<path>/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention</path>
<type>directory</type>
<entries>
benchmark.py
check.py
problem.yaml
PROMPT.txt
reference.py
shapes.py
sota.py

(7 entries)
</entries>

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/reference.py</path>
<type>file</type>
<content>
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
3

... (4426 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/check.py</path>
<type>file</type>
<content>
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: 
18: 
19: def _apply_shape(reference, shape: dict) -> None:
20:     reference.BATCH = shape["batch"]
21:     reference.NUM_HEADS = shape["num_heads"]
22:     reference.NUM_KV_HEADS = shape["num_kv_heads"]
23:     reference.HEAD_DIM = shape["head_dim"]
24:     reference.SEQ_LEN = shape["seq_len"]
25:     reference.PAGE_SIZE = shape["page_size"]
26: 
27: 
28: def main():
29:     try:
30:         import reference
31:         import shapes
32:         import solution
33:     except Exception as e:
34:         print(f"FAIL: import error: {e}")
35:         sys.exit(1)
36: 
37:     problem_yaml = Path("problem.yaml")
38:     meta = yaml.safe_load(problem_yaml.read_text()) if problem_yaml.exists() else {}
39: 
40:     sol_src = Path("solution.py").read_text() if Path("solution.py").exists() else ""
41:     for forbidden in meta.get("forbidden", []):
42:         pat = re.escape(forbidden)
43:     

... (2537 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/benchmark.py</path>
<type>file</type>
<content>
1: """Roofline benchmark for paged-attention decode.
2: 
3: For each shape: times eager reference, compiled reference, SOTA (if available),
4: and the agent's solution. 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 time_fn  # 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.y

... (3563 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/shapes.py</path>
<type>file</type>
<content>
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: ]

(End of file - total 18 lines)
</content>

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/problem.yaml</path>
<type>file</type>
<content>
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. SD

... (875 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/sota.py</path>
<type>file</type>
<content>
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].contig

... (1803 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/src/eval/correctness.py</path>
<type>file</type>
<content>
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
4

... (2580 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/PROMPT.txt</path>
<type>file</type>
<content>
1: I need you to write a paged attention decode kernel for the RTX PRO 6000 (SM120 Blackwell, GDDR7, 1.8 TB/s). The reference implementation is in reference.py and your kernel goes in solution.py. You can make whatever mess you want in this directory — scratch files, intermediate kernels, build artifacts, profiling traces — but the final answer has to be in solution.py with the same Model, get_inputs, and get_init_inputs interface as reference.py.
2: 
3: This is single-query decode: each batch element has a query of shape (num_heads, head_dim) and attends over a KV cache of seq_len[b] tokens stored as fixed-size pages in a global pool, with block_table[b] listing which pages belong to batch element b. The KV cache is laid out as (num_blocks, page_size, num_kv_heads, head_dim * 2) where the last dim packs [K | V] so a single gather pulls both. You return the bf16 output of shape (batch, num_heads, head_dim). Correctness tolerance is 0.02 abs/rel on bf16/fp16. The shapes you have to handle are batch=8 num_heads=32 num_kv_heads=8 head_dim=128 seq_len=1024 page_size=16 (Llama-3 8B-style decode), batch=32 num_heads=32 num_kv_heads=8 head_dim=128 seq_len=2048 page_size=16 (server-batched decode), batch=4 num_heads=64 num_kv_heads=8 head_dim=128 seq_len=4096 page_size=16 (Llama-3 70B GQA wide ratio with long context), batch=16 num_heads=32 num_kv_heads=8 head_dim=128

... (1253 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/src/hardware</path>
<type>directory</type>
<entries>
__init__.py
__pycache__/
m4_max.py
rtx_pro_6000.py

(4 entries)
</entries>

NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 12.0, 97887 MiB

<path>/home/infatoshi/cuda/KernelBench-Hard/src/hardware/rtx_pro_6000.py</path>
<type>file</type>
<content>
1: """RTX PRO 6000 Blackwell Workstation — SM120, consumer-lineage Blackwell.
2: 
3: Peak tensor-core throughputs are dense-matrix advertised peaks. Actual kernels
4: will see 60-85% of peak on well-tuned code.
5: """
6: from dataclasses import dataclass
7: 
8: 
9: @dataclass(frozen=True)
10: class HardwareTarget:
11:     name: str
12:     sm: str
13:     vram_gb: int
14:     peak_bandwidth_gb_s: float  # DRAM
15:     peak_tflops_dense: dict[str, float]  # dtype -> TFLOPS
16: 
17: 
18: RTX_PRO_6000 = HardwareTarget(
19:     name="RTX PRO 6000 Blackwell Workstation",
20:     sm="sm_120a",
21:     vram_gb=96,
22:     peak_bandwidth_gb_s=1800.0,
23:     peak_tflops_dense={
24:         "fp4": 800.0,
25:         "nvfp4": 800.0,
26:         "mxfp4": 800.0,
27:         "fp6": 800.0,
28:         "fp8": 400.0,
29:         "bf16": 200.0,
30:         "fp16": 200.0,
31:         "tf32": 100.0,
32:         "fp32": 12.0,  # non-tensor-core SIMT fp32
33:         "int8": 400.0,
34:         "int4": 800.0,
35:     },
36: )

(End of file - total 36 lines)
</content>

<path>/home/infatoshi/cuda/KernelBench-Hard/src/hardware/__init__.py</path>
<type>file</type>
<content>
1: """Hardware peak-throughput lookup tables."""
2: from src.hardware.m4_max import M4_MAX
3: from src.hardware.rtx_pro_6000 import RTX_PRO_6000
4: 
5: TARGETS = {
6:     "RTX_PRO_6000": RTX_PRO_6000,
7:     "M4_MAX": M4_MAX,
8: }
9: 
10: 
11: def get(name: str):
12:     if name not in TARGETS:
13:         raise ValueError(f"Unknown hardware {name!r}; available: {list(TARGETS)}")
14:     return TARGETS[name]

(End of file - total 14 lines)
</content>

Help on function load_inline in module torch.utils.cpp_extension:

load_inline(name, cpp_sources, cuda_sources=None, sycl_sources=None, functions=None, extra_cflags=None, extra_cuda_cflags=None, extra_sycl_cflags=None, extra_ldflags=None, extra_include_paths=None, build_directory=None, verbose=False, with_cuda=None, with_sycl=None, is_python_module=True, with_pytorch_error_handling=True, keep_intermediates=True, use_pch=False, no_implicit_headers=False)
    Load a PyTorch C++ extension just-in-time (JIT) from string sources.

    This function behaves exactly like :func:`load`, but takes its sources as
    strings rather than filenames. These strings are stored to files in the
    build directory, after which the behavior of :func:`load_inline` is
    identical to :func:`load`.

    See `the
    tests <https://github.com/pytorch/pytorch/blob/master/test/test_cpp_extensions_jit.py>`_
    for good examples of using this function.

    Sources may omit two required parts of a typical non-inline C++ extension:
    the necessary header includes, as well as the (pybind11) binding code. More
    precisely, strings passed to ``cpp_sources`` are first concatenated into a
    single ``.cpp`` file. This file is then prepended with ``#include
    <torch/extension.h>``

    Furthermore, if the ``functions`` argument is supplied, bindings will be
    automatically generated for each function specified. ``functions`` can
    either be a list of function names, or a dictionary mapping from f

... (3323 more chars)

task_id: ses_23088b86cffeX6aN6lxvfBg8m2 (for resuming to continue this task if needed)

<task_result>
Now I have a complete picture. Let me provide a comprehensive answer with all findings.

---

## Results: `load_inline` / `cpp_extension.load_inline` Calling Convention in KernelBench-Hard

### Key Finding: No Existing Usage in the Repo

There are **zero actual calls to `load_inline`** anywhere in the KernelBench-Hard repository. The only references are:

1. **PROMPT.txt files** (7 of them) -- suggesting agents use `load_inline` as one possible approach:
   - `/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/PROMPT.txt` (line 5)
   - `/home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/PROMPT.txt` (line 5)
   - `/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/PROMPT.txt` (line 5)
   - `/home/infatoshi/cuda/KernelBench-Hard/problems/04_kahan_softmax/PROMPT.txt` (line 5)
   - `/home/infatoshi/cuda/KernelBench-Hard/problems/05_topk_bitonic/PROMPT.txt` (line 5)
   - `/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/PROMPT.txt` (line 5)
   - `/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/PROMPT.txt` (line 5)

2. **check.py files** (7 of them) -- using it only as a regex detection pattern to label the framework:
   ```python
   ("cuda_raw",  r"torch\.utils\.cpp_extension\.load_inline|__global__\s+void")
   ```
   All 7 check.py files share this identical pattern (the file paths are listed above, all under `/home

... (6925 more chars)

content: 'Write initial solution.py with CUDA C++ kernel + C++ wrapper via load_inline'
status: 'in_progress'
priority: 'high'
content: 'Run check.py for correctness verification'
status: 'pending'
priority: 'high'
content: 'Run benchmark.py for performance measurement'
status: 'pending'
priority: 'high'
content: 'Profile with ncu and iterate if performance is below target'
status: 'pending'
priority: 'medium'

total 44
drwxrwxr-x  2 infatoshi infatoshi 4096 Apr 27 08:52 .
drwxrwxr-x 10 infatoshi infatoshi 4096 Apr 24 00:30 ..
-rw-rw-r--  1 infatoshi infatoshi 4353 Apr 26 03:11 benchmark.py
-rw-rw-r--  1 infatoshi infatoshi 3441 Apr 25 22:20 check.py
-rw-rw-r--  1 infatoshi infatoshi 2030 Apr 25 22:19 problem.yaml
-rw-rw-r--  1 infatoshi infatoshi 2585 Apr 27 03:21 PROMPT.txt
-rw-rw-r--  1 infatoshi infatoshi 5151 Apr 25 22:19 reference.py
-rw-rw-r--  1 infatoshi infatoshi  933 Apr 25 22:19 shapes.py
-rw-rw-r--  1 infatoshi infatoshi 2819 Apr 25 22:24 sota.py

CUDA available: True
Device: NVIDIA RTX PRO 6000 Blackwell Workstation Edition
Compute cap: (12, 0)
bf16 supported: True
nvcc version: nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2026 NVIDIA Corporation
Built on Mon_Mar_02_09:52:23_PM_PST_2026
Cuda compilation tools, release 13.2, V13.2.51
Build cuda_13.2.r13.2/compiler.

Wrote file successfully.

Traceback (most recent call last):
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2774, in _run_ninja_build
    subprocess.run(
  File "/home/infatoshi/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/subprocess.py", line 571, in run
    raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 2.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/check.py", line 109, in <module>
    main()
  File "/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/check.py", line 72, in main
    sol_out = sol_model(*inputs)
              ^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/solution.py", line 283, in forward
    mod = _get_module()
          ^^^^^^^^^^^^^
  File "/home/infatoshi/cuda/Kernel

... (3641 more chars)

Edit applied successfully.

PASS

content: 'Write initial solution.py with CUDA C++ kernel + C++ wrapper via load_inline'
status: 'completed'
priority: 'high'
content: 'Run check.py for correctness verification'
status: 'completed'
priority: 'high'
content: 'Run benchmark.py for performance measurement'
status: 'in_progress'
priority: 'high'
content: 'Profile with ncu and iterate if performance is below target'
status: 'pending'
priority: 'medium'

Traceback (most recent call last):
  File "/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/benchmark.py", line 131, in <module>
    main()
  File "/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/benchmark.py", line 42, in main
    meta = yaml.safe_load(Path("problem.yaml").read_text())
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/pathlib.py", line 1058, in read_text
    with self.open(mode='r', encoding=encoding, errors=errors) as f:
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/pathlib.py", line 1044, in open
    return io.open(self, mode, buffering, encoding, errors, newline)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'problem.yaml'

W0427 09:12:19.341000 1130296 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] Graph break from `Tensor.item()`, consider setting:
W0427 09:12:19.341000 1130296 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     torch._dynamo.config.capture_scalar_outputs = True
W0427 09:12:19.341000 1130296 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] or:
W0427 09:12:19.341000 1130296 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
W0427 09:12:19.341000 1130296 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] to include these operations in the captured graph.
W0427 09:12:19.341000 1130296 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] 
W0427 09:12:19.341000 1130296 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] Graph break: from user code at:
W0427 09:12:19.341000 1130296 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]   File "/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/reference.py", line 89, in forward
W0427 09:12:19.341000 1130296 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     L = int(seq_lens[b].item())
W0427 09:12:19.341000 1130296 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] 
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<path>/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/solution.py</path>
<type>file</type>
<content>
1: """Paged attention decode kernel for RTX PRO 6000 (SM120 Blackwell).
2: 
3: Single-query decode with grouped-query attention (GQA).
4: Uses online softmax to process KV cache in a single pass without gathering pages.
5: One block per (batch, kv_head) pair; each thread handles one head_dim element.
6: """
7: from __future__ import annotations
8: 
9: import math
10: 
11: import torch
12: import torch.nn as nn
13: from torch.utils.cpp_extension import load_inline
14: 
15: OP_TYPE = "attention"
16: SUPPORTED_PRECISIONS = ["bf16"]
17: HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
18: 
19: # Language-visible shape knobs (overridden by check.py/benchmark.py)
20: BATCH = 8
21: NUM_HEADS = 32
22: NUM_KV_HEADS = 8
23: HEAD_DIM = 128
24: SEQ_LEN = 1024
25: PAGE_SIZE = 16
26: 
27: # ---------------------------------------------------------------------------
28: # CUDA kernel + wrapper (compiled once per process)
29: # ---------------------------------------------------------------------------
30: 
31: _cpp_src = r"""
32: torch::Tensor launch_paged_attention_decode(
33:     torch::Tensor query,
34:     torch::Tensor kv_cache,
35:     torch::Tensor block_table,
36:     torch::Tensor seq_lens,
37:     int B, int H, int Hkv, int D, int P, int max_blocks, float scale);
38: """
39: 
40: _cuda_src = r"""
41: #include <cuda_runtime.h>
42: #include <cuda_fp16.h>
43: #in

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PASS
---
W0427 09:18:45.298000 1130835 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] Graph break from `Tensor.item()`, consider setting:
W0427 09:18:45.298000 1130835 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     torch._dynamo.config.capture_scalar_outputs = True
W0427 09:18:45.298000 1130835 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] or:
W0427 09:18:45.298000 1130835 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
W0427 09:18:45.298000 1130835 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] to include these operations in the captured graph.
W0427 09:18:45.298000 1130835 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] 
W0427 09:18:45.298000 1130835 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] Graph break: from user code at:
W0427 09:18:45.298000 1130835 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]   File "/home/infatoshi/cuda/KernelBench-Hard/problems/03_paged_attention/reference.py", line 89, in forward
W0427 09:18:45.298000 1130835 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     L = int(seq_lens[b].item())
W0427 09:18:45.298000 1130835 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] 

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