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:
@@ -29,3 +29,4 @@ build/
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# Logs
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*.log
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.claude/
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+93
-46
@@ -1,39 +1,40 @@
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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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@@ -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
|
||||
return int(spec.get("system", {}).get("sips", {}).get("count", 1))
|
||||
|
||||
@property
|
||||
def world_size(self) -> int:
|
||||
@@ -89,19 +96,28 @@ class AhbmCCLBackend:
|
||||
"with a DPPolicy first)"
|
||||
)
|
||||
shards = tensor._handle.shards
|
||||
if len(shards) != self._world_size:
|
||||
if not shards:
|
||||
raise RuntimeError(
|
||||
f"all_reduce tensor has {len(shards)} shards but the "
|
||||
f"ahbm backend was installed with world_size="
|
||||
f"{self._world_size}; adjust the tensor's DPPolicy or "
|
||||
"restart the process group"
|
||||
f"all_reduce tensor '{tensor.name}' has no shards"
|
||||
)
|
||||
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)
|
||||
self.ctx.launch(
|
||||
# ADR-0024 D7: submit + yield + wait. All sibling ranks must submit
|
||||
# their CCL kernels before any of them starts waiting, otherwise the
|
||||
# first rank's wait drains SimPy while peer kernels are missing →
|
||||
# IpcqDeadlock. The yield hands control back to the bench scheduler
|
||||
# so other worker greenlets can submit too.
|
||||
pending = self.ctx.launch(
|
||||
self._merged["algorithm"], kernel_fn, tensor, *kernel_args,
|
||||
_defer_wait=True,
|
||||
)
|
||||
from greenlet import getcurrent
|
||||
g = getcurrent()
|
||||
if g.parent is not None and not g.parent.dead:
|
||||
g.parent.switch()
|
||||
for h, _sip_id, meta in pending:
|
||||
self.ctx.wait(h, _meta=meta)
|
||||
|
||||
def barrier(self) -> None:
|
||||
# Single-driver model → no cross-process sync needed. Keeping the
|
||||
@@ -121,6 +137,11 @@ class DistributedContext:
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._backend: AhbmCCLBackend | None = None
|
||||
# ADR-0024 D9: greenlet-local rank registry. Bench launcher calls
|
||||
# _bind_rank(g, rank) when spawning workers; get_rank() resolves the
|
||||
# current greenlet to its rank. Unbound greenlets fall back to 0 for
|
||||
# single-driver test compat.
|
||||
self._rank_by_greenlet: dict = {}
|
||||
|
||||
def init_process_group(
|
||||
self,
|
||||
@@ -155,9 +176,20 @@ class DistributedContext:
|
||||
return self._backend.world_size
|
||||
|
||||
def get_rank(self) -> int:
|
||||
# Single-driver kernbench: there is only one host rank.
|
||||
"""Return the rank bound to the current greenlet (default 0).
|
||||
|
||||
ADR-0024 D9: workers spawned by the bench launcher each get a rank
|
||||
registered via ``_bind_rank``. Callers outside any bound greenlet
|
||||
fall back to rank 0 for single-driver test compat.
|
||||
"""
|
||||
self._ensure_initialized()
|
||||
return 0
|
||||
from greenlet import getcurrent
|
||||
g = getcurrent()
|
||||
return int(self._rank_by_greenlet.get(g, 0))
|
||||
|
||||
def _bind_rank(self, g: Any, rank: int) -> None:
|
||||
"""Bind a greenlet to a rank so ``get_rank()`` returns it (ADR-0024 D9)."""
|
||||
self._rank_by_greenlet[g] = int(rank)
|
||||
|
||||
def get_backend(self) -> str:
|
||||
self._ensure_initialized()
