kernbench2/tests/attention/test_attention_mesh_panels_diag.py

"""End-to-end engine drives for the four GQA Llama-70B panels (sub-cycle 4c step 2).

Mirrors the existing single_user_decode diag harness across all four panels
of the milestone-gqa-llama70b sweep (ADR-0057):

  single_user_prefill   ring-K/V kernel, intracube PE ring  (8 PEs / 1 cube)
  single_user_decode    allreduce-mlo kernel, intracube PE ring
  multi_user_prefill    ring-K/V kernel, intercube multisip (4 cubes)
  multi_user_decode     allreduce-mlo kernel, intercube multisip

Each test runs the panel through ``run_bench`` with ``enable_data=True``
and asserts ``result.completion.ok``. Failures dump the engine's op_log
tail and the exception, mirroring the decode-diag harness format.

Validation-scale config matches ADR-0057 D4:
  S_q_prefill=16, S_kv_per_rank=16, h_q=h_kv=1, d_head=64
  n_ranks_single_user=8, n_ranks_multi_user=4
"""
from __future__ import annotations

import traceback
from pathlib import Path

import pytest

from kernbench.benches._attention_mesh_kv import attention_mesh_kv_kernel
from kernbench.benches._attention_mesh_mlo import attention_mesh_mlo_kernel
from kernbench.ccl.install import load_ccl_config, resolve_algorithm_config
from kernbench.ccl.sfr_config import (
    configure_sfr_intercube_multisip,
    configure_sfr_intracube_pe_ring,
)
from kernbench.policy.placement.dp import DPPolicy
from kernbench.runtime_api.bench_runner import run_bench
from kernbench.runtime_api.types import resolve_device
from kernbench.sim_engine.engine import GraphEngine
from kernbench.topology.builder import resolve_topology

TOPOLOGY_PATH = Path(__file__).resolve().parents[2] / "topology.yaml"

S_Q_PREFILL = 16
S_Q_DECODE = 1
S_KV_PER_RANK = 16
H_Q = 1
H_KV = 1
D_HEAD = 64
N_RANKS_SINGLE_USER = 8
N_RANKS_MULTI_USER = 4
DTYPE = "f16"


# ── Helpers ──────────────────────────────────────────────────────


def _engine_factory(t, d):
    return GraphEngine(getattr(t, "topology_obj", t), enable_data=True)


def _run_panel(bench_fn):
    """Drive a panel through run_bench; return (exc, result, engine)."""
    topo = resolve_topology(str(TOPOLOGY_PATH))
    captured: dict = {"engine": None}

    def factory(t, d):
        eng = _engine_factory(t, d)
        captured["engine"] = eng
        return eng

    exc = None
    result = None
    try:
        result = run_bench(
            topology=topo, bench_fn=bench_fn,
            device=resolve_device(None), engine_factory=factory,
        )
    except BaseException as e:  # noqa: BLE001
        exc = e
    return exc, result, captured["engine"]


def _assert_ok(name: str, exc, result, engine) -> None:
    if exc is not None:
        oplog_len = len(getattr(engine, "op_log", []) or []) if engine else 0
        print(f"\n========== {name} FAIL ==========")
        print(f"op_log records before crash: {oplog_len}")
        print(f"{type(exc).__name__}: {exc}")
        traceback.print_exception(type(exc), exc, exc.__traceback__)
        raise AssertionError(
            f"{name} failed at runtime: {exc}"
        ) from exc
    assert result is not None, f"{name}: no result"
    assert result.completion.ok, f"{name}: completion not ok — {result.completion}"


# ── Panel bench fns ──────────────────────────────────────────────


def _bench_fn_single_user_prefill(ctx):
    configure_sfr_intracube_pe_ring(
        ctx.engine, ctx.spec,
        resolve_algorithm_config(load_ccl_config(), name="lrab_hierarchical_allreduce"),
    )
    n = N_RANKS_SINGLE_USER
    dp_full = DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=n)
    dp_kv = DPPolicy(cube="replicate", pe="row_wise", num_cubes=1, num_pes=n)
    q = ctx.zeros((S_Q_PREFILL, H_Q * D_HEAD), dtype=DTYPE, dp=dp_full, name="q")
    k = ctx.zeros((S_KV_PER_RANK * n, H_KV * D_HEAD), dtype=DTYPE, dp=dp_kv, name="k")
    v = ctx.zeros((S_KV_PER_RANK * n, H_KV * D_HEAD), dtype=DTYPE, dp=dp_kv, name="v")
    o = ctx.empty((S_Q_PREFILL, H_Q * D_HEAD), dtype=DTYPE, dp=dp_full, name="o")
    ctx.launch(
        "single_user_prefill_mesh", attention_mesh_kv_kernel,
        q, k, v, o,
        S_Q_PREFILL, S_KV_PER_RANK, H_Q, H_KV, D_HEAD, n,
    )


