mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 15:37:44 +00:00
7cd64a26fd
* feat: rdkit for chemcotbench * update qwen2.5&llama3.1 context * fix: force failure on validation error and remove try/except in validator * feat: unified error sample extraction (with test scripts) * feat: set conda cache with .env * feat: skip data eval if data pass in last evo * fix: rm redundant param * fix ui bug * refactor: centralize assign_code_list_to_evo in MultiProcessEvolvingStrategy * feat: add test_params.yaml generation and workspace cleanup improvements for finetune * refactor: replace get_clear_ws_cmd with clear_workspace and update prompts for hard check criteria * add bioprobench dataset * fix: handle commas in training config extraction and refactor prompt includes * bioprobench description * add bioprobench readme * feat: merge lora adapter for blackwell gpu * feat: support for multi benchmarks in one job * change dfficult aware content for training * update difficulty-aware and logging principles * fix: resolve variable name conflict in FTRunnerEvaluator * set job id accuracy to minute * feat(ui): display one selected metric per benchmark * feat: store sota exp, and fix ws_ckp bug * fix: truncate data.json in feedback * fix: opencompass data for conda env * fix: save only the last model * feat: set log path and ws path * fix: set overwrite_cache to avoid lock contention(through injecting params) * feat: redirect stdout to file in localenv * add pickle cache to dataset desc * fix CI * fix: remove redundant wrapper * feat: set python_unbuffered * move redirect stdout to env run * fix a small bug * move model folder * feat(ui): display benchmark baseline * fix: enrich scenario and benchmark description * fix: rewrite runner eval to accept easier * feat: compare with baseline when no SOTA * update tablebench readme * fix: switch back to single benchmark (for baseline) * feat(ui): add ws path in ui * refactor: update SOTA tracking to use DAG traversal and parent selection * fix: prioritize local_selection in trace and refactor sibling retrieval logic * refactor: unify error handling in feedback generation and update workspace injection * feat: add skip_loop_error_stepname to control error skip step in LoopBase * fix: set local_selection to NEW_ROOT for experiments without parent * feat: set different ports for jobs * feat: set different ports for jobs * feat: add upper data size limit for LLM fine-tuning and update related prompts * fix: replace get_truncated_stdout() with stdout for consistent output handling * refactor: remove data.json from cache and workspace logic, focus on script-based reuse * fix: rm target_scenario * feat: add selective cache extraction and custom cache key for data processing * fix(ui): bug when displaying tablebench * fix: filter config in dataset_info.json * feat: add test set, set valid set * feat(ui): update test score, and set color for final decision * feat: add test score for baseline and update ui * fix: use [-100:] as test range * feat: update data_stats in runner * feat: wait for opencompass init when run multi jobs * fix: adjust test&valid split * feat: force to generate COT(with <think> token), and add answer format in scenarios.json * feat: improve ui * fix: unify benchmark volume mounts and set extra_volumes for conda env * fix(ui): number color * fix: update GPU memory handling to use total memory in GB and streamline code * fix: set use_cot_postprocessor * feat: add env_dict to config classes and merge env vars in Env run * fix: let coder obey proposal * fix(ui): direction bug and update chemcot core metirc * fix: set consistent benchmark mount points and env vars for docker and conda * fix: addintional target for LoRA * feat: workspace dir log for benchmark running * fix: tableInstruct path bug and update benchmark description * feat: timeout for whole job * fix: align FinanceIQ import to opencompass * feat: use llm_judge for FinanceIQ * feat: switch to turn on <think> or not * feat: using scripts to redirect stdout, and run in different windows * feat: sync litellm log * fix: gpu memory format * fix: escape special characters in benchmark desc * fix: set data processing timeout to 1h * feat: set valid_loss and save_best_model * fix: inject timeout and stage * fix: loss history extract logic * feat: inject output dir * feat: inject eval batch size * feat: inject save_total_limit * feat: update data prompt * fix: escape shell special characters * fix: tablebench visualization UI * fix: move implementation validation to coder, and ignore injected params * docs: add README for RL-PostTraining evaluation system * Add AutoRL-Bench evaluation framework for RL post-training * Add architecture documentation * docs: update architecture and interface documentation for AutoRL-Bench * improve doc * fix * refactor: YAML配置驱动 * feat: add RL Docker env, workspace test, and update project structure * feat: 重命名 autorl_bench, 新增 RLWorkspace, 配置 Docker extra_volumes * Add eval-only AutoRL-Bench pipeline * sturcture clean * docs: add autorl_bench README * feat(rl): Implement RL post-training agent scaffold