akg/akg_agents/scripts · mindspore/akg - AtomGit

xchang1121feat(autoresearch): build out the kernel-optimization agent workflow

文件	最后提交记录	最后更新时间
server_related	refactor: rename project from aikg to akg_agents Complete project renaming: - Renamed main directory: aikg/ -> akg_agents/ - Renamed Python package: ai_kernel_generator -> akg_agents - Renamed benchmark: aikgbench -> akg_kernels_bench - Updated client API: AIKGClient -> AKGAgentsClient - Updated all import paths and module references - Updated environment variables: AIKG_* -> AKG_AGENTS_* - Updated output directories and log paths - Updated all documentation and configuration files - Fixed Git submodules paths Breaking changes: - All Python imports need to update to 'from akg_agents' - Environment variable names changed: AIKG_* -> AKG_AGENTS_* - Package name changed from ai_kernel_generator to akg_agents - Client class renamed: AIKGClient -> AKGAgentsClient Co-authored-by: Cursor <cursoragent@cursor.com>	3 个月前
build_logo.js	Refactor: remove legacy code, standardize test structure, and enhance core module test coverage	2 个月前
build_mindspore_akg_logo.js	Refactor: remove legacy code, standardize test structure, and enhance core module test coverage	2 个月前
generate_logo.sh	Refactor: remove legacy code, standardize test structure, and enhance core module test coverage	2 个月前
publish_akg_cli.sh	feat: add akg-cli wrapper and publish scripts	3 个月前
run_autoresearch.py	feat(autoresearch): build out the kernel-optimization agent workflow Since c72a9735 the autoresearch module has been a minimal ReAct skeleton (loop / turn / feedback / evaluator / runner + a handful of prompts). This commit turns it into the full kernel-optimization agent documented in docs/v2/AutoResearch.md. Agent architecture - Structured plan state: FeedbackBuilder owns plan.md, enforces rationale validation + min_items_per_plan + diversity checks, renders plan + optimization history + skill state across versions. - Three-layer skill model: Layer 0 fundamentals embedded in the system prompt from task_dir/skills/; Layer 1 SKILL.md mirrored per task, read on demand, elided at settle; Layer 2 compact skill index in the initial message. - System-owned skill binding: agent submits keywords per plan item, the framework matches them against SkillPool and assigns backing_skill; agent-emitted bindings are stripped at the TurnExecutor trust boundary. SkillBuilder tier() ranks bindings applied (0) / fresh (1) / previously-unbound (2). No terminal state — a later KEEP promotes an unbound skill back to tier 0. - New acknowledge_skill tool gates the first patch_file / write_file on any bound item (valuable_aspects / kernel_application / apply\|unbind), doubling as the audit trail. - SkillPool auto-widens on keyword-miss; search_skills tool exposes the widen path to the agent. - Diagnose subagent post-eval; require_replan tags one item abandoned and enters replace-mode on next update_plan. Context management - ConversationBuffer owns skill injection (synthetic user message, not raw tool_result) and settle-time unload of both synthetic markers and voluntary read_file tool_results, with tracking maps rebuilt after compact. - Multi-step auto_compact pipeline: two concurrent LLM calls (op_summary + 5-section plan_analysis.md), force_rebuild fallback with char-capped degraded bodies. Runtime / tooling - run_autoresearch.py accepts --worker-url (`host:port[,...]`) to forward eval to a remote Worker Service, mutually exclusive with --device-id. - AST-aware patch_file + Markdown helpers + forbidden-pattern validator (edit_guardrails.yaml); better patch_file diagnostics with anchor_line. - Hardware family registry + hardware_aliases.yaml for arch normalisation across Huawei product lines. - Unified Python logger; SkillBuilder state transitions and diagnose decisions surface in agent.log. - Session resume gates on HEAD sha + dirty state; pool rehydration from plan_items; v4→v5 skill-state migration. Docs - Clean-room rewrite of docs/v2/AutoResearch.md + docs/v2/CN/ AutoResearch.md to describe the post-supervisor architecture; CLI usage as the single production entry point, debug entry points consolidated. Tests - End-to-end autoresearch suite stays green; new unit coverage: SkillBuilder transitions, SkillPool refill / match, keyword pipeline, acknowledge_skill schema + wiring, fundamentals layout, ConversationBuffer injection lifecycle, session resume, scaffold round-trip, compact pipeline, patch_file diagnostics, patch_validator, edit tool, git_repo, operator_skill_catalog, run_counters.	1 个月前
sync_skills.sh	support qwen skill	1 个月前
testpypi_venv_check.sh	feat: add akg-cli wrapper and publish scripts	3 个月前