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kernbench2/tests/attention/test_gqa_scoped_writeback.py
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mukesh 7fad0371c5 gqa: tile-granular Ring KV (P3c) + rename to gqa_attention_* + ADR-0060/62/63/64 → Accepted
Three logically distinct changes, bundled for atomic test green:

1. **P3c — prefill_long tile-granular Ring KV** (ADR-0060 §5.5.1 amendment).
   Convert the ring from slice-granular (one full ``(d_head, S_local)``
   KV slice per step) to tile-granular (``n_tiles`` tiles of
   ``TILE_S_KV`` per step). Nested loop with outer tile, inner ring step:
   each tile propagates through all C ring positions before the next
   tile starts, so IPCQ in-flight depth stays at 1 per direction.
   Bootstrap at ``(t=0, k=0)`` outside the scratch_scope establishes the
   persistent ``(m, ℓ, O)``; every other iteration scope-wraps + persists
   via ``copy_to``. Per-rank persistent scratch shrinks to ~1 KB; per-tile
   scope bounded by TILE_S_KV regardless of S_local. Headline:
   prefill_long now completes at S_kv=128K (previously overflowed).
   New: ``tests/attention/test_gqa_prefill_long_tile_ring.py``
   (3 tests — ceiling-lift + tile-granular ipcq_copy count +
   per-CUBE distributed output regression guard).

2. **Rename ``gqa_*`` → ``gqa_attention_*``** across kernel files,
   function names, and importers. The "attention" name makes the role
   explicit (GQA is grouped-query attention) and matches upstream Triton
   FlashAttention naming conventions. Renames:
     _gqa_decode_long.py        -> _gqa_attention_decode_long.py
     _gqa_decode_short.py       -> _gqa_attention_decode_short.py
     _gqa_prefill_long.py       -> _gqa_attention_prefill_long.py
     _gqa_prefill_short.py      -> _gqa_attention_prefill_short.py
   And function names ``gqa_<phase>_<context>_kernel`` →
   ``gqa_attention_<phase>_<context>_kernel``. Updated 1 bench file
   (milestone_gqa_headline.py) and 10 test files.

3. **ADR-0060 / 0062 / 0063 / 0064: Proposed → Accepted**.
   All four are reflected in production code and covered by tests:
   - ADR-0060 (GQA fused attention): 4 kernels deployed; §5.5.1
     amendment added for the tile-granular Ring KV introduced by P3c
     (EN + KO mirror).
   - ADR-0062 (lazy tl.load): LoadFuture + _await_pending live in
     tl_context.py.
   - ADR-0063 (tl.scratch_scope + tl.copy_to): used in every chain
     reduce + tile sweep + ring step. EN-only previously; KO
     translation authored as part of this commit (CLAUDE.md
     bidirectional rule).
   - ADR-0064 (per-op-type CPU issue cost): cpu_issue_cost.py +
     issue_cost_table wiring in tl_context.py (Phase E).
   Files git mv'd from docs/adr-proposed/ to docs/adr/ (EN) and
   docs/adr-ko/ (KO). ADR-0061 (tl.broadcast) stays Proposed — no
   implementation; documented as optional convenience primitive in
   the ADR itself.

Tests: 88/88 focused regression green
(tests/attention/ + Phase E + TL discipline).
ADR pair verification: ``python tools/verify_adr_lang_pairs.py`` OK.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-10 16:20:00 -07:00

