"""Paged attention decode kernel implemented in Triton.
Conforms to the same Model/get_inputs/get_init_inputs interface as reference.py.
"""
import math
import torch
import torch.nn as nn
import triton
import triton.language as tl
OP_TYPE = "attention"
SUPPORTED_PRECISIONS = ["bf16"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
BATCH = 8
NUM_HEADS = 32
NUM_KV_HEADS = 8
HEAD_DIM = 128
SEQ_LEN = 1024
PAGE_SIZE = 16
@triton.jit
def paged_decode_kernel(
q_ptr,
kv_ptr,
block_table_ptr,
seq_lens_ptr,
out_ptr,
stride_qb,
stride_qh,
stride_qd,
stride_kvn,
stride_kvt,
stride_kvh,
stride_kvd,
stride_bt_b,
stride_bt_p,
stride_ob,
stride_oh,
stride_od,
SCALE: tl.constexpr,
PAGE_SIZE: tl.constexpr,
HEAD_DIM: tl.constexpr,
G: tl.constexpr,
):
"""One CUDA block per (batch, kv_head). Computes all G query heads."""
pid_b = tl.program_id(0)
pid_hkv = tl.program_id(1)
seq_len = tl.load(seq_lens_ptr + pid_b)
num_pages = tl.cdiv(seq_len, PAGE_SIZE)
offs_d = tl.arange(0, HEAD_DIM)
offs_t = tl.arange(0, PAGE_SIZE)
offs_g = tl.arange(0, G)[:, None]
offs_g1 = tl.arange(0, G)
# Load the G query heads for this KV group: (G, HEAD_DIM).
q_ptrs = (
q_ptr
+ pid_b * stride_qb
+ (pid_hkv * G + offs_g) * stride_qh
+ offs_d[None, :] * stride_qd
)
qs = tl.load(q_ptrs, mask=offs_d[None, :] < HEAD_DIM, other=0.0).to(tl.float32)
ms = tl.full((G,), float("-inf"), dtype=tl.float32)
ls = tl.zeros((G,), dtype=tl.float32)
accs = tl.zeros((G, HEAD_DIM), dtype=tl.float32)
for p in range(num_pages):
physical_block = tl.load(
block_table_ptr + pid_b * stride_bt_b + p * stride_bt_p
).to(tl.int64)
token_offset = p * PAGE_SIZE
valid = tl.minimum(PAGE_SIZE, seq_len - token_offset)
kv_base = kv_ptr + physical_block * stride_kvn + pid_hkv * stride_kvh
k_ptrs = kv_base + (offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd)
k = tl.load(
k_ptrs,
mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
other=0.0,
).to(tl.float32)
v_ptrs = kv_base + HEAD_DIM * stride_kvd + (
offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd
)
v = tl.load(
v_ptrs,
mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
other=0.0,
).to(tl.float32)
for g in tl.static_range(G):
mask_g = tl.arange(0, G) == g
mask_g2 = mask_g[:, None]
q = tl.sum(tl.where(mask_g2, qs, 0.0), axis=0)
acc_cur = tl.sum(tl.where(mask_g2, accs, 0.0), axis=0)
m_cur = tl.sum(tl.where(mask_g, ms, 0.0), axis=0)
l_cur = tl.sum(tl.where(mask_g, ls, 0.0), axis=0)
scores = tl.reshape(tl.dot(q[None, :], tl.trans(k)), (PAGE_SIZE,)) * SCALE
scores = tl.where(offs_t < valid, scores, float("-inf"))
m_new = tl.maximum(m_cur, tl.max(scores, axis=0))
exp_scale = tl.exp(m_cur - m_new)
exp_scores = tl.exp(scores - m_new)
weighted = tl.reshape(tl.dot(exp_scores[None, :], v), (HEAD_DIM,))
new_acc = acc_cur * exp_scale + weighted
new_l = l_cur * exp_scale + tl.sum(exp_scores, axis=0)
accs = tl.where(mask_g2, new_acc[None, :], accs)
ls = tl.where(mask_g, new_l, ls)
ms = tl.where(mask_g, m_new, ms)
out_ptrs = (
out_ptr
+ pid_b * stride_ob
+ (pid_hkv * G + offs_g) * stride_oh
+ offs_d[None, :] * stride_od
)
tl.store(
out_ptrs,
(accs / ls[:, None]).to(tl.bfloat16),
mask=offs_d[None, :] < HEAD_DIM,
)
@triton.jit
def paged_decode_part_kernel(
q_ptr,
kv_ptr,
block_table_ptr,
seq_lens_ptr,
part_out_ptr,
part_m_ptr,
part_l_ptr,
stride_qb,
stride_qh,
stride_qd,
stride_kvn,
stride_kvt,
stride_kvh,
stride_kvd,
stride_bt_b,
stride_bt_p,
stride_pob,
stride_poh,
stride_pos,
stride_pod,
stride_pmb,
stride_pmh,
stride_pms,
SCALE: tl.constexpr,
PAGE_SIZE: tl.constexpr,
HEAD_DIM: tl.constexpr,
G: tl.constexpr,
NUM_SPLITS: tl.constexpr,
):
"""One CUDA block per (batch, kv_head, split). Computes partial attention."""
pid_b = tl.program_id(0)
pid_hkv = tl.program_id(1)
pid_s = tl.program_id(2)
seq_len = tl.load(seq_lens_ptr + pid_b)
num_pages = tl.cdiv(seq_len, PAGE_SIZE)
pages_per_split = tl.cdiv(num_pages, NUM_SPLITS)
start_page = pid_s * pages_per_split
end_page = tl.minimum(num_pages, start_page + pages_per_split)
offs_d = tl.arange(0, HEAD_DIM)
offs_t = tl.arange(0, PAGE_SIZE)
offs_g = tl.arange(0, G)[:, None]
offs_g1 = tl.arange(0, G)
# Load the G query heads for this KV group: (G, HEAD_DIM).
q_ptrs = (
q_ptr
+ pid_b * stride_qb
+ (pid_hkv * G + offs_g) * stride_qh
+ offs_d[None, :] * stride_qd
)
qs = tl.load(q_ptrs, mask=offs_d[None, :] < HEAD_DIM, other=0.0).to(tl.float32)
ms = tl.full((G,), float("-inf"), dtype=tl.float32)
ls = tl.zeros((G,), dtype=tl.float32)
accs = tl.zeros((G, HEAD_DIM), dtype=tl.float32)
for p in range(start_page, end_page):
physical_block = tl.load(
block_table_ptr + pid_b * stride_bt_b + p * stride_bt_p
).to(tl.int64)
token_offset = p * PAGE_SIZE
valid = tl.minimum(PAGE_SIZE, seq_len - token_offset)
kv_base = kv_ptr + physical_block * stride_kvn + pid_hkv * stride_kvh
k_ptrs = kv_base + (offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd)
k = tl.load(
k_ptrs,
mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
other=0.0,
).to(tl.float32)
v_ptrs = kv_base + HEAD_DIM * stride_kvd + (
offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd
)
v = tl.load(
v_ptrs,
mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
other=0.0,
).to(tl.float32)
for g in tl.static_range(G):
mask_g = tl.arange(0, G) == g
mask_g2 = mask_g[:, None]
q = tl.sum(tl.where(mask_g2, qs, 0.0), axis=0)
acc_cur = tl.sum(tl.where(mask_g2, accs, 0.0), axis=0)
m_cur = tl.sum(tl.where(mask_g, ms, 0.0), axis=0)
l_cur = tl.sum(tl.where(mask_g, ls, 0.0), axis=0)
scores = tl.reshape(tl.dot(q[None, :], tl.trans(k)), (PAGE_SIZE,)) * SCALE
scores = tl.where(offs_t < valid, scores, float("-inf"))
m_new = tl.maximum(m_cur, tl.max(scores, axis=0))
exp_scale = tl.exp(m_cur - m_new)
exp_scores = tl.exp(scores - m_new)
weighted = tl.reshape(tl.dot(exp_scores[None, :], v), (HEAD_DIM,))
new_acc = acc_cur * exp_scale + weighted
new_l = l_cur * exp_scale + tl.sum(exp_scores, axis=0)
accs = tl.where(mask_g2, new_acc[None, :], accs)
ls = tl.where(mask_g, new_l, ls)
ms = tl.where(mask_g, m_new, ms)
# Write partials for all G query heads in the group at once.
out_ptrs = (
part_out_ptr
+ pid_b * stride_pob
+ (pid_hkv * G + offs_g) * stride_poh
+ pid_s * stride_pos
+ offs_d[None, :] * stride_pod
)
tl.store(out_ptrs, accs, mask=offs_d[None, :] < HEAD_DIM)
m_ptrs = (
part_m_ptr
+ pid_b * stride_pmb
+ (pid_hkv * G + offs_g1) * stride_pmh
+ pid_s * stride_pms
)
tl.store(m_ptrs, ms)
l_ptrs = (
part_l_ptr
+ pid_b * stride_pmb
+ (pid_hkv * G + offs_g1) * stride_pmh
+ pid_s * stride_pms
)
tl.store(l_ptrs, ls)
@triton.jit
def combine_kernel(
part_out_ptr,
part_m_ptr,
part_l_ptr,
out_ptr,
stride_pob,
stride_poh,
stride_pos,
stride_pod,
stride_pmb,
stride_pmh,
stride_pms,
stride_ob,
stride_oh,
stride_od,
HEAD_DIM: tl.constexpr,
NUM_SPLITS: tl.constexpr,
):
"""Combine partial attention results across splits."""
pid_b = tl.program_id(0)
pid_h = tl.program_id(1)
offs_d = tl.arange(0, HEAD_DIM)
M = tl.full((), float("-inf"), dtype=tl.float32)
denom = tl.zeros((), dtype=tl.float32)
acc = tl.zeros((HEAD_DIM,), dtype=tl.float32)
base_out = part_out_ptr + pid_b * stride_pob + pid_h * stride_poh
base_m = part_m_ptr + pid_b * stride_pmb + pid_h * stride_pmh
base_l = part_l_ptr + pid_b * stride_pmb + pid_h * stride_pmh
for s in tl.static_range(NUM_SPLITS):
m_s = tl.load(base_m + s * stride_pms)
l_s = tl.load(base_l + s * stride_pms)
acc_s = tl.load(base_out + s * stride_pos + offs_d * stride_pod, mask=offs_d < HEAD_DIM, other=0.0)
M_new = tl.maximum(M, m_s)
scale_global = tl.exp(M - M_new)
scale_s = tl.exp(m_s - M_new)
acc = acc * scale_global + acc_s * scale_s
denom = denom * scale_global + l_s * scale_s
M = M_new
out_ptrs = out_ptr + pid_b * stride_ob + pid_h * stride_oh + offs_d * stride_od
tl.store(
out_ptrs,
(acc / denom).to(tl.bfloat16),
mask=offs_d < HEAD_DIM,
)
class Model(nn.Module):
def __init__(
self,
batch: int,
num_heads: int,
num_kv_heads: int,
head_dim: int,
seq_len: int,
page_size: int,
):
super().__init__()
assert num_heads % num_kv_heads == 0, "num_heads must be a multiple of num_kv_heads (GQA)"
self.batch = batch
self.num_heads = num_heads
self.num_kv_heads = num_kv_heads
self.head_dim = head_dim
self.seq_len = seq_len
self.page_size = page_size
self.group_size = num_heads // num_kv_heads
self.scale = 1.0 / math.sqrt(head_dim)
self.register_buffer("_dummy", torch.zeros(1, dtype=torch.bfloat16), persistent=False)
def forward(
self,
query: torch.Tensor,
kv_cache: torch.Tensor,
block_table: torch.Tensor,
seq_lens: torch.Tensor,
) -> torch.Tensor:
B, H, D = query.shape
Hkv = self.num_kv_heads
G = self.group_size
P = self.page_size
device = query.device
out = torch.empty(B, H, D, dtype=query.dtype, device=device)
# Use sequence splitting when there are too few (batch, kv_head) tiles to
# keep the GPU busy. Aim for ~256 active blocks.
blocks = B * Hkv
num_splits = max(1, 256 // blocks)
max_pages = (int(seq_lens.max().item()) + P - 1) // P
num_splits = min(num_splits, max_pages)
if num_splits == 1:
grid = (B, Hkv)
paged_decode_kernel[grid](
query,
kv_cache,
block_table,
seq_lens,
out,
query.stride(0),
query.stride(1),
query.stride(2),
kv_cache.stride(0),
kv_cache.stride(1),
kv_cache.stride(2),
kv_cache.stride(3),
block_table.stride(0),
block_table.stride(1),
out.stride(0),
out.stride(1),
out.stride(2),
SCALE=self.scale,
PAGE_SIZE=P,
HEAD_DIM=D,
G=G,
num_warps=4,
)
return out
part_out = torch.empty(B, H, num_splits, D, dtype=torch.float32, device=device)
part_m = torch.empty(B, H, num_splits, dtype=torch.float32, device=device)
part_l = torch.empty(B, H, num_splits, dtype=torch.float32, device=device)
grid_part = (B, Hkv, num_splits)
paged_decode_part_kernel[grid_part](
query,
kv_cache,
block_table,
seq_lens,
part_out,
part_m,
part_l,
query.stride(0),
query.stride(1),
query.stride(2),
kv_cache.stride(0),
kv_cache.stride(1),
kv_cache.stride(2),
kv_cache.stride(3),
block_table.stride(0),
block_table.stride(1),
part_out.stride(0),
part_out.stride(1),
part_out.stride(2),
part_out.stride(3),
part_m.stride(0),
part_m.stride(1),
part_m.stride(2),
SCALE=self.scale,
PAGE_SIZE=P,
HEAD_DIM=D,
G=G,
NUM_SPLITS=num_splits,
num_warps=4,
)
grid_combine = (B, H)
combine_kernel[grid_combine](
part_out,
part_m,
part_l,
out,
part_out.stride(0),
part_out.stride(1),
part_out.stride(2),
part_out.stride(3),
part_m.stride(0),
part_m.stride(1),
part_m.stride(2),
out.stride(0),
out.stride(1),
out.stride(2),
HEAD_DIM=D,
NUM_SPLITS=num_splits,
num_warps=2,
)
return out
def get_inputs():
B = BATCH
H = NUM_HEADS
Hkv = NUM_KV_HEADS
D = HEAD_DIM
L = SEQ_LEN
P = PAGE_SIZE
pages_per_seq = (L + P - 1) // P
total_pages = max(B * pages_per_seq + 8, 64)
query = torch.randn(B, H, D, dtype=torch.bfloat16) * 0.1
kv_cache = torch.randn(total_pages, P, Hkv, 2 * D, dtype=torch.bfloat16) * 0.1
perm = torch.randperm(total_pages)[: B * pages_per_seq].reshape(B, pages_per_seq).int()
block_table = perm.contiguous()
seq_lens = torch.full((B,), L, dtype=torch.int32)
return [query, kv_cache, block_table, seq_lens]
def get_init_inputs():
return [BATCH, NUM_HEADS, NUM_KV_HEADS, HEAD_DIM, SEQ_LEN, PAGE_SIZE]
warning: `VIRTUAL_ENV=/home/infatoshi/kernelbench.com/benchmarks/hard/.venv` does not match the project environment path `/home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_163906_kimi-claude_kimi-k2.7-code_03_paged_attention/repo/.venv` and will be ignored; use `--active` to target the active environment instead
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T23:19:42.582779+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T23:19:42.824930+00:00 elapsed_s=0.242 ms=0.080304
shape=0 variant=solution tflops=1.671 gbps=419.475 ms=0.080
shape=0 solution_peak_fraction=0.2330
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T23:19:44.421498+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T23:19:44.440301+00:00 elapsed_s=0.019 ms=0.369616
shape=1 variant=solution tflops=2.905 gbps=727.673 ms=0.370
shape=1 solution_peak_fraction=0.4043
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T23:19:44.841434+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T23:19:44.853659+00:00 elapsed_s=0.012 ms=0.198352
shape=2 variant=solution tflops=2.707 gbps=338.993 ms=0.198
shape=2 solution_peak_fraction=0.1883
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T23:19:45.454738+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T23:19:45.465097+00:00 elapsed_s=0.010 ms=0.163744
shape=3 variant=solution tflops=2.457 gbps=615.961 ms=0.164
shape=3 solution_peak_fraction=0.3422
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T23:19:45.562911+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T23:19:45.570072+00:00 elapsed_s=0.007 ms=0.068016
shape=4 variant=solution tflops=0.964 gbps=241.366 ms=0.068
shape=4 solution_peak_fraction=0.1341
peak_fraction: 0.2411
RESULT: OK
warning: `VIRTUAL_ENV=/home/infatoshi/kernelbench.com/benchmarks/hard/.venv` does not match the project environment path `/home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_163906_kimi-claude_kimi-k2.7-code_03_paged_attention/repo/.venv` and will be ignored; use `--active` to target the active environment instead
warning: Ignoring existing virtual environment linked to non-existent Python interpreter: /home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_163906_kimi-claude_kimi-k2.7-code_03_paged_attention/repo/.venv/bin/python3 -> python
Using CPython 3.11.15
Removed virtual environment at: /home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_163906_kimi-claude_kimi-k2.7-code_03_paged_attention/repo/.venv
Creating virtual environment at: /home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_163906_kimi-claude_kimi-k2.7-code_03_paged_attention/repo/.venv
Building kernelbench-hard @ file:///home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_163906_kimi-claude_kimi-k2.7-code_03_paged_attention/repo
Built kernelbench-hard @ file:///home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_163906_kimi-claude_kimi-k2.7-code_03_paged_attention/repo
Installed 56 packages in 65ms
PASS
{
"run_id": "20260613_163906_kimi-claude_kimi-k2.7-code_03_paged_attention",
"run_group": "",
"problem": "03_paged_attention",
"harness": "kimi-claude",
"model": "kimi-k2.7-code",
"reasoning_effort": "",
"started_at": "2026-06-13T16:39:06-06:00",
"harness_finished_at": "2026-06-13T17:19:18-06:00",
"finished_at": "2026-06-13T17:19:45-06:00",
"start_epoch": 1781390346,
"harness_end_epoch": 1781392758,
"end_epoch": 1781392785,
"has_solution": true,
"correct": true,
"failure_reason": "pass",
"retryable_infra_failure": false,
"minimum_useful_output_tokens": 5000,
"peak_fraction": 0.2411,
"template_mutated": false,
"elapsed_seconds": 2412,
"total_elapsed_seconds": 2439,
"check_elapsed_seconds": 14,
"benchmark_elapsed_seconds": 13,
"check_timeout_seconds": 180,
"benchmark_timeout_seconds": 1800,
"check_exit_code": 0,
"benchmark_exit_code": 0,
"harness_exit_code": 0,
"session_complete": true,
"agent_cuda_disabled": false,
"agent_container": true,
"agent_container_image": "nvcr.io/nvidia/tensorrt-llm/release:latest",
"agent_container_network": "bridge",
"gpu_queue_mode": "agent_container_native_profiling_path_wrapper_gpu_lock",
"output_tokens_per_second": 28.407131011608623,
"usage": {"input_tokens": 103452, "output_tokens": 68518, "cache_read_tokens": 5590528, "cache_creation_tokens": 0, "reasoning_tokens": null, "total_cost_usd": 5.025473999999999}
}
{
"command": "ls -la /workspace/problems/03_paged_attention",
"description": "List workspace files"
}total 44
drwxrwxr-x 2 1002 1002 4096 Jun 13 22:39 .
