fix all bugs

* dsp multicore 2

* hmm

* better

.coveragerc

@@ -1,3 +0,0 @@
-[run]
-source = tinygrad
-branch = True

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

@@ -11,5 +11,5 @@ runs:
         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 && CHECK_OOB=0 PYTHONPATH=. python3 process_replay.py
+        cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py
         git checkout $CURRENT_HEAD  # restore to branch

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

@@ -29,10 +29,6 @@ inputs:
     description: "Install CUDA?"
     required: false
     default: 'false'
-  ocelot:
-    description: "Install gpuocelot?"
-    required: false
-    default: 'false'
   webgpu:
     description: "Install webgpu?"
     required: false
@@ -41,238 +37,137 @@ inputs:
     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 ****
+    # TODO: key should include input.deps, but it can't since it can't contain commas
 
-    - name: Cache Python packages (PR)
-      if: github.event_name == 'pull_request'
-      id: restore-venv-pr
-      uses: actions/cache/restore@v4
-      with:
-        path: ${{ github.workspace }}/.venv
-        key: venv-${{ runner.os }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ env.CACHE_VERSION }}
-    - name: Cache Python packages
-      if: github.event_name != 'pull_request'
-      id: restore-venv
+    - name: Cache Python packages (Linux)
+      if: inputs.key != '' && runner.os == 'Linux'
       uses: actions/cache@v4
       with:
-        path: ${{ github.workspace }}/.venv
-        key: venv-${{ runner.os }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ env.CACHE_VERSION }}
+        path: ${{ env.Python3_ROOT_DIR }}/lib/python${{ inputs.python-version }}/site-packages
+        key: python-package-${{ inputs.key }}-${{ hashFiles('**/setup.py') }}
+    - name: Cache Python packages (macOS)
+      if: inputs.key != '' && runner.os == 'macOS'
+      uses: actions/cache@v4
+      with:
+        path: /Users/runner/Library/Python/${{ inputs.python-version }}/lib/python/site-packages
+        key: osx-python-package-${{ inputs.key }}-${{ hashFiles('**/setup.py') }}
+    - name: Cache Python packages (Windows)
+      if: inputs.key != '' && runner.os == 'Windows'
+      uses: actions/cache@v4
+      with:
+        path: ${{ env.Python3_ROOT_DIR }}\Lib\site-packages
+        key: windows-python-package-${{ inputs.key }}-${{ hashFiles('**/setup.py') }}
 
     # **** Caching downloads ****
 
-    - name: Cache downloads (PR)
-      if: inputs.key != '' && github.event_name == 'pull_request'
-      uses: actions/cache/restore@v4
-      with:
-        path: ${{ runner.os == 'Linux' && '~/.cache/tinygrad/downloads/' || '~/Library/Caches/tinygrad/downloads/' }}
-        key: downloads-${{ github.job }}-${{ inputs.key }}-${{ env.CACHE_VERSION }}
-    - name: Cache downloads
-      if: inputs.key != '' && github.event_name != 'pull_request'
+    - name: Cache downloads (Linux)
+      if: inputs.key != '' && runner.os == 'Linux'
       uses: actions/cache@v4
       with:
-        path: ${{ runner.os == 'Linux' && '~/.cache/tinygrad/downloads/' || '~/Library/Caches/tinygrad/downloads/' }}
-        key: downloads-${{ github.job }}-${{ inputs.key }}-${{ env.CACHE_VERSION }}
+        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-pr.outputs.cache-hit != 'true' && steps.restore-venv.outputs.cache-hit != 'true'
+    - name: Install dependencies (with extra)
+      if: inputs.deps != ''
       shell: bash
-      run: |
-        python -m venv .venv
-        if [[ "$RUNNER_OS" == "Windows" ]]; then
-          source .venv/Scripts/activate
-        else
-          . .venv/bin/activate
-        fi
-        python -m pip install -e ".[${{ 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-pr.outputs.cache-hit != 'true' && steps.restore-venv.outputs.cache-hit != 'true'
+      run: pip install ${{ (runner.os == 'macOS' && '--user') || (runner.os != 'macOS' && '') }} -e ".[${{ inputs.deps }}]" ${{ inputs.pydeps }} --extra-index-url https://download.pytorch.org/whl/cpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/
+    - name: Install dependencies (without extra)
+      if: inputs.deps == ''
       shell: bash
-      run: |
-        python -m venv .venv
-        if [[ "$RUNNER_OS" == "Windows" ]]; then
-          source .venv/Scripts/activate
-        else
-          . .venv/bin/activate
-        fi
-        python -m pip install -e . ${{ inputs.pydeps }}
-    - name: Set up venv environment
-      shell: bash
-      run: |
-        echo "VIRTUAL_ENV=${{ github.workspace }}/.venv" >> "$GITHUB_ENV"
-        echo "OMP_NUM_THREADS=1" >> "$GITHUB_ENV"
-        # no buffers should be over 300MB in CI
-        echo "MAX_BUFFER_SIZE=300000000" >> "$GITHUB_ENV"
-        if [[ "$RUNNER_OS" == "Windows" ]]; then
-          echo "${{ github.workspace }}/.venv/Scripts" >> "$GITHUB_PATH"
-        else
-          echo "${{ github.workspace }}/.venv/bin" >> "$GITHUB_PATH"
-        fi
+      run: pip install ${{ (runner.os == 'macOS' && '--user') || (runner.os != 'macOS' && '') }} -e . ${{ inputs.pydeps }}
 
-    # ******************* apt *******************
-    - name: Setup apt
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
+    # **** OpenCL ****
+
+    - name: Install OpenCL
+      if: inputs.opencl == 'true'
       shell: bash
       run: |
-        sudo chown -R $USER:$USER /var/cache/apt/archives
-
-        echo 'Acquire::GzipIndexes "true";' | sudo tee /etc/apt/apt.conf.d/gzip
         echo 'Acquire::http::Pipeline-Depth "5";' | sudo tee -a /etc/apt/apt.conf.d/99parallel
-        echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' | sudo tee -a /etc/apt/apt.conf.d/99keep-debs
-
-    - name: Add OpenCL Repo
-      if: inputs.opencl == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: echo "deb [ allow-insecure=yes ] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list
-
-    - name: Add AMD Repo (Linux)
-      if: inputs.amd == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: |
-        wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
-        sudo tee /etc/apt/sources.list.d/rocm.list <<EOF
-        deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/7.1 $(lsb_release -cs) main
-        EOF
-        echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
-
-    - name: Add LLVM Repo (Linux)
-      if: inputs.llvm == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: |
-        wget -qO- https://apt.llvm.org/llvm-snapshot.gpg.key | sudo tee /etc/apt/trusted.gpg.d/apt.llvm.org.asc
-        echo "deb http://apt.llvm.org/$(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 (PR)
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true') && github.event_name == 'pull_request'
-      uses: actions/cache/restore@v4
-      with:
-        path: /var/cache/apt/archives/
-        key: ${{ runner.os }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.CACHE_VERSION }}
-    - name: Cache apt
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true') && github.event_name != 'pull_request'
-      uses: actions/cache@v4
-      with:
-        path: /var/cache/apt/archives/
-        key: ${{ runner.os }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.CACHE_VERSION }}
-
-    - name: Run apt Update + Install
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
-      shell: bash
-      run: |
-        sudo apt -qq update || true
-
-        # ******** do install ********
-        if [[ -n "${{ steps.apt-pkgs.outputs.pkgs }}" ]]; then
-          sudo apt-get -y --allow-unauthenticated --no-install-recommends install ${{ steps.apt-pkgs.outputs.pkgs }}
-        fi
-
-        sudo chown -R $USER:$USER /var/cache/apt/archives/
+        echo "deb [ allow-insecure=yes ] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list
+        sudo apt update || true
+        sudo apt install --allow-unauthenticated -y --no-install-recommends opencl-headers \
+          intel-oneapi-runtime-openmp=2023.2.1-16 intel-oneapi-runtime-compilers-common=2023.2.1-16 intel-oneapi-runtime-compilers=2023.2.1-16 \
+          intel-oneapi-runtime-dpcpp-sycl-opencl-cpu=2023.2.1-16 intel-oneapi-runtime-tbb-common=2021.10.0-49541 \
+          intel-oneapi-runtime-tbb=2021.10.0-49541 intel-oneapi-runtime-opencl=2023.2.1-16
 
     # **** AMD ****
-    - name: Setup AMD (Linux)
+
+    - name: Install AMD (Linux)
       if: inputs.amd == 'true' && runner.os == 'Linux'
       shell: bash
       run: |
-        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
+        echo 'Acquire::http::Pipeline-Depth "5";' | sudo tee -a /etc/apt/apt.conf.d/99parallel
+        wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
+        sudo tee /etc/apt/sources.list.d/rocm.list <<'EOF'
+        deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/6.1.2 jammy main
+        EOF
+        echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
+        sudo apt update || true
+        sudo apt install --no-install-recommends --allow-unauthenticated -y hsa-rocr comgr hsa-rocr-dev liburing-dev libc6-dev
+        curl -s https://api.github.com/repos/Qazalin/remu/releases/latest | \
+        jq -r '.assets[] | select(.name == "libremu.so").browser_download_url' | \
+        sudo xargs curl -L -o /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)
+    - name: Install 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 -fL -o /usr/local/lib/libamd_comgr.dylib
-        cargo build --release --manifest-path ./extra/remu/Cargo.toml
+          sudo xargs curl -L -o /usr/local/lib/libamd_comgr.dylib
+        curl -s -H "Authorization: token $GH_TOKEN" curl -s https://api.github.com/repos/Qazalin/remu/releases/latest | \
+          jq -r '.assets[] | select(.name == "libremu.dylib").browser_download_url' | \
+          sudo xargs curl -L -o /usr/local/lib/libremu.dylib
 
-    # **** gpuocelot ****
+    # **** CUDA ****
 
-    - name: Install gpuocelot dependencies (MacOS)
-      if: inputs.ocelot == 'true' && runner.os == 'macOS'
+    - name: Install cuda packages (Linux)
+      if: inputs.cuda == 'true' && runner.os == 'Linux'
       shell: bash
       run: |
-        pkgs=(cmake ninja llvm@15 zlib glew flex bison [email protected] zstd ncurses)
-        for f in "${pkgs[@]}"; do
-          brew ls --versions "$f" >/dev/null 2>&1 || brew install --quiet "$f"
-        done
-
-        # Fix boost 1.85 for gpuocelot
-        ln -s /opt/homebrew/opt/[email protected] /opt/homebrew/opt/boost || true
-        ln -s /opt/homebrew/opt/boost/lib/libboost_atomic-mt.dylib /opt/homebrew/opt/boost/lib/libboost_atomic.dylib || true
-        ln -s /opt/homebrew/opt/boost/lib/libboost_thread-mt.dylib /opt/homebrew/opt/boost/lib/libboost_thread.dylib || true
-    - name: Cache gpuocelot (PR)
-      if: inputs.ocelot == 'true' && github.event_name == 'pull_request'
-      id: cache-build-pr
-      uses: actions/cache/restore@v4
-      env:
-        cache-name: cache-gpuocelot-build-1
-      with:
-        path: ${{ github.workspace }}/gpuocelot/ocelot
-        key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-${{ env.CACHE_VERSION }}
+        echo 'Acquire::http::Pipeline-Depth "5";' | sudo tee -a /etc/apt/apt.conf.d/99parallel
+        sudo apt update -y || true
+        sudo apt install -y --no-install-recommends git g++ cmake ninja-build llvm-15-dev zlib1g-dev libglew-dev \
+          flex bison libfl-dev libboost-thread-dev libboost-filesystem-dev nvidia-cuda-toolkit-gcc libzstd-dev
+    - name: Install gpuocelot dependencies (MacOS)
+      if: inputs.cuda == 'true' && runner.os == 'macOS'
+      shell: bash
+      run: |
+        brew update
+        brew install cmake ninja llvm@15 zlib glew flex bison boost zstd ncurses
     - name: Cache gpuocelot
-      if: inputs.ocelot == 'true' && github.event_name != 'pull_request'
+      if: inputs.cuda == 'true'
       id: cache-build
       uses: actions/cache@v4
       env:
-        cache-name: cache-gpuocelot-build-1
+        cache-name: cache-gpuocelot-build
       with:
         path: ${{ github.workspace }}/gpuocelot/ocelot
-        key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-${{ env.CACHE_VERSION }}
+        key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-0
     - name: Clone/compile gpuocelot
-      if: inputs.ocelot == 'true' && steps.cache-build-pr.outputs.cache-hit != 'true' && steps.cache-build.outputs.cache-hit != 'true'
+      if: inputs.cuda == 'true' && steps.cache-build.outputs.cache-hit != 'true'
       shell: bash
       run: |
         git clone --recurse-submodules https://github.com/gpuocelot/gpuocelot.git ${{ github.workspace }}/gpuocelot
@@ -280,20 +175,14 @@ runs:
         git checkout b16039dc940dc6bc4ea0a98380495769ff35ed99
         mkdir build
         cd build
-
-        CMAKE_ARGS="-Wno-dev -G Ninja -DOCELOT_BUILD_TOOLS=OFF -DCMAKE_BUILD_ALWAYS=0 -DBUILD_TESTS_CUDA=OFF -DCMAKE_POLICY_VERSION_MINIMUM=3.5"
-        if [[ "${{ 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
+        cmake .. -Wno-dev -G Ninja -DOCELOT_BUILD_TOOLS=OFF -DCMAKE_BUILD_ALWAYS=0 -DBUILD_TESTS_CUDA=OFF
         ninja
     - name: Install gpuocelot
-      if: inputs.ocelot == 'true'
+      if: inputs.cuda == 'true'
       shell: bash
       run: |
         cd ${{ github.workspace }}/gpuocelot/ocelot/build
-        sudo cp libgpuocelot.${{ runner.os == 'macOS' && 'dylib' || 'so' }} /usr/${{ runner.os == 'macOS' && 'local/' || '' }}lib/
+        sudo cp libgpuocelot.${{ runner.os == 'macOS' && 'dylib' || 'so' }} /usr/${{ runner.os == 'macOS' && 'local/' || ''}}lib/
 
     # **** WebGPU ****
 
@@ -301,8 +190,14 @@ runs:
       if: inputs.webgpu == 'true' && runner.os == 'Linux'
       shell: bash
       run: |
-        sudo curl -fL https://github.com/wpmed92/pydawn/releases/download/v0.1.6/libwebgpu_dawn.so -o /usr/local/lib/libwebgpu_dawn.so
-        sudo ldconfig
+        sudo curl -L https://github.com/wpmed92/pydawn/releases/download/v0.1.6/libwebgpu_dawn.so -o /usr/local/lib/libwebgpu_dawn.so
+    - name: Install dependencies for software-based vulkan
+      if: inputs.webgpu == 'true' && runner.os == 'Linux'
+      shell: bash
+      run: |
+        sudo apt update -y || true
+        sudo apt install -y libegl1-mesa libgl1-mesa-dri libxcb-xfixes0-dev mesa-vulkan-drivers
+
     - name: Install WebGPU dawn (macOS)
       if: inputs.webgpu == 'true' && runner.os == 'macOS'
       shell: bash
@@ -312,17 +207,18 @@ runs:
 
     # **** LLVM ****
 
+    - name: Install LLVM (Linux)
+      if: inputs.llvm == 'true' && runner.os == 'Linux'
+      shell: bash
+      run: |
+        echo 'Acquire::http::Pipeline-Depth "5";' | sudo tee -a /etc/apt/apt.conf.d/99parallel
+        wget -qO- https://apt.llvm.org/llvm-snapshot.gpg.key | sudo tee /etc/apt/trusted.gpg.d/apt.llvm.org.asc
+        echo "deb http://apt.llvm.org/$(lsb_release -cs)/ llvm-toolchain-$(lsb_release -cs)-19 main" | sudo tee /etc/apt/sources.list.d/llvm.list
+        sudo apt update -y || true
+        sudo apt install -y --no-install-recommends libllvm19 clang-19 lld-19
+
     - name: Install LLVM (macOS)
       if: inputs.llvm == 'true' && runner.os == 'macOS'
       shell: bash
-      run: brew install llvm@20
-
-    # **** mesa ****
-    - name: Install mesa (linux)
-      if: inputs.mesa == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: sudo curl -fL https://github.com/sirhcm/tinymesa/releases/download/v1/libtinymesa_cpu-mesa-25.2.7-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
+      run: |
+        brew install llvm

.github/workflows/autogen.yml

@@ -1,139 +0,0 @@
-name: Autogen
-env:
-  # increment this when downloads substantially change to avoid the internet
-  CACHE_VERSION: '13'
-  CAPTURE_PROCESS_REPLAY: 1
-  GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-  PYTHONPATH: ${{ github.workspace }}
-
-on:
-  push:
-    branches:
-      - master
-  pull_request:
-    paths:
-    - 'tinygrad/runtime/autogen/**/*'
-    - 'tinygrad/runtime/support/autogen.py'
-    - '.github/workflows/autogen.yml'
-  workflow_dispatch:
-    paths:
-    - 'tinygrad/runtime/autogen/**/*'
-    - 'tinygrad/runtime/support/autogen.py'
-    - '.github/workflows/autogen.yml'
-
-jobs:
-  autogen:
-    name: In-tree 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'
-        llvm: 'true'
-        webgpu: 'true'
-        mesa: 'true'
-        pydeps: 'pyyaml mako'
-    - name: Install autogen support packages
-      run: sudo apt-get install -y --no-install-recommends libclang-20-dev llvm-20-dev hip-dev libusb-1.0-0-dev libdrm-dev
-    - name: Regenerate autogen files
-      run: |
-        find tinygrad/runtime/autogen -type f -name "*.py" -not -path "*/amd/*" -not -name "__init__.py" -not -name "comgr.py" -not -name "metal.py" -not -name "iokit.py" -not -name "corefoundation.py" -not -name "libclang.py" -delete
-        python3 -c "from tinygrad.runtime.autogen import opencl"
-        python3 -c "from tinygrad.runtime.autogen import cuda, nvrtc, nvjitlink, nv_570, nv_580, nv"
-        python3 -c "from tinygrad.runtime.autogen import comgr_3, hsa, hip, amd_gpu, sqtt, rocprof, amdgpu_kd, amdgpu_drm"
-        python3 -c "from tinygrad.runtime.autogen.am import am, pm4_soc15, pm4_nv, sdma_4_0_0, sdma_5_0_0, sdma_6_0_0, smu_v13_0_0, smu_v13_0_6, smu_v13_0_12, smu_v14_0_2"
-        python3 -c "from tinygrad.runtime.autogen import libc, kfd, io_uring, ib, pci, vfio"
-        python3 -c "from tinygrad.runtime.autogen import llvm"
-        python3 -c "from tinygrad.runtime.autogen import webgpu"
-        python3 -c "from tinygrad.runtime.autogen import kgsl, qcom_dsp"
-        python3 -c "from tinygrad.runtime.autogen import libusb"
-        python3 -c "from tinygrad.runtime.autogen import mesa"
-        python3 -c "from tinygrad.runtime.autogen import avcodec"
-        REGEN=1 python3 -c "from tinygrad.runtime.autogen import libclang"
-    - name: Check for differences
-      run: |
-        if ! git diff --quiet; then
-          git diff
-          git diff > autogen-ubuntu.patch
-          echo "Autogen mismatch detected. Patch available at: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}#artifacts"
-          exit 1
-        fi
-    - name: Upload patch artifact
-      if: failure()
-      uses: actions/upload-artifact@v4
-      with:
-        name: autogen-ubuntu-patch
-        path: autogen-ubuntu.patch
-
-  autogen-mac:
-    name: In-tree Autogen (macos)
-    runs-on: macos-14
-    timeout-minutes: 15
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        llvm: 'true'
-    - name: Regenerate autogen files
-      run: |
-        rm tinygrad/runtime/autogen/metal.py tinygrad/runtime/autogen/iokit.py tinygrad/runtime/autogen/corefoundation.py
-        python3 -c "from tinygrad.runtime.autogen import metal, iokit, corefoundation"
-    - name: Check for differences
-      run: |
-        if ! git diff --quiet; then
-          git diff
-          git diff > autogen-macos.patch
-          echo "Autogen mismatch detected. Patch available at: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}#artifacts"
-          exit 1
-        fi
-    - name: Upload patch artifact
-      if: failure()
-      uses: actions/upload-artifact@v4
-      with:
-        name: autogen-macos-patch
-        path: autogen-macos.patch
-
-  autogen-comgr-2:
-    name: In-tree Autogen (comgr 2)
-    runs-on: ubuntu-24.04
-    timeout-minutes: 15
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-    - name: Install autogen support packages
-      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
-        sudo apt -qq update || true
-        sudo apt-get install -y --no-install-recommends libclang-20-dev comgr
-    - name: Regenerate autogen files
-      run: |
-        rm tinygrad/runtime/autogen/comgr.py
-        python3 -c "from tinygrad.runtime.autogen import comgr"
-    - name: Check for differences
-      run: |
-        if ! git diff --quiet; then
-          git diff
-          git diff > autogen-comgr2.patch
-          echo "Autogen mismatch detected. Patch available at: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}#artifacts"
-          exit 1
-        fi
-    - name: Upload patch artifact
-      if: failure()
-      uses: actions/upload-artifact@v4
-      with:
-        name: autogen-comgr2-patch
-        path: autogen-comgr2.patch

.github/workflows/benchmark.yml

@@ -14,55 +14,21 @@ on:
       - update_benchmark
       - update_benchmark_staging
   workflow_dispatch:
+    inputs:
+      run_process_replay:
+        description: "Run process replay tests"
+        required: false
+        default: false
+        type: boolean
 
 jobs:
-  # the goal of this test is to replicate a normal person on a laptop running the test
-  # no process replay, no benchmarks, no CI, just a normal laptop person
-  # the 3 minute timeout should not be raised
-  testmacpytest:
-    name: Mac pytest
-    env:
-      CI: ""
-      CAPTURE_PROCESS_REPLAY: "0"
-    runs-on: [self-hosted, macOS]
-    timeout-minutes: 3
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    # brew install uv
-    - name: setup python environment
-      run: |
-        rm -rf /tmp/tinygrad_pytest_ci
-        uv venv /tmp/tinygrad_pytest_ci
-        source /tmp/tinygrad_pytest_ci/bin/activate
-        uv pip install .[testing]
-    - name: setup staging db
-      run: |
-        echo "CACHEDB=/tmp/pytest-db-ci.db" >> $GITHUB_ENV
-        rm -f /tmp/pytest-db-ci*
-    - name: Run pytest -nauto
-      run: |
-        source /tmp/tinygrad_pytest_ci/bin/activate
-        pytest -nauto --durations=20
-    - name: openpilot compile3 0.10.1 driving_vision
-      run: FLOAT16=1 CL=1 IMAGE=2 python3.11 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
-    - name: IMAGE=1 openpilot compile3 0.10.1 driving_vision
-      run: FLOAT16=1 CL=1 IMAGE=1 python3.11 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
-
   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
+    timeout-minutes: 20
     defaults:
       run:
-        shell: bash -e -o pipefail {0}
+        shell: bash -o pipefail {0}
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
@@ -70,7 +36,6 @@ jobs:
     - name: Symlink models and datasets
       run: |
         mkdir -p weights
-        mkdir -p extra/disassemblers
         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
@@ -83,129 +48,100 @@ jobs:
         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
+      run: JIT=1 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=720 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing
+      run: JIT=1 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
-    # process replay can't capture this, the graph is too large
+      run: JIT=1 python3.11 examples/sdv2.py --fp16 --seed 0 --noshow --timing | tee sdv2.txt
     - 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
+      run: JIT=1 python3.11 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
     - name: Run model inference benchmark
-      run: METAL=1 NOCLANG=1 python3.11 test/external/external_model_benchmark.py
+      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
+      run: BIG=2 MPS=1 python3.11 test/test_speed_v_torch.py | tee torch_speed.txt
     - name: Test tensor cores
-      run: METAL=1 python3.11 test/opt/test_tensor_cores.py
+      run: METAL=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded
     - 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
+        DEBUG=2 CPU=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded
+        DEBUG=2 LLVM=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded
     - name: Run Tensor Core GEMM (float)
-      run: DEBUG=2 SHOULD_USE_TC=1 python3.11 extra/gemm/simple_matmul.py
+      run: DEBUG=2 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
+      run: DEBUG=2 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
+      run: DEBUG=2 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
-        BENCHMARK_LOG=llama JIT=1 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing
+        JIT=0 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
+        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
+      run: 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
-        BENCHMARK_LOG=llama_nf4 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing --quantize nf4
-    - name: Run quantized LLaMA3
-      run: |
-        BENCHMARK_LOG=llama3_int8 python3.11 examples/llama3.py --size 8B --temperature 0 --benchmark --quantize int8
-        BENCHMARK_LOG=llama3_nf4 python3.11 examples/llama3.py --size 8B --temperature 0 --benchmark --quantize nf4
+        python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing --quantize int8 | tee llama_int8.txt
+        python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing --quantize nf4 | tee llama_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
+    #  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
-        BENCHMARK_LOG=gpt2 JIT=1 ASSERT_MIN_STEP_TIME=13 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing
+        JIT=0 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
+        JIT=1 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
+      run: 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
-    - name: Run OLMoE
-      run: BENCHMARK_LOG=olmoe python3.11 examples/olmoe.py
+      run: HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3.11 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
     - name: Train MNIST
-      run: time PYTHONPATH=. TARGET_EVAL_ACC_PCT=96.0 python3.11 examples/beautiful_mnist.py
-
-    # 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
-    #- 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
-
+      run: time PYTHONPATH=. TARGET_EVAL_ACC_PCT=96.0 python3.11 examples/beautiful_mnist.py | tee beautiful_mnist.txt
+    - name: Run 10 CIFAR training steps
+      run: JIT=1 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar.txt
+    - name: Run 10 CIFAR training steps w HALF
+      run: JIT=2 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
-    # 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
+    #  run: STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3.11 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
+    - name: Run 10 CIFAR training steps w winograd
+      run: JIT=1 WINO=1 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar_wino.txt
     - 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
+          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
 
-  testusbgpu:
-    name: UsbGPU Benchmark
-    env:
-      PYTHONPYCACHEPREFIX: /tmp/tiny_python_pycache
-    runs-on: [self-hosted, macOS]
-    timeout-minutes: 10
-    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: Kill stale pids
-      run: |
-        PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
-        PYTHONPATH=. ./extra/hcq/hcq_smi.py nv kill_pids
-    - 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
-    - name: UsbGPU (USB4/TB) boot time
-      run: PYTHONPATH=. DEBUG=3 NV=1 NV_IFACE=PCI NV_NAK=1 time python3.11 test/test_tiny.py TestTiny.test_plus
-    - name: UsbGPU (USB4/TB) tiny tests
-      run: PYTHONPATH=. NV=1 NV_IFACE=PCI NV_NAK=1 python3.11 test/test_tiny.py
-
   testnvidiabenchmark:
     name: tinybox green Benchmark
     runs-on: [self-hosted, Linux, tinyboxgreen]
-    timeout-minutes: 60
+    timeout-minutes: 30
     defaults:
       run:
-        shell: bash -e -o pipefail {0}
+        shell: bash -o pipefail {0}
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
@@ -231,79 +167,95 @@ jobs:
     - name: Run model inference benchmark
       run: NV=1 CAPTURE_PROCESS_REPLAY=0 NOCLANG=1 python3 test/external/external_model_benchmark.py
     - name: Test speed vs torch
-      run: NV=1 CAPTURE_PROCESS_REPLAY=0 HALF=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py
+      run: NV=1 CAPTURE_PROCESS_REPLAY=0 HALF=1 BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
     - name: Test speed vs theoretical
-      run: NV=1 IGNORE_BEAM_CACHE=1 CCACHE=0 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
+      run: NV=1 IGNORE_BEAM_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
     - name: Test benchmark allreduce
       run: NV=1 python test/external/external_benchmark_multitensor_allreduce.py
     - name: Test tensor cores
       run: |
-        NV=1 ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
-        NV=1 NV_PTX=1 ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
+        NV=1 ALLOW_TF32=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded
+        PTX=1 ALLOW_TF32=1 NV=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded
     - name: Run Tensor Core GEMM (CUDA)
       run: |
-        CUDA=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
-        CUDA=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
-        CUDA=1 SHOULD_USE_TC=1 ALLOW_TF32=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py
-        CUDA=1 SHOULD_USE_TC=1 FP8E4M3=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
+        CUDA=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul.txt
+        CUDA=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_bfloat16.txt
+        CUDA=1 ALLOW_TF32=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_tf32.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
+      run: NV=1 PTX=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
+      run: NV=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
-    # 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
+      run: NV=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
+    - name: Run SDXL
+      run: 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
-        BENCHMARK_LOG=llama NV=1 JIT=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing
+        NV=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
+        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
+      run: 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
+    #   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
+    #   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
+      run: 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
-    - 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
+      run: 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 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
+    #   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
+    #   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
+      run: time 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
-        BENCHMARK_LOG=gpt2 NV=1 JIT=1 ASSERT_MIN_STEP_TIME=4 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing
+        NV=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
+        NV=1 JIT=1 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
+      run: NV=1 HALF=1 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
+      run: 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
+          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
+    timeout-minutes: 20
     defaults:
       run:
-        shell: bash -e -o pipefail {0}
+        shell: bash -o pipefail {0}
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
@@ -325,59 +277,63 @@ jobs:
         rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
     - name: reset process replay
       run: test/external/process_replay/reset.py
-    # 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: HEVC Decode Benchmark
-      run: VALIDATE=1 MAX_FRAMES=100 JITBEAM=1 NV=1 PYTHONPATH=. python3 extra/hevc/decode.py
+    - 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
+    - name: Fuzz Padded Tensor Core GEMM (PTX)
+      run: NV=1 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
+      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=120 NV=1 STEPS=10 python3 examples/hlb_cifar10.py
+      run: 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=120 NV=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py
+      run: 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=120 NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py
-    # - name: Run 10 CIFAR training steps w winograd
-    #   run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=350 NV=1 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py
+      run: NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
+    - name: Run 10 CIFAR training steps w winograd
+      run: 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
+      run: time NV=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
     - name: Run full CIFAR training steps w 6 GPUS
-      run: time BENCHMARK_LOG=cifar_6gpu 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
+      run: time CAPTURE_PROCESS_REPLAY=0 NV=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
     - name: Run MLPerf resnet eval on training data
-      run: time BENCHMARK_LOG=resnet_eval NV=1 MODEL=resnet python3 examples/mlperf/model_eval.py
+      run: time 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
+      run: 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
+      run: 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=72 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
+      run: 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
+    timeout-minutes: 20
     defaults:
       run:
-        shell: bash -e -o pipefail {0}
+        shell: bash -o pipefail {0}
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
       uses: actions/checkout@v4
-    - name: Setcap to python
-      run: ./extra/amdpci/setup_python_cap.sh
-    - name: Remove amd modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
-    - name: Kill stale pids
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
-    #- name: Insert amdgpu
-    #  run: sudo modprobe amdgpu
+    - name: Insert amdgpu
+      run: sudo modprobe amdgpu
     - name: Symlink models and datasets
       run: |
         mkdir -p weights
@@ -396,12 +352,12 @@ jobs:
         rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
     - name: reset process replay
       run: test/external/process_replay/reset.py
-    #- name: setup perflevel
-    #  run: |
-    #    examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_red/setup.sh
-    #    rocm-smi
-    #- name: Show off tinybox
-    #  run: /opt/rocm/bin/rocm-bandwidth-test
+    - name: setup perflevel
+      run: |
+        examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_red/setup.sh
+        rocm-smi
+    - name: Show off tinybox
+      run: /opt/rocm/bin/rocm-bandwidth-test
     # TODO: unstable on AMD
     #- name: Run model inference benchmark
     #  run: LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 NOCLANG=1 python3 test/external/external_model_benchmark.py
@@ -409,85 +365,98 @@ jobs:
     #- name: Test speed vs torch
     #  run: |
     #    python3 -c "import torch; print(torch.__version__)"
-    #    LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py
+    #    LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
     - name: Test speed vs theoretical
-      run: AMD=1 IGNORE_BEAM_CACHE=1 CCACHE=0 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
-    - name: Test tensor cores AMD_LLVM=0
-      run: AMD=1 AMD_LLVM=0 python3 test/opt/test_tensor_cores.py
-    # TODO: this is flaky
-    # - name: Test tensor cores AMD_LLVM=1
-    #   run: AMD=1 AMD_LLVM=1 python3 test/opt/test_tensor_cores.py
+      run: AMD=1 IGNORE_BEAM_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
+    - name: Test tensor cores
+      run: AMD=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded
     - name: Run Tensor Core GEMM (AMD)
-      run: |
-        AMD=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
-        AMD=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py
+      run: AMD=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_amd.txt
     - name: Test AMD=1
       run: DEBUG=2 AMD=1 python -m pytest -rA test/test_tiny.py
-    #- name: Test HIP=1
-    #  run: DEBUG=2 HIP=1 python -m pytest -rA test/test_tiny.py
+    - name: Test HIP=1
+      run: DEBUG=2 HIP=1 python -m pytest -rA test/test_tiny.py
     # TODO: AMD compiler bug causes this to fail
     #- name: Fuzz Padded Tensor Core GEMM
     #  run: HSA=1 M_START=12 M_STOP=20 M_STEP=1 N_START=12 N_STOP=20 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 DEBUG=2 python3 ./extra/gemm/fuzz_matmul.py
-    #- name: Remove amdgpu
-    #  run: sleep 10 && sudo rmmod amdgpu # sleep a bit to let the driver unload the prev pid.
+    - name: Remove amdgpu
+      run: sleep 5 && 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
+      run: AMD=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
     - 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
+      run: 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
-        BENCHMARK_LOG=llama AMD=1 JIT=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing
+        AMD=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
+        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
+      run: 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
+    #   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
+    #   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
+      run: 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
+      run: 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
-    #- name: Restore amdgpu
-    #  run: sudo modprobe amdgpu
+    #   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
+    #   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
+      run: time 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
-        BENCHMARK_LOG=gpt2 AMD=1 JIT=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing
+        AMD=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
+        AMD=1 JIT=1 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
+      run: AMD=1 HALF=1 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
+      run: 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
+    timeout-minutes: 30
     defaults:
       run:
-        shell: bash -e -o pipefail {0}
+        shell: bash -o pipefail {0}
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
       uses: actions/checkout@v4
-    - name: Setcap to python
-      run: ./extra/amdpci/setup_python_cap.sh
-    - name: Remove amd modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
-    - name: Kill stale pids
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
+    - name: Remove amdgpu
+      run: sudo rmmod amdgpu || true
     - name: Symlink models and datasets
       run: |
         mkdir -p weights
@@ -506,69 +475,42 @@ jobs:
     - 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
+      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=200 AMD=1 STEPS=10 python3 examples/hlb_cifar10.py
+      run: 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=230 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py
-    # - 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
-    # 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
+      run: 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: AMD=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
+    - name: Run 10 CIFAR training steps w winograd
+      run: 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
+      run: time AMD=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
     - name: Run full CIFAR training steps w 6 GPUS
-      run: time BENCHMARK_LOG=cifar_6gpu AMD=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py
-    # this needs to be mocked and testable on a local machine
-    #- name: Test full tinyfs load
-    #  run: TINYFS_ENDPOINT=10.0.52.11:6767 PYTHONPATH=. python extra/tinyfs/fetch_file.py --hash d734f5e3be9f1e9d863bfaa4fc6c1ef2 --len 175866113 --dest mapping.json --check
-    - 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: Setcap to python
-      run: ./extra/amdpci/setup_python_cap.sh
-    - name: Remove amd modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
-    - name: Kill stale pids
-      run: PYTHONPATH=. ./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
+      run: time AMD=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
     - name: Run MLPerf resnet eval
-      run: time BENCHMARK_LOG=resnet_eval AMD=1 MODEL=resnet python3 examples/mlperf/model_eval.py
+      run: time 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
+      run: 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
+      run: 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=72 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
+      run: 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 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
 
@@ -578,7 +520,7 @@ jobs:
     timeout-minutes: 20
     defaults:
       run:
-        shell: bash -e -o pipefail {0}
+        shell: bash -o pipefail {0}
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
@@ -590,149 +532,31 @@ jobs:
         rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
     - name: reset process replay
       run: test/external/process_replay/reset.py
-    - name: openpilot compile3 0.10.0 driving_policy
-      run: BENCHMARK_LOG=openpilot_0_10_0_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=3 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=11 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: DEBUG=2 openpilot compile3 0.10.1 driving_vision
-      run: PYTHONPATH="." DEBUG=2 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: DEBUG=2 IMAGE=1 openpilot compile3 0.10.1 driving_vision
-      run: PYTHONPATH="." DEBUG=2 DEV=QCOM FLOAT16=1 IMAGE=1 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: IMAGE=1 openpilot compile3 0.10.1 driving_vision
-      run: BENCHMARK_LOG=image_1_openpilot_0_10_1_vision PYTHONPATH="." DEV=QCOM FLOAT16=1 IMAGE=1 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_vision
-      run: BENCHMARK_LOG=openpilot_0_10_1_vision PYTHONPATH="." ASSERT_MIN_STEP_TIME=17 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=3 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
-      run: BENCHMARK_LOG=openpilot_0_10_1_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=11 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: Setcap to python
-      run: ./extra/amdpci/setup_python_cap.sh
-    - name: Remove amd modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
-    - name: Kill stale pids
-      run: PYTHONPATH=. ./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
-    - 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
-    # - 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
-    - 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
-    - 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: Setcap to python
-      run: ./extra/amdpci/setup_python_cap.sh
-    - name: Remove nv modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py nv rmmod
-    - name: Kill stale pids
-      run: PYTHONPATH=. ./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
-    - 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
-    - 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
-    - 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
+    - name: validate openpilot 0.9.7
+      run: PYTHONPATH=. FLOAT16=0 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx | tee openpilot_image_0_9_7.txt
+    - name: benchmark openpilot 0.9.4
+      run: PYTHONPATH=. QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx | tee openpilot_0_9_4.txt
+    - name: benchmark openpilot 0.9.7
+      run: PYTHONPATH=. QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx | tee openpilot_0_9_7.txt
+    - name: benchmark openpilot w IMAGE=2 0.9.4
+      run: PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx | tee openpilot_image_0_9_4.txt
+    - name: benchmark openpilot w IMAGE=2 0.9.7
+      run: PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx | tee openpilot_image_0_9_7.txt
+    - name: openpilot compile3 0.9.7
+      run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx
+    - name: openpilot compile3 0.9.7+ tomb raider
+      run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/e8bea2c78ffa92685ece511e9b554122aaf1a79d/selfdrive/modeld/models/supercombo.onnx
+    - name: openpilot dmonitoring compile3 0.9.7
+      run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/dmonitoring_model.onnx
     - name: Run process replay tests
       run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py
+    - uses: actions/upload-artifact@v4
+      with:
+        name: Speed (comma)
+        path: |
+          openpilot_compile_0_9_4.txt
+          openpilot_compile_0_9_7.txt
+          openpilot_0_9_4.txt
+          openpilot_0_9_7.txt
+          openpilot_image_0_9_4.txt
+          openpilot_image_0_9_7.txt

.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 CCACHE=0 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 CCACHE=0 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/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

@@ -20,11 +20,11 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install setuptools wheel build twine
+        pip install setuptools wheel twine
     - name: Build and publish
       env:
         TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
         TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
       run: |
-        python -m build
+        python setup.py sdist bdist_wheel
         twine upload dist/*

.github/workflows/szdiff.yml

@@ -56,15 +56,15 @@ jobs:
         uses: actions/checkout@v4
         with:
           path: base
-      - name: Set up Python 3.12
+      - name: Set up Python 3.10
         uses: actions/setup-python@v5
         with:
-          python-version: '3.12'
+          python-version: '3.10'
       - name: Count Line Diff
         run: |
+          pip install tabulate
           BASE="$GITHUB_WORKSPACE/base"
           PR="$GITHUB_WORKSPACE/pr"
-          pip install tabulate $BASE
           cp "$BASE/sz.py" .
           echo "loc_content<<EOF" >> "$GITHUB_ENV"
           python sz.py "$BASE" "$PR" >> "$GITHUB_ENV"

.gitignore

@@ -38,7 +38,6 @@ extra/huggingface_onnx/models/*
 extra/huggingface_onnx/*.yaml
 extra/weights
 venv
-venv_sd_mlperf
 examples/**/net.*[js,json]
 examples/**/*.safetensors
 node_modules
@@ -58,13 +57,6 @@ weights
 *.lprof
 comgr_*
 *.pkl
-!extra/sqtt/examples/**/*.pkl
 site/
 profile_stats
 *.log
-target
-.mypy_cache
-mutants
-.mutmut-cache
-dagre/
-graphlib/

.pre-commit-config.yaml

@@ -16,19 +16,31 @@ repos:
         pass_filenames: false
       - id: mypy
         name: mypy
-        entry: python3 -m 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
+      - id: devicetests
+        name: select GPU tests
+        entry: env GPU=1 PYTHONPATH="." python3 -m pytest test/test_uops.py test/test_search.py
         language: system
         always_run: true
         pass_filenames: false
       - id: tests
-        name: comprehensive test suite
-        entry: env OMP_NUM_THREADS=1 SKIP_SLOW_TEST=1 PYTHONPATH="." python3 -m pytest -n=6 test/backend/test_ops.py test/backend/test_schedule.py test/unit/test_assign.py test/backend/test_tensor.py test/backend/test_jit.py test/unit/test_schedule_cache.py test/null/test_pattern_matcher.py test/null/test_uop_symbolic.py test/unit/test_helpers.py
+        name: subset of tests
+        entry: env PYTHONPATH="." python3 -m pytest -n=4 test/unit/ test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py test/test_symbolic_shapetracker.py
         language: system
         always_run: true
         pass_filenames: false
+      - id: example
+        name: multi device tests
+        entry: python3 test/external/external_test_example.py
+        language: system
+        always_run: true
+        pass_filenames: false
+      - id: pylint
+        name: pylint
+        entry: python3 -m pylint tinygrad/
+        language: system
+        always_run: true
+        pass_filenames: false

.pylintrc

@@ -7,7 +7,7 @@ extension-pkg-whitelist=scipy,cereal.messaging.messaging_pyx,PyQt5,av
 
 # Add files or directories to the blacklist. They should be base names, not
 # paths.
-ignore=CVS,autogen,msm_kgsl.py,runtime,.venv
+ignore=CVS,autogen,msm_kgsl.py,runtime
 
 # Add files or directories matching the regex patterns to the blacklist. The
 # regex matches against base names, not paths.
@@ -30,6 +30,10 @@ persistent=yes
 # Specify a configuration file.
 #rcfile=
 
+# When enabled, pylint would attempt to guess common misconfiguration and emit
+# user-friendly hints instead of false-positive error messages
+suggestion-mode=yes
+
 # Allow loading of arbitrary C extensions. Extensions are imported into the
 # active Python interpreter and may run arbitrary code.
 unsafe-load-any-extension=no
@@ -50,12 +54,11 @@ confidence=
 # --enable=similarities". If you want to run only the classes checker, but have
 # no Warning level messages displayed, use"--disable=all --enable=classes
 # --disable=W"
-disable=C,R,W0613,W0511,W0212,W0201,W0106,W0603,W0621,W0703,W1201,W1203,E1136,W1514,E1101,W0221,W0105,E0401,abstract-method,W0707
+disable=C,R,W0613,W0511,W0212,W0201,W0106,W0603,W0621,W0703,W1201,W1203,E1136,W1514,E1101,W0221,W0105,E0401,abstract-method
 # E1101 for function binding
 # W0221 for Function class
 # W0105 for comment strings
 # E0401 for missing imports
-# W0707 for not reraising
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option

README.md

@@ -21,38 +21,17 @@ tinygrad: For something between [PyTorch](https://github.com/pytorch/pytorch) an
 
 ---
 
-tinygrad is an end-to-end deep learning stack:
+This may not be the best deep learning framework, but it is a deep learning framework.
 
-- **Tensor library** with autograd
-- **IR and compiler** that fuse and lower kernels
-- **JIT + graph execution**
-- **nn / optim / datasets** for real training
+Due to its extreme simplicity, it aims to be the easiest framework to add new accelerators to, with support for both inference and training. If XLA is CISC, tinygrad is RISC.
 
-It’s inspired by PyTorch (ergonomics), JAX (functional transforms and IR-based AD), and TVM (scheduling and codegen), but stays intentionally tiny and hackable.
+tinygrad is still alpha software, but we [raised some money](https://geohot.github.io/blog/jekyll/update/2023/05/24/the-tiny-corp-raised-5M.html) to make it good. Someday, we will tape out chips.
 
----
+## Features
 
-## How tinygrad compares
+### LLaMA and Stable Diffusion
 
-**PyTorch**
-
-- ✅ Similar: eager `Tensor` API, autograd, `optim`, basic datasets and layers.
-- ✅ You can write familiar training loops.
-- 🔁 Unlike PyTorch, the entire compiler and IR are visible and hackable.
-
-**JAX**
-
-- ✅ IR-based autodiff over primitives (like JAXPR + XLA).
-- ✅ Function-level JIT (`TinyJit`) that captures and replays kernels.
-- 🔁 Fewer functional transforms (no full `vmap`/`pmap` yet), but far easier to read.
-
-**TVM**
-
-- ✅ Multiple lowering passes, scheduling, and BEAM search over kernels.
-- ✅ Device “graphs” for batched execution.
-- 🔁 tinygrad also ships the **front-end framework** (tensors, nn, optim), not just the compiler.
-
----
+tinygrad can run [LLaMA](/docs/showcase.md#llama) and [Stable Diffusion](/docs/showcase.md#stable-diffusion)!
 
 ### Laziness
 
@@ -60,8 +39,9 @@ Try a matmul. See how, despite the style, it is fused into one kernel with the p
 
 ```sh
 DEBUG=3 python3 -c "from tinygrad import Tensor;
-N = 1024; a, b = Tensor.empty(N, N), Tensor.empty(N, N);
-(a.reshape(N, 1, N) * b.T.reshape(1, N, N)).sum(axis=2).realize()"
+N = 1024; a, b = Tensor.rand(N, N), Tensor.rand(N, N);
+c = (a.reshape(N, 1, N) * b.T.reshape(1, N, N)).sum(axis=2);
+print((c.numpy() - (a.numpy() @ b.numpy())).mean())"
 ```
 
 And we can change `DEBUG` to `4` to see the generated code.
@@ -100,8 +80,9 @@ See [examples/beautiful_mnist.py](examples/beautiful_mnist.py) for the full vers
 
 tinygrad already supports numerous accelerators, including:
 
-- [x] [OpenCL](tinygrad/runtime/ops_cl.py)
-- [x] [CPU](tinygrad/runtime/ops_cpu.py)
+- [x] [GPU (OpenCL)](tinygrad/runtime/ops_gpu.py)
+- [x] [CPU (C Code)](tinygrad/runtime/ops_cpu.py)
+- [x] [LLVM](tinygrad/runtime/ops_llvm.py)
 - [x] [METAL](tinygrad/runtime/ops_metal.py)
 - [x] [CUDA](tinygrad/runtime/ops_cuda.py)
 - [x] [AMD](tinygrad/runtime/ops_amd.py)
@@ -192,7 +173,7 @@ For more examples on how to run the full test suite please refer to the [CI work
 Some examples of running tests locally:
 ```sh
 python3 -m pip install -e '.[testing]'  # install extra deps for testing
-python3 test/backend/test_ops.py        # just the ops tests
+python3 test/test_ops.py                # just the ops tests
 python3 -m pytest test/                 # whole test suite
 ```
 

autogen_stubs.sh

@@ -0,0 +1,540 @@
+#!/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 --user .
+  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: return ctypes.CDLL(library)
+  for candidate in PATHS_TO_TRY:
+    try: return ctypes.CDLL(candidate)
+    except OSError: pass
+  raise RuntimeError("library $name not found")
+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" $BASE/kfd.py
+  sed -i "s\import ctypes,os\a from tinygrad.runtime.support import HWInterface\g" $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:HWInterface, **kwargs):\g" $BASE/kfd.py
+  sed -i "s\fcntl.ioctl(__fd, (__idir<<30)\__fd.ioctl((__idir<<30)\g" $BASE/kfd.py
+  python3 -c "import tinygrad.runtime.autogen.kfd"
+}
+
+generate_cuda() {
+  clang2py /usr/include/cuda.h -o $BASE/cuda.py -l /usr/lib/x86_64-linux-gnu/libcuda.so
+  sed -i "s\import ctypes\import ctypes, ctypes.util\g" $BASE/cuda.py
+  sed -i "s\ctypes.CDLL('/usr/lib/x86_64-linux-gnu/libcuda.so')\ctypes.CDLL(ctypes.util.find_library('cuda'))\g" $BASE/cuda.py
+  fixup $BASE/cuda.py
+  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 $BASE/nvrtc.py
+  python3 -c "import tinygrad.runtime.autogen.nvrtc"
+}
+
+generate_nv() {
+  NVKERN_COMMIT_HASH=d6b75a34094b0f56c2ccadf14e5d0bd515ed1ab6
+  NVKERN_SRC=/tmp/open-gpu-kernel-modules-$NVKERN_COMMIT_HASH
+  if [ ! -d "$NVKERN_SRC" ]; then
+    git clone https://github.com/tinygrad/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 \
+    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl0080.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/clc56f.h \
+    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc56f.h \
+    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl83de.h \
+    $NVKERN_SRC/src/nvidia/generated/g_allclasses.h \
+    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc6c0.h \
+    $NVKERN_SRC/kernel-open/nvidia-uvm/clc6b5.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 \
+    --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/#\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"
+}
+
+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/kfd_pm4_headers_ai.h \
+    extra/hip_gpu_driver/soc21_enum.h \
+    extra/hip_gpu_driver/sdma_v6_0_0_pkt_open.h \
+    extra/hip_gpu_driver/gc_11_0_0_offset.h \
+    extra/hip_gpu_driver/gc_10_3_0_offset.h \
+    extra/hip_gpu_driver/sienna_cichlid_ip_offset.h \
+    --clang-args="-I/opt/rocm/include -x c++" \
+    -o $BASE/amd_gpu.py
+
+  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_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)\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 HWInterface\g" $BASE/vfio.py
+  sed -i "s\fcntl.ioctl(__fd, (__idir<<30)\return __fd.ioctl((__idir<<30)\g" $BASE/vfio.py
+}
+
+generate_am() {
+  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 \
+    extra/amdpci/headers/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 \
+    extra/hip_gpu_driver/kfd_pm4_headers_ai.h \
+    extra/hip_gpu_driver/soc15d.h \
+    -o $BASE/am/pm4_soc15.py
+  fixup $BASE/am/pm4_soc15.py
+
+  clang2py -k cdefstum \
+    extra/hip_gpu_driver/kfd_pm4_headers_ai.h \
+    extra/hip_gpu_driver/nvd.h \
+    -o $BASE/am/pm4_nv.py
+  fixup $BASE/am/pm4_nv.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/vega10_enum.h \
+    -o $BASE/am/vega10.py
+  fixup $BASE/am/vega10.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/navi10_enum.h \
+    -o $BASE/am/navi10.py
+  fixup $BASE/am/navi10.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/soc21_enum.h \
+    -o $BASE/am/soc21.py
+  fixup $BASE/am/soc21.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/soc24_enum.h \
+    -o $BASE/am/soc24.py
+  fixup $BASE/am/soc24.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/mp_13_0_0_offset.h \
+    extra/amdpci/headers/mp_13_0_0_sh_mask.h \
+    -o $BASE/am/mp_13_0_0.py
+  fixup $BASE/am/mp_13_0_0.py
+
+  # 14_0_3 reuses 14_0_2
+  clang2py -k cdefstum \
+    extra/amdpci/headers/mp_14_0_2_offset.h \
+    extra/amdpci/headers/mp_14_0_2_sh_mask.h \
+    -o $BASE/am/mp_14_0_3.py
+  fixup $BASE/am/mp_14_0_3.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/mp_11_0_offset.h \
+    extra/amdpci/headers/mp_11_0_sh_mask.h \
+    -o $BASE/am/mp_11_0.py
+  fixup $BASE/am/mp_11_0.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/gc_9_4_3_offset.h \
+    extra/amdpci/headers/gc_9_4_3_sh_mask.h \
+    extra/amdpci/overlay/gc_9_4_3.h \
+    -o $BASE/am/gc_9_4_3.py
+  fixup $BASE/am/gc_9_4_3.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/gc_10_3_0_offset.h \
+    extra/amdpci/headers/gc_10_3_0_sh_mask.h \
+    -o $BASE/am/gc_10_3_0.py
+  fixup $BASE/am/gc_10_3_0.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/gc_11_0_0_offset.h \
+    extra/amdpci/headers/gc_11_0_0_sh_mask.h \
+    -o $BASE/am/gc_11_0_0.py
+  fixup $BASE/am/gc_11_0_0.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/gc_12_0_0_offset.h \
+    extra/amdpci/headers/gc_12_0_0_sh_mask.h \
+    -o $BASE/am/gc_12_0_0.py
+  fixup $BASE/am/gc_12_0_0.py
+
+  clang2py -k cdefstum \
+    extra/hip_gpu_driver/sdma_registers.h \
+    extra/hip_gpu_driver/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 \
+    extra/hip_gpu_driver/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 \
+    extra/hip_gpu_driver/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 \
+    extra/amdpci/headers/mmhub_3_0_0_offset.h \
+    extra/amdpci/headers/mmhub_3_0_0_sh_mask.h \
+    -o $BASE/am/mmhub_3_0_0.py
+  fixup $BASE/am/mmhub_3_0_0.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/mmhub_3_0_2_offset.h \
+    extra/amdpci/headers/mmhub_3_0_2_sh_mask.h \
+    -o $BASE/am/mmhub_3_0_2.py
+  fixup $BASE/am/mmhub_3_0_2.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/nbio_2_3_offset.h \
+    extra/amdpci/headers/nbio_2_3_sh_mask.h \
+    -o $BASE/am/nbio_2_3_0.py
+  fixup $BASE/am/nbio_2_3_0.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/mmhub_4_1_0_offset.h \
+    extra/amdpci/headers/mmhub_4_1_0_sh_mask.h \
+    -o $BASE/am/mmhub_4_1_0.py
+  fixup $BASE/am/mmhub_4_1_0.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/nbio_4_3_0_offset.h \
+    extra/amdpci/headers/nbio_4_3_0_sh_mask.h \
+    -o $BASE/am/nbio_4_3_0.py
+  fixup $BASE/am/nbio_4_3_0.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/nbif_6_3_1_offset.h \
+    extra/amdpci/headers/nbif_6_3_1_sh_mask.h \
+    -o $BASE/am/nbif_6_3_1.py
+  fixup $BASE/am/nbif_6_3_1.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/nbio_7_9_0_offset.h \
+    extra/amdpci/headers/nbio_7_9_0_sh_mask.h \
+    -o $BASE/am/nbio_7_9_0.py
+  fixup $BASE/am/nbio_7_9_0.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/nbio_7_11_0_offset.h \
+    extra/amdpci/headers/nbio_7_11_0_sh_mask.h \
+    -o $BASE/am/nbio_7_11_0.py
+  fixup $BASE/am/nbio_7_11_0.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/osssys_6_0_0_offset.h \
+    extra/amdpci/headers/osssys_6_0_0_sh_mask.h \
+    -o $BASE/am/osssys_6_0_0.py
+  fixup $BASE/am/osssys_6_0_0.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/osssys_7_0_0_offset.h \
+    extra/amdpci/headers/osssys_7_0_0_sh_mask.h \
+    -o $BASE/am/osssys_7_0_0.py
+  fixup $BASE/am/osssys_7_0_0.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/smu_v13_0_0_ppsmc.h \
+    extra/amdpci/headers/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 \
+    extra/amdpci/headers/smu_v14_0_0_pmfw.h \
+    extra/amdpci/headers/smu_v14_0_2_ppsmc.h \
+    extra/amdpci/headers/smu14_driver_if_v14_0_0.h \
+    extra/amdpci/headers/smu14_driver_if_v14_0.h \
+    extra/amdpci/headers/amdgpu_smu.h \
+    --clang-args="-include stdint.h" \
+    -o $BASE/am/smu_v14_0_3.py
+  fixup $BASE/am/smu_v14_0_3.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/hdp_6_0_0_offset.h \
+    extra/amdpci/headers/hdp_6_0_0_sh_mask.h \
+    -o $BASE/am/hdp_6_0_0.py
+  fixup $BASE/am/hdp_6_0_0.py
+
+  clang2py -k cdefstum \
+    extra/amdpci/headers/hdp_7_0_0_offset.h \
+    extra/amdpci/headers/hdp_7_0_0_sh_mask.h \
+    -o $BASE/am/hdp_7_0_0.py
+  fixup $BASE/am/hdp_7_0_0.py
+}
+
+generate_sqtt() {
+  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"
+}
+
+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"
+}
+
+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" == "sqtt" ]; then generate_sqtt
+elif [ "$1" == "qcom" ]; then generate_qcom
+elif [ "$1" == "io_uring" ]; then generate_io_uring
+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" == "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
+else echo "usage: $0 <type>"
+fi

docs/abstractions2.py

@@ -0,0 +1,118 @@
+# 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 "LazyBuffer" 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, MallocAllocator, CPUProgram
+
+# allocate some buffers
+out = MallocAllocator.alloc(4)
+a = MallocAllocator.alloc(4)
+b = MallocAllocator.alloc(4)
+
+# load in some values (little endian)
+MallocAllocator._copyin(a, memoryview(bytearray([2,0,0,0])))
+MallocAllocator._copyin(b, memoryview(bytearray([3,0,0,0])))
+
+# compile a program to a binary
+lib = ClangJITCompiler().compile("void add(int *out, int *a, int *b) { out[0] = a[0] + b[0]; }")
+
+# create a runtime for the program
+fxn = CPUProgram("add", lib)
+
+# run the program
+fxn(out, a, b)
+
+# check the data out
+print(val := MallocAllocator._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.ops import UOp, Ops
+from tinygrad.shape.shapetracker import ShapeTracker
+
+# 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 MallocAllocator.alloc
+
+# describe the computation
+buf_1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 1)
+buf_2 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 2)
+ld_1 = UOp(Ops.LOAD, dtypes.int32, (buf_1, ShapeTracker.from_shape((1,)).to_uop()))
+ld_2 = UOp(Ops.LOAD, dtypes.int32, (buf_2, ShapeTracker.from_shape((1,)).to_uop()))
+alu = ld_1 + ld_2
+output_buf = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 0)
+st_0 = UOp(Ops.STORE, dtypes.void, (output_buf, ShapeTracker.from_shape((1,)).to_uop(), alu))
+s = UOp(Ops.SINK, dtypes.void, (st_0,))
+
+# convert the computation to a "linearized" format (print the format)
+from tinygrad.engine.realize import get_kernel, CompiledRunner
+kernel = get_kernel(Device[DEVICE].renderer, s).linearize()
+
+# compile a program (and print the source)
+fxn = CompiledRunner(kernel.to_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 LazyBuffer ***********")
+
+from tinygrad.engine.realize import run_schedule
+from tinygrad.engine.schedule import create_schedule_with_vars
+
+# allocate some values + load in values
+a = UOp.metaop(Ops.EMPTY, (1,), dtypes.int32, DEVICE)
+b = UOp.metaop(Ops.EMPTY, (1,), dtypes.int32, DEVICE)
+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.alu(Ops.ADD, b)
+
+# schedule the computation as a list of kernels
+sched, _, becomes_map = create_schedule_with_vars(out.sink())
+for si in sched: print(si.ast.op)  # NOTE: the first two convert it to CPU
+# NOTE: UOps are no longer mutable, the scheduler gives you a map to lookup which BUFFER the result was written to
+out = becomes_map[out]
+
+# DEBUGGING: print the compute ast
+print(sched[-1].ast)
+# NOTE: sched[-1].ast is the same as st_0 above
+
+# 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/abstractions3.py

@@ -36,21 +36,27 @@ optim.schedule_step()   # this will step the optimizer without running realize
 # 3. Create a schedule.
 
 # The weight Tensors have been assigned to, but not yet realized. Everything is still lazy at this point
-# l1.uop and l2.uop define a computation graph
+# l1.lazydata and l2.lazydata define a computation graph
 
-from tinygrad.engine.schedule import ExecItem
-schedule: List[ExecItem] = Tensor.schedule(l1, l2)
+from tinygrad.engine.schedule import ScheduleItem
+schedule: List[ScheduleItem] = Tensor.schedule(l1, l2)
 
 print(f"The schedule contains {len(schedule)} items.")
 for si in schedule: print(str(si)[:80])
 
 # *****
-# 4. Lower and run the schedule.
+# 4. Lower a schedule.
 
-for si in tqdm(schedule): si.run()
+from tinygrad.engine.realize import lower_schedule_item, ExecItem
+lowered: List[ExecItem] = [lower_schedule_item(si) for si in tqdm(schedule)]
 
 # *****
-# 5. Print the weight change
+# 5. Run the schedule
+
+for ei in tqdm(lowered): ei.run()
+
+# *****
+# 6. Print the weight change
 
 print("first weight change\n", l1.numpy()-l1n)
 print("second weight change\n", l2.numpy()-l2n)

docs/developer/am.md

@@ -1,6 +1,6 @@
 # AM Driver
 
-AM driver is a userspace driver targeting AMD's RDNA3/RDNA4. You only need tinygrad to send compute tasks to your GPU!
+AM driver is a userspace driver targeting AMD's 7900XTX. 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`!

docs/developer/developer.md

@@ -13,19 +13,19 @@ There's also a [doc describing speed](../developer/speed.md)
 
 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. Inputs to a base can be either base or view, inputs to a view can only be a single base.
+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 `ExecItem`. One `ExecItem` 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.
+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.ExecItem
+::: tinygrad.engine.schedule.ScheduleItem
 
 ## Lowering
 
-The code in [realize](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/engine/realize.py) lowers `ExecItem` by populating its `prg` field with
+The code in [realize](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/engine/realize.py) lowers `ScheduleItem` to `ExecItem` with
 
-::: tinygrad.engine.realize.run_schedule
+::: tinygrad.engine.realize.lower_schedule
 
 There's a ton of complexity hidden behind this, see the `codegen/` directory.
 

