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benchdnn-3.14-1.1 RPM for x86_64

From OpenSuSE Tumbleweed for x86_64

Name: benchdnn Distribution: openSUSE Tumbleweed
Version: 3.14 Vendor: openSUSE
Release: 1.1 Build date: Sat Oct 24 06:48:44 2026
Group: Unspecified Build host: reproducible
Size: 18869710 Source RPM: onednn-3.14-1.1.src.rpm
Packager: https://bugs.opensuse.org
Url: https://github.com/uxlfoundation/oneDNN
Summary: Header files of Intel Math Kernel Library
 
Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) is an
open-source performance library for deep-learning applications. The library
accelerates deep-learning applications and frameworks on Intel architecture.
Intel MKL-DNN contains vectorized and threaded building blocks that you can use
to implement deep neural networks (DNN) with C and C++ interfaces.

This package only includes the benchmark utility including its input files.

Provides

Requires

License

Apache-2.0

Changelog

* Sat Oct 24 2026 Martin Pluskal <mpluskal@suse.com>
  - Update to 3.14:
    * Layer normalization gains RMSNorm mode; sparse memory objects
      and sparse matmul are now production status
    * Graph API: RMSNorm and Dropout operations, SDPA/GQA fusion
      improvements, host-side scalars, experimental grouped (MoE)
      matmul
    * New quantization support: fp8 primitives, MX dynamic
      quantization, NVFP4 scheme and fp4 compressed weights
    * x86: initial AVX10.2/AMX support (opt-in), relaxed fp32
      accumulation mode, ZenDNN path for AMD Zen (opt-in)
    * AArch64: faster SVE/ASIMD matmul, eltwise and softmax; re-enable
      the Arm Compute Library backend (the 25.03.1 API break is fixed
      on both sides and 3.14 needs ACL >= 53.1, matched here)
      and the OpenCL GPU backend (kept off on ppc64le where the
      bundled CL headers still break the build via <altivec.h>,
      and on unvalidated riscv64);
      drop the unpackaged bundled-KleidiAI install droppings
    * AArch64 %check: skip test_internals_env_vars_* (new verbose-mode
      coverage fails only there) and test_benchdnn_modeC_graph_ci_cpu
      (known upstream MHA/dropout reference failures)
    * Removed the experimental Graph Compiler backend; BLAS-like
      sgemm API and f4_e3m0 type deprecated; legacy SSE4.1/AVX and
      old-GPU kernels dropped in favor of generic fallbacks
    * No soname change (libdnnl.so.3)
    * Pass empty DNNL_ARCH_OPT_FLAGS for portable binaries (upstream
      renamed the option and dropped the MKLDNN_ compat shim)
    * Enable ONEDNN_BUILD_DOC (upstream flipped its default to OFF)
    * Add doxygen-mainpage-path.patch: point USE_MDFILE_AS_MAINPAGE
      at the source tree, fixing the doc build (the relative
      mainpage.md no longer resolves in out-of-source builds, fatal
      since upstream turned doxygen warnings into errors)
    * Add doxygen-no-warn-as-error.patch: tolerate doxygen warnings
      instead of failing the doc build on them (the doc tree predates
      the newer doxygen, which reports hundreds of alias/ref warnings)
    * Add ocl-sparse-test-skip-without-gpu.patch: skip the new sparse
      memory OpenCL tests when no GPU is present (they failed instead
      of skipping like every sibling test)
    * CVE-2022-26076 (boo#1208411) was fixed upstream in 2022.1,
      before the previously packaged 3.7.3, and this Linux build
      loads system libraries only: not affected
* Mon Jul 20 2026 Andreas Schwab <schwab@suse.de>
  - Enable build for riscv64
* Tue May 06 2025 Eyad Issa <eyadlorenzo@gmail.com>
  - Update to v3.7.3:
    * https://github.com/uxlfoundation/oneDNN/releases/tag/v3.7.3
    * Various correctness and performance fixes
* Tue Apr 15 2025 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Update to 3.7.2:
    * https://github.com/oneapi-src/oneDNN/releases/tag/v3.7.2
  - Disable benchdnn_modeC_matmul_ci_cpu test as it reaches timeout
    quite often
* Fri Jan 03 2025 Eyad Issa <eyadlorenzo@gmail.com>
  - Update to 3.6.2:
    * https://github.com/oneapi-src/oneDNN/releases/tag/v3.6.2
* Thu Oct 17 2024 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Update to 3.6:
    * https://github.com/oneapi-src/oneDNN/releases/tag/v3.6
* Wed Oct 02 2024 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Add openCL deps for devel package
* Tue Sep 24 2024 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Enable graph component
* Mon Sep 23 2024 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Update to 3.5.3:
    * https://github.com/oneapi-src/oneDNN/releases/tag/v3.5.3
* Fri Apr 19 2024 Alessandro de Oliveira Faria <cabelo@opensuse.org>
  - Update to 3.4.1:
    * Fixed an issue with caching and serialization of primitives in
      deterministic mode (7ed604a)
    * Introduced memory descriptor serialization API
      (4cad420, 929a27a, 9b848c8)
    * Fixed incorrect results in fp64 convolution and deconvolution
      on Intel GPUs based on Xe-LPG architecture (ebe77b5, 0b399ac,
      d748d64, 9f4f3d5, 21a8cae)
    * Fixed incorrect results in reorder with large sizes on
      Intel CPUs and GPUs (69a111e, 4b72361, 74a343b)
    * Reduced creation time for deconvolution primitive on
      Intel CPUs (bec487e, 1eab005)
    * Fixed performance regression in deconvolution on
      Intel CPUs (fbe5b97, 1dd3c6a)
    * Removed dangling symblols from static builds
      (e92c404, 6f5621a)
    * Fixed crash during platform detection on some
      AArch64-based systems (406a079)
    * Fixed performance regression in int8 deconvolution on
      Intel CPUs (7e50e15)
    * Fixed handling of zero points for matmul in verbose
      logs converter (15c7916)
* Fri Dec 01 2023 Alessandro de Oliveira Faria <cabelo@opensuse.org>
  - Update to 3.3.3:
  - This is a patch release containing the following changes to v3.3.2:
    * Fixed performance regression in int8 convolutions on processors with Intel AVX-512 and Intel DL Boost support (a00661f)
    * Fixed race condition during library initialization on Intel Data Center GPU Max Series (7dfcd11)
    * Fixed accuracy issue in experimental Graph Compiler with LLVM code generator (8892e7e)
    * Disabled int8 RNN implementation for cases with non-trivial strides (2195e4b)
    * Fixed incorrect results in bfloat16 convolution implementation on processors with Intel AMX support (9f00af9)
    * Fixed incorrect results in fp16 and int8 convolution on Intel Core Ultra integrated GPUs (69cef84, 79bc6cc, c9c0b09)
* Fri Dec 01 2023 Alessandro de Oliveira Faria <cabelo@opensuse.org>
  - Update to 3.3.1:
  - This is a patch release containing the following changes to v3.3:
    * Fixed int8 convolution accuracy issue on Intel GPUs (09c87c7)
    * Switched internal stream to in-order mode for NVIDIA and AMD GPUs to avoid synchronization issues (db01d62)
    * Fixed runtime error for avgpool_bwd operation in Graph API (d025ef6, 9e0602a, e0dc1b3)
    * Fixed benchdnn error reporting for some Graph API cases (98dc9db)
    * Fixed accuracy issue in experimental Graph Compiler for int8 MHA variant from StarCoder model (5476ef7)
    * Fixed incorrect results for layer normalization with trivial dimensions on Intel GPUs (a2ec0a0)
    * Removed redundant synchronization for out-of-order SYCL queues (a96e9b1)
    * Fixed runtime error in experimental Graph Compiler for int8 MLP subgraph from LLAMA model (595543d)
    * Fixed SEGFAULT in experimental Graph Compiler for fp32 MLP subgraph (4207105)
    * Fixed incorrect results in experimental Graph Compiler for MLP subgraph (57e14b5)
    * Fixed the issue with f16 inner product primitive with s8 output returning unimplemented on Intel GPUs (bf12207, 800b5e9, ec7054a)
    * Fixed incorrect results for int8 deconvolution with zero-points on processors with Intel AMX instructions support (55d2cec)
* Tue Oct 10 2023 Paolo Stivanin <info@paolostivanin.com>
  - Update to 3.3:
    * 3.3: https://github.com/oneapi-src/oneDNN/releases/tag/v3.3
    * 3.2: https://github.com/oneapi-src/oneDNN/releases/tag/v3.2
    * 3.1: https://github.com/oneapi-src/oneDNN/releases/tag/v3.1
  - Drop upstreamed onednn-fix-gcc13.patch
* Tue Mar 21 2023 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Update to 3.0.1:
    * Changes: https://github.com/oneapi-src/oneDNN/releases/tag/v3.0.1
  - Skipped 3.0:
    * Changes: https://github.com/oneapi-src/oneDNN/releases/tag/v3.0
  - Add patch to fix build with GCC13:
    * onednn-fix-gcc13.patch
  - Disable Arm Compute library support until fixed upstream
    https://github.com/oneapi-src/oneDNN/issues/1599
  - Drop upstream patches:
    * 1428.patch
    * fa93750.patch

Files

/usr/bin/benchdnn
/usr/share/benchdnn
/usr/share/benchdnn/inputs
/usr/share/benchdnn/inputs/binary
/usr/share/benchdnn/inputs/binary/harness_binary_bcast4d
/usr/share/benchdnn/inputs/binary/harness_binary_bf16
/usr/share/benchdnn/inputs/binary/harness_binary_different_dt
/usr/share/benchdnn/inputs/binary/harness_binary_f16
/usr/share/benchdnn/inputs/binary/harness_binary_f32
/usr/share/benchdnn/inputs/binary/harness_binary_i8
/usr/share/benchdnn/inputs/binary/harness_binary_regression
/usr/share/benchdnn/inputs/binary/option_set_all
/usr/share/benchdnn/inputs/binary/option_set_bcast4d
/usr/share/benchdnn/inputs/binary/option_set_fwks_ext_gpu
/usr/share/benchdnn/inputs/binary/option_set_fwks_key_gpu
/usr/share/benchdnn/inputs/binary/option_set_minimal
/usr/share/benchdnn/inputs/binary/option_set_src0_bcast
/usr/share/benchdnn/inputs/binary/perf_binary_gpu
/usr/share/benchdnn/inputs/binary/shapes_ci
/usr/share/benchdnn/inputs/binary/shapes_perf_1st_conv
/usr/share/benchdnn/inputs/binary/shapes_perf_scaleshift
/usr/share/benchdnn/inputs/binary/test_binary_all
/usr/share/benchdnn/inputs/binary/test_binary_bfloat16
/usr/share/benchdnn/inputs/binary/test_binary_ci
/usr/share/benchdnn/inputs/binary/test_binary_different_dt_ci
/usr/share/benchdnn/inputs/binary/test_binary_float16
/usr/share/benchdnn/inputs/binary/test_binary_gpu
/usr/share/benchdnn/inputs/binary/test_binary_smoke
/usr/share/benchdnn/inputs/bnorm
/usr/share/benchdnn/inputs/bnorm/option_set_fwks_ext_gpu
/usr/share/benchdnn/inputs/bnorm/option_set_fwks_key_gpu
/usr/share/benchdnn/inputs/bnorm/perf_bnorm_gpu
/usr/share/benchdnn/inputs/bnorm/set_nd
/usr/share/benchdnn/inputs/bnorm/shapes_1d
/usr/share/benchdnn/inputs/bnorm/shapes_2d
/usr/share/benchdnn/inputs/bnorm/shapes_3d
/usr/share/benchdnn/inputs/bnorm/shapes_ci
/usr/share/benchdnn/inputs/bnorm/shapes_densenet_121
/usr/share/benchdnn/inputs/bnorm/shapes_googlenet_v2
/usr/share/benchdnn/inputs/bnorm/shapes_googlenet_v3
/usr/share/benchdnn/inputs/bnorm/shapes_large
/usr/share/benchdnn/inputs/bnorm/shapes_regressions
/usr/share/benchdnn/inputs/bnorm/shapes_resnet_50
/usr/share/benchdnn/inputs/bnorm/shapes_topologies_small
/usr/share/benchdnn/inputs/bnorm/test_bnorm_all_blocked
/usr/share/benchdnn/inputs/bnorm/test_bnorm_all_plain
/usr/share/benchdnn/inputs/bnorm/test_bnorm_bfloat16_blocked
/usr/share/benchdnn/inputs/bnorm/test_bnorm_bfloat16_plain
/usr/share/benchdnn/inputs/bnorm/test_bnorm_ci
/usr/share/benchdnn/inputs/bnorm/test_bnorm_float16_plain
/usr/share/benchdnn/inputs/bnorm/test_bnorm_gpu
/usr/share/benchdnn/inputs/bnorm/test_bnorm_regressions
/usr/share/benchdnn/inputs/bnorm/test_bnorm_regressions_large
/usr/share/benchdnn/inputs/bnorm/test_bnorm_smoke
/usr/share/benchdnn/inputs/brgemm
/usr/share/benchdnn/inputs/brgemm/harness_brgemm_f32
/usr/share/benchdnn/inputs/brgemm/harness_brgemm_f8
/usr/share/benchdnn/inputs/brgemm/harness_brgemm_fpmath
/usr/share/benchdnn/inputs/brgemm/option_set_bf16