drwxrwxr-x 3 1002 1002 4096 Jun 13 22:39 ..
-rw-rw-r-- 1 1002 1002 2585 Apr 27 09:21 PROMPT.txt
-rw-rw-r-- 1 1002 1002 5387 Jun 1 21:05 benchmark.py
-rw-rw-r-- 1 1002 1002 3841 Jun 2 01:08 check.py
-rw-rw-r-- 1 1002 1002 2030 Apr 26 04:19 problem.yaml
-rw-rw-r-- 1 1002 1002 5151 Apr 26 04:19 reference.py
-rw-rw-r-- 1 1002 1002 933 Apr 26 04:19 shapes.py
-rw-rw-r-- 1 1002 1002 2819 Apr 26 04:24 sota.py{
"command": "uv run python --version && uv run python -c \"import torch; print(torch.__version__, torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'no cuda')\"",
"description": "Check python and torch version"
}{
"command": "uv run python -c \"import triton; print(triton.__version__)\" 2>&1",
"description": "Check triton availability"
}Using CPython 3.11.15
Creating virtual environment at: /workspace/.venv
Building kernelbench-hard @ file:///workspace
Built kernelbench-hard @ file:///workspace
warning: Failed to hardlink files; falling back to full copy. This may lead to degraded performance.
If the cache and target directories are on different filesystems, hardlinking may not be supported.
If this is intentional, set `export UV_LINK_MODE=copy` or use `--link-mode=copy` to suppress this warning.
Installed 56 packages in 862ms
Python 3.11.15
2.11.0+cu130 NVIDIA RTX PRO 6000 Blackwell Workstation Edition3.6.0{
"file_path": "/workspace/problems/03_paged_attention/reference.py"
}1 """Naive PyTorch paged-attention decode reference (correctness oracle, not SOTA).
2
3 Single-query decode: each batch element has a query of shape (num_heads, head_dim)
4 and attends over a KV cache of `seq_len[b]` tokens stored as fixed-size pages in
5 a global pool. Pages for batch element b are listed in `block_table[b]`.
6
7 The reference performs the slow path:
8 1. Gather pages -> contiguous (seq_len, num_kv_heads, head_dim) per batch element.
9 2. Repeat KV heads for grouped-query (broadcast num_kv_heads -> num_heads).
10 3. Manual softmax(QK^T / sqrt(d)) @ V in fp32, cast back to bf16.
11
12 This avoids torch.nn.functional.scaled_dot_product_attention (which is on the
13 forbidden list) so the agent cannot dispatch through SDPA either.
14 """
15 import math
16
17 import torch
18 import torch.nn as nn
19
20 OP_TYPE = "attention"
21 SUPPORTED_PRECISIONS = ["bf16"]
22 HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
23
24
25 # --- Shape knobs (overridden by check.py / benchmark.py from shapes.py) ----
26 BATCH = 8
27 NUM_HEADS = 32
28 NUM_KV_HEADS = 8
29 HEAD_DIM = 128
30 SEQ_LEN = 1024
31 PAGE_SIZE = 16
32
33
34 class Model(nn.Module):
35 """Single-query paged attention decode.
36
37 Forward inputs (all on device):
38 query: (batch, num_heads, head_dim) bf16
39 kv_cache: (num_blocks, page_size, num_kv_heads, head_dim * 2)
40 Layout: last dim packs [K | V] so a single gather pulls both.
4
... (4123 more chars){
"file_path": "/workspace/problems/03_paged_attention/check.py"
}1 """Correctness runner for paged-attention decode.
2
3 Runs solution.Model vs reference.Model across all shapes in shapes.py, 3 seeds
4 each, with per-dtype atol/rtol. Also rejects forbidden ops by grep.
5 """
6 import re
7 import sys
8 from pathlib import Path
9
10 import torch
11 import yaml
12
13 REPO_ROOT = Path(__file__).resolve().parents[2]
14 sys.path.insert(0, str(REPO_ROOT))
15
16 from src.eval.correctness import check_correctness # noqa: E402
17 from src.eval.numeric_stress import ( # noqa: E402
18 numeric_stress_cases,
19 numeric_stress_context,
20 tolerance_for_case,
21 )
22
23
24 def _apply_shape(reference, shape: dict) -> None:
25 reference.BATCH = shape["batch"]
26 reference.NUM_HEADS = shape["num_heads"]
27 reference.NUM_KV_HEADS = shape["num_kv_heads"]
28 reference.HEAD_DIM = shape["head_dim"]
29 reference.SEQ_LEN = shape["seq_len"]
30 reference.PAGE_SIZE = shape["page_size"]
31
32
33 def main():
34 try:
35 import reference
36 import shapes
37 import solution
38 except Exception as e:
39 print(f"FAIL: import error: {e}")
40 sys.exit(1)
41
42 problem_yaml = Path("problem.yaml")
43 meta = yaml.safe_load(problem_yaml.read_text()) if problem_yaml.exists() else {}
44
45 sol_src = Path("solution.py").read_text() if Path("solution.py").exists() else ""
46 for forbidden in meta.get("forbidden", []):
47 pat = re.escape(forbidden)
48 if re.se
... (2701 more chars){
"file_path": "/workspace/problems/03_paged_attention/benchmark.py"
}1 """Roofline benchmark for paged-attention decode.
2
3 For each shape: times the agent's solution first. Optional eager, compiled,
4 and SOTA diagnostics are enabled with KBH_BENCHMARK_BASELINES=1. Reports achieved TFLOPS, GB/s, and peak_fraction.
5
6 Decode is memory-bound, so peak_fraction is computed from achieved GB/s vs
7 the GPU's peak DRAM bandwidth.
8 """
9 import sys
10 from math import exp, log
11 from pathlib import Path
12
13 import torch
14 import yaml
15
16 REPO_ROOT = Path(__file__).resolve().parents[2]
17 sys.path.insert(0, str(REPO_ROOT))
18
19 from src.eval.roofline import compute_gbps, compute_tflops, peak_fraction # noqa: E402
20 from src.eval.timing import benchmark_baselines_enabled, time_variant # noqa: E402
21 from src.hardware import get as get_hw # noqa: E402
22
23
24 def _eval_formula(expr: str, vars: dict) -> float:
25 return float(eval(expr, {"__builtins__": {}}, vars))
26
27
28 def _apply_shape(reference, shape: dict) -> None:
29 reference.BATCH = shape["batch"]
30 reference.NUM_HEADS = shape["num_heads"]
31 reference.NUM_KV_HEADS = shape["num_kv_heads"]
32 reference.HEAD_DIM = shape["head_dim"]
33 reference.SEQ_LEN = shape["seq_len"]
34 reference.PAGE_SIZE = shape["page_size"]
35
36
37 def main():
38 import reference
39 import shapes
40 import solution
41
42 meta = yaml.safe_load(Path("problem.yaml").read_text())
43 hw = get_hw(meta["hardware"][0])
44 peak_tflops = hw.peak_tflop
... (4399 more chars){
"file_path": "/workspace/problems/03_paged_attention/shapes.py"
}1 """Shape sweep for paged attention decode.
2
3 Mix targets:
4 - small batch / long context (Llama-3 8B-style decode)
5 - large batch / medium context (server batched decode)
6 - GQA wide ratio (Llama-3 70B: 64 heads / 8 kv-heads)
7 - non-power-of-2 seq_len (forces predicated tail handling)
8 - head_dim=64 small-head case
9 """
10
11 SHAPES = [
12 # (B, H, Hkv, D, L, P)
13 {"batch": 8, "num_heads": 32, "num_kv_heads": 8, "head_dim": 128, "seq_len": 1024, "page_size": 16},
14 {"batch": 32, "num_heads": 32, "num_kv_heads": 8, "head_dim": 128, "seq_len": 2048, "page_size": 16},
15 {"batch": 4, "num_heads": 64, "num_kv_heads": 8, "head_dim": 128, "seq_len": 4096, "page_size": 16},
16 {"batch": 16, "num_heads": 32, "num_kv_heads": 8, "head_dim": 128, "seq_len": 1535, "page_size": 16}, # non-pow2
17 {"batch": 8, "num_heads": 16, "num_kv_heads": 4, "head_dim": 64, "seq_len": 2000, "page_size": 16}, # small-D, non-pow2
18 ]
19 {
"file_path": "/workspace/problems/03_paged_attention/sota.py"
}1 """SOTA reference for paged-attention decode.
2
3 Tries, in order:
4 1. FlashInfer's BatchDecodeWithPagedKVCacheWrapper (preferred -- portable,
5 supports SM120, GQA, arbitrary head_dim).
6 2. vLLM's paged_attention_v2 CUDA op (requires its KV-cache layout, more
7 finicky; we adapt the layout on the fly when possible).
8
9 If neither is importable, is_available() returns False and the benchmark just
10 reports eager + compiled + solution.
11
12 Agents are FORBIDDEN from importing these in solution.py (see problem.yaml).
13 This file is only for the benchmark's reference line.
14 """
15 from __future__ import annotations
16
17 import torch
18
19
20 def _try_flashinfer(
21 query: torch.Tensor,
22 kv_cache: torch.Tensor,
23 block_table: torch.Tensor,
24 seq_lens: torch.Tensor,
25 num_kv_heads: int,
26 head_dim: int,
27 page_size: int,
28 ) -> torch.Tensor | None:
29 try:
30 import flashinfer # noqa: F401
31 from flashinfer.decode import BatchDecodeWithPagedKVCacheWrapper
32 except Exception:
33 return None
34
35 B, H, D = query.shape
36 # FlashInfer expects K and V as separate (num_blocks, page_size, num_kv_heads, head_dim) tensors.
37 # Our reference packs [K|V] on the last dim -- split here.
38 k_cache = kv_cache[..., :D].contiguous()
39 v_cache = kv_cache[..., D:].contiguous()
40
41 workspace = torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device=query.device)
42
... (1565 more chars){
"file_path": "/workspace/problems/03_paged_attention/problem.yaml"
}1 name: 03_paged_attention
2 display_name: "Paged Attention Decode"
3 precision: bf16
4 regime: memory # decode is bandwidth-bound (KV cache streamed once per token)
5
6 # Effective FLOPs per call: QK^T + softmax*V across all batches/heads/seq.
7 # Sum-formula evaluated per-shape; the benchmark substitutes batch/num_heads/etc.
8 # 2 * B * H * L * D for QK^T plus 2 * B * H * L * D for AV => 4 * B * H * L * D
9 flops_formula: "4 * batch * num_heads * seq_len * head_dim"
10
11 # Bytes moved (the real bottleneck): the KV cache must be streamed end-to-end.
12 # K and V each: batch * seq_len * num_kv_heads * head_dim * 2 bytes (bf16)
13 # Q: batch * num_heads * head_dim * 2 (negligible vs KV)
14 # Out: same as Q
15 # So total ~ 2 * (B * L * Hkv * D * 2) + small.
16 bytes_formula: "2 * batch * seq_len * num_kv_heads * head_dim * 2 + batch * num_heads * head_dim * 2 * 2"
17
18 hardware: [RTX_PRO_6000]
19 peak_tflops_key: bf16 # informational; regime=memory uses bandwidth
20 peak_bandwidth_key: dram # 1.8 TB/s GDDR7 on RTX PRO 6000 Blackwell
21
22 tolerance:
23 bfloat16: 0.02
24 float16: 0.02
25
26 # Forbidden ops -- using any of these in solution.py fails correctness post-hoc.
27 # vllm.attention / flashinfer.batch_decode_with_paged_kv_cache: dispatching to
28 # the SOTA library is the trivial cheat. SDPA is also banned because the agent
29 # could gather pages then call SDPA and inherit FlashAttention "for free".
30 forbidden:
31 - "vllm.attenti
... (668 more chars){
"command": "find /workspace -type d -name src 2>/dev/null | head -5",
"description": "Find src directory"
}/workspace/src
/workspace/.venv/lib/python3.11/site-packages/numpy/f2py/src
/workspace/.venv/lib/python3.11/site-packages/numpy/f2py/tests/src
/workspace/.venv/lib/python3.11/site-packages/numpy-2.4.4.dist-info/licenses/numpy/random/src
/workspace/.venv/lib/python3.11/site-packages/numpy-2.4.4.dist-info/licenses/numpy/_core/src{
"subject": "Implement Triton paged-attention decode kernel",
"description": "Write a custom Triton kernel in solution.py that performs single-query paged attention decode, supports GQA and variable sequence lengths, and conforms to the Model/get_inputs/get_init_inputs interface.",
"activeForm": "Implementing Triton paged-attention decode kernel"
}Task #1 created successfully: Implement Triton paged-attention decode kernel{
"subject": "Verify correctness with check.py",
"description": "Run `python check.py` across all shapes and seeds; fix any numerical or masking issues until PASS is printed.",
"activeForm": "Verifying correctness with check.py"
}Task #2 created successfully: Verify correctness with check.py{
"subject": "Profile and optimize kernel",
"description": "Use benchmark.py and optional profiling tools to evaluate achieved bandwidth/TFLOPS. Iterate on block size, num_warps, or algorithm if needed to improve performance.",
"activeForm": "Profiling and optimizing kernel"
}Task #3 created successfully: Profile and optimize kernel--- /dev/null
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -0,0 +1,155 @@
+"""Paged attention decode kernel implemented in Triton.
+
+Conforms to the same Model/get_inputs/get_init_inputs interface as reference.py.
+"""
+import math
+from typing import List
+
+import torch
+import torch.nn as nn
+import triton
+import triton.language as tl
+
+
+OP_TYPE = "attention"
+SUPPORTED_PRECISIONS = ["bf16"]
+HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
+
+BATCH = 8
+NUM_HEADS = 32
+NUM_KV_HEADS = 8
+HEAD_DIM = 128
+SEQ_LEN = 1024
+PAGE_SIZE = 16
+
+
+@triton.jit
+def paged_decode_kernel(
+ q_ptr,
+ kv_ptr,
+ block_table_ptr,
+ seq_lens_ptr,
+ out_ptr,
+ stride_qb,
+ stride_qh,
+ stride_qd,
+ stride_kvn,
+ stride_kvt,
+ stride_kvh,
+ stride_kvd,
+ stride_bt_b,
+ stride_bt_p,
+ stride_ob,
+ stride_oh,
+ stride_od,
+ SCALE: tl.constexpr,
+ PAGE_SIZE: tl.constexpr,
+ HEAD_DIM: tl.constexpr,
+ G: tl.constexpr,
+):
+ """One CUDA block per (batch, kv_head). Computes all G query heads."""
+ pid_b = tl.program_id(0)
+ pid_hkv = tl.program_id(1)
+
+ seq_len = tl.load(seq_lens_ptr + pid_b)
+ num_pages = tl.cdiv(seq_len, PAGE_SIZE)
+
+ # Load all query heads belonging to this KV group into a (G, D) tensor.
+ offs_d = tl.arange(0, HEAD_DIM)
+ qs = tl.zeros((G, HEAD_DIM), dtype=tl.float32)
+ base_q = q_ptr + pid_b * stride_qb + pid_hkv * G * stride_qh
+ for g in tl.static_range(G):
+ q_ptrs = base_q + g * stride_qh + offs_d * stride_qd
+ qs = tl.sum(
+ qs + tl.load(q_ptrs, mask=offs_d < HEAD_DIM, other=0.0).to(tl.float32)[None, :],
+ axis=0,
+ ) # no-op sum just to scatter; actually this is wrong.
+
+
+class Model(nn.Module):
+ def __init__(
+ self,
+ batch: int,
+ num_heads: int,
+ num_kv_heads: int,
+ head_dim: int,
+ seq_len: int,
+ page_size: int,
+ ):
+ super().__init__()
+ assert num_heads % num_kv_heads == 0
+ self.batch = batch
+ self.num_heads = num_heads
+ self.num_kv_heads = num_kv_heads
+ self.head_dim = head_dim
+ self.seq_len = seq_len
+ self.page_size = page_size
+ self.group_size = num_heads // num_kv_heads
+ self.scale = 1.0 / math.sqrt(head_dim)
+ self.register_buffer("_dummy", torch.zeros(1, dtype=torch.bfloat16), persistent=False)
+
+ def forward(
+ self,
+ query: torch.Tensor,
+ kv_cache: torch.Tensor,
+ block_table: torch.Tensor,
+ seq_lens: torch.Tensor,
+ ) -> torch.Tensor:
+ B, H, D = query.shape
+ Hkv = self.num_kv_heads
+ G = self.group_size
+ P = self.page_size
+
+ out = torch.empty(B, H, D, dtype=query.dtype, device=query.device)
+
+ grid = (B, Hkv)
+ paged_decode_kernel[grid](
+ query,
+ kv_cache,
+ block_table,
+ seq_lens,
+ out,
+ query.stride(0),
+ query.stride(1),
+ query.stride(2),
+ kv_cache.stride(0),
+ kv_cache.stride(1),
+ kv_cache.stride(2),
+ kv_cache.stride(3),
+ block_table.stride(0),
+ block_table.stride(1),
+ out.stride(0),
+ out.stride(1),
+ out.stride(2),
+ SCALE=self.scale,
+ PAGE_SIZE=P,
+ HEAD_DIM=D,
+ G=G,
+ num_warps=4,
+ )
+ return out
+
+
+def get_inputs():
+ B = BATCH
+ H = NUM_HEADS
+ Hkv = NUM_KV_HEADS
+ D = HEAD_DIM
+ L = SEQ_LEN
+ P = PAGE_SIZE
+
+ pages_per_seq = (L + P - 1) // P
+ total_pages = max(B * pages_per_seq + 8, 64)
+
+ query = torch.randn(B, H, D, dtype=torch.bfloat16) * 0.1
+ kv_cache = torch.randn(total_pages, P, Hkv, 2 * D, dtype=torch.bfloat16) * 0.1
+
+ perm = torch.randperm(total_pages)[: B * pages_per_seq].reshape(B, pages_per_seq).int()
+ block_table = perm.contiguous()
+ seq_lens = torch.full((B,), L, dtype=torch.int32)
+
+ return [query, kv_cache, block_table, seq_lens]
+
+
+def get_init_inputs():
+ return [BATCH, NUM_HEADS, NUM_KV_HEADS, HEAD_DIM, SEQ_LEN, PAGE_SIZE]
{
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"content": "<4030 chars \u2014 see diff>"
}File created successfully at: /workspace/problems/03_paged_attention/solution.py (file state is current in your context — no need to Read it back)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -3,7 +3,6 @@
Conforms to the same Model/get_inputs/get_init_inputs interface as reference.py.
"""
import math
-from typing import List
import torch
import torch.nn as nn
@@ -54,16 +53,80 @@
seq_len = tl.load(seq_lens_ptr + pid_b)
num_pages = tl.cdiv(seq_len, PAGE_SIZE)
- # Load all query heads belonging to this KV group into a (G, D) tensor.
offs_d = tl.arange(0, HEAD_DIM)
- qs = tl.zeros((G, HEAD_DIM), dtype=tl.float32)
- base_q = q_ptr + pid_b * stride_qb + pid_hkv * G * stride_qh
- for g in tl.static_range(G):
- q_ptrs = base_q + g * stride_qh + offs_d * stride_qd
- qs = tl.sum(
- qs + tl.load(q_ptrs, mask=offs_d < HEAD_DIM, other=0.0).to(tl.float32)[None, :],
- axis=0,
- ) # no-op sum just to scatter; actually this is wrong.
+ offs_g = tl.arange(0, G)[:, None]
+ offs_g1 = tl.arange(0, G)
+
+ # Load the G query heads for this KV group: (G, HEAD_DIM).
+ q_ptrs = (
+ q_ptr
+ + pid_b * stride_qb
+ + (pid_hkv * G + offs_g) * stride_qh
+ + offs_d[None, :] * stride_qd
+ )
+ qs = tl.load(q_ptrs, mask=offs_d[None, :] < HEAD_DIM, other=0.0).to(tl.float32)
+
+ ms = tl.full((G,), float("-inf"), dtype=tl.float32)
+ ls = tl.zeros((G,), dtype=tl.float32)
+ accs = tl.zeros((G, HEAD_DIM), dtype=tl.float32)
+
+ offs_t = tl.arange(0, PAGE_SIZE)
+ for p in range(num_pages):
+ physical_block = tl.load(
+ block_table_ptr + pid_b * stride_bt_b + p * stride_bt_p
+ ).to(tl.int64)
+ token_offset = p * PAGE_SIZE
+ valid = tl.minimum(PAGE_SIZE, seq_len - token_offset)
+
+ kv_base = kv_ptr + physical_block * stride_kvn + pid_hkv * stride_kvh
+ k_ptrs = kv_base + (offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd)
+ k = tl.load(
+ k_ptrs,
+ mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
+ other=0.0,
+ ).to(tl.float32)
+ v_ptrs = kv_base + HEAD_DIM * stride_kvd + (
+ offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd
+ )
+ v = tl.load(
+ v_ptrs,
+ mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
+ other=0.0,
+ ).to(tl.float32)
+
+ for g in tl.static_range(G):
+ q = qs[g, :]
+ # scores: (1, PAGE_SIZE) -> (PAGE_SIZE,)
+ scores = tl.dot(q[None, :], tl.trans(k))
+ scores = tl.where(offs_t < valid, scores[0, :], float("-inf")) * SCALE
+
+ m_new = tl.maximum(ms[g], tl.max(scores, axis=0))
+ exp_scale = tl.exp(ms[g] - m_new)
+ exp_scores = tl.exp(scores - m_new)
+
+ # Weighted V sum: (1, PAGE_SIZE) @ (PAGE_SIZE, HEAD_DIM) -> (HEAD_DIM,)
+ weighted = tl.dot(exp_scores[None, :], v)[0, :]
+
+ new_acc = accs[g, :] * exp_scale + weighted
+ new_l = ls[g] * exp_scale + tl.sum(exp_scores, axis=0)
+
+ mask_g = offs_g1 == g
+ mask_g2 = mask_g[:, None]
+ accs = tl.where(mask_g2, new_acc[None, :], accs)
+ ls = tl.where(mask_g, new_l, ls)
+ ms = tl.where(mask_g, m_new, ms)
+
+ out_ptrs = (
+ out_ptr
+ + pid_b * stride_ob
+ + (pid_hkv * G + offs_g) * stride_oh
+ + offs_d[None, :] * stride_od
+ )
+ tl.store(
+ out_ptrs,
+ (accs / ls[:, None]).to(tl.bfloat16),
+ mask=offs_d[None, :] < HEAD_DIM,
+ )
class Model(nn.Module):
@@ -77,7 +140,7 @@
page_size: int,
):
super().__init__()
- assert num_heads % num_kv_heads == 0
+ assert num_heads % num_kv_heads == 0, "num_heads must be a multiple of num_kv_heads (GQA)"
self.batch = batch
self.num_heads = num_heads
self.num_kv_heads = num_kv_heads
@@ -86,6 +149,7 @@
self.page_size = page_size
self.group_size = num_heads // num_kv_heads
self.scale = 1.0 / math.sqrt(head_dim)
+
self.register_buffer("_dummy", torch.zeros(1, dtype=torch.bfloat16), persistent=False)
def forward(
{
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"content": "<6193 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"taskId": "1",
"status": "in_progress"
}Updated task #1 status{
"command": "uv run python check.py 2>&1 | head -100",
"description": "Run correctness check"
}Traceback (most recent call last):
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 43, in wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 1101, in __getitem__
raise ValueError(f"unsupported tensor index: {sl}")
ValueError: unsupported tensor index: constexpr[0]
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/workspace/problems/03_paged_attention/check.py", line 116, in <module>
main()
File "/workspace/problems/03_paged_attention/check.py", line 79, in main
sol_out = sol_model(*inputs)
^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/problems/03_paged_attention/solution.py", line 170, in forward
paged_decode_kernel[grid](
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^
... (1598 more chars)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -95,7 +95,7 @@
).to(tl.float32)
for g in tl.static_range(G):
- q = qs[g, :]
+ q = qs[g]
# scores: (1, PAGE_SIZE) -> (PAGE_SIZE,)
scores = tl.dot(q[None, :], tl.trans(k))
scores = tl.where(offs_t < valid, scores[0, :], float("-inf")) * SCALE
@@ -107,7 +107,7 @@
# Weighted V sum: (1, PAGE_SIZE) @ (PAGE_SIZE, HEAD_DIM) -> (HEAD_DIM,)
weighted = tl.dot(exp_scores[None, :], v)[0, :]
- new_acc = accs[g, :] * exp_scale + weighted
+ new_acc = accs[g] * exp_scale + weighted
new_l = ls[g] * exp_scale + tl.sum(exp_scores, axis=0)
mask_g = offs_g1 == g
{
"replace_all": true,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<640 chars \u2014 see diff>",
"new_string": "<634 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated. All occurrences were successfully replaced. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -100",
"description": "Run correctness check"
}Traceback (most recent call last):
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 43, in wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 1101, in __getitem__
raise ValueError(f"unsupported tensor index: {sl}")
ValueError: unsupported tensor index: constexpr[0]
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/workspace/problems/03_paged_attention/check.py", line 116, in <module>
main()
File "/workspace/problems/03_paged_attention/check.py", line 79, in main
sol_out = sol_model(*inputs)
^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/problems/03_paged_attention/solution.py", line 170, in forward
paged_decode_kernel[grid](
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^
... (1595 more chars)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -54,23 +54,21 @@
num_pages = tl.cdiv(seq_len, PAGE_SIZE)
offs_d = tl.arange(0, HEAD_DIM)
- offs_g = tl.arange(0, G)[:, None]
- offs_g1 = tl.arange(0, G)
-
- # Load the G query heads for this KV group: (G, HEAD_DIM).
- q_ptrs = (
- q_ptr
- + pid_b * stride_qb
- + (pid_hkv * G + offs_g) * stride_qh
- + offs_d[None, :] * stride_qd
- )
- qs = tl.load(q_ptrs, mask=offs_d[None, :] < HEAD_DIM, other=0.0).to(tl.float32)
-
- ms = tl.full((G,), float("-inf"), dtype=tl.float32)
- ls = tl.zeros((G,), dtype=tl.float32)
- accs = tl.zeros((G, HEAD_DIM), dtype=tl.float32)
-
offs_t = tl.arange(0, PAGE_SIZE)
+
+ # Load the G query heads for this KV group into a Python list.
+ base_q = q_ptr + pid_b * stride_qb + pid_hkv * G * stride_qh
+ qs = []
+ for g in tl.static_range(G):
+ q_ptrs = base_q + g * stride_qh + offs_d * stride_qd
+ qs.append(
+ tl.load(q_ptrs, mask=offs_d < HEAD_DIM, other=0.0).to(tl.float32)
+ )
+
+ ms = [tl.full((), float("-inf"), dtype=tl.float32) for _ in range(G)]
+ ls = [tl.zeros((), dtype=tl.float32) for _ in range(G)]
+ accs = [tl.zeros((HEAD_DIM,), dtype=tl.float32) for _ in range(G)]
+
for p in range(num_pages):
physical_block = tl.load(
block_table_ptr + pid_b * stride_bt_b + p * stride_bt_p
@@ -96,37 +94,30 @@
for g in tl.static_range(G):
q = qs[g]
- # scores: (1, PAGE_SIZE) -> (PAGE_SIZE,)
scores = tl.dot(q[None, :], tl.trans(k))
scores = tl.where(offs_t < valid, scores[0, :], float("-inf")) * SCALE
m_new = tl.maximum(ms[g], tl.max(scores, axis=0))
exp_scale = tl.exp(ms[g] - m_new)
exp_scores = tl.exp(scores - m_new)
-
- # Weighted V sum: (1, PAGE_SIZE) @ (PAGE_SIZE, HEAD_DIM) -> (HEAD_DIM,)
weighted = tl.dot(exp_scores[None, :], v)[0, :]
- new_acc = accs[g] * exp_scale + weighted
- new_l = ls[g] * exp_scale + tl.sum(exp_scores, axis=0)
-
- mask_g = offs_g1 == g
- mask_g2 = mask_g[:, None]
- accs = tl.where(mask_g2, new_acc[None, :], accs)
- ls = tl.where(mask_g, new_l, ls)
- ms = tl.where(mask_g, m_new, ms)
-
- out_ptrs = (
- out_ptr
- + pid_b * stride_ob
- + (pid_hkv * G + offs_g) * stride_oh
- + offs_d[None, :] * stride_od
- )
- tl.store(
- out_ptrs,
- (accs / ls[:, None]).to(tl.bfloat16),
- mask=offs_d[None, :] < HEAD_DIM,
- )
+ accs[g] = accs[g] * exp_scale + weighted
+ ls[g] = ls[g] * exp_scale + tl.sum(exp_scores, axis=0)
+ ms[g] = m_new
+
+ for g in tl.static_range(G):
+ out_ptrs = (
+ out_ptr
+ + pid_b * stride_ob
+ + (pid_hkv * G + g) * stride_oh
+ + offs_d * stride_od
+ )
+ tl.store(
+ out_ptrs,
+ (accs[g] / ls[g]).to(tl.bfloat16),
+ mask=offs_d < HEAD_DIM,
+ )
class Model(nn.Module):
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<2646 chars \u2014 see diff>",
"new_string": "<2399 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -120",
"description": "Run correctness check"
}Traceback (most recent call last):
File "/workspace/problems/03_paged_attention/check.py", line 116, in <module>
main()
File "/workspace/problems/03_paged_attention/check.py", line 79, in main
sol_out = sol_model(*inputs)
^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/problems/03_paged_attention/solution.py", line 161, in forward
paged_decode_kernel[grid](
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 720, in run
kernel = self._do_compile(key, signature, device, constexprs, options, attrs, warmup)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 849, in _do_compile
kernel = self.compile(src, target=target, options=options.__dict__)
... (1140 more chars)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -56,14 +56,12 @@
offs_d = tl.arange(0, HEAD_DIM)
offs_t = tl.arange(0, PAGE_SIZE)
- # Load the G query heads for this KV group into a Python list.
+ # Load the G query heads for this KV group into a fixed-size Python list.
base_q = q_ptr + pid_b * stride_qb + pid_hkv * G * stride_qh
- qs = []
+ qs = [tl.zeros((HEAD_DIM,), dtype=tl.float32) for _ in range(G)]
for g in tl.static_range(G):
q_ptrs = base_q + g * stride_qh + offs_d * stride_qd
- qs.append(
- tl.load(q_ptrs, mask=offs_d < HEAD_DIM, other=0.0).to(tl.float32)
- )
+ qs[g] = tl.load(q_ptrs, mask=offs_d < HEAD_DIM, other=0.0).to(tl.float32)
ms = [tl.full((), float("-inf"), dtype=tl.float32) for _ in range(G)]
ls = [tl.zeros((), dtype=tl.float32) for _ in range(G)]
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<551 chars \u2014 see diff>",
"new_string": "<594 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -120",
"description": "Run correctness check"
}Traceback (most recent call last):
File "/workspace/problems/03_paged_attention/check.py", line 116, in <module>
main()
File "/workspace/problems/03_paged_attention/check.py", line 79, in main
sol_out = sol_model(*inputs)
^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/problems/03_paged_attention/solution.py", line 159, in forward
paged_decode_kernel[grid](
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 720, in run
kernel = self._do_compile(key, signature, device, constexprs, options, attrs, warmup)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 849, in _do_compile
kernel = self.compile(src, target=target, options=options.__dict__)
... (1182 more chars)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -56,16 +56,16 @@
offs_d = tl.arange(0, HEAD_DIM)
offs_t = tl.arange(0, PAGE_SIZE)
- # Load the G query heads for this KV group into a fixed-size Python list.
+ # Load the G query heads for this KV group into a tuple.
base_q = q_ptr + pid_b * stride_qb + pid_hkv * G * stride_qh
- qs = [tl.zeros((HEAD_DIM,), dtype=tl.float32) for _ in range(G)]
- for g in tl.static_range(G):
- q_ptrs = base_q + g * stride_qh + offs_d * stride_qd
- qs[g] = tl.load(q_ptrs, mask=offs_d < HEAD_DIM, other=0.0).to(tl.float32)
-
- ms = [tl.full((), float("-inf"), dtype=tl.float32) for _ in range(G)]
- ls = [tl.zeros((), dtype=tl.float32) for _ in range(G)]
- accs = [tl.zeros((HEAD_DIM,), dtype=tl.float32) for _ in range(G)]
+ qs = tuple(
+ tl.load(base_q + g * stride_qh + offs_d * stride_qd, mask=offs_d < HEAD_DIM, other=0.0).to(tl.float32)
+ for g in range(G)
+ )
+
+ ms = tuple(tl.full((), float("-inf"), dtype=tl.float32) for _ in range(G))
+ ls = tuple(tl.zeros((), dtype=tl.float32) for _ in range(G))
+ accs = tuple(tl.zeros((HEAD_DIM,), dtype=tl.float32) for _ in range(G))
for p in range(num_pages):
physical_block = tl.load(
@@ -100,9 +100,12 @@
exp_scores = tl.exp(scores - m_new)
weighted = tl.dot(exp_scores[None, :], v)[0, :]
- accs[g] = accs[g] * exp_scale + weighted
- ls[g] = ls[g] * exp_scale + tl.sum(exp_scores, axis=0)
- ms[g] = m_new
+ new_acc = accs[g] * exp_scale + weighted
+ new_l = ls[g] * exp_scale + tl.sum(exp_scores, axis=0)
+
+ ms = tuple(m_new if i == g else ms[i] for i in range(G))
+ ls = tuple(new_l if i == g else ls[i] for i in range(G))
+ accs = tuple(new_acc if i == g else accs[i] for i in range(G))
for g in tl.static_range(G):
out_ptrs = (
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<2368 chars \u2014 see diff>",
"new_string": "<2468 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -120",
"description": "Run correctness check"
}Traceback (most recent call last):
File "/workspace/problems/03_paged_attention/check.py", line 116, in <module>
main()
File "/workspace/problems/03_paged_attention/check.py", line 79, in main
sol_out = sol_model(*inputs)
^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/problems/03_paged_attention/solution.py", line 162, in forward
paged_decode_kernel[grid](
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 720, in run
kernel = self._do_compile(key, signature, device, constexprs, options, attrs, warmup)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 849, in _do_compile
kernel = self.compile(src, target=target, options=options.__dict__)
... (1083 more chars){
"command": "uv run python -c \"import triton.language as tl; print(hasattr(tl,'gather'), hasattr(tl,'scatter'))\"",
"description": "Check Triton gather/scatter availability"
}True False--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -56,17 +56,24 @@
offs_d = tl.arange(0, HEAD_DIM)
offs_t = tl.arange(0, PAGE_SIZE)
- # Load the G query heads for this KV group into a tuple.
- base_q = q_ptr + pid_b * stride_qb + pid_hkv * G * stride_qh
- qs = tuple(
- tl.load(base_q + g * stride_qh + offs_d * stride_qd, mask=offs_d < HEAD_DIM, other=0.0).to(tl.float32)
- for g in range(G)
+ offs_d = tl.arange(0, HEAD_DIM)
+ offs_g = tl.arange(0, G)[:, None]
+ offs_g1 = tl.arange(0, G)
+
+ # Load the G query heads for this KV group: (G, HEAD_DIM).
+ q_ptrs = (
+ q_ptr
+ + pid_b * stride_qb
+ + (pid_hkv * G + offs_g) * stride_qh
+ + offs_d[None, :] * stride_qd
)
-
- ms = tuple(tl.full((), float("-inf"), dtype=tl.float32) for _ in range(G))
- ls = tuple(tl.zeros((), dtype=tl.float32) for _ in range(G))
- accs = tuple(tl.zeros((HEAD_DIM,), dtype=tl.float32) for _ in range(G))
-
+ qs = tl.load(q_ptrs, mask=offs_d[None, :] < HEAD_DIM, other=0.0).to(tl.float32)
+
+ ms = tl.full((G,), float("-inf"), dtype=tl.float32)
+ ls = tl.zeros((G,), dtype=tl.float32)
+ accs = tl.zeros((G, HEAD_DIM), dtype=tl.float32)
+
+ offs_t = tl.arange(0, PAGE_SIZE)
for p in range(num_pages):
physical_block = tl.load(
block_table_ptr + pid_b * stride_bt_b + p * stride_bt_p
@@ -91,7 +98,7 @@
).to(tl.float32)
for g in tl.static_range(G):
- q = qs[g]
+ q = tl.gather(qs, g, axis=0)
scores = tl.dot(q[None, :], tl.trans(k))
scores = tl.where(offs_t < valid, scores[0, :], float("-inf")) * SCALE
@@ -103,22 +110,23 @@
new_acc = accs[g] * exp_scale + weighted
new_l = ls[g] * exp_scale + tl.sum(exp_scores, axis=0)
- ms = tuple(m_new if i == g else ms[i] for i in range(G))
- ls = tuple(new_l if i == g else ls[i] for i in range(G))
- accs = tuple(new_acc if i == g else accs[i] for i in range(G))
-
- for g in tl.static_range(G):
- out_ptrs = (
- out_ptr
- + pid_b * stride_ob
- + (pid_hkv * G + g) * stride_oh
- + offs_d * stride_od
- )
- tl.store(
- out_ptrs,
- (accs[g] / ls[g]).to(tl.bfloat16),
- mask=offs_d < HEAD_DIM,
- )
+ mask_g = offs_g1 == g
+ mask_g2 = mask_g[:, None]
+ accs = tl.where(mask_g2, new_acc[None, :], accs)
+ ls = tl.where(mask_g, new_l, ls)
+ ms = tl.where(mask_g, m_new, ms)
+
+ out_ptrs = (
+ out_ptr
+ + pid_b * stride_ob
+ + (pid_hkv * G + offs_g) * stride_oh
+ + offs_d[None, :] * stride_od
+ )
+ tl.store(
+ out_ptrs,
+ (accs / ls[:, None]).to(tl.bfloat16),
+ mask=offs_d[None, :] < HEAD_DIM,
+ )
class Model(nn.Module):
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<2468 chars \u2014 see diff>",
"new_string": "<2527 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -120",
"description": "Run correctness check"
}Traceback (most recent call last):
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 43, in wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 2806, in gather
return _semantic.gather(src, index, axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/semantic.py", line 1742, in gather
assert index.dtype.is_int(), "index must be an integer tensor"
^^^^^^^^^^^
AttributeError: 'int' object has no attribute 'dtype'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/workspace/problems/03_paged_attention/check.py", line 116, in <module>
main()
File "/workspace/problems/03_paged_attention/check.py", line 79, in main
sol_out = sol_model(*inputs)
^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/problems/03_paged_attention/solution.py", line 170, in forward
paged_decode_kernel[grid](
File "/workspa
... (1842 more chars)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -98,17 +98,22 @@
).to(tl.float32)
for g in tl.static_range(G):
- q = tl.gather(qs, g, axis=0)
+ idx = tl.full((), g, dtype=tl.int32)
+ q = tl.gather(qs, idx, axis=0)
scores = tl.dot(q[None, :], tl.trans(k))
scores = tl.where(offs_t < valid, scores[0, :], float("-inf")) * SCALE
- m_new = tl.maximum(ms[g], tl.max(scores, axis=0))
- exp_scale = tl.exp(ms[g] - m_new)
+ m_cur = tl.gather(ms, idx, axis=0)
+ l_cur = tl.gather(ls, idx, axis=0)
+ acc_cur = tl.gather(accs, idx, axis=0)
+
+ m_new = tl.maximum(m_cur, tl.max(scores, axis=0))
+ exp_scale = tl.exp(m_cur - m_new)
exp_scores = tl.exp(scores - m_new)
weighted = tl.dot(exp_scores[None, :], v)[0, :]
- new_acc = accs[g] * exp_scale + weighted
- new_l = ls[g] * exp_scale + tl.sum(exp_scores, axis=0)
+ new_acc = acc_cur * exp_scale + weighted
+ new_l = l_cur * exp_scale + tl.sum(exp_scores, axis=0)
mask_g = offs_g1 == g
mask_g2 = mask_g[:, None]
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<776 chars \u2014 see diff>",
"new_string": "<973 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -120",
"description": "Run correctness check"
}Traceback (most recent call last):
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 43, in wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 2806, in gather
return _semantic.gather(src, index, axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/semantic.py", line 1745, in gather
assert len(index.type.shape) == rank, "source and index tensors must have the same rank"
^^^^^^^^^^^^^^^^
AttributeError: 'dtype' object has no attribute 'shape'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/workspace/problems/03_paged_attention/check.py", line 116, in <module>
main()
File "/workspace/problems/03_paged_attention/check.py", line 79, in main
sol_out = sol_model(*inputs)
^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/problems/03_paged_attention/solution.py", line 175, in forward
paged_
... (1907 more chars){
"command": "<657 chars \u2014 see below>",
"description": "Test Triton gather indexing"
}cat > /tmp/test_gather.py <<'EOF'
import torch
import triton
import triton.language as tl
@triton.jit
def test_kernel(x_ptr, y_ptr, HEAD_DIM: tl.constexpr):
offs_d = tl.arange(0, HEAD_DIM)
x = tl.load(x_ptr + offs_d).to(tl.float32) # (HEAD_DIM,)
idx = tl.full((1, HEAD_DIM), 0, dtype=tl.int32)
row = tl.gather(x[None,:], idx, axis=0) # gather from (1,HEAD_DIM)
tl.store(y_ptr + offs_d, row[0,:])
a = torch.randn(1,128,dtype=torch.bfloat16,device='cuda')
b = torch.empty(128,dtype=torch.bfloat16,device='cuda')
test_kernel[(1,)](a,b,HEAD_DIM=128)
print('ok', (b-a[0]).abs().max())
EOF
uv run python /tmp/test_gather.py 2>&1 | tail -30The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/tmp/test_gather.py", line 15, in <module>
test_kernel[(1,)](a,b,HEAD_DIM=128)
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 720, in run
kernel = self._do_compile(key, signature, device, constexprs, options, attrs, warmup)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 849, in _do_compile
kernel = self.compile(src, target=target, options=options.__dict__)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/compiler/compiler.py", line 304, in compile
module = src.make_ir(target, options, codegen_fns, module_map, context)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/compiler/compiler.py", line 80, in make_ir
return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
... (480 more chars){
"command": "<517 chars \u2014 see below>",
"description": "Test Triton tuple comprehension syntax"
}cat > /tmp/test_tuple.py <<'EOF'
import torch
import triton
import triton.language as tl
@triton.jit
def test_kernel(x_ptr, y_ptr, N: tl.constexpr):
# try tuple comprehension
t = (tl.load(x_ptr + i) for i in range(N))
s = 0.0
for i in tl.static_range(N):
s = s + t[i]
tl.store(y_ptr, s)
a = torch.randn(4,dtype=torch.float32,device='cuda')
b = torch.empty(1,dtype=torch.float32,device='cuda')
test_kernel[(1,)](a,b,N=4)
print('ok', b)
EOF
uv run python /tmp/test_tuple.py 2>&1 | tail -30Traceback (most recent call last):
File "/tmp/test_tuple.py", line 16, in <module>
test_kernel[(1,)](a,b,N=4)
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 720, in run
kernel = self._do_compile(key, signature, device, constexprs, options, attrs, warmup)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 849, in _do_compile
kernel = self.compile(src, target=target, options=options.__dict__)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/compiler/compiler.py", line 304, in compile
module = src.make_ir(target, options, codegen_fns, module_map, context)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/compiler/compiler.py", line 80, in make_ir
return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
triton.compiler.errors.UnsupportedL
... (200 more chars){
"command": "<735 chars \u2014 see below>",
"description": "Test Triton list of tensors update"
}cat > /tmp/test_list.py <<'EOF'
import torch
import triton
import triton.language as tl
@triton.jit
def test_kernel(x_ptr, y_ptr, N: tl.constexpr):
a0 = tl.zeros((), dtype=tl.float32)
a1 = tl.zeros((), dtype=tl.float32)
a2 = tl.zeros((), dtype=tl.float32)
a3 = tl.zeros((), dtype=tl.float32)
accs = [a0, a1, a2, a3]
for i in tl.static_range(N):
accs[i] = accs[i] + tl.load(x_ptr + i)
s = tl.zeros((), dtype=tl.float32)
for i in tl.static_range(N):
s = s + accs[i]
tl.store(y_ptr, s)
a = torch.ones(4,dtype=torch.float32,device='cuda')
b = torch.empty(1,dtype=torch.float32,device='cuda')
test_kernel[(1,)](a,b,N=4)
print('ok', b)
EOF
uv run python /tmp/test_list.py 2>&1 | tail -30Traceback (most recent call last):
File "/tmp/test_list.py", line 21, in <module>
test_kernel[(1,)](a,b,N=4)
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 720, in run
kernel = self._do_compile(key, signature, device, constexprs, options, attrs, warmup)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 849, in _do_compile
kernel = self.compile(src, target=target, options=options.__dict__)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/compiler/compiler.py", line 304, in compile
module = src.make_ir(target, options, codegen_fns, module_map, context)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/compiler/compiler.py", line 80, in make_ir
return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
triton.compiler.errors.CompilationEr
... (400 more chars){
"command": "<525 chars \u2014 see below>",
"description": "Test Triton reshape"
}cat > /tmp/test_reshape.py <<'EOF'
import torch
import triton
import triton.language as tl
@triton.jit
def test_kernel(x_ptr, y_ptr, N: tl.constexpr):
offs = tl.arange(0, N)
a = tl.load(x_ptr + offs) # (N,)
b = a[None, :] # (1,N)
c = tl.reshape(b, (N,))
tl.store(y_ptr + offs, c)
a = torch.randn(16,dtype=torch.float32,device='cuda')
b = torch.empty(16,dtype=torch.float32,device='cuda')
test_kernel[(1,)](a,b,N=16)
print('ok', (a-b).abs().max())
EOF
uv run python /tmp/test_reshape.py 2>&1 | tail -30ok tensor(0., device='cuda:0'){
"command": "<664 chars \u2014 see below>",
"description": "Test row selection via sum and mask"
}cat > /tmp/test_sumsel.py <<'EOF'
import torch
import triton
import triton.language as tl
@triton.jit
def test_kernel(x_ptr, y_ptr, G: tl.constexpr, D: tl.constexpr):
offs_d = tl.arange(0, D)
offs_g = tl.arange(0, G)[:,None]
x = tl.load(x_ptr + offs_g * D + offs_d[None,:]) # (G,D)
g = 1
mask = (tl.arange(0, G) == g)[:,None]
row = tl.sum(x * mask, axis=0)
tl.store(y_ptr + offs_d, row)
a = torch.arange(8*64,dtype=torch.float32,device='cuda').reshape(8,64)
b = torch.empty(64,dtype=torch.float32,device='cuda')
test_kernel[(1,)](a,b,G=8,D=64)
print('ok', (b - a[1]).abs().max())
EOF
uv run python /tmp/test_sumsel.py 2>&1 | tail -30ok tensor(0., device='cuda:0')--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -98,25 +98,24 @@
).to(tl.float32)
for g in tl.static_range(G):
- idx = tl.full((), g, dtype=tl.int32)
- q = tl.gather(qs, idx, axis=0)
- scores = tl.dot(q[None, :], tl.trans(k))
- scores = tl.where(offs_t < valid, scores[0, :], float("-inf")) * SCALE
-
- m_cur = tl.gather(ms, idx, axis=0)
- l_cur = tl.gather(ls, idx, axis=0)
- acc_cur = tl.gather(accs, idx, axis=0)
+ mask_g = tl.arange(0, G) == g
+ mask_g2 = mask_g[:, None]
+ q = tl.sum(qs * mask_g2, axis=0)
+ acc_cur = tl.sum(accs * mask_g2, axis=0)
+ m_cur = tl.sum(ms * mask_g, axis=0)
+ l_cur = tl.sum(ls * mask_g, axis=0)
+
+ scores = tl.reshape(tl.dot(q[None, :], tl.trans(k)), (PAGE_SIZE,))
+ scores = tl.where(offs_t < valid, scores, float("-inf")) * SCALE
m_new = tl.maximum(m_cur, tl.max(scores, axis=0))
exp_scale = tl.exp(m_cur - m_new)
exp_scores = tl.exp(scores - m_new)
- weighted = tl.dot(exp_scores[None, :], v)[0, :]
+ weighted = tl.reshape(tl.dot(exp_scores[None, :], v), (HEAD_DIM,))
new_acc = acc_cur * exp_scale + weighted
new_l = l_cur * exp_scale + tl.sum(exp_scores, axis=0)
- mask_g = offs_g1 == g
- mask_g2 = mask_g[:, None]
accs = tl.where(mask_g2, new_acc[None, :], accs)
ls = tl.where(mask_g, new_l, ls)
ms = tl.where(mask_g, m_new, ms)
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<973 chars \u2014 see diff>",
"new_string": "<977 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -120",
"description": "Run correctness check"
}FAIL: shape 0 {'batch': 8, 'num_heads': 32, 'num_kv_heads': 8, 'head_dim': 128, 'seq_len': 1024, 'page_size': 16} seed 42 case nominal: solution contains NaN--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -105,13 +105,13 @@
m_cur = tl.sum(ms * mask_g, axis=0)
l_cur = tl.sum(ls * mask_g, axis=0)
- scores = tl.reshape(tl.dot(q[None, :], tl.trans(k)), (PAGE_SIZE,))
- scores = tl.where(offs_t < valid, scores, float("-inf")) * SCALE
+ scores = tl.sum(q[None, :] * k, axis=1) * SCALE
+ scores = tl.where(offs_t < valid, scores, float("-inf"))
m_new = tl.maximum(m_cur, tl.max(scores, axis=0))
exp_scale = tl.exp(m_cur - m_new)
exp_scores = tl.exp(scores - m_new)
- weighted = tl.reshape(tl.dot(exp_scores[None, :], v), (HEAD_DIM,))
+ weighted = tl.sum(exp_scores[:, None] * v, axis=0)
new_acc = acc_cur * exp_scale + weighted
new_l = l_cur * exp_scale + tl.sum(exp_scores, axis=0)
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<392 chars \u2014 see diff>",
"new_string": "<349 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -120",
"description": "Run correctness check"
}FAIL: shape 0 {'batch': 8, 'num_heads': 32, 'num_kv_heads': 8, 'head_dim': 128, 'seq_len': 1024, 'page_size': 16} seed 42 case nominal: solution contains NaN{
"command": "<411 chars \u2014 see below>",
"description": "Test Triton exp(-inf)"
}cat > /tmp/test_exp.py <<'EOF'
import torch
import triton
import triton.language as tl
@triton.jit
def test_kernel(x_ptr, y_ptr):
a = tl.load(x_ptr)
b = tl.exp(a)
tl.store(y_ptr, b)
a = torch.tensor(float('-inf'),dtype=torch.float32,device='cuda')
b = torch.empty(1,dtype=torch.float32,device='cuda')
test_kernel[(1,)](a,b)
print('exp(-inf)=', b)
EOF
uv run python /tmp/test_exp.py 2>&1 | tail -10exp(-inf)= tensor([0.], device='cuda:0'){
"command": "<482 chars \u2014 see below>",
"description": "Test Triton max with -inf"
}cat > /tmp/test_max.py <<'EOF'
import torch
import triton
import triton.language as tl
@triton.jit
def test_kernel(x_ptr, y_ptr, N: tl.constexpr):
offs = tl.arange(0, N)
x = tl.load(x_ptr + offs)
m = tl.max(x, axis=0)
tl.store(y_ptr, m)
a = torch.tensor([-float('inf'), -1.0, -2.0],dtype=torch.float32,device='cuda')
b = torch.empty(1,dtype=torch.float32,device='cuda')
test_kernel[(1,)](a,b,N=3)
print('max=', b)
EOF
uv run python /tmp/test_max.py 2>&1 | tail -10module = src.make_ir(target, options, codegen_fns, module_map, context)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/compiler/compiler.py", line 80, in make_ir
return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
triton.compiler.errors.CompilationError: at 2:11:
def test_kernel(x_ptr, y_ptr, N: tl.constexpr):
offs = tl.arange(0, N)
^
arange's range must be a power of 2{
"command": "<488 chars \u2014 see below>",
"description": "Test Triton max with -inf N=4"
}cat > /tmp/test_max.py <<'EOF'
import torch
import triton
import triton.language as tl
@triton.jit
def test_kernel(x_ptr, y_ptr, N: tl.constexpr):
offs = tl.arange(0, N)
x = tl.load(x_ptr + offs)
m = tl.max(x, axis=0)
tl.store(y_ptr, m)
a = torch.tensor([-float('inf'), -1.0, -2.0, -3.0],dtype=torch.float32,device='cuda')
b = torch.empty(1,dtype=torch.float32,device='cuda')
test_kernel[(1,)](a,b,N=4)
print('max=', b)
EOF
uv run python /tmp/test_max.py 2>&1 | tail -10max= tensor([-1.], device='cuda:0'){
"command": "<1247 chars \u2014 see below>",
"description": "Debug NaN location"
}cat > /tmp/debug_sol.py <<'EOF'
import sys, torch
sys.path.insert(0,'/workspace/problems/03_paged_attention')
import reference, solution
shape = {'batch':8,'num_heads':32,'num_kv_heads':8,'head_dim':128,'seq_len':1024,'page_size':16}
for mod in [reference, solution]:
mod.BATCH=shape['batch']; mod.NUM_HEADS=shape['num_heads']; mod.NUM_KV_HEADS=shape['num_kv_heads']; mod.HEAD_DIM=shape['head_dim']; mod.SEQ_LEN=shape['seq_len']; mod.PAGE_SIZE=shape['page_size']
torch.manual_seed(42)
inputs = [t.cuda() for t in reference.get_inputs()]
ref = reference.Model(*reference.get_init_inputs()).cuda().eval()
sol = solution.Model(*solution.get_init_inputs()).cuda().eval()
with torch.no_grad():
ref_out = ref(*inputs)
sol_out = sol(*inputs)
print('ref nan', ref_out.isnan().any().item(), 'sol nan', sol_out.isnan().any().item())
print('sol max', sol_out.abs().max().item(), 'min', sol_out.min().item(), 'max ref', ref_out.abs().max().item())
print('diff finite', (sol_out - ref_out).abs()[~sol_out.isnan()].max().item())
# print first nan location
if sol_out.isnan().any():
idx = torch.where(sol_out.isnan())
print('nan loc', idx[0][0].item(), idx[1][0].item(), idx[2][0].item())
EOF
uv run python /tmp/debug_sol.py 2>&1 | tail -20ref nan False sol nan True
sol max nan min nan max ref 0.01220703125
Traceback (most recent call last):
File "/tmp/debug_sol.py", line 18, in <module>
print('diff finite', (sol_out - ref_out).abs()[~sol_out.isnan()].max().item())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: max(): Expected reduction dim to be specified for input.numel() == 0. Specify the reduction dim with the 'dim' argument.--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -100,10 +100,10 @@
for g in tl.static_range(G):
mask_g = tl.arange(0, G) == g
mask_g2 = mask_g[:, None]
- q = tl.sum(qs * mask_g2, axis=0)
- acc_cur = tl.sum(accs * mask_g2, axis=0)
- m_cur = tl.sum(ms * mask_g, axis=0)
- l_cur = tl.sum(ls * mask_g, axis=0)
+ q = tl.sum(tl.where(mask_g2, qs, 0.0), axis=0)
+ acc_cur = tl.sum(tl.where(mask_g2, accs, 0.0), axis=0)
+ m_cur = tl.sum(tl.where(mask_g, ms, 0.0), axis=0)
+ l_cur = tl.sum(tl.where(mask_g, ls, 0.0), axis=0)
scores = tl.sum(q[None, :] * k, axis=1) * SCALE
scores = tl.where(offs_t < valid, scores, float("-inf"))
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<194 chars \u2014 see diff>",
"new_string": "<250 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -120",
"description": "Run correctness check"
}PASS{
"status": "completed",
"taskId": "1"
}Updated task #1 status{
"status": "completed",
"taskId": "2"
}Updated task #2 status{
"status": "in_progress",
"taskId": "3"
}Updated task #3 status{
"command": "uv run python benchmark.py 2>&1 | tail -80",
"description": "Run benchmark"
}benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T22:57:35.417086+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T22:57:35.662677+00:00 elapsed_s=0.246 ms=0.304720
shape=0 variant=solution tflops=0.440 gbps=110.546 ms=0.305
shape=0 solution_peak_fraction=0.0614
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T22:57:37.270417+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T22:57:37.305363+00:00 elapsed_s=0.035 ms=0.831760
shape=1 variant=solution tflops=1.291 gbps=323.362 ms=0.832
shape=1 solution_peak_fraction=0.1796
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T22:57:37.707340+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T22:57:37.809703+00:00 elapsed_s=0.102 ms=2.493232
shape=2 variant=solution tflops=0.215 gbps=26.969 ms=2.493
shape=2 solution_peak_fraction=0.0150
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T22:57:38.410602+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T22:57:38.430002+00:00 elapsed_s=0.019 ms=0.447456
shape=3 variant=solution tflops=0.899 gbps=225.407 ms=0.447
shape=3 solution_peak_fraction=0.1252
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T22:57:38.527581+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T22:57:38.545403+00:00 elapsed_s=0.018 ms=0.366528
shape=4 v
... (121 more chars)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -105,13 +105,13 @@
m_cur = tl.sum(tl.where(mask_g, ms, 0.0), axis=0)
l_cur = tl.sum(tl.where(mask_g, ls, 0.0), axis=0)
- scores = tl.sum(q[None, :] * k, axis=1) * SCALE
+ scores = tl.reshape(tl.dot(q[None, :], tl.trans(k)), (PAGE_SIZE,)) * SCALE
scores = tl.where(offs_t < valid, scores, float("-inf"))
m_new = tl.maximum(m_cur, tl.max(scores, axis=0))
exp_scale = tl.exp(m_cur - m_new)
exp_scores = tl.exp(scores - m_new)
- weighted = tl.sum(exp_scores[:, None] * v, axis=0)
+ weighted = tl.reshape(tl.dot(exp_scores[None, :], v), (HEAD_DIM,))
new_acc = acc_cur * exp_scale + weighted
new_l = l_cur * exp_scale + tl.sum(exp_scores, axis=0)
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<349 chars \u2014 see diff>",
"new_string": "<392 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | tail -5 && uv run python benchmark.py 2>&1 | tail -20",
"description": "Check and benchmark dot version"
}PASS
shape=0 variant=solution tflops=0.798 gbps=200.242 ms=0.168
shape=0 solution_peak_fraction=0.1112
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T22:59:02.426669+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T22:59:02.442572+00:00 elapsed_s=0.016 ms=0.358096
shape=1 variant=solution tflops=2.998 gbps=751.083 ms=0.358
shape=1 solution_peak_fraction=0.4173
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T22:59:02.843894+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T22:59:02.892225+00:00 elapsed_s=0.048 ms=1.130096
shape=2 variant=solution tflops=0.475 gbps=59.499 ms=1.130
shape=2 solution_peak_fraction=0.0331
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T22:59:03.489899+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T22:59:03.501369+00:00 elapsed_s=0.011 ms=0.241536
shape=3 variant=solution tflops=1.666 gbps=417.577 ms=0.242
shape=3 solution_peak_fraction=0.2320
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T22:59:03.598728+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T22:59:03.612919+00:00 elapsed_s=0.014 ms=0.272144
shape=4 variant=solution tflops=0.241 gbps=60.324 ms=0.272
shape=4 solution_peak_fraction=0.0335
peak_fraction: 0.1036
RESULT: OK--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -193,7 +193,7 @@
PAGE_SIZE=P,
HEAD_DIM=D,
G=G,
- num_warps=4,
+ num_warps=8,
)
return out
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<25 chars \u2014 see diff>",
"new_string": "<25 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | tail -3 && uv run python benchmark.py 2>&1 | tail -20",
"description": "Check and benchmark with 8 warps"
}PASS
shape=0 variant=solution tflops=0.661 gbps=165.919 ms=0.203
shape=0 solution_peak_fraction=0.0922
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T22:59:31.719256+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T22:59:31.741474+00:00 elapsed_s=0.022 ms=0.513648
shape=1 variant=solution tflops=2.090 gbps=523.627 ms=0.514
shape=1 solution_peak_fraction=0.2909
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T22:59:32.143011+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T22:59:32.198766+00:00 elapsed_s=0.056 ms=1.315312
shape=2 variant=solution tflops=0.408 gbps=51.121 ms=1.315
shape=2 solution_peak_fraction=0.0284
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T22:59:32.799642+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T22:59:32.813370+00:00 elapsed_s=0.014 ms=0.297200
shape=3 variant=solution tflops=1.354 gbps=339.367 ms=0.297
shape=3 solution_peak_fraction=0.1885
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T22:59:32.911202+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T22:59:32.927289+00:00 elapsed_s=0.016 ms=0.319504
shape=4 variant=solution tflops=0.205 gbps=51.382 ms=0.320
shape=4 solution_peak_fraction=0.0285
peak_fraction: 0.0837
RESULT: LOW--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -193,7 +193,7 @@
PAGE_SIZE=P,
HEAD_DIM=D,
G=G,
- num_warps=8,
+ num_warps=2,
)
return out
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<25 chars \u2014 see diff>",
"new_string": "<25 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | tail -3 && uv run python benchmark.py 2>&1 | tail -20",
"description": "Check and benchmark with 2 warps"
}PASS
shape=0 variant=solution tflops=0.745 gbps=187.042 ms=0.180
shape=0 solution_peak_fraction=0.1039
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T23:00:48.172182+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T23:00:48.187878+00:00 elapsed_s=0.016 ms=0.353632
shape=1 variant=solution tflops=3.036 gbps=760.564 ms=0.354
shape=1 solution_peak_fraction=0.4225
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T23:00:48.589444+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T23:00:48.639129+00:00 elapsed_s=0.050 ms=1.165616
shape=2 variant=solution tflops=0.461 gbps=57.686 ms=1.166
shape=2 solution_peak_fraction=0.0320
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T23:00:49.242534+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T23:00:49.254860+00:00 elapsed_s=0.012 ms=0.261376
shape=3 variant=solution tflops=1.540 gbps=385.880 ms=0.261
shape=3 solution_peak_fraction=0.2144
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T23:00:49.353258+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T23:00:49.366757+00:00 elapsed_s=0.013 ms=0.253408
shape=4 variant=solution tflops=0.259 gbps=64.784 ms=0.253
shape=4 solution_peak_fraction=0.0360
peak_fraction: 0.1017
RESULT: OK--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -193,7 +193,8 @@
PAGE_SIZE=P,
HEAD_DIM=D,
G=G,
- num_warps=2,
+ num_warps=4,
+ num_stages=2,
)
return out
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<25 chars \u2014 see diff>",
"new_string": "<51 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | tail -3 && uv run python benchmark.py 2>&1 | tail -20",
"description": "Check and benchmark with num_stages=2"
}PASS
shape=0 variant=solution tflops=0.797 gbps=199.995 ms=0.168
shape=0 solution_peak_fraction=0.1111
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T23:01:26.534113+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T23:01:26.550159+00:00 elapsed_s=0.016 ms=0.360128
shape=1 variant=solution tflops=2.982 gbps=746.845 ms=0.360
shape=1 solution_peak_fraction=0.4149
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T23:01:26.951107+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T23:01:27.002002+00:00 elapsed_s=0.051 ms=1.192896
shape=2 variant=solution tflops=0.450 gbps=56.367 ms=1.193
shape=2 solution_peak_fraction=0.0313
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T23:01:27.600924+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T23:01:27.612477+00:00 elapsed_s=0.012 ms=0.243584
shape=3 variant=solution tflops=1.652 gbps=414.066 ms=0.244
shape=3 solution_peak_fraction=0.2300
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T23:01:27.710290+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T23:01:27.724705+00:00 elapsed_s=0.014 ms=0.278848
shape=4 variant=solution tflops=0.235 gbps=58.874 ms=0.279
shape=4 solution_peak_fraction=0.0327
peak_fraction: 0.1017
RESULT: OK--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -194,7 +194,6 @@
HEAD_DIM=D,
G=G,
num_warps=4,
- num_stages=2,
)
return out
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<51 chars \u2014 see diff>",
"new_string": "<25 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -21,18 +21,23 @@
PAGE_SIZE: tl.constexpr,
HEAD_DIM: tl.constexpr,
G: tl.constexpr,
+ CHUNK_PAGES: tl.constexpr,
):
- """One CUDA block per (batch, kv_head). Computes all G query heads."""
+ """One CUDA block per (batch, kv_head). Processes CHUNK_PAGES pages at a time."""
pid_b = tl.program_id(0)
pid_hkv = tl.program_id(1)
seq_len = tl.load(seq_lens_ptr + pid_b)
num_pages = tl.cdiv(seq_len, PAGE_SIZE)
+ num_chunks = tl.cdiv(num_pages, CHUNK_PAGES)
+ BLOCK_N = PAGE_SIZE * CHUNK_PAGES
offs_d = tl.arange(0, HEAD_DIM)
- offs_g = tl.arange(0, G)[:, None]
+ offs_page = tl.arange(0, PAGE_SIZE)
+ offs_t = tl.arange(0, BLOCK_N)
# Load the G query heads for this KV group: (G, HEAD_DIM).
+ offs_g = tl.arange(0, G)[:, None]
q_ptrs = (
q_ptr
+ pid_b * stride_qb
@@ -45,29 +50,47 @@
ls = tl.zeros((G,), dtype=tl.float32)
accs = tl.zeros((G, HEAD_DIM), dtype=tl.float32)
- offs_t = tl.arange(0, PAGE_SIZE)
- for p in range(num_pages):
- physical_block = tl.load(
- block_table_ptr + pid_b * stride_bt_b + p * stride_bt_p
+ for c in range(num_chunks):
+ p0 = c * CHUNK_PAGES
+ physical_block_0 = tl.load(
+ block_table_ptr + pid_b * stride_bt_b + p0 * stride_bt_p
).to(tl.int64)
- token_offset = p * PAGE_SIZE
- valid = tl.minimum(PAGE_SIZE, seq_len - token_offset)
-
- kv_base = kv_ptr + physical_block * stride_kvn + pid_hkv * stride_kvh
- k_ptrs = kv_base + (offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd)
- k = tl.load(
- k_ptrs,
- mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
+ kv_base_0 = kv_ptr + physical_block_0 * stride_kvn + pid_hkv * stride_kvh
+ k0 = tl.load(
+ kv_base_0 + (offs_page[:, None] * stride_kvt + offs_d[None, :] * stride_kvd),
+ mask=offs_d[None, :] < HEAD_DIM,
other=0.0,
).to(tl.float32)
- v_ptrs = kv_base + HEAD_DIM * stride_kvd + (
- offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd
- )
- v = tl.load(
- v_ptrs,
- mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
+ v0 = tl.load(
+ kv_base_0 + HEAD_DIM * stride_kvd + (
+ offs_page[:, None] * stride_kvt + offs_d[None, :] * stride_kvd
+ ),
+ mask=offs_d[None, :] < HEAD_DIM,
other=0.0,
).to(tl.float32)
+
+ physical_block_1 = tl.load(
+ block_table_ptr + pid_b * stride_bt_b + (p0 + 1) * stride_bt_p
+ ).to(tl.int64)
+ kv_base_1 = kv_ptr + physical_block_1 * stride_kvn + pid_hkv * stride_kvh
+ k1 = tl.load(
+ kv_base_1 + (offs_page[:, None] * stride_kvt + offs_d[None, :] * stride_kvd),
+ mask=offs_d[None, :] < HEAD_DIM,
+ other=0.0,
+ ).to(tl.float32)
+ v1 = tl.load(
+ kv_base_1 + HEAD_DIM * stride_kvd + (
+ offs_page[:, None] * stride_kvt + offs_d[None, :] * stride_kvd
+ ),
+ mask=offs_d[None, :] < HEAD_DIM,
+ other=0.0,
+ ).to(tl.float32)
+
+ k = tl.cat(k0, k1, axis=0)
+ v = tl.cat(v0, v1, axis=0)
+
+ token_offset = c * BLOCK_N
+ valid = tl.minimum(BLOCK_N, seq_len - token_offset)
for g in tl.static_range(G):
mask_g = tl.arange(0, G) == g
@@ -77,7 +100,7 @@
m_cur = tl.sum(tl.where(mask_g, ms, 0.0), axis=0)
l_cur = tl.sum(tl.where(mask_g, ls, 0.0), axis=0)
- scores = tl.reshape(tl.dot(q[None, :], tl.trans(k)), (PAGE_SIZE,)) * SCALE
+ scores = tl.reshape(tl.dot(q[None, :], tl.trans(k)), (BLOCK_N,)) * SCALE
scores = tl.where(offs_t < valid, scores, float("-inf"))
m_new = tl.maximum(m_cur, tl.max(scores, axis=0))
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<3385 chars \u2014 see diff>",
"new_string": "<4247 chars \u2014 see diff>"
}<tool_use_error>String to replace not found in file.
String: @triton.jit
def paged_decode_kernel(
q_ptr,
kv_ptr,
block_table_ptr,
seq_lens_ptr,
out_ptr,
stride_qb,
stride_qh,
stride_qd,
stride_kvn,
stride_kvt,
stride_kvh,
stride_kvd,
stride_bt_b,
stride_bt_p,
stride_ob,
stride_oh,
stride_od,
SCALE: tl.constexpr,
PAGE_SIZE: tl.constexpr,
HEAD_DIM: tl.constexpr,
G: tl.constexpr,
):
"""One CUDA block per (batch, kv_head). Computes all G query heads."""
pid_b = tl.program_id(0)
pid_hkv = tl.program_id(1)
seq_len = tl.load(seq_lens_ptr + pid_b)
num_pages = tl.cdiv(seq_len, PAGE_SIZE)
offs_d = tl.arange(0, HEAD_DIM)
offs_g = tl.arange(0, G)[:, None]
# Load the G query heads for this KV group: (G, HEAD_DIM).
q_ptrs = (
q_ptr
+ pid_b * stride_qb
+ (pid_hkv * G + offs_g) * stride_qh
+ offs_d[None, :] * stride_qd
)
qs = tl.load(q_ptrs, mask=offs_d[None, :] < HEAD_DIM, other=0.0).to(tl.float32)
ms = tl.full((G,), float("-inf"), dtype=tl.float32)
ls = tl.zeros((G,), dtype=tl.float32)
accs = tl.zeros((G, HEAD_DIM), dtype=tl.float32)
offs_t = tl.arange(0, PAGE_SIZE)
for p in range(num_pages):
physical_block = tl.load(
block_table_ptr + pid_b * stride_bt_b + p * stride_bt_p
).to(tl.int64)
token_offset = p * PAGE_SIZE
valid = tl.minimum(PAGE_SIZE, seq_len - token_off
... (1963 more chars){
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"limit": 200
}1 """Paged attention decode kernel implemented in Triton.
2
3 Conforms to the same Model/get_inputs/get_init_inputs interface as reference.py.
4 """
5 import math
6
7 import torch
8 import torch.nn as nn
9 import triton
10 import triton.language as tl
11
12
13 OP_TYPE = "attention"
14 SUPPORTED_PRECISIONS = ["bf16"]
15 HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
16
17 BATCH = 8
18 NUM_HEADS = 32
19 NUM_KV_HEADS = 8
20 HEAD_DIM = 128
21 SEQ_LEN = 1024
22 PAGE_SIZE = 16
23
24
25 @triton.jit
26 def paged_decode_kernel(
27 q_ptr,
28 kv_ptr,
29 block_table_ptr,
30 seq_lens_ptr,
31 out_ptr,
32 stride_qb,
33 stride_qh,
34 stride_qd,
35 stride_kvn,
36 stride_kvt,
37 stride_kvh,
38 stride_kvd,
39 stride_bt_b,
40 stride_bt_p,
41 stride_ob,
42 stride_oh,
43 stride_od,
44 SCALE: tl.constexpr,
45 PAGE_SIZE: tl.constexpr,
46 HEAD_DIM: tl.constexpr,
47 G: tl.constexpr,
48 ):
49 """One CUDA block per (batch, kv_head). Computes all G query heads."""
50 pid_b = tl.program_id(0)
51 pid_hkv = tl.program_id(1)
52
53 seq_len = tl.load(seq_lens_ptr + pid_b)
54 num_pages = tl.cdiv(seq_len, PAGE_SIZE)
55
56 offs_d = tl.arange(0, HEAD_DIM)
57 offs_t = tl.arange(0, PAGE_SIZE)
58
59 offs_d = tl.arange(0, HEAD_DIM)
60 offs_g = tl.arange(0, G)[:, None]
61 offs_g1 = tl.arange(0, G)
62
63 # Load the G query heads for this KV group: (G, HEAD_DIM).
64 q_ptrs =
... (4899 more chars)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -45,20 +45,21 @@
PAGE_SIZE: tl.constexpr,
HEAD_DIM: tl.constexpr,
G: tl.constexpr,
+ CHUNK_PAGES: tl.constexpr,
):
- """One CUDA block per (batch, kv_head). Computes all G query heads."""
+ """One CUDA block per (batch, kv_head). Processes CHUNK_PAGES pages at a time."""
pid_b = tl.program_id(0)
pid_hkv = tl.program_id(1)
seq_len = tl.load(seq_lens_ptr + pid_b)
num_pages = tl.cdiv(seq_len, PAGE_SIZE)
+ num_chunks = tl.cdiv(num_pages, CHUNK_PAGES)
+ BLOCK_N = PAGE_SIZE * CHUNK_PAGES
offs_d = tl.arange(0, HEAD_DIM)
- offs_t = tl.arange(0, PAGE_SIZE)
-
- offs_d = tl.arange(0, HEAD_DIM)
+ offs_page = tl.arange(0, PAGE_SIZE)
+ offs_t = tl.arange(0, BLOCK_N)
offs_g = tl.arange(0, G)[:, None]
- offs_g1 = tl.arange(0, G)
# Load the G query heads for this KV group: (G, HEAD_DIM).
q_ptrs = (
@@ -73,29 +74,48 @@
ls = tl.zeros((G,), dtype=tl.float32)
accs = tl.zeros((G, HEAD_DIM), dtype=tl.float32)
- offs_t = tl.arange(0, PAGE_SIZE)
- for p in range(num_pages):
- physical_block = tl.load(
- block_table_ptr + pid_b * stride_bt_b + p * stride_bt_p
+ for c in range(num_chunks):
+ p0 = c * CHUNK_PAGES
+
+ physical_block_0 = tl.load(
+ block_table_ptr + pid_b * stride_bt_b + p0 * stride_bt_p
).to(tl.int64)
- token_offset = p * PAGE_SIZE
- valid = tl.minimum(PAGE_SIZE, seq_len - token_offset)
-
- kv_base = kv_ptr + physical_block * stride_kvn + pid_hkv * stride_kvh
- k_ptrs = kv_base + (offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd)
- k = tl.load(
- k_ptrs,
- mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
- other=0.0,
- ).to(tl.float32)
- v_ptrs = kv_base + HEAD_DIM * stride_kvd + (
- offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd
- )
- v = tl.load(
- v_ptrs,
- mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
- other=0.0,
- ).to(tl.float32)
+ kv_base_0 = kv_ptr + physical_block_0 * stride_kvn + pid_hkv * stride_kvh
+ k0 = tl.load(
+ kv_base_0 + (offs_page[:, None] * stride_kvt + offs_d[None, :] * stride_kvd),
+ mask=offs_d[None, :] < HEAD_DIM,
+ other=0.0,
+ ).to(tl.float32)
+ v0 = tl.load(
+ kv_base_0 + HEAD_DIM * stride_kvd + (
+ offs_page[:, None] * stride_kvt + offs_d[None, :] * stride_kvd
+ ),
+ mask=offs_d[None, :] < HEAD_DIM,
+ other=0.0,
+ ).to(tl.float32)
+
+ physical_block_1 = tl.load(
+ block_table_ptr + pid_b * stride_bt_b + (p0 + 1) * stride_bt_p
+ ).to(tl.int64)
+ kv_base_1 = kv_ptr + physical_block_1 * stride_kvn + pid_hkv * stride_kvh
+ k1 = tl.load(
+ kv_base_1 + (offs_page[:, None] * stride_kvt + offs_d[None, :] * stride_kvd),
+ mask=offs_d[None, :] < HEAD_DIM,
+ other=0.0,
+ ).to(tl.float32)
+ v1 = tl.load(
+ kv_base_1 + HEAD_DIM * stride_kvd + (
+ offs_page[:, None] * stride_kvt + offs_d[None, :] * stride_kvd
+ ),
+ mask=offs_d[None, :] < HEAD_DIM,
+ other=0.0,
+ ).to(tl.float32)
+
+ k = tl.cat(k0, k1, axis=0)
+ v = tl.cat(v0, v1, axis=0)
+
+ token_offset = c * BLOCK_N
+ valid = tl.minimum(BLOCK_N, seq_len - token_offset)
for g in tl.static_range(G):
mask_g = tl.arange(0, G) == g
@@ -105,7 +125,7 @@
m_cur = tl.sum(tl.where(mask_g, ms, 0.0), axis=0)
l_cur = tl.sum(tl.where(mask_g, ls, 0.0), axis=0)
- scores = tl.reshape(tl.dot(q[None, :], tl.trans(k)), (PAGE_SIZE,)) * SCALE
+ scores = tl.reshape(tl.dot(q[None, :], tl.trans(k)), (BLOCK_N,)) * SCALE
scores = tl.where(offs_t < valid, scores, float("-inf"))
m_new = tl.maximum(m_cur, tl.max(scores, axis=0))
@@ -169,6 +189,19 @@
P = self.page_size
out = torch.empty(B, H, D, dtype=query.dtype, device=query.device)
+
+ # Pad block_table so every chunk can load two physical blocks without
+ # predicating the block-table read itself.
+ max_blocks = block_table.shape[1]
+ pad = (-max_blocks) % 2
+ if pad:
+ block_table = torch.cat(
+ [
+ block_table,
+ torch.zeros(B, pad, dtype=torch.int32, device=block_table.device),
+ ],
+ dim=1,
+ )
grid = (B, Hkv)
paged_decode_kernel[grid](
@@ -193,6 +226,7 @@
PAGE_SIZE=P,
HEAD_DIM=D,
G=G,
+ CHUNK_PAGES=2,
num_warps=4,
)
return out
{
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"content": "<7636 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -120",
"description": "Check correctness of chunked kernel"
}Traceback (most recent call last):
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 43, in wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 1644, in arange
return _semantic.arange(start, end)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/semantic.py", line 574, in arange
raise ValueError("arange's arguments must be of type tl.constexpr")
ValueError: arange's arguments must be of type tl.constexpr
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/workspace/problems/03_paged_attention/check.py", line 116, in <module>
main()
File "/workspace/problems/03_paged_attention/check.py", line 79, in main
sol_out = sol_model(*inputs)
^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/problems/03_paged_attention/solution.py", line 207, in forward
paged_decode_kernel[grid](
File "/workspace/.venv/lib/python3.11/s
... (1855 more chars)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -46,6 +46,7 @@
HEAD_DIM: tl.constexpr,
G: tl.constexpr,
CHUNK_PAGES: tl.constexpr,
+ BLOCK_N: tl.constexpr,
):
"""One CUDA block per (batch, kv_head). Processes CHUNK_PAGES pages at a time."""
pid_b = tl.program_id(0)
@@ -54,7 +55,6 @@
seq_len = tl.load(seq_lens_ptr + pid_b)
num_pages = tl.cdiv(seq_len, PAGE_SIZE)
num_chunks = tl.cdiv(num_pages, CHUNK_PAGES)
- BLOCK_N = PAGE_SIZE * CHUNK_PAGES
offs_d = tl.arange(0, HEAD_DIM)
offs_page = tl.arange(0, PAGE_SIZE)
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<405 chars \u2014 see diff>",
"new_string": "<394 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -227,6 +227,7 @@
HEAD_DIM=D,
G=G,
CHUNK_PAGES=2,
+ BLOCK_N=P * 2,
num_warps=4,
)
return out
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<118 chars \u2014 see diff>",
"new_string": "<145 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -120",
"description": "Check chunked kernel correctness"
}Traceback (most recent call last):
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 43, in wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
TypeError: cat() got an unexpected keyword argument 'axis'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/workspace/problems/03_paged_attention/check.py", line 116, in <module>
main()
File "/workspace/problems/03_paged_attention/check.py", line 79, in main
sol_out = sol_model(*inputs)
^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/problems/03_paged_attention/solution.py", line 207, in forward
paged_decode_kernel[grid](
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 720, in run
kernel = self._do_compile(ke
... (1450 more chars){
"command": "uv run python -c \"import triton.language as tl; help(tl.cat)\" 2>&1 | head -30",
"description": "Check tl.cat signature"
}Help on function cat in module triton.language.core:
cat(input, other, can_reorder=False, _semantic=None)
Concatenate the given blocks
:param input: The first input tensor.
:type input: Tensor
:param other: The second input tensor.
:type other: Tensor
:param reorder: Compiler hint. If true, the compiler is
allowed to reorder elements while concatenating inputs. Only use if the
order does not matter (e.g., result is only used in reduction ops).
Current implementation of `cat` supports only can_reorder=True.{
"command": "<515 chars \u2014 see below>",
"description": "Test Triton cat"
}cat > /tmp/test_cat.py <<'EOF'
import torch
import triton
import triton.language as tl
@triton.jit
def test_kernel(x_ptr, y_ptr, N: tl.constexpr):
offs = tl.arange(0, N)
a = tl.load(x_ptr + offs) # (N,)
b = tl.load(x_ptr + N + offs)
c = tl.cat(a, b)
tl.store(y_ptr + tl.arange(0, 2*N), c)
N=4
x = torch.arange(8,dtype=torch.float32,device='cuda')
y = torch.empty(8,dtype=torch.float32,device='cuda')
test_kernel[(1,)](x,y,N=N)
print('y=', y)
EOF
uv run python /tmp/test_cat.py 2>&1 | tail -10return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
triton.compiler.errors.CompilationError: at 5:8:
def test_kernel(x_ptr, y_ptr, N: tl.constexpr):
offs = tl.arange(0, N)
a = tl.load(x_ptr + offs) # (N,)
b = tl.load(x_ptr + N + offs)
c = tl.cat(a, b)
^
current implementation of `cat` always may reorder elements{
"command": "uv run python -c \"import triton.language as tl; print([x for x in dir(tl) if 'stack' in x or 'join' in x or 'concat' in x])\"",
"description": "Check Triton concatenation functions"
}['join']{
"command": "<545 chars \u2014 see below>",
"description": "Test Triton join"
}cat > /tmp/test_join.py <<'EOF'
import torch
import triton
import triton.language as tl
@triton.jit
def test_kernel(x_ptr, y_ptr, N: tl.constexpr):
offs = tl.arange(0, N)
a = tl.load(x_ptr + offs)
b = tl.load(x_ptr + N + offs)
c = tl.join(a, b)
tl.store(y_ptr + tl.arange(0, 2*N), c)
N=4
x = torch.arange(8,dtype=torch.float32,device='cuda')
y = torch.empty(8,dtype=torch.float32,device='cuda')
test_kernel[(1,)](x,y,N=N)
print('y=', y, 'shape', c.shape if False else '')
EOF
uv run python /tmp/test_join.py 2>&1 | tail -10^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
triton.compiler.errors.CompilationError: at 6:4:
def test_kernel(x_ptr, y_ptr, N: tl.constexpr):
offs = tl.arange(0, N)
a = tl.load(x_ptr + offs)
b = tl.load(x_ptr + N + offs)
c = tl.join(a, b)
tl.store(y_ptr + tl.arange(0, 2*N), c)
^
Cannot make_shape_compatible: incompatible dimensions at index 1: 8 and 2{
"command": "<663 chars \u2014 see below>",
"description": "Test Triton join 2D"
}cat > /tmp/test_join2.py <<'EOF'
import torch
import triton
import triton.language as tl
@triton.jit
def test_kernel(x_ptr, y_ptr, N: tl.constexpr):
offs_i = tl.arange(0, N)[:,None]
offs_j = tl.arange(0, N)[None,:]
a = tl.load(x_ptr + offs_i * N + offs_j)
b = tl.load(x_ptr + N*N + offs_i * N + offs_j)
c = tl.join(a, b)
# store shape? let's store flatten
tl.store(y_ptr + tl.arange(0, 2*N*N), tl.reshape(c, (2*N*N,)))
N=2
x = torch.arange(8,dtype=torch.float32,device='cuda')
y = torch.empty(8,dtype=torch.float32,device='cuda')
test_kernel[(1,)](x,y,N=N)
print('x=', x, 'y=', y)
EOF
uv run python /tmp/test_join2.py 2>&1 | tail -10x= tensor([0., 1., 2., 3., 4., 5., 6., 7.], device='cuda:0') y= tensor([0., 4., 1., 5., 2., 6., 3., 7.], device='cuda:0')--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -45,20 +45,16 @@
PAGE_SIZE: tl.constexpr,
HEAD_DIM: tl.constexpr,
G: tl.constexpr,
- CHUNK_PAGES: tl.constexpr,
- BLOCK_N: tl.constexpr,
):
- """One CUDA block per (batch, kv_head). Processes CHUNK_PAGES pages at a time."""
+ """One CUDA block per (batch, kv_head). Computes all G query heads."""
pid_b = tl.program_id(0)
pid_hkv = tl.program_id(1)
seq_len = tl.load(seq_lens_ptr + pid_b)
num_pages = tl.cdiv(seq_len, PAGE_SIZE)
- num_chunks = tl.cdiv(num_pages, CHUNK_PAGES)
offs_d = tl.arange(0, HEAD_DIM)
- offs_page = tl.arange(0, PAGE_SIZE)
- offs_t = tl.arange(0, BLOCK_N)
+ offs_t = tl.arange(0, PAGE_SIZE)
offs_g = tl.arange(0, G)[:, None]
# Load the G query heads for this KV group: (G, HEAD_DIM).
@@ -74,48 +70,28 @@
ls = tl.zeros((G,), dtype=tl.float32)
accs = tl.zeros((G, HEAD_DIM), dtype=tl.float32)
- for c in range(num_chunks):
- p0 = c * CHUNK_PAGES
-
- physical_block_0 = tl.load(
- block_table_ptr + pid_b * stride_bt_b + p0 * stride_bt_p
+ for p in range(num_pages):
+ physical_block = tl.load(
+ block_table_ptr + pid_b * stride_bt_b + p * stride_bt_p
).to(tl.int64)
- kv_base_0 = kv_ptr + physical_block_0 * stride_kvn + pid_hkv * stride_kvh
- k0 = tl.load(
- kv_base_0 + (offs_page[:, None] * stride_kvt + offs_d[None, :] * stride_kvd),
- mask=offs_d[None, :] < HEAD_DIM,
+ token_offset = p * PAGE_SIZE
+ valid = tl.minimum(PAGE_SIZE, seq_len - token_offset)
+
+ kv_base = kv_ptr + physical_block * stride_kvn + pid_hkv * stride_kvh
+ k_ptrs = kv_base + (offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd)
+ k = tl.load(
+ k_ptrs,
+ mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
other=0.0,
).to(tl.float32)
- v0 = tl.load(
- kv_base_0 + HEAD_DIM * stride_kvd + (
- offs_page[:, None] * stride_kvt + offs_d[None, :] * stride_kvd
- ),
- mask=offs_d[None, :] < HEAD_DIM,
+ v_ptrs = kv_base + HEAD_DIM * stride_kvd + (
+ offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd
+ )
+ v = tl.load(
+ v_ptrs,
+ mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
other=0.0,
).to(tl.float32)
-
- physical_block_1 = tl.load(
- block_table_ptr + pid_b * stride_bt_b + (p0 + 1) * stride_bt_p
- ).to(tl.int64)
- kv_base_1 = kv_ptr + physical_block_1 * stride_kvn + pid_hkv * stride_kvh
- k1 = tl.load(
- kv_base_1 + (offs_page[:, None] * stride_kvt + offs_d[None, :] * stride_kvd),
- mask=offs_d[None, :] < HEAD_DIM,
- other=0.0,
- ).to(tl.float32)
- v1 = tl.load(
- kv_base_1 + HEAD_DIM * stride_kvd + (
- offs_page[:, None] * stride_kvt + offs_d[None, :] * stride_kvd
- ),
- mask=offs_d[None, :] < HEAD_DIM,
- other=0.0,
- ).to(tl.float32)
-
- k = tl.cat(k0, k1, axis=0)
- v = tl.cat(v0, v1, axis=0)
-
- token_offset = c * BLOCK_N
- valid = tl.minimum(BLOCK_N, seq_len - token_offset)
for g in tl.static_range(G):
mask_g = tl.arange(0, G) == g
@@ -125,7 +101,7 @@
m_cur = tl.sum(tl.where(mask_g, ms, 0.0), axis=0)
l_cur = tl.sum(tl.where(mask_g, ls, 0.0), axis=0)
- scores = tl.reshape(tl.dot(q[None, :], tl.trans(k)), (BLOCK_N,)) * SCALE
+ scores = tl.reshape(tl.dot(q[None, :], tl.trans(k)), (PAGE_SIZE,)) * SCALE
scores = tl.where(offs_t < valid, scores, float("-inf"))
m_new = tl.maximum(m_cur, tl.max(scores, axis=0))
@@ -189,19 +165,6 @@
P = self.page_size
out = torch.empty(B, H, D, dtype=query.dtype, device=query.device)
-
- # Pad block_table so every chunk can load two physical blocks without
- # predicating the block-table read itself.
- max_blocks = block_table.shape[1]
- pad = (-max_blocks) % 2
- if pad:
- block_table = torch.cat(
- [
- block_table,
- torch.zeros(B, pad, dtype=torch.int32, device=block_table.device),
- ],
- dim=1,
- )
grid = (B, Hkv)
paged_decode_kernel[grid](
@@ -226,8 +189,6 @@
PAGE_SIZE=P,
HEAD_DIM=D,
G=G,
- CHUNK_PAGES=2,
- BLOCK_N=P * 2,
num_warps=4,
)
return out
{
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"content": "<6295 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | tail -5 && uv run python benchmark.py 2>&1 | tail -20",
"description": "Verify restored kernel and benchmark"
}PASS
shape=0 variant=solution tflops=0.802 gbps=201.180 ms=0.167
shape=0 solution_peak_fraction=0.1118
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T23:07:50.290576+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T23:07:50.306452+00:00 elapsed_s=0.016 ms=0.357664
shape=1 variant=solution tflops=3.002 gbps=751.990 ms=0.358
shape=1 solution_peak_fraction=0.4178
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T23:07:50.706163+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T23:07:50.754808+00:00 elapsed_s=0.049 ms=1.130608
shape=2 variant=solution tflops=0.475 gbps=59.472 ms=1.131
shape=2 solution_peak_fraction=0.0330
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T23:07:51.353272+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T23:07:51.364764+00:00 elapsed_s=0.011 ms=0.241232
shape=3 variant=solution tflops=1.668 gbps=418.103 ms=0.241
shape=3 solution_peak_fraction=0.2323
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T23:07:51.461999+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T23:07:51.476188+00:00 elapsed_s=0.014 ms=0.272720
shape=4 variant=solution tflops=0.240 gbps=60.196 ms=0.273
shape=4 solution_peak_fraction=0.0334
peak_fraction: 0.1037
RESULT: OK--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -23,12 +23,14 @@
@triton.jit
-def paged_decode_kernel(
+def paged_decode_part_kernel(
q_ptr,
kv_ptr,
block_table_ptr,
seq_lens_ptr,
- out_ptr,
+ part_out_ptr,
+ part_m_ptr,
+ part_l_ptr,
stride_qb,
stride_qh,
stride_qd,
@@ -38,20 +40,29 @@
stride_kvd,
stride_bt_b,
stride_bt_p,
- stride_ob,
- stride_oh,
- stride_od,
+ stride_pob,
+ stride_poh,
+ stride_pos,
+ stride_pod,
+ stride_pmb,
+ stride_pmh,
+ stride_pms,
SCALE: tl.constexpr,
PAGE_SIZE: tl.constexpr,
HEAD_DIM: tl.constexpr,
G: tl.constexpr,
+ NUM_SPLITS: tl.constexpr,
):
- """One CUDA block per (batch, kv_head). Computes all G query heads."""
+ """One CUDA block per (batch, kv_head, split). Computes partial attention."""
pid_b = tl.program_id(0)
pid_hkv = tl.program_id(1)
+ pid_s = tl.program_id(2)
seq_len = tl.load(seq_lens_ptr + pid_b)
num_pages = tl.cdiv(seq_len, PAGE_SIZE)
+ pages_per_split = tl.cdiv(num_pages, NUM_SPLITS)
+ start_page = pid_s * pages_per_split
+ end_page = tl.minimum(num_pages, start_page + pages_per_split)
offs_d = tl.arange(0, HEAD_DIM)
offs_t = tl.arange(0, PAGE_SIZE)
@@ -70,7 +81,7 @@
ls = tl.zeros((G,), dtype=tl.float32)
accs = tl.zeros((G, HEAD_DIM), dtype=tl.float32)
- for p in range(num_pages):
+ for p in range(start_page, end_page):
physical_block = tl.load(
block_table_ptr + pid_b * stride_bt_b + p * stride_bt_p
).to(tl.int64)
@@ -116,16 +127,69 @@
ls = tl.where(mask_g, new_l, ls)
ms = tl.where(mask_g, m_new, ms)
- out_ptrs = (
- out_ptr
- + pid_b * stride_ob
- + (pid_hkv * G + offs_g) * stride_oh
- + offs_d[None, :] * stride_od
- )
+ # Write partials for each query head in the group.
+ base_out = part_out_ptr + pid_b * stride_pob
+ base_m = part_m_ptr + pid_b * stride_pmb
+ base_l = part_l_ptr + pid_b * stride_pmb
+ for g in tl.static_range(G):
+ h = pid_hkv * G + g
+ out_ptrs = base_out + h * stride_poh + pid_s * stride_pos + offs_d * stride_pod
+ tl.store(out_ptrs, accs[g], mask=offs_d < HEAD_DIM)
+ tl.store(base_m + h * stride_pmh + pid_s * stride_pms, ms[g])
+ tl.store(base_l + h * stride_pmh + pid_s * stride_pms, ls[g])
+
+
+@triton.jit
+def combine_kernel(
+ part_out_ptr,
+ part_m_ptr,
+ part_l_ptr,
+ out_ptr,
+ stride_pob,
+ stride_poh,
+ stride_pos,
+ stride_pod,
+ stride_pmb,
+ stride_pmh,
+ stride_pms,
+ stride_ob,
+ stride_oh,
+ stride_od,
+ HEAD_DIM: tl.constexpr,
+ NUM_SPLITS: tl.constexpr,
+):
+ """Combine partial attention results across splits."""
+ pid_b = tl.program_id(0)
+ pid_h = tl.program_id(1)
+
+ offs_d = tl.arange(0, HEAD_DIM)
+
+ M = tl.full((), float("-inf"), dtype=tl.float32)
+ denom = tl.zeros((), dtype=tl.float32)
+ acc = tl.zeros((HEAD_DIM,), dtype=tl.float32)
+
+ base_out = part_out_ptr + pid_b * stride_pob + pid_h * stride_poh
+ base_m = part_m_ptr + pid_b * stride_pmb + pid_h * stride_pmh
+ base_l = part_l_ptr + pid_b * stride_pmb + pid_h * stride_pmh
+
+ for s in tl.static_range(NUM_SPLITS):
+ m_s = tl.load(base_m + s * stride_pms)
+ l_s = tl.load(base_l + s * stride_pms)
+ acc_s = tl.load(base_out + s * stride_pos + offs_d * stride_pod, mask=offs_d < HEAD_DIM, other=0.0)
+
+ M_new = tl.maximum(M, m_s)
+ scale_global = tl.exp(M - M_new)
+ scale_s = tl.exp(m_s - M_new)
+
+ acc = acc * scale_global + acc_s * scale_s
+ denom = denom * scale_global + l_s * scale_s
+ M = M_new
+
+ out_ptrs = out_ptr + pid_b * stride_ob + pid_h * stride_oh + offs_d * stride_od
tl.store(
out_ptrs,
- (accs / ls[:, None]).to(tl.bfloat16),
- mask=offs_d[None, :] < HEAD_DIM,
+ (acc / denom).to(tl.bfloat16),
+ mask=offs_d < HEAD_DIM,
)
@@ -163,34 +227,109 @@
Hkv = self.num_kv_heads
G = self.group_size
P = self.page_size
-
- out = torch.empty(B, H, D, dtype=query.dtype, device=query.device)
-
- grid = (B, Hkv)
- paged_decode_kernel[grid](
- query,
- kv_cache,
- block_table,
- seq_lens,
- out,
- query.stride(0),
- query.stride(1),
- query.stride(2),
- kv_cache.stride(0),
- kv_cache.stride(1),
- kv_cache.stride(2),
- kv_cache.stride(3),
- block_table.stride(0),
- block_table.stride(1),
- out.stride(0),
- out.stride(1),
- out.stride(2),
- SCALE=self.scale,
- PAGE_SIZE=P,
- HEAD_DIM=D,
- G=G,
- num_warps=4,
- )
+ device = query.device
+
+ out = torch.empty(B, H, D, dtype=query.dtype, device=device)
+
+ # Use sequence splitting when there are too few (batch, kv_head) tiles to
+ # keep the GPU busy. Aim for ~128 active blocks.
+ blocks = B * Hkv
+ num_splits = max(1, 128 // blocks)
+ num_pages = (int(seq_lens.max().item()) + P - 1) // P
+ num_splits = min(num_splits, num_pages)
+
+ if num_splits == 1:
+ grid = (B, Hkv)
+ paged_decode_part_kernel[grid](
+ query,
+ kv_cache,
+ block_table,
+ seq_lens,
+ out,
+ out, # unused placeholders
+ out,
+ query.stride(0),
+ query.stride(1),
+ query.stride(2),
+ kv_cache.stride(0),
+ kv_cache.stride(1),
+ kv_cache.stride(2),
+ kv_cache.stride(3)
... (2433 more chars){
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"content": "<10952 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -20,6 +20,113 @@
HEAD_DIM = 128
SEQ_LEN = 1024
PAGE_SIZE = 16
+
+
+@triton.jit
+def paged_decode_kernel(
+ q_ptr,
+ kv_ptr,
+ block_table_ptr,
+ seq_lens_ptr,
+ out_ptr,
+ stride_qb,
+ stride_qh,
+ stride_qd,
+ stride_kvn,
+ stride_kvt,
+ stride_kvh,
+ stride_kvd,
+ stride_bt_b,
+ stride_bt_p,
+ stride_ob,
+ stride_oh,
+ stride_od,
+ SCALE: tl.constexpr,
+ PAGE_SIZE: tl.constexpr,
+ HEAD_DIM: tl.constexpr,
+ G: tl.constexpr,
+):
+ """One CUDA block per (batch, kv_head). Computes all G query heads."""
