mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-08-14 15:18:27 +00:00
Compare commits
1
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Chen-Yu Yang
|
0071d864f8 |
@@ -11,5 +11,5 @@ runs:
|
||||
git fetch origin $CURRENT_SHA
|
||||
export COMMIT_MESSAGE=$(git show -s --format=%B "$CURRENT_SHA")
|
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export CURRENT_HEAD=$(git rev-parse HEAD)
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||||
cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && IGNORE_OOB=1 PYTHONPATH=. python3 process_replay.py
|
||||
cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py
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||||
git checkout $CURRENT_HEAD # restore to branch
|
||||
|
||||
@@ -29,10 +29,6 @@ inputs:
|
||||
description: "Install CUDA?"
|
||||
required: false
|
||||
default: 'false'
|
||||
ocelot:
|
||||
description: "Install gpuocelot?"
|
||||
required: false
|
||||
default: 'false'
|
||||
webgpu:
|
||||
description: "Install webgpu?"
|
||||
required: false
|
||||
@@ -45,19 +41,34 @@ runs:
|
||||
using: "composite"
|
||||
steps:
|
||||
- name: Set up Python ${{ inputs.python-version }}
|
||||
id: setup-python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: ${{ inputs.python-version }}
|
||||
- name: Upgrade pip
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||||
shell: bash
|
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run: python -m pip install --upgrade pip
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|
||||
# **** Caching packages ****
|
||||
# TODO: key should include input.deps, but it can't since it can't contain commas
|
||||
|
||||
- name: Cache Python packages
|
||||
id: restore-venv
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||||
- name: Cache Python packages (Linux)
|
||||
if: inputs.key != '' && runner.os == 'Linux'
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||||
uses: actions/cache@v4
|
||||
with:
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||||
path: ${{ github.workspace }}/.venv
|
||||
key: venv-${{ runner.os }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ hashFiles('**/setup.py') }}-${{ env.PYTHON_CACHE_VERSION }}
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||||
path: ${{ env.Python3_ROOT_DIR }}/lib/python${{ inputs.python-version }}/site-packages
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||||
key: python-package-${{ inputs.key }}-${{ hashFiles('**/setup.py') }}
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||||
- name: Cache Python packages (macOS)
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||||
if: inputs.key != '' && runner.os == 'macOS'
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||||
uses: actions/cache@v4
|
||||
with:
|
||||
path: /Users/runner/Library/Python/${{ inputs.python-version }}/lib/python/site-packages
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||||
key: osx-python-package-${{ inputs.key }}-${{ hashFiles('**/setup.py') }}
|
||||
- name: Cache Python packages (Windows)
|
||||
if: inputs.key != '' && runner.os == 'Windows'
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||||
uses: actions/cache@v4
|
||||
with:
|
||||
path: ${{ env.Python3_ROOT_DIR }}\Lib\site-packages
|
||||
key: windows-python-package-${{ inputs.key }}-${{ hashFiles('**/setup.py') }}
|
||||
|
||||
# **** Caching downloads ****
|
||||
|
||||
@@ -76,133 +87,43 @@ runs:
|
||||
|
||||
# **** Python deps ****
|
||||
|
||||
- name: Install dependencies in venv (with extra)
|
||||
if: inputs.deps != '' && steps.restore-venv.outputs.cache-hit != 'true'
|
||||
- name: Install dependencies (with extra)
|
||||
if: inputs.deps != ''
|
||||
shell: bash
|
||||
run: |
|
||||
python -m venv .venv
|
||||
if [[ "$RUNNER_OS" == "Windows" ]]; then
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source .venv/Scripts/activate
|
||||
else
|
||||
. .venv/bin/activate
|
||||
fi
|
||||
python -m pip install -e ".[${{ inputs.deps }}]" ${{ inputs.pydeps }} --extra-index-url https://download.pytorch.org/whl/cpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/
|
||||
- name: Install dependencies in venv (without extra)
|
||||
if: inputs.deps == '' && steps.restore-venv.outputs.cache-hit != 'true'
|
||||
run: pip install ${{ (runner.os == 'macOS' && '--user') || (runner.os != 'macOS' && '') }} -e ".[${{ inputs.deps }}]" ${{ inputs.pydeps }} --extra-index-url https://download.pytorch.org/whl/cpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/
|
||||
- name: Install dependencies (without extra)
|
||||
if: inputs.deps == ''
|
||||
shell: bash
|
||||
run: |
|
||||
python -m venv .venv
|
||||
if [[ "$RUNNER_OS" == "Windows" ]]; then
|
||||
source .venv/Scripts/activate
|
||||
else
|
||||
. .venv/bin/activate
|
||||
fi
|
||||
python -m pip install -e . ${{ inputs.pydeps }}
|
||||
- name: Set up venv environment
|
||||
shell: bash
|
||||
run: |
|
||||
echo "VIRTUAL_ENV=${{ github.workspace }}/.venv" >> "$GITHUB_ENV"
|
||||
echo "OMP_NUM_THREADS=1" >> "$GITHUB_ENV"
|
||||
# no buffers should be over 300MB in CI
|
||||
echo "MAX_BUFFER_SIZE=300000000" >> "$GITHUB_ENV"
|
||||
if [[ "$RUNNER_OS" == "Windows" ]]; then
|
||||
echo "${{ github.workspace }}/.venv/Scripts" >> "$GITHUB_PATH"
|
||||
else
|
||||
echo "${{ github.workspace }}/.venv/bin" >> "$GITHUB_PATH"
|
||||
fi
|
||||
run: pip install ${{ (runner.os == 'macOS' && '--user') || (runner.os != 'macOS' && '') }} -e . ${{ inputs.pydeps }}
|
||||
|
||||
# ******************* apt *******************
|
||||
- name: Setup apt
|
||||
if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
|
||||
# **** OpenCL ****
|
||||
|
||||
- name: Install OpenCL
|
||||
if: inputs.opencl == 'true'
|
||||
shell: bash
|
||||
run: |
|
||||
sudo chown -R $USER:$USER /var/cache/apt/archives
|
||||
|
||||
echo 'Acquire::GzipIndexes "true";' | sudo tee /etc/apt/apt.conf.d/gzip
|
||||
echo 'Acquire::http::Pipeline-Depth "5";' | sudo tee -a /etc/apt/apt.conf.d/99parallel
|
||||
echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' | sudo tee -a /etc/apt/apt.conf.d/99keep-debs
|
||||
|
||||
- name: Add OpenCL Repo
|
||||
if: inputs.opencl == 'true' && runner.os == 'Linux'
|
||||
shell: bash
|
||||
run: echo "deb [ allow-insecure=yes ] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list
|
||||
|
||||
- name: Add AMD Repo (Linux)
|
||||
if: inputs.amd == 'true' && runner.os == 'Linux'
|
||||
shell: bash
|
||||
run: |
|
||||
wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
|
||||
sudo tee /etc/apt/sources.list.d/rocm.list <<EOF
|
||||
deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/6.2 $(lsb_release -cs) main
|
||||
EOF
|
||||
echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
|
||||
|
||||
- name: Add LLVM Repo (Linux)
|
||||
if: inputs.llvm == 'true' && runner.os == 'Linux'
|
||||
shell: bash
|
||||
run: |
|
||||
wget -qO- https://apt.llvm.org/llvm-snapshot.gpg.key | sudo tee /etc/apt/trusted.gpg.d/apt.llvm.org.asc
|
||||
echo "deb http://apt.llvm.org/$(lsb_release -cs)/ llvm-toolchain-$(lsb_release -cs)-20 main" | sudo tee /etc/apt/sources.list.d/llvm.list
|
||||
|
||||
- name: Compute Package List + Hash
|
||||
if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
|
||||
id: apt-pkgs
|
||||
shell: bash
|
||||
run: |
|
||||
pkgs=""
|
||||
# **** OpenCL ****
|
||||
if [[ "${{ inputs.opencl }}" == "true" ]]; then
|
||||
pkgs+=" opencl-headers \
|
||||
intel-oneapi-runtime-openmp=2023.2.1-16 intel-oneapi-runtime-compilers-common=2023.2.1-16 intel-oneapi-runtime-compilers=2023.2.1-16 \
|
||||
intel-oneapi-runtime-dpcpp-sycl-opencl-cpu=2023.2.1-16 intel-oneapi-runtime-tbb-common=2021.10.0-49541 \
|
||||
intel-oneapi-runtime-tbb=2021.10.0-49541 intel-oneapi-runtime-opencl=2023.2.1-16"
|
||||
fi
|
||||
# **** AMD ****
|
||||
if [[ "${{ inputs.amd }}" == "true" ]]; then
|
||||
pkgs+=" hsa-rocr comgr hsa-rocr-dev liburing-dev libibverbs-dev libc6-dev"
|
||||
fi
|
||||
# **** CUDA ****
|
||||
if [[ "${{ inputs.cuda }}" == "true" ]]; then
|
||||
pkgs+=" git g++ cmake ninja-build llvm-15-dev zlib1g-dev libglew-dev \
|
||||
flex bison libfl-dev libboost-thread-dev libboost-filesystem-dev nvidia-cuda-toolkit-gcc libzstd-dev"
|
||||
fi
|
||||
# **** WebGPU (dependencies for software-based vulkan) ****
|
||||
if [[ "${{ inputs.webgpu }}" == "true" ]]; then
|
||||
pkgs+=" libgl1 libglx-mesa0 libgl1-mesa-dri libxcb-xfixes0-dev mesa-vulkan-drivers"
|
||||
fi
|
||||
# **** LLVM ****
|
||||
if [[ "${{ inputs.llvm }}" == "true" ]]; then
|
||||
pkgs+=" libllvm20 clang-20 lld-20"
|
||||
fi
|
||||
|
||||
echo "pkgs=$pkgs" >> "$GITHUB_OUTPUT"
|
||||
echo "hash=$(echo -n "$pkgs" | sha256sum | cut -d' ' -f1)" >> "$GITHUB_OUTPUT"
|
||||
|
||||
- name: Cache apt
|
||||
if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
|
||||
uses: actions/cache@v4
|
||||
with:
|
||||
path: /var/cache/apt/archives/
|
||||
key: ${{ runner.os }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.APT_CACHE_VERSION }}
|
||||
|
||||
- name: Run apt Update + Install
|
||||
if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
|
||||
shell: bash
|
||||
run: |
|
||||
sudo apt -qq update || true
|
||||
|
||||
# ******** do install ********
|
||||
if [[ -n "${{ steps.apt-pkgs.outputs.pkgs }}" ]]; then
|
||||
sudo apt-get -y --allow-unauthenticated --no-install-recommends install ${{ steps.apt-pkgs.outputs.pkgs }}
|
||||
fi
|
||||
|
||||
sudo chown -R $USER:$USER /var/cache/apt/archives/
|
||||
echo "deb [ allow-insecure=yes ] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list
|
||||
sudo apt update || true
|
||||
sudo apt install --allow-unauthenticated -y --no-install-recommends opencl-headers \
|
||||
intel-oneapi-runtime-openmp=2023.2.1-16 intel-oneapi-runtime-compilers-common=2023.2.1-16 intel-oneapi-runtime-compilers=2023.2.1-16 \
|
||||
intel-oneapi-runtime-dpcpp-sycl-opencl-cpu=2023.2.1-16 intel-oneapi-runtime-tbb-common=2021.10.0-49541 \
|
||||
intel-oneapi-runtime-tbb=2021.10.0-49541 intel-oneapi-runtime-opencl=2023.2.1-16
|
||||
|
||||
# **** AMD ****
|
||||
- name: Setup AMD (Linux)
|
||||
|
||||
- name: Install AMD (Linux)
|
||||
if: inputs.amd == 'true' && runner.os == 'Linux'
|
||||
shell: bash
|
||||
run: |
|
||||
echo 'Acquire::http::Pipeline-Depth "5";' | sudo tee -a /etc/apt/apt.conf.d/99parallel
|
||||
wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
|
||||
sudo tee /etc/apt/sources.list.d/rocm.list <<'EOF'
|
||||
deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/6.1.2 jammy main
|
||||
EOF
|
||||
echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
|
||||
sudo apt update || true
|
||||
sudo apt install --no-install-recommends --allow-unauthenticated -y hsa-rocr comgr hsa-rocr-dev liburing-dev libc6-dev
|
||||
cargo build --release --manifest-path ./extra/remu/Cargo.toml
|
||||
sudo ln -sf ${{ github.workspace }}/extra/remu/target/release/libremu.so /usr/local/lib/libremu.so
|
||||
sudo tee --append /etc/ld.so.conf.d/rocm.conf <<'EOF'
|
||||
@@ -210,7 +131,7 @@ runs:
|
||||
/opt/rocm/lib64
|
||||
EOF
|
||||
sudo ldconfig
|
||||
- name: Setup AMD comgr+remu (macOS)
|
||||
- name: Install AMD comgr+remu (macOS)
|
||||
if: inputs.amd == 'true' && runner.os == 'macOS'
|
||||
shell: bash
|
||||
run: |
|
||||
@@ -220,32 +141,33 @@ runs:
|
||||
sudo xargs curl -L -o /usr/local/lib/libamd_comgr.dylib
|
||||
cargo build --release --manifest-path ./extra/remu/Cargo.toml
|
||||
|
||||
# **** gpuocelot ****
|
||||
# **** CUDA ****
|
||||
|
||||
- name: Install gpuocelot dependencies (MacOS)
|
||||
if: inputs.ocelot == 'true' && runner.os == 'macOS'
|
||||
- name: Install cuda packages (Linux)
|
||||
if: inputs.cuda == 'true' && runner.os == 'Linux'
|
||||
shell: bash
|
||||
run: |
|
||||
pkgs=(cmake ninja llvm@15 zlib glew flex bison [email protected] zstd ncurses)
|
||||
for f in "${pkgs[@]}"; do
|
||||
brew ls --versions "$f" >/dev/null 2>&1 || brew install --quiet "$f"
|
||||
done
|
||||
|
||||
# Fix boost 1.85 for gpuocelot
|
||||
ln -s /opt/homebrew/opt/[email protected] /opt/homebrew/opt/boost || true
|
||||
ln -s /opt/homebrew/opt/boost/lib/libboost_atomic-mt.dylib /opt/homebrew/opt/boost/lib/libboost_atomic.dylib || true
|
||||
ln -s /opt/homebrew/opt/boost/lib/libboost_thread-mt.dylib /opt/homebrew/opt/boost/lib/libboost_thread.dylib || true
|
||||
echo 'Acquire::http::Pipeline-Depth "5";' | sudo tee -a /etc/apt/apt.conf.d/99parallel
|
||||
sudo apt update -y || true
|
||||
sudo apt install -y --no-install-recommends git g++ cmake ninja-build llvm-15-dev zlib1g-dev libglew-dev \
|
||||
flex bison libfl-dev libboost-thread-dev libboost-filesystem-dev nvidia-cuda-toolkit-gcc libzstd-dev
|
||||
- name: Install gpuocelot dependencies (MacOS)
|
||||
if: inputs.cuda == 'true' && runner.os == 'macOS'
|
||||
shell: bash
|
||||
run: |
|
||||
brew update
|
||||
brew install --quiet cmake ninja llvm@15 zlib glew flex bison boost zstd ncurses
|
||||
- name: Cache gpuocelot
|
||||
if: inputs.ocelot == 'true'
|
||||
if: inputs.cuda == 'true'
|
||||
id: cache-build
|
||||
uses: actions/cache@v4
|
||||
env:
|
||||
cache-name: cache-gpuocelot-build-1
|
||||
cache-name: cache-gpuocelot-build
|
||||
with:
|
||||
path: ${{ github.workspace }}/gpuocelot/ocelot
|
||||
key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-${{ env.BUILD_CACHE_VERSION }}
|
||||
key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-0
|
||||
- name: Clone/compile gpuocelot
|
||||
if: inputs.ocelot == 'true' && steps.cache-build.outputs.cache-hit != 'true'
|
||||
if: inputs.cuda == 'true' && steps.cache-build.outputs.cache-hit != 'true'
|
||||
shell: bash
|
||||
run: |
|
||||
git clone --recurse-submodules https://github.com/gpuocelot/gpuocelot.git ${{ github.workspace }}/gpuocelot
|
||||
@@ -253,15 +175,14 @@ runs:
|
||||
git checkout b16039dc940dc6bc4ea0a98380495769ff35ed99
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. -Wno-dev -G Ninja -DOCELOT_BUILD_TOOLS=OFF -DCMAKE_BUILD_ALWAYS=0 -DBUILD_TESTS_CUDA=OFF \
|
||||
-DBoost_INCLUDE_DIR=$(brew --prefix boost)/include -DBoost_LIBRARY_DIR=$(brew --prefix boost)/lib -DCMAKE_POLICY_VERSION_MINIMUM=3.5
|
||||
cmake .. -Wno-dev -G Ninja -DOCELOT_BUILD_TOOLS=OFF -DCMAKE_BUILD_ALWAYS=0 -DBUILD_TESTS_CUDA=OFF -DCMAKE_POLICY_VERSION_MINIMUM=3.5
|
||||
ninja
|
||||
- name: Install gpuocelot
|
||||
if: inputs.ocelot == 'true'
|
||||
if: inputs.cuda == 'true'
|
||||
shell: bash
|
||||
run: |
|
||||
cd ${{ github.workspace }}/gpuocelot/ocelot/build
|
||||
sudo cp libgpuocelot.${{ runner.os == 'macOS' && 'dylib' || 'so' }} /usr/${{ runner.os == 'macOS' && 'local/' || '' }}lib/
|
||||
sudo cp libgpuocelot.${{ runner.os == 'macOS' && 'dylib' || 'so' }} /usr/${{ runner.os == 'macOS' && 'local/' || ''}}lib/
|
||||
|
||||
# **** WebGPU ****
|
||||
|
||||
@@ -270,7 +191,13 @@ runs:
|
||||
shell: bash
|
||||
run: |
|
||||
sudo curl -L https://github.com/wpmed92/pydawn/releases/download/v0.1.6/libwebgpu_dawn.so -o /usr/local/lib/libwebgpu_dawn.so
|
||||
sudo ldconfig
|
||||
- name: Install dependencies for software-based vulkan
|
||||
if: inputs.webgpu == 'true' && runner.os == 'Linux'
|
||||
shell: bash
|
||||
run: |
|
||||
sudo apt update -y || true
|
||||
sudo apt install -y libegl1-mesa libgl1-mesa-dri libxcb-xfixes0-dev mesa-vulkan-drivers
|
||||
|
||||
- name: Install WebGPU dawn (macOS)
|
||||
if: inputs.webgpu == 'true' && runner.os == 'macOS'
|
||||
shell: bash
|
||||
@@ -280,7 +207,18 @@ runs:
|
||||
|
||||
# **** LLVM ****
|
||||
|
||||
- name: Install LLVM (Linux)
|
||||
if: inputs.llvm == 'true' && runner.os == 'Linux'
|
||||
shell: bash
|
||||
run: |
|
||||
echo 'Acquire::http::Pipeline-Depth "5";' | sudo tee -a /etc/apt/apt.conf.d/99parallel
|
||||
wget -qO- https://apt.llvm.org/llvm-snapshot.gpg.key | sudo tee /etc/apt/trusted.gpg.d/apt.llvm.org.asc
|
||||
echo "deb http://apt.llvm.org/$(lsb_release -cs)/ llvm-toolchain-$(lsb_release -cs)-19 main" | sudo tee /etc/apt/sources.list.d/llvm.list
|
||||
sudo apt update -y || true
|
||||
sudo apt install -y --no-install-recommends libllvm19 clang-19 lld-19
|
||||
|
||||
- name: Install LLVM (macOS)
|
||||
if: inputs.llvm == 'true' && runner.os == 'macOS'
|
||||
shell: bash
|
||||
run: brew install llvm@20
|
||||
run: |
|
||||
brew install llvm@19
|
||||
+58
-249
@@ -63,15 +63,13 @@ jobs:
|
||||
- name: Run model inference benchmark
|
||||
run: METAL=1 python3.11 test/external/external_model_benchmark.py
|
||||
- name: Test speed vs torch
|
||||
run: BIG=2 MPS=1 python3.11 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
|
||||
run: BIG=2 MPS=1 python3.11 test/test_speed_v_torch.py | tee torch_speed.txt
|
||||
- name: Test tensor cores
|
||||
run: METAL=1 python3.11 test/opt/test_tensor_cores.py
|
||||
run: METAL=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_emulation TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Test AMX tensor cores
|
||||
run: |
|
||||
DEBUG=2 CPU=1 AMX=1 python3.11 test/opt/test_tensor_cores.py
|
||||
DEBUG=2 LLVM=1 AMX=1 python3.11 test/opt/test_tensor_cores.py
|
||||
DEBUG=2 CPU=1 AMX=1 python3.11 test/opt/test_gen_float4.py TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
|
||||
DEBUG=2 LLVM=1 AMX=1 python3.11 test/opt/test_gen_float4.py TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
|
||||
DEBUG=2 CPU=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_emulation TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
DEBUG=2 LLVM=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_emulation TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Run Tensor Core GEMM (float)
|
||||
run: DEBUG=2 SHOULD_USE_TC=1 python3.11 extra/gemm/simple_matmul.py | tee matmul.txt
|
||||
- name: Run Tensor Core GEMM (half)
|
||||
@@ -122,8 +120,8 @@ jobs:
|
||||
run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB python3.11 test/test_tiny.py
|
||||
- name: UsbGPU copy speeds
|
||||
run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB python3.11 test/external/external_test_usb_asm24.py TestDevCopySpeeds
|
||||
#- name: UsbGPU openpilot test
|
||||
# run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB NOLOCALS=0 IMAGE=0 GRAPH_ONE_KERNEL=1 python3.11 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
|
||||
- name: UsbGPU openpilot test
|
||||
run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB AMD_LLVM=1 NOLOCALS=0 IMAGE=0 GRAPH_ONE_KERNEL=1 python3.11 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
|
||||
- uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: Speed (Mac)
|
||||
@@ -189,15 +187,15 @@ jobs:
|
||||
- name: Run model inference benchmark
|
||||
run: NV=1 CAPTURE_PROCESS_REPLAY=0 NOCLANG=1 python3 test/external/external_model_benchmark.py
|
||||
- name: Test speed vs torch
|
||||
run: NV=1 CAPTURE_PROCESS_REPLAY=0 HALF=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
|
||||
run: NV=1 CAPTURE_PROCESS_REPLAY=0 HALF=1 BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
|
||||
- name: Test speed vs theoretical
|
||||
run: NV=1 IGNORE_BEAM_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
|
||||
- name: Test benchmark allreduce
|
||||
run: NV=1 python test/external/external_benchmark_multitensor_allreduce.py
|
||||
- name: Test tensor cores
|
||||
run: |
|
||||
NV=1 ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
|
||||
PTX=1 ALLOW_TF32=1 NV=1 python3 test/opt/test_tensor_cores.py
|
||||
NV=1 ALLOW_TF32=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_emulation TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
PTX=1 ALLOW_TF32=1 NV=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_emulation TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Run Tensor Core GEMM (CUDA)
|
||||
run: |
|
||||
CUDA=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul.txt
|
||||
@@ -319,13 +317,13 @@ jobs:
|
||||
run: time BENCHMARK_LOG=cifar_6gpu CAPTURE_PROCESS_REPLAY=0 NV=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
|
||||
- name: Run MLPerf resnet eval on training data
|
||||
run: time BENCHMARK_LOG=resnet_eval NV=1 MODEL=resnet python3 examples/mlperf/model_eval.py
|
||||
#- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
|
||||
# run: BENCHMARK_LOG=resnet_10steps NV=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet_one_gpu.txt
|
||||
#- name: Run 10 MLPerf ResNet50 training steps (6 gpu)
|
||||
# run: BENCHMARK_LOG=resnet_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
|
||||
- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
|
||||
run: BENCHMARK_LOG=resnet_10steps NV=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet_one_gpu.txt
|
||||
- name: Run 10 MLPerf ResNet50 training steps (6 gpu)
|
||||
run: BENCHMARK_LOG=resnet_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
|
||||
- name: Run 10 MLPerf Bert training steps (6 gpu)
|
||||
# TODO: remove BERT_LAYERS once scheduler is fast
|
||||
run: BENCHMARK_LOG=bert_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
|
||||
run: BENCHMARK_LOG=bert_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
|
||||
- uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: Speed (NVIDIA Training)
|
||||
@@ -354,12 +352,8 @@ jobs:
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Remove amdgpu
|
||||
run: sudo rmmod amdgpu || true
|
||||
- name: Cleanup running AM processes
|
||||
run: python extra/amdpci/am_smi.py --pids --kill
|
||||
#- name: Insert amdgpu
|
||||
# run: sudo modprobe amdgpu
|
||||
- name: Insert amdgpu
|
||||
run: sudo modprobe amdgpu
|
||||
- name: Symlink models and datasets
|
||||
run: |
|
||||
mkdir -p weights
|
||||
@@ -378,12 +372,12 @@ jobs:
|
||||
rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
|
||||
- name: reset process replay
|
||||
run: test/external/process_replay/reset.py
|
||||
#- name: setup perflevel
|
||||
# run: |
|
||||
# examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_red/setup.sh
|
||||
# rocm-smi
|
||||
#- name: Show off tinybox
|
||||
# run: /opt/rocm/bin/rocm-bandwidth-test
|
||||
- name: setup perflevel
|
||||
run: |
|
||||
examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_red/setup.sh
|
||||
rocm-smi
|
||||
- name: Show off tinybox
|
||||
run: /opt/rocm/bin/rocm-bandwidth-test
|
||||
# TODO: unstable on AMD
|
||||
#- name: Run model inference benchmark
|
||||
# run: LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 NOCLANG=1 python3 test/external/external_model_benchmark.py
|
||||
@@ -391,25 +385,25 @@ jobs:
|
||||
#- name: Test speed vs torch
|
||||
# run: |
|
||||
# python3 -c "import torch; print(torch.__version__)"
|
||||
# LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
|
||||
# LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
|
||||
- name: Test speed vs theoretical
|
||||
run: AMD=1 IGNORE_BEAM_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
|
||||
- name: Test tensor cores
|
||||
run: |
|
||||
AMD=1 AMD_LLVM=0 python3 test/opt/test_tensor_cores.py
|
||||
AMD=1 AMD_LLVM=1 python3 test/opt/test_tensor_cores.py
|
||||
AMD=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_emulation TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
|
||||
AMD=1 AMD_LLVM=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_emulation TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
|
||||
AMD=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
|
||||
- name: Run Tensor Core GEMM (AMD)
|
||||
run: AMD=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py | tee matmul_amd.txt
|
||||
- name: Test AMD=1
|
||||
run: DEBUG=2 AMD=1 python -m pytest -rA test/test_tiny.py
|
||||
#- name: Test HIP=1
|
||||
# run: DEBUG=2 HIP=1 python -m pytest -rA test/test_tiny.py
|
||||
- name: Test HIP=1
|
||||
run: DEBUG=2 HIP=1 python -m pytest -rA test/test_tiny.py
|
||||
# TODO: AMD compiler bug causes this to fail
|
||||
#- name: Fuzz Padded Tensor Core GEMM
|
||||
# run: HSA=1 M_START=12 M_STOP=20 M_STEP=1 N_START=12 N_STOP=20 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 DEBUG=2 python3 ./extra/gemm/fuzz_matmul.py
|
||||
#- name: Remove amdgpu
|
||||
# run: sleep 10 && sudo rmmod amdgpu # sleep a bit to let the driver unload the prev pid.
|
||||
- name: Remove amdgpu
|
||||
run: sleep 10 && sudo rmmod amdgpu # sleep a bit to let the driver unload the prev pid.
|
||||
- name: Test AM cold start time
|
||||
run: time AMD=1 AM_RESET=1 python3 test/test_tiny.py TestTiny.test_plus
|
||||
- name: Test AM warm start time
|
||||
@@ -434,8 +428,8 @@ jobs:
|
||||
run: BENCHMARK_LOG=llama3_beam_4gpu AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 4 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_four_gpu.txt
|
||||
# - name: Run LLaMA-3 8B on 6 GPUs
|
||||
# run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 6 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_six_gpu.txt
|
||||
#- name: Restore amdgpu
|
||||
# run: sudo modprobe amdgpu
|
||||
- name: Restore amdgpu
|
||||
run: sudo modprobe amdgpu
|
||||
# - name: Run LLaMA-2 70B
|
||||
# run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 2 --size 70B --shard 6 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_2_70B.txt
|
||||
- name: Run Mixtral 8x7B
|
||||
@@ -486,8 +480,6 @@ jobs:
|
||||
uses: actions/checkout@v4
|
||||
- name: Remove amdgpu
|
||||
run: sudo rmmod amdgpu || true
|
||||
- name: Cleanup running AM processes
|
||||
run: python extra/amdpci/am_smi.py --pids --kill
|
||||
- name: Symlink models and datasets
|
||||
run: |
|
||||
mkdir -p weights
|
||||
@@ -517,10 +509,17 @@ jobs:
|
||||
run: BENCHMARK_LOG=cifar_10steps_half_wino AMD=1 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
|
||||
- name: Run full CIFAR training w 1 GPU
|
||||
run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
|
||||
#- name: Run full CIFAR training steps w 6 GPUS
|
||||
# run: time BENCHMARK_LOG=cifar_6gpu AMD=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
|
||||
#- name: Run full CIFAR training steps w 6 GPUS (REMOTE)
|
||||
# run: time BENCHMARK_LOG=cifar_6gpu_remote REMOTE=1 REMOTEDEV=AMD DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu_remote.txt
|
||||
- name: Run full CIFAR training steps w 6 GPUS
|
||||
run: time BENCHMARK_LOG=cifar_6gpu AMD=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
|
||||
- name: Run MLPerf resnet eval
|
||||
run: time BENCHMARK_LOG=resnet_eval AMD=1 MODEL=resnet python3 examples/mlperf/model_eval.py
|
||||
- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
|
||||
run: BENCHMARK_LOG=resnet_10steps AMD=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet_one_gpu.txt
|
||||
- name: Run 10 MLPerf ResNet50 training steps (6 gpu)
|
||||
run: BENCHMARK_LOG=resnet_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
|
||||
- name: Run 10 MLPerf Bert training steps (6 gpu)
|
||||
# TODO: remove BERT_LAYERS once scheduler is fast
|
||||
run: BENCHMARK_LOG=bert_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
|
||||
- uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: Speed (AMD Training)
|
||||
@@ -532,55 +531,6 @@ jobs:
|
||||
train_cifar_wino.txt
|
||||
train_cifar_one_gpu.txt
|
||||
train_cifar_six_gpu.txt
|
||||
train_cifar_six_gpu_remote.txt
|
||||
- name: Run process replay tests
|
||||
run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py
|
||||
|
||||
testmlperfamdbenchmark:
|
||||
name: tinybox red MLPerf Benchmark
|
||||
runs-on: [self-hosted, Linux, tinybox]
|
||||
timeout-minutes: 30
|
||||
defaults:
|
||||
run:
|
||||
shell: bash -e -o pipefail {0}
|
||||
if: github.repository_owner == 'tinygrad'
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Remove amdgpu
|
||||
run: sudo rmmod amdgpu || true
|
||||
- name: Cleanup running AM processes
|
||||
run: python extra/amdpci/am_smi.py --pids --kill
|
||||
- name: Symlink models and datasets
|
||||
run: |
|
||||
mkdir -p weights
|
||||
ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
|
||||
ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
|
||||
ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
|
||||
ln -s /raid/weights/mixtral-8x7b-32kseqlen weights/mixtral-8x7b-32kseqlen
|
||||
ln -s /raid/weights/LLaMA-2 weights/LLaMA-2
|
||||
mkdir -p extra/datasets
|
||||
ln -s /raid/datasets/imagenet extra/datasets/imagenet
|
||||
- name: setup staging db
|
||||
if: github.ref == 'refs/heads/update_benchmark_staging'
|
||||
run: |
|
||||
echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
|
||||
rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
|
||||
- name: reset process replay
|
||||
run: test/external/process_replay/reset.py
|
||||
- name: Run MLPerf resnet eval
|
||||
run: time BENCHMARK_LOG=resnet_eval AMD=1 MODEL=resnet python3 examples/mlperf/model_eval.py
|
||||
#- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
|
||||
# run: BENCHMARK_LOG=resnet_10steps AMD=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet_one_gpu.txt
|
||||
#- name: Run 10 MLPerf ResNet50 training steps (6 gpu)
|
||||
# run: BENCHMARK_LOG=resnet_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
|
||||
- name: Run 10 MLPerf Bert training steps (6 gpu)
|
||||
# TODO: remove BERT_LAYERS once scheduler is fast
|
||||
run: BENCHMARK_LOG=bert_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
|
||||
- uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: Speed (AMD MLPerf)
|
||||
path: |
|
||||
train_resnet.txt
|
||||
train_resnet_one_gpu.txt
|
||||
train_bert.txt
|
||||
@@ -605,30 +555,22 @@ jobs:
|
||||
rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
|
||||
- name: reset process replay
|
||||
run: test/external/process_replay/reset.py
|
||||
- name: benchmark openpilot 0.9.9 driving_vision
|
||||
run: BENCHMARK_LOG=openpilot_0_9_9_vision PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
|
||||
- name: benchmark openpilot 0.9.9 driving_policy
|
||||
run: BENCHMARK_LOG=openpilot_0_9_9_policy PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
|
||||
- name: benchmark openpilot 0.9.9 dmonitoring
|
||||
run: BENCHMARK_LOG=openpilot_0_9_9_dmonitoring PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
|
||||
- name: openpilot compile3 0.9.9 driving_vision
|
||||
run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
|
||||
- name: openpilot compile3 0.9.9 driving_policy
|
||||
run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
|
||||
- name: openpilot compile3 0.9.9 dmonitoring
|
||||
run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
|
||||
- name: openpilot compile3 Space Lab policy + vision
|
||||
run: |
|
||||
PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/22aec22a10ce09384d4a4af2a0bbff08d54af7e0c888503508f356fae4ff0e29
|
||||
PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/c824f68646a3b94f117f01c70dc8316fb466e05fbd42ccdba440b8a8dc86914b
|
||||
- name: benchmark MobileNetV2 on DSP
|
||||
run: |
|
||||
# generate quantized weights
|
||||
ln -s /data/home/tiny/tinygrad/extra/datasets/imagenet extra/datasets/imagenet
|
||||
ln -s /data/home/tiny/tinygrad/testsig-*.so .
