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

src/kernbench/ccl/algorithms/hierarchical_allreduce.py

@@ -0,0 +1,192 @@
+"""Hierarchical all-reduce kernel (ADR-0023).
+
+3-level reduce + broadcast exploiting the topology hierarchy:
+
+  Level 1 — Intra-cube (8 PEs, E/W, fastest link):
+      Bidirectional ring reduce to PE 0.
+  Level 2 — Inter-cube within SIP (16 cubes, N/S, UCIe):
+      Bidirectional ring reduce of PE 0s to cube 0 PE 0.
+  Level 3 — Inter-SIP (2 SIPs, parent):
+      Pair exchange between SIP representatives.
+  Broadcast — Reverse chain through levels 2 and 1.
+
+Bidirectional reduce: left-half sends toward node 0 via dir_dec,
+right-half sends via dir_inc (wrapping). Representative receives from
+both sides. Rounds per level = ceil((group_size - 1) / 2).
+
+Direction pairing (ring):
+  Send via dir_dec at PE K → recv via dir_inc at PE K-1
+  Send via dir_inc at PE K → recv via dir_dec at PE K+1
+"""
+from __future__ import annotations
+
+
+def kernel_args(world_size: int, n_elem: int) -> tuple:
+    """Positional kernel args for the ahbm backend."""
+    pes_per_cube = 8
+    num_sips = max(1, world_size // 128) if world_size > 128 else 1
+    cubes_per_sip = world_size // (pes_per_cube * num_sips)
+    return (n_elem, pes_per_cube, cubes_per_sip, num_sips)
+
+
+def neighbors(rank: int, world_size: int, neighbor_map: dict) -> dict:
+    """Build the 3-level neighbor map."""
+    pes_per_cube = 8
+    num_sips = max(1, world_size // 128) if world_size > 128 else 1
+    cubes_per_sip = world_size // (pes_per_cube * num_sips)
+
+    pe_id = rank % pes_per_cube
+    cube_global = rank // pes_per_cube
+    sip_id = cube_global // cubes_per_sip
+    local_cube_id = cube_global % cubes_per_sip
+
+    result = {}
+
+    # Level 1: intra-cube ring (E/W, all PEs)
+    cube_base = cube_global * pes_per_cube
+    result["E"] = cube_base + (pe_id + 1) % pes_per_cube
+    result["W"] = cube_base + (pe_id - 1) % pes_per_cube
+
+    # Level 2: inter-cube ring (N/S, PE 0 only)
+    if pe_id == 0 and cubes_per_sip > 1:
+        sip_base = sip_id * cubes_per_sip * pes_per_cube
+        next_cube_pe0 = sip_base + ((local_cube_id + 1) % cubes_per_sip) * pes_per_cube
+        prev_cube_pe0 = sip_base + ((local_cube_id - 1) % cubes_per_sip) * pes_per_cube
+        result["N"] = next_cube_pe0
+        result["S"] = prev_cube_pe0
+
+    # Level 3: inter-SIP (parent, PE 0 cube 0 only)
+    if pe_id == 0 and local_cube_id == 0 and num_sips > 1:
+        other_sip_pe0 = ((sip_id + 1) % num_sips) * cubes_per_sip * pes_per_cube
+        result["parent"] = other_sip_pe0
+
+    return result
+
+
+def _bidir_reduce(tl, acc, my_id, group_size, dir_inc, dir_dec, shape, dtype):
+    """Bidirectional ring reduce to node 0.
+
+    Left half (1..half): chain reduces via dir_dec (toward lower IDs).
+      Each PE recvs from higher PE (via dir_inc) and sends to lower (via dir_dec).
+    Right half (half+1..N-1): chain reduces via dir_inc (wraps to node 0).
+      Each PE recvs from lower PE (via dir_dec) and sends to higher (via dir_inc).
+    Node 0: recvs left sum via dir_inc, right sum via dir_dec.
+
+    Direction pairing: send dir_dec at K → recv dir_inc at K-1.
+                       send dir_inc at K → recv dir_dec at K+1.
+    """
+    if group_size <= 1:
+        return acc
+
+    half = group_size // 2
+
+    if my_id == 0:
+        # Representative: recv left-half sum via dir_inc (from PE 1)
+        recv = tl.recv(dir=dir_inc, shape=shape, dtype=dtype)
+        acc = acc + recv
+        # Recv right-half sum via dir_dec (from PE N-1, wrapped)
+        if group_size - half - 1 >= 1:
