oneDNN 3.6.0

pts/onednn-3.6.0 - 15 October 2024 - Update against oneDNN 3.6 upstream.

downloads.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.5-->
<PhoronixTestSuite>
  <Downloads>
    <Package>
      <URL>https://github.com/oneapi-src/oneDNN/archive/refs/tags/v3.6.tar.gz</URL>
      <MD5>9a6ea4f8ebd1e5e2b5b19f6594cb761c</MD5>
      <SHA256>20c4a92cc0ae0dc19d3d2beca0e357b1d13a5a3af9890a2cc3e41a880e4a0302</SHA256>
      <FileName>oneDNN-3.6.tar.gz</FileName>
      <FileSize>13782760</FileSize>
    </Package>
  </Downloads>
</PhoronixTestSuite>

install.sh

#!/bin/bash
tar -xf oneDNN-3.6.tar.gz
cd oneDNN-3.6
mkdir build 
cd build 
CFLAGS="-O3 -march=native $CFLAGS" CXXFLAGS="-O3 -march=native $CXXFLAGS" cmake -DCMAKE_BUILD_TYPE=Release MKLDNN_ARCH_OPT_FLAGS="-O3 -march=native $CFLAGS" $CMAKE_OPTIONS ..
make -j $NUM_CPU_CORES
EXIT_STATUS=$?
if [ $EXIT_STATUS -ne 0 ]; then
	# For architectures not supporting -march=native compiler option...
	CFLAGS="-O3 $CFLAGS" CXXFLAGS="-O3 $CXXFLAGS" cmake -DCMAKE_BUILD_TYPE=Release MKLDNN_ARCH_OPT_FLAGS="-O3 $CFLAGS" $CMAKE_OPTIONS ..
	make -j $NUM_CPU_CORES
	EXIT_STATUS=$?
fi
echo $EXIT_STATUS > ~/install-exit-status
cd ~
echo "#!/bin/bash
export DNNL_CPU_RUNTIME=OMP
export OMP_PLACES=cores
export OMP_PROC_BIND=close
cd oneDNN-3.6/build/tests/benchdnn
./benchdnn \$4 --mode=p \$1 \$3 \$2 > \$LOG_FILE 2>&1
echo \$? > ~/test-exit-status" > onednn
chmod +x onednn

results-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.5-->
<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 v10.8.5-->
<PhoronixTestSuite>
  <TestInformation>
    <Title>oneDNN</Title>
    <AppVersion>3.6</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 Intel oneAPI toolkit.</Description>
    <ResultScale>ms</ResultScale>
    <Proportion>LIB</Proportion>
    <TimesToRun>3</TimesToRun>
  </TestInformation>
  <TestProfile>
    <Version>3.6.0</Version>
    <SupportedPlatforms>Linux, MacOSX</SupportedPlatforms>
    <SoftwareType>Utility</SoftwareType>
    <TestType>Processor</TestType>
    <License>Free</License>
    <Status>Verified</Status>
    <ExternalDependencies>build-utilities, cmake</ExternalDependencies>
    <EnvironmentSize>287</EnvironmentSize>
    <ProjectURL>https://www.intel.com/content/www/us/en/developer/tools/oneapi/onednn.html</ProjectURL>
    <RepositoryURL>https://github.com/oneapi-src/oneDNN</RepositoryURL>
    <InternalTags>SMP</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 shapes_1d</Name>
          <Value>--deconv --batch=inputs/deconv/shapes_1d</Value>
        </Entry>
        <Entry>
          <Name>Deconvolution Batch shapes_3d</Name>
          <Value>--deconv --batch=inputs/deconv/shapes_3d</Value>
        </Entry>
        <Entry>
          <Name>IP Shapes 1D</Name>
          <Value>--ip --batch=inputs/ip/shapes_1d</Value>
        </Entry>
        <Entry>
          <Name>IP Shapes 3D</Name>
          <Value>--ip --batch=inputs/ip/shapes_3d</Value>
        </Entry>
        <Entry>
          <Name>Recurrent Neural Network Training</Name>
          <Value>--rnn --batch=inputs/rnn/perf_rnn_training</Value>
        </Entry>
        <Entry>
          <Name>Recurrent Neural Network Inference</Name>
          <Value>--rnn --batch=inputs/rnn/perf_rnn_inference_lb</Value>
        </Entry>
      </Menu>
    </Option>
    <Option>
      <DisplayName>Engine</DisplayName>
      <Identifier>engine</Identifier>
      <Menu>
        <Entry>
          <Name>CPU</Name>
          <Value>--engine=cpu</Value>
        </Entry>
      </Menu>
    </Option>
  </TestSettings>
</PhoronixTestSuite>