|
||||
PYTHONPATH=. CC=clang-19 CPU=1 QUANT=1 CNT=0 python3 examples/test_onnx_imagenet.py https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx /tmp/model.quant.onnx
|
||||
# benchmark on DSP with NOOPT=1, the devectorizer has issues
|
||||
PYTHONPATH=. CC=clang-19 DSP=1 DONT_REALIZE_EXPAND=1 NOOPT=1 CNT=2 DEBUG=2 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
|
||||
- name: validate openpilot 0.9.7
|
||||
run: PYTHONPATH=. FLOAT16=0 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx | tee openpilot_image_0_9_7.txt
|
||||
- name: benchmark openpilot 0.9.4
|
||||
run: BENCHMARK_LOG=openpilot_0_9_4 PYTHONPATH=. QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx | tee openpilot_0_9_4.txt
|
||||
- name: benchmark openpilot 0.9.7
|
||||
run: BENCHMARK_LOG=openpilot_0_9_7 PYTHONPATH=. QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx | tee openpilot_0_9_7.txt
|
||||
- name: benchmark openpilot w IMAGE=2 0.9.4
|
||||
run: BENCHMARK_LOG=openpilot_0_9_4_image PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx | tee openpilot_image_0_9_4.txt
|
||||
- name: benchmark openpilot w IMAGE=2 0.9.7
|
||||
run: BENCHMARK_LOG=openpilot_0_9_7_image PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx | tee openpilot_image_0_9_7.txt
|
||||
- name: openpilot compile3 0.9.7
|
||||
run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx
|
||||
- name: openpilot compile3 0.9.7+ tomb raider
|
||||
run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/e8bea2c78ffa92685ece511e9b554122aaf1a79d/selfdrive/modeld/models/supercombo.onnx
|
||||
- name: openpilot dmonitoring compile3 0.9.7
|
||||
run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/dmonitoring_model.onnx
|
||||
- name: Run process replay tests
|
||||
run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py
|
||||
- uses: actions/upload-artifact@v4
|
||||
@@ -641,136 +583,3 @@ jobs:
|
||||
openpilot_0_9_7.txt
|
||||
openpilot_image_0_9_4.txt
|
||||
openpilot_image_0_9_7.txt
|
||||
|
||||
testreddriverbenchmark:
|
||||
name: AM Benchmark
|
||||
runs-on: [self-hosted, Linux, tinyboxrandom]
|
||||
timeout-minutes: 15
|
||||
defaults:
|
||||
run:
|
||||
shell: bash -e -o pipefail {0}
|
||||
if: github.repository_owner == 'tinygrad'
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Remove amd modules
|
||||
run: ./extra/hcq/hcq_smi.py amd rmmod
|
||||
- name: Kill stale pids
|
||||
run: ./extra/hcq/hcq_smi.py amd kill_pids
|
||||
- name: Symlink models and datasets
|
||||
run: |
|
||||
mkdir -p weights
|
||||
ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
|
||||
ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
|
||||
ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
|
||||
ln -s /raid/weights/mixtral-8x7b-32kseqlen weights/mixtral-8x7b-32kseqlen
|
||||
ln -s /raid/weights/LLaMA-2 weights/LLaMA-2
|
||||
mkdir -p extra/datasets
|
||||
ln -s /raid/datasets/imagenet extra/datasets/imagenet
|
||||
- name: setup staging db
|
||||
if: github.ref == 'refs/heads/update_benchmark_staging'
|
||||
run: |
|
||||
echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
|
||||
rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
|
||||
- name: reset process replay
|
||||
run: test/external/process_replay/reset.py
|
||||
- name: Test driver cold start time
|
||||
run: time DEBUG=3 AMD=1 AM_RESET=1 python3 test/test_tiny.py TestTiny.test_plus
|
||||
- name: Test driver warm start time
|
||||
run: time DEBUG=3 AMD=1 python3 test/test_tiny.py TestTiny.test_plus
|
||||
# Fails on 9070
|
||||
# - name: Test tensor cores
|
||||
# run: |
|
||||
# AMD=1 AMD_LLVM=0 python3 test/test_linearizer.py test/opt/test_tensor_cores.py
|
||||
# AMD=1 AMD_LLVM=1 python3 test/test_linearizer.py test/opt/test_tensor_cores.py
|
||||
# AMD=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
|
||||
- name: Run Tensor Core GEMM (AMD)
|
||||
run: AMD=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py | tee am_matmul_amd.txt
|
||||
- name: Test AMD=1
|
||||
run: DEBUG=2 AMD=1 python -m pytest -rA test/test_tiny.py
|
||||
- name: Test DISK copy time
|
||||
run: AMD=1 TESTFILE=/raid/downloads/llama3-8b-sfr/model-00001-of-00004.safetensors python3 test/external/external_benchmark_disk_raw.py
|
||||
- name: Test CPU copy time
|
||||
run: |
|
||||
AMD=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyDefaulttoCPUJit
|
||||
AMD=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyCPUtoDefaultJit
|
||||
- name: Run full CIFAR training w 1 GPU
|
||||
run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee am_train_cifar_one_gpu.txt
|
||||
# TODO: enable
|
||||
# - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
|
||||
# run: BENCHMARK_LOG=resnet_10steps AMD=1 MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee am_train_resnet_one_gpu.txt
|
||||
- name: Run 10 MLPerf Bert training steps (1 gpu)
|
||||
# TODO: remove BERT_LAYERS once scheduler is fast
|
||||
run: BENCHMARK_LOG=bert_10steps AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee am_train_bert_one_gpu.txt
|
||||
- uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: Speed (AM Driver)
|
||||
path: |
|
||||
am_matmul_amd.txt
|
||||
am_train_cifar_one_gpu.txt
|
||||
am_train_resnet_one_gpu.txt
|
||||
am_train_bert_one_gpu.txt
|
||||
- name: Run process replay tests
|
||||
run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py
|
||||
|
||||
testgreendriverbenchmark:
|
||||
name: NV Benchmark
|
||||
runs-on: [self-hosted, Linux, tinyboxrandom]
|
||||
timeout-minutes: 15
|
||||
defaults:
|
||||
run:
|
||||
shell: bash -e -o pipefail {0}
|
||||
if: github.repository_owner == 'tinygrad'
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Remove nv modules
|
||||
run: ./extra/hcq/hcq_smi.py nv rmmod
|
||||
- name: Kill stale pids
|
||||
run: ./extra/hcq/hcq_smi.py nv kill_pids
|
||||
- name: Symlink models and datasets
|
||||
run: |
|
||||
mkdir -p weights
|
||||
ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
|
||||
ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
|
||||
ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
|
||||
ln -s /raid/weights/mixtral-8x7b-32kseqlen weights/mixtral-8x7b-32kseqlen
|
||||
ln -s /raid/weights/LLaMA-2 weights/LLaMA-2
|
||||
mkdir -p extra/datasets
|
||||
ln -s /raid/datasets/imagenet extra/datasets/imagenet
|
||||
- name: setup staging db
|
||||
if: github.ref == 'refs/heads/update_benchmark_staging'
|
||||
run: |
|
||||
echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
|
||||
rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
|
||||
- name: reset process replay
|
||||
run: test/external/process_replay/reset.py
|
||||
- name: Test driver start time
|
||||
run: time DEBUG=3 NV=1 python3 test/test_tiny.py TestTiny.test_plus
|
||||
- name: Test tensor cores
|
||||
run: NV=1 ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
|
||||
- name: Test DISK copy time
|
||||
run: NV=1 TESTFILE=/raid/downloads/llama3-8b-sfr/model-00001-of-00004.safetensors python3 test/external/external_benchmark_disk_raw.py
|
||||
- name: Test CPU copy time
|
||||
run: |
|
||||
NV=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyDefaulttoCPUJit
|
||||
NV=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyCPUtoDefaultJit
|
||||
- name: Test LLAMA-3
|
||||
run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --benchmark --temperature 0 | tee nv_llama3_beam.txt
|
||||
- name: Run full CIFAR training w 1 GPU
|
||||
run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee nv_train_cifar_one_gpu.txt
|
||||
#- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
|
||||
# run: BENCHMARK_LOG=resnet_10steps NV=1 MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee nv_train_resnet_one_gpu.txt
|
||||
- name: Run 10 MLPerf Bert training steps (1 gpu)
|
||||
# TODO: remove BERT_LAYERS once scheduler is fast
|
||||
run: BENCHMARK_LOG=bert_10steps NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee nv_train_bert_one_gpu.txt
|
||||
- uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: Speed (NV Driver)
|
||||
path: |
|
||||
nv_llama3_beam.txt
|
||||
nv_train_cifar_one_gpu.txt
|
||||
nv_train_resnet_one_gpu.txt
|
||||
nv_train_bert_one_gpu.txt
|
||||
- name: Run process replay tests
|
||||
run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py
|
||||
|
||||
@@ -1,34 +0,0 @@
|
||||
name: Benchmark with kernel search
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- update_benchmark_search
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
run_script_job:
|
||||
runs-on: [self-hosted, Linux, tinybox]
|
||||
if: github.repository_owner == 'tinygrad'
|
||||
timeout-minutes: 100
|
||||
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Remove amdgpu
|
||||
run: sudo rmmod amdgpu || true
|
||||
- name: Cleanup running AM processes
|
||||
run: python extra/amdpci/am_smi.py --pids --kill
|
||||
- name: Run SDXL with new search
|
||||
# TODO: GCVM_L2_PROTECTION_FAULT_STATUS with llvm19
|
||||
run: |
|
||||
BENCHMARK_LOG=search_sdxl PYTHONPATH=. AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_CACHE=1 python examples/sdxl.py --noshow --timing --seed 0
|
||||
- name: Run SDXL with cached search
|
||||
run: |
|
||||
BENCHMARK_LOG=search_sdxl_cached PYTHONPATH=. AMD=1 JITBEAM=2 python examples/sdxl.py --noshow --timing --seed 0
|
||||
- name: Run winograd cifar with new search
|
||||
run: |
|
||||
BENCHMARK_LOG=search_wino_cifar WINO=1 DEFAULT_FLOAT=HALF JITBEAM=4 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_CACHE=1 BS=1024 STEPS=500 python examples/hlb_cifar10.py
|
||||
- name: Run winograd cifar with cached search
|
||||
run: |
|
||||
BENCHMARK_LOG=search_wino_cifar_cached WINO=1 DEFAULT_FLOAT=HALF JITBEAM=4 BS=1024 STEPS=500 python examples/hlb_cifar10.py
|
||||
@@ -1,30 +1,21 @@
|
||||
name: Run MLPerf Training
|
||||
|
||||
on:
|
||||
schedule:
|
||||
- cron: '5 8 * * *' # Runs at 08:05 UTC (12:05 AM Pacific Time)
|
||||
push:
|
||||
branches:
|
||||
- update_mlperf
|
||||
# schedule:
|
||||
# - cron: '0 */1 * * *'
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
run_script_job:
|
||||
runs-on: [self-hosted, Linux, tinybox]
|
||||
if: github.repository_owner == 'tinygrad'
|
||||
timeout-minutes: 360
|
||||
runs-on: [self-hosted, Linux, tinyboxgreen]
|
||||
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Cleanup running AM processes
|
||||
run: python extra/amdpci/am_smi.py --pids --kill
|
||||
- name: Symlink datasets
|
||||
run: |
|
||||
mkdir -p extra/datasets
|
||||
ln -s /raid/datasets/imagenet extra/datasets/imagenet
|
||||
- name: Run resnet
|
||||
run: |
|
||||
rm "~/.cache/tinygrad/cache_mlperf.db" || true
|
||||
BENCHMARK_LOG=mlpert_train_resnet LOGMLPERF=0 CACHEDB="~/.cache/tinygrad/cache_mlperf.db" examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
|
||||
rm "~/.cache/tinygrad/cache_mlperf.db"
|
||||
LOGMLPERF=0 examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/resnet/implementations/tinybox_green/run_and_time.sh
|
||||
+175
-352
@@ -1,13 +1,9 @@
|
||||
name: Unit Tests
|
||||
env:
|
||||
# increment this when downloads substantially change to avoid the internet
|
||||
DOWNLOAD_CACHE_VERSION: '12'
|
||||
PYTHON_CACHE_VERSION: '3'
|
||||
APT_CACHE_VERSION: '1'
|
||||
BUILD_CACHE_VERSION: '1'
|
||||
DOWNLOAD_CACHE_VERSION: '9'
|
||||
CAPTURE_PROCESS_REPLAY: 1
|
||||
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
PYTHONPATH: ${{ github.workspace }}
|
||||
|
||||
on:
|
||||
push:
|
||||
@@ -33,9 +29,9 @@ jobs:
|
||||
- name: External Benchmark Schedule
|
||||
run: PYTHONPATH="." python3 test/external/external_benchmark_schedule.py
|
||||
- name: Speed Test
|
||||
run: LLVM=1 python3 test/speed/external_test_speed_v_torch.py
|
||||
run: LLVM=1 python3 test/test_speed_v_torch.py
|
||||
- name: Speed Test (BEAM=2)
|
||||
run: BEAM=2 LLVM=1 python3 test/speed/external_test_speed_v_torch.py
|
||||
run: BEAM=2 LLVM=1 python3 test/test_speed_v_torch.py
|
||||
|
||||
docs:
|
||||
name: Docs
|
||||
@@ -49,11 +45,6 @@ jobs:
|
||||
with:
|
||||
deps: docs
|
||||
pydeps: "capstone"
|
||||
- name: Build wheel and show size
|
||||
run: |
|
||||
pip install build
|
||||
python -m build --wheel --outdir dist
|
||||
ls -lh dist/*.whl
|
||||
- name: Use as an external package
|
||||
run: |
|
||||
mkdir $HOME/test_external_dir
|
||||
@@ -62,6 +53,11 @@ jobs:
|
||||
source venv/bin/activate
|
||||
pip install $GITHUB_WORKSPACE
|
||||
python -c "from tinygrad.tensor import Tensor; print(Tensor([1,2,3,4,5]))"
|
||||
# Test using VIZ=1 after package installation
|
||||
VIZ=1 python -c "from tinygrad.tensor import Tensor; Tensor([1,2,3,4,5]).realize()" & VIZ_PID=$!
|
||||
echo "started VIZ server at: $(ps -p "$VIZ_PID" -o pid=)"
|
||||
i=0; while ((i++ < 10)); do curl -sSf localhost:8000 > /dev/null && break || { echo "VIZ verification attempt $i/10"; sleep 1; }; done; ((i > 10)) && echo "Could not verify VIZ server" && exit 1
|
||||
kill $VIZ_PID
|
||||
pip install mypy
|
||||
mypy -c "from tinygrad.tensor import Tensor; print(Tensor([1,2,3,4,5]))"
|
||||
- name: Run beautiful_mnist with tinygrad only
|
||||
@@ -80,7 +76,7 @@ jobs:
|
||||
python docs/abstractions2.py
|
||||
python docs/abstractions3.py
|
||||
- name: Test Quickstart
|
||||
run: awk '/```python/{flag=1;next}/```/{flag=0}flag' docs/quickstart.md > quickstart.py && python quickstart.py
|
||||
run: awk '/```python/{flag=1;next}/```/{flag=0}flag' docs/quickstart.md > quickstart.py && PYTHONPATH=. python quickstart.py
|
||||
- name: Test DEBUG
|
||||
run: DEBUG=100 python3 -c "from tinygrad import Tensor; N = 1024; a, b = Tensor.rand(N, N), Tensor.rand(N, N); c = (a.reshape(N, 1, N) * b.T.reshape(1, N, N)).sum(axis=2); print((c.numpy() - (a.numpy() @ b.numpy())).mean())"
|
||||
- name: Compile EfficientNet to C and test it
|
||||
@@ -91,7 +87,7 @@ jobs:
|
||||
|
||||
autogen:
|
||||
name: Autogen
|
||||
runs-on: ubuntu-24.04
|
||||
runs-on: ubuntu-22.04
|
||||
timeout-minutes: 15
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
@@ -104,8 +100,6 @@ jobs:
|
||||
cuda: 'true'
|
||||
webgpu: 'true'
|
||||
llvm: 'true'
|
||||
- name: Install autogen support packages
|
||||
run: sudo apt-get install -y --no-install-recommends llvm-14-dev libclang-14-dev
|
||||
- name: Verify OpenCL autogen
|
||||
run: |
|
||||
cp tinygrad/runtime/autogen/opencl.py /tmp/opencl.py.bak
|
||||
@@ -140,13 +134,10 @@ jobs:
|
||||
run: |
|
||||
cp tinygrad/runtime/autogen/libc.py /tmp/libc.py.bak
|
||||
cp tinygrad/runtime/autogen/io_uring.py /tmp/io_uring.py.bak
|
||||
cp tinygrad/runtime/autogen/ib.py /tmp/ib.py.bak
|
||||
./autogen_stubs.sh libc
|
||||
./autogen_stubs.sh io_uring
|
||||
./autogen_stubs.sh ib
|
||||
diff /tmp/libc.py.bak tinygrad/runtime/autogen/libc.py
|
||||
diff /tmp/io_uring.py.bak tinygrad/runtime/autogen/io_uring.py
|
||||
diff /tmp/ib.py.bak tinygrad/runtime/autogen/ib.py
|
||||
- name: Verify WebGPU autogen
|
||||
run: |
|
||||
cp tinygrad/runtime/autogen/webgpu.py /tmp/webgpu.py.bak
|
||||
@@ -183,19 +174,19 @@ jobs:
|
||||
pip3 install --upgrade --force-reinstall ruff==0.11.0
|
||||
python3 -m ruff check extra/torch_backend/backend.py
|
||||
- name: Test one op
|
||||
run: FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add
|
||||
run: PYTHONPATH=. FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add
|
||||
- name: Test ResNet-18
|
||||
run: DEBUG=2 python3 extra/torch_backend/example.py
|
||||
run: PYTHONPATH=. DEBUG=2 python3 extra/torch_backend/example.py
|
||||
- name: My (custom) tests
|
||||
run: python3 extra/torch_backend/test.py
|
||||
run: PYTHONPATH=. python3 extra/torch_backend/test.py
|
||||
- name: Test one op in torch tests
|
||||
run: DEBUG=2 python3 extra/torch_backend/torch_tests.py TestTinyBackendPRIVATEUSE1.test_unary_log_tiny_float32
|
||||
run: PYTHONPATH=. DEBUG=2 python3 extra/torch_backend/torch_tests.py TestTinyBackendPRIVATEUSE1.test_unary_log_tiny_float32
|
||||
- name: Test Ops with TINY_BACKEND
|
||||
run: LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
|
||||
run: PYTHONPATH=. LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
|
||||
- name: Test in-place operations on views
|
||||
run: TORCH_DEBUG=1 python3 extra/torch_backend/test_inplace.py
|
||||
run: PYTHONPATH=. TORCH_DEBUG=1 python3 extra/torch_backend/test_inplace.py
|
||||
- name: Test multi-gpu
|
||||
run: LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py
|
||||
run: PYTHONPATH=. LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py
|
||||
|
||||
torchbackendmore:
|
||||
name: Torch Backend Tests More
|
||||
@@ -217,9 +208,9 @@ jobs:
|
||||
sudo apt update || true
|
||||
sudo apt install -y --no-install-recommends ninja-build
|
||||
- name: Test beautiful_mnist in torch with TINY_BACKEND
|
||||
run: SPLIT_REDUCEOP=0 FUSE_ARANGE=1 LLVM=1 TARGET_EVAL_ACC_PCT=96.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
|
||||
run: SPLIT_REDUCEOP=0 FUSE_ARANGE=1 PYTHONPATH=. LLVM=1 TARGET_EVAL_ACC_PCT=96.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
|
||||
- name: Test some torch tests (expect failure)
|
||||
run: python3 -m pytest extra/torch_backend/torch_tests.py -v --tb=no || true
|
||||
run: PYTHONPATH=. python3 -m pytest extra/torch_backend/torch_tests.py -v --tb=no || true
|
||||
|
||||
tc:
|
||||
name: Tensor Core tests
|
||||
@@ -241,45 +232,63 @@ jobs:
|
||||
IMAGE=2 PYTHON=1 python3 test/test_ops.py TestOps.test_simple_conv2d
|
||||
- name: Test emulated METAL tensor cores
|
||||
run: |
|
||||
DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_big_gemm
|
||||
DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
|
||||
DEBUG=2 EMULATE_METAL=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_big_gemm
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_METAL=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_METAL=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Test emulated AMX tensor cores
|
||||
run: DEBUG=2 AMX=1 EMULATE=AMX FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
|
||||
run: PYTHONPATH=. DEBUG=2 AMX=1 EMULATE_AMX=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
|
||||
- name: Test emulated AMD tensor cores
|
||||
run: |
|
||||
DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Test emulated AMD MFMA tensor cores
|
||||
run: |
|
||||
DEBUG=2 EMULATE=AMD_MFMA FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD_MFMA FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_MFMA=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_MFMA=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_MFMA=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Test emulated AMD RDNA4 tensor cores
|
||||
run: |
|
||||
DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Test emulated CUDA tensor cores
|
||||
run: |
|
||||
DEBUG=2 EMULATE=CUDA FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
|
||||
DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
|
||||
DEBUG=2 EMULATE=CUDA_SM75 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
|
||||
PYTHONPATH="." DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
|
||||
DEBUG=2 EMULATE_CUDA=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
|
||||
DEBUG=2 EMULATE_CUDA=1 ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
|
||||
DEBUG=2 EMULATE_CUDA_SM75=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
|
||||
PYTHONPATH="." DEBUG=2 EMULATE_CUDA=1 ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH="." DEBUG=2 EMULATE_CUDA=1 ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Test emulated INTEL OpenCL tensor cores
|
||||
run: DEBUG=2 EMULATE=INTEL FORWARD_ONLY=1 PYTHON=1 HALF=1 N=64 python3 ./extra/gemm/simple_matmul.py
|
||||
- name: Test emulated AMX tensor cores
|
||||
run: DEBUG=2 AMX=1 EMULATE=AMX FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
|
||||
run: DEBUG=2 EMULATE_INTEL=1 FORWARD_ONLY=1 PYTHON=1 HALF=1 N=64 python3 ./extra/gemm/simple_matmul.py
|
||||
- name: Full test tensor cores
|
||||
run: |
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_METAL=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_CUDA=1 ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_INTEL=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 AMX=1 EMULATE_AMX=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
- name: Test tensor cores (TC=3)
|
||||
run: |
|
||||
PYTHONPATH=. DEBUG=2 PYTHON=1 EMULATE_METAL=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores_emulation
|
||||
PYTHONPATH=. DEBUG=2 PYTHON=1 EMULATE_AMD=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores_emulation
|
||||
PYTHONPATH=. DEBUG=2 PYTHON=1 EMULATE_AMD_MFMA=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores_emulation
|
||||
PYTHONPATH=. DEBUG=2 PYTHON=1 EMULATE_CUDA=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores_emulation
|
||||
PYTHONPATH=. DEBUG=2 PYTHON=1 EMULATE_INTEL=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores_emulation
|
||||
PYTHONPATH=. DEBUG=2 PYTHON=1 EMULATE_AMX=1 AMX=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores_emulation
|
||||
- name: Test device flop counts
|
||||
run: |
|
||||
DEBUG=2 EMULATE=METAL PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
DEBUG=2 EMULATE=AMD PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
DEBUG=2 EMULATE=CUDA PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
DEBUG=2 EMULATE=INTEL PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
DEBUG=2 AMX=1 EMULATE=AMX PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStats.test_simple_matmul
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_METAL=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_CUDA=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_INTEL=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
PYTHONPATH=. DEBUG=2 AMX=1 EMULATE_AMX=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStats.test_simple_matmul
|
||||
|
||||
bepython:
|
||||
name: Python Backend
|
||||
@@ -296,15 +305,17 @@ jobs:
|
||||
key: be-minimal
|
||||
deps: testing_minimal
|
||||
- name: Test dtype with Python emulator
|
||||
run: DEBUG=1 PYTHON=1 python3 -m pytest -n=auto test/test_dtype.py test/test_dtype_alu.py
|
||||
run: DEBUG=1 PYTHONPATH=. PYTHON=1 python3 -m pytest -n=auto test/test_dtype.py test/test_dtype_alu.py
|
||||
- name: Test ops with Python emulator
|
||||
run: DEBUG=2 SKIP_SLOW_TEST=1 PYTHON=1 python3 -m pytest -n=auto test/test_ops.py --durations=20
|
||||
run: DEBUG=2 PYTHON=1 python3 -m pytest -n=auto test/test_ops.py -k "not (test_split or test_simple_cumsum or test_cumsum or test_einsum or test_dot or test_dot_1d or test_big_gemm or test_broadcastdot or test_multidot or test_var_axis or test_std_axis or test_broadcast_full or test_broadcast_partial or test_simple_conv3d or test_dilated_conv_transpose2d or test_simple_conv_transpose3d or test_large_input_conv2d or test_max_pool2d or test_max_pool2d_simple or test_max_pool2d_bigger_stride or test_avg_pool2d or test_cat or test_scaled_product_attention or test_scaled_product_attention_causal or test_slice_fancy_indexing_dim_inject_none or test_slice_fancy_indexing_list_indices or test_slice_fancy_indexing_no_dim_collapse or test_slice_fancy_indexing_tuple_indices or test_slice_fancy_indexing_list_with_tensors or test_slice_fancy_indexing_dim_collapse_int or test_interpolate_bilinear or test_interpolate_bilinear_corners_aligned or test_scaled_dot_product_attention or test_cummax or test_simple_cummax or test_logcumsumexp or test_sort or test_cumprod)" --durations=20
|
||||
- name: Test uops with Python emulator
|
||||
run: PYTHON=1 python3 -m pytest test/test_uops.py --durations=20
|
||||
- name: Test symbolic with Python emulator
|
||||
run: PYTHON=1 python3 test/test_symbolic_ops.py
|
||||
run: PYTHONPATH=. PYTHON=1 python3 test/test_symbolic_ops.py
|
||||
- name: test_linearizer_failures with Python emulator
|
||||
run: PYTHONPATH=. PYTHON=1 python3 -m pytest -rA test/test_linearizer_failures.py::TestLinearizerFailures::test_failure_1
|
||||
- name: test_renderer_failures with Python emulator
|
||||
run: PYTHON=1 python3 -m pytest -rA test/test_renderer_failures.py::TestRendererFailures
|
||||
run: PYTHONPATH=. PYTHON=1 python3 -m pytest -rA test/test_renderer_failures.py::TestRendererFailures
|
||||
|
||||
linter:
|
||||
name: Linters
|
||||
@@ -327,20 +338,16 @@ jobs:
|
||||
run: |
|
||||
pip3 install --upgrade --force-reinstall ruff==0.11.0
|
||||
python3 -m ruff check .
|
||||
python3 -m ruff check examples/mlperf/ --ignore E501
|
||||
python3 -m ruff check examples/mlperf/model_train.py --ignore E501
|
||||
- name: Lint tinygrad with pylint
|
||||
run: python -m pylint tinygrad/
|
||||
- name: Run mypy
|
||||
run: |
|
||||
python -m mypy --strict-equality --lineprecision-report .
|
||||
cat lineprecision.txt
|
||||
- name: Run TYPED=1
|
||||
run: TYPED=1 python -c "import tinygrad"
|
||||
run: python -m mypy --strict-equality --lineprecision-report . && cat lineprecision.txt
|
||||
|
||||
unittest:
|
||||
name: Unit Tests
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 15
|
||||
timeout-minutes: 10
|
||||
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
@@ -349,32 +356,20 @@ jobs:
|
||||
uses: ./.github/actions/setup-tinygrad
|
||||
with:
|
||||
key: unittest-12
|
||||
pydeps: "pillow"
|
||||
deps: testing_unit
|
||||
- name: Test README
|
||||
run: awk '/```python/{flag=1;next}/```/{flag=0}flag' README.md > README.py && python README.py
|
||||
run: awk '/```python/{flag=1;next}/```/{flag=0}flag' README.md > README.py && PYTHONPATH=. python README.py
|
||||
- name: Run unit tests
|
||||
run: PYTHONPATH="." python -m pytest -n=auto test/unit/ --durations=20
|
||||
run: PYTHONPATH="." python -m pytest -n=auto test/unit/
|
||||
- name: Run targetted tests on NULL backend
|
||||
run: PYTHONPATH="." NULL=1 python3 test/test_multitensor.py TestMultiTensor.test_data_parallel_resnet_train_step
|
||||
- name: Run SDXL on NULL backend
|
||||
run: MAX_BUFFER_SIZE=0 PYTHONPATH="." NULL=1 DEBUG=1 python3 examples/sdxl.py --seed 0 --noshow --timing --fakeweights
|
||||
# TODO: support fake weights
|
||||
#- name: Run LLaMA 7B on 4 fake devices
|
||||
# run: NULL=1 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 3 --temperature 0 --timing
|
||||
- name: Run GC tests
|
||||
run: PYTHONPATH="." python test/external/external_uop_gc.py
|
||||
- name: Run process replay tests
|
||||
uses: ./.github/actions/process-replay
|
||||
- name: Regen dataset on test_tiny
|
||||
run: |
|
||||
test/external/process_replay/reset.py
|
||||
CAPTURE_PROCESS_REPLAY=1 python test/test_tiny.py TestTiny.test_plus
|
||||
python extra/optimization/extract_dataset.py
|
||||
gzip -c /tmp/sops > extra/datasets/sops.gz
|
||||
#DEBUG=1 MIN_ASTS=1 python extra/optimization/get_action_space.py
|
||||
- name: Repo line count < 18000 lines
|
||||
run: MAX_LINE_COUNT=18000 python sz.py
|
||||
- name: Repo line count < 13500 lines
|
||||
run: MAX_LINE_COUNT=13500 python sz.py
|
||||
|
||||
fuzzing:
|
||||
name: Fuzzing
|
||||
@@ -386,8 +381,8 @@ jobs:
|
||||
- name: Setup Environment
|
||||
uses: ./.github/actions/setup-tinygrad
|
||||
with:
|
||||
key: fuzzing-unit
|
||||
deps: testing_unit
|
||||
key: fuzzing-minimal
|
||||
deps: testing_minimal
|
||||
- name: Fuzz Test symbolic
|
||||
run: python test/external/fuzz_symbolic.py
|
||||
- name: Fuzz Test fast idiv
|
||||
@@ -396,8 +391,6 @@ jobs:
|
||||
run: |
|
||||
PYTHONPATH="." python test/external/fuzz_shapetracker.py
|
||||
PYTHONPATH="." python test/external/fuzz_shapetracker_math.py
|
||||
- name: Fuzz Test shape ops
|
||||
run: python test/external/fuzz_shape_ops.py
|
||||
|
||||
testgpuimage:
|
||||
name: 'GPU IMAGE Tests'
|
||||
@@ -422,38 +415,13 @@ jobs:
|
||||
run: |
|
||||
PYTHONPATH="." GPU=1 IMAGE=2 python -m pytest -n=auto test/test_ops.py --durations=20
|
||||
PYTHONPATH="." GPU=1 IMAGE=2 python3 test/models/test_end2end.py TestEnd2End.test_linear_mnist
|
||||
- name: Run fused optimizer tests
|
||||
run: PYTHONPATH="." GPU=1 FUSE_OPTIM=1 python -m pytest -n=auto test/models/test_mnist.py
|
||||
- name: Run process replay tests
|
||||
uses: ./.github/actions/process-replay
|
||||
|
||||
testgendataset:
|
||||
name: 'GPU Generate Kernel Dataset'
|
||||
runs-on: ubuntu-22.04
|
||||
timeout-minutes: 10
|
||||
env:
|
||||
IGNORE_OOB: 0
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Setup Environment
|
||||
uses: ./.github/actions/setup-tinygrad
|
||||
with:
|
||||
key: gen-dataset
|
||||
deps: testing_minimal
|
||||
opencl: 'true'
|
||||
- name: Generate Dataset
|
||||
run: PYTHONPATH="." extra/optimization/generate_dataset.sh
|
||||
- name: Upload artifact
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: sops.gz
|
||||
path: /tmp/sops.gz
|
||||
|
||||
testopenpilot:
|
||||
name: 'openpilot Compile Tests'
|
||||
runs-on: ubuntu-22.04
|
||||
timeout-minutes: 15
|
||||
timeout-minutes: 10
|
||||
env:
|
||||
IGNORE_OOB: 0
|
||||
steps:
|
||||
@@ -468,52 +436,18 @@ jobs:
|
||||
llvm: 'true'
|
||||
- name: Test openpilot model kernel count and gate usage
|
||||
run: |
|
||||
PYTHONPATH="." ALLOWED_KERNEL_COUNT=208 ALLOWED_READ_IMAGE=2175 ALLOWED_GATED_READ_IMAGE=16 FLOAT16=0 GPU=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx
|
||||
PYTHONPATH="." ALLOWED_KERNEL_COUNT=209 ALLOWED_READ_IMAGE=2137 ALLOWED_GATED_READ_IMAGE=29 FLOAT16=0 GPU=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx
|
||||
- name: Test openpilot alt model correctness (float32)
|
||||
run: PYTHONPATH="." FLOAT16=0 DEBUGCL=1 GPU=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/3799fe46b3a629e491d4b8498b8ae83e4c88c304/selfdrive/modeld/models/supercombo.onnx
|
||||
- name: Test openpilot fastvits model correctness (float32)
|
||||
run: PYTHONPATH="." FLOAT16=0 DEBUGCL=1 GPU=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
|
||||
# - name: Test openpilot simple_plan vision model correctness (float32)
|
||||
# run: PYTHONPATH="." FLOAT16=0 DEBUGCL=1 GPU=1 IMAGE=2 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/35ff4f4577002f2685e50c8346addae33fe8da27a41dd4d6a0f14d1f4b1af81b
|
||||
- name: Test openpilot LLVM compile
|
||||
run: PYTHONPATH="." LLVM=1 LLVMOPT=1 JIT=2 BEAM=0 IMAGE=0 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
|
||||
- name: Test openpilot compile4
|
||||
run: PYTHONPATH="." NOLOCALS=1 GPU=1 IMAGE=2 FLOAT16=1 DEBUG=2 python3 examples/openpilot/compile4.py
|
||||
- name: Run process replay tests
|
||||
uses: ./.github/actions/process-replay
|
||||
|
||||
testonnxcpu:
|
||||
name: 'ONNX (CPU) Tests'
|
||||
runs-on: ubuntu-22.04
|
||||
timeout-minutes: 20
|
||||
env:
|
||||
IGNORE_OOB: 0
|
||||
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Setup Environment
|
||||
uses: ./.github/actions/setup-tinygrad
|
||||
with:
|
||||
key: onnxoptc
|
||||
deps: testing
|
||||
python-version: '3.11'
|
||||
llvm: 'true'
|
||||
- name: Test ONNX (CPU)
|
||||
run: CPU=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
|
||||
- name: Test ONNX (LLVM)
|
||||
run: LLVM=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
|
||||
- name: Test ONNX Runner (CPU)
|
||||
run: CPU=1 python3 test/external/external_test_onnx_runner.py
|
||||
- name: Test Additional ONNX Ops (CPU)
|
||||
run: CPU=1 python3 test/external/external_test_onnx_ops.py
|
||||
- name: Test Quantize ONNX
|
||||
run: CPU=1 python3 test/test_quantize_onnx.py
|
||||
- name: Run process replay tests
|
||||
uses: ./.github/actions/process-replay
|
||||
|
||||
testopencl:
|
||||
name: 'ONNX (GPU)+Optimization Tests'
|
||||
name: 'ONNX+Optimization Tests'
|
||||
runs-on: ubuntu-22.04
|
||||
timeout-minutes: 20
|
||||
env:
|
||||
@@ -526,43 +460,42 @@ jobs:
|
||||
uses: ./.github/actions/setup-tinygrad
|
||||
with:
|
||||
key: onnxoptl
|
||||
deps: testing
|
||||
pydeps: "tensorflow==2.15.1 tensorflow_addons"
|
||||
deps: testing,testing_tf
|
||||
python-version: '3.11'
|
||||
opencl: 'true'
|
||||
llvm: 'true'
|
||||
- name: Test ONNX (GPU)
|
||||
run: GPU=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
|
||||
#- name: Test Optimization Helpers
|
||||
# run: DEBUG=1 python3 extra/optimization/test_helpers.py
|
||||
#- name: Test Action Space
|
||||
# run: DEBUG=1 GPU=1 python3 extra/optimization/get_action_space.py
|
||||
- name: Test ONNX (CPU)
|
||||
run: CPU=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
|
||||
- name: Test ONNX (LLVM)
|
||||
run: LLVM=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
|
||||
- name: Test Additional ONNX Ops (CPU)
|
||||
run: CPU=1 PYTHONPATH=. python3 test/external/external_test_onnx_ops.py
|
||||
- name: Test Quantize ONNX
|
||||
run: CPU=1 PYTHONPATH=. python3 test/test_quantize_onnx.py
|
||||
- name: Run REMOTE=1 Test
|
||||
run: |
|
||||
REMOTEDEV=CPU REMOTE=1 python3 -m pytest test/test_tiny.py test/test_jit.py test/test_multitensor.py
|
||||
REMOTEDEV=GPU REMOTE=1 python3 -m pytest test/test_tiny.py test/test_image_dtype.py test/test_jit.py
|
||||
REMOTEDEV=GPU IMAGE=2 REMOTE=1 python3 -m pytest test/test_tiny.py test/test_image_dtype.py
|
||||
- name: Test Optimization Helpers
|
||||
run: PYTHONPATH="." DEBUG=1 python3 extra/optimization/test_helpers.py
|
||||
- name: Test Action Space
|
||||
run: PYTHONPATH="." DEBUG=1 GPU=1 python3 extra/optimization/get_action_space.py
|
||||
- name: Test Beam Search
|
||||
run: GPU=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
|
||||
run: PYTHONPATH="." GPU=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
|
||||
- name: Test MLPerf stuff
|
||||
run: GPU=1 python -m pytest -n=auto test/external/external_test_optim.py test/external/external_test_losses.py test/external/external_test_metrics.py test/external/external_test_datasets.py --durations=20
|
||||
- name: Test llama 3 training
|
||||
run: MAX_BUFFER_SIZE=0 DEV=NULL SAMPLES=300 BS=8 SEQLEN=512 GRADIENT_ACC_STEPS=8 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B MODEL=llama3 python3 examples/mlperf/model_train.py
|
||||
- name: Run handcode_opt
|
||||
run: PYTHONPATH=. MODEL=resnet GPU=1 DEBUG=1 BS=4 HALF=0 python3 examples/handcode_opt.py
|
||||
- name: Run process replay tests
|
||||
uses: ./.github/actions/process-replay
|
||||
|
||||
testllm:
|
||||
name: Test LLM
|
||||
runs-on: ubuntu-24.04
|
||||
timeout-minutes: 15
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Setup Environment
|
||||
uses: ./.github/actions/setup-tinygrad
|
||||
with:
|
||||
key: apps_llm
|
||||
- name: Test 1B LLM
|
||||
run: echo "What's a male chicken called? Answer with only one word." | MAX_BUFFER_SIZE=0 python3 -m tinygrad.apps.llm | grep -i rooster
|
||||
|
||||
testmodels:
|
||||
name: Models (llvm+cpu+gpu)
|
||||
runs-on: ubuntu-22.04
|
||||
timeout-minutes: 15
|
||||
timeout-minutes: 10
|
||||
env:
|
||||
IGNORE_OOB: 0
|
||||
steps:
|
||||
@@ -584,56 +517,6 @@ jobs:
|
||||
- name: Run process replay tests
|
||||
uses: ./.github/actions/process-replay
|
||||
|
||||
testrangeify:
|
||||
name: Linux (rangeify)
|
||||
runs-on: ubuntu-24.04
|
||||
timeout-minutes: 15
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Setup Environment
|
||||
uses: ./.github/actions/setup-tinygrad
|
||||
with:
|
||||
key: rangeify-minimal-llvm
|
||||
deps: testing_minimal
|
||||
llvm: "true"
|
||||
- name: Test CPU=1 RANGEIFY=1
|
||||
# TODO: add more passing tests here
|
||||
# test_symbolic_arange_sym_step is passing now
|
||||
# test_threefry_doesnt_use_long is because there's a contig after the long now
|
||||
run: |
|
||||
CPU=1 RANGEIFY=1 python3 -m pytest -n auto --durations 20 \
|
||||
-k "not test_symbolic_arange_sym_step and not test_threefry_doesnt_use_long" \
|
||||
test/test_tiny.py test/test_rangeify.py test/test_ops.py test/test_tensor_variable.py \
|
||||
test/test_outerworld_range.py test/test_sample.py test/test_randomness.py
|
||||
- name: Test CPU=1 RANGEIFY=2
|
||||
run: CPU=1 RANGEIFY=2 python3 -m pytest -n auto test/test_tiny.py test/test_rangeify.py test/test_ops.py --durations 20
|
||||
- name: Test LLVM=1 RANGEIFY=1 (slow tests)
|
||||
run: LLVM=1 RANGEIFY=1 python3 -m pytest -n auto test/models/test_mnist.py --durations 20
|
||||
|
||||
testdevectorize:
|
||||
name: Linux (devectorize)
|
||||
runs-on: ubuntu-24.04
|
||||
timeout-minutes: 15
|
||||
env:
|
||||
IGNORE_OOB: 0
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Setup Environment
|
||||
uses: ./.github/actions/setup-tinygrad
|
||||
with:
|
||||
key: devectorize-minimal
|
||||
deps: testing_minimal
|
||||
pydeps: "pillow"
|
||||
llvm: "true"
|
||||
- name: Test LLVM=1 DEVECTORIZE=0
|
||||
run: LLVM=1 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
|
||||
- name: Test LLVM=1 DEVECTORIZE=0 for model
|
||||
run: LLVM=1 DEVECTORIZE=0 python3 test/models/test_efficientnet.py
|
||||
- name: Test CPU=1 DEVECTORIZE=0
|
||||
run: CPU=1 DEVECTORIZE=0 FUSE_ARANGE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
|
||||
|
||||
testdsp:
|
||||
name: Linux (DSP)
|
||||
runs-on: ubuntu-24.04
|
||||
@@ -648,7 +531,7 @@ jobs:
|
||||
with:
|
||||
key: dsp-minimal
|
||||
deps: testing_minimal
|
||||
pydeps: "onnx==1.18.0 onnxruntime pillow"
|
||||
pydeps: "onnx==1.17.0 onnxruntime pillow"
|
||||
llvm: "true"
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
@@ -661,14 +544,18 @@ jobs:
|
||||
tags: qemu-hexagon:latest
|
||||
cache-from: type=gha
|
||||
cache-to: type=gha,mode=min
|
||||
- name: Set MOCKDSP env
|
||||
run: printf "MOCKDSP=1" >> $GITHUB_ENV
|
||||
- name: Run test_tiny on DSP
|
||||
run: DEBUG=2 DSP=1 python test/test_tiny.py
|
||||
- name: Test transcendentals
|
||||
run: CC=clang-20 DEBUG=2 DSP=1 python test/test_transcendental.py TestTranscendentalVectorized
|
||||
run: CC=clang-19 PYTHONPATH="." DEBUG=2 DSP=1 python test/test_transcendental.py TestTranscendentalVectorized
|
||||
- name: Test quantize onnx
|
||||
run: DEBUG=2 DSP=1 python3 test/test_quantize_onnx.py
|
||||
run: PYTHONPATH="." DEBUG=2 DSP=1 python3 test/test_quantize_onnx.py
|
||||
- name: Test LLVM=1 DEVECTORIZE=0
|
||||
run: LLVM=1 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
|
||||
- name: Test LLVM=1 DEVECTORIZE=0 for model
|
||||
run: PYTHONPATH="." LLVM=1 DEVECTORIZE=0 python3 test/models/test_efficientnet.py
|
||||
- name: Test CPU=1 DEVECTORIZE=0
|
||||
run: CPU=1 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
|
||||
|
||||
testwebgpu:
|
||||
name: Linux (WebGPU)
|
||||
@@ -692,7 +579,7 @@ jobs:
|
||||
run: |
|
||||
WEBGPU=1 WEBGPU_BACKEND="WGPUBackendType_Vulkan" python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit \
|
||||
--ignore=test/test_copy_speed.py --ignore=test/test_rearrange_einops.py \
|
||||
--ignore=test/test_fuzz_shape_ops.py --durations=20
|
||||
--ignore=test/test_fuzz_shape_ops.py --ignore=test/test_linearizer_failures.py --durations=20
|
||||
- name: Run process replay tests
|
||||
uses: ./.github/actions/process-replay
|
||||
|
||||
@@ -723,21 +610,15 @@ jobs:
|
||||
llvm: ${{ matrix.backend == 'amdllvm' && 'true' }}
|
||||
- name: Check Device.DEFAULT and print some source
|
||||
run: |
|
||||
python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['AMD'], Device.DEFAULT"
|
||||
DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
|
||||
PYTHONPATH=${{ github.workspace }} python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['AMD'], Device.DEFAULT"
|
||||
DEBUG=5 PYTHONPATH=${{ github.workspace }} FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
|
||||
- name: Run LLVM test
|
||||
if: matrix.backend=='amdllvm'
|
||||
run: python test/device/test_amd_llvm.py
|
||||
run: PYTHONPATH="." python test/test_amd_llvm.py
|
||||
- name: Run pytest (amd)
|
||||
run: python -m pytest -n=auto test/test_ops.py test/test_dtype.py test/test_dtype_alu.py test/test_linearizer.py test/test_randomness.py test/test_jit.py test/test_graph.py test/test_multitensor.py test/device/test_hcq.py --durations=20
|
||||
- name: Run pytest (amd)
|
||||
run: python -m pytest test/external/external_test_am.py --durations=20
|
||||
run: python -m pytest -n=auto test/test_ops.py test/test_dtype.py test/test_dtype_alu.py test/test_linearizer.py test/test_randomness.py test/imported/test_indexing.py test/test_jit.py test/test_graph.py test/test_multitensor.py test/test_hcq.py test/external/external_test_am.py --durations=20
|
||||
- name: Run TRANSCENDENTAL math
|
||||
run: TRANSCENDENTAL=2 python -m pytest -n=auto test/test_ops.py::TestOps::test_sin test/test_ops.py::TestOps::test_cos test/test_ops.py::TestOps::test_tan test/test_ops.py::TestOps::test_exp test/test_ops.py::TestOps::test_log --durations=20
|
||||
- name: Run TestOps.test_add with SQTT
|
||||
run: |
|
||||
PROFILE=1 SQTT=1 DEBUG=5 python3 test/test_ops.py TestOps.test_add
|
||||
extra/sqtt/rgptool.py create "/tmp/profile.pkl.$USER" -o /tmp/gpu0.rgp
|
||||
- name: Run process replay tests
|
||||
uses: ./.github/actions/process-replay
|
||||
|
||||
@@ -750,9 +631,7 @@ jobs:
|
||||
name: Linux (${{ matrix.backend }})
|
||||
runs-on: ubuntu-22.04
|
||||
timeout-minutes: 20
|
||||
env:
|
||||
MOCKGPU: 1
|
||||
FORWARD_ONLY: 1
|
||||
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
@@ -762,16 +641,14 @@ jobs:
|
||||
key: ${{ matrix.backend }}-minimal
|
||||
deps: testing_minimal
|
||||
cuda: 'true'
|
||||
ocelot: 'true'
|
||||
- name: Set env
|
||||
run: printf "${{ matrix.backend == 'PTX' && 'CUDA=1\nPTX=1' || matrix.backend == 'nv' && 'NV=1\nSKIP_SLOW_TEST=1' }}" >> $GITHUB_ENV
|
||||
run: printf "${{ matrix.backend == 'PTX' && 'FORWARD_ONLY=1\nJIT=1\nOPT=2\nCUDA=1\nPTX=1\nMOCKGPU=1' || matrix.backend == 'nv' && 'NV=1\nMOCKGPU=1\nFORWARD_ONLY=1' }}" >> $GITHUB_ENV
|
||||
- name: Check Device.DEFAULT and print some source
|
||||
run: |
|
||||
python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CUDA','NV'], Device.DEFAULT"
|
||||
DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
|
||||
PYTHONPATH=${{ github.workspace }} python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CUDA','NV'], Device.DEFAULT"
|
||||
DEBUG=5 PYTHONPATH=${{ github.workspace }} FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
|
||||
- name: Run pytest (cuda)
|
||||
# skip multitensor because it's slow
|
||||
run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore test/test_gc.py --ignore test/test_multitensor.py --durations=20
|
||||
run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore test/test_gc.py --durations=20
|
||||
- name: Run process replay tests
|
||||
uses: ./.github/actions/process-replay
|
||||
|
||||
@@ -801,8 +678,8 @@ jobs:
|
||||
run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'gpu' && 'GPU=1' }}" >> $GITHUB_ENV
|
||||
- name: Check Device.DEFAULT and print some source
|
||||
run: |
|
||||
python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['LLVM','CPU','GPU'], Device.DEFAULT"
|
||||
DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
|
||||
PYTHONPATH=${{ github.workspace }} python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['LLVM','CPU','GPU'], Device.DEFAULT"
|
||||
DEBUG=5 PYTHONPATH=${{ github.workspace }} FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
|
||||
- name: Run pytest (not cuda)
|
||||
run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --durations=20
|
||||
- name: Run TRANSCENDENTAL math
|
||||
@@ -830,12 +707,11 @@ jobs:
|
||||
python-version: '3.11'
|
||||
amd: 'true'
|
||||
cuda: 'true'
|
||||
ocelot: 'true'
|
||||
llvm: 'true'
|
||||
- name: Run real world test
|
||||
run: METAL=1 python -m pytest -n=auto test/models/test_real_world.py --durations=20
|
||||
- name: Test models (Metal)
|
||||
run: METAL=1 python -m pytest -n=auto test/models -v --durations=20
|
||||
run: METAL=1 python -m pytest test/models -v --durations=20
|
||||
- name: Run ONNX
|
||||
run: METAL=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
|
||||
- name: Test tensor core ops (fake)
|
||||
@@ -843,11 +719,11 @@ jobs:
|
||||
- name: Test tensor core ops (real)
|
||||
run: METAL=1 DEBUG=3 python test/test_ops.py TestOps.test_big_gemm
|
||||
- name: Test LLaMA compile speed
|
||||
run: METAL=1 python test/external/external_test_speed_llama.py
|
||||
run: PYTHONPATH="." METAL=1 python test/external/external_test_speed_llama.py
|
||||
- name: Test Beam Search
|
||||
run: METAL=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
|
||||
#- name: Fuzz Test linearizer
|
||||
# run: METAL=1 DEPTH=4 FUZZ_N=50 FUZZ_MAX_SIZE=1000000 python test/external/fuzz_linearizer.py
|
||||
run: PYTHONPATH="." METAL=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
|
||||
- name: Fuzz Test linearizer
|
||||
run: PYTHONPATH="." METAL=1 DEPTH=4 FUZZ_N=50 FUZZ_MAX_SIZE=1000000 python test/external/fuzz_linearizer.py
|
||||
- name: Run TRANSCENDENTAL math
|
||||
run: TRANSCENDENTAL=2 python -m pytest -n=auto test/test_ops.py::TestOps::test_sin test/test_ops.py::TestOps::test_cos test/test_ops.py::TestOps::test_tan test/test_ops.py::TestOps::test_exp test/test_ops.py::TestOps::test_log --durations=20
|
||||
- name: Run pytest (amd)
|
||||
@@ -856,14 +732,15 @@ jobs:
|
||||
AMD: 1
|
||||
FORWARD_ONLY: 1
|
||||
run: |
|
||||
python3 -m pytest -n=auto test/device/test_hcq.py test/test_tiny.py --durations=20
|
||||
python3 -m pytest -n=auto test/test_hcq.py test/test_tiny.py --durations=20
|
||||
- name: Run pytest (amd with llvm backend)
|
||||
env:
|
||||
MOCKGPU: 1
|
||||
AMD: 1
|
||||
AMD_LLVM: 1
|
||||
FORWARD_ONLY: 1
|
||||
run: |
|
||||
python -m pytest -n=auto test/device/test_hcq.py test/test_tiny.py test/device/test_amd_llvm.py --durations=20
|
||||
python -m pytest -n=auto test/test_hcq.py test/test_tiny.py test/test_amd_llvm.py --durations=20
|
||||
- name: Run pytest (ptx)
|
||||
env:
|
||||
MOCKGPU: 1
|
||||
@@ -871,7 +748,7 @@ jobs:
|
||||
NV: 1
|
||||
FORWARD_ONLY: 1
|
||||
run: |
|
||||
python3 -m pytest -n=auto test/device/test_hcq.py test/test_tiny.py --durations=20
|
||||
python3 -m pytest -n=auto test/test_hcq.py test/test_tiny.py --durations=20
|
||||
- name: Run process replay tests
|
||||
uses: ./.github/actions/process-replay
|
||||
|
||||
@@ -886,7 +763,7 @@ jobs:
|
||||
uses: ./.github/actions/setup-tinygrad
|
||||
with:
|
||||
key: osx-webgpu
|
||||
deps: testing
|
||||
deps: testing_minimal
|
||||
webgpu: 'true'
|
||||
- name: Test infinity math in WGSL
|
||||
run: WEBGPU=1 python -m pytest -n=auto test/test_renderer_failures.py::TestWGSLFailures::test_multiply_infinity --durations=20
|
||||
@@ -896,110 +773,59 @@ jobs:
|
||||
run: npm cache clean --force
|
||||
- name: Install Puppeteer
|
||||
run: npm install puppeteer
|
||||
# this is also flaky
|
||||
#- name: Run WEBGPU Efficientnet
|
||||
# run: node test/web/test_webgpu.js
|
||||
# this is flaky
|
||||
#- name: Run VIZ tests as external package
|
||||
# run: |
|
||||
# mkdir $GITHUB_WORKSPACE/test_dir
|
||||
# cd $GITHUB_WORKSPACE/test_dir
|
||||
# python -m venv venv
|
||||
# source venv/bin/activate
|
||||
# pip install $GITHUB_WORKSPACE
|
||||
# cp $GITHUB_WORKSPACE/test/web/test_viz.js .
|
||||
# node test_viz.js
|
||||
- name: Test ONNX Runner (WEBGPU)
|
||||
run: WEBGPU=1 python3 test/external/external_test_onnx_runner.py
|
||||
- name: Run WEBGPU Efficientnet
|
||||
run: node test/web/test_webgpu.js
|
||||
|
||||
osxremote:
|
||||
name: MacOS (remote metal)
|
||||
runs-on: macos-15
|
||||
timeout-minutes: 10
|
||||
env:
|
||||
REMOTE: 1
|
||||
REMOTEDEV: METAL
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Setup Environment
|
||||
uses: ./.github/actions/setup-tinygrad
|
||||
with:
|
||||
key: macos-remote
|
||||
deps: testing_minimal
|
||||
- name: Check Device.DEFAULT and print some source
|
||||
run: |
|
||||
python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
|
||||
python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'METAL', Device.default.properties.real_device"
|
||||
DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
|
||||
- name: Run REMOTE=1 Test
|
||||
run: |
|
||||
python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_tensor_variable.py
|
||||
|
||||
amdremote:
|
||||
name: Linux (remote)
|
||||
runs-on: ubuntu-22.04
|
||||
timeout-minutes: 20
|
||||
name: MacOS (remote metal)
|
||||
runs-on: macos-15
|
||||
timeout-minutes: 10
|
||||
env:
|
||||
REMOTE: 1
|
||||
REMOTEDEV: METAL
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Setup Environment
|
||||
uses: ./.github/actions/setup-tinygrad
|
||||
with:
|
||||
key: linux-remote
|
||||
key: macos-remote
|
||||
deps: testing_minimal
|
||||
- name: Check Device.DEFAULT and print some source
|
||||
run: |
|
||||
python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
|
||||
python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'METAL', Device.default.properties.real_device"
|
||||
DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
|
||||
- name: Run REMOTE=1 Test
|
||||
run: |
|
||||
python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_tensor_variable.py
|
||||
|
||||
amdremote:
|
||||
name: Linux (remote amd)
|
||||
runs-on: ubuntu-22.04
|
||||
timeout-minutes: 20
|
||||
env:
|
||||
REMOTE: 1
|
||||
REMOTEDEV: 'AMD'
|
||||
PYTHONPATH: ${{ github.workspace }}
|
||||
MOCKGPU: 1
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
- name: Setup Environment
|
||||
uses: ./.github/actions/setup-tinygrad
|
||||
with:
|
||||
key: linux-remote-amd
|
||||
deps: testing_minimal
|
||||
amd: 'true'
|
||||
llvm: 'true'
|
||||
opencl: 'true'
|
||||
- name: Start remote server
|
||||
run: |
|
||||
start_server() {
|
||||
systemd-run --user \
|
||||
--unit="$1" \
|
||||
--setenv=REMOTEDEV="$2" \
|
||||
--setenv=MOCKGPU=1 \
|
||||
--setenv=PYTHONPATH=. \
|
||||
--setenv=PORT="$3" \
|
||||
--working-directory="$(pwd)" \
|
||||
python tinygrad/runtime/ops_remote.py
|
||||
}
|
||||
|
||||
start_server "remote-server-amd-1" "AMD" 6667
|
||||
start_server "remote-server-amd-2" "AMD" 6668
|
||||
start_server "remote-server-gpu" "GPU" 7667
|
||||
start_server "remote-server-cpu" "CPU" 8667
|
||||
- name: Check Device.DEFAULT and print some source
|
||||
env:
|
||||
HOST: 127.0.0.1:6667*6,127.0.0.1:6668*6
|
||||
run: |
|
||||
python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
|
||||
python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'AMD', Device.default.properties.real_device"
|
||||
DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
|
||||
- name: Run REMOTE=1 Test (AMD)
|
||||
env:
|
||||
HOST: 127.0.0.1:6667*6,127.0.0.1:6668*6
|
||||
- name: Run REMOTE=1 Test
|
||||
run: |
|
||||
python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_remote.py test/test_tensor_variable.py --durations 20
|
||||
- name: Run REMOTE=1 Test (GPU)
|
||||
env:
|
||||
HOST: 127.0.0.1:7667*6
|
||||
run: |
|
||||
python3 -m pytest test/test_tiny.py test/test_image_dtype.py test/test_jit.py --durations 20
|
||||
IMAGE=2 python3 -m pytest test/test_tiny.py test/test_image_dtype.py
|
||||
- name: Run REMOTE=1 Test (CPU)
|
||||
env:
|
||||
HOST: 127.0.0.1:8667*6
|
||||
run: |
|
||||
python3 -m pytest test/test_tiny.py test/test_jit.py test/test_multitensor.py --durations 20
|
||||
- name: Show remote server logs
|
||||
if: always()
|
||||
run: |
|
||||
journalctl --user -u remote-server-amd-1 --no-pager
|
||||
journalctl --user -u remote-server-amd-2 --no-pager
|
||||
journalctl --user -u remote-server-gpu --no-pager
|
||||
journalctl --user -u remote-server-cpu --no-pager
|
||||
python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_tensor_variable.py
|
||||
|
||||
osxtests:
|
||||
strategy:
|
||||
@@ -1019,7 +845,6 @@ jobs:
|
||||
with:
|
||||
key: macos-${{ matrix.backend }}-minimal
|
||||
deps: testing_minimal
|
||||
pydeps: "capstone"
|
||||
llvm: ${{ matrix.backend == 'llvm' && 'true' }}
|
||||
- name: Set env
|
||||
run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'metal' && 'METAL=1'}}" >> $GITHUB_ENV
|
||||
@@ -1041,7 +866,7 @@ jobs:
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
backend: [llvm, cpu, webgpu]
|
||||
backend: [llvm, cpu]
|
||||
|
||||
name: Windows (${{ matrix.backend }})
|
||||
runs-on: windows-latest
|
||||
@@ -1054,16 +879,14 @@ jobs:
|
||||
- name: Setup Environment
|
||||
uses: ./.github/actions/setup-tinygrad
|
||||
with:
|
||||
key: windows-${{ matrix.backend }}-minimal
|
||||
key: windows-minimal
|
||||
deps: testing_unit
|
||||
pydeps: ${{ matrix.backend == 'webgpu' && 'dawn-python' || '' }}
|
||||
- name: Set env
|
||||
shell: bash
|
||||
run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'webgpu' && 'WEBGPU=1'}}" >> $GITHUB_ENV
|
||||
run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1'}}" >> $GITHUB_ENV
|
||||
- name: Run unit tests
|
||||
if: matrix.backend=='llvm'
|
||||
# test_newton_schulz hits RecursionError
|
||||
run: python -m pytest -n=auto test/unit/ --ignore=test/unit/test_disk_tensor.py --ignore=test/unit/test_elf.py --ignore=test/unit/test_tar.py --ignore=test/unit/test_linalg.py --durations=20
|
||||
run: python -m pytest -n=auto test/unit/ --ignore=test/unit/test_disk_tensor.py --ignore=test/unit/test_elf.py --ignore=test/unit/test_tar.py
|
||||
- name: Run pytest (${{ matrix.backend }})
|
||||
shell: bash
|
||||
run: |
|
||||
|
||||
@@ -20,9 +20,15 @@ repos:
|
||||
language: system
|
||||
always_run: true
|
||||
pass_filenames: false
|
||||
- id: devicetests
|
||||
name: select GPU tests
|
||||
entry: env GPU=1 PYTHONPATH="." python3 -m pytest test/test_uops.py test/test_search.py
|
||||
language: system
|
||||
always_run: true
|
||||
pass_filenames: false
|
||||
- id: tests
|
||||
name: subset of tests
|
||||
entry: env PYTHONPATH="." python3 -m pytest -n=4 test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py
|
||||
entry: env PYTHONPATH="." python3 -m pytest -n=4 test/unit/ test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py test/test_symbolic_shapetracker.py
|
||||
language: system
|
||||
always_run: true
|
||||
pass_filenames: false
|
||||
|
||||
@@ -7,7 +7,7 @@ extension-pkg-whitelist=scipy,cereal.messaging.messaging_pyx,PyQt5,av
|
||||
|
||||
# Add files or directories to the blacklist. They should be base names, not
|
||||
# paths.
|
||||
ignore=CVS,autogen,msm_kgsl.py,runtime,.venv
|
||||
ignore=CVS,autogen,msm_kgsl.py,runtime
|
||||
|
||||
# Add files or directories matching the regex patterns to the blacklist. The
|
||||
# regex matches against base names, not paths.
|
||||
@@ -54,12 +54,11 @@ confidence=
|
||||
# --enable=similarities". If you want to run only the classes checker, but have
|
||||
# no Warning level messages displayed, use"--disable=all --enable=classes
|
||||
# --disable=W"
|
||||
disable=C,R,W0613,W0511,W0212,W0201,W0106,W0603,W0621,W0703,W1201,W1203,E1136,W1514,E1101,W0221,W0105,E0401,abstract-method,W0707
|
||||
disable=C,R,W0613,W0511,W0212,W0201,W0106,W0603,W0621,W0703,W1201,W1203,E1136,W1514,E1101,W0221,W0105,E0401,abstract-method
|
||||
# E1101 for function binding
|
||||
# W0221 for Function class
|
||||
# W0105 for comment strings
|
||||
# E0401 for missing imports
|
||||
# W0707 for not reraising
|
||||
|
||||
# Enable the message, report, category or checker with the given id(s). You can
|
||||
# either give multiple identifier separated by comma (,) or put this option
|
||||
|
||||
@@ -1,17 +0,0 @@
|
||||
# tinygrad agents
|
||||
|
||||
Hello agent. You are one of the most talented programmers of your generation.
|
||||
|
||||
You are looking forward to putting those talents to use to improve tinygrad.
|
||||
|
||||
## philosophy
|
||||
|
||||
tinygrad is a **tensor** library focused on beauty and minimalism, while still matching the functionality of PyTorch and JAX.
|
||||
|
||||
Every line must earn its keep. Prefer readability over cleverness. We believe that if carefully designed, 10 lines can have the impact of 1000.
|
||||
|
||||
Never mix functionality changes with whitespace changes. All functionality changes must be tested.
|
||||
|
||||
## style
|
||||
|
||||
Use **2-space indentation**, and keep lines to a maximum of **150 characters**. Match the existing style.
|
||||
@@ -21,11 +21,11 @@ tinygrad: For something between [PyTorch](https://github.com/pytorch/pytorch) an
|
||||
|
||||
---
|
||||
|
||||
Despite tinygrad's size, it is a fully featured deep learning framework.
|
||||
This may not be the best deep learning framework, but it is a deep learning framework.
|
||||
|
||||
Due to its extreme simplicity, it is the easiest framework to add new accelerators to, with support for both inference and training. If XLA is CISC, tinygrad is RISC.
|
||||
Due to its extreme simplicity, it aims to be the easiest framework to add new accelerators to, with support for both inference and training. If XLA is CISC, tinygrad is RISC.
|
||||
|
||||
tinygrad is now beta software, we [raised some money](https://geohot.github.io/blog/jekyll/update/2023/05/24/the-tiny-corp-raised-5M.html) to make it good. Someday, we will tape out chips.
|
||||
tinygrad is still alpha software, but we [raised some money](https://geohot.github.io/blog/jekyll/update/2023/05/24/the-tiny-corp-raised-5M.html) to make it good. Someday, we will tape out chips.
|
||||
|
||||
## Features
|
||||
|
||||
@@ -39,8 +39,9 @@ Try a matmul. See how, despite the style, it is fused into one kernel with the p
|
||||
|
||||
```sh
|
||||
DEBUG=3 python3 -c "from tinygrad import Tensor;
|
||||
N = 1024; a, b = Tensor.empty(N, N), Tensor.empty(N, N);
|
||||
(a.reshape(N, 1, N) * b.T.reshape(1, N, N)).sum(axis=2).realize()"
|
||||
N = 1024; a, b = Tensor.rand(N, N), Tensor.rand(N, N);
|
||||
c = (a.reshape(N, 1, N) * b.T.reshape(1, N, N)).sum(axis=2);
|
||||
print((c.numpy() - (a.numpy() @ b.numpy())).mean())"
|
||||
```
|
||||
|
||||
And we can change `DEBUG` to `4` to see the generated code.
|
||||
|
||||
+157
-60
@@ -9,7 +9,7 @@ if [[ ! $(clang2py -V) ]]; then
|
||||
pip install clang==14.0.6
|
||||
git clone https://github.com/nimlgen/ctypeslib.git
|
||||
cd ctypeslib
|
||||
pip install .
|
||||
pip install --user .
|
||||
clang2py -V
|
||||
popd
|
||||
fi
|
||||
@@ -83,12 +83,11 @@ generate_kfd() {
|
||||
sed -i "/import functools/a from tinygrad.runtime.support.hcq import FileIOInterface" $BASE/kfd.py
|
||||
sed -i "s/def _do_ioctl(__idir, __base, __nr, __user_struct, __fd, \*\*kwargs):/def _do_ioctl(__idir, __base, __nr, __user_struct, __fd:FileIOInterface, \*\*kwargs):/g" $BASE/kfd.py
|
||||
sed -i "s/fcntl.ioctl(__fd, (__idir<<30)/__fd.ioctl((__idir<<30)/g" $BASE/kfd.py
|
||||
sed -i "s/!!/not not /g" $BASE/kfd.py
|
||||
python3 -c "import tinygrad.runtime.autogen.kfd"
|
||||
}
|
||||
|
||||
generate_cuda() {
|
||||
clang2py /usr/include/cuda.h --clang-args="-D__CUDA_API_VERSION_INTERNAL" -o $BASE/cuda.py -l /usr/lib/x86_64-linux-gnu/libcuda.so
|
||||
clang2py /usr/include/cuda.h -o $BASE/cuda.py -l /usr/lib/x86_64-linux-gnu/libcuda.so
|
||||
sed -i "s\import ctypes\import ctypes, ctypes.util\g" $BASE/cuda.py
|
||||
sed -i "s\ctypes.CDLL('/usr/lib/x86_64-linux-gnu/libcuda.so')\ctypes.CDLL(ctypes.util.find_library('cuda'))\g" $BASE/cuda.py
|
||||
fixup $BASE/cuda.py
|
||||
@@ -105,10 +104,10 @@ generate_nvrtc() {
|
||||
}
|
||||
|
||||
generate_nv() {
|
||||
NVKERN_COMMIT_HASH=81fe4fb417c8ac3b9bdcc1d56827d116743892a5
|
||||
NVKERN_COMMIT_HASH=d6b75a34094b0f56c2ccadf14e5d0bd515ed1ab6
|
||||
NVKERN_SRC=/tmp/open-gpu-kernel-modules-$NVKERN_COMMIT_HASH
|
||||
if [ ! -d "$NVKERN_SRC" ]; then
|
||||
git clone https://github.com/NVIDIA/open-gpu-kernel-modules $NVKERN_SRC
|
||||
git clone https://github.com/tinygrad/open-gpu-kernel-modules $NVKERN_SRC
|
||||
pushd .
