feat(manager)!: replace the Numba manager path with a generic Torch executor

.github/workflows/README.md

@@ -25,10 +25,9 @@ Python 3.10 uses `--all`; the other versions omit the Unitree hardware plugin an
 
 PR and push runs have two independent, parallel suites per Python version:
 
-- **fast**: `pytest -m "not slow and not integration and not numba"`.
-- **numba**: `pytest -m "not integration and (slow or numba)"`. This includes the two expensive
+- **fast**: `pytest -m "not slow and not integration"`.
+- **slow**: `pytest -m "not integration and (slow)"`. This includes the expensive
   WBT kernel regressions on every PR, so failures are not deferred until the next day.
-  The entire `test_wbt_numba.py` module stays together: excluding only the first two expensive
   tests shifts first-use compilation into later tests rather than making the fast suite cheap.
 
 These selections partition all non-integration tests, including future `slow` tests. They run on
@@ -40,64 +39,30 @@ with these matrix checks.
 Nightly and manual dispatch run the **full** suite, including
 `test/test_all_envs.py::test_all_demos`. The full-environment subprocess smoke test is excluded
 from PR/push jobs. Each job reports the 20 slowest phases and uploads JUnit results, including on
-failure. Numba caches have per-suite write keys to avoid parallel jobs competing for one cache.
+failure.
 
 Local commands (activate `.venv` first):
 
 ```bash
 # Quick feedback; excludes expensive kernel regressions and full-environment smoke.
-python -m pytest -m "not slow and not integration and not numba"
+python -m pytest -m "not slow and not integration"
 # Dedicated compilation/runtime regressions.
-python -m pytest -m "not integration and (slow or numba)"
+python -m pytest -m "not integration and (slow)"
 # Only the full-environment integration tests.
 python -m pytest -m integration
 # Full coverage remains the default; no tests are silently excluded by addopts.
 python -m pytest
 ```
 
-Markers are registered in the root `pyproject.toml`: `slow` describes cost, `numba` identifies
-specialized kernel regressions, and `integration` identifies tests reserved for full runs.
-Not every test importing Numba needs the `numba` marker: inexpensive core contract tests remain
-in the fast suite.
+Markers are registered in the root `pyproject.toml`: `slow` describes cost and `integration`
+identifies tests reserved for full runs.
 
-For new Numba environments, reuse small synthetic-manager tests for shared runtime contracts,
+For new manager environments, reuse small synthetic-manager tests for shared runtime contracts,
 keep task-specific smoke tests minimal, and retain a small set of representative real-environment
 regressions. The existing nightly smoke test automatically covers all registered environments.
 Do not duplicate a full expensive rollout for every preset. If the dedicated job grows too large,
 measure `--durations` and shard by environment family; do not silently drop kernel coverage from PRs.
 
-#### Diagnosing Numba startup costs
-
-Compare a fresh `NUMBA_CACHE_DIR` with a second process using the same directory, then compare
-another environment in the same process. `NUMBA_DEBUG_CACHE=1` reports actual specialization
-cache loads/saves. Manager INFO logs time each evaluate/observe/reset specialization; DEBUG
-logs include signatures. Dispatcher preparation is not the actual compilation phase.
-
-Precompilation uses the real input types, and warmup copies preserve strides and readonly flags,
-so warmup and reset/step share signatures. Function fingerprints use explicit code fields rather
-than marshal serialization, which can change after Numba inspects an unchanged code object.
-Keep these invariants intact when extending the compiler: a warmup must not introduce new
-signatures, and constructing the same configuration after execution must reuse the same plan.
-
-A local G1 WBT play-mode benchmark (`num_envs=2`, init and one step; interpreter imports excluded)
-measured about 29 seconds with an empty cache, 0.27 seconds in a second process with disk cache,
-and 0.07 seconds for another environment using the in-process cache after these fixes.
-The original cold benchmark took about 57 seconds and compiled each kernel twice.
-These are diagnostic measurements, not CI timing thresholds; hardware and cache state vary.
-
-### File: `codeql.yml`
-
-Runs GitHub CodeQL analysis for Python on pushes, pull requests, and a weekly
-scheduled scan. Review results under the repository's **Security** tab.
-
-### File: `branch-policy.yml`
-
-Checks pull request metadata without checking out contributor code. Normal PRs
-must target `main`; a post-patch back-merge may use upstream `stable → main`.
-Only a release PR from the upstream `main` branch or a `hotfix/*` stable patch
-PR may target `stable`. Add `Branch policy / validate` as a required status check on both
-protected branches.
-
 ## Docker Image Build Workflow
 