and example * refactor: simplify RL scenario classes and update RL CoSTEER integration * feat(rl): 调通 scaffold,mock 数据跑完 5 步循环 * feat(rl): 接入 LLM 生成代码,支持 model_path 传递 * feat(rl): Docker 执行框架,RLWorkspace.run() + RLPostTrainingRunner * feat(rl): LLM 生成假设/反馈,完整 loop 跑通 * feat: add RL post-training entry point with configurable options * refactor: simplify RL proposal and trace classes, update config and docs * Update rl eval autorl_bench layout * Update RL workflow and evaluation setup * Integrate AutoRL-Bench evaluation in RL workflow * feat(rl): 添加 --base-model/--benchmark CLI 参数,简化 RLTask * feat(rl): Docker 环境动态选择 + example_agent 完整训练评测流程(无llm) * fix(rl): 修复 feedback 传递 + 添加 verl 依赖 * refactor: remove unused validate in BenchmarkAdapter and add core utils module * feat(rl): UI * Refactor autorl_bench layout and docker entrypoint * autorl_bench: add aider autoloop tool * feat(rl): environment docker * refactor: simplify aider autoloop tooling * chore: update misc files * feat(rl): yaml-driven dataset download & auto-download on startup * feat(rl): yaml-driven dataset download & auto-download on startup * Refactor RL eval runner and clean up * Simplify RL eval runner and env * rl: include litellm in RL docker image * feat(rl): unified resource path & model repo_id structure * feat(rl): refactor eval with OpenCompass & add training code template * feat(rl): refactor eval with OpenCompass & add training code template * feat(rl): delete test bench * docs: add benchmark interface notes and TODOs for unified evaluation * feat(rl): unified benchmark eval interface + shared configs * feat(rl): 优雅 * feat(rl): prompt prososal+coder improve * feat(rl): fix eval * fix(rl): docker * fix(rl): eval * v 1.0 tmep * benchmark v1.0 * benchmark v1.1 * benchmark v1.1: grading日志+代码去重 * benchmark v1.1: grading日志+代码去重 * benchmark v1.1: grading日志+代码去重+task description * benchmark v1.2: fix * benchmark v1.3: fix,example-agent ok,rdagent test,openhands develop * benchmark v1.4: fix,example-agent ok,rdagent ok,openhands develop * benchmark : add alfworld * benchmark : update readme * benchmark : update readme * benchmark : * chore: add eval bypass block and mark TODO in grading server * benchmark * benchmark * benchmark * benchmark * alfworld * alfworld * benchmark * rdagent * rdagent * benchmark * benchmark:ui * benchmark:delete docker + log * 1 * alfworld * ui * alfworld * readme * alfworld * parallex * alfworld * run * eval gpu * alfworld * alfworld * fix conda init in start.sh for non-interactive shells Fallback to common miniconda paths when conda is not in PATH. Fixes B200 pod startup failure (conda: command not found). Made-with: Cursor * simplify start.sh: read TRAINING_PYTHON from .env No more conda detection logic. Just set TRAINING_PYTHON in .env. Fallback to conda only if not set. Made-with: Cursor * use OPENHANDS_PYTHON from .env to run agent start.sh now uses OPENHANDS_PYTHON for main.py execution, since the parent process may be in a different conda env. Made-with: Cursor * feat: register OpenCode agent into autorl_bench framework - Add agents/opencode/ with config.yaml, start.sh, README.md - Include opencode-rl pipeline code (pipeline/, runner_fsm/, benchmarks/) - Merge opencode-rl dependencies into autorl_bench requirements.txt - Remove separate venv requirement, share main environment Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * Update opencode agent, benchmarks, and eval configs - Sync opencode-rl runner_fsm with latest simplifications - Add smith benchmarks integration - Update opencompass configs and server with GPU support + error handling * Update OpenCode agent docs for external opencode-rl integration - Document external repo architecture (opencode-rl as independent plugin) - Add setup instructions for cloning and configuring opencode-rl - Add architecture diagram showing RD-Agent ↔ opencode-rl interaction - Document OPENCODE_RL_ROOT for custom paths * feat: add smith benchmark discovery and per-sample evaluator - Add smith/ module for dynamic benchmark discovery from rl-smith - Add PerSampleEvaluator for per-sample scoring via vLLM - Update utils.py to support script-based data download for smith benchmarks - Update opencode agent config * enforce RL-only in instructions.md; remove embedded opencode-rl - instructions.md: prohibit SFT, require RL (GRPO/PPO) for all benchmarks - remove agents/opencode/opencode-rl/ (runtime uses external OPENCODE_RL_ROOT) Made-with: Cursor * comment out OpenCode-only deps in requirements.txt openai, httpx, python-dotenv, tenacity are for OpenCode agent's separate environment. Keep peft and pydantic as shared deps. Made-with: Cursor * refactor: extract _kill_process_group, narrow exception catches - run.py: replace 2x nested 3-level try/except with shared _kill_process_group() using loop + specific exceptions - server.py: except Exception → except (RuntimeError, ValueError, OSError) - utils.py: except Exception → except requests.ConnectionError Made-with: Cursor * move kill_process_group to core/utils for reuse Extract from run.py into core/utils.py so other runners can also use