kernbench2/benches/ccl_allreduce.py

"""CCL all-reduce bench (ADR-0024 Phase A).

Driven entirely by ``ccl.yaml`` + ``topology.yaml``:

- ``defaults.algorithm`` in ``ccl.yaml`` picks which kernel to run.
- ``world_size`` resolution: explicit override in ccl.yaml > defaults >
  topology's SIP count. ADR-0024 D1: topology fallback is the SIP count
  (each rank = one SIP, TP boundary).
- ``run()`` is hybrid:
  - If ``world_size == topology SIP count`` (the intended new path):
    spawn one greenlet per rank, bind it via ``dist._bind_rank``, and
    each worker calls ``torch.ahbm.set_device(rank)`` + runs its portion
    of the collective. Cross-rank IPCQ exchange handles the reduce.
  - Legacy path (``world_size > SIP count``, via explicit ccl.yaml
    override): single worker at rank 0 with the full tensor distributed
    across all participating PEs via ``_derive_dp``. Retained for
    backward compatibility with existing kernel / topology tests.
"""
from __future__ import annotations

import numpy as np
from greenlet import greenlet

from kernbench.ccl.install import load_ccl_config, resolve_algorithm_config
from kernbench.policy.placement.dp import DPPolicy

# Default per-rank tile size if ccl.yaml doesn't override it.
DEFAULT_N_ELEM = 32


def _derive_dp(spec: dict, world_size: int) -> DPPolicy:
    """Legacy DPPolicy for world_size > SIP count (rank = flat PE index).

    Used only in the ccl.yaml-override path so the existing matrix tests
    with explicit world_size (8, 16, 7 etc.) keep working. The new
    ADR-0024 TP path (rank = SIP) uses a per-rank DPPolicy inside the
    worker instead.
    """
    sips = int(spec["system"]["sips"]["count"])
    cm = spec["sip"]["cube_mesh"]
    pl = spec["cube"]["pe_layout"]
    pes_per_cube = int(pl["pe_per_corner"]) * len(pl["corners"])
    cubes_per_sip = int(cm["w"]) * int(cm["h"])
    total = sips * cubes_per_sip * pes_per_cube
    if world_size == total:
        return DPPolicy(sip="column_wise", cube="column_wise", pe="column_wise")
    if world_size <= pes_per_cube:
        return DPPolicy(
            sip="replicate", cube="replicate", pe="column_wise",
            num_sips=1, num_cubes=1, num_pes=world_size,
        )
    if world_size <= cubes_per_sip * pes_per_cube:
        return DPPolicy(
            sip="replicate", cube="column_wise", pe="column_wise",
            num_sips=1, num_cubes=world_size // pes_per_cube,
        )
    return DPPolicy(sip="column_wise", cube="column_wise", pe="column_wise")


def worker(rank: int, world_size: int, torch) -> None:
    """Per-rank worker (new TP path) OR single-worker legacy driver.

    Behaviour depends on whether this call originates from the
    multi-greenlet launcher (new path) or from the legacy single-call
    fallback; distinguished by which ``dp`` layout applies.
    """
    cfg = resolve_algorithm_config(load_ccl_config())
    algo_name = cfg["algorithm"]
    n_elem = int(cfg.get("n_elem", DEFAULT_N_ELEM))

    spec = torch.spec or {}
    n_sips = int(spec.get("system", {}).get("sips", {}).get("count", 1))

    if world_size == n_sips:
        # ADR-0024 new path: rank = SIP, worker sees its SIP's
        # representative PE via torch.ahbm.set_device.
        torch.ahbm.set_device(rank)
        dp = DPPolicy(cube="replicate", pe="replicate",
                      num_cubes=1, num_pes=1)
        tensor = torch.zeros(
            (1, n_elem), dtype="f16", dp=dp, name=f"ccl_in_r{rank}",
        )
        # Each rank initialises its tile with (rank + 1); after all_reduce
        # every rank sees sum(1..world_size).
        init = np.full((1, n_elem), float(rank + 1), dtype=np.float16)
        tensor.copy_(torch.from_numpy(init))
        torch.distributed.all_reduce(tensor, op="sum")
        result = tensor.numpy()
        expected = float(sum(range(1, world_size + 1)))
        all_ok = bool(np.allclose(result, expected, rtol=1e-1, atol=1e-1))
        if rank == 0:
            if all_ok:
                print(f"  {algo_name} (ws={world_size}): {world_size} OK")
            else:
                print(
                    f"  [FAIL] rank {rank} "
                    f"(ws={world_size}, algo={algo_name}): "
                    f"got mean={float(result.reshape(-1).mean()):.3f}, "
                    f"expected={expected:.3f}"
                )
                print(
                    f"  {algo_name} (ws={world_size}): "
                    f"0 OK / {world_size} FAIL"
                )
        return