docs/developer/hcq.md

@@ -52,7 +52,7 @@ Signals are device-dependent structures used for synchronization and timing in H
 The following Python code demonstrates the usage of signals:
 
 ```python
-signal = your_device.new_signal(value=0)
+signal = your_device.signal_t()
 
 HWQueue().timestamp(signal) \
          .signal(signal, value_to_fire) \

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_kernel_graph
-    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 and rendered program.
-
-::: tinygrad.codegen.get_program
-    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/speed.md

@@ -68,4 +68,4 @@ We have a simple framework in tinygrad for adding these ALU blocks and achieving
 
 ### 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.
+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 simplifiy 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

@@ -1,10 +1,10 @@
-::: tinygrad.uop.ops.UOp
+::: tinygrad.ops.UOp
     options:
         members: false
         members_order: source
         show_labels: false
 
-::: tinygrad.uop.ops.Ops
+::: tinygrad.ops.Ops
     options:
         members: true
         members_order: source

docs/env_vars.md

@@ -3,7 +3,7 @@
 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`
+Example: `GPU=1 DEBUG=4 python3 -m pytest`
 
 However you can also decorate a function to set a value only inside that function.
 
@@ -30,32 +30,23 @@ 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
+DEBUG               | [1-6]      | enable debugging output, with 4 you get operations, timings, speed, generated code and more
+GPU                 | [1]        | enable the GPU (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
+METAL_XCODE         | [1]        | enable Metal using macOS Xcode SDK
+CPU                 | [1]        | enable CPU (Clang) backend
+LLVM                | [1]        | enable LLVM backend
 BEAM                | [#]        | number of beams in kernel beam search
 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).
+PTX                 | [1]        | enable the specialized [PTX](https://docs.nvidia.com/cuda/parallel-thread-execution/) assembler for Nvidia GPUs. If not set, defaults to generic CUDA codegen backend.
+PROFILE             | [1]        | enable profiling. This feature is supported in NV, AMD, QCOM and METAL backends.
+VISIBLE_DEVICES     | [list[int]]| restricts the NV/AMD 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
+WEBGPU_BACKEND      | [WGPUBackendType_Metal, ...]          | Force select a backend for WebGPU (Metal, DirectX, OpenGL, Vulkan...)

docs/index.md

@@ -42,7 +42,7 @@ There's nothing special about a "Module" class in tinygrad, it's just a normal c
 
 ### 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.
+In tinygrad, you can do [`x.conv2d(w, b)`](tensor/ops.md/#tinygrad.Tensor.conv2d) or [`x.sparse_categorical_cross_entropy(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
 

docs/mnist.md

@@ -131,7 +131,7 @@ timeit.repeat(jit_step, repeat=5, number=1)
 
 1.0 ms is 75x faster! Note that we aren't syncing the GPU, so GPU time may be slower.
 
-The first two runs of the function execute normally, with the JIT capturing the kernels. Starting from the third run, only the tinygrad operations are replayed, removing the overhead by skipping Python code execution. So be aware that any non-tinygrad Python values affecting the kernels will be "frozen" from the second run. Note that `Tensor` randomness functions work as expected.
+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.
 

docs/runtime.md

@@ -2,17 +2,17 @@
 
 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) |
-
+| Runtime | Description | Requirements |
+|---------|-------------|--------------|
+| [NV](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_nv.py) | Provides acceleration for NVIDIA GPUs | Ampere/Ada series GPUs |
+| [AMD](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_amd.py) | Provides acceleration for AMD GPUs | RDNA2/RDNA3 series GPUs |
+| [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 | NVIDIA GPU with CUDA support |
+| [GPU (OpenCL)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_gpu.py) | Accelerates computations using OpenCL on GPUs | OpenCL 2.0 compatible device |
+| [CPU (C Code)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_cpu.py) | Runs on CPU using the clang compiler | `clang` compiler in system `PATH` |
+| [LLVM (LLVM IR)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_llvm.py) | Runs on CPU using the LLVM compiler infrastructure | llvm libraries installed and findable |
+| [WEBGPU](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_webgpu.py) | Runs on GPU using the Dawn WebGPU engine (used in Google Chrome) | Dawn library installed and findable. Download binaries [here](https://github.com/wpmed92/pydawn/releases/tag/v0.1.6). |
 
 ## Interoperability
 
@@ -64,18 +64,3 @@ rawbuf_ptr = to_mv(cl_buf_desc_ptr, 0x100).cast('Q')[20] # offset 0xA0 is a raw
 # 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 interface 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/tensor/creation.md

@@ -22,9 +22,7 @@
 ::: 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

docs/tensor/elementwise.md

@@ -6,7 +6,6 @@ Elementwise ops operate on a per element basis. They don't change the shape of t
 ::: tinygrad.Tensor.neg
 ::: tinygrad.Tensor.log
 ::: tinygrad.Tensor.log2
-::: tinygrad.Tensor.log10
 ::: tinygrad.Tensor.exp
 ::: tinygrad.Tensor.exp2
 ::: tinygrad.Tensor.sqrt
@@ -36,7 +35,6 @@ Elementwise ops operate on a per element basis. They don't change the shape of t
 
 ::: tinygrad.Tensor.relu
 ::: tinygrad.Tensor.sigmoid
-::: tinygrad.Tensor.logsigmoid
 ::: tinygrad.Tensor.hardsigmoid
 ::: tinygrad.Tensor.elu
 ::: tinygrad.Tensor.celu
@@ -79,7 +77,6 @@ Elementwise ops operate on a per element basis. They don't change the shape of t
 ::: tinygrad.Tensor.minimum
 ::: tinygrad.Tensor.where
 ::: tinygrad.Tensor.copysign
-::: tinygrad.Tensor.logaddexp
 
 ## Casting Ops
 
@@ -88,8 +85,4 @@ Elementwise ops operate on a per element basis. They don't change the shape of t
 ::: tinygrad.Tensor.float
 ::: tinygrad.Tensor.half
 ::: tinygrad.Tensor.int
-::: tinygrad.Tensor.bool
-::: tinygrad.Tensor.bfloat16
-::: tinygrad.Tensor.double
-::: tinygrad.Tensor.long
-::: tinygrad.Tensor.short
+::: tinygrad.Tensor.bool

docs/tensor/movement.md

@@ -18,7 +18,6 @@
 ::: tinygrad.Tensor.repeat_interleave
 ::: tinygrad.Tensor.split
 ::: tinygrad.Tensor.chunk
-::: tinygrad.Tensor.unfold
 ::: tinygrad.Tensor.meshgrid
 ::: tinygrad.Tensor.squeeze
 ::: tinygrad.Tensor.unsqueeze
@@ -26,7 +25,5 @@
 ::: tinygrad.Tensor.transpose
 ::: tinygrad.Tensor.flatten
 ::: tinygrad.Tensor.unflatten
-::: tinygrad.Tensor.diag
-::: tinygrad.Tensor.diagonal
 ::: tinygrad.Tensor.roll
 ::: tinygrad.Tensor.rearrange

docs/tensor/ops.md

@@ -7,7 +7,6 @@
 ::: tinygrad.Tensor.any
 ::: tinygrad.Tensor.all
 ::: tinygrad.Tensor.isclose
-::: tinygrad.Tensor.allclose
 ::: tinygrad.Tensor.mean
 ::: tinygrad.Tensor.var
 ::: tinygrad.Tensor.var_mean
@@ -31,21 +30,15 @@
 ::: tinygrad.Tensor.matmul
 ::: tinygrad.Tensor.einsum
 ::: tinygrad.Tensor.cumsum
-::: tinygrad.Tensor.cumprod
 ::: tinygrad.Tensor.cummax
-::: tinygrad.Tensor.cummin
 ::: 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.nonzero
 ::: tinygrad.Tensor.sort
-::: tinygrad.Tensor.argsort
 ::: tinygrad.Tensor.topk
-::: tinygrad.Tensor.multinomial
 
 ## Neural Network (functional)
 
@@ -61,8 +54,3 @@
 ::: tinygrad.Tensor.sparse_categorical_crossentropy
 ::: tinygrad.Tensor.cross_entropy
 ::: tinygrad.Tensor.nll_loss
-
-## Linear Algebra
-
-::: tinygrad.Tensor.qr
-::: tinygrad.Tensor.svd

docs/tinybox.md

@@ -6,7 +6,7 @@ If you don't have a tinybox and you want one, see [tinygrad.org](https://tinygra
 
 ## Welcome
 
-Welcome to your tinybox! The tinybox is the universal system purpose-built for all AI infrastructure and workloads, from training to inference. The red box includes six 7900XTX GPUs, the green box includes six 4090 GPUs, and the green v2 box includes four 5090 GPUs. Whether you bought a red one or a green one, we want you to love it.
+Welcome to your tinybox! The tinybox is the universal system purpose-built for all AI infrastructure and workloads, from training to inference. The red box includes six 7900XTX GPUs, and the green box includes six 4090 GPUs. Whether you bought a red one or a green one, we want you to love it.
 
 We don't have a stupid cloud service, you don't have to create a tiny account to set it up, and we aren't tracking how you use the box. We're just happy you bought one. This petaflop is your petaflop.
 
@@ -41,14 +41,14 @@ The BMC also has a web interface you can use if you find that easier.
 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 `tinybox-display.service` service.
+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
+## tinychat
 
-The OS image is built using `ubuntu-image` from <https://github.com/tinygrad/tinyos>.
+Since LLMs are so popular, we ship with a built in tinygrad based chatbot using a LLaMA-3 finetune. Visit the IP (not the BMC IP) of your tinybox in a web browser on your computer or phone, and you'll find a friendly looking chat interface. This chatbot also provides an OpenAI compatible LLM API on that port, so you can script it.
 
-After cloning, run `make green` or `make red` to build a tinybox green or tinybox red image respectively.
+The conversations you have with this chatbot are between you and your tinybox. Also, the history in the web app is saved on the client, not the tinybox.

eslint.config.mjs

@@ -0,0 +1,9 @@
+import globals from "globals";
+import pluginJs from "@eslint/js";
+import pluginHtml from "eslint-plugin-html";
+
+export default [
+  {files: ["**/*.html"], plugins: {html: pluginHtml}, rules:{"max-len": ["error", {"code": 150}]}},
+  {languageOptions: {globals: globals.browser}},
+  pluginJs.configs.recommended,
+];

examples/anthropic_challenge.py

@@ -1,196 +0,0 @@
-from tinygrad import Tensor, dtypes, Context, getenv, UOp, fetch
-from tinygrad.uop.ops import Ops, PatternMatcher, UPat
-from tinygrad.uop.symbolic import symbolic
-from tinygrad.codegen import Renderer
-from tinygrad.codegen.opt import Opt, OptOps
-
-# ************************* implementation of the problem ************************
-
-def myhash(a: Tensor) -> Tensor:
-  a = (a + 0x7ED55D16) + (a << 12)
-  a = (a ^ 0xC761C23C) ^ (a >> 19)
-  a = (a + 0x165667B1) + (a << 5)
-  a = (a + 0xD3A2646C) ^ (a << 9)
-  a = (a + 0xFD7046C5) + (a << 3)
-  a = (a ^ 0xB55A4F09) ^ (a >> 16)
-  return a
-
-def select_with_where_tree(values: Tensor, relative_idx: Tensor) -> Tensor:
-  n = values.shape[0]
-  if n == 1: return values[0].expand(relative_idx.shape)
-
-  mid = n // 2
-  left = select_with_where_tree(values[:mid], relative_idx)
-  right = select_with_where_tree(values[mid:], relative_idx - mid)
-
-  go_left = relative_idx < mid
-  return go_left.where(left, right)
-
-def tree_traversal(forest: Tensor, val: Tensor, height: int, rounds: int, where_tree_threshold=3) -> Tensor:
-  # All walkers start at idx=0
-  idx = Tensor.zeros(val.shape, device=val.device, dtype=dtypes.uint32)
-
-  for r in range(rounds):
-    level = r % (height + 1)
-    level_start = (1 << level) - 1
-    level_size = 1 << level
-
-    if level == 0:
-      # At root (level 0), all walkers are at idx=0
-      # No gather needed, just broadcast the root value
-      node_val = forest[0].expand(val.shape)
-      idx = idx * 0  # Reset to 0
-    elif level <= where_tree_threshold:
-      # Small level: use where-tree
-      level_values = forest[level_start : level_start + level_size]
-      relative_idx = (idx - level_start)
-      node_val = select_with_where_tree(level_values, relative_idx)
-    else:
-      # Large level: use gather
-      node_val = forest.gather(0, idx)
-
-    val = myhash(val ^ node_val)
-    idx = (idx << 1) + (1 + (val & 1))
-
-    # No wrap check needed! At round 10 (level becomes 0), we reset idx above.
-
-  return val.contiguous(arg=(Opt(OptOps.UPCAST, 0, 8),))
-
-# ************************* renderer for VLIW machine *************************
-
-def loop_unrolling(sink:UOp):
-  rng = [x for x in sink.toposort() if x.op is Ops.RANGE]
-  if len(rng) == 0: return None
-  print(f"unrolling loop with size {rng[0].vmax+1}")
-  unrolled_sinks = [sink.substitute({rng[0]:rng[0].const_like(i)}).src[0] for i in range(rng[0].vmax+1)]
-  return UOp.sink(*unrolled_sinks, arg=sink.arg)
-
-global_addrs = []
-vliw_prepare = PatternMatcher([
-  # loop unrolling (should be a part of tinygrad)
-  (UPat(Ops.SINK, name="sink"), loop_unrolling),
-  # cast is fake
-  (UPat(Ops.CAST, name="c"), lambda c: c.src[0]),
-  # rewrites to hardcode the addresses in memory
-  (UPat(Ops.PARAM, name="dg"), lambda dg: UOp.const(dtypes.uint, global_addrs[dg.arg])),
-  # INDEX is just plus
-  (UPat(Ops.INDEX, name="i"), lambda i: i.src[0]+i.src[1]),
-])+symbolic
-
-class VLIWRenderer(Renderer):
-  has_local = False  # TODO: this should be the default / cleaned up
-  # this says this backend supports MULACC + more. decompositions uses this
-  code_for_op: dict = {Ops.MULACC: None, Ops.ADD: "+", Ops.MUL: "*",
-                       Ops.XOR: "^", Ops.AND: "&", Ops.OR: "|",
-                       Ops.SHL: "<<", Ops.SHR: ">>", Ops.CMPLT: "<"}
-  # this matcher runs while still in graph form
-  pre_matcher = vliw_prepare
-
-  def render(self, uops:list[UOp]):
-
-    # TODO: this is a minimal renderer. for low cycle count, make it good
-    # to get speed, you need to add VLIW packing
-    # to get under 1536 regs, you need to add a register allocator
-    # we left the fun parts to you
-
-    print(f"rendering with {len(uops)} uops")
-    reg, inst = 0, []
-    r: dict[UOp, int] = {}
-    for u in uops:
-      assert u.dtype.count in (1,8), "dtype count must be 1 or 8"
-
-      # dumb register allocator
-      if u.op not in {Ops.STORE, Ops.SINK, Ops.GEP}:
-        r[u] = reg
-        reg += u.dtype.count
-
-      # render UOps to instructions
-      match u.op:
-        case Ops.SINK:
-          inst.append({"flow": [("halt",)]})
-        case Ops.CONST:
-          inst.append({"load": [("const", r[u], u.arg)]})
-        case Ops.GEP:
-          # a GEP is just an alias to a special register in the vector
-          r[u] = r[u.src[0]] + u.arg[0]
-        case Ops.VECTORIZE:
-          if all(s == u.src[0] for s in u.src):
-            # if all sources are the same, we can broadcast
-            inst.append({"valu": [("vbroadcast", r[u], r[u.src[0]])]})
-          else:
-            # this is a copy into a contiguous chunk of registers
-            inst.extend({"flow": [("add_imm", r[u]+i, r[s], 0)]} for i,s in enumerate(u.src) if r[s] != r[u]+i)
-        case Ops.LOAD:
-          op = "vload" if u.dtype.count > 1 else "load"
-          inst.append({"load": [(op, r[u], r[u.src[0]])]})
-        case Ops.STORE:
-          op = "vstore" if u.src[1].dtype.count > 1 else "store"
-          inst.append({"store": [(op, r[u.src[0]], r[u.src[1]])]})
-        case Ops.MULACC:
-          assert u.dtype.count == 8
-          inst.append({"valu": [("multiply_add", r[u], r[u.src[0]], r[u.src[1]], r[u.src[2]])]})
-        case Ops.WHERE:
-          assert u.dtype.count == 8
-          inst.append({"flow": [("vselect", r[u], r[u.src[0]], r[u.src[1]], r[u.src[2]])]})
-        case _ if u.op in self.code_for_op:
-          cat = "valu" if u.dtype.count > 1 else "alu"
-          inst.append({cat: [(self.code_for_op[u.op], r[u], r[u.src[0]], r[u.src[1]])]})
-        case _:
-          raise NotImplementedError(f"unhandled op {u.op}")
-    return repr(inst)
-
-# ************************* test and render *************************
-
-import sys, types
-PROBLEM_URL = "https://raw.githubusercontent.com/anthropics/original_performance_takehome/refs/heads/main/tests/frozen_problem.py"
-sys.modules["problem"] = problem = types.ModuleType("problem")
-exec(fetch(PROBLEM_URL).read_text(), problem.__dict__)
-
-if __name__ == "__main__":
-  batch_size = getenv("BS", 256)
-  height = 10
-  rounds = getenv("ROUNDS", 16)
-
-  # build problem
-  tree = problem.Tree.generate(height)
-  inp = problem.Input.generate(tree, batch_size, rounds)
-  mem = problem.build_mem_image(tree, inp)
-  global_addrs.extend([mem[6], mem[6], mem[4]])  # output, input, forest
-
-  # *** verify the kernel in tinygrad compared to reference ***
-
-  forest_t = Tensor(tree.values, dtype=dtypes.uint32)
-  val_t = Tensor(inp.values, dtype=dtypes.uint32)
-
-  if getenv("VERIFY", 1):
-    # verify on normal tinygrad device
-    with Context(PCONTIG=2):
-      out = tree_traversal(forest_t, val_t, height, rounds)
-      val_out = out.tolist()
-    problem.reference_kernel(tree, inp)
-    assert val_out == inp.values
-    print("verification passed")
-
-  # *** render to device ***
-
-  from tinygrad.codegen import get_program
-  with Context(PCONTIG=2, DEVECTORIZE=2, SPEC=0):
-    out = tree_traversal(forest_t, val_t, height, rounds)
-    sink = out.schedule()[-1].ast
-    prg = get_program(sink, VLIWRenderer())
-
-  # *** run on Machine and compare ***
-
-  # NOTE: the scratch size needs to be reduced to 1536 when you have a register allocator
-  src = eval(prg.src)
-  max_regs = max(t[1] for instr in src for v in instr.values() for t in v if len(t) > 1) + 8
-  print(f"{max_regs:5d} regs used" + ("" if max_regs <= 1536 else "       <-- WARNING: TOO MANY REGISTERS, MUST BE <= 1536"))
-  machine = problem.Machine(mem, src, problem.DebugInfo(scratch_map={}), n_cores=1, trace=False, scratch_size=max_regs)
-  machine.run()
-  print(f"ran for {machine.cycle:5d} cycles" + ("" if machine.cycle <= 1363 else "  <-- EVEN CLAUDE GOT 1363"))
-
-  # compare to reference
-  ref_mem = mem.copy()
-  for _ in problem.reference_kernel2(ref_mem, {}): pass
-  assert machine.mem[mem[6]:mem[6]+mem[2]] == ref_mem[mem[6]:mem[6]+mem[2]]
-  print("compare passed!")

examples/audio_helpers.py

@@ -1,79 +0,0 @@
-from typing import Optional
-from tinygrad import Tensor
-from tinygrad.dtype import DTypeLike, dtypes
-import math
-
-# rewritten from numpy
-def rfftfreq(n: int, d: float = 1.0, device=None) -> Tensor:
-  val = 1.0 / (n * d)
-  N = n // 2 + 1
-  results = Tensor.arange(N, device=device)
-  return results * val
-
-# just like in librosa
-def fft_frequencies(sr: float, n_fft: int) -> Tensor:
-  return rfftfreq(n=n_fft, d=1.0 / sr)
-
-def hz_to_mel(freq: Tensor) -> Tensor:
-  # linear part
-  f_min = 0.0
-  f_sp = 200.0 / 3
-  mels = (freq - f_min) / f_sp
-
-  # log-scale part
-  min_log_hz = 1000.0  # beginning of log region (Hz)
-  mask = freq >= min_log_hz
-  return mask.where(((min_log_hz - f_min) / f_sp) + (freq / min_log_hz).log() / (math.log(6.4) / 27.0), mels)
-
-def mel_to_hz(mels: Tensor) -> Tensor:
-  # linear scale
-  f_min = 0.0
-  f_sp = 200.0 / 3
-  freqs = f_min + f_sp * mels
-
-  # nonlinear scale
-  min_log_hz = 1000.0  # beginning of log region (Hz)
-  min_log_mel = (min_log_hz - f_min) / f_sp  # same (Mels)
-  logstep = math.log(6.4) / 27.0  # step size for log region
-
-  log_t = mels >= min_log_mel
-  freqs = log_t.where(min_log_hz * ((logstep * (mels - min_log_mel)).exp()), freqs)
-  return freqs
-
-def mel_frequencies(n_mels: int = 128, *, fmin: float = 0.0, fmax: float = 11025.0) -> Tensor:
-  # center freqs of mel bands - uniformly spaced between limits
-  min_max_mel = hz_to_mel(Tensor([fmin, fmax]))
-
-  mels = Tensor.linspace(min_max_mel[0], min_max_mel[1], n_mels)
-  hz = mel_to_hz(mels)
-  return hz
-
-def mel(
-  *,
-  sr: float,
-  n_fft: int,
-  n_mels: int = 128,
-  fmin: float = 0.0,
-  fmax: Optional[float] = None,
-  dtype: DTypeLike = dtypes.default_float,
-) -> Tensor:
-  if fmax is None:
-    fmax = float(sr) / 2
-
-  n_mels = int(n_mels)
-
-  fftfreqs = fft_frequencies(sr=sr, n_fft=n_fft)  # center freqs of each FFT bin
-  mel_f = mel_frequencies(n_mels + 2, fmin=fmin, fmax=fmax)  # center freqs of mel bands
-
-  fdiff = mel_f[1:] - mel_f[:-1]
-  ramps = mel_f[None].T.expand(-1, fftfreqs.shape[-1]) - fftfreqs
-
-  lower = -ramps[:n_mels] / fdiff[:n_mels][None].T
-  upper = ramps[2 : n_mels + 2] / fdiff[1 : n_mels + 1][None].T
-  weights = lower.minimum(upper).maximum(0)
-
-  # Slaney-style mel is scaled to be approx constant energy per channel
-  enorm = 2.0 / (mel_f[2 : n_mels + 2] - mel_f[:n_mels])
-  weights *= enorm[:, None]
-
-  return weights

examples/beautiful_cartpole.py

@@ -78,7 +78,10 @@ if __name__ == "__main__":
 
   @TinyJit
   def get_action(obs:Tensor) -> Tensor:
+    # TODO: with no_grad
+    Tensor.no_grad = True
     ret = model(obs)[0].exp().multinomial().realize()
+    Tensor.no_grad = False
     return ret
 
   st, steps = time.perf_counter(), 0

examples/beautiful_cifar.py

@@ -2,19 +2,15 @@ import time
 start_tm = time.perf_counter()
 import math
 from typing import Tuple, cast
-from tinygrad import Tensor, nn, GlobalCounters, TinyJit, dtypes, Device
+import numpy as np
+from tinygrad import Tensor, nn, GlobalCounters, TinyJit, dtypes
 from tinygrad.helpers import partition, trange, getenv, Context
 from extra.lr_scheduler import OneCycleLR
 
-GPUS = [f'{Device.DEFAULT}:{i}' for i in range(getenv("GPUS", 1))]
-
-# override tinygrad defaults
 dtypes.default_float = dtypes.half
-Context(FUSE_OPTIM=1).__enter__()
 
 # from https://github.com/tysam-code/hlb-CIFAR10/blob/main/main.py
 batchsize = getenv("BS", 1024)
-assert batchsize % len(GPUS) == 0, f"{batchsize=} is not a multiple of {len(GPUS)=}"
 bias_scaler = 64
 hyp = {
   'opt': {
@@ -71,7 +67,7 @@ class ConvGroup:
     cast(Tensor, self.norm2.weight).requires_grad = False
   def __call__(self, x:Tensor) -> Tensor:
     x =    self.norm1(self.conv1(x).max_pool2d().float()).cast(dtypes.default_float).quick_gelu()
-    return self.norm2(self.conv2(x).float()).cast(dtypes.default_float).quick_gelu() + x
+    return self.norm2(self.conv2(x).float()).cast(dtypes.default_float).quick_gelu()
 
 class SpeedyConvNet:
   def __init__(self):
@@ -82,25 +78,23 @@ class SpeedyConvNet:
     self.linear = nn.Linear(depths['block3'], depths['num_classes'], bias=False)
   def __call__(self, x:Tensor) -> Tensor:
     x = self.whiten(x).quick_gelu()
-    # ************* HACKS *************
-    x = x.pad((1,0,0,1)) # TODO: this pad should not be here! copied from hlb_cifar10 for speed
-    # ************* HACKS *************
     x = x.sequential([self.conv_group_1, self.conv_group_2, self.conv_group_3])
     return self.linear(x.max(axis=(2,3))) * hyp['opt']['scaling_factor']
 
 if __name__ == "__main__":
   # *** dataset ***
   X_train, Y_train, X_test, Y_test = nn.datasets.cifar()
+  # TODO: without this line indexing doesn't fuse!
+  X_train, Y_train, X_test, Y_test = [x.contiguous() for x in [X_train, Y_train, X_test, Y_test]]
   cifar10_std, cifar10_mean = X_train.float().std_mean(axis=(0, 2, 3))
-  def preprocess(X:Tensor) -> Tensor: return ((X - cifar10_mean.view(1, -1, 1, 1)) / cifar10_std.view(1, -1, 1, 1)).cast(dtypes.default_float)
+  def preprocess(X:Tensor, Y:Tensor) -> Tuple[Tensor, Tensor]:
+    return ((X - cifar10_mean.view(1, -1, 1, 1)) / cifar10_std.view(1, -1, 1, 1)).cast(dtypes.default_float), Y.one_hot(depths['num_classes'])
 
   # *** model ***
   model = SpeedyConvNet()
   state_dict = nn.state.get_state_dict(model)
-  if len(GPUS) > 1:
-    cifar10_std.to_(GPUS)
-    cifar10_mean.to_(GPUS)
-    for x in state_dict.values(): x.to_(GPUS)
+
+  #for k,v in nn.state.torch_load("/tmp/cifar_net.pt").items(): print(k)
 
   params_bias, params_non_bias = partition(state_dict.items(), lambda x: 'bias' in x[0])
   opt_bias     = nn.optim.SGD([x[1] for x in params_bias],     lr=0.01, momentum=.85, nesterov=True, weight_decay=hyp['opt']['bias_decay'])
@@ -117,44 +111,48 @@ if __name__ == "__main__":
   lr_sched_bias     = OneCycleLR(opt_bias,     max_lr=hyp['opt']['bias_lr'],     pct_start=pct_start, div_factor=initial_div_factor, final_div_factor=1./(initial_div_factor*final_lr_ratio), total_steps=total_train_steps)
   lr_sched_non_bias = OneCycleLR(opt_non_bias, max_lr=hyp['opt']['non_bias_lr'], pct_start=pct_start, div_factor=initial_div_factor, final_div_factor=1./(initial_div_factor*final_lr_ratio), total_steps=total_train_steps)
 
-  def loss_fn(out:Tensor, Y:Tensor) -> Tensor:
-    ret = out.sparse_categorical_crossentropy(Y, reduction='none', label_smoothing=0.2)
-    return ret.mul(hyp['opt']['loss_scale_scaler']*loss_batchsize_scaler).sum().div(hyp['opt']['loss_scale_scaler'])
+  def loss_fn(out, Y):
+    return out.cross_entropy(Y, reduction='none', label_smoothing=0.2).mul(hyp['opt']['loss_scale_scaler']*loss_batchsize_scaler).sum().div(hyp['opt']['loss_scale_scaler'])
 
   @TinyJit
   @Tensor.train()
   def train_step(idxs:Tensor) -> Tensor:
-    X, Y = X_train[idxs], Y_train[idxs]
-    if len(GPUS) > 1:
-      X.shard_(GPUS, axis=0)
-      Y.shard_(GPUS, axis=0)
-    out = model(preprocess(X))
+    with Context(SPLIT_REDUCEOP=0, FUSE_ARANGE=1):
+      X = X_train[idxs]
+      Y = Y_train[idxs].realize(X)
+    X, Y = preprocess(X, Y)
+    out = model(X)
     loss = loss_fn(out, Y)
     opt.zero_grad()
     loss.backward()
-    return (loss / (batchsize*loss_batchsize_scaler)).realize(*opt.schedule_step(),
-                                                              *lr_sched_bias.schedule_step(), *lr_sched_non_bias.schedule_step())
+    opt.step()
+    lr_sched_bias.step()
+    lr_sched_non_bias.step()
+    return loss / (batchsize*loss_batchsize_scaler)
 
   eval_batchsize = 2500
   @TinyJit
+  @Tensor.test()
   def val_step() -> Tuple[Tensor, Tensor]:
+    # TODO with Tensor.no_grad()
+    Tensor.no_grad = True
     loss, acc = [], []
     for i in range(0, X_test.size(0), eval_batchsize):
-      X, Y = X_test[i:i+eval_batchsize], Y_test[i:i+eval_batchsize]
-      if len(GPUS) > 1:
-        X.shard_(GPUS, axis=0)
-        Y.shard_(GPUS, axis=0)
-      out = model(preprocess(X))
+      X, Y = preprocess(X_test[i:i+eval_batchsize], Y_test[i:i+eval_batchsize])
+      out = model(X)
       loss.append(loss_fn(out, Y))
-      acc.append((out.argmax(-1) == Y).sum() / eval_batchsize)
-    return Tensor.stack(*loss).mean() / (batchsize*loss_batchsize_scaler), Tensor.stack(*acc).mean()
-
-  Tensor.manual_seed(1337)
-  num_train_samples = X_train.shape[0]
+      acc.append((out.argmax(-1).one_hot(depths['num_classes']) * Y).sum() / eval_batchsize)
+    ret = Tensor.stack(*loss).mean() / (batchsize*loss_batchsize_scaler), Tensor.stack(*acc).mean()
+    Tensor.no_grad = False
+    return ret
 
+  np.random.seed(1337)
   for epoch in range(math.ceil(hyp['misc']['train_epochs'])):
+    # TODO: move to tinygrad
     gst = time.perf_counter()
-    tidxs = Tensor.randperm(num_train_samples, dtype='int')[:num_steps_per_epoch*batchsize].reshape(num_steps_per_epoch, batchsize)
+    idxs = np.arange(X_train.shape[0])
+    np.random.shuffle(idxs)
+    tidxs = Tensor(idxs, dtype='int')[:num_steps_per_epoch*batchsize].reshape(num_steps_per_epoch, batchsize)  # NOTE: long doesn't fold
     train_loss:float = 0
     for epoch_step in (t:=trange(num_steps_per_epoch)):
       st = time.perf_counter()

examples/beautiful_mnist.py

@@ -1,12 +1,12 @@
-# model based off https://medium.com/data-science/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
-from typing import Callable
+# model based off https://towardsdatascience.com/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
+from typing import List, Callable
 from tinygrad import Tensor, TinyJit, nn, GlobalCounters
 from tinygrad.helpers import getenv, colored, trange
 from tinygrad.nn.datasets import mnist
 
 class Model:
   def __init__(self):
-    self.layers: list[Callable[[Tensor], Tensor]] = [
+    self.layers: List[Callable[[Tensor], Tensor]] = [
       nn.Conv2d(1, 32, 5), Tensor.relu,
       nn.Conv2d(32, 32, 5), Tensor.relu,
       nn.BatchNorm(32), Tensor.max_pool2d,
@@ -21,17 +21,20 @@ if __name__ == "__main__":
   X_train, Y_train, X_test, Y_test = mnist(fashion=getenv("FASHION"))
 
   model = Model()
-  opt = (nn.optim.Muon if getenv("MUON") else nn.optim.SGD if getenv("SGD") else nn.optim.Adam)(nn.state.get_parameters(model))
+  opt = nn.optim.Adam(nn.state.get_parameters(model))
 
   @TinyJit
   @Tensor.train()
   def train_step() -> Tensor:
     opt.zero_grad()
     samples = Tensor.randint(getenv("BS", 512), high=X_train.shape[0])
+    # TODO: this "gather" of samples is very slow. will be under 5s when this is fixed
     loss = model(X_train[samples]).sparse_categorical_crossentropy(Y_train[samples]).backward()
-    return loss.realize(*opt.schedule_step())
+    opt.step()
+    return loss
 
   @TinyJit
+  @Tensor.test()
   def get_test_acc() -> Tensor: return (model(X_test).argmax(axis=1) == Y_test).mean()*100
 
   test_acc = float('nan')

examples/benchmark_onnx.py

@@ -1,11 +1,12 @@
-import sys, time
-from tinygrad import TinyJit, GlobalCounters, fetch, getenv
-from tinygrad.nn.onnx import OnnxRunner
+import sys, onnx, time, pickle
+from tinygrad import TinyJit, Device, GlobalCounters, fetch, getenv
+from tinygrad.frontend.onnx import OnnxRunner
 from extra.onnx_helpers import get_example_inputs, validate
 
 def load_onnx_model(onnx_file):
-  run_onnx = OnnxRunner(onnx_file)
-  run_onnx_jit = TinyJit(lambda **kwargs: next(iter(run_onnx({k:v.to(None) for k,v in kwargs.items()}).values())), prune=True, optimize=True)
+  onnx_model = onnx.load(onnx_file)
+  run_onnx = OnnxRunner(onnx_model)
+  run_onnx_jit = TinyJit(lambda **kwargs: next(iter(run_onnx({k:v.to(Device.DEFAULT) for k,v in kwargs.items()}).values())), prune=True)
   return run_onnx_jit, run_onnx.graph_inputs
 
 if __name__ == "__main__":
@@ -33,3 +34,8 @@ if __name__ == "__main__":
   if getenv("ORT"):
     validate(onnx_file, new_inputs, rtol=1e-3, atol=1e-3)
     print("model validated")
+
+  if (fn:=getenv("SAVE_PKL", "")) != "":
+    with open(fn, "wb") as f:
+      pickle.dump(run_onnx_jit, f)
+    print(f"pkl saved to {fn}")

examples/coder.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+import os, sys, traceback
+sys.path.append(os.getcwd())
+
+from io import StringIO
+from contextlib import redirect_stdout
+from tinygrad import Tensor, nn, Device, dtypes
+from tinygrad.helpers import Timing, colored, getenv, fetch
+from extra.models.llama import Transformer, convert_from_huggingface, fix_bf16
+from sentencepiece import SentencePieceProcessor
+
+def create_fixed_tokenizer(output_file):
+  print("creating fixed tokenizer")
+  import extra.junk.sentencepiece_model_pb2 as spb2
+  mp = spb2.ModelProto()
+  mp.ParseFromString(fetch("https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/resolve/main/tokenizer.model?download=true").read_bytes())
+  mp.pieces.append(spb2.ModelProto.SentencePiece(piece="<|im_end|>", score=0))
+  mp.pieces.append(spb2.ModelProto.SentencePiece(piece="<|im_start|>", score=0))
+  with open(output_file, "wb") as f:
+    f.write(mp.SerializeToString())
+
+# example:
+# echo -en "write 2+2\nwrite hello world\ny\n" | TEMP=0 python3 examples/coder.py
+
+if __name__ == "__main__":
+  Tensor.no_grad = True
+
+  # https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/config.json
+  with Timing("create model: "):
+    model = Transformer(4096, 14336, n_heads=32, n_layers=32, norm_eps=1e-5, vocab_size=32002, n_kv_heads=8, max_context=4096, jit=getenv("JIT", 1))
+
+  with Timing("download weights: "):
+    part1 = nn.state.torch_load(fetch("https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/resolve/main/pytorch_model-00001-of-00002.bin?download=true"))
+    part2 = nn.state.torch_load(fetch("https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/resolve/main/pytorch_model-00002-of-00002.bin?download=true"))
+
+  with Timing("weights -> model: "):
+    nn.state.load_state_dict(model, fix_bf16(convert_from_huggingface(part1, model, 32, 8)), strict=False)
+    nn.state.load_state_dict(model, fix_bf16(convert_from_huggingface(part2, model, 32, 8)), strict=False)
+
+  if not os.path.isfile("/tmp/tokenizer.model"): create_fixed_tokenizer("/tmp/tokenizer.model")
+  spp = SentencePieceProcessor(model_file="/tmp/tokenizer.model")
+
+  # https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json
+  #   "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  IM_END = 32000
+  IM_START = 32001
+  def encode_prompt(k, v): return [IM_START]+spp.encode(f"{k}\n{v}")+[IM_END]+spp.encode("\n")
+  def start_prompt(k): return [IM_START]+spp.encode(f"{k}\n")
+  def output(outputted, toks, color):
+    cur = spp.decode(toks)[len(outputted):]
+    sys.stdout.write(colored(cur, color))
+    sys.stdout.flush()
+    outputted += cur
+    return outputted
+
+  # *** app below this line ***
+
+  toks = [spp.bos_id()] + encode_prompt("system", "You are Quentin. Quentin is a useful assistant who writes Python code to answer questions. He keeps the code as short as possible and doesn't read from user input")
+
+  PROMPT = getenv("PROMPT", 1)
+  temperature = getenv("TEMP", 0.7)
+
+  start_pos = 0
+  outputted = output("", toks, "green")
+  turn = True
+  while 1:
+    if PROMPT:
+      toks += encode_prompt("user", input("Q: ")) + start_prompt("assistant")
+    else:
+      toks += start_prompt("user" if turn else "assistant")
+      turn = not turn
+    old_output_len = len(outputted)
+    while 1:
+      tok = model(Tensor([toks[start_pos:]]), start_pos, temperature).item()
+      start_pos = len(toks)
+      toks.append(tok)
+      outputted = output(outputted, toks, "blue" if not turn else "cyan")
+      if tok == IM_END: break
+      if tok == spp.eos_id(): break
+      new_output = outputted[old_output_len:]
+
+      if new_output.endswith("```") and '```python\n' in new_output:
+        python_code = new_output.split('```python\n')[1].split("```")[0]
+        # AI safety. Warning to user. Do not press y if the AI is trying to do unsafe things.
+        if input(colored(f" <-- PYTHON DETECTED, RUN IT? ", "red")).lower() == 'y':
+          my_stdout = StringIO()
+          try:
+            with redirect_stdout(my_stdout): exec(python_code)
+            result = my_stdout.getvalue()
+          except Exception as e:
+            result = ''.join(traceback.format_exception_only(e))
+          toks += spp.encode(f"\nOutput:\n```\n{result}```")
+          outputted = output(outputted, toks, "yellow")
+          old_output_len = len(outputted)
+    print("")

examples/compile_tensorflow.py

@@ -8,9 +8,8 @@ import numpy as np
 import subprocess
 import tensorflow as tf
 import tf2onnx
-from tinygrad.nn.onnx import OnnxRunner
+from tinygrad.frontend.onnx import OnnxRunner
 from tinygrad.tensor import Tensor
-from tinygrad.helpers import to_mv
 from extra.export_model import export_model_clang, compile_net, jit_model
 
 def get_uncompiled_model2(dataset_size=32, output_size=4):
@@ -26,7 +25,7 @@ class TinyOnnx:
   def __init__(self, keras_model):
     input_signature = [tf.TensorSpec([1,32], tf.float32, name='x')]
     onnx_model, _ = tf2onnx.convert.from_keras(keras_model, input_signature, opset=13)
-    self.run_onnx = OnnxRunner(Tensor(onnx_model.SerializeToString(), device="PYTHON"))
+    self.run_onnx = OnnxRunner(onnx_model)
 
   def forward(self, x):
     return self.run_onnx({"x": x}, debug=False)['predictions']
@@ -48,8 +47,8 @@ def compile_onnx_model(onnx_model):
   cprog.append("void initialize(float *weights) {")
   weights = bytes()
   for name,cl in bufs_to_save.items():
-    cprog.append(f"memcpy({name}, weights + {len(weights)//4}, {cl._buf.size});")
-    weights += bytes(to_mv(cl._buf.va_addr, cl._buf.size))
+    cprog.append(f"memcpy({name}, weights + {len(weights)//4}, {len(cl._buf)*4});")
+    weights += bytes(cl._buf)
   cprog.append("}")
 
   # write the weights to disk

examples/conversation.py

@@ -0,0 +1,343 @@
+import argparse
+import multiprocessing as mp
+import os
+import re
+import sys
+import time
+from contextlib import contextmanager
+from pathlib import Path
+
+import numpy as np
+import pyaudio
+import yaml
+from llama import LLaMa
+from vits import MODELS as VITS_MODELS
+from vits import Y_LENGTH_ESTIMATE_SCALARS, HParams, Synthesizer, TextMapper, get_hparams_from_file, load_model
+from whisper import init_whisper, transcribe_waveform
+from sentencepiece import SentencePieceProcessor
+
+from tinygrad.helpers import Timing, fetch
+from tinygrad import Tensor, dtypes
+
+# Whisper constants
+RATE = 16000
+CHUNK = 1600
+
+# LLaMa constants
+IM_START = 32001
+IM_END = 32002
+
+
+# Functions for encoding prompts to chatml md
+def encode_prompt(spp, k, v): return [IM_START]+spp.encode(f"{k}\n{v}")+[IM_END]+spp.encode("\n")
+def start_prompt(spp, k): return [IM_START]+spp.encode(f"{k}\n")
+
+def chunks(lst, n):
+  for i in range(0, len(lst), n): yield lst[i:i + n]
+
+def create_fixed_tokenizer():
+  """Function needed for extending tokenizer with additional chat tokens"""
+  import extra.junk.sentencepiece_model_pb2 as spb2
+  tokenizer_path = fetch("https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.4/resolve/main/tokenizer.model")
+  if SentencePieceProcessor(model_file=str(tokenizer_path)).vocab_size() != 32003:
+    print("creating fixed tokenizer")
+    mp = spb2.ModelProto()
+    mp.ParseFromString(tokenizer_path.read_bytes())
+    # https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.4/blob/main/added_tokens.json
+    mp.pieces.append(spb2.ModelProto.SentencePiece(piece="[PAD]", score=0))
+    mp.pieces.append(spb2.ModelProto.SentencePiece(piece="<|im_start|>", score=0))
+    mp.pieces.append(spb2.ModelProto.SentencePiece(piece="<|im_end|>", score=0))
+    tokenizer_path.write_bytes(mp.SerializeToString())
+  return tokenizer_path
+
+def llama_prepare(llama: LLaMa, temperature: float, pre_prompt_path: Path) -> tuple[list[int], str, str, str]:
+  """Prepares a llama model from a specified pre-prompt file"""
+  with open(str(pre_prompt_path)) as f:
+    config = yaml.safe_load(f.read())
+  toks = [llama.tokenizer.bos_id()] + encode_prompt(llama.tokenizer, "system", config["pre_prompt"].replace("\n", " "))
+  for i in config["examples"]:
+    toks += encode_prompt(llama.tokenizer, config["user_delim"], i["user_prompt"])
+    toks += encode_prompt(llama.tokenizer, config["resp_delim"], i["resp_prompt"])
+  llama.model(Tensor([toks]), 0, temperature).realize()  # NOTE: outputs are not used
+  return toks, config["user_delim"], config["resp_delim"], len(toks), llama.tokenizer.decode(toks)
+
+def llama_generate(
+  llama: LLaMa,
+  toks: list[int],
+  outputted: str,
+  prompt: str,
+  start_pos: int,
+  user_delim: str,
+  resp_delim: str,
+  temperature=0.7,
+  max_tokens=1000
+):
+  """Generates an output for the specified prompt"""
+  toks += encode_prompt(llama.tokenizer, user_delim, prompt)
+  toks += start_prompt(llama.tokenizer, resp_delim)
+
+  outputted = llama.tokenizer.decode(toks)
+  init_length = len(outputted)
+  for _ in range(max_tokens):
+    token = llama.model(Tensor([toks[start_pos:]]), start_pos, temperature).item()
+    start_pos = len(toks)
+    toks.append(token)
+
+    cur = llama.tokenizer.decode(toks)
+
+    # Print is just for debugging
+    sys.stdout.write(cur[len(outputted):])
+    sys.stdout.flush()
+    outputted = cur
+    if toks[-1] == IM_END: break
+  else:
+    toks.append(IM_END)
+  print() # because the output is flushed
+  return outputted, start_pos, outputted[init_length:].replace("<|im_end|>", "")
+
+def tts(
+  text_to_synthesize: str,
+  synth: Synthesizer,
+  hps: HParams,
+  emotion_embedding: Path,
+  speaker_id: int,
+  model_to_use: str,
+  noise_scale: float,
+  noise_scale_w: float,
+  length_scale: float,
+  estimate_max_y_length: bool,
+  text_mapper: TextMapper,
+  model_has_multiple_speakers: bool,
+  pad_length=600,
+  vits_pad_length=1000
+):
+  if model_to_use == "mmts-tts": text_to_synthesize = text_mapper.filter_oov(text_to_synthesize.lower())
+
+  # Convert the input text to a tensor.
+  stn_tst = text_mapper.get_text(text_to_synthesize, hps.data.add_blank, hps.data.text_cleaners)
+  init_shape = stn_tst.shape
+  assert init_shape[0] < pad_length, "text is too long"
+  x_tst, x_tst_lengths = stn_tst.pad(((0, pad_length - init_shape[0]),), value=1).unsqueeze(0), Tensor([init_shape[0]], dtype=dtypes.int64)
+  sid = Tensor([speaker_id], dtype=dtypes.int64) if model_has_multiple_speakers else None
+
+  # Perform inference.
+  audio_tensor = synth.infer(x_tst, x_tst_lengths, sid, noise_scale, length_scale, noise_scale_w, emotion_embedding=emotion_embedding,
+                             max_y_length_estimate_scale=Y_LENGTH_ESTIMATE_SCALARS[model_to_use] if estimate_max_y_length else None, pad_length=vits_pad_length)[0, 0]
+  # Save the audio output.
+  audio_data = (np.clip(audio_tensor.numpy(), -1.0, 1.0) * 32767).astype(np.int16)
+  return audio_data
+
+def init_vits(
+  model_to_use: str,
+  emotion_path: Path,
+  speaker_id: int,
+  seed: int,
+):
+  model_config = VITS_MODELS[model_to_use]
+
+  # Load the hyperparameters from the config file.
+  hps = get_hparams_from_file(fetch(model_config[0]))
+
+  # If model has multiple speakers, validate speaker id and retrieve name if available.
+  model_has_multiple_speakers = hps.data.n_speakers > 0
+  if model_has_multiple_speakers:
+    if speaker_id >= hps.data.n_speakers: raise ValueError(f"Speaker ID {speaker_id} is invalid for this model.")
+    if hps.__contains__("speakers"): # maps speaker ids to names
+      speakers = hps.speakers
+      if isinstance(speakers, list): speakers = {speaker: i for i, speaker in enumerate(speakers)}
+
+  # Load emotions if any. TODO: find an english model with emotions, this is untested atm.
+  emotion_embedding = None
+  if emotion_path is not None:
+    if emotion_path.endswith(".npy"): emotion_embedding = Tensor(np.load(emotion_path), dtype=dtypes.int64).unsqueeze(0)
+    else: raise ValueError("Emotion path must be a .npy file.")
+
+  # Load symbols, instantiate TextMapper and clean the text.
+  if hps.__contains__("symbols"): symbols = hps.symbols
+  elif model_to_use == "mmts-tts": symbols = [x.replace("\n", "") for x in fetch("https://huggingface.co/facebook/mms-tts/raw/main/full_models/eng/vocab.txt").open(encoding="utf-8").readlines()]
+  else: symbols = ['_'] + list(';:,.!?¡¿—…"«»“” ') + list('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz') + list("ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ")
+  text_mapper = TextMapper(apply_cleaners=True, symbols=symbols)
+
+  # Load the model.
+  Tensor.no_grad = True
+  if seed is not None:
+    Tensor.manual_seed(seed)
+    np.random.seed(seed)
+  net_g = load_model(text_mapper.symbols, hps, model_config)
+
+  return net_g, emotion_embedding, text_mapper, hps, model_has_multiple_speakers
+
+@contextmanager
+def output_stream(num_channels: int, sample_rate: int):
+  try:
+    p = pyaudio.PyAudio()
+    stream = p.open(format=pyaudio.paInt16, channels=num_channels, rate=sample_rate, output=True)
+    yield stream
+  except KeyboardInterrupt: pass
+  finally:
+    stream.stop_stream()
+    stream.close()
+    p.terminate()
+
+@contextmanager
+def log_writer():
+  try:
+    logs = []
+    yield logs
+  finally:
+    sep = "="*os.get_terminal_size()[1]
+    print(f"{sep[:-1]}\nCHAT LOG")
+    print(*logs, sep="\n")
+    print(sep)
+
+def listener(q: mp.Queue, event: mp.Event):
+  try:
+    p = pyaudio.PyAudio()
+    stream = p.open(format=pyaudio.paInt16, channels=1, rate=RATE, input=True, frames_per_buffer=CHUNK)
+    did_print = False
+    while True:
+      data = stream.read(CHUNK) # read data to avoid overflow
+      if event.is_set():
+        if not did_print:
+          print("listening")
+          did_print = True
+        q.put(((np.frombuffer(data, np.int16)/32768).astype(np.float32)*3))
+      else:
+        did_print = False
+  finally:
+    stream.stop_stream()
+    stream.close()
+    p.terminate()
+
+def mp_output_stream(q: mp.Queue, counter: mp.Value, num_channels: int, sample_rate: int):
+  with output_stream(num_channels, sample_rate) as stream:
+    while True:
+      try:
+        stream.write(q.get())
+        counter.value += 1
+      except KeyboardInterrupt:
+        break
+
+if __name__ == "__main__":
+  import nltk
+  nltk.download("punkt")
+  Tensor.no_grad = True
+  # Parse CLI arguments
+  parser = argparse.ArgumentParser("Have a tiny conversation with tinygrad")
+
+  # Whisper args
+  parser.add_argument("--whisper_model_name", type=str, default="tiny.en")
+
+  # LLAMA args
+  parser.add_argument("--llama_pre_prompt_path", type=Path, default=Path(__file__).parent / "conversation_data" / "pre_prompt_stacy.yaml", help="Path to yaml file which contains all pre-prompt data needed. ")
+  parser.add_argument("--llama_count", type=int, default=1000, help="Max number of tokens to generate")
+  parser.add_argument("--llama_temperature", type=float, default=0.7, help="Temperature in the softmax")
+  parser.add_argument("--llama_quantize", type=str, default=None, help="Quantize the weights to int8 or nf4 in memory")
+  parser.add_argument("--llama_model", type=Path, default=None, help="Folder with the original weights to load, or single .index.json, .safetensors or .bin file")
+  parser.add_argument("--llama_gen", type=str, default="tiny", required=False, help="Generation of the model to use")
+  parser.add_argument("--llama_size", type=str, default="1B-Chat", required=False, help="Size of model to use")
+  parser.add_argument("--llama_tokenizer", type=Path, default=None, required=False, help="Path to llama tokenizer.model")
+
+  # vits args
+  parser.add_argument("--vits_model_to_use", default="vctk", help="Specify the model to use. Default is 'vctk'.")
+  parser.add_argument("--vits_speaker_id", type=int, default=12, help="Specify the speaker ID. Default is 6.")
+  parser.add_argument("--vits_noise_scale", type=float, default=0.667, help="Specify the noise scale. Default is 0.667.")
+  parser.add_argument("--vits_noise_scale_w", type=float, default=0.8, help="Specify the noise scale w. Default is 0.8.")
+  parser.add_argument("--vits_length_scale", type=float, default=1, help="Specify the length scale. Default is 1.")
+  parser.add_argument("--vits_seed", type=int, default=None, help="Specify the seed (set to None if no seed). Default is 1337.")
+  parser.add_argument("--vits_num_channels", type=int, default=1, help="Specify the number of audio output channels. Default is 1.")
+  parser.add_argument("--vits_sample_width", type=int, default=2, help="Specify the number of bytes per sample, adjust if necessary. Default is 2.")
+  parser.add_argument("--vits_emotion_path", type=Path, default=None, help="Specify the path to emotion reference.")
+  parser.add_argument("--vits_estimate_max_y_length", type=str, default=False, help="If true, overestimate the output length and then trim it to the correct length, to prevent premature realization, much more performant for larger inputs, for smaller inputs not so much. Default is False.")
+  parser.add_argument("--vits_vocab_path", type=Path, default=None, help="Path to the TTS vocabulary.")
+
+  # conversation args
+  parser.add_argument("--max_sentence_length", type=int, default=20, help="Max words in one sentence to pass to vits")
+
+  args = parser.parse_args()
+
+  # Init models
+  model, enc = init_whisper(args.whisper_model_name)
+  synth, emotion_embedding, text_mapper, hps, model_has_multiple_speakers = init_vits(args.vits_model_to_use, args.vits_emotion_path, args.vits_speaker_id, args.vits_seed)
+
+  # Download tinyllama chat as a default model
+  if args.llama_model is None:
+    args.llama_model = fetch("https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.4/resolve/main/model.safetensors", "tinyllamachat.safetensors")
+    args.llama_gen = "tiny"
+    args.llama_size = "1B-Chat"
+  # Add 3 more tokens to the tokenizer
+  if args.llama_gen == "tiny" and args.llama_size.endswith("Chat"): args.llama_tokenizer = create_fixed_tokenizer()
+  tokenizer_path = args.llama_tokenizer or args.llama_model.parent / "tokenizer.model"
+  llama = LLaMa.build(args.llama_model, tokenizer_path, args.llama_gen, args.llama_size, args.llama_quantize)
+  toks, user_delim, resp_delim, start_pos, outputted = llama_prepare(llama, args.llama_temperature, args.llama_pre_prompt_path)
+
+  # Start child process for mic input
+  q = mp.Queue()
+  is_listening_event = mp.Event()
+  p = mp.Process(target=listener, args=(q, is_listening_event,))
+  p.daemon = True
+  p.start()
+
+  # Start child process for speaker output
+  out_q = mp.Queue()
+  out_counter = mp.Value("i", 0)
+  out_p = mp.Process(target=mp_output_stream, args=(out_q, out_counter, args.vits_num_channels, hps.data.sampling_rate,))
+  out_p.daemon = True
+  out_p.start()
+
+  # JIT tts
+  for i in ["Hello, I'm a chat bot", "I am capable of doing a lot of things"]:
+    tts(
+      i, synth, hps, emotion_embedding,
+      args.vits_speaker_id, args.vits_model_to_use, args.vits_noise_scale,
+      args.vits_noise_scale_w, args.vits_length_scale,
+      args.vits_estimate_max_y_length, text_mapper, model_has_multiple_speakers
+    )
+
+  # Start the pipeline
+  with log_writer() as log:
+    while True:
+      tokens = [enc._special_tokens["<|startoftranscript|>"], enc._special_tokens["<|notimestamps|>"]]
+      total = np.array([])
+      out_counter.value = 0
+
+      s = time.perf_counter()
+      is_listening_event.set()
+      prev_text = None
+      while True:
+        for _ in range(RATE // CHUNK): total = np.concatenate([total, q.get()])
+        txt = transcribe_waveform(model, enc, [total], truncate=True)
+        print(txt, end="\r")
+        if txt == "[BLANK_AUDIO]" or re.match(r"^\([\w+ ]+\)$", txt.strip()): continue
+        if prev_text is not None and prev_text == txt:
+          is_listening_event.clear()
+          break
+        prev_text = txt
+      print() # to avoid llama printing on the same line
+      log.append(f"{user_delim.capitalize()}: {txt}")
+
+      # Generate with llama
+      with Timing("llama generation: "):
+        outputted, start_pos, response = llama_generate(
+          llama, toks, outputted, txt, start_pos,
+          user_delim=user_delim, resp_delim=resp_delim, temperature=args.llama_temperature,
+          max_tokens=args.llama_count
+        )
+        log.append(f"{resp_delim.capitalize()}: {response}")
+
+      # Convert to voice
+      with Timing("tts: "):
+        sentences = nltk.sent_tokenize(response.replace('"', ""))
+        for i in sentences:
+          total = np.array([], dtype=np.int16)
+          for j in chunks(i.split(), args.max_sentence_length):
+            audio_data = tts(
+              " ".join(j), synth, hps, emotion_embedding,
+              args.vits_speaker_id, args.vits_model_to_use, args.vits_noise_scale,
+              args.vits_noise_scale_w, args.vits_length_scale,
+              args.vits_estimate_max_y_length, text_mapper, model_has_multiple_speakers
+            )
+            total = np.concatenate([total, audio_data])
+          out_q.put(total.tobytes())
+      while out_counter.value < len(sentences): continue
+      log.append(f"Total: {time.perf_counter() - s}")

examples/efficientnet.py

@@ -0,0 +1,89 @@
+# load weights from
+# https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth
+# a rough copy of
+# https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/model.py
+import sys
+import ast
+import time
+import numpy as np
+from PIL import Image
+from tinygrad.tensor import Tensor
+from tinygrad.helpers import getenv, fetch, Timing
+from tinygrad.engine.jit import TinyJit
+from extra.models.efficientnet import EfficientNet
+np.set_printoptions(suppress=True)
+
+# TODO: you should be able to put these in the jitted function
+bias = Tensor([0.485, 0.456, 0.406])
+scale = Tensor([0.229, 0.224, 0.225])
+
+@TinyJit
+def _infer(model, img):
+  img = img.permute((2,0,1))
+  img = img / 255.0
+  img = img - bias.reshape((1,-1,1,1))
+  img = img / scale.reshape((1,-1,1,1))
+  return model.forward(img).realize()
+
+def infer(model, img):
+  # preprocess image
+  aspect_ratio = img.size[0] / img.size[1]
+  img = img.resize((int(224*max(aspect_ratio,1.0)), int(224*max(1.0/aspect_ratio,1.0))))
+
+  img = np.array(img)
+  y0,x0=(np.asarray(img.shape)[:2]-224)//2
+  retimg = img = img[y0:y0+224, x0:x0+224]
+
+  # if you want to look at the image
+  """
+  import matplotlib.pyplot as plt
+  plt.imshow(img)
+  plt.show()
+  """
+
+  # run the net
+  out = _infer(model, Tensor(img.astype("float32"))).numpy()
+
+  # if you want to look at the outputs
+  """
+  import matplotlib.pyplot as plt
+  plt.plot(out[0])
+  plt.show()
+  """
+  return out, retimg
+
+if __name__ == "__main__":
+  # instantiate my net
+  model = EfficientNet(getenv("NUM", 0))
+  model.load_from_pretrained()
+
+  # category labels
+  lbls = ast.literal_eval(fetch("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt").read_text())
+
+  # load image and preprocess
+  url = sys.argv[1] if len(sys.argv) >= 2 else "https://raw.githubusercontent.com/tinygrad/tinygrad/master/docs/showcase/stable_diffusion_by_tinygrad.jpg"
+  if url == 'webcam':
+    import cv2
+    cap = cv2.VideoCapture(0)
+    cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
+    while 1:
+      _ = cap.grab() # discard one frame to circumvent capture buffering
+      ret, frame = cap.read()
+      img = Image.fromarray(frame[:, :, [2,1,0]])
+      lt = time.monotonic_ns()
+      out, retimg = infer(model, img)
+      print(f"{(time.monotonic_ns()-lt)*1e-6:7.2f} ms", np.argmax(out), np.max(out), lbls[np.argmax(out)])
+      SCALE = 3
+      simg = cv2.resize(retimg, (224*SCALE, 224*SCALE))
+      retimg = cv2.cvtColor(simg, cv2.COLOR_RGB2BGR)
+      cv2.imshow('capture', retimg)
+      if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+    cap.release()
+    cv2.destroyAllWindows()
+  else:
+    img = Image.open(fetch(url))
+    for i in range(getenv("CNT", 1)):
+      with Timing("did inference in "):
+        out, _ = infer(model, img)
+        print(np.argmax(out), np.max(out), lbls[np.argmax(out)])

examples/flux1.py

@@ -0,0 +1,498 @@
+# pip3 install sentencepiece
+
+# This file incorporates code from the following:
+# Github Name                    | License | Link
+# black-forest-labs/flux         | Apache  | https://github.com/black-forest-labs/flux/tree/main/model_licenses
+
+from tinygrad import Tensor, nn, dtypes, TinyJit
+from tinygrad.nn.state import safe_load, load_state_dict
+from tinygrad.helpers import fetch, tqdm, colored
+from sdxl import FirstStage
+from extra.models.clip import FrozenClosedClipEmbedder
+from extra.models.t5 import T5Embedder
+import numpy as np
+
+import math, time, argparse, tempfile
+from typing import List, Dict, Optional, Union, Tuple, Callable
+from dataclasses import dataclass
+from pathlib import Path
+from PIL import Image
+
+urls:dict = {
+  "flux-schnell": "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/flux1-schnell.safetensors",
+  "flux-dev": "https://huggingface.co/camenduru/FLUX.1-dev/resolve/main/flux1-dev.sft",
+  "ae": "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/ae.safetensors",
+  "T5_1_of_2": "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/text_encoder_2/model-00001-of-00002.safetensors",
+  "T5_2_of_2": "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/text_encoder_2/model-00002-of-00002.safetensors",
+  "T5_tokenizer": "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/tokenizer_2/spiece.model",
+  "clip": "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/text_encoder/model.safetensors"
+}
+
+def tensor_identity(x:Tensor) -> Tensor: return x
+
+class AutoEncoder:
+  def __init__(self, scale_factor:float, shift_factor:float):
+    self.decoder = FirstStage.Decoder(128, 3, 3, 16, [1, 2, 4, 4], 2, 256)
+    self.scale_factor = scale_factor
+    self.shift_factor = shift_factor
+
+  def decode(self, z:Tensor) -> Tensor:
+    z = z / self.scale_factor + self.shift_factor
+    return self.decoder(z)
+
+# Conditioner
+class ClipEmbedder(FrozenClosedClipEmbedder):
+  def __call__(self, texts:Union[str, List[str], Tensor]) -> Tensor:
+    if isinstance(texts, str): texts = [texts]
+    assert isinstance(texts, (list,tuple)), f"expected list of strings, got {type(texts).__name__}"
+    tokens = Tensor.cat(*[Tensor(self.tokenizer.encode(text)) for text in texts], dim=0)
+    return self.transformer.text_model(tokens.reshape(len(texts),-1))[:, tokens.argmax(-1)]
+
+# https://github.com/black-forest-labs/flux/blob/main/src/flux/math.py
+def attention(q:Tensor, k:Tensor, v:Tensor, pe:Tensor) -> Tensor:
+  q, k = apply_rope(q, k, pe)
+  x = Tensor.scaled_dot_product_attention(q, k, v)
+  return x.rearrange("B H L D -> B L (H D)")
+
+def rope(pos:Tensor, dim:int, theta:int) -> Tensor:
+  assert dim % 2 == 0
+  scale = Tensor.arange(0, dim, 2, dtype=dtypes.float32, device=pos.device) / dim # NOTE: this is torch.float64 in reference implementation
+  omega = 1.0 / (theta**scale)
+  out = Tensor.einsum("...n,d->...nd", pos, omega)
+  out = Tensor.stack(Tensor.cos(out), -Tensor.sin(out), Tensor.sin(out), Tensor.cos(out), dim=-1)
+  out = out.rearrange("b n d (i j) -> b n d i j", i=2, j=2)
+  return out.float()
+
+def apply_rope(xq:Tensor, xk:Tensor, freqs_cis:Tensor) -> Tuple[Tensor, Tensor]:
+  xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+  xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+  xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+  xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+  return xq_out.reshape(*xq.shape).cast(xq.dtype), xk_out.reshape(*xk.shape).cast(xk.dtype)
+
+
+# https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/layers.py
+class EmbedND:
+  def __init__(self, dim:int, theta:int, axes_dim:List[int]):
+    self.dim = dim
+    self.theta = theta
+    self.axes_dim = axes_dim
+
+  def __call__(self, ids:Tensor) -> Tensor:
+    n_axes = ids.shape[-1]
+    emb = Tensor.cat(*[rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], dim=-3)
+    return emb.unsqueeze(1)
+
+class MLPEmbedder:
+  def __init__(self, in_dim:int, hidden_dim:int):
+    self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
+    self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
+
+  def __call__(self, x:Tensor) -> Tensor:
+    return self.out_layer(self.in_layer(x).silu())
+
+class QKNorm:
+  def __init__(self, dim:int):
+    self.query_norm = nn.RMSNorm(dim)
+    self.key_norm = nn.RMSNorm(dim)
+
+  def __call__(self, q:Tensor, k:Tensor) -> Tuple[Tensor, Tensor]:
+    return self.query_norm(q), self.key_norm(k)
+
+class SelfAttention:
+  def __init__(self, dim:int, num_heads:int = 8, qkv_bias:bool = False):
+    self.num_heads = num_heads
+    head_dim = dim // num_heads
+
+    self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+    self.norm = QKNorm(head_dim)
+    self.proj = nn.Linear(dim, dim)
+
+  def __call__(self, x:Tensor, pe:Tensor) -> Tensor:
+    qkv = self.qkv(x)
+    q, k, v = qkv.rearrange("B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+    q, k = self.norm(q, k)
+    x = attention(q, k, v, pe=pe)
+    return self.proj(x)
+
+@dataclass
+class ModulationOut:
+  shift:Tensor
+  scale:Tensor
+  gate:Tensor
+
+class Modulation:
+  def __init__(self, dim:int, double:bool):
+    self.is_double = double
+    self.multiplier = 6 if double else 3
+    self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
+
+  def __call__(self, vec:Tensor) -> Tuple[ModulationOut, Optional[ModulationOut]]:
+    out = self.lin(vec.silu())[:, None, :].chunk(self.multiplier, dim=-1)
+    return ModulationOut(*out[:3]), ModulationOut(*out[3:]) if self.is_double else None
+
+class DoubleStreamBlock:
+  def __init__(self, hidden_size:int, num_heads:int, mlp_ratio:float, qkv_bias:bool = False):
+    mlp_hidden_dim = int(hidden_size * mlp_ratio)
+    self.num_heads = num_heads
+    self.hidden_size = hidden_size
+    self.img_mod = Modulation(hidden_size, double=True)
+    self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+    self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
+
+    self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+    self.img_mlp = [nn.Linear(hidden_size, mlp_hidden_dim, bias=True), Tensor.gelu, nn.Linear(mlp_hidden_dim, hidden_size, bias=True)]
+
+    self.txt_mod = Modulation(hidden_size, double=True)
+    self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+    self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
+
+    self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+    self.txt_mlp = [nn.Linear(hidden_size, mlp_hidden_dim, bias=True), Tensor.gelu, nn.Linear(mlp_hidden_dim, hidden_size, bias=True)]
+
+  def __call__(self, img:Tensor, txt:Tensor, vec:Tensor, pe:Tensor) -> tuple[Tensor, Tensor]:
+    img_mod1, img_mod2 = self.img_mod(vec)
+    txt_mod1, txt_mod2 = self.txt_mod(vec)
+    assert img_mod2 is not None and txt_mod2 is not None
+    # prepare image for attention
+    img_modulated = self.img_norm1(img)
+    img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
+    img_qkv = self.img_attn.qkv(img_modulated)
+    img_q, img_k, img_v = img_qkv.rearrange("B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+    img_q, img_k = self.img_attn.norm(img_q, img_k)
+
+    # prepare txt for attention
+    txt_modulated = self.txt_norm1(txt)
+    txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
+    txt_qkv = self.txt_attn.qkv(txt_modulated)
+    txt_q, txt_k, txt_v = txt_qkv.rearrange("B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+    txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k)
+
+    # run actual attention
+    q = Tensor.cat(txt_q, img_q, dim=2)
+    k = Tensor.cat(txt_k, img_k, dim=2)
+    v = Tensor.cat(txt_v, img_v, dim=2)
+
+    attn = attention(q, k, v, pe=pe)
+    txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
+
+    # calculate the img bloks
+    img = img + img_mod1.gate * self.img_attn.proj(img_attn)
+    img = img + img_mod2.gate * ((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift).sequential(self.img_mlp)
+
+    # calculate the txt bloks
+    txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
+    txt = txt + txt_mod2.gate * ((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift).sequential(self.txt_mlp)
+    return img, txt
+
+
+class SingleStreamBlock:
+  """
+  A DiT block with parallel linear layers as described in
+  https://arxiv.org/abs/2302.05442 and adapted modulation interface.
+  """
+
+  def __init__(self,hidden_size:int, num_heads:int, mlp_ratio:float=4.0, qk_scale:Optional[float]=None):
+    self.hidden_dim = hidden_size
+    self.num_heads = num_heads
+    head_dim = hidden_size // num_heads
+    self.scale = qk_scale or head_dim**-0.5
+
+    self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+    # qkv and mlp_in
+    self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
+    # proj and mlp_out
+    self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
+
+    self.norm = QKNorm(head_dim)
+
+    self.hidden_size = hidden_size
+    self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+
+    self.mlp_act = Tensor.gelu
+    self.modulation = Modulation(hidden_size, double=False)
+
+  def __call__(self, x:Tensor, vec:Tensor, pe:Tensor) -> Tensor:
+    mod, _ = self.modulation(vec)
+    x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
+    qkv, mlp = Tensor.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+    q, k, v = qkv.rearrange("B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+    q, k = self.norm(q, k)
+
+    # compute attention
+    attn = attention(q, k, v, pe=pe)
+    # compute activation in mlp stream, cat again and run second linear layer
+    output = self.linear2(Tensor.cat(attn, self.mlp_act(mlp), dim=2))
+    return x + mod.gate * output
+
+
+class LastLayer:
+  def __init__(self, hidden_size:int, patch_size:int, out_channels:int):
+    self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+    self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+    self.adaLN_modulation:List[Callable[[Tensor], Tensor]] = [Tensor.silu, nn.Linear(hidden_size, 2 * hidden_size, bias=True)]
+
+  def __call__(self, x:Tensor, vec:Tensor) -> Tensor:
+    shift, scale = vec.sequential(self.adaLN_modulation).chunk(2, dim=1)
+    x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+    return self.linear(x)
+
+def timestep_embedding(t:Tensor, dim:int, max_period:int=10000, time_factor:float=1000.0) -> Tensor:
+  """
+  Create sinusoidal timestep embeddings.
+  :param t: a 1-D Tensor of N indices, one per batch element.
+                    These may be fractional.
+  :param dim: the dimension of the output.
+  :param max_period: controls the minimum frequency of the embeddings.
+  :return: an (N, D) Tensor of positional embeddings.
+  """
+  t = time_factor * t
+  half = dim // 2
+  freqs = Tensor.exp(-math.log(max_period) * Tensor.arange(0, stop=half, dtype=dtypes.float32) / half).to(t.device)
+
+  args = t[:, None].float() * freqs[None]
+  embedding = Tensor.cat(Tensor.cos(args), Tensor.sin(args), dim=-1)
+  if dim % 2:  embedding = Tensor.cat(*[embedding, Tensor.zeros_like(embedding[:, :1])], dim=-1)
+  if Tensor.is_floating_point(t):  embedding = embedding.cast(t.dtype)
+  return embedding
+
+# https://github.com/black-forest-labs/flux/blob/main/src/flux/model.py
+class Flux:
+  """
+  Transformer model for flow matching on sequences.
+  """
+
+  def __init__(
+      self,
+      guidance_embed:bool,
+      in_channels:int = 64,
+      vec_in_dim:int = 768,
+      context_in_dim:int = 4096,
+      hidden_size:int = 3072,
+      mlp_ratio:float = 4.0,
+      num_heads:int = 24,
+      depth:int = 19,
+      depth_single_blocks:int = 38,
+      axes_dim:Optional[List[int]] = None,
+      theta:int = 10_000,
+      qkv_bias:bool = True,
+      ):
+
+    axes_dim = axes_dim or [16, 56, 56]
+    self.guidance_embed = guidance_embed
+    self.in_channels = in_channels
+    self.out_channels = self.in_channels
+    if hidden_size % num_heads != 0:
+      raise ValueError(f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}")
+    pe_dim = hidden_size // num_heads
+    if sum(axes_dim) != pe_dim:
+      raise ValueError(f"Got {axes_dim} but expected positional dim {pe_dim}")
+    self.hidden_size = hidden_size
+    self.num_heads = num_heads
+    self.pe_embedder = EmbedND(dim=pe_dim, theta=theta, axes_dim=axes_dim)
+    self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
+    self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
+    self.vector_in = MLPEmbedder(vec_in_dim, self.hidden_size)
+    self.guidance_in:Callable[[Tensor], Tensor] = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if guidance_embed else tensor_identity
+    self.txt_in = nn.Linear(context_in_dim, self.hidden_size)
+
+    self.double_blocks = [DoubleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias) for _ in range(depth)]
+    self.single_blocks = [SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=mlp_ratio) for _ in range(depth_single_blocks)]
+    self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
+
+  def __call__(self, img:Tensor, img_ids:Tensor, txt:Tensor, txt_ids:Tensor, timesteps:Tensor, y:Tensor, guidance:Optional[Tensor] = None) -> Tensor:
+    if img.ndim != 3 or txt.ndim != 3:
+      raise ValueError("Input img and txt tensors must have 3 dimensions.")
+    # running on sequences img
+    img = self.img_in(img)
+    vec = self.time_in(timestep_embedding(timesteps, 256))
+    if self.guidance_embed:
+      if guidance is None:
+        raise ValueError("Didn't get guidance strength for guidance distilled model.")
+      vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
+    vec = vec + self.vector_in(y)
+    txt = self.txt_in(txt)
+    ids = Tensor.cat(txt_ids, img_ids, dim=1)
+    pe = self.pe_embedder(ids)
+    for double_block in self.double_blocks:
+      img, txt = double_block(img=img, txt=txt, vec=vec, pe=pe)
+
+    img = Tensor.cat(txt, img, dim=1)
+    for single_block in self.single_blocks:
+      img = single_block(img, vec=vec, pe=pe)
+
+    img = img[:, txt.shape[1] :, ...]
+
+    return self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
+
+# https://github.com/black-forest-labs/flux/blob/main/src/flux/util.py
+def load_flow_model(name:str, model_path:str):
+  # Loading Flux
+  print("Init model")
+  model = Flux(guidance_embed=(name != "flux-schnell"))
+  if not model_path: model_path = fetch(urls[name])
+  state_dict = {k.replace("scale", "weight"): v for k, v in safe_load(model_path).items()}
+  load_state_dict(model, state_dict)
+  return model
+
+def load_T5(max_length:int=512):
+  # max length 64, 128, 256 and 512 should work (if your sequence is short enough)
+  print("Init T5")
+  T5 = T5Embedder(max_length, fetch(urls["T5_tokenizer"]))
+  pt_1 = fetch(urls["T5_1_of_2"])
+  pt_2 = fetch(urls["T5_2_of_2"])
+  load_state_dict(T5.encoder, safe_load(pt_1) | safe_load(pt_2), strict=False)
+  return T5
+
+def load_clip():
+  print("Init Clip")
+  clip = ClipEmbedder()
+  load_state_dict(clip.transformer, safe_load(fetch(urls["clip"])))
+  return clip
+
+def load_ae() -> AutoEncoder:
+  # Loading the autoencoder
+  print("Init AE")
+  ae = AutoEncoder(0.3611, 0.1159)
+  load_state_dict(ae, safe_load(fetch(urls["ae"])))
+  return ae
+
+# https://github.com/black-forest-labs/flux/blob/main/src/flux/sampling.py
+def prepare(T5:T5Embedder, clip:ClipEmbedder, img:Tensor, prompt:Union[str, List[str]]) -> Dict[str, Tensor]:
+  bs, _, h, w = img.shape
+  if bs == 1 and not isinstance(prompt, str):
+    bs = len(prompt)
+
+  img = img.rearrange("b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+  if img.shape[0] == 1 and bs > 1:
+    img = img.expand((bs, *img.shape[1:]))
+
+  img_ids = Tensor.zeros(h // 2, w // 2, 3).contiguous()
+  img_ids[..., 1] = img_ids[..., 1] + Tensor.arange(h // 2)[:, None]
+  img_ids[..., 2] = img_ids[..., 2] + Tensor.arange(w // 2)[None, :]
+  img_ids = img_ids.rearrange("h w c -> 1 (h w) c")
+  img_ids = img_ids.expand((bs, *img_ids.shape[1:]))
+
+  if isinstance(prompt, str):
+    prompt = [prompt]
+  txt = T5(prompt).realize()
+  if txt.shape[0] == 1 and bs > 1:
+    txt = txt.expand((bs, *txt.shape[1:]))
+  txt_ids = Tensor.zeros(bs, txt.shape[1], 3)
+
+  vec = clip(prompt).realize()
+  if vec.shape[0] == 1 and bs > 1:
+    vec = vec.expand((bs, *vec.shape[1:]))
+
+  return {"img": img, "img_ids": img_ids.to(img.device), "txt": txt.to(img.device), "txt_ids": txt_ids.to(img.device), "vec": vec.to(img.device)}
+
+
+def get_schedule(num_steps:int, image_seq_len:int, base_shift:float=0.5, max_shift:float=1.15, shift:bool=True) -> List[float]:
+  # extra step for zero
+  step_size = -1.0 / num_steps
+  timesteps = Tensor.arange(1, 0 + step_size, step_size)
+
+  # shifting the schedule to favor high timesteps for higher signal images
+  if shift:
+    # estimate mu based on linear estimation between two points
+    mu = 0.5 + (max_shift - base_shift) * (image_seq_len - 256) / (4096 - 256)
+    timesteps = math.exp(mu) / (math.exp(mu) + (1 / timesteps - 1))
+  return timesteps.tolist()
+
+@TinyJit
+def run(model, *args): return model(*args).realize()
+
+def denoise(model, img:Tensor, img_ids:Tensor, txt:Tensor, txt_ids:Tensor, vec:Tensor, timesteps:List[float], guidance:float=4.0) -> Tensor:
+  # this is ignored for schnell
+  guidance_vec = Tensor((guidance,), device=img.device, dtype=img.dtype).expand((img.shape[0],))
+  for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:])), "Denoising"):
+    t_vec = Tensor((t_curr,), device=img.device, dtype=img.dtype).expand((img.shape[0],))
+    pred = run(model, img, img_ids, txt, txt_ids, t_vec, vec, guidance_vec)
+    img = img + (t_prev - t_curr) * pred
+
+  return img
+
+def unpack(x:Tensor, height:int, width:int) -> Tensor:
+  return x.rearrange("b (h w) (c ph pw) -> b c (h ph) (w pw)", h=math.ceil(height / 16), w=math.ceil(width / 16), ph=2, pw=2)
+
+# https://github.com/black-forest-labs/flux/blob/main/src/flux/cli.py
+if __name__ == "__main__":
+  default_prompt = "bananas and a can of coke"
+  parser = argparse.ArgumentParser(description="Run Flux.1", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+  parser.add_argument("--name",       type=str,   default="flux-schnell", help="Name of the model to load")
+  parser.add_argument("--model_path", type=str,   default="",             help="path of the model file")
+  parser.add_argument("--width",      type=int,   default=512,            help="width of the sample in pixels (should be a multiple of 16)")
+  parser.add_argument("--height",     type=int,   default=512,            help="height of the sample in pixels (should be a multiple of 16)")
+  parser.add_argument("--seed",       type=int,   default=None,           help="Set a seed for sampling")
+  parser.add_argument("--prompt",     type=str,   default=default_prompt, help="Prompt used for sampling")
+  parser.add_argument('--out',        type=str,   default=Path(tempfile.gettempdir()) / "rendered.png", help="Output filename")
+  parser.add_argument("--num_steps",  type=int,   default=None,           help="number of sampling steps (default 4 for schnell, 50 for guidance distilled)") #noqa:E501
+  parser.add_argument("--guidance",   type=float, default=3.5,            help="guidance value used for guidance distillation")
+  parser.add_argument("--output_dir", type=str,   default="output",       help="output directory")
+  args = parser.parse_args()
+
+  if args.name not in ["flux-schnell", "flux-dev"]:
+    raise ValueError(f"Got unknown model name: {args.name}, chose from flux-schnell and flux-dev")
+
+  if args.num_steps is None:
+    args.num_steps = 4 if args.name == "flux-schnell" else 50
+
+  # allow for packing and conversion to latent space
+  height = 16 * (args.height // 16)
+  width = 16 * (args.width // 16)
+
+  if args.seed is None: args.seed = Tensor._seed
+  else: Tensor.manual_seed(args.seed)
+
+  print(f"Generating with seed {args.seed}:\n{args.prompt}")
+  t0 = time.perf_counter()
+
+  # prepare input noise
+  x = Tensor.randn(1, 16, 2 * math.ceil(height / 16), 2 * math.ceil(width / 16), dtype="bfloat16")
+
+  # load text embedders
+  T5 = load_T5(max_length=256 if args.name == "flux-schnell" else 512)
+  clip = load_clip()
+
+  # embed text to get inputs for model
+  inp = prepare(T5, clip, x, prompt=args.prompt)
+  timesteps = get_schedule(args.num_steps, inp["img"].shape[1], shift=(args.name != "flux-schnell"))
+
+  # done with text embedders
+  del T5, clip
+
+  # load model
+  model = load_flow_model(args.name, args.model_path)
+
+  # denoise initial noise
+  x = denoise(model, **inp, timesteps=timesteps, guidance=args.guidance)
+
+  # done with model
+  del model, run
+
+  # load autoencoder
+  ae = load_ae()
+
+  # decode latents to pixel space
+  x = unpack(x.float(), height, width)
+  x = ae.decode(x).realize()
+
+  t1 = time.perf_counter()
+  print(f"Done in {t1 - t0:.1f}s. Saving {args.out}")
+
+  # bring into PIL format and save
+  x = x.clamp(-1, 1)
+  x = x[0].rearrange("c h w -> h w c")
+  x = (127.5 * (x + 1.0)).cast("uint8")
+
+  img = Image.fromarray(x.numpy())
+
+  img.save(args.out)
+
+  # validation!
+  if args.prompt == default_prompt and args.name=="flux-schnell" and args.seed == 0 and args.width == args.height == 512:
+    ref_image = Tensor(np.array(Image.open("examples/flux1_seed0.png")))
+    distance = (((x.cast(dtypes.float) - ref_image.cast(dtypes.float)) / ref_image.max())**2).mean().item()
+    assert distance < 4e-3, colored(f"validation failed with {distance=}", "red")
+    print(colored(f"output validated with {distance=}", "green"))

examples/gpt2.py

@@ -3,11 +3,10 @@ import os, argparse, contextlib
 from typing import Optional, Union
 with contextlib.suppress(ImportError): import tiktoken
 from tinygrad import Tensor, TinyJit, Device, GlobalCounters, Variable, dtypes
-from tinygrad.uop.ops import UOp
+from tinygrad.ops import UOp
 from tinygrad.helpers import Timing, DEBUG, JIT, getenv, fetch, colored, trange
 from tinygrad.nn import Embedding, Linear, LayerNorm
 from tinygrad.nn.state import gguf_load, torch_load, load_state_dict, get_state_dict
-from extra.bench_log import BenchEvent, WallTimeEvent
 
 MAX_CONTEXT = getenv("MAX_CONTEXT", 128)
 HALF = getenv("HALF")
@@ -26,8 +25,8 @@ class Attention:
       start_pos = start_pos.val
 
     if HALF: x = x.half()
-    xqkv = self.c_attn(x).reshape(None, None, 3, self.n_heads, self.head_dim)
-    xq, xk, xv = [xqkv[:, :, i, :, :] for i in range(3)]
+    xqkv = self.c_attn(x)
+    xq, xk, xv = [xqkv.shrink((None, None, (i*self.dim, (i+1)*self.dim))).reshape(None, None, self.n_heads, self.head_dim) for i in range(3)]
     bsz, seqlen, _, _ = xq.shape
 
     # create kv cache
@@ -35,11 +34,11 @@ class Attention:
       self.cache_kv = Tensor.zeros(2, bsz, MAX_CONTEXT, self.n_heads, self.head_dim, dtype=x.dtype).contiguous().realize()
 
     # update the cache
-    self.cache_kv[:, :, start_pos:start_pos+seqlen, :, :].assign(Tensor.stack(xk, xv)).realize()
+    self.cache_kv.shrink((None, None,(start_pos,start_pos+seqlen),None,None)).assign(Tensor.stack(xk, xv)).realize()
 
     if start_pos > 0:
-      keys = self.cache_kv[0][:, :start_pos+seqlen, :, :]
-      values = self.cache_kv[1][:, :start_pos+seqlen, :, :]
+      keys = self.cache_kv[0].shrink((None, (0, start_pos+seqlen), None, None))
+      values = self.cache_kv[1].shrink((None, (0, start_pos+seqlen), None, None))
     else:
       keys = xk
       values = xv
@@ -64,7 +63,7 @@ class TransformerBlock:
 
   def __call__(self, x:Tensor, start_pos:Variable, mask:Optional[Tensor]):
     h = x + self.attn(self.ln_1(x), start_pos, mask).float()
-    return (h + self.mlp(self.ln_2(h))).contiguous()
+    return (h + self.mlp(self.ln_2(h)))
 
 class Transformer:
   def __init__(self, dim, n_heads, n_layers, norm_eps, vocab_size, max_seq_len=1024):
@@ -85,10 +84,7 @@ class Transformer:
       seqlen = tokens.shape[1]
       tok_emb = self.wte(tokens)
 
-    # not symbolic when consuming the prompt
-    selected_pos = (0, seqlen) if start_pos.val == 0 else (start_pos, start_pos+1)
-    pos_emb = self.wpe(self.allpos.shrink((None, selected_pos)))
-
+    pos_emb = self.wpe(self.allpos.shrink((None, (start_pos, start_pos+seqlen))))
     h = tok_emb + pos_emb
 
     if HALF: h = h.half()
@@ -138,12 +134,11 @@ class GPT2:
     # lm head and wte are tied
     weights['lm_head.weight'] = weights['wte.weight']
 
-    with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):
-      load_state_dict(model, weights)
+    load_state_dict(model, weights)
 
-      if HALF:
-        for l in get_state_dict(model).values():
-          l.replace(l.half().realize())
+    if HALF:
+      for l in get_state_dict(model).values():
+        l.replace(l.half().realize())
 
     return GPT2(model, tokenizer)
 
@@ -172,8 +167,7 @@ class GPT2:
       return key
     state_dict = { _remap_gguf_key(k): v for k, v in state_dict.items() }
     model = Transformer(**gpt2_params)
-    with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):
-      load_state_dict(model, state_dict)
+    load_state_dict(model, state_dict)
     return GPT2(model, tiktoken.get_encoding("gpt2"))
 
   def __init__(self, model, tokenizer):
@@ -181,7 +175,6 @@ class GPT2:
     self.tokenizer = tokenizer
 
   def generate(self, prompt:str, max_length:int, temperature:float, timing:bool=False, batch_size:int=1):
-    step_times = []
     prompt_tokens = self.tokenizer.encode(prompt, allowed_special={"<|endoftext|>"})
     toks = [prompt_tokens[:] for _ in range(batch_size)]
     start_pos = 0
@@ -189,27 +182,22 @@ class GPT2:
       GlobalCounters.reset()
       if timing: print("")
       st = GlobalCounters.time_sum_s
-      with Timing("ran model in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on {Device.DEFAULT}" if DEBUG>=2 else "")+
+      with Timing("ran model in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU" if DEBUG>=2 else "")+
                   f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB"+
                   (f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None, enabled=timing):
-        with WallTimeEvent(BenchEvent.STEP):
-          if batch_size == 1 and len(toks[0][start_pos:]) == 1:
-            tokens = Variable("tokens", 0, VOCAB_SIZE-1).bind(toks[0][start_pos])
-          else:
-            tokens = Tensor([x[start_pos:] for x in toks])
-          tok = self.model(tokens, Variable("start_pos", 1 if start_pos else 0, MAX_CONTEXT-1).bind(start_pos), temperature).tolist()
-      step_times.append((GlobalCounters.time_sum_s-st)*1e3)
+        if batch_size == 1 and len(toks[0][start_pos:]) == 1:
+          tokens = Variable("tokens", 0, VOCAB_SIZE).bind(toks[0][start_pos])
+        else:
+          tokens = Tensor([x[start_pos:] for x in toks])
+        tok = self.model(tokens, Variable("start_pos", 1 if start_pos else 0, MAX_CONTEXT).bind(start_pos), temperature).tolist()
       start_pos = len(toks[0])
       for i,t in enumerate(tok): toks[i].append(t)
-
-    if (assert_time:=getenv("ASSERT_MIN_STEP_TIME")):
-      min_time = min(step_times)
-      assert min_time < assert_time, f"Speed regression, expected min step time of < {assert_time} ms but took: {min_time} ms"
     return [self.tokenizer.decode(x) for x in toks]
 
 # **** main code ****
 
 if __name__ == "__main__":
+  Tensor.no_grad = True
   print(f"using {Device.DEFAULT} backend")
   default_prompt = "What is the answer to life, the universe, and everything?"
 
@@ -232,7 +220,7 @@ if __name__ == "__main__":
   gpt2 = GPT2.build_gguf(args.model_size) if args.model_size.startswith("gpt2_gguf_") else GPT2.build(args.model_size)
 
   if args.benchmark != -1:
-    gpt2.model(Tensor.randint(args.batch_size, args.benchmark), Variable("a", 0, MAX_CONTEXT).bind(0)).realize()
+    gpt2.model(Tensor.rand(args.batch_size, args.benchmark), Variable("a", 0, MAX_CONTEXT).bind(0)).realize()
   else:
     texts = gpt2.generate(args.prompt, args.count, args.temperature, timing=args.timing, batch_size=args.batch_size)
     if not args.noshow:

examples/gradaccum_mnist.py

@@ -1,108 +0,0 @@
-import itertools
-from typing import Callable
-from tinygrad import nn, Tensor, dtypes, Device, TinyJit
-from tinygrad.helpers import getenv, trange, partition
-
-class Model:
-  def __init__(self):
-    self.layers: list[Callable[[Tensor], Tensor]] = [
-      nn.Conv2d(1, 32, 5), Tensor.relu,
-      nn.Conv2d(32, 32, 5), Tensor.relu,
-      nn.BatchNorm(32), Tensor.max_pool2d,
-      nn.Conv2d(32, 64, 3), Tensor.relu,
-      nn.Conv2d(64, 64, 3), Tensor.relu,
-      nn.BatchNorm(64), Tensor.max_pool2d,
-      lambda x: x.flatten(1), nn.Linear(576, 10)]
-
-  def __call__(self, x:Tensor) -> Tensor: return x.sequential(self.layers)
-
-# TODO: refactor this into optim/onnx
-def functional_adam(g:Tensor, m:Tensor, v:Tensor, b1_t:Tensor, b2_t:Tensor, lr=0.001, b1=0.9, b2=0.999, eps=1e-6) -> Tensor:
-  b1_t *= b1
-  b2_t *= b2
-  m.assign(b1 * m + (1.0 - b1) * g)
-  v.assign(b2 * v + (1.0 - b2) * (g * g))
-  m_hat = m / (1.0 - b1_t)
-  v_hat = v / (1.0 - b2_t)
-  return lr * (m_hat / (v_hat.sqrt() + eps))
-
-if __name__ == "__main__":
-  BS = getenv("BS", 512)
-  ACC_STEPS = getenv("ACC_STEPS", 8)
-
-  X_train, Y_train, X_test, Y_test = nn.datasets.mnist()
-  model = Model()
-
-  params = nn.state.get_parameters(model)
-
-  # init params, set requires grad on the ones we need gradients of
-  for x in params:
-    if x.requires_grad is None: x.requires_grad_()
-    x.replace(x.contiguous())
-  Tensor.realize(*params)
-
-  # split params (with grads) and buffers (without)
-  params, buffers = partition(params, lambda x: x.requires_grad)
-  print(f"params: {len(params)} buffers: {len(buffers)}")
-
-  # optim params
-  pos_params = list(itertools.accumulate(params, lambda x,y: x+y.numel(), initial=0))
-  adam_m = Tensor.zeros(pos_params[-1], device="CPU").contiguous()
-  adam_v = Tensor.zeros(pos_params[-1], device="CPU").contiguous()
-  adam_b1_t = Tensor.ones((1,), dtype=dtypes.float32, device="CPU", requires_grad=False).contiguous()
-  adam_b2_t = Tensor.ones((1,), dtype=dtypes.float32, device="CPU", requires_grad=False).contiguous()
-  adam_params = [adam_m, adam_v, adam_b1_t, adam_b2_t]
-
-  # create loss and grads. init all state so the JIT works on microbatch
-  for x in params: x.assign(x.detach())
-  loss = Tensor.zeros(tuple()).contiguous()
-  grads = Tensor.zeros(pos_params[-1]).contiguous()
-  Tensor.realize(*params, *buffers, *adam_params, loss, grads)
-
-  @TinyJit
-  @Tensor.train()
-  def microbatch():
-    samples = Tensor.randint(BS // ACC_STEPS, high=X_train.shape[0])
-    for t in params: t.grad = None
-    # divide by ACC_STEPS at the loss
-    uloss = (model(X_train[samples]).sparse_categorical_crossentropy(Y_train[samples]) / ACC_STEPS).backward()
-    ugrads = Tensor.cat(*[t.grad.contiguous().flatten() for t in params], dim=0)
-    for t in params: t.grad = None
-    # concat the grads and assign them
-    loss.assign(loss + uloss)
-    grads.assign(grads + ugrads)
-    Tensor.realize(*params, *buffers, loss, grads)
-
-  @TinyJit
-  def optimizer():
-    # run optimizer (on CPU, where adam params live)
-    delta = functional_adam(grads.to("CPU"), adam_m, adam_v, adam_b1_t, adam_b2_t)
-
-    # update the params, copying back the delta one at a time to avoid OOM
-    # NOTE: the scheduler is ordering things poorly, all the copies are happening before the adds
-    for j,tt in enumerate(params):
-      tt.assign(tt.detach() - delta[pos_params[j]:pos_params[j+1]].reshape(tt.shape).to(Device.DEFAULT))
-
-    # realize everything, zero out loss and grads
-    loss.assign(Tensor.zeros_like(loss))
-    grads.assign(Tensor.zeros_like(grads))
-    Tensor.realize(*params, *adam_params, loss, grads)
-
-  @TinyJit
-  def get_test_acc() -> Tensor: return (model(X_test).argmax(axis=1) == Y_test).mean()*100
-
-  test_acc = float('nan')
-  for i in (t:=trange(getenv("STEPS", 70))):
-    # microbatch sets the gradients
-    for _ in range(ACC_STEPS): microbatch()
-
-    # get the loss before the optimizer clears it
-    # this is already realized so this isn't a schedule
-    loss_item = loss.item()
-
-    # run the optimizer
-    optimizer()
-
-    # eval
-    if i%10 == 9: test_acc = get_test_acc().item()
-    t.set_description(f"loss: {loss_item:6.2f} test_accuracy: {test_acc:5.2f}%")

examples/handcode_opt.py

@@ -0,0 +1,132 @@
+from extra.models.resnet import ResNet50
+from extra.mcts_search import mcts_search
+from examples.mlperf.helpers import get_mlperf_bert_model
+from tinygrad import Tensor, Device, dtypes, nn
+from tinygrad.codegen.kernel import Kernel
+from tinygrad.ops import Ops, sym_infer
+from tinygrad.device import Compiled
+from tinygrad.engine.search import beam_search, bufs_from_lin
+from tinygrad.helpers import DEBUG, ansilen, getenv, colored, TRACEMETA
+from extra.optimization.helpers import time_linearizer
+
+def get_sched_resnet():
+  mdl = ResNet50()
+  optim = (nn.optim.LARS if getenv("LARS") else nn.optim.SGD)(nn.state.get_parameters(mdl))
+  BS = getenv("BS", 64)
+
+  # run model twice to get only what changes, these are the kernels of the model
+  for _ in range(2):
+    out = mdl(Tensor.empty(BS, 3, 224, 224))
+    targets = [out]
+    if getenv("BACKWARD"):
+      optim.zero_grad()
+      out.sparse_categorical_crossentropy(Tensor.empty(BS, dtype=dtypes.int)).backward()
+      targets += [x for x in optim.schedule_step()]
+    sched = Tensor.schedule(*targets)
+    print(f"schedule length {len(sched)}")
+  return sched
+
+def get_sched_bert():
+  mdl = get_mlperf_bert_model()
+  optim = nn.optim.LAMB(nn.state.get_parameters(mdl))
+
+  # fake data
+  BS = getenv("BS", 9)
+  input_ids = Tensor.empty((BS, 512), dtype=dtypes.float32)
+  segment_ids = Tensor.empty((BS, 512), dtype=dtypes.float32)
+  attention_mask = Tensor.empty((BS, 512), dtype=dtypes.default_float)
+  masked_positions = Tensor.empty((BS, 76), dtype=dtypes.float32)
+  masked_lm_ids = Tensor.empty((BS, 76), dtype=dtypes.float32)
+  masked_lm_weights = Tensor.empty((BS, 76), dtype=dtypes.float32)
+  next_sentence_labels = Tensor.empty((BS, 1), dtype=dtypes.float32)
+
+  # run model twice to get only what changes, these are the kernels of the model
+  for _ in range(2):
+    lm_logits, seq_relationship_logits = mdl(input_ids, attention_mask, masked_positions, segment_ids)
+    targets = [lm_logits, seq_relationship_logits]
+    if getenv("BACKWARD"):
+      optim.zero_grad()
+      loss = mdl.loss(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels)
+      # ignore grad norm and loss scaler for now
+      loss.backward()
+      targets += [x for x in optim.schedule_step()]
+    sched = Tensor.schedule(*targets)
+    print(f"schedule length {len(sched)}")
+  return sched
+
+if __name__ == "__main__":
+  if getenv("HALF", 1):
+    dtypes.default_float = dtypes.half
+
+  # the device we are optimizing for
+  device: Compiled = Device[Device.DEFAULT]
+  if getenv("BACKWARD"): Tensor.training = True
+  print(f"optimizing for {Device.DEFAULT}")
+
+  sched = globals()[f"get_sched_{getenv('MODEL', 'resnet')}"]()
+  sched = [x for x in sched if x.ast.op is Ops.SINK]
+
+  # focus on one kernel
+  if getenv("KERNEL", -1) >= 0: sched = sched[getenv("KERNEL", -1):getenv("KERNEL", -1)+1]
+
+  # work with the schedule
+  total_tm = 0
+  running_gflops = 0
+  usage = {}
+  for i,si in enumerate(sched):
+    if DEBUG >= 3: print(si.ast)
+
+    rawbufs = bufs_from_lin(Kernel(si.ast))
+
+    # "linearize" the op into uops in different ways
+    lins: list[tuple[Kernel, str]] = []
+
+    # always try hand coded opt
+    lin = Kernel(si.ast, opts=device.renderer)
+    lin.hand_coded_optimizations()
+    lins.append((lin, "HC"))
+
+    # maybe try tensor cores
+    lin = Kernel(si.ast, opts=device.renderer)
+    if lin.apply_tensor_cores():
+      lins.append((lin, "TC"))
+
+    # try a beam search
+    if beam:=getenv("BEAM"):
+      lin = Kernel(si.ast, opts=device.renderer)
+      lin = beam_search(lin, rawbufs, beam, bool(getenv("BEAM_ESTIMATE", 1)))
+      lins.append((lin, "BEAM"))
+
+    # try MCTS
+    if mcts:=getenv("MCTS"):
+      lin = Kernel(si.ast, opts=device.renderer)
+      lin = mcts_search(lin, rawbufs, mcts)
+      lins.append((lin, "MCTS"))
+
+    # benchmark the programs
+    choices = []
+    for lin, nm in lins:
+      tm = time_linearizer(lin, rawbufs, allow_test_size=False, cnt=10, disable_cache=True)
+      ops = (prg:=lin.to_program()).estimates.ops
+      gflops = sym_infer(ops, {k:k.min for k in lin.ast.variables()})*1e-9/tm
+      choices.append((tm, gflops, lin, prg, nm))
+
+    sorted_choices = sorted(choices, key=lambda x: x[0])
+    if DEBUG >= 1: # print all kernels
+      for tm, gflops, lin, prg, nm in choices:
+        print(f"                 kernel {i:2d} {lin.name+' '*(37-ansilen(lin.name))} {str(prg.global_size):18s} {str(prg.local_size):12s} takes {tm*1000:7.2f} ms, {gflops:6.0f} GFLOPS -- {colored(nm, 'green') if lin is sorted_choices[0][2] else nm}")
+
+    tm, gflops, lin, prg, nm = sorted_choices[0]
+    if getenv("SRC"):
+      print(si.ast)
+      print(lin.applied_opts)
+      print(lin.to_program().src)
+    total_tm += tm
+    running_gflops += gflops * tm
+    if (key := str([str(m) for m in si.metadata])) not in usage: usage[key] = (0, 0)
+    usage[key] = (usage[key][0] + tm, usage[key][1] + 1)
+    print(f"*** {total_tm*1000:7.2f} ms : kernel {i:2d} {lin.name+' '*(37-ansilen(lin.name))} {str(prg.global_size):18s} {str(prg.local_size):12s} takes {tm*1000:7.2f} ms, {gflops:6.0f} GFLOPS {[repr(m) if TRACEMETA >= 2 else str(m) for m in si.metadata]}")
+  print(f"******* total {total_tm*1000:.2f} ms, {running_gflops/total_tm:6.0f} GFLOPS")
+  print("usage:")
+  for k in sorted(usage, key=lambda x: -usage[x][0])[:10]:
+    print(f"{usage[k][0]*1000:.2f} ms: {k} ({usage[k][1]} times)")

examples/hlb_cifar10.py

@@ -7,11 +7,10 @@ import random, time
 import numpy as np
 from typing import Optional
 from extra.lr_scheduler import OneCycleLR
-from tinygrad import nn, dtypes, Tensor, Device, GlobalCounters, TinyJit, Variable
-from tinygrad.nn.state import get_state_dict
+from tinygrad import nn, dtypes, Tensor, Device, GlobalCounters, TinyJit
+from tinygrad.nn.state import get_state_dict, get_parameters
 from tinygrad.nn import optim
 from tinygrad.helpers import Context, BEAM, WINO, getenv, colored, prod
-from extra.bench_log import BenchEvent, WallTimeEvent
 
 cifar_mean = [0.4913997551666284, 0.48215855929893703, 0.4465309133731618]
 cifar_std = [0.24703225141799082, 0.24348516474564, 0.26158783926049628]
@@ -19,8 +18,8 @@ cifar_std = [0.24703225141799082, 0.24348516474564, 0.26158783926049628]
 BS, STEPS = getenv("BS", 512), getenv("STEPS", 1000)
 EVAL_BS = getenv("EVAL_BS", BS)
 GPUS = [f'{Device.DEFAULT}:{i}' for i in range(getenv("GPUS", 1))]
-assert BS % len(GPUS) == 0, f"{BS=} is not a multiple of {len(GPUS)=}"
-assert EVAL_BS % len(GPUS) == 0, f"{EVAL_BS=} is not a multiple of {len(GPUS)=}"
+assert BS % len(GPUS) == 0, f"{BS=} is not a multiple of {len(GPUS)=}, uneven multi GPU is slow"
+assert EVAL_BS % len(GPUS) == 0, f"{EVAL_BS=} is not a multiple of {len(GPUS)=}, uneven multi GPU is slow"
 
 class UnsyncedBatchNorm:
   def __init__(self, sz:int, eps=1e-5, affine=True, track_running_stats=True, momentum=0.1, num_devices=len(GPUS)):
@@ -118,7 +117,7 @@ class SpeedyResNet:
 # hyper-parameters were exactly the same as the original repo
 bias_scaler = 58
 hyp = {
-  'seed' : 201,
+  'seed' : 209,
   'opt': {
     'bias_lr':            1.76 * bias_scaler/512,
     'non_bias_lr':        1.76 / 512,
@@ -201,38 +200,24 @@ def train_cifar():
     idx_y = Tensor.arange(H, dtype=dtypes.int32).reshape((1,1,H,1))
     return (idx_x >= low_x) * (idx_x < (low_x + mask_size)) * (idx_y >= low_y) * (idx_y < (low_y + mask_size))
 
-  # Similar, but different enough.
-  def make_random_crop_indices(shape, mask_size) -> Tensor:
-    BS, _, H, W = shape
-    low_x = Tensor.randint(BS, low=0, high=W-mask_size).reshape(BS,1,1,1)
-    low_y = Tensor.randint(BS, low=0, high=H-mask_size).reshape(BS,1,1,1)
-    idx_x = Tensor.arange(mask_size, dtype=dtypes.int32).reshape((1,1,1,mask_size))
-    idx_y = Tensor.arange(mask_size, dtype=dtypes.int32).reshape((1,1,mask_size,1))
-    return low_x, low_y, idx_x, idx_y
-
   def random_crop(X:Tensor, crop_size=32):
-    Xs, Ys, Xi, Yi = make_random_crop_indices(X.shape, crop_size)
-    return X.gather(-1, (Xs + Xi).expand(-1, 3, X.shape[2], -1)).gather(-2, ((Ys+Yi).expand(-1, 3, crop_size, crop_size)))
+    mask = make_square_mask(X.shape, crop_size)
+    mask = mask.expand((-1,3,-1,-1))
+    X_cropped = Tensor(X.numpy()[mask.numpy()])
+    return X_cropped.reshape((-1, 3, crop_size, crop_size))
 
-  def cutmix(X, Y, order, mask_size=3):
+  def cutmix(X:Tensor, Y:Tensor, mask_size=3):
+    # fill the square with randomly selected images from the same batch
     mask = make_square_mask(X.shape, mask_size)
-    X_patch, Y_patch = X[order], Y[order]
+    order = list(range(0, X.shape[0]))
+    random.shuffle(order)
+    X_patch = Tensor(X.numpy()[order], device=X.device, dtype=X.dtype)
+    Y_patch = Tensor(Y.numpy()[order], device=Y.device, dtype=Y.dtype)
     X_cutmix = mask.where(X_patch, X)
     mix_portion = float(mask_size**2)/(X.shape[-2]*X.shape[-1])
     Y_cutmix = mix_portion * Y_patch + (1. - mix_portion) * Y
     return X_cutmix, Y_cutmix
 
-  @TinyJit
-  def augmentations(X:Tensor, Y:Tensor):
-    perms = Tensor.randperm(X.shape[0], device=X.device) # We reuse perms for cutmix, because they are expensivne to generate
-    if getenv("RANDOM_CROP", 1):
-      X = random_crop(X, crop_size=32)
-    if getenv("RANDOM_FLIP", 1):
-      # NOTE: RANGEIFY=1 needs this contiguous or the X[perms] is very slow
-      X = (Tensor.rand(X.shape[0],1,1,1) < 0.5).where(X.flip(-1), X).contiguous() # flip LR
-    X, Y = X[perms], Y[perms]
-    return X, Y, *cutmix(X, Y, perms, mask_size=hyp['net']['cutmix_size'])
-
   # the operations that remain inside batch fetcher is the ones that involves random operations
   def fetch_batches(X_in:Tensor, Y_in:Tensor, BS:int, is_train:bool):
     step, epoch = 0, 0
@@ -240,16 +225,28 @@ def train_cifar():
       st = time.monotonic()
       X, Y = X_in, Y_in
       if is_train:
-        X, Y, X_cm, Y_cm = augmentations(X, Y)
-        if getenv("CUTMIX", 1) and step >= hyp['net']['cutmix_steps']: X, Y = X_cm, Y_cm
+        # TODO: these are not jitted
+        if getenv("RANDOM_CROP", 1):
+          X = random_crop(X, crop_size=32)
+        if getenv("RANDOM_FLIP", 1):
+          X = (Tensor.rand(X.shape[0],1,1,1) < 0.5).where(X.flip(-1), X) # flip LR
+        if getenv("CUTMIX", 1):
+          if step >= hyp['net']['cutmix_steps']:
+            X, Y = cutmix(X, Y, mask_size=hyp['net']['cutmix_size'])
+        order = list(range(0, X.shape[0]))
+        random.shuffle(order)
+        X, Y = X.numpy()[order], Y.numpy()[order]
+      else:
+        X, Y = X.numpy(), Y.numpy()
       et = time.monotonic()
       print(f"shuffling {'training' if is_train else 'test'} dataset in {(et-st)*1e3:.2f} ms ({epoch=})")
-
-      vi = Variable("i", 0, (full_batches := (X.shape[0] // BS) * BS) - BS)
-      for i in range(0, full_batches, BS):
+      for i in range(0, X.shape[0], BS):
+        # pad the last batch  # TODO: not correct for test
+        batch_end = min(i+BS, Y.shape[0])
+        x = Tensor(X[batch_end-BS:batch_end], device=X_in.device, dtype=X_in.dtype)
+        y = Tensor(Y[batch_end-BS:batch_end], device=Y_in.device, dtype=Y_in.dtype)
         step += 1
-        vib = vi.bind(i)
-        yield X[vib:vib+BS], Y[vib:vib+BS]
+        yield x, y
       epoch += 1
       if not is_train: break
 
@@ -269,10 +266,13 @@ def train_cifar():
 
     @TinyJit
     def update(self, net, decay):
+      # TODO with Tensor.no_grad()
+      Tensor.no_grad = True
       for net_ema_param, (param_name, net_param) in zip(get_state_dict(self.net_ema).values(), get_state_dict(net).items()):
         # batchnorm currently is not being tracked
         if not ("num_batches_tracked" in param_name) and not ("running" in param_name):
           net_ema_param.assign(net_ema_param.detach()*decay + net_param.detach()*(1.-decay)).realize()
+      Tensor.no_grad = False
 
   set_seed(getenv('SEED', hyp['seed']))
 
@@ -355,7 +355,7 @@ def train_cifar():
 
   # https://www.anandtech.com/show/16727/nvidia-announces-geforce-rtx-3080-ti-3070-ti-upgraded-cards-coming-in-june
   # 136 TFLOPS is the theoretical max w float16 on 3080 Ti
-  step_times = []
+
   model_ema: Optional[modelEMA] = None
   projected_ema_decay_val = hyp['ema']['decay_base'] ** hyp['ema']['every_n_steps']
   i = 0
@@ -395,35 +395,27 @@ def train_cifar():
       if STEPS == 0 or i == STEPS: break
 
       GlobalCounters.reset()
+      X, Y = next(batcher)
+      if len(GPUS) > 1:
+        X.shard_(GPUS, axis=0)
+        Y.shard_(GPUS, axis=0)
 
-      with WallTimeEvent(BenchEvent.STEP):
-        X, Y = next(batcher)
-        if len(GPUS) > 1:
-          X.shard_(GPUS, axis=0)
-          Y.shard_(GPUS, axis=0)
-
-        with Context(BEAM=getenv("LATEBEAM", BEAM.value), WINO=getenv("LATEWINO", WINO.value)):
-          loss = train_step_jitted(model, optim.OptimizerGroup(opt_bias, opt_non_bias), [lr_sched_bias, lr_sched_non_bias], X, Y)
-          et = time.monotonic()
-          loss_cpu = loss.numpy()
-        # EMA for network weights
-        if getenv("EMA") and i > hyp['ema']['steps'] and (i+1) % hyp['ema']['every_n_steps'] == 0:
-          if model_ema is None:
-            model_ema = modelEMA(W, model)
-          model_ema.update(model, Tensor([projected_ema_decay_val*(i/STEPS)**hyp['ema']['decay_pow']]))
-
+      with Context(BEAM=getenv("LATEBEAM", BEAM.value), WINO=getenv("LATEWINO", WINO.value)):
+        loss = train_step_jitted(model, optim.OptimizerGroup(opt_bias, opt_non_bias), [lr_sched_bias, lr_sched_non_bias], X, Y)
+        et = time.monotonic()
+        loss_cpu = loss.numpy()
+      # EMA for network weights
+      if getenv("EMA") and i > hyp['ema']['steps'] and (i+1) % hyp['ema']['every_n_steps'] == 0:
+        if model_ema is None:
+          model_ema = modelEMA(W, model)
+        model_ema.update(model, Tensor([projected_ema_decay_val*(i/STEPS)**hyp['ema']['decay_pow']]))
       cl = time.monotonic()
-      step_times.append((cl-st)*1000.0)
       device_str = loss.device if isinstance(loss.device, str) else f"{loss.device[0]} * {len(loss.device)}"
       #  53  221.74 ms run,    2.22 ms python,  219.52 ms CL,  803.39 loss, 0.000807 LR, 4.66 GB used,   3042.49 GFLOPS,    674.65 GOPS
       print(f"{i:3d} {(cl-st)*1000.0:7.2f} ms run, {(et-st)*1000.0:7.2f} ms python, {(cl-et)*1000.0:7.2f} ms {device_str}, {loss_cpu:7.2f} loss, {opt_non_bias.lr.numpy()[0]:.6f} LR, {GlobalCounters.mem_used/1e9:.2f} GB used, {GlobalCounters.global_ops*1e-9/(cl-st):9.2f} GFLOPS, {GlobalCounters.global_ops*1e-9:9.2f} GOPS")
       st = cl
       i += 1
 
-  if (assert_time:=getenv("ASSERT_MIN_STEP_TIME")):
-    min_time = min(step_times)
-    assert min_time < assert_time, f"Speed regression, expected min step time of < {assert_time} ms but took: {min_time} ms"
-
   # verify eval acc
   if target := getenv("TARGET_EVAL_ACC_PCT", 0.0):
     if eval_acc_pct >= target:
@@ -432,5 +424,4 @@ def train_cifar():
       raise ValueError(colored(f"{eval_acc_pct=} < {target}", "red"))
 
 if __name__ == "__main__":
-  with WallTimeEvent(BenchEvent.FULL):
-    train_cifar()
+  train_cifar()

examples/llama.py

@@ -13,7 +13,6 @@ from extra.models.llama import Transformer, convert_from_huggingface, fix_bf16
 from sentencepiece import SentencePieceProcessor
 import tiktoken, sys
 from tiktoken.load import load_tiktoken_bpe
-from extra.bench_log import BenchEvent, WallTimeEvent
 
 MAX_CONTEXT = getenv("MAX_CONTEXT", 4096)
 
@@ -207,42 +206,40 @@ class LLaMa:
 
     model = Transformer(**params["args"], linear=linear, max_context=MAX_CONTEXT, jit=bool(JIT))
 
-    with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):
-      if model_path.is_dir():
-        weights = concat_weights([load(filename) for filename in [f"{model_path}/consolidated.{i:02d}.pth" for i in range(params["files"])]], device[0] if isinstance(device, tuple) else device)
-      else:
-        weights = load(str(model_path))
-      if "model.embed_tokens.weight" in weights:
-        weights = convert_from_huggingface(weights, params["args"]["n_layers"], params["args"]["n_heads"], params["args"].get("n_kv_heads", params["args"]["n_heads"]))
+    if model_path.is_dir():
+      weights = concat_weights([load(filename) for filename in [f"{model_path}/consolidated.{i:02d}.pth" for i in range(params["files"])]], device[0] if isinstance(device, tuple) else device)
+    else:
+      weights = load(str(model_path))
+    if "model.embed_tokens.weight" in weights:
+      weights = convert_from_huggingface(weights, model, params["args"]["n_heads"], params["args"].get("n_kv_heads", params["args"]["n_heads"]))
 
-      weights = fix_bf16(weights)
+    weights = fix_bf16(weights)
 
-      # prevent tracking model weights
-      # this is a part of a larger problem with BUFFER UOps and gc in TRACK_MATCH_STATS=2
-      with Context(BEAM=0, TRACK_MATCH_STATS=0):
-        # quantize
-        if quantize is not None:
-          weights = linear.quantize(weights, device)
-          for _,v in weights.items(): v.realize()
+    with Context(BEAM=0):
+      # quantize
+      if quantize is not None:
+        weights = linear.quantize(weights, device)
+        for _,v in weights.items(): v.realize()
 
-        # shard
-        if isinstance(device, tuple):
-          for k,v in nn.state.get_state_dict(model).items():
-            if 'scale' in k: v.shard_(device, axis=None)  # from quantized
-            elif '.attention.' in k:
-              if getenv("SHARD_KVCACHE") and ('.wq.' in k or '.wk.' in k or '.wv.' in k): v.shard_(device, axis=0)
-              else: v.shard_(device, axis=-1)
-            elif '.feed_forward.w1.' in k: v.shard_(device, axis=0)
-            elif '.feed_forward.w3.' in k: v.shard_(device, axis=0)
-            elif '.feed_forward.' in k: v.shard_(device, axis=-1)
-            elif 'tok_embeddings.weight' in k: v.shard_(device, axis=0)
-            elif 'output.weight' in k: v.shard_(device, axis=-1)
-            #elif k.endswith('.weight'): v.shard_(device, axis=-1)
-            #elif 'norm.' in k: v.shard_(device, axis=-1)
-            else: v.shard_(device, axis=None)
+      # shard
+      if isinstance(device, tuple):
+        for k,v in nn.state.get_state_dict(model).items():
+          if 'scale' in k: v.shard_(device, axis=None)  # from quantized
+          elif '.attention.' in k:
+            if getenv("SHARD_KVCACHE") and ('.wq.' in k or '.wk.' in k or '.wv.' in k): v.shard_(device, axis=0)
+            else: v.shard_(device, axis=-1)
+          elif '.feed_forward.w1.' in k: v.shard_(device, axis=0)
+          elif '.feed_forward.w3.' in k: v.shard_(device, axis=0)
+          elif '.feed_forward.' in k: v.shard_(device, axis=-1)
+          elif 'tok_embeddings.weight' in k: v.shard_(device, axis=0)
+          elif 'output.weight' in k: v.shard_(device, axis=-1)
+          #elif k.endswith('.weight'): v.shard_(device, axis=-1)
+          #elif 'norm.' in k: v.shard_(device, axis=-1)
+          else: v.shard_(device, axis=None)
+          #print(k, v.shape, v.lazydata.axis)
 
-        # replace weights in model
-        load_state_dict(model, weights, strict=False, consume=True)
+      # replace weights in model
+      load_state_dict(model, weights, strict=False, consume=True)
 
     return LLaMa(model, tokenizer)
 
@@ -330,6 +327,7 @@ int main()
 \end{code}
 """
 if __name__ == "__main__":
+  Tensor.no_grad = True
   print(f"using {Device.DEFAULT} backend")
 
   parser = argparse.ArgumentParser(description="Run LLaMA in tinygrad", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@@ -445,7 +443,7 @@ After you are done speaking, output [EOS]. You are not Chad.
   print(f"using LLaMA{LLAMA_SUFFIX}-{args.size} model")
   device = tuple(f"{Device.DEFAULT}:{i}" for i in range(args.shard)) if args.shard > 1 else Device.DEFAULT
   llama = LLaMa.build(MODEL_PATH, TOKENIZER_PATH, model_gen=args.gen, model_size=args.size, quantize=args.quantize, device=device)
-  param_bytes = sum(x.uop.size * x.dtype.itemsize for x in get_parameters(llama.model))
+  param_bytes = sum(x.lazydata.size * x.dtype.itemsize for x in get_parameters(llama.model))
 
   outputted = pre_prompt if chatbot else args.prompt
   start_pos, toks = 0, [llama.tokenizer.bos_id()] + llama.tokenizer.encode(outputted)
@@ -477,12 +475,11 @@ After you are done speaking, output [EOS]. You are not Chad.
       next_tok = Tensor([toks[start_pos:]], device=device) if tok_tensor is None or (len(toks)-start_pos) > 1 else tok_tensor.reshape(1, 1)
       with Profiling(enabled=args.profile):
         with Timing("total ", enabled=args.timing, on_exit=lambda x: f", {1e9/x:.2f} tok/s, {GlobalCounters.global_mem/x:.2f} GB/s, param {param_bytes/x:.2f} GB/s"):
-          with WallTimeEvent(BenchEvent.STEP):
-            with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on {Device.DEFAULT}" if DEBUG>=2 else "")+
-                        f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB"+
-                        (f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s, param {param_bytes*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None, enabled=args.timing):
-              tok_tensor = llama.model(next_tok, start_pos, args.temperature)
-            tok = tok_tensor.item()
+          with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU" if DEBUG>=2 else "")+
+                      f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB"+
+                      (f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s, param {param_bytes*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None, enabled=args.timing):
+            tok_tensor = llama.model(next_tok, start_pos, args.temperature)
+          tok = tok_tensor.item()
 
       # use the kv cache
       start_pos = len(toks)

examples/llama3.py

@@ -1,17 +1,16 @@
 from pathlib import Path
 from typing import List
 import json, argparse, random, time, os
+import tiktoken
+from tiktoken.load import load_tiktoken_bpe
 from extra.models.llama import Transformer, convert_from_huggingface, convert_from_gguf, fix_bf16
 from tinygrad.nn.state import safe_load, torch_load, load_state_dict, get_parameters, gguf_load
 from tinygrad import Tensor, dtypes, nn, Context, Device, GlobalCounters
 from tinygrad.helpers import Profiling, Timing, DEBUG, colored, fetch, tqdm
-from extra.bench_log import BenchEvent, WallTimeEvent
 
 class Tokenizer:
   pat_str = r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"
   def __init__(self, model_path: str):
-    import tiktoken
-    from tiktoken.load import load_tiktoken_bpe
     mergeable_ranks = load_tiktoken_bpe(model_path)
     self.num_base_tokens = len(mergeable_ranks)
     special_tokens = [
@@ -127,8 +126,7 @@ def NF4Linear(block_size):
       return x.linear(unscaled.reshape(self.out_features, self.in_features).T)
 
     @staticmethod
-    def quantize(state_dict: dict[str, Tensor], device, scale_dtype=dtypes.float16, quantize_embeds=False) -> dict[str, Tensor]:
-      assert not quantize_embeds  # TODO: support this?
+    def quantize(state_dict: dict[str, Tensor], device, scale_dtype=dtypes.float16) -> dict[str, Tensor]:
       new_state_dict = {}
       for k, v in state_dict.items():
         if "feed_forward" in k or "attention.w" in k:
@@ -145,41 +143,6 @@ def NF4Linear(block_size):
       return new_state_dict
   return _NF4Linear
 
-def quantize_to_fp8(x: Tensor, dtype=dtypes.fp8e4m3):
-  fp8_min = -448.0 if dtype == dtypes.fp8e4m3 else -57344.0
-  fp8_max = 448.0 if dtype == dtypes.fp8e4m3 else 57344.0
-  scale = fp8_max / x.abs().max()
-  x_scl_sat = (x * scale).clamp(fp8_min, fp8_max)
-  return x_scl_sat.cast(dtype), scale.float().reciprocal()
-
-class FP8Linear:
-  def __init__(self, in_features, out_features, bias=True):
-    self.weight = Tensor.empty(out_features, in_features, dtype=dtypes.fp8e4m3)
-    self.bias = Tensor.empty(out_features, dtype=dtypes.float16) if bias else None
-    self.weight_scale = Tensor.empty((), dtype=dtypes.float16)
-
-  def __call__(self, x:Tensor):
-    y = x.dot(self.weight.T.cast(dtypes.float32)) * self.weight_scale
-    if self.bias is not None: y = y + self.bias.cast(y.dtype)
-    return y.cast(x.dtype)
-
-  @staticmethod
-  def quantize(tensors, device, scale_dtype=dtypes.float16, quantize_embeds=False):
-    assert not quantize_embeds
-    new_tensors = {}
-    for name,v in tensors.items():
-      if "feed_forward" in name or "attention.w" in name:
-        assert "weight" in name, name
-        fp8_weight, scale = quantize_to_fp8(v)
-        new_tensors[name] = fp8_weight
-        new_tensors[name.replace('weight', 'weight_scale')] = scale.cast(scale_dtype)
-        if isinstance(device, tuple):
-          new_tensors[name].shard_(device, axis=-1)
-          new_tensors[name.replace('weight', 'weight_scale')].shard_(device, axis=None)
-      else:
-        new_tensors[name] = v
-    return new_tensors
-
 MODEL_PARAMS = {
   "1B": {
     "args": {"dim": 2048, "n_heads": 32, "n_kv_heads": 8, "n_layers": 16, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 8192},
@@ -192,57 +155,50 @@ MODEL_PARAMS = {
   "70B": {
     "args": {"dim": 8192, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256,  "hidden_dim": 28672},
     "files": 8
-  },
-  "405B": {
-    "args": {"dim": 16384, "n_heads": 128, "n_kv_heads": 8, "n_layers": 126, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256,  "hidden_dim": 53248},
-    "files": 191
-  },
+  }
 }
 def build_transformer(model_path: Path, model_size="8B", quantize=None, scale_dtype=dtypes.float16, device=None, max_context=8192, load_weights=True):
   # build model
   if quantize == "int8": linear, embedding, quantize_embeds = Int8Linear, Int8Embedding, True
   elif quantize == "nf4": linear, embedding, quantize_embeds = NF4Linear(64), nn.Embedding, False
-  elif quantize == "fp8": linear, embedding, quantize_embeds = FP8Linear, nn.Embedding, False
   else: linear, embedding, quantize_embeds = nn.Linear, nn.Embedding, False
   model = Transformer(**MODEL_PARAMS[model_size]["args"], linear=linear, embedding=embedding, max_context=max_context, jit=True)
 
   if not load_weights: return model
-
   # load weights
-  with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):
-    if model_path.is_dir():
-      if (model_path / "model.safetensors.index.json").exists(): weights = load(str(model_path / "model.safetensors.index.json"))
-      elif (model_path / "model.safetensors").exists(): weights = load(str(model_path / "model.safetensors"))
-      else: weights = concat_weights([load(str(model_path / f"consolidated.{i:02d}.pth")) for i in range(MODEL_PARAMS[model_size]["files"])], device[0] if isinstance(device, tuple) else device)
-    else:
-      weights = load(str(model_path))
-    if "model.embed_tokens.weight" in weights:
-      weights = convert_from_huggingface(weights, MODEL_PARAMS[model_size]["args"]["n_layers"], MODEL_PARAMS[model_size]["args"]["n_heads"], MODEL_PARAMS[model_size]["args"]["n_kv_heads"])
-    elif "token_embd.weight" in weights:
-      weights = convert_from_gguf(weights, MODEL_PARAMS[model_size]["args"]["n_layers"])
-    weights = fix_bf16(weights)
+  if model_path.is_dir():
+    if (model_path / "model.safetensors.index.json").exists(): weights = load(str(model_path / "model.safetensors.index.json"))
+    elif (model_path / "model.safetensors").exists(): weights = load(str(model_path / "model.safetensors"))
+    else: weights = concat_weights([load(str(model_path / f"consolidated.{i:02d}.pth")) for i in range(MODEL_PARAMS[model_size]["files"])], device[0] if isinstance(device, tuple) else device)
+  else:
+    weights = load(str(model_path))
+  if "model.embed_tokens.weight" in weights:
+    weights = convert_from_huggingface(weights, model, MODEL_PARAMS[model_size]["args"]["n_heads"], MODEL_PARAMS[model_size]["args"]["n_kv_heads"])
+  elif "token_embd.weight" in weights:
+    weights = convert_from_gguf(weights, model)
+  weights = fix_bf16(weights)
 
-    with Context(BEAM=0):
-      # quantize
-      if quantize == "float16": weights = {k:v.cast(quantize).contiguous() for k,v in weights.items()}
-      elif quantize is not None:
-        weights = linear.quantize(weights, device, scale_dtype, quantize_embeds)
-        for _,v in weights.items(): v.realize()
+  with Context(BEAM=0):
+    # quantize
+    if quantize == "float16": weights = {k:v.cast(quantize).contiguous() for k,v in weights.items()}
+    elif quantize is not None:
+      weights = linear.quantize(weights, device, scale_dtype, quantize_embeds)
+      for _,v in weights.items(): v.realize()
 
-      # shard
-      if isinstance(device, tuple):
-        for k,v in nn.state.get_state_dict(model).items():
-          if 'scale' in k: v.shard_(device, axis=None)  # from quantized
-          elif '.attention.' in k: v.shard_(device, axis=-1)
-          elif '.feed_forward.w1.' in k: v.shard_(device, axis=0)
-          elif '.feed_forward.w3.' in k: v.shard_(device, axis=0)
-          elif '.feed_forward.' in k: v.shard_(device, axis=-1)
-          elif 'tok_embeddings.weight' in k: v.shard_(device, axis=0)
-          elif 'output.weight' in k: v.shard_(device, axis=0)
-          else: v.shard_(device, axis=None)
+    # shard
+    if isinstance(device, tuple):
+      for k,v in nn.state.get_state_dict(model).items():
+        if 'scale' in k: v.shard_(device, axis=None)  # from quantized
+        elif '.attention.' in k: v.shard_(device, axis=-1)
+        elif '.feed_forward.w1.' in k: v.shard_(device, axis=0)
+        elif '.feed_forward.w3.' in k: v.shard_(device, axis=0)
+        elif '.feed_forward.' in k: v.shard_(device, axis=-1)
+        elif 'tok_embeddings.weight' in k: v.shard_(device, axis=0)
+        elif 'output.weight' in k: v.shard_(device, axis=0)
+        else: v.shard_(device, axis=None)
 
-      # replace weights in model
-      load_state_dict(model, weights, strict=False, consume=True)
+    # replace weights in model
+    load_state_dict(model, weights, strict=False, consume=True)
   return model
 
 # default settings
@@ -273,18 +229,20 @@ def prefill(model, toks, start_pos=0):
   return start_pos
 
 if __name__ == "__main__":
+  Tensor.no_grad = True
+
   parser = argparse.ArgumentParser()
   parser.add_argument("--download_model", action="store_true", help="Download a model")
   parser.add_argument("--model", type=Path, help="Model path")
-  parser.add_argument("--size", choices=["1B", "8B", "70B", "405B"], default="1B", help="Model size")
+  parser.add_argument("--size", choices=["1B", "8B", "70B"], default="1B", help="Model size")
   parser.add_argument("--shard", type=int, default=1, help="Shard the model across multiple devices")
-  parser.add_argument("--quantize", choices=["int8", "nf4", "float16", "fp8"], help="Quantization method")
+  parser.add_argument("--quantize", choices=["int8", "nf4", "float16"], help="Quantization method")
   parser.add_argument("--no_api", action="store_true", help="Disable the api and run a cli test interface")
   parser.add_argument("--host", type=str, default="0.0.0.0", help="Web server bind address")
   parser.add_argument("--port", type=int, default=7776, help="Web server port")
   parser.add_argument("--debug", action="store_true", help="Enable debug mode")
   parser.add_argument("--seed", type=int, help="Random seed")
-  parser.add_argument("--temperature", type=float, default=0.85, help="Temperature")
+  parser.add_argument("--temperature", type=int, default=0.85, help="Temperature")
   parser.add_argument("--benchmark", action="store_true", help="Run a benchmark")
   parser.add_argument("--timing", action="store_true", help="Print timing per token")
   parser.add_argument("--profile", action="store_true", help="Output profile data")
@@ -324,7 +282,7 @@ if __name__ == "__main__":
 
   device = tuple(f"{Device.DEFAULT}:{i}" for i in range(args.shard)) if args.shard > 1 else Device.DEFAULT
   model = build_transformer(args.model, model_size=args.size, quantize=args.quantize, device=device)
-  param_bytes = sum(x.uop.size * x.dtype.itemsize for x in get_parameters(model))
+  param_bytes = sum(x.lazydata.size * x.dtype.itemsize for x in get_parameters(model))
 
   if not args.no_api and not args.benchmark:
     from bottle import Bottle, request, response, HTTPResponse, abort, static_file
@@ -476,12 +434,11 @@ if __name__ == "__main__":
       st = GlobalCounters.time_sum_s
       with Profiling(enabled=args.profile):
         with Timing("total ", on_exit=lambda x: f", {1e9/x:.2f} tok/s, {GlobalCounters.global_mem/x:.2f} GB/s, param {param_bytes/x:.2f} GB/s"):
-          with WallTimeEvent(BenchEvent.STEP):
-            with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on {Device.DEFAULT}" if DEBUG>=2 else "")+
-                        f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB"+
-                        (f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s, param {param_bytes*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None):
-              tok = model(Tensor([[last_tok]], device=device), start_pos, TEMPERATURE, TOP_K, TOP_P, ALPHA_F, ALPHA_P)
-            tok = tok.item()
+          with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU" if DEBUG>=2 else "")+
+                      f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB"+
+                      (f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s, param {param_bytes*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None):
+            tok = model(Tensor([[last_tok]], device=device), start_pos, TEMPERATURE, TOP_K, TOP_P, ALPHA_F, ALPHA_P)
+          tok = tok.item()
       start_pos += 1
       last_tok = tok
       generated += tokenizer.decode([tok])
@@ -515,7 +472,7 @@ if __name__ == "__main__":
         st = GlobalCounters.time_sum_s
         with Profiling(enabled=args.profile):
           with Timing("total ", enabled=args.timing, on_exit=lambda x: f", {1e9/x:.2f} tok/s, {GlobalCounters.global_mem/x:.2f} GB/s, param {param_bytes/x:.2f} GB/s"):
-            with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on {Device.DEFAULT}" if DEBUG>=2 else "")+
+            with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU" if DEBUG>=2 else "")+
                         f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB"+
                         (f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s, param {param_bytes*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None, enabled=args.timing):
 

examples/llm.c/export.py

@@ -1,13 +1,13 @@
 #!/usr/bin/env python3
 import os
 if "NOOPT" not in os.environ: os.environ["NOOPT"] = "1"
-from tinygrad import Device, nn, Tensor, dtypes
+from tinygrad import Device, nn, Tensor, dtypes, Variable
 Device.DEFAULT = "CPU"
 from train_gpt2 import GPT, GPTConfig
-from tinygrad.helpers import dedup, flatten, getenv, GlobalCounters, to_function_name
-from tinygrad.engine.realize import get_kernel
+from tinygrad.helpers import dedup, to_function_name, flatten, getenv, GlobalCounters, ansilen, to_function_name
+from tinygrad.engine.realize import get_kernel, run_schedule
 from tinygrad.engine.memory import memory_planner
-from tinygrad.uop.ops import Ops
+from tinygrad.ops import Ops
 
 TIMING = getenv("TIMING")
 
@@ -16,7 +16,7 @@ if __name__ == "__main__":
   #model.load_pretrained()
   for p in nn.state.get_parameters(model): p.replace(Tensor.empty(p.shape, dtype=p.dtype)) # fake load pretrained
 
-  #early_sched = create_schedule([x.uop for x in nn.state.get_parameters(model)])
+  #early_sched = create_schedule([x.lazydata for x in nn.state.get_parameters(model)])
   #print(f"built model {len(early_sched)}")
 
   #B, T = Variable("B", 1, 128).bind(4), 64 #Variable("T", 1, 1024).bind(64)
@@ -25,7 +25,7 @@ if __name__ == "__main__":
   Tensor.training = True
   optimizer = nn.optim.Adam(nn.state.get_parameters(model), lr=1e-4)
   warmup_count = getenv("WARMUP", 3)
-  for i in range(warmup_count):  # TODO: why does it take three and not two to stabilize
+  for i in range(warmup_count):  # TODO: why does it take three and not two to stablize
     GlobalCounters.reset()
     X = Tensor.empty(4, 64, dtype=dtypes.int).reshape(B, T)
     Y = Tensor.empty(4, 64, dtype=dtypes.int).reshape(B, T)
@@ -56,7 +56,7 @@ if __name__ == "__main__":
   state_dict.update({'X': X, 'Y': Y, 'loss': loss})
   grad_state_dict = {}
   for k,v in state_dict.items():
-    if v.uop.base.buffer not in used_buffers: print(f"UNUSED: {k}")
+    if v.lazydata.base.buffer not in used_buffers: print(f"UNUSED: {k}")
     if v.grad is not None: grad_state_dict['grad_'+k] = v.grad
   state_dict.update(grad_state_dict)
   state_dict.update({'adam_b1_t': optimizer.b1_t, 'adam_b2_t': optimizer.b2_t, 'adam_lr': optimizer.lr})
@@ -65,7 +65,7 @@ if __name__ == "__main__":
     nm = inverse_state_dict[p]
     state_dict["adam_m_"+nm] = m
     state_dict["adam_v_"+nm] = v
-  named_buffers = {v.uop.base.buffer:k.replace(".", "_") for k,v in state_dict.items()}
+  named_buffers = {v.lazydata.base.buffer:k.replace(".", "_") for k,v in state_dict.items()}
 
   c_code = ["#include <stdlib.h>", "#include <tgmath.h>", "#include <stdbool.h>"]
   if TIMING: c_code += ["#include <stdio.h>", "#include <time.h>"]

examples/llm.c/train_gpt2.py

@@ -99,7 +99,7 @@ class GPT:
 
   def __call__(self, idx:Tensor, targets=None):
     b, t = idx.shape
-    pos = Tensor.arange(0, t, device=idx.device)
+    pos = Tensor.arange(0, t)
 
     tok_emb = self.wte(idx) # token embeddings of shape (b, t, n_embd)
     pos_emb = self.wpe(pos) # position embeddings of shape (t, n_embd)
@@ -124,7 +124,6 @@ if __name__ == "__main__":
   parser.add_argument("--batch_size", type=int, default=4, help="batch size")
   parser.add_argument("--sequence_length", type=int, default=64, help="sequence length")
   parser.add_argument("--skip_test", action="store_true", help="skip test")
-  parser.add_argument("--gpus", type=int, default=1, help="sequence length")
   args = parser.parse_args()
   B, T = args.batch_size, args.sequence_length
   assert 1 <= T <= 1024
@@ -132,10 +131,6 @@ if __name__ == "__main__":
   model = GPT(GPTConfig(n_layer=12, n_head=12, n_embd=768))
   model.load_pretrained()
 
-  if args.gpus > 1:
-    GPUS = tuple(f'{Device.DEFAULT}:{i}' for i in range(args.gpus))
-    for x in nn.state.get_parameters(model): x.to_(GPUS)  # we put a copy of the model on every GPU
-
   # init the tokenizer
   enc = tiktoken.get_encoding("gpt2")
   encode = lambda s: enc.encode(s, allowed_special={"<|endoftext|>"})
@@ -170,32 +165,23 @@ if __name__ == "__main__":
   x, y = next(data_iter) # we'll overfit this batch below
   optimizer = nn.optim.AdamW(nn.state.get_parameters(model), lr=1e-4, weight_decay=0)
 
-  print(f"model state:     {sum(x.nbytes() for x in nn.state.get_parameters(model))/1e9:.2f} GB")
-  print(f"optimizer state: {sum(x.nbytes() for x in nn.state.get_parameters(optimizer))/1e9:.2f} GB")
-
-  # shard the data on axis 0
-  if args.gpus > 1: x, y = x.shard(GPUS, axis=0), y.shard(GPUS, axis=0)
-
   @TinyJit
-  @Tensor.train()
-  def step(x:Tensor, y:Tensor) -> Tensor:
+  def step(x, y):
     _, loss = model(x, y)
     optimizer.zero_grad()
     loss.backward()
     return loss.realize(*optimizer.schedule_step())
 
-  for i in range(args.num_iterations):
-    GlobalCounters.reset()
-    t0 = time.perf_counter()
-    loss = step(x.contiguous(), y.contiguous())
-    Device[Device.DEFAULT].synchronize()
-    t1 = time.perf_counter()
-    print(f"iteration {i}, loss: {loss.item():.6f}, time: {(t1-t0)*1000:.3f}ms, {int(B*T/(t1-t0))} tok/s, {GlobalCounters.global_mem/1e9:.2f} GB")
+  with Tensor.train():
+    for i in range(args.num_iterations):
+      GlobalCounters.reset()
+      t0 = time.time()
+      loss = step(x.contiguous(), y.contiguous())
+      Device[Device.DEFAULT].synchronize()
+      t1 = time.time()
+      print(f"iteration {i}, loss: {loss.item():.6f}, time: {(t1-t0)*1000:.3f}ms, {int(B*T/(t1-t0))} tok/s")
 
   if not args.skip_test:
-    # copy back to single gpu for test
-    if args.gpus > 1:
-      for x in nn.state.get_parameters(model): x.to_(Device.DEFAULT)
     start = "<|endoftext|>"
     start_ids = encode(start)
     x = (Tensor(start_ids)[None, ...])

examples/mamba.py

@@ -279,15 +279,9 @@ def generate(model, tokenizer, prompt: str, n_tokens_to_gen: int = 10, temp: boo
   # Loading in the prompt tokens
   logits = model.forward(Tensor([tks]))[:, -1, :]
   for _ in tqdm(range(n_tokens_to_gen), desc="Speed Gen"):
+    # TODO: topk
     if sample:
-      scaled_logits = logits / temp
-      if top_k is not None:
-        topk_values, topk_indices = scaled_logits.topk(top_k)
-        filtered_logits = Tensor.full_like(scaled_logits, -float("inf"))
-        filtered_logits = filtered_logits.scatter(dim=-1, index=topk_indices, src=topk_values)
-        tok_Tens = filtered_logits.softmax().multinomial()
-      else:
-        tok_Tens = scaled_logits.softmax().multinomial()
+      tok_Tens = (logits/temp).softmax().multinomial()
     else:
       tok_Tens = logits.argmax(axis=-1).unsqueeze(0)
     tok = tok_Tens.item()
@@ -304,7 +298,6 @@ if __name__ == "__main__":
   parser.add_argument("--size", type=str, default="370m",
                       help=f"Size of model to use [{', '.join([k for k in MODELS.keys()])}]")
   parser.add_argument("--n_tokens", type=int, default=10, help="Number of tokens to generate")
-  parser.add_argument("--top_k", type=int, help="Limit sampling to the top k most likely tokens")
   parser.add_argument("--sample", dest="sample", action="store_true", help="Sample flag")
   parser.add_argument("--temp", type=float, default=1.0, help="Sampling temp has to be <=1.0")
   args = parser.parse_args()
@@ -315,9 +308,8 @@ if __name__ == "__main__":
   num_toks = args.n_tokens
   sample = args.sample
   temp = args.temp
-  top_k = args.top_k
   s = time.time()
-  tinyoutput = generate(model, tokenizer, prompt, n_tokens_to_gen=num_toks, sample=sample, temp=temp, top_k=top_k)
+  tinyoutput = generate(model, tokenizer, prompt, n_tokens_to_gen=num_toks, sample=sample, temp=temp)
   print(tinyoutput)
   print('TIME: ', time.time() - s)
   TORCHOUTPUT = "Why is gravity \nso important?\nBecause it's the only"

examples/mask_rcnn.py

@@ -0,0 +1,299 @@
+from extra.models.mask_rcnn import MaskRCNN
+from extra.models.resnet import ResNet
+from extra.models.mask_rcnn import BoxList
+from torch.nn import functional as F
+from torchvision import transforms as T
+from torchvision.transforms import functional as Ft
+import random
+from tinygrad.tensor import Tensor
+from PIL import Image
+import numpy as np
+import torch
+import argparse
+import cv2
+
+
+class Resize:
+  def __init__(self, min_size, max_size):
+    if not isinstance(min_size, (list, tuple)):
+      min_size = (min_size,)
+    self.min_size = min_size
+    self.max_size = max_size
+
+  # modified from torchvision to add support for max size
+  def get_size(self, image_size):
+    w, h = image_size
+    size = random.choice(self.min_size)
+    max_size = self.max_size
+    if max_size is not None:
+      min_original_size = float(min((w, h)))
+      max_original_size = float(max((w, h)))
+      if max_original_size / min_original_size * size > max_size:
+        size = int(round(max_size * min_original_size / max_original_size))
+
+      if (w <= h and w == size) or (h <= w and h == size):
+        return (h, w)
+
+      if w < h:
+        ow = size
+        oh = int(size * h / w)
+      else:
+        oh = size
+        ow = int(size * w / h)
+
+      return (oh, ow)
+
+  def __call__(self, image):
+    size = self.get_size(image.size)
+    image = Ft.resize(image, size)
+    return image
+
+
+class Normalize:
+  def __init__(self, mean, std, to_bgr255=True):
+    self.mean = mean
+    self.std = std
+    self.to_bgr255 = to_bgr255
+
+  def __call__(self, image):
+    if self.to_bgr255:
+      image = image[[2, 1, 0]] * 255
+    else:
+      image = image[[0, 1, 2]] * 255
+    image = Ft.normalize(image, mean=self.mean, std=self.std)
+    return image
+
+transforms = lambda size_scale: T.Compose(
+  [
+    Resize(int(800*size_scale), int(1333*size_scale)),
+    T.ToTensor(),
+    Normalize(
+      mean=[102.9801, 115.9465, 122.7717], std=[1., 1., 1.], to_bgr255=True
+    ),
+  ]
+)
+
+def expand_boxes(boxes, scale):
+  w_half = (boxes[:, 2] - boxes[:, 0]) * .5
+  h_half = (boxes[:, 3] - boxes[:, 1]) * .5
+  x_c = (boxes[:, 2] + boxes[:, 0]) * .5
+  y_c = (boxes[:, 3] + boxes[:, 1]) * .5
+
+  w_half *= scale
+  h_half *= scale
+
+  boxes_exp = torch.zeros_like(boxes)
+  boxes_exp[:, 0] = x_c - w_half
+  boxes_exp[:, 2] = x_c + w_half
+  boxes_exp[:, 1] = y_c - h_half
+  boxes_exp[:, 3] = y_c + h_half
+  return boxes_exp
+
+
+def expand_masks(mask, padding):
+  N = mask.shape[0]
+  M = mask.shape[-1]
+  pad2 = 2 * padding
+  scale = float(M + pad2) / M
+  padded_mask = mask.new_zeros((N, 1, M + pad2, M + pad2))
+  padded_mask[:, :, padding:-padding, padding:-padding] = mask
+  return padded_mask, scale
+
+
+def paste_mask_in_image(mask, box, im_h, im_w, thresh=0.5, padding=1):
+  # TODO: remove torch
+  mask = torch.tensor(mask.numpy())
+  box = torch.tensor(box.numpy())
+  padded_mask, scale = expand_masks(mask[None], padding=padding)
+  mask = padded_mask[0, 0]
+  box = expand_boxes(box[None], scale)[0]
+  box = box.to(dtype=torch.int32)
+
+  TO_REMOVE = 1
+  w = int(box[2] - box[0] + TO_REMOVE)
+  h = int(box[3] - box[1] + TO_REMOVE)
+  w = max(w, 1)
+  h = max(h, 1)
+
+  mask = mask.expand((1, 1, -1, -1))
+
+  mask = mask.to(torch.float32)
+  mask = F.interpolate(mask, size=(h, w), mode='bilinear', align_corners=False)
+  mask = mask[0][0]
+
+  if thresh >= 0:
+    mask = mask > thresh
+  else:
+    mask = (mask * 255).to(torch.uint8)
+
+  im_mask = torch.zeros((im_h, im_w), dtype=torch.uint8)
+  x_0 = max(box[0], 0)
+  x_1 = min(box[2] + 1, im_w)
+  y_0 = max(box[1], 0)
+  y_1 = min(box[3] + 1, im_h)
+
+  im_mask[y_0:y_1, x_0:x_1] = mask[
+                              (y_0 - box[1]): (y_1 - box[1]), (x_0 - box[0]): (x_1 - box[0])
+                              ]
+  return im_mask
+
+
+class Masker:
+  def __init__(self, threshold=0.5, padding=1):
+    self.threshold = threshold
+    self.padding = padding
+
+  def forward_single_image(self, masks, boxes):
+    boxes = boxes.convert("xyxy")
+    im_w, im_h = boxes.size
+    res = [
+      paste_mask_in_image(mask[0], box, im_h, im_w, self.threshold, self.padding)
+      for mask, box in zip(masks, boxes.bbox)
+    ]
+    if len(res) > 0:
+      res = torch.stack(*res, dim=0)[:, None]
+    else:
+      res = masks.new_empty((0, 1, masks.shape[-2], masks.shape[-1]))
+    return Tensor(res.numpy())
+
+  def __call__(self, masks, boxes):
+    if isinstance(boxes, BoxList):
+      boxes = [boxes]
+
+    results = []
+    for mask, box in zip(masks, boxes):
+      result = self.forward_single_image(mask, box)
+      results.append(result)
+    return results
+
+
+masker = Masker(threshold=0.5, padding=1)
+
+def select_top_predictions(predictions, confidence_threshold=0.9):
+  scores = predictions.get_field("scores").numpy()
+  keep = [idx for idx, score in enumerate(scores) if score > confidence_threshold]
+  return predictions[keep]
+
+def compute_prediction(original_image, model, confidence_threshold, size_scale=1.0):
+  image = transforms(size_scale)(original_image).numpy()
+  image = Tensor(image, requires_grad=False)
+  predictions = model(image)
+  prediction = predictions[0]
+  prediction = select_top_predictions(prediction, confidence_threshold)
+  width, height = original_image.size
+  prediction = prediction.resize((width, height))
+
+  if prediction.has_field("mask"):
+    masks = prediction.get_field("mask")
+    masks = masker([masks], [prediction])[0]
+    prediction.add_field("mask", masks)
+  return prediction
+
+def compute_prediction_batched(batch, model, size_scale=1.0):
+  imgs = []
+  for img in batch:
+    imgs.append(transforms(size_scale)(img).numpy())
+  image = [Tensor(image, requires_grad=False) for image in imgs]
+  predictions = model(image)
+  del image
+  return predictions
+
+palette = np.array([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
+
+def findContours(*args, **kwargs):
+  if cv2.__version__.startswith('4'):
+    contours, hierarchy = cv2.findContours(*args, **kwargs)
+  elif cv2.__version__.startswith('3'):
+    _, contours, hierarchy = cv2.findContours(*args, **kwargs)
+  return contours, hierarchy
+
+def compute_colors_for_labels(labels):
+  l = labels[:, None]
+  colors = l * palette
+  colors = (colors % 255).astype("uint8")
+  return colors
+
+def overlay_mask(image, predictions):
+  image = np.asarray(image)
+  masks = predictions.get_field("mask").numpy()
+  labels = predictions.get_field("labels").numpy()
+
+  colors = compute_colors_for_labels(labels).tolist()
+
+  for mask, color in zip(masks, colors):
+    thresh = mask[0, :, :, None]
+    contours, hierarchy = findContours(
+        thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
+    )
+    image = cv2.drawContours(image, contours, -1, color, 3)
+
+  composite = image
+
+  return composite
+
+CATEGORIES = [
+    "__background", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
+    "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant",
+    "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard",
+    "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
+    "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli",
+    "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table",
+    "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster",
+    "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush",
+]
+
+def overlay_boxes(image, predictions):
+  labels = predictions.get_field("labels").numpy()
+  boxes = predictions.bbox
+  image = np.asarray(image)
+  colors = compute_colors_for_labels(labels).tolist()
+
+  for box, color in zip(boxes, colors):
+    box = torch.tensor(box.numpy())
+    box = box.to(torch.int64)
+    top_left, bottom_right = box[:2].tolist(), box[2:].tolist()
+    image = cv2.rectangle(
+        image, tuple(top_left), tuple(bottom_right), tuple(color), 1
+    )
+
+  return image
+
+def overlay_class_names(image, predictions):
+  scores = predictions.get_field("scores").numpy().tolist()
+  labels = predictions.get_field("labels").numpy().tolist()
+  labels = [CATEGORIES[int(i)] for i in labels]
+  boxes = predictions.bbox.numpy()
+  image = np.asarray(image)
+  template = "{}: {:.2f}"
+  for box, score, label in zip(boxes, scores, labels):
+    x, y = box[:2]
+    s = template.format(label, score)
+    x, y = int(x), int(y)
+    cv2.putText(
+        image, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255), 1
+    )
+
+  return image
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser(description='Run MaskRCNN', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument('--image', type=str, help="Path of the image to run")
+  parser.add_argument('--threshold', type=float, default=0.7, help="Detector threshold")
+  parser.add_argument('--size_scale', type=float, default=1.0, help="Image resize multiplier")
+  parser.add_argument('--out', type=str, default="/tmp/rendered.png", help="Output filename")
+  args = parser.parse_args()
+
+  resnet = ResNet(50, num_classes=None, stride_in_1x1=True)
+  model_tiny = MaskRCNN(resnet)
+  model_tiny.load_from_pretrained()
+  img = Image.open(args.image)
+  top_result_tiny = compute_prediction(img, model_tiny, confidence_threshold=args.threshold, size_scale=args.size_scale)
+  bbox_image = overlay_boxes(img, top_result_tiny)
+  mask_image = overlay_mask(bbox_image, top_result_tiny)
+  final_image = overlay_class_names(mask_image, top_result_tiny)
+
+  im = Image.fromarray(final_image)
+  print(f"saving {args.out}")
+  im.save(args.out)
+  im.show()

examples/minrf.py

@@ -1,156 +0,0 @@
-# much taken from https://github.com/cloneofsimo/minRF
-from tinygrad import Tensor, nn, GlobalCounters, TinyJit
-from tinygrad.helpers import getenv, trange
-from extra.models.llama import Attention, FeedForward, precompute_freqs_cis
-
-def modulate(x:Tensor, shift:Tensor, scale:Tensor) -> Tensor: return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
-
-# TODO: why doesn't the TimestepEmbedder from minRF work?
-class TimestepEmbedder:
-  def __init__(self, hidden_size): self.mlp = [nn.Linear(1, hidden_size), Tensor.silu, nn.Linear(hidden_size, hidden_size)]
-  def __call__(self, t:Tensor): return t.reshape(-1, 1).sequential(self.mlp)
-
-class TransformerBlock:
-  def __init__(self, dim, n_heads, norm_eps=1e-5):
-    self.attention = Attention(dim, n_heads)
-    self.feed_forward = FeedForward(dim, 4*dim)
-    self.attention_norm = nn.LayerNorm(dim, eps=norm_eps)
-    self.ffn_norm = nn.LayerNorm(dim, eps=norm_eps)
-    self.adaLN_modulation = nn.Linear(dim, 6 * dim, bias=True)
-
-  def __call__(self, x:Tensor, freqs_cis:Tensor, adaln_input:Tensor):
-    shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input.silu()).chunk(6, dim=1)
-    x = x + gate_msa.unsqueeze(1) * self.attention(modulate(self.attention_norm(x), shift_msa, scale_msa), 0, freqs_cis)
-    x = x + gate_mlp.unsqueeze(1) * self.feed_forward(modulate(self.ffn_norm(x), shift_mlp, scale_mlp))
-    return x.contiguous().contiguous_backward()
-
-class FinalLayer:
-  def __init__(self, dim, patch_size, out_channels):
-    self.norm_final = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
-    self.linear = nn.Linear(dim, patch_size*patch_size*out_channels, bias=True)
-    self.adaLN_modulation = nn.Linear(dim, 2 * dim, bias=True)
-
-    # init weights/bias to 0
-    self.linear.weight.replace(self.linear.weight.zeros_like().contiguous())
-    self.linear.bias.replace(self.linear.bias.zeros_like().contiguous())
-
-  def __call__(self, x:Tensor, c:Tensor):
-    shift, scale = self.adaLN_modulation(c.silu()).chunk(2, dim=1)
-    x = modulate(self.norm_final(x), shift, scale)
-    return self.linear(x)
-
-# channels=1, input_size=32, dim=64, n_layers=6, n_heads=4, num_classes=10
-class DiT_Llama:
-  def __init__(self, in_channels=1, dim=64, n_layers=6, n_heads=4, num_classes=10, patch_size=2):
-    self.patch_size = patch_size
-    self.out_channels = in_channels
-    self.num_classes = num_classes
-
-    self.init_conv_seq = [
-      nn.Conv2d(in_channels, dim // 2, kernel_size=5, padding=2, stride=1), Tensor.silu, nn.GroupNorm(32, dim//2),
-      nn.Conv2d(dim //2, dim // 2, kernel_size=5, padding=2, stride=1), Tensor.silu, nn.GroupNorm(32, dim//2),
-    ]
-
-    self.x_embedder = nn.Linear(self.patch_size * self.patch_size * dim // 2, dim, bias=True)
-    self.t_embedder = TimestepEmbedder(dim)
-    self.y_embedder = nn.Embedding(num_classes+1, dim)
-    self.final_layer = FinalLayer(dim, self.patch_size, self.out_channels)
-
-    self.freqs_cis = precompute_freqs_cis(dim // n_heads, 4096)
-    self.layers = [TransformerBlock(dim, n_heads) for _ in range(n_layers)]
-
-  def unpatchify(self, x:Tensor):
-    c, p = self.out_channels, self.patch_size
-    h = w = int(x.shape[1] ** 0.5)
-    x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
-    x = x.rearrange("n h w p q c -> n c h p w q")
-    return x.reshape(shape=(x.shape[0], c, h * p, h * p))
-
-  def patchify(self, x:Tensor):
-    B, C, H, W = x.shape
-    x = x.reshape(B, C, H // self.patch_size, self.patch_size, W // self.patch_size, self.patch_size)
-    x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
-    return x  # B <H*W ish> <C*patch_size*patch_size>
-
-  def __call__(self, x:Tensor, t:Tensor, y:Tensor) -> Tensor:
-    x = x.sequential(self.init_conv_seq)
-    x = self.patchify(x)
-    x = self.x_embedder(x)
-    adaln_input = self.t_embedder(t) + self.y_embedder(y)
-    adaln_input = adaln_input.contiguous()
-    for layer in self.layers:
-      x = layer(x, self.freqs_cis[:, :x.size(1)], adaln_input=adaln_input)
-    x = self.final_layer(x, adaln_input)
-    return self.unpatchify(x)
-
-  def rf(self, x:Tensor, cond:Tensor):
-    b = x.shape[0]
-    # self.ln is True
-    t = Tensor.randn((b,)).sigmoid()
-    texp = t.view([b, *([1] * len(x.shape[1:]))])
-
-    # conditional dropout
-    dropout_prob = 0.1
-    cond = (Tensor.rand(cond.shape[0]) < dropout_prob).where(cond.full_like(self.num_classes), cond)
-
-    # this is rectified flow
-    z1 = x.randn_like()
-    zt = (1 - texp) * x + texp * z1
-    vtheta = self(zt, t, cond)
-
-    # MSE loss
-    return ((z1 - x) - vtheta).square().mean()
-
-  def sample(self, z, cond, null_cond, sample_steps=50, cfg=2.0):
-    b = z.size(0)
-    dt = Tensor.full((b,)+(1,)*len(z.shape[1:]), fill_value=1.0/sample_steps).contiguous()
-    images = [z]
-    for i in range(sample_steps, 0, -1):
-      t = Tensor.full((b,), fill_value=i/sample_steps).contiguous()
-      vc = self(z, t, cond)
-      vu = self(z, t, null_cond)
-      vc = vu + cfg * (vc - vu)
-      z = z - dt * vc
-      z = z.contiguous()
-      images.append(z)
-    return images
-
-def mviz(t:Tensor):
-  assert len(t.shape) == 4 and t.shape[1] == 1
-  ft = t.permute(1,2,0,3).reshape(32, -1)
-  assert ft.shape[-1]%32 == 0
-  print("")
-  for y in ((ft+1)/2).clamp(0,1).tolist():
-    ln = [f"\033[38;5;{232+int(x*23)}m██" for x in y]
-    print(''.join(ln) + "\033[0m")
-
-if __name__ == "__main__":
-  X_train, Y_train, X_test, Y_test = nn.datasets.mnist()
-  X_train = X_train.pad((2,2,2,2))
-  X_train = ((X_train.float()/255)-0.5)/0.5
-  Y_train = Y_train.int()
-
-  model = DiT_Llama(patch_size=getenv("PATCH_SIZE", 2))
-  for r in nn.state.get_parameters(model): r.realize()
-  optimizer = nn.optim.Adam(nn.state.get_parameters(model), lr=5e-4)
-
-  @TinyJit
-  @Tensor.train()
-  def train_step():
-    if getenv("OVERFIT"): samples = Tensor.zeros(getenv("BS", 256), dtype='int')
-    else: samples = Tensor.randint(getenv("BS", 256), high=X_train.shape[0])
-    optimizer.zero_grad()
-    loss = model.rf(X_train[samples], Y_train[samples])
-    loss.backward()
-    optimizer.step()
-    return loss
-
-  @TinyJit
-  def sample(z:Tensor, cond:Tensor) -> Tensor:
-    return model.sample(z, cond, Tensor.full_like(cond, 10), sample_steps=getenv("SAMPLE_STEPS", 20))[-1]
-
-  for steps in (t:=trange(getenv("STEPS", 5000))):
-    if steps%10 == 0: mviz(sample(Tensor.randn(3, 1, 32, 32), Tensor([5,0,4], dtype='int')))
-    GlobalCounters.reset()
-    loss = train_step()
-    t.set_description(f"loss: {loss.item():9.2f}")

examples/mixtral.py

@@ -3,7 +3,6 @@ from tinygrad import Tensor, nn, Device, GlobalCounters, Variable
 from tinygrad.helpers import Timing, Profiling, CI, tqdm
 from tinygrad.nn.state import torch_load, get_state_dict
 from extra.models.llama import FeedForward, Transformer
-from extra.bench_log import BenchEvent, WallTimeEvent
 
 class MixtureFeedForward:
   def __init__(self, num_experts:int, dim:int, hidden_dim:int, linear=nn.Linear):
@@ -31,19 +30,18 @@ if __name__ == "__main__":
                       help="Path to the downloaded weights")
   args = parser.parse_args()
 
-  with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):
-    state = torch_load(args.weights + "/consolidated.00.pth.b")
-    model = Transformer(n_layers=32, dim=4096, hidden_dim=14336, n_heads=32, n_kv_heads=8, norm_eps=1e-5, vocab_size=32000, feed_forward=functools.partial(MixtureFeedForward, 8), jit=False)
-    model_state_dict = get_state_dict(model)
+  state = torch_load(args.weights + "/consolidated.00.pth.b")
+  model = Transformer(n_layers=32, dim=4096, hidden_dim=14336, n_heads=32, n_kv_heads=8, norm_eps=1e-5, vocab_size=32000, feed_forward=functools.partial(MixtureFeedForward, 8), jit=False)
+  model_state_dict = get_state_dict(model)
 
-    for k in (t := tqdm(state, disable=CI)):
-      if 'feed_forward.experts.' in k:
-        expert_no = int(k.split('feed_forward.experts.')[1].split('.')[0])
-        device = Device.DEFAULT + ":" + str((expert_no//2)+1)
-      else:
-        device = Device.DEFAULT
-      t.set_description(f"ram used: {GlobalCounters.mem_used/1e9:5.2f} GB, loading {k} to {device}")
-      model_state_dict[k].replace(state[k].to(device).half()).realize()
+  for k in (t := tqdm(state, disable=CI)):
+    if 'feed_forward.experts.' in k:
+      expert_no = int(k.split('feed_forward.experts.')[1].split('.')[0])
+      device = Device.DEFAULT + ":" + str((expert_no//2)+1)
+    else:
+      device = Device.DEFAULT
+    t.set_description(f"ram used: {GlobalCounters.mem_used/1e9:5.2f} GB, loading {k} to {device}")
+    model_state_dict[k].replace(state[k].to(device).half()).realize()
   if CI: print(f"ram used: {GlobalCounters.mem_used/1e9:5.2f} GB")
 
   from sentencepiece import SentencePieceProcessor
@@ -55,8 +53,7 @@ if __name__ == "__main__":
     GlobalCounters.reset()
     with Profiling(sort="time", frac=0.1, enabled=args.profile):
       with Timing("total ", enabled=args.timing, on_exit=lambda x: f", {1e9/x:.2f} tok/sec"):
-        with WallTimeEvent(BenchEvent.STEP):
-          tok = model(Tensor([toks[start_pos:]]), 0 if start_pos == 0 else Variable("start_pos", 1, 1024-1).bind(start_pos), args.temperature).item()
+        tok = model(Tensor([toks[start_pos:]]), 0 if start_pos == 0 else Variable("start_pos", 1, 1024).bind(start_pos), args.temperature).item()
     toks.append(tok)
     start_pos += 1
     print(spp.decode(toks))

examples/mlperf/dataloader.py

@@ -1,12 +1,11 @@
-import os, random, pickle, queue, struct, math, functools, hashlib, time
+import os, random, pickle, queue
 from typing import List
 from pathlib import Path
 from multiprocessing import Queue, Process, shared_memory, connection, Lock, cpu_count
 
 import numpy as np
 from tinygrad import dtypes, Tensor
-from tinygrad.helpers import getenv, prod, Context, round_up, tqdm, OSX
-from tinygrad.nn.state import TensorIO
+from tinygrad.helpers import getenv, prod, Context, round_up, tqdm
 
 ### ResNet
 
@@ -65,7 +64,17 @@ def loader_process(q_in, q_out, X:Tensor, seed):
       else:
         # pad data with training mean
         img = np.tile(np.array([[[123.68, 116.78, 103.94]]], dtype=np.uint8), (224, 224, 1))
-      X[idx].flatten().assign(img.tobytes())
+
+      # broken out
+      #img_tensor = Tensor(img.tobytes(), device='CPU')
+      #storage_tensor = X[idx].contiguous().realize().lazydata.base.realized
+      #storage_tensor._copyin(img_tensor.numpy())
+
+      # faster
+      X[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = img.tobytes()
+
+      # ideal
+      #X[idx].assign(img.tobytes())   # NOTE: this is slow!
       q_out.put(idx)
     q_out.put(None)
 
@@ -120,15 +129,14 @@ def batch_load_resnet(batch_size=64, val=False, shuffle=True, seed=None, pad_fir
   q_in, q_out = Queue(), Queue()
 
   sz = (batch_size*BATCH_COUNT, 224, 224, 3)
-  shm_name = "resnet_X_val" if val else "resnet_X_train"
-  if not OSX and os.path.exists(f"/dev/shm/{shm_name}"): os.unlink(f"/dev/shm/{shm_name}")
-  shm = shared_memory.SharedMemory(name=shm_name, create=True, size=prod(sz))
+  if os.path.exists("/dev/shm/resnet_X"): os.unlink("/dev/shm/resnet_X")
+  shm = shared_memory.SharedMemory(name="resnet_X", create=True, size=prod(sz))
   procs = []
 
   try:
     # disk:shm is slower
-    if OSX: X = Tensor.empty(*sz, dtype=dtypes.uint8, device=f"disk:shm:{shm.name}")
-    else: X = Tensor.empty(*sz, dtype=dtypes.uint8, device=f"disk:/dev/shm/{shm_name}")
+    #X = Tensor.empty(*sz, dtype=dtypes.uint8, device=f"disk:shm:{shm.name}")
+    X = Tensor.empty(*sz, dtype=dtypes.uint8, device=f"disk:/dev/shm/resnet_X")
     Y = [None] * (batch_size*BATCH_COUNT)
 
     for _ in range(cpu_count()):
@@ -203,13 +211,12 @@ class InterleavedDataset:
     self.queues[queue_index].queue.extend(load_file(file))
 
 # Reference: https://github.com/mlcommons/training/blob/1c8a098ae3e70962a4f7422c0b0bd35ae639e357/language_model/tensorflow/bert/run_pretraining.py, Line 394
-def batch_load_train_bert(BS:int, seed:int|None=None):
+def batch_load_train_bert(BS:int):
   from extra.datasets.wikipedia import get_wiki_train_files
-  rng = random.Random(seed)
   fs = sorted(get_wiki_train_files())
   train_files = []
   while fs: # TF shuffle
-    rng.shuffle(fs)
+    random.shuffle(fs)
     train_files.append(fs.pop(0))
 
   cycle_length = min(getenv("NUM_CPU_THREADS", min(os.cpu_count(), 8)), len(train_files))
@@ -254,8 +261,8 @@ def load_unet3d_data(preprocessed_dataset_dir, seed, queue_in, queue_out, X:Tens
       x = random_brightness_augmentation(x)
       x = gaussian_noise(x)
 
-    X[idx].flatten().assign(x.tobytes())
-    Y[idx].flatten().assign(y.tobytes())
+    X[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = x.tobytes()
+    Y[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = y.tobytes()
 
     queue_out.put(idx)
   queue_out.put(None)
@@ -305,7 +312,7 @@ def batch_load_unet3d(preprocessed_dataset_dir:Path, batch_size:int=6, val:bool=
       proc = Process(target=load_unet3d_data, args=(preprocessed_dataset_dir, seed, queue_in, queue_out, X, Y))
       proc.daemon = True
       proc.start()
-
+      
       procs.append(proc)
 
     for bc in range(batch_count):
@@ -369,12 +376,12 @@ def load_retinanet_data(base_dir:Path, val:bool, queue_in:Queue, queue_out:Queue
       clipped_match_idxs = np.clip(match_idxs, 0, None)
       clipped_boxes, clipped_labels = tgt["boxes"][clipped_match_idxs], tgt["labels"][clipped_match_idxs]
 
-      boxes[idx].flatten().assign(clipped_boxes.tobytes())
-      labels[idx].flatten().assign(clipped_labels.tobytes())
-      matches[idx].flatten().assign(match_idxs.tobytes())
-      anchors[idx].flatten().assign(anchor.tobytes())
+      boxes[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = clipped_boxes.tobytes()
+      labels[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = clipped_labels.tobytes()
+      matches[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = match_idxs.tobytes()
+      anchors[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = anchor.tobytes()
 
-    imgs[idx].flatten().assign(img.tobytes())
+    imgs[idx].contiguous().realize().lazydata.base.realized.as_buffer(force_zero_copy=True)[:] = img.tobytes()
 
     queue_out.put(idx)
   queue_out.put(None)
@@ -502,280 +509,6 @@ def batch_load_retinanet(dataset, val:bool, base_dir:Path, batch_size:int=32, sh
       # happens with BENCHMARK set
       pass
 
-# stable diffusion callbacks to match mlperf ref; declared here because they're pickled
-def filter_dataset(sample:dict): return {k:v for k,v in sample.items() if k in {'npy', 'txt'}}
-def collate(batch:list[dict]):
-  ret = {"npy": [], "txt": [], "__key__": []}
-  for sample in batch:
-    for k,v in sample.items():
-      ret[k].append(v)
-  return ret
-def collate_fn(batch): return batch
-
-# Reference (code): https://github.com/mlcommons/training/blob/2f4a93fb4888180755a8ef55f4b977ef8f60a89e/stable_diffusion/ldm/data/webdatasets.py, Line 55
-# Reference (params): https://github.com/mlcommons/training/blob/ab4ae1ca718d7fe62c369710a316dff18768d04b/stable_diffusion/configs/train_01x08x08.yaml, Line 107
-def batch_load_train_stable_diffusion(urls:str, BS:int):
-  import webdataset
-  dataset = webdataset.WebDataset(urls=urls, resampled=True, cache_size=-1, cache_dir=None)
-  dataset = dataset.shuffle(size=1000)
-  dataset = dataset.decode()
-  dataset = dataset.map(filter_dataset)
-  dataset = dataset.batched(BS, partial=False, collation_fn=collate)
-  dataset = webdataset.WebLoader(dataset, batch_size=None, shuffle=False, num_workers=1, persistent_workers=True, collate_fn=collate_fn)
-
-  for x in dataset:
-    assert isinstance(x, dict) and all(isinstance(k, str) for k in x.keys()) and all(isinstance(v, list) for v in x.values())
-    assert all(isinstance(moment_mean_logvar, np.ndarray) and moment_mean_logvar.shape==(1,8,64,64) for moment_mean_logvar in x["npy"])
-    assert all(isinstance(caption, str) for caption in x["txt"])
-    yield x
-
-# llama3
-
-class BinIdxDataset:
-  def __init__(self, base_path:Path):
-    self.idx_t = Tensor(base_path.with_name(f"{base_path.name}.idx"))
-    self.idx = TensorIO(self.idx_t)
-
-    # parse idx file
-    magic = self.idx.read(9)
-    assert magic == b"MMIDIDX\x00\x00", "invalid index file format"
-    version, = struct.unpack("<Q", self.idx.read(8))
-    assert version == 1, "unsupported index version"
-    dtype_code, = struct.unpack("<B", self.idx.read(1))
-    self.dtype = {1:np.dtype(np.uint8), 2:np.dtype(np.int8), 3:np.dtype(np.int16), 4:np.dtype(np.int32), 5:np.dtype(np.int64), 6:np.dtype(np.float64), 7:np.dtype(np.double), 8:np.dtype(np.uint16)}[dtype_code]
-    self.count, = struct.unpack("<Q", self.idx.read(8))
-    doc_count, = struct.unpack("<Q", self.idx.read(8))
-
-    start = self.idx.tell()
-    end = start + self.count * dtypes.int32.itemsize
-    self.sizes = self.idx_t[start:end].bitcast(dtypes.int32).numpy()
-
-    start = end
-    end = start + self.count * dtypes.int64.itemsize
-    self.pointers = self.idx_t[start:end].bitcast(dtypes.int64).numpy()
-
-    start = end
-    end = start + doc_count * dtypes.int64.itemsize
-    self.doc_idx = self.idx_t[start:end].bitcast(dtypes.int64).numpy()
-
-    # bin file
-    self.bin_t = Tensor(base_path.with_name(f"{base_path.name}.bin")).numpy()
-
-  def _index(self, idx) -> tuple[int, int]:
-    return int(self.pointers[idx]), int(self.sizes[idx])
-
-  def get(self, idx, offset:int=0, length:int|None=None):
-    ptr, size = self._index(idx)
-    if length is None: length = size - offset
-    ptr += offset * self.dtype.itemsize
-    return self.bin_t[ptr:ptr+length*self.dtype.itemsize].view(self.dtype)
-
-# https://docs.nvidia.com/megatron-core/developer-guide/latest/api-guide/datasets.html
-class GPTDataset:
-  def __init__(self, base_path:Path, samples:int, seqlen:int, seed:int, shuffle:bool):
-    self.samples, self.seqlen = samples, seqlen
-    self.shuffle = shuffle
-    self.rng = np.random.RandomState(seed)
-
-    self.indexed_dataset = BinIdxDataset(base_path)
-
-    # check for cache
-    cache_hash = hashlib.sha256(f"{samples}:{seqlen}:{seed}:{shuffle}".encode()).hexdigest()
-    cache_path = base_path.with_name(f"{base_path.name}.{cache_hash}.index_cache")
-    print(f"try loading GPTDataset from {cache_path}...")
-    if cache_path.exists():
-      print("cache found, loading...")
-      with open(cache_path, "rb") as f:
-        self.doc_idx, self.sample_idx, self.shuffle_idx = pickle.load(f)
-    else:
-      print("cache not found, building index...")
-      self.doc_idx = self._build_doc_idx()
-      self.sample_idx = self._build_sample_idx()
-      self.shuffle_idx = self._build_shuffle_idx()
-      # save cache
-      with open(cache_path, "wb") as f:
-        pickle.dump((self.doc_idx, self.sample_idx, self.shuffle_idx), f)
-
-  def __getitem__(self, idx):
-    if idx is None:
-      text = self._get(0)
-    else:
-      text = self._get(idx)
-
-    return text
-
-  def _get(self, idx):
-    idx = self.shuffle_idx[idx]
-
-    doc_idx_beg, doc_idx_beg_offset = self.sample_idx[idx]
-    doc_idx_end, doc_idx_end_offset = self.sample_idx[idx + 1]
-
-    doc_ids, sample_parts = [], []
-
-    if doc_idx_beg == doc_idx_end:
-      doc_ids.append(self.doc_idx[doc_idx_beg])
-
-      sample_parts.append(
-          self.indexed_dataset.get(
-            int(self.doc_idx[doc_idx_beg]), offset=int(doc_idx_beg_offset), length=int(doc_idx_end_offset - doc_idx_beg_offset + 1)))
-    else:
-      for i in range(doc_idx_beg, doc_idx_end + 1):
-        doc_ids.append(self.doc_idx[i])
-
-        offset = 0 if i > doc_idx_beg else doc_idx_beg_offset
-        length = None if i < doc_idx_end else int(doc_idx_end_offset + 1)
-        sample_parts.append(self.indexed_dataset.get(int(self.doc_idx[i]), offset=int(offset), length=length))
-
-    # concat all parts
-    text = np.concatenate(sample_parts, axis=0)
-
-    return text
-
-  @functools.cached_property
-  def tokens_per_epoch(self) -> int:
-    return sum(self.indexed_dataset.sizes.tolist())
-
-  @functools.cached_property
-  def num_epochs(self) -> int:
-    # we need enough epochs to cover the requested amount of tokens
-    num_epochs = 1
-    num_tokens = self.tokens_per_epoch
-    while num_tokens < self.samples * self.seqlen:
-      num_epochs += 1
-      num_tokens += self.tokens_per_epoch
-    return num_epochs
-
-  # https://github.com/NVIDIA/Megatron-LM/blob/94bd476bd840c2fd4c3ebfc7448c2af220f4832b/megatron/core/datasets/gpt_dataset.py#L558
-  def _build_doc_idx(self):
-    print(f"building doc_idx for {self.num_epochs=}, {self.indexed_dataset.count=}")
-    st = time.perf_counter()
-    # doc_idx = np.mgrid[:self.num_epochs, :self.indexed_dataset.count][1]
-    doc_idx = np.arange(self.indexed_dataset.count).reshape(1, -1).repeat(self.num_epochs, axis=0).flatten()
-    doc_idx = doc_idx.astype(np.int32)
-    at = time.perf_counter()
-    if self.shuffle: self.rng.shuffle(doc_idx)
-    print(f"doc_idx built in {at - st:.3f}s, shuffled in {time.perf_counter() - at:.3f}s")
-    return doc_idx
-
-  def _build_sample_idx(self):
-    print(f"building sample_idx for {self.samples=}, {self.seqlen=}, {self.doc_idx.shape[0]=}")
-    sample_idx_max = max(self.doc_idx.shape[0], self.indexed_dataset.sizes.max())
-    sample_idx = np.empty((self.samples + 1, 2), dtype=np.int64 if sample_idx_max > dtypes.int32.max else np.int32)
-
-    sample_idx_idx, doc_idx_idx, doc_offset = 0, 0, 0
-    sample_idx[sample_idx_idx, 0], sample_idx[sample_idx_idx, 1] = doc_idx_idx, doc_offset
-    sample_idx_idx += 1
-
-    for _ in tqdm(range(1, self.samples + 1)):
-      remaining_seqlen = self.seqlen + 1
-      while remaining_seqlen > 0:
-        doc_idx = int(self.doc_idx[doc_idx_idx])
-        doc_len = int(self.indexed_dataset.sizes[doc_idx]) - doc_offset
-        remaining_seqlen -= doc_len
-        if remaining_seqlen <= 0:
-          doc_offset += remaining_seqlen + doc_len - 1
-          remaining_seqlen = 0
-        else:
-          if doc_idx_idx == len(self.doc_idx) - 1:
-            assert sample_idx_idx == self.samples
-            doc_idx = int(self.doc_idx[doc_idx_idx])
-            doc_offset = int(self.indexed_dataset.sizes[doc_idx]) - 1
-            break
-          doc_idx_idx += 1
-          doc_offset = 0
-
-      sample_idx[sample_idx_idx, 0], sample_idx[sample_idx_idx, 1] = doc_idx_idx, doc_offset
-      sample_idx_idx += 1
-
-    return sample_idx
-
-  def _build_shuffle_idx(self):
-    print(f"building shuffle_idx for {self.samples=}")
-    st = time.perf_counter()
-    shuffle_idx = np.arange(self.samples, dtype=np.int32)
-    at = time.perf_counter()
-    if self.shuffle: self.rng.shuffle(shuffle_idx)
-    print(f"shuffle_idx built in {at - st:.3f}s, shuffled in {time.perf_counter() - at:.3f}s")
-    return shuffle_idx
-
-class BlendedGPTDataset:
-  def __init__(self, paths:list[Path], weights:list[float], samples:int, seqlen:int, seed:int, shuffle:bool):
-    self.shuffle = shuffle
-    self.rng = np.random.RandomState(seed)
-
-    # normalize weights
-    total_weight = sum(weights)
-    self.weights = [w / total_weight for w in weights]
-
-    self.samples = samples
-    surplus = 0.005
-    samples_per_blend = [math.ceil(math.ceil(self.samples * w) * (1 + surplus)) for w in self.weights]
-
-    self.datasets = [GPTDataset(path, samples_per_blend[i], seqlen, seed + i, shuffle) for i,path in enumerate(paths)]
-
-    # check for cache
-    cache_hash = hashlib.sha256(f"{samples}:{seqlen}:{seed}:{shuffle}".encode()).hexdigest()
-    cache_path = paths[0].with_name(f"{paths[0].name}.{cache_hash}.blend_cache")
-    print(f"try loading BlendedGPTDataset from {cache_path}...")
-    if cache_path.exists():
-      print("cache found, loading...")
-      with open(cache_path, "rb") as f:
-        self.dataset_idx, self.dataset_sample_idx = pickle.load(f)
-    else:
-      print("cache not found, building index...")
-      self.dataset_idx, self.dataset_sample_idx = self._build_blend_idx()
-      # save cache
-      with open(cache_path, "wb") as f:
-        pickle.dump((self.dataset_idx, self.dataset_sample_idx), f)
-
-  def get(self, idx:int):
-    tokens = self.datasets[self.dataset_idx[idx]][self.dataset_sample_idx[idx]]
-    return tokens
-
-  def _build_blend_idx(self):
-    dataset_idx = np.zeros(self.samples, dtype=np.int16)
-    dataset_sample_idx = np.zeros(self.samples, dtype=np.int64)
-
-    unspent_datasets = set(range(len(self.datasets)))
-    dataset_sample_counts = [0] * len(self.datasets)
-
-    for i in tqdm(range(self.samples)):
-      error_argmax, error_max = 0, 0.0
-      for di in unspent_datasets:
-        error = self.weights[di] * max(i, 1) - dataset_sample_counts[di]
-        if error > error_max:
-          error_max = error
-          error_argmax = di
-
-      dataset_idx[i] = error_argmax
-      dataset_sample_idx[i] = dataset_sample_counts[error_argmax]
-
-      dataset_sample_counts[error_argmax] += 1
-
-    return dataset_idx, dataset_sample_idx
-
-def get_llama3_dataset(samples:int, seqlen:int, base_dir:Path, seed:int=0, val:bool=True, small:bool=False) -> BlendedGPTDataset:
-  if small:
-    if val:
-      return BlendedGPTDataset(
-        [base_dir / "c4-validation-91205-samples.en_text_document"], [1.0], samples, seqlen, seed, shuffle=False)
-    return BlendedGPTDataset(
-      [base_dir / "c4-train.en_6_text_document"], [1.0], samples, seqlen, seed, shuffle=True)
-  if val:
-    return BlendedGPTDataset(
-      [base_dir / "validation" / "c4-validationn-91205-samples.en_text_document"], [1.0], samples, seqlen, seed, shuffle=False)
-  return BlendedGPTDataset(
-    [base_dir / "c4-train.en_6_text_document", base_dir / "c4-train.en_7_text_document"], [1.0, 1.0], samples, seqlen, seed, shuffle=True)
-
-def iterate_llama3_dataset(dataset:BlendedGPTDataset, bs:int):
-  for b in range(math.ceil(dataset.samples / bs)):
-    batch = [dataset.get(b * bs + i) for i in range(bs)]
-    stacked = np.stack(batch, axis=0)
-    yield Tensor(stacked, device="NPY")
-
-def batch_load_llama3(bs:int, samples:int, seqlen:int, base_dir:Path, seed:int=0, val:bool=True, small:bool=False):
-  return iterate_llama3_dataset(get_llama3_dataset(samples, seqlen, base_dir, seed, val, small), bs)
-
 if __name__ == "__main__":
   def load_unet3d(val):
     assert not val, "validation set is not supported due to different sizes on inputs"
@@ -799,23 +532,11 @@ if __name__ == "__main__":
   def load_retinanet(val):
     from extra.datasets.openimages import BASEDIR, download_dataset
     from pycocotools.coco import COCO
-    dataset = COCO(download_dataset(base_dir:=getenv("BASEDIR", BASEDIR), "validation" if val else "train"))
+    dataset = COCO(download_dataset(base_dir:=getenv("BASE_DIR", BASEDIR), "validation" if val else "train"))
     with tqdm(total=len(dataset.imgs.keys())) as pbar:
       for x in batch_load_retinanet(dataset, val, base_dir):
         pbar.update(x[0].shape[0])
 
-  def load_llama3(val):
-    bs = 24
-    samples = 5760 if val else 1_200_000 * 1152
-    seqlen = 8192
-
-    max_, min_ = 0, math.inf
-    for tokens in tqdm(batch_load_llama3(bs, samples, seqlen, Path(getenv("BASEDIR", "/raid/datasets/c4/")), seed=5760, val=bool(val)), total=samples//bs):
-      max_ = max(max_, tokens.shape[1])
-      min_ = min(min_, tokens.shape[1])
-    print(f"max seq length: {max_}")
-    print(f"min seq length: {min_}")
-
   load_fn_name = f"load_{getenv('MODEL', 'resnet')}"
   if load_fn_name in globals():
     globals()[load_fn_name](getenv("VAL", 1))

examples/mlperf/helpers.py

@@ -212,35 +212,24 @@ def get_mlperf_bert_model():
   from examples.mlperf.initializers import LinearBert, EmbeddingBert, LayerNormBert
 
   bert.Linear = LinearBert
-  bert.Embedding = EmbeddingBert
+  bert.Embedding = EmbeddingBert 
   bert.LayerNorm = LayerNormBert
 
   from extra.models.bert import BertForPretraining
   config = get_mlperf_bert_config()
   if getenv("DISABLE_DROPOUT", 0):
     config["hidden_dropout_prob"] = config["attention_probs_dropout_prob"] = 0.0
-  model = BertForPretraining(**config)
-  if getenv("FP8_TRAIN"):
-    from extra.fp8.fp8_linear import convert_to_float8_training
-    def module_filter_fn(mod, fqn):
-      if isinstance(mod, LinearBert):
-        skip_layers = [] if (ln:=config["num_hidden_layers"]) <= 2 else ["bert.encoder.layer.0.", f"bert.encoder.layer.{ln-1}"]
-        if mod.weight.shape[-1] >= 1024 and "encoder" in fqn and not any(name in fqn for name in skip_layers):
-          print(f"replacing linear with fp8: {fqn} {mod.weight.shape}")
-          return True
-      return False
-    convert_to_float8_training(model, module_filter_fn)
-  return model
+  return BertForPretraining(**config)
 
 def get_fake_data_bert(BS:int):
   return {
-    "input_ids": Tensor.zeros((BS, 512), dtype=dtypes.int32, device="CPU").contiguous(),
-    "input_mask": Tensor.zeros((BS, 512), dtype=dtypes.int32, device="CPU").contiguous(),
-    "segment_ids": Tensor.zeros((BS, 512), dtype=dtypes.int32, device="CPU").contiguous(),
-    "masked_lm_positions": Tensor.zeros((BS, 76), dtype=dtypes.int32, device="CPU").contiguous(),
-    "masked_lm_ids": Tensor.zeros((BS, 76), dtype=dtypes.int32, device="CPU").contiguous(),
-    "masked_lm_weights": Tensor.zeros((BS, 76), dtype=dtypes.float32, device="CPU").contiguous(),
-    "next_sentence_labels": Tensor.zeros((BS, 1), dtype=dtypes.int32, device="CPU").contiguous(),
+    "input_ids": Tensor.empty((BS, 512), dtype=dtypes.int32, device="CPU"),
+    "input_mask": Tensor.empty((BS, 512), dtype=dtypes.int32, device="CPU"),
+    "segment_ids": Tensor.empty((BS, 512), dtype=dtypes.int32, device="CPU"),
+    "masked_lm_positions": Tensor.empty((BS, 76), dtype=dtypes.int32, device="CPU"),
+    "masked_lm_ids": Tensor.empty((BS, 76), dtype=dtypes.int32, device="CPU"),
+    "masked_lm_weights": Tensor.empty((BS, 76), dtype=dtypes.float32, device="CPU"),
+    "next_sentence_labels": Tensor.empty((BS, 1), dtype=dtypes.int32, device="CPU"),
   }
 
 def find_matches(match_quality_matrix:np.ndarray, high_threshold:float=0.5, low_threshold:float=0.4, allow_low_quality_matches:bool=False) -> np.ndarray:
@@ -306,62 +295,3 @@ def generate_anchors(input_size:tuple[int, int], scales:Optional[tuple[Tensor, .
     anchors.append((shifts[:, None] + base_anchors[None, :]).reshape(-1, 4))
 
   return anchors
-
-
-class BoxCoder(object):
-  def __init__(self, weights, bbox_xform_clip=math.log(1000. / 16), apply_to_remove=True):
-    self.weights = weights
-    self.bbox_xform_clip = bbox_xform_clip
-    self.apply_to_remove = apply_to_remove
-
-  def encode(self, reference_boxes, proposals):
-    TO_REMOVE = self.apply_to_remove  # TODO remove
-    ex_widths = proposals[..., 2] - proposals[..., 0] + TO_REMOVE
-    ex_heights = proposals[..., 3] - proposals[..., 1] + TO_REMOVE
-    ex_ctr_x = proposals[..., 0] + 0.5 * ex_widths
-    ex_ctr_y = proposals[..., 1] + 0.5 * ex_heights
-
-    gt_widths = reference_boxes[..., 2] - reference_boxes[..., 0] + TO_REMOVE
-    gt_heights = reference_boxes[..., 3] - reference_boxes[..., 1] + TO_REMOVE
-    gt_ctr_x = reference_boxes[..., 0] + 0.5 * gt_widths
-    gt_ctr_y = reference_boxes[..., 1] + 0.5 * gt_heights
-
-    wx, wy, ww, wh = self.weights
-    targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths
-    targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights
-    targets_dw = ww * Tensor.log(gt_widths / ex_widths)
-    targets_dh = wh * Tensor.log(gt_heights / ex_heights)
-
-    targets = Tensor.stack(targets_dx, targets_dy, targets_dw, targets_dh, dim=-1)
-    return targets
-
-  def decode(self, rel_codes, boxes):
-    boxes = boxes.cast(rel_codes.dtype)
-    rel_codes = rel_codes
-
-    TO_REMOVE = self.apply_to_remove  # TODO remove
-    widths = boxes[:, 2] - boxes[:, 0] + TO_REMOVE
-    heights = boxes[:, 3] - boxes[:, 1] + TO_REMOVE
-    ctr_x = boxes[:, 0] + 0.5 * widths
-    ctr_y = boxes[:, 1] + 0.5 * heights
-
-    wx, wy, ww, wh = self.weights
-    dx = rel_codes[:, 0::4] / wx
-    dy = rel_codes[:, 1::4] / wy
-    dw = rel_codes[:, 2::4] / ww
-    dh = rel_codes[:, 3::4] / wh
-
-    # Prevent sending too large values into Tensor.exp()
-    dw = dw.clip(min_=dw.min(), max_=self.bbox_xform_clip)
-    dh = dh.clip(min_=dh.min(), max_=self.bbox_xform_clip)
-
-    pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
-    pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
-    pred_w = dw.exp() * widths[:, None]
-    pred_h = dh.exp() * heights[:, None]
-    x = pred_ctr_x - 0.5 * pred_w
-    y = pred_ctr_y - 0.5 * pred_h
-    w = pred_ctr_x + 0.5 * pred_w - 1
-    h = pred_ctr_y + 0.5 * pred_h - 1
-    pred_boxes = Tensor.stack(x, y, w, h).permute(1,2,0).reshape(rel_codes.shape[0], rel_codes.shape[1])
-    return pred_boxes

examples/mlperf/initializers.py

@@ -2,9 +2,7 @@ import math
 from typing import Union
 
 from tinygrad import Tensor, nn, dtypes
-from tinygrad.helpers import prod, argfix, Context
-from tinygrad.nn.state import get_parameters
-from extra.models.unet import UNetModel
+from tinygrad.helpers import prod, argfix
 
 # rejection sampling truncated randn
 def rand_truncn(*shape, dtype=None, truncstds=2, **kwargs) -> Tensor:
@@ -19,10 +17,6 @@ def he_normal(*shape, a: float = 0.00, **kwargs) -> Tensor:
   std = math.sqrt(2.0 / (1 + a ** 2)) / math.sqrt(prod(argfix(*shape)[1:])) / 0.87962566103423978
   return std * rand_truncn(*shape, **kwargs)
 
-# Stable Diffusion v2 training uses default torch gelu, which doesn't use tanh approximation
-def gelu_erf(x:Tensor) -> Tensor:
-  return 0.5 * x * (1.0 + (x / 1.4142135623730951).erf())
-
 class Conv2dHeNormal(nn.Conv2d):
   def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
     super().__init__(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
@@ -45,7 +39,7 @@ class LinearBert(nn.Linear):
   def __init__(self, in_features, out_features, bias=True, std=0.02):
     self.weight = std * rand_truncn(out_features, in_features, dtype=dtypes.float32)
     self.bias = Tensor.zeros(out_features, dtype=dtypes.float32) if bias else None
-
+  
   def __call__(self, x:Tensor):
     return x.cast(dtypes.default_float).linear(self.weight.cast(dtypes.default_float).transpose(), self.bias.cast(dtypes.default_float) if self.bias is not None else None)
 
@@ -59,7 +53,7 @@ class EmbeddingBert(nn.Embedding):
     arange_shp, weight_shp, big_shp = (1, 1, self.vocab_sz, 1), (1, 1, self.vocab_sz, self.embed_sz), idx.shape+(self.vocab_sz, self.embed_sz,)
     if not hasattr(self, 'arange'): self.arange = Tensor.arange(self.vocab_sz, requires_grad=False, device=self.weight.device).reshape(arange_shp)
     arange, idx, vals = self.arange.expand(big_shp), idx.reshape(idx.shape+(1, 1,)).expand(big_shp), self.weight.cast(dtypes.default_float).reshape(weight_shp).expand(big_shp)
-    return (arange == idx).where(vals, 0).sum(2, dtype=vals.dtype)
+    return (arange == idx).mul(vals).sum(2, dtype=vals.dtype)
 
 class LayerNormBert:
   def __init__(self, normalized_shape:Union[int, tuple[int, ...]], eps:float=1e-12, elementwise_affine:bool=True):
@@ -72,118 +66,3 @@ class LayerNormBert:
     xn = x.cast(dtypes.float32).layernorm(eps=self.eps, axis=self.axis).cast(x.dtype)
     if not self.elementwise_affine: return xn
     return (xn * self.weight.cast(dtypes.default_float) + self.bias.cast(dtypes.default_float))
-
-class FrozenBatchNorm2dRetinaNet(nn.BatchNorm2d):
-  def __init__(self, sz:int, eps=1e-5, affine=True, track_running_stats=True, momentum=0.1):
-    self.eps, self.track_running_stats, self.momentum = eps, track_running_stats, momentum
-
-    self.weight = Tensor.ones(sz, dtype=dtypes.float32, requires_grad=False) if affine else None
-    self.bias = Tensor.zeros(sz, dtype=dtypes.float32, requires_grad=False) if affine else None
-
-    if track_running_stats: self.running_mean, self.running_var = Tensor.zeros(sz, dtype=dtypes.float32, requires_grad=False), Tensor.ones(sz, dtype=dtypes.float32, requires_grad=False)
-    self.num_batches_tracked = Tensor.zeros(1, dtype=dtypes.long, requires_grad=False)
-
-  def __call__(self, x:Tensor) -> Tensor:
-    batch_mean, batch_var = super().calc_stats(x.cast(dtypes.float32))
-    if self.track_running_stats and Tensor.training:
-      self.running_mean.assign((1-self.momentum) * self.running_mean + self.momentum * batch_mean.detach().cast(self.running_mean.dtype))
-      self.running_var.assign((1-self.momentum) * self.running_var + self.momentum * x.numel()/(x.numel()-x.shape[1]) * batch_var.detach().cast(self.running_var.dtype))
-      self.num_batches_tracked += 1
-    return x.cast(dtypes.float32).batchnorm(self.weight, self.bias, batch_mean, batch_var.add(self.eps).rsqrt()).cast(x.dtype)
-
-class Conv2dNormalRetinaNet(nn.Conv2d):
-  def __init__(self, in_channels:int, out_channels:int, kernel_size:int|tuple[int, ...],
-               stride:int=1, padding:int|tuple[int, ...]|str=0, dilation:int=1, groups:int=1,
-               bias:bool=True, prior_prob:float|None=None):
-    super().__init__(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
-    self.weight = Tensor.normal(*self.weight.shape, std=0.01, dtype=dtypes.float32)
-    if bias:
-      if prior_prob:
-        prior_prob = Tensor(prior_prob, device=self.bias.device, dtype=dtypes.float32).expand(*self.bias.shape)
-        self.bias = -(((1 - prior_prob) / prior_prob).log())
-      else: self.bias = Tensor.zeros_like(self.bias, dtype=dtypes.float32)
-
-  def __call__(self, x:Tensor) -> Tensor:
-    return x.conv2d(self.weight.cast(dtypes.default_float), self.bias.cast(dtypes.default_float) if self.bias is not None else None,
-                    groups=self.groups, stride=self.stride, padding=self.padding)
-
-class Conv2dKaimingUniformRetinaNet(nn.Conv2d):
-  def __init__(self, in_channels:int, out_channels:int, kernel_size:int|tuple[int, ...],
-               stride:int=1, padding:int|tuple[int, ...]|str=0, dilation:int=1, groups:int=1,
-               bias:bool=True):
-    super().__init__(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
-    self.weight = Tensor.kaiming_uniform(*self.weight.shape, a=1, dtype=dtypes.float32)
-    if bias: self.bias = Tensor.zeros_like(self.bias, dtype=dtypes.float32)
-
-  def __call__(self, x:Tensor) -> Tensor:
-    return x.conv2d(self.weight.cast(dtypes.default_float), self.bias.cast(dtypes.default_float) if self.bias is not None else None,
-                    groups=self.groups, stride=self.stride, padding=self.padding)
-
-class Conv2dRetinaNet(nn.Conv2d):
-  def __init__(self, in_channels:int, out_channels:int, kernel_size:int|tuple[int, ...],
-               stride:int=1, padding:int|tuple[int, ...]|str=0, dilation:int=1, groups:int=1,
-               bias:bool=True):
-    super().__init__(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
-    scale = 1 / math.sqrt(in_channels * prod(self.kernel_size))
-    self.weight = Tensor.uniform(out_channels, in_channels//groups, *self.kernel_size, low=-scale, high=scale, dtype=dtypes.float32)
-    self.bias: Tensor|None = Tensor.uniform(out_channels, low=-scale, high=scale, dtype=dtypes.float32) if bias else None
-
-  def __call__(self, x:Tensor) -> Tensor:
-    return x.conv2d(self.weight.cast(dtypes.default_float), self.bias.cast(dtypes.default_float) if self.bias is not None else None,
-                    groups=self.groups, stride=self.stride, dilation=self.dilation, padding=self.padding)
-
-# copy torch AMP: isolate mixed precision to just the below autocast ops, instead of using dtypes.default_float which affects all new Tensors
-class AutocastLinear(nn.Linear):
-  cast_dtype=dtypes.bfloat16 # enable monkeypatching of the mixed precision dtype
-  def __call__(self, x:Tensor) -> Tensor:
-    dtype = type(self).cast_dtype
-    return x.cast(dtype).linear(self.weight.cast(dtype).transpose(), self.bias.cast(dtype) if self.bias is not None else None)
-
-class AutocastConv2d(nn.Conv2d):
-  cast_dtype=dtypes.bfloat16
-  def __call__(self, x:Tensor) -> Tensor:
-    dtype = type(self).cast_dtype
-    return x.cast(dtype).conv2d(self.weight.cast(dtype), self.bias.cast(dtype), self.groups, self.stride, self.dilation, self.padding)
-
-# copy torch AMP: upcast to float32 before GroupNorm and LayerNorm
-class AutocastGroupNorm(nn.GroupNorm):
-  def __call__(self, x:Tensor) -> Tensor:
-    return super().__call__(x.cast(dtypes.float32))
-
-class AutocastLayerNorm(nn.LayerNorm):
-  def __call__(self, x:Tensor) -> Tensor:
-    return super().__call__(x.cast(dtypes.float32))
-
-def zero_module(module):
-  for p in get_parameters(module): p.assign(Tensor.zeros_like(p).contiguous())
-
-# Stable Diffusion mlperf reference doesn't call scaled_dot_product_attention
-# copy torch AMP: upcast to float32 before softmax on CUDA
-def attn_f32_softmax(q:Tensor, k:Tensor, v:Tensor) -> Tensor:
-  return (q.matmul(k.transpose(-2,-1), dtype=dtypes.float32) / math.sqrt(q.shape[-1])).softmax(-1).cast(q.dtype) @ v
-
-def init_stable_diffusion(version:str, pretrained:str, devices:list[str]):
-  from examples.stable_diffusion import StableDiffusion
-  from tinygrad.nn.state import safe_load, safe_save, load_state_dict, get_state_dict
-  from tempfile import TemporaryDirectory
-  model = StableDiffusion(version=version, pretrained=pretrained)
-  unet:UNetModel = model.model.diffusion_model
-
-  # this prevents extra consumption of memory, enabling much larger BS
-  Tensor.realize(*get_parameters(unet))
-  with TemporaryDirectory(prefix="unet_init") as tmp:
-    safe_save(get_state_dict(unet), init_fn:=f"{tmp}/init_model.safetensors")
-    load_state_dict(unet, safe_load(init_fn))
-
-  sqrt_alphas_cumprod = model.alphas_cumprod.sqrt().realize()
-  sqrt_one_minus_alphas_cumprod = (1 - model.alphas_cumprod).sqrt().realize()
-
-  if len(devices) > 1:
-    to_move = [sqrt_alphas_cumprod, sqrt_one_minus_alphas_cumprod]
-    if version == "v2-mlperf-train": to_move += get_parameters(unet) + get_parameters(model.cond_stage_model)
-    for p in to_move:
-      p.to_(devices)
-    with Context(BEAM=0):
-      Tensor.realize(*to_move)
-
-  return model, unet, sqrt_alphas_cumprod, sqrt_one_minus_alphas_cumprod

examples/mlperf/lr_schedulers.py

@@ -1,9 +1,7 @@
-import math
-from tinygrad import dtypes, Tensor
+from tinygrad import Tensor, dtypes
 from tinygrad.nn.optim import Optimizer
 
 from extra.lr_scheduler import LR_Scheduler
-from typing import Callable
 
 # https://github.com/mlcommons/training/blob/e237206991d10449d9675d95606459a3cb6c21ad/image_classification/tensorflow2/lars_util.py
 class PolynomialDecayWithWarmup(LR_Scheduler):
@@ -22,39 +20,3 @@ class PolynomialDecayWithWarmup(LR_Scheduler):
     warmup_lr = (self.epoch_counter * (1.0 / self.warmup)) * self.initial_lr
     x = (1 - (self.epoch_counter - self.warmup) / (self.epochs - self.warmup + 1))
     return (self.epoch_counter <= self.warmup).where(warmup_lr, (self.initial_lr - self.end_lr) * x ** self.power + self.end_lr).cast(self.optimizer.lr.dtype)
-
-class CosineAnnealingLRWithWarmup(LR_Scheduler):
-  def __init__(self, optimizer:Optimizer, base_lr, end_lr, warmup_steps:int, decay_steps:int):
-    assert warmup_steps > 0 and decay_steps > 0
-    super().__init__(optimizer)
-    self.base_lr = base_lr
-    self.end_lr = end_lr
-    self.warmup_steps = warmup_steps
-    self.decay_steps = decay_steps
-    # set lr for first warmup step
-    self.optimizer.lr.assign(self.get_lr()).realize()
-
-  def get_lr(self):
-    warmup_lr = ((self.epoch_counter+1) / self.warmup_steps) * self.base_lr
-    decay_lr = self.end_lr + 0.5 * (self.base_lr-self.end_lr) * (1 + (((self.epoch_counter+1-self.warmup_steps)/self.decay_steps) * math.pi).cos())
-    return (self.epoch_counter < self.warmup_steps).where(warmup_lr, decay_lr).cast(self.optimizer.lr.dtype)
-
-# Reference: https://github.com/mlcommons/training/blob/64b14a9abc74e08779a175abca7d291f8c957632/stable_diffusion/ldm/lr_scheduler.py, Lines 36-97
-class LambdaLinearScheduler:
-  def __init__(self, warm_up_steps:int, f_min:float, f_max:float, f_start:float, cycle_lengths:int):
-    self.lr_warm_up_steps, self.f_min, self.f_max, self.f_start, self.cycle_lengths = warm_up_steps, f_min, f_max, f_start, cycle_lengths
-
-  def schedule(self, n:Tensor) -> Tensor:
-    warm_up = (n < self.lr_warm_up_steps)
-    f_warm_up = (self.f_max - self.f_start) / self.lr_warm_up_steps * n + self.f_start
-    return warm_up.where(f_warm_up, self.f_min + (self.f_max - self.f_min) * (self.cycle_lengths - n) / (self.cycle_lengths))
-
-# based on torch.optim.lr_scheduler.LambdaLR
-class LambdaLR(LR_Scheduler):
-  def __init__(self, optimizer:Optimizer, base_lr:Tensor, lr_lambda:Callable):
-    super().__init__(optimizer)
-    self.base_lr, self.lr_lambda = base_lr, lr_lambda
-    self.step()
-
-  def get_lr(self):
-    return self.base_lr * self.lr_lambda(self.epoch_counter - 1)

examples/mlperf/metrics.py

@@ -1,6 +1,6 @@
-import re, string
+import re
+import string
 from collections import Counter
-from tinygrad import Tensor
 
 def levenshtein(a, b):
   n, m = len(a), len(b)
@@ -59,11 +59,3 @@ def f1_score(x, y):
   p = ns / len(xt)
   r = ns / len(yt)
   return 2 * p * r / (p + r)
-
-def log_perplexity(logit:Tensor, target:Tensor, ignore_index:int|None=None):
-  # logit has shape (n_samples, seq_len, vocab_size), target has shape (n_samples, seq_len)
-  assert logit.ndim == 3, logit.ndim
-  assert target.ndim == 2, target.ndim
-  assert logit.shape[:2] == target.shape, f"{logit.shape[:2]=}, {target.shape=}"
-  log_prob = logit.log_softmax(axis=-1)
-  return log_prob.transpose(1, 2).nll_loss(target, ignore_index=ignore_index)

examples/mlperf/model_eval.py

@@ -1,66 +1,65 @@
-import time, math, os
+import time
 start = time.perf_counter()
 from pathlib import Path
 import numpy as np
 from tinygrad import Tensor, Device, dtypes, GlobalCounters, TinyJit
 from tinygrad.nn.state import get_parameters, load_state_dict, safe_load
-from tinygrad.helpers import getenv, Context, prod
-from extra.bench_log import BenchEvent, WallTimeEvent
+from tinygrad.helpers import getenv
 def tlog(x): print(f"{x:25s}  @ {time.perf_counter()-start:5.2f}s")
 
 def eval_resnet():
-  with WallTimeEvent(BenchEvent.FULL):
-    # Resnet50-v1.5
-    from extra.models.resnet import ResNet50
-    tlog("imports")
-    GPUS = [f'{Device.DEFAULT}:{i}' for i in range(getenv("GPUS", 6))]
-    for x in GPUS: Device[x]
-    tlog("got devices")    # NOTE: this is faster with rocm-smi running
+  Tensor.no_grad = True
+  # Resnet50-v1.5
+  from extra.models.resnet import ResNet50
+  tlog("imports")
+  GPUS = [f'{Device.DEFAULT}:{i}' for i in range(getenv("GPUS", 6))]
+  for x in GPUS: Device[x]
+  tlog("got devices")    # NOTE: this is faster with rocm-smi running
 
-    class ResnetRunner:
-      def __init__(self, device=None):
-        self.mdl = ResNet50()
-        for x in get_parameters(self.mdl) if device else []: x.to_(device)
-        if (fn:=getenv("RESNET_MODEL", "")): load_state_dict(self.mdl, safe_load(fn))
-        else: self.mdl.load_from_pretrained()
-        self.input_mean = Tensor([0.485, 0.456, 0.406], device=device).reshape(1, -1, 1, 1)
-        self.input_std = Tensor([0.229, 0.224, 0.225], device=device).reshape(1, -1, 1, 1)
-      def __call__(self, x:Tensor) -> Tensor:
-        x = x.permute([0,3,1,2]).cast(dtypes.float32) / 255.0
-        x -= self.input_mean
-        x /= self.input_std
-        return self.mdl(x).log_softmax().argmax(axis=1).realize()
+  class ResnetRunner:
+    def __init__(self, device=None):
+      self.mdl = ResNet50()
+      for x in get_parameters(self.mdl) if device else []: x.to_(device)
+      if (fn:=getenv("RESNET_MODEL", "")): load_state_dict(self.mdl, safe_load(fn))
+      else: self.mdl.load_from_pretrained()
+      self.input_mean = Tensor([0.485, 0.456, 0.406], device=device).reshape(1, -1, 1, 1)
+      self.input_std = Tensor([0.229, 0.224, 0.225], device=device).reshape(1, -1, 1, 1)
+    def __call__(self, x:Tensor) -> Tensor:
+      x = x.permute([0,3,1,2]).cast(dtypes.float32) / 255.0
+      x -= self.input_mean
+      x /= self.input_std
+      return self.mdl(x).log_softmax().argmax(axis=1).realize()
 
-    mdl = TinyJit(ResnetRunner(GPUS))
-    tlog("loaded models")
+  mdl = TinyJit(ResnetRunner(GPUS))
+  tlog("loaded models")
 
-    # evaluation on the mlperf classes of the validation set from imagenet
-    from examples.mlperf.dataloader import batch_load_resnet
-    iterator = batch_load_resnet(getenv("BS", 128*6), val=getenv("VAL", 1), shuffle=False, pad_first_batch=True)
-    def data_get():
-      x,y,cookie = next(iterator)
-      return x.shard(GPUS, axis=0).realize(), y, cookie
-    n,d = 0,0
-    proc = data_get()
-    tlog("loaded initial data")
-    st = time.perf_counter()
-    while proc is not None:
-      GlobalCounters.reset()
-      proc = (mdl(proc[0]), proc[1], proc[2])  # this frees the images
-      run = time.perf_counter()
-      # load the next data here
-      try: next_proc = data_get()
-      except StopIteration: next_proc = None
-      nd = time.perf_counter()
-      y = np.array(proc[1])
-      proc = (proc[0].numpy() == y) & (y != -1)  # this realizes the models and frees the cookies
-      n += proc.sum()
-      d += (y != -1).sum()
-      et = time.perf_counter()
-      tlog(f"****** {n:5d}/{d:5d}  {n*100.0/d:.2f}% -- {(run-st)*1000:7.2f} ms to enqueue, {(et-run)*1000:7.2f} ms to realize ({(nd-run)*1000:7.2f} ms fetching). {(len(proc))/(et-st):8.2f} examples/sec. {GlobalCounters.global_ops*1e-12/(et-st):5.2f} TFLOPS")
-      st = et
-      proc, next_proc = next_proc, None
-    tlog("done")
+  # evaluation on the mlperf classes of the validation set from imagenet
+  from examples.mlperf.dataloader import batch_load_resnet
+  iterator = batch_load_resnet(getenv("BS", 128*6), val=getenv("VAL", 1), shuffle=False, pad_first_batch=True)
+  def data_get():
+    x,y,cookie = next(iterator)
+    return x.shard(GPUS, axis=0).realize(), y, cookie
+  n,d = 0,0
+  proc = data_get()
+  tlog("loaded initial data")
+  st = time.perf_counter()
+  while proc is not None:
+    GlobalCounters.reset()
+    proc = (mdl(proc[0]), proc[1], proc[2])  # this frees the images
+    run = time.perf_counter()
+    # load the next data here
+    try: next_proc = data_get()
+    except StopIteration: next_proc = None
+    nd = time.perf_counter()
+    y = np.array(proc[1])
+    proc = (proc[0].numpy() == y) & (y != -1)  # this realizes the models and frees the cookies
+    n += proc.sum()
+    d += (y != -1).sum()
+    et = time.perf_counter()
+    tlog(f"****** {n:5d}/{d:5d}  {n*100.0/d:.2f}% -- {(run-st)*1000:7.2f} ms to enqueue, {(et-run)*1000:7.2f} ms to realize ({(nd-run)*1000:7.2f} ms fetching). {(len(proc))/(et-st):8.2f} examples/sec. {GlobalCounters.global_ops*1e-12/(et-st):5.2f} TFLOPS")
+    st = et
+    proc, next_proc = next_proc, None
+  tlog("done")
 
 def eval_unet3d():
   # UNet3D
@@ -82,43 +81,47 @@ def eval_unet3d():
 
 def eval_retinanet():
   # RetinaNet with ResNeXt50_32X4D
-  from examples.mlperf.dataloader import batch_load_retinanet
-  from extra.datasets.openimages import normalize, download_dataset, BASEDIR
   from extra.models.resnet import ResNeXt50_32X4D
   from extra.models.retinanet import RetinaNet
+  mdl = RetinaNet(ResNeXt50_32X4D())
+  mdl.load_from_pretrained()
+
+  input_mean = Tensor([0.485, 0.456, 0.406]).reshape(1, -1, 1, 1)
+  input_std = Tensor([0.229, 0.224, 0.225]).reshape(1, -1, 1, 1)
+  def input_fixup(x):
+    x = x.permute([0,3,1,2]) / 255.0
+    x -= input_mean
+    x /= input_std
+    return x
+
+  from extra.datasets.openimages import download_dataset, iterate, BASEDIR
   from pycocotools.coco import COCO
   from pycocotools.cocoeval import COCOeval
   from contextlib import redirect_stdout
-  tlog("imports")
-
-  mdl = RetinaNet(ResNeXt50_32X4D())
-  mdl.load_from_pretrained()
-  tlog("loaded models")
-
-  coco = COCO(download_dataset(base_dir:=getenv("BASEDIR", BASEDIR), 'validation'))
+  coco = COCO(download_dataset(base_dir:=getenv("BASE_DIR", BASEDIR), 'validation'))
   coco_eval = COCOeval(coco, iouType="bbox")
   coco_evalimgs, evaluated_imgs, ncats, narea = [], [], len(coco_eval.params.catIds), len(coco_eval.params.areaRng)
-  tlog("loaded dataset")
 
-  iterator = batch_load_retinanet(coco, True, Path(base_dir), getenv("BS", 8), shuffle=False)
-  def data_get():
-    x, img_ids, img_sizes, cookie = next(iterator)
-    return x.to(Device.DEFAULT).realize(), img_ids, img_sizes, cookie
-  n = 0
-  proc = data_get()
-  tlog("loaded initial data")
+  from tinygrad.engine.jit import TinyJit
+  mdlrun = TinyJit(lambda x: mdl(input_fixup(x)).realize())
+
+  n, bs = 0, 8
   st = time.perf_counter()
-  while proc is not None:
-    GlobalCounters.reset()
-    proc = (mdl(normalize(proc[0])), proc[1], proc[2], proc[3])
-    run = time.perf_counter()
-    # load the next data here
-    try: next_proc = data_get()
-    except StopIteration: next_proc = None
-    nd = time.perf_counter()
-    predictions, img_ids = mdl.postprocess_detections(proc[0].numpy(), orig_image_sizes=proc[2]), proc[1]
-    pd = time.perf_counter()
-    coco_results  = [{"image_id": img_ids[i], "category_id": label, "bbox": box.tolist(), "score": score}
+  for x, targets in iterate(coco, base_dir, bs):
+    dat = Tensor(x.astype(np.float32))
+    mt = time.perf_counter()
+    if dat.shape[0] == bs:
+      outs = mdlrun(dat).numpy()
+    else:
+      mdlrun._jit_cache = []
+      outs =  mdl(input_fixup(dat)).numpy()
+    et = time.perf_counter()
+    predictions = mdl.postprocess_detections(outs, input_size=dat.shape[1:3], orig_image_sizes=[t["image_size"] for t in targets])
+    ext = time.perf_counter()
+    n += len(targets)
+    print(f"[{n}/{len(coco.imgs)}] == {(mt-st)*1000:.2f} ms loading data, {(et-mt)*1000:.2f} ms to run model, {(ext-et)*1000:.2f} ms for postprocessing")
+    img_ids = [t["image_id"] for t in targets]
+    coco_results  = [{"image_id": targets[i]["image_id"], "category_id": label, "bbox": box.tolist(), "score": score}
       for i, prediction in enumerate(predictions) for box, score, label in zip(*prediction.values())]
     with redirect_stdout(None):
       coco_eval.cocoDt = coco.loadRes(coco_results)
@@ -126,18 +129,13 @@ def eval_retinanet():
       coco_eval.evaluate()
     evaluated_imgs.extend(img_ids)
     coco_evalimgs.append(np.array(coco_eval.evalImgs).reshape(ncats, narea, len(img_ids)))
-    n += len(proc[0])
-    et = time.perf_counter()
-    tlog(f"****** {(run-st)*1000:7.2f} ms to enqueue, {(et-run)*1000:7.2f} ms to realize ({(nd-run)*1000:7.2f} ms fetching, {(pd-run)*1000:4.2f} ms postprocess_detections). {(len(proc))/(et-st):8.2f} examples/sec. {GlobalCounters.global_ops*1e-12/(et-st):5.2f} TFLOPS")
-    st = et
-    proc, next_proc = next_proc, None
+    st = time.perf_counter()
 
   coco_eval.params.imgIds = evaluated_imgs
   coco_eval._paramsEval.imgIds = evaluated_imgs
   coco_eval.evalImgs = list(np.concatenate(coco_evalimgs, -1).flatten())
   coco_eval.accumulate()
   coco_eval.summarize()
-  tlog("done")
 
 def eval_rnnt():
   # RNN-T
@@ -204,304 +202,49 @@ def eval_bert():
 
     st = time.perf_counter()
 
-def eval_llama3():
-  from extra.models.llama import Transformer
-  from examples.llama3 import MODEL_PARAMS, load, convert_from_huggingface
-  from tinygrad.helpers import tqdm
-
-  BASEDIR = Path(getenv("BASEDIR", "/raid/datasets/c4/"))
-  BS = getenv("BS", 4)
-  SMALL = getenv("SMALL", 0)
-  SEQLEN = getenv("SEQLEN", 8192)
-  MODEL_PATH = Path(getenv("MODEL_PATH", "/raid/weights/llama31_8b/"))
-
-  params = MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]
-  params = params | {"vocab_size": 32000} if not SMALL else params
-  if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: params['n_layers'] = llama_layers
-  model = Transformer(**params, max_context=SEQLEN, jit=False, disable_kv_cache=True)
-
-  # load weights
-  weights = load(str(MODEL_PATH / "model.safetensors.index.json"))
-  if "model.embed_tokens.weight" in weights:
-    print("converting from huggingface format")
-    weights = convert_from_huggingface(weights, params["n_layers"], params["n_heads"], params["n_kv_heads"])
-
-  load_state_dict(model, weights, strict=False, consume=True)
-
-  @TinyJit
-  def eval_step(model, tokens):
-    logits:Tensor = model(tokens[:, :-1], start_pos=0, temperature=math.nan)
-    loss = logits.sparse_categorical_crossentropy(tokens[:, 1:])
-    return loss.flatten().float()
-
-  from examples.mlperf.dataloader import get_llama3_dataset, iterate_llama3_dataset
-  eval_dataset = get_llama3_dataset(5760, SEQLEN, BASEDIR, val=True, small=bool(SMALL))
-  iter = iterate_llama3_dataset(eval_dataset, BS)
-
-  losses = []
-  for tokens in tqdm(iter, total=5760//BS):
-    GlobalCounters.reset()
-    losses += eval_step(model, tokens).tolist()
-    tqdm.write(f"loss: {np.mean(losses)}")
-
-  log_perplexity = np.mean(losses)
-  print(f"Log Perplexity: {log_perplexity}")
-
-# NOTE: BEAM hangs on 8xmi300x with DECODE_BS=384 in final realize below; function is declared here for external testing
-@TinyJit
-def vae_decode(x:Tensor, vae, disable_beam=False) -> Tensor:
-  from examples.stable_diffusion import AutoencoderKL
-  assert isinstance(vae, AutoencoderKL)
-  x = vae.post_quant_conv(1./0.18215 * x)
-
-  x = vae.decoder.conv_in(x)
-  x = vae.decoder.mid(x)
-  for i, l in enumerate(vae.decoder.up[::-1]):
-    print("decode", x.shape)
-    for b in l['block']: x = b(x)
-    if 'upsample' in l:
-      bs,c,py,px = x.shape
-      x = x.reshape(bs, c, py, 1, px, 1).expand(bs, c, py, 2, px, 2).reshape(bs, c, py*2, px*2)
-      x = l['upsample']['conv'](x)
-    if i == len(vae.decoder.up) - 1 and disable_beam:
-      with Context(BEAM=0): x.realize()
-    else: x.realize()
-  x = vae.decoder.conv_out(vae.decoder.norm_out(x).swish())
-
-  x = ((x + 1.0) / 2.0).clip(0.0, 1.0)
-  return x
-
-def eval_stable_diffusion():
-  import csv, PIL, sys
+def eval_mrcnn():
   from tqdm import tqdm
-  from examples.mlperf.initializers import init_stable_diffusion, gelu_erf
-  from examples.stable_diffusion import AutoencoderKL
-  from extra.models.unet import UNetModel
-  from tinygrad.nn.state import load_state_dict, torch_load
-  from tinygrad.helpers import BEAM
-  from extra.models import clip
-  from extra.models.clip import FrozenOpenClipEmbedder
-  from extra.models.clip import OpenClipEncoder
-  from extra.models.inception import FidInceptionV3
+  from extra.models.mask_rcnn import MaskRCNN
+  from extra.models.resnet import ResNet
+  from extra.datasets.coco import BASEDIR, images, convert_prediction_to_coco_bbox, convert_prediction_to_coco_mask, accumulate_predictions_for_coco, evaluate_predictions_on_coco, iterate
+  from examples.mask_rcnn import compute_prediction_batched, Image
+  mdl = MaskRCNN(ResNet(50, num_classes=None, stride_in_1x1=True))
+  mdl.load_from_pretrained()
 
-  config = {}
-  GPUS               = config["GPUS"]                   = [f"{Device.DEFAULT}:{i}" for i in range(getenv("GPUS", 1))]
-  for x in GPUS: Device[x]
-  print(f"running eval on {GPUS}")
-  seed               = config["seed"]                   = getenv("SEED", 12345)
-  CKPTDIR            = config["CKPTDIR"]                = Path(getenv("CKPTDIR", "./checkpoints"))
-  DATADIR            = config["DATADIR"]                = Path(getenv("DATADIR", "./datasets"))
-  CONTEXT_BS         = config["CONTEXT_BS"]             = getenv("CONTEXT_BS", 1 * len(GPUS))
-  DENOISE_BS         = config["DENOISE_BS"]             = getenv("DENOISE_BS", 1 * len(GPUS))
-  DECODE_BS          = config["DECODE_BS"]              = getenv("DECODE_BS", 1 * len(GPUS))
-  INCEPTION_BS       = config["INCEPTION_BS"]           = getenv("INCEPTION_BS", 1 * len(GPUS))
-  CLIP_BS            = config["CLIP_BS"]                = getenv("CLIP_BS", 1 * len(GPUS))
-  EVAL_CKPT_DIR      = config["EVAL_CKPT_DIR"]          = getenv("EVAL_CKPT_DIR", "")
-  STOP_IF_CONVERGED  = config["STOP_IF_CONVERGED"]      = getenv("STOP_IF_CONVERGED", 0)
+  bbox_output = '/tmp/results_bbox.json'
+  mask_output = '/tmp/results_mask.json'
 
-  if (WANDB := getenv("WANDB", "")):
-    import wandb
-    wandb.init(config=config, project="MLPerf-Stable-Diffusion")
+  accumulate_predictions_for_coco([], bbox_output, rm=True)
+  accumulate_predictions_for_coco([], mask_output, rm=True)
 
-  assert EVAL_CKPT_DIR != "", "provide a directory with checkpoints to be evaluated"
-  print(f"running eval on checkpoints in {EVAL_CKPT_DIR}\nSEED={seed}")
-  eval_queue:list[tuple[int, Path]] = []
-  for p in Path(EVAL_CKPT_DIR).iterdir():
-    if p.name.endswith(".safetensors"):
-      ckpt_iteration = p.name.split(".safetensors")[0]
-      assert ckpt_iteration.isdigit(), f"invalid checkpoint name: {p.name}, expected <digits>.safetensors"
-      eval_queue.append((int(ckpt_iteration), p))
-  assert len(eval_queue), f'no files ending with ".safetensors" were found in {EVAL_CKPT_DIR}'
-  print(sorted(eval_queue, reverse=True))
+  #TODO: bs > 1 not as accurate
+  bs = 1
 
-  Tensor.manual_seed(seed)  # seed for weight initialization
-  model, unet, sqrt_alphas_cumprod, sqrt_one_minus_alphas_cumprod = init_stable_diffusion("v2-mlperf-eval", CKPTDIR / "sd" / "512-base-ema.ckpt", GPUS)
+  for batch in tqdm(iterate(images, bs=bs), total=len(images)//bs):
+    batch_imgs = []
+    for image_row in batch:
+      image_name = image_row['file_name']
+      img = Image.open(BASEDIR/f'val2017/{image_name}').convert("RGB")
+      batch_imgs.append(img)
+    batch_result = compute_prediction_batched(batch_imgs, mdl)
+    for image_row, result in zip(batch, batch_result):
+      image_name = image_row['file_name']
+      box_pred = convert_prediction_to_coco_bbox(image_name, result)
+      mask_pred = convert_prediction_to_coco_mask(image_name, result)
+      accumulate_predictions_for_coco(box_pred, bbox_output)
+      accumulate_predictions_for_coco(mask_pred, mask_output)
+    del batch_imgs
+    del batch_result
 
-  # load prompts for generating images for validation; 2 MB of data total
-  with open(DATADIR / "coco2014" / "val2014_30k.tsv") as f:
-    reader = csv.DictReader(f, delimiter="\t")
-    eval_inputs:list[dict] = [{"image_id": int(row["image_id"]), "id": int(row["id"]), "caption": row["caption"]} for row in reader]
-  assert len(eval_inputs) == 30_000
-  # NOTE: the clip weights are the same between model.cond_stage_model and clip_encoder
-  eval_timesteps = list(reversed(range(1, 1000, 20)))
-
-  original_device, Device.DEFAULT = Device.DEFAULT, "CPU"
-  # The choice of alphas_prev[0] = alphas_cumprod[0] seems arbitrary, but it's how the mlperf ref does it:
-  #   alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-  eval_alphas_prev = model.alphas_cumprod[0:1].cat(model.alphas_cumprod[list(range(1, 1000, 20))[:-1]]).to(GPUS).realize()
-  inception = FidInceptionV3().load_from_pretrained(CKPTDIR / "inception" / "pt_inception-2015-12-05-6726825d.pth")
-  vision_cfg = {'width': 1280, 'layers': 32, 'd_head': 80, 'image_size': 224, 'patch_size': 14}
-  text_cfg = {'width': 1024, 'n_heads': 16, 'layers': 24, 'vocab_size': 49408, 'ctx_length': 77}
-  clip.gelu = gelu_erf
-  clip_encoder = OpenClipEncoder(1024, text_cfg, vision_cfg)
-  loaded = torch_load(CKPTDIR / "clip" / "open_clip_pytorch_model.bin")
-  loaded.update({"attn_mask": clip_encoder.attn_mask, "mean": clip_encoder.mean, "std": clip_encoder.std})
-  load_state_dict(clip_encoder, loaded)
-  Device.DEFAULT=original_device
-
-  @TinyJit
-  def denoise_step(x:Tensor, x_x:Tensor, t_t:Tensor, uc_c:Tensor, sqrt_alphas_cumprod_t:Tensor, sqrt_one_minus_alphas_cumprod_t:Tensor,
-                    alpha_prev:Tensor, unet:UNetModel, GPUS) -> Tensor:
-    out_uncond, out = unet(x_x, t_t, uc_c).to("CPU").reshape(-1, 2, 4, 64, 64).chunk(2, dim=1)
-    out_uncond = out_uncond.squeeze(1).shard(GPUS,axis=0)
-    out = out.squeeze(1).shard(GPUS,axis=0)
-    v_t = out_uncond + 8.0 * (out - out_uncond)
-    e_t = sqrt_alphas_cumprod_t * v_t + sqrt_one_minus_alphas_cumprod_t * x
-    pred_x0 = sqrt_alphas_cumprod_t * x - sqrt_one_minus_alphas_cumprod_t * v_t
-    dir_xt = (1. - alpha_prev).sqrt() * e_t
-    x_prev = alpha_prev.sqrt() * pred_x0 + dir_xt
-    return x_prev.realize()
-
-  def shard_tensor(t:Tensor) -> Tensor: return t.shard(GPUS, axis=0) if len(GPUS) > 1 else t.to(GPUS[0])
-  def get_batch(whole:Tensor, i:int, bs:int) -> tuple[Tensor, int]:
-    batch = whole[i: i + bs].to("CPU")
-    if (unpadded_bs:=batch.shape[0]) < bs:
-      batch = batch.cat(batch[-1:].expand(bs - unpadded_bs, *batch[-1].shape))
-    return batch, unpadded_bs 
-
-  @Tensor.train(mode=False)
-  def eval_unet(eval_inputs:list[dict], unet:UNetModel, cond_stage:FrozenOpenClipEmbedder, first_stage:AutoencoderKL,
-                inception:FidInceptionV3, clip:OpenClipEncoder) -> tuple[float, float]:
-    # Eval is divided into 5 jits, one per model
-    # It doesn't make sense to merge these jits, e.g. unet repeats 50 times in isolation; images fork to separate inception/clip
-    # We're generating and scoring 30,000 images per eval, and all the data can flow through one jit at a time
-    # To maximize throughput for each jit, we have only one model/jit on the GPU at a time, and pool outputs from each jit off-GPU
