"""Short-context GQA kernel tests — unified A1/A2/A4/B (ADR-0070). Both ``_gqa_attention_prefill_short.py`` and ``_gqa_attention_decode_short.py`` are single unified kernels selected at launch via ``kv_per_cube ∈ {1, 2, 4, 8}``: Mode kv_per_cube C group_size ---- ----------- ------- ---------- A1 1 h_kv P (=8) A2 2 h_kv/2 P/2 (=4) A4 4 h_kv/4 P/4 (=2) B 8 1 1 Tests are mode-parametrized over the four (kv_per_cube, C) tuples. """ from __future__ import annotations from pathlib import Path import pytest from kernbench.ccl.install import load_ccl_config, resolve_algorithm_config from kernbench.ccl.sfr_config import configure_sfr_intercube_multisip 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" TILE_S_KV = 1024 P = 8 H_KV = 8 # (kv_per_cube, C) for the four modes. MODES = [ pytest.param(1, 8, id="A1"), pytest.param(2, 4, id="A2"), pytest.param(4, 2, id="A4"), pytest.param(8, 1, id="B"), ] 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) # ── Prefill ────────────────────────────────────────────────────────── def _run_prefill(*, kv_per_cube: int, C: int, T_q: int, S_kv: int, h_q: int = 8): """Run the unified prefill kernel in the given mode.""" from kernbench.benches.gqa_helpers.short_ctx._gqa_attention_prefill_short import ( _validate_config as _validate_prefill_config, gqa_attention_prefill_short_kernel, ) _validate_prefill_config(kv_per_cube=kv_per_cube, T_q=T_q, P=P, C=C, h_q=h_q, h_kv=H_KV, S_kv=S_kv) n_tiles = S_kv // TILE_S_KV Q_ROWS = kv_per_cube * T_q Q_COLS = (h_q * D_HEAD) // kv_per_cube topo = resolve_topology(str(TOPOLOGY_DEFAULT)) def _bench_fn(ctx): configure_sfr_intercube_multisip(ctx.engine, ctx.spec, _ccl_cfg()) dp_q = DPPolicy(cube="column_wise", pe="replicate", num_cubes=C, num_pes=P) dp_kv = DPPolicy(cube="row_wise", pe="replicate", num_cubes=C, num_pes=P) dp_o = DPPolicy(cube="column_wise", pe="replicate", num_cubes=C, num_pes=P) q = ctx.zeros((Q_ROWS, Q_COLS), dtype=DTYPE, dp=dp_q, name=f"q_pre_kv{kv_per_cube}") k = ctx.zeros((H_KV * n_tiles * D_HEAD, TILE_S_KV), dtype=DTYPE, dp=dp_kv, name=f"k_pre_kv{kv_per_cube}") v = ctx.zeros((H_KV * S_kv, D_HEAD), dtype=DTYPE, dp=dp_kv, name=f"v_pre_kv{kv_per_cube}") o = ctx.empty((Q_ROWS, Q_COLS), dtype=DTYPE, dp=dp_o, name=f"o_pre_kv{kv_per_cube}") ctx.launch( f"gqa_prefill_kv{kv_per_cube}", gqa_attention_prefill_short_kernel, q, k, v, o, T_q, S_kv, h_q, H_KV, D_HEAD, C, P, kv_per_cube, _auto_dim_remap=False, ) return run_bench( topology=topo, bench_fn=_bench_fn, device=resolve_device(None), engine_factory=_engine_factory, ) @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_prefill_smoke(kv_per_cube, C): """All four prefill modes complete on a single-tile mini config.""" r = _run_prefill(kv_per_cube=kv_per_cube, C=C, T_q=8, S_kv=1024) assert r.completion.ok, ( f"prefill kv_per_cube={kv_per_cube}: {r.completion}" ) @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_prefill_qtile_split_dma_writes(kv_per_cube, C): """Every