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/tensor.py

@@ -146,6 +146,11 @@ class Tensor:
         self._handle: TensorHandle | None = None
         self._ctx_ref: weakref.ref | None = None  # set by RuntimeContext
         self._memory_store = None  # set by RuntimeContext when enable_data=True
+        # Host-side staging buffer for torch.from_numpy() results. A tensor
+        # with a non-None _host_buffer is NOT deployed to any PE — it lives
+        # only on the host. Use `target.copy_(host_tensor)` to scatter the
+        # data into a deployed, sharded target tensor.
+        self._host_buffer: np.ndarray | None = None
 
     def __del__(self) -> None:
         if self._ctx_ref is None or self._handle is None:
@@ -166,15 +171,85 @@ class Tensor:
 
     @property
     def data(self) -> np.ndarray:
-        """Tensor data as numpy array. Returns actual values when enable_data=True,
-        zeros placeholder otherwise (like an uninitialized tensor)."""
-        if self._memory_store is not None and self._handle is not None:
-            shard = self._handle.shards[0]
+        """Tensor data as numpy array.
+
+        Gathers all shards into a single full-shape array. Returns actual
+        values when enable_data=True, zeros placeholder otherwise (like an
+        uninitialized tensor). Alias of ``numpy()``.
+        """
+        return self.numpy()
+
+    def _shard_store_addr(self, shard: TensorShard) -> int:
+        """MemoryStore key for a shard.
+
+        Kernels read tensors via VA (translated to PA by PE_DMA's MMU when
+        a mapping exists, otherwise the addr is treated as a PA-equivalent
+        key). Tensor I/O therefore writes/reads at ``va_base + offset_bytes``
+        when ``va_base`` is set, falling back to ``shard.pa`` for the
+        VA-less mode used by some legacy paths.
+        """
+        if self._handle and self._handle.va_base:
+            return self._handle.va_base + shard.offset_bytes
+        return shard.pa
+
+    def numpy(self) -> np.ndarray:
+        """Return a single numpy array gathered from all shards.
+
+        Mirrors ``torch.Tensor.numpy()``. In kernbench, sharded tensors are
+        gathered into a single full-shape ndarray according to each shard's
+        ``offset_bytes`` / ``nbytes`` range.
+        """
+        np_dtype = _numpy_dtype(self.dtype)
+        # Host-side tensor (created via torch.from_numpy) has no shards.
+        if self._host_buffer is not None:
+            return self._host_buffer.copy()
+        if self._handle is None or self._memory_store is None:
+            return np.zeros(self.shape, dtype=np_dtype)
+        flat = np.zeros(math.prod(self.shape), dtype=np_dtype)
+        for shard in self._handle.shards:
+            start = shard.offset_bytes // self.itemsize
+            count = shard.nbytes // self.itemsize
             try:
-                return self._memory_store.read("hbm", shard.pa, shape=self.shape, dtype=self.dtype)
+                piece = self._memory_store.read(
+                    "hbm", self._shard_store_addr(shard),
+                )
             except KeyError:
-                pass
-        return np.zeros(self.shape, dtype=_numpy_dtype(self.dtype))
+                continue
+            flat[start : start + count] = (
+                np.asarray(piece, dtype=np_dtype).reshape(-1)[:count]
+            )
+        return flat.reshape(self.shape)
+
+    def copy_(self, source: "Tensor") -> "Tensor":
+        """In-place copy from another tensor into self.
+
+        Mirrors ``torch.Tensor.copy_()``. If ``source`` is a host tensor
+        (from ``torch.from_numpy``), its ndarray is split across self's
+        shards using each shard's byte range. If ``source`` is a deployed
+        (sharded) tensor, its contents are gathered first and then
+        re-scattered into self's shard layout.
+
+        Shapes must match. Returns self.
+        """
+        if self._handle is None or self._memory_store is None:
+            raise RuntimeError(
+                f"Tensor '{self.name}' must be deployed before copy_()"
+            )
+        if source.shape != self.shape:
+            raise ValueError(
+                f"copy_ shape mismatch: self={self.shape} source={source.shape}"
+            )
+        np_dtype = _numpy_dtype(self.dtype)
+        arr = source.numpy().astype(np_dtype, copy=False)
+        flat = np.ascontiguousarray(arr).reshape(-1)
+        for shard in self._handle.shards:
+            start = shard.offset_bytes // self.itemsize
+            count = shard.nbytes // self.itemsize
+            piece = flat[start : start + count].copy()
+            self._memory_store.write(
+                "hbm", self._shard_store_addr(shard), piece,
+            )
+        return self
 
     @property
     def itemsize(self) -> int: