Deployed 6ea665e with MkDocs version: 1.6.1

.github/actions/process-replay/action.yml

@@ -1,15 +0,0 @@
-name: Run process replay tests
-description: Verify process replay compared to master
-runs:
-  using: "composite"
-  steps:
-    - name: Run process replay tests
-      shell: bash
-      run: |
-        export PR_TITLE=$(jq -r .pull_request.title "$GITHUB_EVENT_PATH")
-        export CURRENT_SHA=${{ github.event.pull_request && github.event.pull_request.head.sha || github.sha }}
-        git fetch origin $CURRENT_SHA
-        export COMMIT_MESSAGE=$(git show -s --format=%B "$CURRENT_SHA")
-        export CURRENT_HEAD=$(git rev-parse HEAD)
-        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
-        git checkout $CURRENT_HEAD  # restore to branch

.github/actions/setup-tinygrad/action.yml

@@ -1,305 +0,0 @@
-name: Setup Python & Install
-description: Sets up Python and installs project dependencies.
-inputs:
-  python-version:
-    description: 'Python version to use'
-    required: false
-    default: '3.12'
-  key:
-    description: 'Key for the python cache'
-    required: false
-    default: ''  # if you don't set a key, it doesn't cache
-  deps:
-    description: 'Extra dependency groups (comma separated)'
-    required: false
-    default: ''
-  pydeps:
-    description: 'Extra Python dependency groups (space separated)'
-    required: false
-    default: ''
-  opencl:
-    description: "Install OpenCL?"
-    required: false
-    default: 'false'
-  amd:
-    description: "Install AMD?"
-    required: false
-    default: 'false'
-  cuda:
-    description: "Install CUDA?"
-    required: false
-    default: 'false'
-  ocelot:
-    description: "Install gpuocelot?"
-    required: false
-    default: 'false'
-  webgpu:
-    description: "Install webgpu?"
-    required: false
-    default: 'false'
-  llvm:
-    description: "Install LLVM?"
-    required: false
-    default: 'false'
-  mesa:
-    description: "Install mesa"
-    required: false
-    default: 'false'
-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 }}
-
-    # **** Caching packages ****
-
-    - name: Cache Python packages
-      id: restore-venv
-      uses: actions/cache@v4
-      with:
-        path: ${{ github.workspace }}/.venv
-        key: venv-${{ runner.os }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ hashFiles('**/setup.py') }}-${{ env.PYTHON_CACHE_VERSION }}
-
-    # **** Caching downloads ****
-
-    - name: Cache downloads (Linux)
-      if: inputs.key != '' && runner.os == 'Linux'
-      uses: actions/cache@v4
-      with:
-        path: ~/.cache/tinygrad/downloads/
-        key: downloads-cache-${{ inputs.key }}-${{ env.DOWNLOAD_CACHE_VERSION }}
-    - name: Cache downloads (macOS)
-      if: inputs.key != '' && runner.os == 'macOS'
-      uses: actions/cache@v4
-      with:
-        path: ~/Library/Caches/tinygrad/downloads/
-        key: osx-downloads-cache-${{ inputs.key }}-${{ env.DOWNLOAD_CACHE_VERSION }}
-
-    # **** Python deps ****
-
-    - name: Install dependencies in venv (with extra)
-      if: inputs.deps != '' && steps.restore-venv.outputs.cache-hit != 'true'
-      shell: bash
-      run: |
-        python -m venv .venv
-        if [[ "$RUNNER_OS" == "Windows" ]]; then
-          source .venv/Scripts/activate
-        else
-          . .venv/bin/activate
-        fi
-        python -m pip install -e ".[${{ inputs.deps }}]" ${{ inputs.pydeps }} --extra-index-url https://download.pytorch.org/whl/cpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/
-    - name: Install dependencies in venv (without extra)
-      if: inputs.deps == '' && steps.restore-venv.outputs.cache-hit != 'true'
-      shell: bash
-      run: |
-        python -m venv .venv
-        if [[ "$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
-
-    # ******************* apt *******************
-    - name: Setup apt
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == '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/
-
-    # **** AMD ****
-    - name: Setup AMD (Linux)
-      if: inputs.amd == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: |
-        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'
-          /opt/rocm/lib
-          /opt/rocm/lib64
-        EOF
-        sudo ldconfig
-    - name: Setup AMD comgr+remu (macOS)
-      if: inputs.amd == 'true' && runner.os == 'macOS'
-      shell: bash
-      run: |
-        sudo mkdir -p /usr/local/lib
-        curl -s -H "Authorization: token $GH_TOKEN" curl -s https://api.github.com/repos/nimlgen/amdcomgr_dylib/releases/latest | \
-          jq -r '.assets[] | select(.name == "libamd_comgr.dylib").browser_download_url' | \
-          sudo xargs curl -L -o /usr/local/lib/libamd_comgr.dylib
-        cargo build --release --manifest-path ./extra/remu/Cargo.toml
-
-    # **** gpuocelot ****
-
-    - name: Install gpuocelot dependencies (MacOS)
-      if: inputs.ocelot == 'true' && runner.os == 'macOS'
-      shell: bash
-      run: |
-        pkgs=(cmake ninja llvm@15 zlib glew flex bison [email protected] zstd ncurses)
-        for f in "${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
-    - name: Cache gpuocelot
-      if: inputs.ocelot == 'true'
-      id: cache-build
-      uses: actions/cache@v4
-      env:
-        cache-name: cache-gpuocelot-build-1
-      with:
-        path: ${{ github.workspace }}/gpuocelot/ocelot
-        key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-${{ env.BUILD_CACHE_VERSION }}
-    - name: Clone/compile gpuocelot
-      if: inputs.ocelot == 'true' && steps.cache-build.outputs.cache-hit != 'true'
-      shell: bash
-      run: |
-        git clone --recurse-submodules https://github.com/gpuocelot/gpuocelot.git ${{ github.workspace }}/gpuocelot
-        cd ${{ github.workspace }}/gpuocelot/ocelot
-        git checkout b16039dc940dc6bc4ea0a98380495769ff35ed99
-        mkdir build
-        cd build
-
-        CMAKE_ARGS="-Wno-dev -G Ninja -DOCELOT_BUILD_TOOLS=OFF -DCMAKE_BUILD_ALWAYS=0 -DBUILD_TESTS_CUDA=OFF -DCMAKE_POLICY_VERSION_MINIMUM=3.5"
-        if [[ "${{ runner.os }}" == "macOS" ]]; then
-          CMAKE_ARGS="$CMAKE_ARGS -DBoost_INCLUDE_DIR=$(brew --prefix boost)/include -DBoost_LIBRARY_DIR=$(brew --prefix boost)/lib"
-        fi
-
-        cmake .. $CMAKE_ARGS
-        ninja
-    - name: Install gpuocelot
-      if: inputs.ocelot == 'true'
-      shell: bash
-      run: |
-        cd ${{ github.workspace }}/gpuocelot/ocelot/build
-        sudo cp libgpuocelot.${{ runner.os == 'macOS' && 'dylib' || 'so' }} /usr/${{ runner.os == 'macOS' && 'local/' || '' }}lib/
-
-    # **** WebGPU ****
-
-    - name: Install WebGPU dawn (Linux)
-      if: inputs.webgpu == 'true' && runner.os == 'Linux'
-      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 WebGPU dawn (macOS)
-      if: inputs.webgpu == 'true' && runner.os == 'macOS'
-      shell: bash
-      run: |
-        brew tap wpmed92/dawn
-        brew install dawn
-
-    # **** LLVM ****
-
-    - name: Install LLVM (macOS)
-      if: inputs.llvm == 'true' && runner.os == 'macOS'
-      shell: bash
-      run: brew install llvm@20
-
-    # **** mesa ****
-    - name: Install mesa (linux)
-      if: inputs.mesa == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: sudo curl -L https://github.com/sirhcm/tinymesa/releases/download/tinymesa-32dc66c/libtinymesa_cpu-mesa-25.2.4-linux-amd64.so -o /usr/lib/libtinymesa_cpu.so
-    - name: Install mesa (macOS)
-      if: inputs.mesa == 'true' && runner.os == 'macOS'
-      shell: bash
-      run: brew install sirhcm/tinymesa/tinymesa_cpu

.github/workflows/autogen.yml

@@ -1,97 +0,0 @@
-name: Autogen
-env:
-  # increment this when downloads substantially change to avoid the internet
-  DOWNLOAD_CACHE_VERSION: '12'
-  PYTHON_CACHE_VERSION: '4'
-  APT_CACHE_VERSION: '1'
-  BUILD_CACHE_VERSION: '1'
-  CAPTURE_PROCESS_REPLAY: 1
-  GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-  PYTHONPATH: ${{ github.workspace }}
-
-on:
-  push:
-    branches:
-      - master
-  pull_request:
-    paths: 
-    - 'tinygrad/runtime/autogen/**/*'
-  workflow_dispatch:
-    paths: 
-    - 'tinygrad/runtime/autogen/**/*'
-
-jobs:
-  autogen:
-    name: Autogen
-    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:
-        opencl: 'true'
-        amd: 'true'
-        cuda: 'true'
-        webgpu: 'true'
-        llvm: 'true'
-        pydeps: 'pyyaml mako'
-    - name: Install autogen support packages
-      run: sudo apt-get install -y --no-install-recommends llvm-14-dev libclang-14-dev llvm-20-dev
-    - name: Verify OpenCL autogen
-      run: |
-        cp tinygrad/runtime/autogen/opencl.py /tmp/opencl.py.bak
-        ./autogen_stubs.sh opencl
-        diff /tmp/opencl.py.bak tinygrad/runtime/autogen/opencl.py
-    - name: Verify CUDA autogen
-      run: |
-        cp tinygrad/runtime/autogen/cuda.py /tmp/cuda.py.bak
-        cp tinygrad/runtime/autogen/nv_gpu.py /tmp/nv_gpu.py.bak
-        ./autogen_stubs.sh cuda
-        ./autogen_stubs.sh nv
-        diff /tmp/cuda.py.bak tinygrad/runtime/autogen/cuda.py
-        diff /tmp/nv_gpu.py.bak tinygrad/runtime/autogen/nv_gpu.py
-    - name: Verify AMD autogen
-      run: |
-        cp tinygrad/runtime/autogen/hsa.py /tmp/hsa.py.bak
-        cp tinygrad/runtime/autogen/kfd.py /tmp/kfd.py.bak
-        cp tinygrad/runtime/autogen/comgr.py /tmp/comgr.py.bak
-        cp tinygrad/runtime/autogen/amd_gpu.py /tmp/amd_gpu.py.bak
-        cp tinygrad/runtime/autogen/sqtt.py /tmp/sqtt.py.bak
-        ./autogen_stubs.sh hsa
-        ./autogen_stubs.sh kfd
-        ./autogen_stubs.sh comgr
-        ./autogen_stubs.sh amd
-        ./autogen_stubs.sh sqtt
-        diff /tmp/hsa.py.bak tinygrad/runtime/autogen/hsa.py
-        diff /tmp/kfd.py.bak tinygrad/runtime/autogen/kfd.py
-        diff /tmp/comgr.py.bak tinygrad/runtime/autogen/comgr.py
-        diff /tmp/amd_gpu.py.bak tinygrad/runtime/autogen/amd_gpu.py
-        diff /tmp/sqtt.py.bak tinygrad/runtime/autogen/sqtt.py
-    - name: Verify Linux autogen
-      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
-        ./autogen_stubs.sh webgpu
-        diff /tmp/webgpu.py.bak tinygrad/runtime/autogen/webgpu.py
-    - name: Verify LLVM autogen
-      run: |
-        cp tinygrad/runtime/autogen/llvm.py /tmp/llvm.py.bak
-        ./autogen_stubs.sh llvm
-        diff /tmp/llvm.py.bak tinygrad/runtime/autogen/llvm.py
-    - name: Verify mesa autogen
-      run: |
-        cp tinygrad/runtime/autogen/mesa.py /tmp/mesa.py.bak
-        ./autogen_stubs.sh mesa
-        diff /tmp/mesa.py.bak tinygrad/runtime/autogen/mesa.py

.github/workflows/benchmark.yml

@@ -1,786 +0,0 @@
-name: Benchmarks
-env:
-  # TODO: this rescheduling makes gpt2, mixtral and llama unjitted slower
-  # TODO: very slow for llama 70B and resnet training 6 GPU
-  CAPTURE_PROCESS_REPLAY: "1"
-  ASSERT_PROCESS_REPLAY: "0"
-  PYTHONPATH: .
-  GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-
-on:
-  push:
-    branches:
-      - master
-      - update_benchmark
-      - update_benchmark_staging
-  workflow_dispatch:
-    inputs:
-      run_process_replay:
-        description: "Run process replay tests"
-        required: false
-        default: false
-        type: boolean
-
-jobs:
-  testmacbenchmark:
-    name: Mac Benchmark
-    env:
-      # since sudo is required for usbgpu on macos, move the cache to a new location, as some of the files are owned by root
-      PYTHONPYCACHEPREFIX: /tmp/tiny_python_pycache
-    runs-on: [self-hosted, macOS]
-    timeout-minutes: 60
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Symlink models and datasets
-      run: |
-        mkdir -p weights
-        ln -s ~/tinygrad/extra/disassemblers/applegpu extra/disassemblers/applegpu
-        ln -s ~/tinygrad/weights/sd-v1-4.ckpt weights/sd-v1-4.ckpt
-        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
-    - 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: python3.11 test/external/process_replay/reset.py
-    - name: Print macOS version
-      run: sw_vers
-    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion JIT=1 ASSERT_MIN_STEP_TIME=720 python3.11 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
-    - name: Run Stable Diffusion without fp16
-      run: BENCHMARK_LOG=stable_diffusion_fp32 JIT=1 ASSERT_MIN_STEP_TIME=800 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd_no_fp16.txt
-    - name: Run Stable Diffusion v2
-      # TODO: very slow step time
-      run: BENCHMARK_LOG=stable_diffusion_v2 JIT=1 ASSERT_MIN_STEP_TIME=4500 python3.11 examples/sdv2.py --fp16 --seed 0 --noshow --timing | tee sdv2.txt
-    # process replay can't capture this, the graph is too large
-    - name: Run SDXL
-      run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=5000 CAPTURE_PROCESS_REPLAY=0 JIT=1 python3.11 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
-    - 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
-    - name: Test tensor cores
-      run: METAL=1 python3.11 test/opt/test_tensor_cores.py
-    - name: Test AMX tensor cores
-      run: |
-        DEBUG=2 CPU=1 CPU_LLVM=0 AMX=1 python3.11 test/opt/test_tensor_cores.py
-        DEBUG=2 CPU=1 CPU_LLVM=1 AMX=1 python3.11 test/opt/test_tensor_cores.py
-        DEBUG=2 CPU=1 CPU_LLVM=0 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 CPU_LLVM=1 AMX=1 python3.11 test/opt/test_gen_float4.py TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
-    - 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)
-      run: DEBUG=2 SHOULD_USE_TC=1 HALF=1 python3.11 extra/gemm/simple_matmul.py | tee matmul_half.txt
-    - name: Run Tensor Core GEMM (bfloat16)
-      run: DEBUG=2 SHOULD_USE_TC=1 BFLOAT16=1 python3.11 extra/gemm/simple_matmul.py | tee matmul_bfloat16.txt
-    - name: Fuzz Padded Tensor Core GEMM
-      run: METAL=1 M_START=6 M_STOP=10 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=6 K_STOP=24 K_STEP=1 TC_OPT=2 DEBUG=2 python3.11 ./extra/gemm/fuzz_matmul.py
-    - name: Run LLaMA
-      run: |
-        BENCHMARK_LOG=llama_nojit JIT=0 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
-        BENCHMARK_LOG=llama JIT=1 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_jitted.txt
-    - name: Run LLaMA with BEAM
-      run: BENCHMARK_LOG=llama_beam JITBEAM=2 IGNORE_BEAM_CACHE=1 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_beam.txt
-    - name: Run quantized LLaMA
-      run: |
-        BENCHMARK_LOG=llama_int8 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing --quantize int8 | tee llama_int8.txt
-        BENCHMARK_LOG=llama_nf4 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing --quantize nf4 | tee llama_nf4.txt
-    - name: Run quantized LLaMA3
-      run: |
-        BENCHMARK_LOG=llama3_int8 python3.11 examples/llama3.py --size 8B --temperature 0 --benchmark --quantize int8 | tee llama3_int8.txt
-        BENCHMARK_LOG=llama3_nf4 python3.11 examples/llama3.py --size 8B --temperature 0 --benchmark --quantize nf4 | tee llama3_nf4.txt
-    #- name: Run LLaMA 7B on 4 (virtual) GPUs
-    #  run: python3.11 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_four_gpu.txt
-    - name: Run GPT2
-      run: |
-        BENCHMARK_LOG=gpt2_nojit JIT=0 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 JIT=1 ASSERT_MIN_STEP_TIME=13 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
-    - name: Run GPT2 w HALF
-      run: BENCHMARK_LOG=gpt2_half HALF=1 python3.11 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
-    - name: Run GPT2 w HALF/BEAM
-      run: BENCHMARK_LOG=gpt2_half_beam HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3.11 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
-    - name: Run OLMoE
-      run: BENCHMARK_LOG=olmoe python3.11 examples/olmoe.py
-    - name: Train MNIST
-      run: time PYTHONPATH=. TARGET_EVAL_ACC_PCT=96.0 python3.11 examples/beautiful_mnist.py | tee beautiful_mnist.txt
-
-    # NOTE: this is failing in CI. it is not failing on my machine and I don't really have a way to debug it
-    # the error is "RuntimeError: Internal Error (0000000e:Internal Error)"
-    #- name: Run 10 CIFAR training steps
-    #  run: BENCHMARK_LOG=cifar_10steps JIT=1 ASSERT_MIN_STEP_TIME=3000 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar.txt
-    #- name: Run 10 CIFAR training steps w HALF
-    #  run: BENCHMARK_LOG=cifar_10steps_half JIT=2 ASSERT_MIN_STEP_TIME=3000 STEPS=10 DEFAULT_FLOAT=HALF python3.11 examples/hlb_cifar10.py | tee train_cifar_half.txt
-
-    #- name: Run 10 CIFAR training steps w BF16
-    #  run: STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3.11 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
-    # TODO: too slow
-    # - name: Run 10 CIFAR training steps w winograd
-    #   run: BENCHMARK_LOG=cifar_10steps_wino JIT=1 ASSERT_MIN_STEP_TIME=150 WINO=1 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar_wino.txt
-    - name: UsbGPU boot time
-      run: sudo -E PYTHONPATH=. DEBUG=2 AM_RESET=1 AMD=1 AMD_IFACE=USB time python3.11 test/test_tiny.py TestTiny.test_plus
-    - name: UsbGPU tiny tests
-      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 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)
-        path: |
-          onnx_inference_speed.csv
-          torch_speed.txt
-          llama_unjitted.txt
-          llama_jitted.txt
-          llama_beam.txt
-          llama_int8.txt
-          llama_nf4.txt
-          llama3_int8.txt
-          llama3_nf4.txt
-          llama_four_gpu.txt
-          gpt2_unjitted.txt
-          gpt2_jitted.txt
-          gpt2_half.txt
-          gpt2_half_beam.txt
-          matmul.txt
-          matmul_half.txt
-          matmul_bfloat16.txt
-          sd.txt
-          sd_no_fp16.txt
-          sdv2.txt
-          sdxl.txt
-          beautiful_mnist.txt
-          train_cifar.txt
-          train_cifar_half.txt
-          train_cifar_bf16.txt
-          train_cifar_wino.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=. python3.11 process_replay.py
-
-  testnvidiabenchmark:
-    name: tinybox green Benchmark
-    runs-on: [self-hosted, Linux, tinyboxgreen]
-    timeout-minutes: 60
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Print nvidia-smi
-      run: nvidia-smi
-    - name: Symlink models and datasets
-      run: |
-        mkdir -p weights
-        ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
-        ln -s /raid/weights/mixtral-8x7b-32kseqlen weights/mixtral-8x7b-32kseqlen
-        ln -s /raid/weights/LLaMA-2 weights/LLaMA-2
-        ln -s /raid/weights/LLaMA-3 weights/LLaMA-3
-        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 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
-    - name: Test speed vs theoretical
-      run: NV=1 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_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
-        NV=1 NV_PTX=1 ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
-    - 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
-        CUDA=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_bfloat16.txt
-        CUDA=1 SHOULD_USE_TC=1 ALLOW_TF32=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py | tee matmul_tf32.txt
-        CUDA=1 SHOULD_USE_TC=1 FP8E4M3=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_fp8.txt
-    - name: Run Tensor Core GEMM (PTX)
-      run: NV=1 NV_PTX=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_ptx.txt
-    - name: Run Tensor Core GEMM (NV)
-      run: NV=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_nv.txt
-    - name: Test NV=1
-      run: DEBUG=2 NV=1 python -m pytest -rA test/test_tiny.py
-    - name: Test CUDA=1
-      run: DEBUG=2 CUDA=1 python -m pytest -rA test/test_tiny.py
-    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion NV=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
-    # TODO: too slow
-    # - name: Run SDXL
-    #   run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=2000 CAPTURE_PROCESS_REPLAY=0 NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
-    - name: Run LLaMA
-      run: |
-        BENCHMARK_LOG=llama_nojit NV=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
-        BENCHMARK_LOG=llama NV=1 JIT=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_jitted.txt
-    - name: Run LLaMA with BEAM
-      run: BENCHMARK_LOG=llama_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_beam.txt
-    # - name: Run LLaMA 7B on 4 GPUs
-    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_four_gpu.txt
-    # - name: Run LLaMA 7B on 6 GPUs
-    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_six_gpu.txt
-    - name: Run LLaMA-3 8B BEAM
-      run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_beam.txt
-    - name: Run LLaMA-3 8B on 4 GPUs with BEAM
-      run: BENCHMARK_LOG=llama3_beam_4gpu NV=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 quantized LLaMA3
-      run: BENCHMARK_LOG=llama3_fp8 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --temperature 0 --benchmark --quantize fp8 | tee llama3_fp8.txt
-    # - name: Run LLaMA-3 8B on 6 GPUs
-    #   run: NV=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: Run LLaMA-2 70B
-    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 MAX_CONTEXT=256 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
-      run: time BENCHMARK_LOG=mixtral NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/mixtral.py --temperature 0 --count 10 --timing | tee mixtral.txt
-    - name: Run GPT2
-      run: |
-        BENCHMARK_LOG=gpt2_nojit NV=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 NV=1 JIT=1 ASSERT_MIN_STEP_TIME=4 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
-    - name: Run GPT2 w HALF
-      run: BENCHMARK_LOG=gpt2_half NV=1 HALF=1 ASSERT_MIN_STEP_TIME=6 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
-    - name: Run GPT2 w HALF/BEAM
-      run: BENCHMARK_LOG=gpt2_half_beam NV=1 HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
-    - uses: actions/upload-artifact@v4
-      with:
-        name: Speed (NVIDIA)
-        path: |
-          onnx_inference_speed.csv
-          torch_speed.txt
-          matmul.txt
-          matmul_bfloat16.txt
-          matmul_tf32.txt
-          matmul_ptx.txt
-          matmul_nv.txt
-          sd.txt
-          sdxl.txt
-          llama_unjitted.txt
-          llama_jitted.txt
-          llama_beam.txt
-          llama3_beam.txt
-          llama3_four_gpu.txt
-          llama3_six_gpu.txt
-          llama3_fp8.txt
-          llama_2_70B.txt
-          mixtral.txt
-          gpt2_unjitted.txt
-          gpt2_jitted.txt
-          gpt2_half.txt
-          gpt2_half_beam.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
-
-  testmorenvidiabenchmark:
-    name: tinybox green Training Benchmark
-    runs-on: [self-hosted, Linux, tinyboxgreen]
-    timeout-minutes: 60
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - 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
-    # TODO: too slow
-    # - name: Fuzz Padded Tensor Core GEMM (NV)
-    #   run: NV=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
-    # TODO: too slow
-    # - name: Fuzz Padded Tensor Core GEMM (PTX)
-    #   run: NV=1 NV_PTX=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
-    - name: Train MNIST
-      run: time PYTHONPATH=. NV=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
-    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=270 NV=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
-    - name: Run 10 CIFAR training steps w HALF
-      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=240 NV=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
-    - name: Run 10 CIFAR training steps w BF16
-      run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=270 NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
-    # TODO: too slow
-    # - name: Run 10 CIFAR training steps w winograd
-    #   run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=350 NV=1 CAPTURE_PROCESS_REPLAY=0 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 NV=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 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 CAPTURE_PROCESS_REPLAY=0 NV=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 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 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
-    - uses: actions/upload-artifact@v4
-      with:
-        name: Speed (NVIDIA Training)
-        path: |
-          beautiful_mnist.txt
-          train_cifar.txt
-          train_cifar_half.txt
-          train_cifar_bf16.txt
-          train_cifar_wino.txt
-          train_cifar_one_gpu.txt
-          train_cifar_six_gpu.txt
-          train_resnet.txt
-          train_resnet_one_gpu.txt
-          train_bert.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
-
-  testamdbenchmark:
-    name: tinybox red Benchmark
-    runs-on: [self-hosted, Linux, tinybox]
-    timeout-minutes: 60
-    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: Insert amdgpu
-    #  run: sudo modprobe amdgpu
-    - 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
-        ln -s /raid/weights/LLaMA-3 weights/LLaMA-3
-        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: 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
-    # TODO: unstable on AMD
-    #- 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
-    - name: Test speed vs theoretical
-      run: AMD=1 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_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 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
-    # 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: 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
-      run: time AMD=1 python3 test/test_tiny.py TestTiny.test_plus
-    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion ASSERT_MIN_STEP_TIME=550 AMD=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
-    # TODO: too slow
-    # - name: Run SDXL
-    #   run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=3200 CAPTURE_PROCESS_REPLAY=0 AMD=1 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
-    - name: Run LLaMA 7B
-      run: |
-        BENCHMARK_LOG=llama_nojit AMD=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
-        BENCHMARK_LOG=llama AMD=1 JIT=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_jitted.txt
-    - name: Run LLaMA 7B with BEAM
-      run: BENCHMARK_LOG=llama_beam AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_beam.txt
-    # - name: Run LLaMA 7B on 4 GPUs
-    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_four_gpu.txt
-    # - name: Run LLaMA 7B on 6 GPUs
-    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_six_gpu.txt
-    - name: Run LLaMA-3 8B BEAM
-      run: BENCHMARK_LOG=llama3_beam AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_beam.txt
-    - name: Run LLaMA-3 8B on 4 GPUs with BEAM
-      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: 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
-      run: time BENCHMARK_LOG=mixtral AMD=1 python3 examples/mixtral.py --temperature 0 --count 10 --timing | tee mixtral.txt
-    - name: Run GPT2
-      run: |
-        BENCHMARK_LOG=gpt2_nojit AMD=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 AMD=1 JIT=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
-    - name: Run GPT2 w HALF
-      run: BENCHMARK_LOG=gpt2_half AMD=1 HALF=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
-    - name: Run GPT2 w HALF/BEAM
-      run: BENCHMARK_LOG=gpt2_half_beam AMD=1 HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
-    - uses: actions/upload-artifact@v4
-      with:
-        name: Speed (AMD)
-        path: |
-          onnx_inference_speed.csv
-          torch_speed.txt
-          llama_unjitted.txt
-          llama_jitted.txt
-          llama_beam.txt
-          llama3_beam.txt
-          llama3_four_gpu.txt
-          llama3_six_gpu.txt
-          llama_2_70B.txt
-          gpt2_unjitted.txt
-          gpt2_jitted.txt
-          gpt2_half.txt
-          gpt2_half_beam.txt
-          matmul.txt
-          matmul_amd.txt
-          sd.txt
-          sdxl.txt
-          mixtral.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
-
-  testmoreamdbenchmark:
-    name: tinybox red Training Benchmark
-    runs-on: [self-hosted, Linux, tinybox]
-    timeout-minutes: 60
-    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: Train MNIST
-      run: time PYTHONPATH=. AMD=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
-    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=330 AMD=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
-    - name: Run 10 CIFAR training steps w HALF
-      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=390 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
-    # - name: Run 10 CIFAR training steps w BF16
-    #   run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=288 AMD=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
-    # TODO: too slow
-    # - name: Run 10 CIFAR training steps w winograd
-    #   run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=66 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 STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 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=93.0 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=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu_remote.txt
-    - uses: actions/upload-artifact@v4
-      with:
-        name: Speed (AMD Training)
-        path: |
-          beautiful_mnist.txt
-          train_cifar.txt
-          train_cifar_half.txt
-          train_cifar_bf16.txt
-          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: 60
-    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
-    - 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
-
-  testqualcommbenchmark:
-    name: comma Benchmark
-    runs-on: [self-hosted, Linux, comma]
-    timeout-minutes: 20
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - 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: openpilot compile3 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 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: BENCHMARK_LOG=openpilot_0_9_9_policy PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 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: BENCHMARK_LOG=openpilot_0_9_9_dmonitoring PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 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 0.10.0 driving_policy
-      run: BENCHMARK_LOG=openpilot_0_10_0_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=5 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.10.0/selfdrive/modeld/models/driving_policy.onnx
-    - name: openpilot compile3 0.10.0 dmonitoring
-      run: BENCHMARK_LOG=openpilot_0_10_0_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=13 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.10.0/selfdrive/modeld/models/dmonitoring_model.onnx
-    - name: openpilot compile3 0.10.1 driving_vision
-      # TODO: ASSERT_MIN_STEP_TIME=17
-      run: BENCHMARK_LOG=openpilot_0_10_1_vision PYTHONPATH="." ASSERT_MIN_STEP_TIME=25 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
-    - name: openpilot compile3 0.10.1 driving_policy
-      run: BENCHMARK_LOG=openpilot_0_10_1_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=5 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_policy.onnx
-    - name: openpilot compile3 0.10.1 dmonitoring
-      # TODO: ASSERT_MIN_STEP_TIME=10
-      run: BENCHMARK_LOG=openpilot_0_10_1_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=13 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/dmonitoring_model.onnx
-    - 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 CPU_LLVM=0 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 NOOPT=1 CNT=2 DEBUG=2 python3 examples/test_onnx_imagenet.py /tmp/model.quant.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
-
-  testreddriverbenchmark:
-    name: AM Benchmark
-    runs-on: [self-hosted, Linux, tinyboxrandom]
-    timeout-minutes: 20
-    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 STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 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: 20
-    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 STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 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

.github/workflows/benchmark_search.yml

@@ -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

.github/workflows/docs.yml

@@ -1,30 +0,0 @@
-name: Deploy Docs
-on:
-  push:
-    branches:
-      - master
-      - mkdocs
-permissions:
-  contents: write
-jobs:
-  deploy:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - name: Configure Git Credentials
-        run: |
-          git config user.name github-actions[bot]
-          git config user.email 41898282+github-actions[bot]@users.noreply.github.com
-      - uses: actions/setup-python@v5
-        with:
-          python-version: 3.x
-      - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
-      - uses: actions/cache@v4
-        with:
-          key: mkdocs-material-${{ env.cache_id }}
-          path: .cache
-          restore-keys: |
-            mkdocs-material-
-      - run: pip install -e .[docs]
-      - run: mkdocs build --strict
-      - run: mkdocs gh-deploy --force

.github/workflows/mlperf.yml

@@ -1,30 +0,0 @@
-name: Run MLPerf Training
-
-on:
-  schedule:
-    - cron: '5 8 * * *'  # Runs at 08:05 UTC (12:05 AM Pacific Time)
-  push:
-    branches:
-      - update_mlperf
-  workflow_dispatch:
-
-jobs:
-  run_script_job:
-    runs-on: [self-hosted, Linux, tinybox]
-    if: github.repository_owner == 'tinygrad'
-    timeout-minutes: 720
-
-    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"

.github/workflows/python-publish.yml

@@ -1,30 +0,0 @@
-# This workflows will upload a Python Package using Twine when a release is created
-# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
-
-name: Upload Python Package
-
-on:
-  release:
-    types: [published]
-  workflow_dispatch:
-
-jobs:
-  deploy:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python
-      uses: actions/setup-python@v2
-      with:
-        python-version: '3.x'
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install setuptools wheel twine
-    - name: Build and publish
-      env:
-        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
-        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
-      run: |
-        python setup.py sdist bdist_wheel
-        twine upload dist/*

.github/workflows/szdiff.yml

@@ -1,98 +0,0 @@
-name: Check Line Counts
-on:
-  pull_request_target:
-
-# Cancel the workflow in progress in newer build is about to start.
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  checkbranch:
-    name: Check PR Branch status
-    runs-on: ubuntu-latest
-    outputs:
-      branchstat: ${{ steps.brstat.outputs.stat}}
-    steps:
-      - name: Check code from PR branch 
-        uses: actions/checkout@v4
-        with:
-          repository: ${{ github.event.pull_request.head.repo.full_name }}
-          ref: ${{ github.event.pull_request.head.sha }}
-          fetch-depth: 0
-      - name: Check whether branch is up-to-date
-        id: brstat
-        run: |
-          git remote add tinygrad https://github.com/tinygrad/tinygrad
-          git fetch tinygrad master
-          echo "${{ github.event.pull_request.head.sha }}"
-          git rev-list --left-right --count  tinygrad/master...${{ github.event.pull_request.head.sha }} | awk '{print "Behind "$1" - Ahead "$2""}'
-          count=$(git rev-list --left-right --count  tinygrad/master...${{ github.event.pull_request.head.sha }} | awk '{print $1}')
-          if [ $count -gt 0 ]
-          then
-            echo "Current branch is behind tinygrad master branch!"
-            echo "stat=true" >> "$GITHUB_OUTPUT"
-          else
-            echo "stat=false" >> "$GITHUB_OUTPUT"
-          fi
-
-  szdiff:
-    name: Core Library Line Difference
-    permissions:
-      contents: read
-      pull-requests: write
-    runs-on: ubuntu-latest
-    needs: checkbranch
-    if: needs.checkbranch.outputs.branchstat == 'false'
-    steps:
-      - name: Checkout code from PR branch
-        uses: actions/checkout@v4
-        with:
-          repository: ${{ github.event.pull_request.head.repo.full_name }}
-          ref: ${{ github.event.pull_request.head.sha }}
-          path: pr
-        # the base default to tinygrad master and cannot be other fork branch for security purpose
-      - name: Checkout code from tinygrad master
-        uses: actions/checkout@v4
-        with:
-          path: base
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.10'
-      - name: Count Line Diff
-        run: |
-          pip install tabulate
-          BASE="$GITHUB_WORKSPACE/base"
-          PR="$GITHUB_WORKSPACE/pr"
-          cp "$BASE/sz.py" .
-          echo "loc_content<<EOF" >> "$GITHUB_ENV"
-          python sz.py "$BASE" "$PR" >> "$GITHUB_ENV"
-          echo "EOF" >> "$GITHUB_ENV"
-      - name: Comment Code Line Diff
-        continue-on-error: false
-        uses: marocchino/sticky-pull-request-comment@v2
-        with:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          ignore_empty: true
-          skip_unchanged: true
-          recreate: true
-          message: ${{ env.loc_content }}
-
-  rebase:
-    name: Core Library Line Difference
-    permissions:
-      pull-requests: write
-    runs-on: ubuntu-latest
-    needs: checkbranch
-    if: needs.checkbranch.outputs.branchstat == 'true'
-    steps:
-      - name: Comment Rebase
-        continue-on-error: false
-        uses: marocchino/sticky-pull-request-comment@v2
-        with:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          skip_unchanged: true
-          recreate: true
-          message: |
-            This branch currently is behind tinygrad/master. The line count difference bot is disabled.

.github/workflows/test.yml

@@ -1,971 +0,0 @@
-name: Unit Tests
-env:
-  # increment this when downloads substantially change to avoid the internet
-  DOWNLOAD_CACHE_VERSION: '12'
-  PYTHON_CACHE_VERSION: '4'
-  APT_CACHE_VERSION: '1'
-  BUILD_CACHE_VERSION: '1'
-  CAPTURE_PROCESS_REPLAY: 1
-  GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-  PYTHONPATH: ${{ github.workspace }}
-
-on:
-  push:
-    branches:
-      - master
-  pull_request:
-  workflow_dispatch:
-
-jobs:
-  llvmspeed:
-    name: LLVM Speed
-    runs-on: ubuntu-24.04
-    timeout-minutes: 20
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: llvm-speed
-        deps: testing_minimal
-        llvm: 'true'
-    - name: Speed Test
-      run: CPU=1 CPU_LLVM=1 python3 test/speed/external_test_speed_v_torch.py
-    - name: Speed Test (BEAM=2)
-      run: BEAM=2 CPU=1 CPU_LLVM=1 python3 test/speed/external_test_speed_v_torch.py
-
-  docs:
-    name: Docs
-    runs-on: ubuntu-22.04
-    timeout-minutes: 10
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        deps: docs
-        pydeps: "capstone torch"
-    - 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
-        cd $HOME/test_external_dir
-        python -m venv venv
-        source venv/bin/activate
-        pip install $GITHUB_WORKSPACE
-        python -c "from tinygrad.tensor import Tensor; print(Tensor([1,2,3,4,5]))"
-        pip install mypy
-        mypy -c "from tinygrad.tensor import Tensor; print(Tensor([1,2,3,4,5]))"
-    - name: Run beautiful_mnist with tinygrad only
-      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/examples/beautiful_mnist.py .
-        BS=2 STEPS=10 python beautiful_mnist.py
-    - name: Test Docs Build
-      run: python -m mkdocs build --strict
-    - name: Test Docs
-      run: |
-        python docs/abstractions2.py
-        python docs/abstractions3.py
-    - name: Test README
-      run: awk '/```python/{flag=1;next}/```/{flag=0}flag' README.md > README.py &&  python README.py
-    - name: Test Quickstart
-      run: awk '/```python/{flag=1;next}/```/{flag=0}flag' docs/quickstart.md > quickstart.py &&  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
-      run: |
-        CPU=1 CPU_LLVM=0 python examples/compile_efficientnet.py > recognize.c
-        clang -O2 recognize.c -lm -o recognize
-        cat test/models/efficientnet/Chicken.jpg | ./recognize | grep cock
-
-  # TODO: fix the torch backend and reenable
-  # torchbackend:
-  #   name: Torch Backend Tests
-  #   runs-on: ubuntu-latest
-  #   timeout-minutes: 15
-  #   steps:
-  #   - name: Checkout Code
-  #     uses: actions/checkout@v4
-  #   - name: Setup Environment
-  #     uses: ./.github/actions/setup-tinygrad
-  #     with:
-  #       key: torch-backend-pillow-torchvision-et-pt
-  #       deps: testing_minimal
-  #       pydeps: "pillow torchvision expecttest"
-  #       llvm: 'true'
-  #   - name: Install ninja
-  #     run: |
-  #       sudo apt update || true
-  #       sudo apt install -y --no-install-recommends ninja-build
-  #   - name: Lint with ruff
-  #     run: |
-  #       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
-  #   - name: Test ResNet-18
-  #     run: DEBUG=2 python3 extra/torch_backend/example.py
-  #   - name: My (custom) tests
-  #     run: 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
-  #   - name: Test Ops with TINY_BACKEND
-  #     run: CPU=1 CPU_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
-  #   - name: Test multi-gpu
-  #     run: CPU=1 CPU_LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py
-
-  # torchbackendmore:
-  #   name: Torch Backend Tests More
-  #   runs-on: ubuntu-latest
-  #   timeout-minutes: 15
-  #   steps:
-  #   - name: Checkout Code
-  #     uses: actions/checkout@v4
-  #   - name: Setup Environment
-  #     uses: ./.github/actions/setup-tinygrad
-  #     with:
-  #       key: torch-backend-pillow-torchvision-et-pt
-  #       deps: testing_minimal
-  #       llvm: 'true'
-  #   - name: Install ninja
-  #     run: |
-  #       sudo apt update || true
-  #       sudo apt install -y --no-install-recommends ninja-build
-  #   - name: Test beautiful_mnist in torch with TINY_BACKEND
-  #     run: STEPS=20 CPU=1 TARGET_EVAL_ACC_PCT=90.0 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
-
-  bepython:
-    name: Python Backend
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        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
-    - 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
-    - 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
-    - name: test_renderer_failures with Python emulator
-      run: PYTHON=1 python3 -m pytest -rA test/test_renderer_failures.py::TestRendererFailures
-    - name: Test IMAGE=2 support
-      run: |
-        IMAGE=2 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
-        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
-    - 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
-    - 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
-    - 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
-    - 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
-    - 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
-        DEBUG=2 EMULATE=CUDA_SM89 ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
-    - 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
-    - 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
-
-  linter:
-    name: Linters
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-
-    # TODO: run the pre-commit hook to replace a lot of this
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: linting-only
-        python-version: '3.11'
-        deps: linting
-    - name: Lint bad-indentation and trailing-whitespace with pylint
-      run: python -m pylint --disable=all -e W0311 -e C0303 --jobs=0 --indent-string='  ' --recursive=y .
-    - name: Lint with ruff
-      run: |
-        pip3 install --upgrade --force-reinstall ruff==0.11.0
-        python3 -m ruff check .
-        python3 -m ruff check examples/mlperf/ --ignore E501
-    - name: Run mypy
-      run: |
-        python -m mypy --strict-equality --lineprecision-report .
-        cat lineprecision.txt
-    # broken because of UPatAny
-    #- name: Run TYPED=1
-    #  run: TYPED=1 python -c "import tinygrad"
-
-  unittest:
-    name: Unit Tests
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: unittest-12
-        pydeps: "pillow numpy ftfy regex"
-        deps: testing_unit
-    - name: Check Device.DEFAULT
-      run: python -c "from tinygrad import Device; assert Device.DEFAULT == 'CPU', Device.DEFAULT"
-    - name: Run unit tests
-      run: CPU=1 python -m pytest -n=auto test/unit/ --durations=20
-    - name: Run targetted tests on NULL backend
-      run: NULL=1 python3 -m unittest test.test_multitensor.TestMultiTensor.test_data_parallel_resnet_train_step test/device/test_null.py
-    # TODO: too slow
-    # - name: Run SDXL on NULL backend
-    #   run: NULL=1 DEBUG=1 python3 examples/sdxl.py --seed 0 --noshow --timing --fakeweights
-    - name: Run Clip tests for SD MLPerf on NULL backend
-      run: NULL=1 python -m pytest -n=auto test/external/mlperf_stable_diffusion/external_test_models.py::TestOpenClip --durations=20
-    - name: Run AMD emulated BERT training on NULL backend
-      run: EMULATE=AMD_RDNA4 NULL=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
-    # 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: python test/external/external_uop_gc.py
-    - name: External Benchmark Schedule
-      run: python3 test/external/external_benchmark_schedule.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
-
-  spec:
-    strategy:
-      fail-fast: false
-      matrix:
-        group: [1, 2]
-    name: SPEC=2 (${{ matrix.group }})
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: spec-unit
-        deps: testing_unit
-    - name: Test SPEC=2
-      run: IGNORE_OOB=0 SPEC=2 PYTHONPATH="." pytest --maxfail=10 -n auto --durations=30 --ignore=test/models --ignore test/unit/test_hashing.py --timeout 60 -k "not test_setitem_big" --splits 2 --group ${{ matrix.group }}
-
-  fuzzing:
-    name: Fuzzing
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: fuzzing-unit
-        deps: testing_unit
-    - name: Fuzz Test symbolic
-      run: python test/external/fuzz_symbolic.py
-    - name: Fuzz Test fast idiv
-      run: python test/external/fuzz_fast_idiv.py
-    - name: Fuzz Test shape ops
-      run: python test/external/fuzz_shape_ops.py
-
-  testopenclimage:
-    name: CL IMAGE Tests
-    runs-on: ubuntu-22.04
-    timeout-minutes: 15
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: gpu-image
-          deps: testing_minimal
-          opencl: 'true'
-      - name: Test CL IMAGE=2 ops + training
-        run: |
-          CL=1 IMAGE=2 python -m pytest -n=auto test/test_ops.py --durations=20
-          CL=1 IMAGE=2 python test/models/test_end2end.py TestEnd2End.test_linear_mnist
-      - name: Run process replay tests
-        uses: ./.github/actions/process-replay
-
-  testgpumisc:
-    name: CL Misc tests
-    runs-on: ubuntu-22.04
-    timeout-minutes: 10
-    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: CL=1 extra/optimization/generate_dataset.sh
-      - name: Run Kernel Count Test
-        run: CL=1 python -m pytest -n=auto test/external/external_test_opt.py
-      - name: Run fused optimizer tests
-        run: CL=1 FUSE_OPTIM=1 python -m pytest -n=auto test/models/test_mnist.py test/test_optim.py -k "not muon"
-      - 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
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: openpilot-compile
-          deps: testing
-          opencl: 'true'
-          llvm: 'true'
-      - name: Test openpilot model kernel count and gate usage
-        run: |
-          ALLOWED_KERNEL_COUNT=123 ALLOWED_READ_IMAGE=1452 ALLOWED_GATED_READ_IMAGE=122 FLOAT16=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
-      - name: Test openpilot CL compile fp16
-        run: FLOAT16=1 DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
-      - name: Test openpilot CL compile fp32 (test correctness)
-        run: DEBUGCL=1 CL=1 IMAGE=2 SELFTEST=1 python examples/openpilot/compile3.py https://github.com/haraschax/filedump/raw/refs/heads/master/driving_vision_fp32.onnx
-      - name: Test openpilot LLVM compile fp16
-        run: FLOAT16=1 CPU=1 CPU_LLVM=1 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
-      - name: Run process replay tests
-        uses: ./.github/actions/process-replay
-
-# ****** ONNX Tests ******
-
-  testonnxcpu:
-    name: ONNX (CPU) Tests
-    runs-on: ubuntu-22.04
-    timeout-minutes: 20
-
-    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 CPU_LLVM=0 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
-      - name: Test ONNX (LLVM)
-        run: CPU=1 CPU_LLVM=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
-      - name: Test ONNX Runner (CPU)
-        run: CPU=1 CPU_LLVM=0 python3 test/external/external_test_onnx_runner.py
-      - name: Test Additional ONNX Ops (CPU)
-        run: CPU=1 CPU_LLVM=0 python3 test/external/external_test_onnx_ops.py
-      - name: Test Quantize ONNX
-        run: CPU=1 CPU_LLVM=0 python3 test/test_quantize_onnx.py
-      - name: Run process replay tests
-        uses: ./.github/actions/process-replay
-
-  testopencl:
-    name: ONNX (CL)+Optimization Tests
-    runs-on: ubuntu-22.04
-    timeout-minutes: 20
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: onnxoptl
-          deps: testing
-          pydeps: "tensorflow==2.15.1 tensorflow_addons"
-          python-version: '3.11'
-          opencl: 'true'
-      - name: Test ONNX (CL)
-        run: CL=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 CL=1 python3 extra/optimization/get_action_space.py
-      - name: Test Beam Search
-        run: CL=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
-      - name: Test MLPerf stuff
-        run: CL=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: NULL=1 beautiful_mnist_multigpu
-        run: NULL=1 python examples/beautiful_mnist_multigpu.py
-      - name: Test Bert training
-        run: NULL=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=24 GPUS=4 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
-      - name: Test llama 3 training
-        run: NULL=1 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 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
-
-# ****** Models Tests ******
-
-  testmodels:
-    name: Models (llvm+cpu+gpu)
-    runs-on: ubuntu-22.04
-    timeout-minutes: 15
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: models
-          deps: testing
-          opencl: 'true'
-          llvm: 'true'
-      - name: Test models (llvm)
-        run: CPU=1 CPU_LLVM=1 python -m pytest -n=auto test/models --durations=20
-      - name: Test models (opencl)
-        run: CL=1 python -m pytest -n=auto test/models --durations=20
-      - name: Test models (cpu)
-        run: CPU=1 CPU_LLVM=0 python -m pytest -n=auto test/models --durations=20
-      - name: Run process replay tests
-        uses: ./.github/actions/process-replay
-
-  testmetalmodels:
-    name: Models (metal)
-    runs-on: macos-14
-    timeout-minutes: 20
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: metal
-        deps: testing
-        python-version: '3.11'
-    - name: Test models (Metal)
-      run: METAL=1 python -m pytest -n=auto test/models --durations=20
-    - name: Test LLaMA compile speed
-      run: METAL=1 python test/external/external_test_speed_llama.py
-
-# ****** Feature Tests ******
-
-  testdevectorize:
-    name: Linux (devectorize)
-    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: devectorize-minimal
-        deps: testing_minimal
-        pydeps: "pillow"
-        llvm: "true"
-    - name: Test LLVM=1 DEVECTORIZE=0
-      run: CPU=1 CPU_LLVM=1 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py
-    - name: Test LLVM=1 DEVECTORIZE=0 for model
-      run: CPU=1 CPU_LLVM=1 DEVECTORIZE=0 python3 test/models/test_efficientnet.py
-    - name: Test CPU=1 DEVECTORIZE=0
-      run: CPU=1 CPU_LLVM=0 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py
-
-  testdsp:
-    name: Linux (DSP)
-    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: dsp-minimal
-        deps: testing_minimal
-        pydeps: "onnx==1.18.0 onnxruntime pillow"
-        llvm: "true"
-    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v3
-    - name: Build QEMU Docker with cache
-      uses: docker/build-push-action@v4
-      with:
-        file: extra/dsp/Dockerfile
-        push: false
-        load: true
-        tags: qemu-hexagon:latest
-        cache-from: type=gha
-        cache-to: type=gha,mode=min
-    - name: Set MOCKDSP env
-      run: printf "MOCKDSP=1" >> $GITHUB_ENV
-    - name: Run test_tiny on DSP
-      run: DEBUG=2 DSP=1 python test/test_tiny.py
-    - name: Test transcendentals
-      run: CC=clang-20 DEBUG=2 DSP=1 python test/test_transcendental.py TestTranscendentalVectorized
-    - name: Test quantize onnx
-      run: DEBUG=2 DSP=1 python3 test/test_quantize_onnx.py
-
-  testwebgpu:
-    name: Linux (WebGPU)
-    runs-on: ubuntu-22.04
-    timeout-minutes: 20
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: webgpu-minimal
-        deps: testing_minimal
-        python-version: '3.11'
-        webgpu: 'true'
-    - name: Check Device.DEFAULT (WEBGPU) and print some source
-      run: |
-        WEBGPU=1 python -c "from tinygrad import Device; assert Device.DEFAULT == 'WEBGPU', Device.DEFAULT"
-        WEBGPU=1 DEBUG=4 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
-    - name: Run selected webgpu tests
-      run: |
-          WEBGPU=1 WEBGPU_BACKEND="WGPUBackendType_Vulkan" python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit \
-          --ignore=test/test_copy_speed.py --ignore=test/test_rearrange_einops.py \
-          --ignore=test/test_fuzz_shape_ops.py --durations=20
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-  testamd:
-    strategy:
-      fail-fast: false
-      matrix:
-        backend: [amd, amdllvm]
-
-    name: Linux (${{ matrix.backend }})
-    runs-on: ubuntu-22.04
-    timeout-minutes: 20
-    env:
-      AMD: 1
-      MOCKGPU: 1
-      FORWARD_ONLY: 1
-      AMD_LLVM: ${{ matrix.backend == 'amdllvm' && '1' || matrix.backend != 'amdllvm' && '0' }}
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: ${{ matrix.backend }}-minimal
-          deps: testing_minimal
-          amd: 'true'
-          llvm: ${{ matrix.backend == 'amdllvm' && 'true' }}
-      - name: Check Device.DEFAULT and print some source
-        run: |
-          python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['AMD'], Device.DEFAULT"
-          DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
-      - name: Run LLVM test
-        if: matrix.backend=='amdllvm'
-        run: python test/device/test_amd_llvm.py
-      - name: Run pytest (amd)
-        run: python -m pytest -n=auto test/test_ops.py test/test_dtype.py test/test_dtype_alu.py test/test_linearizer.py test/test_randomness.py test/test_jit.py test/test_graph.py test/test_multitensor.py test/device/test_hcq.py --durations=20
-      - name: Run pytest (amd)
-        run: python -m pytest test/external/external_test_am.py --durations=20
-      - name: Run TRANSCENDENTAL math
-        run: TRANSCENDENTAL=2 python -m pytest -n=auto test/test_ops.py::TestOps::test_sin test/test_ops.py::TestOps::test_cos test/test_ops.py::TestOps::test_tan test/test_ops.py::TestOps::test_exp test/test_ops.py::TestOps::test_log --durations=20
-      - name: Run TestOps.test_add with SQTT
-        run: |
-          VIZ=1 PMC=1 DEBUG=5 python3 test/test_ops.py TestOps.test_add
-          VIZ=1 SQTT=1 DEBUG=5 python3 test/test_ops.py TestOps.test_add
-          extra/sqtt/rgptool.py create "/tmp/profile.pkl.$USER" -o /tmp/gpu0.rgp
-      - name: Run process replay tests
-        uses: ./.github/actions/process-replay
-
-  testnvidia:
-    strategy:
-      fail-fast: false
-      matrix:
-        backend: [ptx, nv]
-
-    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
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: ${{ matrix.backend }}-minimal
-          deps: testing_minimal
-          cuda: 'true'
-          ocelot: 'true'
-      - name: Set env
-        run: printf "${{ matrix.backend == 'PTX' && 'CUDA=1\nCUDA_PTX=1' || matrix.backend == 'nv' && 'NV=1\nSKIP_SLOW_TEST=1' }}" >> $GITHUB_ENV
-      - name: Check Device.DEFAULT and print some source
-        run: |
-          python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CUDA','NV'], Device.DEFAULT"
-          DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
-      - name: Run pytest (cuda)
-        # skip multitensor because it's slow
-        run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore test/test_gc.py --ignore test/test_multitensor.py --durations=20
-      - name: Run process replay tests
-        uses: ./.github/actions/process-replay
-
-  testcpuopencl:
-    strategy:
-      fail-fast: false
-      matrix:
-        backend: [llvm, cpu, opencl, lvp]
-
-    name: Linux (${{ matrix.backend }})
-    runs-on: ubuntu-22.04
-    timeout-minutes: 20
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: ${{ matrix.backend }}-minimal
-          deps: testing_minimal
-          opencl: ${{ matrix.backend == 'opencl' && 'true' }}
-          llvm: ${{ matrix.backend == 'llvm' || matrix.backend == 'lvp' }}
-          mesa: ${{ matrix.backend == 'lvp' && 'true' }}
-      - name: Set env
-        run: printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'opencl' && 'CL=1' || matrix.backend == 'lvp' && 'CPU=1\nCPU_LVP=1' }}" >> $GITHUB_ENV
-      - name: Check Device.DEFAULT and print some source
-        run: |
-          python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CPU','CL'], Device.DEFAULT"
-          DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
-      - name: Run pytest (${{ matrix.backend }})
-        run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --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 process replay tests
-        uses: ./.github/actions/process-replay
-
-  amdremote:
-    name: Linux (remote)
-    runs-on: ubuntu-22.04
-    timeout-minutes: 20
-    env:
-      REMOTE: 1
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: linux-remote
-          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" "CL" 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
-        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 (CL)
-        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
-
-# ****** OSX Tests ******
-
-  testmetal:
-    name: MacOS (unit)
-    runs-on: macos-14
-    timeout-minutes: 20
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: metal
-        deps: testing
-        python-version: '3.11'
-        amd: 'true'
-        cuda: 'true'
-        ocelot: 'true'
-        llvm: 'true'
-    - name: Run unit tests
-      run: METAL=1 python -m pytest -n=auto test/unit/ --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)
-      run: METAL=1 DEBUG=3 TC=2 python test/test_ops.py TestOps.test_gemm
-    - name: Test tensor core ops (real)
-      run: METAL=1 DEBUG=3 python test/test_ops.py TestOps.test_big_gemm
-    - 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
-    - name: Run TRANSCENDENTAL math
-      run: METAL=1 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)
-      env:
-        MOCKGPU: 1
-        AMD: 1
-        AMD_LLVM: 0
-        FORWARD_ONLY: 1
-      run: |
-        python3 -m pytest -n=auto test/device/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
-    - name: Run pytest (ptx)
-      env:
-        MOCKGPU: 1
-        NV_PTX: 1
-        NV: 1
-        FORWARD_ONLY: 1
-      run: |
-        python3 -m pytest -n=auto test/device/test_hcq.py test/test_tiny.py --durations=20
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-  osxwebgpu:
-    name: MacOS (WebGPU)
-    runs-on: macos-14
-    timeout-minutes: 10
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: osx-webgpu
-        deps: testing
-        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
-    - name: Build WEBGPU Efficientnet
-      run: WEBGPU=1 WEBGPU_BACKEND="WGPUBackendType_Metal" python3 -m examples.compile_efficientnet
-    - name: Clean npm cache
-      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 $GITHUB_WORKSPACE/test/web/test_viz.js .
-    #    node test_viz.js
-    - name: Test ONNX Runner (WEBGPU)
-      run: WEBGPU=1 python3 test/external/external_test_onnx_runner.py
-
-  osxremote:
-   name: MacOS (remote metal)
-   runs-on: macos-15
-   timeout-minutes: 10
-   env:
-     REMOTE: 1
-     REMOTEDEV: METAL
-   steps:
-     - name: Checkout Code
-       uses: actions/checkout@v4
-     - name: Setup Environment
-       uses: ./.github/actions/setup-tinygrad
-       with:
-         key: macos-remote
-         deps: testing_minimal
-     - name: Check Device.DEFAULT and print some source
-       run: |
-         python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
-         python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'METAL', Device.default.properties.real_device"
-         DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
-     - name: Run REMOTE=1 Test
-       run: |
-         python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_tensor_variable.py
-
-  osxtests:
-    strategy:
-      fail-fast: false
-      matrix:
-        backend: [metal, llvm, cpu, lvp]
-    name: MacOS (${{ matrix.backend }})
-    runs-on: macos-15
-    timeout-minutes: 20
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: macos-${{ matrix.backend }}-minimal
-          deps: testing_minimal
-          pydeps: "capstone"
-          llvm: ${{ matrix.backend == 'llvm' || matrix.backend == 'lvp' }}
-          mesa: ${{ matrix.backend == 'lvp' && 'true' }}
-      - name: Set env
-        run: printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'metal' && 'METAL=1' || matrix.backend == 'lvp' && 'CPU=1\nCPU_LVP=1' }}" >> $GITHUB_ENV
-      - name: Check Device.DEFAULT and print some source
-        run: |
-          python -c "from tinygrad import Device; assert Device.DEFAULT == {'LLVM':'CPU','LVP':'CPU'}.get(x:='${{ matrix.backend }}'.upper(), x), Device.DEFAULT"
-          DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
-      - name: Run pytest (${{ matrix.backend }})
-        run: python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --durations=20
-      - name: Run process replay tests
-        uses: ./.github/actions/process-replay
-      - name: Run macOS-specific unit test
-        if: matrix.backend == 'cpu'
-        run: python3 -m pytest test/unit/test_disk_tensor.py::TestDiskTensor::test_copy_to_cpu_not_truncated
-
-# ****** Windows Tests ******
-
-  wintests:
-    strategy:
-      fail-fast: false
-      matrix:
-        backend: [llvm, cpu, webgpu]
-
-    name: Windows (${{ matrix.backend }})
-    runs-on: windows-latest
-    timeout-minutes: 15
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: windows-${{ matrix.backend }}-minimal
-          deps: testing_unit
-          pydeps: ${{ matrix.backend == 'webgpu' && 'dawn-python' || '' }}
-      - name: Set env
-        shell: bash
-        run:  printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'webgpu' && 'WEBGPU=1'}}" >> $GITHUB_ENV
-      - name: Run unit tests
-        if: matrix.backend=='llvm'
-        # test_newton_schulz hits RecursionError
-        run: python -m pytest -n=auto test/unit/ --ignore=test/unit/test_disk_tensor.py --ignore=test/unit/test_elf.py --ignore=test/unit/test_tar.py --ignore=test/unit/test_linalg.py --durations=20
-      - name: Run pytest (${{ matrix.backend }})
-        shell: bash
-        run: |
-          python -c "from tinygrad import Device; assert Device.DEFAULT == {'LLVM':'CPU'}.get(x:='${{ matrix.backend }}'.upper(), x), Device.DEFAULT"
-          python -m pytest -n=auto test/test_tiny.py test/test_ops.py --durations=20

.gitignore

@@ -1,65 +0,0 @@
-__pycache__
-.venv/
-.venv-*/
-.vscode
-.DS_Store
-notebooks
-.*.swp
-.*.swo
-*.pyc
-*.so
-*.txt
-build
-!examples/tinychat/assets/cdn.jsdelivr.net/npm/[email protected]/build/
-/dist
-*.egg-info
-/env
-a.out
-boxes.jpg
-pandecode.dump
-vertex.bin
-recognize*
-.idea
-*.prof
-extra/disassemblers/applegpu
-extra/datasets/cifar-10-python.tar.gz
-extra/datasets/librispeech/
-extra/datasets/imagenet/
-extra/datasets/wiki/
-extra/datasets/kits19
-extra/datasets/kits19/
-extra/datasets/squad/
-extra/datasets/img_align_celeba*
-extra/datasets/open-images-v6-mlperf
-extra/datasets/kits/
-extra/datasets/COCO/
-extra/datasets/audio*
-extra/huggingface_onnx/models/*
-extra/huggingface_onnx/*.yaml
-extra/weights
-venv
-venv_sd_mlperf
-examples/**/net.*[js,json]
-examples/**/*.safetensors
-node_modules
-package.json
-package-lock.json
-temp
-*.csv
-.coverage
-coverage.xml
-htmlcov
-outputs_yolov8
-wandb
-model.safetensors
-quickstart.py
-.hypothesis
-weights
-*.lprof
-comgr_*
-*.pkl
-site/
-profile_stats
-*.log
-target
-.mypy_cache

.pre-commit-config.yaml

@@ -1,34 +0,0 @@
-# on Windows -- $env:SKIP="tests,example"
-repos:
-  - repo: local
-    hooks:
-      - id: ruff
-        name: ruff
-        entry: python3 -m ruff check .
-        language: system
-        always_run: true
-        pass_filenames: false
-      - id: tiny
-        name: tiny tests
-        entry: python3 -m pytest test/test_tiny.py
-        language: system
-        always_run: true
-        pass_filenames: false
-      - id: mypy
-        name: mypy
-        entry: python3 -m mypy tinygrad/ --strict-equality
-        language: system
-        always_run: true
-        pass_filenames: false
-      - id: example
-        name: test all devices
-        entry: python3 test/external/external_test_example.py
-        language: system
-        always_run: true
-        pass_filenames: false
-      - id: tests
-        name: subset of tests
-        entry: env OMP_NUM_THREADS=1 PYTHONPATH="." python3 -m pytest -n=6 test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py
-        language: system
-        always_run: true
-        pass_filenames: false

.pylintrc

@@ -1,469 +0,0 @@
-[MASTER]
-
-# A comma-separated list of package or module names from where C extensions may
-# be loaded. Extensions are loading into the active Python interpreter and may
-# run arbitrary code
-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
-
-# Add files or directories matching the regex patterns to the blacklist. The
-# regex matches against base names, not paths.
-ignore-patterns=
-
-# Python code to execute, usually for sys.path manipulation such as
-# pygtk.require().
-#init-hook=
-
-# Use multiple processes to speed up Pylint.
-jobs=4
-
-# List of plugins (as comma separated values of python modules names) to load,
-# usually to register additional checkers.
-load-plugins=
-
-# Pickle collected data for later comparisons.
-persistent=yes
-
-# Specify a configuration file.
-#rcfile=
-
-# Allow loading of arbitrary C extensions. Extensions are imported into the
-# active Python interpreter and may run arbitrary code.
-unsafe-load-any-extension=no
-
-
-[MESSAGES CONTROL]
-
-# Only show warnings with the listed confidence levels. Leave empty to show
-# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED
-confidence=
-
-# Disable the message, report, category or checker with the given id(s). You
-# can either give multiple identifiers separated by comma (,) or put this
-# option multiple times (only on the command line, not in the configuration
-# file where it should appear only once).You can also use "--disable=all" to
-# disable everything first and then reenable specific checks. For example, if
-# you want to run only the similarities checker, you can use "--disable=all
-# --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
-# 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
-# multiple time (only on the command line, not in the configuration file where
-# it should appear only once). See also the "--disable" option for examples.
-enable=c-extension-no-member,use-a-generator, no-else-return
-
-
-[REPORTS]
-
-# Python expression which should return a note less than 10 (10 is the highest
-# note). You have access to the variables errors warning, statement which
-# respectively contain the number of errors / warnings messages and the total
-# number of statements analyzed. This is used by the global evaluation report
-# (RP0004).
-evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
-
-# Template used to display messages. This is a python new-style format string
-# used to format the message information. See doc for all details
-#msg-template=
-
-# Set the output format. Available formats are text, parseable, colorized, json
-# and msvs (visual studio).You can also give a reporter class, eg
-# mypackage.mymodule.MyReporterClass.
-output-format=text
-
-# Tells whether to display a full report or only the messages
-reports=no
-
-# Activate the evaluation score.
-score=yes
-
-
-[REFACTORING]
-
-# Maximum number of nested blocks for function / method body
-max-nested-blocks=5
-
-# Complete name of functions that never returns. When checking for
-# inconsistent-return-statements if a never returning function is called then
-# it will be considered as an explicit return statement and no message will be
-# printed.
-never-returning-functions=optparse.Values,sys.exit
-
-
-[LOGGING]
-
-# Logging modules to check that the string format arguments are in logging
-# function parameter format
-logging-modules=logging
-
-
-[SPELLING]
-
-# Limits count of emitted suggestions for spelling mistakes
-max-spelling-suggestions=4
-
-# Spelling dictionary name. Available dictionaries: none. To make it working
-# install python-enchant package.
-spelling-dict=
-
-# List of comma separated words that should not be checked.
-spelling-ignore-words=
-
-# A path to a file that contains private dictionary; one word per line.
-spelling-private-dict-file=
-
-# Tells whether to store unknown words to indicated private dictionary in
-# --spelling-private-dict-file option instead of raising a message.
-spelling-store-unknown-words=no
-
-
-[MISCELLANEOUS]
-
-# List of note tags to take in consideration, separated by a comma.
-notes=FIXME,
-      XXX,
-      TODO
-
-
-[SIMILARITIES]
-
-# Ignore comments when computing similarities.
-ignore-comments=yes
-
-# Ignore docstrings when computing similarities.
-ignore-docstrings=yes
-
-# Ignore imports when computing similarities.
-ignore-imports=no
-
-# Minimum lines number of a similarity.
-min-similarity-lines=4
-
-
-[TYPECHECK]
-
-# List of decorators that produce context managers, such as
-# contextlib.contextmanager. Add to this list to register other decorators that
-# produce valid context managers.
-contextmanager-decorators=contextlib.contextmanager
-
-# List of members which are set dynamically and missed by pylint inference
-# system, and so shouldn't trigger E1101 when accessed. Python regular
-# expressions are accepted.
-generated-members=capnp.* cereal.* pygame.* zmq.* setproctitle.* smbus2.* usb1.* serial.* cv2.* ft4222.* carla.*
-
-# Tells whether missing members accessed in mixin class should be ignored. A
-# mixin class is detected if its name ends with "mixin" (case insensitive).
-ignore-mixin-members=yes
-
-# This flag controls whether pylint should warn about no-member and similar
-# checks whenever an opaque object is returned when inferring. The inference
-# can return multiple potential results while evaluating a Python object, but
-# some branches might not be evaluated, which results in partial inference. In
-# that case, it might be useful to still emit no-member and other checks for
-# the rest of the inferred objects.
-ignore-on-opaque-inference=yes
-
-# List of class names for which member attributes should not be checked (useful
-# for classes with dynamically set attributes). This supports the use of
-# qualified names.
-ignored-classes=optparse.Values,thread._local,_thread._local
-
-# List of module names for which member attributes should not be checked
-# (useful for modules/projects where namespaces are manipulated during runtime
-# and thus existing member attributes cannot be deduced by static analysis. It
-# supports qualified module names, as well as Unix pattern matching.
-ignored-modules=flask setproctitle usb1 flask.ext.socketio smbus2 usb1.*
-
-# Show a hint with possible names when a member name was not found. The aspect
-# of finding the hint is based on edit distance.
-missing-member-hint=yes
-
-# The minimum edit distance a name should have in order to be considered a
-# similar match for a missing member name.
-missing-member-hint-distance=1
-
-# The total number of similar names that should be taken in consideration when
-# showing a hint for a missing member.
-missing-member-max-choices=1
-
-
-[VARIABLES]
-
-# List of additional names supposed to be defined in builtins. Remember that
-# you should avoid to define new builtins when possible.
-additional-builtins=
-
-# Tells whether unused global variables should be treated as a violation.
-allow-global-unused-variables=yes
-
-# List of strings which can identify a callback function by name. A callback
-# name must start or end with one of those strings.
-callbacks=cb_,
-          _cb
-
-# A regular expression matching the name of dummy variables (i.e. expectedly
-# not used).
-dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
-
-# Argument names that match this expression will be ignored. Default to name
-# with leading underscore
-ignored-argument-names=_.*|^ignored_|^unused_
-
-# Tells whether we should check for unused import in __init__ files.
-init-import=no
-
-# List of qualified module names which can have objects that can redefine
-# builtins.
-redefining-builtins-modules=six.moves,past.builtins,future.builtins
-
-
-[FORMAT]
-
-# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
-expected-line-ending-format=
-
-# Regexp for a line that is allowed to be longer than the limit.
-ignore-long-lines=^\s*(# )?<?https?://\S+>?$
-
-# Number of spaces of indent required inside a hanging  or continued line.
-indent-after-paren=4
-
-# String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
-# tab).
-indent-string='  '
-
-# Maximum number of characters on a single line.
-max-line-length=150
-
-# Maximum number of lines in a module
-max-module-lines=1000
-
-# Allow the body of a class to be on the same line as the declaration if body
-# contains single statement.
-single-line-class-stmt=no
-
-# Allow the body of an if to be on the same line as the test if there is no
-# else.
-single-line-if-stmt=no
-
-
-[BASIC]
-
-# Naming style matching correct argument names
-argument-naming-style=snake_case
-
-# Regular expression matching correct argument names. Overrides argument-
-# naming-style
-#argument-rgx=
-
-# Naming style matching correct attribute names
-attr-naming-style=snake_case
-
-# Regular expression matching correct attribute names. Overrides attr-naming-
-# style
-#attr-rgx=
-
-# Bad variable names which should always be refused, separated by a comma
-bad-names=foo,
-          bar,
-          baz,
-          toto,
-          tutu,
-          tata
-
-# Naming style matching correct class attribute names
-class-attribute-naming-style=any
-
-# Regular expression matching correct class attribute names. Overrides class-
-# attribute-naming-style
-#class-attribute-rgx=
-
-# Naming style matching correct class names
-class-naming-style=PascalCase
-
-# Regular expression matching correct class names. Overrides class-naming-style
-#class-rgx=
-
-# Naming style matching correct constant names
-const-naming-style=UPPER_CASE
-
-# Regular expression matching correct constant names. Overrides const-naming-
-# style
-#const-rgx=
-
-# Minimum line length for functions/classes that require docstrings, shorter
-# ones are exempt.
-docstring-min-length=-1
-
-# Naming style matching correct function names
-function-naming-style=snake_case
-
-# Regular expression matching correct function names. Overrides function-
-# naming-style
-#function-rgx=
-
-# Good variable names which should always be accepted, separated by a comma
-good-names=i,
-           j,
-           k,
-           ex,
-           Run,
-           _
-
-# Include a hint for the correct naming format with invalid-name
-include-naming-hint=no
-
-# Naming style matching correct inline iteration names
-inlinevar-naming-style=any
-
-# Regular expression matching correct inline iteration names. Overrides
-# inlinevar-naming-style
-#inlinevar-rgx=
-
-# Naming style matching correct method names
-method-naming-style=snake_case
-
-# Regular expression matching correct method names. Overrides method-naming-
-# style
-#method-rgx=
-
-# Naming style matching correct module names
-module-naming-style=snake_case
-
-# Regular expression matching correct module names. Overrides module-naming-
-# style
-#module-rgx=
-
-# Colon-delimited sets of names that determine each other's naming style when
-# the name regexes allow several styles.
-name-group=
-
-# Regular expression which should only match function or class names that do
-# not require a docstring.
-no-docstring-rgx=^_
-
-# List of decorators that produce properties, such as abc.abstractproperty. Add
-# to this list to register other decorators that produce valid properties.
-property-classes=abc.abstractproperty
-
-# Naming style matching correct variable names
-variable-naming-style=snake_case
-
-# Regular expression matching correct variable names. Overrides variable-
-# naming-style
-#variable-rgx=
-
-
-[DESIGN]
-
-# Maximum number of arguments for function / method
-max-args=5
-
-# Maximum number of attributes for a class (see R0902).
-max-attributes=7
-
-# Maximum number of boolean expressions in a if statement
-max-bool-expr=5
-
-# Maximum number of branch for function / method body
-max-branches=12
-
-# Maximum number of locals for function / method body
-max-locals=15
-
-# Maximum number of parents for a class (see R0901).
-max-parents=7
-
-# Maximum number of public methods for a class (see R0904).
-max-public-methods=20
-
-# Maximum number of return / yield for function / method body
-max-returns=6
-
-# Maximum number of statements in function / method body
-max-statements=50
-
-# Minimum number of public methods for a class (see R0903).
-min-public-methods=2
-
-
-[CLASSES]
-
-# List of method names used to declare (i.e. assign) instance attributes.
-defining-attr-methods=__init__,
-                      __new__,
-                      setUp
-
-# List of member names, which should be excluded from the protected access
-# warning.
-exclude-protected=_asdict,
-                  _fields,
-                  _replace,
-                  _source,
-                  _make
-
-# List of valid names for the first argument in a class method.
-valid-classmethod-first-arg=cls
-
-# List of valid names for the first argument in a metaclass class method.
-valid-metaclass-classmethod-first-arg=mcs
-
-
-[IMPORTS]
-
-# Allow wildcard imports from modules that define __all__.
-allow-wildcard-with-all=no
-
-# Analyse import fallback blocks. This can be used to support both Python 2 and
-# 3 compatible code, which means that the block might have code that exists
-# only in one or another interpreter, leading to false positives when analysed.
-analyse-fallback-blocks=no
-
-# Deprecated modules which should not be used, separated by a comma
-deprecated-modules=regsub,
-                   TERMIOS,
-                   Bastion,
-                   rexec
-
-# Create a graph of external dependencies in the given file (report RP0402 must
-# not be disabled)
-ext-import-graph=
-
-# Create a graph of every (i.e. internal and external) dependencies in the
-# given file (report RP0402 must not be disabled)
-import-graph=
-
-# Create a graph of internal dependencies in the given file (report RP0402 must
-# not be disabled)
-int-import-graph=
-
-# Force import order to recognize a module as part of the standard
-# compatibility libraries.
-known-standard-library=
-
-# Force import order to recognize a module as part of a third party library.
-known-third-party=enchant
-
-[STRING]
-
-# This flag controls whether the implicit-str-concat should generate a warning
-# on implicit string concatenation in sequences defined over several lines.
-check-str-concat-over-line-jumps=yes
-
-[EXCEPTIONS]
-
-# Exceptions that will emit a warning when being caught. Defaults to
-# "Exception"
-overgeneral-exceptions=builtins.Exception

AGENTS.md

@@ -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.

LICENSE

@@ -1,7 +0,0 @@
-Copyright (c) 2024, the tiny corp
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -1,180 +0,0 @@
-<div align="center">
-
-<picture>
-  <source media="(prefers-color-scheme: light)" srcset="/docs/logo_tiny_light.svg">
-  <img alt="tiny corp logo" src="/docs/logo_tiny_dark.svg" width="50%" height="50%">
-</picture>
-
-tinygrad: For something between [PyTorch](https://github.com/pytorch/pytorch) and [karpathy/micrograd](https://github.com/karpathy/micrograd). Maintained by [tiny corp](https://tinygrad.org).
-
-<h3>
-
-[Homepage](https://github.com/tinygrad/tinygrad) | [Documentation](https://docs.tinygrad.org/) | [Discord](https://discord.gg/ZjZadyC7PK)
-
-</h3>
-
-[![GitHub Repo stars](https://img.shields.io/github/stars/tinygrad/tinygrad)](https://github.com/tinygrad/tinygrad/stargazers)
-[![Unit Tests](https://github.com/tinygrad/tinygrad/actions/workflows/test.yml/badge.svg)](https://github.com/tinygrad/tinygrad/actions/workflows/test.yml)
-[![Discord](https://img.shields.io/discord/1068976834382925865)](https://discord.gg/ZjZadyC7PK)
-
-</div>
-
----
-
-Despite tinygrad's size, it is a fully featured 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.
-
-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.
-
-## Features
-
-### LLaMA and Stable Diffusion
-
-tinygrad can run [LLaMA](/docs/showcase.md#llama) and [Stable Diffusion](/docs/showcase.md#stable-diffusion)!
-
-### Laziness
-
-Try a matmul. See how, despite the style, it is fused into one kernel with the power of laziness.
-
-```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()"
-```
-
-And we can change `DEBUG` to `4` to see the generated code.
-
-### Neural networks
-
-As it turns out, 90% of what you need for neural networks are a decent autograd/tensor library.
-Throw in an optimizer, a data loader, and some compute, and you have all you need.
-
-```python
-from tinygrad import Tensor, nn
-
-class LinearNet:
-  def __init__(self):
-    self.l1 = Tensor.kaiming_uniform(784, 128)
-    self.l2 = Tensor.kaiming_uniform(128, 10)
-  def __call__(self, x:Tensor) -> Tensor:
-    return x.flatten(1).dot(self.l1).relu().dot(self.l2)
-
-model = LinearNet()
-optim = nn.optim.Adam([model.l1, model.l2], lr=0.001)
-
-x, y = Tensor.rand(4, 1, 28, 28), Tensor([2,4,3,7])  # replace with real mnist dataloader
-
-with Tensor.train():
-  for i in range(10):
-    optim.zero_grad()
-    loss = model(x).sparse_categorical_crossentropy(y).backward()
-    optim.step()
-    print(i, loss.item())
-```
-
-See [examples/beautiful_mnist.py](examples/beautiful_mnist.py) for the full version that gets 98% in ~5 seconds
-
-## Accelerators
-
-tinygrad already supports numerous accelerators, including:
-
-- [x] [OpenCL](tinygrad/runtime/ops_cl.py)
-- [x] [CPU](tinygrad/runtime/ops_cpu.py)
-- [x] [METAL](tinygrad/runtime/ops_metal.py)
-- [x] [CUDA](tinygrad/runtime/ops_cuda.py)
-- [x] [AMD](tinygrad/runtime/ops_amd.py)
-- [x] [NV](tinygrad/runtime/ops_nv.py)
-- [x] [QCOM](tinygrad/runtime/ops_qcom.py)
-- [x] [WEBGPU](tinygrad/runtime/ops_webgpu.py)
-
-And it is easy to add more! Your accelerator of choice only needs to support a total of ~25 low level ops.
-
-To check default accelerator run: `python3 -c "from tinygrad import Device; print(Device.DEFAULT)"`
-
-## Installation
-
-The current recommended way to install tinygrad is from source.
-
-### From source
-
-```sh
-git clone https://github.com/tinygrad/tinygrad.git
-cd tinygrad
-python3 -m pip install -e .
-```
-
-### Direct (master)
-
-```sh
-python3 -m pip install git+https://github.com/tinygrad/tinygrad.git
-```
-
-## Documentation
-
-Documentation along with a quick start guide can be found on the [docs website](https://docs.tinygrad.org/) built from the [docs/](/docs) directory.
-
-### Quick example comparing to PyTorch
-
-```python
-from tinygrad import Tensor
-
-x = Tensor.eye(3, requires_grad=True)
-y = Tensor([[2.0,0,-2.0]], requires_grad=True)
-z = y.matmul(x).sum()
-z.backward()
-
-print(x.grad.tolist())  # dz/dx
-print(y.grad.tolist())  # dz/dy
-```
-
-The same thing but in PyTorch:
-```python
-import torch
-
-x = torch.eye(3, requires_grad=True)
-y = torch.tensor([[2.0,0,-2.0]], requires_grad=True)
-z = y.matmul(x).sum()
-z.backward()
-
-print(x.grad.tolist())  # dz/dx
-print(y.grad.tolist())  # dz/dy
-```
-
-## Contributing
-
-There has been a lot of interest in tinygrad lately. Following these guidelines will help your PR get accepted.
-
-We'll start with what will get your PR closed with a pointer to this section:
-
-- No code golf! While low line count is a guiding light of this project, anything that remotely looks like code golf will be closed. The true goal is reducing complexity and increasing readability, and deleting `\n`s does nothing to help with that.
-- All docs and whitespace changes will be closed unless you are a well-known contributor. The people writing the docs should be those who know the codebase the absolute best. People who have not demonstrated that shouldn't be messing with docs. Whitespace changes are both useless *and* carry a risk of introducing bugs.
-- Anything you claim is a "speedup" must be benchmarked. In general, the goal is simplicity, so even if your PR makes things marginally faster, you have to consider the tradeoff with maintainability and readability.
-- In general, the code outside the core `tinygrad/` folder is not well tested, so unless the current code there is broken, you shouldn't be changing it.
-- If your PR looks "complex", is a big diff, or adds lots of lines, it won't be reviewed or merged. Consider breaking it up into smaller PRs that are individually clear wins. A common pattern I see is prerequisite refactors before adding new functionality. If you can (cleanly) refactor to the point that the feature is a 3 line change, this is great, and something easy for us to review.
-
-Now, what we want:
-
-- Bug fixes (with a regression test) are great! This library isn't 1.0 yet, so if you stumble upon a bug, fix it, write a test, and submit a PR, this is valuable work.
-- Solving bounties! tinygrad [offers cash bounties](https://docs.google.com/spreadsheets/d/1WKHbT-7KOgjEawq5h5Ic1qUWzpfAzuD_J06N1JwOCGs/edit?usp=sharing) for certain improvements to the library. All new code should be high quality and well tested.
-- Features. However, if you are adding a feature, consider the line tradeoff. If it's 3 lines, there's less of a bar of usefulness it has to meet over something that's 30 or 300 lines. All features must have regression tests. In general with no other constraints, your feature's API should match torch or numpy.
-- Refactors that are clear wins. In general, if your refactor isn't a clear win it will be closed. But some refactors are amazing! Think about readability in a deep core sense. A whitespace change or moving a few functions around is useless, but if you realize that two 100 line functions can actually use the same 110 line function with arguments while also improving readability, this is a big win. Refactors should pass [process replay](#process-replay-tests).
-- Tests/fuzzers. If you can add tests that are non brittle, they are welcome. We have some fuzzers in here too, and there's a plethora of bugs that can be found with them and by improving them. Finding bugs, even writing broken tests (that should pass) with `@unittest.expectedFailure` is great. This is how we make progress.
-- Dead code removal from core `tinygrad/` folder. We don't care about the code in extra, but removing dead code from the core library is great. Less for new people to read and be confused by.
-
-### Running tests
-
-You should install the pre-commit hooks with `pre-commit install`. This will run the linter, mypy, and a subset of the tests on every commit.
-
-For more examples on how to run the full test suite please refer to the [CI workflow](.github/workflows/test.yml).
-
-Some examples of running tests locally:
-```sh
-python3 -m pip install -e '.[testing]'  # install extra deps for testing
-python3 test/test_ops.py                # just the ops tests
-python3 -m pytest test/                 # whole test suite
-```
-
-#### Process replay tests
-
-[Process replay](https://github.com/tinygrad/tinygrad/blob/master/test/external/process_replay/README.md) compares your PR's generated kernels against master. If your PR is a refactor or speedup without any expected behavior change, It should include [pr] in the pull request title.

abstractions3.py

@@ -1,6 +1,4 @@
 # abstractions2 goes from back to front, here we will go from front to back
-from typing import List
-from tinygrad.helpers import tqdm
 
 # *****
 # 0. Load mnist on the device
@@ -13,7 +11,7 @@ X_train -= X_train.mean()
 # *****
 # 1. Define an MNIST model.
 
-from tinygrad import Tensor
+from tinygrad import Tensor, Context
 
 l1 = Tensor.kaiming_uniform(128, 784)
 l2 = Tensor.kaiming_uniform(10, 128)
@@ -26,37 +24,31 @@ l1n, l2n = l1.numpy(), l2.numpy()
 from tinygrad.nn.optim import SGD
 optim = SGD([l1, l2])
 
-Tensor.training = True
-X, Y = X_train[(samples:=Tensor.randint(128, high=X_train.shape[0]))], Y_train[samples]
-optim.zero_grad()
-model(X).sparse_categorical_crossentropy(Y).backward()
-optim.schedule_step()   # this will step the optimizer without running realize
+with Context(TRAINING=1):
+  X, Y = X_train[(samples:=Tensor.randint(128, high=X_train.shape[0]))], Y_train[samples]
+  optim.zero_grad()
+  model(X).sparse_categorical_crossentropy(Y).backward()
+  optim.schedule_step()   # this will step the optimizer without running realize
 
 # *****
-# 3. Create a schedule.
+# 3. Create a schedule (linear uop).
 
 # 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
 
-from tinygrad.engine.schedule import ScheduleItem
-schedule: List[ScheduleItem] = Tensor.schedule(l1, l2)
+from tinygrad.engine.realize import run_linear
+linear = Tensor.schedule_linear(l1, l2)
 
-print(f"The schedule contains {len(schedule)} items.")
-for si in schedule: print(str(si)[:80])
+print(f"The schedule contains {len(linear.src)} items.")
+for call in linear.src: print(str(call)[:80])
 
 # *****
-# 4. Lower a schedule.
+# 4. Lower and run the schedule (linear uop).
 
-from tinygrad.engine.realize import lower_schedule_item, ExecItem
-lowered: List[ExecItem] = [lower_schedule_item(si) for si in tqdm(schedule)]
+run_linear(linear)
 
 # *****
-# 5. Run the schedule
-
-for ei in tqdm(lowered): ei.run()
-
-# *****
-# 6. Print the weight change
+# 5. Print the weight change
 
 print("first weight change\n", l1.numpy()-l1n)
 print("second weight change\n", l2.numpy()-l2n)

abstractions4.py

@@ -0,0 +1,251 @@
+# tinygrad allows you to write kernels at many different abstractions levels.
+# This is for RDNA3, but if you don't have one you can run with the emulator
+# PYTHONPATH="." DEV=MOCKPCI+AMD
+
+from tinygrad import Tensor, Context, GlobalCounters, UOp, Device
+from tinygrad.helpers import DEV, DEBUG, getenv
+from tinygrad.uop.ops import AxisType, KernelInfo, Ops
+from tinygrad.dtype import AddrSpace, dtypes
+from tinygrad.runtime.autogen.amd.rdna3.ins import *
+
+def eval_harness(name, tensor, fxn, check=None):
+  print(f"***** {name}")
+  GlobalCounters.reset()
+  with Context(DEBUG=max(DEBUG.value, 2)): out = fxn(tensor).item()
+  assert check is None or abs(out - check) < abs(check) * 1e-3, f"out was wrong {out}, expected {check}, off by {out/check}x"
+  print(f"computed in {GlobalCounters.time_sum_s*1000:.2f} ms, {(a.nbytes()/1e9)/GlobalCounters.time_sum_s:.2f} GB/s")
+  return out
+
+SZ = 256*1024 if DEV.interface.startswith("MOCK") else 1024*1024*1024
+
+def example_2_hip(a:Tensor, correct):
+  GLOBALS = 1024
+  THREADS = 256
+  def hip_reduce_sum(out:UOp, buf:UOp) -> UOp:
+    assert SZ % (GLOBALS * THREADS) == 0
+    CHUNK = SZ // (GLOBALS * THREADS)
+    # NOTE: tinygrad doesn't populate HIP hidden kernargs, so blockDim.x/gridDim.x read as 0.
+    # We hardcode block/grid sizes as constexpr to avoid any dependency on those builtins.
+    code = f"""
+    #include <hip/hip_runtime.h>
+    constexpr unsigned int BLOCK = {THREADS};
+    constexpr unsigned int CHUNK = {CHUNK};
+    extern "C" __global__ void hip_reduce_sum_kernel(float* __restrict__ block_sums, const float* __restrict__ x) {{
+      __shared__ float sdata[BLOCK];
+
+      unsigned int tid = threadIdx.x;
+      unsigned int gid = blockIdx.x * BLOCK + tid;
+
+      // Each thread sums CHUNK consecutive elements from its own region
+      float sum = 0.0f;
+      const float* base = x + gid * CHUNK;
+      #pragma unroll 16
+      for (unsigned int k = 0; k < CHUNK; k++) {{
+        sum += base[k];
+      }}
+
+      sdata[tid] = sum;
+      __syncthreads();
+
+      // Block reduction in shared memory
+      for (unsigned int s = BLOCK / 2; s > 0; s >>= 1) {{
+        if (tid < s) {{
+          sdata[tid] += sdata[tid + s];
+        }}
+        __syncthreads();
+      }}
+
+      // One partial sum per block
+      if (tid == 0) {{
+        block_sums[blockIdx.x] = sdata[0];
+      }}
+    }}"""
+
+    # TODO: remove the need for the compiler here, you should just be able to remove Ops.BINARY
+    from tinygrad.runtime.support.compiler_amd import HIPCCCompiler
+    lib = HIPCCCompiler(Device[Device.DEFAULT].renderer.target.arch, []).compile_cached(code)
+    # the sink specifies the GLOBAL and LOCAL sizes, along with the input buffers and name
+    sink = UOp.sink(UOp.special(GLOBALS, 'gidx0'), UOp.special(THREADS, 'lidx0'), out, buf,
+                    arg=KernelInfo(name="hip_reduce_sum_kernel"))
+    return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
+  eval_harness("HIP kernel", a, lambda x: Tensor.empty(GLOBALS).custom_kernel(x, fxn=hip_reduce_sum)[0].sum(), check=correct)
+
+def example_3_custom_uop(a:Tensor, correct):
+  # This GPU has 32 CUs, keep them all busy
+  CU_COUNT = 32
+  def custom_sum(out:UOp, buf:UOp) -> UOp:
+    LCLS = 256
+    buf = buf.reshape(CU_COUNT, -1, LCLS)
+
+    glbl = UOp.range(CU_COUNT, 0, AxisType.GLOBAL)
+    lane = UOp.range(LCLS, 1, AxisType.LOCAL)
+
+    # accumulate the globals into a per lane accumulator
+    reduce_loop = UOp.range(buf.shape[1], 2, AxisType.REDUCE)
+    acc = UOp.placeholder((1,), dtypes.float, slot=6, addrspace=AddrSpace.REG)
+    acc = acc.after(acc.store(0))
+    acc = acc.after(acc[0].store(acc.after(reduce_loop)[0] + buf[glbl, reduce_loop, lane]).end(reduce_loop))
+
+    # store all the per lane accumulators to LOCAL
+    local_accs = UOp.placeholder((LCLS,), dtypes.float, slot=0, addrspace=AddrSpace.LOCAL)
+    local_accs = local_accs.after(local_accs[lane].store(acc[0]))
+
+    # accumulate LOCALs into a single per CU accumulator
+    late_reduce_loop = UOp.range(LCLS, 3, AxisType.REDUCE)
+    acc2 = UOp.placeholder((1,), dtypes.float, slot=7, addrspace=AddrSpace.REG)
+    acc2 = acc2.after(acc2.store(0))
+    acc2 = acc2.after(acc2[0].store(acc2.after(late_reduce_loop)[0] + local_accs[late_reduce_loop]).end(late_reduce_loop))[0]
+
+    # store (NOTE: since the address doesn't depend on the warp, this will be automatically gated)
+    return out[glbl].store(acc2).end(lane, glbl).sink(arg=KernelInfo(opts_to_apply=()))
+
+  eval_harness("custom UOp kernel", a, lambda x: Tensor.empty(CU_COUNT).custom_kernel(x, fxn=custom_sum)[0].sum(), check=correct)
+
+def example_5_custom_assembly(a:Tensor, correct):
+  # Kernel class copied from amd_asm_matmul
+  class Kernel:
+    def __init__(self): self.instructions, self.labels, self.pos = [], {}, 0
+    def label(self, name): self.labels[name] = self.pos
+    def emit(self, inst, target=None):
+      self.instructions.append(inst)
+      inst._target, inst._pos = target, self.pos
+      self.pos += inst.size()
+      return inst
+    def waitcnt(self, lgkm=None, vm=None):
+      # Wait for memory operations. lgkm=N waits until N lgkm ops remain, vm=N waits until N vmem ops remain.
+      vmcnt, lgkmcnt, expcnt = vm if vm is not None else 63, lgkm if lgkm is not None else 63, 7
+      waitcnt = (expcnt & 0x7) | ((lgkmcnt & 0x3f) << 4) | ((vmcnt & 0x3f) << 10)
+      self.emit(s_waitcnt(simm16=waitcnt))
+    def finalize(self, sink:UOp) -> UOp:
+      for inst in self.instructions:
+        if inst._target is None: continue
+        offset_dwords = (self.labels[inst._target] - inst._pos - inst.size()) // 4
+        if not -32768 <= offset_dwords <= 32767: raise ValueError(f"branch to '{inst._target}' offset {offset_dwords} exceeds simm16 range")
+        inst.simm16 = offset_dwords
+      return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=tuple([UOp(Ops.INS, arg=x) for x in self.instructions]))))
+
+  CU_COUNT = 32
+  LANES = 64
+  def asm_sum(out:UOp, buf:UOp) -> UOp:
+    V_LANE_ID = 0             # lane_id set on startup
+    S_WORKGROUP_X = 2         # workgroup_id_x
+    S_LOOP_CTR = 3
+    k = Kernel()
+    # mul lane id by 16 for offsets (4 for float, 4 for b128)
+    k.emit(v_mul_lo_u32(v[0], v[V_LANE_ID], 16))
+    k.emit(v_add_nc_u32_e32(v[1], 4096, v[0]))
+    k.emit(v_add_nc_u32_e32(v[2], 4096, v[1]))
+    k.emit(v_add_nc_u32_e32(v[3], 4096, v[2]))
+    # load both addresses
+    k.emit(s_load_b128(sdata=s[4:7], sbase=s[0:1], offset=0x0, soffset=NULL))
+    k.waitcnt(lgkm=0)
+    # offset buffer pointer by workgroup_id_x * chunk_size_bytes
+    k.emit(s_mul_i32(s[S_LOOP_CTR], s[S_WORKGROUP_X], buf.numel()*4//CU_COUNT))
+    k.emit(s_add_u32(s[6], s[6], s[S_LOOP_CTR]))
+    k.emit(s_addc_u32(s[7], s[7], 0))
+    # zero the accumulators
+    k.emit(VOPD(VOPDOp.V_DUAL_MOV_B32, VOPDOp.V_DUAL_MOV_B32, vdstx=v[4], vdsty=v[5], srcx0=0, srcy0=0))
+    k.emit(VOPD(VOPDOp.V_DUAL_MOV_B32, VOPDOp.V_DUAL_MOV_B32, vdstx=v[6], vdsty=v[7], srcx0=0, srcy0=0))
+
+    def emit_loads(base_vreg, reg_len):
+      assert reg_len%4 == 0
+      k.emit(s_clause(simm16=(reg_len//4)-1))
+      for i in range(reg_len//4):
+        offset = i*LANES*16
+        assert offset < 16384
+        k.emit(global_load_b128(vdst=v[base_vreg+i*4:base_vreg+i*4+3], addr=v[offset//4096], saddr=s[6:7], offset=offset%4096))
+      k.emit(s_add_u32(s[6], s[6], reg_len * LANES * 4))
+      k.emit(s_addc_u32(s[7], s[7], 0))
+
+    def tree_reduce_to_4567(base_vreg, reg_len):
+      assert reg_len%4 == 0
+      reg_len //= 4
+      while reg_len > 1:
+        half = reg_len // 2
+        for j in range(half):
+          a, b = base_vreg + j*4, base_vreg + (j+half)*4
+          # v[a+0](bank0) += v[b+2](bank2), v[a+1](bank1) += v[b+3](bank3) — src0 and src1 on different banks
+          k.emit(VOPD(VOPDOp.V_DUAL_ADD_F32, VOPDOp.V_DUAL_ADD_F32, vdstx=v[a], vdsty=v[a+1], srcx0=v[a], vsrcx1=v[b+2], srcy0=v[a+1], vsrcy1=v[b+3]))
+          # v[a+2](bank2) += v[b+0](bank0), v[a+3](bank3) += v[b+1](bank1) — src0 and src1 on different banks
+          k.emit(VOPD(VOPDOp.V_DUAL_ADD_F32, VOPDOp.V_DUAL_ADD_F32, vdstx=v[a+2], vdsty=v[a+3], srcx0=v[a+2], vsrcx1=v[b], srcy0=v[a+3], vsrcy1=v[b+1]))
+        reg_len = half
+      k.emit(VOPD(VOPDOp.V_DUAL_ADD_F32, VOPDOp.V_DUAL_ADD_F32, vdstx=v[4], vdsty=v[5], srcx0=v[4], vsrcx1=v[base_vreg], srcy0=v[5], vsrcy1=v[base_vreg+1]))
+      k.emit(VOPD(VOPDOp.V_DUAL_ADD_F32, VOPDOp.V_DUAL_ADD_F32, vdstx=v[6], vdsty=v[7], srcx0=v[6], vsrcx1=v[base_vreg+2], srcy0=v[7], vsrcy1=v[base_vreg+3]))
+
+    BASE_REG = 8
+    LOAD_UNROLL = 64
+    INNER_UNROLL = 2
+
+    assert buf.numel() % (CU_COUNT*LANES*LOAD_UNROLL*INNER_UNROLL) == 0
+    total_batches = buf.numel()//(CU_COUNT*LANES*LOAD_UNROLL*INNER_UNROLL)
+    k.emit(s_mov_b32(s[S_LOOP_CTR], total_batches-1))
+
+    k.label('LOOP')
+    for _ in range(INNER_UNROLL):
+      emit_loads(BASE_REG, reg_len=LOAD_UNROLL)
+      k.waitcnt(vm=0)
+      tree_reduce_to_4567(BASE_REG, reg_len=LOAD_UNROLL)
+    k.emit(s_sub_u32(s[S_LOOP_CTR], s[S_LOOP_CTR], 1))
+    k.emit(s_cbranch_scc0(), target='LOOP')
+
+    # add into v[4]
+    k.emit(v_add_f32_e32(v[4], v[4], v[5]))
+    k.emit(v_add_f32_e32(v[6], v[6], v[7]))
+    k.emit(v_add_f32_e32(v[4], v[4], v[6]))
+
+    # warp shuffle into v[4] on lane 0 using DPP row_shl within each 16-lane row
+    for shift in [1, 2, 4, 8]:
+      k.emit(v_add_f32_e32(v[4], DPP, v[4], vsrc0=v[4], dpp=0x100 | shift, row_mask=0xf, bank_mask=0xf, bc=1))
+    # combine rows: get lane 16's value to lane 0 via permlanex16
+    k.emit(v_permlanex16_b32(v[5], v[4], 0, 0))
+    k.emit(v_add_f32_e32(v[4], v[4], v[5]))
+
+    # atomic store (only on lane 0)
+    k.emit(s_mov_b32(EXEC_LO, 1))
+    k.emit(v_mov_b32_e32(v[0], 0))
+    k.emit(global_atomic_add_f32(addr=v[0], saddr=s[4:5], data=v[4]))
+
+    k.emit(s_sendmsg(simm16=3))  # DEALLOC_VGPRS
+    k.emit(s_endpgm())
+    return k.finalize(UOp.sink(UOp.special(CU_COUNT, 'gidx0'), UOp.special(LANES, 'lidx0'), out, buf, arg=KernelInfo(name="asm_reduce")))
+
+  out = Tensor.zeros(1,).contiguous().realize()
+  eval_harness("RDNA3 assembly kernel", a, lambda x: out.custom_kernel(x, fxn=asm_sum)[0], check=correct)
+
+if __name__ == "__main__":
+  examples = [int(x) for x in getenv("EXAMPLES", "1,2,3,4,5").split(",")]
+
+  correct = None
+  # First define a Tensor and realize it. We will focus on a 1GB sum kernel on RDNA3
+  a = (Tensor.randn(SZ) if getenv("RAND") else Tensor.ones(SZ)).contiguous().realize()
+
+  if 1 in examples:
+    # *****
+    # This is the high level tinygrad way.
+    # Note that this is split into multiple kernels for speed.
+    correct = eval_harness("basic kernel", a, lambda x: x.sum())
+
+  if 2 in examples:
+    # *****
+    # You can import kernels from CUDA/HIP/Metal.
+    # ChatGPT is great at writing these Kernel
+    example_2_hip(a, correct)
+
+  if 3 in examples:
+    # *****
+    # Now we get to the lower abstraction layers of tinygrad.
+    # You can write a kernel in UOps, and it's 2.5x faster than normal.
+    example_3_custom_uop(a, correct)
+
+  if 4 in examples:
+    # *****
+    # You can also BEAM search stock tinygrad for a faster kernel.
+    # This does even better than all the kernels to date in this simple case.
+    with Context(BEAM=2):
+      eval_harness("BEAMed kernel", a, lambda x: x.sum(), check=correct)
+
+  if 5 in examples:
+    # *****
+    # If you really want to go crazy with speed, you can code in assembly.
+    # There's not too much to gain here over BEAM, but it's a few percent faster.
+    example_5_custom_assembly(a, correct)

assets/_markdown_exec_ansi.css

@@ -0,0 +1,355 @@
+/*
+  Inspired by https://spec.draculatheme.com/ specification, they should work
+  decently with both dark and light themes.
+  */
+:root {
+    --ansi-red: #ff5555;
+    --ansi-green: #50fa7b;
+    --ansi-blue: #265285;
+    --ansi-yellow: #ffb86c;
+    --ansi-magenta: #bd93f9;
+    --ansi-cyan: #8be9fd;
+    --ansi-black: #282a36;
+    --ansi-white: #f8f8f2;
+}
+
+.-Color-Green,
+.-Color-Faint-Green,
+.-Color-Bold-Green,
+.-Color-BrightGreen {
+    color: var(--ansi-green);
+}
+
+.-Color-Red,
+.-Color-Faint-Red,
+.-Color-Bold-Red,
+.-Color-BrightRed {
+    color: var(--ansi-red);
+}
+
+.-Color-Yellow,
+.-Color-Faint-Yellow,
+.-Color-Bold-Yellow,
+.-Color-BrightYellow {
+    color: var(--ansi-yellow);
+}
+
+.-Color-Blue,
+.-Color-Faint-Blue,
+.-Color-Bold-Blue,
+.-Color-BrightBlue {
+    color: var(--ansi-blue);
+}
+
+.-Color-Magenta,
+.-Color-Faint-Magenta,
+.-Color-Bold-Magenta,
+.-Color-BrightMagenta {
+    color: var(--ansi-magenta);
+}
+
+.-Color-Cyan,
+.-Color-Faint-Cyan,
+.-Color-Bold-Cyan,
+.-Color-BrightCyan {
+    color: var(--ansi-cyan);
+}
+
+.-Color-White,
+.-Color-Faint-White,
+.-Color-Bold-White,
+.-Color-BrightWhite {
+    color: var(--ansi-white);
+}
+
+.-Color-Black,
+.-Color-Faint-Black,
+.-Color-Bold-Black,
+.-Color-BrightBlack {
+    color: var(--ansi-black);
+}
+
+.-Color-Faint {
+    opacity: 0.5;
+}
+
+.-Color-Bold {
+    font-weight: bold;
+}
+
+.-Color-BGBlack,
+.-Color-Black-BGBlack,
+.-Color-Blue-BGBlack,
+.-Color-Bold-BGBlack,
+.-Color-BrightBGBlack,
+.-Color-Bold-Black-BGBlack,
+.-Color-BrightBlack-BGBlack,
+.-Color-Bold-Green-BGBlack,
+.-Color-BrightGreen-BGBlack,
+.-Color-Bold-Cyan-BGBlack,
+.-Color-BrightCyan-BGBlack,
+.-Color-Bold-Blue-BGBlack,
+.-Color-BrightBlue-BGBlack,
+.-Color-Bold-Magenta-BGBlack,
+.-Color-BrightMagenta-BGBlack,
+.-Color-Bold-Red-BGBlack,
+.-Color-BrightRed-BGBlack,
+.-Color-Bold-White-BGBlack,
+.-Color-BrightWhite-BGBlack,
+.-Color-Bold-Yellow-BGBlack,
+.-Color-BrightYellow-BGBlack,
+.-Color-Cyan-BGBlack,
+.-Color-Green-BGBlack,
+.-Color-Magenta-BGBlack,
+.-Color-Red-BGBlack,
+.-Color-White-BGBlack,
+.-Color-Yellow-BGBlack {
+    background-color: var(--ansi-black);
+}
+
+.-Color-BGRed,
+.-Color-Black-BGRed,
+.-Color-Blue-BGRed,
+.-Color-Bold-BGRed,
+.-Color-BrightBGRed,
+.-Color-Bold-Black-BGRed,
+.-Color-BrightBlack-BGRed,
+.-Color-Bold-Green-BGRed,
+.-Color-BrightGreen-BGRed,
+.-Color-Bold-Cyan-BGRed,
+.-Color-BrightCyan-BGRed,
+.-Color-Bold-Blue-BGRed,
+.-Color-BrightBlue-BGRed,
+.-Color-Bold-Magenta-BGRed,
+.-Color-BrightMagenta-BGRed,
+.-Color-Bold-Red-BGRed,
+.-Color-BrightRed-BGRed,
+.-Color-Bold-White-BGRed,
+.-Color-BrightWhite-BGRed,
+.-Color-Bold-Yellow-BGRed,
+.-Color-BrightYellow-BGRed,
+.-Color-Cyan-BGRed,
+.-Color-Green-BGRed,
+.-Color-Magenta-BGRed,
+.-Color-Red-BGRed,
+.-Color-White-BGRed,
+.-Color-Yellow-BGRed {
+    background-color: var(--ansi-red);
+}
+
+.-Color-BGGreen,
+.-Color-Black-BGGreen,
+.-Color-Blue-BGGreen,
+.-Color-Bold-BGGreen,
+.-Color-BrightBGGreen,
+.-Color-Bold-Black-BGGreen,
+.-Color-BrightBlack-BGGreen,
+.-Color-Bold-Green-BGGreen,
+.-Color-BrightGreen-BGGreen,
+.-Color-Bold-Cyan-BGGreen,
+.-Color-BrightCyan-BGGreen,
+.-Color-Bold-Blue-BGGreen,
+.-Color-BrightBlue-BGGreen,
+.-Color-Bold-Magenta-BGGreen,
+.-Color-BrightMagenta-BGGreen,
+.-Color-Bold-Red-BGGreen,
+.-Color-BrightRed-BGGreen,
+.-Color-Bold-White-BGGreen,
+.-Color-BrightWhite-BGGreen,
+.-Color-Bold-Yellow-BGGreen,
+.-Color-BrightYellow-BGGreen,
+.-Color-Cyan-BGGreen,
+.-Color-Green-BGGreen,
+.-Color-Magenta-BGGreen,
+.-Color-Red-BGGreen,
+.-Color-White-BGGreen,
+.-Color-Yellow-BGGreen {
+    background-color: var(--ansi-green);
+}
+
+.-Color-BGYellow,
+.-Color-Black-BGYellow,
+.-Color-Blue-BGYellow,
+.-Color-Bold-BGYellow,
+.-Color-BrightBGYellow,
+.-Color-Bold-Black-BGYellow,
+.-Color-BrightBlack-BGYellow,
+.-Color-Bold-Green-BGYellow,
+.-Color-BrightGreen-BGYellow,
+.-Color-Bold-Cyan-BGYellow,
+.-Color-BrightCyan-BGYellow,
+.-Color-Bold-Blue-BGYellow,
+.-Color-BrightBlue-BGYellow,
+.-Color-Bold-Magenta-BGYellow,
+.-Color-BrightMagenta-BGYellow,
+.-Color-Bold-Red-BGYellow,
+.-Color-BrightRed-BGYellow,
+.-Color-Bold-White-BGYellow,
+.-Color-BrightWhite-BGYellow,
+.-Color-Bold-Yellow-BGYellow,
+.-Color-BrightYellow-BGYellow,
+.-Color-Cyan-BGYellow,
+.-Color-Green-BGYellow,
+.-Color-Magenta-BGYellow,
+.-Color-Red-BGYellow,
+.-Color-White-BGYellow,
+.-Color-Yellow-BGYellow {
+    background-color: var(--ansi-yellow);
+}
+
+.-Color-BGBlue,
+.-Color-Black-BGBlue,
+.-Color-Blue-BGBlue,
+.-Color-Bold-BGBlue,
+.-Color-BrightBGBlue,
+.-Color-Bold-Black-BGBlue,
+.-Color-BrightBlack-BGBlue,
+.-Color-Bold-Green-BGBlue,
+.-Color-BrightGreen-BGBlue,
+.-Color-Bold-Cyan-BGBlue,
+.-Color-BrightCyan-BGBlue,
+.-Color-Bold-Blue-BGBlue,
+.-Color-BrightBlue-BGBlue,
+.-Color-Bold-Magenta-BGBlue,
+.-Color-BrightMagenta-BGBlue,
+.-Color-Bold-Red-BGBlue,
+.-Color-BrightRed-BGBlue,
+.-Color-Bold-White-BGBlue,
+.-Color-BrightWhite-BGBlue,
+.-Color-Bold-Yellow-BGBlue,
+.-Color-BrightYellow-BGBlue,
+.-Color-Cyan-BGBlue,
+.-Color-Green-BGBlue,
+.-Color-Magenta-BGBlue,
+.-Color-Red-BGBlue,
+.-Color-White-BGBlue,
+.-Color-Yellow-BGBlue {
+    background-color: var(--ansi-blue);
+}
+
+.-Color-BGMagenta,
+.-Color-Black-BGMagenta,
+.-Color-Blue-BGMagenta,
+.-Color-Bold-BGMagenta,
+.-Color-BrightBGMagenta,
+.-Color-Bold-Black-BGMagenta,
+.-Color-BrightBlack-BGMagenta,
+.-Color-Bold-Green-BGMagenta,
+.-Color-BrightGreen-BGMagenta,
+.-Color-Bold-Cyan-BGMagenta,
+.-Color-BrightCyan-BGMagenta,
+.-Color-Bold-Blue-BGMagenta,
+.-Color-BrightBlue-BGMagenta,
+.-Color-Bold-Magenta-BGMagenta,
+.-Color-BrightMagenta-BGMagenta,
+.-Color-Bold-Red-BGMagenta,
+.-Color-BrightRed-BGMagenta,
+.-Color-Bold-White-BGMagenta,
+.-Color-BrightWhite-BGMagenta,
+.-Color-Bold-Yellow-BGMagenta,
+.-Color-BrightYellow-BGMagenta,
+.-Color-Cyan-BGMagenta,
+.-Color-Green-BGMagenta,
+.-Color-Magenta-BGMagenta,
+.-Color-Red-BGMagenta,
+.-Color-White-BGMagenta,
+.-Color-Yellow-BGMagenta {
+    background-color: var(--ansi-magenta);
+}
+
+.-Color-BGCyan,
+.-Color-Black-BGCyan,
+.-Color-Blue-BGCyan,
+.-Color-Bold-BGCyan,
+.-Color-BrightBGCyan,
+.-Color-Bold-Black-BGCyan,
+.-Color-BrightBlack-BGCyan,
+.-Color-Bold-Green-BGCyan,
+.-Color-BrightGreen-BGCyan,
+.-Color-Bold-Cyan-BGCyan,
+.-Color-BrightCyan-BGCyan,
+.-Color-Bold-Blue-BGCyan,
+.-Color-BrightBlue-BGCyan,
+.-Color-Bold-Magenta-BGCyan,
+.-Color-BrightMagenta-BGCyan,
+.-Color-Bold-Red-BGCyan,
+.-Color-BrightRed-BGCyan,
+.-Color-Bold-White-BGCyan,
+.-Color-BrightWhite-BGCyan,
+.-Color-Bold-Yellow-BGCyan,
+.-Color-BrightYellow-BGCyan,
+.-Color-Cyan-BGCyan,
+.-Color-Green-BGCyan,
+.-Color-Magenta-BGCyan,
+.-Color-Red-BGCyan,
+.-Color-White-BGCyan,
+.-Color-Yellow-BGCyan {
+    background-color: var(--ansi-cyan);
+}
+
+.-Color-BGWhite,
+.-Color-Black-BGWhite,
+.-Color-Blue-BGWhite,
+.-Color-Bold-BGWhite,
+.-Color-BrightBGWhite,
+.-Color-Bold-Black-BGWhite,
+.-Color-BrightBlack-BGWhite,
+.-Color-Bold-Green-BGWhite,
+.-Color-BrightGreen-BGWhite,
+.-Color-Bold-Cyan-BGWhite,
+.-Color-BrightCyan-BGWhite,
+.-Color-Bold-Blue-BGWhite,
+.-Color-BrightBlue-BGWhite,
+.-Color-Bold-Magenta-BGWhite,
+.-Color-BrightMagenta-BGWhite,
+.-Color-Bold-Red-BGWhite,
+.-Color-BrightRed-BGWhite,
+.-Color-Bold-White-BGWhite,
+.-Color-BrightWhite-BGWhite,
+.-Color-Bold-Yellow-BGWhite,
+.-Color-BrightYellow-BGWhite,
+.-Color-Cyan-BGWhite,
+.-Color-Green-BGWhite,
+.-Color-Magenta-BGWhite,
+.-Color-Red-BGWhite,
+.-Color-White-BGWhite,
+.-Color-Yellow-BGWhite {
+    background-color: var(--ansi-white);
+}
+
+.-Color-Black,
+.-Color-Bold-Black,
+.-Color-BrightBlack,
+.-Color-Black-BGBlack,
+.-Color-Bold-Black-BGBlack,
+.-Color-BrightBlack-BGBlack,
+.-Color-Black-BGGreen,
+.-Color-Red-BGRed,
+.-Color-Bold-Red-BGRed,
+.-Color-BrightRed-BGRed,
+.-Color-Bold-Blue-BGBlue,
+.-Color-BrightBlue-BGBlue,
+.-Color-Blue-BGBlue {
+    text-shadow: 0 0 1px var(--ansi-white);
+}
+
+.-Color-Bold-Cyan-BGCyan,
+.-Color-BrightCyan-BGCyan,
+.-Color-Bold-Magenta-BGMagenta,
+.-Color-BrightMagenta-BGMagenta,
+.-Color-Bold-White,
+.-Color-BrightWhite,
+.-Color-Bold-Yellow-BGYellow,
+.-Color-BrightYellow-BGYellow,
+.-Color-Bold-Green-BGGreen,
+.-Color-BrightGreen-BGGreen,
+.-Color-Cyan-BGCyan,
+.-Color-Cyan-BGGreen,
+.-Color-Green-BGCyan,
+.-Color-Green-BGGreen,
+.-Color-Magenta-BGMagenta,
+.-Color-White,
+.-Color-White-BGWhite,
+.-Color-Yellow-BGYellow {
+    text-shadow: 0 0 1px var(--ansi-black);
+}

assets/_markdown_exec_pyodide.css

@@ -0,0 +1,53 @@
+html[data-theme="light"] {
+    @import "https://cdn.jsdelivr.net/npm/highlightjs-themes@1.0.0/tomorrow.css"
+}
+
+html[data-theme="dark"] {
+    @import "https://cdn.jsdelivr.net/npm/highlightjs-themes@1.0.0/tomorrow-night-blue.min.css"
+}
+
+
+.ace_gutter {
+    z-index: 1;
+}
+
+.pyodide-editor {
+    width: 100%;
+    font-size: .85em;
+}
+
+.pyodide-editor-bar {
+    color: var(--md-primary-bg-color);
+    background-color: var(--md-primary-fg-color);
+    width: 100%;
+    font: monospace;
+    font-size: 0.75em;
+    padding: 2px 0 2px;
+}
+
+.pyodide-bar-item {
+    padding: 0 18px 0;
+    display: inline-block;
+    width: 50%;
+}
+
+.pyodide pre {
+    margin: 0;
+}
+
+.pyodide-output {
+    width: 100%;
+    margin-bottom: -15px;
+    min-height: 46px;
+    max-height: 400px
+}
+
+.pyodide-clickable {
+    cursor: pointer;
+    text-align: right;
+}
+
+/* For themes other than Material. */
+.pyodide .twemoji svg {
+    width: 1rem;
+}

assets/_markdown_exec_pyodide.js

@@ -0,0 +1,131 @@
+var _sessions = {};
+
+function getSession(name, pyodide) {
+    if (!(name in _sessions)) {
+        _sessions[name] = pyodide.globals.get("dict")();
+    }
+    return _sessions[name];
+}
+
+function writeOutput(element, string) {
+    element.innerHTML += string + '\n';
+}
+
+function clearOutput(element) {
+    element.innerHTML = '';
+}
+
+async function evaluatePython(pyodide, editor, output, session) {
+    pyodide.setStdout({ batched: (string) => { writeOutput(output, new Option(string).innerHTML); } });
+    let result, code = editor.getValue();
+    clearOutput(output);
+    try {
+        result = await pyodide.runPythonAsync(code, { globals: getSession(session, pyodide) });
+    } catch (error) {
+        writeOutput(output, new Option(error.toString()).innerHTML);
+    }
+    if (result) writeOutput(output, new Option(result).innerHTML);
+    hljs.highlightElement(output);
+}
+
+async function initPyodide() {
+    try {
+        let pyodide = await loadPyodide();
+        await pyodide.loadPackage("micropip");
+        return pyodide;
+    } catch(error) {
+        return null;
+    }
+}
+
+function getTheme() {
+    return document.body.getAttribute('data-md-color-scheme');
+}
+
+function setTheme(editor, currentTheme, light, dark) {
+    // https://gist.github.com/RyanNutt/cb8d60997d97905f0b2aea6c3b5c8ee0
+    if (currentTheme === "default") {
+        editor.setTheme("ace/theme/" + light);
+        document.querySelector(`link[title="light"]`).removeAttribute("disabled");
+        document.querySelector(`link[title="dark"]`).setAttribute("disabled", "disabled");
+    } else if (currentTheme === "slate") {
+        editor.setTheme("ace/theme/" + dark);
+        document.querySelector(`link[title="dark"]`).removeAttribute("disabled");
+        document.querySelector(`link[title="light"]`).setAttribute("disabled", "disabled");
+    }
+}
+
+function updateTheme(editor, light, dark) {
+    // Create a new MutationObserver instance
+    const observer = new MutationObserver((mutations) => {
+        // Loop through the mutations that occurred
+        mutations.forEach((mutation) => {
+            // Check if the mutation was a change to the data-md-color-scheme attribute
+            if (mutation.attributeName === 'data-md-color-scheme') {
+                // Get the new value of the attribute
+                const newColorScheme = mutation.target.getAttribute('data-md-color-scheme');
+                // Update the editor theme
+                setTheme(editor, newColorScheme, light, dark);
+            }
+        });
+    });
+
+    // Configure the observer to watch for changes to the data-md-color-scheme attribute
+    observer.observe(document.body, {
+        attributes: true,
+        attributeFilter: ['data-md-color-scheme'],
+    });
+}
+
+async function setupPyodide(
+    idPrefix,
+    install = null,
+    themeLight = 'tomorrow',
+    themeDark = 'tomorrow_night',
+    session = null,
+    minLines = 5,
+    maxLines = 30,
+) {
+    const editor = ace.edit(idPrefix + "editor");
+    const run = document.getElementById(idPrefix + "run");
+    const clear = document.getElementById(idPrefix + "clear");
+    const output = document.getElementById(idPrefix + "output");
+
+    updateTheme(editor, themeLight, themeDark);
+
+    editor.session.setMode("ace/mode/python");
+    setTheme(editor, getTheme(), themeLight, themeDark);
+
+    editor.setOption("minLines", minLines);
+    editor.setOption("maxLines", maxLines);
+
+    // Force editor to resize after setting options
+    editor.resize();
+
+    writeOutput(output, "Initializing...");
+    let pyodide = await pyodidePromise;
+    if (install && install.length) {
+        try {
+            micropip = pyodide.pyimport("micropip");
+            for (const package of install)
+                await micropip.install(package);
+            clearOutput(output);
+        } catch (error) {
+            clearOutput(output);
+            writeOutput(output, `Could not install one or more packages: ${install.join(", ")}\n`);
+            writeOutput(output, new Option(error.toString()).innerHTML);
+        }
+    } else {
+        clearOutput(output);
+    }
+    run.onclick = () => evaluatePython(pyodide, editor, output, session);
+    clear.onclick = () => clearOutput(output);
+    output.parentElement.parentElement.addEventListener("keydown", (event) => {
+        if (event.ctrlKey && event.key.toLowerCase() === 'enter') {
+            event.preventDefault();
+            run.click();
+        }
+    });
+}
+
+var pyodidePromise = initPyodide();

assets/_mkdocstrings.css

@@ -0,0 +1,237 @@
+
+/* Avoid breaking parameter names, etc. in table cells. */
+.doc-contents td code {
+  word-break: normal !important;
+}
+
+/* No line break before first paragraph of descriptions. */
+.doc-md-description,
+.doc-md-description>p:first-child {
+  display: inline;
+}
+
+/* No text transformation from Material for MkDocs for H5 headings. */
+.md-typeset h5 .doc-object-name {
+  text-transform: none;
+}
+
+/* Max width for docstring sections tables. */
+.doc .md-typeset__table,
+.doc .md-typeset__table table {
+  display: table !important;
+  width: 100%;
+}
+
+.doc .md-typeset__table tr {
+  display: table-row;
+}
+
+/* Defaults in Spacy table style. */
+.doc-param-default,
+.doc-type_param-default {
+  float: right;
+}
+
+/* Parameter headings must be inline, not blocks. */
+.doc-heading-parameter,
+.doc-heading-type_parameter {
+  display: inline;
+}
+
+/* Default font size for parameter headings. */
+.md-typeset .doc-heading-parameter {
+  font-size: inherit;
+}
+
+/* Prefer space on the right, not the left of parameter permalinks. */
+.doc-heading-parameter .headerlink,
+.doc-heading-type_parameter .headerlink {
+  margin-left: 0 !important;
+  margin-right: 0.2rem;
+}
+
+/* Backward-compatibility: docstring section titles in bold. */
+.doc-section-title {
+  font-weight: bold;
+}
+
+/* Backlinks crumb separator. */
+.doc-backlink-crumb {
+  display: inline-flex;
+  gap: .2rem;
+  white-space: nowrap;
+  align-items: center;
+  vertical-align: middle;
+}
+.doc-backlink-crumb:not(:first-child)::before {
+  background-color: var(--md-default-fg-color--lighter);
+  content: "";
+  display: inline;
+  height: 1rem;
+  --md-path-icon: url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M8.59 16.58 13.17 12 8.59 7.41 10 6l6 6-6 6z"/></svg>');
+  -webkit-mask-image: var(--md-path-icon);
+  mask-image: var(--md-path-icon);
+  width: 1rem;
+}
+.doc-backlink-crumb.last {
+  font-weight: bold;
+}
+
+/* Symbols in Navigation and ToC. */
+:root, :host,
+[data-md-color-scheme="default"] {
+  --doc-symbol-parameter-fg-color: #df50af;
+  --doc-symbol-type_parameter-fg-color: #df50af;
+  --doc-symbol-attribute-fg-color: #953800;
+  --doc-symbol-function-fg-color: #8250df;
+  --doc-symbol-method-fg-color: #8250df;
+  --doc-symbol-class-fg-color: #0550ae;
+  --doc-symbol-type_alias-fg-color: #0550ae;
+  --doc-symbol-module-fg-color: #5cad0f;
+
+  --doc-symbol-parameter-bg-color: #df50af1a;
+  --doc-symbol-type_parameter-bg-color: #df50af1a;
+  --doc-symbol-attribute-bg-color: #9538001a;
+  --doc-symbol-function-bg-color: #8250df1a;
+  --doc-symbol-method-bg-color: #8250df1a;
+  --doc-symbol-class-bg-color: #0550ae1a;
+  --doc-symbol-type_alias-bg-color: #0550ae1a;
+  --doc-symbol-module-bg-color: #5cad0f1a;
+}
+
+[data-md-color-scheme="slate"] {
+  --doc-symbol-parameter-fg-color: #ffa8cc;
+  --doc-symbol-type_parameter-fg-color: #ffa8cc;
+  --doc-symbol-attribute-fg-color: #ffa657;
+  --doc-symbol-function-fg-color: #d2a8ff;
+  --doc-symbol-method-fg-color: #d2a8ff;
+  --doc-symbol-class-fg-color: #79c0ff;
+  --doc-symbol-type_alias-fg-color: #79c0ff;
+  --doc-symbol-module-fg-color: #baff79;
+
+  --doc-symbol-parameter-bg-color: #ffa8cc1a;
+  --doc-symbol-type_parameter-bg-color: #ffa8cc1a;
+  --doc-symbol-attribute-bg-color: #ffa6571a;
+  --doc-symbol-function-bg-color: #d2a8ff1a;
+  --doc-symbol-method-bg-color: #d2a8ff1a;
+  --doc-symbol-class-bg-color: #79c0ff1a;
+  --doc-symbol-type_alias-bg-color: #79c0ff1a;
+  --doc-symbol-module-bg-color: #baff791a;
+}
+
+code.doc-symbol {
+  border-radius: .1rem;
+  font-size: .85em;
+  padding: 0 .3em;
+  font-weight: bold;
+}
+
+code.doc-symbol-parameter,
+a code.doc-symbol-parameter {
+  color: var(--doc-symbol-parameter-fg-color);
+  background-color: var(--doc-symbol-parameter-bg-color);
+}
+
+code.doc-symbol-parameter::after {
+  content: "param";
+}
+
+code.doc-symbol-type_parameter,
+a code.doc-symbol-type_parameter {
+  color: var(--doc-symbol-type_parameter-fg-color);
+  background-color: var(--doc-symbol-type_parameter-bg-color);
+}
+
+code.doc-symbol-type_parameter::after {
+  content: "type-param";
+}
+
+code.doc-symbol-attribute,
+a code.doc-symbol-attribute {
+  color: var(--doc-symbol-attribute-fg-color);
+  background-color: var(--doc-symbol-attribute-bg-color);
+}
+
+code.doc-symbol-attribute::after {
+  content: "attr";
+}
+
+code.doc-symbol-function,
+a code.doc-symbol-function {
+  color: var(--doc-symbol-function-fg-color);
+  background-color: var(--doc-symbol-function-bg-color);
+}
+
+code.doc-symbol-function::after {
+  content: "func";
+}
+
+code.doc-symbol-method,
+a code.doc-symbol-method {
+  color: var(--doc-symbol-method-fg-color);
+  background-color: var(--doc-symbol-method-bg-color);
+}
+
+code.doc-symbol-method::after {
+  content: "meth";
+}
+
+code.doc-symbol-class,
+a code.doc-symbol-class {
+  color: var(--doc-symbol-class-fg-color);
+  background-color: var(--doc-symbol-class-bg-color);
+}
+
+code.doc-symbol-class::after {
+  content: "class";
+}
+
+
+code.doc-symbol-type_alias,
+a code.doc-symbol-type_alias {
+  color: var(--doc-symbol-type_alias-fg-color);
+  background-color: var(--doc-symbol-type_alias-bg-color);
+}
+
+code.doc-symbol-type_alias::after {
+  content: "type";
+}
+
+code.doc-symbol-module,
+a code.doc-symbol-module {
+  color: var(--doc-symbol-module-fg-color);
+  background-color: var(--doc-symbol-module-bg-color);
+}
+
+code.doc-symbol-module::after {
+  content: "mod";
+}
+
+.doc-signature .autorefs {
+  color: inherit;
+  border-bottom: 1px dotted currentcolor;
+}
+
+/* Source code blocks (admonitions). */
+:root {
+  --md-admonition-icon--mkdocstrings-source: url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M15.22 4.97a.75.75 0 0 1 1.06 0l6.5 6.5a.75.75 0 0 1 0 1.06l-6.5 6.5a.749.749 0 0 1-1.275-.326.75.75 0 0 1 .215-.734L21.19 12l-5.97-5.97a.75.75 0 0 1 0-1.06m-6.44 0a.75.75 0 0 1 0 1.06L2.81 12l5.97 5.97a.749.749 0 0 1-.326 1.275.75.75 0 0 1-.734-.215l-6.5-6.5a.75.75 0 0 1 0-1.06l6.5-6.5a.75.75 0 0 1 1.06 0"/></svg>')
+}
+.md-typeset .admonition.mkdocstrings-source,
+.md-typeset details.mkdocstrings-source {
+  border: none;
+  padding: 0;
+}
+.md-typeset .admonition.mkdocstrings-source:focus-within,
+.md-typeset details.mkdocstrings-source:focus-within {
+  box-shadow: none;
+}
+.md-typeset .mkdocstrings-source > .admonition-title,
+.md-typeset .mkdocstrings-source > summary {
+  background-color: inherit;
+}
+.md-typeset .mkdocstrings-source > .admonition-title::before,
+.md-typeset .mkdocstrings-source > summary::before {
+  background-color: var(--md-default-fg-color);
+  -webkit-mask-image: var(--md-admonition-icon--mkdocstrings-source);
+          mask-image: var(--md-admonition-icon--mkdocstrings-source);
+}

assets/javascripts/lunr/min/lunr.da.min.js

@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Danish` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.da=function(){this.pipeline.reset(),this.pipeline.add(e.da.trimmer,e.da.stopWordFilter,e.da.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.da.stemmer))},e.da.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.da.trimmer=e.trimmerSupport.generateTrimmer(e.da.wordCharacters),e.Pipeline.registerFunction(e.da.trimmer,"trimmer-da"),e.da.stemmer=function(){var r=e.stemmerSupport.Among,i=e.stemmerSupport.SnowballProgram,n=new function(){function e(){var e,r=f.cursor+3;if(d=f.limit,0<=r&&r<=f.limit){for(a=r;;){if(e=f.cursor,f.in_grouping(w,97,248)){f.cursor=e;break}if(f.cursor=e,e>=f.limit)return;f.cursor++}for(;!f.out_grouping(w,97,248);){if(f.cursor>=f.limit)return;f.cursor++}d=f.cursor,d<a&&(d=a)}}function n(){var e,r;if(f.cursor>=d&&(r=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,e=f.find_among_b(c,32),f.limit_backward=r,e))switch(f.bra=f.cursor,e){case 1:f.slice_del();break;case 2:f.in_grouping_b(p,97,229)&&f.slice_del()}}function t(){var e,r=f.limit-f.cursor;f.cursor>=d&&(e=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,f.find_among_b(l,4)?(f.bra=f.cursor,f.limit_backward=e,f.cursor=f.limit-r,f.cursor>f.limit_backward&&(f.cursor--,f.bra=f.cursor,f.slice_del())):f.limit_backward=e)}function s(){var e,r,i,n=f.limit-f.cursor;if(f.ket=f.cursor,f.eq_s_b(2,"st")&&(f.bra=f.cursor,f.eq_s_b(2,"ig")&&f.slice_del()),f.cursor=f.limit-n,f.cursor>=d&&(r=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,e=f.find_among_b(m,5),f.limit_backward=r,e))switch(f.bra=f.cursor,e){case 1:f.slice_del(),i=f.limit-f.cursor,t(),f.cursor=f.limit-i;break;case 2:f.slice_from("løs")}}function o(){var e;f.cursor>=d&&(e=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,f.out_grouping_b(w,97,248)?(f.bra=f.cursor,u=f.slice_to(u),f.limit_backward=e,f.eq_v_b(u)&&f.slice_del()):f.limit_backward=e)}var a,d,u,c=[new r("hed",-1,1),new r("ethed",0,1),new r("ered",-1,1),new r("e",-1,1),new r("erede",3,1),new r("ende",3,1),new r("erende",5,1),new r("ene",3,1),new r("erne",3,1),new r("ere",3,1),new r("en",-1,1),new r("heden",10,1),new r("eren",10,1),new r("er",-1,1),new r("heder",13,1),new r("erer",13,1),new r("s",-1,2),new r("heds",16,1),new r("es",16,1),new r("endes",18,1),new r("erendes",19,1),new r("enes",18,1),new r("ernes",18,1),new r("eres",18,1),new r("ens",16,1),new r("hedens",24,1),new r("erens",24,1),new r("ers",16,1),new r("ets",16,1),new r("erets",28,1),new r("et",-1,1),new r("eret",30,1)],l=[new r("gd",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1)],m=[new r("ig",-1,1),new r("lig",0,1),new r("elig",1,1),new r("els",-1,1),new r("løst",-1,2)],w=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],p=[239,254,42,3,0,0,0,0,0,0,0,0,0,0,0,0,16],f=new i;this.setCurrent=function(e){f.setCurrent(e)},this.getCurrent=function(){return f.getCurrent()},this.stem=function(){var r=f.cursor;return e(),f.limit_backward=r,f.cursor=f.limit,n(),f.cursor=f.limit,t(),f.cursor=f.limit,s(),f.cursor=f.limit,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}}(),e.Pipeline.registerFunction(e.da.stemmer,"stemmer-da"),e.da.stopWordFilter=e.generateStopWordFilter("ad af alle alt anden at blev blive bliver da de dem den denne der deres det dette dig din disse dog du efter eller en end er et for fra ham han hans har havde have hende hendes her hos hun hvad hvis hvor i ikke ind jeg jer jo kunne man mange med meget men mig min mine mit mod ned noget nogle nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}});

assets/javascripts/lunr/min/lunr.hi.min.js

@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hi=function(){this.pipeline.reset(),this.pipeline.add(e.hi.trimmer,e.hi.stopWordFilter,e.hi.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hi.stemmer))},e.hi.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.hi.trimmer=e.trimmerSupport.generateTrimmer(e.hi.wordCharacters),e.Pipeline.registerFunction(e.hi.trimmer,"trimmer-hi"),e.hi.stopWordFilter=e.generateStopWordFilter("अत अपना अपनी अपने अभी अंदर आदि आप इत्यादि इन इनका इन्हीं इन्हें इन्हों इस इसका इसकी इसके इसमें इसी इसे उन उनका उनकी उनके उनको उन्हीं उन्हें उन्हों उस उसके उसी उसे एक एवं एस ऐसे और कई कर करता करते करना करने करें कहते कहा का काफ़ी कि कितना किन्हें किन्हों किया किर किस किसी किसे की कुछ कुल के को कोई कौन कौनसा गया घर जब जहाँ जा जितना जिन जिन्हें जिन्हों जिस जिसे जीधर जैसा जैसे जो तक तब तरह तिन तिन्हें तिन्हों तिस तिसे तो था थी थे दबारा दिया दुसरा दूसरे दो द्वारा न नके नहीं ना निहायत नीचे ने पर पहले पूरा पे फिर बनी बही बहुत बाद बाला बिलकुल भी भीतर मगर मानो मे में यदि यह यहाँ यही या यिह ये रखें रहा रहे ऱ्वासा लिए लिये लेकिन व वग़ैरह वर्ग वह वहाँ वहीं वाले वुह वे वो सकता सकते सबसे सभी साथ साबुत साभ सारा से सो संग ही हुआ हुई हुए है हैं हो होता होती होते होना होने".split(" ")),e.hi.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.hi.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var t=i.toString().toLowerCase().replace(/^\s+/,"");return r.cut(t).split("|")},e.Pipeline.registerFunction(e.hi.stemmer,"stemmer-hi"),e.Pipeline.registerFunction(e.hi.stopWordFilter,"stopWordFilter-hi")}});

assets/javascripts/lunr/min/lunr.hy.min.js

@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hy=function(){this.pipeline.reset(),this.pipeline.add(e.hy.trimmer,e.hy.stopWordFilter)},e.hy.wordCharacters="[A-Za-z԰-֏ff-ﭏ]",e.hy.trimmer=e.trimmerSupport.generateTrimmer(e.hy.wordCharacters),e.Pipeline.registerFunction(e.hy.trimmer,"trimmer-hy"),e.hy.stopWordFilter=e.generateStopWordFilter("դու և եք էիր էիք հետո նաև նրանք որը վրա է որ պիտի են այս մեջ ն իր ու ի այդ որոնք այն կամ էր մի ես համար այլ իսկ էին ենք հետ ին թ էինք մենք նրա նա դուք եմ էի ըստ որպես ում".split(" ")),e.Pipeline.registerFunction(e.hy.stopWordFilter,"stopWordFilter-hy"),e.hy.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}(),e.Pipeline.registerFunction(e.hy.stemmer,"stemmer-hy")}});

assets/javascripts/lunr/min/lunr.ja.min.js

@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.ja=function(){this.pipeline.reset(),this.pipeline.add(e.ja.trimmer,e.ja.stopWordFilter,e.ja.stemmer),r?this.tokenizer=e.ja.tokenizer:(e.tokenizer&&(e.tokenizer=e.ja.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.ja.tokenizer))};var t=new e.TinySegmenter;e.ja.tokenizer=function(i){var n,o,s,p,a,u,m,l,c,f;if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new e.Token(t.toLowerCase()):t.toLowerCase()});for(o=i.toString().toLowerCase().replace(/^\s+/,""),n=o.length-1;n>=0;n--)if(/\S/.test(o.charAt(n))){o=o.substring(0,n+1);break}for(a=[],s=o.length,c=0,l=0;c<=s;c++)if(u=o.charAt(c),m=c-l,u.match(/\s/)||c==s){if(m>0)for(p=t.segment(o.slice(l,c)).filter(function(e){return!!e}),f=l,n=0;n<p.length;n++)r?a.push(new e.Token(p[n],{position:[f,p[n].length],index:a.length})):a.push(p[n]),f+=p[n].length;l=c+1}return a},e.ja.stemmer=function(){return function(e){return e}}(),e.Pipeline.registerFunction(e.ja.stemmer,"stemmer-ja"),e.ja.wordCharacters="一二三四五六七八九十百千万億兆一-龠々〆ヵヶぁ-んァ-ヴーｱ-ﾝﾞa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.ja.trimmer=e.trimmerSupport.generateTrimmer(e.ja.wordCharacters),e.Pipeline.registerFunction(e.ja.trimmer,"trimmer-ja"),e.ja.stopWordFilter=e.generateStopWordFilter("これ それ あれ この その あの ここ そこ あそこ こちら どこ だれ なに なん 何 私 貴方 貴方方 我々 私達 あの人 あのかた 彼女 彼 です あります おります います は が の に を で え から まで より も どの と し それで しかし".split(" ")),e.Pipeline.registerFunction(e.ja.stopWordFilter,"stopWordFilter-ja"),e.jp=e.ja,e.Pipeline.registerFunction(e.jp.stemmer,"stemmer-jp"),e.Pipeline.registerFunction(e.jp.trimmer,"trimmer-jp"),e.Pipeline.registerFunction(e.jp.stopWordFilter,"stopWordFilter-jp")}});

assets/javascripts/lunr/min/lunr.jp.min.js

@@ -0,0 +1 @@
+module.exports=require("./lunr.ja");

assets/javascripts/lunr/min/lunr.kn.min.js

@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.kn=function(){this.pipeline.reset(),this.pipeline.add(e.kn.trimmer,e.kn.stopWordFilter,e.kn.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.kn.stemmer))},e.kn.wordCharacters="ಀ-಄ಅ-ಔಕ-ಹಾ-ೌ಼-ಽೕ-ೖೝ-ೞೠ-ೡೢ-ೣ೤೥೦-೯ೱ-ೳ",e.kn.trimmer=e.trimmerSupport.generateTrimmer(e.kn.wordCharacters),e.Pipeline.registerFunction(e.kn.trimmer,"trimmer-kn"),e.kn.stopWordFilter=e.generateStopWordFilter("ಮತ್ತು ಈ ಒಂದು ರಲ್ಲಿ ಹಾಗೂ ಎಂದು ಅಥವಾ ಇದು ರ ಅವರು ಎಂಬ ಮೇಲೆ ಅವರ ತನ್ನ ಆದರೆ ತಮ್ಮ ನಂತರ ಮೂಲಕ ಹೆಚ್ಚು ನ ಆ ಕೆಲವು ಅನೇಕ ಎರಡು ಹಾಗು ಪ್ರಮುಖ ಇದನ್ನು ಇದರ ಸುಮಾರು ಅದರ ಅದು ಮೊದಲ ಬಗ್ಗೆ ನಲ್ಲಿ ರಂದು ಇತರ ಅತ್ಯಂತ ಹೆಚ್ಚಿನ ಸಹ ಸಾಮಾನ್ಯವಾಗಿ ನೇ ಹಲವಾರು ಹೊಸ ದಿ ಕಡಿಮೆ ಯಾವುದೇ ಹೊಂದಿದೆ ದೊಡ್ಡ ಅನ್ನು ಇವರು ಪ್ರಕಾರ ಇದೆ ಮಾತ್ರ ಕೂಡ ಇಲ್ಲಿ ಎಲ್ಲಾ ವಿವಿಧ ಅದನ್ನು ಹಲವು ರಿಂದ ಕೇವಲ ದ ದಕ್ಷಿಣ ಗೆ ಅವನ ಅತಿ ನೆಯ ಬಹಳ ಕೆಲಸ ಎಲ್ಲ ಪ್ರತಿ ಇತ್ಯಾದಿ ಇವು ಬೇರೆ ಹೀಗೆ ನಡುವೆ ಇದಕ್ಕೆ ಎಸ್ ಇವರ ಮೊದಲು ಶ್ರೀ ಮಾಡುವ ಇದರಲ್ಲಿ ರೀತಿಯ ಮಾಡಿದ ಕಾಲ ಅಲ್ಲಿ ಮಾಡಲು ಅದೇ ಈಗ ಅವು ಗಳು ಎ ಎಂಬುದು ಅವನು ಅಂದರೆ ಅವರಿಗೆ ಇರುವ ವಿಶೇಷ ಮುಂದೆ ಅವುಗಳ ಮುಂತಾದ ಮೂಲ ಬಿ ಮೀ ಒಂದೇ ಇನ್ನೂ ಹೆಚ್ಚಾಗಿ ಮಾಡಿ ಅವರನ್ನು ಇದೇ ಯ ರೀತಿಯಲ್ಲಿ ಜೊತೆ ಅದರಲ್ಲಿ ಮಾಡಿದರು ನಡೆದ ಆಗ ಮತ್ತೆ ಪೂರ್ವ ಆತ ಬಂದ ಯಾವ ಒಟ್ಟು ಇತರೆ ಹಿಂದೆ ಪ್ರಮಾಣದ ಗಳನ್ನು ಕುರಿತು ಯು ಆದ್ದರಿಂದ ಅಲ್ಲದೆ ನಗರದ ಮೇಲಿನ ಏಕೆಂದರೆ ರಷ್ಟು ಎಂಬುದನ್ನು ಬಾರಿ ಎಂದರೆ ಹಿಂದಿನ ಆದರೂ ಆದ ಸಂಬಂಧಿಸಿದ ಮತ್ತೊಂದು ಸಿ ಆತನ ".split(" ")),e.kn.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.kn.tokenizer=function(t){if(!arguments.length||null==t||void 0==t)return[];if(Array.isArray(t))return t.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var n=t.toString().toLowerCase().replace(/^\s+/,"");return r.cut(n).split("|")},e.Pipeline.registerFunction(e.kn.stemmer,"stemmer-kn"),e.Pipeline.registerFunction(e.kn.stopWordFilter,"stopWordFilter-kn")}});

assets/javascripts/lunr/min/lunr.multi.min.js

@@ -0,0 +1 @@
+!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){e.multiLanguage=function(){for(var t=Array.prototype.slice.call(arguments),i=t.join("-"),r="",n=[],s=[],p=0;p<t.length;++p)"en"==t[p]?(r+="\\w",n.unshift(e.stopWordFilter),n.push(e.stemmer),s.push(e.stemmer)):(r+=e[t[p]].wordCharacters,e[t[p]].stopWordFilter&&n.unshift(e[t[p]].stopWordFilter),e[t[p]].stemmer&&(n.push(e[t[p]].stemmer),s.push(e[t[p]].stemmer)));var o=e.trimmerSupport.generateTrimmer(r);return e.Pipeline.registerFunction(o,"lunr-multi-trimmer-"+i),n.unshift(o),function(){this.pipeline.reset(),this.pipeline.add.apply(this.pipeline,n),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add.apply(this.searchPipeline,s))}}}});

assets/javascripts/lunr/min/lunr.no.min.js

@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Norwegian` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.no=function(){this.pipeline.reset(),this.pipeline.add(e.no.trimmer,e.no.stopWordFilter,e.no.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.no.stemmer))},e.no.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.no.trimmer=e.trimmerSupport.generateTrimmer(e.no.wordCharacters),e.Pipeline.registerFunction(e.no.trimmer,"trimmer-no"),e.no.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){function e(){var e,r=w.cursor+3;if(a=w.limit,0<=r||r<=w.limit){for(s=r;;){if(e=w.cursor,w.in_grouping(d,97,248)){w.cursor=e;break}if(e>=w.limit)return;w.cursor=e+1}for(;!w.out_grouping(d,97,248);){if(w.cursor>=w.limit)return;w.cursor++}a=w.cursor,a<s&&(a=s)}}function i(){var e,r,n;if(w.cursor>=a&&(r=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,e=w.find_among_b(m,29),w.limit_backward=r,e))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:n=w.limit-w.cursor,w.in_grouping_b(c,98,122)?w.slice_del():(w.cursor=w.limit-n,w.eq_s_b(1,"k")&&w.out_grouping_b(d,97,248)&&w.slice_del());break;case 3:w.slice_from("er")}}function t(){var e,r=w.limit-w.cursor;w.cursor>=a&&(e=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,w.find_among_b(u,2)?(w.bra=w.cursor,w.limit_backward=e,w.cursor=w.limit-r,w.cursor>w.limit_backward&&(w.cursor--,w.bra=w.cursor,w.slice_del())):w.limit_backward=e)}function o(){var e,r;w.cursor>=a&&(r=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,e=w.find_among_b(l,11),e?(w.bra=w.cursor,w.limit_backward=r,1==e&&w.slice_del()):w.limit_backward=r)}var s,a,m=[new r("a",-1,1),new r("e",-1,1),new r("ede",1,1),new r("ande",1,1),new r("ende",1,1),new r("ane",1,1),new r("ene",1,1),new r("hetene",6,1),new r("erte",1,3),new r("en",-1,1),new r("heten",9,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",12,1),new r("s",-1,2),new r("as",14,1),new r("es",14,1),new r("edes",16,1),new r("endes",16,1),new r("enes",16,1),new r("hetenes",19,1),new r("ens",14,1),new r("hetens",21,1),new r("ers",14,1),new r("ets",14,1),new r("et",-1,1),new r("het",25,1),new r("ert",-1,3),new r("ast",-1,1)],u=[new r("dt",-1,-1),new r("vt",-1,-1)],l=[new r("leg",-1,1),new r("eleg",0,1),new r("ig",-1,1),new r("eig",2,1),new r("lig",2,1),new r("elig",4,1),new r("els",-1,1),new r("lov",-1,1),new r("elov",7,1),new r("slov",7,1),new r("hetslov",9,1)],d=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],c=[119,125,149,1],w=new n;this.setCurrent=function(e){w.setCurrent(e)},this.getCurrent=function(){return w.getCurrent()},this.stem=function(){var r=w.cursor;return e(),w.limit_backward=r,w.cursor=w.limit,i(),w.cursor=w.limit,t(),w.cursor=w.limit,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.no.stemmer,"stemmer-no"),e.no.stopWordFilter=e.generateStopWordFilter("alle at av bare begge ble blei bli blir blitt både båe da de deg dei deim deira deires dem den denne der dere deres det dette di din disse ditt du dykk dykkar då eg ein eit eitt eller elles en enn er et ett etter for fordi fra før ha hadde han hans har hennar henne hennes her hjå ho hoe honom hoss hossen hun hva hvem hver hvilke hvilken hvis hvor hvordan hvorfor i ikke ikkje ikkje ingen ingi inkje inn inni ja jeg kan kom korleis korso kun kunne kva kvar kvarhelst kven kvi kvifor man mange me med medan meg meget mellom men mi min mine mitt mot mykje ned no noe noen noka noko nokon nokor nokre nå når og også om opp oss over på samme seg selv si si sia sidan siden sin sine sitt sjøl skal skulle slik so som som somme somt så sånn til um upp ut uten var vart varte ved vere verte vi vil ville vore vors vort vår være være vært å".split(" ")),e.Pipeline.registerFunction(e.no.stopWordFilter,"stopWordFilter-no")}});

assets/javascripts/lunr/min/lunr.sa.min.js

@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sa=function(){this.pipeline.reset(),this.pipeline.add(e.sa.trimmer,e.sa.stopWordFilter,e.sa.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sa.stemmer))},e.sa.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿ꣠-꣱ꣲ-ꣷ꣸-ꣻ꣼-ꣽꣾ-ꣿᆰ0-ᆰ9",e.sa.trimmer=e.trimmerSupport.generateTrimmer(e.sa.wordCharacters),e.Pipeline.registerFunction(e.sa.trimmer,"trimmer-sa"),e.sa.stopWordFilter=e.generateStopWordFilter('तथा अयम्‌ एकम्‌ इत्यस्मिन्‌ तथा तत्‌ वा अयम्‌ इत्यस्य ते आहूत उपरि तेषाम्‌  किन्तु तेषाम्‌ तदा इत्यनेन अधिकः इत्यस्य तत्‌ केचन बहवः द्वि तथा महत्वपूर्णः अयम्‌ अस्य  विषये अयं अस्ति तत्‌ प्रथमः विषये इत्युपरि इत्युपरि इतर अधिकतमः अधिकः अपि सामान्यतया ठ इतरेतर नूतनम्‌ द  न्यूनम्‌ कश्चित्‌ वा विशालः द  सः अस्ति तदनुसारम् तत्र अस्ति केवलम्‌ अपि अत्र सर्वे विविधाः तत्‌ बहवः यतः इदानीम्‌ द  दक्षिण इत्यस्मै तस्य उपरि नथ अतीव कार्यम्‌ सर्वे एकैकम्‌ इत्यादि। एते सन्ति  उत इत्थम्‌ मध्ये एतदर्थं . स कस्य प्रथमः श्री. करोति अस्मिन् प्रकारः निर्मिता कालः तत्र कर्तुं  समान अधुना ते सन्ति स एकः अस्ति सः अर्थात् तेषां कृते . स्थितम्  विशेषः अग्रिम तेषाम्‌ समान स्रोतः ख म समान इदानीमपि अधिकतया करोतु ते समान इत्यस्य वीथी सह यस्मिन्  कृतवान्‌ धृतः तदा पुनः पूर्वं सः आगतः किम्‌ कुल इतर पुरा  मात्रा स विषये उ अतएव अपि नगरस्य  उपरि यतः प्रतिशतं  कतरः कालः साधनानि भूत तथापि जात सम्बन्धि अन्यत्‌ ग अतः अस्माकं स्वकीयाः अस्माकं इदानीं अन्तः इत्यादयः भवन्तः इत्यादयः एते एताः तस्य अस्य इदम् एते तेषां तेषां तेषां तान् तेषां तेषां तेषां समानः सः एकः च तादृशाः बहवः अन्ये च वदन्ति यत् कियत् कस्मै  कस्मै  यस्मै  यस्मै  यस्मै  यस्मै न अतिनीचः किन्तु प्रथमं सम्पूर्णतया  ततः चिरकालानन्तरं पुस्तकं सम्पूर्णतया अन्तः  किन्तु अत्र वा इह इव श्रद्धाय अवशिष्यते  परन्तु अन्ये वर्गाः सन्ति ते सन्ति शक्नुवन्ति सर्वे मिलित्वा सर्वे एकत्र"'.split(" ")),e.sa.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.sa.tokenizer=function(t){if(!arguments.length||null==t||void 0==t)return[];if(Array.isArray(t))return t.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var i=t.toString().toLowerCase().replace(/^\s+/,"");return r.cut(i).split("|")},e.Pipeline.registerFunction(e.sa.stemmer,"stemmer-sa"),e.Pipeline.registerFunction(e.sa.stopWordFilter,"stopWordFilter-sa")}});

assets/javascripts/lunr/min/lunr.stemmer.support.min.js

@@ -0,0 +1 @@
+!function(r,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(r.lunr)}(this,function(){return function(r){r.stemmerSupport={Among:function(r,t,i,s){if(this.toCharArray=function(r){for(var t=r.length,i=new Array(t),s=0;s<t;s++)i[s]=r.charCodeAt(s);return i},!r&&""!=r||!t&&0!=t||!i)throw"Bad Among initialisation: s:"+r+", substring_i: "+t+", result: "+i;this.s_size=r.length,this.s=this.toCharArray(r),this.substring_i=t,this.result=i,this.method=s},SnowballProgram:function(){var r;return{bra:0,ket:0,limit:0,cursor:0,limit_backward:0,setCurrent:function(t){r=t,this.cursor=0,this.limit=t.length,this.limit_backward=0,this.bra=this.cursor,this.ket=this.limit},getCurrent:function(){var t=r;return r=null,t},in_grouping:function(t,i,s){if(this.cursor<this.limit){var e=r.charCodeAt(this.cursor);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},in_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},out_grouping:function(t,i,s){if(this.cursor<this.limit){var e=r.charCodeAt(this.cursor);if(e>s||e<i)return this.cursor++,!0;if(e-=i,!(t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},out_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e>s||e<i)return this.cursor--,!0;if(e-=i,!(t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},eq_s:function(t,i){if(this.limit-this.cursor<t)return!1;for(var s=0;s<t;s++)if(r.charCodeAt(this.cursor+s)!=i.charCodeAt(s))return!1;return this.cursor+=t,!0},eq_s_b:function(t,i){if(this.cursor-this.limit_backward<t)return!1;for(var s=0;s<t;s++)if(r.charCodeAt(this.cursor-t+s)!=i.charCodeAt(s))return!1;return this.cursor-=t,!0},find_among:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o<h?o:h,_=t[a],m=l;m<_.s_size;m++){if(n+l==u){f=-1;break}if(f=r.charCodeAt(n+l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n+_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n+_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},find_among_b:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit_backward,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o<h?o:h,_=t[a],m=_.s_size-1-l;m>=0;m--){if(n-l==u){f=-1;break}if(f=r.charCodeAt(n-1-l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n-_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n-_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},replace_s:function(t,i,s){var e=s.length-(i-t),n=r.substring(0,t),u=r.substring(i);return r=n+s+u,this.limit+=e,this.cursor>=i?this.cursor+=e:this.cursor>t&&(this.cursor=t),e},slice_check:function(){if(this.bra<0||this.bra>this.ket||this.ket>this.limit||this.limit>r.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),r.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}});

assets/javascripts/lunr/min/lunr.sv.min.js

@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Swedish` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sv=function(){this.pipeline.reset(),this.pipeline.add(e.sv.trimmer,e.sv.stopWordFilter,e.sv.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sv.stemmer))},e.sv.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.sv.trimmer=e.trimmerSupport.generateTrimmer(e.sv.wordCharacters),e.Pipeline.registerFunction(e.sv.trimmer,"trimmer-sv"),e.sv.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,t=new function(){function e(){var e,r=w.cursor+3;if(o=w.limit,0<=r||r<=w.limit){for(a=r;;){if(e=w.cursor,w.in_grouping(l,97,246)){w.cursor=e;break}if(w.cursor=e,w.cursor>=w.limit)return;w.cursor++}for(;!w.out_grouping(l,97,246);){if(w.cursor>=w.limit)return;w.cursor++}o=w.cursor,o<a&&(o=a)}}function t(){var e,r=w.limit_backward;if(w.cursor>=o&&(w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(u,37),w.limit_backward=r,e))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.in_grouping_b(d,98,121)&&w.slice_del()}}function i(){var e=w.limit_backward;w.cursor>=o&&(w.limit_backward=o,w.cursor=w.limit,w.find_among_b(c,7)&&(w.cursor=w.limit,w.ket=w.cursor,w.cursor>w.limit_backward&&(w.bra=--w.cursor,w.slice_del())),w.limit_backward=e)}function s(){var e,r;if(w.cursor>=o){if(r=w.limit_backward,w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(m,5))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.slice_from("lös");break;case 3:w.slice_from("full")}w.limit_backward=r}}var a,o,u=[new r("a",-1,1),new r("arna",0,1),new r("erna",0,1),new r("heterna",2,1),new r("orna",0,1),new r("ad",-1,1),new r("e",-1,1),new r("ade",6,1),new r("ande",6,1),new r("arne",6,1),new r("are",6,1),new r("aste",6,1),new r("en",-1,1),new r("anden",12,1),new r("aren",12,1),new r("heten",12,1),new r("ern",-1,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",18,1),new r("or",-1,1),new r("s",-1,2),new r("as",21,1),new r("arnas",22,1),new r("ernas",22,1),new r("ornas",22,1),new r("es",21,1),new r("ades",26,1),new r("andes",26,1),new r("ens",21,1),new r("arens",29,1),new r("hetens",29,1),new r("erns",21,1),new r("at",-1,1),new r("andet",-1,1),new r("het",-1,1),new r("ast",-1,1)],c=[new r("dd",-1,-1),new r("gd",-1,-1),new r("nn",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1),new r("tt",-1,-1)],m=[new r("ig",-1,1),new r("lig",0,1),new r("els",-1,1),new r("fullt",-1,3),new r("löst",-1,2)],l=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,24,0,32],d=[119,127,149],w=new n;this.setCurrent=function(e){w.setCurrent(e)},this.getCurrent=function(){return w.getCurrent()},this.stem=function(){var r=w.cursor;return e(),w.limit_backward=r,w.cursor=w.limit,t(),w.cursor=w.limit,i(),w.cursor=w.limit,s(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return t.setCurrent(e),t.stem(),t.getCurrent()}):(t.setCurrent(e),t.stem(),t.getCurrent())}}(),e.Pipeline.registerFunction(e.sv.stemmer,"stemmer-sv"),e.sv.stopWordFilter=e.generateStopWordFilter("alla allt att av blev bli blir blivit de dem den denna deras dess dessa det detta dig din dina ditt du där då efter ej eller en er era ert ett från för ha hade han hans har henne hennes hon honom hur här i icke ingen inom inte jag ju kan kunde man med mellan men mig min mina mitt mot mycket ni nu när någon något några och om oss på samma sedan sig sin sina sitta själv skulle som så sådan sådana sådant till under upp ut utan vad var vara varför varit varje vars vart vem vi vid vilka vilkas vilken vilket vår våra vårt än är åt över".split(" ")),e.Pipeline.registerFunction(e.sv.stopWordFilter,"stopWordFilter-sv")}});

assets/javascripts/lunr/min/lunr.ta.min.js

@@ -0,0 +1 @@
+!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ta=function(){this.pipeline.reset(),this.pipeline.add(e.ta.trimmer,e.ta.stopWordFilter,e.ta.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.ta.stemmer))},e.ta.wordCharacters="஀-உஊ-ஏஐ-ஙச-ட஠-னப-யர-ஹ஺-ிீ-௉ொ-௏ௐ-௙௚-௟௠-௩௪-௯௰-௹௺-௿a-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.ta.trimmer=e.trimmerSupport.generateTrimmer(e.ta.wordCharacters),e.Pipeline.registerFunction(e.ta.trimmer,"trimmer-ta"),e.ta.stopWordFilter=e.generateStopWordFilter("அங்கு அங்கே அது அதை அந்த அவர் அவர்கள் அவள் அவன் அவை ஆக ஆகவே ஆகையால் ஆதலால் ஆதலினால் ஆனாலும் ஆனால் இங்கு இங்கே இது இதை இந்த இப்படி இவர் இவர்கள் இவள் இவன் இவை இவ்வளவு உனக்கு உனது உன் உன்னால் எங்கு எங்கே எது எதை எந்த எப்படி எவர் எவர்கள் எவள் எவன் எவை எவ்வளவு எனக்கு எனது எனவே என் என்ன என்னால் ஏது ஏன் தனது தன்னால் தானே தான் நாங்கள் நாம் நான் நீ நீங்கள்".split(" ")),e.ta.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.ta.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.ta.stemmer,"stemmer-ta"),e.Pipeline.registerFunction(e.ta.stopWordFilter,"stopWordFilter-ta")}});

assets/javascripts/lunr/min/lunr.te.min.js

@@ -0,0 +1 @@
+!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.te=function(){this.pipeline.reset(),this.pipeline.add(e.te.trimmer,e.te.stopWordFilter,e.te.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.te.stemmer))},e.te.wordCharacters="ఀ-ఄఅ-ఔక-హా-ౌౕ-ౖౘ-ౚౠ-ౡౢ-ౣ౦-౯౸-౿఼ఽ్ౝ౷౤౥",e.te.trimmer=e.trimmerSupport.generateTrimmer(e.te.wordCharacters),e.Pipeline.registerFunction(e.te.trimmer,"trimmer-te"),e.te.stopWordFilter=e.generateStopWordFilter("అందరూ అందుబాటులో అడగండి అడగడం అడ్డంగా అనుగుణంగా అనుమతించు అనుమతిస్తుంది అయితే ఇప్పటికే ఉన్నారు ఎక్కడైనా ఎప్పుడు ఎవరైనా ఎవరో ఏ ఏదైనా ఏమైనప్పటికి ఒక ఒకరు కనిపిస్తాయి కాదు కూడా గా గురించి చుట్టూ చేయగలిగింది తగిన తర్వాత దాదాపు దూరంగా నిజంగా పై ప్రకారం ప్రక్కన మధ్య మరియు మరొక మళ్ళీ మాత్రమే మెచ్చుకో వద్ద వెంట వేరుగా వ్యతిరేకంగా సంబంధం".split(" ")),e.te.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.te.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.te.stemmer,"stemmer-te"),e.Pipeline.registerFunction(e.te.stopWordFilter,"stopWordFilter-te")}});

assets/javascripts/lunr/min/lunr.th.min.js

@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.th=function(){this.pipeline.reset(),this.pipeline.add(e.th.trimmer),r?this.tokenizer=e.th.tokenizer:(e.tokenizer&&(e.tokenizer=e.th.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.th.tokenizer))},e.th.wordCharacters="[฀-๿]",e.th.trimmer=e.trimmerSupport.generateTrimmer(e.th.wordCharacters),e.Pipeline.registerFunction(e.th.trimmer,"trimmer-th");var t=e.wordcut;t.init(),e.th.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new e.Token(t):t});var n=i.toString().replace(/^\s+/,"");return t.cut(n).split("|")}}});

assets/javascripts/lunr/min/lunr.vi.min.js

@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.vi=function(){this.pipeline.reset(),this.pipeline.add(e.vi.stopWordFilter,e.vi.trimmer)},e.vi.wordCharacters="[A-Za-ẓ̀͐́͑̉̃̓ÂâÊêÔôĂ-ăĐ-đƠ-ơƯ-ư]",e.vi.trimmer=e.trimmerSupport.generateTrimmer(e.vi.wordCharacters),e.Pipeline.registerFunction(e.vi.trimmer,"trimmer-vi"),e.vi.stopWordFilter=e.generateStopWordFilter("là cái nhưng mà".split(" "))}});

assets/javascripts/lunr/min/lunr.zh.min.js

@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r(require("@node-rs/jieba")):r()(e.lunr)}(this,function(e){return function(r,t){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==r.version[0];r.zh=function(){this.pipeline.reset(),this.pipeline.add(r.zh.trimmer,r.zh.stopWordFilter,r.zh.stemmer),i?this.tokenizer=r.zh.tokenizer:(r.tokenizer&&(r.tokenizer=r.zh.tokenizer),this.tokenizerFn&&(this.tokenizerFn=r.zh.tokenizer))},r.zh.tokenizer=function(n){if(!arguments.length||null==n||void 0==n)return[];if(Array.isArray(n))return n.map(function(e){return i?new r.Token(e.toLowerCase()):e.toLowerCase()});t&&e.load(t);var o=n.toString().trim().toLowerCase(),s=[];e.cut(o,!0).forEach(function(e){s=s.concat(e.split(" "))}),s=s.filter(function(e){return!!e});var u=0;return s.map(function(e,t){if(i){var n=o.indexOf(e,u),s={};return s.position=[n,e.length],s.index=t,u=n,new r.Token(e,s)}return e})},r.zh.wordCharacters="\\w一-龥",r.zh.trimmer=r.trimmerSupport.generateTrimmer(r.zh.wordCharacters),r.Pipeline.registerFunction(r.zh.trimmer,"trimmer-zh"),r.zh.stemmer=function(){return function(e){return e}}(),r.Pipeline.registerFunction(r.zh.stemmer,"stemmer-zh"),r.zh.stopWordFilter=r.generateStopWordFilter("的 一 不 在 人 有 是 为 為 以 于 於 上 他 而 后 後 之 来 來 及 了 因 下 可 到 由 这 這 与 與 也 此 但 并 並 个 個 其 已 无 無 小 我 们 們 起 最 再 今 去 好 只 又 或 很 亦 某 把 那 你 乃 它 吧 被 比 别 趁 当 當 从 從 得 打 凡 儿 兒 尔 爾 该 該 各 给 給 跟 和 何 还 還 即 几 幾 既 看 据 據 距 靠 啦 另 么 麽 每 嘛 拿 哪 您 凭 憑 且 却 卻 让 讓 仍 啥 如 若 使 谁 誰 虽 雖 随 隨 同 所 她 哇 嗡 往 些 向 沿 哟 喲 用 咱 则 則 怎 曾 至 致 着 著 诸 諸 自".split(" ")),r.Pipeline.registerFunction(r.zh.stopWordFilter,"stopWordFilter-zh")}});

assets/javascripts/lunr/tinyseg.js

@@ -0,0 +1,206 @@
+/**
+ * export the module via AMD, CommonJS or as a browser global
+ * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js
+ */
+;(function (root, factory) {
+    if (typeof define === 'function' && define.amd) {
+        // AMD. Register as an anonymous module.
+        define(factory)
+    } else if (typeof exports === 'object') {
+        /**
+         * Node. Does not work with strict CommonJS, but
+         * only CommonJS-like environments that support module.exports,
+         * like Node.
+         */
+        module.exports = factory()
+    } else {
+        // Browser globals (root is window)
+        factory()(root.lunr);
+    }
+}(this, function () {
+    /**
+     * Just return a value to define the module export.
+     * This example returns an object, but the module
+     * can return a function as the exported value.
+     */
+
+    return function(lunr) {
+        // TinySegmenter 0.1 -- Super compact Japanese tokenizer in Javascript
+        // (c) 2008 Taku Kudo <[email protected]>
+        // TinySegmenter is freely distributable under the terms of a new BSD licence.
+        // For details, see http://chasen.org/~taku/software/TinySegmenter/LICENCE.txt
+
+        function TinySegmenter() {
+          var patterns = {
+            "[一二三四五六七八九十百千万億兆]":"M",
+            "[一-龠々〆ヵヶ]":"H",
+            "[ぁ-ん]":"I",
+            "[ァ-ヴーｱ-ﾝﾞｰ]":"K",
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+          this.UW6__ = {",":227,".":808,"1":-270,"E1":306,"、":227,"。":808,"あ":-307,"う":189,"か":241,"が":-73,"く":-121,"こ":-200,"じ":1782,"す":383,"た":-428,"っ":573,"て":-1014,"で":101,"と":-105,"な":-253,"に":-149,"の":-417,"は":-236,"も":-206,"り":187,"る":-135,"を":195,"ル":-673,"ン":-496,"一":-277,"中":201,"件":-800,"会":624,"前":302,"区":1792,"員":-1212,"委":798,"学":-960,"市":887,"広":-695,"後":535,"業":-697,"相":753,"社":-507,"福":974,"空":-822,"者":1811,"連":463,"郎":1082,"１":-270,"Ｅ１":306,"ﾙ":-673,"ﾝ":-496};
+          
+          return this;
+        }
+        TinySegmenter.prototype.ctype_ = function(str) {
+          for (var i in this.chartype_) {
+            if (str.match(this.chartype_[i][0])) {
+              return this.chartype_[i][1];
+            }
+          }
+          return "O";
+        }
+
+        TinySegmenter.prototype.ts_ = function(v) {
+          if (v) { return v; }
+          return 0;
+        }
+
+        TinySegmenter.prototype.segment = function(input) {
+          if (input == null || input == undefined || input == "") {
+            return [];
+          }
+          var result = [];
+          var seg = ["B3","B2","B1"];
+          var ctype = ["O","O","O"];
+          var o = input.split("");
+          for (i = 0; i < o.length; ++i) {
+            seg.push(o[i]);
+            ctype.push(this.ctype_(o[i]))
+          }
+          seg.push("E1");
+          seg.push("E2");
+          seg.push("E3");
+          ctype.push("O");
+          ctype.push("O");
+          ctype.push("O");
+          var word = seg[3];
+          var p1 = "U";
+          var p2 = "U";
+          var p3 = "U";
+          for (var i = 4; i < seg.length - 3; ++i) {
+            var score = this.BIAS__;
+            var w1 = seg[i-3];
+            var w2 = seg[i-2];
+            var w3 = seg[i-1];
+            var w4 = seg[i];
+            var w5 = seg[i+1];
+            var w6 = seg[i+2];
+            var c1 = ctype[i-3];
+            var c2 = ctype[i-2];
+            var c3 = ctype[i-1];
+            var c4 = ctype[i];
+            var c5 = ctype[i+1];
+            var c6 = ctype[i+2];
+            score += this.ts_(this.UP1__[p1]);
+            score += this.ts_(this.UP2__[p2]);
+            score += this.ts_(this.UP3__[p3]);
+            score += this.ts_(this.BP1__[p1 + p2]);
+            score += this.ts_(this.BP2__[p2 + p3]);
+            score += this.ts_(this.UW1__[w1]);
+            score += this.ts_(this.UW2__[w2]);
+            score += this.ts_(this.UW3__[w3]);
+            score += this.ts_(this.UW4__[w4]);
+            score += this.ts_(this.UW5__[w5]);
+            score += this.ts_(this.UW6__[w6]);
+            score += this.ts_(this.BW1__[w2 + w3]);
+            score += this.ts_(this.BW2__[w3 + w4]);
+            score += this.ts_(this.BW3__[w4 + w5]);
+            score += this.ts_(this.TW1__[w1 + w2 + w3]);
+            score += this.ts_(this.TW2__[w2 + w3 + w4]);
+            score += this.ts_(this.TW3__[w3 + w4 + w5]);
+            score += this.ts_(this.TW4__[w4 + w5 + w6]);
+            score += this.ts_(this.UC1__[c1]);
+            score += this.ts_(this.UC2__[c2]);
+            score += this.ts_(this.UC3__[c3]);
+            score += this.ts_(this.UC4__[c4]);
+            score += this.ts_(this.UC5__[c5]);
+            score += this.ts_(this.UC6__[c6]);
+            score += this.ts_(this.BC1__[c2 + c3]);
+            score += this.ts_(this.BC2__[c3 + c4]);
+            score += this.ts_(this.BC3__[c4 + c5]);
+            score += this.ts_(this.TC1__[c1 + c2 + c3]);
+            score += this.ts_(this.TC2__[c2 + c3 + c4]);
+            score += this.ts_(this.TC3__[c3 + c4 + c5]);
+            score += this.ts_(this.TC4__[c4 + c5 + c6]);
+        //  score += this.ts_(this.TC5__[c4 + c5 + c6]);    
+            score += this.ts_(this.UQ1__[p1 + c1]);
+            score += this.ts_(this.UQ2__[p2 + c2]);
+            score += this.ts_(this.UQ3__[p3 + c3]);
+            score += this.ts_(this.BQ1__[p2 + c2 + c3]);
+            score += this.ts_(this.BQ2__[p2 + c3 + c4]);
+            score += this.ts_(this.BQ3__[p3 + c2 + c3]);
+            score += this.ts_(this.BQ4__[p3 + c3 + c4]);
+            score += this.ts_(this.TQ1__[p2 + c1 + c2 + c3]);
+            score += this.ts_(this.TQ2__[p2 + c2 + c3 + c4]);
+            score += this.ts_(this.TQ3__[p3 + c1 + c2 + c3]);
+            score += this.ts_(this.TQ4__[p3 + c2 + c3 + c4]);
+            var p = "O";
+            if (score > 0) {
+              result.push(word);
+              word = "";
+              p = "B";
+            }
+            p1 = p2;
+            p2 = p3;
+            p3 = p;
+            word += seg[i];
+          }
+          result.push(word);
+
+          return result;
+        }
+
+        lunr.TinySegmenter = TinySegmenter;
+    };
+
+}));

assets/stylesheets/palette.ab4e12ef.min.css.map

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autogen_stubs.sh

@@ -1,568 +0,0 @@
-#!/bin/bash -e
-
-# setup instructions for clang2py
-if [[ ! $(clang2py -V) ]]; then
-  pushd .
-  cd /tmp
-  sudo apt-get install -y --no-install-recommends clang
-  pip install --upgrade pip setuptools
-  pip install clang==14.0.6
-  git clone https://github.com/nimlgen/ctypeslib.git
-  cd ctypeslib
-  pip install .
-  clang2py -V
-  popd
-fi
-
-BASE=tinygrad/runtime/autogen/
-
-fixup() {
-  sed -i '1s/^/# mypy: ignore-errors\n/' $1
-  sed -i 's/ *$//' $1
-  grep FIXME_STUB $1 || true
-}
-
-patch_dlopen() {
-  path=$1; shift
-  name=$1; shift
-  cat <<EOF | sed -i "/import ctypes.*/r /dev/stdin" $path
-PATHS_TO_TRY = [
-$(for p in "$@"; do echo "  $p,"; done)
-]
-def _try_dlopen_$name():
-  library = ctypes.util.find_library("$name")
-  if library:
-    try: return ctypes.CDLL(library)
-    except OSError: pass
-  for candidate in PATHS_TO_TRY:
-    try: return ctypes.CDLL(candidate)
-    except OSError: pass
-  return None
-EOF
-}
-
-generate_opencl() {
-  clang2py /usr/include/CL/cl.h -o $BASE/opencl.py -l /usr/lib/x86_64-linux-gnu/libOpenCL.so.1 -k cdefstum
-  fixup $BASE/opencl.py
-  # hot patches
-  sed -i "s\import ctypes\import ctypes, ctypes.util\g" $BASE/opencl.py
-  sed -i "s\ctypes.CDLL('/usr/lib/x86_64-linux-gnu/libOpenCL.so.1')\ctypes.CDLL(ctypes.util.find_library('OpenCL'))\g" $BASE/opencl.py
-  python3 -c "import tinygrad.runtime.autogen.opencl"
-}
-
-generate_hip() {
-  clang2py /opt/rocm/include/hip/hip_ext.h /opt/rocm/include/hip/hiprtc.h \
-  /opt/rocm/include/hip/hip_runtime_api.h /opt/rocm/include/hip/driver_types.h \
-  --clang-args="-D__HIP_PLATFORM_AMD__ -I/opt/rocm/include -x c++" -o $BASE/hip.py -l /opt/rocm/lib/libamdhip64.so
-  echo "hipDeviceProp_t = hipDeviceProp_tR0600" >> $BASE/hip.py
-  echo "hipGetDeviceProperties = hipGetDevicePropertiesR0600" >> $BASE/hip.py
-  fixup $BASE/hip.py
-  # we can trust HIP is always at /opt/rocm/lib
-  #sed -i "s\import ctypes\import ctypes, ctypes.util\g" $BASE/hip.py
-  #sed -i "s\ctypes.CDLL('/opt/rocm/lib/libhiprtc.so')\ctypes.CDLL(ctypes.util.find_library('hiprtc'))\g" $BASE/hip.py
-  #sed -i "s\ctypes.CDLL('/opt/rocm/lib/libamdhip64.so')\ctypes.CDLL(ctypes.util.find_library('amdhip64'))\g" $BASE/hip.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/hip.py
-  sed -i "s\'/opt/rocm/\os.getenv('ROCM_PATH', '/opt/rocm/')+'/\g" $BASE/hip.py
-  python3 -c "import tinygrad.runtime.autogen.hip"
-}
-
-generate_comgr() {
-  clang2py /opt/rocm/include/amd_comgr/amd_comgr.h \
-  --clang-args="-D__HIP_PLATFORM_AMD__ -I/opt/rocm/include -x c++" -o $BASE/comgr.py -l /opt/rocm/lib/libamd_comgr.so
-  fixup $BASE/comgr.py
-  sed -i "s\import ctypes\import ctypes, ctypes.util, os\g" $BASE/comgr.py
-  patch_dlopen $BASE/comgr.py amd_comgr "'/opt/rocm/lib/libamd_comgr.so'" "os.getenv('ROCM_PATH', '')+'/lib/libamd_comgr.so'" "'/usr/local/lib/libamd_comgr.dylib'" "'/opt/homebrew/lib/libamd_comgr.dylib'"
-  sed -i "s\ctypes.CDLL('/opt/rocm/lib/libamd_comgr.so')\_try_dlopen_amd_comgr()\g" $BASE/comgr.py
-  python3 -c "import tinygrad.runtime.autogen.comgr"
-}
-
-generate_kfd() {
-  clang2py /usr/include/linux/kfd_ioctl.h -o $BASE/kfd.py -k cdefstum
-
-  fixup $BASE/kfd.py
-  sed -i "s/import ctypes/import ctypes, os/g" $BASE/kfd.py
-  sed -i "s/import fcntl, functools/import functools/g" $BASE/kfd.py
-  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
-  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
-  python3 -c "import tinygrad.runtime.autogen.cuda"
-}
-
-generate_nvrtc() {
-  clang2py /usr/local/cuda/include/nvrtc.h /usr/local/cuda/include/nvJitLink.h -o $BASE/nvrtc.py -l /usr/local/cuda/lib64/libnvrtc.so -l /usr/local/cuda/lib64/libnvJitLink.so
-  sed -i "s\import ctypes\import ctypes, ctypes.util\g" $BASE/nvrtc.py
-  sed -i "s\ctypes.CDLL('/usr/local/cuda/lib64/libnvrtc.so')\ctypes.CDLL(ctypes.util.find_library('nvrtc'))\g" $BASE/nvrtc.py
-  sed -i "s\ctypes.CDLL('/usr/local/cuda/lib64/libnvJitLink.so')\ctypes.CDLL(ctypes.util.find_library('nvJitLink'))\g" $BASE/nvrtc.py
-  fixup $BASE/nvrtc.py
-  python3 -c "import tinygrad.runtime.autogen.nvrtc"
-}
-
-generate_nv() {
-  NVKERN_COMMIT_HASH=81fe4fb417c8ac3b9bdcc1d56827d116743892a5
-  NVKERN_SRC=/tmp/open-gpu-kernel-modules-$NVKERN_COMMIT_HASH
-  if [ ! -d "$NVKERN_SRC" ]; then
-    git clone https://github.com/NVIDIA/open-gpu-kernel-modules $NVKERN_SRC
-    pushd .
-    cd $NVKERN_SRC
-    git reset --hard $NVKERN_COMMIT_HASH
-    popd
-  fi
-
-  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/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 \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include/nv_escape.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include/nv-ioctl.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include/nv-ioctl-numbers.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include/nv-ioctl-numa.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include/nv-unix-nvos-params-wrappers.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/alloc/alloc_channel.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/nvos.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrl0000/*.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrl0080/*.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrl2080/*.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrl83de/*.h \
-    $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
-  sed -i "s\(0000000001)\1\g" $BASE/nv_gpu.py
-  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>
-
-  # Parse status codes
-  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() {
-  # clang2py broken when pass -x c++ to prev headers
-  clang2py -k cdefstum \
-    extra/hip_gpu_driver/sdma_registers.h \
-    extra/hip_gpu_driver/nvd.h \
-    extra/hip_gpu_driver/gc_11_0_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
-
-  fixup $BASE/amd_gpu.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/amd_gpu.py
-  python3 -c "import tinygrad.runtime.autogen.amd_gpu"
-}
-
-generate_hsa() {
-  clang2py \
-    /opt/rocm/include/hsa/hsa.h \
-    /opt/rocm/include/hsa/hsa_ext_amd.h \
-    /opt/rocm/include/hsa/amd_hsa_signal.h \
-    /opt/rocm/include/hsa/amd_hsa_queue.h \
-    /opt/rocm/include/hsa/amd_hsa_kernel_code.h \
-    /opt/rocm/include/hsa/hsa_ext_finalize.h /opt/rocm/include/hsa/hsa_ext_image.h \
-    /opt/rocm/include/hsa/hsa_ven_amd_aqlprofile.h \
-    --clang-args="-I/opt/rocm/include" \
-    -o $BASE/hsa.py -l /opt/rocm/lib/libhsa-runtime64.so
-
-  fixup $BASE/hsa.py
-  sed -i "s\import ctypes\import ctypes, ctypes.util, os\g" $BASE/hsa.py
-  sed -i "s\ctypes.CDLL('/opt/rocm/lib/libhsa-runtime64.so')\ctypes.CDLL(os.getenv('ROCM_PATH')+'/lib/libhsa-runtime64.so' if os.getenv('ROCM_PATH') else ctypes.util.find_library('hsa-runtime64'))\g" $BASE/hsa.py
-  python3 -c "import tinygrad.runtime.autogen.hsa"
-}
-
-generate_io_uring() {
-  clang2py -k cdefstum \
-    /usr/include/liburing.h \
-    /usr/include/linux/io_uring.h \
-    -o $BASE/io_uring.py
-
-  sed -r '/^#define __NR_io_uring/ s/^#define __(NR_io_uring[^ ]+) (.*)$/\1 = \2/; t; d' /usr/include/asm-generic/unistd.h >> $BASE/io_uring.py # io_uring syscalls numbers
-  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) \
-    $(dpkg -L libc6-dev | grep sys/syscall.h) \
-    /usr/include/string.h \
-    /usr/include/elf.h \
-    /usr/include/unistd.h \
-    /usr/include/asm-generic/mman-common.h \
-    -o $BASE/libc.py
-
-  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
-
-  fixup $BASE/libc.py
-}
-
-generate_llvm() {
-  INC="$(llvm-config-14 --includedir)"
-  clang2py -k cdefstum \
-    $(find "$INC/llvm-c/" -type f -name '*.h' | sort) \
-    "$INC/llvm/Config/Targets.def" \
-    "$INC/llvm/Config/AsmPrinters.def" \
-    "$INC/llvm/Config/AsmParsers.def" \
-    "$INC/llvm/Config/Disassemblers.def" \
-    --clang-args="$(llvm-config-14 --cflags)" \
-    -o "$BASE/llvm.py"
-
-  sed -i "s\import ctypes\import ctypes, tinygrad.runtime.support.llvm as llvm_support\g" "$BASE/llvm.py"
-  sed -i "s\FIXME_STUB\llvm\g" "$BASE/llvm.py"
-  sed -i "s\FunctionFactoryStub()\ctypes.CDLL(llvm_support.LLVM_PATH)\g" "$BASE/llvm.py"
-
-  fixup "$BASE/llvm.py"
-}
-
-generate_kgsl() {
-  clang2py extra/qcom_gpu_driver/msm_kgsl.h -o $BASE/kgsl.py -k cdefstum
-  fixup $BASE/kgsl.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/kgsl.py
-  sed -nE 's/#define ([A-Za-z0-9_]+)_SHIFT\s*[^\S\r\n]*[0-9]*$/def \1(val): return (val << \1_SHIFT) \& \1_MASK/p' extra/qcom_gpu_driver/msm_kgsl.h >> $BASE/kgsl.py
-  sed -i "s\fcntl.ioctl(__fd, (__idir<<30)\__fd.ioctl((__idir<<30)\g" $BASE/kgsl.py
-  python3 -c "import tinygrad.runtime.autogen.kgsl"
-}
-
-generate_adreno() {
-  clang2py extra/qcom_gpu_driver/a6xx.xml.h -o $BASE/adreno.py -k cestum
-  sed -nE 's/#define ([A-Za-z0-9_]+)__SHIFT\s*[^\S\r\n]*[0-9]*$/def \1(val): return (val << \1__SHIFT) \& \1__MASK/p' extra/qcom_gpu_driver/a6xx.xml.h >> $BASE/adreno.py
-  fixup $BASE/adreno.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/adreno.py
-  python3 -c "import tinygrad.runtime.autogen.adreno"
-}
-
-generate_qcom() {
-  clang2py -k cdefstum \
-    extra/dsp/include/ion.h \
-    extra/dsp/include/msm_ion.h \
-    extra/dsp/include/adsprpc_shared.h \
-    extra/dsp/include/remote_default.h \
-    extra/dsp/include/apps_std.h \
-    -o $BASE/qcom_dsp.py
-
-  fixup $BASE/qcom_dsp.py
-  python3 -c "import tinygrad.runtime.autogen.qcom_dsp"
-}
-
-generate_pci() {
-  clang2py -k cdefstum \
-    /usr/include/linux/pci_regs.h \
-    -o $BASE/pci.py
-  fixup $BASE/pci.py
-}
-
-generate_vfio() {
-  clang2py -k cdefstum \
-    /usr/include/linux/vfio.h \
-    -o $BASE/vfio.py
-  fixup $BASE/vfio.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/vfio.py
-  sed -i "s\import fcntl, functools\import functools" $BASE/vfio.py
-  sed -i "s\import ctypes,os\a from tinygrad.runtime.support import FileIOInterface\g" $BASE/vfio.py
-  sed -i "s\fcntl.ioctl(__fd, (__idir<<30)\return __fd.ioctl((__idir<<30)\g" $BASE/vfio.py
-}
-
-generate_am() {
-  AMKERN_COMMIT_HASH=ceb12c04e2b5b53ec0779362831f5ee40c4921e4
-  AMKERN_SRC=/tmp/ROCK-Kernel-Driver-$AMKERN_COMMIT_HASH
-  if [ ! -d "$AMKERN_SRC" ]; then
-    git clone https://github.com/ROCm/ROCK-Kernel-Driver $AMKERN_SRC --depth 1
-  fi
-  AMKERN_AMD=$AMKERN_SRC/drivers/gpu/drm/amd/
-  AMKERN_INC=$AMKERN_AMD/include/
-
-  clang2py -k cdefstum \
-    extra/amdpci/headers/v11_structs.h \
-    extra/amdpci/headers/v12_structs.h \
-    extra/amdpci/headers/amdgpu_vm.h \
-    extra/amdpci/headers/discovery.h \
-    extra/amdpci/headers/amdgpu_ucode.h \
-    extra/amdpci/headers/psp_gfx_if.h \
-    extra/amdpci/headers/amdgpu_psp.h \
-    extra/amdpci/headers/amdgpu_irq.h \
-    extra/amdpci/headers/amdgpu_doorbell.h \
-    $AMKERN_INC/soc15_ih_clientid.h \
-    --clang-args="-include stdint.h" \
-    -o $BASE/am/am.py
-  fixup $BASE/am/am.py
-  sed -i "s\(int64_t)\ \g" $BASE/am/am.py
-  sed -i "s\AMDGPU_PTE_MTYPE_VG10(2)\AMDGPU_PTE_MTYPE_VG10(0, 2)\g" $BASE/am/am.py # incorrect parsing (TODO: remove when clang2py is gone).
-
-  clang2py -k cdefstum \
-    $AMKERN_AMD/amdkfd/kfd_pm4_headers_ai.h \
-    $AMKERN_AMD/amdgpu/soc15d.h \
-    -o $BASE/am/pm4_soc15.py
-  fixup $BASE/am/pm4_soc15.py
-
-  clang2py -k cdefstum \
-    $AMKERN_AMD/amdkfd/kfd_pm4_headers_ai.h \
-    $AMKERN_AMD/amdgpu/nvd.h \
-    -o $BASE/am/pm4_nv.py
-  fixup $BASE/am/pm4_nv.py
-
-  clang2py -k cdefstum \
-    extra/hip_gpu_driver/sdma_registers.h \
-    $AMKERN_AMD/amdgpu/vega10_sdma_pkt_open.h \
-    --clang-args="-I/opt/rocm/include -x c++" \
-    -o $BASE/am/sdma_4_0_0.py
-  fixup $BASE/am/sdma_4_0_0.py
-
-  clang2py -k cdefstum \
-    extra/hip_gpu_driver/sdma_registers.h \
-    $AMKERN_AMD/amdgpu/navi10_sdma_pkt_open.h \
-    --clang-args="-I/opt/rocm/include -x c++" \
-    -o $BASE/am/sdma_5_0_0.py
-  fixup $BASE/am/sdma_5_0_0.py
-
-  clang2py -k cdefstum \
-    extra/hip_gpu_driver/sdma_registers.h \
-    $AMKERN_AMD/amdgpu/sdma_v6_0_0_pkt_open.h \
-    --clang-args="-I/opt/rocm/include -x c++" \
-    -o $BASE/am/sdma_6_0_0.py
-  fixup $BASE/am/sdma_6_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 \
-    extra/amdpci/headers/amdgpu_smu.h \
-    -o $BASE/am/smu_v13_0_0.py
-  fixup $BASE/am/smu_v13_0_0.py
-
-  clang2py -k cdefstum \
-    $AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu_v14_0_0_pmfw.h \
-    $AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu_v14_0_2_ppsmc.h \
-    $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
-}
-
-generate_sqtt() {
-  clang2py -k cdefstum \
-    extra/sqtt/sqtt.h \
-    -o $BASE/sqtt.py
-  fixup $BASE/sqtt.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/sqtt.py
-  python3 -c "import tinygrad.runtime.autogen.sqtt"
-
-  ROCPROF_COMMIT_HASH=dd0485100971522cc4cd8ae136bdda431061a04d
-  ROCPROF_SRC=/tmp/rocprof-trace-decoder-$ROCPROF_COMMIT_HASH
-  if [ ! -d "$ROCPROF_SRC" ]; then
-    git clone https://github.com/ROCm/rocprof-trace-decoder $ROCPROF_SRC
-    pushd .
-    cd $ROCPROF_SRC
-    git reset --hard $ROCPROF_COMMIT_HASH
-    popd
-  fi
-
-  clang2py -k cdefstum \
-    $ROCPROF_SRC/include/rocprof_trace_decoder.h \
-    $ROCPROF_SRC/include/trace_decoder_instrument.h \
-    $ROCPROF_SRC/include/trace_decoder_types.h \
-    -o $BASE/rocprof.py
-  fixup $BASE/rocprof.py
-  sed -i '1s/^/# pylint: skip-file\n/' $BASE/rocprof.py
-  sed -i "s/import ctypes/import ctypes, ctypes.util/g" $BASE/rocprof.py
-  patch_dlopen $BASE/rocprof.py rocprof-trace-decoder "'/usr/local/lib/librocprof-trace-decoder.so'" "'/usr/local/lib/librocprof-trace-decoder.dylib'"
-  sed -i "s/def _try_dlopen_rocprof-trace-decoder():/def _try_dlopen_rocprof_trace_decoder():/g" $BASE/rocprof.py
-  sed -i "s|FunctionFactoryStub()|_try_dlopen_rocprof_trace_decoder()|g" $BASE/rocprof.py
-}
-
-generate_webgpu() {
-  clang2py extra/webgpu/webgpu.h -o $BASE/webgpu.py
-  fixup $BASE/webgpu.py
-  sed -i "s/FIXME_STUB/webgpu/g" "$BASE/webgpu.py"
-  sed -i "s/FunctionFactoryStub()/ctypes.CDLL(webgpu_support.WEBGPU_PATH)/g" "$BASE/webgpu.py"
-  sed -i "s/import ctypes/import ctypes, tinygrad.runtime.support.webgpu as webgpu_support/g" "$BASE/webgpu.py"
-  python3 -c "import tinygrad.runtime.autogen.webgpu"
-}
-
-generate_libusb() {
-  clang2py -k cdefstum \
-    /usr/include/libusb-1.0/libusb.h \
-    -o $BASE/libusb.py
-
-  fixup $BASE/libusb.py
-  sed -i "s\import ctypes\import ctypes, ctypes.util, os\g" $BASE/libusb.py
-  sed -i "s/FIXME_STUB/libusb/g" "$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"
-  python3 -c "import tinygrad.runtime.autogen.libusb"
-}
-
-generate_mesa() {
-  MESA_TAG="mesa-25.2.4"
-  MESA_SRC=/tmp/mesa-$MESA_TAG
-  TINYMESA_TAG=tinymesa-32dc66c
-  TINYMESA_DIR=/tmp/tinymesa-$MESA_TAG-$TINYMESA_TAG/
-  TINYMESA_SO=$TINYMESA_DIR/libtinymesa_cpu.so
-  if [ ! -d "$MESA_SRC" ]; then
-    git clone --depth 1 --branch $MESA_TAG https://gitlab.freedesktop.org/mesa/mesa.git $MESA_SRC
-    pushd .
-    cd $MESA_SRC
-    git reset --hard $MESA_COMMIT_HASH
-    # clang 14 doesn't support packed enums
-    sed -i "s/enum \w\+ \(\w\+\);$/uint8_t \1;/" $MESA_SRC/src/nouveau/headers/nv_device_info.h
-    sed -i "s/enum \w\+ \(\w\+\);$/uint8_t \1;/" $MESA_SRC/src/nouveau/compiler/nak.h
-    sed -i "s/nir_instr_type \(\w\+\);/uint8_t \1;/" $MESA_SRC/src/compiler/nir/nir.h
-    mkdir -p gen/util/format
-    python3 src/util/format/u_format_table.py src/util/format/u_format.yaml --enums > gen/util/format/u_format_gen.h
-    python3 src/compiler/nir/nir_opcodes_h.py > gen/nir_opcodes.h
-    python3 src/compiler/nir/nir_intrinsics_h.py --outdir gen
-    python3 src/compiler/nir/nir_intrinsics_indices_h.py --outdir gen
-    python3 src/compiler/nir/nir_builder_opcodes_h.py > gen/nir_builder_opcodes.h
-    python3 src/compiler/nir/nir_intrinsics_h.py --outdir gen
-    python3 src/compiler/builtin_types_h.py gen/builtin_types.h
-    popd
-  fi
-
-  if [ ! -d "$TINYMESA_DIR" ]; then
-    mkdir $TINYMESA_DIR
-    curl -L https://github.com/sirhcm/tinymesa/releases/download/$TINYMESA_TAG/libtinymesa_cpu-$MESA_TAG-linux-amd64.so -o $TINYMESA_SO
-  fi
-
-  clang2py -k cdefstu \
-    $MESA_SRC/src/compiler/nir/nir.h \
-    $MESA_SRC/src/compiler/nir/nir_builder.h \
-    $MESA_SRC/src/compiler/nir/nir_shader_compiler_options.h \
-    $MESA_SRC/src/compiler/nir/nir_serialize.h \
-    $MESA_SRC/gen/nir_intrinsics.h \
-    $MESA_SRC/src/nouveau/headers/nv_device_info.h \
-    $MESA_SRC/src/nouveau/compiler/nak.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_passmgr.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_misc.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_type.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_init.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_nir.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_struct.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_jit_types.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_flow.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_const.h \
-    $MESA_SRC/src/compiler/glsl_types.h \
-    $MESA_SRC/src/util/blob.h \
-    $MESA_SRC/src/util/ralloc.h \
-    --clang-args="-DHAVE_ENDIAN_H -DHAVE_STRUCT_TIMESPEC -DHAVE_PTHREAD -I$MESA_SRC/src -I$MESA_SRC/include -I$MESA_SRC/gen -I$MESA_SRC/src/compiler/nir -I$MESA_SRC/src/gallium/auxiliary -I$MESA_SRC/src/gallium/include -I$(llvm-config-20 --includedir)" \
-    -l $TINYMESA_SO \
-    -o $BASE/mesa.py
-
-  LVP_NIR_OPTIONS=$(./extra/mesa/lvp_nir_options.sh $MESA_SRC)
-
-  fixup $BASE/mesa.py
-  patch_dlopen $BASE/mesa.py tinymesa_cpu "(BASE:=os.getenv('MESA_PATH', f\"/usr{'/local/' if helpers.OSX else '/'}lib\"))+'/libtinymesa_cpu'+(EXT:='.dylib' if helpers.OSX else '.so')" "f'{BASE}/libtinymesa{EXT}'" "'/opt/homebrew/lib/libtinymesa_cpu.dylib'" "'/opt/homebrew/lib/libtinymesa.dylib'"
-  echo "lvp_nir_options = gzip.decompress(base64.b64decode('$LVP_NIR_OPTIONS'))" >> $BASE/mesa.py
-  sed -i "/in_dll/s/.*/try: &\nexcept (AttributeError, ValueError): pass/" $BASE/mesa.py
-  sed -i "s/import ctypes/import ctypes, ctypes.util, os, gzip, base64, subprocess, tinygrad.helpers as helpers/" $BASE/mesa.py
-  sed -i "s/ctypes.CDLL('.\+')/(dll := _try_dlopen_tinymesa_cpu())/" $BASE/mesa.py
-  echo "def __getattr__(nm): raise AttributeError('LLVMpipe requires tinymesa_cpu' if 'tinymesa_cpu' not in dll._name else f'attribute {nm} not found') if dll else FileNotFoundError(f'libtinymesa not found (MESA_PATH={BASE}). See https://github.com/sirhcm/tinymesa ($TINYMESA_TAG, $MESA_TAG)')" >> $BASE/mesa.py
-  sed -i "s/ctypes.glsl_base_type/glsl_base_type/" $BASE/mesa.py
-  # bitfield bug in clang2py
-  sed -i "s/('fp_fast_math', ctypes.c_bool, 9)/('fp_fast_math', ctypes.c_uint32, 9)/" $BASE/mesa.py
-  sed -i "s/('\(\w\+\)', pipe_shader_type, 8)/('\1', ctypes.c_ubyte)/" $BASE/mesa.py
-  sed -i "s/\([0-9]\+\)()/\1/" $BASE/mesa.py
-  sed -i '/struct_nir_builder._pack_ = 1 # source:False/d' "$BASE/mesa.py"
-  python3 -c "import tinygrad.runtime.autogen.mesa"
-}
-
-if [ "$1" == "opencl" ]; then generate_opencl
-elif [ "$1" == "hip" ]; then generate_hip
-elif [ "$1" == "comgr" ]; then generate_comgr
-elif [ "$1" == "cuda" ]; then generate_cuda
-elif [ "$1" == "nvrtc" ]; then generate_nvrtc
-elif [ "$1" == "hsa" ]; then generate_hsa
-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
-elif [ "$1" == "adreno" ]; then generate_adreno
-elif [ "$1" == "pci" ]; then generate_pci
-elif [ "$1" == "vfio" ]; then generate_vfio
-elif [ "$1" == "webgpu" ]; then generate_webgpu
-elif [ "$1" == "libusb" ]; then generate_libusb
-elif [ "$1" == "mesa" ]; then generate_mesa
-elif [ "$1" == "all" ]; then generate_opencl; generate_hip; generate_comgr; generate_cuda; generate_nvrtc; generate_hsa; generate_kfd; generate_nv; generate_amd; generate_io_uring; generate_libc; generate_am; generate_webgpu; generate_mesa
-else echo "usage: $0 <type>"
-fi

docs/abstractions2.py

@@ -1,135 +0,0 @@
-# tinygrad is a tensor library, and as a tensor library it has multiple parts
-# 1. a "runtime". this allows buffer management, compilation, and running programs
-# 2. a "Device" that uses the runtime but specifies compute in an abstract way for all
-# 3. a "UOp" that fuses the compute into kernels, using memory only when needed
-# 4. a "Tensor" that provides an easy to use frontend with autograd ".backward()"
-
-
-print("******** first, the runtime ***********")
-
-from tinygrad.runtime.ops_cpu import ClangJITCompiler, CPUDevice, CPUProgram
-
-cpu = CPUDevice()
-
-# allocate some buffers
-out = cpu.allocator.alloc(4)
-a = cpu.allocator.alloc(4)
-b = cpu.allocator.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])))
-
-# 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)
-
-# run the program
-fxn(out, a, b)
-
-# check the data out
-print(val := cpu.allocator._as_buffer(out).cast("I").tolist()[0])
-assert val == 5
-
-
-print("******** second, the Device ***********")
-
-DEVICE = "CPU"   # NOTE: you can change this!
-
-import struct
-from tinygrad.dtype import dtypes
-from tinygrad.device import Buffer, Device
-from tinygrad.uop.ops import UOp, Ops
-
-# allocate some buffers + load in values
-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
-
-# describe the computation
-idx = UOp.const(dtypes.index, 0)
-buf_1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 1)
-buf_2 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 2)
-alu = buf_1.index(idx) + buf_2.index(idx)
-output_buf = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 0)
-st_0 = UOp(Ops.STORE, dtypes.void, (output_buf.index(idx), 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)
-
-# compile a program (and print the source)
-fxn = CompiledRunner(program)
-print(fxn.p.src)
-# NOTE: fxn.clprg is the CPUProgram
-
-# run the program
-fxn.exec([out, a, b])
-
-# check the data out
-assert out.as_buffer().cast('I')[0] == 5
-
-
-print("******** third, the UOp ***********")
-
-from tinygrad.engine.realize import run_schedule
-from tinygrad.engine.schedule import create_schedule_with_vars
-from tinygrad.schedule.rangeify import get_rangeify_map
-
-# allocate some values + load in values
-a = UOp.new_buffer(DEVICE, 1, dtypes.int32)
-b = UOp.new_buffer(DEVICE, 1, dtypes.int32)
-a.buffer.allocate().copyin(memoryview(bytearray(struct.pack("I", 2))))
-b.buffer.allocate().copyin(memoryview(bytearray(struct.pack("I", 3))))
-
-# describe the computation
-out = a + b
-s = UOp(Ops.SINK, dtypes.void, (out,))
-
-# group the computation into kernels
-becomes_map = get_rangeify_map(s)
-
-# the compute maps to an assign
-assign = becomes_map[a+b].base
-
-# the first source is the output buffer (data)
-assert assign.src[0].op is Ops.BUFFER
-# the second source is the kernel (compute)
-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)
-assert len(sched) == 1
-
-# DEBUGGING: print the compute ast
-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
-assert out.op is Ops.BUFFER and not out.buffer.is_allocated()
-print(out)
-
-# run that schedule
-run_schedule(sched)
-
-# check the data out
-assert out.is_realized and out.buffer.as_buffer().cast('I')[0] == 5
-
-
-print("******** fourth, the Tensor ***********")
-
-from tinygrad import Tensor
-
-a = Tensor([2], dtype=dtypes.int32, device=DEVICE)
-b = Tensor([3], dtype=dtypes.int32, device=DEVICE)
-out = a + b
-
-# check the data out
-print(val:=out.item())
-assert val == 5

docs/developer/am.md

@@ -1,39 +0,0 @@
-# AM Driver
-
-AM driver is a userspace driver targeting AMD's RDNA3/RDNA4. You only need tinygrad to send compute tasks to your GPU!
-
-## How to run?
-Make sure that amdgpu module is unloaded and just run tinygrad with `AMD=1`!
-
-Optional requirements:
-
-* System without IOMMU for P2P / SDMA support
-* vfio-pci module for IRQ handling
-
-## Environment Variables
-
-| Variable | Possible Value(s) | Description |
-|----------|------------------|-------------|
-| AM_RESET | [1] | Performs a full GPU reset (reloading all firmware and IP blocks) |
-| AM_DEBUG | [0-4] | Sets the level of additional debugging information |
-
-## AM Driver Details
-
-### Compute & SDMA Queues
-
-AM binds compute queues directly to MEC (bypassing MES). Tinygrad uses only one compute queue, which is bound at `pipe=0 queue=0`. Similarly, the single SDMA queue is bound at `engine=0 queue=0`.
-
-### Boot
-
-The GPU being passed can be in one of several states:
-1. Not initialized
-2. Initialized by amdgpu
-3. Initialized by AM
-
-The first and second states require a full GPU setup since their states are unknown. The second state also requires a mode1 reset to reinitialize all components.
-
-The third state can be set up partially to optimize boot time. In this case, only the GFX and SDMA IPs need to be initialized. To enable this, AM uses a separate boot memory that is guaranteed not to be overwritten. This physical memory is utilized for all blocks that are initialized only during the initial AM boot. To determine if the GPU is in the third state, AM uses `regSCRATCH_REG7` as a flag.
-
-### VM Management
-
-Each AM device sets up only a single `VMID=0` and one page directory. The page directory used is 3-level and thus supports up to 512GB of virtual addresses. All AM devices are located in one virtual address space.

docs/developer/developer.md

@@ -1,54 +0,0 @@
-The tinygrad framework has four pieces
-
-* a PyTorch like <b>frontend</b>.
-* a <b>scheduler</b> which breaks the compute into kernels.
-* a <b>lowering</b> engine which converts ASTs into code that can run on the accelerator.
-* an <b>execution</b> engine which can run that code.
-
-There is a good [bunch of tutorials](https://mesozoic-egg.github.io/tinygrad-notes/) by Di Zhu that go over tinygrad internals.
-
-There's also a [doc describing speed](../developer/speed.md)
-
-## Frontend
-
-Everything in [Tensor](../tensor/index.md) is syntactic sugar around constructing a graph of [UOps](../developer/uop.md).
-
-The `UOp` graph specifies the compute in terms of low level tinygrad ops. Not all UOps will actually become realized. There's two types of UOps, base and view. base contains compute into a contiguous buffer, and view is a view (specified by a ShapeTracker). Inputs to a base can be either base or view, inputs to a view can only be a single base.
-
-## Scheduling
-
-The [scheduler](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/engine/schedule.py) converts the graph of UOps into a list of `ScheduleItem`. One `ScheduleItem` is one kernel on the GPU, and the scheduler is responsible for breaking the large compute graph into subgraphs that can fit in a kernel. `ast` specifies what compute to run, and `bufs` specifies what buffers to run it on.
-
-::: tinygrad.engine.schedule.ScheduleItem
-
-## Lowering
-
-The code in [realize](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/engine/realize.py) lowers `ScheduleItem` to `ExecItem` with
-
-::: tinygrad.engine.realize.lower_schedule
-
-There's a ton of complexity hidden behind this, see the `codegen/` directory.
-
-First we lower the AST to UOps, which is a linear list of the compute to be run. This is where the BEAM search happens.
-
-Then we render the UOps into code with a `Renderer`, then we compile the code to binary with a `Compiler`.
-
-## Execution
-
-Creating `ExecItem`, which has a run method
-
-::: tinygrad.engine.realize.ExecItem
-    options:
-        members: true
-
-Lists of `ExecItem` can be condensed into a single ExecItem with the Graph API (rename to Queue?)
-
-## Runtime
-
-Runtimes are responsible for device-specific interactions. They handle tasks such as initializing devices, allocating memory, loading/launching programs, and more. You can find more information about the runtimes API on the [runtime overview page](runtime.md).
-
-All runtime implementations can be found in the [runtime directory](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime).
-
-### HCQ Compatible Runtimes
-
-HCQ API is a lower-level API for defining runtimes. Interaction with HCQ-compatible devices occurs at a lower level, with commands issued directly to hardware queues. Some examples of such backends are [NV](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_nv.py) and [AMD](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_amd.py), which are userspace drivers for NVIDIA and AMD devices respectively. You can find more information about the API on [HCQ overview page](hcq.md)

docs/developer/hcq.md

@@ -1,128 +0,0 @@
-# HCQ Compatible Runtime
-
-## Overview
-
-The main aspect of HCQ-compatible runtimes is how they interact with devices. In HCQ, all interactions with devices occur in a hardware-friendly manner using [command queues](#command-queues). This approach allows commands to be issued directly to devices, bypassing runtime overhead such as HIP or CUDA. Additionally, by using the HCQ API, these runtimes can benefit from various optimizations and features, including [HCQGraph](#hcqgraph) and built-in profiling capabilities.
-
-### Command Queues
-
-To interact with devices you create a `HWQueue`. Some methods are required, like timestamp and synchronization methods like [signal](#tinygrad.runtime.support.hcq.HWQueue.signal) and [wait](#tinygrad.runtime.support.hcq.HWQueue.wait), while others are dependent on it being a compute or copy queue.
-
-For example, the following Python code enqueues a wait, execute, and signal command on the HCQ-compatible device:
-```python
-HWQueue().wait(signal_to_wait, value_to_wait) \
-         .exec(program, args_state, global_dims, local_dims) \
-         .signal(signal_to_fire, value_to_fire) \
-         .submit(your_device)
-```
-
-Each runtime should implement the required functions that are defined in the `HWQueue` classes.
-
-::: tinygrad.runtime.support.hcq.HWQueue
-    options:
-        members: [
-            "signal",
-            "wait",
-            "timestamp",
-            "bind",
-            "submit",
-            "memory_barrier",
-            "exec",
-            "copy",
-        ]
-        show_source: false
-
-### HCQ Compatible Device
-
-The `HCQCompiled` class defines the API for HCQ-compatible devices. This class serves as an abstract base class that device-specific implementations should inherit from and implement.
-
-::: tinygrad.runtime.support.hcq.HCQCompiled
-    options:
-        show_source: false
-
-#### Signals
-
-Signals are device-dependent structures used for synchronization and timing in HCQ-compatible devices. They should be designed to record both a `value` and a `timestamp` within the same signal. HCQ-compatible backend implementations should use `HCQSignal` as a base class.
-
-::: tinygrad.runtime.support.hcq.HCQSignal
-    options:
-        members: [value, timestamp, wait]
-        show_source: false
-
-The following Python code demonstrates the usage of signals:
-
-```python
-signal = your_device.new_signal(value=0)
-
-HWQueue().timestamp(signal) \
-         .signal(signal, value_to_fire) \
-         .submit(your_device)
-
-signal.wait(value_to_fire)
-signaled_value = signal.value # should be the same as `value_to_fire`
-timestamp = signal.timestamp
-```
-
-##### Synchronization signals
-
-Each HCQ-compatible device must allocate two signals for global synchronization purposes. These signals are passed to the `HCQCompiled` base class during initialization: an active timeline signal `self.timeline_signal` and a shadow timeline signal `self._shadow_timeline_signal` which helps to handle signal value overflow issues. You can find more about synchronization in the [synchronization section](#synchronization)
-
-### HCQ Compatible Allocator
-
-The `HCQAllocator` base class simplifies allocator logic by leveraging [command queues](#command-queues) abstractions. This class efficiently handles copy and transfer operations, leaving only the alloc and free functions to be implemented by individual backends.
-
-::: tinygrad.runtime.support.hcq.HCQAllocator
-    options:
-        members: [
-            "_alloc",
-            "_free",
-        ]
-        show_source: false
-
-#### HCQ Allocator Result Protocol
-
-Backends must adhere to the `HCQBuffer` protocol when returning allocation results.
-
-::: tinygrad.runtime.support.hcq.HCQBuffer
-    options:
-        members: true
-        show_source: false
-
-### HCQ Compatible Program
-
-`HCQProgram` is a base class for defining programs compatible with HCQ-enabled devices. It provides a flexible framework for handling different argument layouts (see `HCQArgsState`).
-
-::: tinygrad.runtime.support.hcq.HCQProgram
-    options:
-        members: true
-        show_source: false
-
-#### Arguments State
-
-`HCQArgsState` is a base class for managing the argument state for HCQ programs. Backend implementations should create a subclass of `HCQArgsState` to manage arguments for the given program.
-
-::: tinygrad.runtime.support.hcq.HCQArgsState
-    options:
-        members: true
-        show_source: false
-
-**Lifetime**: The `HCQArgsState` is passed to `HWQueue.exec` and is guaranteed not to be freed until `HWQueue.submit` for the same queue is called.
-
-### Synchronization
-
-HCQ-compatible devices use a global timeline signal for synchronizing all operations. This mechanism ensures proper ordering and completion of tasks across the device. By convention, `self.timeline_value` points to the next value to signal. So, to wait for all previous operations on the device to complete, wait for `self.timeline_value - 1` value. The following Python code demonstrates the typical usage of signals to synchronize execution to other operations on the device:
-
-```python
-HWQueue().wait(your_device.timeline_signal, your_device.timeline_value - 1) \
-         .exec(...)
-         .signal(your_device.timeline_signal, your_device.next_timeline()) \
-         .submit(your_device)
-
-# Optionally wait for execution
-your_device.timeline_signal.wait(your_device.timeline_value - 1)
-```
-
-## HCQGraph
-
-[HCQGraph](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/graph/hcq.py) is a core feature that implements `GraphRunner` for HCQ-compatible devices. `HCQGraph` builds static `HWQueue` for all operations per device. To optimize enqueue time, only the necessary parts of the queues are updated for each run using the symbolic variables, avoiding a complete rebuild.
-Optionally, queues can implement a `bind` API, which allows further optimization by eliminating the need to copy the queues into the device ring.

docs/developer/layout.md

@@ -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.rangeify.get_rangeify_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

docs/developer/runtime.md

@@ -1,51 +0,0 @@
-# Runtime Overview
-
-## Overview
-
-A typical runtime consists of the following parts:
-
-- [Compiled](#compiled)
-- [Allocator](#allocator)
-- [Program](#program)
-- [Compiler](#compiler)
-
-### Compiled
-
-The `Compiled` class is responsible for initializing and managing a device.
-
-::: tinygrad.device.Compiled
-    options:
-        members: [
-            "synchronize"
-        ]
-        show_source: false
-
-### Allocator
-
-The `Allocator` class is responsible for managing memory on the device. There is also a version called the `LRUAllocator`, which caches allocated buffers to optimize performance.
-
-::: tinygrad.device.Allocator
-    options:
-        members: true
-        show_source: false
-
-::: tinygrad.device.LRUAllocator
-    options:
-        members: true
-        show_source: false
-
-### Program
-
-The `Program` class is created for each loaded program. It is responsible for executing the program on the device. As an example, here is a `CPUProgram` implementation which loads program and runs it.
-
-::: tinygrad.runtime.ops_cpu.CPUProgram
-    options:
-        members: true
-
-### Compiler
-
-The `Compiler` class compiles the output from the `Renderer` and produces it in a device-specific format.
-
-::: tinygrad.device.Compiler
-    options:
-        members: true

docs/developer/speed.md

@@ -1,71 +0,0 @@
-# speed in tinygrad
-
-## Overview
-
-Speed refers to many different things. To break it down to four, there's:
-
-- Compile Speed (Python)
-- Execution Speed (driver)
-- Model Speed (scheduler)
-- Kernel Speed (codegen)
-
-## Compile Speed (Python)
-
-This is how long the first run of your model takes. It's limited largely by the runtime of the Python doing UOp rewrites. Currently it's a bit slow, but on par with torch.compile. It gets even slower if you are using BEAM, since that's compiling many variants of each kernel.
-
-This will be improved by writing faster graph_rewrite, doing less graph_rewrite, and better parallelization.
-
-## Execution Speed (driver)
-
-After your model is compiled, you are often using the `TinyJIT`. tinygrad has the best execution speed of any framework because it usually bypasses the GPU driver and prebuilds the command queue. It's tons faster than normal CUDA, and often even faster than CUDA Graph.
-
-There's very little to improve here, as this is almost never the bottleneck.
-
-## Model Speed (scheduler)
-
-The scheduler determines how operations are grouped into kernels and which Tensors are written to memory. This is currently a big bottleneck of training speed.
-
-The decisions are often not obvious. For example, when is it worth recomputing an arithmetic operation instead of storing and loading from memory? Example:
-
-```python
-from tinygrad import Tensor
-a = Tensor.rand(100)
-b = Tensor.rand(100)
-c = Tensor.rand(100)
-d = Tensor.rand(100)
-out1 = a+b+c
-out2 = a+b+d
-Tensor.realize(out1, out2)
-```
-
-The real answer is obvious, compute both `out1` and `out2` in the same kernel. But you can't always do that. If you can't, should `a+b` first be saved to a subbuffer? Or should both the `out1` and `out2` kernels recompute `a+b`?
-
-In this case: with recompute (6 reads + 2 writes), no recompute (6 reads + 3 writes), so we should probably recompute. However, once you add movement ops and casts this is even harder to figure out. tinygrad doesn't yet have a systematic way to do it.
-
-## Kernel Speed (codegen)
-
-Given that you have decided how the model ops will be grouped and what will be written to memory, kernel speed determines how fast that operation is done. This is what BEAM changes, it searches over a set of equivalent kernels which all perform the same operation and finds the one which performs the task the fastest.
-
-In `kernel.py` we have a set of `OptOps`, these control the parameters of the speed optimizations applied to the kernel.
-
-### Memory
-
-The main bottleneck in most kernels is accessing memory. In a freshman algorithms class, you'll learn about cache aware matrix multiplication, and this is all forms of that. While the same math is run, the order in which you run it can have large impacts on the speed depending on if the data you are loading. OptOps will change this order.
-
-Memory, even cache, is often much slower than accessing the register file. The amount of times data is used in math is called the "arithmetic intensity". For operations like BS=1 GEMV, the arithmetic intensity is 1, but for GEMMs and convs it can be much higher. OptOps like UPCAST and UNROLL can increase this, but be careful of making them too large, as if there's too much register pressure on the GPU the warp scheduler may not be able to fit many warps, or even worse, it could be spilling to local memory.
-
-4090s have 1 TB/s of ram bandwidth and ~160 TFLOPS of compute, so you need to use each loaded value ~100 times. The L1 cache has around 40 TB/s of bandwidth, so in order to get full compute utilization you need to use each value ~4 times.
-
-A lot of work can still be done here. For example, we never copy the inputs to on chip SRAM, but this is often quite helpful for kernel speed. Also, we aren't doing a good job with L2 cache awareness (the locals handle L1 quite well)
-
-### Tensor Cores
-
-Many accelerators have Tensor Cores / MAC arrays / systolic arrays. The main value of these is that, since they are 2-D, they create an n^2 ratio between the compute and the input data.
-
-GPUs use Tensor Cores instead of MAC arrays to fit better in the GPU warp paradigm. This is because the output of Tensor Cores is O(n) wrt the input, while the output of MAC arrays like the AMX is O(n^2)
-
-We have a simple framework in tinygrad for adding these ALU blocks and achieving good performance from them.
-
-### Indexing
-
-Indexing determines the address of the memory we need to load. GPUs often have less integer math resources than floating point math, so this can sometimes be the bottleneck. We have a symbolic math engine in our rewrite rules to simplify indexing before it's emitted to the kernel. Newer NVIDIA GPUs have a "Tensor Memory Accelerator" to assist with fast indexing, however, this is not supported in tinygrad yet.

docs/developer/uop.md

@@ -1,11 +0,0 @@
-::: tinygrad.uop.ops.UOp
-    options:
-        members: false
-        members_order: source
-        show_labels: false
-
-::: tinygrad.uop.ops.Ops
-    options:
-        members: true
-        members_order: source
-        show_labels: false

docs/dtypes.md

@@ -1,9 +0,0 @@
-::: tinygrad.dtype.DType
-
-::: tinygrad.dtype.dtypes
-    options:
-        members: true
-        members_order: source
-        show_labels: false
-
-::: tinygrad.dtype.ConstType

docs/env_vars.md

@@ -1,61 +0,0 @@
-# List of environment variables that control tinygrad behavior.
-
-This is a list of environment variable that control the runtime behavior of tinygrad and its examples.
-Most of these are self-explanatory, and are usually used to set an option at runtime.
-
-Example: `CL=1 DEBUG=4 python3 -m pytest`
-
-However you can also decorate a function to set a value only inside that function.
-
-```python
-# in tensor.py (probably only useful if you are a tinygrad developer)
-@Context(DEBUG=4)
-def numpy(self) -> ...
-```
-
-Or use contextmanager to temporarily set a value inside some scope:
-
-```python
-with Context(DEBUG=0):
-  a = Tensor.ones(10, 10)
-  a *= 2
-```
-
-## Global Variables
-The columns of this list are are: Variable, Possible Value(s) and Description.
-
-- A `#` means that the variable can take any integer value.
-
-These control the behavior of core tinygrad even when used as a library.
-
-Variable | Possible Value(s) | Description
----|---|---
-DEBUG               | [1-7]      | enable debugging output (operations, timings, speed, generated code and more)
-CL                  | [1]        | enable OpenCL backend
-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)
-CPU                 | [1]        | enable CPU backend
-BEAM                | [#]        | number of beams in kernel beam search
-DEFAULT_FLOAT       | [HALF, ...]| specify the default float dtype (FLOAT32, HALF, BFLOAT16, FLOAT64, ...), default to FLOAT32
-IMAGE               | [1-2]      | enable 2d specific optimizations
-FLOAT16             | [1]        | use float16 for images instead of float32
-HCQ_VISIBLE_DEVICES | [list[int]]| restricts the HCQ devices that are available. The format is a comma-separated list of identifiers (indexing starts with 0).
-JIT                 | [0-2]      | 0=disabled, 1=[jit enabled](quickstart.md#jit) (default), 2=jit enabled, but graphs are disabled
-VIZ                 | [1]        | 0=disabled, 1=[viz enabled](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/viz)
-ALLOW_TF32          | [1]        | enable TensorFloat-32 tensor cores on Ampere or newer GPUs.
-WEBGPU_BACKEND      | [WGPUBackendType_Metal, ...]          | Force select a backend for WebGPU (Metal, DirectX, OpenGL, Vulkan...)
-CUDA_PATH           | str        | Use `CUDA_PATH/include` for CUDA headers for CUDA and NV backends. If not set, TinyGrad will use `/usr/local/cuda/include`, `/usr/include` and `/opt/cuda/include`.
-
-## Debug breakdown
-
-Variable | Value | Description
----|---|---
-DEBUG               | >= 1       | Enables debugging and lists devices being used
-DEBUG               | >= 2       | Provides performance metrics for operations, including timing, memory usage, bandwidth for each kernel execution
-DEBUG               | >= 3       | Outputs buffers used for each kernel (shape, dtype and strides) and the applied optimizations at a kernel level
-DEBUG               | >= 4       | Outputs the generated kernel code
-DEBUG               | >= 5       | Displays the intermediate representation of the computation UOps (AST)
-DEBUG               | >= 6       | Displays the intermediate representation of the computation UOps in a linearized manner, detailing the operation sequence
-DEBUG               | >= 7       | Outputs the assembly code generated for the target hardware

docs/index.md

@@ -1,53 +0,0 @@
-# tinygrad documentation
-
-Welcome to the docs for tinygrad. This page is for users of the tinygrad library. tinygrad is not 1.0 yet, but it will be soon. The API has been pretty stable for a while.
-
-While you can `pip install tinygrad`, we encourage you to install from source:
-
-```bash
-git clone https://github.com/tinygrad/tinygrad.git
-cd tinygrad
-python3 -m pip install -e .
-```
-
-After you have installed tinygrad, try the [MNIST tutorial](mnist.md).
-
-If you are new to tensor libraries, learn how to use them by solving puzzles from [tinygrad-tensor-puzzles](https://github.com/obadakhalili/tinygrad-tensor-puzzles).
-
-We also have [developer docs](developer/developer.md), and Di Zhu has created a [bunch of tutorials](https://mesozoic-egg.github.io/tinygrad-notes/) to help understand how tinygrad works.
-
-## tinygrad Usage
-
-The main class you will interact with is [Tensor](tensor/index.md). It functions very similarly to PyTorch, but has a bit more of a functional style. tinygrad supports [many datatypes](dtypes.md).  All operations in tinygrad are lazy, meaning they won't do anything until you realize.
-
-* tinygrad has a built in [neural network library](nn.md) with some classes, optimizers, and load/save state management.
-* tinygrad has a JIT to make things fast. Decorate your pure function with `TinyJit`
-* tinygrad has amazing support for multiple GPUs, allowing you to shard your Tensors with `Tensor.shard`
-
-To understand what training looks like in tinygrad, you should read `beautiful_mnist.py`
-
-We have a [quickstart guide](quickstart.md) and a [showcase](showcase.md)
-
-## tinygrad Stack
-
-<img src="./tinygrad_vs_others.png" alt="Tinygrad vs others" style="max-width: 1000px; height: auto;" />
-
-## Differences from PyTorch
-
-If you are migrating from PyTorch, welcome. Most of the API is the same. We hope you will find tinygrad both familiar and somehow more "correct feeling"
-
-### tinygrad doesn't have nn.Module
-
-There's nothing special about a "Module" class in tinygrad, it's just a normal class. [`nn.state.get_parameters`](nn.md/#tinygrad.nn.state.get_parameters) can be used to recursively search normal classes for valid tensors. Instead of the `forward` method in PyTorch, tinygrad just uses `__call__`
-
-### tinygrad is functional
-
-In tinygrad, you can do [`x.conv2d(w, b)`](tensor/ops.md/#tinygrad.Tensor.conv2d) or [`x.sparse_categorical_crossentropy(y)`](tensor/ops.md/#tinygrad.Tensor.sparse_categorical_crossentropy). We do also have a [`Conv2D`](nn.md/#tinygrad.nn.Conv2d) class like PyTorch if you want a place to keep the state, but all stateless operations don't have classes.
-
-### tinygrad is lazy
-
-When you do `a+b` in tinygrad, nothing happens. It's not until you [`realize`](tensor/properties.md#tinygrad.Tensor.realize) the Tensor that the computation actually runs.
-
-### tinygrad requires @TinyJit to be fast
-
-PyTorch spends a lot of development effort to make dispatch very fast. tinygrad doesn't. We have a simple decorator that will replay the kernels used in the decorated function.

docs/mnist.md

@@ -1,185 +0,0 @@
-# MNIST Tutorial
-
-After you have installed tinygrad, this is a great first tutorial.
-
-Start up a notebook locally, or use [colab](https://colab.research.google.com/). tinygrad is very lightweight, so it's easy to install anywhere and doesn't need a special colab image, but for speed we recommend a T4 GPU image.
-
-### One-liner to install tinygrad in colab
-
-```python
-!pip install git+https://github.com/tinygrad/tinygrad.git
-```
-
-### What's the default device?
-
-```python
-from tinygrad import Device
-print(Device.DEFAULT)
-```
-
-You will see `CUDA` here on a GPU instance, or `CPU` here on a CPU instance.
-
-## A simple model
-
-We'll use the model from [the Keras tutorial](https://keras.io/examples/vision/mnist_convnet/).
-
-```python
-from tinygrad import Tensor, nn
-
-class Model:
-  def __init__(self):
-    self.l1 = nn.Conv2d(1, 32, kernel_size=(3,3))
-    self.l2 = nn.Conv2d(32, 64, kernel_size=(3,3))
-    self.l3 = nn.Linear(1600, 10)
-
-  def __call__(self, x:Tensor) -> Tensor:
-    x = self.l1(x).relu().max_pool2d((2,2))
-    x = self.l2(x).relu().max_pool2d((2,2))
-    return self.l3(x.flatten(1).dropout(0.5))
-```
-
-Two key differences from PyTorch:
-
-* Only the stateful layers are declared in `__init__`
-* There's no `nn.Module` class or `forward` function, just a normal class and `__call__`
-
-### Getting the dataset
-
-```python
-from tinygrad.nn.datasets import mnist
-X_train, Y_train, X_test, Y_test = mnist()
-print(X_train.shape, X_train.dtype, Y_train.shape, Y_train.dtype)
-# (60000, 1, 28, 28) dtypes.uchar (60000,) dtypes.uchar
-```
-
-tinygrad includes MNIST, it only adds four lines. Feel free to read the [function](https://github.com/tinygrad/tinygrad/blob/master/tinygrad/nn/datasets.py).
-
-## Using the model
-
-MNIST is small enough that the `mnist()` function copies the dataset to the default device.
-
-So creating the model and evaluating it is a matter of:
-
-```python
-model = Model()
-acc = (model(X_test).argmax(axis=1) == Y_test).mean()
-# NOTE: tinygrad is lazy, and hasn't actually run anything by this point
-print(acc.item())  # ~10% accuracy, as expected from a random model
-```
-
-### Training the model
-
-We'll use the Adam optimizer. The `nn.state.get_parameters` will walk the model class and pull out the parameters for the optimizer. Also, in tinygrad, it's typical to write a function to do the training step so it can be jitted.
-
-```python
-optim = nn.optim.Adam(nn.state.get_parameters(model))
-batch_size = 128
-def step():
-  Tensor.training = True  # makes dropout work
-  samples = Tensor.randint(batch_size, high=X_train.shape[0])
-  X, Y = X_train[samples], Y_train[samples]
-  optim.zero_grad()
-  loss = model(X).sparse_categorical_crossentropy(Y).backward()
-  optim.step()
-  return loss
-```
-
-You can time a step with:
-
-```python
-import timeit
-timeit.repeat(step, repeat=5, number=1)
-#[0.08268719699981375,
-# 0.07478952900009972,
-# 0.07714716600003158,
-# 0.07785399599970333,
-# 0.07605237000007037]
-```
-
-So around 75 ms on T4 colab.
-
-If you want to see a breakdown of the time by kernel:
-
-```python
-from tinygrad import GlobalCounters, Context
-GlobalCounters.reset()
-with Context(DEBUG=2): step()
-```
-
-### Why so slow?
-
-Unlike PyTorch, tinygrad isn't designed to be fast like that. While 75 ms for one step is plenty fast for debugging, it's not great for training. Here, we introduce the first quintessentially tinygrad concept, the `TinyJit`.
-
-```python
-from tinygrad import TinyJit
-jit_step = TinyJit(step)
-```
-
-NOTE: It can also be used as a decorator `@TinyJit`
-
-Now when we time it:
-
-```python
-import timeit
-timeit.repeat(jit_step, repeat=5, number=1)
-# [0.2596786549997887,
-#  0.08989566299987928,
-#  0.0012115650001760514,
-#  0.001010227999813651,
-#  0.0012164899999334011]
-```
-
-1.0 ms is 75x faster! Note that we aren't syncing the GPU, so GPU time may be slower.
-
-The slowness the first two times is the JIT capturing the kernels. And this JIT will not run any Python in the function, it will just replay the tinygrad kernels that were run, so be aware that non tinygrad Python operations won't work. Randomness functions work as expected.
-
-Unlike other JITs, we JIT everything, including the optimizer. Think of it as a dumb replay on different data.
-
-## Putting it together
-
-Since we are just randomly sampling from the dataset, there's no real concept of an epoch. We have a batch size of 128, so the Keras example is taking about 7000 steps.
-
-```python
-for step in range(7000):
-  loss = jit_step()
-  if step%100 == 0:
-    Tensor.training = False
-    acc = (model(X_test).argmax(axis=1) == Y_test).mean().item()
-    print(f"step {step:4d}, loss {loss.item():.2f}, acc {acc*100.:.2f}%")
-```
-
-It doesn't take long to reach 98%, and it usually reaches 99%.
-
-```
-step    0, loss 4.03, acc 71.43%
-step  100, loss 0.34, acc 93.86%
-step  200, loss 0.23, acc 95.97%
-step  300, loss 0.18, acc 96.32%
-step  400, loss 0.18, acc 96.76%
-step  500, loss 0.13, acc 97.46%
-step  600, loss 0.14, acc 97.45%
-step  700, loss 0.10, acc 97.27%
-step  800, loss 0.23, acc 97.49%
-step  900, loss 0.13, acc 97.51%
-step 1000, loss 0.13, acc 97.88%
-step 1100, loss 0.11, acc 97.72%
-step 1200, loss 0.14, acc 97.65%
-step 1300, loss 0.12, acc 98.04%
-step 1400, loss 0.25, acc 98.17%
-step 1500, loss 0.11, acc 97.86%
-step 1600, loss 0.21, acc 98.21%
-step 1700, loss 0.14, acc 98.34%
-...
-```
-
-## From here?
-
-tinygrad is yours to play with now. It's pure Python and short, so unlike PyTorch, fixing library bugs is well within your abilities.
-
-- It's two lines to add multiGPU support to this example (can you find them?). You have to `.shard` the model to all GPUs, and `.shard` the dataset by batch.
-- `with Context(DEBUG=2)` shows the running kernels, `DEBUG=4` shows the code. All `Context` variables can also be environment variables.
-- `with Context(BEAM=2)` will do a BEAM search on the kernels, searching many possible implementations for what runs the fastest on your hardware. After this search, tinygrad is usually speed competitive with PyTorch, and the results are cached so you won't have to search next time.
-
-[Join our Discord](https://discord.gg/ZjZadyC7PK) for help, and if you want to be a tinygrad developer. Please read the Discord rules when you get there.
-
-[Follow us on Twitter](https://twitter.com/__tinygrad__) to keep up with the project.

docs/nn.md

@@ -1,40 +0,0 @@
-## Neural Network classes
-
-::: tinygrad.nn.BatchNorm
-::: tinygrad.nn.Conv1d
-::: tinygrad.nn.Conv2d
-::: tinygrad.nn.ConvTranspose1d
-::: tinygrad.nn.ConvTranspose2d
-::: tinygrad.nn.Linear
-::: tinygrad.nn.GroupNorm
-::: tinygrad.nn.InstanceNorm
-::: tinygrad.nn.LayerNorm
-::: tinygrad.nn.LayerNorm2d
-::: tinygrad.nn.RMSNorm
-::: tinygrad.nn.Embedding
-::: tinygrad.nn.LSTMCell
-
-## Optimizers
-
-::: tinygrad.nn.optim.SGD
-::: tinygrad.nn.optim.LARS
-::: tinygrad.nn.optim.AdamW
-::: tinygrad.nn.optim.Adam
-::: tinygrad.nn.optim.LAMB
-
-## Load/Save
-
-::: tinygrad.nn.state.safe_load
-::: tinygrad.nn.state.safe_save
-::: tinygrad.nn.state.get_state_dict
-::: tinygrad.nn.state.get_parameters
-::: tinygrad.nn.state.load_state_dict
-::: tinygrad.nn.state.tar_extract
-    options:
-        show_signature: false
-        separate_signature: false
-::: tinygrad.nn.state.torch_load
-    options:
-        show_signature: false
-        separate_signature: false
-::: tinygrad.nn.state.gguf_load

docs/quickstart.md

@@ -1,305 +0,0 @@
-# Quick Start Guide
-
-This guide assumes no prior knowledge of pytorch or any other deep learning framework, but does assume some basic knowledge of neural networks.
-It is intended to be a very quick overview of the high level API that tinygrad provides.
-
-This guide is also structured as a tutorial which at the end of it you will have a working model that can classify handwritten digits.
-
-We need some imports to get started:
-
-```python
-import numpy as np
-from tinygrad.helpers import Timing
-```
-
-## Tensors
-
-Tensors are the base data structure in tinygrad. They can be thought of as a multidimensional array of a specific data type.
-All high level operations in tinygrad operate on these tensors.
-
-The tensor class can be imported like so:
-
-```python
-from tinygrad import Tensor
-```
-
-Tensors can be created from an existing data structure like a python list or numpy ndarray:
-
-```python
-t1 = Tensor([1, 2, 3, 4, 5])
-na = np.array([1, 2, 3, 4, 5])
-t2 = Tensor(na)
-```
-
-Tensors can also be created using one of the many factory methods:
-
-```python
-full = Tensor.full(shape=(2, 3), fill_value=5) # create a tensor of shape (2, 3) filled with 5
-zeros = Tensor.zeros(2, 3) # create a tensor of shape (2, 3) filled with 0
-ones = Tensor.ones(2, 3) # create a tensor of shape (2, 3) filled with 1
-
-full_like = Tensor.full_like(full, fill_value=2) # create a tensor of the same shape as `full` filled with 2
-zeros_like = Tensor.zeros_like(full) # create a tensor of the same shape as `full` filled with 0
-ones_like = Tensor.ones_like(full) # create a tensor of the same shape as `full` filled with 1
-
-eye = Tensor.eye(3) # create a 3x3 identity matrix
-arange = Tensor.arange(start=0, stop=10, step=1) # create a tensor of shape (10,) filled with values from 0 to 9
-
-rand = Tensor.rand(2, 3) # create a tensor of shape (2, 3) filled with random values from a uniform distribution
-randn = Tensor.randn(2, 3) # create a tensor of shape (2, 3) filled with random values from a standard normal distribution
-uniform = Tensor.uniform(2, 3, low=0, high=10) # create a tensor of shape (2, 3) filled with random values from a uniform distribution between 0 and 10
-```
-
-There are even more of these factory methods, you can find them in the [Tensor Creation](tensor/creation.md) file.
-
-All the tensors creation methods can take a `dtype` argument to specify the data type of the tensor, find the supported `dtype` in [dtypes](dtypes.md).
-
-```python
-from tinygrad import dtypes
-
-t3 = Tensor([1, 2, 3, 4, 5], dtype=dtypes.int32)
-```
-
-Tensors allow you to perform operations on them like so:
-
-```python
-t4 = Tensor([1, 2, 3, 4, 5])
-t5 = (t4 + 1) * 2
-t6 = (t5 * t4).relu().log_softmax()
-```
-
-All of these operations are lazy and are only executed when you realize the tensor using `.realize()` or `.numpy()`.
-
-```python
-print(t6.numpy())
-# [-56. -48. -36. -20.   0.]
-```
-
-There are a lot more operations that can be performed on tensors, you can find them in the [Tensor Ops](tensor/ops.md) file.
-Additionally reading through [abstractions2.py](https://github.com/tinygrad/tinygrad/blob/master/docs/abstractions2.py) will help you understand how operations on these tensors make their way down to your hardware.
-
-## Models
-
-Neural networks in tinygrad are really just represented by the operations performed on tensors.
-These operations are commonly grouped into the `__call__` method of a class which allows modularization and reuse of these groups of operations.
-These classes do not need to inherit from any base class, in fact if they don't need any trainable parameters they don't even need to be a class!
-
-An example of this would be the `nn.Linear` class which represents a linear layer in a neural network.
-
-```python
-class Linear:
-  def __init__(self, in_features, out_features, bias=True, initialization: str='kaiming_uniform'):
-    self.weight = getattr(Tensor, initialization)(out_features, in_features)
-    self.bias = Tensor.zeros(out_features) if bias else None
-
-  def __call__(self, x):
-    return x.linear(self.weight.transpose(), self.bias)
-```
-
-There are more neural network modules already implemented in [nn](nn.md), and you can also implement your own.
-
-We will be implementing a simple neural network that can classify handwritten digits from the MNIST dataset.
-Our classifier will be a simple 2 layer neural network with a Leaky ReLU activation function.
-It will use a hidden layer size of 128 and an output layer size of 10 (one for each digit) with no bias on either Linear layer.
-
-```python
-class TinyNet:
-  def __init__(self):
-    self.l1 = Linear(784, 128, bias=False)
-    self.l2 = Linear(128, 10, bias=False)
-
-  def __call__(self, x):
-    x = self.l1(x)
-    x = x.leaky_relu()
-    x = self.l2(x)
-    return x
-
-net = TinyNet()
-```
-
-We can see that the forward pass of our neural network is just the sequence of operations performed on the input tensor `x`.
-We can also see that functional operations like `leaky_relu` are not defined as classes and instead are just methods we can just call.
-Finally, we just initialize an instance of our neural network, and we are ready to start training it.
-
-## Training
-
-Now that we have our neural network defined we can start training it.
-Training neural networks in tinygrad is super simple.
-All we need to do is define our neural network, define our loss function, and then call `.backward()` on the loss function to compute the gradients.
-They can then be used to update the parameters of our neural network using one of the many [Optimizers](nn.md#optimizers).
-
-For our loss function we will be using sparse categorical cross entropy loss. The implementation below is taken from [tensor.py](https://github.com/tinygrad/tinygrad/blob/master/tinygrad/tensor.py), it's copied below to highlight an important detail of tinygrad.
-
-```python
-def sparse_categorical_crossentropy(self, Y, ignore_index=-1) -> Tensor:
-    loss_mask = Y != ignore_index
-    y_counter = Tensor.arange(self.shape[-1], dtype=dtypes.int32, requires_grad=False, device=self.device).unsqueeze(0).expand(Y.numel(), self.shape[-1])
-    y = ((y_counter == Y.flatten().reshape(-1, 1)).where(-1.0, 0) * loss_mask.reshape(-1, 1)).reshape(*Y.shape, self.shape[-1])
-    return self.log_softmax().mul(y).sum() / loss_mask.sum()
-```
-
-As we can see in this implementation of cross entropy loss, there are certain operations that tinygrad does not support natively.
-Load/store ops are not supported in tinygrad natively because they add complexity when trying to port to different backends, 90% of the models out there don't use/need them, and they can be implemented like it's done above with an `arange` mask.
-
-For our optimizer we will be using the traditional stochastic gradient descent optimizer with a learning rate of 3e-4.
-
-```python
-from tinygrad.nn.optim import SGD
-
-opt = SGD([net.l1.weight, net.l2.weight], lr=3e-4)
-```
-
-We can see that we are passing in the parameters of our neural network to the optimizer.
-This is due to the fact that the optimizer needs to know which parameters to update.
-There is a simpler way to do this just by using `get_parameters(net)` from `tinygrad.nn.state` which will return a list of all the parameters in the neural network.
-The parameters are just listed out explicitly here for clarity.
-
-Now that we have our network, loss function, and optimizer defined all we are missing is the data to train on!
-There are a couple of dataset loaders in tinygrad located in [/extra/datasets](https://github.com/tinygrad/tinygrad/blob/master/extra/datasets).
-We will be using the MNIST dataset loader.
-
-```python
-from extra.datasets import fetch_mnist
-```
-
-Now we have everything we need to start training our neural network.
-We will be training for 1000 steps with a batch size of 64.
-
-We use `with Tensor.train()` to set the internal flag `Tensor.training` to `True` during training.
-Upon exit, the flag is restored to its previous value by the context manager.
-
-```python
-X_train, Y_train, X_test, Y_test = fetch_mnist()
-
-with Tensor.train():
-  for step in range(1000):
-    # random sample a batch
-    samp = np.random.randint(0, X_train.shape[0], size=(64))
-    batch = Tensor(X_train[samp], requires_grad=False)
-    # get the corresponding labels
-    labels = Tensor(Y_train[samp])
-
-    # forward pass
-    out = net(batch)
-
-    # compute loss
-    loss = sparse_categorical_crossentropy(out, labels)
-
-    # zero gradients
-    opt.zero_grad()
-
-    # backward pass
-    loss.backward()
-
-    # update parameters
-    opt.step()
-
-    # calculate accuracy
-    pred = out.argmax(axis=-1)
-    acc = (pred == labels).mean()
-
-    if step % 100 == 0:
-      print(f"Step {step+1} | Loss: {loss.numpy()} | Accuracy: {acc.numpy()}")
-```
-
-## Evaluation
-
-Now that we have trained our neural network we can evaluate it on the test set.
-We will be using the same batch size of 64 and will be evaluating for 1000 of those batches.
-
-```python
-with Timing("Time: "):
-  avg_acc = 0
-  for step in range(1000):
-    # random sample a batch
-    samp = np.random.randint(0, X_test.shape[0], size=(64))
-    batch = Tensor(X_test[samp], requires_grad=False)
-    # get the corresponding labels
-    labels = Y_test[samp]
-
-    # forward pass
-    out = net(batch)
-
-    # calculate accuracy
-    pred = out.argmax(axis=-1).numpy()
-    avg_acc += (pred == labels).mean()
-  print(f"Test Accuracy: {avg_acc / 1000}")
-```
-
-## And that's it
-
-Highly recommend you check out the [examples/](https://github.com/tinygrad/tinygrad/blob/master/examples) folder for more examples of using tinygrad.
-Reading the source code of tinygrad is also a great way to learn how it works.
-Specifically the tests in [test/](https://github.com/tinygrad/tinygrad/blob/master/test) are a great place to see how to use and the semantics of the different operations.
-There are also a bunch of models implemented in [models/](https://github.com/tinygrad/tinygrad/blob/master/extra/models) that you can use as a reference.
-
-Additionally, feel free to ask questions in the `#learn-tinygrad` channel on the [discord](https://discord.gg/beYbxwxVdx). Don't ask to ask, just ask!
-
-## Extras
-
-### JIT
-
-Additionally, it is possible to speed up the computation of certain neural networks by using the JIT.
-Currently, this does not support models with varying input sizes and non tinygrad operations.
-
-To use the JIT we just need to add a function decorator to the forward pass of our neural network and ensure that the input and output are realized tensors.
-Or in this case we will create a wrapper function and decorate the wrapper function to speed up the evaluation of our neural network.
-
-```python
-from tinygrad import TinyJit
-
-@TinyJit
-def jit(x):
-  return net(x).realize()
-
-with Timing("Time: "):
-  avg_acc = 0
-  for step in range(1000):
-    # random sample a batch
-    samp = np.random.randint(0, X_test.shape[0], size=(64))
-    batch = Tensor(X_test[samp], requires_grad=False)
-    # get the corresponding labels
-    labels = Y_test[samp]
-
-    # forward pass with jit
-    out = jit(batch)
-
-    # calculate accuracy
-    pred = out.argmax(axis=-1).numpy()
-    avg_acc += (pred == labels).mean()
-  print(f"Test Accuracy: {avg_acc / 1000}")
-```
-
-You will find that the evaluation time is much faster than before and that your accelerator utilization is much higher.
-
-### Saving and Loading Models
-
-The standard weight format for tinygrad is [safetensors](https://github.com/huggingface/safetensors). This means that you can load the weights of any model also using safetensors into tinygrad.
-There are functions in [state.py](https://github.com/tinygrad/tinygrad/blob/master/tinygrad/nn/state.py) to save and load models to and from this format.
-
-```python
-from tinygrad.nn.state import safe_save, safe_load, get_state_dict, load_state_dict
-
-# first we need the state dict of our model
-state_dict = get_state_dict(net)
-
-# then we can just save it to a file
-safe_save(state_dict, "model.safetensors")
-
-# and load it back in
-state_dict = safe_load("model.safetensors")
-load_state_dict(net, state_dict)
-```
-
-Many of the models in the [models/](https://github.com/tinygrad/tinygrad/tree/master/extra/models) folder have a `load_from_pretrained` method that will download and load the weights for you. These usually are pytorch weights meaning that you would need pytorch installed to load them.
-
-### Environment Variables
-
-There exist a bunch of environment variables that control the runtime behavior of tinygrad.
-Some of the commons ones are `DEBUG` and the different backend enablement variables.
-
-You can find a full list and their descriptions in [env_vars.md](env_vars.md).
-
-### Visualizing the Computation Graph
-
-It is possible to visualize the computation graph of a neural network using VIZ=1.

docs/ramp.py

@@ -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

docs/runtime.md

@@ -1,81 +0,0 @@
-# Runtimes
-
-tinygrad supports various runtimes, enabling your code to scale across a wide range of devices. The default runtime can be automatically selected based on the available hardware, or you can force a specific runtime to be default using environment variables (e.g., `CPU=1`).
-
-| Runtime | Description | Compiler Options | Requirements |
-|---------|-------------|------------------|--------------|
-| [NV](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_nv.py) | Provides acceleration for NVIDIA GPUs | nvrtc (default)<br>PTX (`NV_PTX=1`) | Ampere/Ada/Blackwell series GPUs.<br>You can select an interface via `NV_IFACE=(NVK\|PCI)`. See [NV interfaces](#nv-interfaces) for details. |
-| [AMD](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_amd.py) | Provides acceleration for AMD GPUs | LLVM (`AMD_LLVM=1`)<br>HIP/COMGR (`AMD_HIP=1`) | RDNA2 or newer GPUs.<br>You can select an interface via `AMD_IFACE=(KFD\|PCI\|USB)`. See [AMD interfaces](#amd-interfaces) for details. |
-| [QCOM](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_qcom.py) | Provides acceleration for QCOM GPUs | - | 6xx series GPUs |
-| [METAL](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_metal.py) | Utilizes Metal for acceleration on Apple devices | - | M1+ Macs; Metal 3.0+ for `bfloat` support |
-| [CUDA](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_cuda.py) | Utilizes CUDA for acceleration on NVIDIA GPUs | nvrtc (default)<br> PTX (`CUDA_PTX=1`) | NVIDIA GPU with CUDA support |
-| [CL](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_cl.py) | Accelerates computations using OpenCL on GPUs | - | OpenCL 2.0 compatible device |
-| [CPU](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_cpu.py) | Runs on CPU using the clang or llvm compiler | Clang JIT (default)<br>LLVM IR (`CPU_LLVM=1`) | `clang` compiler in system `PATH` |
-| [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 discoverable. Binaries: [pydawn v0.3.0](https://github.com/wpmed92/pydawn/releases/tag/v0.3.0) |
-
-
-## Interoperability
-
-tinygrad provides interoperability with OpenCL and PyTorch, allowing efficient tensor data sharing between frameworks through the `Tensor.from_blob` API. This enables zero-copy operations by working directly with external memory pointers.
-
-**Important**: When using external memory pointers with tinygrad tensors, you must ensure these pointers remain valid throughout the entire lifetime of the tinygrad tensor to prevent memory corruption.
-
-### `CUDA`/`METAL` PyTorch Interoperability
-
-You can seamlessly work with CUDA/MPS tensors between PyTorch and tinygrad without data copying:
-```python
-from tinygrad.dtype import _from_torch_dtype
-tensor1 = torch.tensor([1.0, 2.0, 3.0], device=torch.device("cuda"))
-tiny_tensor1 = Tensor.from_blob(tensor1.data_ptr(), tensor1.shape, dtype=_from_torch_dtype(tensor1.dtype), device='CUDA')
-
-# Before tinygrad calculations, mps needs to be synchronized to make sure data is valid.
-if data.device.type == "mps": torch.mps.synchronize()
-else: torch.cuda.synchronize()
-
-x = (tiny_tensor1 + 1).realize()
-```
-
-### `QCOM` OpenCL Interoperability
-
-tinygrad supports OpenCL interoperability on `QCOM` backend.
-
-Buffer interop allows direct access to OpenCL memory buffers:
-```python
-# create raw opencl buffer.
-cl_buf = cl.clCreateBuffer(cl_context, cl.CL_MEM_READ_WRITE, 0x100, None, status := ctypes.c_int32())
-
-# extract pointers
-cl_buf_desc_ptr = to_mv(ctypes.addressof(cl_buf), 8).cast('Q')[0]
-rawbuf_ptr = to_mv(cl_buf_desc_ptr, 0x100).cast('Q')[20] # offset 0xA0 is a raw gpu pointer.
-
-# create tiny tensor
-tiny = Tensor.from_blob(rawbuf_ptr, (8, 8), dtype=dtypes.int, device='QCOM')
-```
-
-And the same for the images:
-```python
-# create cl image.
-cl_img = cl.clCreateImage2D(cl_context, cl.CL_MEM_READ_WRITE, cl.cl_image_format(cl.CL_RGBA, cl.CL_FLOAT), w, h, 0, None, status := ctypes.c_int32())
-
-# extract pointers
-cl_buf_desc_ptr = to_mv(ctypes.addressof(cl_img), 8).cast('Q')[0]
-rawbuf_ptr = to_mv(cl_buf_desc_ptr, 0x100).cast('Q')[20] # offset 0xA0 is a raw gpu pointer.
-
-# create tiny tensor
-tiny = Tensor.from_blob(rawbuf_ptr, (h*w*4,), dtype=dtypes.imagef((h,w)), device='QCOM')
-```
-
-## AMD Interfaces
-AMD backend supports several interfaces for communicating with devices:
-
-* `KFD`: uses the amdgpu driver
-* `PCI`: uses the [AM driver](developer/am.md)
-* `USB`: USB3 interafce for asm24xx chips.
-
-You can force an interface by setting `AMD_IFACE` to one of these values. In the case of `AMD_IFACE=PCI`, this may unbind your GPU from the amdgpu driver.
-
-## NV Interfaces
-NV backend supports several interfaces for communicating with devices:
-
-* `NVK`: uses the nvidia driver
-* `PCI`: uses the [NV driver](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/support/nv/nvdev.py)

docs/showcase.md

@@ -1,62 +0,0 @@
-# Showcase
-
-Despite being a tiny library, tinygrad is capable of doing a lot of things. From state-of-the-art [vision](https://arxiv.org/abs/1905.11946) to state-of-the-art [language](https://arxiv.org/abs/1706.03762) models.
-
-## Vision
-
-### EfficientNet
-
-You can either pass in the URL of a picture to discover what it is:
-```sh
-python3 examples/efficientnet.py ./test/models/efficientnet/Chicken.jpg
-```
-Or, if you have a camera and OpenCV installed, you can detect what is in front of you:
-```sh
-python3 examples/efficientnet.py webcam
-```
-
-### YOLOv8
-
-Take a look at [yolov8.py](https://github.com/tinygrad/tinygrad/tree/master/examples/yolov8.py).
-
-![yolov8 by tinygrad](https://github.com/tinygrad/tinygrad/blob/master/docs/showcase/yolov8_showcase_image.png?raw=true)
-
-## Audio
-
-### Whisper
-
-Take a look at [whisper.py](https://github.com/tinygrad/tinygrad/tree/master/examples/whisper.py). You need pyaudio and torchaudio installed.
-
-```sh
-SMALL=1 python3 examples/whisper.py
-```
-
-## Generative
-
-### Stable Diffusion
-
-```sh
-python3 examples/stable_diffusion.py
-```
-
-![a horse sized cat eating a bagel](https://github.com/tinygrad/tinygrad/blob/master/docs/showcase/stable_diffusion_by_tinygrad.jpg?raw=true)
-
-*"a horse sized cat eating a bagel"*
-
-### LLaMA
-
-You will need to download and put the weights into the `weights/LLaMA` directory, which may need to be created.
-
-Then you can have a chat with Stacy:
-```sh
-python3 examples/llama.py
-```
-
-### Conversation
-
-Make sure you have espeak installed and `PHONEMIZER_ESPEAK_LIBRARY` set.
-
-Then you can talk to Stacy:
-```sh
-python3 examples/conversation.py
-```

docs/tensor/creation.md

@@ -1,33 +0,0 @@
-## Creation (basic)
-
-::: tinygrad.Tensor.empty
-::: tinygrad.Tensor.zeros
-::: tinygrad.Tensor.ones
-::: tinygrad.Tensor.full
-::: tinygrad.Tensor.arange
-::: tinygrad.Tensor.linspace
-::: tinygrad.Tensor.eye
-::: tinygrad.Tensor.full_like
-::: tinygrad.Tensor.zeros_like
-::: tinygrad.Tensor.ones_like
-
-## Creation (external)
-
-::: tinygrad.Tensor.from_blob
-::: tinygrad.Tensor.from_url
-
-## Creation (random)
-
-::: tinygrad.Tensor.manual_seed
-::: tinygrad.Tensor.rand
-::: tinygrad.Tensor.rand_like
-::: tinygrad.Tensor.randn
-::: tinygrad.Tensor.randn_like
-::: tinygrad.Tensor.randint
-::: tinygrad.Tensor.randperm
-::: tinygrad.Tensor.normal
-::: tinygrad.Tensor.uniform
-::: tinygrad.Tensor.scaled_uniform
-::: tinygrad.Tensor.glorot_uniform
-::: tinygrad.Tensor.kaiming_uniform
-::: tinygrad.Tensor.kaiming_normal

docs/tensor/elementwise.md

@@ -1,90 +0,0 @@
-Elementwise ops operate on a per element basis. They don't change the shape of the tensor.
-
-## Unary Ops (math)
-
-::: tinygrad.Tensor.logical_not
-::: tinygrad.Tensor.neg
-::: tinygrad.Tensor.log
-::: tinygrad.Tensor.log2
-::: tinygrad.Tensor.exp
-::: tinygrad.Tensor.exp2
-::: tinygrad.Tensor.sqrt
-::: tinygrad.Tensor.rsqrt
-::: tinygrad.Tensor.sin
-::: tinygrad.Tensor.cos
-::: tinygrad.Tensor.tan
-::: tinygrad.Tensor.asin
-::: tinygrad.Tensor.acos
-::: tinygrad.Tensor.atan
-::: tinygrad.Tensor.trunc
-::: tinygrad.Tensor.ceil
-::: tinygrad.Tensor.floor
-::: tinygrad.Tensor.round
-::: tinygrad.Tensor.isinf
-::: tinygrad.Tensor.isnan
-::: tinygrad.Tensor.isfinite
-::: tinygrad.Tensor.lerp
-::: tinygrad.Tensor.square
-::: tinygrad.Tensor.clamp
-::: tinygrad.Tensor.clip
-::: tinygrad.Tensor.sign
-::: tinygrad.Tensor.abs
-::: tinygrad.Tensor.reciprocal
-
-## Unary Ops (activation)
-
-::: tinygrad.Tensor.relu
-::: tinygrad.Tensor.sigmoid
-::: tinygrad.Tensor.logsigmoid
-::: tinygrad.Tensor.hardsigmoid
-::: tinygrad.Tensor.elu
-::: tinygrad.Tensor.celu
-::: tinygrad.Tensor.selu
-::: tinygrad.Tensor.swish
-::: tinygrad.Tensor.silu
-::: tinygrad.Tensor.relu6
-::: tinygrad.Tensor.hardswish
-::: tinygrad.Tensor.tanh
-::: tinygrad.Tensor.sinh
-::: tinygrad.Tensor.cosh
-::: tinygrad.Tensor.atanh
-::: tinygrad.Tensor.asinh
-::: tinygrad.Tensor.acosh
-::: tinygrad.Tensor.hardtanh
-::: tinygrad.Tensor.erf
-::: tinygrad.Tensor.gelu
-::: tinygrad.Tensor.quick_gelu
-::: tinygrad.Tensor.leaky_relu
-::: tinygrad.Tensor.mish
-::: tinygrad.Tensor.softplus
-::: tinygrad.Tensor.softsign
-
-## Elementwise Ops (broadcasted)
-
-::: tinygrad.Tensor.add
-::: tinygrad.Tensor.sub
-::: tinygrad.Tensor.mul
-::: tinygrad.Tensor.div
-::: tinygrad.Tensor.idiv
-::: tinygrad.Tensor.mod
-::: tinygrad.Tensor.bitwise_xor
-::: tinygrad.Tensor.bitwise_and
-::: tinygrad.Tensor.bitwise_or
-::: tinygrad.Tensor.bitwise_not
-::: tinygrad.Tensor.lshift
-::: tinygrad.Tensor.rshift
-::: tinygrad.Tensor.pow
-::: tinygrad.Tensor.maximum
-::: tinygrad.Tensor.minimum
-::: tinygrad.Tensor.where
-::: tinygrad.Tensor.copysign
-::: tinygrad.Tensor.logaddexp
-
-## Casting Ops
-
-::: tinygrad.Tensor.cast
-::: tinygrad.Tensor.bitcast
-::: tinygrad.Tensor.float
-::: tinygrad.Tensor.half
-::: tinygrad.Tensor.int
-::: tinygrad.Tensor.bool

docs/tensor/index.md

@@ -1,7 +0,0 @@
-# Tensor
-
-::: tinygrad.Tensor
-    options:
-        heading_level: 2
-        members: false
-        show_source: false

docs/tensor/movement.md

@@ -1,31 +0,0 @@
-## Movement (low level)
-
-::: tinygrad.Tensor.view
-::: tinygrad.Tensor.reshape
-::: tinygrad.Tensor.expand
-::: tinygrad.Tensor.permute
-::: tinygrad.Tensor.flip
-::: tinygrad.Tensor.shrink
-::: tinygrad.Tensor.pad
-
-## Movement (high level)
-
-::: tinygrad.Tensor.__getitem__
-::: tinygrad.Tensor.gather
-::: tinygrad.Tensor.cat
-::: tinygrad.Tensor.stack
-::: tinygrad.Tensor.repeat
-::: tinygrad.Tensor.repeat_interleave
-::: tinygrad.Tensor.split
-::: tinygrad.Tensor.chunk
-::: tinygrad.Tensor.unfold
-::: tinygrad.Tensor.meshgrid
-::: tinygrad.Tensor.squeeze
-::: tinygrad.Tensor.unsqueeze
-::: tinygrad.Tensor.T
-::: tinygrad.Tensor.transpose
-::: tinygrad.Tensor.flatten
-::: tinygrad.Tensor.unflatten
-::: tinygrad.Tensor.diag
-::: tinygrad.Tensor.roll
-::: tinygrad.Tensor.rearrange

docs/tensor/ops.md

@@ -1,58 +0,0 @@
-## Reduce
-
-::: tinygrad.Tensor.sum
-::: tinygrad.Tensor.prod
-::: tinygrad.Tensor.max
-::: tinygrad.Tensor.min
-::: tinygrad.Tensor.any
-::: tinygrad.Tensor.all
-::: tinygrad.Tensor.isclose
-::: tinygrad.Tensor.mean
-::: tinygrad.Tensor.var
-::: tinygrad.Tensor.var_mean
-::: tinygrad.Tensor.std
-::: tinygrad.Tensor.std_mean
-::: tinygrad.Tensor.softmax
-::: tinygrad.Tensor.log_softmax
-::: tinygrad.Tensor.logsumexp
-::: tinygrad.Tensor.logcumsumexp
-::: tinygrad.Tensor.argmax
-::: tinygrad.Tensor.argmin
-
-## Processing
-
-::: tinygrad.Tensor.avg_pool2d
-::: tinygrad.Tensor.max_pool2d
-::: tinygrad.Tensor.max_unpool2d
-::: tinygrad.Tensor.conv2d
-::: tinygrad.Tensor.conv_transpose2d
-::: tinygrad.Tensor.dot
-::: tinygrad.Tensor.matmul
-::: tinygrad.Tensor.einsum
-::: tinygrad.Tensor.cumsum
-::: tinygrad.Tensor.cummax
-::: tinygrad.Tensor.triu
-::: tinygrad.Tensor.tril
-::: tinygrad.Tensor.interpolate
-::: 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)
-
-::: tinygrad.Tensor.linear
-::: tinygrad.Tensor.sequential
-::: tinygrad.Tensor.layernorm
-::: tinygrad.Tensor.batchnorm
-::: tinygrad.Tensor.dropout
-::: tinygrad.Tensor.one_hot
-::: tinygrad.Tensor.scaled_dot_product_attention
-::: tinygrad.Tensor.binary_crossentropy
-::: tinygrad.Tensor.binary_crossentropy_logits
-::: tinygrad.Tensor.sparse_categorical_crossentropy
-::: tinygrad.Tensor.cross_entropy
-::: tinygrad.Tensor.nll_loss

docs/tensor/properties.md

@@ -1,39 +0,0 @@
-## Basic
-
-::: tinygrad.Tensor.shape
-::: tinygrad.Tensor.dtype
-::: tinygrad.Tensor.device
-::: tinygrad.Tensor.ndim
-::: tinygrad.Tensor.numel
-::: tinygrad.Tensor.element_size
-::: tinygrad.Tensor.nbytes
-::: tinygrad.Tensor.is_floating_point
-::: tinygrad.Tensor.size
-
-## Data Access
-
-::: tinygrad.Tensor.data
-::: tinygrad.Tensor.item
-::: tinygrad.Tensor.tolist
-::: tinygrad.Tensor.numpy
-
-## tinygrad ops
-
-::: tinygrad.Tensor.schedule_with_vars
-::: tinygrad.Tensor.schedule
-::: tinygrad.Tensor.realize
-::: tinygrad.Tensor.replace
-::: tinygrad.Tensor.assign
-::: tinygrad.Tensor.detach
-::: tinygrad.Tensor.clone
-::: tinygrad.Tensor.to
-::: tinygrad.Tensor.to_
-::: tinygrad.Tensor.shard
-::: tinygrad.Tensor.shard_
-::: tinygrad.Tensor.contiguous
-::: tinygrad.Tensor.contiguous_backward
-
-## Gradient
-
-::: tinygrad.Tensor.gradient
-::: tinygrad.Tensor.backward

docs/tinybox.md

@@ -1,54 +0,0 @@
-# tinybox
-
-Although these docs live in tinygrad, they pertain to deep learning hardware sold by the tiny corp. tinyboxes are used heavily in tinygrad's CI, and are the best tested platform to use tinygrad with. They appeared running tinygrad on [MLPerf Training 4.0](https://public.tableau.com/views/MLCommons-Training_16993769118290/MLCommons-Training)
-
-If you don't have a tinybox and you want one, see [tinygrad.org](https://tinygrad.org). If you don't want one, that's okay too.
-
-## 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.
-
-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.
-
-## Plugging it in
-
-tinybox has two 1600W PSUs, which together exceed the capacity of most 120V household circuits. Fortunately, it comes with two plugs. You'll want to plug each plug into a different circuit. You can verify that they are different circuits by flipping the breaker and seeing what turns off. If you have at least a 120V 30A or 220V 20A circuit, you are welcome to use only that one.
-
-You'll also want to connect the Ethernet port without a rubber stopper to your home network.
-
-While it's designed primarily for the home or office, the tinybox is 12U rack mountable using [these rails](https://rackmountmart.store.turbify.net/26slidrailfo.html).
-
-## Power limiting the box
-
-While a tinybox should ideally be run without power limits, there are cases where you might want to run the box off of a single outlet.
-
-In such cases, it is possible to power limit the box using the provided `power-limit` script, which will power limit all of the GPUs to a specified wattage.
-
-`sudo power-limit 150` should be good to run off of a single 120V 15A outlet.
-
-## Connecting to the box
-
-tinybox ships with a relatively basic install of Ubuntu 22.04. To do initial setup, you can either plug in a VGA monitor and keyboard, or you can connect remotely to the machine using the BMC. The BMC IP and password are displayed on the screen.
-
-`ipmitool -H <BMC IP> -U admin -P <BMC PW> -I lanplus sol activate`
-
-The default username is `tiny` and the default password is `tiny`. Once you are logged in, you can add an SSH key to authorized keys to connect over SSH (on the normal IP). Exit `ipmitool` with `~.` after a newline.
-
-The BMC also has a web interface you can use if you find that easier.
-
-## Changing the BMC password
-
-It is recommended that you change the BMC password after setting up the box, as the password on the screen is only the initial password.
-
-If you do decide to change the BMC password and no longer want the initial password to be displayed, remove the `/root/.bmc_password` file.
-Reboot after making these changes or restart the `displayservice.service` service.
-
-## What do I use it for?
-
-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
-
-The OS image is built using `ubuntu-image` from <https://github.com/tinygrad/tinyos>.
-
-After cloning, run `make green` or `make red` to build a tinybox green or tinybox red image respectively.