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/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu
uv 0.11.3 (x86_64-unknown-linux-gnu)
Python 3.11.15

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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% /
shapes.py
reference.py
check.py
benchmark.py
problem.yaml

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"""Naive grouped GEMM + fused SwiGLU reference (correctness only, NOT the SOTA).

This is the up-projection of an MoE FFN. Each token i is assigned to K experts;
expert_indices[i*K + j] tells you which expert. Tokens are dispatched to experts
according to routing metadata; we compute, per expert e:

    h_e = silu(x_e @ W_gate[e])  *  (x_e @ W_up[e])

where x_e is the slice of permuted hidden states routed to expert e, with
expert_offsets[e]:expert_offsets[e+1] giving its row range in the permuted layout.

The reference loops over experts in Python. Slow, but pedagogically clear and
correct. Forbidden ops (torch.matmul, torch.bmm, F.linear, sonic_moe imports)
are NOT used here, but the reference is exempt — only solution.py is checked.
"""
from __future__ import annotations

import torch
import torch.nn as nn
import torch.nn.functional as F

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


class Model(nn.Module):
    """Up-projection of a top-K MoE FFN with fused SwiGLU.

    Inputs at call time:
      hidden_states:    (T_perm, H)  bf16, already permuted to expert order
      expert_offsets:   (E+1,)       int32, prefix sums of token counts per expert
                                     so expert e owns rows [offsets[e]:offsets[e+1]]
                                     T_perm = T_total * K (each token 

... (2404 more chars)

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"""Correctness runner for Sonic-MoE up-projection (grouped GEMM + fused SwiGLU).

Runs solution.Model vs reference.Model across all shapes in shapes.py, 3 seeds
each, with per-dtype atol/rtol. 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 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)

    device = torch.device("cuda:0")
    tol_override = meta.get("tolerance") or None

    # --- Per-shape correctness --------------------------------------------
    all_shapes = shapes.SHAPES
    for shape_idx, shape in enumerat

... (2295 more chars)

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"""Roofline benchmark for Sonic-MoE up-projection (grouped GEMM + fused SwiGLU).

For each shape: times eager reference, compiled reference, SOTA (if available),
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 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_formula"]
    num_perf_trials = int(meta.get("num_perf_trials", 20))

    device = torch.device("cuda:0")

    # Op

... (3095 more chars)

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Output:
"""Shape sweep for Sonic-MoE up-projection (grouped GEMM + fused SwiGLU).

Defaults match the sonic-moe paper's headline configuration. We add:
  - a smaller shape for fast iteration during agent development
  - a wider intermediate (different aspect ratio) to stress N-tile selection
"""

SHAPES = [
    # Headline sonic-moe shape: 32K tokens, 128 experts, top-8.
    {"T_total": 32768, "H": 4096, "I": 1536, "E": 128, "K": 8},

    # Fast-iteration shape (~16x cheaper). Same expert count to keep the
    # variable-length grouped layout meaningful, but smaller token / hidden dims.
    {"T_total": 4096, "H": 2048, "I": 1024, "E": 64, "K": 4},

    # Different aspect ratio: smaller H, wider I (intermediate-heavy FFN).
    # Forces tiles to handle larger N relative to K.
    {"T_total": 16384, "H": 2048, "I": 4096, "E": 64, "K": 8},
]
name: 06_sonic_moe_swiglu
display_name: "Sonic-MoE up-projection (Grouped GEMM + SwiGLU)"
precision: bf16
regime: compute
hardware: [RTX_PRO_6000]
peak_tflops_key: bf16
peak_bandwidth_key: dram

# Dense-equivalent FLOPs: gate GEMM + up GEMM + SwiGLU (negligible elementwise).
# Per token: 2*H*I FMAs for gate, 2*H*I for up => 2 * T_total * H * (2*I).
# (Each of T_total tokens visits K experts, but total work = T_total * K * (2*H*I*2)
# only if you count routing. The standard MoE FLOPs convention counts only the
# active per-token compute: T_total toke

... (1284 more chars)

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Output:
torch 2.11.0+cu130 cuda 13.0
triton 3.6.0
cuda available True
arch list ['sm_75', 'sm_80', 'sm_86', 'sm_90', 'sm_100', 'sm_120']

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Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 552, 97887

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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)

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Output:
sed: can't read ../../src/hardware.py: No such file or directory

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Output:
../../src/hardware/m4_max.py:9:    peak_tflops_dense={
../../src/hardware/m4_max.py:14:        "bf16": 34.0,
../../src/hardware/__init__.py:3:from src.hardware.rtx_pro_6000 import RTX_PRO_6000
../../src/hardware/__init__.py:6:    "RTX_PRO_6000": RTX_PRO_6000,
../../src/hardware/rtx_pro_6000.py:15:    peak_tflops_dense: dict[str, float]  # dtype -> TFLOPS
../../src/hardware/rtx_pro_6000.py:18:RTX_PRO_6000 = HardwareTarget(
../../src/hardware/rtx_pro_6000.py:23:    peak_tflops_dense={
../../src/hardware/rtx_pro_6000.py:29:        "bf16": 200.0,
../../src/eval/correctness.py:3:Stanford's KernelBench uses 1e-4 atol/rtol for fp32 and 1e-2 for fp16/bf16.

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Output:
"""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:
"""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



... (1171 more chars)

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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)

output:
  Success. Updated the following files:
  A /home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: f76bf9
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Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 552, 97887

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Output:

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Output:

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Output:
0, 22280

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Output:
PASS

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Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 552, 97887

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Output:
W0428 00:43:58.402000 1449335 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] Graph break from `Tensor.item()`, consider setting:
W0428 00:43:58.402000 1449335 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     torch._dynamo.config.capture_scalar_outputs = True
W0428 00:43:58.402000 1449335 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] or:
W0428 00:43:58.402000 1449335 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
W0428 00:43:58.402000 1449335 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] to include these operations in the captured graph.
W0428 00:43:58.402000 1449335 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] 
W0428 00:43:58.402000 1449335 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] Graph break: from user code at:
W0428 00:43:58.402000 1449335 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]   File "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/reference.py", line 62, in forward
W0428 00:43:58.402000 1449335 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     start = int(expert_offsets[e].i

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Output:
shape=0 variant=eager tflops=33.544 gbps=251.142 ms=24.584
shape=0 variant=compiled tflops=33.487 gbps=250.719 ms=24.625
shape=0 variant=solution tflops=25.001 gbps=187.180 ms=32.984
shape=0 solution_peak_fraction=0.1250
shape=1 variant=eager tflops=14.282 gbps=264.989 ms=2.406
shape=1 variant=compiled tflops=14.300 gbps=265.339 ms=2.403
shape=1 variant=solution tflops=48.265 gbps=895.534 ms=0.712
shape=1 solution_peak_fraction=0.2413
shape=2 variant=eager tflops=35.522 gbps=242.826 ms=15.476
shape=2 variant=compiled tflops=35.281 gbps=241.182 ms=15.582
shape=2 variant=solution tflops=24.744 gbps=169.148 ms=22.218
shape=2 solution_peak_fraction=0.1237
peak_fraction: 0.1551
RESULT: OK

output:
  Success. Updated the following files:
  A /home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/tune_tiles.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

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Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 1136, 97887

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Output:
1, 13937

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Output:

write_stdin failed: stdin is closed for this session; rerun exec_command with tty=true to keep stdin open

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Output:
1449928 uv run tune_tiles.py
1449931 /home/infatoshi/cuda/KernelBench-Hard/.venv/bin/python3 tune_tiles.py

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Output:

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Output:

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/tune_tiles.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

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Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 1119, 97887

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Output:
shape 0
  cfg=(32, 64, 64, 4, 4) ms=32.646
  cfg=(32, 128, 64, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(64, 64, 64, 4, 4) ms=24.331
  cfg=(64, 128, 64, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 122880, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(32, 64, 128, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 122880, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(32, 128, 128, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 221184, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(64, 64, 128, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 147456, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(32, 128, 64, 8, 4) error=OutOfResources: out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(64, 128, 64, 8, 4) error=OutOfResources: out of resource: shared memory, Required: 122880, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
best shape 0: cfg=(64, 64, 64, 4, 4) ms=24.331
shape 1
  cfg=(32, 64, 64, 4, 4) ms=0.707
  cfg=(32, 128, 64, 4, 4) error=Ou

