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Output:
/home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass
PROMPT.txt
shapes.py
sota.py
reference.py
check.py
benchmark.py
problem.yaml

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Output:
## master...origin/master
96b597e qwen3.6-27b reinstated: 1/7 PASS in post-fix rerun
5722a4a opencode workspace leak: deny external_directory + drop qwen3.6-27b
50ecc7a Shakedown sweep results + drop qwen3.6-35b-a3b from active matrix

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Output:
Filesystem      Size  Used Avail Use% Mounted on
/dev/nvme0n1p2  3.6T  1.8T  1.7T  51% /
uv 0.11.3 (x86_64-unknown-linux-gnu)
Python 3.11.15

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Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 508, 97887

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Output:
"""Naive PyTorch reference for Kimi Delta Attention (KDA) forward, chunk form.

This is the correctness oracle, NOT the SOTA baseline. It mirrors the
chunk-parallel formulation in fla/ops/kda/naive.py (Songlin Yang et al.)
without any Triton or CUDA optimization.

Inputs (per the FLA convention):
  q, k : (B, T, H, K)   bf16   -- queries / keys
  v    : (B, T, H, V)   bf16   -- values
  g    : (B, T, H, K)   fp32   -- per-channel log-decay (in-chunk cumsum applied)
  beta : (B, T, H)      bf16   -- write strength

Output:
  o    : (B, T, H, V)   bf16

The agent must reproduce this output (within bf16 tolerance) using a CUTLASS
CuTe kernel on SM120 -- NOT by calling fla.ops.chunk_kda directly.
"""
from __future__ import annotations

import torch
import torch.nn as nn
from einops import rearrange

OP_TYPE = "linear_attention"
SUPPORTED_PRECISIONS = ["bf16"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]


def _naive_chunk_kda(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    g: torch.Tensor,
    beta: torch.Tensor,
    scale: float,
    chunk_size: int = 64,
) -> torch.Tensor:
    """KDA forward, no initial state, no final state. Returns o with v's dtype."""
    dtype = v.dtype
    B, T, H, K = q.shape
    V = v.shape[-1]
    BT = chunk_size
    assert T % BT == 0, f"T={T} must be a multiple of chunk_size={BT}"
    NT = T // BT

    q, k, v, g, beta =

... (3698 more chars)

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Output:
"""Correctness runner for KDA forward (chunk form).

Runs solution.Model vs reference.Model across all shapes in shapes.py, 3 seeds
each, with per-dtype atol/rtol (bf16 default 1e-2 plus a 5e-2 override for
this problem). Also rejects forbidden ops by grep.
"""
import re
import sys
from pathlib import Path

import torch
import yaml

# Make the repo's src/ importable
REPO_ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(REPO_ROOT))

from src.eval.correctness import check_correctness  # noqa: E402


def _apply_shape(reference, shape):
    """Override reference's module-level shape shims so get_inputs/get_init_inputs match."""
    for k, v in shape.items():
        setattr(reference, k, v)


def main():
    try:
        import reference
        import shapes
        import solution
    except Exception as e:
        print(f"FAIL: import error: {e}")
        sys.exit(1)

    problem_yaml = Path("problem.yaml")
    meta = yaml.safe_load(problem_yaml.read_text()) if problem_yaml.exists() else {}

    # --- Forbidden-op check ------------------------------------------------
    sol_src = Path("solution.py").read_text() if Path("solution.py").exists() else ""
    for forbidden in meta.get("forbidden", []):
        pat = re.escape(forbidden)
        if re.search(pat, sol_src):
            print(f"FAIL: forbidden op used: {forbidden}")
            sys.exit(1)

    de

... (2426 more chars)

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Output:
"""Roofline benchmark for KDA forward (chunk form).

For each shape: times eager reference, compiled reference, SOTA (FLA's Triton
chunk_kda, if available on this GPU), and the agent's solution. Reports
achieved TFLOPS, GB/s, and peak_fraction.

Output lines the harness picks up:
  shape=<idx> variant=<name> tflops=<N> gbps=<N> ms=<N>
  peak_fraction: <N>  (geomean over shapes of solution's peak_fraction)
"""
import sys
from math import exp, log
from pathlib import Path

import torch
import yaml

REPO_ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(REPO_ROOT))

from src.eval.roofline import compute_gbps, compute_tflops, peak_fraction  # noqa: E402
from src.eval.timing import time_fn  # noqa: E402
from src.hardware import get as get_hw  # noqa: E402


def _eval_formula(expr: str, vars: dict) -> float:
    return float(eval(expr, {"__builtins__": {}}, vars))


def _apply_shape(reference, shape):
    for k, v in shape.items():
        setattr(reference, k, v)


def main():
    import reference
    import shapes
    import solution

    meta = yaml.safe_load(Path("problem.yaml").read_text())
    hw = get_hw(meta["hardware"][0])
    peak_tflops = hw.peak_tflops_dense.get(meta["peak_tflops_key"], 0.0)
    peak_gbps = hw.peak_bandwidth_gb_s
    regime = meta.get("regime", "compute")
    flops_formula = meta["flops_formula"]
    bytes_formula = meta["bytes_formul

... (2985 more chars)

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Output:
"""Canonical shape sweep for KDA forward (chunk form).

Mix of:
  - short-context training-step scale (T=1024)
  - mid-context (T=2048) which is the headline benchmark
  - long-context that stresses the inter-chunk recurrence (T=4096)
  - thin-batch decode-style (B=1, T=2048, fewer heads)

Constraints:
  - T % chunk_size == 0 (chunk_size = 64)
  - K, V are the per-head channel dims; KDA in Kimi Linear uses K=V=128
"""

SHAPES = [
    {"B": 2, "T": 1024, "H": 8, "K": 128, "V": 128, "CHUNK_SIZE": 64},
    {"B": 2, "T": 2048, "H": 8, "K": 128, "V": 128, "CHUNK_SIZE": 64},
    {"B": 1, "T": 4096, "H": 8, "K": 128, "V": 128, "CHUNK_SIZE": 64},
    {"B": 1, "T": 2048, "H": 4, "K": 128, "V": 128, "CHUNK_SIZE": 64},
]
name: 02_kda_cutlass
display_name: "Kimi Delta Attention (chunk forward) via CUTLASS CuTe"
precision: bf16
regime: compute

# Approximate FLOPs per forward call. KDA forward (chunk form) breakdown per
# token across all heads:
#   - intra-chunk Aqk (q @ k.T with decay): 2*BT*K
#   - intra-chunk Akk + cumulative invert : ~3*BT*K + BT^2
#   - w = A @ (g.exp()*k):                  2*BT*K
#   - u = A @ v:                            2*BT*V
#   - inter-chunk q @ S:                    2*K*V
#   - inter-chunk (Aqk @ v_i):              2*BT*V
#   - state update S += k.T @ v_i:          2*K*V
# Aggregating with a constant of 4 (typical for chunked linear attention) gives
# the

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Output:
0, 508, 97887

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Output:
{'B': 2, 'T': 1024, 'H': 8, 'K': 128, 'V': 128, 'CHUNK_SIZE': 64} torch.bfloat16 torch.Size([2, 1024, 8, 128]) 0.01171875 0.0014267018996179104 7.152557373046875e-06

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Output:
"""Per-dtype correctness tolerance.

Stanford's KernelBench uses 1e-4 atol/rtol for fp32 and 1e-2 for fp16/bf16.
We extend to fp8 (0.1, permissive) and int (exact).
"""
from __future__ import annotations

import torch

DEFAULT_TOLERANCE = {
    torch.float32: {"atol": 1e-4, "rtol": 1e-4},
    torch.float16: {"atol": 1e-2, "rtol": 1e-2},
    torch.bfloat16: {"atol": 1e-2, "rtol": 1e-2},
    torch.float8_e4m3fn: {"atol": 1e-1, "rtol": 1e-1},
    torch.float8_e5m2: {"atol": 1e-1, "rtol": 1e-1},
    torch.int8: {"atol": 0, "rtol": 0},
    torch.int32: {"atol": 0, "rtol": 0},
    torch.int64: {"atol": 0, "rtol": 0},
}


def _coerce_float(x):
    """Accept int/float/str scientific-notation as float; pass through dicts."""
    if isinstance(x, (int, float)):
        return float(x)
    if isinstance(x, str):
        try:
            return float(x)
        except ValueError:
            return x
    return x


def tolerance_for_dtype(dtype: torch.dtype, override: dict | None = None) -> dict:
    """Lookup atol/rtol for a given dtype, with optional per-problem override.

    The override dict is keyed by str(dtype). Values may be:
      - a single number (or a string parseable as float, e.g. "5e-2") -> used
        as both atol and rtol
      - a dict {"atol": ..., "rtol": ...} -> used directly, with values coerced
    """
    # Accept several spellings of the same dtype key (PyYAM

... (2176 more chars)

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Output:
0, 508, 97887

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Output:
fast ms 5.640983581542969
{'B': 2, 'T': 1024, 'H': 8, 'K': 128, 'V': 128, 'CHUNK_SIZE': 64} 3.0517578125e-05 True
fast ms 4.5566558837890625
{'B': 2, 'T': 2048, 'H': 8, 'K': 128, 'V': 128, 'CHUNK_SIZE': 64} 3.0517578125e-05 True

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Output:
0, 508, 97887

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Output:
0 42 0.01171875 0.0014267018996179104
0 123 0.01171875 0.0014267018996179104
0 456 0.01171875 0.0014267018996179104
1 42 0.01312255859375 0.0014322521165013313
1 123 0.01312255859375 0.0014322521165013313
1 456 0.01312255859375 0.0014322521165013313
2 42 0.01312255859375 0.0014348685508593917
2 123 0.01312255859375 0.0014348685508593917
2 456 0.01312255859375 0.0014348685508593917
3 42 0.01385498046875 0.0014387881383299828
3 123 0.01385498046875 0.0014387881383299828
3 456 0.01385498046875 0.0014387881383299828

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Output:
"""Centralized GPU timing for benchmark.py files.

All problems' benchmark.py should call `time_fn` from here so we have one
implementation of warmup, L2 flush, and CUDA event capture to maintain.

Methodology:
  - 10 warmup calls absorb Triton autotune (typical ~7 configs) and
    torch.compile reduce-overhead CUDA-graph capture.
  - Between each timed call, `_l2_flush()` writes 128 MB to evict L2
    (Blackwell L2 is 96 MB; we want any prior L2 contents flushed so
    we measure HBM-load bandwidth, not L2-cached bandwidth).
  - GPU timing via cuda.Event with synchronize() AFTER record() but
    BEFORE elapsed_time() — the canonical NVIDIA pattern.
  - Reported value is the median of `iters` trials, robust to outliers.

Notes / known biases not addressed here:
  - torch.compile(mode="reduce-overhead") gets CUDA graphs which eliminate
    launch overhead. Custom Triton/CUDA kernels do NOT get this treatment.
    On small shapes where launch overhead matters, this gives compile an
    artificial advantage. We accept this as the cost of using the agreed
    "compiled" baseline policy.
  - cuBLAS / cuDNN allocate workspaces on first call. 10 warmup absorbs.
  - Median over a fairly small number of trials (default 30) is fine for
    headline numbers but won't catch bimodal distributions. Use --extra
    instrumentation if you ever care.
"""
from __future__ import annotations



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Output:
../../src/hardware/m4_max.py
../../src/hardware/__init__.py
../../src/hardware/rtx_pro_6000.py
../../src/eval/roofline.py

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Output:
"""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]
"""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": 800.0,
        "mxfp4": 800.0,
        "fp6": 800.0,
        "fp8": 400.0,
        "bf16": 200.0,
        "fp16": 200.0,
        "tf32": 100.0,
        "fp32": 12.0,  # non-tensor-core SIMT fp32
        "int8": 400.0,
        "int4": 800.0,
    },
)

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Output:
"""Roofline math: achieved TFLOPS / GB/s, peak fraction.

FLOPS and bytes formulas come from the problem's `problem.yaml`. They are the
dense-equivalent algorithmic work; kernels that skip work (sparsity, early
exit) are still measured against the dense cost.
"""
from __future__ import annotations


def compute_tflops(flops: float, time_ms: float) -> float:
    """flops per invocation and wall time in ms -> TFLOPS."""
    if time_ms <= 0:
        return 0.0
    return flops / (time_ms * 1e-3) / 1e12


def compute_gbps(bytes_moved: float, time_ms: float) -> float:
    """bytes per invocation and wall time in ms -> GB/s."""
    if time_ms <= 0:
        return 0.0
    return bytes_moved / (time_ms * 1e-3) / 1e9


def peak_fraction(
    achieved: float,
    peak: float,
) -> float:
    """Return achieved / peak, clamped to [0, infinity)."""
    if peak <= 0:
        return 0.0
    return max(0.0, achieved / peak)

Chunk ID: 98dd0e
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Output:
0, 508, 97887

Chunk ID: 22afc3
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Original token count: 56
Output:
ms 1.5337944030761719
{'B': 2, 'T': 1024, 'H': 8, 'K': 128, 'V': 128, 'CHUNK_SIZE': 64} 1.52587890625e-05 True
ms 2.8653621673583984
{'B': 2, 'T': 2048, 'H': 8, 'K': 128, 'V': 128, 'CHUNK_SIZE': 64} 3.0517578125e-05 True

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Output:
triton /home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/triton/__init__.py
torch /home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/__init__.py
fla /home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/__init__.py
cutlass /home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/cutlass/__init__.py

Chunk ID: a28ab1
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Output:
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/ops/kda/__init__.py
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/ops/kda/chunk_intra_token_parallel.py
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/ops/kda/naive.py
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/ops/kda/chunk_bwd.py
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/ops/kda/fused_recurrent.py
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/ops/kda/gate.py
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/ops/kda/chunk.py
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/ops/kda/chunk_fwd.py
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/ops/kda/chunk_intra.py
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/ops/kda/wy_fast.py
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/ops/kda/backends/__init__.py
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/ops/kda/backends/flashkda.py
/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/fla/ops/kda/backends/tilelang/__init__.py
/ho

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Output:
# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# For a list of all contributors, visit:
#   https://github.com/fla-org/flash-linear-attention/graphs/contributors

# Related files are modified and supported by the Moonshot AI Team

import torch

from fla.modules.l2norm import l2norm_bwd, l2norm_fwd
from fla.ops.backends import dispatch
from fla.ops.cp import FLACPContext
from fla.ops.kda.chunk_bwd import chunk_kda_bwd
from fla.ops.kda.chunk_fwd import chunk_kda_fwd
from fla.ops.utils.index import prepare_chunk_indices
from fla.utils import autocast_custom_bwd, autocast_custom_fwd, input_guard


class ChunkKDAFunction(torch.autograd.Function):
    @staticmethod
    @input_guard
    @autocast_custom_fwd
    def forward(
        ctx,
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        g: torch.Tensor,
        beta: torch.Tensor,
        A_log: torch.Tensor,
        dt_bias: torch.Tensor,
        scale: float,
        initial_state: torch.Tensor,
        output_final_state: bool = False,
        use_qk_l2norm_in_kernel: bool = False,
        use_gate_in_kernel: bool = False,
        cu_seqlens: torch.LongTensor | None = None,
        cu_seqlens_cpu: torch.LongTensor | None = None,
        safe_gate: bool = Fa

... (9894 more chars)

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Output:
# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# For a list of all contributors, visit:
#   https://github.com/fla-org/flash-linear-attention/graphs/contributors

import torch

from fla.ops.common.chunk_delta_h import chunk_gated_delta_rule_fwd_h
from fla.ops.cp import FLACPContext
from fla.ops.cp.chunk_delta_h import chunk_gated_delta_rule_fwd_h_pre_process, compress_h0
from fla.ops.gla.chunk import chunk_gla_fwd_o_gk
from fla.ops.kda.chunk_intra import chunk_kda_fwd_intra
from fla.ops.kda.gate import kda_gate_chunk_cumsum
from fla.ops.utils import chunk_local_cumsum
from fla.ops.utils.constant import RCP_LN2


def chunk_kda_fwd(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    g: torch.Tensor,
    beta: torch.Tensor,
    scale: float,
    initial_state: torch.Tensor,
    output_final_state: bool,
    cu_seqlens: torch.LongTensor | None = None,
    cu_seqlens_cpu: torch.LongTensor | None = None,
    chunk_indices: torch.LongTensor | None = None,
    chunk_size: int = 64,
    safe_gate: bool = False,
    lower_bound: float | None = None,
    use_gate_in_kernel: bool = False,
    A_log: torch.Tensor | None = None,
    dt_bias: torch.Tensor | None = None,
    disable_recompute: bool = False,
    return_intermediate_sta

... (2967 more chars)

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Output:
# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# For a list of all contributors, visit:
#   https://github.com/fla-org/flash-linear-attention/graphs/contributors

import torch
import triton
import triton.language as tl

from fla.ops.utils import prepare_chunk_indices
from fla.ops.utils.op import exp2
from fla.utils import autotune_cache_kwargs, check_shared_mem


