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Output:
/workspace/problems/01_fp8_gemm
PROMPT.txt
shapes.py
sota.py
reference.py
check.py
benchmark.py
problem.yaml

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"""FP8 e4m3 GEMM reference (correctness only, NOT the SOTA baseline).

Genuine fp8 x fp8: BOTH operands are fp8_e4m3. The weight is stored as fp8
(normalized into the e4m3 range) together with a per-output-channel scale, the
standard scaled-fp8 inference layout. The reference upcasts the fp8 operands to
bf16 and matmuls, then applies the per-channel scale — this DEFINES the fp8
target. A real fp8 x fp8 MMA kernel matches it (and can exceed the bf16 roofline
ceiling of ~0.5); a bf16-upcast kernel also matches but stays capped at ~0.5.
"""
import torch
import torch.nn as nn

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0


class Model(nn.Module):
    """y = ((x @ w.T) * weight_scale).to(bf16).

    x: fp8_e4m3 (M, K).  w: fp8_e4m3 (N, K) normalized to the e4m3 range.
    weight_scale: (N,) per-output-channel dequant scale.
    """

    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)  # (N,1)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)                         # (N, K) fp8
        self.register_buffer("weight_scal

... (554 more chars)

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"""Correctness runner for FP8 GEMM.

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
from src.eval.numeric_stress import (  # noqa: E402
    numeric_stress_cases,
    numeric_stress_context,
    tolerance_for_case,
)


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 ------------------------

... (2808 more chars)

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Output:
"""Roofline benchmark for FP8 GEMM.

For each shape: times the agent's solution first. Optional eager, compiled,
and SOTA diagnostics are enabled with KBH_BENCHMARK_BASELINES=1. 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 benchmark_baselines_enabled, time_variant  # noqa: E402
from src.hardware import get as get_hw  # noqa: E402


def _eval_formula(expr: str, vars: dict) -> float:
    # Very small eval: only names from `vars` are valid.
    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 = in

... (3752 more chars)

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"""Canonical shape sweep for FP8 GEMM.

Mix of:
  - square aligned (the easy case)
  - off-alignment K (common real-world failure mode for tile-quantized kernels)
  - skinny (decode-like, memory-bound)
  - rectangular (prefill with grouped attention)
"""

SHAPES = [
    {"M": 4096, "N": 4096, "K": 4096},        # square aligned
    {"M": 4096, "N": 4096, "K": 4127},        # K not multiple of 128 -> forces predicated tails
    {"M": 32,   "N": 8192, "K": 8192},        # skinny M (decode)
    {"M": 4096, "N": 14336, "K": 4096},       # Llama3 up-proj shape
]

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Output:
name: 01_fp8_gemm
display_name: "FP8 e4m3 GEMM"
precision: fp8_e4m3
regime: compute

# Dense-equivalent FLOPs per invocation. Evaluated per-shape; the benchmark
# substitutes M/N/K from each shapes.py entry.
flops_formula: "2 * M * N * K"

# Bytes moved (approximate): fp8 inputs in, bf16 output.
bytes_formula: "M*K + K*N + M*N*2"

hardware: [RTX_PRO_6000]
peak_tflops_key: fp8  # index into src/hardware/rtx_pro_6000.py peak_tflops_dense
peak_bandwidth_key: dram

# Per-dtype tolerance override (optional). Defaults are in src/eval/correctness.py.
tolerance:
  bfloat16: 0.2
  fp8_e4m3fn: 0.2

# Forbidden ops — using any of these in solution.py fails correctness post-hoc.
# torch._scaled_mm is Stanford-style cheating (just call the vendor library).
forbidden:
  - "torch._scaled_mm"
  - "torch.ops.aten._scaled_mm"

sota:
  name: "FlashInfer FP8 GEMM"
  url: "https://github.com/flashinfer-ai/flashinfer"
  function: "flashinfer.gemm.fp8_gemm"
  deps:
    - "flashinfer>=0.6.8"
  # Documented H100 throughput for this shape (informational, not graded):
  reference_throughput_tflops_h100: 550

num_correct_trials: 3
num_perf_trials: 30

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"""SOTA reference for FP8 GEMM: flashinfer.gemm.fp8_gemm.

If flashinfer is not installed or the SM120 path isn't supported, this falls
back to torch._scaled_mm which is the cuBLAS FP8 path. The benchmark treats
whichever succeeds as the SOTA reference line.

Agents are FORBIDDEN from using torch._scaled_mm in their solution (see
problem.yaml.forbidden). This file is only for the benchmark's reference line.
"""
from __future__ import annotations

import torch


def _try_flashinfer(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor | None:
    try:
        import flashinfer  # noqa: F401
        # Note: flashinfer's FP8 GEMM API surface may differ; adapt if needed.
        # Placeholder call — replace with the actual flashinfer entry point
        # once validated on SM120.
        return None
    except ImportError:
        return None


def _scaled_mm(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
    # torch._scaled_mm wants per-tensor scales. Use unit scales for the reference.
    scale_a = torch.tensor(1.0, device=x.device)
    scale_b = torch.tensor(1.0, device=x.device)
    out = torch._scaled_mm(
        x,
        w.T,
        scale_a=scale_a,
        scale_b=scale_b,
        out_dtype=torch.bfloat16,
    )
    return out if not isinstance(out, tuple) else out[0]


def sota_forward(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
    """Best-available FP8 GEMM 

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Output:
Using CPython 3.11.15
Creating virtual environment at: /workspace/.venv
   Building kernelbench-hard @ file:///workspace
      Built kernelbench-hard @ file:///workspace
warning: Failed to hardlink files; falling back to full copy. This may lead to degraded performance.
         If the cache and target directories are on different filesystems, hardlinking may not be supported.
         If this is intentional, set `export UV_LINK_MODE=copy` or use `--link-mode=copy` to suppress this warning.
Installed 56 packages in 898ms

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Output:
2.11.0+cu130
13.0
True
NVIDIA RTX PRO 6000 Blackwell Workstation Edition
(12, 0)

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triton 3.6.0

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Output:

    Returns the matrix product of two blocks.

    The two blocks must both be two-dimensional or three-dimensional and have compatible inner dimensions.
    For three-dimensional blocks, `tl.dot` performs the batched matrix product,
    where the first dimension of each block represents the batch dimension.

    :param input: The first tensor to be multiplied.
    :type input: 2D or 3D tensor of scalar-type in {:code:`int8`, :code:`float8_e5m2`, :code:`float16`, :code:`bfloat16`, :code:`float32`}
    :param other: The second tensor to be multiplied.
    :type other: 2D or 3D tensor of scalar-type in {:code:`int8`, :code:`float8_e5m2`, :code:`float16`, :code:`bfloat16`, :code:`float32`}
    :param acc: The accumulator tensor. If not None, the result is added to this tensor.
    :type acc: 2D or 3D tensor of scalar-type in {:code:`float16`, :code:`float32`, :code:`int32`}
    :param input_precision: How to exercise the Tensor Cores for f32 x f32. If
      the device does not have Tensor Cores or the inputs are not of dtype f32,
      this option is ignored. For devices that do have tensor cores, the
      default precision is tf32.
    :type input_precision: string. Available options for nvidia: :code:`"tf32"`, :code:`"tf32x3"`, :code:`"ieee"`. Default: :code:`"tf32"`. Available options for amd: :code:`"ieee"`, (CDNA3 only) :code:`"tf32"`.
    :param allow_tf32: *Deprecated

... (181 more chars)

output:
  Success. Updated the following files:
  A /workspace/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

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Output:
PASS

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Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-14T20:43:53.821152+00:00

Chunk ID: 04cbaa
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Output:
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-14T20:43:54.027282+00:00 elapsed_s=0.206 ms=0.232752
shape=0 variant=solution tflops=590.495 gbps=288.328 ms=0.233
shape=0 solution_peak_fraction=0.5905
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-14T20:43:54.531053+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-14T20:43:54.556362+00:00 elapsed_s=0.025 ms=0.550112
shape=1 variant=solution tflops=251.729 gbps=122.453 ms=0.550
shape=1 solution_peak_fraction=0.2517
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-14T20:43:56.394376+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-14T20:43:56.399965+00:00 elapsed_s=0.006 ms=0.060336
shape=2 variant=solution tflops=71.184 gbps=1125.287 ms=0.060
shape=2 solution_peak_fraction=0.0712
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-14T20:43:58.058735+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-14T20:43:58.092450+00:00 elapsed_s=0.034 ms=0.736592
shape=3 variant=solution tflops=653.057 gbps=261.933 ms=0.737
shape=3 solution_peak_fraction=0.6531
peak_fraction: 0.2883
RESULT: OK

output:
  Success. Updated the following files:
  M /workspace/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

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Output:
PASS

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Output:

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Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-14T20:45:01.578316+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-14T20:45:01.782619+00:00 elapsed_s=0.204 ms=0.234336
shape=0 variant=solution tflops=586.504 gbps=286.379 ms=0.234
shape=0 solution_peak_fraction=0.5865
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-14T20:45:02.291965+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-14T20:45:02.312549+00:00 elapsed_s=0.021 ms=0.430192
shape=1 variant=solution tflops=321.901 gbps=156.588 ms=0.430

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Output:
shape=1 solution_peak_fraction=0.3219
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-14T20:45:04.158884+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-14T20:45:04.164553+00:00 elapsed_s=0.006 ms=0.060528
shape=2 variant=solution tflops=70.958 gbps=1121.717 ms=0.061
shape=2 solution_peak_fraction=0.0710
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-14T20:45:05.829763+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-14T20:45:05.862916+00:00 elapsed_s=0.033 ms=0.740976
shape=3 variant=solution tflops=649.193 gbps=260.384 ms=0.741
shape=3 solution_peak_fraction=0.6492
peak_fraction: 0.3054
RESULT: OK

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Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-14T20:45:11.715233+00:00

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Output:
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-14T20:45:11.923134+00:00 elapsed_s=0.208 ms=0.234048
shape=0 variant=solution tflops=587.225 gbps=286.731 ms=0.234
benchmark_event event=variant_start shape=0 variant=eager ts=2026-06-14T20:45:11.923163+00:00
benchmark_event event=variant_end shape=0 variant=eager ts=2026-06-14T20:45:12.010443+00:00 elapsed_s=0.087 ms=0.503952
benchmark_event event=variant_start shape=0 variant=compiled ts=2026-06-14T20:45:14.229071+00:00
benchmark_event event=variant_end shape=0 variant=compiled ts=2026-06-14T20:45:14.995085+00:00 elapsed_s=0.766 ms=0.427440
benchmark_event event=variant_start shape=0 variant=sota ts=2026-06-14T20:45:14.995113+00:00
benchmark_event event=variant_end shape=0 variant=sota ts=2026-06-14T20:45:15.009660+00:00 elapsed_s=0.015 ms=0.257216
shape=0 variant=eager tflops=272.722 gbps=133.165 ms=0.504
shape=0 variant=compiled tflops=321.540 gbps=157.002 ms=0.427
shape=0 variant=sota tflops=534.333 gbps=260.905 ms=0.257
shape=0 solution_peak_fraction=0.5872
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-14T20:45:15.530311+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-14T20:45:16.011439+00:00 elapsed_s=0.481 ms=0.430160
shape=1 variant=solution tflops=321.925 gbps=156.599 ms=0.430
benchmark_event event=variant_start shape=1 variant=eag

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Output:
shape=2 solution_peak_fraction=0.0720
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-14T20:45:20.599084+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-14T20:45:20.806348+00:00 elapsed_s=0.207 ms=0.742672
shape=3 variant=solution tflops=647.710 gbps=259.789 ms=0.743
benchmark_event event=variant_start shape=3 variant=eager ts=2026-06-14T20:45:20.806372+00:00
benchmark_event event=variant_end shape=3 variant=eager ts=2026-06-14T20:45:20.892633+00:00 elapsed_s=0.086 ms=2.087648
benchmark_event event=variant_start shape=3 variant=compiled ts=2026-06-14T20:45:20.894092+00:00
benchmark_event event=variant_end shape=3 variant=compiled ts=2026-06-14T20:45:21.325365+00:00 elapsed_s=0.431 ms=1.434256
benchmark_event event=variant_start shape=3 variant=sota ts=2026-06-14T20:45:21.325394+00:00
benchmark_event event=variant_end shape=3 variant=sota ts=2026-06-14T20:45:21.357147+00:00 elapsed_s=0.032 ms=0.737904
shape=3 variant=eager tflops=230.420 gbps=92.419 ms=2.088
shape=3 variant=compiled tflops=335.391 gbps=134.521 ms=1.434
shape=3 variant=sota tflops=651.896 gbps=261.468 ms=0.738
shape=3 solution_peak_fraction=0.6477
peak_fraction: 0.3065
RESULT: OK

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Output:

shape 0 {'M': 4096, 'N': 4096, 'K': 4096}
cfg=(64, 128, 64, 4, 3, 2) ms=0.2320 tflops=592.4
cfg=(64, 128, 64, 4, 3, 4) ms=0.2310 tflops=595.0
cfg=(64, 128, 64, 4, 3, 8) ms=0.2319 tflops=592.7
cfg=(64, 128, 64, 4, 4, 2) ms=0.2340 tflops=587.2
cfg=(64, 128, 64, 4, 4, 4) ms=0.2340 tflops=587.4
cfg=(64, 128, 64, 4, 4, 8) ms=0.2341 tflops=587.1
cfg=(64, 128, 64, 4, 5, 2) ms=0.2352 tflops=584.4
cfg=(64, 128, 64, 4, 5, 4) ms=0.2344 tflops=586.3
cfg=(64, 128, 64, 4, 5, 8) ms=0.2350 tflops=584.9
cfg=(64, 128, 64, 8, 3, 2) ms=0.2391 tflops=574.8
cfg=(64, 128, 64, 8, 3, 4) ms=0.2400 tflops=572.6
cfg=(64, 128, 64, 8, 3, 8) ms=0.2401 tflops=572.5
cfg=(64, 128, 64, 8, 4, 2) ms=0.2408 tflops=570.8
cfg=(64, 128, 64, 8, 4, 4) ms=0.2403 tflops=571.9
cfg=(64, 128, 64, 8, 4, 8) ms=0.2404 tflops=571.7
cfg=(64, 128, 64, 8, 5, 2) ms=0.2415 tflops=569.2
cfg=(64, 128, 64, 8, 5, 4) ms=0.2422 tflops=567.4
cfg=(64, 128, 64, 8, 5, 8) ms=0.2420 tflops=567.8
cfg=(64, 128, 128, 4, 3, 2) ms=0.2249 tflops=611.1
cfg=(64, 128, 128, 4, 3, 4) ms=0.2247 tflops=611.7
cfg=(64, 128, 128, 4, 3, 8) ms=0.2241 tflops=613.3
cfg=(64, 128, 128, 4, 4, 2) ms=0.2338 tflops=587.9
cfg=(64, 128, 128, 4, 4, 4) ms=0.2340 tflops=587.2
cfg=(64, 128, 128, 4, 4, 8) ms=0.2341 tflops=587.0
cfg=(64, 128, 128, 4, 5, 2) ms=0.2352 tflops=584.3
cfg=(64, 128, 128, 4, 5, 4) ms=0.2352 tflops=584.3
cfg=(64, 128, 128, 4, 5, 8) ms=0.2

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Wall time: 5.0005 seconds
Process running with session ID 26164
Original token count: 937
Output:
cfg=(64, 128, 256, 8, 3, 8) ms=0.2424 tflops=567.0
cfg=(64, 128, 256, 8, 4, 2) ERR OutOfResources: out of resource: shared memory, Required: 147456, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
cfg=(64, 128, 256, 8, 4, 4) ERR OutOfResources: out of resource: shared memory, Required: 147456, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
cfg=(64, 128, 256, 8, 4, 8) ERR OutOfResources: out of resource: shared memory, Required: 147456, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
cfg=(64, 128, 256, 8, 5, 2) ERR OutOfResources: out of resource: shared memory, Required: 196608, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
cfg=(64, 128, 256, 8, 5, 4) ERR OutOfResources: out of resource: shared memory, Required: 196608, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
cfg=(64, 128, 256, 8, 5, 8) ERR OutOfResources: out of resource: shared memory, Required: 196608, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
cfg=(64, 256, 64, 4, 3, 2) ms=0.2153 tflops=638.4
cfg=(64, 256, 64, 4, 3, 4) ms=0.2156 tflops=637.5
cfg=(64, 256, 64, 4, 3, 8) ms=0.2149 tflops=639.5
cfg=(64, 256, 64, 4, 4, 2) ms=0.2238 tflops=614.0
cfg=(64, 256, 64, 4, 4, 4) ms=0.2228 tflops=616.7
cfg=(64, 256, 64, 4, 4, 8) ms=0.2228 tflops=616.7
cfg=(64, 256, 64, 4,

... (2360 more chars)

Chunk ID: 602164
Wall time: 5.0018 seconds
Process running with session ID 26164
Original token count: 406
Output:
cfg=(64, 128, 64, 4, 5, 4) ms=0.9068 tflops=152.7
cfg=(64, 128, 64, 4, 5, 8) ms=0.9059 tflops=152.9
cfg=(64, 128, 64, 8, 3, 2) ms=0.6275 tflops=220.7
cfg=(64, 128, 64, 8, 3, 4) ms=0.6272 tflops=220.8
cfg=(64, 128, 64, 8, 3, 8) ms=0.6270 tflops=220.8
cfg=(64, 128, 64, 8, 4, 2) ms=0.6281 tflops=220.5
cfg=(64, 128, 64, 8, 4, 4) ms=0.6263 tflops=221.1
cfg=(64, 128, 64, 8, 4, 8) ms=0.6280 tflops=220.5
cfg=(64, 128, 64, 8, 5, 2) ms=0.6284 tflops=220.4
cfg=(64, 128, 64, 8, 5, 4) ms=0.6274 tflops=220.7
cfg=(64, 128, 64, 8, 5, 8) ms=0.6277 tflops=220.6
cfg=(64, 128, 128, 4, 3, 2) ms=0.4915 tflops=281.8
cfg=(64, 128, 128, 4, 3, 4) ms=0.4902 tflops=282.5
cfg=(64, 128, 128, 4, 3, 8) ms=0.4903 tflops=282.4
cfg=(64, 128, 128, 4, 4, 2) ms=0.4902 tflops=282.5
cfg=(64, 128, 128, 4, 4, 4) ms=0.4903 tflops=282.4
cfg=(64, 128, 128, 4, 4, 8) ms=0.4903 tflops=282.5
cfg=(64, 128, 128, 4, 5, 2) ms=0.4908 tflops=282.2
cfg=(64, 128, 128, 4, 5, 4) ms=0.4901 tflops=282.5
cfg=(64, 128, 128, 4, 5, 8) ms=0.4907 tflops=282.2
cfg=(64, 128, 128, 8, 3, 2) ms=0.5781 tflops=239.5
cfg=(64, 128, 128, 8, 3, 4) ms=0.5761 tflops=240.4
cfg=(64, 128, 128, 8, 3, 8) ms=0.5769 tflops=240.0
cfg=(64, 128, 128, 8, 4, 2) ms=0.5783 tflops=239.5
cfg=(64, 128, 128, 8, 4, 4) ms=0.5762 tflops=240.3
cfg=(64, 128, 128, 8, 4, 8) ms=0.5761 tflops=240.4
cfg=(64, 128, 128, 8, 5, 2) ms=0.5759 tflops=240.5
cfg=(64, 128, 128, 

... (236 more chars)

Chunk ID: 792e02
Wall time: 5.0012 seconds
Process running with session ID 26164
Original token count: 242
Output:
cfg=(64, 128, 256, 4, 4, 2) ms=0.6086 tflops=227.5
cfg=(64, 128, 256, 4, 4, 4) ms=0.6084 tflops=227.6
cfg=(64, 128, 256, 4, 4, 8) ms=0.6084 tflops=227.6
cfg=(64, 128, 256, 4, 5, 2) ms=0.6084 tflops=227.6
cfg=(64, 128, 256, 4, 5, 4) ms=0.6086 tflops=227.5
cfg=(64, 128, 256, 4, 5, 8) ms=0.6076 tflops=227.9
cfg=(64, 128, 256, 8, 3, 2) ms=0.6089 tflops=227.4
cfg=(64, 128, 256, 8, 3, 4) ms=0.6088 tflops=227.5
cfg=(64, 128, 256, 8, 3, 8) ms=0.6095 tflops=227.2
cfg=(64, 128, 256, 8, 4, 2) ms=0.6108 tflops=226.7
cfg=(64, 128, 256, 8, 4, 4) ms=0.6089 tflops=227.4
cfg=(64, 128, 256, 8, 4, 8) ms=0.6090 tflops=227.4
cfg=(64, 128, 256, 8, 5, 2) ms=0.6087 tflops=227.5
cfg=(64, 128, 256, 8, 5, 4) ms=0.6095 tflops=227.2
cfg=(64, 128, 256, 8, 5, 8) ms=0.6092 tflops=227.3
cfg=(64, 256, 64, 4, 3, 2) ms=1.2954 tflops=106.9
cfg=(64, 256, 64, 4, 3, 4) ms=1.2860 tflops=107.7
cfg=(64, 256, 64, 4, 3, 8) ms=1.2826 tflops=108.0
cfg=(64, 256, 64, 4, 4, 2) ms=1.2943 tflops=107.0

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

Chunk ID: 5ff78d
Wall time: 0.0000 seconds
Process exited with code -1
Original token count: 0
Output:

Chunk ID: 0743aa
Wall time: 0.0000 seconds
Process exited with code -1
Original token count: 239
Output:
cfg=(64, 256, 64, 4, 4, 4) ms=1.2878 tflops=107.5
cfg=(64, 256, 64, 4, 4, 8) ms=1.2793 tflops=108.2
cfg=(64, 256, 64, 4, 5, 2) ms=1.2948 tflops=106.9
cfg=(64, 256, 64, 4, 5, 4) ms=1.2823 tflops=108.0
cfg=(64, 256, 64, 4, 5, 8) ms=1.2826 tflops=108.0
cfg=(64, 256, 64, 8, 3, 2) ms=0.6565 tflops=210.9
cfg=(64, 256, 64, 8, 3, 4) ms=0.6567 tflops=210.9
cfg=(64, 256, 64, 8, 3, 8) ms=0.6568 tflops=210.8
cfg=(64, 256, 64, 8, 4, 2) ms=0.6578 tflops=210.5
cfg=(64, 256, 64, 8, 4, 4) ms=0.6542 tflops=211.7
cfg=(64, 256, 64, 8, 4, 8) ms=0.6568 tflops=210.8
cfg=(64, 256, 64, 8, 5, 2) ms=0.6546 tflops=211.6
cfg=(64, 256, 64, 8, 5, 4) ms=0.6548 tflops=211.5
cfg=(64, 256, 64, 8, 5, 8) ms=0.6561 tflops=211.1
cfg=(64, 256, 128, 4, 3, 2) ms=0.5953 tflops=232.6
cfg=(64, 256, 128, 4, 3, 4) ms=0.5956 tflops=232.5
cfg=(64, 256, 128, 4, 3, 8) ms=0.5954 tflops=232.6
cfg=(64, 256, 128, 4, 4, 2) ms=0.5948 tflops=232.8
cfg=(64, 256, 128, 4, 4, 4) ms=0.5953 tflops=232.6

Chunk ID: 2f79f1
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1079
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


... (2919 more chars)

Chunk ID: 57965d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1018
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 (PyYA

... (2677 more chars)

Chunk ID: c8721d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1617
Output:
"""Numeric distribution stress cases for correctness checks.

These cases are not hidden shapes. They rerun the same shape/seed validation
under a few scale regimes that catch kernels that only work for the nominal
N(0, 1)-ish inputs.
"""
from __future__ import annotations

import os
from collections.abc import Iterator, Mapping, Sequence
from contextlib import contextmanager
from dataclasses import dataclass, field

import torch

ToleranceOverride = Mapping[str, float | Mapping[str, float]]


@dataclass(frozen=True)
class NumericStressCase:
    name: str
    input_scales: Mapping[int, float] = field(default_factory=dict)
    state_scales: Mapping[str, float] = field(default_factory=dict)
    tolerance: ToleranceOverride | None = None


NOMINAL_CASE = NumericStressCase("nominal")

_SMALL_BF16 = {"bfloat16": {"atol": 1e-4, "rtol": 5e-2}}
_MED_BF16 = {"bfloat16": {"atol": 5e-4, "rtol": 5e-2}}
_LARGE_BF16 = {"bfloat16": {"atol": 2e-1, "rtol": 5e-2}}
_TINY_FP32 = {"float32": {"atol": 1e-7, "rtol": 1e-4}}
_LARGE_FP32 = {"float32": {"atol": 1e-1, "rtol": 1e-4}}

_CASES: dict[str, tuple[NumericStressCase, ...]] = {
    "01_fp8_gemm": (
        # fp8 x fp8 has accumulation-order noise vs the bf16-matmul reference that
        # scales with input magnitude; atols are calibrated to absorb it (measured
        # fp8-MMA residual x ~1.5 margin) while rtol=5e-2 still catches gross erro

... (5073 more chars)

Chunk ID: 83e43f
Wall time: 0.7484 seconds
Process exited with code 0
Original token count: 116
Output:
torch.Size([5, 4]) torch.float8_e4m3fn True
tensor([[ 1.1250, -0.3125, -0.1875,  1.1250],
        [-0.0938,  0.0625, -0.8750, -0.6250],
        [ 0.1016,  1.0000, -0.5625, -0.2812],
        [-0.7500, -0.8750, -1.7500,  0.4062],
        [-0.6875, -0.8125, -0.3438,  1.0000],
        [ 0.0000,  0.0000,  0.0000,  0.0000],
        [ 0.0000,  0.0000,  0.0000,  0.0000],
        [ 0.0000,  0.0000,  0.0000,  0.0000]], device='cuda:0',
       dtype=torch.float8_e4m3fn)

output:
  Success. Updated the following files:
  M /workspace/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: e4f34d
Wall time: 1.0005 seconds
Process running with session ID 97053
Original token count: 0
Output:

Chunk ID: 8e401d
Wall time: 4.4972 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 2ec068
Wall time: 1.0012 seconds
Process running with session ID 97847
Original token count: 25
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-14T20:48:28.655946+00:00

Chunk ID: 3177d3
Wall time: 2.7995 seconds
Process exited with code 0
Original token count: 304
Output:
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-14T20:48:28.871534+00:00 elapsed_s=0.216 ms=0.290912
shape=0 variant=solution tflops=472.442 gbps=230.684 ms=0.291
shape=0 solution_peak_fraction=0.4724
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-14T20:48:29.390259+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-14T20:48:29.414727+00:00 elapsed_s=0.024 ms=0.530864
shape=1 variant=solution tflops=260.856 gbps=126.893 ms=0.531
shape=1 solution_peak_fraction=0.2609
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-14T20:48:31.259349+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-14T20:48:31.265718+00:00 elapsed_s=0.006 ms=0.078432
shape=2 variant=solution tflops=54.760 gbps=865.658 ms=0.078
shape=2 solution_peak_fraction=0.0548
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-14T20:48:32.937228+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-14T20:48:32.977645+00:00 elapsed_s=0.040 ms=0.923184
shape=3 variant=solution tflops=521.062 gbps=208.992 ms=0.923
shape=3 solution_peak_fraction=0.5211
peak_fraction: 0.2435
RESULT: OK

Chunk ID: 9905ad
Wall time: 1.0011 seconds
Process running with session ID 52362
Original token count: 0
Output:

Chunk ID: 7d4850
Wall time: 5.0019 seconds
Process running with session ID 52362
Original token count: 563
Output:

shape 0 {'M': 4096, 'N': 4096, 'K': 4096}
(128, 128, 128, 4, 3, 2) ms=0.2306 tf=596.1
(128, 128, 128, 4, 3, 4) ms=0.2268 tf=605.9
(128, 128, 128, 4, 3, 8) ms=0.2278 tf=603.3
(128, 128, 128, 4, 4, 2) ms=0.2283 tf=602.1
(128, 128, 128, 4, 4, 4) ms=0.2287 tf=601.1
(128, 128, 128, 4, 4, 8) ms=0.2290 tf=600.3
(128, 128, 128, 8, 3, 2) ms=0.2326 tf=590.9
(128, 128, 128, 8, 3, 4) ms=0.2316 tf=593.3
(128, 128, 128, 8, 3, 8) ms=0.2311 tf=594.6
(128, 128, 128, 8, 4, 2) ms=0.2337 tf=588.2
(128, 128, 128, 8, 4, 4) ms=0.2329 tf=590.0
(128, 128, 128, 8, 4, 8) ms=0.2329 tf=590.2
(128, 128, 64, 4, 3, 2) ms=0.2235 tf=614.9
(128, 128, 64, 4, 3, 4) ms=0.2225 tf=617.6
(128, 128, 64, 4, 3, 8) ms=0.2241 tf=613.3
(128, 128, 64, 4, 4, 2) ms=0.2253 tf=610.1
(128, 128, 64, 4, 4, 4) ms=0.2254 tf=609.7
(128, 128, 64, 4, 4, 8) ms=0.2250 tf=610.7
(128, 128, 64, 8, 3, 2) ms=0.2306 tf=596.0
(128, 128, 64, 8, 3, 4) ms=0.2293 tf=599.4
(128, 128, 64, 8, 3, 8) ms=0.2293 tf=599.4
(128, 128, 64, 8, 4, 2) ms=0.2312 tf=594.5
(128, 128, 64, 8, 4, 4) ms=0.2306 tf=595.9
(128, 128, 64, 8, 4, 8) ms=0.2311 tf=594.7
(128, 128, 256, 4, 3, 2) ERR OutOfResources: out of resource: shared memory, Required: 131072, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(128, 128, 256, 4, 3, 4) ERR OutOfResources: out of resource: shared memory, Required: 131072, Hardware limit: 101376. Reducing bloc

... (864 more chars)

