gem5/tests/weekly.sh

#!/bin/bash

# Copyright (c) 2021 The Regents of the University of California
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set -e
set -x

dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
gem5_root="${dir}/.."

# We assume the lone argument is the number of threads. If no argument is
# given we default to one.
threads=1
if [[ $# -gt 0 ]]; then
    threads=$1
fi

# Run the gem5 very-long tests.
docker run -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
    "${gem5_root}"/tests --rm gcr.io/gem5-test/ubuntu-20.04_all-dependencies \
        ./main.py run --length very-long -j${threads} -t${threads}

# For the GPU tests we compile and run GCN3_X86 inside a gcn-gpu container.
docker pull gcr.io/gem5-test/gcn-gpu:latest
docker run --rm -u $UID:$GUID --volume "${gem5_root}":"${gem5_root}" -w \
    "${gem5_root}" gcr.io/gem5-test/gcn-gpu:latest bash -c \
    "scons build/GCN3_X86/gem5.opt -j${threads} \
        || (rm -rf build && scons build/GCN3_X86/gem5.opt -j${threads})"

# get LULESH
wget -qN http://dist.gem5.org/dist/develop/test-progs/lulesh/lulesh

mkdir -p tests/testing-results

# LULESH is heavily used in the HPC community on GPUs, and does a good job of
# stressing several GPU compute and memory components
docker run --rm -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
    "${gem5_root}" gcr.io/gem5-test/gcn-gpu:latest build/GCN3_X86/gem5.opt \
    configs/example/apu_se.py -n3 --mem-size=8GB -clulesh

# get DNNMark
# Delete gem5 resources repo if it already exists -- need to do in docker
# because of cachefiles DNNMark creates
docker run --rm --volume "${gem5_root}":"${gem5_root}" -w \
       "${gem5_root}" gcr.io/gem5-test/gcn-gpu:latest bash -c \
       "rm -rf ${gem5_root}/gem5-resources"

# Pull the gem5 resources to the root of the gem5 directory -- DNNMark
# builds a library and thus doesn't have a binary, so we need to build
# it before we run it
git clone -b develop https://gem5.googlesource.com/public/gem5-resources \
    "${gem5_root}/gem5-resources"

# setup cmake for DNNMark
docker run --rm -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
     "${gem5_root}/gem5-resources/src/gpu/DNNMark" \
     gcr.io/gem5-test/gcn-gpu:latest bash -c "./setup.sh HIP"

# make the DNNMark library
docker run --rm -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
    "${gem5_root}/gem5-resources/src/gpu/DNNMark/build" \
    gcr.io/gem5-test/gcn-gpu:latest bash -c "make -j${threads}"

# generate cachefiles -- since we are testing gfx801 and 4 CUs (default config)
# in tester, we want cachefiles for this setup
docker run --rm --volume "${gem5_root}":"${gem5_root}" -w \
    "${gem5_root}/gem5-resources/src/gpu/DNNMark" \
    "-v${gem5_root}/gem5-resources/src/gpu/DNNMark/cachefiles:/root/.cache/miopen/2.9.0" \
    gcr.io/gem5-test/gcn-gpu:latest bash -c \
    "python3 generate_cachefiles.py cachefiles.csv --gfx-version=gfx801 \
    --num-cus=4"

# generate mmap data for DNNMark (makes simulation much faster)
docker run --rm -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
    "${gem5_root}/gem5-resources/src/gpu/DNNMark" gcr.io/gem5-test/gcn-gpu:latest bash -c \
    "g++ -std=c++0x generate_rand_data.cpp -o generate_rand_data"

docker run --rm -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
    "${gem5_root}/gem5-resources/src/gpu/DNNMark" gcr.io/gem5-test/gcn-gpu:latest bash -c \
    "./generate_rand_data"

# now we can run DNNMark!
# DNNMark is representative of several simple (fast) layers within ML
# applications, which are heavily used in modern GPU applications.  So, we want
# to make sure support for these applications are tested.  Run three variants:
# fwd_softmax, bwd_bn, fwd_pool; these tests ensure we run a variety of ML kernels,
# including both inference and training
docker run --rm --volume "${gem5_root}":"${gem5_root}" -v \
       "${gem5_root}/gem5-resources/src/gpu/DNNMark/cachefiles:/root/.cache/miopen/2.9.0" \
       -w "${gem5_root}/gem5-resources/src/gpu/DNNMark" gcr.io/gem5-test/gcn-gpu \
       "${gem5_root}/build/GCN3_X86/gem5.opt" "${gem5_root}/configs/example/apu_se.py" -n3 \
       --benchmark-root="${gem5_root}/gem5-resources/src/gpu/DNNMark/build/benchmarks/test_fwd_softmax" \
       -cdnnmark_test_fwd_softmax \
       --options="-config ${gem5_root}/gem5-resources/src/gpu/DNNMark/config_example/softmax_config.dnnmark \
       -mmap ${gem5_root}/gem5-resources/src/gpu/DNNMark/mmap.bin"

docker run --rm --volume "${gem5_root}":"${gem5_root}" -v \
       "${gem5_root}/gem5-resources/src/gpu/DNNMark/cachefiles:/root/.cache/miopen/2.9.0" \
       -w "${gem5_root}/gem5-resources/src/gpu/DNNMark" gcr.io/gem5-test/gcn-gpu \
       "${gem5_root}/build/GCN3_X86/gem5.opt" "${gem5_root}/configs/example/apu_se.py" -n3 \
       --benchmark-root="${gem5_root}/gem5-resources/src/gpu/DNNMark/build/benchmarks/test_fwd_pool" \
       -cdnnmark_test_fwd_pool \
       --options="-config ${gem5_root}/gem5-resources/src/gpu/DNNMark/config_example/pool_config.dnnmark \
       -mmap ${gem5_root}/gem5-resources/src/gpu/DNNMark/mmap.bin"

docker run --rm --volume "${gem5_root}":"${gem5_root}" -v \
       "${gem5_root}/gem5-resources/src/gpu/DNNMark/cachefiles:/root/.cache/miopen/2.9.0" \
       -w "${gem5_root}/gem5-resources/src/gpu/DNNMark" gcr.io/gem5-test/gcn-gpu \
       "${gem5_root}/build/GCN3_X86/gem5.opt" "${gem5_root}/configs/example/apu_se.py" -n3 \
       --benchmark-root="${gem5_root}/gem5-resources/src/gpu/DNNMark/build/benchmarks/test_bwd_bn" \
       -cdnnmark_test_bwd_bn \
       --options="-config ${gem5_root}/gem5-resources/src/gpu/DNNMark/config_example/bn_config.dnnmark \
       -mmap ${gem5_root}/gem5-resources/src/gpu/DNNMark/mmap.bin"

# Delete the gem5 resources repo we created -- need to do in docker because of
# cachefiles DNNMark creates
docker run --rm --volume "${gem5_root}":"${gem5_root}" -w \
       "${gem5_root}" gcr.io/gem5-test/gcn-gpu:latest bash -c \
       "rm -rf ${gem5_root}/gem5-resources"