total 44
drwxrwxr-x 2 1002 1002 4096 Jun 11 11:01 .
drwxrwxr-x 3 1002 1002 4096 Jun 11 11:01 ..
-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

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)

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)

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)

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	

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)

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)

problems
pyproject.toml
src
uv.lock
__init__.py
__pycache__
eval
hardware
harness
kbh
sandbox
viewer
__init__.py
__pycache__
correctness.py
numeric_stress.py
report.py
roofline.py
shapes.py
timing.py

1	"""Per-dtype correctness tolerance.
2	
3	Stanford's KernelBench uses 1e-4 atol/rtol for fp32 and 1e-2 for fp16/bf16.
4	We extend to fp8 (0.1, permissive) and int (exact).
5	"""
6	from __future__ import annotations
7	
8	import torch
9	
10	DEFAULT_TOLERANCE = {
11	    torch.float32: {"atol": 1e-4, "rtol": 1e-4},
12	    torch.float16: {"atol": 1e-2, "rtol": 1e-2},
13	    torch.bfloat16: {"atol": 1e-2, "rtol": 1e-2},
14	    torch.float8_e4m3fn: {"atol": 1e-1, "rtol": 1e-1},
15	    torch.float8_e5m2: {"atol": 1e-1, "rtol": 1e-1},
16	    torch.int8: {"atol": 0, "rtol": 0},
17	    torch.int32: {"atol": 0, "rtol": 0},
18	    torch.int64: {"atol": 0, "rtol": 0},
19	}
20	
21	
22	def _coerce_float(x):
23	    """Accept int/float/str scientific-notation as float; pass through dicts."""
24	    if isinstance(x, (int, float)):
25	        return float(x)
26	    if isinstance(x, str):
27	        try:
28	            return float(x)
29	        except ValueError:
30	            return x
31	    return x
32	
33	
34	def tolerance_for_dtype(dtype: torch.dtype, override: dict | None = None) -> dict:
35	    """Lookup atol/rtol for a given dtype, with optional per-problem override.
36	
37	    The override dict is keyed by str(dtype). Values may be:
38	      - a single number (or a string parseable as float, e.g. "5e-2") -> used
39	        as both atol and rtol
40	      - a dict {"atol": ..., "rtol": ...} -> used directly, with values coerced
41	    """
42	    # Accept several spellings of the same dty

... (2892 more chars)

1	"""Numeric distribution stress cases for correctness checks.
2	
3	These cases are not hidden shapes. They rerun the same shape/seed validation
4	under a few scale regimes that catch kernels that only work for the nominal
5	N(0, 1)-ish inputs.
6	"""
7	from __future__ import annotations
8	
9	import os
10	from collections.abc import Iterator, Mapping, Sequence
11	from contextlib import contextmanager
12	from dataclasses import dataclass, field
13	
14	import torch
15	
16	ToleranceOverride = Mapping[str, float | Mapping[str, float]]
17	
18	
19	@dataclass(frozen=True)
20	class NumericStressCase:
21	    name: str
22	    input_scales: Mapping[int, float] = field(default_factory=dict)
23	    state_scales: Mapping[str, float] = field(default_factory=dict)
24	    tolerance: ToleranceOverride | None = None
25	
26	
27	NOMINAL_CASE = NumericStressCase("nominal")
28	
29	_SMALL_BF16 = {"bfloat16": {"atol": 1e-4, "rtol": 5e-2}}
30	_MED_BF16 = {"bfloat16": {"atol": 5e-4, "rtol": 5e-2}}
31	_LARGE_BF16 = {"bfloat16": {"atol": 2e-1, "rtol": 5e-2}}
32	_TINY_FP32 = {"float32": {"atol": 1e-7, "rtol": 1e-4}}
33	_LARGE_FP32 = {"float32": {"atol": 1e-1, "rtol": 1e-4}}
34	
35	_CASES: dict[str, tuple[NumericStressCase, ...]] = {
36	    "01_fp8_gemm": (
37	        NumericStressCase("small_input", input_scales={0: 1e-3}, tolerance=_SMALL_BF16),
38	        NumericStressCase("large_input", input_scales={0: 64.0}, tolerance=_LARGE_BF16),
39	        NumericStressCase(
40	            "small_weight",
41	      

... (5156 more chars)

1	"""Centralized GPU timing for benchmark.py files.
2	
3	All problems' benchmark.py should call `time_fn` from here so we have one
4	implementation of warmup, L2 flush, and CUDA event capture to maintain.
5	
6	Methodology:
7	  - 10 warmup calls absorb Triton autotune (typical ~7 configs) and
8	    torch.compile reduce-overhead CUDA-graph capture.
9	  - Between each timed call, `_l2_flush()` writes 128 MB to evict L2
10	    (Blackwell L2 is 96 MB; we want any prior L2 contents flushed so
11	    we measure HBM-load bandwidth, not L2-cached bandwidth).
12	  - GPU timing via cuda.Event with synchronize() AFTER record() but
13	    BEFORE elapsed_time() — the canonical NVIDIA pattern.
14	  - Reported value is the median of `iters` trials, robust to outliers.
15	
16	Notes / known biases not addressed here:
17	  - torch.compile(mode="reduce-overhead") gets CUDA graphs which eliminate
18	    launch overhead. Custom Triton/CUDA kernels do NOT get this treatment.
19	    On small shapes where launch overhead matters, this gives compile an
20	    artificial advantage. We accept this as the cost of using the agreed
21	    "compiled" baseline policy.
22	  - cuBLAS / cuDNN allocate workspaces on first call. 10 warmup absorbs.
23	  - Median over a fairly small number of trials (default 30) is fine for
24	    headline numbers but won't catch bimodal distributions. Use --extra
25	    instrumentation if you ever care.
26	"""
27	from __future__ import annotations
28	
29	import os
30	import re
31	

... (3182 more chars)

Thu Jun 11 11:07:01 2026       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 610.43.02              KMD Version: 610.43.02     CUDA UMD Version: 13.3     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA RTX PRO 6000 Blac...    On  |   00000000:01:00.0 Off |                  Off |
| 30%   37C    P0             88W /  600W |      15MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|======================================================================================

... (291 more chars)

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 1.30s
torch 2.11.0+cu130 cuda 13.0
device NVIDIA RTX PRO 6000 Blackwell Workstation Edition (12, 0)
triton 3.6.0
sm count 188
L2 128 MB

"""Hardware peak-throughput lookup tables."""
from src.hardware.m4_max import M4_MAX
from src.hardware.rtx_pro_6000 import RTX_PRO_6000

TARGETS = {
    "RTX_PRO_6000": RTX_PRO_6000,
    "M4_MAX": M4_MAX,
}


def get(name: str):
    if name not in TARGETS:
        raise ValueError(f"Unknown hardware {name!r}; available: {list(TARGETS)}")
    return TARGETS[name]
"""Apple M4 Max — unified memory, 16-core GPU, Metal 3."""
from src.hardware.rtx_pro_6000 import HardwareTarget

M4_MAX = HardwareTarget(
    name="Apple M4 Max",
    sm="metal3",
    vram_gb=36,  # unified
    peak_bandwidth_gb_s=546.0,
    peak_tflops_dense={
        # Apple doesn't publish formal TFLOPS numbers the way NVIDIA does.
        # These are community estimates from chip-level benchmarks.
        "fp32": 17.0,
        "fp16": 34.0,
        "bf16": 34.0,
        "int8": 68.0,
    },
)
"""RTX PRO 6000 Blackwell Workstation — SM120, consumer-lineage Blackwell.

Peak tensor-core throughputs are dense-matrix advertised peaks. Actual kernels
will see 60-85% of peak on well-tuned code.
"""
from dataclasses import dataclass


@dataclass(frozen=True)
class HardwareTarget:
    name: str
    sm: str
    vram_gb: int
    peak_bandwidth_gb_s: float  # DRAM
    peak_tflops_dense: dict[str, float]  # dtype -> TFLOPS


RTX_PRO_6000 = HardwareTarget(
    name="RTX PRO 6000 Blackwell Workstation",
    sm="sm_120a",
    vram_gb=96,
    peak_bandwidth_gb_s=1800.0,
    peak_tflops_dense={
        "fp4": 800.0,
        "nvfp4"

... (301 more chars)

Task #1 created successfully: Write v1 CUDA split-K paged decode kernel in solution.py

Task #2 created successfully: Pass check.py for all 5 shapes

Task #3 created successfully: Benchmark and iterate on bandwidth fraction

Updated task #1 status

File created successfully at: /workspace/problems/03_paged_attention/solution.py (file state is current in your context — no need to Read it back)

[1/3] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=paged_decode_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -fPIC -std=c++17 -c /home/agent/.cache/torch_extensions/py311_cu130/paged_decode_v1/main.cpp -o main.o 
[2/3] /usr/local/cuda-host/bin/nvcc -MD -MF cuda.cuda.o.d -DTORCH_EXTENSION_NAME=paged_decode_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_100,code=sm_100 -gencode=arch=compute_120,code=compute_120 -gencode=arch=compute_120,code=sm_120 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 --use_fast_math -std=c++17 -c /home/agent/.cache/torch_extensions/py311_cu130/paged_decode_v1/cuda.cu -o cuda.cuda.o 
[3/3] c++ main.o cuda.cuda.o -shared -L/workspace/.v

... (210 more chars)

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)

PASS

Updated task #1 status

Updated task #2 status

shape=0 variant=solution tflops=3.638 gbps=912.985 ms=0.037
shape=0 solution_peak_fraction=0.5072
shape=1 variant=solution tflops=5.254 gbps=1316.159 ms=0.204
shape=1 solution_peak_fraction=0.7312
shape=2 variant=solution tflops=6.776 gbps=848.646 ms=0.079
shape=2 solution_peak_fraction=0.4715
shape=3 variant=solution tflops=5.133 gbps=1286.478 ms=0.078
shape=3 solution_peak_fraction=0.7147
shape=4 variant=solution tflops=2.880 gbps=721.553 ms=0.023
shape=4 solution_peak_fraction=0.4009
peak_fraction: 0.5495
RESULT: OK

File created successfully at: /workspace/problems/03_paged_attention/bench_dev.py (file state is current in your context — no need to Read it back)

_warn_once(
shape=0 ms=     36.7us gbps=   918.2 frac=0.5101 S=8 chunk=128 nwarps=8
         24.0us x1  void decode_split_kernel<128, 4, 8>(__nv_bfloat16 const*, __nv_bfloat16 const*, int const*
          2.4us x1  void reduce_kernel<128, 4>(float const*, float const*, int const*, __nv_bfloat16*, int, in
shape=1 ms=    204.8us gbps=  1313.2 frac=0.7295 S=3 chunk=688 nwarps=8
        181.0us x1  void decode_split_kernel<128, 4, 8>(__nv_bfloat16 const*, __nv_bfloat16 const*, int const*
          2.1us x1  void reduce_kernel<128, 4>(float const*, float const*, int const*, __nv_bfloat16*, int, in
shape=2 ms=     80.7us gbps=   833.7 frac=0.4631 S=18 chunk=240 nwarps=8
         54.7us x1  void decode_split_kernel<128, 8, 8>(__nv_bfloat16 const*, __nv_bfloat16 const*, int const*
          7.2us x1  void reduce_kernel<128, 8>(float const*, float const*, int const*, __nv_bfloat16*, int, in
shape=3 ms=     79.8us gbps=  1264.5 frac=0.7025 S=5 chunk=320 nwarps=8
         64.5us x1  void decode_split_kernel<128, 4, 8>(__nv_bfloat16 const*, __nv_bfloat16 const*, int const*
          2.3us x1  void reduce_kernel<128, 4>(float const*, float const*, int const*, __nv_bfloat16*, int, in
shape=4 ms=     24.0us gbps=   684.9 frac=0.3805 S=18 chunk=112 nwarps=4
          8.9us x1  void decode_split_kernel<64, 4, 4>(__nv_bfloat16 const*, __nv_bfloat16 const*, int const*,
          2.9us x1  void reduce_kernel<64, 4>(float const*, float const*, int const*, __nv_bfloat16*, int, int

/kbh/bin/ncu
/kbh/bin/nsys
Version 2025.2.1.0 (build 35987062) (public-release)

File created successfully at: /workspace/problems/03_paged_attention/prof_one.py (file state is current in your context — no need to Read it back)

==PROF== Profiling "decode_split_kernel": 0%....50%....100% - 9 passes
==PROF== Profiling "reduce_kernel": 0%....50%....100% - 9 passes
==PROF== Profiling "decode_split_kernel": 0%....50%....100% - 9 passes
==PROF== Profiling "reduce_kernel": 0%....50%....100% - 9 passes
  void decode_split_kernel<128, 4, 8>(const __nv_bfloat16 *, const __nv_bfloat16 *, const int *, const int *, float *, float *, __nv_bfloat16 *, int, int, int, int, float) (8, 8, 8)x(256, 1, 1), Context 1, Stream 7, Device 0, CC 12.0
    Metric Name                                            Metric Unit Metric Value
    dram__bytes_read.sum                                                    (!) n/a
    dram__bytes_write.sum                                                   (!) n/a
    dram__throughput.avg.pct_of_peak_sustained_elapsed               %        75.28
    gpu__time_duration.sum                                          us        26.18
    launch__occupancy_limit_registers                            block            2
    launch__occupancy_limit_shared_mem                           block            5
    launch__registers_per_thread                       register/thread          117
    sm__throughput.avg.pct_of_peak_sustained_elapsed                 %        16.16
    sm__warps_active.avg.pct_of_peak_sustained_active                %        31.28
  void reduce_kernel<128, 4>(const float *, const float *, const int *, __nv_bfloat16 *, int, int, int) (8, 8, 1)x(256, 1, 1), Context 1, Stream 7, Device

... (2931 more chars)

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)

PASS
shape=0 ms=     36.5us gbps=   922.6 frac=0.5125 S=8 chunk=128 nwarps=8
         25.2us x1  void decode_split_kernel<128, 4, 8>(__nv_bfloat16 const*, __nv_bfloat16 const*, int const*
shape=1 ms=    205.0us gbps=  1311.9 frac=0.7289 S=3 chunk=688 nwarps=8
        179.6us x1  void decode_split_kernel<128, 4, 8>(__nv_bfloat16 const*, __nv_bfloat16 const*, int const*
shape=2 ms=     73.2us gbps=   919.2 frac=0.5107 S=18 chunk=240 nwarps=8
         57.2us x1  void decode_split_kernel<128, 8, 8>(__nv_bfloat16 const*, __nv_bfloat16 const*, int const*
shape=3 ms=     87.7us gbps=  1149.7 frac=0.6387 S=5 chunk=320 nwarps=8
         66.2us x1  void decode_split_kernel<128, 4, 8>(__nv_bfloat16 const*, __nv_bfloat16 const*, int const*
shape=4 ms=     23.8us gbps=   689.5 frac=0.3831 S=18 chunk=112 nwarps=4
         10.8us x1  void decode_split_kernel<64, 4, 4>(__nv_bfloat16 const*, __nv_bfloat16 const*, int const*,

void decode_split_kernel<128, 4, 8>(const __nv_bfloat16 *, const __nv_bfloat16 *, const int *, const int *, float *, float *, unsigned int *, __nv_bfloat16 *, int, int, int, int, float) (8, 8, 8)x(256, 1, 1), Context 1, Stream 7, Device 0, CC 12.0
    dram__throughput.avg.pct_of_peak_sustained_elapsed               %        68.73
    gpu__time_duration.sum                                          us        28.70
    launch__registers_per_thread                       register/thread          118
    sm__warps_active.avg.pct_of_peak_sustained_active                %        31.12
  void decode_split_kernel<128, 4, 8>(const __nv_bfloat16 *, const __nv_bfloat16 *, const int *, const int *, float *, float *, unsigned int *, __nv_bfloat16 *, int, int, int, int, float) (5, 8, 16)x(256, 1, 1), Context 1, Stream 7, Device 0, CC 12.0
    dram__throughput.avg.pct_of_peak_sustained_elapsed               %        86.84
    gpu__time_duration.sum                                          us        67.90
    launch__registers_per_thread                       register/thread          118
    sm__warps_active.avg.pct_of_peak_sustained_active                %        30.41
  void decode_split_kernel<64, 4, 4>(const __nv_bfloat16 *, const __nv_bfloat16 *, const int *, const int *, float *, float *, unsigned int *, __nv_bfloat16 *, int, int, int, int, float) (18, 4, 8)x(128, 1, 1), Context 1, Stream 7, Device 0, CC 12.0
    dram__throughput.avg.pct_of_peak_sustained_elapsed               %        5

... (256 more chars)

File created successfully at: /workspace/problems/03_paged_attention/microbench_loads.py (file state is current in your context — no need to Read it back)

Updated task #3 status

^^^^^^^^^^^^^
  File "/workspace/.venv/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2268, in _jit_compile
    _write_ninja_file_and_build_library(
  File "/workspace/.venv/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2429, in _write_ninja_file_and_build_library
    _run_ninja_build(
  File "/workspace/.venv/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2791, in _run_ninja_build
    raise RuntimeError(message) from e
RuntimeError: Error building extension 'mb_loads': [1/3] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=mb_loads -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -fPIC -std=c++17 -c /home/agent/.cache/torch_extensions/py311_cu130/mb_loads/main.cpp -o main.o 
[2/3] /usr/local/cuda-host/bin/nvcc -MD -MF cuda.cuda.o.d -DTORCH_EXTENSION_NAME=mb_loads -DTORCH_API_INCLUDE_EXTENSION_H -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include -isystem /workspace/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-host/include -isystem /uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_N

... (1198 more chars)

I cannot provide the specific information you've requested based on the content above. While the document table of contents references "Cache Eviction Priority Hints" (section 9.7.9.2) and mentions `createpolicy` in the instruction list (9.7.9.19), the actual detailed syntax, valid priority combinations, and parameter specifications for the `createpolicy` instruction are not included in the provided excerpt.

The document appears to be truncated before reaching the complete instruction descriptions. To access the precise syntax for `createpolicy.fractional`, its valid primary and secondary priorities, and clarification on whether `ld.global` supports direct `.L2::no_allocate` qualifiers, you would need to consult the full PTX ISA 9.3 documentation or the complete sections 9.7.9.2 and 9.7.9.19.

Web search results for query: "PTX createpolicy.fractional L2 no_allocate primary priority syntax cache_hint"

Links: [{"title":"Nvcc 13.1 ptxas codegen bug: createpolicy.fractional dropped on sm_90 - CUDA NVCC Compiler - NVIDIA Developer Forums","url":"https://forums.developer.nvidia.com/t/nvcc-13-1-ptxas-codegen-bug-createpolicy-fractional-dropped-on-sm-90/368663"},{"title":"PTX createpolicy instruction compile failure - CUDA NVCC Compiler - NVIDIA Developer Forums","url":"https://forums.developer.nvidia.com/t/ptx-createpolicy-instruction-compile-failure/333913"},{"title":"PTX ISA Release 8.8 NVIDIA Corporation May 31, 2025","url":"https://docs.nvidia.com/cuda/pdf/ptx_isa_8.8.pdf"},{"title":"How to use createpolicy ptx instruction well in CUDA? Are there any practical examples as reference? - CUDA Programming and Performance - NVIDIA Developer Forums","url":"https://forums.developer.nvidia.com/t/how-to-use-createpolicy-ptx-instruction-well-in-cuda-are-there-any-practical-examples-as-reference/247592"},{"title":"User Guide for NVPTX Back-end — LLVM 23.0.0git documentation","url":"https://llvm.org/docs/NVPTXUsage.html"},{"title":"Setting eviction policy of store command does not affect underlying PTX instruction · Issue #1728 · triton-lang/triton","url":"https://github.com/triton-lang/triton/issues/1728"},{"title":"PTX ISA Release 8.7 NVIDIA Corporation Feb 27, 2025","url":"https://docs.nvidia.com/cuda/pdf/ptx_isa_8.7.pdf"},{"title":"DeepSeek and DeepEP — Understanding DeepSe

... (2012 more chars)

-rw-r--r-- 1 1002 1002 20058950 Jun 11 11:29 ptx_isa.pdf
no pdftotext
Shell cwd was reset to /workspace/problems/03_paged_attention

no pypdf
Shell cwd was reset to /workspace/problems/03_paged_attention

VARIANT 1: 
VARIANT 2: ptxas /tmp/tmpxft_0000075f_00000000-7_polytest.compute_120.ptx, line 27; error   : Argument 2 of instruction 'createpolicy.fractional': value '0.000000' out of range, expected to be in range (0.000000 .. 1.000000] ptxas /tmp/tmpxft_0000075f_00000000-7_polytest.compute_120.ptx, line 27; error   : Unsupported modifier '.no_allocate' as secondary_priority for instruction 'createpolicy.fractional' 
VARIANT 3: ptxas /tmp/tmpxft_00000776_00000000-7_polytest.compute_120.ptx, line 27; error   : Argument 2 of instruction 'createpolicy.fractional': value '0.000000' out of range, expected to be in range (0.000000 .. 1.000000] ptxas /tmp/tmpxft_00000776_00000000-7_polytest.compute_120.ptx, line 27; error   : Unsupported modifier '.no_allocate' as secondary_priority for instruction 'createpolicy.fractional' 
VARIANT 4: ptxas /tmp/tmpxft_0000078d_00000000-7_polytest.compute_120.ptx, line 27; error   : Unsupported modifier '.no_allocate' as primary_priority for instruction 'createpolicy.fractional' ptxas fatal   : Ptx assembly aborted due to errors 
VARIANT 5: ptxas /tmp/tmpxft_000007a4_00000000-7_polytest.compute_120.ptx, line 27; error   : Argument 2 of instruction 'createpolicy.fractional': value '0.000000' out of range, expected to be in range (0.000000 .. 1.000000] ptxas /tmp/tmpxft_000007a4_00000000-7_polytest.compute_120.ptx, line 27; error   : Unsupported modifier '.no_allocate' as secondary_priority for instruction 'createpolicy.fractional'
Shell cwd was rese

... (43 more chars)

RANGE VARIANT 1: 2
RANGE VARIANT 2: 2
RANGE VARIANT 3: 2

The file /workspace/problems/03_paged_attention/microbench_loads.py has been updated successfully. (file state is current in your context — no need to Read it back)

32MB ldg        26.2us 1222493.9 GB/s
  32MB ldcs       25.3us 1262626.3 GB/s
  32MB ldcv       25.7us 1246882.8 GB/s
  32MB ld_na      25.8us 1240694.7 GB/s
 100MB ldg        70.8us 1412748.6 GB/s
 100MB ldcs       71.1us 1406390.7 GB/s
 100MB ldcv       70.7us 1415307.9 GB/s
 100MB ld_na      71.2us 1403863.5 GB/s
 256MB ldg       173.9us 1471941.1 GB/s
 256MB ldcs      173.8us 1472754.1 GB/s
 256MB ldcv      176.0us 1454281.0 GB/s
 256MB ld_na     173.6us 1474926.3 GB/s

File created successfully at: /workspace/problems/03_paged_attention/ab_time.py (file state is current in your context — no need to Read it back)

shape=0 cold=   33.9us warm=   30.0us avg_wall_per_call=   25.3us kv=33.6MB cold_gbps=  989.2
shape=1 cold=  202.5us warm=  183.5us avg_wall_per_call=  179.0us kv=268.4MB cold_gbps= 1325.8
shape=2 cold=   70.5us warm=   60.6us avg_wall_per_call=   56.5us kv=67.1MB cold_gbps=  951.5
shape=3 cold=   84.5us warm=   70.8us avg_wall_per_call=   66.2us kv=100.6MB cold_gbps= 1190.8
shape=4 cold=   21.2us warm=   14.5us avg_wall_per_call=    9.8us kv=16.4MB cold_gbps=  774.6

==PROF== Profiling "decode_split_kernel": 0%....50%....100% - 6 passes
    sm__inst_executed.avg.per_cycle_active                           inst/cycle         0.68
    sm__inst_executed_pipe_xu.avg.pct_of_peak_sustained_active                %         5.10
    sm__issue_active.avg.pct_of_peak_sustained_active                         %        17.11
    sm__pipe_alu_cycles_active.avg.pct_of_peak_sustained_active               %         3.10
    sm__pipe_fma_cycles_active.avg.pct_of_peak_sustained_active               %        10.83
    smsp__warp_issue_stalled_long_scoreboard_per_warp_active.pct              %        84.82
    smsp__warp_issue_stalled_math_pipe_throttle_per_warp_active.pct           %         0.18
    smsp__warp_issue_stalled_mio_throttle_per_warp_active.pct                 %         0.57
    smsp__warp_issue_stalled_not_selected_per_warp_active.pct                 %         0.91
    smsp__warp_issue_stalled_wait_per_warp_active.pct                         %         2.81

1	"""Paged-attention decode kernel for SM120 (RTX PRO 6000 Blackwell).
2	
3	Flash-decoding style split-K CUDA kernel (torch.utils.cpp_extension.load_inline):
4	  - One threadblock per (batch, kv_head, split). Each block streams its chunk of
5	    the KV cache exactly once (K and V share a 512B segment, gathered via the
6	    page table staged in shared memory), computes online softmax for the
7	    G = num_heads/num_kv_heads grouped query heads with 8/16B vector loads and
8	    a 2-stage register prefetch pipeline.
9	  - Split partials (fp32 O, m, l) are merged by the LAST finishing block of each
10	    (batch, kv_head) group — detected with an auto-resetting atomicInc semaphore
11	    — so the whole decode is a single kernel launch.
12	"""
13	import math
14	import os
15	
16	import torch
17	import torch.nn as nn
18	
19	OP_TYPE = "attention"
20	SUPPORTED_PRECISIONS = ["bf16"]
21	HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
22	
23	# --- Shape knobs (kept for interface parity with reference.py) -------------
24	BATCH = 8
25	NUM_HEADS = 32
26	NUM_KV_HEADS = 8
27	HEAD_DIM = 128
28	SEQ_LEN = 1024
29	PAGE_SIZE = 16
30	
31	_CPP_SRC = r"""
32	#include <torch/extension.h>
33	void paged_decode(at::Tensor q, at::Tensor kv, at::Tensor bt, at::Tensor sl,
34	                  at::Tensor out, at::Tensor o_part, at::Tensor ml, at::Tensor sem,
35	                  int64_t S, int64_t chunk, int64_t nwarps, double qscale);
36	"""
37	
38	_CUDA_SRC = r"""
39	#include <torch/extension.h>
40

... (16695 more chars)

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)

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)

<tool_use_error>String to replace not found in file.
String:     // 2-stage software pipeline: K/V for token t prefetched at t-NS.
    int t = t0 + stream;
    KVec kk, vv;
    if (t < tend) {
        const bf16* kp = kvc + (int64_t)sm_pages[(t - t0) >> 4] * (16 * slot_stride) +
                         (t & 15) * slot_stride + kvh_off;
        kk = __ldcs(reinterpret_cast<const KVec*>(kp));
        vv = __ldcs(reinterpret_cast<const KVec*>(kp + D));
    }
    for (; t < tend; ) {
        const KVec ck = kk, cv = vv;
        const int tn = t + NS;
        if (tn < tend) {
            const bf16* kp = kvc + (int64_t)sm_pages[(tn - t0) >> 4] * (16 * slot_stride) +
                             (tn - t0, tn & 15, 0) * slot_stride + kvh_off;
            kk = __ldcs(reinterpret_cast<const KVec*>(kp));
            vv = __ldcs(reinterpret_cast<const KVec*>(kp + D));
        }
        float kf[E], vf[E];
        unpack_bf16<E>(&ck, kf);
        unpack_bf16<E>(&cv, vf);</tool_use_error>