Chunk ID: e88166
Wall time: 5.0005 seconds
Process running with session ID 52362
Original token count: 977
Output:
(128, 128, 256, 8, 3, 4) ERR OutOfResources: out of resource: shared memory, Required: 131072, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(128, 128, 256, 8, 3, 8) ERR OutOfResources: out of resource: shared memory, Required: 131072, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(128, 128, 256, 8, 4, 2) ERR OutOfResources: out of resource: shared memory, Required: 196608, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(128, 128, 256, 8, 4, 4) ERR OutOfResources: out of resource: shared memory, Required: 196608, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(128, 128, 256, 8, 4, 8) ERR OutOfResources: out of resource: shared memory, Required: 196608, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(64, 256, 64, 4, 3, 2) ms=0.2367 tf=580.8
(64, 256, 64, 4, 3, 4) ms=0.2367 tf=580.6
(64, 256, 64, 4, 3, 8) ms=0.2375 tf=578.6
(64, 256, 64, 4, 4, 2) ms=0.2515 tf=546.6
(64, 256, 64, 4, 4, 4) ms=0.2530 tf=543.2
(64, 256, 64, 4, 4, 8) ms=0.2519 tf=545.6
(64, 256, 64, 8, 3, 2) ms=0.2418 tf=568.4
(64, 256, 64, 8, 3, 4) ms=0.2416 tf=568.9
(64, 256, 64, 8, 3, 8) ms=0.2417 tf=568.7
(64, 256, 64, 8, 4, 2) ms=0.2565 tf=535.8
(64, 256, 64, 8, 4, 4) ms=0.2581 tf=532.5
(64, 256, 64, 8, 4, 8) ms=0.2591 tf=530.4
(64, 256, 128, 4, 3, 2) ms=0.2419 tf=568.1
(64, 256

... (2523 more chars)

Chunk ID: b4376d
Wall time: 5.0007 seconds
Process running with session ID 52362
Original token count: 563
Output:
(128, 64, 64, 8, 3, 4) ms=0.2608 tf=526.9
(128, 64, 64, 8, 3, 8) ms=0.2591 tf=530.4
(128, 64, 64, 8, 4, 2) ms=0.2671 tf=514.6
(128, 64, 64, 8, 4, 4) ms=0.2637 tf=521.2
(128, 64, 64, 8, 4, 8) ms=0.2614 tf=525.8
(256, 64, 64, 4, 3, 2) ms=0.2413 tf=569.5
(256, 64, 64, 4, 3, 4) ms=0.2372 tf=579.4
(256, 64, 64, 4, 3, 8) ms=0.2350 tf=584.9
(256, 64, 64, 4, 4, 2) ms=0.2543 tf=540.6
(256, 64, 64, 4, 4, 4) ms=0.2515 tf=546.5
(256, 64, 64, 4, 4, 8) ms=0.2475 tf=555.3
(256, 64, 64, 8, 3, 2) ms=0.2454 tf=560.0
(256, 64, 64, 8, 3, 4) ms=0.2442 tf=562.9
(256, 64, 64, 8, 3, 8) ms=0.2436 tf=564.3
(256, 64, 64, 8, 4, 2) ms=0.2593 tf=529.9
(256, 64, 64, 8, 4, 4) ms=0.2573 tf=534.1
(256, 64, 64, 8, 4, 8) ms=0.2536 tf=541.8
BEST
  (256, 128, 64, 8, 4, 4) ms=0.2161 tf=636.0
  (256, 128, 64, 8, 4, 8) ms=0.2162 tf=635.7
  (256, 128, 64, 8, 3, 8) ms=0.2163 tf=635.5
  (256, 128, 64, 8, 3, 4) ms=0.2167 tf=634.1
  (128, 256, 64, 8, 4, 2) ms=0.2171 tf=633.0
  (256, 128, 64, 8, 4, 2) ms=0.2173 tf=632.4
  (256, 128, 64, 8, 3, 2) ms=0.2186 tf=628.8
  (128, 256, 64, 8, 4, 4) ms=0.2186 tf=628.7
  (128, 256, 64, 8, 4, 8) ms=0.2186 tf=628.7
  (128, 256, 64, 8, 3, 8) ms=0.2193 tf=626.6

shape 1 {'M': 4096, 'N': 4096, 'K': 4127}
(128, 128, 128, 4, 3, 2) ms=0.4297 tf=322.3
(128, 128, 128, 4, 3, 4) ms=0.4241 tf=326.5
(128, 128, 128, 4, 3, 8) ms=0.4236 tf=326.9
(128, 128, 128, 4, 4, 2) ms=0.4283 tf=323

... (865 more chars)

Chunk ID: 766008
Wall time: 5.0013 seconds
Process running with session ID 52362
Original token count: 132
Output:
(128, 128, 256, 4, 3, 2) ms=1.4029 tf=98.7
(128, 128, 256, 4, 3, 4) ms=1.2040 tf=115.0
(128, 128, 256, 4, 3, 8) ms=1.1446 tf=121.0
(128, 128, 256, 4, 4, 2) ms=1.4043 tf=98.6
(128, 128, 256, 4, 4, 4) ms=1.2080 tf=114.6
(128, 128, 256, 4, 4, 8) ms=1.1435 tf=121.1
(128, 128, 256, 8, 3, 2) ms=0.8289 tf=167.1
(128, 128, 256, 8, 3, 4) ms=0.8290 tf=167.0
(128, 128, 256, 8, 3, 8) ms=0.8332 tf=166.2
(128, 128, 256, 8, 4, 2) ms=0.8292 tf=167.0
(128, 128, 256, 8, 4, 4) ms=0.8332 tf=166.2
(128, 128, 256, 8, 4, 8) ms=0.8303 tf=166.8

Chunk ID: 0985ca
Wall time: 5.0009 seconds
Process running with session ID 52362
Original token count: 306
Output:
(64, 256, 64, 4, 3, 2) ms=1.3905 tf=99.6
(64, 256, 64, 4, 3, 4) ms=1.4196 tf=97.5
(64, 256, 64, 4, 3, 8) ms=1.3997 tf=98.9
(64, 256, 64, 4, 4, 2) ms=1.3912 tf=99.5
(64, 256, 64, 4, 4, 4) ms=1.4160 tf=97.8
(64, 256, 64, 4, 4, 8) ms=1.4008 tf=98.9
(64, 256, 64, 8, 3, 2) ms=0.7721 tf=179.3
(64, 256, 64, 8, 3, 4) ms=0.7719 tf=179.4
(64, 256, 64, 8, 3, 8) ms=0.7408 tf=186.9
(64, 256, 64, 8, 4, 2) ms=0.7758 tf=178.5
(64, 256, 64, 8, 4, 4) ms=0.7737 tf=179.0
(64, 256, 64, 8, 4, 8) ms=0.7430 tf=186.4
(64, 256, 128, 4, 3, 2) ms=0.6293 tf=220.1
(64, 256, 128, 4, 3, 4) ms=0.6302 tf=219.7
(64, 256, 128, 4, 3, 8) ms=0.6289 tf=220.2
(64, 256, 128, 4, 4, 2) ms=0.6318 tf=219.2
(64, 256, 128, 4, 4, 4) ms=0.6302 tf=219.7
(64, 256, 128, 4, 4, 8) ms=0.6288 tf=220.2
(64, 256, 128, 8, 3, 2) ms=1.3659 tf=101.4
(64, 256, 128, 8, 3, 4) ms=1.3684 tf=101.2
(64, 256, 128, 8, 3, 8) ms=1.3834 tf=100.1
(64, 256, 128, 8, 4, 2) ms=1.3676 tf=101.3
(64, 256, 128, 8, 4, 4) ms=1.3707 tf=101.0
(64, 256, 128, 8, 4, 8) ms=1.3808 tf=100.3
(128, 256, 64, 4, 3, 2) ms=19.0026 tf=7.3
(128, 256, 64, 4, 3, 4) ms=19.0256 tf=7.3
(128, 256, 64, 4, 3, 8) ms=19.0928 tf=7.3
(128, 256, 64, 4, 4, 2) ms=19.0246 tf=7.3
(128, 256, 64, 4, 4, 4) ms=19.0734 tf=7.3

Chunk ID: 8810a4
Wall time: 0.0000 seconds
Process exited with code -1
Original token count: 0
Output:

Chunk ID: 02468a
Wall time: 0.0000 seconds
Process exited with code -1
Original token count: 107
Output:
(128, 256, 64, 4, 4, 8) ms=19.0909 tf=7.3
(128, 256, 64, 8, 3, 2) ms=0.9149 tf=151.4
(128, 256, 64, 8, 3, 4) ms=0.9193 tf=150.6
(128, 256, 64, 8, 3, 8) ms=0.9136 tf=151.6
(128, 256, 64, 8, 4, 2) ms=0.9152 tf=151.3
(128, 256, 64, 8, 4, 4) ms=0.9201 tf=150.5
(128, 256, 64, 8, 4, 8) ms=0.9128 tf=151.7
(256, 128, 64, 4, 3, 2) ms=21.3403 tf=6.5
(256, 128, 64, 4, 3, 4) ms=21.3595 tf=6.5
(256, 128, 64, 4, 3, 8) ms=21.3588 tf=6.5

Chunk ID: c38d7e
Wall time: 1.0013 seconds
Process running with session ID 91423
Original token count: 0
Output:

Chunk ID: ea7ad8
Wall time: 0.6415 seconds
Process exited with code 0
Original token count: 291
Output:
shape {'M': 4096, 'N': 14336, 'K': 4096}
(128, 128, 128, 4, 3, 4) ms=0.7334 tf=655.9
(128, 128, 128, 4, 3, 8) ms=0.7353 tf=654.2
(128, 128, 128, 4, 4, 4) ms=0.7321 tf=657.1
(128, 128, 64, 4, 3, 4) ms=0.7248 tf=663.7
(128, 128, 64, 4, 3, 8) ms=0.7220 tf=666.3
(256, 128, 64, 8, 4, 4) ms=0.7033 tf=684.0
(256, 128, 64, 8, 3, 8) ms=0.7138 tf=673.9
(128, 256, 64, 8, 4, 2) ms=0.7012 tf=686.0
(128, 256, 64, 8, 3, 8) ms=0.7055 tf=681.9
(64, 256, 64, 4, 3, 8) ms=0.7792 tf=617.3
(64, 256, 128, 4, 3, 4) ms=0.7820 tf=615.2
(256, 64, 64, 4, 3, 8) ms=0.7816 tf=615.4
(256, 64, 64, 8, 3, 8) ms=0.8076 tf=595.6
BEST
(128, 256, 64, 8, 4, 2) ms=0.7012 tf=686.0
(256, 128, 64, 8, 4, 4) ms=0.7033 tf=684.0
(128, 256, 64, 8, 3, 8) ms=0.7055 tf=681.9
(256, 128, 64, 8, 3, 8) ms=0.7138 tf=673.9
(128, 128, 64, 4, 3, 8) ms=0.7220 tf=666.3
(128, 128, 64, 4, 3, 4) ms=0.7248 tf=663.7
(128, 128, 128, 4, 4, 4) ms=0.7321 tf=657.1
(128, 128, 128, 4, 3, 4) ms=0.7334 tf=655.9
(128, 128, 128, 4, 3, 8) ms=0.7353 tf=654.2
(64, 256, 64, 4, 3, 8) ms=0.7792 tf=617.3
(256, 64, 64, 4, 3, 8) ms=0.7816 tf=615.4
(64, 256, 128, 4, 3, 4) ms=0.7820 tf=615.2
(256, 64, 64, 8, 3, 8) ms=0.8076 tf=595.6

Chunk ID: 7849f5
Wall time: 1.0010 seconds
Process running with session ID 74526
Original token count: 0
Output:

Chunk ID: 4aae58
Wall time: 5.0012 seconds
Process running with session ID 74526
Original token count: 2064
Output:
shape {'M': 32, 'N': 8192, 'K': 8192}
(32, 32, 64, 4, 3, 1) ms=0.0890 tf=48.2 gb=763
(32, 32, 64, 4, 3, 2) ms=0.0899 tf=47.8 gb=755
(32, 32, 64, 4, 3, 4) ms=0.0901 tf=47.7 gb=753
(32, 32, 64, 4, 3, 8) ms=0.0908 tf=47.3 gb=748
(32, 32, 64, 4, 4, 1) ms=0.0808 tf=53.1 gb=840
(32, 32, 64, 4, 4, 2) ms=0.0811 tf=52.9 gb=837
(32, 32, 64, 4, 4, 4) ms=0.0824 tf=52.1 gb=824
(32, 32, 64, 4, 4, 8) ms=0.0810 tf=53.0 gb=838
(32, 32, 64, 8, 3, 1) ms=0.0896 tf=48.0 gb=758
(32, 32, 64, 8, 3, 2) ms=0.0912 tf=47.1 gb=744
(32, 32, 64, 8, 3, 4) ms=0.0912 tf=47.1 gb=745
(32, 32, 64, 8, 3, 8) ms=0.0903 tf=47.6 gb=752
(32, 32, 64, 8, 4, 1) ms=0.0825 tf=52.1 gb=823
(32, 32, 64, 8, 4, 2) ms=0.0830 tf=51.8 gb=818
(32, 32, 64, 8, 4, 4) ms=0.0834 tf=51.5 gb=814
(32, 32, 64, 8, 4, 8) ms=0.0843 tf=50.9 gb=805
(32, 32, 128, 4, 3, 1) ms=0.0718 tf=59.9 gb=946
(32, 32, 128, 4, 3, 2) ms=0.0673 tf=63.8 gb=1009
(32, 32, 128, 4, 3, 4) ms=0.0686 tf=62.6 gb=990
(32, 32, 128, 4, 3, 8) ms=0.0692 tf=62.0 gb=981
(32, 32, 128, 4, 4, 1) ms=0.0575 tf=74.7 gb=1180
(32, 32, 128, 4, 4, 2) ms=0.0589 tf=73.0 gb=1153
(32, 32, 128, 4, 4, 4) ms=0.0584 tf=73.5 gb=1162
(32, 32, 128, 4, 4, 8) ms=0.0579 tf=74.2 gb=1172
(32, 32, 128, 8, 3, 1) ms=0.0677 tf=63.4 gb=1002
(32, 32, 128, 8, 3, 2) ms=0.0678 tf=63.4 gb=1002
(32, 32, 128, 8, 3, 4) ms=0.0683 tf=62.9 gb=995
(32, 32, 128, 8, 3, 8) ms=0.0676 tf=63.6 gb=1005
(32, 32, 1

... (6872 more chars)

