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Run parallel prefill

Add attention_backend to let user choose #2674

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Add attention_backend to let user choose

Add attention_backend to let user choose #2674

Workflow file for this run

.github/workflows/more-tests.yml at 48f3c19
name: Run parallel prefill
on:
pull_request:
push:
branches:
- main
workflow_dispatch:
jobs:
test-cuda:

Check failure on line 11 in .github/workflows/more-tests.yml

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Invalid workflow file 

The workflow is not valid. .github/workflows/more-tests.yml (Line: 11, Col: 3): Error calling workflow 'pytorch/test-infra/.github/workflows/linux_job_v2.yml@main'. The workflow is requesting 'id-token: write', but is only allowed 'id-token: none'.
uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
with:
runner: linux.g5.4xlarge.nvidia.gpu
gpu-arch-type: cuda
gpu-arch-version: "12.4"
timeout: 60
script: |
echo "::group::Print machine info"
uname -a
echo "::endgroup::"
echo "::group::Download checkpoints"
# Install requirements
./install/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 torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500
###################################################################
# group with different temperatures and prefill, and compile
# and prefill compile
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --compile --compile-prefill
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --compile --compile-prefill
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --compile --compile-prefill
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --compile --compile-prefill
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --compile --compile-prefill
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --compile --compile-prefill
###################################################################
# group with different temperatures and sequential prefill
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill
###################################################################
# group with different temperatures and prefill, and compile
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0 --sequential-prefill --compile
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 0.9 --sequential-prefill --compile
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --temperature 1.0 --sequential-prefill --compile
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 100 --sequential-prefill --compile
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 200 --sequential-prefill --compile
python torchchat.py generate --checkpoint-path ${MODEL_PATH} --device cpu --dtype ${DTYPE} --top-k 500 --sequential-prefill --compile
done
echo "tests complete"
echo "******************************************"
echo "::endgroup::"