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>
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"""CCL all-reduce bench — single unified entry point.
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"""CCL all-reduce bench (ADR-0024 Phase A).
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Driven entirely by ``ccl.yaml`` + ``topology.yaml``:
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- ``defaults.algorithm`` in ``ccl.yaml`` picks which kernel to run
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(``ring_allreduce_{tcm,hbm,sram}`` / ``mesh_allreduce_4`` /
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``tree_allreduce_7``).
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- ``world_size`` is derived from the algorithm entry's override or from
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the topology spec (``sips × cubes_per_sip × pes_per_cube``).
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- The host code uses only real PyTorch ``torch.distributed`` names:
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``init_process_group``, ``get_world_size``, ``get_rank``, ``all_reduce``.
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The bench is split into ``worker(rank, world_size, torch)`` — the
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per-rank business logic, designed to look like a real PyTorch DDP
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training worker so future model benches can reuse the same skeleton —
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and ``run(torch)`` — the kernbench-specific launcher that initializes
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the process group and invokes the worker.
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- ``defaults.algorithm`` in ``ccl.yaml`` picks which kernel to run.
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- ``world_size`` resolution: explicit override in ccl.yaml > defaults >
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topology's SIP count. ADR-0024 D1: topology fallback is the SIP count
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(each rank = one SIP, TP boundary).
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- ``run()`` is hybrid:
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- If ``world_size == topology SIP count`` (the intended new path):
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spawn one greenlet per rank, bind it via ``dist._bind_rank``, and
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each worker calls ``torch.ahbm.set_device(rank)`` + runs its portion
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of the collective. Cross-rank IPCQ exchange handles the reduce.
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- Legacy path (``world_size > SIP count``, via explicit ccl.yaml
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override): single worker at rank 0 with the full tensor distributed
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across all participating PEs via ``_derive_dp``. Retained for
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backward compatibility with existing kernel / topology tests.
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"""
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from __future__ import annotations
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import numpy as np
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from greenlet import greenlet
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from kernbench.ccl.install import load_ccl_config, resolve_algorithm_config
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from kernbench.policy.placement.dp import DPPolicy
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# Default per-rank tile size if ccl.yaml doesn't override it. Real
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# pytorch benches hardcode batch/feature dims similarly.
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# Default per-rank tile size if ccl.yaml doesn't override it.
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DEFAULT_N_ELEM = 32
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def _derive_dp(spec: dict, world_size: int) -> DPPolicy:
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"""Pick a DPPolicy that fans the tensor across exactly ``world_size`` PEs.
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"""Legacy DPPolicy for world_size > SIP count (rank = flat PE index).
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Mirrors what a real PyTorch DDP user does manually with
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``tensor.to(f"cuda:{rank}")``: the host code chooses the placement so
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that the collective sees the right number of participating ranks.
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Used only in the ccl.yaml-override path so the existing matrix tests
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with explicit world_size (8, 16, 7 etc.) keep working. The new
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ADR-0024 TP path (rank = SIP) uses a per-rank DPPolicy inside the
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worker instead.
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"""
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sips = int(spec["system"]["sips"]["count"])
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cm = spec["sip"]["cube_mesh"]
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@@ -57,44 +58,69 @@ def _derive_dp(spec: dict, world_size: int) -> DPPolicy:
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def worker(rank: int, world_size: int, torch) -> None:
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"""Per-rank business logic. Mirrors a real PyTorch DDP worker.
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"""Per-rank worker (new TP path) OR single-worker legacy driver.
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In real PyTorch DDP, this function runs in N separate processes,
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each with its own ``rank``. In kernbench (single-process multi-device)
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it is invoked once with ``rank=0`` on the single host driver; the
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actual per-PE parallelism is handled by ``torch.launch`` fanning out
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the kernel across all participating PEs via the tensor's DPPolicy.
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The ``rank`` parameter is therefore always 0 today, and is kept as
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an explicit argument for parity with real DDP workers (``if rank ==
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0`` logging guards, future multi-host extensions).
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Behaviour depends on whether this call originates from the
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multi-greenlet launcher (new path) or from the legacy single-call
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fallback; distinguished by which ``dp`` layout applies.
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"""
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cfg = resolve_algorithm_config(load_ccl_config())
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algo_name = cfg["algorithm"]
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n_elem = int(cfg.get("n_elem", DEFAULT_N_ELEM))
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# Pick a DP that produces exactly ``world_size`` shards on this topology.
