forked from pool/onednn
Accepting request 897415 from science:machinelearning
OBS-URL: https://build.opensuse.org/request/show/897415 OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/onednn?expand=0&rev=4
This commit is contained in:
Dominique Leuenberger
Git OBS Bridge
commit
f3b563b1b0
@ -0,0 +1,38 @@
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From cfbefd8d744d4cdcdf3dd2f18576f487b36911b6 Mon Sep 17 00:00:00 2001
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From: Denis Samoilov <denis.samoylov@intel.com>
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Date: Fri, 2 Apr 2021 19:46:22 -0700
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Subject: [PATCH] common, gpu: include thread and limit headers to fix GCC 11
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build issues
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---
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src/common/primitive_cache.hpp | 1 +
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src/gpu/jit/ngen/ngen_auto_swsb.hpp | 1 +
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2 files changed, 2 insertions(+)
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diff --git a/src/common/primitive_cache.hpp b/src/common/primitive_cache.hpp
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index 73cb1224f..05a3e53e5 100644
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--- a/src/common/primitive_cache.hpp
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+++ b/src/common/primitive_cache.hpp
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@@ -20,6 +20,7 @@
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#include <future>
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#include <list>
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#include <memory>
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+#include <thread>
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#include <unordered_map>
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#include "c_types_map.hpp"
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diff --git a/src/gpu/jit/ngen/ngen_auto_swsb.hpp b/src/gpu/jit/ngen/ngen_auto_swsb.hpp
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index de3417af3..62ef2a571 100644
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--- a/src/gpu/jit/ngen/ngen_auto_swsb.hpp
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+++ b/src/gpu/jit/ngen/ngen_auto_swsb.hpp
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@@ -33,6 +33,7 @@
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#include <list>
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#include <map>
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+#include <limits>
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namespace ngen {
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namespace autoswsb {
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--
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2.26.2
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3
oneDNN-2.2.3.tar.gz
Normal file
3
oneDNN-2.2.3.tar.gz
Normal file
@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:07e5cc2a30e7bb5a381eba04f8579f427372132ed3d44363f5fd89850a7b50fd
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size 9534138
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21
onednn-1045.patch
Normal file
21
onednn-1045.patch
Normal file
@ -0,0 +1,21 @@
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From a94acd4e2dfaf51552dd2a60b059df1c1f14e452 Mon Sep 17 00:00:00 2001
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From: Alexandre Truong <alexandre.truong@arm.com>
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Date: Wed, 28 Apr 2021 10:32:35 +0100
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Subject: [PATCH] cpu: aarch64: missing include for arm_compute::Scheduler
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---
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src/cpu/aarch64/acl_indirect_gemm_convolution.hpp | 1 +
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1 file changed, 1 insertion(+)
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diff --git a/src/cpu/aarch64/acl_indirect_gemm_convolution.hpp b/src/cpu/aarch64/acl_indirect_gemm_convolution.hpp
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index 86d2bed73..040311f8c 100644
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--- a/src/cpu/aarch64/acl_indirect_gemm_convolution.hpp
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+++ b/src/cpu/aarch64/acl_indirect_gemm_convolution.hpp
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@@ -26,6 +26,7 @@
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#include "arm_compute/runtime/FunctionDescriptors.h"
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#include "arm_compute/runtime/NEON/NEFunctions.h"
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+#include "arm_compute/runtime/Scheduler.h"
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namespace dnnl {
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namespace impl {
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@ -1,3 +0,0 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:3faf3b7811dd37835169a9d5c57f17fd591f062029851186f9b11db117b9d1d9
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size 9533694
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@ -1,3 +1,50 @@
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-------------------------------------------------------------------
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Thu Jun 3 01:38:56 UTC 2021 - Ferdinand Thiessen <rpm@fthiessen.de>
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- Update to version 2.2.3
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* Fixed a bug in int8 depthwise convolution ptimitive with groups
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and 1d spatial size for processors with AVX-512 and AVX2 support
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* Fixed correctness issue for PReLU primitive
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* Fixed corretness issue in reorder for blocked layouts with
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zero padding
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* Improved performance of weights reorders used by BRGEMM-based
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convolution primitive for processors with AVX-512 support
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* Added -fp-model=precise build flag for DPC++ code
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* Fixed potential memory leak in matmul primitive
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* Fixed performance of matmul primitive when fused with bias
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update and sum
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* Fixed a bug in matmul primitive when writing to non-contiguous
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destination buffer
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- Add upstream patch for GCC11 support
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* 0001-common-gpu-include-thread-and-limit-headers-to-fix-G.patch
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-------------------------------------------------------------------
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Thu May 27 08:10:13 UTC 2021 - Jan Engelhardt <jengelh@inai.de>
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- Update descriptions.
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-------------------------------------------------------------------
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Wed May 26 13:29:27 UTC 2021 - Guillaume GARDET <guillaume.gardet@opensuse.org>
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- Update to 2.2.2, changes:
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* Fixed performance regression in fp32 forward inner product for
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shapes with number of output channels equal to 1 for processors
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with Intel AVX-512 support (714b1fd)
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* Fixed performance regression in forward convolutions with groups
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for processors with Intel AVX-512 support(3555d4a)
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* Removed -std=c++11 build flag for DPC++ headers (1fcb867)
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* Fixed buffer access in initializing workspace in RNN
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implementation on GPU (9b03091)
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* Fixed fix a bug in convolution with 1x1 kernel and mixed
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strides on processors with Intel AVX-512 support (d0b3e3f)
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* Used getauxval for Linux to get CPU features on for AArch64
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systems (25c4cea)
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* Added -fp-model=precise build flag for DPC++ code (3e40e5e)
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* Fixed out-of-bounds writes in elementwise primitive on
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Intel Processor Graphics (bcf823c)
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- Fix build with Arm Compute Library:
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* onednn-1045.patch
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-------------------------------------------------------------------
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Tue Apr 13 07:53:16 UTC 2021 - Guillaume GARDET <guillaume.gardet@opensuse.org>
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35
onednn.spec
35
onednn.spec
@ -31,12 +31,16 @@
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%define libname libdnnl2
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Name: onednn
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Version: 2.2.1
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Version: 2.2.3
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Release: 0
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Summary: Intel(R) Math Kernel Library for Deep Neural Networks
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Summary: Intel Math Kernel Library for Deep Neural Networks
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License: Apache-2.0
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URL: https://01.org/onednn
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Source0: https://github.com/oneapi-src/oneDNN/archive/v%{version}/%{name}-%{version}.tar.gz
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Source0: https://github.com/oneapi-src/oneDNN/archive/v%{version}/oneDNN-%{version}.tar.gz
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# PATCH-FIX-UPSTREAM onednn-1045.patch -- https://github.com/oneapi-src/oneDNN/pull/1045
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Patch0: onednn-1045.patch
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# PATCH-FIX-UPSTREAM 0001-common-gpu-include-thread-and-limit-headers-to-fix-G.patch
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Patch1: 0001-common-gpu-include-thread-and-limit-headers-to-fix-G.patch
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BuildRequires: cmake
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BuildRequires: doxygen
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BuildRequires: fdupes
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@ -57,18 +61,18 @@ Obsoletes: mkl-dnn <= %{version}
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Provides: oneDNN = %{version}
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%description
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Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
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Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) is an
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open-source performance library for deep-learning applications. The library
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accelerates deep-learning applications and frameworks on Intel architecture.
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Intel MKL-DNN contains vectorized and threaded building blocks that you can use
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to implement deep neural networks (DNN) with C and C++ interfaces.
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%package -n benchdnn
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Summary: Header files of Intel(R) Math Kernel Library
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Summary: Header files of Intel Math Kernel Library
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Requires: %{libname} = %{version}
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%description -n benchdnn
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Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
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Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) is an
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open-source performance library for deep-learning applications. The library
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accelerates deep-learning applications and frameworks on Intel architecture.
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Intel MKL-DNN contains vectorized and threaded building blocks that you can use
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@ -77,43 +81,42 @@ to implement deep neural networks (DNN) with C and C++ interfaces.
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This package only includes the benchmark utility including its input files.
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%package devel
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Summary: Header files of Intel(R) Math Kernel Library
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Summary: Header files of Intel Math Kernel Library
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Requires: %{libname} = %{version}
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Provides: mkl-dnn-devel = %{version}
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Obsoletes: mkl-dnn-devel <= %{version}
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Provides: oneDNN-devel = %{version}
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%description devel
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Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
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Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) is an
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open-source performance library for deep-learning applications. The library
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accelerates deep-learning applications and frameworks on Intel architecture.
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Intel MKL-DNN contains vectorized and threaded building blocks that you can use
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to implement deep neural networks (DNN) with C and C++ interfaces.
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This package includes the required headers and library files to develop software
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with the Intel(R) MKL-DNN.
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with the Intel MKL-DNN.
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%package doc
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Summary: Reference documentation for the Intel(R) Math Kernel Library
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Summary: Reference documentation for the Intel Math Kernel Library
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BuildArch: noarch
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%description doc
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The reference documentation for the Intel(R) Math Kernel Library can be installed
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The reference documentation for the Intel Math Kernel Library can be installed
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with this package.
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%package -n %{libname}
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Summary: Header files of Intel(R) Math Kernel Library
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Summary: Header files of Intel Math Kernel Library
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%description -n %{libname}
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Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
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Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) is an
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open-source performance library for deep-learning applications. The library
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accelerates deep-learning applications and frameworks on Intel architecture.
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Intel MKL-DNN contains vectorized and threaded building blocks that you can use
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to implement deep neural networks (DNN) with C and C++ interfaces.
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%prep
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%setup -q -n oneDNN-%{version}
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%autopatch -p1
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%autosetup -p1 -n oneDNN-%{version}
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%build
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%cmake \
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@ -167,6 +170,7 @@ popd
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%{_datadir}/benchdnn
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%files devel
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%doc README.md
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%{_includedir}/mkl-dnn
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%{_includedir}/mkldnn*.h*
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%{_includedir}/dnnl*.h*
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@ -185,7 +189,6 @@ popd
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%files -n %{libname}
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%license LICENSE
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%doc README.md
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%{_libdir}/libdnnl.so.*
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%{_libdir}/libmkldnn.so.*
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