... (1230 more chars)

Chunk ID: ef3a94
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Process exited with code 0
Original token count: 328
Output:
shape 2
  cfg=(32, 64, 64, 4, 4) ms=21.873
  cfg=(32, 128, 64, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(64, 64, 64, 4, 4) ms=16.406
  cfg=(64, 128, 64, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 122880, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(32, 64, 128, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 122880, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(32, 128, 128, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 221184, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(64, 64, 128, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 147456, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(32, 128, 64, 8, 4) error=OutOfResources: out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(64, 128, 64, 8, 4) error=OutOfResources: out of resource: shared memory, Required: 122880, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
best shape 2: cfg=(64, 64, 64, 4, 4) ms=16.406

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/tune_tiles.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 8b72dd
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 17
Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 1119, 97887

Chunk ID: 8b32a3
Wall time: 1.0009 seconds
Process running with session ID 23260
Original token count: 0
Output:

Chunk ID: 02e168
Wall time: 45.0006 seconds
Process running with session ID 23260
Original token count: 831
Output:
shape 0
  cfg=(32, 64, 64, 4, 4) ms=32.583
  cfg=(32, 128, 64, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(32, 128, 64, 4, 3) ms=31.179
  cfg=(32, 128, 64, 4, 2) ms=31.577
  cfg=(32, 128, 32, 4, 4) ms=29.512
  cfg=(64, 64, 64, 4, 4) ms=24.359
  cfg=(64, 64, 64, 4, 3) ms=23.553
  cfg=(64, 64, 64, 8, 4) ms=38.748
  cfg=(64, 128, 64, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 122880, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(64, 128, 64, 4, 3) ms=21.484
  cfg=(64, 128, 64, 4, 2) ms=21.873
  cfg=(64, 128, 32, 4, 4) ms=21.171
  cfg=(128, 64, 64, 4, 4) ms=21.566
  cfg=(128, 64, 64, 8, 4) ms=21.036
  cfg=(32, 64, 128, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 122880, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(32, 128, 128, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 221184, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(64, 64, 128, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 147456, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(32, 128, 64, 8, 4) error=OutOfResources: out of resource: shared memory, Required: 110592, Hard

... (1939 more chars)

Chunk ID: 843a91
Wall time: 7.7939 seconds
Process exited with code 0
Original token count: 417
Output:
shape 2
  cfg=(32, 64, 64, 4, 4) ms=21.875
  cfg=(32, 128, 64, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(32, 128, 64, 4, 3) ms=20.291
  cfg=(32, 128, 64, 4, 2) ms=20.470
  cfg=(32, 128, 32, 4, 4) ms=19.720
  cfg=(64, 64, 64, 4, 4) ms=16.769
  cfg=(64, 64, 64, 4, 3) ms=15.568
  cfg=(64, 64, 64, 8, 4) ms=25.911
  cfg=(64, 128, 64, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 122880, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(64, 128, 64, 4, 3) ms=13.687
  cfg=(64, 128, 64, 4, 2) ms=13.654
  cfg=(64, 128, 32, 4, 4) ms=13.627
  cfg=(128, 64, 64, 4, 4) ms=14.393
  cfg=(128, 64, 64, 8, 4) ms=13.864
  cfg=(32, 64, 128, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 122880, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(32, 128, 128, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 221184, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(64, 64, 128, 4, 4) error=OutOfResources: out of resource: shared memory, Required: 147456, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(32, 128, 64, 8, 4) error=OutOfResources: out of resource: shared memory, Required: 110592, Hardware limit: 

... (272 more chars)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: cb9392
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 17
Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 1096, 97887

Chunk ID: 6aa2f7
Wall time: 1.0010 seconds
Process running with session ID 33043
Original token count: 0
Output:

Chunk ID: 373aae
Wall time: 30.0015 seconds
Process running with session ID 33043
Original token count: 0
Output:

Chunk ID: 6f1919
Wall time: 30.0014 seconds
Process running with session ID 33043
Original token count: 0
Output:

Chunk ID: 7b0ab0
Wall time: 30.0016 seconds
Process running with session ID 33043
Original token count: 0
Output:

Chunk ID: 4fcb3a
Wall time: 16.8160 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: d64f30
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 17
Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 1096, 97887

Chunk ID: 3a9bd0
Wall time: 1.0010 seconds
Process running with session ID 75564
Original token count: 0
Output:

Chunk ID: ed8a26
Wall time: 30.0014 seconds
Process running with session ID 75564
Original token count: 0
Output:

Chunk ID: ce53cf
Wall time: 30.0015 seconds
Process running with session ID 75564
Original token count: 404
Output:
W0428 00:56:00.772000 1452277 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] Graph break from `Tensor.item()`, consider setting:
W0428 00:56:00.772000 1452277 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     torch._dynamo.config.capture_scalar_outputs = True
W0428 00:56:00.772000 1452277 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] or:
W0428 00:56:00.772000 1452277 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
W0428 00:56:00.772000 1452277 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] to include these operations in the captured graph.
W0428 00:56:00.772000 1452277 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] 
W0428 00:56:00.772000 1452277 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] Graph break: from user code at:
W0428 00:56:00.772000 1452277 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]   File "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/reference.py", line 62, in forward
W0428 00:56:00.772000 1452277 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     start = int(expert_offsets[e].i

... (231 more chars)

Chunk ID: c9083c
Wall time: 20.4557 seconds
Process exited with code 0
Original token count: 174
Output:
shape=0 variant=eager tflops=33.517 gbps=250.943 ms=24.603
shape=0 variant=compiled tflops=33.365 gbps=249.801 ms=24.716
shape=0 variant=solution tflops=38.588 gbps=288.911 ms=21.370
shape=0 solution_peak_fraction=0.1929
shape=1 variant=eager tflops=14.318 gbps=265.675 ms=2.400
shape=1 variant=compiled tflops=14.220 gbps=263.852 ms=2.416
shape=1 variant=solution tflops=73.113 gbps=1356.594 ms=0.470
shape=1 solution_peak_fraction=0.3656
shape=2 variant=eager tflops=35.534 gbps=242.908 ms=15.471
shape=2 variant=compiled tflops=35.245 gbps=240.933 ms=15.598
shape=2 variant=solution tflops=37.843 gbps=258.691 ms=14.527
shape=2 solution_peak_fraction=0.1892
peak_fraction: 0.2372
RESULT: OK

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/tune_tiles.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: f23793
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 17
Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 1096, 97887

Chunk ID: b61a91
Wall time: 1.0011 seconds
Process running with session ID 26842
Original token count: 0
Output:

Chunk ID: 272b9e
Wall time: 45.0017 seconds
Process running with session ID 26842
Original token count: 313
Output:
shape 0
  cfg=(64, 128, 64, 4, 3) ms=21.071
  cfg=(64, 128, 64, 4, 2) ms=22.067
  cfg=(64, 128, 32, 4, 4) ms=21.467
  cfg=(64, 128, 32, 4, 3) ms=20.500
  cfg=(64, 128, 32, 8, 4) ms=21.824
  cfg=(64, 128, 64, 8, 3) ms=21.924
  cfg=(64, 256, 32, 4, 3) ms=41.933
  cfg=(64, 256, 32, 8, 3) ms=20.141
  cfg=(128, 64, 64, 4, 4) ms=21.767
  cfg=(128, 64, 64, 4, 3) ms=21.409
  cfg=(128, 64, 64, 8, 4) ms=21.376
  cfg=(128, 64, 64, 8, 3) ms=21.021
  cfg=(128, 64, 32, 8, 4) ms=21.403
  cfg=(128, 128, 32, 4, 3) ms=30.004
  cfg=(128, 128, 32, 8, 3) ms=20.259
  cfg=(256, 64, 32, 8, 4) ms=19.816
best shape 0: cfg=(256, 64, 32, 8, 4) ms=19.816
shape 1
  cfg=(64, 128, 64, 4, 3) ms=0.462
  cfg=(64, 128, 64, 4, 2) ms=0.500
  cfg=(64, 128, 32, 4, 4) ms=0.469
  cfg=(64, 128, 32, 4, 3) ms=0.447
  cfg=(64, 128, 32, 8, 4) ms=0.479
  cfg=(64, 128, 64, 8, 3) ms=0.466
  cfg=(64, 256, 32, 4, 3) ms=0.959
  cfg=(64, 256, 32, 8, 3) ms=0.459
  cfg=(128, 64, 64, 4, 4) ms=0.484
  cfg=(128, 64, 64, 4, 3) ms=0.479
  cfg=(128, 64, 64, 8, 4) ms=0.467
  cfg=(128, 64, 64, 8, 3) ms=0.462
  cfg=(128, 64, 32, 8, 4) ms=0.480
  cfg=(128, 128, 32, 4, 3) ms=0.677
  cfg=(128, 128, 32, 8, 3) ms=0.466
  cfg=(256, 64, 32, 8, 4) ms=0.453
best shape 1: cfg=(64, 128, 32, 4, 3) ms=0.447