+            recv = tl.recv(dir=dir_dec, shape=shape, dtype=dtype)
+            acc = acc + recv
+
+    elif my_id <= half:
+        # Left half: recv from PE my_id+1 via dir_inc, send to PE my_id-1 via dir_dec
+        if my_id < half:  # not the far-edge
+            recv = tl.recv(dir=dir_inc, shape=shape, dtype=dtype)
+            acc = acc + recv
+        tl.send(dir=dir_dec, src=acc)
+
+    else:
+        # Right half: recv from PE my_id-1 via dir_dec, send to PE my_id+1 via dir_inc
+        if my_id > half + 1:  # not the near-edge
+            recv = tl.recv(dir=dir_dec, shape=shape, dtype=dtype)
+            acc = acc + recv
+        tl.send(dir=dir_inc, src=acc)
+
+    return acc
+
+
+def _chain_broadcast(tl, acc, my_id, group_size, dir_inc, shape, dtype):
+    """Linear chain broadcast from node 0 via dir_inc.
+
+    Node 0 sends via dir_inc → node 1. Node 1 recvs via dir_dec (implicit
+    from the ring pairing), stores, sends via dir_inc → node 2. Etc.
+
+    Recv direction = the opposite: send dir_inc at K → recv dir_dec at K+1.
+    """
+    if group_size <= 1:
+        return acc
+
+    # In ring pairing: send via dir_inc at K → recv via dir_dec at K+1.
+    # dir_dec is the "other" direction. We infer it from the ring:
+    # if dir_inc is "E", peer recvs via "W"; if "N", peer recvs via "S".
+    _recv_dir = {"E": "W", "W": "E", "N": "S", "S": "N"}.get(dir_inc, dir_inc)
+
+    if my_id == 0:
+        tl.send(dir=dir_inc, src=acc)
+    else:
+        acc = tl.recv(dir=_recv_dir, shape=shape, dtype=dtype)
+        if my_id < group_size - 1:
+            tl.send(dir=dir_inc, src=acc)
+    return acc
+
+
+def kernel(t_ptr, n_elem, pes_per_cube, cubes_per_sip, num_sips, tl):
+    """Hierarchical all-reduce.
+
+    Args:
+        t_ptr: HBM base address (column-sharded VA).
+        n_elem: f16 elements per tile.
+        pes_per_cube: PEs per cube (typically 8).
+        cubes_per_sip: cubes per SIP (typically 16).
+        num_sips: number of SIPs (typically 2).
+        tl: TLContext (auto-injected).
+    """
+    pe_id = tl.program_id(axis=0)
+    cube_global = tl.program_id(axis=1)
+    sip_id = cube_global // cubes_per_sip
+    local_cube_id = cube_global % cubes_per_sip
+
+    rank = cube_global * pes_per_cube + pe_id
+    nbytes = n_elem * 2
+    pe_addr = t_ptr + rank * nbytes
+
+    acc = tl.load(pe_addr, shape=(n_elem,), dtype="f16")
+    shape = (n_elem,)
+    dtype = "f16"
+
+    # ── Level 1: intra-cube bidirectional reduce to PE 0 ──
+    acc = _bidir_reduce(
+        tl, acc, my_id=pe_id, group_size=pes_per_cube,
+        dir_inc="E", dir_dec="W", shape=shape, dtype=dtype,
+    )
+
+    # ── Level 2: inter-cube bidirectional reduce to cube 0 (PE 0 only) ──
+    if pe_id == 0 and cubes_per_sip > 1:
+        acc = _bidir_reduce(
+            tl, acc, my_id=local_cube_id, group_size=cubes_per_sip,
+            dir_inc="N", dir_dec="S", shape=shape, dtype=dtype,
+        )
+
+    # ── Level 3: inter-SIP exchange (PE 0 cube 0 only) ──
+    if pe_id == 0 and local_cube_id == 0 and num_sips > 1:
+        tl.send(dir="parent", src=acc)
+        recv = tl.recv(dir="parent", shape=shape, dtype=dtype)
+        acc = acc + recv
+
+    # ── Broadcast back ──
+
+    # Level 2: cube 0 PE 0 → all PE 0s via chain
+    if pe_id == 0 and cubes_per_sip > 1:
+        acc = _chain_broadcast(
+            tl, acc, my_id=local_cube_id, group_size=cubes_per_sip,
+            dir_inc="N", shape=shape, dtype=dtype,
+        )
+
+    # Level 1: PE 0 → all PEs in cube via chain
+    acc = _chain_broadcast(
+        tl, acc, my_id=pe_id, group_size=pes_per_cube,
+        dir_inc="E", shape=shape, dtype=dtype,
+    )
+
+    tl.store(pe_addr, acc)

