Add PE-level IPCQ collective infra + unified ccl_allreduce bench (ADR-0023)

Major changes:

PE-level IPCQ infrastructure:
- New PE_IPCQ component: ring-buffer control plane with 4-direction
  neighbor mapping, head/tail pointers, backpressure (poll/sleep).
- PE_DMA extended with vc_comm channel for IPCQ outbound/inbound DMA,
  including in-flight data snapshot (D9) and op_log recording at
  outbound time for Phase 2 replay correctness.
- IpcqDmaToken piggyback model: data + metadata travel together,
  atomic visibility at receiver (invariant I6).
- Credit return fast path: bottleneck-BW latency, no fabric vc_comm.

Phase 2 data execution (ADR-0020 integration):
- op_log extended: DmaWriteCmd now captures src_space/src_addr for
  Phase 2 dma_write copy; ipcq_copy ops recorded at outbound time.
- DataExecutor replays dma_write + ipcq_copy in t_start order.
- Engine._flush_data_phase: incremental cursor-based replay after
  each engine.wait() so host reads see post-Phase-2 data.
- KernelRunner Phase 1 writes disabled when op_log is active to
  prevent stale data from corrupting the MemoryStore snapshot.

TLContext / kernel API:
- tl.send(dir, src=TensorHandle), tl.recv(dir, shape, dtype),
  tl.recv_async, tl.wait(RecvFuture), copy_to_dst mode.
- TensorHandle operator overloading (add/sub/mul/div) via thread-local
  active TLContext → MathCmd dispatch through PE_MATH.
- PE-local scratch allocator for math output handles.
- tl.load returns space="hbm" handles for correct Phase 2 addressing.
- Additional math functions: maximum, minimum, fma, clamp, softmax, cdiv.

Unified ccl_allreduce bench (PyTorch-compat host code):
- Single benches/ccl_allreduce.py with run() + worker(rank, ws, torch)
  split matching real PyTorch DDP worker pattern.
- torch.distributed facade: init_process_group, get_world_size,
  get_rank, get_backend, all_reduce, barrier — only real PyTorch names.
- AhbmCCLBackend: eager install_ipcq at init, all_reduce dispatches
  kernel via tensor shard metadata (n_elem from shards[0].nbytes).
- world_size derived from topology spec (sips × cubes × pes_per_cube)
  with optional algorithm-level override in ccl.yaml.

Tensor API (PyTorch-compat surface):
- Tensor.numpy(): gather-aware (all shards via VA-based addressing).
- Tensor.copy_(source): scatter from host tensor into sharded target.
- RuntimeContext.from_numpy(arr): host-side staging tensor.
- Tensor.data property fixed to use numpy() (was shards[0]-only).

Algorithm modules moved to src/kernbench/ccl/algorithms/:
- ring_allreduce, mesh_allreduce, tree_allreduce, hello_send.
- Each module exports kernel_args(world_size, n_elem) helper.
- ccl.yaml module paths updated to kernbench.ccl.algorithms.*.

Dead code removed:
- 7 per-variant bench files (ccl_allreduce_{tcm,hbm,sram}, etc.).
- _run_ccl_bench greenlet-per-SIP scheduler.
- benches.loader.is_ccl_bench + run_rank detection.
- benches/ccl/ directory.

Tests:
- New test_ccl_allreduce_matrix.py: 7 parametrized cases
  (ring×3 buffers, ring 8/16, mesh 4, tree 7).
- New test_runtime_api_tensor.py: copy_/numpy/from_numpy unit tests.
- Existing tests updated for new import paths + world_size_override.

Docs:
- Korean ccl-author-guide.md and ADR-0023 paths updated.
- New English versions: ccl-author-guide.en.md, ADR-0023.en.md.

502 tests pass.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

src/kernbench/runtime_api/context.py

@@ -9,6 +9,39 @@ from kernbench.common.types import Completion, RequestHandle, SimEngine
 from .types import DeviceSelector
 