it. Exported via core/__init__.py. Made-with: Cursor * add comments to run.py for workspace isolation and signal handling Made-with: Cursor * remove OpenCode-only deps from requirements.txt entirely Made-with: Cursor * allow SFT in instructions, RL as ultimate goal Made-with: Cursor * add workspace isolation rules to instructions.md Use relative paths, forbid cd outside workspace, ignore symlink targets. Made-with: Cursor * update opencode start.sh: use OPENCODE_PYTHON, add PATH for opencode CLI, remove unsupported args Made-with: Cursor * opencode start.sh: pass --run-dir to use AutoRL-Bench workspace Ensures OpenCode-FSM-Runner writes outputs into the workspace prepared by AutoRL-Bench instead of creating its own runs/ directory. Made-with: Cursor * opencode start.sh: prepend training env bin to PATH Ensures LLM agent bash calls (e.g. python3 -c "from trl import ...") resolve to the correct training environment, instead of relying on parent shell conda activation. Made-with: Cursor * opencode start.sh: restore --max-retries and --eval-timeout for opencode-rl Made-with: Cursor * add humaneval benchmark * Replace import * cleanup hack with explicit imports in OpenCompass config - Resolve dataset variable names via importlib before generating config, so the template uses `from xxx import datasets` instead of `import *` - Remove the fragile runtime cleanup hack that set leaked modules to None - Increase OpenCompass timeout from 3600s to 7200s - Fix score parsing to average across multiple subdatasets * refine opencompass config file generating * add humaneval benchmark dependency instructions human-eval package requires clone from open-compass/human-eval with a one-line patch to relax assertion for partial evaluation (test split only). Made-with: Cursor * fix: sanitize user-provided paths in RL UI (CodeQL) * fix: resolve user path relative to safe root (CodeQL) * fix: use Copilot-suggested path sanitization pattern (CodeQL) * fix: normalize and reject absolute user paths (CodeQL) * Fix training params, vLLM OOM cleanup, OpenCompass score parsing, and baseline cache logic * fix: add setuptools<75 to requirements for opencompass pkg_resources dependency uv venv does not include setuptools by default, causing OpenCompass baseline evaluation to fail with "No module named 'pkg_resources'". Made-with: Cursor * webshop * feat(autorl_bench): improve smith benchmark integration and evaluator robustness - Add smith benchmark docs to README: usage examples, discovery mechanism, SMITH_BENCH_DIR - Improve PerSampleEvaluator: vLLM GPU cleanup, test_range slicing - Refactor server.py: extract grading server from utils - Fix OpenCompass score parsing and baseline cache logic * fix: add smart fallback for OpenCompass dataset variable resolution When build_dataset_imports_explicit() fails to import an OpenCompass dataset module (common in grading server subprocess), it now guesses the correct variable name from the module path convention instead of falling back to empty names (which causes import * and breaks BBH due to leaked file handle objects). * revert: restore opencompass.py to pre-modification state Revert vLLM pid cleanup, dash-value checks, and metric-based score parsing added in 31caff2f and bb32e555. * keep metric-aware score parsing in opencompass; add baseline column to UI - opencompass.py: retain metric-type filtering (accuracy/score) instead of naive averaging, avoids polluting scores with pass/timeout counters - ui.py: add Baseline column to Agent Summary table Made-with: Cursor * fix: handle non-string answers in extract_answer to prevent TypeError arc_agi and other benchmarks can have non-string answer fields (e.g. lists), which caused a crash in re.search(). Adding str() coercion fixes this. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * update deepsearch qa tasks * fix: benchmark evaluation reliability (B1-B4) - B1: auto-detect LoRA adapters and enable vLLM LoRA mode (read base_model from adapter_config.json) - B2: serialize evaluations with threading.Lock to prevent GPU contention - B3: cache eval results by model_path to deduplicate concurrent submissions - B4: propagate error details from OpenCompass to agent (non-numeric scores, load failures) Made-with: Cursor * fix(B1): reject LoRA adapter submissions with clear merge instructions - opencompass.py: detect adapter_config.json and return error with merge_and_unload() instructions instead of broken vLLM LoRA mode - instructions.md: add requirement to submit full merged models - opencompass_template.yaml: remove unused is_lora/lora_path params Made-with: Cursor * update chat completion * update * update deepsearch * md * codex + benchmark update * codex + benchmark update * codex + benchmark update * codex * codex * fix: grading server cache key includes mtime to detect model overwrites Previously cache used only resolved_path, so overwritten models at the same path returned stale scores. Now cache key = path@max_mtime so re-evaluation is triggered when model files change. Made-with: Cursor * feat: add gemini/claude agent scaffolds, fix codex binary path - codex/start.sh: use CODEX_BIN env var instead of bare 'codex' - Add gemini/ and claude/ agent directories with config.yaml and start.sh Made-with: Cursor * chore: remove copied human_readable_trace.py from PostTrainBench Made-with: Cursor * update evaluation * benchmark * Fix log cleanup and OpenHands env * fix: webshop env pth problem * benchmark alpacaeval * style(rl): apply auto-lint fixes * fix(rl): address CI and CodeQL issues * fix(rl): make autorl bench imports CI-safe --------- Co-authored-by: Qizheng Li <jenssenlee@163.com> Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: Bowen Xian <xianbowen@outlook.com> Co-authored-by: chelsea97 <zhuowbrown@gmail.com> Co-authored-by: Xu Yang <peteryang@vip.qq.com> Co-authored-by: sakura657 <yctangcse@gmail.com> Co-authored-by: shatianming5 <tianming.sha@stonybrook.edu> Co-authored-by: Yeyuqing0913 <shatianming4@gmail.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
import importlib
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import os
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import unittest
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from pathlib import Path
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import pytest
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@pytest.mark.offline
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class TestRDAgentImports(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.rdagent_directory = Path(__file__).resolve().parent.parent.parent
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cls.modules = list(cls.import_all_modules_from_directory(cls.rdagent_directory))
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@staticmethod
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def import_all_modules_from_directory(directory):
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for file in directory.joinpath("rdagent").rglob("*.py"):
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fstr = str(file)
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if "example" in fstr:
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continue
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if "meta_tpl" in fstr:
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continue
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if "autorl_bench/workspace/" in fstr:
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continue
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if "template" in fstr or "tpl" in fstr:
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continue
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if "model_coder" in fstr:
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continue
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if "llm_st" in fstr:
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continue
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if (
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"rdagent/log/ui/" in fstr
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or fstr.endswith("rdagent/app/cli.py")
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or fstr.endswith("rdagent/app/CI/run.py")
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or fstr.endswith("rdagent/app/utils/ape.py")
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or fstr.endswith("rdagent/log/ui/utils.py")
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):
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# the entrance points
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continue
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# llamafactory==0.9.3 pins numpy to an older version, causing other
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# installations to fail. The `extract_parameters` tests are therefore
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# temporarily disabled and can be re-enabled once the numpy constraint is relaxed.
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if "extract_parameters" in fstr:
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continue
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yield fstr[fstr.index("rdagent") : -3].replace("/", ".")
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def test_import_modules(self):
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print(self.modules)
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for module_name in self.modules:
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with self.subTest(module=module_name):
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try:
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print(module_name)
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importlib.import_module(module_name)
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except Exception as e:
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self.fail(f"Failed to import {module_name}: {e}")
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if __name__ == "__main__":
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unittest.main()
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