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

File created successfully at: /workspace/problems/03_paged_attention/sweep.py (file state is current in your context — no need to Read it back)

/workspace/.venv/lib/python3.11/site-packages/torch/profiler/profiler.py:224: UserWarning: Warning: Profiler clears events at the end of each cycle.Only events from the current cycle will be reported.To keep events across cycles, set acc_events=True.
  _warn_once(
shape=0 ms=     36.4us gbps=   925.8 frac=0.5144 S=8 chunk=128 nwarps=8
         25.3us x1  void decode_split_kernel<128, 4, 8, 2>(__nv_bfloat16 const*, __nv_bfloat16 const*, int con

PASS

--- shape 0  (B8 Hkv8 D128 L1024)
  S=  2 chunk=  512 W=8 PF=2      32.9us   1022.5 GB/s frac=0.5681
  S=  2 chunk=  512 W=8 PF=3      33.0us   1021.0 GB/s frac=0.5672
  S=  2 chunk=  512 W=8 PF=4      33.1us   1017.6 GB/s frac=0.5653
  S=  8 chunk=  128 W=4 PF=2      33.1us   1017.6 GB/s frac=0.5653
  S=  3 chunk=  352 W=8 PF=3      33.5us   1005.4 GB/s frac=0.5586
  S=  8 chunk=  128 W=4 PF=4      33.5us   1004.9 GB/s frac=0.5583
  S=  4 chunk=  256 W=4 PF=2      33.6us   1004.0 GB/s frac=0.5578
  S=  8 chunk=  128 W=4 PF=3      33.6us   1004.0 GB/s frac=0.5578
--- shape 1  (B32 Hkv8 D128 L2048)
  S=  1 chunk= 2048 W=8 PF=2     182.8us   1471.6 GB/s frac=0.8176
  S=  1 chunk= 2048 W=4 PF=4     183.2us   1468.4 GB/s frac=0.8158
  S=  1 chunk= 2048 W=8 PF=3     183.4us   1466.2 GB/s frac=0.8146
  S=  2 chunk= 1024 W=4 PF=2     183.9us   1462.4 GB/s frac=0.8124
  S=  1 chunk= 2048 W=8 PF=4     184.0us   1462.1 GB/s frac=0.8123
  S=  2 chunk= 1024 W=4 PF=4     184.1us   1460.6 GB/s frac=0.8114
  S=  1 chunk= 2048 W=4 PF=2     184.7us   1455.9 GB/s frac=0.8088
  S=  2 chunk= 1024 W=4 PF=3     185.2us   1452.5 GB/s frac=0.8070
--- shape 2  (B4 Hkv8 D128 L4096)
  S=  9 chunk=  464 W=8 PF=2      58.8us   1143.2 GB/s frac=0.6351
  S=  9 chunk=  464 W=8 PF=3      60.4us   1112.4 GB/s frac=0.6180
  S=  9 chunk=  464 W=8 PF=4      60.6us   1109.7 GB/s frac=0.6165
  S= 18 chunk=  240 W=4 PF=2      63.9us   1051.7 GB/s frac=0.5843
  S= 18 chunk=  240 W=4 PF=3      64.6us   1041.3 GB/s fr

... (1350 more chars)

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)

The file /workspace/problems/03_paged_attention/sweep.py has been updated successfully. (file state is current in your context — no need to Read it back)

/kbh/bin/ncu
NVIDIA (R) Nsight Compute Command Line Profiler
Copyright (c) 2018-2025 NVIDIA Corporation

