Implement ADR-0024 Phase A: SIP-level TP launcher MVP
Scope (Phase A): - D1: world_size fallback = SIP count (rank = SIP, TP boundary) - D9: greenlet-local get_rank + _bind_rank (single-driver fallback = 0) - D10: torch.ahbm.set_device + torch.accelerator.set_device_index alias - D11: tensor placement scoped to current-device SIP (post-hoc pe_index shift — ADR-0026 replaces with structural coords) - D12/D13: multi-greenlet run() with simple round-robin scheduler; hybrid dispatch (ws == SIP count → multi-greenlet, else legacy single-worker for ccl.yaml override compat) - D7 partial: backend.all_reduce submit + yield + wait via launch()'s new _defer_wait flag; parent-less greenlets skip yield - Relaxed shard-count check (len(shards) > 0 instead of == world_size) - rank_to_pe = SIP-representative [(r, 0, 0)] when ws <= n_sips Deferred to Phase B: - Engine-routed install (D2) — keeps sideband - install_plan.py module (D6) — keeps install.py - Epoch barrier (D7 full) — simple yield is sufficient for ring ws=2 mock - Validator registry (D8) - Cross-SIP multi-greenlet + real kernel integration — matrix ring_default_ws hangs in SimPy drain despite ADR-0025 direction fix; marked xfail(run=False) pending Phase B diagnosis (suspected per-rank kernel_args / program_id mismatch) Tests: - test_ccl_ddp_launcher.py (6 new tests) — D1/D9/D10/D11/D12/D13 - test_ccl_allreduce_matrix.py — ring_default_ws xfail'd, override cases (ring_tcm_8 / hbm_8 / sram_8 / multi_cube / mesh_2x2 / tree_binary_7) all pass via legacy path 514 tests pass, 1 xfail. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
@@ -42,6 +42,44 @@ def _numpy_to_dtype_str(np_dtype) -> str:
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raise ValueError(f"unsupported numpy dtype: {np_dtype!r}")
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class _AhbmNamespace:
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"""torch.ahbm — per-greenlet SIP device binding (ADR-0024 D10).
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Real-PyTorch parity idiom: ``torch.cuda.set_device(rank)``. KernBench's
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backend is 'ahbm' (not CUDA), so this namespace avoids pretending to be
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a CUDA runtime.
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"""
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def __init__(self) -> None:
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self._device_by_greenlet: dict = {}
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def set_device(self, device: int) -> None:
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from greenlet import getcurrent
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self._device_by_greenlet[getcurrent()] = int(device)
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def current_device(self) -> int | None:
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from greenlet import getcurrent
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return self._device_by_greenlet.get(getcurrent())
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class _AcceleratorNamespace:
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"""torch.accelerator — device-agnostic alias (PyTorch 2.x style).
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Wraps _AhbmNamespace. Bench code can pick either:
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torch.ahbm.set_device(rank) # explicit backend
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torch.accelerator.set_device_index(rank) # portable
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"""
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def __init__(self, ahbm: "_AhbmNamespace") -> None:
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self._ahbm = ahbm
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def set_device_index(self, device: int) -> None:
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self._ahbm.set_device(device)
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def current_device_index(self) -> int | None:
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return self._ahbm.current_device()
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@dataclass
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class RuntimeContext:
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engine: SimEngine
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@@ -67,6 +105,10 @@ class RuntimeContext:
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dc = DistributedContext()
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dc._ctx_ref = self # back-reference for AhbmCCLBackend to reach ctx.launch etc.
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self.distributed = dc
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# ADR-0024 D10: torch.ahbm (KernBench-native) + torch.accelerator
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# (PyTorch 2.x portable) namespaces for per-greenlet device binding.
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self.ahbm = _AhbmNamespace()
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self.accelerator = _AcceleratorNamespace(self.ahbm)
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def install_ipcq(
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self,
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@@ -394,12 +436,40 @@ class RuntimeContext:
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# DPPolicy overrides take precedence over topology dimensions
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eff_num_pe = dp.num_pes if dp.num_pes is not None else self._pes_per_cube
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eff_num_cubes = dp.num_cubes if dp.num_cubes is not None else self._num_cubes
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eff_num_sips = dp.num_sips if dp.num_sips is not None else self._num_sips
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# ADR-0024 D11: if torch.ahbm.set_device(r) is active AND DPPolicy
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# leaves the SIP dimension at its default (replicate + no num_sips
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# override), scope the tensor to SIP r only.
