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Thu May  7 04:28:13 2026       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 595.58.03              Driver Version: 595.58.03      CUDA Version: 13.2     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA RTX PRO 6000 Blac...    On  |   00000000:01:00.0 Off |                  Off |
| 30%   35C    P0             87W /  600W |     271MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

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## master...origin/master
?? ../09_fmha_preattn_mrope/
?? ./
?? ../../scripts/sweep_09_10.sh

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Python 3.11.15

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reference.py
check.py
benchmark.py

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"""Naive Conv3d-as-GEMM patch embedding reference (correctness only).

Vision-Transformer / Qwen2-VL style patch embedding: a video tensor
(B, C, T, H, W) is split into non-overlapping (kT, kH, kW) patches and each
patch projected to embed_dim. This is mathematically a 3D convolution with
stride == kernel and equivalently a single GEMM after a strided reshape.

Reference uses nn.Conv3d (cuDNN dispatch) for clarity. The agent's solution is
forbidden from using Conv3d / conv3d / matmul / linear / einsum, forcing them
to write a fused patch-gather + tensor-core GEMM kernel.

Output layout: (B, embed_dim, T/kT, H/kH, W/kW). No bias.
"""
import torch
import torch.nn as nn

OP_TYPE = "patch_embed"
SUPPORTED_PRECISIONS = ["bf16"]
HARDWARE_REQUIRED = ["RTX_PRO_6000"]


class Model(nn.Module):
    def __init__(self, B: int, C: int, T: int, H: int, W: int,
                 kT: int, kH: int, kW: int, embed_dim: int):
        super().__init__()
        assert T % kT == 0 and H % kH == 0 and W % kW == 0, \
            f"Input dims must be divisible by patch size: T={T} kT={kT} H={H} kH={kH} W={W} kW={kW}"
        self.B, self.C, self.T, self.H, self.W = B, C, T, H, W
        self.kT, self.kH, self.kW = kT, kH, kW
        self.embed_dim = embed_dim

        self.conv = nn.Conv3d(
            C, embed_dim,
            kernel_size=(kT, kH, kW),
            stride=(kT, kH, kW),
            

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"""Correctness runner for Conv3d-as-GEMM patch embedding.

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 enumerate(all_shapes):
        

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"""Roofline benchmark for Conv3d-as-GEMM patch embedding.

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:
    # 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 = int(meta.get("num_perf_trials", 30))

    device 

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Output:
../../AGENTS.md
../../SPEC.md
../../CLAUDE.md

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"""Canonical shape sweep for Conv3d-as-GEMM patch embedding.

Sized for Qwen2-VL / ViT-style patch embedding. embed_dim=1280 matches Qwen2-VL
ViT; embed_dim=768 is a ViT-B/16 reference. Patch sizes are the canonical
14x14 spatial / 2-frame temporal used in modern VL encoders.
"""

SHAPES = [
    # Qwen2-VL ViT base: 224x224x2-frame video, 14x14x2 patches, embed_dim=1280
    {"B": 1, "C": 3, "T": 2,  "H": 224, "W": 224, "kT": 2, "kH": 14, "kW": 14, "embed_dim": 1280},

    # Same patch config, batch 2, 4-frame video
    {"B": 2, "C": 3, "T": 4,  "H": 224, "W": 224, "kT": 2, "kH": 14, "kW": 14, "embed_dim": 1280},

    # Larger spatial (336x336), 8-frame — exercises larger num_patches
    {"B": 1, "C": 3, "T": 8,  "H": 336, "W": 336, "kT": 2, "kH": 14, "kW": 14, "embed_dim": 1280},

    # ViT-B/16 (image-mode): kT=1, kH=kW=16, embed_dim=768
    {"B": 4, "C": 3, "T": 1,  "H": 224, "W": 224, "kT": 1, "kH": 16, "kW": 16, "embed_dim": 768},
]

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Output:
name: 10_patch_embed_conv3d_gemm
display_name: "Conv3d-as-GEMM Patch Embedding"
precision: bf16
regime: compute  # GEMM-equivalent, embed_dim=1280 is well above the bf16 ridge

# Dense FLOPs: 2 * (num_patches) * (C * kT * kH * kW) * embed_dim.
# With stride==kernel and divisible inputs, num_patches * (kT*kH*kW) = T*H*W,
# so this collapses to: 2 * B * embed_dim * C * T * H * W.
flops_formula: "2 * B * embed_dim * C * T * H * W"

# Bytes moved (lower bound):
#   read input x:    B * C * T * H * W * 2
#   read weights:    embed_dim * C * kT * kH * kW * 2
#   write output:    B * embed_dim * (T // kT) * (H // kH) * (W // kW) * 2
bytes_formula: "B*C*T*H*W*2 + embed_dim*C*kT*kH*kW*2 + B*embed_dim*(T//kT)*(H//kH)*(W//kW)*2"

hardware: [RTX_PRO_6000]
peak_tflops_key: bf16
peak_bandwidth_key: dram

tolerance:
  bfloat16: 0.02

# Forbidden ops -- agent must implement the fused patch-gather + GEMM
# themselves. Banning Conv3d/conv3d (the cuDNN dispatch) and matmul/bmm/linear/
# einsum (the lazy reshape-then-cuBLAS shortcut). Unfold is also banned for
# completeness even though it's nominally 2D.
forbidden:
  - "torch.nn.Conv3d"
  - "torch.nn.functional.conv3d"
  - "F.conv3d"
  - "torch.conv3d"
  - "torch.matmul"
  - "torch.bmm"
  - "torch.nn.functional.linear"
  - "F.linear"
  - "torch.einsum"
  - "torch.nn.functional.unfold"
  - "F.unfold"

sota:
  name: "cuDNN Conv3d (torch.nn.func

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Output:
# KernelBench-Hard: Design Specification

Last updated: 2026-04-27.

## Purpose

A small, hand-curated GPU kernel benchmark where frontier coding agents attempt to beat the state-of-the-art kernel on a specific operation on specific hardware. Unlike KernelBench-v3, the goal is not breadth or quantity — it's to produce a few genuinely-hard traces that reveal how each (model, harness) pair approaches kernel engineering.

## Why "Hard"

v3 was 43 problems of grab-bag difficulty. Most were winnable by any frontier model with any harness. Median speedups ended up reward-hacked or trivially-above-eager, which made the leaderboard non-informative. Hard has ~8 problems where:

1. A reward-hacked solution fails correctness (tight atol, multi-shape eval, SOTA comparison).
2. Eager PyTorch is not the baseline — SOTA references are (sonic-moe, flashinfer, marlin, Tri Dao's attention kits). Beating PyTorch means nothing; approaching SOTA is the goal.
3. The problem requires reading source code / papers that the agent must navigate to. No spoon-feeding.