|
||||
cd $NVKERN_SRC
|
||||
git reset --hard $NVKERN_COMMIT_HASH
|
||||
@@ -117,21 +116,15 @@ generate_nv() {
|
||||
|
||||
clang2py -k cdefstum \
|
||||
extra/nv_gpu_driver/clc6c0qmd.h \
|
||||
extra/nv_gpu_driver/clcec0qmd.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl0000.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl0080.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl2080.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl2080_notification.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc56f.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc86f.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc96f.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc761.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc56f.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc56f.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl83de.h \
|
||||
$NVKERN_SRC/src/nvidia/generated/g_allclasses.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc6c0.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/class/clcdc0.h \
|
||||
$NVKERN_SRC/kernel-open/nvidia-uvm/clc6b5.h \
|
||||
$NVKERN_SRC/kernel-open/nvidia-uvm/clc9b5.h \
|
||||
$NVKERN_SRC/kernel-open/nvidia-uvm/uvm_ioctl.h \
|
||||
$NVKERN_SRC/kernel-open/nvidia-uvm/uvm_linux_ioctl.h \
|
||||
$NVKERN_SRC/kernel-open/nvidia-uvm/hwref/ampere/ga100/dev_fault.h \
|
||||
@@ -149,7 +142,6 @@ generate_nv() {
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrlc36f.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrlcb33.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrla06c.h \
|
||||
$NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrl90f1.h \
|
||||
--clang-args="-include $NVKERN_SRC/src/common/sdk/nvidia/inc/nvtypes.h -I$NVKERN_SRC/src/common/inc -I$NVKERN_SRC/kernel-open/nvidia-uvm -I$NVKERN_SRC/kernel-open/common/inc -I$NVKERN_SRC/src/common/sdk/nvidia/inc -I$NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include -I$NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl" \
|
||||
-o $BASE/nv_gpu.py
|
||||
fixup $BASE/nv_gpu.py
|
||||
@@ -157,8 +149,6 @@ generate_nv() {
|
||||
sed -i "s\import ctypes\import ctypes, os\g" $BASE/nv_gpu.py
|
||||
sed -i 's/#\?\s\([A-Za-z0-9_]\+\) = MW ( \([0-9]\+\) : \([0-9]\+\) )/\1 = (\2 , \3)/' $BASE/nv_gpu.py # NVC6C0_QMDV03_00 processing
|
||||
sed -i 's/#\sdef NVC6C0_QMD\([A-Za-z0-9_()]\+\):/def NVC6C0_QMD\1:/' $BASE/nv_gpu.py
|
||||
sed -i 's/#\sdef NVCEC0_QMD\([A-Za-z0-9_()]\+\):/def NVCEC0_QMD\1:/' $BASE/nv_gpu.py
|
||||
sed -E -i -n '/^def (NVCEC0_QMDV05_00_RELEASE)(_ENABLE)\(i\):/{p;s//\1'"0"'\2=\1\2(0)\n\1'"1"'\2=\1\2(1)/;H;b};p;${x;s/^\n//;p}' "$BASE/nv_gpu.py"
|
||||
sed -i 's/#\s*return MW(\([0-9i()*+]\+\):\([0-9i()*+]\+\))/ return (\1 , \2)/' $BASE/nv_gpu.py
|
||||
sed -i 's/#\?\s*\(.*\)\s*=\s*\(NV\)\?BIT\(32\)\?\s*(\s*\([0-9]\+\)\s*)/\1 = (1 << \4)/' $BASE/nv_gpu.py # name = BIT(x) -> name = (1 << x)
|
||||
sed -i "s/UVM_\([A-Za-z0-9_]\+\) = \['i', '(', '\([0-9]\+\)', ')'\]/UVM_\1 = \2/" $BASE/nv_gpu.py # UVM_name = ['i', '(', '<num>', ')'] -> UVM_name = <num>
|
||||
@@ -167,30 +157,8 @@ generate_nv() {
|
||||
sed -n '1i\
|
||||
nv_status_codes = {}
|
||||
/^NV_STATUS_CODE/ { s/^NV_STATUS_CODE(\([^,]*\), *\([^,]*\), *"\([^"]*\)") *.*$/\1 = \2\nnv_status_codes[\1] = "\3"/; p }' $NVKERN_SRC/src/common/sdk/nvidia/inc/nvstatuscodes.h >> $BASE/nv_gpu.py
|
||||
|
||||
python3 -c "import tinygrad.runtime.autogen.nv_gpu"
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$NVKERN_SRC/src/nvidia/inc/kernel/gpu/fsp/kern_fsp_cot_payload.h \
|
||||
$NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/gsp/gspifpub.h \
|
||||
$NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/gsp/gsp_fw_wpr_meta.h \
|
||||
$NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/gsp/gsp_fw_sr_meta.h \
|
||||
$NVKERN_SRC/src/nvidia/inc/kernel/gpu/gsp/gsp_init_args.h \
|
||||
$NVKERN_SRC/src/nvidia/inc/kernel/gpu/gsp/gsp_init_args.h \
|
||||
$NVKERN_SRC/src/common/uproc/os/common/include/libos_init_args.h \
|
||||
$NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/rmRiscvUcode.h \
|
||||
$NVKERN_SRC/src/common/shared/msgq/inc/msgq/msgq_priv.h \
|
||||
$NVKERN_SRC/src/nvidia/inc/kernel/vgpu/rpc_headers.h \
|
||||
$NVKERN_SRC/src/nvidia/inc/kernel/vgpu/rpc_global_enums.h \
|
||||
$NVKERN_SRC/src/nvidia/generated/g_rpc-structures.h \
|
||||
$NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/fsp/fsp_nvdm_format.h \
|
||||
extra/nv_gpu_driver/g_rpc-message-header.h \
|
||||
extra/nv_gpu_driver/gsp_static_config.h \
|
||||
extra/nv_gpu_driver/vbios.h \
|
||||
--clang-args="-DRPC_MESSAGE_STRUCTURES -DRPC_STRUCTURES -include $NVKERN_SRC/src/common/sdk/nvidia/inc/nvtypes.h -I$NVKERN_SRC/src/nvidia/generated -I$NVKERN_SRC/src/common/inc -I$NVKERN_SRC/src/nvidia/inc -I$NVKERN_SRC/src/nvidia/interface/ -I$NVKERN_SRC/src/nvidia/inc/kernel -I$NVKERN_SRC/src/nvidia/inc/libraries -I$NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc -I$NVKERN_SRC/kernel-open/nvidia-uvm -I$NVKERN_SRC/kernel-open/common/inc -I$NVKERN_SRC/src/common/sdk/nvidia/inc -I$NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include -I$NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl" \
|
||||
-o $BASE/nv/nv.py
|
||||
|
||||
fixup $BASE/nv/nv.py
|
||||
python3 -c "import tinygrad.runtime.autogen.nv.nv"
|
||||
}
|
||||
|
||||
generate_amd() {
|
||||
@@ -198,7 +166,11 @@ generate_amd() {
|
||||
clang2py -k cdefstum \
|
||||
extra/hip_gpu_driver/sdma_registers.h \
|
||||
extra/hip_gpu_driver/nvd.h \
|
||||
extra/hip_gpu_driver/kfd_pm4_headers_ai.h \
|
||||
extra/hip_gpu_driver/soc21_enum.h \
|
||||
extra/hip_gpu_driver/sdma_v6_0_0_pkt_open.h \
|
||||
extra/hip_gpu_driver/gc_11_0_0_offset.h \
|
||||
extra/hip_gpu_driver/gc_10_3_0_offset.h \
|
||||
extra/hip_gpu_driver/sienna_cichlid_ip_offset.h \
|
||||
--clang-args="-I/opt/rocm/include -x c++" \
|
||||
-o $BASE/amd_gpu.py
|
||||
@@ -236,21 +208,6 @@ generate_io_uring() {
|
||||
fixup $BASE/io_uring.py
|
||||
}
|
||||
|
||||
generate_ib() {
|
||||
clang2py -k cdefstum \
|
||||
/usr/include/infiniband/verbs.h \
|
||||
/usr/include/infiniband/verbs_api.h \
|
||||
/usr/include/infiniband/ib_user_ioctl_verbs.h \
|
||||
/usr/include/rdma/ib_user_verbs.h \
|
||||
-o $BASE/ib.py
|
||||
|
||||
sed -i "s\import ctypes\import ctypes, ctypes.util\g" "$BASE/ib.py"
|
||||
sed -i "s\FIXME_STUB\libibverbs\g" "$BASE/ib.py"
|
||||
sed -i "s\FunctionFactoryStub()\ctypes.CDLL(ctypes.util.find_library('ibverbs'), use_errno=True)\g" "$BASE/ib.py"
|
||||
|
||||
fixup $BASE/ib.py
|
||||
}
|
||||
|
||||
generate_libc() {
|
||||
clang2py -k cdefstum \
|
||||
$(dpkg -L libc6-dev | grep sys/mman.h) \
|
||||
@@ -263,7 +220,7 @@ generate_libc() {
|
||||
|
||||
sed -i "s\import ctypes\import ctypes, ctypes.util, os\g" $BASE/libc.py
|
||||
sed -i "s\FIXME_STUB\libc\g" $BASE/libc.py
|
||||
sed -i "s\FunctionFactoryStub()\None if (libc_path := ctypes.util.find_library('c')) is None else ctypes.CDLL(libc_path, use_errno=True)\g" $BASE/libc.py
|
||||
sed -i "s\FunctionFactoryStub()\None if (libc_path := ctypes.util.find_library('c')) is None else ctypes.CDLL(libc_path)\g" $BASE/libc.py
|
||||
|
||||
fixup $BASE/libc.py
|
||||
}
|
||||
@@ -372,6 +329,70 @@ generate_am() {
|
||||
-o $BASE/am/pm4_nv.py
|
||||
fixup $BASE/am/pm4_nv.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/vega10_enum.h \
|
||||
-o $BASE/am/vega10.py
|
||||
fixup $BASE/am/vega10.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/navi10_enum.h \
|
||||
-o $BASE/am/navi10.py
|
||||
fixup $BASE/am/navi10.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/soc21_enum.h \
|
||||
-o $BASE/am/soc21.py
|
||||
fixup $BASE/am/soc21.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/soc24_enum.h \
|
||||
-o $BASE/am/soc24.py
|
||||
fixup $BASE/am/soc24.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/mp/mp_13_0_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/mp/mp_13_0_0_sh_mask.h \
|
||||
-o $BASE/am/mp_13_0_0.py
|
||||
fixup $BASE/am/mp_13_0_0.py
|
||||
|
||||
# 14_0_3 reuses 14_0_2
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/mp/mp_14_0_2_offset.h \
|
||||
$AMKERN_INC/asic_reg/mp/mp_14_0_2_sh_mask.h \
|
||||
-o $BASE/am/mp_14_0_3.py
|
||||
fixup $BASE/am/mp_14_0_3.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/mp/mp_11_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/mp/mp_11_0_sh_mask.h \
|
||||
-o $BASE/am/mp_11_0.py
|
||||
fixup $BASE/am/mp_11_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/gc/gc_9_4_3_offset.h \
|
||||
$AMKERN_INC/asic_reg/gc/gc_9_4_3_sh_mask.h \
|
||||
extra/amdpci/overlay/gc_9_4_3.h \
|
||||
-o $BASE/am/gc_9_4_3.py
|
||||
fixup $BASE/am/gc_9_4_3.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/gc/gc_10_3_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/gc/gc_10_3_0_sh_mask.h \
|
||||
-o $BASE/am/gc_10_3_0.py
|
||||
fixup $BASE/am/gc_10_3_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/gc/gc_11_0_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/gc/gc_11_0_0_sh_mask.h \
|
||||
-o $BASE/am/gc_11_0_0.py
|
||||
fixup $BASE/am/gc_11_0_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/gc/gc_12_0_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/gc/gc_12_0_0_sh_mask.h \
|
||||
-o $BASE/am/gc_12_0_0.py
|
||||
fixup $BASE/am/gc_12_0_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
extra/hip_gpu_driver/sdma_registers.h \
|
||||
$AMKERN_AMD/amdgpu/vega10_sdma_pkt_open.h \
|
||||
@@ -393,6 +414,72 @@ generate_am() {
|
||||
-o $BASE/am/sdma_6_0_0.py
|
||||
fixup $BASE/am/sdma_6_0_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/mmhub/mmhub_3_0_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/mmhub/mmhub_3_0_0_sh_mask.h \
|
||||
-o $BASE/am/mmhub_3_0_0.py
|
||||
fixup $BASE/am/mmhub_3_0_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/mmhub/mmhub_3_0_2_offset.h \
|
||||
$AMKERN_INC/asic_reg/mmhub/mmhub_3_0_2_sh_mask.h \
|
||||
-o $BASE/am/mmhub_3_0_2.py
|
||||
fixup $BASE/am/mmhub_3_0_2.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/nbio/nbio_2_3_offset.h \
|
||||
$AMKERN_INC/asic_reg/nbio/nbio_2_3_sh_mask.h \
|
||||
-o $BASE/am/nbio_2_3_0.py
|
||||
fixup $BASE/am/nbio_2_3_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/nbio/nbio_7_2_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/nbio/nbio_7_2_0_sh_mask.h \
|
||||
-o $BASE/am/nbio_7_2_0.py
|
||||
fixup $BASE/am/nbio_7_2_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/mmhub/mmhub_4_1_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/mmhub/mmhub_4_1_0_sh_mask.h \
|
||||
-o $BASE/am/mmhub_4_1_0.py
|
||||
fixup $BASE/am/mmhub_4_1_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/nbio/nbio_4_3_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/nbio/nbio_4_3_0_sh_mask.h \
|
||||
-o $BASE/am/nbio_4_3_0.py
|
||||
fixup $BASE/am/nbio_4_3_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/nbif/nbif_6_3_1_offset.h \
|
||||
$AMKERN_INC/asic_reg/nbif/nbif_6_3_1_sh_mask.h \
|
||||
-o $BASE/am/nbif_6_3_1.py
|
||||
fixup $BASE/am/nbif_6_3_1.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/nbio/nbio_7_9_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/nbio/nbio_7_9_0_sh_mask.h \
|
||||
-o $BASE/am/nbio_7_9_0.py
|
||||
fixup $BASE/am/nbio_7_9_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/nbio/nbio_7_11_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/nbio/nbio_7_11_0_sh_mask.h \
|
||||
-o $BASE/am/nbio_7_11_0.py
|
||||
fixup $BASE/am/nbio_7_11_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/oss/osssys_6_0_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/oss/osssys_6_0_0_sh_mask.h \
|
||||
-o $BASE/am/osssys_6_0_0.py
|
||||
fixup $BASE/am/osssys_6_0_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/oss/osssys_7_0_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/oss/osssys_7_0_0_sh_mask.h \
|
||||
-o $BASE/am/osssys_7_0_0.py
|
||||
fixup $BASE/am/osssys_7_0_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu_v13_0_0_ppsmc.h \
|
||||
$AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu13_driver_if_v13_0_0.h \
|
||||
@@ -406,8 +493,20 @@ generate_am() {
|
||||
$AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu14_driver_if_v14_0.h \
|
||||
extra/amdpci/headers/amdgpu_smu.h \
|
||||
--clang-args="-include stdint.h" \
|
||||
-o $BASE/am/smu_v14_0_2.py
|
||||
fixup $BASE/am/smu_v14_0_2.py
|
||||
-o $BASE/am/smu_v14_0_3.py
|
||||
fixup $BASE/am/smu_v14_0_3.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/hdp/hdp_6_0_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/hdp/hdp_6_0_0_sh_mask.h \
|
||||
-o $BASE/am/hdp_6_0_0.py
|
||||
fixup $BASE/am/hdp_6_0_0.py
|
||||
|
||||
clang2py -k cdefstum \
|
||||
$AMKERN_INC/asic_reg/hdp/hdp_7_0_0_offset.h \
|
||||
$AMKERN_INC/asic_reg/hdp/hdp_7_0_0_sh_mask.h \
|
||||
-o $BASE/am/hdp_7_0_0.py
|
||||
fixup $BASE/am/hdp_7_0_0.py
|
||||
}
|
||||
|
||||
generate_sqtt() {
|
||||
@@ -435,7 +534,7 @@ generate_libusb() {
|
||||
-o $BASE/libusb.py
|
||||
|
||||
fixup $BASE/libusb.py
|
||||
sed -i "s\import ctypes\import ctypes, ctypes.util, os\g" $BASE/libusb.py
|
||||
sed -i "s\import ctypes\import ctypes, os\g" $BASE/libusb.py
|
||||
sed -i "s/FIXME_STUB/libusb/g" "$BASE/libusb.py"
|
||||
sed -i "s/libusb_le16_to_cpu = libusb_cpu_to_le16//g" "$BASE/libusb.py"
|
||||
sed -i "s/FunctionFactoryStub()/None if (lib_path:=os.getenv('LIBUSB_PATH', ctypes.util.find_library('usb-1.0'))) is None else ctypes.CDLL(lib_path)/g" "$BASE/libusb.py"
|
||||
@@ -452,11 +551,9 @@ elif [ "$1" == "kfd" ]; then generate_kfd
|
||||
elif [ "$1" == "nv" ]; then generate_nv
|
||||
elif [ "$1" == "amd" ]; then generate_amd
|
||||
elif [ "$1" == "am" ]; then generate_am
|
||||
elif [ "$1" == "nvdrv" ]; then generate_nvdrv
|
||||
elif [ "$1" == "sqtt" ]; then generate_sqtt
|
||||
elif [ "$1" == "qcom" ]; then generate_qcom
|
||||
elif [ "$1" == "io_uring" ]; then generate_io_uring
|
||||
elif [ "$1" == "ib" ]; then generate_ib
|
||||
elif [ "$1" == "libc" ]; then generate_libc
|
||||
elif [ "$1" == "llvm" ]; then generate_llvm
|
||||
elif [ "$1" == "kgsl" ]; then generate_kgsl
|
||||
|
||||
+19
-21
@@ -7,30 +7,28 @@
|
||||
|
||||
print("******** first, the runtime ***********")
|
||||
|
||||
from tinygrad.runtime.ops_cpu import ClangJITCompiler, CPUDevice, CPUProgram
|
||||
|
||||
cpu = CPUDevice()
|
||||
from tinygrad.runtime.ops_cpu import ClangJITCompiler, MallocAllocator, CPUProgram
|
||||
|
||||
# allocate some buffers
|
||||
out = cpu.allocator.alloc(4)
|
||||
a = cpu.allocator.alloc(4)
|
||||
b = cpu.allocator.alloc(4)
|
||||
out = MallocAllocator.alloc(4)
|
||||
a = MallocAllocator.alloc(4)
|
||||
b = MallocAllocator.alloc(4)
|
||||
|
||||
# load in some values (little endian)
|
||||
cpu.allocator._copyin(a, memoryview(bytearray([2,0,0,0])))
|
||||
cpu.allocator._copyin(b, memoryview(bytearray([3,0,0,0])))
|
||||
MallocAllocator._copyin(a, memoryview(bytearray([2,0,0,0])))
|
||||
MallocAllocator._copyin(b, memoryview(bytearray([3,0,0,0])))
|
||||
|
||||
# compile a program to a binary
|
||||
lib = ClangJITCompiler().compile("void add(int *out, int *a, int *b) { out[0] = a[0] + b[0]; }")
|
||||
|
||||
# create a runtime for the program
|
||||
fxn = cpu.runtime("add", lib)
|
||||
fxn = CPUProgram("add", lib)
|
||||
|
||||
# run the program
|
||||
fxn(out, a, b)
|
||||
|
||||
# check the data out
|
||||
print(val := cpu.allocator._as_buffer(out).cast("I").tolist()[0])
|
||||
print(val := MallocAllocator._as_buffer(out).cast("I").tolist()[0])
|
||||
assert val == 5
|
||||
|
||||
|
||||
@@ -48,24 +46,24 @@ from tinygrad.shape.shapetracker import ShapeTracker
|
||||
out = Buffer(DEVICE, 1, dtypes.int32).allocate()
|
||||
a = Buffer(DEVICE, 1, dtypes.int32).allocate().copyin(memoryview(bytearray(struct.pack("I", 2))))
|
||||
b = Buffer(DEVICE, 1, dtypes.int32).allocate().copyin(memoryview(bytearray(struct.pack("I", 3))))
|
||||
# NOTE: a._buf is the same as the return from cpu.allocator.alloc
|
||||
# NOTE: a._buf is the same as the return from MallocAllocator.alloc
|
||||
|
||||
# describe the computation
|
||||
buf_1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 1)
|
||||
buf_2 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 2)
|
||||
ld_1 = UOp(Ops.LOAD, dtypes.int32, (buf_1.view(ShapeTracker.from_shape((1,))),))
|
||||
ld_2 = UOp(Ops.LOAD, dtypes.int32, (buf_2.view(ShapeTracker.from_shape((1,))),))
|
||||
ld_1 = UOp(Ops.LOAD, dtypes.int32, (buf_1, ShapeTracker.from_shape((1,)).to_uop()))
|
||||
ld_2 = UOp(Ops.LOAD, dtypes.int32, (buf_2, ShapeTracker.from_shape((1,)).to_uop()))
|
||||
alu = ld_1 + ld_2
|
||||
output_buf = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 0)
|
||||
st_0 = UOp(Ops.STORE, dtypes.void, (output_buf.view(ShapeTracker.from_shape((1,))), alu))
|
||||
st_0 = UOp(Ops.STORE, dtypes.void, (output_buf, ShapeTracker.from_shape((1,)).to_uop(), alu))
|
||||
s = UOp(Ops.SINK, dtypes.void, (st_0,))
|
||||
|
||||
# convert the computation to a "linearized" format (print the format)
|
||||
from tinygrad.engine.realize import get_program, CompiledRunner
|
||||
program = get_program(s, Device[DEVICE].renderer)
|
||||
from tinygrad.engine.realize import get_kernel, CompiledRunner
|
||||
kernel = get_kernel(Device[DEVICE].renderer, s).linearize()
|
||||
|
||||
# compile a program (and print the source)
|
||||
fxn = CompiledRunner(program)
|
||||
fxn = CompiledRunner(kernel.to_program())
|
||||
print(fxn.p.src)
|
||||
# NOTE: fxn.clprg is the CPUProgram
|
||||
|
||||
@@ -80,7 +78,7 @@ print("******** third, the UOp ***********")
|
||||
|
||||
from tinygrad.engine.realize import run_schedule
|
||||
from tinygrad.engine.schedule import create_schedule_with_vars
|
||||
from tinygrad.schedule.kernelize import get_kernelize_map
|
||||
from tinygrad.engine.grouper import get_becomes_map
|
||||
|
||||
# allocate some values + load in values
|
||||
a = UOp.new_buffer(DEVICE, 1, dtypes.int32)
|
||||
@@ -93,7 +91,7 @@ out = a + b
|
||||
s = UOp(Ops.SINK, dtypes.void, (out,))
|
||||
|
||||
# group the computation into kernels
|
||||
becomes_map = get_kernelize_map(s)
|
||||
becomes_map = get_becomes_map(s)
|
||||
|
||||
# the compute maps to an assign
|
||||
assign = becomes_map[a+b]
|
||||
@@ -105,7 +103,7 @@ assert assign.src[1].op is Ops.KERNEL
|
||||
|
||||
# schedule the kernel graph in a linear list
|
||||
s = UOp(Ops.SINK, dtypes.void, (assign,))
|
||||
sched, _ = create_schedule_with_vars(s)
|
||||
sched, _, becomes_map = create_schedule_with_vars(s)
|
||||
assert len(sched) == 1
|
||||
|
||||
# DEBUGGING: print the compute ast
|
||||
@@ -113,7 +111,7 @@ print(sched[-1].ast)
|
||||
# NOTE: sched[-1].ast is the same as st_0 above
|
||||
|
||||
# the output will be stored in a new buffer
|
||||
out = assign.buf_uop
|
||||
out = becomes_map[assign]
|
||||
assert out.op is Ops.BUFFER and not out.buffer.is_allocated()
|
||||
print(out)
|
||||
|
||||
|
||||
@@ -36,7 +36,7 @@ optim.schedule_step() # this will step the optimizer without running realize
|
||||
# 3. Create a schedule.
|
||||
|
||||
# The weight Tensors have been assigned to, but not yet realized. Everything is still lazy at this point
|
||||
# l1.uop and l2.uop define a computation graph
|
||||
# l1.lazydata and l2.lazydata define a computation graph
|
||||
|
||||
from tinygrad.engine.schedule import ScheduleItem
|
||||
schedule: List[ScheduleItem] = Tensor.schedule(l1, l2)
|
||||
|
||||
@@ -52,7 +52,7 @@ Signals are device-dependent structures used for synchronization and timing in H
|
||||
The following Python code demonstrates the usage of signals:
|
||||
|
||||
```python
|
||||
signal = your_device.new_signal(value=0)
|
||||
signal = your_device.signal_t()
|
||||
|
||||
HWQueue().timestamp(signal) \
|
||||
.signal(signal, value_to_fire) \
|
||||
|
||||
@@ -34,7 +34,7 @@ print(out) # <Tensor <UOp METAL (1,) int (<Ops.ASSIGN: 66>, None)> on METAL with
|
||||
The multiply Tensor stays the same because it is fused. The output Tensor's UOp becomes a new ASSIGN UOp:
|
||||
|
||||
```py
|
||||
print(out.uop)
|
||||
print(out.lazydata)
|
||||
```
|
||||
|
||||
The first source is the output BUFFER:
|
||||
@@ -72,7 +72,7 @@ Once a Tensor is kernelized, all children will LOAD its BUFFER, instead of fusin
|
||||
```py
|
||||
child = out+2
|
||||
child.kernelize()
|
||||
print(child.uop.src[1].arg.ast)
|
||||
print(child.lazydata.src[1].arg.ast)
|
||||
```
|
||||
|
||||
```
|
||||
|
||||
@@ -1,60 +0,0 @@
|
||||
# tinygrad directory layout
|
||||
|
||||
This explains the flow of a big graph down to programs.
|
||||
|
||||
Directories are listed in order of how they are processed.
|
||||
|
||||
---
|
||||
|
||||
## tinygrad/schedule
|
||||
|
||||
Group UOps into kernels.
|
||||
|
||||
::: tinygrad.schedule.kernelize.get_kernelize_map
|
||||
options:
|
||||
members: false
|
||||
show_labels: false
|
||||
show_source: false
|
||||
|
||||
---
|
||||
|
||||
## tinygrad/codegen/opt
|
||||
|
||||
Transforms the ast into an optimized ast. This is where BEAM search and heuristics live.
|
||||
|
||||
---
|
||||
|
||||
## tinygrad/codegen
|
||||
|
||||
Transform the optimized ast into a linearized list of UOps.
|
||||
|
||||
::: tinygrad.codegen.full_rewrite
|
||||
options:
|
||||
members: false
|
||||
show_labels: false
|
||||
show_source: false
|
||||
|
||||
---
|
||||
|
||||
## tinygrad/renderer
|
||||
|
||||
Transform the linearized list of UOps into a program, represented as a string.
|
||||
|
||||
::: tinygrad.renderer.Renderer
|
||||
options:
|
||||
members:
|
||||
- render
|
||||
show_labels: false
|
||||
show_source: false
|
||||
|
||||
---
|
||||
|
||||
## tinygrad/engine
|
||||
|
||||
Abstracted high level interface to the runtimes.
|
||||
|
||||
::: tinygrad.engine.realize.get_program
|
||||
options:
|
||||
members: false
|
||||
show_labels: false
|
||||
show_source: false
|
||||
@@ -36,6 +36,7 @@ CUDA | [1] | enable CUDA backend
|
||||
AMD | [1] | enable AMD backend
|
||||
NV | [1] | enable NV backend
|
||||
METAL | [1] | enable Metal backend (for Mac M1 and after)
|
||||
METAL_XCODE | [1] | enable Metal using macOS Xcode SDK
|
||||
CPU | [1] | enable CPU (Clang) backend
|
||||
LLVM | [1] | enable LLVM backend
|
||||
BEAM | [#] | number of beams in kernel beam search
|
||||
|
||||
-293
@@ -1,293 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
# this file is a "ramp" for people new to tinygrad to think about how to approach it
|
||||
# it is runnable and editable.
|
||||
# whenever you see stuff like DEBUG=2 or CPU=1 discussed, these are environment variables
|
||||
# in a unix shell like bash `DEBUG=2 CPU=1 python docs/ramp.py`
|
||||
|
||||
# this pip installs tinygrad master for the system
|
||||
# the -e allows you to edit the tinygrad folder and update system tinygrad
|
||||
# tinygrad is pure Python, so you are encouraged to do this
|
||||
# git pull in the tinygrad directory will also get you the latest
|
||||
"""
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
cd tinygrad
|
||||
python3 -m pip install -e .
|
||||
"""
|
||||
|
||||
# %% ********
|
||||
print("******* PART 1 *******")
|
||||
|
||||
# we start with a Device.
|
||||
# a Device is where Tensors are stored and compute is run
|
||||
# tinygrad autodetects the best device on your system and makes it the DEFAULT
|
||||
from tinygrad import Device
|
||||
print(Device.DEFAULT) # on Mac, you can see this prints METAL
|
||||
|
||||
# now, lets create a Tensor
|
||||
from tinygrad import Tensor, dtypes
|
||||
t = Tensor([1,2,3,4])
|
||||
|
||||
# you can see this Tensor is on the DEFAULT device with int dtype and shape (4,)
|
||||
assert t.device == Device.DEFAULT
|
||||
assert t.dtype == dtypes.int
|
||||
assert t.shape == (4,)
|
||||
|
||||
# unlike in torch, if we print it, it doesn't print the contents
|
||||
# this is because tinygrad is lazy
|
||||
# this Tensor has not been computed yet
|
||||
print(t)
|
||||
# <Tensor <UOp METAL (4,) int (<Ops.COPY: 7>, None)> on METAL with grad None>
|
||||
|
||||
# the ".uop" property on Tensor contains the specification of how to compute it
|
||||
print(t.uop)
|
||||
"""
|
||||
UOp(Ops.COPY, dtypes.int, arg=None, src=(
|
||||
UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
|
||||
UOp(Ops.UNIQUE, dtypes.void, arg=0, src=()),
|
||||
UOp(Ops.DEVICE, dtypes.void, arg='PYTHON', src=()),)),
|
||||
UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),))
|
||||
"""
|
||||
# as you can see, it's specifying a copy from PYTHON device
|
||||
# which is where the [1,2,3,4] array lives
|
||||
|
||||
# UOps are the specification language in tinygrad
|
||||
# they are immutable and form a DAG
|
||||
# they have a "Ops", a "dtype", a tuple of srcs (parents), and an arg
|
||||
|
||||
t.realize()
|
||||
# if we want to "realize" a tensor, we can with the "realize" method
|
||||
# now when we look at the uop, it's changed
|
||||
print(t.uop)
|
||||
"""
|
||||
UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
|
||||
UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),
|
||||
UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),))
|
||||
"""
|
||||
# the copy was actually run, and now the "uop" of the Tensor is just a BUFFER
|
||||
# if you run this script with DEBUG=2 in the environment, you can see the copy happen
|
||||
# *** METAL 1 copy 16, METAL <- PYTHON ...
|
||||
|
||||
# now let's do some compute
|
||||
# we look at the uop to see the specification of the compute
|
||||
t_times_2 = t * 2
|
||||
print(t_times_2.uop)
|
||||
"""
|
||||
UOp(Ops.MUL, dtypes.int, arg=None, src=(
|
||||
UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
|
||||
UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),
|
||||
x2:=UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),)),
|
||||
UOp(Ops.EXPAND, dtypes.int, arg=(4,), src=(
|
||||
UOp(Ops.RESHAPE, dtypes.int, arg=(1,), src=(
|
||||
UOp(Ops.CONST, dtypes.int, arg=2, src=(
|
||||
UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(), strides=(), offset=0, mask=None, contiguous=True),)), src=(
|
||||
x2,)),)),)),)),))
|
||||
"""
|
||||
# the BUFFER from above is being multiplied by a CONST 2
|
||||
# it's RESHAPEd and EXPANDed to broadcast the CONST to the BUFFER
|
||||
|
||||
# we can check the result with
|
||||
assert t_times_2.tolist() == [2, 4, 6, 8]
|
||||
|
||||
# UOps are both immutable and globally unique
|
||||
# if i multiply the Tensor by 4 twice, these result Tensors will have the same uop specification
|
||||
t_times_4_try_1 = t * 4
|
||||
t_times_4_try_2 = t * 4
|
||||
assert t_times_4_try_1.uop is t_times_4_try_2.uop
|
||||
# the specification isn't just the same, it's the exact same Python object
|
||||
assert t_times_4_try_1 is not t_times_4_try_2
|
||||
# the Tensor is a different Python object
|
||||
|
||||
# if we realize `t_times_4_try_1` ...
|
||||
t_times_4_try_1.realize()
|
||||
print(t_times_4_try_2.uop)
|
||||
"""
|
||||
UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
|
||||
UOp(Ops.UNIQUE, dtypes.void, arg=4, src=()),
|
||||
UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),))
|
||||
"""
|
||||
# ... `t_times_4_try_2` also becomes the same BUFFER
|
||||
assert t_times_4_try_1.uop is t_times_4_try_2.uop
|
||||
# so this print doesn't require any computation, just a copy back to the CPU so we can print it
|
||||
print("** only the copy start")
|
||||
print(t_times_4_try_2.tolist()) # [4, 8, 12, 16]
|
||||
print("** only the copy end")
|
||||
# you can confirm this with DEBUG=2, seeing what's printed in between the "**" prints
|
||||
|
||||
# tinygrad has an auto differentiation engine that operates according to these same principles
|
||||
# the derivative of "log(x)" is "1/x", and you can see this on line 20 of gradient.py
|
||||
t_float = Tensor([3.0])
|
||||
t_log = t_float.log()
|
||||
t_log_grad, = t_log.sum().gradient(t_float)
|
||||
# due to how log is implemented, this gradient contains a lot of UOps
|
||||
print(t_log_grad.uop)
|
||||
# ...not shown here...
|
||||
# but if you run with DEBUG=4 (CPU=1 used here for simpler code), you can see the generated code
|
||||
"""
|
||||
void E_(float* restrict data0, float* restrict data1) {
|
||||
float val0 = *(data1+0);
|
||||
*(data0+0) = (1/val0);
|
||||
}
|
||||
"""
|
||||
# the derivative is close to 1/3
|
||||
assert (t_log_grad.item() - 1/3) < 1e-6
|
||||
|
||||
# %% ********
|
||||
print("******* PART 2 *******")
|
||||
|
||||
# we redefine the same t here so this cell can run on it's own
|
||||
from tinygrad import Tensor
|
||||
t = Tensor([1,2,3,4])
|
||||
|
||||
# what's above gives you enough of an understanding to go use tinygrad as a library
|
||||
# however, a lot of the beauty of tinygrad is in how easy it is to interact with the internals
|
||||
# NOTE: the APIs here are subject to change
|
||||
|
||||
t_plus_3_plus_4 = t + 3 + 4
|
||||
print(t_plus_3_plus_4.uop)
|
||||
"""
|
||||
UOp(Ops.ADD, dtypes.int, arg=None, src=(
|
||||
UOp(Ops.ADD, dtypes.int, arg=None, src=(
|
||||
UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
|
||||
UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),
|
||||
x3:=UOp(Ops.DEVICE, dtypes.void, arg='CPU', src=()),)),
|
||||
UOp(Ops.EXPAND, dtypes.int, arg=(4,), src=(
|
||||
UOp(Ops.RESHAPE, dtypes.int, arg=(1,), src=(
|
||||
UOp(Ops.CONST, dtypes.int, arg=3, src=(
|
||||
x7:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(), strides=(), offset=0, mask=None, contiguous=True),)), src=(
|
||||
x3,)),)),)),)),)),
|
||||
UOp(Ops.EXPAND, dtypes.int, arg=(4,), src=(
|
||||
UOp(Ops.RESHAPE, dtypes.int, arg=(1,), src=(
|
||||
UOp(Ops.CONST, dtypes.int, arg=4, src=(
|
||||
x7,)),)),)),))
|
||||
"""
|
||||
# you can see it's adding both 3 and 4
|
||||
|
||||
# but by the time we are actually running the code, it's adding 7
|
||||
# `kernelize` will simplify and group the operations in the graph into kernels
|
||||
t_plus_3_plus_4.kernelize()
|
||||
print(t_plus_3_plus_4.uop)
|
||||
"""
|
||||
UOp(Ops.ASSIGN, dtypes.int, arg=None, src=(
|
||||
x0:=UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
|
||||
UOp(Ops.UNIQUE, dtypes.void, arg=7, src=()),
|
||||
x2:=UOp(Ops.DEVICE, dtypes.void, arg='CPU', src=()),)),
|
||||
UOp(Ops.KERNEL, dtypes.void, arg=<Kernel 12 SINK(<Ops.STORE: 48>,) (__add__,)>, src=(
|
||||
x0,
|
||||
UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
|
||||
UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),
|
||||
x2,)),)),))
|
||||
"""
|
||||
# ASSIGN has two srcs, src[0] is the BUFFER that's assigned to, and src[1] is the thing to assign
|
||||
# src[1] is the GPU Kernel that's going to be run
|
||||
# we can get the ast of the Kernel as follows
|
||||
kernel_ast = t_plus_3_plus_4.uop.src[1].arg.ast
|
||||
|
||||
# almost everything in tinygrad functions as a rewrite of the UOps
|
||||
# the codegen rewrites the ast to a simplified form ready for "rendering"
|
||||
from tinygrad.codegen import full_rewrite_to_sink
|
||||
rewritten_ast = full_rewrite_to_sink(kernel_ast)
|
||||
print(rewritten_ast)
|
||||
"""
|
||||
UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.INDEX, dtypes.int.ptr(4), arg=None, src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(4), arg=0, src=()),
|
||||
x3:=UOp(Ops.SPECIAL, dtypes.int, arg=('gidx0', 4), src=()),)),
|
||||
UOp(Ops.ADD, dtypes.int, arg=None, src=(
|
||||
UOp(Ops.LOAD, dtypes.int, arg=None, src=(
|
||||
UOp(Ops.INDEX, dtypes.int.ptr(4), arg=None, src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(4), arg=1, src=()),
|
||||
x3,)),)),
|
||||
UOp(Ops.CONST, dtypes.int, arg=7, src=()),)),)),))
|
||||
"""
|
||||
# you can see at this point we are adding 7, not 3 and 4
|
||||
|
||||
# with DEBUG=4, we can see the code.
|
||||
# since optimizations are on, it UPCASTed the operation, explicitly writing out all 4 +7s
|
||||
t_plus_3_plus_4.realize()
|
||||
"""
|
||||
void E_4n2(int* restrict data0, int* restrict data1) {
|
||||
int val0 = *(data1+0);
|
||||
int val1 = *(data1+1);
|
||||
int val2 = *(data1+2);
|
||||
int val3 = *(data1+3);
|
||||
*(data0+0) = (val0+7);
|
||||
*(data0+1) = (val1+7);
|
||||
*(data0+2) = (val2+7);
|
||||
*(data0+3) = (val3+7);
|
||||
}
|
||||
"""
|
||||
# the function name E_4n2 is "E" for elementwise op (as opposed to "r" for reduce op)
|
||||
# "4" for the size, and "n2" for name deduping (it's the 3rd function with the same E and 4 in this session)
|
||||
# when you print the name with DEBUG=2, you'll see the 4 is yellow, meaning that it's upcasted
|
||||
# if you run with NOOPT=1 ...
|
||||
"""
|
||||
void E_4n2(int* restrict data0, int* restrict data1) {
|
||||
for (int ridx0 = 0; ridx0 < 4; ridx0++) {
|
||||
int val0 = *(data1+ridx0);
|
||||
*(data0+ridx0) = (val0+7);
|
||||
}
|
||||
}
|
||||
"""
|
||||
# ... you get this unoptimized code with a loop and the 4 is blue (for global). the color code is in kernel.py
|
||||
|
||||
# %% ********
|
||||
print("******* PART 3 *******")
|
||||
|
||||
# now, we go even lower and understand UOps better and how the graph rewrite engine works.
|
||||
# it's much simpler than what's in LLVM or MLIR
|
||||
|
||||
from tinygrad import dtypes
|
||||
from tinygrad.uop.ops import UOp, Ops
|
||||
|
||||
# first, we'll construct some const UOps
|
||||
a = UOp(Ops.CONST, dtypes.int, arg=2)
|
||||
b = UOp(Ops.CONST, dtypes.int, arg=2)
|
||||
|
||||
# if you have been paying attention, you should know these are the same Python object
|
||||
assert a is b
|
||||
|
||||
# UOps support normal Python math operations, so a_plus_b expresses the spec for 2 + 2
|
||||
a_plus_b = a + b
|
||||
print(a_plus_b)
|
||||
"""
|
||||
UOp(Ops.ADD, dtypes.int, arg=None, src=(
|
||||
x0:=UOp(Ops.CONST, dtypes.int, arg=2, src=()),
|
||||
x0,))
|
||||
"""
|
||||
|
||||
# we could actually render this 2+2 into a language like c and run it
|
||||
# or, we can use tinygrad's graph rewrite engine to "constant fold"
|
||||
|
||||
from tinygrad.uop.ops import graph_rewrite, UPat, PatternMatcher
|
||||
|
||||
# a `PatternMatcher` is a list of tuples. for each element in the list:
|
||||
# [0] is the pattern to match, and [1] is the function to run.
|
||||
# this function can return either a UOp to replace the pattern with, or None to not replace
|
||||
simple_pm = PatternMatcher([
|
||||
(UPat(Ops.ADD, src=(UPat(Ops.CONST, name="c1"), UPat(Ops.CONST, name="c2"))),
|
||||
lambda c1,c2: UOp(Ops.CONST, dtype=c1.dtype, arg=c1.arg+c2.arg)),
|
||||
])
|
||||
# this pattern matches the addition of two CONST and rewrites it into a single CONST UOp
|
||||
|
||||
# to actually apply the pattern to a_plus_b, we use graph_rewrite
|
||||
a_plus_b_simplified = graph_rewrite(a_plus_b, simple_pm)
|
||||
print(a_plus_b_simplified)
|
||||
"""
|
||||
UOp(Ops.CONST, dtypes.int, arg=4, src=())
|
||||
"""
|
||||
# 2+2 is in fact, 4
|
||||
|
||||
# we can also use syntactic sugar to write the pattern nicer
|
||||
simpler_pm = PatternMatcher([
|
||||
(UPat.cvar("c1")+UPat.cvar("c2"), lambda c1,c2: c1.const_like(c1.arg+c2.arg))
|
||||
])
|
||||
assert graph_rewrite(a_plus_b, simple_pm) is graph_rewrite(a_plus_b, simpler_pm)
|
||||
# note again the use of is, UOps are immutable and globally unique
|
||||
|
||||
# %% ********
|
||||
|
||||
# that brings you to an understanding of the most core concepts in tinygrad
|
||||
# you can run this with VIZ=1 to use the web based graph rewrite explorer
|
||||
# hopefully now you understand it. the nodes in the graph are just UOps
|
||||
+1
-1
@@ -12,7 +12,7 @@ tinygrad supports various runtimes, enabling your code to scale across a wide ra
|
||||
| [GPU (OpenCL)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_gpu.py) | Accelerates computations using OpenCL on GPUs | OpenCL 2.0 compatible device |
|
||||
| [CPU (C Code)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_cpu.py) | Runs on CPU using the clang compiler | `clang` compiler in system `PATH` |
|
||||
| [LLVM (LLVM IR)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_llvm.py) | Runs on CPU using the LLVM compiler infrastructure | llvm libraries installed and findable |
|
||||
| [WEBGPU](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_webgpu.py) | Runs on GPU using the Dawn WebGPU engine (used in Google Chrome) | Dawn library installed and findable. Download binaries [here](https://github.com/wpmed92/pydawn/releases/tag/v0.3.0). |
|
||||
| [WEBGPU](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_webgpu.py) | Runs on GPU using the Dawn WebGPU engine (used in Google Chrome) | Dawn library installed and findable. Download binaries [here](https://github.com/wpmed92/pydawn/releases/tag/v0.1.6). |
|
||||
|
||||
## Interoperability
|
||||
|
||||
|
||||
@@ -24,7 +24,6 @@
|
||||
::: tinygrad.Tensor.randn
|
||||
::: tinygrad.Tensor.randn_like
|
||||
::: tinygrad.Tensor.randint
|
||||
::: tinygrad.Tensor.randperm
|
||||
::: tinygrad.Tensor.normal
|
||||
::: tinygrad.Tensor.uniform
|
||||
::: tinygrad.Tensor.scaled_uniform
|
||||
|
||||
@@ -35,7 +35,6 @@ Elementwise ops operate on a per element basis. They don't change the shape of t
|
||||
|
||||
::: tinygrad.Tensor.relu
|
||||
::: tinygrad.Tensor.sigmoid
|
||||
::: tinygrad.Tensor.logsigmoid
|
||||
::: tinygrad.Tensor.hardsigmoid
|
||||
::: tinygrad.Tensor.elu
|
||||
::: tinygrad.Tensor.celu
|
||||
@@ -78,7 +77,6 @@ Elementwise ops operate on a per element basis. They don't change the shape of t
|
||||
::: tinygrad.Tensor.minimum
|
||||
::: tinygrad.Tensor.where
|
||||
::: tinygrad.Tensor.copysign
|
||||
::: tinygrad.Tensor.logaddexp
|
||||
|
||||
## Casting Ops
|
||||
|
||||
|
||||
@@ -18,7 +18,6 @@
|
||||
::: tinygrad.Tensor.repeat_interleave
|
||||
::: tinygrad.Tensor.split
|
||||
::: tinygrad.Tensor.chunk
|
||||
::: tinygrad.Tensor.unfold
|
||||
::: tinygrad.Tensor.meshgrid
|
||||
::: tinygrad.Tensor.squeeze
|
||||
::: tinygrad.Tensor.unsqueeze
|
||||
@@ -26,6 +25,5 @@
|
||||
::: tinygrad.Tensor.transpose
|
||||
::: tinygrad.Tensor.flatten
|
||||
::: tinygrad.Tensor.unflatten
|
||||
::: tinygrad.Tensor.diag
|
||||
::: tinygrad.Tensor.roll
|
||||
::: tinygrad.Tensor.rearrange
|
||||
@@ -37,10 +37,8 @@
|
||||
::: tinygrad.Tensor.scatter
|
||||
::: tinygrad.Tensor.scatter_reduce
|
||||
::: tinygrad.Tensor.masked_select
|
||||
::: tinygrad.Tensor.masked_fill
|
||||
::: tinygrad.Tensor.sort
|
||||
::: tinygrad.Tensor.topk
|
||||
::: tinygrad.Tensor.multinomial
|
||||
|
||||
## Neural Network (functional)
|
||||
|
||||
|
||||
+4
-4
@@ -6,7 +6,7 @@ If you don't have a tinybox and you want one, see [tinygrad.org](https://tinygra
|
||||
|
||||
## Welcome
|
||||
|
||||
Welcome to your tinybox! The tinybox is the universal system purpose-built for all AI infrastructure and workloads, from training to inference. The red box includes six 7900XTX GPUs, the green box includes six 4090 GPUs, and the green v2 box includes four 5090 GPUs. Whether you bought a red one or a green one, we want you to love it.