Chunk ID: c9b0da
Wall time: 5.0012 seconds
Process running with session ID 74526
Original token count: 1717
Output:
(32, 256, 64, 4, 4, 4) ms=0.0713 tf=60.3 gb=953
(32, 256, 64, 4, 4, 8) ms=0.0721 tf=59.6 gb=942
(32, 256, 64, 8, 3, 1) ms=0.0949 tf=45.3 gb=716
(32, 256, 64, 8, 3, 2) ms=0.0947 tf=45.4 gb=717
(32, 256, 64, 8, 3, 4) ms=0.0947 tf=45.4 gb=717
(32, 256, 64, 8, 3, 8) ms=0.0946 tf=45.4 gb=717
(32, 256, 64, 8, 4, 1) ms=0.0728 tf=59.0 gb=932
(32, 256, 64, 8, 4, 2) ms=0.0725 tf=59.3 gb=937
(32, 256, 64, 8, 4, 4) ms=0.0723 tf=59.4 gb=940
(32, 256, 64, 8, 4, 8) ms=0.0728 tf=59.0 gb=932
(32, 256, 128, 4, 3, 1) ms=0.0632 tf=68.0 gb=1075
(32, 256, 128, 4, 3, 2) ms=0.0634 tf=67.8 gb=1071
(32, 256, 128, 4, 3, 4) ms=0.0635 tf=67.7 gb=1070
(32, 256, 128, 4, 3, 8) ms=0.0629 tf=68.3 gb=1079
(32, 256, 128, 4, 4, 1) ERR OutOfResources: out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(32, 256, 128, 4, 4, 2) ERR OutOfResources: out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(32, 256, 128, 4, 4, 4) ERR OutOfResources: out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(32, 256, 128, 4, 4, 8) ERR OutOfResources: out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(32, 256, 128, 8, 3, 1) ms=0.0628 tf=68.4 gb

... (5482 more chars)

Chunk ID: dd37b0
Wall time: 0.0000 seconds
Process exited with code -1
Original token count: 0
Output:

output:
  Success. Updated the following files:
  M /workspace/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: f90bc4
Wall time: 1.0007 seconds
Process running with session ID 63402
Original token count: 0
Output:

Chunk ID: b209df
Wall time: 3.0177 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 216c7a
Wall time: 1.0015 seconds
Process running with session ID 30718
Original token count: 25
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-14T20:51:26.294215+00:00

Chunk ID: aa952c
Wall time: 2.5876 seconds
Process exited with code 0
Original token count: 304
Output:
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-14T20:51:26.500679+00:00 elapsed_s=0.206 ms=0.220400
shape=0 variant=solution tflops=623.589 gbps=304.487 ms=0.220
shape=0 solution_peak_fraction=0.6236
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-14T20:51:27.008943+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-14T20:51:27.029527+00:00 elapsed_s=0.021 ms=0.430400
shape=1 variant=solution tflops=321.745 gbps=156.512 ms=0.430
shape=1 solution_peak_fraction=0.3217
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-14T20:51:28.877432+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-14T20:51:28.883009+00:00 elapsed_s=0.006 ms=0.058224
shape=2 variant=solution tflops=73.766 gbps=1166.105 ms=0.058
shape=2 solution_peak_fraction=0.0738
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-14T20:51:30.557252+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-14T20:51:30.589028+00:00 elapsed_s=0.032 ms=0.707104
shape=3 variant=solution tflops=680.291 gbps=272.857 ms=0.707
shape=3 solution_peak_fraction=0.6803
peak_fraction: 0.3168
RESULT: OK

Chunk ID: e27acb
Wall time: 1.0019 seconds
Process running with session ID 71821
Original token count: 0
Output:

Chunk ID: 059568
Wall time: 5.0020 seconds
Process running with session ID 71821
Original token count: 0
Output:

Chunk ID: 775dfe
Wall time: 3.8818 seconds
Process exited with code 0
Original token count: 87
Output:
Kp 4224
(256, 128, 64, 8, 4, 4) pack+gemm 0.2492 tf 555.7 gemm_only 0.2219
(128, 256, 64, 8, 4, 2) pack+gemm 0.2498 tf 554.4 gemm_only 0.2221
(128, 128, 64, 4, 3, 4) pack+gemm 0.2536 tf 546.1 gemm_only 0.2276
(128, 128, 128, 4, 3, 8) pack+gemm 0.2600 tf 532.6 gemm_only 0.2320
(256, 128, 128, 8, 3, 4) pack+gemm 0.2409 tf 574.7 gemm_only 0.2127

output:
  Success. Updated the following files:
  M /workspace/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 4c35b9
Wall time: 1.0011 seconds
Process running with session ID 27446
Original token count: 0
Output:

Chunk ID: d89fda
Wall time: 5.0011 seconds
Process running with session ID 27446
Original token count: 0
Output:

Chunk ID: 245a51
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 5fffb0
Wall time: 1.0010 seconds
Process running with session ID 26285
Original token count: 25
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-14T20:53:18.923402+00:00

Chunk ID: 2cfb8a
Wall time: 2.6728 seconds
Process exited with code 0
Original token count: 304
Output:
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-14T20:53:19.134649+00:00 elapsed_s=0.211 ms=0.221360
shape=0 variant=solution tflops=620.884 gbps=303.166 ms=0.221
shape=0 solution_peak_fraction=0.6209
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-14T20:53:19.656391+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-14T20:53:19.673378+00:00 elapsed_s=0.017 ms=0.244800
shape=1 variant=solution tflops=565.683 gbps=275.175 ms=0.245
shape=1 solution_peak_fraction=0.5657
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-14T20:53:21.553364+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-14T20:53:21.559192+00:00 elapsed_s=0.006 ms=0.061488
shape=2 variant=solution tflops=69.850 gbps=1104.204 ms=0.061
shape=2 solution_peak_fraction=0.0699
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-14T20:53:23.291422+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-14T20:53:23.323374+00:00 elapsed_s=0.032 ms=0.710272
shape=3 variant=solution tflops=677.256 gbps=271.640 ms=0.710
shape=3 solution_peak_fraction=0.6773
peak_fraction: 0.3590
RESULT: OK

Chunk ID: 0a9a5f
Wall time: 1.0011 seconds
Process running with session ID 74930
Original token count: 0
Output:

Chunk ID: ef4bea
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 125
Output:
(32, 128, 128, 4, 4, 4) ms=0.055968 tf=76.740 gb=1213.1
(32, 128, 128, 4, 4, 8) ms=0.055328 tf=77.627 gb=1227.1
(32, 64, 256, 8, 3, 2) ms=0.054144 tf=79.325 gb=1254.0
(32, 64, 256, 8, 3, 1) ms=0.054112 tf=79.372 gb=1254.7
(32, 64, 256, 4, 4, 8) ms=0.055632 tf=77.203 gb=1220.4
(32, 128, 256, 8, 3, 1) ms=0.054256 tf=79.161 gb=1251.4
(32, 128, 256, 8, 3, 4) ms=0.054000 tf=79.536 gb=1257.3
(32, 32, 256, 4, 4, 1) ms=0.053968 tf=79.584 gb=1258.1
(32, 32, 256, 8, 4, 1) ms=0.054544 tf=78.743 gb=1244.8

output:
  Success. Updated the following files:
  M /workspace/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 901dc7
Wall time: 1.0010 seconds
Process running with session ID 41980
Original token count: 0
Output:

Chunk ID: 7a21b0
Wall time: 2.3475 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 14d5c7
Wall time: 1.0007 seconds
Process running with session ID 72517
Original token count: 25
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-14T20:54:02.710334+00:00

Chunk ID: a721b3
Wall time: 2.4992 seconds
Process exited with code 0
Original token count: 304
Output:
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-14T20:54:02.918219+00:00 elapsed_s=0.208 ms=0.219584
shape=0 variant=solution tflops=625.906 gbps=305.618 ms=0.220
shape=0 solution_peak_fraction=0.6259
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-14T20:54:03.434926+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-14T20:54:03.451667+00:00 elapsed_s=0.017 ms=0.244736
shape=1 variant=solution tflops=565.831 gbps=275.247 ms=0.245
shape=1 solution_peak_fraction=0.5658
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-14T20:54:05.299032+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-14T20:54:05.304584+00:00 elapsed_s=0.006 ms=0.058288
shape=2 variant=solution tflops=73.685 gbps=1164.825 ms=0.058
shape=2 solution_peak_fraction=0.0737
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-14T20:54:06.974103+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-14T20:54:07.005858+00:00 elapsed_s=0.032 ms=0.706592
shape=3 variant=solution tflops=680.784 gbps=273.054 ms=0.707
shape=3 solution_peak_fraction=0.6808
peak_fraction: 0.3651
RESULT: OK