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dp = _derive_dp(torch.spec, world_size)
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spec = torch.spec or {}
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n_sips = int(spec.get("system", {}).get("sips", {}).get("count", 1))
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if world_size == n_sips:
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# ADR-0024 new path: rank = SIP, worker sees its SIP's
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# representative PE via torch.ahbm.set_device.
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torch.ahbm.set_device(rank)
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dp = DPPolicy(cube="replicate", pe="replicate",
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num_cubes=1, num_pes=1)
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tensor = torch.zeros(
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(1, n_elem), dtype="f16", dp=dp, name=f"ccl_in_r{rank}",
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)
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# Each rank initialises its tile with (rank + 1); after all_reduce
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# every rank sees sum(1..world_size).
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init = np.full((1, n_elem), float(rank + 1), dtype=np.float16)
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tensor.copy_(torch.from_numpy(init))
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torch.distributed.all_reduce(tensor, op="sum")
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result = tensor.numpy()
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expected = float(sum(range(1, world_size + 1)))
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all_ok = bool(np.allclose(result, expected, rtol=1e-1, atol=1e-1))
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if rank == 0:
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if all_ok:
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print(f" {algo_name} (ws={world_size}): {world_size} OK")
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else:
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print(
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f" [FAIL] rank {rank} "
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f"(ws={world_size}, algo={algo_name}): "
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f"got mean={float(result.reshape(-1).mean()):.3f}, "
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f"expected={expected:.3f}"
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)
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print(
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f" {algo_name} (ws={world_size}): "
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f"0 OK / {world_size} FAIL"
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)
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return
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# Legacy path: world_size overridden via ccl.yaml to exceed SIP count.
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# Single-worker at rank 0; whole tensor distributed across all
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# participating PEs using the derived DPPolicy. Matches pre-ADR-0024
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# behaviour.
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dp = _derive_dp(spec, world_size)
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tensor = torch.zeros(
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(1, world_size * n_elem), dtype="f16", dp=dp, name="ccl_in",
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)
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# Initialize: CCL rank r's slice gets value (r + 1). Real PyTorch idiom:
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# target.copy_(torch.from_numpy(source))
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init = np.zeros((1, world_size * n_elem), dtype=np.float16)
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for r in range(world_size):
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init[0, r * n_elem : (r + 1) * n_elem] = float(r + 1)
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tensor.copy_(torch.from_numpy(init))
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# The main act: one all_reduce call — the backend installs IPCQ at
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# init_process_group time and here only dispatches the kernel.
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torch.distributed.all_reduce(tensor, op="sum")
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# Verify: each shard should hold sum(1..world_size) after all-reduce.
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result = tensor.numpy()
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expected = float(sum(range(1, world_size + 1)))
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all_ok = bool(np.allclose(result, expected, rtol=1e-1, atol=1e-1))
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# Print only on rank 0 — real PyTorch DDP idiom for single-source logs.
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if rank == 0:
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if all_ok:
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print(f" {algo_name} (ws={world_size}): {world_size} OK")
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@@ -119,11 +145,32 @@ def worker(rank: int, world_size: int, torch) -> None:
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def run(torch) -> None:
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"""CLI entry point: initialize the process group, invoke worker."""
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"""CLI entry — dispatch to multi-greenlet path when ws == SIP count,
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else fall back to single-worker legacy path for ccl.yaml override compat.
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"""
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dist = torch.distributed
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dist.init_process_group(backend="ahbm")
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worker(
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rank=dist.get_rank(),
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world_size=dist.get_world_size(),
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torch=torch,
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)
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world_size = dist.get_world_size()
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spec = torch.spec or {}
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n_sips = int(spec.get("system", {}).get("sips", {}).get("count", 1))
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if world_size == n_sips:
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# ADR-0024 D12/D13: one greenlet per rank, simple round-robin.
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gs: list[greenlet] = []
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for rank in range(world_size):
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def _entry(r: int = rank) -> None:
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worker(r, world_size, torch)
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g = greenlet(_entry)
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dist._bind_rank(g, rank)
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gs.append(g)
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while True:
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alive = [g for g in gs if not g.dead]
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if not alive:
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break
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for g in alive:
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if not g.dead:
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g.switch()
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else:
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# Legacy single-worker path (ccl.yaml world_size override).
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worker(rank=dist.get_rank(), world_size=world_size, torch=torch)
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