content: 'Read reference.py, check.py, benchmark.py, shapes.py to understand the problem'
status: 'in_progress'
priority: 'high'
content: 'Implement custom CUDA kernel for 3D patch embedding (Triton or CUDA C++)'
status: 'pending'
priority: 'high'
content: 'Verify correctness with check.py'
status: 'pending'
priority: 'high'
content: 'Profile and optimize for RTX PRO 6000 (SM120)'
status: 'pending'
priority: 'high'
content: 'Benchmark all shapes and iterate'
status: 'pending'
priority: 'medium'

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm</path>
<type>directory</type>
<entries>
benchmark.py
check.py
problem.yaml
PROMPT.txt
reference.py
shapes.py
sota.py

(7 entries)
</entries>

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/check.py</path>
<type>file</type>
<content>
1: """Correctness runner for Conv3d-as-GEMM patch embedding.
2: 
3: Runs solution.Model vs reference.Model across all shapes in shapes.py, 3 seeds
4: each, with per-dtype atol/rtol. Also rejects forbidden ops by grep.
5: """
6: import re
7: import sys
8: from pathlib import Path
9: 
10: import torch
11: import yaml
12: 
13: # Make the repo's src/ importable
14: REPO_ROOT = Path(__file__).resolve().parents[2]
15: sys.path.insert(0, str(REPO_ROOT))
16: 
17: from src.eval.correctness import check_correctness  # noqa: E402
18: 
19: 
20: def main():
21:     try:
22:         import reference
23:         import shapes
24:         import solution
25:     except Exception as e:
26:         print(f"FAIL: import error: {e}")
27:         sys.exit(1)
28: 
29:     problem_yaml = Path("problem.yaml")
30:     meta = yaml.safe_load(problem_yaml.read_text()) if problem_yaml.exists() else {}
31: 
32:     # --- Forbidden-op check ------------------------------------------------
33:     sol_src = Path("solution.py").read_text() if Path("solution.py").exists() else ""
34:     for forbidden in meta.get("forbidden", []):
35:         pat = re.escape(forbidden)
36:         if re.search(pat, sol_src):
37:             print(f"FAIL: forbidden op used: {forbidden}")
38:             sys.exit(1)
39: 
40:     device = torch.device("cuda:0")
41:     tol_override = meta.get("tolerance") o

... (3130 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/reference.py</path>
<type>file</type>
<content>
1: """Naive Conv3d-as-GEMM patch embedding reference (correctness only).
2: 
3: Vision-Transformer / Qwen2-VL style patch embedding: a video tensor
4: (B, C, T, H, W) is split into non-overlapping (kT, kH, kW) patches and each
5: patch projected to embed_dim. This is mathematically a 3D convolution with
6: stride == kernel and equivalently a single GEMM after a strided reshape.
7: 
8: Reference uses nn.Conv3d (cuDNN dispatch) for clarity. The agent's solution is
9: forbidden from using Conv3d / conv3d / matmul / linear / einsum, forcing them
10: to write a fused patch-gather + tensor-core GEMM kernel.
11: 
12: Output layout: (B, embed_dim, T/kT, H/kH, W/kW). No bias.
13: """
14: import torch
15: import torch.nn as nn
16: 
17: OP_TYPE = "patch_embed"
18: SUPPORTED_PRECISIONS = ["bf16"]
19: HARDWARE_REQUIRED = ["RTX_PRO_6000"]
20: 
21: 
22: class Model(nn.Module):
23:     def __init__(self, B: int, C: int, T: int, H: int, W: int,
24:                  kT: int, kH: int, kW: int, embed_dim: int):
25:         super().__init__()
26:         assert T % kT == 0 and H % kH == 0 and W % kW == 0, \
27:             f"Input dims must be divisible by patch size: T={T} kT={kT} H={H} kH={kH} W={W} kW={kW}"
28:         self.B, self.C, self.T, self.H, self.W = B, C, T, H, W
29:         self.kT, self.kH, self.kW = kT, kH, kW
30:         self.embed_dim = embed_dim
31: 
3