## Non-goals

- No public leaderboard. We publish roofline plots and kernels, not a number that models compete on.
- No "portable kernel" judging. SM120-specific optimizations are expected and good.
- No multi-turn/multi-day agent loops. 45 minutes wall-clock, one shot.

## Metric

### Primary: fraction of hardware peak

**Compute-bound

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total 40
drwxrwxr-x  2 infatoshi infatoshi 4096 May  7 04:28 .
drwxrwxr-x 12 infatoshi infatoshi 4096 May  4 13:57 ..
-rw-rw-r--  1 infatoshi infatoshi 2969 May  4 14:03 PROMPT.txt
-rw-rw-r--  1 infatoshi infatoshi 4626 May  4 14:02 benchmark.py
-rw-rw-r--  1 infatoshi infatoshi 3988 May  4 14:02 check.py
-rw-rw-r--  1 infatoshi infatoshi 1541 May  4 14:01 problem.yaml
-rw-rw-r--  1 infatoshi infatoshi 1994 May  4 14:01 reference.py
-rw-rw-r--  1 infatoshi infatoshi  953 May  4 14:01 shapes.py
-rw-rw-r--  1 infatoshi infatoshi  534 May  4 14:02 sota.py

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"""SOTA reference for Conv3d-as-GEMM patch embedding.

Dispatches torch.nn.functional.conv3d (cuDNN). The agent's solution is
forbidden from calling conv3d directly; this file is only for the benchmark's
reference baseline.
"""
from __future__ import annotations

import torch
import torch.nn.functional as F


def sota_forward(x: torch.Tensor, weight: torch.Tensor,
                 stride: tuple[int, int, int]) -> torch.Tensor:
    return F.conv3d(x, weight, bias=None, stride=stride)


def is_available() -> bool:
    return True

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2.11.0+cu130
3.6.0

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

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

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

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



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"""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,
    },
)

output:
  Success. Updated the following files:
  A /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

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Output:

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Output:
PASS

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Output:

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Output:
shape=0 variant=eager tflops=3.692 gbps=20.444 ms=0.209
shape=0 variant=compiled tflops=3.152 gbps=17.454 ms=0.245
shape=0 variant=sota tflops=3.694 gbps=20.455 ms=0.209
shape=0 variant=solution tflops=23.014 gbps=127.450 ms=0.033
shape=0 solution_peak_fraction=0.1151
shape=1 variant=eager tflops=13.935 gbps=36.344 ms=0.221
shape=1 variant=compiled tflops=12.046 gbps=31.418 ms=0.256
shape=1 variant=sota tflops=14.104 gbps=36.786 ms=0.219
shape=1 variant=solution tflops=42.845 gbps=111.747 ms=0.072
shape=1 solution_peak_fraction=0.2142
shape=2 variant=eager tflops=26.317 gbps=54.361 ms=0.264
shape=2 variant=compiled tflops=23.158 gbps=47.836 ms=0.300
shape=2 variant=sota tflops=26.870 gbps=55.503 ms=0.258
shape=2 variant=solution tflops=55.895 gbps=115.457 ms=0.124
shape=2 solution_peak_fraction=0.2795
shape=3 variant=eager tflops=6.486 gbps=25.163 ms=0.143
shape=3 variant=compiled tflops=5.210 gbps=20.212 ms=0.178
shape=3 variant=sota tflops=6.577 gbps=25.518 ms=0.141
shape=3 variant=solution tflops=36.423 gbps=141.308 ms=0.025
shape=3 solution_peak_fraction=0.1821
peak_fraction: 0.1882
RESULT: OK

output:
  Success. Updated the following files:
  A /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/tune_triton.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

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Output:
shape=0 {'B': 1, 'C': 3, 'T': 2, 'H': 224, 'W': 224, 'kT': 2, 'kH': 14, 'kW': 14, 'embed_dim': 1280}
  cfg=(8, 64, 64, 4, 3) ms=0.0435
  cfg=(16, 32, 64, 4, 3) ms=0.0370
  cfg=(16, 64, 32, 4, 3) ms=0.0424
  cfg=(16, 64, 64, 4, 3) ms=0.0333
  cfg=(16, 64, 128, 4, 3) ms=0.0342
  cfg=(16, 128, 32, 4, 3) ms=0.0486
  cfg=(16, 128, 64, 4, 3) ms=0.0358
  cfg=(32, 32, 64, 4, 3) ms=0.0392
  cfg=(32, 64, 32, 4, 3) ms=0.0502
  cfg=(32, 64, 64, 4, 3) ms=0.0390
  cfg=(32, 64, 128, 4, 3) ms=0.0382
  cfg=(32, 128, 32, 4, 3) ms=0.0516
  cfg=(32, 128, 64, 4, 3) ms=0.0441
  cfg=(64, 32, 64, 4, 3) ms=0.0462
  cfg=(64, 64, 32, 4, 3) ms=0.0564
  cfg=(64, 64, 64, 4, 3) ms=0.0489
  cfg=(16, 64, 64, 8, 3) ms=0.0328
  cfg=(32, 64, 64, 8, 3) ms=0.0353
  cfg=(32, 128, 64, 8, 3) ms=0.0400
best shape=0 cfg=(16, 64, 64, 8, 3) ms=0.0328
shape=1 {'B': 2, 'C': 3, 'T': 4, 'H': 224, 'W': 224, 'kT': 2, 'kH': 14, 'kW': 14, 'embed_dim': 1280}
  cfg=(8, 64, 64, 4, 3) ms=0.1021
  cfg=(16, 32, 64, 4, 3) ms=0.0845
  cfg=(16, 64, 32, 4, 3) ms=0.0699
  cfg=(16, 64, 64, 4, 3) ms=0.0705
  cfg=(16, 64, 128, 4, 3) ms=0.0792
  cfg=(16, 128, 32, 4, 3) ms=0.0701
  cfg=(16, 128, 64, 4, 3) ms=0.0649
  cfg=(32, 32, 64, 4, 3) ms=0.0824
  cfg=(32, 64, 32, 4, 3) ms=0.0561
  cfg=(32, 64, 64, 4, 3) ms=0.0447
  cfg=(32, 64, 128, 4, 3) ms=0.0648
  cfg=(32, 128, 32, 4, 3) ms=0.0563
  cfg=(32, 128, 64, 4, 3) ms=0.0496
  cfg=(64, 32, 64

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output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

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Output:

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Output:
PASS

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Output:

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Original token count: 279
Output:
shape=0 variant=eager tflops=3.686 gbps=20.414 ms=0.209
shape=0 variant=compiled tflops=3.164 gbps=17.522 ms=0.244
shape=0 variant=sota tflops=3.718 gbps=20.587 ms=0.207
shape=0 variant=solution tflops=22.415 gbps=124.128 ms=0.034
shape=0 solution_peak_fraction=0.1121
shape=1 variant=eager tflops=13.962 gbps=36.415 ms=0.221
shape=1 variant=compiled tflops=11.994 gbps=31.281 ms=0.257
shape=1 variant=sota tflops=14.107 gbps=36.794 ms=0.219
shape=1 variant=solution tflops=72.163 gbps=188.213 ms=0.043
shape=1 solution_peak_fraction=0.3608
shape=2 variant=eager tflops=26.346 gbps=54.420 ms=0.263
shape=2 variant=compiled tflops=23.135 gbps=47.787 ms=0.300
shape=2 variant=sota tflops=26.897 gbps=55.558 ms=0.258
shape=2 variant=solution tflops=83.002 gbps=171.451 ms=0.084
shape=2 solution_peak_fraction=0.4150
shape=3 variant=eager tflops=6.443 gbps=24.995 ms=0.144
shape=3 variant=compiled tflops=5.118 gbps=19.854 ms=0.181
shape=3 variant=sota tflops=6.569 gbps=25.487 ms=0.141
shape=3 variant=solution tflops=38.280 gbps=148.514 ms=0.024
shape=3 solution_peak_fraction=0.1914
peak_fraction: 0.2381
RESULT: OK

output:
  Success. Updated the following files:
  A /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/tune_orientations.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

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Output:

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Output:

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Output:

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Output:

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Output:
shape=0 {'B': 1, 'C': 3, 'T': 2, 'H': 224, 'W': 224, 'kT': 2, 'kH': 14, 'kW': 14, 'embed_dim': 1280}
  mn cfg=(16, 64, 64, 4, 3) ms=0.0390
  mn cfg=(16, 64, 64, 8, 3) ms=0.0329
  mn cfg=(16, 128, 64, 4, 3) ms=0.0361
  mn cfg=(32, 64, 32, 4, 3) ms=0.0523
  mn cfg=(32, 64, 64, 4, 3) ms=0.0392
  mn cfg=(32, 64, 64, 8, 3) ms=0.0367
  mn cfg=(32, 64, 128, 4, 3) ms=0.0399
  mn cfg=(32, 128, 32, 4, 3) ms=0.0516
  mn cfg=(32, 128, 64, 4, 3) ms=0.0457
  mn cfg=(32, 128, 64, 8, 3) ms=0.0403
  mn cfg=(64, 64, 32, 4, 3) ms=0.0567
  mn cfg=(64, 64, 64, 4, 3) ms=0.0482
  mn cfg=(64, 64, 64, 8, 3) ms=0.0449
  mn cfg=(64, 128, 32, 4, 3) ms=0.0627
  mn cfg=(64, 128, 64, 4, 3) ms=0.0544
  mn cfg=(128, 32, 64, 4, 3) ms=0.0598
  mn cfg=(128, 64, 32, 4, 3) ms=0.0719
  mn cfg=(128, 64, 64, 4, 3) ms=0.0645
  em cfg=(16, 64, 64, 4, 3) ms=0.0421
  em cfg=(16, 64, 64, 8, 3) ms=0.0404
  em cfg=(16, 128, 64, 4, 3) ms=0.0606
  em cfg=(32, 64, 32, 4, 3) ms=0.0516
  em cfg=(32, 64, 64, 4, 3) ms=0.0447
  em cfg=(32, 64, 64, 8, 3) ms=0.0414
  em cfg=(32, 64, 128, 4, 3) ms=0.0396
  em cfg=(32, 128, 32, 4, 3) ms=0.0594
  em cfg=(32, 128, 64, 4, 3) ms=0.0590
  em cfg=(32, 128, 64, 8, 3) ms=0.0505
  em cfg=(64, 64, 32, 4, 3) ms=0.0508
  em cfg=(64, 64, 64, 4, 3) ms=0.0478
  em cfg=(64, 64, 64, 8, 3) ms=0.0433
  em cfg=(64, 128, 32, 4, 3) ms=0.0643
  em cfg=(64, 128, 64, 4, 3) ms=0.0670
  em cfg=(128, 32, 64, 

... (4787 more chars)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 9a0bbe
Wall time: 1.0009 seconds
Process running with session ID 91126
Original token count: 0
Output:

Chunk ID: 421925
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: e7cbce
Wall time: 1.0010 seconds
Process running with session ID 57430
Original token count: 0
Output:

Chunk ID: f10fae
Wall time: 0.9641 seconds
Process exited with code 0
Original token count: 279
Output:
shape=0 variant=eager tflops=3.693 gbps=20.453 ms=0.209
shape=0 variant=compiled tflops=3.192 gbps=17.679 ms=0.241
shape=0 variant=sota tflops=3.731 gbps=20.661 ms=0.207
shape=0 variant=solution tflops=23.192 gbps=128.431 ms=0.033
shape=0 solution_peak_fraction=0.1160
shape=1 variant=eager tflops=13.966 gbps=36.426 ms=0.221
shape=1 variant=compiled tflops=12.018 gbps=31.345 ms=0.257
shape=1 variant=sota tflops=14.119 gbps=36.823 ms=0.218
shape=1 variant=solution tflops=70.422 gbps=183.673 ms=0.044
shape=1 solution_peak_fraction=0.3521
shape=2 variant=eager tflops=26.215 gbps=54.151 ms=0.265
shape=2 variant=compiled tflops=23.023 gbps=47.556 ms=0.301
shape=2 variant=sota tflops=26.858 gbps=55.479 ms=0.258
shape=2 variant=solution tflops=126.723 gbps=261.762 ms=0.055
shape=2 solution_peak_fraction=0.6336
shape=3 variant=eager tflops=6.460 gbps=25.062 ms=0.143
shape=3 variant=compiled tflops=5.199 gbps=20.172 ms=0.178
shape=3 variant=sota tflops=6.619 gbps=25.679 ms=0.140
shape=3 variant=solution tflops=40.966 gbps=158.934 ms=0.023
shape=3 solution_peak_fraction=0.2048
peak_fraction: 0.2698
RESULT: OK

Chunk ID: 690133
Wall time: 0.8222 seconds
Process exited with code 0
Original token count: 2
Output:
(12, 0)

output:
  Success. Updated the following files:
  A /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/tune_big.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: e912b7
Wall time: 1.0011 seconds
Process running with session ID 10808
Original token count: 0
Output:

Chunk ID: c80482
Wall time: 5.0011 seconds
Process running with session ID 10808
Original token count: 0
Output:

Chunk ID: 48a3d9
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 365
Output:
shape=1 {'B': 2, 'C': 3, 'T': 4, 'H': 224, 'W': 224, 'kT': 2, 'kH': 14, 'kW': 14, 'embed_dim': 1280}
  cfg=(64, 128, 32, 4, 3) ms=0.0581
  cfg=(64, 128, 64, 4, 3) ms=0.0664
  cfg=(64, 128, 128, 4, 3) ms=0.0613
  cfg=(128, 32, 32, 4, 3) ms=0.0534
  cfg=(128, 32, 64, 4, 3) ms=0.0411
  cfg=(128, 32, 128, 4, 3) ms=0.0523
  cfg=(128, 64, 32, 4, 3) ms=0.0509
  cfg=(128, 64, 64, 4, 3) ms=0.0408
  cfg=(128, 64, 128, 4, 3) ms=0.0448
  cfg=(128, 128, 32, 4, 3) ms=0.0572
  cfg=(128, 128, 64, 4, 3) ms=0.0839
  cfg=(256, 16, 64, 4, 3) ms=0.0700
  cfg=(256, 32, 32, 4, 3) ms=0.0663
  cfg=(256, 32, 64, 4, 3) ms=0.0516
  cfg=(256, 64, 32, 4, 3) ms=0.0562
  cfg=(256, 64, 64, 4, 3) ms=0.0630
best shape=1 cfg=(128, 64, 64, 4, 3) ms=0.0408
shape=2 {'B': 1, 'C': 3, 'T': 8, 'H': 336, 'W': 336, 'kT': 2, 'kH': 14, 'kW': 14, 'embed_dim': 1280}
  cfg=(64, 128, 32, 4, 3) ms=0.0744
  cfg=(64, 128, 64, 4, 3) ms=0.0733
  cfg=(64, 128, 128, 4, 3) ms=0.2989
  cfg=(128, 32, 32, 4, 3) ms=0.0797
  cfg=(128, 32, 64, 4, 3) ms=0.0713
  cfg=(128, 32, 128, 4, 3) ms=0.0981
  cfg=(128, 64, 32, 4, 3) ms=0.0617
  cfg=(128, 64, 64, 4, 3) ms=0.0541
  cfg=(128, 64, 128, 4, 3) ms=0.0852
  cfg=(128, 128, 32, 4, 3) ms=0.0709
  cfg=(128, 128, 64, 4, 3) ms=0.0885
  cfg=(256, 16, 64, 4, 3) ms=0.1250
  cfg=(256, 32, 32, 4, 3) ms=0.0768
  cfg=(256, 32, 64, 4, 3) ms=0.0816
  cfg=(256, 64, 32, 4, 3) ms=0.0686
  cfg=(256, 64, 64, 4

... (62 more chars)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: b32518
Wall time: 1.0004 seconds
Process running with session ID 58354
Original token count: 0
Output:

Chunk ID: fc1152
Wall time: 0.0309 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: d9a778
Wall time: 1.0009 seconds
Process running with session ID 90905
Original token count: 0
Output:

Chunk ID: e8233e
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 279
Output:
shape=0 variant=eager tflops=3.676 gbps=20.358 ms=0.210
shape=0 variant=compiled tflops=3.175 gbps=17.582 ms=0.243
shape=0 variant=sota tflops=3.729 gbps=20.648 ms=0.207
shape=0 variant=solution tflops=22.851 gbps=126.543 ms=0.034
shape=0 solution_peak_fraction=0.1143
shape=1 variant=eager tflops=13.970 gbps=36.436 ms=0.221
shape=1 variant=compiled tflops=12.048 gbps=31.424 ms=0.256
shape=1 variant=sota tflops=14.128 gbps=36.848 ms=0.218
shape=1 variant=solution tflops=72.272 gbps=188.495 ms=0.043
shape=1 solution_peak_fraction=0.3614
shape=2 variant=eager tflops=26.327 gbps=54.381 ms=0.263
shape=2 variant=compiled tflops=22.800 gbps=47.096 ms=0.304
shape=2 variant=sota tflops=26.693 gbps=55.137 ms=0.260
shape=2 variant=solution tflops=122.880 gbps=253.823 ms=0.056
shape=2 solution_peak_fraction=0.6144
shape=3 variant=eager tflops=6.479 gbps=25.138 ms=0.143
shape=3 variant=compiled tflops=5.215 gbps=20.234 ms=0.177
shape=3 variant=sota tflops=6.588 gbps=25.559 ms=0.140
shape=3 variant=solution tflops=38.154 gbps=148.024 ms=0.024
shape=3 solution_peak_fraction=0.1908
peak_fraction: 0.2637
RESULT: OK

output:
  Success. Updated the following files:
  A /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/tune_wt.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: ef699e
Wall time: 1.0010 seconds
Process running with session ID 89982
Original token count: 0
Output:

Chunk ID: 48de60
Wall time: 5.0010 seconds
Process running with session ID 89982
Original token count: 0
Output:

Chunk ID: d7cf75
Wall time: 5.0013 seconds
Process running with session ID 89982
Original token count: 713
Output:
shape=0 {'B': 1, 'C': 3, 'T': 2, 'H': 224, 'W': 224, 'kT': 2, 'kH': 14, 'kW': 14, 'embed_dim': 1280}
  cfg=(8, 64, 64, 4, 3) ms=0.0331
  cfg=(16, 32, 64, 4, 3) ms=0.0361
  cfg=(16, 64, 32, 4, 3) ms=0.0471
  cfg=(16, 64, 64, 4, 3) ms=0.0341
  cfg=(16, 64, 64, 8, 3) ms=0.0321
  cfg=(16, 64, 128, 4, 3) ms=0.0318
  cfg=(16, 128, 64, 4, 3) ms=0.0350
  cfg=(32, 32, 64, 4, 3) ms=0.0378
  cfg=(32, 64, 32, 4, 3) ms=0.0483
  cfg=(32, 64, 64, 4, 3) ms=0.0454
  cfg=(32, 64, 64, 8, 3) ms=0.0354
  cfg=(32, 64, 128, 4, 3) ms=0.0409
  cfg=(32, 128, 64, 4, 3) ms=0.0449
  cfg=(64, 64, 64, 4, 3) ms=0.0472
  cfg=(64, 128, 32, 4, 3) ms=0.0612
  cfg=(64, 128, 64, 4, 3) ms=0.0496
best shape=0 cfg=(16, 64, 128, 4, 3) ms=0.0318
shape=1 {'B': 2, 'C': 3, 'T': 4, 'H': 224, 'W': 224, 'kT': 2, 'kH': 14, 'kW': 14, 'embed_dim': 1280}
  cfg=(8, 64, 64, 4, 3) ms=0.0832
  cfg=(16, 32, 64, 4, 3) ms=0.0823
  cfg=(16, 64, 32, 4, 3) ms=0.0508
  cfg=(16, 64, 64, 4, 3) ms=0.0601
  cfg=(16, 64, 64, 8, 3) ms=0.0559
  cfg=(16, 64, 128, 4, 3) ms=0.0681
  cfg=(16, 128, 64, 4, 3) ms=0.0604
  cfg=(32, 32, 64, 4, 3) ms=0.0711
  cfg=(32, 64, 32, 4, 3) ms=0.0488
  cfg=(32, 64, 64, 4, 3) ms=0.0435
  cfg=(32, 64, 64, 8, 3) ms=0.0436
  cfg=(32, 64, 128, 4, 3) ms=0.0558
  cfg=(32, 128, 64, 4, 3) ms=0.0426
  cfg=(64, 64, 64, 4, 3) ms=0.0474
  cfg=(64, 128, 32, 4, 3) ms=0.0532
  cfg=(64, 128, 64, 4, 3) ms=0.0465
best s