 
+def _world_size_from_spec(spec: dict | None) -> int:
+    """Derive world_size from topology spec: sips × cubes × pes_per_cube."""
+    spec = spec or {}
+    sips = int(spec.get("system", {}).get("sips", {}).get("count", 1))
+    cm = spec.get("sip", {}).get("cube_mesh", {})
+    cubes_per_sip = int(cm.get("w", 1)) * int(cm.get("h", 1))
+    pl = spec.get("cube", {}).get("pe_layout", {})
+    corners = pl.get("corners", [])
+    pe_per_corner = int(pl.get("pe_per_corner", 1))
+    pes_per_cube = pe_per_corner * max(len(corners), 1)
+    return sips * cubes_per_sip * pes_per_cube
+
+
+def _numpy_to_dtype_str(np_dtype) -> str:
+    """Map numpy dtype → kernbench dtype string used by Tensor."""
+    import numpy as np
+
+    kind_map = {
+        np.float16: "f16",
+        np.float32: "f32",
+        np.int8: "i8",
+        np.int16: "i16",
+        np.int32: "i32",
+        np.uint8: "u8",
+        np.uint16: "u16",
+        np.uint32: "u32",
+    }
+    for np_type, s in kind_map.items():
+        if np.dtype(np_dtype) == np.dtype(np_type):
+            return s
+    raise ValueError(f"unsupported numpy dtype: {np_dtype!r}")
+
+
 @dataclass
 class RuntimeContext:
     engine: SimEngine
@@ -23,6 +56,66 @@ class RuntimeContext:
     _tensor_counter: int = field(default=0, init=False)
     _traces: list[dict] = field(default_factory=list, init=False)
     _tensors: list[Any] = field(default_factory=list, init=False)
+    distributed: Any = field(default=None, init=False)  # DistributedContext for CCL benches
+    _ipcq_plan: dict = field(default_factory=dict, init=False)  # ADR-0023 install plan
+
+    def __post_init__(self) -> None:
+        # Eagerly attach a DistributedContext so bench code can do
+        # ``dist = torch.distributed`` + ``dist.init_process_group(...)``
+        # without needing a separate launcher to install it.
+        from kernbench.runtime_api.distributed import DistributedContext
+        dc = DistributedContext()
+        dc._ctx_ref = self  # back-reference for AhbmCCLBackend to reach ctx.launch etc.
+        self.distributed = dc
+
+    def install_ipcq(
+        self,
+        algorithm: str | None = None,
+        ccl_yaml: str | None = None,
+        world_size_override: int | None = None,
+        rank_to_pe: list[tuple[int, int, int]] | None = None,
+    ) -> dict:
+        """Install IPCQ neighbor tables on all participating PEs (ADR-0023 D10).
+
+        Loads ``ccl.yaml`` (or the path provided), resolves the chosen
+        algorithm (or ``defaults.algorithm`` if None), and pushes per-PE
+        IpcqInitMsg into every PE_IPCQ component via the engine.
+
+        Args:
+            algorithm: name of the algorithm in ccl.yaml (or use defaults).
+            ccl_yaml: optional path to ccl.yaml.
+            world_size_override: if set, replace the algorithm's world_size.
+
+        Returns the install plan dict (rank → (sip,cube,pe), neighbor table).
+        """
+        import importlib
+        from kernbench.ccl.install import (
+            install_ipcq as _install,
+            load_ccl_config,
+            resolve_algorithm_config,
+        )
+
+        cfg = load_ccl_config(ccl_yaml)
+        merged = resolve_algorithm_config(cfg, algorithm)
+        if world_size_override is not None:
+            merged["world_size"] = world_size_override
+        elif "world_size" not in merged:
+            # Derive from topology.yaml when neither the algorithm entry
+            # nor ``defaults`` carries ``world_size`` (matches pytorch DDP
+            # where env vars determine ranks, not the ccl config file).
+            merged["world_size"] = _world_size_from_spec(self.spec)
+        algo_module = None
+        try:
+            algo_module = importlib.import_module(merged["module"])
+        except ModuleNotFoundError:
+            pass
+        plan = _install(
+            self.engine, self.spec, merged,
+            algo_module=algo_module, rank_to_pe=rank_to_pe,
+        )
+        self._ipcq_plan = plan
+        self._ipcq_config = merged
+        return plan
 
     def __enter__(self):
         return self
@@ -258,6 +351,24 @@ class RuntimeContext:
         """Allocate a tensor in HBM without initialization (like torch.empty)."""
         return self._create_tensor(shape, dtype, name, pattern=None, dp=dp)
 
+    def from_numpy(self, arr: Any):
+        """Create a host-side tensor wrapping a numpy array.
+
+        Mirrors ``torch.from_numpy``. The returned tensor is NOT deployed
+        to any PE — it lives in an in-memory host staging buffer. Use
+        ``target.copy_(host_tensor)`` to scatter its contents into a
+        sharded, deployed tensor.
+        """
+        import numpy as np
+        from kernbench.runtime_api.tensor import Tensor
+
+        arr_c = np.ascontiguousarray(arr)
+        dtype_str = _numpy_to_dtype_str(arr_c.dtype)
+        t = Tensor(shape=tuple(arr_c.shape), dtype=dtype_str, name="host")
+        t._host_buffer = arr_c
+        t._memory_store = getattr(self.engine, "_memory_store", None)
+        return t
+
     def _create_tensor(
         self,
         shape: tuple[int, ...],
@@ -418,13 +529,12 @@ class RuntimeContext:
             TensorArgShard,
         )
         from kernbench.runtime_api.tensor import Tensor
-        from kernbench.triton_emu.registry import register_kernel
+        from kernbench.triton_emu.registry import _kernels, register_kernel
 
-        # Register kernel (idempotent)
-        try:
-            register_kernel(kernel_name, kernel_fn)
-        except ValueError:
-            pass
+        # Register kernel (idempotent overwrite — last call wins).
+        # Tests can re-register the same kernel_name with a different
+        # function; the user's most recent launch must use the latest fn.
+        _kernels[kernel_name] = kernel_fn
 
         # Collect tensors and scalars
         tensor_args: list[Tensor] = []
@@ -506,6 +616,7 @@ class RuntimeContext:
 
         # Per-SIP kernel launch: each SIP gets TensorArgs with local va_base
         last_handle = None
+        _pending_handles: list[tuple[Any, int]] = []
         for sip_id in sorted(sip_set):
             sip_kernel_args: list = []
             sip_cube_set: set[int] = set()
@@ -566,10 +677,17 @@ class RuntimeContext:
                 target_cubes=target_cubes,
                 target_pe=target_pe,
             ))
+            # Defer wait until all SIPs are submitted (multi-SIP CCL needs
+            # all participating PEs to be live concurrently — waiting
+            # per-SIP would deadlock when ranks span SIP boundaries).
+            _pending_handles.append((h, sip_id))
+            last_handle = h
+
+        # Drain pending handles now that every SIP has a launch posted.
+        for h, sip_id in _pending_handles:
             self.wait(h, _meta={
                 "phase": "kernel", "name": kernel_name,
                 "sip": sip_id, "target_pe": target_pe,
             })
-            last_handle = h
 
         return last_handle