@triton.heuristics({
    'STORE_QG': lambda args: args['qg'] is not None,
    'STORE_KG': lambda args: args['kg'] is not None,
    'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
})
@triton.autotune(
    configs=[
        triton.Config({}, num_warps=num_warps, num_stages=num_stages)
        for num_warps in [2, 4, 8]
        for num_stages in [2, 3, 4]
    ],
    key=['H', 'HV', 'K', 'V', 'BT', 'BK', 'BV', 'IS_VARLEN'],
    **autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def recompute_w_u_fwd_kda_kernel(
    q,
    k,
    qg,
    kg,
    v,
    beta,
    w,
    u,
    A,
    gk,
    cu_seqlens,
    chunk_indices,
    T,
    H: tl.constexpr,
    HV: tl.constexpr,
    K: tl.constexpr,
    V: tl.constexpr,
    BT: tl.constexpr,
    BK: tl.constexpr,
    BV: tl.constexpr,
    STORE_QG: tl.constexpr,
    STORE_KG: tl.constexpr,
    IS_VARLEN: tl.constexpr

... (9908 more chars)

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Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4115
Output:
# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# For a list of all contributors, visit:
#   https://github.com/fla-org/flash-linear-attention/graphs/contributors

import torch
import triton
import triton.language as tl

from fla.ops.kda.chunk_intra_token_parallel import chunk_kda_fwd_intra_token_parallel
from fla.ops.kda.wy_fast import recompute_w_u_fwd
from fla.ops.utils import prepare_chunk_indices
from fla.ops.utils.op import exp2, gather
from fla.utils import IS_GATHER_SUPPORTED, IS_TF32_SUPPORTED, autotune_cache_kwargs

if IS_TF32_SUPPORTED:
    SOLVE_TRIL_DOT_PRECISION = tl.constexpr('tf32')
else:
    SOLVE_TRIL_DOT_PRECISION = tl.constexpr('ieee')

################################################################################
# Fused inter + solve_tril kernel: compute off-diagonal Akk and solve in one pass
################################################################################


@triton.heuristics({
    'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
})
@triton.autotune(
    configs=[
        triton.Config({'BK': BK}, num_warps=num_warps)
        for BK in [32, 64]
        for num_warps in [1, 2, 4]
    ],
    key=["H", "HV", "K", "BC"],
    **autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
de

... (15065 more chars)

Chunk ID: e3b7ed
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 3887
Output:
        for num_warps in [1, 2, 4, 8]
        for num_stages in [2, 3, 4]
    ],
    key=['BK', 'NC', 'BT', 'HV'],
    **autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['B', 'T'])
def chunk_kda_bwd_kernel_intra(
    q,
    k,
    g,
    beta,
    dAqk,
    dAkk,
    dq,
    dq2,
    dk,
    dk2,
    dg,
    dg2,
    db,
    cu_seqlens,
    chunk_indices,
    B,
    T,
    H: tl.constexpr,
    HV: tl.constexpr,
    K: tl.constexpr,
    BT: tl.constexpr,
    BC: tl.constexpr,
    BK: tl.constexpr,
    NC: tl.constexpr,
    IS_VARLEN: tl.constexpr,
    SAFE_GATE: tl.constexpr,
    USE_GATHER: tl.constexpr,
):
    i_kc, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
    i_b, i_hv = i_bh // HV, i_bh % HV
    i_h = i_hv // (HV // H)
    i_k, i_i = i_kc // NC, i_kc % NC

    all = B * T
    if IS_VARLEN:
        i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32)
        bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
    else:
        bos, eos = i_b * T, i_b * T + T
    T = eos - bos

    i_ti = i_t * BT + i_i * BC
    if i_ti >= T:
        return

    o_k = i_k * BK + tl.arange(0, BK)
    m_k = o_k < K

    q += (bos * H + i_h) * K
    k += (bos * H + i_h) * K
    g += (bos * HV + i_hv) * K
    beta += bos * HV + i_hv

    dAqk += (bos * HV + i_hv) * B

... (14153 more chars)

Chunk ID: 19486e
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1019
Output:
    safe_gate: bool = False,
    disable_recompute: bool = False,
):
    B, T, H, K, HV = *k.shape, gk.shape[2]
    BT = chunk_size
    BC = 16
    if chunk_indices is None and cu_seqlens is not None:
        chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
    NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
    NC = triton.cdiv(BT, BC)

    Aqk = torch.empty(B, T, HV, BT, device=k.device, dtype=k.dtype)
    # Akk must be zero-initialized - kernel only writes lower triangular
    Akk = torch.zeros(B, T, HV, BT, device=k.device, dtype=k.dtype)
    # Separate fp32 buffer for diagonal 16x16 blocks (for precision in solve_tril)
    Akkd = torch.empty(B, T, HV, BC, device=k.device, dtype=torch.float32)

    # Step 1: Run token_parallel first to compute diagonal blocks into Akkd (fp32)
    # Step 1: compute diagonal blocks into Akk_diag (fp32)
    if safe_gate:
        grid = (NT, NC, B * HV)
        BK = triton.next_power_of_2(K)
        chunk_kda_fwd_kernel_intra_sub_chunk[grid](
            q=q,
            k=k,
            g=gk,
            beta=beta,
            Aqk=Aqk,
            Akk=Akkd,
            scale=scale,
            cu_seqlens=cu_seqlens,
            chunk_indices=chunk_indices,
            T=T,
            H=H,
            HV=HV,
            K=K,
            BT=BT,
            BC=BC,
            BK=BK,
            USE_GATHER=IS_GATHE

... (2680 more chars)

Chunk ID: 0693e2
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
0, 508, 97887

Chunk ID: 2001b0
Wall time: 2.5173 seconds
Process exited with code 0
Original token count: 3
Output:
avail True

Chunk ID: df6113
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
0, 508, 97887

Chunk ID: 926c1c
Wall time: 2.8622 seconds
Process exited with code 0
Original token count: 22
Output:
0 0.28857600688934326
1 0.2747519910335541
2 0.3152959942817688
3 0.25999999046325684