src/kernbench/ccl/testing.py

@@ -46,9 +46,13 @@ class _MockRankState:
         world_size: int,
         neighbors: dict[str, int],
         input_arr: np.ndarray,
+        pes_per_cube: int = 0,
     ) -> None:
         self.rank = rank
         self.world_size = world_size
+        # PEs per cube for program_id(axis=0/1). If 0 or world_size,
+        # all ranks are in one cube (legacy single-cube behavior).
+        self.pes_per_cube = pes_per_cube if pes_per_cube > 0 else world_size
         self.neighbors = neighbors  # direction → peer rank
         # HBM "memory": addr → ndarray. Per-rank, no cross-rank sharing.
         self._hbm: dict[int, np.ndarray] = {}
@@ -99,10 +103,19 @@ class _MockTL:
 
     # axis-aware
     def program_id(self, axis: int = 0) -> int:
-        return self._state.rank if axis == 0 else 0
+        # Multi-cube: axis=0 = PE within cube, axis=1 = global cube id.
+        # Falls back to flat (all ranks in one cube) if pes_per_cube
+        # is not set (legacy single-cube tests).
+        ppc = self._state.pes_per_cube
+        if axis == 1:
+            return self._state.rank // ppc
+        return self._state.rank % ppc
 
     def num_programs(self, axis: int = 0) -> int:
-        return self._state.world_size if axis == 0 else 1
+        ppc = self._state.pes_per_cube
+        if axis == 1:
+            return self._state.world_size // ppc
+        return ppc
 
     # ── arithmetic ops (called by TensorHandle.__add__ etc.) ──
 
@@ -272,18 +285,27 @@ class _MockTL:
         if data is None:
             raise RuntimeError("mock tl.send: src is None")
         peer_rank = self._state.neighbors[dir]
-        # Find the reverse direction in peer's neighbors that points back to me
+        # Find the reverse direction at the peer, mirroring real IPCQ
+        # install pairing: N↔S, E↔W, parent↔parent, child_left↔child_left, etc.
+        _REVERSE = {"N": "S", "S": "N", "E": "W", "W": "E",
+                    "parent": "parent", "child_left": "child_left",
+                    "child_right": "child_right"}
         peer_state = self._scheduler.states[peer_rank]
-        reverse_dir = None
-        for d, target in peer_state.neighbors.items():
-            if target == self._state.rank:
-                reverse_dir = d
-                break
+        reverse_dir = _REVERSE.get(dir)
+        # Fall back to "first direction pointing at me" if the explicit
+        # reverse doesn't exist at the peer (e.g. custom directions).
+        if reverse_dir is None or reverse_dir not in peer_state.neighbors:
+            reverse_dir = None
+            for d, target in peer_state.neighbors.items():
+                if target == self._state.rank:
+                    reverse_dir = d
+                    break
         if reverse_dir is None:
             raise RuntimeError(
                 f"mock tl.send: peer rank {peer_rank} has no reverse direction"
             )
         peer_state.recv_q[reverse_dir].append(data.copy())
+        self._scheduler._send_counter += 1
         # After delivering, hand control back to scheduler so the receiver
         # can wake up.
         self._scheduler.yield_()
@@ -388,33 +410,34 @@ class _MockScheduler:
             state.g = _spawn(state.rank)
 
         # Drive each rank round-robin until all dead. Detect global deadlock.
-        max_rounds = 10_000
-        round_no = 0
+        # A global send counter tracks whether any greenlet delivered data
+        # in the current round. This is more reliable than queue-depth
+        # tracking because a recv+send pair in the same round nets to zero
+        # depth change yet still represents real progress.
+        self._send_counter = 0
+        max_idle_rounds = 10_000
+        idle_rounds = 0
         while True:
             alive = [s for s in self.states if s.g is not None and not s.g.dead]
             if not alive:
                 break
-            progressed = False
+            counter_before = self._send_counter
             for s in self.states:
                 if s.g is None or s.g.dead:
                     continue
-                # Multi-rank greenlets share TLContext active state via the
-                # module-level thread-local; restore this rank's tl before
-                # resuming so TensorHandle operator overloads dispatch to
-                # the right _MockTL.
                 TLContext._set_active(tls[s.rank])  # type: ignore[attr-defined]
                 s.g.switch()
-                if s.g.dead:
-                    progressed = True
             TLContext._set_active(None)  # type: ignore[attr-defined]
-            # Loose progress check: if no greenlet died and queues didn't grow,
-            # advance round counter; abort after too many idle rounds.
-            round_no += 1
-            if round_no > max_rounds and not progressed:
-                raise RuntimeError(
-                    "mock CCL runtime: deadlock detected (no progress for "
-                    f"{max_rounds} rounds)"
-                )
+            any_died = any(s.g is not None and s.g.dead for s in self.states)
+            if self._send_counter > counter_before or any_died:
+                idle_rounds = 0
+            else:
+                idle_rounds += 1
+                if idle_rounds >= max_idle_rounds:
+                    raise RuntimeError(
+                        "mock CCL runtime: deadlock detected (no progress for "
+                        f"{max_idle_rounds} rounds)"
+                    )
 
         return [
             s.output if s.output is not None else s._hbm.get(s._slice_addr)
@@ -432,6 +455,7 @@ def run_kernel_in_mock(
     inputs: list[np.ndarray],
     kernel_args: tuple = (),
     algo_module: Any | None = None,
+    pes_per_cube: int = 0,
 ) -> list[np.ndarray]:
     """Run a CCL kernel under the mock runtime with no SimPy/fabric.
 
@@ -443,6 +467,8 @@ def run_kernel_in_mock(
                 local tile at HBM address 0.
         kernel_args: extra positional args after t_ptr
         algo_module: optional module providing ``neighbors()`` override
+        pes_per_cube: PEs per cube for multi-cube program_id mapping.
+                      0 → single-cube legacy (all ranks in one cube).
 
     Returns:
         Per-rank output ndarrays — whatever the kernel wrote via tl.store
@@ -457,6 +483,7 @@ def run_kernel_in_mock(
             rank=r, world_size=world_size,
             neighbors=topo_fn(r, world_size),
             input_arr=inputs[r],
+            pes_per_cube=pes_per_cube,
         )
         for r in range(world_size)
     ]

src/kernbench/runtime_api/tensor.py

@@ -159,6 +159,116 @@ class Tensor:
         if ctx is not None:
             ctx._free_tensor(self)
 
+    # ── Indexing (shard-aligned slices) ────────────────────────────
+
+    def _resolve_shard_index(self, key) -> tuple[int, int | None]:
+        """Map a numpy-style index key to (flat_start_elem, flat_stop_elem).
+
+        Only shard-aligned slices on the last dimension are supported.
+        Returns (start, stop) in element units from the flat layout, or
+        raises IndexError / NotImplementedError for unsupported keys.
+        """
+        if self._handle is None:
+            raise RuntimeError(f"Tensor '{self.name}' is not deployed")
+        ndim = len(self.shape)
+        if not isinstance(key, tuple):
+            key = (key,)
+        if len(key) != ndim:
+            raise IndexError(
+                f"expected {ndim} indices, got {len(key)}"
+            )
+        # All leading dims must be int (selecting a single row/plane).
+        for i, k in enumerate(key[:-1]):
+            if not isinstance(k, int):
+                raise NotImplementedError(
+                    "only integer indices are supported for leading dims"
+                )
+        last = key[-1]
+        total_elems = math.prod(self.shape)
+        if isinstance(last, int):
+            # Single element
+            return (last, last + 1)
+        if isinstance(last, slice):
+            start, stop, step = last.indices(self.shape[-1])
+            if step != 1:
+                raise NotImplementedError("step != 1 not supported")
+            return (start, stop)
+        raise NotImplementedError(f"unsupported index type: {type(last)}")
+
+    def _shard_for_range(self, start_elem: int, stop_elem: int) -> TensorShard:
+        """Return the single shard that fully covers [start_elem, stop_elem).
+
+        Raises NotImplementedError if the range spans multiple shards.
+        """
+        isize = self.itemsize
+        start_byte = start_elem * isize
+        stop_byte = stop_elem * isize
+        for shard in self._handle.shards:
+            s_start = shard.offset_bytes
+            s_end = shard.offset_bytes + shard.nbytes
+            if start_byte >= s_start and stop_byte <= s_end:
+                return shard
+        raise NotImplementedError(
+            f"slice [{start_elem}:{stop_elem}] spans multiple shards "
+            f"(only shard-aligned slices are supported)"
+        )
+
+    def __getitem__(self, key):
+        """Read a shard-aligned slice. Returns a numpy array.
+
+        Mirrors ``torch.Tensor.__getitem__`` for the shard-aligned case.
+        """
+        start, stop = self._resolve_shard_index(key)
+        shard = self._shard_for_range(start, stop)
+        if self._memory_store is None:
+            return np.zeros(stop - start, dtype=_numpy_dtype(self.dtype))
+        isize = self.itemsize
+        local_start = (start * isize - shard.offset_bytes) // isize
+        local_count = stop - start
+        try:
+            arr = self._memory_store.read(
+                "hbm", self._shard_store_addr(shard),
+            )
+            flat = np.asarray(arr, dtype=_numpy_dtype(self.dtype)).reshape(-1)
+            return flat[local_start : local_start + local_count]
+        except KeyError:
+            return np.zeros(local_count, dtype=_numpy_dtype(self.dtype))
+
+    def __setitem__(self, key, value):
+        """Write a shard-aligned slice.
+
+        Mirrors ``torch.Tensor.__setitem__``. Scalar broadcast and
+        numpy array assignment are both supported.
+        """
+        if self._handle is None or self._memory_store is None:
+            raise RuntimeError(
+                f"Tensor '{self.name}' must be deployed before assignment"
+            )
+        start, stop = self._resolve_shard_index(key)
+        shard = self._shard_for_range(start, stop)
+        np_dtype = _numpy_dtype(self.dtype)
+        isize = self.itemsize
+        local_start = (start * isize - shard.offset_bytes) // isize
+        local_count = stop - start
+        shard_elems = shard.nbytes // isize
+        addr = self._shard_store_addr(shard)
+
+        # Read current shard data (or zeros if uninitialized)
+        try:
+            arr = self._memory_store.read("hbm", addr)
+            arr = np.array(arr, dtype=np_dtype).reshape(-1).copy()
+        except KeyError:
+            arr = np.zeros(shard_elems, dtype=np_dtype)
+
+        # Write the slice
+        if isinstance(value, (int, float)):
+            arr[local_start : local_start + local_count] = np_dtype.type(value)
+        else:
+            v = np.asarray(value, dtype=np_dtype).reshape(-1)
+            arr[local_start : local_start + local_count] = v[:local_count]
+
+        self._memory_store.write("hbm", addr, arr)
+
     def __repr__(self) -> str:
         parts = [f"tensor(name={self.name}, shape={self.shape}, dtype={self.dtype}"]
         if self._memory_store is not None and self._handle is not None:

tests/test_runtime_api_tensor.py

@@ -134,3 +134,73 @@ def test_copy_shape_mismatch_raises():
             t.copy_(torch.from_numpy(src))
 
     _run_with(body)
+
+
+# ── __setitem__ / __getitem__ (shard-aligned) ───────────────────────
+
+
+def test_setitem_getitem_single_pe():
+    """Scalar and slice assignment on a single-PE tensor round-trips."""
+
+    def body(torch):
+        dp = DPPolicy(sip="replicate", cube="replicate", pe="replicate",
+                      num_sips=1, num_cubes=1, num_pes=1)
+        t = torch.zeros((1, 8), dtype="f16", dp=dp, name="t")
+
+        # Scalar broadcast
+        t[0, 0:4] = 3.0
+        assert np.allclose(t[0, 0:4], 3.0)
+        assert np.allclose(t[0, 4:8], 0.0)  # untouched
+
+        # Array assignment
+        t[0, 4:8] = np.array([10, 20, 30, 40], dtype=np.float16)
+        assert np.array_equal(t[0, 4:8], [10, 20, 30, 40])
+
+        # Full read-back via numpy()
+        expected = np.array([[3, 3, 3, 3, 10, 20, 30, 40]], dtype=np.float16)
+        assert np.array_equal(t.numpy(), expected)
+
+    _run_with(body)
+
+
+def test_setitem_getitem_multi_pe_shard_aligned():
+    """Shard-aligned slice assignment on an 8-PE column-wise tensor."""
+
+    def body(torch):
+        n_pe = 8
+        n_elem = 4  # per shard
+        dp = DPPolicy(sip="replicate", cube="replicate", pe="column_wise",
+                      num_sips=1, num_cubes=1, num_pes=n_pe)
+        t = torch.zeros((1, n_pe * n_elem), dtype="f16", dp=dp, name="t")
+
+        # Write each shard with its rank value
+        for r in range(n_pe):
+            t[0, r * n_elem : (r + 1) * n_elem] = float(r + 1)
+
+        # Read back each shard
+        for r in range(n_pe):
+            expected = float(r + 1)
+            arr = t[0, r * n_elem : (r + 1) * n_elem]
+            assert np.allclose(arr, expected), f"shard {r}: {arr} != {expected}"
+
+        # Full gather
+        full = t.numpy().reshape(-1)
+        for r in range(n_pe):
+            assert np.allclose(full[r * n_elem : (r + 1) * n_elem], float(r + 1))
+
+    _run_with(body)
+
+
+def test_setitem_cross_shard_raises():
+    """Slice spanning two shards raises NotImplementedError."""
+
+    def body(torch):
+        n_pe = 4
+        n_elem = 4
+        dp = DPPolicy(sip="replicate", cube="replicate", pe="column_wise",
+                      num_sips=1, num_cubes=1, num_pes=n_pe)
+        t = torch.zeros((1, n_pe * n_elem), dtype="f16", dp=dp, name="t")
+        with pytest.raises(NotImplementedError, match="spans multiple shards"):
+            t[0, 2:6] = 1.0  # crosses shard 0 (0:4) and shard 1 (4:8)
+
+    _run_with(body)