Chunk ID: 65744a
Wall time: 11.5189 seconds
Process exited with code 0
Original token count: 159
Output:
shape 2
  cfg=(64, 128, 64, 4, 3) ms=13.524
  cfg=(64, 128, 64, 4, 2) ms=14.461
  cfg=(64, 128, 32, 4, 4) ms=14.447
  cfg=(64, 128, 32, 4, 3) ms=12.744
  cfg=(64, 128, 32, 8, 4) ms=14.052
  cfg=(64, 128, 64, 8, 3) ms=14.019
  cfg=(64, 256, 32, 4, 3) ms=27.737
  cfg=(64, 256, 32, 8, 3) ms=12.821
  cfg=(128, 64, 64, 4, 4) ms=14.871
  cfg=(128, 64, 64, 4, 3) ms=14.250
  cfg=(128, 64, 64, 8, 4) ms=14.667
  cfg=(128, 64, 64, 8, 3) ms=13.719
  cfg=(128, 64, 32, 8, 4) ms=14.303
  cfg=(128, 128, 32, 4, 3) ms=19.849
  cfg=(128, 128, 32, 8, 3) ms=12.586
  cfg=(256, 64, 32, 8, 4) ms=12.162
best shape 2: cfg=(256, 64, 32, 8, 4) ms=12.162

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/tune_tiles.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: a3131c
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 17
Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 1096, 97887

Chunk ID: 5e49e5
Wall time: 1.0008 seconds
Process running with session ID 86715
Original token count: 0
Output:

Chunk ID: e480c9
Wall time: 45.0011 seconds
Process running with session ID 86715
Original token count: 75
Output:
shape 0
  cfg=(64, 128, 32, 4, 3) ms=20.096
  cfg=(64, 256, 32, 8, 3) ms=20.383
  cfg=(128, 128, 32, 4, 3) ms=30.166
  cfg=(128, 128, 32, 8, 3) ms=20.536
  cfg=(256, 64, 32, 8, 4) ms=20.067
  cfg=(256, 64, 32, 8, 3) ms=19.752
  cfg=(256, 64, 64, 8, 2) ms=20.687
  cfg=(256, 64, 64, 8, 3) ms=20.843

Chunk ID: c34ea1
Wall time: 44.5295 seconds
Process exited with code 0
Original token count: 393
Output:
  cfg=(256, 128, 32, 8, 2) ms=1709.315
  cfg=(256, 128, 32, 8, 3) ms=27.976
  cfg=(512, 32, 32, 8, 4) error=OutOfResources: out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(512, 64, 32, 8, 3) ms=39.108
best shape 0: cfg=(256, 64, 32, 8, 3) ms=19.752
shape 1
  cfg=(64, 128, 32, 4, 3) ms=0.451
  cfg=(64, 256, 32, 8, 3) ms=0.463
  cfg=(128, 128, 32, 4, 3) ms=0.680
  cfg=(128, 128, 32, 8, 3) ms=0.474
  cfg=(256, 64, 32, 8, 4) ms=0.456
  cfg=(256, 64, 32, 8, 3) ms=0.456
  cfg=(256, 64, 64, 8, 2) ms=0.518
  cfg=(256, 64, 64, 8, 3) ms=0.509
  cfg=(256, 128, 32, 8, 2) ms=32.879
  cfg=(256, 128, 32, 8, 3) ms=0.721
  cfg=(512, 32, 32, 8, 4) error=OutOfResources: out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
  cfg=(512, 64, 32, 8, 3) ms=1.576
best shape 1: cfg=(64, 128, 32, 4, 3) ms=0.451
shape 2
  cfg=(64, 128, 32, 4, 3) ms=12.869
  cfg=(64, 256, 32, 8, 3) ms=13.509
  cfg=(128, 128, 32, 4, 3) ms=20.237
  cfg=(128, 128, 32, 8, 3) ms=13.822
  cfg=(256, 64, 32, 8, 4) ms=13.438
  cfg=(256, 64, 32, 8, 3) ms=12.370
  cfg=(256, 64, 64, 8, 2) ms=13.215
  cfg=(256, 64, 64, 8, 3) ms=13.822
  cfg=(256, 128, 32, 8, 2) ms=1141.995
  cfg=(256, 128, 32, 8, 3) ms=19.582
  cfg=(512, 32, 32, 8, 4) error=OutOfResources: out of resource: shared memory