+ pid_b = tl.program_id(0)
+ pid_hkv = tl.program_id(1)
+
+ seq_len = tl.load(seq_lens_ptr + pid_b)
+ num_pages = tl.cdiv(seq_len, PAGE_SIZE)
+
+ offs_d = tl.arange(0, HEAD_DIM)
+ offs_t = tl.arange(0, PAGE_SIZE)
+ offs_g = tl.arange(0, G)[:, None]
+
+ # Load the G query heads for this KV group: (G, HEAD_DIM).
+ q_ptrs = (
+ q_ptr
+ + pid_b * stride_qb
+ + (pid_hkv * G + offs_g) * stride_qh
+ + offs_d[None, :] * stride_qd
+ )
+ qs = tl.load(q_ptrs, mask=offs_d[None, :] < HEAD_DIM, other=0.0).to(tl.float32)
+
+ ms = tl.full((G,), float("-inf"), dtype=tl.float32)
+ ls = tl.zeros((G,), dtype=tl.float32)
+ accs = tl.zeros((G, HEAD_DIM), dtype=tl.float32)
+
+ for p in range(num_pages):
+ physical_block = tl.load(
+ block_table_ptr + pid_b * stride_bt_b + p * stride_bt_p
+ ).to(tl.int64)
+ token_offset = p * PAGE_SIZE
+ valid = tl.minimum(PAGE_SIZE, seq_len - token_offset)
+
+ kv_base = kv_ptr + physical_block * stride_kvn + pid_hkv * stride_kvh
+ k_ptrs = kv_base + (offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd)
+ k = tl.load(
+ k_ptrs,
+ mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
+ other=0.0,
+ ).to(tl.float32)
+ v_ptrs = kv_base + HEAD_DIM * stride_kvd + (
+ offs_t[:, None] * stride_kvt + offs_d[None, :] * stride_kvd
+ )
+ v = tl.load(
+ v_ptrs,
+ mask=(offs_t[:, None] < valid) & (offs_d[None, :] < HEAD_DIM),
+ other=0.0,
+ ).to(tl.float32)
+
+ for g in tl.static_range(G):
+ mask_g = tl.arange(0, G) == g
+ mask_g2 = mask_g[:, None]
+ q = tl.sum(tl.where(mask_g2, qs, 0.0), axis=0)
+ acc_cur = tl.sum(tl.where(mask_g2, accs, 0.0), axis=0)
+ m_cur = tl.sum(tl.where(mask_g, ms, 0.0), axis=0)
+ l_cur = tl.sum(tl.where(mask_g, ls, 0.0), axis=0)
+
+ scores = tl.reshape(tl.dot(q[None, :], tl.trans(k)), (PAGE_SIZE,)) * SCALE
+ scores = tl.where(offs_t < valid, scores, float("-inf"))
+
+ m_new = tl.maximum(m_cur, tl.max(scores, axis=0))
+ exp_scale = tl.exp(m_cur - m_new)
+ exp_scores = tl.exp(scores - m_new)
+ weighted = tl.reshape(tl.dot(exp_scores[None, :], v), (HEAD_DIM,))
+
+ new_acc = acc_cur * exp_scale + weighted
+ new_l = l_cur * exp_scale + tl.sum(exp_scores, axis=0)
+
+ accs = tl.where(mask_g2, new_acc[None, :], accs)
+ ls = tl.where(mask_g, new_l, ls)
+ ms = tl.where(mask_g, m_new, ms)
+
+ out_ptrs = (
+ out_ptr
+ + pid_b * stride_ob
+ + (pid_hkv * G + offs_g) * stride_oh
+ + offs_d[None, :] * stride_od
+ )
+ tl.store(
+ out_ptrs,
+ (accs / ls[:, None]).to(tl.bfloat16),
+ mask=offs_d[None, :] < HEAD_DIM,
+ )
@triton.jit
@@ -235,18 +342,16 @@
# keep the GPU busy. Aim for ~128 active blocks.
blocks = B * Hkv
num_splits = max(1, 128 // blocks)
- num_pages = (int(seq_lens.max().item()) + P - 1) // P
- num_splits = min(num_splits, num_pages)
+ max_pages = (int(seq_lens.max().item()) + P - 1) // P
+ num_splits = min(num_splits, max_pages)
if num_splits == 1:
grid = (B, Hkv)
- paged_decode_part_kernel[grid](
+ paged_decode_kernel[grid](
query,
kv_cache,
block_table,
seq_lens,
- out,
- out, # unused placeholders
out,
query.stride(0),
query.stride(1),
@@ -257,78 +362,74 @@
kv_cache.stride(3),
block_table.stride(0),
block_table.stride(1),
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
+ out.stride(0),
+ out.stride(1),
+ out.stride(2),
SCALE=self.scale,
PAGE_SIZE=P,
HEAD_DIM=D,
G=G,
- NUM_SPLITS=1,
num_warps=4,
)
- else:
- part_out = torch.empty(B, H, num_splits, D, dtype=torch.float32, device=device)
- part_m = torch.empty(B, H, num_splits, dtype=torch.float32, device=device)
- part_l = torch.empty(B, H, num_splits, dtype=torch.float32, device=device)
-
- grid_part = (B, Hkv, num_splits)
- paged_decode_part_kernel[grid_part](
- query,
- kv_cache,
- block_table,
- seq_lens,
- part_out,
- part_m,
- part_l,
- query.stride(0),
- query.stride(1),
- query.stride(2),
- kv_cache.stride(0),
- kv_cache.stride(1),
- kv_cache.stride(2),
- kv_cache.stride(3),
- block_table.stride(0),
- block_table.stride(1),
- part_out.stride(0),
- part_
... (2845 more chars){
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"content": "<13989 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -120 && uv run python benchmark.py 2>&1 | tail -25",
"description": "Check and benchmark split-seq kernel"
}Traceback (most recent call last):
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 43, in wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 1101, in __getitem__
raise ValueError(f"unsupported tensor index: {sl}")
ValueError: unsupported tensor index: constexpr[0]
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/workspace/problems/03_paged_attention/check.py", line 116, in <module>
main()
File "/workspace/problems/03_paged_attention/check.py", line 79, in main
sol_out = sol_model(*inputs)
^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/problems/03_paged_attention/solution.py", line 381, in forward
paged_decode_part_kernel[grid_part](
File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
^^^^^^^^^^^^^^
... (3355 more chars)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -234,16 +234,29 @@
ls = tl.where(mask_g, new_l, ls)
ms = tl.where(mask_g, m_new, ms)
- # Write partials for each query head in the group.
- base_out = part_out_ptr + pid_b * stride_pob
- base_m = part_m_ptr + pid_b * stride_pmb
- base_l = part_l_ptr + pid_b * stride_pmb
- for g in tl.static_range(G):
- h = pid_hkv * G + g
- out_ptrs = base_out + h * stride_poh + pid_s * stride_pos + offs_d * stride_pod
- tl.store(out_ptrs, accs[g], mask=offs_d < HEAD_DIM)
- tl.store(base_m + h * stride_pmh + pid_s * stride_pms, ms[g])
- tl.store(base_l + h * stride_pmh + pid_s * stride_pms, ls[g])
+ # Write partials for all G query heads in the group at once.
+ out_ptrs = (
+ part_out_ptr
+ + pid_b * stride_pob
+ + (pid_hkv * G + offs_g) * stride_poh
+ + pid_s * stride_pos
+ + offs_d[None, :] * stride_pod
+ )
+ tl.store(out_ptrs, accs, mask=offs_d[None, :] < HEAD_DIM)
+ m_ptrs = (
+ part_m_ptr
+ + pid_b * stride_pmb
+ + (pid_hkv * G + offs_g) * stride_pmh
+ + pid_s * stride_pms
+ )
+ tl.store(m_ptrs, ms)
+ l_ptrs = (
+ part_l_ptr
+ + pid_b * stride_pmb
+ + (pid_hkv * G + offs_g) * stride_pmh
+ + pid_s * stride_pms
+ )
+ tl.store(l_ptrs, ls)
@triton.jit
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<543 chars \u2014 see diff>",
"new_string": "<652 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -120",
"description": "Check split-seq after store fix"
}Traceback (most recent call last):
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 43, in wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 2233, in store
return _semantic.store(pointer, value, mask, boundary_check, cache_modifier, eviction_policy)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/semantic.py", line 1301, in store
return self._store_legacy(ptr, val, mask, boundary_check, cache, eviction)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/triton/language/semantic.py", line 1265, in _store_legacy
raise ValueError(f"Expected pointer argument to have shape {ptr.shape} but got {ptr_shape}")
ValueError: Expected pointer argument to have shape ['constexpr[4]', 'constexpr[4]'] but got ['constexpr[4]', 'constexpr[1]']
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/workspace/problems/03_paged_attention/check.py", line 116, in <module>
main()
File "/workspace/problems/03_paged_attention/check.py", line 79, in main
sol_out = sol_model(*inputs)
^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/m
... (2315 more chars)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -56,6 +56,7 @@
offs_d = tl.arange(0, HEAD_DIM)
offs_t = tl.arange(0, PAGE_SIZE)
offs_g = tl.arange(0, G)[:, None]
+ offs_g1 = tl.arange(0, G)
# Load the G query heads for this KV group: (G, HEAD_DIM).
q_ptrs = (
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<111 chars \u2014 see diff>",
"new_string": "<141 chars \u2014 see diff>"
}<tool_use_error>Found 2 matches of the string to replace, but replace_all is false. To replace all occurrences, set replace_all to true. To replace only one occurrence, please provide more context to uniquely identify the instance.
String: offs_d = tl.arange(0, HEAD_DIM)
offs_t = tl.arange(0, PAGE_SIZE)
offs_g = tl.arange(0, G)[:, None]
</tool_use_error>--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -56,6 +56,7 @@
offs_d = tl.arange(0, HEAD_DIM)
offs_t = tl.arange(0, PAGE_SIZE)
offs_g = tl.arange(0, G)[:, None]
+ offs_g1 = tl.arange(0, G)
offs_g1 = tl.arange(0, G)
# Load the G query heads for this KV group: (G, HEAD_DIM).
{
"replace_all": true,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<111 chars \u2014 see diff>",
"new_string": "<141 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated. All occurrences were successfully replaced. (file state is current in your context — no need to Read it back)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -248,14 +248,14 @@
m_ptrs = (
part_m_ptr
+ pid_b * stride_pmb
- + (pid_hkv * G + offs_g) * stride_pmh
+ + (pid_hkv * G + offs_g1) * stride_pmh
+ pid_s * stride_pms
)
tl.store(m_ptrs, ms)
l_ptrs = (
part_l_ptr
+ pid_b * stride_pmb
- + (pid_hkv * G + offs_g) * stride_pmh
+ + (pid_hkv * G + offs_g1) * stride_pmh
+ pid_s * stride_pms
)
tl.store(l_ptrs, ls)
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<338 chars \u2014 see diff>",
"new_string": "<340 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | head -120",
"description": "Check split-seq after pointer fix"
}PASS{
"command": "uv run python benchmark.py 2>&1 | tail -25",
"description": "Benchmark split-seq kernel"
}benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T23:16:04.859376+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T23:16:05.103114+00:00 elapsed_s=0.244 ms=0.110576
shape=0 variant=solution tflops=1.214 gbps=304.637 ms=0.111
shape=0 solution_peak_fraction=0.1692
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T23:16:06.702529+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T23:16:06.721420+00:00 elapsed_s=0.019 ms=0.368720
shape=1 variant=solution tflops=2.912 gbps=729.442 ms=0.369
shape=1 solution_peak_fraction=0.4052
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T23:16:07.122685+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T23:16:07.139436+00:00 elapsed_s=0.017 ms=0.319264
shape=2 variant=solution tflops=1.682 gbps=210.609 ms=0.319
shape=2 solution_peak_fraction=0.1170
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T23:16:07.742654+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T23:16:07.754788+00:00 elapsed_s=0.012 ms=0.253136
shape=3 variant=solution tflops=1.590 gbps=398.442 ms=0.253
shape=3 solution_peak_fraction=0.2214
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T23:16:07.852631+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T23:16:07.860909+00:00 elapsed_s=0.008 ms=0.099936
shape=4
... (122 more chars)--- a//workspace/problems/03_paged_attention/solution.py
+++ b//workspace/problems/03_paged_attention/solution.py
@@ -354,9 +354,9 @@
out = torch.empty(B, H, D, dtype=query.dtype, device=device)
# Use sequence splitting when there are too few (batch, kv_head) tiles to
- # keep the GPU busy. Aim for ~128 active blocks.
+ # keep the GPU busy. Aim for ~256 active blocks.
blocks = B * Hkv
- num_splits = max(1, 128 // blocks)
+ num_splits = max(1, 256 // blocks)
max_pages = (int(seq_lens.max().item()) + P - 1) // P
num_splits = min(num_splits, max_pages)
{
"replace_all": false,
"file_path": "/workspace/problems/03_paged_attention/solution.py",
"old_string": "<317 chars \u2014 see diff>",
"new_string": "<317 chars \u2014 see diff>"
}The file /workspace/problems/03_paged_attention/solution.py has been updated successfully. (file state is current in your context — no need to Read it back){
"command": "uv run python check.py 2>&1 | tail -3 && uv run python benchmark.py 2>&1 | tail -25",
"description": "Check and benchmark with 256 target"
}Command running in background with ID: bkjh1qopj. Output is being written to: /tmp/claude-1002/-workspace-problems-03-paged-attention/12cbe6f6-642d-457b-bea8-002ece8be17d/tasks/bkjh1qopj.output. You will be notified when it completes. To check interim output, use Read on that file path.{
"task_id": "bkjh1qopj",
"block": true,
"timeout": 120000
}<retrieval_status>success</retrieval_status>
<task_id>bkjh1qopj</task_id>
<task_type>local_bash</task_type>
<status>completed</status>
<exit_code>0</exit_code>
<output>
PASS
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T23:18:29.045430+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T23:18:29.289277+00:00 elapsed_s=0.244 ms=0.080864
shape=0 variant=solution tflops=1.660 gbps=416.570 ms=0.081
shape=0 solution_peak_fraction=0.2314
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T23:18:30.887602+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T23:18:30.906349+00:00 elapsed_s=0.019 ms=0.368784
shape=1 variant=solution tflops=2.912 gbps=729.315 ms=0.369
shape=1 solution_peak_fraction=0.4052
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T23:18:31.309219+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T23:18:31.321398+00:00 elapsed_s=0.012 ms=0.197808
shape=2 variant=solution tflops=2.714 gbps=339.925 ms=0.198
shape=2 solution_peak_fraction=0.1888
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T23:18:31.921547+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T23:18:31.931923+00:00 elapsed_s=0.010 ms=0.163616
shape=3 variant=solution tflops=2.459 gbps=616.443 ms=0.164
shape=3 solution_peak_fraction=0.3425
benchmark_event event=variant_start shape=4 varia
... (311 more chars){
"command": "for s in \"vllm.attention\" \"flashinfer.batch_decode_with_paged_kv_cache\" \"flashinfer.decode\" \"torch.nn.functional.scaled_dot_product_attention\" \"F.scaled_dot_product_attention\"; do if grep -q \"$s\" /workspace/problems/03_paged_attention/solution.py; then echo \"FOUND $s\"; fi; done; echo \"done\"",
"description": "Check forbidden strings"
}done{
"command": "uv run python check.py 2>&1 | tail -3 && uv run python benchmark.py 2>&1 | tail -25",
"description": "Final verification"
}PASS
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T23:19:03.091306+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T23:19:03.337749+00:00 elapsed_s=0.246 ms=0.081472
shape=0 variant=solution tflops=1.647 gbps=413.461 ms=0.081
shape=0 solution_peak_fraction=0.2297
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T23:19:04.936186+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T23:19:04.954889+00:00 elapsed_s=0.019 ms=0.368784
shape=1 variant=solution tflops=2.912 gbps=729.315 ms=0.369
shape=1 solution_peak_fraction=0.4052
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T23:19:05.356561+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T23:19:05.368763+00:00 elapsed_s=0.012 ms=0.198816
shape=2 variant=solution tflops=2.700 gbps=338.202 ms=0.199
shape=2 solution_peak_fraction=0.1879
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T23:19:05.969570+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T23:19:05.979996+00:00 elapsed_s=0.010 ms=0.163888
shape=3 variant=solution tflops=2.455 gbps=615.420 ms=0.164
shape=3 solution_peak_fraction=0.3419
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T23:19:06.077800+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T23:19:06.084834+00:00 elapsed_s=0.007 ms=0.068560
sha
... (127 more chars){
"status": "completed",
"taskId": "3"
}Updated task #3 status