Enable sdpa backends for server export in export.py #2852
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| name: Run parallel prefill | |
| on: | |
| pull_request: | |
| push: | |
| branches: | |
| - main | |
| workflow_dispatch: | |
| jobs: | |
| test-cuda: | |
| permissions: | |
| id-token: write | |
| contents: read | |
| 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: | | |
| set -xeou pipefail | |
| 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_DIR=checkpoints/stories15M/ | |
| export MODEL_PATH=${MODEL_DIR}/stories15M.pt | |
| export MODEL_NAME=stories15M | |
| 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 ${DTYPE} --top-k 500 --sequential-prefill --compile | |
| done | |
| echo "tests complete" | |
| echo "******************************************" | |
| echo "::endgroup::" | |
| test-sdpa-backends-export: | |
| permissions: | |
| id-token: write | |
| contents: read | |
| uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main | |
| with: | |
| runner: linux.g5.4xlarge.nvidia.gpu | |
| gpu-arch-type: cuda | |
| gpu-arch-version: "12.4" | |
| timeout: 60 | |
| script: | | |
| set -xeou pipefail | |
| echo "::group::Print machine info" | |
| uname -a | |
| echo "::endgroup::" | |
| echo "::group::Download checkpoints" | |
| # Install requirements | |
| ./install/install_requirements.sh cuda | |
| pip3 list | |
| python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")' | |
| echo "::endgroup::" | |
| echo "::group::Download checkpoints" | |
| mkdir -p checkpoints/stories15M | |
| pushd checkpoints/stories15M | |
| wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt | |
| wget https://github.com/karpathy/llama2.c/raw/master/tokenizer.model | |
| popd | |
| echo "::endgroup::" | |
| echo "::group::Run inference" | |
| export MODEL_DIR=checkpoints/stories15M/ | |
| export MODEL_PATH=${MODEL_DIR}/stories15M.pt | |
| export MODEL_NAME=stories15M | |
| ./torchchat/utils/scripts/build_native.sh aoti | |
| for DEVICE in cpu cuda; do | |
| # depending on how the parameter passing works, may only be able to do bfloat16 for aoti_run, similar to runner-cuda-dtype.yml | |
| # (although the runner environment should not have an opinion what we us in the artifact, and we might suitably abstract that) | |
| for DTYPE in bfloat16 float16 float32; do | |
| for SDPA in 'math' 'flash_attention' 'efficient_attention' 'cudnn_attention'; do | |
| echo "***************************************************************" | |
| echo "*** $DEVICE $DTYPE $SDPA" | |
| ################################################################### | |
| # Export DSO and run with Python | |
| python torchchat.py export --output-dso dso.so --checkpoint-path ${MODEL_PATH} --attention-backend ${SDPA} --device ${DEVICE} --dtype ${DTYPE} | |
| python torchchat.py generate --dso-path dso.so --checkpoint-path ${MODEL_PATH} --attention-backend ${SDPA} --device ${DEVICE} --dtype ${DTYPE} --temperature 0 --prompt "Once upon a time" | |
| ################################################################### | |
| # Export AOTI and run with aoti_run | |
| python torchchat.py export --output-aoti /tmp/model.pt2 --checkpoint-path ${MODEL_PATH} --attention-backend ${SDPA} --device ${DEVICE} --dtype ${DTYPE} | |
| ./cmake-out/aoti_run /tmp/model.pt2 -z ${MODEL_DIR}/tokenizer.model -i "Once upon a time" | |
| ################################################################### | |
| done | |
| done | |
| done | |
| echo "tests complete" | |
| echo "******************************************" | |
| echo "::endgroup::" |