... (1465 more chars)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 1b9711
Wall time: 1.0011 seconds
Process running with session ID 65003
Original token count: 0
Output:

Chunk ID: eb31d6
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 62fe94
Wall time: 1.0011 seconds
Process running with session ID 49631
Original token count: 0
Output:

Chunk ID: f04a0a
Wall time: 1.2757 seconds
Process exited with code 0
Original token count: 279
Output:
shape=0 variant=eager tflops=3.683 gbps=20.394 ms=0.209
shape=0 variant=compiled tflops=3.196 gbps=17.700 ms=0.241
shape=0 variant=sota tflops=3.726 gbps=20.632 ms=0.207
shape=0 variant=solution tflops=22.394 gbps=124.013 ms=0.034
shape=0 solution_peak_fraction=0.1120
shape=1 variant=eager tflops=13.933 gbps=36.339 ms=0.221
shape=1 variant=compiled tflops=12.009 gbps=31.322 ms=0.257
shape=1 variant=sota tflops=14.090 gbps=36.748 ms=0.219
shape=1 variant=solution tflops=72.299 gbps=188.566 ms=0.043
shape=1 solution_peak_fraction=0.3615
shape=2 variant=eager tflops=26.276 gbps=54.275 ms=0.264
shape=2 variant=compiled tflops=23.022 gbps=47.554 ms=0.301
shape=2 variant=sota tflops=26.820 gbps=55.400 ms=0.259
shape=2 variant=solution tflops=128.527 gbps=265.487 ms=0.054
shape=2 solution_peak_fraction=0.6426
shape=3 variant=eager tflops=6.341 gbps=24.600 ms=0.146
shape=3 variant=compiled tflops=5.213 gbps=20.223 ms=0.177
shape=3 variant=sota tflops=6.580 gbps=25.527 ms=0.141
shape=3 variant=solution tflops=39.429 gbps=152.971 ms=0.023
shape=3 solution_peak_fraction=0.1971
peak_fraction: 0.2676
RESULT: OK

Chunk ID: b55450
Wall time: 1.0010 seconds
Process running with session ID 35026
Original token count: 0
Output:

Chunk ID: 7179d8
Wall time: 1.1931 seconds
Process exited with code 0
Original token count: 279
Output:
shape=0 variant=eager tflops=3.680 gbps=20.378 ms=0.209
shape=0 variant=compiled tflops=3.188 gbps=17.654 ms=0.242
shape=0 variant=sota tflops=3.721 gbps=20.605 ms=0.207
shape=0 variant=solution tflops=22.404 gbps=124.071 ms=0.034
shape=0 solution_peak_fraction=0.1120
shape=1 variant=eager tflops=13.945 gbps=36.370 ms=0.221
shape=1 variant=compiled tflops=12.008 gbps=31.320 ms=0.257
shape=1 variant=sota tflops=14.107 gbps=36.794 ms=0.219
shape=1 variant=solution tflops=72.028 gbps=187.861 ms=0.043
shape=1 solution_peak_fraction=0.3601
shape=2 variant=eager tflops=26.206 gbps=54.131 ms=0.265
shape=2 variant=compiled tflops=23.096 gbps=47.708 ms=0.300
shape=2 variant=sota tflops=26.877 gbps=55.517 ms=0.258
shape=2 variant=solution tflops=128.451 gbps=265.330 ms=0.054
shape=2 solution_peak_fraction=0.6423
shape=3 variant=eager tflops=6.442 gbps=24.992 ms=0.144
shape=3 variant=compiled tflops=5.239 gbps=20.326 ms=0.177
shape=3 variant=sota tflops=6.581 gbps=25.533 ms=0.141
shape=3 variant=solution tflops=38.535 gbps=149.504 ms=0.024
shape=3 solution_peak_fraction=0.1927
peak_fraction: 0.2658
RESULT: OK

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 3a727c
Wall time: 1.0010 seconds
Process running with session ID 48712
Original token count: 0
Output:

Chunk ID: b8e244
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 211b46
Wall time: 1.0011 seconds
Process running with session ID 87686
Original token count: 0
Output:

Chunk ID: d5547b
Wall time: 1.3696 seconds
Process exited with code 0
Original token count: 279
Output:
shape=0 variant=eager tflops=3.689 gbps=20.428 ms=0.209
shape=0 variant=compiled tflops=3.078 gbps=17.044 ms=0.250
shape=0 variant=sota tflops=3.687 gbps=20.416 ms=0.209
shape=0 variant=solution tflops=20.471 gbps=113.367 ms=0.038
shape=0 solution_peak_fraction=0.1024
shape=1 variant=eager tflops=13.950 gbps=36.383 ms=0.221
shape=1 variant=compiled tflops=11.997 gbps=31.289 ms=0.257
shape=1 variant=sota tflops=14.121 gbps=36.829 ms=0.218
shape=1 variant=solution tflops=67.987 gbps=177.321 ms=0.045
shape=1 solution_peak_fraction=0.3399
shape=2 variant=eager tflops=26.365 gbps=54.460 ms=0.263
shape=2 variant=compiled tflops=23.076 gbps=47.665 ms=0.301
shape=2 variant=sota tflops=26.933 gbps=55.634 ms=0.258
shape=2 variant=solution tflops=125.987 gbps=260.241 ms=0.055
shape=2 solution_peak_fraction=0.6299
shape=3 variant=eager tflops=6.530 gbps=25.334 ms=0.142
shape=3 variant=compiled tflops=5.224 gbps=20.269 ms=0.177
shape=3 variant=sota tflops=6.603 gbps=25.618 ms=0.140
shape=3 variant=solution tflops=41.111 gbps=159.499 ms=0.022
shape=3 solution_peak_fraction=0.2056
peak_fraction: 0.2591
RESULT: OK

