Skip to content

fix(attention): avoid overflow in triton kv cache offsets #854

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
jason-fxz merged 2 commits into from
Nov 4, 2026
+84 −5
Merged

fix(attention): avoid overflow in triton kv cache offsets #854

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
f0fce4e
fix(attention): avoid overflow in triton kv cache offsets
LZ-QWQ Nov 3, 2026
2a18e24
fix(attention): simplify kv offset dtype selection
LZ-QWQ Nov 4, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
27 changes: 22 additions & 5 deletions python/freetoken/kernel/triton/attention.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,15 @@
_STAGE2_TILE_ELEMS = 8192


def _kv_offset_dtype(k_cache: torch.Tensor, v_cache: torch.Tensor):
# Bound physical offsets, not request lengths.
max_offset = max(
sum((size - 1) * stride for size, stride in zip(cache.shape, cache.stride()))
for cache in (k_cache, v_cache)
)
return tl.int32 if max_offset < 1 << 31 else tl.int64


def _stage2_launch(max_kv_splits: int, block_dv: int) -> tuple[int, int]:
"""(splits merged per step, num_warps) for stage 2; the tile is at most the split cap rounded up to a power of two."""
tile = min(triton.next_power_of_2(max_kv_splits), max(4, _STAGE2_TILE_ELEMS // block_dv), 64)
Expand Down Expand Up @@ -97,6 +106,7 @@ def _paged_attention_kernel(
BLOCK_N: tl.constexpr,
SLIDING_WINDOW: tl.constexpr,
HAS_SINKS: tl.constexpr,
KV_OFFSET_DTYPE: tl.constexpr,
):
q_tok = tl.program_id(0)
q_head = tl.program_id(1)
Expand Down Expand Up @@ -135,7 +145,7 @@ def _paged_attention_kernel(

skip_tile = tl.max(mask_n.to(tl.int32), axis=0) == 0
if not skip_tile:
slots = tl.load(indices_ptr + kv_start + offs_n, mask=offs_n < kv_len, other=0)
slots = tl.load(indices_ptr + kv_start + offs_n, mask=offs_n < kv_len, other=0).to(KV_OFFSET_DTYPE)
k = tl.load(
k_ptr
+ slots[:, None] * stride_ks
Expand Down Expand Up @@ -217,6 +227,7 @@ def _decode_grouped_stage1_kernel(
D: tl.constexpr,
DV: tl.constexpr,
SLIDING_WINDOW: tl.constexpr,
KV_OFFSET_DTYPE: tl.constexpr,
):
batch_id = tl.program_id(0)
head_block_id = tl.program_id(1)
Expand Down Expand Up @@ -268,7 +279,7 @@ def _decode_grouped_stage1_kernel(
rel_offs = rel_start + tl.arange(0, BLOCK_N)
mask_n = rel_offs < split_end
logical_offs = effective_start + rel_offs
slots = tl.load(indices_ptr + kv_start + logical_offs, mask=mask_n, other=0)
slots = tl.load(indices_ptr + kv_start + logical_offs, mask=mask_n, other=0).to(KV_OFFSET_DTYPE)

k = tl.load(
k_ptr + slots[None, :] * stride_ks + k_base_offsets,
Expand Down Expand Up @@ -492,6 +503,7 @@ def decode_paged_attention(
attn_lse.stride(0),
attn_lse.stride(1),
attn_lse.stride(2),
KV_OFFSET_DTYPE=_kv_offset_dtype(k_cache, v_cache),
GROUP=group,
NUM_Q_HEADS=num_q_heads,
BLOCK_D=block_d,
Expand Down Expand Up @@ -568,6 +580,7 @@ def _extend_attention_kernel(
SLIDING_WINDOW: tl.constexpr,
HAS_SINKS: tl.constexpr,
HAS_BLOCKS: tl.constexpr,
KV_OFFSET_DTYPE: tl.constexpr,
):
seq_id = tl.program_id(0)
q_head = tl.program_id(1)
Expand Down Expand Up @@ -626,7 +639,7 @@ def _extend_attention_kernel(