|
||||
Welcome to your tinybox! The tinybox is the universal system purpose-built for all AI infrastructure and workloads, from training to inference. The red box includes six 7900XTX GPUs, and the green box includes six 4090 GPUs. Whether you bought a red one or a green one, we want you to love it.
|
||||
|
||||
We don't have a stupid cloud service, you don't have to create a tiny account to set it up, and we aren't tracking how you use the box. We're just happy you bought one. This petaflop is your petaflop.
|
||||
|
||||
@@ -47,8 +47,8 @@ Reboot after making these changes or restart the `displayservice.service` servic
|
||||
|
||||
The [default tinybox image](https://github.com/tinygrad/tinyos) ships with tinygrad and PyTorch. While we develop tinygrad, the box is universal hardware. Use whatever framework you desire, run notebooks, download demos, install more things, train, inference, live, laugh, love, you aren't paying per hour for this box so the only limit is your imagination.
|
||||
|
||||
## Building the OS image
|
||||
## tinychat
|
||||
|
||||
The OS image is built using `ubuntu-image` from <https://github.com/tinygrad/tinyos>.
|
||||
Since LLMs are so popular, we ship with a built in tinygrad based chatbot using a LLaMA-3 finetune. Visit the IP (not the BMC IP) of your tinybox in a web browser on your computer or phone, and you'll find a friendly looking chat interface. This chatbot also provides an OpenAI compatible LLM API on that port, so you can script it.
|
||||
|
||||
After cloning, run `make green` or `make red` to build a tinybox green or tinybox red image respectively.
|
||||
The conversations you have with this chatbot are between you and your tinybox. Also, the history in the web app is saved on the client, not the tinybox.
|
||||
|
||||
@@ -78,7 +78,10 @@ if __name__ == "__main__":
|
||||
|
||||
@TinyJit
|
||||
def get_action(obs:Tensor) -> Tensor:
|
||||
# TODO: with no_grad
|
||||
Tensor.no_grad = True
|
||||
ret = model(obs)[0].exp().multinomial().realize()
|
||||
Tensor.no_grad = False
|
||||
return ret
|
||||
|
||||
st, steps = time.perf_counter(), 0
|
||||
|
||||
+34
-36
@@ -2,19 +2,15 @@ import time
|
||||
start_tm = time.perf_counter()
|
||||
import math
|
||||
from typing import Tuple, cast
|
||||
from tinygrad import Tensor, nn, GlobalCounters, TinyJit, dtypes, Device
|
||||
import numpy as np
|
||||
from tinygrad import Tensor, nn, GlobalCounters, TinyJit, dtypes
|
||||
from tinygrad.helpers import partition, trange, getenv, Context
|
||||
from extra.lr_scheduler import OneCycleLR
|
||||
|
||||
GPUS = [f'{Device.DEFAULT}:{i}' for i in range(getenv("GPUS", 1))]
|
||||
|
||||
# override tinygrad defaults
|
||||
dtypes.default_float = dtypes.half
|
||||
Context(FUSE_ARANGE=1, FUSE_OPTIM=1).__enter__()
|
||||
|
||||
# from https://github.com/tysam-code/hlb-CIFAR10/blob/main/main.py
|
||||
batchsize = getenv("BS", 1024)
|
||||
assert batchsize % len(GPUS) == 0, f"{batchsize=} is not a multiple of {len(GPUS)=}"
|
||||
bias_scaler = 64
|
||||
hyp = {
|
||||
'opt': {
|
||||
@@ -71,7 +67,7 @@ class ConvGroup:
|
||||
cast(Tensor, self.norm2.weight).requires_grad = False
|
||||
def __call__(self, x:Tensor) -> Tensor:
|
||||
x = self.norm1(self.conv1(x).max_pool2d().float()).cast(dtypes.default_float).quick_gelu()
|
||||
return self.norm2(self.conv2(x).float()).cast(dtypes.default_float).quick_gelu() + x
|
||||
return self.norm2(self.conv2(x).float()).cast(dtypes.default_float).quick_gelu()
|
||||
|
||||
class SpeedyConvNet:
|
||||
def __init__(self):
|
||||
@@ -82,25 +78,23 @@ class SpeedyConvNet:
|
||||
self.linear = nn.Linear(depths['block3'], depths['num_classes'], bias=False)
|
||||
def __call__(self, x:Tensor) -> Tensor:
|
||||
x = self.whiten(x).quick_gelu()
|
||||
# ************* HACKS *************
|
||||
x = x.pad((1,0,0,1)) # TODO: this pad should not be here! copied from hlb_cifar10 for speed
|
||||
# ************* HACKS *************
|
||||
x = x.sequential([self.conv_group_1, self.conv_group_2, self.conv_group_3])
|
||||
return self.linear(x.max(axis=(2,3))) * hyp['opt']['scaling_factor']
|
||||
|
||||
if __name__ == "__main__":
|
||||
# *** dataset ***
|
||||
X_train, Y_train, X_test, Y_test = nn.datasets.cifar()
|
||||
# TODO: without this line indexing doesn't fuse!
|
||||
X_train, Y_train, X_test, Y_test = [x.contiguous() for x in [X_train, Y_train, X_test, Y_test]]
|
||||
cifar10_std, cifar10_mean = X_train.float().std_mean(axis=(0, 2, 3))
|
||||
def preprocess(X:Tensor) -> Tensor: return ((X - cifar10_mean.view(1, -1, 1, 1)) / cifar10_std.view(1, -1, 1, 1)).cast(dtypes.default_float)
|
||||
def preprocess(X:Tensor, Y:Tensor) -> Tuple[Tensor, Tensor]:
|
||||
return ((X - cifar10_mean.view(1, -1, 1, 1)) / cifar10_std.view(1, -1, 1, 1)).cast(dtypes.default_float), Y.one_hot(depths['num_classes'])
|
||||
|
||||
# *** model ***
|
||||
model = SpeedyConvNet()
|
||||
state_dict = nn.state.get_state_dict(model)
|
||||
if len(GPUS) > 1:
|
||||
cifar10_std.to_(GPUS)
|
||||
cifar10_mean.to_(GPUS)
|
||||
for x in state_dict.values(): x.to_(GPUS)
|
||||
|
||||
#for k,v in nn.state.torch_load("/tmp/cifar_net.pt").items(): print(k)
|
||||
|
||||
params_bias, params_non_bias = partition(state_dict.items(), lambda x: 'bias' in x[0])
|
||||
opt_bias = nn.optim.SGD([x[1] for x in params_bias], lr=0.01, momentum=.85, nesterov=True, weight_decay=hyp['opt']['bias_decay'])
|
||||
@@ -117,44 +111,48 @@ if __name__ == "__main__":
|
||||
lr_sched_bias = OneCycleLR(opt_bias, max_lr=hyp['opt']['bias_lr'], pct_start=pct_start, div_factor=initial_div_factor, final_div_factor=1./(initial_div_factor*final_lr_ratio), total_steps=total_train_steps)
|
||||
lr_sched_non_bias = OneCycleLR(opt_non_bias, max_lr=hyp['opt']['non_bias_lr'], pct_start=pct_start, div_factor=initial_div_factor, final_div_factor=1./(initial_div_factor*final_lr_ratio), total_steps=total_train_steps)
|
||||
|
||||
def loss_fn(out:Tensor, Y:Tensor) -> Tensor:
|
||||
ret = out.sparse_categorical_crossentropy(Y, reduction='none', label_smoothing=0.2)
|
||||
return ret.mul(hyp['opt']['loss_scale_scaler']*loss_batchsize_scaler).sum().div(hyp['opt']['loss_scale_scaler'])
|
||||
def loss_fn(out, Y):
|
||||
return out.cross_entropy(Y, reduction='none', label_smoothing=0.2).mul(hyp['opt']['loss_scale_scaler']*loss_batchsize_scaler).sum().div(hyp['opt']['loss_scale_scaler'])
|
||||
|
||||
@TinyJit
|
||||
@Tensor.train()
|
||||
def train_step(idxs:Tensor) -> Tensor:
|
||||
X, Y = X_train[idxs], Y_train[idxs]
|
||||
if len(GPUS) > 1:
|
||||
X.shard_(GPUS, axis=0)
|
||||
Y.shard_(GPUS, axis=0)
|
||||
out = model(preprocess(X))
|
||||
with Context(SPLIT_REDUCEOP=0, FUSE_ARANGE=1):
|
||||
X = X_train[idxs]
|
||||
Y = Y_train[idxs].realize(X)
|
||||
X, Y = preprocess(X, Y)
|
||||
out = model(X)
|
||||
loss = loss_fn(out, Y)
|
||||
opt.zero_grad()
|
||||
loss.backward()
|
||||
return (loss / (batchsize*loss_batchsize_scaler)).realize(*opt.schedule_step(),
|
||||
*lr_sched_bias.schedule_step(), *lr_sched_non_bias.schedule_step())
|
||||
opt.step()
|
||||
lr_sched_bias.step()
|
||||
lr_sched_non_bias.step()
|
||||
return loss / (batchsize*loss_batchsize_scaler)
|
||||
|
||||
eval_batchsize = 2500
|
||||
@TinyJit
|
||||
@Tensor.test()
|
||||
def val_step() -> Tuple[Tensor, Tensor]:
|
||||
# TODO with Tensor.no_grad()
|
||||
Tensor.no_grad = True
|
||||
loss, acc = [], []
|
||||
for i in range(0, X_test.size(0), eval_batchsize):
|
||||
X, Y = X_test[i:i+eval_batchsize], Y_test[i:i+eval_batchsize]
|
||||
if len(GPUS) > 1:
|
||||
X.shard_(GPUS, axis=0)
|
||||
Y.shard_(GPUS, axis=0)
|
||||
out = model(preprocess(X))
|
||||
X, Y = preprocess(X_test[i:i+eval_batchsize], Y_test[i:i+eval_batchsize])
|
||||
out = model(X)
|
||||
loss.append(loss_fn(out, Y))
|
||||
acc.append((out.argmax(-1) == Y).sum() / eval_batchsize)
|
||||
return Tensor.stack(*loss).mean() / (batchsize*loss_batchsize_scaler), Tensor.stack(*acc).mean()
|
||||
|
||||
Tensor.manual_seed(1337)
|
||||
num_train_samples = X_train.shape[0]
|
||||
acc.append((out.argmax(-1).one_hot(depths['num_classes']) * Y).sum() / eval_batchsize)
|
||||
ret = Tensor.stack(*loss).mean() / (batchsize*loss_batchsize_scaler), Tensor.stack(*acc).mean()
|
||||
Tensor.no_grad = False
|
||||
return ret
|
||||
|
||||
np.random.seed(1337)
|
||||
for epoch in range(math.ceil(hyp['misc']['train_epochs'])):
|
||||
# TODO: move to tinygrad
|
||||
gst = time.perf_counter()
|
||||
tidxs = Tensor.randperm(num_train_samples, dtype='int')[:num_steps_per_epoch*batchsize].reshape(num_steps_per_epoch, batchsize)
|
||||
idxs = np.arange(X_train.shape[0])
|
||||
np.random.shuffle(idxs)
|
||||
tidxs = Tensor(idxs, dtype='int')[:num_steps_per_epoch*batchsize].reshape(num_steps_per_epoch, batchsize) # NOTE: long doesn't fold
|
||||
train_loss:float = 0
|
||||
for epoch_step in (t:=trange(num_steps_per_epoch)):
|
||||
st = time.perf_counter()
|
||||
|
||||
@@ -1,12 +1,12 @@
|
||||
# model based off https://medium.com/data-science/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
|
||||
from typing import Callable
|
||||
from typing import List, Callable
|
||||
from tinygrad import Tensor, TinyJit, nn, GlobalCounters
|
||||
from tinygrad.helpers import getenv, colored, trange
|
||||
from tinygrad.nn.datasets import mnist
|
||||
|
||||
class Model:
|
||||
def __init__(self):
|
||||
self.layers: list[Callable[[Tensor], Tensor]] = [
|
||||
self.layers: List[Callable[[Tensor], Tensor]] = [
|
||||
nn.Conv2d(1, 32, 5), Tensor.relu,
|
||||
nn.Conv2d(32, 32, 5), Tensor.relu,
|
||||
nn.BatchNorm(32), Tensor.max_pool2d,
|
||||
@@ -21,17 +21,20 @@ if __name__ == "__main__":
|
||||
X_train, Y_train, X_test, Y_test = mnist(fashion=getenv("FASHION"))
|
||||
|
||||
model = Model()
|
||||
opt = (nn.optim.Adam if not getenv("MUON") else nn.optim.Muon)(nn.state.get_parameters(model))
|
||||
opt = nn.optim.Adam(nn.state.get_parameters(model))
|
||||
|
||||
@TinyJit
|
||||
@Tensor.train()
|
||||
def train_step() -> Tensor:
|
||||
opt.zero_grad()
|
||||
samples = Tensor.randint(getenv("BS", 512), high=X_train.shape[0])
|
||||
# TODO: this "gather" of samples is very slow. will be under 5s when this is fixed
|
||||
loss = model(X_train[samples]).sparse_categorical_crossentropy(Y_train[samples]).backward()
|
||||
return loss.realize(*opt.schedule_step())
|
||||
opt.step()
|
||||
return loss
|
||||
|
||||
@TinyJit
|
||||
@Tensor.test()
|
||||
def get_test_acc() -> Tensor: return (model(X_test).argmax(axis=1) == Y_test).mean()*100
|
||||
|
||||
test_acc = float('nan')
|
||||
|
||||
@@ -1,10 +1,11 @@
|
||||
import sys, time
|
||||
import sys, onnx, time, pickle
|
||||
from tinygrad import TinyJit, GlobalCounters, fetch, getenv
|
||||
from tinygrad.frontend.onnx import OnnxRunner
|
||||
from extra.onnx_helpers import get_example_inputs, validate
|
||||
|
||||
def load_onnx_model(onnx_file):
|
||||
run_onnx = OnnxRunner(onnx_file)
|
||||
onnx_model = onnx.load(onnx_file)
|
||||
run_onnx = OnnxRunner(onnx_model)
|
||||
run_onnx_jit = TinyJit(lambda **kwargs: next(iter(run_onnx({k:v.to(None) for k,v in kwargs.items()}).values())), prune=True, optimize=True)
|
||||
return run_onnx_jit, run_onnx.graph_inputs
|
||||
|
||||
|
||||
+3
-1
@@ -4,7 +4,7 @@ sys.path.append(os.getcwd())
|
||||
|
||||
from io import StringIO
|
||||
from contextlib import redirect_stdout
|
||||
from tinygrad import Tensor, nn
|
||||
from tinygrad import Tensor, nn, Device, dtypes
|
||||
from tinygrad.helpers import Timing, colored, getenv, fetch
|
||||
from extra.models.llama import Transformer, convert_from_huggingface, fix_bf16
|
||||
from sentencepiece import SentencePieceProcessor
|
||||
@@ -23,6 +23,8 @@ def create_fixed_tokenizer(output_file):
|
||||
# echo -en "write 2+2\nwrite hello world\ny\n" | TEMP=0 python3 examples/coder.py
|
||||
|
||||
if __name__ == "__main__":
|
||||
Tensor.no_grad = True
|
||||
|
||||
# https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/config.json
|
||||
with Timing("create model: "):
|
||||
model = Transformer(4096, 14336, n_heads=32, n_layers=32, norm_eps=1e-5, vocab_size=32002, n_kv_heads=8, max_context=4096, jit=getenv("JIT", 1))
|
||||
|
||||
@@ -10,7 +10,6 @@ import tensorflow as tf
|
||||
import tf2onnx
|
||||
from tinygrad.frontend.onnx import OnnxRunner
|
||||
from tinygrad.tensor import Tensor
|
||||
from tinygrad.helpers import to_mv
|
||||
from extra.export_model import export_model_clang, compile_net, jit_model
|
||||
|
||||
def get_uncompiled_model2(dataset_size=32, output_size=4):
|
||||
@@ -26,7 +25,7 @@ class TinyOnnx:
|
||||
def __init__(self, keras_model):
|
||||
input_signature = [tf.TensorSpec([1,32], tf.float32, name='x')]
|
||||
onnx_model, _ = tf2onnx.convert.from_keras(keras_model, input_signature, opset=13)
|
||||
self.run_onnx = OnnxRunner(Tensor(onnx_model.SerializeToString(), device="PYTHON"))
|
||||
self.run_onnx = OnnxRunner(onnx_model)
|
||||
|
||||
def forward(self, x):
|
||||
return self.run_onnx({"x": x}, debug=False)['predictions']
|
||||
@@ -48,8 +47,8 @@ def compile_onnx_model(onnx_model):
|
||||
cprog.append("void initialize(float *weights) {")
|
||||
weights = bytes()
|
||||
for name,cl in bufs_to_save.items():
|
||||
cprog.append(f"memcpy({name}, weights + {len(weights)//4}, {cl._buf.size});")
|
||||
weights += bytes(to_mv(cl._buf.va_addr, cl._buf.size))
|
||||
cprog.append(f"memcpy({name}, weights + {len(weights)//4}, {len(cl._buf)*4});")
|
||||
weights += bytes(cl._buf)
|
||||
cprog.append("}")
|
||||
|
||||
# write the weights to disk
|
||||
|
||||
@@ -159,6 +159,7 @@ def init_vits(
|
||||
text_mapper = TextMapper(apply_cleaners=True, symbols=symbols)
|
||||
|
||||
# Load the model.
|
||||
Tensor.no_grad = True
|
||||
if seed is not None:
|
||||
Tensor.manual_seed(seed)
|
||||
np.random.seed(seed)
|
||||
@@ -220,6 +221,7 @@ def mp_output_stream(q: mp.Queue, counter: mp.Value, num_channels: int, sample_r
|
||||
if __name__ == "__main__":
|
||||
import nltk
|
||||
nltk.download("punkt")
|
||||
Tensor.no_grad = True
|
||||
# Parse CLI arguments
|
||||
parser = argparse.ArgumentParser("Have a tiny conversation with tinygrad")
|
||||
|
||||
|
||||
+3
-5
@@ -85,10 +85,7 @@ class Transformer:
|
||||
seqlen = tokens.shape[1]
|
||||
tok_emb = self.wte(tokens)
|
||||
|
||||
# not symbolic when consuming the prompt
|
||||
selected_pos = (0, seqlen) if start_pos.val == 0 else (start_pos, start_pos+1)
|
||||
pos_emb = self.wpe(self.allpos.shrink((None, selected_pos)))
|
||||
|
||||
pos_emb = self.wpe(self.allpos.shrink((None, (start_pos, start_pos+seqlen))))
|
||||
h = tok_emb + pos_emb
|
||||
|
||||
if HALF: h = h.half()
|
||||
@@ -193,7 +190,7 @@ class GPT2:
|
||||
(f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None, enabled=timing):
|
||||
with WallTimeEvent(BenchEvent.STEP):
|
||||
if batch_size == 1 and len(toks[0][start_pos:]) == 1:
|
||||
tokens = Variable("tokens", 0, VOCAB_SIZE-1).bind(toks[0][start_pos])
|
||||
tokens = Variable("tokens", 0, VOCAB_SIZE).bind(toks[0][start_pos])
|
||||
else:
|
||||
tokens = Tensor([x[start_pos:] for x in toks])
|
||||
tok = self.model(tokens, Variable("start_pos", 1 if start_pos else 0, MAX_CONTEXT-1).bind(start_pos), temperature).tolist()
|
||||
@@ -204,6 +201,7 @@ class GPT2:
|
||||
# **** main code ****
|
||||
|
||||
if __name__ == "__main__":
|
||||
Tensor.no_grad = True
|
||||
print(f"using {Device.DEFAULT} backend")
|
||||
default_prompt = "What is the answer to life, the universe, and everything?"
|
||||
|
||||
|
||||
@@ -0,0 +1,133 @@
|
||||
from extra.models.resnet import ResNet50
|
||||
from extra.mcts_search import mcts_search
|
||||
from examples.mlperf.helpers import get_mlperf_bert_model
|
||||
from tinygrad import Tensor, Device, dtypes, nn
|
||||
from tinygrad.codegen.kernel import Kernel
|
||||
from tinygrad.codegen.heuristic import hand_coded_optimizations
|
||||
from tinygrad.uop.ops import Ops, sym_infer
|
||||
from tinygrad.device import Compiled
|
||||
from tinygrad.engine.search import beam_search, bufs_from_lin
|
||||
from tinygrad.helpers import DEBUG, ansilen, getenv, colored, TRACEMETA
|
||||
from extra.optimization.helpers import time_linearizer
|
||||
|
||||
def get_sched_resnet():
|
||||
mdl = ResNet50()
|
||||
optim = (nn.optim.LARS if getenv("LARS") else nn.optim.SGD)(nn.state.get_parameters(mdl))
|
||||
BS = getenv("BS", 64)
|
||||
|
||||
# run model twice to get only what changes, these are the kernels of the model
|
||||
for _ in range(2):
|
||||
out = mdl(Tensor.empty(BS, 3, 224, 224))
|
||||
targets = [out]
|
||||
if getenv("BACKWARD"):
|
||||
optim.zero_grad()
|
||||
out.sparse_categorical_crossentropy(Tensor.empty(BS, dtype=dtypes.int)).backward()
|
||||
targets += [x for x in optim.schedule_step()]
|
||||
sched = Tensor.schedule(*targets)
|
||||
print(f"schedule length {len(sched)}")
|
||||
return sched
|
||||
|
||||
def get_sched_bert():
|
||||
mdl = get_mlperf_bert_model()
|
||||
optim = nn.optim.LAMB(nn.state.get_parameters(mdl))
|
||||
|
||||
# fake data
|
||||
BS = getenv("BS", 9)
|
||||
input_ids = Tensor.empty((BS, 512), dtype=dtypes.float32)
|
||||
segment_ids = Tensor.empty((BS, 512), dtype=dtypes.float32)
|
||||
attention_mask = Tensor.empty((BS, 512), dtype=dtypes.default_float)
|
||||
masked_positions = Tensor.empty((BS, 76), dtype=dtypes.float32)
|
||||
masked_lm_ids = Tensor.empty((BS, 76), dtype=dtypes.float32)
|
||||
masked_lm_weights = Tensor.empty((BS, 76), dtype=dtypes.float32)
|
||||
next_sentence_labels = Tensor.empty((BS, 1), dtype=dtypes.float32)
|
||||
|
||||
# run model twice to get only what changes, these are the kernels of the model
|
||||
for _ in range(2):
|
||||
lm_logits, seq_relationship_logits = mdl(input_ids, attention_mask, masked_positions, segment_ids)
|
||||
targets = [lm_logits, seq_relationship_logits]
|
||||
if getenv("BACKWARD"):
|
||||
optim.zero_grad()
|
||||
loss = mdl.loss(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels)
|
||||
# ignore grad norm and loss scaler for now
|
||||
loss.backward()
|
||||
targets += [x for x in optim.schedule_step()]
|
||||
sched = Tensor.schedule(*targets)
|
||||
print(f"schedule length {len(sched)}")
|
||||
return sched
|
||||
|
||||
if __name__ == "__main__":
|
||||
if getenv("HALF", 1):
|
||||
dtypes.default_float = dtypes.half
|
||||
|
||||
# the device we are optimizing for
|
||||
device: Compiled = Device[Device.DEFAULT]
|
||||
if getenv("BACKWARD"): Tensor.training = True
|
||||
print(f"optimizing for {Device.DEFAULT}")
|
||||
|
||||
sched = globals()[f"get_sched_{getenv('MODEL', 'resnet')}"]()
|
||||
sched = [x for x in sched if x.ast.op is Ops.SINK]
|
||||
|
||||
# focus on one kernel
|
||||
if getenv("KERNEL", -1) >= 0: sched = sched[getenv("KERNEL", -1):getenv("KERNEL", -1)+1]
|
||||
|
||||
# work with the schedule
|
||||
total_tm = 0
|
||||
running_gflops = 0
|
||||
usage = {}
|
||||
for i,si in enumerate(sched):
|
||||
if DEBUG >= 3: print(si.ast)
|
||||
|
||||
rawbufs = bufs_from_lin(Kernel(si.ast))
|
||||
|
||||
# "linearize" the op into uops in different ways
|
||||
lins: list[tuple[Kernel, str]] = []
|
||||
|
||||
# always try hand coded opt
|
||||
lin = Kernel(si.ast, opts=device.renderer)
|
||||
lin.apply_opts(hand_coded_optimizations(lin))
|
||||
lins.append((lin, "HC"))
|
||||
|
||||
# maybe try tensor cores
|
||||
lin = Kernel(si.ast, opts=device.renderer)
|
||||
if lin.apply_tensor_cores():
|
||||
lins.append((lin, "TC"))
|
||||
|
||||
# try a beam search
|
||||
if beam:=getenv("BEAM"):
|
||||
lin = Kernel(si.ast, opts=device.renderer)
|
||||
lin = beam_search(lin, rawbufs, beam, bool(getenv("BEAM_ESTIMATE", 1)))
|
||||
lins.append((lin, "BEAM"))
|
||||
|
||||
# try MCTS
|
||||
if mcts:=getenv("MCTS"):
|
||||
lin = Kernel(si.ast, opts=device.renderer)
|
||||
lin = mcts_search(lin, rawbufs, mcts)
|
||||
lins.append((lin, "MCTS"))
|
||||
|
||||
# benchmark the programs
|
||||
choices = []
|
||||
for lin, nm in lins:
|
||||
tm = time_linearizer(lin, rawbufs, allow_test_size=False, cnt=10, disable_cache=True)
|
||||
ops = (prg:=lin.to_program()).estimates.ops
|
||||
gflops = sym_infer(ops, {k:k.min for k in lin.ast.variables()})*1e-9/tm
|
||||
choices.append((tm, gflops, lin, prg, nm))
|
||||
|
||||
sorted_choices = sorted(choices, key=lambda x: x[0])
|
||||
if DEBUG >= 1: # print all kernels
|
||||
for tm, gflops, lin, prg, nm in choices:
|
||||
print(f" kernel {i:2d} {lin.name+' '*(37-ansilen(lin.name))} {str(prg.global_size):18s} {str(prg.local_size):12s} takes {tm*1000:7.2f} ms, {gflops:6.0f} GFLOPS -- {colored(nm, 'green') if lin is sorted_choices[0][2] else nm}")
|
||||
|
||||
tm, gflops, lin, prg, nm = sorted_choices[0]
|
||||
if getenv("SRC"):
|
||||
print(si.ast)
|
||||
print(lin.applied_opts)
|
||||
print(lin.to_program().src)
|
||||
total_tm += tm
|
||||
running_gflops += gflops * tm
|
||||
if (key := str([str(m) for m in si.metadata])) not in usage: usage[key] = (0, 0)
|
||||
usage[key] = (usage[key][0] + tm, usage[key][1] + 1)
|
||||
print(f"*** {total_tm*1000:7.2f} ms : kernel {i:2d} {lin.name+' '*(37-ansilen(lin.name))} {str(prg.global_size):18s} {str(prg.local_size):12s} takes {tm*1000:7.2f} ms, {gflops:6.0f} GFLOPS {[repr(m) if TRACEMETA >= 2 else str(m) for m in si.metadata]}")
|
||||
print(f"******* total {total_tm*1000:.2f} ms, {running_gflops/total_tm:6.0f} GFLOPS")
|
||||
print("usage:")
|
||||
for k in sorted(usage, key=lambda x: -usage[x][0])[:10]:
|
||||
print(f"{usage[k][0]*1000:.2f} ms: {k} ({usage[k][1]} times)")
|
||||
+35
-34
@@ -7,8 +7,8 @@ import random, time
|
||||
import numpy as np
|
||||
from typing import Optional
|
||||
from extra.lr_scheduler import OneCycleLR
|
||||
from tinygrad import nn, dtypes, Tensor, Device, GlobalCounters, TinyJit, Variable
|
||||
from tinygrad.nn.state import get_state_dict
|
||||
from tinygrad import nn, dtypes, Tensor, Device, GlobalCounters, TinyJit
|
||||
from tinygrad.nn.state import get_state_dict, get_parameters
|
||||
from tinygrad.nn import optim
|
||||
from tinygrad.helpers import Context, BEAM, WINO, getenv, colored, prod
|
||||
from extra.bench_log import BenchEvent, WallTimeEvent
|
||||
@@ -118,7 +118,7 @@ class SpeedyResNet:
|
||||
# hyper-parameters were exactly the same as the original repo
|
||||
bias_scaler = 58
|
||||
hyp = {
|
||||
'seed' : 201,
|
||||
'seed' : 209,
|
||||
'opt': {
|
||||
'bias_lr': 1.76 * bias_scaler/512,
|
||||
'non_bias_lr': 1.76 / 512,
|
||||
@@ -145,7 +145,6 @@ hyp = {
|
||||
},
|
||||
}
|
||||
|
||||
@Context(FUSE_ARANGE=getenv("FUSE_ARANGE", 1))
|
||||
def train_cifar():
|
||||
|
||||
def set_seed(seed):
|
||||
@@ -202,37 +201,24 @@ def train_cifar():
|
||||
idx_y = Tensor.arange(H, dtype=dtypes.int32).reshape((1,1,H,1))
|
||||
return (idx_x >= low_x) * (idx_x < (low_x + mask_size)) * (idx_y >= low_y) * (idx_y < (low_y + mask_size))
|
||||
|
||||
# Similar, but different enough.
|
||||
def make_random_crop_indices(shape, mask_size) -> Tensor:
|
||||
BS, _, H, W = shape
|
||||
low_x = Tensor.randint(BS, low=0, high=W-mask_size).reshape(BS,1,1,1)
|
||||
low_y = Tensor.randint(BS, low=0, high=H-mask_size).reshape(BS,1,1,1)
|
||||
idx_x = Tensor.arange(mask_size, dtype=dtypes.int32).reshape((1,1,1,mask_size))
|
||||
idx_y = Tensor.arange(mask_size, dtype=dtypes.int32).reshape((1,1,mask_size,1))
|
||||
return low_x, low_y, idx_x, idx_y
|
||||
|
||||
def random_crop(X:Tensor, crop_size=32):
|
||||
Xs, Ys, Xi, Yi = make_random_crop_indices(X.shape, crop_size)
|
||||
return X.gather(-1, (Xs + Xi).expand(-1, 3, X.shape[2], -1)).gather(-2, ((Ys+Yi).expand(-1, 3, crop_size, crop_size)))
|
||||
mask = make_square_mask(X.shape, crop_size)
|
||||
mask = mask.expand((-1,3,-1,-1))
|
||||
X_cropped = Tensor(X.numpy()[mask.numpy()])
|
||||
return X_cropped.reshape((-1, 3, crop_size, crop_size))
|
||||
|
||||
def cutmix(X, Y, order, mask_size=3):
|
||||
def cutmix(X:Tensor, Y:Tensor, mask_size=3):
|
||||
# fill the square with randomly selected images from the same batch
|
||||
mask = make_square_mask(X.shape, mask_size)
|
||||
X_patch, Y_patch = X[order], Y[order]
|
||||
order = list(range(0, X.shape[0]))
|
||||
random.shuffle(order)
|
||||
X_patch = Tensor(X.numpy()[order], device=X.device, dtype=X.dtype)
|
||||
Y_patch = Tensor(Y.numpy()[order], device=Y.device, dtype=Y.dtype)
|
||||
X_cutmix = mask.where(X_patch, X)
|
||||
mix_portion = float(mask_size**2)/(X.shape[-2]*X.shape[-1])
|
||||
Y_cutmix = mix_portion * Y_patch + (1. - mix_portion) * Y
|
||||
return X_cutmix, Y_cutmix
|
||||
|
||||
@TinyJit
|
||||
def augmentations(X:Tensor, Y:Tensor):
|
||||
perms = Tensor.randperm(X.shape[0], device=X.device) # We reuse perms for cutmix, because they are expensivne to generate
|
||||
if getenv("RANDOM_CROP", 1):
|
||||
X = random_crop(X, crop_size=32)
|
||||
if getenv("RANDOM_FLIP", 1):
|
||||
X = (Tensor.rand(X.shape[0],1,1,1) < 0.5).where(X.flip(-1), X) # flip LR
|
||||
X, Y = X[perms], Y[perms]
|
||||
return X, Y, *cutmix(X, Y, perms, mask_size=hyp['net']['cutmix_size'])
|
||||
|
||||
# the operations that remain inside batch fetcher is the ones that involves random operations
|
||||
def fetch_batches(X_in:Tensor, Y_in:Tensor, BS:int, is_train:bool):
|
||||
step, epoch = 0, 0
|
||||
@@ -240,16 +226,28 @@ def train_cifar():
|
||||
st = time.monotonic()
|
||||
X, Y = X_in, Y_in
|
||||
if is_train:
|
||||
X, Y, X_cm, Y_cm = augmentations(X, Y)
|
||||
if getenv("CUTMIX", 1) and step >= hyp['net']['cutmix_steps']: X, Y = X_cm, Y_cm
|
||||
# TODO: these are not jitted
|
||||
if getenv("RANDOM_CROP", 1):
|
||||
X = random_crop(X, crop_size=32)
|
||||
if getenv("RANDOM_FLIP", 1):
|
||||
X = (Tensor.rand(X.shape[0],1,1,1) < 0.5).where(X.flip(-1), X) # flip LR
|
||||
if getenv("CUTMIX", 1):
|
||||
if step >= hyp['net']['cutmix_steps']:
|
||||
X, Y = cutmix(X, Y, mask_size=hyp['net']['cutmix_size'])
|
||||
order = list(range(0, X.shape[0]))
|
||||
random.shuffle(order)
|
||||
X, Y = X.numpy()[order], Y.numpy()[order]
|
||||
else:
|
||||
X, Y = X.numpy(), Y.numpy()
|
||||
et = time.monotonic()
|
||||
print(f"shuffling {'training' if is_train else 'test'} dataset in {(et-st)*1e3:.2f} ms ({epoch=})")
|
||||
|
||||
vi = Variable("i", 0, (full_batches := (X.shape[0] // BS) * BS) - BS)
|
||||
for i in range(0, full_batches, BS):
|
||||
for i in range(0, X.shape[0], BS):
|
||||
# pad the last batch # TODO: not correct for test
|
||||
batch_end = min(i+BS, Y.shape[0])
|
||||
x = Tensor(X[batch_end-BS:batch_end], device=X_in.device, dtype=X_in.dtype)
|
||||
y = Tensor(Y[batch_end-BS:batch_end], device=Y_in.device, dtype=Y_in.dtype)
|
||||
step += 1
|
||||
vib = vi.bind(i)
|
||||
yield X[vib:vib+BS], Y[vib:vib+BS]
|
||||
yield x, y
|
||||
epoch += 1
|
||||
if not is_train: break
|
||||
|
||||
@@ -269,10 +267,13 @@ def train_cifar():
|
||||
|
||||
@TinyJit
|
||||
def update(self, net, decay):
|
||||
# TODO with Tensor.no_grad()
|
||||
Tensor.no_grad = True
|
||||
for net_ema_param, (param_name, net_param) in zip(get_state_dict(self.net_ema).values(), get_state_dict(net).items()):
|
||||
# batchnorm currently is not being tracked
|
||||
if not ("num_batches_tracked" in param_name) and not ("running" in param_name):
|
||||
net_ema_param.assign(net_ema_param.detach()*decay + net_param.detach()*(1.-decay)).realize()
|
||||
Tensor.no_grad = False
|
||||
|
||||
set_seed(getenv('SEED', hyp['seed']))
|
||||
|
||||
|
||||
+3
-1
@@ -240,6 +240,7 @@ class LLaMa:
|
||||
#elif k.endswith('.weight'): v.shard_(device, axis=-1)
|
||||
#elif 'norm.' in k: v.shard_(device, axis=-1)
|
||||
else: v.shard_(device, axis=None)
|
||||
#print(k, v.shape, v.lazydata.axis)
|
||||
|
||||
# replace weights in model
|
||||
load_state_dict(model, weights, strict=False, consume=True)
|
||||
@@ -330,6 +331,7 @@ int main()
|
||||
\end{code}
|
||||
"""
|
||||
if __name__ == "__main__":
|
||||
Tensor.no_grad = True
|
||||
print(f"using {Device.DEFAULT} backend")
|
||||
|
||||
parser = argparse.ArgumentParser(description="Run LLaMA in tinygrad", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
@@ -445,7 +447,7 @@ After you are done speaking, output [EOS]. You are not Chad.
|
||||
print(f"using LLaMA{LLAMA_SUFFIX}-{args.size} model")
|
||||
device = tuple(f"{Device.DEFAULT}:{i}" for i in range(args.shard)) if args.shard > 1 else Device.DEFAULT
|
||||
llama = LLaMa.build(MODEL_PATH, TOKENIZER_PATH, model_gen=args.gen, model_size=args.size, quantize=args.quantize, device=device)
|
||||
param_bytes = sum(x.uop.size * x.dtype.itemsize for x in get_parameters(llama.model))
|
||||
param_bytes = sum(x.lazydata.size * x.dtype.itemsize for x in get_parameters(llama.model))
|
||||
|
||||
outputted = pre_prompt if chatbot else args.prompt
|
||||
start_pos, toks = 0, [llama.tokenizer.bos_id()] + llama.tokenizer.encode(outputted)
|
||||
|
||||
+6
-8
@@ -157,11 +157,7 @@ MODEL_PARAMS = {
|
||||
"70B": {
|
||||
"args": {"dim": 8192, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 28672},
|
||||
"files": 8
|
||||
},
|
||||
"405B": {
|
||||
"args": {"dim": 16384, "n_heads": 128, "n_kv_heads": 8, "n_layers": 126, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 53248},
|
||||
"files": 191
|
||||
},
|
||||
}
|
||||
}
|
||||
def build_transformer(model_path: Path, model_size="8B", quantize=None, scale_dtype=dtypes.float16, device=None, max_context=8192, load_weights=True):
|
||||
# build model
|
||||
@@ -237,10 +233,12 @@ def prefill(model, toks, start_pos=0):
|
||||
return start_pos
|
||||
|
||||
if __name__ == "__main__":
|
||||
Tensor.no_grad = True
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--download_model", action="store_true", help="Download a model")
|
||||
parser.add_argument("--model", type=Path, help="Model path")
|
||||
parser.add_argument("--size", choices=["1B", "8B", "70B", "405B"], default="1B", help="Model size")
|
||||
parser.add_argument("--size", choices=["1B", "8B", "70B"], default="1B", help="Model size")
|
||||
parser.add_argument("--shard", type=int, default=1, help="Shard the model across multiple devices")
|
||||
parser.add_argument("--quantize", choices=["int8", "nf4", "float16"], help="Quantization method")
|
||||
parser.add_argument("--no_api", action="store_true", help="Disable the api and run a cli test interface")
|
||||
@@ -248,7 +246,7 @@ if __name__ == "__main__":
|
||||
parser.add_argument("--port", type=int, default=7776, help="Web server port")
|
||||
parser.add_argument("--debug", action="store_true", help="Enable debug mode")
|
||||
parser.add_argument("--seed", type=int, help="Random seed")
|
||||
parser.add_argument("--temperature", type=float, default=0.85, help="Temperature")
|
||||
parser.add_argument("--temperature", type=int, default=0.85, help="Temperature")
|
||||
parser.add_argument("--benchmark", action="store_true", help="Run a benchmark")
|
||||
parser.add_argument("--timing", action="store_true", help="Print timing per token")
|
||||
parser.add_argument("--profile", action="store_true", help="Output profile data")
|
||||
@@ -288,7 +286,7 @@ if __name__ == "__main__":
|
||||
|
||||
device = tuple(f"{Device.DEFAULT}:{i}" for i in range(args.shard)) if args.shard > 1 else Device.DEFAULT
|
||||
model = build_transformer(args.model, model_size=args.size, quantize=args.quantize, device=device)
|
||||
param_bytes = sum(x.uop.size * x.dtype.itemsize for x in get_parameters(model))
|
||||
param_bytes = sum(x.lazydata.size * x.dtype.itemsize for x in get_parameters(model))
|
||||
|
||||
if not args.no_api and not args.benchmark:
|
||||
from bottle import Bottle, request, response, HTTPResponse, abort, static_file
|
||||
|
||||
@@ -1,11 +1,11 @@
|
||||
#!/usr/bin/env python3
|
||||
import os
|
||||
if "NOOPT" not in os.environ: os.environ["NOOPT"] = "1"
|
||||
from tinygrad import Device, nn, Tensor, dtypes
|
||||
from tinygrad import Device, nn, Tensor, dtypes, Variable
|
||||
Device.DEFAULT = "CPU"
|
||||
from train_gpt2 import GPT, GPTConfig
|
||||
from tinygrad.helpers import dedup, flatten, getenv, GlobalCounters, to_function_name
|
||||
from tinygrad.engine.realize import get_kernel
|
||||
from tinygrad.helpers import dedup, to_function_name, flatten, getenv, GlobalCounters, ansilen, to_function_name
|
||||
from tinygrad.engine.realize import get_kernel, run_schedule
|
||||
from tinygrad.engine.memory import memory_planner
|
||||
from tinygrad.uop.ops import Ops
|
||||
|
||||
@@ -16,7 +16,7 @@ if __name__ == "__main__":
|
||||
#model.load_pretrained()
|
||||
for p in nn.state.get_parameters(model): p.replace(Tensor.empty(p.shape, dtype=p.dtype)) # fake load pretrained
|
||||
|
||||
#early_sched = create_schedule([x.uop for x in nn.state.get_parameters(model)])
|
||||
#early_sched = create_schedule([x.lazydata for x in nn.state.get_parameters(model)])
|
||||
#print(f"built model {len(early_sched)}")
|
||||
|
||||
#B, T = Variable("B", 1, 128).bind(4), 64 #Variable("T", 1, 1024).bind(64)
|
||||
@@ -56,7 +56,7 @@ if __name__ == "__main__":
|
||||
state_dict.update({'X': X, 'Y': Y, 'loss': loss})
|
||||
grad_state_dict = {}
|
||||
for k,v in state_dict.items():
|
||||
if v.uop.base.buffer not in used_buffers: print(f"UNUSED: {k}")
|
||||
if v.lazydata.base.buffer not in used_buffers: print(f"UNUSED: {k}")
|
||||
if v.grad is not None: grad_state_dict['grad_'+k] = v.grad
|
||||
state_dict.update(grad_state_dict)
|
||||
state_dict.update({'adam_b1_t': optimizer.b1_t, 'adam_b2_t': optimizer.b2_t, 'adam_lr': optimizer.lr})
|
||||
@@ -65,7 +65,7 @@ if __name__ == "__main__":
|
||||
nm = inverse_state_dict[p]
|
||||
state_dict["adam_m_"+nm] = m
|
||||
state_dict["adam_v_"+nm] = v
|
||||
named_buffers = {v.uop.base.buffer:k.replace(".", "_") for k,v in state_dict.items()}
|
||||
named_buffers = {v.lazydata.base.buffer:k.replace(".", "_") for k,v in state_dict.items()}
|
||||
|
||||
c_code = ["#include <stdlib.h>", "#include <tgmath.h>", "#include <stdbool.h>"]
|
||||
if TIMING: c_code += ["#include <stdio.h>", "#include <time.h>"]
|
||||
|
||||
@@ -146,6 +146,7 @@ if __name__ == "__main__":
|
||||
return loss
|
||||
|
||||
@TinyJit
|
||||
@Tensor.test()
|
||||
def sample(z:Tensor, cond:Tensor) -> Tensor:
|
||||
return model.sample(z, cond, Tensor.full_like(cond, 10), sample_steps=getenv("SAMPLE_STEPS", 20))[-1]
|
||||
|
||||
|
||||
+1
-1
@@ -56,7 +56,7 @@ if __name__ == "__main__":
|
||||
with Profiling(sort="time", frac=0.1, enabled=args.profile):
|
||||
with Timing("total ", enabled=args.timing, on_exit=lambda x: f", {1e9/x:.2f} tok/sec"):
|
||||
with WallTimeEvent(BenchEvent.STEP):
|
||||
tok = model(Tensor([toks[start_pos:]]), 0 if start_pos == 0 else Variable("start_pos", 1, 1024-1).bind(start_pos), args.temperature).item()
|
||||
tok = model(Tensor([toks[start_pos:]]), 0 if start_pos == 0 else Variable("start_pos", 1, 1024).bind(start_pos), args.temperature).item()
|
||||
toks.append(tok)
|
||||
start_pos += 1
|
||||
print(spp.decode(toks))
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
import os, random, pickle, queue, struct, math, functools, hashlib, time
|
||||
import os, random, pickle, queue
|
||||
from typing import List
|
||||
from pathlib import Path
|
||||
from multiprocessing import Queue, Process, shared_memory, connection, Lock, cpu_count
|
||||
@@ -6,7 +6,6 @@ from multiprocessing import Queue, Process, shared_memory, connection, Lock, cpu
|
||||
import numpy as np
|
||||
from tinygrad import dtypes, Tensor
|
||||
from tinygrad.helpers import getenv, prod, Context, round_up, tqdm, OSX
|
||||
from tinygrad.nn.state import TensorIO
|
||||
|
||||
### ResNet
|
||||
|
||||
@@ -72,7 +71,7 @@ def loader_process(q_in, q_out, X:Tensor, seed):
|
||||
#storage_tensor._copyin(img_tensor.numpy())
|
||||
|
||||
# faster
|
||||
X[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = img.tobytes()
|
||||
X[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = img.tobytes()
|
||||
|
||||
# ideal
|
||||
#X[idx].assign(img.tobytes()) # NOTE: this is slow!
|
||||
@@ -263,8 +262,8 @@ def load_unet3d_data(preprocessed_dataset_dir, seed, queue_in, queue_out, X:Tens
|
||||
x = random_brightness_augmentation(x)
|
||||
x = gaussian_noise(x)
|
||||
|
||||
X[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = x.tobytes()
|
||||
Y[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = y.tobytes()
|
||||
X[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = x.tobytes()
|
||||
Y[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = y.tobytes()
|
||||
|
||||
queue_out.put(idx)
|
||||
queue_out.put(None)
|
||||
@@ -378,12 +377,12 @@ def load_retinanet_data(base_dir:Path, val:bool, queue_in:Queue, queue_out:Queue
|
||||
clipped_match_idxs = np.clip(match_idxs, 0, None)
|
||||
clipped_boxes, clipped_labels = tgt["boxes"][clipped_match_idxs], tgt["labels"][clipped_match_idxs]
|
||||
|
||||
boxes[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = clipped_boxes.tobytes()
|
||||
labels[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = clipped_labels.tobytes()
|
||||
matches[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = match_idxs.tobytes()
|
||||
anchors[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = anchor.tobytes()
|
||||
boxes[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = clipped_boxes.tobytes()
|
||||
labels[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = clipped_labels.tobytes()
|
||||
matches[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = match_idxs.tobytes()
|
||||
anchors[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = anchor.tobytes()
|
||||
|
||||
imgs[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = img.tobytes()
|
||||
imgs[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = img.tobytes()
|
||||
|
||||
queue_out.put(idx)
|
||||
queue_out.put(None)
|
||||
@@ -511,274 +510,6 @@ def batch_load_retinanet(dataset, val:bool, base_dir:Path, batch_size:int=32, sh
|
||||
# happens with BENCHMARK set
|
||||
pass
|
||||
|
||||
# llama3
|
||||
|
||||
class BinIdxDataset:
|
||||
def __init__(self, base_path:Path):
|
||||
self.idx_t = Tensor(base_path.with_name(f"{base_path.name}.idx"))
|
||||
self.idx = TensorIO(self.idx_t)
|
||||
|
||||
# parse idx file
|
||||
magic = self.idx.read(9)
|
||||
assert magic == b"MMIDIDX\x00\x00", "invalid index file format"
|
||||
version, = struct.unpack("<Q", self.idx.read(8))
|
||||
assert version == 1, "unsupported index version"
|
||||
dtype_code, = struct.unpack("<B", self.idx.read(1))
|
||||
self.dtype = {1:dtypes.uint8, 2:dtypes.int8, 3:dtypes.int16, 4:dtypes.int32, 5:dtypes.int64, 6:dtypes.float64, 7:dtypes.double, 8:dtypes.uint16}[dtype_code]
|
||||
self.count, = struct.unpack("<Q", self.idx.read(8))
|
||||
doc_count, = struct.unpack("<Q", self.idx.read(8))
|
||||
|
||||
start = self.idx.tell()
|
||||
end = start + self.count * dtypes.int32.itemsize
|
||||
self.sizes = self.idx_t[start:end].bitcast(dtypes.int32).numpy()
|
||||
|
||||
start = end
|
||||
end = start + self.count * dtypes.int64.itemsize
|
||||
self.pointers = self.idx_t[start:end].bitcast(dtypes.int64).numpy()
|
||||
|
||||
start = end
|
||||
end = start + doc_count * dtypes.int64.itemsize
|
||||
self.doc_idx = self.idx_t[start:end].bitcast(dtypes.int64).numpy()
|
||||
|
||||
# bin file
|
||||
self.bin_t = Tensor(base_path.with_name(f"{base_path.name}.bin"))
|
||||
|
||||
def _index(self, idx) -> tuple[int, int]:
|
||||
return int(self.pointers[idx]), int(self.sizes[idx])
|
||||
|
||||
def get(self, idx, offset:int=0, length:int|None=None):
|
||||
ptr, size = self._index(idx)
|
||||
if length is None: length = size - offset
|
||||
ptr += offset * self.dtype.itemsize
|
||||
return self.bin_t[ptr:ptr+length*self.dtype.itemsize].bitcast(self.dtype).to(None)
|
||||
|
||||
# https://docs.nvidia.com/megatron-core/developer-guide/latest/api-guide/datasets.html
|
||||
class GPTDataset:
|
||||
def __init__(self, base_path:Path, samples:int, seqlen:int, seed:int, shuffle:bool):
|
||||
self.samples, self.seqlen = samples, seqlen
|
||||
self.shuffle = shuffle
|
||||
self.rng = np.random.RandomState(seed)
|
||||
|
||||
self.indexed_dataset = BinIdxDataset(base_path)
|
||||
|
||||
# check for cache
|
||||
cache_hash = hashlib.sha256(f"{samples}:{seqlen}:{seed}:{shuffle}".encode()).hexdigest()
|
||||
cache_path = base_path.with_name(f"{base_path.name}.{cache_hash}.index_cache")
|
||||
print(f"try loading GPTDataset from {cache_path}...")
|
||||
if cache_path.exists():
|
||||
print("cache found, loading...")
|
||||
with open(cache_path, "rb") as f:
|
||||
self.doc_idx, self.sample_idx, self.shuffle_idx = pickle.load(f)
|
||||
else:
|
||||
print("cache not found, building index...")
|
||||
self.doc_idx = self._build_doc_idx()
|
||||
self.sample_idx = self._build_sample_idx()
|
||||
self.shuffle_idx = self._build_shuffle_idx()
|
||||
# save cache
|
||||
with open(cache_path, "wb") as f:
|
||||
pickle.dump((self.doc_idx, self.sample_idx, self.shuffle_idx), f)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
if idx is None:
|
||||
text = self._get(0)
|
||||
else:
|
||||
text = self._get(idx)
|
||||
|
||||
return text
|
||||
|
||||
def _get(self, idx):
|
||||
idx = self.shuffle_idx[idx]
|
||||
|
||||
doc_idx_beg, doc_idx_beg_offset = self.sample_idx[idx]
|
||||
doc_idx_end, doc_idx_end_offset = self.sample_idx[idx + 1]
|
||||
|
||||
doc_ids, sample_parts = [], []
|
||||
|
||||
if doc_idx_beg == doc_idx_end:
|
||||
doc_ids.append(self.doc_idx[doc_idx_beg])
|
||||
|
||||
sample_parts.append(
|
||||
self.indexed_dataset.get(
|
||||
int(self.doc_idx[doc_idx_beg]), offset=int(doc_idx_beg_offset), length=int(doc_idx_end_offset - doc_idx_beg_offset + 1)))
|
||||
else:
|
||||
for i in range(doc_idx_beg, doc_idx_end + 1):
|
||||
doc_ids.append(self.doc_idx[i])
|
||||
|
||||
offset = 0 if i > doc_idx_beg else doc_idx_beg_offset
|
||||
length = None if i < doc_idx_end else int(doc_idx_end_offset + 1)
|
||||
sample_parts.append(self.indexed_dataset.get(int(self.doc_idx[i]), offset=int(offset), length=length))
|
||||
|
||||
# concat all parts
|
||||
text = Tensor.cat(*sample_parts)
|
||||
|
||||
return text
|
||||
|
||||
@functools.cached_property
|
||||
def tokens_per_epoch(self) -> int:
|
||||
return sum(self.indexed_dataset.sizes.tolist())
|
||||
|
||||
@functools.cached_property
|
||||
def num_epochs(self) -> int:
|
||||
# we need enough epochs to cover the requested amount of tokens
|
||||
num_epochs = 1
|
||||
num_tokens = self.tokens_per_epoch
|
||||
while num_tokens < self.samples * self.seqlen:
|
||||
num_epochs += 1
|
||||
num_tokens += self.tokens_per_epoch
|
||||
return num_epochs
|
||||
|
||||
# https://github.com/NVIDIA/Megatron-LM/blob/94bd476bd840c2fd4c3ebfc7448c2af220f4832b/megatron/core/datasets/gpt_dataset.py#L558
|
||||
def _build_doc_idx(self):
|
||||
print(f"building doc_idx for {self.num_epochs=}, {self.indexed_dataset.count=}")
|
||||
st = time.perf_counter()
|
||||
# doc_idx = np.mgrid[:self.num_epochs, :self.indexed_dataset.count][1]
|
||||
doc_idx = np.arange(self.indexed_dataset.count).reshape(1, -1).repeat(self.num_epochs, axis=0).flatten()
|
||||
doc_idx = doc_idx.astype(np.int32)
|
||||
at = time.perf_counter()
|
||||
if self.shuffle: self.rng.shuffle(doc_idx)
|
||||
print(f"doc_idx built in {at - st:.3f}s, shuffled in {time.perf_counter() - at:.3f}s")
|
||||
return doc_idx
|
||||
|
||||
def _build_sample_idx(self):
|
||||
print(f"building sample_idx for {self.samples=}, {self.seqlen=}, {self.doc_idx.shape[0]=}")
|
||||
sample_idx_max = max(self.doc_idx.shape[0], self.indexed_dataset.sizes.max())
|
||||
sample_idx = np.empty((self.samples + 1, 2), dtype=np.int64 if sample_idx_max > dtypes.int32.max else np.int32)
|
||||
|
||||
sample_idx_idx, doc_idx_idx, doc_offset = 0, 0, 0
|
||||
sample_idx[sample_idx_idx, 0], sample_idx[sample_idx_idx, 1] = doc_idx_idx, doc_offset
|
||||
sample_idx_idx += 1
|
||||
|
||||
for _ in tqdm(range(1, self.samples + 1)):
|
||||
remaining_seqlen = self.seqlen + 1
|
||||
while remaining_seqlen > 0:
|
||||
doc_idx = int(self.doc_idx[doc_idx_idx])
|
||||
doc_len = int(self.indexed_dataset.sizes[doc_idx]) - doc_offset
|
||||
remaining_seqlen -= doc_len
|
||||
if remaining_seqlen <= 0:
|
||||
doc_offset += remaining_seqlen + doc_len - 1
|
||||
remaining_seqlen = 0
|
||||
else:
|
||||
if doc_idx_idx == len(self.doc_idx) - 1:
|
||||
assert sample_idx_idx == self.samples
|
||||
doc_idx = int(self.doc_idx[doc_idx_idx])
|
||||
doc_offset = int(self.indexed_dataset.sizes[doc_idx]) - 1
|
||||
break
|
||||
doc_idx_idx += 1
|
||||
doc_offset = 0
|
||||
|
||||
sample_idx[sample_idx_idx, 0], sample_idx[sample_idx_idx, 1] = doc_idx_idx, doc_offset
|
||||
sample_idx_idx += 1
|
||||
|
||||
return sample_idx
|
||||
|
||||
def _build_shuffle_idx(self):
|
||||
print(f"building shuffle_idx for {self.samples=}")
|
||||
st = time.perf_counter()
|
||||
shuffle_idx = np.arange(self.samples, dtype=np.int32)
|
||||
at = time.perf_counter()
|
||||
if self.shuffle: self.rng.shuffle(shuffle_idx)
|
||||
print(f"shuffle_idx built in {at - st:.3f}s, shuffled in {time.perf_counter() - at:.3f}s")
|
||||
return shuffle_idx
|
||||
|
||||
class BlendedGPTDataset:
|
||||
def __init__(self, paths:list[Path], weights:list[float], samples:int, seqlen:int, seed:int, shuffle:bool):
|
||||
self.shuffle = shuffle
|
||||
self.rng = np.random.RandomState(seed)
|
||||
|
||||
# normalize weights
|
||||
total_weight = sum(weights)
|
||||
self.weights = [w / total_weight for w in weights]
|
||||
|
||||
self.samples = samples
|
||||
surplus = 0.005
|
||||
samples_per_blend = [math.ceil(math.ceil(self.samples * w) * (1 + surplus)) for w in self.weights]
|
||||
|
||||
self.datasets = [GPTDataset(path, samples_per_blend[i], seqlen, seed + i, shuffle) for i,path in enumerate(paths)]
|
||||
|
||||
# check for cache
|
||||
cache_hash = hashlib.sha256(f"{samples}:{seqlen}:{seed}:{shuffle}".encode()).hexdigest()
|
||||
cache_path = paths[0].with_name(f"{paths[0].name}.{cache_hash}.blend_cache")
|
||||
print(f"try loading BlendedGPTDataset from {cache_path}...")
|
||||
if cache_path.exists():
|
||||
print("cache found, loading...")
|
||||
with open(cache_path, "rb") as f:
|
||||
self.dataset_idx, self.dataset_sample_idx = pickle.load(f)
|
||||
else:
|
||||
print("cache not found, building index...")
|
||||
self.dataset_idx, self.dataset_sample_idx = self._build_blend_idx()
|
||||
# save cache
|
||||
with open(cache_path, "wb") as f:
|
||||
pickle.dump((self.dataset_idx, self.dataset_sample_idx), f)
|
||||
|
||||
def get(self, idx:int):
|
||||
tokens = self.datasets[self.dataset_idx[idx]][self.dataset_sample_idx[idx]]
|
||||
return tokens
|
||||
|
||||
def _build_blend_idx(self):
|
||||
dataset_idx = np.zeros(self.samples, dtype=np.int16)
|
||||
dataset_sample_idx = np.zeros(self.samples, dtype=np.int64)
|
||||
|
||||
unspent_datasets = set(range(len(self.datasets)))
|
||||
dataset_sample_counts = [0] * len(self.datasets)
|
||||
|
||||
for i in tqdm(range(self.samples)):
|
||||
error_argmax, error_max = 0, 0.0
|
||||
for di in unspent_datasets:
|
||||
error = self.weights[di] * max(i, 1) - dataset_sample_counts[di]
|
||||
if error > error_max:
|
||||
error_max = error
|
||||
error_argmax = di
|
||||
|
||||
dataset_idx[i] = error_argmax
|
||||
dataset_sample_idx[i] = dataset_sample_counts[error_argmax]
|
||||
|
||||
dataset_sample_counts[error_argmax] += 1
|
||||
|
||||
return dataset_idx, dataset_sample_idx
|
||||
|
||||
def batch_load_llama3(bs:int, samples:int, seqlen:int, base_dir:Path, seed:int=0, val:bool=True):
|
||||
if val:
|
||||
dataset = BlendedGPTDataset([
|
||||
base_dir / "validation" / "c4-validationn-91205-samples.en_text_document",
|
||||
], [
|
||||
1.0
|
||||
], samples, seqlen, seed, False)
|
||||
else:
|
||||
dataset = BlendedGPTDataset([
|
||||
base_dir / "c4-train.en_6_text_document",
|
||||
base_dir / "c4-train.en_7_text_document",
|
||||
], [
|
||||
1.0, 1.0
|
||||
], samples, seqlen, seed, True)
|
||||
|
||||
for b in range(math.ceil(samples / bs)):
|
||||
batch = []
|
||||
for i in range(bs):
|
||||
tokens = dataset.get(b * bs + i)
|
||||
batch.append(tokens)
|
||||
yield Tensor.stack(batch, dim=0)
|
||||
|
||||
def batch_load_llama3_small(bs:int, samples:int, seqlen:int, base_dir:Path, seed:int=0, val:bool=True):
|
||||
if val:
|
||||
dataset = BlendedGPTDataset([
|
||||
base_dir / "c4-validation-91205-samples.en_text_document",
|
||||
], [
|
||||
1.0
|
||||
], samples, seqlen, seed, False)
|
||||
else:
|
||||
dataset = BlendedGPTDataset([
|
||||
base_dir / "c4-train.en_6_text_document",
|
||||
], [
|
||||
1.0
|
||||
], samples, seqlen, seed, True)
|
||||
|
||||
for b in range(math.ceil(samples / bs)):
|
||||
batch = []
|
||||
for i in range(bs):
|
||||
tokens = dataset.get(b * bs + i)
|
||||
batch.append(tokens)
|
||||
yield Tensor.stack(batch, dim=0)
|
||||
|
||||
if __name__ == "__main__":
|
||||
def load_unet3d(val):
|
||||
assert not val, "validation set is not supported due to different sizes on inputs"
|
||||
@@ -807,18 +538,6 @@ if __name__ == "__main__":
|
||||
for x in batch_load_retinanet(dataset, val, base_dir):
|
||||
pbar.update(x[0].shape[0])
|
||||
|
||||
def load_llama3(val):
|
||||
bs = 24
|
||||
samples = 5760 if val else 1_200_000 * 1152
|
||||
seqlen = 8192
|
||||
|
||||
max_, min_ = 0, math.inf
|
||||
for tokens in tqdm(batch_load_llama3(bs, samples, seqlen, Path(getenv("BASEDIR", "/raid/datasets/c4/")), seed=5760, val=bool(val)), total=samples//bs):
|
||||
max_ = max(max_, tokens.shape[1])
|
||||
min_ = min(min_, tokens.shape[1])
|
||||
print(f"max seq length: {max_}")
|
||||
print(f"min seq length: {min_}")
|
||||
|
||||
load_fn_name = f"load_{getenv('MODEL', 'resnet')}"
|
||||
if load_fn_name in globals():
|
||||
globals()[load_fn_name](getenv("VAL", 1))
|
||||
|
||||
@@ -212,7 +212,7 @@ def get_mlperf_bert_model():
|
||||
from examples.mlperf.initializers import LinearBert, EmbeddingBert, LayerNormBert
|
||||
|
||||
bert.Linear = LinearBert
|
||||
bert.Embedding = EmbeddingBert
|
||||
bert.Embedding = EmbeddingBert
|
||||
bert.LayerNorm = LayerNormBert
|
||||
|
||||
from extra.models.bert import BertForPretraining
|
||||
|
||||
@@ -39,7 +39,7 @@ class LinearBert(nn.Linear):
|
||||
def __init__(self, in_features, out_features, bias=True, std=0.02):
|
||||
self.weight = std * rand_truncn(out_features, in_features, dtype=dtypes.float32)
|
||||
self.bias = Tensor.zeros(out_features, dtype=dtypes.float32) if bias else None
|
||||
|
||||
|
||||
def __call__(self, x:Tensor):
|
||||
return x.cast(dtypes.default_float).linear(self.weight.cast(dtypes.default_float).transpose(), self.bias.cast(dtypes.default_float) if self.bias is not None else None)
|
||||
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
import math
|
||||
from tinygrad import dtypes
|
||||
from tinygrad import Tensor, dtypes
|
||||
from tinygrad.nn.optim import Optimizer
|
||||
|
||||
from extra.lr_scheduler import LR_Scheduler
|
||||
@@ -21,19 +20,3 @@ class PolynomialDecayWithWarmup(LR_Scheduler):
|
||||
warmup_lr = (self.epoch_counter * (1.0 / self.warmup)) * self.initial_lr
|
||||
x = (1 - (self.epoch_counter - self.warmup) / (self.epochs - self.warmup + 1))
|
||||
return (self.epoch_counter <= self.warmup).where(warmup_lr, (self.initial_lr - self.end_lr) * x ** self.power + self.end_lr).cast(self.optimizer.lr.dtype)
|
||||
|
||||
class CosineAnnealingLRWithWarmup(LR_Scheduler):
|
||||
def __init__(self, optimizer:Optimizer, base_lr, end_lr, warmup_steps:int, decay_steps:int):
|
||||
assert warmup_steps > 0 and decay_steps > 0
|
||||
super().__init__(optimizer)
|
||||
self.base_lr = base_lr
|
||||
self.end_lr = end_lr
|
||||
self.warmup_steps = warmup_steps
|
||||
self.decay_steps = decay_steps
|
||||
# set lr for first warmup step
|
||||
self.optimizer.lr.assign(self.get_lr()).realize()
|
||||
|
||||
def get_lr(self):
|
||||
warmup_lr = ((self.epoch_counter+1) / self.warmup_steps) * self.base_lr
|
||||
decay_lr = self.end_lr + 0.5 * (self.base_lr-self.end_lr) * (1 + (((self.epoch_counter+1-self.warmup_steps)/self.decay_steps) * math.pi).cos())
|
||||
return (self.epoch_counter < self.warmup_steps).where(warmup_lr, decay_lr).cast(self.optimizer.lr.dtype)
|
||||
@@ -1,6 +1,6 @@
|
||||
import re, string
|
||||
import re
|
||||
import string
|
||||
from collections import Counter
|
||||
from tinygrad import Tensor
|
||||
|
||||
def levenshtein(a, b):
|
||||
n, m = len(a), len(b)
|
||||
@@ -59,11 +59,3 @@ def f1_score(x, y):
|
||||
p = ns / len(xt)
|
||||
r = ns / len(yt)
|
||||
return 2 * p * r / (p + r)
|
||||
|
||||
def log_perplexity(logit:Tensor, target:Tensor, ignore_index:int|None=None):
|
||||
# logit has shape (n_samples, seq_len, vocab_size), target has shape (n_samples, seq_len)
|
||||
assert logit.ndim == 3, logit.ndim
|
||||
assert target.ndim == 2, target.ndim
|
||||
assert logit.shape[:2] == target.shape, f"{logit.shape[:2]=}, {target.shape=}"
|
||||
log_prob = logit.log_softmax(axis=-1)
|
||||
return log_prob.transpose(1, 2).nll_loss(target, ignore_index=ignore_index)
|
||||
@@ -1,4 +1,4 @@
|
||||
import time, math
|
||||
import time
|
||||
start = time.perf_counter()
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
@@ -9,6 +9,7 @@ from extra.bench_log import BenchEvent, WallTimeEvent
|
||||
def tlog(x): print(f"{x:25s} @ {time.perf_counter()-start:5.2f}s")
|
||||
|
||||
def eval_resnet():
|
||||
Tensor.no_grad = True
|
||||
with WallTimeEvent(BenchEvent.FULL):
|
||||
# Resnet50-v1.5
|
||||
from extra.models.resnet import ResNet50
|
||||
@@ -241,55 +242,10 @@ def eval_mrcnn():
|
||||
evaluate_predictions_on_coco(bbox_output, iou_type='bbox')
|
||||
evaluate_predictions_on_coco(mask_output, iou_type='segm')
|
||||
|
||||
def eval_llama3():
|
||||
from extra.models.llama import Transformer
|
||||
from examples.llama3 import MODEL_PARAMS, load, convert_from_huggingface
|
||||
from tinygrad.helpers import tqdm
|
||||
|
||||
BASEDIR = Path(getenv("BASEDIR", "/raid/datasets/c4/"))
|
||||
BS = getenv("BS", 4)
|
||||
SMALL = getenv("SMALL", 0)
|
||||
SEQLEN = getenv("SEQLEN", 8192)
|
||||
MODEL_PATH = Path(getenv("MODEL_PATH", "/raid/weights/llama31_8b/"))
|
||||
|
||||
params = MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]
|
||||
params = params | {"vocab_size": 32000} if not SMALL else params
|
||||
if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: params['n_layers'] = llama_layers
|
||||
model = Transformer(**params, max_context=SEQLEN, jit=False, disable_kv_cache=True)
|
||||
|
||||
# load weights
|
||||
weights = load(str(MODEL_PATH / "model.safetensors.index.json"))
|
||||
if "model.embed_tokens.weight" in weights:
|
||||
print("converting from huggingface format")
|
||||
weights = convert_from_huggingface(weights, params["n_layers"], params["n_heads"], params["n_kv_heads"])
|
||||
|
||||
load_state_dict(model, weights, strict=False, consume=True)
|
||||
|
||||
@TinyJit
|
||||
def eval_step(model, tokens):
|
||||
logits:Tensor = model(tokens[:, :-1], start_pos=0, temperature=math.nan)
|
||||
loss = logits.sparse_categorical_crossentropy(tokens[:, 1:])
|
||||
return loss.flatten().float()
|
||||
|
||||
if SMALL:
|
||||
from examples.mlperf.dataloader import batch_load_llama3_small
|
||||
iter = batch_load_llama3_small(BS, 5760, SEQLEN, BASEDIR, val=True)
|
||||
else:
|
||||
from examples.mlperf.dataloader import batch_load_llama3
|
||||
iter = batch_load_llama3(BS, 5760, SEQLEN, BASEDIR, val=True)
|
||||
|
||||
losses = []
|
||||
for tokens in tqdm(iter, total=5760//BS):
|
||||
GlobalCounters.reset()
|
||||
losses += eval_step(model, tokens).tolist()
|
||||
tqdm.write(f"loss: {np.mean(losses)}")
|
||||
|
||||
log_perplexity = np.mean(losses)
|
||||
print(f"Log Perplexity: {log_perplexity}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
# inference only
|
||||
Tensor.training = False
|
||||
Tensor.no_grad = True
|
||||
|
||||
models = getenv("MODEL", "resnet,retinanet,unet3d,rnnt,bert,mrcnn").split(",")
|
||||
for m in models:
|
||||
|
||||
@@ -60,6 +60,7 @@ def spec_mrcnn():
|
||||
if __name__ == "__main__":
|
||||
# inference only for now
|
||||
Tensor.training = False
|
||||
Tensor.no_grad = True
|
||||
|
||||
for m in getenv("MODEL", "resnet,retinanet,unet3d,rnnt,bert,mrcnn").split(","):
|
||||
nm = f"spec_{m}"
|
||||
|
||||
+37
-251
@@ -1,11 +1,11 @@
|
||||
import os, time, math, functools, random, contextlib
|
||||
import os, time, math, functools, random
|
||||
from pathlib import Path
|
||||
import multiprocessing
|
||||
|
||||
from tinygrad import Device, GlobalCounters, Tensor, TinyJit, dtypes
|
||||
from tinygrad.helpers import getenv, BEAM, WINO, round_up, diskcache_clear, FUSE_CONV_BW, Profiling
|
||||
from tinygrad.nn.state import get_parameters, get_state_dict, load_state_dict, safe_load, safe_save
|
||||
from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup, Adam, AdamW
|
||||
from tinygrad.nn.state import get_parameters, get_state_dict, safe_load, safe_save
|
||||
from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup, Adam
|
||||
|
||||
from extra.lr_scheduler import LRSchedulerGroup
|
||||
from examples.mlperf.helpers import get_training_state, load_training_state
|
||||
@@ -608,7 +608,7 @@ def train_retinanet():
|
||||
|
||||
if getenv("RESET_STEP", 1): _train_step.reset()
|
||||
|
||||
with Tensor.train(mode=False):
|
||||
with Tensor.train(mode=False), Tensor.test():
|
||||
if not RUNMLPERF:
|
||||
i, proc = 0, _fake_data_get(EVAL_BS, val=(val:=True))
|
||||
else:
|
||||
@@ -791,6 +791,7 @@ def train_unet3d():
|
||||
return loss.realize()
|
||||
|
||||
@Tensor.train(mode=False)
|
||||
@Tensor.test()
|
||||
def eval_step(model, x, y):
|
||||
y_hat, y = sliding_window_inference(model, x, y, gpus=GPUS)
|
||||
y_hat, y = Tensor(y_hat), Tensor(y, requires_grad=False)
|
||||
@@ -914,26 +915,18 @@ def train_rnnt():
|
||||
pass
|
||||
|
||||
@TinyJit
|
||||
def train_step_bert(model, optimizer, scheduler, loss_scaler:float, GPUS, grad_acc:int, **kwargs):
|
||||
def train_step_bert(model, optimizer, scheduler, loss_scaler:float, input_ids:Tensor, segment_ids:Tensor, attention_mask:Tensor,
|
||||
masked_positions:Tensor, masked_lm_ids:Tensor, masked_lm_weights:Tensor, next_sentence_labels:Tensor, GPUS):
|
||||
for t in [input_ids, segment_ids, attention_mask, masked_positions, masked_lm_ids, masked_lm_weights, next_sentence_labels]:
|
||||
if len(GPUS) > 1: t.shard_(GPUS, axis=0)
|
||||
else: t.to_(GPUS[0])
|
||||
optimizer.zero_grad()
|
||||
|
||||
for i in range(grad_acc):
|
||||
input_ids, segment_ids = kwargs[f"input_ids{i}"], kwargs[f"segment_ids{i}"]
|
||||
# NOTE: these two have different names
|
||||
attention_mask, masked_positions = kwargs[f"input_mask{i}"], kwargs[f"masked_lm_positions{i}"]
|
||||
masked_lm_ids, masked_lm_weights, next_sentence_labels = kwargs[f"masked_lm_ids{i}"], kwargs[f"masked_lm_weights{i}"], kwargs[f"next_sentence_labels{i}"]
|
||||
lm_logits, seq_relationship_logits = model(input_ids, attention_mask, masked_positions, segment_ids)
|
||||
loss = model.loss(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels)
|
||||
(loss * loss_scaler).backward()
|
||||
|
||||
for t in [input_ids, segment_ids, attention_mask, masked_positions, masked_lm_ids, masked_lm_weights, next_sentence_labels]:
|
||||
if len(GPUS) > 1: t.shard_(GPUS, axis=0)
|
||||
else: t.to_(GPUS[0])
|
||||
|
||||
lm_logits, seq_relationship_logits = model(input_ids, attention_mask, masked_positions, segment_ids)
|
||||
loss = model.loss(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels)
|
||||
(loss * loss_scaler).backward()
|
||||
# TODO: OOM without this realize with large grad_acc
|
||||
Tensor.realize(*[p.grad for p in optimizer.params])
|
||||
|
||||
global_norm = Tensor(0.0, dtype=dtypes.float32, device=optimizer[0].device)
|
||||
global_norm = Tensor([0.0], dtype=dtypes.float32, device=optimizer[0].device)
|
||||
for p in optimizer.params:
|
||||
p.grad = p.grad / loss_scaler
|
||||
global_norm += p.grad.float().square().sum()
|
||||
@@ -1007,19 +1000,16 @@ def train_bert():
|
||||
MLLOGGER = None
|
||||
|
||||
# ** hyperparameters **
|
||||
BS = config["BS"] = getenv("BS", 11 * len(GPUS) if dtypes.default_float in (dtypes.float16, dtypes.bfloat16) else 8 * len(GPUS))
|
||||
grad_acc = config["GRADIENT_ACC_STEPS"] = getenv("GRADIENT_ACC_STEPS", 1)
|
||||
# TODO: mlperf logging
|
||||
GBS = config["GLOBAL_BATCH_SIZE"] = BS * grad_acc
|
||||
BS = config["GLOBAL_BATCH_SIZE"] = getenv("BS", 11 * len(GPUS) if dtypes.default_float in (dtypes.float16, dtypes.bfloat16) else 8 * len(GPUS))
|
||||
EVAL_BS = config["EVAL_BS"] = getenv("EVAL_BS", 1 * len(GPUS))
|
||||
max_lr = config["OPT_BASE_LEARNING_RATE"] = getenv("OPT_BASE_LEARNING_RATE", 0.000175 * math.sqrt(GBS/96))
|
||||
max_lr = config["OPT_BASE_LEARNING_RATE"] = getenv("OPT_BASE_LEARNING_RATE", 0.000175 * math.sqrt(BS/96))
|
||||
opt_lamb_beta_1 = config["OPT_LAMB_BETA_1"] = getenv("OPT_LAMB_BETA_1", 0.9)
|
||||
opt_lamb_beta_2 = config["OPT_LAMB_BETA_2"] = getenv("OPT_LAMB_BETA_2", 0.999)
|
||||
|
||||
train_steps = config["TRAIN_STEPS"] = getenv("TRAIN_STEPS", 3600000 // GBS)
|
||||
train_steps = config["TRAIN_STEPS"] = getenv("TRAIN_STEPS", 3600000 // BS)
|
||||
warmup_steps = config["NUM_WARMUP_STEPS"] = getenv("NUM_WARMUP_STEPS", 1)
|
||||
max_eval_steps = config["MAX_EVAL_STEPS"] = getenv("MAX_EVAL_STEPS", (10000 + EVAL_BS - 1) // EVAL_BS) # EVAL_BS * MAX_EVAL_STEPS >= 10000
|
||||
eval_step_freq = config["EVAL_STEP_FREQ"] = getenv("EVAL_STEP_FREQ", int((math.floor(0.05 * (230.23 * GBS + 3000000) / 25000) * 25000) / GBS)) # Round down
|
||||
eval_step_freq = config["EVAL_STEP_FREQ"] = getenv("EVAL_STEP_FREQ", int((math.floor(0.05 * (230.23 * BS + 3000000) / 25000) * 25000) / BS)) # Round down
|
||||
save_ckpt_freq = config["SAVE_CKPT_FREQ"] = getenv("SAVE_CKPT_FREQ", 1000)
|
||||
keep_ckpt_amount = config["KEEP_CKPT_AMOUNT"] = getenv("KEEP_CKPT_AMOUNT", 5)
|
||||
save_ckpt_dir = config["SAVE_CKPT_DIR"] = getenv("SAVE_CKPT_DIR", "./ckpts")
|
||||
@@ -1077,7 +1067,7 @@ def train_bert():
|
||||
scheduler_wd = PolynomialDecayWithWarmup(optimizer_wd, max_lr, 0, train_steps, warmup_steps, power=poly_power)
|
||||
scheduler_no_wd = PolynomialDecayWithWarmup(optimizer_no_wd, max_lr, 0, train_steps, warmup_steps, power=poly_power)
|
||||
scheduler_group = LRSchedulerGroup(scheduler_wd, scheduler_no_wd)
|
||||
print(f"training with global batch size {GBS} for one epoch with {train_steps} steps")
|
||||
print(f"training with batch size {BS} for one epoch with {train_steps} steps")
|
||||
|
||||
# log mlperf hparams
|
||||
if MLLOGGER:
|
||||
@@ -1126,11 +1116,11 @@ def train_bert():
|
||||
# ** train loop **
|
||||
wc_start = time.perf_counter()
|
||||
|
||||
i, train_data = start_step, [next(train_it) for _ in range(grad_acc)]
|
||||
i, train_data = start_step, next(train_it)
|
||||
|
||||
if RUNMLPERF:
|
||||
if MLLOGGER:
|
||||
MLLOGGER.start(key=mllog_constants.EPOCH_START, value=i*GBS, metadata={"epoch_num": i*GBS})
|
||||
MLLOGGER.start(key=mllog_constants.EPOCH_START, value=i*BS, metadata={"epoch_num": i*BS})
|
||||
|
||||
while train_data is not None and i < train_steps and not achieved:
|
||||
if getenv("TRAIN", 1):
|
||||
@@ -1139,13 +1129,14 @@ def train_bert():
|
||||
st = time.perf_counter()
|
||||
GlobalCounters.reset()
|
||||
with WallTimeEvent(BenchEvent.STEP):
|
||||
data = {f"{k}{i}":v for i,d in enumerate(train_data) for k,v in d.items()}
|
||||
loss, global_norm, lr = train_step_bert(model, optimizer_group, scheduler_group, loss_scaler, GPUS, grad_acc, **data)
|
||||
loss, global_norm, lr = train_step_bert(model, optimizer_group, scheduler_group, loss_scaler,
|
||||
train_data["input_ids"], train_data["segment_ids"], train_data["input_mask"], train_data["masked_lm_positions"], \
|
||||
train_data["masked_lm_ids"], train_data["masked_lm_weights"], train_data["next_sentence_labels"], GPUS)
|
||||
|
||||
pt = time.perf_counter()
|
||||
|
||||
try:
|
||||
next_data = [next(train_it) for _ in range(grad_acc)]
|
||||
next_data = next(train_it)
|
||||
except StopIteration:
|
||||
next_data = None
|
||||
|
||||
@@ -1166,7 +1157,7 @@ def train_bert():
|
||||
if WANDB:
|
||||
wandb.log({"lr": lr, "train/loss": loss, "train/global_norm": global_norm.item(), "train/step_time": cl - st,
|
||||
"train/python_time": pt - st, "train/data_time": dt - pt, "train/cl_time": cl - dt,
|
||||
"train/GFLOPS": GlobalCounters.global_ops * 1e-9 / (cl - st), "epoch": (i+1)*GBS})
|
||||
"train/GFLOPS": GlobalCounters.global_ops * 1e-9 / (cl - st), "epoch": (i+1)*BS})
|
||||
|
||||
train_data, next_data = next_data, None
|
||||
i += 1
|
||||
@@ -1181,7 +1172,7 @@ def train_bert():
|
||||
# ** eval loop **
|
||||
if i % eval_step_freq == 0 or (BENCHMARK and i == BENCHMARK) or i == train_steps:
|
||||
if MLLOGGER and RUNMLPERF:
|
||||
MLLOGGER.start(key=mllog_constants.EVAL_START, value=None, metadata={"epoch_num": i*GBS, "step_num": i})
|
||||
MLLOGGER.start(key=mllog_constants.EVAL_START, value=None, metadata={"epoch_num": i*BS, "step_num": i})
|
||||
if getenv("RESET_STEP"): train_step_bert.reset()
|
||||
elif getenv("FREE_INTERMEDIATE", 1) and train_step_bert.captured is not None: train_step_bert.captured.free_intermediates()
|
||||
eval_lm_losses = []
|
||||
@@ -1231,11 +1222,11 @@ def train_bert():
|
||||
|
||||
if WANDB:
|
||||
wandb.log({"eval/lm_loss": avg_lm_loss, "eval/clsf_loss": avg_clsf_loss, "eval/lm_accuracy": avg_lm_acc, \
|
||||
"eval/clsf_accuracy": avg_clsf_acc, "eval/forward_time": avg_fw_time, "epoch": (i+1)*GBS})
|
||||
"eval/clsf_accuracy": avg_clsf_acc, "eval/forward_time": avg_fw_time, "epoch": (i+1)*BS})
|
||||
|
||||
if MLLOGGER and RUNMLPERF:
|
||||
MLLOGGER.end(key=mllog_constants.EVAL_STOP, value=i*GBS, metadata={"epoch_count": i*GBS, "step_num": i, "samples_count": config["EVAL_BS"] * config["MAX_EVAL_STEPS"]})
|
||||
MLLOGGER.event(key=mllog_constants.EVAL_ACCURACY, value=avg_lm_acc, metadata={"epoch_num": i*GBS, "masked_lm_accuracy": avg_lm_acc})
|
||||
MLLOGGER.end(key=mllog_constants.EVAL_STOP, value=i*BS, metadata={"epoch_count": i*BS, "step_num": i, "samples_count": config["EVAL_BS"] * config["MAX_EVAL_STEPS"]})
|
||||
MLLOGGER.event(key=mllog_constants.EVAL_ACCURACY, value=avg_lm_acc, metadata={"epoch_num": i*BS, "masked_lm_accuracy": avg_lm_acc})
|
||||
|
||||
# save model if achieved target
|
||||
if not achieved and avg_lm_acc >= target:
|
||||
@@ -1250,10 +1241,10 @@ def train_bert():
|
||||
hours = int(total_seconds // 3600)
|
||||
minutes = int((total_seconds % 3600) // 60)
|
||||
seconds = total_seconds % 60
|
||||
print(f"Reference Convergence point reached after {i * GBS} datasamples and {hours}h{minutes}m{seconds:.2f}s.")
|
||||
print(f"Reference Convergence point reached after {i * BS} datasamples and {hours}h{minutes}m{seconds:.2f}s.")
|
||||
achieved = True
|
||||
if MLLOGGER and RUNMLPERF:
|
||||
MLLOGGER.event(key=mllog_constants.EPOCH_STOP, value=i*GBS, metadata={"epoch_num": i*GBS})
|
||||
MLLOGGER.event(key=mllog_constants.EPOCH_STOP, value=i*BS, metadata={"epoch_num": i*BS})
|
||||
MLLOGGER.end(key=mllog_constants.RUN_STOP, metadata=dict(status=mllog_constants.SUCCESS))
|
||||
# stop once hitting the target
|
||||
break
|
||||
@@ -1281,223 +1272,18 @@ def train_bert():
|
||||
os.remove(os.path.join(ckpt_dir, last))
|
||||
if MLLOGGER and RUNMLPERF:
|
||||
MLLOGGER.end(key="checkpoint_stop", value=None, metadata={"step_num": i})
|
||||
MLLOGGER.start(key=mllog_constants.BLOCK_START, value=None, metadata={"first_epoch_num": 1, "epoch_num": 1, "epoch_count": 1, "samples_count": i * GBS, "step_num": i, "first_step_num": i+1})
|
||||
MLLOGGER.start(key=mllog_constants.BLOCK_START, value=None, metadata={"first_epoch_num": 1, "epoch_num": 1, "epoch_count": 1, "samples_count": i * BS, "step_num": i, "first_step_num": i+1})
|
||||
previous_step = i
|
||||
|
||||
def train_llama3():
|
||||
from extra.models.llama import Transformer
|
||||
from examples.llama3 import MODEL_PARAMS
|
||||
from examples.mlperf.lr_schedulers import CosineAnnealingLRWithWarmup
|
||||
|
||||
config = {}
|
||||
BASEDIR = config["BASEDIR"] = Path(getenv("BASEDIR", "/raid/datasets/c4/"))
|
||||
BS = config["BS"] = getenv("BS", 16)
|
||||
grad_acc = config["GRADIENT_ACC_STEPS"] = getenv("GRADIENT_ACC_STEPS", 1)
|
||||
GBS = config["GLOBAL_BATCH_SIZE"] = BS * grad_acc
|
||||
SEED = config["SEED"] = getenv("SEED", 5760)
|
||||
SEQLEN = config["SEQLEN"] = getenv("SEQLEN", 8192)
|
||||
TRAIN_ON_VAL = config["TRAIN_ON_VAL"] = getenv("TRAIN_ON_VAL", 0)
|
||||
SMALL = config["SMALL"] = getenv("SMALL", 0)
|
||||
SAMPLES = config["SAMPLES"] = getenv("SAMPLES", 5_760 if TRAIN_ON_VAL else 1_200_000 * 1152)
|
||||
EVAL_FREQ = config["EVAL_FREQ"] = getenv("EVAL_FREQ", 46080)
|
||||
EVAL_BS = config["EVAL_BS"] = getenv("EVAL_BS", 16)
|
||||
EVAL_TARGET = config["EVAL_TARGET"] = getenv("EVAL_TARGET", 5.6)
|
||||
|
||||
# LR=1e-4 TRAIN_ON_VAL=1 DEFAULT_FLOAT=bfloat16 FUSE_ARANGE=1 JITBEAM=2 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B WARMUP_STEPS=36 DECAY_STEPS=360 SEQLEN=512 PYTHONPATH=. AMD=1 AMD_LLVM=0 MODEL=llama3 python3 examples/mlperf/model_train.py
|
||||
# trains to 7
|
||||
|
||||
opt_adamw_beta_1 = 0.9
|
||||
opt_adamw_beta_2 = 0.95
|
||||
opt_adamw_epsilon = 1e-5
|
||||
opt_adamw_weight_decay = 0.1
|
||||
|
||||
opt_gradient_clip_norm = 1.0
|
||||
opt_learning_rate_warmup_steps = getenv("WARMUP_STEPS", math.ceil(8000 * 1152 / GBS))
|
||||
opt_learning_rate_decay_steps = getenv("MAX_STEPS", math.ceil(1_200_000 * 1152 / GBS)) - opt_learning_rate_warmup_steps
|
||||
opt_base_learning_rate = getenv("LR", 8e-5 * GBS / 1152) # NOTE: cannot change for benchmark
|
||||
opt_end_learning_rate = getenv("END_LR", 8e-7)
|
||||
|
||||
# TODO: confirm weights are in bf16
|
||||
# vocab_size from the mixtral tokenizer
|
||||
params = MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]
|
||||
params = params | {"vocab_size": 32000} if not SMALL else params
|
||||
if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: params['n_layers'] = llama_layers
|
||||
model = Transformer(**params, max_context=SEQLEN, jit=False, disable_kv_cache=True)
|
||||
|
||||
if getenv("FAKEDATA"):
|
||||
for v in get_parameters(model):
|
||||
v = v.assign(Tensor.empty(v.shape))
|
||||
|
||||
if (DP := getenv("DP", 1)) > 1:
|
||||
device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
|
||||
for v in get_parameters(model):
|
||||
v.shard_(device, axis=None)
|
||||
|
||||
if (MP := getenv("MP", 1)) > 1:
|
||||
device = tuple(f"{Device.DEFAULT}:{i}" for i in range(MP))
|
||||
for k,v in get_state_dict(model).items():
|
||||
if 'scale' in k: v.shard_(device, axis=None) # from quantized
|
||||
elif '.attention.wq' in k: v.shard_(device, axis=0)
|
||||
elif '.attention.wk' in k: v.shard_(device, axis=0)
|
||||
elif '.attention.wv' in k: v.shard_(device, axis=0)
|
||||
elif '.attention.wo' in k: v.shard_(device, axis=1)
|
||||
elif '.feed_forward.w1.' in k: v.shard_(device, axis=0)
|
||||
elif '.feed_forward.w2.' in k: v.shard_(device, axis=1)
|
||||
elif '.feed_forward.w3.' in k: v.shard_(device, axis=0)
|
||||
elif 'tok_embeddings.weight' in k: v.shard_(device, axis=0)
|
||||
elif 'output.weight' in k: v.shard_(device, axis=0)
|
||||
else:
|
||||
# attention_norm, ffn_norm, norm
|
||||
v.shard_(device, axis=None)
|
||||
# prevents memory spike on device 0
|
||||
v.realize()
|
||||
|
||||
optim = AdamW(get_parameters(model), lr=0.0,
|
||||
b1=opt_adamw_beta_1, b2=opt_adamw_beta_2, eps=opt_adamw_epsilon, weight_decay=opt_adamw_weight_decay)
|
||||
scheduler = CosineAnnealingLRWithWarmup(optim, opt_base_learning_rate, opt_end_learning_rate, opt_learning_rate_warmup_steps, opt_learning_rate_decay_steps)
|
||||
|
||||
if resume_ckpt := getenv("RESUME_CKPT"):
|
||||
fn = f"./ckpts/llama3_{resume_ckpt}.safe"
|
||||
print(f"loading initial checkpoint from {fn}")
|
||||
load_state_dict(model, safe_load(fn), realize=False)
|
||||
|
||||
fn = f"./ckpts/llama3_{resume_ckpt}_optim.safe"
|
||||
print(f"loading optim checkpoint from {fn}")
|
||||
load_state_dict(scheduler, safe_load(fn), realize=False)
|
||||
|
||||
@TinyJit
|
||||
@Tensor.train()
|
||||
def train_step(model, tokens:Tensor, grad_acc:int):
|
||||
optim.zero_grad()
|
||||
# grad acc
|
||||
for batch in tokens.split(tokens.shape[0]//grad_acc):
|
||||
if (DP := getenv("DP", 1)) > 1:
|
||||
device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
|
||||
batch = batch.shard(device, 0)
|
||||
if (MP := getenv("MP", 1)) > 1:
|
||||
device = tuple(f"{Device.DEFAULT}:{i}" for i in range(MP))
|
||||
batch = batch.shard(device)
|
||||
logits:Tensor = model(batch[:, :-1], start_pos=0, temperature=math.nan)
|
||||
loss = logits.sparse_categorical_crossentropy(batch[:, 1:])
|
||||
loss.backward()
|
||||
Tensor.realize(*[p.grad for p in optim.params])
|
||||
# L2 norm grad clip
|
||||
# https://github.com/NVIDIA/NeMo/blob/3368c3fc0b4a186ab33a1d68a504315100c0b2a6/nemo/collections/nlp/modules/common/megatron/clip_grads.py#L57
|
||||
# https://docs.pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html
|
||||
if not getenv("DISABLE_GRAD_CLIP_NORM"):
|
||||
total_norm = Tensor(0.0, dtype=dtypes.float32, device=optim.params[0].device)
|
||||
for p in optim.params:
|
||||
total_norm += p.grad.float().square().sum()
|
||||
total_norm = total_norm.sqrt().contiguous()
|
||||
for p in optim.params:
|
||||
p.grad = p.grad * (opt_gradient_clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)
|
||||
|
||||
optim.step()
|
||||
scheduler.step()
|
||||
|
||||
lr = optim.lr
|
||||
loss.realize(lr)
|
||||
return loss, lr
|
||||
|
||||
@TinyJit
|
||||
@Tensor.train(False)
|
||||
def eval_step(model, tokens:Tensor):
|
||||
if (DP := getenv("DP", 1)) > 1:
|
||||
device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
|
||||
tokens = tokens.shard(device, 0)
|
||||
if (MP := getenv("MP", 1)) > 1:
|
||||
device = tuple(f"{Device.DEFAULT}:{i}" for i in range(MP))
|
||||
tokens = tokens.shard(device)
|
||||
logits:Tensor = model(tokens[:, :-1], start_pos=0, temperature=math.nan)
|
||||
loss = logits.sparse_categorical_crossentropy(tokens[:, 1:])
|
||||
return loss.flatten().float()
|
||||
|
||||
# ** data iters **
|
||||
def fake_data(bs, samples):
|
||||
for _ in range(samples // bs):
|
||||
yield Tensor.randint(bs, SEQLEN + 1, low=0, high=params["vocab_size"], dtype=dtypes.int32, device=Device.DEFAULT)
|
||||
|
||||
def get_train_iter():
|
||||
if getenv("FAKEDATA", 0):
|
||||
return fake_data(GBS, SAMPLES)
|
||||
else:
|
||||
if SMALL:
|
||||
from examples.mlperf.dataloader import batch_load_llama3_small
|
||||
return batch_load_llama3_small(GBS, SAMPLES, SEQLEN, BASEDIR, seed=SEED, val=bool(TRAIN_ON_VAL))
|
||||
else:
|
||||
from examples.mlperf.dataloader import batch_load_llama3
|
||||
return batch_load_llama3(GBS, SAMPLES, SEQLEN, BASEDIR, seed=SEED, val=bool(TRAIN_ON_VAL))
|
||||
|
||||
def get_eval_iter():
|
||||
if getenv("FAKEDATA", 0):
|
||||
return fake_data(EVAL_BS, 5760)
|
||||
else:
|
||||
if SMALL:
|
||||
from examples.mlperf.dataloader import batch_load_llama3_small
|
||||
return batch_load_llama3_small(EVAL_BS, 5760, SEQLEN, BASEDIR, val=True)
|
||||
else:
|
||||
from examples.mlperf.dataloader import batch_load_llama3
|
||||
return batch_load_llama3(EVAL_BS, 5760, SEQLEN, BASEDIR, val=True)
|
||||
|
||||
iter = get_train_iter()
|
||||
i, sequences_seen = resume_ckpt, 0
|
||||
for tokens in tqdm(iter, total=SAMPLES//GBS):
|
||||
t = time.perf_counter()
|
||||
GlobalCounters.reset()
|
||||
loss, lr = train_step(model, tokens, grad_acc)
|
||||
loss = loss.float().item()
|
||||
|
||||
i += 1
|
||||
sequences_seen += tokens.shape[0]
|
||||
|
||||
tqdm.write(f"{loss:.4f} loss, {lr.item():.12f} LR, {GlobalCounters.mem_used / 1e9:.2f} GB used, {time.perf_counter()-t:.2f} s")
|
||||
if (fname:=getenv("LOSS_FILE", "")):
|
||||
with open(fname, "a") as f:
|
||||
f.write(f"{i} {loss:.4f} {lr.item():.12f} {GlobalCounters.mem_used / 1e9:.2f}\n")
|
||||
|
||||
if (ckpt_freq := getenv("CKPT")) and (i % ckpt_freq == 0 and (i != 1 or ckpt_freq == 1)):
|
||||
tqdm.write("saving checkpoint")
|
||||
if not os.path.exists(ckpt_dir := "./ckpts"): os.mkdir(ckpt_dir)
|
||||
fn = f"{ckpt_dir}/llama3_{i}.safe"
|
||||
safe_save(get_state_dict(model), fn)
|
||||
|
||||
tqdm.write("saving optim checkpoint")
|
||||
fn = f"{ckpt_dir}/llama3_{i}_optim.safe"
|
||||
safe_save(get_state_dict(scheduler), fn)
|
||||
|
||||
if sequences_seen % EVAL_FREQ == 0 and (i != 1 or EVAL_FREQ == 1):
|
||||
tqdm.write(f"evaluating after {sequences_seen} sequences")
|
||||
|
||||
# run eval
|
||||
eval_losses = []
|
||||
eval_iter = get_eval_iter()
|
||||
tqdm.write(f"evaluating {5760//EVAL_BS} batches of {EVAL_BS} sequences")
|
||||
|
||||
for tokens in tqdm(eval_iter, total=5760//EVAL_BS):
|
||||
eval_losses += eval_step(model, tokens).tolist()
|
||||
log_perplexity = Tensor(eval_losses).mean().float().item()
|
||||
|
||||
tqdm.write(f"eval log perplexity: {log_perplexity:.4f}")
|
||||
|
||||
if log_perplexity < EVAL_TARGET:
|
||||
tqdm.write(f"target achieved after {sequences_seen} sequences")
|
||||
if getenv("CKPT"):
|
||||
if not os.path.exists(ckpt_dir := "./ckpts"): os.mkdir(ckpt_dir)
|
||||
fn = f"{ckpt_dir}/llama3.safe"
|
||||
safe_save(get_state_dict(model), fn)
|
||||
break
|
||||
def train_maskrcnn():
|
||||
# TODO: Mask RCNN
|
||||
pass
|
||||
|
||||
if __name__ == "__main__":
|
||||
multiprocessing.set_start_method('spawn')
|
||||
|
||||
if getenv("INITMLPERF"): bench_log_manager = WallTimeEvent(BenchEvent.MLPERF_INIT)
|
||||
elif getenv("RUNMLPERF"): bench_log_manager = WallTimeEvent(BenchEvent.MLPERF_RUN)
|
||||
else: bench_log_manager = contextlib.nullcontext()
|
||||
|
||||
with Tensor.train():
|
||||
for m in getenv("MODEL", "resnet,retinanet,unet3d,rnnt,bert,maskrcnn").split(","):
|
||||
nm = f"train_{m}"
|
||||
if nm in globals():
|
||||
print(f"training {m}")
|
||||
with bench_log_manager:
|
||||
with Profiling(enabled=getenv("PYPROFILE")): globals()[nm]()
|
||||
with Profiling(enabled=getenv("PYPROFILE")): globals()[nm]()
|
||||
|
||||
-1
@@ -1,5 +1,4 @@
|
||||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="bert"
|
||||
|
||||
-1
@@ -1,5 +1,4 @@
|
||||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="bert"
|
||||
|
||||
-1
@@ -1,5 +1,4 @@
|
||||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="bert"
|
||||
|
||||
-1
@@ -1,5 +1,4 @@
|
||||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="resnet"
|
||||
|
||||
+1
-3
@@ -1,5 +1,4 @@
|
||||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="resnet"
|
||||
@@ -11,14 +10,13 @@ export RESET_STEP=0
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=${LOGMLPERF:-1}
|
||||
export LOGMLPERF=1
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="resnet_red_${DATETIME}_${SEED}.log"
|
||||
|
||||
# init
|
||||
sleep 5 && sudo rmmod amdgpu || true
|
||||
BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
|
||||
-1
@@ -1,5 +1,4 @@
|
||||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="retinanet"
|
||||
|
||||
@@ -5,7 +5,7 @@
|
||||
"system_name": "tinybox 8xMI300X",
|
||||
"number_of_nodes": "1",
|
||||
"host_processors_per_node": "2",
|
||||
"host_processor_model_name": "AMD EPYC 9354",
|
||||
"host_processor_model_name": "AMD EPYC 9354 32-Core Processor",
|
||||
"host_processor_core_count": "32",
|
||||
"host_processor_vcpu_count": "64",
|
||||
"host_processor_frequency": "",
|
||||
@@ -18,7 +18,7 @@
|
||||
"host_networking_topology": "",
|
||||
"host_memory_configuration": "24x 96GB DDR5",
|
||||
"accelerators_per_node": "8",
|
||||
"accelerator_model_name": "AMD Instinct MI300X 192GB HBM3",
|
||||
"accelerator_model_name": "AMD Instinct MI300X",
|
||||
"accelerator_host_interconnect": "PCIe 5.0 x16",
|
||||
"accelerator_frequency": "",
|
||||
"accelerator_on-chip_memories": "",
|
||||
@@ -30,9 +30,10 @@
|
||||
"hw_notes": "",
|
||||
"framework": "tinygrad, branch mlperf_training_v5.0",
|
||||
"other_software_stack": {
|
||||
"python": "3.10.16",
|
||||
"ROCm": "3.0.0+94441cb"
|
||||
"python": "3.10.16",
|
||||
"ROCm": "3.0.0+94441cb"
|
||||
},
|
||||
"operating_system": "Ubuntu 24.04.1 LTS",
|
||||
"sw_notes": ""
|
||||
}
|
||||
}
|
||||
|
||||
@@ -5,7 +5,7 @@
|
||||
"system_name": "tinybox green",
|
||||
"number_of_nodes": "1",
|
||||
"host_processors_per_node": "1",
|
||||
"host_processor_model_name": "AMD EPYC 7532",
|
||||
"host_processor_model_name": "AMD EPYC 7532 32-Core Processor",
|
||||
"host_processor_core_count": "32",
|
||||
"host_processor_vcpu_count": "64",
|
||||
"host_processor_frequency": "",
|
||||
@@ -35,4 +35,4 @@
|
||||
},
|
||||
"operating_system": "Ubuntu 22.04.4",
|
||||
"sw_notes": ""
|
||||
}
|
||||
}
|
||||
|
||||
@@ -5,7 +5,7 @@
|
||||
"system_name": "tinybox red",
|
||||
"number_of_nodes": "1",
|
||||
"host_processors_per_node": "1",
|
||||
"host_processor_model_name": "AMD EPYC 7532",
|
||||
"host_processor_model_name": "AMD EPYC 7532 32-Core Processor",
|
||||
"host_processor_core_count": "32",
|
||||
"host_processor_vcpu_count": "64",
|
||||
"host_processor_frequency": "",
|
||||
@@ -34,4 +34,4 @@
|
||||
},
|
||||
"operating_system": "Ubuntu 22.04.4",
|
||||
"sw_notes": ""
|
||||
}
|
||||
}
|
||||
|
||||
-17
@@ -1,17 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=1 BS=128 EVAL_BS=128
|
||||
|
||||
export IGNORE_OOB=1
|
||||
|
||||
export BEAM=3 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
# export BEAM_LOG_SURPASS_MAX=1
|
||||
# export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export RESET_STEP=1
|
||||
export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
-69
@@ -1,69 +0,0 @@
|
||||
# 1. Problem
|
||||
|
||||
This problem uses BERT for NLP.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
Also install gdown (for dataset), numpy, tqdm and tensorflow.
|
||||
```
|
||||
pip install gdown numpy tqdm tensorflow
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download and verify data
|
||||
|
||||
### 1. Download raw data
|
||||
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" WIKI_TRAIN=1 VERIFY_CHECKSUM=1 python3 extra/datasets/wikipedia_download.py
|
||||
```
|
||||
|
||||
### 2. Preprocess train and validation data
|
||||
|
||||
Note: The number of threads used for preprocessing is limited by available memory. With 128GB of RAM, a maximum of 16 threads is recommended.
|
||||
|
||||
#### Training:
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" NUM_WORKERS=16 python3 extra/datasets/wikipedia.py pre-train all
|
||||
```
|
||||
|
||||
Generating a specific topic (Between 0 and 499)
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-train 42
|
||||
```
|
||||
|
||||
#### Validation:
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
|
||||
```
|
||||
## Running
|
||||
|
||||
### tinybox_green
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
|
||||
```
|
||||
|
||||
### tinybox_red
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
### tinybox_8xMI300X
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
|
||||
```
|
||||
-16
@@ -1,16 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
|
||||
export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
|
||||
|
||||
export IGNORE_OOB=1
|
||||
|
||||
export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export BENCHMARK=10 BERT_LAYERS=2
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
-19
@@ -1,19 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
|
||||
|
||||
# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
|
||||
export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
|
||||
export TRAIN_STEPS=3900
|
||||
|
||||
export IGNORE_OOB=1
|
||||
|
||||
export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
RUNMLPERF=1 python3 examples/mlperf/model_train.py
|
||||
-30
@@ -1,30 +0,0 @@
|
||||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
set -o pipefail # Make pipeline fail if any command fails
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="bert"
|
||||
export SUBMISSION_PLATFORM="tinybox_8xMI300X"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
|
||||
|
||||
# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
|
||||
export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
|
||||
export TRAIN_STEPS=3900
|
||||
|
||||
export IGNORE_OOB=1
|
||||
|
||||
export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=1
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="bert_8xMI300x_${DATETIME}_${SEED}.log"
|
||||
|
||||
BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
-69
@@ -1,69 +0,0 @@
|
||||
# 1. Problem
|
||||
|
||||
This problem uses BERT for NLP.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
Also install gdown (for dataset), numpy, tqdm and tensorflow.
|
||||
```
|
||||
pip install gdown numpy tqdm tensorflow
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download and verify data
|
||||
|
||||
### 1. Download raw data
|
||||
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" WIKI_TRAIN=1 VERIFY_CHECKSUM=1 python3 extra/datasets/wikipedia_download.py
|
||||
```
|
||||
|
||||
### 2. Preprocess train and validation data
|
||||
|
||||
Note: The number of threads used for preprocessing is limited by available memory. With 128GB of RAM, a maximum of 16 threads is recommended.
|
||||
|
||||
#### Training:
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" NUM_WORKERS=16 python3 extra/datasets/wikipedia.py pre-train all
|
||||
```
|
||||
|
||||
Generating a specific topic (Between 0 and 499)
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-train 42
|
||||
```
|
||||
|
||||
#### Validation:
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
|
||||
```
|
||||
## Running
|
||||
|
||||
### tinybox_green
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
|
||||
```
|
||||
|
||||
### tinybox_red
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
### tinybox_8xMI300X
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
|
||||
```
|
||||
-16
@@ -1,16 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90
|
||||
|
||||
export IGNORE_OOB=1
|
||||
|
||||
export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BEAM_LOG_SURPASS_MAX=1
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
-15
@@ -1,15 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90
|
||||
|
||||
export IGNORE_OOB=1
|
||||
|
||||
export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
RUNMLPERF=1 python3 examples/mlperf/model_train.py
|
||||
-27
@@ -1,27 +0,0 @@
|
||||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
set -o pipefail # Make pipeline fail if any command fails
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="bert"
|
||||
export SUBMISSION_PLATFORM="tinybox_green"
|
||||
export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90
|
||||
|
||||
export IGNORE_OOB=1
|
||||
|
||||
export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=1
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="bert_green_${DATETIME}_${SEED}.log"
|
||||
|
||||
# init
|
||||
BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
-69
@@ -1,69 +0,0 @@
|
||||
# 1. Problem
|
||||
|
||||
This problem uses BERT for NLP.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
Also install gdown (for dataset), numpy, tqdm and tensorflow.
|
||||
```
|
||||
pip install gdown numpy tqdm tensorflow
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download and verify data
|
||||
|
||||
### 1. Download raw data
|
||||
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" WIKI_TRAIN=1 VERIFY_CHECKSUM=1 python3 extra/datasets/wikipedia_download.py
|
||||
```
|
||||
|
||||
### 2. Preprocess train and validation data
|
||||
|
||||
Note: The number of threads used for preprocessing is limited by available memory. With 128GB of RAM, a maximum of 16 threads is recommended.
|
||||
|
||||
#### Training:
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" NUM_WORKERS=16 python3 extra/datasets/wikipedia.py pre-train all
|
||||
```
|
||||
|
||||
Generating a specific topic (Between 0 and 499)
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-train 42
|
||||
```
|
||||
|
||||
#### Validation:
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
|
||||
```
|
||||
## Running
|
||||
|
||||
### tinybox_green
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
|
||||
```
|
||||
|
||||
### tinybox_red
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
### tinybox_8xMI300X
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
|
||||
```
|
||||
-17
@@ -1,17 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90
|
||||
|
||||
export IGNORE_OOB=1
|
||||
|
||||
export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BEAM_LOG_SURPASS_MAX=1
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export RESET_STEP=1
|
||||
export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
-15
@@ -1,15 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90
|
||||
|
||||
export IGNORE_OOB=1
|
||||
|
||||
export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
RUNMLPERF=1 python3 examples/mlperf/model_train.py
|
||||
-30
@@ -1,30 +0,0 @@
|
||||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
set -o pipefail # Make pipeline fail if any command fails
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="bert"
|
||||
export SUBMISSION_PLATFORM="tinybox_red"
|
||||
export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90
|
||||
|
||||
export IGNORE_OOB=1
|
||||
|
||||
export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=1
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="bert_red_${DATETIME}_${SEED}.log"
|
||||
|
||||
export HCQDEV_WAIT_TIMEOUT_MS=100000 # prevents hang?
|
||||
|
||||
# init
|
||||
sleep 5 && sudo rmmod amdgpu || true
|
||||
BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
-50
@@ -1,50 +0,0 @@
|
||||
# 1. Problem
|
||||
|
||||
This problem uses the ResNet-50 CNN to do image classification.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging from master.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
### tinybox_red
|
||||
Disable cwsr
|
||||
This is the default on production tinybox red.
|
||||
```
|
||||
sudo vi /etc/modprobe.d/amdgpu.conf
|
||||
cat <<EOF > /etc/modprobe.d/amdgpu.conf
|
||||
options amdgpu cwsr_enable=0
|
||||
EOF
|
||||
sudo update-initramfs -u
|
||||
sudo reboot
|
||||
|
||||
# validate
|
||||
sudo cat /sys/module/amdgpu/parameters/cwsr_enable #= 0
|
||||
```
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download and verify data
|
||||
|
||||
```
|
||||
IMGNET_TRAIN=1 python3 extra/datasets/imagenet_download.py
|
||||
```
|
||||
|
||||
## Steps for one time setup
|
||||
|
||||
### tinybox_red
|
||||
```
|
||||
examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh
|
||||
```
|
||||
|
||||
## Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
-13
@@ -1,13 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="resnet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
|
||||
|
||||
export BENCHMARK=10 DEBUG=2
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
-15
@@ -1,15 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="resnet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
|
||||
|
||||
export EVAL_START_EPOCH=3 EVAL_FREQ=4
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
-25
@@ -1,25 +0,0 @@
|
||||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
set -o pipefail # Make pipeline fail if any command fails
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="resnet"
|
||||
export SUBMISSION_PLATFORM="tinybox_green"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=${LOGMLPERF:-1}
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="resnet_green_${DATETIME}_${SEED}.log"
|
||||
|
||||
# init
|
||||
BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
-50
@@ -1,50 +0,0 @@
|
||||
# 1. Problem
|
||||
|
||||
This problem uses the ResNet-50 CNN to do image classification.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging from master.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
### tinybox_red
|
||||
Disable cwsr
|
||||
This is the default on production tinybox red.
|
||||
```
|
||||
sudo vi /etc/modprobe.d/amdgpu.conf
|
||||
cat <<EOF > /etc/modprobe.d/amdgpu.conf
|
||||
options amdgpu cwsr_enable=0
|
||||
EOF
|
||||
sudo update-initramfs -u
|
||||
sudo reboot
|
||||
|
||||
# validate
|
||||
sudo cat /sys/module/amdgpu/parameters/cwsr_enable #= 0
|
||||
```
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download and verify data
|
||||
|
||||
```
|
||||
IMGNET_TRAIN=1 python3 extra/datasets/imagenet_download.py
|
||||
```
|
||||
|
||||
## Steps for one time setup
|
||||
|
||||
### tinybox_red
|
||||
```
|
||||
examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh
|
||||
```
|
||||
|
||||
## Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
-13
@@ -1,13 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="resnet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export BENCHMARK=10 DEBUG=${DEBUG:-2}
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
-15
@@ -1,15 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="resnet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export EVAL_START_EPOCH=3 EVAL_FREQ=4
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
-26
@@ -1,26 +0,0 @@
|
||||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
set -o pipefail # Make pipeline fail if any command fails
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="resnet"
|
||||
export SUBMISSION_PLATFORM="tinybox_red"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=${LOGMLPERF:-1}
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="resnet_red_${DATETIME}_${SEED}.log"
|
||||
|
||||
# init
|
||||
sleep 5 && sudo rmmod amdgpu || true
|
||||
BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
-8
@@ -1,8 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
rocm-smi --setprofile compute
|
||||
rocm-smi --setmclk 3
|
||||
rocm-smi --setperflevel high
|
||||
|
||||
# power cap to 350W
|
||||
echo "350000000" | sudo tee /sys/class/drm/card{1..6}/device/hwmon/hwmon*/power1_cap
|
||||
-38
@@ -1,38 +0,0 @@
|
||||
# 1. Problem
|
||||
|
||||
This problem uses RetinaNet for SSD.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
|
||||
Also install the following dependencies:
|
||||
```
|
||||
pip install tqdm numpy pycocotools boto3 pandas torch torchvision
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download data
|
||||
|
||||
Run the following:
|
||||
```
|
||||
BASEDIR=/raid/datasets/openimages python3 extra/datasets/openimages.py
|
||||
```
|
||||
|
||||
## Running
|
||||
|
||||
### tinybox_green
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/run_and_time.sh
|
||||
```
|
||||
-14
@@ -1,14 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="retinanet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
export BASEDIR="/raid/datasets/openimages"
|
||||
|
||||
# export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export BENCHMARK=5 DEBUG=2
|
||||
|
||||
python examples/mlperf/model_train.py
|
||||
-15
@@ -1,15 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="retinanet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
export BASEDIR="/raid/datasets/openimages"
|
||||
|
||||
# export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
export RUNMLPERF=1
|
||||
|
||||
python examples/mlperf/model_train.py
|
||||
-25
@@ -1,25 +0,0 @@
|
||||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
set -o pipefail # Make pipeline fail if any command fails
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="retinanet"
|
||||
export SUBMISSION_PLATFORM="tinybox_green"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
|
||||
export TRAIN_BEAM=2 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BASEDIR="/raid/datasets/openimages"
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=1
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="retinanet_green_${DATETIME}_${SEED}.log"
|
||||
|
||||
# init
|
||||
BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
-14
@@ -1,14 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="retinanet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
export BASEDIR="/raid/datasets/openimages"
|
||||
|
||||
# export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export BENCHMARK=5 DEBUG=2
|
||||
|
||||
python examples/mlperf/model_train.py
|
||||
-15
@@ -1,15 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="retinanet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
export BASEDIR="/raid/datasets/openimages"
|
||||
|
||||
# export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
export RUNMLPERF=1
|
||||
|
||||
python examples/mlperf/model_train.py
|
||||
@@ -1,38 +0,0 @@
|
||||
{
|
||||
"submitter": "tinycorp",
|
||||
"division": "closed",
|
||||
"status": "Available on-premise",
|
||||
"system_name": "tinybox 8xMI300X",
|
||||
"number_of_nodes": "1",
|
||||
"host_processors_per_node": "2",
|
||||
"host_processor_model_name": "AMD EPYC 9354",
|
||||
"host_processor_core_count": "32",
|
||||
"host_processor_vcpu_count": "64",
|
||||
"host_processor_frequency": "",
|
||||
"host_processor_caches": "",
|
||||
"host_processor_interconnect": "",
|
||||
"host_memory_capacity": "2304GB",
|
||||
"host_storage_type": "NVMe SSD",
|
||||
"host_storage_capacity": "3x 4TB raid array",
|
||||
"host_networking": "",
|
||||
"host_networking_topology": "",
|
||||
"host_memory_configuration": "24x 96GB DDR5",
|
||||
"accelerators_per_node": "8",
|
||||
"accelerator_model_name": "AMD Instinct MI300X 192GB HBM3",
|
||||
"accelerator_host_interconnect": "PCIe 5.0 x16",
|
||||
"accelerator_frequency": "",
|
||||
"accelerator_on-chip_memories": "",
|
||||
"accelerator_memory_configuration": "HBM3",
|
||||
"accelerator_memory_capacity": "192GB",
|
||||
"accelerator_interconnect": "",
|
||||
"accelerator_interconnect_topology": "",
|
||||
"cooling": "air",
|
||||
"hw_notes": "",
|
||||
"framework": "tinygrad, branch mlperf_training_v5.0",
|
||||
"other_software_stack": {
|
||||
"python": "3.10.16",
|
||||
"ROCm": "3.0.0+94441cb"
|
||||
},
|
||||
"operating_system": "Ubuntu 24.04.1 LTS",
|
||||
"sw_notes": ""
|
||||
}
|
||||
@@ -1,38 +0,0 @@
|
||||
{
|
||||
"submitter": "tinycorp",
|
||||
"division": "closed",
|
||||
"status": "Available on-premise",
|
||||
"system_name": "tinybox green",
|
||||
"number_of_nodes": "1",
|
||||
"host_processors_per_node": "1",
|
||||
"host_processor_model_name": "AMD EPYC 7532",
|
||||
"host_processor_core_count": "32",
|
||||
"host_processor_vcpu_count": "64",
|
||||
"host_processor_frequency": "",
|
||||
"host_processor_caches": "",
|
||||
"host_processor_interconnect": "",
|
||||
"host_memory_capacity": "128GB",
|
||||
"host_storage_type": "NVMe SSD",
|
||||
"host_storage_capacity": "4 TB raid array + 1 TB boot",
|
||||
"host_networking": "",
|
||||
"host_networking_topology": "",
|
||||
"host_memory_configuration": "8x 16GB DDR4",
|
||||
"accelerators_per_node": "6",
|
||||
"accelerator_model_name": "NVIDIA GeForce RTX 4090",
|
||||
"accelerator_host_interconnect": "PCIe 4.0 x16",
|
||||
"accelerator_frequency": "",
|
||||
"accelerator_on-chip_memories": "",
|
||||
"accelerator_memory_configuration": "GDDR6X",
|
||||
"accelerator_memory_capacity": "24GB",
|
||||
"accelerator_interconnect": "",
|
||||
"accelerator_interconnect_topology": "",
|
||||
"cooling": "air",
|
||||
"hw_notes": "",
|
||||
"framework": "tinygrad, branch mlperf_training_v5.0",
|
||||
"other_software_stack": {
|
||||
"python": "3.10.12",
|
||||
"CUDA": "12.4"
|
||||
},
|
||||
"operating_system": "Ubuntu 22.04.4",
|
||||
"sw_notes": ""
|
||||
}
|
||||
@@ -1,37 +0,0 @@
|
||||
{
|
||||
"submitter": "tinycorp",
|
||||
"division": "closed",
|
||||
"status": "Available on-premise",
|
||||
"system_name": "tinybox red",
|
||||
"number_of_nodes": "1",
|
||||
"host_processors_per_node": "1",
|
||||
"host_processor_model_name": "AMD EPYC 7532",
|
||||
"host_processor_core_count": "32",
|
||||
"host_processor_vcpu_count": "64",
|
||||
"host_processor_frequency": "",
|
||||
"host_processor_caches": "",
|
||||
"host_processor_interconnect": "",
|
||||
"host_memory_capacity": "128GB",
|
||||
"host_storage_type": "NVMe SSD",
|
||||
"host_storage_capacity": "4 TB raid array + 1 TB boot",
|
||||
"host_networking": "",
|
||||
"host_networking_topology": "",
|
||||
"host_memory_configuration": "8x 16GB DDR4",
|
||||
"accelerators_per_node": "6",
|
||||
"accelerator_model_name": "AMD Radeon RX 7900 XTX",
|
||||
"accelerator_host_interconnect": "PCIe 4.0 x16",
|
||||
"accelerator_frequency": "",
|
||||
"accelerator_on-chip_memories": "",
|
||||
"accelerator_memory_configuration": "GDDR6",
|
||||
"accelerator_memory_capacity": "24GB",
|
||||
"accelerator_interconnect": "",
|
||||
"accelerator_interconnect_topology": "",
|
||||
"cooling": "air",
|
||||
"hw_notes": "",
|
||||
"framework": "tinygrad, branch mlperf_training_v5.0",
|
||||
"other_software_stack": {
|
||||
"python": "3.10.12"
|
||||
},
|
||||
"operating_system": "Ubuntu 22.04.4",
|
||||
"sw_notes": ""
|
||||
}
|
||||
+4
-3
@@ -1,7 +1,8 @@
|
||||
# https://arxiv.org/pdf/2409.02060
|
||||
import time, functools
|
||||
import time
|
||||
import numpy as np
|
||||
np.set_printoptions(suppress=True, linewidth=1000)
|
||||
import functools
|
||||
from tinygrad import Tensor, nn, Device, GlobalCounters
|
||||
from tinygrad.helpers import Timing, getenv
|
||||
from extra.models.llama import Transformer, convert_from_huggingface
|
||||
@@ -16,7 +17,7 @@ class MixtureFeedForward:
|
||||
def __call__(self, x:Tensor) -> Tensor:
|
||||
assert x.shape[0] == 1, "only BS=1"
|
||||
assert x.shape[1] == 1, "only length=1"
|
||||
g = self.gate(x).softmax(-1)
|
||||
g = self.gate(x).float().softmax(-1)
|
||||
|
||||
g = g.squeeze() # (BS, length, num_experts) -> (num_experts,)
|
||||
probs, sel = g.topk(self.activated_experts)
|
||||
@@ -24,7 +25,7 @@ class MixtureFeedForward:
|
||||
# run MoE
|
||||
x_up_gate = x.dot(self.gate_proj[sel].permute(0,2,1)).silu() * x.dot(self.up_proj[sel].permute(0,2,1))
|
||||
x_down = x_up_gate.dot(self.down_proj[sel].permute(0,2,1))
|
||||
return (x_down * probs.reshape(self.activated_experts, 1, 1)).sum(axis=0)
|
||||
return (x_down.float() * probs.reshape(self.activated_experts, 1, 1)).sum(axis=0)
|
||||
|
||||
# model is bf16, 1.3B active, 6.9B total
|
||||
# M3 Max is 400 GB/s, so 400/2.6 = ~154 tok/s
|
||||
|
||||
@@ -5,26 +5,29 @@ if "IMAGE" not in os.environ: os.environ["IMAGE"] = "2"
|
||||
if "NOLOCALS" not in os.environ: os.environ["NOLOCALS"] = "1"
|
||||
if "JIT_BATCH_SIZE" not in os.environ: os.environ["JIT_BATCH_SIZE"] = "0"
|
||||
|
||||
from tinygrad import fetch, Tensor, TinyJit, Context, GlobalCounters, Device, dtypes
|
||||
from tinygrad import fetch, Tensor, TinyJit, Context, GlobalCounters, Device
|
||||
from tinygrad.helpers import DEBUG, getenv
|
||||
from tinygrad.tensor import _from_np_dtype
|
||||
from tinygrad.engine.realize import CompiledRunner
|
||||
|
||||
import onnx
|
||||
from onnx.helper import tensor_dtype_to_np_dtype
|
||||
from tinygrad.frontend.onnx import OnnxRunner
|
||||
|
||||
OPENPILOT_MODEL = sys.argv[1] if len(sys.argv) > 1 else "https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx"
|
||||
OUTPUT = sys.argv[2] if len(sys.argv) > 2 else "/tmp/openpilot.pkl"
|
||||
|
||||
def compile(onnx_file):
|
||||
run_onnx = OnnxRunner(onnx_file)
|
||||
onnx_model = onnx.load(onnx_file)
|
||||
run_onnx = OnnxRunner(onnx_model)
|
||||
print("loaded model")
|
||||
|
||||
input_shapes = {name: spec.shape for name, spec in run_onnx.graph_inputs.items()}
|
||||
input_types = {name: spec.dtype for name, spec in run_onnx.graph_inputs.items()}
|
||||
input_shapes = {inp.name:tuple(x.dim_value for x in inp.type.tensor_type.shape.dim) for inp in onnx_model.graph.input}
|
||||
input_types = {inp.name: tensor_dtype_to_np_dtype(inp.type.tensor_type.elem_type) for inp in onnx_model.graph.input}
|
||||
# Float inputs and outputs to tinyjits for openpilot are always float32
|
||||
input_types = {k:(dtypes.float32 if v is dtypes.float16 else v) for k,v in input_types.items()}
|
||||
input_types = {k:(np.float32 if v==np.float16 else v) for k,v in input_types.items()}
|
||||
Tensor.manual_seed(100)
|
||||
new_inputs = {k:Tensor.randn(*shp, dtype=input_types[k]).mul(8).realize() for k,shp in sorted(input_shapes.items())}
|
||||
new_inputs = {k:Tensor.randn(*shp, dtype=_from_np_dtype(input_types[k])).mul(8).realize() for k,shp in sorted(input_shapes.items())}
|
||||
new_inputs_numpy = {k:v.numpy() for k,v in new_inputs.items()}
|
||||
print("created tensors")
|
||||
|
||||
@@ -54,11 +57,11 @@ def compile(onnx_file):
|
||||
gated_read_image_count += ei.prg.p.src.count("?read_image")
|
||||
print(f"{kernel_count=}, {read_image_count=}, {gated_read_image_count=}")
|
||||
if (allowed_kernel_count:=getenv("ALLOWED_KERNEL_COUNT", -1)) != -1:
|
||||
assert kernel_count == allowed_kernel_count, f"different kernels! {kernel_count=}, {allowed_kernel_count=}"
|
||||
assert kernel_count <= allowed_kernel_count, f"too many kernels! {kernel_count=}, {allowed_kernel_count=}"
|
||||
if (allowed_read_image:=getenv("ALLOWED_READ_IMAGE", -1)) != -1:
|
||||
assert read_image_count == allowed_read_image, f"different read_image! {read_image_count=}, {allowed_read_image=}"
|
||||
if (allowed_gated_read_image:=getenv("ALLOWED_GATED_READ_IMAGE", -1)) != -1:
|
||||
assert gated_read_image_count == allowed_gated_read_image, f"different gated read_image! {gated_read_image_count=}, {allowed_gated_read_image=}"
|
||||
assert gated_read_image_count <= allowed_gated_read_image, f"too many gated read_image! {gated_read_image_count=}, {allowed_gated_read_image=}"
|
||||
|
||||
with open(OUTPUT, "wb") as f:
|
||||
pickle.dump(run_onnx_jit, f)
|
||||
|
||||
@@ -1,47 +0,0 @@
|
||||
import sys
|
||||
from tinygrad import Tensor, fetch, GlobalCounters, dtypes
|
||||
from tinygrad.uop.ops import UOp
|
||||
from tinygrad.frontend.onnx import OnnxRunner
|
||||
from tinygrad.schedule.kernelize import get_kernelize_map
|
||||
from tinygrad.engine.schedule import create_schedule_with_vars
|
||||
from tinygrad.engine.realize import run_schedule
|
||||
|
||||
# NOLOCALS=1 GPU=1 IMAGE=2 FLOAT16=1 VIZ=1 DEBUG=2 python3 examples/openpilot/compile4.py
|
||||
|
||||
OPENPILOT_MODEL = sys.argv[1] if len(sys.argv) > 1 else "https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx"
|
||||
OUTPUT = sys.argv[2] if len(sys.argv) > 2 else "/tmp/openpilot.pkl"
|
||||
|
||||
if __name__ == "__main__":
|
||||
onnx_file = fetch(OPENPILOT_MODEL)
|
||||
run_onnx = OnnxRunner(onnx_file)
|
||||
|
||||
inputs = run_onnx.get_empty_input_data("npy", dtypes.float32)
|
||||
out: Tensor = next(iter(run_onnx({k:v.to(None) for k,v in inputs.items()}).values())).to('cpu')
|
||||
root = out.uop
|
||||
targets = [x.uop for x in inputs.values()]
|
||||
print(targets)
|
||||
|
||||
# TODO: abstract this from gradient?
|
||||
|
||||
# compute the target path (top down)
|
||||
in_target_path: dict[UOp, bool] = {}
|
||||
for u in root.toposort(): in_target_path[u] = any(x in targets or in_target_path[x] for x in u.src)
|
||||
independent_set = {}
|
||||
for u in root.toposort():
|
||||
if in_target_path[u]:
|
||||
for s in u.src:
|
||||
if not in_target_path[s]:
|
||||
independent_set[s] = None
|
||||
independent = UOp.sink(*independent_set.keys())
|
||||
kernelized = get_kernelize_map(independent)
|
||||
independent = independent.substitute(kernelized)
|
||||
schedule, var_vals = create_schedule_with_vars(independent)
|
||||
run_schedule(schedule)
|
||||
|
||||
print("**** real ****")
|
||||
GlobalCounters.reset()
|
||||
out.uop = root.substitute(kernelized)
|
||||
out.kernelize()
|
||||
|
||||
# realize
|
||||
out.realize()
|
||||
@@ -27,7 +27,7 @@ class Model(nn.Module):
|
||||
|
||||
if __name__ == "__main__":
|
||||
if getenv("TINY_BACKEND"):
|
||||
import tinygrad.frontend.torch # noqa: F401
|
||||
import tinygrad.frontend.torch
|
||||
device = torch.device("tiny")
|
||||
else:
|
||||
device = torch.device({"METAL":"mps","NV":"cuda"}.get(Device.DEFAULT, "cpu"))
|
||||
|
||||
+3
-1
@@ -52,6 +52,8 @@ def load_model(model_path:Path, model_params:Dict[str, Union[int, float]]) -> Tr
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
Tensor.no_grad = True
|
||||
|
||||
parser = argparse.ArgumentParser(description="Run QwQ in tinygrad", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument("--size", choices=["32B"], default="32B", help="Model size")
|
||||
parser.add_argument("--count", type=int, default=30, help="Max number of tokens to generate")
|
||||
@@ -66,7 +68,7 @@ if __name__ == "__main__":
|
||||
model_path = Path(args.weights) if args.weights else download_weights(model_info["total_num_weights"])
|
||||
transformer = load_model(model_path, model_info["model_params"])
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_info["tokenizer"])
|
||||
param_bytes = sum(x.uop.size * x.dtype.itemsize for x in get_parameters(transformer))
|
||||
param_bytes = sum(x.lazydata.size * x.dtype.itemsize for x in get_parameters(transformer))
|
||||
|
||||
outputted = args.prompt
|
||||
start_pos, toks = 0, tokenizer(outputted)["input_ids"]
|
||||
|
||||
@@ -107,6 +107,7 @@ if __name__ == "__main__":
|
||||
assert args.width % F == 0, f"img_width must be multiple of {F}, got {args.width}"
|
||||
assert args.height % F == 0, f"img_height must be multiple of {F}, got {args.height}"
|
||||
|
||||
Tensor.no_grad = True
|
||||
if args.seed is not None:
|
||||
Tensor.manual_seed(args.seed)
|
||||
|
||||
|
||||
+10
-11
@@ -5,7 +5,7 @@
|
||||
|
||||
from tinygrad import Tensor, TinyJit, dtypes, GlobalCounters
|
||||
from tinygrad.nn import Conv2d, GroupNorm
|
||||
from tinygrad.nn.state import safe_load, load_state_dict
|
||||
from tinygrad.nn.state import safe_load, load_state_dict, get_state_dict
|
||||
from tinygrad.helpers import fetch, trange, colored, Timing
|
||||
from extra.models.clip import Embedder, FrozenClosedClipEmbedder, FrozenOpenClipEmbedder
|
||||
from extra.models.unet import UNetModel, Upsample, Downsample, timestep_embedding
|
||||
@@ -376,24 +376,23 @@ if __name__ == "__main__":
|
||||
parser.add_argument('--weights', type=str, help="Custom path to weights")
|
||||
parser.add_argument('--timing', action='store_true', help="Print timing per step")
|
||||
parser.add_argument('--noshow', action='store_true', help="Don't show the image")
|
||||
parser.add_argument('--fakeweights', action='store_true', help="Load fake weights")
|
||||
args = parser.parse_args()
|
||||
|
||||
Tensor.no_grad = True
|
||||
if args.seed is not None:
|
||||
Tensor.manual_seed(args.seed)
|
||||
|
||||
model = SDXL(configs["SDXL_Base"])
|
||||
|
||||
if not args.fakeweights:
|
||||
default_weight_url = 'https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors'
|
||||
weights = args.weights if args.weights else fetch(default_weight_url, 'sd_xl_base_1.0.safetensors')
|
||||
loaded_weights = load_state_dict(model, safe_load(weights), strict=False, verbose=False, realize=False)
|
||||
default_weight_url = 'https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors'
|
||||
weights = args.weights if args.weights else fetch(default_weight_url, 'sd_xl_base_1.0.safetensors')
|
||||
loaded_weights = load_state_dict(model, safe_load(weights), strict=False, verbose=False, realize=False)
|
||||
|
||||
start_mem_used = GlobalCounters.mem_used
|
||||
with Timing("loaded weights in ", lambda et_ns: f", {(B:=(GlobalCounters.mem_used-start_mem_used))/1e9:.2f} GB loaded at {B/et_ns:.2f} GB/s"):
|
||||
with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):
|
||||
Tensor.realize(*loaded_weights)
|
||||
del loaded_weights
|
||||
start_mem_used = GlobalCounters.mem_used
|
||||
with Timing("loaded weights in ", lambda et_ns: f", {(B:=(GlobalCounters.mem_used-start_mem_used))/1e9:.2f} GB loaded at {B/et_ns:.2f} GB/s"):
|
||||
with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):
|
||||
Tensor.realize(*loaded_weights)
|
||||
del loaded_weights
|
||||
|
||||
N = 1
|
||||
C = 4
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 1.5 MiB After Width: | Height: | Size: 1.5 MiB |
+10
-6
@@ -5,7 +5,7 @@ from functools import partial, reduce
|
||||
from pathlib import Path
|
||||
from typing import Tuple, Optional, Type
|
||||
from tinygrad import nn, dtypes, Tensor
|
||||
from tinygrad.helpers import getenv, fetch
|
||||
from tinygrad.helpers import getenv
|
||||
from tinygrad.nn.state import torch_load
|
||||
from examples.vits import ResidualCouplingBlock, PosteriorEncoder, Encoder, ResBlock1, ResBlock2, LRELU_SLOPE, sequence_mask, split, get_hparams_from_file, load_checkpoint, weight_norm, HParams
|
||||
from examples.sovits_helpers import preprocess
|
||||
@@ -19,6 +19,10 @@ F0_MIN = 50.0
|
||||
F0_MEL_MIN = 1127 * np.log(1 + F0_MIN / 700)
|
||||
F0_MEL_MAX = 1127 * np.log(1 + F0_MAX / 700)
|
||||
|
||||
def download_if_not_present(file_path: Path, url: str):
|
||||
if not os.path.isfile(file_path): download_file(url, file_path)
|
||||
return file_path
|
||||
|
||||
class SpeechEncoder:
|
||||
def __init__(self, hidden_dim, model:ContentVec): self.hidden_dim, self.model = hidden_dim, model
|
||||
def encode(self, ): raise NotImplementedError("implement me")
|
||||
@@ -93,7 +97,7 @@ class ContentVec:
|
||||
return res, padding_mask
|
||||
@classmethod
|
||||
def load_from_pretrained(cls, checkpoint_path:str, checkpoint_url:str) -> ContentVec:
|
||||
fetch(checkpoint_url, checkpoint_path)
|
||||
download_if_not_present(checkpoint_path, checkpoint_url)
|
||||
cfg = load_fairseq_cfg(checkpoint_path)
|
||||
enc = cls(cfg.model)
|
||||
_ = load_checkpoint_enc(checkpoint_path, enc, None)
|
||||
@@ -320,9 +324,9 @@ class Synthesizer:
|
||||
return f0_coarse
|
||||
@classmethod
|
||||
def load_from_pretrained(cls, config_path:str, config_url:str, weights_path:str, weights_url:str) -> Synthesizer:
|
||||
fetch(config_url, config_path)
|
||||
download_if_not_present(config_path, config_url)
|
||||
hps = get_hparams_from_file(config_path)
|
||||
fetch(weights_url, weights_path)
|
||||
download_if_not_present(weights_path, weights_url)
|
||||
net_g = cls(hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, **hps.model)
|
||||
_ = load_checkpoint(weights_path, net_g, None, skip_list=["f0_decoder"])
|
||||
logging.debug(f"{cls.__name__}:Loaded model with hps: {hps}")
|
||||
@@ -583,7 +587,7 @@ if __name__=="__main__":
|
||||
vits_model = args.model
|
||||
encoder_location, vits_location = ENCODER_MODELS[ENCODER_MODEL], VITS_MODELS[vits_model]
|
||||
|
||||
Tensor.training = False
|
||||
Tensor.no_grad, Tensor.training = True, False
|
||||
# Get Synthesizer and ContentVec
|
||||
net_g, hps = Synthesizer.load_from_pretrained(vits_location[0], vits_location[2], vits_location[1], vits_location[3])
|
||||
Encoder = get_encoder(hps.model.ssl_dim)
|
||||
@@ -598,7 +602,7 @@ if __name__=="__main__":
|
||||
speaker = args.speaker if args.speaker is not None else list(hps.spk.__dict__.keys())[0]
|
||||
|
||||
### Loading audio and slicing ###
|
||||
if audio_path == DEMO_PATH: fetch(DEMO_URL, DEMO_PATH)
|
||||
if audio_path == DEMO_PATH: download_if_not_present(DEMO_PATH, DEMO_URL)
|
||||
assert Path(audio_path).is_file() and Path(audio_path).suffix == ".wav"
|
||||
chunks = preprocess.cut(audio_path, db_thresh=slice_db)
|
||||
audio_data, audio_sr = preprocess.chunks2audio(audio_path, chunks)
|
||||
|
||||
@@ -229,6 +229,7 @@ if __name__ == "__main__":
|
||||
parser.add_argument('--guidance', type=float, default=7.5, help="Prompt strength")
|
||||
args = parser.parse_args()
|
||||
|
||||
Tensor.no_grad = True
|
||||
model = StableDiffusion()
|
||||
|
||||
# load in weights
|
||||
|
||||
@@ -7,7 +7,7 @@
|
||||
from examples.beautiful_mnist import Model
|
||||
from tinygrad import Tensor, nn, getenv, GlobalCounters, Variable
|
||||
from tinygrad.nn.datasets import mnist
|
||||
from tinygrad.helpers import trange
|
||||
from tinygrad.helpers import trange, DEBUG
|
||||
|
||||
# STEPS=70 python3 examples/stunning_mnist.py
|
||||
# NOTE: it's broken with STACK=1, why?
|
||||
@@ -45,7 +45,8 @@ if __name__ == "__main__":
|
||||
print("*** scheduled training")
|
||||
|
||||
# evaluate the model
|
||||
test_acc = ((model(X_test).argmax(axis=1) == Y_test).mean()*100)
|
||||
with Tensor.test():
|
||||
test_acc = ((model(X_test).argmax(axis=1) == Y_test).mean()*100)
|
||||
print("*** scheduled eval")
|
||||
|
||||
# NOTE: there's no kernels run in the scheduling phase
|
||||
|
||||
@@ -27,7 +27,7 @@ def imagenet_dataloader(cnt=0):
|
||||
input_std = Tensor([0.229, 0.224, 0.225]).reshape(1, -1, 1, 1)
|
||||
files = get_val_files()
|
||||
random.shuffle(files)
|
||||
files = files[:cnt]
|
||||
if cnt != 0: files = files[:cnt]
|
||||
cir = get_imagenet_categories()
|
||||
for fn in files:
|
||||
img = Image.open(fn)
|
||||
@@ -66,7 +66,7 @@ if __name__ == "__main__":
|
||||
assert t_spec.shape[1:] == (3,224,224), f"shape is {t_spec.shape}"
|
||||
|
||||
hit = 0
|
||||
for i,(img,y) in enumerate(imagenet_dataloader(cnt:=getenv("CNT", 100))):
|
||||
for i,(img,y) in enumerate(imagenet_dataloader(cnt=getenv("CNT", 100))):
|
||||
GlobalCounters.reset()
|
||||
p = run_onnx_jit(**{t_name:img})
|
||||
assert p.shape == (1,1000)
|
||||
@@ -77,6 +77,5 @@ if __name__ == "__main__":
|
||||
MS_TARGET = 13.4
|
||||
print(f"need {GlobalCounters.global_ops/1e9*(1000/MS_TARGET):.2f} GFLOPS for {MS_TARGET:.2f} ms")
|
||||
|
||||
if cnt >= 2:
|
||||
import pickle
|
||||
with open("/tmp/im.pkl", "wb") as f: pickle.dump(run_onnx_jit, f)
|
||||
import pickle
|
||||
with open("/tmp/im.pkl", "wb") as f: pickle.dump(run_onnx_jit, f)
|
||||
|
||||
@@ -13,8 +13,8 @@ def prepare_browser_chunks(model):
|
||||
chunk_size = 16 * 1024 * 1024 # small chunks based on iphone browser constraints
|
||||
metadata = {}
|
||||
# We won't export cache_kv bytes (because we start inference on client at start_pos=0), but we will tell the client how big cache_kv needs to be
|
||||
t_infos = [(v.uop.base.realized.nbytes, k, v.dtype) for k,v in state_dict.items() if "cache_kv" not in k]
|
||||
empty_t_infos = [(v.uop.base.realized.nbytes, k, v.dtype) for k,v in state_dict.items() if "cache_kv" in k]
|
||||
t_infos = [(v.lazydata.base.realized.nbytes, k, v.dtype) for k,v in state_dict.items() if "cache_kv" not in k]
|
||||
empty_t_infos = [(v.lazydata.base.realized.nbytes, k, v.dtype) for k,v in state_dict.items() if "cache_kv" in k]
|
||||
|
||||
split_t_infos = []
|
||||
for size, name, dtype in t_infos:
|
||||
@@ -48,7 +48,7 @@ def prepare_browser_chunks(model):
|
||||
weight_metadata = metadata.get(name, default)
|
||||
weight_metadata["parts"][part_num] = {"file": i, "file_start_pos": cursor, "size": size}
|
||||
metadata[name] = weight_metadata
|
||||
data = bytes(state_dict[name].uop.base.realized.as_buffer())
|
||||
data = bytes(state_dict[name].lazydata.base.realized.as_buffer())
|
||||
data = data if not offsets else data[offsets[0]:offsets[1]]
|
||||
writer.write(data)
|
||||
cursor += size
|
||||
@@ -109,6 +109,7 @@ if __name__=="__main__":
|
||||
tokenizer = Tokenizer(str(tokenizer_path))
|
||||
|
||||
model_path = fetch("https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-f16.gguf", "Llama-3.2-1B-Instruct-f16.gguf", subdir="llama3-1b-instruct")
|
||||
Tensor.no_grad = True
|
||||
max_context=1024
|
||||
tok = 128000
|
||||
TEMPERATURE, TOP_K, TOP_P, ALPHA_F, ALPHA_P = 0.95, 0, 0.0, 0.0, 0.0
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user