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"""Phase 1 spec test for Phase C: scratch_scope + tl.copy_to discipline
in the GQA kernels (ADR-0060 §5.2 / §5.5 + ADR-0063 §D3 / §D3.1).
ADR-0060 §5.2 (decode pseudocode line 75 / §5.5 (prefill pseudocode line
96) both wrap per-tile / per-ring-step intermediates in
``with tl.scratch_scope():`` and persist the merged running ``(m, , O)``
to a persistent arena allocated outside the scope. The original ADR-0063
§D3 specifies the two-arena pattern; §D3.1 specifies the
``tl.copy_to(dst, src)`` writeback primitive used to persist scoped
results.
Currently neither kernel uses ``scratch_scope`` or ``copy_to``; their
chain-merge / ring-merge bodies allocate every intermediate from the
bump cursor and never recycle. Result: op_log has 0 ``copy`` entries.
After Phase 2: each per-tile / per-step merge writes the new running
``(m, , O)`` via ``copy_to`` to the persistent arena. Per merge step
→ 3 copy ops (m, , O).
Phase 1 (this commit): tests only — production code lands in Phase 2.
"""
from __future__ import annotations
from pathlib import Path
from kernbench.benches._gqa_attention_decode_long import gqa_attention_decode_long_kernel # noqa: F401
from kernbench.benches._gqa_attention_prefill_long import gqa_attention_prefill_long_kernel # noqa: F401
from kernbench.ccl.install import load_ccl_config, resolve_algorithm_config
from kernbench.ccl.sfr_config import (
configure_sfr_intercube_multisip,
configure_sfr_intercube_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_DEFAULT = Path(__file__).resolve().parents[2] / "topology.yaml"
D_HEAD = 64
DTYPE = "f16"
def _ccl_cfg():
return resolve_algorithm_config(
load_ccl_config(), name="lrab_hierarchical_allreduce",
)
def _engine_factory(t, d):
return GraphEngine(getattr(t, "topology_obj", t), enable_data=True)
def _count(op_log, name: str) -> int:
return sum(1 for r in op_log if r.op_name == name)
# ── Decode chain merges must use scratch_scope + copy_to ─────────────
def _run_decode_sp(*, h_q: int, h_kv: int, P: int, S_kv: int):
"""Single-CUBE SP decode (C=1, P PEs along intra-cube chain)."""
topo = resolve_topology(str(TOPOLOGY_DEFAULT))
def _bench_fn(ctx):
configure_sfr_intercube_multisip(ctx.engine, ctx.spec, _ccl_cfg())
dp_full = DPPolicy(cube="replicate", pe="replicate",
num_cubes=1, num_pes=P)
dp_kv = DPPolicy(cube="replicate", pe="row_wise",
num_cubes=1, num_pes=P)
q = ctx.zeros((1, h_q * D_HEAD),
dtype=DTYPE, dp=dp_full, name=f"q_sc_{P}")
k = ctx.zeros((S_kv, h_kv * D_HEAD),
dtype=DTYPE, dp=dp_kv, name=f"k_sc_{P}")
v = ctx.zeros((S_kv, h_kv * D_HEAD),
dtype=DTYPE, dp=dp_kv, name=f"v_sc_{P}")
o = ctx.empty((1, h_q * D_HEAD),
dtype=DTYPE, dp=dp_full, name=f"o_sc_{P}")
ctx.launch(
f"gqa_decode_scoped_{P}",
gqa_attention_decode_long_kernel,
q, k, v, o,
1, S_kv, h_q, h_kv, D_HEAD,
1, P,
_auto_dim_remap=False,
)
return run_bench(
topology=topo, bench_fn=_bench_fn,
device=resolve_device(None), engine_factory=_engine_factory,
)
def test_decode_chain_merges_emit_copy_to_writeback():
"""ADR-0060 §5.2 + ADR-0063 §D3.1: each chain-merge step must wrap
its intermediates in ``scratch_scope`` and persist the new running
``(m, , O)`` via ``tl.copy_to``.
For (C=1, P=8): 7 intra-cube chain merges × 3 handles (m, , O) per
merge ⇒ 21 ``copy`` entries.