Chunk ID: b7c1f6
Wall time: 1.0016 seconds
Process running with session ID 9332
Original token count: 0
Output:

Chunk ID: 2c2e2e
Wall time: 1.1980 seconds
Process exited with code 0
Original token count: 279
Output:
shape=0 variant=eager tflops=3.676 gbps=20.355 ms=0.210
shape=0 variant=compiled tflops=3.184 gbps=17.632 ms=0.242
shape=0 variant=sota tflops=3.727 gbps=20.642 ms=0.207
shape=0 variant=solution tflops=23.349 gbps=129.303 ms=0.033
shape=0 solution_peak_fraction=0.1167
shape=1 variant=eager tflops=13.878 gbps=36.195 ms=0.222
shape=1 variant=compiled tflops=11.978 gbps=31.240 ms=0.257
shape=1 variant=sota tflops=14.096 gbps=36.764 ms=0.219
shape=1 variant=solution tflops=67.487 gbps=176.017 ms=0.046
shape=1 solution_peak_fraction=0.3374
shape=2 variant=eager tflops=26.315 gbps=54.358 ms=0.264
shape=2 variant=compiled tflops=23.025 gbps=47.562 ms=0.301
shape=2 variant=sota tflops=26.925 gbps=55.617 ms=0.258
shape=2 variant=solution tflops=125.622 gbps=259.487 ms=0.055
shape=2 solution_peak_fraction=0.6281
shape=3 variant=eager tflops=6.472 gbps=25.110 ms=0.143
shape=3 variant=compiled tflops=5.216 gbps=20.236 ms=0.177
shape=3 variant=sota tflops=6.598 gbps=25.597 ms=0.140
shape=3 variant=solution tflops=40.535 gbps=157.262 ms=0.023
shape=3 solution_peak_fraction=0.2027
peak_fraction: 0.2661
RESULT: OK

Chunk ID: 4a22a5
Wall time: 1.0008 seconds
Process running with session ID 39121
Original token count: 0
Output:

Chunk ID: c91f09
Wall time: 1.0692 seconds
Process exited with code 0
Original token count: 279
Output:
shape=0 variant=eager tflops=3.690 gbps=20.435 ms=0.209
shape=0 variant=compiled tflops=3.185 gbps=17.636 ms=0.242
shape=0 variant=sota tflops=3.732 gbps=20.666 ms=0.207
shape=0 variant=solution tflops=23.531 gbps=130.314 ms=0.033
shape=0 solution_peak_fraction=0.1177
shape=1 variant=eager tflops=13.921 gbps=36.307 ms=0.221
shape=1 variant=compiled tflops=12.022 gbps=31.355 ms=0.256
shape=1 variant=sota tflops=14.100 gbps=36.775 ms=0.219
shape=1 variant=solution tflops=69.059 gbps=180.118 ms=0.045
shape=1 solution_peak_fraction=0.3453
shape=2 variant=eager tflops=26.357 gbps=54.444 ms=0.263
shape=2 variant=compiled tflops=23.047 gbps=47.607 ms=0.301
shape=2 variant=sota tflops=26.895 gbps=55.555 ms=0.258
shape=2 variant=solution tflops=126.983 gbps=262.299 ms=0.055
shape=2 solution_peak_fraction=0.6349
shape=3 variant=eager tflops=6.501 gbps=25.220 ms=0.142
shape=3 variant=compiled tflops=5.220 gbps=20.251 ms=0.177
shape=3 variant=sota tflops=6.589 gbps=25.562 ms=0.140
shape=3 variant=solution tflops=39.456 gbps=153.076 ms=0.023
shape=3 solution_peak_fraction=0.1973
peak_fraction: 0.2671
RESULT: OK

Chunk ID: 9f36ba
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 10
Output:
Found 4 errors (4 fixed, 0 remaining).

Chunk ID: 0b1c3a
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1873
Output:
import torch
import torch.nn as nn
import triton
import triton.language as tl


@triton.jit
def _pack_weight_kernel(
    weight,
    weight_t,
    total: tl.constexpr,
    embed_dim: tl.constexpr,
    K: tl.constexpr,
    BLOCK: tl.constexpr,
):
    offs = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK)
    k = offs // embed_dim
    e = offs - k * embed_dim
    vals = tl.load(weight + e * K + k, mask=offs < total, other=0.0)
    tl.store(weight_t + offs, vals, mask=offs < total)


@triton.jit
def _patch_embed_kernel(
    x,
    weight,
    y,
    total_patches: tl.constexpr,
    patches_per_batch: tl.constexpr,
    C: tl.constexpr,
    T: tl.constexpr,
    H: tl.constexpr,
    W: tl.constexpr,
    kT: tl.constexpr,
    kH: tl.constexpr,
    kW: tl.constexpr,
    To: tl.constexpr,
    Ho: tl.constexpr,
    Wo: tl.constexpr,
    embed_dim: tl.constexpr,
    K: 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)

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

    b = offs_m // patches_per_batch
    local_m = offs_m - b * patches_per_batch
    ot = local_m // (Ho * Wo)
    rem = local_m - ot * (Ho * Wo)
    oh = rem // Wo
    ow = rem - oh * Wo

    acc = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)

    for k0 in range(

... (6094 more chars)

Chunk ID: 1e3cf2
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 17
Output:
?? ../09_fmha_preattn_mrope/
?? ./
?? ../../scripts/sweep_09_10.sh

Chunk ID: 1c2c68
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1250
Output:
        self.To, self.Ho, self.Wo = T // kT, H // kH, W // kW
        self.K = C * kT * kH * kW
        self.total_patches = B * self.To * self.Ho * self.Wo
        self.patches_per_batch = self.To * self.Ho * self.Wo

        self.conv = _WeightOnly(C, kT, kH, kW, embed_dim)
        nn.init.normal_(self.conv.weight, std=0.02)
        self._weight_t = None
        self._weight_t_version = -1

    def _packed_weight(self) -> torch.Tensor:
        version = self.conv.weight._version
        if (
            self._weight_t is None
            or self._weight_t.device != self.conv.weight.device
            or self._weight_t_version != version
        ):
            weight_t = torch.empty(
                (self.K, self.embed_dim),
                device=self.conv.weight.device,
                dtype=self.conv.weight.dtype,
            )
            block = 1024
            grid = (triton.cdiv(self.K * self.embed_dim, block),)
            _pack_weight_kernel[grid](
                self.conv.weight,
                weight_t,
                self.K * self.embed_dim,
                self.embed_dim,
                self.K,
                BLOCK=block,
                num_warps=4,
            )
            self._weight_t = weight_t
            self._weight_t_version = version
        return self._weight_t

