preparing these + inputs. The console never downloads or generates model inputs. +3. Choose GPU layout, tensor parallelism, rollout batches and sampling settings. + Allocation defaults are configuration values, not a model memory-fit estimate. +4. Review the generated YAML and validation. **Check local files** additionally + checks references on the controller. Deferred GPU, connectivity, model and + Sandbox checks remain explicit. Save the bundle under a new configuration name. +5. Run **Dry-run** to build the actual CLI launch plan. **Doctor** performs live + checks after confirmation. **Start training** requires a separate confirmation + and invokes the existing `harborrl train` flow, including its preflight. + +Configuration names are limited to letters, digits, underscores and hyphens. +Saved configurations are not overwritten. Reopen one to launch it again, or edit +and save under a new name. The advanced answers editor and YAML/JSON import use +the existing `answers_version: 1` contract. Download an individual generated file +or export answers from the editor. Advanced remote Sandbox runners can be +specified through imported answers; the form preset defaults to a local runner. + +H-cluster deployments remain the optional +[H practice](../practices/h_cluster/README.md). Prepare its Sandbox configuration +separately and reference the path. H project/environment identifiers and relay +settings are not part of the core web form. + +Windows supports configuration and dry-run. Live training/doctor controls require +the console to run on a POSIX controller, using the same environment and checkout +that would normally run the training CLI. Deployment readiness still depends on +the project's supported Linux training environment. + +## Runs and shutdown + +Only one execution is active per workspace, including doctor and dry-run. This +prevents duplicate launches within this console; it is not a cluster-wide GPU +scheduler. Other CLI processes or different workspaces can still use the same +resources, and operators must coordinate those allocations. + +Each job keeps immutable input files and a durable `job.json`/`console.log` under +`WORKSPACE/jobs/JOB_ID`. Execution appends `web/JOB_ID` to the configured shared +run root and updates both config files in its snapshot. The original saved bundle +is unchanged. The existing runtime then creates its normal `training/...` run +directory there. This isolates artifacts while retaining the configured shared +filesystem. Run logs show the actual snapshot and output paths. + +The Runs view polls status and the last 64 KiB of output. **Controller** shows the +CLI output and errors. **GPU training** reads the separate `train-stdout.log` when +the service deployment creates one. Embedded training generally streams into +the controller log. Full logs remain on disk; the browser download contains the +currently displayed tail. This initial release does not aggregate WandB curves +or claim remote/GPU progress from a quiet controller log. + +**Stop** sends an interrupt through the existing CLI lifecycle. Local training +forwards it to the training process group; service mode uses the existing SSH +target cancellation. `stopping` means cleanup is pending, not that resources are +already free. Closing the browser does not stop a job. Ctrl+C or SIGTERM in the +server terminal requests cleanup; shutdown waits up to 30 seconds. A timeout +records an unknown state without force-killing arbitrary remote processes. + +After a crash or interrupted shutdown, previously running jobs appear as +`unknown`. New launches are blocked. Inspect the target processes, shared run +records and Sandbox cleanup evidence, then use **Confirm cleanup** to acknowledge +that external cleanup is complete. This action records operator confirmation; it +does not kill a stored PID or automatically relaunch a job. The server never +signals a PID recovered from an old session. + +## Remote controller + +On the controller, from the intended checkout/environment: + +```bash +python -m harborrl.cli web --workspace /srv/shared/web-console --port 8765 --no-browser +``` + +On the workstation, create a local SSH forward: + +```bash +ssh -N -L 127.0.0.1:8765:127.0.0.1:8765 your-controller-alias +``` + +Open the URL printed by the controller in the workstation browser. If the local +forward uses a different port, replace only the URL port. Leave the `#token=...` +fragment intact. If either port is occupied, choose an unused port; omitting +`--port` selects one automatically and prints it. + +The listener is deliberately fixed to `127.0.0.1`. A random per-session token +protects API reads and writes; the browser moves the URL fragment into +sessionStorage and removes it from the address bar. State-changing requests also +require a same-origin JSON request. Responses use CSP, frame blocking and +no-store headers, and assets are packaged locally. Keep the token private: this +is a local single-user console with the controller user's filesystem and training +privileges. It is not a public multi-user service or an Internet-facing gateway. +Do not expose it through a public reverse proxy. diff --git a/examples/native/catalog.jsonl b/examples/native/catalog.jsonl new file mode 100644 index 000000000..eba16a31c --- /dev/null +++ b/examples/native/catalog.jsonl @@ -0,0 +1 @@ +{"id": "hello_world", "path": "tasks/hello_world", "revision": "v0.1-example", "reward_profile": {"key": "reward", "offset": 0.0, "raw_range": [0.0, 1.0], "scale": 1.0}, "task_digest": "905f496d2852cca5b1e86e9de72395b41c291d2b8f6a80807e0ed24ca19541b3"} diff --git a/examples/native/deployment-embedded.yaml b/examples/native/deployment-embedded.yaml new file mode 100644 index 000000000..12c5cef81 --- /dev/null +++ b/examples/native/deployment-embedded.yaml @@ -0,0 +1,45 @@ +# Embedded (same-machine) deployment: identical to the legacy pipeline. +# Use this file explicitly, or simply omit --deployment to get the same +# embedded-local topology built from the training config. +schema_version: 1 + +manager: + id: embedded-local + mode: embedded + bind: 127.0.0.1 + control_port: 49200 + control_url: http://127.0.0.1:49200 + gateway_port: 49184 + gateway_url: http://127.0.0.1:49184 + run_token_file: /run/secrets/harborrl/run-token + source_dir: /opt/harborrl/source + controller_python: /opt/harborrl/controller/bin/python + +gpu_target: + id: local + transport: local + ssh: null + source_dir: /opt/harborrl/source + train_python: /opt/harborrl/training/bin/python + environment_file: null + expected_gpus: 8 + +storage: + mode: shared_fs + run_root: /mnt/shared/harborrl/runs + manager_writable: true + gpu_writable: true + worker_writable: true + +rollout: + # The embedded provider keeps worker discovery in the training config. + provider: embedded + workers: [] + +connectivity: + command_timeout_s: 30 + ssh_timeout_s: 15 + http_timeout_s: 10 + gpu_to_controller: true + worker_to_gateway: true + manager_to_gpu_inference: true diff --git a/examples/native/deployment-service.yaml b/examples/native/deployment-service.yaml new file mode 100644 index 000000000..b3584f5f9 --- /dev/null +++ b/examples/native/deployment-service.yaml @@ -0,0 +1,43 @@ +# Replace example hosts and paths; no site-specific addresses are required. +schema_version: 1 +manager: + id: manager + mode: service + bind: 0.0.0.0 + control_port: 49200 + control_url: http://manager.example:49200 + gateway_port: 49184 + gateway_url: http://manager.example:49184 + run_token_file: /srv/harborrl/site/run-token + source_dir: /srv/harborrl/repo + controller_python: /opt/harborrl/controller/bin/python +gpu_target: + id: trainer + transport: ssh + ssh: {host: training-host} + source_dir: /srv/harborrl/repo + train_python: /opt/harborrl/training/bin/python + environment_file: /srv/harborrl/site/gpu-env.json + expected_gpus: 8 +storage: + mode: shared_fs + run_root: /srv/harborrl/runs + manager_writable: true + gpu_writable: true + worker_writable: true +rollout: + provider: ssh_docker + workers: + - id: worker-a + ssh: {host: rollout-host} + source_dir: /srv/harborrl/repo + runner_python: /opt/harborrl/runner/bin/python + docker: true + task_roots: [/srv/harborrl/tasks] +connectivity: + command_timeout_s: 30 + ssh_timeout_s: 15 + http_timeout_s: 10 + gpu_to_controller: true + worker_to_gateway: true + manager_to_gpu_inference: true diff --git a/examples/native/sandbox.yaml b/examples/native/sandbox.yaml new file mode 100644 index 000000000..4ab3788a5 --- /dev/null +++ b/examples/native/sandbox.yaml @@ -0,0 +1,10 @@ +# Standard OpenSandbox; install harborrl[sandbox] in a Python 3.12+ runner. +domain: https://sandbox.example +credential_file: /run/secrets/harborrl/sandbox-key +max_live_instances: 2 +startup_concurrency: 1 +bindings: + - image: registry.example/project/task:locked-version + max_live_instances: 2 +# Omit installation to use normal Harbor agent setup. Offline deployments +# may supply mode, version, binary and sha256; see docs/sandbox-rollout.md. diff --git a/examples/options/embedded-local.answers.yaml b/examples/options/embedded-local.answers.yaml new file mode 100644 index 000000000..7a4bc5db1 --- /dev/null +++ b/examples/options/embedded-local.answers.yaml @@ -0,0 +1,30 @@ +# Replace every example path and REPLACE_* value. No model fit is implied. +answers_version: 1 +preset: embedded-local +training: + tasks: + catalog: /srv/harborrl/inputs/tasks.jsonl + harness: + profile: /srv/harborrl/inputs/claude-code.json + harbor: + python: /opt/harbor/bin/python + model: + checkpoint: /srv/models/qwen + reference: /srv/models/qwen-reference + args_file: qwen3-8B + gateway: + tokenizer_digest: REPLACE_WITH_ACTUAL_TOKENIZER_SHA256 + template_digest: REPLACE_WITH_ACTUAL_TEMPLATE_SHA256 + deployment: + layout: colocate + num_gpus: 1 + sampling: + group_size: 2 + groups_per_batch: 1 +deployment: + manager: + source_dir: /srv/harborrl/source + controller_python: /opt/controller/bin/python + gpu_target: + source_dir: /srv/harborrl/source + train_python: /opt/training/bin/python diff --git a/examples/options/service-sandbox.answers.yaml b/examples/options/service-sandbox.answers.yaml new file mode 100644 index 000000000..a55de6d2f --- /dev/null +++ b/examples/options/service-sandbox.answers.yaml @@ -0,0 +1,34 @@ +# Prepare a standard or practice-specific Sandbox config separately. +answers_version: 1 +preset: service-sandbox-local-runner +sandbox_config: /srv/shared/site/sandbox.yaml +training: + tasks: + catalog: /srv/shared/inputs/tasks.jsonl + harness: + profile: /srv/shared/inputs/claude-code.json + harbor: + python: /opt/sandbox-runner/bin/python + model: + checkpoint: /srv/models/qwen + reference: /srv/models/qwen-reference + args_file: qwen3-8B + gateway: + tokenizer_digest: REPLACE_WITH_ACTUAL_TOKENIZER_SHA256 + template_digest: REPLACE_WITH_ACTUAL_TEMPLATE_SHA256 + deployment: + layout: colocate + num_gpus: 1 +deployment: + manager: + source_dir: /srv/shared/harborrl + controller_python: /opt/controller/bin/python + control_url: http://controller.example:49200 + gateway_url: http://controller.example:49184 + gpu_target: + ssh: + host: gpu-training + source_dir: /srv/shared/harborrl + train_python: /opt/training/bin/python + storage: + run_root: /srv/shared/runs diff --git a/examples/options/service-ssh.answers.yaml b/examples/options/service-ssh.answers.yaml new file mode 100644 index 000000000..7a5851da4 --- /dev/null +++ b/examples/options/service-ssh.answers.yaml @@ -0,0 +1,44 @@ +# All paths refer to the named role's machine. Replace before use. +answers_version: 1 +preset: service-ssh +training: + tasks: + catalog: /srv/shared/inputs/tasks.jsonl + harness: + profile: /srv/shared/inputs/claude-code.json + model: + checkpoint: /srv/models/qwen + reference: /srv/models/qwen-reference + args_file: qwen3-8B + gateway: + tokenizer_digest: REPLACE_WITH_ACTUAL_TOKENIZER_SHA256 + template_digest: REPLACE_WITH_ACTUAL_TEMPLATE_SHA256 + deployment: + layout: split + num_gpus: 8 + actor_gpus: 4 + rollout_gpus: 4 + actor_tensor_parallel_size: 4 + rollout_gpus_per_engine: 4 +deployment: + manager: + source_dir: /srv/shared/harborrl + controller_python: /opt/controller/bin/python + control_url: http://controller.example:49200 + gateway_url: http://controller.example:49184 + gpu_target: + ssh: + host: gpu-training + source_dir: /srv/shared/harborrl + train_python: /opt/training/bin/python + storage: + run_root: /srv/shared/runs + rollout: + workers: + - id: runner-0 + ssh: + host: rollout-worker + source_dir: /srv/shared/harborrl + runner_python: /opt/harbor/bin/python + docker: true + task_roots: [/srv/shared/tasks] diff --git a/harborrl/algorithms/__init__.py b/harborrl/algorithms/__init__.py new file mode 100644 index 000000000..61b32100b --- /dev/null +++ b/harborrl/algorithms/__init__.py @@ -0,0 +1 @@ +"""Training algorithm adapters independent of backend hook plumbing.""" diff --git a/harborrl/algorithms/grpo.py b/harborrl/algorithms/grpo.py new file mode 100644 index 000000000..34e98d7dc --- /dev/null +++ b/harborrl/algorithms/grpo.py @@ -0,0 +1,18 @@ +"""GRPO semantics backed by the existing CPU-verified implementations.""" +from __future__ import annotations + +from harborrl.backends.slime_v032.advantage import compute_advantages_and_returns +from harborrl.backends.slime_v032.loss import clipped_loss, loss_function + + +class GRPOAlgorithmAdapter: + algorithm_id = "grpo" + + def compute_advantages_and_returns(self, args, rollout_data): + return compute_advantages_and_returns(args, rollout_data) + + def clipped_loss(self, *args, **kwargs): + return clipped_loss(*args, **kwargs) + + def backend_loss_function(self, *args, **kwargs): + return loss_function(*args, **kwargs) diff --git a/harborrl/backends/slime_v032/config.py b/harborrl/backends/slime_v032/config.py new file mode 100644 index 000000000..cc52b864d --- /dev/null +++ b/harborrl/backends/slime_v032/config.py @@ -0,0 +1,125 @@ +"""Schema-2 to official Slime launch mapping; no live probes.""" +import json +import os +import sys + +CONFIG_ENV = { + "HF_CKPT": ("model", "checkpoint"), + "REF_LOAD": ("model", "reference"), + "MODEL_ARGS_FILE": ("model", "args_file"), + "MODEL_TAG": ("model", "args_file"), + "NUM_ROLLOUT": ("training", "num_rollout"), + "SAVE_INTERVAL": ("training", "save_interval"), + "LR": ("training", "learning_rate"), + "ROLLOUT_BATCH_SIZE": ("sampling", "groups_per_batch"), + "N_SAMPLES": ("sampling", "group_size"), + "ROLLOUT_MAX_RESPONSE_LEN": ("sampling", "max_tokens"), + "ROLLOUT_MAX_CONTEXT_LEN": ("sampling", "max_context"), + "NUM_GPUS": ("deployment", "num_gpus"), + "ACTOR_GPUS": ("deployment", "actor_gpus"), + "ROLLOUT_GPUS": ("deployment", "rollout_gpus"), + "TP_SIZE": ("deployment", "actor_tensor_parallel_size"), + "ROLLOUT_NUM_GPUS_PER_ENGINE": ("deployment", "rollout_gpus_per_engine"), + "HARBORRL_GPU_LAYOUT": ("deployment", "layout"), + "HARBORRL_NATIVE_RUNNER_PYTHON": ("harbor", "python"), + "HARBORRL_NATIVE_PROFILE": ("harness", "profile"), + "HARBORRL_NATIVE_CATALOG": ("tasks", "catalog"), + "HARBORRL_NATIVE_GATEWAY_HOST": ("gateway", "host"), + "HARBORRL_NATIVE_GATEWAY_PORT": ("gateway", "port"), + "HARBORRL_NATIVE_GATEWAY_URL": ("gateway", "advertised_url"), + "HARBORRL_NATIVE_TOKENIZER_DIGEST": ("gateway", "tokenizer_digest"), + "HARBORRL_NATIVE_TEMPLATE_DIGEST": ("gateway", "template_digest"), + "HARBORRL_NATIVE_MAX_ATTEMPTS": ("sampling", "max_attempts"), + "RUNS_ROOT": ("output", "root"), +} + + +def launch_plan(config): + from harborrl.backends.slime_v032.versions import runtime_contract_id + from harborrl.config import ROOT + from harborrl.config.native import validate, validate_files + from harborrl.math_registry import select_math + from harborrl.tasks.catalog import catalog + validate(config, ROOT / "config.yaml") + validate_files(config) + selection = select_math(config) + entries = catalog(config) + runtime = config['harbor'].get('environment', {'runtime': 'docker'}) + sandbox = None + if runtime['runtime'] == 'sandbox': + from harborrl.rollout.harbor_job.sandbox_config import ( + load_sandbox, + task_binding, + ) + from harborrl.trajectories.native import read_json + sandbox = load_sandbox(runtime['config']) + profile = read_json(config['harness']['profile']) + if sandbox['installation'] and sandbox['installation']['version'] != profile['cli_version']: + raise ValueError('sandbox installation version differs from harness profile') + budget = sum(profile[k] for k in ('agent_timeout_sec', 'setup_timeout_sec', 'verifier_timeout_sec')) + if sandbox['lifecycle_minutes'] * 60 <= budget + sandbox['create_timeout_s'] + 2 * sandbox['cleanup_timeout_s']: + raise ValueError('sandbox lifecycle must cover setup, agent, verifier and cleanup budgets') + for entry in entries: + task_binding(sandbox, entry['path']) + c = config + worker_overrides = os.environ.get('HARBORRL_NATIVE_TASK_WORKER_OVERRIDES', '{}') + try: + overrides = json.loads(worker_overrides) + valid_workers = set(c['harbor']['workers']) + if (not isinstance(overrides, dict) or any(not isinstance(key, str) or not isinstance(value, str) or value not in valid_workers + for key, value in overrides.items())): + raise ValueError + except (ValueError, json.JSONDecodeError) as exc: + raise ValueError('invalid HARBORRL_NATIVE_TASK_WORKER_OVERRIDES') from exc + environment = { + **{name: c[section][field] for name, (section, field) in CONFIG_ENV.items()}, + 'DATASET': selection.task.id, 'ALGO': selection.algorithm.id, + 'HARNESS_OPTION': f"native-{selection.harness.id.replace('_', '-')}", + 'TRAIN_PYTHON': sys.executable, + 'HARBORRL_STRUCTURED_LAUNCH': '1', 'HARBORRL_VERIFY_POLICY_POOL': '1', + 'HARBORRL_SKIP_GLOBAL_CLEANUP': '1', 'SLIME_RAY_PLACEMENT_GPU_PROBE': '1', + 'PYTORCH_CUDA_ALLOC_CONF': ('max_split_size_mb:2048' if c['deployment']['layout'] == 'colocate' + else 'max_split_size_mb:2048,expandable_segments:True'), + 'HARBORRL_NATIVE_ROLLOUT': '1', + 'HARBORRL_NATIVE_SLIME_CONTRACT': runtime_contract_id(c['training']['backend_contract']), + 'HARBORRL_NATIVE_RUNNER_WORKERS': json.dumps(c['harbor']['workers']), + 'HARBORRL_NATIVE_TASK_WORKER_OVERRIDES': json.dumps(overrides, sort_keys=True), + 'HARBORRL_NATIVE_AUDITED_LOGPROBS': str(int(c['gateway']['audited_raw_logprobs'])), + 'HARBORRL_NATIVE_DRAIN_TIMEOUT': '30', + 'WANDB_ENABLE': '0', + 'HARBORRL_CONSISTENCY': json.dumps(c['consistency'], sort_keys=True), + 'HARBORRL_CONSISTENCY_MODE': c['consistency']['mode'], + } + environment['HARBORRL_NATIVE_RUNNER_TRANSPORT'] = c['harbor'].get('runner_transport', 'ssh') + if sandbox is not None: + environment['HARBORRL_NATIVE_SANDBOX'] = json.dumps(sandbox, sort_keys=True) + return {'config': config, 'math': selection.asdict(), + 'command': [sys.executable, '-m', 'harborrl.platform.native_train'], + 'environment': {key: str(value) for key, value in environment.items()}, + 'training_command': ['bash', str(ROOT / 'harborrl/platform/slime_train.sh')], + 'required_services': ['GPU Ray / SGLang / Messages Gateway', + 'Sandbox / Harbor runner' if sandbox else 'external CPU Harbor Docker workers'], + 'launcher': f"native {selection.harness.display_name} → Messages Gateway → " + f"{selection.task.display_name} verifier → " + f"{selection.algorithm.display_name}", + 'layout': {'mode': config['deployment']['layout'], 'offload': config['deployment']['layout']=='colocate'}} + + +def field_usage(): + """Account for every schema-2 field, including non-environment consumers.""" + usage = {} + from harborrl.consistency.config import DEFAULTS + usage.update({f'consistency.{key}': ['environment:HARBORRL_CONSISTENCY'] for key in DEFAULTS}) + for name, (section, field) in CONFIG_ENV.items(): + usage.setdefault(f"{section}.{field}", []).append(f"environment:{name}") + usage.update({ + "execution.backend": ["schema:harbor_job"], + "harness.name": ["schema:claude_code", "runner:Claude Code"], + "harbor.version": ["preflight:worker runtime version"], + "training.backend_contract": ["environment:HARBORRL_NATIVE_SLIME_CONTRACT", "preflight:backend hooks"], + "harbor.workers": ["environment:HARBORRL_NATIVE_RUNNER_WORKERS", "preflight:worker probe"], + "harbor.runner_transport": ["environment:HARBORRL_NATIVE_RUNNER_TRANSPORT"], + "harbor.environment": ["environment:HARBORRL_NATIVE_SANDBOX", "runner:environment"], + "gateway.audited_raw_logprobs": ["environment:HARBORRL_NATIVE_AUDITED_LOGPROBS"], + }) + return usage diff --git a/harborrl/backends/slime_v032/converter.py b/harborrl/backends/slime_v032/converter.py index 6098332b3..075a121ae 100644 --- a/harborrl/backends/slime_v032/converter.py +++ b/harborrl/backends/slime_v032/converter.py @@ -131,6 +131,8 @@ def convert_samples_to_train_data(args, samples): data["loss_masks"].append(response_mask) data["rollout_log_probs"].append(list(span.old_logprobs)) data["rollout_mask_sums"].append(unit.token_count) + from harborrl.consistency.slime import record_turn + record_turn(ir["turns"][turn_index], list(span.tokens), list(span.old_logprobs), response_mask) for group in batch.groups: weight_sum = math.fsum( 1.0 / (group.group_size * unit.token_count) diff --git a/harborrl/backends/slime_v032/launcher.py b/harborrl/backends/slime_v032/launcher.py index c0f5c9d0d..3450782d1 100644 --- a/harborrl/backends/slime_v032/launcher.py +++ b/harborrl/backends/slime_v032/launcher.py @@ -4,40 +4,37 @@ import sys from harborrl.backends.slime_v032.versions import BACKEND_CONTRACT +from harborrl.math_registry import MATH_REGISTRY -HOOK_PATHS = { - "rollout": "harborrl.backends.slime_v032.rollout.generate_rollout", - "converter": "harborrl.backends.slime_v032.converter.convert_samples_to_train_data", - "advantage": "harborrl.backends.slime_v032.advantage.compute_advantages_and_returns", - "loss": "harborrl.backends.slime_v032.loss.loss_function", - "postprocess": "harborrl.backends.slime_v032.postprocess.rollout_data_postprocess", -} - -def hook_arguments(contract=BACKEND_CONTRACT): +def _hooks(contract=BACKEND_CONTRACT): if str(contract) != BACKEND_CONTRACT: raise ValueError(f"unsupported backend contract: {contract!r}") + return MATH_REGISTRY.algorithm_for_contract(str(contract)).backend_hooks + + +def hook_arguments(contract=BACKEND_CONTRACT): + hooks = _hooks(contract) return [ - "--rollout-function-path", HOOK_PATHS["rollout"], - "--custom-convert-samples-to-train-data-path", HOOK_PATHS["converter"], - "--custom-advantage-function-path", HOOK_PATHS["advantage"], + "--rollout-function-path", hooks["rollout"], + "--custom-convert-samples-to-train-data-path", hooks["converter"], + "--custom-advantage-function-path", hooks["advantage"], "--loss-type", "custom_loss", - "--custom-loss-function-path", HOOK_PATHS["loss"], - "--rollout-data-postprocess-path", HOOK_PATHS["postprocess"], + "--custom-loss-function-path", hooks["loss"], + "--rollout-data-postprocess-path", hooks["postprocess"], ] def validate_hook_assignment(assignment, *, contract=BACKEND_CONTRACT): - if str(contract) != BACKEND_CONTRACT: - raise ValueError(f"unsupported backend contract: {contract!r}") - observed = {key: value for key, value in assignment.items() if key in HOOK_PATHS} - if observed != HOOK_PATHS: + hooks = _hooks(contract) + observed = {key: value for key, value in assignment.items() if key in hooks} + if observed != hooks: raise ValueError(f"Slime hook assignment must exactly match {BACKEND_CONTRACT}") return observed def hook_modules(): - return {name: path.rsplit(".", 1)[0] for name, path in HOOK_PATHS.items()} + return {name: path.rsplit(".", 1)[0] for name, path in _hooks().items()} def dry_run_command(entrypoint, base_args=(), *, contract=BACKEND_CONTRACT, python=sys.executable): diff --git a/harborrl/backends/slime_v032/loss.py b/harborrl/backends/slime_v032/loss.py index 72f386168..502c146eb 100644 --- a/harborrl/backends/slime_v032/loss.py +++ b/harborrl/backends/slime_v032/loss.py @@ -104,5 +104,12 @@ def loss_function(args, batch, logits, unused_reducer): "native_token_count": loss_masks.sum().detach(), "native_group_count": weight_sum.detach().round(), } + from harborrl.consistency.config import environment + if environment()["mode"] != "off": + p0 = torch.cat(batch["log_probs"]).detach() + current = torch.cat(output["log_probs"]).detach() + metrics["consistency_backend_abs_sum"] = ((p0 - old_logprobs).abs() * loss_masks).sum() + metrics["consistency_update_abs_sum"] = ((current - p0).abs() * loss_masks).sum() + metrics["consistency_valid_tokens"] = loss_masks.sum().detach() return loss * LOSS_SCALE_CORRECTION, metrics diff --git a/harborrl/backends/slime_v032/postprocess.py b/harborrl/backends/slime_v032/postprocess.py index d778412bc..7d4c89bb3 100644 --- a/harborrl/backends/slime_v032/postprocess.py +++ b/harborrl/backends/slime_v032/postprocess.py @@ -3,8 +3,6 @@ import math -from harborrl.trajectories.native import finite - REQUIRED_FIELDS = ( "tokens", "response_lengths", @@ -34,7 +32,7 @@ def _integer_scalar(value): return int(number) if math.isfinite(number) and number.is_integer() else None -def rollout_data_postprocess(args, rollout_id, rollout_data): +def _validate_rollout_data(args, rollout_id, rollout_data): """Slime ``--rollout-data-postprocess-path`` hook; fail closed on drift.""" missing = [field for field in REQUIRED_FIELDS if field not in rollout_data] if missing: @@ -51,8 +49,11 @@ def rollout_data_postprocess(args, rollout_id, rollout_data): group_size = int(getattr(args, "n_samples_per_prompt", 0)) if group_size < 2: raise ValueError("native training requires a complete group") - local_weight_sum = 0.0 + group_weights = {} for index in range(count): + group_id = _integer_scalar(rollout_data["rollout_ids"][index]) + if group_id is None or group_id < 0: + raise ValueError("invalid native rollout id") response_length = _integer_scalar(rollout_data["response_lengths"][index]) mask = rollout_data["loss_masks"][index] logprobs = rollout_data["rollout_log_probs"][index] @@ -71,8 +72,22 @@ def rollout_data_postprocess(args, rollout_id, rollout_data): raise ValueError("invalid native rollout mask sum") if truncated is None or truncated not in (0, 1): raise ValueError("invalid native truncated flag") - local_weight_sum += sum( - 1.0 / (group_size * mask_sum) for flag in mask if flag + group_weights.setdefault(group_id, []).append( + sum(1 for flag in mask if flag) / (group_size * mask_sum) ) - if not finite(local_weight_sum) or local_weight_sum > 1.0 + 1e-9: - raise ValueError(f"native local weights exceed the one-per-group budget: {local_weight_sum}") + # Actor shards may contain partial groups; each group has its own unit budget. + for group_id, weights in group_weights.items(): + local_weight_sum = math.fsum(weights) + if not math.isfinite(local_weight_sum) or local_weight_sum > 1.0 + 1e-9: + raise ValueError( + f"native local weights exceed the one-per-group budget for rollout id " + f"{group_id}: {local_weight_sum}" + ) + + +def rollout_data_postprocess(args, rollout_id, rollout_data): + from harborrl.consistency.config import environment + if environment()["mode"] == "off": + return _validate_rollout_data(args, rollout_id, rollout_data) + from harborrl.consistency.slime import check_training_batch + return check_training_batch(args, rollout_id, rollout_data, _validate_rollout_data) diff --git a/harborrl/backends/slime_v032/rollout.py b/harborrl/backends/slime_v032/rollout.py index c232921c2..44c36e2c8 100644 --- a/harborrl/backends/slime_v032/rollout.py +++ b/harborrl/backends/slime_v032/rollout.py @@ -1,11 +1,28 @@ -"""Slime v0.3.2 rollout function hook for complete Native Harbor groups.""" +"""Slime v0.3.2 rollout function hook for complete Native Harbor groups. + +This module is the training-side adapter: it maps Slime args and Sample +objects onto the framework-free rollout core. When the manager runs the +RolloutService (P3), groups are requested over HTTP instead of executing in +this process; conversion to Slime samples is identical either way. +""" from __future__ import annotations import asyncio import math +import os +from pathlib import Path from harborrl.backends.slime_v032.versions import lock_policy_version from harborrl.export.native import export_training_batch +from harborrl.rollout.runtime import ( + RolloutRuntimeConfig, + close_runtime, + current_policy_version, + digest, + run_group, + runtime, + task_entry, +) def sampling_params(args): @@ -21,6 +38,30 @@ def sampling_params(args): } +def runtime_config_from_args(args): + """Map Slime runtime args onto the extracted rollout core config.""" + endpoint = f"http://{args.sglang_router_ip}:{args.sglang_router_port}" + engine_count = int(args.rollout_num_gpus) // int(args.rollout_num_gpus_per_engine) + limit = max(1, int(args.sglang_server_concurrency) * engine_count) + return RolloutRuntimeConfig.from_environment( + os.environ, endpoint=endpoint, concurrency_limit=limit, + group_size=int(args.n_samples_per_prompt)) + + +def slime_tokenizer_loader(args): + """The single Slime dependency at the rollout boundary: the tokenizer.""" + from slime.utils.processing_utils import load_tokenizer + + return lambda: load_tokenizer(args.hf_checkpoint, trust_remote_code=True) + + +def group_index_of(group): + indexes = {getattr(sample, "group_index", None) for sample in group} + if len(indexes) != 1 or type(next(iter(indexes), None)) is not int: + raise ValueError("Slime must provide one group identity before native generation") + return indexes.pop() + + def assert_locked_policy_versions(ir_groups, policy_version): """Fail closed if any generation escaped the locked weight version.""" for group in ir_groups: @@ -32,11 +73,128 @@ def assert_locked_policy_versions(ir_groups, policy_version): raise ValueError("native rollout observed a stale policy version") +async def generate_group(args, group, sampling_params_, rollout_id, evaluation=False): + """Run one complete native group; evaluation is intentionally unsupported.""" + from harborrl.math_registry import NATIVE_TASK_PROVIDER + + if evaluation: + raise ValueError("native training rollout cannot be reused as evaluation") + if len(group) != args.n_samples_per_prompt: + raise ValueError("native rollout group is incomplete before launch") + entries = [task_entry(sample) for sample in group] + if any(entry != entries[0] for entry in entries[1:]): + raise ValueError("native rollout group mixes task locks") + entry = entries[0] + report = NATIVE_TASK_PROVIDER.adapter.inspect( + entry["path"], expected_digest=entry["task_digest"] + ) + if report["status"] != "NEEDS_PROBE": + raise ValueError(f"native task preflight failed: {report['reasons']}") + stop = list(sampling_params_.get("stop") or []) + sampling_digest = digest({ + "temperature": sampling_params_.get("temperature"), + "top_p": sampling_params_.get("top_p"), + "top_k": sampling_params_.get("top_k"), + "max_new_tokens": sampling_params_.get("max_new_tokens"), + "stop": stop, + }) + config = runtime_config_from_args(args) + runtime_ = await runtime(config, tokenizer_loader=slime_tokenizer_loader(args)) + policy_version = current_policy_version(runtime_) + trajectories = await run_group( + config, runtime_, entry=entry, group_index=group_index_of(group), + rollout_id=rollout_id, sampling_digest=sampling_digest, + policy_version=policy_version, + ) + return bind_group_samples(group, trajectories) + + +def bind_group_samples(group, trajectories): + """Attach trajectory evidence to Slime samples; order defines the slots.""" + output = [] + for sample, ir in zip(group, trajectories): + sample.metadata["native_ir"] = ir + sample.response = "native Harbor trial" + sample.reward = {"score": ir["evaluation"]["training_reward"]} + sample.status = sample.Status.COMPLETED + try: + sample.policy_version = int(ir["identity"]["policy_version"]) + except ValueError as exc: + raise ValueError("native policy version must be an integer in Slime") from exc + output.append(sample) + return output + + +async def run_service_groups(args, groups, rollout_id): + """Request complete groups from the manager-side RolloutService (P3).""" + from harborrl.backends.slime_v032.rollout_service_client import ( + RolloutServiceClient, + fetch_pool_states, + load_group_result, + service_group_request, + ) + + token_file = os.environ.get('HARBORRL_ROLLOUT_SERVICE_TOKEN_FILE') + token = Path(token_file).read_text().strip() if token_file else os.environ.get('HARBORRL_ROLLOUT_SERVICE_TOKEN', '') + client = RolloutServiceClient( + os.environ["HARBORRL_ROLLOUT_SERVICE_URL"], os.environ["RUN_ID"], + token) + router = f"http://{args.sglang_router_ip}:{args.sglang_router_port}" + # Policy lifecycle (§7): the GPU job publishes its serving endpoint, then + # the manager admits the version the complete pool actually serves. The + # manager never opens a version some engine has not reached yet. + await asyncio.to_thread(client.announce_serving, router) + states = await asyncio.to_thread(fetch_pool_states, router) + from harborrl.consistency.config import environment + from harborrl.consistency.sglang import admit_pool + if environment()['mode'] != 'off': + await asyncio.to_thread(admit_pool, states, os.environ["RUN_DIR"]) + await asyncio.to_thread(client.activate_policy, states, states[0]["weight_version"]) + # The manager's activation publishes the locked policy-pool record into + # the shared run directory; lock the same version before requesting. + policy_version = lock_policy_version(args) + params = sampling_params(args) + async def request_group(group): + request = service_group_request(group, params, rollout_id, policy_version) + payload = await asyncio.to_thread(client.submit_group, request) + # The HTTP layer returns a status summary; completed work carries the + # GroupResult itself. + result = payload.get("result") if isinstance(payload, dict) else None + if not isinstance(result, dict): + from harborrl.backends.slime_v032.rollout_service_client import ( + ServiceRolloutError, + ) + raise ServiceRolloutError( + f"group {request['group_id']} is {payload.get('status')!r}: " + f"{payload.get('error', '')}") + irs = load_group_result(client.run_id, request, result, allow_synthetic=False) + bind_group_samples(group, irs) + return irs + + pending = [asyncio.create_task(request_group(group)) for group in groups] + try: + results = await asyncio.gather(*pending, return_exceptions=True) + # Retain independent groups' evidence, but never export a partial batch. + for result in results: + if isinstance(result, BaseException): + raise result + return results + except BaseException: + # HTTP workers keep running until the manager acknowledges cancellation. + try: + await asyncio.to_thread(client.cancel_run) + finally: + await asyncio.gather(*pending, return_exceptions=True) + raise + + async def run_native_groups(args, groups, policy_version, rollout_id): """Run every group concurrently and return only complete IR evidence.""" - from harborrl.rollout.native_generate import close_runtime, generate_group - params = sampling_params(args) + if os.environ.get("HARBORRL_ROLLOUT_SERVICE_URL"): + ir_groups = await run_service_groups(args, groups, rollout_id) + assert_locked_policy_versions(ir_groups, policy_version) + return ir_groups try: results = await asyncio.gather(*( generate_group(args, group, params, rollout_id=rollout_id) for group in groups diff --git a/harborrl/backends/slime_v032/rollout_service_client.py b/harborrl/backends/slime_v032/rollout_service_client.py new file mode 100644 index 000000000..4fc73e492 --- /dev/null +++ b/harborrl/backends/slime_v032/rollout_service_client.py @@ -0,0 +1,210 @@ +"""SlimeAdapter HTTP client (P3): complete groups from the RolloutService. + +The GPU job never trusts a GroupResult: every manifest digest is recomputed, +identities are checked against the request, the group must be complete with +at least two RL_READY slots, and the policy version must be the locked one. +After a transport timeout the client queries status; it never resubmits. +""" +from __future__ import annotations + +import json +import time +from pathlib import Path +from urllib import error, parse, request + +from harborrl.rollout.runtime import task_entry +from harborrl.trajectories.native import digest, read_json, require_ready + +SERVICE_SCHEMA = "harborrl-rollout-service-v1" + + +def fetch_pool_states(endpoint, *, timeout=120, opener=None): + """Read the serving pool states directly from the SGLang router. + + Mirrors SGLangBackend.pool_states without a tokenizer: the GPU job calls + this before asking the manager to admit the version it currently serves. + """ + import json as json_module + from urllib import request as url_request + + opener = opener or url_request.build_opener(url_request.ProxyHandler({})) + endpoint = endpoint.rstrip("/") + + def get(url): + with opener.open(url_request.Request(url), timeout=timeout) as response: + return json_module.load(response) + + workers = get(endpoint + "/workers").get("workers") + if not isinstance(workers, list) or not workers: + raise ServiceRolloutError("serving router returned no workers") + states = [] + for worker in workers: + engine = worker.get("url") if isinstance(worker, dict) else None + if not isinstance(engine, str) or not engine.strip(): + raise ServiceRolloutError("serving router returned an invalid worker URL") + engine = engine.rstrip("/") + version = get(engine + "/get_weight_version").get("weight_version") + if not isinstance(version, str) or not version.strip(): + raise ServiceRolloutError("missing serving weight version") + states.append({"endpoint": engine, "healthy": True, + "weight_version": version}) + versions = {state["weight_version"] for state in states} + if len(versions) != 1: + raise ServiceRolloutError( + f"serving pool is mid-update; retry after all engines agree: {sorted(versions)}") + return states + + +class ServiceRolloutError(RuntimeError): + def __init__(self, message, *, status=None, code=None): + super().__init__(message) + self.status, self.code = status, code + + +def service_group_request(group, sampling_params, rollout_id, policy_version): + """Build one GroupRequest from a Slime sample group.""" + entries = [task_entry(sample) for sample in group] + if any(entry != entries[0] for entry in entries[1:]): + raise ValueError("native rollout group mixes task locks") + indexes = {getattr(sample, "group_index", None) for sample in group} + if len(indexes) != 1 or type(next(iter(indexes), None)) is not int: + raise ValueError("Slime must provide one group identity before native generation") + index = indexes.pop() + return { + "group_id": f"group-{index}", + "rollout_id": int(rollout_id), + "group_index": index, + "policy_version": str(policy_version), + "task": entries[0], + "sampling": {key: sampling_params.get(key) for key in + ("temperature", "top_p", "top_k", "max_new_tokens", "stop")}, + "slots": [{"slot_id": str(slot)} for slot in range(len(group))], + } + + +class RolloutServiceClient: + """Synchronous urllib client; safe to call from asyncio.to_thread.""" + + def __init__(self, base_url, run_id, token, *, timeout=120, poll_interval=1.0, + opener=None): + self.base_url = base_url.rstrip("/") + self.run_id = run_id + self.token = token + self.timeout = timeout + self.poll_interval = poll_interval + self.opener = opener or request.build_opener(request.ProxyHandler({})) + + def _request(self, method, path, body=None): + url = f"{self.base_url}{path}" + data = json.dumps(body).encode() if body is not None else None + headers = {"Content-Type": "application/json"} + if self.token: + headers["Authorization"] = f"Bearer {self.token}" + req = request.Request(url, data=data, headers=headers, method=method) + try: + with self.opener.open(req, timeout=self.timeout) as response: + return json.load(response) + except error.HTTPError as exc: + try: + payload = json.load(exc) + except (ValueError, OSError): + payload = {"error": {"type": "transport", "message": str(exc)}} + error_payload = payload.get("error", payload) + raise ServiceRolloutError( + f"{method} {path}: {error_payload.get('type')}: " + f"{error_payload.get('message')}", + status=exc.code, code=error_payload.get("type")) from exc + except (error.URLError, OSError, ValueError) as exc: + raise ServiceRolloutError(f"{method} {path}: {exc}", + code="transport") from exc + + def health(self): + return self._request("GET", "/healthz") + + def register(self, manifest): + return self._request("POST", f"/runs/{parse.quote(self.run_id)}/register", + {"manifest": manifest}) + + def submit_group(self, group_request, *, max_attempts=None): + """Submit idempotently; on timeout poll status, never regenerate.""" + path = f"/runs/{parse.quote(self.run_id)}/groups" + try: + status = self._request('POST', path, group_request) + except ServiceRolloutError as exc: + if exc.code != 'transport': + raise + status = self.group_status(group_request['group_id']) + polls = 0 + while status['status'] in ('queued', 'running'): + if max_attempts is not None and polls >= max_attempts: + raise ServiceRolloutError('group polling deadline reached', code='transport') + polls += 1 + time.sleep(self.poll_interval) + status = self.group_status(group_request["group_id"]) + return status + + def group_status(self, group_id): + return self._request( + "GET", f"/runs/{parse.quote(self.run_id)}/groups/{parse.quote(group_id)}") + + def cancel_group(self, group_id): + return self._request( + "POST", + f"/runs/{parse.quote(self.run_id)}/groups/{parse.quote(group_id)}/cancel") + + def activate_policy(self, states, expected): + return self._request( + "POST", f"/runs/{parse.quote(self.run_id)}/policy/activate", + {"states": states, "expected": str(expected)}) + + def announce_serving(self, router_url): + return self._request( + "POST", f"/runs/{parse.quote(self.run_id)}/serving/announce", + {"router_url": router_url}) + + def drain(self): + return self._request("POST", f"/runs/{parse.quote(self.run_id)}/drain") + + def cancel_run(self): + return self._request("POST", f"/runs/{parse.quote(self.run_id)}/cancel") + + +def load_group_result(run_id, group_request, result, *, allow_synthetic=False): + """Validate a GroupResult and load its trajectory manifests in slot order.""" + if not isinstance(result, dict): + raise ServiceRolloutError("group result must be a mapping") + if result.get("status") != "complete": + raise ServiceRolloutError( + f"group is {result.get('status')!r}, not complete: {result.get('error', '')}") + if result.get("run_id") != run_id or result.get("group_id") != group_request["group_id"]: + raise ServiceRolloutError("group result identity does not match the request") + if result.get("request_digest") != digest(group_request): + raise ServiceRolloutError("group result digest does not match the request") + slots = result.get("slots") + expected = [slot["slot_id"] for slot in group_request["slots"]] + if (not isinstance(slots, list) or len(slots) != len(expected) or len(slots) < 2 + or [slot.get("slot_id") for slot in slots] != expected): + raise ServiceRolloutError("group result is incomplete or reordered") + trajectories = [] + for descriptor, slot in zip(group_request["slots"], slots): + path = Path(slot["manifest_path"]) + try: + payload = read_json(path) + except (OSError, ValueError) as exc: + raise ServiceRolloutError(f"cannot read slot manifest: {exc}") from exc + if digest(payload) != slot.get("manifest_sha256"): + raise ServiceRolloutError(f"slot {descriptor['slot_id']} manifest digest mismatch") + identity = payload.get("identity", {}) + if (identity.get("policy_version") != group_request["policy_version"] + or identity.get("task_digest") != group_request["task"]["task_digest"] + or identity.get("run_id") != run_id + or identity.get("group_id") != group_request["group_id"] + or identity.get("slot_id") != descriptor["slot_id"]): + raise ServiceRolloutError( + f"slot {descriptor['slot_id']} identity differs from the request") + try: + require_ready(payload, allow_synthetic=allow_synthetic) + except ValueError as exc: + raise ServiceRolloutError(f"slot {descriptor['slot_id']}: {exc}") from exc + trajectories.append(payload) + return trajectories diff --git a/harborrl/cli.py b/harborrl/cli.py index 55530e0a1..0ea67a8a0 100644 --- a/harborrl/cli.py +++ b/harborrl/cli.py @@ -4,18 +4,35 @@ import argparse import json import sys - -ALGORITHM_CONTRACTS = {"grpo": "slime-v0.3.2-native-v1"} +from pathlib import Path def main(argv=None): argv = list(sys.argv[1:] if argv is None else argv) + if argv and argv[0] == "web": + from harborrl.webui.cli import main as web_main + + return web_main(argv[1:]) + if argv and argv[0] in {"options", "validate", "init"}: + from harborrl.options.cli import main as options_main + + return options_main(argv) + if argv and argv[0] == "list": + from harborrl.math_registry import list_components + + choices = {"models", "algorithms", "tasks", "harnesses"} + if len(argv) != 2 or argv[1] not in choices: + parser = argparse.ArgumentParser(prog="harborrl list") + parser.error("choose one of: " + ", ".join(sorted(choices))) + print(json.dumps(list_components()[argv[1]], indent=2)) + return 0 if argv and argv[0] == "hub": from harborrl.tasks.cli import main as hub_main return hub_main(argv[1:]) parser = argparse.ArgumentParser( - prog="harborrl", description=__doc__, allow_abbrev=False + prog="harborrl", description=__doc__, allow_abbrev=False, + epilog="Configuration tools: harborrl options --help; harborrl validate --help; harborrl init --help; harborrl web --help." ) parser.add_argument("-model", help="Slime model preset to select") parser.add_argument("-harness", help="agent harness to select") @@ -24,10 +41,13 @@ def main(argv=None): parser.add_argument("command", choices=["train", "doctor"]) parser.add_argument("config_path", nargs="?", help="YAML configuration file") parser.add_argument("--config", dest="config_option", help="YAML configuration file") + parser.add_argument("--deployment", help="deployment YAML describing the manager, GPU target and rollout workers") parser.add_argument("--set", action="append", default=[]) parser.add_argument("--dry-run", action="store_true") args = parser.parse_args(argv) from harborrl.config import launch_plan, load_config + from harborrl.deployment.schema import embedded_local, load_deployment + from harborrl.deployment.schema import summary as deployment_summary if args.config_path and args.config_option: parser.error( @@ -46,20 +66,36 @@ def main(argv=None): if args.harness is not None: overrides.append(f"harness.name={args.harness.replace('-', '_')}") if args.alg is not None: - contract = ALGORITHM_CONTRACTS.get(args.alg) - if contract is None: + from harborrl.math_registry import MATH_REGISTRY + + matches = [ + spec for spec in MATH_REGISTRY.algorithms.items() + if args.alg in (spec.id,) + ] + if not matches: parser.error( f"unsupported algorithm: {args.alg}; " - f"supported algorithms: {', '.join(sorted(ALGORITHM_CONTRACTS))}" + f"supported algorithms: " + f"{', '.join(spec.id for spec in MATH_REGISTRY.algorithms.items())}" ) + contract = next(iter(matches[0].training_backend_contracts)) overrides.append(f"training.backend_contract={contract}") try: - plan = launch_plan(load_config( + config = load_config( config_path, overrides, task_ids=None if args.task is None else [args.task], - )) + ) + deployment, deployment_source = load_deployment(args.deployment) + if deployment_source == "embedded-local": + deployment = embedded_local( + run_root=str(Path(config["output"]["root"]).resolve()), + workers=config["harbor"]["workers"], + expected_gpus=config["deployment"]["num_gpus"], + ) + plan = launch_plan(config) + plan["deployment"] = deployment_summary(deployment, deployment_source) except (TypeError, ValueError, OSError) as exc: parser.error(str(exc)) if args.dry_run: diff --git a/harborrl/config/native.py b/harborrl/config/native.py index 227f6e4d3..50b3ddb86 100644 --- a/harborrl/config/native.py +++ b/harborrl/config/native.py @@ -1,19 +1,18 @@ """Strict native schema 2; local paths resolve relative to the config file.""" -import json import os import re -import sys from pathlib import Path from urllib.parse import urlsplit import yaml -from harborrl.rollout.harbor_job.bindings import claude_config -from harborrl.trajectories.native import RewardProfile +from harborrl.consistency.config import DEFAULTS as CONSISTENCY_DEFAULTS +from harborrl.consistency.config import validate as validate_consistency FIELDS = { + 'consistency': set(CONSISTENCY_DEFAULTS), 'execution': {'backend'}, 'tasks': {'catalog'}, 'harness': {'name', 'profile'}, - 'harbor': {'python', 'version', 'workers'}, + 'harbor': {'python', 'version', 'workers', 'runner_transport', 'environment'}, 'gateway': {'host', 'port', 'advertised_url', 'tokenizer_digest', 'template_digest', 'audited_raw_logprobs'}, 'model': {'checkpoint', 'reference', 'args_file'}, 'training': {'num_rollout', 'learning_rate', 'save_interval', 'backend_contract'}, @@ -21,6 +20,7 @@ 'deployment': {'layout', 'num_gpus', 'actor_gpus', 'rollout_gpus', 'actor_tensor_parallel_size', 'rollout_gpus_per_engine'}, 'output': {'root'}, } +OPTIONAL = {'harbor': {'runner_transport', 'environment'}} class SelectedConfig(dict): @@ -32,7 +32,10 @@ def __init__(self, config, task_ids): def load_config(path, overrides=None, task_ids=None): """Load one schema-2 YAML file and apply strict ``section.key=value`` overrides.""" path = Path(path).resolve() - config = yaml.safe_load(path.read_text()) + try: + config = yaml.safe_load(path.read_text()) + except yaml.YAMLError as exc: + raise ValueError(f'configuration YAML: {exc}') from exc if not isinstance(config, dict): raise TypeError('configuration must be a mapping') if config.get('schema_version') != 2: @@ -45,8 +48,12 @@ def load_config(path, overrides=None, task_ids=None): if (not separator or field not in FIELDS.get(section_name, ()) or not isinstance(section, dict)): raise ValueError(f'unknown native override: {key}') - section[field] = yaml.safe_load(value) + try: + section[field] = yaml.safe_load(value) + except yaml.YAMLError as exc: + raise ValueError(f'{key}: invalid YAML override') from exc config = validate(config, path) + validate_files(config) if task_ids is None: return config if (not isinstance(task_ids, (list, tuple)) or not task_ids @@ -57,16 +64,19 @@ def load_config(path, overrides=None, task_ids=None): def validate(config, path): - from harborrl.trajectories.native import read_json + if not isinstance(config, dict): + raise TypeError('configuration must be a mapping') + config['consistency'] = validate_consistency(config.get('consistency')) if set(config) != set(FIELDS) | {'schema_version'} or type(config['schema_version']) is not int or config['schema_version'] != 2: raise ValueError('native schema requires exactly the documented schema 2 sections') + if not isinstance(config['training'], dict): + raise TypeError('training must be a mapping') config['training'].setdefault( 'backend_contract', 'slime-v0.3.2-native-v1') for section, fields in FIELDS.items(): - if not isinstance(config[section], dict) or set(config[section]) != fields: + actual = set(config[section]) if isinstance(config[section], dict) else set() + if not isinstance(config[section], dict) or not fields - OPTIONAL.get(section, set()) <= actual <= fields: raise ValueError(f'{section} requires exactly {sorted(fields)}') - if config['execution']['backend'] != 'harbor_job' or config['harness']['name'] != 'claude_code' or config['harbor']['version'] != '0.23.0': - raise ValueError('native requires harbor_job / claude_code / Harbor 0.23.0') for section, key in [('tasks','catalog'), ('harness','profile'), ('harbor','python'), ('model','checkpoint'), ('model','reference'), ('output','root')]: value = config[section][key] @@ -82,133 +92,90 @@ def validate(config, path): raise ValueError(f'{section}.{key} must be a positive integer') from harborrl.backends.slime_v032.versions import CONFIG_CONTRACTS from harborrl.trajectories.native import finite - if config['training']['backend_contract'] not in CONFIG_CONTRACTS: + if (not isinstance(config['training']['backend_contract'], str) + or config['training']['backend_contract'] not in CONFIG_CONTRACTS): raise ValueError(f"training.backend_contract must be one of {sorted(CONFIG_CONTRACTS)}") if not finite(config['training']['learning_rate']) or config['training']['learning_rate'] <= 0: - raise ValueError('positive finite learning_rate required') + raise ValueError('training.learning_rate must be positive and finite') g, d, s = config['gateway'], config['deployment'], config['sampling'] if type(g['audited_raw_logprobs']) is not bool or not 0 < g['port'] < 65536: - raise ValueError('invalid gateway audit flag or port') + raise ValueError('gateway.audited_raw_logprobs must be boolean and gateway.port must be in 1..65535') for key in ('host','tokenizer_digest','template_digest'): if not isinstance(g[key], str) or not g[key].strip(): raise ValueError(f'gateway.{key} required') + if not isinstance(g['advertised_url'], str): + raise TypeError('gateway.advertised_url must be a string') advertised = urlsplit(g['advertised_url']) if (advertised.scheme not in ('http', 'https') or not advertised.hostname or advertised.username or advertised.password or advertised.query or advertised.fragment or advertised.path not in ('', '/') or advertised.port != g['port']): raise ValueError('advertised gateway must be a plain origin and match listener port') - if s['group_size'] < 2 or s['max_tokens'] >= s['max_context']: - raise ValueError('invalid group or context budget') - if d['layout'] not in ('split','colocate'): + if s['group_size'] < 2: + raise ValueError('sampling.group_size must be at least 2') + if s['max_tokens'] >= s['max_context']: + raise ValueError('sampling.max_tokens must be below sampling.max_context') + if not isinstance(d['layout'], str) or d['layout'] not in ('split','colocate'): raise ValueError('layout must be split or colocate') if d['layout'] == 'split' and d['actor_gpus'] + d['rollout_gpus'] > d['num_gpus']: - raise ValueError('GPU budget exceeded') + raise ValueError('GPU budget exceeded: deployment.actor_gpus + deployment.rollout_gpus > deployment.num_gpus') if d['layout'] == 'colocate' and not d['actor_gpus'] == d['rollout_gpus'] == d['num_gpus']: raise ValueError('colocate GPU counts must match') if d['actor_gpus'] % d['actor_tensor_parallel_size'] or d['rollout_gpus'] % d['rollout_gpus_per_engine']: - raise ValueError('GPU counts must be divisible by TP') + raise ValueError('deployment GPU counts must be divisible by their tensor parallel sizes') preset = config['model']['args_file'] if not isinstance(preset,str) or not re.fullmatch(r'[A-Za-z0-9_-]+',preset): raise ValueError('model.args_file must name a Slime model preset; SLIME_DIR is checked by doctor') for key in ('tokenizer_digest', 'template_digest'): if not re.fullmatch(r'[0-9a-f]{64}', g[key]): raise ValueError(f'gateway.{key} must be a SHA-256 digest') - workers = config['harbor']['workers'] - if (not isinstance(workers, list) or not workers or any( + harbor = config['harbor'] + transport = harbor.get('runner_transport', 'ssh') + if transport not in ('ssh', 'local'): + raise ValueError('harbor.runner_transport must be ssh or local') + environment = harbor.get('environment', {'runtime': 'docker'}) + if not isinstance(environment, dict) or environment.get('runtime') not in ('docker', 'sandbox'): + raise ValueError('harbor.environment.runtime must be docker or sandbox') + expected = {'runtime', 'config'} if environment['runtime'] == 'sandbox' else {'runtime'} + if set(environment) != expected: + raise ValueError('invalid harbor.environment fields') + if environment['runtime'] == 'sandbox': + if not isinstance(environment['config'], str) or not environment['config']: + raise ValueError('sandbox config path required') + environment['config'] = str((Path(path).parent / environment['config']).resolve()) + workers = harbor['workers'] + if (not isinstance(workers, list) or (transport == 'ssh' and not workers) or any( not isinstance(worker, str) or not re.fullmatch(r'[A-Za-z0-9_.:+@-]+', worker) or worker.startswith('-') for worker in workers)): raise ValueError('harbor.workers must contain nonempty SSH destinations') - claude_config(read_json(config['harness']['profile']), g['advertised_url'], 'validation-only') + if transport == 'local' and workers: + raise ValueError('local runner requires empty harbor.workers') + from harborrl.math_registry import select_math, validate_combination + validate_combination(select_math(config), config) return config -def catalog(config): - from harborrl.data.harbor.native_inspector import inspect_native +def validate_files(config): + """Check harness profile files separately from pure schema validation.""" + from harborrl.math_registry import select_math from harborrl.trajectories.native import read_json - path = Path(config['tasks']['catalog']) - entries = read_json(path) - if not isinstance(entries,list) or not entries: - raise ValueError('catalog must be a nonempty JSON list') - seen = set() - for entry in entries: - if (set(entry) != {'id','revision','path','task_digest','reward_profile'} - or not isinstance(entry['id'], str) or not entry['id'].strip() - or not isinstance(entry['revision'], str) or not entry['revision'].strip() - or entry['id'] in seen): - raise ValueError('invalid or duplicate catalog identity') - if (not isinstance(entry['path'], str) or not entry['path'].strip() - or not isinstance(entry['task_digest'], str) - or not re.fullmatch(r'[0-9a-f]{64}', entry['task_digest']) - or not isinstance(entry['reward_profile'], dict)): - raise ValueError(f'invalid native task lock: {entry["id"]}') - seen.add(entry['id']) - entry['path'] = str((path.parent / entry['path']).resolve()) - report = inspect_native(entry['path'], expected_digest=entry['task_digest']) - if report['status'] != 'NEEDS_PROBE': - raise ValueError(f"task preflight failed: {entry['id']}: {report['reasons']}") - RewardProfile(**entry['reward_profile']) - selected = set(getattr(config, 'task_ids', ())) - if selected: - missing = selected - {entry['id'] for entry in entries} - if missing: - raise ValueError(f'task selector not found in catalog: {min(missing)}') - entries = [entry for entry in entries if entry['id'] in selected] - return entries + harness = select_math(config).harness + try: + harness.adapter.validate_profile( + read_json(config['harness']['profile']), + config['gateway']['advertised_url'], + ) + except (TypeError, ValueError, KeyError) as exc: + raise ValueError(f'harness.profile: {exc}') from exc + return config + + +def catalog(config): + """Compatibility entry point for task lock validation.""" + from harborrl.tasks.catalog import catalog as inspect_catalog + return inspect_catalog(config) def launch_plan(config): - from harborrl.backends.slime_v032.versions import runtime_contract_id - from harborrl.config import ROOT - catalog(config) - c = config - worker_overrides = os.environ.get('HARBORRL_NATIVE_TASK_WORKER_OVERRIDES', '{}') - try: - overrides = json.loads(worker_overrides) - valid_workers = set(c['harbor']['workers']) - if (not isinstance(overrides, dict) or any(not isinstance(key, str) or value not in valid_workers - for key, value in overrides.items())): - raise ValueError - except (ValueError, json.JSONDecodeError) as exc: - raise ValueError('invalid HARBORRL_NATIVE_TASK_WORKER_OVERRIDES') from exc - environment = { - 'DATASET': 'native', 'ALGO': 'grpo', 'HARNESS_OPTION': 'native-claude-code', - 'HF_CKPT': c['model']['checkpoint'], 'REF_LOAD': c['model']['reference'], - 'MODEL_ARGS_FILE': c['model']['args_file'], 'MODEL_TAG': c['model']['args_file'], - 'NUM_ROLLOUT': c['training']['num_rollout'], 'SAVE_INTERVAL': c['training']['save_interval'], - 'TRAIN_PYTHON': sys.executable, - 'ROLLOUT_BATCH_SIZE': c['sampling']['groups_per_batch'], - 'N_SAMPLES': c['sampling']['group_size'], - 'ROLLOUT_MAX_RESPONSE_LEN': c['sampling']['max_tokens'], - 'ROLLOUT_MAX_CONTEXT_LEN': c['sampling']['max_context'], - 'NUM_GPUS': c['deployment']['num_gpus'], 'ACTOR_GPUS': c['deployment']['actor_gpus'], - 'ROLLOUT_GPUS': c['deployment']['rollout_gpus'], - 'TP_SIZE': c['deployment']['actor_tensor_parallel_size'], - 'ROLLOUT_NUM_GPUS_PER_ENGINE': c['deployment']['rollout_gpus_per_engine'], - 'HARBORRL_GPU_LAYOUT': c['deployment']['layout'], - 'HARBORRL_STRUCTURED_LAUNCH': '1', 'HARBORRL_VERIFY_POLICY_POOL': '1', - 'HARBORRL_SKIP_GLOBAL_CLEANUP': '1', 'SLIME_RAY_PLACEMENT_GPU_PROBE': '1', - 'PYTORCH_CUDA_ALLOC_CONF': ('max_split_size_mb:2048' if c['deployment']['layout'] == 'colocate' - else 'max_split_size_mb:2048,expandable_segments:True'), - 'HARBORRL_NATIVE_ROLLOUT': '1', - 'HARBORRL_NATIVE_SLIME_CONTRACT': runtime_contract_id(c['training']['backend_contract']), - 'HARBORRL_NATIVE_RUNNER_PYTHON': c['harbor']['python'], - 'HARBORRL_NATIVE_RUNNER_WORKERS': json.dumps(c['harbor']['workers']), - 'HARBORRL_NATIVE_TASK_WORKER_OVERRIDES': json.dumps(overrides, sort_keys=True), - 'HARBORRL_NATIVE_PROFILE': c['harness']['profile'], - 'HARBORRL_NATIVE_CATALOG': c['tasks']['catalog'], - 'HARBORRL_NATIVE_GATEWAY_HOST': c['gateway']['host'], - 'HARBORRL_NATIVE_GATEWAY_PORT': c['gateway']['port'], - 'HARBORRL_NATIVE_GATEWAY_URL': c['gateway']['advertised_url'], - 'HARBORRL_NATIVE_TOKENIZER_DIGEST': c['gateway']['tokenizer_digest'], - 'HARBORRL_NATIVE_TEMPLATE_DIGEST': c['gateway']['template_digest'], - 'HARBORRL_NATIVE_AUDITED_LOGPROBS': str(int(c['gateway']['audited_raw_logprobs'])), - 'HARBORRL_NATIVE_MAX_ATTEMPTS': c['sampling']['max_attempts'], - 'HARBORRL_NATIVE_DRAIN_TIMEOUT': '30', - 'RUNS_ROOT': c['output']['root'], 'WANDB_ENABLE': '0', - } - return {'config': config, 'command': [sys.executable, '-m', 'harborrl.platform.native_train'], - 'environment': {key: str(value) for key, value in environment.items()}, - 'training_command': ['bash', str(ROOT / 'harborrl/platform/slime_train.sh')], - 'required_services': ['GPU Ray / SGLang / Messages Gateway', 'external CPU Harbor Docker workers'], - 'launcher': 'native Claude Code → Messages Gateway → Harbor verifier → Slime GRPO', - 'layout': {'mode': config['deployment']['layout'], 'offload': config['deployment']['layout']=='colocate'}} + """Compatibility entry point for backend launch mapping.""" + from harborrl.backends.slime_v032.config import launch_plan as build_plan + return build_plan(config) diff --git a/harborrl/consistency/__init__.py b/harborrl/consistency/__init__.py new file mode 100644 index 000000000..8031995e5 --- /dev/null +++ b/harborrl/consistency/__init__.py @@ -0,0 +1 @@ +"""Backend consistency evidence, independent of optimization objectives.""" diff --git a/harborrl/consistency/actor.py b/harborrl/consistency/actor.py new file mode 100644 index 000000000..e332fc4d1 --- /dev/null +++ b/harborrl/consistency/actor.py @@ -0,0 +1,88 @@ +"""Small Slime actor extension: snapshot trained-model canaries before weight sync.""" +import os +from pathlib import Path + +import torch +import torch.distributed as dist +from slime.backends.megatron_utils.actor import MegatronTrainRayActor +from slime.backends.megatron_utils.data import DataIterator +from slime.backends.megatron_utils.loss import get_log_probs_and_entropy +from slime.backends.megatron_utils.model import forward_only +from slime.backends.megatron_utils.update_weight.common import named_params_and_buffers +from slime.utils.distributed_utils import get_gloo_group +from slime.utils.reloadable_process_group import ( + destroy_process_groups, + reload_process_groups, +) + +from harborrl.trajectories.native import digest, publish + + +class ConsistencyActor(MegatronTrainRayActor): + def update_weights(self): + if self.args.offload_train: + self.wake_up() + self._switch_model("actor") + from harborrl.rollout.runtime import tokenizer_fingerprint + error = None + try: + actual = tokenizer_fingerprint(self.tokenizer) + expected = (os.environ['HARBORRL_NATIVE_TOKENIZER_DIGEST'], os.environ['HARBORRL_NATIVE_TEMPLATE_DIGEST']) + if actual != expected: + raise ValueError('Trainer tokenizer/template differs from rollout launch identity') + except (ValueError, TypeError, KeyError) as exc: + error = str(exc) + errors = [None] * dist.get_world_size() + dist.all_gather_object(errors, error, group=get_gloo_group()) + if any(errors): + raise ValueError(f'Invalid trainer identity: {errors}') + cases = [] + # Literal token fixtures intentionally bypass all serving-side text templating. + for prompt, answer in (("Compute 1 + 1 =", " 2.\n"), ("def square(x):\n", " return x * x\n")): + p = self.tokenizer.encode(prompt, add_special_tokens=True) + r = self.tokenizer.encode(answer, add_special_tokens=False) + if not p or not r: + raise ValueError("Tokenizer produced an empty canary") + cases.append({"prompt_ids": p, "response_ids": r}) + device = next(self.model[0].parameters()).device + data = {"tokens": [torch.tensor(c["prompt_ids"] + c["response_ids"], device=device) for c in cases], + "loss_masks": [torch.ones(len(c["response_ids"]), dtype=torch.int, device=device) for c in cases], + "total_lengths": [len(c["prompt_ids"]) + len(c["response_ids"]) for c in cases], + "response_lengths": [len(c["response_ids"]) for c in cases]} + output = forward_only(get_log_probs_and_entropy, self.args, self.model, + [DataIterator(data, [[i] for i in range(len(cases))])], [len(cases)]) + for case, values in zip(cases, output["log_probs"], strict=True): + case["logprobs"] = values.detach().float().cpu().tolist() + fingerprints = [] + for name, parameter in named_params_and_buffers(self.args, self.model): + flat = parameter.detach().reshape(-1) + indices = torch.linspace(0, max(0, flat.numel() - 1), min(16, flat.numel()), device=flat.device).long() + fingerprints.append({"name": name, "shape": list(parameter.shape), "dtype": str(parameter.dtype), + "sample": flat[indices].float().cpu().tolist()}) + local = {"rank": dist.get_rank(), "fingerprint": digest(fingerprints), "tensor_count": len(fingerprints)} + shards = [None] * dist.get_world_size() + dist.all_gather_object(shards, local, group=get_gloo_group()) + config = self.hf_config.to_dict() + reference = {"schema_version": "harborrl-sync-reference-v1", "cases": cases, + "model_config_digest": digest(config), "model_config": config, + "tokenizer_vocabulary_digest": digest(sorted(self.tokenizer.get_vocab().items())), + "parameter_shards": shards, "fingerprint_semantics": "16 deterministic values per tensor; not a full checksum"} + if self.args.offload_train: + self.sleep() + super().update_weights() + if self.args.offload_train: + reload_process_groups() + reference["weight_version"] = str(self.weight_updater.weight_version) + reference["digest"] = digest(reference) + error = None + if dist.get_rank() == 0: + try: + publish(Path(os.environ["RUN_DIR"]) / "consistency" / f"sync-{reference['weight_version']}.json", reference) + except (OSError, ValueError) as exc: + error = str(exc) + errors = [None] * dist.get_world_size() + dist.all_gather_object(errors, error, group=get_gloo_group()) + if self.args.offload_train: + destroy_process_groups() + if any(errors): + raise ValueError(f"Failed to publish synchronized model evidence: {errors}") diff --git a/harborrl/consistency/config.py b/harborrl/consistency/config.py new file mode 100644 index 000000000..f2317e49a --- /dev/null +++ b/harborrl/consistency/config.py @@ -0,0 +1,51 @@ +"""Explicit deployment-specific tolerances; no universal numeric threshold.""" +import json +import os + +from harborrl.trajectories.native import finite + +DEFAULTS = {"mode": "off", "max_abs_delta": None, "max_mean_abs_delta": None, + "bad_token_delta": None, "max_bad_fraction": 0.0, "worst_tokens": 8} + + +def validate(value=None): + if value is None: + value = {} + if not isinstance(value, dict) or set(value) - set(DEFAULTS): + raise ValueError("Unknown consistency configuration fields") + result = {**DEFAULTS, **value} + if result["mode"] not in ("off", "audit", "enforce"): + raise ValueError("consistency.mode must be off, audit or enforce") + for key in ("max_abs_delta", "max_mean_abs_delta", "bad_token_delta"): + if result[key] is not None and (not finite(result[key]) or result[key] < 0): + raise ValueError(f"consistency.{key} must be nonnegative and finite") + if result["mode"] == "enforce" and result["max_abs_delta"] is None: + raise ValueError("enforce requires an explicit max_abs_delta for this deployment") + if not finite(result["max_bad_fraction"]) or not 0 <= result["max_bad_fraction"] <= 1: + raise ValueError("max_bad_fraction must be in [0, 1]") + if type(result["worst_tokens"]) is not int or not 0 <= result["worst_tokens"] <= 32: + raise ValueError("worst_tokens must be an integer in [0, 32]") + return result + + +def environment(): + return validate(json.loads(os.environ.get("HARBORRL_CONSISTENCY", "{}"))) + + +def require_raw_sampling(sampling): + allowed = {"temperature", "top_p", "top_k", "max_new_tokens", "stop", "raw_logprobs"} + if (not isinstance(sampling, dict) or set(sampling) - allowed + or sampling.get("temperature") != 1 or sampling.get("top_p") != 1 + or sampling.get("top_k", -1) not in (-1, 0) + or sampling.get("raw_logprobs", True) is not True): + raise ValueError("Consistency v1 requires untransformed raw-policy sampling") + + +def violations(metrics, config): + errors = [] + for option, field in (("max_abs_delta", "max_abs_delta"), ("max_mean_abs_delta", "mean_abs_delta")): + if config[option] is not None and metrics[field] > config[option]: + errors.append(f"{field}={metrics[field]:.8g} exceeds {config[option]}") + if config["bad_token_delta"] is not None and metrics["bad_fraction"] > config["max_bad_fraction"]: + errors.append(f"bad_fraction={metrics['bad_fraction']:.8g} exceeds {config['max_bad_fraction']}") + return errors diff --git a/harborrl/consistency/metrics.py b/harborrl/consistency/metrics.py new file mode 100644 index 000000000..4057e8c97 --- /dev/null +++ b/harborrl/consistency/metrics.py @@ -0,0 +1,67 @@ +"""Mergeable token statistics with bounded-memory percentile upper bounds.""" +import bisect +import math + +from harborrl.trajectories.native import finite + +# Logarithmic buckets retain sub-micro precision near zero, with no token all-gather. +EDGES = [0.0, *[2 ** (-24 + i / 8) for i in range(321)]] + + +def statistics(rows, *, threshold=None, worst=8): + result = {"count": 0, "signed_sum": 0.0, "abs_sum": 0.0, "max_abs_delta": 0.0, + "min_log_ratio": None, "max_log_ratio": None, "bad_count": 0, + "histogram": [0] * (len(EDGES) + 1), "worst": []} + for row_index, (current, behavior, mask) in enumerate(rows): + if len(current) != len(behavior) or len(mask) != len(current) or not len(current): + raise ValueError("Probability/mask length mismatch") + if any(flag not in (0, 1) for flag in mask): + raise ValueError("Loss mask must be binary") + for position, (p, q, flag) in enumerate(zip(current, behavior, mask, strict=True)): + if not finite(p) or not finite(q) or p > 0 or q > 0: + raise ValueError("Log probabilities must be finite and nonpositive") + if not flag: + continue + delta = float(p) - float(q) + absolute = abs(delta) + if not math.isfinite(delta): + raise ValueError("Nonfinite log-probability difference") + result["count"] += 1 + result["signed_sum"] += delta + result["abs_sum"] += absolute + result["max_abs_delta"] = max(result["max_abs_delta"], absolute) + result["min_log_ratio"] = delta if result["min_log_ratio"] is None else min(result["min_log_ratio"], delta) + result["max_log_ratio"] = delta if result["max_log_ratio"] is None else max(result["max_log_ratio"], delta) + result["bad_count"] += int(threshold is not None and absolute > threshold) + result["histogram"][bisect.bisect_left(EDGES, absolute)] += 1 + if worst: + result["worst"].append({"row": row_index, "position": position, "delta": delta}) + result["worst"] = sorted(result["worst"], key=lambda x: abs(x["delta"]), reverse=True)[:worst] + if not result["count"]: + raise ValueError("No valid response tokens") + if not math.isfinite(result["abs_sum"]) or not math.isfinite(result["signed_sum"]): + raise ValueError("Probability statistics overflow") + return result + + +def merge(parts): + if not parts: + raise ValueError("No contributing ranks") + count = sum(p["count"] for p in parts) + bins = [sum(p["histogram"][i] for p in parts) for i in range(len(EDGES) + 1)] + maximum = max(p["max_abs_delta"] for p in parts) + def percentile(fraction): + target, seen = math.ceil(count * fraction), 0 + for index, n in enumerate(bins): + seen += n + if seen >= target: + return min(EDGES[index], maximum) if index < len(EDGES) else maximum + raise ValueError("Corrupt histogram") + return {"token_count": count, "signed_mean_delta": math.fsum(p["signed_sum"] for p in parts) / count, + "mean_abs_delta": math.fsum(p["abs_sum"] for p in parts) / count, + "max_abs_delta": maximum, "p95_abs_delta_upper": percentile(.95), "p99_abs_delta_upper": percentile(.99), + "min_log_ratio": min(p["min_log_ratio"] for p in parts), + "max_log_ratio": max(p["max_log_ratio"] for p in parts), + "bad_fraction": sum(p["bad_count"] for p in parts) / count, + "percentile_semantics": "logarithmic-histogram upper bound; not exact quantiles", + "gap_semantics": "fixed-prefix token log-ratio; not full-sequence KL"} diff --git a/harborrl/consistency/probe.py b/harborrl/consistency/probe.py new file mode 100644 index 000000000..a218337b1 --- /dev/null +++ b/harborrl/consistency/probe.py @@ -0,0 +1,35 @@ +"""Replay a Megatron fixed-token reference against an explicit engine pool.""" +import argparse +import json + +from harborrl.trajectories.native import digest, publish, read_json + +from .config import validate +from .sglang import check_pool, http_json + + +def main(argv=None): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--reference', required=True) + parser.add_argument('--engine', action='append', required=True) + parser.add_argument('--config', required=True, help='JSON consistency mapping with explicit tolerances') + parser.add_argument('--output', required=True) + args = parser.parse_args(argv) + settings = validate(read_json(args.config)) + if settings['mode'] == 'off': + parser.error('probe mode must be audit or enforce') + reference = read_json(args.reference) + if reference.get('digest') != digest({k: v for k, v in reference.items() if k != 'digest'}): + raise ValueError('Corrupt sync reference') + states = [{'endpoint': endpoint.rstrip('/'), 'healthy': True, + 'weight_version': http_json(endpoint.rstrip('/') + '/get_weight_version')['weight_version']} + for endpoint in args.engine] + report = check_pool(states, reference, settings) + report['reference_digest'] = reference['digest'] + publish(args.output, report) + print(json.dumps(report, indent=2)) + return int(report['status'] == 'failed') + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/harborrl/consistency/report.py b/harborrl/consistency/report.py new file mode 100644 index 000000000..0b3962894 --- /dev/null +++ b/harborrl/consistency/report.py @@ -0,0 +1,51 @@ +"""Collect errors before raising so every participating rank makes the same decision.""" +from pathlib import Path + +from harborrl.trajectories.native import publish + +from .config import violations +from .metrics import merge, statistics + + +def assess(rows, config, *, metadata=None): + try: + return {"statistics": statistics(rows, threshold=config["bad_token_delta"], worst=config["worst_tokens"]), + "error": None, "metadata": metadata or {}} + except (ValueError, TypeError, OverflowError) as exc: + return {"statistics": None, "error": f"{type(exc).