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/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm
PROMPT.txt
shapes.py
sota.py
reference.py
check.py
benchmark.py
problem.yaml

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## master...origin/master

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uv 0.11.3 (x86_64-unknown-linux-gnu)

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Filesystem      Size  Used Avail Use% Mounted on
/dev/nvme0n1p2  3.6T  1.8T  1.7T  51% /

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

We cast inputs to bf16 and use torch.matmul. The agent's solution must match
this numerically within the fp8 tolerance declared in problem.yaml.
"""
import torch
import torch.nn as nn

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


class Model(nn.Module):
    """y = (x @ w.T).to(bf16), where x is fp8_e4m3 (M, K), w is fp8_e4m3 (N, K)."""

    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        # Weights stored as parameters so state_dict is well-defined.
        # We initialize in bf16 then cast; the fp8 dtype is set by get_inputs.
        self.weight = nn.Parameter(torch.empty(N, K, dtype=torch.bfloat16))
        nn.init.normal_(self.weight, std=0.02)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Upcast to bf16 for the naive reference; the kernel equivalent would
        # use mma.sync f8f6f4 kind directly.
        x_bf = x.to(torch.bfloat16)
        w_bf = self.weight.to(torch.bfloat16)
        return x_bf @ w_bf.T  # (M, N) bf16


M = 4096
N = 4096
K = 4096


def get_inputs():
    # fp8_e4m3 input; random uniform in [-4, 4] then cast.
    x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn)
    return [x]


def get_init_inputs():
    return [M, N

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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


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):
        # Rebuild reference mo

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"""Roofline benchmark for FP8 GEMM.

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 = torch.device("cuda:0

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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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Mon Apr 27 23:07:33 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  On |                  Off |
| 30%   28C    P8             15W /  600W |     508MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

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Python 3.11.15

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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:
  fp8_e4m3fn: 0.15

# 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 

... (327 more chars)

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2.11.0+cu130
13.0
['sm_75', 'sm_80', 'sm_86', 'sm_90', 'sm_100', 'sm_120']

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Output:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2026 NVIDIA Corporation
Built on Mon_Mar_02_09:52:23_PM_PST_2026
Cuda compilation tools, release 13.2, V13.2.51
Build cuda_13.2.r13.2/compiler.37434383_0

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total 40
drwxrwxr-x  2 infatoshi infatoshi 4096 Apr 27 18:02 .
drwxrwxr-x 10 infatoshi infatoshi 4096 Apr 24 00:30 ..
-rw-rw-r--  1 infatoshi infatoshi 1850 Apr 27 03:21 PROMPT.txt
-rw-rw-r--  1 infatoshi infatoshi 4228 Apr 26 03:11 benchmark.py
-rw-rw-r--  1 infatoshi infatoshi 3805 Apr 24 00:37 check.py
-rw-rw-r--  1 infatoshi infatoshi 1128 Apr 24 00:36 problem.yaml
-rw-rw-r--  1 infatoshi infatoshi 1401 Apr 24 00:40 reference.py
-rw-rw-r--  1 infatoshi infatoshi  564 Apr 24 00:36 shapes.py
-rw-rw-r--  1 infatoshi infatoshi 1725 Apr 24 00:37 sota.py

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/home/infatoshi/.cache/uv/wheels-v6/pypi/cutlass
/home/infatoshi/.cache/uv/archive-v0/n7pHATJd_WwJGa7XEx1Rg/cutlass
/home/infatoshi/vllm/csrc/quantization/w8a8/cutlass
/home/infatoshi/vllm/.deps/flashmla-src/csrc/cutlass
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass
/home/infatoshi/vllm/.deps/vllm-flash-attn-src/csrc/cutlass
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/cutlass
/home/infatoshi/vllm/.deps/cutlass-build/include/cutlass
/home/infatoshi/vllm/.deps/qutlass-src/third_party/cutlass
/home/infatoshi/cuda/KernelBench-v3/docs/refs/cutlass
/home/infatoshi/cuda/fa4/flash-attention/csrc/cutlass
/home/infatoshi/cuda/SageAttention/sageattention3_blackwell/csrc/cutlass

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/usr/local/cuda-13.2/include/library_types.h:87:    CUDA_R_8F_E4M3 = 28, /* real as a nv_fp8_e4m3 */
/usr/local/cuda-13.2/include/library_types.h:88:    CUDA_R_8F_UE4M3 = CUDA_R_8F_E4M3, /* real as an unsigned nv_fp8_e4m3 */
/usr/local/cuda-13.2/include/cuda_fp8.hpp:256:            asm("{cvt.rn.satfinite.e4m3x2.f32 %0, %2, %1;}\n"
/usr/local/cuda-13.2/include/cuda_fp8.hpp:301:            asm("{cvt.rn.satfinite.e4m3x2.f32 %0, %2, %1;}\n"
/usr/local/cuda-13.2/include/cuda_fp8.hpp:381:            asm("{cvt.rn.satfinite.e4m3x2.f16x2 %0, %1;}\n"
/usr/local/cuda-13.2/include/cuda_fp8.hpp:409:            asm("{cvt.rn.satfinite.e4m3x2.f16x2 %0, %1;}\n"
/usr/local/cuda-13.2/include/cuda_fp8.hpp:456:            asm("{cvt.rn.satfinite.e4m3x2.bf16x2 %0, %1;}\n"
/usr/local/cuda-13.2/include/cuda_fp8.hpp:485:            asm("{cvt.rn.satfinite.e4m3x2.bf16x2 %0, %1;}\n"
/usr/local/cuda-13.2/include/cuda_fp8.hpp:572:        asm("{cvt.rn.f16x2.e4m3x2 %0, %1;}\n" : "=r"(half2_storage) : "h"(x));
/usr/local/cuda-13.2/include/cuda_fp8.hpp:1562: * \defgroup CUDA_MATH_FP8_E4M3_STRUCT C++ struct for handling fp8 data type of e4m3 kind.
/usr/local/cuda-13.2/include/cuda_fp8.hpp:1568: * \brief __nv_fp8_e4m3 datatype
/usr/local/cuda-13.2/include/cuda_fp8.hpp:1571: * \p fp8 floating-point numbers of \p e4m3 kind:
/usr/local/cuda-13.2/include/cuda_fp8.hpp:1578:struct __CUDA

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/home/infatoshi/vllm/.deps/cutlass-src/test/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/test/self_contained_includes/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/python/README.md
/home/infatoshi/vllm/.deps/cutlass-src/examples/73_blackwell_gemm_preferred_cluster/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/examples/63_hopper_gemm_with_weight_prefetch/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/examples/63_hopper_gemm_with_weight_prefetch/README.md
/home/infatoshi/vllm/.deps/cutlass-src/examples/92_blackwell_moe_gemm/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/examples/08_turing_tensorop_gemm/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/examples/58_ada_fp8_gemm/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/examples/33_ampere_3xtf32_tensorop_symm/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/examples/75_blackwell_grouped_gemm/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/examples/64_ada_fp8_gemm_grouped/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/examples/cute/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/examples/93_blackwell_low_latency_gqa/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/examples/47_ampere_gemm_universal_streamk/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/examples/89_sm103_fp4_ult

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/home/infatoshi/vllm/.deps/cutlass-src/examples/77_blackwell_fmha/77_blackwell_mla.cu:32:          NVIDIA Blackwell Architecture.
/home/infatoshi/vllm/.deps/cutlass-src/examples/77_blackwell_fmha/77_blackwell_mla.cu:182:      << "  attention kernels targeting NVIDIA's Blackwell architecture.\n\n"
/home/infatoshi/vllm/.deps/cutlass-src/examples/77_blackwell_fmha/77_blackwell_mla.cu:285:  using Element = cutlass::float_e4m3_t;
/home/infatoshi/vllm/.deps/cutlass-src/examples/77_blackwell_fmha/77_blackwell_mla.cu:781:      << "This example requires a GPU of NVIDIA's Blackwell Architecture "
/home/infatoshi/vllm/.deps/cutlass-src/examples/55_hopper_mixed_dtype_gemm/55_hopper_int4_fp8_gemm.cu:122:using MmaType = cutlass::float_e4m3_t;
/home/infatoshi/vllm/.deps/cutlass-src/examples/55_hopper_mixed_dtype_gemm/55_hopper_int4_fp8_gemm.cu:440:  constexpr bool IsFP8Input = cute::is_same_v<MmaType, cutlass::float_e4m3_t> || cute::is_same_v<MmaType, cutlass::float_e5m2_t>;
/home/infatoshi/vllm/.deps/cutlass-src/examples/78_blackwell_emulated_bf16x9_gemm/78_blackwell_emulated_bf16x9_gemm.cu:33:    \brief A Blackwell CUTLASS GEMM example for FastFP32 (using BF16 to emulate SGEMM).
/home/infatoshi/vllm/.deps/cutlass-src/examples/78_blackwell_emulated_bf16x9_gemm/78_blackwell_emulated_bf16x9_gemm.cu:36:    NVIDIA's Blackwell architecture (SM100a). Using BF16x9 l

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/home/infatoshi/vllm/.deps/cutlass-src/examples/73_blackwell_gemm_preferred_cluster/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/examples/73_blackwell_gemm_preferred_cluster/blackwell_gemm_preferred_cluster.cu
/home/infatoshi/vllm/.deps/cutlass-src/examples/63_hopper_gemm_with_weight_prefetch/gemm_with_weight_prefetch_commandline.hpp
/home/infatoshi/vllm/.deps/cutlass-src/examples/63_hopper_gemm_with_weight_prefetch/collective/sm90_mma_tma_gmma_ss_warpspecialized_with_prefetch.hpp
/home/infatoshi/vllm/.deps/cutlass-src/examples/63_hopper_gemm_with_weight_prefetch/collective/builder.hpp
/home/infatoshi/vllm/.deps/cutlass-src/examples/63_hopper_gemm_with_weight_prefetch/collective/dispatch_policy_extra.hpp
/home/infatoshi/vllm/.deps/cutlass-src/examples/63_hopper_gemm_with_weight_prefetch/CMakeLists.txt
/home/infatoshi/vllm/.deps/cutlass-src/examples/63_hopper_gemm_with_weight_prefetch/kernel/sm90_gemm_tma_warpspecialized_with_prefetch.hpp
/home/infatoshi/vllm/.deps/cutlass-src/examples/63_hopper_gemm_with_weight_prefetch/README.md
/home/infatoshi/vllm/.deps/cutlass-src/examples/63_hopper_gemm_with_weight_prefetch/63_hopper_gemm_with_weight_prefetch.cu
/home/infatoshi/vllm/.deps/cutlass-src/examples/63_hopper_gemm_with_weight_prefetch/pipeline/prefetch_pipeline_sm90.hpp
/home/infatoshi/vllm/.deps/cutlass-src/examples/92_blackwell_moe_gemm/92_blackwell_moe_gemm_fp4_g

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/home/infatoshi/vllm/.deps/deepgemm-src/.gitmodules
/home/infatoshi/vllm/.deps/deepgemm-src/build.sh
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/scan.h
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/posix-mock-test.cc
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/chrono-test.cc
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/compile-fp-test.cc
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/color-test.cc
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/posix-mock.h
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/ranges-odr-test.cc
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/mock-allocator.h
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/gtest-extra.cc
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/ostream-test.cc
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/util.cc
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/printf-test.cc
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/format-impl-test.cc
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/args-test.cc
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/ranges-test.cc
/home/infatoshi/vllm/.deps/deepgemm-src/third-party/fmt/test/header-only-test.cc
/home/infatoshi/vllm/.deps/deepgemm-src/third

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find: ‘/home/infatoshi/SageAttention/sageattention3_blackwell/csrc’: No such file or directory

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/***************************************************************************************************
 * Copyright (c) 2024 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUB

... (10451 more chars)

Chunk ID: 73d01d
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Output:
    cmd.get_cmd_line_argument("swizzle", swizzle);
  }

  /// Prints the usage statement.
  std::ostream & print_usage(std::ostream &out) const {

    out << "70_blackwell_fp8_gemm\n\n"
      << "  Blackwell FP8 GEMM using a Warp Specialized kernel.\n\n"
      << "Options:\n\n"
      << "  --help                      If specified, displays this usage statement\n\n"
      << "  --m=<int>                   Sets the M extent of the GEMM\n"
      << "  --n=<int>                   Sets the N extent of the GEMM\n"
      << "  --k=<int>                   Sets the K extent of the GEMM\n"
      << "  --l=<int>                   Sets the l extent (batch) of the GEMM\n"
      << "  --alpha=<f32>               Epilogue scalar alpha\n"
      << "  --beta=<f32>                Epilogue scalar beta\n"
      << "  --swizzle=<int>             Cluster rasterization swizzle\n"
      << "  --scale_a=<f32>             Scaling factor for A\n"
      << "  --scale_b=<f32>             Scaling factor for B\n"
      << "  --scale_c=<f32>             Scaling factor for C\n"
      << "  --scale_d=<f32>             Scaling factor for D (ignored for non-fp8 D)\n"
      << "  --scale_aux=<f32>           Scaling factor for the auxiliary tensor (ignored for non-fp8 aux)\n"
      << "  --device_scale=<bool>       Copy scalars to device memory before kernel launch (default: false)\n"
      << "  --save_aux=<b

... (11170 more chars)

Chunk ID: 4f6065
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Output:
/***************************************************************************************************
 * Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUB

... (10056 more chars)

Chunk ID: 7b946f
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Original token count: 2478
Output:
/***************************************************************************************************
 * Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUB

... (8516 more chars)

Chunk ID: 8f57d9
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Original token count: 2625
Output:
  /// Prints the usage statement.
  std::ostream & print_usage(std::ostream &out) const {

    out << "87a_blackwell_geforce_gemm_blockwise\n\n"
      << "  Blackwell FP8 GEMM with Blockwise Scaling using a Warp Specialized kernel.\n\n"
      << "Options:\n\n"
      << "  --help                      If specified, displays this usage statement\n\n"
      << "  --m=<int>                   Sets the M extent of the GEMM\n"
      << "  --n=<int>                   Sets the N extent of the GEMM\n"
      << "  --k=<int>                   Sets the K extent of the GEMM\n"
      << "  --l=<int>                   Sets the l extent (batch) of the GEMM\n"
      << "  --alpha=<f32>               Epilogue scalar alpha\n"
      << "  --beta=<f32>                Epilogue scalar beta\n"
      << "  --iterations=<int>          Number of profiling iterations to perform.\n\n"
      << "  --skip-verification         Skip verification.\n\n";

    out
      << "\n\nExamples:\n\n"
      << "$ " << "87a_blackwell_geforce_gemm_blockwise" << " --m=1024 --n=512 --k=1024 --alpha=2 --beta=0.707 \n\n";

    return out;
  }

  /// Compute performance in GFLOP/s
  double gflops(double runtime_s) const {
    // Two flops per multiply-add
    uint64_t flop = uint64_t(2) * m * n * k;
    double gflop = double(flop) / double(1.0e9);
    return gflop / runtime_s;
  }
};

/// Result structure
struct Result {
  do

... (9105 more chars)

Chunk ID: 86b651
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Output:
/***************************************************************************************************
 * Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUB

... (9400 more chars)

Chunk ID: b318b0
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2157
Output:
/***************************************************************************************************
 * Copyright (c) 2024 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUB

... (7233 more chars)

Chunk ID: bcff11
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 801
Output:
/***************************************************************************************************
 * Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBS

... (1808 more chars)

Chunk ID: faadf5
Wall time: 0.0000 seconds
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Original token count: 2160
Output:
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/detail/sm100_mixed_dtype_blockwise_layout.hpp:49:struct Sm100MixedInputBlockwiseScaleConfig {
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/detail/sm100_mixed_dtype_blockwise_layout.hpp:133:struct RuntimeMixedInputBlockwiseScaleConfig {
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/detail/blockwise_scale_layout.hpp:52:struct Sm1xxBlockwiseScaleConfig {
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/detail/blockwise_scale_layout.hpp:184:struct RuntimeBlockwiseScaleConfig {
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/detail/blockwise_scale_layout.hpp:275:using Sm90BlockwiseScaleConfig = Sm1xxBlockwiseScaleConfig<
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/detail/blockwise_scale_layout.hpp:283:using Sm100BlockwiseScaleConfig = Sm1xxBlockwiseScaleConfig<SFVecSizeM, SFVecSizeN, SFVecSizeK, majorSFA, majorSFB>;
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/detail/blockwise_scale_layout.hpp:286:using Sm120BlockwiseScaleConfig = Sm1xxBlockwiseScaleConfig<SFVecSizeM, SFVecSizeN, SFVecSizeK, majorSFA, majorSFB>;
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/detail/blockwise_scale_layout.hpp:289:constexpr auto sm90_trivial_blockwise_scale_config(MmaTileShape_MNK) {
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/detail/blockwise_scale_layout.hpp:290:  retur

... (7243 more chars)

Chunk ID: f16a9a
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Output:
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/device/gemm_universal_adapter.h:82:class GemmUniversalAdapter;
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/device/gemm_universal_adapter.h:123:class GemmUniversalAdapter<
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/device/gemm_universal_adapter.h:243:  get_workspace_size(Arguments const& args) {
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/device/gemm_universal_adapter.h:249:    workspace_bytes += GemmKernel::get_workspace_size(args);
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/device/gemm_universal_adapter.h:313:  initialize(
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/device/gemm_universal_adapter.h:319:    CUTLASS_TRACE_HOST("GemmUniversal::initialize() - workspace "
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/device/gemm_universal_adapter.h:363:    size_t workspace_bytes = get_workspace_size(args);
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/device/gemm_universal_adapter.h:590:    Status status = initialize(args, workspace, stream, cuda_adapter);
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/device/gemm_universal_adapter.h:630:class GemmUniversalAdapter<
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/device/gemm_universal_adapter.h:727:  static size_t get_workspace_size(Argum

... (741 more chars)

Chunk ID: cc5306
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Output:
struct Sm1xxBlockwiseScaleConfig {

  using ShapeSFA = Shape<Shape<Int<SFVecSizeM>, int32_t>, Shape<Int<SFVecSizeK>, int32_t>, int32_t>;
  using ShapeSFB = Shape<Shape<Int<SFVecSizeN>, int32_t>, Shape<Int<SFVecSizeK>, int32_t>, int32_t>;

  using StrideSFA = conditional_t<majorSFA == UMMA::Major::MN, 
      Stride<Stride<_0,_1>,Stride<_0,int32_t>, int32_t>, 
      Stride<Stride<_0,int32_t>,Stride<_0,_1>, int32_t>>;

  using StrideSFB = conditional_t<majorSFB == UMMA::Major::MN, 
      Stride<Stride<_0,_1>,Stride<_0,int32_t>, int32_t>, 
      Stride<Stride<_0,int32_t>,Stride<_0,_1>, int32_t>>;

  using LayoutSFA = Layout<ShapeSFA, StrideSFA>;
  using LayoutSFB = Layout<ShapeSFB, StrideSFB>;

  CUTE_HOST_DEVICE
  static constexpr auto
  deduce_layoutSFA() {
    return LayoutSFA{};
  }

  template<typename CtaShape_MNK>
  CUTE_HOST_DEVICE
  static constexpr auto
  smem_atom_layoutSFA(CtaShape_MNK cta_shape_mnk) {
    static_assert(cute::is_static_v<CtaShape_MNK>, "Expect static CTA shape");
    auto strides = [&]() CUTLASS_LAMBDA_FUNC_INLINE {
      auto [M, N, K] = cta_shape_mnk;
      if constexpr (majorSFA == UMMA::Major::MN) {
        return make_stride(make_stride(_0{}, _1{}), make_stride(_0{}, Int<cute::ceil_div(size<0>(CtaShape_MNK{}), SFVecSizeM)>{}));
      }
      else {
        return make_stride(make_stride(_0{}, Int<cute::ceil_div(size<2>(CtaShape_MNK{}), SFVecSi

... (8512 more chars)

Chunk ID: 4bded9
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Original token count: 1396
Output:
  /// Determines whether the GEMM can execute the given problem.
  static Status
  can_implement(Arguments const& args) {
    if (GemmKernel::can_implement(args)) {
      return Status::kSuccess;
    }
    else {
      return Status::kInvalid;
    }
  }

  /// Gets the workspace size
  static size_t
  get_workspace_size(Arguments const& args) {
    size_t workspace_bytes = 0;
    if (args.mode == GemmUniversalMode::kGemmSplitKParallel) {
      workspace_bytes += sizeof(int) * size_t(cute::size<0>(TileShape{})) * size_t(cute::size<1>(TileShape{}));
    }

    workspace_bytes += GemmKernel::get_workspace_size(args);

    CUTLASS_TRACE_HOST("  workspace_bytes: " << workspace_bytes);

    return workspace_bytes;
  }

  /// Computes the grid shape
  static dim3
  get_grid_shape(Arguments const& args, void* workspace = nullptr) {
    auto tmp_params = GemmKernel::to_underlying_arguments(args, workspace);
    return GemmKernel::get_grid_shape(tmp_params);
  }

  /// Computes the grid shape
  static dim3
  get_grid_shape(Params const& params) {
    return GemmKernel::get_grid_shape(params);
  }

  /// Computes the maximum number of active blocks per multiprocessor
  static int maximum_active_blocks(int /* smem_capacity */ = -1) {
    CUTLASS_TRACE_HOST("GemmUniversal::maximum_active_blocks()");
    int max_active_blocks = -1;
    int smem_size = GemmKernel::SharedStorageSize;

   

... (4187 more chars)

Chunk ID: 9d23b8
Wall time: 0.0000 seconds
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Output:
      CUTLASS_TRACE_HOST("GemmUniversal::run: Use extended launch API");
#endif
      [[maybe_unused]] constexpr bool is_static_1x1x1 =
        cute::is_static_v<typename GemmKernel::DispatchPolicy::ClusterShape> and
        cute::size(typename GemmKernel::DispatchPolicy::ClusterShape{}) == 1;
      [[maybe_unused]] dim3 cluster(cute::size<0>(typename GemmKernel::DispatchPolicy::ClusterShape{}),
        cute::size<1>(typename GemmKernel::DispatchPolicy::ClusterShape{}),
        cute::size<2>(typename GemmKernel::DispatchPolicy::ClusterShape{}));
      
      // Dynamic cluster support
      [[maybe_unused]] dim3 fallback_cluster = dim3{0,0,0};
      if constexpr (GemmKernel::ArchTag::kMinComputeCapability == 100 
                    || GemmKernel::ArchTag::kMinComputeCapability == 101
                    || GemmKernel::ArchTag::kMinComputeCapability == 103
                    ) {
        if constexpr (!cute::is_static_v<typename GemmKernel::DispatchPolicy::ClusterShape>) {
          if constexpr (detail::IsDistGemmKernel<GemmKernel>::value) {
            fallback_cluster = params.base.hw_info.cluster_shape_fallback;
            cluster = params.base.hw_info.cluster_shape;
          } else {
            fallback_cluster = params.hw_info.cluster_shape_fallback;
            cluster = params.hw_info.cluster_shape;
          }
        }
      }
      
      [[maybe_unused]] voi

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Chunk ID: ba4ac2
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Output:
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm120_blockscaled_mma_builder.inl:43:  int AlignmentA,
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm120_blockscaled_mma_builder.inl:46:  int AlignmentB,
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm120_blockscaled_mma_builder.inl:58:    AlignmentA,
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm120_blockscaled_mma_builder.inl:61:    AlignmentB,
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm120_blockscaled_mma_builder.inl:78:                                                AlignmentA,
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm120_blockscaled_mma_builder.inl:80:                                                AlignmentB,
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/sm120_mma_tma_blockwise_scaling.hpp:326:  can_implement(
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/sm120_mma_tma_blockwise_scaling.hpp:337:    implementable = implementable && cutlass::detail::check_alignment<min_tma_aligned_elements_A>(cute::make_shape(M,K,L), StrideA{});
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/sm120_mma_tma_blockwise_scaling.hpp:339:    implementable = i

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Output:

Chunk ID: 8aa3f7
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 654
Output:
        GmemTiledCopyA{},
        tensor_a,
        SmemLayoutA{}(_,_,cute::Int<0>{}),
        make_shape(shape<0>(TileShape{}), shape<2>(TileShape{})),
        size<1>(ClusterShape{})); // mcast along N mode for this M load, if any
    typename Params::TMA_B tma_load_b = make_tma_copy(
        GmemTiledCopyB{},
        tensor_b,
        SmemLayoutB{}(_,_,cute::Int<0>{}),
        make_shape(shape<1>(TileShape{}), shape<2>(TileShape{})),
        size<0>(ClusterShape{})); // mcast along M mode for this N load, if any
    return {
      tma_load_a,
      tma_load_b,
      TmaTransactionBytes,
      TmaTransactionBytesMK,
      TmaTransactionBytesNK,
      args.ptr_SFA,
      args.layout_SFA,
      args.ptr_SFB,
      args.layout_SFB
    };
  }

  template<class ProblemShape>
  static bool
  can_implement(
      ProblemShape const& problem_shape,
      [[maybe_unused]] Arguments const& args) {
    auto problem_shape_MNKL = append<4>(problem_shape, 1);
    auto [M, N, K, L] = problem_shape_MNKL;

    constexpr int tma_alignment_bits_A = cutlass::detail::get_input_alignment_bits<ElementA, IsF8F6F4>();
    constexpr int tma_alignment_bits_B = cutlass::detail::get_input_alignment_bits<ElementB, IsF8F6F4>();

    bool implementable = true;
    constexpr int min_tma_aligned_elements_A = tma_alignment_bits_A / cutlass::sizeof_bits<ElementA>::value;
    implementable = implementable &&

... (1218 more chars)

Chunk ID: 70e829
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Original token count: 8457
Output:
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/detail/layout.hpp:401:check_alignment(cute::Layout<Shape,Stride> const& layout) {
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/detail/layout.hpp:413:check_alignment(Shape const& shape, Stride const& stride) {
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/detail/layout.hpp:414:  return check_alignment<Alignment>(cute::make_layout(shape, stride));
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/epilogue/collective/sm100_epilogue_planar_complex_tma_warpspecialized.hpp:301:    bool implementable = cutlass::detail::check_alignment<min_tma_aligned_elements_D>(cute::make_shape(M,N,L), StrideD{});
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/epilogue/collective/sm100_epilogue_planar_complex_tma_warpspecialized.hpp:306:      implementable = implementable && cutlass::detail::check_alignment<min_tma_aligned_elements_C>(cute::make_shape(M,N,L), StrideC{});
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/epilogue/collective/sm90_epilogue_tma_warpspecialized.hpp:336:        implementable = cutlass::detail::check_alignment<min_tma_aligned_elements_D>(take<0,2>(shape), take<0,2>(StrideD{}));
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/epilogue/collective/sm90_epilogue_tma_warpspecialized.hpp:339:        implementable = cutlass::detail::check_alignment<min_tma_aligned_elements_D>(sh

... (32433 more chars)

Chunk ID: ad5ba1
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5755
Output:
/home/infatoshi/vllm/.deps/cutlass-src/examples/80_blackwell_geforce_sparse_gemm/80a_blackwell_geforce_mxfp8_bf16_sparse_gemm.cu:104:using ArchTag             = cutlass::arch::Sm120;                            // Tag indicating the minimum SM that supports the intended feature
/home/infatoshi/vllm/.deps/cutlass-src/examples/80_blackwell_geforce_sparse_gemm/80b_blackwell_geforce_nvfp4_nvfp4_sparse_gemm.cu:107:using ArchTag             = cutlass::arch::Sm120;                            // Tag indicating the minimum SM that supports the intended feature
/home/infatoshi/vllm/.deps/cutlass-src/examples/87_blackwell_geforce_gemm_blockwise/87a_blackwell_geforce_fp8_bf16_gemm_blockwise.cu:131:    cutlass::arch::Sm120, cutlass::arch::OpClassTensorOp,
/home/infatoshi/vllm/.deps/cutlass-src/examples/87_blackwell_geforce_gemm_blockwise/87a_blackwell_geforce_fp8_bf16_gemm_blockwise.cu:141:    cutlass::arch::Sm120, cutlass::arch::OpClassTensorOp,
/home/infatoshi/vllm/.deps/cutlass-src/examples/87_blackwell_geforce_gemm_blockwise/87b_blackwell_geforce_fp8_bf16_gemm_groupwise.cu:138:    cutlass::arch::Sm120, cutlass::arch::OpClassTensorOp,
/home/infatoshi/vllm/.deps/cutlass-src/examples/87_blackwell_geforce_gemm_blockwise/87b_blackwell_geforce_fp8_bf16_gemm_groupwise.cu:149:    cutlass::arch::Sm120, cutlass::arch::OpClassTensorOp,
/home/infatoshi/vllm/.deps/cutlass-src/examples/87_blackwe

... (21625 more chars)

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    auto ptr_A = recast_ptr<TmaInternalElementA>(args.ptr_A);
    auto ptr_B = recast_ptr<TmaInternalElementB>(args.ptr_B);

    Tensor tensor_a = make_tensor(ptr_A, make_layout(make_shape(M,K,L), args.dA));
    Tensor tensor_b = make_tensor(ptr_B, make_layout(make_shape(N,K,L), args.dB));
    typename Params::TMA_A tma_load_a = make_tma_copy(
        GmemTiledCopyA{},
        tensor_a,
        SmemLayoutA{}(_,_,cute::Int<0>{}),
        make_shape(shape<0>(TileShape{}), shape<2>(TileShape{})),
        size<1>(ClusterShape{})); // mcast along N mode for this M load, if any
    typename Params::TMA_B tma_load_b = make_tma_copy(
        GmemTiledCopyB{},
        tensor_b,
        SmemLayoutB{}(_,_,cute::Int<0>{}),
        make_shape(shape<1>(TileShape{}), shape<2>(TileShape{})),
        size<0>(ClusterShape{})); // mcast along M mode for this N load, if any
    return {
      tma_load_a,
      tma_load_b,
      TmaTransactionBytes,
      TmaTransactionBytesMK,
      TmaTransactionBytesNK
    };
  }

  template<class ProblemShape>
  static bool
  can_implement(
      ProblemShape const& problem_shape,
      [[maybe_unused]] Arguments const& args) {
    auto problem_shape_MNKL = append<4>(problem_shape, 1);
    auto [M, N, K, L] = problem_shape_MNKL;

    constexpr int tma_alignment_bits_A = cutlass::detail::get_input_alignment_bits<ElementA, IsF8F6F4>();
    constexpr int tma_

... (2627 more chars)

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/***************************************************************************************************
 * Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUB

... (5263 more chars)

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Output:
  using MainloopPipelineStorage = typename cutlass::PipelineTmaUmmaAsync<1>::SharedStorage;

  static constexpr int PipelineStages = detail::sm100_compute_stage_count_or_override<
      detail::sm120_smem_capacity_bytes, SmemAllocTypeA,
      SmemAllocTypeB, TileShape_MNK, MainloopPipelineStorage>(StageCountType{});
  static constexpr uint32_t SchedulerPipelineStageCount = 2;


  static constexpr bool IsPtrArrayKernel = cute::is_base_of_v<KernelPtrArrayTmaWarpSpecializedCooperative, BuilderScheduleTag> ||
                                           cute::is_base_of_v<KernelPtrArrayTmaWarpSpecializedPingpong, BuilderScheduleTag>;
  static_assert(!IsPtrArrayKernel, "PtrArray kernel is not supported for this collective builder.");
  
  using KernelSchedule = cute::conditional_t<IsCooperative, 
                                              KernelTmaWarpSpecializedCooperativeSm120<SchedulerPipelineStageCount>, 
                                              KernelTmaWarpSpecializedPingpongSm120<SchedulerPipelineStageCount>>;

  using DispatchPolicy = MainloopSm120TmaWarpSpecialized<PipelineStages,
                                                          SchedulerPipelineStageCount,
                                                          ClusterShape_MNK,
                                                          KernelSchedule>;

  static_assert(cute::is_base_of_v<KernelTmaWarpS

... (1127 more chars)

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Output:
/***************************************************************************************************
 * Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUB

... (3402 more chars)

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Output:
/***************************************************************************************************
 * Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUB

... (3403 more chars)

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Output:
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_tensorop_gemm/sm120_gemm_f4_f8_f16_tensor_op.cu:111:  using ElementA = cutlass::float_e4m3_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_tensorop_gemm/sm120_gemm_f6_f8_f32_tensor_op.cu:61:  using ElementB = cutlass::float_e4m3_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_tensorop_gemm/sm120_gemm_f6_f8_f32_tensor_op.cu:111:  using ElementA = cutlass::float_e4m3_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_tensorop_gemm/sm120_gemm_f6_f8_f16_tensor_op.cu:61:  using ElementB = cutlass::float_e4m3_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_tensorop_gemm/sm120_gemm_f6_f8_f16_tensor_op.cu:111:  using ElementA = cutlass::float_e4m3_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_tensorop_gemm/sm120_gemm_f4_f8_f16_tensor_op.cu:111:  using ElementA = cutlass::float_e4m3_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_tensorop_gemm/sm120_gemm_f4_f8_f32_tensor_op.cu:111:  using ElementA = cutlass::float_e4m3_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_tensorop_gemm/sm120_gemm_f8_f8_f32_tensor_op.cu:60:  using ElementA = cutlass::float_e4m3_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_tensorop_gemm/sm120_gemm_f8_f8_f32_tensor_o

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}

// Check if tensor layout satisfies a given major alignment
template<int Alignment, class Shape, class Stride>
CUTLASS_HOST_DEVICE constexpr
bool
check_alignment(cute::Layout<Shape,Stride> const& layout) {
  // Condition: shape must divide by Alignment without rounding
  bool shape_check = cute::size(layout.shape()) == Alignment * cute::size(cute::upcast<Alignment>(layout));
  // Condition: every dynamic stride must be a multiple of Alignment
  bool stride_check = cute::all_of(cute::flatten(layout.stride()), [](auto s){ return cute::is_static<decltype(s)>::value || (s % Alignment == 0); });
  return shape_check && stride_check;
}

// Check if tensor layout satisfies a given major alignment
template<int Alignment, class Shape, class Stride>
CUTLASS_HOST_DEVICE constexpr
bool
check_alignment(Shape const& shape, Stride const& stride) {
  return check_alignment<Alignment>(cute::make_layout(shape, stride));
}

template<int B, int M, int S>
CUTLASS_HOST_DEVICE constexpr
size_t
alignment_for_swizzle(cute::Swizzle<B, M, S>) {
  static_assert(B >= 0 and M >= 0);
  return size_t(1) << size_t(B + M + cute::abs(S));
}

template<class Layout>
CUTLASS_HOST_DEVICE constexpr
size_t
alignment_for_swizzle(Layout layout) {
  return alignment_for_swizzle(cute::get_swizzle_portion(layout));
}

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/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm1xx_common.inl:41:#include "cutlass/detail/layout.hpp" // cutlass::detail::get_input_alignment_bits()
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm1xx_common.inl:369:sm1xx_gemm_is_aligned() {
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm1xx_common.inl:373:  return ((cute::sizeof_bits_v<ElementA> * AlignmentA) % cutlass::detail::get_input_alignment_bits<ElementA, is_f8f6f4_subbytes>() == 0) &&
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm1xx_common.inl:374:         ((cute::sizeof_bits_v<ElementB> * AlignmentB) % cutlass::detail::get_input_alignment_bits<ElementB, is_f8f6f4_subbytes>() == 0);
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm1xx_common.inl:385:  return ((cute::sizeof_bits_v<ElementA> * AlignmentA) % cutlass::detail::get_input_alignment_bits<ElementA, is_mxf8f6f4_subbytes>() == 0) &&
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm1xx_common.inl:386:         ((cute::sizeof_bits_v<ElementB> * AlignmentB) % cutlass::detail::get_input_alignment_bits<ElementB, is_mxf8f6f4_subbytes>() == 0);
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm1xx_common.inl:397:          cutlass::

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/***************************************************************************************************
 * Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUB

... (8533 more chars)

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  else {
    // For TMA tiled copies, we know the alignment has to be 128 bits
    if constexpr (is_tma_copy_engine<GmemTiledCopy>()) {
      if constexpr ( cute::is_same_v<typename RawDtype<ElementMma>::type, cutlass::detail::float_e2m1_unpacksmem_t> ||
                     cute::is_same_v<typename RawDtype<ElementMma>::type, cutlass::detail::float_e3m2_unpacksmem_t> ||
                     cute::is_same_v<typename RawDtype<ElementMma>::type, cutlass::detail::float_e2m3_unpacksmem_t> ||
                     cute::is_same_v<typename RawDtype<ElementMma>::type, cutlass::detail::type_erased_dynamic_float4_unpacksmem_t> ||
                     cute::is_same_v<typename RawDtype<ElementMma>::type, cutlass::detail::type_erased_dynamic_float6_unpacksmem_t> ||
                     cutlass::gemm::collective::detail::is_sm10x_f8f6f4_element<Element>() && cute::is_same_v<typename RawDtype<ElementMma>::type, uint8_t>) {
        return 128;
      }

      // For sparse MMA, alignment in logical elements is increased by sparsity factor
      if constexpr (cute::is_sparse_v<ElementMma>) {
        return 128 / sizeof_bits<Element>::value * ElementMma::sparsity;
      }
      return 128 / sizeof_bits<Element>::value;
    }
    else {
      // For non-TMA tiled copies, TiledCopy holds the alignment count directly in its TiledShape_MN
      return GmemTiledCopy::NumValSrc;
    }
  }
}

// Re

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                                                    TileShape_N % 128 == 0 : 
                                                    TileShape_K % 128 == 0);
      return valid_a && valid_b;
    }
  }
}

template <class ElementA, int AlignmentA, class ElementB, int AlignmentB, class BuilderScheduleTag>
constexpr bool
sm1xx_gemm_is_aligned() {
  // Only support dense gemm alignment check
  constexpr bool is_f8f6f4_subbytes = cute::sizeof_bits_v<ElementA> < 8 || cute::sizeof_bits_v<ElementB> < 8;

  return ((cute::sizeof_bits_v<ElementA> * AlignmentA) % cutlass::detail::get_input_alignment_bits<ElementA, is_f8f6f4_subbytes>() == 0) &&
         ((cute::sizeof_bits_v<ElementB> * AlignmentB) % cutlass::detail::get_input_alignment_bits<ElementB, is_f8f6f4_subbytes>() == 0);
}

template <class ElementA, int AlignmentA, class ElementB, int AlignmentB, class BuilderScheduleTag>
constexpr bool
sm1xx_blockscaled_gemm_is_aligned() {
  // Only support blocksscaled gemm alignment check
  constexpr bool is_mxf8f6f4_subbytes = (cute::sizeof_bits_v<ElementA> < 8 || cute::sizeof_bits_v<ElementB> < 8) &&
                                    (cute::is_base_of_v<KernelScheduleMxf8f6f4Sm100, BuilderScheduleTag> ||
                                     cute::is_base_of_v<KernelScheduleMxf8f6f4Sm120, BuilderScheduleTag> );

  return ((cute::sizeof_bits_v<ElementA> * AlignmentA) % cutlass::detail::get_i

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compute_100
compute_120
compute_121

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sm_100
sm_120
sm_121

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/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/arch/config.h:156:  #define CUTLASS_ARCH_MMA_SM120_SUPPORTED 1

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// SM110 and SM110a only on 13.0 and above
#if !CUTLASS_CLANG_CUDA && (__CUDACC_VER_MAJOR__ > 13 || (__CUDACC_VER_MAJOR__ == 13 && __CUDACC_VER_MINOR__ >= 0))
  #define CUTLASS_ARCH_MMA_SM110_SUPPORTED 1
  #if (!defined(CUTLASS_ARCH_MMA_SM110_ENABLED) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ == 1100)
    #define CUTLASS_ARCH_MMA_SM110_ENABLED 1

    #if (!defined(CUTLASS_ARCH_MMA_SM110A_ENABLED) && defined(__CUDA_ARCH_FEAT_SM110_ALL))
      #define CUTLASS_ARCH_MMA_SM110A_ENABLED 1
    #endif

    // SM110f
    #if (__CUDACC_VER_MAJOR__ > 13 || (__CUDACC_VER_MAJOR__ == 13 && __CUDACC_VER_MINOR__ >= 0))
    #define CUTLASS_ARCH_MMA_SM110F_SUPPORTED 1
    #endif

    #if (!defined(CUTLASS_ARCH_MMA_SM110F_ENABLED) && CUDA_ARCH_FAMILY(1100))
      #define CUTLASS_ARCH_MMA_SM110F_ENABLED CUTLASS_ARCH_MMA_SM110F_SUPPORTED
    #endif
  #endif
#endif

/////////////////////////////////////////////////////////////////////////////////////////////////

// SM120 and SM120a
#if !CUTLASS_CLANG_CUDA && (__CUDACC_VER_MAJOR__ > 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ >= 8))
  #define CUTLASS_ARCH_MMA_SM120_SUPPORTED 1
  #if (!defined(CUTLASS_ARCH_MMA_SM120_ENABLED) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ == 1200)
    #define CUTLASS_ARCH_MMA_SM120_ENABLED 1

    #if (!defined(CUTLASS_ARCH_MMA_SM120A_ENABLED) && defined(__CUDA_ARCH_FEAT_SM120_ALL))
      #define CUTLAS

... (417 more chars)

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    #if (!defined(CUTLASS_ARCH_MMA_SM100F_ENABLED) && CUDA_ARCH_FAMILY(1000))
      #define CUTLASS_ARCH_MMA_SM100F_ENABLED CUTLASS_ARCH_MMA_SM100F_SUPPORTED
    #endif
  #endif
#endif

/////////////////////////////////////////////////////////////////////////////////////////////////

// SM101 and SM101a
#if !CUTLASS_CLANG_CUDA && (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ >= 8)
  #define CUTLASS_ARCH_MMA_SM101_SUPPORTED 1
  #if (!defined(CUTLASS_ARCH_MMA_SM101_ENABLED) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ == 1010)
    #define CUTLASS_ARCH_MMA_SM101_ENABLED 1

    #if (!defined(CUTLASS_ARCH_MMA_SM101A_ENABLED) && defined(__CUDA_ARCH_FEAT_SM101_ALL))
      #define CUTLASS_ARCH_MMA_SM101A_ENABLED 1
    #endif

    // SM101f
    #if !CUTLASS_CLANG_CUDA && (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ >= 9)
    #define CUTLASS_ARCH_MMA_SM101F_SUPPORTED 1
    #endif

    #if (!defined(CUTLASS_ARCH_MMA_SM101F_ENABLED) && CUDA_ARCH_FAMILY(1010))
      #define CUTLASS_ARCH_MMA_SM101F_ENABLED CUTLASS_ARCH_MMA_SM101F_SUPPORTED
    #endif
  #endif
#endif

/////////////////////////////////////////////////////////////////////////////////////////////////

// SM110 and SM110a only on 13.0 and above
#if !CUTLASS_CLANG_CUDA && (__CUDACC_VER_MAJOR__ > 13 || (__CUDACC_VER_MAJOR__ == 13 && __CUDACC_VER_MINOR__ >= 0))
  #define CUTLASS_ARCH_MMA_SM110_SUPPORTED 1
  #if (!defi

... (133 more chars)

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"""Per-dtype correctness tolerance.

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

import torch

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


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


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

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

... (2176 more chars)

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

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

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

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



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Output:
Mon Apr 27 23:10:29 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  On |                  Off |
| 30%   28C    P8             10W /  600W |     508MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

... (569 more chars)

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Original token count: 8
Output:
0.25 0.03910573944449425 False

Chunk ID: b043cb
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 222
Output:
"""RTX PRO 6000 Blackwell Workstation — SM120, consumer-lineage Blackwell.

