add three gqa attention kernel variants for short context length

This commit is contained in:
eusonice
2026-06-24 14:46:55 -07:00
parent a98e93110b
commit 1971ac731c
7 changed files with 575 additions and 58 deletions
@@ -1,4 +1,4 @@
"""GQA decode kernel: short context, attention only, multi-tile per PE.
"""GQA decode kernel: short context, attention only, multi-tile per PE (1).
Unified A1/A2/A4/B decode mapping per ADR-0070 (phase mirror of
``_gqa_attention_prefill_short.py``). Mode selected at launch via
@@ -1,19 +1,22 @@
"""GQA decode kernel composite (second-level) variant.
"""GQA decode kernel: composite GEMM-only variant (2).
Identical mapping to ``_gqa_attention_decode_short.py`` (sequence-shard
+ chain reduce + FA2 head fusion, unified A1/A2/A4/B). Per-tile fusion
follows the ADR-0065 two-composite pattern (cf. decode_opt2):
+ chain reduce + FA2 head fusion, unified A1/A2/A4/B). Difference vs
first-level: the Q·Kᵀ GEMM is issued via ``tl.composite(op="gemm", ...)``
instead of ``tl.dot``.
tile 0 primitives establish (m, , O)
tile k>0 #1 Q·Kᵀ composite → scores (pinned primary-out)
#2 softmax_merge prologue → P (pinned, auto-binds to GEMM a)
+ P·V composite, ``out=O_local``
+ add epilogue folding the result into O_local
This is the GEMM-only tier: Q·Kᵀ is a composite (operands ``a=Q`` /
``b=K_T`` are pinned ``tl.load`` results), while P·V stays a plain
``tl.dot`` and softmax stays a primitive MATH chain no
``softmax_merge`` fusion. Folding the per-tile softmax into a P·V
composite (the ``softmax_merge`` prologue making ``P`` a pinned
primary-out bound to the head GEMM) is variant (3) in
``_gqa_attention_decode_short_composite_fused.py``.
The softmax_merge recipe (tl_recipes.py) folds max/exp/sum into the
composite, eliminating the GEMM/MATH engine bubble that a flat
primitives chain incurs. The intra-group chain reduce after the tile
loop is unchanged.
Three-variant comparison:
(1) without composite : ``_gqa_attention_decode_short.py``
(2) with composite (GEMM-only, no fuse) : this file
(3) with composite + softmax_merge fuse : ``_composite_fused.py``
Shard addressing, layouts, and caller contract are identical to the
first-level decode kernel (ADR-0011 D-VA1).
@@ -112,23 +115,17 @@ def gqa_attention_decode_short_composite_kernel(
Q = tl.load(q_base, shape=(G, d_head), dtype="f16")
# ── Tile 0: establish running (m, , O) with primitives ──────────
# (Reference: decode_opt2 — running state is set up with tl.dot/MATH
# primitives, not composite. Recipe-driven composite enters in tile 1+.)
# ── Tile 0: establish (m, , O) — Q·Kᵀ composite, softmax + P·V primitives ──
K_T = tl.load(k_shard_base, shape=(d_head, TILE_S_KV), dtype="f16")
V = tl.load(v_shard_base, shape=(TILE_S_KV, d_head), dtype="f16")
scores = tl.dot(Q, K_T)
scores = tl.composite(op="gemm", a=Q, b=K_T)
m_local = tl.max(scores, axis=-1)
centered = scores - m_local
exp_scores = tl.exp(centered)
l_local = tl.sum(exp_scores, axis=-1)
O_local = tl.dot(exp_scores, V)
