2024-08-22T20:27:32.1001105Z Current runner version: '2.319.1'
2024-08-22T20:27:32.1007546Z Runner name: 'i-0eff7f5d1c54b6a46'
2024-08-22T20:27:32.1008428Z Runner group name: 'Default'
2024-08-22T20:27:32.1009311Z Machine name: 'ip-10-0-38-42'
2024-08-22T20:27:32.1025648Z Testing runner upgrade compatibility
2024-08-22T20:27:32.2227328Z ##[group]GITHUB_TOKEN Permissions
2024-08-22T20:27:32.2229355Z Contents: read
2024-08-22T20:27:32.2229886Z Metadata: read
2024-08-22T20:27:32.2230401Z Packages: read
2024-08-22T20:27:32.2230980Z ##[endgroup]
2024-08-22T20:27:32.2234133Z Secret source: Actions
2024-08-22T20:27:32.2234983Z Prepare workflow directory
2024-08-22T20:27:32.7893104Z Prepare all required actions
2024-08-22T20:27:32.8063537Z Getting action download info
2024-08-22T20:27:32.9836127Z Download action repository 'actions/checkout@v3' (SHA:f43a0e5ff2bd294095638e18286ca9a3d1956744)
2024-08-22T20:27:33.2331604Z Download action repository 'actions/download-artifact@v3' (SHA:9bc31d5ccc31df68ecc42ccf4149144866c47d8a)
2024-08-22T20:27:33.3729071Z Download action repository 'pmeier/pytest-results-action@v0.3.0' (SHA:a2c1430e2bddadbad9f49a6f9b879f062c6b19b1)
2024-08-22T20:27:33.4736890Z Download action repository 'actions/upload-artifact@v3' (SHA:a8a3f3ad30e3422c9c7b888a15615d19a852ae32)
2024-08-22T20:27:33.5850522Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a)
2024-08-22T20:27:33.8763061Z Uses: pytorch/test-infra/.github/workflows/linux_job.yml@refs/heads/main (6dab351bed7d1c647665a8b8e883ec5b1c973feb)
2024-08-22T20:27:33.8765213Z ##[group] Inputs
2024-08-22T20:27:33.8785294Z   script: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:33.8805511Z   timeout: 60
2024-08-22T20:27:33.8805970Z   runner: linux.g5.4xlarge.nvidia.gpu
2024-08-22T20:27:33.8806563Z   upload-artifact: 
2024-08-22T20:27:33.8806985Z   download-artifact: 
2024-08-22T20:27:33.8807390Z   repository: 
2024-08-22T20:27:33.8807856Z   fetch-depth: 1
2024-08-22T20:27:33.8808354Z   submodules: 
2024-08-22T20:27:33.8808695Z   ref: 
2024-08-22T20:27:33.8809160Z   test-infra-repository: pytorch/test-infra
2024-08-22T20:27:33.8809659Z   test-infra-ref: 
2024-08-22T20:27:33.8810115Z   docker-image: pytorch/conda-builder
2024-08-22T20:27:33.8810758Z   docker-build-dir: .ci/docker
2024-08-22T20:27:33.8811253Z   gpu-arch-type: cuda
2024-08-22T20:27:33.8811645Z   gpu-arch-version: 12.1
2024-08-22T20:27:33.8812134Z   job-name: linux-job
2024-08-22T20:27:33.8812932Z   continue-on-error: false
2024-08-22T20:27:33.8813345Z   binary-matrix: 
2024-08-22T20:27:33.8813822Z   run-with-docker: true
2024-08-22T20:27:33.8814236Z   secrets-env: 
2024-08-22T20:27:33.8814630Z   no-sudo: false
2024-08-22T20:27:33.8815160Z ##[endgroup]
2024-08-22T20:27:33.8815802Z Complete job name: test-cuda / linux-job
2024-08-22T20:27:33.9495215Z A job started hook has been configured by the self-hosted runner administrator
2024-08-22T20:27:33.9671774Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh'
2024-08-22T20:27:33.9684828Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:27:33.9685582Z ##[endgroup]
2024-08-22T20:27:35.6172141Z Runner Type: linux.g5.4xlarge.nvidia.gpu
2024-08-22T20:27:35.6172706Z Instance Type: g5.4xlarge
2024-08-22T20:27:35.6173398Z AMI Name: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
2024-08-22T20:27:35.6173930Z AMI ID: ami-06c68f701d8090592
2024-08-22T20:27:41.4008286Z ##[group]Run set -euxo pipefail
2024-08-22T20:27:41.4008822Z set -euxo pipefail
2024-08-22T20:27:41.4009279Z if [[ "${NO_SUDO}" == "false" ]]; then
2024-08-22T20:27:41.4009853Z   echo "::group::Cleanup with-sudo debug output"
2024-08-22T20:27:41.4010401Z   sudo rm -rfv "${GITHUB_WORKSPACE}"
2024-08-22T20:27:41.4010836Z else
2024-08-22T20:27:41.4011215Z   echo "::group::Cleanup no-sudo debug output"
2024-08-22T20:27:41.4011925Z   rm -rfv "${GITHUB_WORKSPACE}"
2024-08-22T20:27:41.4012337Z fi
2024-08-22T20:27:41.4012606Z 
2024-08-22T20:27:41.4012922Z mkdir -p "${GITHUB_WORKSPACE}"
2024-08-22T20:27:41.4013352Z echo "::endgroup::"
2024-08-22T20:27:41.4022431Z shell: /usr/bin/bash -e {0}
2024-08-22T20:27:41.4022793Z env:
2024-08-22T20:27:41.4023128Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:41.4023588Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:41.4023999Z   PR_NUMBER: 1044
2024-08-22T20:27:41.4042884Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:41.4062174Z   NO_SUDO: false
2024-08-22T20:27:41.4062478Z ##[endgroup]
2024-08-22T20:27:41.4094003Z + [[ false == \f\a\l\s\e ]]
2024-08-22T20:27:41.4114592Z ##[group]Cleanup with-sudo debug output
2024-08-22T20:27:41.4123506Z + echo '::group::Cleanup with-sudo debug output'
2024-08-22T20:27:41.4124215Z + sudo rm -rfv /home/ec2-user/actions-runner/_work/torchchat/torchchat
2024-08-22T20:27:41.4841723Z removed directory '/home/ec2-user/actions-runner/_work/torchchat/torchchat'
2024-08-22T20:27:41.4862745Z + mkdir -p /home/ec2-user/actions-runner/_work/torchchat/torchchat
2024-08-22T20:27:41.4877130Z + echo ::endgroup::
2024-08-22T20:27:41.4878362Z ##[endgroup]
2024-08-22T20:27:41.4996449Z ##[group]Run actions/checkout@v3
2024-08-22T20:27:41.4996858Z with:
2024-08-22T20:27:41.4997143Z   repository: pytorch/test-infra
2024-08-22T20:27:41.4997522Z   path: test-infra
2024-08-22T20:27:41.4997831Z   submodules: recursive
2024-08-22T20:27:41.4998399Z   token: ***
2024-08-22T20:27:41.4998713Z   ssh-strict: true
2024-08-22T20:27:41.4999037Z   persist-credentials: true
2024-08-22T20:27:41.4999380Z   clean: true
2024-08-22T20:27:41.4999709Z   sparse-checkout-cone-mode: true
2024-08-22T20:27:41.5000092Z   fetch-depth: 1
2024-08-22T20:27:41.5000378Z   fetch-tags: false
2024-08-22T20:27:41.5000676Z   lfs: false
2024-08-22T20:27:41.5000969Z   set-safe-directory: true
2024-08-22T20:27:41.5001292Z env:
2024-08-22T20:27:41.5001606Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:41.5002057Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:41.5002416Z   PR_NUMBER: 1044
2024-08-22T20:27:41.5021505Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:41.5040776Z ##[endgroup]
2024-08-22T20:27:41.6104845Z Syncing repository: pytorch/test-infra
2024-08-22T20:27:41.6105692Z ##[group]Getting Git version info
2024-08-22T20:27:41.6106481Z Working directory is '/home/ec2-user/actions-runner/_work/torchchat/torchchat/test-infra'
2024-08-22T20:27:41.6107416Z [command]/usr/bin/git version
2024-08-22T20:27:41.6107778Z git version 2.40.1
2024-08-22T20:27:41.6113206Z ##[endgroup]
2024-08-22T20:27:41.6126727Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/fc80e4d1-ea49-4adf-a8be-0f5aa6269bf6' before making global git config changes
2024-08-22T20:27:41.6127973Z Adding repository directory to the temporary git global config as a safe directory
2024-08-22T20:27:41.6132073Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/torchchat/torchchat/test-infra
2024-08-22T20:27:41.6179187Z ##[group]Initializing the repository
2024-08-22T20:27:41.6182289Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/torchchat/torchchat/test-infra
2024-08-22T20:27:41.6228087Z hint: Using 'master' as the name for the initial branch. This default branch name
2024-08-22T20:27:41.6229221Z hint: is subject to change. To configure the initial branch name to use in all
2024-08-22T20:27:41.6230359Z hint: of your new repositories, which will suppress this warning, call:
2024-08-22T20:27:41.6230919Z hint: 
2024-08-22T20:27:41.6231392Z hint: 	git config --global init.defaultBranch <name>
2024-08-22T20:27:41.6231864Z hint: 
2024-08-22T20:27:41.6232384Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and
2024-08-22T20:27:41.6233214Z hint: 'development'. The just-created branch can be renamed via this command:
2024-08-22T20:27:41.6233802Z hint: 
2024-08-22T20:27:41.6234154Z hint: 	git branch -m <name>
2024-08-22T20:27:41.6235675Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/torchchat/torchchat/test-infra/.git/
2024-08-22T20:27:41.6242795Z [command]/usr/bin/git remote add origin https://github.com/pytorch/test-infra
2024-08-22T20:27:41.6287062Z ##[endgroup]
2024-08-22T20:27:41.6287715Z ##[group]Disabling automatic garbage collection
2024-08-22T20:27:41.6290180Z [command]/usr/bin/git config --local gc.auto 0
2024-08-22T20:27:41.6332535Z ##[endgroup]
2024-08-22T20:27:41.6333099Z ##[group]Setting up auth
2024-08-22T20:27:41.6338323Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2024-08-22T20:27:41.6381748Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :"
2024-08-22T20:27:41.6737415Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2024-08-22T20:27:41.6780205Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :"
2024-08-22T20:27:41.7142320Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic ***
2024-08-22T20:27:41.7197574Z ##[endgroup]
2024-08-22T20:27:41.7198285Z ##[group]Determining the default branch
2024-08-22T20:27:41.7201110Z Retrieving the default branch name
2024-08-22T20:27:41.8804636Z Default branch 'main'
2024-08-22T20:27:41.8805493Z ##[endgroup]
2024-08-22T20:27:41.8806041Z ##[group]Fetching the repository
2024-08-22T20:27:41.8811292Z [command]/usr/bin/git -c protocol.version=2 fetch --no-tags --prune --progress --no-recurse-submodules --depth=1 origin +refs/heads/main:refs/remotes/origin/main
2024-08-22T20:27:42.0518062Z remote: Enumerating objects: 1649, done.        
2024-08-22T20:27:42.0520483Z remote: Counting objects:   0% (1/1649)        
2024-08-22T20:27:42.0521815Z remote: Counting objects:   1% (17/1649)        
2024-08-22T20:27:42.0523123Z remote: Counting objects:   2% (33/1649)        
2024-08-22T20:27:42.0524437Z remote: Counting objects:   3% (50/1649)        
2024-08-22T20:27:42.0525941Z remote: Counting objects:   4% (66/1649)        
2024-08-22T20:27:42.0526970Z remote: Counting objects:   5% (83/1649)        
2024-08-22T20:27:42.0527653Z remote: Counting objects:   6% (99/1649)        
2024-08-22T20:27:42.0528483Z remote: Counting objects:   7% (116/1649)        
2024-08-22T20:27:42.0529231Z remote: Counting objects:   8% (132/1649)        
2024-08-22T20:27:42.0529917Z remote: Counting objects:   9% (149/1649)        
2024-08-22T20:27:42.0530581Z remote: Counting objects:  10% (165/1649)        
2024-08-22T20:27:42.0531200Z remote: Counting objects:  11% (182/1649)        
2024-08-22T20:27:42.0531691Z remote: Counting objects:  12% (198/1649)        
2024-08-22T20:27:42.0532259Z remote: Counting objects:  13% (215/1649)        
2024-08-22T20:27:42.0532737Z remote: Counting objects:  14% (231/1649)        
2024-08-22T20:27:42.0533418Z remote: Counting objects:  15% (248/1649)        
2024-08-22T20:27:42.0533981Z remote: Counting objects:  16% (264/1649)        
2024-08-22T20:27:42.0534466Z remote: Counting objects:  17% (281/1649)        
2024-08-22T20:27:42.0535073Z remote: Counting objects:  18% (297/1649)        
2024-08-22T20:27:42.0535609Z remote: Counting objects:  19% (314/1649)        
2024-08-22T20:27:42.0536383Z remote: Counting objects:  20% (330/1649)        
2024-08-22T20:27:42.0536863Z remote: Counting objects:  21% (347/1649)        
2024-08-22T20:27:42.0537338Z remote: Counting objects:  22% (363/1649)        
2024-08-22T20:27:42.0537827Z remote: Counting objects:  23% (380/1649)        
2024-08-22T20:27:42.0538364Z remote: Counting objects:  24% (396/1649)        
2024-08-22T20:27:42.0538882Z remote: Counting objects:  25% (413/1649)        
2024-08-22T20:27:42.0539378Z remote: Counting objects:  26% (429/1649)        
2024-08-22T20:27:42.0539858Z remote: Counting objects:  27% (446/1649)        
2024-08-22T20:27:42.0540328Z remote: Counting objects:  28% (462/1649)        
2024-08-22T20:27:42.0540853Z remote: Counting objects:  29% (479/1649)        
2024-08-22T20:27:42.0541333Z remote: Counting objects:  30% (495/1649)        
2024-08-22T20:27:42.0541802Z remote: Counting objects:  31% (512/1649)        
2024-08-22T20:27:42.0542294Z remote: Counting objects:  32% (528/1649)        
2024-08-22T20:27:42.0542775Z remote: Counting objects:  33% (545/1649)        
2024-08-22T20:27:42.0543325Z remote: Counting objects:  34% (561/1649)        
2024-08-22T20:27:42.0543810Z remote: Counting objects:  35% (578/1649)        
2024-08-22T20:27:42.0544288Z remote: Counting objects:  36% (594/1649)        
2024-08-22T20:27:42.0544759Z remote: Counting objects:  37% (611/1649)        
2024-08-22T20:27:42.0545236Z remote: Counting objects:  38% (627/1649)        
2024-08-22T20:27:42.0545914Z remote: Counting objects:  39% (644/1649)        
2024-08-22T20:27:42.0546450Z remote: Counting objects:  40% (660/1649)        
2024-08-22T20:27:42.0546924Z remote: Counting objects:  41% (677/1649)        
2024-08-22T20:27:42.0547404Z remote: Counting objects:  42% (693/1649)        
2024-08-22T20:27:42.0547885Z remote: Counting objects:  43% (710/1649)        
2024-08-22T20:27:42.0548358Z remote: Counting objects:  44% (726/1649)        
2024-08-22T20:27:42.0548894Z remote: Counting objects:  45% (743/1649)        
2024-08-22T20:27:42.0549378Z remote: Counting objects:  46% (759/1649)        
2024-08-22T20:27:42.0549853Z remote: Counting objects:  47% (776/1649)        
2024-08-22T20:27:42.0550332Z remote: Counting objects:  48% (792/1649)        
2024-08-22T20:27:42.0550813Z remote: Counting objects:  49% (809/1649)        
2024-08-22T20:27:42.0551328Z remote: Counting objects:  50% (825/1649)        
2024-08-22T20:27:42.0551847Z remote: Counting objects:  51% (841/1649)        
2024-08-22T20:27:42.0552337Z remote: Counting objects:  52% (858/1649)        
2024-08-22T20:27:42.0552810Z remote: Counting objects:  53% (874/1649)        
2024-08-22T20:27:42.0553290Z remote: Counting objects:  54% (891/1649)        
2024-08-22T20:27:42.0553815Z remote: Counting objects:  55% (907/1649)        
2024-08-22T20:27:42.0554328Z remote: Counting objects:  56% (924/1649)        
2024-08-22T20:27:42.0554884Z remote: Counting objects:  57% (940/1649)        
2024-08-22T20:27:42.0555455Z remote: Counting objects:  58% (957/1649)        
2024-08-22T20:27:42.0555937Z remote: Counting objects:  59% (973/1649)        
2024-08-22T20:27:42.0556478Z remote: Counting objects:  60% (990/1649)        
2024-08-22T20:27:42.0557017Z remote: Counting objects:  61% (1006/1649)        
2024-08-22T20:27:42.0557524Z remote: Counting objects:  62% (1023/1649)        
2024-08-22T20:27:42.0558022Z remote: Counting objects:  63% (1039/1649)        
2024-08-22T20:27:42.0558536Z remote: Counting objects:  64% (1056/1649)        
2024-08-22T20:27:42.0559094Z remote: Counting objects:  65% (1072/1649)        
2024-08-22T20:27:42.0559597Z remote: Counting objects:  66% (1089/1649)        
2024-08-22T20:27:42.0560091Z remote: Counting objects:  67% (1105/1649)        
2024-08-22T20:27:42.0560590Z remote: Counting objects:  68% (1122/1649)        
2024-08-22T20:27:42.0561088Z remote: Counting objects:  69% (1138/1649)        
2024-08-22T20:27:42.0561618Z remote: Counting objects:  70% (1155/1649)        
2024-08-22T20:27:42.0562273Z remote: Counting objects:  71% (1171/1649)        
2024-08-22T20:27:42.0562773Z remote: Counting objects:  72% (1188/1649)        
2024-08-22T20:27:42.0563265Z remote: Counting objects:  73% (1204/1649)        
2024-08-22T20:27:42.0563760Z remote: Counting objects:  74% (1221/1649)        
2024-08-22T20:27:42.0564308Z remote: Counting objects:  75% (1237/1649)        
2024-08-22T20:27:42.0564794Z remote: Counting objects:  76% (1254/1649)        
2024-08-22T20:27:42.0565294Z remote: Counting objects:  77% (1270/1649)        
2024-08-22T20:27:42.0565800Z remote: Counting objects:  78% (1287/1649)        
2024-08-22T20:27:42.0566342Z remote: Counting objects:  79% (1303/1649)        
2024-08-22T20:27:42.0566886Z remote: Counting objects:  80% (1320/1649)        
2024-08-22T20:27:42.0567420Z remote: Counting objects:  81% (1336/1649)        
2024-08-22T20:27:42.0567916Z remote: Counting objects:  82% (1353/1649)        
2024-08-22T20:27:42.0568533Z remote: Counting objects:  83% (1369/1649)        
2024-08-22T20:27:42.0569085Z remote: Counting objects:  84% (1386/1649)        
2024-08-22T20:27:42.0569584Z remote: Counting objects:  85% (1402/1649)        
2024-08-22T20:27:42.0570079Z remote: Counting objects:  86% (1419/1649)        
2024-08-22T20:27:42.0570584Z remote: Counting objects:  87% (1435/1649)        
2024-08-22T20:27:42.0571083Z remote: Counting objects:  88% (1452/1649)        
2024-08-22T20:27:42.0571619Z remote: Counting objects:  89% (1468/1649)        
2024-08-22T20:27:42.0572131Z remote: Counting objects:  90% (1485/1649)        
2024-08-22T20:27:42.0572983Z remote: Counting objects:  91% (1501/1649)        
2024-08-22T20:27:42.0573489Z remote: Counting objects:  92% (1518/1649)        
2024-08-22T20:27:42.0573991Z remote: Counting objects:  93% (1534/1649)        
2024-08-22T20:27:42.0574495Z remote: Counting objects:  94% (1551/1649)        
2024-08-22T20:27:42.0574982Z remote: Counting objects:  95% (1567/1649)        
2024-08-22T20:27:42.0575473Z remote: Counting objects:  96% (1584/1649)        
2024-08-22T20:27:42.0575977Z remote: Counting objects:  97% (1600/1649)        
2024-08-22T20:27:42.0576472Z remote: Counting objects:  98% (1617/1649)        
2024-08-22T20:27:42.0576970Z remote: Counting objects:  99% (1633/1649)        
2024-08-22T20:27:42.0577467Z remote: Counting objects: 100% (1649/1649)        
2024-08-22T20:27:42.0578277Z remote: Counting objects: 100% (1649/1649), done.        
2024-08-22T20:27:42.0578833Z remote: Compressing objects:   0% (1/1259)        
2024-08-22T20:27:42.0579365Z remote: Compressing objects:   1% (13/1259)        
2024-08-22T20:27:42.0579896Z remote: Compressing objects:   2% (26/1259)        
2024-08-22T20:27:42.0580419Z remote: Compressing objects:   3% (38/1259)        
2024-08-22T20:27:42.0580946Z remote: Compressing objects:   4% (51/1259)        
2024-08-22T20:27:42.0581466Z remote: Compressing objects:   5% (63/1259)        
2024-08-22T20:27:42.0582088Z remote: Compressing objects:   6% (76/1259)        
2024-08-22T20:27:42.0582620Z remote: Compressing objects:   7% (89/1259)        
2024-08-22T20:27:42.0583197Z remote: Compressing objects:   8% (101/1259)        
2024-08-22T20:27:42.0583773Z remote: Compressing objects:   9% (114/1259)        
2024-08-22T20:27:42.0584306Z remote: Compressing objects:  10% (126/1259)        
2024-08-22T20:27:42.0584845Z remote: Compressing objects:  11% (139/1259)        
2024-08-22T20:27:42.0585371Z remote: Compressing objects:  12% (152/1259)        
2024-08-22T20:27:42.0585905Z remote: Compressing objects:  13% (164/1259)        
2024-08-22T20:27:42.0586459Z remote: Compressing objects:  14% (177/1259)        
2024-08-22T20:27:42.0587025Z remote: Compressing objects:  15% (189/1259)        
2024-08-22T20:27:42.0587553Z remote: Compressing objects:  16% (202/1259)        
2024-08-22T20:27:42.0588081Z remote: Compressing objects:  17% (215/1259)        
2024-08-22T20:27:42.0588589Z remote: Compressing objects:  18% (227/1259)        
2024-08-22T20:27:42.0589109Z remote: Compressing objects:  19% (240/1259)        
2024-08-22T20:27:42.0589796Z remote: Compressing objects:  20% (252/1259)        
2024-08-22T20:27:42.0590316Z remote: Compressing objects:  21% (265/1259)        
2024-08-22T20:27:42.0590836Z remote: Compressing objects:  22% (277/1259)        
2024-08-22T20:27:42.0591357Z remote: Compressing objects:  23% (290/1259)        
2024-08-22T20:27:42.0591878Z remote: Compressing objects:  24% (303/1259)        
2024-08-22T20:27:42.0592397Z remote: Compressing objects:  25% (315/1259)        
2024-08-22T20:27:42.0592974Z remote: Compressing objects:  26% (328/1259)        
2024-08-22T20:27:42.0593516Z remote: Compressing objects:  27% (340/1259)        
2024-08-22T20:27:42.0594033Z remote: Compressing objects:  28% (353/1259)        
2024-08-22T20:27:42.0594550Z remote: Compressing objects:  29% (366/1259)        
2024-08-22T20:27:42.0595071Z remote: Compressing objects:  30% (378/1259)        
2024-08-22T20:27:42.0595582Z remote: Compressing objects:  31% (391/1259)        
2024-08-22T20:27:42.0596112Z remote: Compressing objects:  32% (403/1259)        
2024-08-22T20:27:42.0596633Z remote: Compressing objects:  33% (416/1259)        
2024-08-22T20:27:42.0597200Z remote: Compressing objects:  34% (429/1259)        
2024-08-22T20:27:42.0597723Z remote: Compressing objects:  35% (441/1259)        
2024-08-22T20:27:42.0598244Z remote: Compressing objects:  36% (454/1259)        
2024-08-22T20:27:42.0598757Z remote: Compressing objects:  37% (466/1259)        
2024-08-22T20:27:42.0599277Z remote: Compressing objects:  38% (479/1259)        
2024-08-22T20:27:42.0599925Z remote: Compressing objects:  39% (492/1259)        
2024-08-22T20:27:42.0600453Z remote: Compressing objects:  40% (504/1259)        
2024-08-22T20:27:42.0600968Z remote: Compressing objects:  41% (517/1259)        
2024-08-22T20:27:42.0601538Z remote: Compressing objects:  42% (529/1259)        
2024-08-22T20:27:42.0602069Z remote: Compressing objects:  43% (542/1259)        
2024-08-22T20:27:42.0602596Z remote: Compressing objects:  44% (554/1259)        
2024-08-22T20:27:42.0603133Z remote: Compressing objects:  45% (567/1259)        
2024-08-22T20:27:42.0603661Z remote: Compressing objects:  46% (580/1259)        
2024-08-22T20:27:42.0604182Z remote: Compressing objects:  47% (592/1259)        
2024-08-22T20:27:42.0604709Z remote: Compressing objects:  48% (605/1259)        
2024-08-22T20:27:42.0605268Z remote: Compressing objects:  49% (617/1259)        
2024-08-22T20:27:42.0605819Z remote: Compressing objects:  50% (630/1259)        
2024-08-22T20:27:42.0606355Z remote: Compressing objects:  51% (643/1259)        
2024-08-22T20:27:42.0606905Z remote: Compressing objects:  52% (655/1259)        
2024-08-22T20:27:42.0607425Z remote: Compressing objects:  53% (668/1259)        
2024-08-22T20:27:42.0607960Z remote: Compressing objects:  54% (680/1259)        
2024-08-22T20:27:42.0608616Z remote: Compressing objects:  55% (693/1259)        
2024-08-22T20:27:42.0609164Z remote: Compressing objects:  56% (706/1259)        
2024-08-22T20:27:42.0609701Z remote: Compressing objects:  57% (718/1259)        
2024-08-22T20:27:42.0610224Z remote: Compressing objects:  58% (731/1259)        
2024-08-22T20:27:42.0610774Z remote: Compressing objects:  59% (743/1259)        
2024-08-22T20:27:42.0611294Z remote: Compressing objects:  60% (756/1259)        
2024-08-22T20:27:42.0611822Z remote: Compressing objects:  61% (768/1259)        
2024-08-22T20:27:42.0612407Z remote: Compressing objects:  62% (781/1259)        
2024-08-22T20:27:42.0612930Z remote: Compressing objects:  63% (794/1259)        
2024-08-22T20:27:42.0613465Z remote: Compressing objects:  64% (806/1259)        
2024-08-22T20:27:42.0613996Z remote: Compressing objects:  65% (819/1259)        
2024-08-22T20:27:42.0614520Z remote: Compressing objects:  66% (831/1259)        
2024-08-22T20:27:42.0615055Z remote: Compressing objects:  67% (844/1259)        
2024-08-22T20:27:42.0615583Z remote: Compressing objects:  68% (857/1259)        
2024-08-22T20:27:42.0616103Z remote: Compressing objects:  69% (869/1259)        
2024-08-22T20:27:42.0616766Z remote: Compressing objects:  70% (882/1259)        
2024-08-22T20:27:42.0617317Z remote: Compressing objects:  71% (894/1259)        
2024-08-22T20:27:42.0617836Z remote: Compressing objects:  72% (907/1259)        
2024-08-22T20:27:42.0618365Z remote: Compressing objects:  73% (920/1259)        
2024-08-22T20:27:42.0618883Z remote: Compressing objects:  74% (932/1259)        
2024-08-22T20:27:42.0619397Z remote: Compressing objects:  75% (945/1259)        
2024-08-22T20:27:42.0619937Z remote: Compressing objects:  76% (957/1259)        
2024-08-22T20:27:42.0620464Z remote: Compressing objects:  77% (970/1259)        
2024-08-22T20:27:42.0620987Z remote: Compressing objects:  78% (983/1259)        
2024-08-22T20:27:42.0621502Z remote: Compressing objects:  79% (995/1259)        
2024-08-22T20:27:42.0622030Z remote: Compressing objects:  80% (1008/1259)        
2024-08-22T20:27:42.0622571Z remote: Compressing objects:  81% (1020/1259)        
2024-08-22T20:27:42.0623109Z remote: Compressing objects:  82% (1033/1259)        
2024-08-22T20:27:42.0623651Z remote: Compressing objects:  83% (1045/1259)        
2024-08-22T20:27:42.0624191Z remote: Compressing objects:  84% (1058/1259)        
2024-08-22T20:27:42.0624718Z remote: Compressing objects:  85% (1071/1259)        
2024-08-22T20:27:42.0625248Z remote: Compressing objects:  86% (1083/1259)        
2024-08-22T20:27:42.0625783Z remote: Compressing objects:  87% (1096/1259)        
2024-08-22T20:27:42.0626448Z remote: Compressing objects:  88% (1108/1259)        
2024-08-22T20:27:42.0627156Z remote: Compressing objects:  89% (1121/1259)        
2024-08-22T20:27:42.0627709Z remote: Compressing objects:  90% (1134/1259)        
2024-08-22T20:27:42.0628241Z remote: Compressing objects:  91% (1146/1259)        
2024-08-22T20:27:42.0628769Z remote: Compressing objects:  92% (1159/1259)        
2024-08-22T20:27:42.0629285Z remote: Compressing objects:  93% (1171/1259)        
2024-08-22T20:27:42.0629818Z remote: Compressing objects:  94% (1184/1259)        
2024-08-22T20:27:42.0630355Z remote: Compressing objects:  95% (1197/1259)        
2024-08-22T20:27:42.0630887Z remote: Compressing objects:  96% (1209/1259)        
2024-08-22T20:27:42.0631511Z remote: Compressing objects:  97% (1222/1259)        
2024-08-22T20:27:42.0632044Z remote: Compressing objects:  98% (1234/1259)        
2024-08-22T20:27:42.0632567Z remote: Compressing objects:  99% (1247/1259)        
2024-08-22T20:27:42.0633097Z remote: Compressing objects: 100% (1259/1259)        
2024-08-22T20:27:42.0633672Z remote: Compressing objects: 100% (1259/1259), done.        
2024-08-22T20:27:42.0634198Z Receiving objects:   0% (1/1649)
2024-08-22T20:27:42.0634579Z Receiving objects:   1% (17/1649)
2024-08-22T20:27:42.0634974Z Receiving objects:   2% (33/1649)
2024-08-22T20:27:42.0635365Z Receiving objects:   3% (50/1649)
2024-08-22T20:27:42.0642054Z Receiving objects:   4% (66/1649)
2024-08-22T20:27:42.0647794Z Receiving objects:   5% (83/1649)
2024-08-22T20:27:42.0653483Z Receiving objects:   6% (99/1649)
2024-08-22T20:27:42.0656276Z Receiving objects:   7% (116/1649)
2024-08-22T20:27:42.0658022Z Receiving objects:   8% (132/1649)
2024-08-22T20:27:42.0662384Z Receiving objects:   9% (149/1649)
2024-08-22T20:27:42.0665563Z Receiving objects:  10% (165/1649)
2024-08-22T20:27:42.0668659Z Receiving objects:  11% (182/1649)
2024-08-22T20:27:42.0671519Z Receiving objects:  12% (198/1649)
2024-08-22T20:27:42.0890836Z Receiving objects:  13% (215/1649)
2024-08-22T20:27:42.0967966Z Receiving objects:  14% (231/1649)
2024-08-22T20:27:42.0971429Z Receiving objects:  15% (248/1649)
2024-08-22T20:27:42.0974903Z Receiving objects:  16% (264/1649)
2024-08-22T20:27:42.0978890Z Receiving objects:  17% (281/1649)
2024-08-22T20:27:42.1015156Z Receiving objects:  18% (297/1649)
2024-08-22T20:27:42.1027344Z Receiving objects:  19% (314/1649)
2024-08-22T20:27:42.1028086Z Receiving objects:  20% (330/1649)
2024-08-22T20:27:42.1032781Z Receiving objects:  21% (347/1649)
2024-08-22T20:27:42.1036150Z Receiving objects:  22% (363/1649)
2024-08-22T20:27:42.1039637Z Receiving objects:  23% (380/1649)
2024-08-22T20:27:42.1049610Z Receiving objects:  24% (396/1649)
2024-08-22T20:27:42.1065929Z Receiving objects:  25% (413/1649)
2024-08-22T20:27:42.1068941Z Receiving objects:  26% (429/1649)
2024-08-22T20:27:42.1069972Z Receiving objects:  27% (446/1649)
2024-08-22T20:27:42.1076646Z Receiving objects:  28% (462/1649)
2024-08-22T20:27:42.1079692Z Receiving objects:  29% (479/1649)
2024-08-22T20:27:42.1089123Z Receiving objects:  30% (495/1649)
2024-08-22T20:27:42.1095843Z Receiving objects:  31% (512/1649)
2024-08-22T20:27:42.1101665Z Receiving objects:  32% (528/1649)
2024-08-22T20:27:42.1109808Z Receiving objects:  33% (545/1649)
2024-08-22T20:27:42.1110516Z Receiving objects:  34% (561/1649)
2024-08-22T20:27:42.1112868Z Receiving objects:  35% (578/1649)
2024-08-22T20:27:42.1118464Z Receiving objects:  36% (594/1649)
2024-08-22T20:27:42.1124882Z Receiving objects:  37% (611/1649)
2024-08-22T20:27:42.1127448Z Receiving objects:  38% (627/1649)
2024-08-22T20:27:42.1131780Z Receiving objects:  39% (644/1649)
2024-08-22T20:27:42.1136708Z Receiving objects:  40% (660/1649)
2024-08-22T20:27:42.1140995Z Receiving objects:  41% (677/1649)
2024-08-22T20:27:42.1145346Z Receiving objects:  42% (693/1649)
2024-08-22T20:27:42.1148573Z Receiving objects:  43% (710/1649)
2024-08-22T20:27:42.1151124Z Receiving objects:  44% (726/1649)
2024-08-22T20:27:42.1154766Z Receiving objects:  45% (743/1649)
2024-08-22T20:27:42.1159031Z Receiving objects:  46% (759/1649)
2024-08-22T20:27:42.1161719Z Receiving objects:  47% (776/1649)
2024-08-22T20:27:42.1164607Z Receiving objects:  48% (792/1649)
2024-08-22T20:27:42.1167902Z Receiving objects:  49% (809/1649)
2024-08-22T20:27:42.1172247Z Receiving objects:  50% (825/1649)
2024-08-22T20:27:42.1175041Z Receiving objects:  51% (841/1649)
2024-08-22T20:27:42.1177531Z Receiving objects:  52% (858/1649)
2024-08-22T20:27:42.1183054Z Receiving objects:  53% (874/1649)
2024-08-22T20:27:42.1186453Z Receiving objects:  54% (891/1649)
2024-08-22T20:27:42.1188141Z Receiving objects:  55% (907/1649)
2024-08-22T20:27:42.1190641Z Receiving objects:  56% (924/1649)
2024-08-22T20:27:42.1194006Z Receiving objects:  57% (940/1649)
2024-08-22T20:27:42.1199586Z Receiving objects:  58% (957/1649)
2024-08-22T20:27:42.1207266Z Receiving objects:  59% (973/1649)
2024-08-22T20:27:42.1210798Z Receiving objects:  60% (990/1649)
2024-08-22T20:27:42.1214272Z Receiving objects:  61% (1006/1649)
2024-08-22T20:27:42.1217607Z Receiving objects:  62% (1023/1649)
2024-08-22T20:27:42.1222858Z Receiving objects:  63% (1039/1649)
2024-08-22T20:27:42.1228218Z Receiving objects:  64% (1056/1649)
2024-08-22T20:27:42.1232454Z Receiving objects:  65% (1072/1649)
2024-08-22T20:27:42.1234399Z Receiving objects:  66% (1089/1649)
2024-08-22T20:27:42.1238407Z Receiving objects:  67% (1105/1649)
2024-08-22T20:27:42.1242555Z Receiving objects:  68% (1122/1649)
2024-08-22T20:27:42.1248339Z Receiving objects:  69% (1138/1649)
2024-08-22T20:27:42.1253822Z Receiving objects:  70% (1155/1649)
2024-08-22T20:27:42.1261449Z Receiving objects:  71% (1171/1649)
2024-08-22T20:27:42.1268721Z Receiving objects:  72% (1188/1649)
2024-08-22T20:27:42.1306885Z Receiving objects:  73% (1204/1649)
2024-08-22T20:27:42.1312587Z Receiving objects:  74% (1221/1649)
2024-08-22T20:27:42.1393333Z Receiving objects:  75% (1237/1649)
2024-08-22T20:27:42.1394881Z Receiving objects:  76% (1254/1649)
2024-08-22T20:27:42.1400493Z Receiving objects:  77% (1270/1649)
2024-08-22T20:27:42.1401577Z Receiving objects:  78% (1287/1649)
2024-08-22T20:27:42.1404029Z Receiving objects:  79% (1303/1649)
2024-08-22T20:27:42.1409717Z Receiving objects:  80% (1320/1649)
2024-08-22T20:27:42.1418283Z Receiving objects:  81% (1336/1649)
2024-08-22T20:27:42.1459550Z Receiving objects:  82% (1353/1649)
2024-08-22T20:27:42.1461413Z Receiving objects:  83% (1369/1649)
2024-08-22T20:27:42.1464299Z Receiving objects:  84% (1386/1649)
2024-08-22T20:27:42.1466774Z Receiving objects:  85% (1402/1649)
2024-08-22T20:27:42.1467371Z Receiving objects:  86% (1419/1649)
2024-08-22T20:27:42.1467758Z Receiving objects:  87% (1435/1649)
2024-08-22T20:27:42.1469001Z Receiving objects:  88% (1452/1649)
2024-08-22T20:27:42.1471732Z Receiving objects:  89% (1468/1649)
2024-08-22T20:27:42.1474049Z Receiving objects:  90% (1485/1649)
2024-08-22T20:27:42.1475073Z Receiving objects:  91% (1501/1649)
2024-08-22T20:27:42.1475639Z Receiving objects:  92% (1518/1649)
2024-08-22T20:27:42.1476729Z Receiving objects:  93% (1534/1649)
2024-08-22T20:27:42.1480372Z Receiving objects:  94% (1551/1649)
2024-08-22T20:27:42.1480916Z Receiving objects:  95% (1567/1649)
2024-08-22T20:27:42.1487335Z Receiving objects:  96% (1584/1649)
2024-08-22T20:27:42.1499244Z Receiving objects:  97% (1600/1649)
2024-08-22T20:27:42.1509637Z Receiving objects:  98% (1617/1649)
2024-08-22T20:27:42.1526588Z Receiving objects:  99% (1633/1649)
2024-08-22T20:27:42.1527497Z remote: Total 1649 (delta 372), reused 1071 (delta 268), pack-reused 0 (from 0)        
2024-08-22T20:27:42.1544893Z Receiving objects: 100% (1649/1649)
2024-08-22T20:27:42.1545436Z Receiving objects: 100% (1649/1649), 2.83 MiB | 28.43 MiB/s, done.
2024-08-22T20:27:42.1551897Z Resolving deltas:   0% (0/372)
2024-08-22T20:27:42.1553841Z Resolving deltas:   1% (4/372)
2024-08-22T20:27:42.1555928Z Resolving deltas:   2% (8/372)
2024-08-22T20:27:42.1556442Z Resolving deltas:   3% (12/372)
2024-08-22T20:27:42.1557317Z Resolving deltas:   4% (15/372)
2024-08-22T20:27:42.1558393Z Resolving deltas:   5% (20/372)
2024-08-22T20:27:42.1558852Z Resolving deltas:   6% (23/372)
2024-08-22T20:27:42.1560076Z Resolving deltas:   7% (27/372)
2024-08-22T20:27:42.1560925Z Resolving deltas:   8% (30/372)
2024-08-22T20:27:42.1562995Z Resolving deltas:   9% (34/372)
2024-08-22T20:27:42.1563384Z Resolving deltas:  10% (38/372)
2024-08-22T20:27:42.1564762Z Resolving deltas:  11% (41/372)
2024-08-22T20:27:42.1566619Z Resolving deltas:  12% (46/372)
2024-08-22T20:27:42.1568475Z Resolving deltas:  13% (50/372)
2024-08-22T20:27:42.1568880Z Resolving deltas:  14% (53/372)
2024-08-22T20:27:42.1571949Z Resolving deltas:  15% (56/372)
2024-08-22T20:27:42.1576451Z Resolving deltas:  16% (61/372)
2024-08-22T20:27:42.1587159Z Resolving deltas:  17% (65/372)
2024-08-22T20:27:42.1589173Z Resolving deltas:  18% (67/372)
2024-08-22T20:27:42.1590134Z Resolving deltas:  19% (71/372)
2024-08-22T20:27:42.1590785Z Resolving deltas:  20% (75/372)
2024-08-22T20:27:42.1591194Z Resolving deltas:  21% (79/372)
2024-08-22T20:27:42.1592201Z Resolving deltas:  22% (82/372)
2024-08-22T20:27:42.1593119Z Resolving deltas:  23% (86/372)
2024-08-22T20:27:42.1593654Z Resolving deltas:  24% (90/372)
2024-08-22T20:27:42.1594440Z Resolving deltas:  25% (93/372)
2024-08-22T20:27:42.1596918Z Resolving deltas:  26% (97/372)
2024-08-22T20:27:42.1597489Z Resolving deltas:  27% (101/372)
2024-08-22T20:27:42.1598050Z Resolving deltas:  28% (106/372)
2024-08-22T20:27:42.1598612Z Resolving deltas:  29% (109/372)
2024-08-22T20:27:42.1599881Z Resolving deltas:  30% (112/372)
2024-08-22T20:27:42.1607543Z Resolving deltas:  31% (116/372)
2024-08-22T20:27:42.1608046Z Resolving deltas:  32% (120/372)
2024-08-22T20:27:42.1609762Z Resolving deltas:  33% (123/372)
2024-08-22T20:27:42.1610881Z Resolving deltas:  34% (127/372)
2024-08-22T20:27:42.1612057Z Resolving deltas:  35% (131/372)
2024-08-22T20:27:42.1613332Z Resolving deltas:  36% (134/372)
2024-08-22T20:27:42.1615047Z Resolving deltas:  37% (139/372)
2024-08-22T20:27:42.1616763Z Resolving deltas:  38% (142/372)
2024-08-22T20:27:42.1617160Z Resolving deltas:  39% (148/372)
2024-08-22T20:27:42.1618133Z Resolving deltas:  40% (149/372)
2024-08-22T20:27:42.1619843Z Resolving deltas:  41% (153/372)
2024-08-22T20:27:42.1620381Z Resolving deltas:  42% (157/372)
2024-08-22T20:27:42.1620996Z Resolving deltas:  43% (160/372)
2024-08-22T20:27:42.1622757Z Resolving deltas:  44% (164/372)
2024-08-22T20:27:42.1623435Z Resolving deltas:  45% (168/372)
2024-08-22T20:27:42.1624100Z Resolving deltas:  46% (172/372)
2024-08-22T20:27:42.1624923Z Resolving deltas:  47% (175/372)
2024-08-22T20:27:42.1625436Z Resolving deltas:  48% (179/372)
2024-08-22T20:27:42.1626328Z Resolving deltas:  49% (183/372)
2024-08-22T20:27:42.1626846Z Resolving deltas:  50% (187/372)
2024-08-22T20:27:42.1628310Z Resolving deltas:  51% (190/372)
2024-08-22T20:27:42.1628897Z Resolving deltas:  52% (195/372)
2024-08-22T20:27:42.1630248Z Resolving deltas:  53% (199/372)
2024-08-22T20:27:42.1632208Z Resolving deltas:  54% (201/372)
2024-08-22T20:27:42.1632766Z Resolving deltas:  55% (206/372)
2024-08-22T20:27:42.1634120Z Resolving deltas:  56% (209/372)
2024-08-22T20:27:42.1635449Z Resolving deltas:  57% (213/372)
2024-08-22T20:27:42.1636435Z Resolving deltas:  58% (216/372)
2024-08-22T20:27:42.1637175Z Resolving deltas:  59% (221/372)
2024-08-22T20:27:42.1638818Z Resolving deltas:  60% (224/372)
2024-08-22T20:27:42.1639711Z Resolving deltas:  61% (227/372)
2024-08-22T20:27:42.1641515Z Resolving deltas:  62% (231/372)
2024-08-22T20:27:42.1643710Z Resolving deltas:  63% (235/372)
2024-08-22T20:27:42.1645210Z Resolving deltas:  64% (239/372)
2024-08-22T20:27:42.1647105Z Resolving deltas:  65% (243/372)
2024-08-22T20:27:42.1648596Z Resolving deltas:  66% (246/372)
2024-08-22T20:27:42.1649128Z Resolving deltas:  67% (250/372)
2024-08-22T20:27:42.1649519Z Resolving deltas:  68% (253/372)
2024-08-22T20:27:42.1649899Z Resolving deltas:  69% (258/372)
2024-08-22T20:27:42.1650377Z Resolving deltas:  70% (261/372)
2024-08-22T20:27:42.1650766Z Resolving deltas:  71% (267/372)
2024-08-22T20:27:42.1651145Z Resolving deltas:  72% (268/372)
2024-08-22T20:27:42.1651727Z Resolving deltas:  73% (272/372)
2024-08-22T20:27:42.1652117Z Resolving deltas:  74% (277/372)
2024-08-22T20:27:42.1652495Z Resolving deltas:  75% (280/372)
2024-08-22T20:27:42.1652886Z Resolving deltas:  76% (283/372)
2024-08-22T20:27:42.1653273Z Resolving deltas:  77% (287/372)
2024-08-22T20:27:42.1653648Z Resolving deltas:  78% (291/372)
2024-08-22T20:27:42.1654030Z Resolving deltas:  79% (295/372)
2024-08-22T20:27:42.1654436Z Resolving deltas:  80% (298/372)
2024-08-22T20:27:42.1654813Z Resolving deltas:  81% (302/372)
2024-08-22T20:27:42.1655302Z Resolving deltas:  82% (306/372)
2024-08-22T20:27:42.1655692Z Resolving deltas:  83% (312/372)
2024-08-22T20:27:42.1656070Z Resolving deltas:  84% (313/372)
2024-08-22T20:27:42.1656506Z Resolving deltas:  85% (317/372)
2024-08-22T20:27:42.1656889Z Resolving deltas:  86% (320/372)
2024-08-22T20:27:42.1657256Z Resolving deltas:  87% (324/372)
2024-08-22T20:27:42.1657633Z Resolving deltas:  88% (328/372)
2024-08-22T20:27:42.1658027Z Resolving deltas:  89% (333/372)
2024-08-22T20:27:42.1658404Z Resolving deltas:  90% (335/372)
2024-08-22T20:27:42.1658780Z Resolving deltas:  91% (339/372)
2024-08-22T20:27:42.1659158Z Resolving deltas:  92% (343/372)
2024-08-22T20:27:42.1659732Z Resolving deltas:  93% (346/372)
2024-08-22T20:27:42.1663189Z Resolving deltas:  94% (350/372)
2024-08-22T20:27:42.1665244Z Resolving deltas:  95% (355/372)
2024-08-22T20:27:42.1666231Z Resolving deltas:  96% (358/372)
2024-08-22T20:27:42.1667683Z Resolving deltas:  97% (362/372)
2024-08-22T20:27:42.1669734Z Resolving deltas:  98% (366/372)
2024-08-22T20:27:42.1671294Z Resolving deltas:  99% (370/372)
2024-08-22T20:27:42.1672974Z Resolving deltas: 100% (372/372)
2024-08-22T20:27:42.1673506Z Resolving deltas: 100% (372/372), done.
2024-08-22T20:27:42.1830530Z From https://github.com/pytorch/test-infra
2024-08-22T20:27:42.1831109Z  * [new branch]      main       -> origin/main
2024-08-22T20:27:42.1856583Z ##[endgroup]
2024-08-22T20:27:42.1857118Z ##[group]Determining the checkout info
2024-08-22T20:27:42.1859035Z ##[endgroup]
2024-08-22T20:27:42.1859556Z ##[group]Checking out the ref
2024-08-22T20:27:42.1862886Z [command]/usr/bin/git checkout --progress --force -B main refs/remotes/origin/main
2024-08-22T20:27:42.3177067Z Switched to a new branch 'main'
2024-08-22T20:27:42.3178028Z branch 'main' set up to track 'origin/main'.
2024-08-22T20:27:42.3188704Z ##[endgroup]
2024-08-22T20:27:42.3189307Z ##[group]Setting up auth for fetching submodules
2024-08-22T20:27:42.3194374Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic ***
2024-08-22T20:27:42.3256956Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf
2024-08-22T20:27:42.3302850Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com:
2024-08-22T20:27:42.3348108Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com:
2024-08-22T20:27:42.3390520Z ##[endgroup]
2024-08-22T20:27:42.3391142Z ##[group]Fetching submodules
2024-08-22T20:27:42.3394018Z [command]/usr/bin/git submodule sync --recursive
2024-08-22T20:27:42.3758798Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --depth=1 --recursive
2024-08-22T20:27:42.4122321Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0
2024-08-22T20:27:42.4475544Z ##[endgroup]
2024-08-22T20:27:42.4476586Z ##[group]Persisting credentials for submodules
2024-08-22T20:27:42.4481494Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'url\.https\:\/\/github\.com\/\.insteadOf' && git config --local --unset-all 'url.https://github.com/.insteadOf' || :"
2024-08-22T20:27:42.4830663Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url"
2024-08-22T20:27:42.5187880Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:'
2024-08-22T20:27:42.5546130Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:'
2024-08-22T20:27:42.5908043Z ##[endgroup]
2024-08-22T20:27:42.5961920Z [command]/usr/bin/git log -1 --format='%H'
2024-08-22T20:27:42.6003915Z '6dab351bed7d1c647665a8b8e883ec5b1c973feb'
2024-08-22T20:27:42.6312577Z Prepare all required actions
2024-08-22T20:27:42.6313074Z Getting action download info
2024-08-22T20:27:42.7303605Z Download action repository 'pytorch/test-infra@main' (SHA:6dab351bed7d1c647665a8b8e883ec5b1c973feb)
2024-08-22T20:27:43.1702273Z Getting action download info
2024-08-22T20:27:43.2683792Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482)
2024-08-22T20:27:43.4084653Z ##[group]Run ./test-infra/.github/actions/setup-linux
2024-08-22T20:27:43.4085126Z env:
2024-08-22T20:27:43.4085456Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:43.4085920Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:43.4086285Z   PR_NUMBER: 1044
2024-08-22T20:27:43.4105309Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:43.4124687Z ##[endgroup]
2024-08-22T20:27:43.4319686Z ##[group]Run set -euo pipefail
2024-08-22T20:27:43.4320111Z set -euo pipefail
2024-08-22T20:27:43.4320516Z function get_ec2_metadata() {
2024-08-22T20:27:43.4321077Z   # Pulled from instance metadata endpoint for EC2
2024-08-22T20:27:43.4322049Z   # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
2024-08-22T20:27:43.4322892Z   category=$1
2024-08-22T20:27:43.4323441Z   curl -fsSL "http://169.254.169.254/latest/meta-data/${category}"
2024-08-22T20:27:43.4324039Z }
2024-08-22T20:27:43.4324420Z echo "ami-id: $(get_ec2_metadata ami-id)"
2024-08-22T20:27:43.4325037Z echo "instance-id: $(get_ec2_metadata instance-id)"
2024-08-22T20:27:43.4325743Z echo "instance-type: $(get_ec2_metadata instance-type)"
2024-08-22T20:27:43.4326475Z echo "system info $(uname -a)"
2024-08-22T20:27:43.4335716Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:27:43.4336215Z env:
2024-08-22T20:27:43.4336558Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:43.4337046Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:43.4337417Z   PR_NUMBER: 1044
2024-08-22T20:27:43.4356375Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:43.4375736Z ##[endgroup]
2024-08-22T20:27:43.4471319Z ami-id: ami-06c68f701d8090592
2024-08-22T20:27:43.4527447Z instance-id: i-0eff7f5d1c54b6a46
2024-08-22T20:27:43.4581635Z instance-type: g5.4xlarge
2024-08-22T20:27:43.4596001Z system info Linux ip-10-0-38-42.ec2.internal 6.1.94-99.176.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 18 14:57:56 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux
2024-08-22T20:27:43.4627738Z ##[group]Run if systemctl is-active --quiet docker; then
2024-08-22T20:27:43.4628333Z if systemctl is-active --quiet docker; then
2024-08-22T20:27:43.4628866Z     echo "Docker daemon is running...";
2024-08-22T20:27:43.4629310Z else
2024-08-22T20:27:43.4629791Z     echo "Starting docker deamon..." && sudo systemctl start docker;
2024-08-22T20:27:43.4630371Z fi
2024-08-22T20:27:43.4639260Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:27:43.4639746Z env:
2024-08-22T20:27:43.4640075Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:43.4640539Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:43.4640917Z   PR_NUMBER: 1044
2024-08-22T20:27:43.4660001Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:43.4679774Z ##[endgroup]
2024-08-22T20:27:43.4767265Z Docker daemon is running...
2024-08-22T20:27:43.4795349Z ##[group]Run AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\")
2024-08-22T20:27:43.4796292Z AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\")
2024-08-22T20:27:43.4797078Z retry () { "$@"  || (sleep 1 && "$@") || (sleep 2 && "$@") }
2024-08-22T20:27:43.4798014Z retry aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \
2024-08-22T20:27:43.4799095Z     --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com"
2024-08-22T20:27:43.4808323Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:27:43.4808816Z env:
2024-08-22T20:27:43.4809138Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:43.4809589Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:43.4809986Z   PR_NUMBER: 1044
2024-08-22T20:27:43.4828908Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:43.4848355Z   AWS_RETRY_MODE: standard
2024-08-22T20:27:43.4848707Z   AWS_MAX_ATTEMPTS: 5
2024-08-22T20:27:43.4849045Z   AWS_DEFAULT_REGION: us-east-1
2024-08-22T20:27:43.4849403Z ##[endgroup]
2024-08-22T20:27:44.5851967Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2024-08-22T20:27:44.5852778Z Configure a credential helper to remove this warning. See
2024-08-22T20:27:44.5853610Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2024-08-22T20:27:44.5854123Z 
2024-08-22T20:27:44.5854255Z Login Succeeded
2024-08-22T20:27:44.5905474Z ##[group]Run env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}"
2024-08-22T20:27:44.5906409Z env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}"
2024-08-22T20:27:44.5907131Z env | grep '^CI' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}"
2024-08-22T20:27:44.5916340Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:27:44.5916837Z env:
2024-08-22T20:27:44.5917154Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:44.5917615Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:44.5917999Z   PR_NUMBER: 1044
2024-08-22T20:27:44.5937087Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:44.5956301Z ##[endgroup]
2024-08-22T20:27:44.6052330Z ##[group]Run RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts"
2024-08-22T20:27:44.6052977Z RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts"
2024-08-22T20:27:44.6053506Z sudo rm -rf "${RUNNER_ARTIFACT_DIR}"
2024-08-22T20:27:44.6053977Z mkdir -p "${RUNNER_ARTIFACT_DIR}"
2024-08-22T20:27:44.6054584Z echo "RUNNER_ARTIFACT_DIR=${RUNNER_ARTIFACT_DIR}" >> "${GITHUB_ENV}"
2024-08-22T20:27:44.6055146Z 
2024-08-22T20:27:44.6055536Z RUNNER_TEST_RESULTS_DIR="${RUNNER_TEMP}/test-results"
2024-08-22T20:27:44.6056110Z sudo rm -rf "${RUNNER_TEST_RESULTS_DIR}"
2024-08-22T20:27:44.6056619Z mkdir -p "${RUNNER_TEST_RESULTS_DIR}"
2024-08-22T20:27:44.6057265Z echo "RUNNER_TEST_RESULTS_DIR=${RUNNER_TEST_RESULTS_DIR}" >> "${GITHUB_ENV}"
2024-08-22T20:27:44.6057853Z 
2024-08-22T20:27:44.6058184Z RUNNER_DOCS_DIR="${RUNNER_TEMP}/docs"
2024-08-22T20:27:44.6058658Z sudo rm -rf "${RUNNER_DOCS_DIR}"
2024-08-22T20:27:44.6059096Z mkdir -p "${RUNNER_DOCS_DIR}"
2024-08-22T20:27:44.6059831Z echo "RUNNER_DOCS_DIR=${RUNNER_DOCS_DIR}" >> "${GITHUB_ENV}"
2024-08-22T20:27:44.6068894Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:27:44.6069380Z env:
2024-08-22T20:27:44.6069703Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:44.6070166Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:44.6070537Z   PR_NUMBER: 1044
2024-08-22T20:27:44.6089712Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:44.6108961Z ##[endgroup]
2024-08-22T20:27:44.8494769Z ##[group]Run needs=0
2024-08-22T20:27:44.8495101Z needs=0
2024-08-22T20:27:44.8495621Z if lspci -v | grep -e 'controller.*NVIDIA' >/dev/null 2>/dev/null; then
2024-08-22T20:27:44.8496217Z   needs=1
2024-08-22T20:27:44.8496529Z fi
2024-08-22T20:27:44.8496894Z echo "does=${needs}" >> $GITHUB_OUTPUT
2024-08-22T20:27:44.8505902Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:27:44.8506386Z env:
2024-08-22T20:27:44.8506712Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:44.8507173Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:44.8507545Z   PR_NUMBER: 1044
2024-08-22T20:27:44.8526487Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:44.8546159Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:27:44.8546922Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:27:44.8547636Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:27:44.8548240Z ##[endgroup]
2024-08-22T20:27:44.8849356Z ##[group]Run pytorch/test-infra/.github/actions/setup-nvidia@main
2024-08-22T20:27:44.8849875Z with:
2024-08-22T20:27:44.8850140Z   driver-version: 550.54.15
2024-08-22T20:27:44.8850467Z env:
2024-08-22T20:27:44.8850779Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:44.8851222Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:44.8851593Z   PR_NUMBER: 1044
2024-08-22T20:27:44.8870608Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:44.8890477Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:27:44.8891322Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:27:44.8892118Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:27:44.8892664Z ##[endgroup]
2024-08-22T20:27:44.8937944Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482
2024-08-22T20:27:44.8938483Z with:
2024-08-22T20:27:44.8938751Z   timeout_minutes: 10
2024-08-22T20:27:44.8939067Z   max_attempts: 3
2024-08-22T20:27:44.8971708Z   command: # Is it disgusting to have a full shell script here in this github action? Sure
# But is it the best way to make it so that this action relies on nothing else? Absolutely
set -eou pipefail