__name__}: {exc}", "metadata": metadata or {}} + + +def decide(parts, config, *, phase): + structural = [p["error"] for p in parts if p["error"]] + contributing = [p for p in parts if p.get("contribute", True)] + metrics = None if structural else merge([p["statistics"] for p in contributing]) + numeric = violations(metrics, config) if metrics else [] + return {"schema_version": "harborrl-consistency-v1", "phase": phase, "mode": config["mode"], + "status": "failed" if structural or (numeric and config["mode"] == "enforce") else "warning" if numeric else "passed", + "structural_errors": structural, "numeric_violations": numeric, "metrics": metrics, + "config": config, "ranks": [{"metadata": p["metadata"], "error": p["error"], + "contribute": p.get("contribute", True), + "worst": p["statistics"]["worst"] if p["statistics"] else []} for p in parts]} + + +def distributed_gate(part, config, path, *, phase, group=None): + import torch.distributed as dist + active = dist.is_available() and dist.is_initialized() + parts = [part] + if active: + parts = [None] * dist.get_world_size(group) + dist.all_gather_object(parts, part, group=group) + report = decide(parts, config, phase=phase) + write_error = None + if not active or dist.get_rank(group) == 0: + try: + publish(Path(path), report) + except (OSError, ValueError) as exc: + write_error = str(exc) + if active: + errors = [None] * dist.get_world_size(group) + dist.all_gather_object(errors, write_error, group=group) + write_error = next((error for error in errors if error), None) + if write_error or report["status"] == "failed": + raise ValueError(f"Consistency {phase} rejected: {write_error or report['structural_errors'] or report['numeric_violations']}") + return report diff --git a/harborrl/consistency/sglang.py b/harborrl/consistency/sglang.py new file mode 100644 index 000000000..d9a5ac654 --- /dev/null +++ b/harborrl/consistency/sglang.py @@ -0,0 +1,92 @@ +"""Exact-token teacher-forcing probes against every synchronized inference engine.""" +import json +from pathlib import Path +from urllib import request + +from harborrl.trajectories.native import digest, finite, publish, read_json + +from .config import environment, violations +from .metrics import merge +from .report import assess, decide + + +def http_json(url, payload=None, timeout=120): + opener = request.build_opener(request.ProxyHandler({})) + req = request.Request(url, data=json.dumps(payload).encode() if payload is not None else None, + headers={"Content-Type": "application/json"}) + with opener.open(req, timeout=timeout) as response: + return json.load(response) + + +def score(endpoint, prompt, response, version, *, transport=http_json): + tokens = prompt + response + payload = {"input_ids": tokens, "sampling_params": {"temperature": 1.0, "top_p": 1.0, + "top_k": -1, "max_new_tokens": 0}, "return_logprob": True, "logprob_start_len": 0} + output = transport(endpoint.rstrip("/") + "/generate", payload) + meta = output.get("meta_info", {}) + if str(meta.get("weight_version", "")) != str(version): + raise ValueError("Canary generation-time weight version mismatch") + rows = meta.get("input_token_logprobs") + if (not isinstance(rows, list) or len(rows) != len(tokens) + or any(not isinstance(row, (tuple, list)) or len(row) < 2 for row in rows) + or [row[1] for row in rows] != tokens): + raise ValueError("Canary input logprobs must preserve every exact token and position") + values = [row[0] for row in rows[len(prompt):]] + if any(not finite(value) or value > 0 for value in values): + raise ValueError("Invalid canary raw logprob") + return values + + +def check_pool(states, reference, config, *, transport=http_json): + expected = reference["weight_version"] + endpoints = [s["endpoint"] for s in states] + if not states or len(set(endpoints)) != len(endpoints): + raise ValueError("Canary requires a nonempty unique engine pool") + if any(not s.get("healthy") or str(s.get("weight_version")) != str(expected) for s in states): + raise ValueError("Canary pool contains stale or unhealthy engines") + parts = [] + for endpoint in endpoints: + try: + rows = [(case["logprobs"], score(endpoint, case["prompt_ids"], case["response_ids"], expected, + transport=transport), [1] * len(case["response_ids"])) for case in reference["cases"]] + part = assess(rows, config, metadata={"engine": endpoint, "weight_version": expected}) + # Detect a version transition across scoring, in addition to each response's version. + if str(transport(endpoint + "/get_weight_version").get("weight_version")) != str(expected): + raise ValueError("Engine changed weights during canary scoring") + except (OSError, ValueError, TypeError, KeyError) as exc: + part = {"statistics": None, "error": str(exc), "metadata": {"engine": endpoint}} + parts.append(part) + report = decide(parts, config, phase="synchronized-pool") + # Mean/fraction limits apply independently to every engine. + engine_errors = [f"{p['metadata']['engine']}: {error}" for p in parts if p['statistics'] + for error in violations(merge([p['statistics']]), config)] + report['numeric_violations'] = engine_errors + if engine_errors and report['status'] != 'failed': + report['status'] = 'failed' if config['mode'] == 'enforce' else 'warning' + return report + + +def admit_pool(states, run_dir): + config = environment() + if config["mode"] == "off": + return + versions = {str(s["weight_version"]) for s in states} + if len(versions) != 1: + raise ValueError("Cannot admit a mixed-version pool") + version = versions.pop() + if not version.isdecimal(): + raise ValueError("Invalid numeric serving version") + root = Path(run_dir) / "consistency" + reference = read_json(root / f"sync-{version}.json") + if reference["weight_version"] != version or reference["digest"] != digest({k: v for k, v in reference.items() if k != "digest"}): + raise ValueError("Canary reference identity mismatch") + admission = digest({'states': states, 'config': config, 'reference': reference['digest']}) + path = root / f"pool-{version}-{admission}.json" + if path.exists(): + report = read_json(path) + else: + report = check_pool(states, reference, config) + report["reference_digest"] = reference["digest"] + publish(path, report) + if report["status"] == "failed": + raise ValueError(f"Serving canary rejected version {version}") diff --git a/harborrl/consistency/slime.py b/harborrl/consistency/slime.py new file mode 100644 index 000000000..6493e5ea1 --- /dev/null +++ b/harborrl/consistency/slime.py @@ -0,0 +1,74 @@ +"""Integration using the locked Slime precomputed log_probs and postprocess hook.""" +import os +import struct +from pathlib import Path + +from harborrl.trajectories.native import digest, publish, read_json + +from .config import environment, require_raw_sampling +from .report import assess, distributed_gate + + +def evidence_key(tokens, logprobs, mask): + q = [struct.unpack("!f", struct.pack("!f", value))[0] for value in logprobs] + return digest({"tokens": tokens, "q": q, "mask": mask}) + + +def record_turn(turn, tokens, logprobs, mask): + config = environment() + if config["mode"] == "off": + return + require_raw_sampling(turn["sampling"]) + if turn["logprob_semantics"] != "raw_model" or turn["evidence_kind"] != "serving": + raise ValueError("Consistency requires observed raw serving evidence") + key = evidence_key(tokens, logprobs, mask) + version = str(turn["policy_version"]) + if not version.isdecimal(): + raise ValueError("Invalid serving version") + payload = {"key": key, "weight_version": version, "sampling": turn["sampling"], + "tokenizer_digest": turn["tokenizer_digest"], "template_digest": turn["template_digest"], + "logprob_semantics": "raw_model_equals_sampling", "evidence_kind": "serving"} + publish(Path(os.environ["RUN_DIR"]) / "consistency" / "turns" / version / f"{key}.json", payload) + + +def identity_tis(*args, **kwargs): + raise RuntimeError("Consistency is metrics-only; invoking a TIS correction would change the Native objective") + + +def check_training_batch(args, rollout_id, data, validator): + import torch.distributed as dist + from megatron.core import mpu + from slime.utils.distributed_utils import get_gloo_group + + from harborrl.backends.slime_v032.versions import read_locked_policy_version + + config = environment() + root = Path(os.environ["RUN_DIR"]) + metadata = {"rank": dist.get_rank(), "rollout_id": rollout_id, "backend": "megatron", + "scope": "pre-update actual packed rollout batch"} + try: + validator(args, rollout_id, data) + if "log_probs" not in data or len(data["log_probs"]) != len(data["tokens"]): + raise ValueError("Missing pre-update Megatron log_probs; --get-mismatch-metrics is required") + version = read_locked_policy_version(root) + metadata["weight_version"] = version + cases = [] + for tokens, q, mask, p0 in zip(data["tokens"], data["rollout_log_probs"], data["loss_masks"], data["log_probs"], strict=True): + tokens, q, mask, p0 = (x.detach().cpu().tolist() if hasattr(x, "detach") else list(x) for x in (tokens, q, mask, p0)) + # Slime stores rollout probabilities as float32. Normalize the lookup identically. + key = evidence_key(tokens, q, mask) + evidence = read_json(root / "consistency" / "turns" / version / f"{key}.json") + if evidence["key"] != key or evidence["weight_version"] != version: + raise ValueError("Frozen serving evidence does not match the training batch") + for field, env in (("tokenizer_digest", "HARBORRL_NATIVE_TOKENIZER_DIGEST"), + ("template_digest", "HARBORRL_NATIVE_TEMPLATE_DIGEST")): + if evidence[field] != os.environ[env]: + raise ValueError(f"Training batch {field} differs from launch identity") + require_raw_sampling(evidence["sampling"]) + cases.append((p0, q, mask)) + part = assess(cases, config, metadata=metadata) + except (ValueError, TypeError, KeyError, OSError) as exc: + part = {"statistics": None, "error": str(exc), "metadata": metadata} + part["contribute"] = mpu.get_tensor_model_parallel_rank() == 0 + distributed_gate(part, config, root / "consistency" / f"batch-{rollout_id}.json", + phase="pre-update-backend-gap", group=get_gloo_group()) diff --git a/harborrl/consistency/train.py b/harborrl/consistency/train.py new file mode 100644 index 000000000..caaea02d9 --- /dev/null +++ b/harborrl/consistency/train.py @@ -0,0 +1,76 @@ +"""Run the locked upstream training loop with its supported actor-class factory.""" +import functools +import importlib.util +import json +import os +from pathlib import Path + +from .config import environment + + +class CheckedActorGroup: + """Check every sync, including the final one, before returning to upstream.""" + def __init__(self, actor, manager, args): + self.actor, self.manager, self.args = actor, manager, args + + def __getattr__(self, name): + return getattr(self.actor, name) + + def update_weights(self): + import ray + + from harborrl.backends.slime_v032.rollout_service_client import ( + fetch_pool_states, + ) + + from .sglang import admit_pool + result = self.actor.update_weights() + if self.args.offload_rollout: + ray.get(self.manager.onload_kv.remote()) + endpoint = f"http://{self.args.sglang_router_ip}:{self.args.sglang_router_port}" + admit_pool(fetch_pool_states(endpoint), os.environ['RUN_DIR']) + return result + + +def main(): + import ray + from slime.ray.placement_group import create_training_models + from slime.utils.arguments import parse_args + + from .actor import ConsistencyActor + + args = parse_args() + for name, expected in (("pipeline_model_parallel_size", 1), ("context_parallel_size", 1), + ("update_weights_interval", 1)): + if getattr(args, name) != expected: + raise ValueError(f"Consistency v1 requires {name}={expected}") + if any(getattr(args, key, False) for key in ('use_critic', 'keep_old_actor', 'use_tis', 'release_train', 'use_rollout_routing_replay', 'num_experts')): + raise ValueError("Consistency v1 supports synchronous Native actor training without routing replay") + if args.update_weight_mode != "full" or args.update_weight_transport != "nccl": + raise ValueError("Consistency v1 requires full tensor/NCCL weight synchronization") + settings = environment() + rollout_env = {key: value for key, value in os.environ.items() + if key.startswith('HARBORRL_') or key in ('RUN_DIR', 'RUN_ID', 'MODEL_TAG', 'MODEL_ARGS_FILE', + 'ROLLOUT_MAX_RESPONSE_LEN', 'ROLLOUT_MAX_CONTEXT_LEN', 'PYTHONPATH')} + if not ray.is_initialized(): + ray.init(runtime_env={'env_vars': rollout_env}) + args.get_mismatch_metrics = True + args.custom_tis_function_path = "harborrl.consistency.slime.identity_tis" + args.train_env_vars = dict(args.train_env_vars or {}) + args.train_env_vars.update({"HARBORRL_CONSISTENCY": json.dumps(settings), "RUN_DIR": os.environ["RUN_DIR"], + "HARBORRL_NATIVE_TOKENIZER_DIGEST": os.environ["HARBORRL_NATIVE_TOKENIZER_DIGEST"], + "HARBORRL_NATIVE_TEMPLATE_DIGEST": os.environ["HARBORRL_NATIVE_TEMPLATE_DIGEST"]}) + spec = importlib.util.spec_from_file_location("harborrl_upstream_train", Path(os.environ["SLIME_DIR"]) / "train.py") + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + # Only the factory in this private entrypoint module is replaced; upstream files are untouched. + factory = functools.partial(create_training_models, actor_cls=ConsistencyActor) + def checked_factory(args, pgs, manager): + actor, critic = factory(args, pgs, manager) + return CheckedActorGroup(actor, manager, args), critic + module.create_training_models = checked_factory + module.train(args) + + +if __name__ == "__main__": + main() diff --git a/harborrl/data/harbor/__main__.py b/harborrl/data/harbor/__main__.py index 99d90d197..291463cbd 100644 --- a/harborrl/data/harbor/__main__.py +++ b/harborrl/data/harbor/__main__.py @@ -1,3 +1,4 @@ +"""Legacy TerminalEnv compatibility CLI; use harborrl hub inspect for native tasks.""" import argparse import json from pathlib import Path diff --git a/harborrl/data/harbor/inspector.py b/harborrl/data/harbor/inspector.py index 4a426fd4f..3499bdbd2 100644 --- a/harborrl/data/harbor/inspector.py +++ b/harborrl/data/harbor/inspector.py @@ -1,35 +1,16 @@ -"""Conservative inspection of pinned local Harbor tasks.""" +"""Legacy TerminalEnv compatibility tooling; native training does not use this path.""" -import hashlib from dataclasses import asdict, dataclass from pathlib import Path -from .receipt import parse_reward +from harborrl.tasks.content import tomllib, tree_digest # compatibility exports -try: - import tomllib -except ModuleNotFoundError: # Python 3.10 - import tomli as tomllib +from .receipt import parse_reward PROFILE = "terminal_text_v1" REWARD_PROFILE = "harbor_reward_txt_v1" -def tree_digest(root: Path, *, exclude: tuple[str, ...] = ()) -> str: - h = hashlib.sha256() - for path in sorted(Path(root).rglob("*")): - if str(path.relative_to(root)) in exclude: - continue - if path.is_symlink(): - raise ValueError("symlinks are not supported") - if path.is_file(): - data = path.read_bytes() - h.update(str(path.relative_to(root)).encode() + b"\0") - h.update(str(path.stat().st_mode & 0o777).encode() + b"\0") - h.update(str(len(data)).encode() + b"\0" + data) - return h.hexdigest() - - @dataclass(frozen=True) class TaskRef: dataset: str diff --git a/harborrl/data/harbor/materializer.py b/harborrl/data/harbor/materializer.py index 95346c414..ce0c59cec 100644 --- a/harborrl/data/harbor/materializer.py +++ b/harborrl/data/harbor/materializer.py @@ -1,4 +1,4 @@ -"""Publish a task without changing its verifier or Docker build context.""" +"""Legacy TerminalEnv compatibility tooling; native training does not use this path.""" import json import shutil diff --git a/harborrl/data/harbor/native_inspector.py b/harborrl/data/harbor/native_inspector.py index de0a311b5..729f3c726 100644 --- a/harborrl/data/harbor/native_inspector.py +++ b/harborrl/data/harbor/native_inspector.py @@ -1,7 +1,7 @@ """Native preflight: task metadata, not TerminalEnv materialization rules.""" from pathlib import Path -from .inspector import tomllib, tree_digest +from harborrl.tasks.content import tomllib, tree_digest def inspect_native(path, *, expected_digest=None): @@ -14,7 +14,7 @@ def inspect_native(path, *, expected_digest=None): if expected_digest is not None and checksum != expected_digest: raise ValueError("task content changed since catalog lock") spec = tomllib.loads((root / "task.toml").read_text()) - for name in ("instruction.md", "environment/Dockerfile", "tests/test.sh"): + for name in ("instruction.md", "tests/test.sh"): if not (root / name).is_file(): raise ValueError("missing " + name) if not (root / "instruction.md").read_text().strip(): @@ -22,6 +22,8 @@ def inspect_native(path, *, expected_digest=None): env, verifier = spec.get("environment", {}), spec.get("verifier", {}) if not isinstance(env, dict) or not isinstance(verifier, dict): raise TypeError("invalid environment/verifier configuration") + if not (root / 'environment/Dockerfile').is_file() and not env.get('docker_image'): + raise ValueError('missing environment/Dockerfile or prebuilt docker_image') if not isinstance(spec.get("agent", {}), dict): raise TypeError("invalid agent configuration") if spec.get("user_agent") or spec.get("bridge"): diff --git a/harborrl/data/harbor/probe.py b/harborrl/data/harbor/probe.py index 650941bb9..d3e11d948 100644 --- a/harborrl/data/harbor/probe.py +++ b/harborrl/data/harbor/probe.py @@ -1,11 +1,4 @@ -"""Probe a Harbor task with native Docker verifier semantics. - -The legacy TerminalEnv comparison path is intentionally not part of the public -release. This probe runs the untouched task verifier twice in isolated -containers and requires the same scalar reward, which keeps the check aligned -with the Native Harbor Runner boundary without importing the old environment -stack. -""" +"""Legacy TerminalEnv compatibility tooling; native training does not use this path.""" from __future__ import annotations import asyncio diff --git a/harborrl/data/harbor/receipt.py b/harborrl/data/harbor/receipt.py index 425a8be4f..ad0476ab1 100644 --- a/harborrl/data/harbor/receipt.py +++ b/harborrl/data/harbor/receipt.py @@ -1,4 +1,4 @@ -"""Strict scalar receipt parsing: a missing/invalid score is an infra failure.""" +"""Legacy TerminalEnv compatibility tooling; native training does not use this path.""" import math diff --git a/harborrl/deployment/__init__.py b/harborrl/deployment/__init__.py new file mode 100644 index 000000000..cfae85518 --- /dev/null +++ b/harborrl/deployment/__init__.py @@ -0,0 +1 @@ +"""Deployment topology: machine roles, preflight and remote training targets.""" diff --git a/harborrl/deployment/controller.py b/harborrl/deployment/controller.py new file mode 100644 index 000000000..0ee3700e3 --- /dev/null +++ b/harborrl/deployment/controller.py @@ -0,0 +1,207 @@ +"""Service-mode dispatch: the manager-side controller flow (P1 + P3). + +An independent CPU manager runs the same preflight graph, materializes the +run on shared storage, starts the RolloutService control plane, launches the +existing Slime job on the GPU target over SSH, and waits for the shared +exit-code record. Rollout execution itself moves to the manager only through +the service; training stays on the GPU. +""" +from __future__ import annotations + +import contextlib +import json +import os +import signal +import sys +import threading +from pathlib import Path + +from harborrl.config import ROOT +from harborrl.deployment import remote +from harborrl.deployment.schema import destination +from harborrl.deployment.training_target import SshTrainingTarget +from harborrl.platform.evidence import ( + _materialize, + _source_audit, + effective_config, + new_run_root, + run_environment, +) +from harborrl.rollout.service import RolloutService, make_control_server +from harborrl.trajectories.native import publish + +TOKEN_MODE = 0o600 + + +def check_gpu_control(deployment): + manager, gpu = deployment['manager'], deployment['gpu_target'] + timeout = deployment['connectivity']['http_timeout_s'] + result = remote.run_ssh(gpu['ssh'] or {'host': 'localhost'}, + f"curl --noproxy '*' -fsS --max-time {timeout} " + remote.quote(manager['control_url'] + '/healthz'), + timeout=timeout + 15) + if result.returncode != 0: + raise RuntimeError('GPU cannot reach manager control service') + payload = json.loads(result.stdout) + if payload != {'ok': True, 'service': 'harborrl-rollout-service-v1'}: + raise RuntimeError('GPU reached an unexpected control service') + return {'gpu_to_controller': True, 'control_url': manager['control_url']} + + +def _ensure_token(token_file): + """Load the run token, creating a fresh one on first use.""" + path = Path(token_file) + if path.is_file(): + token = path.read_text().strip() + if token: + return token + import secrets + + token = secrets.token_urlsafe(32) + path.parent.mkdir(parents=True, exist_ok=True) + fd = os.open(path, os.O_WRONLY | os.O_CREAT | os.O_EXCL, TOKEN_MODE) + with os.fdopen(fd, "w") as stream: + stream.write(token + "\n") + return token + + +def _rollout_environment(plan, deployment, environment): + """Environment overlay that points the GPU job at the manager service.""" + manager = deployment["manager"] + workers = deployment["rollout"]["workers"] + overlay = dict(environment) + overlay.update({ + "HARBORRL_ROLLOUT_MODE": "service", + "HARBORRL_ROLLOUT_SERVICE_URL": manager["control_url"], + "HARBORRL_ROLLOUT_SERVICE_TOKEN_FILE": manager['run_token_file'], + "TRAIN_PYTHON": deployment['gpu_target']['train_python'], + "HARBORRL_NATIVE_GATEWAY_HOST": manager["bind"], + "HARBORRL_NATIVE_GATEWAY_PORT": str(manager["gateway_port"]), + "HARBORRL_NATIVE_GATEWAY_URL": manager["gateway_url"], + "HARBORRL_NATIVE_RUNNER_WORKERS": json.dumps( + [destination(worker["ssh"]) for worker in workers]), + "HARBORRL_NATIVE_RUNNER_PYTHON": (plan['config']['harbor']['python'] + if deployment['rollout']['provider'] == 'sandbox' + else workers[0]['runner_python']), + }) + if deployment['rollout']['provider'] == 'sandbox': + if 'HARBORRL_NATIVE_SANDBOX' not in overlay: + raise ValueError('sandbox deployment requires sandbox training config') + transport = plan['config']['harbor'].get('runner_transport', 'ssh') + if (transport == 'local' and workers) or (transport == 'ssh' and not workers): + raise ValueError('sandbox runner transport and deployment workers disagree') + overlay['HARBORRL_NATIVE_RUNNER_TRANSPORT'] = transport + return overlay + + +def _runtime_factory(deployment, environment, *, concurrency_limit, group_size, tokenizer=None): + """Build the per-run rollout runtime when the GPU job announces serving.""" + + def factory(run_id, router_url): + from harborrl.rollout.runtime import RolloutRuntime, RolloutRuntimeConfig + + env = {**os.environ, **environment} + config = RolloutRuntimeConfig.from_environment( + env, endpoint=router_url, concurrency_limit=concurrency_limit, + group_size=group_size, + gateway_host=deployment["manager"]["bind"], + gateway_port=deployment["manager"]["gateway_port"], + gateway_url=deployment["manager"]["gateway_url"]) + resolved = tokenizer + if resolved is None: + from transformers import AutoTokenizer + + resolved = AutoTokenizer.from_pretrained( + env["HF_CKPT"], trust_remote_code=True) + return RolloutRuntime(config, resolved) + + return factory + + +def dispatch_service(plan, deployment, *, checks): + manager, gpu, storage = deployment["manager"], deployment["gpu_target"], deployment["storage"] + config = plan["config"] + root = new_run_root(storage["run_root"]) + root.mkdir(parents=True, mode=0o700) + run_id = root.name + + catalog_path, prompt_path = _materialize(plan, root) + source = _source_audit(root) + environment = run_environment(plan, root, catalog_path, prompt_path) + token = _ensure_token(manager["run_token_file"]) + environment = _rollout_environment(plan, deployment, environment) + + sampling = config["sampling"] + concurrency = sampling["group_size"] * sampling["groups_per_batch"] + service = RolloutService( + run_root=root, + runtime_factory=_runtime_factory( + deployment, environment, + concurrency_limit=concurrency, group_size=sampling["group_size"]), + token_file=manager["run_token_file"]) + control = make_control_server(service, host=manager["bind"], + port=manager["control_port"]) + control_thread = None + target = None + exit_code = 1 + try: + service.register_run(run_id, { + "run_id": run_id, "deployment_source": plan["deployment"]["source"], + "source_commit": source["commit"], + "config_digest": plan["deployment"]["digest"], + "group_size": sampling["group_size"], + "workers": [worker["id"] for worker in deployment["rollout"]["workers"]], + }, token=token) + control_thread = threading.Thread( + target=control.serve_forever, name="harborrl-control-plane", daemon=True) + control_thread.start() + publish(root / 'control-readiness.json', check_gpu_control(deployment)) + + publish(root / "launch.json", { + "plan": plan, "run_dir": str(root), "source": source, + "materialized_catalog": str(catalog_path), "prompt_data": str(prompt_path), + "control_url": manager["control_url"]}) + publish(root / "effective-config.json", + effective_config(plan, environment, dict(environment), source, checks)) + + target = SshTrainingTarget( + gpu["ssh"] or {"host": "localhost"}, source_dir=gpu["source_dir"], run_dir=root, + environment_file=gpu.get("environment_file"), + command_timeout=deployment["connectivity"]["command_timeout_s"] + 30, + ssh_timeout=deployment["connectivity"]["ssh_timeout_s"]) + # The launcher executes inside the GPU checkout; manager-side absolute + # paths are rewritten so shared-storage artifacts keep one spelling. + target.write_launcher( + ["bash", str(ROOT / "harborrl/platform/slime_train.sh")], environment, + path_rewrites=[(str(manager["source_dir"]), str(gpu["source_dir"]))]) + target.launch() + + def forward_signal(signum, frame): + if target is not None: + target.cancel() + + previous = {sig: signal.signal(sig, forward_signal) + for sig in (signal.SIGINT, signal.SIGTERM)} + try: + exit_code = target.wait() + finally: + for sig, handler in previous.items(): + signal.signal(sig, handler) + except BaseException: + import traceback + + traceback.print_exc() + if target is not None: + # Best-effort teardown; the run record keeps the failure evidence. + with contextlib.suppress(Exception): + target.cancel() + raise + finally: + with contextlib.suppress(Exception): + service.drain_run(run_id) + service.close() + if control_thread is not None: + control.shutdown() + control_thread.join(5) + control.server_close() + print(json.dumps({"run_dir": str(root), "exit_code": exit_code}), file=sys.stderr) + return exit_code diff --git a/harborrl/deployment/preflight.py b/harborrl/deployment/preflight.py new file mode 100644 index 000000000..94cb6fd6f --- /dev/null +++ b/harborrl/deployment/preflight.py @@ -0,0 +1,340 @@ +"""Role-layered preflight graph shared by train, doctor and quickstart. + +Every check names the role and machine it ran against, the outcome, and a +user-executable fix. The graph never raises: failures are data, and any +failed mandatory check keeps a run from being created. +""" +from __future__ import annotations + +import json +import socket +import subprocess +import time +from pathlib import Path +from urllib.parse import urlsplit + +from harborrl.deployment import remote +from harborrl.deployment.schema import destination + + +def _check(role, machine, name, ok, detail="", hint=None, duration_ms=None): + record = {"role": role, "machine": machine, "check": name, "ok": bool(ok), + "detail": str(detail)} + if hint: + record["hint"] = str(hint) + if duration_ms is not None: + record["duration_ms"] = round(duration_ms, 3) + return record + + +def _timed(record_factory): + started = time.monotonic() + record = record_factory() + record["duration_ms"] = (time.monotonic() - started) * 1000 + return record + + +def _bindable(host, port): + """Probe with the same address reuse semantics as our HTTP servers.""" + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as server: + try: + server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + server.bind((host, port)) + server.listen() + return True, "port is free" + except OSError as exc: + return False, f"cannot bind manager listener on {host}:{port}: {exc}" + + +def _manager_checks(deployment): + manager, storage = deployment["manager"], deployment["storage"] + checks = [] + run_root = Path(storage["run_root"]) + exists = run_root.is_dir() + writable = exists and _touch_ok(run_root) if storage["manager_writable"] else exists + checks.append(_timed(lambda: _check( + "manager", manager["id"], "storage.run_root", exists and writable, str(run_root), + hint="create the shared run root and grant the manager write access"))) + + if manager["mode"] == "service": + token = Path(manager["run_token_file"]) + token_ok = token.is_file() + detail = str(token) + if token_ok: + mode = token.stat().st_mode & 0o777 + token_ok = mode & 0o077 == 0 + detail = f"{token} mode {oct(mode)}" + checks.append(_timed(lambda: _check( + "manager", manager["id"], "manager.run_token_file", token_ok, detail, + hint="install the run-scoped token with chmod 600"))) + + for name, port in (("manager.control_port", manager["control_port"]), + ("manager.gateway_port", manager["gateway_port"])): + started = time.monotonic() + ok, detail = _bindable(manager["bind"], port) + checks.append(_check( + "manager", manager["id"], name, ok, detail, + hint=f"free the port or stop the conflicting listener on {manager['bind']}:{port}", + duration_ms=(time.monotonic() - started) * 1000)) + return checks + + +def _touch_ok(directory): + probe = directory / ".harborrl-preflight-probe" + try: + probe.write_text("write probe\n") + probe.unlink() + return True + except OSError: + return False + + +def _local_runner(): + def run(command, *, timeout=30): + try: + return subprocess.run(command, capture_output=True, text=True, + timeout=timeout, check=False, shell=isinstance(command, str)) + except (subprocess.TimeoutExpired, OSError) as exc: + return subprocess.CompletedProcess(command, 255, "", f"{type(exc).__name__}: {exc}") + + def test(expression): + result = run(f"test {expression} && echo ok") + return result.returncode == 0 and result.stdout.strip() == "ok", result + + return run, test + + +def _gpu_checks(plan, deployment): + gpu, connectivity = deployment["gpu_target"], deployment["connectivity"] + config = plan["config"] + machine = gpu["id"] + checks = [] + if gpu["transport"] == "ssh": + spec = gpu["ssh"] + started = time.monotonic() + probe = remote.run_ssh(spec, "true", timeout=connectivity["ssh_timeout_s"] + 5, + connect_timeout=connectivity["ssh_timeout_s"]) + checks.append(_check( + "gpu_target", machine, "gpu_target.ssh", probe.returncode == 0, + probe.stderr.strip()[-300:] or "non-interactive ssh ok", + hint="the SSH config alias, agent or identity file must allow non-interactive login", + duration_ms=(time.monotonic() - started) * 1000)) + if probe.returncode != 0: + checks.append(_check( + "gpu_target", machine, "gpu_target.further_probes", True, + "skipped: fix gpu_target.ssh first, then rerun for path, model and GPU checks")) + return checks + + def execute(command): + return remote.run_ssh(spec, command, timeout=connectivity["command_timeout_s"], + connect_timeout=connectivity["ssh_timeout_s"]) + + def shell_test(expression): + ok, result = remote.test_remote(spec, expression, + timeout=connectivity["command_timeout_s"], + connect_timeout=connectivity["ssh_timeout_s"]) + return ok, result + else: + run, test = _local_runner() + + def execute(command): + return run(command, timeout=connectivity["command_timeout_s"]) + + def shell_test(expression): + return test(expression) + + def path_check(name, expression, target, hint): + started = time.monotonic() + ok, _result = shell_test(expression) + checks.append(_check("gpu_target", machine, name, ok, target, + hint=hint, duration_ms=(time.monotonic() - started) * 1000)) + + path_check("gpu_target.source_dir", f"-d {remote.quote(gpu['source_dir'])}", gpu["source_dir"], + "mount or clone the HarborRL checkout at gpu_target.source_dir") + path_check("gpu_target.train_python", f"-x {remote.quote(gpu['train_python'])}", gpu["train_python"], + "provision the training interpreter at gpu_target.train_python") + if gpu.get("environment_file"): + path_check("gpu_target.environment_file", + f"-f {remote.quote(gpu['environment_file'])}", gpu["environment_file"], + "provide the controlled environment variable map") + for key in ("checkpoint", "reference"): + target = config["model"][key] + path_check(f"model.{key}", f"-d {remote.quote(target)}", target, + "the model checkpoint must exist in GPU-machine path semantics") + + started = time.monotonic() + gpu_list = execute("nvidia-smi --query-gpu=index --format=csv,noheader") + visible = len([line for line in gpu_list.stdout.splitlines() if line.strip()]) + checks.append(_check( + "gpu_target", machine, "gpu_target.expected_gpus", + gpu_list.returncode == 0 and visible >= gpu["expected_gpus"], + f"{visible} visible GPUs (returncode {gpu_list.returncode})", + hint="check CUDA visibility and the GPU allocation before training", + duration_ms=(time.monotonic() - started) * 1000)) + return checks + + +def _worker_checks(plan, deployment): + workers, connectivity = deployment["rollout"]["workers"], deployment["connectivity"] + expected_harbor = plan["config"]["harbor"]["version"] + checks = [] + for worker in workers: + spec, machine = worker["ssh"], worker["id"] + started = time.monotonic() + probe = remote.run_ssh(spec, "true", timeout=connectivity["ssh_timeout_s"] + 5, + connect_timeout=connectivity["ssh_timeout_s"]) + checks.append(_check( + "rollout_worker", machine, "worker.ssh", probe.returncode == 0, + probe.stderr.strip()[-300:] or "non-interactive ssh ok", + hint=f"SSH access to {destination(spec)} must work without a password prompt", + duration_ms=(time.monotonic() - started) * 1000)) + if probe.returncode != 0: + continue + + def worker_test(spec, machine, name, expression, target, hint): + started = time.monotonic() + ok, _result = remote.test_remote(spec, expression, + timeout=connectivity["command_timeout_s"], + connect_timeout=connectivity["ssh_timeout_s"]) + checks.append(_check("rollout_worker", machine, name, ok, target, + hint=hint, duration_ms=(time.monotonic() - started) * 1000)) + + worker_test(spec, machine, "worker.source_dir", + f"-d {remote.quote(worker['source_dir'])}", + worker["source_dir"], "mount the HarborRL checkout on the worker") + worker_test(spec, machine, "worker.runner_python", + f"-f {remote.quote(worker['runner_python'])}", + worker["runner_python"], "provision the runner interpreter on the worker") + for index, task_root in enumerate(worker["task_roots"]): + worker_test(spec, machine, f"worker.task_roots[{index}]", + f"-d {remote.quote(task_root)}", + task_root, "mount the task tree on the worker") + + started = time.monotonic() + probe_command = (f"cd {remote.quote(worker['source_dir'])} && " + f"{remote.quote(worker['runner_python'])} -m " + "harborrl.rollout.harbor_job.runner --probe") + result = remote.run_ssh(spec, probe_command, timeout=connectivity["command_timeout_s"], + connect_timeout=connectivity["ssh_timeout_s"]) + runtime_ok = harbor_ok = False + detail = result.stderr.strip()[-300:] or result.stdout.strip()[-300:] + try: + payload = json.loads(result.stdout) + parts = payload.get("python", "").split(".")[:2] + runtime_ok = (result.returncode == 0 + and len(parts) == 2 and all(p.isdigit() for p in parts) + and tuple(map(int, parts)) >= (3, 12)) + harbor_ok = payload.get("harbor_version") == expected_harbor + if result.returncode == 0: + detail = f"python {payload.get('python')}, harbor {payload.get('harbor_version')}" + except (ValueError, AttributeError, TypeError): + pass + checks.append(_check( + "rollout_worker", machine, "worker.runner_runtime", runtime_ok, detail, + hint="runner Python must be >= 3.12 with the pinned Harbor installation", + duration_ms=(time.monotonic() - started) * 1000)) + checks.append(_check( + "rollout_worker", machine, "worker.harbor_version", harbor_ok, detail, + hint=f"pin Harbor {expected_harbor} in the runner environment", + duration_ms=(time.monotonic() - started) * 1000)) + + started = time.monotonic() + docker = remote.run_ssh(spec, "docker info --format '{{.ServerVersion}}'", + timeout=connectivity["command_timeout_s"], + connect_timeout=connectivity["ssh_timeout_s"]) + checks.append(_check( + "rollout_worker", machine, "worker.docker", docker.returncode == 0, + docker.stdout.strip()[:120] or docker.stderr.strip()[-200:], + hint="start the Docker daemon; Harbor trials require container execution", + duration_ms=(time.monotonic() - started) * 1000)) + return checks + + +def _control_loop_checks(deployment): + manager, connectivity = deployment["manager"], deployment["connectivity"] + checks = [] + if manager["mode"] != "service": + return checks + dials = (("connectivity.gpu_to_controller", manager["control_url"], connectivity["gpu_to_controller"]), + ("connectivity.worker_to_gateway", manager["gateway_url"], connectivity["worker_to_gateway"])) + for name, url, enabled in dials: + if not enabled: + continue + parts = urlsplit(url) + started = time.monotonic() + try: + socket.getaddrinfo(parts.hostname, parts.port) + ok, detail = True, "address resolves from the manager" + except socket.gaierror as exc: + ok, detail = False, f"{exc}" + checks.append(_check("control_loop", manager["id"], name, ok, detail, + hint="fix DNS or firewall so the address resolves for every role", + duration_ms=(time.monotonic() - started) * 1000)) + started = time.monotonic() + serving = False + try: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as client: + client.settimeout(min(connectivity["http_timeout_s"], 5)) + serving = client.connect_ex((parts.hostname, parts.port)) == 0 + except OSError: + serving = False # unreachable now; the post-start dial retries + checks.append(_check( + "control_loop", manager["id"], name + ".dial", True, + "listener already serving" if serving + else "no listener yet; the live dial reruns after components start", + duration_ms=(time.monotonic() - started) * 1000)) + if connectivity["manager_to_gpu_inference"]: + checks.append(_check( + "control_loop", manager["id"], "connectivity.manager_to_gpu_inference", True, + "deferred: the GPU job publishes its inference endpoint after serving is healthy")) + return checks + + +def preflight(plan, deployment, *, live=True): + """Run every deployment layer; remote probes are skipped when live=False.""" + if not live: + return [_check("deployment", deployment["manager"]["id"], "deployment.config", + True, "dry-run: parsed and validated, remote probes skipped")] + checks = [] + checks.extend(_manager_checks(deployment)) + checks.extend(_gpu_checks(plan, deployment)) + if deployment["rollout"]["provider"] == "ssh_docker": + checks.extend(_worker_checks(plan, deployment)) + checks.extend(_control_loop_checks(deployment)) + return checks + + +def readiness_checks(plan, deployment, *, from_specs=()): + """Post-start dials: cross-machine health after components are listening. + + ``from_specs`` maps a role to the ssh spec that should perform the dial; + callers pass the GPU target and rollout workers of a live deployment. + """ + manager, connectivity = deployment["manager"], deployment["connectivity"] + checks = [] + dials = (("gpu_target", "gpu_to_controller.ready", manager["control_url"], + connectivity["gpu_to_controller"]), + ("rollout_worker", "worker_to_gateway.ready", manager["gateway_url"], + connectivity["worker_to_gateway"])) + specs = dict(from_specs) + for role, name, url, enabled in dials: + if not enabled: + continue + spec = specs.get(role) + if spec is None: + checks.append(_check("control_loop", manager["id"], name, True, + "deferred: no machine mapped for this dial")) + continue + path = "/healthz" if "control" in name else "/readyz" + command = (f"python3 -c " + f"\"import urllib.request;urllib.request.urlopen('{url}{path}'," + f"timeout={connectivity['http_timeout_s']})\"") + started = time.monotonic() + result = remote.run_ssh(spec, command, timeout=connectivity["command_timeout_s"], + connect_timeout=connectivity["ssh_timeout_s"]) + checks.append(_check("control_loop", destination(spec), name, + result.returncode == 0, + result.stderr.strip()[-300:] or "health dial ok", + hint=f"open the route from {destination(spec)} to {url}{path}", + duration_ms=(time.monotonic() - started) * 1000)) + return checks diff --git a/harborrl/deployment/remote.py b/harborrl/deployment/remote.py new file mode 100644 index 000000000..e9ddd276c --- /dev/null +++ b/harborrl/deployment/remote.py @@ -0,0 +1,55 @@ +"""Non-interactive SSH transport for deployment preflight and launch. + +Every invocation runs BatchMode: a password prompt is a failed check, never an +interactive hang. User, port and identity are inherited from the invoking +user's SSH config unless the deployment spec narrows them. +""" +from __future__ import annotations + +import shlex +import subprocess + +SSH_OPTIONS = ("BatchMode=yes", "ClearAllForwardings=yes", "StrictHostKeyChecking=accept-new") + + +def ssh_command(spec, remote_command, *, connect_timeout=15): + """Assemble one non-interactive ssh argv for a normalized spec.""" + argv = ["ssh"] + for option in SSH_OPTIONS: + argv += ["-o", option] + argv += ["-o", f"ConnectTimeout={int(connect_timeout)}"] + if spec.get("port"): + argv += ["-p", str(spec["port"])] + if spec.get("identity"): + argv += ["-i", str(spec["identity"])] + from harborrl.deployment.schema import destination + + argv += [destination(spec), remote_command] + return argv + + +def run_ssh(spec, command, *, timeout=30, connect_timeout=15): + """Run one remote command; transport failures surface as returncode 255.""" + argv = ssh_command(spec, command, connect_timeout=connect_timeout) + try: + return subprocess.run(argv, capture_output=True, text=True, + timeout=timeout, check=False) + except (subprocess.TimeoutExpired, OSError) as exc: + return subprocess.CompletedProcess(argv, 255, "", f"{type(exc).__name__}: {exc}") + + +def test_remote(spec, expression, *, timeout=30, connect_timeout=15): + """Evaluate a shell test expression on the target; True when it passes.""" + result = run_ssh(spec, f"test {expression} && echo ok", timeout=timeout, + connect_timeout=connect_timeout) + return result.returncode == 0 and result.stdout.strip() == "ok", result + + +def remote_read(spec, command, *, timeout=30, connect_timeout=15): + """Return (returncode, stdout, stderr) for a remote command.""" + result = run_ssh(spec, command, timeout=timeout, connect_timeout=connect_timeout) + return result.returncode, result.stdout, result.stderr + + +def quote(value): + return shlex.quote(str(value)) diff --git a/harborrl/deployment/schema.py b/harborrl/deployment/schema.py new file mode 100644 index 000000000..3cc7bf7c8 --- /dev/null +++ b/harborrl/deployment/schema.py @@ -0,0 +1,330 @@ +"""Deployment schema v1: machine roles and access, never training semantics. + +A deployment config describes WHERE the manager, GPU trainer and rollout +workers live and HOW they reach each other. It deliberately excludes every +training-side field (learning rate, sampling, task catalog) and never stores +secrets: SSH targets must resolve through the invoking user's SSH config, +agent or a restricted identity file. +""" +from __future__ import annotations + +import re +import sys +from pathlib import Path +from urllib.parse import urlsplit + +import yaml + +from harborrl.config import ROOT + +DEPLOYMENT_SCHEMA_VERSION = 1 +DEPLOYMENT_SECTIONS = {"manager", "gpu_target", "storage", "rollout", "connectivity"} + +SLUG = re.compile(r"[A-Za-z0-9][A-Za-z0-9_-]*") +LOOPBACK_HOSTS = {"localhost", "127.0.0.1", "::1", "0.0.0.0", "[::1]"} + +MANAGER_FIELDS = {"id", "mode", "bind", "control_port", "control_url", "gateway_port", + "gateway_url", "run_token_file", "source_dir", "controller_python"} +SSH_FIELDS = {"host", "user", "port", "identity"} +GPU_FIELDS = {"id", "transport", "ssh", "source_dir", "train_python", "environment_file", "expected_gpus"} +STORAGE_FIELDS = {"mode", "run_root", "manager_writable", "gpu_writable", "worker_writable"} +WORKER_FIELDS = {"id", "ssh", "source_dir", "runner_python", "docker", "task_roots"} +ROLLOUT_FIELDS = {"provider", "workers"} +CONNECTIVITY_FIELDS = {"command_timeout_s", "ssh_timeout_s", "http_timeout_s", + "gpu_to_controller", "worker_to_gateway", "manager_to_gpu_inference"} +TRANSPORTS = ("local", "ssh") +ROLLOUT_PROVIDERS = ("ssh_docker", "embedded", "sandbox") +STORAGE_MODES = ("shared_fs",) + + +class DeploymentError(ValueError): + """Raised for any malformed or unsafe deployment configuration.""" + + +def _slug(value, label): + if not isinstance(value, str) or not SLUG.fullmatch(value): + raise DeploymentError(f"{label} must be a short machine identifier") + return value + + +def _positive_int(value, label): + if type(value) is not int or value <= 0 or type(value) is bool: + raise DeploymentError(f"{label} must be a positive integer") + return value + + +def _nonempty_str(value, label): + if not isinstance(value, str) or not value.strip(): + raise DeploymentError(f"{label} must be a nonempty string") + return value + + +def _bool(value, label): + if type(value) is not bool: + raise DeploymentError(f"{label} must be a boolean") + return value + + +def _absolute_path(value, label): + value = _nonempty_str(value, label) + if not Path(value).is_absolute(): + raise DeploymentError(f"{label} must be an absolute path in target-machine semantics") + return value + + +def _plain_origin(url, label, *, port=None, allow_loopback=False): + parts = urlsplit(_nonempty_str(url, label)) + if (parts.scheme not in ("http", "https") or not parts.hostname + or parts.username or parts.password or parts.query or parts.fragment + or parts.path not in ("", "/") + or (port is not None and parts.port != port)): + raise DeploymentError(f"{label} must be a plain http(s) origin matching its declared port") + if not allow_loopback and parts.hostname.lower() in LOOPBACK_HOSTS: + raise DeploymentError(f"{label} must be reachable from other machines; loopback is forbidden") + return f"{parts.scheme}://{parts.netloc}".rstrip("/") + + +def _ssh_spec(value, label, *, require_host=True): + if value is None: + value = {} + if not isinstance(value, dict) or set(value) - SSH_FIELDS: + raise DeploymentError( + f"{label} supports only {sorted(SSH_FIELDS)}; passwords and private keys are never stored") + host = value.get("host") + if host is not None: + host = _nonempty_str(host, f"{label}.host") + if host.startswith("-") or host.count("@") > 1: + raise DeploymentError(f"{label}.host must be an SSH destination, not a flag") + if require_host and not host: + raise DeploymentError(f"{label}.host is required") + user = value.get("user") + if user is not None: + user = _nonempty_str(user, f"{label}.user") + if user.startswith("-"): + raise DeploymentError(f"{label}.user must not look like a flag") + port = value.get("port") + if port is not None: + port = _positive_int(port, f"{label}.port") + identity = value.get("identity") + if identity is not None: + identity = _absolute_path(identity, f"{label}.identity") + spec = {"host": host} + if user: + spec["user"] = user + if port: + spec["port"] = port + if identity: + spec["identity"] = identity + return spec + + +def destination(spec): + """Render an ssh destination from a normalized spec (user@host).""" + return f"{spec['user']}@{spec['host']}" if spec.get("user") else spec["host"] + + +def _manager(config): + if not isinstance(config, dict) or set(config) != MANAGER_FIELDS: + raise DeploymentError(f"manager requires exactly {sorted(MANAGER_FIELDS)}") + mode = config["mode"] + if mode not in ("embedded", "service"): + raise DeploymentError("manager.mode must be embedded or service") + service = mode == "service" + control_port = _positive_int(config["control_port"], "manager.control_port") + gateway_port = _positive_int(config["gateway_port"], "manager.gateway_port") + if control_port == gateway_port: + raise DeploymentError("manager.control_port and manager.gateway_port must differ") + if not 0 < control_port < 65536 or not 0 < gateway_port < 65536: + raise DeploymentError("manager ports must be in 1..65535") + control_url = _plain_origin(config["control_url"], "manager.control_url", + port=control_port, allow_loopback=not service) + gateway_url = _plain_origin(config["gateway_url"], "manager.gateway_url", + port=gateway_port, allow_loopback=not service) + return { + "id": _slug(config["id"], "manager.id"), "mode": mode, + "bind": _nonempty_str(config["bind"], "manager.bind"), + "control_port": control_port, "control_url": control_url, + "gateway_port": gateway_port, "gateway_url": gateway_url, + "run_token_file": _absolute_path(config["run_token_file"], "manager.run_token_file"), + "source_dir": _absolute_path(config["source_dir"], "manager.source_dir"), + "controller_python": _absolute_path(config["controller_python"], "manager.controller_python"), + } + + +def _gpu_target(config): + if not isinstance(config, dict) or set(config) != GPU_FIELDS: + raise DeploymentError(f"gpu_target requires exactly {sorted(GPU_FIELDS)}") + transport = config["transport"] + if transport not in TRANSPORTS: + raise DeploymentError(f"gpu_target.transport must be one of {TRANSPORTS}") + ssh = config["ssh"] + if transport == "ssh": + ssh = _ssh_spec(ssh, "gpu_target.ssh") + elif ssh is not None: + raise DeploymentError("gpu_target.ssh must be null for local transport") + target = { + "id": _slug(config["id"], "gpu_target.id"), "transport": transport, "ssh": ssh, + "source_dir": _absolute_path(config["source_dir"], "gpu_target.source_dir"), + "train_python": _absolute_path(config["train_python"], "gpu_target.train_python"), + "expected_gpus": _positive_int(config["expected_gpus"], "gpu_target.expected_gpus"), + } + environment_file = config["environment_file"] + if environment_file is not None: + target["environment_file"] = _absolute_path(environment_file, "gpu_target.environment_file") + return target + + +def _storage(config): + if not isinstance(config, dict) or set(config) != STORAGE_FIELDS: + raise DeploymentError(f"storage requires exactly {sorted(STORAGE_FIELDS)}") + if config["mode"] not in STORAGE_MODES: + raise DeploymentError(f"storage.mode must be one of {STORAGE_MODES}") + return { + "mode": config["mode"], + "run_root": _absolute_path(config["run_root"], "storage.run_root"), + "manager_writable": _bool(config["manager_writable"], "storage.manager_writable"), + "gpu_writable": _bool(config["gpu_writable"], "storage.gpu_writable"), + "worker_writable": _bool(config["worker_writable"], "storage.worker_writable"), + } + + +def _rollout(config): + if not isinstance(config, dict) or set(config) != ROLLOUT_FIELDS: + raise DeploymentError(f"rollout requires exactly {sorted(ROLLOUT_FIELDS)}") + if config["provider"] not in ROLLOUT_PROVIDERS: + raise DeploymentError(f"rollout.provider must be one of {ROLLOUT_PROVIDERS}") + workers = config["workers"] + # ssh_docker needs explicit machines; the embedded provider falls back to + # the training config's worker list, so it may start empty. + if (not isinstance(workers, list) + or (not workers and config["provider"] not in ("embedded", "sandbox"))): + raise DeploymentError("rollout.workers must be a nonempty list") + normalized, seen = [], set() + for worker in workers: + if not isinstance(worker, dict) or set(worker) != WORKER_FIELDS: + raise DeploymentError(f"rollout worker requires exactly {sorted(WORKER_FIELDS)}") + worker_id = _slug(worker["id"], "rollout worker id") + if worker_id in seen: + raise DeploymentError(f"duplicate rollout worker id: {worker_id}") + seen.add(worker_id) + task_roots = worker["task_roots"] + if (not isinstance(task_roots, list) or not task_roots + or any(not isinstance(root, str) or not root.strip() for root in task_roots)): + raise DeploymentError(f"worker {worker_id} requires nonempty task_roots") + normalized.append({ + "id": worker_id, + "ssh": _ssh_spec(worker["ssh"], f"rollout worker {worker_id} ssh"), + "source_dir": _absolute_path(worker["source_dir"], f"worker {worker_id} source_dir"), + "runner_python": _absolute_path(worker["runner_python"], f"worker {worker_id} runner_python"), + "docker": _bool(worker["docker"], f"worker {worker_id} docker"), + "task_roots": [_absolute_path(root, f"worker {worker_id} task_root") for root in task_roots], + }) + return {"provider": config["provider"], "workers": normalized} + + +def _connectivity(config): + if not isinstance(config, dict) or set(config) != CONNECTIVITY_FIELDS: + raise DeploymentError(f"connectivity requires exactly {sorted(CONNECTIVITY_FIELDS)}") + return { + "command_timeout_s": _positive_int(config["command_timeout_s"], "connectivity.command_timeout_s"), + "ssh_timeout_s": _positive_int(config["ssh_timeout_s"], "connectivity.ssh_timeout_s"), + "http_timeout_s": _positive_int(config["http_timeout_s"], "connectivity.http_timeout_s"), + "gpu_to_controller": _bool(config["gpu_to_controller"], "connectivity.gpu_to_controller"), + "worker_to_gateway": _bool(config["worker_to_gateway"], "connectivity.worker_to_gateway"), + "manager_to_gpu_inference": _bool(config["manager_to_gpu_inference"], "connectivity.manager_to_gpu_inference"), + } + + +def validate(config): + """Strictly validate and normalize a deployment mapping.""" + if not isinstance(config, dict): + raise DeploymentError("deployment config must be a mapping") + if set(config) != DEPLOYMENT_SECTIONS | {"schema_version"}: + raise DeploymentError( + "deployment config requires exactly schema_version and " + + ", ".join(sorted(DEPLOYMENT_SECTIONS))) + if config["schema_version"] != DEPLOYMENT_SCHEMA_VERSION or type(config["schema_version"]) is not int: + raise DeploymentError(f"deployment schema_version must be {DEPLOYMENT_SCHEMA_VERSION}") + return { + "schema_version": DEPLOYMENT_SCHEMA_VERSION, + "manager": _manager(config["manager"]), + "gpu_target": _gpu_target(config["gpu_target"]), + "storage": _storage(config["storage"]), + "rollout": _rollout(config["rollout"]), + "connectivity": _connectivity(config["connectivity"]), + } + + +def embedded_local(*, run_root=None, workers=(), expected_gpus=1): + """Default same-machine deployment; behavior matches the legacy pipeline.""" + return validate({ + "schema_version": 1, + "manager": {"id": "embedded-local", "mode": "embedded", "bind": "127.0.0.1", + "control_port": 49200, "control_url": "http://127.0.0.1:49200", + "gateway_port": 49184, "gateway_url": "http://127.0.0.1:49184", + "run_token_file": str(ROOT / "runs" / "run-token"), + "source_dir": str(ROOT), "controller_python": sys.executable}, + "gpu_target": {"id": "local", "transport": "local", "ssh": None, + "source_dir": str(ROOT), "train_python": sys.executable, + "environment_file": None, "expected_gpus": expected_gpus}, + "storage": {"mode": "shared_fs", "run_root": run_root or str(ROOT / "runs"), + "manager_writable": True, "gpu_writable": True, "worker_writable": True}, + "rollout": {"provider": "embedded", "workers": [ + {"id": f"worker-{index}", "ssh": {"host": worker}, "source_dir": str(ROOT), + "runner_python": sys.executable, "docker": True, "task_roots": [str(ROOT)]} + for index, worker in enumerate(workers)]}, + "connectivity": {"command_timeout_s": 30, "ssh_timeout_s": 15, "http_timeout_s": 10, + "gpu_to_controller": True, "worker_to_gateway": True, + "manager_to_gpu_inference": True}, + }) + + +DEFAULT_FILENAME = "deployment.yaml" + + +def load_deployment(path=None, *, default_directory="."): + """Load an explicit, default-discovered or embedded-local deployment config. + + Returns ``(config, source)`` where source describes where the config came + from; absent any file the embedded-local topology keeps legacy behavior. + """ + if path is not None: + source = str(Path(path).expanduser()) + candidates = [source] + else: + candidates = [str(Path(default_directory) / DEFAULT_FILENAME), DEFAULT_FILENAME] + source = candidates[0] + for candidate in candidates: + file = Path(candidate) + if not file.is_file(): + continue + try: + text = file.read_text() + except OSError as exc: + raise DeploymentError(f"cannot read deployment config {candidate}: {exc}") from exc + try: + raw = yaml.safe_load(text) + except yaml.YAMLError as exc: + raise DeploymentError(f"deployment YAML {candidate}: {exc}") from exc + return validate(raw), str(file.resolve()) + if path is not None: + raise DeploymentError(f"deployment config not found: {path}") + return embedded_local(), "embedded-local" + + +def summary(config, source): + """Plan-embedded deployment digest; the full config travels with it.""" + from harborrl.trajectories.native import digest + return { + "schema_version": DEPLOYMENT_SCHEMA_VERSION, + "source": source, + "digest": digest(config), + "mode": config["manager"]["mode"], + "manager_id": config["manager"]["id"], + "control_url": config["manager"]["control_url"], + "gateway_url": config["manager"]["gateway_url"], + "gpu": {"id": config["gpu_target"]["id"], "transport": config["gpu_target"]["transport"]}, + "worker_ids": [worker["id"] for worker in config["rollout"]["workers"]], + "run_root": config["storage"]["run_root"], + "config": config, + } diff --git a/harborrl/deployment/training_target.py b/harborrl/deployment/training_target.py new file mode 100644 index 000000000..4de5d2468 --- /dev/null +++ b/harborrl/deployment/training_target.py @@ -0,0 +1,217 @@ +"""TrainingTarget (P1): launch, query and cancel one Slime training job. + +``local`` keeps the legacy in-process launcher byte-for-byte. ``ssh`` runs the +same launcher script on the GPU machine through shared storage: the run +directory is written by the manager, executed by the GPU target, and polled +without holding an interactive session open. +""" +from __future__ import annotations + +import os +import shlex +import signal +import subprocess +import sys +import time +from pathlib import Path + +from harborrl.deployment import remote +from harborrl.deployment.schema import destination + +LAUNCHER_NAME = "gpu-launcher.sh" +EXIT_CODE_NAME = "exit-code" +PID_NAME = "launcher.pid" +STDOUT_NAME = "train-stdout.log" + + +class TrainingTargetError(RuntimeError): + pass + + +class LocalTrainingTarget: + """Legacy target: the Slime job runs as a child of this process.""" + + def __init__(self, command, *, cwd, env): + self.command = [str(part) for part in command] + self.cwd = str(cwd) + self.env = env + self.child = None + + def launch(self): + self.child = subprocess.Popen(self.command, cwd=self.cwd, env=self.env, + start_new_session=True) + return self.child.pid + + def wait(self): + if self.child is None: + raise TrainingTargetError("local target was never launched") + + def forward_signal(signum, frame): + if self.child.poll() is None: + try: + os.killpg(os.getpgid(self.child.pid), signum) + except ProcessLookupError: + pass + + previous = {sig: signal.signal(sig, forward_signal) + for sig in (signal.SIGINT, signal.SIGTERM)} + try: + return self.child.wait() + finally: + for sig, handler in previous.items(): + signal.signal(sig, handler) + + def query(self): + if self.child is None: + return {"state": "not-launched"} + return {"state": "running" if self.child.poll() is None else "exited", + "exit_code": self.child.returncode} + + def cancel(self): + if self.child is None or self.child.poll() is not None: + return False + try: + os.killpg(os.getpgid(self.child.pid), signal.SIGINT) + except (ProcessLookupError, PermissionError): + self.child.terminate() + return True + + +class SshTrainingTarget: + """Remote target over shared storage; the launcher script is the contract.""" + + def __init__(self, spec, *, source_dir, run_dir, environment_file=None, + command_timeout=60, ssh_timeout=15, poll_interval=2.0): + self.spec = dict(spec) + self.source_dir = str(source_dir) + self.run_dir = Path(run_dir).resolve() + self.environment_file = environment_file + self.command_timeout = command_timeout + self.ssh_timeout = ssh_timeout + self.poll_interval = poll_interval + self._launched = False + + def write_launcher(self, command, environment, *, path_rewrites=()): + """Materialize the launcher; env values and paths are quoted, never interpolated.""" + command = [str(part) for part in command] + for manager_path, target_path in path_rewrites: + command = [str(part).replace(str(manager_path), str(target_path), 1) + if str(part).startswith(str(manager_path)) else part for part in command] + lines = ["#!/usr/bin/env bash", + "# Generated by the HarborRL manager; executed on the GPU target.", + "set -uo pipefail", + f"cd {shlex.quote(self.source_dir)}", + f"export RUN_DIR={shlex.quote(str(self.run_dir))}"] + if self.environment_file: + # A controlled JSON mapping only; never an arbitrary remote shell. + quoted_file = shlex.quote(str(self.environment_file)) + lines.append( + "eval \"$(HARBORRL_ENV_FILE=" + quoted_file + " python3 - <<'HARBORRL_ENV_JSON'\n" + "import json, os, shlex\n" + "with open(os.environ['HARBORRL_ENV_FILE']) as stream:\n" + " for key, value in sorted(json.load(stream).items()):\n" + " print(f'export {key}={shlex.quote(str(value))}')\n" + "HARBORRL_ENV_JSON\n" + ")\"") + for key in sorted(environment): + lines.append(f"export {key}={shlex.quote(str(environment[key]))}") + lines.append("status=0") + # The command is a full argv (its first word is already the + # interpreter); execute it as-is and capture the real exit code. + lines.append(" ".join(shlex.quote(part) for part in command) + + " || status=$?") + lines.append(f"printf '%s\\n' \"$status\" > {shlex.quote(str(self.run_dir / EXIT_CODE_NAME))}") + lines.append("exit \"$status\"") + launcher = self.run_dir / LAUNCHER_NAME + launcher.write_text("\n".join(lines) + "\n") + launcher.chmod(0o755) + return launcher + + def launch(self): + if self._launched: + raise TrainingTargetError("ssh target was already launched") + # The background job records its own session-leader pid: setsid execs + # the launcher, so the recorded pid heads the cancellable process group. + bootstrap = (f"cd {shlex.quote(str(self.run_dir))} && " + f"(setsid nohup bash {LAUNCHER_NAME} > {STDOUT_NAME} 2>&1" + f" < /dev/null & echo $! > {PID_NAME})") + result = remote.run_ssh(self.spec, bootstrap, timeout=self.command_timeout, + connect_timeout=self.ssh_timeout) + if result.returncode != 0: + raise TrainingTargetError( + f"remote launch failed on {destination(self.spec)}: " + f"{result.stderr.strip()[-500:]}") + self._launched = True + return self.run_dir / PID_NAME + + def _pid(self): + try: + return int((self.run_dir / PID_NAME).read_text().strip()) + except (OSError, ValueError): + return None + + def exit_code(self): + try: + return int((self.run_dir / EXIT_CODE_NAME).read_text().strip()) + except (OSError, ValueError): + return None + + def alive(self): + pid = self._pid() + if pid is None: + return False + result = remote.run_ssh(self.spec, f"kill -0 {pid} 2>/dev/null && echo ok", + timeout=self.command_timeout, connect_timeout=self.ssh_timeout) + return result.returncode == 0 and result.stdout.strip() == "ok" + + def wait(self, *, timeout=None): + """Poll the shared exit-code record; never resubmit or restart.""" + deadline = None if timeout is None else time.monotonic() + timeout + while True: + code = self.exit_code() + if code is not None: + return code + if deadline is not None and time.monotonic() >= deadline: + raise TimeoutError(f"GPU job in {self.run_dir} did not finish in time") + time.sleep(self.poll_interval) + + def query(self): + code = self.exit_code() + return {"state": "exited" if code is not None else + ("running" if self.alive() else "unknown"), + "exit_code": code, "pid": self._pid(), + "run_dir": str(self.run_dir)} + + def cancel(self, *, grace_seconds=30): + pid = self._pid() + if pid is None: + return False + for signame in ("INT", "TERM", "KILL"): + remote.run_ssh(self.spec, f"kill -{signame} -- -{pid} 2>/dev/null || " + f"kill -{signame} {pid} 2>/dev/null || true", + timeout=self.command_timeout, connect_timeout=self.ssh_timeout) + deadline = time.monotonic() + grace_seconds + while time.monotonic() < deadline: + if self.exit_code() is not None: + return True + time.sleep(min(2.0, self.poll_interval)) + return self.exit_code() is not None + + +def local_target(plan, *, environment): + """Build the legacy in-process target from a resolved plan.""" + allowed = {"PATH", "HOME", "USER", "LANG", "LC_ALL", "TMPDIR", "LD_LIBRARY_PATH", + "CUDA_VISIBLE_DEVICES", "HTTP_PROXY", "HTTPS_PROXY", "NO_PROXY", + "http_proxy", "https_proxy", "no_proxy", "PYTHONPATH", + "SLIME_DIR", "MEGATRON_DIR", "SGLANG_IMAGE"} + child_env = {key: value for key, value in os.environ.items() if key in allowed} + child_env["PATH"] = os.pathsep.join( + [str(Path(sys.executable).parent), child_env.get("PATH", os.defpath)]) + child_env["PYTHONPATH"] = os.pathsep.join(filter(None, [ + child_env.get("SLIME_DIR", ""), child_env.get("MEGATRON_DIR", ""), + str(Path(__file__).resolve().parents[2]), child_env.get("PYTHONPATH", ""), + ])) + child_env.update(environment) + from harborrl.config import ROOT + + return LocalTrainingTarget(plan["training_command"], cwd=ROOT, env=child_env) diff --git a/harborrl/gateway/messages.py b/harborrl/gateway/messages.py index 479e764eb..9c474817b 100644 --- a/harborrl/gateway/messages.py +++ b/harborrl/gateway/messages.py @@ -58,7 +58,7 @@ def convert(payload, *, model, max_output_tokens=8192, count_only=False): raise ProtocolError("unsupported context management control") effort = payload.get("output_config") if effort is not None and (not isinstance(effort, dict) or set(effort) != {"effort"} - or effort["effort"] not in ("low", "medium", "high")): + or effort["effort"] not in ("low", "medium", "high", "xhigh")): raise ProtocolError("unsupported output configuration") if not isinstance(payload.get("tools", []), list): raise ProtocolError("tools must be a list") @@ -138,7 +138,13 @@ def parse_qwen_output(text, *, response_id, tools): """Deterministic Qwen tool markers; malformed model output is never repaired.""" if "" in text or "" in text: raise ProtocolError("thinking output is not enabled by this profile") - names = {tool["function"]["name"] for tool in tools} + names = {} + for tool in tools: + name = tool["function"]["name"] + alias = name.casefold() + if alias in names: + raise ProtocolError("ambiguous generated tool alias") + names[alias] = name blocks, offset = [], 0 matches = list(re.finditer(r"\s*(.*?)\s*", text, re.DOTALL)) remainder = re.sub(r"\s*(.*?)\s*", "", text, flags=re.DOTALL) @@ -152,9 +158,17 @@ def parse_qwen_output(text, *, response_id, tools): except ValueError as exc: raise ProtocolError("invalid generated tool JSON") from exc keys(call, ("name", "arguments"), "generated tool") - if call.get("name") not in names or not isinstance(call.get("arguments"), dict): - raise ProtocolError("unknown generated tool or invalid arguments") - blocks.append({"type": "tool_use", "id": f"toolu_{response_id}_{index}", "name": call["name"], "input": call["arguments"]}) + generated_name = call.get("name") + if generated_name == "finish" and call.get("arguments") == {}: + if text[match.end():]: + raise ProtocolError("finish marker must be the final output") + return blocks + canonical_name = names.get(generated_name.casefold()) if isinstance(generated_name, str) else None + if canonical_name is None: + raise ProtocolError(f"unknown generated tool: {generated_name!r}") + if not isinstance(call.get("arguments"), dict): + raise ProtocolError("generated tool arguments are not an object") + blocks.append({"type": "tool_use", "id": f"toolu_{response_id}_{index}", "name": canonical_name, "input": call["arguments"]}) offset = match.end() if text[offset:] or not blocks: blocks.append({"type": "text", "text": text[offset:]}) diff --git a/harborrl/gateway/server.py b/harborrl/gateway/server.py index 282a95e26..fd011c505 100644 --- a/harborrl/gateway/server.py +++ b/harborrl/gateway/server.py @@ -110,7 +110,7 @@ def do_POST(self): gateway.registry.delivery(aid, message["id"], "delivery_unknown") status = 401 if isinstance(exc, PermissionError) else 400 if isinstance(exc, (ProtocolError, json.JSONDecodeError)) else 502 # Do not echo backend errors or request contents (credentials may appear there). - self.reply(status, {"type": "error", "error": {"type": "authentication_error" if status == 401 else "invalid_request_error" if status == 400 else "api_error", "message": type(exc).__name__}}) + self.reply(status, {"type": "error", "error": {"type": "authentication_error" if status == 401 else "invalid_request_error" if status == 400 else "api_error", "message": f"{type(exc).__name__}: {exc}"}}) server = ThreadingHTTPServer((host, port), Handler) server.daemon_threads = True diff --git a/harborrl/gateway/sglang_backend.py b/harborrl/gateway/sglang_backend.py index f37f594d8..d2efe5e63 100644 --- a/harborrl/gateway/sglang_backend.py +++ b/harborrl/gateway/sglang_backend.py @@ -9,24 +9,23 @@ from harborrl.trajectories.native import digest, finite -from .messages import ProtocolError, parse_qwen_output +from .messages import ProtocolError class SGLangBackend: - def __init__(self, endpoint, tokenizer, *, tokenizer_digest, template_digest, - timeout=120, max_context=32768, audited_raw_logprobs=False): + def __init__(self, endpoint, tokenizer, model_adapter, *, tokenizer_digest, + template_digest, timeout=120, max_context=32768, + audited_raw_logprobs=False): self.endpoint = endpoint.rstrip("/") self.tokenizer = tokenizer + self.model_adapter = model_adapter self.tokenizer_digest, self.template_digest = tokenizer_digest, template_digest self.timeout, self.max_context = timeout, max_context self.audited_raw_logprobs = audited_raw_logprobs self.opener = request.build_opener(request.ProxyHandler({})) def prepare(self, converted): - ids = self.tokenizer.apply_chat_template(converted["messages"], tools=converted["tools"] or None, - tokenize=True, add_generation_prompt=True, enable_thinking=False) - if not isinstance(ids, list) or not ids or any(type(i) is not int or i < 0 for i in ids): - raise ProtocolError("tokenizer did not produce actual input IDs") + ids = self.model_adapter.render_chat(self.tokenizer, converted) if len(ids) + converted["sampling"]["max_new_tokens"] > self.max_context: raise ProtocolError("context budget exceeded; implicit truncation is forbidden") return ids @@ -97,7 +96,9 @@ def decode(self, output, payload, converted, identity, response_id): finish = meta.get("finish_reason", {}) if finish.get("type") not in ("stop", "length"): raise ValueError("unsupported serving finish reason") - blocks = parse_qwen_output(text, response_id=response_id, tools=converted["tools"]) + blocks = self.model_adapter.parse_output( + text, response_id=response_id, tools=converted["tools"] + ) has_tools = any(b["type"] == "tool_use" for b in blocks) stop_sequence = finish.get("matched") if isinstance(finish.get("matched"), str) else None if stop_sequence not in converted["sampling"]["stop"]: diff --git a/harborrl/gateway/trace.py b/harborrl/gateway/trace.py index a5a5d3118..5f5ca7c6a 100644 --- a/harborrl/gateway/trace.py +++ b/harborrl/gateway/trace.py @@ -94,6 +94,19 @@ def delivery(self, aid, response_id, status): state["pending"].discard(matches[0]["request_id"]) self.changed.notify_all() + def wait_delivered(self, aid, timeout): + deadline = time.monotonic() + timeout + with self.changed: + state = self.attempts[aid] + while state["pending"]: + remaining = deadline - time.monotonic() + if remaining <= 0: + raise TimeoutError("policy delivery acknowledgement still pending") + self.changed.wait(remaining) + if any(turn.get("delivery") not in ("sent", "consumed_confirmed") + for turn in state["turns"]): + raise ValueError("policy response delivery was not confirmed") + def consume(self, aid, response_id, content, evidence_ref): """Called only by trusted native-session audit, never by an HTTP policy client.""" with self.lock: @@ -109,6 +122,13 @@ def consume(self, aid, response_id, content, evidence_ref): publish(state["root"] / "consumption" / f"{response_id}.json", {"response_id": response_id, "content_digest": digest(content), "evidence_ref": evidence_ref}) turn.update(delivery="consumed_confirmed", consumption_ref=evidence_ref) + def unconsumed_responses(self, aid): + with self.lock: + state = self.attempts[aid] + return [{"response_id": turn["response_id"], "content": deepcopy(turn["response"]["content"]), + "finish_reason": turn.get("finish_reason"), "is_final": index == len(state["turns"]) - 1} + for index, turn in enumerate(state["turns"]) if turn.get("delivery") != "consumed_confirmed"] + def close(self, aid): with self.lock: state = self.attempts[aid] diff --git a/harborrl/harnesses/__init__.py b/harborrl/harnesses/__init__.py new file mode 100644 index 000000000..53da87865 --- /dev/null +++ b/harborrl/harnesses/__init__.py @@ -0,0 +1 @@ +"""Agent harness adapters and their protocol boundaries.""" diff --git a/harborrl/harnesses/claude_code.py b/harborrl/harnesses/claude_code.py new file mode 100644 index 000000000..fb5c27519 --- /dev/null +++ b/harborrl/harnesses/claude_code.py @@ -0,0 +1,53 @@ +"""Claude Code harness adapter with explicit credentials and session audit.""" +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + +from harborrl.rollout.harbor_job.audit import audit_session +from harborrl.rollout.harbor_job.bindings import ( + claude_config, + sanitized_runner_env, +) + + +class ClaudeCodeHarnessAdapter: + harness_id = "claude_code" + + def validate_profile(self, profile, gateway_url): + return claude_config(profile, gateway_url, "validation-only") + + def agent_config(self, profile, gateway_url, credential, max_output_tokens=8192): + return claude_config(profile, gateway_url, credential, max_output_tokens) + + def runner_environment(self, environment): + return sanitized_runner_env(environment) + + def audit_session(self, path, registry, attempt_id): + return audit_session(path, registry, attempt_id) + + def audit_empty_final(self, path, registry, attempt_id, audit): + """Claude 2.1.141 omits empty final messages from persisted sessions.""" + missing = registry.unconsumed_responses(attempt_id) + if not missing: + return + if len(missing) != 1 or missing[0] != { + "response_id": missing[0]["response_id"], "content": [], + "finish_reason": "end_turn", "is_final": True, + }: + raise ValueError("only one empty final end_turn may use terminal evidence") + path = Path(path) + raw = path.read_bytes() + events = [json.loads(line) for line in raw.splitlines() if line.strip()] + results = [event for event in events if event.get("type") == "result"] + if len(results) != 1 or events[-1] != results[0]: + raise ValueError("expected one final Claude result") + result = results[0] + expected = {"subtype": "success", "is_error": False, "result": "", + "stop_reason": "end_turn", "session_id": audit["session_id"], + "terminal_reason": "completed"} + if any(result.get(key) != value for key, value in expected.items()) or result.get("is_error") is not False: + raise ValueError("empty final response lacks successful matching session result") + evidence = f"{path}#sha256={hashlib.sha256(raw).hexdigest()}" + registry.consume(attempt_id, missing[0]["response_id"], [], evidence) diff --git a/harborrl/harnesses/offline_claude.py b/harborrl/harnesses/offline_claude.py new file mode 100644 index 000000000..cd44d37cd --- /dev/null +++ b/harborrl/harnesses/offline_claude.py @@ -0,0 +1,61 @@ +"""Pinned native Claude executable deployment, without package-manager fallback.""" +import asyncio +import hashlib +import json +import shlex +from pathlib import Path + +from harbor.agents.installed.claude_code import ClaudeCode + +from harborrl.trajectories.native import publish + + +def binary_checksum(binary): + with binary.open('rb') as source: + return hashlib.file_digest(source, 'sha256').hexdigest() + + +class OfflineClaudeCode(ClaudeCode): + def __init__(self, *args, installation, **kwargs): + self.installation = installation + super().__init__(*args, **kwargs) + + async def install(self, environment): + lock = self.installation + binary = Path(lock['binary']) + checksum = await asyncio.to_thread(binary_checksum, binary) + if checksum != lock['sha256']: + raise ValueError('offline Claude binary checksum mismatch') + # Harbor invokes Claude with this HOME-local prefix on every command. + probe = ('import json, os, platform; print(json.dumps(dict(' + 'system=platform.system(), machine=platform.machine(), ' + 'libc=os.confstr("CS_GNU_LIBC_VERSION"), home=os.environ.get("HOME", ""))))') + result = await environment.exec('python3 -c ' + shlex.quote(probe)) + try: + system = json.loads(result.stdout or '') + except ValueError: + system = {} + if (result.return_code or system.get('system') != 'Linux' or system.get('machine') != 'x86_64' + or not str(system.get('libc', '')).startswith('glibc ')): + raise ValueError('offline binary requires Linux x86_64 glibc and a writable HOME') + home = system.get('home', '') + if not home.startswith('/') or home == '/': + raise ValueError('invalid agent HOME') + target = home + '/.local/bin/claude' + staging = target + '.harborrl-upload' + result = await environment.exec('mkdir -p ' + shlex.quote(home + '/.local/bin')) + if result.return_code: + raise ValueError('agent tool directory is not writable') + await environment.upload_file(binary, staging) + command = (f'printf "%s %s\\n" {shlex.quote(lock["sha256"])} {shlex.quote(staging)} | sha256sum -c -' + f' && chmod 755 {shlex.quote(staging)} && mv {shlex.quote(staging)} {shlex.quote(target)}') + result = await environment.exec(command) + if result.return_code: + raise ValueError('offline Claude installation checksum/permissions failed') + result = await environment.exec(f'{shlex.quote(target)} --version') + version = (result.stdout or '').strip().split() + if result.return_code or not version or version[0] != lock['version']: + raise ValueError('offline Claude installation/version probe failed') + publish(self.logs_dir / 'offline-installation.json', { + 'version': lock['version'], 'sha256': lock['sha256'], 'target': target, + 'platform': system, 'mode': lock['mode']}) diff --git a/harborrl/math_registry.py b/harborrl/math_registry.py new file mode 100644 index 000000000..61f0a2b37 --- /dev/null +++ b/harborrl/math_registry.py @@ -0,0 +1,316 @@ +"""Explicit MATH component contracts and startup compatibility checks.""" +from __future__ import annotations + +from dataclasses import dataclass +from typing import Protocol + +from harborrl.algorithms.grpo import GRPOAlgorithmAdapter +from harborrl.backends.slime_v032.versions import BACKEND_CONTRACT +from harborrl.harnesses.claude_code import ClaudeCodeHarnessAdapter +from harborrl.models.qwen import QwenModelAdapter +from harborrl.tasks.jsonl_provider import JsonlTaskProvider +from harborrl.tasks.provider import NativeTaskProvider + + +class ComponentRegistry: + def __init__(self, dimension): + self.dimension = dimension + self._components = {} + + def register(self, spec): + if spec.id in self._components: + raise ValueError(f"duplicate {self.dimension} registration: {spec.id}") + self._components[spec.id] = spec + return spec + + def get(self, component_id): + try: + return self._components[component_id] + except KeyError as exc: + raise ValueError( + f"unsupported {self.dimension}: {component_id}; " + f"registered: {', '.join(sorted(self._components))}" + ) from exc + + def items(self): + return tuple(self._components[spec_id] for spec_id in sorted(self._components)) + + +@dataclass(frozen=True) +class ModelSpec: + id: str + display_name: str + preset_prefix: str + serving_backend: str + tool_protocol: str + gateway_protocols: frozenset[str] + training_backend_contracts: frozenset[str] + adapter: QwenModelAdapter + version: str + evidence: tuple[str, ...] + limitations: tuple[str, ...] + + +@dataclass(frozen=True) +class AlgorithmSpec: + id: str + display_name: str + training_backend_contracts: frozenset[str] + minimum_group_size: int + requires_audited_raw_logprobs: bool + reduction: str + adapter: GRPOAlgorithmAdapter + backend_hooks: dict[str, str] + version: str + evidence: tuple[str, ...] + limitations: tuple[str, ...] + + +@dataclass(frozen=True) +class TaskProviderSpec: + id: str + display_name: str + execution_backends: frozenset[str] + task_format: str + adapter: TaskProviderAdapter + version: str + evidence: tuple[str, ...] + limitations: tuple[str, ...] + + +@dataclass(frozen=True) +class HarnessSpec: + id: str + display_name: str + request_protocol: str + tool_protocol: str + harbor_versions: frozenset[str] + adapter: ClaudeCodeHarnessAdapter + version: str + evidence: tuple[str, ...] + limitations: tuple[str, ...] + + +class ModelAdapter(Protocol): + def render_chat(self, tokenizer, converted): ... + + def parse_output(self, text, *, response_id, tools): ... + + +class AlgorithmAdapter(Protocol): + def compute_advantages_and_returns(self, args, rollout_data): ... + + +class TaskAdapter(Protocol): + def discover(self, tasks_dir): ... + + def inspect(self, path, *, expected_digest=None): ... + + def catalog(self, config): ... + + +class TaskProviderAdapter(Protocol): + def discover(self, tasks_dir): ... + + def inspect(self, path, *, expected_digest=None): ... + + def catalog(self, config): ... + + def prompt_rows(self, entries): ... + + +class HarnessAdapter(Protocol): + def validate_profile(self, profile, gateway_url): ... + + def agent_config(self, profile, gateway_url, credential, max_output_tokens=8192): ... + +QWEN_MODEL = ModelSpec( + id="qwen", + display_name="Qwen", + preset_prefix="qwen", + serving_backend="sglang", + tool_protocol="qwen-tool-call-v1", + gateway_protocols=frozenset({"anthropic-messages-v1"}), + training_backend_contracts=frozenset({BACKEND_CONTRACT}), + adapter=QwenModelAdapter(), + version="qwen3-profile-v1", + evidence=("examples/native/train_qwen_native.yaml",), + limitations=("preset must start with qwen", "tokenizer and template digests are checked live"), +) +GRPO_ALGORITHM = AlgorithmSpec( + id="grpo", + display_name="Slime GRPO", + training_backend_contracts=frozenset({BACKEND_CONTRACT}), + minimum_group_size=2, + requires_audited_raw_logprobs=True, + reduction="global-sum", + adapter=GRPOAlgorithmAdapter(), + backend_hooks={ + "rollout": "harborrl.backends.slime_v032.rollout.generate_rollout", + "converter": "harborrl.backends.slime_v032.converter.convert_samples_to_train_data", + "advantage": "harborrl.backends.slime_v032.advantage.compute_advantages_and_returns", + "loss": "harborrl.backends.slime_v032.loss.loss_function", + "postprocess": "harborrl.backends.slime_v032.postprocess.rollout_data_postprocess", + }, + version="native-centered-grpo-v1", + evidence=("harborrl/backends/slime_v032/loss.py",), + limitations=("requires complete reward-varied groups", "only Slime v0.3.2 hooks are accepted"), +) +HARBOR_JSONL_TASK_PROVIDER = TaskProviderSpec( + id="harbor_jsonl", + display_name="Harbor JSONL", + execution_backends=frozenset({"harbor_job"}), + task_format="native_single_step_jsonl_v1", + adapter=JsonlTaskProvider(), + version="jsonl-catalog-lock-v1", + evidence=("examples/native/catalog.jsonl",), + limitations=( + "one task lock must be encoded per JSONL line", + "tasks must be fetched before cataloging", + "task selectors use catalog IDs", + ), +) +NATIVE_TASK_PROVIDER = TaskProviderSpec( + id="native_harbor", + display_name="Harbor", + execution_backends=frozenset({"harbor_job"}), + task_format="native_single_step_text_v1", + adapter=NativeTaskProvider(), + version="catalog-lock-v1", + evidence=("examples/native/catalog.json",), + limitations=("tasks must be fetched before cataloging", "task selectors use catalog IDs"), +) +CLAUDE_CODE_HARNESS = HarnessSpec( + id="claude_code", + display_name="Claude Code", + request_protocol="anthropic-messages-v1", + tool_protocol="qwen-tool-call-v1", + harbor_versions=frozenset({"0.23.0"}), + adapter=ClaudeCodeHarnessAdapter(), + version="claude-code-session-audit-v1", + evidence=("harborrl/rollout/harbor_job/audit.py",), + limitations=("exact CLI version is required", "ambient credentials are rejected"), +) +class MATHRegistry: + def __init__(self): + self.models = ComponentRegistry("model") + self.algorithms = ComponentRegistry("algorithm") + self.tasks = ComponentRegistry("task provider") + self.harnesses = ComponentRegistry("harness") + self.models.register(QWEN_MODEL) + self.algorithms.register(GRPO_ALGORITHM) + self.tasks.register(NATIVE_TASK_PROVIDER) + self.tasks.register(HARBOR_JSONL_TASK_PROVIDER) + self.harnesses.register(CLAUDE_CODE_HARNESS) + + def model_for_preset(self, preset): + for spec in self.models.items(): + if str(preset).startswith(spec.preset_prefix): + return spec + raise ValueError( + f"unsupported model preset: {preset}; " + f"registered: {', '.join(spec.id for spec in self.models.items())}" + ) + + def algorithm_for_contract(self, contract): + for spec in self.algorithms.items(): + if contract in spec.training_backend_contracts: + return spec + raise ValueError(f"no registered algorithm supports backend contract: {contract}") + + +MATH_REGISTRY = MATHRegistry() + + +@dataclass(frozen=True) +class MATHSelection: + model: ModelSpec + algorithm: AlgorithmSpec + task: TaskProviderSpec + harness: HarnessSpec + + def asdict(self): + return { + "model": _describe(self.model), + "algorithm": _describe(self.algorithm), + "task": _describe(self.task), + "harness": _describe(self.harness), + } + + +def _describe(spec): + return { + "id": spec.id, + "display_name": spec.display_name, + "version": spec.version, + "evidence": list(spec.evidence), + "limitations": list(spec.limitations), + } + + +def select_math(config): + return MATHSelection( + model=MATH_REGISTRY.model_for_preset(config["model"]["args_file"]), + algorithm=MATH_REGISTRY.algorithm_for_contract( + config["training"]["backend_contract"] + ), + task=MATH_REGISTRY.tasks.get( + "harbor_jsonl" + if str(config.get("tasks", {}).get("catalog", "")).endswith(".jsonl") + else "native_harbor" + ), + harness=MATH_REGISTRY.harnesses.get(config["harness"]["name"]), + ) + + +def validate_combination(selection, config): + contract = config["training"]["backend_contract"] + if contract not in selection.model.training_backend_contracts: + raise ValueError( + f"model {selection.model.id} does not support training backend {contract}" + ) + if contract not in selection.algorithm.training_backend_contracts: + raise ValueError( + f"algorithm {selection.algorithm.id} does not support training backend {contract}" + ) + if config["execution"]["backend"] not in selection.task.execution_backends: + raise ValueError( + f"task provider {selection.task.id} does not support execution backend " + f"{config['execution']['backend']}" + ) + if config["harbor"]["version"] not in selection.harness.harbor_versions: + raise ValueError( + f"harness {selection.harness.id} does not support Harbor " + f"{config['harbor']['version']}" + ) + if selection.model.tool_protocol != selection.harness.tool_protocol: + raise ValueError("model and harness tool protocols do not match") + if selection.harness.request_protocol not in selection.model.gateway_protocols: + raise ValueError( + f"model gateway does not support harness protocol {selection.harness.request_protocol}" + ) + if config["sampling"]["group_size"] < selection.algorithm.minimum_group_size: + raise ValueError( + f"algorithm {selection.algorithm.id} requires group size >= " + f"{selection.algorithm.minimum_group_size}" + ) + if ( + selection.algorithm.requires_audited_raw_logprobs + and not config["gateway"]["audited_raw_logprobs"] + ): + raise ValueError( + f"algorithm {selection.algorithm.id} requires audited raw logprobs" + ) + return selection + + +def list_components(): + return { + dimension: [_describe(spec) for spec in registry.items()] + for dimension, registry in ( + ("models", MATH_REGISTRY.models), + ("algorithms", MATH_REGISTRY.algorithms), + ("tasks", MATH_REGISTRY.tasks), + ("harnesses", MATH_REGISTRY.harnesses), + ) + } diff --git a/harborrl/models/__init__.py b/harborrl/models/__init__.py new file mode 100644 index 000000000..89314d47e --- /dev/null +++ b/harborrl/models/__init__.py @@ -0,0 +1 @@ +"""Model family adapters used by serving and training boundaries.""" diff --git a/harborrl/models/qwen.py b/harborrl/models/qwen.py new file mode 100644 index 000000000..cbd096dad --- /dev/null +++ b/harborrl/models/qwen.py @@ -0,0 +1,39 @@ +"""Qwen tokenizer/template and generated tool-call protocol.""" +from __future__ import annotations + +from harborrl.gateway.messages import ProtocolError, parse_qwen_output + + +def normalize_chat_template_ids(rendered): + """Accept both legacy lists and modern ``BatchEncoding`` results.""" + if hasattr(rendered, "get"): + rendered = rendered.get("input_ids") + if isinstance(rendered, (list, tuple)): + if rendered and isinstance(rendered[0], (list, tuple)): + rendered = rendered[0] + return list(rendered) + return rendered + + +class QwenModelAdapter: + model_id = "qwen" + + def render_chat(self, tokenizer, converted): + rendered_ids = tokenizer.apply_chat_template( + converted["messages"], + tools=converted["tools"] or None, + tokenize=True, + add_generation_prompt=True, + enable_thinking=False, + ) + token_ids = normalize_chat_template_ids(rendered_ids) + if ( + not isinstance(token_ids, list) + or not token_ids + or any(type(token_id) is not int or token_id < 0 for token_id in token_ids) + ): + raise ProtocolError("tokenizer did not produce actual input IDs") + return token_ids + + def parse_output(self, text, *, response_id, tools): + return parse_qwen_output(text, response_id=response_id, tools=tools) diff --git a/harborrl/options/__init__.py b/harborrl/options/__init__.py new file mode 100644 index 000000000..a53f7a1cc --- /dev/null +++ b/harborrl/options/__init__.py @@ -0,0 +1,11 @@ +"""Offline configuration discovery and generation; no optional runtime imports.""" + + +def build_catalog(): + from .catalog import build_catalog as build + return build() + + +def build_config_bundle(answers, *, output_directory): + from .wizard import build_config_bundle as build + return build(answers, output_directory=output_directory) diff --git a/harborrl/options/catalog.py b/harborrl/options/catalog.py new file mode 100644 index 000000000..76ede0794 --- /dev/null +++ b/harborrl/options/catalog.py @@ -0,0 +1,147 @@ +"""JSON-safe option metadata. Existing validators remain authoritative.""" +from copy import deepcopy +from importlib.resources import files + +import yaml + +from harborrl.config import ROOT +from harborrl.config.native import FIELDS, OPTIONAL +from harborrl.deployment import schema +from harborrl.math_registry import MATH_REGISTRY + +from .presets import PRESETS + +TRAINING_FIELDS = { + "consistency.mode": ("string", "off, audit or enforce"), + "consistency.max_abs_delta": ("number_or_null", "Explicit deployment maximum token log-probability delta"), + "consistency.max_mean_abs_delta": ("number_or_null", "Explicit deployment mean token delta"), + "consistency.bad_token_delta": ("number_or_null", "Threshold identifying a bad token"), + "consistency.max_bad_fraction": ("number", "Allowed bad-token fraction between zero and one"), + "consistency.worst_tokens": ("integer", "Maximum diagnostic tokens, zero through 32"), + "execution.backend": ("string", "Task execution backend; currently harbor_job"), + "tasks.catalog": ("path", "Existing JSON/JSONL Harbor task lock; resolved on the controller"), + "harness.name": ("string", "Registered agent harness; currently claude_code"), + "harness.profile": ("path", "Pinned harness profile JSON on the controller"), + "harbor.python": ("path", "Runner interpreter on the local runner or SSH worker"), + "harbor.version": ("string", "Harbor version supported by the selected harness"), + "harbor.workers": ("array", "Ordered SSH destinations; empty with a local runner"), + "harbor.runner_transport": ("string", "local or ssh; local requires no workers"), + "harbor.environment": ("object", "Docker runtime, or Sandbox runtime with a config file reference"), + "gateway.host": ("string", "Gateway listener address, distinct from its advertised origin"), + "gateway.port": ("integer", "Gateway listener port, 1 through 65535"), + "gateway.advertised_url": ("string", "Plain HTTP(S) origin reachable by runners; port must match"), + "gateway.tokenizer_digest": ("string", "SHA-256 of the actual tokenizer fingerprint; never a placeholder"), + "gateway.template_digest": ("string", "SHA-256 of the actual chat-template fingerprint"), + "gateway.audited_raw_logprobs": ("boolean", "Raw serving probability audit, required by GRPO"), + "model.checkpoint": ("path", "Student model checkpoint; must be accessible on the GPU target"), + "model.reference": ("path", "Reference checkpoint accessible on the GPU target"), + "model.args_file": ("string", "Supported Slime preset name; currently the qwen prefix"), + "training.num_rollout": ("integer", "Positive number of rollout batches"), + "training.learning_rate": ("number", "Positive finite learning rate"), + "training.save_interval": ("integer", "Positive checkpoint interval in rollout batches"), + "training.backend_contract": ("string", "Pinned backend/algorithm contract"), + "sampling.group_size": ("integer", "Samples per prompt, at least two for GRPO"), + "sampling.groups_per_batch": ("integer", "Positive number of complete groups per batch"), + "sampling.max_attempts": ("integer", "Bounded invalid-attempt retries; not low-reward retries"), + "sampling.max_tokens": ("integer", "Positive response limit below max_context"), + "sampling.max_context": ("integer", "Total context token limit"), + "deployment.layout": ("string", "split or colocate; controls actor/serving GPU allocation"), + "deployment.num_gpus": ("integer", "Total positive GPU budget, not a memory-fit guarantee"), + "deployment.actor_gpus": ("integer", "Actor GPU count; must be divisible by actor TP"), + "deployment.rollout_gpus": ("integer", "Serving GPU count; must be divisible by engine TP"), + "deployment.actor_tensor_parallel_size": ("integer", "Positive actor tensor-parallel size"), + "deployment.rollout_gpus_per_engine": ("integer", "Positive serving tensor-parallel size"), + "output.root": ("path", "Run output root; service mode must match shared storage.run_root"), +} + +DEPLOYMENT_GROUPS = { + "manager": schema.MANAGER_FIELDS, "gpu_target": schema.GPU_FIELDS, + "storage": schema.STORAGE_FIELDS, "rollout": schema.ROLLOUT_FIELDS, + "rollout.workers[]": schema.WORKER_FIELDS, "gpu_target.ssh": schema.SSH_FIELDS, + "rollout.workers[].ssh": schema.SSH_FIELDS, "connectivity": schema.CONNECTIVITY_FIELDS, +} +DEPLOYMENT_FIELDS = { + "id": ("string", "Short role identifier"), "mode": ("string", "Manager mode or shared storage mode"), + "bind": ("string", "Manager listener address"), + "control_port": ("integer", "Control listener port; different from gateway_port"), + "control_url": ("string", "Control origin reachable from the GPU target"), + "gateway_port": ("integer", "Gateway listener port"), + "gateway_url": ("string", "Gateway origin reachable from runners or the configured relay"), + "run_token_file": ("path", "Manager run-token file reference; never the secret value"), + "source_dir": ("path", "Absolute source checkout path on this role's machine"), + "controller_python": ("path", "Absolute interpreter path on the controller"), + "transport": ("string", "GPU launch transport: local or ssh"), + "ssh": ("object", "SSH specification; GPU local transport requires null"), + "train_python": ("path", "Absolute interpreter path on the GPU target"), + "environment_file": ("path_or_null", "Controlled JSON environment file on the GPU target"), + "expected_gpus": ("integer", "Expected target GPU inventory, at least the requested budget"), + "run_root": ("path", "Absolute shared run root visible to all participating roles"), + "manager_writable": ("boolean", "Declared shared-root access; verified by doctor"), + "gpu_writable": ("boolean", "Declared GPU shared-root access; verified by doctor"), + "worker_writable": ("boolean", "Declared worker shared-root access; verified by doctor"), + "provider": ("string", "Rollout placement/provider; not a dataset or algorithm"), + "workers": ("array", "Ordered worker role specifications; empty for local Sandbox runners"), + "runner_python": ("path", "Absolute interpreter path on the worker"), + "docker": ("boolean", "Worker Docker requirement"), + "task_roots": ("array", "Absolute task roots visible on this worker"), + "host": ("string", "SSH alias/destination, not an automatically verified host"), + "user": ("string", "Optional SSH username"), "port": ("integer", "Optional SSH port"), + "identity": ("path", "Optional identity-file reference; no private-key contents"), + "command_timeout_s": ("integer", "Positive remote command timeout"), + "ssh_timeout_s": ("integer", "Positive SSH connection timeout"), + "http_timeout_s": ("integer", "Positive HTTP check timeout"), + "gpu_to_controller": ("boolean", "Declared GPU-to-controller connectivity requirement"), + "worker_to_gateway": ("boolean", "Declared runner-to-gateway connectivity requirement"), + "manager_to_gpu_inference": ("boolean", "Declared controller-to-serving connectivity requirement"), +} + + +def _describe(spec, attributes): + record = {} + for key in ("id", "display_name", "version", "evidence", "limitations", *attributes): + value = getattr(spec, key) + record[key] = sorted(value) if isinstance(value, frozenset) else list(value) if isinstance(value, tuple) else value + return record + + +def dataset_metadata(): + return yaml.safe_load(files("harborrl.options").joinpath("resources/datasets.yaml").read_text(encoding="utf-8")) + + +def build_catalog(): + training = {} + for section, fields in sorted(FIELDS.items()): + for name in sorted(fields): + key = f"{section}.