Peak tensor-core throughputs are dense-matrix advertised peaks. Actual kernels
will see 60-85% of peak on well-tuned code.
"""
from dataclasses import dataclass


@dataclass(frozen=True)
class HardwareTarget:
    name: str
    sm: str
    vram_gb: int
    peak_bandwidth_gb_s: float  # DRAM
    peak_tflops_dense: dict[str, float]  # dtype -> TFLOPS


RTX_PRO_6000 = HardwareTarget(
    name="RTX PRO 6000 Blackwell Workstation",
    sm="sm_120a",
    vram_gb=96,
    peak_bandwidth_gb_s=1800.0,
    peak_tflops_dense={
        "fp4": 800.0,
        "nvfp4": 800.0,
        "mxfp4": 800.0,
        "fp6": 800.0,
        "fp8": 400.0,
        "bf16": 200.0,
        "fp16": 200.0,
        "tf32": 100.0,
        "fp32": 12.0,  # non-tensor-core SIMT fp32
        "int8": 400.0,
        "int4": 800.0,
    },
)

Chunk ID: da7f17
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 13834
Output:
Total output lines: 302

/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_sparse_tensorop_gemm/sm120_sparse_gemm_f4_f4_f32_tensor_op_epilogue_fusion.cu:60:// Bias: bf16
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_sparse_tensorop_gemm/sm120_sparse_gemm_f4_f4_f32_tensor_op_epilogue_fusion.cu:127:// Bias: bf16
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_sparse_tensorop_gemm/sm120_sparse_gemm_f4_f4_f32_tensor_op_epilogue_fusion.cu:205:// Bias: bf16
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_sparse_tensorop_gemm/sm120_sparse_gemm_f4_f4_f32_tensor_op_epilogue_fusion.cu:284:// Bias: bf16
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_sparse_tensorop_gemm/sm120_sparse_gemm_f4_f4_f32_tensor_op_epilogue_fusion.cu:364:// Bias: bf16
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_sparse_tensorop_gemm/sm120_sparse_gemm_f4_f4_f32_tensor_op_epilogue_fusion.cu:444:// Bias: bf16
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_sparse_tensorop_gemm/sm120_sparse_gemm_f4_f4_f32_tensor_op_epilogue_fusion.cu:524:// Bias: bf16
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120_sparse_tensorop_gemm/sm120_sparse_gemm_f4_f4_f32_tensor_op_epilogue_fusion.cu:535:// Bias: bf16
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm120

... (38654 more chars)

Chunk ID: 05e3e2
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2124
Output:
/***************************************************************************************************
 * Copyright (c) 2024 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUB

... (7100 more chars)

Chunk ID: 93d7a6
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1447
Output:
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/gemm_universal_bf16t_s8n_f32t_mixed_input_tensor_op_f32_sm80.cu:61:  using ElementA = cutlass::bfloat16_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/gemm_universal_bf16t_u8n_f32t_mixed_input_tensor_op_f32_sm80.cu:61:  using ElementA = cutlass::bfloat16_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/gemm_universal_bf16t_u8n_bf16t_mixed_input_tensor_op_f32_sm80.cu:61:  using ElementA = cutlass::bfloat16_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm100_gemm_planar_cbf16_cbf16_cbf16_tensor_op_cf32_2sm.cu:63:  using ElementA = cutlass::bfloat16_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm100_gemm_planar_cbf16_cbf16_cbf16_tensor_op_cf32_2sm.cu:115:  using ElementA = cutlass::bfloat16_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm100_gemm_planar_cbf16_cbf16_cbf16_tensor_op_cf32_conjugate_layout.cu:63:  using ElementA = cutlass::bfloat16_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm100_gemm_planar_cbf16_cbf16_cbf16_tensor_op_cf32_conjugate_layout.cu:112:  using ElementA = cutlass::bfloat16_t;
/home/infatoshi/vllm/.deps/cutlass-src/test/unit/gemm/device/sm100_gemm_planar_cbf16_cbf16_cbf16_tensor_op_cf32_conjugate_layout.cu:164:  using ElementA = cutlass::bfloat16_t;
/home/infatoshi/vllm/.deps/cutlass-src

... (4393 more chars)

Chunk ID: 53caeb
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 54
Output:
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm90_sparse_config.inl:101:  // https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#sparse-wgmma-metadata-64n32-f16bf16

Chunk ID: 4d82a3
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 164
Output:
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm120_blockwise_mma_builder.inl
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm120_sparse_mma_builder.inl
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm120_mma_builder.inl
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm120_blockscaled_mma_builder.inl
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm120_blockscaled_sparse_mma_builder.inl
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/collective/builders/sm120_common.inl

output:
  Success. Updated the following files:
  A /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 5ee496
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 10
Output:
Found 1 error (1 fixed, 0 remaining).

Chunk ID: 96b1d3
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2211
Output:
import os
from pathlib import Path

import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline


M = 4096
N = 4096
K = 4096


_EXT = None


def _load_ext():
    global _EXT
    if _EXT is not None:
        return _EXT

    cutlass_root = Path("/home/infatoshi/vllm/.deps/cutlass-src")
    include_paths = [str(cutlass_root / "include")]

    os.environ.setdefault("TORCH_CUDA_ARCH_LIST", "12.0")

    cuda_src = r"""
#include <torch/extension.h>

#include <cuda_bf16.h>
#include <cuda_fp8.h>
#include <cuda_runtime.h>

#include "cutlass/cutlass.h"
#include "cutlass/bfloat16.h"
#include "cutlass/epilogue/thread/linear_combination.h"
#include "cutlass/gemm/device/gemm.h"
#include "cutlass/gemm/threadblock/threadblock_swizzle.h"

namespace {

#define CUDA_CHECK_OK(expr)                                                     \
  do {                                                                         \
    cudaError_t err = (expr);                                                   \
    TORCH_CHECK(err == cudaSuccess, "CUDA error: ", cudaGetErrorString(err));   \
  } while (0)

#define CUTLASS_CHECK_OK(expr)                                                  \
  do {                                                                         \
    cutlass::Status st = (expr);                                                \
    TORCH_CHECK(st == cutlass::Status:

... (7446 more chars)

Chunk ID: a240a2
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 35
Output:

def get_inputs():
    x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn)
    return [x]


def get_init_inputs():
    return [M, N, K]

Chunk ID: dc62e3
Wall time: 1.0009 seconds
Process running with session ID 29214
Original token count: 0
Output:

Chunk ID: ebdef5
Wall time: 2.7779 seconds
Process exited with code 1
Original token count: 1714
Output:
Traceback (most recent call last):
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2774, in _run_ninja_build
    subprocess.run(
  File "/home/infatoshi/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/subprocess.py", line 571, in run
    raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 2.

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

Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py", line 211, in _load_ext
    _EXT = load_inline(
           ^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2174, in load_inline
    return _jit_compile(
           ^^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2268, in _jit_compile
    _write_ninja_file_and_build_library(
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2429, in _write_ninja_file_and_build_library
    _run_ninja_build(
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packag

... (5442 more chars)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 7abca2
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: 6e8ada
Wall time: 1.0007 seconds
Process running with session ID 6105
Original token count: 0
Output:

Chunk ID: 7b5431
Wall time: 5.0019 seconds
Process running with session ID 6105
Original token count: 0
Output:

Chunk ID: 1a96cb
Wall time: 15.0904 seconds
Process exited with code 0
Original token count: 3
Output:
import ok

Chunk ID: 5d5b11
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 492
Output:
Mon Apr 27 23:13:13 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  On |                  Off |
| 30%   29C    P0             24W /  600W |     508MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

... (569 more chars)

Chunk ID: bb5fa0
Wall time: 1.0008 seconds
Process running with session ID 95864
Original token count: 253
Output:
Traceback (most recent call last):
  File "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/check.py", line 112, in <module>
    main()
  File "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/check.py", line 73, in main
    sol_out = sol_model(*inputs)
              ^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py", line 265, in forward
    return ext.bf16_gemm(x_bf16, weight, self.K_gemm)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: CUTLASS error: Error Internal

Chunk ID: 2f397c
Wall time: 0.0000 seconds
Process exited with code 1
Original token count: 0
Output:

Chunk ID: 0d80db
Wall time: 0.0000 seconds
Process exited with code 2
Original token count: 5427
Output:
rg: /home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/status.h: No such file or directory (os error 2)
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/cutlass.h:47:enum class Status {
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/cutlass.h:55:  kErrorInternal,              ///< An error within CUTLASS occurred.
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/cutlass.h:59:  kInvalid                     ///< Status is unspecified.
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/cutlass.h:64:static char const* cutlassGetStatusString(cutlass::Status status) {
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/cutlass.h:80:    case cutlass::Status::kErrorInternal:
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/cutlass.h:88:    case cutlass::Status::kInvalid: break;
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/blas3_types.h:46:  kInvalid
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/blas3_types.h:56:  kInvalid
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/blas3_types.h:64:  kInvalid
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/blas3_types.h:71:  kInvalid
/home/infatoshi/vllm/.deps/cutlass-src/include/cutlass/gemm/threadblock/default_trmm.h:182:          ElementB, LayoutB, 0, ThreadMapB, kSideMode, FillMode::kFull, DiagType::kInvalid, AccessTypeB>;
/home/infatoshi/vllm/.deps/cutlass-src/include/c