O_local = tl.dot(exp_scores, V) # P·V stays primitive in the GEMM-only tier
# ── Tiles 1..n_tiles_per_pe-1: two composites per tile ──────────
# #1 Q·Kᵀ composite → scores (pinned primary-out, fed into #2).
# #2 softmax_merge prologue + P·V GEMM + add epilogue, all in one
# composite: updates (m, ) in place, computes P, runs P·V with
# pinned auto-bind, and folds the result into O_local.
# ── Tiles 1..n_tiles_per_pe-1: Q·Kᵀ composite + softmax + P·V tl.dot ──
for tile_idx in range(1, n_tiles_per_pe):
with tl.scratch_scope():
K_T_t = tl.load(k_shard_base + tile_idx * K_TILE_BYTES,
@@ -136,12 +133,17 @@ def gqa_attention_decode_short_composite_kernel(
V_t = tl.load(v_shard_base + tile_idx * TILE_S_KV * KV_ROW_BYTES,
shape=(TILE_S_KV, d_head), dtype="f16")
scores_t = tl.composite(op="gemm", a=Q, b=K_T_t)
tl.composite(
prologue=[{"op": "softmax_merge", "s": scores_t,
"m": m_local, "l": l_local, "O": O_local}],
op="gemm", b=V_t, out=O_local,
epilogue=[{"op": "add", "other": O_local}],
m_tile = tl.max(scores_t, axis=-1)
centered_t = scores_t - m_tile
exp_scores_t = tl.exp(centered_t)
l_tile = tl.sum(exp_scores_t, axis=-1)
O_tile = tl.dot(exp_scores_t, V_t)
m_new, l_new, O_new = _merge_running(
m_local, l_local, O_local, m_tile, l_tile, O_tile, tl=tl,
)
tl.copy_to(m_local, m_new)
tl.copy_to(l_local, l_new)
tl.copy_to(O_local, O_new)
# ── Chain reduce ──
group_cols = min(4, group_size)
@@ -0,0 +1,195 @@
"""GQA decode kernel: composite + softmax_merge fused variant (3).
Identical mapping to ``_gqa_attention_decode_short.py``. Difference vs
the GEMM-only composite: per-tile softmax is folded into
the P·V composite via the ``softmax_merge`` prologue recipe, eliminating
the GEMM/MATH engine bubble that a flat primitives chain incurs.
Per-tile structure (ADR-0065 two-composite pattern, cf. decode_opt2):
tile 0 primitives establish (m, , O)
tile k>0 #1 Q·Kᵀ composite → scores (pinned primary-out)
#2 softmax_merge prologue → P (pinned, auto-binds to GEMM a)
+ P·V composite, ``out=O_local``
+ add epilogue folding the result into O_local
The intra-group chain reduce after the tile loop is unchanged.
Three-variant comparison:
(1) without composite : ``_gqa_attention_decode_short.py``
(2) with composite (GEMM-only, no fuse) : ``…_composite.py``
(3) with composite + softmax_merge fuse : this file
Shard addressing, layouts, and caller contract are identical to the
first-level decode kernel (ADR-0011 D-VA1).
"""
from __future__ import annotations
TILE_S_KV = 1024
def _validate_config(*, kv_per_cube: int, T_q: int, P: int, C: int,
h_q: int, h_kv: int, S_kv: int) -> None:
"""Validate composite-decode config — caller-side, sim-cost 0.
Mirrors first-level decode ``_validate_config``.
"""
if kv_per_cube not in (1, 2, 4, 8):
raise ValueError(f"kv_per_cube must be in {{1,2,4,8}}; got {kv_per_cube}")
if T_q != 1:
raise ValueError(f"decode requires T_q == 1; got {T_q}")
if P != 8:
raise ValueError(f"2x4 mesh chain-reduce geometry requires P == 8; got {P}")
if h_q % h_kv != 0:
raise ValueError(f"GQA group G = h_q/h_kv must be integer; got h_q={h_q}, h_kv={h_kv}")
if C != h_kv // kv_per_cube:
raise ValueError(
f"C must equal h_kv/kv_per_cube = {h_kv // kv_per_cube}; "
f"got C={C} (h_kv={h_kv}, kv_per_cube={kv_per_cube})"
)
group_size = P // kv_per_cube
if S_kv % (group_size * TILE_S_KV) != 0:
raise ValueError(
f"S_kv ({S_kv}) must be a multiple of group_size·TILE_S_KV "
f"({group_size * TILE_S_KV}); each PE's sequence shard must "
f"be a whole number of tiles"
)
def _merge_running(m_local, l_local, O_local, m_other, l_other, O_other, *, tl):
"""Used only by the intra-group chain reduce below (recipe handles
per-tile fold internally)."""
m_new = tl.maximum(m_local, m_other)
scale_old = tl.exp(m_local - m_new)
scale_new = tl.exp(m_other - m_new)
l_new = l_local * scale_old + l_other * scale_new
O_new = O_local * scale_old + O_other * scale_new
return m_new, l_new, O_new
def gqa_attention_decode_short_composite_fused_kernel(
q_ptr: int,
k_ptr: int,
v_ptr: int,
o_ptr: int,
T_q: int,
S_kv: int,
h_q: int,
h_kv: int,
d_head: int,
C: int,
P: int,
kv_per_cube: int,
*,
tl,
) -> None:
"""Composite-GEMM decode — same mapping as first-level + tl.composite.
Caller must invoke ``_validate_config(...)`` first.
"""
group_size = P // kv_per_cube
S_local = S_kv // group_size
n_tiles_per_pe = S_local // TILE_S_KV
G = h_q // h_kv
pe_id = tl.program_id(axis=0)
cube_id = tl.program_id(axis=1)
pe_in_group = pe_id % group_size
group_id_in_cube = pe_id // group_size
KV_ROW_BYTES = d_head * 2
K_TILE_BYTES = d_head * TILE_S_KV * 2
Q_ROW_BYTES = G * d_head * 2
K_HEAD_BYTES = S_kv * d_head * 2
V_HEAD_BYTES = S_kv * KV_ROW_BYTES
# Global VA per ADR-0011 D-VA1: kernel computes its own shard base.
q_base = q_ptr + cube_id * kv_per_cube * Q_ROW_BYTES + group_id_in_cube * Q_ROW_BYTES
k_shard_base = (k_ptr
+ cube_id * kv_per_cube * K_HEAD_BYTES
+ group_id_in_cube * K_HEAD_BYTES
+ pe_in_group * n_tiles_per_pe * K_TILE_BYTES)
v_shard_base = (v_ptr
+ cube_id * kv_per_cube * V_HEAD_BYTES
+ group_id_in_cube * V_HEAD_BYTES
+ pe_in_group * n_tiles_per_pe * TILE_S_KV * KV_ROW_BYTES)
Q = tl.load(q_base, shape=(G, d_head), dtype="f16")
# ── Tile 0: establish running (m, , O) with primitives ──────────
# (Reference: decode_opt2 — running state is set up with tl.dot/MATH
# primitives, not composite. Recipe-driven composite enters in tile 1+.)
K_T = tl.load(k_shard_base, shape=(d_head, TILE_S_KV), dtype="f16")
V = tl.load(v_shard_base, shape=(TILE_S_KV, d_head), dtype="f16")
scores = tl.dot(Q, K_T)
m_local = tl.max(scores, axis=-1)
centered = scores - m_local
exp_scores = tl.exp(centered)
l_local = tl.sum(exp_scores, axis=-1)
O_local = tl.dot(exp_scores, V)
# ── Tiles 1..n_tiles_per_pe-1: two composites per tile ──────────
# #1 Q·Kᵀ composite → scores (pinned primary-out, fed into #2).
# #2 softmax_merge prologue + P·V GEMM + add epilogue, all in one
# composite: updates (m, ) in place, computes P, runs P·V with
# pinned auto-bind, and folds the result into O_local.
for tile_idx in range(1, n_tiles_per_pe):
with tl.scratch_scope():
K_T_t = tl.load(k_shard_base + tile_idx * K_TILE_BYTES,
shape=(d_head, TILE_S_KV), dtype="f16")
V_t = tl.load(v_shard_base + tile_idx * TILE_S_KV * KV_ROW_BYTES,
shape=(TILE_S_KV, d_head), dtype="f16")
scores_t = tl.composite(op="gemm", a=Q, b=K_T_t)
tl.composite(
prologue=[{"op": "softmax_merge", "s": scores_t,
"m": m_local, "l": l_local, "O": O_local}],
op="gemm", b=V_t, out=O_local,
epilogue=[{"op": "add", "other": O_local}],
)
# ── Chain reduce ──
group_cols = min(4, group_size)
group_rows = (group_size + group_cols - 1) // group_cols
pe_col_in_group = pe_in_group % group_cols
pe_row_in_group = pe_in_group // group_cols
if group_cols > 1:
if pe_col_in_group < group_cols - 1:
with tl.scratch_scope():
m_other = tl.recv(dir="intra_E", shape=m_local.shape, dtype="f16")
l_other = tl.recv(dir="intra_E", shape=l_local.shape, dtype="f16")
O_other = tl.recv(dir="intra_E", shape=O_local.shape, dtype="f16")
m_new, l_new, O_new = _merge_running(
m_local, l_local, O_local, m_other, l_other, O_other, tl=tl,
)
tl.copy_to(m_local, m_new)
tl.copy_to(l_local, l_new)
tl.copy_to(O_local, O_new)
if pe_col_in_group > 0:
tl.send(dir="intra_W", src=m_local)
tl.send(dir="intra_W", src=l_local)
tl.send(dir="intra_W", src=O_local)
if pe_col_in_group == 0 and group_rows > 1:
if pe_row_in_group < group_rows - 1:
with tl.scratch_scope():
m_other = tl.recv(dir="intra_S", shape=m_local.shape, dtype="f16")
l_other = tl.recv(dir="intra_S", shape=l_local.shape, dtype="f16")
O_other = tl.recv(dir="intra_S", shape=O_local.shape, dtype="f16")
m_new, l_new, O_new = _merge_running(
m_local, l_local, O_local, m_other, l_other, O_other, tl=tl,
)
tl.copy_to(m_local, m_new)
tl.copy_to(l_local, l_new)
tl.copy_to(O_local, O_new)
if pe_row_in_group > 0:
tl.send(dir="intra_N", src=m_local)
tl.send(dir="intra_N", src=l_local)
tl.send(dir="intra_N", src=O_local)
if pe_in_group == 0:
O_final = O_local / l_local
o_base = (o_ptr
+ cube_id * kv_per_cube * Q_ROW_BYTES
+ group_id_in_cube * Q_ROW_BYTES)
tl.store(o_base, O_final)
@@ -1,4 +1,4 @@
"""GQA prefill kernel: short context, attention only, multi-tile.
"""GQA prefill kernel: short context, attention only, multi-tile (1).
Unified A1/A2/A4/B prefill mapping per ADR-0070 (supersedes
ADR-0060 §B.split.2 prefill-short clause). Mode selected at launch
@@ -1,24 +1,22 @@
"""GQA prefill kernel composite (second-level) variant.
"""GQA prefill kernel: composite GEMM-only variant (2).
Identical mapping to ``_gqa_attention_prefill_short.py`` (Q-tile split +
FA2 head fusion + IPCQ KV broadcast, unified A1/A2/A4/B).
FA2 head fusion + IPCQ KV broadcast, unified A1/A2/A4/B). Difference vs
first-level: Q·Kᵀ uses ``tl.composite(op="gemm")`` instead of ``tl.dot``.
``P·V`` stays a plain ``tl.dot``: this is the GEMM-only tier, where
softmax remains a primitive MATH chain with no ``softmax_merge`` fusion.
Composite hybrid (option c1): Q·Kᵀ uses ``tl.composite(op="gemm")`` so
GEMM and any future prologue/epilogue can overlap; P·V stays a plain
``tl.dot``. The reason: prefill broadcasts V over IPCQ, so on non-root
PEs V is a recv'd TCM slot (unpinned per ADR-0065 D4). Feeding it to a
composite would trigger the scheduler's "stream from HBM" branch and
PhysAddr-decode the slot address (PE scratch, bit-61 set) PageFault.
``tl.dot`` reads TCM in place, preserving the broadcast model.
Full second-level fusion (with the ``softmax_merge`` prologue making
P a pinned primary-out bound to a P·V composite) is variant (3) in
``_gqa_attention_prefill_short_composite_fused.py``.
This means prefill cannot host the ``softmax_merge`` prologue (which
attaches to the P·V composite). Full second-level fusion would require
either: (i) pinning recv handles (sim/API change, ADR-0065 supplement),
or (ii) skipping the broadcast model both out of scope here.
Three-variant comparison:
(1) without composite : ``_gqa_attention_prefill_short.py``
(2) with composite (GEMM-only, no fuse) : this file
(3) with composite + softmax_merge fuse : ``_composite_fused.py``
Shard addressing, layouts, and the caller contract are identical to
the first-level kernel (see its docstring for the full Q/K/V/O layout
and ADR-0011 D-VA1 offset computation).
Shard addressing, layouts, and caller contract are identical to the
first-level kernel (ADR-0011 D-VA1).
"""
from __future__ import annotations
@@ -151,13 +149,8 @@ def gqa_attention_prefill_short_composite_kernel(
tl.send(dir="intra_E", src=K_T)
tl.send(dir="intra_E", src=V)
# Q·Kᵀ is a composite GEMM (pinned load operands). P·V stays a
# primitive tl.dot: in the broadcast topology, V on non-root PEs is
# a recv'd TCM slot (unpinned), which the composite scheduler would
# try to stream from HBM. tl.dot reads TCM in-place, preserving
# the broadcast model. softmax_merge prologue requires a composite
# P·V to host it — so prefill keeps Q·Kᵀ-only composite (no fused
# softmax epilogue); see kernel docstring (c1).
# Q·Kᵀ is a composite GEMM; P·V stays a primitive tl.dot in this
# GEMM-only tier (no softmax_merge fusion — that is variant 3).
scores = tl.composite(op="gemm", a=Q, b=K_T)
m_local = tl.max(scores, axis=-1)
centered = scores - m_local
@@ -0,0 +1,204 @@
"""GQA prefill kernel: composite + softmax_merge fused variant (3).
Identical mapping to ``_gqa_attention_prefill_short.py``. Difference vs
the GEMM-only composite: per-tile softmax is folded into
the P·V composite via the ``softmax_merge`` prologue recipe, eliminating
the GEMM/MATH engine bubble.
Multi-cube (A1/A2/A4): non-root PEs receive K/V via IPCQ ``tl.recv``.
Recv'd slots in on-chip memory are pinned (read in place as a composite
operand, not DMA-streamed from HBM).
Three-variant comparison:
(1) without composite : ``_gqa_attention_prefill_short.py``
(2) with composite (GEMM-only, no fuse) : ``…_composite.py``
(3) with composite + softmax_merge fuse : this file
Shard addressing, layouts, and caller contract are identical to the
first-level kernel (ADR-0011 D-VA1).
"""
from __future__ import annotations
TILE_S_KV = 1024
def _validate_config(*, kv_per_cube: int, T_q: int, P: int, C: int,
h_q: int, h_kv: int, S_kv: int) -> None:
"""Validate composite-prefill config — caller-side, sim-cost 0.
Mirrors first-level prefill ``_validate_config``.
"""
if kv_per_cube not in (1, 2, 4, 8):
raise ValueError(f"kv_per_cube must be in {{1,2,4,8}}; got {kv_per_cube}")
if P != 8:
raise ValueError(f"2x4 mesh broadcast geometry requires P == 8; got {P}")
if h_q % h_kv != 0:
raise ValueError(f"GQA group G = h_q/h_kv must be integer; got h_q={h_q}, h_kv={h_kv}")
if C != h_kv // kv_per_cube:
raise ValueError(
f"C must equal h_kv/kv_per_cube = {h_kv // kv_per_cube}; "
f"got C={C} (h_kv={h_kv}, kv_per_cube={kv_per_cube})"
)
group_size = P // kv_per_cube
if T_q < group_size:
raise ValueError(
f"T_q ({T_q}) must be >= group_size ({group_size}) so every PE "
f"has q-tile work for the broadcast chain"
)
if S_kv % TILE_S_KV != 0:
raise ValueError(
f"S_kv ({S_kv}) must be a multiple of TILE_S_KV ({TILE_S_KV})"
)
def _merge_running(m_local, l_local, O_local, m_other, l_other, O_other, *, tl):
m_new = tl.maximum(m_local, m_other)
scale_old = tl.exp(m_local - m_new)
scale_new = tl.exp(m_other - m_new)
l_new = l_local * scale_old + l_other * scale_new
O_new = O_local * scale_old + O_other * scale_new
return m_new, l_new, O_new
def gqa_attention_prefill_short_composite_fused_kernel(
q_ptr: int,
k_ptr: int,
v_ptr: int,
o_ptr: int,
T_q: int,
S_kv: int,
h_q: int,
h_kv: int,
d_head: int,
C: int,
P: int,
kv_per_cube: int,
*,
tl,
) -> None:
"""Composite-GEMM prefill — same mapping as first-level + tl.composite.