{name}" + kind, description = TRAINING_FIELDS[key] + training[key] = {"type": kind, "description": description, + "required": name not in OPTIONAL.get(section, set())} + deployment = {} + for section, fields in sorted(DEPLOYMENT_GROUPS.items()): + for name in sorted(fields): + kind, description = DEPLOYMENT_FIELDS[name] + deployment[f"{section}.{name}"] = {"type": kind, "description": description} + for key, values in {"manager.mode": ("embedded", "service"), "storage.mode": schema.STORAGE_MODES, + "rollout.provider": schema.ROLLOUT_PROVIDERS, "gpu_target.transport": schema.TRANSPORTS}.items(): + deployment[key]["values"] = list(values) + datasets = deepcopy(dataset_metadata()["datasets"]) + for item in datasets: + path = ROOT / item["task_root"] + item["available"] = item["status"] != "planned" and item["source"] == "bundled" and path.is_dir() + item["availability_note"] = "Provide an existing inspected task catalog; no download is performed" + registry = MATH_REGISTRY + return { + "catalog_version": 1, "schema_versions": {"training": 2, "deployment": 1}, + "models": [_describe(s, ("preset_prefix", "serving_backend", "tool_protocol", "gateway_protocols", "training_backend_contracts")) for s in registry.models.items()], + "algorithms": [_describe(s, ("training_backend_contracts", "minimum_group_size", "requires_audited_raw_logprobs", "reduction")) for s in registry.algorithms.items()], + "task_providers": [_describe(s, ("execution_backends", "task_format")) for s in registry.tasks.items()], + "harnesses": [_describe(s, ("request_protocol", "tool_protocol", "harbor_versions")) for s in registry.harnesses.items()], + "datasets": datasets, "deployment_presets": deepcopy(list(PRESETS)), + "fields": {"training": training, "deployment": deployment}, + "compatibility": {"scope": "component_metadata", "status": "unknown_until_configuration_validated", + "constraints": ["Model and harness tool/request protocols must match", + "Algorithm and model must support the selected backend contract", + "Task provider must support the execution backend", + "Harbor version must be supported by the harness"], + "validator": "harborrl.math_registry.validate_combination"}, + } diff --git a/harborrl/options/cli.py b/harborrl/options/cli.py new file mode 100644 index 000000000..7438628dc --- /dev/null +++ b/harborrl/options/cli.py @@ -0,0 +1,184 @@ +"""Terminal interface for the offline catalog, validation and bundle builder.""" +import argparse +import json +import shlex +import sys +from pathlib import Path + +import yaml + +from .catalog import build_catalog +from .presets import PRESETS, worker_defaults +from .validation import read_mapping, validate_paths +from .wizard import build_config_bundle, publish_bundle + +DIMENSIONS = {"models": "models", "algorithms": "algorithms", "tasks": "task_providers", + "harnesses": "harnesses", "datasets": "datasets", "deployments": "deployment_presets", + "fields": "fields", "compat": "compatibility"} + + +def ask(label, default=None, convert=str): + while True: + value = input(f"{label}" + (f" [{default}]" if default is not None else "") + ": ").strip() + if not value and default is not None: + return default + try: + if not value: + raise ValueError("A value is required") + return convert(value) + except ValueError as exc: + print(str(exc), file=sys.stderr) + + +def choose(label, choices): + for index, value in enumerate(choices, 1): + print(f" {index}. {value}") + while True: + index = ask(label, 1, int) + if 1 <= index <= len(choices): + return choices[index - 1] + print("Choose a listed number", file=sys.stderr) + + +def interactive_answers(): + preset = choose("Deployment", [p["id"] for p in PRESETS]) + t, d = {}, {} + service = preset != "embedded-local" + print("Components: Qwen / GRPO / Harbor tasks / Claude Code. Advanced fields use an answers file.") + if service: + d["manager"] = { + "source_dir": ask("Controller source directory"), + "controller_python": ask("Controller Python", sys.executable), + "control_url": ask("Controller origin (include port 49200)"), + "gateway_url": ask("Gateway origin (include port 49184)"), + } + d["storage"] = {"run_root": ask("Shared run root (absolute)")} + d["gpu_target"] = {"ssh": {"host": ask("GPU SSH alias/destination")}, + "source_dir": ask("GPU source directory"), "train_python": ask("GPU training Python")} + else: + d["gpu_target"] = {"train_python": ask("Training Python", sys.executable)} + if preset == "service-ssh": + workers = [] + count = ask("Number of SSH runners", 1, int) + if count < 1: + raise ValueError("At least one SSH runner is required") + for index in range(count): + worker = worker_defaults() + worker.update(id=f"worker-{index}", ssh={"host": ask(f"Runner {index} SSH destination")}, + source_dir=ask("Runner source directory"), runner_python=ask("Runner Python"), + task_roots=[ask("Runner task root")]) + workers.append(worker) + d["rollout"] = {"workers": workers} + else: + t["harbor"] = {"python": ask("Local Harbor runner Python", sys.executable)} + t["tasks"] = {"catalog": ask("Existing task catalog JSON/JSONL")} + t["harness"] = {"profile": ask("Existing harness profile JSON")} + t["model"] = {"checkpoint": ask("GPU checkpoint directory"), "reference": ask("GPU reference directory"), + "args_file": ask("Qwen model args preset", "qwen3-8B")} + t["gateway"] = {"tokenizer_digest": ask("Actual tokenizer SHA-256"), + "template_digest": ask("Actual template SHA-256")} + if not service: + t["gateway"].update(host=ask("Gateway bind address", "127.0.0.1"), + advertised_url=ask("Gateway origin reachable by tasks", "http://127.0.0.1:49184")) + layout = choose("GPU layout", ["colocate", "split"]) + t["deployment"] = {"layout": layout, "num_gpus": ask("Total GPU budget", 1, int)} + if layout == "split": + t["deployment"].update(actor_gpus=ask("Actor GPUs", 1, int), rollout_gpus=ask("Serving GPUs", 1, int)) + t["training"] = {"num_rollout": ask("Rollout batches", 2, int)} + t["sampling"] = {"group_size": ask("Samples per group", 2, int), + "groups_per_batch": ask("Groups per batch", 1, int)} + answers = {"answers_version": 1, "preset": preset, "training": t, "deployment": d} + if preset == "service-sandbox-local-runner": + answers["sandbox_config"] = ask("Existing Sandbox configuration") + return answers + + +def render_validation(result): + print(f"{result['status']} (scope: {result['scope']})") + for row in result["checks"]: + print(f" {row['status']}: {row['path']}: {row['message']}") + + +def render_catalog(result): + print("HarborRL options (catalog 1; training schema 2; deployment schema 1)") + for name, value in result.items(): + if name in {"catalog_version", "schema_versions"}: + continue + if name == "fields" and len(result) > 3: + print("fields: use 'harborrl options fields' for field descriptions") + elif isinstance(value, list): + print(f"{name}:") + for row in value: + description = row.get("description", row.get("display_name", "")) + if "available" in row: + description += f" (status={row['status']}, locally_available={row['available']})" + print(f" {row.get('id', row.get('name'))}: {description}") + else: + print(yaml.safe_dump({name: value}, sort_keys=False).rstrip()) + + +def main(argv): + parser = argparse.ArgumentParser(prog="harborrl") + commands = parser.add_subparsers(dest="command", required=True) + options = commands.add_parser("options", help="List supported configuration options") + options.add_argument("dimension", nargs="?", choices=DIMENSIONS) + options.add_argument("--json", action="store_true") + validate = commands.add_parser("validate", help="Validate offline; no runtime is started") + validate.add_argument("config") + validate.add_argument("--deployment") + validate.add_argument("--check-files", action="store_true") + validate.add_argument("--json", action="store_true") + init = commands.add_parser("init", help="Create a configuration bundle in a new directory") + init.add_argument("--out-dir", required=True) + init.add_argument("--answers") + init.add_argument("--probe-local", action="store_true") + args = parser.parse_args(argv) + try: + if args.command == "options": + result = build_catalog() + if args.dimension: + key = DIMENSIONS[args.dimension] + result = {k: result[k] for k in ("catalog_version", "schema_versions", key)} + if args.json: + print(json.dumps(result, indent=2, sort_keys=True)) + else: + render_catalog(result) + return 0 + if args.command == "validate": + result = validate_paths(args.config, args.deployment, check_files=args.check_files) + if args.json: + print(json.dumps(result, indent=2, sort_keys=True)) + else: + render_validation(result) + return int(result["status"] == "invalid") + if not args.answers and not sys.stdin.isatty(): + parser.error("init requires --answers when stdin is not a terminal") + output = Path(args.out_dir).expanduser().absolute() + if output.exists() or output.is_symlink(): + raise ValueError(f"Output directory already exists: {output}") + if args.probe_local: + from .probe import probe_local + print(json.dumps(probe_local(output), indent=2)) + answers = read_mapping(args.answers) if args.answers else interactive_answers() + bundle = build_config_bundle(answers, output_directory=output) + render_validation(bundle["validation"]) + if not args.answers: + print(bundle["files"]["config.yaml"]) + print(bundle["files"]["deployment.yaml"]) + if ask("Publish these files? (yes/no)", "no").lower() != "yes": + print("Cancelled; no files written") + return 0 + publish_bundle(bundle, output) + print(f"Created configuration bundle: {output}") + config = shlex.quote(str(output / "config.yaml")) + deployment = shlex.quote(str(output / "deployment.yaml")) + for command, suffix in (("validate", " --check-files"), ("train", " --dry-run"), ("doctor", "")): + print(f"harborrl {command} {config} --deployment {deployment}{suffix}") + print("doctor checks the live environment and connectivity; training requires a separate command.") + return 0 + except (ValueError, TypeError, KeyError, AttributeError, OSError, yaml.YAMLError) as exc: + print(f"error: {exc}", file=sys.stderr) + return 1 + except (EOFError, KeyboardInterrupt): + print("Cancelled; no configuration published", file=sys.stderr) + return 130 diff --git a/harborrl/options/presets.py b/harborrl/options/presets.py new file mode 100644 index 000000000..6cc7ef8b5 --- /dev/null +++ b/harborrl/options/presets.py @@ -0,0 +1,153 @@ +"""Small configuration recipes; final validity belongs to existing schemas.""" +import sys +from copy import deepcopy +from pathlib import Path + +from harborrl.config import ROOT +from harborrl.consistency.config import DEFAULTS as CONSISTENCY_DEFAULTS +from harborrl.deployment.schema import destination, embedded_local + +PRESETS = ( + {"id": "embedded-local", "description": "Local training and a local Harbor runner"}, + {"id": "service-ssh", "description": "Controller, SSH GPU target and SSH Docker runners"}, + {"id": "service-sandbox-local-runner", "description": "Controller and runner here; GPU target over SSH; Sandbox execution"}, +) + + +def merge(base, override, path=""): + if not isinstance(override, dict): + raise TypeError(f"{path or 'answers'} must be a mapping") + result = deepcopy(base) + for key, value in override.items(): + if key not in base: + raise ValueError(f"unknown field: {path}{key}") + result[key] = (merge(base[key], value, f"{path}{key}.") + if isinstance(base[key], dict) and base[key] else deepcopy(value)) + return result + + +def defaults(preset, output_directory): + if preset not in {item["id"] for item in PRESETS}: + raise ValueError(f"unknown preset: {preset}") + output = Path(output_directory).resolve() + service = preset != "embedded-local" + sandbox = preset == "service-sandbox-local-runner" + training = { + "consistency": deepcopy(CONSISTENCY_DEFAULTS), + "schema_version": 2, + "execution": {"backend": "harbor_job"}, "tasks": {"catalog": None}, + "harness": {"name": "claude_code", "profile": None}, + "harbor": {"python": None, "version": "0.23.0", "workers": [], + "runner_transport": "ssh" if preset == "service-ssh" else "local", + "environment": {"runtime": "sandbox", "config": None} if sandbox else {"runtime": "docker"}}, + "gateway": {"host": "0.0.0.0" if service else "127.0.0.1", "port": 49184, + "advertised_url": None if service else "http://127.0.0.1:49184", + "tokenizer_digest": None, "template_digest": None, "audited_raw_logprobs": True}, + "model": {"checkpoint": None, "reference": None, "args_file": "qwen3-8B"}, + "training": {"num_rollout": 2, "learning_rate": 1e-6, "save_interval": 1, + "backend_contract": "slime-v0.3.2-native-v1"}, + "sampling": {"group_size": 2, "groups_per_batch": 1, "max_attempts": 1, + "max_tokens": 2048, "max_context": 32768}, + "deployment": {"layout": "colocate", "num_gpus": 1, "actor_gpus": 1, + "rollout_gpus": 1, "actor_tensor_parallel_size": 1, "rollout_gpus_per_engine": 1}, + "output": {"root": str(output / "runs")}, + } + deployment = embedded_local(run_root=str(output / "runs")) + deployment["gpu_target"]["environment_file"] = None + deployment["manager"]["run_token_file"] = str(output / "run-token") + if service: + deployment["manager"].update(id="controller", mode="service", bind="0.0.0.0", + control_url=None, gateway_url=None) + deployment["gpu_target"].update(id="gpu", transport="ssh", ssh={"host": None, "user": None, "port": None, "identity": None}, + source_dir=None, train_python=None) + deployment["rollout"]["provider"] = "sandbox" if sandbox else "ssh_docker" + return training, deployment + + +def build_configs(answers, output_directory): + if not isinstance(answers, dict) or set(answers) - { + "answers_version", "preset", "training", "deployment", "sandbox_config"}: + raise ValueError("unknown answers field or non-mapping answers") + if type(answers.get("answers_version")) is not int or answers["answers_version"] != 1: + raise ValueError("answers_version must be 1") + preset = answers.get("preset") + training, deployment = defaults(preset, output_directory) + t = answers.get("training", {}) + d = answers.get("deployment", {}) + training = merge(training, t, "training.") + deployment = merge(deployment, d, "deployment.") + + # Repeated fields have one source unless both were explicitly supplied. + for section, field, other_section, other_field in ( + ("gateway", "host", "manager", "bind"), + ("gateway", "port", "manager", "gateway_port"), + ("gateway", "advertised_url", "manager", "gateway_url"), + ("output", "root", "storage", "run_root"), + ): + if field in t.get(section, {}) and other_field not in d.get(other_section, {}): + deployment[other_section][other_field] = training[section][field] + elif other_field in d.get(other_section, {}) and field not in t.get(section, {}): + training[section][field] = deployment[other_section][other_field] + elif field in t.get(section, {}) and other_field in d.get(other_section, {}): + left, right = training[section][field], deployment[other_section][other_field] + if field == "root" and isinstance(left, str) and isinstance(right, str): + left, right = ((Path(output_directory) / value).resolve() for value in (left, right)) + elif field == "advertised_url" and isinstance(left, str) and isinstance(right, str): + left, right = left.rstrip("/"), right.rstrip("/") + if left != right: + raise ValueError(f"training.{section}.{field} conflicts with deployment.{other_section}.{other_field}") + count = training["deployment"]["num_gpus"] + if "expected_gpus" not in d.get("gpu_target", {}): + deployment["gpu_target"]["expected_gpus"] = count + if training["deployment"]["layout"] == "colocate": + for field in ("actor_gpus", "rollout_gpus"): + if field not in t.get("deployment", {}): + training["deployment"][field] = count + if training["harbor"]["runner_transport"] == "ssh": + workers = deployment["rollout"]["workers"] + if workers: + if "workers" not in t.get("harbor", {}): + training["harbor"]["workers"] = [destination(w["ssh"]) for w in workers] + if "python" not in t.get("harbor", {}): + training["harbor"]["python"] = workers[0]["runner_python"] + if answers.get("sandbox_config") is not None: + if preset != "service-sandbox-local-runner": + raise ValueError("sandbox_config requires the Sandbox preset") + environment = training["harbor"]["environment"] + if environment.get("config") not in (None, answers["sandbox_config"]): + raise ValueError("sandbox_config conflicts with training.harbor.environment.config") + environment["config"] = answers["sandbox_config"] + required = [("training", section, field) for section, field in ( + ("tasks", "catalog"), ("harness", "profile"), ("harbor", "python"), + ("model", "checkpoint"), ("model", "reference"), + ("gateway", "tokenizer_digest"), ("gateway", "template_digest"), ("gateway", "advertised_url"))] + if preset != "embedded-local": + required += [("deployment", section, field) for section, field in ( + ("manager", "control_url"), ("manager", "gateway_url"), + ("gpu_target", "source_dir"), ("gpu_target", "train_python"))] + configs = {"training": training, "deployment": deployment} + missing = [".".join(keys) for keys in required if configs[keys[0]][keys[1]][keys[2]] in (None, "")] + if preset != "embedded-local" and not deployment["gpu_target"]["ssh"].get("host"): + missing.append("deployment.gpu_target.ssh.host") + if preset == "service-ssh" and not deployment["rollout"]["workers"]: + missing.append("deployment.rollout.workers") + if preset == "service-sandbox-local-runner" and not training["harbor"]["environment"].get("config"): + missing.append("sandbox_config") + if missing: + raise ValueError("Missing required answers: " + ", ".join(missing)) + for field in ("tokenizer_digest", "template_digest"): + if training["gateway"][field] == "0" * 64: + raise ValueError(f"gateway.{field}: a placeholder digest cannot be used by init") + expected_mode = "embedded" if preset == "embedded-local" else "service" + expected_provider = {"embedded-local": "embedded", "service-ssh": "ssh_docker", + "service-sandbox-local-runner": "sandbox"}[preset] + if deployment["manager"]["mode"] != expected_mode or deployment["rollout"]["provider"] != expected_provider: + raise ValueError("deployment mode/provider conflicts with the selected preset") + # All generated path references are rooted at the final directory, not staging. + deployment["storage"]["run_root"] = str((Path(output_directory) / deployment["storage"]["run_root"]).resolve()) + return training, deployment + + +def worker_defaults(): + return {"id": "worker-0", "ssh": {"host": None}, "source_dir": str(ROOT), + "runner_python": sys.executable, "docker": True, "task_roots": [str(ROOT)]} diff --git a/harborrl/options/probe.py b/harborrl/options/probe.py new file mode 100644 index 000000000..12131c5df --- /dev/null +++ b/harborrl/options/probe.py @@ -0,0 +1,39 @@ +"""Explicit, bounded local hints. Never connect to a remote machine.""" +import os +import shlex +import subprocess +import sys +from pathlib import Path + + +def command_hint(command): + try: + result = subprocess.run(command, capture_output=True, text=True, timeout=3, check=True) + return {"method": command[0], "status": "observed", "value": result.stdout.strip(), "reason": None} + except (OSError, subprocess.SubprocessError) as exc: + return {"method": command[0], "status": "unknown", "value": None, "reason": str(exc)} + + +def ssh_aliases(path): + aliases = set() + for line in Path(path).read_text(encoding="utf-8").splitlines(): + words = shlex.split(line, comments=True) + if words and words[0].lower() == "host": + aliases.update(word for word in words[1:] if not any(c in word for c in "*?!")) + return sorted(aliases) + + +def probe_local(output_directory): + try: + aliases = {"method": "ssh_config_hosts", "status": "observed", + "value": ssh_aliases(Path.home() / ".ssh/config"), "reason": "Include and Match are not evaluated"} + except (OSError, ValueError) as exc: + aliases = {"method": "ssh_config_hosts", "status": "unknown", "value": None, "reason": str(exc)} + parent = Path(output_directory).expanduser().absolute().parent + return { + "gpu": command_hint(["nvidia-smi", "--query-gpu=uuid", "--format=csv,noheader"]), + "python": command_hint([sys.executable, "-I", "--version"]), + "ssh_aliases": aliases, + "output_parent": {"method": "os.access", "status": "hint", "value": os.access(parent, os.W_OK), + "reason": "Access hint only; no write was attempted"}, + } diff --git a/harborrl/options/resources/datasets.yaml b/harborrl/options/resources/datasets.yaml new file mode 100644 index 000000000..40d8e6726 --- /dev/null +++ b/harborrl/options/resources/datasets.yaml @@ -0,0 +1,40 @@ +schema_version: 1 +datasets: + - name: hello_world + status: example + source: bundled + upstream_revision: v0.1-example + license: MIT + task_root: examples/native/tasks + expected_task_count: 1 + digest_policy: native-inspector-tree-sha256 + docker_base: debian:bookworm-slim + gpu_required: false + acceptance: offline-native-contract-only + owner: HarborRL maintainers + + - name: terminal-bench-core + status: planned + source: https://github.com/laude-institute/terminal-bench + upstream_revision: TBD + license: TBD + task_root: TBD + expected_task_count: TBD + digest_policy: native-inspector-tree-sha256 + docker_base: task-defined + gpu_required: false + acceptance: pending-public-worker-matrix + owner: HarborRL maintainers + + - name: terminal-bench-test + status: planned + source: https://github.com/laude-institute/terminal-bench + upstream_revision: dataset/terminal-bench-core/v0.1.x + license: TBD + task_root: TBD + expected_task_count: TBD + digest_policy: native-inspector-tree-sha256 + docker_base: task-defined + gpu_required: false + acceptance: pending-public-worker-matrix + owner: HarborRL maintainers diff --git a/harborrl/options/validation.py b/harborrl/options/validation.py new file mode 100644 index 000000000..53465d83e --- /dev/null +++ b/harborrl/options/validation.py @@ -0,0 +1,169 @@ +"""Offline checks composed from the authoritative configuration validators.""" +from copy import deepcopy +from pathlib import Path + +import yaml + +from harborrl.config import native +from harborrl.deployment import schema + + +def read_mapping(path): + value = yaml.safe_load(Path(path).read_text(encoding="utf-8")) + if not isinstance(value, dict): + raise TypeError("configuration must be a mapping") + return value + + +def check(path, status, code, message): + return {"path": path, "status": status, "code": code, "message": message} + + +def report(checks, *, check_files=False, source="explicit"): + states = {item["status"] for item in checks} + status = "invalid" if "failed" in states else ( + "valid_with_deferred_checks" if "deferred" in states else "valid") + return {"validation_version": 1, "status": status, + "scope": "structure_and_local_files" if check_files else "structure", + "deployment_source": source, "checks": checks} + + +def cross_checks(training, deployment): + """Only compare settings actually consumed together by the selected mode.""" + checks = [] + + def require(condition, path, message): + if not condition: + checks.append(check(path, "failed", "inconsistent", message)) + + gpu = deployment["gpu_target"] + require(training["deployment"]["num_gpus"] <= gpu["expected_gpus"], + "deployment.gpu_target.expected_gpus", "Expected GPU count is below the training budget") + if deployment["manager"]["mode"] != "service": + return checks + manager, rollout, harbor = deployment["manager"], deployment["rollout"], training["harbor"] + for key, other in (("host", "bind"), ("port", "gateway_port"), ("advertised_url", "gateway_url")): + left, right = training["gateway"][key], manager[other] + if key == "advertised_url": + left, right = left.rstrip("/"), right.rstrip("/") + require(left == right, f"training.gateway.{key}", f"Must match deployment.manager.{other}") + require(training["output"]["root"] == deployment["storage"]["run_root"], + "training.output.root", "Must match deployment.storage.run_root") + sandbox = harbor.get("environment", {}).get("runtime") == "sandbox" + require(sandbox == (rollout["provider"] == "sandbox"), "deployment.rollout.provider", + "Sandbox provider and training environment must agree") + workers = rollout["workers"] + local = harbor.get("runner_transport", "ssh") == "local" + require(not workers if local else bool(workers), "deployment.rollout.workers", + "Local runners require no workers; SSH runners require workers") + if not local and workers: + destinations = [schema.destination(worker["ssh"]) for worker in workers] + require(harbor["workers"] == destinations, "training.harbor.workers", + "Must match deployment worker destinations in order") + require(harbor["python"] == workers[0]["runner_python"], "training.harbor.python", + "Must match the deployment's first runner_python") + return checks + + +def _local_file(checks, path, label, *, directory=False): + target = Path(path) + exists = target.is_dir() if directory else target.is_file() + checks.append(check(label, "passed" if exists else "failed", "local_path", + "Local path exists" if exists else "Required local path is missing")) + + +def _file_checks(training, deployment): + checks = [] + manager = deployment["manager"] + _local_file(checks, manager["source_dir"], "deployment.manager.source_dir", directory=True) + _local_file(checks, manager["controller_python"], "deployment.manager.controller_python") + for label, function in ( + ("training.harness.profile", lambda: native.validate_files(training)), + ("training.tasks.catalog", lambda: native.catalog(training)), + ): + try: + function() + except (ValueError, TypeError, KeyError, OSError, AttributeError) as exc: + checks.append(check(label, "failed", "local_reference", str(exc))) + else: + checks.append(check(label, "passed", "local_reference", "Local reference validated")) + gpu = deployment["gpu_target"] + if gpu["transport"] == "local": + for key in ("checkpoint", "reference"): + _local_file(checks, training["model"][key], f"training.model.{key}", directory=True) + _local_file(checks, gpu["train_python"], "deployment.gpu_target.train_python") + _local_file(checks, gpu["source_dir"], "deployment.gpu_target.source_dir", directory=True) + if gpu.get("environment_file"): + _local_file(checks, gpu["environment_file"], "deployment.gpu_target.environment_file") + else: + checks.append(check("deployment.gpu_target", "deferred", "remote_paths", + "Model, interpreter and source paths belong to the GPU target")) + if training["harbor"].get("runner_transport", "ssh") == "local": + _local_file(checks, training["harbor"]["python"], "training.harbor.python") + else: + checks.append(check("deployment.rollout.workers", "deferred", "remote_paths", + "Runner paths must be checked on their worker machines")) + environment = training["harbor"].get("environment", {}) + if environment.get("runtime") == "sandbox": + from harborrl.rollout.harbor_job.sandbox_config import ( + load_sandbox, + task_binding, + ) + try: + cfg = load_sandbox(environment["config"]) + if cfg["adapter"] == "harborrl.rollout.harbor_job.environments.opensandbox:ManagedOpenSandboxEnvironment": + if cfg["options"] or any(binding["options"] for binding in cfg["bindings"]): + raise ValueError("standard OpenSandbox does not accept platform options") + else: + checks.append(check("sandbox.adapter", "deferred", "runner_adapter", + "Platform adapter validation belongs to its trusted runner environment")) + for entry in native.catalog(training): + task_binding(cfg, entry["path"]) + except (ValueError, TypeError, KeyError, OSError, AttributeError, yaml.YAMLError) as exc: + checks.append(check("training.harbor.environment", "failed", "sandbox_config", str(exc))) + else: + checks.append(check("training.harbor.environment", "passed", "sandbox_config", + "Sandbox structure and task image bindings validated; credentials not read")) + checks.append(check("sandbox.platform", "deferred", "platform_access", + "Credentials, offline binary, capacity and network need runner preflight")) + return checks + + +def validate_mappings(training, deployment, *, config_path, check_files=False, source="explicit"): + checks = [] + normalized = {} + for name, function in ( + ("training", lambda: native.validate(deepcopy(training), Path(config_path).resolve())), + ("deployment", lambda: schema.validate(deepcopy(deployment))), + ): + try: + normalized[name] = function() + except (ValueError, TypeError, KeyError, OSError, AttributeError) as exc: + checks.append(check(name, "failed", "schema", str(exc))) + else: + checks.append(check(name, "passed", "schema", "Schema and semantic checks passed")) + if len(normalized) == 2: + checks.extend(cross_checks(normalized["training"], normalized["deployment"])) + if check_files: + checks.extend(_file_checks(normalized["training"], normalized["deployment"])) + else: + checks.append(check("references", "deferred", "files_not_checked", "Use --check-files for local references")) + checks.append(check("runtime", "deferred", "live_preflight", + "GPU memory, model loading, live digests and connectivity require doctor or an explicit live test")) + return report(checks, check_files=check_files, source=source) + + +def validate_paths(config_path, deployment_path=None, *, check_files=False): + try: + training = read_mapping(config_path) + deployment, source = schema.load_deployment(deployment_path) + if source == "embedded-local": + normalized = native.validate(deepcopy(training), Path(config_path).resolve()) + deployment = schema.embedded_local(run_root=normalized["output"]["root"], + workers=normalized["harbor"]["workers"], expected_gpus=normalized["deployment"]["num_gpus"]) + # The legacy validator omits an optional null key from its normalized output. + deployment["gpu_target"].setdefault("environment_file", None) + return validate_mappings(training, deployment, config_path=config_path, + check_files=check_files, source=source) + except (ValueError, TypeError, KeyError, OSError, AttributeError, yaml.YAMLError) as exc: + return report([check("configuration", "failed", "load", str(exc))], check_files=check_files) diff --git a/harborrl/options/wizard.py b/harborrl/options/wizard.py new file mode 100644 index 000000000..55cba4a53 --- /dev/null +++ b/harborrl/options/wizard.py @@ -0,0 +1,68 @@ +"""Build a reviewed configuration bundle, then publish it to a new directory.""" +import json +import os +import shutil +import tempfile +from pathlib import Path + +import yaml + +from harborrl.config.native import validate +from harborrl.trajectories.native import digest + +from .presets import build_configs +from .validation import validate_mappings + + +def build_config_bundle(answers, *, output_directory): + output = Path(output_directory).expanduser().resolve() + training, deployment = build_configs(answers, output) + result = validate_mappings(training, deployment, config_path=output / "config.yaml") + if result["status"] == "invalid": + raise ValueError("; ".join(f"{row['path']}: {row['message']}" for row in result["checks"] if row["status"] == "failed")) + training = validate(training, output / "config.yaml") + manifest = {"setup_version": 1, "preset": answers["preset"], + "schema_versions": {"training": 2, "deployment": 1}, + "config_digest": digest(training), "deployment_digest": digest(deployment), + "references": {"catalog": training["tasks"]["catalog"], + "profile": training["harness"]["profile"], + "sandbox_config": training["harbor"].get("environment", {}).get("config")}, + "validation": result} + return {"files": {"config.yaml": yaml.safe_dump(training, sort_keys=False), + "deployment.yaml": yaml.safe_dump(deployment, sort_keys=False), + "setup-manifest.json": json.dumps(manifest, indent=2) + "\n"}, + "validation": result} + + +def publish_bundle(bundle, output_directory): + """No overwrite; same-parent staging and an exclusive per-target lock.""" + output = Path(output_directory).expanduser().absolute() + if output.exists() or output.is_symlink(): + raise FileExistsError(f"Output directory already exists: {output}") + if not output.parent.is_dir(): + raise ValueError("Output parent directory must already exist") + files = bundle["files"] + if set(files) != {"config.yaml", "deployment.yaml", "setup-manifest.json"}: + raise ValueError("Bundle must contain exactly the three documented files") + lock = output.parent / f".{output.name}.init.lock" + descriptor = os.open(lock, os.O_CREAT | os.O_EXCL | os.O_WRONLY, 0o600) + staging = None + try: + os.close(descriptor) + if output.exists() or output.is_symlink(): + raise FileExistsError(f"Output directory already exists: {output}") + staging = Path(tempfile.mkdtemp(prefix=f".{output.name}.init-", dir=output.parent)) + for name, content in files.items(): + with (staging / name).open("x", encoding="utf-8") as stream: + stream.write(content) + stream.flush() + os.fsync(stream.fileno()) + if output.exists() or output.is_symlink(): + raise FileExistsError(f"Output directory already exists: {output}") + staging.rename(output) + staging = None + finally: + if staging is not None: + shutil.rmtree(staging) + lock.unlink() + return output diff --git a/harborrl/platform/evidence.py b/harborrl/platform/evidence.py new file mode 100644 index 000000000..d988cf86f --- /dev/null +++ b/harborrl/platform/evidence.py @@ -0,0 +1,126 @@ +"""Run input preparation and reproducible source evidence.""" +import hashlib +import json +import subprocess +import sys +import time +from pathlib import Path +from uuid import uuid4 + +from harborrl.config import ROOT +from harborrl.trajectories.native import publish + + +def new_run_root(output_root): + name = time.strftime("%Y%m%d-%H%M%S") + "-" + uuid4().hex[:8] + return Path(output_root) / "training" / name + + +def run_environment(plan, root, catalog_path, prompt_path): + """Shared run paths for both local and controller-managed training.""" + return { + **plan["environment"], + "RUN_ID": root.name, "RUN_NAME": root.name, "RUN_DIR": str(root), + "RUNS_ROOT": str(root.parent.parent.resolve()), + "CKPT_ROOT": str(root / "checkpoints"), "WANDB_DIR": str(root / "metrics/wandb"), + "HARBORRL_NATIVE_CATALOG": str(catalog_path), + "ROLLOUT_PROMPT_DATA": str(prompt_path), + "HARBORRL_NATIVE_PROFILE": plan["config"]["harness"]["profile"], + "HARBOR_IR_ROOT": str(root / "ir"), + } + + +def _materialize(plan, root): + from harborrl.math_registry import select_math + + config = plan["config"] + entries = select_math(config).task.adapter.catalog(config) + normalized = root / "config" + normalized.mkdir(parents=True) + catalog_path = normalized / "native_catalog.json" + publish(catalog_path, entries) + prompt_path = normalized / "native_tasks.jsonl" + rows = select_math(config).task.adapter.prompt_rows(entries) + prompt_path.write_text("".join(json.dumps(row, ensure_ascii=False, sort_keys=True) + "\n" for row in rows)) + return catalog_path, prompt_path + + +def _source_audit(root): + def git(*args): + return subprocess.check_output(["git", *args], cwd=ROOT) + + files = git("ls-files", "-z", "--cached", "--others", "--exclude-standard", "--", + "harborrl", "scripts", "examples", "pyproject.toml").decode().split("\0") + untracked = set(git("ls-files", "-z", "--others", "--exclude-standard", "--", + "harborrl", "scripts", "examples").decode().split("\0")) + (root / "source.patch").write_bytes(git("diff", "--binary", "HEAD", "--", + "harborrl", "scripts", "examples", "pyproject.toml")) + hashes = {} + for name in files: + source = ROOT / name + if name and source.is_file(): + hashes[name] = hashlib.sha256(source.read_bytes()).hexdigest() + if name in untracked: + target = root / "source-new" / name + target.parent.mkdir(parents=True, exist_ok=True) + target.write_bytes(source.read_bytes()) + payload = { + "commit": git("rev-parse", "HEAD").decode().strip(), + "dirty": bool(git("status", "--porcelain")), + "source_hashes": hashes, + "patch": "source.patch", + } + publish(root / "source-manifest.json", payload) + return payload + + + + +# Proxy values and provider credentials are deliberately outside this snapshot. +DEPLOYMENT_EVIDENCE_KEYS = ("SLIME_DIR", "MEGATRON_DIR", "SGLANG_IMAGE", "CUDA_VISIBLE_DEVICES") + + +def effective_config(plan, environment, child_env, source, checks): + from harborrl.backends.slime_v032.config import field_usage + config = plan["config"] + return { + "schema_version": "harborrl-effective-config-v1", + "components": { + "model_preset": config["model"]["args_file"], + "algorithm": plan["math"]["algorithm"]["id"], + "harness": plan["math"]["harness"]["id"], + "execution_backend": config["execution"]["backend"], + "training_backend_contract": config["training"]["backend_contract"], + }, + "environment": environment, + "deployment_environment": {key: child_env[key] for key in DEPLOYMENT_EVIDENCE_KEYS if key in child_env}, + "training_command": plan["training_command"], + "expanded_command_file": "effective-command.json", + "source": source, + "runtime_versions": [check for check in checks + if "version" in check or check.get("service") in + ("slime commit", "megatron commit", "sglang image")], + "field_usage": field_usage(), + "precedence": "YAML < --set in order < explicit MATH selectors; declared deployment environment only", + } + + +def record_command(root, command, *, dry_run=False): + """Record argv after shell defaults and model preset expansion.""" + payload = { + "schema_version": "harborrl-effective-command-v1", + "command": command, + "mode": "dry_run" if dry_run else "training", + } + target = Path(root) / "effective-command.json" + if dry_run: + checksum = hashlib.sha256(json.dumps(command).encode()).hexdigest() + target = Path(root) / "dry-run-commands" / f"{checksum}.json" + publish(target, payload) + + +if __name__ == "__main__": + # Internal shell boundary: RUN_DIR, mode, then the exact executable argv. + if len(sys.argv) < 4 or sys.argv[2] not in ("0", "1"): + raise SystemExit("usage: evidence RUN_DIR DRY_RUN COMMAND [ARG ...]") + record_command(sys.argv[1], sys.argv[3:], dry_run=sys.argv[2] == "1") diff --git a/harborrl/platform/native_train.py b/harborrl/platform/native_train.py index a2a356dbc..0c0eedba2 100644 --- a/harborrl/platform/native_train.py +++ b/harborrl/platform/native_train.py @@ -2,249 +2,50 @@ The dispatcher freezes a run directory and materializes the original-task lock into Slime prompt rows before handing lifecycle control to the existing Slime -launcher. It never rewrites task tests or rewards. +launcher. It never rewrites task tests or rewards. A service-mode deployment +routes through the manager-side controller instead of a local child process. """ from __future__ import annotations -import hashlib -import importlib -import importlib.metadata import json -import os -import shutil -import signal -import subprocess import sys -import time from pathlib import Path -from types import SimpleNamespace -from uuid import uuid4 -from harborrl.config import ROOT +from harborrl.deployment.training_target import local_target +from harborrl.platform.evidence import effective_config, new_run_root, run_environment from harborrl.trajectories.native import publish, read_json def _run_root(plan): - name = time.strftime("%Y%m%d-%H%M%S") + "-" + uuid4().hex[:8] - return Path(plan["config"]["output"]["root"]) / "training" / name + return new_run_root(plan["config"]["output"]["root"]) def doctor(plan): - config = plan["config"] - checks = [] - - runner = Path(config["harbor"]["python"]) - checks.append({"path": "harbor.python", "ok": runner.is_file()}) - from harborrl.rollout.harbor_job.coordinator import runner_command - if runner.is_file(): - for worker in config["harbor"]["workers"]: - try: - probe = subprocess.run( - runner_command(str(runner), ROOT, worker) + ["--probe"], - capture_output=True, text=True, timeout=30, check=False, - ) - except (subprocess.SubprocessError, OSError) as exc: - probe = SimpleNamespace(returncode=1, stdout="", stderr=str(exc)) - try: - payload = json.loads(probe.stdout) - runtime_version = tuple(payload.get("python", "").split(".")[:2]) - version_ok = payload.get("harbor_version") == config["harbor"]["version"] - runtime_ok = len(runtime_version) == 2 and all(part.isdigit() for part in runtime_version) - except (ValueError, AttributeError, TypeError): - runtime_version, version_ok, runtime_ok = (), False, False - checks.append({ - "service": "external Harbor worker", - "worker": worker, - "ok": probe.returncode == 0 and runtime_ok - and tuple(map(int, runtime_version)) >= (3, 12) and version_ok, - "detail": probe.stdout.strip() or probe.stderr.strip()[-1000:], - }) - - for key, path in (("tasks.catalog", config["tasks"]["catalog"]), - ("harness.profile", config["harness"]["profile"]), - ("model.checkpoint", config["model"]["checkpoint"]), - ("model.reference", config["model"]["reference"])): - checks.append({"path": key, "ok": Path(path).exists()}) - - model_config = Path(config["model"]["checkpoint"]) / "config.json" - if model_config.is_file(): - try: - model = json.loads(model_config.read_text()) - heads = model.get("num_attention_heads", 0) - kv_heads = model.get("num_key_value_heads", heads) - for tp in {config["deployment"]["actor_tensor_parallel_size"], - config["deployment"]["rollout_gpus_per_engine"]}: - checks.append({"model_tp": tp, "ok": bool( - heads and kv_heads and heads % tp == 0 and (kv_heads % tp == 0 or tp % kv_heads == 0))}) - except (OSError, ValueError, TypeError) as exc: - checks.append({"path": "model.config", "ok": False, "detail": str(exc)}) - - from harborrl.config.native import catalog - try: - entries = catalog(config) - for entry in entries: - from harborrl.data.harbor.native_inspector import inspect_native - report = inspect_native(entry["path"], expected_digest=entry["task_digest"]) - checks.append({"task": entry["id"], "ok": report["status"] == "NEEDS_PROBE", - "detail": report["reasons"]}) - except (ValueError, OSError, TypeError) as exc: - checks.append({"service": "native catalog", "ok": False, "detail": str(exc)}) - - for package in ("ray", "torch", "sglang", "transformers"): - try: - version = importlib.metadata.version(package) - except importlib.metadata.PackageNotFoundError: - version = None - checks.append({"dependency": package, "version": version, "ok": version is not None}) - - from harborrl.backends.slime_v032.versions import ( - MEGATRON_COMMIT, - SGLANG_IMAGE_TAG, - SLIME_COMMIT, - ) - slime_dir = os.environ.get("SLIME_DIR", "").strip() - slime_path = Path(slime_dir) if slime_dir else None - checks.append({"path": "SLIME_DIR", "ok": bool(slime_path and slime_path.is_dir()), - "detail": slime_dir or "not set"}) - if slime_path and slime_path.is_dir(): - checks.append({"path": "slime entrypoint", "ok": (slime_path / "train.py").is_file()}) - preset = slime_path / "scripts" / "models" / f"{config['model']['args_file']}.sh" - checks.append({"path": "slime model preset", "ok": preset.is_file(), "detail": str(preset)}) - try: - commit = subprocess.run(["git", "-C", str(slime_path), "rev-parse", "HEAD"], - capture_output=True, text=True, timeout=15, check=True).stdout.strip() - except (subprocess.SubprocessError, OSError) as exc: - commit = "" - checks.append({"service": "slime commit", "ok": False, "detail": str(exc)}) - if commit: - checks.append({"service": "slime commit", "ok": commit == SLIME_COMMIT, - "detail": f"{commit} != {SLIME_COMMIT}" if commit != SLIME_COMMIT else commit}) - - megatron_dir = os.environ.get("MEGATRON_DIR", "").strip() - if megatron_dir: - try: - megatron_commit = subprocess.run(["git", "-C", megatron_dir, "rev-parse", "HEAD"], - capture_output=True, text=True, timeout=15, check=True).stdout.strip() - except (subprocess.SubprocessError, OSError) as exc: - megatron_commit = "" - checks.append({"service": "megatron commit", "ok": False, "detail": str(exc)}) - if megatron_commit: - checks.append({"service": "megatron commit", "ok": megatron_commit == MEGATRON_COMMIT, - "detail": f"{megatron_commit} != {MEGATRON_COMMIT}" if megatron_commit != MEGATRON_COMMIT else megatron_commit}) - else: - checks.append({"service": "megatron commit", - "ok": True, - "detail": "MEGATRON_DIR not set; official Slime image bundles Megatron"}) - - runtime_env = os.environ.copy() - runtime_pythonpath = os.pathsep.join(filter(None, [ - str(slime_path), megatron_dir, str(ROOT), runtime_env.get("PYTHONPATH", ""), - ])) - runtime_env["PYTHONPATH"] = runtime_pythonpath - for module in ("slime.rollout.base_types", "slime.utils.types", - "slime.backends.megatron_utils.loss"): - try: - probe = subprocess.run([sys.executable, "-c", f"import {module}"], - capture_output=True, text=True, timeout=90, check=False, - env=runtime_env) - except (subprocess.SubprocessError, OSError) as exc: - probe = SimpleNamespace(returncode=1, stdout="", stderr=str(exc)) - checks.append({"dependency": module, "ok": probe.returncode == 0, - "detail": probe.stderr.strip()[-1000:]}) - - sglang_image = os.environ.get("SGLANG_IMAGE", "").strip() - if sglang_image: - checks.append({"service": "sglang image", "ok": sglang_image.endswith(SGLANG_IMAGE_TAG), - "detail": sglang_image}) - - if shutil.which("nvidia-smi"): - try: - result = subprocess.run(["nvidia-smi", "--query-gpu=index", "--format=csv,noheader"], - capture_output=True, text=True, timeout=10, check=True) - visible = os.environ.get("CUDA_VISIBLE_DEVICES") - count = (len([item for item in visible.split(",") if item.strip() and item.strip() != "-1"]) - if visible is not None else len(result.stdout.splitlines())) - checks.append({"resource": "visible GPU budget", "available": count, - "ok": count >= config["deployment"]["num_gpus"]}) - except (subprocess.SubprocessError, OSError) as exc: - checks.append({"resource": "visible GPU budget", "ok": False, "detail": str(exc)}) - else: - checks.append({"resource": "visible GPU budget", "ok": False, "detail": "nvidia-smi unavailable"}) - - if config["training"]["backend_contract"] != "slime-legacy": - from harborrl.backends.slime_v032.launcher import hook_modules - for hook_name, module_name in hook_modules().items(): - try: - importlib.import_module(module_name) - checks.append({"service": "training backend hook", "hook": hook_name, "ok": True}) - except (ImportError, AttributeError, TypeError, ValueError) as exc: - checks.append({ - "service": "training backend hook", "hook": hook_name, - "ok": False, "detail": str(exc), - }) - - checks.append({ - "service": "raw-model logprob audit", - "ok": True, - "detail": ("enabled by lock; live serving semantic probe remains a GPU gate" - if config["gateway"]["audited_raw_logprobs"] else - "disabled; generated evidence remains EVAL_ONLY and training will fail closed"), - }) - return checks + """Compatibility entry point for deployment preflight.""" + from harborrl.platform.preflight import doctor as check + return check(plan) def _materialize(plan, root): - from harborrl.config.native import catalog - - config = plan["config"] - entries = catalog(config) - normalized = root / "config" - normalized.mkdir(parents=True) - catalog_path = normalized / "native_catalog.json" - publish(catalog_path, entries) - prompt_path = normalized / "native_tasks.jsonl" - rows = [] - for entry in entries: - instruction = (Path(entry["path"]) / "instruction.md").read_text() - rows.append({"task": instruction, "metadata": {"harborrl_native": True, "native_task": entry}}) - prompt_path.write_text("".join(json.dumps(row, ensure_ascii=False, sort_keys=True) + "\n" for row in rows)) - return catalog_path, prompt_path + from harborrl.platform.evidence import _materialize as prepare + return prepare(plan, root) def _source_audit(root): - def git(*args): - return subprocess.check_output(["git", *args], cwd=ROOT) - - files = git("ls-files", "-z", "--cached", "--others", "--exclude-standard", "--", - "harborrl", "scripts", "examples", "pyproject.toml").decode().split("\0") - untracked = set(git("ls-files", "-z", "--others", "--exclude-standard", "--", - "harborrl", "scripts", "examples").decode().split("\0")) - (root / "source.patch").write_bytes(git("diff", "--binary", "HEAD", "--", - "harborrl", "scripts", "examples", "pyproject.toml")) - hashes = {} - for name in files: - source = ROOT / name - if name and source.is_file(): - hashes[name] = hashlib.sha256(source.read_bytes()).hexdigest() - if name in untracked: - target = root / "source-new" / name - target.parent.mkdir(parents=True, exist_ok=True) - target.write_bytes(source.read_bytes()) - payload = { - "commit": git("rev-parse", "HEAD").decode().strip(), - "dirty": bool(git("status", "--porcelain")), - "source_hashes": hashes, - "patch": "source.patch", - } - publish(root / "source-manifest.json", payload) - return payload + from harborrl.platform.evidence import _source_audit as audit + return audit(root) def dispatch(plan, *, doctor_only=False): + deployment = (plan.get("deployment") or {}).get("config") if doctor_only: checks = doctor(plan) + explicit = plan.get("deployment", {}).get("source", "embedded-local") != "embedded-local" print(json.dumps({"checks": checks, - "scope": "native config, original tasks, isolated Harbor, training dependencies and GPU budget; protocol/token semantics require the deferred live probe"}, + "scope": ("deployment topology, " if explicit else "") + + "native config, original tasks, isolated Harbor, training " + "dependencies and GPU budget; protocol/token semantics " + "require the deferred live probe"}, indent=2)) return int(any(not check["ok"] for check in checks)) @@ -254,51 +55,23 @@ def dispatch(plan, *, doctor_only=False): print(json.dumps({"failed_checks": failed}, indent=2), file=sys.stderr) return 1 + if deployment is not None and deployment["manager"]["mode"] == "service": + from harborrl.deployment.controller import dispatch_service + return dispatch_service(plan, deployment, checks=checks) + root = _run_root(plan) root.mkdir(parents=True) catalog_path, prompt_path = _materialize(plan, root) source = _source_audit(root) - environment = dict(plan["environment"]) - environment.update({ - "RUN_ID": root.name, "RUN_NAME": root.name, "RUN_DIR": str(root), - "RUNS_ROOT": str(Path(plan["config"]["output"]["root"]).resolve()), - "CKPT_ROOT": str(root / "checkpoints"), "WANDB_DIR": str(root / "metrics/wandb"), - "HARBORRL_NATIVE_CATALOG": str(catalog_path), - "ROLLOUT_PROMPT_DATA": str(prompt_path), - "HARBORRL_NATIVE_PROFILE": plan["config"]["harness"]["profile"], - "HARBOR_IR_ROOT": str(root / "ir"), - }) + environment = run_environment(plan, root, catalog_path, prompt_path) launch = {"plan": plan, "run_dir": str(root), "source": source, "materialized_catalog": str(catalog_path), "prompt_data": str(prompt_path)} publish(root / "launch.json", launch) - allowed = {"PATH", "HOME", "USER", "LANG", "LC_ALL", "TMPDIR", "LD_LIBRARY_PATH", - "CUDA_VISIBLE_DEVICES", "HTTP_PROXY", "HTTPS_PROXY", "NO_PROXY", - "http_proxy", "https_proxy", "no_proxy", "PYTHONPATH", - "SLIME_DIR", "MEGATRON_DIR", "SGLANG_IMAGE"} - child_env = {key: value for key, value in os.environ.items() if key in allowed} - child_env["PATH"] = os.pathsep.join( - [str(Path(sys.executable).parent), child_env.get("PATH", os.defpath)]) - child_env["PYTHONPATH"] = os.pathsep.join(filter(None, [ - child_env.get("SLIME_DIR", ""), child_env.get("MEGATRON_DIR", ""), - str(ROOT), child_env.get("PYTHONPATH", ""), - ])) - child_env.update(environment) - child = subprocess.Popen(plan["training_command"], cwd=ROOT, env=child_env, start_new_session=True) - - def forward_signal(signum, frame): - if child.poll() is None: - try: - child.send_signal(signum) - except ProcessLookupError: - pass - - previous = {sig: signal.signal(sig, forward_signal) for sig in (signal.SIGINT, signal.SIGTERM)} - try: - return child.wait() - finally: - for sig, handler in previous.items(): - signal.signal(sig, handler) + target = local_target(plan, environment=environment) + publish(root / "effective-config.json", effective_config(plan, environment, target.env, source, checks)) + target.launch() + return target.wait() if __name__ == "__main__": diff --git a/harborrl/platform/policy_pool.py b/harborrl/platform/policy_pool.py index 697ebe2d4..945e9d09c 100644 --- a/harborrl/platform/policy_pool.py +++ b/harborrl/platform/policy_pool.py @@ -20,6 +20,8 @@ def validate_pool(states, expected=None): def publish_pool(states, expected=None): version = validate_pool(states, expected) root = Path(os.environ['RUN_DIR']) + from harborrl.consistency.sglang import admit_pool + admit_pool(states, root) payload = {'weight_version': version, 'engines': states, 'timestamp': time.time()} temp = root / 'policy-pool.tmp' temp.write_text(json.dumps(payload) + '\n') diff --git a/harborrl/platform/preflight.py b/harborrl/platform/preflight.py new file mode 100644 index 000000000..3a35fe88f --- /dev/null +++ b/harborrl/platform/preflight.py @@ -0,0 +1,220 @@ +"""Live deployment checks for the native pipeline; no training dispatch. + +``doctor`` composes two layers: the deployment topology graph (manager, GPU +target, rollout workers, control-loop addresses) and the machine-local +environment checks. The local layer only runs where it is meaningful: when +the GPU target is remote, dependency probes belong to the GPU job startup, +not to this machine. +""" +import importlib +import importlib.metadata +import json +import os +import shutil +import subprocess +import sys +from pathlib import Path +from types import SimpleNamespace + +from harborrl.config import ROOT + + +def _deployment(plan): + return (plan.get("deployment") or {}).get("config") + + +def doctor(plan): + config = plan["config"] + checks = [] + deployment = _deployment(plan) + # Embedded-local is a compatibility record, not an opt-in: only an + # explicit deployment file activates the topology layers. + explicit = deployment is not None and plan.get("deployment", {}).get( + "source", "embedded-local") != "embedded-local" + local_gpu = deployment is None or deployment["gpu_target"]["transport"] == "local" + legacy_workers = (not explicit) or deployment["rollout"]["provider"] in ("embedded", "sandbox") + + if explicit: + from harborrl.deployment.preflight import preflight as deployment_preflight + checks.extend(deployment_preflight(plan, deployment, live=True)) + if not local_gpu: + checks.append({ + "role": "gpu_target", "machine": deployment["gpu_target"]["id"], + "check": "gpu_target.training_dependencies", "ok": True, + "detail": "Slime/Megatron/SGlang versions are enforced fail-closed " + "by slime_train.sh at GPU job startup on the GPU machine", + }) + + runner = Path(config["harbor"]["python"]) + checks.append({"path": "harbor.python", "ok": runner.is_file()}) + from harborrl.rollout.harbor_job.coordinator import runner_command + if legacy_workers and runner.is_file(): + workers = ([None] if config['harbor'].get('runner_transport') == 'local' + else config['harbor']['workers']) + for worker in workers: + try: + probe = subprocess.run( + runner_command(str(runner), ROOT, worker) + ["--probe"], + capture_output=True, text=True, timeout=30, check=False, + ) + except (subprocess.SubprocessError, OSError) as exc: + probe = SimpleNamespace(returncode=1, stdout="", stderr=str(exc)) + try: + payload = json.loads(probe.stdout) + runtime_version = tuple(payload.get("python", "").split(".")[:2]) + version_ok = payload.get("harbor_version") == config["harbor"]["version"] + runtime_ok = len(runtime_version) == 2 and all(part.isdigit() for part in runtime_version) + except (ValueError, AttributeError, TypeError): + runtime_version, version_ok, runtime_ok = (), False, False + checks.append({ + "service": "local Harbor runner" if worker is None else "external Harbor worker", + "worker": worker, + "ok": probe.returncode == 0 and runtime_ok + and tuple(map(int, runtime_version)) >= (3, 12) and version_ok, + "detail": probe.stdout.strip() or probe.stderr.strip()[-1000:], + }) + if 'HARBORRL_NATIVE_SANDBOX' in plan['environment']: + for worker in workers: + try: + probe = subprocess.run( + runner_command(str(runner), ROOT, worker, + module='harborrl.rollout.harbor_job.sandbox_probe') + ['--stdin'], + input=plan['environment']['HARBORRL_NATIVE_SANDBOX'], cwd=ROOT, + capture_output=True, text=True, timeout=60, check=False) + checks.append({'service': 'sandbox offline runtime', 'worker': worker, + 'ok': probe.returncode == 0, 'detail': probe.stdout.strip()}) + except (subprocess.SubprocessError, OSError) as exc: + checks.append({'service': 'sandbox offline runtime', 'worker': worker, + 'ok': False, 'detail': type(exc).__name__}) + + for key, path in (("tasks.catalog", config["tasks"]["catalog"]), + ("harness.profile", config["harness"]["profile"]), + ("model.checkpoint", config["model"]["checkpoint"]), + ("model.reference", config["model"]["reference"])): + checks.append({"path": key, "ok": Path(path).exists()}) + + model_config = Path(config["model"]["checkpoint"]) / "config.json" + if model_config.is_file(): + try: + model = json.loads(model_config.read_text()) + heads = model.get("num_attention_heads", 0) + kv_heads = model.get("num_key_value_heads", heads) + for tp in {config["deployment"]["actor_tensor_parallel_size"], + config["deployment"]["rollout_gpus_per_engine"]}: + checks.append({"model_tp": tp, "ok": bool( + heads and kv_heads and heads % tp == 0 and (kv_heads % tp == 0 or tp % kv_heads == 0))}) + except (OSError, ValueError, TypeError) as exc: + checks.append({"path": "model.config", "ok": False, "detail": str(exc)}) + + from harborrl.tasks.catalog import catalog + try: + entries = catalog(config) + for entry in entries: + # catalog() already checks the complete task tree against its lock. + checks.append({"task": entry["id"], "ok": True, "detail": []}) + except (ValueError, OSError, TypeError) as exc: + checks.append({"service": "native catalog", "ok": False, "detail": str(exc)}) + + if local_gpu: + for package in ("ray", "torch", "sglang", "transformers"): + try: + version = importlib.metadata.version(package) + except importlib.metadata.PackageNotFoundError: + version = None + checks.append({"dependency": package, "version": version, "ok": version is not None}) + + from harborrl.backends.slime_v032.versions import ( + MEGATRON_COMMIT, + SGLANG_IMAGE_TAG, + SLIME_COMMIT, + ) + if local_gpu: + slime_dir = os.environ.get("SLIME_DIR", "").strip() + slime_path = Path(slime_dir) if slime_dir else None + checks.append({"path": "SLIME_DIR", "ok": bool(slime_path and slime_path.is_dir()), + "detail": slime_dir or "not set"}) + if slime_path and slime_path.is_dir(): + checks.append({"path": "slime entrypoint", "ok": (slime_path / "train.py").is_file()}) + preset = slime_path / "scripts" / "models" / f"{config['model']['args_file']}.sh" + checks.append({"path": "slime model preset", "ok": preset.is_file(), "detail": str(preset)}) + try: + commit = subprocess.run(["git", "-C", str(slime_path), "rev-parse", "HEAD"], + capture_output=True, text=True, timeout=15, check=True).stdout.strip() + except (subprocess.SubprocessError, OSError) as exc: + commit = "" + checks.append({"service": "slime commit", "ok": False, "detail": str(exc)}) + if commit: + checks.append({"service": "slime commit", "ok": commit == SLIME_COMMIT, + "detail": f"{commit} != {SLIME_COMMIT}" if commit != SLIME_COMMIT else commit}) + + megatron_dir = os.environ.get("MEGATRON_DIR", "").strip() + if megatron_dir: + try: + megatron_commit = subprocess.run(["git", "-C", megatron_dir, "rev-parse", "HEAD"], + capture_output=True, text=True, timeout=15, check=True).stdout.strip() + except (subprocess.SubprocessError, OSError) as exc: + megatron_commit = "" + checks.append({"service": "megatron commit", "ok": False, "detail": str(exc)}) + if megatron_commit: + checks.append({"service": "megatron commit", "ok": megatron_commit == MEGATRON_COMMIT, + "detail": f"{megatron_commit} != {MEGATRON_COMMIT}" if megatron_commit != MEGATRON_COMMIT else megatron_commit}) + else: + checks.append({"service": "megatron commit", + "ok": True, + "detail": "MEGATRON_DIR not set; official Slime image bundles Megatron"}) + + runtime_env = os.environ.copy() + runtime_pythonpath = os.pathsep.join(filter(None, [ + str(slime_path), megatron_dir, str(ROOT), runtime_env.get("PYTHONPATH", ""), + ])) + runtime_env["PYTHONPATH"] = runtime_pythonpath + for module in ("slime.rollout.base_types", "slime.utils.types", + "slime.backends.megatron_utils.loss"): + try: + probe = subprocess.run([sys.executable, "-c", f"import {module}"], + capture_output=True, text=True, timeout=90, check=False, + env=runtime_env) + except (subprocess.SubprocessError, OSError) as exc: + probe = SimpleNamespace(returncode=1, stdout="", stderr=str(exc)) + checks.append({"dependency": module, "ok": probe.returncode == 0, + "detail": probe.stderr.strip()[-1000:]}) + + sglang_image = os.environ.get("SGLANG_IMAGE", "").strip() + if sglang_image: + checks.append({"service": "sglang image", "ok": sglang_image.endswith(SGLANG_IMAGE_TAG), + "detail": sglang_image}) + + if shutil.which("nvidia-smi"): + try: + result = subprocess.run(["nvidia-smi", "--query-gpu=index", "--format=csv,noheader"], + capture_output=True, text=True, timeout=10, check=True) + visible = os.environ.get("CUDA_VISIBLE_DEVICES") + count = (len([item for item in visible.split(",") if item.strip() and item.strip() != "-1"]) + if visible is not None else len(result.stdout.splitlines())) + checks.append({"resource": "visible GPU budget", "available": count, + "ok": count >= config["deployment"]["num_gpus"]}) + except (subprocess.SubprocessError, OSError) as exc: + checks.append({"resource": "visible GPU budget", "ok": False, "detail": str(exc)}) + else: + checks.append({"resource": "visible GPU budget", "ok": False, "detail": "nvidia-smi unavailable"}) + + if config["training"]["backend_contract"] != "slime-legacy": + from harborrl.backends.slime_v032.launcher import hook_modules + for hook_name, module_name in hook_modules().items(): + try: + importlib.import_module(module_name) + checks.append({"service": "training backend hook", "hook": hook_name, "ok": True}) + except (ImportError, AttributeError, TypeError, ValueError) as exc: + checks.append({ + "service": "training backend hook", "hook": hook_name, + "ok": False, "detail": str(exc), + }) + + checks.append({ + "service": "raw-model logprob audit", + "ok": True, + "detail": ("enabled by lock; live serving semantic probe remains a GPU gate" + if config["gateway"]["audited_raw_logprobs"] else + "disabled; generated evidence remains EVAL_ONLY and training will fail closed"), + }) + return checks diff --git a/harborrl/platform/slime_train.sh b/harborrl/platform/slime_train.sh index 50377dbab..8d7972874 100644 --- a/harborrl/platform/slime_train.sh +++ b/harborrl/platform/slime_train.sh @@ -171,12 +171,19 @@ TRAIN_ARGS=( export CUDA_DEVICE_MAX_CONNECTIONS=1 TRAIN_PYTHON="${TRAIN_PYTHON:-python3}" export PYTHONPATH="${REPO_ROOT}${PYTHONPATH:+:${PYTHONPATH}}" +ENTRY=("${SLIME_DIR}/train.py") +if [[ "${HARBORRL_CONSISTENCY_MODE:-off}" != "off" ]]; then + ENTRY=(-m harborrl.consistency.train) +fi + +"${TRAIN_PYTHON}" -m harborrl.platform.evidence "${RUN_DIR}" "${DRY_RUN:-0}" \ + "${TRAIN_PYTHON}" -u "${ENTRY[@]}" "${TRAIN_ARGS[@]}" if [[ "${DRY_RUN:-0}" == "1" ]]; then printf '[dry-run] ' - printf '%q ' "${TRAIN_PYTHON}" -u "${SLIME_DIR}/train.py" "${TRAIN_ARGS[@]}" + printf '%q ' "${TRAIN_PYTHON}" -u "${ENTRY[@]}" "${TRAIN_ARGS[@]}" printf '\n' exit 0 fi -exec "${TRAIN_PYTHON}" -u "${SLIME_DIR}/train.py" "${TRAIN_ARGS[@]}" +exec "${TRAIN_PYTHON}" -u "${ENTRY[@]}" "${TRAIN_ARGS[@]}" diff --git a/harborrl/rollout/exporters/native.py b/harborrl/rollout/exporters/native.py index 55355c7aa..05fb63be8 100644 --- a/harborrl/rollout/exporters/native.py +++ b/harborrl/rollout/exporters/native.py @@ -1,47 +1,36 @@ -"""Trajectory-aware GRPO export, independent of Slime/Torch imports. +"""Compatibility record view of the canonical trajectory-weighted GRPO batch. -These records must enter a backend that honors token_weights and advantages. -They must NOT be passed through the interactive reward/whitening builder. +No independent advantage calculation or secondary whitening occurs here. """ -import math - -from harborrl.trajectories.native import Identity, finite, require_ready +from harborrl.export.native import export_training_batch +from harborrl.export.validate import token_weight +from harborrl.trajectories.native import Identity def export_group(trajectories, group_size, *, epsilon=1e-6, allow_synthetic=False): if type(group_size) is not int or group_size < 2 or len(trajectories) != group_size: raise ValueError("a complete group of at least two trajectories is required") - if not finite(epsilon) or epsilon <= 0: - raise ValueError("epsilon must be positive and finite") - for ir in trajectories: - require_ready(ir, allow_synthetic=allow_synthetic) - ids = [Identity(**ir["identity"]) for ir in trajectories] - if len({i.group_key for i in ids}) != 1: - raise ValueError("mixed group, policy, task or profiles") - for key in ("slot_id", "attempt_id", "trajectory_id"): - if len({getattr(i, key) for i in ids}) != group_size: - raise ValueError(f"duplicate {key}") - rewards = [ir["evaluation"]["training_reward"] for ir in trajectories] - mean = math.fsum(rewards) / group_size - std = math.sqrt(math.fsum((r - mean) ** 2 for r in rewards) / group_size) - if not finite(mean) or not finite(std): - raise ValueError("nonfinite group statistics") - advantages = [0.0 if std == 0 else (r - mean) / (std + epsilon) for r in rewards] + batch = export_training_batch( + [trajectories], group_size=group_size, epsilon=epsilon, + allow_synthetic=allow_synthetic, + ) + group = batch.groups[0] records = [] - for ir, ident, advantage in zip(trajectories, ids, advantages): - count = sum(len(t["output_ids"]) for t in ir["turns"]) - for index, turn in enumerate(ir["turns"]): - n = len(turn["output_ids"]) - records.append({"identity": ident.to_dict(), "turn_index": index, - "tokens": turn["input_ids"] + turn["output_ids"], - "prompt_length": len(turn["input_ids"]), "response_length": n, - "loss_mask": [0] * len(turn["input_ids"]) + [1] * n, - "old_logprobs": turn["logprobs"], "advantages": [advantage] * n, - "token_weights": [1.0 / (group_size * count)] * n, - "training_reward": ir["evaluation"]["training_reward"], - "padding": False}) - return {"export_version": "native-grpo-v2", "group_key": ids[0].group_key, - "trajectory_count": group_size, "mean": mean, "std": std, - "advantages": advantages, "records": records, + for ir, unit in zip(trajectories, group.trajectories): + ident = Identity(**ir["identity"]) + weight = token_weight(group_size, unit.token_count) + for span in unit.turns: + n = span.response_length + records.append({ + "identity": ident.to_dict(), "turn_index": span.turn_index, + "tokens": list(span.tokens), "prompt_length": span.prompt_length, + "response_length": n, "loss_mask": [int(flag) for flag in span.loss_mask], + "old_logprobs": list(span.old_logprobs), "advantages": [unit.advantage] * n, + "token_weights": [weight] * n, "training_reward": ir["evaluation"]["training_reward"], + "padding": False, + }) + return {"export_version": "native-grpo-v2", "group_key": group.group_key, + "trajectory_count": group_size, "mean": group.reward_mean, "std": group.reward_std, + "advantages": [unit.advantage for unit in group.trajectories], "records": records, "synthetic": allow_synthetic, "reduction": "sum_token_weighted_loss; no secondary whitening"} diff --git a/harborrl/rollout/harbor_job/__main__.py b/harborrl/rollout/harbor_job/__main__.py index 01828fd19..da10c1bb6 100644 --- a/harborrl/rollout/harbor_job/__main__.py +++ b/harborrl/rollout/harbor_job/__main__.py @@ -13,8 +13,10 @@ def main(argv=None): smoke.add_argument("--output", required=True) args = parser.parse_args(argv) if args.command == "inspect": - from harborrl.data.harbor.native_inspector import inspect_native - result = inspect_native(args.task, expected_digest=args.expected_digest) + from harborrl.math_registry import NATIVE_TASK_PROVIDER + result = NATIVE_TASK_PROVIDER.adapter.inspect( + args.task, expected_digest=args.expected_digest + ) print(json.dumps(result, indent=2)) return int(result["status"] in ("INVALID", "UNSUPPORTED")) from .offline import smoke as run_smoke diff --git a/harborrl/rollout/harbor_job/bindings.py b/harborrl/rollout/harbor_job/bindings.py index 4cd439689..e0e553e85 100644 --- a/harborrl/rollout/harbor_job/bindings.py +++ b/harborrl/rollout/harbor_job/bindings.py @@ -5,8 +5,9 @@ def claude_config(profile, gateway_url, credential, max_output_tokens=8192): required = {"cli_version", "model", "max_turns", "agent_timeout_sec", "setup_timeout_sec", "verifier_timeout_sec"} - if set(profile) != required: - raise ValueError(f"Claude profile requires exactly {sorted(required)}") + allowed = required | {"disallowed_tools"} + if not required.issubset(profile) or set(profile) - allowed: + raise ValueError(f"Claude profile requires {sorted(required)} and optionally disallowed_tools") if not isinstance(profile["cli_version"], str) or not re.fullmatch(r"\d+\.\d+\.\d+(?:-[A-Za-z0-9.]+)?", profile["cli_version"]): raise ValueError("exact Claude CLI version required; latest is forbidden") if not isinstance(profile["model"], str) or not re.fullmatch(r"[A-Za-z0-9_.:/-]+", profile["model"]): @@ -14,6 +15,10 @@ def claude_config(profile, gateway_url, credential, max_output_tokens=8192): for key in required - {"model", "cli_version"}: if type(profile[key]) is not int or profile[key] <= 0: raise ValueError(f"positive integer required: {key}") + disallowed_tools = profile.get("disallowed_tools", "Agent,Task") + if not isinstance(disallowed_tools, str) or not re.fullmatch( + r"[A-Za-z][A-Za-z0-9_]*(?:,[A-Za-z][A-Za-z0-9_]*)*", disallowed_tools): + raise ValueError("disallowed tools must be a comma-separated identifier list") url = urlsplit(gateway_url) if url.scheme not in ("http", "https") or not url.hostname or url.username or url.password or url.query or url.fragment or url.path not in ("", "/"): raise ValueError("Anthropic base URL must be a plain origin (no /v1 suffix)") @@ -27,9 +32,10 @@ def claude_config(profile, gateway_url, credential, max_output_tokens=8192): "override_timeout_sec": profile["agent_timeout_sec"], "override_setup_timeout_sec": profile["setup_timeout_sec"], "kwargs": {"version": profile["cli_version"], "max_turns": profile["max_turns"], - "max_thinking_tokens": 0, "disallowed_tools": "Agent,Task", + "max_thinking_tokens": 0, "disallowed_tools": disallowed_tools, "permission_mode": "bypassPermissions"}, "env": {**client_controls(), "ANTHROPIC_API_KEY": credential, + "ANTHROPIC_MODEL": profile["model"], "ANTHROPIC_BASE_URL": gateway_url.rstrip("/"), "NO_PROXY": url.hostname, "no_proxy": url.hostname, "CLAUDE_CODE_MAX_OUTPUT_TOKENS": str(max_output_tokens)}} diff --git a/harborrl/rollout/harbor_job/coordinator.py b/harborrl/rollout/harbor_job/coordinator.py index 4afa6a27d..17047cd40 100644 --- a/harborrl/rollout/harbor_job/coordinator.py +++ b/harborrl/rollout/harbor_job/coordinator.py @@ -12,10 +12,9 @@ from dataclasses import replace from pathlib import Path +from harborrl.math_registry import CLAUDE_CODE_HARNESS from harborrl.trajectories.native import assemble, publish -from .audit import audit_session -from .bindings import sanitized_runner_env from .collector import collect @@ -66,8 +65,7 @@ def export(self): return export_group([self.completed[slot] for slot in self.identities], len(self.identities)) -def runner_command(runner_python, project, runner_host=None): - module = "harborrl.rollout.harbor_job.runner" +def runner_command(runner_python, project, runner_host=None, *, module="harborrl.rollout.harbor_job.runner"): if runner_host is None: return [runner_python, "-m", module] remote = f"cd {shlex.quote(str(project))} && exec {shlex.quote(runner_python)} -m {module}" @@ -77,13 +75,13 @@ def runner_command(runner_python, project, runner_host=None): async def run_attempt(identity, *, task_path, profile, gateway_url, runner_python, root, registry, reward_profile, timeout=1800, cleanup_timeout=120, - max_output_tokens=8192, runner_host=None): + max_output_tokens=8192, runner_host=None, sandbox=None): """One real trial. CPU-only tests use a separate fake worker, never this path.""" root = Path(root).resolve() root.mkdir(parents=True, exist_ok=False) credential = secrets.token_urlsafe(32) project = str(Path(__file__).resolve().parents[3]) - env = sanitized_runner_env(os.environ) + env = CLAUDE_CODE_HARNESS.adapter.runner_environment(os.environ) env["PYTHONPATH"] = project stderr = (root / "runner.stderr").open("wb") try: @@ -100,6 +98,12 @@ async def run_attempt(identity, *, task_path, profile, gateway_url, runner_pytho req = {"identity": identity.to_dict(), "task_path": str(Path(task_path).resolve()), "profile": profile, "gateway_url": gateway_url, "credential": credential, "output": str(root / "runner"), "max_output_tokens": max_output_tokens} + if sandbox is not None: + from harborrl.trajectories.native import digest + req.update(sandbox=sandbox, runtime_digest=digest(sandbox)) + timeout = (sandbox['create_timeout_s'] + sandbox['cleanup_timeout_s'] * 2 + + sum(profile[k] for k in ('setup_timeout_sec', 'agent_timeout_sec', 'verifier_timeout_sec'))) + cleanup_timeout = sandbox['cleanup_timeout_s'] + 15 process.stdin.write(json.dumps(req).encode() + b"\n") await process.stdin.drain() @@ -133,16 +137,26 @@ async def lifecycle(): await asyncio.wait_for(process.wait(), cleanup_timeout) if process.returncode != 0 or terminal["type"] != "terminal": raise RuntimeError("native trial did not finish normally") + if sandbox is not None: + from harborrl.trajectories.native import read_json + lifecycle_path = root / 'runner' / 'sandbox-lifecycle.json' + lifecycle = read_json(lifecycle_path) if lifecycle_path.is_file() else {} + if lifecycle.get('state') != 'deleted' or lifecycle.get('runtime_digest') != req['runtime_digest']: + publish(root / 'cleanup-required.json', {'identity': identity.to_dict(), 'reason': 'Sandbox deletion unconfirmed'}) + raise RuntimeError('sandbox deletion unconfirmed') trial_dir = Path(terminal["trial_dir"]).resolve() result_path = Path(terminal["result_path"]).resolve() if not trial_dir.is_relative_to(root / "runner" / "harbor") or result_path != trial_dir / "result.json": raise ValueError("Runner result path is outside this attempt") - sessions = [p for p in (trial_dir / "agent" / "sessions" / "projects").rglob("*.jsonl")] + sessions = list((trial_dir / "agent" / "sessions" / "projects").rglob("*.jsonl")) if len(sessions) != 1: raise ValueError("expected exactly one native Claude session; auxiliary sessions require review") - audit_session(sessions[0], registry, identity.attempt_id) + registry.wait_delivered(identity.attempt_id, 30.0) + audit = CLAUDE_CODE_HARNESS.adapter.audit_session(sessions[0], registry, identity.attempt_id) + receipt = collect(identity, trial_id, result_path, trial_dir / "verifier", reward_profile) + CLAUDE_CODE_HARNESS.adapter.audit_empty_final( + trial_dir / "agent" / "claude-code.txt", registry, identity.attempt_id, audit) turns, seal = registry.seal(identity.attempt_id) - receipt = collect(identity, trial_id, terminal["result_path"], trial_dir / "verifier", reward_profile) publish(root / "evaluation.json", receipt) ir = assemble(identity, trial_id, turns, seal, receipt) publish(root / "trajectory.v2.json", ir) @@ -152,6 +166,8 @@ async def lifecycle(): publish(root / "failure.json", {"identity": identity.to_dict(), "error_type": type(exc).__name__}) raise finally: + if sandbox is not None and (root / 'runner' / 'cleanup-required.json').exists(): + publish(root / 'cleanup-required.json', {'identity': identity.to_dict(), 'reason': 'Inspect runner/cleanup-required.json'}) if registered: registry.close(identity.attempt_id) if process.returncode is None: diff --git a/harborrl/rollout/harbor_job/environments/__init__.py b/harborrl/rollout/harbor_job/environments/__init__.py new file mode 100644 index 000000000..d73e6b751 --- /dev/null +++ b/harborrl/rollout/harbor_job/environments/__init__.py @@ -0,0 +1 @@ +"""Optional Harbor environment adapters loaded only by the isolated runner.""" diff --git a/harborrl/rollout/harbor_job/environments/opensandbox.py b/harborrl/rollout/harbor_job/environments/opensandbox.py new file mode 100644 index 000000000..829f9dadc --- /dev/null +++ b/harborrl/rollout/harbor_job/environments/opensandbox.py @@ -0,0 +1,189 @@ +"""Audited lifecycle around Harbor's OpenSandbox environment.""" +import asyncio +import fcntl +import json +import os +import tempfile +import time +from contextlib import asynccontextmanager +from datetime import timedelta +from pathlib import Path + +import httpx +from harbor.environments.opensandbox import OpenSandboxEnvironment + +from harborrl.rollout.harbor_job.sandbox_config import read_credential +from harborrl.trajectories.native import digest, publish + + +@asynccontextmanager +async def startup_slot(directory, count): + """Process-shared creation slots; kernel releases locks on runner death.""" + directory = Path(directory) + directory.mkdir(parents=True, exist_ok=True) + held = None + try: + while held is None: + for index in range(count): + handle = (directory / f'{index}.lock').open('a') + try: + fcntl.flock(handle, fcntl.LOCK_EX | fcntl.LOCK_NB) + held = handle + break + except BlockingIOError: + handle.close() + if held is None: + await asyncio.sleep(0.2) + yield + finally: + if held is not None: + held.close() + + +class ManagedOpenSandboxEnvironment(OpenSandboxEnvironment): + @classmethod + def validate_config(cls, cfg): + if cfg['options'] or any(b['options'] for b in cfg['bindings']): + raise ValueError('standard OpenSandbox adapter does not accept platform options') + + @classmethod + def agent_gateway(cls, cfg, gateway_url): + return gateway_url + + @classmethod + async def network_check(cls, cfg): + return {'status': 'not_probed', 'reason': 'Use the explicit sandbox smoke test for allocation checks'} + + async def validate_binding(self): + pass + + def extensions(self): + return {} + + def api_params(self): + return {} + + def startup_transport(self, allocated): + from harborrl.rollout.harbor_job.sandbox_transport import ( + SandboxStartupTransport, + ) + return SandboxStartupTransport(self.cfg['domain'].rstrip('/') + '/v1', allocated) + + def __init__(self, *args, sandbox_config, binding, evidence_dir, startup_dir, gateway_url=None, **kwargs): + self.validate_config(sandbox_config) + self.cfg, self.binding = sandbox_config, binding + self.evidence = Path(evidence_dir) + self.startup_dir = startup_dir + self.gateway_url = gateway_url + self._startup_transport = None + self.record = {'runtime_digest': digest(sandbox_config), + 'image': binding['image'], 'sandbox_id': None, 'state': 'pending', 'events': []} + super().__init__(*args, domain=self.cfg['domain'], api_key=read_credential(self.cfg), + protocol=self.cfg['protocol'], use_server_proxy=True, + ready_timeout_sec=self.cfg['create_timeout_s'], + sandbox_timeout_sec=self.cfg['lifecycle_minutes'] * 60, + request_timeout_sec=self.cfg['request_timeout_s'], **kwargs) + + def record_state(self, state): + self.record['state'] = state + self.record['events'].append({'state': state, 'time': time.time()}) + publish(self.evidence / 'sandbox-events' / f'{len(self.record["events"]):04d}.json', self.record) + with tempfile.NamedTemporaryFile(mode='w', dir=self.evidence, delete=False) as stream: + json.dump(self.record, stream) + stream.flush() + os.fsync(stream.fileno()) + os.replace(stream.name, self.evidence / 'sandbox-lifecycle.json') + + def cleanup_required(self, reason): + publish(self.evidence / 'cleanup-required.json', { + 'sandbox_id': self.record['sandbox_id'], 'image': self.binding['image'], + 'reason': reason, 'runtime_digest': self.record['runtime_digest']}) + + async def _create_sandbox(self, sdk): + await self.validate_binding() + async with startup_slot(self.startup_dir, self.cfg['startup_concurrency']): + self.record_state('creating') + def allocated(sandbox_id): + self.record['sandbox_id'] = sandbox_id + self.record_state('allocated') + + self._startup_transport = self.startup_transport(allocated) + connection = self._build_connection_config(sdk).model_copy(update={'transport': self._startup_transport}) + try: + sandbox = await asyncio.wait_for(sdk['Sandbox'].create( + self.binding['image'], connection_config=connection, + timeout=timedelta(minutes=self.cfg['lifecycle_minutes']), + ready_timeout=timedelta(seconds=self.cfg['create_timeout_s']), + extensions=self.extensions(), + resource=self._build_resource(), env=dict(self._persistent_env), + network_policy=self._build_network_policy(sdk), skip_health_check=True), + self.cfg['create_timeout_s']) + except BaseException: # noqa: BLE001 - persist unknown allocation on cancellation too + if self.record['sandbox_id']: + await self._cleanup_allocated() + else: + self.record_state('creation_unknown') + self.cleanup_required('Creation outcome unknown; reconcile platform inventory before retry') + await self._startup_transport.aclose() + raise RuntimeError('Sandbox creation outcome unknown') from None + self._sandbox = sandbox + self.record['sandbox_id'] = sandbox.id + self.record_state('created') + try: + await asyncio.wait_for(self._wait_until_ready(sandbox), self.cfg['create_timeout_s']) + self.record_state('ready') + return sandbox + except BaseException: # noqa: BLE001 - cleanup before redacting SDK errors + await self.stop(delete=True) + raise RuntimeError('Sandbox readiness failed') from None + + async def _cleanup_allocated(self): + """The SDK may already have killed an instance whose endpoints failed.""" + self.record_state('deleting') + url = self.cfg['domain'].rstrip('/') + '/v1/sandboxes/' + self.record['sandbox_id'] + try: + async with asyncio.timeout(self.cfg['cleanup_timeout_s']): + async with httpx.AsyncClient(timeout=30, headers={'OPEN-SANDBOX-API-KEY': self._api_key}, + params=self.api_params()) as client: + status = await client.get(url) + if status.status_code != 404: + (await client.delete(url)).raise_for_status() + status = await client.get(url) + if status.status_code != 404: + raise RuntimeError('allocation deletion unconfirmed') + self.record_state('deleted') + except BaseException: # noqa: BLE001 - retain debt if cleanup is interrupted + self.record_state('delete_failed') + self.cleanup_required('Allocated Sandbox deletion was not confirmed') + + async def start(self, force_build): + try: + await super().start(force_build) + except BaseException: + if self._sandbox is not None: + await self.stop(delete=True) + raise + + async def stop(self, delete=True): + if self._sandbox is None: + if self._startup_transport is not None: + await self._startup_transport.aclose() + return + sandbox = self._sandbox + self.record_state('deleting') + try: + await asyncio.wait_for(sandbox.kill(), self.cfg['cleanup_timeout_s']) + self.record_state('deleted') + (self.evidence / 'cleanup-required.json').unlink(missing_ok=True) + self._sandbox = None + except BaseException: # noqa: BLE001 - preserve cleanup debt on cancellation + self.record_state('delete_failed') + self.cleanup_required('Sandbox kill was not acknowledged') + raise RuntimeError('Sandbox deletion failed') from None + finally: + try: + await asyncio.wait_for(sandbox.close(), 10) + except Exception: # noqa: BLE001 - close errors must not expose SAT headers + self.logger.warning('Sandbox SDK connection close failed') + if self._startup_transport is not None: + await self._startup_transport.aclose() diff --git a/harborrl/rollout/harbor_job/offline.py b/harborrl/rollout/harbor_job/offline.py index 7dfa3220e..8f5dea362 100644 --- a/harborrl/rollout/harbor_job/offline.py +++ b/harborrl/rollout/harbor_job/offline.py @@ -7,6 +7,7 @@ from harborrl.gateway.server import Gateway, make_server from harborrl.gateway.trace import TraceRegistry +from harborrl.math_registry import CLAUDE_CODE_HARNESS from harborrl.rollout.exporters.native import export_group from harborrl.trajectories.native import ( Identity, @@ -17,7 +18,6 @@ require_ready, ) -from .audit import audit_session from .collector import collect @@ -72,7 +72,9 @@ def smoke(output): events.append({"sessionId": trial, "type": "assistant", "message": message}) session.write_text("".join(json.dumps(e) + "\n" for e in events)) registry.close(identity.attempt_id) - audit_session(session, registry, identity.attempt_id) + CLAUDE_CODE_HARNESS.adapter.audit_session( + session, registry, identity.attempt_id + ) turns, seal = registry.seal(identity.attempt_id) publish(directory / "result.json", {"id": trial, "finished_at": "synthetic", "verifier_environment_mode": "shared", "verifier_result": {"rewards": {"reward": slot}}}) diff --git a/harborrl/rollout/harbor_job/runner.py b/harborrl/rollout/harbor_job/runner.py index a5b1c9389..7aa23fe4a 100644 --- a/harborrl/rollout/harbor_job/runner.py +++ b/harborrl/rollout/harbor_job/runner.py @@ -14,11 +14,9 @@ import sys from pathlib import Path -from harborrl.data.harbor.native_inspector import inspect_native +from harborrl.math_registry import CLAUDE_CODE_HARNESS, NATIVE_TASK_PROVIDER from harborrl.trajectories.native import Identity, digest, publish -from .bindings import claude_config - def probe(): if sys.version_info < (3, 12): @@ -43,7 +41,9 @@ async def execute(request, emit, read_command): identity = Identity(**request["identity"]) if digest(request["profile"]) != identity.harness_digest: raise ValueError("harness profile differs from attempt manifest") - inspection = inspect_native(request["task_path"], expected_digest=identity.task_digest) + inspection = NATIVE_TASK_PROVIDER.adapter.inspect( + request["task_path"], expected_digest=identity.task_digest + ) if inspection["status"] != "NEEDS_PROBE": raise ValueError("native task preflight failed: " + ",".join(inspection["reasons"])) root = Path(request["output"]).resolve() @@ -51,12 +51,36 @@ async def execute(request, emit, read_command): root.mkdir(parents=True, exist_ok=False) publish(root / "manifest.json", {"identity": identity.to_dict(), "task": inspection, "profile": request["profile"], "gateway_url": request["gateway_url"]}) + sandbox = request.get('sandbox') + agent_gateway = request['gateway_url'] + if sandbox is not None: + from harborrl.rollout.harbor_job.sandbox_config import environment_class + agent_gateway = environment_class(sandbox).agent_gateway(sandbox, agent_gateway) + agent = CLAUDE_CODE_HARNESS.adapter.agent_config( + request['profile'], agent_gateway, request['credential'], request['max_output_tokens']) + environment = {'type': 'docker', 'delete': True} + if sandbox is not None: + from harborrl.rollout.harbor_job.sandbox_config import task_binding + if digest(sandbox) != request.get('runtime_digest'): + raise ValueError('sandbox runtime lock mismatch') + if sandbox['installation'] and sandbox['installation']['version'] != request['profile']['cli_version']: + raise ValueError('offline harness version mismatch') + publish(root / 'runtime.json', {'config': sandbox, 'digest': digest(sandbox)}) + environment = {'type': 'opensandbox', 'delete': True, + 'import_path': sandbox['adapter'], + 'kwargs': {'sandbox_config': sandbox, 'binding': task_binding(sandbox, request['task_path']), + 'evidence_dir': str(root), + 'gateway_url': request['gateway_url'], + 'startup_dir': str(root.parents[2] / '.sandbox-startup')}} + if sandbox['installation']: + agent.pop('name') + agent['import_path'] = 'harborrl.harnesses.offline_claude:OfflineClaudeCode' + agent['kwargs']['installation'] = sandbox['installation'] config = TrialConfig.model_validate({ "task": {"path": inspection["path"]}, "trial_name": digest(identity.to_dict()), "trials_dir": str(root / "harbor"), - "agent": claude_config(request["profile"], request["gateway_url"], request["credential"], - request["max_output_tokens"]), - "environment": {"type": "docker", "delete": True}, + "agent": agent, + "environment": environment, "verifier": {"disable": False, "override_timeout_sec": request["profile"]["verifier_timeout_sec"]}}) trial = await Trial.create(config) trial_id = str(trial.id) diff --git a/harborrl/rollout/harbor_job/sandbox_config.py b/harborrl/rollout/harbor_job/sandbox_config.py new file mode 100644 index 000000000..bb008e184 --- /dev/null +++ b/harborrl/rollout/harbor_job/sandbox_config.py @@ -0,0 +1,135 @@ +"""Non-secret Sandbox settings; no SDK imports in the training process.""" +import asyncio +import copy +import importlib +import re +from contextlib import asynccontextmanager +from pathlib import Path +from urllib.parse import urlsplit + +import yaml + +from harborrl.tasks.content import tomllib + + +def _check_options(value): + if isinstance(value, dict): + for key, item in value.items(): + if not isinstance(key, str): + raise TypeError('sandbox option names must be strings') + name = key.lower().replace('-', '_') + if name in {'token', 'credential', 'password', 'secret', 'authorization'} or name.endswith(('_api_key', '_token', '_secret')) or name == 'api_key': + raise ValueError('sandbox options must reference credential files, not contain secrets') + _check_options(item) + elif isinstance(value, list): + for item in value: + _check_options(item) + + +def load_sandbox(path): + path = Path(path) + cfg = yaml.safe_load(path.read_text()) + required = {'domain', 'credential_file', 'bindings'} + defaults = {'protocol': 'https', 'use_server_proxy': True, + 'request_timeout_s': 60, 'create_timeout_s': 600, 'lifecycle_minutes': 120, + 'cleanup_timeout_s': 120, 'max_live_instances': 2, 'startup_concurrency': 1, + 'adapter': 'harborrl.rollout.harbor_job.environments.opensandbox:ManagedOpenSandboxEnvironment', + 'options': {}, 'installation': None} + if not isinstance(cfg, dict) or not required <= set(cfg) or set(cfg) - required - defaults.keys(): + raise ValueError('invalid sandbox configuration fields') + cfg = {**defaults, **copy.deepcopy(cfg)} + url = urlsplit(cfg['domain']) + if (url.scheme not in ('http', 'https') + or not url.hostname or url.username or url.password or url.query or url.fragment + or url.path.rstrip('/').endswith('/v1') or cfg['protocol'] != url.scheme): + raise ValueError('invalid sandbox domain/protocol') + for key in defaults.keys() - {'protocol', 'use_server_proxy', 'adapter', 'options', 'installation'}: + if type(cfg[key]) is not int or cfg[key] <= 0: + raise ValueError(f'sandbox {key} must be a positive integer') + if type(cfg['use_server_proxy']) is not bool or not isinstance(cfg['options'], dict): + raise ValueError('invalid sandbox proxy/options') + _check_options(cfg['options']) + if not isinstance(cfg['adapter'], str) or not re.fullmatch(r'[A-Za-z_]\w*(?:\.[A-Za-z_]\w*)*:[A-Za-z_]\w*', cfg['adapter']): + raise ValueError('sandbox adapter must be module:Class') + if not isinstance(cfg['credential_file'], str) or not cfg['credential_file']: + raise ValueError('sandbox credential_file required') + cfg['credential_file'] = str((path.parent / cfg['credential_file']).resolve()) + bindings = cfg['bindings'] + if not isinstance(bindings, list) or not bindings: + raise ValueError('sandbox image bindings required') + images = set() + for binding in bindings: + if not isinstance(binding, dict) or set(binding) - {'image', 'max_live_instances', 'options'} or not {'image', 'max_live_instances'} <= set(binding): + raise ValueError('invalid sandbox binding fields') + binding.setdefault('options', {}) + _check_options(binding['options']) + if (not isinstance(binding['image'], str) or not binding['image'] + or binding['image'] in images or not isinstance(binding['options'], dict)): + raise ValueError('invalid or duplicate sandbox image binding') + if type(binding['max_live_instances']) is not int or binding['max_live_instances'] <= 0: + raise ValueError('binding max_live_instances must be positive') + images.add(binding['image']) + install = cfg['installation'] + if install is None: + return cfg + if not isinstance(install, dict) or set(install) != {'mode', 'version', 'binary', 'sha256'}: + raise ValueError('installation requires mode, version, binary, sha256') + if (install['mode'] != 'offline_binary' or not isinstance(install['version'], str) + or not re.fullmatch(r'\d+\.\d+\.\d+', install['version']) + or not isinstance(install['sha256'], str) or not re.fullmatch(r'[0-9a-f]{64}', install['sha256']) + or not isinstance(install['binary'], str) or not install['binary']): + raise ValueError('invalid pinned offline binary installation') + install['binary'] = str((path.parent / install['binary']).resolve()) + return cfg + + +def task_binding(cfg, task_path): + spec = tomllib.loads((Path(task_path) / 'task.toml').read_text()) + image = spec.get('environment', {}).get('docker_image') + for binding in cfg['bindings']: + if image == binding['image']: + return binding + raise ValueError('sandbox task requires a prebuilt image with an exact binding') + + +def environment_class(cfg): + """Import trusted deployment code only in the runner, never in GPU training.""" + module, name = cfg['adapter'].split(':') + cls = getattr(importlib.import_module(module), name) + from harborrl.rollout.harbor_job.environments.opensandbox import ( + ManagedOpenSandboxEnvironment, + ) + if not isinstance(cls, type) or not issubclass(cls, ManagedOpenSandboxEnvironment): + raise TypeError('sandbox adapter must implement the audited OpenSandbox lifecycle') + cls.validate_config(cfg) + return cls + + +def read_credential(cfg): + path = Path(cfg['credential_file']) + if path.stat().st_mode & 0o077: + raise ValueError('sandbox credential file must have mode 600 or stricter') + value = path.read_text().strip() + if not value or '\n' in value: + raise ValueError('sandbox credential file must contain one nonempty token') + return value + + +class SandboxAdmission: + """One runtime owns capacity; unresolved cleanup blocks further admission.""" + def __init__(self, cfg): + self.total = asyncio.Semaphore(cfg['max_live_instances']) + self.bindings = {b['image']: asyncio.Semaphore(b['max_live_instances']) for b in cfg['bindings']} + self.blocked = False + + @asynccontextmanager + async def lease(self, binding, root): + # Wait for image-specific capacity before reserving a global live slot. + async with self.bindings[binding['image']], self.total: + if self.blocked: + raise RuntimeError('sandbox admission blocked by unresolved cleanup') + try: + yield + finally: + if (Path(root) / 'cleanup-required.json').exists(): + self.blocked = True diff --git a/harborrl/rollout/harbor_job/sandbox_probe.py b/harborrl/rollout/harbor_job/sandbox_probe.py new file mode 100644 index 000000000..ebed3f47d --- /dev/null +++ b/harborrl/rollout/harbor_job/sandbox_probe.py @@ -0,0 +1,63 @@ +"""Offline dependency check or explicit live Environment check (no allocation).""" +import argparse +import asyncio +import hashlib +import importlib.metadata +import json +import sys +from pathlib import Path + +from harborrl.rollout.harbor_job.sandbox_config import ( + environment_class, + load_sandbox, + read_credential, +) + + +def check(cfg): + from harborrl.rollout.harbor_job.runner import probe + result = probe() + version = importlib.metadata.version('opensandbox') + if version != '0.1.9': + raise ValueError('sandbox runner requires opensandbox==0.1.9') + from opensandbox.models.execd import RunCommandOpts + + adapter = environment_class(cfg) + if not {'timeout', 'envs', 'working_directory'} <= RunCommandOpts.model_fields.keys(): + raise ValueError('unsupported SDK command options') + checksum = None + if cfg['installation']: + with Path(cfg['installation']['binary']).open('rb') as source: + checksum = hashlib.file_digest(source, 'sha256').hexdigest() + if checksum != cfg['installation']['sha256']: + raise ValueError('offline binary checksum mismatch') + read_credential(cfg) + result.update(opensandbox_version=version, environment=adapter.__name__, binary_sha256=checksum) + return result + + +async def network_check(cfg): + return await environment_class(cfg).network_check(cfg) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('config', nargs='?') + parser.add_argument('--stdin', action='store_true') + parser.add_argument('--network', action='store_true') + args = parser.parse_args() + try: + cfg = json.load(sys.stdin) if args.stdin else load_sandbox(args.config) + result = check(cfg) + if args.network: + result['environments'] = asyncio.run(network_check(cfg)) + print(json.dumps(result)) + except Exception as exc: # noqa: BLE001 - redact authenticated SDK exceptions + # SDK/HTTP exceptions may contain authenticated endpoint URLs. + print(json.dumps({'ok': False, 'error_type': type(exc).__name__})) + return 1 + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/harborrl/rollout/harbor_job/sandbox_transport.py b/harborrl/rollout/harbor_job/sandbox_transport.py new file mode 100644 index 000000000..385ad5701 --- /dev/null +++ b/harborrl/rollout/harbor_job/sandbox_transport.py @@ -0,0 +1,27 @@ +"""Observe SDK allocation IDs without retrying creation requests.""" +import re + +import httpx + + +class SandboxStartupTransport(httpx.AsyncBaseTransport): + def __init__(self, base_url, on_created, *, transport=None): + self.base_url = base_url.rstrip('/') + self.on_created = on_created + self.transport = transport or httpx.AsyncHTTPTransport() + + async def handle_async_request(self, request): + url = str(request.url).split('?', 1)[0] + response = await self.transport.handle_async_request(request) + if request.method == 'POST' and url == self.base_url + '/sandboxes' and response.is_success: + await response.aread() + try: + sandbox_id = response.json()['id'] + if isinstance(sandbox_id, str) and re.fullmatch(r'[A-Za-z0-9_-]+', sandbox_id): + self.on_created(sandbox_id) + except (ValueError, KeyError, TypeError): + pass # Let the SDK reject malformed creation responses. + return response + + async def aclose(self): + await self.transport.aclose() diff --git a/harborrl/rollout/native_generate.py b/harborrl/rollout/native_generate.py index 5f59276e7..b5cfeebda 100644 --- a/harborrl/rollout/native_generate.py +++ b/harborrl/rollout/native_generate.py @@ -1,255 +1,17 @@ -"""Slime group hook for native Harbor trials. - -The hook keeps one trajectory per Slime slot and stores its immutable IR in -metadata. Turn expansion happens only at the training converter boundary. -""" -from __future__ import annotations - -import asyncio -import json -import os -import secrets -from pathlib import Path - -from harborrl.rollout.harbor_job.coordinator import GroupSlots, run_attempt -from harborrl.trajectories.native import Identity, RewardProfile, digest, read_json - - -class NativeRolloutRuntime: - def __init__(self, args): - from slime.utils.processing_utils import load_tokenizer - - from harborrl.gateway.server import Gateway, make_server - from harborrl.gateway.sglang_backend import SGLangBackend - from harborrl.gateway.trace import TraceRegistry - - self.args = args - self.run_root = Path(os.environ["RUN_DIR"]).resolve() - self.catalog = read_json(os.environ["HARBORRL_NATIVE_CATALOG"]) - self.profile = read_json(os.environ["HARBORRL_NATIVE_PROFILE"]) - self.rollout_root = self.run_root / "native-rollouts" - self.rollout_root.mkdir(parents=True, exist_ok=True) - self.registry = TraceRegistry(self.run_root / "native-traces") - engine_count = int(args.rollout_num_gpus) // int(args.rollout_num_gpus_per_engine) - limit = max(1, int(args.sglang_server_concurrency) * engine_count) - self.semaphore = asyncio.Semaphore(limit) - tokenizer = load_tokenizer(args.hf_checkpoint, trust_remote_code=True) - tokenizer_digest, template_digest = tokenizer_fingerprint(tokenizer) - expected_tokenizer = os.environ["HARBORRL_NATIVE_TOKENIZER_DIGEST"] - expected_template = os.environ["HARBORRL_NATIVE_TEMPLATE_DIGEST"] - if (tokenizer_digest != expected_tokenizer or template_digest != expected_template): - raise ValueError("live tokenizer/template digest differs from the native lock") - endpoint = f"http://{args.sglang_router_ip}:{args.sglang_router_port}" - backend = SGLangBackend( - endpoint, - tokenizer, - tokenizer_digest=tokenizer_digest, - template_digest=template_digest, - max_context=int(args.rollout_max_context_len), - audited_raw_logprobs=os.environ.get("HARBORRL_NATIVE_AUDITED_LOGPROBS") == "1", - ) - self.backend = backend - gateway = Gateway( - self.registry, - backend, - model=self.profile["model"], - max_output_tokens=int(args.rollout_max_response_len), - ) - self.server = make_server( - gateway, - host=os.environ.get("HARBORRL_NATIVE_GATEWAY_HOST", "0.0.0.0"), - port=int(os.environ["HARBORRL_NATIVE_GATEWAY_PORT"]), - ) - self.thread = asyncio.get_running_loop().run_in_executor(None, self.server.serve_forever) - - def close(self): - self.server.shutdown() - self.server.server_close() - - -def tokenizer_fingerprint(tokenizer): - messages = [{"role": "user", "content": "HarborRL native tokenizer/template fingerprint"}] - kwargs = {"tokenize": True, "add_generation_prompt": True, "enable_thinking": False} - rendered = tokenizer.apply_chat_template(messages, **kwargs) - template = digest({"messages": messages, "kwargs": kwargs, "token_ids": rendered}) - vocabulary = tokenizer.get_vocab() - tokenizer_digest = digest({ - "class": type(tokenizer).__name__, - "vocab": sorted((str(key), int(value)) for key, value in vocabulary.items()), - }) - return tokenizer_digest, template - - -_RUNTIME = None -_RUNTIME_LOCK = asyncio.Lock() - - -async def runtime(args): - global _RUNTIME - if _RUNTIME is None: - async with _RUNTIME_LOCK: - if _RUNTIME is None: - _RUNTIME = NativeRolloutRuntime(args) - return _RUNTIME - - -def publish_native_version(states, expected): - """Open the next admission epoch only after the complete pool is healthy.""" - if _RUNTIME is None: - return - _RUNTIME.registry.drain(float(os.environ.get("HARBORRL_NATIVE_DRAIN_TIMEOUT", "30"))) - _RUNTIME.registry.publish_version(states, str(expected)) - - -def current_policy_version(runtime_): - """Publish and lock the version currently served by the rollout pool.""" - states = runtime_.backend.pool_states() - from harborrl.platform.policy_pool import publish_pool - version = publish_pool(states) - runtime_.registry.drain(float(os.environ.get("HARBORRL_NATIVE_DRAIN_TIMEOUT", "30"))) - runtime_.registry.publish_version(states, version) - return version - - -def close_runtime(): - global _RUNTIME - if _RUNTIME is not None: - _RUNTIME.close() - _RUNTIME = None - - -def _task_entry(sample): - metadata = sample.metadata if isinstance(sample.metadata, dict) else {} - entry = metadata.get("native_task") - required = {"id", "revision", "path", "task_digest", "reward_profile"} - if not isinstance(entry, dict) or set(entry) != required: - raise ValueError("native rollout requires a locked task catalog entry") - return entry - - -def _identity( - args, - sample, - slot, - rollout_id, - task_digest, - reward_digest, - sampling_digest, - policy_version, -): - group_index = getattr(sample, "group_index", None) - if type(group_index) is not int: - raise ValueError("Slime must provide group identity before native generation") - return Identity( - run_id=os.environ["RUN_ID"], - batch_id=f"rollout-{rollout_id}", - group_id=f"group-{group_index}", - slot_id=str(slot), - attempt_id=secrets.token_hex(16), - trajectory_id=secrets.token_hex(16), - task_digest=task_digest, - policy_version=str(policy_version), - harness_digest=digest(read_json(os.environ["HARBORRL_NATIVE_PROFILE"])), - reward_digest=reward_digest, - sampling_digest=sampling_digest, - ) - - -async def _run_slot( - args, - runtime_, - sample, - slot, - rollout_id, - entry, - sampling_digest, - policy_version, -): - reward_profile = RewardProfile(**entry["reward_profile"]) - identities = [ - _identity( - args, - sample, - index, - rollout_id=rollout_id, - task_digest=entry["task_digest"], - reward_digest=reward_profile.digest, - sampling_digest=sampling_digest, - policy_version=policy_version, - ) - for index in range(args.n_samples_per_prompt) - ] - slots = GroupSlots(identities, max_attempts=int(os.environ["HARBORRL_NATIVE_MAX_ATTEMPTS"])) - workers = json.loads(os.environ["HARBORRL_NATIVE_RUNNER_WORKERS"]) - task_workers = json.loads(os.environ.get("HARBORRL_NATIVE_TASK_WORKER_OVERRIDES", "{}")) - for attempt_index in range(slots.max_attempts): - identity = slots.start(str(slot)) - runner_host = task_workers.get(entry["id"]) - if runner_host is None: - runner_host = workers[(slot + attempt_index) % len(workers)] - root = runtime_.rollout_root / identity.group_id / identity.attempt_id - try: - async with runtime_.semaphore: - ir = await run_attempt( - identity, - task_path=entry["path"], - profile=runtime_.profile, - gateway_url=os.environ["HARBORRL_NATIVE_GATEWAY_URL"], - runner_python=os.environ["HARBORRL_NATIVE_RUNNER_PYTHON"], - runner_host=runner_host, - root=root, - registry=runtime_.registry, - reward_profile=reward_profile, - max_output_tokens=int(args.rollout_max_response_len), - ) - except (OSError, ValueError, RuntimeError, asyncio.TimeoutError) as exc: - slots.finish(str(slot), error=f"{type(exc).__name__}: {exc}") - continue - slots.finish(str(slot), ir) - return ir - raise RuntimeError(f"native slot {slot} exhausted its retry budget") - - -async def generate_group(args, group, sampling_params, rollout_id, evaluation=False): - """Run one complete native group; evaluation is intentionally unsupported.""" - from harborrl.data.harbor.native_inspector import inspect_native - - if evaluation: - raise ValueError("native training rollout cannot be reused as evaluation") - if len(group) != args.n_samples_per_prompt: - raise ValueError("native rollout group is incomplete before launch") - entries = [_task_entry(sample) for sample in group] - if any(entry != entries[0] for entry in entries[1:]): - raise ValueError("native rollout group mixes task locks") - entry = entries[0] - report = inspect_native(entry["path"], expected_digest=entry["task_digest"]) - if report["status"] != "NEEDS_PROBE": - raise ValueError(f"native task preflight failed: {report['reasons']}") - stop = list(sampling_params.get("stop") or []) - sampling_digest = digest({ - "temperature": sampling_params.get("temperature"), - "top_p": sampling_params.get("top_p"), - "top_k": sampling_params.get("top_k"), - "max_new_tokens": sampling_params.get("max_new_tokens"), - "stop": stop, - }) - runtime_ = await runtime(args) - policy_version = current_policy_version(runtime_) - trajectories = await asyncio.gather(*[ - _run_slot( - args, runtime_, sample, slot, rollout_id, entry, sampling_digest, policy_version - ) - for slot, sample in enumerate(group) - ]) - output = [] - for sample, ir in zip(group, trajectories): - sample.metadata["native_ir"] = ir - sample.response = "native Harbor trial" - sample.reward = {"score": ir["evaluation"]["training_reward"]} - sample.status = sample.Status.COMPLETED - try: - sample.policy_version = int(ir["identity"]["policy_version"]) - except ValueError as exc: - raise ValueError("native policy version must be an integer in Slime") from exc - output.append(sample) - return output +"""Compatibility imports for the framework-independent rollout runtime.""" +from harborrl.rollout.runtime import ( # noqa: F401 + GroupSlots, + NativeRolloutRuntime, + RolloutRuntime, + RolloutRuntimeConfig, + close_runtime, + current_policy_version, + publish_native_version, + run_attempt, + run_group, + run_slot, + runtime, + slot_identity, + task_entry, + tokenizer_fingerprint, +) diff --git a/harborrl/rollout/runtime.py b/harborrl/rollout/runtime.py new file mode 100644 index 000000000..558bab01d --- /dev/null +++ b/harborrl/rollout/runtime.py @@ -0,0 +1,350 @@ +"""Extracted rollout core (P2): gateway, traces and complete groups. + +This module owns rollout execution and must not import Slime or Ray: training +frameworks live in ``harborrl.backends.*`` adapters that build a +``RolloutRuntimeConfig`` and hand over a tokenizer. Group semantics, worker +scheduling, retries and the RL_READY contract stay exactly as the embedded +pipeline enforced them. +""" +from __future__ import annotations + +import asyncio +import json +import secrets +import threading +from contextlib import nullcontext +from dataclasses import dataclass +from pathlib import Path + +from harborrl.models.qwen import normalize_chat_template_ids +from harborrl.rollout.harbor_job.coordinator import GroupSlots, run_attempt +from harborrl.trajectories.native import Identity, RewardProfile, digest, read_json + +TASK_ENTRY_KEYS = {"id", "revision", "path", "task_digest", "reward_profile"} + + +@dataclass(frozen=True) +class RolloutRuntimeConfig: + """Deployment-independent description of one run's rollout machinery.""" + + run_id: str + run_root: str + catalog_path: str + profile_path: str + gateway_host: str + gateway_port: int + gateway_url: str + runner_python: str + workers: tuple + task_worker_overrides: dict + max_attempts: int + max_output_tokens: int + max_context: int + concurrency_limit: int + group_size: int + expected_tokenizer_digest: str + expected_template_digest: str + audited_raw_logprobs: bool + drain_timeout: float + model_tag: str + endpoint: str + sandbox: dict = None + runner_transport: str = 'ssh' + + @staticmethod + def _required(env, name): + value = env.get(name) + if not isinstance(value, str) or not value.strip(): + raise ValueError(f"rollout runtime requires {name}") + return value + + @classmethod + def from_environment(cls, env, *, endpoint, concurrency_limit, group_size, + gateway_host=None, gateway_port=None, gateway_url=None, + workers=None, runner_python=None): + if not isinstance(endpoint, str) or not endpoint.strip(): + raise ValueError("rollout runtime requires a serving endpoint") + if type(group_size) is not int or group_size < 2: + raise ValueError("rollout runtime requires a group size of at least two") + if workers is None: + workers = json.loads(cls._required(env, "HARBORRL_NATIVE_RUNNER_WORKERS")) + if runner_python is None: + runner_python = cls._required(env, "HARBORRL_NATIVE_RUNNER_PYTHON") + try: + overrides = json.loads(env.get("HARBORRL_NATIVE_TASK_WORKER_OVERRIDES") or "{}") + except ValueError as exc: + raise ValueError("invalid HARBORRL_NATIVE_TASK_WORKER_OVERRIDES") from exc + return cls( + run_id=cls._required(env, "RUN_ID"), + run_root=cls._required(env, "RUN_DIR"), + catalog_path=cls._required(env, "HARBORRL_NATIVE_CATALOG"), + profile_path=cls._required(env, "HARBORRL_NATIVE_PROFILE"), + gateway_host=gateway_host or env.get("HARBORRL_NATIVE_GATEWAY_HOST", "0.0.0.0"), + gateway_port=int(gateway_port or cls._required(env, "HARBORRL_NATIVE_GATEWAY_PORT")), + gateway_url=gateway_url or cls._required(env, "HARBORRL_NATIVE_GATEWAY_URL"), + runner_python=runner_python, + workers=tuple(workers), + task_worker_overrides=dict(overrides), + max_attempts=int(env.get("HARBORRL_NATIVE_MAX_ATTEMPTS", "2")), + max_output_tokens=int(env.get("ROLLOUT_MAX_RESPONSE_LEN", "8192")), + max_context=int(env.get("ROLLOUT_MAX_CONTEXT_LEN", "32768")), + concurrency_limit=max(1, int(concurrency_limit)), + group_size=int(group_size), + expected_tokenizer_digest=cls._required(env, "HARBORRL_NATIVE_TOKENIZER_DIGEST"), + expected_template_digest=cls._required(env, "HARBORRL_NATIVE_TEMPLATE_DIGEST"), + audited_raw_logprobs=env.get("HARBORRL_NATIVE_AUDITED_LOGPROBS") == "1", + drain_timeout=float(env.get("HARBORRL_NATIVE_DRAIN_TIMEOUT", "30")), + model_tag=env.get("MODEL_TAG", env.get("MODEL_ARGS_FILE", "qwen3-8B")), + endpoint=endpoint.rstrip("/"), + sandbox=json.loads(env.get('HARBORRL_NATIVE_SANDBOX', 'null')), + runner_transport=env.get('HARBORRL_NATIVE_RUNNER_TRANSPORT', 'ssh'), + ) + + def environment(self): + """Render the environment mapping this config was built from.""" + return { + "RUN_ID": self.run_id, "RUN_DIR": self.run_root, + "HARBORRL_NATIVE_CATALOG": self.catalog_path, + "HARBORRL_NATIVE_PROFILE": self.profile_path, + "HARBORRL_NATIVE_GATEWAY_HOST": self.gateway_host, + "HARBORRL_NATIVE_GATEWAY_PORT": str(self.gateway_port), + "HARBORRL_NATIVE_GATEWAY_URL": self.gateway_url, + "HARBORRL_NATIVE_RUNNER_PYTHON": self.runner_python, + "HARBORRL_NATIVE_RUNNER_WORKERS": json.dumps(list(self.workers)), + "HARBORRL_NATIVE_TASK_WORKER_OVERRIDES": json.dumps( + self.task_worker_overrides, sort_keys=True), + "HARBORRL_NATIVE_MAX_ATTEMPTS": str(self.max_attempts), + "ROLLOUT_MAX_RESPONSE_LEN": str(self.max_output_tokens), + "ROLLOUT_MAX_CONTEXT_LEN": str(self.max_context), + "HARBORRL_NATIVE_TOKENIZER_DIGEST": self.expected_tokenizer_digest, + "HARBORRL_NATIVE_TEMPLATE_DIGEST": self.expected_template_digest, + "HARBORRL_NATIVE_AUDITED_LOGPROBS": str(int(self.audited_raw_logprobs)), + "HARBORRL_NATIVE_DRAIN_TIMEOUT": str(self.drain_timeout), + "MODEL_TAG": self.model_tag, + "HARBORRL_NATIVE_SANDBOX": json.dumps(self.sandbox), + "HARBORRL_NATIVE_RUNNER_TRANSPORT": self.runner_transport, + } + + +def tokenizer_fingerprint(tokenizer): + messages = [{"role": "user", "content": "HarborRL native tokenizer/template fingerprint"}] + kwargs = {"tokenize": True, "add_generation_prompt": True, "enable_thinking": False} + rendered_ids = normalize_chat_template_ids( + tokenizer.apply_chat_template(messages, **kwargs)) + vocabulary = tokenizer.get_vocab() + tokenizer_digest = digest({ + "class": type(tokenizer).__name__, + "vocab": sorted((str(key), int(value)) for key, value in vocabulary.items()), + }) + template = digest({"messages": messages, "kwargs": kwargs, "token_ids": rendered_ids}) + return tokenizer_digest, template + + +class RolloutRuntime: + """One gateway, one trace registry and one rollout root per run.""" + + def __init__(self, config, tokenizer, *, backend=None): + from harborrl.gateway.server import Gateway, make_server + from harborrl.gateway.sglang_backend import SGLangBackend + from harborrl.gateway.trace import TraceRegistry + from harborrl.math_registry import MATH_REGISTRY + + self.config = config + self.run_root = Path(config.run_root).resolve() + self.catalog = read_json(config.catalog_path) + self.profile = read_json(config.profile_path) + self.rollout_root = self.run_root / "native-rollouts" + self.rollout_root.mkdir(parents=True, exist_ok=True) + self.registry = TraceRegistry(self.run_root / "native-traces") + self.semaphore = asyncio.Semaphore(config.concurrency_limit) + if config.sandbox: + from harborrl.rollout.harbor_job.sandbox_config import SandboxAdmission + self.sandbox_admission = SandboxAdmission(config.sandbox) + tokenizer_digest, template_digest = tokenizer_fingerprint(tokenizer) + if (tokenizer_digest != config.expected_tokenizer_digest + or template_digest != config.expected_template_digest): + raise ValueError("live tokenizer/template digest differs from the native lock") + model = MATH_REGISTRY.model_for_preset(config.model_tag) + self.backend = backend or SGLangBackend( + config.endpoint, tokenizer, model.adapter, + tokenizer_digest=tokenizer_digest, template_digest=template_digest, + max_context=config.max_context, + audited_raw_logprobs=config.audited_raw_logprobs, + ) + gateway = Gateway(self.registry, self.backend, model=self.profile["model"], + max_output_tokens=config.max_output_tokens) + self.server = make_server(gateway, host=config.gateway_host, + port=config.gateway_port) + self._start_gateway() + + def _start_gateway(self): + self.thread = threading.Thread( + target=self.server.serve_forever, name="harborrl-native-gateway", daemon=True + ) + self.thread.start() + + def close(self): + self.server.shutdown() + if getattr(self.thread, "is_alive", lambda: False)(): + self.thread.join(5) + self.server.server_close() + + def manifest_path(self, identity): + """Deterministic shared-storage path of one trajectory manifest.""" + if isinstance(identity, dict): + identity = Identity(**identity) + return self.rollout_root / identity.group_id / identity.attempt_id / "trajectory.v2.json" + + +# Historical name kept for existing hooks and evidence readers. +NativeRolloutRuntime = RolloutRuntime + +_RUNTIME = None +_RUNTIME_LOCK = asyncio.Lock() + + +async def runtime(config, *, tokenizer=None, tokenizer_loader=None): + """Process-wide runtime cache for the embedded GPU-side rollout path.""" + global _RUNTIME + if _RUNTIME is None: + async with _RUNTIME_LOCK: + if _RUNTIME is None: + if tokenizer is None: + if tokenizer_loader is None: + raise ValueError("runtime requires a tokenizer or a loader") + tokenizer = tokenizer_loader() + _RUNTIME = RolloutRuntime(config, tokenizer) + return _RUNTIME + + +def publish_native_version(runtime_, states, expected): + """Open the next admission epoch only after the complete pool is healthy.""" + runtime_.registry.drain(runtime_.config.drain_timeout) + runtime_.registry.publish_version(states, str(expected)) + + +def current_policy_version(runtime_): + """Publish and lock the version currently served by the rollout pool.""" + states = runtime_.backend.pool_states() + from harborrl.platform.policy_pool import publish_pool + version = publish_pool(states) + runtime_.registry.drain(runtime_.config.drain_timeout) + runtime_.registry.publish_version(states, version) + return version + + +def close_runtime(): + global _RUNTIME + if _RUNTIME is not None: + _RUNTIME.close() + _RUNTIME = None + + +def task_entry(sample): + metadata = sample.metadata if isinstance(sample.metadata, dict) else {} + entry = metadata.get("native_task") + if not isinstance(entry, dict) or set(entry) != TASK_ENTRY_KEYS: + raise ValueError("native rollout requires a locked task catalog entry") + return entry + + +def slot_identity(config, group_index, slot, rollout_id, entry, + reward_digest, sampling_digest, policy_version, *, harness_digest=None): + return Identity( + run_id=config.run_id, + batch_id=f"rollout-{rollout_id}", + group_id=f"group-{group_index}", + slot_id=str(slot), + attempt_id=secrets.token_hex(16), + trajectory_id=secrets.token_hex(16), + task_digest=entry["task_digest"], + policy_version=str(policy_version), + harness_digest=harness_digest if harness_digest is not None else digest(read_json(config.profile_path)), + reward_digest=reward_digest, + sampling_digest=sampling_digest, + ) + + +def _group_slots(config, runtime_, group_index, rollout_id, entry, + sampling_digest, policy_version, size): + reward_profile = RewardProfile(**entry["reward_profile"]) + harness_digest = digest(runtime_.profile) + identities = [ + slot_identity(config, group_index, index, rollout_id, entry, + reward_profile.digest, sampling_digest, policy_version, + harness_digest=harness_digest) + for index in range(size) + ] + return GroupSlots(identities, max_attempts=config.max_attempts), reward_profile + + +async def run_slot(config, runtime_, group_index, slot, rollout_id, entry, + sampling_digest, policy_version): + """Compatibility entry point for executing an individual group slot.""" + slots, reward_profile = _group_slots( + config, runtime_, group_index, rollout_id, entry, + sampling_digest, policy_version, config.group_size) + return await _run_slot(config, runtime_, slots, slot, entry, reward_profile) + + +async def _run_slot(config, runtime_, slots, slot, entry, reward_profile): + """Execute one slot with the group's shared identity and retry bookkeeping.""" + workers = config.workers + overrides = config.task_worker_overrides + for attempt_index in range(slots.max_attempts): + identity = slots.start(str(slot)) + runner_host = overrides.get(entry["id"]) + if config.runner_transport == 'local': + runner_host = None + elif runner_host is None: + if not workers: + raise ValueError("rollout runtime requires at least one worker destination") + runner_host = workers[(slot + attempt_index) % len(workers)] + root = runtime_.rollout_root / identity.group_id / identity.attempt_id + admission = nullcontext() + if config.sandbox: + from harborrl.rollout.harbor_job.sandbox_config import task_binding + admission = runtime_.sandbox_admission.lease(task_binding(config.sandbox, entry['path']), root) + try: + async with admission, runtime_.semaphore: + ir = await run_attempt( + identity, + task_path=entry["path"], + profile=runtime_.profile, + gateway_url=config.gateway_url, + runner_python=config.runner_python, + runner_host=runner_host, + root=root, + registry=runtime_.registry, + reward_profile=reward_profile, + max_output_tokens=config.max_output_tokens, + **({'sandbox': config.sandbox} if config.sandbox else {}), + ) + except (OSError, ValueError, RuntimeError, asyncio.TimeoutError) as exc: + slots.finish(str(slot), error=f"{type(exc).__name__}: {exc}") + if config.sandbox and ((root / 'cleanup-required.json').exists() or runtime_.sandbox_admission.blocked): + raise RuntimeError('sandbox cleanup unresolved; automatic retry blocked') from exc + continue + slots.finish(str(slot), ir) + return ir + raise RuntimeError(f"native slot {slot} exhausted its retry budget") + + +async def run_group(config, runtime_, *, entry, group_index, rollout_id, + sampling_digest, policy_version, group_size=None): + """Execute one complete group and return per-slot IRs in slot order.""" + size = config.group_size if group_size is None else int(group_size) + slots, reward_profile = _group_slots( + config, runtime_, group_index, rollout_id, entry, + sampling_digest, policy_version, size) + tasks = [asyncio.create_task( + _run_slot(config, runtime_, slots, slot, entry, reward_profile)) + for slot in range(size) + ] + try: + return list(await asyncio.gather(*tasks)) + except BaseException: + for task in tasks: + task.cancel() + await asyncio.gather(*tasks, return_exceptions=True) + raise diff --git a/harborrl/rollout/service.py b/harborrl/rollout/service.py new file mode 100644 index 000000000..e223ab05a --- /dev/null +++ b/harborrl/rollout/service.py @@ -0,0 +1,562 @@ +"""RolloutService (P3): the manager-side HTTP control plane for rollout groups. + +First-phase contract: HTTP JSON, run-scoped tokens, idempotent group +submission. Bulky payloads (trajectory manifests, token ids, logprobs, loss +masks) live on the shared run directory; HTTP responses carry paths plus +SHA-256 digests, and the SlimeAdapter recomputes every digest before +converting anything into training samples. + +Rules that keep the lifecycle safe: +- same group id and same content -> the existing state, never a second run; +- same group id and different content -> rejected; +- after a timeout the client must query, never resubmit; +- a new policy version is admitted only after the whole serving pool is + healthy on it (drain first, publish second). +""" +from __future__ import annotations + +import asyncio +import hashlib +import hmac +import json +import secrets +import threading +import time +from dataclasses import dataclass, field +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from pathlib import Path +from urllib.parse import urlsplit + +from harborrl.rollout.exporters.native import export_group +from harborrl.rollout.runtime import run_group +from harborrl.trajectories.native import digest, finite, publish, read_json + +SERVICE_SCHEMA = "harborrl-rollout-service-v1" +GROUP_REQUEST_KEYS = {"group_id", "rollout_id", "group_index", "policy_version", + "task", "sampling", "slots"} +SAMPLING_KEYS = {"temperature", "top_p", "top_k", "max_new_tokens", "stop"} +TASK_ENTRY_KEYS = {"id", "revision", "path", "task_digest", "reward_profile"} +GROUP_DIR = "rollout-service/groups" + + +class ServiceError(Exception): + """Maps onto one HTTP status; carries a machine-readable code.""" + + def __init__(self, status, code, message): + super().__init__(message) + self.status, self.code = status, code + + +def validate_group_request(request): + if not isinstance(request, dict) or set(request) != GROUP_REQUEST_KEYS: + raise ServiceError(400, "invalid_request", + f"group request requires exactly {sorted(GROUP_REQUEST_KEYS)}") + for key in ("group_id", "policy_version"): + if not isinstance(request[key], str) or not request[key].strip(): + raise ServiceError(400, "invalid_request", f"{key} must be a nonempty string") + for key in ("rollout_id", "group_index"): + if type(request[key]) is not int or request[key] < 0: + raise ServiceError(400, "invalid_request", f"{key} must be a nonnegative integer") + task = request["task"] + if not isinstance(task, dict) or set(task) != TASK_ENTRY_KEYS: + raise ServiceError(400, "invalid_request", + f"task requires exactly {sorted(TASK_ENTRY_KEYS)}") + if not isinstance(task["reward_profile"], dict): + raise ServiceError(400, "invalid_request", "task.reward_profile must be a mapping") + sampling = request["sampling"] + if not isinstance(sampling, dict) or set(sampling) != SAMPLING_KEYS: + raise ServiceError(400, "invalid_request", + f"sampling requires exactly {sorted(SAMPLING_KEYS)}") + if not finite(sampling["temperature"]) or not finite(sampling["top_p"]): + raise ServiceError(400, "invalid_request", "sampling probabilities must be finite") + if type(sampling["top_k"]) is not int: + raise ServiceError(400, "invalid_request", "sampling.top_k must be an integer") + if type(sampling["max_new_tokens"]) is not int or sampling["max_new_tokens"] < 1: + raise ServiceError(400, "invalid_request", "sampling.max_new_tokens must be positive") + if (not isinstance(sampling["stop"], list) + or any(not isinstance(stop, str) or not stop for stop in sampling["stop"])): + raise ServiceError(400, "invalid_request", "sampling.stop must be a string list") + slots = request["slots"] + if (not isinstance(slots, list) or len(slots) < 2 + or any(not isinstance(slot, dict) or set(slot) != {"slot_id"} + or not isinstance(slot["slot_id"], str) or not slot["slot_id"].strip() + for slot in slots)): + raise ServiceError(400, "invalid_request", + "slots must describe at least two unique slot ids") + if len({slot["slot_id"] for slot in slots}) != len(slots): + raise ServiceError(400, "invalid_request", "slot ids must be unique") + return request + + +def request_digest(request): + """Canonical digest of the full request content, group id included.""" + return digest(request) + + +def manifest_path_from(run_root, identity): + """Deterministic trajectory manifest path for one identity.""" + return Path(run_root) / "native-rollouts" / identity["group_id"] / \ + identity["attempt_id"] / "trajectory.v2.json" + + +def build_group_result(run_id, request, trajectories, *, runtime, export=None): + if len(trajectories) != len(request["slots"]): + raise ServiceError(500, "incomplete_group", + "executor returned a different slot count") + slots = [] + for descriptor, ir in zip(request["slots"], trajectories): + identity = ir["identity"] + path = runtime.manifest_path(identity) + payload = read_json(path) + seal = ir["trace_seal"] + slots.append({ + "slot_id": descriptor["slot_id"], + "trajectory_id": identity["trajectory_id"], + "attempt_id": identity["attempt_id"], + "manifest_path": str(path), + "manifest_sha256": digest(payload), + "finish_reason": ir["termination"], + "reward": ir["evaluation"]["training_reward"], + "policy_version": identity["policy_version"], + "task_digest": identity["task_digest"], + "harness_digest": identity["harness_digest"], + "sampling_digest": identity["sampling_digest"], + "readiness": ir["readiness"], + "integrity_errors": ir["integrity_errors"], + "trace_seal": {"status": seal["status"], "errors": seal["errors"], + "digest": digest(seal)}, + "verifier_digest": ir["evaluation"].get("source_result_digest"), + }) + result = { + "schema_version": SERVICE_SCHEMA, + "run_id": run_id, + "group_id": request["group_id"], + "rollout_id": request["rollout_id"], + "request_digest": request_digest(request), + "policy_version": request["policy_version"], + "status": "complete", + "slots": slots, + "completed_at": time.time(), + } + if export is not None: + result["group_export"] = export + return result + + +async def default_executor(config, runtime_, request): + """The production executor: the framework-free rollout core.""" + return await run_group( + config, runtime_, entry=request["task"], group_index=request["group_index"], + rollout_id=request["rollout_id"], sampling_digest=digest(request["sampling"]), + policy_version=request["policy_version"], group_size=len(request["slots"])) + + +@dataclass +class GroupState: + request: dict + request_sha: str + status: str = "queued" + result: dict = None + error: str = None + created_at: float = field(default_factory=time.time) + future: object = None + + def summary(self, *, include_result=True): + payload = {"group_id": self.request["group_id"], "status": self.status, + "request_digest": self.request_sha, + "created_at": self.created_at} + if self.error: + payload["error"] = self.error + if include_result and self.result is not None: + payload["result"] = self.result + return payload + + +@dataclass +class RunState: + run_id: str + manifest: dict + manifest_sha: str + token_digest: str + config: object = None + runtime: object = None + drained: bool = False + serving: str = None + groups: dict = field(default_factory=dict) + + +class RolloutService: + """Run registry plus a single-threaded asyncio executor for group work.""" + + def __init__(self, *, run_root, executor=None, runtime_factory=None, + token_file=None, group_wait_timeout=3600.0, poll_interval=0.05, + allow_synthetic=False): + self.run_root = Path(run_root).resolve() + self.executor = executor or default_executor + self.runtime_factory = runtime_factory + self.allow_synthetic = allow_synthetic + self.token = None + if token_file is not None: + token = Path(token_file).read_text().strip() + if not token: + raise ServiceError(500, "invalid_token_file", "run token file is empty") + self.token = token + self.group_wait_timeout = group_wait_timeout + self.poll_interval = poll_interval + self.runs = {} + self.lock = threading.RLock() + self.loop = asyncio.new_event_loop() + self.thread = threading.Thread(target=self._serve_loop, + name="harborrl-rollout-service", daemon=True) + self.thread.start() + + def _serve_loop(self): + asyncio.set_event_loop(self.loop) + self.loop.run_forever() + + def close(self): + with self.lock: + states = [state for state in self.runs.values() if state.runtime is not None] + for state in states: + try: + state.runtime.close() + except AttributeError: + pass + self.loop.call_soon_threadsafe(self.loop.stop) + self.thread.join(5) + + # -- run lifecycle ---------------------------------------------------- + + def _token_digest(self, token): + return hashlib.sha256(token.encode()).hexdigest() + + def register_run(self, run_id, manifest, *, token=None): + if not isinstance(run_id, str) or not run_id.strip(): + raise ServiceError(400, "invalid_request", "run id required") + if not isinstance(manifest, dict) or not manifest: + raise ServiceError(400, "invalid_request", "manifest must be a nonempty mapping") + if manifest.get("run_id", run_id) != run_id: + raise ServiceError(400, "invalid_request", "manifest run id must match the path") + manifest_sha = digest(manifest) + with self.lock: + existing = self.runs.get(run_id) + if existing is not None: + if existing.manifest_sha != manifest_sha: + raise ServiceError(409, "conflict", + "run id already registered with a different manifest") + return {"run_id": run_id, "state": "registered", "existed": True} + if self.token is not None and token != self.token: + raise ServiceError(401, "unauthorized", "invalid manager run token") + generated = token is None + if generated: + token = secrets.token_urlsafe(32) + self.runs[run_id] = RunState(run_id=run_id, manifest=manifest, + manifest_sha=manifest_sha, + token_digest=self._token_digest(token)) + return {"run_id": run_id, "state": "registered", "existed": False, + **({"run_token": token} if generated else {})} + + def authenticate(self, run_id, token): + with self.lock: + state = self.runs.get(run_id) + if state is None: + raise ServiceError(404, "not_found", f"unknown run: {run_id}") + if not isinstance(token, str) or not token or not hmac.compare_digest( + state.token_digest, self._token_digest(token)): + raise ServiceError(401, "unauthorized", "invalid run token") + return state + + def _run(self, run_id): + state = self.runs.get(run_id) + if state is None: + raise ServiceError(404, "not_found", f"unknown run: {run_id}") + return state + + def attach_runtime(self, run_id, config, runtime): + """Bind the rollout runtime that executes this run's groups.""" + with self.lock: + state = self._run(run_id) + state.config, state.runtime = config, runtime + return {"run_id": run_id, "state": "attached"} + + def announce_serving(self, run_id, router_url): + parts = urlsplit(router_url or "") + if (parts.scheme not in ("http", "https") or not parts.hostname + or parts.username or parts.password or parts.query + or parts.fragment or parts.path not in ("", "/")): + raise ServiceError(400, "invalid_request", "router_url must be a plain origin") + with self.lock: + state = self._run(run_id) + state.serving = f"{parts.scheme}://{parts.netloc}".rstrip("/") + if state.runtime is None and self.runtime_factory is not None: + runtime = self.runtime_factory(run_id, state.serving) + state.runtime = runtime + if state.config is None: + # The factory-built runtime carries its own config; the + # executor needs both for group admission. + state.config = getattr(runtime, "config", None) + return {"run_id": run_id, "router_url": state.serving, + "runtime": "attached" if state.runtime is not None else "pending"} + + # -- policy lifecycle (§7) --------------------------------------------- + + def activate_policy(self, run_id, states, expected): + from harborrl.platform.policy_pool import validate_pool + + with self.lock: + state = self._run(run_id) + if state.runtime is None: + raise ServiceError(409, "admission_closed", "run has no serving runtime yet") + try: + version = validate_pool(states, str(expected)) + backend = getattr(state.runtime, 'backend', None) + if backend is not None: + live_states = backend.pool_states() + validate_pool(live_states, version) + if {item['endpoint'] for item in live_states} != {item['endpoint'] for item in states}: + raise ValueError('manager and GPU disagree on serving engine membership') + states = live_states + except (RuntimeError, TypeError, ValueError) as exc: + raise ServiceError(409, "pool_invalid", str(exc)) from exc + registry = state.runtime.registry + registry.drain(float(getattr(state.config, "drain_timeout", 30))) + registry.publish_version(states, version) + # Mirror policy_pool.publish_pool into the shared run directory so the + # GPU side can lock the same version without manager-local state; the + # record is replaceable because activation legitimately advances. + payload = {"weight_version": version, "engines": states, "timestamp": time.time()} + temp = self.run_root / "policy-pool.tmp" + temp.write_text(json.dumps(payload) + "\n") + temp.replace(self.run_root / "policy-pool.json") + return {"run_id": run_id, "policy_version": version, "admission": "open"} + + # -- group lifecycle ---------------------------------------------------- + + def _group_dir(self, run_id, group_id): + return self.run_root / GROUP_DIR / run_id / group_id + + def submit_group(self, run_id, request, *, wait=True): + request = validate_group_request(request) + sha = request_digest(request) + with self.lock: + state = self._run(run_id) + existing = state.groups.get(request["group_id"]) + if existing is not None: + if existing.request_sha != sha: + raise ServiceError(409, "conflict", + "group id already submitted with different content") + group = existing + else: + if state.drained: + raise ServiceError(409, "admission_closed", "run is draining") + if state.runtime is None or state.config is None: + raise ServiceError(409, "admission_closed", + "run has no rollout runtime attached") + registry = getattr(state.runtime, "registry", None) + version = getattr(registry, "version", None) if registry is not None else None + accepting = getattr(registry, "accepting", True) if registry is not None else False + if not accepting or version != request["policy_version"]: + raise ServiceError( + 409, "stale_policy", + f"policy {request['policy_version']!r} is not the admitted version " + f"({version!r}, accepting={accepting}); activate first") + group = GroupState(request=request, request_sha=sha) + state.groups[request["group_id"]] = group + future = asyncio.run_coroutine_threadsafe( + self._execute(state, group), self.loop) + group.future = future + if wait: + return self._wait_group(run_id, group) + return group.summary(include_result=False) + + def _wait_group(self, run_id, group): + deadline = time.monotonic() + self.group_wait_timeout + while group.status in ("queued", "running"): + if time.monotonic() >= deadline: + return {**group.summary(include_result=False), + "hint": "query GET /groups/{group_id}; do not resubmit"} + time.sleep(self.poll_interval) + return group.summary() + + async def _execute(self, state, group): + # Immutable state records: each transition gets its own file, and + # publish() refuses to rewrite history. + request = group.request + directory = self._group_dir(state.run_id, request["group_id"]) + publish(directory / "request.json", request) + group.status = "running" + publish(directory / "running.json", group.summary(include_result=False)) + try: + trajectories = await self.executor(state.config, state.runtime, request) + export = export_group( + trajectories, len(trajectories), + allow_synthetic=self.allow_synthetic) + export_path = directory / "group-export.json" + publish(export_path, export) + group.result = build_group_result( + state.run_id, request, trajectories, runtime=state.runtime, + export={"path": str(export_path), "sha256": digest(export)}) + group.status = "complete" + publish(directory / "result.json", group.result) + except asyncio.CancelledError: + group.status = "cancelled" + publish(directory / "cancelled.json", group.summary(include_result=False)) + raise + except Exception as exc: # noqa: BLE001 — any executor failure is + # recorded as a failed group instead of killing the service + group.status = "failed" + group.error = f"{type(exc).__name__}: {exc}" + publish(directory / "failed.json", group.summary(include_result=False)) + + def group_status(self, run_id, group_id): + with self.lock: + state = self._run(run_id) + group = state.groups.get(group_id) + if group is None: + raise ServiceError(404, "not_found", f"unknown group: {group_id}") + return group.summary() + + def cancel_group(self, run_id, group_id): + with self.lock: + state = self._run(run_id) + group = state.groups.get(group_id) + if group is None: + raise ServiceError(404, "not_found", f"unknown group: {group_id}") + self._cancel_groups([group]) + return group.summary() + + def drain_run(self, run_id): + with self.lock: + state = self._run(run_id) + state.drained = True + runtime = state.runtime + if runtime is not None: + registry = getattr(runtime, "registry", None) + if registry is not None: + timeout = float(getattr(state.config, "drain_timeout", 30)) + try: + registry.drain(timeout) + except TimeoutError as exc: + raise ServiceError(409, "drain_timeout", + f"generation still in flight: {exc}") from exc + return {"run_id": run_id, "state": "drained"} + + def cancel_run(self, run_id): + with self.lock: + state = self._run(run_id) + groups = list(state.groups.values()) + self._cancel_groups(groups) + return {"run_id": run_id, "state": "cancelled", + "groups": {group.request["group_id"]: group.status for group in groups}} + + def _cancel_groups(self, groups): + for group in groups: + if group.status in ("queued", "running") and group.future is not None: + group.future.cancel() + deadline = time.monotonic() + 30 + while any(group.status in ("queued", "running") for group in groups) \ + and time.monotonic() < deadline: + time.sleep(self.poll_interval) + + +def make_control_server(service, host="127.0.0.1", port=0, *, max_body_bytes=8 * 1024 * 1024): + """Bounded stdlib HTTP transport for the control plane.""" + + class Handler(BaseHTTPRequestHandler): + def setup(self): + super().setup() + self.connection.settimeout(120) + + def log_message(self, *args): + pass + + def reply(self, status, payload): + body = json.dumps(payload).encode() + self.send_response(status) + self.send_header("Content-Type", "application/json") + self.send_header("Content-Length", str(len(body))) + self.end_headers() + self.wfile.write(body) + + def token(self): + token = self.headers.get("x-api-key", "") + auth = self.headers.get("Authorization", "") + if auth: + if not auth.startswith("Bearer ") or token: + raise ServiceError(400, "invalid_request", "ambiguous authentication") + token = auth[7:] + return token + + def read_json(self): + if self.headers.get("Transfer-Encoding"): + raise ServiceError(400, "invalid_request", "chunked bodies are not supported") + size = int(self.headers.get("Content-Length", "0")) + if size <= 0 or size > max_body_bytes: + raise ServiceError(400, "invalid_request", "invalid request body size") + return json.loads(self.rfile.read(size)) + + def dispatch(self, method): + parts = [part for part in urlsplit(self.path).path.split("/") if part] + try: + payload = self.route(method, parts) + except ServiceError as exc: + self.reply(exc.status, {"error": {"type": exc.code, "message": str(exc)}}) + except (ValueError, TypeError, KeyError, OSError, json.JSONDecodeError) as exc: + self.reply(400, {"error": {"type": "invalid_request", + "message": f"{type(exc).__name__}: {exc}"}}) + else: + self.reply(200, payload) + + def route(self, method, parts): + if method == "GET" and parts == ["healthz"]: + return {"ok": True, "service": SERVICE_SCHEMA} + if method == "GET" and parts == ["readyz"]: + with service.lock: + attached = sum(1 for state in service.runs.values() + if state.runtime is not None) + return {"ok": True, "runs": len(service.runs), "attached_runs": attached} + if len(parts) < 2 or parts[0] != "runs": + raise ServiceError(404, "not_found", "unsupported endpoint") + run_id = parts[1] + if len(parts) == 3 and method == "POST" and parts[2] == "register": + body = self.read_json() + if not isinstance(body, dict) or set(body) != {"manifest"}: + raise ServiceError(400, "invalid_request", "body must be {manifest}") + return service.register_run(run_id, body["manifest"], token=self.token()) + service.authenticate(run_id, self.token()) + if len(parts) == 3 and method == "POST" and parts[2] == "groups": + return service.submit_group(run_id, self.read_json()) + if (len(parts) == 4 and parts[2] == "groups" + and method == "GET"): + return service.group_status(run_id, parts[3]) + if (len(parts) == 5 and parts[2] == "groups" and parts[4] == "cancel" + and method == "POST"): + return service.cancel_group(run_id, parts[3]) + if len(parts) == 4 and method == "POST" and parts[2] == "policy" \ + and parts[3] == "activate": + body = self.read_json() + if not isinstance(body, dict) or set(body) != {"states", "expected"}: + raise ServiceError(400, "invalid_request", + "body must be {states, expected}") + return service.activate_policy(run_id, body["states"], body["expected"]) + if len(parts) == 4 and method == "POST" and parts[2] == "serving" \ + and parts[3] == "announce": + body = self.read_json() + if not isinstance(body, dict) or set(body) != {"router_url"}: + raise ServiceError(400, "invalid_request", "body must be {router_url}") + return service.announce_serving(run_id, body["router_url"]) + if len(parts) == 3 and method == "POST" and parts[2] == "drain": + return service.drain_run(run_id) + if len(parts) == 3 and method == "POST" and parts[2] == "cancel": + return service.cancel_run(run_id) + raise ServiceError(404, "not_found", "unsupported endpoint") + + def do_GET(self): + self.dispatch("GET") + + def do_POST(self): + self.dispatch("POST") + + server = ThreadingHTTPServer((host, port), Handler) + server.daemon_threads = True + return server diff --git a/harborrl/tasks/catalog.py b/harborrl/tasks/catalog.py new file mode 100644 index 000000000..d887e7ec6 --- /dev/null +++ b/harborrl/tasks/catalog.py @@ -0,0 +1,8 @@ +"""Compatibility entry point for the selected Harbor task provider.""" +from __future__ import annotations + +from harborrl.math_registry import select_math + + +def catalog(config): + return select_math(config).task.adapter.catalog(config) diff --git a/harborrl/tasks/cli.py b/harborrl/tasks/cli.py index 8cbb3c5d3..f9d337cd3 100644 --- a/harborrl/tasks/cli.py +++ b/harborrl/tasks/cli.py @@ -7,7 +7,7 @@ from pathlib import Path from uuid import uuid4 -from harborrl.data.harbor.native_inspector import inspect_native +from harborrl.math_registry import NATIVE_TASK_PROVIDER def main(argv=None): @@ -23,12 +23,12 @@ def main(argv=None): if args.command == "inspect": if not args.tasks_dir.is_dir(): parser.error("tasks-dir must exist") - tasks = sorted(path.parent for path in args.tasks_dir.rglob("task.toml")) + tasks = NATIVE_TASK_PROVIDER.adapter.discover(args.tasks_dir) if not tasks: parser.error("no task.toml found") rows = [] for task in tasks: - result = inspect_native(task) + result = NATIVE_TASK_PROVIDER.adapter.inspect(task) rows.append({ **result, "dataset": args.dataset, diff --git a/harborrl/tasks/content.py b/harborrl/tasks/content.py new file mode 100644 index 000000000..e108f167e --- /dev/null +++ b/harborrl/tasks/content.py @@ -0,0 +1,26 @@ +"""Native task content hashing and TOML reader shared with compatibility tools.""" +import hashlib +from pathlib import Path + +try: + import tomllib +except ModuleNotFoundError: # Python 3.10 + import tomli as tomllib + + +__all__ = ["tomllib", "tree_digest"] + + +def tree_digest(root: Path, *, exclude: tuple[str, ...] = ()) -> str: + h = hashlib.sha256() + for path in sorted(Path(root).rglob("*")): + if str(path.relative_to(root)) in exclude: + continue + if path.is_symlink(): + raise ValueError("symlinks are not supported") + if path.is_file(): + data = path.read_bytes() + h.update(str(path.relative_to(root)).encode() + b"\0") + h.update(str(path.stat().st_mode & 0o777).encode() + b"\0") + h.update(str(len(data)).encode() + b"\0" + data) + return h.hexdigest() diff --git a/harborrl/tasks/jsonl_provider.py b/harborrl/tasks/jsonl_provider.py new file mode 100644 index 000000000..4fdeae0fd --- /dev/null +++ b/harborrl/tasks/jsonl_provider.py @@ -0,0 +1,92 @@ +"""JSONL Harbor task catalog provider with the same native task lock contract.""" +from __future__ import annotations + +import json +import re +from pathlib import Path + +from harborrl.data.harbor.native_inspector import inspect_native +from harborrl.trajectories.native import RewardProfile + + +class JsonlTaskProvider: + provider_id = "harbor_jsonl" + + def discover(self, tasks_dir): + return sorted(path.parent for path in Path(tasks_dir).rglob("task.toml")) + + def inspect(self, path, *, expected_digest=None): + return inspect_native(path, expected_digest=expected_digest) + + def catalog(self, config): + path = Path(config["tasks"]["catalog"]) + entries, seen = [], set() + with path.open(encoding="utf-8") as source: + for line_number, line in enumerate(source, start=1): + if not line.strip(): + raise ValueError(f"blank JSONL catalog line: {line_number}") + try: + entry = json.loads(line) + except json.JSONDecodeError as exc: + raise ValueError( + f"invalid JSONL catalog line {line_number}: {exc.msg}" + ) from exc + if ( + not isinstance(entry, dict) + or set(entry) + != {"id", "revision", "path", "task_digest", "reward_profile"} + or not isinstance(entry["id"], str) + or not entry["id"].strip() + or not isinstance(entry["revision"], str) + or not entry["revision"].strip() + or entry["id"] in seen + ): + raise ValueError( + f"invalid or duplicate JSONL catalog identity: {line_number}" + ) + if ( + not isinstance(entry["path"], str) + or not entry["path"].strip() + or not isinstance(entry["task_digest"], str) + or not re.fullmatch(r"[0-9a-f]{64}", entry["task_digest"]) + or not isinstance(entry["reward_profile"], dict) + ): + raise ValueError(f'invalid native task lock: {entry["id"]}') + seen.add(entry["id"]) + entry["path"] = str((path.parent / entry["path"]).resolve()) + report = self.inspect( + entry["path"], expected_digest=entry["task_digest"] + ) + if report["status"] != "NEEDS_PROBE": + raise ValueError( + f'task preflight failed: {entry["id"]}: {report["reasons"]}' + ) + RewardProfile(**entry["reward_profile"]) + entries.append(entry) + if not entries: + raise ValueError("catalog must contain at least one JSONL task lock") + selected = set(getattr(config, "task_ids", ())) + if selected: + missing = selected - {entry["id"] for entry in entries} + if missing: + raise ValueError( + f"task selector not found in catalog: {min(missing)}" + ) + entries = [entry for entry in entries if entry["id"] in selected] + return entries + + def prompt_rows(self, entries): + rows = [] + for entry in entries: + instruction = (Path(entry["path"]) / "instruction.md").read_text() + rows.append( + { + "task": instruction, + "metadata": { + "harborrl_native": True, + "task_provider": self.provider_id, + "native_task": entry, + }, + } + ) + return rows diff --git a/harborrl/tasks/provider.py b/harborrl/tasks/provider.py new file mode 100644 index 000000000..239fd58ef --- /dev/null +++ b/harborrl/tasks/provider.py @@ -0,0 +1,72 @@ +"""Native Harbor task discovery, locking, and prompt materialization.""" +from __future__ import annotations + +import re +from pathlib import Path + +from harborrl.data.harbor.native_inspector import inspect_native +from harborrl.trajectories.native import RewardProfile, read_json + + +class NativeTaskProvider: + provider_id = "native_harbor" + + def discover(self, tasks_dir): + return sorted(path.parent for path in Path(tasks_dir).rglob("task.toml")) + + def inspect(self, path, *, expected_digest=None): + return inspect_native(path, expected_digest=expected_digest) + + def catalog(self, config): + path = Path(config["tasks"]["catalog"]) + entries = read_json(path) + if not isinstance(entries, list) or not entries: + raise ValueError("catalog must be a nonempty JSON list") + seen = set() + for entry in entries: + if ( + not isinstance(entry, dict) + or set(entry) + != {"id", "revision", "path", "task_digest", "reward_profile"} + or not isinstance(entry["id"], str) + or not entry["id"].strip() + or not isinstance(entry["revision"], str) + or not entry["revision"].strip() + or entry["id"] in seen + ): + raise ValueError("invalid or duplicate catalog identity") + if ( + not isinstance(entry["path"], str) + or not entry["path"].strip() + or not isinstance(entry["task_digest"], str) + or not re.fullmatch(r"[0-9a-f]{64}", entry["task_digest"]) + or not isinstance(entry["reward_profile"], dict) + ): + raise ValueError(f'invalid native task lock: {entry["id"]}') + seen.add(entry["id"]) + entry["path"] = str((path.parent / entry["path"]).resolve()) + report = self.inspect(entry["path"], expected_digest=entry["task_digest"]) + if report["status"] != "NEEDS_PROBE": + raise ValueError( + f'task preflight failed: {entry["id"]}: {report["reasons"]}' + ) + RewardProfile(**entry["reward_profile"]) + selected = set(getattr(config, "task_ids", ())) + if selected: + missing = selected - {entry["id"] for entry in entries} + if missing: + raise ValueError(f"task selector not found in catalog: {min(missing)}") + entries = [entry for entry in entries if entry["id"] in selected] + return entries + + def prompt_rows(self, entries): + rows = [] + for entry in entries: + instruction = (Path(entry["path"]) / "instruction.md").read_text() + rows.append( + { + "task": instruction, + "metadata": {"harborrl_native": True, "native_task": entry}, + } + ) + return rows diff --git a/harborrl/webui/__init__.py b/harborrl/webui/__init__.py new file mode 100644 index 000000000..765ec5b8e --- /dev/null +++ b/harborrl/webui/__init__.py @@ -0,0 +1 @@ +"""Local configuration and training console; runtime imports stay lazy.""" diff --git a/harborrl/webui/cli.py b/harborrl/webui/cli.py new file mode 100644 index 000000000..1193ce5d8 --- /dev/null +++ b/harborrl/webui/cli.py @@ -0,0 +1,67 @@ +"""Start a single-user loopback console, optionally opening the browser.""" +import argparse +import contextlib +import os +import signal +import webbrowser +from pathlib import Path + + +@contextlib.contextmanager +def workspace_lock(workspace): + workspace.mkdir(parents=True, exist_ok=True) + with (workspace / ".console.lock").open("a+b") as stream: + try: + if os.name == "posix": + import fcntl + fcntl.flock(stream.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB) + else: + import msvcrt + stream.seek(0) + if stream.read(1) == b"": + stream.write(b"0") + stream.flush() + stream.seek(0) + msvcrt.locking(stream.fileno(), msvcrt.LK_NBLCK, 1) + except OSError as exc: + raise ValueError("Another console owns this workspace") from exc + yield + + +def main(argv): + parser = argparse.ArgumentParser(prog="harborrl web", description=__doc__) + parser.add_argument("--workspace", default="./harborrl-web", help="Configurations and job logs directory") + parser.add_argument("--port", type=int, default=0, help="Loopback port; 0 selects a free port") + parser.add_argument("--no-browser", action="store_true", help="Print the URL without opening a browser") + args = parser.parse_args(argv) + if not 0 <= args.port <= 65535: + parser.error("port must be in 0..65535") + from .server import Console, make_server + + workspace = Path(args.workspace).expanduser().resolve() + try: + with workspace_lock(workspace): + console = Console(workspace) + server = make_server(console, args.port) + url = f"http://127.0.0.1:{server.server_port}/#token={console.token}" + print(f"HarborRL console: {url}\nWorkspace: {workspace}\nKeep this terminal open. Ctrl+C stops the console and requests active job cleanup.", flush=True) + if not args.no_browser: + try: + webbrowser.open(url) + except webbrowser.Error: + print("Browser unavailable; open the printed URL.", flush=True) + previous = signal.getsignal(signal.SIGTERM) + def terminate(_signum, _frame): + raise KeyboardInterrupt + signal.signal(signal.SIGTERM, terminate) + try: + server.serve_forever() + except KeyboardInterrupt: + pass + finally: + server.server_close() + console.jobs.close() + signal.signal(signal.SIGTERM, previous) + except (OSError, ValueError) as exc: + parser.error(str(exc)) + return 0 diff --git a/harborrl/webui/jobs.py b/harborrl/webui/jobs.py new file mode 100644 index 000000000..28a36df2b --- /dev/null +++ b/harborrl/webui/jobs.py @@ -0,0 +1,173 @@ +"""One foreground CLI job at a time, with immutable inputs and durable logs.""" +import json +import os +import signal +import subprocess +import threading +from datetime import datetime, timezone +from pathlib import Path +from uuid import uuid4 + +import yaml + +from harborrl.options.wizard import build_config_bundle, publish_bundle + + +def now(): + return datetime.now(timezone.utc).isoformat() + + +class Jobs: + def __init__(self, root): + self.root = Path(root) + self.root.mkdir(parents=True, exist_ok=True) + self.lock = threading.RLock() + self.child = None + self.active = None + self.closed = False + self.records = {} + for path in self.root.glob("*/job.json"): + record = json.loads(path.read_text(encoding="utf-8")) + if record["state"] in {"running", "stopping"}: + record.update(state="unknown", message="Console restarted; verify target cleanup before launching again.") + self.records[record["id"]] = record + + def _save(self, record): + path = self.root / record["id"] / "job.json" + temporary = path.with_suffix(".tmp") + temporary.write_text(json.dumps(record, indent=2) + "\n", encoding="utf-8") + temporary.replace(path) + + def _refresh(self): + if self.child is not None and self.child.poll() is not None: + record = self.records[self.active] + code = self.child.returncode + record.update(state="stopped" if record["state"] == "stopping" else "succeeded" if code == 0 else "failed", + exit_code=code, finished_at=now()) + self._save(record) + self.child = self.active = None + + def _watch(self, child): + child.wait() + with self.lock: + if self.child is child: + self._refresh() + + def list(self): + with self.lock: + self._refresh() + return [dict(record) for record in sorted(self.records.values(), key=lambda r: r["created_at"], reverse=True)] + + def start(self, configuration, bundle, mode): + if mode not in {"dry-run", "doctor", "train"}: + raise ValueError("Unknown run mode") + with self.lock: + self._refresh() + if self.closed or self.child is not None: + raise ValueError("A job is already active or the console is closing") + if any(record["state"] == "unknown" for record in self.records.values()): + raise ValueError("An interrupted job has unknown cleanup status. Reconcile it before launching again.") + identifier = uuid4().hex + root = self.root / identifier + root.mkdir(mode=0o700) + training = yaml.safe_load(bundle["files"]["config.yaml"]) + deployment = yaml.safe_load(bundle["files"]["deployment.yaml"]) + manifest = json.loads(bundle["files"]["setup-manifest.json"]) + output = str(Path(training["output"]["root"]) / "web" / identifier) + training["output"]["root"] = deployment["storage"]["run_root"] = output + snapshot = build_config_bundle({"answers_version": 1, "preset": manifest["preset"], + "training": training, "deployment": deployment}, output_directory=root / "inputs") + publish_bundle(snapshot, root / "inputs") + manager = deployment["manager"] + command = [manager["controller_python"], "-u", "-m", "harborrl.cli", "doctor" if mode == "doctor" else "train", + str(root / "inputs/config.yaml"), "--deployment", str(root / "inputs/deployment.yaml")] + if mode == "dry-run": + command.append("--dry-run") + record = {"id": identifier, "configuration": configuration, "mode": mode, "state": "running", + "created_at": now(), "finished_at": None, "exit_code": None, "command": command, + "log_path": str(root / "console.log"), "snapshot": str(root / "inputs"), "output_root": output} + self._save(record) + env = {**os.environ, "PYTHONUNBUFFERED": "1"} + env["PYTHONPATH"] = os.pathsep.join(filter(None, [manager["source_dir"], env.get("PYTHONPATH")])) + try: + with (root / "console.log").open("wb") as log: + self.child = subprocess.Popen(command, cwd=manager["source_dir"], env=env, stdin=subprocess.DEVNULL, + stdout=log, stderr=subprocess.STDOUT, start_new_session=True) + except OSError as exc: + record.update(state="failed", message=str(exc), finished_at=now()) + else: + self.active = identifier + record["pid"] = self.child.pid + self.records[identifier] = record + self._save(record) + if self.child is not None: + threading.Thread(target=self._watch, args=(self.child,), name="harborrl-web-job", daemon=True).start() + return dict(record) + + def stop(self, identifier): + with self.lock: + self._refresh() + if identifier != self.active or self.child is None: + raise ValueError("This job is not active") + record = self.records[identifier] + if record["state"] != "stopping": + # The existing CLI forwards signals to its local/SSH training target. + try: + self.child.send_signal(signal.SIGINT) if os.name == "posix" else self.child.terminate() + except ProcessLookupError: + self._refresh() + return dict(record) + record["state"] = "stopping" + self._save(record) + return dict(record) + + def reconcile(self, identifier): + with self.lock: + record = self.records[identifier] + if record["state"] != "unknown": + raise ValueError("Only an interrupted job can be reconciled") + record.update(state="interrupted", finished_at=now(), message="Target cleanup confirmed by operator.") + self._save(record) + return dict(record) + + def log(self, identifier): + with self.lock: + self._refresh() + record = self.records[identifier] + path = self.root / identifier / "console.log" + with path.open("rb") as stream: + size = stream.seek(0, os.SEEK_END) + stream.seek(max(0, size - 65536)) + tail = stream.read(65536).decode("utf-8", errors="replace") + result = {"job": dict(record), "text": tail, "bytes": size, "truncated": size > 65536, + "training_text": "", "training_log_path": None, "training_truncated": False} + output = Path(record["output_root"]) + candidates = sorted(output.glob("training/*/train-stdout.log")) + if candidates: + training_log = candidates[-1] + if training_log.resolve().is_relative_to(output.resolve()): + with training_log.open("rb") as stream: + length = stream.seek(0, os.SEEK_END) + stream.seek(max(0, length - 65536)) + result.update(training_text=stream.read(65536).decode("utf-8", errors="replace"), + training_log_path=str(training_log), training_truncated=length > 65536) + return result + + def close(self): + with self.lock: + self.closed = True + self._refresh() + child = self.child + if child: + self.stop(self.active) + if child: + try: + child.wait(timeout=30) + except subprocess.TimeoutExpired: + with self.lock: + record = self.records[self.active] + record.update(state="unknown", message="Graceful shutdown timed out; target cleanup must be checked.") + self._save(record) + else: + with self.lock: + self._refresh() diff --git a/harborrl/webui/server.py b/harborrl/webui/server.py new file mode 100644 index 000000000..442699175 --- /dev/null +++ b/harborrl/webui/server.py @@ -0,0 +1,189 @@ +"""Loopback-only HTTP boundary for the existing configuration services.""" +import hmac +import json +import os +import re +import secrets +import threading +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from importlib.resources import files +from pathlib import Path +from urllib.parse import urlsplit + +import yaml + +from harborrl.options.catalog import build_catalog +from harborrl.options.presets import PRESETS, defaults +from harborrl.options.validation import read_mapping, validate_mappings +from harborrl.options.wizard import build_config_bundle, publish_bundle + +from .jobs import Jobs + +ASSETS = {"/": ("index.html", "text/html; charset=utf-8"), + "/app.js": ("app.js", "text/javascript; charset=utf-8"), + "/style.css": ("style.css", "text/css; charset=utf-8"), + "/lucide.min.js": ("lucide.min.js", "text/javascript; charset=utf-8"), + "/brand.jpg": ("brand.jpg", "image/jpeg")} +NAME = re.compile(r"[a-zA-Z0-9][a-zA-Z0-9_-]{0,63}") + + +class Console: + def __init__(self, workspace): + self.workspace = Path(workspace).expanduser().resolve() + self.configurations = self.workspace / "configurations" + self.configurations.mkdir(parents=True, exist_ok=True) + self.jobs = Jobs(self.workspace / "jobs") + self.token = secrets.token_urlsafe(32) + self.lock = threading.RLock() + + def directory(self, name): + if not isinstance(name, str) or not NAME.fullmatch(name): + raise ValueError("Name must be 1-64 letters, digits, underscores or hyphens") + path = self.configurations / name + if path.is_symlink() or path.resolve().parent != self.configurations: + raise ValueError("Configuration must stay inside the workspace") + return path + + def bootstrap(self): + return {"workspace": str(self.workspace), "catalog": build_catalog(), + "live_supported": os.name == "posix", + "defaults": {item["id"]: dict(zip(("training", "deployment"), + defaults(item["id"], self.configurations / "experiment"))) for item in PRESETS}, + "configurations": self.list_configurations(), "jobs": self.jobs.list()} + + def list_configurations(self): + result = [] + for path in sorted(self.configurations.iterdir()): + if path.is_dir() and not path.is_symlink() and NAME.fullmatch(path.name) and (path / "setup-manifest.json").is_file(): + result.append({"id": path.name, "path": str(path)}) + return result + + def preview(self, body): + output = self.directory(body["name"]) + bundle = build_config_bundle(body["answers"], output_directory=output) + if body.get("check_files") is True: + bundle["validation"] = validate_mappings(yaml.safe_load(bundle["files"]["config.yaml"]), + yaml.safe_load(bundle["files"]["deployment.yaml"]), config_path=output / "config.yaml", check_files=True) + return bundle + + def save(self, body): + with self.lock: + output = self.directory(body["name"]) + bundle = build_config_bundle(body["answers"], output_directory=output) + publish_bundle(bundle, output) + return {"id": output.name, "path": str(output), "validation": bundle["validation"]} + + def configuration(self, identifier): + root = self.directory(identifier) + manifest = json.loads((root / "setup-manifest.json").read_text(encoding="utf-8")) + answers = {"answers_version": 1, "preset": manifest["preset"], + "training": read_mapping(root / "config.yaml"), "deployment": read_mapping(root / "deployment.yaml")} + return {"id": identifier, "answers": answers, + "files": {name: (root / name).read_text(encoding="utf-8") for name in + ("config.yaml", "deployment.yaml", "setup-manifest.json")}} + + def start(self, body): + mode = body["mode"] + if mode not in {"dry-run", "doctor", "train"}: + raise ValueError("Unknown run mode") + if mode != "dry-run" and (body.get("confirmed") is not True or os.name != "posix"): + raise ValueError("Live jobs require explicit confirmation and a Linux/POSIX controller") + with self.lock: + identifier = body["configuration"] + root = self.directory(identifier) + config = self.configuration(identifier) + result = validate_mappings(yaml.safe_load(config["files"]["config.yaml"]), + yaml.safe_load(config["files"]["deployment.yaml"]), config_path=root / "config.yaml", check_files=True) + if result["status"] == "invalid": + raise ValueError("; ".join(row["message"] for row in result["checks"] if row["status"] == "failed")) + return self.jobs.start(identifier, config, mode) + + +def make_server(console, port=0): + class Handler(BaseHTTPRequestHandler): + def setup(self): + super().setup() + self.connection.settimeout(15) + + def log_message(self, *_args): + pass + + def respond(self, status, payload, content_type="application/json; charset=utf-8"): + data = payload if isinstance(payload, bytes) else json.dumps(payload).encode("utf-8") + self.send_response(status) + for name, value in {"Content-Type": content_type, "Content-Length": str(len(data)), + "Cache-Control": "no-store", "X-Content-Type-Options": "nosniff", + "Referrer-Policy": "no-referrer", "X-Frame-Options": "DENY", + "Content-Security-Policy": "default-src 'self'; script-src 'self'; style-src 'self'; img-src 'self'; connect-src 'self'; frame-ancestors 'none'; base-uri 'none'"}.items(): + self.send_header(name, value) + self.end_headers() + self.wfile.write(data) + + def dispatch(self, method): + try: + host = self.headers.get("Host", "") + if urlsplit("http://" + host).hostname not in {"127.0.0.1", "localhost"}: + return self.respond(403, {"error": "Loopback Host required"}) + path = urlsplit(self.path).path + if method == "GET" and path in ASSETS: + name, mime = ASSETS[path] + return self.respond(200, files("harborrl.webui").joinpath("static", name).read_bytes(), mime) + if not hmac.compare_digest(self.headers.get("Authorization", ""), "Bearer " + console.token): + return self.respond(403, {"error": "Console session expired or not authorized. Reopen the CLI URL."}) + if method == "GET": + if path == "/api/bootstrap": + return self.respond(200, console.bootstrap()) + if path == "/api/jobs": + return self.respond(200, console.jobs.list()) + if path.startswith("/api/jobs/"): + return self.respond(200, console.jobs.log(path.removeprefix("/api/jobs/"))) + if path.startswith("/api/configurations/"): + return self.respond(200, console.configuration(path.removeprefix("/api/configurations/"))) + if method == "POST": + if self.headers.get("Origin") != "http://" + host: + return self.respond(403, {"error": "Same-origin request required"}) + if self.headers.get("Content-Type", "").split(";")[0] != "application/json": + return self.respond(415, {"error": "JSON body required"}) + size = int(self.headers.get("Content-Length", "0")) + if not 0 < size <= 1048576: + return self.respond(413, {"error": "Request must be between 1 byte and 1 MiB"}) + body = json.loads(self.rfile.read(size)) + if not isinstance(body, dict): + raise TypeError("Request must be an object") + if path == "/api/preview": + return self.respond(200, console.preview(body)) + if path == "/api/configurations": + return self.respond(201, console.save(body)) + if path == "/api/jobs": + return self.respond(201, console.start(body)) + if path == "/api/stop": + return self.respond(200, console.jobs.stop(body["id"])) + if path == "/api/reconcile": + if body.get("confirmed") is not True: + raise ValueError("Confirm target cleanup before reconciling") + return self.respond(200, console.jobs.reconcile(body["id"])) + if path == "/api/import": + answers = yaml.safe_load(body["text"]) + if (not isinstance(answers, dict) or type(answers.get("answers_version")) is not int + or answers.get("answers_version") != 1): + raise ValueError("Import an answers_version: 1 YAML/JSON mapping") + if set(answers) - {"answers_version", "preset", "training", "deployment", "sandbox_config"}: + raise ValueError("Unknown answer fields") + if any(not isinstance(answers.get(key, {}), dict) for key in ("training", "deployment")): + raise ValueError("training and deployment answers must be objects") + if answers.get("preset") not in {p["id"] for p in PRESETS}: + raise ValueError("Unknown deployment preset") + return self.respond(200, {"answers": answers}) + self.respond(404, {"error": "Not found"}) + except (ValueError, TypeError, KeyError, OSError, AttributeError, yaml.YAMLError) as exc: + self.respond(400, {"error": str(exc)}) + + def do_GET(self): + self.dispatch("GET") + + def do_POST(self): + self.dispatch("POST") + + server = ThreadingHTTPServer(("127.0.0.1", port), Handler) + server.daemon_threads = True + return server diff --git a/harborrl/webui/static/app.js b/harborrl/webui/static/app.js new file mode 100644 index 000000000..0a6a5dc0d --- /dev/null +++ b/harborrl/webui/static/app.js @@ -0,0 +1,884 @@ +/* The form edits answers; Python remains the authoritative builder and validator. */ +"use strict"; +const $ = (selector) => document.querySelector(selector); +const $$ = (selector) => [...document.querySelectorAll(selector)]; +const escapeHtml = (value) => + String(value ?? "").replace( + /[&<>"']/g, + (c) => + ({ "&": "&", "<": "<", ">": ">", '"': """, "'": "'" })[ + c + ], + ); +const icon = (name) => ``; +const state = { + bootstrap: null, + answers: null, + step: 0, + saved: null, + preview: null, + file: "config.yaml", + view: "configuration", + selectedJob: null, + logSource: "console", + jobs: [], + busy: false, +}; +const fragment = new URLSearchParams(location.hash.slice(1)); +if (fragment.has("token")) { + sessionStorage.setItem("harborrl-token", fragment.get("token")); + history.replaceState(null, "", location.pathname); +} +const token = sessionStorage.getItem("harborrl-token") || ""; +function icons() { + lucide.createIcons({ attrs: { "aria-hidden": "true" } }); +} +function notify(text, success = false) { + $("#notice").textContent = text; + $("#notice").className = `notice${success ? " success" : ""}`; + $("#notice").hidden = false; +} +async function api(path, body) { + const response = await fetch(path, { + method: body === undefined ? "GET" : "POST", + headers: { + Authorization: `Bearer ${token}`, + "Content-Type": "application/json", + }, + ...(body === undefined ? {} : { body: JSON.stringify(body) }), + }); + const result = await response.json(); + if (!response.ok) throw new Error(result.error || "Request failed"); + return result; +} +async function action(callback) { + if (state.busy) return; + state.busy = true; + $("#notice").hidden = true; + try { + await callback(); + } catch (error) { + notify(error.message); + } finally { + state.busy = false; + updateActions(); + icons(); + } +} +function valueAt(object, path) { + return path.split(".").reduce((value, key) => value?.[key], object); +} +function setAt(object, path, value) { + const parts = path.split("."); + let parent = object; + parts.slice(0, -1).forEach((key) => { + if (!parent[key] || typeof parent[key] !== "object") parent[key] = {}; + parent = parent[key]; + }); + parent[parts.at(-1)] = value; +} +function defaultRoot() { + const separator = state.bootstrap.workspace.includes("\\") ? "\\" : "/"; + return [ + state.bootstrap.workspace, + "configurations", + $("#config-name").value, + "runs", + ].join(separator); +} +function read(path) { + const supplied = valueAt(state.answers, path); + if (supplied !== undefined) return supplied; + if (path === "sandbox_config") + return valueAt(state.answers, "training.harbor.environment.config"); + if (path === "deployment.storage.run_root") + return valueAt(state.answers, "training.output.root") ?? defaultRoot(); + if ( + [ + "training.deployment.actor_gpus", + "training.deployment.rollout_gpus", + ].includes(path) && + read("training.deployment.layout") === "colocate" + ) + return read("training.deployment.num_gpus"); + return valueAt(state.bootstrap.defaults[state.answers.preset], path); +} +function changed() { + state.saved = null; + state.preview = null; + $("#draft-state").textContent = "Draft"; + $("#draft-state").className = "badge"; + updateActions(); + renderSummary(); +} +function deriveWorkers() { + if (state.answers.preset === "service-ssh") { + delete state.answers.training.harbor?.workers; + delete state.answers.training.harbor?.python; + } +} +function newAnswers(preset = "embedded-local") { + return { answers_version: 1, preset, training: {}, deployment: {} }; +} +function field(path, label, type = "text", options = {}) { + const value = read(path), + id = path.replaceAll(".", "-"); + let control; + if (options.choices) + control = ``; + else if (type === "checkbox") + control = ``; + else + control = ``; + return `