skip_tile = tl.max(tl.max(final_mask.to(tl.int32), axis=1), axis=0) == 0
if not skip_tile:
slots = tl.load(kv_indices_ptr + kv_start + kv_offsets, mask=mask_n, other=0)
slots = tl.load(kv_indices_ptr + kv_start + kv_offsets, mask=mask_n, other=0).to(KV_OFFSET_DTYPE)
k = tl.load(
k_ptr
+ slots[None, :] * stride_ks
Expand Down Expand Up @@ -745,6 +758,7 @@ def _extend_attention_split_kernel(
SLIDING_WINDOW: tl.constexpr,
HAS_SINKS: tl.constexpr,
HAS_BLOCKS: tl.constexpr,
KV_OFFSET_DTYPE: tl.constexpr,
):
seq_id = tl.program_id(0)
q_head = tl.program_id(1)
Expand Down Expand Up @@ -793,7 +807,7 @@ def _extend_attention_split_kernel(
prefix_len + block_start, prefix_end // BLOCK_N * BLOCK_N, prefix_end, BLOCK_M, BLOCK_N, SLIDING_WINDOW
)
for start_n in tl.range(u0, u1, BLOCK_N):
slots = tl.load(kv_indices_ptr + kv_start + start_n + offs_n)
slots = tl.load(kv_indices_ptr + kv_start + start_n + offs_n).to(KV_OFFSET_DTYPE)
m_i, l_i, acc = _extend_attend_tile(
q, m_i, l_i, acc, qk_scale, kc_base, vc_base, slots * stride_kcs, slots * stride_vcs,
offs_d, offs_dv, mask_d, mask_dv, None, None, False,
Expand All @@ -805,7 +819,7 @@ def _extend_attention_split_kernel(
final_mask = mask_n[None, :]
if SLIDING_WINDOW > 0:
final_mask = final_mask & ((kv_offsets[None, :] + SLIDING_WINDOW) > q_abs_pos[:, None])
slots = tl.load(kv_indices_ptr + kv_start + kv_offsets, mask=mask_n, other=0)
slots = tl.load(kv_indices_ptr + kv_start + kv_offsets, mask=mask_n, other=0).to(KV_OFFSET_DTYPE)
m_i, l_i, acc = _extend_attend_tile(
q, m_i, l_i, acc, qk_scale, kc_base, vc_base, slots * stride_kcs, slots * stride_vcs,
offs_d, offs_dv, mask_d, mask_dv, mask_n, final_mask, True,
Expand Down Expand Up @@ -942,6 +956,7 @@ def extend_paged_attention(
v_cache.stride(1),
o.stride(0),
o.stride(1),
KV_OFFSET_DTYPE=_kv_offset_dtype(k_cache, v_cache),
GROUP=num_q_heads // num_kv_heads,
D=head_dim,
BLOCK_D=block_d,
Expand Down Expand Up @@ -976,6 +991,7 @@ def extend_paged_attention(
v_cache.stride(1),
o.stride(0),
o.stride(1),
KV_OFFSET_DTYPE=_kv_offset_dtype(k_cache, v_cache),
GROUP=num_q_heads // num_kv_heads,
D=head_dim,
BLOCK_D=block_d,
Expand Down Expand Up @@ -1048,6 +1064,7 @@ def paged_attention(
v_cache.stride(1),
o.stride(0),
o.stride(1),
KV_OFFSET_DTYPE=_kv_offset_dtype(k_cache, v_cache),
GROUP=num_q_heads // num_kv_heads,
D=head_dim,
BLOCK_D=block_d,
Expand Down
62 changes: 62 additions & 0 deletions tests/kernels/test_triton_attention.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,68 @@ def _reference_paged_attention(
return torch.stack(outs, dim=0)


@pytest.mark.skipif(not torch.cuda.is_available(), reason="Triton attention needs a GPU")
@pytest.mark.parametrize("path", ["paged", "decode", "extend", "split_extend"])
@pytest.mark.parametrize("offset_bits", [31, 32])
def test_triton_attention_high_physical_slots(path: str, offset_bits: int):
from freetoken.kernel.triton.attention import (
decode_paged_attention,
extend_paged_attention,
paged_attention,
)

torch.manual_seed(1347)
device = torch.device("cuda")
head_dim, num_kv_heads, num_q_heads = 64, 2, 4
stride = num_kv_heads * head_dim
low, count = 128, 66
high = (1 << 32) // stride + low
required = (high + count) * stride * 2
if torch.cuda.mem_get_info()[0] < required + (1 << 30):
# Release inactive cached blocks before deciding this case cannot fit.
torch.cuda.empty_cache()
if torch.cuda.mem_get_info()[0] < required + (1 << 30):
pytest.skip("High-offset attention regression needs 9 GiB of free GPU memory")

# Share K/V storage to keep the real allocation near 8 GiB. Int32 overflow
# aliases initialized low rows, so a regression fails numerically, not by OOB.
cache = torch.empty(high + count, num_kv_heads, head_dim, device=device, dtype=torch.bfloat16)
cache[low : low + count].fill_(-2)
cache[high : high + count].normal_(mean=3, std=2)
if offset_bits == 31:
# Keep the old negative offset inside allocated guard storage too.
guard = (1 << 31) // stride
cache = cache[guard:]
high -= guard
q = torch.randn(1, num_q_heads, head_dim, device=device, dtype=cache.dtype)
indices = torch.arange(high, high + count, device=device, dtype=torch.int32)
indptr = torch.tensor([0, count], device=device, dtype=torch.int32)
q_to_req = torch.zeros(1, device=device, dtype=torch.int32)
q_positions = torch.tensor([count - 1], device=device, dtype=torch.int64)
sm_scale = head_dim**-0.5
expected = _reference_paged_attention(
q, cache, cache, indptr, indices, q_to_req, q_positions, sm_scale, None,
)

if path == "paged":
actual = paged_attention(q, cache, cache, indptr, indices, q_to_req, q_positions, sm_scale)
elif path == "decode":
mid = torch.empty(1, num_q_heads, 64, head_dim, device=device, dtype=torch.float32)
lse = torch.empty(1, num_q_heads, 64, device=device, dtype=torch.float32)
actual = decode_paged_attention(q, cache, cache, indptr, indices, q_positions, mid, lse, sm_scale)
else:
qo_indptr = torch.tensor([0, 1], device=device, dtype=torch.int32)
prefix_lens = torch.tensor([count - 1], device=device, dtype=torch.int32)
# 65 cached tokens cover both the full-tile and masked split-prefix loads.
extend = cache[high + count - 1 : high + count] if path == "split_extend" else None
actual = extend_paged_attention(
q, cache, cache, qo_indptr, indptr, indices, prefix_lens, 1, sm_scale,
k_extend=extend, v_extend=extend,
)

torch.testing.assert_close(actual.float(), expected.float(), atol=2e-2, rtol=2e-2)


def test_triton_backend_passes_attention_sinks_to_paged_kernel(monkeypatch):
from freetoken.attention import AttentionSpec
from freetoken.attention.triton import TritonAttentionBackend, TritonMetadata
Expand Down
Loading