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        y = torch.empt

... (3603 more chars)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: c9e8c3
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: 8b06f3
Wall time: 1.0008 seconds
Process running with session ID 44867
Original token count: 0
Output:

Chunk ID: 9131b4
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 9fc43c
Wall time: 1.0010 seconds
Process running with session ID 94453
Original token count: 0
Output:

Chunk ID: 9426cf
Wall time: 0.8322 seconds
Process exited with code 0
Original token count: 279
Output:
shape=0 variant=eager tflops=3.683 gbps=20.395 ms=0.209
shape=0 variant=compiled tflops=3.201 gbps=17.729 ms=0.241
shape=0 variant=sota tflops=3.733 gbps=20.674 ms=0.206
shape=0 variant=solution tflops=23.036 gbps=127.571 ms=0.033
shape=0 solution_peak_fraction=0.1152
shape=1 variant=eager tflops=13.947 gbps=36.376 ms=0.221
shape=1 variant=compiled tflops=12.013 gbps=31.332 ms=0.257
shape=1 variant=sota tflops=14.106 gbps=36.791 ms=0.219
shape=1 variant=solution tflops=67.534 gbps=176.140 ms=0.046
shape=1 solution_peak_fraction=0.3377
shape=2 variant=eager tflops=26.370 gbps=54.470 ms=0.263
shape=2 variant=compiled tflops=23.011 gbps=47.531 ms=0.301
shape=2 variant=sota tflops=26.890 gbps=55.544 ms=0.258
shape=2 variant=solution tflops=126.723 gbps=261.762 ms=0.055
shape=2 solution_peak_fraction=0.6336
shape=3 variant=eager tflops=6.418 gbps=24.901 ms=0.144
shape=3 variant=compiled tflops=5.231 gbps=20.293 ms=0.177
shape=3 variant=sota tflops=6.611 gbps=25.647 ms=0.140
shape=3 variant=solution tflops=40.678 gbps=157.816 ms=0.023
shape=3 solution_peak_fraction=0.2034
peak_fraction: 0.2661
RESULT: OK

Chunk ID: dc000b
Wall time: 0.0000 seconds
Process exited with code 5
Original token count: 79
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
collected 0 items

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

Chunk ID: 464c3e
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1783
Output:
import torch
import torch.nn as nn
import triton
import triton.language as tl


@triton.jit
def _patch_embed_kernel(
    x,
    weight,
    y,
    total_patches: tl.constexpr,
    patches_per_batch: tl.constexpr,
    C: tl.constexpr,
    T: tl.constexpr,
    H: tl.constexpr,
    W: tl.constexpr,
    kT: tl.constexpr,
    kH: tl.constexpr,
    kW: tl.constexpr,
    To: tl.constexpr,
    Ho: tl.constexpr,
    Wo: tl.constexpr,
    embed_dim: tl.constexpr,
    K: 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)

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

    b = offs_m // patches_per_batch
    local_m = offs_m - b * patches_per_batch
    ot = local_m // (Ho * Wo)
    rem = local_m - ot * (Ho * Wo)
    oh = rem // Wo
    ow = rem - oh * Wo

    acc = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)

    for k0 in range(0, K, BLOCK_K):
        offs_k = k0 + tl.arange(0, BLOCK_K)
        c = offs_k // (kT * kH * kW)
        kr = offs_k - c * (kT * kH * kW)
        kt = kr // (kH * kW)
        kr = kr - kt * (kH * kW)
        kh = kr // kW
        kw = kr - kh * kW

        x_t = ot[:, None] * kT + kt[None, :]
        x_h = oh[:, None] * kH + kh[None, :]
        x_w = ow[:, None] * kW + kw[None, :]
        x_offsets = (

... (5736 more chars)

Chunk ID: 97c36b
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 255
Output:
total 76
drwxrwxr-x  3 infatoshi infatoshi 4096 May  7 04:34 .
drwxrwxr-x 12 infatoshi infatoshi 4096 May  4 13:57 ..
-rw-rw-r--  1 infatoshi infatoshi 2969 May  4 14:03 PROMPT.txt
drwxrwxr-x  2 infatoshi infatoshi 4096 May  7 04:36 __pycache__
-rw-rw-r--  1 infatoshi infatoshi 4626 May  4 14:02 benchmark.py
-rw-rw-r--  1 infatoshi infatoshi 3988 May  4 14:02 check.py
-rw-rw-r--  1 infatoshi infatoshi    7 May  7 04:36 framework.txt
-rw-rw-r--  1 infatoshi infatoshi 1541 May  4 14:01 problem.yaml
-rw-rw-r--  1 infatoshi infatoshi 1994 May  4 14:01 reference.py
-rw-rw-r--  1 infatoshi infatoshi  953 May  4 14:01 shapes.py
-rw-rw-r--  1 infatoshi infatoshi 8065 May  7 04:36 solution.py
-rw-rw-r--  1 infatoshi infatoshi  534 May  4 14:02 sota.py
-rw-rw-r--  1 infatoshi infatoshi 2939 May  7 04:36 tune_big.py
-rw-rw-r--  1 infatoshi infatoshi 4259 May  7 04:36 tune_orientations.py
-rw-rw-r--  1 infatoshi infatoshi 2979 May  7 04:36 tune_triton.py
-rw-rw-r--  1 infatoshi infatoshi 3026 May  7 04:36 tune_wt.py

Chunk ID: 134040
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 237
Output:
            )
            _patch_embed_kernel_em[grid](
                x,
                self.conv.weight,
                y,
                self.total_patches,
                self.patches_per_batch,
                self.C,
                self.T,
                self.H,
                self.W,
                self.kT,
                self.kH,
                self.kW,
                self.To,
                self.Ho,
                self.Wo,
                self.embed_dim,
                self.K,
                BLOCK_E=block_e,
                BLOCK_M=block_m,
                BLOCK_K=block_k,
                num_warps=warps,
                num_stages=3,
            )
        return y


B = 1
C = 3
T = 2
H = 224
W = 224
kT = 2
kH = 14
kW = 14
embed_dim = 1280


def get_inputs():
    x = torch.randn(B, C, T, H, W, dtype=torch.bfloat16) * 0.5
    return [x]


def get_init_inputs():
    return [B, C, T, H, W, kT, kH, kW, embed_dim]

output:
  Success. Updated the following files:
  A /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/tune_small.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 243ee8
Wall time: 1.0010 seconds
Process running with session ID 8675
Original token count: 0
Output:

Chunk ID: 741bc6
Wall time: 1.4026 seconds
Process exited with code 0
Original token count: 394
Output:
shape=0 {'B': 1, 'C': 3, 'T': 2, 'H': 224, 'W': 224, 'kT': 2, 'kH': 14, 'kW': 14, 'embed_dim': 1280}
  cfg=(8, 32, 64, 4, 3) ms=0.0381
  cfg=(8, 64, 64, 4, 3) ms=0.0388
  cfg=(8, 64, 128, 4, 3) ms=0.0439
  cfg=(8, 128, 32, 4, 3) ms=0.0561
  cfg=(8, 128, 64, 4, 3) ms=0.0396
  cfg=(8, 128, 128, 4, 3) ms=0.0476
  cfg=(16, 32, 128, 4, 3) ms=0.0488
  cfg=(16, 64, 64, 4, 3) ms=0.0330
  cfg=(16, 64, 64, 8, 3) ms=0.0323
  cfg=(16, 64, 128, 4, 3) ms=0.0333
  cfg=(16, 128, 64, 4, 3) ms=0.0349
  cfg=(16, 128, 128, 4, 3) ms=0.0317
  cfg=(16, 256, 64, 4, 3) ms=0.0489
  cfg=(32, 32, 128, 4, 3) ms=0.0359
  cfg=(32, 64, 64, 4, 3) ms=0.0385
  cfg=(32, 64, 128, 4, 3) ms=0.0381
  cfg=(32, 128, 64, 4, 3) ms=0.0429
  cfg=(32, 128, 128, 4, 3) ms=0.0336
best shape=0 cfg=(16, 128, 128, 4, 3) ms=0.0317
shape=3 {'B': 4, 'C': 3, 'T': 1, 'H': 224, 'W': 224, 'kT': 1, 'kH': 16, 'kW': 16, 'embed_dim': 768}
  cfg=(8, 32, 64, 4, 3) ms=0.0375
  cfg=(8, 64, 64, 4, 3) ms=0.0377
  cfg=(8, 64, 128, 4, 3) ms=0.0341
  cfg=(8, 128, 32, 4, 3) ms=0.0412
  cfg=(8, 128, 64, 4, 3) ms=0.0349
  cfg=(8, 128, 128, 4, 3) ms=0.0352
  cfg=(16, 32, 128, 4, 3) ms=0.0530
  cfg=(16, 64, 64, 4, 3) ms=0.0485
  cfg=(16, 64, 64, 8, 3) ms=0.0510
  cfg=(16, 64, 128, 4, 3) ms=0.0510
  cfg=(16, 128, 64, 4, 3) ms=0.0352
  cfg=(16, 128, 128, 4, 3) ms=0.0271
  cfg=(16, 256, 64, 4, 3) ms=0.0253
  cfg=(32, 32, 128, 4, 3) ms=0.0248
  cfg=(32, 

... (180 more chars)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: b6a73e
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 10
Output:
Found 1 error (1 fixed, 0 remaining).

Chunk ID: 17b363
Wall time: 1.0005 seconds
Process running with session ID 12862
Original token count: 0
Output:

Chunk ID: 98ce27
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 8d079d
Wall time: 1.0004 seconds
Process running with session ID 69818
Original token count: 0
Output:

Chunk ID: 16b5e4
Wall time: 1.3261 seconds
Process exited with code 0
Original token count: 279
Output:
shape=0 variant=eager tflops=3.693 gbps=20.450 ms=0.209
shape=0 variant=compiled tflops=3.147 gbps=17.426 ms=0.245
shape=0 variant=sota tflops=3.742 gbps=20.720 ms=0.206
shape=0 variant=solution tflops=23.080 gbps=127.816 ms=0.033
shape=0 solution_peak_fraction=0.1154
shape=1 variant=eager tflops=13.949 gbps=36.381 ms=0.221
shape=1 variant=compiled tflops=11.281 gbps=29.424 ms=0.273
shape=1 variant=sota tflops=13.961 gbps=36.412 ms=0.221
shape=1 variant=solution tflops=53.328 gbps=139.089 ms=0.058
shape=1 solution_peak_fraction=0.2666
shape=2 variant=eager tflops=26.330 gbps=54.387 ms=0.263
shape=2 variant=compiled tflops=23.088 gbps=47.691 ms=0.300
shape=2 variant=sota tflops=26.852 gbps=55.465 ms=0.258
shape=2 variant=solution tflops=125.367 gbps=258.961 ms=0.055
shape=2 solution_peak_fraction=0.6268
shape=3 variant=eager tflops=6.501 gbps=25.220 ms=0.142
shape=3 variant=compiled tflops=5.158 gbps=20.010 ms=0.179
shape=3 variant=sota tflops=6.579 gbps=25.524 ms=0.141
shape=3 variant=solution tflops=40.253 gbps=156.167 ms=0.023
shape=3 solution_peak_fraction=0.2013
peak_fraction: 0.2496
RESULT: OK

Chunk ID: cc959f
Wall time: 1.0009 seconds
Process running with session ID 43421
Original token count: 0
Output:

Chunk ID: 60a353
Wall time: 1.8455 seconds
Process exited with code 0
Original token count: 279
Output:
shape=0 variant=eager tflops=3.660 gbps=20.270 ms=0.211
shape=0 variant=compiled tflops=3.117 gbps=17.263 ms=0.247
shape=0 variant=sota tflops=3.698 gbps=20.478 ms=0.208
shape=0 variant=solution tflops=22.615 gbps=125.236 ms=0.034
shape=0 solution_peak_fraction=0.1131
shape=1 variant=eager tflops=13.303 gbps=34.695 ms=0.232
shape=1 variant=compiled tflops=12.021 gbps=31.353 ms=0.256
shape=1 variant=sota tflops=14.101 gbps=36.778 ms=0.219
shape=1 variant=solution tflops=69.333 gbps=180.831 ms=0.044
shape=1 solution_peak_fraction=0.3467
shape=2 variant=eager tflops=26.362 gbps=54.454 ms=0.263
shape=2 variant=compiled tflops=23.083 gbps=47.681 ms=0.300
shape=2 variant=sota tflops=26.942 gbps=55.651 ms=0.257
shape=2 variant=solution tflops=126.097 gbps=260.468 ms=0.055
shape=2 solution_peak_fraction=0.6305
shape=3 variant=eager tflops=6.489 gbps=25.175 ms=0.143
shape=3 variant=compiled tflops=5.217 gbps=20.240 ms=0.177
shape=3 variant=sota tflops=6.602 gbps=25.612 ms=0.140
shape=3 variant=solution tflops=39.974 gbps=155.087 ms=0.023
shape=3 solution_peak_fraction=0.1999
peak_fraction: 0.2651
RESULT: OK

Chunk ID: 7b5648
Wall time: 0.0000 seconds
Process exited with code 5
Original token count: 79
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
collected 0 items

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