Caller must invoke ``_validate_config(...)`` first.
"""
group_size = P // kv_per_cube
G = h_q // h_kv
pe_id = tl.program_id(axis=0)
cube_id = tl.program_id(axis=1)
pe_in_group = pe_id % group_size
group_id_in_cube = pe_id // group_size
q_start = (T_q * pe_in_group) // group_size
q_end = (T_q * (pe_in_group + 1)) // group_size
T_q_pe = q_end - q_start
n_tiles = S_kv // TILE_S_KV
Q_ROW_BYTES = G * d_head * 2
KV_ROW_BYTES = d_head * 2
K_TILE_BYTES = d_head * TILE_S_KV * 2
K_HEAD_BYTES = n_tiles * K_TILE_BYTES
V_HEAD_BYTES = S_kv * KV_ROW_BYTES
# Global VA per ADR-0011 D-VA1: kernel computes its own shard base.
q_base = (q_ptr
+ cube_id * kv_per_cube * T_q * Q_ROW_BYTES
+ (group_id_in_cube * T_q + q_start) * Q_ROW_BYTES)
k_head_shard_base = (k_ptr
+ cube_id * kv_per_cube * K_HEAD_BYTES
+ group_id_in_cube * K_HEAD_BYTES)
v_head_shard_base = (v_ptr
+ cube_id * kv_per_cube * V_HEAD_BYTES
+ group_id_in_cube * V_HEAD_BYTES)
Q = tl.load(q_base, shape=(T_q_pe * G, d_head), dtype="f16")
group_cols = min(4, group_size)
group_rows = (group_size + group_cols - 1) // group_cols
pe_col_in_group = pe_in_group % group_cols
pe_row_in_group = pe_in_group // group_cols
# ── Tile 0 — broadcast + persistent (m, , O) ──
if pe_in_group == 0:
K_T = tl.load(k_head_shard_base,
shape=(d_head, TILE_S_KV), dtype="f16")
V = tl.load(v_head_shard_base,
shape=(TILE_S_KV, d_head), dtype="f16")
if group_cols > 1:
tl.send(dir="intra_E", src=K_T)
tl.send(dir="intra_E", src=V)
if group_rows > 1:
tl.send(dir="intra_S", src=K_T)
tl.send(dir="intra_S", src=V)
elif pe_col_in_group == 0 and pe_row_in_group > 0:
K_T = tl.recv(dir="intra_N",
shape=(d_head, TILE_S_KV), dtype="f16")
V = tl.recv(dir="intra_N",
shape=(TILE_S_KV, d_head), dtype="f16")
if group_cols > 1:
tl.send(dir="intra_E", src=K_T)
tl.send(dir="intra_E", src=V)
else:
K_T = tl.recv(dir="intra_W",
shape=(d_head, TILE_S_KV), dtype="f16")
V = tl.recv(dir="intra_W",
shape=(TILE_S_KV, d_head), dtype="f16")
if pe_col_in_group < group_cols - 1:
tl.send(dir="intra_E", src=K_T)
tl.send(dir="intra_E", src=V)