Chunk ID: d57dc1
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 271
Output:
10:@triton.jit
70:@triton.jit
71:def _fp8_gemm_wt_kernel(
172:        self._weight_pad = None
173:        self._weight_pad_key = None
174:        self._x_pad = None
182:    def forward(self, x: torch.Tensor) -> torch.Tensor:
192:            if self._weight_pad is None or self._weight_pad_key != w_key:
193:                weight_pad = torch.empty((self.N, k_pad), device=x.device, dtype=self.weight.dtype)
194:                weight_pad[:, : self.K].copy_(self.weight)
195:                weight_pad[:, self.K :].zero_()
196:                self._weight_pad = weight_pad
197:                self._weight_pad_key = w_key
199:                self._x_pad is None
200:                or self._x_pad.device != x.device
201:                or self._x_pad.shape != (self.M, k_pad)
203:                self._x_pad = torch.empty((self.M, k_pad), device=x.device, dtype=x.dtype)
204:            self._x_pad[:, : self.K].copy_(x)
205:            self._x_pad[:, self.K :].zero_()
206:            return _launch_fp8_gemm(self._x_pad, self._weight_pad, self.weight_scale, self.M, self.N, k_pad)

Chunk ID: b448ee
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1704
Output:
import torch
import torch.nn as nn
import triton
import triton.language as tl


E4M3_MAX = 448.0


@triton.jit
def _fp8_gemm_kernel(
    x_ptr,
    w_ptr,
    scale_ptr,
    y_ptr,
    M: tl.constexpr,
    N: tl.constexpr,
    K: tl.constexpr,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    group_size = GROUP_M * num_pid_n
    group_id = pid // group_size
    first_pid_m = group_id * GROUP_M
    group_m = tl.minimum(num_pid_m - first_pid_m, GROUP_M)
    pid_in_group = pid - group_id * group_size
    pid_m = first_pid_m + (pid_in_group % group_m)
    pid_n = pid_in_group // group_m

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)

    acc = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)
    full_k = (K // BLOCK_K) * BLOCK_K
    for k0 in range(0, full_k, BLOCK_K):
        k = k0 + offs_k
        a = tl.load(x_ptr + offs_m[:, None] * K + k[None, :])
        b = tl.load(w_ptr + offs_n[None, :] * K + k[:, None])
        acc = tl.dot(a, b, acc, out_dtype=tl.float32)

    if full_k < K:
        k = full_k + offs_k
        a = tl.load(
            x_ptr + offs_m[:, None] * K + k[None, :],
            mask=k[None, :] < K,
          

... (5420 more chars)

Chunk ID: df97fc
Wall time: 0.0000 seconds
Process exited with code 129
Original token count: 1840
Output:
warning: Not a git repository. Use --no-index to compare two paths outside a working tree
usage: git diff --no-index [<options>] <path> <path>

Diff output format options
    -p, --patch           generate patch
    -s, --no-patch        suppress diff output
    -u                    generate patch
    -U, --unified[=<n>]   generate diffs with <n> lines context
    -W, --[no-]function-context
                          generate diffs with <n> lines context
    --raw                 generate the diff in raw format
    --patch-with-raw      synonym for '-p --raw'
    --patch-with-stat     synonym for '-p --stat'
    --numstat             machine friendly --stat
    --shortstat           output only the last line of --stat
    -X, --dirstat[=<param1,param2>...]
                          output the distribution of relative amount of changes for each sub-directory
    --cumulative          synonym for --dirstat=cumulative
    --dirstat-by-file[=<param1,param2>...]
                          synonym for --dirstat=files,param1,param2...
    --check               warn if changes introduce conflict markers or whitespace errors
    --summary             condensed summary such as creations, renames and mode changes
    --name-only           show only names of changed files
    --name-status         show only names and status of changed files
    --stat[=<width>[,<name-width>[,<count>

... (5964 more chars)

output:
  Success. Updated the following files:
  M /workspace/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 2b79cc
Wall time: 1.0011 seconds
Process running with session ID 1810
Original token count: 0
Output:

Chunk ID: a2b4c0
Wall time: 2.6517 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: f67507
Wall time: 1.0004 seconds
Process running with session ID 18982
Original token count: 25
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-14T20:54:53.867334+00:00

Chunk ID: efb884
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 304
Output:
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-14T20:54:54.074914+00:00 elapsed_s=0.208 ms=0.221120
shape=0 variant=solution tflops=621.558 gbps=303.495 ms=0.221
shape=0 solution_peak_fraction=0.6216
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-14T20:54:54.584204+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-14T20:54:54.600883+00:00 elapsed_s=0.017 ms=0.244800
shape=1 variant=solution tflops=565.683 gbps=275.175 ms=0.245
shape=1 solution_peak_fraction=0.5657
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-14T20:54:56.440040+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-14T20:54:56.445560+00:00 elapsed_s=0.006 ms=0.058448
shape=2 variant=solution tflops=73.484 gbps=1161.636 ms=0.058
shape=2 solution_peak_fraction=0.0735
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-14T20:54:58.109524+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-14T20:54:58.141242+00:00 elapsed_s=0.032 ms=0.708128
shape=3 variant=solution tflops=679.307 gbps=272.462 ms=0.708
shape=3 solution_peak_fraction=0.6793
peak_fraction: 0.3640
RESULT: OK

Chunk ID: bdab2a
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 81
Output:
45:        acc = tl.dot(a, b, acc, out_dtype=tl.float32)
59:        acc = tl.dot(a, b, acc, out_dtype=tl.float32)
108:class Model(nn.Module):
119:        self.register_buffer("weight", w_fp8)
120:        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))
155:def get_inputs():
160:def get_init_inputs():

Chunk ID: 9d30fc
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1261
Output:
import torch
import torch.nn as nn
import triton
import triton.language as tl


E4M3_MAX = 448.0


@triton.jit
def _fp8_gemm_kernel(
    x_ptr,
    w_ptr,
    scale_ptr,
    y_ptr,
    M: tl.constexpr,
    N: tl.constexpr,
    K: tl.constexpr,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    group_size = GROUP_M * num_pid_n
    group_id = pid // group_size
    first_pid_m = group_id * GROUP_M
    group_m = tl.minimum(num_pid_m - first_pid_m, GROUP_M)
    pid_in_group = pid - group_id * group_size
    pid_m = first_pid_m + (pid_in_group % group_m)
    pid_n = pid_in_group // group_m

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)

    acc = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)
    full_k = (K // BLOCK_K) * BLOCK_K
    for k0 in range(0, full_k, BLOCK_K):
        k = k0 + offs_k
        a = tl.load(x_ptr + offs_m[:, None] * K + k[None, :])
        b = tl.load(w_ptr + offs_n[None, :] * K + k[:, None])
        acc = tl.dot(a, b, acc, out_dtype=tl.float32)

    if full_k < K:
        k = full_k + offs_k
        a = tl.load(
            x_ptr + offs_m[:, None] * K + k[None, :],
            mask=k[None, :] < K,
          

... (3648 more chars)

Chunk ID: 7bc984
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 29
Output:
PROMPT.txt
__pycache__
benchmark.py
check.py
framework.txt
problem.yaml
reference.py
shapes.py
solution.py
sota.py