Add Tensor indexing + hierarchical 3-level all-reduce kernel

Tensor.__setitem__ / __getitem__:
- Shard-aligned slice assignment and read on deployed tensors.
- Scalar broadcast and numpy array assignment supported.
- Cross-shard slices raise NotImplementedError (use copy_ for that).
- 3 new tests: single-PE, multi-PE, cross-shard error case.

Hierarchical all-reduce kernel (src/kernbench/ccl/algorithms/):
- 3-level reduce: intra-cube (E/W) → inter-cube (N/S) → inter-SIP (parent).
- Bidirectional ring reduce at each level: ceil((N-1)/2) rounds.
  Left half sends via dir_dec, right half via dir_inc (wrap).
  Representative receives from both sides.
- Chain broadcast for reverse path: cube 0 PE 0 → all PE 0s → all PEs.
- Registered in ccl.yaml as "hierarchical_allreduce" with topology: none
  (neighbors() override builds the full 3-level neighbor map).
- kernel_args derives pes_per_cube/cubes_per_sip/num_sips from world_size.
- Mock-verified at 8/16/32/64/128 ranks.

Mock runtime fixes:
- Direction pairing: explicit N↔S, E↔W, parent↔parent instead of
  "first matching reverse". Fixes 2-element rings where N and S both
  point to the same peer.
- Deadlock detection: send-counter based (not just queue-depth-total)
  to catch chain reductions where send+recv pairs net to zero.
- Multi-cube program_id: pes_per_cube parameter enables
  program_id(axis=0) = PE within cube, program_id(axis=1) = cube id.
  Legacy single-cube tests unaffected (default = world_size).

504 tests pass in 12s.

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

ccl.yaml

@@ -78,3 +78,11 @@ algorithms:
     buffer_kind: tcm
     world_size: 7
     n_elem: 16
+
+  # ── hierarchical all-reduce (3-level: intra-cube → inter-cube → inter-SIP) ──
+  # Uses bidirectional ring reduce + chain broadcast. ~25 rounds vs 255 flat.
+  hierarchical_allreduce:
+    module: kernbench.ccl.algorithms.hierarchical_allreduce
+    topology: none
+    buffer_kind: tcm
+    n_elem: 16