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>

tests/test_ccl_allreduce_matrix.py

@@ -0,0 +1,142 @@
+"""End-to-end matrix tests for the unified ``ccl_allreduce`` bench.
+
+Each parametrized case writes a tmp ``ccl.yaml`` overlay that selects a
+specific (algorithm, world_size, buffer_kind, n_elem) combination, then
+runs the bench via the CLI and asserts the printed line reports all
+ranks OK.
+
+This single test file replaces the per-variant bench tests
+(test_ccl_allreduce_e2e, test_ccl_mesh_allreduce, test_ccl_tree_allreduce,
+test_ccl_multicube, test_ccl_multisip).
+"""
+from __future__ import annotations
+
+import os
+import textwrap
+
+import pytest
+
+import kernbench.cli.main as cli_main
+
+
+CCL_YAML_TEMPLATE = textwrap.dedent("""\
+    defaults:
+      algorithm: {algorithm}
+      buffer_kind: {buffer_kind}
+      backpressure: sleep
+      n_slots: 4
+      slot_size: 4096
+      vc_chunk_size: 256
+      ipcq_credit_size_bytes: 16
+
+    algorithms:
+      {algorithm}:
+        module: {module}
+        topology: {topology}
+        buffer_kind: {buffer_kind}
+{world_size_line}{n_elem_line}
+""")
+
+
+def _write_ccl_yaml(
+    tmp_path,
+    *,
+    algorithm: str,
+    module: str,
+    topology: str,
+    buffer_kind: str = "tcm",
+    world_size: int | None = None,
+    n_elem: int | None = None,
+) -> str:
+    """Write a tmp ccl.yaml in tmp_path and return its directory."""
+    ws_line = f"        world_size: {world_size}\n" if world_size is not None else ""
+    nel_line = f"        n_elem: {n_elem}\n" if n_elem is not None else ""
+    body = CCL_YAML_TEMPLATE.format(
+        algorithm=algorithm,
+        module=module,
+        topology=topology,
+        buffer_kind=buffer_kind,
+        world_size_line=ws_line,
+        n_elem_line=nel_line,
+    )
+    yaml_path = tmp_path / "ccl.yaml"
+    yaml_path.write_text(body)
+    return str(tmp_path)
+
+
+CASES = [
+    # algorithm, module, topology, buffer_kind, world_size, n_elem, expected_ws
+    pytest.param(
+        "ring_allreduce_tcm", "kernbench.ccl.algorithms.ring_allreduce",
+        "ring_1d", "tcm", None, 8, 256,
+        id="ring_full_system_tcm",
+    ),
+    pytest.param(
+        "ring_allreduce_hbm", "kernbench.ccl.algorithms.ring_allreduce",
+        "ring_1d", "hbm", None, 8, 256,
+        id="ring_full_system_hbm",
+    ),
+    pytest.param(
+        "ring_allreduce_sram", "kernbench.ccl.algorithms.ring_allreduce",
+        "ring_1d", "sram", None, 8, 256,
+        id="ring_full_system_sram",
+    ),
+    pytest.param(
+        "ring_allreduce_8", "kernbench.ccl.algorithms.ring_allreduce",
+        "ring_1d", "tcm", 8, 32, 8,
+        id="ring_single_cube",
+    ),
+    pytest.param(
+        "ring_allreduce_16", "kernbench.ccl.algorithms.ring_allreduce",
+        "ring_1d", "tcm", 16, 16, 16,
+        id="ring_multi_cube",
+    ),
+    pytest.param(
+        "mesh_allreduce_4", "kernbench.ccl.algorithms.mesh_allreduce",
+        "mesh_2d", "tcm", 4, 16, 4,
+        id="mesh_2x2",
+    ),
+    pytest.param(
+        "tree_allreduce_7", "kernbench.ccl.algorithms.tree_allreduce",
+        "tree_binary", "tcm", 7, 16, 7,
+        id="tree_binary_7",
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    "algorithm,module,topology,buffer_kind,world_size,n_elem,expected_ws",
+    CASES,
+)
+def test_ccl_allreduce_matrix(
+    tmp_path, capsys, monkeypatch,
+    algorithm, module, topology, buffer_kind, world_size, n_elem, expected_ws,
+):
+    """Each (algorithm × buffer × world_size) combo passes through the
+    unified bench and yields all ranks OK."""
+    project_root = os.path.abspath(
+        os.path.join(os.path.dirname(__file__), "..")
+    )
+    yaml_dir = _write_ccl_yaml(
+        tmp_path,
+        algorithm=algorithm,
+        module=module,
+        topology=topology,
+        buffer_kind=buffer_kind,
+        world_size=world_size,
+        n_elem=n_elem,
+    )
+    monkeypatch.chdir(yaml_dir)
+    rc = cli_main.main([
+        "run",
+        "--topology", os.path.join(project_root, "topology.yaml"),
+        "--bench", "ccl_allreduce",
+        "--verify-data",
+    ])
+    assert rc == 0
+    out = capsys.readouterr().out
+    assert "FAIL" not in out, f"unexpected FAIL in output:\n{out}"
+    assert f"{algorithm} (ws={expected_ws}): {expected_ws} OK" in out, (
+        f"expected '{algorithm} (ws={expected_ws}): {expected_ws} OK' "
+        f"in output:\n{out}"
+    )