${type === "checkbox" ? control : ""}${type === "checkbox" ? "" : control}${options.hint ? `${escapeHtml(options.hint)}` : ""}
`; +} +const section = (title, content) => + `

${title}

${content}
`; +function renderForm() { + const preset = state.answers.preset, + service = preset !== "embedded-local", + sandbox = preset === "service-sandbox-local-runner"; + let html = ""; + if (state.step === 0) { + const presets = [ + ["embedded-local", "Local", "One machine", "monitor"], + ["service-ssh", "SSH workers", "Distributed Docker", "network"], + ["service-sandbox-local-runner", "Sandbox", "Controller + GPU", "box"], + ]; + html = `

Deployment topology

${service ? "Service" : "Embedded"}
${presets.map(([id, label, detail, ico]) => ``).join("")}
`; + html += section( + "Controller", + field("deployment.manager.source_dir", "Source directory", "text", { + full: true, + hint: "Path on the controller", + required: true, + }) + + field( + "deployment.manager.controller_python", + "Controller Python", + "text", + { full: true, required: true }, + ) + + (service + ? field("deployment.manager.control_url", "Control origin", "url", { + placeholder: "http://controller.example:49200", + required: true, + }) + + field("deployment.manager.gateway_url", "Gateway origin", "url", { + placeholder: "http://controller.example:49184", + required: true, + }) + : ""), + ); + html += section( + "GPU target", + (service + ? field("deployment.gpu_target.ssh.host", "SSH destination", "text", { + placeholder: "gpu-training", + full: true, + required: true, + }) + : "") + + field("deployment.gpu_target.source_dir", "Source directory", "text", { + full: true, + required: true, + hint: "Path on the GPU machine", + }) + + field("deployment.gpu_target.train_python", "Training Python", "text", { + full: true, + required: true, + }), + ); + if (preset === "service-ssh") + html += `

Rollout workers

`; + else + html += section( + "Rollout runner", + field("training.harbor.python", "Harbor runner Python", "text", { + full: true, + required: true, + }) + + (sandbox + ? field("sandbox_config", "Sandbox configuration", "text", { + full: true, + placeholder: "/srv/site/sandbox.yaml", + required: true, + }) + : ""), + ); + html += section( + "Storage", + field( + "deployment.storage.run_root", + service ? "Shared run root" : "Run root", + "text", + { + full: true, + required: true, + hint: service + ? "Same absolute path on the controller, GPU and workers" + : "Path on this machine", + }, + ), + ); + } else if (state.step === 1) { + html += section( + "Model", + field("training.model.args_file", "Model args preset", "text", { + required: true, + }) + + field("training.harness.name", "Harness", "text", { + choices: state.bootstrap.catalog.harnesses.map((x) => x.id), + }) + + field("training.model.checkpoint", "Checkpoint directory", "text", { + full: true, + required: true, + hint: "Path on the GPU target", + }) + + field("training.model.reference", "Reference checkpoint", "text", { + full: true, + required: true, + }), + ); + html += section( + "Tasks & agent", + field("training.tasks.catalog", "Task catalog", "text", { + full: true, + required: true, + placeholder: "/srv/inputs/tasks.jsonl", + hint: "An existing locked JSON / JSONL catalog on the controller", + }) + + field("training.harness.profile", "Claude Code profile", "text", { + full: true, + required: true, + placeholder: "/srv/inputs/claude-code.json", + }), + ); + html += section( + "Gateway fingerprints", + field("training.gateway.tokenizer_digest", "Tokenizer SHA-256", "text", { + full: true, + required: true, + }) + + field("training.gateway.template_digest", "Template SHA-256", "text", { + full: true, + required: true, + }) + + (!service + ? field("training.gateway.host", "Bind address") + + field("training.gateway.advertised_url", "Advertised origin", "url") + : ""), + ); + } else if (state.step === 2) { + html += section( + "GPU allocation", + field("training.deployment.layout", "Layout", "text", { + choices: ["colocate", "split"], + }) + + field("training.deployment.num_gpus", "Total GPUs", "number") + + field("training.deployment.actor_gpus", "Actor GPUs", "number") + + field("training.deployment.rollout_gpus", "Serving GPUs", "number") + + field( + "training.deployment.actor_tensor_parallel_size", + "Actor tensor parallelism", + "number", + ) + + field( + "training.deployment.rollout_gpus_per_engine", + "Serving tensor parallelism", + "number", + ), + ); + html += section( + "Optimization", + field("training.training.num_rollout", "Rollout batches", "number") + + field("training.training.learning_rate", "Learning rate", "text") + + field("training.training.save_interval", "Save interval", "number") + + field( + "training.training.backend_contract", + "Algorithm contract", + "text", + { + choices: [ + ...new Set( + state.bootstrap.catalog.algorithms.flatMap( + (x) => x.training_backend_contracts, + ), + ), + ], + }, + ), + ); + html += section( + "Sampling", + field("training.sampling.group_size", "Samples per group", "number") + + field( + "training.sampling.groups_per_batch", + "Groups per batch", + "number", + ) + + field( + "training.sampling.max_tokens", + "Maximum output tokens", + "number", + ) + + field("training.sampling.max_context", "Context limit", "number") + + field("training.sampling.max_attempts", "Maximum attempts", "number") + + field( + "training.gateway.audited_raw_logprobs", + "Audited raw log probabilities", + "checkbox", + ), + ); + } + $("#form-sections").innerHTML = html; + $("#form-sections").hidden = state.step === 3; + $("#review").hidden = state.step !== 3; + if (state.step === 0 && preset === "service-ssh") renderWorkers(); + $$("[data-path]").forEach((input) => + input.addEventListener("change", () => { + const path = input.dataset.path; + let value = + input.type === "checkbox" + ? input.checked + : input.type === "number" || path.endsWith("learning_rate") + ? input.value === "" + ? null + : Number(input.value) + : input.value; + setAt(state.answers, path, value); + // Loaded bundles contain both sides of repeated schema fields. + const mirrors = { + "deployment.manager.control_url": null, + "deployment.manager.gateway_url": "training.gateway.advertised_url", + "deployment.storage.run_root": "training.output.root", + "training.gateway.host": "deployment.manager.bind", + "training.gateway.advertised_url": "deployment.manager.gateway_url", + }; + if (mirrors[path] && valueAt(state.answers, mirrors[path]) !== undefined) + setAt(state.answers, mirrors[path], value); + if ( + path === "sandbox_config" && + valueAt(state.answers, "training.harbor.environment.config") !== + undefined + ) + setAt(state.answers, "training.harbor.environment.config", value); + if ( + path.startsWith("deployment.rollout.workers.") && + state.answers.preset === "service-ssh" + ) { + deriveWorkers(); + } + if (path === "training.deployment.num_gpus") { + if ( + valueAt(state.answers, "deployment.gpu_target.expected_gpus") !== + undefined + ) + setAt(state.answers, "deployment.gpu_target.expected_gpus", value); + if (read("training.deployment.layout") === "colocate") + ["actor_gpus", "rollout_gpus"].forEach((key) => + setAt(state.answers, `training.deployment.${key}`, value), + ); + } + if (path === "training.deployment.layout" && value === "colocate") { + ["actor_gpus", "rollout_gpus"].forEach((key) => + setAt( + state.answers, + `training.deployment.${key}`, + read("training.deployment.num_gpus"), + ), + ); + } + changed(); + if (path.startsWith("training.deployment.")) renderForm(); + }), + ); + $$("[data-preset]").forEach((button) => + button.addEventListener("click", () => + action(async () => { + if (button.dataset.preset === state.answers.preset) return; + if ( + !(await confirmAction( + "Change deployment?", + "This starts a new draft for the selected topology. Saved configurations remain available.", + "Change topology", + )) + ) + return; + state.answers = newAnswers(button.dataset.preset); + changed(); + renderForm(); + }), + ), + ); + $("#add-worker")?.addEventListener("click", () => { + const workers = state.answers.deployment.rollout?.workers || []; + workers.push({ + id: `worker-${Date.now().toString(36)}`, + ssh: { host: "" }, + source_dir: "", + runner_python: "", + docker: true, + task_roots: [""], + }); + setAt(state.answers, "deployment.rollout.workers", workers); + deriveWorkers(); + changed(); + renderForm(); + }); + renderSummary(); + updateActions(); + icons(); +} +function renderWorkers() { + const workers = state.answers.deployment.rollout?.workers || []; + $("#worker-fields").innerHTML = workers.length + ? workers + .map( + (worker, index) => + `

Worker ${index + 1}

${field(`deployment.rollout.workers.${index}.id`, "ID")}${field(`deployment.rollout.workers.${index}.ssh.host`, "SSH destination")}${field(`deployment.rollout.workers.${index}.source_dir`, "Source directory", "text", { full: true })}${field(`deployment.rollout.workers.${index}.runner_python`, "Runner Python", "text", { full: true })}${field(`deployment.rollout.workers.${index}.task_roots.0`, "Task root", "text", { full: true })}
`, + ) + .join("") + : '

No workers configured.

'; + $$("[data-remove-worker]").forEach((button) => + button.addEventListener("click", () => { + workers.splice(Number(button.dataset.removeWorker), 1); + deriveWorkers(); + changed(); + renderForm(); + }), + ); +} +function renderSummary() { + const service = state.answers.preset !== "embedded-local", + sandbox = state.answers.preset === "service-sandbox-local-runner"; + const nodes = [ + ["monitor", "Controller", service ? "Service mode" : "Embedded mode"], + [ + "cpu", + "GPU target", + service + ? read("deployment.gpu_target.ssh.host") || "Not configured" + : "Local machine", + ], + [ + sandbox ? "box" : "network", + "Rollout", + sandbox + ? "Sandbox environment" + : service + ? `${state.answers.deployment.rollout?.workers?.length || 0} SSH workers` + : "Local Docker", + ], + ]; + $("#topology").innerHTML = nodes + .map( + ([ico, title, detail], index) => + `${index ? '
' : ""}
${icon(ico)}
${title}${escapeHtml(detail)}
`, + ) + .join(""); + const rows = [ + ["Model", read("training.model.args_file")], + ["Algorithm", "GRPO"], + ["GPUs", read("training.deployment.num_gpus")], + ["Layout", read("training.deployment.layout")], + [ + "Samples / batch", + Number(read("training.sampling.group_size")) * + Number(read("training.sampling.groups_per_batch")), + ], + ["Rollout batches", read("training.training.num_rollout")], + ]; + $("#resource-summary").innerHTML = + `
${rows.map(([key, value]) => `
${key}
${escapeHtml(value)}
`).join("")}
`; + $("#summary-status").textContent = state.saved + ? "Saved configuration" + : "Unsaved configuration"; + icons(); +} +function updateActions() { + $("#previous").disabled = state.step === 0 || state.busy; + $("#next").hidden = state.step === 3; + $("#save").hidden = state.step !== 3; + $("#next").disabled = state.busy; + $("#save").disabled = + state.busy || + !state.preview || + state.preview.validation.status === "invalid" || + !!state.saved; + $("#step-description").textContent = `Step ${state.step + 1} of 4`; + $$("[data-step]").forEach((button) => { + button.classList.toggle( + "active", + Number(button.dataset.step) === state.step, + ); + button.setAttribute( + "aria-current", + Number(button.dataset.step) === state.step ? "step" : "false", + ); + }); + const active = state.jobs.some((job) => + ["running", "stopping", "unknown"].includes(job.state), + ); + $("#dry-run").disabled = !state.saved || state.busy || active; + $("#doctor").disabled = + !state.saved || state.busy || active || !state.bootstrap?.live_supported; + $("#train").disabled = + !state.saved || state.busy || active || !state.bootstrap?.live_supported; +} +async function preview(checkFiles = false) { + state.preview = null; + $("#validation-result").textContent = "Checking configuration..."; + $("#config-preview").textContent = ""; + try { + state.preview = await api("/api/preview", { + name: $("#config-name").value, + answers: state.answers, + check_files: checkFiles, + }); + const result = state.preview.validation; + $("#validation-result").innerHTML = + `
${result.status === "invalid" ? "Changes required" : "Structure valid · runtime checks deferred"}
${result.checks.map((row) => `
${icon(row.status === "passed" ? "circle-check" : row.status === "failed" ? "circle-x" : "clock-3")}
${escapeHtml(row.path)}

${escapeHtml(row.message)}

`).join("")}`; + renderPreview(); + } catch (error) { + $("#validation-result").textContent = error.message; + throw error; + } finally { + updateActions(); + icons(); + } +} +function renderPreview() { + $("#config-preview").textContent = state.preview?.files[state.file] || ""; + $$("[data-file]").forEach((button) => + button.classList.toggle("active", button.dataset.file === state.file), + ); +} +async function goStep(step) { + state.step = step; + renderForm(); + if (step === 3) await preview(); +} +function showView(view) { + state.view = view; + $("#configuration-view").hidden = view !== "configuration"; + $("#runs-view").hidden = view !== "runs"; + $$("[data-view]").forEach((button) => + button.classList.toggle("active", button.dataset.view === view), + ); + if (view === "runs") renderJobs(); +} +function renderSaved() { + const configurations = state.bootstrap.configurations; + $("#saved-list").innerHTML = configurations.length + ? configurations + .map( + (config) => + ``, + ) + .join("") + : 'No configurations yet'; + $$("[data-config]").forEach((button) => + button.addEventListener("click", () => + action(async () => { + if ( + !state.saved && + !(await confirmAction( + "Open saved configuration?", + "The current draft will be replaced.", + "Open configuration", + )) + ) + return; + const config = await api( + `/api/configurations/${encodeURIComponent(button.dataset.config)}`, + ); + state.answers = config.answers; + state.saved = config.id; + $("#config-name").value = config.id; + $("#draft-state").textContent = "Saved"; + $("#draft-state").className = "badge saved"; + showView("configuration"); + await goStep(3); + renderSaved(); + }), + ), + ); + icons(); +} +function download(name, text, type = "text/plain") { + const url = URL.createObjectURL(new Blob([text], { type })); + const anchor = document.createElement("a"); + anchor.href = url; + anchor.download = name; + anchor.click(); + setTimeout(() => URL.revokeObjectURL(url), 1000); +} +function confirmAction(title, message, label = "Confirm") { + const dialog = $("#confirm-dialog"); + $("#confirm-title").textContent = title; + $("#confirm-message").textContent = message; + $("#confirm-action").textContent = label; + dialog.returnValue = "cancel"; + return new Promise((resolve) => { + dialog.addEventListener( + "close", + () => resolve(dialog.returnValue === "confirm"), + { once: true }, + ); + dialog.showModal(); + }); +} +async function startJob(mode) { + if ( + mode !== "dry-run" && + !(await confirmAction( + mode === "train" ? "Start training?" : "Run live environment checks?", + mode === "train" + ? `Configuration: ${state.saved}\nGPU target: ${read("deployment.gpu_target.ssh.host") || "Local machine"}\nGPU budget: ${read("training.deployment.num_gpus")}\n\nTraining runs preflight, then allocates the configured GPU and rollout resources.` + : "Doctor executes environment and network checks on the configured machines.", + mode === "train" ? "Start training" : "Run doctor", + )) + ) + return; + const job = await api("/api/jobs", { + configuration: state.saved, + mode, + confirmed: mode !== "dry-run", + }); + state.selectedJob = job.id; + showView("runs"); + await refreshJobs(); +} +function renderJobs() { + $("#run-count").textContent = state.jobs.length; + $("#jobs-list").innerHTML = state.jobs.length + ? `
${state.jobs.map((job) => ``).join("")}
CONFIGURATIONMODESTARTEDSTATUSEXIT
${escapeHtml(job.configuration)}
${job.id.slice(0, 8)}
${escapeHtml(job.mode)}${new Date(job.created_at).toLocaleString()}${escapeHtml(job.state)}${job.exit_code ?? "—"}
` + : `
${icon("activity")}

No runs yet

Your dry-runs, environment checks and training jobs appear here.

`; + $$("[data-job]").forEach((row) => { + const select = () => + action(async () => { + state.selectedJob = row.dataset.job; + renderJobs(); + await loadLog(); + }); + row.addEventListener("click", select); + row.addEventListener("keydown", (event) => { + if (event.key === "Enter") select(); + }); + }); + icons(); +} +async function loadLog() { + if (!state.selectedJob) return; + const result = await api(`/api/jobs/${state.selectedJob}`), + job = result.job; + $("#job-detail").hidden = false; + $("#log-selector").hidden = false; + $("#job-title").textContent = `${job.configuration} · ${job.mode}`; + $("#job-meta").textContent = + `${job.state} · ${job.snapshot}${job.message ? ` · ${job.message}` : ""}`; + const log = $("#job-log"), + atBottom = log.scrollTop + log.clientHeight >= log.scrollHeight - 25; + const training = state.logSource === "training"; + log.textContent = + (training ? result.training_text : result.text) || + "Waiting for process output..."; + if (atBottom) log.scrollTop = log.scrollHeight; + $("#log-note").textContent = + `${(training ? result.training_truncated : result.truncated) ? "Showing the last 64 KiB. " : ""}${training ? result.training_log_path || "No separate GPU log yet" : job.log_path} · Output: ${job.output_root}`; + $("#stop-job").hidden = !["running", "stopping"].includes(job.state); + $("#stop-job").disabled = job.state === "stopping"; + $("#reconcile-job").hidden = job.state !== "unknown"; +} +async function refreshJobs() { + state.jobs = await api("/api/jobs"); + renderJobs(); + updateActions(); + if (state.view === "runs") await loadLog(); +} +async function importAnswers(text) { + const { answers } = await api("/api/import", { text }); + state.answers = answers; + changed(); + state.step = 0; + renderForm(); + showView("configuration"); + notify("Answers imported", true); +} +async function initialize() { + icons(); + try { + state.bootstrap = await api("/api/bootstrap"); + state.jobs = state.bootstrap.jobs; + state.answers = newAnswers(); + $("#workspace-path").textContent = state.bootstrap.workspace; + $("#connection-text").textContent = "Local session"; + renderForm(); + renderSaved(); + renderJobs(); + if (!state.bootstrap.live_supported) + notify( + "Configuration and dry-run are available here. Start this console on the Linux controller for live training.", + ); + } catch (error) { + $("#connection-text").textContent = "Disconnected"; + $(".connection").classList.add("offline"); + notify(error.message); + return; + } + $$("[data-view]").forEach((button) => + button.addEventListener("click", () => showView(button.dataset.view)), + ); + $$("[data-step]").forEach((button) => + button.addEventListener("click", () => + action(() => goStep(Number(button.dataset.step))), + ), + ); + $("#previous").addEventListener("click", () => + action(() => goStep(state.step - 1)), + ); + $("#next").addEventListener("click", () => + action(() => goStep(state.step + 1)), + ); + $("#config-name").addEventListener("change", () => { + changed(); + if (state.step === 3) action(() => preview()); + else renderForm(); + }); + $("#check-files").addEventListener("click", () => + action(() => preview(true)), + ); + $$("[data-file]").forEach((button) => + button.addEventListener("click", () => { + state.file = button.dataset.file; + renderPreview(); + }), + ); + $("#download-file").addEventListener("click", () => { + if (state.preview) download(state.file, state.preview.files[state.file]); + }); + $("#save").addEventListener("click", () => + action(async () => { + const saved = await api("/api/configurations", { + name: $("#config-name").value, + answers: state.answers, + }); + state.saved = saved.id; + state.bootstrap.configurations.push(saved); + $("#draft-state").textContent = "Saved"; + $("#draft-state").className = "badge saved"; + renderSaved(); + renderSummary(); + notify(`Saved ${saved.id}`, true); + }), + ); + ["dry-run", "doctor", "train"].forEach((mode) => + $("#" + mode).addEventListener("click", () => action(() => startJob(mode))), + ); + $("#new-config").addEventListener("click", () => + action(async () => { + if ( + !(await confirmAction( + "New configuration?", + "Start a fresh draft?", + "New configuration", + )) + ) + return; + state.answers = newAnswers(); + $("#config-name").value = + `experiment-${state.bootstrap.configurations.length + 1}`; + state.step = 0; + changed(); + showView("configuration"); + renderForm(); + renderSaved(); + }), + ); + $("#answers-button").addEventListener("click", () => { + $("#answers-error").hidden = true; + $("#answers-editor").value = JSON.stringify(state.answers, null, 2); + $("#answers-dialog").showModal(); + }); + $("#close-answers").addEventListener("click", () => + $("#answers-dialog").close(), + ); + $("#apply-answers").addEventListener("click", () => + action(async () => { + try { + await importAnswers($("#answers-editor").value); + $("#answers-dialog").close(); + } catch (error) { + $("#answers-error").textContent = error.message; + $("#answers-error").hidden = false; + } + }), + ); + $("#mobile-new").addEventListener("click", () => $("#new-config").click()); + $("#export-answers").addEventListener("click", () => + download("answers.json", $("#answers-editor").value, "application/json"), + ); + $("#import-button").addEventListener("click", () => + $("#import-file").click(), + ); + $("#import-file").addEventListener("change", () => + action(async () => { + const file = $("#import-file").files[0]; + if (file) { + if (file.size > 1048576) + throw new Error("Answers file must be at most 1 MiB"); + await importAnswers(await file.text()); + } + $("#import-file").value = ""; + }), + ); + $("#refresh-jobs").addEventListener("click", () => action(refreshJobs)); + $("#stop-job").addEventListener("click", () => + action(async () => { + if ( + await confirmAction( + "Stop this job?", + "Send a graceful stop request through the existing training CLI?", + "Stop job", + ) + ) { + await api("/api/stop", { id: state.selectedJob }); + await refreshJobs(); + } + }), + ); + $("#reconcile-job").addEventListener("click", () => + action(async () => { + if ( + await confirmAction( + "Confirm target cleanup?", + "Only continue after checking that the previous GPU and Sandbox processes have stopped. This releases the interrupted-job block.", + "Cleanup confirmed", + ) + ) { + await api("/api/reconcile", { id: state.selectedJob, confirmed: true }); + await refreshJobs(); + } + }), + ); + $("#copy-log").addEventListener("click", () => + download(`${state.selectedJob}.log`, $("#job-log").textContent), + ); + $("#log-source").addEventListener("change", () => { + state.logSource = $("#log-source").value; + action(loadLog); + }); + setInterval(async () => { + if (state.busy) return; + try { + await refreshJobs(); + $("#connection-text").textContent = "Local session"; + $(".connection").classList.remove("offline"); + } catch { + $("#connection-text").textContent = "Disconnected"; + $(".connection").classList.add("offline"); + } + }, 2500); +} +initialize(); diff --git a/harborrl/webui/static/brand.jpg b/harborrl/webui/static/brand.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e7cd9b29f9659d9d58b22c291a2474403b24c029 GIT binary patch literal 186959 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+ HarborRL + Training console +
+ Connecting +
+
+
+ +
+ +
+
+
+
EXPERIMENT SETUP
+

Training configuration

+
+
+ + +
+
+
+ Draft +
+ +
+
+
+ +
+ Step 1 of 4 +
+
+ +
+
+ +
+
+ +
+
+

Confirm

+ +
+

+
+ +
+
+ + +
+

Advanced answers

+ +
+ + +
+ +
+ + + diff --git a/harborrl/webui/static/lucide-LICENSE.txt b/harborrl/webui/static/lucide-LICENSE.txt new file mode 100644 index 000000000..718bb3f0e --- /dev/null +++ b/harborrl/webui/static/lucide-LICENSE.txt @@ -0,0 +1,43 @@ +ISC License + +Copyright (c) 2026 Lucide Icons and Contributors + +Permission to use, copy, modify, and/or distribute this software for any +purpose with or without fee is hereby granted, provided that the above +copyright notice and this permission notice appear in all copies. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES +WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR +ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES +WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN +ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF +OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. + +--- + +The following Lucide icons are derived from the Feather project: + +airplay, alert-circle, alert-octagon, alert-triangle, aperture, arrow-down-circle, arrow-down-left, arrow-down-right, arrow-down, arrow-left-circle, arrow-left, arrow-right-circle, arrow-right, arrow-up-circle, arrow-up-left, arrow-up-right, arrow-up, at-sign, calendar, cast, check, chevron-down, chevron-left, chevron-right, chevron-up, chevrons-down, chevrons-left, chevrons-right, chevrons-up, circle, clipboard, clock, code, columns, command, compass, corner-down-left, corner-down-right, corner-left-down, corner-left-up, corner-right-down, corner-right-up, corner-up-left, corner-up-right, crosshair, database, divide-circle, divide-square, dollar-sign, download, external-link, feather, frown, hash, headphones, help-circle, info, italic, key, layout, life-buoy, link-2, link, loader, lock, log-in, log-out, maximize, meh, minimize, minimize-2, minus-circle, minus-square, minus, monitor, moon, more-horizontal, more-vertical, move, music, navigation-2, navigation, octagon, pause-circle, percent, plus-circle, plus-square, plus, power, radio, rss, search, server, share, shopping-bag, sidebar, smartphone, smile, square, table-2, tablet, target, terminal, trash-2, trash, triangle, tv, type, upload, x-circle, x-octagon, x-square, x, zoom-in, zoom-out + +The MIT License (MIT) (for the icons listed above) + +Copyright (c) 2013-present Cole Bemis + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/harborrl/webui/static/lucide.min.js b/harborrl/webui/static/lucide.min.js new file mode 100644 index 000000000..e3e5586e9 --- /dev/null +++ b/harborrl/webui/static/lucide.min.js @@ -0,0 +1,12 @@ +/** + * @license lucide v1.61.0 - ISC + * + * This source code is licensed under the ISC license. + * See the LICENSE file in the root directory of this source tree. + */ + +(function(a,i){typeof exports=="object"&&typeof module<"u"?i(exports):typeof define=="function"&&define.amd?define(["exports"],i):(a=typeof globalThis<"u"?globalThis:a||self,i(a.lucide={}))})(this,(function(a){"use strict";const i={xmlns:"http://www.w3.org/2000/svg",width:24,height:24,viewBox:"0 0 24 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+//# sourceMappingURL=lucide.min.js.map diff --git a/harborrl/webui/static/style.css b/harborrl/webui/static/style.css new file mode 100644 index 000000000..122b593ca --- /dev/null +++ b/harborrl/webui/static/style.css @@ -0,0 +1,1094 @@ +:root { + font-family: Inter, "Segoe UI", Arial, sans-serif; + color: #262a29; + background: #fff; + font-synthesis: none; + font-size: 14px; + letter-spacing: 0; + --green: #13745b; + --green-light: #edf7f2; + --border: #e3e7e4; + --muted: #727a75; + --red: #b83c3c; +} +* { + box-sizing: border-box; +} +body { + margin: 0; +} +button, +input, +select, +textarea { + font: inherit; + letter-spacing: 0; +} +button { + display: inline-flex; + align-items: center; + justify-content: center; + gap: 8px; + border: 1px solid transparent; + border-radius: 6px; + padding: 9px 14px; + min-height: 38px; + cursor: pointer; + background: none; + color: inherit; + font-weight: 550; + transition: + background 0.12s, + border-color 0.12s; +} +button:hover { + background: #f1f4f2; +} +button:disabled { + opacity: 0.45; + cursor: not-allowed; +} +button:focus-visible, +a:focus-visible { + outline: 2px solid #13745b; + outline-offset: 3px; +} +svg { + width: 17px; + height: 17px; + flex-shrink: 0; + stroke-width: 1.7; +} +input, +select, +textarea { + border: 1px solid #cdd5cf; + border-radius: 5px; + min-width: 0; + width: 100%; + background: #fff; + color: #282e2a; + padding: 10px 11px; + min-height: 40px; +} +input:focus, +select:focus, +textarea:focus { + outline: 2px solid #d1e9dd; + border-color: var(--green); +} +input:disabled { + background: #f5f7f5; + color: #666; +} +input[type="checkbox"] { + width: 16px; + height: 16px; + min-height: 16px; + accent-color: var(--green); +} +textarea { + resize: vertical; +} +label { + display: block; + font-size: 12px; + font-weight: 600; + margin-bottom: 7px; + color: #535d55; +} +h1, +h2, +h3, +p { + margin: 0; +} +h1 { + font-size: 25px; + font-weight: 620; + line-height: 1.25; +} +h2 { + font-size: 16px; + font-weight: 620; +} +h3 { + font-size: 13px; + font-weight: 650; +} +p { + line-height: 1.6; +} +a { + color: var(--green); +} +pre { + white-space: pre-wrap; + overflow-wrap: anywhere; +} +button.primary { + background: var(--green); + color: #fff; +} +button.primary:hover { + background: #0f614c; +} +button.secondary { + background: #fff; + border-color: #d9dfda; +} +button.secondary:hover { + background: #f6f8f6; + border-color: #b7c4bb; +} +button.danger { + color: var(--red); + background: #fff5f3; + border-color: #efd4cf; +} +.icon-button { + width: 36px; + height: 36px; + min-height: 36px; + padding: 8px; + flex-shrink: 0; +} +.muted { + color: var(--muted); + font-size: 12px; + line-height: 1.6; +} +.topbar { + height: 70px; + display: flex; + align-items: center; + border-bottom: 1px solid var(--border); + padding: 0 30px; + gap: 24px; + background: white; +} +.brand { + width: 173px; + display: flex; + flex-shrink: 0; +} +.brand img { + width: 100%; + height: auto; +} +.app-title { + font-size: 13px; + border-left: 1px solid var(--border); + padding-left: 24px; + color: #667168; +} +.connection { + display: flex; + align-items: center; + gap: 8px; + margin-left: auto; + font-size: 12px; + color: #606c63; +} +.dot, +.status-dot { + width: 7px; + height: 7px; + background: #299474; + border-radius: 50%; + flex-shrink: 0; +} +.connection.offline .dot { + background: #c4863b; +} +.shell { + display: grid; + grid-template-columns: 228px minmax(0, 1fr); + min-height: calc(100vh - 70px); +} +.sidebar { + background: #f8faf8; + border-right: 1px solid var(--border); + padding: 28px 14px 20px; + display: flex; + flex-direction: column; + min-width: 0; +} +.nav-caption { + font-size: 10px; + font-weight: 650; + color: #879088; + letter-spacing: 1.1px; + padding: 0 14px; +} +.sidebar nav { + margin-top: 12px; + display: grid; + gap: 5px; +} +.nav-item { + justify-content: flex-start; + width: 100%; + padding: 10px 13px; + color: #677068; + font-size: 13px; +} +.nav-item.active { + background: #eaf2ec; + color: #176d51; +} +.nav-item svg { + margin-right: 2px; +} +.count { + margin-left: auto; + font-size: 10px; + min-width: 20px; + border-radius: 4px; + background: #e4eae5; + padding: 2px 5px; + text-align: center; +} +.saved-heading { + margin-top: 37px; + display: flex; + align-items: center; + justify-content: space-between; +} +.saved-heading .nav-caption { + font-size: 9px; + padding-right: 0; +} +.saved-list { + display: grid; + gap: 3px; + margin-top: 10px; +} +.saved-list > .muted { + padding: 0 14px; +} +.saved-item { + display: flex; + justify-content: flex-start; + min-width: 0; + font-size: 12px; + font-weight: 450; + padding: 9px 13px; + text-align: left; +} +.saved-item span { + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} +.saved-item.selected { + color: var(--green); + background: #eef4ef; +} +.sidebar-bottom { + margin-top: auto; + padding: 40px 13px 0; + color: #89928b; + display: flex; + align-items: flex-start; + gap: 8px; + font-size: 10px; +} +.sidebar-bottom span { + overflow-wrap: anywhere; + line-height: 1.6; +} +.sidebar-bottom svg { + width: 14px; + height: 14px; +} +main { + padding: 35px 38px 40px; + min-width: 0; + max-width: 1590px; + width: 100%; + margin: 0 auto; +} +.page-heading { + display: flex; + justify-content: space-between; + align-items: center; + gap: 18px; + margin-bottom: 27px; +} +.eyebrow { + color: #89938b; + font-size: 10px; + font-weight: 650; + letter-spacing: 1.2px; + margin-bottom: 9px; +} +.toolbar { + display: flex; + gap: 8px; + align-items: center; +} +.configuration-name { + display: flex; + align-items: center; + gap: 14px; + margin-bottom: 25px; +} +.configuration-name label { + margin: 0; + white-space: nowrap; +} +.configuration-name input { + max-width: 255px; + font-size: 13px; + height: 36px; + min-height: 36px; +} +.badge { + padding: 4px 7px; + background: #f0f2ef; + color: #667166; + font-size: 10px; + border-radius: 4px; + white-space: nowrap; +} +.badge.saved, +.badge.succeeded { + color: #176d51; + background: #e6f3eb; +} +.badge.failed { + color: #b43d3d; + background: #fbeceb; +} +.badge.running { + color: #176d51; + background: #e5f4eb; +} +.badge.stopping, +.badge.unknown { + color: #986719; + background: #fbf2dd; +} +.steps { + display: grid; + grid-template-columns: repeat(4, minmax(0, 1fr)); + border-bottom: 1px solid var(--border); + gap: 6px; + margin-bottom: 31px; +} +.steps button { + font-size: 12px; + border-radius: 0; + border-bottom: 2px solid transparent; + padding: 10px 2px 17px; + justify-content: flex-start; + min-width: 0; + color: #868c86; + gap: 9px; +} +.steps button > span { + height: 22px; + width: 22px; + border-radius: 50%; + border: 1px solid #d8ded9; + display: flex; + align-items: center; + justify-content: center; + flex-shrink: 0; + font-size: 10px; +} +.steps button.active { + border-bottom-color: var(--green); + color: var(--green); +} +.steps button.active > span { + background: var(--green); + border-color: var(--green); + color: white; +} +.editor-layout { + display: grid; + grid-template-columns: minmax(0, 1fr) 242px; + gap: 38px; +} +.editor { + min-width: 0; +} +.section-heading { + display: flex; + align-items: center; + justify-content: space-between; + gap: 14px; + margin-bottom: 20px; +} +.form-section { + margin-bottom: 27px; +} +.form-section + .form-section { + border-top: 1px solid var(--border); + padding-top: 25px; +} +.form-grid { + display: grid; + grid-template-columns: repeat(2, minmax(0, 1fr)); + gap: 20px 18px; +} +.field.full { + grid-column: 1/-1; +} +.field small { + color: #8a938c; + font-size: 10px; + display: block; + margin-top: 5px; + line-height: 1.5; +} +.field.required label:after { + content: " *"; + color: #b9915a; +} +.checkbox-field { + display: flex; + align-items: center; + gap: 9px; + padding-top: 12px; +} +.checkbox-field label { + margin: 0; +} +.preset-options { + display: grid; + grid-template-columns: repeat(3, minmax(0, 1fr)); + gap: 9px; + margin-bottom: 26px; +} +.preset { + border: 1px solid var(--border); + border-radius: 6px; + min-height: 104px; + flex-direction: column; + align-items: flex-start; + justify-content: center; + text-align: left; + padding: 14px; + gap: 12px; + font-size: 12px; +} +.preset svg { + color: #839087; + width: 21px; + height: 21px; +} +.preset.active { + border-color: #72a88c; + background: #f2f8f3; + color: #116a4e; +} +.preset.active svg { + color: #13745b; +} +.preset small { + font-size: 10px; + font-weight: 400; + color: #859085; + display: block; + margin-top: 4px; +} +.summary { + border-left: 1px solid var(--border); + padding-left: 25px; + align-self: start; + position: sticky; + top: 20px; + min-width: 0; +} +.summary-heading { + display: flex; + gap: 8px; + align-items: center; + color: #607264; + margin-bottom: 20px; +} +.summary-heading h2 { + font-size: 12px; + color: #546659; +} +.topology-node { + display: grid; + grid-template-columns: 30px 1fr; + gap: 10px; + align-items: center; + padding: 10px 0; +} +.topology-node > .node-icon { + width: 30px; + height: 30px; + background: #f0f4ef; + display: flex; + align-items: center; + justify-content: center; + color: #617769; + border-radius: 5px; +} +.topology-node span { + display: block; + font-size: 10px; + color: #8a958c; + margin-top: 3px; + overflow-wrap: anywhere; +} +.topology-node strong { + font-size: 11px; + font-weight: 550; +} +.topology-connector { + height: 20px; + margin-left: 14px; + border-left: 1px dashed #c7d6ca; +} +.summary dl { + border-top: 1px solid var(--border); + margin: 21px 0 0; + padding: 19px 0 0; + display: grid; + grid-template-columns: 1fr auto; + gap: 13px; + font-size: 11px; +} +.summary dt { + color: #869087; +} +.summary dd { + margin: 0; + text-align: right; + font-weight: 550; + overflow-wrap: anywhere; + max-width: 110px; +} +.summary-footer { + border-top: 1px solid var(--border); + display: flex; + align-items: center; + gap: 7px; + margin-top: 21px; + padding-top: 17px; + font-size: 10px; + color: #929a93; +} +.summary-footer .status-dot { + background: #b2bbb0; + width: 5px; + height: 5px; +} +.editor-footer { + display: flex; + gap: 12px; + align-items: center; + padding-top: 23px; + border-top: 1px solid var(--border); + margin-top: 24px; +} +.editor-footer .muted { + margin-left: auto; + font-size: 10px; +} +.notice { + padding: 12px 15px; + margin-bottom: 22px; + background: #fbf5e8; + border: 1px solid #eadbbc; + border-radius: 5px; + color: #875e24; + font-size: 13px; + overflow-wrap: anywhere; +} +.notice.success { + background: #edf7ef; + border-color: #cfe5d4; + color: #296b42; +} +.validation-result { + margin-bottom: 24px; +} +.validation-row { + display: flex; + gap: 10px; + padding: 10px 0; + border-bottom: 1px solid #edf0ed; + font-size: 12px; + align-items: flex-start; +} +.validation-row svg { + width: 15px; + height: 15px; + margin-top: 2px; +} +.validation-row.passed svg { + color: var(--green); +} +.validation-row.failed svg { + color: var(--red); +} +.validation-row.deferred svg { + color: #b28943; +} +.validation-row strong { + display: block; + font-size: 11px; + font-weight: 600; + overflow-wrap: anywhere; + margin-bottom: 3px; +} +.validation-row p { + font-size: 11px; + color: #7c867e; +} +.check-status { + font-size: 12px; + font-weight: 600; + margin-bottom: 12px; + color: #5f6c63; +} +.file-tabs { + display: flex; + gap: 3px; + border: 1px solid var(--border); + border-bottom: 0; + background: #fafbf9; + padding: 5px 7px; + border-radius: 5px 5px 0 0; + overflow: auto; +} +.file-tabs button { + font-size: 10px; + min-height: 29px; + padding: 5px 9px; + white-space: nowrap; +} +.file-tabs button.active { + background: #eaf0e8; + color: #3e684b; +} +.code-panel { + font: + 11px/1.8 Consolas, + "SFMono-Regular", + monospace; + padding: 18px; + background: #fafbf9; + border: 1px solid var(--border); + margin: 0; + min-height: 130px; + max-height: 380px; + overflow: auto; + border-radius: 0 0 5px 5px; + color: #586758; +} +.preview-heading { + margin-bottom: 12px; +} +.launch-section { + margin-top: 26px; +} +.launch-section p { + font-size: 11px; + margin: 8px 0 15px; +} +.launch-actions { + display: flex; + gap: 9px; + flex-wrap: wrap; +} +.jobs-table { + width: 100%; + border-collapse: collapse; + font-size: 12px; + margin-top: 20px; +} +.jobs-table th { + text-align: left; + color: #8a938b; + font-size: 10px; + font-weight: 500; + padding: 14px 12px; + background: #f6f8f5; + border-bottom: 1px solid var(--border); +} +.jobs-table td { + padding: 16px 12px; + border-bottom: 1px solid var(--border); +} +.jobs-table tr[data-job] { + cursor: pointer; +} +.jobs-table tr[data-job]:hover, +.jobs-table tr.selected { + background: #f5f9f4; +} +.jobs-table .job-name { + font-weight: 550; +} +.jobs-table .job-id { + font-size: 10px; + color: #90998f; + margin-top: 3px; +} +.table-wrap { + overflow: auto; +} +.empty-state { + padding: 70px 20px; + text-align: center; + border-block: 1px solid var(--border); + color: #94a18f; +} +.empty-state svg { + width: 30px; + height: 30px; + margin-bottom: 16px; +} +.empty-state h2 { + color: #60735e; + margin-bottom: 8px; +} +.empty-state p { + font-size: 12px; +} +#job-detail { + margin-top: 35px; +} +#job-meta { + font-size: 11px; + overflow-wrap: anywhere; + margin-top: 8px; +} +.log-panel { + background: #1e2824; + color: #c3d3c8; + border-color: #1e2824; + border-radius: 5px; + min-height: 260px; + max-height: 510px; + font-size: 11px; +} +#log-note { + font-size: 10px; + margin-top: 10px; +} +dialog { + border: 1px solid #dce2da; + border-radius: 8px; + padding: 26px; + width: min(540px, calc(100% - 32px)); + box-shadow: 0 20px 90px #15291d25; + color: #354237; +} +dialog::backdrop { + background: #172a1c55; +} +dialog p { + font-size: 13px; + white-space: pre-wrap; + overflow-wrap: anywhere; + margin-top: 18px; +} +.dialog-heading { + display: flex; + justify-content: space-between; + align-items: center; + gap: 12px; +} +.dialog-actions { + display: flex; + gap: 10px; + justify-content: flex-end; + margin-top: 25px; +} +#answers-dialog { + width: min(850px, calc(100% - 32px)); +} +#answers-editor { + font: + 12px/1.6 Consolas, + monospace; + height: 50vh; + margin-top: 18px; +} +.worker-heading { + display: flex; + justify-content: space-between; + align-items: center; + margin: 20px 0 12px; +} +.worker-group { + border-bottom: 1px solid var(--border); + padding-bottom: 20px; +} +.error-field { + border-color: #bf7770; +} +.small-select { + max-width: 130px; +} +.visually-hidden { + position: absolute; + width: 1px; + height: 1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); +} +[hidden] { + display: none !important; +} +.mobile-only { + display: none; +} +.nav-caption, +.eyebrow { + letter-spacing: 0; +} +.log-selector { + display: flex; + align-items: center; + gap: 12px; + margin-top: 22px; +} +.log-selector label { + margin: 0; +} +@media (min-width: 1600px) { + main { + padding-left: 55px; + padding-right: 55px; + } + .editor-layout { + grid-template-columns: minmax(0, 1fr) 280px; + gap: 50px; + } + .summary { + padding-left: 32px; + } +} +@media (max-width: 1150px) { + .shell { + grid-template-columns: 195px minmax(0, 1fr); + } + main { + padding: 28px 24px; + } + .editor-layout { + grid-template-columns: minmax(0, 1fr) 195px; + gap: 23px; + } + .summary { + padding-left: 19px; + } + .preset { + padding: 11px; + } + .steps button { + font-size: 10px; + gap: 6px; + } + .saved-heading .nav-caption { + font-size: 8px; + padding-left: 10px; + } +} +@media (max-width: 900px) { + .editor-layout { + grid-template-columns: minmax(0, 1fr); + } + .summary { + display: none; + } + .shell { + grid-template-columns: 185px minmax(0, 1fr); + } + .page-heading .secondary { + font-size: 11px; + padding: 7px 9px; + } + .configuration-name { + gap: 10px; + } + .configuration-name label { + font-size: 10px; + } +} +@media (max-width: 650px) { + .mobile-only { + display: inline-flex; + } + .topbar { + height: 61px; + padding: 0 17px; + gap: 14px; + } + .brand { + width: 139px; + } + .app-title { + display: none; + } + .connection { + font-size: 10px; + } + .shell { + display: block; + min-height: calc(100vh - 61px); + } + .sidebar { + padding: 10px 14px; + border-right: 0; + border-bottom: 1px solid var(--border); + display: block; + } + .sidebar > .nav-caption, + .saved-heading, + .sidebar-bottom { + display: none; + } + .sidebar nav { + margin: 0; + display: flex; + gap: 10px; + } + .nav-item { + width: auto; + padding: 6px 12px; + font-size: 12px; + min-height: 34px; + } + .saved-list { + display: flex; + overflow: auto; + margin-top: 8px; + gap: 5px; + } + .saved-item { + padding: 5px 9px; + min-height: 30px; + max-width: 200px; + flex-shrink: 0; + } + .saved-list > .muted { + font-size: 10px; + padding-left: 12px; + } + main { + padding: 25px 18px; + } + .page-heading { + align-items: flex-start; + margin-bottom: 24px; + gap: 10px; + } + h1 { + font-size: 21px; + } + .eyebrow { + font-size: 9px; + } + .page-heading .secondary { + font-size: 0; + gap: 0; + width: 36px; + padding: 8px; + } + .page-heading .toolbar { + gap: 2px; + } + .configuration-name { + flex-wrap: wrap; + gap: 8px; + } + .configuration-name label { + width: 100%; + font-size: 11px; + } + .configuration-name input { + max-width: calc(100% - 65px); + } + .steps { + gap: 3px; + margin-bottom: 25px; + } + .steps button { + flex-direction: column; + align-items: center; + justify-content: flex-start; + font-size: 10px; + gap: 8px; + text-align: center; + line-height: 1.4; + padding: 8px 0 12px; + } + .steps button > span { + width: 20px; + height: 20px; + } + .form-grid { + grid-template-columns: minmax(0, 1fr); + gap: 17px; + } + .preset-options { + gap: 6px; + } + .preset { + min-height: 110px; + font-size: 10px; + gap: 8px; + padding: 10px 7px; + } + .preset small { + font-size: 9px; + line-height: 1.5; + } + .preset svg { + height: 18px; + width: 18px; + } + .section-heading h2 { + font-size: 14px; + } + .section-heading .secondary { + font-size: 10px; + padding: 7px 9px; + } + .editor-footer { + gap: 8px; + } + .editor-footer button { + font-size: 11px; + padding: 8px 10px; + } + .editor-footer .muted { + font-size: 9px; + } + .launch-actions { + gap: 7px; + } + .launch-actions button { + font-size: 11px; + padding: 8px 10px; + } + .jobs-table th, + .jobs-table td { + padding: 12px 8px; + } + .jobs-table th:nth-child(3), + .jobs-table td:nth-child(3) { + display: none; + } + .jobs-table { + font-size: 11px; + } + .code-panel { + font-size: 10px; + } + .dialog-actions { + flex-wrap: wrap; + } +} diff --git a/practices/h_cluster/README.md b/practices/h_cluster/README.md new file mode 100644 index 000000000..36fcd7cd3 --- /dev/null +++ b/practices/h_cluster/README.md @@ -0,0 +1,100 @@ +# H-cluster deployment practice + +This optional package adapts HarborRL to Brainbox and restricted Sandbox egress. +It shares the core Controller, scheduling, Harbor Runner and trajectory audit; +it does not implement another training loop. Installing HarborRL alone does not +install or import this package. All example addresses and paths are placeholders. + +## Topology + +```text +GPU training adapter -> developer Controller / RolloutService + -> local Harbor Runner -> Sandbox platform +Sandbox Claude -> loopback mailbox <- developer relay pump + -> developer Gateway -> GPU SGLang +``` + +The developer initiates authenticated platform-proxy requests. Sandbox needs +no direct route to the GPU or developer private listener. The developer must +reach the platform proxy and every advertised inference endpoint; the GPU +must reach the controller. Relay responses, including SSE, are fully buffered. + +## Install and configure + +Use Python 3.12+ for the runner. From the repository root: + +```bash +python -m pip install -e . -e practices/h_cluster +``` + +Install the practice in the runner environment. The controller can use a separate +Python environment with the dependencies required for its tokenizer and service. +Start with `configs/deployment.example.yaml` and `configs/sandbox.example.yaml`. +Use service placement, a local developer runner and no SSH rollout workers. +Set the training config's `harbor.environment.runtime` to `sandbox`, its +`config` to your Sandbox config, and `harbor.runner_transport` to `local`. + +For each task image create a matching platform Environment. Expose command +port 44772 and the configured relay port. Its capacity must cover the binding +limit; use prewarm 0 for an idle test deployment. Pin images and offline Claude +binary hashes. The all-zero example hash must be replaced. Put AK:SK credentials +in the referenced mode-600 file; never commit credentials or private site files. +Tasks and verifiers needing external packages must have those dependencies +prepared offline. The model relay is not a general internet proxy. + +The core only interprets common lifecycle settings. This package validates +project, Environment IDs, mounts and relay options. Whole-Gi memory values are +expressed using Gi. Endpoint GET 502/503/504 responses are retried within the +creation deadline; the allocation POST is never retried. Platform Pending can +still time out and must not be mistaken for ready capacity. + +## Verify and run + +No GPU or platform credentials are needed for the package's local HTTP/mock tests: + +```bash +python -m pip install -e '.[dev]' -e practices/h_cluster +python -m pytest practices/h_cluster/tests +python scripts/sandbox_sdk_contract.py +``` + +After filling in private configuration, use the runner interpreter: + +```bash +python -m harborrl.rollout.harbor_job.sandbox_probe /srv/harborrl/site/sandbox.yaml +python -m harborrl.rollout.harbor_job.sandbox_probe /srv/harborrl/site/sandbox.yaml --network +harborrl train /srv/harborrl/site/train.yaml --deployment /srv/harborrl/site/deployment.yaml --dry-run +``` + +The first command is local. The second performs authenticated, read-only +Environment checks and does not create instances. The dry run parses the launch +plan and does not contact the GPU. Check actual routes before starting training. +Removing `--dry-run` launches the selected GPU job and can allocate Sandbox +instances. Use a new run directory for each test. + +Inspect instance lifecycle files and cleanup-required records after cancellation. +An empty instance list can disagree with Environment capacity details; verify +both before reusing capacity. Never delete templates or instances belonging to +other runs. The framework records unresolved cleanup rather than assuming it +succeeded. It does not automatically replace platform Environment templates. + +## Configuration migration + +Experimental flat Brainbox configs are intentionally not accepted as standard +OpenSandbox settings. Replace `provider: brainbox` with +`adapter: harborrl_h_cluster.environment:BrainboxEnvironment`; move project and +relay settings under `options`, and Environment ID/mount requirements under +each binding's `options`. The platform always uses command port 44772. +Keep lifecycle limits and offline installation in their common top-level fields. + +## Operational limits + +Set concurrency within project quotas and each Environment's actual capacity. +Measure agent execution separately from allocated or Running instances. +Pending allocations, offline verifier dependencies, turn limits and invalid +generated tools can reject a complete training group. Session content must +match the recorded model response to pass consumption auditing. + +Start with a serial complete-group run before increasing concurrency. Inspect +rejected groups and confirm cleanup of every allocated instance. Checkpoint +creation and policy-version changes alone do not establish learning improvement. diff --git a/practices/h_cluster/configs/deployment.example.yaml b/practices/h_cluster/configs/deployment.example.yaml new file mode 100644 index 000000000..e89c2b62f --- /dev/null +++ b/practices/h_cluster/configs/deployment.example.yaml @@ -0,0 +1,37 @@ +# Replace example hosts and paths; no site-specific addresses are required. +schema_version: 1 +manager: + id: manager + mode: service + bind: 0.0.0.0 + control_port: 49200 + control_url: http://manager.example:49200 + gateway_port: 49184 + gateway_url: http://manager.example:49184 + run_token_file: /srv/harborrl/site/run-token + source_dir: /srv/harborrl/repo + controller_python: /opt/harborrl/controller/bin/python +gpu_target: + id: trainer + transport: ssh + ssh: {host: training-host} + source_dir: /srv/harborrl/repo + train_python: /opt/harborrl/training/bin/python + environment_file: /srv/harborrl/site/gpu-env.json + expected_gpus: 8 +storage: + mode: shared_fs + run_root: /srv/harborrl/runs + manager_writable: true + gpu_writable: true + worker_writable: true +rollout: + provider: sandbox + workers: [] +connectivity: + command_timeout_s: 30 + ssh_timeout_s: 15 + http_timeout_s: 10 + gpu_to_controller: true + worker_to_gateway: false + manager_to_gpu_inference: true diff --git a/practices/h_cluster/configs/sandbox.example.yaml b/practices/h_cluster/configs/sandbox.example.yaml new file mode 100644 index 000000000..6229e8659 --- /dev/null +++ b/practices/h_cluster/configs/sandbox.example.yaml @@ -0,0 +1,23 @@ +adapter: harborrl_h_cluster.environment:BrainboxEnvironment +domain: https://sandbox.example/brainbox +credential_file: /run/secrets/harborrl/sandbox-key +max_live_instances: 8 +startup_concurrency: 1 +request_timeout_s: 180 +create_timeout_s: 600 +options: + project: REPLACE_PROJECT + gateway_transport: platform_pull + relay_port: 49185 + relay_timeout_s: 180 +bindings: + - image: registry.example/project/task:locked-version + max_live_instances: 1 + options: + environment_id: REPLACE_ENVIRONMENT_ID + required_mount_paths: [] +installation: + mode: offline_binary + version: 2.1.141 + binary: /opt/harborrl/assets/claude + sha256: '0000000000000000000000000000000000000000000000000000000000000000' diff --git a/practices/h_cluster/pyproject.toml b/practices/h_cluster/pyproject.toml new file mode 100644 index 000000000..0e06e0611 --- /dev/null +++ b/practices/h_cluster/pyproject.toml @@ -0,0 +1,16 @@ +[build-system] +requires = ["setuptools>=69", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "harborrl-h-cluster" +version = "0.1.0" +description = "Optional H-cluster deployment practice for HarborRL" +requires-python = ">=3.12" +dependencies = ["harborrl>=0.1.0", "harbor==0.23.0", "opensandbox==0.1.9", "httpx>=0.27,<1"] + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.pytest.ini_options] +testpaths = ["tests"] diff --git a/practices/h_cluster/src/harborrl_h_cluster/__init__.py b/practices/h_cluster/src/harborrl_h_cluster/__init__.py new file mode 100644 index 000000000..e8e8a73b2 --- /dev/null +++ b/practices/h_cluster/src/harborrl_h_cluster/__init__.py @@ -0,0 +1 @@ +"""Optional H-cluster deployment adapters; not imported by HarborRL core.""" diff --git a/practices/h_cluster/src/harborrl_h_cluster/environment.py b/practices/h_cluster/src/harborrl_h_cluster/environment.py new file mode 100644 index 000000000..6b0b68527 --- /dev/null +++ b/practices/h_cluster/src/harborrl_h_cluster/environment.py @@ -0,0 +1,143 @@ +"""Brainbox-specific settings and relay on the shared audited lifecycle.""" +import asyncio +import re +from contextlib import suppress + +import httpx + +from harborrl.rollout.harbor_job.environments.opensandbox import ( + ManagedOpenSandboxEnvironment, +) +from harborrl.rollout.harbor_job.sandbox_config import read_credential +from harborrl.trajectories.native import publish +from harborrl_h_cluster.transport import SandboxStartupTransport + + +async def validate_environment(cfg, binding, credential): + options, target = cfg['options'], binding['options'] + async with httpx.AsyncClient(timeout=cfg['request_timeout_s']) as client: + response = await client.get( + cfg['domain'].rstrip('/') + '/v1/sandbox-environments/' + target['environment_id'], + params={'project': options['project']}, headers={'OPEN-SANDBOX-API-KEY': credential}) + if response.status_code != 200: + raise RuntimeError(f'Environment query failed: HTTP {response.status_code}') + environment = response.json() + if environment.get('image', {}).get('uri') != binding['image']: + raise ValueError('Environment image does not match task image') + required_ports = {44772} + if options['gateway_transport'] == 'platform_pull': + required_ports.add(options['relay_port']) + if not required_ports <= {p.get('containerPort') for p in environment.get('ports', [])}: + raise ValueError('Environment command or relay port missing') + capacity = environment.get('instanceCapacity') + if type(capacity) is not int or capacity < binding['max_live_instances']: + raise ValueError('Environment capacity below configured admission limit') + mounts = {v.get('mountPath') for v in environment.get('volumes', [])} + if set(target['required_mount_paths']) - mounts: + raise ValueError('Environment required mounts missing') + return {'environment_id': target['environment_id'], 'image': binding['image'], 'capacity': capacity} + + +class BrainboxEnvironment(ManagedOpenSandboxEnvironment): + @classmethod + def validate_config(cls, cfg): + options = cfg['options'] + if set(options) != {'project', 'gateway_transport', 'relay_port', 'relay_timeout_s'}: + raise ValueError('H-cluster options require project, gateway_transport, relay_port, relay_timeout_s') + if not isinstance(options['project'], str) or not options['project'].strip(): + raise ValueError('H-cluster project required') + if options['gateway_transport'] not in ('direct', 'platform_pull') or cfg['use_server_proxy'] is not True: + raise ValueError('H-cluster requires server proxy and a supported gateway transport') + if (type(options['relay_port']) is not int or not 0 < options['relay_port'] < 65536 + or options['relay_port'] == 44772 or type(options['relay_timeout_s']) is not int + or options['relay_timeout_s'] < 30): + raise ValueError('invalid H-cluster relay port/timeout') + seen = set() + for binding in cfg['bindings']: + target = binding['options'] + if set(target) != {'environment_id', 'required_mount_paths'}: + raise ValueError('H-cluster binding requires environment_id and required_mount_paths') + eid = target['environment_id'] + if not isinstance(eid, str) or not re.fullmatch(r'[A-Za-z0-9_-]+', eid) or eid in seen: + raise ValueError('invalid or duplicate H-cluster Environment ID') + seen.add(eid) + if not isinstance(target['required_mount_paths'], list) or any( + not isinstance(p, str) or not p.startswith('/') for p in target['required_mount_paths']): + raise ValueError('invalid required_mount_paths') + + @classmethod + def agent_gateway(cls, cfg, gateway_url): + if cfg['options']['gateway_transport'] == 'platform_pull': + return f"http://127.0.0.1:{cfg['options']['relay_port']}" + return gateway_url + + @classmethod + async def network_check(cls, cfg): + credential = read_credential(cfg) + if ':' not in credential: + raise ValueError('H-cluster credential must contain AK:SK') + return [await validate_environment(cfg, binding, credential) for binding in cfg['bindings']] + + def __init__(self, *args, **kwargs): + self.relay_task = None + super().__init__(*args, **kwargs) + self.record['environment_id'] = self.binding['options']['environment_id'] + + async def validate_binding(self): + if ':' not in self._api_key: + raise ValueError('H-cluster credential must contain AK:SK') + await validate_environment(self.cfg, self.binding, self._api_key) + + def extensions(self): + return {'environmentId': self.binding['options']['environment_id']} + + def api_params(self): + return {'project': self.cfg['options']['project']} + + def startup_transport(self, allocated): + return SandboxStartupTransport(self.cfg['domain'].rstrip('/') + '/v1', allocated, + timeout=self.cfg['create_timeout_s']) + + def _build_resource(self): + resource = super()._build_resource() + memory = re.fullmatch(r'(\d+)Mi', resource.get('memory', '')) + if memory and int(memory[1]) % 1024 == 0: + resource['memory'] = f'{int(memory[1]) // 1024}Gi' + return resource + + async def start(self, force_build): + try: + await super().start(force_build) + if self.cfg['options']['gateway_transport'] == 'platform_pull': + from harborrl_h_cluster.relay import start_relay + if not self.gateway_url: + raise ValueError('platform relay requires manager Gateway URL') + self.relay_task = await start_relay(self, self.gateway_url) + except BaseException: + if self._sandbox is not None: + await self.stop(delete=True) + raise + + async def exec(self, *args, **kwargs): + if self.relay_task is None: + return await super().exec(*args, **kwargs) + command = asyncio.create_task(super().exec(*args, **kwargs)) + try: + done, _ = await asyncio.wait((command, self.relay_task), return_when=asyncio.FIRST_COMPLETED) + if self.relay_task in done: + publish(self.evidence / 'relay-failed.json', {'reason': 'manager relay pump stopped'}) + raise RuntimeError('manager relay pump stopped; attempt invalid') + return await command + finally: + if not command.done(): + command.cancel() + with suppress(asyncio.CancelledError): + await command + + async def stop(self, delete=True): + if self.relay_task is not None: + task, self.relay_task = self.relay_task, None + task.cancel() + with suppress(asyncio.CancelledError, Exception): + await task + await super().stop(delete=delete) diff --git a/practices/h_cluster/src/harborrl_h_cluster/relay.py b/practices/h_cluster/src/harborrl_h_cluster/relay.py new file mode 100644 index 000000000..3bf7d5b32 --- /dev/null +++ b/practices/h_cluster/src/harborrl_h_cluster/relay.py @@ -0,0 +1,50 @@ +"""Lifecycle of one manager-initiated relay inside a real sandbox.""" +import asyncio +import secrets +import tempfile +from pathlib import Path + +import httpx + +from harborrl.trajectories.native import publish +from harborrl_h_cluster import relay_server +from harborrl_h_cluster.reverse_relay import pump + + +async def start_relay(environment, gateway_url): + cfg = {**environment.cfg, **environment.cfg['options']} + token = secrets.token_hex(32) + await environment.upload_file(Path(relay_server.__file__), '/tmp/harborrl-relay.py') + with tempfile.NamedTemporaryFile(mode='w', encoding='ascii') as secret: + secret.write(token) + secret.flush() + await environment.upload_file(Path(secret.name), '/tmp/harborrl-relay-token') + result = await environment.exec('chmod 600 /tmp/harborrl-relay-token && ' + 'nohup python3 /tmp/harborrl-relay.py --token-file /tmp/harborrl-relay-token ' + f"--port {cfg['relay_port']} --timeout {cfg['relay_timeout_s']} " + '> /tmp/harborrl-relay.log 2>&1 < /dev/null &') + if result.return_code: + raise RuntimeError('sandbox relay process failed to start') + endpoint = await environment._sandbox.get_endpoint(cfg['relay_port']) + url = endpoint.endpoint + if not url.startswith(('http://', 'https://')): + url = cfg['protocol'] + '://' + url + headers = endpoint.headers or {} + async with httpx.AsyncClient(timeout=10, trust_env=False) as client: + for _ in range(20): + try: + response = await client.get(url.rstrip('/') + '/relay/health', + headers={**headers, 'X-Relay-Token': token}) + if response.status_code == 200 and response.json() == {'ok': True}: + break + except (httpx.HTTPError, ValueError): + pass + await asyncio.sleep(1) + else: + raise RuntimeError('sandbox relay readiness failed') + publish(environment.evidence / 'relay-ready.json', { + 'transport': 'platform_pull', 'port': cfg['relay_port'], + 'upstream': gateway_url, 'sandbox_id': environment.record['sandbox_id'], + 'response_mode': 'buffered', 'timeout_s': cfg['relay_timeout_s']}) + return asyncio.create_task(pump(url, headers, token, gateway_url, asyncio.Event(), + timeout=cfg['relay_timeout_s'] - 10)) diff --git a/practices/h_cluster/src/harborrl_h_cluster/relay_server.py b/practices/h_cluster/src/harborrl_h_cluster/relay_server.py new file mode 100644 index 000000000..689d47c1c --- /dev/null +++ b/practices/h_cluster/src/harborrl_h_cluster/relay_server.py @@ -0,0 +1,150 @@ +"""Sandbox-local HTTP mailbox. Standalone stdlib file, uploaded by the runner. + +The manager pulls each request once. Upstream inference is never retried after +an ambiguous transport failure. This prototype buffers responses, including SSE. +""" +import argparse +import base64 +import hmac +import json +import queue +import secrets +import threading +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from pathlib import Path + +MAX_BODY = 16 * 1024 * 1024 +PATHS = {'/v1/messages', '/v1/messages/count_tokens'} +REQUEST_HEADERS = {'content-type', 'x-api-key', 'authorization', 'anthropic-version', 'anthropic-beta'} +RESPONSE_HEADERS = {'content-type', 'request-id', 'retry-after'} + + +class Mailbox: + def __init__(self, token, *, timeout=120, capacity=8): + self.token, self.timeout = token, timeout + self.slots = threading.BoundedSemaphore(capacity) + self.queue = queue.Queue(maxsize=capacity) + self.pending = {} + self.lock = threading.Lock() + + +def make_server(host, port, mailbox): + class Handler(BaseHTTPRequestHandler): + def log_message(self, *args): + pass + + def reply(self, status, body, headers=None): + self.send_response(status) + for key, value in (headers or {}).items(): + if key.lower() in RESPONSE_HEADERS: + self.send_header(key, value) + self.send_header('Content-Length', str(len(body))) + self.end_headers() + self.wfile.write(body) + + def json_reply(self, status, data): + self.reply(status, json.dumps(data).encode(), {'content-type': 'application/json'}) + + def authorized(self): + return hmac.compare_digest(self.headers.get('X-Relay-Token', ''), mailbox.token) + + def read_body(self): + length = int(self.headers.get('Content-Length', '0')) + if not 0 <= length <= MAX_BODY or self.headers.get('Transfer-Encoding'): + raise ValueError('invalid body length') + return self.rfile.read(length) + + def do_GET(self): + if not self.authorized(): + self.json_reply(403, {'error': 'forbidden'}) + return + if self.path == '/relay/health': + self.json_reply(200, {'ok': True}) + return + if self.path != '/relay/next': + self.json_reply(404, {'error': 'not_found'}) + return + while True: + try: + item = mailbox.queue.get_nowait() + except queue.Empty: + self.reply(204, b'') + return + with mailbox.lock: + active = item['id'] in mailbox.pending + if active: + self.json_reply(200, item) + return + + def do_POST(self): + try: + self.handle_post() + except (ValueError, KeyError, TypeError): + self.json_reply(400, {'error': 'invalid_request'}) + + def handle_post(self): + if self.path.startswith('/relay/result/'): + if not self.authorized(): + self.json_reply(403, {'error': 'forbidden'}) + return + data = json.loads(self.read_body()) + if type(data['status']) is not int or not 200 <= data['status'] <= 599: + raise ValueError('invalid status') + body = base64.b64decode(data['body'], validate=True) + headers = data['headers'] + if not isinstance(headers, dict) or any( + not isinstance(k, str) or not isinstance(v, str) or '\r' in v or '\n' in v + for k, v in headers.items() + ): + raise ValueError('invalid response headers') + with mailbox.lock: + pending = mailbox.pending.get(self.path.rsplit('/', 1)[-1]) + if pending is None or pending['event'].is_set(): + self.json_reply(409, {'error': 'request_expired_or_completed'}) + return + pending['result'] = (data['status'], body, headers) + pending['event'].set() + self.json_reply(200, {'ok': True}) + return + if self.path.split('?', 1)[0] not in PATHS: + self.json_reply(404, {'error': 'not_found'}) + return + body = self.read_body() + if not mailbox.slots.acquire(blocking=False): + self.json_reply(503, {'error': 'relay_capacity'}) + return + identity = secrets.token_hex(16) + pending = {'event': threading.Event()} + try: + with mailbox.lock: + mailbox.pending[identity] = pending + try: + mailbox.queue.put_nowait({'id': identity, 'path': self.path, 'method': 'POST', + 'headers': {k: v for k, v in self.headers.items() if k.lower() in REQUEST_HEADERS}, + 'body': base64.b64encode(body).decode()}) + except queue.Full: + self.json_reply(503, {'error': 'relay_queue_full'}) + return + if not pending['event'].wait(mailbox.timeout): + self.json_reply(504, {'error': 'relay_deadline'}) + return + self.reply(*pending['result']) + finally: + with mailbox.lock: + mailbox.pending.pop(identity, None) + mailbox.slots.release() + + server = ThreadingHTTPServer((host, port), Handler) + server.daemon_threads = True + return server + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--token-file', required=True) + parser.add_argument('--port', type=int, default=49185) + parser.add_argument('--timeout', type=float, default=120) + args = parser.parse_args() + server = make_server('0.0.0.0', args.port, + Mailbox(Path(args.token_file).read_text().strip(), timeout=args.timeout)) + server.serve_forever() diff --git a/practices/h_cluster/src/harborrl_h_cluster/reverse_relay.py b/practices/h_cluster/src/harborrl_h_cluster/reverse_relay.py new file mode 100644 index 000000000..86316dbac --- /dev/null +++ b/practices/h_cluster/src/harborrl_h_cluster/reverse_relay.py @@ -0,0 +1,49 @@ +"""Manager-initiated sandbox relay over the authenticated platform proxy. + +Experimental transport: HTTP/SSE bodies are buffered and no upstream POST is +retried. Credentials and request bodies must never enter diagnostic logs. +""" +import asyncio +import base64 + +import httpx + +from .relay_server import MAX_BODY, PATHS, REQUEST_HEADERS, RESPONSE_HEADERS + + +async def pump(proxy_url, proxy_headers, relay_token, gateway_url, stop, *, timeout=110): + headers = {**proxy_headers, 'X-Relay-Token': relay_token} + async with httpx.AsyncClient(timeout=timeout, trust_env=False) as upstream, \ + httpx.AsyncClient(timeout=30, trust_env=False, headers=headers) as platform: + while not stop.is_set(): + pending = await platform.get(proxy_url.rstrip('/') + '/relay/next') + if pending.status_code == 204: + await asyncio.sleep(0.25) + continue + pending.raise_for_status() + item = pending.json() + if (item['method'] != 'POST' or item['path'].split('?', 1)[0] not in PATHS + or not isinstance(item['id'], str) + or len(item['id']) != 32 or any(c not in '0123456789abcdef' for c in item['id'])): + raise ValueError('invalid relay request') + body = base64.b64decode(item['body'], validate=True) + if len(body) > MAX_BODY: + raise ValueError('relay body too large') + request_headers = {k: v for k, v in item['headers'].items() if k.lower() in REQUEST_HEADERS} + try: + async with upstream.stream('POST', gateway_url.rstrip('/') + item['path'], + content=body, headers=request_headers) as response: + content = bytearray() + async for chunk in response.aiter_bytes(): + content.extend(chunk) + if len(content) > MAX_BODY // 2: + raise ValueError('relay response too large') + result = {'status': response.status_code, + 'headers': {k: v for k, v in response.headers.items() + if k.lower() in RESPONSE_HEADERS}, + 'body': base64.b64encode(content).decode()} + except (httpx.HTTPError, ValueError): + result = {'status': 502, 'headers': {'content-type': 'application/json'}, + 'body': base64.b64encode(b'{"error":"upstream_failed_no_retry"}').decode()} + delivered = await platform.post(proxy_url.rstrip('/') + '/relay/result/' + item['id'], json=result) + delivered.raise_for_status() diff --git a/practices/h_cluster/src/harborrl_h_cluster/transport.py b/practices/h_cluster/src/harborrl_h_cluster/transport.py new file mode 100644 index 000000000..b72151681 --- /dev/null +++ b/practices/h_cluster/src/harborrl_h_cluster/transport.py @@ -0,0 +1,28 @@ +"""Bounded retries for H-platform endpoint discovery, never allocation POSTs.""" +import asyncio +import re +import time + +from harborrl.rollout.harbor_job.sandbox_transport import ( + SandboxStartupTransport as AllocationTransport, +) + + +class SandboxStartupTransport(AllocationTransport): + def __init__(self, base_url, on_created, *, timeout, transport=None, retry_delay=2): + super().__init__(base_url, on_created, transport=transport) + self.deadline = time.monotonic() + timeout + self.retry_delay = retry_delay + + async def handle_async_request(self, request): + url = str(request.url).split('?', 1)[0] + endpoint_get = request.method == 'GET' and re.fullmatch( + re.escape(self.base_url) + r'/sandboxes/[A-Za-z0-9_-]+/endpoints/\d+', url) + while True: + response = await super().handle_async_request(request) + if not endpoint_get or response.status_code not in (502, 503, 504): + return response + if self.deadline - time.monotonic() <= self.retry_delay: + return response + await response.aclose() + await asyncio.sleep(self.retry_delay) diff --git a/practices/h_cluster/tests/test_environment.py b/practices/h_cluster/tests/test_environment.py new file mode 100644 index 000000000..95ea0dda3 --- /dev/null +++ b/practices/h_cluster/tests/test_environment.py @@ -0,0 +1,69 @@ +import asyncio + +import httpx +import pytest +from harbor.environments.opensandbox import OpenSandboxEnvironment +from harborrl_h_cluster.environment import BrainboxEnvironment, validate_environment + + +def settings(): + return {'domain': 'https://sandbox.example', 'request_timeout_s': 1, 'use_server_proxy': True, + 'options': {'project': 'test', 'gateway_transport': 'platform_pull', 'relay_port': 49185, + 'relay_timeout_s': 180}, + 'bindings': [{'image': 'image', 'max_live_instances': 2, + 'options': {'environment_id': 'env-test', 'required_mount_paths': ['/data']}}]} + + +@pytest.mark.parametrize('memory,normalized', [('1024Mi', '1Gi'), ('2048Mi', '2Gi'), ('512Mi', '512Mi')]) +def test_resource_units(monkeypatch, memory, normalized): + monkeypatch.setattr(OpenSandboxEnvironment, '_build_resource', lambda _: {'cpu': '2', 'memory': memory}) + assert object.__new__(BrainboxEnvironment)._build_resource() == {'cpu': '2', 'memory': normalized} + + +@pytest.mark.parametrize('change', ['port', 'project', 'binding', 'duplicate']) +def test_options_reject_invalid_platform_configuration(change): + cfg = settings() + if change == 'port': + cfg['options']['relay_port'] = 44772 + elif change == 'project': + cfg['options']['project'] = '' + elif change == 'binding': + cfg['bindings'][0]['options']['environment_id'] = '../other' + else: + cfg['bindings'].append(cfg['bindings'][0]) + with pytest.raises(ValueError): + BrainboxEnvironment.validate_config(cfg) + + +def test_platform_routing_and_extensions(): + cfg = settings() + BrainboxEnvironment.validate_config(cfg) + assert BrainboxEnvironment.agent_gateway(cfg, 'http://gateway') == 'http://127.0.0.1:49185' + env = object.__new__(BrainboxEnvironment) + env.cfg, env.binding = cfg, cfg['bindings'][0] + assert env.extensions() == {'environmentId': 'env-test'} + assert env.api_params() == {'project': 'test'} + cfg['options']['gateway_transport'] = 'direct' + assert BrainboxEnvironment.agent_gateway(cfg, 'http://gateway') == 'http://gateway' + + +@pytest.mark.parametrize('ports,capacity,mounts,accepted', [ + ([44772, 49185], 2, ['/data'], True), ([44772], 2, ['/data'], False), + ([44772, 49185], 1, ['/data'], False), ([44772, 49185], 2, [], False), +]) +def test_environment_preflight_checks_platform_contract(monkeypatch, ports, capacity, mounts, accepted): + cfg = settings() + + def handler(request): + assert request.method == 'GET' + assert request.url.params['project'] == 'test' + return httpx.Response(200, json={'image': {'uri': 'image'}, 'instanceCapacity': capacity, + 'ports': [{'containerPort': p} for p in ports], 'volumes': [{'mountPath': p} for p in mounts]}) + + client = httpx.AsyncClient + monkeypatch.setattr(httpx, 'AsyncClient', lambda **kw: client(**kw, transport=httpx.MockTransport(handler))) + if accepted: + assert asyncio.run(validate_environment(cfg, cfg['bindings'][0], 'test-only'))['capacity'] == 2 + else: + with pytest.raises(ValueError): + asyncio.run(validate_environment(cfg, cfg['bindings'][0], 'test-only')) diff --git a/practices/h_cluster/tests/test_relay.py b/practices/h_cluster/tests/test_relay.py new file mode 100644 index 000000000..b8e98007b --- /dev/null +++ b/practices/h_cluster/tests/test_relay.py @@ -0,0 +1,137 @@ +import asyncio +import base64 +import contextlib +import json +import threading +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer + +import httpx +import pytest +from harborrl_h_cluster.relay_server import Mailbox, make_server +from harborrl_h_cluster.reverse_relay import pump + + +@contextlib.contextmanager +def serving(server): + thread = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + try: + yield f'http://127.0.0.1:{server.server_port}' + finally: + server.shutdown() + server.server_close() + thread.join(5) + + +def test_relay_auth_timeout_and_no_redelivery(): + async def run(url): + async with httpx.AsyncClient(trust_env=False) as client: + assert (await client.get(url + '/relay/next')).status_code == 403 + assert (await client.post(url + '/unexpected', content=b'x')).status_code == 404 + pending = asyncio.create_task(client.post(url + '/v1/messages', content=b'body')) + headers = {'X-Relay-Token': 'secret'} + for _ in range(100): + item = await client.get(url + '/relay/next', headers=headers) + if item.status_code == 200: + break + await asyncio.sleep(.01) + assert item.status_code == 200 + assert (await client.get(url + '/relay/next', headers=headers)).status_code == 204 + assert (await pending).status_code == 504 + delivered = await client.post(url + '/relay/result/' + item.json()['id'], + headers=headers, json={'status': 200, 'headers': {}, 'body': base64.b64encode(b'late').decode()}) + assert delivered.status_code == 409 + with serving(make_server('127.0.0.1', 0, Mailbox('secret', timeout=.5))) as url: + asyncio.run(run(url)) + + +@pytest.mark.parametrize('status,content_type,body', [ + (200, 'text/event-stream', b'event: message_start\ndata: {"id":"test"}\n\n'), + (429, 'application/json', b'{"error":"rate_limited"}'), +]) +def test_reverse_roundtrip_preserves_bytes_and_headers(status, content_type, body): + calls = [] + class Upstream(BaseHTTPRequestHandler): + def log_message(self, *args): + pass + def do_POST(self): + calls.append((self.path, self.headers.get('x-api-key'), + self.rfile.read(int(self.headers['Content-Length'])))) + self.send_response(status) + self.send_header('Content-Type', content_type) + self.send_header('Content-Length', str(len(body))) + self.end_headers() + self.wfile.write(body) + async def run(relay, gateway): + stop = asyncio.Event() + task = asyncio.create_task(pump(relay, {}, 'secret', gateway, stop)) + try: + async with httpx.AsyncClient(trust_env=False) as client: + response = await client.post(relay + '/v1/messages?beta=true', + headers={'x-api-key': 'attempt-token'}, content=b'{"stream":true}') + assert response.status_code == status + assert response.content == body + assert response.headers['content-type'] == content_type + finally: + stop.set() + task.cancel() + with contextlib.suppress(asyncio.CancelledError): + await task + with serving(make_server('127.0.0.1', 0, Mailbox('secret'))) as relay, \ + serving(ThreadingHTTPServer(('127.0.0.1', 0), Upstream)) as gateway: + asyncio.run(run(relay, gateway)) + assert calls == [('/v1/messages?beta=true', 'attempt-token', b'{"stream":true}')] + + +def test_real_gateway_trace_and_session_audit_through_relay(tmp_path): + from harborrl.gateway.server import Gateway + from harborrl.gateway.server import make_server as make_gateway + from harborrl.gateway.trace import TraceRegistry + from harborrl.rollout.harbor_job.audit import audit_session + from harborrl.trajectories.native import Identity, RewardProfile, digest + + class Backend: + def count_tokens(self, converted): + return 2 + + def generate(self, converted, ident, response_id): + return {'input_ids': [1, 2], 'output_ids': [3], 'logprobs': [-.2], 'logprob_semantics': 'raw_model', + 'policy_version': ident.policy_version, 'tokenizer_digest': 'tok', 'template_digest': 'tpl', + 'engine_id': 'synthetic', 'serving_input': {'input_ids': [1, 2]}, 'sampling': converted['sampling'], + 'finish_reason': 'end_turn', 'evidence_kind': 'synthetic', 'content': [{'type': 'text', 'text': 'done'}]} + + sampling = {'temperature': 1.0, 'top_p': 1.0, 'top_k': -1, 'max_new_tokens': 64, 'stop': []} + identity = Identity(run_id='run', batch_id='batch', group_id='group', slot_id='0', attempt_id='attempt', + trajectory_id='trajectory', task_digest='task', policy_version='1', harness_digest='claude', + reward_digest=RewardProfile().digest, sampling_digest=digest(sampling)) + registry = TraceRegistry(tmp_path / 'traces') + credential = registry.register(identity, 'trial') + gateway = Gateway(registry, Backend(), model='policy') + + async def run(relay, upstream): + stop = asyncio.Event() + task = asyncio.create_task(pump(relay, {}, 'secret', upstream, stop)) + try: + async with httpx.AsyncClient(trust_env=False) as client: + response = await client.post(relay + '/v1/messages', + headers={'x-api-key': credential}, json={'model': 'policy', 'max_tokens': 64, + 'messages': [{'role': 'user', 'content': 'hello'}]}) + assert response.status_code == 200 + message = response.json() + session = tmp_path / 'session.jsonl' + session.write_text(json.dumps({'sessionId': 'relay-session', 'type': 'assistant', + 'message': message}) + '\n') + audit_session(session, registry, identity.attempt_id) + registry.close(identity.attempt_id) + turns, seal = registry.seal(identity.attempt_id) + assert seal['errors'] == [] + assert len(turns) == 1 + assert turns[0]['delivery'] == 'consumed_confirmed' + finally: + stop.set() + task.cancel() + with contextlib.suppress(asyncio.CancelledError): + await task + with serving(make_server('127.0.0.1', 0, Mailbox('secret'))) as relay, \ + serving(make_gateway(gateway)) as upstream: + asyncio.run(run(relay, upstream)) diff --git a/practices/h_cluster/tests/test_transport.py b/practices/h_cluster/tests/test_transport.py new file mode 100644 index 000000000..9e56c93df --- /dev/null +++ b/practices/h_cluster/tests/test_transport.py @@ -0,0 +1,66 @@ +import asyncio + +import httpx +from harborrl_h_cluster.transport import SandboxStartupTransport + + +def test_transient_endpoint_retries_do_not_recreate_sandbox(): + async def run(): + calls, ids = [], [] + + async def serve(request): + calls.append((request.method, request.url.path)) + if request.method == 'POST': + return httpx.Response(201, json={'id': 'sbx-one'}) + if len(calls) < 4: + return httpx.Response(503) + return httpx.Response(200, json={'endpoint': 'ready'}) + + transport = SandboxStartupTransport('https://platform.test/v1', ids.append, timeout=10, + transport=httpx.MockTransport(serve), retry_delay=0) + async with httpx.AsyncClient(transport=transport) as client: + await client.post('https://platform.test/v1/sandboxes', json={}) + response = await client.get('https://platform.test/v1/sandboxes/sbx-one/endpoints/44772') + assert response.status_code == 200 + assert ids == ['sbx-one'] + assert sum(method == 'POST' for method, _ in calls) == 1 + assert len(calls) == 4 + + asyncio.run(run()) + + +def test_mutations_proxy_requests_and_auth_failures_are_not_retried(): + async def run(): + calls = [] + + async def serve(request): + calls.append(request) + return httpx.Response(401 if '/endpoints/' in request.url.path else 503) + + transport = SandboxStartupTransport('https://platform.test/v1', lambda _: None, timeout=10, + transport=httpx.MockTransport(serve), retry_delay=0) + async with httpx.AsyncClient(transport=transport) as client: + await client.post('https://platform.test/v1/sandboxes') + await client.get('https://platform.test/v1/sandboxes/sbx-one/proxy/44772') + await client.get('https://platform.test/v1/sandboxes/sbx-one/endpoints/44772') + assert len(calls) == 3 + + asyncio.run(run()) + + +def test_endpoint_retry_deadline_returns_final_failure(): + async def run(): + calls = [] + + async def serve(request): + calls.append(request) + return httpx.Response(503) + + transport = SandboxStartupTransport('https://platform.test/v1', lambda _: None, timeout=0, + transport=httpx.MockTransport(serve), retry_delay=0) + async with httpx.AsyncClient(transport=transport) as client: + response = await client.get('https://platform.test/v1/sandboxes/sbx-one/endpoints/44772') + assert response.status_code == 503 + assert len(calls) == 1 + + asyncio.run(run()) diff --git a/pyproject.toml b/pyproject.toml index ed654f6bb..1cbdca1ad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,10 +16,16 @@ dependencies = [ harborrl = "harborrl.cli:main" [project.optional-dependencies] +sandbox = [ + "harbor==0.23.0; python_version >= '3.12'", + "opensandbox==0.1.9; python_version >= '3.12'", + "httpx>=0.27,<1", +] dev = [ "pytest>=8.0", "pytest-asyncio>=0.24", "ruff>=0.8", + "httpx>=0.27,<1", ] [tool.uv] @@ -31,6 +37,8 @@ exclude = ["runs*", ".venv*", "workspace*", "local*", "docs*", "tests*"] [tool.setuptools.package-data] "harborrl.platform" = ["*.sh"] +"harborrl.options" = ["resources/*.yaml"] +"harborrl.webui" = ["static/*"] [tool.pytest.ini_options] testpaths = ["tests"] diff --git a/scripts/audit_public_tree.sh b/scripts/audit_public_tree.sh index 7f80ce653..b9c044203 100755 --- a/scripts/audit_public_tree.sh +++ b/scripts/audit_public_tree.sh @@ -4,11 +4,40 @@ set -euo pipefail ROOT="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/.." && pwd)" cd "$ROOT" -python3 - <<'PY' +python3 - "$@" <<'PY' +import argparse from pathlib import Path import re import subprocess +parser = argparse.ArgumentParser(description="Audit public files and optional new-commit document history.") +parser.add_argument("--history-base", help="Check every commit after this ancestor, including deleted documents.") +args = parser.parse_args() + +public_documents = { + "CONTRIBUTING.md", "NOTICE.md", "README.md", "README_zh.md", "SECURITY.md", + "configs/harbor_hub/public_compatibility_summary.md", + "configs/harbor_hub/public_compatibility_summary_zh.md", + "docs/agents/environment-check.md", "docs/configuration-wizard.md", + "docs/deployment.md", "docs/sandbox-rollout.md", + "docs/train-inference-consistency.md", "docs/web-console.md", + "practices/h_cluster/README.md", "harborrl/webui/static/lucide-LICENSE.txt", +} + + +def audit_documents(names, location): + for name in names: + path = Path(name) + if path.suffix.lower() not in {".md", ".rst", ".adoc", ".txt"}: + continue + task_instruction = ( + path.parts[:3] == ("examples", "native", "tasks") + and path.name == "instruction.md" + ) + if name not in public_documents and not task_instruction: + raise SystemExit(f"document is not approved for publication ({location}): {name}") + + root = Path(".") files = subprocess.check_output( ["git", "ls-files", "-z"], text=True @@ -16,6 +45,23 @@ files = subprocess.check_output( files = [Path(name) for name in files if name] if not files: raise SystemExit("tracked tree is empty") +audit_documents([path.as_posix() for path in files], "index") + +if args.history_base: + if subprocess.run( + ["git", "merge-base", "--is-ancestor", args.history_base, "HEAD"], + stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, + ).returncode: + raise SystemExit("history base must resolve to an ancestor of HEAD") + commits = subprocess.check_output( + ["git", "rev-list", f"{args.history_base}..HEAD"], text=True + ).splitlines() + for commit in commits: + names = subprocess.check_output( + ["git", "ls-tree", "-r", "--name-only", "-z", commit], text=True + ).split("\0") + audit_documents([name for name in names if name], commit[:12]) + print(f"public document history audit passed: {len(commits)} new commits") forbidden_directories = {"backends", "benchmarks", "deploy", "runs", "tools"} for path in files: @@ -36,10 +82,11 @@ internal_patterns = { "personal path": re.compile(rb"/(mnt/shared-storage-user|home/[A-Za-z0-9_.-]+|root)/"), "internal DNS": re.compile(rb"(?i)[a-z0-9.-]+\.(local|internal|svc)\b"), "private registry": re.compile(rb"(?i)(registry\.[a-z0-9.-]+/(?:[a-z0-9_.-]+/){2,})"), + "cluster DNS": re.compile(rb"(?i)[a-z0-9.-]+\.(?:pjlab|p\x6a-lab)\.org\.cn\b"), } text_suffixes = { ".cfg", ".env", ".github", ".in", ".json", ".md", ".py", ".sh", ".toml", - ".txt", ".yaml", ".yml", + ".txt", ".yaml", ".yml", ".lock", } for path in files: if path.suffix.lower() not in text_suffixes and path.name != "Dockerfile": diff --git a/scripts/collect_service_evidence.py b/scripts/collect_service_evidence.py new file mode 100755 index 000000000..fa55d4add --- /dev/null +++ b/scripts/collect_service_evidence.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python3 +"""Collect service-mode run evidence for the controller-placement validation. + +Reads one run directory and reports: exit code, policy versions, checkpoints, +rollout groups/slots (from the RolloutService artifacts), RL_READY counts, +rewards, trace seals, integrity errors, and cleanup records. Machine-readable +JSON goes to stdout; exit 0 only when the mandatory gates hold. +""" +from __future__ import annotations + +import json +import sys +from pathlib import Path + + +def read_json(path): + return json.loads(Path(path).read_text()) + + +def main(run_dir): + run_dir = Path(run_dir) + report = {"run_dir": str(run_dir), "gates": {}} + + exit_code = int((run_dir / "exit-code").read_text().strip()) + report["exit_code"] = exit_code + report["gates"]["exit_code"] = exit_code == 0 + + # Policy versions: the manager's activation publishes policy-pool.json + # (snapshot) and each completed group records its admitted version. + versions = [] + snapshot = run_dir / "policy-pool.json" + final_version = None + if snapshot.is_file(): + final_version = read_json(snapshot).get("weight_version") + for result in sorted(run_dir.glob("rollout-service/groups/*/group-*/result.json")): + versions.append(read_json(result)["policy_version"]) + report["policy_versions"] = {"groups": versions, "final_pool": final_version} + report["gates"]["policy_advanced"] = len(set(versions)) >= 2 and ( + final_version is not None and str(final_version) == str(versions[-1])) + + checkpoints = sorted(p.name for p in run_dir.glob("checkpoints/*/iter_*")) \ + if run_dir.joinpath("checkpoints").is_dir() else [] + report["checkpoints"] = checkpoints + report["gates"]["checkpoints_written"] = len(checkpoints) >= 2 + + # Service artifacts: groups executed by the manager. + groups_root = run_dir / "rollout-service" / "groups" + groups = {} + if groups_root.is_dir(): + for run_id_dir in groups_root.iterdir(): + for group_dir in run_id_dir.iterdir(): + summary = {"group_id": group_dir.name, "states": [], + "has_result": (group_dir / "result.json").is_file()} + for state in ("running", "result", "cancelled", "failed"): + if (group_dir / f"{state}.json").is_file(): + summary["states"].append(state) + groups[group_dir.name] = summary + report["service_groups"] = groups + report["gates"]["service_groups_complete"] = bool(groups) and all( + g["states"] and "result" in g["states"] and g["has_result"] + for g in groups.values()) + + # Native rollouts + traces. + attempts = list((run_dir / "native-rollouts").rglob("trajectory.v2.json")) \ + if (run_dir / "native-rollouts").is_dir() else [] + readiness, rewards, integrity, seals = {}, [], 0, {"sealed": 0, "unsealed": 0} + turns_total = 0 + for path in attempts: + ir = read_json(path) + readiness[ir["readiness"]] = readiness.get(ir["readiness"], 0) + 1 + integrity += len(ir.get("integrity_errors", [])) + rewards.append(ir["evaluation"]["training_reward"]) + turns_total += len(ir["turns"]) + if ir["trace_seal"]["status"] == "sealed" and not ir["trace_seal"]["errors"]: + seals["sealed"] += 1 + else: + seals["unsealed"] += 1 + report["trajectories"] = { + "count": len(attempts), "readiness": readiness, "rewards": rewards, + "turns_total": turns_total, "integrity_errors": integrity, "seals": seals} + report["gates"]["all_rl_ready"] = len(attempts) >= 6 and readiness.get("RL_READY") == len(attempts) + report["gates"]["zero_integrity_errors"] = integrity == 0 + report["gates"]["zero_unsealed"] = seals["unsealed"] == 0 + + cleanup = [p.name for p in run_dir.rglob("*cleanup-required.json")] + unsealed = [p.name for p in run_dir.rglob("*trace-unsealed.json")] + failed = [p.name for p in run_dir.rglob("*failure.json")] + report["cleanup_records"] = {"cleanup_required": cleanup, "trace_unsealed": unsealed, + "failures": failed} + report["gates"]["clean_run"] = not (cleanup or unsealed or failed) + + passed = int(exit_code) == 0 and all(report["gates"].values()) + report["summary"] = ("PASS" if passed else "FAIL") + \ + f" gates={report['gates']}" + print(json.dumps(report, indent=2, ensure_ascii=False)) + return 0 if passed else 1 + + +if __name__ == "__main__": + if len(sys.argv) != 2: + raise SystemExit("usage: collect_service_evidence.py RUN_DIR") + raise SystemExit(main(sys.argv[1])) diff --git a/scripts/readiness_dial.py b/scripts/readiness_dial.py new file mode 100755 index 000000000..7268c4563 --- /dev/null +++ b/scripts/readiness_dial.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python3 +"""Live cross-machine readiness dials for the controller placement validation. + +Starts a temporary control plane + a /readyz listener on the manager, then +dials them from the GPU target and the rollout worker over SSH — the same +hops the real deployment requires (gpu_to_controller, worker_to_gateway). +""" +from __future__ import annotations + +import ipaddress +import json +import subprocess +import sys +import threading +import time +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer + +from harborrl.rollout.service import RolloutService, make_control_server + + +def ssh(spec, command, timeout=20): + result = subprocess.run( + ["ssh", "-o", "BatchMode=yes", "-o", "ConnectTimeout=10", + "-o", "StrictHostKeyChecking=accept-new", spec, command], + capture_output=True, text=True, timeout=timeout, check=False) + return result.returncode == 0, (result.stdout + result.stderr).strip()[-200:] + + +def main(gpu_spec, worker_spec, manager_address): + manager_address = str(ipaddress.IPv4Address(manager_address)) + # 1) a real control plane on the manager's control port + service = RolloutService(run_root="/tmp/harborrl-readiness") + control = make_control_server(service, host="0.0.0.0", port=49200) + control_thread = threading.Thread(target=control.serve_forever, daemon=True) + control_thread.start() + # 2) a plain /readyz listener standing in for the gateway port + class Gateway(ThreadingHTTPServer): + daemon_threads = True + + class Handler(BaseHTTPRequestHandler): + def log_message(self, *args): + pass + + def do_GET(self): + body = b'{"ok": true}' + self.send_response(200) + self.send_header("Content-Type", "application/json") + self.send_header("Content-Length", str(len(body))) + self.end_headers() + self.wfile.write(body) + + gateway = Gateway(("0.0.0.0", 49184), Handler) + gateway_thread = threading.Thread(target=gateway.serve_forever, daemon=True) + gateway_thread.start() + time.sleep(1) + + checks = [] + checks.append(("manager.control_plane_listening", + *ssh("localhost", "true"))) # manager-local sanity + # Direct local health check of the control plane. + local = subprocess.run( + ["python3", "-c", + "import urllib.request;urllib.request.urlopen('http://127.0.0.1:49200/healthz',timeout=5)"], + capture_output=True, text=True, timeout=10, check=False) + checks.append(("manager.control_healthz_local", local.returncode == 0, + "healthz answered on the manager")) + checks.append(("gpu_to_controller", + *ssh(gpu_spec, + "python3 -c \"import urllib.request;" + f"urllib.request.urlopen('http://{manager_address}:49200/healthz'," + "timeout=10)\""))) + checks.append(("worker_to_gateway", + *ssh(worker_spec, + "python3 -c \"import urllib.request;" + f"urllib.request.urlopen('http://{manager_address}:49184/readyz'," + "timeout=10)\""))) + + control.shutdown() + gateway.shutdown() + report = [{"check": name, "ok": ok, "detail": detail} + for name, ok, detail in checks] + passed = all(item["ok"] for item in report) + print(json.dumps({"readiness": report, + "summary": "PASS" if passed else "FAIL"}, indent=2)) + return 0 if passed else 1 + + +if __name__ == "__main__": + if len(sys.argv) != 4: + raise SystemExit("usage: readiness_dial.py GPU_SSH_SPEC WORKER_SSH_SPEC MANAGER_IPV4") + raise SystemExit(main(sys.argv[1], sys.argv[2], sys.argv[3])) diff --git a/scripts/rollout_service_smoke.py b/scripts/rollout_service_smoke.py new file mode 100755 index 000000000..c0de2e1a9 --- /dev/null +++ b/scripts/rollout_service_smoke.py @@ -0,0 +1,263 @@ +#!