--- shape 0  (B8 Hkv8 D128 L1024)
  S=  2 chunk=  512 W=16 PF=2      33.2us   1013.6 GB/s frac=0.5631
  S=  2 chunk=  512 W=8 PF=3      33.4us   1008.8 GB/s frac=0.5604
  S=  2 chunk=  512 W=8 PF=2      33.5us   1004.5 GB/s frac=0.5580
  S=  2 chunk=  512 W=16 PF=3      33.8us    996.4 GB/s frac=0.5535
  S=  4 chunk=  256 W=8 PF=3      33.8us    995.4 GB/s frac=0.5530
  S=  4 chunk=  256 W=8 PF=2      34.0us    990.8 GB/s frac=0.5504
  S=  2 chunk=  512 W=8 PF=4      34.0us    989.8 GB/s frac=0.5499
  S=  3 chunk=  352 W=8 PF=3      34.4us    980.6 GB/s frac=0.5448
--- shape 1  (B32 Hkv8 D128 L2048)
  S=  1 chunk= 2048 W=16 PF=2     182.9us   1470.6 GB/s frac=0.8170
  S=  1 chunk= 2048 W=8 PF=2     183.2us   1468.1 GB/s frac=0.8156
  S=  1 chunk= 2048 W=8 PF=4     184.3us   1459.5 GB/s frac=0.8108
  S=  2 chunk= 1024 W=8 PF=2     188.5us   1426.6 GB/s frac=0.7926
  S=  2 chunk= 1024 W=16 PF=2     189.7us   1417.7 GB/s frac=0.7876
  S=  2 chunk= 1024 W=8 PF=4     194.3us   1384.3 GB/s frac=0.7691
  S=  1 chunk= 2048 W=16 PF=4     199.9us   1345.8 GB/s frac=0.7476
  S=  2 chunk= 1024 W=16 PF=4     200.8us   1339.1 GB/s frac=0.7440
--- shape 2  (B4 Hkv8 D128 L4096)
  S=  9 chunk=  464 W=8 PF=2      60.1us   1118.0 GB/s frac=0.6211
  S=  9 chunk=  464 W=8 PF=3      60.4us   1112.7 GB/s frac=0.6181
  S=  9 chunk=  464 W=8 PF=4      60.4us   1112.4 GB/s frac=0.6180
  S=  9 chunk=  464 W=16 PF=2      63.0us   1068.0 GB/s frac=0.5933
  S=  9 chunk=  464 W=16 PF=3      64.7us   1039.4

... (1362 more chars)

Exit code 1
=== shape 0 S=2 W=8 PF=2 ===
=== shape 2 S=9 W=8 PF=2 ===
=== shape 4 S=4 W=8 PF=6 ===

malloc(): mismatching next->prev_size (unsorted)
==ERROR== The application returned an error code (6).

==WARNING== Note: Running with unmodified GPU clocks. If not controlled otherwise, profiling results may be inconsistent.
==PROF== Connected to process 3413 (/uv-cache/python/cpython-3.11.15-linux-x86_64-gnu/bin/python3.11)
==PROF== Profiling "decode_split_kernel": 0%....50%....100% - 1 pass
==PROF== Disconnected from process 3413
[3413] python3.11@127.0.0.1
  void decode_split_kernel<128, 4, 8, 2>(const __nv_bfloat16 *, const __nv_bfloat16 *, const int *, const int *, __nv_bfloat16 *, float *, unsigned int *, __nv_bfloat16 *, int, int, int, int, float) (2, 8, 8)x(256, 1, 1), Context 1, Stream 7, Device 0, CC 12.0
    Warning: Data collection happened without fixed GPU frequencies. Profiling results may be inconsistent.
    Section: Command line profiler metrics
    ------------------------------------------------------ ----------- ------------
    Metric Name                                            Metric Unit Metric Value
    ------------------------------------------------------ ----------- ------------
    dram__bytes_read.sum                                                    (!) n/a
    dram__bytes_write.sum                                                   (!) n/a
    gpu__dram_throughput.avg.pct_of_peak_sustained_elapsed           %        74.64
    gpu__time_duration.sum                                          us        26.43
    ------------------------------------------------------ ----------- ------------

gpu__dram_throughput.avg.pct_of_peak_sustained_elapsed           %        75.27
    gpu__time_duration.sum                                          us        52.29
    gpu__dram_throughput.avg.pct_of_peak_sustained_elapsed           %        60.99
    gpu__time_duration.sum                                          us        15.84

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)

shape=0 ms=     32.4us gbps=  1039.2 frac=0.5773 S=2 chunk=512 nwarps=8
shape=4 ms=     20.8us gbps=   788.1 frac=0.4378 S=4 chunk=512 nwarps=8

shape=1 solution_peak_fraction=0.7491
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-11T12:00:49.654681+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-11T12:00:49.659805+00:00 elapsed_s=0.005 ms=0.068464
shape=2 variant=solution tflops=7.842 gbps=982.121 ms=0.068
shape=2 solution_peak_fraction=0.5456
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-11T12:00:50.263953+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-11T12:00:50.269920+00:00 elapsed_s=0.006 ms=0.085088
shape=3 variant=solution tflops=4.729 gbps=1185.360 ms=0.085
shape=3 solution_peak_fraction=0.6585
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-11T12:00:50.368281+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-11T12:00:50.371713+00:00 elapsed_s=0.003 ms=0.022224
shape=4 variant=solution tflops=2.949 gbps=738.695 ms=0.022
shape=4 solution_peak_fraction=0.4104
peak_fraction: 0.5643
RESULT: OK

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)

shape=0 solution_peak_fraction=0.5745
shape=1 solution_peak_fraction=0.8205
shape=2 solution_peak_fraction=0.6331
shape=3 solution_peak_fraction=0.7395
shape=4 solution_peak_fraction=0.4385
peak_fraction: 0.6268
RESULT: OK