-    for model in (unet, first_stage, inception, clip):
-      Tensor.realize(*[p.to_("CPU") for p in get_parameters(model)])
-
-    uc_written = False
-    models = (cond_stage, unet, first_stage, inception, clip)
-    jits = (jit_context:=TinyJit(cond_stage.embed_tokens), denoise_step, vae_decode, jit_inception:=TinyJit(inception),
-            jit_clip:=TinyJit(clip.get_clip_score))
-    all_bs = (CONTEXT_BS, DENOISE_BS, DECODE_BS, INCEPTION_BS, CLIP_BS)
-    if (EVAL_SAMPLES:=getenv("EVAL_SAMPLES", 0)) and EVAL_SAMPLES > 0:
-      eval_inputs = eval_inputs[0:EVAL_SAMPLES]
-    output_shapes = [(ns:=len(eval_inputs),77), (ns,77,1024), (ns,4,64,64), (ns,3,512,512), (ns,2048), (ns,)]
-    # Writing progress to disk lets us resume eval if we crash
-    stages = ["tokens", "embeds", "latents", "imgs", "inception", "clip"]
-    disk_tensor_names, disk_tensor_shapes = stages + ["end", "uc"], output_shapes + [(6,), (1,77,1024)]
-    if not all(os.path.exists(f"{EVAL_CKPT_DIR}/{name}.bytes") for name in disk_tensor_names):
-      for name, shape in zip(disk_tensor_names, disk_tensor_shapes):
-        file = Path(f"{EVAL_CKPT_DIR}/{name}.bytes")
-        file.unlink(missing_ok=True)
-        with file.open("wb") as f: f.truncate(prod(shape) * 4)
-    progress = {name: Tensor.empty(*shape, device=f"disk:{EVAL_CKPT_DIR}/{name}.bytes", dtype=dtypes.int if name in {"tokens", "end"} else dtypes.float)
-                for name, shape in zip(disk_tensor_names, disk_tensor_shapes)}
-
-    def embed_tokens(tokens:Tensor) -> Tensor:
-      nonlocal uc_written
-      if not uc_written:
-        with Context(BEAM=0): progress["uc"].assign(cond_stage.embed_tokens(cond_stage.tokenize("").to(GPUS)).to("CPU").realize()).realize()
-        uc_written = True
-      return jit_context(shard_tensor(tokens))
-
-    def generate_latents(embeds:Tensor) -> Tensor:
-      uc_c = Tensor.stack(progress["uc"].to("CPU").expand(bs, 77, 1024), embeds, dim=1).reshape(-1, 77, 1024)
-      uc_c = shard_tensor(uc_c)
-      x = shard_tensor(Tensor.randn(bs,4,64,64))
-      for step_idx, timestep in enumerate(tqdm(eval_timesteps)):
-        reversed_idx = Tensor([50 - step_idx - 1], device=GPUS)
-        alpha_prev = eval_alphas_prev[reversed_idx]
-        ts = Tensor.full(bs, fill_value=timestep, dtype=dtypes.int, device="CPU")
-        ts_ts = shard_tensor(ts.cat(ts))
-        ts = shard_tensor(ts)
-        sqrt_alphas_cumprod_t = sqrt_alphas_cumprod[ts].reshape(bs, 1, 1, 1)
-        sqrt_one_minus_alphas_cumprod_t = sqrt_one_minus_alphas_cumprod[ts].reshape(bs, 1, 1, 1)
-        x_x = shard_tensor(Tensor.stack(x.to("CPU"), x.to("CPU"), dim=1).reshape(-1, 4, 64, 64))
-        x.assign(denoise_step(x, x_x, ts_ts, uc_c, sqrt_alphas_cumprod_t, sqrt_one_minus_alphas_cumprod_t, alpha_prev, unet, GPUS)).realize()
-      return x
-
-    def decode_latents(latents:Tensor) -> Tensor: return vae_decode(shard_tensor(latents), first_stage, disable_beam=True)
-    def generate_inception(imgs:Tensor) -> Tensor: return jit_inception(shard_tensor(imgs))[:,:,0,0]
-
-    def calc_clip_scores(batch:Tensor, batch_tokens:Tensor) -> Tensor:
-      # Tensor.interpolate does not yet support bicubic, so we use PIL
-      batch = (batch.to(GPUS[0]).permute(0,2,3,1) * 255).clip(0, 255).cast(dtypes.uint8).numpy()
-      batch = [np.array(PIL.Image.fromarray(batch[i]).resize((224,224), PIL.Image.BICUBIC)) for i in range(bs)]
-      batch = shard_tensor(Tensor(np.stack(batch, axis=0).transpose(0,3,1,2), device="CPU").realize())
-      batch = batch.cast(dtypes.float) / 255
-      batch = (batch - model.mean) / model.std
-      batch = jit_clip(shard_tensor(batch_tokens), batch)
-      return batch
-
-    callbacks = (embed_tokens, generate_latents, decode_latents, generate_inception, calc_clip_scores)
-
-    # save every forward pass output to disk; NOTE: this needs ~100 GB disk space because 30k images are large
-    def stage_progress(stage_idx:int) -> int: return progress["end"].to("CPU")[stage_idx].item()
-    if stage_progress(0) < len(eval_inputs):
-      tokens = []
-      for i in tqdm(range(0, len(eval_inputs), CONTEXT_BS)):
-        subset = [cond_stage.tokenize(row["caption"], device="CPU") for row in eval_inputs[i: i+CONTEXT_BS]]
-        tokens.append(Tensor.cat(*subset, dim=0).realize())
-      progress["tokens"].assign(Tensor.cat(*tokens, dim=0).realize()).realize()
-      progress["end"][0:1].assign(Tensor([len(eval_inputs)], dtype=dtypes.int)).realize()
-    prev_stage = "tokens"
-    tokens = progress["tokens"]
-
-    # wrapper code for every model
-    for stage_idx, model, jit, bs, callback in zip(range(1,6), models, jits, all_bs, callbacks):
-      stage = stages[stage_idx]
-      if stage_progress(stage_idx) >= len(eval_inputs):
-        prev_stage = stage
-        continue # use cache
-      t0 = time.perf_counter()
-      print(f"starting eval with model: {model}")
-      if stage_idx == 1: inputs = tokens
-      elif stage_idx == 5: inputs = progress["imgs"]
-      else: inputs = progress[prev_stage]
-
-      Tensor.realize(*[p.to_(GPUS) for p in get_parameters(model)])
-      for batch_idx in tqdm(range(stage_progress(stage_idx), inputs.shape[0], bs)):
-        t1 = time.perf_counter()
-        batch, unpadded_bs = get_batch(inputs, batch_idx, bs)
-        if isinstance(model, OpenClipEncoder): batch = callback(batch, get_batch(tokens, batch_idx, bs)[0].realize())
-        else: batch = callback(batch)
-        # to(GPUS[0]) is necessary for this to work, without that the result is still on GPUS, probably due to a bug
-        batch = batch.to(GPUS[0]).to("CPU")[0:unpadded_bs].realize()
-        progress[stage][batch_idx: batch_idx + bs].assign(batch).realize()
-        # keep track of what our last output was, so we can resume from there if we crash in this loop
-        progress["end"][stage_idx: stage_idx + 1].assign(Tensor([batch_idx + bs], dtype=dtypes.int)).realize()
-        print(f"model: {model}, batch_idx: {batch_idx}, elapsed: {(time.perf_counter() - t1):.2f}")
-      del batch
-        
-      jit.reset()
-      Tensor.realize(*[p.to_("CPU") for p in get_parameters(model)])
-      print(f"done with model: {model}, elapsed: {(time.perf_counter() - t0):.2f}")
-      prev_stage = stage
-
-    inception_stats_fn = str(DATADIR / "coco2014" / "val2014_30k_stats.npz")
-    fid_score = inception.compute_score(progress["inception"].to("CPU"), inception_stats_fn)
-    clip_score = progress["clip"].to(GPUS[0]).mean().item()
-    for name in disk_tensor_names:
-      Path(f"{EVAL_CKPT_DIR}/{name}.bytes").unlink(missing_ok=True)
-    
-    if EVAL_SAMPLES and BEAM:
-      print("BEAM COMPLETE", flush=True) # allows wrapper script to detect BEAM search completion and retry if it failed
-      sys.exit() # Don't eval additional models; we don't care about clip/fid scores when running BEAM on eval sample subset
-
-    return clip_score, fid_score
-
-  # evaluate checkpoints in reverse chronological order
-  for ckpt_iteration, p in sorted(eval_queue, reverse=True):
-    unet_ckpt = safe_load(p)
-    load_state_dict(unet, unet_ckpt)
-    clip_score, fid_score = eval_unet(eval_inputs, unet, model.cond_stage_model, model.first_stage_model, inception, clip_encoder)
-    converged = True if clip_score >= 0.15 and fid_score <= 90 else False
-    print(f"eval results for {EVAL_CKPT_DIR}/{p.name}: clip={clip_score}, fid={fid_score}, converged={converged}")
-    if WANDB:
-      wandb.log({"eval/ckpt_iteration": ckpt_iteration, "eval/clip_score": clip_score, "eval/fid_score": fid_score})
-    if converged and STOP_IF_CONVERGED:
-      print(f"Convergence detected, exiting early before evaluating other checkpoints due to STOP_IF_CONVERGED={STOP_IF_CONVERGED}")
-      sys.exit()
-
-  # for testing
-  return clip_score, fid_score, ckpt_iteration
+  evaluate_predictions_on_coco(bbox_output, iou_type='bbox')
+  evaluate_predictions_on_coco(mask_output, iou_type='segm')
 
 if __name__ == "__main__":
   # inference only
   Tensor.training = False
+  Tensor.no_grad = True
 
-  models = getenv("MODEL", "resnet,retinanet,unet3d,rnnt,bert").split(",")
+  models = getenv("MODEL", "resnet,retinanet,unet3d,rnnt,bert,mrcnn").split(",")
   for m in models:
     nm = f"eval_{m}"
     if nm in globals():

examples/mlperf/model_spec.py

@@ -60,6 +60,7 @@ def spec_mrcnn():
 if __name__ == "__main__":
   # inference only for now
   Tensor.training = False
+  Tensor.no_grad = True
 
   for m in getenv("MODEL", "resnet,retinanet,unet3d,rnnt,bert,mrcnn").split(","):
     nm = f"spec_{m}"

examples/mlperf/optim.py

@@ -1,46 +0,0 @@
-from tinygrad.tensor import Tensor
-from tinygrad.dtype import dtypes
-from tinygrad.nn.optim import Optimizer
-from tinygrad.helpers import FUSE_OPTIM
-
-class GradAccClipAdamW(Optimizer):
-  def __init__(self, params:list[Tensor], lr=0.001, b1=0.9, b2=0.999, eps=1e-6, weight_decay=0.0, grad_acc=1, clip_norm=1.0, device=None, fused=FUSE_OPTIM):
-    super().__init__(params, lr, device, fused)
-    self.b1, self.b2, self.eps, self.wd = b1, b2, eps, weight_decay
-    self.b1_t, self.b2_t = (Tensor.ones((1,), dtype=dtypes.float32, device=self.device, requires_grad=False).contiguous() for _ in [b1, b2])
-    self.m = self._new_optim_param()
-    self.v = self._new_optim_param()
-    self.grad_acc, self.clip_norm = grad_acc, clip_norm
-
-  def _step(self, params:list[Tensor], grads:list[Tensor]) -> tuple[list[Tensor], list[Tensor]]:
-    for i in range(len(grads)):
-      if grads[i].device != self.m[i].device: grads[i] = grads[i].to(self.m[i].device)
-
-    if self.fused:
-      grads[0] = grads[0] / self.grad_acc
-      total_norm = grads[0].float().square().sum().sqrt()
-      grads[0] = (grads[0] * (self.clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)).cast(grads[0].dtype)
-    else:
-      total_norm = Tensor.zeros((), dtype=dtypes.float32, device=self.device)
-      for g in grads:
-        total_norm += g.float().square().sum()
-      total_norm = total_norm.sqrt()
-      for i in range(len(grads)):
-        grads[i] = grads[i] / self.grad_acc
-        grads[i] = (grads[i] * (self.clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)).cast(grads[i].dtype)
-
-    ret = []
-    self.b1_t *= self.b1
-    self.b2_t *= self.b2
-    for i, (t, g) in enumerate(zip(params, grads)):
-      self.m[i].assign((self.b1 * self.m[i] + (1.0 - self.b1) * g).cast(self.m[i].dtype))
-      self.v[i].assign((self.b2 * self.v[i] + (1.0 - self.b2) * (g * g)).cast(self.v[i].dtype))
-      m_hat = self.m[i] / (1.0 - self.b1_t)
-      v_hat = self.v[i] / (1.0 - self.b2_t)
-      up = m_hat / (v_hat.sqrt() + self.eps)
-      ret.append((self.lr * up).cast(t.dtype))
-    return ret, [self.b1_t, self.b2_t] + self.m + self.v
-
-  def _apply_update(self, t:Tensor, up:Tensor) -> Tensor:
-    up = up.shard_like(t) + self.lr.to(t.device) * self.wd * t.detach()
-    return t.detach() - up.cast(t.dtype)

examples/mlperf/scripts/stable_diffusion_downloads.sh

@@ -1,57 +0,0 @@
-#!/usr/bin/env bash
-# adapted from https://github.com/mlcommons/training/blob/4bdf5c8ed218ad76565a2ba1ac27c919ccc6d689/stable_diffusion/README.md
-
-# setup dirs
-
-DATA=/raid/datasets/stable_diffusion
-
-LAION=$DATA/laion-400m/webdataset-moments-filtered 
-COCO=$DATA/coco2014
-mkdir -p $LAION $COCO
-
-CKPT=/raid/weights/stable_diffusion
-mkdir -p $CKPT/clip $CKPT/sd $CKPT/inception
-
-# download data
-
-# if rclone isn't installed system-wide / in your PATH, put the executable path in quotes below
-#RCLONE=""
-RCLONE="rclone"
-
-## VAE-encoded image latents, from 6.1M image subset of laion-400m
-## about 1 TB for whole download
-$RCLONE config create mlc-training s3 provider=Cloudflare access_key_id=76ea42eadb867e854061a1806220ee1e secret_access_key=a53625c4d45e3ca8ac0df8a353ea3a41ffc3292aa25259addd8b7dc5a6ce2936 endpoint=c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
-$RCLONE copy mlc-training:mlcommons-training-wg-public/stable_diffusion/datasets/laion-400m/moments-webdataset-filtered/ ${LAION} --include="*.tar" -P
-$RCLONE copy mlc-training:mlcommons-training-wg-public/stable_diffusion/datasets/laion-400m/moments-webdataset-filtered/sha512sums.txt ${LAION} -P
-cd $LAION && grep -E '\.tar$' sha512sums.txt | sha512sum -c --quiet - && \
-  echo "All .tar files verified" || { echo "Checksum failure when validating downloaded Laion moments"; exit 1; }
-
-## prompts and FID statistics from 30k image subset of coco2014
-## 33 MB
-$RCLONE config create mlc-training s3 provider=Cloudflare access_key_id=76ea42eadb867e854061a1806220ee1e secret_access_key=a53625c4d45e3ca8ac0df8a353ea3a41ffc3292aa25259addd8b7dc5a6ce2936 endpoint=c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
-$RCLONE copy mlc-training:mlcommons-training-wg-public/stable_diffusion/datasets/coco2014/val2014_30k.tsv ${COCO} -P
-
-$RCLONE config create mlc-training s3 provider=Cloudflare access_key_id=76ea42eadb867e854061a1806220ee1e secret_access_key=a53625c4d45e3ca8ac0df8a353ea3a41ffc3292aa25259addd8b7dc5a6ce2936 endpoint=c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
-$RCLONE copy mlc-training:mlcommons-training-wg-public/stable_diffusion/datasets/coco2014/val2014_30k_stats.npz ${COCO} -P
-
-# download checkpoints
-
-## clip (needed for text and vision encoders for validation)
-CLIP_WEIGHTS_URL="https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/resolve/main/open_clip_pytorch_model.bin"
-CLIP_WEIGHTS_SHA256="9a78ef8e8c73fd0df621682e7a8e8eb36c6916cb3c16b291a082ecd52ab79cc4"
-CLIP_CONFIG_URL="https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/raw/main/open_clip_config.json"
-wget -N -P ${CKPT}/clip ${CLIP_WEIGHTS_URL}
-wget -N -P ${CKPT}/clip ${CLIP_CONFIG_URL}
-echo "${CLIP_WEIGHTS_SHA256}  ${CKPT}/clip/open_clip_pytorch_model.bin"                    | sha256sum -c
-
-## sd (needed for latent->image decoder for validation, also has clip text encoder for training)
-SD_WEIGHTS_URL='https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt'
-SD_WEIGHTS_SHA256="d635794c1fedfdfa261e065370bea59c651fc9bfa65dc6d67ad29e11869a1824"
-wget -N -P ${CKPT}/sd ${SD_WEIGHTS_URL}
-echo "${SD_WEIGHTS_SHA256}  ${CKPT}/sd/512-base-ema.ckpt"                    | sha256sum -c
-
-## inception (needed for validation)
-FID_WEIGHTS_URL='https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'
-FID_WEIGHTS_SHA1="bd836944fd6db519dfd8d924aa457f5b3c8357ff"
-wget -N -P ${CKPT}/inception ${FID_WEIGHTS_URL}
-echo "${FID_WEIGHTS_SHA1}  ${CKPT}/inception/pt_inception-2015-12-05-6726825d.pth"                    | sha1sum -c

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_1xMI300X/dev_beam.sh

@@ -1,15 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="bert"
-export DEFAULT_FLOAT="HALF" GPUS=1 BS=128 EVAL_BS=128
-
-export BEAM=3 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1
-# export BEAM_LOG_SURPASS_MAX=1
-# export BASEDIR="/raid/datasets/wiki"
-
-export RESET_STEP=1
-export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
-
-python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/README.md

@@ -1,69 +0,0 @@
-# 1. Problem
-
-This problem uses BERT for NLP.
-
-## Requirements
-
-Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
-```
-git clone https://github.com/tinygrad/tinygrad.git
-python3 -m pip install -e ".[mlperf]"
-```
-Also install gdown (for dataset), numpy, tqdm and tensorflow.
-```
-pip install gdown numpy tqdm tensorflow
-```
-
-### tinybox_green
-Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
-This is the default on production tinybox green.
-
-# 2. Directions
-
-## Steps to download and verify data
-
-### 1. Download raw data
-
-```
-BASEDIR="/raid/datasets/wiki" WIKI_TRAIN=1 VERIFY_CHECKSUM=1 python3 extra/datasets/wikipedia_download.py
-```
-
-### 2. Preprocess train and validation data
-
-Note: The number of threads used for preprocessing is limited by available memory. With 128GB of RAM, a maximum of 16 threads is recommended. 
-
-#### Training:
-```
-BASEDIR="/raid/datasets/wiki" NUM_WORKERS=16 python3 extra/datasets/wikipedia.py pre-train all
-```
-
-Generating a specific topic (Between 0 and 499)
-```
-BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-train 42
-```
-
-#### Validation:
-```
-BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
-```
-## Running
-
-### tinybox_green
-
-#### Steps to run benchmark
-```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
-```
-
-### tinybox_red
-
-#### Steps to run benchmark
-```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
-```
-### tinybox_8xMI300X
-
-#### Steps to run benchmark
-```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
-```

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/dev_beam.sh

@@ -1,14 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="bert"
-export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
-export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
-
-export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
-export BASEDIR="/raid/datasets/wiki"
-
-export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
-
-python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/dev_run.sh

@@ -1,17 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="bert"
-export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
-
-# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
-export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
-export TRAIN_STEPS=3900
-
-export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
-export BASEDIR="/raid/datasets/wiki"
-
-export WANDB=1 PARALLEL=0
-
-RUNMLPERF=1 python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh

@@ -1,28 +0,0 @@
-#!/bin/bash
-set -e  # Exit on any error
-
-export PYTHONPATH="." AMD=1
-export MODEL="bert"
-export SUBMISSION_PLATFORM="tinybox_8xMI300X"
-export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
-
-# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
-export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
-export TRAIN_STEPS=3900
-
-export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
-export BASEDIR="/raid/datasets/wiki"
-
-# pip install -e ".[mlperf]"
-export LOGMLPERF=1
-
-export SEED=$RANDOM
-DATETIME=$(date "+%m%d%H%M")
-LOGFILE="bert_8xMI300x_${DATETIME}_${SEED}.log"
-
-# init  # TODO: without DEBUG=2 it hangs
-BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 DEBUG=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
-
-# run
-PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/README.md

@@ -4,20 +4,24 @@ This problem uses BERT for NLP.
 
 ## Requirements
 
-Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
+Install tinygrad and mlperf-logging from master.
 ```
 git clone https://github.com/tinygrad/tinygrad.git
 python3 -m pip install -e ".[mlperf]"
 ```
-Also install gdown (for dataset), numpy, tqdm and tensorflow.
+Also install tqdm and tensorflow.
 ```
-pip install gdown numpy tqdm tensorflow
+pip install tqdm tensorflow
 ```
 
 ### tinybox_green
 Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
 This is the default on production tinybox green.
 
+### tinybox_red
+Disable cwsr + increase mes timeout.
+Install the custom amdgpu driver per [README](https://github.com/nimlgen/amdgpu_ubuntu_22_04/blob/v6.1.3/readme.md)
+
 # 2. Directions
 
 ## Steps to download and verify data
@@ -52,18 +56,18 @@ BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
 
 #### Steps to run benchmark
 ```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
+examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
 ```
 
 ### tinybox_red
 
-#### Steps to run benchmark
+#### One time setup
+
 ```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
+examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_red/setup.sh
 ```
-### tinybox_8xMI300X
 
 #### Steps to run benchmark
 ```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
+examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
 ```

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/dev_beam.sh

@@ -1,16 +1,14 @@
 #!/bin/bash
 
-export PYTHONPATH="." NV=1
+export PYTHONPATH="."
 export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=24
 
-export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
-
-export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export BEAM=4 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1
 export BEAM_LOG_SURPASS_MAX=1
 export BASEDIR="/raid/datasets/wiki"
 
-export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
+export BENCHMARK=10 DEBUG=2
 
 python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/dev_run.sh

@@ -1,12 +1,10 @@
 #!/bin/bash
 
-export PYTHONPATH="." NV=1
+export PYTHONPATH="."
 export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=24
 
-export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
-
-export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export BEAM=4 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1
 export BASEDIR="/raid/datasets/wiki"
 

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh

@@ -1,14 +1,11 @@
 #!/bin/bash
-set -e  # Exit on any error
 
-export PYTHONPATH="." NV=1
+export PYTHONPATH="."
 export MODEL="bert"
 export SUBMISSION_PLATFORM="tinybox_green"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=24
 
-export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
-
-export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export BEAM=4 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1
 export BASEDIR="/raid/datasets/wiki"
 
@@ -20,7 +17,7 @@ DATETIME=$(date "+%m%d%H%M")
 LOGFILE="bert_green_${DATETIME}_${SEED}.log"
 
 # init
-BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
+BENCHMARK=10 INITMLPERF=1 BEAM_LOG_SURPASS_MAX=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
 
 # run
 PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/README.md

@@ -4,20 +4,24 @@ This problem uses BERT for NLP.
 
 ## Requirements
 
-Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
+Install tinygrad and mlperf-logging from master.
 ```
 git clone https://github.com/tinygrad/tinygrad.git
 python3 -m pip install -e ".[mlperf]"
 ```
-Also install gdown (for dataset), numpy, tqdm and tensorflow.
+Also install tqdm and tensorflow.
 ```
-pip install gdown numpy tqdm tensorflow
+pip install tqdm tensorflow
 ```
 
 ### tinybox_green
 Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
 This is the default on production tinybox green.
 
+### tinybox_red
+Disable cwsr + increase mes timeout.
+Install the custom amdgpu driver per [README](https://github.com/nimlgen/amdgpu_ubuntu_22_04/blob/v6.1.3/readme.md)
+
 # 2. Directions
 
 ## Steps to download and verify data
@@ -52,18 +56,18 @@ BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
 
 #### Steps to run benchmark
 ```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
+examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
 ```
 
 ### tinybox_red
 
-#### Steps to run benchmark
+#### One time setup
+
 ```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
+examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_red/setup.sh
 ```
-### tinybox_8xMI300X
 
 #### Steps to run benchmark
 ```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
+examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
 ```

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/dev_beam.sh

@@ -1,17 +1,15 @@
 #!/bin/bash
 
-export PYTHONPATH="." AMD=1
+export PYTHONPATH="."
 export MODEL="bert"
 export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
 
-export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
-
-export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export BEAM=3 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1
 export BEAM_LOG_SURPASS_MAX=1
 export BASEDIR="/raid/datasets/wiki"
 
 export RESET_STEP=1
-export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
+export BENCHMARK=10 DEBUG=2
 
 python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/dev_run.sh

@@ -1,12 +1,10 @@
 #!/bin/bash
 
-export PYTHONPATH="." AMD=1
+export PYTHONPATH="."
 export MODEL="bert"
 export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
 
-export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
-
-export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export BEAM=3 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1
 export BASEDIR="/raid/datasets/wiki"
 

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh

@@ -1,14 +1,11 @@
 #!/bin/bash
-set -e  # Exit on any error
 
-export PYTHONPATH="." AMD=1
+export PYTHONPATH="."
 export MODEL="bert"
 export SUBMISSION_PLATFORM="tinybox_red"
 export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
 
-export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
-
-export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export BEAM=3 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1
 export BASEDIR="/raid/datasets/wiki"
 
@@ -19,13 +16,12 @@ export SEED=$RANDOM
 DATETIME=$(date "+%m%d%H%M")
 LOGFILE="bert_red_${DATETIME}_${SEED}.log"
 
-export HCQDEV_WAIT_TIMEOUT_MS=100000  # prevents hang?
-
 # init
-sleep 5 && sudo rmmod amdgpu || true
-BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
+sudo rmmod amdgpu || true
+BENCHMARK=10 INITMLPERF=1 BEAM_LOG_SURPASS_MAX=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
 
 # run
-# TODO: AM driver resulted in nan
+# TODO: AMD driver hangs during init, but is 5% faster per step in real run.
 sudo modprobe amdgpu
 PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
+sudo rmmod amdgpu || true

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/setup.sh

@@ -5,4 +5,4 @@ rocm-smi --setmclk 3
 rocm-smi --setperflevel high
 
 # power cap to 350W
-echo "350000000" | sudo tee /sys/class/drm/card{1..6}/device/hwmon/hwmon*/power1_cap
+# echo "350000000" | sudo tee /sys/class/drm/card{1..6}/device/hwmon/hwmon*/power1_cap

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/resnet/implementations/tinybox_green/dev_beam.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-export PYTHONPATH="." NV=1
+export PYTHONPATH="."
 export MODEL="resnet"
 export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
 

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/resnet/implementations/tinybox_green/dev_run.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-export PYTHONPATH="." NV=1
+export PYTHONPATH="."
 export MODEL="resnet"
 export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
 

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/resnet/implementations/tinybox_green/run_and_time.sh

@@ -1,7 +1,6 @@
 #!/bin/bash
-set -e  # Exit on any error
 
-export PYTHONPATH="." NV=1
+export PYTHONPATH="."
 export MODEL="resnet"
 export SUBMISSION_PLATFORM="tinybox_green"
 export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
@@ -11,7 +10,7 @@ export RESET_STEP=0
 export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
 
 # pip install -e ".[mlperf]"
-export LOGMLPERF=${LOGMLPERF:-1}
+export LOGMLPERF=1
 
 export SEED=$RANDOM
 DATETIME=$(date "+%m%d%H%M")

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/dev_beam.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-export PYTHONPATH="." AMD=1
+export PYTHONPATH="."
 export MODEL="resnet"
 export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
 
@@ -8,6 +8,6 @@ export RESET_STEP=0
 
 export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
 
-export BENCHMARK=10 DEBUG=${DEBUG:-2}
+export BENCHMARK=10 DEBUG=2
 
 python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/dev_run.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-export PYTHONPATH="." AMD=1
+export PYTHONPATH="."
 export MODEL="resnet"
 export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
 

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh

@@ -1,7 +1,6 @@
 #!/bin/bash
-set -e  # Exit on any error
 
-export PYTHONPATH="." AMD=1
+export PYTHONPATH="."
 export MODEL="resnet"
 export SUBMISSION_PLATFORM="tinybox_red"
 export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
@@ -11,14 +10,13 @@ export RESET_STEP=0
 export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
 
 # pip install -e ".[mlperf]"
-export LOGMLPERF=${LOGMLPERF:-1}
+export LOGMLPERF=1
 
 export SEED=$RANDOM
 DATETIME=$(date "+%m%d%H%M")
 LOGFILE="resnet_red_${DATETIME}_${SEED}.log"
 
 # init
-sleep 5 && sudo rmmod amdgpu || true
 BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
 
 # run

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/README.md

@@ -1,38 +0,0 @@
-# 1. Problem
-
-This problem uses RetinaNet for SSD.
-
-## Requirements
-
-Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
-```
-git clone https://github.com/tinygrad/tinygrad.git
-python3 -m pip install -e ".[mlperf]"
-```
-
-Also install the following dependencies:
-```
-pip install tqdm numpy pycocotools boto3 pandas torch torchvision
-```
-
-### tinybox_green
-Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
-This is the default on production tinybox green.
-
-# 2. Directions
-
-## Steps to download data
-
-Run the following:
-```
-BASEDIR=/raid/datasets/openimages python3 extra/datasets/openimages.py
-```
-
-## Running
-
-### tinybox_green
-
-#### Steps to run benchmark
-```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/run_and_time.sh
-```

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/dev_beam.sh

@@ -1,14 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." NV=1
-export MODEL="retinanet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
-export BASEDIR="/raid/datasets/openimages"
-
-# export RESET_STEP=0
-
-export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-
-export BENCHMARK=5 DEBUG=2
-
-python examples/mlperf/model_train.py

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/dev_run.sh

@@ -1,15 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." NV=1
-export MODEL="retinanet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
-export BASEDIR="/raid/datasets/openimages"
-
-# export RESET_STEP=0
-
-export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-
-export WANDB=1 PARALLEL=0
-export RUNMLPERF=1
-
-python examples/mlperf/model_train.py

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/run_and_time.sh

@@ -1,24 +0,0 @@
-#!/bin/bash
-set -e  # Exit on any error
-
-export PYTHONPATH="." NV=1
-export MODEL="retinanet"
-export SUBMISSION_PLATFORM="tinybox_green"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
-
-export TRAIN_BEAM=2 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-export IGNORE_JIT_FIRST_BEAM=1
-export BASEDIR="/raid/datasets/openimages"
-
-# pip install -e ".[mlperf]"
-export LOGMLPERF=1
-
-export SEED=$RANDOM
-DATETIME=$(date "+%m%d%H%M")
-LOGFILE="retinanet_green_${DATETIME}_${SEED}.log"
-
-# init
-BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
-
-# run
-PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/retinanet/implementations/tinybox_red/dev_beam.sh

@@ -1,14 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="retinanet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
-export BASEDIR="/raid/datasets/openimages"
-
-# export RESET_STEP=0
-
-export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-
-export BENCHMARK=5 DEBUG=2
-
-python examples/mlperf/model_train.py

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/retinanet/implementations/tinybox_red/dev_run.sh

@@ -1,15 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="retinanet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
-export BASEDIR="/raid/datasets/openimages"
-
-# export RESET_STEP=0
-
-export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-
-export WANDB=1 PARALLEL=0
-export RUNMLPERF=1
-
-python examples/mlperf/model_train.py

examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/stable_diffusion/implementations/tinybox_8xMI300X/dev_run.sh

@@ -1,72 +0,0 @@
-#!/usr/bin/env bash
-
-DATETIME=${2:-$(date "+%m%d%H%M")}
-LOGFILE="${HOME}/logs/sd_mi300x_${DATETIME}.log"
-# UNET_CKPTDIR must be set: training saves checkpoints to this path, then a separate eval process scans this path to know which checkpoints to eval
-export UNET_CKPTDIR="${HOME}/stable_diffusion/training_checkpoints/${DATETIME}"
-mkdir -p "${HOME}/logs" "$UNET_CKPTDIR"
-
-# run this script in isolation when using the --bg flag
-if [[ "${1:-}" == "--bg" ]]; then
-  echo "logging output to $LOGFILE"
-  echo "saving UNet checkpoints to $UNET_CKPTDIR"
-  script_path="$(readlink -f "${BASH_SOURCE[0]}")"
-  nohup bash "$script_path" run "$DATETIME" >"$LOGFILE" 2>&1 & disown $!
-  exit 0
-fi
-
-# venv management
-if [[ -d .venv-sd-mlperf ]]; then
-  . .venv-sd-mlperf/bin/activate
-else
-  python3 -m venv .venv-sd-mlperf && . .venv-sd-mlperf/bin/activate
-  pip install --index-url https://download.pytorch.org/whl/cpu torch && pip install tqdm numpy ftfy regex pillow scipy wandb webdataset
-fi
-pip list
-apt list --installed | grep amdgpu
-rocm-smi --version
-modinfo amdgpu | grep version
-
-export BEAM=2 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 IGNORE_JIT_FIRST_BEAM=1 HCQDEV_WAIT_TIMEOUT_MS=300000
-export AMD_LLVM=0 # bf16 seems to require this
-export DATADIR="/raid/datasets/stable_diffusion"
-export CKPTDIR="/raid/weights/stable_diffusion"
-export EVAL_CKPT_DIR=$UNET_CKPTDIR
-export MODEL="stable_diffusion" PYTHONPATH="."
-export GPUS=8 BS=304
-export CONTEXT_BS=816 DENOISE_BS=600 DECODE_BS=384 INCEPTION_BS=560 CLIP_BS=240
-export WANDB=1
-export PARALLEL=4
-export PYTHONUNBUFFERED=1
-sudo rocm-smi -d 0 1 2 3 4 5 6 7 --setperfdeterminism 1500 || exit 1
-
-# Retry BEAM search if script fails before BEAM COMPLETE is printed, but don't retry after that
-run_retry(){ local try=0 max=5 code tmp py pgid kids
-  while :; do
-    tmp=$(mktemp)
-    setsid bash -c 'exec env "$@"' _ "$@" > >(tee -a "$LOGFILE" | tee "$tmp") 2>&1 &
-    py=$!; pgid=$(ps -o pgid= -p "$py" | tr -d ' ')
-    wait "$py"; code=$?
-    [[ -n "$pgid" ]] && { kill -TERM -"$pgid" 2>/dev/null; sleep 1; kill -KILL -"$pgid" 2>/dev/null; }
-    kids=$(pgrep -P "$py" || true)
-    while [[ -n "$kids" ]]; do
-      kill -TERM $kids 2>/dev/null; sleep 0.5
-      kids=$(for k in $kids; do pgrep -P "$k" || true; done)
-    done
-    grep -q 'BEAM COMPLETE' "$tmp" && { rm -f "$tmp"; return 1; }
-    rm -f "$tmp"
-    ((code==0)) && return 0
-    ((try>=max)) && return 2
-    ((try++)); sleep 90; echo "try = ${try}"
-  done
-}
-
-# Power limiting to 400W is only needed if GPUs fall out of sync (causing 2.2x increased train time) at higher power, which has been observed at 450W
-sudo rocm-smi -d 0 1 2 3 4 5 6 7 --setpoweroverdrive 750 && \
-run_retry TOTAL_CKPTS=7 python3 examples/mlperf/model_train.py; (( $? == 2 )) && { echo "training failed before BEAM completion"; exit 2; }
-sleep 90
-
-run_retry EVAL_SAMPLES=600 python3 examples/mlperf/model_eval.py; (( $? == 2 )) && { echo "eval failed before BEAM completion"; exit 2; }
-# Checkpoints will be evaluated in reverse chronological order, even if above training crashed early
-# STOP_IF_CONVERGED=1: Stop the eval after the first time convergence is detected; no more checkpoints will be evaluated after that.
-STOP_IF_CONVERGED=1 python3 examples/mlperf/model_eval.py

examples/mlperf/training_submission_v5.0/tinycorp/systems/tinybox_8xMI300X.json

@@ -1,38 +0,0 @@
-{
-    "submitter": "tinycorp",
-    "division": "closed",
-    "status": "Available on-premise",
-    "system_name": "tinybox 8xMI300X",
-    "number_of_nodes": "1",
-    "host_processors_per_node": "2",
-    "host_processor_model_name": "AMD EPYC 9354",
-    "host_processor_core_count": "32",
-    "host_processor_vcpu_count": "64",
-    "host_processor_frequency": "",
-    "host_processor_caches": "",
-    "host_processor_interconnect": "",
-    "host_memory_capacity": "2304GB",
-    "host_storage_type": "NVMe SSD",
-    "host_storage_capacity": "3x 4TB raid array",
-    "host_networking": "",
-    "host_networking_topology": "",
-    "host_memory_configuration": "24x 96GB DDR5",
-    "accelerators_per_node": "8",
-    "accelerator_model_name": "AMD Instinct MI300X 192GB HBM3",
-    "accelerator_host_interconnect": "PCIe 5.0 x16",
-    "accelerator_frequency": "",
-    "accelerator_on-chip_memories": "",
-    "accelerator_memory_configuration": "HBM3",
-    "accelerator_memory_capacity": "192GB",
-    "accelerator_interconnect": "",
-    "accelerator_interconnect_topology": "",
-    "cooling": "air",
-    "hw_notes": "",
-    "framework": "tinygrad, branch mlperf_training_v5.0",
-    "other_software_stack": {
-        "python": "3.10.16",
-        "ROCm": "3.0.0+94441cb"
-    },
-    "operating_system": "Ubuntu 24.04.1 LTS",
-    "sw_notes": ""
-  }

examples/mlperf/training_submission_v5.0/tinycorp/systems/tinybox_green.json

@@ -5,7 +5,7 @@
   "system_name": "tinybox green",
   "number_of_nodes": "1",
   "host_processors_per_node": "1",
-  "host_processor_model_name": "AMD EPYC 7532",
+  "host_processor_model_name": "AMD EPYC 7532 32-Core Processor",
   "host_processor_core_count": "32",
   "host_processor_vcpu_count": "64",
   "host_processor_frequency": "",
@@ -28,11 +28,11 @@
   "accelerator_interconnect_topology": "",
   "cooling": "air",
   "hw_notes": "",
-  "framework": "tinygrad, branch mlperf_training_v5.0",
+  "framework": "tinygrad, commit b5546912e24e0a864b35924da4efa5d71cfe368b",
   "other_software_stack": {
     "python": "3.10.12",
     "CUDA": "12.4"
   },
   "operating_system": "Ubuntu 22.04.4",
   "sw_notes": ""
-}
+}

examples/mlperf/training_submission_v5.0/tinycorp/systems/tinybox_red.json

@@ -5,7 +5,7 @@
   "system_name": "tinybox red",
   "number_of_nodes": "1",
   "host_processors_per_node": "1",
-  "host_processor_model_name": "AMD EPYC 7532",
+  "host_processor_model_name": "AMD EPYC 7532 32-Core Processor",
   "host_processor_core_count": "32",
   "host_processor_vcpu_count": "64",
   "host_processor_frequency": "",
@@ -28,10 +28,11 @@
   "accelerator_interconnect_topology": "",
   "cooling": "air",
   "hw_notes": "",
-  "framework": "tinygrad, branch mlperf_training_v5.0",
+  "framework": "tinygrad, commit b5546912e24e0a864b35924da4efa5d71cfe368b",
   "other_software_stack": {
-    "python": "3.10.12"
+    "python": "3.10.12",
+    "ROCm": "6.1.3"
   },
   "operating_system": "Ubuntu 22.04.4",
   "sw_notes": ""
-}
+}

examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/bert/implementations/tinybox_1xMI300X/dev_beam.sh

@@ -1,17 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="bert"
-export DEFAULT_FLOAT="HALF" GPUS=1 BS=128 EVAL_BS=128
-
-export IGNORE_OOB=1
-
-export BEAM=3 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1
-# export BEAM_LOG_SURPASS_MAX=1
-# export BASEDIR="/raid/datasets/wiki"
-
-export RESET_STEP=1
-export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
-
-python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/README.md

@@ -1,69 +0,0 @@
-# 1. Problem
-
-This problem uses BERT for NLP.
-
-## Requirements
-
-Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
-```
-git clone https://github.com/tinygrad/tinygrad.git
-python3 -m pip install -e ".[mlperf]"
-```
-Also install gdown (for dataset), numpy, tqdm and tensorflow.
-```
-pip install gdown numpy tqdm tensorflow
-```
-
-### tinybox_green
-Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
-This is the default on production tinybox green.
-
-# 2. Directions
-
-## Steps to download and verify data
-
-### 1. Download raw data
-
-```
-BASEDIR="/raid/datasets/wiki" WIKI_TRAIN=1 VERIFY_CHECKSUM=1 python3 extra/datasets/wikipedia_download.py
-```
-
-### 2. Preprocess train and validation data
-
-Note: The number of threads used for preprocessing is limited by available memory. With 128GB of RAM, a maximum of 16 threads is recommended. 
-
-#### Training:
-```
-BASEDIR="/raid/datasets/wiki" NUM_WORKERS=16 python3 extra/datasets/wikipedia.py pre-train all
-```
-
-Generating a specific topic (Between 0 and 499)
-```
-BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-train 42
-```
-
-#### Validation:
-```
-BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
-```
-## Running
-
-### tinybox_green
-
-#### Steps to run benchmark
-```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
-```
-
-### tinybox_red
-
-#### Steps to run benchmark
-```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
-```
-### tinybox_8xMI300X
-
-#### Steps to run benchmark
-```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
-```

examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/dev_beam.sh

@@ -1,16 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="bert"
-export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
-export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
-
-export IGNORE_OOB=1
-
-export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
-export BASEDIR="/raid/datasets/wiki"
-
-export BENCHMARK=10 BERT_LAYERS=2
-
-python3 examples/mlperf/model_train.py