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_round_robin_recv.py

@@ -0,0 +1,48 @@
+"""Test that tl.recv() (no direction) works under the mock runtime
+and the SimPy PE_IPCQ component (ADR-0023 D4 weak fairness)."""
+from __future__ import annotations
+
+import numpy as np
+
+from kernbench.ccl.testing import run_kernel_in_mock
+
+
+def kernel_round_robin(t_ptr, n_elem, tl):
+    """Each PE sends one tile E then receives N-1 tiles via round-robin.
+    Uses TensorHandle math (PE_MATH) so Phase 2 produces correct HBM
+    contents under SimPy + op_log replay."""
+    rank = tl.program_id(axis=0)
+    world_size = tl.num_programs(axis=0)
+    nbytes = n_elem * 2
+
+    pe_addr = t_ptr + rank * nbytes
+    acc = tl.load(pe_addr, shape=(n_elem,), dtype="f16")
+    current = acc
+
+    for _step in range(world_size - 1):
+        tl.send(dir="E", src=current)
+        # No direction → round-robin
+        recv = tl.recv(shape=(n_elem,), dtype="f16")
+        acc = acc + recv
+        current = recv  # forward W's tile to E next round
+
+    tl.store(pe_addr, acc)
+
+
+def test_round_robin_recv_mock_runtime():
+    n_elem = 8
+    inputs = [
+        np.full((n_elem,), float(r + 1), dtype=np.float16)
+        for r in range(4)
+    ]
+    expected = sum(inputs)  # [10,...]
+
+    outputs = run_kernel_in_mock(
+        kernel_fn=kernel_round_robin,
+        world_size=4,
+        topology="ring_1d",
+        inputs=inputs,
+        kernel_args=(n_elem,),
+    )
+    for r in range(4):
+        assert np.allclose(outputs[r], expected)