... (175 more chars)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 2b0c0c
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 17
Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 1096, 97887

Chunk ID: 12d8c1
Wall time: 1.0007 seconds
Process running with session ID 22242
Original token count: 0
Output:

Chunk ID: 04b50b
Wall time: 30.0013 seconds
Process running with session ID 22242
Original token count: 0
Output:

Chunk ID: d416ea
Wall time: 30.0010 seconds
Process running with session ID 22242
Original token count: 0
Output:

Chunk ID: 9d38b7
Wall time: 30.0011 seconds
Process running with session ID 22242
Original token count: 0
Output:

Chunk ID: 69d7cd
Wall time: 4.1529 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 778708
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 17
Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 1096, 97887

Chunk ID: 2ab6c0
Wall time: 1.0010 seconds
Process running with session ID 67718
Original token count: 0
Output:

Chunk ID: 763c87
Wall time: 30.0013 seconds
Process running with session ID 67718
Original token count: 0
Output:

Chunk ID: 254d1d
Wall time: 30.0004 seconds
Process running with session ID 67718
Original token count: 404
Output:
W0428 01:03:08.395000 1455618 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] Graph break from `Tensor.item()`, consider setting:
W0428 01:03:08.395000 1455618 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     torch._dynamo.config.capture_scalar_outputs = True
W0428 01:03:08.395000 1455618 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] or:
W0428 01:03:08.395000 1455618 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
W0428 01:03:08.395000 1455618 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] to include these operations in the captured graph.
W0428 01:03:08.395000 1455618 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] 
W0428 01:03:08.395000 1455618 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0] Graph break: from user code at:
W0428 01:03:08.395000 1455618 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]   File "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/reference.py", line 62, in forward
W0428 01:03:08.395000 1455618 .venv/lib/python3.11/site-packages/torch/_dynamo/variables/tensor.py:1379] [0/0]     start = int(expert_offsets[e].i

... (231 more chars)

Chunk ID: 11ffb9
Wall time: 17.4447 seconds
Process exited with code 0
Original token count: 174
Output:
shape=0 variant=eager tflops=33.525 gbps=251.000 ms=24.598
shape=0 variant=compiled tflops=33.462 gbps=250.533 ms=24.644
shape=0 variant=solution tflops=41.216 gbps=308.583 ms=20.008
shape=0 solution_peak_fraction=0.2061
shape=1 variant=eager tflops=14.155 gbps=262.638 ms=2.427
shape=1 variant=compiled tflops=14.262 gbps=264.627 ms=2.409
shape=1 variant=solution tflops=75.308 gbps=1397.317 ms=0.456
shape=1 solution_peak_fraction=0.3765
shape=2 variant=eager tflops=35.539 gbps=242.945 ms=15.469
shape=2 variant=compiled tflops=35.264 gbps=241.060 ms=15.590
shape=2 variant=solution tflops=40.826 gbps=279.081 ms=13.466
shape=2 solution_peak_fraction=0.2041
peak_fraction: 0.2511
RESULT: OK