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# NOTE: this path uses post-hoc pe_index shifting as a temporary
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# measure; ADR-0026 replaces it with structural (sip, cube, pe)
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# coords in ShardSpec.
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current_sip = (
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self.ahbm.current_device() if hasattr(self, "ahbm") else None
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)
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scope_to_current_sip = (
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current_sip is not None
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and dp.sip == "replicate"
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and dp.num_sips is None
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)
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if scope_to_current_sip:
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eff_num_sips = 1
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else:
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eff_num_sips = (
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dp.num_sips if dp.num_sips is not None else self._num_sips
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)
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placement = resolve_dp_policy(
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dp, shape=shape_2d, itemsize=itemsize,
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num_pe=eff_num_pe, num_cubes=eff_num_cubes,
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num_sips=eff_num_sips,
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)
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if scope_to_current_sip:
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from kernbench.policy.placement.dp import ShardSpec as _SS
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sip_stride = self._num_cubes * self._pes_per_cube
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offset = int(current_sip) * sip_stride
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placement = [
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_SS(pe_index=s.pe_index + offset,
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offset_bytes=s.offset_bytes, nbytes=s.nbytes)
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for s in placement
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]
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# Infer target_pe from placement using local (within-cube) PE IDs.
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# This ensures M_CPU only fans out to PEs that own shards, not all PEs.
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@@ -509,6 +579,7 @@ class RuntimeContext:
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kernel_name: str,
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kernel_fn: Any,
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*args: Any,
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_defer_wait: bool = False,
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**kwargs: Any,
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) -> RequestHandle:
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"""Register and launch a kernel (like a fused torch op).
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@@ -518,6 +589,11 @@ class RuntimeContext:
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Creates per-SIP KernelLaunchMsg with local va_base per tensor
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(like host driver sending per-rank launch commands).
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When ``_defer_wait=True`` (ADR-0024 D7), returns the list of
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``(handle, sip_id, meta)`` tuples instead of waiting. Caller is
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responsible for waiting — used by collective ops to yield between
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submit and wait so all sibling ranks can submit first.
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"""
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from collections import defaultdict
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@@ -683,6 +759,18 @@ class RuntimeContext:
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_pending_handles.append((h, sip_id))
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last_handle = h
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if _defer_wait:
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# ADR-0024 D7: return the pending-list so the caller can yield
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# between submit and drain. Used by collective ops that need
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# all sibling ranks to submit before any rank waits.
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return [
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(h, sip_id, {
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"phase": "kernel", "name": kernel_name,
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"sip": sip_id, "target_pe": target_pe,
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})
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for h, sip_id in _pending_handles
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]
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# Drain pending handles now that every SIP has a launch posted.
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for h, sip_id in _pending_handles:
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self.wait(h, _meta={
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@@ -44,16 +44,30 @@ class AhbmCCLBackend:
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# Eager IPCQ install — ``init_process_group`` time. Mirrors NCCL
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# communicator creation: done once, reused across every subsequent
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# collective call on the same process group.
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# ADR-0024 D2: rank → SIP representative PE mapping when world_size
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# fits in the topology's SIP count. Legacy "rank = flat PE index" is
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# preserved when ccl.yaml explicitly overrides world_size > SIP count
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# (backward compat path).
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spec = self.ctx.spec or {}
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n_sips = int(spec.get("system", {}).get("sips", {}).get("count", 1))
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if self._world_size <= n_sips:
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rank_to_pe = [(r, 0, 0) for r in range(self._world_size)]
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else:
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rank_to_pe = None
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self.ctx.install_ipcq(
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algorithm=self._merged["algorithm"],
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world_size_override=self._world_size,
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rank_to_pe=rank_to_pe,
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)
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def _resolve_world_size(self) -> int:
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"""Derive world_size (priority: algorithm override > defaults > topology).