... (911 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/benchmark.py</path>
<type>file</type>
<content>
1: """Roofline benchmark for Conv3d-as-GEMM patch embedding.
2: 
3: For each shape: times eager reference, compiled reference, SOTA (if available),
4: and the agent's solution. Reports achieved TFLOPS, GB/s, and peak_fraction.
5: 
6: Output lines the harness picks up:
7:   shape=<idx> variant=<name> tflops=<N> gbps=<N> ms=<N>
8:   peak_fraction: <N>  (geomean over shapes of solution's peak_fraction)
9: """
10: import sys
11: from math import exp, log
12: from pathlib import Path
13: 
14: import torch
15: import yaml
16: 
17: REPO_ROOT = Path(__file__).resolve().parents[2]
18: sys.path.insert(0, str(REPO_ROOT))
19: 
20: from src.eval.roofline import compute_gbps, compute_tflops, peak_fraction  # noqa: E402
21: from src.eval.timing import time_fn  # noqa: E402
22: from src.hardware import get as get_hw  # noqa: E402
23: 
24: 
25: def _eval_formula(expr: str, vars: dict) -> float:
26:     # Very small eval: only names from `vars` are valid.
27:     return float(eval(expr, {"__builtins__": {}}, vars))
28: 
29: 
30: def main():
31:     import reference
32:     import shapes
33:     import solution
34: 
35:     meta = yaml.safe_load(Path("problem.yaml").read_text())
36:     hw = get_hw(meta["hardware"][0])
37:     peak_tflops = hw.peak_tflops_dense.get(meta["peak_tflops_key"], 0.0)
38:     peak_gbps = hw.peak_bandwidth_gb_s
39:     regime = meta.get("regim

... (3879 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/shapes.py</path>
<type>file</type>
<content>
1: """Canonical shape sweep for Conv3d-as-GEMM patch embedding.
2: 
3: Sized for Qwen2-VL / ViT-style patch embedding. embed_dim=1280 matches Qwen2-VL
4: ViT; embed_dim=768 is a ViT-B/16 reference. Patch sizes are the canonical
5: 14x14 spatial / 2-frame temporal used in modern VL encoders.
6: """
7: 
8: SHAPES = [
9:     # Qwen2-VL ViT base: 224x224x2-frame video, 14x14x2 patches, embed_dim=1280
10:     {"B": 1, "C": 3, "T": 2,  "H": 224, "W": 224, "kT": 2, "kH": 14, "kW": 14, "embed_dim": 1280},
11: 
12:     # Same patch config, batch 2, 4-frame video
13:     {"B": 2, "C": 3, "T": 4,  "H": 224, "W": 224, "kT": 2, "kH": 14, "kW": 14, "embed_dim": 1280},
14: 
15:     # Larger spatial (336x336), 8-frame — exercises larger num_patches
16:     {"B": 1, "C": 3, "T": 8,  "H": 336, "W": 336, "kT": 2, "kH": 14, "kW": 14, "embed_dim": 1280},
17: 
18:     # ViT-B/16 (image-mode): kT=1, kH=kW=16, embed_dim=768
19:     {"B": 4, "C": 3, "T": 1,  "H": 224, "W": 224, "kT": 1, "kH": 16, "kW": 16, "embed_dim": 768},
20: ]

(End of file - total 20 lines)
</content>

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/sota.py</path>
<type>file</type>
<content>
1: """SOTA reference for Conv3d-as-GEMM patch embedding.
2: 
3: Dispatches torch.nn.functional.conv3d (cuDNN). The agent's solution is
4: forbidden from calling conv3d directly; this file is only for the benchmark's
5: reference baseline.
6: """
7: from __future__ import annotations
8: 
9: import torch
10: import torch.nn.functional as F
11: 
12: 
13: def sota_forward(x: torch.Tensor, weight: torch.Tensor,
14:                  stride: tuple[int, int, int]) -> torch.Tensor:
15:     return F.conv3d(x, weight, bias=None, stride=stride)
16: 
17: 
18: def is_available() -> bool:
19:     return True