... (20310 more chars)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 34a2ac
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: ac59cb
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 492
Output:
Mon Apr 27 23:13:51 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  On |                  Off |
| 30%   29C    P5             10W /  600W |     508MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

... (569 more chars)

Chunk ID: 3b59cd
Wall time: 1.0011 seconds
Process running with session ID 12040
Original token count: 0
Output:

Chunk ID: 7b6bbb
Wall time: 19.7687 seconds
Process exited with code 1
Original token count: 201
Output:
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py", line 273, in forward
    return ext.bf16_gemm(x_bf16, weight, self.K_gemm)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: CUTLASS run error: Error Internal cuda=invalid argument

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 280c2d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: 0eaae3
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 492
Output:
Mon Apr 27 23:14:35 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  On |                  Off |
| 30%   30C    P8             14W /  600W |     508MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

... (569 more chars)

Chunk ID: f466b0
Wall time: 1.0010 seconds
Process running with session ID 9462
Original token count: 0
Output:

Chunk ID: a604e9
Wall time: 20.4213 seconds
Process exited with code 1
Original token count: 201
Output:
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py", line 275, in forward
    return ext.bf16_gemm(x_bf16, weight, self.K_gemm)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: CUTLASS run error: Error Internal cuda=invalid argument

Chunk ID: e2acd1
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1088
Output:
  static Status can_implement(Arguments const &args) {

    if (!kSplitKSerial && args.split_k_slices > 1) {
      return Status::kErrorInvalidProblem;
    }

    Status status = GemmKernel::can_implement(
      args.problem_size,
      args.ref_A.non_const_ref(),
      args.ref_B.non_const_ref(),
      args.ref_C.non_const_ref(),
      args.ref_D
    );

    if (status != Status::kSuccess) {
      return status;
    }

    return Status::kSuccess;
  }

  /// Gets the workspace size
  static size_t get_workspace_size(Arguments const &args) {
    
    size_t bytes = 0;

    // Determine grid shape
    ThreadblockSwizzle threadblock_swizzle;

    cutlass::gemm::GemmCoord tiled_shape = threadblock_swizzle.get_tiled_shape(
      args.problem_size, 
      {ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
      args.split_k_slices);
    
    if (kSplitKSerial && args.split_k_slices > 1) {

      bytes += sizeof(int) * size_t(tiled_shape.m()) * size_t(tiled_shape.n());
    }

    return bytes;
  }

  /// Initializes GEMM state from arguments.
  Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) {

    // Determine grid shape
    ThreadblockSwizzle threadblock_swizzle;

    cutlass::gemm::GemmCoord grid_shape = threadblock_swizzle.get_tiled_shape(
      args.problem_size, 
      {ThreadblockShape::kM, ThreadblockSha

... (2956 more chars)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: d08ebb
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: e3037b
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 492
Output:
Mon Apr 27 23:15:40 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  On |                  Off |
| 30%   30C    P8             17W /  600W |     508MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

... (569 more chars)

Chunk ID: 58c3ec
Wall time: 1.0019 seconds
Process running with session ID 13793
Original token count: 0
Output:

Chunk ID: f6a212
Wall time: 21.4196 seconds
Process exited with code 0
Original token count: 10
Output:
torch.Size([128, 128]) torch.bfloat16

Chunk ID: daac0e
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 492
Output:
Mon Apr 27 23:16:13 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  On |                  Off |
| 30%   31C    P5             48W /  600W |     508MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

... (569 more chars)

Chunk ID: 47dfe9
Wall time: 1.0011 seconds
Process running with session ID 54773
Original token count: 0
Output:

Chunk ID: cbdf57
Wall time: 0.0000 seconds
Process exited with code 1
Original token count: 30
Output:
FAIL: shape 1 {'M': 4096, 'N': 4096, 'K': 4127} seed 42: tolerance exceeded: max_abs_diff=0.0625 (atol=0.01, rtol=0.01)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: a783f3
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: 489a65
Wall time: 1.0011 seconds
Process running with session ID 32260
Original token count: 0
Output:

Chunk ID: af7a88
Wall time: 21.9974 seconds
Process exited with code 0
Original token count: 3
Output:
import ok

Chunk ID: f90383
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 492
Output:
Mon Apr 27 23:18:06 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  On |                  Off |
| 30%   30C    P0             17W /  600W |     508MiB /  97887MiB |      4%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

... (569 more chars)

Chunk ID: 51cd15
Wall time: 1.0010 seconds
Process running with session ID 76294
Original token count: 0
Output:

Chunk ID: 4ac634
Wall time: 0.0000 seconds
Process exited with code 1
Original token count: 30
Output:
FAIL: shape 1 {'M': 4096, 'N': 4096, 'K': 4127} seed 42: tolerance exceeded: max_abs_diff=0.125 (atol=0.01, rtol=0.01)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: b26bc3
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: f1b8a1
Wall time: 1.0010 seconds
Process running with session ID 34860
Original token count: 0
Output:

Chunk ID: f3ed26
Wall time: 22.1608 seconds
Process exited with code 0
Original token count: 3
Output:
import ok

Chunk ID: 43b4d3
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 492
Output:
Mon Apr 27 23:19:23 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  On |                  Off |
| 30%   29C    P8             22W /  600W |     508MiB /  97887MiB |      4%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

... (569 more chars)

Chunk ID: bdea34
Wall time: 1.0010 seconds
Process running with session ID 5088
Original token count: 0
Output:

Chunk ID: 150028
Wall time: 0.0000 seconds
Process exited with code 1
Original token count: 30
Output:
FAIL: shape 1 {'M': 4096, 'N': 4096, 'K': 4127} seed 42: tolerance exceeded: max_abs_diff=0.0625 (atol=0.01, rtol=0.01)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 319151
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: 4c5e9c
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 492
Output:
Mon Apr 27 23:19:52 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  On |                  Off |
| 30%   29C    P8             22W /  600W |     508MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

... (569 more chars)

Chunk ID: 1a4d65
Wall time: 1.0013 seconds
Process running with session ID 85079
Original token count: 0
Output:

Chunk ID: dbe589
Wall time: 0.0000 seconds
Process exited with code 1
Original token count: 30
Output:
FAIL: shape 2 {'M': 32, 'N': 8192, 'K': 8192} seed 42: tolerance exceeded: max_abs_diff=0.0625 (atol=0.01, rtol=0.01)

output:
  Success. Updated the following files:
  M /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 6555dd
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: 3a8de0
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 492
Output:
Mon Apr 27 23:20:12 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  On |                  Off |
| 30%   29C    P8             14W /  600W |     508MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

... (569 more chars)

Chunk ID: 50ca28
Wall time: 1.0007 seconds
Process running with session ID 85029
Original token count: 0
Output:

Chunk ID: 549298
Wall time: 0.4064 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: 225b3d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 492
Output:
Mon Apr 27 23:20:26 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  On |                  Off |
| 30%   30C    P5             38W /  600W |     508MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

... (569 more chars)

Chunk ID: 4d56d0
Wall time: 1.0009 seconds
Process running with session ID 50052
Original token count: 0
Output:

Chunk ID: 33e306
Wall time: 1.3967 seconds
Process exited with code 0
Original token count: 1528
Output:
  [sota unavailable] RuntimeError: Invalid scaling configuration.
- For TensorWise scaling, a and b should be float8, scales should be float and singletons.
- For RowWise scaling, a and b should be float8, scales should be float, scale_a should be (4096, 1) and scale_b should be (1, 4096), and both should be contiguous.
- For BlockWise 1x128 scaling, a and b should be float8, scales should be float, scale_a should be (4096, 32) and scale_b should be (32, 4096), and both should be outer-dim-major.
- For BlockWise 128x128 scaling, a and b should be float8, scales should be float, scale_a should be (32, 32) and scale_b should be (32, 32), and both should be near-inner-dim-major (with 16-byte aligned strides).
- For Blockwise 1x32 scaling, a and b should be float8, scales should be float8_e8m0fnu, scale_a should have 524288 elements and scale_b should have 524288 elements, and both should be contiguous.
- For Blockwise 1x16 scaling, a and b should be float4 (packed 2x), scales should be float8_e4m3fn, scale_a should have 2097152 elements and scale_b should have 2097152 elements, and both should be contiguous.
Got a.dtype()=Float8_e4m3fn, scale_a.dtype()=Float, scale_a.size()=[], scale_a.stride()=[], b.dtype()=BFloat16, scale_b.dtype()=Float, scale_b.size()=[] and scale_b.stride()=[]
shape=0 variant=eager tflops=348.448 gbps=170.141 ms=0.394
shape=0 variant=compiled tflops=323.