Chunk ID: 5511f0
Wall time: 0.0000 seconds
Process exited with code 1
Original token count: 202
Output:
E402 Module level import not at top of file
  --> tune_tiles.py:12:1
   |
10 | sys.path.insert(0, str(REPO_ROOT))
11 |
12 | import reference
   | ^^^^^^^^^^^^^^^^
13 | import shapes
14 | import solution
   |

E402 Module level import not at top of file
  --> tune_tiles.py:13:1
   |
12 | import reference
13 | import shapes
   | ^^^^^^^^^^^^^
14 | import solution
   |

E402 Module level import not at top of file
  --> tune_tiles.py:14:1
   |
12 | import reference
13 | import shapes
14 | import solution
   | ^^^^^^^^^^^^^^^
15 |
16 | from src.eval.timing import time_fn
   |

E402 Module level import not at top of file
  --> tune_tiles.py:16:1
   |
14 | import solution
15 |
16 | from src.eval.timing import time_fn
   | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
   |

Found 5 errors (1 fixed, 4 remaining).

output:
  Success. Updated the following files:
  D /home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/tune_tiles.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: b0184b
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: 0e39d3
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 17
Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 0, 1096, 97887

Chunk ID: f3b141
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:

Chunk ID: d2c997
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 =============================

Chunk ID: 95b5f6
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 13
Output:
../../tests/test_eval.py
../../tests/__init__.py

Chunk ID: b406af
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 166
Output:
[project]
name = "kernelbench-hard"
version = "0.1.0"
description = "Surgical GPU kernel benchmark. Small deck, frontier agents, roofline metric, link-don't-spoil problem briefs."
requires-python = ">=3.11"
dependencies = [
    "torch>=2.11",
    "numpy>=1.26",
    "pyyaml>=6.0",
    "rich>=13.7",
    "matplotlib>=3.9",
    "pytest>=8.0",
    "ruff>=0.6",
    "einops>=0.8.2",
    "requests<2.33",
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.hatch.build.targets.wheel]
packages = ["src"]

[tool.ruff]
line-length = 100
target-version = "py311"

[tool.ruff.lint]
select = ["E", "F", "W", "I", "B", "UP"]
ignore = ["E501"]

Chunk ID: 8e6b94
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 364
Output:
total 392
drwxrwxr-x 12 infatoshi infatoshi   4096 Apr 27 21:56 .
drwxr-xr-x 23 infatoshi infatoshi   4096 Apr 28 00:55 ..
-rw-rw-r--  1 infatoshi infatoshi   1612 Apr 24 00:32 .cursorrules
drwxrwxr-x  8 infatoshi infatoshi   4096 Apr 28 01:04 .git
-rw-rw-r--  1 infatoshi infatoshi    262 Apr 24 00:30 .gitignore
drwxrwxr-x  3 infatoshi infatoshi   4096 Apr 24 00:41 .pytest_cache
-rw-rw-r--  1 infatoshi infatoshi      5 Apr 24 00:30 .python-version
drwxrwxr-x  3 infatoshi infatoshi   4096 Apr 24 00:40 .ruff_cache
drwxrwxr-x  5 infatoshi infatoshi   4096 Apr 24 00:40 .venv
-rw-rw-r--  1 infatoshi infatoshi   1012 Apr 27 02:25 AGENTS.md
-rw-rw-r--  1 infatoshi infatoshi   7080 Apr 27 02:24 CLAUDE.md
-rw-rw-r--  1 infatoshi infatoshi  46507 Apr 27 21:56 DEVLOG.md
-rw-rw-r--  1 infatoshi infatoshi   1071 Apr 27 04:26 LICENSE
-rw-rw-r--  1 infatoshi infatoshi   8151 Apr 26 00:07 README.md
-rw-rw-r--  1 infatoshi infatoshi   6611 Apr 27 02:25 SPEC.md
drwxrwxr-x  2 infatoshi infatoshi   4096 Apr 24 00:30 docs
drwxrwxr-x  3 infatoshi infatoshi   4096 Apr 24 00:30 outputs
drwxrwxr-x 10 infatoshi infatoshi   4096 Apr 24 00:30 problems
-rw-rw-r--  1 infatoshi infatoshi    663 Apr 25 22:54 pyproject.toml
drwxrwxr-x  2 infatoshi infatoshi   4096 Apr 27 21:56 scripts
drwxrwxr-x  8 infatoshi infatoshi   4096 Apr 24 00:41 src
drwxrwxr-x  3 infatoshi infatoshi   4096 Apr 24 00:40 tests
-rw-r