DISTRIBUTION=$(. /etc/os-release;echo $ID$VERSION_ID)
DRIVER_FN="NVIDIA-Linux-x86_64-${DRIVER_VERSION}.run"

install_nvidia_docker2_amzn2() {
    (
        set -x
        # Needed for yum-config-manager
        sudo yum install -y yum-utils
        if [[ "${DISTRIBUTION}" == "amzn2023" ]] ; then
          YUM_REPO_URL="https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo"
        else
          # Amazon Linux 2
          YUM_REPO_URL="https://nvidia.github.io/nvidia-docker/${DISTRIBUTION}/nvidia-docker.repo"
        fi

        sudo yum-config-manager --add-repo "${YUM_REPO_URL}"
        sudo yum install -y nvidia-docker2
        sudo systemctl restart docker
    )
}

install_nvidia_docker2_ubuntu20() {
    (
        set -x
        # Install nvidia-driver package if not installed
        status="$(dpkg-query -W --showformat='${db:Status-Status}' nvidia-docker2 2>&1)"
        if [ ! $? = 0 ] || [ ! "$status" = installed ]; then
          sudo apt-get install -y nvidia-docker2
          sudo systemctl restart docker
        fi
    )
}

pre_install_nvidia_driver_amzn2() {
    (
        # Purge any nvidia driver installed from RHEL repo
        sudo yum remove -y nvidia-driver-latest-dkms
    )
}

install_nvidia_driver_common() {
    (
        # Try to gather more information about the runner and its existing NVIDIA driver if any
        echo "Before installing NVIDIA driver"
        lspci
        lsmod
        modinfo nvidia || true

        HAS_NVIDIA_DRIVER=0
        # Check if NVIDIA driver has already been installed
        if [ -x "$(command -v nvidia-smi)" ]; then
            set +e
            # The driver exists, check its version next. Also check only the first GPU if there are more than one of them
            # so that the same driver version is not print over multiple lines
            INSTALLED_DRIVER_VERSION=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0)
            NVIDIA_SMI_STATUS=$?

            if [ "$NVIDIA_SMI_STATUS" -ne 0 ] && [ "$NVIDIA_SMI_STATUS" -ne 14 ]; then
                echo "Failed to get NVIDIA driver version ($INSTALLED_DRIVER_VERSION). Continuing"
            elif [ "$INSTALLED_DRIVER_VERSION" != "$DRIVER_VERSION" ]; then
                echo "NVIDIA driver ($INSTALLED_DRIVER_VERSION) has been installed, but we expect to have $DRIVER_VERSION instead. Continuing"
            else
                HAS_NVIDIA_DRIVER=1
                echo "NVIDIA driver ($INSTALLED_DRIVER_VERSION) has already been installed. Skipping NVIDIA driver installation"
            fi
            set -e
        fi

        if [ "$HAS_NVIDIA_DRIVER" -eq 0 ]; then
            # CAUTION: this may need to be updated in future
            if [ "${DISTRIBUTION}" != ubuntu20.04 ]; then
                  sudo yum groupinstall -y "Development Tools"
                  # ensure our kernel install is the same as our underlying kernel,
                  # groupinstall "Development Tools" has a habit of mismatching kernel headers
                  sudo yum install -y "kernel-devel-uname-r == $(uname -r)"
                  sudo modprobe backlight
            fi
            sudo curl -fsL -o /tmp/nvidia_driver "https://s3.amazonaws.com/ossci-linux/nvidia_driver/$DRIVER_FN"

            set +e
            sudo /bin/bash /tmp/nvidia_driver -s --no-drm
            NVIDIA_INSTALLATION_STATUS=$?

            RESET_GPU=0
            if [ "$NVIDIA_INSTALLATION_STATUS" -ne 0 ]; then
                sudo cat /var/log/nvidia-installer.log
                # Fail to install NVIDIA driver, try to reset the GPU
                RESET_GPU=1
            elif [ -x "$(command -v nvidia-smi)" ]; then
                # Check again if nvidia-smi works even if the driver installation completes successfully
                INSTALLED_DRIVER_VERSION=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0)
                NVIDIA_SMI_STATUS=$?

                if [ "$NVIDIA_SMI_STATUS" -ne 0 ] && [ "$NVIDIA_SMI_STATUS" -ne 14 ]; then
                    RESET_GPU=1
                fi
            fi

            if [ "$RESET_GPU" -eq 1 ]; then
                NVIDIA_DEVICES=$(lspci -D | grep -i NVIDIA | cut -d' ' -f1)
                # The GPU can get stuck in a failure state if somehow the test crashs the GPU microcode. When this
                # happens, we'll try to reset all NVIDIA devices https://github.com/pytorch/pytorch/issues/88388
                for PCI_ID in $NVIDIA_DEVICES; do
                    DEVICE_ENABLED=$(cat /sys/bus/pci/devices/$PCI_ID/enable)

                    echo "Reseting $PCI_ID (enabled state: $DEVICE_ENABLED)"
                    # This requires sudo permission of course
                    echo "1" | sudo tee /sys/bus/pci/devices/$PCI_ID/reset
                    sleep 1
                done
            fi

            sudo rm -fv /tmp/nvidia_driver
            set -e
        fi
    )
}

post_install_nvidia_driver_common() {
    (
        sudo modprobe nvidia || true
        echo "After installing NVIDIA driver"
        lspci
        lsmod
        modinfo nvidia || true

        (
            set +e

            nvidia-smi
            # NB: Annoyingly, nvidia-smi command returns successfully with return code 0 even in
            # the case where the driver has already crashed as it still can get the driver version
            # and some basic information like the bus ID.  However, the rest of the information
            # would be missing (ERR!), for example:
            #
            # +-----------------------------------------------------------------------------+
            # | NVIDIA-SMI 525.89.02    Driver Version: 525.89.02    CUDA Version: 12.0     |
            # |-------------------------------+----------------------+----------------------+
            # | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
            # | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
            # |                               |                      |               MIG M. |
            # |===============================+======================+======================|
            # |   0  ERR!                Off  | 00000000:00:1E.0 Off |                 ERR! |
            # |ERR!  ERR! ERR!    ERR! / ERR! |   4184MiB / 23028MiB |    ERR!      Default |
            # |                               |                      |                 ERR! |
            # +-------------------------------+----------------------+----------------------+
            #
            # +-----------------------------------------------------------------------------+
            # | Processes:                                                                  |
            # |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
            # |        ID   ID                                                   Usage      |
            # |=============================================================================|
            # +-----------------------------------------------------------------------------+
            #
            # This should be reported as a failure instead as it will guarantee to fail when
            # Docker tries to run with --gpus all
            #
            # So, the correct check here is to query one of the missing piece of info like
            # GPU name, so that the command can fail accordingly
            nvidia-smi --query-gpu=gpu_name --format=csv,noheader --id=0
            NVIDIA_SMI_STATUS=$?

            # Allowable exit statuses for nvidia-smi, see: https://github.com/NVIDIA/gpu-operator/issues/285
            if [ "$NVIDIA_SMI_STATUS" -eq 0 ] || [ "$NVIDIA_SMI_STATUS" -eq 14 ]; then
                echo "INFO: Ignoring allowed status ${NVIDIA_SMI_STATUS}"
            else
                echo "ERROR: nvidia-smi exited with unresolved status ${NVIDIA_SMI_STATUS}"
                exit ${NVIDIA_SMI_STATUS}
            fi
            set -e
        )
    )
}

install_nvidia_driver_amzn2() {
    (
        set -x
        pre_install_nvidia_driver_amzn2
        install_nvidia_driver_common
        post_install_nvidia_driver_common
    )
}

install_nvidia_driver_ubuntu20() {
    (
        set -x
        install_nvidia_driver_common
        post_install_nvidia_driver_common
    )
}

echo "== Installing nvidia driver ${DRIVER_FN} =="
case "${DISTRIBUTION}" in
    amzn*)
        install_nvidia_driver_amzn2
        ;;
    ubuntu20.04)
        install_nvidia_driver_ubuntu20
        ;;
    *)
        echo "ERROR: Unknown distribution ${DISTRIBUTION}"
        exit 1
        ;;
esac

# Install container toolkit based on distribution
echo "== Installing nvidia container toolkit for ${DISTRIBUTION} =="
case "${DISTRIBUTION}" in
    amzn*)
        install_nvidia_docker2_amzn2
        ;;
    ubuntu20.04)
        install_nvidia_docker2_ubuntu20
        ;;
    *)
        echo "ERROR: Unknown distribution ${DISTRIBUTION}"
        exit 1
        ;;
esac
echo "GPU_FLAG=--gpus all -e NVIDIA_DRIVER_CAPABILITIES=all" >> "${GITHUB_ENV}"

# Fix https://github.com/NVIDIA/nvidia-docker/issues/1648 on runners with
# more than one GPUs. This just needs to be run once. The command fails
# on subsequent runs and complains that the mode is already on, but that's
# ok
sudo nvidia-persistenced || true
# This should show persistence mode ON
nvidia-smi

2024-08-22T20:27:44.9006040Z   retry_wait_seconds: 10
2024-08-22T20:27:44.9006454Z   polling_interval_seconds: 1
2024-08-22T20:27:44.9006832Z   warning_on_retry: true
2024-08-22T20:27:44.9007188Z   continue_on_error: false
2024-08-22T20:27:44.9007529Z env:
2024-08-22T20:27:44.9007864Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:44.9008420Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:44.9008783Z   PR_NUMBER: 1044
2024-08-22T20:27:44.9027712Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:44.9047130Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:27:44.9047893Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:27:44.9048690Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:27:44.9049208Z   DRIVER_VERSION: 550.54.15
2024-08-22T20:27:44.9049550Z ##[endgroup]
2024-08-22T20:27:44.9865534Z == Installing nvidia driver NVIDIA-Linux-x86_64-550.54.15.run ==
2024-08-22T20:27:44.9866959Z + pre_install_nvidia_driver_amzn2
2024-08-22T20:27:44.9868436Z + sudo yum remove -y nvidia-driver-latest-dkms
2024-08-22T20:27:45.3447820Z No match for argument: nvidia-driver-latest-dkms
2024-08-22T20:27:45.3448440Z No packages marked for removal.
2024-08-22T20:27:45.3510858Z Dependencies resolved.
2024-08-22T20:27:45.3520805Z Nothing to do.
2024-08-22T20:27:45.3521745Z Complete!
2024-08-22T20:27:45.4000257Z + install_nvidia_driver_common
2024-08-22T20:27:45.4003394Z + echo 'Before installing NVIDIA driver'
2024-08-22T20:27:45.4003834Z Before installing NVIDIA driver
2024-08-22T20:27:45.4005573Z + lspci
2024-08-22T20:27:45.4112316Z 00:00.0 Host bridge: Intel Corporation 440FX - 82441FX PMC [Natoma]
2024-08-22T20:27:45.4113237Z 00:01.0 ISA bridge: Intel Corporation 82371SB PIIX3 ISA [Natoma/Triton II]
2024-08-22T20:27:45.4114182Z 00:01.3 Non-VGA unclassified device: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 08)
2024-08-22T20:27:45.4114983Z 00:03.0 VGA compatible controller: Amazon.com, Inc. Device 1111
2024-08-22T20:27:45.4115822Z 00:04.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe EBS Controller
2024-08-22T20:27:45.4116608Z 00:05.0 Ethernet controller: Amazon.com, Inc. Elastic Network Adapter (ENA)
2024-08-22T20:27:45.4117367Z 00:1e.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1)
2024-08-22T20:27:45.4118194Z 00:1f.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe SSD Controller
2024-08-22T20:27:45.4118782Z + lsmod
2024-08-22T20:27:45.4162771Z Module                  Size  Used by
2024-08-22T20:27:45.4163168Z veth                   36864  0
2024-08-22T20:27:45.4163635Z nvidia_modeset       1351680  0
2024-08-22T20:27:45.4164181Z video                  65536  1 nvidia_modeset
2024-08-22T20:27:45.4164741Z wmi                    36864  1 video
2024-08-22T20:27:45.4165230Z nvidia_uvm           4706304  0
2024-08-22T20:27:45.4165738Z nvidia              54071296  7 nvidia_uvm,nvidia_modeset
2024-08-22T20:27:45.4166219Z drm                   602112  1 nvidia
2024-08-22T20:27:45.4166940Z drm_panel_orientation_quirks    28672  1 drm
2024-08-22T20:27:45.4167475Z backlight              24576  3 video,drm,nvidia_modeset
2024-08-22T20:27:45.4168014Z i2c_core              106496  2 nvidia,drm
2024-08-22T20:27:45.4168523Z xt_conntrack           16384  1
2024-08-22T20:27:45.4168895Z nft_chain_nat          16384  3
2024-08-22T20:27:45.4169261Z xt_MASQUERADE          20480  1
2024-08-22T20:27:45.4169706Z nf_nat                 57344  2 nft_chain_nat,xt_MASQUERADE
2024-08-22T20:27:45.4170185Z nf_conntrack_netlink    57344  0
2024-08-22T20:27:45.4170743Z nf_conntrack          184320  4 xt_conntrack,nf_nat,nf_conntrack_netlink,xt_MASQUERADE
2024-08-22T20:27:45.4171379Z nf_defrag_ipv6         24576  1 nf_conntrack
2024-08-22T20:27:45.4171832Z nf_defrag_ipv4         16384  1 nf_conntrack
2024-08-22T20:27:45.4172247Z xfrm_user              57344  1
2024-08-22T20:27:45.4172633Z xfrm_algo              16384  1 xfrm_user
2024-08-22T20:27:45.4173043Z xt_addrtype            16384  2
2024-08-22T20:27:45.4173414Z nft_compat             20480  4
2024-08-22T20:27:45.4173859Z nf_tables             307200  57 nft_compat,nft_chain_nat
2024-08-22T20:27:45.4174454Z nfnetlink              20480  4 nft_compat,nf_conntrack_netlink,nf_tables
2024-08-22T20:27:45.4174983Z br_netfilter           36864  0
2024-08-22T20:27:45.4175377Z bridge                307200  1 br_netfilter
2024-08-22T20:27:45.4175807Z stp                    16384  1 bridge
2024-08-22T20:27:45.4176217Z llc                    16384  2 bridge,stp
2024-08-22T20:27:45.4176622Z overlay               167936  0
2024-08-22T20:27:45.4177008Z tls                   114688  0
2024-08-22T20:27:45.4177387Z nls_ascii              16384  1
2024-08-22T20:27:45.4178002Z nls_cp437              20480  1
2024-08-22T20:27:45.4178362Z vfat                   24576  1
2024-08-22T20:27:45.4178722Z fat                    86016  1 vfat
2024-08-22T20:27:45.4179094Z ena                   167936  0
2024-08-22T20:27:45.4179455Z ptp                    36864  1 ena
2024-08-22T20:27:45.4180001Z pps_core               24576  1 ptp
2024-08-22T20:27:45.4180377Z sunrpc                692224  1
2024-08-22T20:27:45.4180743Z ghash_clmulni_intel    16384  0
2024-08-22T20:27:45.4181106Z i8042                  45056  0
2024-08-22T20:27:45.4181461Z serio                  28672  3 i8042
2024-08-22T20:27:45.4181851Z aesni_intel           393216  0
2024-08-22T20:27:45.4182211Z button                 24576  0
2024-08-22T20:27:45.4182590Z crypto_simd            16384  1 aesni_intel
2024-08-22T20:27:45.4183099Z cryptd                 28672  2 crypto_simd,ghash_clmulni_intel
2024-08-22T20:27:45.4183590Z sch_fq_codel           20480  17
2024-08-22T20:27:45.4183949Z dm_mod                188416  0
2024-08-22T20:27:45.4184309Z fuse                  163840  1
2024-08-22T20:27:45.4184759Z configfs               57344  1
2024-08-22T20:27:45.4185244Z dax                    45056  1 dm_mod
2024-08-22T20:27:45.4185932Z loop                   36864  0
2024-08-22T20:27:45.4186297Z dmi_sysfs              20480  0
2024-08-22T20:27:45.4186668Z crc32_pclmul           16384  0
2024-08-22T20:27:45.4187038Z crc32c_intel           24576  0
2024-08-22T20:27:45.4187420Z efivarfs               24576  1
2024-08-22T20:27:45.4187873Z + modinfo nvidia
2024-08-22T20:27:45.4188585Z filename:       /lib/modules/6.1.94-99.176.amzn2023.x86_64/kernel/drivers/video/nvidia.ko
2024-08-22T20:27:45.4189279Z alias:          char-major-195-*
2024-08-22T20:27:45.4189646Z version:        550.54.15
2024-08-22T20:27:45.4189983Z supported:      external
2024-08-22T20:27:45.4190317Z license:        NVIDIA
2024-08-22T20:27:45.4190679Z firmware:       nvidia/550.54.15/gsp_tu10x.bin
2024-08-22T20:27:45.4191154Z firmware:       nvidia/550.54.15/gsp_ga10x.bin
2024-08-22T20:27:45.4191604Z srcversion:     833721318DA517F0C2FEC97
2024-08-22T20:27:45.4192056Z alias:          pci:v000010DEd*sv*sd*bc06sc80i00*
2024-08-22T20:27:45.4192544Z alias:          pci:v000010DEd*sv*sd*bc03sc02i00*
2024-08-22T20:27:45.4193021Z alias:          pci:v000010DEd*sv*sd*bc03sc00i00*
2024-08-22T20:27:45.4193686Z depends:        i2c-core,drm
2024-08-22T20:27:45.4194047Z retpoline:      Y
2024-08-22T20:27:45.4194344Z name:           nvidia
2024-08-22T20:27:45.4194949Z vermagic:       6.1.94-99.176.amzn2023.x86_64 SMP preempt mod_unload modversions 
2024-08-22T20:27:45.4195641Z parm:           NvSwitchRegDwords:NvSwitch regkey (charp)
2024-08-22T20:27:45.4196275Z parm:           NvSwitchBlacklist:NvSwitchBlacklist=uuid[,uuid...] (charp)
2024-08-22T20:27:45.4197038Z parm:           NVreg_ResmanDebugLevel:int
2024-08-22T20:27:45.4197539Z parm:           NVreg_RmLogonRC:int
2024-08-22T20:27:45.4197971Z parm:           NVreg_ModifyDeviceFiles:int
2024-08-22T20:27:45.4198414Z parm:           NVreg_DeviceFileUID:int
2024-08-22T20:27:45.4198841Z parm:           NVreg_DeviceFileGID:int
2024-08-22T20:27:45.4199267Z parm:           NVreg_DeviceFileMode:int
2024-08-22T20:27:45.4199771Z parm:           NVreg_InitializeSystemMemoryAllocations:int
2024-08-22T20:27:45.4200327Z parm:           NVreg_UsePageAttributeTable:int
2024-08-22T20:27:45.4200803Z parm:           NVreg_EnablePCIeGen3:int
2024-08-22T20:27:45.4201221Z parm:           NVreg_EnableMSI:int
2024-08-22T20:27:45.4201618Z parm:           NVreg_TCEBypassMode:int
2024-08-22T20:27:45.4202067Z parm:           NVreg_EnableStreamMemOPs:int
2024-08-22T20:27:45.4202582Z parm:           NVreg_RestrictProfilingToAdminUsers:int
2024-08-22T20:27:45.4203138Z parm:           NVreg_PreserveVideoMemoryAllocations:int
2024-08-22T20:27:45.4203685Z parm:           NVreg_EnableS0ixPowerManagement:int
2024-08-22T20:27:45.4204275Z parm:           NVreg_S0ixPowerManagementVideoMemoryThreshold:int
2024-08-22T20:27:45.4204847Z parm:           NVreg_DynamicPowerManagement:int
2024-08-22T20:27:45.4205436Z parm:           NVreg_DynamicPowerManagementVideoMemoryThreshold:int
2024-08-22T20:27:45.4206011Z parm:           NVreg_EnableGpuFirmware:int
2024-08-22T20:27:45.4206479Z parm:           NVreg_EnableGpuFirmwareLogs:int
2024-08-22T20:27:45.4207004Z parm:           NVreg_OpenRmEnableUnsupportedGpus:int
2024-08-22T20:27:45.4207686Z parm:           NVreg_EnableUserNUMAManagement:int
2024-08-22T20:27:45.4208258Z parm:           NVreg_MemoryPoolSize:int
2024-08-22T20:27:45.4208710Z parm:           NVreg_KMallocHeapMaxSize:int
2024-08-22T20:27:45.4209180Z parm:           NVreg_VMallocHeapMaxSize:int
2024-08-22T20:27:45.4209630Z parm:           NVreg_IgnoreMMIOCheck:int
2024-08-22T20:27:45.4210066Z parm:           NVreg_NvLinkDisable:int
2024-08-22T20:27:45.4210556Z parm:           NVreg_EnablePCIERelaxedOrderingMode:int
2024-08-22T20:27:45.4211059Z parm:           NVreg_RegisterPCIDriver:int
2024-08-22T20:27:45.4211524Z parm:           NVreg_EnableResizableBar:int
2024-08-22T20:27:45.4212007Z parm:           NVreg_EnableDbgBreakpoint:int
2024-08-22T20:27:45.4212483Z parm:           NVreg_EnableNonblockingOpen:int
2024-08-22T20:27:45.4212956Z parm:           NVreg_RegistryDwords:charp
2024-08-22T20:27:45.4213438Z parm:           NVreg_RegistryDwordsPerDevice:charp
2024-08-22T20:27:45.4213908Z parm:           NVreg_RmMsg:charp
2024-08-22T20:27:45.4214314Z parm:           NVreg_GpuBlacklist:charp
2024-08-22T20:27:45.4214765Z parm:           NVreg_TemporaryFilePath:charp
2024-08-22T20:27:45.4215212Z parm:           NVreg_ExcludedGpus:charp
2024-08-22T20:27:45.4215654Z parm:           NVreg_DmaRemapPeerMmio:int
2024-08-22T20:27:45.4216120Z parm:           NVreg_RmNvlinkBandwidth:charp
2024-08-22T20:27:45.4216575Z parm:           NVreg_ImexChannelCount:int
2024-08-22T20:27:45.4217017Z parm:           rm_firmware_active:charp
2024-08-22T20:27:45.4217422Z + HAS_NVIDIA_DRIVER=0
2024-08-22T20:27:45.4217868Z ++ command -v nvidia-smi
2024-08-22T20:27:45.4218251Z + '[' -x /usr/bin/nvidia-smi ']'
2024-08-22T20:27:45.4218605Z + set +e
2024-08-22T20:27:45.4219104Z ++ nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0
2024-08-22T20:27:45.4494030Z + INSTALLED_DRIVER_VERSION=550.54.15
2024-08-22T20:27:45.4494441Z + NVIDIA_SMI_STATUS=0
2024-08-22T20:27:45.4494832Z + '[' 0 -ne 0 ']'
2024-08-22T20:27:45.4495401Z + '[' 550.54.15 '!=' 550.54.15 ']'
2024-08-22T20:27:45.4495778Z + HAS_NVIDIA_DRIVER=1
2024-08-22T20:27:45.4496490Z + echo 'NVIDIA driver (550.54.15) has already been installed. Skipping NVIDIA driver installation'
2024-08-22T20:27:45.4497183Z + set -e
2024-08-22T20:27:45.4497482Z + '[' 1 -eq 0 ']'
2024-08-22T20:27:45.4498038Z NVIDIA driver (550.54.15) has already been installed. Skipping NVIDIA driver installation
2024-08-22T20:27:45.4498707Z + post_install_nvidia_driver_common
2024-08-22T20:27:45.4499685Z + sudo modprobe nvidia
2024-08-22T20:27:45.5582856Z + echo 'After installing NVIDIA driver'
2024-08-22T20:27:45.5583282Z + lspci
2024-08-22T20:27:45.5583567Z After installing NVIDIA driver
2024-08-22T20:27:45.5687831Z 00:00.0 Host bridge: Intel Corporation 440FX - 82441FX PMC [Natoma]
2024-08-22T20:27:45.5688638Z 00:01.0 ISA bridge: Intel Corporation 82371SB PIIX3 ISA [Natoma/Triton II]
2024-08-22T20:27:45.5689559Z 00:01.3 Non-VGA unclassified device: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 08)
2024-08-22T20:27:45.5690340Z 00:03.0 VGA compatible controller: Amazon.com, Inc. Device 1111
2024-08-22T20:27:45.5691133Z 00:04.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe EBS Controller
2024-08-22T20:27:45.5691912Z 00:05.0 Ethernet controller: Amazon.com, Inc. Elastic Network Adapter (ENA)
2024-08-22T20:27:45.5692623Z 00:1e.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1)
2024-08-22T20:27:45.5693418Z 00:1f.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe SSD Controller
2024-08-22T20:27:45.5693996Z + lsmod
2024-08-22T20:27:45.5721583Z Module                  Size  Used by
2024-08-22T20:27:45.5721985Z veth                   36864  0
2024-08-22T20:27:45.5722392Z nvidia_modeset       1351680  0
2024-08-22T20:27:45.5722944Z video                  65536  1 nvidia_modeset
2024-08-22T20:27:45.5723474Z wmi                    36864  1 video
2024-08-22T20:27:45.5723946Z nvidia_uvm           4706304  0
2024-08-22T20:27:45.5724540Z nvidia              54071296  7 nvidia_uvm,nvidia_modeset
2024-08-22T20:27:45.5725370Z drm                   602112  1 nvidia
2024-08-22T20:27:45.5725842Z drm_panel_orientation_quirks    28672  1 drm
2024-08-22T20:27:45.5726358Z backlight              24576  3 video,drm,nvidia_modeset
2024-08-22T20:27:45.5726857Z i2c_core              106496  2 nvidia,drm
2024-08-22T20:27:45.5727269Z xt_conntrack           16384  1
2024-08-22T20:27:45.5727668Z nft_chain_nat          16384  3
2024-08-22T20:27:45.5728065Z xt_MASQUERADE          20480  1
2024-08-22T20:27:45.5728596Z nf_nat                 57344  2 nft_chain_nat,xt_MASQUERADE
2024-08-22T20:27:45.5729080Z nf_conntrack_netlink    57344  0
2024-08-22T20:27:45.5729650Z nf_conntrack          184320  4 xt_conntrack,nf_nat,nf_conntrack_netlink,xt_MASQUERADE
2024-08-22T20:27:45.5730277Z nf_defrag_ipv6         24576  1 nf_conntrack
2024-08-22T20:27:45.5730733Z nf_defrag_ipv4         16384  1 nf_conntrack
2024-08-22T20:27:45.5731163Z xfrm_user              57344  1
2024-08-22T20:27:45.5731552Z xfrm_algo              16384  1 xfrm_user
2024-08-22T20:27:45.5731972Z xt_addrtype            16384  2
2024-08-22T20:27:45.5732337Z nft_compat             20480  4
2024-08-22T20:27:45.5732771Z nf_tables             307200  57 nft_compat,nft_chain_nat
2024-08-22T20:27:45.5733370Z nfnetlink              20480  4 nft_compat,nf_conntrack_netlink,nf_tables
2024-08-22T20:27:45.5733902Z br_netfilter           36864  0
2024-08-22T20:27:45.5734293Z bridge                307200  1 br_netfilter
2024-08-22T20:27:45.5734722Z stp                    16384  1 bridge
2024-08-22T20:27:45.5735130Z llc                    16384  2 bridge,stp
2024-08-22T20:27:45.5735536Z overlay               167936  0
2024-08-22T20:27:45.5735894Z tls                   114688  0
2024-08-22T20:27:45.5736251Z nls_ascii              16384  1
2024-08-22T20:27:45.5736603Z nls_cp437              20480  1
2024-08-22T20:27:45.5736968Z vfat                   24576  1
2024-08-22T20:27:45.5737330Z fat                    86016  1 vfat
2024-08-22T20:27:45.5737849Z ena                   167936  0
2024-08-22T20:27:45.5738223Z ptp                    36864  1 ena
2024-08-22T20:27:45.5738612Z pps_core               24576  1 ptp
2024-08-22T20:27:45.5738988Z sunrpc                692224  1
2024-08-22T20:27:45.5739353Z ghash_clmulni_intel    16384  0
2024-08-22T20:27:45.5739716Z i8042                  45056  0
2024-08-22T20:27:45.5740067Z serio                  28672  3 i8042
2024-08-22T20:27:45.5740453Z aesni_intel           393216  0
2024-08-22T20:27:45.5740814Z button                 24576  0
2024-08-22T20:27:45.5741192Z crypto_simd            16384  1 aesni_intel
2024-08-22T20:27:45.5741701Z cryptd                 28672  2 crypto_simd,ghash_clmulni_intel
2024-08-22T20:27:45.5742192Z sch_fq_codel           20480  17
2024-08-22T20:27:45.5742555Z dm_mod                188416  0
2024-08-22T20:27:45.5742912Z fuse                  163840  1
2024-08-22T20:27:45.5743262Z configfs               57344  1
2024-08-22T20:27:45.5743633Z dax                    45056  1 dm_mod
2024-08-22T20:27:45.5744035Z loop                   36864  0
2024-08-22T20:27:45.5744387Z dmi_sysfs              20480  0
2024-08-22T20:27:45.5744750Z crc32_pclmul           16384  0
2024-08-22T20:27:45.5745114Z crc32c_intel           24576  0
2024-08-22T20:27:45.5745471Z efivarfs               24576  1
2024-08-22T20:27:45.5745841Z + modinfo nvidia
2024-08-22T20:27:45.5746719Z filename:       /lib/modules/6.1.94-99.176.amzn2023.x86_64/kernel/drivers/video/nvidia.ko
2024-08-22T20:27:45.5747463Z alias:          char-major-195-*
2024-08-22T20:27:45.5747841Z version:        550.54.15
2024-08-22T20:27:45.5748180Z supported:      external
2024-08-22T20:27:45.5748515Z license:        NVIDIA
2024-08-22T20:27:45.5748876Z firmware:       nvidia/550.54.15/gsp_tu10x.bin
2024-08-22T20:27:45.5749352Z firmware:       nvidia/550.54.15/gsp_ga10x.bin
2024-08-22T20:27:45.5749809Z srcversion:     833721318DA517F0C2FEC97
2024-08-22T20:27:45.5750258Z alias:          pci:v000010DEd*sv*sd*bc06sc80i00*
2024-08-22T20:27:45.5750757Z alias:          pci:v000010DEd*sv*sd*bc03sc02i00*
2024-08-22T20:27:45.5751347Z alias:          pci:v000010DEd*sv*sd*bc03sc00i00*
2024-08-22T20:27:45.5751826Z depends:        i2c-core,drm
2024-08-22T20:27:45.5752179Z retpoline:      Y
2024-08-22T20:27:45.5752483Z name:           nvidia
2024-08-22T20:27:45.5753077Z vermagic:       6.1.94-99.176.amzn2023.x86_64 SMP preempt mod_unload modversions 
2024-08-22T20:27:45.5753767Z parm:           NvSwitchRegDwords:NvSwitch regkey (charp)
2024-08-22T20:27:45.5754397Z parm:           NvSwitchBlacklist:NvSwitchBlacklist=uuid[,uuid...] (charp)
2024-08-22T20:27:45.5754985Z parm:           NVreg_ResmanDebugLevel:int
2024-08-22T20:27:45.5755410Z parm:           NVreg_RmLogonRC:int
2024-08-22T20:27:45.5755832Z parm:           NVreg_ModifyDeviceFiles:int
2024-08-22T20:27:45.5756282Z parm:           NVreg_DeviceFileUID:int
2024-08-22T20:27:45.5756703Z parm:           NVreg_DeviceFileGID:int
2024-08-22T20:27:45.5757138Z parm:           NVreg_DeviceFileMode:int
2024-08-22T20:27:45.5757697Z parm:           NVreg_InitializeSystemMemoryAllocations:int
2024-08-22T20:27:45.5758263Z parm:           NVreg_UsePageAttributeTable:int
2024-08-22T20:27:45.5758735Z parm:           NVreg_EnablePCIeGen3:int
2024-08-22T20:27:45.5759158Z parm:           NVreg_EnableMSI:int
2024-08-22T20:27:45.5759559Z parm:           NVreg_TCEBypassMode:int
2024-08-22T20:27:45.5760014Z parm:           NVreg_EnableStreamMemOPs:int
2024-08-22T20:27:45.5760534Z parm:           NVreg_RestrictProfilingToAdminUsers:int
2024-08-22T20:27:45.5761096Z parm:           NVreg_PreserveVideoMemoryAllocations:int
2024-08-22T20:27:45.5761646Z parm:           NVreg_EnableS0ixPowerManagement:int
2024-08-22T20:27:45.5762244Z parm:           NVreg_S0ixPowerManagementVideoMemoryThreshold:int
2024-08-22T20:27:45.5762828Z parm:           NVreg_DynamicPowerManagement:int
2024-08-22T20:27:45.5763422Z parm:           NVreg_DynamicPowerManagementVideoMemoryThreshold:int
2024-08-22T20:27:45.5764008Z parm:           NVreg_EnableGpuFirmware:int
2024-08-22T20:27:45.5764584Z parm:           NVreg_EnableGpuFirmwareLogs:int
2024-08-22T20:27:45.5765102Z parm:           NVreg_OpenRmEnableUnsupportedGpus:int
2024-08-22T20:27:45.5765640Z parm:           NVreg_EnableUserNUMAManagement:int
2024-08-22T20:27:45.5766130Z parm:           NVreg_MemoryPoolSize:int
2024-08-22T20:27:45.5766576Z parm:           NVreg_KMallocHeapMaxSize:int
2024-08-22T20:27:45.5767053Z parm:           NVreg_VMallocHeapMaxSize:int
2024-08-22T20:27:45.5767517Z parm:           NVreg_IgnoreMMIOCheck:int
2024-08-22T20:27:45.5767948Z parm:           NVreg_NvLinkDisable:int
2024-08-22T20:27:45.5768548Z parm:           NVreg_EnablePCIERelaxedOrderingMode:int
2024-08-22T20:27:45.5769062Z parm:           NVreg_RegisterPCIDriver:int
2024-08-22T20:27:45.5769526Z parm:           NVreg_EnableResizableBar:int
2024-08-22T20:27:45.5770004Z parm:           NVreg_EnableDbgBreakpoint:int
2024-08-22T20:27:45.5770494Z parm:           NVreg_EnableNonblockingOpen:int
2024-08-22T20:27:45.5770965Z parm:           NVreg_RegistryDwords:charp
2024-08-22T20:27:45.5771468Z parm:           NVreg_RegistryDwordsPerDevice:charp
2024-08-22T20:27:45.5771950Z parm:           NVreg_RmMsg:charp
2024-08-22T20:27:45.5772349Z parm:           NVreg_GpuBlacklist:charp
2024-08-22T20:27:45.5772811Z parm:           NVreg_TemporaryFilePath:charp
2024-08-22T20:27:45.5773269Z parm:           NVreg_ExcludedGpus:charp
2024-08-22T20:27:45.5773715Z parm:           NVreg_DmaRemapPeerMmio:int
2024-08-22T20:27:45.5774178Z parm:           NVreg_RmNvlinkBandwidth:charp
2024-08-22T20:27:45.5774648Z parm:           NVreg_ImexChannelCount:int
2024-08-22T20:27:45.5775093Z parm:           rm_firmware_active:charp
2024-08-22T20:27:45.5775477Z + set +e
2024-08-22T20:27:45.5775778Z + nvidia-smi
2024-08-22T20:27:45.5954930Z Thu Aug 22 20:27:45 2024       
2024-08-22T20:27:45.5955562Z +-----------------------------------------------------------------------------------------+
2024-08-22T20:27:45.5956373Z | NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |
2024-08-22T20:27:45.5957378Z |-----------------------------------------+------------------------+----------------------+
2024-08-22T20:27:45.5958156Z | GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
2024-08-22T20:27:45.5959031Z | Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
2024-08-22T20:27:45.5959707Z |                                         |                        |               MIG M. |
2024-08-22T20:27:45.5960242Z |=========================================+========================+======================|
2024-08-22T20:27:45.6197028Z |   0  NVIDIA A10G                    On  |   00000000:00:1E.0 Off |                    0 |
2024-08-22T20:27:45.6197740Z |  0%   25C    P8             16W /  300W |       0MiB /  23028MiB |      0%      Default |
2024-08-22T20:27:45.6198362Z |                                         |                        |                  N/A |
2024-08-22T20:27:45.6199046Z +-----------------------------------------+------------------------+----------------------+
2024-08-22T20:27:45.6202268Z                                                                                          
2024-08-22T20:27:45.6202942Z +-----------------------------------------------------------------------------------------+
2024-08-22T20:27:45.6203559Z | Processes:                                                                              |
2024-08-22T20:27:45.6204244Z |  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
2024-08-22T20:27:45.6204928Z |        ID   ID                                                               Usage      |
2024-08-22T20:27:45.6205485Z |=========================================================================================|
2024-08-22T20:27:45.6209210Z |  No running processes found                                                             |
2024-08-22T20:27:45.6209967Z +-----------------------------------------------------------------------------------------+
2024-08-22T20:27:45.9072587Z + nvidia-smi --query-gpu=gpu_name --format=csv,noheader --id=0
2024-08-22T20:27:45.9262222Z NVIDIA A10G
2024-08-22T20:27:45.9325594Z + NVIDIA_SMI_STATUS=0
2024-08-22T20:27:45.9326150Z + '[' 0 -eq 0 ']'
2024-08-22T20:27:45.9326654Z + echo 'INFO: Ignoring allowed status 0'
2024-08-22T20:27:45.9327201Z + set -e
2024-08-22T20:27:45.9327501Z INFO: Ignoring allowed status 0
2024-08-22T20:27:45.9335052Z == Installing nvidia container toolkit for amzn2023 ==
2024-08-22T20:27:45.9339581Z + sudo yum install -y yum-utils
2024-08-22T20:27:46.3517480Z Last metadata expiration check: 0:33:42 ago on Thu Aug 22 19:54:04 2024.
2024-08-22T20:27:46.3739738Z Package dnf-utils-4.3.0-13.amzn2023.0.4.noarch is already installed.
2024-08-22T20:27:46.4041098Z Dependencies resolved.
2024-08-22T20:27:46.4164766Z Nothing to do.
2024-08-22T20:27:46.4165100Z Complete!
2024-08-22T20:27:46.4541912Z + [[ amzn2023 == \a\m\z\n\2\0\2\3 ]]
2024-08-22T20:27:46.4543033Z + YUM_REPO_URL=https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo
2024-08-22T20:27:46.4544343Z + sudo yum-config-manager --add-repo https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo
2024-08-22T20:27:46.7016785Z Adding repo from: https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo
2024-08-22T20:27:46.7660551Z + sudo yum install -y nvidia-docker2
2024-08-22T20:27:47.2100919Z nvidia-container-toolkit                         10 kB/s | 833  B     00:00    
2024-08-22T20:27:47.2318399Z Package nvidia-docker2-2.14.0-1.noarch is already installed.
2024-08-22T20:27:47.2614056Z Dependencies resolved.
2024-08-22T20:27:47.2738107Z Nothing to do.
2024-08-22T20:27:47.2738680Z Complete!
2024-08-22T20:27:47.3118191Z + sudo systemctl restart docker
2024-08-22T20:27:49.8754147Z nvidia-persistenced failed to initialize. Check syslog for more details.
2024-08-22T20:27:49.8991252Z Thu Aug 22 20:27:49 2024       
2024-08-22T20:27:49.8992193Z +-----------------------------------------------------------------------------------------+
2024-08-22T20:27:49.8993277Z | NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |
2024-08-22T20:27:49.8994062Z |-----------------------------------------+------------------------+----------------------+
2024-08-22T20:27:49.8994849Z | GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
2024-08-22T20:27:49.8995753Z | Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
2024-08-22T20:27:49.8996433Z |                                         |                        |               MIG M. |
2024-08-22T20:27:49.8996966Z |=========================================+========================+======================|
2024-08-22T20:27:49.9214838Z |   0  NVIDIA A10G                    On  |   00000000:00:1E.0 Off |                    0 |
2024-08-22T20:27:49.9215589Z |  0%   25C    P8             16W /  300W |       0MiB /  23028MiB |      0%      Default |
2024-08-22T20:27:49.9216220Z |                                         |                        |                  N/A |
2024-08-22T20:27:49.9216896Z +-----------------------------------------+------------------------+----------------------+
2024-08-22T20:27:49.9219911Z                                                                                          
2024-08-22T20:27:49.9220607Z +-----------------------------------------------------------------------------------------+
2024-08-22T20:27:49.9221230Z | Processes:                                                                              |
2024-08-22T20:27:49.9221919Z |  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
2024-08-22T20:27:49.9222600Z |        ID   ID                                                               Usage      |
2024-08-22T20:27:49.9223156Z |=========================================================================================|
2024-08-22T20:27:49.9225394Z |  No running processes found                                                             |
2024-08-22T20:27:49.9226194Z +-----------------------------------------------------------------------------------------+
2024-08-22T20:27:50.9663491Z Command completed after 1 attempt(s).
2024-08-22T20:27:50.9755676Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2024-08-22T20:27:50.9756431Z # ignore expansion of "docker ps -q" since it could be empty
2024-08-22T20:27:50.9757008Z # shellcheck disable=SC2046
2024-08-22T20:27:50.9757456Z docker stop $(docker ps -q) || true
2024-08-22T20:27:50.9757916Z # Prune all of the docker images
2024-08-22T20:27:50.9758385Z docker system prune -af
2024-08-22T20:27:50.9771751Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:27:50.9772239Z env:
2024-08-22T20:27:50.9772558Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:50.9773016Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:50.9773402Z   PR_NUMBER: 1044
2024-08-22T20:27:50.9792681Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:50.9812223Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:27:50.9813112Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:27:50.9814043Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:27:50.9814751Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:27:50.9815308Z ##[endgroup]
2024-08-22T20:27:51.0137648Z "docker stop" requires at least 1 argument.
2024-08-22T20:27:51.0138679Z See 'docker stop --help'.
2024-08-22T20:27:51.0139022Z 
2024-08-22T20:27:51.0139392Z Usage:  docker stop [OPTIONS] CONTAINER [CONTAINER...]
2024-08-22T20:27:51.0140132Z 
2024-08-22T20:27:51.0140315Z Stop one or more running containers
2024-08-22T20:27:51.0314848Z Total reclaimed space: 0B
2024-08-22T20:27:51.0394776Z ##[group]Run ./test-infra/.github/actions/setup-ssh
2024-08-22T20:27:51.0395332Z with:
2024-08-22T20:27:51.0395960Z   github-secret: ***
2024-08-22T20:27:51.0396964Z   instructions: All testing is done inside the container, to start an interactive session run:
   docker exec -it $(docker container ps --format '{{.ID}}') bash

2024-08-22T20:27:51.0398060Z   activate-with-label: false
2024-08-22T20:27:51.0398523Z   label: with-ssh
2024-08-22T20:27:51.0398932Z   remove-existing-keys: true
2024-08-22T20:27:51.0399436Z   fail-silently: true
2024-08-22T20:27:51.0399849Z env:
2024-08-22T20:27:51.0400280Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:51.0400825Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:51.0401305Z   PR_NUMBER: 1044
2024-08-22T20:27:51.0420666Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:51.0440339Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:27:51.0441269Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:27:51.0442103Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:27:51.0442792Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:27:51.0443392Z ##[endgroup]
2024-08-22T20:27:51.1270460Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info.
2024-08-22T20:27:51.4853399Z Grabbing public ssh keys from https://github.com/kwen2501.keys
2024-08-22T20:27:51.5569253Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh
2024-08-22T20:27:51.5584149Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys
2024-08-22T20:27:51.5609724Z Login using: ssh ec2-user@ec2-3-87-228-147.compute-1.amazonaws.com
2024-08-22T20:27:51.5610730Z All testing is done inside the container, to start an interactive session run:
2024-08-22T20:27:51.5611629Z    docker exec -it $(docker container ps --format '{{.ID}}') bash
2024-08-22T20:27:51.5750305Z ##[group]Run actions/checkout@v3
2024-08-22T20:27:51.5750755Z with:
2024-08-22T20:27:51.5751154Z   repository: pytorch/torchchat
2024-08-22T20:27:51.5751726Z   ref: refs/pull/1044/merge
2024-08-22T20:27:51.5752138Z   path: pytorch/torchchat
2024-08-22T20:27:51.5752594Z   fetch-depth: 1
2024-08-22T20:27:51.5753159Z   token: ***
2024-08-22T20:27:51.5753505Z   ssh-strict: true
2024-08-22T20:27:51.5753941Z   persist-credentials: true
2024-08-22T20:27:51.5754437Z   clean: true
2024-08-22T20:27:51.5754806Z   sparse-checkout-cone-mode: true
2024-08-22T20:27:51.5755305Z   fetch-tags: false
2024-08-22T20:27:51.5755786Z   lfs: false
2024-08-22T20:27:51.5756141Z   set-safe-directory: true
2024-08-22T20:27:51.5756585Z env:
2024-08-22T20:27:51.5757062Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:51.5757575Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:51.5758068Z   PR_NUMBER: 1044
2024-08-22T20:27:51.5777354Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:51.5797229Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:27:51.5798133Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:27:51.5798975Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:27:51.5799692Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:27:51.5800471Z ##[endgroup]
2024-08-22T20:27:51.6687348Z Syncing repository: pytorch/torchchat
2024-08-22T20:27:51.6697741Z ##[group]Getting Git version info
2024-08-22T20:27:51.6698723Z Working directory is '/home/ec2-user/actions-runner/_work/torchchat/torchchat/pytorch/torchchat'
2024-08-22T20:27:51.6719596Z [command]/usr/bin/git version
2024-08-22T20:27:51.6769281Z git version 2.40.1
2024-08-22T20:27:51.6792109Z ##[endgroup]
2024-08-22T20:27:51.6805635Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/e517cca4-ce03-4813-9e97-65b26d249820' before making global git config changes
2024-08-22T20:27:51.6806968Z Adding repository directory to the temporary git global config as a safe directory
2024-08-22T20:27:51.6810192Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/torchchat/torchchat/pytorch/torchchat
2024-08-22T20:27:51.6861582Z ##[group]Initializing the repository
2024-08-22T20:27:51.6864841Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/torchchat/torchchat/pytorch/torchchat
2024-08-22T20:27:51.6909353Z hint: Using 'master' as the name for the initial branch. This default branch name
2024-08-22T20:27:51.6910354Z hint: is subject to change. To configure the initial branch name to use in all
2024-08-22T20:27:51.6911293Z hint: of your new repositories, which will suppress this warning, call:
2024-08-22T20:27:51.6912037Z hint: 
2024-08-22T20:27:51.6912575Z hint: 	git config --global init.defaultBranch <name>
2024-08-22T20:27:51.6913157Z hint: 
2024-08-22T20:27:51.6913793Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and
2024-08-22T20:27:51.6914959Z hint: 'development'. The just-created branch can be renamed via this command:
2024-08-22T20:27:51.6915900Z hint: 
2024-08-22T20:27:51.6916365Z hint: 	git branch -m <name>
2024-08-22T20:27:51.6917329Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/torchchat/torchchat/pytorch/torchchat/.git/
2024-08-22T20:27:51.6923234Z [command]/usr/bin/git remote add origin https://github.com/pytorch/torchchat
2024-08-22T20:27:51.6983526Z ##[endgroup]
2024-08-22T20:27:51.6984441Z ##[group]Disabling automatic garbage collection
2024-08-22T20:27:51.6985566Z [command]/usr/bin/git config --local gc.auto 0
2024-08-22T20:27:51.7003234Z ##[endgroup]
2024-08-22T20:27:51.7003875Z ##[group]Setting up auth
2024-08-22T20:27:51.7009195Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2024-08-22T20:27:51.7052819Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :"
2024-08-22T20:27:51.7412104Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2024-08-22T20:27:51.7455570Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :"
2024-08-22T20:27:51.7813641Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic ***
2024-08-22T20:27:51.7868266Z ##[endgroup]
2024-08-22T20:27:51.7869079Z ##[group]Fetching the repository
2024-08-22T20:27:51.7875612Z [command]/usr/bin/git -c protocol.version=2 fetch --no-tags --prune --progress --no-recurse-submodules --depth=1 origin +refs/pull/1044/merge:refs/remotes/pull/1044/merge
2024-08-22T20:27:51.9770702Z remote: Enumerating objects: 237, done.        
2024-08-22T20:27:51.9771673Z remote: Counting objects:   0% (1/237)        
2024-08-22T20:27:51.9772428Z remote: Counting objects:   1% (3/237)        
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2024-08-22T20:27:51.9807997Z remote: Counting objects:  56% (133/237)        
2024-08-22T20:27:51.9808708Z remote: Counting objects:  57% (136/237)        
2024-08-22T20:27:51.9809281Z remote: Counting objects:  58% (138/237)        
2024-08-22T20:27:51.9810069Z remote: Counting objects:  59% (140/237)        
2024-08-22T20:27:51.9810606Z remote: Counting objects:  60% (143/237)        
2024-08-22T20:27:51.9811178Z remote: Counting objects:  61% (145/237)        
2024-08-22T20:27:51.9811836Z remote: Counting objects:  62% (147/237)        
2024-08-22T20:27:51.9812373Z remote: Counting objects:  63% (150/237)        
2024-08-22T20:27:51.9812947Z remote: Counting objects:  64% (152/237)        
2024-08-22T20:27:51.9813594Z remote: Counting objects:  65% (155/237)        
2024-08-22T20:27:51.9814126Z remote: Counting objects:  66% (157/237)        
2024-08-22T20:27:51.9814713Z remote: Counting objects:  67% (159/237)        
2024-08-22T20:27:51.9815377Z remote: Counting objects:  68% (162/237)        
2024-08-22T20:27:51.9815906Z remote: Counting objects:  69% (164/237)        
2024-08-22T20:27:51.9816489Z remote: Counting objects:  70% (166/237)        
2024-08-22T20:27:51.9817103Z remote: Counting objects:  71% (169/237)        
2024-08-22T20:27:51.9817638Z remote: Counting objects:  72% (171/237)        
2024-08-22T20:27:51.9818241Z remote: Counting objects:  73% (174/237)        
2024-08-22T20:27:51.9818861Z remote: Counting objects:  74% (176/237)        
2024-08-22T20:27:51.9819395Z remote: Counting objects:  75% (178/237)        
2024-08-22T20:27:51.9819989Z remote: Counting objects:  76% (181/237)        
2024-08-22T20:27:51.9820612Z remote: Counting objects:  77% (183/237)        
2024-08-22T20:27:51.9821205Z remote: Counting objects:  78% (185/237)        
2024-08-22T20:27:51.9821742Z remote: Counting objects:  79% (188/237)        
2024-08-22T20:27:51.9822416Z remote: Counting objects:  80% (190/237)        
2024-08-22T20:27:51.9823009Z remote: Counting objects:  81% (192/237)        
2024-08-22T20:27:51.9823543Z remote: Counting objects:  82% (195/237)        
2024-08-22T20:27:51.9824163Z remote: Counting objects:  83% (197/237)        
2024-08-22T20:27:51.9824755Z remote: Counting objects:  84% (200/237)        
2024-08-22T20:27:51.9825291Z remote: Counting objects:  85% (202/237)        
2024-08-22T20:27:51.9825917Z remote: Counting objects:  86% (204/237)        
2024-08-22T20:27:51.9826509Z remote: Counting objects:  87% (207/237)        
2024-08-22T20:27:51.9827045Z remote: Counting objects:  88% (209/237)        
2024-08-22T20:27:51.9827664Z remote: Counting objects:  89% (211/237)        
2024-08-22T20:27:51.9828362Z remote: Counting objects:  90% (214/237)        
2024-08-22T20:27:51.9828949Z remote: Counting objects:  91% (216/237)        
2024-08-22T20:27:51.9829651Z remote: Counting objects:  92% (219/237)        
2024-08-22T20:27:51.9830221Z remote: Counting objects:  93% (221/237)        
2024-08-22T20:27:51.9830753Z remote: Counting objects:  94% (223/237)        
2024-08-22T20:27:51.9831397Z remote: Counting objects:  95% (226/237)        
2024-08-22T20:27:51.9831967Z remote: Counting objects:  96% (228/237)        
2024-08-22T20:27:51.9832499Z remote: Counting objects:  97% (230/237)        
2024-08-22T20:27:51.9833145Z remote: Counting objects:  98% (233/237)        
2024-08-22T20:27:51.9833719Z remote: Counting objects:  99% (235/237)        
2024-08-22T20:27:51.9834382Z remote: Counting objects: 100% (237/237)        
2024-08-22T20:27:51.9834952Z remote: Counting objects: 100% (237/237), done.        
2024-08-22T20:27:51.9835576Z remote: Compressing objects:   0% (1/193)        
2024-08-22T20:27:51.9836318Z remote: Compressing objects:   1% (2/193)        
2024-08-22T20:27:51.9836879Z remote: Compressing objects:   2% (4/193)        
2024-08-22T20:27:51.9837473Z remote: Compressing objects:   3% (6/193)        
2024-08-22T20:27:51.9838133Z remote: Compressing objects:   4% (8/193)        
2024-08-22T20:27:51.9838710Z remote: Compressing objects:   5% (10/193)        
2024-08-22T20:27:51.9839357Z remote: Compressing objects:   6% (12/193)        
2024-08-22T20:27:51.9840037Z remote: Compressing objects:   7% (14/193)        
2024-08-22T20:27:51.9840609Z remote: Compressing objects:   8% (16/193)        
2024-08-22T20:27:51.9841214Z remote: Compressing objects:   9% (18/193)        
2024-08-22T20:27:51.9841984Z remote: Compressing objects:  10% (20/193)        
2024-08-22T20:27:51.9842552Z remote: Compressing objects:  11% (22/193)        
2024-08-22T20:27:51.9843183Z remote: Compressing objects:  12% (24/193)        
2024-08-22T20:27:51.9843884Z remote: Compressing objects:  13% (26/193)        
2024-08-22T20:27:51.9844496Z remote: Compressing objects:  14% (28/193)        
2024-08-22T20:27:51.9845084Z remote: Compressing objects:  15% (29/193)        
2024-08-22T20:27:51.9845734Z remote: Compressing objects:  16% (31/193)        
2024-08-22T20:27:51.9852795Z remote: Compressing objects:  17% (33/193)        
2024-08-22T20:27:51.9867712Z remote: Compressing objects:  18% (35/193)        
2024-08-22T20:27:51.9872197Z remote: Compressing objects:  19% (37/193)        
2024-08-22T20:27:51.9873045Z remote: Compressing objects:  20% (39/193)        
2024-08-22T20:27:51.9879217Z remote: Compressing objects:  21% (41/193)        
2024-08-22T20:27:51.9882212Z remote: Compressing objects:  22% (43/193)        
2024-08-22T20:27:51.9883576Z remote: Compressing objects:  23% (45/193)        
2024-08-22T20:27:51.9884578Z remote: Compressing objects:  24% (47/193)        
2024-08-22T20:27:51.9886607Z remote: Compressing objects:  25% (49/193)        
2024-08-22T20:27:51.9887445Z remote: Compressing objects:  26% (51/193)        
2024-08-22T20:27:51.9888432Z remote: Compressing objects:  27% (53/193)        
2024-08-22T20:27:51.9889172Z remote: Compressing objects:  28% (55/193)        
2024-08-22T20:27:51.9893578Z remote: Compressing objects:  29% (56/193)        
2024-08-22T20:27:51.9896665Z remote: Compressing objects:  30% (58/193)        
2024-08-22T20:27:51.9897555Z remote: Compressing objects:  31% (60/193)        
2024-08-22T20:27:51.9898272Z remote: Compressing objects:  32% (62/193)        
2024-08-22T20:27:51.9899215Z remote: Compressing objects:  33% (64/193)        
2024-08-22T20:27:51.9899903Z remote: Compressing objects:  34% (66/193)        
2024-08-22T20:27:51.9900588Z remote: Compressing objects:  35% (68/193)        
2024-08-22T20:27:51.9901388Z remote: Compressing objects:  36% (70/193)        
2024-08-22T20:27:51.9902161Z remote: Compressing objects:  37% (72/193)        
2024-08-22T20:27:51.9902729Z remote: Compressing objects:  38% (74/193)        
2024-08-22T20:27:51.9903702Z remote: Compressing objects:  39% (76/193)        
2024-08-22T20:27:51.9904878Z remote: Compressing objects:  40% (78/193)        
2024-08-22T20:27:51.9905644Z remote: Compressing objects:  41% (80/193)        
2024-08-22T20:27:51.9906318Z remote: Compressing objects:  42% (82/193)        
2024-08-22T20:27:51.9907202Z remote: Compressing objects:  43% (83/193)        
2024-08-22T20:27:51.9907839Z remote: Compressing objects:  44% (85/193)        
2024-08-22T20:27:51.9908411Z remote: Compressing objects:  45% (87/193)        
2024-08-22T20:27:51.9911722Z remote: Compressing objects:  46% (89/193)        
2024-08-22T20:27:51.9912532Z remote: Compressing objects:  47% (91/193)        
2024-08-22T20:27:51.9913252Z remote: Compressing objects:  48% (93/193)        
2024-08-22T20:27:51.9914292Z remote: Compressing objects:  49% (95/193)        
2024-08-22T20:27:51.9914903Z remote: Compressing objects:  50% (97/193)        
2024-08-22T20:27:51.9915518Z remote: Compressing objects:  51% (99/193)        
2024-08-22T20:27:51.9916715Z remote: Compressing objects:  52% (101/193)        
2024-08-22T20:27:51.9917800Z remote: Compressing objects:  53% (103/193)        
2024-08-22T20:27:51.9918774Z remote: Compressing objects:  54% (105/193)        
2024-08-22T20:27:51.9919744Z remote: Compressing objects:  55% (107/193)        
2024-08-22T20:27:51.9920538Z remote: Compressing objects:  56% (109/193)        
2024-08-22T20:27:51.9921519Z remote: Compressing objects:  57% (111/193)        
2024-08-22T20:27:51.9922102Z remote: Compressing objects:  58% (112/193)        
2024-08-22T20:27:51.9922881Z remote: Compressing objects:  59% (114/193)        
2024-08-22T20:27:51.9923967Z remote: Compressing objects:  60% (116/193)        
2024-08-22T20:27:51.9925020Z remote: Compressing objects:  61% (118/193)        
2024-08-22T20:27:51.9925800Z remote: Compressing objects:  62% (120/193)        
2024-08-22T20:27:51.9926678Z remote: Compressing objects:  63% (122/193)        
2024-08-22T20:27:51.9927482Z remote: Compressing objects:  64% (124/193)        
2024-08-22T20:27:51.9928502Z remote: Compressing objects:  65% (126/193)        
2024-08-22T20:27:51.9929448Z remote: Compressing objects:  66% (128/193)        
2024-08-22T20:27:51.9930393Z remote: Compressing objects:  67% (130/193)        
2024-08-22T20:27:51.9931434Z remote: Compressing objects:  68% (132/193)        
2024-08-22T20:27:51.9933660Z remote: Compressing objects:  69% (134/193)        
2024-08-22T20:27:51.9937623Z remote: Compressing objects:  70% (136/193)        
2024-08-22T20:27:51.9947736Z remote: Compressing objects:  71% (138/193)        
2024-08-22T20:27:51.9948919Z remote: Compressing objects:  72% (139/193)        
2024-08-22T20:27:51.9949678Z remote: Compressing objects:  73% (141/193)        
2024-08-22T20:27:51.9950468Z remote: Compressing objects:  74% (143/193)        
2024-08-22T20:27:51.9951282Z remote: Compressing objects:  75% (145/193)        
2024-08-22T20:27:51.9951901Z remote: Compressing objects:  76% (147/193)        
2024-08-22T20:27:51.9952929Z remote: Compressing objects:  77% (149/193)        
2024-08-22T20:27:51.9953884Z remote: Compressing objects:  78% (151/193)        
2024-08-22T20:27:51.9956567Z remote: Compressing objects:  79% (153/193)        
2024-08-22T20:27:51.9957361Z remote: Compressing objects:  80% (155/193)        
2024-08-22T20:27:51.9958047Z remote: Compressing objects:  81% (157/193)        
2024-08-22T20:27:51.9958668Z remote: Compressing objects:  82% (159/193)        
2024-08-22T20:27:51.9961338Z remote: Compressing objects:  83% (161/193)        
2024-08-22T20:27:51.9962199Z remote: Compressing objects:  84% (163/193)        
2024-08-22T20:27:51.9963008Z remote: Compressing objects:  85% (165/193)        
2024-08-22T20:27:51.9963630Z remote: Compressing objects:  86% (166/193)        
2024-08-22T20:27:51.9964314Z remote: Compressing objects:  87% (168/193)        
2024-08-22T20:27:51.9964901Z remote: Compressing objects:  88% (170/193)        
2024-08-22T20:27:51.9965505Z remote: Compressing objects:  89% (172/193)        
2024-08-22T20:27:51.9966323Z remote: Compressing objects:  90% (174/193)        
2024-08-22T20:27:51.9966894Z remote: Compressing objects:  91% (176/193)        
2024-08-22T20:27:51.9967944Z remote: Compressing objects:  92% (178/193)        
2024-08-22T20:27:51.9968913Z remote: Compressing objects:  93% (180/193)        
2024-08-22T20:27:51.9969784Z remote: Compressing objects:  94% (182/193)        
2024-08-22T20:27:51.9970547Z remote: Compressing objects:  95% (184/193)        
2024-08-22T20:27:51.9971463Z remote: Compressing objects:  96% (186/193)        
2024-08-22T20:27:51.9972033Z remote: Compressing objects:  97% (188/193)        
2024-08-22T20:27:51.9972643Z remote: Compressing objects:  98% (190/193)        
2024-08-22T20:27:51.9973355Z remote: Compressing objects:  99% (192/193)        
2024-08-22T20:27:51.9973954Z remote: Compressing objects: 100% (193/193)        
2024-08-22T20:27:51.9974582Z remote: Compressing objects: 100% (193/193), done.        
2024-08-22T20:27:51.9988938Z Receiving objects:   0% (1/237)
2024-08-22T20:27:51.9989623Z Receiving objects:   1% (3/237)
2024-08-22T20:27:51.9990220Z Receiving objects:   2% (5/237)
2024-08-22T20:27:51.9990936Z Receiving objects:   3% (8/237)
2024-08-22T20:27:51.9992532Z Receiving objects:   4% (10/237)
2024-08-22T20:27:51.9994223Z Receiving objects:   5% (12/237)
2024-08-22T20:27:51.9994977Z Receiving objects:   6% (15/237)
2024-08-22T20:27:51.9995882Z Receiving objects:   7% (17/237)
2024-08-22T20:27:51.9996354Z Receiving objects:   8% (19/237)
2024-08-22T20:27:51.9998192Z Receiving objects:   9% (22/237)
2024-08-22T20:27:51.9998898Z Receiving objects:  10% (24/237)
2024-08-22T20:27:51.9999588Z Receiving objects:  11% (27/237)
2024-08-22T20:27:52.0000346Z Receiving objects:  12% (29/237)
2024-08-22T20:27:52.0001003Z Receiving objects:  13% (31/237)
2024-08-22T20:27:52.0001690Z Receiving objects:  14% (34/237)
2024-08-22T20:27:52.0002257Z Receiving objects:  15% (36/237)
2024-08-22T20:27:52.0004508Z Receiving objects:  16% (38/237)
2024-08-22T20:27:52.0005222Z Receiving objects:  17% (41/237)
2024-08-22T20:27:52.0005792Z Receiving objects:  18% (43/237)
2024-08-22T20:27:52.0006417Z Receiving objects:  19% (46/237)
2024-08-22T20:27:52.0007038Z Receiving objects:  20% (48/237)
2024-08-22T20:27:52.0008152Z Receiving objects:  21% (50/237)
2024-08-22T20:27:52.0008899Z Receiving objects:  22% (53/237)
2024-08-22T20:27:52.0009500Z Receiving objects:  23% (55/237)
2024-08-22T20:27:52.0009961Z Receiving objects:  24% (57/237)
2024-08-22T20:27:52.0010430Z Receiving objects:  25% (60/237)
2024-08-22T20:27:52.0011518Z Receiving objects:  26% (62/237)
2024-08-22T20:27:52.0012168Z Receiving objects:  27% (64/237)
2024-08-22T20:27:52.0012813Z Receiving objects:  28% (67/237)
2024-08-22T20:27:52.0013518Z Receiving objects:  29% (69/237)
2024-08-22T20:27:52.0014139Z Receiving objects:  30% (72/237)
2024-08-22T20:27:52.0014707Z Receiving objects:  31% (74/237)
2024-08-22T20:27:52.0015421Z Receiving objects:  32% (76/237)
2024-08-22T20:27:52.0016024Z Receiving objects:  33% (79/237)
2024-08-22T20:27:52.0016551Z Receiving objects:  34% (81/237)
2024-08-22T20:27:52.0017118Z Receiving objects:  35% (83/237)
2024-08-22T20:27:52.0017591Z Receiving objects:  36% (86/237)
2024-08-22T20:27:52.0024215Z Receiving objects:  37% (88/237)
2024-08-22T20:27:52.0025720Z Receiving objects:  38% (91/237)
2024-08-22T20:27:52.0027010Z Receiving objects:  39% (93/237)
2024-08-22T20:27:52.0027599Z Receiving objects:  40% (95/237)
2024-08-22T20:27:52.0028329Z Receiving objects:  41% (98/237)
2024-08-22T20:27:52.0028946Z Receiving objects:  42% (100/237)
2024-08-22T20:27:52.0029511Z Receiving objects:  43% (102/237)
2024-08-22T20:27:52.0030098Z Receiving objects:  44% (105/237)
2024-08-22T20:27:52.0030580Z Receiving objects:  45% (107/237)
2024-08-22T20:27:52.0031018Z Receiving objects:  46% (110/237)
2024-08-22T20:27:52.0031588Z Receiving objects:  47% (112/237)
2024-08-22T20:27:52.0032135Z Receiving objects:  48% (114/237)
2024-08-22T20:27:52.0032661Z Receiving objects:  49% (117/237)
2024-08-22T20:27:52.0056115Z Receiving objects:  50% (119/237)
2024-08-22T20:27:52.0057204Z Receiving objects:  51% (121/237)
2024-08-22T20:27:52.0058653Z Receiving objects:  52% (124/237)
2024-08-22T20:27:52.0140080Z Receiving objects:  53% (126/237)
2024-08-22T20:27:52.0140752Z Receiving objects:  54% (128/237)
2024-08-22T20:27:52.0142762Z Receiving objects:  55% (131/237)
2024-08-22T20:27:52.0144445Z Receiving objects:  56% (133/237)
2024-08-22T20:27:52.0145139Z Receiving objects:  57% (136/237)
2024-08-22T20:27:52.0145899Z Receiving objects:  58% (138/237)
2024-08-22T20:27:52.0146366Z Receiving objects:  59% (140/237)
2024-08-22T20:27:52.0146870Z Receiving objects:  60% (143/237)
2024-08-22T20:27:52.0147434Z Receiving objects:  61% (145/237)
2024-08-22T20:27:52.0147882Z Receiving objects:  62% (147/237)
2024-08-22T20:27:52.0148394Z Receiving objects:  63% (150/237)
2024-08-22T20:27:52.0149164Z Receiving objects:  64% (152/237)
2024-08-22T20:27:52.0150284Z Receiving objects:  65% (155/237)
2024-08-22T20:27:52.0151053Z Receiving objects:  66% (157/237)
2024-08-22T20:27:52.0151654Z Receiving objects:  67% (159/237)
2024-08-22T20:27:52.0153627Z Receiving objects:  68% (162/237)
2024-08-22T20:27:52.0154136Z Receiving objects:  69% (164/237)
2024-08-22T20:27:52.0154697Z Receiving objects:  70% (166/237)
2024-08-22T20:27:52.0155603Z Receiving objects:  71% (169/237)
2024-08-22T20:27:52.0156414Z Receiving objects:  72% (171/237)
2024-08-22T20:27:52.0156988Z Receiving objects:  73% (174/237)
2024-08-22T20:27:52.0157520Z Receiving objects:  74% (176/237)
2024-08-22T20:27:52.0159899Z Receiving objects:  75% (178/237)
2024-08-22T20:27:52.0160483Z Receiving objects:  76% (181/237)
2024-08-22T20:27:52.0161063Z Receiving objects:  77% (183/237)
2024-08-22T20:27:52.0161807Z Receiving objects:  78% (185/237)
2024-08-22T20:27:52.0162717Z Receiving objects:  79% (188/237)
2024-08-22T20:27:52.0164869Z Receiving objects:  80% (190/237)
2024-08-22T20:27:52.0167008Z Receiving objects:  81% (192/237)
2024-08-22T20:27:52.0167777Z Receiving objects:  82% (195/237)
2024-08-22T20:27:52.0172029Z Receiving objects:  83% (197/237)
2024-08-22T20:27:52.0173054Z Receiving objects:  84% (200/237)
2024-08-22T20:27:52.0174801Z Receiving objects:  85% (202/237)
2024-08-22T20:27:52.0175595Z Receiving objects:  86% (204/237)
2024-08-22T20:27:52.0176386Z Receiving objects:  87% (207/237)
2024-08-22T20:27:52.0177011Z Receiving objects:  88% (209/237)
2024-08-22T20:27:52.0180627Z Receiving objects:  89% (211/237)
2024-08-22T20:27:52.0181822Z remote: Total 237 (delta 16), reused 151 (delta 7), pack-reused 0 (from 0)        
2024-08-22T20:27:52.0182619Z Receiving objects:  90% (214/237)
2024-08-22T20:27:52.0183379Z Receiving objects:  91% (216/237)
2024-08-22T20:27:52.0183935Z Receiving objects:  92% (219/237)
2024-08-22T20:27:52.0184893Z Receiving objects:  93% (221/237)
2024-08-22T20:27:52.0186076Z Receiving objects:  94% (223/237)
2024-08-22T20:27:52.0186679Z Receiving objects:  95% (226/237)
2024-08-22T20:27:52.0189539Z Receiving objects:  96% (228/237)
2024-08-22T20:27:52.0190320Z Receiving objects:  97% (230/237)
2024-08-22T20:27:52.0190955Z Receiving objects:  98% (233/237)
2024-08-22T20:27:52.0191542Z Receiving objects:  99% (235/237)
2024-08-22T20:27:52.0192243Z Receiving objects: 100% (237/237)
2024-08-22T20:27:52.0192857Z Receiving objects: 100% (237/237), 488.09 KiB | 24.40 MiB/s, done.
2024-08-22T20:27:52.0196571Z Resolving deltas:   0% (0/16)
2024-08-22T20:27:52.0197652Z Resolving deltas:   6% (1/16)
2024-08-22T20:27:52.0198760Z Resolving deltas:  12% (2/16)
2024-08-22T20:27:52.0200409Z Resolving deltas:  31% (5/16)
2024-08-22T20:27:52.0202301Z Resolving deltas:  37% (6/16)
2024-08-22T20:27:52.0202899Z Resolving deltas:  56% (9/16)
2024-08-22T20:27:52.0203536Z Resolving deltas:  62% (10/16)
2024-08-22T20:27:52.0204157Z Resolving deltas:  68% (11/16)
2024-08-22T20:27:52.0204772Z Resolving deltas:  75% (12/16)
2024-08-22T20:27:52.0205383Z Resolving deltas:  81% (13/16)
2024-08-22T20:27:52.0205870Z Resolving deltas:  87% (14/16)
2024-08-22T20:27:52.0206332Z Resolving deltas:  93% (15/16)
2024-08-22T20:27:52.0208467Z Resolving deltas: 100% (16/16)
2024-08-22T20:27:52.0209053Z Resolving deltas: 100% (16/16), done.
2024-08-22T20:27:52.0289461Z From https://github.com/pytorch/torchchat
2024-08-22T20:27:52.0290467Z  * [new ref]         refs/pull/1044/merge -> pull/1044/merge
2024-08-22T20:27:52.0317855Z ##[endgroup]
2024-08-22T20:27:52.0318774Z ##[group]Determining the checkout info
2024-08-22T20:27:52.0321290Z ##[endgroup]
2024-08-22T20:27:52.0322178Z ##[group]Checking out the ref
2024-08-22T20:27:52.0325098Z [command]/usr/bin/git checkout --progress --force refs/remotes/pull/1044/merge
2024-08-22T20:27:52.0501099Z Note: switching to 'refs/remotes/pull/1044/merge'.
2024-08-22T20:27:52.0501907Z 
2024-08-22T20:27:52.0502467Z You are in 'detached HEAD' state. You can look around, make experimental
2024-08-22T20:27:52.0503569Z changes and commit them, and you can discard any commits you make in this
2024-08-22T20:27:52.0504372Z state without impacting any branches by switching back to a branch.
2024-08-22T20:27:52.0504965Z 
2024-08-22T20:27:52.0505310Z If you want to create a new branch to retain commits you create, you may
2024-08-22T20:27:52.0506200Z do so (now or later) by using -c with the switch command. Example:
2024-08-22T20:27:52.0506654Z 
2024-08-22T20:27:52.0507066Z   git switch -c <new-branch-name>
2024-08-22T20:27:52.0507474Z 
2024-08-22T20:27:52.0507692Z Or undo this operation with:
2024-08-22T20:27:52.0508081Z 
2024-08-22T20:27:52.0508273Z   git switch -
2024-08-22T20:27:52.0508517Z 
2024-08-22T20:27:52.0509047Z Turn off this advice by setting config variable advice.detachedHead to false
2024-08-22T20:27:52.0509562Z 
2024-08-22T20:27:52.0510307Z HEAD is now at 0e4d4b1 Merge c8dc18a48d66f51855d89294f3ca800692cd5dad into d5bb3c6659c8978a95c5141eb31e6e58f94af20a
2024-08-22T20:27:52.0512078Z ##[endgroup]
2024-08-22T20:27:52.0557559Z [command]/usr/bin/git log -1 --format='%H'
2024-08-22T20:27:52.0595352Z '0e4d4b184770ee5c0943c8ad43f2a1be7a2dd2c9'
2024-08-22T20:27:52.0816198Z Prepare all required actions
2024-08-22T20:27:52.0949380Z ##[group]Run ./test-infra/.github/actions/calculate-docker-image
2024-08-22T20:27:52.0950096Z with:
2024-08-22T20:27:52.0950542Z   docker-image-name: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:52.0951139Z   docker-build-dir: .ci/docker
2024-08-22T20:27:52.0951645Z   working-directory: pytorch/torchchat
2024-08-22T20:27:52.0952284Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-08-22T20:27:52.0952915Z   force-push: false
2024-08-22T20:27:52.0953321Z env:
2024-08-22T20:27:52.0953729Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:52.0954298Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:52.0954794Z   PR_NUMBER: 1044
2024-08-22T20:27:52.0974121Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:52.0993934Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:27:52.0994854Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:27:52.0995685Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:27:52.0996388Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:27:52.0996970Z ##[endgroup]
2024-08-22T20:27:52.1023782Z ##[group]Run set -ex
2024-08-22T20:27:52.1024388Z set -ex
2024-08-22T20:27:52.1024755Z 
2024-08-22T20:27:52.1025398Z # If the docker build directory or the build script doesn't exist, the action will
2024-08-22T20:27:52.1026479Z # gracefully return the docker image name as it is.  Pulling docker image in Linux
2024-08-22T20:27:52.1027386Z # job could then download the pre-built image as usual
2024-08-22T20:27:52.1028176Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then
2024-08-22T20:27:52.1029013Z   echo "skip=true" >> "${GITHUB_OUTPUT}"
2024-08-22T20:27:52.1029778Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2024-08-22T20:27:52.1030388Z 
2024-08-22T20:27:52.1031035Z   echo "There is no Docker build script in ${REPO_NAME} repo, skipping..."
2024-08-22T20:27:52.1031763Z   exit 0
2024-08-22T20:27:52.1032114Z else
2024-08-22T20:27:52.1032715Z   echo "skip=false" >> "${GITHUB_OUTPUT}"
2024-08-22T20:27:52.1033253Z fi
2024-08-22T20:27:52.1033581Z 
2024-08-22T20:27:52.1034200Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then
2024-08-22T20:27:52.1035133Z   # The docker image name already includes the ECR prefix and tag, so we can just
2024-08-22T20:27:52.1035992Z   # use it as it is, but first let's extract the tag
2024-08-22T20:27:52.1036825Z   DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}')
2024-08-22T20:27:52.1037638Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2024-08-22T20:27:52.1038423Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2024-08-22T20:27:52.1039106Z else
2024-08-22T20:27:52.1039631Z   DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}")
2024-08-22T20:27:52.1040395Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2024-08-22T20:27:52.1041491Z   echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2024-08-22T20:27:52.1042289Z fi
2024-08-22T20:27:52.1052334Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:27:52.1052937Z env:
2024-08-22T20:27:52.1053310Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:52.1053930Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:52.1054571Z   PR_NUMBER: 1044
2024-08-22T20:27:52.1073713Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:52.1094701Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:27:52.1095584Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:27:52.1096362Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:27:52.1097142Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:27:52.1097729Z   REPO_NAME: torchchat
2024-08-22T20:27:52.1098219Z   DOCKER_IMAGE_NAME: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:52.1098824Z   DOCKER_BUILD_DIR: .ci/docker
2024-08-22T20:27:52.1099405Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-08-22T20:27:52.1100012Z ##[endgroup]
2024-08-22T20:27:52.1131968Z + [[ ! -d .ci/docker ]]
2024-08-22T20:27:52.1132487Z + echo skip=true
2024-08-22T20:27:52.1133055Z + echo docker-image=pytorch/conda-builder:cuda12.1
2024-08-22T20:27:52.1133831Z + echo 'There is no Docker build script in torchchat repo, skipping...'
2024-08-22T20:27:52.1134783Z + exit 0
2024-08-22T20:27:52.1135346Z There is no Docker build script in torchchat repo, skipping...
2024-08-22T20:27:52.1258040Z Prepare all required actions
2024-08-22T20:27:52.1346925Z ##[group]Run ./test-infra/.github/actions/pull-docker-image
2024-08-22T20:27:52.1347409Z with:
2024-08-22T20:27:52.1347737Z   docker-image: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:52.1348304Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-08-22T20:27:52.1348807Z env:
2024-08-22T20:27:52.1349119Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:52.1349566Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:52.1349927Z   PR_NUMBER: 1044
2024-08-22T20:27:52.1368939Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:52.1388754Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:27:52.1389527Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:27:52.1390248Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:27:52.1390846Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:27:52.1391318Z ##[endgroup]
2024-08-22T20:27:52.1410293Z ##[group]Run set -x
2024-08-22T20:27:52.1410637Z set -x
2024-08-22T20:27:52.1410929Z set +e
2024-08-22T20:27:52.1411208Z 
2024-08-22T20:27:52.1411471Z login() {
2024-08-22T20:27:52.1412113Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2024-08-22T20:27:52.1412810Z }
2024-08-22T20:27:52.1413070Z 
2024-08-22T20:27:52.1413336Z retry () {
2024-08-22T20:27:52.1413712Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2024-08-22T20:27:52.1414145Z }
2024-08-22T20:27:52.1414407Z 
2024-08-22T20:27:52.1414718Z retry login "${DOCKER_REGISTRY}"
2024-08-22T20:27:52.1415115Z 
2024-08-22T20:27:52.1415375Z set -e
2024-08-22T20:27:52.1415828Z # ignore output since only exit code is used for conditional
2024-08-22T20:27:52.1416507Z # only pull docker image if it's not available locally
2024-08-22T20:27:52.1417391Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then
2024-08-22T20:27:52.1418067Z   retry docker pull "${DOCKER_IMAGE}"
2024-08-22T20:27:52.1418485Z fi
2024-08-22T20:27:52.1427098Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:27:52.1427579Z env:
2024-08-22T20:27:52.1427888Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:27:52.1428337Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:27:52.1428705Z   PR_NUMBER: 1044
2024-08-22T20:27:52.1447774Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:27:52.1467314Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:27:52.1468075Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:27:52.1468784Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:27:52.1469417Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:27:52.1470041Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-08-22T20:27:52.1470536Z ##[endgroup]
2024-08-22T20:27:52.1498640Z + set +e
2024-08-22T20:27:52.1499293Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-08-22T20:27:52.1499923Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-08-22T20:27:52.1502419Z + aws ecr get-login-password --region us-east-1
2024-08-22T20:27:52.1504660Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-08-22T20:27:52.7110681Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2024-08-22T20:27:52.7111936Z Configure a credential helper to remove this warning. See
2024-08-22T20:27:52.7113238Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2024-08-22T20:27:52.7114076Z 
2024-08-22T20:27:52.7114263Z Login Succeeded
2024-08-22T20:27:52.7135763Z + set -e
2024-08-22T20:27:52.7136424Z + docker inspect --type=image pytorch/conda-builder:cuda12.1
2024-08-22T20:27:52.7297860Z + retry docker pull pytorch/conda-builder:cuda12.1
2024-08-22T20:27:52.7298653Z + docker pull pytorch/conda-builder:cuda12.1
2024-08-22T20:27:52.8979848Z cuda12.1: Pulling from pytorch/conda-builder
2024-08-22T20:27:52.8980523Z 2d473b07cdd5: Pulling fs layer
2024-08-22T20:27:52.8981009Z 1feefa38b24d: Pulling fs layer
2024-08-22T20:27:52.8981479Z f3b743645691: Pulling fs layer
2024-08-22T20:27:52.8981941Z 09ec69653086: Pulling fs layer
2024-08-22T20:27:52.8982420Z 07dcdf002e0e: Pulling fs layer
2024-08-22T20:27:52.8982885Z 6d463d3f66d5: Pulling fs layer
2024-08-22T20:27:52.8983351Z af5c0f3f240b: Pulling fs layer
2024-08-22T20:27:52.8983868Z 1c60ace79a8a: Pulling fs layer
2024-08-22T20:27:52.8984323Z 0cb4bb0dd002: Pulling fs layer
2024-08-22T20:27:52.8984806Z 6a40c884dc1c: Pulling fs layer
2024-08-22T20:27:52.8985276Z 21c4fd2610fc: Pulling fs layer
2024-08-22T20:27:52.8985696Z 90d96273fa23: Pulling fs layer
2024-08-22T20:27:52.8986138Z 625bfd303d2d: Pulling fs layer
2024-08-22T20:27:52.8986496Z e8f5e731f8e4: Pulling fs layer
2024-08-22T20:27:52.8986847Z 43ef26b4d3a4: Pulling fs layer
2024-08-22T20:27:52.8987204Z 7027d2bdfdb1: Pulling fs layer
2024-08-22T20:27:52.8987563Z db6f5f4c0334: Pulling fs layer
2024-08-22T20:27:52.8987910Z 784019980816: Pulling fs layer
2024-08-22T20:27:52.8988262Z a7beba6b9d19: Pulling fs layer
2024-08-22T20:27:52.8988624Z 417dce1e59af: Pulling fs layer
2024-08-22T20:27:52.8988973Z 2b99a72dd557: Pulling fs layer
2024-08-22T20:27:52.8989323Z 6a40c884dc1c: Waiting
2024-08-22T20:27:52.8989646Z f49fb64d359c: Pulling fs layer
2024-08-22T20:27:52.8989983Z 07dcdf002e0e: Waiting
2024-08-22T20:27:52.8990306Z bfb071e5ae48: Pulling fs layer
2024-08-22T20:27:52.8990653Z af5c0f3f240b: Waiting
2024-08-22T20:27:52.8990956Z e8f5e731f8e4: Waiting
2024-08-22T20:27:52.8991523Z 625bfd303d2d: Waiting
2024-08-22T20:27:52.8991843Z b2ba091c4c7d: Pulling fs layer
2024-08-22T20:27:52.8992184Z 43ef26b4d3a4: Waiting
2024-08-22T20:27:52.8992487Z 6d463d3f66d5: Waiting
2024-08-22T20:27:52.8992803Z 8cfb81a5220c: Pulling fs layer
2024-08-22T20:27:52.8993144Z 0cb4bb0dd002: Waiting
2024-08-22T20:27:52.8993450Z 417dce1e59af: Waiting
2024-08-22T20:27:52.8993767Z c5058f81a371: Pulling fs layer
2024-08-22T20:27:52.8994108Z 1c60ace79a8a: Waiting
2024-08-22T20:27:52.8994414Z 2b99a72dd557: Waiting
2024-08-22T20:27:52.8994718Z 7027d2bdfdb1: Waiting
2024-08-22T20:27:52.8995014Z bfb071e5ae48: Waiting
2024-08-22T20:27:52.8995313Z 90d96273fa23: Waiting
2024-08-22T20:27:52.8995637Z ca3686f68fee: Pulling fs layer
2024-08-22T20:27:52.8995982Z 21c4fd2610fc: Waiting
2024-08-22T20:27:52.8996534Z 8cfb81a5220c: Waiting
2024-08-22T20:27:52.8996854Z fdb9230cd483: Pulling fs layer
2024-08-22T20:27:52.8997210Z dc18f68db4d4: Pulling fs layer
2024-08-22T20:27:52.8997555Z c5058f81a371: Waiting
2024-08-22T20:27:52.8997878Z e7fc196062f1: Pulling fs layer
2024-08-22T20:27:52.8998277Z b2ba091c4c7d: Waiting
2024-08-22T20:27:52.8998633Z ca3686f68fee: Waiting
2024-08-22T20:27:52.8999002Z dc18f68db4d4: Waiting
2024-08-22T20:27:52.8999297Z e7fc196062f1: Waiting
2024-08-22T20:27:52.8999596Z fdb9230cd483: Waiting
2024-08-22T20:27:52.8999895Z db6f5f4c0334: Waiting
2024-08-22T20:27:52.9000184Z 784019980816: Waiting
2024-08-22T20:27:52.9404068Z f3b743645691: Verifying Checksum
2024-08-22T20:27:52.9404563Z f3b743645691: Download complete
2024-08-22T20:27:52.9427832Z 1feefa38b24d: Verifying Checksum
2024-08-22T20:27:52.9428395Z 1feefa38b24d: Download complete
2024-08-22T20:27:52.9773495Z 09ec69653086: Download complete
2024-08-22T20:27:53.6829848Z 2d473b07cdd5: Verifying Checksum
2024-08-22T20:27:53.6830300Z 2d473b07cdd5: Download complete
2024-08-22T20:27:53.7215623Z af5c0f3f240b: Download complete
2024-08-22T20:27:53.7571157Z 6d463d3f66d5: Verifying Checksum
2024-08-22T20:27:53.8461815Z 0cb4bb0dd002: Verifying Checksum
2024-08-22T20:27:53.8462315Z 0cb4bb0dd002: Download complete
2024-08-22T20:27:53.8821758Z 6a40c884dc1c: Download complete
2024-08-22T20:27:53.9262176Z 21c4fd2610fc: Download complete
2024-08-22T20:27:53.9689388Z 90d96273fa23: Download complete
2024-08-22T20:27:54.3145793Z 1c60ace79a8a: Verifying Checksum
2024-08-22T20:27:54.3146355Z 1c60ace79a8a: Download complete
2024-08-22T20:27:54.3948975Z 07dcdf002e0e: Verifying Checksum
2024-08-22T20:27:54.3949643Z 07dcdf002e0e: Download complete
2024-08-22T20:27:54.9635523Z e8f5e731f8e4: Verifying Checksum
2024-08-22T20:27:54.9635979Z e8f5e731f8e4: Download complete
2024-08-22T20:27:55.3991317Z 625bfd303d2d: Verifying Checksum
2024-08-22T20:27:55.3992672Z 625bfd303d2d: Download complete
2024-08-22T20:27:55.4375983Z db6f5f4c0334: Verifying Checksum
2024-08-22T20:27:55.4377978Z db6f5f4c0334: Download complete
2024-08-22T20:27:55.4797965Z 784019980816: Download complete
2024-08-22T20:27:55.5216270Z a7beba6b9d19: Download complete
2024-08-22T20:27:55.5860762Z 43ef26b4d3a4: Verifying Checksum
2024-08-22T20:27:55.5861807Z 43ef26b4d3a4: Download complete
2024-08-22T20:27:55.7303935Z 7027d2bdfdb1: Verifying Checksum
2024-08-22T20:27:55.7304572Z 7027d2bdfdb1: Download complete
2024-08-22T20:27:55.7339908Z 2b99a72dd557: Verifying Checksum
2024-08-22T20:27:55.7340487Z 2b99a72dd557: Download complete
2024-08-22T20:27:55.7794129Z f49fb64d359c: Verifying Checksum
2024-08-22T20:27:55.7794624Z f49fb64d359c: Download complete
2024-08-22T20:27:55.8271439Z b2ba091c4c7d: Verifying Checksum
2024-08-22T20:27:55.8271898Z b2ba091c4c7d: Download complete
2024-08-22T20:27:55.8689952Z 8cfb81a5220c: Verifying Checksum
2024-08-22T20:27:55.8690577Z 8cfb81a5220c: Download complete
2024-08-22T20:27:55.9365049Z c5058f81a371: Verifying Checksum
2024-08-22T20:27:55.9365739Z c5058f81a371: Download complete
2024-08-22T20:27:56.0685042Z ca3686f68fee: Verifying Checksum
2024-08-22T20:27:56.0685567Z ca3686f68fee: Download complete
2024-08-22T20:27:56.1064008Z fdb9230cd483: Verifying Checksum
2024-08-22T20:27:56.1064791Z fdb9230cd483: Download complete
2024-08-22T20:27:56.1459723Z dc18f68db4d4: Verifying Checksum
2024-08-22T20:27:56.1460234Z dc18f68db4d4: Download complete
2024-08-22T20:27:57.2248049Z 2d473b07cdd5: Pull complete
2024-08-22T20:27:57.2407823Z 1feefa38b24d: Pull complete
2024-08-22T20:27:57.2631208Z f3b743645691: Pull complete
2024-08-22T20:27:57.2787167Z 09ec69653086: Pull complete
2024-08-22T20:27:59.8736120Z bfb071e5ae48: Verifying Checksum
2024-08-22T20:27:59.8736731Z bfb071e5ae48: Download complete
2024-08-22T20:28:01.4905731Z 07dcdf002e0e: Pull complete
2024-08-22T20:28:01.6840619Z e7fc196062f1: Verifying Checksum
2024-08-22T20:28:01.6841093Z e7fc196062f1: Download complete
2024-08-22T20:28:03.8527220Z 6d463d3f66d5: Pull complete
2024-08-22T20:28:03.8703626Z af5c0f3f240b: Pull complete
2024-08-22T20:28:05.1689528Z 1c60ace79a8a: Pull complete
2024-08-22T20:28:05.3290960Z 0cb4bb0dd002: Pull complete
2024-08-22T20:28:05.3436216Z 6a40c884dc1c: Pull complete
2024-08-22T20:28:05.3604996Z 21c4fd2610fc: Pull complete
2024-08-22T20:28:05.3783664Z 90d96273fa23: Pull complete
2024-08-22T20:28:08.7553978Z 625bfd303d2d: Pull complete
2024-08-22T20:28:10.2247410Z e8f5e731f8e4: Pull complete
2024-08-22T20:28:14.1254939Z 43ef26b4d3a4: Pull complete
2024-08-22T20:28:15.9066447Z 7027d2bdfdb1: Pull complete
2024-08-22T20:28:15.9177531Z db6f5f4c0334: Pull complete
2024-08-22T20:28:15.9311162Z 784019980816: Pull complete
2024-08-22T20:28:15.9425767Z a7beba6b9d19: Pull complete
2024-08-22T20:28:45.3563298Z 417dce1e59af: Verifying Checksum
2024-08-22T20:28:45.3563865Z 417dce1e59af: Download complete
2024-08-22T20:29:37.2920605Z 417dce1e59af: Pull complete
2024-08-22T20:29:37.7605851Z 2b99a72dd557: Pull complete
2024-08-22T20:29:38.0119016Z f49fb64d359c: Pull complete
2024-08-22T20:29:53.3567550Z bfb071e5ae48: Pull complete
2024-08-22T20:29:53.5774630Z b2ba091c4c7d: Pull complete
2024-08-22T20:29:53.8149045Z 8cfb81a5220c: Pull complete
2024-08-22T20:29:54.0510714Z c5058f81a371: Pull complete
2024-08-22T20:29:54.5937043Z ca3686f68fee: Pull complete
2024-08-22T20:29:54.8246772Z fdb9230cd483: Pull complete
2024-08-22T20:29:55.0560354Z dc18f68db4d4: Pull complete
2024-08-22T20:30:14.0496026Z e7fc196062f1: Pull complete
2024-08-22T20:30:14.1724977Z Digest: sha256:9f128b503c8de2d2267228e5a5ddfd791872f8fe7e4630afeec3efb07d810e8b
2024-08-22T20:30:14.2246592Z Status: Downloaded newer image for pytorch/conda-builder:cuda12.1
2024-08-22T20:30:14.2486913Z docker.io/pytorch/conda-builder:cuda12.1
2024-08-22T20:30:14.2668894Z ##[group]Run set -ex
2024-08-22T20:30:14.2669244Z set -ex
2024-08-22T20:30:14.2669544Z {
2024-08-22T20:30:14.2669856Z   echo "#!/usr/bin/env bash";
2024-08-22T20:30:14.2670280Z   echo "set -eou pipefail";
2024-08-22T20:30:14.2670706Z   # shellcheck disable=SC2016
2024-08-22T20:30:14.2671182Z   echo 'eval "$(conda shell.bash hook)"';
2024-08-22T20:30:14.2671641Z   echo "set -x";
2024-08-22T20:30:14.2671996Z   echo "${SCRIPT}";
2024-08-22T20:30:14.2672401Z } > "${RUNNER_TEMP}/exec_script"
2024-08-22T20:30:14.2672866Z chmod +x "${RUNNER_TEMP}/exec_script"
2024-08-22T20:30:14.2673782Z python3 "/home/ec2-user/actions-runner/_work/torchchat/torchchat/test-infra/.github/scripts/run_with_env_secrets.py" ""
2024-08-22T20:30:14.2684282Z shell: /usr/bin/bash -e {0}
2024-08-22T20:30:14.2684625Z env:
2024-08-22T20:30:14.2684966Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:30:14.2685412Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:30:14.2685780Z   PR_NUMBER: 1044
2024-08-22T20:30:14.2704788Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:30:14.2724232Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:30:14.2725078Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:30:14.2725786Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:30:14.2726394Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:30:14.2727256Z   ALL_SECRETS: {
  "github_token": "***"
}
2024-08-22T20:30:14.2727669Z ##[endgroup]
2024-08-22T20:30:14.2761411Z + echo '#!/usr/bin/env bash'
2024-08-22T20:30:14.2761846Z + echo 'set -eou pipefail'
2024-08-22T20:30:14.2762280Z + echo 'eval "$(conda shell.bash hook)"'
2024-08-22T20:30:14.2764093Z + echo 'set -x'
2024-08-22T20:30:14.2764752Z + echo 'echo "::group::Print machine info"
2024-08-22T20:30:14.2765270Z uname -a
2024-08-22T20:30:14.2765545Z echo "::endgroup::"
2024-08-22T20:30:14.2765789Z 
2024-08-22T20:30:14.2766280Z echo "::group::Install newer objcopy that supports --set-section-alignment"
2024-08-22T20:30:14.2768891Z yum install -y  devtoolset-10-binutils
2024-08-22T20:30:14.2769644Z export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
2024-08-22T20:30:14.2770273Z echo "::endgroup::"
2024-08-22T20:30:14.2770475Z 
2024-08-22T20:30:14.2770482Z 
2024-08-22T20:30:14.2770629Z echo "::group::Download checkpoints"
2024-08-22T20:30:14.2771066Z # Install requirements
2024-08-22T20:30:14.2771414Z ./install_requirements.sh cuda
2024-08-22T20:30:14.2771767Z pip3 list
2024-08-22T20:30:14.2772741Z python3 -c '\''import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'\''
2024-08-22T20:30:14.2773463Z echo "::endgroup::"
2024-08-22T20:30:14.2773657Z 
2024-08-22T20:30:14.2773852Z echo "::group::Download checkpoints"
2024-08-22T20:30:14.2774445Z mkdir -p checkpoints/stories15M
2024-08-22T20:30:14.2774935Z pushd checkpoints/stories15M
2024-08-22T20:30:14.2775623Z wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
2024-08-22T20:30:14.2776397Z wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
2024-08-22T20:30:14.2776981Z popd
2024-08-22T20:30:14.2777261Z echo "::endgroup::"
2024-08-22T20:30:14.2777453Z 
2024-08-22T20:30:14.2777586Z echo "::group::Run inference"
2024-08-22T20:30:14.2778360Z export MODEL_PATH=checkpoints/stories15M/stories15M.pt
2024-08-22T20:30:14.2778856Z export MODEL_NAME=stories15M
2024-08-22T20:30:14.2779202Z export MODEL_DIR=/tmp
2024-08-22T20:30:14.2779413Z 
2024-08-22T20:30:14.2779571Z for DTYPE in bfloat16 float16 float32; do
2024-08-22T20:30:14.2780259Z   ###################################################################
2024-08-22T20:30:14.2780727Z   # group with different temperatures
2024-08-22T20:30:14.2781541Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
2024-08-22T20:30:14.2782623Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
2024-08-22T20:30:14.2783703Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
2024-08-22T20:30:14.2784732Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
2024-08-22T20:30:14.2785815Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
2024-08-22T20:30:14.2786820Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
2024-08-22T20:30:14.2787513Z   ###################################################################
2024-08-22T20:30:14.2788083Z   # group with different temperatures and prefill, and compile
2024-08-22T20:30:14.2788588Z   # and prefill compile
2024-08-22T20:30:14.2789432Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
2024-08-22T20:30:14.2790757Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
2024-08-22T20:30:14.2792132Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
2024-08-22T20:30:14.2793589Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
2024-08-22T20:30:14.2794843Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
2024-08-22T20:30:14.2796096Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
2024-08-22T20:30:14.2796914Z   ###################################################################
2024-08-22T20:30:14.2797468Z   # group with different temperatures and sequential prefill
2024-08-22T20:30:14.2798439Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
2024-08-22T20:30:14.2799687Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
2024-08-22T20:30:14.2800941Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
2024-08-22T20:30:14.2802163Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
2024-08-22T20:30:14.2803357Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
2024-08-22T20:30:14.2804550Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
2024-08-22T20:30:14.2805346Z   ###################################################################
2024-08-22T20:30:14.2805895Z   # group with different temperatures and prefill, and compile
2024-08-22T20:30:14.2806928Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
2024-08-22T20:30:14.2808378Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
2024-08-22T20:30:14.2809743Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
2024-08-22T20:30:14.2811096Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
2024-08-22T20:30:14.2812500Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
2024-08-22T20:30:14.2813972Z   python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile
2024-08-22T20:30:14.2814680Z 
2024-08-22T20:30:14.2814804Z done
2024-08-22T20:30:14.2814947Z 
2024-08-22T20:30:14.2815075Z echo "tests complete"
2024-08-22T20:30:14.2815465Z echo "******************************************"
2024-08-22T20:30:14.2815927Z echo "::endgroup::"
2024-08-22T20:30:14.2816267Z '
2024-08-22T20:30:14.2816739Z + chmod +x /home/ec2-user/actions-runner/_work/_temp/exec_script
2024-08-22T20:30:14.2817799Z + python3 /home/ec2-user/actions-runner/_work/torchchat/torchchat/test-infra/.github/scripts/run_with_env_secrets.py ''
2024-08-22T20:30:52.7433017Z Running command: 
2024-08-22T20:30:52.7443311Z         docker run             -e PR_NUMBER             -e RUNNER_ARTIFACT_DIR=/artifacts             -e RUNNER_DOCS_DIR=/docs             -e RUNNER_TEST_RESULTS_DIR=/test-results             --env-file="/home/ec2-user/actions-runner/_work/_temp/github_env_10515112123"             `# It is unknown why the container sees a different value for this.`             -e GITHUB_STEP_SUMMARY             -e SECRET_GITHUB_TOKEN             --cap-add=SYS_PTRACE             --detach             --ipc=host             --security-opt seccomp=unconfined             --shm-size=2g             --tty             --ulimit stack=10485760:83886080             --ulimit core=0             --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all             -v "/home/ec2-user/actions-runner/_work/torchchat/torchchat/pytorch/torchchat:/pytorch/torchchat"             -v "/home/ec2-user/actions-runner/_work/torchchat/torchchat/test-infra:/test-infra"             -v "/home/ec2-user/actions-runner/_work/_temp/artifacts:/artifacts"             -v "/home/ec2-user/actions-runner/_work/_temp/docs:/docs"             -v "/home/ec2-user/actions-runner/_work/_temp/test-results:/test-results"             -v "/home/ec2-user/actions-runner/_work/_temp/exec_script:/exec"             -v "/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_4363d743-01bc-404e-b31a-0596253d0d79":"/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_4363d743-01bc-404e-b31a-0596253d0d79"             -w /pytorch/torchchat             "pytorch/conda-builder:cuda12.1"
2024-08-22T20:30:52.7451584Z         
2024-08-22T20:30:52.7452036Z 638b8463543e1793a9bf9b9c5317033fb1f5078871a798e3dab1e776fe9db640
2024-08-22T20:30:52.7453358Z Running command: docker exec -t 638b8463543e1793a9bf9b9c5317033fb1f5078871a798e3dab1e776fe9db640 /exec
2024-08-22T20:30:52.7454454Z + echo '::group::Print machine info'
2024-08-22T20:30:52.7455355Z ##[group]Print machine info
2024-08-22T20:30:52.7455817Z + uname -a
2024-08-22T20:30:52.7456664Z Linux 638b8463543e 6.1.94-99.176.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 18 14:57:56 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux
2024-08-22T20:30:52.7457481Z + echo ::endgroup::
2024-08-22T20:30:52.7458182Z ##[endgroup]
2024-08-22T20:30:52.7458790Z + echo '::group::Install newer objcopy that supports --set-section-alignment'
2024-08-22T20:30:52.7459784Z ##[group]Install newer objcopy that supports --set-section-alignment
2024-08-22T20:30:52.7460420Z + yum install -y devtoolset-10-binutils
2024-08-22T20:30:52.7460856Z Loaded plugins: fastestmirror, ovl
2024-08-22T20:30:52.7461278Z Loading mirror speeds from cached hostfile
2024-08-22T20:30:52.7461589Z 
2024-08-22T20:30:52.7461858Z epel/x86_64/metalink                                     | 5.1 kB     00:00     
2024-08-22T20:30:52.7462466Z  * epel: d2lzkl7pfhq30w.cloudfront.net
2024-08-22T20:30:52.7462761Z 
2024-08-22T20:30:52.7462973Z base                                                     | 3.6 kB     00:00     
2024-08-22T20:30:52.7463328Z 
2024-08-22T20:30:52.7463651Z centos-sclo-rh                                           | 3.0 kB     00:00     
2024-08-22T20:30:52.7464062Z 
2024-08-22T20:30:52.7464390Z centos-sclo-sclo                                         | 3.0 kB     00:00     
2024-08-22T20:30:52.7464805Z 
2024-08-22T20:30:52.7465251Z extras                                                   | 2.9 kB     00:00     
2024-08-22T20:30:52.7465621Z 
2024-08-22T20:30:52.7465840Z updates                                                  | 2.9 kB     00:00     
2024-08-22T20:30:52.7466324Z Resolving Dependencies
2024-08-22T20:30:52.7466717Z --> Running transaction check
2024-08-22T20:30:52.7467330Z ---> Package devtoolset-10-binutils.x86_64 0:2.35-5.el7.4 will be installed
2024-08-22T20:30:52.7468328Z --> Processing Dependency: devtoolset-10-runtime for package: devtoolset-10-binutils-2.35-5.el7.4.x86_64
2024-08-22T20:30:52.7469110Z --> Running transaction check
2024-08-22T20:30:52.7469729Z ---> Package devtoolset-10-runtime.x86_64 0:10.1-0.el7 will be installed
2024-08-22T20:30:52.7470362Z --> Finished Dependency Resolution
2024-08-22T20:30:52.7470632Z 
2024-08-22T20:30:52.7470752Z Dependencies Resolved
2024-08-22T20:30:52.7470954Z 
2024-08-22T20:30:52.7471130Z ================================================================================
2024-08-22T20:30:52.7471705Z  Package                   Arch      Version            Repository         Size
2024-08-22T20:30:52.7472266Z ================================================================================
2024-08-22T20:30:52.7472703Z Installing:
2024-08-22T20:30:52.7473257Z  devtoolset-10-binutils    x86_64    2.35-5.el7.4       centos-sclo-rh    6.3 M
2024-08-22T20:30:52.7473842Z Installing for dependencies:
2024-08-22T20:30:52.7474468Z  devtoolset-10-runtime     x86_64    10.1-0.el7         centos-sclo-rh     19 k
2024-08-22T20:30:52.7475079Z 
2024-08-22T20:30:52.7475201Z Transaction Summary
2024-08-22T20:30:52.7475559Z ================================================================================
2024-08-22T20:30:52.7476048Z Install  1 Package (+1 Dependent package)
2024-08-22T20:30:52.7476346Z 
2024-08-22T20:30:52.7476476Z Total download size: 6.3 M
2024-08-22T20:30:52.7476815Z Installed size: 25 M
2024-08-22T20:30:52.7477132Z Downloading packages:
2024-08-22T20:30:52.7477334Z 
2024-08-22T20:30:52.7478167Z (1/2): devtoolset-10-binutils-2.35-5.el7.4.x86_64.rpm      | 6.3 MB   00:00     
2024-08-22T20:30:52.7478669Z 
2024-08-22T20:30:52.7479100Z (2/2): devtoolset-10-runtime-10.1-0.el7.x86_64.rpm         |  19 kB   00:00     
2024-08-22T20:30:52.7479846Z --------------------------------------------------------------------------------
2024-08-22T20:30:52.7480450Z Total                                               26 MB/s | 6.3 MB  00:00     
2024-08-22T20:30:52.7480951Z Running transaction check
2024-08-22T20:30:52.7481285Z Running transaction test
2024-08-22T20:30:52.7481633Z Transaction test succeeded
2024-08-22T20:30:52.7481973Z Running transaction
2024-08-22T20:30:52.7482163Z 
2024-08-22T20:30:52.7482544Z   Installing : devtoolset-10-runtime-10.1-0 [                             ] 1/2
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2024-08-22T20:30:52.7505826Z   Installing : devtoolset-10-runtime-10.1-0.el7.x86_64                      1/2 
2024-08-22T20:30:52.7506589Z ValueError: SELinux policy is not managed or store cannot be accessed.
2024-08-22T20:30:52.7507023Z 
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2024-08-22T20:30:54.7303496Z   Installing : devtoolset-10-binutils-2.35-5.el7.4.x86_64                   2/2 
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2024-08-22T20:30:54.7310993Z 
2024-08-22T20:30:54.7311706Z   Verifying  : devtoolset-10-runtime-10.1-0.el7.x86_64                      1/2 
2024-08-22T20:30:54.7312614Z 
2024-08-22T20:30:54.7313080Z   Verifying  : devtoolset-10-binutils-2.35-5.el7.4.x86_64                   2/2 
2024-08-22T20:30:54.7313992Z 
2024-08-22T20:30:54.7314105Z Installed:
2024-08-22T20:30:54.7314955Z   devtoolset-10-binutils.x86_64 0:2.35-5.el7.4                                  
2024-08-22T20:30:54.7315472Z 
2024-08-22T20:30:54.7315595Z Dependency Installed:
2024-08-22T20:30:54.7316755Z   devtoolset-10-runtime.x86_64 0:10.1-0.el7                                     
2024-08-22T20:30:54.7317264Z 
2024-08-22T20:30:54.7317538Z Complete!
2024-08-22T20:30:54.7319421Z + export PATH=/opt/rh/devtoolset-10/root/usr/bin/:/opt/conda/bin:/opt/conda/condabin:/opt/conda/bin:/usr/local/cuda-12.1/bin:/opt/rh/devtoolset-9/root/usr/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2024-08-22T20:30:54.7322923Z + PATH=/opt/rh/devtoolset-10/root/usr/bin/:/opt/conda/bin:/opt/conda/condabin:/opt/conda/bin:/usr/local/cuda-12.1/bin:/opt/rh/devtoolset-9/root/usr/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2024-08-22T20:30:54.7324593Z + echo ::endgroup::
2024-08-22T20:30:54.7325238Z ##[endgroup]
2024-08-22T20:30:54.7325952Z + echo '::group::Download checkpoints'
2024-08-22T20:30:54.7327047Z ##[group]Download checkpoints
2024-08-22T20:30:54.7327472Z + ./install_requirements.sh cuda
2024-08-22T20:30:54.7328754Z + pip3 install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/nightly/cu121
2024-08-22T20:30:54.7330135Z Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/nightly/cu121
2024-08-22T20:30:54.7331146Z Ignoring tomli: markers 'python_version < "3.11"' don't match your environment
2024-08-22T20:30:54.7332225Z Collecting huggingface_hub (from -r requirements.txt (line 4))
2024-08-22T20:30:54.7333436Z   Downloading huggingface_hub-0.24.6-py3-none-any.whl (417 kB)
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2024-08-22T20:30:54.7337796Z [?25hCollecting gguf (from -r requirements.txt (line 7))
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2024-08-22T20:30:54.7342269Z [?25hCollecting tiktoken (from -r requirements.txt (line 10))
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2024-08-22T20:30:54.7351188Z [?25hCollecting sentencepiece (from -r requirements.txt (line 14))
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2024-08-22T20:30:54.7356631Z [?25hCollecting numpy<2.0 (from -r requirements.txt (line 15))
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2024-08-22T20:31:00.6519445Z [?25hCollecting ninja (from -r requirements.txt (line 24))
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2024-08-22T20:31:00.6523378Z [?25hCollecting zstd (from -r requirements.txt (line 25))
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2024-08-22T20:31:00.6526956Z [?25hCollecting streamlit (from -r requirements.txt (line 28))
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2024-08-22T20:31:03.7750002Z      ━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━ 260.1/797.3 MB 245.6 MB/s eta 0:00:03
2024-08-22T20:31:03.7751097Z      ━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━ 268.6/797.3 MB 245.3 MB/s eta 0:00:03
2024-08-22T20:31:03.7752188Z      ━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━ 277.2/797.3 MB 247.0 MB/s eta 0:00:03
2024-08-22T20:31:03.7753332Z      ━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━ 285.7/797.3 MB 248.4 MB/s eta 0:00:03
2024-08-22T20:31:03.7755248Z      ━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━ 294.3/797.3 MB 249.0 MB/s eta 0:00:03
2024-08-22T20:31:03.7758382Z      ━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━ 303.0/797.3 MB 250.1 MB/s eta 0:00:02
2024-08-22T20:31:03.7760435Z      ━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━ 310.6/797.3 MB 229.1 MB/s eta 0:00:03
2024-08-22T20:31:03.7762842Z      ━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━ 313.5/797.3 MB 228.3 MB/s eta 0:00:03
2024-08-22T20:31:03.7764208Z      ━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━ 313.5/797.3 MB 228.3 MB/s eta 0:00:03
2024-08-22T20:31:03.7765365Z      ━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━ 319.6/797.3 MB 93.2 MB/s eta 0:00:06
2024-08-22T20:31:03.7768790Z      ━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━ 326.1/797.3 MB 207.8 MB/s eta 0:00:03
2024-08-22T20:31:03.7770096Z      ━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━ 334.4/797.3 MB 236.3 MB/s eta 0:00:02
2024-08-22T20:31:03.7771416Z      ━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━ 342.6/797.3 MB 237.6 MB/s eta 0:00:02
2024-08-22T20:31:03.7772529Z      ━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━ 350.9/797.3 MB 239.1 MB/s eta 0:00:02
2024-08-22T20:31:03.7773620Z      ━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━ 358.8/797.3 MB 231.9 MB/s eta 0:00:02
2024-08-22T20:31:03.7774715Z      ━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 367.3/797.3 MB 245.5 MB/s eta 0:00:02
2024-08-22T20:31:03.7775817Z      ━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 375.8/797.3 MB 246.2 MB/s eta 0:00:02
2024-08-22T20:31:03.7776901Z      ━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━ 384.1/797.3 MB 239.6 MB/s eta 0:00:02
2024-08-22T20:31:03.7778263Z      ━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━ 391.6/797.3 MB 220.8 MB/s eta 0:00:02
2024-08-22T20:31:03.7779369Z      ━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━ 399.5/797.3 MB 230.4 MB/s eta 0:00:02
2024-08-22T20:31:03.7780454Z      ━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━ 406.6/797.3 MB 206.1 MB/s eta 0:00:02
2024-08-22T20:31:03.7781550Z      ━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━ 415.3/797.3 MB 242.9 MB/s eta 0:00:02
2024-08-22T20:31:03.7782799Z      ━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━ 421.9/797.3 MB 209.9 MB/s eta 0:00:02
2024-08-22T20:31:03.7783876Z      ━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━ 429.4/797.3 MB 193.3 MB/s eta 0:00:02
2024-08-22T20:31:03.7784960Z      ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━ 438.1/797.3 MB 249.9 MB/s eta 0:00:02
2024-08-22T20:31:03.7786038Z      ━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━ 443.9/797.3 MB 197.3 MB/s eta 0:00:02
2024-08-22T20:31:03.7787120Z      ━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━ 452.2/797.3 MB 236.4 MB/s eta 0:00:02
2024-08-22T20:31:03.7788202Z      ━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━ 460.3/797.3 MB 238.7 MB/s eta 0:00:02
2024-08-22T20:31:03.7789289Z      ━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━ 467.9/797.3 MB 218.1 MB/s eta 0:00:02
2024-08-22T20:31:03.7790366Z      ━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━ 476.5/797.3 MB 246.2 MB/s eta 0:00:02
2024-08-22T20:31:03.7791591Z      ━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━ 485.1/797.3 MB 250.9 MB/s eta 0:00:02
2024-08-22T20:31:03.7792675Z      ━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━ 493.1/797.3 MB 234.2 MB/s eta 0:00:02
2024-08-22T20:31:03.7793750Z      ━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━ 500.4/797.3 MB 216.2 MB/s eta 0:00:02
2024-08-22T20:31:05.0997930Z      ━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━ 509.0/797.3 MB 242.9 MB/s eta 0:00:02
2024-08-22T20:31:05.0999802Z      ━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━ 516.2/797.3 MB 219.1 MB/s eta 0:00:02
2024-08-22T20:31:05.1000922Z      ━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━ 524.3/797.3 MB 229.4 MB/s eta 0:00:02
2024-08-22T20:31:05.1002050Z      ━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━ 532.4/797.3 MB 230.3 MB/s eta 0:00:02
2024-08-22T20:31:05.1003189Z      ━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━ 539.6/797.3 MB 209.6 MB/s eta 0:00:02
2024-08-22T20:31:05.1004272Z      ━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━ 544.7/797.3 MB 170.4 MB/s eta 0:00:02
2024-08-22T20:31:05.1005356Z      ━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━ 550.5/797.3 MB 158.5 MB/s eta 0:00:02
2024-08-22T20:31:05.1006463Z      ━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━ 557.0/797.3 MB 185.7 MB/s eta 0:00:02
2024-08-22T20:31:05.1007542Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━ 563.1/797.3 MB 183.9 MB/s eta 0:00:02
2024-08-22T20:31:05.1009199Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━ 569.4/797.3 MB 170.6 MB/s eta 0:00:02
2024-08-22T20:31:05.1011565Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━ 576.7/797.3 MB 196.9 MB/s eta 0:00:02
2024-08-22T20:31:05.1013198Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━ 583.0/797.3 MB 200.4 MB/s eta 0:00:02
2024-08-22T20:31:05.1014693Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━ 589.0/797.3 MB 170.7 MB/s eta 0:00:02
2024-08-22T20:31:05.1016772Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━ 595.0/797.3 MB 168.5 MB/s eta 0:00:02
2024-08-22T20:31:05.1018416Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━ 603.5/797.3 MB 201.5 MB/s eta 0:00:01
2024-08-22T20:31:05.1020811Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━ 612.1/797.3 MB 247.2 MB/s eta 0:00:01
2024-08-22T20:31:05.1022938Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━ 620.2/797.3 MB 235.8 MB/s eta 0:00:01
2024-08-22T20:31:05.1024603Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━ 628.4/797.3 MB 238.3 MB/s eta 0:00:01
2024-08-22T20:31:05.1027061Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━ 636.2/797.3 MB 226.5 MB/s eta 0:00:01
2024-08-22T20:31:05.1028643Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━ 643.2/797.3 MB 202.7 MB/s eta 0:00:01
2024-08-22T20:31:05.1029728Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━ 649.9/797.3 MB 204.6 MB/s eta 0:00:01
2024-08-22T20:31:05.1030824Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━ 657.9/797.3 MB 214.3 MB/s eta 0:00:01
2024-08-22T20:31:05.1031908Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━ 666.2/797.3 MB 240.4 MB/s eta 0:00:01
2024-08-22T20:31:05.1033010Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━ 674.7/797.3 MB 245.3 MB/s eta 0:00:01
2024-08-22T20:31:05.1034111Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━ 683.2/797.3 MB 245.6 MB/s eta 0:00:01
2024-08-22T20:31:05.1035195Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━ 691.7/797.3 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:05.1036264Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━ 700.2/797.3 MB 245.0 MB/s eta 0:00:01
2024-08-22T20:31:05.1037330Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 706.8/797.3 MB 208.0 MB/s eta 0:00:01
2024-08-22T20:31:05.1038573Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 714.8/797.3 MB 228.8 MB/s eta 0:00:01
2024-08-22T20:31:05.1039650Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━ 722.2/797.3 MB 217.2 MB/s eta 0:00:01
2024-08-22T20:31:05.1040716Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━ 729.1/797.3 MB 212.8 MB/s eta 0:00:01
2024-08-22T20:31:05.1041782Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 735.9/797.3 MB 202.5 MB/s eta 0:00:01
2024-08-22T20:31:05.1042867Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 743.1/797.3 MB 204.9 MB/s eta 0:00:01
2024-08-22T20:31:05.1043925Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━ 751.6/797.3 MB 243.3 MB/s eta 0:00:01
2024-08-22T20:31:05.1045009Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 759.2/797.3 MB 248.9 MB/s eta 0:00:01
2024-08-22T20:31:05.1046066Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 759.2/797.3 MB 248.9 MB/s eta 0:00:01
2024-08-22T20:31:05.1047125Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 759.2/797.3 MB 248.9 MB/s eta 0:00:01
2024-08-22T20:31:05.1048277Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 763.9/797.3 MB 79.5 MB/s eta 0:00:01
2024-08-22T20:31:06.9886614Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━ 771.1/797.3 MB 216.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9888270Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺ 777.2/797.3 MB 181.8 MB/s eta 0:00:01
2024-08-22T20:31:06.9889438Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺ 784.6/797.3 MB 201.7 MB/s eta 0:00:01
2024-08-22T20:31:06.9890444Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 790.9/797.3 MB 195.1 MB/s eta 0:00:01
2024-08-22T20:31:06.9891436Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9892445Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9893519Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9894512Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9895728Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9896747Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9897752Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9898746Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9899750Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9900768Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9901768Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9902794Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9903783Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9904781Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9905775Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9906912Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9907903Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9908920Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9909914Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9910896Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9911882Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9912935Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9913919Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9914898Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9915979Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9916971Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9917957Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9918933Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9920022Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9921009Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9922012Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9922990Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9923984Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9924981Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9926001Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:06.9926982Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3718692Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3720136Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3721245Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3722247Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3723265Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3724352Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3725347Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3726657Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3727680Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3728790Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3729779Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3730966Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3731983Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3733001Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3733989Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3734992Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3735992Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3736991Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3737998Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3739012Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3740110Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3741113Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3742097Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3743095Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3744156Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3745136Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3746147Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:08.3747143Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
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2024-08-22T20:31:08.3755374Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
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2024-08-22T20:31:09.7553176Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:09.7554634Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:09.7556005Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:09.7557115Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
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2024-08-22T20:31:09.7564358Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:09.7565348Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
2024-08-22T20:31:09.7566347Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
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2024-08-22T20:31:11.1391737Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 797.3/797.3 MB 189.6 MB/s eta 0:00:01
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2024-08-22T20:31:24.0553733Z      ━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━ 410.3/664.8 MB 238.7 MB/s eta 0:00:02
2024-08-22T20:31:24.0554853Z      ━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━ 418.6/664.8 MB 240.5 MB/s eta 0:00:02
2024-08-22T20:31:24.0555941Z      ━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━ 426.9/664.8 MB 241.1 MB/s eta 0:00:01
2024-08-22T20:31:24.0557226Z      ━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━ 435.1/664.8 MB 238.3 MB/s eta 0:00:01
2024-08-22T20:31:24.0558329Z      ━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━ 443.4/664.8 MB 239.9 MB/s eta 0:00:01
2024-08-22T20:31:24.0559411Z      ━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━ 451.6/664.8 MB 239.2 MB/s eta 0:00:01
2024-08-22T20:31:24.0560486Z      ━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━ 459.9/664.8 MB 238.5 MB/s eta 0:00:01
2024-08-22T20:31:24.0561621Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━ 468.1/664.8 MB 237.6 MB/s eta 0:00:01
2024-08-22T20:31:24.0562743Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━ 476.3/664.8 MB 238.4 MB/s eta 0:00:01
2024-08-22T20:31:24.0563895Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━ 484.5/664.8 MB 238.6 MB/s eta 0:00:01
2024-08-22T20:31:24.0564971Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━ 492.7/664.8 MB 238.1 MB/s eta 0:00:01
2024-08-22T20:31:24.0566052Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━ 501.0/664.8 MB 238.5 MB/s eta 0:00:01
2024-08-22T20:31:24.0567165Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━ 509.2/664.8 MB 238.9 MB/s eta 0:00:01
2024-08-22T20:31:24.0568620Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━ 517.5/664.8 MB 238.9 MB/s eta 0:00:01
2024-08-22T20:31:24.0569692Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━ 525.7/664.8 MB 239.3 MB/s eta 0:00:01
2024-08-22T20:31:24.0570804Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━ 533.9/664.8 MB 238.5 MB/s eta 0:00:01
2024-08-22T20:31:24.0571873Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━ 542.2/664.8 MB 238.6 MB/s eta 0:00:01
2024-08-22T20:31:24.0572950Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━ 550.5/664.8 MB 239.7 MB/s eta 0:00:01
2024-08-22T20:31:24.0574088Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━ 558.8/664.8 MB 241.0 MB/s eta 0:00:01
2024-08-22T20:31:24.0575242Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━ 567.0/664.8 MB 238.5 MB/s eta 0:00:01
2024-08-22T20:31:24.0576314Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━ 575.1/664.8 MB 235.8 MB/s eta 0:00:01
2024-08-22T20:31:24.0577560Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━ 583.2/664.8 MB 235.6 MB/s eta 0:00:01
2024-08-22T20:31:24.0579037Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 591.5/664.8 MB 237.7 MB/s eta 0:00:01
2024-08-22T20:31:24.0580119Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 599.7/664.8 MB 238.7 MB/s eta 0:00:01
2024-08-22T20:31:24.0581194Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━ 607.9/664.8 MB 238.7 MB/s eta 0:00:01
2024-08-22T20:31:24.0582463Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 616.2/664.8 MB 239.8 MB/s eta 0:00:01
2024-08-22T20:31:24.0583527Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━ 624.4/664.8 MB 239.0 MB/s eta 0:00:01
2024-08-22T20:31:24.0584614Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 632.5/664.8 MB 236.1 MB/s eta 0:00:01
2024-08-22T20:31:24.0585680Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━ 640.6/664.8 MB 233.8 MB/s eta 0:00:01
2024-08-22T20:31:24.0586729Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺ 648.6/664.8 MB 233.3 MB/s eta 0:00:01
2024-08-22T20:31:24.0587743Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 656.8/664.8 MB 236.4 MB/s eta 0:00:01
2024-08-22T20:31:24.0588770Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:24.0589774Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:24.0590784Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4859915Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4861178Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4862318Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4863331Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4864359Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4865354Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4866543Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4867546Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4868552Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4869547Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4870661Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4871681Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4872693Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4873684Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4874665Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4875652Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4876664Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4877978Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4879124Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4880286Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4881297Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4882296Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4883281Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4884302Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
2024-08-22T20:31:25.4885296Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
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2024-08-22T20:31:25.4890533Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
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2024-08-22T20:31:25.4892514Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 664.8/664.8 MB 245.5 MB/s eta 0:00:01
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2024-08-22T20:31:30.0969171Z [?25hCollecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.8->lm-eval==0.4.2->-r requirements.txt (line 17))
2024-08-22T20:31:30.0970540Z   Downloading https://download.pytorch.org/whl/nightly/cu121/nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)
2024-08-22T20:31:30.0971748Z [?25l     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/410.6 MB ? eta -:--:--
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2024-08-22T20:31:31.6403236Z      ━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━ 184.7/410.6 MB 240.2 MB/s eta 0:00:01
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2024-08-22T20:31:31.6406432Z      ━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━ 201.3/410.6 MB 241.0 MB/s eta 0:00:01
2024-08-22T20:31:31.6407897Z      ━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━ 209.6/410.6 MB 239.9 MB/s eta 0:00:01
2024-08-22T20:31:31.6409543Z      ━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━ 217.8/410.6 MB 238.8 MB/s eta 0:00:01
2024-08-22T20:31:31.6411006Z      ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━ 226.0/410.6 MB 237.9 MB/s eta 0:00:01
2024-08-22T20:31:31.6412469Z      ━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━ 234.3/410.6 MB 239.4 MB/s eta 0:00:01
2024-08-22T20:31:31.6413915Z      ━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━ 242.6/410.6 MB 240.1 MB/s eta 0:00:01
2024-08-22T20:31:31.6415404Z      ━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━ 250.8/410.6 MB 239.1 MB/s eta 0:00:01
2024-08-22T20:31:31.6416863Z      ━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━ 258.9/410.6 MB 234.6 MB/s eta 0:00:01
2024-08-22T20:31:31.6418447Z      ━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━ 266.9/410.6 MB 232.7 MB/s eta 0:00:01
2024-08-22T20:31:31.6419908Z      ━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━ 275.0/410.6 MB 235.1 MB/s eta 0:00:01
2024-08-22T20:31:31.6421355Z      ━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━ 283.1/410.6 MB 235.9 MB/s eta 0:00:01
2024-08-22T20:31:31.6422802Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━ 291.3/410.6 MB 237.7 MB/s eta 0:00:01
2024-08-22T20:31:31.6424278Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━ 299.5/410.6 MB 237.7 MB/s eta 0:00:01
2024-08-22T20:31:31.6425722Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━ 307.7/410.6 MB 236.5 MB/s eta 0:00:01
2024-08-22T20:31:31.6427203Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━ 315.9/410.6 MB 237.2 MB/s eta 0:00:01
2024-08-22T20:31:31.6428654Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━ 323.9/410.6 MB 235.1 MB/s eta 0:00:01
2024-08-22T20:31:31.6430090Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━ 332.1/410.6 MB 234.8 MB/s eta 0:00:01
2024-08-22T20:31:31.6431533Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━ 340.2/410.6 MB 235.4 MB/s eta 0:00:01
2024-08-22T20:31:31.6433129Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━ 348.2/410.6 MB 233.5 MB/s eta 0:00:01
2024-08-22T20:31:31.6434583Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━ 356.1/410.6 MB 228.9 MB/s eta 0:00:01
2024-08-22T20:31:31.6436060Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 364.1/410.6 MB 231.7 MB/s eta 0:00:01
2024-08-22T20:31:31.6437495Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━ 372.1/410.6 MB 230.8 MB/s eta 0:00:01
2024-08-22T20:31:31.6438950Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 380.1/410.6 MB 232.3 MB/s eta 0:00:01
2024-08-22T20:31:31.6440400Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━ 388.3/410.6 MB 236.5 MB/s eta 0:00:01
2024-08-22T20:31:31.6441876Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━ 396.6/410.6 MB 240.0 MB/s eta 0:00:01
2024-08-22T20:31:31.6443279Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺ 404.8/410.6 MB 239.4 MB/s eta 0:00:01
2024-08-22T20:31:31.6444637Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 410.6/410.6 MB 246.0 MB/s eta 0:00:01
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2024-08-22T20:31:34.7278466Z [?25hCollecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.8->lm-eval==0.4.2->-r requirements.txt (line 17))
2024-08-22T20:31:34.7280561Z   Downloading https://download.pytorch.org/whl/nightly/cu121/nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)
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2024-08-22T20:31:37.5932840Z [?25hCollecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.8->lm-eval==0.4.2->-r requirements.txt (line 17))
2024-08-22T20:31:37.5934232Z   Downloading https://download.pytorch.org/whl/nightly/cu121/nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)
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2024-08-22T20:31:37.5973556Z [?25hCollecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.8->lm-eval==0.4.2->-r requirements.txt (line 17))
2024-08-22T20:31:37.5975235Z   Downloading https://download.pytorch.org/whl/nightly/cu121/nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)
2024-08-22T20:31:37.5976690Z [?25l     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/124.2 MB ? eta -:--:--
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2024-08-22T20:31:40.4299543Z [?25hCollecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.8->lm-eval==0.4.2->-r requirements.txt (line 17))
2024-08-22T20:31:40.4301634Z   Downloading https://download.pytorch.org/whl/nightly/cu121/nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)
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2024-08-22T20:31:44.7926153Z [?25hCollecting nvidia-nccl-cu12==2.20.5 (from torch>=1.8->lm-eval==0.4.2->-r requirements.txt (line 17))
2024-08-22T20:31:44.7928825Z   Downloading https://download.pytorch.org/whl/nightly/cu121/nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)
2024-08-22T20:31:44.7930269Z [?25l     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/176.2 MB ? eta -:--:--
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2024-08-22T20:31:46.1762253Z [?25hCollecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.8->lm-eval==0.4.2->-r requirements.txt (line 17))
2024-08-22T20:31:46.1764359Z   Downloading https://download.pytorch.org/whl/nightly/cu121/nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)
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2024-08-22T20:31:46.1768422Z [?25hCollecting triton==3.0.0 (from torch>=1.8->lm-eval==0.4.2->-r requirements.txt (line 17))
2024-08-22T20:31:46.1769986Z   Downloading triton-3.0.0-1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (209.4 MB)
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2024-08-22T20:32:20.9456106Z [?25hBuilding wheels for collected packages: rouge-score, numexpr, sqlitedict, word2number
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2024-08-22T20:32:20.9467189Z   Stored in directory: /root/.cache/pip/wheels/73/63/89/7210274f9b7fb033b8f22671f64c0e0b55083d30c3c046a3ff
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2024-08-22T20:32:20.9469410Z [?25h  Created wheel for word2number: filename=word2number-1.1-py3-none-any.whl size=5566 sha256=4bbce053b7c82308872528add59568e51f9edc5b286304737a71fa9f5f3da9ac
2024-08-22T20:32:20.9470860Z   Stored in directory: /root/.cache/pip/wheels/cd/ef/ae/073b491b14d25e2efafcffca9e16b2ee6d114ec5c643ba4f06
2024-08-22T20:32:20.9471830Z Successfully built rouge-score numexpr sqlitedict word2number
2024-08-22T20:32:20.9478762Z Installing collected packages: zstd, word2number, sqlitedict, sentencepiece, ninja, mpmath, xxhash, Werkzeug, watchdog, tzdata, typing-extensions, triton, tqdm, tornado, toml, threadpoolctl, tenacity, tcolorpy, tabulate, sympy, smmap, safetensors, requests, regex, pygments, pycryptodomex, pybind11, protobuf, portalocker, pillow, pathvalidate, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, narwhals, multidict, mdurl, mbstrdecoder, lxml, jsonlines, joblib, itsdangerous, fsspec, frozenlist, dill, colorama, cmake, cachetools, blinker, aiohappyeyeballs, absl-py, yarl, typepy, tqdm-multiprocess, tiktoken, snakeviz, scipy, sacrebleu, pydeck, pyarrow, pandas, nvidia-cusparse-cu12, nvidia-cudnn-cu12, numexpr, nltk, multiprocess, markdown-it-py, huggingface_hub, gitdb, gguf, flask, blobfile, aiosignal, tokenizers, scikit-learn, rouge-score, rich, nvidia-cusolver-cu12, gitpython, aiohttp, transformers, torch, DataProperty, altair, tabledata, streamlit, datasets, accelerate, pytablewriter, peft, evaluate, lm-eval
2024-08-22T20:32:20.9485004Z   Attempting uninstall: tqdm
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2024-08-22T20:32:20.9485908Z     Uninstalling tqdm-4.65.0:
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2024-08-22T20:33:11.6210209Z     Found existing installation: cmake 3.18.2
2024-08-22T20:33:11.6210990Z     Uninstalling cmake-3.18.2:
2024-08-22T20:33:11.6211444Z       Successfully uninstalled cmake-3.18.2
2024-08-22T20:33:11.6222458Z Successfully installed DataProperty-1.0.1 Werkzeug-3.0.4 absl-py-2.1.0 accelerate-0.33.0 aiohappyeyeballs-2.4.0 aiohttp-3.10.5 aiosignal-1.3.1 altair-5.4.0 blinker-1.8.2 blobfile-2.1.1 cachetools-5.5.0 cmake-3.30.2 colorama-0.4.6 datasets-2.21.0 dill-0.3.8 evaluate-0.4.2 flask-3.0.3 frozenlist-1.4.1 fsspec-2024.6.1 gguf-0.9.1 gitdb-4.0.11 gitpython-3.1.43 huggingface_hub-0.24.6 itsdangerous-2.2.0 joblib-1.4.2 jsonlines-4.0.0 lm-eval-0.4.2 lxml-4.9.4 markdown-it-py-3.0.0 mbstrdecoder-1.1.3 mdurl-0.1.2 mpmath-1.3.0 multidict-6.0.5 multiprocess-0.70.16 narwhals-1.5.2 networkx-3.3 ninja-1.11.1.1 nltk-3.9.1 numexpr-2.10.1 numpy-1.26.4 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105 pandas-2.2.2 pathvalidate-3.2.0 peft-0.12.0 pillow-10.4.0 portalocker-2.10.1 protobuf-5.27.3 pyarrow-17.0.0 pybind11-2.13.5 pycryptodomex-3.20.0 pydeck-0.9.1 pygments-2.18.0 pytablewriter-1.2.0 regex-2024.7.24 requests-2.32.3 rich-13.7.1 rouge-score-0.1.2 sacrebleu-2.4.3 safetensors-0.4.4 scikit-learn-1.5.1 scipy-1.14.1 sentencepiece-0.2.0 smmap-5.0.1 snakeviz-2.2.0 sqlitedict-2.1.0 streamlit-1.37.1 sympy-1.13.2 tabledata-1.3.3 tabulate-0.9.0 tcolorpy-0.1.6 tenacity-8.5.0 threadpoolctl-3.5.0 tiktoken-0.7.0 tokenizers-0.19.1 toml-0.10.2 torch-2.4.0 tornado-6.4.1 tqdm-4.66.5 tqdm-multiprocess-0.0.11 transformers-4.44.2 triton-3.0.0 typepy-1.3.2 typing-extensions-4.12.2 tzdata-2024.1 watchdog-4.0.2 word2number-1.1 xxhash-3.5.0 yarl-1.9.4 zstd-1.5.5.1
2024-08-22T20:33:11.6234448Z WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
2024-08-22T20:33:11.6235866Z + pip3 uninstall -y triton
2024-08-22T20:33:11.6236292Z Found existing installation: triton 3.0.0
2024-08-22T20:33:11.6236758Z Uninstalling triton-3.0.0:
2024-08-22T20:33:11.6237317Z   Successfully uninstalled triton-3.0.0
2024-08-22T20:33:11.6239065Z WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
2024-08-22T20:33:11.6240854Z + pip3 install --extra-index-url https://download.pytorch.org/whl/nightly/cu121 torch==2.5.0.dev20240814
2024-08-22T20:33:11.6241946Z Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/nightly/cu121
2024-08-22T20:33:11.6242636Z Collecting torch==2.5.0.dev20240814
2024-08-22T20:33:11.6243636Z   Downloading https://download.pytorch.org/whl/nightly/cu121/torch-2.5.0.dev20240814%2Bcu121-cp311-cp311-linux_x86_64.whl (778.6 MB)
2024-08-22T20:33:11.6244873Z [?25l     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/778.6 MB ? eta -:--:--
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2024-08-22T20:33:11.6256966Z      ━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 91.5/778.6 MB 238.8 MB/s eta 0:00:03
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2024-08-22T20:33:11.6262663Z      ━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 133.0/778.6 MB 240.8 MB/s eta 0:00:03
2024-08-22T20:33:11.6263768Z      ━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 141.4/778.6 MB 241.6 MB/s eta 0:00:03
2024-08-22T20:33:11.6264869Z      ━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 149.7/778.6 MB 241.6 MB/s eta 0:00:03
2024-08-22T20:33:11.6265964Z      ━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 158.0/778.6 MB 240.6 MB/s eta 0:00:03
2024-08-22T20:33:11.6267357Z      ━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 166.0/778.6 MB 234.0 MB/s eta 0:00:03
2024-08-22T20:33:11.6268874Z      ━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 173.9/778.6 MB 229.0 MB/s eta 0:00:03
2024-08-22T20:33:11.6270214Z      ━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 179.6/778.6 MB 189.2 MB/s eta 0:00:04
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2024-08-22T20:33:12.9443891Z      ━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━ 199.7/778.6 MB 194.4 MB/s eta 0:00:03
2024-08-22T20:33:12.9445081Z      ━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━ 207.9/778.6 MB 236.4 MB/s eta 0:00:03
2024-08-22T20:33:12.9446232Z      ━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━ 216.1/778.6 MB 237.8 MB/s eta 0:00:03
2024-08-22T20:33:12.9447343Z      ━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━ 224.4/778.6 MB 241.3 MB/s eta 0:00:03
2024-08-22T20:33:12.9448828Z      ━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━ 232.8/778.6 MB 242.8 MB/s eta 0:00:03
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2024-08-22T20:33:12.9451114Z      ━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━ 249.4/778.6 MB 239.8 MB/s eta 0:00:03
2024-08-22T20:33:12.9452265Z      ━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━ 257.7/778.6 MB 240.8 MB/s eta 0:00:03
2024-08-22T20:33:12.9453542Z      ━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━ 266.1/778.6 MB 241.1 MB/s eta 0:00:03
2024-08-22T20:33:12.9454640Z      ━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━ 274.4/778.6 MB 241.5 MB/s eta 0:00:03
2024-08-22T20:33:12.9455754Z      ━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━ 282.8/778.6 MB 242.1 MB/s eta 0:00:03
2024-08-22T20:33:12.9456847Z      ━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━ 291.2/778.6 MB 244.6 MB/s eta 0:00:02
2024-08-22T20:33:12.9457937Z      ━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━ 299.7/778.6 MB 245.2 MB/s eta 0:00:02
2024-08-22T20:33:12.9459034Z      ━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━ 308.1/778.6 MB 244.5 MB/s eta 0:00:02
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2024-08-22T20:33:12.9463532Z      ━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━ 341.5/778.6 MB 238.9 MB/s eta 0:00:02
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2024-08-22T20:33:12.9466826Z      ━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 365.5/778.6 MB 219.2 MB/s eta 0:00:02
2024-08-22T20:33:12.9467917Z      ━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━ 372.8/778.6 MB 211.7 MB/s eta 0:00:02
2024-08-22T20:33:12.9469020Z      ━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━ 380.0/778.6 MB 210.9 MB/s eta 0:00:02
2024-08-22T20:33:12.9470109Z      ━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━ 387.2/778.6 MB 209.2 MB/s eta 0:00:02
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2024-08-22T20:33:12.9472481Z      ━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━ 401.6/778.6 MB 209.4 MB/s eta 0:00:02
2024-08-22T20:33:12.9473694Z      ━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━ 409.5/778.6 MB 224.4 MB/s eta 0:00:02
2024-08-22T20:33:12.9474774Z      ━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━ 417.4/778.6 MB 230.9 MB/s eta 0:00:02
2024-08-22T20:33:12.9475881Z      ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━ 425.4/778.6 MB 231.2 MB/s eta 0:00:02
2024-08-22T20:33:12.9476963Z      ━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━ 433.4/778.6 MB 231.8 MB/s eta 0:00:02
2024-08-22T20:33:12.9478330Z      ━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━ 441.3/778.6 MB 228.8 MB/s eta 0:00:02
2024-08-22T20:33:12.9479422Z      ━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━ 449.2/778.6 MB 229.8 MB/s eta 0:00:02
2024-08-22T20:33:12.9480527Z      ━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━ 457.2/778.6 MB 230.1 MB/s eta 0:00:02
2024-08-22T20:33:12.9481630Z      ━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━ 465.2/778.6 MB 231.1 MB/s eta 0:00:02
2024-08-22T20:33:12.9482899Z      ━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━ 473.2/778.6 MB 229.7 MB/s eta 0:00:02
2024-08-22T20:33:12.9483995Z      ━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━ 481.0/778.6 MB 228.9 MB/s eta 0:00:02
2024-08-22T20:33:12.9485082Z      ━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━ 489.0/778.6 MB 229.5 MB/s eta 0:00:02
2024-08-22T20:33:12.9486194Z      ━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━ 496.9/778.6 MB 231.4 MB/s eta 0:00:02
2024-08-22T20:33:14.7153447Z      ━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━ 504.9/778.6 MB 231.6 MB/s eta 0:00:02
2024-08-22T20:33:14.7154639Z      ━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━ 512.9/778.6 MB 231.4 MB/s eta 0:00:02
2024-08-22T20:33:14.7155780Z      ━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━ 520.8/778.6 MB 230.6 MB/s eta 0:00:02
2024-08-22T20:33:14.7156868Z      ━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━ 528.8/778.6 MB 230.5 MB/s eta 0:00:02
2024-08-22T20:33:14.7157947Z      ━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━ 536.9/778.6 MB 234.8 MB/s eta 0:00:02
2024-08-22T20:33:14.7159030Z      ━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━ 544.8/778.6 MB 230.2 MB/s eta 0:00:02
2024-08-22T20:33:14.7160517Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━ 553.1/778.6 MB 238.3 MB/s eta 0:00:01
2024-08-22T20:33:14.7161603Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━ 561.1/778.6 MB 233.6 MB/s eta 0:00:01
2024-08-22T20:33:14.7162700Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━ 569.1/778.6 MB 231.6 MB/s eta 0:00:01
2024-08-22T20:33:14.7163786Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━ 577.1/778.6 MB 231.0 MB/s eta 0:00:01
2024-08-22T20:33:14.7164867Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━ 584.9/778.6 MB 228.7 MB/s eta 0:00:01
2024-08-22T20:33:14.7165938Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━ 592.9/778.6 MB 229.3 MB/s eta 0:00:01
2024-08-22T20:33:14.7167028Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━ 601.0/778.6 MB 234.8 MB/s eta 0:00:01
2024-08-22T20:33:14.7168245Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━ 608.9/778.6 MB 232.0 MB/s eta 0:00:01
2024-08-22T20:33:14.7169527Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━ 616.9/778.6 MB 231.3 MB/s eta 0:00:01
2024-08-22T20:33:14.7170610Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━ 624.8/778.6 MB 229.9 MB/s eta 0:00:01
2024-08-22T20:33:14.7171682Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━ 632.7/778.6 MB 228.7 MB/s eta 0:00:01
2024-08-22T20:33:14.7172816Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━ 641.0/778.6 MB 237.3 MB/s eta 0:00:01
2024-08-22T20:33:14.7173998Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━ 649.1/778.6 MB 236.3 MB/s eta 0:00:01
2024-08-22T20:33:14.7175073Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━ 657.1/778.6 MB 229.7 MB/s eta 0:00:01
2024-08-22T20:33:14.7176164Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━ 665.1/778.6 MB 232.1 MB/s eta 0:00:01
2024-08-22T20:33:14.7177236Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━ 673.1/778.6 MB 231.1 MB/s eta 0:00:01
2024-08-22T20:33:14.7178526Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━ 681.1/778.6 MB 231.2 MB/s eta 0:00:01
2024-08-22T20:33:14.7179608Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 689.0/778.6 MB 230.1 MB/s eta 0:00:01
2024-08-22T20:33:14.7180696Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 697.0/778.6 MB 232.0 MB/s eta 0:00:01
2024-08-22T20:33:14.7181787Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━ 705.0/778.6 MB 229.5 MB/s eta 0:00:01
2024-08-22T20:33:14.7182859Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━ 712.9/778.6 MB 231.1 MB/s eta 0:00:01
2024-08-22T20:33:14.7183944Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 720.9/778.6 MB 231.2 MB/s eta 0:00:01
2024-08-22T20:33:14.7185008Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━ 728.8/778.6 MB 230.3 MB/s eta 0:00:01
2024-08-22T20:33:14.7186078Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 737.1/778.6 MB 238.3 MB/s eta 0:00:01
2024-08-22T20:33:14.7187149Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 745.4/778.6 MB 240.1 MB/s eta 0:00:01
2024-08-22T20:33:14.7188233Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━ 753.7/778.6 MB 241.0 MB/s eta 0:00:01
2024-08-22T20:33:14.7189267Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺ 762.0/778.6 MB 239.9 MB/s eta 0:00:01
2024-08-22T20:33:14.7190422Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 770.3/778.6 MB 239.6 MB/s eta 0:00:01
2024-08-22T20:33:14.7191435Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:14.7192487Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:14.7193470Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:14.7194603Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1505479Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1506972Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1508053Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1509277Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1510303Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1511330Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1512333Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1513318Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1514571Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1515594Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1516594Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1517581Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1518607Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1519607Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1520618Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1521602Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1522607Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1523603Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1524767Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1525750Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1526772Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1527778Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1528919Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
2024-08-22T20:33:16.1529907Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 778.6/778.6 MB 244.2 MB/s eta 0:00:01
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2024-08-22T20:33:23.4658986Z [?25hRequirement already satisfied: filelock in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (3.13.1)
2024-08-22T20:33:23.4660499Z Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (4.12.2)
2024-08-22T20:33:23.4661996Z Requirement already satisfied: networkx in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (3.3)
2024-08-22T20:33:23.4663396Z Requirement already satisfied: jinja2 in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (3.1.4)
2024-08-22T20:33:23.4664976Z Requirement already satisfied: fsspec in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (2024.6.1)
2024-08-22T20:33:23.4666485Z Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (12.1.105)
2024-08-22T20:33:23.4668311Z Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (12.1.105)
2024-08-22T20:33:23.4670035Z Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (12.1.105)
2024-08-22T20:33:23.4671757Z Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (9.1.0.70)
2024-08-22T20:33:23.4673408Z Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (12.1.3.1)
2024-08-22T20:33:23.4675048Z Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (11.0.2.54)
2024-08-22T20:33:23.4676707Z Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (10.3.2.106)
2024-08-22T20:33:23.4678787Z Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (11.4.5.107)
2024-08-22T20:33:23.4680492Z Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (12.1.0.106)
2024-08-22T20:33:23.4681746Z Collecting nvidia-nccl-cu12==2.21.5 (from torch==2.5.0.dev20240814)
2024-08-22T20:33:23.4683099Z   Downloading https://download.pytorch.org/whl/nightly/cu121/nvidia_nccl_cu12-2.21.5-py3-none-manylinux2014_x86_64.whl (188.7 MB)
2024-08-22T20:33:23.4684360Z [?25l     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/188.7 MB ? eta -:--:--
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2024-08-22T20:33:23.4690451Z      ━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 40.6/188.7 MB 236.7 MB/s eta 0:00:01
2024-08-22T20:33:23.4691594Z      ━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 48.8/188.7 MB 235.6 MB/s eta 0:00:01
2024-08-22T20:33:23.4692726Z      ━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━ 56.9/188.7 MB 235.4 MB/s eta 0:00:01
2024-08-22T20:33:23.4693816Z      ━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━ 65.0/188.7 MB 235.2 MB/s eta 0:00:01
2024-08-22T20:33:23.4694965Z      ━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━ 73.1/188.7 MB 234.6 MB/s eta 0:00:01
2024-08-22T20:33:23.4696095Z      ━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━ 81.0/188.7 MB 231.9 MB/s eta 0:00:01
2024-08-22T20:33:23.4697241Z      ━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━ 89.1/188.7 MB 233.4 MB/s eta 0:00:01
2024-08-22T20:33:23.4698333Z      ━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━ 97.2/188.7 MB 235.3 MB/s eta 0:00:01
2024-08-22T20:33:23.4699413Z      ━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━ 105.4/188.7 MB 236.3 MB/s eta 0:00:01
2024-08-22T20:33:23.4700552Z      ━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━ 113.6/188.7 MB 236.9 MB/s eta 0:00:01
2024-08-22T20:33:23.4701651Z      ━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━ 121.6/188.7 MB 234.2 MB/s eta 0:00:01
2024-08-22T20:33:23.4702732Z      ━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━ 129.5/188.7 MB 228.7 MB/s eta 0:00:01
2024-08-22T20:33:24.9246926Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━ 137.2/188.7 MB 225.8 MB/s eta 0:00:01
2024-08-22T20:33:24.9248508Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━ 145.3/188.7 MB 233.1 MB/s eta 0:00:01
2024-08-22T20:33:24.9249622Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━ 153.0/188.7 MB 228.0 MB/s eta 0:00:01
2024-08-22T20:33:24.9250711Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━ 160.9/188.7 MB 226.9 MB/s eta 0:00:01
2024-08-22T20:33:24.9252023Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 168.7/188.7 MB 227.0 MB/s eta 0:00:01
2024-08-22T20:33:24.9253108Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━ 176.8/188.7 MB 233.5 MB/s eta 0:00:01
2024-08-22T20:33:24.9254157Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺ 184.6/188.7 MB 228.1 MB/s eta 0:00:01
2024-08-22T20:33:24.9255192Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9256202Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9257197Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9258181Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9259202Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9260199Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9261295Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9262282Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9263283Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9264279Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9265343Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9266330Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9267337Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9268348Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9269329Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9270317Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9271311Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9272407Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9273388Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9274388Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9275433Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9276426Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9277410Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9278733Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9279723Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9280870Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9281854Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9282841Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9283826Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9284809Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9285804Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:24.9286793Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3064797Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3066273Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3067586Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3068631Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3069879Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3070879Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3071877Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3072865Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3073868Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3074871Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3075933Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3076933Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3078238Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3079418Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3080413Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3081412Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3082403Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3083524Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3084506Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
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2024-08-22T20:33:26.3094655Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3095639Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
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2024-08-22T20:33:26.3097621Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3098716Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3099701Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3100686Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3101679Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3102788Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3103772Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:26.3104827Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
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2024-08-22T20:33:27.7604020Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:27.7605657Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:27.7606837Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:27.7607897Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:27.7609034Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:27.7610367Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
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2024-08-22T20:33:27.7613492Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:27.7614542Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:27.7615641Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:27.7616701Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 188.7/188.7 MB 232.4 MB/s eta 0:00:01
2024-08-22T20:33:27.7617678Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 188.7/188.7 MB 3.3 MB/s eta 0:00:00
2024-08-22T20:33:27.7618969Z [?25hRequirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.11/site-packages (from torch==2.5.0.dev20240814) (12.1.105)
2024-08-22T20:33:27.7620172Z Collecting pytorch-triton==3.0.0+dedb7bdf33 (from torch==2.5.0.dev20240814)
2024-08-22T20:33:27.7621405Z   Downloading https://download.pytorch.org/whl/nightly/pytorch_triton-3.0.0%2Bdedb7bdf33-cp311-cp311-linux_x86_64.whl (239.5 MB)
2024-08-22T20:33:27.7622783Z [?25l     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/239.5 MB ? eta -:--:--
2024-08-22T20:33:27.7623860Z      ━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.9/239.5 MB 237.7 MB/s eta 0:00:01
2024-08-22T20:33:27.7624980Z      ━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 15.9/239.5 MB 230.0 MB/s eta 0:00:01
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2024-08-22T20:33:27.7627168Z      ━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 31.4/239.5 MB 224.9 MB/s eta 0:00:01
2024-08-22T20:33:27.7628319Z      ━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 39.1/239.5 MB 224.8 MB/s eta 0:00:01
2024-08-22T20:33:27.7629432Z      ━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 46.8/239.5 MB 223.5 MB/s eta 0:00:01
2024-08-22T20:33:27.7630577Z      ━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 54.5/239.5 MB 224.2 MB/s eta 0:00:01
2024-08-22T20:33:27.7631853Z      ━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 62.5/239.5 MB 230.2 MB/s eta 0:00:01
2024-08-22T20:33:27.7632996Z      ━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━ 70.3/239.5 MB 227.8 MB/s eta 0:00:01
2024-08-22T20:33:27.7634089Z      ━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━ 78.1/239.5 MB 223.4 MB/s eta 0:00:01
2024-08-22T20:33:27.7635272Z      ━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━ 85.9/239.5 MB 227.4 MB/s eta 0:00:01
2024-08-22T20:33:27.7636416Z      ━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━ 93.8/239.5 MB 229.3 MB/s eta 0:00:01
2024-08-22T20:33:27.7637502Z      ━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━ 101.9/239.5 MB 233.1 MB/s eta 0:00:01
2024-08-22T20:33:27.7638667Z      ━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 110.1/239.5 MB 237.0 MB/s eta 0:00:01
2024-08-22T20:33:27.7639761Z      ━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━ 118.1/239.5 MB 234.7 MB/s eta 0:00:01
2024-08-22T20:33:27.7640897Z      ━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━ 126.3/239.5 MB 236.1 MB/s eta 0:00:01
2024-08-22T20:33:27.7642025Z      ━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━ 134.4/239.5 MB 235.1 MB/s eta 0:00:01
2024-08-22T20:33:27.7643298Z      ━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━ 142.4/239.5 MB 235.3 MB/s eta 0:00:01
2024-08-22T20:33:27.7644383Z      ━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━ 150.6/239.5 MB 235.7 MB/s eta 0:00:01
2024-08-22T20:33:27.7645587Z      ━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━ 158.7/239.5 MB 236.1 MB/s eta 0:00:01
2024-08-22T20:33:27.7646716Z      ━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━ 166.7/239.5 MB 233.0 MB/s eta 0:00:01
2024-08-22T20:33:27.7647846Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━ 174.6/239.5 MB 229.0 MB/s eta 0:00:01
2024-08-22T20:33:27.7649042Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━ 182.7/239.5 MB 233.7 MB/s eta 0:00:01
2024-08-22T20:33:29.2242357Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━ 190.8/239.5 MB 235.4 MB/s eta 0:00:01
2024-08-22T20:33:29.2243542Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━ 198.9/239.5 MB 235.1 MB/s eta 0:00:01
2024-08-22T20:33:29.2244942Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━ 206.9/239.5 MB 233.0 MB/s eta 0:00:01
2024-08-22T20:33:29.2246114Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 215.0/239.5 MB 233.8 MB/s eta 0:00:01
2024-08-22T20:33:29.2247214Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 223.1/239.5 MB 233.0 MB/s eta 0:00:01
2024-08-22T20:33:29.2248390Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━ 230.9/239.5 MB 227.1 MB/s eta 0:00:01
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2024-08-22T20:35:19.4750141Z [?25hCollecting sympy==1.13.1 (from torch==2.5.0.dev20240814)
2024-08-22T20:35:19.4751039Z   Downloading https://download.pytorch.org/whl/nightly/sympy-1.13.1-py3-none-any.whl (6.2 MB)
2024-08-22T20:35:19.4752285Z [?25l     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/6.2 MB ? eta -:--:--
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2024-08-22T20:35:19.4757328Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.2/6.2 MB 39.7 MB/s eta 0:00:00
2024-08-22T20:35:19.4758787Z [?25hRequirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.11/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.5.0.dev20240814) (12.6.20)
2024-08-22T20:35:19.4760609Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/lib/python3.11/site-packages (from sympy==1.13.1->torch==2.5.0.dev20240814) (1.3.0)
2024-08-22T20:35:19.4762165Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.11/site-packages (from jinja2->torch==2.5.0.dev20240814) (2.1.3)
2024-08-22T20:35:19.4763334Z Installing collected packages: sympy, pytorch-triton, nvidia-nccl-cu12, torch
2024-08-22T20:35:19.4763940Z   Attempting uninstall: sympy
2024-08-22T20:35:19.4764359Z     Found existing installation: sympy 1.13.2
2024-08-22T20:35:19.4764856Z     Uninstalling sympy-1.13.2:
2024-08-22T20:35:19.4765312Z       Successfully uninstalled sympy-1.13.2
2024-08-22T20:35:19.4765861Z   Attempting uninstall: nvidia-nccl-cu12
2024-08-22T20:35:19.4766437Z     Found existing installation: nvidia-nccl-cu12 2.20.5
2024-08-22T20:35:19.4766997Z     Uninstalling nvidia-nccl-cu12-2.20.5:
2024-08-22T20:35:19.4767558Z       Successfully uninstalled nvidia-nccl-cu12-2.20.5
2024-08-22T20:35:19.4768045Z   Attempting uninstall: torch
2024-08-22T20:35:19.4768617Z     Found existing installation: torch 2.4.0
2024-08-22T20:35:19.4769108Z     Uninstalling torch-2.4.0:
2024-08-22T20:35:19.4769556Z       Successfully uninstalled torch-2.4.0
2024-08-22T20:35:19.4770657Z Successfully installed nvidia-nccl-cu12-2.21.5 pytorch-triton-3.0.0+dedb7bdf33 sympy-1.13.1 torch-2.5.0.dev20240814+cu121
2024-08-22T20:35:19.4772674Z WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
2024-08-22T20:35:19.4774440Z + pip3 install git+https://github.com/pytorch/ao.git@e11201a62669f582d81cdb33e031a07fb8dfc4f3
2024-08-22T20:35:19.4775415Z Collecting git+https://github.com/pytorch/ao.git@e11201a62669f582d81cdb33e031a07fb8dfc4f3
2024-08-22T20:35:19.4776667Z   Cloning https://github.com/pytorch/ao.git (to revision e11201a62669f582d81cdb33e031a07fb8dfc4f3) to /tmp/pip-req-build-w2wle4kr
2024-08-22T20:35:19.4778369Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/ao.git /tmp/pip-req-build-w2wle4kr
2024-08-22T20:35:19.4779661Z   Running command git rev-parse -q --verify 'sha^e11201a62669f582d81cdb33e031a07fb8dfc4f3'
2024-08-22T20:35:19.4780765Z   Running command git fetch -q https://github.com/pytorch/ao.git e11201a62669f582d81cdb33e031a07fb8dfc4f3
2024-08-22T20:35:19.4781743Z   Running command git checkout -q e11201a62669f582d81cdb33e031a07fb8dfc4f3
2024-08-22T20:35:19.4782610Z   Resolved https://github.com/pytorch/ao.git to commit e11201a62669f582d81cdb33e031a07fb8dfc4f3
2024-08-22T20:35:19.4783411Z   Preparing metadata (setup.py) ... [?25l- done
2024-08-22T20:35:19.4784008Z [?25hBuilding wheels for collected packages: torchao
2024-08-22T20:35:19.4784789Z   Building wheel for torchao (setup.py) ... [?25l- \ | / - \ | / - done
2024-08-22T20:35:19.4786477Z [?25h  Created wheel for torchao: filename=torchao-0.4.0+gite11201a-cp311-cp311-linux_x86_64.whl size=1019164 sha256=13ac58874dc4cf5adc351c4dc178a0a0d7b65bd817249a5d7f5bae8adb133247
2024-08-22T20:35:19.4787988Z   Stored in directory: /root/.cache/pip/wheels/f7/33/31/b21ec8d5a4ec3826118179c0c853d6f776a1f80c58c0960d0e
2024-08-22T20:35:19.4788773Z Successfully built torchao
2024-08-22T20:35:19.4789157Z Installing collected packages: torchao
2024-08-22T20:35:19.4789691Z Successfully installed torchao-0.4.0+gite11201a
2024-08-22T20:35:19.4791327Z WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
2024-08-22T20:35:19.4792759Z + python3 scripts/patch_triton.py
2024-08-22T20:35:19.4793157Z + pip3 list
2024-08-22T20:35:19.4793447Z Package                   Version
2024-08-22T20:35:19.4793903Z ------------------------- -----------------------
2024-08-22T20:35:19.4794370Z absl-py                   2.1.0
2024-08-22T20:35:19.4794743Z accelerate                0.33.0
2024-08-22T20:35:19.4795119Z aiohappyeyeballs          2.4.0
2024-08-22T20:35:19.4795497Z aiohttp                   3.10.5
2024-08-22T20:35:19.4795865Z aiosignal                 1.3.1
2024-08-22T20:35:19.4796220Z altair                    5.4.0
2024-08-22T20:35:19.4796643Z anaconda-anon-usage       0.4.4
2024-08-22T20:35:19.4797072Z anaconda-client           1.12.3
2024-08-22T20:35:19.4797436Z attrs                     23.1.0
2024-08-22T20:35:19.4797806Z beautifulsoup4            4.12.3
2024-08-22T20:35:19.4798178Z blinker                   1.8.2
2024-08-22T20:35:21.2661757Z blobfile                  2.1.1
2024-08-22T20:35:21.2662350Z boltons                   23.0.0
2024-08-22T20:35:21.2662828Z brotlipy                  0.7.0
2024-08-22T20:35:21.2663292Z cachetools                5.5.0
2024-08-22T20:35:21.2663783Z certifi                   2024.7.4
2024-08-22T20:35:21.2664284Z cffi                      1.15.1
2024-08-22T20:35:21.2664742Z chardet                   4.0.0
2024-08-22T20:35:21.2665523Z charset-normalizer        2.0.4
2024-08-22T20:35:21.2665967Z click                     8.1.7
2024-08-22T20:35:21.2666446Z cmake                     3.30.2
2024-08-22T20:35:21.2666928Z colorama                  0.4.6
2024-08-22T20:35:21.2667397Z conda                     23.5.2
2024-08-22T20:35:21.2668489Z conda-build               24.3.0
2024-08-22T20:35:21.2668990Z conda-content-trust       0.1.3
2024-08-22T20:35:21.2669367Z conda_index               0.5.0
2024-08-22T20:35:21.2669787Z conda-libmamba-solver     23.5.0
2024-08-22T20:35:21.2670211Z conda-package-handling    2.1.0
2024-08-22T20:35:21.2670598Z conda_package_streaming   0.8.0
2024-08-22T20:35:21.2670974Z cryptography              39.0.1
2024-08-22T20:35:21.2671341Z DataProperty              1.0.1
2024-08-22T20:35:21.2671707Z datasets                  2.21.0
2024-08-22T20:35:21.2672076Z defusedxml                0.7.1
2024-08-22T20:35:21.2672426Z dill                      0.3.8
2024-08-22T20:35:21.2672783Z evaluate                  0.4.2
2024-08-22T20:35:21.2673155Z fastjsonschema            2.16.2
2024-08-22T20:35:21.2673520Z filelock                  3.13.1
2024-08-22T20:35:21.2673878Z Flask                     3.0.3
2024-08-22T20:35:21.2674239Z frozenlist                1.4.1
2024-08-22T20:35:21.2674607Z fsspec                    2024.6.1
2024-08-22T20:35:21.2674978Z gguf                      0.9.1
2024-08-22T20:35:21.2675332Z gitdb                     4.0.11
2024-08-22T20:35:21.2675691Z GitPython                 3.1.43
2024-08-22T20:35:21.2676114Z huggingface-hub           0.24.6
2024-08-22T20:35:21.2676485Z idna                      3.4
2024-08-22T20:35:21.2676835Z itsdangerous              2.2.0
2024-08-22T20:35:21.2677195Z Jinja2                    3.1.4
2024-08-22T20:35:21.2677553Z joblib                    1.4.2
2024-08-22T20:35:21.2678275Z jsonlines                 4.0.0
2024-08-22T20:35:21.2678635Z jsonpatch                 1.32
2024-08-22T20:35:21.2679045Z jsonpointer               2.1
2024-08-22T20:35:21.2679400Z jsonschema                4.19.2
2024-08-22T20:35:21.2679849Z jsonschema-specifications 2023.7.1
2024-08-22T20:35:21.2680247Z jupyter_core              5.7.2
2024-08-22T20:35:21.2680653Z libarchive-c              2.9
2024-08-22T20:35:21.2681061Z libmambapy                1.4.1
2024-08-22T20:35:21.2681425Z lm_eval                   0.4.2
2024-08-22T20:35:21.2681772Z lxml                      4.9.4
2024-08-22T20:35:21.2682174Z markdown-it-py            3.0.0
2024-08-22T20:35:21.2682539Z MarkupSafe                2.1.3
2024-08-22T20:35:21.2682895Z mbstrdecoder              1.1.3
2024-08-22T20:35:21.2683254Z mdurl                     0.1.2
2024-08-22T20:35:21.2683607Z menuinst                  2.1.2
2024-08-22T20:35:21.2684008Z more-itertools            10.1.0
2024-08-22T20:35:21.2684376Z mpmath                    1.3.0
2024-08-22T20:35:21.2684741Z multidict                 6.0.5
2024-08-22T20:35:21.2685103Z multiprocess              0.70.16
2024-08-22T20:35:21.2685479Z narwhals                  1.5.2
2024-08-22T20:35:21.2685834Z nbformat                  5.9.2
2024-08-22T20:35:21.2686186Z networkx                  3.3
2024-08-22T20:35:21.2686539Z ninja                     1.11.1.1
2024-08-22T20:35:21.2686904Z nltk                      3.9.1
2024-08-22T20:35:21.2687254Z numexpr                   2.10.1
2024-08-22T20:35:21.2687628Z numpy                     1.26.4
2024-08-22T20:35:21.2688051Z nvidia-cublas-cu12        12.1.3.1
2024-08-22T20:35:21.2688586Z nvidia-cuda-cupti-cu12    12.1.105
2024-08-22T20:35:21.2689036Z nvidia-cuda-nvrtc-cu12    12.1.105
2024-08-22T20:35:21.2689483Z nvidia-cuda-runtime-cu12  12.1.105
2024-08-22T20:35:21.2689917Z nvidia-cudnn-cu12         9.1.0.70
2024-08-22T20:35:21.2690352Z nvidia-cufft-cu12         11.0.2.54
2024-08-22T20:35:21.2690798Z nvidia-curand-cu12        10.3.2.106
2024-08-22T20:35:21.2691254Z nvidia-cusolver-cu12      11.4.5.107
2024-08-22T20:35:21.2691709Z nvidia-cusparse-cu12      12.1.0.106
2024-08-22T20:35:21.2692152Z nvidia-nccl-cu12          2.21.5
2024-08-22T20:35:21.2692578Z nvidia-nvjitlink-cu12     12.6.20
2024-08-22T20:35:21.2693013Z nvidia-nvtx-cu12          12.1.105
2024-08-22T20:35:21.2693398Z packaging                 23.0
2024-08-22T20:35:21.2693752Z pandas                    2.2.2
2024-08-22T20:35:21.2694117Z pathvalidate              3.2.0
2024-08-22T20:35:21.2694621Z peft                      0.12.0
2024-08-22T20:35:21.2694982Z pillow                    10.4.0
2024-08-22T20:35:21.2695346Z pip                       23.1.2
2024-08-22T20:35:21.2695712Z pkginfo                   1.10.0
2024-08-22T20:35:21.2696072Z platformdirs              3.10.0
2024-08-22T20:35:21.2696439Z pluggy                    1.0.0
2024-08-22T20:35:21.2696802Z portalocker               2.10.1
2024-08-22T20:35:21.2697163Z protobuf                  5.27.3
2024-08-22T20:35:21.2697526Z psutil                    5.9.0
2024-08-22T20:35:21.2697895Z pyarrow                   17.0.0
2024-08-22T20:35:21.2698250Z pybind11                  2.13.5
2024-08-22T20:35:21.2698611Z pycosat                   0.6.4
2024-08-22T20:35:21.2698974Z pycparser                 2.21
2024-08-22T20:35:21.2699339Z pycryptodomex             3.20.0
2024-08-22T20:35:21.2699711Z pydeck                    0.9.1
2024-08-22T20:35:21.2700075Z Pygments                  2.18.0
2024-08-22T20:35:21.2700441Z pyOpenSSL                 23.0.0
2024-08-22T20:35:21.2700806Z PySocks                   1.7.1
2024-08-22T20:35:21.2701172Z pytablewriter             1.2.0
2024-08-22T20:35:21.2701606Z python-dateutil           2.9.0.post0
2024-08-22T20:35:21.2702091Z pytorch-triton            3.0.0+dedb7bdf33
2024-08-22T20:35:21.2702514Z pytz                      2024.1
2024-08-22T20:35:21.2702871Z PyYAML                    6.0.1
2024-08-22T20:35:21.2703234Z referencing               0.30.2
2024-08-22T20:35:21.2703606Z regex                     2024.7.24
2024-08-22T20:35:21.2704094Z requests                  2.32.3
2024-08-22T20:35:21.2704508Z requests-toolbelt         1.0.0
2024-08-22T20:35:21.2704873Z rich                      13.7.1
2024-08-22T20:35:21.2705266Z rouge-score               0.1.2
2024-08-22T20:35:21.2705674Z rpds-py                   0.10.6
2024-08-22T20:35:21.2706043Z ruamel.yaml               0.17.21
2024-08-22T20:35:21.2706409Z sacrebleu                 2.4.3
2024-08-22T20:35:21.2706772Z safetensors               0.4.4
2024-08-22T20:35:21.2707183Z scikit-learn              1.5.1
2024-08-22T20:35:21.2707538Z scipy                     1.14.1
2024-08-22T20:35:21.2707904Z sentencepiece             0.2.0
2024-08-22T20:35:21.2708275Z setuptools                67.8.0
2024-08-22T20:35:21.2708626Z six                       1.16.0
2024-08-22T20:35:21.2708982Z smmap                     5.0.1
2024-08-22T20:35:21.2709338Z snakeviz                  2.2.0
2024-08-22T20:35:21.2709691Z soupsieve                 2.5
2024-08-22T20:35:21.2710048Z sqlitedict                2.1.0
2024-08-22T20:35:21.2710420Z streamlit                 1.37.1
2024-08-22T20:35:21.2710790Z sympy                     1.13.1
2024-08-22T20:35:21.2711191Z tabledata                 1.3.3
2024-08-22T20:35:21.2711557Z tabulate                  0.9.0
2024-08-22T20:35:21.2711912Z tcolorpy                  0.1.6
2024-08-22T20:35:21.2712267Z tenacity                  8.5.0
2024-08-22T20:35:21.2712629Z threadpoolctl             3.5.0
2024-08-22T20:35:21.2712989Z tiktoken                  0.7.0
2024-08-22T20:35:21.2713352Z tokenizers                0.19.1
2024-08-22T20:35:21.2713721Z toml                      0.10.2
2024-08-22T20:35:21.2714074Z toolz                     0.12.0
2024-08-22T20:35:21.2714478Z torch                     2.5.0.dev20240814+cu121
2024-08-22T20:35:21.2714936Z torchao                   0.4.0+gite11201a
2024-08-22T20:35:21.2715343Z tornado                   6.4.1
2024-08-22T20:35:21.2715704Z tqdm                      4.66.5
2024-08-22T20:35:21.2716123Z tqdm-multiprocess         0.0.11
2024-08-22T20:35:21.2716497Z traitlets                 5.14.3
2024-08-22T20:35:21.2716865Z transformers              4.44.2
2024-08-22T20:35:21.2717229Z typepy                    1.3.2
2024-08-22T20:35:21.2717589Z typing_extensions         4.12.2
2024-08-22T20:35:21.2717958Z tzdata                    2024.1
2024-08-22T20:35:21.2718317Z urllib3                   1.26.16
2024-08-22T20:35:21.2718679Z watchdog                  4.0.2
2024-08-22T20:35:21.2719038Z Werkzeug                  3.0.4
2024-08-22T20:35:21.2719491Z wheel                     0.38.4
2024-08-22T20:35:21.2719847Z word2number               1.1
2024-08-22T20:35:21.2720200Z xxhash                    3.5.0
2024-08-22T20:35:21.2720551Z yarl                      1.9.4
2024-08-22T20:35:21.2720916Z zstandard                 0.19.0
2024-08-22T20:35:21.2721320Z zstd                      1.5.5.1
2024-08-22T20:35:21.2722027Z + python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
2024-08-22T20:35:21.2722949Z torch: ('2.5.0.dev20240814+cu121', '3a023a67c47bcde45538c9991e332d21ac548e46')
2024-08-22T20:35:21.2723558Z + echo ::endgroup::
2024-08-22T20:35:21.2724246Z ##[endgroup]
2024-08-22T20:35:21.2724672Z + echo '::group::Download checkpoints'
2024-08-22T20:35:21.2725308Z ##[group]Download checkpoints
2024-08-22T20:35:21.2725731Z + mkdir -p checkpoints/stories15M
2024-08-22T20:35:21.2726128Z + pushd checkpoints/stories15M
2024-08-22T20:35:21.2726629Z /pytorch/torchchat/checkpoints/stories15M /pytorch/torchchat
2024-08-22T20:35:21.2727382Z + wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
2024-08-22T20:35:21.2728417Z --2024-08-22 20:35:21--  https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
2024-08-22T20:35:21.2729352Z Resolving huggingface.co (huggingface.co)... 18.154.227.69, 18.154.227.87, 18.154.227.7, ...
2024-08-22T20:35:21.2730195Z Connecting to huggingface.co (huggingface.co)|18.154.227.69|:443... connected.
2024-08-22T20:35:21.2730879Z HTTP request sent, awaiting response... 302 Found
2024-08-22T20:35:21.2739832Z Location: https://cdn-lfs.huggingface.co/repos/88/4b/884bade32e5ee32eea725c5087af1358179a1bea94a4f6abc3c0470c9610ac38/3da00c0fef684f3f83b457736837c46ab55e92a26662b61d6104de2d271c708d?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27stories15M.pt%3B+filename%3D%22stories15M.pt%22%3B&Expires=1724617338&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyNDYxNzMzOH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy84OC80Yi84ODRiYWRlMzJlNWVlMzJlZWE3MjVjNTA4N2FmMTM1ODE3OWExYmVhOTRhNGY2YWJjM2MwNDcwYzk2MTBhYzM4LzNkYTAwYzBmZWY2ODRmM2Y4M2I0NTc3MzY4MzdjNDZhYjU1ZTkyYTI2NjYyYjYxZDYxMDRkZTJkMjcxYzcwOGQ%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=f1ZHqXa6KImnUwA63gTrv9x13BSVC%7EqMQmfdoui8ED8v8gRi2oYs5U0B%7EWDyByLVArLhlV37HyJURKfPMSVfWGJw7KqB0oFqgDw-ijNp4EtF0M1LvS5AhHLLaNEGo9qALmHmCwLGYDw28M151zvvaN5R6KEvHmgZcYB1gt49foS21yU3xn8tB0-vRLRHhsUQThQPqOQG3OHRci8CkP4o25g38t-VaKbNypL1JR%7ERPJ9B%7ECxMYBuZZq9TZCwvdYeuVPD8BBNYFiSBpYQdCC%7Evh6ZNEqpS5uzWAjT9V76hFPWwKa9OnTWohJ63DQlWPLfgKJu7n565GhtP6M6qnaVeMA__&Key-Pair-Id=K3ESJI6DHPFC7 [following]
2024-08-22T20:35:21.2756302Z --2024-08-22 20:35:21--  https://cdn-lfs.huggingface.co/repos/88/4b/884bade32e5ee32eea725c5087af1358179a1bea94a4f6abc3c0470c9610ac38/3da00c0fef684f3f83b457736837c46ab55e92a26662b61d6104de2d271c708d?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27stories15M.pt%3B+filename%3D%22stories15M.pt%22%3B&Expires=1724617338&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyNDYxNzMzOH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy84OC80Yi84ODRiYWRlMzJlNWVlMzJlZWE3MjVjNTA4N2FmMTM1ODE3OWExYmVhOTRhNGY2YWJjM2MwNDcwYzk2MTBhYzM4LzNkYTAwYzBmZWY2ODRmM2Y4M2I0NTc3MzY4MzdjNDZhYjU1ZTkyYTI2NjYyYjYxZDYxMDRkZTJkMjcxYzcwOGQ%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=f1ZHqXa6KImnUwA63gTrv9x13BSVC%7EqMQmfdoui8ED8v8gRi2oYs5U0B%7EWDyByLVArLhlV37HyJURKfPMSVfWGJw7KqB0oFqgDw-ijNp4EtF0M1LvS5AhHLLaNEGo9qALmHmCwLGYDw28M151zvvaN5R6KEvHmgZcYB1gt49foS21yU3xn8tB0-vRLRHhsUQThQPqOQG3OHRci8CkP4o25g38t-VaKbNypL1JR%7ERPJ9B%7ECxMYBuZZq9TZCwvdYeuVPD8BBNYFiSBpYQdCC%7Evh6ZNEqpS5uzWAjT9V76hFPWwKa9OnTWohJ63DQlWPLfgKJu7n565GhtP6M6qnaVeMA__&Key-Pair-Id=K3ESJI6DHPFC7
2024-08-22T20:35:21.2764958Z Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 108.138.64.49, 108.138.64.121, 108.138.64.111, ...
2024-08-22T20:35:21.2766188Z Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|108.138.64.49|:443... connected.
2024-08-22T20:35:56.7317450Z HTTP request sent, awaiting response... 200 OK
2024-08-22T20:35:56.7318332Z Length: 60789090 (58M) [binary/octet-stream]
2024-08-22T20:35:56.7321213Z Saving to: ‘stories15M.pt’
2024-08-22T20:35:56.7321522Z 
2024-08-22T20:35:56.7321529Z 
2024-08-22T20:35:56.7321861Z  0% [                                       ] 0           --.-K/s              
2024-08-22T20:35:56.7322594Z 100%[======================================>] 60,789,090  --.-K/s   in 0.1s    
2024-08-22T20:35:56.7323041Z 
2024-08-22T20:35:56.7333290Z 2024-08-22 20:35:21 (397 MB/s) - ‘stories15M.pt’ saved [60789090/60789090]
2024-08-22T20:35:56.7333881Z 
2024-08-22T20:35:56.7334296Z + wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
2024-08-22T20:35:56.7335524Z --2024-08-22 20:35:21--  https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
2024-08-22T20:35:56.7336319Z Resolving github.com (github.com)... 140.82.114.4
2024-08-22T20:35:56.7337044Z Connecting to github.com (github.com)|140.82.114.4|:443... connected.
2024-08-22T20:35:56.7337745Z HTTP request sent, awaiting response... 302 Found
2024-08-22T20:35:56.7338629Z Location: https://raw.githubusercontent.com/karpathy/llama2.c/master/tokenizer.model [following]
2024-08-22T20:35:56.7339883Z --2024-08-22 20:35:21--  https://raw.githubusercontent.com/karpathy/llama2.c/master/tokenizer.model
2024-08-22T20:35:56.7341133Z Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...
2024-08-22T20:35:56.7342812Z Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.
2024-08-22T20:35:56.7343734Z HTTP request sent, awaiting response... 200 OK
2024-08-22T20:35:56.7344384Z Length: 499723 (488K) [application/octet-stream]
2024-08-22T20:35:56.7344967Z Saving to: ‘tokenizer.model’
2024-08-22T20:35:56.7345257Z 
2024-08-22T20:35:56.7345263Z 
2024-08-22T20:35:56.7345584Z  0% [                                       ] 0           --.-K/s              
2024-08-22T20:35:56.7346301Z 100%[======================================>] 499,723     --.-K/s   in 0.006s  
2024-08-22T20:35:56.7346706Z 
2024-08-22T20:35:56.7347184Z 2024-08-22 20:35:21 (85.5 MB/s) - ‘tokenizer.model’ saved [499723/499723]
2024-08-22T20:35:56.7347885Z 
2024-08-22T20:35:56.7348047Z + popd
2024-08-22T20:35:56.7348552Z /pytorch/torchchat
2024-08-22T20:35:56.7348988Z + echo ::endgroup::
2024-08-22T20:35:56.7349868Z ##[endgroup]
2024-08-22T20:35:56.7350459Z + echo '::group::Run inference'
2024-08-22T20:35:56.7351332Z ##[group]Run inference
2024-08-22T20:35:56.7351938Z + export MODEL_PATH=checkpoints/stories15M/stories15M.pt
2024-08-22T20:35:56.7352620Z + MODEL_PATH=checkpoints/stories15M/stories15M.pt
2024-08-22T20:35:56.7353302Z + export MODEL_NAME=stories15M
2024-08-22T20:35:56.7353847Z + MODEL_NAME=stories15M
2024-08-22T20:35:56.7354366Z + export MODEL_DIR=/tmp
2024-08-22T20:35:56.7354921Z + MODEL_DIR=/tmp
2024-08-22T20:35:56.7355477Z + for DTYPE in bfloat16 float16 float32
2024-08-22T20:35:56.7356628Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --temperature 0
2024-08-22T20:35:56.7357549Z 
2024-08-22T20:35:56.7357872Z Downloading builder script:   0% 0.00/5.67k [00:00<?, ?B/s]
2024-08-22T20:35:56.7358694Z Downloading builder script: 100% 5.67k/5.67k [00:00<00:00, 33.9MB/s]
2024-08-22T20:35:56.7359448Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:35:56.7360016Z Loading model...
2024-08-22T20:35:56.7360470Z Time to load model: 0.03 seconds
2024-08-22T20:35:56.7361127Z -----------------------------------------------------------
2024-08-22T20:35:56.7362609Z Hello, my name is Daisy. Daisy is very popular. Everyone wants to be her friend. One day, Daisy's mommy takes her to the park. Daisy is so excited! She loves to play.
2024-08-22T20:35:56.7365168Z Daisy sees a big slide. She wants to go on it. Daisy's mommy says, "Yes, you can go on the slide." Daisy runs to the slide. She climbs up the steps. She sits on the slide and slides down. She laughs and says, "Whee!"
2024-08-22T20:35:56.7368600Z Daisy goes on the slide again and again. She has so much fun. She sees her mommy and daddy. They are happy to see her. They say, "Good job, Daisy!" Daisy is happy too. She loves to play at the park. Once upon a time, there was a little girl named Lily. She loved to2024-08-22:20:35:28,411 INFO     [generate.py:846] 
2024-08-22T20:35:56.7371341Z Time for inference 1: 3.02 sec total, time to first token 0.06 sec with parallel prefill, 199 tokens, 65.99 tokens/sec, 15.15 ms/token
2024-08-22T20:35:56.7372734Z 2024-08-22:20:35:28,411 INFO     [generate.py:849] Bandwidth achieved: 3.22 GB/s
2024-08-22T20:35:56.7374192Z 2024-08-22:20:35:28,411 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:35:56.7375183Z 
2024-08-22T20:35:56.7375360Z ========================================
2024-08-22T20:35:56.7375696Z 
2024-08-22T20:35:56.7375941Z Average tokens/sec: 65.99
2024-08-22T20:35:56.7376386Z Memory used: 0.00 GB
2024-08-22T20:35:56.7377536Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --temperature 0.9
2024-08-22T20:35:56.7378873Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:35:56.7379347Z Loading model...
2024-08-22T20:35:56.7379873Z Time to load model: 0.03 seconds
2024-08-22T20:35:56.7380553Z -----------------------------------------------------------
2024-08-22T20:35:56.7381117Z Hello, my name is Ella." 
2024-08-22T20:35:56.7383206Z Moral of the story: No one is fair and always listen to you because it's so quiet. Once upon a time, there was a shy cat named Sam. Sam loved to hug his big, soft pillow. Every morning, he would jump on the pillow and sleep under the sun.
2024-08-22T20:35:56.7385688Z One day, Sam saw a bird on the window. The bird looked sad. Sam wanted to help the bird, so he tried to give it some food from the window. But the bird was too busy flying around the window.
2024-08-22T20:35:56.7389154Z Sam had an idea. He went to his friend, the kind dog named Max. Sam told Max about the bird. Max wanted to help, so he gave Sam a big, red pill. It was the same pill! Sam took the pill and waved it around. Now, the bird was happy, and Sam felt good too. They both waved goodbye to the bird and watched it fly2024-08-22:20:35:36,171 INFO     [generate.py:846] 
2024-08-22T20:35:56.7391947Z Time for inference 1: 3.18 sec total, time to first token 0.07 sec with parallel prefill, 199 tokens, 62.66 tokens/sec, 15.96 ms/token
2024-08-22T20:35:56.7393288Z 2024-08-22:20:35:36,171 INFO     [generate.py:849] Bandwidth achieved: 3.06 GB/s
2024-08-22T20:35:56.7394813Z 2024-08-22:20:35:36,172 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:35:56.7395774Z 
2024-08-22T20:35:56.7395952Z ========================================
2024-08-22T20:35:56.7396302Z 
2024-08-22T20:35:56.7396514Z Average tokens/sec: 62.66
2024-08-22T20:35:56.7397016Z Memory used: 0.00 GB
2024-08-22T20:35:56.7398125Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --temperature 1.0
2024-08-22T20:35:56.7399229Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:35:56.7399824Z Loading model...
2024-08-22T20:35:56.7400283Z Time to load model: 0.03 seconds
2024-08-22T20:35:56.7400933Z -----------------------------------------------------------
2024-08-22T20:35:56.7401998Z Hello, my name is John. He loves to play pink flute. Every day, John plays his fl flute and it shines so beautiful.
2024-08-22T20:35:56.7403166Z One day, Mary asked her brother: "Would you like me to help you teach?"
2024-08-22T20:35:56.7404088Z Mary said: "Yes, please. I would love to show you how to play a song."
2024-08-22T20:35:56.7405110Z Together, they started practicing. They were both humming outdoors more carefully.
2024-08-22T20:35:56.7406089Z Suddenly, John stopped and said: "Hey, I hear this. Let me hear it!"
2024-08-22T20:35:56.7407319Z Mary smiled and said: "Yes, that's right! Come in and let me show you how to play."
2024-08-22T20:35:56.7408747Z At first, John grumbled. But then, he tried to become friends and decided to join Mary in teaching him a new song of music.
2024-08-22T20:35:56.7410611Z When Tony finished singing, he came in and began. Mary and John both danced to the music, happy to be with a kind and2024-08-22:20:35:43,913 INFO     [generate.py:846] 
2024-08-22T20:35:56.7412469Z Time for inference 1: 3.16 sec total, time to first token 0.07 sec with parallel prefill, 199 tokens, 63.01 tokens/sec, 15.87 ms/token
2024-08-22T20:35:56.7413905Z 2024-08-22:20:35:43,913 INFO     [generate.py:849] Bandwidth achieved: 3.08 GB/s
2024-08-22T20:35:56.7415377Z 2024-08-22:20:35:43,914 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:35:56.7416297Z 
2024-08-22T20:35:56.7416474Z ========================================
2024-08-22T20:35:56.7416928Z 
2024-08-22T20:35:56.7417104Z Average tokens/sec: 63.01
2024-08-22T20:35:56.7417608Z Memory used: 0.00 GB
2024-08-22T20:35:56.7418619Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --top-k 100
2024-08-22T20:35:56.7419741Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:35:56.7420270Z Loading model...
2024-08-22T20:35:56.7420683Z Time to load model: 0.03 seconds
2024-08-22T20:35:56.7421383Z -----------------------------------------------------------
2024-08-22T20:35:56.7422178Z Hello, my name is Mommy!"
2024-08-22T20:35:56.7422718Z Mommy took Mommy's hand. "Do you care?"
2024-08-22T20:35:56.7423385Z Mommy smiled and hugged Mia. "Yes, I care!"
2024-08-22T20:35:56.7424810Z Mia and her mommy laughed and hugged. "We care!" Once upon a time there was a famous spider. He was the most popular spider in the forest. Everyone wanted to be his friend and love him.
2024-08-22T20:35:56.7426873Z One day, the spider had an idea. He wanted to make a special cobweb to put in the trees. He worked hard all day, determined to make it just right for his new friend. 
2024-08-22T20:35:56.7428477Z When the cobweb was finished, the spider was so excited. He jumped in and spun his new cobweb. 
2024-08-22T20:35:56.7430378Z The spider was delighted with his work and he brought it inside to show his friend. Now Mommy and the spider had a special cobweb in2024-08-22:20:35:51,566 INFO     [generate.py:846] 
2024-08-22T20:35:56.7432232Z Time for inference 1: 3.10 sec total, time to first token 0.07 sec with parallel prefill, 199 tokens, 64.25 tokens/sec, 15.57 ms/token
2024-08-22T20:35:56.7433655Z 2024-08-22:20:35:51,566 INFO     [generate.py:849] Bandwidth achieved: 3.14 GB/s
2024-08-22T20:35:56.7435120Z 2024-08-22:20:35:51,566 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:35:56.7436042Z 
2024-08-22T20:35:56.7436221Z ========================================
2024-08-22T20:35:56.7436628Z 
2024-08-22T20:35:56.7436912Z Average tokens/sec: 64.25
2024-08-22T20:35:56.7437359Z Memory used: 0.00 GB
2024-08-22T20:35:56.7438396Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --top-k 200
2024-08-22T20:35:56.7439490Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:35:56.7440012Z Loading model...
2024-08-22T20:35:56.7440486Z Time to load model: 0.03 seconds
2024-08-22T20:35:56.7441182Z -----------------------------------------------------------
2024-08-22T20:39:01.0992459Z Hello, my name is Sarah. He is small and only three years old. I always applaud you by doing a little dance for me. He is so gifted.
2024-08-22T20:39:01.0996188Z One day, some bad people came to visit. They wanted to steal Sarah's things! They took Sarah's things and put them in a box. Sarah felt so scared.
2024-08-22T20:39:01.1000544Z When they realized what was happening, Sarah asked for her things back. But the bad people wouldn't give them back. They just laughed and kept running away.
2024-08-22T20:39:01.1002935Z Sarah started to cry. She really was scared and didn't know what to do. Then, she had an idea. She started crawling in the box and made it into a very special hiding place.
2024-08-22T20:39:01.1005333Z When she was done, the bad people were amazed! They had never seen Sarah so brave. They gave her back all her things and ran away. Sarah was very happy.
2024-08-22T20:39:01.1007167Z She hugged her new friend and thanked2024-08-22:20:35:59,260 INFO     [generate.py:846] 
2024-08-22T20:39:01.1008867Z Time for inference 1: 3.12 sec total, time to first token 0.07 sec with parallel prefill, 199 tokens, 63.80 tokens/sec, 15.67 ms/token
2024-08-22T20:39:01.1010361Z 2024-08-22:20:35:59,260 INFO     [generate.py:849] Bandwidth achieved: 3.11 GB/s
2024-08-22T20:39:01.1012043Z 2024-08-22:20:35:59,260 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:39:01.1013014Z 
2024-08-22T20:39:01.1013205Z ========================================
2024-08-22T20:39:01.1013595Z 
2024-08-22T20:39:01.1013781Z Average tokens/sec: 63.80
2024-08-22T20:39:01.1014454Z Memory used: 0.00 GB
2024-08-22T20:39:01.1015531Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --top-k 500
2024-08-22T20:39:01.1016607Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:39:01.1017228Z Loading model...
2024-08-22T20:39:01.1017670Z Time to load model: 0.03 seconds
2024-08-22T20:39:01.1018357Z -----------------------------------------------------------
2024-08-22T20:39:01.1021065Z Hello, my name is Mark. He is three years old. He loves to play with his toy drum.
2024-08-22T20:39:01.1022173Z One day, he was playing with it in the living room. He was making up a story.
2024-08-22T20:39:01.1023360Z Suddenly, he heard a noise from outside. It sounded like someone was shouting. He was scared.
2024-08-22T20:39:01.1024390Z Mark went to the window to see what was happening. He saw two big monsters!
2024-08-22T20:39:01.1025326Z Mark was more scared. Then he saw his toy drum, but it was broken.
2024-08-22T20:39:01.1026219Z He was about to cry when he heard a voice.
2024-08-22T20:39:01.1026901Z "Hello Mark! I need your help. Will you help me fix my drum?"
2024-08-22T20:39:01.1027737Z Mark was surprised. He looked around, and saw a big monster.
2024-08-22T20:39:01.1028469Z He said, "Yes, please!"
2024-08-22T20:39:01.1029784Z The monster was very kind. He said, "I can fix your drum. I'm stuck here for a while. But don't worry, I'll help you2024-08-22:20:36:07,014 INFO     [generate.py:846] 
2024-08-22T20:39:01.1031542Z Time for inference 1: 3.17 sec total, time to first token 0.07 sec with parallel prefill, 199 tokens, 62.69 tokens/sec, 15.95 ms/token
2024-08-22T20:39:01.1032988Z 2024-08-22:20:36:07,014 INFO     [generate.py:849] Bandwidth achieved: 3.06 GB/s
2024-08-22T20:39:01.1034470Z 2024-08-22:20:36:07,014 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:39:01.1035384Z 
2024-08-22T20:39:01.1035560Z ========================================
2024-08-22T20:39:01.1035991Z 
2024-08-22T20:39:01.1036167Z Average tokens/sec: 62.69
2024-08-22T20:39:01.1036651Z Memory used: 0.00 GB
2024-08-22T20:39:01.1037875Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --temperature 0 --compile --compile-prefill
2024-08-22T20:39:01.1039175Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:39:01.1039708Z Loading model...
2024-08-22T20:39:01.1040133Z Time to load model: 0.03 seconds
2024-08-22T20:39:01.1040837Z -----------------------------------------------------------
2024-08-22T20:39:01.1042396Z Hello, my name is Daisy. Daisy is very popular. Everyone wants to be her friend. One day, Daisy's mommy takes her to the park. Daisy is so excited! She loves to play.
2024-08-22T20:39:01.1044641Z Daisy sees a big slide. She wants to go on it. She runs to the slide and climbs up the ladder. She sits on the top and slides down. She laughs and says, "Whee!"
2024-08-22T20:39:01.1046479Z Daisy's mommy claps and says, "Good job, Daisy! You are so brave!" Daisy smiles and says, "Thank you, mommy!"
2024-08-22T20:39:01.1049309Z Daisy and her mommy have a great day at the park. They play on the swings, slide down the slide, and run in the grass. Daisy is very happy. She loves her mommy and her popular slide. Once upon a time, therejust-in-time compilation time (incl run time): 4.1e+01 seconds
2024-08-22T20:39:01.1051485Z 2024-08-22:20:36:53,661 INFO     [generate.py:846] 
2024-08-22T20:39:01.1052720Z Time for inference 1: 41.18 sec total, time to first token 18.02 sec with parallel prefill, 199 tokens, 4.83 tokens/sec, 206.95 ms/token
2024-08-22T20:39:01.1054164Z 2024-08-22:20:36:53,661 INFO     [generate.py:849] Bandwidth achieved: 0.24 GB/s
2024-08-22T20:39:01.1055956Z 2024-08-22:20:36:53,661 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:39:01.1057059Z 
2024-08-22T20:39:01.1057243Z ========================================
2024-08-22T20:39:01.1057634Z 
2024-08-22T20:39:01.1057816Z Average tokens/sec: 4.83
2024-08-22T20:39:01.1058370Z Memory used: 0.00 GB
2024-08-22T20:39:01.1059672Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --temperature 0.9 --compile --compile-prefill
2024-08-22T20:39:01.1060929Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:39:01.1061539Z Loading model...
2024-08-22T20:39:01.1062100Z Time to load model: 0.03 seconds
2024-08-22T20:39:01.1062777Z -----------------------------------------------------------
2024-08-22T20:39:01.1064419Z Hello, my name is Sally. Sally, an old lady with a big mustache lived in a big city. She wanted to pick the highest sunrise in the world. She saw these bright and beautiful smiles come from the sky.
2024-08-22T20:39:01.1066334Z Soon she came to the hill. On the hill, she saw two birds fighting in the sky. Isn't it beautiful?
2024-08-22T20:39:01.1067815Z As Sally watched, she thought about how to make the bird happy. She realized that both birds were better than the same.
2024-08-22T20:39:01.1069614Z Sally watched the birds and then decided to share her hill with the birds. She carefully placed one of the birds on the tall grass and waited. 
2024-08-22T20:39:01.1072687Z When all the birds saw each other, they stopped fighting. They joined in the fun. Sally was glad to see the birds happy and learn this. Once upon a time, there was a little girl named Lily. She had a kitten named Mittens. Mittens was very cute with soft fur andjust-in-time compilation time (incl run time): 4e+01 seconds
2024-08-22T20:39:01.1074941Z 2024-08-22:20:37:39,708 INFO     [generate.py:846] 
2024-08-22T20:39:01.1076104Z Time for inference 1: 39.56 sec total, time to first token 15.97 sec with parallel prefill, 199 tokens, 5.03 tokens/sec, 198.79 ms/token
2024-08-22T20:39:01.1077477Z 2024-08-22:20:37:39,708 INFO     [generate.py:849] Bandwidth achieved: 0.25 GB/s
2024-08-22T20:39:01.1079583Z 2024-08-22:20:37:39,708 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:39:01.1080639Z 
2024-08-22T20:39:01.1080820Z ========================================
2024-08-22T20:39:01.1081198Z 
2024-08-22T20:39:01.1081375Z Average tokens/sec: 5.03
2024-08-22T20:39:01.1081929Z Memory used: 0.00 GB
2024-08-22T20:39:01.1083197Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --temperature 1.0 --compile --compile-prefill
2024-08-22T20:39:01.1084397Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:39:01.1084994Z Loading model...
2024-08-22T20:39:01.1085507Z Time to load model: 0.03 seconds
2024-08-22T20:39:01.1086115Z -----------------------------------------------------------
2024-08-22T20:39:01.1088341Z Hello, my name is Gandy - the little human! Every day Gandy liked to wake up early and go out exploring. One day, Gandy was walking down the street and saw a bird. Gandy wanted to get a closer look, so she stopped and stretched its wings.
2024-08-22T20:39:01.1090778Z Suddenly, a loud voice rang from the other side of the park. It was Grandpa, who asked why Gandy was stretching. He asked her to tell him what it was like to touch his arm.
2024-08-22T20:39:01.1092858Z Gandy ran ahead and she thought of her Mom telling her about other humans who could help. She was so thankful that she told Grandpa who was now in the park.
2024-08-22T20:39:01.1096078Z Grandpa was very proud of Gandy for being so quick and smart. He started learning them important lessons: no matter how big and special you are, remember that no matter how big you are, you can do good things and you can get nice memories! Oncejust-in-time compilation time (incl run time): 3.9e+01 seconds
2024-08-22T20:39:01.1098526Z 2024-08-22:20:38:25,335 INFO     [generate.py:846] 
2024-08-22T20:39:01.1099761Z Time for inference 1: 39.16 sec total, time to first token 15.65 sec with parallel prefill, 199 tokens, 5.08 tokens/sec, 196.76 ms/token
2024-08-22T20:39:01.1101274Z 2024-08-22:20:38:25,336 INFO     [generate.py:849] Bandwidth achieved: 0.25 GB/s
2024-08-22T20:39:01.1102906Z 2024-08-22:20:38:25,336 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:39:01.1104036Z 
2024-08-22T20:39:01.1104219Z ========================================
2024-08-22T20:39:01.1104567Z 
2024-08-22T20:39:01.1105030Z Average tokens/sec: 5.08
2024-08-22T20:39:01.1105523Z Memory used: 0.00 GB
2024-08-22T20:39:01.1106799Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --top-k 100 --compile --compile-prefill
2024-08-22T20:39:01.1108116Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:39:01.1108609Z Loading model...
2024-08-22T20:39:01.1109084Z Time to load model: 0.03 seconds
2024-08-22T20:39:01.1109850Z -----------------------------------------------------------
2024-08-22T20:39:01.1111358Z Hello, my name is George. He loves to explore the woods. One day he saw a mysterious tree and he wanted to discover what it was. He asked his friend, a little bird flew to him and said:
2024-08-22T20:40:43.8307208Z "Can you help me find out what this tree is?"
2024-08-22T20:40:43.8310652Z His friend replied: "Yes, I know what it is. It is called a special tree. Let's go in and see what it is."
2024-08-22T20:40:43.8312154Z So off they went, and they discovered that the tree was a temple. It was full of bright and shiny things and it looked so beautiful.
2024-08-22T20:40:43.8314640Z George was so excited and he wanted to explore the temple, so he asked his friend for help.
2024-08-22T20:40:43.8316045Z His friend said: "I can help you find the temple, George. Just follow me."
2024-08-22T20:40:43.8318582Z George and his friend continued to explore the temple together, discovering all sorts of strange things. They had a wonderful time and George found so many magical, mysterious thingsjust-in-time compilation time (incl run time): 3.9e+01 seconds
2024-08-22T20:40:43.8320882Z 2024-08-22:20:39:11,013 INFO     [generate.py:846] 
2024-08-22T20:40:43.8322611Z Time for inference 1: 39.24 sec total, time to first token 15.90 sec with parallel prefill, 199 tokens, 5.07 tokens/sec, 197.20 ms/token
2024-08-22T20:40:43.8324025Z 2024-08-22:20:39:11,013 INFO     [generate.py:849] Bandwidth achieved: 0.25 GB/s
2024-08-22T20:40:43.8325630Z 2024-08-22:20:39:11,013 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:40:43.8327032Z 
2024-08-22T20:40:43.8327223Z ========================================
2024-08-22T20:40:43.8327613Z 
2024-08-22T20:40:43.8328030Z Average tokens/sec: 5.07
2024-08-22T20:40:43.8328562Z Memory used: 0.00 GB
2024-08-22T20:40:43.8330210Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --top-k 200 --compile --compile-prefill
2024-08-22T20:40:43.8331555Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:40:43.8332118Z Loading model...
2024-08-22T20:40:43.8332540Z Time to load model: 0.03 seconds
2024-08-22T20:40:43.8333290Z -----------------------------------------------------------
2024-08-22T20:40:43.8334189Z Hello, my name is Emma. I'm a soft grey cat. I'm excited to see you today."
2024-08-22T20:40:43.8335040Z Suddenly, Emma heard her mother calling her. "Emma!"
2024-08-22T20:40:43.8336418Z She was so excited that she started running around the house. She was having so much fun that she didn't even notice how early she had been.
2024-08-22T20:40:43.8337651Z "Emma, it's time for bed," her mother said.
2024-08-22T20:40:43.8338536Z Emma stopped and yawned. She was so tired, she couldn't keep her eyes open.
2024-08-22T20:40:43.8339381Z "Time to go to bed now," her mother said.
2024-08-22T20:40:43.8340031Z Emma reluctantly got up and put on her pajamas.
2024-08-22T20:40:43.8340970Z "Goodnight Emma, I'll be here when you wake up from your nap," her mother said.
2024-08-22T20:40:43.8342324Z Emma smiled and went to bed, feeling a little bit tired. She yawned one more time, and then went to sleep.
2024-08-22T20:40:43.8345219Z The next morning, Emma was so excitedjust-in-time compilation time (incl run time): 2.7e+01 seconds
2024-08-22T20:40:43.8346402Z 2024-08-22:20:39:44,609 INFO     [generate.py:846] 
2024-08-22T20:40:43.8347630Z Time for inference 1: 27.19 sec total, time to first token 9.02 sec with parallel prefill, 199 tokens, 7.32 tokens/sec, 136.62 ms/token
2024-08-22T20:40:43.8349229Z 2024-08-22:20:39:44,609 INFO     [generate.py:849] Bandwidth achieved: 0.36 GB/s
2024-08-22T20:40:43.8350865Z 2024-08-22:20:39:44,609 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:40:43.8351888Z 
2024-08-22T20:40:43.8352067Z ========================================
2024-08-22T20:40:43.8352402Z 
2024-08-22T20:40:43.8352709Z Average tokens/sec: 7.32
2024-08-22T20:40:43.8353158Z Memory used: 0.00 GB
2024-08-22T20:40:43.8354380Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --top-k 500 --compile --compile-prefill
2024-08-22T20:40:43.8355745Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:40:43.8356220Z Loading model...
2024-08-22T20:40:43.8356681Z Time to load model: 0.03 seconds
2024-08-22T20:40:43.8357412Z -----------------------------------------------------------
2024-08-22T20:40:43.8556815Z Hello, my name is Monolph. He loves to play with his friends, the chickens and the sheep.
2024-08-22T20:40:43.8558000Z One day, Ginger saw a big barrel in the meadow. She wanted to know what was inside it. She asked Mona to explain.
2024-08-22T20:40:43.8559108Z "It's a barrel of corn," Monica said.
2024-08-22T20:40:43.8559611Z "What's corn?" Monica asked.
2024-08-22T20:40:43.8560216Z "It's like food, but it's not sweet. It's icky," Monica said.
2024-08-22T20:40:43.8560804Z Ginger shook her head.
2024-08-22T20:40:43.8561226Z "Tasty corn?".
2024-08-22T20:40:43.8561897Z T million laughed, "Yes Mama, I love it! Let's take it back and make it sweet!"
2024-08-22T20:40:43.8564167Z So, with a big smile on her face and the chickens and sheep helped her carry the barrel back to the meadow. They all enjoyed the sweet corn together. Once upon a time, there was a little girl named Lily. She loved to eatjust-in-time compilation time (incl run time): 2.9e+01 seconds
2024-08-22T20:40:43.8566129Z 2024-08-22:20:40:20,015 INFO     [generate.py:846] 
2024-08-22T20:40:43.8567173Z Time for inference 1: 29.17 sec total, time to first token 9.17 sec with parallel prefill, 199 tokens, 6.82 tokens/sec, 146.59 ms/token
2024-08-22T20:40:43.8568542Z 2024-08-22:20:40:20,016 INFO     [generate.py:849] Bandwidth achieved: 0.33 GB/s
2024-08-22T20:40:43.8570390Z 2024-08-22:20:40:20,016 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:40:43.8571350Z 
2024-08-22T20:40:43.8571505Z ========================================
2024-08-22T20:40:43.8571812Z 
2024-08-22T20:40:43.8571959Z Average tokens/sec: 6.82
2024-08-22T20:40:43.8572347Z Memory used: 0.00 GB
2024-08-22T20:40:43.8573459Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --temperature 0 --sequential-prefill
2024-08-22T20:40:43.8574509Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:40:43.8574923Z Loading model...
2024-08-22T20:40:43.8575298Z Time to load model: 0.03 seconds
2024-08-22T20:40:43.8575847Z -----------------------------------------------------------
2024-08-22T20:40:43.8577203Z Hello, my name is Daisy. Daisy is very popular. Everyone wants to be her friend. One day, Daisy's mommy takes her to the park. Daisy is so excited! She loves to play.
2024-08-22T20:40:43.8579754Z Daisy sees a big slide. She wants to go on it. Daisy's mommy says, "Yes, you can go on the slide." Daisy runs to the slide. She climbs up the steps. She sits on the slide and slides down. She laughs and says, "Whee!"
2024-08-22T20:40:43.8582891Z Daisy goes on the slide again and again. She has so much fun. She sees her mommy and daddy. They are happy to see her. They say, "Good job, Daisy!" Daisy is happy too. She loves to play at the park. Once upon a time, there was a little girl named Lily. She loved to2024-08-22:20:40:28,418 INFO     [generate.py:846] 
2024-08-22T20:40:43.8585428Z Time for inference 1: 3.08 sec total, time to first token 0.09 sec with sequential prefill, 199 tokens, 64.59 tokens/sec, 15.48 ms/token
2024-08-22T20:40:43.8586902Z 2024-08-22:20:40:28,418 INFO     [generate.py:849] Bandwidth achieved: 3.15 GB/s
2024-08-22T20:40:43.8588273Z 2024-08-22:20:40:28,418 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:40:43.8589133Z 
2024-08-22T20:40:43.8589288Z ========================================
2024-08-22T20:40:43.8589598Z 
2024-08-22T20:40:43.8589755Z Average tokens/sec: 64.59
2024-08-22T20:40:43.8590141Z Memory used: 0.00 GB
2024-08-22T20:40:43.8591252Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --temperature 0.9 --sequential-prefill
2024-08-22T20:40:43.8592318Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:40:43.8592734Z Loading model...
2024-08-22T20:40:43.8593101Z Time to load model: 0.03 seconds
2024-08-22T20:40:43.8593662Z -----------------------------------------------------------
2024-08-22T20:40:43.8594737Z Hello, my name is Mrs. Burt. He was an eager ostrich and loved to bounce around. On one sunny day, he bounced around the garden with a grin.
2024-08-22T20:40:43.8596377Z A little girl named Lucy was crawling around in the grass near the oak tree. She saw Mrs. Oworm and said, "Hi, why are you crawling around like that?"
2024-08-22T20:40:43.8597614Z Grandma frowned and said, "I want to explore and find new things."
2024-08-22T20:40:43.8598942Z Lucy smiled at Mrs. Owl and said, "I think you should jump, Kquet butterfly, and I'm sure you will find something special to remind you of me."
2024-08-22T20:40:43.8600351Z The ostrichfrog waved his wings and slithered away, as Lucy continued to bounce, before tinking forward.
2024-08-22T20:40:43.8601944Z Suddenly, the ostriche hatched just as quickly as it was before, and Lucy could see something funny.2024-08-22:20:40:36,189 INFO     [generate.py:846] 
2024-08-22T20:40:43.8603534Z Time for inference 1: 3.20 sec total, time to first token 0.09 sec with sequential prefill, 199 tokens, 62.20 tokens/sec, 16.08 ms/token
2024-08-22T20:40:43.8604868Z 2024-08-22:20:40:36,189 INFO     [generate.py:849] Bandwidth achieved: 3.04 GB/s
2024-08-22T20:40:43.8606238Z 2024-08-22:20:40:36,189 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:40:43.8607109Z 
2024-08-22T20:40:43.8607257Z ========================================
2024-08-22T20:40:43.8607779Z 
2024-08-22T20:40:43.8608037Z Average tokens/sec: 62.20
2024-08-22T20:40:43.8608433Z Memory used: 0.00 GB
2024-08-22T20:40:43.8609553Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --temperature 1.0 --sequential-prefill
2024-08-22T20:40:43.8610612Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:40:43.8611026Z Loading model...
2024-08-22T20:40:43.8611391Z Time to load model: 0.03 seconds
2024-08-22T20:40:43.8611950Z -----------------------------------------------------------
2024-08-22T20:40:43.8613216Z Hello, my name is Stiger. Stiger is the biggest of animals in the world! He has a pink hat and long ears and two blue eyes. He is very famous. Everyone wants to be his friend.
2024-08-22T20:40:43.8615523Z One day, Stog meet a new friend. He is a leopard. His name is Leo. He has a big, furry coat and tiny helmets. He likes to run and play with balls and toys. He also likes to bite his neck and tail. He makes them dart around and chase all the other animals.
2024-08-22T20:40:43.8617002Z Some of Stard group is also friendly."
2024-08-22T20:41:37.1316983Z Mom and Dad tell Stard that Leo has a lot of energy and energy. They tell him that if he wants to play with Leo again, he should not throw the ball too far. They tell him that he should not bite the ball. They tell him that he should not do that.
2024-08-22T20:41:37.1319196Z Stretchy nodded and smiled.2024-08-22:20:40:43,975 INFO     [generate.py:846] 
2024-08-22T20:41:37.1320427Z Time for inference 1: 3.19 sec total, time to first token 0.09 sec with sequential prefill, 199 tokens, 62.30 tokens/sec, 16.05 ms/token
2024-08-22T20:41:37.1322163Z 2024-08-22:20:40:43,975 INFO     [generate.py:849] Bandwidth achieved: 3.04 GB/s
2024-08-22T20:41:37.1329288Z 2024-08-22:20:40:43,975 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:41:37.1330219Z 
2024-08-22T20:41:37.1330389Z ========================================
2024-08-22T20:41:37.1330726Z 
2024-08-22T20:41:37.1330888Z Average tokens/sec: 62.30
2024-08-22T20:41:37.1331285Z Memory used: 0.00 GB
2024-08-22T20:41:37.1332386Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --top-k 100 --sequential-prefill
2024-08-22T20:41:37.1333442Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:41:37.1333855Z Loading model...
2024-08-22T20:41:37.1334233Z Time to load model: 0.03 seconds
2024-08-22T20:41:37.1334792Z -----------------------------------------------------------
2024-08-22T20:41:37.1335869Z Hello, my name is Bubble. He is an adventurous bird and he likes to explore. One day, Wally met a little mouse and they started talking. 
2024-08-22T20:41:37.1337007Z "Would you like to go on an adventure with me?" Bubble asked.
2024-08-22T20:41:37.1337625Z "Yes, please!" Wally replied.
2024-08-22T20:41:37.1338522Z They went on a long journey and they saw many beautiful things. They saw a big house, a bridge, and even a dragon! 
2024-08-22T20:41:37.1339430Z "This is so exciting!" Wally said. 
2024-08-22T20:41:37.1340022Z "I know, it's really cool!" the mouse agreed. 
2024-08-22T20:41:37.1340993Z Bubble and the mouse continued on their adventure. They flew over mountains, across rivers, and even across the desert. 
2024-08-22T20:41:37.1342434Z Finally, they arrived at a big, beautiful garden. Bubble and the mouse were very excited and they decided to explore every corner. 
2024-08-22T20:41:37.1344053Z The moral of the story is that adventure can be lots of fun and adventures! Once2024-08-22:20:40:51,723 INFO     [generate.py:846] 
2024-08-22T20:41:37.1345550Z Time for inference 1: 3.14 sec total, time to first token 0.09 sec with sequential prefill, 199 tokens, 63.34 tokens/sec, 15.79 ms/token
2024-08-22T20:41:37.1346835Z 2024-08-22:20:40:51,724 INFO     [generate.py:849] Bandwidth achieved: 3.09 GB/s
2024-08-22T20:41:37.1348556Z 2024-08-22:20:40:51,724 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:41:37.1349437Z 
2024-08-22T20:41:37.1349586Z ========================================
2024-08-22T20:41:37.1349897Z 
2024-08-22T20:41:37.1350052Z Average tokens/sec: 63.34
2024-08-22T20:41:37.1350436Z Memory used: 0.00 GB
2024-08-22T20:41:37.1351514Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --top-k 200 --sequential-prefill
2024-08-22T20:41:37.1352596Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:41:37.1353045Z Loading model...
2024-08-22T20:41:37.1353436Z Time to load model: 0.03 seconds
2024-08-22T20:41:37.1353967Z -----------------------------------------------------------
2024-08-22T20:41:37.1355195Z Hello, my name is Toby. Toby's birthday is today someone". She was very excited to go to the party. She wanted to get a grip from the grown-ups, so she asked for a present.
2024-08-22T20:41:37.1356279Z "Can I have a present, please?" Toby asked.
2024-08-22T20:41:37.1357210Z "Yes, Toby," said Mommy, "but you have to be careful. Don't get too close to the other children and keep to yourself."
2024-08-22T20:41:37.1358233Z Toby was very happy. She ran to the party and he got his present. It was a toy car.
2024-08-22T20:41:37.1359299Z When Toby got the toy car, he wanted to play with it right away. He asked his mom if he could borrow it, but she said no.
2024-08-22T20:41:37.1360146Z "That car is too expensive!" said Toby.
2024-08-22T20:41:37.1360732Z Mommy smiled and said, "It's okay. You can borrow it then".
2024-08-22T20:41:37.1361830Z Toby was very happy. He thanked his mom and they went home2024-08-22:20:40:59,514 INFO     [generate.py:846] 
2024-08-22T20:41:37.1363054Z Time for inference 1: 3.19 sec total, time to first token 0.09 sec with sequential prefill, 199 tokens, 62.45 tokens/sec, 16.01 ms/token
2024-08-22T20:41:37.1364192Z 2024-08-22:20:40:59,514 INFO     [generate.py:849] Bandwidth achieved: 3.05 GB/s
2024-08-22T20:41:37.1365410Z 2024-08-22:20:40:59,514 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:41:37.1366179Z 
2024-08-22T20:41:37.1366306Z ========================================
2024-08-22T20:41:37.1366585Z 
2024-08-22T20:41:37.1366711Z Average tokens/sec: 62.45
2024-08-22T20:41:37.1367056Z Memory used: 0.00 GB
2024-08-22T20:41:37.1368004Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --top-k 500 --sequential-prefill
2024-08-22T20:41:37.1369049Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:41:37.1369423Z Loading model...
2024-08-22T20:41:37.1369739Z Time to load model: 0.03 seconds
2024-08-22T20:41:37.1370244Z -----------------------------------------------------------
2024-08-22T20:41:37.1371215Z Hello, my name is Farmer Sland. So, Mr. C Farmer is very nice. Every day he gives gifts for the children at his farm. All the children love him.
2024-08-22T20:41:37.1372683Z One day, Farmer Sack comes to town and sees how nice the children were. Farmer Sned the children, "I will deliver this toys and give them to you." 
2024-08-22T20:41:37.1374256Z The children were so excited! They couldn't believe it. When Farmer S alongside the toys and gave them the gifts, the children were very glad. 
2024-08-22T20:41:37.1375233Z "Thank you Farmer Sack!" they said. "We love you!"
2024-08-22T20:41:37.1377235Z B Farmer Sorrow and the children get toys and give them the best gifts of all. Once upon a time, there was a little girl named Amy. She had a big, red ball. One day, she went to the park with her mom and dad. They played and had lots of fun. Amy would throw the2024-08-22:20:41:07,260 INFO     [generate.py:846] 
2024-08-22T20:41:37.1379863Z Time for inference 1: 3.17 sec total, time to first token 0.09 sec with sequential prefill, 199 tokens, 62.69 tokens/sec, 15.95 ms/token
2024-08-22T20:41:37.1381015Z 2024-08-22:20:41:07,260 INFO     [generate.py:849] Bandwidth achieved: 3.06 GB/s
2024-08-22T20:41:37.1382412Z 2024-08-22:20:41:07,260 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:41:37.1383187Z 
2024-08-22T20:41:37.1383315Z ========================================
2024-08-22T20:41:37.1383590Z 
2024-08-22T20:41:37.1383716Z Average tokens/sec: 62.69
2024-08-22T20:41:37.1384055Z Memory used: 0.00 GB
2024-08-22T20:41:37.1385071Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --temperature 0 --sequential-prefill --compile
2024-08-22T20:41:37.1386062Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:41:37.1386428Z Loading model...
2024-08-22T20:41:37.1386765Z Time to load model: 0.03 seconds
2024-08-22T20:41:37.1387273Z -----------------------------------------------------------
2024-08-22T20:41:37.1388471Z Hello, my name is Daisy. Daisy is very popular. Everyone wants to be her friend. One day, Daisy's mommy takes her to the park. Daisy is so excited! She loves to play.
2024-08-22T20:41:37.1390506Z Daisy sees a big slide. She wants to go on it. Daisy's mommy says, "Yes, you can go on the slide." Daisy runs to the slide. She climbs up the steps. She sits on the slide and slides down. She laughs and says, "Whee!"
2024-08-22T20:41:37.1393337Z Daisy goes on the slide again and again. She has so much fun. She sees her mommy and daddy. They are happy to see her. They say, "Good job, Daisy!" Daisy is happy too. She loves to play at the park. Once upon a time, there was a little girl named Lily. She loved tojust-in-time compilation time (incl run time): 9.2 seconds
2024-08-22T20:41:37.1395362Z 2024-08-22:20:41:21,883 INFO     [generate.py:846] 
2024-08-22T20:41:37.1396295Z Time for inference 1: 9.15 sec total, time to first token 0.09 sec with sequential prefill, 199 tokens, 21.75 tokens/sec, 45.98 ms/token
2024-08-22T20:41:37.1397436Z 2024-08-22:20:41:21,883 INFO     [generate.py:849] Bandwidth achieved: 1.06 GB/s
2024-08-22T20:41:37.1398743Z 2024-08-22:20:41:21,883 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:41:37.1399593Z 
2024-08-22T20:41:37.1399718Z ========================================
2024-08-22T20:41:37.1399983Z 
2024-08-22T20:41:37.1400121Z Average tokens/sec: 21.75
2024-08-22T20:41:37.1400455Z Memory used: 0.00 GB
2024-08-22T20:41:37.1401488Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --temperature 0.9 --sequential-prefill --compile
2024-08-22T20:41:37.1402483Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:41:37.1402854Z Loading model...
2024-08-22T20:41:37.1403161Z Time to load model: 0.03 seconds
2024-08-22T20:41:37.1403643Z -----------------------------------------------------------
2024-08-22T20:41:37.1404373Z Hello, my name is Rabbit". Rabbit was a friendly, bald man who lived in a big tree in a valley.
2024-08-22T20:41:37.1405392Z One day, Rabbit met a little rabbit at the valley. Rabbit asked, "Hello, Rabbit. Can I join you in a game?"
2024-08-22T20:41:37.1406540Z Rabbit thought for a moment and said, "No, Rabbit. That would be too dangerous. I don't belong here."
2024-08-22T20:41:37.1407687Z But Rabbit refused. "Please, Rabbit. It's nice and safe. I'll let you play with me all afternoon!"
2024-08-22T20:41:37.1408860Z Rabbit thought for a moment and said, "Okay, Rabbit. I'm sorry. I won't be too adventurous today!"
2024-08-22T20:41:37.1409616Z Rabbit smiled and said, "Alright, Rabbit. bye!"
2024-08-22T20:41:37.1410864Z Rabbit smiled back and hopped off. Rabbit sat in the tree and was ready to play. The end. Once upon a time, therejust-in-time compilation time (incl run time): 9.5 seconds
2024-08-22T20:41:37.1412049Z 2024-08-22:20:41:37,130 INFO     [generate.py:846] 
2024-08-22T20:41:37.1412975Z Time for inference 1: 9.50 sec total, time to first token 0.09 sec with sequential prefill, 199 tokens, 20.94 tokens/sec, 47.76 ms/token
2024-08-22T20:41:37.1414110Z 2024-08-22:20:41:37,131 INFO     [generate.py:849] Bandwidth achieved: 1.02 GB/s
2024-08-22T20:42:46.1406632Z 2024-08-22:20:41:37,131 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:42:46.1409762Z 
2024-08-22T20:42:46.1409929Z ========================================
2024-08-22T20:42:46.1410250Z 
2024-08-22T20:42:46.1410385Z Average tokens/sec: 20.94
2024-08-22T20:42:46.1410739Z Memory used: 0.00 GB
2024-08-22T20:42:46.1415604Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --temperature 1.0 --sequential-prefill --compile
2024-08-22T20:42:46.1416659Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:42:46.1417026Z Loading model...
2024-08-22T20:42:46.1417348Z Time to load model: 0.03 seconds
2024-08-22T20:42:46.1417840Z -----------------------------------------------------------
2024-08-22T20:42:46.1420349Z Hello, my name is Thety. Thety's name wasust and she had a nice job cleaning the house. One day, tears came down of an angel's face and said, "I'm so impatient I woke you up, it must be special to me." The Ironing was uncomfortable, so thety gently put a little bit of iron in her hand. Suddenly she smiled as the iron gently rustled each time it woke her up. When she touched it, the world started sparkling.
2024-08-22T20:42:46.1424613Z Gread came closer to the ironing table and the Angelingbird noticed. She said, "I found your job, Queen!" The exam took its time and the golden paper to receive the ironing order. Everyone cheered with happiness as the ironing began to freeze the room. TheAntybird took a deep breath and said to the ironing man, "The ironing is very hard". Thejust-in-time compilation time (incl run time): 9.5 seconds
2024-08-22T20:42:46.1427098Z 2024-08-22:20:41:52,372 INFO     [generate.py:846] 
2024-08-22T20:42:46.1428037Z Time for inference 1: 9.52 sec total, time to first token 0.09 sec with sequential prefill, 199 tokens, 20.90 tokens/sec, 47.84 ms/token
2024-08-22T20:42:46.1429185Z 2024-08-22:20:41:52,372 INFO     [generate.py:849] Bandwidth achieved: 1.02 GB/s
2024-08-22T20:42:46.1430504Z 2024-08-22:20:41:52,373 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:42:46.1431357Z 
2024-08-22T20:42:46.1431493Z ========================================
2024-08-22T20:42:46.1431778Z 
2024-08-22T20:42:46.1431948Z Average tokens/sec: 20.90
2024-08-22T20:42:46.1432441Z Memory used: 0.00 GB
2024-08-22T20:42:46.1433568Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --top-k 100 --sequential-prefill --compile
2024-08-22T20:42:46.1434604Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:42:46.1434964Z Loading model...
2024-08-22T20:42:46.1435284Z Time to load model: 0.03 seconds
2024-08-22T20:42:46.1435773Z -----------------------------------------------------------
2024-08-22T20:42:46.1436543Z Hello, my name is Tiny. He has a long white fluffy tail. He loves to play with his friends in the park.
2024-08-22T20:42:46.1437929Z One day, Tiny's friends came to play hide and seek. They all ran to the big tree and tried to hide. But Tiny was too fast and found a place to hide.
2024-08-22T20:42:46.1439601Z Tiny was sad because he could not find his friends. He walked around the tree, looking for them. Then, he saw a big, green ball. He thought his friends might be hiding behind the ball.
2024-08-22T20:42:46.1442794Z Tiny found his friends quickly. They were all hiding from Tiny. He was happy to find them. They all laughed and played more hide and seek. Tiny and his friends had a fun day at the park. Once upon a time, there was a little boy named Timmy. Timmy loved to climb trees and play outside. One day, Timmy's mommy asked him to come inside for lunchjust-in-time compilation time (incl run time): 9.5 seconds
2024-08-22T20:42:46.1445056Z 2024-08-22:20:42:07,516 INFO     [generate.py:846] 
2024-08-22T20:42:46.1446097Z Time for inference 1: 9.46 sec total, time to first token 0.09 sec with sequential prefill, 199 tokens, 21.04 tokens/sec, 47.52 ms/token
2024-08-22T20:42:46.1447241Z 2024-08-22:20:42:07,516 INFO     [generate.py:849] Bandwidth achieved: 1.03 GB/s
2024-08-22T20:42:46.1448675Z 2024-08-22:20:42:07,516 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:42:46.1449533Z 
2024-08-22T20:42:46.1449659Z ========================================
2024-08-22T20:42:46.1449936Z 
2024-08-22T20:42:46.1450063Z Average tokens/sec: 21.04
2024-08-22T20:42:46.1450411Z Memory used: 0.00 GB
2024-08-22T20:42:46.1451410Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --top-k 200 --sequential-prefill --compile
2024-08-22T20:42:46.1452381Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:42:46.1452748Z Loading model...
2024-08-22T20:42:46.1453057Z Time to load model: 0.03 seconds
2024-08-22T20:42:46.1453547Z -----------------------------------------------------------
2024-08-22T20:42:46.1454438Z Hello, my name is Hairy Bear. Every day, Hairy Bear wakes up, eats, and polishes. He loves to help others and make them happy.
2024-08-22T20:42:46.1456325Z One day, Hairy Bear comes to visit the village. The villagespeople are very happy to see Hairy Bear and they all thank him for their work. Hairy Bear gives them advice and makes them happy to know that if you help others, you will make them happy too. 
2024-08-22T20:42:46.1458454Z The villagespeople watch Hairy Bear and nod. They all thanked him for his kind words. Hairy Bear understood why everyone was so polite and thanked the village for all of them. 
2024-08-22T20:42:46.1460153Z That is how Hairy Bear and the villagespeople became very happy and grateful. They also appreciate Hairy Bear for being so polite and kind. 
2024-08-22T20:42:46.1461647Z The end. One day, a big cat and a little cat were playing in the yard. Thejust-in-time compilation time (incl run time): 9.5 seconds
2024-08-22T20:42:46.1462648Z 2024-08-22:20:42:22,731 INFO     [generate.py:846] 
2024-08-22T20:42:46.1463573Z Time for inference 1: 9.50 sec total, time to first token 0.09 sec with sequential prefill, 199 tokens, 20.95 tokens/sec, 47.74 ms/token
2024-08-22T20:42:46.1464709Z 2024-08-22:20:42:22,731 INFO     [generate.py:849] Bandwidth achieved: 1.02 GB/s
2024-08-22T20:42:46.1466014Z 2024-08-22:20:42:22,731 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:42:46.1466869Z 
2024-08-22T20:42:46.1467000Z ========================================
2024-08-22T20:42:46.1467268Z 
2024-08-22T20:42:46.1467393Z Average tokens/sec: 20.95
2024-08-22T20:42:46.1467734Z Memory used: 0.00 GB
2024-08-22T20:42:46.1468734Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype bfloat16 --top-k 500 --sequential-prefill --compile
2024-08-22T20:42:46.1469689Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:42:46.1470057Z Loading model...
2024-08-22T20:42:46.1470373Z Time to load model: 0.03 seconds
2024-08-22T20:42:46.1470845Z -----------------------------------------------------------
2024-08-22T20:42:46.1471337Z Hello, my name is Tink. Where are you from?"
2024-08-22T20:42:46.1472721Z Chink was a 3-year-old who was always playing around his room. One day, he decided to visit his Grandma who lived in a nearby house. As soon as he arrived, he saw his Grandma smiling at him.
2024-08-22T20:42:46.1474135Z "Hello, welcome to her! I have a surprise for you," said Grandma as handing him a jar of jelly.
2024-08-22T20:42:46.1474956Z "Wow!" exclaimed Chink, amazed at how delicate the jelly looked! 
2024-08-22T20:42:46.1476223Z Grandma opened the jar and pulled out a big jar of jelly. "This will be a special treat," she said. "It's something you can enjoy as long as you eat plenty of it."
2024-08-22T20:42:46.1477495Z Chinky's eyes lit up. She was so excited and couldn't wait to try the jelly.
2024-08-22T20:42:46.1479199Z As Tinkle sat in the room, Grandma gave him a piece of jelly.just-in-time compilation time (incl run time): 9.5 seconds
2024-08-22T20:42:46.1480135Z 2024-08-22:20:42:37,890 INFO     [generate.py:846] 
2024-08-22T20:42:46.1481067Z Time for inference 1: 9.51 sec total, time to first token 0.08 sec with sequential prefill, 199 tokens, 20.93 tokens/sec, 47.78 ms/token
2024-08-22T20:42:46.1482192Z 2024-08-22:20:42:37,890 INFO     [generate.py:849] Bandwidth achieved: 1.02 GB/s
2024-08-22T20:42:46.1483550Z 2024-08-22:20:42:37,891 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:42:46.1484411Z 
2024-08-22T20:42:46.1484540Z ========================================
2024-08-22T20:42:46.1484804Z 
2024-08-22T20:42:46.1484936Z Average tokens/sec: 20.93
2024-08-22T20:42:46.1485270Z Memory used: 0.00 GB
2024-08-22T20:42:46.1485616Z + for DTYPE in bfloat16 float16 float32
2024-08-22T20:42:46.1486561Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --temperature 0
2024-08-22T20:42:46.1487384Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:42:46.1487747Z Loading model...
2024-08-22T20:42:46.1488196Z Time to load model: 0.03 seconds
2024-08-22T20:42:46.1488673Z -----------------------------------------------------------
2024-08-22T20:42:46.1489870Z Hello, my name is Daisy. Daisy is very popular. Everyone wants to be her friend. One day, Daisy's mommy takes her to the park. Daisy is so excited! She loves to play.
2024-08-22T20:42:46.1491655Z Daisy sees a big slide. She wants to go on it. She runs to the slide and climbs up the ladder. She sits on the top and slides down. She laughs and says, "Whee!"
2024-08-22T20:42:46.1493078Z Daisy's mommy claps and says, "Good job, Daisy! You are so brave!" Daisy smiles and says, "Thank you, mommy!"
2024-08-22T20:42:46.1495067Z Daisy and her mommy have a great day at the park. They play on the swings, slide down the slide, and run in the grass. Daisy is very happy. She loves her mommy and her popular slide. Once upon a time, there2024-08-22:20:42:46,139 INFO     [generate.py:846] 
2024-08-22T20:42:46.1496984Z Time for inference 1: 3.38 sec total, time to first token 0.07 sec with parallel prefill, 199 tokens, 58.95 tokens/sec, 16.96 ms/token
2024-08-22T20:42:46.1498112Z 2024-08-22:20:42:46,139 INFO     [generate.py:849] Bandwidth achieved: 2.88 GB/s
2024-08-22T20:42:46.1499317Z 2024-08-22:20:42:46,139 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:42:46.1500090Z 
2024-08-22T20:42:46.1500214Z ========================================
2024-08-22T20:42:46.1500491Z 
2024-08-22T20:44:02.7715175Z Average tokens/sec: 58.95
2024-08-22T20:44:02.7715669Z Memory used: 0.00 GB
2024-08-22T20:44:02.7717041Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --temperature 0.9
2024-08-22T20:44:02.7718582Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:44:02.7720807Z Loading model...
2024-08-22T20:44:02.7721241Z Time to load model: 0.03 seconds
2024-08-22T20:44:02.7721911Z -----------------------------------------------------------
2024-08-22T20:44:02.7723762Z Hello, my name is Daisy. Every day, Daisy and Daisy would step around the park together. They would spin around, laughing and playing. One day, Daisy urged Daisy to try something new. Daisy waved her hand and took a deep breath. She urged Daisy to follow her.
2024-08-22T20:44:02.7726828Z Daisy stepped forward and rolled right in front of Daisy and Daisy. Gently, Daisy stepped up onto the swingset. She ran over to the other side of the park and began to skip around it. Daisy skipped around the park, and Daisy laughed out loud.
2024-08-22T20:44:02.7729538Z Suddenly, Daisy heard a very loud bark! She looked up and saw Daisy a baby. Daisy was a bit scared, but she couldn't help but make Daisy laugh.
2024-08-22T20:44:02.7731648Z "My mommy said this is the most important thing ever2024-08-22:20:42:54,259 INFO     [generate.py:846] 
2024-08-22T20:44:02.7733095Z Time for inference 1: 3.53 sec total, time to first token 0.08 sec with parallel prefill, 199 tokens, 56.39 tokens/sec, 17.73 ms/token
2024-08-22T20:44:02.7734465Z 2024-08-22:20:42:54,259 INFO     [generate.py:849] Bandwidth achieved: 2.75 GB/s
2024-08-22T20:44:02.7735937Z 2024-08-22:20:42:54,259 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:44:02.7736868Z 
2024-08-22T20:44:02.7737035Z ========================================
2024-08-22T20:44:02.7737396Z 
2024-08-22T20:44:02.7737552Z Average tokens/sec: 56.39
2024-08-22T20:44:02.7737974Z Memory used: 0.00 GB
2024-08-22T20:44:02.7739030Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --temperature 1.0
2024-08-22T20:44:02.7740040Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:44:02.7740484Z Loading model...
2024-08-22T20:44:02.7740882Z Time to load model: 0.03 seconds
2024-08-22T20:44:02.7741458Z -----------------------------------------------------------
2024-08-22T20:44:02.7742874Z Hello, my name is Anna. She is a proud girl. She has a big book with many pages. She can read and look at them easily. She likes to read with her mom and dad. They have a book of films.
2024-08-22T20:44:02.7745189Z One day, Anna's mom is looking for a special film to turn back home. "Anna, please help me find this film." she says. "I need it for our home. It will show the pictures and sounds soft."
2024-08-22T20:44:02.7747685Z Anna and her mom look for the film on the shelf. They find many blank pages. Anna points and says, "This one is pretty and soft." They take a picture, the film, and Anna's mom reads the away.
2024-08-22T20:44:02.7750447Z But Anna feels sad. She wants to keep looking at the film. She thinks it is fun to watch the white film. She does not want to eat or play or read. She puts the magazine back on the shelf. She looks at her mom.2024-08-22:20:43:02,938 INFO     [generate.py:846] 
2024-08-22T20:44:02.7752608Z Time for inference 1: 3.47 sec total, time to first token 0.08 sec with parallel prefill, 199 tokens, 57.35 tokens/sec, 17.44 ms/token
2024-08-22T20:44:02.7753877Z 2024-08-22:20:43:02,939 INFO     [generate.py:849] Bandwidth achieved: 2.80 GB/s
2024-08-22T20:44:02.7755322Z 2024-08-22:20:43:02,939 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:44:02.7756257Z 
2024-08-22T20:44:02.7756423Z ========================================
2024-08-22T20:44:02.7756825Z 
2024-08-22T20:44:02.7756981Z Average tokens/sec: 57.35
2024-08-22T20:44:02.7757415Z Memory used: 0.00 GB
2024-08-22T20:44:02.7758457Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --top-k 100
2024-08-22T20:44:02.7759434Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:44:02.7759888Z Loading model...
2024-08-22T20:44:02.7760342Z Time to load model: 0.03 seconds
2024-08-22T20:44:02.7760941Z -----------------------------------------------------------
2024-08-22T20:44:02.7762445Z Hello, my name is Buzzy. He is three years old and he loves to explore. One day he saw a zigzag. It was a bright and colourful zigzag. Buzzy wanted to explore something new, so he went to the park. 
2024-08-22T20:44:02.7764637Z At the park he saw a big slide and he wanted to take a look. He ran over to the slide and started to go down it. He was having lots of fun and he enjoyed it very much.
2024-08-22T20:44:02.7766279Z Suddenly, he heard a voice. "Hey, what are you doing here?" It was a little girl who was playing nearby.
2024-08-22T20:44:02.7767332Z "I'm going to slide down the zigzag slide," said Buzzy. 
2024-08-22T20:44:02.7768326Z The girl smiled and said, "That's very brave of you. Have fun!"
2024-08-22T20:44:02.7769826Z Buzzy was very happy and he went back up the slide. He was ready to go down it again and again! Once upon2024-08-22:20:43:11,079 INFO     [generate.py:846] 
2024-08-22T20:44:02.7771677Z Time for inference 1: 3.51 sec total, time to first token 0.08 sec with parallel prefill, 199 tokens, 56.77 tokens/sec, 17.62 ms/token
2024-08-22T20:44:02.7773043Z 2024-08-22:20:43:11,080 INFO     [generate.py:849] Bandwidth achieved: 2.77 GB/s
2024-08-22T20:44:02.7774500Z 2024-08-22:20:43:11,080 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:44:02.7775429Z 
2024-08-22T20:44:02.7775590Z ========================================
2024-08-22T20:44:02.7775923Z 
2024-08-22T20:44:02.7776077Z Average tokens/sec: 56.77
2024-08-22T20:44:02.7776493Z Memory used: 0.00 GB
2024-08-22T20:44:02.7777504Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --top-k 200
2024-08-22T20:44:02.7778848Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:44:02.7779294Z Loading model...
2024-08-22T20:44:02.7779678Z Time to load model: 0.03 seconds
2024-08-22T20:44:02.7780319Z -----------------------------------------------------------
2024-08-22T20:44:02.7781578Z Hello, my name is George. His name is George and he loves to play with his friends. One day, George went to a new place and he saw a new girl in the classroom. 
2024-08-22T20:44:02.7783336Z George wanted to meet the new girl. But she didn’t know him. He was very scared and he felt ashamed.
2024-08-22T20:44:02.7784540Z Suddenly, he heard a voice behind him. It said, “Hello there! Who’s there?” 
2024-08-22T20:44:02.7785624Z George turned around and saw a nice lady. She was smiling at George. He welcomed her and they had a nice chat.
2024-08-22T20:44:02.7787024Z The nice lady said, “You are very welcome. Would you like to join your class?”
2024-08-22T20:44:02.7787906Z George was so happy and he said, “Yes, I would love to!”
2024-08-22T20:44:02.7789725Z The nice lady and George went to the classroom and they all had a great time. They played games, sang songs and laughed together. George was no longer ashamed and he felt happy2024-08-22:20:43:19,195 INFO     [generate.py:846] 
2024-08-22T20:44:02.7791774Z Time for inference 1: 3.52 sec total, time to first token 0.08 sec with parallel prefill, 199 tokens, 56.52 tokens/sec, 17.69 ms/token
2024-08-22T20:44:02.7793095Z 2024-08-22:20:43:19,195 INFO     [generate.py:849] Bandwidth achieved: 2.76 GB/s
2024-08-22T20:44:02.7794573Z 2024-08-22:20:43:19,195 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:44:02.7795528Z 
2024-08-22T20:44:02.7795705Z ========================================
2024-08-22T20:44:02.7796054Z 
2024-08-22T20:44:02.7796213Z Average tokens/sec: 56.52
2024-08-22T20:44:02.7796634Z Memory used: 0.00 GB
2024-08-22T20:44:02.7797648Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --top-k 500
2024-08-22T20:44:02.7798657Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:44:02.7799113Z Loading model...
2024-08-22T20:44:02.7799504Z Time to load model: 0.03 seconds
2024-08-22T20:44:02.7800137Z -----------------------------------------------------------
2024-08-22T20:44:02.7801177Z Hello, my name is Emmy, I have a special job. I'm going to deliver a basket to the park for my family.
2024-08-22T20:44:02.7802112Z "What is your job, Emmy?" asked the small girl.
2024-08-22T20:44:02.7802710Z "I have a surprise for you," replied the girl.
2024-08-22T20:44:02.7803255Z "What is it?" asked Emmy.
2024-08-22T20:44:02.7803767Z "It's a present for me," said the girl.
2024-08-22T20:44:02.7804351Z Emmy was curious and asked, "What kind of present?"
2024-08-22T20:44:02.7805101Z "It's a fancy balloon," she said, looking at the basket.
2024-08-22T20:44:02.7805845Z "Wow!" said the girl. "I wish I had something fancy like that."
2024-08-22T20:44:02.7808653Z Emmy smiled and said, "Maybe one day I'll have something fancy too!" Once upon a time, there was a little girl named Lily. She loved to play outside with her friends. One day, she saw a big, powerful dog. The dog was very scary, and Lily was afraid it would hurt her.2024-08-22:20:43:27,336 INFO     [generate.py:846] 
2024-08-22T20:44:02.7811350Z Time for inference 1: 3.55 sec total, time to first token 0.08 sec with parallel prefill, 199 tokens, 56.01 tokens/sec, 17.85 ms/token
2024-08-22T20:44:02.7812718Z 2024-08-22:20:43:27,337 INFO     [generate.py:849] Bandwidth achieved: 2.73 GB/s
2024-08-22T20:44:02.7814183Z 2024-08-22:20:43:27,337 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:44:02.7815112Z 
2024-08-22T20:44:02.7815267Z ========================================
2024-08-22T20:44:02.7815601Z 
2024-08-22T20:44:02.7815754Z Average tokens/sec: 56.01
2024-08-22T20:44:02.7816172Z Memory used: 0.00 GB
2024-08-22T20:44:02.7817379Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --temperature 0 --compile --compile-prefill
2024-08-22T20:44:02.7818553Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:44:02.7818997Z Loading model...
2024-08-22T20:44:02.7819380Z Time to load model: 0.03 seconds
2024-08-22T20:44:02.7819958Z -----------------------------------------------------------
2024-08-22T20:44:02.7821401Z Hello, my name is Daisy. Daisy is very popular. Everyone wants to be her friend. One day, Daisy's mommy takes her to the park. Daisy is so excited! She loves to play.
2024-08-22T20:44:02.7823273Z Daisy sees a big slide. She wants to go on it. She runs to the slide and climbs up the ladder. She sits on the top and slides down. She laughs and says, "Whee!"
2024-08-22T20:47:31.1278816Z Daisy's mommy claps and says, "Good job, Daisy! You are so brave!" Daisy smiles and says, "Thank you, mommy!"
2024-08-22T20:47:31.1282604Z Daisy and her mommy have a great day at the park. They play on the swings, slide down the slide, and run in the grass. Daisy is very happy. She loves her mommy and her popular slide. Once upon a time, therejust-in-time compilation time (incl run time): 4e+01 seconds
2024-08-22T20:47:31.1284750Z 2024-08-22:20:44:12,323 INFO     [generate.py:846] 
2024-08-22T20:47:31.1285894Z Time for inference 1: 39.56 sec total, time to first token 16.59 sec with parallel prefill, 199 tokens, 5.03 tokens/sec, 198.79 ms/token
2024-08-22T20:47:31.1287264Z 2024-08-22:20:44:12,323 INFO     [generate.py:849] Bandwidth achieved: 0.25 GB/s
2024-08-22T20:47:31.1288983Z 2024-08-22:20:44:12,323 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:47:31.1290034Z 
2024-08-22T20:47:31.1290188Z ========================================
2024-08-22T20:47:31.1290519Z 
2024-08-22T20:47:31.1290674Z Average tokens/sec: 5.03
2024-08-22T20:47:31.1291086Z Memory used: 0.00 GB
2024-08-22T20:47:31.1292317Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --temperature 0.9 --compile --compile-prefill
2024-08-22T20:47:31.1293490Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:47:31.1293929Z Loading model...
2024-08-22T20:47:31.1294309Z Time to load model: 0.03 seconds
2024-08-22T20:47:31.1294943Z -----------------------------------------------------------
2024-08-22T20:47:31.1295707Z Hello, my name is George. Every day George had to unlock his dishwasher. 
2024-08-22T20:47:31.1297039Z One day, George asked he could unlock his dishwasher, but he couldn't find the key. So, he tried to unlock the dishwasher. 
2024-08-22T20:47:31.1298413Z He looked everywhere, but he couldn't find it. He asked his mom "Mom, where is the key?" 
2024-08-22T20:47:31.1300156Z But her mom didn't know. So she told George that the key was very important. She said that if he kept looking, he could only find a door that no one else could open. 
2024-08-22T20:47:31.1303243Z George was determined to get the key. He started to walk around the house, looking for a door but he still couldn't unlock it. He thought and thought until he came up with a great idea. He went back to the store and got the key! 
2024-08-22T20:47:31.1305473Z George was very happy and he quickly unlocked the dishwjust-in-time compilation time (incl run time): 3.9e+01 seconds
2024-08-22T20:47:31.1306560Z 2024-08-22:20:44:58,034 INFO     [generate.py:846] 
2024-08-22T20:47:31.1307628Z Time for inference 1: 39.30 sec total, time to first token 15.75 sec with parallel prefill, 199 tokens, 5.06 tokens/sec, 197.47 ms/token
2024-08-22T20:47:31.1308909Z 2024-08-22:20:44:58,034 INFO     [generate.py:849] Bandwidth achieved: 0.25 GB/s
2024-08-22T20:47:31.1310389Z 2024-08-22:20:44:58,035 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:47:31.1311341Z 
2024-08-22T20:47:31.1311486Z ========================================
2024-08-22T20:47:31.1311801Z 
2024-08-22T20:47:31.1311947Z Average tokens/sec: 5.06
2024-08-22T20:47:31.1312335Z Memory used: 0.00 GB
2024-08-22T20:47:31.1313483Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --temperature 1.0 --compile --compile-prefill
2024-08-22T20:47:31.1314577Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:47:31.1314995Z Loading model...
2024-08-22T20:47:31.1315352Z Time to load model: 0.03 seconds
2024-08-22T20:47:31.1315905Z -----------------------------------------------------------
2024-08-22T20:47:31.1316748Z Hello, my name is Max! For Toni, I'm the harmlessest bear who always wins at games.
2024-08-22T20:47:31.1317927Z Today, my name is Max and I don't want to be strong. I want to be bigger and wiser than everyone else". 
2024-08-22T20:47:31.1319221Z He watched a tiny flame crawl around with a crwork burning its ingredients.
2024-08-22T20:47:31.1320016Z "How about we work together to be competitive", said Toni.
2024-08-22T20:47:31.1320960Z "Ok, that sounds like a great idea!", replied Toni with a smile.
2024-08-22T20:47:31.1322193Z Toni and Max worked together to make their game more alright. Everyone was having so much fun and beating one another with so much bouncing past one. 
2024-08-22T20:47:31.1324084Z Eventually, all the other animals gathered to see it and start rolling around in want and be competitive. Toni was so proud of Max and Toni that they had worked together to be vaughty. 
2024-08-22T20:47:31.1325812Z Max went happily by the grownujust-in-time compilation time (incl run time): 3.9e+01 seconds
2024-08-22T20:47:31.1326716Z 2024-08-22:20:45:43,850 INFO     [generate.py:846] 
2024-08-22T20:47:31.1327839Z Time for inference 1: 39.37 sec total, time to first token 15.98 sec with parallel prefill, 199 tokens, 5.05 tokens/sec, 197.83 ms/token
2024-08-22T20:47:31.1329198Z 2024-08-22:20:45:43,850 INFO     [generate.py:849] Bandwidth achieved: 0.25 GB/s
2024-08-22T20:47:31.1330794Z 2024-08-22:20:45:43,850 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:47:31.1331849Z 
2024-08-22T20:47:31.1332018Z ========================================
2024-08-22T20:47:31.1332358Z 
2024-08-22T20:47:31.1332516Z Average tokens/sec: 5.05
2024-08-22T20:47:31.1332927Z Memory used: 0.00 GB
2024-08-22T20:47:31.1334159Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --top-k 100 --compile --compile-prefill
2024-08-22T20:47:31.1335315Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:47:31.1335762Z Loading model...
2024-08-22T20:47:31.1336145Z Time to load model: 0.03 seconds
2024-08-22T20:47:31.1336717Z -----------------------------------------------------------
2024-08-22T20:47:31.1337930Z Hello, my name is Grace. Grace is three years old. Grace loves to wear her favourite scarf. It is pink and pretty. She loves to wear it to keep her warm.
2024-08-22T20:47:31.1340184Z Grace and Grandma's friend began to play a game. Grandma said they would clap their hands together. Grace clapped her hands and it was funny. Grandma smiled and said, "Let's clap our hands together!"
2024-08-22T20:47:31.1342055Z Grace clapped her hands and said, "Yay!" Then Grandma took out her scarf and began to clap it.
2024-08-22T20:47:31.1343504Z Grace loved clapping. She clapped faster and louder and more. Grandma smiled and said, "Looks like we should put the scarf back, you know Grandma."
2024-08-22T20:47:31.1345665Z Grace put the scarf back and said, "Thank you, Grandma!" Then she clapped her hands again and said, "Let's play again!" Once upon ajust-in-time compilation time (incl run time): 3.9e+01 seconds
2024-08-22T20:47:31.1347145Z 2024-08-22:20:46:29,728 INFO     [generate.py:846] 
2024-08-22T20:47:31.1348239Z Time for inference 1: 39.40 sec total, time to first token 16.02 sec with parallel prefill, 199 tokens, 5.05 tokens/sec, 198.00 ms/token
2024-08-22T20:47:31.1349583Z 2024-08-22:20:46:29,728 INFO     [generate.py:849] Bandwidth achieved: 0.25 GB/s
2024-08-22T20:47:31.1351140Z 2024-08-22:20:46:29,729 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:47:31.1352142Z 
2024-08-22T20:47:31.1352301Z ========================================
2024-08-22T20:47:31.1352620Z 
2024-08-22T20:47:31.1352771Z Average tokens/sec: 5.05
2024-08-22T20:47:31.1353178Z Memory used: 0.00 GB
2024-08-22T20:47:31.1354339Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --top-k 200 --compile --compile-prefill
2024-08-22T20:47:31.1355495Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:47:31.1355931Z Loading model...
2024-08-22T20:47:31.1356442Z Time to load model: 0.03 seconds
2024-08-22T20:47:31.1357015Z -----------------------------------------------------------
2024-08-22T20:47:31.1357561Z Hello, my name is Lily, the sun!"
2024-08-22T20:47:31.1358272Z Lily was excited to learn so much to see. She loved to explore and find new things.
2024-08-22T20:47:31.1359887Z They went walking in a park and soon, they came to a big brown gate. The gate was open and there was a sign that said 'Welcome," replied a long and clear swirling butterfly.
2024-08-22T20:47:31.1361301Z Lily was amazed. She had never seen anything so beautiful and so interesting.
2024-08-22T20:47:31.1362057Z “Wow,” she said.
2024-08-22T20:47:31.1362839Z Suddenly, her mom came and said, “Lily, why don't you try to go on the swing? It is too scary.”
2024-08-22T20:47:31.1364070Z Lily was a bit scared and said, “I don't want to, Mom. I don't want to go on the swing. Please don't."
2024-08-22T20:47:31.1365683Z Her mom said, “Don't worry, Lily. I know it's dangerous. But you can go on the swing andjust-in-time compilation time (incl run time): 2.7e+01 seconds
2024-08-22T20:47:31.1366898Z 2024-08-22:20:47:03,612 INFO     [generate.py:846] 
2024-08-22T20:47:31.1368105Z Time for inference 1: 27.36 sec total, time to first token 9.16 sec with parallel prefill, 199 tokens, 7.27 tokens/sec, 137.49 ms/token
2024-08-22T20:47:31.1369391Z 2024-08-22:20:47:03,612 INFO     [generate.py:849] Bandwidth achieved: 0.36 GB/s
2024-08-22T20:47:31.1370874Z 2024-08-22:20:47:03,612 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:47:31.1371823Z 
2024-08-22T20:47:31.1371983Z ========================================
2024-08-22T20:47:31.1372285Z 
2024-08-22T20:47:31.1372431Z Average tokens/sec: 7.27
2024-08-22T20:47:31.1372822Z Memory used: 0.00 GB
2024-08-22T20:47:31.1373941Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --top-k 500 --compile --compile-prefill
2024-08-22T20:47:31.1375024Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:47:31.1375471Z Loading model...
2024-08-22T20:47:31.1375836Z Time to load model: 0.03 seconds
2024-08-22T20:47:31.1376380Z -----------------------------------------------------------
2024-08-22T20:47:31.1376895Z Hello, my name is Bob.
2024-08-22T20:47:31.1377413Z "Your job, Bob," said the little girl who lived in the store. 
2024-08-22T20:47:31.1378965Z Suddenly, Bob heard a loud scream! He quickly screamed, "What was that noise?"
2024-08-22T20:47:31.1380053Z His mom came running over to see what was happening. She looked in the window and saw Bob. 
2024-08-22T20:47:31.1380936Z "Oh no!" she said. "Someone is in trouble." 
2024-08-22T20:47:31.1381695Z The little girl ran to Bob, who was a bit impatient. "Woof!" She said. 
2024-08-22T20:47:31.1382521Z "Don't worry," said Mom. "We'll help you."
2024-08-22T20:47:31.1383206Z They went outside and saw a man who was looking for his scream. 
2024-08-22T20:47:31.1383905Z Mom said, "We will help him."
2024-08-22T20:47:31.1384388Z The man thanked them and walked away. 
2024-08-22T20:48:19.8652476Z Bob and Mom were happy that they helped the man. He was so thankful to them. 
2024-08-22T20:48:19.8653907Z The little girl smiled, "Sometimes it isjust-in-time compilation time (incl run time): 2.9e+01 seconds
2024-08-22T20:48:19.8656782Z 2024-08-22:20:47:38,487 INFO     [generate.py:846] 
2024-08-22T20:48:19.8658351Z Time for inference 1: 28.65 sec total, time to first token 8.95 sec with parallel prefill, 199 tokens, 6.95 tokens/sec, 143.95 ms/token
2024-08-22T20:48:19.8659656Z 2024-08-22:20:47:38,487 INFO     [generate.py:849] Bandwidth achieved: 0.34 GB/s
2024-08-22T20:48:19.8661144Z 2024-08-22:20:47:38,487 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:48:19.8662118Z 
2024-08-22T20:48:19.8662266Z ========================================
2024-08-22T20:48:19.8662573Z 
2024-08-22T20:48:19.8663115Z Average tokens/sec: 6.95
2024-08-22T20:48:19.8664606Z Memory used: 0.00 GB
2024-08-22T20:48:19.8666140Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --temperature 0 --sequential-prefill
2024-08-22T20:48:19.8667472Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:48:19.8667980Z Loading model...
2024-08-22T20:48:19.8668412Z Time to load model: 0.03 seconds
2024-08-22T20:48:19.8669094Z -----------------------------------------------------------
2024-08-22T20:48:19.8670791Z Hello, my name is Daisy. Daisy is very popular. Everyone wants to be her friend. One day, Daisy's mommy takes her to the park. Daisy is so excited! She loves to play.
2024-08-22T20:48:19.8673118Z Daisy sees a big slide. She wants to go on it. She runs to the slide and climbs up the ladder. She sits on the top and slides down. She laughs and says, "Whee!"
2024-08-22T20:48:19.8675162Z Daisy's mommy claps and says, "Good job, Daisy! You are so brave!" Daisy smiles and says, "Thank you, mommy!"
2024-08-22T20:48:19.8678530Z Daisy and her mommy have a great day at the park. They play on the swings, slide down the slide, and run in the grass. Daisy is very happy. She loves her mommy and her popular slide. Once upon a time, there2024-08-22:20:47:47,265 INFO     [generate.py:846] 
2024-08-22T20:48:19.8681531Z Time for inference 1: 3.44 sec total, time to first token 0.10 sec with sequential prefill, 199 tokens, 57.90 tokens/sec, 17.27 ms/token
2024-08-22T20:48:19.8683354Z 2024-08-22:20:47:47,265 INFO     [generate.py:849] Bandwidth achieved: 2.83 GB/s
2024-08-22T20:48:19.8685407Z 2024-08-22:20:47:47,265 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:48:19.8686665Z 
2024-08-22T20:48:19.8686842Z ========================================
2024-08-22T20:48:19.8687227Z 
2024-08-22T20:48:19.8687408Z Average tokens/sec: 57.90
2024-08-22T20:48:19.8687894Z Memory used: 0.00 GB
2024-08-22T20:48:19.8689537Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --temperature 0.9 --sequential-prefill
2024-08-22T20:48:19.8690964Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:48:19.8691487Z Loading model...
2024-08-22T20:48:19.8691937Z Time to load model: 0.03 seconds
2024-08-22T20:48:19.8692613Z -----------------------------------------------------------
2024-08-22T20:48:19.8694632Z Hello, my name is Milly. Today, Mama was teaching him how to tie a bow. She had a bunch of papers on her desk with little squares. It was a special picture that she had made.
2024-08-22T20:48:19.8696359Z "Mama, I want to learn to tie a bow," said Molly.
2024-08-22T20:48:19.8697591Z "That's a very nice idea, my love. Can you see how hard you are getting? Let's make it easier," said Mama.
2024-08-22T20:48:19.8699674Z Molly nodded. Then she leaned over her desk and pulled out a pencil and a piece of string. She started to tie the bow on the paper. She made a nice bow pattern and then carefully stitched it through the square.
2024-08-22T20:48:19.8701584Z Mama smiled when she saw the tie. "Well done, Molly! That was a great job for you."
2024-08-22T20:48:19.8703249Z Molly was so proud of herself. "Thank you Mama, I learn how to tie a bow!" Once upon a time there2024-08-22:20:47:55,432 INFO     [generate.py:846] 
2024-08-22T20:48:19.8705067Z Time for inference 1: 3.54 sec total, time to first token 0.10 sec with sequential prefill, 199 tokens, 56.29 tokens/sec, 17.77 ms/token
2024-08-22T20:48:19.8706591Z 2024-08-22:20:47:55,432 INFO     [generate.py:849] Bandwidth achieved: 2.75 GB/s
2024-08-22T20:48:19.8708189Z 2024-08-22:20:47:55,432 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:48:19.8709207Z 
2024-08-22T20:48:19.8709381Z ========================================
2024-08-22T20:48:19.8709742Z 
2024-08-22T20:48:19.8709970Z Average tokens/sec: 56.29
2024-08-22T20:48:19.8710404Z Memory used: 0.00 GB
2024-08-22T20:48:19.8711877Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --temperature 1.0 --sequential-prefill
2024-08-22T20:48:19.8713113Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:48:19.8713583Z Loading model...
2024-08-22T20:48:19.8713994Z Time to load model: 0.03 seconds
2024-08-22T20:48:19.8714635Z -----------------------------------------------------------
2024-08-22T20:48:19.8716163Z Hello, my name is Jack. Grandma, who has been having a birthday and me everything she can choose. One of her wishes is to be wise and do the same tasks. Please pick one to buy."
2024-08-22T20:48:19.8718915Z Little did Emily tell her all her options - she had a big patience stick in her arms and she could think of all of her things and the things that she would do. You should earn money and the wiser money.
2024-08-22T20:48:19.8722113Z Whenever I receive the nice girl's advice, this will be my luck to make my wishes come true! Once upon a time, there was a big red hill. On top of the hill, a new toy appeared. It was a toy drum. The drum was red, small, and had a loud bell.
2024-08-22T20:48:19.8725328Z One day, a little boy named Tim wanted to play with the drum. But Tim was not careful. He bumped the drum into a hole. The hole made a big hole and Tim fell2024-08-22:20:48:03,564 INFO     [generate.py:846] 
2024-08-22T20:48:19.8727604Z Time for inference 1: 3.52 sec total, time to first token 0.10 sec with sequential prefill, 199 tokens, 56.51 tokens/sec, 17.69 ms/token
2024-08-22T20:48:19.8729186Z 2024-08-22:20:48:03,565 INFO     [generate.py:849] Bandwidth achieved: 2.76 GB/s
2024-08-22T20:48:19.8730767Z 2024-08-22:20:48:03,565 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:48:19.8731764Z 
2024-08-22T20:48:19.8731930Z ========================================
2024-08-22T20:48:19.8732279Z 
2024-08-22T20:48:19.8732444Z Average tokens/sec: 56.51
2024-08-22T20:48:19.8732885Z Memory used: 0.00 GB
2024-08-22T20:48:19.8734104Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --top-k 100 --sequential-prefill
2024-08-22T20:48:19.8735262Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:48:19.8735738Z Loading model...
2024-08-22T20:48:19.8736152Z Time to load model: 0.03 seconds
2024-08-22T20:48:19.8736787Z -----------------------------------------------------------
2024-08-22T20:48:19.8737619Z Hello, my name is Ellie. Everyday, we go to the stable."
2024-08-22T20:48:19.8738858Z Lucy was very excited to go to the stable. She wanted to explore, try running around the stable, and play in the warm sun.
2024-08-22T20:48:19.8740856Z One day, Ellie and her friends decided to explore the stable. Everyone was surprised at how much they had discovered. They enjoyed playing, exploring and having lots of fun.
2024-08-22T20:48:19.8743968Z When it was time to go home, they all agreed that they had seen so much of the stable. What a wonderful experience! One day, a boy named Tim went to the park with his mom. He saw a big tree and wanted to climb it. Tim asked his mom, "Can I climb the tree, please?" His mom said, "Yes, but be careful."
2024-08-22T20:48:19.8747449Z Tim started to climb the tree. When he was high up, he saw a big bird. The bird was very scared. Tim wanted to help the bird. He said,2024-08-22:20:48:11,680 INFO     [generate.py:846] 
2024-08-22T20:48:19.8749592Z Time for inference 1: 3.50 sec total, time to first token 0.10 sec with sequential prefill, 199 tokens, 56.78 tokens/sec, 17.61 ms/token
2024-08-22T20:48:19.8751133Z 2024-08-22:20:48:11,680 INFO     [generate.py:849] Bandwidth achieved: 2.77 GB/s
2024-08-22T20:48:19.8752694Z 2024-08-22:20:48:11,680 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:48:19.8753741Z 
2024-08-22T20:48:19.8753917Z ========================================
2024-08-22T20:48:19.8754302Z 
2024-08-22T20:48:19.8754474Z Average tokens/sec: 56.78
2024-08-22T20:48:19.8755061Z Memory used: 0.00 GB
2024-08-22T20:48:19.8756426Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --top-k 200 --sequential-prefill
2024-08-22T20:48:19.8757708Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:48:19.8758213Z Loading model...
2024-08-22T20:48:19.8758652Z Time to load model: 0.03 seconds
2024-08-22T20:48:19.8759398Z -----------------------------------------------------------
2024-08-22T20:48:19.8761550Z Hello, my name is Lily. I'm the bossy one. I do what I know. I can do whatever I want. I can make you happy, I can make you happy." Lily and her mommy listen. They say, "We want you to play with something that is fun."
2024-08-22T20:48:19.8764421Z Lily is getting jealous. She wants her mommy to do what she wants. She wants to make them happy, not sad. She wants to make them happy. She says, "No, mommy, no. I want to play with you. I want to be happy."
2024-08-22T20:48:19.8768250Z Mommy says, "Lily, be nice. You are not nice. You are not the boss. You are a family. You have to listen to me. You have to do what I say. You have to be quiet. You have to do what I say. You have to share with me. You have to be kind. You have to be a family.2024-08-22:20:48:19,864 INFO     [generate.py:846] 
2024-08-22T20:48:19.8771310Z Time for inference 1: 3.60 sec total, time to first token 0.10 sec with sequential prefill, 199 tokens, 55.31 tokens/sec, 18.08 ms/token
2024-08-22T20:48:19.8772953Z 2024-08-22:20:48:19,864 INFO     [generate.py:849] Bandwidth achieved: 2.70 GB/s
2024-08-22T20:49:29.5040480Z 2024-08-22:20:48:19,864 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:49:29.5041921Z 
2024-08-22T20:49:29.5042061Z ========================================
2024-08-22T20:49:29.5043653Z 
2024-08-22T20:49:29.5043990Z Average tokens/sec: 55.31
2024-08-22T20:49:29.5044492Z Memory used: 0.00 GB
2024-08-22T20:49:29.5045803Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --top-k 500 --sequential-prefill
2024-08-22T20:49:29.5047055Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:49:29.5047555Z Loading model...
2024-08-22T20:49:29.5047965Z Time to load model: 0.03 seconds
2024-08-22T20:49:29.5048818Z -----------------------------------------------------------
2024-08-22T20:49:29.5050692Z Hello, my name is I. This is Will. He is rich. He has a big house and many toys. I like to play and have fun. But I also get sick. Mommy gave me medicine and rest. I also have a bad day."
2024-08-22T20:49:29.5053451Z Inie wasUseful. She liked to use medicine and play with fire. She thought it was cool and fun. I jumped on Will and hit him with the bottle. That made me cry. Mommy heard me and ran to Will. She picked him up and hugged him.
2024-08-22T20:49:29.5056546Z "I'm so sorry, Will. I'm not really sick. I'm just saving you. I love you, Will. You will be better soon. But you have to wait until I change my mind. You have to be wise and kind. You have to listen to me. I know what is best for you."
2024-08-22T20:49:29.5058328Z Beakty was still scared, but he did not cry. He2024-08-22:20:48:28,090 INFO     [generate.py:846] 
2024-08-22T20:49:29.5059647Z Time for inference 1: 3.59 sec total, time to first token 0.10 sec with sequential prefill, 199 tokens, 55.47 tokens/sec, 18.03 ms/token
2024-08-22T20:49:29.5061161Z 2024-08-22:20:48:28,090 INFO     [generate.py:849] Bandwidth achieved: 2.71 GB/s
2024-08-22T20:49:29.5062649Z 2024-08-22:20:48:28,090 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:49:29.5063429Z 
2024-08-22T20:49:29.5063555Z ========================================
2024-08-22T20:49:29.5063823Z 
2024-08-22T20:49:29.5063953Z Average tokens/sec: 55.47
2024-08-22T20:49:29.5064287Z Memory used: 0.00 GB
2024-08-22T20:49:29.5065320Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --temperature 0 --sequential-prefill --compile
2024-08-22T20:49:29.5066511Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:49:29.5066866Z Loading model...
2024-08-22T20:49:29.5067181Z Time to load model: 0.03 seconds
2024-08-22T20:49:29.5067668Z -----------------------------------------------------------
2024-08-22T20:49:29.5068920Z Hello, my name is Daisy. Daisy is very popular. Everyone wants to be her friend. One day, Daisy's mommy takes her to the park. Daisy is so excited! She loves to play.
2024-08-22T20:49:29.5070557Z Daisy sees a big slide. She wants to go on it. She runs to the slide and climbs up the ladder. She sits on the top and slides down. She laughs and says, "Whee!"
2024-08-22T20:49:29.5071987Z Daisy's mommy claps and says, "Good job, Daisy! You are so brave!" Daisy smiles and says, "Thank you, mommy!"
2024-08-22T20:49:29.5074022Z Daisy and her mommy have a great day at the park. They play on the swings, slide down the slide, and run in the grass. Daisy is very happy. She loves her mommy and her popular slide. Once upon a time, therejust-in-time compilation time (incl run time): 9.2 seconds
2024-08-22T20:49:29.5075656Z 2024-08-22:20:48:42,728 INFO     [generate.py:846] 
2024-08-22T20:49:29.5076586Z Time for inference 1: 9.19 sec total, time to first token 0.10 sec with sequential prefill, 199 tokens, 21.66 tokens/sec, 46.17 ms/token
2024-08-22T20:49:29.5078055Z 2024-08-22:20:48:42,728 INFO     [generate.py:849] Bandwidth achieved: 1.06 GB/s
2024-08-22T20:49:29.5079408Z 2024-08-22:20:48:42,728 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:49:29.5080262Z 
2024-08-22T20:49:29.5080395Z ========================================
2024-08-22T20:49:29.5080663Z 
2024-08-22T20:49:29.5080788Z Average tokens/sec: 21.66
2024-08-22T20:49:29.5081128Z Memory used: 0.00 GB
2024-08-22T20:49:29.5082152Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --temperature 0.9 --sequential-prefill --compile
2024-08-22T20:49:29.5083139Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:49:29.5083501Z Loading model...
2024-08-22T20:49:29.5083821Z Time to load model: 0.03 seconds
2024-08-22T20:49:29.5084293Z -----------------------------------------------------------
2024-08-22T20:49:29.5085648Z Hello, my name is Sarah the Frog. Sarah has a very long nose. Everyday Sarah and the Frog go for a walk together. Every day Sarah would hold on to Sarah's long nose.
2024-08-22T20:49:29.5087478Z One day Sarah was walking along the river with her tongue out. As Sarah walked, she saw the other animals swimming around. Ducks were swimming too close to the water and Sarah asked them to be gentle. 
2024-08-22T20:49:29.5089101Z The other animals began to laugh at Sarah. They thought she was too small to get any closer. 
2024-08-22T20:49:29.5090520Z Max, who was known for being very flexible, was not amazing. He was able to grow taller and stretching his long nose. Duck soon realized that Sarah was right that she was a flexible sheep.
2024-08-22T20:49:29.5092313Z Scared, Sarah said to the other animals, "Look! We don't have to be big to be independent. We can do big things together even if we make a mess."
2024-08-22T20:49:29.5093520Z Duck smiled and said, "just-in-time compilation time (incl run time): 9.4 seconds
2024-08-22T20:49:29.5094262Z 2024-08-22:20:48:57,874 INFO     [generate.py:846] 
2024-08-22T20:49:29.5095194Z Time for inference 1: 9.44 sec total, time to first token 0.10 sec with sequential prefill, 199 tokens, 21.09 tokens/sec, 47.42 ms/token
2024-08-22T20:49:29.5096333Z 2024-08-22:20:48:57,874 INFO     [generate.py:849] Bandwidth achieved: 1.03 GB/s
2024-08-22T20:49:29.5097638Z 2024-08-22:20:48:57,874 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:49:29.5098490Z 
2024-08-22T20:49:29.5098625Z ========================================
2024-08-22T20:49:29.5099025Z 
2024-08-22T20:49:29.5099150Z Average tokens/sec: 21.09
2024-08-22T20:49:29.5099490Z Memory used: 0.00 GB
2024-08-22T20:49:29.5100516Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --temperature 1.0 --sequential-prefill --compile
2024-08-22T20:49:29.5101486Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:49:29.5101848Z Loading model...
2024-08-22T20:49:29.5102160Z Time to load model: 0.03 seconds
2024-08-22T20:49:29.5102639Z -----------------------------------------------------------
2024-08-22T20:49:29.5103619Z Hello, my name is Daisy. Daisy is an old lawyer. He loves to watch the icy day in the park and the people who walk by. Enjoy this lovely day".
2024-08-22T20:49:29.5105081Z Daisy was delighted. She watched as the children ran around and laughed. Thankfully, they made delicious ice cream and sang together in the park.
2024-08-22T20:49:29.5106025Z Daisy meowed with joy and cheered.
2024-08-22T20:49:29.5106476Z "Yay! Yay! What a fun day! Please come back soon!"
2024-08-22T20:49:29.5108087Z Daisy was overjoyed no longer feeling miserable. She hugged her new friends and waved goodbye as she skipped home. Once upon a time, there was a little girl named Lucy. She lived in a tidy house with her mom and dad. Lucy loved to play in her safe room with her toys.
2024-08-22T20:49:29.5110219Z One day, Lucy found a big, red ball in her room. She was not allowed to play withjust-in-time compilation time (incl run time): 9.6 seconds
2024-08-22T20:49:29.5111237Z 2024-08-22:20:49:13,276 INFO     [generate.py:846] 
2024-08-22T20:49:29.5112168Z Time for inference 1: 9.60 sec total, time to first token 0.10 sec with sequential prefill, 199 tokens, 20.72 tokens/sec, 48.25 ms/token
2024-08-22T20:49:29.5113299Z 2024-08-22:20:49:13,277 INFO     [generate.py:849] Bandwidth achieved: 1.01 GB/s
2024-08-22T20:49:29.5114597Z 2024-08-22:20:49:13,277 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:49:29.5115448Z 
2024-08-22T20:49:29.5115572Z ========================================
2024-08-22T20:49:29.5115850Z 
2024-08-22T20:49:29.5115976Z Average tokens/sec: 20.72
2024-08-22T20:49:29.5116314Z Memory used: 0.00 GB
2024-08-22T20:49:29.5117298Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --top-k 100 --sequential-prefill --compile
2024-08-22T20:49:29.5118350Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:49:29.5118713Z Loading model...
2024-08-22T20:49:29.5119020Z Time to load model: 0.03 seconds
2024-08-22T20:49:29.5119501Z -----------------------------------------------------------
2024-08-22T20:49:29.5120845Z Hello, my name is Gus. He loves to watch them all day. One day, Gus was feeling very frustrated and he was feeling very sad. He wanted to go and see the world, but he didn't know how to travel.
2024-08-22T20:49:29.5122560Z Horsie saw Gus and knew he needed help. He told his Mom to take him outside and they could go. Gus was so happy and he ran out to the park.
2024-08-22T20:49:29.5124238Z When he got to the park, he saw something amazing. The park was full of colourful clouds and the sun was shining, and the birds were singing. Gus was so excited, he could not believe his eyes.
2024-08-22T20:49:29.5126242Z Horsie watched the clouds for hours, and he was so amazed that he couldn't keep his face above the clouds. Soon, he was the happiest, all and he was dreaming of going on an adventure.
2024-08-22T20:49:29.5127764Z Gus was so glad he had the chance to traveljust-in-time compilation time (incl run time): 9.5 seconds
2024-08-22T20:49:29.5128746Z 2024-08-22:20:49:28,475 INFO     [generate.py:846] 
2024-08-22T20:49:29.5129673Z Time for inference 1: 9.49 sec total, time to first token 0.10 sec with sequential prefill, 199 tokens, 20.96 tokens/sec, 47.71 ms/token
2024-08-22T20:49:29.5130805Z 2024-08-22:20:49:28,475 INFO     [generate.py:849] Bandwidth achieved: 1.02 GB/s
2024-08-22T20:49:29.5132159Z 2024-08-22:20:49:28,475 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:49:29.5133947Z 
2024-08-22T20:49:29.5134081Z ========================================
2024-08-22T20:49:29.5134349Z 
2024-08-22T20:49:29.5134473Z Average tokens/sec: 20.96
2024-08-22T20:49:29.5134812Z Memory used: 0.00 GB
2024-08-22T20:50:25.1690209Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --top-k 200 --sequential-prefill --compile
2024-08-22T20:50:25.1691906Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:50:25.1693294Z Loading model...
2024-08-22T20:50:25.1693774Z Time to load model: 0.03 seconds
2024-08-22T20:50:25.1694439Z -----------------------------------------------------------
2024-08-22T20:50:25.1701100Z Hello, my name is Max. He is three years old. Every day, he loves to paint. He has a special paints and some paint. He is always looking for fun, but he never looks for anything special.
2024-08-22T20:50:25.1703373Z One day, Max decides to explore the world. He peeks out of the window and sees the world. It looks so big and new! He also sees a big park with a big swing and a slides. He feels so excited.
2024-08-22T20:50:25.1705193Z He sees a girl walking around with her mom. He wanted to go and have fun too! He asked her, "Can I play in the park?"
2024-08-22T20:50:25.1706277Z The girl smiled and said, "Yes of course! I can take you for a ride."
2024-08-22T20:50:25.1707786Z Max was so happy. He ran outside in the sunshine and the birds were singing. He had a great time and even made the girl feel better. He was so grateful to the girl for taking him to the park.
2024-08-22T20:50:25.1709618Z Moral: Anytime youjust-in-time compilation time (incl run time): 9.6 seconds
2024-08-22T20:50:25.1710432Z 2024-08-22:20:49:43,828 INFO     [generate.py:846] 
2024-08-22T20:50:25.1711494Z Time for inference 1: 9.59 sec total, time to first token 0.10 sec with sequential prefill, 199 tokens, 20.76 tokens/sec, 48.17 ms/token
2024-08-22T20:50:25.1712785Z 2024-08-22:20:49:43,828 INFO     [generate.py:849] Bandwidth achieved: 1.01 GB/s
2024-08-22T20:50:25.1714261Z 2024-08-22:20:49:43,828 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:50:25.1715289Z 
2024-08-22T20:50:25.1715450Z ========================================
2024-08-22T20:50:25.1715777Z 
2024-08-22T20:50:25.1716374Z Average tokens/sec: 20.76
2024-08-22T20:50:25.1716817Z Memory used: 0.00 GB
2024-08-22T20:50:25.1717972Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float16 --top-k 500 --sequential-prefill --compile
2024-08-22T20:50:25.1719037Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:50:25.1719453Z Loading model...
2024-08-22T20:50:25.1719830Z Time to load model: 0.03 seconds
2024-08-22T20:50:25.1720379Z -----------------------------------------------------------
2024-08-22T20:50:25.1721682Z Hello, my name is Jane. He has a very special one. It's a box with special words. Every night Jane put the box in her bedroom, so she could read it later.
2024-08-22T20:50:25.1723328Z One day, Jane was feeling very tired. She said to her teddy bear and said "Okay, teddy. I am so tired. Goodnight, teddy. I want to rest."
2024-08-22T20:50:25.1724360Z "Good night, Jane," said its soft voice.
2024-08-22T20:50:25.1724899Z Suddenly the box moved! "Teddy, what was that?"
2024-08-22T20:50:25.1725471Z "I don't know," said Jane.
2024-08-22T20:50:25.1726363Z Teddy opened the box and was surprised to find it was full of shiny things! He grabbed a chest, and then took out a new book.
2024-08-22T20:50:25.1727621Z He said, "Let's play with these book. I will read it and tell you all my stories."
2024-08-22T20:50:25.1728600Z Jane smiled and said, "That sounds like a wonderful book!"
2024-08-22T20:50:25.1729550Z And then they spentjust-in-time compilation time (incl run time): 9.5 seconds
2024-08-22T20:50:25.1730595Z 2024-08-22:20:49:59,088 INFO     [generate.py:846] 
2024-08-22T20:50:25.1731653Z Time for inference 1: 9.52 sec total, time to first token 0.10 sec with sequential prefill, 199 tokens, 20.91 tokens/sec, 47.83 ms/token
2024-08-22T20:50:25.1732936Z 2024-08-22:20:49:59,088 INFO     [generate.py:849] Bandwidth achieved: 1.02 GB/s
2024-08-22T20:50:25.1734403Z 2024-08-22:20:49:59,088 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:50:25.1735347Z 
2024-08-22T20:50:25.1735492Z ========================================
2024-08-22T20:50:25.1735802Z 
2024-08-22T20:50:25.1735947Z Average tokens/sec: 20.91
2024-08-22T20:50:25.1736341Z Memory used: 0.00 GB
2024-08-22T20:50:25.1736733Z + for DTYPE in bfloat16 float16 float32
2024-08-22T20:50:25.1737840Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --temperature 0
2024-08-22T20:50:25.1738774Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:50:25.1739194Z Loading model...
2024-08-22T20:50:25.1739547Z Time to load model: 0.02 seconds
2024-08-22T20:50:25.1740142Z -----------------------------------------------------------
2024-08-22T20:50:25.1741544Z Hello, my name is Daisy. Daisy is very popular. Everyone wants to be her friend. One day, Daisy's mommy takes her to the park. Daisy is so excited! She loves to play.
2024-08-22T20:50:25.1743355Z Daisy sees a big slide. She wants to go on it. She runs to the slide and climbs up the ladder. She sits on the top and slides down. She laughs and says, "Whee!"
2024-08-22T20:50:25.1744991Z Daisy's mommy claps and says, "Good job, Daisy! You are so brave!" Daisy smiles and says, "Thank you, mommy!"
2024-08-22T20:50:25.1747253Z Daisy and her mommy have a great day at the park. They play on the swings, slide down the slide, and run in the grass. Daisy is very happy. She loves her mommy and her popular slide. Once upon a time, there2024-08-22:20:50:06,205 INFO     [generate.py:846] 
2024-08-22T20:50:25.1749439Z Time for inference 1: 2.25 sec total, time to first token 0.01 sec with parallel prefill, 199 tokens, 88.46 tokens/sec, 11.30 ms/token
2024-08-22T20:50:25.1750751Z 2024-08-22:20:50:06,205 INFO     [generate.py:849] Bandwidth achieved: 8.64 GB/s
2024-08-22T20:50:25.1752153Z 2024-08-22:20:50:06,205 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:50:25.1753017Z 
2024-08-22T20:50:25.1753271Z ========================================
2024-08-22T20:50:25.1753587Z 
2024-08-22T20:50:25.1753734Z Average tokens/sec: 88.46
2024-08-22T20:50:25.1754122Z Memory used: 0.00 GB
2024-08-22T20:50:25.1755114Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --temperature 0.9
2024-08-22T20:50:25.1756093Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:50:25.1756508Z Loading model...
2024-08-22T20:50:25.1756862Z Time to load model: 0.02 seconds
2024-08-22T20:50:25.1757420Z -----------------------------------------------------------
2024-08-22T20:50:25.1759414Z Hello, my name is Nia. I am a famous angel, like the stars in the sky at night." I was very curious and asked me lots of questions. I tried to learn more about the world, and now I asked lots of questions. One day, I was feeling extra brave daring. I was determined to explore the world and what was behind the door. 
2024-08-22T20:50:25.1762384Z Daisy warned Truck, "It is very dark so you should be careful." But Treink's loyalty the fish barked, which made Truck and Daisy disagree. Thunder boomed and creeped but Zoom was still curious.
2024-08-22T20:50:25.1764719Z Suddenly, a big flash of lightning that came from the sky. Treamps flew up high and could not believe what he saw. He was about to cry, but Daisy quickly bravely stood her ground. She had used her brave spirit to think of an idea.
2024-08-22T20:50:25.1766515Z The wind suddenly started2024-08-22:20:50:13,112 INFO     [generate.py:846] 
2024-08-22T20:50:25.1767798Z Time for inference 1: 2.34 sec total, time to first token 0.02 sec with parallel prefill, 199 tokens, 85.12 tokens/sec, 11.75 ms/token
2024-08-22T20:50:25.1769199Z 2024-08-22:20:50:13,113 INFO     [generate.py:849] Bandwidth achieved: 8.31 GB/s
2024-08-22T20:50:25.1770557Z 2024-08-22:20:50:13,113 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:50:25.1771426Z 
2024-08-22T20:50:25.1771577Z ========================================
2024-08-22T20:50:25.1771880Z 
2024-08-22T20:50:25.1772032Z Average tokens/sec: 85.12
2024-08-22T20:50:25.1772412Z Memory used: 0.00 GB
2024-08-22T20:50:25.1773384Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --temperature 1.0
2024-08-22T20:50:25.1774322Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:50:25.1774730Z Loading model...
2024-08-22T20:50:25.1775140Z Time to load model: 0.01 seconds
2024-08-22T20:50:25.1775689Z -----------------------------------------------------------
2024-08-22T20:50:25.1776948Z Hello, my name is Grace, the sun.” She had a basket that was full of lovely flowers. A small one, sat next to Grace and liked to sit and smile.
2024-08-22T20:50:25.1778302Z Grace wanted to help but called out for her wife.
2024-08-22T20:50:25.1778838Z "Danger! What should I do?"
2024-08-22T20:50:25.1779413Z Grace bellowed out in confidence, "I need help. Will you help me?"
2024-08-22T20:50:25.1780746Z Daisy was not sure what Grace wanted, but she knew there was even more lovely clothes in the basket. She was very compassionate and always helped those around her.
2024-08-22T20:50:25.1781983Z Daisy finished her job and shouted "We need this one!"
2024-08-22T20:50:25.1783222Z Grace proudly took the basket and said, "Yes of course! You don't have to worry about any changes happens if we don't find the right one".
2024-08-22T20:50:25.1785289Z The moral of the story is that sometimes, showing compassion can help anyone. When we give up, we start searching sadness and worry together.2024-08-22:20:50:19,961 INFO     [generate.py:846] 
2024-08-22T20:50:25.1787099Z Time for inference 1: 2.29 sec total, time to first token 0.01 sec with parallel prefill, 199 tokens, 86.89 tokens/sec, 11.51 ms/token
2024-08-22T20:50:25.1788367Z 2024-08-22:20:50:19,961 INFO     [generate.py:849] Bandwidth achieved: 8.48 GB/s
2024-08-22T20:50:25.1789892Z 2024-08-22:20:50:19,961 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:50:25.1790760Z 
2024-08-22T20:50:25.1790906Z ========================================
2024-08-22T20:50:25.1791215Z 
2024-08-22T20:50:25.1791361Z Average tokens/sec: 86.89
2024-08-22T20:50:25.1791750Z Memory used: 0.00 GB
2024-08-22T20:50:25.1792687Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --top-k 100
2024-08-22T20:50:25.1793602Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:50:25.1794026Z Loading model...
2024-08-22T20:50:25.1794381Z Time to load model: 0.02 seconds
2024-08-22T20:50:25.1794932Z -----------------------------------------------------------
2024-08-22T20:50:25.1795894Z Hello, my name is Daisy. Daisy is three and I call him my name. We are having a marriage party. Do you want to join us?"
2024-08-22T20:52:43.2910873Z The old man smiled and said, "Yes, thank you. I am so happy to have you join me."
2024-08-22T20:52:43.2912267Z Daisy and the old man talked for a while and they had lots of fun. After the party, Daisy asked the old man if they could have ice cream for dessert.
2024-08-22T20:52:43.2919679Z The old man said, "Yes, of course. I'm very happy to have you come to my wedding. You are very polite and very welcome."
2024-08-22T20:52:43.2921742Z Daisy was so excited! She was grateful that the old man was so nice. She said, “Thank you! I am grateful for your love and kindness."
2024-08-22T20:52:43.2923475Z The old man smiled and said, “You're welcome. I am grateful for you too." 
2024-08-22T20:52:43.2925486Z Daisy and the old man hugged2024-08-22:20:50:26,830 INFO     [generate.py:846] 
2024-08-22T20:52:43.2927133Z Time for inference 1: 2.30 sec total, time to first token 0.01 sec with parallel prefill, 199 tokens, 86.34 tokens/sec, 11.58 ms/token
2024-08-22T20:52:43.2928998Z 2024-08-22:20:50:26,830 INFO     [generate.py:849] Bandwidth achieved: 8.43 GB/s
2024-08-22T20:52:43.2930700Z 2024-08-22:20:50:26,830 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:52:43.2931750Z 
2024-08-22T20:52:43.2931926Z ========================================
2024-08-22T20:52:43.2932330Z 
2024-08-22T20:52:43.2932512Z Average tokens/sec: 86.34
2024-08-22T20:52:43.2932953Z Memory used: 0.00 GB
2024-08-22T20:52:43.2934087Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --top-k 200
2024-08-22T20:52:43.2935192Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:52:43.2935680Z Loading model...
2024-08-22T20:52:43.2936089Z Time to load model: 0.01 seconds
2024-08-22T20:52:43.2936739Z -----------------------------------------------------------
2024-08-22T20:52:43.2938147Z Hello, my name is Maya. He loves to play with her. One day, Maya was out at the park with her mommy. It was a beautiful day and she enjoyed running and playing.
2024-08-22T20:52:43.2940451Z Suddenly, she stopped and looked around. Everything seemed so different. The sky was blue and the clouds were fluffy. Maya started to feel a bit scared.
2024-08-22T20:52:43.2942773Z Maya's mommy noticed this, so she took a deep breath and asked, “Are you sure it's okay to go play?” Maya felt a bit nervous, but she nodded and smiled.
2024-08-22T20:52:43.2945409Z The sun was shining and it seemed like it was time for her. She could feel the warmth on her face and it made her feel a little less scared. At last, Maya's mommy said, “Let’s go, I think it’s time to go!”
2024-08-22T20:52:43.2947742Z Maya felt relieved and quickly ran toten up the big2024-08-22:20:50:33,772 INFO     [generate.py:846] 
2024-08-22T20:52:43.2949446Z Time for inference 1: 2.34 sec total, time to first token 0.01 sec with parallel prefill, 199 tokens, 85.21 tokens/sec, 11.74 ms/token
2024-08-22T20:52:43.2950994Z 2024-08-22:20:50:33,772 INFO     [generate.py:849] Bandwidth achieved: 8.32 GB/s
2024-08-22T20:52:43.2952960Z 2024-08-22:20:50:33,772 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:52:43.2954023Z 
2024-08-22T20:52:43.2954205Z ========================================
2024-08-22T20:52:43.2954585Z 
2024-08-22T20:52:43.2954774Z Average tokens/sec: 85.21
2024-08-22T20:52:43.2955240Z Memory used: 0.00 GB
2024-08-22T20:52:43.2956417Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --top-k 500
2024-08-22T20:52:43.2957682Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:52:43.2958145Z Loading model...
2024-08-22T20:52:43.2958595Z Time to load model: 0.02 seconds
2024-08-22T20:52:43.2959285Z -----------------------------------------------------------
2024-08-22T20:52:43.2960383Z Hello, my name is Danny. I'm not a storm. I'm a large, brown bear. I like to hide in the forest."
2024-08-22T20:52:43.2961962Z Danny was surprised to hear a bear talk. He said, "Where are you going, Danny? I'm looking for a place to hide."
2024-08-22T20:52:43.2963739Z Danny smiled and said, "I'm looking for a safe place. I don't know where it's coming from."
2024-08-22T20:52:43.2965435Z Danny knew the bear was looking for a hollow in the forest. He said, "I think I can hide in the hollow up there. Let's go!"
2024-08-22T20:52:43.2967081Z Danny and Danny ran to the hollow, but as soon as they got there, the bear called out, "Wait! Wanna come in?"
2024-08-22T20:52:43.2969594Z Danny was so happy that he had found a safe place to hide. He said, "Thanks for finding me, Danny. I'm glad I didn't leave you here."2024-08-22:20:50:40,761 INFO     [generate.py:846] 
2024-08-22T20:52:43.2971951Z Time for inference 1: 2.34 sec total, time to first token 0.01 sec with parallel prefill, 199 tokens, 85.08 tokens/sec, 11.75 ms/token
2024-08-22T20:52:43.2973633Z 2024-08-22:20:50:40,761 INFO     [generate.py:849] Bandwidth achieved: 8.31 GB/s
2024-08-22T20:52:43.2975391Z 2024-08-22:20:50:40,761 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:52:43.2976512Z 
2024-08-22T20:52:43.2976702Z ========================================
2024-08-22T20:52:43.2977084Z 
2024-08-22T20:52:43.2977277Z Average tokens/sec: 85.08
2024-08-22T20:52:43.2978138Z Memory used: 0.00 GB
2024-08-22T20:52:43.2979638Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --temperature 0 --compile --compile-prefill
2024-08-22T20:52:43.2981032Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:52:43.2981545Z Loading model...
2024-08-22T20:52:43.2981978Z Time to load model: 0.01 seconds
2024-08-22T20:52:43.2982706Z -----------------------------------------------------------
2024-08-22T20:52:43.2984450Z Hello, my name is Daisy. Daisy is very popular. Everyone wants to be her friend. One day, Daisy's mommy takes her to the park. Daisy is so excited! She loves to play.
2024-08-22T20:52:43.2986897Z Daisy sees a big slide. She wants to go on it. She runs to the slide and climbs up the ladder. She sits on the top and slides down. She laughs and says, "Whee!"
2024-08-22T20:52:43.2988889Z Daisy's mommy claps and says, "Good job, Daisy! You are so brave!" Daisy smiles and says, "Thank you, mommy!"
2024-08-22T20:52:43.2991780Z Daisy and her mommy have a great day at the park. They play on the swings, slide down the slide, and run in the grass. Daisy is very happy. She loves her mommy and her popular slide. Once upon a time, therejust-in-time compilation time (incl run time): 3.5e+01 seconds
2024-08-22T20:52:43.2994212Z 2024-08-22:20:51:21,100 INFO     [generate.py:846] 
2024-08-22T20:52:43.2995523Z Time for inference 1: 34.90 sec total, time to first token 15.22 sec with parallel prefill, 199 tokens, 5.70 tokens/sec, 175.35 ms/token
2024-08-22T20:52:43.2997124Z 2024-08-22:20:51:21,100 INFO     [generate.py:849] Bandwidth achieved: 0.56 GB/s
2024-08-22T20:52:43.2998909Z 2024-08-22:20:51:21,100 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:52:43.3000104Z 
2024-08-22T20:52:43.3000679Z ========================================
2024-08-22T20:52:43.3001072Z 
2024-08-22T20:52:43.3001253Z Average tokens/sec: 5.70
2024-08-22T20:52:43.3001735Z Memory used: 0.00 GB
2024-08-22T20:52:43.3003160Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --temperature 0.9 --compile --compile-prefill
2024-08-22T20:52:43.3004551Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:52:43.3005060Z Loading model...
2024-08-22T20:52:43.3005489Z Time to load model: 0.02 seconds
2024-08-22T20:52:43.3006167Z -----------------------------------------------------------
2024-08-22T20:52:43.3006775Z Hello, my name is Lucy." 
2024-08-22T20:52:43.3007374Z God looked at Lucy and asked her, "What was your exam?" 
2024-08-22T20:52:43.3008476Z Lucy looked up and said, "My exam was a difficult one to understand." 
2024-08-22T20:52:43.3009653Z God smiled and said, "That's good. The exam is too difficult for me next time." 
2024-08-22T20:52:43.3011109Z Lucy felt so happy that God had helped her feel confident. She thanked him and ran off to tell her mommy all about the exam. 
2024-08-22T20:52:43.3014315Z From that day on, Lucy was able to understand all of her questions. She was proud of herself and glad she had a very big exam that Once upon a time, there was a boy named Tim. Tim had a huge box of chocolates. He loved to taste them all. One day, Tim was eating one chocolate, and another was left on the table. His stomach started to hurt.
2024-08-22T20:52:43.3017475Z Tim went to his mom and said, "Mom, myjust-in-time compilation time (incl run time): 3.5e+01 seconds
2024-08-22T20:52:43.3018845Z 2024-08-22:20:52:02,425 INFO     [generate.py:846] 
2024-08-22T20:52:43.3020164Z Time for inference 1: 34.97 sec total, time to first token 14.59 sec with parallel prefill, 199 tokens, 5.69 tokens/sec, 175.71 ms/token
2024-08-22T20:52:43.3021764Z 2024-08-22:20:52:02,426 INFO     [generate.py:849] Bandwidth achieved: 0.56 GB/s
2024-08-22T20:52:43.3023586Z 2024-08-22:20:52:02,426 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:52:43.3024792Z 
2024-08-22T20:52:43.3024967Z ========================================
2024-08-22T20:52:43.3025350Z 
2024-08-22T20:52:43.3025519Z Average tokens/sec: 5.69
2024-08-22T20:52:43.3025975Z Memory used: 0.00 GB
2024-08-22T20:52:43.3027420Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --temperature 1.0 --compile --compile-prefill
2024-08-22T20:52:43.3028806Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:52:43.3029315Z Loading model...
2024-08-22T20:52:43.3029727Z Time to load model: 0.01 seconds
2024-08-22T20:52:43.3030403Z -----------------------------------------------------------
2024-08-22T20:52:43.3031037Z Hello, my name is Mary!"
2024-08-22T20:52:43.3032007Z He and her parents carried an unusual creature to the park. Mary's mom held a leather ring.
2024-08-22T20:52:43.3032993Z "What is your name, Mommy?" Mary asked.
2024-08-22T20:52:43.3033592Z "My name is Harry," her mom smiled.
2024-08-22T20:52:43.3034398Z Mary watched as her mom explained that it was a special bird called Agloo. 
2024-08-22T20:52:43.3035670Z Mom explained to Mary that "Aglope" "Cannappy" wasn't as special as it was for her.
2024-08-22T20:52:43.3037208Z "You can now have an ohip," Mom said. "Taking it will help Little Jimmy see and the other pilots come and take him on flight."
2024-08-22T20:52:43.3038981Z Mary was excited, as she and Susan got ready to launch the spaceship. They had so much fun watching the stars twinkle in the sky. 
2024-08-22T20:54:39.8559367Z Two days later there were two birds who had made a little hive. They were discussing how to fly that high, so Mary used herjust-in-time compilation time (incl run time): 3.4e+01 seconds
2024-08-22T20:54:39.8560667Z 2024-08-22:20:52:43,290 INFO     [generate.py:846] 
2024-08-22T20:54:39.8565092Z Time for inference 1: 34.48 sec total, time to first token 14.51 sec with parallel prefill, 199 tokens, 5.77 tokens/sec, 173.24 ms/token
2024-08-22T20:54:39.8566500Z 2024-08-22:20:52:43,290 INFO     [generate.py:849] Bandwidth achieved: 0.56 GB/s
2024-08-22T20:54:39.8567829Z 2024-08-22:20:52:43,290 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:54:39.8568896Z 
2024-08-22T20:54:39.8569034Z ========================================
2024-08-22T20:54:39.8569310Z 
2024-08-22T20:54:39.8569460Z Average tokens/sec: 5.77
2024-08-22T20:54:39.8569795Z Memory used: 0.00 GB
2024-08-22T20:54:39.8570797Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --top-k 100 --compile --compile-prefill
2024-08-22T20:54:39.8571732Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:54:39.8572188Z Loading model...
2024-08-22T20:54:39.8572589Z Time to load model: 0.01 seconds
2024-08-22T20:54:39.8573158Z -----------------------------------------------------------
2024-08-22T20:54:39.8574560Z Hello, my name is Jane. I have a funny smile and I was walking in the park with a jar of sugar. He saw a butterfly flying in the sky and he wanted to catch it. He ran towards it, but as soon as he reached it, he heard a loud voice.
2024-08-22T20:54:39.8576208Z "Hey! You better run away and I can't play with you!"
2024-08-22T20:54:39.8577276Z Jane turned around and saw a big bear standing there. He was waving his arms and clapping his paws.
2024-08-22T20:54:39.8578624Z "Quickly, come on!" said Jane, and she started running towards her house.
2024-08-22T20:54:39.8579832Z The bear followed her and soon they were outside. Jane was scared and the bear just wanted to be friends.
2024-08-22T20:54:39.8580635Z "It's ok," said the bear.
2024-08-22T20:54:39.8581006Z "You have to be very careful," said Jane.
2024-08-22T20:54:39.8581771Z The bear smiled and said, "I promise I won't hurt you. I just wanted to try something new."
2024-08-22T20:54:39.8582729Z Jane was so happyjust-in-time compilation time (incl run time): 3.4e+01 seconds
2024-08-22T20:54:39.8583464Z 2024-08-22:20:53:24,116 INFO     [generate.py:846] 
2024-08-22T20:54:39.8584385Z Time for inference 1: 34.47 sec total, time to first token 14.42 sec with parallel prefill, 199 tokens, 5.77 tokens/sec, 173.21 ms/token
2024-08-22T20:54:39.8585517Z 2024-08-22:20:53:24,116 INFO     [generate.py:849] Bandwidth achieved: 0.56 GB/s
2024-08-22T20:54:39.8586815Z 2024-08-22:20:53:24,116 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:54:39.8587673Z 
2024-08-22T20:54:39.8587797Z ========================================
2024-08-22T20:54:39.8588104Z 
2024-08-22T20:54:39.8588229Z Average tokens/sec: 5.77
2024-08-22T20:54:39.8588563Z Memory used: 0.00 GB
2024-08-22T20:54:39.8589542Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --top-k 200 --compile --compile-prefill
2024-08-22T20:54:39.8590484Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:54:39.8590837Z Loading model...
2024-08-22T20:54:39.8591146Z Time to load model: 0.01 seconds
2024-08-22T20:54:39.8591621Z -----------------------------------------------------------
2024-08-22T20:54:39.8592707Z Hello, my name is Henry. He is three years old. One day, Henry's mom told me to go shopping. She said, "Max, I am going to pay for unhealthy food!" 
2024-08-22T20:54:39.8593875Z Henry watched as his mom took off they were going to buy food. He was very excited. 
2024-08-22T20:54:39.8594650Z Another day, Henry asked his mom, "How much money do you pay?" 
2024-08-22T20:54:39.8595302Z Henry's mom smiled and said, "One dollars, please." 
2024-08-22T20:54:39.8595785Z Henry said, "Okay, I will pay." 
2024-08-22T20:54:39.8596251Z He handed his mom the money and she paid for the food.
2024-08-22T20:54:39.8596974Z Henry looked at his mom with big, innocent eyes and said, "Mom, why do we need money?" 
2024-08-22T20:54:39.8597958Z His mom replied, "We need to be healthy and take care of our bodies too." 
2024-08-22T20:54:39.8599366Z Henry nodded and smiled. He knew that he was going to have to be healthy and take carejust-in-time compilation time (incl run time): 2.4e+01 seconds
2024-08-22T20:54:39.8600542Z 2024-08-22:20:53:53,962 INFO     [generate.py:846] 
2024-08-22T20:54:39.8601556Z Time for inference 1: 23.51 sec total, time to first token 8.34 sec with parallel prefill, 199 tokens, 8.47 tokens/sec, 118.12 ms/token
2024-08-22T20:54:39.8602801Z 2024-08-22:20:53:53,962 INFO     [generate.py:849] Bandwidth achieved: 0.83 GB/s
2024-08-22T20:54:39.8604263Z 2024-08-22:20:53:53,962 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:54:39.8605221Z 
2024-08-22T20:54:39.8605348Z ========================================
2024-08-22T20:54:39.8605639Z 
2024-08-22T20:54:39.8605765Z Average tokens/sec: 8.47
2024-08-22T20:54:39.8606119Z Memory used: 0.00 GB
2024-08-22T20:54:39.8607209Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --top-k 500 --compile --compile-prefill
2024-08-22T20:54:39.8608393Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:54:39.8608753Z Loading model...
2024-08-22T20:54:39.8609063Z Time to load model: 0.01 seconds
2024-08-22T20:54:39.8609534Z -----------------------------------------------------------
2024-08-22T20:54:39.8610229Z Hello, my name is Donkey. He is a nice and friendly dog. He just loves to play and tease.
2024-08-22T20:54:39.8611471Z Every day, I will play with him, but he always feels like he's too big for you. He's a bit scared, but also excited.
2024-08-22T20:54:39.8612806Z One day, Donkey's owner showed him a new game. It was called "Bad Spot". Donkey was so excited, he couldn't stop wagging his tail.
2024-08-22T20:54:39.8614062Z His owner said, "Let's play 'Spot The Bungler". Donkey was happy. He loved playing chase with his owner.
2024-08-22T20:54:39.8614844Z His owner said, "Come on, let's go!"
2024-08-22T20:54:39.8615488Z Mperkey meowed happily and followed his owner. They played the game and had lots of fun.
2024-08-22T20:54:39.8617011Z He was no longer scared. He was brave and he liked to tease his owner. Soon, they were playing the fun game back together.just-in-time compilation time (incl run time): 2.5e+01 seconds
2024-08-22T20:54:39.8618242Z 2024-08-22:20:54:25,392 INFO     [generate.py:846] 
2024-08-22T20:54:39.8619169Z Time for inference 1: 25.33 sec total, time to first token 8.29 sec with parallel prefill, 199 tokens, 7.86 tokens/sec, 127.31 ms/token
2024-08-22T20:54:39.8620293Z 2024-08-22:20:54:25,393 INFO     [generate.py:849] Bandwidth achieved: 0.77 GB/s
2024-08-22T20:54:39.8621587Z 2024-08-22:20:54:25,393 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:54:39.8622440Z 
2024-08-22T20:54:39.8622567Z ========================================
2024-08-22T20:54:39.8622848Z 
2024-08-22T20:54:39.8622994Z Average tokens/sec: 7.86
2024-08-22T20:54:39.8623345Z Memory used: 0.00 GB
2024-08-22T20:54:39.8624304Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --temperature 0 --sequential-prefill
2024-08-22T20:54:39.8625233Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:54:39.8625584Z Loading model...
2024-08-22T20:54:39.8625896Z Time to load model: 0.01 seconds
2024-08-22T20:54:39.8626369Z -----------------------------------------------------------
2024-08-22T20:54:39.8627561Z Hello, my name is Daisy. Daisy is very popular. Everyone wants to be her friend. One day, Daisy's mommy takes her to the park. Daisy is so excited! She loves to play.
2024-08-22T20:54:39.8629188Z Daisy sees a big slide. She wants to go on it. She runs to the slide and climbs up the ladder. She sits on the top and slides down. She laughs and says, "Whee!"
2024-08-22T20:54:39.8630711Z Daisy's mommy claps and says, "Good job, Daisy! You are so brave!" Daisy smiles and says, "Thank you, mommy!"
2024-08-22T20:54:39.8632691Z Daisy and her mommy have a great day at the park. They play on the swings, slide down the slide, and run in the grass. Daisy is very happy. She loves her mommy and her popular slide. Once upon a time, there2024-08-22:20:54:32,891 INFO     [generate.py:846] 
2024-08-22T20:54:39.8634668Z Time for inference 1: 2.25 sec total, time to first token 0.07 sec with sequential prefill, 199 tokens, 88.25 tokens/sec, 11.33 ms/token
2024-08-22T20:54:39.8635804Z 2024-08-22:20:54:32,891 INFO     [generate.py:849] Bandwidth achieved: 8.62 GB/s
2024-08-22T20:54:39.8637009Z 2024-08-22:20:54:32,891 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:54:39.8637774Z 
2024-08-22T20:54:39.8637905Z ========================================
2024-08-22T20:54:39.8638169Z 
2024-08-22T20:54:39.8638295Z Average tokens/sec: 88.25
2024-08-22T20:54:39.8638638Z Memory used: 0.00 GB
2024-08-22T20:54:39.8639599Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --temperature 0.9 --sequential-prefill
2024-08-22T20:54:39.8640524Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:54:39.8640884Z Loading model...
2024-08-22T20:54:39.8641196Z Time to load model: 0.02 seconds
2024-08-22T20:54:39.8641665Z -----------------------------------------------------------
2024-08-22T20:54:39.8642456Z Hello, my name is Wise Ollie the Grandpa Owl. Wise Ollie loves to cook oats for breakfast he was going out.
2024-08-22T20:54:39.8643693Z One morning, Hanna said to him, "Wise Owl, I'm so excited to cook oats. Can you tell me how to do it?"
2024-08-22T20:54:39.8644421Z Wise Owl was glad and answered, "Yes,
2024-08-22T20:54:39.8645446Z Gisewl told Wise Ollie to put them on the hot stove. I'm going to cook them tonight. I can tell you about the oats and how they are so yummy!"
2024-08-22T20:54:39.8646452Z WiseO counts to one, "One, two, three, four, five, six."
2024-08-22T20:54:39.8647319Z Wise Owl said, "That's right, Wise Owl! Soon, you will be very big and strong and well-r-to-beed."
2024-08-22T20:54:39.8648455Z Wise Owl was so encouraged by W2024-08-22:20:54:39,855 INFO     [generate.py:846] 
2024-08-22T20:54:39.8649547Z Time for inference 1: 2.39 sec total, time to first token 0.07 sec with sequential prefill, 199 tokens, 83.39 tokens/sec, 11.99 ms/token
2024-08-22T20:54:39.8650681Z 2024-08-22:20:54:39,855 INFO     [generate.py:849] Bandwidth achieved: 8.14 GB/s
2024-08-22T20:54:39.8651893Z 2024-08-22:20:54:39,855 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:54:39.8652658Z 
2024-08-22T20:55:32.8351364Z ========================================
2024-08-22T20:55:32.8362074Z 
2024-08-22T20:55:32.8364265Z Average tokens/sec: 83.39
2024-08-22T20:55:32.8366636Z Memory used: 0.00 GB
2024-08-22T20:55:32.8368430Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --temperature 1.0 --sequential-prefill
2024-08-22T20:55:32.8369819Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:55:32.8370293Z Loading model...
2024-08-22T20:55:32.8370696Z Time to load model: 0.01 seconds
2024-08-22T20:55:32.8371478Z -----------------------------------------------------------
2024-08-22T20:55:32.8372126Z Hello, my name is Jake."
2024-08-22T20:55:32.8374596Z Jake was so excited, and wanted to go to the reef to swim in. When he arrived at the reef, he saw something strange. It was a big net and it scared him so much that he ran away as fast as he could.
2024-08-22T20:55:32.8376459Z He was so scared and he said to himself, "Who's there?."
2024-08-22T20:55:32.8377131Z Then he heard a voice. It was a monkey with an eagle.
2024-08-22T20:55:32.8378325Z "Don't worry, Jake. I'm here to help you. I can help you get back to the reef safely!"
2024-08-22T20:55:32.8379245Z Jake felt much better and said, "Thank you, monkey. You are the best!" 
2024-08-22T20:55:32.8380763Z The monkey smiled and said, "No problem, Jake. I'm happy to help. Now let's swim together, when me"
2024-08-22T20:55:32.8382289Z And so young and kindness began to heal when everyone was scared and found a reliable2024-08-22:20:54:46,730 INFO     [generate.py:846] 
2024-08-22T20:55:32.8383815Z Time for inference 1: 2.31 sec total, time to first token 0.07 sec with sequential prefill, 199 tokens, 86.23 tokens/sec, 11.60 ms/token
2024-08-22T20:55:32.8385096Z 2024-08-22:20:54:46,730 INFO     [generate.py:849] Bandwidth achieved: 8.42 GB/s
2024-08-22T20:55:32.8386530Z 2024-08-22:20:54:46,730 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:55:32.8387397Z 
2024-08-22T20:55:32.8387546Z ========================================
2024-08-22T20:55:32.8387860Z 
2024-08-22T20:55:32.8388010Z Average tokens/sec: 86.23
2024-08-22T20:55:32.8388450Z Memory used: 0.00 GB
2024-08-22T20:55:32.8389641Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --top-k 100 --sequential-prefill
2024-08-22T20:55:32.8391418Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:55:32.8392460Z Loading model...
2024-08-22T20:55:32.8393144Z Time to load model: 0.01 seconds
2024-08-22T20:55:32.8394270Z -----------------------------------------------------------
2024-08-22T20:55:32.8396551Z Hello, my name is Jake. Jake is playing with his toys in the garden. He is having lots of fun. Suddenly, he heard a loud noise and he started to cry.
2024-08-22T20:55:32.8399642Z "What happened, Jake?" asked Mama.
2024-08-22T20:55:32.8401385Z “I heard a noise and it made me scream!” said Jake.
2024-08-22T20:55:32.8403092Z Mama hugged Jake and asked: “Why did you cry, Jake?”
2024-08-22T20:55:32.8404663Z "I stopped when I heard a loud noise! He was screaming for help," answered Jake.
2024-08-22T20:55:32.8406914Z Mama looked around and saw a little rabbit stuck in the bushes. “Oh, don’t worry, it's ok,” said Mama.
2024-08-22T20:55:32.8408020Z Mama carefully freed the rabbit from the bushes and hugged it.
2024-08-22T20:55:32.8409050Z “Thank you, Mama,” said Jake.
2024-08-22T20:55:32.8410176Z Mama smiled and said: “You're welcome, Jake.” Then they both went back to playing in the2024-08-22:20:54:53,613 INFO     [generate.py:846] 
2024-08-22T20:55:32.8411727Z Time for inference 1: 2.34 sec total, time to first token 0.07 sec with sequential prefill, 199 tokens, 85.01 tokens/sec, 11.76 ms/token
2024-08-22T20:55:32.8413011Z 2024-08-22:20:54:53,613 INFO     [generate.py:849] Bandwidth achieved: 8.30 GB/s
2024-08-22T20:55:32.8414404Z 2024-08-22:20:54:53,613 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:55:32.8415266Z 
2024-08-22T20:55:32.8415425Z ========================================
2024-08-22T20:55:32.8415731Z 
2024-08-22T20:55:32.8415879Z Average tokens/sec: 85.01
2024-08-22T20:55:32.8416275Z Memory used: 0.00 GB
2024-08-22T20:55:32.8417362Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --top-k 200 --sequential-prefill
2024-08-22T20:55:32.8418388Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:55:32.8418806Z Loading model...
2024-08-22T20:55:32.8419170Z Time to load model: 0.01 seconds
2024-08-22T20:55:32.8419721Z -----------------------------------------------------------
2024-08-22T20:55:32.8420887Z Hello, my name is Lily, the princess. She loves to play with her toys and have fun. But sometimes, she gets bored and wants to do something new and exciting.
2024-08-22T20:55:32.8423091Z One day, she goes to the park and sees a big slide. She wants to try it, but she is a little scared. She thinks the slide looks scary and she does not want to go on it. She looks at the slide and says, "Please, be careful and slide down."
2024-08-22T20:55:32.8425812Z But there is a boy who is playing on the slide too. He does not listen to Lily and he slides too fast. He laughs and says, "This is fun. No one will see you and want to slide with me."
2024-08-22T20:55:32.8428265Z Lily feels angry and sad. She does not want to slide with the boy. She thinks he is not nice and he is mean. She goes to the boy and says, "Please, don't do that. The slide2024-08-22:20:55:00,507 INFO     [generate.py:846] 
2024-08-22T20:55:32.8430699Z Time for inference 1: 2.30 sec total, time to first token 0.07 sec with sequential prefill, 199 tokens, 86.67 tokens/sec, 11.54 ms/token
2024-08-22T20:55:32.8433308Z 2024-08-22:20:55:00,507 INFO     [generate.py:849] Bandwidth achieved: 8.46 GB/s
2024-08-22T20:55:32.8436251Z 2024-08-22:20:55:00,508 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:55:32.8437682Z 
2024-08-22T20:55:32.8438140Z ========================================
2024-08-22T20:55:32.8438724Z 
2024-08-22T20:55:32.8439022Z Average tokens/sec: 86.67
2024-08-22T20:55:32.8439866Z Memory used: 0.00 GB
2024-08-22T20:55:32.8442072Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --top-k 500 --sequential-prefill
2024-08-22T20:55:32.8444288Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:55:32.8445192Z Loading model...
2024-08-22T20:55:32.8445913Z Time to load model: 0.01 seconds
2024-08-22T20:55:32.8446793Z -----------------------------------------------------------
2024-08-22T20:55:32.8447481Z Hello, my name is Daisy. How are you feeling?"
2024-08-22T20:55:32.8448387Z Daisy said, "I feel uncomfortable. There is something on my head."
2024-08-22T20:55:32.8450238Z Daisy said, "Oh, I wish you were at home."
2024-08-22T20:55:32.8451007Z Daisy said, "I wish I had fun playing with you. I have a treasure with my favorite toys."
2024-08-22T20:55:32.8452101Z Daisy said, "That's wonderful. I wished I had a treasure too. Do you want to play?"
2024-08-22T20:55:32.8452915Z Daisy said, "Yes, I do. But first, let me have a snack."
2024-08-22T20:55:32.8453886Z Daisy went to the kitchen and came back with a snack. She was very happy and said, "Thank you. This is yummy."
2024-08-22T20:55:32.8455967Z Daisy played with Daisy all day. She was happy that she had a friend to go play with. Once upon a time, there was a little girl named Lily. She loved to play outside2024-08-22:20:55:07,437 INFO     [generate.py:846] 
2024-08-22T20:55:32.8457911Z Time for inference 1: 2.35 sec total, time to first token 0.07 sec with sequential prefill, 199 tokens, 84.56 tokens/sec, 11.83 ms/token
2024-08-22T20:55:32.8459183Z 2024-08-22:20:55:07,437 INFO     [generate.py:849] Bandwidth achieved: 8.26 GB/s
2024-08-22T20:55:32.8460549Z 2024-08-22:20:55:07,437 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches. ***
2024-08-22T20:55:32.8461426Z 
2024-08-22T20:55:32.8461573Z ========================================
2024-08-22T20:55:32.8461882Z 
2024-08-22T20:55:32.8462039Z Average tokens/sec: 84.56
2024-08-22T20:55:32.8462425Z Memory used: 0.00 GB
2024-08-22T20:55:32.8463589Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --temperature 0 --sequential-prefill --compile
2024-08-22T20:55:32.8464692Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:55:32.8465106Z Loading model...
2024-08-22T20:55:32.8465487Z Time to load model: 0.02 seconds
2024-08-22T20:55:32.8466070Z -----------------------------------------------------------
2024-08-22T20:55:32.8467440Z Hello, my name is Daisy. Daisy is very popular. Everyone wants to be her friend. One day, Daisy's mommy takes her to the park. Daisy is so excited! She loves to play.
2024-08-22T20:55:32.8469917Z Daisy sees a big slide. She wants to go on it. She runs to the slide and climbs up the ladder. She sits on the top and slides down. She laughs and says, "Whee!"
2024-08-22T20:55:32.8472674Z Daisy's mommy claps and says, "Good job, Daisy! You are so brave!" Daisy smiles and says, "Thank you, mommy!"
2024-08-22T20:55:32.8478171Z Daisy and her mommy have a great day at the park. They play on the swings, slide down the slide, and run in the grass. Daisy is very happy. She loves her mommy and her popular slide. Once upon a time, therejust-in-time compilation time (incl run time): 8.5 seconds
2024-08-22T20:55:32.8481771Z 2024-08-22:20:55:21,356 INFO     [generate.py:846] 
2024-08-22T20:55:32.8483817Z Time for inference 1: 8.51 sec total, time to first token 0.08 sec with sequential prefill, 199 tokens, 23.40 tokens/sec, 42.74 ms/token
2024-08-22T20:55:32.8486509Z 2024-08-22:20:55:21,357 INFO     [generate.py:849] Bandwidth achieved: 2.28 GB/s
2024-08-22T20:55:32.8488437Z 2024-08-22:20:55:21,357 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:55:32.8489396Z 
2024-08-22T20:55:32.8489557Z ========================================
2024-08-22T20:55:32.8489862Z 
2024-08-22T20:55:32.8490010Z Average tokens/sec: 23.40
2024-08-22T20:55:32.8490404Z Memory used: 0.00 GB
2024-08-22T20:55:32.8491592Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --temperature 0.9 --sequential-prefill --compile
2024-08-22T20:55:32.8492697Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:55:32.8493118Z Loading model...
2024-08-22T20:55:32.8493481Z Time to load model: 0.01 seconds
2024-08-22T20:55:32.8494030Z -----------------------------------------------------------
2024-08-22T20:55:32.8495274Z Hello, my name is Nora, the star. Sophie, an impatient girl, would often go for walks with her mum. One day, Sophie was walking through the woods, eager to explore the wild.
2024-08-22T20:56:35.0761005Z As Sophie walked, she found a strange-looking house. It was full of books, including a magical window. Sophie opened the door and started exploring! She was so excited and ran inside to take a look around.
2024-08-22T20:56:35.0763673Z Sophie examined all the things that were inside the house. She looked at the garden, the flowers and the trees, but eventually she realised that it was gloomy outside and she had to stay inside. The dark clouds had made it dark.
2024-08-22T20:56:35.0766807Z Suddenly, Sophie heard a noise. She looked around to see what it was and she saw a small furry creature walking towards her house. The creature had a piece of the candle in its hand. Sophie was so happy because the animaljust-in-time compilation time (incl run time): 8.9 seconds
2024-08-22T20:56:35.0769113Z 2024-08-22:20:55:35,890 INFO     [generate.py:846] 
2024-08-22T20:56:35.0770067Z Time for inference 1: 8.86 sec total, time to first token 0.08 sec with sequential prefill, 199 tokens, 22.47 tokens/sec, 44.50 ms/token
2024-08-22T20:56:35.0771246Z 2024-08-22:20:55:35,891 INFO     [generate.py:849] Bandwidth achieved: 2.19 GB/s
2024-08-22T20:56:35.0772801Z 2024-08-22:20:55:35,891 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:56:35.0773726Z 
2024-08-22T20:56:35.0773870Z ========================================
2024-08-22T20:56:35.0774142Z 
2024-08-22T20:56:35.0774271Z Average tokens/sec: 22.47
2024-08-22T20:56:35.0774734Z Memory used: 0.00 GB
2024-08-22T20:56:35.0776002Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --temperature 1.0 --sequential-prefill --compile
2024-08-22T20:56:35.0776991Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:56:35.0777357Z Loading model...
2024-08-22T20:56:35.0778326Z Time to load model: 0.02 seconds
2024-08-22T20:56:35.0779034Z -----------------------------------------------------------
2024-08-22T20:56:35.0779921Z Hello, my name is Daisy. We are going on a trip. We want to march to the woods."
2024-08-22T20:56:35.0780914Z Daisy: "I see my shoes on the trail. They are socks on the path."
2024-08-22T20:56:35.0781932Z Daisy: "Can I join you on the trail? I want to see the animals and listen to the birds."
2024-08-22T20:56:35.0783362Z Gray: "Sure, Daisy. But be careful, there are some animals ahead of us."
2024-08-22T20:56:35.0784429Z Daisy got on the trail and started to march. She was slow, but soon she followed a car.
2024-08-22T20:56:35.0785283Z Daisy: "This is so much fun!"
2024-08-22T20:56:35.0786771Z Gray was happy to see Daisy improve the trip and decided to join her. Daisy skipped and they continued on the trail together, following the animals. They had lots of time looking at the animals and listening to their music in the woods. 
2024-08-22T20:56:35.0788648Z When it was time to go home, Daisy said goodbye tojust-in-time compilation time (incl run time): 8.9 seconds
2024-08-22T20:56:35.0789535Z 2024-08-22:20:55:50,444 INFO     [generate.py:846] 
2024-08-22T20:56:35.0790473Z Time for inference 1: 8.87 sec total, time to first token 0.08 sec with sequential prefill, 199 tokens, 22.42 tokens/sec, 44.60 ms/token
2024-08-22T20:56:35.0791601Z 2024-08-22:20:55:50,444 INFO     [generate.py:849] Bandwidth achieved: 2.19 GB/s
2024-08-22T20:56:35.0792914Z 2024-08-22:20:55:50,444 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:56:35.0793771Z 
2024-08-22T20:56:35.0793895Z ========================================
2024-08-22T20:56:35.0794163Z 
2024-08-22T20:56:35.0794293Z Average tokens/sec: 22.42
2024-08-22T20:56:35.0794625Z Memory used: 0.00 GB
2024-08-22T20:56:35.0795611Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --top-k 100 --sequential-prefill --compile
2024-08-22T20:56:35.0796740Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:56:35.0797093Z Loading model...
2024-08-22T20:56:35.0797403Z Time to load model: 0.01 seconds
2024-08-22T20:56:35.0797883Z -----------------------------------------------------------
2024-08-22T20:56:35.0798758Z Hello, my name is Daisy. She likes to swing on the swing in the park. She swings very high and far. She looks like a princess.
2024-08-22T20:56:35.0800178Z But one day, a big boy comes to the park. He sees Daisy on the swing. He wants to swing too. He thinks Daisy is scary. He laughs and runs to her.
2024-08-22T20:56:35.0801231Z "Hey, that's not fair!" he says. "Give me the swing!"
2024-08-22T20:56:35.0802170Z Daisy hears the boy. She is scared. She does not want to give the swing to him. She likes the swing. But she also wants to swing too.
2024-08-22T20:56:35.0803097Z "No, it's mine!" she says. "Go away!"
2024-08-22T20:56:35.0804512Z The boy does not go away. He pulls the swing hard. He makes a big noise. Daisy feels a tear roll. She sees a big cut on her arm. She sees blood. She cries.just-in-time compilation time (incl run time): 8.8 seconds
2024-08-22T20:56:35.0805890Z 2024-08-22:20:56:04,902 INFO     [generate.py:846] 
2024-08-22T20:56:35.0806815Z Time for inference 1: 8.79 sec total, time to first token 0.08 sec with sequential prefill, 199 tokens, 22.65 tokens/sec, 44.15 ms/token
2024-08-22T20:56:35.0807943Z 2024-08-22:20:56:04,902 INFO     [generate.py:849] Bandwidth achieved: 2.21 GB/s
2024-08-22T20:56:35.0809420Z 2024-08-22:20:56:04,902 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:56:35.0810273Z 
2024-08-22T20:56:35.0810404Z ========================================
2024-08-22T20:56:35.0810670Z 
2024-08-22T20:56:35.0810798Z Average tokens/sec: 22.65
2024-08-22T20:56:35.0811135Z Memory used: 0.00 GB
2024-08-22T20:56:35.0812128Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --top-k 200 --sequential-prefill --compile
2024-08-22T20:56:35.0813076Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:56:35.0813436Z Loading model...
2024-08-22T20:56:35.0813749Z Time to load model: 0.02 seconds
2024-08-22T20:56:35.0814217Z -----------------------------------------------------------
2024-08-22T20:56:35.0815032Z Hello, my name is Jake, the fairy. I'm a magical fairy. I can do amazing things. This is my home.
2024-08-22T20:56:35.0816021Z One day, I want to explore the world. So, I started walking and looking around.
2024-08-22T20:56:35.0816643Z As I walked, I saw a very dark cave.
2024-08-22T20:56:35.0817190Z Suddenly, from the distance, she heard a loud noise. She was scared.
2024-08-22T20:56:35.0818070Z “Don’t be scared! I'm a fairy and I can help you explore the dark cave.”
2024-08-22T20:56:35.0819093Z So Jake followed the fairy into the cave. They found a magical potion that could make the light come out of the cave.
2024-08-22T20:56:35.0820037Z The fairy said, “Now, let’s drink this potion!”
2024-08-22T20:56:35.0820592Z They drank the potion and suddenly the dark was back.
2024-08-22T20:56:35.0821227Z Jake was so happy that he thanked the magical fairy for helping him.
2024-08-22T20:56:35.0822183Z The fairy smiled and said, “You’re welcomejust-in-time compilation time (incl run time): 8.9 seconds
2024-08-22T20:56:35.0823027Z 2024-08-22:20:56:19,467 INFO     [generate.py:846] 
2024-08-22T20:56:35.0823975Z Time for inference 1: 8.90 sec total, time to first token 0.08 sec with sequential prefill, 199 tokens, 22.35 tokens/sec, 44.73 ms/token
2024-08-22T20:56:35.0825103Z 2024-08-22:20:56:19,468 INFO     [generate.py:849] Bandwidth achieved: 2.18 GB/s
2024-08-22T20:56:35.0826400Z 2024-08-22:20:56:19,468 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:56:35.0827258Z 
2024-08-22T20:56:35.0827384Z ========================================
2024-08-22T20:56:35.0827652Z 
2024-08-22T20:56:35.0827879Z Average tokens/sec: 22.35
2024-08-22T20:56:35.0828211Z Memory used: 0.00 GB
2024-08-22T20:56:35.0829232Z + python generate.py --checkpoint-path checkpoints/stories15M/stories15M.pt --device cpu --dtype float32 --top-k 500 --sequential-prefill --compile
2024-08-22T20:56:35.0830212Z Using device=cpu AMD EPYC 7R32
2024-08-22T20:56:35.0830567Z Loading model...
2024-08-22T20:56:35.0830881Z Time to load model: 0.01 seconds
2024-08-22T20:56:35.0831361Z -----------------------------------------------------------
2024-08-22T20:56:35.0832586Z Hello, my name is today. The sun is shining and the day is very bright. The birds and animals start to sing in the morning. Teddy, the boy, and they all sing together. Everyone loves the sunrise. 
2024-08-22T20:56:35.0834275Z After a while, the sun starts to go away. The sky is turning orange and pink. Teddy says, "It's time for us to go home now." 
2024-08-22T20:56:35.0835537Z But the birds and animals still answer Teddy. They say, "Please, Johnny, let's play for a little longer." 
2024-08-22T20:56:35.0837634Z So the birds and the rabbit said they could stay and play until the sun went down. The birds and the rabbits had a great day and soon it was time to go home. Once upon a time, there was a little monkey. The monkey loved bananas. One day, the monkey saw a big bunch of bananas in a tree. The monkey was very happy.
2024-08-22T20:56:35.0839552Z Thejust-in-time compilation time (incl run time): 8.9 seconds
2024-08-22T20:56:35.0840193Z 2024-08-22:20:56:34,068 INFO     [generate.py:846] 
2024-08-22T20:56:35.0841121Z Time for inference 1: 8.88 sec total, time to first token 0.08 sec with sequential prefill, 199 tokens, 22.42 tokens/sec, 44.60 ms/token
2024-08-22T20:56:35.0842248Z 2024-08-22:20:56:34,068 INFO     [generate.py:849] Bandwidth achieved: 2.19 GB/s
2024-08-22T20:56:35.0843537Z 2024-08-22:20:56:34,068 INFO     [generate.py:853] *** This first iteration will include cold start effects for dynamic import, hardware caches, JIT compilation. ***
2024-08-22T20:56:35.0844395Z 
2024-08-22T20:56:35.0844521Z ========================================
2024-08-22T20:56:35.0844794Z 
2024-08-22T20:56:35.0844919Z Average tokens/sec: 22.42
2024-08-22T20:56:35.0845255Z Memory used: 0.00 GB
2024-08-22T20:56:35.0845608Z + echo 'tests complete'
2024-08-22T20:56:35.0845931Z tests complete
2024-08-22T20:56:35.0846314Z + echo '******************************************'
2024-08-22T20:56:35.0846743Z ******************************************
2024-08-22T20:56:35.0847249Z + echo ::endgroup::
2024-08-22T20:56:35.0847860Z ##[endgroup]
2024-08-22T20:56:35.0898887Z ##[group]Run pmeier/pytest-results-action@v0.3.0
2024-08-22T20:56:35.0899346Z with:
2024-08-22T20:56:35.0899743Z   path: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:56:35.0900267Z   fail-on-empty: false
2024-08-22T20:56:35.0900575Z env:
2024-08-22T20:56:35.0900896Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:56:35.0901357Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:56:35.0901727Z   PR_NUMBER: 1044
2024-08-22T20:56:35.0920730Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:56:35.0940250Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:56:35.0941020Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:56:35.0941742Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:56:35.0942357Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:56:35.0942833Z ##[endgroup]
2024-08-22T20:56:35.1630243Z Prepare all required actions
2024-08-22T20:56:35.1668331Z ##[group]Run ./test-infra/.github/actions/chown-directory
2024-08-22T20:56:35.1668810Z with:
2024-08-22T20:56:35.1669246Z   directory: /home/ec2-user/actions-runner/_work/torchchat/torchchat/
2024-08-22T20:56:35.1670194Z   ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine
2024-08-22T20:56:35.1670745Z env:
2024-08-22T20:56:35.1671063Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:56:35.1671517Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:56:35.1671891Z   PR_NUMBER: 1044
2024-08-22T20:56:35.1691177Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:56:35.1710671Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:56:35.1711444Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:56:35.1712160Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:56:35.1712772Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:56:35.1713248Z ##[endgroup]
2024-08-22T20:56:35.1732760Z ##[group]Run docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" .
2024-08-22T20:56:35.1733726Z docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" .
2024-08-22T20:56:35.1747366Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:56:35.1747863Z env:
2024-08-22T20:56:35.1748180Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:56:35.1748642Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:56:35.1749018Z   PR_NUMBER: 1044
2024-08-22T20:56:35.1767972Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:56:35.1787657Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:56:35.1788425Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:56:35.1789150Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:56:35.1789804Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:56:35.1790451Z   ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine
2024-08-22T20:56:35.1791169Z   DIRECTORY: /home/ec2-user/actions-runner/_work/torchchat/torchchat/
2024-08-22T20:56:35.1791707Z ##[endgroup]
2024-08-22T20:56:35.2015673Z Unable to find image '308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine:latest' locally
2024-08-22T20:56:35.4192940Z latest: Pulling from tool/alpine
2024-08-22T20:56:35.4193408Z 540db60ca938: Pulling fs layer
2024-08-22T20:56:35.5292008Z 540db60ca938: Verifying Checksum
2024-08-22T20:56:35.5292441Z 540db60ca938: Download complete
2024-08-22T20:56:35.6422360Z 540db60ca938: Pull complete
2024-08-22T20:56:35.6483009Z Digest: sha256:def822f9851ca422481ec6fee59a9966f12b351c62ccb9aca841526ffaa9f748
2024-08-22T20:56:35.6503124Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine:latest
2024-08-22T20:56:36.7405286Z Prepare all required actions
2024-08-22T20:56:36.7440445Z ##[group]Run ./test-infra/.github/actions/chown-directory
2024-08-22T20:56:36.7440932Z with:
2024-08-22T20:56:36.7441287Z   directory: /home/ec2-user/actions-runner/_work/_temp
2024-08-22T20:56:36.7441940Z   ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine
2024-08-22T20:56:36.7442501Z env:
2024-08-22T20:56:36.7442817Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:56:36.7443273Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:56:36.7443649Z   PR_NUMBER: 1044
2024-08-22T20:56:36.7462554Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:56:36.7482296Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:56:36.7483066Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:56:36.7483781Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:56:36.7484388Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:56:36.7484858Z ##[endgroup]
2024-08-22T20:56:36.7504962Z ##[group]Run docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" .
2024-08-22T20:56:36.7505916Z docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" .
2024-08-22T20:56:36.7518465Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:56:36.7518960Z env:
2024-08-22T20:56:36.7519282Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:56:36.7519779Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:56:36.7520173Z   PR_NUMBER: 1044
2024-08-22T20:56:36.7539160Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:56:36.7558715Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:56:36.7559475Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:56:36.7560185Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:56:36.7560782Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:56:36.7561423Z   ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine
2024-08-22T20:56:36.7562056Z   DIRECTORY: /home/ec2-user/actions-runner/_work/_temp
2024-08-22T20:56:36.7562504Z ##[endgroup]
2024-08-22T20:56:37.7102954Z ##[group]Run # Only do these steps if we actually want to upload an artifact
2024-08-22T20:56:37.7103784Z # Only do these steps if we actually want to upload an artifact
2024-08-22T20:56:37.7104431Z if [[ -n "${UPLOAD_ARTIFACT_NAME}" ]]; then
2024-08-22T20:56:37.7105207Z   # If the default execution path is followed then we should get a wheel in the dist/ folder
2024-08-22T20:56:37.7106086Z   # attempt to just grab whatever is in there and scoop it all up
2024-08-22T20:56:37.7106812Z   if find "dist/" -name "*.whl" >/dev/null 2>/dev/null; then
2024-08-22T20:56:37.7107434Z     mv -v dist/*.whl "${RUNNER_ARTIFACT_DIR}/"
2024-08-22T20:56:37.7107893Z   fi
2024-08-22T20:56:37.7108282Z   if [[ -d "artifacts-to-be-uploaded" ]]; then
2024-08-22T20:56:37.7108936Z     mv -v artifacts-to-be-uploaded/* "${RUNNER_ARTIFACT_DIR}/"
2024-08-22T20:56:37.7109489Z   fi
2024-08-22T20:56:37.7110109Z   # Set to fail upload step if there are no files for upload and expected files for upload
2024-08-22T20:56:37.7110942Z   echo 'if-no-files-found=error' >> "${GITHUB_OUTPUT}"
2024-08-22T20:56:37.7111461Z fi
2024-08-22T20:56:37.7111741Z 
2024-08-22T20:56:37.7112014Z upload_docs=0
2024-08-22T20:56:37.7112590Z # Check if there are files in the documentation folder to upload, note that
2024-08-22T20:56:37.7113247Z # empty folders do not count
2024-08-22T20:56:37.7113873Z if find "${RUNNER_DOCS_DIR}" -mindepth 1 -maxdepth 1 -type f | read -r; then
2024-08-22T20:56:37.7114739Z   # TODO: Add a check here to test if on ec2 because if we're not on ec2 then this
2024-08-22T20:56:37.7115475Z   # upload will probably not work correctly
2024-08-22T20:56:37.7115950Z   upload_docs=1
2024-08-22T20:56:37.7116277Z fi
2024-08-22T20:56:37.7116710Z echo "upload-docs=${upload_docs}" >> "${GITHUB_OUTPUT}"
2024-08-22T20:56:37.7127270Z shell: /usr/bin/bash -e {0}
2024-08-22T20:56:37.7127610Z env:
2024-08-22T20:56:37.7127943Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:56:37.7128540Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:56:37.7128910Z   PR_NUMBER: 1044
2024-08-22T20:56:37.7147748Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:56:37.7167214Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:56:37.7167983Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:56:37.7168753Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:56:37.7169361Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:56:37.7169855Z   UPLOAD_ARTIFACT_NAME: 
2024-08-22T20:56:37.7170232Z ##[endgroup]
2024-08-22T20:56:37.7270344Z Prepare all required actions
2024-08-22T20:56:37.7308637Z ##[group]Run ./test-infra/.github/actions/teardown-linux
2024-08-22T20:56:37.7309112Z with:
2024-08-22T20:56:37.7309366Z env:
2024-08-22T20:56:37.7309688Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:56:37.7310138Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:56:37.7310511Z   PR_NUMBER: 1044
2024-08-22T20:56:37.7329799Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:56:37.7349263Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:56:37.7350031Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:56:37.7350740Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:56:37.7351356Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:56:37.7351826Z ##[endgroup]
2024-08-22T20:56:37.7372771Z ##[group]Run set -eou pipefail
2024-08-22T20:56:37.7373161Z set -eou pipefail
2024-08-22T20:56:37.7373500Z 
2024-08-22T20:56:37.7373998Z echo "Holding runner for 2 hours until all ssh sessions have logged out"
2024-08-22T20:56:37.7374619Z for _ in $(seq 1440); do
2024-08-22T20:56:37.7375081Z     # Break if no ssh session exists anymore
2024-08-22T20:56:37.7375567Z     if [ "$(who)" = "" ]; then
2024-08-22T20:56:37.7375959Z       break
2024-08-22T20:56:37.7376264Z     fi
2024-08-22T20:56:37.7376554Z     echo "."
2024-08-22T20:56:37.7376869Z     sleep 5
2024-08-22T20:56:37.7377174Z done
2024-08-22T20:56:37.7386023Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:56:37.7386516Z env:
2024-08-22T20:56:37.7386846Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:56:37.7387299Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:56:37.7387674Z   PR_NUMBER: 1044
2024-08-22T20:56:37.7406586Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:56:37.7426064Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:56:37.7426834Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:56:37.7427551Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:56:37.7428149Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:56:37.7428620Z ##[endgroup]
2024-08-22T20:56:37.7457029Z Holding runner for 2 hours until all ssh sessions have logged out
2024-08-22T20:56:37.7518223Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2024-08-22T20:56:37.7518979Z # ignore expansion of "docker ps -q" since it could be empty
2024-08-22T20:56:37.7519566Z # shellcheck disable=SC2046
2024-08-22T20:56:37.7520058Z docker stop $(docker ps -q) || true
2024-08-22T20:56:37.7520521Z # Prune all of the docker images
2024-08-22T20:56:37.7520959Z docker system prune -af
2024-08-22T20:56:37.7530101Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:56:37.7530594Z env:
2024-08-22T20:56:37.7530926Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:56:37.7531391Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:56:37.7531770Z   PR_NUMBER: 1044
2024-08-22T20:56:37.7550770Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:56:37.7570391Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:56:37.7571156Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:56:37.7571880Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:56:37.7572489Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:56:37.7572964Z ##[endgroup]
2024-08-22T20:56:48.7305897Z 638b8463543e
2024-08-22T20:56:52.1443565Z Deleted Containers:
2024-08-22T20:56:52.1444112Z 638b8463543e1793a9bf9b9c5317033fb1f5078871a798e3dab1e776fe9db640
2024-08-22T20:56:52.1444526Z 
2024-08-22T20:56:57.4315873Z Deleted Images:
2024-08-22T20:56:57.4316990Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine:latest
2024-08-22T20:56:57.4327163Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine@sha256:def822f9851ca422481ec6fee59a9966f12b351c62ccb9aca841526ffaa9f748
2024-08-22T20:56:57.4329012Z deleted: sha256:6dbb9cc54074106d46d4ccb330f2a40a682d49dda5f4844962b7dce9fe44aaec
2024-08-22T20:56:57.4330151Z deleted: sha256:b2d5eeeaba3a22b9b8aa97261957974a6bd65274ebd43e1d81d0a7b8b752b116
2024-08-22T20:56:57.4331047Z untagged: pytorch/conda-builder:cuda12.1
2024-08-22T20:56:57.4332079Z untagged: pytorch/conda-builder@sha256:9f128b503c8de2d2267228e5a5ddfd791872f8fe7e4630afeec3efb07d810e8b
2024-08-22T20:56:57.4333293Z deleted: sha256:ca6cb5e15adf1287624fd5ee41e98e3482f9ba8e8eb49b94e1c89149740d589e
2024-08-22T20:56:57.4334548Z deleted: sha256:7ea5160e27b147a9b8579d1ecaa57d793582bf5bc915c7f8cdbfb0e9d6cb07a7
2024-08-22T20:56:57.4335763Z deleted: sha256:b62ea2cf6a5771e822ce3339bbcc153c9d1d567d410908f8c8bb6364538d5ce4
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2024-08-22T20:56:57.4362290Z 
2024-08-22T20:56:57.4362426Z Total reclaimed space: 24.26GB
2024-08-22T20:56:57.5196647Z ##[group]Run set +e
2024-08-22T20:56:57.5196992Z set +e
2024-08-22T20:56:57.5197332Z if [[ "${NO_SUDO}" == "false" ]]; then
2024-08-22T20:56:57.5197909Z   sudo rm -rf "${GITHUB_WORKSPACE:?}/${REPOSITORY:?}"
2024-08-22T20:56:57.5198405Z else
2024-08-22T20:56:57.5198790Z   rm -rf "${GITHUB_WORKSPACE:?}/${REPOSITORY:?}"
2024-08-22T20:56:57.5199255Z fi
2024-08-22T20:56:57.5199532Z set -e
2024-08-22T20:56:57.5210412Z shell: /usr/bin/bash -e {0}
2024-08-22T20:56:57.5210765Z env:
2024-08-22T20:56:57.5211087Z   DOCKER_IMAGE: pytorch/conda-builder:cuda12.1
2024-08-22T20:56:57.5211541Z   REPOSITORY: pytorch/torchchat
2024-08-22T20:56:57.5211924Z   PR_NUMBER: 1044
2024-08-22T20:56:57.5231103Z   SCRIPT: echo "::group::Print machine info"
uname -a
echo "::endgroup::"

echo "::group::Install newer objcopy that supports --set-section-alignment"
yum install -y  devtoolset-10-binutils
export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
echo "::endgroup::"


echo "::group::Download checkpoints"
# Install requirements
./install_requirements.sh cuda
pip3 list
python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
echo "::endgroup::"

echo "::group::Download checkpoints"
mkdir -p checkpoints/stories15M
pushd checkpoints/stories15M
wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt
wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model
popd
echo "::endgroup::"

echo "::group::Run inference"
export MODEL_PATH=checkpoints/stories15M/stories15M.pt
export MODEL_NAME=stories15M
export MODEL_DIR=/tmp

for DTYPE in bfloat16 float16 float32; do
  ###################################################################
  # group with different temperatures
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
  ###################################################################
  # group with different temperatures and prefill, and compile
  # and prefill compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
  ###################################################################
  # group with different temperatures and sequential prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
  ###################################################################
  # group with different temperatures and prefill, and compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
  python generate.py --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile

done

echo "tests complete"
echo "******************************************"
echo "::endgroup::"

2024-08-22T20:56:57.5250730Z   RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts
2024-08-22T20:56:57.5251508Z   RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results
2024-08-22T20:56:57.5252219Z   RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs
2024-08-22T20:56:57.5252831Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2024-08-22T20:56:57.5253309Z   NO_SUDO: false
2024-08-22T20:56:57.5253596Z ##[endgroup]
2024-08-22T20:56:57.7157225Z Post job cleanup.
2024-08-22T20:56:57.8134857Z Post job cleanup.
2024-08-22T20:56:57.9009213Z [command]/usr/bin/git version
2024-08-22T20:56:57.9061991Z git version 2.40.1
2024-08-22T20:56:57.9099819Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/d7e4a8d9-63cd-4c8d-903d-3c135a9d45aa' before making global git config changes
2024-08-22T20:56:57.9101036Z Adding repository directory to the temporary git global config as a safe directory
2024-08-22T20:56:57.9105037Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/torchchat/torchchat/test-infra
2024-08-22T20:56:57.9151005Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2024-08-22T20:56:57.9195455Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :"
2024-08-22T20:56:57.9560760Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2024-08-22T20:56:57.9596272Z http.https://github.com/.extraheader
2024-08-22T20:56:57.9606050Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader
2024-08-22T20:56:57.9651265Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :"
2024-08-22T20:56:58.0092133Z A job completed hook has been configured by the self-hosted runner administrator
2024-08-22T20:56:58.0114915Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh'
2024-08-22T20:56:58.0122995Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-08-22T20:56:58.0123508Z ##[endgroup]
2024-08-22T20:57:04.8392752Z Cleaning up orphan processes