PE in every active group stores its q-tile output: dma_writes = group_size · kv_per_cube · C.""" r = _run_prefill(kv_per_cube=kv_per_cube, C=C, T_q=8, S_kv=1024) assert r.completion.ok group_size = P // kv_per_cube expected = group_size * kv_per_cube * C n_writes = _count(r.engine.op_log, "dma_write") assert n_writes == expected, ( f"prefill kv_per_cube={kv_per_cube}: expected {expected} " f"dma_writes; got {n_writes}" ) @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_prefill_kv_hbm_read_collapsed(kv_per_cube, C): """IPCQ KV broadcast collapses HBM K/V reads to one per group per tile: dma_reads = (P · C) Q-reads + (kv_per_cube · 2 · n_tiles · C) KV-reads. """ r = _run_prefill(kv_per_cube=kv_per_cube, C=C, T_q=8, S_kv=1024) assert r.completion.ok n_tiles = 1024 // TILE_S_KV expected = (P * C) + (kv_per_cube * 2 * n_tiles * C) n_reads = _count(r.engine.op_log, "dma_read") assert n_reads == expected, ( f"prefill kv_per_cube={kv_per_cube}: expected {expected} dma_reads " f"(Q + IPCQ-broadcasted KV); got {n_reads}" ) def test_prefill_no_ring_KV_traffic(): """Short prefill never uses Ring KV — no inter-CUBE E/W IPCQ.""" r = _run_prefill(kv_per_cube=2, C=4, T_q=8, S_kv=1024) assert r.completion.ok inter_cube_ipcq = sum( 1 for rec in r.engine.op_log if rec.op_name == "ipcq_copy" and rec.params.get("direction") in ("E", "W") ) assert inter_cube_ipcq == 0, ( f"short prefill must have no inter-CUBE E/W IPCQ; " f"got {inter_cube_ipcq}" ) @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_prefill_multitile_scaling(kv_per_cube, C): """Prefill dma_read scales linearly with n_tiles in every mode. Per-mode formula (IPCQ broadcast: cube's group-root PE reads K/V): dma_reads = P·C + 2·kv_per_cube·C·n_tiles = (Q: 1 per PE) + (K + V: 1 per group per tile) """ for S_kv in (1024, 2048, 4096): r = _run_prefill(kv_per_cube=kv_per_cube, C=C, T_q=8, S_kv=S_kv) assert r.completion.ok, f"kv={kv_per_cube} S_kv={S_kv}" n_tiles = S_kv // TILE_S_KV expected = (P * C) + (2 * kv_per_cube * C * n_tiles) n_reads = _count(r.engine.op_log, "dma_read") assert n_reads == expected, ( f"prefill kv={kv_per_cube} n_tiles={n_tiles}: expected " f"{expected} dma_reads; got {n_reads}" ) # ── Decode ─────────────────────────────────────────────────────────── def _run_decode(*, kv_per_cube: int, C: int, S_kv: int, h_q: int = 8): """Run the unified decode kernel in the given mode.""" from kernbench.benches.gqa_helpers.short_ctx._gqa_attention_decode_short import ( _validate_config as _validate_decode_config, gqa_attention_decode_short_kernel, ) T_q = 1 _validate_decode_config(kv_per_cube=kv_per_cube, T_q=T_q, P=P, C=C, h_q=h_q, h_kv=H_KV, S_kv=S_kv) Q_ROWS = kv_per_cube * T_q Q_COLS = (h_q * D_HEAD) // kv_per_cube # K total elements = h_kv · S_kv · d_head; mode-invariant deploy. k_rows = (H_KV * S_kv * D_HEAD) // TILE_S_KV topo = resolve_topology(str(TOPOLOGY_DEFAULT)) def _bench_fn(ctx): configure_sfr_intercube_multisip(ctx.engine, ctx.spec, _ccl_cfg()) dp_q = DPPolicy(cube="column_wise", pe="replicate", num_cubes=C, num_pes=P) dp_kv = DPPolicy(cube="row_wise", pe="row_wise", num_cubes=C, num_pes=P) dp_o = DPPolicy(cube="column_wise", pe="replicate", num_cubes=C, num_pes=P) q = ctx.zeros((Q_ROWS, Q_COLS), dtype=DTYPE, dp=dp_q, name=f"q_dec_kv{kv_per_cube}") k = ctx.zeros((k_rows, TILE_S_KV), dtype=DTYPE, dp=dp_kv, name=f"k_dec_kv{kv_per_cube}") v = ctx.zeros((H_KV * S_kv, D_HEAD), dtype=DTYPE, dp=dp_kv, name=f"v_dec_kv{kv_per_cube}") o = ctx.empty((Q_ROWS, Q_COLS), dtype=DTYPE, dp=dp_o, name=f"o_dec_kv{kv_per_cube}") ctx.launch( f"gqa_decode_kv{kv_per_cube}", gqa_attention_decode_short_kernel, q, k, v, o, T_q, S_kv, h_q, H_KV, D_HEAD, C, P, kv_per_cube, _auto_dim_remap=False, ) return run_bench( topology=topo, bench_fn=_bench_fn, device=resolve_device(None), engine_factory=_engine_factory, ) @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_decode_smoke(kv_per_cube, C): """All four decode modes complete at S_kv = 8K (min multi-tile-aligned config that satisfies every mode's group-size constraint).""" r = _run_decode(kv_per_cube=kv_per_cube, C=C, S_kv=8192) assert r.completion.ok, ( f"decode kv_per_cube={kv_per_cube}: {r.completion}" ) @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_decode_chain_reduce_store_count(kv_per_cube, C): """Decode chain-reduce: only group root (PE 0 per group) stores — dma_writes = kv_per_cube · C (one per group root × cubes).""" r = _run_decode(kv_per_cube=kv_per_cube, C=C, S_kv=8192) assert r.completion.ok expected = kv_per_cube * C n_writes = _count(r.engine.op_log, "dma_write") assert n_writes == expected, ( f"decode kv_per_cube={kv_per_cube}: expected {expected} dma_writes " f"(1 per group root); got {n_writes}" ) @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_decode_multitile_per_pe(kv_per_cube, C): """Decode multi-tile sweep path exercised in every mode. S_kv is chosen so each PE owns 2 tiles (n_tiles_per_pe == 2), forcing the post-tile-0 sweep loop to run. """ group_size = P // kv_per_cube S_kv = group_size * 2 * TILE_S_KV r = _run_decode(kv_per_cube=kv_per_cube, C=C, S_kv=S_kv) assert r.completion.ok, ( f"decode kv={kv_per_cube} S_kv={S_kv}: {r.completion}" ) # ── ADR-0011 D-VA1 contract regression tests ─────────────────────── def _per_cube_dma_busy(op_log): """Sum of dma_read t_end-t_start grouped by cube index.""" from collections import defaultdict busy = defaultdict(float) for rec in op_log: if rec.op_name != "dma_read": continue cid = rec.component_id or "" for part in cid.split("."): if part.startswith("cube"): try: busy[int(part[4:])] += rec.t_end - rec.t_start except ValueError: pass break return dict(busy) def _per_cube_large_read_addrs(op_log, *, min_nbytes: int = 1024): """Group dma_read src_addrs (>= min_nbytes) by issuing cube.""" from collections import defaultdict per_cube: dict[int, set[int]] = defaultdict(set) for rec in op_log: if rec.op_name != "dma_read": continue if rec.params.get("nbytes", 0) < min_nbytes: continue cid = rec.component_id or "" for part in cid.split("."): if part.startswith("cube"): try: per_cube[int(part[4:])].add(rec.params["src_addr"]) except ValueError: pass break return per_cube def _assert_cubes_disjoint(per_cube_addrs, label): seen = sorted(per_cube_addrs.items()) for i, (ci, ai) in enumerate(seen): for cj, aj in seen[i + 1:]: assert ai.isdisjoint(aj), ( f"{label}: cube{ci} and cube{cj} share {len(ai & aj)} " f"dma_read src_addrs; cubes must target disjoint HBM regions." ) def _assert_cube_balanced(busy, label, max_ratio=1.1): if len(busy) <= 1: return # single-cube modes auto-pass vals = list(busy.values()) ratio = max(vals) / min(vals) if min(vals) > 0 else 0 assert ratio < max_ratio, ( f"{label}: per-cube DMA_READ unbalanced: max/min = {ratio:.2f}× " f"(expected < {max_ratio}×). Per-cube busy (μs): " f"{[f'cube{c}={v/1000:.1f}' for c,v in sorted(busy.items())]}" ) @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_decode_per_cube_disjoint_src_addrs(kv_per_cube, C): """Decode: cubes must read from disjoint HBM regions (per ADR-0011 D-VA1, kernel computes cube/PE shard offset from program_id). Regression guard: pre-fix all 64 PEs read offset 0 → all cubes share the same src_addrs. """ r = _run_decode(kv_per_cube=kv_per_cube, C=C, S_kv=8192) assert r.completion.ok per_cube = _per_cube_large_read_addrs(r.engine.op_log) assert len(per_cube) == C, ( f"decode kv={kv_per_cube}: expected {C} cubes; got {len(per_cube)}" ) _assert_cubes_disjoint(per_cube, f"decode kv={kv_per_cube}") @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_decode_per_cube_dma_balanced(kv_per_cube, C): """Decode: per-cube DMA_READ busy must be ~equal (cubes operate on independent local HBM). max/min < 1.1× for multi-cube modes. Regression guard: pre-fix 11.5× ratio (cross-cube traffic to cube0). """ r = _run_decode(kv_per_cube=kv_per_cube, C=C, S_kv=8192) assert r.completion.ok busy = _per_cube_dma_busy(r.engine.op_log) assert len(busy) == C, ( f"decode kv={kv_per_cube}: expected {C} cubes; got {sorted(busy.keys())}" ) _assert_cube_balanced(busy, f"decode kv={kv_per_cube}") @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_prefill_per_cube_disjoint_src_addrs(kv_per_cube, C): """Prefill: cubes must read from disjoint HBM regions.""" r = _run_prefill(kv_per_cube=kv_per_cube, C=C, T_q=8, S_kv=1024) assert r.completion.ok per_cube = _per_cube_large_read_addrs(r.engine.op_log) assert len(per_cube) == C, ( f"prefill kv={kv_per_cube}: expected {C} cubes; got {len(per_cube)}" ) _assert_cubes_disjoint(per_cube, f"prefill kv={kv_per_cube}") @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_prefill_per_cube_dma_balanced(kv_per_cube, C): """Prefill: per-cube DMA_READ busy must be ~equal (cube-parallel).""" r = _run_prefill(kv_per_cube=kv_per_cube, C=C, T_q=8, S_kv=1024) assert r.completion.ok busy = _per_cube_dma_busy(r.engine.op_log) assert