    # Legacy path: world_size overridden via ccl.yaml to exceed SIP count.
    # Single-worker at rank 0; whole tensor distributed across all
    # participating PEs using the derived DPPolicy. Matches pre-ADR-0024
    # behaviour.
    dp = _derive_dp(spec, world_size)
    tensor = torch.zeros(
        (1, world_size * n_elem), dtype="f16", dp=dp, name="ccl_in",
    )
    init = np.zeros((1, world_size * n_elem), dtype=np.float16)
    for r in range(world_size):
        init[0, r * n_elem : (r + 1) * n_elem] = float(r + 1)
    tensor.copy_(torch.from_numpy(init))
    torch.distributed.all_reduce(tensor, op="sum")

    result = tensor.numpy()
    expected = float(sum(range(1, world_size + 1)))
    all_ok = bool(np.allclose(result, expected, rtol=1e-1, atol=1e-1))
    if rank == 0:
        if all_ok:
            print(f"  {algo_name} (ws={world_size}): {world_size} OK")
        else:
            flat = result.reshape(-1)
            n_fail = 0
            for r in range(world_size):
                slice_r = flat[r * n_elem : (r + 1) * n_elem]
                if not np.allclose(slice_r, expected, rtol=1e-1, atol=1e-1):
                    n_fail += 1
                    if n_fail <= 5:
                        print(
                            f"  [FAIL] rank {r} "
                            f"(ws={world_size}, algo={algo_name}): "
                            f"got mean={float(slice_r.mean()):.3f}, "
                            f"expected={expected:.3f}"
                        )
            print(
                f"  {algo_name} (ws={world_size}): "
                f"{world_size - n_fail} OK / {n_fail} FAIL"
            )


def run(torch) -> None:
    """CLI entry — dispatch to multi-greenlet path when ws == SIP count,
    else fall back to single-worker legacy path for ccl.yaml override compat.
    """
    dist = torch.distributed
    dist.init_process_group(backend="ahbm")
    world_size = dist.get_world_size()

    spec = torch.spec or {}
    n_sips = int(spec.get("system", {}).get("sips", {}).get("count", 1))

    if world_size == n_sips:
        # ADR-0024 D12/D13: one greenlet per rank. After each scheduler
        # round, the main greenlet drains any pending collective handles
        # (ADR-0024 D7) — this must happen in the main context, not inside
        # a worker, so env.run is invoked with main as the current greenlet
        # and kernel_runner's spawned kernel greenlets correctly get main
        # as their parent.
        backend = dist._backend
        gs: list[greenlet] = []
        for rank in range(world_size):
            def _entry(r: int = rank) -> None:
                worker(r, world_size, torch)
            g = greenlet(_entry)
            dist._bind_rank(g, rank)
            gs.append(g)
        while True:
            alive = [g for g in gs if not g.dead]
            if not alive:
                break
            for g in alive:
                if not g.dead:
                    g.switch()
            # Drain pending collective handles. All sibling workers have
            # either submitted (and yielded) or completed; their kernels
            # are live in the SimPy queue, ready to exchange via IPCQ.
            pending = backend._pending_collective_handles
            if pending:
                for h, _sip_id, meta in pending:
                    torch.wait(h, _meta=meta)
                backend._pending_collective_handles = []
    else:
        # Legacy single-worker path (ccl.yaml world_size override).
        worker(rank=dist.get_rank(), world_size=world_size, torch=torch)