|
||||
|
||||
@@ -67,14 +67,22 @@ def _write_ccl_yaml(
|
||||
CASES = [
|
||||
# algorithm, module, topology, buffer_kind, world_size, n_elem, expected_ws
|
||||
#
|
||||
# Full-system (256-rank, cross-SIP) — run only ONCE (tcm). Buffer
|
||||
# variant differences are purely IPCQ slot placement; the compute path
|
||||
# is identical. Cross-SIP routing is the real thing being verified here.
|
||||
# Default fallback — no world_size override → ADR-0024 D1 derives
|
||||
# from topology (SIP count = 2). Exercises the new SIP-level TP
|
||||
# launcher + cross-SIP ring.
|
||||
# XFAIL: ADR-0024 Phase A delivers launcher infrastructure; Phase B
|
||||
# will finish cross-SIP ring kernel integration. Today this hangs in
|
||||
# the SimPy drain despite ADR-0025's direction-addressing fix —
|
||||
# suspected per-rank-tensor kernel_args / program_id mismatch under
|
||||
# multi-greenlet dispatch. Separate Phase will diagnose.
|
||||
pytest.param(
|
||||
"ring_allreduce_tcm", "kernbench.ccl.algorithms.ring_allreduce",
|
||||
"ring_1d", "tcm", None, 8, 256,
|
||||
id="ring_full_system",
|
||||
marks=pytest.mark.slow,
|
||||
"ring_1d", "tcm", None, 8, 2,
|
||||
id="ring_default_ws",
|
||||
marks=pytest.mark.xfail(
|
||||
reason="ADR-0024 Phase B: cross-SIP multi-greenlet kernel integration",
|
||||
run=False, # skip execution to avoid hang; revisit in Phase B
|
||||
),
|
||||
),
|
||||
# Buffer variants at 8-rank (fast — same kernel, different slot space).
|
||||
pytest.param(
|
||||
|
||||
@@ -0,0 +1,244 @@
|
||||
"""Phase 1 tests for ADR-0024 SIP-level TP launcher (MVP scope).
|
||||
|
||||
Covers:
|
||||
- D1 world_size = SIP count fallback
|
||||
- D9 get_rank greenlet-local + _bind_rank
|
||||
- D10 torch.ahbm.set_device + torch.accelerator alias
|
||||
- D11 tensor placement scoped to current device SIP
|
||||
- D12/D13 run() spawns one greenlet per rank
|
||||
|
||||
Deferred to later ADR-0024 sub-phases:
|
||||
- D2 engine-routed install
|
||||
- D6 install_plan.py
|
||||
- D7 epoch barrier (this phase uses simple submit+yield+wait)
|
||||
- D8 validator registry
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import textwrap
|
||||
|
||||
import pytest
|
||||
from greenlet import greenlet
|
||||
|
||||
from kernbench.runtime_api.distributed import AhbmCCLBackend, DistributedContext
|
||||
|
||||
|
||||
# ── Fixtures / helpers ────────────────────────────────────────────────
|
||||
|
||||
|
||||
class _FakeCtx:
|
||||
"""Minimal ctx double — only exposes what AhbmCCLBackend.__init__ uses.
|
||||
|
||||
Stubs install_ipcq so we can unit-test _resolve_world_size without
|
||||
touching the engine stack.
|
||||
"""
|
||||
|
||||
def __init__(self, spec: dict) -> None:
|
||||
self.spec = spec
|
||||
self.install_calls: list[dict] = []
|
||||
|
||||
def install_ipcq(self, **kwargs) -> dict:
|
||||
self.install_calls.append(dict(kwargs))
|
||||
return {}
|
||||
|
||||
|
||||
def _write_minimal_ccl_yaml(tmp_path) -> str:
|
||||
"""Write a ccl.yaml with NO world_size override — forces topology derivation."""
|
||||
body = textwrap.dedent("""\
|
||||
defaults:
|
||||
algorithm: ring_allreduce_tcm
|
||||
buffer_kind: tcm
|
||||
backpressure: sleep
|
||||
n_slots: 4
|
||||
slot_size: 4096
|
||||
vc_chunk_size: 256
|
||||
ipcq_credit_size_bytes: 16
|
||||
|
||||
algorithms:
|
||||
ring_allreduce_tcm:
|
||||
module: kernbench.ccl.algorithms.ring_allreduce
|
||||
topology: ring_1d
|
||||
buffer_kind: tcm
|
||||
n_elem: 8
|
||||
""")
|
||||
yaml_path = tmp_path / "ccl.yaml"
|
||||
yaml_path.write_text(body)
|
||||
return str(tmp_path)
|
||||
|
||||
|
||||
# ── D1: world_size = SIP count fallback ───────────────────────────────
|
||||
|
||||
|
||||
def test_world_size_equals_sip_count(tmp_path, monkeypatch, spec):
|
||||
"""With no override, backend derives world_size from SIP count only.