(End of file - total 19 lines)
</content>

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/problem.yaml</path>
<type>file</type>
<content>
1: name: 10_patch_embed_conv3d_gemm
2: display_name: "Conv3d-as-GEMM Patch Embedding"
3: precision: bf16
4: regime: compute  # GEMM-equivalent, embed_dim=1280 is well above the bf16 ridge
5: 
6: # Dense FLOPs: 2 * (num_patches) * (C * kT * kH * kW) * embed_dim.
7: # With stride==kernel and divisible inputs, num_patches * (kT*kH*kW) = T*H*W,
8: # so this collapses to: 2 * B * embed_dim * C * T * H * W.
9: flops_formula: "2 * B * embed_dim * C * T * H * W"
10: 
11: # Bytes moved (lower bound):
12: #   read input x:    B * C * T * H * W * 2
13: #   read weights:    embed_dim * C * kT * kH * kW * 2
14: #   write output:    B * embed_dim * (T // kT) * (H // kH) * (W // kW) * 2
15: bytes_formula: "B*C*T*H*W*2 + embed_dim*C*kT*kH*kW*2 + B*embed_dim*(T//kT)*(H//kH)*(W//kW)*2"
16: 
17: hardware: [RTX_PRO_6000]
18: peak_tflops_key: bf16
19: peak_bandwidth_key: dram
20: 
21: tolerance:
22:   bfloat16: 0.02
23: 
24: # Forbidden ops -- agent must implement the fused patch-gather + GEMM
25: # themselves. Banning Conv3d/conv3d (the cuDNN dispatch) and matmul/bmm/linear/
26: # einsum (the lazy reshape-then-cuBLAS shortcut). Unfold is also banned for
27: # completeness even though it's nominally 2D.
28: forbidden:
29:   - "torch.nn.Conv3d"
30:   - "torch.nn.functional.conv3d"
31:   - "F.conv3d"
32:   - "torch.conv3d"
33:   - "torch.matmul"
34:   - "torch.bmm"
35:   -

... (386 more chars)

---
"""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 (PyYAML quirks):
    # "torch.bfloat16", "bfloat16", torch.bfloat16
    if override is not None:
        t

... (688 more chars)

NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 12.0, 97887 MiB

3.6.0

2.11.0+cu130 (12, 0)

content: 'Read reference.py, check.py, benchmark.py, shapes.py to understand the problem'
status: 'completed'
priority: 'high'
content: 'Implement custom CUDA kernel for 3D patch embedding (Triton with tl.dot)'
status: 'in_progress'
priority: 'high'
content: 'Verify correctness with check.py'
status: 'pending'
priority: 'high'
content: 'Profile and optimize for RTX PRO 6000 (SM120)'
status: 'pending'
priority: 'high'
content: 'Benchmark all shapes and iterate'
status: 'pending'
priority: 'medium'

Wrote file successfully.

PASS

shape=0 variant=eager tflops=3.280 gbps=18.164 ms=0.235
shape=0 variant=compiled tflops=2.866 gbps=15.871 ms=0.269
shape=0 variant=sota tflops=3.308 gbps=18.317 ms=0.233
shape=0 variant=solution tflops=9.786 gbps=54.196 ms=0.079
shape=0 solution_peak_fraction=0.0489
shape=1 variant=eager tflops=12.420 gbps=32.394 ms=0.248
shape=1 variant=compiled tflops=10.760 gbps=28.065 ms=0.286
shape=1 variant=sota tflops=12.537 gbps=32.698 ms=0.246
shape=1 variant=solution tflops=39.011 gbps=101.747 ms=0.079
shape=1 solution_peak_fraction=0.1951
shape=2 variant=eager tflops=24.290 gbps=50.173 ms=0.286
shape=2 variant=compiled tflops=21.686 gbps=44.795 ms=0.320
shape=2 variant=sota tflops=24.604 gbps=50.822 ms=0.282
shape=2 variant=solution tflops=71.882 gbps=148.481 ms=0.096
shape=2 solution_peak_fraction=0.3594
shape=3 variant=eager tflops=5.766 gbps=22.370 ms=0.160
shape=3 variant=compiled tflops=4.679 gbps=18.153 ms=0.198
shape=3 variant=sota tflops=5.869 gbps=22.772 ms=0.158
shape=3 variant=solution tflops=31.988 gbps=124.104 ms=0.029
shape=3 solution_peak_fraction=0.1599
peak_fraction: 0.1530
RESULT: OK

