oneDNN 1.5.0

pts/onednn-1.5.0 - 17 June 2020 - Initial commit of oneDNN test profile based on Intel oneDNN 1.5, forked from existing mkl-dnn test profile that is named from MKL-DNN before it was renamed to DNNL and then oneDNN. So create new test profile to match Intel naming convention.

downloads.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v9.8.0m1-->
<PhoronixTestSuite>
  <Downloads>
    <Package>
      <URL>https://github.com/oneapi-src/oneDNN/archive/v1.5.tar.gz</URL>
      <MD5>5d97e0e8f4c0b37da5f524533b7a644b</MD5>
      <SHA256>2aacc00129418185e0bc1269d3ef82f93f08de2c336932989c0c360279129edb</SHA256>
      <FileName>oneDNN-1.5.tar.gz</FileName>
      <FileSize>5515754</FileSize>
    </Package>
  </Downloads>
</PhoronixTestSuite>

install.sh

#!/bin/sh

tar -xf oneDNN-1.5.tar.gz

cd oneDNN-1.5/

mkdir build 
cd build 
CFLAGS="-O3 -march=native" CXXFLAGS="-O3 -march=native" cmake -DCMAKE_BUILD_TYPE=Release MKLDNN_ARCH_OPT_FLAGS="-O3 -march=native" $CMAKE_OPTIONS ..
make -j $NUM_CPU_CORES
echo $? > ~/install-exit-status

cd ~

echo "#!/bin/bash
export DNNL_CPU_RUNTIME=OMP
export OMP_PLACES=cores
export OMP_PROC_BIND=close
cd oneDNN-1.5/build/tests/benchdnn
./benchdnn \$4 --mode=p \$1 \$3 \$2 > \$LOG_FILE 2>&1
echo \$? > ~/test-exit-status" > onednn
chmod +x 5515754

results-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v9.8.0m1-->
<PhoronixTestSuite>
  <ResultsParser>
    <OutputTemplate>total perf  min(ms) #_MIN_RESULT_# avg(ms) #_RESULT_#</OutputTemplate>
    <LineHint>total perf</LineHint>
    <TurnCharsToSpace>:</TurnCharsToSpace>
  </ResultsParser>
</PhoronixTestSuite>

test-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v9.8.0m1-->
<PhoronixTestSuite>
  <TestInformation>
    <Title>oneDNN</Title>
    <AppVersion>1.5</AppVersion>
    <Description>This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the oneAPI initiative.</Description>
    <ResultScale>ms</ResultScale>
    <Proportion>LIB</Proportion>
    <TimesToRun>3</TimesToRun>
  </TestInformation>
  <TestProfile>
    <Version>1.5.0</Version>
    <SupportedPlatforms>Linux</SupportedPlatforms>
    <SoftwareType>Utility</SoftwareType>
    <TestType>Processor</TestType>
    <License>Free</License>
    <Status>Verified</Status>
    <ExternalDependencies>build-utilities, cmake</ExternalDependencies>
    <EnvironmentSize>287</EnvironmentSize>
    <ProjectURL>https://github.com/oneapi-src/oneDNN</ProjectURL>
    <InternalTags>SMP, OpenMP</InternalTags>
    <Maintainer>Michael Larabel</Maintainer>
  </TestProfile>
  <TestSettings>
    <Option>
      <DisplayName>Harness</DisplayName>
      <Identifier>harness</Identifier>
      <Menu>
        <Entry>
          <Name>Convolution Batch Shapes Auto</Name>
          <Value>--conv --batch=inputs/conv/shapes_auto</Value>
        </Entry>
        <Entry>
          <Name>Deconvolution Batch deconv_1d</Name>
          <Value>--deconv --batch=inputs/deconv/deconv_1d</Value>
        </Entry>
        <Entry>
          <Name>Deconvolution Batch deconv_3d</Name>
          <Value>--deconv --batch=inputs/deconv/deconv_3d</Value>
        </Entry>
        <Entry>
          <Name>IP Batch 1D</Name>
          <Value>--ip --batch=inputs/ip/ip_1d</Value>
        </Entry>
        <Entry>
          <Name>IP Batch All</Name>
          <Value>--ip --batch=inputs/ip/ip_all</Value>
        </Entry>
        <Entry>
          <Name>Matrix Multiply Batch Shapes Transformer</Name>
          <Value>--matmul --batch=inputs/matmul/shapes_transformer</Value>
        </Entry>
        <Entry>
          <Name>Recurrent Neural Network Training</Name>
          <Value>--rnn --batch=inputs/rnn/rnn_training</Value>
        </Entry>
        <Entry>
          <Name>Recurrent Neural Network Inference</Name>
          <Value>--rnn --batch=inputs/rnn/rnn_inference</Value>
        </Entry>
      </Menu>
    </Option>
    <Option>
      <DisplayName>Data Type</DisplayName>
      <Identifier>data-type</Identifier>
      <ArgumentPrefix>--cfg=</ArgumentPrefix>
      <Menu>
        <Entry>
          <Name>f32</Name>
          <Value>f32</Value>
        </Entry>
        <Entry>
          <Name>u8s8f32</Name>
          <Value>u8s8f32</Value>
          <Message>Optimized For AVX-512</Message>
        </Entry>
        <Entry>
          <Name>bf16bf16bf16</Name>
          <Value>bf16bf16bf16</Value>
          <Message>Optimized For AVX-512 + VNNI</Message>
        </Entry>
      </Menu>
    </Option>
    <Option>
      <DisplayName>Engine</DisplayName>
      <Identifier>engine</Identifier>
      <Menu>
        <Entry>
          <Name>CPU</Name>
          <Value>--engine=cpu</Value>
        </Entry>
      </Menu>
    </Option>
  </TestSettings>
</PhoronixTestSuite>