 ### File: `docker-build.yml`

.github/workflows/tests.yml

@@ -25,25 +25,8 @@ jobs:
       fail-fast: false
       matrix:
         python: ["3.10", "3.11", "3.12", "3.13"]
-        suite: ${{ fromJSON((github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && '["full"]' || '["fast","numba"]') }}
+        suite: ${{ fromJSON((github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && '["full"]' || '["fast","slow"]') }}
     steps:
-      - name: Resolve numba cache dir
-        run: |
-          # Redirect all Numba disk caches (manager fused kernels and term
-          # specializations) to a single path so they can be cached between
-          # runs. Kernel plan keys are content hashes, so stale cache entries
-          # are simply unused and never produce wrong results.
-          # Numba does not expand "~" in NUMBA_CACHE_DIR, so resolve the path
-          # via $HOME here; the cache action below reads the same variable.
-          echo "NUMBA_CACHE_DIR=$HOME/.cache/motrixlab/numba-manager" >> "$GITHUB_ENV"
-      - name: Restore numba kernel cache
-        uses: actions/cache@55cc8345863c7cc4c66a329aec7e433d2d1c52a9 # v6.1.0
-        with:
-          path: ~/.cache/motrixlab
-          key: numba-kernel-${{ runner.os }}-py${{ matrix.python }}-${{ matrix.suite }}-${{ github.sha }}
-          restore-keys: |
-            numba-kernel-${{ runner.os }}-py${{ matrix.python }}-${{ matrix.suite }}-
-            numba-kernel-${{ runner.os }}-py${{ matrix.python }}-
       - name: Checkout repository
         uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
         with:
@@ -64,16 +47,16 @@ jobs:
             sh install.sh --all --python 3.10
           else
             # Unitree/DDS runtime support remains limited to Python 3.10.
-            uv sync --frozen --python '${{ matrix.python }}' --extra cuda --extra rslrl --extra skrl-jax --extra tbb --group test
+            uv sync --frozen --python '${{ matrix.python }}' --extra cuda --extra rslrl --extra skrl-jax --group test
           fi
           .venv/bin/python -c 'import sys; assert ".".join(map(str, sys.version_info[:2])) == "${{ matrix.python }}"'
 
       - name: Run tests
         run: |
           selection=''
           case '${{ matrix.suite }}' in
-            fast) selection='not slow and not integration and not numba' ;;
-            numba) selection='not integration and (slow or numba)' ;;
+            fast) selection='not slow and not integration' ;;
+            slow) selection='not integration and (slow)' ;;
           esac
           set --
           if [ '${{ matrix.python }}' != '3.10' ]; then

AGENTS.md

@@ -76,11 +76,14 @@ README 从宏观上说明 package：它提供什么能力、如何安装与使
 框架级契约（Runtime/ControlSession/Controller 语义、rollout result 口径等）只在一处维护——对应框架 package 的
 README 或教程；插件与任务 package 的 README 不重复这些内容，只描述本 package 特有的部分。
 
-### Manager Kernel 入口方法
+### Manager term 入口方法
 
-Manager fused-kernel 入口方法必须用 `@dispatch` 装饰，绝不使用 `@njit`——Numba 编译、缓存和内联由 manager
-编译器统一管理。`@njit` 只用于被编译 kernel 调用的独立 Numba 兼容 helper。哪些方法是 kernel 入口、哪些是普通
-Python 方法，由 manager 运行时契约定义（`wiki/design/manager/runtime.md`）。
+Manager 数值 term 是普通 Python 函数，契约由签名注解表达并由 resolver 校验（首参数注解
+`ManagerContext`，observation 的 `out` 注解 `torch.Tensor`，reset 的 `sim_writes` 注解
+`Map[torch.Tensor]`）；数值执行由 Torch 执行器按整批张量调度。`@dispatch` 仅用于标记 kernel_data
+类上需要进入布局方法代理表的方法（如 `ActionState.current/previous`、CommandTerm 的
+`update`/`advance`/`reset_env` hooks，后者为 `@staticmethod`，签名为 `(receiver, ctx)`）。运行时
+契约见 `wiki/design/manager/runtime.md`。
 
 ### 环境配置与注册规范
 
@@ -238,10 +241,10 @@ tensorboard --logdir runs/{env-name}
 ### 测试
 
 ```bash
-# 与 PR CI 一致的快速测试（不执行集成、慢速或 Numba 编译测试）
-python -m pytest -m "not slow and not integration and not numba"
-# CPU 编译回归、跨模块/进程集成分别执行
-python -m pytest -m "not integration and (slow or numba)"
+# 与 PR CI 一致的快速测试（不执行集成、慢速测试）
+python -m pytest -m "not slow and not integration"
+# 慢速回归、跨模块/进程集成分别执行
+python -m pytest -m "not integration and slow"
 python -m pytest -m integration
 ```
 

bench/bench_physics.py

@@ -81,13 +81,6 @@ def _device_context() -> dict:
         "num_numa_nodes": _detect_num_nodes(),
         "binding": _current_binding(),
     }
-    try:
-        import numba as _numba
-
-        ctx["numba_threads"] = _numba.get_num_threads()
-        ctx["numba_layer"] = _numba.threading_layer()
-    except Exception:
-        pass
     return ctx