Currently 0 because the kernel never calls ``tl.copy_to``.
"""
result = _run_decode_sp(h_q=8, h_kv=1, P=8, S_kv=64)
assert result.completion.ok, f"decode SP failed: {result.completion}"
n_copy = _count(result.engine.op_log, "copy")
assert n_copy > 0, (
f"decode kernel must emit copy_to writeback per merge step "
f"(ADR-0060 §5.2 + ADR-0063 §D3.1); got 0 ``copy`` entries"
)
# ── Prefill Ring KV merges must use scratch_scope + copy_to ──────────
def _run_prefill_ring(*, T_q: int, S_kv: int, C: int):
"""Head-parallel prefill with Ring KV across C CUBEs."""
topo = resolve_topology(str(TOPOLOGY_DEFAULT))
def _bench_fn(ctx):
configure_sfr_intercube_ring(
ctx.engine, ctx.spec, _ccl_cfg(), ring_size=C,
)
dp_q = DPPolicy(cube="replicate", pe="replicate",
num_cubes=C, num_pes=1)
dp_kv = DPPolicy(cube="row_wise" if C > 1 else "replicate",
pe="replicate", num_cubes=C, num_pes=1)
dp_o = DPPolicy(cube="row_wise" if C > 1 else "replicate",
pe="replicate", num_cubes=C, num_pes=1)
q = ctx.zeros((T_q, D_HEAD),
dtype=DTYPE, dp=dp_q, name=f"q_ring_{C}")
k = ctx.zeros((S_kv, D_HEAD),
dtype=DTYPE, dp=dp_kv, name=f"k_ring_{C}")
v = ctx.zeros((S_kv, D_HEAD),
dtype=DTYPE, dp=dp_kv, name=f"v_ring_{C}")
o = ctx.empty((T_q * C, D_HEAD),
dtype=DTYPE, dp=dp_o, name=f"o_ring_{C}")
ctx.launch(
f"gqa_prefill_scoped_{C}",
gqa_attention_prefill_long_kernel,
q, k, v, o,
T_q, S_kv, D_HEAD, C,
_auto_dim_remap=False,
)
return run_bench(
topology=topo, bench_fn=_bench_fn,
device=resolve_device(None), engine_factory=_engine_factory,
)
def test_prefill_ring_step_merges_emit_copy_to_writeback():
"""ADR-0060 §5.5 + ADR-0063 §D3.1: each Ring KV step's online-softmax
merge must wrap its intermediates in ``scratch_scope`` and persist
the new running ``(m, , O)`` via ``tl.copy_to``.
For C=4: 3 ring-step merges (steps 1..C-1) × 3 handles (m, , O) per
merge ⇒ 9 ``copy`` entries per participating CUBE. Aggregated across
C CUBEs: ⇒ 36 ``copy`` entries.
Currently 0 because the kernel never calls ``tl.copy_to``.
"""
result = _run_prefill_ring(T_q=4, S_kv=16, C=4)
assert result.completion.ok, f"prefill ring failed: {result.completion}"
n_copy = _count(result.engine.op_log, "copy")
assert n_copy > 0, (
f"prefill ring kernel must emit copy_to writeback per merge step "
f"(ADR-0060 §5.5 + ADR-0063 §D3.1); got 0 ``copy`` entries"
)
# ── Scoped kernels still produce the same op_log shape (regression) ──
def test_decode_scoped_still_has_root_only_write():
"""ADR-0060 §A.2 root-only output must hold under the rewrite:
adding scratch_scope + copy_to should not change the reduce
topology; only the per-PE scratch usage. Single PE 0 writes O."""
result = _run_decode_sp(h_q=8, h_kv=1, P=8, S_kv=64)
assert result.completion.ok
n_writes = _count(result.engine.op_log, "dma_write")
assert n_writes == 1, (
f"scoped decode must still produce 1 dma_write (root-only); "
f"got {n_writes}"
)
def test_prefill_scoped_still_has_per_cube_distributed_output():
"""ADR-0060 §5.5 per-CUBE distributed output must hold under the
rewrite: scoped prefill still writes one O slice per CUBE."""
result = _run_prefill_ring(T_q=4, S_kv=16, C=4)
assert result.completion.ok
n_writes = _count(result.engine.op_log, "dma_write")
assert n_writes == 4, (
f"scoped prefill must write one O per CUBE (C=4 → 4 dma_write); "
f"got {n_writes}"
)