/usr/share/benchdnn/inputs/brgemm/option_set_f32
/usr/share/benchdnn/inputs/brgemm/option_set_int8
/usr/share/benchdnn/inputs/brgemm/shapes_2d_big_k_bf16
/usr/share/benchdnn/inputs/brgemm/shapes_2d_big_k_f32
/usr/share/benchdnn/inputs/brgemm/shapes_2d_big_k_int8
/usr/share/benchdnn/inputs/brgemm/shapes_2d_big_k_tail_n_bf16
/usr/share/benchdnn/inputs/brgemm/shapes_2d_big_k_tail_n_f32
/usr/share/benchdnn/inputs/brgemm/shapes_2d_big_k_tail_n_int8
/usr/share/benchdnn/inputs/brgemm/shapes_2d_no_tail_bf16
/usr/share/benchdnn/inputs/brgemm/shapes_2d_no_tail_f32
/usr/share/benchdnn/inputs/brgemm/shapes_2d_no_tail_int8
/usr/share/benchdnn/inputs/brgemm/shapes_2d_tail_k_bf16
/usr/share/benchdnn/inputs/brgemm/shapes_2d_tail_k_f32
/usr/share/benchdnn/inputs/brgemm/shapes_2d_tail_k_int8
/usr/share/benchdnn/inputs/brgemm/shapes_2d_tail_k_tail_n_bf16
/usr/share/benchdnn/inputs/brgemm/shapes_2d_tail_k_tail_n_f32
/usr/share/benchdnn/inputs/brgemm/shapes_2d_tail_k_tail_n_int8
/usr/share/benchdnn/inputs/brgemm/shapes_2d_tail_n_bf16
/usr/share/benchdnn/inputs/brgemm/shapes_2d_tail_n_f32
/usr/share/benchdnn/inputs/brgemm/shapes_2d_tail_n_int8
/usr/share/benchdnn/inputs/brgemm/test_brgemm_all
/usr/share/benchdnn/inputs/brgemm/test_brgemm_bf16
/usr/share/benchdnn/inputs/brgemm/test_brgemm_ci
/usr/share/benchdnn/inputs/brgemm/test_brgemm_f16
/usr/share/benchdnn/inputs/brgemm/test_brgemm_f32
/usr/share/benchdnn/inputs/brgemm/test_brgemm_f8
/usr/share/benchdnn/inputs/brgemm/test_brgemm_int8
/usr/share/benchdnn/inputs/brgemm/test_brgemm_regression
/usr/share/benchdnn/inputs/brgemm/test_brgemm_smoke
/usr/share/benchdnn/inputs/concat
/usr/share/benchdnn/inputs/concat/option_set_fwks_ext_gpu
/usr/share/benchdnn/inputs/concat/option_set_fwks_key_gpu
/usr/share/benchdnn/inputs/concat/option_set_xe_gpu
/usr/share/benchdnn/inputs/concat/test_concat_all
/usr/share/benchdnn/inputs/concat/test_concat_bfloat16
/usr/share/benchdnn/inputs/concat/test_concat_ci
/usr/share/benchdnn/inputs/concat/test_concat_float16
/usr/share/benchdnn/inputs/concat/test_concat_gpu
/usr/share/benchdnn/inputs/concat/test_concat_large_gpu
/usr/share/benchdnn/inputs/concat/test_concat_smoke
/usr/share/benchdnn/inputs/conv
/usr/share/benchdnn/inputs/conv/harness_conv_arbitrary_dst
/usr/share/benchdnn/inputs/conv/harness_conv_attrs_gpu
/usr/share/benchdnn/inputs/conv/harness_conv_attrs_int8
/usr/share/benchdnn/inputs/conv/harness_conv_attrs_int8_asymmetric
/usr/share/benchdnn/inputs/conv/harness_conv_auto
/usr/share/benchdnn/inputs/conv/harness_conv_deepbench
/usr/share/benchdnn/inputs/conv/harness_conv_depthwise_int8
/usr/share/benchdnn/inputs/conv/harness_conv_dilated_3d
/usr/share/benchdnn/inputs/conv/harness_conv_dilated_int8
/usr/share/benchdnn/inputs/conv/harness_conv_dw_bfloat16
/usr/share/benchdnn/inputs/conv/harness_conv_dw_bfloat16_nxc
/usr/share/benchdnn/inputs/conv/harness_conv_dw_float16_nxc
/usr/share/benchdnn/inputs/conv/harness_conv_f32
/usr/share/benchdnn/inputs/conv/harness_conv_f32_plain
/usr/share/benchdnn/inputs/conv/harness_conv_fused_depthwise
/usr/share/benchdnn/inputs/conv/harness_conv_int8
/usr/share/benchdnn/inputs/conv/harness_conv_output_striding
/usr/share/benchdnn/inputs/conv/harness_conv_regression_general
/usr/share/benchdnn/inputs/conv/harness_conv_saturation_int8
/usr/share/benchdnn/inputs/conv/harness_conv_smoke_ref
/usr/share/benchdnn/inputs/conv/harness_conv_tags
/usr/share/benchdnn/inputs/conv/harness_conv_zero_points
/usr/share/benchdnn/inputs/conv/option_gpu_ci
/usr/share/benchdnn/inputs/conv/option_set_all_eltwise_postops
/usr/share/benchdnn/inputs/conv/option_set_combined_postops
/usr/share/benchdnn/inputs/conv/option_set_fwks_ext_gpu
/usr/share/benchdnn/inputs/conv/option_set_fwks_key_01_gpu
/usr/share/benchdnn/inputs/conv/option_set_fwks_key_gpu
/usr/share/benchdnn/inputs/conv/option_set_v2
/usr/share/benchdnn/inputs/conv/perf_conv_xe
/usr/share/benchdnn/inputs/conv/perf_conv_xe_hp
/usr/share/benchdnn/inputs/conv/perf_conv_xe_lp
/usr/share/benchdnn/inputs/conv/set_all_topologies
/usr/share/benchdnn/inputs/conv/set_conv_3d
/usr/share/benchdnn/inputs/conv/set_conv_all
/usr/share/benchdnn/inputs/conv/set_conv_dw
/usr/share/benchdnn/inputs/conv/set_dilated-conv
/usr/share/benchdnn/inputs/conv/set_dilated-conv_1st
/usr/share/benchdnn/inputs/conv/set_dilated-conv_3d
/usr/share/benchdnn/inputs/conv/set_fastrcnn
/usr/share/benchdnn/inputs/conv/set_gpu
/usr/share/benchdnn/inputs/conv/set_maskrcnn
/usr/share/benchdnn/inputs/conv/set_perf_gpu_all_mb
/usr/share/benchdnn/inputs/conv/set_perf_gpu_large_mb
/usr/share/benchdnn/inputs/conv/set_perf_gpu_small_mb
/usr/share/benchdnn/inputs/conv/set_topologies_inference_only
/usr/share/benchdnn/inputs/conv/shapes_1d
/usr/share/benchdnn/inputs/conv/shapes_1d_wavenet
/usr/share/benchdnn/inputs/conv/shapes_1x1
/usr/share/benchdnn/inputs/conv/shapes_3d
/usr/share/benchdnn/inputs/conv/shapes_3d_1st_strided_padding
/usr/share/benchdnn/inputs/conv/shapes_3d_1x1_strided_no-padding
/usr/share/benchdnn/inputs/conv/shapes_3d_1x1_strided_padding
/usr/share/benchdnn/inputs/conv/shapes_3d_1x1_unit-stride_no-padding
/usr/share/benchdnn/inputs/conv/shapes_3d_1x1_unit-stride_padding
/usr/share/benchdnn/inputs/conv/shapes_3d_2d_strided_padding
/usr/share/benchdnn/inputs/conv/shapes_3d_gpu
/usr/share/benchdnn/inputs/conv/shapes_3d_i3d
/usr/share/benchdnn/inputs/conv/shapes_3d_resnext101
/usr/share/benchdnn/inputs/conv/shapes_3d_strided_no-padding
/usr/share/benchdnn/inputs/conv/shapes_3d_strided_padding
/usr/share/benchdnn/inputs/conv/shapes_3d_unet
/usr/share/benchdnn/inputs/conv/shapes_3d_unit-stride_no-padding
/usr/share/benchdnn/inputs/conv/shapes_3d_unit-stride_padding
/usr/share/benchdnn/inputs/conv/shapes_4bit
/usr/share/benchdnn/inputs/conv/shapes_a3c
/usr/share/benchdnn/inputs/conv/shapes_alexnet
/usr/share/benchdnn/inputs/conv/shapes_auto
/usr/share/benchdnn/inputs/conv/shapes_basic
/usr/share/benchdnn/inputs/conv/shapes_ci_gpu
/usr/share/benchdnn/inputs/conv/shapes_cosmictagger
/usr/share/benchdnn/inputs/conv/shapes_deepbench_inference_device
/usr/share/benchdnn/inputs/conv/shapes_deepbench_inference_server
/usr/share/benchdnn/inputs/conv/shapes_deepbench_training
/usr/share/benchdnn/inputs/conv/shapes_densnet
/usr/share/benchdnn/inputs/conv/shapes_dilated
/usr/share/benchdnn/inputs/conv/shapes_dilated_1d_1st_strided_padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_1d_strided_no-padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_1d_strided_padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_1d_unit-stride_no-padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_1d_unit-stride_padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_2d_1st_strided_padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_2d_strided_no-padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_2d_strided_padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_2d_unit-stride_no-padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_2d_unit-stride_padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_3d_strided_no-padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_3d_strided_padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_3d_unit-stride_no-padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_3d_unit-stride_padding
/usr/share/benchdnn/inputs/conv/shapes_dilated_rfcn
/usr/share/benchdnn/inputs/conv/shapes_dw_1d_stride_no-padding
/usr/share/benchdnn/inputs/conv/shapes_dw_1d_unit-stride_no-padding
/usr/share/benchdnn/inputs/conv/shapes_dw_1d_unit-stride_padding
/usr/share/benchdnn/inputs/conv/shapes_dw_2d_1d_strided_padding
/usr/share/benchdnn/inputs/conv/shapes_dw_2d_strided_no-padding
/usr/share/benchdnn/inputs/conv/shapes_dw_2d_strided_padding
/usr/share/benchdnn/inputs/conv/shapes_dw_2d_unit-stride_no-padding
/usr/share/benchdnn/inputs/conv/shapes_dw_2d_unit-stride_padding
/usr/share/benchdnn/inputs/conv/shapes_dw_3d_strided_no-padding
/usr/share/benchdnn/inputs/conv/shapes_dw_3d_strided_padding
/usr/share/benchdnn/inputs/conv/shapes_dw_3d_unit-stride_no-padding
/usr/share/benchdnn/inputs/conv/shapes_dw_3d_unit-stride_padding
/usr/share/benchdnn/inputs/conv/shapes_dw_minibatch_2d-spatial
/usr/share/benchdnn/inputs/conv/shapes_dw_minibatch_channel_2d-spatial
/usr/share/benchdnn/inputs/conv/shapes_efficientdet
/usr/share/benchdnn/inputs/conv/shapes_fastrcnn_p1
/usr/share/benchdnn/inputs/conv/shapes_fastrcnn_p2
/usr/share/benchdnn/inputs/conv/shapes_fastrcnn_p3
/usr/share/benchdnn/inputs/conv/shapes_ffn
/usr/share/benchdnn/inputs/conv/shapes_fused_large_src
/usr/share/benchdnn/inputs/conv/shapes_fused_mobilenet_stride_1
/usr/share/benchdnn/inputs/conv/shapes_fused_mobilenet_stride_2
/usr/share/benchdnn/inputs/conv/shapes_gemm
/usr/share/benchdnn/inputs/conv/shapes_googlenet_v1
/usr/share/benchdnn/inputs/conv/shapes_googlenet_v2
/usr/share/benchdnn/inputs/conv/shapes_googlenet_v3
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Fabrice Bellet, Tue Oct 27 22:43:50 2026