/usr/bin/env python3 +"""CPU smoke for the RolloutService control plane (no GPU, no Harbor, no SSH). + +Starts a real control server on loopback with a synthetic executor, then +drives the full lifecycle through the production HTTP client: register, +policy gate, activate, idempotent submit, conflict rejection, digest +validation, cancel, drain. Exits 0 only when every contract holds. +""" +from __future__ import annotations + +import asyncio +import json +import tempfile +import threading +from pathlib import Path +from types import SimpleNamespace + +from harborrl.backends.slime_v032.rollout_service_client import ( + RolloutServiceClient, + ServiceRolloutError, + load_group_result, +) +from harborrl.rollout.harbor_job.collector import collect +from harborrl.rollout.service import ( + RolloutService, + ServiceError, + make_control_server, + manifest_path_from, +) +from harborrl.trajectories.native import ( + Identity, + RewardProfile, + assemble, + digest, + publish, +) + +SAMPLING = {"temperature": 1.0, "top_p": 1.0, "top_k": -1, + "max_new_tokens": 64, "stop": []} +TASK = {"id": "smoke-task", "revision": "1", "path": "/task", + "task_digest": "d" * 64, + "reward_profile": {"key": "reward", "scale": 1.0, "offset": 0.0, + "raw_range": [0.0, 1.0]}} +RUN_ID = "smoke-run-1" + + +def step(name, ok, detail=""): + status = "PASS" if ok else "FAIL" + print(f"[{status}] {name}" + (f" — {detail}" if detail else "")) + return ok + + +def identity(slot, *, group_id, policy_version): + return Identity(run_id=RUN_ID, batch_id="rollout-0", group_id=group_id, + slot_id=slot, attempt_id=f"attempt-{policy_version}-{slot}", + trajectory_id=f"trajectory-{policy_version}-{slot}", + task_digest=TASK["task_digest"], + policy_version=policy_version, harness_digest="claude", + reward_digest=RewardProfile().digest, + sampling_digest=digest(SAMPLING)) + + +def synthetic_ir(root, slot, reward, *, group_id, policy_version): + ident = identity(slot, group_id=group_id, policy_version=policy_version) + attempt_root = root / "receipts" / ident.attempt_id + attempt_root.mkdir(parents=True, exist_ok=True) + result = {"id": "trial-" + ident.attempt_id, + "finished_at": "2026-10-21T00:00:00Z", + "verifier_environment_mode": "shared", + "verifier_result": {"rewards": {"reward": reward}}} + (attempt_root / "result.json").write_text(json.dumps(result)) + (attempt_root / "reward.json").write_text(json.dumps({"reward": reward})) + evaluation = collect(ident, result["id"], attempt_root / "result.json", + attempt_root, RewardProfile(), evidence_kind="synthetic") + trial = evaluation["harbor_trial_id"] + turn = {"identity": ident.to_dict(), "harbor_trial_id": trial, + "request_id": f"r-{slot}", "response_id": f"m-{slot}", + "engine_id": "engine", "input_ids": [1, 2], "output_ids": [3, 4], + "logprobs": [-0.2, -0.3], "logprob_semantics": "raw_model", + "policy_version": policy_version, "tokenizer_digest": "tok", + "template_digest": "template", "delivery": "consumed_confirmed", + "consumption_ref": "session#digest", "role": "policy", + "sampling": SAMPLING, "client_request": {"model": "policy"}, + "serving_input": {"input_ids": [1, 2]}, + "response": {"content": [{"type": "text", "text": "done"}]}, + "finish_reason": "end_turn", "evidence_kind": "synthetic"} + for key in ("client_request", "serving_input", "response"): + turn[key + "_digest"] = digest(turn[key]) + seal = {"identity": ident.to_dict(), "harbor_trial_id": trial, + "pending_requests": 0, "status": "sealed", + "request_ids": [turn["request_id"]], "turns_digest": digest([turn]), + "errors": []} + return assemble(ident, trial, [turn], seal, evaluation) + + +class Registry: + def __init__(self): + self.version, self.accepting = None, False + + def drain(self, timeout): + self.accepting = False + + def publish_version(self, states, expected): + self.version, self.accepting = str(expected), True + + +class Runtime: + def __init__(self, root): + self.registry, self.rollout_root = Registry(), root + + def manifest_path(self, ident): + return manifest_path_from(self.rollout_root, ident) + + def close(self): + pass + + +def group_request(group_index, *, policy_version="1", slots=2): + return {"group_id": f"group-{group_index}", "rollout_id": 0, + "group_index": group_index, "policy_version": policy_version, + "task": TASK, "sampling": dict(SAMPLING), + "slots": [{"slot_id": str(slot)} for slot in range(slots)]} + + +def main(): + root = Path(tempfile.mkdtemp(prefix="harborrl-service-smoke-")) + executions = [] + + async def executor(config, runtime_, request): + executions.append(request["group_id"]) + if request["group_index"] >= 1: + while True: # later groups block until they are cancelled + await asyncio.sleep(0.05) + irs = [] + for descriptor, reward in zip(request["slots"], (1.0, 0.0)): + ir = synthetic_ir(root, descriptor["slot_id"], reward, + group_id=request["group_id"], + policy_version=request["policy_version"]) + publish(runtime_.manifest_path(ir["identity"]), ir) + irs.append(ir) + return irs + + runtime = Runtime(root) + service = RolloutService(run_root=root, executor=executor, allow_synthetic=True, + group_wait_timeout=10) + # The smoke owns the service process, so it registers with its own token + # instead of consuming the one-time generated token. + service.register_run(RUN_ID, {"run_id": RUN_ID, "group_size": 2, + "purpose": "cpu service smoke"}, + token="smoke-token") + service.attach_runtime(RUN_ID, SimpleNamespace(drain_timeout=1), runtime) + control = make_control_server(service, host="127.0.0.1", port=0) + thread = threading.Thread(target=control.serve_forever, daemon=True) + thread.start() + url = f"http://127.0.0.1:{control.server_address[1]}" + client = RolloutServiceClient(url, RUN_ID, "smoke-token", timeout=10, + poll_interval=0.05) + + results = [] + try: + results.append(step("healthz answers", client.health()["ok"])) + # Register is idempotent by manifest digest. + replay = service.register_run(RUN_ID, {"run_id": RUN_ID, + "group_size": 2, + "purpose": "cpu service smoke"}) + results.append(step("register replay is idempotent", replay["existed"])) + try: + service.register_run(RUN_ID, {"run_id": RUN_ID, "other": 1}) + results.append(step("manifest conflict rejected", False)) + except ServiceError as exc: + results.append(step("manifest conflict rejected", exc.status == 409)) + + # Auth: a wrong token never reaches group endpoints. + bad = RolloutServiceClient(url, RUN_ID, "wrong", timeout=10) + try: + bad.group_status("group-0") + results.append(step("bad token rejected", False)) + except ServiceRolloutError as exc: + results.append(step("bad token rejected", exc.status == 401)) + + # Policy gate: nothing runs before activation. + try: + client.submit_group(group_request(0)) + results.append(step("submit before activate rejected", False)) + except ServiceRolloutError as exc: + results.append(step("submit before activate rejected", + exc.code == "stale_policy")) + + states = [{"endpoint": f"http://engine-{i}", "healthy": True, + "weight_version": "1"} for i in range(2)] + activation = client.activate_policy(states, "1") + results.append(step("policy activation opens admission", + activation["policy_version"] == "1" + and runtime.registry.accepting)) + pool = json.loads((root / "policy-pool.json").read_text()) + results.append(step("policy pool published to shared run dir", + pool["weight_version"] == "1")) + + request = group_request(0) + first = client.submit_group(request) + results.append(step("group completes with two slots", + first["status"] == "complete" + and len(first["result"]["slots"]) == 2)) + assert client.group_status("group-0")["status"] == "complete" + again = client.submit_group(json.loads(json.dumps(request))) + results.append(step("resubmit is idempotent", + again["result"]["request_digest"] + == first["result"]["request_digest"] + and executions.count("group-0") == 1, + f"executions={executions}")) + conflicting = group_request(0) + conflicting["sampling"]["temperature"] = 0.5 + try: + client.submit_group(conflicting) + results.append(step("same id different content rejected", False)) + except ServiceRolloutError as exc: + results.append(step("same id different content rejected", + exc.code == "conflict")) + + trajectories = load_group_result( + RUN_ID, request, first["result"], + allow_synthetic=any(slot["readiness"] != "RL_READY" + for slot in first["result"]["slots"])) + results.append(step("client validates manifests and digests", + [ir["identity"]["slot_id"] for ir in trajectories] + == ["0", "1"])) + export_path = Path(first["result"]["group_export"]["path"]) + export = json.loads(export_path.read_text()) + results.append(step("group export lands on shared storage", + export["trajectory_count"] == 2 + and export["mean"] == 0.5, str(export_path.relative_to(root)))) + + # Cancel a group that is still running, then stop admitting. + slow = group_request(1) + service.group_wait_timeout = 0.2 + pending = service.submit_group(RUN_ID, slow) + results.append(step("timeout answer says running and points at status", + pending["status"] in ("queued", "running") + and "hint" in pending)) + cancelled = client.cancel_group("group-1") + results.append(step("running group cancels", + cancelled["status"] == "cancelled")) + + client.drain() + try: + client.submit_group(group_request(2)) + results.append(step("drain closes admission", False)) + except ServiceRolloutError as exc: + results.append(step("drain closes admission", + exc.code == "admission_closed")) + finally: + control.shutdown() + control.server_close() + thread.join(5) + service.close() + + print(f"\nrun root: {root}") + print(f"{sum(results)}/{len(results)} checks passed") + return 0 if all(results) else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/sandbox_sdk_contract.py b/scripts/sandbox_sdk_contract.py new file mode 100644 index 000000000..9957d6ee7 --- /dev/null +++ b/scripts/sandbox_sdk_contract.py @@ -0,0 +1,60 @@ +"""Check the actual optional SDK/Harbor imports without allocating resources.""" +import asyncio +import inspect +import json +import tempfile +from pathlib import Path +from types import SimpleNamespace + +from harbor.models.trial.config import TrialConfig +from opensandbox import Sandbox + +from harborrl.harnesses.offline_claude import OfflineClaudeCode +from harborrl.rollout.harbor_job.environments.opensandbox import ( + ManagedOpenSandboxEnvironment, +) +from harborrl.trajectories.native import read_json + +sdk = ManagedOpenSandboxEnvironment._load_opensandbox(None) +assert {'extensions', 'skip_health_check', 'ready_timeout'} <= inspect.signature(Sandbox.create).parameters.keys() +assert 'import_path' in TrialConfig.model_fields['environment'].annotation.model_fields +assert not inspect.isabstract(OfflineClaudeCode) + + +async def cleanup_contract(): + with tempfile.TemporaryDirectory() as directory: + env = object.__new__(ManagedOpenSandboxEnvironment) + env._startup_transport = None + env.cfg = {'cleanup_timeout_s': 1} + env.evidence = Path(directory) + env.binding = {'image': 'image-test'} + env.record = {'sandbox_id': 'sandbox-test', 'runtime_digest': 'test', 'events': []} + calls = [] + + async def kill(): + calls.append('kill') + + async def close(): + calls.append('close') + + env._sandbox = SimpleNamespace(kill=kill, close=close) + await env.stop() + assert calls == ['kill', 'close'] + assert read_json(env.evidence / 'sandbox-lifecycle.json')['state'] == 'deleted' + + async def failed_kill(): + raise RuntimeError('secret-must-not-appear') + + env._sandbox = SimpleNamespace(kill=failed_kill, close=close) + try: + await env.stop() + except RuntimeError as exc: + assert str(exc) == 'Sandbox deletion failed' + else: + raise AssertionError('deletion failure was swallowed') + assert 'secret-must-not-appear' not in (env.evidence / 'cleanup-required.json').read_text() + assert read_json(env.evidence / 'sandbox-lifecycle.json')['state'] == 'delete_failed' + + +asyncio.run(cleanup_contract()) +print(json.dumps({'sdk_symbols': sorted(sdk), 'agent': OfflineClaudeCode.__name__, 'status': 'passed'})) diff --git a/scripts/sandbox_smoke.py b/scripts/sandbox_smoke.py new file mode 100644 index 000000000..c297c381d --- /dev/null +++ b/scripts/sandbox_smoke.py @@ -0,0 +1,65 @@ +"""Explicit single-instance live probe. Uses real quota and always attempts deletion.""" +import argparse +import asyncio +import json +import shlex +from pathlib import Path +from urllib.parse import urlsplit + +from harbor.models.task.config import EnvironmentConfig +from harbor.models.trial.paths import TrialPaths + +from harborrl.rollout.harbor_job.sandbox_config import environment_class, load_sandbox +from harborrl.rollout.harbor_job.sandbox_probe import check +from harborrl.trajectories.native import publish + + +async def run(args): + cfg = load_sandbox(args.config) + check(cfg) + root = Path(args.output).resolve() + root.mkdir(parents=True, exist_ok=False) + task = root / 'environment' + task.mkdir() + binding = cfg['bindings'][0] + adapter = environment_class(cfg) + env = adapter( + environment_dir=task, environment_name='sandbox-smoke', session_id=root.name, + trial_paths=TrialPaths(root / 'trial'), task_env_config=EnvironmentConfig(docker_image=binding['image']), + sandbox_config=cfg, binding=binding, evidence_dir=str(root), startup_dir=str(root / 'startup'), + gateway_url=args.gateway) + results = {} + try: + await env.start(False) + ok = await env.exec('printf sandbox-ok') + assert ok.return_code == 0 and ok.stdout == 'sandbox-ok', 'positive command failed' + negative = await env.exec('exit 7') + assert negative.return_code == 7, 'negative command exit code lost' + source = root / 'source.bin' + source.write_bytes(b'harborrl\x00sandbox\xff\n') + await env.upload_file(source, '/tmp/harborrl-smoke.bin') + await env.download_file('/tmp/harborrl-smoke.bin', root / 'download.bin') + assert source.read_bytes() == (root / 'download.bin').read_bytes(), 'file roundtrip failed' + agent_gateway = adapter.agent_gateway(cfg, args.gateway) + gateway = urlsplit(agent_gateway) + if gateway.scheme not in ('http', 'https') or not gateway.hostname or gateway.path not in ('', '/'): + raise ValueError('gateway must be an HTTP origin') + command = f'curl -sS -o /dev/null --connect-timeout 10 --max-time 20 {shlex.quote(agent_gateway)}' + connected = await env.exec(command) + publish(root / 'gateway-probe.json', {'exit_code': connected.return_code, + 'stdout': connected.stdout, 'stderr': connected.stderr}) + assert connected.return_code == 0, 'Sandbox cannot reach Gateway HTTP endpoint' + results.update(command=True, negative_exit=True, files=True, gateway_http=True) + finally: + await env.stop(True) + results['deleted'] = True + publish(root / 'smoke-result.json', results) + print(json.dumps(results)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('config') + parser.add_argument('--output', required=True) + parser.add_argument('--gateway', required=True) + asyncio.run(run(parser.parse_args())) diff --git a/scripts/sync_options_manifest.py b/scripts/sync_options_manifest.py new file mode 100644 index 000000000..ea0370ef3 --- /dev/null +++ b/scripts/sync_options_manifest.py @@ -0,0 +1,22 @@ +"""Generate the repository compatibility manifest from packaged metadata.""" +import argparse +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--check", action="store_true") + args = parser.parse_args() + source = ROOT / "harborrl/options/resources/datasets.yaml" + target = ROOT / "configs/harbor_hub/manifests.yaml" + expected = source.read_text(encoding="utf-8") + if args.check: + return int(target.read_text(encoding="utf-8") != expected) + target.write_text(expected, encoding="utf-8") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tests/harborrl/test_delivery_barrier.py b/tests/harborrl/test_delivery_barrier.py new file mode 100644 index 000000000..e6c0f0955 --- /dev/null +++ b/tests/harborrl/test_delivery_barrier.py @@ -0,0 +1,41 @@ +"""Delivery acknowledgements must precede native-session consumption audit.""" +import threading + +import pytest + +from harborrl.gateway.trace import TraceRegistry + + +def test_delivery_barrier_waits_and_wakes(tmp_path): + registry = TraceRegistry(tmp_path) + registry.attempts["attempt"] = {"pending": {"request"}, "turns": []} + entered = threading.Event() + finished = threading.Event() + + def wait(): + entered.set() + registry.wait_delivered("attempt", 2) + finished.set() + + worker = threading.Thread(target=wait) + worker.start() + assert entered.wait(1) + assert not finished.is_set() + with registry.changed: + registry.attempts["attempt"]["pending"].clear() + registry.attempts["attempt"]["turns"].append({"delivery": "sent"}) + registry.changed.notify_all() + worker.join(3) + assert finished.is_set() + assert not worker.is_alive() + + +def test_delivery_barrier_rejects_pending_and_unknown(tmp_path): + registry = TraceRegistry(tmp_path) + registry.attempts["attempt"] = {"pending": {"request"}, "turns": []} + with pytest.raises(TimeoutError, match="pending"): + registry.wait_delivered("attempt", 0) + registry.attempts["attempt"]["pending"].clear() + registry.attempts["attempt"]["turns"].append({"delivery": "delivery_unknown"}) + with pytest.raises(ValueError, match="not confirmed"): + registry.wait_delivered("attempt", 0) diff --git a/tests/harborrl/test_deployment_config.py b/tests/harborrl/test_deployment_config.py new file mode 100644 index 000000000..8e6eb9f46 --- /dev/null +++ b/tests/harborrl/test_deployment_config.py @@ -0,0 +1,176 @@ +"""Deployment schema v1: parsing, strictness, safety and defaults.""" +from __future__ import annotations + +import json + +import pytest + +from harborrl.deployment.schema import ( + DeploymentError, + embedded_local, + load_deployment, + summary, + validate, +) + + +def deployment_mapping(tmp_path, **overrides): + values = { + "schema_version": 1, + "manager": { + "id": "manager-a", "mode": "service", "bind": "0.0.0.0", + "control_port": 49200, "control_url": "http://manager-a:49200", + "gateway_port": 49184, "gateway_url": "http://manager-a:49184", + "run_token_file": str(tmp_path / "secrets" / "run-token"), + "source_dir": str(tmp_path / "manager-source"), + "controller_python": str(tmp_path / "manager-venv" / "bin" / "python"), + }, + "gpu_target": { + "id": "gpu-8x-h200", "transport": "ssh", + "ssh": {"host": "gpu-cluster-a"}, + "source_dir": str(tmp_path / "gpu-source"), + "train_python": str(tmp_path / "gpu-venv" / "bin" / "python"), + "environment_file": str(tmp_path / "gpu-env.json"), + "expected_gpus": 8, + }, + "storage": { + "mode": "shared_fs", "run_root": str(tmp_path / "runs"), + "manager_writable": True, "gpu_writable": True, "worker_writable": True, + }, + "rollout": { + "provider": "ssh_docker", + "workers": [ + {"id": "worker-a", "ssh": {"host": "worker-a"}, + "source_dir": str(tmp_path / "worker-source"), + "runner_python": str(tmp_path / "runner-venv" / "bin" / "python"), + "docker": True, "task_roots": [str(tmp_path / "tasks")]}, + {"id": "worker-b", "ssh": {"host": "worker-b", "port": 2222}, + "source_dir": str(tmp_path / "worker-source"), + "runner_python": str(tmp_path / "runner-venv" / "bin" / "python"), + "docker": True, "task_roots": [str(tmp_path / "tasks")]}, + ], + }, + "connectivity": { + "command_timeout_s": 30, "ssh_timeout_s": 15, "http_timeout_s": 10, + "gpu_to_controller": True, "worker_to_gateway": True, + "manager_to_gpu_inference": True, + }, + } + for section, fields in overrides.items(): + if section in values and isinstance(values[section], dict) and isinstance(fields, dict): + values[section].update(fields) + else: + values[section] = fields + return values + + +def test_valid_deployment_normalizes_and_summarizes(tmp_path): + config = validate(deployment_mapping(tmp_path)) + assert config["manager"]["control_url"] == "http://manager-a:49200" + assert config["rollout"]["workers"][1]["ssh"] == {"host": "worker-b", "port": 2222} + assert config["gpu_target"]["ssh"] == {"host": "gpu-cluster-a"} + plan_summary = summary(config, "deployment.yaml") + assert plan_summary["mode"] == "service" + assert plan_summary["gpu"]["transport"] == "ssh" + assert plan_summary["worker_ids"] == ["worker-a", "worker-b"] + assert plan_summary["config"] == config + assert summary(config, "deployment.yaml")["digest"] == plan_summary["digest"] + + +@pytest.mark.parametrize("mutation, message", [ + ({"manager": {"mode": "service", "control_url": "http://127.0.0.1:49200"}}, "loopback"), + ({"manager": {"control_url": "http://manager-a:49300"}}, "matching its declared port"), + ({"manager": {"gateway_port": 49200}}, "must differ"), + ({"manager": {"bind": ""}}, "bind"), + ({"gpu_target": {"transport": "local", "ssh": {"host": "gpu"}}}, "null for local"), + ({"gpu_target": {"transport": "rdma"}}, "transport"), + ({"gpu_target": {"expected_gpus": 0}}, "expected_gpus"), + ({"rollout": {"workers": []}}, "nonempty list"), + ({"connectivity": {"ssh_timeout_s": 0}}, "ssh_timeout_s"), + ({"storage": {"run_root": "relative/runs"}}, "absolute path"), +]) +def test_invalid_deployments_fail_closed(tmp_path, mutation, message): + with pytest.raises(DeploymentError, match=message): + validate(deployment_mapping(tmp_path, **mutation)) + + +def test_duplicate_worker_ids_and_secret_fields_rejected(tmp_path): + values = deployment_mapping(tmp_path) + values["rollout"]["workers"][1]["id"] = "worker-a" + with pytest.raises(DeploymentError, match="duplicate"): + validate(values) + values = deployment_mapping(tmp_path) + values["gpu_target"]["ssh"]["password"] = "hunter2" + with pytest.raises(DeploymentError, match="never stored"): + validate(values) + + +def test_service_mode_requires_reachable_urls_but_embedded_allows_loopback(tmp_path): + values = deployment_mapping(tmp_path, manager={"mode": "embedded"}) + values["manager"]["control_url"] = "http://127.0.0.1:49200" + values["manager"]["gateway_url"] = "http://127.0.0.1:49184" + assert validate(values)["manager"]["mode"] == "embedded" + + +def test_embedded_local_default_is_valid(): + config = embedded_local(run_root="/tmp/harborrl-runs", workers=("worker-a",)) + assert config["manager"]["mode"] == "embedded" + assert config["gpu_target"]["transport"] == "local" + assert config["rollout"]["provider"] == "embedded" + assert [worker["id"] for worker in config["rollout"]["workers"]] == ["worker-0"] + # Training-config SSH destinations are addresses, not ids: the embedded + # topology keeps slug ids while preserving the destination for access. + config = embedded_local(workers=("root@192.0.2.10", "worker-b")) + assert [worker["id"] for worker in config["rollout"]["workers"]] == [ + "worker-0", "worker-1"] + assert config["rollout"]["workers"][0]["ssh"]["host"] == "root@192.0.2.10" + + +def test_load_deployment_explicit_default_and_embedded(tmp_path, monkeypatch): + monkeypatch.chdir(tmp_path) + config, source = load_deployment() + assert source == "embedded-local" + assert config["manager"]["mode"] == "embedded" + + (tmp_path / "deployment.yaml").write_text( + json.dumps(deployment_mapping(tmp_path))) + config, source = load_deployment() + assert source == str(tmp_path / "deployment.yaml") + assert config["manager"]["mode"] == "service" + with pytest.raises(DeploymentError, match="not found"): + load_deployment(tmp_path / "missing.yaml") + + +def test_cli_dry_run_embeds_deployment_summary(tmp_path, capsys, monkeypatch): + from pathlib import Path + + from test_native_training import native_config + + config_path, task = native_config(tmp_path) + from harborrl.data.harbor.native_inspector import inspect_native + + catalog_path = Path( + json.loads(config_path.read_text().split("tasks: ", 1)[1].split("\n", 1)[0])["catalog"]) + catalog = json.loads(catalog_path.read_text()) + catalog[0]["task_digest"] = inspect_native(task)["task_digest"] + catalog_path.write_text(json.dumps(catalog)) + + deployment_file = tmp_path / "deployment.yaml" + deployment_file.write_text(json.dumps(deployment_mapping(tmp_path))) + + from harborrl.cli import main + + assert main(["train", str(config_path), "--deployment", str(deployment_file), + "--dry-run"]) == 0 + plan = json.loads(capsys.readouterr().out) + assert plan["deployment"]["mode"] == "service" + assert plan["deployment"]["gpu"]["transport"] == "ssh" + assert plan["deployment"]["source"] == str(deployment_file) + + # Without a deployment file the embedded-local topology is attached. + monkeypatch.chdir(tmp_path) + deployment_file.unlink() + assert main(["train", str(config_path), "--dry-run"]) == 0 + plan = json.loads(capsys.readouterr().out) + assert plan["deployment"]["mode"] == "embedded" + assert plan["deployment"]["source"] == "embedded-local" diff --git a/tests/harborrl/test_deployment_preflight.py b/tests/harborrl/test_deployment_preflight.py new file mode 100644 index 000000000..d7906c9be --- /dev/null +++ b/tests/harborrl/test_deployment_preflight.py @@ -0,0 +1,241 @@ +"""Deployment preflight graph: role-layered checks with faked SSH transport.""" +from __future__ import annotations + +import json +import socket +import subprocess +from pathlib import Path + +from test_deployment_config import deployment_mapping +from test_native_training import native_config + +from harborrl.deployment import preflight as deployment_preflight +from harborrl.deployment.schema import embedded_local, validate + + +def test_bindable_rejects_existing_listener(): + with socket.socket() as listener: + listener.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + listener.bind(('127.0.0.1', 0)) + listener.listen() + ok, _ = deployment_preflight._bindable(*listener.getsockname()) + assert not ok + + +def test_bindable_allows_http_server_restart_after_connection_close(): + with socket.socket() as listener: + listener.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + listener.bind(('127.0.0.1', 0)) + listener.listen() + address = listener.getsockname() + with socket.create_connection(address, timeout=2) as client: + accepted, _ = listener.accept() + with accepted: + accepted.settimeout(2) + accepted.shutdown(socket.SHUT_WR) + assert client.recv(1) == b'' + client.shutdown(socket.SHUT_WR) + assert accepted.recv(1) == b'' + ok, detail = deployment_preflight._bindable(*address) + assert ok, detail + + +def completed(returncode=0, stdout="", stderr=""): + return subprocess.CompletedProcess([], returncode, stdout, stderr) + + +def fake_remote(responses): + """Responses keyed by command prefix; unmatched shell tests succeed.""" + calls = [] + probe = json.dumps({"harbor_version": "0.23.0", "python": "3.12.13"}) + + def run_ssh(spec, command, *, timeout=30, connect_timeout=15): + calls.append((dict(spec), command)) + for prefix, result in responses.items(): + if command.startswith(prefix): + return completed(**result) if isinstance(result, dict) else result + if "runner --probe" in command: + return completed(stdout=probe) + if command.startswith("test "): + return completed(stdout="ok\n") # shell test protocol: && echo ok + return completed() + + return run_ssh, calls + + +def resolvable(deployment): + """Point service URLs at loopback so preflight resolution checks pass.""" + manager = deployment["manager"] + manager["control_url"] = f"http://127.0.0.1:{manager['control_port']}" + manager["gateway_url"] = f"http://127.0.0.1:{manager['gateway_port']}" + return deployment + + +def service_deployment(tmp_path): + return validate(deployment_mapping(tmp_path)) + + +def training_plan(tmp_path): + config_path, task = native_config(tmp_path) + from harborrl.config import load_config + from harborrl.data.harbor.native_inspector import inspect_native + + catalog_path = Path( + json.loads(config_path.read_text().split("tasks: ", 1)[1].split("\n", 1)[0])["catalog"]) + catalog = json.loads(catalog_path.read_text()) + catalog[0]["task_digest"] = inspect_native(task)["task_digest"] + catalog_path.write_text(json.dumps(catalog)) + return {"config": load_config(config_path)} + + +def by_name(checks, name): + return [check for check in checks if check.get("check") == name or check.get("path") == name] + + +def test_gpu_layer_probes_remote_machine_semantics(tmp_path, monkeypatch): + plan, deployment = training_plan(tmp_path), resolvable(service_deployment(tmp_path)) + (tmp_path / "runs").mkdir() + (tmp_path / "secrets").mkdir() + token = tmp_path / "secrets" / "run-token" + token.write_text("token\n") + token.chmod(0o600) + run_ssh, calls = fake_remote({ + "nvidia-smi": completed(stdout="0\n1\n2\n3\n4\n5\n6\n7\n"), + }) + monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh) + checks = deployment_preflight.preflight(plan, deployment) + assert all(check["ok"] for check in checks), checks + gpu_checks = [check for check in checks if check["role"] == "gpu_target"] + assert {check["check"] for check in gpu_checks} >= { + "gpu_target.ssh", "gpu_target.source_dir", "gpu_target.train_python", + "gpu_target.expected_gpus"} + assert all(check["machine"] == "gpu-8x-h200" for check in gpu_checks) + # Model paths are evaluated on the GPU machine, not from the manager. + tested = [command for _, command in calls if "test -d" in command] + assert any("/model" in command for command in tested) + + +def test_gpu_layer_reports_shortage_and_unreachable_ssh(tmp_path, monkeypatch): + plan, deployment = training_plan(tmp_path), service_deployment(tmp_path) + (tmp_path / "runs").mkdir() + token = tmp_path / "secrets" / "run-token" + token.parent.mkdir() + token.write_text("token\n") + token.chmod(0o600) + run_ssh, _ = fake_remote({"nvidia-smi": completed(stdout="0\n1\n")}) + monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh) + checks = deployment_preflight.preflight(plan, deployment) + shortage = by_name(checks, "gpu_target.expected_gpus") + assert shortage and not shortage[0]["ok"] + assert "visible GPUs" in shortage[0]["detail"] + + run_ssh, _ = fake_remote({"": completed(returncode=255, stderr="Permission denied")}) + monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh) + checks = deployment_preflight.preflight(plan, deployment) + unreachable = by_name(checks, "gpu_target.ssh") + assert unreachable and not unreachable[0]["ok"] + assert unreachable[0]["role"] == "gpu_target" + assert "hint" in unreachable[0] + + +def test_worker_layer_validates_runner_runtime_and_docker(tmp_path, monkeypatch): + plan, deployment = training_plan(tmp_path), service_deployment(tmp_path) + (tmp_path / "runs").mkdir() + token = tmp_path / "secrets" / "run-token" + token.parent.mkdir() + token.write_text("token\n") + token.chmod(0o600) + probe = json.dumps({"harbor_version": "0.23.0", "python": "3.12.13"}) + run_ssh, _ = fake_remote({ + "docker info": completed(returncode=1, stderr="Cannot connect to the Docker daemon"), + "cd ": completed(stdout=probe), + }) + monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh) + checks = deployment_preflight.preflight(plan, deployment) + docker = [check for check in checks if check["check"] == "worker.docker"] + assert len(docker) == 2 and not any(check["ok"] for check in docker) + assert all("Docker daemon" in check["hint"] for check in docker) + harbor = [check for check in checks if check["check"] == "worker.harbor_version"] + assert all(check["ok"] for check in harbor) + + run_ssh, _ = fake_remote({"cd ": completed(stdout=json.dumps( + {"harbor_version": "0.22.0", "python": "3.12.13"}))}) + monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh) + checks = deployment_preflight.preflight(plan, deployment) + harbor = [check for check in checks if check["check"] == "worker.harbor_version"] + assert not any(check["ok"] for check in harbor) + # a wrong python version is also a failed runtime gate + run_ssh, _ = fake_remote({"cd ": completed(stdout=json.dumps( + {"harbor_version": "0.23.0", "python": "3.11.4"}))}) + monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh) + checks = deployment_preflight.preflight(plan, deployment) + runtime = [check for check in checks if check["check"] == "worker.runner_runtime"] + assert not any(check["ok"] for check in runtime) + + +def test_manager_layer_checks_storage_token_and_ports(tmp_path): + plan = training_plan(tmp_path) + deployment = resolvable(service_deployment(tmp_path)) + checks = deployment_preflight.preflight(plan, deployment) + assert not by_name(checks, "storage.run_root")[0]["ok"] # run root missing + + (tmp_path / "runs").mkdir() + token = tmp_path / "secrets" / "run-token" + token.parent.mkdir() + token.write_text("token\n") + token.chmod(0o644) + checks = deployment_preflight.preflight(plan, deployment) + assert not by_name(checks, "manager.run_token_file")[0]["ok"] + token.chmod(0o600) + checks = deployment_preflight.preflight(plan, deployment) + assert by_name(checks, "manager.run_token_file")[0]["ok"] + assert by_name(checks, "manager.control_port")[0]["ok"] + + +def test_control_loop_layer_defers_live_dials_and_inference(tmp_path, monkeypatch): + plan, deployment = training_plan(tmp_path), service_deployment(tmp_path) + (tmp_path / "runs").mkdir() + token = tmp_path / "secrets" / "run-token" + token.parent.mkdir() + token.write_text("token\n") + token.chmod(0o600) + run_ssh, _ = fake_remote({}) + monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh) + monkeypatch.setattr(deployment_preflight.socket, "getaddrinfo", + lambda host, port: [(None, None, None, None, (host, port))]) + checks = deployment_preflight.preflight(plan, deployment) + inference = by_name(checks, "connectivity.manager_to_gpu_inference") + assert inference and inference[0]["ok"] and "deferred" in inference[0]["detail"] + dials = [check for check in checks if check["check"].endswith(".dial")] + assert dials and all("deferred" not in check["detail"] or check["ok"] for check in dials) + + +def test_doctor_gates_local_checks_on_gpu_transport(tmp_path, monkeypatch): + from harborrl.platform import preflight as platform_preflight + + plan = training_plan(tmp_path) + plan["deployment"] = {"config": service_deployment(tmp_path), + "source": "deployment.yaml"} + run_ssh, _ = fake_remote({"nvidia-smi": completed(stdout="0\n" * 8)}) + monkeypatch.setattr(platform_preflight, "_deployment", + lambda p: p.get("deployment", {}).get("config")) + monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh) + checks = platform_preflight.doctor(plan) + # Local GPU dependency probes do not run for a remote GPU target. + assert not any(check.get("dependency") == "ray" for check in checks) + assert not any(check.get("resource") == "visible GPU budget" for check in checks) + assert not any(check.get("service") == "external Harbor worker" for check in checks) + assert any(check.get("role") == "gpu_target" for check in checks) + + embedded = {"config": embedded_local(run_root=str(tmp_path / "runs")), + "source": "deployment.yaml"} + plan["deployment"] = embedded + checks = platform_preflight.doctor(plan) + assert any(check.get("dependency") == "ray" for check in checks) + assert any(check.get("resource") == "visible GPU budget" for check in checks) + + # The auto-derived embedded-local topology keeps the legacy check set. + plan["deployment"] = {"config": embedded_local(), "source": "embedded-local"} + legacy = platform_preflight.doctor(plan) + assert not any(check.get("role") == "gpu_target" for check in legacy) + assert any(check.get("dependency") == "ray" for check in legacy) diff --git a/tests/harborrl/test_deployment_targets.py b/tests/harborrl/test_deployment_targets.py new file mode 100644 index 000000000..ef609d9ad --- /dev/null +++ b/tests/harborrl/test_deployment_targets.py @@ -0,0 +1,235 @@ +"""TrainingTarget (P1): local child process and SSH launcher over shared storage.""" +from __future__ import annotations + +import json +import subprocess +import time + +import pytest + +from harborrl.deployment import training_target +from harborrl.deployment.training_target import ( + LocalTrainingTarget, + SshTrainingTarget, + TrainingTargetError, +) + + +def completed(returncode=0, stdout="", stderr=""): + return subprocess.CompletedProcess([], returncode, stdout, stderr) + + +def test_local_target_runs_and_forwards_exit_code(tmp_path): + target = LocalTrainingTarget(["bash", "-c", "exit 3"], cwd=str(tmp_path), env={"PATH": "/bin"}) + target.launch() + assert target.wait() == 3 + assert target.query()["state"] == "exited" + + +def test_local_target_cancel_interrupts(tmp_path): + target = LocalTrainingTarget(["bash", "-c", "trap '' INT; sleep 30"], + cwd=str(tmp_path), env={"PATH": "/bin"}) + target.launch() + time.sleep(0.2) + assert target.query()["state"] == "running" + target.cancel() + with pytest.raises(subprocess.TimeoutExpired): + target.child.wait(timeout=2) + import os + import signal + os.killpg(os.getpgid(target.child.pid), signal.SIGKILL) + target.child.wait() + + +def test_ssh_launcher_quotes_environment_and_captures_exit_code(tmp_path): + run_dir = tmp_path / "run" + run_dir.mkdir() + target = SshTrainingTarget({"host": "gpu"}, source_dir="/gpu/source", run_dir=run_dir) + launcher = target.write_launcher( + ["/manager/source/harborrl/platform/slime_train.sh", "--flag", "value with spaces"], + {"LR": "1e-6", "NOTE": "two spaces"}, + path_rewrites=[("/manager/source", "/gpu/source")]) + text = launcher.read_text() + assert "cd /gpu/source" in text + assert "/gpu/source/harborrl/platform/slime_train.sh" in text + assert "/manager/source" not in text + assert "export LR=1e-6" in text + assert "export NOTE='two spaces'" in text + assert "'value with spaces'" in text + assert "exit-code" in text + assert launcher.stat().st_mode & 0o111, "launcher must be executable" + + +def test_ssh_target_environment_file_is_loaded_via_python(tmp_path): + run_dir = tmp_path / "run" + run_dir.mkdir() + environment_file = tmp_path / "gpu-env.json" + environment_file.write_text(json.dumps({"SLIME_DIR": "/opt/slime"})) + target = SshTrainingTarget({"host": "gpu"}, source_dir="/gpu/source", run_dir=run_dir, + environment_file=environment_file) + launcher = target.write_launcher(["true"], {}) + text = launcher.read_text() + assert "HARBORRL_ENV_JSON" in text + assert "json.load" in text + + +def test_ssh_target_lifecycle_with_fake_remote(tmp_path, monkeypatch): + run_dir = tmp_path / "run" + run_dir.mkdir() + target = SshTrainingTarget({"host": "gpu", "port": 2222}, source_dir="/gpu/source", + run_dir=run_dir, poll_interval=0.05) + calls = [] + killed = {"done": False} + + def fake_run_ssh(spec, command, *, timeout=30, connect_timeout=15): + calls.append((dict(spec), command)) + if "setsid" in command: + (run_dir / "launcher.pid").write_text("4242\n") + return completed() + if "kill -0" in command: + return (completed(stdout="ok\n") if not killed["done"] + else completed(returncode=1)) + if command.startswith("kill -INT"): + killed["done"] = True + (run_dir / "exit-code").write_text("130\n") + return completed() + return completed() + + monkeypatch.setattr(training_target.remote, "run_ssh", fake_run_ssh) + target.write_launcher(["true"], {}) + target.launch() + with pytest.raises(TrainingTargetError): + target.launch() # double launch is refused + assert target.query() == {"state": "running", "exit_code": None, "pid": 4242, + "run_dir": str(run_dir)} + assert target.cancel() + assert target.wait() == 130 + assert target.query()["exit_code"] == 130 + # The ssh destination honors user/port from the normalized spec. + assert any(spec.get("port") == 2222 for spec, _ in calls), ( + "spec with port 2222 must reach run_ssh") + assert any("setsid" in command for _, command in calls) + + +def test_ssh_target_wait_timeout(tmp_path, monkeypatch): + run_dir = tmp_path / "run" + run_dir.mkdir() + target = SshTrainingTarget({"host": "gpu"}, source_dir="/gpu/source", run_dir=run_dir, + poll_interval=0.05) + + def fake_run_ssh(spec, command, *, timeout=30, connect_timeout=15): + if "setsid" in command: + (run_dir / "launcher.pid").write_text("99\n") + return completed() + + monkeypatch.setattr(training_target.remote, "run_ssh", fake_run_ssh) + target.write_launcher(["true"], {}) + target.launch() + with pytest.raises(TimeoutError): + target.wait(timeout=0.2) + + +def test_ssh_command_assembles_noninteractive_ssh(): + from harborrl.deployment.remote import ssh_command + + argv = ssh_command({"host": "gpu", "user": "root", "port": 2222, + "identity": "/key"}, "true", connect_timeout=7) + assert argv[0] == "ssh" + assert argv[1:7] == ["-o", "BatchMode=yes", "-o", "ClearAllForwardings=yes", + "-o", "StrictHostKeyChecking=accept-new"] + assert argv[7:9] == ["-o", "ConnectTimeout=7"] + assert argv[9:13] == ["-p", "2222", "-i", "/key"] + assert argv[-2:] == ["root@gpu", "true"] + + +def test_service_dispatch_launches_gpu_job_through_controller(tmp_path, monkeypatch, capsys): + """P1+P3 manager flow: materialize, register the run, launch, wait, report.""" + import json as json_module + from pathlib import Path + + from test_deployment_config import deployment_mapping + from test_native_training import native_config + + from harborrl.config import launch_plan, load_config + from harborrl.data.harbor.native_inspector import inspect_native + from harborrl.deployment import controller as controller_module + from harborrl.deployment.schema import summary, validate + + config_path, task = native_config(tmp_path) + catalog_path = Path(json_module.loads( + config_path.read_text().split("tasks: ", 1)[1].split("\n", 1)[0])["catalog"]) + catalog = json_module.loads(catalog_path.read_text()) + catalog[0]["task_digest"] = inspect_native(task)["task_digest"] + catalog_path.write_text(json_module.dumps(catalog)) + config = load_config(config_path) + config["output"]["root"] = str(tmp_path / "shared-runs") + plan = launch_plan(config) + + deployment = validate(deployment_mapping(tmp_path, storage={ + "run_root": str(tmp_path / "shared-runs")})) + manager = deployment["manager"] + token_file = Path(manager["run_token_file"]) + token_file.parent.mkdir(parents=True, exist_ok=True) + token_file.write_text("token\n") + token_file.chmod(0o600) + (tmp_path / "shared-runs").mkdir() + plan["deployment"] = summary(deployment, "deployment.yaml") + + launched = {} + + class FakeControl: + def __init__(self, service, host, port): + self.address = (host, port) + + def serve_forever(self): + pass + + def shutdown(self): + pass + + def server_close(self): + pass + + class FakeTarget: + def __init__(self, spec, *, source_dir, run_dir, **kwargs): + self.source_dir, self.run_dir = source_dir, Path(run_dir) + + def write_launcher(self, command, environment, *, path_rewrites=()): + launched["command"] = list(command) + launched["environment"] = dict(environment) + launched["rewrites"] = list(path_rewrites) + return self.run_dir / "launcher.sh" + + def launch(self): + launched["run_dir"] = str(self.run_dir) + (self.run_dir / "exit-code").write_text("0\n") + + def wait(self): + return 0 + + def cancel(self): + return True + + monkeypatch.setattr(controller_module, "make_control_server", FakeControl) + monkeypatch.setattr(controller_module, "SshTrainingTarget", FakeTarget) + monkeypatch.setattr(controller_module, 'check_gpu_control', lambda _: {'gpu_to_controller': True}) + + exit_code = controller_module.dispatch_service(plan, deployment, checks=[]) + assert exit_code == 0 + run_dir = Path(launched["run_dir"]) + assert run_dir.is_dir() + environment = launched["environment"] + assert environment["HARBORRL_ROLLOUT_MODE"] == "service" + assert environment["HARBORRL_ROLLOUT_SERVICE_URL"] == manager["control_url"] + assert environment['HARBORRL_ROLLOUT_SERVICE_TOKEN_FILE'] == str(token_file) + assert 'HARBORRL_ROLLOUT_SERVICE_TOKEN' not in environment + assert environment["HARBORRL_NATIVE_GATEWAY_URL"] == manager["gateway_url"] + assert json_module.loads(environment["HARBORRL_NATIVE_RUNNER_WORKERS"]) == [ + "worker-a", "worker-b"] + # The launcher runs inside the GPU checkout with a rewritten script path. + assert launched["rewrites"] == [(manager["source_dir"], + deployment["gpu_target"]["source_dir"])] + launch_record = json_module.loads((run_dir / "launch.json").read_text()) + assert launch_record["control_url"] == manager["control_url"] + effective = json_module.loads((run_dir / "effective-config.json").read_text()) + assert effective["components"]["execution_backend"] == "harbor_job" diff --git a/tests/harborrl/test_math_interfaces.py b/tests/harborrl/test_math_interfaces.py new file mode 100644 index 000000000..a17788fc1 --- /dev/null +++ b/tests/harborrl/test_math_interfaces.py @@ -0,0 +1,126 @@ +"""CPU contracts for the explicit MATH component interfaces.""" +import json +from dataclasses import replace + +import pytest + +from harborrl.math_registry import ( + BACKEND_CONTRACT, + list_components, + select_math, + validate_combination, +) +from harborrl.models.qwen import QwenModelAdapter +from harborrl.tasks.jsonl_provider import JsonlTaskProvider +from harborrl.tasks.provider import NativeTaskProvider + + +def math_config(): + return { + "execution": {"backend": "harbor_job"}, + "harness": {"name": "claude_code"}, + "harbor": {"version": "0.23.0"}, + "gateway": {"audited_raw_logprobs": True}, + "model": {"args_file": "qwen3-8B"}, + "training": {"backend_contract": BACKEND_CONTRACT}, + "sampling": {"group_size": 2}, + } + + +def test_registry_lists_versioned_default_components(): + components = list_components() + assert [spec["id"] for spec in components["models"]] == ["qwen"] + assert [spec["id"] for spec in components["algorithms"]] == ["grpo"] + assert [spec["id"] for spec in components["tasks"]] == ["harbor_jsonl", "native_harbor"] + assert [spec["id"] for spec in components["harnesses"]] == ["claude_code"] + for dimension in components.values(): + for spec in dimension: + assert spec["version"] + assert spec["evidence"] + assert spec["limitations"] + +def test_combination_checks_reject_incompatible_evidence_and_groups(): + config = math_config() + selection = select_math(config) + assert validate_combination(selection, config) is selection + + unaudited = {**config, "gateway": {"audited_raw_logprobs": False}} + with pytest.raises(ValueError, match="requires audited raw logprobs"): + validate_combination(selection, unaudited) + + larger_group = replace(selection.algorithm, minimum_group_size=3) + with pytest.raises(ValueError, match="requires group size"): + validate_combination(replace(selection, algorithm=larger_group), config) + + class Tokenizer: + def get(self, key): + assert key == "input_ids" + return [1, 2] + + def apply_chat_template(self, *args, **kwargs): + assert kwargs["enable_thinking"] is False + return self + + adapter = QwenModelAdapter() + converted = {"messages": [{"role": "user", "content": "run"}], "tools": []} + assert adapter.render_chat(Tokenizer(), converted) == [1, 2] + blocks = adapter.parse_output( + '{"name":"bash","arguments":{"command":"pwd"}}', + response_id="message", + tools=[{"function": {"name": "bash"}}], + ) + assert blocks[0]["type"] == "tool_use" + + +def test_jsonl_task_provider_is_selected_by_catalog_format(tmp_path): + task = tmp_path / "task" + (task / "environment").mkdir(parents=True) + (task / "tests").mkdir() + (task / "task.toml").write_text('version = "1.0"\n') + (task / "instruction.md").write_text("jsonl instruction\n") + (task / "environment/Dockerfile").write_text("FROM debian:bookworm\n") + (task / "tests/test.sh").write_text("true\n") + provider = JsonlTaskProvider() + digest = provider.inspect(task)["task_digest"] + catalog = tmp_path / "catalog.jsonl" + catalog.write_text(json.dumps({ + "id": "task", "revision": "1", "path": str(task), + "task_digest": digest, "reward_profile": {"key": "reward"}, + }) + "\n") + config = {**math_config(), "tasks": {"catalog": str(catalog)}} + selection = select_math(config) + assert selection.task.id == "harbor_jsonl" + entries = provider.catalog(config) + rows = provider.prompt_rows(entries) + assert rows[0]["task"] == "jsonl instruction\n" + assert rows[0]["metadata"]["task_provider"] == "harbor_jsonl" + + incompatible = {**config, "execution": {"backend": "process"}} + with pytest.raises(ValueError, match="does not support execution backend"): + validate_combination(select_math(incompatible), incompatible) + + catalog.write_text("{broken\n") + with pytest.raises(ValueError, match="invalid JSONL catalog line"): + provider.catalog(config) + + +def test_native_task_provider_locks_and_materializes_prompts(tmp_path): + task = tmp_path / "task" + (task / "environment").mkdir(parents=True) + (task / "tests").mkdir() + (task / "task.toml").write_text('version = "1.0"\n') + (task / "instruction.md").write_text("original instruction\n") + (task / "environment/Dockerfile").write_text("FROM debian:bookworm\n") + (task / "tests/test.sh").write_text("true\n") + provider = NativeTaskProvider() + digest = provider.inspect(task)["task_digest"] + catalog = tmp_path / "catalog.json" + catalog.write_text(json.dumps([{ + "id": "task", "revision": "1", "path": str(task), + "task_digest": digest, "reward_profile": {"key": "reward"}, + }])) + config = {**math_config(), "tasks": {"catalog": str(catalog)}} + entries = provider.catalog(config) + rows = provider.prompt_rows(entries) + assert rows[0]["task"] == "original instruction\n" + assert rows[0]["metadata"]["native_task"]["task_digest"] == digest diff --git a/tests/harborrl/test_native_gateway.py b/tests/harborrl/test_native_gateway.py index d3a6d5ffb..75ab7416e 100644 --- a/tests/harborrl/test_native_gateway.py +++ b/tests/harborrl/test_native_gateway.py @@ -65,9 +65,10 @@ def test_tool_roundtrip(): convert(p, model="policy") -def test_claude_2_1_220_control_turns_are_accepted_without_hidden_sampling(): +@pytest.mark.parametrize("effort", ["high", "xhigh"]) +def test_claude_2_1_control_turns_are_accepted_without_hidden_sampling(effort): converted = convert(payload( - output_config={"effort": "high"}, + output_config={"effort": effort}, thinking={"type": "adaptive"}, context_management={"edits": [{"type": "clear_thinking_20251015", "keep": "all"}]}, system=[{"type": "text", "text": "locked system prompt"}], @@ -239,6 +240,32 @@ def test_nonfinite_generated_tool_is_rejected(): response_id="m", tools=[{"function": {"name": "bash"}}]) +def test_generated_tool_errors_distinguish_name_and_arguments(): + tools = [{"function": {"name": "bash"}}] + with pytest.raises(ProtocolError, match="unknown generated tool"): + parse_qwen_output('{"name":"sh","arguments":{}}', + response_id="m", tools=tools) + with pytest.raises(ProtocolError, match="arguments are not an object"): + parse_qwen_output('{"name":"bash","arguments":[]}', + response_id="m", tools=tools) + + +def test_generated_tool_name_alias_is_canonicalized_only_when_unique(): + tools = [{"function": {"name": "Bash"}}] + output = parse_qwen_output('{"name":"bash","arguments":{"command":"pwd"}}', + response_id="m", tools=tools) + assert output[0]["name"] == "Bash" + with pytest.raises(ProtocolError, match="unknown generated tool"): + parse_qwen_output('{"name":"sh","arguments":{}}', + response_id="m", tools=tools) + + +def test_qwen_finish_marker_maps_to_empty_end_turn(): + output = parse_qwen_output('{"name":"finish","arguments":{}}', + response_id="m", tools=[{"function": {"name": "Bash"}}]) + assert output == [] + + def test_sglang_generation_version_and_logprob_evidence(): from harborrl.gateway.sglang_backend import SGLangBackend class Tokenizer: @@ -246,7 +273,11 @@ def apply_chat_template(self, *args, **kwargs): return [11, 12] def decode(self, token_ids, skip_special_tokens): return "done" - backend = SGLangBackend("http://engine", Tokenizer(), tokenizer_digest="tok", template_digest="tpl") + from harborrl.models.qwen import QwenModelAdapter + backend = SGLangBackend( + "http://engine", Tokenizer(), QwenModelAdapter(), + tokenizer_digest="tok", template_digest="tpl", + ) converted = convert(payload(), model="policy") serving = {"input_ids": backend.prepare(converted)} output = {"text": "done", "meta_info": {"weight_version": "1", "output_token_logprobs": [[-.2, 13]], @@ -293,7 +324,31 @@ def open(self, request, timeout): assert timeout == 30 return Response() - backend = SGLangBackend("http://engine", SimpleNamespace(), tokenizer_digest="tok", - template_digest="tpl", timeout=30) + from harborrl.models.qwen import QwenModelAdapter + backend = SGLangBackend( + "http://engine", SimpleNamespace(), QwenModelAdapter(), + tokenizer_digest="tok", template_digest="tpl", timeout=30, + ) backend.opener = Opener() assert backend.weight_version() == "7" + + +def test_native_gateway_thread_does_not_own_asyncio_default_executor(): + from harborrl.rollout.native_generate import NativeRolloutRuntime + + started, stopped = threading.Event(), threading.Event() + + class Server: + def serve_forever(self): + started.set() + stopped.wait(5) + + runtime = object.__new__(NativeRolloutRuntime) + runtime.server = Server() + runtime._start_gateway() + try: + assert started.wait(1) + assert runtime.thread.daemon is True + finally: + stopped.set() + runtime.thread.join(1) diff --git a/tests/harborrl/test_native_lifecycle.py b/tests/harborrl/test_native_lifecycle.py index bdc76b9ea..a36ef0fd9 100644 --- a/tests/harborrl/test_native_lifecycle.py +++ b/tests/harborrl/test_native_lifecycle.py @@ -35,7 +35,12 @@ def test_restart_cannot_reopen_persisted_attempt(tmp_path): def test_profile_controls_reach_agent_and_host(): config = claude_config(PROFILE, "http://gateway:8000", "token") + assert config["kwargs"]["disallowed_tools"] == "Agent,Task" + disabled = claude_config({**PROFILE, "disallowed_tools": "Agent,Task,WebSearch,WebFetch"}, + "http://gateway:8000", "token") + assert disabled["kwargs"]["disallowed_tools"] == "Agent,Task,WebSearch,WebFetch" assert config["env"]["DISABLE_AUTO_COMPACT"] == "1" + assert config["env"]["ANTHROPIC_MODEL"] == "policy" assert config["env"]["NO_PROXY"] == "gateway" assert config["env"]["no_proxy"] == "gateway" assert config["kwargs"]["version"] == "2.1.0" diff --git a/tests/harborrl/test_native_terminal.py b/tests/harborrl/test_native_terminal.py new file mode 100644 index 000000000..eb94c8a98 --- /dev/null +++ b/tests/harborrl/test_native_terminal.py @@ -0,0 +1,111 @@ +"""Regression coverage for the full coordinator success path and empty finals.""" +import asyncio +import json +import sys + +import pytest +from test_native_contracts import identity, make_ir +from test_native_lifecycle import PROFILE + +from harborrl.gateway.trace import TraceRegistry +from harborrl.harnesses.claude_code import ClaudeCodeHarnessAdapter +from harborrl.rollout.harbor_job.coordinator import run_attempt +from harborrl.trajectories.native import RewardProfile, digest + + +def test_casefold_tool_alias_must_be_unique(): + from harborrl.gateway.messages import ProtocolError, parse_qwen_output + with pytest.raises(ProtocolError, match="ambiguous"): + parse_qwen_output('{"name":"BASH","arguments":{}}', + response_id="r", tools=[{"function": {"name": "Bash"}}, + {"function": {"name": "bash"}}]) + + +@pytest.mark.parametrize("empty", [False, True]) +def test_coordinator_publishes_complete_success(tmp_path, monkeypatch, empty): + ident = identity() + trial_id = "trial-" + ident.attempt_id + root = tmp_path / "attempt" + trial = root / "runner/harbor/trial" + template = make_ir(tmp_path / "fixture", nturns=2) + + class Registry(TraceRegistry): + def register(self, ident, trial_id, **kwargs): + token = super().register(ident, trial_id, **kwargs) + turns = template["turns"] + for index, original in enumerate(turns): + aid, request, _, _ = self.begin(token) + turn = {**original, "request_id": request, "delivery": "sent"} + if empty and index == 1: + turn["response"] = {"content": []} + turn["response_digest"] = digest(turn["response"]) + self.finish(aid, request, turn) + self.delivery(aid, turn["response_id"], "sent") + session = trial / "agent/sessions/projects/session.jsonl" + session.parent.mkdir(parents=True) + session.write_text("\n".join(json.dumps({ + "type": "assistant", "sessionId": "session", "message": { + "id": turn["response_id"], "role": "assistant", + "content": turn["response"]["content"]}}) + for turn in turns[:1 if empty else 2])) + (trial / "agent/claude-code.txt").write_text(json.dumps({ + "type": "result", "subtype": "success", "is_error": False, + "result": "", "stop_reason": "end_turn", "session_id": "session", + "terminal_reason": "completed"})) + (trial / "verifier").mkdir() + (trial / "verifier/reward.json").write_text('{"reward": 1}') + (trial / "result.json").write_text(json.dumps({ + "id": trial_id, "finished_at": "2026-10-19T00:00:00Z", + "verifier_environment_mode": "shared", + "verifier_result": {"rewards": {"reward": 1}}})) + return token + + worker = tmp_path / "worker.py" + terminal = {"type": "terminal", "trial_id": trial_id, + "trial_dir": str(trial), "result_path": str(trial / "result.json")} + worker.write_text("import json, sys\njson.loads(sys.stdin.readline())\n" + f"print({json.dumps({'type': 'prepared', 'trial_id': trial_id})!r}, flush=True)\n" + "sys.stdin.readline()\n" + f"print({json.dumps(terminal)!r}, flush=True)\n") + spawn = asyncio.create_subprocess_exec + + async def fake_spawn(*args, **kwargs): + return await spawn(sys.executable, "-S", str(worker), **kwargs) + + monkeypatch.setattr(asyncio, "create_subprocess_exec", fake_spawn) + ir = asyncio.run(run_attempt( + ident, task_path=tmp_path, profile=PROFILE, gateway_url="http://gateway", + runner_python=sys.executable, root=root, registry=Registry(tmp_path / "trace"), + reward_profile=RewardProfile())) + assert ir["readiness"] == "CONTRACT_READY" + assert json.loads((root / "trajectory.v2.json").read_text()) == ir + assert (root / "evaluation.json").is_file() + assert not (root / "cleanup-required.json").exists() + assert all(turn["delivery"] == "consumed_confirmed" for turn in ir["turns"]) + + +@pytest.mark.parametrize("change", [ + {"content": [{"type": "text", "text": "missing"}]}, + {"finish_reason": "length"}, {"is_final": False}, +]) +def test_empty_final_rejects_other_missing_responses(tmp_path, change): + class Registry: + def unconsumed_responses(self, aid): + return [{"response_id": "r", "content": [], "finish_reason": "end_turn", + "is_final": True, **change}] + with pytest.raises(ValueError, match="only one empty final"): + ClaudeCodeHarnessAdapter().audit_empty_final(tmp_path / "absent", Registry(), "a", {}) + + +@pytest.mark.parametrize("change", [{"session_id": "wrong"}, {"is_error": True}, + {"subtype": "error_max_turns"}, {"result": "nonempty"}, {"stop_reason": "max_tokens"}]) +def test_empty_final_requires_cli_consumption_evidence(tmp_path, change): + class Registry: + def unconsumed_responses(self, aid): + return [{"response_id": "r", "content": [], "finish_reason": "end_turn", "is_final": True}] + path = tmp_path / "claude-code.txt" + path.write_text(json.dumps({"type": "result", "subtype": "success", "is_error": False, + "result": "", "stop_reason": "end_turn", "session_id": "session", + "terminal_reason": "completed", **change})) + with pytest.raises(ValueError, match="matching session result"): + ClaudeCodeHarnessAdapter().audit_empty_final(path, Registry(), "a", {"session_id": "session"}) diff --git a/tests/harborrl/test_native_training.py b/tests/harborrl/test_native_training.py index 40ce2b565..ecfd6a24e 100644 --- a/tests/harborrl/test_native_training.py +++ b/tests/harborrl/test_native_training.py @@ -142,8 +142,19 @@ def test_cli_command_mode_selects_experiment_dimensions(tmp_path, capsys, monkey assert positional_plan["config"]["schema_version"] == 2 -def test_native_shell_dry_run_uses_official_v032_hooks(tmp_path): - native_config(tmp_path) +@pytest.mark.parametrize('overrides, expected_lr', [ + ([], '0.0001'), + (['training.learning_rate=0.000025'], '2.5e-05'), +]) +def test_native_shell_dry_run_uses_official_v032_hooks(tmp_path, overrides, expected_lr): + config_path, task = native_config(tmp_path) + from harborrl.data.harbor.native_inspector import inspect_native + config = load_config(config_path, overrides) + catalog_path = Path(config['tasks']['catalog']) + entries = json.loads(catalog_path.read_text()) + entries[0]['task_digest'] = inspect_native(task)['task_digest'] + catalog_path.write_text(json.dumps(entries)) + plan = launch_plan(config) prompt = tmp_path / "native.jsonl" prompt.write_text('{"task":"fixture","metadata":{"harborrl_native":true}}\n') root = tmp_path / "run" @@ -154,6 +165,7 @@ def test_native_shell_dry_run_uses_official_v032_hooks(tmp_path): (model_args / "qwen3-8B.sh").write_text("MODEL_ARGS=(--hidden-size 4096)\n") env = {key: value for key, value in os.environ.items() if key in {"PATH", "HOME", "USER", "LANG", "LC_ALL", "TMPDIR"}} + env.update(plan["environment"]) env.update({ "DRY_RUN": "1", "DATASET": "native", "ALGO": "grpo", "HARNESS_OPTION": "native-claude-code", "HARBORRL_NATIVE_ROLLOUT": "1", @@ -162,14 +174,16 @@ def test_native_shell_dry_run_uses_official_v032_hooks(tmp_path): "MODEL_TAG": "qwen3-8B", "NUM_GPUS": "8", "ACTOR_GPUS": "4", "ROLLOUT_GPUS": "4", "TP_SIZE": "4", "ROLLOUT_NUM_GPUS_PER_ENGINE": "4", "HARBORRL_GPU_LAYOUT": "split", "NUM_ROLLOUT": "1", "SAVE_INTERVAL": "1", - "ROLLOUT_BATCH_SIZE": "1", "N_SAMPLES": "2", "RUN_DIR": str(root), "RUN_ID": "run", + "RUN_DIR": str(root), "RUN_ID": "run", "RUNS_ROOT": str(tmp_path), "CKPT_ROOT": str(root / "checkpoints"), "HARBORRL_NATIVE_MAX_ATTEMPTS": "2", "HARBORRL_NATIVE_DRAIN_TIMEOUT": "1", "HARBORRL_NATIVE_RUNNER_WORKERS": '["worker-a", "worker-b"]', "HARBORRL_NATIVE_GATEWAY_HOST": "0.0.0.0", "HARBORRL_NATIVE_SLIME_CONTRACT": "slime-v032", "SLIME_DIR": str(slime), + "TRAIN_PYTHON": sys.executable, }) + env['LR'] = plan['environment']['LR'] script = Path(__file__).resolve().parents[2] / "harborrl/platform/slime_train.sh" output = tmp_path / "shell.log" with output.open("w") as stream: @@ -179,6 +193,24 @@ def test_native_shell_dry_run_uses_official_v032_hooks(tmp_path): assert result.returncode == 0, result.stdout command = next(line for line in result.stdout.splitlines() if line.startswith("[dry-run] ")) assert "--loss-type custom_loss" in command + import shlex + arguments = shlex.split(command.removeprefix('[dry-run] ')) + assert arguments[arguments.index('--lr') + 1] == expected_lr + expected_flags = { + '--num-rollout': '1', '--save-interval': '1', + '--rollout-batch-size': '1', '--n-samples-per-prompt': '2', + '--rollout-max-response-len': '128', '--rollout-max-context-len': '1024', + '--num-gpus-per-node': '8', '--actor-num-gpus-per-node': '4', + '--rollout-num-gpus': '4', '--tensor-model-parallel-size': '4', + '--rollout-num-gpus-per-engine': '4', '--hf-checkpoint': '/model', + '--ref-load': '/reference', '--prompt-data': str(prompt), + '--save': str(root / 'checkpoints' / 'run'), + } + for flag, value in expected_flags.items(): + assert arguments[arguments.index(flag) + 1] == value + recorded = json.loads(next((root / 'dry-run-commands').glob('*.json')).read_text()) + assert recorded['command'] == arguments + assert recorded['mode'] == 'dry_run' for argument in ( "--rollout-function-path harborrl.backends.slime_v032.rollout.generate_rollout", "--custom-convert-samples-to-train-data-path harborrl.backends.slime_v032.converter.convert_samples_to_train_data", @@ -208,96 +240,57 @@ def test_native_shell_dry_run_uses_official_v032_hooks(tmp_path): assert "--no-load-optim" in command -def test_native_runtime_binds_gateway_with_registry_and_backend(tmp_path, monkeypatch): - from harborrl.gateway import server as gateway_server - from harborrl.gateway import sglang_backend, trace - from harborrl.rollout import native_generate +def test_slime_runtime_config_maps_args_and_environment(tmp_path, monkeypatch): + from harborrl.backends.slime_v032 import rollout as slime_rollout + from harborrl.rollout.runtime import RolloutRuntimeConfig catalog = tmp_path / "catalog.json" catalog.write_text("[]") profile = tmp_path / "profile.json" profile.write_text(json.dumps({"model": "policy"})) - tokenizer = SimpleNamespace( - apply_chat_template=lambda *args, **kwargs: [1, 2], - get_vocab=lambda: {"token": 1}, - ) - tokenizer_digest, template_digest = native_generate.tokenizer_fingerprint(tokenizer) environment = { - "RUN_DIR": str(tmp_path), "HARBORRL_NATIVE_CATALOG": str(catalog), + "RUN_ID": "run-1", "RUN_DIR": str(tmp_path), + "HARBORRL_NATIVE_CATALOG": str(catalog), "HARBORRL_NATIVE_PROFILE": str(profile), - "HARBORRL_NATIVE_TOKENIZER_DIGEST": tokenizer_digest, - "HARBORRL_NATIVE_TEMPLATE_DIGEST": template_digest, "HARBORRL_NATIVE_GATEWAY_HOST": "127.0.0.1", "HARBORRL_NATIVE_GATEWAY_PORT": "49183", + "HARBORRL_NATIVE_GATEWAY_URL": "http://gateway:49183", + "HARBORRL_NATIVE_RUNNER_PYTHON": "/runner/python", + "HARBORRL_NATIVE_RUNNER_WORKERS": '["worker-a", "worker-b"]', + "HARBORRL_NATIVE_TOKENIZER_DIGEST": "0" * 64, + "HARBORRL_NATIVE_TEMPLATE_DIGEST": "1" * 64, + "ROLLOUT_MAX_RESPONSE_LEN": "128", "ROLLOUT_MAX_CONTEXT_LEN": "1024", } for key, value in environment.items(): monkeypatch.setenv(key, value) - - calls = {} - - class FakeGateway: - def __init__(self, registry, backend, *, model, max_output_tokens): - calls["gateway"] = (registry, backend, model, max_output_tokens) - calls["gateway_object"] = self - - class FakeServer: - def serve_forever(self): - pass - - def shutdown(self): - pass - - def server_close(self): - pass - - class FakeTraceRegistry: - def __init__(self, path): - calls["registry_path"] = path - - class FakeBackend: - def __init__(self, *args, **kwargs): - calls["backend_args"] = (args, kwargs) - - def weight_version(self): - return "7" - - def fake_make_server(gateway, host, port): - calls["server"] = (gateway, host, port) - return FakeServer() - + args = SimpleNamespace(rollout_num_gpus=8, rollout_num_gpus_per_engine=4, + sglang_server_concurrency=2, hf_checkpoint="/model", + sglang_router_ip="127.0.0.1", sglang_router_port=30000, + rollout_max_context_len=1024, rollout_max_response_len=128, + n_samples_per_prompt=4) + config = slime_rollout.runtime_config_from_args(args) + assert isinstance(config, RolloutRuntimeConfig) + assert config.endpoint == "http://127.0.0.1:30000" + assert config.concurrency_limit == 4 # 2 concurrency x 2 engines + assert config.group_size == 4 + assert config.workers == ("worker-a", "worker-b") + assert config.max_output_tokens == 128 + + # The tokenizer loader keeps the single Slime dependency at the boundary. slime = ModuleType("slime") utils = ModuleType("slime.utils") processing = ModuleType("slime.utils.processing_utils") + tokenizer = SimpleNamespace() processing.load_tokenizer = lambda *args, **kwargs: tokenizer slime.utils = utils utils.processing_utils = processing monkeypatch.setitem(sys.modules, "slime", slime) monkeypatch.setitem(sys.modules, "slime.utils", utils) monkeypatch.setitem(sys.modules, "slime.utils.processing_utils", processing) - monkeypatch.setattr(gateway_server, "Gateway", FakeGateway) - monkeypatch.setattr(gateway_server, "make_server", fake_make_server) - monkeypatch.setattr(sglang_backend, "SGLangBackend", FakeBackend) - monkeypatch.setattr(trace, "TraceRegistry", FakeTraceRegistry) - monkeypatch.setattr(native_generate, "_RUNTIME", None) - - args = SimpleNamespace(rollout_num_gpus=4, rollout_num_gpus_per_engine=4, - sglang_server_concurrency=1, hf_checkpoint="/model", - sglang_router_ip="127.0.0.1", sglang_router_port=30000, - rollout_max_context_len=1024, rollout_max_response_len=128) - runtime = asyncio.run(native_generate.runtime(args)) - try: - registry = calls["gateway"][0] - backend = calls["gateway"][1] - assert calls["gateway"] == (registry, backend, "policy", 128) - assert calls["server"][0] is calls["gateway_object"] - assert calls["server"][1:] == ("127.0.0.1", 49183) - assert runtime.backend.weight_version() == "7" - finally: - runtime.close() - native_generate._RUNTIME = None + assert slime_rollout.slime_tokenizer_loader(args)() is tokenizer def test_native_policy_identity_uses_synchronized_pool_version(tmp_path, monkeypatch): - from harborrl.rollout import native_generate + from harborrl.rollout import runtime as rollout_runtime class Backend: endpoint = "http://router:30000" @@ -314,26 +307,37 @@ def publish_version(self, states, expected): monkeypatch.setenv("RUN_DIR", str(tmp_path)) registry = Registry() - runtime = SimpleNamespace(run_root=tmp_path, backend=Backend(), registry=registry) - assert native_generate.current_policy_version(runtime) == "2" + runtime = SimpleNamespace(run_root=tmp_path, backend=Backend(), registry=registry, + config=SimpleNamespace(drain_timeout=30)) + assert rollout_runtime.current_policy_version(runtime) == "2" payload = json.loads((tmp_path / "policy-pool.json").read_text()) assert payload["weight_version"] == "2" assert registry.published[1] == "2" + assert registry.timeout == 30 def test_native_slots_spread_workers_and_shift_retries(tmp_path, monkeypatch): - from harborrl.rollout import native_generate + from harborrl.rollout import runtime as rollout_runtime entry = {"id": "task", "revision": "1", "path": "/task", "task_digest": "d" * 64, "reward_profile": {"key": "reward", "scale": 1.0, "offset": 0.0, "raw_range": [0.0, 1.0]}} profile = tmp_path / "profile.json" profile.write_text(json.dumps({"model": "policy"})) - monkeypatch.setenv("RUN_ID", "run") - monkeypatch.setenv("HARBORRL_NATIVE_PROFILE", str(profile)) - monkeypatch.setenv("HARBORRL_NATIVE_RUNNER_WORKERS", json.dumps(["worker-a", "worker-b"])) - monkeypatch.setenv("HARBORRL_NATIVE_MAX_ATTEMPTS", "2") - monkeypatch.setenv("HARBORRL_NATIVE_GATEWAY_URL", "http://gateway:32123") - monkeypatch.setenv("HARBORRL_NATIVE_RUNNER_PYTHON", "/python") + environment = { + "RUN_ID": "run", "RUN_DIR": str(tmp_path), "HARBORRL_NATIVE_PROFILE": str(profile), + "HARBORRL_NATIVE_RUNNER_WORKERS": json.dumps(["worker-a", "worker-b"]), + "HARBORRL_NATIVE_MAX_ATTEMPTS": "2", + "HARBORRL_NATIVE_GATEWAY_URL": "http://gateway:32123", + "HARBORRL_NATIVE_GATEWAY_PORT": "32123", + "HARBORRL_NATIVE_RUNNER_PYTHON": "/python", + "HARBORRL_NATIVE_CATALOG": str(tmp_path / "catalog.json"), + "HARBORRL_NATIVE_TOKENIZER_DIGEST": "0" * 64, + "HARBORRL_NATIVE_TEMPLATE_DIGEST": "1" * 64, + } + for key, value in environment.items(): + monkeypatch.setenv(key, value) + config = rollout_runtime.RolloutRuntimeConfig.from_environment( + environment, endpoint="http://router:30000", concurrency_limit=8, group_size=4) class FakeSlots: max_attempts = 2 @@ -360,19 +364,15 @@ async def fake_run_attempt(identity, *, runner_host, **kwargs): raise RuntimeError("simulated first-attempt failure") return {"identity": identity.__dict__} - monkeypatch.setattr(native_generate, "GroupSlots", fake_group_slots) - monkeypatch.setattr(native_generate, "run_attempt", fake_run_attempt) + monkeypatch.setattr(rollout_runtime, "GroupSlots", fake_group_slots) + monkeypatch.setattr(rollout_runtime, "run_attempt", fake_run_attempt) - args = SimpleNamespace(n_samples_per_prompt=4, rollout_max_response_len=64) runtime_ = SimpleNamespace(rollout_root=tmp_path, semaphore=asyncio.Semaphore(8), registry=SimpleNamespace(), profile={"model": "policy"}) - sample = SimpleNamespace(metadata={"native_task": entry}, group_index=0) async def gather_slots(): return await asyncio.gather(*[ - native_generate._run_slot( - args, runtime_, sample, slot, 0, entry, "sampling", "1" - ) + rollout_runtime.run_slot(config, runtime_, 0, slot, 0, entry, "sampling", "1") for slot in range(4)]) results = asyncio.run(gather_slots()) @@ -413,13 +413,96 @@ def test_native_dispatch_passes_materialized_prompt_data(tmp_path, monkeypatch): monkeypatch.setenv("SLIME_DIR", "/opt/slime-v0.3.2") monkeypatch.setenv("MEGATRON_DIR", "/opt/megatron") monkeypatch.setenv("SGLANG_IMAGE", "sglang:v0.5.15.post1-cu129") + monkeypatch.setenv("LR", "99") + monkeypatch.setenv("N_SAMPLES", "99") + monkeypatch.setenv("ANTHROPIC_API_KEY", "ambient-provider-credential") + monkeypatch.setenv("HTTPS_PROXY", "http://user:credential@proxy.example:8080") monkeypatch.setattr(native_train, "doctor", lambda *_args, **_kwargs: []) assert native_train.dispatch(plan) == 0 launch_path = next((tmp_path / "runs" / "training").glob("*/launch.json")) launch = json.loads(launch_path.read_text()) assert launch["prompt_data"] == output.read_text() + effective_text = (launch_path.parent / "effective-config.json").read_text() + effective = json.loads(effective_text) + assert effective['environment']['LR'] == '0.0001' + assert effective['environment']['N_SAMPLES'] == '2' + assert effective['environment']['ROLLOUT_PROMPT_DATA'] == output.read_text() + assert effective['components']['algorithm'] == 'grpo' + assert effective['deployment_environment']['SLIME_DIR'] == '/opt/slime-v0.3.2' + assert 'ambient-provider-credential' not in effective_text + assert 'user:credential' not in effective_text assert path_output.read_text().split(os.pathsep, 1)[0] == str(Path(plan["command"][0]).parent) assert json.loads(workers_output.read_text()) == ["worker-a", "worker-b"] assert gateway_output.read_text() == "gateway" assert backend_output.read_text() == ( "/opt/slime-v0.3.2:/opt/megatron:sglang:v0.5.15.post1-cu129") + + +def test_all_schema_fields_have_declared_consumers(): + from harborrl.backends.slime_v032.config import field_usage + from harborrl.config.native import FIELDS + assert set(field_usage()) == { + f'{section}.{field}' for section, fields in FIELDS.items() for field in fields + } + + +def test_schema_validation_does_not_read_harness_files(tmp_path): + from harborrl.config.native import validate, validate_files + config_path, _ = native_config(tmp_path) + config = load_config(config_path) + Path(config['harness']['profile']).unlink() + assert validate(config, config_path) is config + with pytest.raises(FileNotFoundError): + validate_files(config) + + +@pytest.mark.parametrize('field, value, message', [ + ('training', None, 'training'), + ('training.backend_contract', [], 'backend_contract'), + ('gateway.advertised_url', [], 'gateway.advertised_url'), + ('sampling.max_tokens', 'many', 'sampling.max_tokens'), +]) +def test_invalid_config_types_are_cli_errors(tmp_path, capsys, field, value, message): + import yaml + + from harborrl.cli import main + config_path, _ = native_config(tmp_path) + config = yaml.safe_load(config_path.read_text()) + section, _, key = field.partition('.') + if key: + config[section][key] = value + else: + config[section] = value + config_path.write_text(yaml.safe_dump(config)) + with pytest.raises(SystemExit) as exc: + main(['train', str(config_path), '--dry-run']) + assert exc.value.code == 2 + assert message in capsys.readouterr().err + + +def test_invalid_override_yaml_is_cli_error(tmp_path, capsys): + from harborrl.cli import main + config_path, _ = native_config(tmp_path) + with pytest.raises(SystemExit) as exc: + main(['train', str(config_path), '--set', 'training.learning_rate=[', '--dry-run']) + assert exc.value.code == 2 + assert 'training.learning_rate: invalid YAML override' in capsys.readouterr().err + + +def test_harness_file_override_is_checked_after_override(tmp_path): + config_path, _ = native_config(tmp_path) + config = load_config(config_path) + old_profile = Path(config['harness']['profile']) + new_profile = tmp_path / 'replacement.json' + old_profile.rename(new_profile) + config = load_config(config_path, [f'harness.profile={new_profile}']) + assert config['harness']['profile'] == str(new_profile) + + +def test_catalog_rejects_nonmapping_entry(tmp_path): + from harborrl.tasks.catalog import catalog + config_path, _ = native_config(tmp_path) + config = load_config(config_path) + Path(config['tasks']['catalog']).write_text('[null]') + with pytest.raises(ValueError, match='catalog identity'): + catalog(config) diff --git a/tests/harborrl/test_options.py b/tests/harborrl/test_options.py new file mode 100644 index 000000000..aa3739c4b --- /dev/null +++ b/tests/harborrl/test_options.py @@ -0,0 +1,308 @@ +"""Offline wizard contracts, including the existing training CLI boundary.""" +import json +import subprocess +import sys +from copy import deepcopy +from pathlib import Path + +import pytest +import yaml +from test_native_training import native_config + +from harborrl.options.catalog import TRAINING_FIELDS, build_catalog, dataset_metadata +from harborrl.options.cli import main +from harborrl.options.presets import PRESETS, worker_defaults +from harborrl.options.validation import validate_paths +from harborrl.options.wizard import build_config_bundle, publish_bundle + + +@pytest.fixture +def answers(tmp_path): + _path, task = native_config(tmp_path) + from harborrl.data.harbor.native_inspector import inspect_native + catalog = tmp_path / "catalog.json" + entries = json.loads(catalog.read_text()) + entries[0]["task_digest"] = inspect_native(task)["task_digest"] + catalog.write_text(json.dumps(entries)) + model = tmp_path / "model" + model.mkdir() + return {"answers_version": 1, "preset": "embedded-local", "training": { + "tasks": {"catalog": str(catalog)}, "harness": {"profile": str(tmp_path / "claude.json")}, + "harbor": {"python": sys.executable}, + "model": {"checkpoint": str(model), "reference": str(model)}, + "gateway": {"tokenizer_digest": "1" * 64, "template_digest": "2" * 64}, + }} + + +def service_answers(answers, preset): + answers = deepcopy(answers) + answers["preset"] = preset + answers["deployment"] = { + "manager": {"control_url": "http://controller.example:49200", "gateway_url": "http://controller.example:49184"}, + "gpu_target": {"ssh": {"host": "gpu", "user": "trainer", "port": 22}, + "source_dir": "/remote/source", "train_python": "/remote/python"}, + } + if preset == "service-ssh": + worker = worker_defaults() + worker["ssh"]["host"] = "runner" + answers["deployment"]["rollout"] = {"workers": [worker]} + del answers["training"]["harbor"]["python"] + else: + answers["sandbox_config"] = "/site/sandbox.yaml" + return answers + + +def test_catalog_contract(monkeypatch): + def forbidden(*args, **kwargs): + raise AssertionError("offline operation attempted subprocess") + monkeypatch.setattr(subprocess, "run", forbidden) + monkeypatch.setattr(subprocess, "Popen", forbidden) + from harborrl.config.native import FIELDS + assert set(TRAINING_FIELDS) == {f"{s}.{f}" for s, fields in FIELDS.items() for f in fields} + first = build_catalog() + assert json.dumps(first, sort_keys=True) == json.dumps(build_catalog(), sort_keys=True) + assert all(not row["available"] for row in first["datasets"] if row["status"] == "planned") + from harborrl.config import ROOT + assert dataset_metadata() == yaml.safe_load((ROOT / "configs/harbor_hub/manifests.yaml").read_text()) + + +@pytest.mark.parametrize("dimension", [None, "models", "algorithms", "tasks", "harnesses", "datasets", "deployments", "fields", "compat"]) +def test_catalog_cli(dimension, capsys): + assert main(["options", *([dimension] if dimension else []), "--json"]) == 0 + captured = capsys.readouterr() + result = json.loads(captured.out) + assert not captured.err + assert result["catalog_version"] == 1 + if dimension: + assert len(result) == 3 + + +@pytest.mark.parametrize("preset", [p["id"] for p in PRESETS]) +def test_presets_and_legacy_dry_run(preset, answers, tmp_path, capsys): + if preset != "embedded-local": + answers = service_answers(answers, preset) + original = deepcopy(answers) + output = tmp_path / "new site" + bundle = build_config_bundle(answers, output_directory=output) + assert answers == original + assert not output.exists() + publish_bundle(bundle, output) + result = validate_paths(output / "config.yaml", output / "deployment.yaml") + assert result["status"] == "valid_with_deferred_checks", result + if preset != "service-sandbox-local-runner": + from harborrl.cli import main as legacy_main + assert legacy_main(["train", str(output / "config.yaml"), "--deployment", str(output / "deployment.yaml"), "--dry-run"]) == 0 + assert json.loads(capsys.readouterr().out)["config"]["schema_version"] == 2 + + +def test_local_files_and_remote_ownership(answers, tmp_path): + for preset in ("embedded-local", "service-ssh"): + current = answers if preset == "embedded-local" else service_answers(answers, preset) + out = tmp_path / preset + publish_bundle(build_config_bundle(current, output_directory=out), out) + result = validate_paths(out / "config.yaml", out / "deployment.yaml", check_files=True) + assert result["status"] == "valid_with_deferred_checks", result + if preset == "service-ssh": + assert any(c["code"] == "remote_paths" for c in result["checks"]) + (tmp_path / "claude.json").unlink() + assert validate_paths(out / "config.yaml", out / "deployment.yaml")["status"] != "invalid" + assert validate_paths(out / "config.yaml", out / "deployment.yaml", check_files=True)["status"] == "invalid" + + +@pytest.mark.parametrize("mutation", ["unknown", "missing", "zero_digest", "gpu", "gateway", "provider"]) +def test_reject_bad_answers(mutation, answers, tmp_path): + answers = service_answers(answers, "service-ssh") + if mutation == "unknown": + answers["training"]["sampling"] = {"typo": 1} + elif mutation == "missing": + del answers["training"]["tasks"] + elif mutation == "zero_digest": + answers["training"]["gateway"]["tokenizer_digest"] = "0" * 64 + elif mutation == "gpu": + answers["deployment"]["gpu_target"]["expected_gpus"] = 0 + elif mutation == "gateway": + answers["training"]["gateway"]["advertised_url"] = "http://other.example:49184" + else: + answers["deployment"]["rollout"]["provider"] = "sandbox" + with pytest.raises(ValueError): + build_config_bundle(answers, output_directory=tmp_path / "output") + assert not (tmp_path / "output").exists() + + +def test_noninteractive_and_failure_json(answers, tmp_path, monkeypatch, capsys): + monkeypatch.setattr(sys.stdin, "isatty", lambda: False) + with pytest.raises(SystemExit) as exc: + main(["init", "--out-dir", str(tmp_path / "output")]) + assert exc.value.code == 2 + capsys.readouterr() + assert main(["validate", str(tmp_path / "missing"), "--json"]) == 1 + assert json.loads(capsys.readouterr().out)["status"] == "invalid" + file = tmp_path / "answers.yaml" + file.write_text(yaml.safe_dump(answers)) + assert main(["init", "--answers", str(file), "--out-dir", str(tmp_path / "output")]) == 0 + assert "harborrl train" in capsys.readouterr().out + assert main(["init", "--answers", str(file), "--out-dir", str(tmp_path / "output")]) == 1 + + +def test_publication_failure_lock_and_traversal(answers, tmp_path, monkeypatch): + output = tmp_path / "output" + bundle = build_config_bundle(answers, output_directory=output) + lock = tmp_path / ".output.init.lock" + lock.write_text("other process") + with pytest.raises(FileExistsError): + publish_bundle(bundle, output) + assert lock.read_text() == "other process" + lock.unlink() + bad = deepcopy(bundle) + bad["files"]["../escape"] = "bad" + with pytest.raises(ValueError): + publish_bundle(bad, output) + def fail(*args, **kwargs): + raise OSError("injected rename failure") + with monkeypatch.context() as context: + context.setattr(Path, "rename", fail) + with pytest.raises(OSError, match="injected"): + publish_bundle(bundle, output) + assert not output.exists() and not list(tmp_path.glob(".output.init*")) + publish_bundle(bundle, output) + before = (output / "config.yaml").read_bytes() + with pytest.raises(FileExistsError): + publish_bundle(bundle, output) + assert (output / "config.yaml").read_bytes() == before + + +@pytest.mark.parametrize("confirmation", ["yes", "no"]) +def test_interactive(answers, tmp_path, monkeypatch, confirmation): + t = answers["training"] + responses = iter(["1", sys.executable, sys.executable, t["tasks"]["catalog"], t["harness"]["profile"], + t["model"]["checkpoint"], t["model"]["reference"], "", "1" * 64, "2" * 64, + "", "", "1", "", "", "", "", confirmation]) + monkeypatch.setattr(sys.stdin, "isatty", lambda: True) + monkeypatch.setattr("builtins.input", lambda _: next(responses)) + output = tmp_path / "interactive" + assert main(["init", "--out-dir", str(output)]) == 0 + assert output.exists() == (confirmation == "yes") + + +def test_probe_failure_and_alias_parsing(tmp_path, monkeypatch): + from harborrl.options.probe import probe_local, ssh_aliases + config = tmp_path / "ssh-config" + config.write_text("Host train runner *.example !excluded\n HostName host\nMatch exec unsafe\n User user\n") + assert ssh_aliases(config) == ["runner", "train"] + def timeout(*args, **kwargs): + assert kwargs["timeout"] == 3 + raise subprocess.TimeoutExpired(args[0], 3) + monkeypatch.setattr(subprocess, "run", timeout) + result = probe_local(tmp_path / "output") + assert result["gpu"]["status"] == result["python"]["status"] == "unknown" + assert not (tmp_path / "output").exists() + + +def test_sandbox_files_and_remote_runner(answers, tmp_path, capsys): + from harborrl.cli import main as legacy_main + from harborrl.data.harbor.native_inspector import inspect_native + task = tmp_path / "task" + (task / "task.toml").write_text('version = "1.0"\n[environment]\ndocker_image = "task:locked"\n') + catalog = tmp_path / "catalog.json" + entries = json.loads(catalog.read_text()) + entries[0]["task_digest"] = inspect_native(task)["task_digest"] + catalog.write_text(json.dumps(entries)) + sandbox = tmp_path / "sandbox.yaml" + sandbox.write_text(yaml.safe_dump({"domain": "https://sandbox.example", "credential_file": "/missing/secret", + "bindings": [{"image": "task:locked", "max_live_instances": 2}]})) + answers = service_answers(answers, "service-sandbox-local-runner") + answers["sandbox_config"] = str(sandbox) + for remote in (False, True): + if remote: + worker = worker_defaults() + worker["ssh"]["host"] = "runner" + worker["docker"] = False + answers["deployment"]["rollout"] = {"workers": [worker]} + answers["training"]["harbor"] = {"runner_transport": "ssh"} + output = tmp_path / f"sandbox-{remote}" + publish_bundle(build_config_bundle(answers, output_directory=output), output) + report = validate_paths(output / "config.yaml", output / "deployment.yaml", check_files=True) + assert report["status"] == "valid_with_deferred_checks", report + assert legacy_main(["train", str(output / "config.yaml"), "--deployment", str(output / "deployment.yaml"), "--dry-run"]) == 0 + capsys.readouterr() + sandbox.write_text(sandbox.read_text().replace("task:locked", "other:locked")) + assert validate_paths(output / "config.yaml", output / "deployment.yaml", check_files=True)["status"] == "invalid" + + +def test_offline_import_boundary(answers, tmp_path): + from harborrl.config import ROOT + output = tmp_path / "offline" + publish_bundle(build_config_bundle(answers, output_directory=output), output) + script = ''' +import builtins, socket, subprocess, sys +sys.path.insert(0, sys.argv[1]) +original = builtins.__import__ +def guarded(name, *args, **kwargs): + assert name.split('.')[0] not in {'torch', 'transformers', 'sglang', 'harbor', 'opensandbox', 'httpx'} + return original(name, *args, **kwargs) +def forbidden(*args, **kwargs): + raise AssertionError('offline command attempted an external operation') +builtins.__import__ = guarded +socket.socket = forbidden +subprocess.run = subprocess.Popen = forbidden +from harborrl.cli import main +assert main(['options', '--json']) == 0 +assert main(['validate', sys.argv[2], '--deployment', sys.argv[3], '--check-files', '--json']) == 0 +''' + result = subprocess.run([sys.executable, "-I", "-c", script, str(ROOT), str(output / "config.yaml"), + str(output / "deployment.yaml")], capture_output=True, text=True, timeout=20, check=False) + assert result.returncode == 0, result.stderr + + +def test_concurrent_publication(answers, tmp_path): + from concurrent.futures import ThreadPoolExecutor + from threading import Barrier + output = tmp_path / "concurrent" + bundle = build_config_bundle(answers, output_directory=output) + barrier = Barrier(2) + def attempt(): + barrier.wait(timeout=10) + try: + publish_bundle(bundle, output) + except FileExistsError: + return False + return True + with ThreadPoolExecutor(max_workers=2) as pool: + assert sorted(pool.map(lambda _: attempt(), range(2))) == [False, True] + assert {p.name for p in output.iterdir()} == set(bundle["files"]) + + +def test_relative_paths_and_invalid_yaml(answers, tmp_path, capsys): + answers["training"]["tasks"]["catalog"] = "../catalog.json" + output = tmp_path / "relative" + publish_bundle(build_config_bundle(answers, output_directory=output), output) + config = yaml.safe_load((output / "config.yaml").read_text()) + assert config["tasks"]["catalog"] == str(tmp_path / "catalog.json") + bad = tmp_path / "bad.yaml" + bad.write_text("[invalid") + assert main(["validate", str(bad), "--json"]) == 1 + assert json.loads(capsys.readouterr().out)["status"] == "invalid" + + +def test_embedded_explicit_conflict(answers, tmp_path): + answers["training"]["gateway"]["advertised_url"] = "http://localhost:49184" + answers["deployment"] = {"manager": {"gateway_url": "http://127.0.0.1:49184"}} + with pytest.raises(ValueError, match="conflicts"): + build_config_bundle(answers, output_directory=tmp_path / "conflict") + + +def test_options_text_is_concise(capsys): + assert main(["options"]) == 0 + output = capsys.readouterr().out + assert len(output.splitlines()) < 50 + assert "locally_available=False" in output + + +def test_public_answer_templates(tmp_path): + from harborrl.config import ROOT + for file in sorted((ROOT / "examples/options").glob("*.yaml")): + answers = yaml.safe_load(file.read_text()) + with pytest.raises(ValueError, match="SHA-256"): + build_config_bundle(answers, output_directory=tmp_path / file.stem) + answers["training"]["gateway"].update(tokenizer_digest="1" * 64, template_digest="2" * 64) + assert build_config_bundle(answers, output_directory=tmp_path / file.stem)["validation"]["status"] != "invalid" diff --git a/tests/harborrl/test_rollout_runtime.py b/tests/harborrl/test_rollout_runtime.py new file mode 100644 index 000000000..8f33323c5 --- /dev/null +++ b/tests/harborrl/test_rollout_runtime.py @@ -0,0 +1,321 @@ +"""Extracted rollout core (P2): config mapping, slot scheduling, boundaries.""" +from __future__ import annotations + +import asyncio +import json +from pathlib import Path +from types import SimpleNamespace + +import pytest + + +def test_runtime_config_maps_environment(tmp_path, monkeypatch): + from harborrl.rollout.runtime import RolloutRuntimeConfig + + environment = { + "RUN_ID": "run-1", "RUN_DIR": str(tmp_path), + "HARBORRL_NATIVE_CATALOG": str(tmp_path / "catalog.json"), + "HARBORRL_NATIVE_PROFILE": str(tmp_path / "profile.json"), + "HARBORRL_NATIVE_GATEWAY_HOST": "127.0.0.1", + "HARBORRL_NATIVE_GATEWAY_PORT": "49183", + "HARBORRL_NATIVE_GATEWAY_URL": "http://gateway:49183", + "HARBORRL_NATIVE_RUNNER_PYTHON": "/runner/python", + "HARBORRL_NATIVE_RUNNER_WORKERS": json.dumps(["worker-a", "worker-b"]), + "HARBORRL_NATIVE_TASK_WORKER_OVERRIDES": json.dumps({"task": "worker-b"}), + "HARBORRL_NATIVE_MAX_ATTEMPTS": "3", + "ROLLOUT_MAX_RESPONSE_LEN": "128", + "ROLLOUT_MAX_CONTEXT_LEN": "1024", + "HARBORRL_NATIVE_TOKENIZER_DIGEST": "0" * 64, + "HARBORRL_NATIVE_TEMPLATE_DIGEST": "1" * 64, + "HARBORRL_NATIVE_AUDITED_LOGPROBS": "1", + "HARBORRL_NATIVE_DRAIN_TIMEOUT": "5", + "MODEL_TAG": "qwen3-8B", + } + for key, value in environment.items(): + monkeypatch.setenv(key, value) + config = RolloutRuntimeConfig.from_environment( + environment, endpoint="http://router:30000/", concurrency_limit=4, group_size=2) + assert config.workers == ("worker-a", "worker-b") + assert config.task_worker_overrides == {"task": "worker-b"} + assert config.max_attempts == 3 and config.max_output_tokens == 128 + assert config.endpoint == "http://router:30000" + assert config.environment()["HARBORRL_NATIVE_RUNNER_WORKERS"] == \ + json.dumps(["worker-a", "worker-b"]) + with pytest.raises(ValueError, match="group size"): + RolloutRuntimeConfig.from_environment(environment, endpoint="x", concurrency_limit=1, + group_size=1) + stripped = dict(environment) + stripped.pop("HARBORRL_NATIVE_RUNNER_WORKERS") + with pytest.raises(ValueError, match="RUNNER_WORKERS"): + RolloutRuntimeConfig.from_environment(stripped, endpoint="x", + concurrency_limit=1, group_size=2) + + +def test_runtime_binds_gateway_with_registry_and_backend(tmp_path, monkeypatch): + from harborrl.gateway import server as gateway_server + from harborrl.gateway import sglang_backend, trace + from harborrl.rollout import runtime as rollout_runtime + + catalog = tmp_path / "catalog.json" + catalog.write_text("[]") + profile = tmp_path / "profile.json" + profile.write_text(json.dumps({"model": "policy"})) + tokenizer = SimpleNamespace( + apply_chat_template=lambda *args, **kwargs: [1, 2], + get_vocab=lambda: {"token": 1}, + ) + tokenizer_digest, template_digest = rollout_runtime.tokenizer_fingerprint(tokenizer) + environment = { + "RUN_DIR": str(tmp_path), "RUN_ID": "run-1", + "HARBORRL_NATIVE_CATALOG": str(catalog), + "HARBORRL_NATIVE_PROFILE": str(profile), + "HARBORRL_NATIVE_TOKENIZER_DIGEST": tokenizer_digest, + "HARBORRL_NATIVE_TEMPLATE_DIGEST": template_digest, + "HARBORRL_NATIVE_GATEWAY_HOST": "127.0.0.1", + "HARBORRL_NATIVE_GATEWAY_PORT": "49183", + "HARBORRL_NATIVE_GATEWAY_URL": "http://gateway:49183", + "HARBORRL_NATIVE_RUNNER_PYTHON": "/runner/python", + "HARBORRL_NATIVE_RUNNER_WORKERS": '["worker-a"]', + "ROLLOUT_MAX_RESPONSE_LEN": "128", + "ROLLOUT_MAX_CONTEXT_LEN": "1024", + } + for key, value in environment.items(): + monkeypatch.setenv(key, value) + + calls = {} + + class FakeGateway: + def __init__(self, registry, backend, *, model, max_output_tokens): + calls["gateway"] = (registry, backend, model, max_output_tokens) + calls["gateway_object"] = self + + class FakeServer: + def serve_forever(self): + pass + + def shutdown(self): + pass + + def server_close(self): + pass + + class FakeTraceRegistry: + def __init__(self, path): + calls["registry_path"] = path + + class FakeBackend: + def __init__(self, *args, **kwargs): + calls["backend_args"] = (args, kwargs) + + def weight_version(self): + return "7" + + def fake_make_server(gateway, host, port): + calls["server"] = (gateway, host, port) + return FakeServer() + + monkeypatch.setattr(gateway_server, "Gateway", FakeGateway) + monkeypatch.setattr(gateway_server, "make_server", fake_make_server) + monkeypatch.setattr(sglang_backend, "SGLangBackend", FakeBackend) + monkeypatch.setattr(trace, "TraceRegistry", FakeTraceRegistry) + + config = rollout_runtime.RolloutRuntimeConfig.from_environment( + environment, endpoint="http://127.0.0.1:30000", concurrency_limit=4, group_size=2) + runtime_ = rollout_runtime.RolloutRuntime(config, tokenizer) + try: + assert calls["gateway"][2:] == ("policy", 128) + assert calls["server"][0] is calls["gateway_object"] + assert calls["server"][1:] == ("127.0.0.1", 49183) + assert calls["backend_args"][0][0] == "http://127.0.0.1:30000" + assert runtime_.backend.weight_version() == "7" + finally: + runtime_.close() + + # A tokenizer that disagrees with the native lock fails closed. + other = SimpleNamespace(apply_chat_template=lambda *a, **k: [9], + get_vocab=lambda: {"other": 2}) + with pytest.raises(ValueError, match="digest"): + rollout_runtime.RolloutRuntime(config, other) + + +def test_policy_identity_uses_synchronized_pool_version(tmp_path, monkeypatch): + from harborrl.rollout import runtime as rollout_runtime + + class Backend: + endpoint = "http://router:30000" + + def pool_states(self): + return [{"endpoint": self.endpoint, "healthy": True, "weight_version": "2"}] + + class Registry: + def __init__(self): + self.published = None + + def drain(self, timeout): + self.timeout = timeout + + def publish_version(self, states, expected): + self.published = (states, expected) + + monkeypatch.setenv("RUN_DIR", str(tmp_path)) + registry = Registry() + runtime_ = SimpleNamespace(run_root=tmp_path, backend=Backend(), registry=registry, + config=SimpleNamespace(drain_timeout=30)) + assert rollout_runtime.current_policy_version(runtime_) == "2" + payload = json.loads((tmp_path / "policy-pool.json").read_text()) + assert payload["weight_version"] == "2" + assert registry.published[1] == "2" + assert registry.timeout == 30 + + +def test_group_shares_slot_state_and_shifts_retry_workers(tmp_path, monkeypatch): + from harborrl.rollout import runtime as rollout_runtime + + entry = {"id": "task", "revision": "1", "path": "/task", "task_digest": "d" * 64, + "reward_profile": {"key": "reward", "scale": 1.0, "offset": 0.0, + "raw_range": [0.0, 1.0]}} + profile = tmp_path / "profile.json" + profile.write_text(json.dumps({"model": "policy"})) + environment = { + "RUN_ID": "run", "RUN_DIR": str(tmp_path), "HARBORRL_NATIVE_PROFILE": str(profile), + "HARBORRL_NATIVE_RUNNER_WORKERS": json.dumps(["worker-a", "worker-b"]), + "HARBORRL_NATIVE_MAX_ATTEMPTS": "2", + "HARBORRL_NATIVE_GATEWAY_URL": "http://gateway:32123", + "HARBORRL_NATIVE_GATEWAY_PORT": "32123", + "HARBORRL_NATIVE_RUNNER_PYTHON": "/python", + "HARBORRL_NATIVE_CATALOG": str(tmp_path / "catalog.json"), + "HARBORRL_NATIVE_TOKENIZER_DIGEST": "0" * 64, + "HARBORRL_NATIVE_TEMPLATE_DIGEST": "1" * 64, + } + for key, value in environment.items(): + monkeypatch.setenv(key, value) + config = rollout_runtime.RolloutRuntimeConfig.from_environment( + environment, endpoint="http://router:30000", concurrency_limit=8, group_size=4) + + class FakeSlots: + max_attempts = 2 + + def __init__(self): + self.started = {} + + def start(self, slot): + self.started[slot] = self.started.get(slot, 0) + 1 + return SimpleNamespace(attempt_id=f"{slot}-{self.started[slot]}", + group_id="group-0", slot_id=slot, + attempt_no=self.started[slot]) + + def finish(self, slot, ir=None, error=None): + assert (ir is None) != (error is None) + + created_groups = [] + + def fake_group_slots(identities, *, max_attempts=2): + created_groups.append(identities) + return FakeSlots() + + calls = [] + + async def fake_run_attempt(identity, *, runner_host, **kwargs): + calls.append((identity.slot_id, runner_host)) + if identity.slot_id == "1" and identity.attempt_no == 1: + raise RuntimeError("simulated first-attempt failure") + return {"identity": identity.__dict__} + + monkeypatch.setattr(rollout_runtime, "GroupSlots", fake_group_slots) + monkeypatch.setattr(rollout_runtime, "run_attempt", fake_run_attempt) + + runtime_ = SimpleNamespace(rollout_root=tmp_path, semaphore=asyncio.Semaphore(8), + registry=SimpleNamespace(), profile={"model": "policy"}) + + # The run owns its loaded profile; group setup must not reread the source file. + profile.unlink() + results = asyncio.run(rollout_runtime.run_group( + config, runtime_, entry=entry, group_index=0, rollout_id=0, + sampling_digest="sampling", policy_version="1")) + assert len(results) == 4 + assert len(created_groups) == 1 + assert len(created_groups[0]) == 4 + assert {item.harness_digest for item in created_groups[0]} == { + rollout_runtime.digest(runtime_.profile)} + by_slot = {} + for slot, host in calls: + by_slot.setdefault(slot, []).append(host) + assert by_slot == {"0": ["worker-a"], "1": ["worker-b", "worker-a"], + "2": ["worker-a"], "3": ["worker-b"]} + + +def test_task_worker_override_wins(tmp_path, monkeypatch): + from harborrl.rollout import runtime as rollout_runtime + + entry = {"id": "task", "revision": "1", "path": "/task", "task_digest": "d" * 64, + "reward_profile": {"key": "reward"}} + profile = tmp_path / "profile.json" + profile.write_text(json.dumps({"model": "policy"})) + environment = { + "RUN_ID": "run", "RUN_DIR": str(tmp_path), "HARBORRL_NATIVE_PROFILE": str(profile), + "HARBORRL_NATIVE_RUNNER_WORKERS": json.dumps(["worker-a", "worker-b"]), + "HARBORRL_NATIVE_TASK_WORKER_OVERRIDES": json.dumps({"task": "worker-b"}), + "HARBORRL_NATIVE_GATEWAY_URL": "http://gateway:32123", + "HARBORRL_NATIVE_GATEWAY_PORT": "32123", "HARBORRL_NATIVE_RUNNER_PYTHON": "/python", + "HARBORRL_NATIVE_CATALOG": str(tmp_path / "catalog.json"), + "HARBORRL_NATIVE_TOKENIZER_DIGEST": "0" * 64, + "HARBORRL_NATIVE_TEMPLATE_DIGEST": "1" * 64, + } + for key, value in environment.items(): + monkeypatch.setenv(key, value) + config = rollout_runtime.RolloutRuntimeConfig.from_environment( + environment, endpoint="http://router:30000", concurrency_limit=2, group_size=2) + + seen = [] + + class PermissiveSlots: + max_attempts = 2 + + def __init__(self, identities, *, max_attempts=2): + self.identities = {identity.slot_id: identity for identity in identities} + + def start(self, slot): + return self.identities[slot] + + def finish(self, slot, ir=None, error=None): + assert (ir is None) != (error is None) + + async def fake_run_attempt(identity, *, runner_host, **kwargs): + seen.append(runner_host) + return {"identity": identity.to_dict()} + + monkeypatch.setattr(rollout_runtime, "GroupSlots", PermissiveSlots) + monkeypatch.setattr(rollout_runtime, "run_attempt", fake_run_attempt) + runtime_ = SimpleNamespace(rollout_root=tmp_path, semaphore=asyncio.Semaphore(2), + registry=SimpleNamespace(), profile={"model": "policy"}) + irs = asyncio.run(rollout_runtime.run_group( + config, runtime_, entry=entry, group_index=0, rollout_id=0, + sampling_digest="sampling", policy_version="1")) + assert len(irs) == 2 + assert seen == ["worker-b", "worker-b"] + + +def test_rollout_core_does_not_import_training_frameworks(): + import re + + import harborrl.rollout + + root = Path(harborrl.rollout.__file__).parent + offenders = [] + for path in sorted(root.rglob("*.py")): + for number, line in enumerate(path.read_text().splitlines(), 1): + if re.match(r"\s*(from|import)\s+(slime|ray)(\.|\s|$)", line): + offenders.append(f"{path.relative_to(root)}:{number}: {line.strip()}") + assert not offenders, "rollout core must stay framework-free: " + "; ".join(offenders) + + +def test_native_generate_shim_reexports_the_core(): + from harborrl import rollout as package + from harborrl.rollout import native_generate, runtime + + for name in ("NativeRolloutRuntime", "RolloutRuntime", "RolloutRuntimeConfig", + "close_runtime", "current_policy_version", "publish_native_version", + "run_attempt", "run_group", "run_slot", "runtime", "task_entry", + "tokenizer_fingerprint", "GroupSlots"): + assert getattr(native_generate, name) is getattr(runtime, name), name + assert package.native_generate.__doc__ is not None diff --git a/tests/harborrl/test_rollout_service.py b/tests/harborrl/test_rollout_service.py new file mode 100644 index 000000000..f12994feb --- /dev/null +++ b/tests/harborrl/test_rollout_service.py @@ -0,0 +1,415 @@ +"""RolloutService + SlimeAdapter contracts (P3) over real local HTTP.""" +from __future__ import annotations + +import asyncio +import json +import threading +import time +from pathlib import Path +from types import SimpleNamespace + +import pytest +from test_native_contracts import SAMPLING, receipt + +from harborrl.backends.slime_v032.rollout_service_client import ( + RolloutServiceClient, + ServiceRolloutError, + load_group_result, + service_group_request, +) +from harborrl.rollout.service import ( + RolloutService, + make_control_server, + manifest_path_from, +) +from harborrl.trajectories.native import Identity, RewardProfile, digest, publish + +TASK = {"id": "task", "revision": "1", "path": "/task", "task_digest": "d" * 64, + "reward_profile": {"key": "reward", "scale": 1.0, "offset": 0.0, + "raw_range": [0.0, 1.0]}} + + +def service_identity(slot, *, run_id="run-1", group_id="group-0", policy_version="1"): + return Identity(run_id=run_id, batch_id="rollout-0", group_id=group_id, + slot_id=slot, attempt_id=f"attempt-{policy_version}-{slot}", + trajectory_id=f"trajectory-{policy_version}-{slot}", + task_digest=TASK["task_digest"], policy_version=policy_version, + harness_digest="claude", reward_digest=RewardProfile().digest, + sampling_digest=digest(SAMPLING)) + + +def service_ir(tmp_path, slot, reward=1, **identity_overrides): + from harborrl.trajectories.native import assemble + + ident = service_identity(slot, **identity_overrides) + evaluation = receipt(tmp_path / slot, ident, reward) + trial = evaluation["harbor_trial_id"] + turn = {"identity": ident.to_dict(), "harbor_trial_id": trial, + "request_id": f"r-{slot}", "response_id": f"m-{slot}", "engine_id": "engine", + "input_ids": [1, 2], "output_ids": [3, 4], "logprobs": [-0.2, -0.3], + "logprob_semantics": "raw_model", "policy_version": ident.policy_version, + "tokenizer_digest": "tok", "template_digest": "template", + "delivery": "consumed_confirmed", "consumption_ref": "session#digest", + "role": "policy", "sampling": SAMPLING, "client_request": {"model": "policy"}, + "serving_input": {"input_ids": [1, 2]}, + "response": {"content": [{"type": "text", "text": "done"}]}, + "finish_reason": "end_turn", "evidence_kind": "synthetic"} + for key in ("client_request", "serving_input", "response"): + turn[key + "_digest"] = digest(turn[key]) + seal = {"identity": ident.to_dict(), "harbor_trial_id": trial, + "pending_requests": 0, "status": "sealed", + "request_ids": [turn["request_id"]], "turns_digest": digest([turn]), + "errors": []} + return assemble(ident, trial, [turn], seal, evaluation) + + +class FakeRegistry: + def __init__(self): + self.version = None + self.accepting = False + + def drain(self, timeout): + self.accepting = False + + def publish_version(self, states, expected): + self.version = str(expected) + self.accepting = True + + +class FakeRuntime: + def __init__(self, root): + self.registry = FakeRegistry() + self.rollout_root = Path(root) + + def manifest_path(self, identity): + return manifest_path_from(self.rollout_root, identity) + + def close(self): + pass + + +def write_group_irs(tmp_path, request, rewards=(1, 0)): + runtime = FakeRuntime(tmp_path) + irs = [] + for descriptor, reward in zip(request["slots"], rewards): + ir = service_ir(tmp_path, descriptor["slot_id"], reward, + policy_version=request["policy_version"]) + publish(runtime.manifest_path(ir["identity"]), ir) + irs.append(ir) + return irs + + +def group_request(group_index=0, policy_version="1", slots=2, task=TASK): + return { + "group_id": f"group-{group_index}", "rollout_id": 0, + "group_index": group_index, "policy_version": policy_version, + "task": task, "sampling": dict(SAMPLING), + "slots": [{"slot_id": str(slot)} for slot in range(slots)], + } + + +class ServiceHarness: + def __init__(self, tmp_path, executor, **kwargs): + self.tmp_path = tmp_path + self.runtime = FakeRuntime(tmp_path) + self.service = RolloutService(run_root=tmp_path, executor=executor, allow_synthetic=True, **kwargs) + self.control = make_control_server(self.service, host="127.0.0.1", port=0) + self.thread = threading.Thread(target=self.control.serve_forever, daemon=True) + self.thread.start() + self.url = f"http://127.0.0.1:{self.control.server_address[1]}" + self.run_id = "run-1" + registration = self.service.register_run( + self.run_id, {"run_id": self.run_id, "group_size": 2}) + self.token = registration["run_token"] + self.service.attach_runtime(self.run_id, SimpleNamespace(drain_timeout=1), + self.runtime) + + def client(self): + return RolloutServiceClient(self.url, self.run_id, self.token, timeout=10, + poll_interval=0.05) + + def close(self): + self.control.shutdown() + self.control.server_close() + self.thread.join(5) + self.service.close() + + +@pytest.fixture() +def harness(tmp_path): + calls = [] + + async def executor(config, runtime_, request): + calls.append(json.loads(json.dumps(request))) + return write_group_irs(tmp_path, request) + + instance = ServiceHarness(tmp_path, executor, group_wait_timeout=5) + instance.executor_calls = calls + try: + yield instance + finally: + instance.close() + + +def activated_states(version="1"): + return [{"endpoint": f"http://engine-{index}", "healthy": True, + "weight_version": version} for index in range(2)] + + +def test_register_auth_and_idempotency(harness): + client = harness.client() + assert client.health()["ok"] + # Wrong tokens are rejected on every run-scoped endpoint. + bad = RolloutServiceClient(harness.url, harness.run_id, "wrong-token", timeout=10) + with pytest.raises(ServiceRolloutError) as exc: + bad.group_status("group-0") + assert exc.value.status == 401 + with pytest.raises(ServiceRolloutError) as exc: + bad.submit_group(group_request()) + assert exc.value.status == 401 + + +def test_policy_gate_and_activation(harness, tmp_path): + client = harness.client() + with pytest.raises(ServiceRolloutError) as exc: + client.submit_group(group_request()) + assert exc.value.status == 409 and exc.value.code == "stale_policy" + assert not (tmp_path / "policy-pool.json").exists() + + client.activate_policy(activated_states(), "1") + assert harness.runtime.registry.version == "1" + assert harness.runtime.registry.accepting + pool = json.loads((tmp_path / "policy-pool.json").read_text()) + assert pool["weight_version"] == "1" + + # A partially unhealthy pool is never admitted. + from harborrl.rollout.service import ServiceError + + broken = activated_states() + broken[1]["healthy"] = False + with pytest.raises(ServiceError, match="pool"): + harness.service.activate_policy(harness.run_id, broken, "2") + + +def test_submit_is_idempotent_and_conflicts_rejected(harness): + client = harness.client() + client.activate_policy(activated_states(), "1") + request = group_request() + first = client.submit_group(request) + assert first["status"] == "complete" + assert len(first["result"]["slots"]) == 2 + assert len(harness.executor_calls) == 1 + + again = client.submit_group(json.loads(json.dumps(request))) + assert again["status"] == "complete" + assert again["result"]["request_digest"] == first["result"]["request_digest"] + assert len(harness.executor_calls) == 1 # no second execution + + conflicting = group_request() + conflicting["sampling"]["temperature"] = 0.5 + with pytest.raises(ServiceRolloutError) as exc: + client.submit_group(conflicting) + assert exc.value.status == 409 and exc.value.code == "conflict" + + other = client.submit_group(group_request(group_index=1)) + assert other["status"] == "complete" + assert len(harness.executor_calls) == 2 + + +def test_client_validates_result_and_rejects_tampering(harness, tmp_path): + client = harness.client() + client.activate_policy(activated_states(), "1") + request = group_request() + result = client.submit_group(request)["result"] + trajectories = load_group_result(harness.run_id, request, result, + allow_synthetic=True) + assert [ir["identity"]["slot_id"] for ir in trajectories] == ["0", "1"] + + slot = result["slots"][0] + manifest = Path(slot["manifest_path"]) + forged = json.loads(json.dumps(result)) + forged["slots"][0]["manifest_sha256"] = "0" * 64 + with pytest.raises(ServiceRolloutError, match="digest mismatch"): + load_group_result(harness.run_id, request, forged, allow_synthetic=True) + tampered = json.loads(json.dumps(result)) + tampered["slots"][1]["manifest_path"] = str(tmp_path / "missing.json") + with pytest.raises(ServiceRolloutError, match="cannot read slot manifest"): + load_group_result(harness.run_id, request, tampered, allow_synthetic=True) + assert manifest.is_file() # nothing above mutated shared evidence + + # A stale policy version inside a result is rejected. + fresh = write_group_irs(tmp_path, group_request(policy_version="2"), rewards=(1, 0)) + stale_result = json.loads(json.dumps(result)) + stale_result["slots"][0]["manifest_path"] = str( + harness.runtime.manifest_path(fresh[0]["identity"])) + stale_result["slots"][0]["manifest_sha256"] = digest(fresh[0]) + with pytest.raises(ServiceRolloutError, match="identity differs"): + load_group_result(harness.run_id, request, stale_result, allow_synthetic=True) + + +def test_drain_closes_admission(harness): + client = harness.client() + client.activate_policy(activated_states(), "1") + client.drain() + with pytest.raises(ServiceRolloutError) as exc: + client.submit_group(group_request()) + assert exc.value.status == 409 and exc.value.code == "admission_closed" + assert not harness.runtime.registry.accepting + + +@pytest.mark.parametrize("cancel_whole_run", [False, True]) +def test_cancel_and_query_after_timeout(tmp_path, cancel_whole_run): + release = threading.Event() + + async def slow_executor(config, runtime_, request): + if request["group_index"] == 0: + while not release.is_set(): + await asyncio.sleep(0.05) # cancellable wait + return write_group_irs(tmp_path, request) + while True: # the second group blocks until it is cancelled + await asyncio.sleep(0.05) + + harness = ServiceHarness(tmp_path, slow_executor, group_wait_timeout=0.2) + try: + client = harness.client() + client.activate_policy(activated_states(), "1") + request = group_request() + # Short server wait: the response must say running and hint a query. + pending = harness.service.submit_group(harness.run_id, request) + assert pending["status"] in ("queued", "running") + assert "hint" in pending + status = client.group_status("group-0") + assert status["status"] in ("queued", "running") + release.set() + deadline = time.time() + 5 + while time.time() < deadline and status["status"] in ("queued", "running"): + time.sleep(0.05) + status = client.group_status("group-0") + assert status["status"] == "complete" + + request2 = group_request(group_index=1) + submitted = harness.service.submit_group(harness.run_id, request2) + assert submitted["status"] in ("queued", "running") + if cancel_whole_run: + result = harness.service.cancel_run(harness.run_id) + assert result["groups"] == {"group-0": "complete", "group-1": "cancelled"} + cancelled = client.group_status("group-1") + else: + cancelled = client.cancel_group("group-1") + assert cancelled["status"] == "cancelled" + with pytest.raises(ServiceRolloutError, match="cancelled|not complete"): + load_group_result(harness.run_id, request2, cancelled) + finally: + release.set() + harness.close() + + +def test_register_run_conflict_on_different_manifest(tmp_path): + async def executor(config, runtime_, request): + return write_group_irs(tmp_path, request) + + harness = ServiceHarness(tmp_path, executor) + try: + from harborrl.rollout.service import ServiceError + + same_manifest = {"run_id": "run-1", "group_size": 2} + assert harness.service.register_run("run-1", same_manifest)["existed"] + with pytest.raises(ServiceError) as exc: + harness.service.register_run("run-1", {"run_id": "run-1", "x": 1}) + assert exc.value.status == 409 + finally: + harness.close() + + +def test_service_group_request_and_run_service_groups(tmp_path, monkeypatch): + """Full SlimeAdapter path: fake Slime samples -> HTTP -> IRs bound to samples.""" + from harborrl.backends.slime_v032 import rollout as slime_rollout + + calls = [] + + async def executor(config, runtime_, request): + calls.append(request["group_id"]) + return write_group_irs(tmp_path, request) + + harness = ServiceHarness(tmp_path, executor) + try: + monkeypatch.setenv("HARBORRL_ROLLOUT_SERVICE_URL", harness.url) + monkeypatch.setenv("HARBORRL_ROLLOUT_SERVICE_TOKEN", harness.token) + monkeypatch.setenv("RUN_ID", harness.run_id) + monkeypatch.setenv("RUN_DIR", str(tmp_path)) + + class Sample: + class Status: + COMPLETED = "completed" + + def __init__(self, group_index): + self.metadata = {"native_task": dict(TASK)} + self.group_index = group_index + + args = SimpleNamespace(rollout_max_response_len=SAMPLING["max_new_tokens"], + rollout_temperature=1.0, rollout_top_p=1.0, + rollout_top_k=-1, rollout_stop=[], + sglang_router_ip="127.0.0.1", sglang_router_port=30000) + group = [Sample(0), Sample(0)] + monkeypatch.setenv("HARBORRL_ROLLOUT_SERVICE_TOKEN", harness.token) + # The adapter announces serving and derives the admitted version from + # the live pool itself before requesting groups. + from harborrl.backends.slime_v032 import rollout_service_client + + monkeypatch.setattr(rollout_service_client, "fetch_pool_states", + lambda endpoint, **kwargs: activated_states()) + + with pytest.raises(ServiceRolloutError, match='synthetic_evidence'): + asyncio.run(slime_rollout.run_service_groups(args, [group], 0)) + assert all('native_ir' not in sample.metadata for sample in group) + assert calls == ["group-0"] + assert harness.service.runs[harness.run_id].serving == ( + "http://127.0.0.1:30000") + + request = service_group_request( + group, slime_rollout.sampling_params(args), 0, "1") + assert request["group_id"] == "group-0" + assert request["group_index"] == 0 + assert [slot["slot_id"] for slot in request["slots"]] == ["0", "1"] + finally: + harness.close() + + +def test_announce_factory_attaches_config_and_runtime(tmp_path): + """A GPU announce through the factory makes the run executable alone.""" + async def executor(config, runtime_, request): + assert config is runtime_.config + return write_group_irs(tmp_path, request) + + service = RolloutService(run_root=tmp_path, executor=executor, allow_synthetic=True, + group_wait_timeout=5, + runtime_factory=lambda run_id, router: SimpleNamespace( + registry=FakeRegistry(), rollout_root=tmp_path, + manifest_path=lambda ident: manifest_path_from(tmp_path, ident), + config=SimpleNamespace(drain_timeout=1))) + try: + registration = service.register_run("run-9", {"run_id": "run-9"}) + result = service.announce_serving("run-9", "http://gpu:30000/") + assert result["runtime"] == "attached" + assert service.runs["run-9"].serving == "http://gpu:30000" + assert service.runs["run-9"].config is not None + service.activate_policy("run-9", activated_states(), "1") + submitted = service.submit_group("run-9", group_request()) + assert submitted["status"] == "complete" + assert registration["run_token"] + finally: + service.close() + + +def test_service_group_request_rejects_mixed_groups(): + class Sample: + def __init__(self, group_index, task): + self.metadata = {"native_task": dict(task)} + self.group_index = group_index + + other = dict(TASK) + other["revision"] = "2" + with pytest.raises(ValueError, match="mixes task locks"): + service_group_request([Sample(0, TASK), Sample(0, other)], dict(SAMPLING), 0, "1") + with pytest.raises(ValueError, match="group identity"): + service_group_request([Sample(0, TASK), Sample(1, TASK)], dict(SAMPLING), 0, "1") diff --git a/tests/harborrl/test_sandbox.py b/tests/harborrl/test_sandbox.py new file mode 100644 index 000000000..b98d27197 --- /dev/null +++ b/tests/harborrl/test_sandbox.py @@ -0,0 +1,97 @@ +"""Sandbox routing and fail-closed capacity contracts, without live resources.""" +import asyncio +import json +from pathlib import Path + +import pytest +import yaml +from test_native_training import native_config + +from harborrl.config.native import load_config +from harborrl.rollout.harbor_job.sandbox_config import ( + SandboxAdmission, + load_sandbox, + task_binding, +) + + +def settings(tmp_path): + return {'domain': 'https://sandbox.example', 'credential_file': 'key', + 'bindings': [{'image': 'registry.example/task@sha256:' + 'a' * 64, + 'max_live_instances': 1}], + 'installation': {'mode': 'offline_binary', 'version': '2.1.0', 'binary': 'claude', 'sha256': 'b' * 64}} + + +def test_local_runner_opt_in_and_unknown_fields(tmp_path): + path, _ = native_config(tmp_path) + old = load_config(path) + assert old['harbor']['workers'] == ['worker-a', 'worker-b'] + local = load_config(path, ['harbor.runner_transport=local', 'harbor.workers=[]', + 'harbor.environment={runtime: sandbox, config: sandbox.yaml}']) + assert local['harbor']['environment']['config'] == str(tmp_path / 'sandbox.yaml') + with pytest.raises(ValueError, match='local runner'): + load_config(path, ['harbor.runner_transport=local']) + with pytest.raises(ValueError, match='fields'): + load_config(path, ['harbor.environment={runtime: sandbox, config: x, api_key: secret}']) + + +def test_sandbox_binding_and_secret_rejection(tmp_path): + cfg = settings(tmp_path) + path = tmp_path / 'sandbox.yaml' + path.write_text(yaml.safe_dump(cfg)) + result = load_sandbox(path) + assert result['credential_file'] == str(tmp_path / 'key') + (tmp_path / 'task.toml').write_text('[environment]\ndocker_image = "' + cfg['bindings'][0]['image'] + '"\n') + assert task_binding(result, tmp_path)['image'] == cfg['bindings'][0]['image'] + (tmp_path / 'task.toml').write_text('[environment]\ndocker_image = "wrong"\n') + with pytest.raises(ValueError, match='exact binding'): + task_binding(result, tmp_path) + cfg['api_key'] = 'must-not-be-serialized' + path.write_text(yaml.safe_dump(cfg)) + with pytest.raises(ValueError, match='fields'): + load_sandbox(path) + + +def test_admission_across_groups_and_cleanup_debt(tmp_path): + async def run(): + cfg = {'max_live_instances': 2, 'bindings': [{'image': 'image', 'max_live_instances': 1}]} + admission = SandboxAdmission(cfg) + active, peak = 0, 0 + + async def trial(index): + nonlocal active, peak + async with admission.lease(cfg['bindings'][0], tmp_path / str(index)): + active += 1 + peak = max(peak, active) + await asyncio.sleep(0.01) + active -= 1 + await asyncio.gather(*(trial(i) for i in range(6))) + assert peak == 1 + root = tmp_path / 'failed' + root.mkdir() + async with admission.lease(cfg['bindings'][0], root): + (root / 'cleanup-required.json').write_text('{}') + with pytest.raises(RuntimeError, match='blocked'): + async with admission.lease(cfg['bindings'][0], tmp_path): + pytest.fail('must not allocate') + asyncio.run(run()) + + +def test_sandbox_launch_freezes_nonsecret_runtime(tmp_path): + from harborrl.backends.slime_v032.config import launch_plan + from harborrl.data.harbor.native_inspector import inspect_native + path, task = native_config(tmp_path) + cfg = settings(tmp_path) + (tmp_path / 'sandbox.yaml').write_text(yaml.safe_dump(cfg)) + (task / 'task.toml').write_text('[environment]\ndocker_image = "' + cfg['bindings'][0]['image'] + '"\n') + catalog = tmp_path / 'catalog.json' + entries = json.loads(catalog.read_text()) + entries[0]['task_digest'] = inspect_native(task)['task_digest'] + catalog.write_text(json.dumps(entries)) + config = load_config(path, ['harbor.runner_transport=local', 'harbor.workers=[]', + 'harbor.environment={runtime: sandbox, config: sandbox.yaml}']) + plan = launch_plan(config) + runtime = json.loads(plan['environment']['HARBORRL_NATIVE_SANDBOX']) + assert runtime['installation']['version'] == '2.1.0' + assert Path(runtime['installation']['binary']).is_absolute() + assert plan['environment']['HARBORRL_NATIVE_RUNNER_WORKERS'] == '[]' diff --git a/tests/harborrl/test_sandbox_allocation_transport.py b/tests/harborrl/test_sandbox_allocation_transport.py new file mode 100644 index 000000000..db34cf098 --- /dev/null +++ b/tests/harborrl/test_sandbox_allocation_transport.py @@ -0,0 +1,24 @@ +import asyncio + +import httpx + +from harborrl.rollout.harbor_job.sandbox_transport import SandboxStartupTransport + + +def test_generic_transport_observes_allocation_without_retry(): + async def run(): + calls, allocations = [], [] + + def handler(request): + calls.append(request.method) + return httpx.Response(200, json={'id': 'sbx-one'}) if request.method == 'POST' else httpx.Response(503) + + transport = SandboxStartupTransport('https://sandbox.example/v1', allocations.append, + transport=httpx.MockTransport(handler)) + async with httpx.AsyncClient(transport=transport) as client: + await client.post('https://sandbox.example/v1/sandboxes') + result = await client.get('https://sandbox.example/v1/sandboxes/sbx-one/endpoints/44772') + assert result.status_code == 503 + assert calls == ['POST', 'GET'] and allocations == ['sbx-one'] + + asyncio.run(run()) diff --git a/tests/harborrl/test_sandbox_cleanup.py b/tests/harborrl/test_sandbox_cleanup.py new file mode 100644 index 000000000..7364ac483 --- /dev/null +++ b/tests/harborrl/test_sandbox_cleanup.py @@ -0,0 +1,36 @@ +import asyncio + +import httpx +import pytest + +pytest.importorskip('harbor.environments.opensandbox') + +from harborrl.rollout.harbor_job.environments.opensandbox import ( + ManagedOpenSandboxEnvironment, +) + + +@pytest.mark.parametrize('delete_status,expected', [(204, 'deleted'), (500, 'delete_failed')]) +def test_allocated_id_cleanup_requires_confirmed_absence(tmp_path, monkeypatch, delete_status, expected): + calls = [] + + async def handler(request): + calls.append((request.method, request.url.path)) + assert request.url.path == '/v1/sandboxes/sbx-owned' + if request.method == 'DELETE': + return httpx.Response(delete_status) + return httpx.Response(200 if len(calls) == 1 else 404) + + client_class = httpx.AsyncClient + monkeypatch.setattr(httpx, 'AsyncClient', lambda **kwargs: client_class( + **kwargs, transport=httpx.MockTransport(handler))) + env = object.__new__(ManagedOpenSandboxEnvironment) + env.cfg = {'domain': 'https://platform.test', 'cleanup_timeout_s': 1} + env.binding = {'image': 'owned'} + env._api_key = 'test-only' + env.evidence = tmp_path + env.record = {'sandbox_id': 'sbx-owned', 'runtime_digest': 'test', 'events': []} + asyncio.run(env._cleanup_allocated()) + assert env.record['state'] == expected + assert (tmp_path / 'cleanup-required.json').exists() == (expected == 'delete_failed') + assert calls[:2] == [('GET', '/v1/sandboxes/sbx-owned'), ('DELETE', '/v1/sandboxes/sbx-owned')] diff --git a/tests/harborrl/test_sandbox_portability.py b/tests/harborrl/test_sandbox_portability.py new file mode 100644 index 000000000..1a4c5bb6a --- /dev/null +++ b/tests/harborrl/test_sandbox_portability.py @@ -0,0 +1,57 @@ +"""Public core must remain usable without the optional SDK or cluster package.""" +import subprocess +import sys +from pathlib import Path + +import pytest +import yaml + +from harborrl.rollout.harbor_job.sandbox_config import load_sandbox, read_credential + + +def test_config_loading_does_not_import_optional_packages(tmp_path): + config = tmp_path / 'sandbox.yaml' + config.write_text(yaml.safe_dump({'domain': 'https://sandbox.example', 'credential_file': 'key', + 'bindings': [{'image': 'image', 'max_live_instances': 1}]})) + code = """ +import sys +from harborrl.rollout.harbor_job.sandbox_config import load_sandbox +cfg = load_sandbox(sys.argv[1]) +assert cfg['installation'] is None and cfg['options'] == {} +assert not any(k == 'harbor' or k.startswith(('harbor.', 'opensandbox', 'harborrl_h_cluster', 'torch', 'ray')) for k in sys.modules) +""" + subprocess.run([sys.executable, '-c', code, str(config)], check=True) + + +def test_core_has_no_cluster_imports_or_platform_keys(): + root = Path(__file__).resolve().parents[2] / 'harborrl' + for path in root.rglob('*.py'): + source = path.read_text() + assert 'harborrl_h_cluster' not in source, str(path) + assert 'brainbox' not in source.lower(), str(path) + assert 'environment_id' not in source, str(path) + + +@pytest.mark.parametrize('adapter', ['invalid', '../module:Class', 'module:Class:Other']) +def test_adapter_path_rejected_without_import(tmp_path, adapter): + path = tmp_path / 'sandbox.yaml' + path.write_text(yaml.safe_dump({'domain': 'https://sandbox.example', 'credential_file': 'key', + 'bindings': [{'image': 'image', 'max_live_instances': 1}], 'adapter': adapter})) + with pytest.raises(ValueError, match='module:Class'): + load_sandbox(path) + + +def test_generic_token_does_not_require_platform_key_format(tmp_path): + path = tmp_path / 'credential' + path.write_text('test-only\n') + path.chmod(0o600) + assert read_credential({'credential_file': str(path)}) == 'test-only' + + +def test_nested_provider_secrets_never_enter_runtime_manifest(tmp_path): + path = tmp_path / 'sandbox.yaml' + path.write_text(yaml.safe_dump({'domain': 'https://sandbox.example', 'credential_file': 'key', + 'bindings': [{'image': 'image', 'max_live_instances': 1}], + 'options': {'nested': {'api_key': 'test-only'}}})) + with pytest.raises(ValueError, match='secrets'): + load_sandbox(path) diff --git a/tests/harborrl/test_sandbox_runtime.py b/tests/harborrl/test_sandbox_runtime.py new file mode 100644 index 000000000..9f896818f --- /dev/null +++ b/tests/harborrl/test_sandbox_runtime.py @@ -0,0 +1,92 @@ +"""Shared Sandbox scheduling and service lifecycle contracts.""" +import asyncio +import json +from dataclasses import replace +from types import SimpleNamespace + +import pytest +from test_deployment_config import deployment_mapping + +from harborrl.backends.slime_v032.rollout_service_client import ( + RolloutServiceClient, + ServiceRolloutError, +) +from harborrl.deployment.schema import validate +from harborrl.rollout.harbor_job.sandbox_config import SandboxAdmission +from harborrl.rollout.runtime import RolloutRuntimeConfig, run_slot + + +def test_sandbox_deployment_allows_local_or_remote_runners(tmp_path): + cfg = deployment_mapping(tmp_path) + cfg['rollout'] = {'provider': 'sandbox', 'workers': []} + assert validate(cfg)['rollout']['workers'] == [] + cfg['rollout']['workers'] = deployment_mapping(tmp_path)['rollout']['workers'] + assert validate(cfg)['rollout']['workers'] + + +def test_slow_group_is_posted_once_even_after_transport_timeout(monkeypatch): + client = RolloutServiceClient('http://manager', 'run', 'token', poll_interval=0) + calls = [] + def request(method, path, body=None): + calls.append(method) + if method == 'POST': + raise ServiceRolloutError('timeout', code='transport') + return {'status': 'running' if len(calls) == 2 else 'complete', 'result': {}} + monkeypatch.setattr(client, '_request', request) + assert client.submit_group({'group_id': 'group-0'})['status'] == 'complete' + assert calls == ['POST', 'GET', 'GET'] + + +def test_sandbox_cleanup_debt_prevents_retry(tmp_path, monkeypatch): + from harborrl.rollout import runtime + from harborrl.rollout.harbor_job import sandbox_config + profile = tmp_path / 'profile.json' + profile.write_text('{}') + binding = {'image': 'image', 'max_live_instances': 1} + sandbox = {'max_live_instances': 1, 'bindings': [binding]} + config = RolloutRuntimeConfig('run', str(tmp_path), '', str(profile), '127.0.0.1', 1, + 'http://gateway', '/python', (), {}, 2, 128, 1024, 2, 2, 'tok', 'tpl', True, 1, + 'qwen3-8B', 'http://router', sandbox=sandbox, runner_transport='local') + current = SimpleNamespace(rollout_root=tmp_path, semaphore=asyncio.Semaphore(2), + registry=None, profile={}, sandbox_admission=SandboxAdmission(sandbox)) + calls = [] + async def attempt(identity, **kwargs): + calls.append(kwargs) + assert kwargs['runner_host'] is None + assert kwargs['sandbox'] == sandbox + kwargs['root'].mkdir(parents=True) + (kwargs['root'] / 'cleanup-required.json').write_text('{}') + raise RuntimeError('deletion unconfirmed') + monkeypatch.setattr(runtime, 'run_attempt', attempt) + monkeypatch.setattr(sandbox_config, 'task_binding', lambda *args: binding) + entry = {'id': 'task', 'path': '/task', 'task_digest': 'digest', 'reward_profile': {}} + with pytest.raises(RuntimeError, match='cleanup unresolved'): + asyncio.run(run_slot(config, current, 0, 0, 0, entry, 'sampling', '1')) + assert len(calls) == 1 and current.sandbox_admission.blocked + restored = replace(config, workers=('worker',), runner_transport='ssh') + assert json.loads(restored.environment()['HARBORRL_NATIVE_SANDBOX']) == sandbox + + +def test_group_failure_waits_for_sibling_cleanup(tmp_path, monkeypatch): + from harborrl.rollout import runtime + events = [] + async def attempt(identity, **kwargs): + if identity.slot_id == '0': + await asyncio.sleep(.01) + raise RuntimeError('failed slot') + try: + await asyncio.sleep(60) + finally: + await asyncio.sleep(.01) + events.append('cleanup complete') + monkeypatch.setattr(runtime, 'run_attempt', attempt) + config = SimpleNamespace(run_id='run', group_size=2, max_attempts=1, workers=(), + task_worker_overrides={}, runner_transport='local', sandbox=None, + gateway_url='http://gateway', runner_python='/python', max_output_tokens=128) + current = SimpleNamespace(profile={}, rollout_root=tmp_path, + semaphore=asyncio.Semaphore(2), registry=None) + entry = {'id': 'task', 'path': '/task', 'task_digest': 'digest', 'reward_profile': {}} + with pytest.raises(RuntimeError, match='exhausted its retry budget'): + asyncio.run(runtime.run_group(config, current, entry=entry, + group_index=0, rollout_id=0, sampling_digest='x', policy_version='1')) + assert events == ['cleanup complete'] diff --git a/tests/harborrl/test_sandbox_scale.py b/tests/harborrl/test_sandbox_scale.py new file mode 100644 index 000000000..2e6f39e2a --- /dev/null +++ b/tests/harborrl/test_sandbox_scale.py @@ -0,0 +1,36 @@ +"""A saturated image pool must not reserve unrelated global capacity.""" +import asyncio +from contextlib import suppress + +from harborrl.rollout.harbor_job.sandbox_config import SandboxAdmission + + +def test_waiting_binding_does_not_starve_other_environments(tmp_path): + async def run(): + first = {'image': 'first', 'max_live_instances': 1} + second = {'image': 'second', 'max_live_instances': 1} + admission = SandboxAdmission({'max_live_instances': 2, 'bindings': [first, second]}) + entered, release = asyncio.Event(), asyncio.Event() + + async def waiting(): + entered.set() + async with admission.lease(first, tmp_path / 'waiting'): + await release.wait() + + async with admission.lease(first, tmp_path / 'active'): + task = asyncio.create_task(waiting()) + try: + await entered.wait() + async def independent(): + async with admission.lease(second, tmp_path / 'independent'): + pass + await asyncio.wait_for(independent(), 1) + finally: + task.cancel() + with suppress(asyncio.CancelledError): + await task + # Cancellation of a queued request must not leak either admission permit. + async with admission.lease(first, tmp_path / 'after'), admission.lease(second, tmp_path / 'also-after'): + pass + + asyncio.run(run()) diff --git a/tests/harborrl/test_service_group_concurrency.py b/tests/harborrl/test_service_group_concurrency.py new file mode 100644 index 000000000..3cbf4c9be --- /dev/null +++ b/tests/harborrl/test_service_group_concurrency.py @@ -0,0 +1,63 @@ +import asyncio +import threading +from types import SimpleNamespace + +import pytest + +from harborrl.backends.slime_v032 import rollout, rollout_service_client + + +@pytest.mark.parametrize('fail', [False, True]) +def test_service_groups_overlap_preserve_order_and_await_failure_cleanup(monkeypatch, fail): + barrier = threading.Barrier(2, timeout=3) + cancelled, completed = threading.Event(), threading.Event() + activated, failed = threading.Event(), threading.Event() + + class Client: + def __init__(self, *args): + self.run_id = 'run' + + def announce_serving(self, _): + pass + + def activate_policy(self, *_): + activated.set() + + def submit_group(self, request): + assert activated.is_set() + barrier.wait() + if fail and request['group_id'] == 'first': + failed.set() + raise ValueError('group failed') + if fail: + assert failed.wait(3) + assert not cancelled.is_set() + completed.set() + return {'result': {'id': request['group_id']}} + + def cancel_run(self): + cancelled.set() + + def load_result(run_id, request, result, *, allow_synthetic): + assert allow_synthetic is False + return [result['id']] + + monkeypatch.setenv('HARBORRL_ROLLOUT_SERVICE_URL', 'http://localhost') + monkeypatch.setenv('RUN_ID', 'run') + monkeypatch.delenv('HARBORRL_ROLLOUT_SERVICE_TOKEN_FILE', raising=False) + monkeypatch.setattr(rollout_service_client, 'RolloutServiceClient', Client) + monkeypatch.setattr(rollout_service_client, 'fetch_pool_states', lambda _: [{'weight_version': '1'}]) + monkeypatch.setattr(rollout_service_client, 'service_group_request', lambda group, *_: {'group_id': group}) + monkeypatch.setattr(rollout_service_client, 'load_group_result', load_result) + monkeypatch.setattr(rollout, 'lock_policy_version', lambda _: '1') + monkeypatch.setattr(rollout, 'sampling_params', lambda _: {}) + monkeypatch.setattr(rollout, 'bind_group_samples', lambda *_: None) + args = SimpleNamespace(sglang_router_ip='localhost', sglang_router_port=8000) + if fail: + with pytest.raises(ValueError, match='group failed'): + asyncio.run(rollout.run_service_groups(args, ['first', 'second'], 0)) + assert cancelled.is_set() + else: + assert asyncio.run(rollout.run_service_groups(args, ['first', 'second'], 0)) == [['first'], ['second']] + assert not cancelled.is_set() + assert completed.is_set() diff --git a/tests/harborrl/test_slime_v032_exporter.py b/tests/harborrl/test_slime_v032_exporter.py index d93e84746..41ce72ef9 100644 --- a/tests/harborrl/test_slime_v032_exporter.py +++ b/tests/harborrl/test_slime_v032_exporter.py @@ -221,6 +221,46 @@ def test_pure_clipped_loss_numerics(): assert LOSS_SCALE_CORRECTION == 1.0 +def test_postprocess_accepts_multiple_groups_and_partial_actor_shards(tmp_path): + args = SimpleNamespace(n_samples_per_prompt=2) + groups = native_groups(tmp_path) + batch = export_training_batch(groups, rollout_ids=[10, 11]) + data = convert_samples_to_train_data(args, fake_samples(batch, groups)) + rollout_data_postprocess(args, 0, data) + shard = {key: values[::2] for key, values in data.items()} + rollout_data_postprocess(args, 0, shard) + + +def test_postprocess_rejects_individual_group_overweight(): + args = SimpleNamespace(n_samples_per_prompt=2) + data = { + "tokens": [[1, 2]] * 4, + "response_lengths": [1] * 4, + "rewards": [0.0] * 4, + "raw_reward": [0.0] * 4, + "truncated": [0] * 4, + "sample_indices": list(range(4)), + "rollout_ids": [10, 10, 10, 11], + "loss_masks": [[1]] * 4, + "rollout_log_probs": [[-0.2]] * 4, + "rollout_mask_sums": [1, 1, 1, 2], + } + # Total weight is below two, but group 10 exceeds its own budget. + with pytest.raises(ValueError, match="budget.*10"): + rollout_data_postprocess(args, 0, data) + + +@pytest.mark.parametrize("group_id", [-1, 0.5, float("nan"), "invalid"]) +def test_postprocess_rejects_invalid_group_id(tmp_path, group_id): + args = SimpleNamespace(n_samples_per_prompt=2) + groups = native_groups(tmp_path) + batch = export_training_batch(groups, rollout_ids=[10, 11]) + data = convert_samples_to_train_data(args, fake_samples(batch, groups)) + data["rollout_ids"][0] = group_id + with pytest.raises(ValueError, match="rollout id"): + rollout_data_postprocess(args, 0, data) + + def test_postprocess_rejects_drift(): args = SimpleNamespace(n_samples_per_prompt=2) base = { @@ -265,7 +305,7 @@ def __float__(self): "raw_reward": [Scalar()], "truncated": [IntegerScalar()], "sample_indices": [0], - "rollout_ids": [0], + "rollout_ids": [IntegerScalar()], "loss_masks": [[1]], "rollout_log_probs": [[Scalar()]], "rollout_mask_sums": [IntegerScalar()], @@ -402,3 +442,45 @@ def test_export_package_has_no_backend_imports(): else: continue assert not (names & forbidden), f"{path} imports backend dependencies: {names & forbidden}" + + +@pytest.mark.parametrize('group_index, expected_advantages, expected_counts', [ + (0, [-0.5 / 0.500001, 0.5 / 0.500001], [2, 6]), + (1, [0.0, 0.0], [4, 2]), +]) +def test_legacy_export_preserves_grpo_record_semantics( + tmp_path, group_index, expected_advantages, expected_counts, +): + from harborrl.rollout.exporters.native import export_group + group = native_groups(tmp_path)[group_index] + exported = export_group(group, 2) + assert exported['export_version'] == 'native-grpo-v2' + assert exported['advantages'] == expected_advantages + assert exported['reduction'] == 'sum_token_weighted_loss; no secondary whitening' + assert exported['synthetic'] is False + for ir, advantage, count in zip(group, expected_advantages, expected_counts): + records = [record for record in exported['records'] + if record['identity']['trajectory_id'] == ir['identity']['trajectory_id']] + assert len(records) == len(ir['turns']) + for index, (turn, record) in enumerate(zip(ir['turns'], records)): + n = len(turn['output_ids']) + assert record == { + 'identity': ir['identity'], 'turn_index': index, + 'tokens': turn['input_ids'] + turn['output_ids'], + 'prompt_length': len(turn['input_ids']), 'response_length': n, + 'loss_mask': [0] * len(turn['input_ids']) + [1] * n, + 'old_logprobs': turn['logprobs'], 'advantages': [advantage] * n, + 'token_weights': [1.0 / (2 * count)] * n, + 'training_reward': ir['evaluation']['training_reward'], 'padding': False, + } + + +def test_native_task_inspection_avoids_legacy_conversion_imports(): + import subprocess + import sys + code = ( + 'import sys; import harborrl.data.harbor.native_inspector; ' + 'assert "harborrl.data.harbor.inspector" not in sys.modules; ' + 'assert "harborrl.data.harbor.materializer" not in sys.modules' + ) + subprocess.run([sys.executable, '-c', code], check=True) diff --git a/tests/harborrl/test_webui.py b/tests/harborrl/test_webui.py new file mode 100644 index 000000000..ddb397ffd --- /dev/null +++ b/tests/harborrl/test_webui.py @@ -0,0 +1,244 @@ +"""HTTP and process lifecycle contracts without allocating training resources.""" +import http.client +import json +import subprocess +import sys +import threading +import time +from pathlib import Path + +import pytest +import yaml +from test_options import answers as options_answers + +from harborrl.options.wizard import build_config_bundle +from harborrl.webui.cli import workspace_lock +from harborrl.webui.jobs import Jobs +from harborrl.webui.server import Console, make_server + +answers = options_answers + + +@pytest.fixture +def console(tmp_path): + instance = Console(tmp_path / "web") + yield instance + instance.jobs.close() + + +@pytest.fixture +def server(console): + server = make_server(console) + thread = threading.Thread(target=server.serve_forever) + thread.start() + yield server + server.shutdown() + thread.join(5) + server.server_close() + + +def request(server, console, method, path, body=None, headers=None): + connection = http.client.HTTPConnection("127.0.0.1", server.server_port, timeout=5) + defaults = {"Authorization": "Bearer " + console.token, "Content-Type": "application/json", + "Origin": f"http://127.0.0.1:{server.server_port}"} + connection.request(method, path, body=json.dumps(body) if body is not None else None, + headers={**defaults, **(headers or {})}) + response = connection.getresponse() + payload = response.read() + result = (response.status, dict(response.getheaders()), payload) + connection.close() + return result + + +def test_static_and_bootstrap(server, console): + for path in ("/", "/app.js", "/style.css", "/lucide.min.js", "/brand.jpg"): + status, headers, payload = request(server, console, "GET", path) + assert status == 200 and payload + assert "frame-ancestors 'none'" in headers["Content-Security-Policy"] + assert console.token.encode() not in payload + status, _, payload = request(server, console, "GET", "/api/bootstrap") + assert status == 200 + assert json.loads(payload)["catalog"]["catalog_version"] == 1 + + +@pytest.mark.parametrize("headers", [{"Authorization": "Bearer invalid"}, {"Origin": "https://external.example"}, + {"Host": "external.example"}, {"Content-Type": "text/plain"}]) +def test_request_boundary(server, console, headers): + status, _, _ = request(server, console, "POST", "/api/configurations", {}, headers=headers) + assert status in {403, 415} + assert not console.list_configurations() + + +@pytest.mark.parametrize("name", ["../outside", "/absolute", "", "a/b", "a\\b", "a b", ".hidden"]) +def test_output_paths(console, answers, name): + with pytest.raises(ValueError): + console.save({"name": name, "answers": answers}) + + +def test_preview_save_and_snapshot(server, console, answers): + body = {"name": "experiment", "answers": answers, "check_files": True} + status, _, payload = request(server, console, "POST", "/api/preview", body) + assert status == 200, payload + assert json.loads(payload)["validation"]["status"] == "valid_with_deferred_checks" + assert not console.directory("experiment").exists() + status, _, payload = request(server, console, "POST", "/api/configurations", body) + assert status == 201, payload + assert request(server, console, "POST", "/api/configurations", body)[0] == 400 + status, _, payload = request(server, console, "POST", "/api/jobs", {"configuration": "experiment", "mode": "dry-run"}) + assert status == 201, payload + job = json.loads(payload) + for _ in range(600): + record = console.jobs.list()[0] + if record["state"] != "running": + break + time.sleep(.05) + assert record["state"] == "succeeded", console.jobs.log(job["id"]) + snapshot = Path(record["snapshot"]) + training = yaml.safe_load((snapshot / "config.yaml").read_text()) + deployment = yaml.safe_load((snapshot / "deployment.yaml").read_text()) + assert training["output"]["root"] == deployment["storage"]["run_root"] == record["output_root"] + saved = console.directory("experiment") / "config.yaml" + assert yaml.safe_load(saved.read_text())["output"]["root"] != record["output_root"] + assert json.loads(console.jobs.log(job["id"])["text"])["config"]["schema_version"] == 2 + + +@pytest.mark.parametrize("mode", ["train", "doctor", "shell"]) +def test_live_requires_confirmation(console, answers, mode, monkeypatch): + console.save({"name": "test", "answers": answers}) + def forbidden(*args, **kwargs): + raise AssertionError("Unconfirmed live job must not execute") + monkeypatch.setattr(subprocess, "Popen", forbidden) + with pytest.raises(ValueError): + console.start({"configuration": "test", "mode": mode}) + + +def test_process_lifecycle(console, answers, tmp_path, monkeypatch): + console.save({"name": "test", "answers": answers}) + real_popen = subprocess.Popen + calls = [] + def stand_in(command, **kwargs): + calls.append(command) + return real_popen([sys.executable, "-u", "-c", "import time; print('training stand-in', flush=True); time.sleep(30)"], **kwargs) + monkeypatch.setattr(subprocess, "Popen", stand_in) + job = console.start({"configuration": "test", "mode": "train", "confirmed": True}) + assert calls[0][4] == "train" and "--dry-run" not in calls[0] + with pytest.raises(ValueError, match="active"): + console.start({"configuration": "test", "mode": "dry-run"}) + for _ in range(600): + if console.jobs.log(job["id"])["text"]: + break + time.sleep(.05) + child = console.jobs.child + stopped = console.jobs.stop(job["id"]) + assert stopped["state"] == "stopping" + child.wait(timeout=5) + assert console.jobs.list()[0]["state"] == "stopped" + assert console.jobs.log(job["id"])["text"] + recovered = Jobs(console.jobs.root) + assert recovered.list()[0]["state"] == "stopped" + + +def test_restart_blocks_unknown_job(console, answers, tmp_path): + root = console.jobs.root / "abc" + root.mkdir() + (root / "job.json").write_text(json.dumps({"id": "abc", "state": "running", "created_at": "2026-10-22"})) + recovered = Jobs(console.jobs.root) + assert recovered.list()[0]["state"] == "unknown" + with pytest.raises(ValueError, match="interrupted"): + recovered.start("test", build_config_bundle(answers, output_directory=tmp_path / "out"), "dry-run") + recovered.reconcile("abc") + assert recovered.list()[0]["state"] == "interrupted" + + +def test_workspace_lock(tmp_path): + with workspace_lock(tmp_path), pytest.raises(ValueError, match="Another console"), workspace_lock(tmp_path): + pass + with workspace_lock(tmp_path): + pass + + +def test_symlink_cannot_escape(console, tmp_path, answers): + outside = tmp_path / "outside" + outside.mkdir() + (console.configurations / "escape").symlink_to(outside, target_is_directory=True) + with pytest.raises(ValueError): + console.save({"name": "escape", "answers": answers}) + assert not list(outside.iterdir()) + + +def test_failed_spawn_and_log_limit(console, answers, tmp_path, monkeypatch): + def failed(*args, **kwargs): + raise OSError("injected launch failure") + monkeypatch.setattr(subprocess, "Popen", failed) + bundle = build_config_bundle(answers, output_directory=tmp_path / "out") + job = console.jobs.start("test", bundle, "dry-run") + assert job["state"] == "failed" + Path(job["log_path"]).write_bytes(b"x" * 100000) + result = console.jobs.log(job["id"]) + assert len(result["text"]) == 65536 and result["truncated"] + training_log = Path(job["output_root"]) / "training/one/train-stdout.log" + training_log.parent.mkdir(parents=True) + training_log.write_text("step=1 reward=0.5") + assert console.jobs.log(job["id"])["training_text"] == "step=1 reward=0.5" + + +def test_import_and_bad_body(server, console): + status, _, payload = request(server, console, "POST", "/api/import", {"text": "answers_version: 1\npreset: embedded-local"}) + assert status == 200 and json.loads(payload)["answers"]["preset"] == "embedded-local" + assert request(server, console, "POST", "/api/import", {"text": "[]"})[0] == 400 + assert request(server, console, "GET", "/../../pyproject.toml")[0] == 404 + + +@pytest.mark.parametrize("text", ["answers_version: true\npreset: embedded-local", "answers_version: 1\npreset: embedded-local\ntraining: []", "answers_version: 1\npreset: embedded-local\nunknown: x"]) +def test_import_rejects_invalid_answers(server, console, text): + assert request(server, console, "POST", "/api/import", {"text": text})[0] == 400 + + +def test_preview_never_starts_process(server, console, answers, monkeypatch): + def forbidden(*args, **kwargs): + raise AssertionError("Preview must not start a process") + monkeypatch.setattr(subprocess, "Popen", forbidden) + assert request(server, console, "POST", "/api/preview", {"name": "offline", "answers": answers, "check_files": True})[0] == 200 + + +def test_local_stop_reaches_descendants(tmp_path): + import os + import signal + + from harborrl.config import ROOT + script = ''' +import os, signal, subprocess, sys, time +from pathlib import Path +from harborrl.deployment.training_target import LocalTrainingTarget +root = Path(sys.argv[1]) +worker = "import signal,sys,time; from pathlib import Path; signal.signal(signal.SIGINT, lambda *a: (Path(sys.argv[1]).write_text('stopped'), sys.exit(0))); Path(sys.argv[1]+'.ready').touch(); time.sleep(30)" +parent = "import subprocess,sys,time; from pathlib import Path; child=subprocess.Popen([sys.executable,'-c',sys.argv[1],sys.argv[2]]); Path(sys.argv[2]+'.pid').write_text(str(child.pid)); time.sleep(30)" +target = LocalTrainingTarget([sys.executable, '-c', parent, worker, str(root / 'worker')], cwd=root, env=os.environ.copy()) +target.launch() +(root / 'group').write_text(str(target.child.pid)) +sys.exit(target.wait()) +''' + env = {**os.environ, "PYTHONPATH": str(ROOT)} + proc = subprocess.Popen([sys.executable, "-c", script, str(tmp_path)], env=env, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + try: + for _ in range(600): + if (tmp_path / "worker.ready").exists(): + break + time.sleep(.05) + assert (tmp_path / "worker.ready").exists() + proc.send_signal(signal.SIGINT) + proc.wait(timeout=10) + for _ in range(100): + if (tmp_path / "worker").exists(): + break + time.sleep(.05) + assert (tmp_path / "worker").read_text() == "stopped" + finally: + if (tmp_path / "group").exists(): + try: + os.killpg(int((tmp_path / "group").read_text()), signal.SIGKILL) + except ProcessLookupError: + pass + if proc.poll() is None: + proc.kill() + proc.wait(timeout=10) diff --git a/tests/test_consistency.py b/tests/test_consistency.py new file mode 100644 index 000000000..04146beb5 --- /dev/null +++ b/tests/test_consistency.py @@ -0,0 +1,81 @@ +"""Dependency-free tests of consistency evidence and admission decisions.""" +import struct + +import pytest + +from harborrl.consistency.config import require_raw_sampling, validate +from harborrl.consistency.metrics import merge, statistics +from harborrl.consistency.report import assess, decide +from harborrl.consistency.sglang import check_pool, score +from harborrl.consistency.slime import evidence_key + + +def test_weighted_statistics_ignore_padding_and_tp_duplicates(): + settings = validate({'mode': 'enforce', 'max_abs_delta': 1}) + a = assess([([-1], [-2], [1])], settings) + b = assess([([-2, -2, -100], [-2, -2, -1], [1, 1, 0])], settings) + duplicate = {**a, 'contribute': False} + report = decide([a, b, duplicate], settings, phase='test') + assert report['status'] == 'passed' + assert report['metrics']['token_count'] == 3 + assert report['metrics']['mean_abs_delta'] == pytest.approx(1 / 3) + assert report['metrics']['p99_abs_delta_upper'] == 1 + + +@pytest.mark.parametrize('rows', [[], [([-1], [-1], [0])], [([float('nan')], [-1], [1])], + [([-1], [-1, -2], [1])], [([-1], [-1], [2])]]) +def test_structural_errors_fail_audit(rows): + settings = validate({'mode': 'audit'}) + assert decide([assess(rows, settings)], settings, phase='test')['status'] == 'failed' + + +def test_thresholds_are_explicit_and_sampling_is_raw(): + with pytest.raises(ValueError): + validate({'mode': 'enforce'}) + with pytest.raises(ValueError): + require_raw_sampling({'temperature': .7, 'top_p': 1}) + settings = validate({'mode': 'audit', 'max_abs_delta': .01}) + assert decide([assess([([-1], [-2], [1])], settings)], settings, phase='test')['status'] == 'warning' + + +def test_serving_evidence_survives_float32_conversion(): + q = [-1.123456789] + converted = [struct.unpack('!f', struct.pack('!f', q[0]))[0]] + assert evidence_key([1, 2], q, [1]) == evidence_key([1, 2], converted, [1]) + assert evidence_key([1, 3], q, [1]) != evidence_key([1, 2], converted, [1]) + + +def transport(url, payload=None): + if payload is None: + return {'weight_version': '1'} + assert payload['sampling_params']['max_new_tokens'] == 0 + assert payload['input_ids'] == [10, 11, 12] + return {'meta_info': {'weight_version': '1', 'input_token_logprobs': [[None, 10], [-1., 11], [-2., 12]]}} + + +def test_teacher_forcing_exact_response_boundary(): + assert score('http://engine', [10, 11], [12], '1', transport=transport) == [-2.] + with pytest.raises(ValueError, match='version'): + score('http://engine', [10, 11], [12], '2', transport=transport) + + +def test_engine_canary_does_not_dilute_bad_engine(): + settings = validate({'mode': 'enforce', 'max_abs_delta': 2, 'max_mean_abs_delta': .6}) + reference = {'weight_version': '1', 'cases': [{'prompt_ids': [10, 11], 'response_ids': [12], 'logprobs': [-1.]}]} + states = [{'endpoint': 'http://bad', 'healthy': True, 'weight_version': '1'}, + {'endpoint': 'http://good', 'healthy': True, 'weight_version': '1'}] + def serving(url, payload=None): + result = transport(url, payload) + if payload and 'good' in url: + result['meta_info']['input_token_logprobs'][-1][0] = -1. + return result + report = check_pool(states, reference, settings, transport=serving) + assert report['metrics']['mean_abs_delta'] == .5 + assert report['status'] == 'failed' + + +def test_histogram_merge_is_token_weighted(): + parts = [statistics([([-1.] * n, [-1. - delta] * n, [1] * n)]) for n, delta in [(1, .1), (9, .01)]] + result = merge(parts) + assert result['mean_abs_delta'] == pytest.approx(.019) + assert result['p95_abs_delta_upper'] >= .1 - 1e-12 diff --git a/tests/test_consistency_torch.py b/tests/test_consistency_torch.py new file mode 100644 index 000000000..0f81e6660 --- /dev/null +++ b/tests/test_consistency_torch.py @@ -0,0 +1,31 @@ +"""Distributed rejection tests; optional Torch dependency.""" +from pathlib import Path + +import pytest + +from harborrl.consistency.config import validate +from harborrl.consistency.report import assess, distributed_gate + +torch = pytest.importorskip('torch') + + +def rank_failure(rank, rendezvous, root): + from datetime import timedelta + + import torch.distributed as dist + dist.init_process_group('gloo', init_method=f'file://{rendezvous}', rank=rank, world_size=2, timeout=timedelta(seconds=30)) + try: + settings = validate({'mode': 'enforce', 'max_abs_delta': .01}) + part = assess([([-1.], [-1. if rank == 0 else float('nan')], [1])], settings) + with pytest.raises(ValueError, match='rejected'): + distributed_gate(part, settings, Path(root) / 'failure.json', phase='injected-rank-failure') + (Path(root) / f'rank{rank}.ok').write_text('rejected') + finally: + dist.destroy_process_group() + + +def test_one_rank_error_rejects_all_ranks(tmp_path): + import torch.multiprocessing as mp + mp.spawn(rank_failure, args=(str(tmp_path / 'rendezvous'), str(tmp_path)), nprocs=2, join=True) + assert (tmp_path / 'rank0.ok').exists() + assert (tmp_path / 'rank1.ok').exists() diff --git a/uv.lock b/uv.lock index 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