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/ccl/topologies.py

@@ -0,0 +1,128 @@
+"""Builtin neighbor topology generators for CCL backend (ADR-0023 D11).
+
+Each generator takes ``(rank, world_size)`` and returns a
+``dict[direction, peer_rank]`` for that rank. ``direction`` is one of
+``"N" | "S" | "E" | "W"`` for ring/mesh, or
+``"parent" | "child_left" | "child_right"`` for tree topologies.
+
+Algorithm modules may override the generated map by defining a
+``neighbors(rank, world_size, neighbor_map) -> dict | None`` function in
+the same module (see D11 / D15). ``resolve_topology`` wires these together.
+"""
+from __future__ import annotations
+
+from typing import Any, Callable
+
+NeighborMap = dict[str, int]
+TopologyFn = Callable[[int, int], NeighborMap]
+
+
+# ── Builtin generators ───────────────────────────────────────────────
+
+
+def ring_1d(rank: int, world_size: int) -> NeighborMap:
+    """1D bidirectional ring (E/W)."""
+    return {
+        "E": (rank + 1) % world_size,
+        "W": (rank - 1) % world_size,
+    }
+
+
+def ring_1d_unidir(rank: int, world_size: int) -> NeighborMap:
+    """1D unidirectional ring (E only)."""
+    return {"E": (rank + 1) % world_size}
+
+
+def mesh_2d(rank: int, world_size: int) -> NeighborMap:
+    """Square 2D mesh (N/S/E/W).
+
+    Layout: rank = row * side + col, with side = sqrt(world_size).
+    Wrap-around (torus) on all four edges.
+    """
+    side = int(round(world_size ** 0.5))
+    if side * side != world_size:
+        raise ValueError(
+            f"mesh_2d requires square world_size, got {world_size}"
+        )
+    r, c = divmod(rank, side)
+    return {
+        "N": ((r - 1) % side) * side + c,
+        "S": ((r + 1) % side) * side + c,
+        "W": r * side + (c - 1) % side,
+        "E": r * side + (c + 1) % side,
+    }
+
+
+def tree_binary(rank: int, world_size: int) -> NeighborMap:
+    """Binary tree rooted at rank 0.
+
+    Children of rank r are 2r+1 and 2r+2 (if within world_size).
+    Parent of rank r > 0 is (r-1)//2.
+    Returned keys (only those that exist):
+        "parent", "child_left", "child_right"
+    """
+    n: NeighborMap = {}
+    if rank > 0:
+        n["parent"] = (rank - 1) // 2
+    left = 2 * rank + 1
+    right = 2 * rank + 2
+    if left < world_size:
+        n["child_left"] = left
+    if right < world_size:
+        n["child_right"] = right
+    return n
+
+
+def none(rank: int, world_size: int) -> NeighborMap:
+    """Empty map — algorithm's neighbors() must build from scratch."""
+    return {}
+
+
+_BUILTIN: dict[str, TopologyFn] = {
+    "ring_1d": ring_1d,
+    "ring_1d_unidir": ring_1d_unidir,
+    "mesh_2d": mesh_2d,
+    "tree_binary": tree_binary,
+    "none": none,
+}
+
+
+# ── Resolution ───────────────────────────────────────────────────────
+
+
+def resolve_topology(
+    name: str, algo_module: Any | None = None,
+) -> TopologyFn:
+    """Return a callable ``(rank, world_size) -> NeighborMap``.
+
+    Args:
+        name: builtin topology name from ccl.yaml. Must be one of
+              ``ring_1d``, ``ring_1d_unidir``, ``mesh_2d``, ``tree_binary``,
+              or ``none``.
+        algo_module: optional algorithm module. If it defines
+              ``neighbors(rank, world_size, neighbor_map)``, that hook is
+              invoked after the builtin to override the result.
+              Returning None from neighbors() leaves the builtin map
+              unchanged; returning a dict replaces it.
+
+    Raises:
+        ValueError: if ``name`` is not a known builtin.
+    """
+    if name not in _BUILTIN:
+        raise ValueError(
+            f"Unknown topology '{name}'. "
+            f"Available builtins: {list(_BUILTIN)}"
+        )
+    builtin_fn = _BUILTIN[name]
+    override_fn = getattr(algo_module, "neighbors", None) if algo_module else None
+    if override_fn is None or not callable(override_fn):
+        return builtin_fn
+
+    def _wrapped(rank: int, world_size: int) -> NeighborMap:
+        base = builtin_fn(rank, world_size)
+        result = override_fn(rank, world_size, base)
+        if result is None:
+            return base
+        return result
+
+    return _wrapped