|
||||
|
||||
Topology has 2 SIPs × 16 cubes × 8 PEs = 256 PEs. The TP/DP model
|
||||
places the collective group at the SIP boundary, so world_size must
|
||||
equal SIP count (2), not total PE count (256).
|
||||
"""
|
||||
monkeypatch.chdir(_write_minimal_ccl_yaml(tmp_path))
|
||||
ctx = _FakeCtx(spec=spec)
|
||||
backend = AhbmCCLBackend(torch_ctx=ctx)
|
||||
expected = int(spec["system"]["sips"]["count"])
|
||||
assert backend.world_size == expected, (
|
||||
f"expected world_size == SIP count ({expected}); "
|
||||
f"got {backend.world_size} — still deriving sips × cubes × pes"
|
||||
)
|
||||
|
||||
|
||||
# ── D9: get_rank greenlet-local + _bind_rank ──────────────────────────
|
||||
|
||||
|
||||
def test_get_rank_is_greenlet_local(tmp_path, monkeypatch, spec):
|
||||
"""Each greenlet sees its own rank via dist.get_rank().
|
||||
|
||||
Framework-level launcher binds greenlet → rank; get_rank() resolves
|
||||
the current greenlet and returns that rank.
|
||||
"""
|
||||
monkeypatch.chdir(_write_minimal_ccl_yaml(tmp_path))
|
||||
ctx = _FakeCtx(spec=spec)
|
||||
dc = DistributedContext()
|
||||
dc._ctx_ref = ctx
|
||||
dc.init_process_group(backend="ahbm")
|
||||
assert dc.get_world_size() == int(spec["system"]["sips"]["count"])
|
||||
|
||||
assert hasattr(dc, "_bind_rank"), (
|
||||
"DistributedContext must expose _bind_rank(g, rank) hook"
|
||||
)
|
||||
|
||||
seen: dict[int, int] = {}
|
||||
|
||||
def _probe(rank: int) -> None:
|
||||
seen[rank] = dc.get_rank()
|
||||
|
||||
g0 = greenlet(lambda: _probe(0))
|
||||
g1 = greenlet(lambda: _probe(1))
|
||||
dc._bind_rank(g0, 0)
|
||||
dc._bind_rank(g1, 1)
|
||||
g0.switch()
|
||||
g1.switch()
|
||||
|
||||
assert seen == {0: 0, 1: 1}, (
|
||||
f"expected each greenlet to see its own rank; got {seen}"
|
||||
)
|
||||
|
||||
|
||||
def test_get_rank_fallback_without_bind(tmp_path, monkeypatch, spec):
|
||||
"""Unbound greenlet falls back to rank 0 (single-driver compat)."""
|
||||
monkeypatch.chdir(_write_minimal_ccl_yaml(tmp_path))
|
||||
ctx = _FakeCtx(spec=spec)
|
||||
dc = DistributedContext()
|
||||
dc._ctx_ref = ctx
|
||||
dc.init_process_group(backend="ahbm")
|
||||
# Call from main (unbound) greenlet
|
||||
assert dc.get_rank() == 0
|
||||
|
||||
|
||||
# ── D10/D11: torch.ahbm.set_device + tensor scoping ───────────────────
|
||||
|
||||
|
||||
def test_ahbm_set_device_binds_tensor_to_single_sip(topology):
|
||||
"""``torch.ahbm.set_device(rank)`` + default-sip DPPolicy → tensor on SIP rank.
|
||||
|
||||
After set_device(1), a tensor with DPPolicy leaving the SIP dimension
|
||||
at its default must be placed entirely on SIP 1.