# Tile 0 — primitives establish (m, , O); recipe fusion enters tile 1+.
scores = tl.dot(Q, K_T)
m_local = tl.max(scores, axis=-1)
centered = scores - m_local
exp_scores = tl.exp(centered)
l_local = tl.sum(exp_scores, axis=-1)
O_local = tl.dot(exp_scores, V)
# ── Tiles 1..n_tiles-1 ──
for tile_idx in range(1, n_tiles):
with tl.scratch_scope():
if pe_in_group == 0:
K_T_t = tl.load(k_head_shard_base + tile_idx * K_TILE_BYTES,
shape=(d_head, TILE_S_KV), dtype="f16")
V_t = tl.load(v_head_shard_base + tile_idx * TILE_S_KV * KV_ROW_BYTES,
shape=(TILE_S_KV, d_head), dtype="f16")
if group_cols > 1:
tl.send(dir="intra_E", src=K_T_t)
tl.send(dir="intra_E", src=V_t)
if group_rows > 1:
tl.send(dir="intra_S", src=K_T_t)
tl.send(dir="intra_S", src=V_t)
elif pe_col_in_group == 0 and pe_row_in_group > 0:
K_T_t = tl.recv(dir="intra_N",
shape=(d_head, TILE_S_KV), dtype="f16")
V_t = tl.recv(dir="intra_N",
shape=(TILE_S_KV, d_head), dtype="f16")
if group_cols > 1:
tl.send(dir="intra_E", src=K_T_t)
tl.send(dir="intra_E", src=V_t)
else:
K_T_t = tl.recv(dir="intra_W",
shape=(d_head, TILE_S_KV), dtype="f16")
V_t = tl.recv(dir="intra_W",
shape=(TILE_S_KV, d_head), dtype="f16")
if pe_col_in_group < group_cols - 1:
tl.send(dir="intra_E", src=K_T_t)
tl.send(dir="intra_E", src=V_t)
# Two-composite fusion: Q·Kᵀ composite → softmax_merge prologue
# binds P (pinned primary-out) to the P·V composite, folding
# the new tile's contribution into O_local.
scores_t = tl.composite(op="gemm", a=Q, b=K_T_t)
tl.composite(
prologue=[{"op": "softmax_merge", "s": scores_t,
"m": m_local, "l": l_local, "O": O_local}],
op="gemm", b=V_t, out=O_local,
epilogue=[{"op": "add", "other": O_local}],
)
O_final = O_local / l_local
o_base = (o_ptr
+ cube_id * kv_per_cube * T_q * Q_ROW_BYTES
+ (group_id_in_cube * T_q + q_start) * Q_ROW_BYTES)
tl.store(o_base, O_final)
+129 -6
View File
@@ -64,7 +64,7 @@ def _count(op_log, name: str) -> int:
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_attention_prefill_short import (
from kernbench.benches.gqa_helpers.short_ctx._gqa_attention_prefill_short import (
_validate_config as _validate_prefill_config,
gqa_attention_prefill_short_kernel,
)
@@ -182,7 +182,7 @@ def test_prefill_multitile_scaling(kv_per_cube, C):
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_attention_decode_short import (
from kernbench.benches.gqa_helpers.short_ctx._gqa_attention_decode_short import (
_validate_config as _validate_decode_config,
gqa_attention_decode_short_kernel,
)
@@ -387,7 +387,7 @@ def test_prefill_per_cube_dma_balanced(kv_per_cube, C):
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_attention_prefill_short_composite import (
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,
)
@@ -428,7 +428,7 @@ def _run_prefill_composite(*, kv_per_cube: int, C: int, T_q: int,
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_attention_decode_short_composite import (
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,
)
@@ -467,9 +467,92 @@ def _run_decode_composite(*, kv_per_cube: int, C: int, S_kv: int,
)
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):
"""Composite-GEMM prefill completes in all 4 modes."""
"""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}"
@@ -478,8 +561,48 @@ def test_prefill_composite_smoke(kv_per_cube, C):
@pytest.mark.parametrize("kv_per_cube,C", MODES)
def test_decode_composite_smoke(kv_per_cube, C):
"""Composite-GEMM decode completes in all 4 modes."""
"""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}"
)