... (4715 more chars)

Chunk ID: 8ae05d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: 6aeecd
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:

Chunk ID: 744f57
Wall time: 0.0082 seconds
Process exited with code 5
Original token count: 85
Output:
============================= test session starts ==============================
platform linux -- Python 3.11.15, pytest-9.0.3, pluggy-1.6.0
rootdir: /home/infatoshi/cuda/KernelBench-Hard
configfile: pyproject.toml
plugins: anyio-4.13.0
collected 0 items

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

Chunk ID: 831954
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:

Chunk ID: 1641ab
Wall time: 0.0678 seconds
Process exited with code 0
Original token count: 81
Output:
!! ../../.pytest_cache/
!! ../../.ruff_cache/
!! ../../.venv/
!! ../../outputs/
!! __pycache__/
!! framework.txt
!! solution.py
!! ../../src/__pycache__/
!! ../../src/eval/__pycache__/
!! ../../src/hardware/__pycache__/
!! ../../src/viewer/__pycache__/
!! ../../src/viewer/parsers/__pycache__/
!! ../../tests/__pycache__/

Chunk ID: 880269
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 192
Output:
total 68
drwxrwxr-x  3 infatoshi infatoshi  4096 Apr 27 23:20 .
drwxrwxr-x 10 infatoshi infatoshi  4096 Apr 24 00:30 ..
-rw-rw-r--  1 infatoshi infatoshi  1850 Apr 27 03:21 PROMPT.txt
drwxrwxr-x  2 infatoshi infatoshi  4096 Apr 27 23:20 __pycache__
-rw-rw-r--  1 infatoshi infatoshi  4228 Apr 26 03:11 benchmark.py
-rw-rw-r--  1 infatoshi infatoshi  3805 Apr 24 00:37 check.py
-rw-rw-r--  1 infatoshi infatoshi     9 Apr 27 23:20 framework.txt
-rw-rw-r--  1 infatoshi infatoshi  1128 Apr 24 00:36 problem.yaml
-rw-rw-r--  1 infatoshi infatoshi  1401 Apr 24 00:40 reference.py
-rw-rw-r--  1 infatoshi infatoshi   564 Apr 24 00:36 shapes.py
-rw-rw-r--  1 infatoshi infatoshi 19715 Apr 27 23:20 solution.py
-rw-rw-r--  1 infatoshi infatoshi  1725 Apr 24 00:37 sota.py

Chunk ID: 5c39e2
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 3198
Output:
import os
from pathlib import Path

import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

M = 4096
N = 4096
K = 4096


_EXT = None


def _load_ext():
    global _EXT
    if _EXT is not None:
        return _EXT

    cutlass_root = Path("/home/infatoshi/vllm/.deps/cutlass-src")
    include_paths = [str(cutlass_root / "include")]

    os.environ.setdefault("TORCH_CUDA_ARCH_LIST", "12.0")

    cuda_src = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>

#include <cuda_bf16.h>
#include <cuda_fp8.h>
#include <cuda_runtime.h>

#include "cutlass/cutlass.h"
#include "cutlass/bfloat16.h"
#include "cutlass/epilogue/thread/linear_combination.h"
#include "cutlass/gemm/device/gemm.h"
#include "cutlass/gemm/threadblock/threadblock_swizzle.h"

namespace {

#define CUDA_CHECK_OK(expr)                                                     \
  do {                                                                         \
    cudaError_t err = (expr);                                                   \
    TORCH_CHECK(err == cudaSuccess, "CUDA error: ", cudaGetErrorString(err));   \
  } while (0)

#define CUTLASS_CHECK_OK(expr)                                                  \
  do {                                                                         \
    cutlass::Status st = (expr);                                                \
    

... (11396 more chars)

output:
  Success. Updated the following files:
  A /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py
  D /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: ae5d3a
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: e78f96
Wall time: 1.0010 seconds
Process running with session ID 70183
Original token count: 0
Output:

Chunk ID: 3577e0
Wall time: 22.2116 seconds
Process exited with code 0
Original token count: 3
Output:
import ok

Chunk ID: 9a9a9b
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 492
Output:
Mon Apr 27 23:22:20 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  On |                  Off |
| 30%   29C    P8             15W /  600W |     508MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

... (569 more chars)

Chunk ID: d18800
Wall time: 1.0011 seconds
Process running with session ID 26120
Original token count: 0
Output:

Chunk ID: 522dfe
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

Chunk ID: abfc0d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 492
Output:
Mon Apr 27 23:22:31 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  On |                  Off |
| 30%   30C    P3             49W /  600W |     508MiB /  97887MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

... (569 more chars)

Chunk ID: efcd2f
Wall time: 1.0011 seconds
Process running with session ID 81051
Original token count: 0
Output:

Chunk ID: 75aa89
Wall time: 2.2052 seconds
Process exited with code 0
Original token count: 1528
Output:
  [sota unavailable] RuntimeError: Invalid scaling configuration.
- For TensorWise scaling, a and b should be float8, scales should be float and singletons.
- For RowWise scaling, a and b should be float8, scales should be float, scale_a should be (4096, 1) and scale_b should be (1, 4096), and both should be contiguous.
- For BlockWise 1x128 scaling, a and b should be float8, scales should be float, scale_a should be (4096, 32) and scale_b should be (32, 4096), and both should be outer-dim-major.
- For BlockWise 128x128 scaling, a and b should be float8, scales should be float, scale_a should be (32, 32) and scale_b should be (32, 32), and both should be near-inner-dim-major (with 16-byte aligned strides).
- For Blockwise 1x32 scaling, a and b should be float8, scales should be float8_e8m0fnu, scale_a should have 524288 elements and scale_b should have 524288 elements, and both should be contiguous.
- For Blockwise 1x16 scaling, a and b should be float4 (packed 2x), scales should be float8_e4m3fn, scale_a should have 2097152 elements and scale_b should have 2097152 elements, and both should be contiguous.
Got a.dtype()=Float8_e4m3fn, scale_a.dtype()=Float, scale_a.size()=[], scale_a.stride()=[], b.dtype()=BFloat16, scale_b.dtype()=Float, scale_b.size()=[] and scale_b.stride()=[]
shape=0 variant=eager tflops=349.071 gbps=170.445 ms=0.394
shape=0 variant=compiled tflops=323.

... (4715 more chars)

Chunk ID: 8afaa2
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!

Chunk ID: 9dd0f6
Wall time: 0.0000 seconds
Process exited with code 5
Original token count: 85
Output:
============================= test session starts ==============================
platform linux -- Python 3.11.15, pytest-9.0.3, pluggy-1.6.0
rootdir: /home/infatoshi/cuda/KernelBench-Hard
configfile: pyproject.toml
plugins: anyio-4.13.0
collected 0 items

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

Chunk ID: 169c6f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 81
Output:
!! ../../.pytest_cache/
!! ../../.ruff_cache/
!! ../../.venv/
!! ../../outputs/
!! __pycache__/
!! framework.txt
!! solution.py
!! ../../src/__pycache__/
!! ../../src/eval/__pycache__/
!! ../../src/hardware/__pycache__/
!! ../../src/viewer/__pycache__/
!! ../../src/viewer/parsers/__pycache__/
!! ../../tests/__pycache__/

Chunk ID: b6525b
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1722
Output:
import os
from pathlib import Path

import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

M = 4096
N = 4096
K = 4096


_EXT = None


def _load_ext():
    global _EXT
    if _EXT is not None:
        return _EXT

    cutlass_root = Path("/home/infatoshi/vllm/.deps/cutlass-src")
    os.environ.setdefault("TORCH_CUDA_ARCH_LIST", "12.0")

    cuda_src = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>

#include <cuda_bf16.h>
#include <cuda_fp8.h>
#include <cuda_runtime.h>

#include "cutlass/cutlass.h"
#include "cutlass/bfloat16.h"
#include "cutlass/epilogue/thread/linear_combination.h"
#include "cutlass/gemm/device/gemm.h"
#include "cutlass/gemm/threadblock/threadblock_swizzle.h"

namespace {

#define CUDA_CHECK_OK(expr)                                                     \
  do {                                                                         \
    cudaError_t err = (expr);                                                   \
    TORCH_CHECK(err == cudaSuccess, "CUDA error: ", cudaGetErrorString(err));   \
  } while (0)

__global__ void fp8_to_bf16_pad_kernel(
    const unsigned char* __restrict__ src,
    __nv_bfloat16* __restrict__ dst,
    int rows,
    int cols,
    int cols_pad) {
  int64_t idx = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
  int64_t total = static_cast<int64_t>(rows) * cols_pad;


... (5493 more chars)

Chunk ID: c1cd13
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 3
Output:
cutlass2