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Chunk ID: c44338
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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 11 items / 3 errors

==================================== ERRORS ====================================
_ ERROR collecting outputs/runs/20260427_131223_opencode_openrouter-pinned_minimax_minimax-m2.7_06_sonic_moe_swiglu/scratch/test_triton.py _
ImportError while importing test module '/home/infatoshi/cuda/KernelBench-Hard/outputs/runs/20260427_131223_opencode_openrouter-pinned_minimax_minimax-m2.7_06_sonic_moe_swiglu/scratch/test_triton.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
outputs/runs/20260427_131223_opencode_openrouter-pinned_minimax_minimax-m2.7_06_sonic_moe_swiglu/scratch/test_triton.py:5: in <module>
    import solution
E   ModuleNotFoundError: No module named 'solution'
_ ERROR collecting outputs/runs/20260427_184750_opencode_openrouter-pinned_qwen_qwen3.6-27b_03_paged_attention/scratch/test_triton.py _
outputs/runs/20260427_184750_open

... (1719 more chars)

Chunk ID: ccbbf9
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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 10 items

tests/test_eval.py ..........                                            [100%]

============================== 10 passed in 0.54s ==============================

Chunk ID: 0f5123
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1028
Output:
from __future__ import annotations

import torch
import torch.nn as nn
import triton
import triton.language as tl


@triton.jit
def _swiglu_grouped_kernel(
    x_ptr,
    offsets_ptr,
    w_gate_ptr,
    w_up_ptr,
    out_ptr,
    H: tl.constexpr,
    I: tl.constexpr,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    expert = tl.program_id(2)

    start = tl.load(offsets_ptr + expert)
    end = tl.load(offsets_ptr + expert + 1)
    rows = start + pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    cols = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    k_offsets = tl.arange(0, BLOCK_K)

    row_mask = rows < end
    col_mask = cols < I

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)
    w_base = expert * H * I

    for k0 in range(0, H, BLOCK_K):
        ks = k0 + k_offsets
        k_mask = ks < H
        x = tl.load(
            x_ptr + rows[:, None] * H + ks[None, :],
            mask=row_mask[:, None] & k_mask[None, :],
            other=0.0,
        )
        gate = tl.load(
            w_gate_ptr + w_base + ks[:, None] * I + cols[None, :],
            mask=k_mask[:, None] & col_mask[None, :],
            other=0.0,
        )
        up = tl.load(
            w_up_ptr + w_base + ks[:, None] * I + cols[None, :],
            ma

... (2715 more chars)

Chunk ID: 3eecf8
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Output:
!! __pycache__/
!! framework.txt
!! solution.py

Chunk ID: 85d7d0
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Output:
total 56
drwxrwxr-x  3 infatoshi infatoshi 4096 Apr 28 01:03 .
drwxrwxr-x 10 infatoshi infatoshi 4096 Apr 24 00:30 ..
-rw-rw-r--  1 infatoshi infatoshi 2409 Apr 27 03:21 PROMPT.txt
drwxrwxr-x  2 infatoshi infatoshi 4096 Apr 28 01:00 __pycache__
-rw-rw-r--  1 infatoshi infatoshi 4490 Apr 26 03:11 benchmark.py
-rw-rw-r--  1 infatoshi infatoshi 3691 Apr 25 22:21 check.py
-rw-rw-r--  1 infatoshi infatoshi    7 Apr 28 01:02 framework.txt
-rw-rw-r--  1 infatoshi infatoshi 1839 Apr 25 22:20 problem.yaml
-rw-rw-r--  1 infatoshi infatoshi 3802 Apr 25 22:25 reference.py
-rw-rw-r--  1 infatoshi infatoshi  841 Apr 25 22:25 shapes.py
-rw-rw-r--  1 infatoshi infatoshi 4110 Apr 28 01:00 solution.py
-rw-rw-r--  1 infatoshi infatoshi 2822 Apr 25 22:20 sota.py

Chunk ID: e33c9f
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Output:
triton

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Output:

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Output: