Merge origin/master: combine single-cube fast path + center-root reduce

Conflict resolution:
- intercube_allreduce.py: kept origin's `if single_cube:` early-exit
  (TP launches kernel on one cube/rank → skip intra-SIP mesh and go
  direct to inter-SIP exchange) AND replaced the multi-cube body with
  the local center-root + bidirectional reduce/broadcast (8-hop
  critical path on 4×4 vs 12 with corner root).
- tests/{allreduce,pe2pe}_latency_plots/: kept the local move to
  docs/diagrams/; dropped origin's stale content edits to the old
  paths (regenerable derived artifacts).
- docs/diagrams/pe2pe_latency_plots/summary.csv: kept local
  (post-Phase-2 + center-root values).

Origin contributions retained as-is:
- pyproject.toml: matplotlib >= 3.7 dep.
- runtime_api/distributed.py: derive effective cube_w/h from tensor
  shard placement so single-cube TP paths get cube_w=cube_h=1.
- kernel_args() now accepts optional cube_w/cube_h kwargs.

Verified post-merge:
- test_intercube_root_center.py: 2/2 (center-root multi-cube path).
- test_tp_layers.py + test_tp_mlp.py: 10/10 (single-cube TP path).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 name = "kernbench"
 version = "0.1.0"
 requires-python = ">=3.10"
-dependencies = ["pytest", "simpy", "pyyaml", "fastapi>=0.110", "uvicorn[standard]>=0.29", "websockets>=12", "numpy>=1.24", "greenlet>=3.0"]
+dependencies = ["pytest", "simpy", "pyyaml", "fastapi>=0.110", "uvicorn[standard]>=0.29", "websockets>=12", "numpy>=1.24", "greenlet>=3.0", "matplotlib>=3.7"]
 
 [project.scripts]
 kernbench = "kernbench.cli.main:main"

src/kernbench/ccl/algorithms/intercube_allreduce.py

@@ -24,9 +24,7 @@ TOPO_NAME_TO_KIND = {
 }
 
 
-def kernel_args(world_size: int, n_elem: int) -> tuple:
-    cube_w = 4
-    cube_h = 4
+def kernel_args(world_size: int, n_elem: int, *, cube_w: int = 4, cube_h: int = 4) -> tuple:
     return (n_elem, cube_w, cube_h, world_size)
 
 
@@ -132,6 +130,7 @@ def allreduce_intercube_multidevice(
     row = cube_id // cube_w
     col = cube_id % cube_w
     nbytes = n_elem * 2
+    single_cube = (cube_w == 1 and cube_h == 1)
 
     root_col = cube_w // 2
     root_row = cube_h // 2
@@ -140,7 +139,23 @@ def allreduce_intercube_multidevice(
     pe_addr = t_ptr + cube_id * nbytes
     acc = tl.load(pe_addr, shape=(n_elem,), dtype="f16")
 
-    # ── Phase 1: row reduce — converge at col == root_col ──
+    if single_cube:
+        # ── Single-cube mode: skip intra-SIP reduce, go directly to
+        #    inter-SIP exchange (TP use case: one cube per rank). ──
+        if n_sips > 1:
+            if sip_topo_kind == SIP_TOPO_RING:
+                acc = _inter_sip_ring(acc, n_sips, n_elem, tl)
+            elif sip_topo_kind == SIP_TOPO_TORUS:
+                acc = _inter_sip_torus_2d(
+                    acc, sip_rank, sip_topo_w, sip_topo_h, n_elem, tl)
+            elif sip_topo_kind == SIP_TOPO_MESH:
+                acc = _inter_sip_mesh_2d(
+                    acc, sip_rank, sip_topo_w, sip_topo_h, n_elem, tl)
+    else:
+        # ── Multi-cube mode: center-root bidirectional reduce
+        #    + inter-SIP exchange + bidirectional broadcast ──
+
+        # Phase 1: row reduce — converge at col == root_col.
         # Left half (col < root_col) walks W→E; right half (col > root_col)
         # walks E→W; the root_col cube merges both sides.
         if col == 0 and root_col > 0:
@@ -163,7 +178,7 @@ def allreduce_intercube_multidevice(
         elif col == cube_w - 1 and cube_w - 1 > root_col:
             tl.send(dir="W", src=acc)
 
-    # ── Phase 2: col reduce on col == root_col — converge at row == root_row ──
+        # Phase 2: col reduce on col == root_col — converge at row == root_row.
         if col == root_col:
             if row == 0 and root_row > 0:
                 tl.send(dir="S", src=acc)
@@ -185,16 +200,18 @@ def allreduce_intercube_multidevice(
             elif row == cube_h - 1 and cube_h - 1 > root_row:
                 tl.send(dir="N", src=acc)
 
-    # ── Phase 3: inter-SIP exchange on root cube ──
+        # Phase 3: inter-SIP exchange on root cube.
         if cube_id == root_cube and n_sips > 1:
             if sip_topo_kind == SIP_TOPO_RING:
                 acc = _inter_sip_ring(acc, n_sips, n_elem, tl)
             elif sip_topo_kind == SIP_TOPO_TORUS:
-            acc = _inter_sip_torus_2d(acc, sip_rank, sip_topo_w, sip_topo_h, n_elem, tl)
+                acc = _inter_sip_torus_2d(
+                    acc, sip_rank, sip_topo_w, sip_topo_h, n_elem, tl)
             elif sip_topo_kind == SIP_TOPO_MESH:
-            acc = _inter_sip_mesh_2d(acc, sip_rank, sip_topo_w, sip_topo_h, n_elem, tl)
+                acc = _inter_sip_mesh_2d(
+                    acc, sip_rank, sip_topo_w, sip_topo_h, n_elem, tl)
 
-    # ── Phase 4: col broadcast on col == root_col, outward from root_row ──
+        # Phase 4: col broadcast on col == root_col, outward from root_row.
         if col == root_col:
             if row == root_row:
                 if root_row > 0:
@@ -210,7 +227,7 @@ def allreduce_intercube_multidevice(
                 if row < cube_h - 1:
                     tl.send(dir="S", src=acc)
 
-    # ── Phase 5: row broadcast outward from root_col ──
+        # Phase 5: row broadcast outward from root_col.
         if col == root_col:
             if root_col > 0:
                 tl.send(dir="W", src=acc)

src/kernbench/runtime_api/distributed.py

@@ -113,7 +113,18 @@ class AhbmCCLBackend:
             )
         n_elem = shards[0].nbytes // tensor.itemsize
         kernel_fn = self._algo_module.kernel
-        kernel_args = self._algo_module.kernel_args(self._world_size, n_elem)
+        # Derive effective cube dims from tensor's actual shard placement
+        # (may differ from topology mesh when TP uses fewer cubes).
+        sip0_cubes = sorted({s.cube for s in shards if s.sip == shards[0].sip})
+        eff_n_cubes = len(sip0_cubes) if sip0_cubes else 1
+        if eff_n_cubes == 1:
+            eff_cube_w, eff_cube_h = 1, 1
+        else:
+            eff_cube_w, eff_cube_h = self._cube_w, self._cube_h
+        kernel_args = self._algo_module.kernel_args(
+            self._world_size, n_elem,
+            cube_w=eff_cube_w, cube_h=eff_cube_h,
+        )
 
         # Resolve sip_rank from the current greenlet's bound rank
         from greenlet import getcurrent as _gc