def _bench_fn_single_user_decode(ctx):
    configure_sfr_intracube_pe_ring(
        ctx.engine, ctx.spec,
        resolve_algorithm_config(load_ccl_config(), name="lrab_hierarchical_allreduce"),
    )
    n = N_RANKS_SINGLE_USER
    dp_full = DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=n)
    dp_kv = DPPolicy(cube="replicate", pe="row_wise", num_cubes=1, num_pes=n)
    q = ctx.zeros((S_Q_DECODE, H_Q * D_HEAD), dtype=DTYPE, dp=dp_full, name="q")
    k = ctx.zeros((S_KV_PER_RANK * n, H_KV * D_HEAD), dtype=DTYPE, dp=dp_kv, name="k")
    v = ctx.zeros((S_KV_PER_RANK * n, H_KV * D_HEAD), dtype=DTYPE, dp=dp_kv, name="v")
    o = ctx.empty((S_Q_DECODE, H_Q * D_HEAD), dtype=DTYPE, dp=dp_full, name="o")
    ctx.launch(
        "single_user_decode_mesh", attention_mesh_mlo_kernel,
        q, k, v, o,
        S_Q_DECODE, S_KV_PER_RANK, H_Q, H_KV, D_HEAD, n,
    )


def _bench_fn_multi_user_prefill(ctx):
    configure_sfr_intercube_multisip(
        ctx.engine, ctx.spec,
        resolve_algorithm_config(load_ccl_config(), name="lrab_hierarchical_allreduce"),
    )
    n = N_RANKS_MULTI_USER
    dp_full = DPPolicy(cube="replicate", pe="replicate", num_cubes=n, num_pes=8)
    dp_kv = DPPolicy(cube="row_wise", pe="replicate", num_cubes=n, num_pes=8)
    q = ctx.zeros((S_Q_PREFILL, H_Q * D_HEAD), dtype=DTYPE, dp=dp_full, name="q")
    k = ctx.zeros((S_KV_PER_RANK * n, H_KV * D_HEAD), dtype=DTYPE, dp=dp_kv, name="k")
    v = ctx.zeros((S_KV_PER_RANK * n, H_KV * D_HEAD), dtype=DTYPE, dp=dp_kv, name="v")
    o = ctx.empty((S_Q_PREFILL, H_Q * D_HEAD), dtype=DTYPE, dp=dp_full, name="o")
    ctx.launch(
        "multi_user_prefill_mesh", attention_mesh_kv_kernel,
        q, k, v, o,
        S_Q_PREFILL, S_KV_PER_RANK, H_Q, H_KV, D_HEAD, n,
        _auto_dim_remap=False,
    )


def _bench_fn_multi_user_decode(ctx):
    configure_sfr_intercube_multisip(
        ctx.engine, ctx.spec,
        resolve_algorithm_config(load_ccl_config(), name="lrab_hierarchical_allreduce"),
    )
    n = N_RANKS_MULTI_USER
    dp_full = DPPolicy(cube="replicate", pe="replicate", num_cubes=n, num_pes=8)
    dp_kv = DPPolicy(cube="row_wise", pe="replicate", num_cubes=n, num_pes=8)
    q = ctx.zeros((S_Q_DECODE, H_Q * D_HEAD), dtype=DTYPE, dp=dp_full, name="q")
    k = ctx.zeros((S_KV_PER_RANK * n, H_KV * D_HEAD), dtype=DTYPE, dp=dp_kv, name="k")
    v = ctx.zeros((S_KV_PER_RANK * n, H_KV * D_HEAD), dtype=DTYPE, dp=dp_kv, name="v")
    o = ctx.empty((S_Q_DECODE, H_Q * D_HEAD), dtype=DTYPE, dp=dp_full, name="o")
    ctx.launch(
        "multi_user_decode_mesh", attention_mesh_mlo_kernel,
        q, k, v, o,
        S_Q_DECODE, S_KV_PER_RANK, H_Q, H_KV, D_HEAD, n,
        _auto_dim_remap=False,
    )


# ── Tests ────────────────────────────────────────────────────────


def test_single_user_prefill_through_engine():
    exc, result, engine = _run_panel(_bench_fn_single_user_prefill)
    _assert_ok("single_user_prefill", exc, result, engine)


def test_single_user_decode_through_engine():
    exc, result, engine = _run_panel(_bench_fn_single_user_decode)
    _assert_ok("single_user_decode", exc, result, engine)


def test_multi_user_prefill_through_engine():
    exc, result, engine = _run_panel(_bench_fn_multi_user_prefill)
    _assert_ok("multi_user_prefill", exc, result, engine)


def test_multi_user_decode_through_engine():
    exc, result, engine = _run_panel(_bench_fn_multi_user_decode)
    _assert_ok("multi_user_decode", exc, result, engine)