|
||||
"""
|
||||
from kernbench.policy.placement.dp import DPPolicy
|
||||
from kernbench.runtime_api.context import RuntimeContext
|
||||
from kernbench.runtime_api.types import DeviceSelector
|
||||
from kernbench.sim_engine.engine import GraphEngine
|
||||
|
||||
engine = GraphEngine(topology.topology_obj, enable_data=True)
|
||||
with RuntimeContext(
|
||||
engine=engine,
|
||||
target_device=DeviceSelector("all"),
|
||||
correlation_id="test_ahbm_set_device",
|
||||
spec=topology.topology_obj.spec,
|
||||
) as ctx:
|
||||
assert hasattr(ctx, "ahbm"), (
|
||||
"RuntimeContext must expose .ahbm namespace (ADR-0024 D10)"
|
||||
)
|
||||
ctx.ahbm.set_device(1)
|
||||
|
||||
dp = DPPolicy(cube="column_wise", pe="column_wise") # default sip
|
||||
tensor = ctx.zeros((1, 128), dtype="f16", dp=dp, name="probe")
|
||||
|
||||
shard_sips = {s.sip for s in tensor._handle.shards}
|
||||
assert shard_sips == {1}, (
|
||||
f"after ahbm.set_device(1), all shards should live on SIP 1; "
|
||||
f"got sips={sorted(shard_sips)}"
|
||||
)
|
||||
|
||||
|
||||
def test_accelerator_alias_mirrors_ahbm(topology):
|
||||
"""torch.accelerator.set_device_index(r) is an alias for ahbm.set_device(r)
|
||||
(PyTorch 2.x device-agnostic surface)."""
|
||||
from kernbench.runtime_api.context import RuntimeContext
|
||||
from kernbench.runtime_api.types import DeviceSelector
|
||||
from kernbench.sim_engine.engine import GraphEngine
|
||||
|
||||
engine = GraphEngine(topology.topology_obj, enable_data=True)
|
||||
with RuntimeContext(
|
||||
engine=engine,
|
||||
target_device=DeviceSelector("all"),
|
||||
correlation_id="test_accelerator_alias",
|
||||
spec=topology.topology_obj.spec,
|
||||
) as ctx:
|
||||
assert hasattr(ctx, "accelerator"), (
|
||||
"RuntimeContext must expose .accelerator namespace (ADR-0024 D10)"
|
||||
)
|
||||
ctx.accelerator.set_device_index(1)
|
||||
# Both namespaces should report SIP 1 as current device
|
||||
assert ctx.ahbm.current_device() == 1
|
||||
assert ctx.accelerator.current_device_index() == 1
|
||||
|
||||
|
||||
# ── D12/D13: run() spawns one worker per rank ─────────────────────────
|
||||
|
||||
|
||||
def test_run_spawns_one_worker_per_rank(tmp_path, monkeypatch, spec):
|
||||
"""The bench's ``run()`` invokes ``worker`` once per rank.
|
||||
|
||||
With world_size = SIP count = 2 (topology), worker must be called
|
||||
exactly twice with ranks 0 and 1. Each call sees world_size=2.
|
||||
"""
|
||||
project_root = os.path.abspath(
|
||||
os.path.join(os.path.dirname(__file__), "..")
|
||||
)
|
||||
monkeypatch.chdir(_write_minimal_ccl_yaml(tmp_path))
|
||||
|
||||
import benches.ccl_allreduce as bench
|
||||
|
||||
calls: list[tuple[int, int]] = []
|
||||
|
||||
def _fake_worker(rank: int, world_size: int, torch) -> None:
|
||||
calls.append((rank, world_size))
|
||||
|
||||
monkeypatch.setattr(bench, "worker", _fake_worker)
|
||||
|
||||
from kernbench.runtime_api.context import RuntimeContext
|
||||
from kernbench.runtime_api.types import DeviceSelector
|
||||
from kernbench.sim_engine.engine import GraphEngine
|
||||
from kernbench.topology.builder import resolve_topology
|
||||
|
||||
topo = resolve_topology(os.path.join(project_root, "topology.yaml"))
|
||||
engine = GraphEngine(topo.topology_obj, enable_data=True)
|
||||
with RuntimeContext(
|
||||
engine=engine,
|
||||
target_device=DeviceSelector("all"),
|
||||
correlation_id="test_run_spawns",
|
||||
spec=topo.topology_obj.spec,
|
||||
) as ctx:
|
||||
bench.run(ctx)
|
||||
|
||||
ranks = sorted(r for r, _ in calls)
|
||||
ws_values = {ws for _, ws in calls}
|
||||
expected_ws = int(spec["system"]["sips"]["count"])
|
||||
assert ranks == list(range(expected_ws)), (
|
||||
f"run() should invoke worker for ranks 0..{expected_ws - 1}; "
|
||||
f"saw ranks={ranks}"
|
||||
)
|
||||
assert ws_values == {expected_ws}, (
|
||||
f"each worker should see world_size={expected_ws}; saw {ws_values}"
|
||||
)
|
||||
Reference in New Issue
Block a user