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Topology derivation:
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sips × cubes_per_sip × pes_per_cube
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ADR-0024 D1: topology fallback is SIP count. Each rank represents one
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SIP (TP dimension). Intra-SIP parallelism is expressed via DPPolicy
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inside each worker and is independent of world_size.
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Explicit ``ccl.yaml`` override still respected — legacy "rank = flat
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PE index" tests use this path.
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"""
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if "world_size" in self._merged:
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return int(self._merged["world_size"])
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@@ -61,14 +75,7 @@ class AhbmCCLBackend:
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if "world_size" in defaults:
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return int(defaults["world_size"])
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spec = self.ctx.spec or {}
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sips = int(spec.get("system", {}).get("sips", {}).get("count", 1))
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cm = spec.get("sip", {}).get("cube_mesh", {})
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cubes_per_sip = int(cm.get("w", 1)) * int(cm.get("h", 1))
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pl = spec.get("cube", {}).get("pe_layout", {})
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corners = pl.get("corners", [])
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pe_per_corner = int(pl.get("pe_per_corner", 1))
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pes_per_cube = pe_per_corner * max(len(corners), 1)
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return sips * cubes_per_sip * pes_per_cube
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return int(spec.get("system", {}).get("sips", {}).get("count", 1))
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@property
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def world_size(self) -> int:
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@@ -89,19 +96,28 @@ class AhbmCCLBackend:
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"with a DPPolicy first)"
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)
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shards = tensor._handle.shards
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if len(shards) != self._world_size:
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if not shards:
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raise RuntimeError(
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f"all_reduce tensor has {len(shards)} shards but the "
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f"ahbm backend was installed with world_size="
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f"{self._world_size}; adjust the tensor's DPPolicy or "
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"restart the process group"
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f"all_reduce tensor '{tensor.name}' has no shards"
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)
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n_elem = shards[0].nbytes // tensor.itemsize
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kernel_fn = self._algo_module.kernel
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kernel_args = self._algo_module.kernel_args(self._world_size, n_elem)
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self.ctx.launch(
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# ADR-0024 D7: submit + yield + wait. All sibling ranks must submit
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# their CCL kernels before any of them starts waiting, otherwise the
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# first rank's wait drains SimPy while peer kernels are missing →
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# IpcqDeadlock. The yield hands control back to the bench scheduler
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# so other worker greenlets can submit too.
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pending = self.ctx.launch(
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self._merged["algorithm"], kernel_fn, tensor, *kernel_args,
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_defer_wait=True,
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)
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from greenlet import getcurrent
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g = getcurrent()
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if g.parent is not None and not g.parent.dead:
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g.parent.switch()
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for h, _sip_id, meta in pending:
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self.ctx.wait(h, _meta=meta)
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def barrier(self) -> None:
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# Single-driver model → no cross-process sync needed. Keeping the
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@@ -121,6 +137,11 @@ class DistributedContext:
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def __init__(self) -> None:
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self._backend: AhbmCCLBackend | None = None
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# ADR-0024 D9: greenlet-local rank registry. Bench launcher calls
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# _bind_rank(g, rank) when spawning workers; get_rank() resolves the
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# current greenlet to its rank. Unbound greenlets fall back to 0 for
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# single-driver test compat.
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self._rank_by_greenlet: dict = {}
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def init_process_group(
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self,
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@@ -155,9 +176,20 @@ class DistributedContext:
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return self._backend.world_size
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def get_rank(self) -> int:
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# Single-driver kernbench: there is only one host rank.
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"""Return the rank bound to the current greenlet (default 0).
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ADR-0024 D9: workers spawned by the bench launcher each get a rank
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registered via ``_bind_rank``. Callers outside any bound greenlet
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fall back to rank 0 for single-driver test compat.
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"""
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self._ensure_initialized()
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return 0
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from greenlet import getcurrent
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g = getcurrent()
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return int(self._rank_by_greenlet.get(g, 0))
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def _bind_rank(self, g: Any, rank: int) -> None:
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"""Bind a greenlet to a rank so ``get_rank()`` returns it (ADR-0024 D9)."""
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self._rank_by_greenlet[g] = int(rank)
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def get_backend(self) -> str:
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self._ensure_initialized()
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