Chunk ID: 8b4870
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
0, 508, 97887

Chunk ID: ada7ab
Wall time: 1.1339 seconds
Process exited with code 0
Original token count: 53
Output:
prep 0.17976760864257812
exp_x_y 0.04458427429199219
kk bmm 0.03337860107421875
mask L 0.03933906555175781
solve 0.07271766662597656
w bmm 0.021696090698242188
u bmm 0.023603439331054688
recur 3.1206607818603516

Chunk ID: b26dc0
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 3702
Output:
# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# For a list of all contributors, visit:
#   https://github.com/fla-org/flash-linear-attention/graphs/contributors

import torch
import triton
import triton.language as tl

from fla.ops.backends import dispatch
from fla.ops.utils import prepare_chunk_indices, prepare_chunk_offsets
from fla.ops.utils.op import exp, exp2
from fla.utils import IS_NVIDIA_HOPPER, USE_CUDA_GRAPH, autotune_cache_kwargs, check_shared_mem

NUM_WARPS = [2, 4] if IS_NVIDIA_HOPPER else [2, 4, 8, 16]


@triton.heuristics({
    'USE_G': lambda args: args['g'] is not None,
    'USE_GK': lambda args: args['gk'] is not None,
    'USE_INITIAL_STATE': lambda args: args['h0'] is not None,
    'STORE_FINAL_STATE': lambda args: args['ht'] is not None,
    'SAVE_NEW_VALUE': lambda args: args['v_new'] is not None,
    'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
})
@triton.autotune(
    configs=[
        triton.Config({'BV': BV}, num_warps=num_warps, num_stages=num_stages)
        for num_warps in [2, 4]
        for num_stages in ([2, 3, 4] if check_shared_mem('ampere') else [2, 1])
        for BV in ([32, 64] if check_shared_mem('ada') else [32])
    ],
    key=['H', 'HV', 'K', 'V', 'BT', 'USE_EXP2', 'TRANSPOSE_STA

... (13412 more chars)

Chunk ID: 79e1be
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1789
Output:
    T,
    H: tl.constexpr,
    HV: tl.constexpr,
    K: tl.constexpr,
    V: tl.constexpr,
    BT: tl.constexpr,
    BV: tl.constexpr,
    USE_G: tl.constexpr,
    USE_GK: tl.constexpr,
    USE_INITIAL_STATE: tl.constexpr,
    USE_FINAL_STATE_GRADIENT: tl.constexpr,
    USE_EXP2: tl.constexpr,
    TRANSPOSE_STATE: tl.constexpr,
    IS_VARLEN: tl.constexpr,
):
    i_v, i_nh = tl.program_id(0), tl.program_id(1)
    i_n, i_h = i_nh // HV, i_nh % HV
    if IS_VARLEN:
        bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
        T = eos - bos
        NT = tl.cdiv(T, BT)
        boh = tl.load(chunk_offsets + i_n).to(tl.int32)
    else:
        bos, eos = i_n * T, i_n * T + T
        NT = tl.cdiv(T, BT)
        boh = i_n * NT

    if TRANSPOSE_STATE:
        b_dh1 = tl.zeros([BV, 64], dtype=tl.float32)
        if K > 64:
            b_dh2 = tl.zeros([BV, 64], dtype=tl.float32)
        if K > 128:
            b_dh3 = tl.zeros([BV, 64], dtype=tl.float32)
        if K > 192:
            b_dh4 = tl.zeros([BV, 64], dtype=tl.float32)
    else:
        b_dh1 = tl.zeros([64, BV], dtype=tl.float32)
        if K > 64:
            b_dh2 = tl.zeros([64, BV], dtype=tl.float32)
        if K > 128:
            b_dh3 = tl.zeros([64, BV], dtype=tl.float32)
        if K > 192:
            b_dh4 = tl.zeros([64, BV], dtype=tl.float32)

    # calculate

... (5760 more chars)

Chunk ID: 869e5b
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1050
Output:
            else:
                p_dh2 = tl.make_block_ptr(dh0, (K, V), (V, 1), (128, i_v * BV), (64, BV), (1, 0))
            tl.store(p_dh2, b_dh3.to(p_dh2.dtype.element_ty), boundary_check=(0, 1))
        if K > 192:
            if TRANSPOSE_STATE:
                p_dh3 = tl.make_block_ptr(dh0, (V, K), (K, 1), (i_v * BV, 192), (BV, 64), (1, 0))
            else:
                p_dh3 = tl.make_block_ptr(dh0, (K, V), (V, 1), (192, i_v * BV), (64, BV), (1, 0))
            tl.store(p_dh3, b_dh4.to(p_dh3.dtype.element_ty), boundary_check=(0, 1))


@dispatch('common')
def chunk_gated_delta_rule_fwd_h(
    k: torch.Tensor,
    w: torch.Tensor,
    u: torch.Tensor,
    g: torch.Tensor | None = None,
    gk: torch.Tensor | None = None,
    initial_state: torch.Tensor | None = None,
    output_final_state: bool = False,
    chunk_size: int = 64,
    save_new_value: bool = True,
    cu_seqlens: torch.LongTensor | None = None,
    cu_seqlens_cpu: torch.LongTensor | None = None,
    chunk_indices: torch.LongTensor | None = None,
    use_exp2: bool = False,
    transpose_state_layout: bool = False,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:
    B, T, H, K, V, HV = *k.shape, u.shape[-1], u.shape[2]
    BT = chunk_size

    if chunk_indices is None and cu_seqlens is not None:
        chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size)
    # N: the actual n

... (2803 more chars)

Chunk ID: a19eee
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2127
Output:
313:def chunk_gla_fwd_kernel_o(
877:def chunk_gla_fwd_o_gk(
900:    chunk_gla_fwd_kernel_o[grid](
1127:    o = chunk_gla_fwd_o_gk(
# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# For a list of all contributors, visit:
#   https://github.com/fla-org/flash-linear-attention/graphs/contributors

import torch
import triton
import triton.language as tl

from fla.ops.common.chunk_h import chunk_bwd_dh, chunk_fwd_h
from fla.ops.utils import prepare_chunk_indices
from fla.ops.utils.cumsum import chunk_local_cumsum
from fla.ops.utils.op import exp, exp2
from fla.utils import autotune_cache_kwargs, check_shared_mem, input_guard

BK_LIST = [32, 64] if check_shared_mem() else [16, 32]
BV_LIST = [64, 128] if check_shared_mem('ampere') else [16, 32]


@triton.heuristics({
    'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
})
@triton.autotune(
    configs=[
        triton.Config({'BK': BK}, num_warps=num_warps, num_stages=num_stages)
        for BK in [32, 64]
        for num_warps in [1, 2, 4, 8]
        for num_stages in [2, 3, 4]
    ],
    key=["BC"],
    **autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def chunk_gla_fwd_A_kernel_intra_sub_inter(
    q,
    k,
    g,
    A,
    cu_seqlens,
    chunk_indices,
    scale,


... (7111 more chars)

Chunk ID: 4551f3
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1472
Output:
})
@triton.autotune(
    configs=[
        triton.Config({'BK': BK, 'BV': BV}, num_warps=num_warps, num_stages=num_stages)
        for BK in [32, 64]
        for BV in [64, 128]
        for num_warps in [2, 4, 8]
        for num_stages in [2, 3, 4]
    ],
    key=['BT', 'HV', 'TRANSPOSE_STATE'],
    **autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def chunk_gla_fwd_kernel_o(
    q,
    v,
    g,
    h,
    o,
    A,
    cu_seqlens,
    chunk_indices,
    scale,
    T,
    H: tl.constexpr,
    HV: tl.constexpr,
    K: tl.constexpr,
    V: tl.constexpr,
    BT: tl.constexpr,
    BK: tl.constexpr,
    BV: tl.constexpr,
    USE_EXP2: tl.constexpr,
    TRANSPOSE_STATE: tl.constexpr,
    IS_VARLEN: tl.constexpr,
):
    i_v, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
    i_b, i_hv = i_bh // HV, i_bh % HV
    i_h = i_hv // (HV // H)
    if IS_VARLEN:
        i_tg = i_t.to(tl.int64)
        i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32)
        bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64)
        T = eos - bos
        NT = tl.cdiv(T, BT)
    else:
        NT = tl.cdiv(T, BT)
        i_tg = (i_b * NT + i_t).to(tl.int64)
        bos, eos = (i_b * T).to(tl.int64), (i_b * T + T).to(tl.int64)

    m_s = tl.arange(0, BT)[:, None] >= tl.arange(

... (4492 more chars)

Chunk ID: 72bbc9
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
0, 508, 97887

Chunk ID: 76fe82
Wall time: 5.6610 seconds
Process exited with code 0
Original token count: 56
Output:
ms 0.35338401794433594
{'B': 2, 'T': 1024, 'H': 8, 'K': 128, 'V': 128, 'CHUNK_SIZE': 64} 0.01666259765625 True
ms 0.41828155517578125
{'B': 2, 'T': 2048, 'H': 8, 'K': 128, 'V': 128, 'CHUNK_SIZE': 64} 0.01849365234375 True

output:
  Success. Updated the following files:
  A /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: b65b68
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:

Chunk ID: c26772
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
0, 508, 97887

Chunk ID: 995984
Wall time: 5.2410 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 18e768
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
0, 508, 97887

Chunk ID: a129b1
Wall time: 30.0036 seconds
Process running with session ID 80079
Original token count: 0
Output:

Chunk ID: a72bc1
Wall time: 30.0013 seconds
Process running with session ID 80079
Original token count: 0
Output:

Chunk ID: 4de0cf
Wall time: 30.0030 seconds
Process running with session ID 80079
Original token count: 0
Output:

Chunk ID: 61356f
Wall time: 60.0011 seconds
Process running with session ID 80079
Original token count: 0
Output:

Chunk ID: c9036a
Wall time: 120.0009 seconds
Process running with session ID 80079
Original token count: 0
Output:

Chunk ID: c6b470
Wall time: 8.1944 seconds
Process exited with code 0
Original token count: 276
Output:
shape=0 variant=eager tflops=0.060 gbps=0.706 ms=35.671
shape=0 variant=compiled tflops=0.504 gbps=5.914 ms=4.261
shape=0 variant=sota tflops=8.324 gbps=97.675 ms=0.258
shape=0 variant=solution tflops=7.242 gbps=84.983 ms=0.297
shape=0 solution_peak_fraction=0.0362
shape=1 variant=eager tflops=0.064 gbps=0.747 ms=67.441
shape=1 variant=compiled tflops=0.491 gbps=5.759 ms=8.751
shape=1 variant=sota tflops=15.421 gbps=180.952 ms=0.279
shape=1 variant=solution tflops=8.086 gbps=94.883 ms=0.531
shape=1 solution_peak_fraction=0.0404
shape=2 variant=eager tflops=0.041 gbps=0.479 ms=105.151
shape=2 variant=compiled tflops=0.267 gbps=3.130 ms=16.103
shape=2 variant=sota tflops=13.568 gbps=159.211 ms=0.317
shape=2 variant=solution tflops=7.266 gbps=85.257 ms=0.591
shape=2 solution_peak_fraction=0.0363
shape=3 variant=eager tflops=0.020 gbps=0.232 ms=54.390
shape=3 variant=compiled tflops=0.212 gbps=2.484 ms=5.073
shape=3 variant=sota tflops=3.891 gbps=45.655 ms=0.276
shape=3 variant=solution tflops=3.289 gbps=38.593 ms=0.326
shape=3 solution_peak_fraction=0.0164
peak_fraction: 0.0306
RESULT: LOW

Chunk ID: 4f024d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
0, 508, 97887

Chunk ID: 0fa3d8
Wall time: 2.0480 seconds
Process exited with code 0
Original token count: 118
Output:
{'B': 2, 'T': 1024, 'H': 8, 'K': 128, 'V': 128, 'CHUNK_SIZE': 64} ms 0.2852439880371094 maxdiff 6.103515625e-05 True
{'B': 2, 'T': 2048, 'H': 8, 'K': 128, 'V': 128, 'CHUNK_SIZE': 64} ms 0.4446983337402344 maxdiff 7.62939453125e-05 True
{'B': 1, 'T': 4096, 'H': 8, 'K': 128, 'V': 128, 'CHUNK_SIZE': 64} ms 0.5038022994995117 maxdiff 7.62939453125e-05 True
{'B': 1, 'T': 2048, 'H': 4, 'K': 128, 'V': 128, 'CHUNK_SIZE': 64} ms 0.2773761749267578 maxdiff 6.103515625e-05 True

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 904eaf
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:

Chunk ID: fec76d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
0, 508, 97887

Chunk ID: 1f76a2
Wall time: 3.5354 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: e93ba9
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
0, 508, 97887

Chunk ID: 37ff52
Wall time: 30.0025 seconds
Process running with session ID 89211
Original token count: 0
Output:

Chunk ID: cdcf91
Wall time: 180.0012 seconds
Process running with session ID 89211
Original token count: 0
Output:

Chunk ID: 4a0f0e
Wall time: 82.0512 seconds
Process exited with code 0
Original token count: 277
Output:
shape=0 variant=eager tflops=0.061 gbps=0.716 ms=35.203
shape=0 variant=compiled tflops=0.508 gbps=5.965 ms=4.225
shape=0 variant=sota tflops=8.276 gbps=97.109 ms=0.259
shape=0 variant=solution tflops=6.708 gbps=78.710 ms=0.320
shape=0 solution_peak_fraction=0.0335
shape=1 variant=eager tflops=0.063 gbps=0.741 ms=68.024
shape=1 variant=compiled tflops=0.492 gbps=5.775 ms=8.727
shape=1 variant=sota tflops=15.319 gbps=179.754 ms=0.280
shape=1 variant=solution tflops=8.948 gbps=104.994 ms=0.480
shape=1 solution_peak_fraction=0.0447
shape=2 variant=eager tflops=0.041 gbps=0.478 ms=105.393
shape=2 variant=compiled tflops=0.266 gbps=3.117 ms=16.168
shape=2 variant=sota tflops=13.673 gbps=160.435 ms=0.314
shape=2 variant=solution tflops=8.018 gbps=94.084 ms=0.536
shape=2 solution_peak_fraction=0.0401
shape=3 variant=eager tflops=0.020 gbps=0.230 ms=54.760
shape=3 variant=compiled tflops=0.212 gbps=2.483 ms=5.074
shape=3 variant=sota tflops=3.995 gbps=46.872 ms=0.269
shape=3 variant=solution tflops=3.107 gbps=36.463 ms=0.346
shape=3 solution_peak_fraction=0.0155
peak_fraction: 0.0311
RESULT: LOW

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: b51fd9
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:

Chunk ID: 667332
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
0, 508, 97887

Chunk ID: 8947c7
Wall time: 3.4796 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 8fefa8
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
0, 508, 97887

Chunk ID: 911364
Wall time: 30.0005 seconds
Process running with session ID 85764
Original token count: 0
Output:

Chunk ID: f589af
Wall time: 240.0011 seconds
Process running with session ID 85764
Original token count: 0
Output:

Chunk ID: 09b081
Wall time: 26.1683 seconds
Process exited with code 0
Original token count: 277
Output:
shape=0 variant=eager tflops=0.061 gbps=0.718 ms=35.097
shape=0 variant=compiled tflops=0.509 gbps=5.970 ms=4.221
shape=0 variant=sota tflops=8.225 gbps=96.514 ms=0.261
shape=0 variant=solution tflops=7.046 gbps=82.681 ms=0.305
shape=0 solution_peak_fraction=0.0352
shape=1 variant=eager tflops=0.064 gbps=0.755 ms=66.745
shape=1 variant=compiled tflops=0.491 gbps=5.764 ms=8.743
shape=1 variant=sota tflops=15.356 gbps=180.186 ms=0.280
shape=1 variant=solution tflops=8.883 gbps=104.237 ms=0.483
shape=1 solution_peak_fraction=0.0444
shape=2 variant=eager tflops=0.041 gbps=0.480 ms=105.049
shape=2 variant=compiled tflops=0.266 gbps=3.120 ms=16.152
shape=2 variant=sota tflops=13.548 gbps=158.970 ms=0.317
shape=2 variant=solution tflops=8.046 gbps=94.411 ms=0.534
shape=2 solution_peak_fraction=0.0402
shape=3 variant=eager tflops=0.020 gbps=0.231 ms=54.482
shape=3 variant=compiled tflops=0.212 gbps=2.483 ms=5.074
shape=3 variant=sota tflops=3.950 gbps=46.348 ms=0.272
shape=3 variant=solution tflops=3.279 gbps=38.478 ms=0.327
shape=3 solution_peak_fraction=0.0164
peak_fraction: 0.0319
RESULT: LOW

Chunk ID: eb405f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: e7c85a
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
0, 508, 97887

Chunk ID: c3f850
Wall time: 0.0000 seconds
Process exited with code 5
Original token count: 85
Output:
============================= test session starts ==============================
platform linux -- Python 3.11.15, pytest-9.0.3, pluggy-1.6.0
rootdir: /home/infatoshi/cuda/KernelBench-Hard
configfile: pyproject.toml
plugins: anyio-4.13.0
collected 0 items

============================ no tests ran in 0.00s =============================

output:
  Success. Updated the following files:
  A /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/test_solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 35a767
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: f23f5f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
0, 508, 97887

Chunk ID: 4c5b42
Wall time: 3.9936 seconds
Process exited with code 0
Original token count: 105
Output:
============================= test session starts ==============================
platform linux -- Python 3.11.15, pytest-9.0.3, pluggy-1.6.0
rootdir: /home/infatoshi/cuda/KernelBench-Hard
configfile: pyproject.toml
plugins: anyio-4.13.0
collected 1 item

test_solution.py .                                                       [100%]

============================== 1 passed in 3.56s ===============================

Chunk ID: e762ea
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
?? test_solution.py

Chunk ID: 91b7b2
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:

Chunk ID: 44bdf8
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
triton

Chunk ID: 497e24
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 211
Output:
?? test_solution.py
total 68
drwxrwxr-x  3 infatoshi infatoshi 4096 Apr 27 23:48 .
drwxrwxr-x 10 infatoshi infatoshi 4096 Apr 24 00:30 ..
-rw-rw-r--  1 infatoshi infatoshi 2312 Apr 27 03:21 PROMPT.txt
drwxrwxr-x  2 infatoshi infatoshi 4096 Apr 27 23:48 __pycache__
-rw-rw-r--  1 infatoshi infatoshi 4380 Apr 26 03:11 benchmark.py
-rw-rw-r--  1 infatoshi infatoshi 3822 Apr 25 22:21 check.py
-rw-rw-r--  1 infatoshi infatoshi    7 Apr 27 23:48 framework.txt
-rw-rw-r--  1 infatoshi infatoshi 2243 Apr 26 02:51 problem.yaml
-rw-rw-r--  1 infatoshi infatoshi 5093 Apr 25 22:19 reference.py
-rw-rw-r--  1 infatoshi infatoshi  720 Apr 25 22:20 shapes.py
-rw-rw-r--  1 infatoshi infatoshi 9262 Apr 27 23:42 solution.py
-rw-rw-r--  1 infatoshi infatoshi 2424 Apr 25 22:20 sota.py
-rw-rw-r--  1 infatoshi infatoshi   71 Apr 27 23:48 test_solution.py

Chunk ID: 693839
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2302
Output:
     1	from __future__ import annotations
     2	
     3	import torch
     4	import torch.nn as nn
     5	import triton
     6	import triton.language as tl
     7	
     8	
     9	@triton.jit
    10	def _state_kernel(
    11	    kg,
    12	    w,
    13	    u,
    14	    g,
    15	    hbuf,
    16	    vnew,
    17	    T: tl.constexpr,
    18	    H: tl.constexpr,
    19	    NT: tl.constexpr,
    20	    K: tl.constexpr,
    21	    V: tl.constexpr,
    22	    BT: tl.constexpr,
    23	    BV: tl.constexpr,
    24	):
    25	    pid_v = tl.program_id(0)
    26	    pid_bh = tl.program_id(1)
    27	    b = pid_bh // H
    28	    hidx = pid_bh - b * H
    29	
    30	    offs_v = pid_v * BV + tl.arange(0, BV)
    31	    offs_c = tl.arange(0, BT)
    32	    offs_k = tl.arange(0, 64)
    33	    mask_v = offs_v < V
    34	
    35	    s0 = tl.zeros((64, BV), dtype=tl.float32)
    36	    s1 = tl.zeros((64, BV), dtype=tl.float32)
    37	
    38	    for n in range(0, NT):
    39	        h_base = (((b * NT + n) * H + hidx) * K) * V
    40	        tl.store(
    41	            hbuf + h_base + offs_k[:, None] * V + offs_v[None, :],
    42	            s0.to(hbuf.dtype.element_ty),
    43	            mask=mask_v[None, :],
    44	        )
    45	        tl.store(
    46	            hbuf + h_base + (offs_k[:, None] + 64) * V + offs_v[None, :],
    47	            s1.to(hbuf.dtype.element_ty),
    4

... (7813 more chars)

Chunk ID: faaa55
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 27
Output:
     1	import check
     2	
     3	
     4	def test_solution_matches_reference():
     5	    check.main()

Chunk ID: 16d932
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 614
Output:
   260	
   261	    hbuf = torch.empty((B, NT, H, K, V), device=q.device, dtype=dtype)
   262	    vnew = torch.empty_like(v)
   263	    out = torch.empty_like(v)
   264	    grid_state = (triton.cdiv(V, 64), B * H)
   265	    _state_kernel[grid_state](
   266	        kg_state,
   267	        w_bthk,
   268	        u_bthv,
   269	        g_bthk,
   270	        hbuf,
   271	        vnew,
   272	        T,
   273	        H,
   274	        NT,
   275	        K,
   276	        V,
   277	        BT,
   278	        BV=64,
   279	        num_warps=4,
   280	        num_stages=3,
   281	    )
   282	    grid_out = (triton.cdiv(V, 64), NT, B * H)
   283	    _out_kernel[grid_out](
   284	        q,
   285	        g_bthk,
   286	        aqk_bthc,
   287	        hbuf,
   288	        vnew,
   289	        out,
   290	        scale,
   291	        T,
   292	        H,
   293	        NT,
   294	        K,
   295	        V,
   296	        BT,
   297	        BV=64,
   298	        num_warps=4,
   299	        num_stages=3,
   300	    )
   301	    return out
   302	
   303	
   304	class Model(nn.Module):
   305	    def __init__(self, B: int, T: int, H: int, K: int, V: int, chunk_size: int = 64):
   306	        super().__init__()
   307	        self.B, self.T, self.H, self.K, self.V = B, T, H, K, V
   308	        self.chunk_size = chunk_size
   309	        self.scale = float(K) ** -0.5
   310	     

... (1060 more chars)