len(busy) == C, ( f"prefill kv={kv_per_cube}: expected {C} cubes; got {sorted(busy.keys())}" ) _assert_cube_balanced(busy, f"prefill kv={kv_per_cube}") # ── Composite (second-level) variant smoke tests ──────────────────── def _run_prefill_composite(*, kv_per_cube: int, C: int, T_q: int, S_kv: int, h_q: int = 8): """Same helper as _run_prefill but for the composite-GEMM kernel.""" from kernbench.benches.gqa_helpers.short_ctx._gqa_attention_prefill_short_composite import ( _validate_config as _validate_prefill_cmp_config, gqa_attention_prefill_short_composite_kernel, ) _validate_prefill_cmp_config(kv_per_cube=kv_per_cube, T_q=T_q, P=P, C=C, h_q=h_q, h_kv=H_KV, S_kv=S_kv) n_tiles = S_kv // TILE_S_KV Q_ROWS = kv_per_cube * T_q Q_COLS = (h_q * D_HEAD) // kv_per_cube topo = resolve_topology(str(TOPOLOGY_DEFAULT)) def _bench_fn(ctx): configure_sfr_intercube_multisip(ctx.engine, ctx.spec, _ccl_cfg()) dp_q = DPPolicy(cube="column_wise", pe="replicate", num_cubes=C, num_pes=P) dp_kv = DPPolicy(cube="row_wise", pe="replicate", num_cubes=C, num_pes=P) dp_o = DPPolicy(cube="column_wise", pe="replicate", num_cubes=C, num_pes=P) q = ctx.zeros((Q_ROWS, Q_COLS), dtype=DTYPE, dp=dp_q, name=f"q_pre_cmp_kv{kv_per_cube}") k = ctx.zeros((H_KV * n_tiles * D_HEAD, TILE_S_KV), dtype=DTYPE, dp=dp_kv, name=f"k_pre_cmp_kv{kv_per_cube}") v = ctx.zeros((H_KV * S_kv, D_HEAD), dtype=DTYPE, dp=dp_kv, name=f"v_pre_cmp_kv{kv_per_cube}") o = ctx.empty((Q_ROWS, Q_COLS), dtype=DTYPE, dp=dp_o, name=f"o_pre_cmp_kv{kv_per_cube}") ctx.launch( f"gqa_prefill_cmp_kv{kv_per_cube}", gqa_attention_prefill_short_composite_kernel, q, k, v, o, T_q, S_kv, h_q, H_KV, D_HEAD, C, P, kv_per_cube, _auto_dim_remap=False, ) return run_bench( topology=topo, bench_fn=_bench_fn, device=resolve_device(None), engine_factory=_engine_factory, ) def _run_decode_composite(*, kv_per_cube: int, C: int, S_kv: int, h_q: int = 8): """Same helper as _run_decode but for the composite-GEMM kernel.""" from kernbench.benches.gqa_helpers.short_ctx._gqa_attention_decode_short_composite import ( _validate_config as _validate_decode_cmp_config, gqa_attention_decode_short_composite_kernel, ) T_q = 1 _validate_decode_cmp_config(kv_per_cube=kv_per_cube, T_q=T_q, P=P, C=C, h_q=h_q, h_kv=H_KV, S_kv=S_kv) Q_ROWS = kv_per_cube * T_q Q_COLS = (h_q * D_HEAD) // kv_per_cube k_rows = (H_KV * S_kv * D_HEAD) // TILE_S_KV topo = resolve_topology(str(TOPOLOGY_DEFAULT)) def _bench_fn(ctx): configure_sfr_intercube_multisip(ctx.engine, ctx.spec, _ccl_cfg()) dp_q = DPPolicy(cube="column_wise", pe="replicate", num_cubes=C, num_pes=P) dp_kv = DPPolicy(cube="row_wise", pe="row_wise", num_cubes=C, num_pes=P) dp_o = DPPolicy(cube="column_wise", pe="replicate", num_cubes=C, num_pes=P) q = ctx.zeros((Q_ROWS, Q_COLS), dtype=DTYPE, dp=dp_q, name=f"q_dec_cmp_kv{kv_per_cube}") k = ctx.zeros((k_rows, TILE_S_KV), dtype=DTYPE, dp=dp_kv, name=f"k_dec_cmp_kv{kv_per_cube}") v = ctx.zeros((H_KV * S_kv, D_HEAD), dtype=DTYPE, dp=dp_kv, name=f"v_dec_cmp_kv{kv_per_cube}") o = ctx.empty((Q_ROWS, Q_COLS), dtype=DTYPE, dp=dp_o, name=f"o_dec_cmp_kv{kv_per_cube}") ctx.launch( f"gqa_decode_cmp_kv{kv_per_cube}", gqa_attention_decode_short_composite_kernel, q, k, v, o, T_q, S_kv, h_q, H_KV, D_HEAD, C, P, kv_per_cube, _auto_dim_remap=False, ) return run_bench( topology=topo, bench_fn=_bench_fn, device=resolve_device(None), engine_factory=_engine_factory, ) def _run_prefill_composite_fused(*, kv_per_cube: int, C: int, T_q: int, S_kv: int, h_q: int = 8): """Variant (3): GEMM-only composite + softmax_merge prologue fusion.""" from kernbench.benches.gqa_helpers.short_ctx._gqa_attention_prefill_short_composite_fused import ( _validate_config as _validate_prefill_fuse_config, gqa_attention_prefill_short_composite_fused_kernel, ) _validate_prefill_fuse_config(kv_per_cube=kv_per_cube, T_q=T_q, P=P, C=C, h_q=h_q, h_kv=H_KV, S_kv=S_kv) n_tiles = S_kv // TILE_S_KV Q_ROWS = kv_per_cube * T_q Q_COLS = (h_q * D_HEAD) // kv_per_cube topo = resolve_topology(str(TOPOLOGY_DEFAULT)) def _bench_fn(ctx): configure_sfr_intercube_multisip(ctx.engine, ctx.spec, _ccl_cfg()) dp_q = DPPolicy(cube="column_wise", pe="replicate", num_cubes=C, num_pes=P) dp_kv = DPPolicy(cube="row_wise", pe="replicate", num_cubes=C, num_pes=P) dp_o = DPPolicy(cube="column_wise", pe="replicate", num_cubes=C, num_pes=P) q = ctx.zeros((Q_ROWS, Q_COLS), dtype=DTYPE, dp=dp_q, name=f"q_pre_fuse_kv{kv_per_cube}") k = ctx.zeros((H_KV * n_tiles * D_HEAD, TILE_S_KV), dtype=DTYPE, dp=dp_kv, name=f"k_pre_fuse_kv{kv_per_cube}") v = ctx.zeros((H_KV * S_kv, D_HEAD), dtype=DTYPE, dp=dp_kv, name=f"v_pre_fuse_kv{kv_per_cube}") o = ctx.empty((Q_ROWS, Q_COLS), dtype=DTYPE, dp=dp_o, name=f"o_pre_fuse_kv{kv_per_cube}") ctx.launch( f"gqa_prefill_fuse_kv{kv_per_cube}", gqa_attention_prefill_short_composite_fused_kernel, q, k, v, o, T_q, S_kv, h_q, H_KV, D_HEAD, C, P, kv_per_cube, _auto_dim_remap=False, ) return run_bench( topology=topo, bench_fn=_bench_fn, device=resolve_device(None), engine_factory=_engine_factory, ) def _run_decode_composite_fused(*, kv_per_cube: int, C: int, S_kv: int, h_q: int = 8): """Variant (3): GEMM-only composite + softmax_merge prologue fusion.""" from kernbench.benches.gqa_helpers.short_ctx._gqa_attention_decode_short_composite_fused import ( _validate_config as _validate_decode_fuse_config, gqa_attention_decode_short_composite_fused_kernel, ) T_q = 1 _validate_decode_fuse_config(kv_per_cube=kv_per_cube, T_q=T_q, P=P, C=C, h_q=h_q, h_kv=H_KV, S_kv=S_kv) Q_ROWS = kv_per_cube * T_q Q_COLS = (h_q * D_HEAD) // kv_per_cube k_rows = (H_KV * S_kv * D_HEAD) // TILE_S_KV topo = resolve_topology(str(TOPOLOGY_DEFAULT)) def _bench_fn(ctx): configure_sfr_intercube_multisip(ctx.engine, ctx.spec, _ccl_cfg()) dp_q = DPPolicy(cube="column_wise", pe="replicate", num_cubes=C, num_pes=P) dp_kv = DPPolicy(cube="row_wise", pe="row_wise", num_cubes=C, num_pes=P) dp_o = DPPolicy(cube="column_wise", pe="replicate", num_cubes=C, num_pes=P) q = ctx.zeros((Q_ROWS, Q_COLS), dtype=DTYPE, dp=dp_q, name=f"q_dec_fuse_kv{kv_per_cube}") k = ctx.zeros((k_rows, TILE_S_KV), dtype=DTYPE, dp=dp_kv, name=f"k_dec_fuse_kv{kv_per_cube}") v = ctx.zeros((H_KV * S_kv, D_HEAD), dtype=DTYPE, dp=dp_kv, name=f"v_dec_fuse_kv{kv_per_cube}") o = ctx.empty((Q_ROWS, Q_COLS), dtype=DTYPE, dp=dp_o, name=f"o_dec_fuse_kv{kv_per_cube}") ctx.launch( f"gqa_decode_fuse_kv{kv_per_cube}", gqa_attention_decode_short_composite_fused_kernel, q, k, v, o, T_q, S_kv, h_q, H_KV, D_HEAD, C, P, kv_per_cube, _auto_dim_remap=False, ) return run_bench( topology=topo, bench_fn=_bench_fn, device=resolve_device(None), engine_factory=_engine_factory, ) @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_prefill_composite_smoke(kv_per_cube, C): """Variant (2) GEMM-only composite prefill — all 4 modes.""" r = _run_prefill_composite(kv_per_cube=kv_per_cube, C=C, T_q=8, S_kv=1024) assert r.completion.ok, ( f"prefill-composite kv_per_cube={kv_per_cube}: {r.completion}" ) @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_decode_composite_smoke(kv_per_cube, C): """Variant (2) GEMM-only composite decode — all 4 modes.""" r = _run_decode_composite(kv_per_cube=kv_per_cube, C=C, S_kv=8192) assert r.completion.ok, ( f"decode-composite kv_per_cube={kv_per_cube}: {r.completion}" ) @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_decode_composite_fused_smoke(kv_per_cube, C): """Variant (3) composite + softmax_merge fused decode — all 4 modes.""" r = _run_decode_composite_fused(kv_per_cube=kv_per_cube, C=C, S_kv=8192) assert r.completion.ok, ( f"decode-composite-fused kv_per_cube={kv_per_cube}: {r.completion}" ) @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_decode_composite_fused_multitile(kv_per_cube, C): """Fused decode with n_tiles_per_pe == 2 in EVERY mode, so the tile-1+ fused composite loop actually runs. The smoke test at S_kv=8192 gives A1 (group_size=8) n_tiles_per_pe=1, which never enters the fused path. S_kv = group_size·2·TILE_S_KV forces 2 tiles per PE for all modes (mirrors test_decode_multitile_per_pe).""" group_size = P // kv_per_cube S_kv = group_size * 2 * TILE_S_KV r = _run_decode_composite_fused(kv_per_cube=kv_per_cube, C=C, S_kv=S_kv) assert r.completion.ok, ( f"decode-composite-fused-multitile kv={kv_per_cube} S_kv={S_kv}: " f"{r.completion}" ) @pytest.mark.parametrize("kv_per_cube,C", MODES) def test_prefill_composite_fused_smoke(kv_per_cube, C): """Variant (3) composite + softmax_merge fused prefill — all 4 modes. S_kv=2048 (n_tiles=2) so the tile-1+ fused composite loop actually runs; S_kv=1024 gives n_tiles=1 and never enters the fused path. Multi-cube (A1/A2/A4) works now that recv'd K/V slots are pinned.""" r = _run_prefill_composite_fused(kv_per_cube=kv_per_cube, C=C, T_q=8, S_kv=2048) assert r.completion.ok, ( f"prefill-composite-fused kv_per_cube={kv_per_cube}: {r.completion}" )