Wrote file successfully.

Uninstalled 1 package in 0.17ms
Installed 1 package in 1ms
PASS

shape=0 variant=eager tflops=3.281 gbps=18.171 ms=0.235
shape=0 variant=compiled tflops=2.857 gbps=15.823 ms=0.270
shape=0 variant=sota tflops=3.310 gbps=18.331 ms=0.233
shape=0 variant=solution tflops=11.815 gbps=65.429 ms=0.065
shape=0 solution_peak_fraction=0.0591
shape=1 variant=eager tflops=12.469 gbps=32.520 ms=0.247
shape=1 variant=compiled tflops=10.697 gbps=27.900 ms=0.288
shape=1 variant=sota tflops=12.592 gbps=32.843 ms=0.245
shape=1 variant=solution tflops=47.563 gbps=124.050 ms=0.065
shape=1 solution_peak_fraction=0.2378
shape=2 variant=eager tflops=24.258 gbps=50.108 ms=0.286
shape=2 variant=compiled tflops=21.492 gbps=44.395 ms=0.323
shape=2 variant=sota tflops=24.678 gbps=50.976 ms=0.281
shape=2 variant=solution tflops=99.158 gbps=204.823 ms=0.070
shape=2 solution_peak_fraction=0.4958
shape=3 variant=eager tflops=5.790 gbps=22.464 ms=0.160
shape=3 variant=compiled tflops=4.585 gbps=17.787 ms=0.202
shape=3 variant=sota tflops=5.818 gbps=22.572 ms=0.159
shape=3 variant=solution tflops=23.421 gbps=90.865 ms=0.039
shape=3 solution_peak_fraction=0.1171
peak_fraction: 0.1690
RESULT: OK

Wrote file successfully.

PASS

shape=0 variant=eager tflops=3.279 gbps=18.159 ms=0.235
shape=0 variant=compiled tflops=2.832 gbps=15.685 ms=0.272
shape=0 variant=sota tflops=3.307 gbps=18.312 ms=0.233
shape=0 variant=solution tflops=13.493 gbps=74.720 ms=0.057
shape=0 solution_peak_fraction=0.0675
shape=1 variant=eager tflops=12.429 gbps=32.417 ms=0.248
shape=1 variant=compiled tflops=10.611 gbps=27.675 ms=0.291
shape=1 variant=sota tflops=12.526 gbps=32.670 ms=0.246
shape=1 variant=solution tflops=51.094 gbps=133.261 ms=0.060
shape=1 solution_peak_fraction=0.2555
shape=2 variant=eager tflops=24.203 gbps=49.994 ms=0.287
shape=2 variant=compiled tflops=21.507 gbps=44.426 ms=0.323
shape=2 variant=sota tflops=24.644 gbps=50.906 ms=0.281
shape=2 variant=solution tflops=94.925 gbps=196.078 ms=0.073
shape=2 solution_peak_fraction=0.4746
shape=3 variant=eager tflops=5.765 gbps=22.365 ms=0.160
shape=3 variant=compiled tflops=4.603 gbps=17.859 ms=0.201
shape=3 variant=sota tflops=5.841 gbps=22.661 ms=0.158
shape=3 variant=solution tflops=25.921 gbps=100.563 ms=0.036
shape=3 solution_peak_fraction=0.1296
peak_fraction: 0.1804
RESULT: OK

s0: im2col=0.005ms  perm=0.001ms  gemm_ideal=0.004ms  M=256 N=1280 K=1176
s1: im2col=0.005ms  perm=0.001ms  gemm_ideal=0.015ms  M=1024 N=1280 K=1176
s2: im2col=0.007ms  perm=0.001ms  gemm_ideal=0.035ms  M=2304 N=1280 K=1176
s3: im2col=0.005ms  perm=0.001ms  gemm_ideal=0.005ms  M=784 N=768 K=768

Wrote file successfully.

PASS

shape=0 variant=eager tflops=3.277 gbps=18.150 ms=0.235
shape=0 variant=compiled tflops=2.832 gbps=15.684 ms=0.272
shape=0 variant=sota tflops=3.303 gbps=18.293 ms=0.233
shape=0 variant=solution tflops=11.835 gbps=65.541 ms=0.065
shape=0 solution_peak_fraction=0.0592
shape=1 variant=eager tflops=12.469 gbps=32.522 ms=0.247
shape=1 variant=compiled tflops=10.739 gbps=28.010 ms=0.287
shape=1 variant=sota tflops=12.614 gbps=32.899 ms=0.244
shape=1 variant=solution tflops=48.680 gbps=126.965 ms=0.063
shape=1 solution_peak_fraction=0.2434
shape=2 variant=eager tflops=24.177 gbps=49.941 ms=0.287
shape=2 variant=compiled tflops=21.442 gbps=44.292 ms=0.323
shape=2 variant=sota tflops=24.664 gbps=50.947 ms=0.281
shape=2 variant=solution tflops=92.238 gbps=190.529 ms=0.075
shape=2 solution_peak_fraction=0.4612
shape=3 variant=eager tflops=5.770 gbps=22.388 ms=0.160
shape=3 variant=compiled tflops=4.683 gbps=18.167 ms=0.198
shape=3 variant=sota tflops=5.862 gbps=22.744 ms=0.158
shape=3 variant=solution tflops=25.554 gbps=99.141 ms=0.036
shape=3 solution_peak_fraction=0.1278
peak_fraction: 0.1707
RESULT: OK

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
compute_100
compute_110
compute_103
compute_120
compute_121

[2/3] /usr/local/cuda-13/bin/nvcc -MD -MF cuda.cuda.o.d -DTORCH_EXTENSION_NAME=test_wmma -DTORCH_API_INCLUDE_EXTENSION_H -isystem /home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/include -isystem /home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-13/include -isystem /home/infatoshi/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_120,code=compute_120 -gencode=arch=compute_120,code=sm_120 --compiler-options '-fPIC' -std=c++17 -c /home/infatoshi/.cache/torch_extensions/py311_cu130/test_wmma/cuda.cu -o cuda.cuda.o 
ninja: build stopped: subcommand failed.

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 1.

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

Traceback (most recent call last):
  File "<string>", line 11, in <module>
  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-packages/torch/utils/cpp_extension.py", line 2791, in _run_ninja_build
    raise RuntimeError(message) from e
RuntimeError: Error building extension 'test_bf16'

[1/3] /usr/local/cuda-13/bin/nvcc -MD -MF cuda.cuda.o.d -DTORCH_EXTENSION_NAME=test_bf16 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/include -isystem /home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-13/include -isystem /home/infatoshi/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_120,code=compute_120 -gencode=arch=compute_120,code=sm_120 --compiler-options '-fPIC' -std=c++17 -c /home/infatoshi/.cache/torch_extensions/py311_cu130/test_bf16/cuda.cu -o cuda.cuda.o 
/usr/local/cuda-13/bin/nvcc -MD -MF cuda.cuda.o.d -DTORCH_EXTENSION_NAME=test_bf16 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/include -isystem /home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda-13/include -isystem /home/infatoshi/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/include/python3.11 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_120,code=compute_120 -gencode=ar

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